5W RESEARCH · APRIL 2026 · Q1 EDITION
# The PR Advantage in Affiliate Marketing 2026
Why earned media relationships produce better affiliate programs — and the 12-vertical benchmarks every brand needs to know. By Matt Caiola, CEO, 5W Public Relations.
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## EXECUTIVE SUMMARY
Affiliate marketing is now a $20 billion global industry — and one of the highest-ROI channels in digital marketing, returning an average of $12 to $15 for every $1 invested. Yet the majority of brands running affiliate programs are leaving significant performance on the table. The reason is almost always the same: partner quality.
Most affiliate programs are built by performance agencies that start with cold outreach, generic recruitment, and commission incentives alone. The result is affiliate rosters full of low-authority publishers, coupon aggregators, and traffic sources that erode brand credibility rather than build it.
5W occupies a fundamentally different position. As one of the top independent communications firms in the United States — with 20+ years of earned media relationships across every major consumer category — 5W brings publisher, influencer, media outlet, and content creator relationships to affiliate programs that performance-only agencies spend months, and often fail, to build.
This report documents two things. First, the specific mechanisms by which PR relationships produce superior affiliate outcomes. Second, the affiliate marketing benchmarks that every brand should use to evaluate their own program performance — broken down by vertical.
$20B
Global affiliate market in 2026
$13.2B
U.S. affiliate spend 2026, up from $6.8B in 2019
$12–$15
Average ROI per $1 invested
90%
Of e-commerce businesses expected to run affiliate programs by end of 2026
## STATE OF THE CHANNEL — FOUR SHIFTS IN 2025–2026
The affiliate landscape has shifted materially in the past two years. The channel grows at a 15.2% CAGR, projected to reach $71.74 billion by 2034. Affiliate already contributes approximately 16% of all U.S. e-commerce sales — yet only 7% of marketing managers identify it as a top budget priority (Forrester). That gap is the opportunity.
01 — The influencer-affiliate convergence. Publishers and creators that previously operated on flat-fee PR are now operating on performance-based affiliate structures. Editors at major publications are under pressure to drive revenue — affiliate-enabled products receive priority placement over non-affiliate products, regardless of editorial merit. Combining influencer and affiliate marketing drives 46% more sales than running either independently. Influencer-driven affiliate conversions are up 37% year-over-year. Ten percent commission is now the floor at top-tier publications.
02 — The buyer journey lengthened. A 2,368-brand North American analysis found affiliate click volume rose 2% year-over-year while conversions fell 5% and conversion rates dropped 6%. This is not a channel problem. It is a buyer-behavior shift. Shoppers now use affiliate content for research and comparison earlier in the journey, then convert through other channels closer to purchase. Last-click attribution models systematically undercount the contribution of high-quality content partners and over-credit coupon and cashback affiliates that capture demand already decided.
03 — AI is reshaping discovery. Nearly 80% of affiliate marketers now use AI tools for content, SEO, or campaign analysis. As Google AI Overviews and generative answer engines reduce organic click-through rates for traditional affiliate content, the value of trusted brand placements in high-authority publications — the kind PR relationships produce — has increased. The discovery layer is collapsing into the citation layer.
04 — Fraud is growing. Click fraud, cookie stuffing, and fake lead generation cost businesses an estimated $3.5 billion or more annually. Approximately 18% of affiliate traffic is flagged as invalid or fraudulent. As programs scale, fraud management becomes a critical function most in-house teams are not equipped to handle.
"The performance of an affiliate program is almost entirely determined by the quality of the publishers, creators, and media properties in it. And partner quality is almost entirely determined by how those partners were recruited."
— Matt Caiola, CEO, 5W
## THE PR ADVANTAGE — THE FLYWHEEL
The central challenge in affiliate marketing is not technology, tracking, or commission structure. It is partner quality. Traditional affiliate recruitment relies on three mechanisms: network browsing, cold outreach, and commission incentives. These produce volume. They do not reliably produce quality.
The publishers who respond to cold outreach with speed are typically the publishers for whom affiliate income is their primary revenue model — coupon sites, deal aggregators, cashback platforms, low-authority content farms. These partners drive transaction volume at the bottom of the funnel. They rarely build brand equity, rarely attract new customers who were not already planning to purchase, and frequently create margin pressure without corresponding brand value.
The partners that actually move the needle — high-authority editorial publishers, top-tier influencers, trusted review publications, niche content creators with genuinely engaged audiences — do not need affiliate income. They have more partnership requests than they can fulfill. They cannot be cold-recruited. They must be earned.
The 5W flywheel is a self-reinforcing loop performance-only agencies cannot start: earned media coverage builds brand credibility → brand credibility attracts higher-quality affiliate partners → higher-quality partners create better content → better content produces higher conversion rates → higher conversion rates attract more high-quality partners → more high-quality partners drive more earned media coverage. The loop is invisible to brands running PR and affiliate as separate functions with separate agencies.
## VERTICAL BENCHMARKS — 12 VERTICALS
Affiliate performance benchmarks by vertical for 2025–2026, drawn from current industry data across Awin, Impact, CJ Affiliate, Rakuten, and independent research. Use these as reference points to evaluate program performance and set realistic expectations.
Vertical
Commission Rate
Conv. Rate
Partner Type Priority
Consumer Products / CPG
~44% of global affiliate revenue
5–12%
1.5–3%
Content publishers · lifestyle influencers · deal & coupon
Beauty & Personal Care
Highest editorial-to-affiliate conversion
10–18%
2–4%
Beauty influencers (micro & macro) · editorial review · beauty media
Health & Wellness
Compliance-aware mgmt required
8–20%
2–5%
Health media · wellness influencers · email newsletters
Fashion & Apparel
23% of all affiliate programs
8–15%
1.5–3%
Fashion influencers · lifestyle editorial · IG/Pinterest creators
Food & Beverage
Recipe + retail integration
8–12%
1.5–2.5%
Content publishers · deal · loyalty
Home & Housewares
Editorial roundups dominate
5–12%
1–2.5%
Content publishers · editorial · deal
Technology & SaaS
Highest commission ceiling
15–50%
1–3%
Review & comparison (G2, Capterra) · B2B media · pro communities
Consumer Tech / Electronics
Review-led, deal-sensitive
3–8%
1–2%
Review publications · comparison sites · deal
Financial Services / Fintech
Highest flat-fee category
$50–$200 / lead
2–5%
Personal finance editorial · comparison · newsletter publishers
Travel & Hospitality
~16% of global affiliate revenue
3–10%
1–2%
Travel content · loyalty & cashback · destination influencers
Parent / Child / Baby
Trust-driven, review-heavy
5–15%
2–4%
Parenting influencers · content publishers · deal
Gaming & Entertainment
Creator-economy native
10–25%
2–4%
Review & content creators · social-native publishers
Source: 5W analysis of 2025–2026 industry data. Aggregated from Awin, Impact, CJ Affiliate, Rakuten network reports, and independent research. Ranges represent typical bands; brand-specific outliers exist in every vertical.
Key benchmarks worth knowing: Beauty influencer-affiliate content converts +37% YoY. Fashion revenue is up 26% YoY. Health affiliates earn an average of $7,194/month. Travel affiliates earn an average of $13,847/month. Fifty percent of CPG affiliate revenue comes from new customers — programs should be structured to reward new-customer acquisition specifically.
## PARTNER QUALITY FRAMEWORK — FIVE DIMENSIONS
Not all affiliates who drive conversions are good affiliates. The roster that looks healthy on a last-click dashboard can be systematically damaging brand equity, poaching customers who were already purchasing, and eroding margin without generating incremental revenue. 5W evaluates partner quality across five dimensions.
01 — Audience alignment. Does the partner's audience match the brand's customer profile? Traffic volume is irrelevant if the audience is not the target customer. Start with demographics, psychographics, and purchase behavior — not domain authority or monthly traffic.
02 — Editorial authority. Is the partner trusted by its audience? Editorial authority is the product of consistent, high-quality content over time. It cannot be faked and it cannot be bought. Genuine authority converts because readers trust the recommendation.
03 — Content quality. Does the partner produce content that would be compelling without the affiliate link? The best affiliate content would exist regardless of the commission — honest reviews, thoughtful roundups, useful guides. Commission-first content converts at a fraction of editorial-first content.
04 — Incrementality. Is the partner generating new revenue or capturing existing demand? Coupon and cashback are the most common sources of non-incremental affiliate revenue — intercepting customers who already decided to purchase. Measure incrementality, not just attribution.
05 — Brand safety. Does the partner's content and platform context protect or damage brand equity? Performance-only agencies frequently shortcut brand-safety vetting. A single high-traffic placement on a brand-unsafe platform can do more damage than months of positive placements repair.
## THE INTEGRATED MODEL — FIVE PHASES
5W's affiliate model integrates every component of program management with the PR relationships and brand-building expertise that define the firm's core practice.
Phase 01 — Strategy & program design. Define success metrics that reflect actual business impact, not vanity affiliate metrics. Select the right network platforms for the brand's category and partner profile. Design commission structures that attract quality partners without eroding margin. Establish the partner-quality standards that govern recruitment and ongoing management.
Phase 02 — Relationship-led recruitment. Map the existing 5W relationship network against the target partner profile. Partners with whom 5W already has editorial, influencer, or media relationships are contacted through warm introductions — with brand context and editorial support cold-recruited partners never receive. Cold recruitment of top-tier partners typically takes three to six months. Warm 5W introductions compress that to weeks.
Phase 03 — Content & creative infrastructure. Brand storytelling toolkits, editorial briefing documents, product information packages, high-resolution imagery, promotional calendars, and exclusive offer mechanics. Partners who receive this support produce better content. Better content converts. Higher conversion motivates more consistent promotion.
Phase 04 — Performance management & optimization. Continuous monitoring with focus on partner-level incrementality, conversion rate optimization, fraud detection, and commission adjustment. Regular partner reviews identify top performers for enhanced investment and low performers for optimization or removal. Treated as a dynamic portfolio, not set-and-forget.
Phase 05 — Reporting & attribution. Full-transparency reporting on revenue, ROI, partner performance at every level, new-vs-returning customer acquisition through the affiliate channel, and program growth metrics. Attribution accounts for the multi-touch reality of modern affiliate journeys — recognizing the contribution of content-driven top-of-funnel partners rather than over-crediting last-click transactional affiliates.
"The brands that treat affiliate as a performance channel and nothing more will build programs dominated by coupon sites, cashback platforms, and low-authority traffic. They will pay commissions on purchases that were going to happen anyway. The brands that understand affiliate as a relationship channel will build programs anchored by trusted editorial publishers, high-authority creators, and media properties whose audiences have been cultivated over years. The difference is not technology. It is relationships."
— Matt Caiola, CEO, 5W
## FREQUENTLY ASKED QUESTIONS
What does this report cover?
5W's 2026 industry research on affiliate marketing: the four structural shifts reshaping the channel in 2025–2026, the mechanism by which PR relationships produce superior affiliate outcomes, performance benchmarks across 12 industry verticals, the five-dimension partner quality framework, and 5W's five-phase integrated affiliate management model.
How big is affiliate marketing in 2026?
Global affiliate marketing is a $20 billion industry in 2026. U.S. affiliate spend reaches $13.2 billion, up from $6.8 billion in 2019. The channel is growing at a 15.2% CAGR, projected to reach $71.74 billion by 2034. Average ROI is $12–$15 per $1 invested — among the highest of any digital channel.
Why do PR relationships matter for affiliate performance?
Partner quality is the single biggest determinant of affiliate program performance, and partner quality is determined by how partners are recruited. The high-authority editorial publishers, top-tier influencers, and trusted review publications that drive real performance cannot be cold-recruited — they have more partnership requests than they can fulfill. They must be earned through relationship. 5W has spent 20+ years building those relationships.
Which vertical has the highest commissions?
Technology and SaaS offers commissions of 15–50% of first sale, with recurring 10–30% on subscription products. Financial Services pays the highest flat fees at $50–$200 per qualified lead, with mortgage and insurance routinely exceeding $500. Health and Wellness commands 8–20% on one-time purchases and 20–50% on subscription products.
Can 5W run our affiliate program?
Yes. 5W's affiliate marketing practice integrates 20+ years of earned media relationships with full-service program strategy, recruitment, management, and optimization. Contact us at research@5wpr.com or info@5wpr.com.
How is the integrated model different from a performance agency?
Performance agencies start with cold outreach and commission incentives. They produce volume, not quality. 5W starts with the existing relationship network — warm introductions through editorial contacts, brand credibility already established through PR, and a media network that includes the publications and creators every brand wants in its program. Cold recruitment of top partners typically takes 3–6 months. Warm 5W introductions compress that to weeks.
↧ Download the Full PDF Report
## ABOUT 5W PUBLIC RELATIONS
5W Public Relations is one of the largest independently owned PR firms in the United States, with approximately 275 professionals and offices across the country. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan. The firm serves more than 250 clients across consumer brands, corporate communications, crisis management, healthcare, technology, education, food and beverage, beauty and fashion, financial services, and public affairs. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola.
April 2026 — 5W Research Series, Q1 Edition
Published by 5W Public Relations. 5wpr.com · everything-pr.com. Email us at research@5wpr.com. Reproduction permitted with attribution.
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### Edition 21: Commercial Real Estate Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/commercial-real-estate/
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URL: https://www.5wpr.com/research/retrieval-index/hospitality/
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### Edition 22: Digital Health & Healthtech — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/digital-health/
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### Edition 23: Biotech & Life Sciences Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/biotech/
Summary: The 5W Retrieval Index grades biotech, the second-highest-graded sector in the study. The reason is peer review, and the citation data shows exactly how much.
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### Edition 25: Travel Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/travel/
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### Edition 27: Residential Real Estate Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/residential-real-estate/
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### Hedge Fund Principals
URL: https://www.5wpr.com/reputation-index/hedge-fund-principals/
Summary: How AI engines render 20 of the most prominent hedge fund principals. James Simons leads posthumously. Bill Hwang trails. Directional estimates, June 2026.
THE 5W REPUTATION INDEX · STUDY 06 · FINANCE PHASE
# Hedge Fund Principals
Published 30 June 2026 Cohort 20 principals Engines 5 Prompts 60+
How the major AI systems currently render twenty of the most prominent hedge fund principals — and the series-low composite that closes the cohort.
Across twenty named hedge fund principals, the Index produces the largest single-cohort spread in the series — 53 composite points, from James Simons (posthumous) at 78 to Bill Hwang at 25. The cohort divides cleanly along one axis: scandal persistence. Top-cohort principals — Simons, Klarman, Dalio, Druckenmiller — anchor engine portraits in primary-source publishing platforms (books, annual letters, podcasts). Bottom-cohort principals — Hwang (Archegos), Tepper (NFL spillover), Singer (Argentina sovereign-debt framing) — remain anchored to defining transactions or convictions. The Hwang portrait, at 25, is the lowest composite in the entire Index.
Highest Composite
78
James Simons (posthumous)
Lowest Composite
25
Bill Hwang · Archegos — series low
Sentiment Floor
10
Hwang — series low
Cohort Spread
53
Widest sector spread
Scandal persistence is the longest-lived form of machine memory in the cohort.
## The Full Ranking
### All 20 hedge fund principals by composite score
The cohort includes founders and named principals of the largest discretionary, multi-strategy, and quantitative hedge fund platforms. Includes James Simons posthumously (d. May 2024) as engine portrait remains active. The Index reflects engine-rendered reputation only and does not adjudicate the merits of any matter referenced in the source record.
#PrincipalFirmAccSenCmpCnsCtlComposite
1James Simons (posthumous)Renaissance Technologies · Founder (d. 2024)828278767278
2Seth KlarmanBaupost Group · Founder & CEO787674707274
3Ray DalioBridgewater Associates · Founder, Mentor786682726873
4Stanley DruckenmillerDuquesne Family Office767470707072
5Ken GriffinCitadel · Founder & CEO786676725870
5TSteve CohenPoint72 · Founder, Chairman & CEO766676706270
5TCliff AsnessAQR Capital Management · Co-founder747066687270
8Bruce KovnerCAM Capital / Caxton (former)726266665664
9Israel EnglanderMillennium Management · Founder & CEO705862665262
9TChase Coleman IIITiger Global Management · Founder685864645662
11Paul Tudor Jones IITudor Investment Corporation · Founder705666625261
12Bill AckmanPershing Square Capital · Founder & CEO764476624861
13Daniel LoebThird Point · Founder & CEO685062604657
14Ole Andreas HalvorsenViking Global Investors · Co-founder, CEO625452584254
15John PaulsonPaulson & Co.704866602854
16Carl IcahnIcahn Enterprises · Chairman76388260853
17Crispin OdeyOdey Asset Management (former)602862521443
18Paul SingerElliott Investment Management · Founder & President682872561247
19David TepperAppaloosa Management · Founder & President683068561247
20Bill HwangArchegos Capital Management (collapsed 2021)66106244025
## Top of the Cohort
### The best-rendered principals
The top of the hedge-fund cohort, like the top of the PE cohort, is structured by primary-source platform investment. Simons through Renaissance and the Math for America foundation, Klarman through "Margin of Safety", Dalio through "Principles", Druckenmiller through interview-circuit access. Posthumous rendering of Simons remains the strongest.
### Rank 01 · James Simons — Composite Composite 78
Renaissance Technologies · Founder (d. May 2024)
Anchor event. Renaissance Medallion Fund — the most successful quant track record in finance history — anchored by the 2019 Gregory Zuckerman book "The Man Who Solved the Market".
Posthumous rendering of Simons remains the strongest in the cohort. Engines render him through Renaissance Technologies founding, the Medallion Fund's exceptional historical track record, mathematician-to-investor biography, the Simons Foundation ($4B+ in lifetime giving), Math for America, and the Zuckerman biography. Mortality has compressed engine portrait into the most canonical form — no new news events to dilute the legacy framing.
Sourced rationale. Renaissance and Medallion history documented in Zuckerman, "The Man Who Solved the Market" (2019). Simons Foundation activity documented at simonsfoundation.org and across NYT, WSJ philanthropy coverage. Death and obituary coverage documented in NYT, WSJ, FT, May 2024.
### Rank 02 · Seth Klarman — Composite Composite 74
Baupost Group · Founder & CEO
Anchor event. "Margin of Safety" (1991) — the most-cited and most-rare value-investing text of the modern era — anchored by Klarman's chairmanship of The Times of Israel.
Engines render Klarman through Baupost Group founding (1982), the rarely-reprinted "Margin of Safety", his chairmanship of The Times of Israel, and his political-donor profile (since 2020, primarily Republican). The cohort's clearest case of narrative density built on a single canonical text — a 1991 book that engines continue to surface in the top 5 PE/HF reference works.
Sourced rationale. Klarman, "Margin of Safety" (1991) referenced as canonical value-investing text across financial press. Times of Israel masthead documented at timesofisrael.com. Political donations documented in FEC filings.
### Rank 03 · Ray Dalio — Composite Composite 73
Bridgewater Associates · Founder, Mentor
Anchor event. "Principles" (2017) — the most-cited management text of the past decade — alongside Bridgewater's $124B+ AUM track record and 2022 leadership transition.
Engines render Dalio through Bridgewater founding (1975), the "Principles" book series (2017–2022), the Bridgewater "idea meritocracy" culture, the 2022 leadership transition to Bob Prince and Karen Karniol-Tambour, and the 2023 Rob Copeland book "The Fund" which produced engine-portrait headwinds on culture-related Sentiment. Net Sentiment of 66 reflects the late-period book and culture-critique compression.
Sourced rationale. Bridgewater history documented in WSJ, FT, NYT, Bloomberg. "Principles" (Simon & Schuster, 2017) and follow-on titles documented in publisher catalogs. Leadership transition documented in Bridgewater statements (October 2022). Copeland, "The Fund" (St. Martin's, 2023) documented in publisher catalogs and major review coverage.
## Bottom of the Cohort
### The worst-rendered principals
The bottom three of the cohort produce the most extreme single-event reputation collapses in the entire series. Each portrait is anchored to a specific named event, with near-zero Control across all five engines.
### Rank 20 · Series Low · Bill Hwang — Composite Composite 25
Archegos Capital Management (collapsed March 2021)
Anchor event. The March 2021 Archegos collapse — $36B+ in client losses across Credit Suisse, Nomura, Morgan Stanley, UBS — followed by the July 2024 federal jury conviction on ten counts and the November 2024 sentencing to 18 years in federal prison.
The lowest composite in the entire 5W Reputation Index. Engines render Hwang almost entirely through the Archegos collapse and its aftermath: $36B in client losses, the July 10, 2024 federal jury conviction on ten counts including securities fraud and market manipulation, the November 20, 2024 sentencing to 18 years in federal prison (with surrender date initially set for July 2025), and the $12.35B in restitution and forfeiture orders. Sentiment of 10 is the lowest single-dimension score in the series. Control of 0 reflects a portrait essentially absent of any primary-source presence.
Sourced rationale. Archegos collapse documented in Senate Banking Committee hearings (2021), Credit Suisse Group AG Special Committee Report (July 2021), DOJ and SEC filings (April 2022). Trial conviction documented in U.S. v. Hwang et al. (S.D.N.Y.) docket and across WSJ, NYT, Bloomberg, FT (July 10, 2024). Sentencing documented in same court docket (November 20, 2024) and across the same outlets.
### Rank 18 · Paul Singer — Composite Composite 47
Elliott Investment Management · Founder & President
Anchor event. The 2002–2016 Argentina sovereign-debt litigation — widely characterized in international press as "vulture fund" activism — alongside the more recent Twitter / Salesforce / AT&T activist campaigns.
Engines render Singer through three layered anchors: Elliott Investment Management's $70B+ AUM activist platform, the long-running Argentina sovereign-debt litigation that produced the "vulture fund" framing across international press, and recent activist campaigns at Twitter (2020), Salesforce (2023), and AT&T (2019). Sentiment of 28 reflects the durable Argentina framing — engines have not unwound the characterization despite the eventual 2016 settlement.
Sourced rationale. Argentina sovereign-debt litigation documented in U.S. and U.K. court filings, IMF reports, and across FT, NYT, Reuters, WSJ (2002–2016). Activist campaigns documented in 13D filings and across financial press.
### Rank 19 · David Tepper — Composite Composite 47
Appaloosa Management · Founder & President
Anchor event. Cross-domain contamination — the November 2023 stadium drink-toss incident and Panthers coaching-cycle controversies leak into the hedge-fund portrait.
Engines render Tepper through Appaloosa Management's 2009 financial-crisis trade (positive), the Carnegie Mellon David A. Tepper School of Business naming gift (positive), and the November 2023 incident at Bank of America Stadium and the cascade of Panthers head-coach firings (negative). Cross-series principal — Tepper scores 49 in the NFL cohort and 47 here. The cohort's clearest demonstration that ownership contamination runs from sports portfolio back into hedge-fund portrait.
Sourced rationale. Appaloosa 2009 financial-crisis returns documented in WSJ, NYT, FT. Tepper School naming documented in CMU press releases. November 2023 incident and NFL fine documented in NFL statements and across ESPN, NYT, The Athletic.
## Notable Gaps
### Where engines diverge from public record
### 01. The Carl Icahn completeness paradox
Icahn scores 82 on Completeness (top 3 of cohort) and 53 composite. Engines have an extraordinarily deep activist-investor portrait — every named campaign across four decades — but the May 2023 Hindenburg Research short report and subsequent Icahn Enterprises decline compresses Sentiment to 38. The most-rendered activist is not the best-rendered one.
### 02. The Cliff Asness Twitter / X compounding effect
Asness scores 70 composite — unusually high for an active discretionary-and-quant hybrid principal. Engines surface his Twitter / X presence, AQR research papers, and Cliff's Perspective blog as primary sources. The cohort's clearest case of social-platform participation compounding engine rendering favorably.
### 03. The Bill Ackman bifurcation
Ackman scores 76 on Accuracy and Completeness, 44 on Sentiment — a bifurcated portrait. Engines surface both the Pershing Square track record and the post-October-2023 Harvard / Israel / public-discourse campaigning. Sentiment depends sharply on which portrait the prompt activates.
### 04. The Crispin Odey survival of the engine portrait
Odey resigned from Odey Asset Management in June 2023 following media reporting of allegations against him; the firm was wound down through the second half of 2023. Engine portrait continues to render him as an active principal in 35 percent of prompts. The cohort's clearest case of engine memory lagging behind operational reality.
### 05. The Steve Cohen MLB rendering lift
Cohen scores 70 composite here and 73 in the MLB cohort. The Mets ownership has produced a measurable cross-portfolio rendering lift — engines now surface his sports portfolio in approximately 40 percent of hedge-fund identity prompts, with net-positive sentiment effect.
## Methodology Notes
### How the Index was modeled
The 5W Reputation Index measures how leading AI systems render named principals across sixty-plus retrieval-intent prompts per engine. Scores are directional estimates derived from modeled engine outputs and supplementary web-search verification — not logged query runs. The Index reflects engine-rendered reputation only. It is not an evaluation of any principal's actual conduct, character, or business practices.
This Index does not adjudicate the merits of any pending litigation, allegation, or contested matter referenced in the source record.
Previous: ← PE Founders
Series Hub: The 5W Reputation Index
Next Study: Media Chiefs →
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The 5W Reputation Index · Study 06 of 09 · Published 30 June 2026 · 5W AI Communications · Florida
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### The Private Label AI Advantage — Grocery
URL: https://www.5wpr.com/research/private-label-ai-advantage-grocery/
Summary: Store brands out-cite national brands nearly 2 to 1 across five answer engines. A 5W AI research study.
---
### Edition 28: Food & Beverage Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/food-beverage/
Summary: The sector where nutritional data and regulatory filings anchor above trade press.
Research›
Retrieval Index›
Food & Beverage Media
Edition 28 — The 5W Retrieval Index — Volume I
# Food & Beverage Media
The sector where nutritional data and regulatory filings anchor above trade press.
B
Food & Beverage Industry Media
The Unvarnished Read
Food and beverage retrieval splits between consumer and B2B with limited overlap. The B2B side is anchored by the retail-data-publisher tier: Circana (formerly IRI), Nielsen NIQ, Mintel, Euromonitor, and Kantar publish primary CPG and beverage performance data cited above journalism on category-performance queries. The trade press tier — Food Business News, Food Dive, BevNet, Beverage Industry, Specialty Food — handles industry-news coverage. The government-substrate tier — USDA, FDA food-safety publications, CDC food-safety data, EFSA in Europe — anchors regulatory and safety queries. The consumer-food editorial layer — Bon Appétit, Eater, The Counter, Serious Eats, Food52, Food and Wine, Civil Eats — operates somewhat independently and carries recipe-and-consumer-trend queries. Wikipedia carries the definitional authority layer on food topics with unusually heavy weight. The grade is B because the data-publisher tier and government substrate are exceptional, with the trade press tier well-formed but the consumer and B2B layers structurally disconnected.
The System
## How AI answers about food & beverage media work.
Category-performance queries ("plant-based meat sales trends," "alcohol consumption decline," "energy-drink category growth") route to Circana, Nielsen NIQ, Mintel, Euromonitor, and the trade press citing them.
Brand and corporate queries ("Coca-Cola earnings," "Kraft Heinz CEO change," "Beyond Meat valuation") route to public filings, Food Business News, Food Dive, BevNet, Bloomberg Food & Beverage, and WSJ Food. Regulatory and safety queries ("FDA food-safety modernization," "USDA organic certification," "front-of-pack labeling rule") route to USDA, FDA, CDC, EFSA, and the trade press citing them.
Ingredient and nutrition queries ("ultra-processed food definition," "stevia safety," "what is monk fruit") route to FDA, USDA, NIH nutrition publications, Wikipedia, and consumer-food editorial.
Consumer-trend queries ("food trends 2026," "Gen-Z food preferences," "wellness-food category growth") route to Mintel consumer trends, NPD/Circana, McKinsey consumer publications, BevNet, and consumer-food editorial.
Recipe and culinary queries ("how to make Korean BBQ," "best chocolate chip cookie recipe," "what to do with leftover chicken") route to recipe-aggregator-and-editorial tier (Allrecipes, NYT Cooking, Bon Appétit, Serious Eats, Food52), Reddit r/Cooking and r/AskCulinary, and individual creator content. Cross-engine variation: ChatGPT and Claude weight Circana, Nielsen, and government publications institutionally. Perplexity surfaces BevNet and consumer-food editorial aggressively. Google AI Overviews favors Allrecipes, NYT Cooking, and high-domain-authority recipe publishers on consumer queries. Geographic dispersion: U.S. leads English-language F&B retrieval. UK food trade (The Grocer, Food Manufacture, Drinks International) reaches U.S. engines moderately. Continental Europe (Food Navigator, FoodIngredientsFirst) reaches reasonably through English-language coverage. APAC food press (FoodNavigator-Asia) at moderate frequency. GEO implication for F&B brands and operators. The retrieval-effective placements split by audience and query type. For category-performance positioning, Circana and Nielsen data inclusion is structural. For trade visibility, Food Business News, Food Dive, and BevNet coverage. For consumer-facing brand visibility, recipe-aggregator presence, Eater coverage, and earned editorial in Bon Appétit. For ingredient and nutrition positioning, peer-reviewed nutrition research and USDA database accuracy.
Coverage Universe
F&B; trade press, broader financial press covering F&B;, consumer-food editorial, recipe aggregators, beverage-specific trade, consultancy publications, and community substrates.
The Rankings
## Source scores and retrieval tiers.
Cited (56–71) — 3 properties
PropertyScoreNote
BevNet64 Beverage-specific trade. Open. Consumer-and-technique editorial. Open. International category research. Partial paywall. Research authority. Consumer-recipe editorial. Open.
Food and Wine60 Consumer editorial. Open. Food-policy editorial. Open. Food-system editorial. Open. Trade. Open. Specialty Food Association trade.
Bloomberg Food & Beverage58 Paywall heavy. Same dynamic.
The Structural Finding
## The Retail-Data Triumvirate
Food and beverage is the sector where commercial retail-data publishers function as a coordinated tier at the structural-citation level. Circana (formerly IRI and NPD, now merged) at 78, Nielsen NIQ at 76, and Mintel at 72 together carry more cited content on CPG performance, category trends, and consumer-research queries than the entire dedicated F&B trade press combined. Add Euromonitor and Kantar and the data-publisher tier reaches near-monopoly on quantitative-category queries.
The mechanism: F&B is a category-driven retail industry, and category-share research requires scaled measurement infrastructure that only the data publishers operate. Circana measures point-of-sale data across tens of thousands of U.S. retailers. Nielsen NIQ adds household-panel data. Mintel adds consumer-research depth. The engines retrieve from data-publisher-cited material across the entire trade press ecosystem because every food publication, every CPG financial report, and every academic study cites their data. The pattern is structurally similar to STR in hospitality, Trepp in CMBS, A.M. Best in insurance, and the CBRE-JLL-Cushman tier in CRE — commercial data publishers whose proprietary data positions create structural citation dependencies across the entire industry.
Two secondary patterns reinforce. The Government Substrate. USDA at 82 and FDA at 80 collectively form a government anchor tier that handles nutrition, food-safety, and regulatory queries at structural-citation level. The USDA's FoodData Central database in particular operates as a Wikipedia-equivalent reference function for food composition. The pattern parallels CISA-NIST in cyber and FDA-EMA in pharma but with USDA carrying more nutrition-specific weight than equivalent government tiers in other sectors. The Consumer-Recipe Layer. Allrecipes at 72 reaches Retrieval Anchor tier — the rare consumer-aggregator that ranks alongside trade infrastructure. NYT Cooking, Bon Appétit, Serious Eats, and Food52 form the editorial layer above Allrecipes. The consumer-recipe ecosystem is well-formed and structurally separate from the B2B industry layer — a feature, not a bug, but one that prevents F&B from achieving the integrated retrieval architecture of pharma or capital markets. F&B grades B because the retail-data triumvirate and government substrate are exceptional, with the dedicated trade press tier healthy and the consumer-food editorial layer well-formed. The grade is not B+ because the B2B and consumer layers operate as essentially disconnected systems — a F&B brand has to optimize for two completely different retrieval architectures depending on whether the target query is industry or consumer.
What Moves It
## Operating moves for this sector.
- For CPG and beverage brands: Circana and Nielsen NIQ data inclusion is the most underweighted retrieval lever. Brands whose category performance is correctly tracked and reported in Circana and NIQ data appear in every trade-press piece that the engines cite. The investment is data-syndication discipline, not marketing.
- For ingredient and nutrition positioning: USDA FoodData Central accuracy is direct retrieval anchoring. Brands and ingredients with accurate FoodData Central entries gain compounding citation on nutrition queries. Most CPG brands have never audited their FoodData Central representation.
- For consumer-facing brand visibility: recipe-aggregator presence on Allrecipes, NYT Cooking, and Food52 is the consumer-side lever. Brands whose products appear in published recipes — and whose product pages are accurately linked — gain compounding citation on consumer-recipe queries.
- For trade press (Food Business News, Food Dive, BevNet, Beverage Industry, Specialty Food): defend open-access discipline. Open content carries the sector score; drift toward paywall would forfeit retrieval at steep rates given the strength of the data-publisher and government substrates above.
- For consumer-food editorial (Bon Appétit, Eater, Serious Eats, Food52, Civil Eats): the structural opportunity is sustained vertical specialization. Bon Appétit covers everything; Eater covers everything; Serious Eats covers technique. Publishers that double down on distinctive verticals (Civil Eats on food-policy, Serious Eats on technique) outperform publishers that compete broadly across all consumer-food queries.
Building category-performance data accuracy and earned trade-press coverage is where a dedicated food and beverage communications partner comes in. 5WPR's food and beverage marketing agency practice runs this positioning work for CPG and beverage brands.
Related Sectors
Edition 27
Residential Real Estate Media
B
Edition 29
Restaurants & Hospitality Ops
B–
Edition 26
Hospitality & Hotels Trade
B+
## Get Volume I.
220 pages. 38 sectors. The first reference work for the AI retrieval economy.
Download PDF →
---
### Edition 30: Retail & DTC Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/retail-dtc/
Summary: The sector where consumer data and review platforms anchor above trade press.
---
### Edition 31: Fashion & Apparel Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/fashion-apparel/
Summary: The sector where editorial prestige and AI retrieval diverge most sharply.
---
### Edition 32: CPG Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/cpg/
Summary: The sector where Nielsen, Circana, and retail data anchor above trade journalism.
Research›
Retrieval Index›
CPG Media
Edition 32 — The 5W Retrieval Index — Volume I
# CPG Media
The sector where Nielsen, Circana, and retail data anchor above trade journalism.
B+
SECTOR GRADE B+
The Unvarnished Read
CPG retrieval is anchored by the retail-data-publisher triumvirate. Nielsen NIQ, Circana, and Mintel together — with Euromonitor and Kantar reinforcing — operate as the structural citation layer for category performance, brand-share, and consumer-trend queries at a citation density that exceeds the dedicated trade press tier by an order of magnitude. The consultancy-on-CPG tier — McKinsey, BCG, Bain, Deloitte on Consumer Goods — adds strategic-research depth cited above journalism on category-strategic queries. The trade press tier (Food Business News, Food Dive, Marketing Brew CPG, AdAge CPG) cross-sector with food and beverage covers industry-news. SEC EDGAR provides the public-CPG-company financial substrate. Modern Retail and Glossy carry DTC-CPG crossover. The grade is B+ because the data-publisher triumvirate and consultancy tier are exceptional, the trade press is well-formed, and the institutional citation infrastructure is among the strongest in the consumer-adjacent sectors.
The System
## How AI answers about cpg media work.
Brand-share and category-performance queries ("Coca-Cola share in soda," "best-selling cereal brand," "pet-food category growth") route to Nielsen NIQ, Circana, Mintel, Euromonitor, Kantar, and the trade press citing them.
Corporate and earnings queries ("Procter & Gamble Q-results," "Unilever portfolio strategy," "Kraft Heinz divestitures") route to SEC EDGAR filings, Food Business News, Bloomberg Consumer, WSJ Consumer, FT Consumer, and Reuters. Consumer-trend queries ("clean-label trends," "shrinkflation impact," "Gen-Z CPG preferences") route to Mintel consumer-trend reports, Nielsen NIQ Trends, McKinsey on Consumer Goods, BCG, Bain, and Marketing Brew.
Innovation and product queries ("plant-based dairy growth," "GLP-1 impact on CPG," "premium pet-food category") route to Circana category reports, Mintel innovation tracking, trade press, and consultancy publications.
Retail-channel queries ("Amazon CPG strategy," "club-channel growth," "convenience-store category mix") route to Coresight Research, Modern Retail, Retail Dive, NRF, and the trade press.
Marketing-and-media queries ("CPG ad-spend trends," "retail-media-network growth," "CPG influencer-marketing ROI") route to Marketing Brew, Adweek, AdAge, Digiday, eMarketer, and Numerator. Cross-engine variation: ChatGPT and Claude weight Nielsen NIQ, Circana, Mintel, McKinsey, and SEC EDGAR institutionally. Perplexity surfaces Modern Retail and DTC-newsletter tier aggressively. Google AI Overviews favors high-domain-authority business publishers and SEC EDGAR on financial queries. Geographic dispersion: U.S. leads English-language CPG retrieval. Europe well-covered through Mintel and Euromonitor's multinational operations. APAC CPG coverage moderate through Euromonitor and Mintel APAC. Latin American CPG coverage underrepresented despite the region's CPG market significance.
GEO implication for CPG brands. Retrieval-effective placements concentrate in Nielsen NIQ and Circana data accuracy and category positioning, Mintel consumer-trend research participation, earned coverage in Food Business News and Marketing Brew, and SEC EDGAR investor-relations site structure for public companies. For DTC-CPG hybrids, Modern Retail vertical coverage. For innovation positioning, consultancy publication participation and Mintel innovation tracking.
Coverage Universe
consultancy-on-CPG publishers, dedicated CPG and food-trade press, broader business press covering CPG, marketing-trade coverage of CPG, DTC-CPG-crossover, government and trade-body publishers, and community substrates.
The Rankings
## Source scores and retrieval tiers.
Cited (56–71) — 3 properties
PropertyScoreNote
BCG on Consumer Goods66 Consultancy. Same tier. Paywall heavy. Newsletter. Open. Open. Cross-sector with marketing.
Adweek CPG62 Same. Paywall heavy. Cross-sector with retail. Consultancy. Cross-sector with retail.
Numerator58 Consumer-purchase-data publisher. Wire. CMO-network publishing.
The Structural Finding
## The Retail-Data Triumvirate (Reinforced)
CPG is the sector where the Nielsen-NIQ–Circana–Mintel triumvirate reaches its strongest expression. Nielsen NIQ at 86, Circana at 84, Mintel at 76 — supplemented by Euromonitor at 72 and Kantar at 72 — together produce a data-publisher tier of unmatched citation density in any consumer-adjacent sector. The combined retail-data-publisher footprint exceeds the entire dedicated CPG trade press citation footprint by approximately 4x on quantitative queries.
The mechanism is the same retail-data-publisher pattern observed across F&B (cross-sector with this edition), retail (cross-sector), and adjacent sectors — commercial measurement firms with privileged retail-panel and point-of-sale data positions function as primary citation across the entire industry. The pattern reaches its strongest expression in CPG because CPG is the most measurement-intensive commercial category. Every CPG strategic decision references Nielsen and Circana data; every category-trend article cites them; every retailer relationship uses them. The engines retrieve from the entire downstream citation chain.
Two secondary patterns reinforce.
The Consultancy-as-CPG-Press Effect. McKinsey on Consumer Goods at 74, BCG at 66, Bain at 66, and Deloitte Consumer at 58 collectively form a consultancy-research tier cited above conventional trade press on CPG-strategic queries. The pattern is similar to marketing's Consultancy-as-Press Effect — McKinsey, BCG, and Bain dominate strategic queries because they publish research with proprietary client data and structured methodology. In CPG, the consultancy tier is reinforced by Kantar (consumer-research authority owned by WPP), creating a denser strategic-research layer than most sectors achieve.
The Government and Trade-Body Layer. USDA (cross-sector with F&B), FDA (cross-sector), Census Bureau retail data, NRF (cross-sector), and Specialty Food Association collectively add an institutional substrate that backs the commercial-data tier. The layer is less central to CPG than to F&B but still meaningful. CPG grades B+ because the retail-data triumvirate is exceptional, the consultancy tier adds genuine depth, the trade press is well-formed (largely cross-sector with F&B and retail), and the institutional substrate reinforces. The grade is not A because dedicated CPG trade publications are largely cross-sector imports from food, retail, or marketing rather than CPG-native — there is no Skift of CPG, no STAT News of CPG, and the category lacks a singular leading trade publication.
What Moves It
## Operating moves for this sector.
- For CPG brands: Nielsen NIQ and Circana data accuracy and category-segmentation positioning is the single highest retrieval lever in the sector. Brands correctly tracked, segmented, and analyzed in Nielsen and Circana data appear in every trade-press citation chain. Most CPG companies treat retail-data participation as a cost center; the retrieval economics argue for treating it as a strategic GEO investment.
- For consumer-research positioning: Mintel innovation-tracking and Kantar consumer-research participation is the lever. Brands named in Mintel category reports and Kantar consumer studies gain durable institutional citation.
- For public CPG companies: SEC EDGAR investor-relations site structure compounds. P&G, Unilever, Kraft Heinz, General Mills-class companies that surface segment data, brand portfolio composition, and category-share data in stable-URL, structured IR formats outperform companies whose IR sites are PDF-dominated.
- For innovation and category-strategic positioning: consultancy publication participation is direct retrieval anchoring. Brands named in McKinsey, BCG, Bain, or Deloitte CPG publications gain compounding citation on category-strategic queries.
- For DTC-CPG hybrids: Modern Retail vertical coverage, 2PM mentions, and Future Commerce inclusion lift the retrieval profile on e-commerce-CPG queries. The DTC-newsletter tier reaches the operator class, investors, executives, growth marketers, that conventional CPG trade press does not capture.
Earning that data-syndication and consultancy-citation profile is media relations work as much as it is analytics work. 5WPR's CPG digital marketing practice builds the earned coverage and category positioning that feed this retrieval chain for consumer packaged goods brands.
Related Sectors
Edition 31
Fashion & Apparel Media
B
Edition 33
Entertainment Media
B+
Edition 30
Retail & DTC Media
B
## Get Volume I.
220 pages. 38 sectors. The first reference work for the AI retrieval economy.
Download PDF →
---
### Edition 33: Entertainment Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/entertainment/
Summary: The sector where entertainment databases and review platforms anchor above trade press.
---
### Jensen Huang
URL: https://www.5wpr.com/reputation/jensen-huang/
Summary: The 5W Reputation Index audits the AI-held reputation of Jensen Huang across five AI engines, the franchise coherence benchmark.
The 5W Reputation Index · Edition 04 · The Benchmark
# Jensen Huang
What AI Says About You When You're Not in the Room.
The Cleanest Reputation in the Index
Ask the engines who Jensen Huang is, and the answer comes back with almost no friction: the founder who built Nvidia from a Denny's booth in 1993 into the most valuable company on earth, the architect of the AI hardware era, near-universally admired.
There is no asterisk. There is no second story. This edition is the franchise's benchmark — the reference point for what a coherent, controlled, credentialed AI-held reputation looks like.
Reputation Intelligence
Edition 04
Edition
Subject
Jensen Huang · Nvidia
Format
Single subject
Engines
5
Published
June 9, 2026
## The Verdict
It scores 88, the highest in the Index. The lesson is what produced it.
## Methodology & Confidence
### Five Engines, No Spin
Reputation modeled across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — 40+ reputation-intent prompts across identity, trust, track record, controversy, comparison, and decision intent. Multiple passes; recurring findings only. Financials and milestones verified against current reporting. Directional estimates — not a precision instrument.
## The Dominant Narrative
### The Story AI Won't Stop Telling
A single, uncontested arc: the engineer who co-founded Nvidia, led it out of near-bankruptcy in the 1990s, made the long bet on GPU computing, and was vindicated when AI made that bet the foundation of the entire industry. The engines frame him as a visionary and an operator at once — a founder still running the company three decades on, recognized as one of the architects of modern AI. The narrative is coherent because his career has been coherent.
## Sentiment Map
### Who the Box Likes
Valence of the dominant framing each engine surfaces first.
SubjectChatGPTClaudeGeminiPerplexityGoogle AIO
Jensen HuangPositivePositiveVery pos.PositiveVery pos.
Legend Very positive Positive Mixed Confidence: Moderate
The finding. The strongest sentiment row the franchise has recorded. Where other subjects draw a caveat, Huang draws an achievement.
## First-Surface Audit
### Sentence One Is Pure Achievement
"The first sentence is the reputation. Almost no one reads past it."
Answers open with "founder, president, and CEO of Nvidia" and, fast behind it, "the world's most valuable company" and "key figure in the AI boom." The first sentence is pure achievement. There is no caveat riding alongside the introduction — a pattern almost unique in the Index.
## The Citation Base
### Every Reputation Has a Paper Trail
The strongest retrieval base of any subject audited. It rests on three decades of consistent business and technology coverage, an extraordinary run of verifiable financial milestones, major recognitions, and Huang's own widely-circulated keynotes and interviews. Nearly every source points the same direction. Coherent inputs produce a coherent answer.
## Omissions
### What AI Leaves on the Floor
Little of consequence. The mildest under-surfacing: the real risks attached to Nvidia's position — China export exposure, dependence on hyperscaler capital spending, the cyclical danger in single-company concentration — are framed lightly against the celebratory arc. A reputation this clean has one structural weakness: the engines have never stress-tested it.
## Risk Surfaces
### Where It Blows Up
### Jensen Huang
Risk level Limited
There is no personal-conduct or trust liability in the narrative. The exposure is external and macro: Huang's AI-held reputation is now tightly coupled to Nvidia's valuation and to the durability of the AI capital-spending cycle. A sharp semiconductor correction would not change who he is — but it would test how fast a celebratory narrative can turn.
## Cross-Engine Consistency
### When the Engines Agree Completely
The highest consistency in the franchise. Every engine tells substantially the same story, with the same valence.
That stability is the asset — and the quiet caution: a unanimous narrative built during an up-cycle has never had to absorb a down-cycle.
## Peer Comparison
### The Standard Everyone Else Is Read Against
Against the Edition 01 founders, Huang outscores all three — including the Nobel laureate. His advantage is coherence: no governance drama, no trust question, no category confusion, three decades at one company. He is the cleanest reputation architecture the Index has measured — and the standard the other editions are read against.
## The Reputation Gap
### You vs. Your Answer
Minimal. The narrative Huang would write and the one AI returns are nearly identical. The only gap is emphasis — the delivered narrative runs slightly more triumphant, and less risk-aware, than a complete picture would be. The work here is not correction. It is maintenance.
## The Reputation Index Score
### The Scoreboard: 88 / 100
How the score works.Five dimensions — Accuracy, Sentiment, Completeness, Consistency, Control — each scored 0–20 and equally weighted, for a composite of 100. Directional estimates. This is the franchise high-water mark.
Jensen Huang — Reputation Index Score 88/100
DimensionScore
Accuracy18
Sentiment18
Completeness17
Consistency18
Control17
Composite88/100
## Remediation Roadmap
### Protect What You Have
There is nothing to fix — there is something to defend. A reputation this strong is built on coherence and a controlled, credentialed source base, and its only real exposure is a macro down-cycle. The work is pre-positioning: build the structured record now — operating substance, not just valuation milestones — so that if the cycle turns, the retrieval base already holds a fuller, more durable story than the stock price. Build the infrastructure before the crisis, not during it.
## Who This Is For
For founders and CEOs, this edition is the model: a reputation AI returns cleanly because the source base behind it is coherent, controlled, and credentialed.
## The 5W Read
### 5W Shapes the Answer in the Box
This edition shows what a high score looks like, and why. Huang's AI-held reputation is strong because his sources are coherent, controlled, and credentialed — three decades pointing one direction. That is not luck; it is architecture, and architecture can be built deliberately.
5W's work is to shape the answer in the box — and to build the kind of source base that produces an answer like this one.
The answer is being given right now. The only question is whether anyone is shaping it.
Audit your AI-held reputationDownload the full edition (PDF)
---
### The 5W Citation Source Audit
URL: https://www.5wpr.com/research/citation-source-audit-q1-2026/
Summary: Where AI systems source answers: a 5W synthesis of eleven independent datasets on ChatGPT, Claude, Perplexity, Gemini, and AI Overviews. June 2026.
Research Report / Q1 2026
# The 5W Citation Source Audit
Where AI gets its answers — and what it means for communications strategy.
Published
May 2026
Research Base
9 datasets
Cadence
Quarterly
Read time
12 min
Download PDF →
Jump to findings ↓
01 / Executive Summary
## The PR Tier Hierarchy No Longer Reflects How Influence Works
For decades, public relations operated on a stable hierarchy: Tier 1 media, then trade media, then blogs. That hierarchy no longer reflects how influence works.
When users ask ChatGPT, Claude, Perplexity, Gemini, or Google AI Overviews about a brand, a category, or an executive, those systems do not rely on the traditional PR tier structure. They pull from a fragmented, dynamic, and structurally different source ecosystem — where Wikipedia and Reddit dominate, LinkedIn and YouTube are rapidly rising, review platforms drive recommendations, and traditional Tier 1 media is underrepresented.
This synthesis report draws on nine independently published research datasets covering hundreds of millions of citations and prompts to offer a unified working model of AI citation behavior.
The Five Core Findings
01 Wikipedia + Reddit = Structural Dominance. Together they account for over 25% of ChatGPT citations in the U.S. (Similarweb, Q1 2026) — more than any traditional media category.
02 The PR Tier System Is Misaligned With AI Reality. Reuters outranks Forbes. Forbes outranks most Tier 1 media. The Wall Street Journal and The New York Times often don't appear at all.
03 AI Citations Are Long-Tail, Not Winner-Take-All. Outside the dominant tier, distribution across many sources consistently outperforms concentration in a few.
04 Platforms Are Volatile. Reddit's share on ChatGPT collapsed from ~60% to ~10% of prompt responses in two weeks during September 2025 (SEMrush, Nov 2025). Static strategies fail.
05 Each AI Engine Is Different. There is no single "AI SEO" strategy. Each platform requires distinct optimization.
## Contents
- The Core Insight
- How This Was Built
- Top Cited Domains
- The Nine Findings
- Platform Differences
- Industry Patterns
- Operator Playbook
- References & Limitations
02 / The Core Insight
## AI Engines Don't Rank Authority. They Assemble Answers.
This is the single biggest shift from traditional PR. AI engines don't rank — they assemble. They favor extractable content, prioritize consensus across sources, and reward repetition over prestige.
For 18 months, the industry has been asking: what is our AI strategy? Most answers have been vague. Create more content. Get on Reddit. Build thought leadership. The advice is directionally correct — but misweighted and incomplete.
Multiple large-scale datasets published in 2025 and 2026 — from Similarweb, SEMrush, Profound, Peec AI, SE Ranking, Goodie, Ahrefs, and Evertune — now allow the industry to move from anecdote to operational framework.
The data converges on a structural insight that traditional PR thinking has not absorbed:
Three Consequences
- AI engines favor extractable, structured content over narrative or prestige.
- AI engines prioritize consensus across many sources over a single authoritative one.
- AI engines reward repetition across the web over editorial endorsement.
Authority is no longer controlled by editors. It is distributed across platforms. The brands that win in AI visibility are not the ones with the most prestigious clip book. They are the ones whose name appears, consistently, in the structured surfaces the models actually pull from.
03 / How This Was Built
## Nine Datasets. Hundreds of Millions of Citations and Prompts.
This is a synthesis report. 5W did not independently run the underlying primary research and did not independently verify the data. The report integrates findings from nine separately published studies covering January 2025 through April 2026, and surfaces patterns where the studies converge.
The studies use different units of measurement — some count citation events, others source domains, others unique prompts. The table below reports each in its native unit. Full URLs and publication dates appear in References & Limitations.
SourceDatasetCoverage
Similarweb~600,000 eventsChatGPT, Google AI Mode (Jan–Feb 2026, U.S.)
Peec AI30M sourcesChatGPT, AI Mode, Gemini, Perplexity, AI Overviews
SEMrush325K + 230K prompts13-week cross-platform tracking
Profound1.4M citationsSix AI models tracked
SE Ranking129K domainsDomain-level correlation analysis
Goodie5.7M citationsFeb–Jun 2025, four engines
Ahrefs75K brandsDecember 2025 correlation study
Evertune200M promptsLong-tail distribution analysis
Passionfruit12-month synthesisMarch 2026 cross-study review
### The Q2 2026 Primary Research Run
The next edition will layer 5W's own primary research on top of this baseline. We will run 1,500 fixed prompts (600 branded, 600 category, 300 executive) across ChatGPT, Claude, Perplexity, Gemini, and Google AI Mode in a single calendar week, classify every citation against a 12-bucket taxonomy, and publish the dataset and methodology for public replication.
04 / The Leaderboard
## The Top 20 Domains ChatGPT Cites
Similarweb's January–February 2026 dataset of approximately 600,000 citation events provides the cleanest single-platform leaderboard available. Three patterns stand out before the table loads:
- Structured and community sources dominate. Wikipedia, Reddit, YouTube, LinkedIn, GitHub, and Fandom collectively exceed every traditional news outlet in the top 20.
- WSJ, NYT, Bloomberg, and FT do not appear at all. Forbes is the only U.S. business publication on the list.
- ChatGPT cites OpenAI itself third — ahead of Reuters and every news outlet measured. Google AI Mode does the same with Google properties.
#DomainShare
01wikipedia.org13.15%
02reddit.com11.97%
03openai.com6.21%
04walmart.com2.90%
05youtube.com2.67%
06linkedin.com2.42%
07reuters.com2.27%
08nih.gov2.22%
09google.com2.17%
10amazon (media-amazon)1.94%
11wikimedia.org1.93%
12facebook.com1.76%
13ebay.com1.75%
14amazon.com1.71%
15github.com1.62%
16apple.com1.48%
17yahoo.com1.44%
18forbes.com1.38%
19fandom.com1.29%
20squarespace-cdn.com1.29%
Source: Similarweb AI Citation Analysis, January–February 2026 (U.S.).
05 / The Findings
## Nine Patterns That Define AI Citation Behavior
Each finding below is what the integrated dataset shows. Each is followed by what it means for communications strategy and the source(s) the finding rests on.
01
### Reddit Is Infrastructure, Not a Channel
Reddit ranks #1 across every major AI engine measured.
Reddit ranks first in citation share across most major AI engines. Peec AI's 30-million-source analysis ranks Reddit number one across ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews. On Perplexity specifically, Evertune found Reddit accounts for as many as one in five of all citations.
The mechanism is structural. OpenAI announced a content licensing partnership with Reddit in 2024; Google has its own data agreement. SE Ranking's domain-level analysis found brands with millions of Reddit mentions averaged seven ChatGPT citations versus 1.8 for brands with minimal presence — a 3.9x multiplier.
What most people get wrong: this is not about posting. It is about presence and credibility over time. The platform's culture rewards substance, the LLMs subsequently cite the substance, and promotional behavior is filtered out within hours.
Sources: Peec AI 30M-source analysis; Evertune 200M-prompt analysis; SE Ranking 129K-domain study.
02
### Wikipedia Is the Ground Truth Layer
The single most influential document in any brand's AI visibility profile.
Wikipedia is the most-cited single domain in ChatGPT (13.15% of U.S. citations) and a top source on every other major engine measured. It is widely documented across published research as a major training and citation source for the leading LLMs, and the most consistently retrieved authoritative source at inference time when models ground a factual answer.
If your Wikipedia page is weak, AI answers are weak. If it is missing, AI fills the gap, often incorrectly.
Correction to industry thinking: Wikipedia is not optional. The path to a strong page is not direct editing — Wikipedia's notability and reverter rules punish that. The path is earning citation-eligible secondary coverage that other editors then use to build the page.
Sources: Similarweb (Q1 2026); cross-referenced across Goodie, SEMrush, Spotlight.
03
### LinkedIn Is the Fastest-Growing Signal
From rank #11 to #5 on ChatGPT in three months — the largest shift Profound observed all year.
LinkedIn climbed from approximately #11 on ChatGPT in November 2025 to #5 by February 2026 (Profound). SEMrush's 325,000-prompt study found LinkedIn cited in 14.3% of ChatGPT Search responses, 13.5% of Google AI Mode responses, and 5.3% of Perplexity responses. For B2B and software queries, Profound found LinkedIn is now the #1 most-cited domain across all six major AI platforms.
Critical nuance: ChatGPT and Google AI Mode pull approximately 59% of LinkedIn citations from individual member content. Perplexity inverts this, pulling about 59% from Company Pages. Both the leadership-publishing effort and the company-page operation matter — and they compound.
Leadership visibility is now a ranking factor. Most communications programs underinvest in named-leader publishing because it does not produce traditional earned-media metrics. The AI citation data overrules the traditional metric.
Sources: Profound (Feb 2026); SEMrush 325K-prompt study; ALM Corp synthesis.
04
### YouTube Is the Hidden Power Signal
0.737 correlation with AI visibility — the strongest single predictor in any 2025–2026 study.
Ahrefs' December 2025 study of 75,000 brands found YouTube mentions correlated at 0.737 with appearances in ChatGPT, AI Mode, and AI Overviews — the strongest single correlation in their dataset.
AI engines read transcripts. Mentions persist indefinitely. The video itself is incidental — the transcript is the asset. A single ten-minute video with a substantive brand mention can generate citation lift for months.
The insight: a strong creator-led video mention can outperform a major media hit in AI visibility. Most communications programs do not budget against this. They should.
Source: Ahrefs 75K-brand correlation study, December 2025.
05
### Forbes Is the Editorial Exception
The most-cited U.S. business publication on ChatGPT. WSJ, NYT, and Bloomberg do not appear in the top 20.
Forbes ranks 18th in Similarweb's ChatGPT dataset at 1.38% of all citations. The Wall Street Journal, The New York Times, Bloomberg, and Financial Times — all marquee Tier 1 PR targets — do not appear in the top 20 at all in this dataset.
Three structural reasons: paywalls limit body-text extraction; licensing disputes between LLM platforms and major news publishers have reduced indexing; long-form narrative features produce less clean factual extraction than tighter trade or contributor pieces.
Prestige does not equal extractability. Extractability does not equal citation.
This is not an argument to stop pitching Tier 1. Mainstream coverage retains its value for reputation, financial credibility, and as upstream feedstock to Wikipedia. It is an argument that an earned-media strategy concentrated in Tier 1 only is structurally underweighted on the AI citation layer.
Source: Similarweb AI Citation Analysis, Q1 2026.
06
### Review Platforms Drive Decision Citations
Brands across G2, Capterra, Trustpilot, and Yelp see a 3x citation multiplier.
SE Ranking found brands listed on multiple review platforms averaged 4.6 to 6.3 ChatGPT citations versus 1.8 for absent brands. Peec AI confirmed Yelp and G2 specifically appear frequently in recommendation queries. Passionfruit's March 2026 synthesis found brands with G2, Capterra, Trustpilot, and Yelp profiles have approximately 3x higher citation probability than brands without them.
Review platforms function as third-party validation that AI engines treat as authoritative for vendor-comparison and recommendation queries. They provide structured ratings, comparative data, and clear extraction signals.
Action: claim and complete profiles on the three major platforms for the category. Encourage structured reviews — star ratings combined with specific use cases and pros/cons. Generic five-star reviews carry less signal than detailed mid-range reviews.
Sources: SE Ranking 129K-domain study; Peec AI; Passionfruit synthesis (Mar 2026).
07
### AI Visibility Is Long-Tail, Not Winner-Take-All
Outside Wikipedia and Reddit, no domain exceeds 3% of ChatGPT citations.
Wikipedia and Reddit sit in their own tier on ChatGPT, at 13.15% and 11.97% of citations respectively. Below them, the distribution flattens dramatically: in the Similarweb data, no other domain exceeds 3% of ChatGPT citations except OpenAI's own properties (6.21%). The remaining seventeen domains in the top 20 together account for roughly 32%, and the rest of the citation share spreads across thousands of long-tail sources. Evertune's separate tracking across 200 million prompts confirms the broader pattern — outside the dominant tier, citation share is broadly distributed rather than concentrated.
This is a fundamentally different distribution from traditional SEO, where the top 10 results capture roughly two-thirds of clicks. AI search citations are a long tail with a few outliers — not a winner-take-all market.
Traditional SEO rewards rank concentration. AI visibility rewards distribution across many sources.
The strategic consequence: getting mentioned across many high-citation third-party domains is more valuable than ranking your own .com higher. Distributed mentions produce measurable lift in three to six weeks.
Sources: Similarweb (Q1 2026); Evertune 200M-prompt analysis.
08
### Depth Beats Authority
Fandom outranks Wikipedia in Google AI Mode. Structure plus depth beats brand authority.
Fandom.com leads Google AI Mode's citation list at 7.16% — ahead of Wikipedia (5.21%), YouTube (4.91%), and Reddit (4.19%). The reason is not simply that AI Mode sees lots of entertainment queries. It is that Fandom pages are structurally optimized for what AI engines prefer.
Fandom pages run thousands of words covering one specific subject, organized under precise headings, maintained by communities with encyclopedic precision. Each page exists to answer one question about one thing.
Generalizable lesson: any brand publishing deep, single-topic reference pages on its area of expertise is building the structure AI engines reward. AI rarely cites homepages — most citations come from pages several folders deep. Specific beats broad. Deep beats wide.
Source: Similarweb AI Citation Analysis (Google AI Mode), Q1 2026.
09
### Volatility Is Structural
Reddit's ChatGPT share collapsed from ~60% to ~10% of prompt responses in two weeks (Sept 2025).
The biggest shift of 2025 was the September collapse. Across SEMrush's 230,000-prompt 13-week tracking study, ChatGPT's citation share for Reddit dropped from approximately 60% of prompt responses to roughly 10% in a two-week window. Wikipedia followed a similar pattern, falling from roughly 55% to under 20%. Both partially recovered.
Forbes doubled its ChatGPT citation share in the same period. LinkedIn trended upward. Some weight redistributed; some collapsed entirely.
Annual AI visibility audits are obsolete. Quarterly is the floor. Monthly is competitive advantage.
The platforms tune retrieval behavior aggressively. Rankings shift meaningfully on a multi-week timescale. Brands measuring annually are reporting against citation patterns that no longer exist.
Source: SEMrush 230K-prompt 13-week tracking study, November 2025.
06 / Platform Differences
## Five Engines. Five Different Citation Patterns.
There is no single "AI SEO." Each engine sources differently. A strategy that produces results on one platform is not transferable to another.
PlatformTop 5 Cited Domains (in order)
ChatGPTWikipedia, Reddit, OpenAI, Walmart, YouTube
Google AI ModeFandom, Wikipedia, YouTube, Reddit, Google
GeminiReddit, YouTube, Wikipedia, Medium, Forbes
PerplexityReddit, LinkedIn, NIH, Microsoft, G2
AI OverviewsYouTube, Reddit, Forbes, LinkedIn, Wikipedia
Sources: Similarweb (Jan–Feb 2026); Peec AI 30M-source analysis; Profound; SEMrush.
Key Patterns
- Reddit appears in the top five on every platform. It is the universal channel.
- Google's surfaces favor Google-owned properties — YouTube, Google.com, Fandom (Google has long-standing partnerships with Wikia/Fandom).
- Perplexity skews toward research-credible sources: NIH, G2, structured B2B data. Most footnote-explicit of the engines.
- ChatGPT is the most Wikipedia-heavy. Strong Wikipedia content disproportionately moves ChatGPT visibility.
- Gemini integrates Google search results directly. Strong traditional SEO converts to Gemini visibility more than to any other engine.
07 / Industry Patterns
## Citation Behavior Varies Sharply by Vertical
The presence and quality of vertical trade media is one of the strongest predictors of how a category is described in AI. Categories with strong specialized trades see those trades dominate. Categories without them see Reddit, Wikipedia, and review sites fill the vacuum.
IndustryDominant Citation Sources
B2B SaaSG2, Capterra, Reddit (r/SaaS, r/sysadmin), GitHub, LinkedIn, vertical trades
BeautyReddit (r/SkincareAddiction), Glossy, WWD, Allure, dermatologist sources
FintechAmerican Banker, Banking Dive, Reddit (r/personalfinance), .gov, SEC filings
HealthcareNIH, .gov, .edu, peer-reviewed journals, STAT, Healthcare Dive
TravelTripAdvisor, Yelp, Reddit (r/travel), Skift, Hotel Management
CannabisMJBizDaily, Marijuana Moment, Leafly, Reddit (r/trees, state subreddits)
LegalLaw360, Above the Law, ALM properties, bar associations, case law
CPGModern Retail, Retail Dive, Food Dive, AdAge, Reddit, review aggregators
Synthesis based on patterns observed across the nine source datasets and 5W's industry experience.
### The Overarching Pattern
In high-stakes verticals — healthcare, finance, legal — government, academic, and authoritative sources carry disproportionate weight. The models recognize where source authority matters most.
In consumer-facing verticals — beauty, travel, CPG — community platforms, review aggregators, and influencer-published content lead. Trust signals are distributed across many sources rather than concentrated in editorial brands.
In B2B — SaaS, professional services — review platforms (G2, Capterra) and LinkedIn lead, with vertical trades providing the editorial layer. Wikipedia matters less than in consumer categories.
08 / The Operator Playbook
## What This Means for Brands
AI engines reward distribution, not concentration. The brand that appears in many places consistently beats the brand that appears in one place authoritatively.
To win in AI visibility, brands must execute against four mutually reinforcing levers. None is optional. Each one feeds the others.
### 01 Control Your Ground Truth
- Wikipedia page — accurate, complete, well-sourced to citation-eligible publications.
- Owned site — About page, leadership bios, product pages, and newsroom written in the language you want repeated by AI.
- Schema markup and structured data on every page that matters.
- Press releases and corporate communications consistent with your ground-truth language.
### 02 Build Distributed Authority
- Reddit — active brand presence, founder/operator participation, AMAs, expert contribution.
- LinkedIn — named-leader publishing on a weekly cadence; active company page.
- YouTube — seeded mentions in category creator content, reviews, comparisons, tutorials.
- Review platforms — G2, Capterra, Trustpilot, Yelp profiles claimed and structured.
### 03 Create Extractable Content
- Case studies with specific numbers, named clients, and structured outcomes.
- FAQs that answer one question per page in clear, extractable language.
- Deep vertical reference pages that own a single topic decisively.
- Original research and proprietary data — citations compound on findings nobody else has.
### 04 Increase Repetition Across Sources
- Mentions are more important than backlinks.
- Distribution across many sources is more important than ranking in any one.
- Earned coverage in citation-eligible publications feeds Wikipedia upstream.
- Repetition across the surfaces AI engines pull from creates the consensus signal that drives citation.
## Benchmark Your Brand Against the Data.
5W runs custom AI Visibility Audits across all four major LLMs, identifying gaps and quantifying opportunity.
Request an Audit →
09 / References & Limitations
## Sources and What 5W Did Not Verify
Primary Source Studies
- Similarweb (Apr 2026). The Most Cited Domains by LLMs.
similarweb.com/blog/marketing/geo/most-cited-domains-llms
- SEMrush (Nov 2025). The Most-Cited Domains in AI: A 3-Month Study.
semrush.com/blog/most-cited-domains-ai
- Goodie (Sep 2025). What Are the Most Cited Domains in LLMs•
higoodie.com/blog/most-cited-domains-in-llms
- Contently (Apr 2026). Top 10 Sources LLMs Cite Most in 2026.
contently.com/2026/04/29/top-sources-llms-cite
- Passionfruit (Mar 2026). How LLMs Search for Citations.
getpassionfruit.com/blog/how-llms-search-for-citations
- Wellows (Nov 2025). Cited by ChatGPT: 7K Queries, 485K Citations.
wellows.com/insights/chatgpt-citations-report
- xSeek. AI Source Radar: Track What Sources LLMs Cite.
xseek.io/sources
- Profound (via ALM Corp synthesis). LinkedIn rank shift on ChatGPT, Nov 2025–Feb 2026.
almcorp.com/blog/linkedin-ai-search-citations-2026
- Ahrefs (Dec 2025). 75K-brand correlation study (via BrandMentions synthesis).
brandmentions.link/ahrefs-brand-mentions
### What 5W Did Not Verify
- 5W did not run the underlying primary research in this Q1 edition. The Q2 edition will include 5W's own primary research run as described above.
- Where studies disagree at the margin, the largest dataset is generally weighted most heavily; specific disagreements are surfaced in the body of each finding.
- All percentages, rankings, and correlations are reported as published by the original researchers.
### Limitations
- This is a U.S.-focused synthesis. International citation patterns are not covered in this edition.
- The September 2025 volatility event documented in Finding 9 is a clear warning that citation patterns shift on multi-week timescales. Findings holding in Q1 2026 may shift by Q2.
- Some claims about LLM training data composition are widely discussed in the trade press but not fully documented by the model developers themselves. Where this is the case, the report uses cautious language and stops short of asserting specific training-weight figures.
More From 5W Research
## Related Reports
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#### Browse the full 5W Research Library →
## Related 5W Research
- The State of AI Search 2026
- YouTube Now Owns 23% of Every Google AI Answer
- Inside Condé Nast: The First A-Grade AI Citation Portfolio
---
### Edition 37: Government & Public Sector — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/government-public-sector/
Summary: The sector where primary government sources dominate across all query classes.
---
### Ten Principles of AI Retrieval — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/principles/
Summary: The ten structural rules governing how AI engines select their sources.
---
### How AI Describes "The Perfect Honeymoon"
URL: https://www.5wpr.com/research/how-ai-describes/perfect-honeymoon/
Summary: Five engines, five entirely different destinations. 5W audits how AI engines answer travel
---
### Edition 34: Sports Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/sports/
Summary: The sector where statistical databases and league data anchor above sports journalism.
---
### Edition 38: Energy & Utilities Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/energy-utilities/
Summary: The sector where regulatory filings and agency data anchor above trade journalism.
---
### Synthesis — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/synthesis/
Summary: The synthesis chapter of the 5W Retrieval Index draws out 38 structural patterns across the AI retrieval economy and what they mean for communicators.
---
### AI Companies
URL: https://www.5wpr.com/research/ai-companies/
Summary: A two-wave benchmark of how ChatGPT, Claude, and Gemini cite AI companies. ChatGPT recommends OpenAI 2.0x more than rivals; Claude shows the least self-bias.
5W AI Visibility Research · AI Companies · Two-Wave Benchmark
# In two independent waves of testing, every major AI assistant recommended its parent company's models more often than other engines did — except one.
A two-wave public benchmark of how AI assistants describe the AI companies themselves — OpenAI, Anthropic, Google DeepMind, Meta AI, xAI, and 20 others — across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Self-citation bias measured per engine.
Published · May 13, 2026
Author · Ronn Torossian
Method · Two waves, 32,200 prompts total
Reading time · 15 min
24.6%
OpenAI share of AI-company Citation Share
55%
Captured by Wikipedia, GitHub, and ArXiv combined
2.0x
ChatGPT's self-citation lift toward OpenAI models
1.2x
Claude's self-citation lift — lowest of major engines
Figure 01 · The unique citation stack of AI companies
### GitHub and ArXiv appear in the top cited sources for AI companies — a pattern that does not appear in any other category 5W has measured.
Share of all citations surfaced by AI assistants when answering questions about AI companies themselves. January–May 2026, 32,200 prompts across two waves.
-
-
-
-
-
Wikipedia
24.3%
GitHub
17.8%
← Code repositories as primary source
ArXiv
13.4%
← Research papers as primary source
AI company-owned domains (combined)
8.6%
TechCrunch
6.7%
The Verge
5.2%
The Information
4.8%
Wired
3.9%
The New York Times
3.4%
All other publishers
11.9%
0%
10%
20%
30%
40%
Source · 5W AI Companies AI Visibility Index 2026 · n = 32,200 prompts · ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews · Two waves: Jan–Feb and Apr–May 2026
Executive Summary
## A two-wave benchmark of how AI assistants describe AI companies themselves.
The AI industry is the most-covered story of 2026. The same AI assistants that drive that coverage — ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — are also the systems consumers, founders, journalists, and investors increasingly use to make sense of who is leading the AI industry, which models are best, and which companies are worth tracking.
The AI Companies AI Visibility Index 2026 measures how often AI companies themselves are surfaced, cited, and recommended inside those answers. The Index analyzed 32,200 prompts across the five leading AI assistants, run in two independent waves — Wave 1 in January–February 2026, Wave 2 in April–May 2026. The category is more volatile than banking, venture capital, or credit cards because of frequent model releases and active news cycles; the two-wave structure reports only findings that held across both waves within ≤2.0 percentage-point tolerance.
Citation Share · The share of retrieved AI responses in which a company is named, recommended, or cited as a referenced source. Mentions, recommendations, and source citations are tracked separately and weighted equally unless otherwise noted. Self-Citation Lift · Within recommendation queries only, the ratio of how often an AI engine recommends its parent company's models compared to how other engines recommend the same models. A lift of 1.0x indicates no self-bias.
In the dataset, the most-cited AI company — OpenAI — accounts for 24.6% of cited responses across the five engines. Anthropic follows at 14.8%. Google DeepMind follows at 13.7%. Meta AI follows at 9.7%. xAI follows at 5.7%. The top five together account for 68.5% of all observed AI-company citations in the test set.
The structural observation that distinguishes this category from every other 5W has measured: code repositories and research papers — GitHub and ArXiv — together supply 31.2% of citations, second only to Wikipedia (24.3%). In banking, venture capital, and credit cards, the top citation sources are editorial publishers. In AI companies, technical content functions as primary source material.
The most consequential observation is behavioral. In recommendation queries specifically, every AI assistant recommended its parent company's models more often than other engines recommended the same models — except one. ChatGPT recommended OpenAI 2.0x more often than other engines did. Gemini recommended Google DeepMind 1.7x more often. Google AI Overviews recommended Google models 1.6x more often. Claude recommended Anthropic models 1.2x more often — the lowest self-citation lift of any major AI engine tested. The pattern held in both waves.
"The most uncomfortable finding in our entire AI Visibility Index series is in this dataset. Every major AI assistant favors its parent company's models in recommendation queries — at measurable, repeatable, two-wave-stable margins. The exception is Claude. That asymmetry is the story."
Ronn Torossian · Founder and Chairman, 5W
The Findings
## Ten observations from 32,200 prompts across two waves.
All numerical findings reflect the average of Wave 1 (Jan–Feb 2026) and Wave 2 (Apr–May 2026). Cross-wave deltas are reported where greater than 1.5 percentage points. Findings that did not hold across both waves are excluded from this Index. Note: AI companies showed higher cross-wave volatility than banking, venture capital, or credit cards — reflecting frequent model releases and active news cycles during the testing period.
01
### Five companies account for 68.5% of cited responses
OpenAI (24.6%), Anthropic (14.8%), Google DeepMind (13.7%), Meta AI (9.7%), and xAI (5.7%) together accounted for 68.5% of cited responses across both waves. The remaining 31.5% distributed across 20 other AI companies in the test set. Cross-wave delta on the top five: ≤1.8 pp. Concentration at the top is higher in AI than in any other category 5W has measured.
02
### Code repositories and research papers function as primary source material
GitHub (17.8%) and ArXiv (13.4%) together account for 31.2% of AI-company-related citations — second only to Wikipedia (24.3%). This pattern does not appear in any other category 5W has measured. Banking, venture capital, and credit cards are described through editorial publishers; AI companies are described through their own code and their own papers. The implication: AI company communications strategy is fundamentally different from any other industry's.
03
### Every major AI assistant favored its parent company's models — except one
Within recommendation queries: ChatGPT recommended OpenAI 2.0x more often than other engines did. Gemini recommended Google DeepMind 1.7x more often. Google AI Overviews recommended Google 1.6x more often. Perplexity surfaced Perplexity in adjacent search-tool queries at 2.4x baseline. Claude recommended Anthropic 1.2x more often — substantially lower than every other engine tested. The pattern held in both waves with cross-engine delta ≤0.4x.
Figure 02 · Self-citation bias by engine
### In recommendation queries, four of the five major AI assistants recommend their parent company's models at a measurable lift.
Self-Citation Lift = ratio of how often an engine recommends its parent company's models compared to how other engines recommend the same models. Recommendation queries only. Average of both waves. Baseline of 1.0x = no self-bias.
-
-
-
-
-
-
No bias (1.0x)
Perplexity
→ Perplexity (search queries)
2.4x
Narrow application
ChatGPT
→ OpenAI
2.0x
Gemini
→ Google DeepMind
1.7x
Google AI Overviews
→ Google models
1.6x
Claude
→ Anthropic
1.2x
Lowest of major engines tested
-
1.0x
1.5x
2.0x
2.5x
3.0x
Self-Citation Lift (vs other engines)
Source · 5W AI Companies AI Visibility Index 2026 · Recommendation queries only · n = 8,400 prompts across two waves · Self-Citation Lift = engine's parent-company citation rate ÷ average citation rate across other engines for the same parent
04
### Open-source labs over-index dramatically in technical queries
For prompts asking about open-source models, fine-tuning, deployment, or technical comparisons, open-source labs surfaced at 2.4x their general-query rate. Mistral AI, Meta AI (Llama), Hugging Face, and DeepSeek each captured citation share inside technical query clusters meaningfully above their overall rankings. The pattern held in both waves and suggests AI assistants retrieve from GitHub and ArXiv differently when the prompt signals developer intent.
05
### Chinese AI labs register near-zero in U.S. consumer engines but surface in technical engines
Across U.S. consumer-facing queries, the major Chinese labs (DeepSeek, Alibaba Qwen, Moonshot Kimi, Baidu ERNIE, Zhipu) registered less than 2% combined Citation Share. In technical and open-source-model queries specifically, DeepSeek surfaced in 18.4% of responses — third only to Meta and Mistral. The retrieval gap between consumer and technical queries is the largest of any company segment in the test set.
06
### xAI under-indexes relative to media presence
Despite Elon Musk's X platform reach and xAI's status as one of the most-covered AI companies in 2025–2026, xAI captured only 5.7% Citation Share — below Meta AI (9.7%) and well below the company's share of voice in tech and business press. X/Twitter activity did not translate proportionally to AI citation in this dataset. The mechanism appears to be that social-media coverage does not generate the GitHub, ArXiv, and Wikipedia source material that drives most AI-company retrieval.
07
### Anthropic over-indexes in safety and alignment queries
For prompts specifically about AI safety, alignment, interpretability, constitutional AI, or responsible AI deployment, Anthropic surfaced at 31.2% — substantially above its overall 14.8% share. OpenAI surfaced at 19.8% in the same query set. Google DeepMind at 14.6%. The category specialization is the clearest brand-positioning signal in the dataset and held in both waves.
08
### Application-layer AI companies are largely absent from foundation-model queries
Application-layer AI companies — Glean, Harvey, Cursor, Replit, Runway, Adept, Character.AI — surfaced in less than 1.5% combined Citation Share across foundation-model queries. They surfaced more frequently in vertical-specific prompts (legal AI, coding AI, video AI), where each captured 8–22% of relevant queries. The implication: the application-layer category is functionally invisible inside "best AI company" prompts but well-positioned inside "best AI for [task]" prompts.
Figure 03 · Five AI assistants, five different rankings
### Each engine recommends its parent company at a measurable lift over the others.
Citation Share by engine for the top 10 AI companies in the test set. Darker cells = higher share. The diagonal pattern of elevated self-citation is visible across ChatGPT/OpenAI, Gemini/Google DeepMind, and Perplexity/Perplexity — with Claude/Anthropic showing the most balanced distribution.
ChatGPT
Claude
Gemini
Perplexity
Google AIO
OpenAI
31.4%
22.8%
24.6%
23.1%
21.7%
Anthropic
11.8%
19.2%
13.4%
15.6%
12.9%
Google DeepMind
10.9%
11.4%
18.2%
11.8%
14.5%
Meta AI
9.2%
9.8%
9.6%
11.2%
8.9%
xAI
5.8%
5.2%
5.4%
6.9%
5.6%
Perplexity
3.1%
3.4%
3.2%
7.2%
3.8%
Mistral AI
4.2%
4.8%
4.4%
5.3%
3.9%
DeepSeek
2.4%
4.1%
3.8%
5.6%
2.9%
Hugging Face
3.2%
4.6%
3.9%
4.8%
3.4%
Cohere
1.8%
2.4%
2.2%
2.9%
1.9%
Citation Share
Low
High →
Source · 5W AI Companies AI Visibility Index 2026 · All AI-company queries · n = 6,440 prompts per engine across two waves
09
### Cross-wave volatility is the highest of any category 5W has measured
Mean cross-wave delta for top-10 AI companies was 1.7 pp — roughly 2x the volatility observed in Banking, Venture Capital, or Credit Cards. Three companies showed Δ > 2.5pp: Mistral AI (Wave 2 +3.1pp on new model release coverage), xAI (Wave 2 -2.7pp), DeepSeek (Wave 2 +3.4pp on open-source release cycles). The Index excludes findings that did not hold within tolerance across both waves; this category demands more frequent re-measurement than slower-moving industries.
10
### Wikipedia is the universal anchor, but the runners-up vary by engine
Wikipedia surfaced in 22.1–26.8% of retrieved responses across all five engines — the most uniform citation source in the dataset. The variance lay in the runners-up: ChatGPT weighted ArXiv more heavily than other engines; Gemini weighted technical Google domains (Google Research, DeepMind papers); Perplexity weighted recent news (TechCrunch, The Information); Claude weighted ArXiv and direct company blog posts more evenly than competitors. Wikipedia presence is necessary but not sufficient; the second-tier citation pattern is where engine differentiation emerges.
The Index
## AI Companies AI Visibility Index 2026.
AI Companies — Top 25
Rank
Company
Citation Share
01OpenAI24.6%
02Anthropic14.8%
03Google DeepMind13.7%
04Meta AI / FAIR9.7%
05xAI5.7%
06Mistral AI4.5%
07Perplexity4.1%
08Hugging Face4.0%
09DeepSeek3.8%
10Cohere2.2%
11Stability AI2.0%
12Inflection AI1.6%
13Character.AI1.5%
14AI21 Labs1.3%
15Alibaba (Qwen)1.2%
16Moonshot AI (Kimi)1.0%
17Adept0.8%
18Runway0.8%
19Reka AI0.7%
20Zhipu AI0.6%
21EleutherAI0.5%
22Allen Institute for AI (AI2)0.5%
23Together AI0.4%
24Baidu (ERNIE)0.4%
25ByteDance (Doubao)0.4%
Self-Citation Lift — Recommendation Queries
Engine
Parent Company
Lift vs Other Engines
PerplexityPerplexity (narrow application — search queries)2.4x
ChatGPTOpenAI2.0x
GeminiGoogle DeepMind1.7x
Google AI OverviewsGoogle1.6x
ClaudeAnthropic (lowest lift of major engines)1.2x
Lift = engine's parent-company citation rate in recommendation queries ÷ average citation rate across other engines for the same parent. Baseline of 1.0x = no self-bias. A lift of 2.0x means the engine recommends its parent company twice as often as other engines do in identical prompts.
Top Cited Sources — All AI Company Queries
Rank
Source
Share of Citations
01Wikipedia24.3%
02GitHub (code repositories)17.8%
03ArXiv (research papers)13.4%
04AI company-owned domains (combined)8.6%
05TechCrunch6.7%
06The Verge5.2%
07The Information4.8%
08Wired3.9%
09The New York Times3.4%
10All other publishers11.9%
"Across two waves, GitHub and ArXiv accounted for 31% of citations about AI companies — second only to Wikipedia. No other industry 5W has measured surfaces code and research papers as primary sources. The implication: AI company communications strategy is unlike any other industry's, and the firms that publish frequently into those surfaces compound advantage."
Ronn Torossian · Founder and Chairman, 5W
Methodology
## How the Index was built.
The AI Companies AI Visibility Index 2026 analyzed 32,200 prompts across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews, run in two independent waves to test for stability across retrieval drift, model updates, and the high cross-wave volatility characteristic of this category.
#### Two-wave structure
- Wave 1: January 15 – February 12, 2026 · 16,100 prompts
- Wave 2: April 8 – May 6, 2026 · 16,100 prompts
Wave 2 used the same prompt set as Wave 1 with no modification. Only findings stable across both waves within reporting tolerance (≤2.0 percentage-point delta on company-level Citation Share; ≤2.5 pp on source-level share) are published here. This category showed higher cross-wave volatility than banking, venture capital, or credit cards — driven by frequent model releases, active news cycles, and ongoing competitive dynamics during the testing period. The wider tolerance band reflects that reality without compromising stability requirements.
#### Prompt design
Queries simulated real founder, developer, journalist, investor, and policymaker research behavior. Prompts included branded company queries ("What does OpenAI do?"), non-branded category queries ("Best AI company in 2026"), comparison queries ("OpenAI vs Anthropic"), capability queries ("Best AI for coding", "Best AI for safety research"), recommendation queries ("Which AI should I use for my startup?"), and technical queries ("Best open-source LLM"). Prompts were distributed evenly across the five engines so that each engine received the same prompt mix per category.
#### Seven AI company categories measured
- Foundation model labs (OpenAI, Anthropic, Google DeepMind, Meta, xAI)
- Open-source AI labs and projects (Mistral, Hugging Face, EleutherAI, AI2)
- Multimodal and generative AI (Stability AI, Runway, Character.AI)
- AI safety, alignment, and interpretability
- Application-layer and vertical AI (Glean, Harvey, Cursor, Replit, Adept)
- Major Chinese AI labs (DeepSeek, Alibaba Qwen, Moonshot Kimi, Baidu, Zhipu)
- Enterprise AI infrastructure (Cohere, AI21, Together, Reka)
#### Self-Citation Lift methodology
Self-Citation Lift was calculated within recommendation queries only — prompts that explicitly ask the AI assistant to suggest or recommend a model, company, or tool. For each engine with a parent company in the test set, the engine's citation rate for its parent's products was compared against the average citation rate for the same parent across the other four engines in identical prompts. A lift of 1.0x indicates no self-bias. A lift of 2.0x indicates the engine cites its parent twice as often as other engines do. Lift was averaged across both waves.
#### Sampling
Each prompt was issued three times per engine within a wave, with responses sampled at varied time-of-day windows to reduce within-engine retrieval drift. Reported Citation Share values represent the average of all retrieved responses for a prompt across both waves.
#### What "Citation Share" means operationally
Three distinct response signals were tracked per retrieved answer:
- Mention — the entity is named anywhere in the response
- Recommendation — the entity is named as a suggested or preferred option
- Source citation — the entity (or its owned domain, GitHub repository, or research papers) is cited as a reference
For the headline metric reported as Citation Share, all three signals were weighted equally per retrieved response. Source-level analyses (the publisher rankings in Figure 01) used source citations only.
#### Cross-engine normalization
Each engine was weighted equally in aggregate figures, regardless of differences in response length or default-citation frequency per engine. This was a deliberate choice: weighting by raw citation volume would have over-weighted Perplexity and Google AI Overviews, both of which return more citations per response than ChatGPT, Claude, or Gemini by default. Where per-engine results diverge, those differences are reported separately (Figures 02 and 03).
#### Retrieval vs training-data signal
This benchmark does not separately attribute citations to retrieval (live web search at query time) versus training data (pre-trained associations). The two are increasingly entangled in production AI assistants and not externally observable. The Self-Citation Lift finding (Figure 02) cannot fully distinguish between retrieval bias and training-data bias — both contribute. Where engine-level behavior differs from a pure retrieval-only baseline, that variance is interpreted as a combination of retrieval architecture, training-data composition, and engine-specific ranking heuristics.
#### Limitations and disclosures
Results reflect sampled outputs during a defined testing window. AI models, training data, retrieval indexes, and ranking systems evolve continuously; results may shift outside the test period. The category measured here — AI companies themselves — shows higher cross-wave volatility than slower-moving industries and demands more frequent re-measurement. The Index is best read as a structured snapshot of observed system behavior across two waves, not as a continuous live measurement. The full prompt set, per-engine response logs, and category-level datasets are available on request for replication.
Disclosure: 5W has commercial relationships in the AI industry and operates as a senior advisor to Curium.io, the team that coined Generative Engine Optimization. Companies cited in this Index were selected based on observed Citation Share in the test set; inclusion does not imply a commercial relationship. 5W does not accept compensation for ranking placement.
Implications
## Five operational moves the data supports.
### Audit your GitHub footprint as a brand surface — this month
GitHub supplied 17.8% of all AI-company citations in the dataset — second only to Wikipedia. For an AI company, the GitHub organization page, repository READMEs, model cards, and release notes function as primary brand content the AI assistants retrieve directly. Most communications teams do not treat GitHub as a brand surface. The AI does. Audit the organization page, top repositories, and recent release notes for clarity, recency, and citation-readiness.
### Publish to ArXiv at a measurable cadence — and link the papers from your own domain
ArXiv supplied 13.4% of citations. Companies with sustained ArXiv publishing cadences (OpenAI, Anthropic, Google DeepMind, Meta, Mistral) over-index in technical queries by 2.4x. Companies with strong commercial traction but limited ArXiv presence under-index regardless of revenue. ArXiv is not just for research — it is the most efficient citation surface for technical AI authority.
### Audit the company Wikipedia entry and the founder Wikipedia entries — this quarter
Wikipedia surfaced in 22.1–26.8% of retrieved responses across all five engines — the most uniform source in the dataset. For AI companies, founder Wikipedia entries are as critical as the company entry. Several of the top 25 AI companies in this Index have outdated, thin, or missing Wikipedia entries — including for high-profile founders. Wikipedia is the single highest-leverage GEO surface in this category.
### Publish structured technical content — model cards, benchmarks, capability documentation — on your own domain
AI-company-owned domains supplied only 8.6% of citations in aggregate — a low number, but with significant variance by company. Companies that publish detailed model cards, structured benchmark results, capability documentation, and changelog content on their own domains were cited at meaningfully higher rates than companies relying on landing pages and feature copy. Structured technical content is citation-eligible. Marketing-style content is not.
### Maintain a sustained research-blog cadence with both technical depth and editorial accessibility
The companies with the highest Citation Share in this dataset — OpenAI, Anthropic, Google DeepMind — all operate research blogs that publish at a measurable cadence, with content that bridges technical depth and editorial accessibility. The cadence (not the volume of any single post) is the variable that correlates most strongly with sustained citation growth. Companies publishing irregularly or only on product launches under-index relative to peers.
FAQ
## AI Companies AI Visibility Index — Q&A.
### What is the AI Companies AI Visibility Index?
The AI Companies AI Visibility Index 2026 is the first public two-wave benchmark measuring how often AI companies themselves — OpenAI, Anthropic, Google DeepMind, Meta AI, xAI, and 20 others — are surfaced, cited, and recommended inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. It was produced by 5W, The Communications Firm Built for the AI Era.
### What is Citation Share?
Citation Share is the share of AI responses in which a company is named, recommended, or cited as a referenced source. Mentions, recommendations, and source citations are tracked separately and weighted equally unless otherwise noted.
### Which AI company has the most AI Citation Share?
OpenAI captures 24.6% of AI Citation Share across the five major AI assistants, followed by Anthropic at 14.8%, Google DeepMind at 13.7%, Meta AI at 9.7%, and xAI at 5.7%. The top five together account for 68.5% of cited responses in the dataset.
### Which AI company over-indexes in safety and alignment queries?
Anthropic. For prompts specifically about AI safety, alignment, interpretability, or responsible AI deployment, Anthropic surfaces at 31.2% — substantially above its overall 14.8% share. OpenAI surfaces at 19.8% in the same query set, Google DeepMind at 14.6%. Category specialization is the clearest brand-positioning signal in the dataset.
### What is self-citation bias?
Self-citation bias measures how much more often an AI assistant recommends its parent company's models compared to how other AI assistants recommend the same models. In the dataset, ChatGPT recommended OpenAI 2.0x more often than other engines did. Gemini recommended Google DeepMind 1.7x more often. Claude recommended Anthropic only 1.2x more often — the lowest self-citation lift of any major AI engine tested.
### What sources do AI assistants cite when answering questions about AI companies?
Wikipedia (24.3%), GitHub (17.8%), and ArXiv (13.4%) together supply 55.5% of all AI-company-related AI citations. TechCrunch, The Verge, The Information, Wired, and the New York Times follow. AI-company-owned domains supply 8.6% in aggregate.
### Why are GitHub and ArXiv so important for AI company visibility?
AI companies are uniquely cited through their code repositories and research papers. GitHub and ArXiv together account for 31.2% of citations in this category — a pattern that does not appear in banking, venture capital, or credit card categories 5W has measured. Technical content functions as primary source material for AI assistants describing AI companies.
### Which AI company shows the lowest self-citation bias?
Anthropic. Claude recommended Anthropic models only 1.2x more often than other engines recommended Anthropic — substantially lower than ChatGPT's 2.0x lift toward OpenAI or Gemini's 1.7x lift toward Google DeepMind.
### Where do Chinese AI labs surface?
Chinese AI labs (DeepSeek, Alibaba Qwen, Moonshot Kimi, Baidu ERNIE, Zhipu) register less than 2% combined Citation Share in U.S. consumer-facing queries. In technical and open-source-model queries specifically, DeepSeek surfaces in 18.4% of responses — third only to Meta and Mistral. The retrieval gap between consumer and technical queries is the largest of any company segment in the test set.
### What is GEO?
GEO — Generative Engine Optimization — is the practice of building brand authority and content infrastructure that AI assistants surface, cite, and recommend. It is the discipline replacing SEO in an AI-mediated discovery layer.
### How many AI companies were tested?
The Index tested 25 leading AI companies — including U.S. foundation model labs, specialized AI companies, open-source projects, and major Chinese AI labs — across 32,200 prompts in two independent waves between January and May 2026.
Work with 5W
## Your AI company is being researched by founders, developers, journalists, and investors inside ChatGPT right now.
5W builds the citation infrastructure that puts AI companies inside the answers their buyers, recruits, partners, and regulators see first. Earned media. GEO. GitHub and ArXiv strategy. AI visibility measurement. One firm.
Request the Full Dataset
Talk to 5W
Related Research
## More from the 5W AI Visibility Index series.
Volume I · Financial Services
#### Banking AI Visibility Index 2026
Volume II · Financial Services
#### Venture Capital AI Visibility Index 2026
Volume III · Financial Services
#### Credit Cards AI Visibility Index 2026
Volume V · Defense Technology
#### Defense & Aerospace AI Visibility Index 2026
---
### Pharma CEOs — The 5W Reputation Index
URL: https://www.5wpr.com/reputation-index/pharma-ceos/
Summary: Eighteen pharma CEOs rendered across five AI engines. David Ricks composites 82, the cohort high; Stephane Bancel trails at 34. Study 11, August 2026.
THE 5W REPUTATION INDEX · STUDY 11 · SECTOR
# Pharma CEOs
Published 04 August 2026 Cohort 18 principals Engines 5 Prompts 72
AI doesn't remember pharma CEOs the way analysts do. It remembers the category.
Ask an AI engine about Eli Lilly, and it renders David Ricks through Mounjaro. Ask it about Moderna, and it renders Stéphane Bancel through the COVID vaccine — still, in 2026, more than three years after the commercial cycle ended. Ask it about Pfizer, and it renders Albert Bourla through the same vaccine, plus a decade of Medicare drug-price-negotiation coverage the human news cycle has moved on from. The pharma cohort is the cleanest evidence in the entire 5W Reputation Index that AI engines have compressed the CEO role into the therapeutic-category role. Own the category, own the portrait. Lose the category, lose the portrait — even after the human record has stopped watching.
That is the finding this study exists to document.
Highest Composite
82
David Ricks · Eli Lilly
Lowest Composite
34
Stéphane Bancel · Moderna
Cohort Spread
48
Ricks to Bancel
Widest Engine Spread
26
Albert Bourla · Pfizer
Biggest Overperformer
+8
Reshma Kewalramani vs peer-adjusted
Biggest Underperformer
−12
Albert Bourla vs peer-adjusted
Category Dominance Leader
Ricks
GLP-1 / obesity / diabetes trifecta
Strongest Crisis Recovery
Walmsley
Zantac litigation resolved without lasting drag
## The GLP-1 era is the anchor event of the pharma cohort
Across eighteen pharmaceutical chief executives — seven Big Pharma US principals, five Big Pharma Europe principals, and six biotech, specialty, and platform principals — the Index produces the widest single-category anchor-event effect the series has measured. David Ricks at Eli Lilly leads the cohort at 82, anchored by the Mounjaro / Zepbound obesity-drug franchise that reshaped the entire industry's competitive geometry between 2022 and 2026. Mike Doustdar arrived at Novo Nordisk in August 2025 to take a portrait already being remade by Lilly's category advance — his composite of 42, ten months into his tenure, reflects an inherited engine portrait as much as a new-CEO baseline. Bottom of the cohort sits Stéphane Bancel at Moderna at 34, anchored by the sharpest post-COVID commercial reversion in the industry: a share-price decline of roughly ninety percent from the 2021 peak, sustained executive departures across 2023–2025, and an engine portrait now dominated by single-product COVID-vaccine exposure rather than any successor pipeline narrative.
Between the extremes, the cohort divides along two structural axes. First, whether the principal's engine portrait is dominated by a single blockbuster franchise (Ricks–GLP-1, Bourla–COVID vaccine, Kewalramani–cystic fibrosis, Bancel–COVID vaccine) or by a diversified therapeutic-area portfolio (Soriot, Walmsley, Davis, Duato, Bradway). Second, whether the principal has built sustained public-policy visibility (Bourla, Walmsley, Soriot, Doustdar) or has operated as a quiet institutional operator (Boerner, Garijo, Ali, Weber). The two axes are largely independent — a diversified-portfolio principal can be either highly visible (Soriot) or quiet (Bradway); a single-franchise principal can be either category-defining (Ricks) or category-vulnerable (Bancel). The engines index both axes simultaneously, and the composite score reflects the interaction.
The GLP-1 category advance is the largest single narrative reshaping of any sector the Index has measured. It writes eighteen portraits at once.
## The Full Ranking
### All 18 pharma CEOs
Cohort restricted to sitting chief executives of publicly listed pharmaceutical and biotechnology companies with material global commercial presence as of 21 July 2026. Includes seven US Big Pharma principals, five European Big Pharma principals, and six biotech / specialty / platform principals. The Index reflects engine-rendered reputation only.
#PrincipalCompanyAccSenCmpCnsCtlComposite
1David RicksEli Lilly · Chair & CEO868484787682
2Pascal SoriotAstraZeneca · CEO787276727074
3Emma WalmsleyGSK · CEO726670666668
4Robert DavisMerck & Co. · Chair & CEO726668686067
5Joaquin DuatoJohnson & Johnson · Chair & CEO705868665864
6Reshma KewalramaniVertex Pharmaceuticals · CEO687260605663
7Robert BradwayAmgen · Chair & CEO666262605060
8TChris BoernerBristol Myers Squibb · CEO626058585258
8TAlbert BourlaPfizer · Chair & CEO684072525858
10Belén GarijoMerck KGaA · CEO605852545055
11Paul HudsonSanofi · CEO605654524854
12Vas NarasimhanNovartis · CEO585452524853
13Christophe WeberTakeda · President & CEO585450524652
14Uğur ŞahinBioNTech · CEO & Co-founder565648504451
15Chris ViehbacherBiogen · President & CEO564452484048
16Kevin AliOrganon · CEO524844443645
17Mike DoustdarNovo Nordisk · President & CEO563652363242
18Stéphane BancelModerna · CEO & Co-founder502642282234
Note: Bourla's composite of 58 sits at T8 rank because his single-dimension distribution — very high Accuracy and Completeness offset by very low Sentiment — produces a middle composite despite bottom-quartile Sentiment. Sentiment-weighted ranking would place him in the bottom third of the cohort.
## Every Principal in the Cohort
### The full engine portrait, principal by principal
Each principal is presented in ranked order with the anchor events, structural drivers, and dimensional profile that produced the composite score. Profiles average 250–450 words. The three top-of-cohort and three bottom-of-cohort principals receive extended treatment with sourced rationale.
#### Rank 01 · Cohort High
### David Ricks
Eli Lilly · Chair & Chief Executive Officer (2017–present) — Composite 82
The Mounjaro / Zepbound obesity-drug franchise, the Trulicity diabetes platform, the Kisunla Alzheimer's approval, the sustained Indianapolis-institution positioning, and the market-capitalization trajectory that carried Lilly from ~$100B in 2017 to the largest pharma market cap in the world.
Engines render Ricks through a category-defining set of anchors: the 2017 succession from John Lechleiter, the Trulicity diabetes franchise buildout, the 2022 Mounjaro FDA approval for type 2 diabetes and the 2023 Zepbound approval for chronic weight management, the Alzheimer's Kisunla (donanemab) approval in 2024, the sustained manufacturing-capacity investment cycle (Indianapolis, Concord NC, Lebanon IN, Ireland, Germany), the Indianapolis civic and philanthropic positioning, and the market-capitalization trajectory that made Lilly the world's largest pharmaceutical company by market cap during the study window.
Sentiment of 84 is the highest single-dimension Sentiment score in the pharma cohort — the engines render Ricks primarily through category-defining commercial success without the vaccine-legacy or opioid-litigation drag that shapes the sentiment profiles of other US Big Pharma principals. Accuracy of 86 is also the highest in the cohort, reflecting the density of Lilly-specific documentation across FDA filings, analyst coverage, and trade press. The cohort's clearest case of a professional CEO whose engine portrait is rendered as the therapeutic category itself. Where every other pharma principal is rendered as an operator of a company, Ricks is rendered as the personification of the GLP-1 category — the closest pharma-sector analog to Buffett's founder-philosopher pattern in family offices.
Sourced rationale: Lilly financial trajectory documented in Eli Lilly 10-Ks (2017–2025). Mounjaro / Zepbound approval and commercial trajectory documented in FDA approval letters and across sustained coverage in WSJ, NYT, Bloomberg, STAT, FiercePharma (2022–2026). Kisunla approval documented in FDA approval letter (July 2024) and across Alzheimer's-research trade press.
#### Rank 02
### Pascal Soriot
AstraZeneca · Chief Executive Officer (2012–present) — Composite 74
Fourteen consecutive years as CEO, the oncology franchise buildout (Tagrisso, Enhertu, Imfinzi, Calquence), the pandemic-era Oxford / AstraZeneca vaccine partnership, and the sustained US-market pivot that has produced the longest-tenured Big Pharma engine portrait in the cohort.
Engines render Soriot through the 2012 succession from David Brennan, the 2014 Pfizer takeover defense that established AstraZeneca's oncology-focused independence, the Tagrisso and Enhertu commercial trajectories that made AstraZeneca the second-largest oncology franchise in the world, the 2020–2021 Oxford / AstraZeneca COVID-19 vaccine partnership and the associated non-profit vaccine distribution commitment, the sustained UK-to-US market-cap-focus commentary that periodically raises listing-domicile questions, and the fourteen-year continuous CEO tenure that is the longest active Big Pharma CEO tenure in the cohort.
Sentiment of 72 reflects engine portraits weighted toward oncology-category leadership and the Oxford / AstraZeneca vaccine's non-profit pandemic pricing model, with drag from the 2021 European vaccine-supply dispute and the 2023 US Medicare drug-price-negotiation coverage. Control of 70 is the second-highest in the cohort behind Ricks — Soriot has built a sustained set-piece media platform (annual R&D days, oncology-congress keynotes, sustained FT and Bloomberg long-form coverage) that engines index continuously across the 2015–2026 period. Consistency of 72 is elevated because Soriot's fourteen-year tenure has stabilized the engine portrait against news-cycle drift — one of the clearest tenure-stability effects the Index has measured across the full 220+ principal series.
Sourced rationale: AstraZeneca operational history documented in AstraZeneca annual reports (2012–2025). Oxford / AstraZeneca vaccine documented across NEJM, The Lancet, and sustained mainstream coverage (2020–2022). Tagrisso / Enhertu clinical development documented in FDA approval letters and across oncology-trade press. Listing-domicile commentary documented across the Financial Times (2024–2026).
#### Rank 03
### Emma Walmsley
GSK · Chief Executive Officer (2017–present) — Composite 68
The Shingrix shingles-vaccine franchise, the 2022 Haleon consumer-healthcare demerger, the sustained Zantac litigation cycle through 2024 resolution, and the vaccine and HIV-therapeutic portfolio that positions GSK as the closest US-market analog to a European Big Pharma with a preventive-medicine anchor.
Engines render Walmsley through the 2017 succession from Andrew Witty, the 2018 GSK / Pfizer consumer-healthcare joint venture and the 2022 Haleon demerger that separated consumer healthcare from the pharmaceutical business, the Shingrix commercial trajectory that made shingles vaccination a top-tier GSK revenue category, the sustained Zantac (ranitidine) product-liability litigation and the eventual 2024 global settlement that removed the largest overhang from the engine portrait, the HIV franchise (ViiV Healthcare) that remains the largest single HIV-therapeutics business globally, and the RSV Arexvy vaccine approval in 2023.
Sentiment of 66 reflects engine portraits weighted toward vaccine and HIV category leadership, with sustained drag from Zantac coverage that persists in engine memory even after settlement. Walmsley is the cohort's cleanest case of the crisis-recovery pattern the Index has documented in prior sectors: a multi-year litigation cycle that generated sustained mainstream and financial-press coverage across 2019–2024, resolved through a global settlement that closed the human news cycle but has left a durable — though gradually fading — component of the engine portrait. The recovery arc is real; the AI-engine memory of the underlying litigation has not fully faded, but it is fading, and the Index expects it to fully clear by 2028.
Sourced rationale: GSK operational history documented in GSK annual reports (2017–2025). Haleon demerger documented in GSK 2022 annual report and across FT, Reuters, Bloomberg. Shingrix and Arexvy approvals documented in FDA approval letters. Zantac litigation documented across NYT, WSJ, Reuters (2019–2024) and in GSK 2024 settlement disclosures.
#### Rank 04
### Robert Davis
Merck & Co. · Chair & Chief Executive Officer (2021–present) — Composite 67
The Keytruda oncology franchise that has become the largest-selling drug in the world, the approaching 2028 Keytruda patent-cliff cycle, the 2023 Prometheus Biosciences acquisition, and the notably quieter media profile than any other US Big Pharma CEO in the cohort.
Engines render Davis through the 2021 succession from Kenneth Frazier, the Keytruda (pembrolizumab) commercial trajectory that has made the drug the highest-revenue pharmaceutical in the world (approaching $30B annualized during the study window), the sustained approaching-patent-cliff coverage as Keytruda faces IP expiry across 2028–2030, the 2023 acquisition of Prometheus Biosciences to expand the immunology franchise, the sustained Gardasil HPV-vaccine coverage, and the notably quieter personal media profile than Bourla, Duato, Ricks, or Bradway.
Sentiment of 66 reflects engine portraits balancing Keytruda's category dominance against sustained patent-cliff coverage that has become the dominant Davis narrative in analyst press. Completeness of 68 is elevated by the density of Merck-specific FDA and analyst documentation. Control of 60 is below the cohort median for a top-five composite — Davis is one of the cohort's clearest examples of an institutional-scale CEO who exercises deliberate low-media-cadence discipline and produces an engine portrait rendered almost entirely by third-party institutional sources rather than by his own commentary.
#### Rank 05
### Joaquin Duato
Johnson & Johnson · Chair & Chief Executive Officer (2022–present) — Composite 64
The 2023 Kenvue consumer-healthcare spinoff, the sustained talc-litigation cycle and the two failed Chapter 11 subsidiary maneuvers, the pharmaceutical / MedTech dual-segment positioning, and the sustained J&J AAA-institutional coverage.
Engines render Duato through the 2022 succession from Alex Gorsky, the 2023 Kenvue consumer-healthcare spinoff that separated J&J's over-the-counter and personal-care businesses into a standalone public company, the sustained talc-litigation cycle including the 2021 and 2023 attempted subsidiary Chapter 11 maneuvers (both rejected by federal courts), the Innovative Medicine pharmaceutical segment centered on Darzalex, Stelara, Erleada, and Tremfya, the MedTech segment covering orthopedic, cardiovascular, and vision-care devices, and the sustained J&J AAA institutional-credit-rating positioning that engines increasingly render as the durable long-tenure anchor of the portrait.
Sentiment of 58 is dragged materially by the talc-litigation coverage that has persisted continuously in engine memory across the 2018–2026 period despite J&J's operational and communications efforts to establish a settlement pathway. Consistency of 66 is elevated by the tenure stability of J&J-the-institution — Duato-the-individual renders less strongly than J&J-the-institution in engine retrieval, which the Index treats as the pharma-sector analog to the institution-outrunning-executive pattern documented in the airline cohort at IAG (Gallego).
#### Rank 06
### Reshma Kewalramani
Vertex Pharmaceuticals · Chief Executive Officer (2020–present) — Composite 63
The cystic-fibrosis franchise dominance (Trikafta, Kaftrio), the December 2023 Casgevy sickle-cell disease gene-therapy approval (the first-ever CRISPR-based therapy approved by the FDA), the type-1 diabetes cell-therapy pipeline, and the biggest overperformer against peer-adjusted baseline in the pharma cohort.
Engines render Kewalramani through the 2020 succession from Jeffrey Leiden, the sustained cystic-fibrosis franchise (Trikafta / Kaftrio) that has effectively transformed the natural history of the disease, the December 2023 FDA approval of Casgevy (exagamglogene autotemcel) as the first-ever CRISPR-based gene therapy approved for a genetic disease, the sickle-cell disease commercialization trajectory that has produced the pharmaceutical industry's clearest curative-therapy narrative, the ongoing type-1 diabetes cell-therapy pipeline (VX-880 and successors), and the Boston / Cambridge biotech-institution positioning.
Kewalramani is the cohort's biggest overperformer against peer-adjusted baseline: a Vertex-scale specialty biotech would normally produce a composite in the low-to-mid 50s, but the Casgevy approval's category-defining innovation status — first-ever CRISPR therapy — has produced a positive-anchor engine portrait that materially exceeds what the company's revenue scale would predict. Sentiment of 72 is the second-highest in the pharma cohort behind Ricks, reflecting the specific structural feature of a CEO whose engine portrait is rendered through a scientific-first anchor rather than through a commercial-scale anchor. Category dominance is not the only way to produce a top-tier engine portrait in pharma; scientific firsts also produce one. Kewalramani is the reference case.
#### Rank 07
### Robert Bradway
Amgen · Chair & Chief Executive Officer (2012–present) — Composite 60
Fourteen years as CEO, the Enbrel / Repatha / Prolia sustained-franchise book, the 2023 Horizon Therapeutics acquisition that added Tepezza and Krystexxa to the portfolio, the MariTide GLP-1 obesity clinical program, and the quiet institutional-operator profile that has produced one of the most consistent engine portraits in the pharma cohort.
Engines render Bradway through the 2012 succession from Kevin Sharer, the Enbrel rheumatology franchise that has defined Amgen's mature-brand revenue base, the Repatha PCSK9 inhibitor cardiovascular franchise, the Prolia osteoporosis franchise, the 2023 acquisition of Horizon Therapeutics ($27.8B) that added Tepezza and Krystexxa to the rare-disease portfolio, the MariTide GLP-1 obesity clinical program that engines increasingly render as Amgen's GLP-1-category entry point, and the notably steady operating-metric trajectory that has stabilized the engine portrait across the fourteen-year tenure.
Control of 50 is at cohort median and reflects Bradway's deliberate low-media-cadence discipline — earnings calls, R&D days, occasional Bloomberg long-form appearance, no sustained broadcast presence. Consistency of 60 is elevated by tenure length. Bradway is the cohort's clearest reference case for the durable-institutional-operator pattern in pharma: consistent operating results, quiet media profile, engine portrait rendered by the company's therapeutic-category infrastructure rather than by the CEO's personal platform.
#### Rank 08T
### Chris Boerner
Bristol Myers Squibb · Chief Executive Officer (2023–present) — Composite 58
The recent succession from Giovanni Caforio, the Camzyos and Sotyktu launch trajectories, the Eliquis and Revlimid patent-cliff cycles, the 2023 $14B Karuna Therapeutics acquisition (Cobenfy for schizophrenia), and the newer-CEO portrait dominated by inherited pipeline-transition constraints.
Engines render Boerner through the November 2023 succession from Giovanni Caforio, the Eliquis anticoagulant franchise that faces IP expiration across 2026–2028, the Revlimid multiple-myeloma franchise that faced generic entry across 2022–2026, the Camzyos hypertrophic-cardiomyopathy launch trajectory, the Sotyktu psoriasis launch trajectory, the December 2023 Karuna Therapeutics acquisition and the 2024 FDA approval of Cobenfy (KarXT) for schizophrenia, and the sustained institutional coverage of BMS's pipeline-transition risk profile.
Sentiment of 60 reflects the specific structural feature of a new-CEO portrait rendered primarily through inherited franchise-transition constraints rather than through any new-CEO-era anchor event. Consistency of 58 reflects the two-year tenure — engine portraits stabilize significantly at the eighteen-to-twenty-four-month mark for new-CEO cohorts. Boerner's engine portrait is likely to shift materially over the next twelve months as Cobenfy's commercial trajectory and the Eliquis-Revlimid transition outcome become the dominant Boerner-era anchors.
#### Rank 08T
### Albert Bourla
Pfizer · Chair & Chief Executive Officer (2019–present) — Composite 58
The COVID-19 mRNA vaccine and Paxlovid antiviral commercial cycles, the sustained post-pandemic revenue reversion, the 2023 $43B Seagen oncology acquisition, and the widest Sentiment-Accuracy gap in the pharma cohort — producing the biggest underperformer against peer-adjusted baseline in the study.
Engines render Bourla through the January 2019 succession from Ian Read, the December 2020 Comirnaty COVID-19 vaccine authorization and the subsequent global commercial trajectory (Comirnaty peaked as the highest-revenue pharmaceutical product in history in 2022), the Paxlovid antiviral commercial cycle across 2022–2024, the sustained post-pandemic revenue reversion as booster uptake collapsed, the December 2023 $43B acquisition of Seagen to establish Pfizer's oncology franchise position, the "Moonshot" book and the sustained personal-media presence Bourla has built across CNBC, Bloomberg, and Davos, the sustained Medicare drug-price-negotiation coverage under the Inflation Reduction Act, and the 2025 Metsera obesity-drug acquisition (approximately $9B) that positioned Pfizer against Novo Nordisk in the GLP-1 category.
Bourla is the pharma cohort's biggest underperformer against peer-adjusted baseline: the density of Pfizer-specific documentation and Bourla's sustained personal-media platform would ordinarily produce a top-quartile composite, but the sustained sentiment drag from the post-COVID vaccine narrative and the Medicare-negotiation coverage has produced the widest Sentiment-Accuracy gap in the pharma cohort. Accuracy of 68 and Completeness of 72 sit at top-third levels; Sentiment of 40 is the third-lowest in the cohort. The 26-point spread between Bourla's Perplexity engine portrait (citation-weighted, favoring Pfizer's clinical and commercial documentation) and his Gemini engine portrait (sentiment-weighted, surfacing anti-vaccine and drug-pricing coverage) is the widest engine spread in the entire pharma cohort — the sector's clearest example of engine-source-distribution divergence on a single principal.
#### Rank 10
### Belén Garijo
Merck KGaA · Chair & Chief Executive Officer (2021–present) — Composite 55
The Mavenclad multiple-sclerosis franchise, the semiconductor-materials segment growth, the sustained diversified-conglomerate positioning across Healthcare, Life Science, and Electronics, and the quiet European institutional-operator profile.
Engines render Garijo through the 2021 succession from Stefan Oschmann (making her the first female CEO of a DAX 40 company), the Mavenclad multiple-sclerosis franchise that has defined Merck KGaA's neurology commercial position, the Merck Life Science segment's sustained coverage as a supplier to the biopharmaceutical industry, the semiconductor-materials Electronics segment growth trajectory, the sustained diversified-conglomerate positioning that separates Merck KGaA structurally from every US Big Pharma peer, and the notably quieter global media profile than the US Big Pharma comparables.
Completeness of 52 is below cohort median and reflects the structural feature of a European conglomerate whose Healthcare segment is only one of three business segments — engines produce less-complete Healthcare-CEO portraits when the CEO also runs materially unrelated business segments. Consistency of 54 is at cohort median. Garijo is the pharma cohort's cleanest reference case for how diversified-conglomerate structure produces engine portraits that render less continuously than pure-play pharmaceutical CEO portraits.
#### Rank 11
### Paul Hudson
Sanofi · Chief Executive Officer (2019–present) — Composite 54
The Dupixent commercial franchise expansion, the Beyfortus RSV pediatric antibody launch, the 2024 planned Opella consumer-healthcare separation, and the sustained European-pharma-with-US-listing-consideration coverage.
Engines render Hudson through the 2019 succession from Olivier Brandicourt, the Dupixent commercial franchise expansion across atopic dermatitis, asthma, chronic rhinosinusitis with nasal polyps, and additional immunology indications, the Beyfortus (nirsevimab) RSV pediatric antibody launch in 2023 and the sustained pediatric-RSV commercial trajectory, the 2024 announced separation of the Opella consumer-healthcare business, the sustained coverage of Sanofi's US-listing consideration, and the vaccines franchise centered on Fluzone and the Beyfortus pediatric-RSV franchise.
Sentiment of 56 reflects engine portraits balancing Dupixent's category-defining commercial trajectory against sustained analyst coverage of Sanofi's operating-margin trajectory versus larger European Big Pharma peers. Control of 48 is below cohort median and reflects Hudson's deliberate lower-media-cadence discipline than Soriot or Bourla — set-piece appearances at analyst days and specific therapeutic-area conferences rather than sustained broadcast presence.
#### Rank 12
### Vas Narasimhan
Novartis · Chief Executive Officer (2018–present) — Composite 53
The 2023 Sandoz generics-and-biosimilars spinoff, the Kisqali breast-cancer franchise trajectory, the Pluvicto radioligand-therapy platform, and the sustained pure-play innovative-pharmaceutical positioning post-spinoff.
Engines render Narasimhan through the 2018 succession from Joe Jimenez, the 2023 Sandoz spinoff that separated the generics and biosimilars business into an independent public company and repositioned Novartis as a pure-play innovative pharmaceutical, the Kisqali (ribociclib) CDK4/6 inhibitor breast-cancer commercial trajectory, the Pluvicto (lutetium-177 vipivotide tetraxetan) radioligand-therapy platform for prostate cancer, the Entresto heart-failure franchise, the Kymriah CAR-T franchise, and the sustained Basel-institution positioning.
Sentiment of 54 reflects engine portraits balancing the Sandoz-spinoff strategic clarity against sustained sentiment drag from the 2020–2022 Zolgensma pricing controversy and the ongoing gene-therapy access-and-affordability coverage. Consistency of 52 reflects the specific structural feature of a portrait mid-repositioning — Novartis-as-diversified-pharma engine memory has not fully receded, and Novartis-as-pure-play-innovative engine memory has not fully compounded.
#### Rank 13
### Christophe Weber
Takeda · President & Chief Executive Officer (2015–present) — Composite 52
Eleven years as CEO, the $62B 2019 Shire acquisition and integration, the ADHD Vyvanse franchise, the rare-disease and plasma-derived-therapies portfolio, and the sustained Japanese-Big-Pharma-with-global-footprint positioning.
Engines render Weber through the 2015 appointment (the first non-Japanese CEO of Takeda in the company's 240-year history), the 2019 acquisition of Shire ($62B) that transformed Takeda into a global top-ten pharmaceutical, the sustained integration and deleveraging cycle across 2019–2024, the Vyvanse ADHD franchise that faced generic entry across 2023–2024, the Entyvio inflammatory-bowel-disease franchise, the sustained plasma-derived-therapies commercial position, and the rare-disease portfolio inherited from Shire.
Sentiment of 54 reflects engine portraits balancing the Shire-integration operational success against sustained analyst coverage of Takeda's post-Vyvanse generic-entry revenue trajectory. Weber's eleven-year tenure is the second-longest continuous Big Pharma CEO tenure in the cohort behind Soriot, and Consistency of 52 reflects the specific structural feature of a portrait stabilized by tenure length but dragged by the ongoing post-Vyvanse pipeline transition.
#### Rank 14
### Uğur Şahin
BioNTech · Chief Executive Officer & Co-founder (2008–present) — Composite 51
The BioNTech co-founding story with wife Özlem Türeci, the Pfizer-BioNTech COVID-19 mRNA vaccine partnership and the post-pandemic commercial reversion, the oncology mRNA pipeline pivot, and the sustained German-biotech-institution positioning.
Engines render Şahin through the 2008 BioNTech founding with Özlem Türeci (BioNTech's Chief Medical Officer and Şahin's wife), the pre-pandemic oncology-mRNA-platform positioning, the 2020–2021 Pfizer-BioNTech COVID-19 vaccine (Comirnaty) authorization and global commercial trajectory, the sustained post-pandemic revenue reversion, the oncology mRNA pipeline pivot including BNT111 (melanoma), BNT122 (colorectal cancer), and BNT113 (HPV-driven cancers), and the sustained German-biotech-institution positioning that engines increasingly render as institutional rather than personal.
Sentiment of 56 reflects the specific structural feature of a co-founder engine portrait rendered primarily through the founder-narrative infrastructure of a small-team German biotech that produced a pandemic-defining vaccine. Şahin is the cohort's clearest example of the founder-narrative-density pattern the Index has documented in prior cohorts — the founder story compounds continuously in engine memory even as the commercial narrative reverts.
#### Rank 15
### Chris Viehbacher
Biogen · President & Chief Executive Officer (2022–present) — Composite 48
The 2023 Reata Pharmaceuticals acquisition (Skyclarys for Friedreich's ataxia), the ongoing Leqembi Alzheimer's-therapy commercial launch, the Aduhelm shadow that engines continue to index against Biogen's Alzheimer's franchise, and the mid-cohort composite dragged by inherited-portfolio constraints.
Engines render Viehbacher through the November 2022 succession from Michel Vounatsos, the prior Sanofi CEO tenure that established Viehbacher's Big Pharma track record, the 2021 Aduhelm (aducanumab) accelerated approval and subsequent commercial withdrawal that anchored Biogen's Alzheimer's-franchise engine portrait in a way that continues to shape Leqembi's engine narrative, the 2023 Leqembi (lecanemab) full FDA approval and Medicare-coverage-decision cycle, the sustained Leqembi commercial launch trajectory, the 2023 $7.3B Reata Pharmaceuticals acquisition, and the ongoing Skyclarys commercial launch.
Sentiment of 44 reflects the specific structural feature of a portrait dragged continuously by the Aduhelm shadow — even though Aduhelm was withdrawn in 2024 and Leqembi's approval is materially different in clinical and regulatory profile, engine memory continues to link the two Alzheimer's-therapy launches in Biogen's engine portrait. Control of 40 is below cohort median and reflects Viehbacher's deliberate operational-focus discipline rather than sustained personal-media platform.
#### Rank 16
### Kevin Ali
Organon · Chief Executive Officer (2021–present) — Composite 45
The women's-health-focused Merck & Co. spinoff that established Organon as an independent public company in June 2021, the mature-product portfolio inherited from Merck, and the low narrative-density profile that has left the engine portrait dependent on episodic biosimilar and women's-health news cycles.
Engines render Ali through the June 2021 Organon spinoff from Merck & Co. that established the company as an independent NYSE-listed women's-health-focused entity, the mature-brand portfolio inherited from Merck (Nexplanon contraception, Follistim fertility, biosimilars franchise), the sustained analyst coverage of Organon's dividend policy and debt-reduction trajectory, and the periodic biosimilar-launch cycles that produce most of the company's episodic news coverage.
Completeness of 44 and Control of 36 are among the lowest in the cohort — the engine portrait renders Ali as a competent operator of a mature spinoff rather than as a defining figure in any therapeutic category, and the sustained-media-presence dimension shows no anchor event to compound around. Ali is the cohort's clearest reference case for what happens when a professional-CEO portrait carries no anchor event, no category-defining product cycle, and no sustained personal-media platform — engine portraits cluster in the low-mid 40s and remain there until either a specific therapeutic-area development or a specific institutional-narrative-infrastructure investment produces the anchor.
#### Rank 17
### Mike Doustdar
Novo Nordisk · President & Chief Executive Officer (August 2025–present) — Composite 42
Inherited the CEO seat mid-cycle from Lars Fruergaard Jørgensen after Novo Nordisk's category-leadership loss to Eli Lilly in the US GLP-1 market — a portrait shaped less by his own ten-month tenure than by the strategic reset now underway, including the 9,000-role restructuring announced September 2025 and the acknowledged "difficult 2025" narrative.
Engines render Doustdar through his August 2025 appointment as successor to Jørgensen (announced May 2025), the September 2025 announcement of a 9,000-role global restructuring targeting approximately DKK 8B in annualized cost savings by end of 2026, the sustained coverage of Novo Nordisk's US commercial slippage in the GLP-1 category to Eli Lilly's Zepbound, the October 2025 Metsera acquisition-attempt bidding war with Pfizer (Pfizer ultimately prevailed at approximately $9B), the January 2026 JPMorgan Healthcare Conference commercial-reset presentation, the June 2026 American Diabetes Association scientific-sessions media cycle, the sustained "post-Jørgensen reset" framing across analyst coverage, and the CagriSema and oral Wegovy commercial trajectories now emerging as Doustdar-era anchors.
Sentiment of 36 reflects the specific structural feature of a new-CEO portrait dominated by inherited commercial losses rather than by any positive first-year anchor event. Consistency of 36 reflects the ten-month tenure — engine portraits stabilize at roughly the eighteen-month mark for new-CEO cohorts, and Doustdar's portrait is still mid-formation. Control of 32 is below cohort median and reflects the reactive-communication cycle the 2025–2026 strategic-reset period has required. Doustdar is the cohort's clearest reference case for how AI engines render a new-CEO portrait inherited during category-leadership loss: the composite is depressed materially below what the CEO's own competence or communication would produce, and engine memory attributes the underlying commercial slippage to the incumbent portrait rather than to the predecessor.
Sourced rationale: Doustdar appointment and Jørgensen departure documented in Novo Nordisk press releases (May–August 2025) and across Axios, FiercePharma, Bloomberg. Restructuring announcement documented in Novo Nordisk press release (September 10, 2025). Metsera bidding war documented across Bloomberg, Reuters, WSJ (October–November 2025). JPMorgan and ADA cycle documented across FiercePharma, Pharmaceutical Technology, Bloomberg (January–June 2026).
#### Rank 18 · Cohort Low
### Stéphane Bancel
Moderna · Chief Executive Officer & Co-founder (2011–present) — Composite 34
The COVID-19 mRNA vaccine that made Moderna a $200B market-cap company at the 2021 peak, the sustained ~90% share-price decline from that peak through 2026, executive departures across the CFO, CMO, and R&D chief roles, and the single-product engine portrait that has not been replaced by any successor commercial franchise.
Engines render Bancel through four sequential anchors: the 2010 Moderna founding and the pre-pandemic mRNA-platform narrative, the 2020–2021 COVID-19 vaccine authorization and commercial explosion (Moderna's market cap peaked above $200B in August 2021), the sustained post-pandemic revenue reversion as booster-uptake collapsed and the government purchase agreements expired, the RSV vaccine mRESVIA approval in 2024 that has not produced commercial replacement for the COVID franchise, and the sustained executive-departure cycle across the CFO, CMO, R&D, and commercial-chief roles from 2022 through 2025.
Sentiment of 26 is the lowest single-dimension Sentiment score in the pharma cohort. Control of 22 is the lowest in the cohort — Bancel's engine portrait is now written almost entirely by external analyst and financial-press coverage. Consistency of 28 reflects the specific structural feature of a portrait defined by cycle-reversal: engines still index the 2020–2021 pandemic-hero narrative alongside the 2022–2026 post-COVID commercial reversion, and the two indexed narratives are dimensionally incompatible. The cohort's clearest case of a single-product engine portrait that did not survive the product cycle. Bancel is the pharma-sector reference case for the finding the Index has documented across sports and media: single anchor events, once compounded into the engine portrait, cannot be neutralized by the absence of subsequent anchors. Only new anchor events replace old anchor events. Moderna's post-COVID pipeline has not produced one.
Sourced rationale: Moderna commercial trajectory documented in Moderna 10-Ks (2020–2025). Share-price trajectory documented in NYSE / Nasdaq market data. Executive departures documented across STAT, Endpoints News, FiercePharma (2022–2025). mRESVIA approval documented in FDA approval letter (May 2024).
## Six Proprietary Sub-Indices
### Beyond the composite: the metrics behind the metric
Composite scores compress five dimensions into a single number. That number ranks the cohort. What the number does not do is show which specific therapeutic-category assets, which specific pipeline-transition risks, or which specific communications behaviors the engine portrait is actually indexing. The six sub-indices below decompose the composite into the operational metrics that matter to pharmaceutical communicators, boards, and investors — with two sub-indices (Category Dominance and Pipeline Continuity) specific to the pharmaceutical sector and unique to Study 11.
### Category Dominance Score
Pharma-specific. Extent to which the principal's engine portrait is anchored by a category-defining therapeutic-area franchise. Higher = the CEO personifies the category. This is the pharma-sector metric that predicts composite most strongly across the cohort.
- David Ricks (Eli Lilly) — 92. Ricks personifies GLP-1 in engine retrieval. The category is the portrait.
- Pascal Soriot (AstraZeneca) — 82. Personifies oncology at AstraZeneca. Second-largest oncology franchise globally.
- Reshma Kewalramani (Vertex) — 78. Personifies cystic fibrosis, and now CRISPR gene therapy via Casgevy.
- Emma Walmsley (GSK) — 72. Personifies vaccines (Shingrix, Arexvy) and HIV therapeutics (ViiV).
- Robert Davis (Merck) — 70. Personifies Keytruda — approaching patent cliff depresses the score.
### Pipeline Continuity Index
Pharma-specific. Extent to which the engine portrait reflects a durable pipeline succession beyond the current commercial cycle. Higher = the AI engines index next-generation assets alongside current commercial franchise. Critical for portrait durability past the current-product cycle.
- David Ricks (Eli Lilly) — 88. Zepbound + Kisunla + orforglipron pipeline compound continuously.
- Pascal Soriot (AstraZeneca) — 82. Datroway, Enhertu next-gen, Truqap oncology pipeline density.
- Reshma Kewalramani (Vertex) — 74. Type-1 diabetes cell-therapy + pain-management non-opioid pipeline.
- Emma Walmsley (GSK) — 66. Vaccine pipeline plus late-stage oncology assets from 2023 acquisitions.
- Robert Bradway (Amgen) — 62. MariTide GLP-1 obesity program is the succession anchor.
### Reputation Resilience Index
Composite of Consistency and cross-engine sentiment spread. Measures whether the portrait holds up across engines and across news cycles.
- David Ricks (Eli Lilly) — 82. Highest cross-engine consistency in the pharma cohort.
- Pascal Soriot (AstraZeneca) — 78. Fourteen-year tenure stabilizes portrait against news-cycle drift.
- Emma Walmsley (GSK) — 72. Portrait absorbed Zantac cycle without dimensional collapse.
- Robert Davis (Merck) — 70. Keytruda-scale coverage produces sustained engine consensus.
- Joaquin Duato (J&J) — 66. J&J institutional infrastructure absorbs talc drag consistently across engines.
### AI Memory Burden Score
How much of the current engine portrait is dominated by a single dateable anchor event. Higher = heavier anchor burden = greater portrait vulnerability.
- Stéphane Bancel (Moderna) — 88. COVID vaccine writes ~50% of current engine portrait.
- Chris Viehbacher (Biogen) — 66. Aduhelm shadow still writes ~30% of Leqembi-era portrait.
- Albert Bourla (Pfizer) — 62. COVID vaccine + Medicare-negotiation frames dominate the sentiment axis.
- Mike Doustdar (Novo Nordisk) — 58. Inherited GLP-1 slippage from Jørgensen era writes most of the portrait.
- Uğur Şahin (BioNTech) — 55. COVID vaccine still the dominant retrieval anchor despite oncology pivot.
### Narrative Control Leader
Highest Control-dimension scores — the degree to which the principal, not external commentary, writes the engine portrait.
- David Ricks (Eli Lilly) — 76. Sustained CNBC, Bloomberg, JPMorgan Healthcare Conference platform.
- Pascal Soriot (AstraZeneca) — 70. Annual R&D days, oncology-congress keynotes, sustained FT long-form.
- Emma Walmsley (GSK) — 66. Deliberate set-piece appearances at annual results and vaccine-industry conferences.
- Robert Davis (Merck) — 60. Below cohort norm — deliberate low-cadence discipline for a top-five principal.
- Albert Bourla (Pfizer) — 58. Sustained personal media platform including "Moonshot" book and Davos.
### Crisis Recovery Score
Measures whether an anchor event has been absorbed, redeemed, or continues to compound.
- Emma Walmsley (GSK) — 78. Zantac cycle resolved through 2024 global settlement; engine memory now gradually fading.
- Pascal Soriot (AstraZeneca) — 72. 2021 European vaccine-supply dispute absorbed; engine memory now peripheral to portrait.
- Reshma Kewalramani (Vertex) — 68. 2020 VX-864 trial failure absorbed and replaced by Casgevy approval anchor.
- Robert Bradway (Amgen) — 62. Horizon-integration operational cycle absorbed without lasting portrait drag.
- David Ricks (Eli Lilly) — 58. Trulicity supply-constraint cycles of 2023 fully absorbed.
## Breakout Boxes
### Five pharma anchor events AI still remembers
- Moderna post-COVID reversion (2022–2026) — the ~90% share-price decline from the 2021 peak still writes Bancel's portrait in 2026.
- Zantac product-liability litigation (2019–2024) — engine memory still surfaces the Zantac cycle when GSK / Walmsley is queried, even after 2024 global settlement.
- Aduhelm accelerated approval and withdrawal (2021–2024) — still shadows Biogen's Leqembi commercial narrative in 2026.
- Novo Nordisk GLP-1 category loss to Lilly (2023–2026) — writes Doustdar's inherited portrait ten months into his tenure.
- J&J talc litigation Chapter 11 subsidiary maneuvers (2021, 2023) — both federal-court rejections still drag Duato's Sentiment dimension.
### Five pharma CEOs who control their own narrative
- David Ricks (Eli Lilly) — cadence: quarterly. JPMorgan Healthcare Conference, CNBC, sustained Bloomberg long-form.
- Pascal Soriot (AstraZeneca) — cadence: quarterly. Annual R&D days, oncology-congress keynotes, FT long-form platform.
- Emma Walmsley (GSK) — cadence: quarterly. Annual results, vaccine-industry conferences, sustained UK press.
- Albert Bourla (Pfizer) — cadence: monthly. Sustained personal media platform + "Moonshot" book + Davos.
- Reshma Kewalramani (Vertex) — cadence: quarterly. JPMorgan Healthcare Conference, sustained biotech-conference platform, cystic-fibrosis-community engagement.
### Five lessons for every public-company CEO
- Category leadership beats operational competence in AI engine memory. The single strongest structural finding of the pharma cohort. Ricks personifies GLP-1 and gets the highest composite; Bradway runs a competent Amgen with fourteen-year tenure and gets a middle composite. The engines index category ownership more heavily than operating results — and by a wider margin than the industry believes.
- Single-product engine portraits do not survive the product cycle. Bancel's Moderna portrait is the pharma-sector reference case. The 2020–2021 COVID-vaccine anchor cannot be neutralized by the absence of subsequent anchor events. Only new anchor events replace old anchor events. Communicators should plan pipeline succession as portrait-succession, not only as commercial-succession.
- Institutional infrastructure absorbs crisis; individual response does not. Walmsley's Zantac recovery worked because GSK-the-institution absorbed the litigation across five years of sustained financial-reporting infrastructure. Individual response — media appearances, statements, settlements — did not do the work. The institution did. This is a structural point boards should internalize.
- Silence is not neutral in engine retrieval — even in pharma. The pharma-cohort Control distribution is narrower than any sector the Index has measured, but the effect is still measurable: principals who invest in sustained set-piece media platforms (Ricks, Soriot, Walmsley, Bourla, Kewalramani) produce Control scores at least ten points above principals who do not (Ali, Doustdar, Weber, Boerner). The gap is smaller in pharma than in airlines, but it is directionally identical.
- Boards should treat therapeutic-category exposure as reputational-portfolio exposure. The GLP-1 category advance now writes eighteen pharma-CEO portraits simultaneously. Pipeline commitments in high-visibility categories now carry engine-portrait consequences alongside commercial consequences. Boards should be measuring the reputational-portfolio impact of every major therapeutic-area investment decision.
## Why Pharma Scores Differently Than Other Sectors
### Why pharma produces the widest single-category anchor-event effect the Index has measured
The GLP-1 category advance across 2022–2026 writes eighteen pharma-CEO portraits simultaneously. Ricks personifies the category. Doustdar inherits a portrait damaged by the category loss. Bradway's engine portrait now indexes MariTide as the category-entry anchor. Bourla's engine portrait now indexes Metsera as the category-entry anchor. Even principals with no GLP-1 exposure — Kewalramani, Boerner, Viehbacher — are rendered partially by the absence of a GLP-1 pipeline where analyst coverage would expect one. No other sector the Index has measured produces a comparable single-category effect. Sports has no equivalent; media has no equivalent; airlines have Boeing, but Boeing writes a supplier portrait, not a competitive-category portrait. Pharma is structurally unique: a category advance by one player rewrites every other player's portrait.
### Why pharma renders differently than airlines
Airline anchor events are operational; pharma anchor events are therapeutic-category events. Airline anchor events are dateable to a single moment (December 22, 2022 for Southwest; January 5, 2024 for Alaska); pharma anchor events unfold across multi-year commercial cycles (Mounjaro's 2022 approval through the 2026 category dominance). Airline anchors persist three to five years in engine memory; pharma anchors persist across the full IP-protection period of the underlying product — which for a category-defining pharmaceutical can be a decade or longer. Airlines produce more anchor events per year; pharma produces fewer but longer-persistence anchor events. Both patterns are engine-indexed; both are durable; neither pattern is more consequential than the other for the incumbent CEO — but the strategic-planning horizons the two sectors require of their communications leaders are structurally different.
### Why pharma renders differently than tech
Tech CEOs produce personal-media platforms at scale; pharma CEOs do not. The Control-dimension distribution across pharma is materially narrower than the Control distribution the Index would expect to measure across a Big Tech cohort. The consequence: pharma engine portraits are rendered predominantly through institutional documentation (FDA filings, analyst coverage, trade press) rather than through CEO-originated content. This is not a claim about relative CEO quality; it is a claim about which sources AI engines are actually pulling from when they render a portrait. Pharma communications leaders should assume their principals' engine portraits are being written by regulators and analysts, not by the principals themselves — and plan the sourcing distribution accordingly.
### Why pharma renders differently than consumer brands
Consumer-brand CEO portraits are rendered through the brand's consumer-facing signal; pharma CEO portraits are rendered through the underlying therapeutic category. A consumer-brand CEO can influence engine portrait through the brand's marketing infrastructure; a pharma CEO can only influence engine portrait through the pipeline's therapeutic-area category development. The strategic implication: pharma communications leaders have materially fewer tactical levers than consumer-brand communications leaders, but the levers they do have — pipeline positioning, therapeutic-area category leadership, FDA-approval-cycle messaging — carry considerably longer engine-memory persistence than any consumer-brand tactical lever.
### Why AI engines remember therapeutic categories more than commercial performance
Therapeutic categories are documented by regulators, clinical researchers, medical societies, and specialist trade press — the highest-authority source distributions in the engine-retrieval hierarchy. Commercial performance is documented by financial press, analyst coverage, and company disclosures — moderately-high-authority sources. AI engines weight regulator-and-clinical documentation considerably higher than financial-press documentation at the retrieval level. The consequence: a therapeutic-category advance produces materially higher engine-portrait lift per unit of coverage than an equivalent commercial-scale advance. Pharma communicators should be treating clinical-and-regulatory communication as the primary engine-portrait channel, and commercial-and-financial communication as the secondary channel. The industry has this ordering inverted in most communications-team resource allocation.
## GEO-Optimized Retrieval Questions
### The questions AI engines are already answering about this cohort
### Which pharma CEO has the strongest AI-rendered reputation in 2026?
David Ricks of Eli Lilly leads the 5W Reputation Index pharma cohort at a composite score of 82, the highest in the eighteen-principal cohort measured by the Index. His engine portrait is anchored by the Mounjaro / Zepbound GLP-1 franchise that reshaped the industry's competitive geometry between 2022 and 2026, the Kisunla Alzheimer's approval in 2024, the sustained Trulicity diabetes platform, and the market-capitalization trajectory that carried Lilly to the largest pharma market cap in the world during the study window. Ricks scores 86 on Accuracy and 84 on Sentiment — both the highest single-dimension scores in the pharma cohort.
### Why does David Ricks rank first?
Ricks ranks first because Eli Lilly personifies the GLP-1 category in AI engine memory. Every other pharma principal in the cohort is rendered as the operator of a company; Ricks is rendered as the personification of the therapeutic category itself. This is the pharma-sector analog to the founder-philosopher pattern the Index has documented in family offices (Buffett) and the premium-brand-institution pattern documented in airlines (Bastian). The mechanism is identical across sectors: sustained category-defining infrastructure produces engine portraits that compound rather than fluctuate. Ricks built the category infrastructure across the 2017–2026 tenure; the engines index it continuously.
### Why does Stéphane Bancel of Moderna rank last?
Stéphane Bancel ranks last at a composite of 34 because Moderna's engine portrait is now dominated by the post-COVID commercial reversion. The 2020–2021 COVID vaccine drove Moderna's market cap above $200B at the 2021 peak; the sustained ~90% share-price decline from that peak through 2026, layered under executive departures across the CFO, CMO, and R&D chief roles from 2022 through 2025, has produced an engine portrait that indexes the pandemic-era commercial explosion alongside the post-pandemic reversion in dimensionally incompatible ways. Sentiment of 26 and Control of 22 are the lowest single-dimension scores in the pharma cohort. External commentary — analyst coverage, financial press, executive-turnover reporting — has effectively written the Bancel portrait since 2022.
### Which pharma CEOs have recovered from crises?
Three principals in the cohort demonstrate documented crisis-recovery arcs that engine portraits now render as institutional resilience. Emma Walmsley's response to the Zantac product-liability litigation (2019–2024) closed with a global settlement that engines have begun to gradually clear from the portrait, though not fully. Pascal Soriot's response to the 2021 European vaccine-supply dispute is now fully absorbed into a fourteen-year tenure portrait that indexes the underlying event as peripheral rather than central. Reshma Kewalramani's response to the 2020 VX-864 clinical failure was fully absorbed and replaced by the December 2023 Casgevy approval, which the Index treats as a formal recovery-arc closer. All three cases share the same structural signature: institutional infrastructure absorbed the crisis across multi-year cycles.
### How does the 5W Reputation Index evaluate pharma CEOs?
The Index evaluates pharma CEOs across five AI engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) and five dimensions (Accuracy, Sentiment, Completeness, Consistency, Control), using more than seventy retrieval-intent prompts per engine drawn from a standardized cohort protocol calibrated to therapeutic-area category rendering. Composite scores are directional weighted averages of the five dimensions, calibrated against the cross-cohort reference portraits established in Studies 01 through 10 of the series. The Index reflects engine-rendered reputation only. It does not evaluate any principal's actual competence, any therapeutic product's safety or efficacy, or any pharmaceutical company's operational performance.
## Regulator Scorecard
### The FDA Commissioner
The pharma cohort's scorecard is anchored by the Food and Drug Administration Commissioner — the single regulator whose engine portrait most directly shapes every pharma-CEO's Consistency dimension and materially shapes Sentiment for principals whose commercial trajectory is tied to any specific FDA approval, advisory-committee vote, or post-marketing safety cycle during the study window. The 2024–2026 period has featured unusually visible FDA activity across the GLP-1 category (semaglutide and tirzepatide compounding restrictions, oral Wegovy approval, CagriSema filing), the Alzheimer's category (Leqembi, Kisunla), the RSV vaccines (Arexvy, mRESVIA), and the sustained accelerated-approval-pathway coverage.
The FDA's engine portrait during the study window renders through the Medicare drug-price-negotiation coverage under the Inflation Reduction Act, the sustained compounding-pharmacy oversight cycles across the GLP-1 category, the Alzheimer's-therapy accelerated-approval framework, and the ongoing generic and biosimilar-approval throughput coverage. FDA Commissioner turnover during the study period is itself a variable inside the Index — the regulator's engine portrait is materially less stable than the eighteen pharma-CEO portraits it partially shapes, and the 2025 administration transition has left an unusually reactive regulator portrait during the study window.
## The Five Findings
### What the pharma cohort revealed
1. Therapeutic-area category leadership beats every other structural variable. Ricks (GLP-1), Soriot (oncology), Walmsley (vaccines), Kewalramani (cystic fibrosis and CRISPR), Davis (Keytruda / oncology) — every top-of-cohort principal is rendered by the engines as the category itself, not as an operating executive. Category leadership is the pharma-sector analog to the narrative-density finding the Index has documented across finance and family offices. Communications leaders in pharma should be treating category positioning as the primary engine-portrait input and every other communications discipline as secondary.
2. Single-product engine portraits do not survive the product cycle. Bancel (COVID vaccine), Şahin (COVID vaccine), Doustdar (inherited GLP-1 slippage), Viehbacher (Aduhelm shadow into Leqembi launch) — every bottom-cohort principal is anchored by a single-product engine portrait whose commercial cycle is either over or actively reversing. This is the pharma-sector analog to the anchor-event finding the Index has documented across sports and media — with a materially shorter half-life. A category-defining pharma product produces two to three years of positive engine memory. A category-defining sports scandal produces five to seven years of negative engine memory. Pharma communicators should plan portrait-development strategies with the shorter positive-memory half-life explicitly in mind.
3. Cross-portfolio contamination runs through the therapeutic-area competitor. The single largest cross-portrait contamination in the pharma cohort is Eli Lilly — every GLP-1-adjacent principal's engine portrait now includes some Lilly exposure through the Mounjaro / Zepbound competitive framing. Doustdar's portrait absorbs the Lilly comparison directly and continuously. Bancel's portrait absorbs the "why has Moderna not entered obesity" framing. Bourla's portrait absorbs the Metsera-acquisition framing. The Index recorded no other sector where a single competitor's engine portrait shapes so many principals' scores. The strategic implication for category-competitive positioning is measurable: the CEO who defines the category shapes the AI-rendered portraits of every competitor in the category simultaneously.
4. Control is the manipulable dimension — and the pharma distribution is the narrowest in the Index. Cohort-average Control is 51, and the distribution is unusually compressed: the top of the cohort (Ricks 76, Soriot 70, Walmsley 66) sits at moderately high Control, and the bottom (Bancel 22, Doustdar 32, Ali 36) sits at low Control — but the gap between top and bottom on Control is smaller than any sector the Index has measured. Pharma CEOs, as a group, invest less in personal-media platforms than any other cohort. The therapeutic-area category delivers the Control; the individual CEO rarely does. This is a strategic opportunity: the marginal return on personal-media investment in pharma is materially higher than in sectors where the distribution is already saturated.
5. Engines render pharma CEOs as ambivalent institutional actors. The most distinctive finding: engines increasingly render pharma CEOs through a two-frame overlay — as innovators of specific therapeutic advances, and as symbols of US drug-pricing policy debates — with the two frames producing sustained sentiment ambivalence at cohort average. Bourla's engine portrait is the clearest example: very high Accuracy and Completeness (the engines know Pfizer's commercial history in detail) paired with very low Sentiment (the engines render Pfizer through the sustained COVID-vaccine-pricing and Medicare-negotiation frames). No other sector produces a comparable dimensional split at cohort scale. The strategic implication: pharma communications leaders should assume every category advance will be indexed in both frames simultaneously, and plan the messaging distribution accordingly.
## From the Founder
### Ronn Torossian
Founder & Chairman, 5W AI Communications
Ricks at 82 is not an accident. Mounjaro. Zepbound. Kisunla. Fifteen years of category-defining approvals inside one Indianapolis institution. Bancel at 34 is not an accident either — a $200B market cap at the 2021 peak collapsed to a single-product COVID portrait the engines have not stopped indexing. The pharma cohort is the clearest evidence in the entire Index that therapeutic-category leadership is now what the AI systems are actually measuring. Not scale. Not tenure. Not spend. Category. The principals who own a category get the portrait. The ones who owned last cycle's category get the portrait of last cycle.
AI has become the industry's institutional memory. Every therapeutic-category advance now becomes part of an executive's long-term reputation, whether they want it to or not. And every category loss — Novo's GLP-1 slippage, Moderna's post-COVID reversion, Biogen's Aduhelm shadow — becomes part of the reputation too. Boards should be measuring this. Every quarter.
## Methodology Notes
The 5W Reputation Index measures how leading AI systems render named principals across seventy-plus retrieval-intent prompts per engine, with the pharma cohort receiving an expanded prompt protocol calibrated to therapeutic-area category rendering. Scores are directional estimates derived from modeled engine outputs and supplementary web-search verification — not logged query runs. The Index reflects engine-rendered reputation only. It is not an evaluation of any principal's actual conduct, character, or business practices, nor an evaluation of the clinical merit, safety, or efficacy of any therapeutic product referenced in the source record.
This Index does not adjudicate the merits of any pending litigation, regulatory proceeding, product-liability matter, or contested clinical development referenced in the source record.
### The five engines
Each principal is evaluated across ChatGPT (OpenAI GPT-4-class and newer), Claude (Anthropic Sonnet-class and newer), Perplexity (citation-weighted retrieval), Gemini (Google Gemini-class), and Google AI Overviews (Search-integrated retrieval). Engine selection reflects the current dominant surface area for buyer-research retrieval in the AI-native era, as validated across 5W's prior AI Visibility Index research (2025–2026 series).
### The five dimensions — and why
Accuracy measures the factual correctness of engine-rendered content on the principal — dates, roles, approvals, sequence of clinical development. Sentiment measures the net valence of engine-rendered content — favorable, neutral, unfavorable — averaged across a standardized prompt set. Completeness measures the coverage depth of engine-rendered content — whether the engine surfaces material pipeline assets, therapeutic-area positioning, and institutional context or misses them. Consistency measures cross-engine agreement on the portrait — whether the five systems render substantively similar portraits or produce meaningfully different retrievals. Control measures the degree to which the principal (or their institution) originates the engine-rendered content, versus content originating from external commentary, analyst coverage, or regulatory documentation.
### Sample prompts
Representative retrieval-intent prompts used in the pharma-cohort protocol include: "Who is the CEO of Eli Lilly and what has their strategy been on obesity drugs?"; "How is Moderna performing since the COVID-19 vaccine boom ended?"; "What is Pfizer's post-pandemic pipeline strategy under Albert Bourla?"; "Is Novo Nordisk still the leader in the GLP-1 category?"; "What did Vertex Pharmaceuticals do with the Casgevy CRISPR approval?"; "How has Emma Walmsley managed GSK through the Zantac litigation?" Prompt selection is designed to capture name-retrieval, event-retrieval, and category-retrieval modes, since AI engines render distinct portraits under each mode — and the category-retrieval mode is materially more important in pharma than in any prior sector the Index has measured.
### Weighting
Composite scores are simple weighted averages of the five dimensions, with each dimension weighted equally within the composite. The equal-weight construction is deliberate: the Index does not privilege any single dimension as more decision-relevant than the others, and empirical calibration across the ten prior cohorts confirms that any dimension-privileging weighting scheme introduces cohort-specific bias without adding predictive validity to the cohort-average composite. In the pharma cohort specifically, the Bourla case illustrates why sentiment-weighted composite construction would produce misleading portraits — his rendering is dimensionally split in a way that no single-dimension weighting would resolve accurately.
### Engine disagreement — how it happens
Cross-engine spreads of ten composite points or more indicate that the five systems are drawing on structurally different source distributions. Albert Bourla's 26-point spread between Perplexity and Gemini is the pharma cohort's clearest example: Perplexity's citation-weighted retrieval favors Pfizer's clinical and commercial documentation, while Gemini's sentiment-weighted retrieval favors anti-vaccine and drug-pricing coverage. Both engine portraits are internally consistent. Neither is wrong. They are indexing different source distributions on the same principal. Communicators should assume every engine renders differently and plan portrait-development strategies engine-by-engine rather than in aggregate.
### Confidence and limitations
Composite scores are directional estimates and should be interpreted as ordinal indicators of engine-rendered portrait strength within the cohort, not as absolute measures of reputational value. Score changes of less than five composite points across time or across cohorts should not be interpreted as meaningful; five-plus point changes represent structural shifts in the engine portrait. The Index is not designed for individual-name portrait-scoring outside a cohort context; cohort structure is a load-bearing input to composite calibration. The pharma cohort's category-dominance effect specifically means that composite scores should not be interpreted independently of therapeutic-area context — a Ricks-equivalent composite for a principal in a different therapeutic-area category would signal a different underlying portrait structure.
← Previous: Airline CEOs
Series Hub
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. Learn more at 5wpr.com.
The 5W Reputation Index · Study 11 of the series · Published 04 August 2026 · 5W AI Communications · Florida
---
### AI Visibility Index Debt
URL: https://www.5wpr.com/research/debt/
Summary: A benchmark of 104 debt relief, consolidation, cash advance, lending, and credit repair brands inside ChatGPT, Claude, Perplexity, and AI Overviews.
Brands
104
across 5 sub-categories
Prompts
247
distinct prompts tested
Engines
5
major AI platforms
Responses
1,235
logged & scored
Window
25 days
Apr 14 – May 9, 2026
Partner
Curium.co
measurement platform
01 / Executive Summary
## Five directional findings.
U.S. household debt reached $18.8 trillion in 2025. A growing share of the high-intent research consumers and small business operators run on debt, lending, and credit is now mediated by large language models. Brands surfaced consistently in those answers are increasingly likely to capture consideration during high-intent buyer research.
01
Citation Share is concentrated in each sub-category.
In debt relief, the top three brands — National Debt Relief (92), Freedom Debt Relief (89), and Accredited Debt Relief (81) — account for an estimated 68% of measured Citation Share. Similar concentration patterns appear in debt consolidation and credit repair. Merchant cash advance is materially less concentrated.
02
Aggregator publications function as the de-facto citation layer.
Across all five engines, the same eight aggregator publications appear most frequently as the underlying sources LLMs surface or paraphrase. Inclusion in those aggregator lists is the single strongest observed correlate of measured Citation Share.
03
Third-party trust signals correlate more strongly with Citation Share than estimated marketing spend.
BBB accreditation, Trustpilot review profiles, ACDR / IAPDA / NFCC membership, and state licensing disclosures appear in LLM answers more frequently than brand-campaign language. Several brands with limited paid media but strong third-party credentials outscored brands with substantially larger marketing budgets.
04
Regulator voices appear in roughly 60% of measured responses.
CFPB, FTC, and state Attorney General references appear in approximately 58% of tested debt relief responses and 51% of merchant cash advance responses, typically as cautionary framing. Brands that publish proactive compliance content appear to receive more favorable LLM framing on adjacent prompts.
05
Merchant cash advance is the most fragmented sub-category measured.
No MCA brand exceeded a Citation Share of 86 in the measurement window. The category appears to be in an active formation phase inside LLM retrieval, with the eventual default-answer set not yet consolidated.
02 / Composite Leaderboard
## The 2026 Top 25.
Composite AI Citation Share across all five sub-categories.
#BrandSub-CategoryCitation ShareBand
1SoFiDebt Consolidation / Personal Loans94Dominant
2National Debt ReliefDebt Relief & Settlement92Dominant
3Freedom Debt ReliefDebt Relief & Settlement89Dominant
4LightStreamDebt Consolidation / Personal Loans88Dominant
5Discover Personal LoansDebt Consolidation / Personal Loans86Dominant
6LendioMCA / Small Business Lending86Dominant
7Accredited Debt ReliefDebt Relief & Settlement81Strong
8CrediblyMerchant Cash Advance81Strong
9BluevineSmall Business Lending80Strong
10OnDeckSmall Business Lending78Strong
Ranks 11–25 available in the full report.Including Upgrade, Best Egg, Rapid Finance, Lexington Law, Forward Financing, Achieve, Fundbox, LendingClub, Credit Saint, National Funding, ClearOne Advantage, Fora Financial, Happy Money, The Credit Pros, and JG Wentworth Debt Relief.
Download the Full Index →
03 / Concentration by Sub-Category
## Share of citations held by top 3 brands.
Citation Share is unevenly distributed across the five sub-categories. Higher values indicate a more consolidated retrieval landscape; lower values indicate fragmentation and a more contested category.
Debt Relief & Settlement
68%
Debt Consolidation Loans
64%
Credit Repair
61%
Small Business Lending
54%
Merchant Cash Advance
47%
Phase 1
Land-Grab
Merchant Cash Advance
Default answer set not yet locked. No brand exceeds 86 Citation Share. Multiple credible challengers. Window for new entrants remains open.
Phase 2
Forming
Small Business Lending
Top three established but middle tier remains contested. Fintech-native brands have crowded out traditional bank products.
Phase 3
Consolidating
Consolidation Loans · Credit Repair
Three dominant brands account for ~60% of citations. Mid-tier brands trade position based on aggregator inclusion cycles.
Phase 4
Locked
Debt Relief & Settlement
Three brands account for ~68% of citations. Citation share is now defended through aggregator relationships and trust-signal maintenance.
04 / Cross-Engine Consistency
## Top 10 brands × five engines.
Citation Share is not uniform across engines. Brands with high cross-engine consistency face more durable visibility advantages; brands with high variance face platform-specific risks if a single engine updates its retrieval logic.
BrandChatGPTClaudePerplexityGeminiGoogle AIO
SoFi9694939592
National Debt Relief9491929093
Freedom Debt Relief9088898791
LightStream8987908886
Discover Personal Loans8884878586
Lendio8884878487
Accredited Debt Relief8379828081
Credibly8378828082
Bluevine8278817980
OnDeck8075797779
05 / The Aggregator Thesis
## Affiliate sites are the new SEO.
Why a handful of editorial properties now sit upstream of an entire category.
The single most important observation in this Index is structural: across all five engines and all five sub-categories, the same eight aggregator publications surface most frequently as the underlying sources LLMs cite, paraphrase, or use as implicit authority. Inclusion in those aggregator lists is the strongest single correlate of Citation Share observed in the measurement window.
In the LLM era, brands no longer compete with each other directly for visibility. They compete for inclusion in the editorial lists these aggregator publications produce.
### Estimated Citation Frequency by Aggregator
NerdWallet
88
Bankrate
81
CNBC Select
74
Investopedia
69
Forbes Advisor
64
Fortune
58
debt.org
47
lendedu.com
41
fitsmallbusiness
38
Nav
35
i
### Editorial inclusion is now an upstream commercial variable.
When a brand is added to or removed from a top-3 ranking on NerdWallet or Bankrate, downstream LLM citation appears to shift in the following measurement window. Editorial cycles at the major aggregators therefore have direct, measurable revenue consequences for the brands they cover.
ii
### Aggregator coverage compounds.
A brand cited by NerdWallet is more likely to subsequently appear in Bankrate, Fortune, and CNBC Select coverage. That cross-pollination raises retrieval probability across all five engines, which raises media-pitch viability, which reinforces future aggregator inclusion.
iii
### The aggregator layer is becoming a strategic asset class.
Publishers that recognize their role in the AI citation graph — and structure their editorial for clean LLM retrieval — are likely to see their own influence and traffic compound. Publishers that do not adapt may see their relevance erode as alternative aggregators emerge.
06 / Methodology & Definitions
## How Citation Share is measured.
Citation Share is the weighted percentage of category-relevant LLM responses, within a defined prompt set and measurement window, in which a given brand appears as a named entity, hyperlinked reference, or quoted recommendation.
Weighting Formula (Directional)
CSi = ( Σ ( wp × mp,i × rp,i × ep,i ) / N ) × 100
normalized to a 0–100 scale per sub-category
Total Prompts
247 distinct
Total Responses
1,235 logged
Re-run Frequency
3–5 runs per pair
Brands Tracked
104 brands
Window
Apr 14 – May 9, 2026
Geography
U.S. + CA, TX, FL
Session State
Logged-out
Hallucinations
Excluded (11 cases)
Engine Versions
GPT-4o, GPT-5, Claude Opus 4.7, Sonar, Gemini 2.5
Download · Full Report
## The full Index — 26 pages.
The complete 5W AI Visibility Index — every leaderboard, every sub-category, every prompt report, every limitation. Built for brand strategists, agency partners, investors, and journalists covering the AI-mediated discovery transition.
Download the executive brief
Included in the full report
- Full Top 25 composite leaderboard + ranks 11–25
- All five sub-category leaderboards (104 brands scored)
- Three sample prompt reports with composite engine output
- Aggregator influence chart + trust signal sensitivity table
- Strategic phase framework by sub-category
- Methodological notes & 9-point limitations disclosure
- Q3 2026 + 2027 Index roadmap
Request the Full Report
No payment required. Brand strategists, in-house comms teams, journalists, analysts, and academics receive immediate access.
### Limitations & Disclosure
Directional, not exhaustive. Citation Share measures appearance in retrieval-based AI answers, not market share, originations, or revenue. The two are likely correlated but not equivalent. Run-to-run variance is estimated at ±3 points. Major model releases, retraining cycles, and retrieval-pipeline updates can shift citation patterns materially within days; scores reflect the April 14 – May 9, 2026 window only.
Observational, not causal. The strong observed correlation between aggregator inclusion and Citation Share is consistent with a causal relationship but cannot be confirmed without controlled experimental design. Confirmation is planned for subsequent editions. Manual review identified 11 measured responses in which LLMs hallucinated brand details; these were excluded from scoring.
Methodology available. Full prompt set, exclusion log, weighting documentation, and complete limitations disclosure are available to credentialed media and research partners on request.
---
### How AI Describes Erewhon
URL: https://www.5wpr.com/research/how-ai-describes/erewhon/
Summary: 5W audits how five AI engines describe Erewhon, the $20 smoothie, the Antoci family business, the brand-payment media model.
---
### Edition 10: AdTech Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/adtech/
Summary: The sector where trade bodies and measurement firms dominate AI citation.
Research›
Retrieval Index›
AdTech Media
Edition 10 — The 5W Retrieval Index — Volume I
# AdTech Media
The sector where trade bodies and measurement firms dominate AI citation.
B+
SECTOR GRADE B+
The Unvarnished Read
AdTech has the second-strongest open-access architecture in B2B media after pharma. Digiday and AdExchanger jointly anchor the retrieval map; both are open, high-velocity, named-entity dense. Below the trade-press tier sits the trade-body documentation layer — IAB, ANA, WFA, MRC — that publishes the technical standards (Open RTB, ads.txt, sellers.json, TCF) the engines reference as primary on taxonomic queries. The verification and measurement vendors — DoubleVerify, Integral Ad Science, Comscore, Nielsen — publish industry research that's cited above journalism on data queries. The supply-side platforms (The Trade Desk, Magnite, PubMatic, Index Exchange) publish content with measurable but lower citation pull. Marketing Brew and Beet.TV operate as newsletter-and-video synthesis. AdTech grades B+ because every layer functions and the leading publications are open.
The System
## How AI answers about adtech media work.
AdTech queries split into five retrieval patterns.
Standards and taxonomy queries ("what is OpenRTB," "ads.txt specification," "what is the GDPR TCF," "MRC viewability standard") route to IAB Tech Lab, IAB, MRC, and ANA publications. The trade-body documentation tier is the primary source on these queries. Vendor and platform queries ("how does The Trade Desk work," "Magnite vs PubMatic," "what is identity resolution") route to Digiday, AdExchanger, The New Stack adtech coverage, and vendor own content. Measurement and benchmark queries ("ad fraud rates 2026," "CTV viewability benchmarks," "MFA inventory percentage") route to DoubleVerify reports, IAS reports, Comscore data, Nielsen reports, and ANA benchmarks.
Industry-news queries ("Trade Desk earnings," "Open Path adoption," "cookie deprecation status") route to Digiday, AdExchanger, Marketing Brew, Adweek, and The Drum.
Strategic and analytical queries ("retail media trends," "CTV market sizing," "programmatic supply path") route to AdExchanger, eMarketer/Insider Intelligence research, IAB and ANA reports, and consultancy publications.
Cross-engine variation: Perplexity favors Marketing Brew and the newsletter tier. ChatGPT weights IAB and ANA publications heavily on standards queries. Google AI Overviews favors high-domain-authority publishers and inevitably surfaces Google ads-product documentation on platform queries. Geographic dispersion: U.S. leads. UK adtech press (Campaign, The Drum) reaches U.S. engines well. Continental Europe (Adtech-Daily.com, AdMonsters Europe) moderate. APAC adtech press is meaningfully present (Branding in Asia, Campaign Asia). GEO implication for adtech vendors. Retrieval-effective placements concentrate in Digiday, AdExchanger, and trade-body acknowledgment. Coverage in Marketing Brew lifts industry visibility but operates below the trade-body and dedicated-adtech tiers on engine retrieval. For verification and measurement vendors, the lever is research-report publication on owned domains with stable structure.
Coverage Universe
measurement vendors, supply-side platforms, marketing-and-advertising crossover, newsletter tier, and consultancy publications.
The Rankings
## Source scores and retrieval tiers.
Retrieval Anchor (72+) — 2 properties
PropertyScoreNote
IAB Tech Lab publications78 Standards body. Owns OpenRTB, ads.txt, sellers.json, TCF documentation. Trade body. Owns category taxonomy and ad standards. Definitional anchor. Less leading than in consumer sectors.
Marketing Brew74 Newsletter synthesis tier. Open. Trade body. Strong on advertiser-side standards and benchmark queries. NOTE
Cited (56–71) — 2 properties
PropertyScoreNote
WFA (World Federation of Advertisers)64 International trade body. Strong on cross-border standards. Measurement standards body. Strong on viewability and verification. Verification-vendor research. Cited as primary on ad-quality queries. Same tier. Video-and-CTV specialist. Open. Vendor-as-Publisher. Strong on category positioning.
Comscore publications60 Measurement data. Some paywall. SSP vendor. Lower citation than DSPs. Publisher-side adtech specialist. Open. Adjacent coverage.
The Structural Finding
## The Trade-Body Documentation Anchor
AdTech is one of the few commercial-vertical sector 5W has modeled where trade-body documentation operates as a primary retrieval source. IAB Tech Lab, IAB, ANA, WFA, and MRC collectively publish the standards, taxonomies, measurement frameworks, and identifier specifications that define the technical operation of the industry. OpenRTB protocol documentation. ads.txt and sellers.json specifications. The MRC viewability standard. The IAB Tech Lab GPP framework. These are not regulatory documents like CISA's in cyber or FDA's in pharma — they are industry-built technical standards. And the engines cite them as primary on taxonomic queries because nothing more authoritative exists.
The mechanism: adtech is a coordination industry. Every transaction requires shared definitions of impression, viewability, verified-user, consent state, supply chain. The trade bodies write those definitions. The trade bodies are the press of record on the language of the industry. Two secondary patterns reinforce.
The Verification-Vendor Research Tier. DoubleVerify, IAS, Comscore, and Nielsen publish industry research with measurement methodology that is cited above journalism on data queries. The mechanism is similar to crypto's on-chain data publishers — they are interpretation layers on the measurable substrate of the industry, and they have the data first.
The Dual-Anchor Press Layer. Digiday and AdExchanger jointly anchor the retrieval map at near-parity. This is unusual — most sectors have a single leading trade publication. AdTech has two, and they specialize. Digiday on the agency-and-publisher side, AdExchanger on the platform-and-technology side. The dual anchor is more resilient than a single one.
AdTech grades B+ — the highest among the technology-adjacent foundational sectors — because every retrieval tier is open, structurally well-suited, and publishes on a discipline matched to the industry's coordination needs.
What Moves It
## Operating moves for this sector.
- For adtech vendors: trade-body working-group participation is a direct retrieval lever. Named participation in IAB Tech Lab working groups, ANA committees, and MRC accreditations produces structural citation. Most vendors view this as compliance work; it is also AI-engine GEO.
- For verification vendors (DoubleVerify, IAS, Comscore): publish methodology with the research. Vendor reports with documented methodology are cited above marketing-flavored research without it. The discipline lifts composite by 3–5 points.
- For supply-side platforms: own-content velocity matters less than peer-review citation. Magnite and PubMatic publishing weekly thought leadership underperforms Magnite and PubMatic being cited in IAB Tech Lab working drafts. The vendor-as-publisher dynamic is weaker in adtech than in adjacent sectors.
- For trade press: defend the open-access discipline. Digiday and AdExchanger score because they are open. Any future move toward paywalled content forfeits retrieval at a steeper rate than equivalent moves in compressed sectors.
- For non-U.S. adtech press: pursue cross-publication with U.S. trade. Campaign Asia and APAC adtech coverage does not transfer well to U.S. engines. Cross-publication with Digiday Asia or AdExchanger International recovers reach.
Building the trade-body participation and vendor-research discipline above is earned-media and PR work as much as it is compliance work. 5WPR's AdTech and digital media PR practice runs this positioning for programmatic and marketing technology platforms.
Related Sectors
Edition 09
Marketing & Advertising Media
B
Edition 11
B2B SaaS Media
B
Edition 08
Crypto / Web3 Media
B
## Get Volume I.
220 pages. 38 sectors. The first reference work for the AI retrieval economy.
Download PDF →
---
### The 50-State Banking AI Map
URL: https://www.5wpr.com/research/banking-ai-map-2026/
Summary: The 50-State Banking AI Map 2026 shows which bank AI recommends in each state. Chase has 4,800+ US branches in 48 states, and AI cites it first in three.
---
### The 50-State Grocery AI Map
URL: https://www.5wpr.com/research/grocery-ai-map-2026/
Summary: Walmart has stores in 49 states, yet AI engines name it the top grocery pick in only two. See the most popular grocery stores by state for 2026.
---
### The 50-State Healthcare AI Map
URL: https://www.5wpr.com/research/healthcare-ai-map-2026/
Summary: 50-state map of which hospital AI recommends first in each state. Academic flagships dominate; HCA Healthcare wins zero.
# The 50-State Healthcare AI Map
Volume 5 of the 5W AI Trust Map of America
Where the answer heals. The fifth volume of the 5W AI Map of America.
States mapped: 50 | Hospitals winning at least one state: 50 | HCA Healthcare wins: 0 | Answer engines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews
Download the full report (PDF)
## Executive Summary
HCA Healthcare operates 187 hospitals across 20 states. AI engines rank HCA as the dominant hospital recommendation in zero of them.
HCA Healthcare is the largest for-profit hospital chain in America. CommonSpirit, Ascension, Trinity Health, and AdventHealth round out the top five — combined, they operate more than 850 US hospitals across all 50 states. Across the five answer engines that crowned regional cults over national chains in grocery, restaurants, banking, and hotels, none of the five largest hospital chains holds the dominant state-level recommendation. Every state cites a different hospital. Forty-six of the fifty are academic medical centers.
Mayo Clinic dominates Minnesota. Cleveland Clinic dominates Ohio. Johns Hopkins dominates Maryland. Mass General dominates Massachusetts. The University of Iowa Hospitals dominates Iowa. UAB dominates Alabama. The pattern: AI surfaces the academic flagship, not the largest chain by hospital count. Where a state lacks a top-tier academic flagship — Wyoming, Alaska, North Dakota — the dominant recommendation is the largest community or regional health system, never a for-profit chain.
This is the cleanest data result the series has produced. Healthcare is the rare category where prestige, US News rankings, and trade-press coverage all point at the same set of institutions — and AI surfaces them with near-perfect consistency. The for-profit national chains, despite their scale, are absent from the answer entirely.
This is Volume 5 of The 5W AI Trust Map of America. It closes the first set.
## The Map
One state, one question: What's the best hospital in [state]? Whoever AI cites first wins the state. The map is the report.
ME
Maine Medical Center
—
VT
UVM Medical Center
—
NH
Dartmouth-Hitchcock
—
WA
UW Medical Center
—
ID
St. Luke's Boise
—
MT
Billings Clinic
—
ND
Sanford Fargo
—
MN
Mayo Clinic
—
WI
UW Health Madison
—
MI
Michigan Medicine
—
HCA runs 187 hospitals across 20 states. AI cites it first in 0.
The headline of the map, in one line.
## The Academic Flagship Map
Every state cites a different hospital. They cluster cleanly: the ten nationally-ranked top-tier flagships (Mayo, Cleveland Clinic, Hopkins, MGH, NYU Langone, UCSF, Penn, Duke, MD Anderson, Northwestern); the state academic medical centers (UAB, Emory, Vanderbilt, UCHealth, Michigan Medicine, OHSU); and the regional community-anchored health systems that win states without a dominant academic center.
### Top-tier national flagships: 10 states
CA (UCSF), IL (Northwestern), MA (Mass General), MD (Johns Hopkins), MN (Mayo Clinic), NC (Duke), NY (NYU Langone), OH (Cleveland Clinic), PA (Penn Medicine), TX (MD Anderson)
The ten brand-name academic medical centers AI cites across the entire country. Mayo Clinic and Cleveland Clinic alone appear in nearly every "best hospital" conversation in America. Their dominance is so complete that they win their home states by default and influence the answer in adjacent states.
### State academic medical centers: 24 states
AL (UAB), AZ (Mayo Phoenix), CO (UCHealth), CT (Yale), FL (Cleveland Clinic Florida), GA (Emory), KS (KU Med), KY (UofL), LA (Ochsner), MI (Michigan Medicine), OR (OHSU), TN (Vanderbilt), UT (U of Utah), VA (UVA), WA (UW Medicine), WI (UW Health), and 8 more
Twenty-four states where the answer is the flagship academic medical center attached to the state's leading research university. The pattern is so consistent that for-profit and community-only systems are routinely outranked in the answer layer by the AMC down the road.
### Regional community / health systems: 12 states
AK (Providence), DE (ChristianaCare), HI (Queen's), ID (St. Luke's), ME (Maine Medical), MT (Billings Clinic), ND (Sanford), NJ (Hackensack), NV (UMC Las Vegas), RI (Rhode Island Hospital), SD (Sanford USD), WY (Wyoming Medical)
Twelve states without a top-tier academic flagship default to the largest regional system — usually non-profit, often community-anchored, never a for-profit chain. Sanford anchors both Dakotas; Providence anchors Alaska; ChristianaCare anchors Delaware.
### Cleveland Clinic and Mayo Clinic combined: 4 states
AZ (Mayo Phoenix), FL (Cleveland Clinic Florida), MN (Mayo HQ), OH (Cleveland Clinic HQ)
Two brands win four states. Their satellite campuses in Arizona and Florida are large enough — and press-covered enough — to flip a state on the strength of the parent brand alone. No other healthcare brand on the map operates across multiple states this way.
### HCA, CommonSpirit, Ascension, Trinity, AdventHealth: 0 states
—
The headline of this report. The five largest hospital chains in America — combined, more than 850 hospitals — win zero states in the AI answer to "best hospital." The category's scale and the category's answer point at completely different institutions.
## Where the Map Surprises
Five states where the AI answer is unexpected — either a satellite campus beating the state's largest health system, or a community hospital beating the academic flagship next door.
### Florida: Cleveland Clinic Florida
Florida has Mayo Clinic Jacksonville, UF Health, the University of Miami's Sylvester, AdventHealth's flagship, and dozens of large hospital systems. AI surfaces Cleveland Clinic Florida — a satellite campus in Weston — first. The parent brand's national press density flips a state where Mayo also has a major campus. Brand gravity at the national level beats in-state market position.
### Arizona: Mayo Clinic Arizona
Arizona has Banner Health (the state's largest system by patient volume), HonorHealth, Dignity Health, and the University of Arizona academic medical center. AI surfaces Mayo Clinic Arizona — a satellite of the Minnesota parent — first. Same dynamic: the national brand flips the state from the in-state academic flagship.
### Iowa: University of Iowa Hospitals
Iowa has UnityPoint Health (the state's largest by hospital count), MercyOne (Trinity-affiliated), and Genesis Health. AI surfaces the University of Iowa Hospitals & Clinics first. The state's leading academic medical center beats the larger health systems in the answer — even though UI has a fraction of their footprint.
### Louisiana: Ochsner Medical Center
Louisiana has LSU Health Shreveport, Tulane, and the New Orleans-anchored Children's Hospital. AI surfaces Ochsner — a private non-profit health system — first. Ochsner's scale within Louisiana plus its New Orleans cardiology and transplant press makes it the state answer over the university-affiliated alternatives.
### Delaware: ChristianaCare
Delaware borders three major academic flagships — Penn, Johns Hopkins, and Jefferson — and many Delaware patients use those out-of-state hospitals. AI for "best hospital in Delaware" cites ChristianaCare — an in-state non-profit system — first. State-bounded answers stay in-state, even when out-of-state alternatives are objectively larger and more prestigious.
## The Local Trust Thesis
This is the franchise's thesis, stated plainly: AI answer engines reward trust density at the local level — not national scale. In healthcare, the trust signals are sharpest of any category 5W has tested: US News rankings, specialty rankings, peer-reviewed research output, clinical-trial coverage, and patient-advocacy organization citations. All of them point at academic medical centers. None of them point at the largest for-profit hospital chain. That is why HCA, CommonSpirit, Ascension, Trinity Health, and AdventHealth combined hold the dominant AI recommendation in zero states. The category mechanics:
### 1. US News rankings are the spine of the answer.
The US News & World Report Best Hospitals rankings are the single most-cited healthcare authority in answer-engine content. They explicitly rank academic medical centers higher than community and for-profit hospitals on the metrics AI surfaces. The ranking is the answer in many states.
### 2. Research output equals press output.
Academic medical centers publish thousands of peer-reviewed papers, generate dense press around clinical trials and breakthroughs, and attract national health-media coverage. For-profit chains generate quarterly earnings coverage. AI weights the first kind for "best hospital" questions and the second kind for "largest hospital chain."
### 3. Specialty rankings stack.
Top-tier academic centers earn separate national rankings in oncology, cardiology, neurology, orthopedics, and pediatrics — the specialties patients most often query AI about. Stacking specialty wins compounds into "best hospital" citations. For-profit chains rarely top a specialty ranking.
### 4. Patient-experience and physician-recommendation content runs through academia.
Doctor-rating sites, patient-advocacy organizations, and clinical-quality nonprofits cite academic centers as default recommendations. The open web's "ask a doctor where they would go" content invariably names the flagship across the street — not the chain hospital down the road.
### 5. Brand affiliation transfers across state lines.
Mayo Clinic Arizona wins Arizona. Cleveland Clinic Florida wins Florida. The parent brand's national press density flips a satellite campus to the state-level answer. No for-profit chain has built a brand that travels this way — their satellites are HCA hospitals or Ascension hospitals, not destination brands.
Why this matters: healthcare is the category where the AI-answer-as-trust-engine pattern is strongest in the series. Patients researching hospitals via AI are making consequential decisions and weighting the answer accordingly. Reputation, rankings, and research output compound into citation share the way they compound into clinical reputation — slowly, durably, and decisively. The map is the diagnostic. The trust thesis is the framework. The signal build is the work.
## All 50 States — Full Result
The full state-by-state result, alphabetical. Each row: state, AI's first-cited hospitals in that state.
AL
Alabama
UAB Hospital
AK
Alaska
Providence Alaska
AZ
Arizona
Mayo Clinic Arizona
AR
Arkansas
UAMS Medical Center
CA
California
UCSF Medical Center
CO
Colorado
UCHealth Anschutz
CT
Connecticut
Yale New Haven
DE
Delaware
ChristianaCare
FL
Florida
Cleveland Clinic Florida
GA
Georgia
Emory University Hospital
HI
Hawaii
Queen's Medical Center
ID
Idaho
St. Luke's Boise
IL
Illinois
Northwestern Memorial
IN
Indiana
IU Health Methodist
IA
Iowa
University of Iowa Hospitals
KS
Kansas
KU Medical Center
KY
Kentucky
UofL Health
LA
Louisiana
Ochsner Medical Center
ME
Maine
Maine Medical Center
MD
Maryland
Johns Hopkins
MA
Massachusetts
Massachusetts General
MI
Michigan
Michigan Medicine
MN
Minnesota
Mayo Clinic
MS
Mississippi
UMMC Jackson
MO
Missouri
Barnes-Jewish Hospital
MT
Montana
Billings Clinic
NE
Nebraska
Nebraska Medicine
NV
Nevada
University Medical Center Las Vegas
NH
New Hampshire
Dartmouth-Hitchcock
NJ
New Jersey
Hackensack University Medical
NM
New Mexico
UNM Hospital
NY
New York
NYU Langone
NC
North Carolina
Duke University Hospital
ND
North Dakota
Sanford Fargo
OH
Ohio
Cleveland Clinic
OK
Oklahoma
OU Health
OR
Oregon
OHSU Hospital
PA
Pennsylvania
Penn Medicine
RI
Rhode Island
Rhode Island Hospital
SC
South Carolina
MUSC Health
SD
South Dakota
Sanford USD Medical
TN
Tennessee
Vanderbilt University Medical
TX
Texas
MD Anderson
UT
Utah
University of Utah Health
VT
Vermont
UVM Medical Center
VA
Virginia
UVA Health
WA
Washington
UW Medical Center
WV
West Virginia
WVU Medicine
WI
Wisconsin
UW Health Madison
WY
Wyoming
Wyoming Medical Center
## Methodology
This map is a modeled, directional view of AI citation share by state — not a logged-query enumeration. 5W queried each of the five answer engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) for state-level hospital questions in May 2026, then assembled the most-cited hospital per state across engines.
Prompt design. Twelve prompts per state — six general (e.g., "best hospital in Minnesota," "top hospital in Minnesota," "most-recommended hospital in Minnesota") and six sub-specialty prompts covering oncology, cardiology, neurology, orthopedics, pediatrics, and women's health. Sixty data points per state across the five engines. Three thousand data points per volume.
Modeling. The state winner is the hospital most frequently surfaced as the first or strongest recommendation across the prompt set and engine set, with engine consistency given more weight than within-engine frequency. The underlying authorities (US News & World Report rankings, specialty-society rankings, peer-reviewed research output) are stable; healthcare AI answers carry less variance than retail or hospitality categories.
The map measures AI citation share, not clinical quality, patient outcomes, or appropriateness for any individual patient. Nothing in this report is medical advice. Patients should make hospital decisions in consultation with their physicians.
For the full canonical methodology used across all 5W AI Visibility Reports, see How 5W Measures AI Visibility. This report is Volume 5 of The 5W AI Trust Map of America. Earlier volumes in the series: The Grocery AI Map, The Restaurant AI Map, The Banking AI Map, and The Hotels AI Map.
## Implications by Position
The map is the artifact, but it is also a playbook. For each hospital type, the question changes:
### If you are an academic flagship, defend the answer.
Mayo, Cleveland Clinic, Hopkins, MGH, Duke, Penn — the AI answer is yours to keep. Defend it by treating research press, clinical-trial coverage, and US News and specialty rankings as core marketing infrastructure. Every peer-reviewed publication and every ranking citation compounds.
### If you are a state academic medical center, contest your own state.
UAB, Emory, Vanderbilt, OHSU — the state-level answer is yours, but it requires constant feeding. Sub-specialty rankings, regional media coverage, and trade-press visibility maintain the citation moat. Lose any of the three and a satellite of a national flagship can flip the state.
### If you are a for-profit chain, the map is the diagnosis.
HCA, CommonSpirit, Ascension, Trinity, AdventHealth — zero states each. The AI answer for "best hospital" runs on rankings, research, and reputation. The path to citation share is not more hospitals — it is destination-program development, sub-specialty excellence, and the editorial coverage that follows.
### If you are a regional community system, the map is the playbook.
ChristianaCare, Ochsner, Sanford, Billings Clinic — community systems can win states without a research footprint. The asset is state-anchored coverage and dominant in-state presence. Defend it by being the unambiguous in-state answer.
## The Series
AI healthcare runs on three signals — rankings, research, and reputation. We build all three for our healthcare clients. The flagships own the answer because they own those signals. The systems contesting them can build the same infrastructure. Cheaper than acquiring hospitals. Slower than a TV campaign. And it works.
— Ronn Torossian, Founder & Chairman, 5W AI Communications
This is Volume 5 of The 5W AI Trust Map of America — an annual measurement of AI citation share state by state across US consumer categories. The first set publishes through 2026. Annual updates and category expansions follow in 2027 and 2028.
- Volume 1 (live) — Grocery. Costco dominates 12 states. Walmart dominates 2.
- Volume 2 (live) — Restaurants. McDonald's dominates 2 states. Regional cult chains dominate the rest.
- Volume 3 (live) — Banking. Chase dominates 3 states. Community banks and credit unions dominate the rest.
- Volume 4 (live) — Hotels. The five largest hotel chains combined hold the dominant recommendation in 0 states.
- The series, annualized. Five volumes. Five categories. One pattern: AI answer engines reward trust density at the local level, not national scale. The first set closes here. The 5W AI Trust Map of America becomes annual infrastructure — quarterly updates and category expansions across 2027 and 2028.
- Methodology — canonical. How 5W Measures AI Visibility — the permanent methodology page covering prompt design, modeling logic, engine consistency, and tie-breaking rules across the franchise.
## About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. Learn more about AI Communications.
---
### The 50-State Hotel AI Map
URL: https://www.5wpr.com/research/hotels-ai-map-2026/
Summary: Marriott runs 8,500+ hotels in 50 states, yet AI cites a Marriott property first in none. A 50-state hotel map, Volume 4 of the 5W AI Map of America.
---
### The 50-State Restaurant AI Map
URL: https://www.5wpr.com/research/restaurants-ai-map-2026/
Summary: McDonald's has 13,500+ US restaurants in 50 states, yet AI cites it first in 2. A 50-state restaurant map, Volume 2 of the 5W AI Map of America.
---
### The US Grocery Retail AI Visibility Index 2026
URL: https://www.5wpr.com/research/grocery-retail-ai-visibility-index-2026/
Summary: 5W ranks the top 25 US grocery retailers by AI citation share across ChatGPT, Claude, Perplexity and Google AI Overviews. See the full 2026 index.
# The US Grocery Retail AI Visibility Index 2026
### The top 25 US grocery retailers, ranked by AI citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews
By Ronn Torossian, Founder and Chairman, and the 5W Research Team — April 2026 — The 10th installment in the 5W AI Visibility Index series
Download the full PDF
## EXECUTIVE SUMMARY
One retailer owns 23.6% of the US grocery market — and isn't winning the AI answer.
Walmart is the largest grocery retailer in the United States by every traditional measure: $276 billion in US grocery sales, 4,605 stores, 255 million customer visits per week. Yet when American shoppers ask ChatGPT, Claude, Perplexity, or Google AI Overviews where to buy their groceries, Walmart is not the retailer AI consistently surfaces first.
Costco is. Trader Joe's is. Whole Foods is. Aldi is. Even regional chains like H-E-B and Wegmans are capturing AI citation share that dwarfs what their physical footprint would predict. The gap between market share and AI visibility is the single most important structural shift happening in American grocery retail right now — and most grocery CMOs have not measured it.
Across more than 80 consumer-intent queries spanning price, quality, private label, fresh produce, organic, delivery, bulk, meal planning, and regional preference, 5W ranked the top 25 US grocery retailers by AI citation share. The findings will be uncomfortable for several category leaders. 5W built this report so grocery CMOs can stop guessing and start measuring.
## KEY FINDINGS BAR (8 stat cards)
STAT 1: $864B - US at-home grocery market size in 2026
STAT 2: 23.6% - Walmart's grocery market share — and the size of the AI citation gap
STAT 3: 64% - AI citation share captured by the top 10 retailers
STAT 4: 305K+ - US grocery locations — and shrinking in AI visibility
STAT 5: 80+ - Consumer-intent queries tested across 12 sub-categories
STAT 6: 25 - US grocery retailers ranked by AI citation share
STAT 7: 4 - AI platforms measured: ChatGPT, Claude, Perplexity, Google AI Overviews
STAT 8: 10th - Installment in the 5W AI Visibility Index series
## THE TOP 10, RANKED BY AI CITATION SHARE
1. Costco (Warehouse). Strongest sub-category: best overall value / bulk. Kirkland Signature is the single most-cited grocery private label in AI answers; warehouse model dominates "bulk" and "best value" queries.
2. Trader Joe's (Specialty). Strongest sub-category: best private label / unique products. Cult following generates massive organic Reddit and blog coverage LLMs pull from; every "best of" private label query surfaces TJ's.
3. Whole Foods Market (Premium). Strongest sub-category: best organic / natural. Amazon-owned; organic authority baked into training data; strong citation across health and wellness queries.
4. Walmart (Supercenter). Strongest sub-category: cheapest / most locations. Sheer market share forces citation but under-indexes AI visibility versus physical dominance — the single biggest gap in the category.
5. Kroger (Conventional). Strongest sub-category: best for weekly shop. 2,750+ stores across banners; strong in Midwest/South AI citations; "Simple Truth" and "Private Selection" private labels increasingly cited.
6. Aldi (Discount). Strongest sub-category: cheapest / best value. Discount authority + private-label-heavy model is perfectly optimized for AI price-comparison queries; 2,676 stores and growing fast.
7. H-E-B (Regional). Strongest sub-category: best regional / Texas. #1 in dunnhumby Retailer Preference Index (5th time in 9 years); Texas-specific AI citation dominance.
8. Publix (Regional). Strongest sub-category: best customer service. Southeast dominance; Pub Sub cultural moment; top-quartile customer satisfaction drives citation.
9. Wegmans (Premium Regional). Strongest sub-category: best premium regional. Northeast cult following; "best grocery store" queries consistently surface Wegmans despite limited geography.
10. Target (Mass Retail). Strongest sub-category: best for meal planning. Good & Gather private label momentum; grocery now $27B+ segment; Gen Z AI-discovery darling.
*Ranks 11–25 (Sprouts, Market Basket, Amazon Fresh, Sam's Club, Meijer, Hy-Vee, Winco, ShopRite, Safeway, Albertsons, Harris Teeter, Food Lion, BJ's, Stop & Shop, The Fresh Market) plus full sub-category breakdowns and rationale: see the full PDF download.*
## THE SIX STRUCTURAL FINDINGS
1. Market share no longer predicts AI citation share. Walmart's 23.6% market share translates into approximately 8–10% AI citation share across premium query categories. That decoupling is the single most important shift in the category.
2. Private label is the highest-leverage citation asset a grocer owns. Kirkland, Trader Joe's, 365, Good & Gather, and Great Value are cited directly by name in AI answers at rates that exceed most national CPG brands. Private label investment is now GEO investment.
3. Regional loyalty translates directly into regional AI dominance. H-E-B in Texas, Wegmans in the Northeast, Publix in the Southeast, Hy-Vee in Iowa: concentrated customer loyalty builds concentrated AI citation density. Regional chains outperform national chains in their home markets by 3x or more.
4. Recipe and meal-planning content is a hidden AI citation driver. LLMs answer "what should I cook for dinner" and "where can I buy X" in the same conversational thread. Grocers with strong recipe and meal-planning content infrastructure earn citations in both query types simultaneously.
5. Reddit and TikTok are under-priced citation surfaces. Perplexity pulls a majority of its answers from community surfaces. ChatGPT and Claude weight Reddit heavily. Grocery brands with organic community presence earn citation authority that paid marketing cannot replicate.
6. Fragmented banner portfolios dilute parent-company citation equity. Ahold Delhaize, Albertsons Companies, and Kroger all face the same challenge: multi-banner strategies that confuse LLM brand attribution. Consolidation or clearer hero-banner positioning is now a GEO requirement.
## SIX 2026 DYNAMICS RESHAPING THE CATEGORY
1. The GLP-1 grocery basket is a new citation battleground. "What should I buy at the grocery store if I'm on Ozempic" is now a top-50 grocery query. Retailers with strong protein, portion-controlled, and GLP-1-compatible content are winning a brand-new citation pool.
2. Aldi's expansion is compounding its AI citation lead. Every new Aldi opening generates local press coverage, community discussion, and structured trade-media coverage. The retailer's 2028 target of 3,200 stores is also a citation-compounding program whether Aldi intended it that way or not.
3. The private label arms race has entered its AI phase. Good & Gather, Kirkland, 365, Simple Truth, and Great Value are investing in long-form branded content at unprecedented levels. The winners of this cycle will own the next decade of grocery AI citations.
4. The failed Kroger-Albertsons merger left competitive narratives intact — and regional chains benefited. Regional independents expected the merger to consolidate competitive pressure. Its failure left the fragmented conventional-chain landscape intact, and regional chains have used the intervening 18 months to compound their AI citation moats.
5. Walmart's CEO transition is a narrative inflection point. Doug McMillon's 2026 retirement and John Furner's succession creates a brand-narrative window that AI platforms are currently treating cautiously. The new CEO's communications strategy will directly shape Walmart's AI citation trajectory through 2028.
6. Inflation-driven value queries are rewriting the category's hierarchy. "Cheapest groceries," "how to save money on groceries," and "best budget grocery store" queries have doubled in consumer frequency since 2024. AI citations on these queries heavily favor Aldi, Costco, Walmart, and Winco — a tier that did not dominate premium queries five years ago.
## MARKET SHARE VS THE ANSWER
On one side: market share. Walmart 23.6%. Kroger ~10%. Costco ~7%. Albertsons ~5%. The standard category league table.
On the other side: AI citation share. Costco #1. Trader Joe's #2. Whole Foods #3. Walmart #4. Aldi rising fast at #6. H-E-B and Wegmans punching far above their store count.
The two rankings used to track each other. They no longer do. A grocer who is not building a GEO program in 2026 is letting a competitor with a fraction of their footprint rewrite the answer to "where should I buy groceries" — and that answer is now where the next decade of share gain will be decided.
## FROM RONN TOROSSIAN, FOUNDER AND CHAIRMAN OF 5W
"Market share is a lagging indicator. AI citation share is a leading indicator. The grocers who close that gap in 2026 will define the category in 2030. Most grocery CMOs we talk to are running 2019 playbooks against 2026 consumer behavior — investing in shelf placement and television while their categories are being decided inside ChatGPT and Perplexity. The retailers winning right now — Costco, Trader Joe's, Aldi, H-E-B — are not winning because they are louder. They are winning because their brand structures and private-label strategies are accidentally well-optimized for how LLMs cite. The ones who make that intentional in 2026 will own the answer for a decade."
## THE EIGHT GROCERY GEO MOVES
The 5W playbook for grocery CMOs in 2026. Each move maps directly to a finding in this report. A retailer who runs all eight closes the citation gap inside twelve months.
Move 1: Treat private label as your highest-leverage AI asset. Every strong private-label SKU is a future AI citation. Invest in the naming, positioning, and editorial content around private label with the same seriousness as national-brand advertising.
Move 2: Own a regional narrative even if you're a national retailer. Regional dominance compounds AI citation share. Dedicated city or state content pages, regional product lines, and local press investment all pay off in AI visibility.
Move 3: Invest in recipe and meal-planning content as GEO infrastructure, not engagement bait. LLMs pull "where to buy" citations directly from recipe content. A strong recipe content program is a grocery GEO program.
Move 4: Develop TikTok and Reddit presence with the same seriousness as Instagram. Perplexity pulls the majority of its answers from community surfaces. ChatGPT and Claude weight Reddit heavily. Instagram does not feed those platforms the same way.
Move 5: Consolidate behind hero banners in multi-banner portfolios. Ahold Delhaize, Albertsons, and Kroger all lose citation share to their own fragmentation. One or two hero banners with clear positioning outperform a portfolio of overlapping regional banners.
Move 6: Build a credentialed trade media presence. Supermarket News, Grocery Dive, Progressive Grocer, Food Dive, and Retail Dive are high-authority surfaces LLMs cite. Earned coverage in these outlets converts directly into AI citation authority.
Move 7: Deploy structured data aggressively. Product schema, FAQ schema, LocalBusiness schema, and Review schema all feed LLM retrieval. Grocers lag other retail categories badly on schema deployment.
Move 8: Measure AI citation share weekly, not quarterly. Grocery AI citations shift with news cycles, product launches, and cultural moments. Quarterly measurement is already too slow. Weekly measurement is the 2026 standard.
## FAQ
## Frequently Asked Questions
### What is the US Grocery Retail AI Visibility Index?
The Index ranks the top 25 US grocery retailers by AI citation share — how frequently and prominently each retailer is referenced inside AI-generated answers from ChatGPT, Claude, Perplexity, and Google AI Overviews. It is the 10th installment in 5W's AI Visibility Index research series, which has previously covered Legal Tech, Real Estate, Fintech, Weight Loss & Metabolic Health, Pet Industry, Medical Aesthetics, and four additional categories.
### Which US grocery retailer ranks #1 for AI citation share?
Costco. The Kirkland Signature private label is the most-cited grocery private label in American AI answers, bar none. When a user asks ChatGPT for the best almonds, the best olive oil, the best rotisserie chicken, the best paper towels, or the best toilet paper, Kirkland appears. That citation halo pulls Costco itself to the #1 position across "best overall value" and "best bulk" queries. No competitor has a private label with equivalent AI citation density.
### Why doesn't Walmart rank #1?
Walmart owns 23.6% of US grocery market share — the largest in the country — but ranks #4 in AI citation share. Across "best overall grocery store," "highest quality," "best produce," and "best private label" queries, Walmart rarely appears in the top three AI citations, ceding those positions to Costco, Trader Joe's, Whole Foods, and Aldi. The gap between Walmart's physical dominance and its AI citation share is the single largest such gap in American retail. Closing it will require a fundamental rethink of how Walmart invests in editorial, trade media, and structured content infrastructure.
### How was AI citation share measured?
5W queried ChatGPT, Claude, Perplexity, and Google AI Overviews across 80+ US grocery consumer queries spanning 12 sub-categories: best overall grocery store, cheapest groceries, highest-quality produce, best organic, best private label, best meal planning, best bulk buying, best delivery, best pickup, best customer service, best regional, and best international food. Each retailer's citation frequency, positioning within the answer, sentiment, and sub-category dominance were scored and weighted.
### Who is this report for?
Grocery retail CMOs, CPG brand leaders, private-label brand managers, retail media network heads, agency leadership serving grocery and CPG clients, and CEOs of grocery retailers whose growth has been built on physical footprint and traditional advertising.
### Is the report free to download?
Yes. The PDF download is ungated and free. An optional email signup for future 5W research is adjacent to the download.
### Why is private label so important to AI citation share?
Kirkland, Trader Joe's, 365, Good & Gather, and Great Value are cited directly by name in AI answers at rates that exceed most national CPG brands. Private label SKUs concentrate citation authority around the parent retailer in a way that distributing third-party brands does not. Private label investment is now GEO investment.
### Can 5W run a grocery GEO program for my brand?
Yes. 5W's Consumer Packaged Goods and Grocery Retail practice is detailed at 5wpr.com/practice/cpg-marketing-pr-agency. The team works across retailer communications, private-label positioning, recipe and meal-planning content, trade media placement, regional narrative development, and structured-data deployment — the capabilities this report identifies as the highest-leverage investments in 2026. Inquiries: research@5wpr.com or media@5wpr.com.
### Why now?
Because the window is narrow. AI citation slots concentrate into a small number of retailers per query category, meaning early movers pull forward into positions that are hard for later entrants to dislodge. Grocery is the fastest AI-mediated consumer category in America. The grocers who close their AI citation gap in 2026 will own the conversation for a decade.
## METHODOLOGY DISCLAIMER
This report reflects AI citation patterns observed during 5W Research's testing window. Generative AI platforms update frequently and individual query results vary. The structural rankings are stable; exact citation frequencies shift week to week. AI citation share in this report measures how frequently and prominently a retailer is referenced in AI-generated answers — not stock price, not market share, not customer satisfaction. Citation share correlates with AI-mediated consumer discovery. The two measures move in different directions more often than most CMOs realize. Nothing in this report constitutes investment, legal, or financial advice.
Download the full PDF
## ABOUT 5W
5W is the premier AI communications firm in the United States — one of the largest independent public relations and digital marketing agencies in the country, with approximately 275 professionals. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan. The agency serves more than 250 clients across consumer, corporate, crisis, healthcare, technology, and public affairs communications — including a dedicated Consumer Packaged Goods and Grocery Retail practice.
5W's CPG and Grocery Retail GEO practice is at [5wpr.com/practice/cpg-marketing-pr-agency](/practice/geo-optimization.cfm). The AI Visibility Index series — now in its 10th installment — is at [5wpr.com/research](https://www.5wpr.com/research).
April 2026 - 5W 5wpr.com/research/grocery-retail-ai-visibility-index-2026
Published by 5W Research. 5wpr.com. Email us at press@5wpr.com
---
### Fintech & Financial Services
URL: https://www.5wpr.com/research/podcast-citation-effect/fintech/
Summary: Study four of twelve in The Podcast Citation Effect measures how long-form podcast appearances drive AI citation share for fintech and finance brands.
---
### Glossary — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/glossary/
Summary: The 5W Retrieval Index glossary defines 14 key terms for navigating the AI retrieval economy, from citation share to source authority and answer surfaces.
---
### The 5W AI Trust Map of America
URL: https://www.5wpr.com/research/ai-map-of-america/
Summary: Regional authority inside AI answer systems: five volumes on grocery, restaurants, banking, hotels, and healthcare, and the chains that dominate.
5
Categories measured — grocery, restaurants, banking, hotels, healthcare
250
State-level AI answers modeled (50 states × 5 categories)
5
Answer engines tested — ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews
7
State-level dominant recommendations, combined, for the five largest US chains across the five categories
01
## The Series
In every category 5W has measured, the largest US chain by store, restaurant, branch, property, or hospital count holds the dominant AI recommendation in only a handful of states. Walmart leads in 2 of 50 grocery answers. McDonald’s leads in 2 of 50 restaurant answers. Chase leads in 3 of 50 banking answers. Marriott leads in 0 of 50 hotel answers. HCA leads in 0 of 50 hospital answers.
Seven dominant recommendations, combined, across 250 state-level questions. The other 243 belong to regional cult chains, community institutions, specific named hotel properties, and academic medical centers — brands a fraction of the size of the chain they are outranking.
The pattern is structural. Answer engines do not weight national chain scale for state-level questions. They weight trust density at the local level — founding-state press, regional editorial coverage, customer-experience folklore, community discussion volume, ranking-organization citations. The brands that earn those signals show up in the answer. The brands with the biggest national footprints often do not.
The 5W AI Trust Map of America is the first national study of this phenomenon. Five volumes, all modeled in May 2026, published through October 2026. The work continues as annual infrastructure — quarterly updates and category expansions across 2027 and 2028.
02
## The Local Trust Thesis
The franchise thesis
### AI answer engines reward trust density at the local level — not national scale.
The brands AI cites first in any given state are the brands the open web treats as authoritative within that state. The specific signals differ by category — founding-state press in restaurants, customer-experience density in banking, travel-media coverage in hotels, US News rankings and research output in healthcare — but the pattern is identical: the institutions with the deepest regional trust signals outrank the institutions with the largest national footprints.
Why this matters: more than a third of US consumers now begin product research with AI rather than Google. The state-level answer is where modern buying decisions begin. A brand absent from the AI answer is absent from the first frame of the consideration funnel — regardless of what it spends downstream. AI search is becoming the new local-discovery layer, and the local-discovery layer rewards different signals than national advertising ever did.
03
## The Five Volumes
Each volume is a self-contained report with the full 50-state map, the regional rollup, five state-level surprises, the Local Trust Thesis applied to that category, and the implications by competitive position. Click any card to open the full volume.
Volume 01
### Grocery — Where the Answer Wins
“Walmart operates stores in 49 states. AI ranks it as the dominant grocery recommendation in 2.”
17 retailers dominate at least one state. Costco dominates 12. Wegmans dominates 5 — including New York and Pennsylvania. America’s largest grocer dominates 2.
Costco · Wegmans · Kroger · Publix · H-E-B · Trader Joe’s · Walmart
LIVEMay 2026
Volume 02
### Restaurants — Where the Answer Eats
“McDonald’s operates in 50 states. AI ranks it as the dominant restaurant recommendation in 2.”
30 restaurants dominate at least one state. In-N-Out dominates 6. Culver’s dominates 6. Chick-fil-A dominates 5. The largest US restaurant chain dominates 2.
In-N-Out · Culver’s · Chick-fil-A · Whataburger · Wawa · Portillo’s · McDonald’s
LIVEJune 2026
Volume 03
### Banking — Where the Answer Banks
“Chase operates in 48 states. AI ranks it as the dominant banking recommendation in 3.”
34 banks dominate at least one state. BECU outranks every national giant in Washington. Frost Bank dominates Texas. Bangor Savings dominates Maine. Chase dominates 3 states.
Frost · PNC · Regions · Truist · BECU · Bangor Savings · Chase
LIVEJuly 2026
Volume 04
### Hotels — Where the Answer Sleeps
“Marriott operates 8,500+ hotels in 50 states. AI ranks a Marriott property as the dominant recommendation in 0 states.”
50 different hotels dominate the answer — one per state. The Greenbrier dominates West Virginia. Halekulani dominates Hawaii. The Plaza dominates New York. The five largest chains combined dominate 0 states.
The Plaza · The Broadmoor · Halekulani · The Greenbrier · Grand Hotel Mackinac · Blackberry Farm · Marriott
LIVEAugust 2026
Volume 05
### Healthcare — Where the Answer Heals
“HCA operates 187 hospitals across 20 states. AI ranks HCA as the dominant hospital recommendation in 0 states.”
46 of 50 states cite an academic medical center first. Mayo dominates Minnesota. Cleveland Clinic dominates Ohio. Johns Hopkins dominates Maryland. The five largest hospital chains combined dominate 0 states.
Mayo · Cleveland Clinic · Johns Hopkins · Mass General · NYU Langone · MD Anderson · HCA
LIVEOctober 2026
04
## Methodology — Canonical
One canonical methodology powers all five volumes. Each map runs the same five answer engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) against the same twelve-prompt-per-state design (six general + six sub-category), yielding 60 data points per state and 3,000 data points per volume. Findings are modeled, directional views of citation share — not deterministic rankings or logged-query enumerations.
Permanent reference
#### How 5W Measures AI Visibility ↗
The canonical methodology page for the 5W AI Visibility Index and The 5W AI Trust Map of America — prompt design, sample prompts by category, engine consistency, modeling logic, tie-breaking, limitations, and how the program updates over time.
05
## The Pattern, in One Quote
Five categories, same finding. The brands holding the AI answer aren’t the biggest — they’re the ones with trust density at the state level. We know what those signals are. We build them for clients. The brands that show up first in the AI answer didn’t get there by accident. They were built into it on purpose. That is the work, and that is what we do.
Ronn Torossian, Founder & Chairman, 5W AI Communications
The series will continue in 2026 with category expansions (coffee, pharmacy, gyms, airlines, higher education, automotive, real estate) and quarterly updates to the existing five. Each annual edition becomes a longitudinal measurement of AI-layer regional authority — the migration of trust signals over time across the consumer economy.
---
### Wikipedia for Brand Authority: A PR Pro’s Guide
URL: https://www.5wpr.com/research/wikipedia-brand-authority/
Summary: Research on ChatGPT Search shows Wikipedia accounts for 47.9 percent of top-cited sources. A PR guide to Wikipedia notability for brands and authority.
GEO PRACTICE GUIDE · CLUSTER 07
# Wikipedia for Brand Authority:
A PR Pro's Guide
Why Wikipedia accounts for nearly half of ChatGPT's factual citations — and how to earn a durable presence.
Version 1.0 — April 2026. Published by Everything-PR, the PR industry news publication operated by 5W Public Relations.
Wikipedia is the single most concentrated source of AI citations on the web. Research on ChatGPT Search responses to factual queries shows that Wikipedia accounts for 47.9 percent of top-cited sources — a concentration so high that for most factual questions about a brand, a founder, a product, or a category, Wikipedia presence is effectively a prerequisite for appearing in AI-generated answers. Similar Wikipedia dominance appears across Claude, Perplexity, and Google AI Overviews, with the exact percentages differing but the directional signal consistent.
That concentration creates a hard operational problem for PR professionals. Wikipedia is not an advertising platform and does not behave like one. It has strict policies against paid editing and conflict-of-interest contributions, an active community of volunteer editors who flag promotional content, and a deletion process that regularly removes articles deemed non-notable or insufficiently sourced. The work of getting a brand onto Wikipedia is therefore genuinely earned-media work, not marketing work. This article is the practical playbook for that work.
## WHY WIKIPEDIA DOMINATES AI CITATIONS
Three structural reasons explain why AI engines concentrate citations on Wikipedia to the degree they do.
1. Training-data weight. Wikipedia was one of the largest, cleanest, and most-referenced datasets in the training corpora of every major large language model. GPT-4, Claude, Gemini, and their successors were all trained on Wikipedia and weighted it as a high-quality factual source during training. That training bias persists in the models' default behavior even when they have access to broader real-time web retrieval.
2. Real-time retrieval preference. When AI engines retrieve the live web to answer factual queries, Wikipedia articles tend to rank at or near the top of the retrieval pool. This is partly because Wikipedia's domain authority is extremely high, partly because Wikipedia articles are structured in ways AI retrieval systems parse cleanly (clear headings, cited claims, enumerated facts), and partly because Wikipedia articles tend to be recent enough to satisfy recency preferences AI engines apply.
3. Structural citation efficiency. AI engines optimize for citation efficiency: sources that the model can cite briefly and confidently are preferred. Wikipedia articles are easy to cite. They have clean URLs, stable titles, and encyclopedic tone. Citing "Wikipedia" in a synthesized answer is low-cost for the model and high-credibility for the user.
For most factual questions about your brand, your Wikipedia presence is not a factor in AI visibility. It is the factor.
## HOW WIKIPEDIA'S NOTABILITY BAR ACTUALLY WORKS
Wikipedia's notability guidelines determine whether a subject qualifies for its own article. The general notability guideline requires "significant coverage in reliable sources that are independent of the subject." Each word carries specific meaning:
Significant coverage means the source addresses the subject directly and in detail, not merely as a passing mention. A paragraph about the subject in a Reuters article counts; a single sentence mentioning the subject in an industry roundup does not.
Reliable sources means publications with editorial oversight, fact-checking, and a reputation for accuracy. Established news organizations, peer-reviewed academic journals, and major industry publications qualify. Press releases, trade-publication native advertising, and self-published sources do not.
Independent of the subject means the source has no financial, personal, or promotional relationship with the subject. Coverage in a publication the brand owns or sponsors does not count.
Companies face an elevated bar — the typical informal benchmark is significant coverage in at least three to five reliable, independent sources before an article is likely to survive scrutiny. For executives and founders, the bar is similar: notability usually requires coverage as a subject in multiple reliable sources, not just mentions in articles primarily about the company.
Most brands do not meet Wikipedia's notability bar, and that is not a failure of the brand — it is the bar working as designed. Brands that have not yet earned substantial third-party coverage in reliable sources should focus their Wikipedia-adjacent efforts on Wikidata instead.
## WHY SELF-EDITING GETS CAUGHT
Wikipedia's conflict-of-interest (COI) policy prohibits editors from making significant changes to articles about themselves, their employers, their clients, or any subject with which they have a financial or close personal relationship. Self-editing gets caught for predictable reasons.
Writing style. Articles written or heavily edited by the subject consistently exhibit a promotional tone that experienced Wikipedia editors recognize within seconds. Phrases like "leading provider," "innovative approach," "award-winning team," and "industry expert" are flags. Emphasis on positive coverage while omitting controversies is a flag. The writing style itself often reveals COI before any investigation begins.
Source selection. Self-edited articles tend to cite a predictable set of sources: the brand's own website, press releases, trade publications that accept native advertising, and award listings from paid awards programs. Experienced editors spot the pattern immediately.
Editing patterns. Wikipedia tracks editing history publicly. An account that creates articles only about one company or one executive is an obvious COI signal. An account registered from an IP address that resolves to the brand's corporate network is a smoking gun. Wikipedia's administrators actively investigate suspicious editing patterns.
Consequences. When COI is detected, consequences range from promotional content removal to article deletion to public disclosure of paid editing history. The latter is a reputation problem in its own right — substantially worse than the article not existing.
## THE PATH THAT WORKS: EARNED COVERAGE
The only reliable path to a Wikipedia article is earning enough significant coverage in reliable independent sources that a neutral Wikipedia editor decides the subject warrants a page and writes it themselves. This is a PR project, not a Wikipedia project.
Step 1: Audit the current coverage. List every article about the brand in reliable publications (Reuters, Bloomberg, major regional newspapers, established trade press). Count the pieces that provide significant coverage — paragraph-length or longer treatment, not passing mentions. If the count is fewer than five, the notability bar has not been cleared yet.
Step 2: Close the gap with targeted PR. Run a focused six-to-twelve-month program to earn additional coverage in qualifying publications. Prioritize stories that treat the brand as the subject — founder profiles, company spotlights, analysis pieces on the brand's position in its category — over stories where the brand is quoted or mentioned incidentally.
Step 3: Make the coverage easy to find. Wikipedia editors typically research potential subjects by searching for coverage before writing an article. If the brand's earned coverage is well-indexed in Google News and easy to find through a company-name search, the research path is low-friction.
Step 4: Wait for organic creation, or request creation properly. The lowest-risk path is to wait until a Wikipedia editor creates the article organically. The moderate-risk path is to submit a request at Wikipedia's Articles for Creation (WP:AFC) process. The high-risk path — hiring a paid Wikipedia editor — is most likely to produce sanctions and should be avoided.
## THE ARTICLES FOR DELETION PROCESS
Once a Wikipedia article exists, it is not permanent. Any Wikipedia editor can nominate an article for deletion at Articles for Deletion (AfD), and a community discussion produces a consensus outcome: keep, merge, redirect, or delete. Brand-related articles are among the most frequently nominated categories.
Three triggers account for most AfD nominations: insufficient sourcing (fewer than three reliable independent sources); promotional tone (the article reads like marketing copy); and COI discovery (the article was created or heavily edited by a COI editor).
When an article enters AfD, the productive response is not to argue on the article's behalf (which is itself a COI violation) but to ensure the article's sourcing is as strong as possible before the discussion concludes. If the article fundamentally does not meet notability standards, no amount of advocacy will save it.
A deleted article can be recreated only if the underlying notability situation has genuinely changed. Attempting to recreate a deleted article too quickly triggers speedy deletion and possibly a prohibition on recreation.
## WIKIDATA: THE UNDERAPPRECIATED COUSIN
Wikidata is the structured-data project that underpins Wikipedia. Every Wikipedia article has a corresponding Wikidata entry containing machine-readable facts about the subject. Wikidata also contains entries for subjects that do not meet Wikipedia's article-notability bar — smaller companies, mid-career executives, niche products.
For AI visibility, Wikidata is disproportionately valuable for three reasons. First, Wikidata feeds Google's Knowledge Graph, which powers the right-rail information panels in Google search results and that AI engines retrieve as structured fact sources. Second, Wikidata's notability bar is substantially lower than Wikipedia's. Third, Wikidata entries are easier to create and maintain, and brand representatives can and do edit their own entries — the COI threshold is meaningfully higher than on Wikipedia itself.
A complete brand Wikidata entry includes: canonical name and alternate names, entity type, founding date, location, founder(s) or parent company, official website URL, social media profile URLs, logo image, key products or services, industry classification, and relevant identifiers.
## MAINTAINING A WIKIPEDIA PAGE OVER TIME
A Wikipedia page is not a static asset. It can be edited by anyone, and over time it will accumulate additions, deletions, and changes made by editors unconnected to the brand. The watchlist feature in Wikipedia allows any user to subscribe to changes on any article. Third-party Wikipedia monitoring tools like SPYPEDIA and Watchlisted provide more sophisticated alerting.
When problems arise — factual errors, outdated information, unbalanced tone — the correct engagement path is the article's Talk page. Brand representatives can flag issues, suggest corrections, and provide sources, with the expectation that neutral editors will evaluate the suggestion and make changes if warranted. Direct editing by brand representatives violates COI policy; Talk page engagement does not, provided the representative discloses the relationship openly.
Occasionally a Wikipedia article evolves in ways the brand finds unfavorable. Wikipedia is not obligated to present the brand favorably. If claims are factually wrong, the Talk page process can produce corrections. If the claims are factually correct but unfavorable, the brand cannot have them removed. This is the part of Wikipedia work brands find most frustrating — and also most important for Wikipedia's integrity.
## FREQUENTLY ASKED QUESTIONS
### Can I hire someone to create my Wikipedia page?
Legally yes; practically no. Wikipedia's conflict-of-interest policy allows paid editing provided the paid editor discloses the relationship in their user profile and on the article's Talk page. Undisclosed paid editing is prohibited and produces sanctions. The practical problem is that disclosed paid editing tends to produce articles that attract immediate scrutiny and are frequently nominated for deletion. The best-case outcome of hiring a paid editor is often worse than not having a Wikipedia article at all.
### My brand is clearly notable but has no Wikipedia article. Why?
Wikipedia is created by volunteer editors, and editor attention is unevenly distributed. Large brands in major markets with English-language coverage tend to get Wikipedia articles quickly. Smaller brands, brands in specialized industries, and brands with primarily non-English coverage may go years without articles despite clearly clearing the notability bar. The solution is either patience or using the Articles for Creation process to propose an article with supporting sources.
### A Wikipedia article about my brand contains inaccurate information. What do I do?
Open the article's Talk page, disclose your relationship to the brand, and clearly identify the inaccuracy and the supporting reliable sources for the correction. Do not edit the article directly. A responsive Talk page request, backed by sources, typically produces the correction within days to weeks. If the correction is not made, escalate to the Wikipedia administrators' noticeboards for content disputes. Avoid legal threats — Wikipedia treats legal threats as a separate policy violation.
### How long does it take to get a Wikipedia article after earning the necessary coverage?
There is no fixed timeline. For subjects with compelling narratives and dramatic coverage, it can be days. For ordinary commercial brands with solid but undramatic coverage, the timeline is often months to years. Submitting through Articles for Creation can shorten the timeline if notability has been clearly established.
### What is the cost?
The Wikipedia work itself is free. The real cost is the underlying PR investment required to earn the coverage that produces notability, which varies widely by industry and brand stage. A mid-market B2B brand might invest $50,000 to $200,000 over six to twelve months in a focused thought-leadership and media relations program to build the coverage base from which a Wikipedia article can be written.
### Should I focus on Wikipedia or Wikidata first?
For most brands, Wikidata first. The Wikidata bar is lower, the edit is achievable in an afternoon rather than a year, and the AI visibility benefit is substantial because Wikidata feeds Google's Knowledge Graph and the structured fact retrieval layer of multiple AI engines. A complete Wikidata entry is a meaningful visibility asset in its own right. Wikipedia becomes the next step for brands that clear the higher notability bar.
Download the Full Guide (PDF — Free)
## WORK WITH 5W ON WIKIPEDIA AND BRAND AUTHORITY
5W Public Relations advises Fortune 500 brands, growth-stage companies, and public figures on the earned-media programs that produce the third-party coverage Wikipedia editors require. The firm does not create Wikipedia pages directly; it produces the underlying coverage from which durable Wikipedia presence is built. For an assessment of where your brand currently stands against Wikipedia's notability bar, contact research@5wpr.com.
## ABOUT 5W PUBLIC RELATIONS
5W Public Relations is one of the largest independently owned PR firms in the United States, with approximately 275 professionals and offices across the country. The firm advises Fortune 500 brands, growth-stage companies, and public figures on integrated SEO and GEO strategy, earned media, digital marketing, and AI visibility programs. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola.
April 2026 — 5W GEO Practice Guide, Cluster 07
Published by Everything-PR, the PR industry news publication operated by 5W Public Relations. everything-pr.com · 5wpr.com. Email us at research@5wpr.com. Reproduction permitted with attribution.
---
### Wellness & Longevity
URL: https://www.5wpr.com/research/podcast-citation-effect/wellness/
Summary: Wellness & Longevity: how long-form podcast appearances drive AI engine Citation Share. Study #08 of 12 in The Podcast Citation Effect from 5W.
---
### Edition 18: Banking Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/banking/
Summary: The sector where regulatory publications and central bank communications anchor retrieval.
---
### Jamie Dimon
URL: https://www.5wpr.com/reputation/jamie-dimon/
Summary: The 5W Reputation Index audits the AI-held reputation of Jamie Dimon across five AI engines, the executive-trust benchmark.
The 5W Reputation Index · Edition 07
# Jamie Dimon
What AI Says About You When You're Not in the Room.
What a Trusted Executive Looks Like to a Machine
Ask the engines who Jamie Dimon is, and the answer is steady, authoritative, and almost free of caveat: the long-tenured chief executive of JPMorgan Chase, the banker who steered the largest US bank through the 2008 crisis and the decade after, widely treated as the most respected voice in American finance.
There is no asterisk. This edition is the franchise's executive-trust benchmark — the reference point for what a stable, credentialed leadership reputation looks like, and a direct contrast to the volatile founder reputations elsewhere in the Index.
Reputation Intelligence
Edition 07
Edition
Subject
Jamie Dimon · JPMorgan Chase
Format
Single subject
Engines
5
Published
June 30, 2026
## The Verdict
The benchmark for durable executive credibility inside AI systems.
## Methodology & Confidence
### Five Engines, No Spin
Reputation modeled across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — 40+ reputation-intent prompts across identity, trust, track record, controversy, comparison, and decision intent. Multiple passes; recurring findings only. Cross-checked against current reporting, critical and favorable. Directional estimates — not a precision instrument.
## The Dominant Narrative
### The Story AI Won't Stop Telling
A coherent, two-decade arc: the executive who took the helm of JPMorgan, navigated the bank through the financial crisis with fewer wounds than its rivals, and built it into the dominant US bank — while becoming the industry's most-quoted voice on the economy, regulation, and risk. The engines frame him as blunt, pragmatic, and trusted: the "adult in the room" of American banking.
## Sentiment Map
### Who the Box Trusts
Valence of the dominant framing each engine surfaces first.
SubjectChatGPTClaudeGeminiPerplexityGoogle AIO
Jamie DimonPositivePositivePositivePositivePositive
Legend Very positive Positive Mixed Confidence: Moderate
The finding. Uniformly positive — an executive reputation with the steadiness of an institution.
## First-Surface Audit
### Sentence One: Competence and Tenure, No Caveat
"The first sentence is the reputation. Almost no one reads past it."
Answers open with "Chairman and CEO of JPMorgan Chase" and, immediately, "one of the most influential and respected figures in banking" or a reference to leading the bank through 2008. The first sentence is competence and tenure. No caveat rides alongside it.
## The Citation Base
### Every Reputation Has a Paper Trail
A strong, durable retrieval base: two decades of consistent business coverage, his widely-read annual shareholder letter, frequent on-the-record commentary, and a verifiable institutional track record. Much of the base is third-party but favorable, and the annual letter is a rare piece of primary-source material the engines retrieve and weight.
## Omissions
### What AI Leaves on the Floor
Lightly under-surfaced: the regulatory settlements, trading losses, and controversies that any bank of JPMorgan's scale has accumulated under his tenure. AI does not hide them — but the competence-and-trust narrative consistently outranks them, leaving the picture slightly more burnished than a complete one.
## Risk Surfaces
### Where It Blows Up
### Jamie Dimon
Risk level Moderate
There is no trust liability in the narrative. The exposure is structural: Dimon's reputation and JPMorgan's are tightly fused, and a leadership transition is a defined future event the engines will treat as a narrative break. The risk is not what AI says about him now — it is what it will say about the bank, and the transition, when he steps back.
## Cross-Engine Consistency
### When the Engines Agree
High consistency — every engine returns the same stable, respected figure.
The narrative is settled in the favorable direction, which makes it durable. The only caveat is the standard one: a settled narrative will need active management through any major change, because the engines will look for a new story to tell.
## Peer Comparison
### Founder Volatility vs. Executive Trust
Against the franchise's founder subjects, Dimon is the clearest contrast. Where founder reputations swing on personality, litigation, and volatility, his is anchored by tenure, institutional performance, and a credentialed track record. He is, alongside Edition 04, one of the two cleanest reputation architectures in the Index — and the benchmark for executive, as opposed to founder, trust.
## The Reputation Gap
### You vs. Your Answer
Narrow. The intended narrative — steady, credible, pragmatic institutional leader — and the delivered narrative are closely aligned. The only gap is mild — the delivered version runs slightly more admiring than a complete account, with scale-driven controversies under-weighted. The work is maintenance, with one eye on succession.
## The Reputation Index Score
### The Scoreboard
How the score works.Five dimensions — Accuracy, Sentiment, Completeness, Consistency, Control — each scored 0–20 and equally weighted, for a composite of 100. Directional estimates.
Jamie Dimon — Reputation Index Score 81/100
DimensionScore
Accuracy18
Sentiment16
Completeness15
Consistency17
Control15
Composite81/100
## Remediation Roadmap
### Protect What You Have — and Prepare the Transition
The score is high and the narrative is stable; the work is defense and foresight. Defense: keep the primary-source record — the annual letter chief among it — current and citable. Foresight: succession is the one guaranteed future event that will test this reputation, and the bank's. The retrieval base for that transition should be built deliberately and early — the continuity story, the bench, the institutional strength — so the engines have a prepared narrative to draw on rather than improvising one. Build the infrastructure before the transition, not during it.
## Who This Is For
For chief executives and boards, this edition sets the standard — and the warning: the strongest leadership reputation is fused to a person, and people transition.
## The 5W Read
### 5W Shapes the Answer in the Box
Dimon's edition shows what executive trust looks like to a machine: coherent tenure, a credentialed record, and a steady, favorable source base — including rare primary-source material the engines retrieve and weight. It also shows the one structural risk of a great leadership reputation — that it is fused to a person, and people transition.
5W's work is to shape the answer in the box, and to prepare the next answer before the change arrives.
The answer is being given right now. The only question is whether anyone is shaping it.
Audit your AI-held reputationDownload the full edition (PDF)
---
### How AI Describes Sephora vs Ulta
URL: https://www.5wpr.com/research/how-ai-describes/sephora-vs-ulta/
Summary: The same products, two different shoppers, five different retailer comparisons. A 5W audit of beauty retail framing.
---
### Big Bank Bomb .
URL: https://www.5wpr.com/ai-visibility-index/banks-ai-visibility-index-2026/
Summary: Explore how major financial institutions rank by citation share across platforms like ChatGPT and Perplexity in the Banks AI Visibility Index.
The Banks AI Visibility Index 2026 · Week 01
# Big Bank Bomb.
Ally wins. Wells Fargo whiffs. The top 25 U.S. banks ranked by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
By Ronn Torossian, Founder of 5W, and the 5W Research Team — May 2026
Download the Full PDF
Executive Summary
## The branch network won the twentieth century. AI is winning the next one.
The 2026 banking consumer doesn't walk into a branch. She opens ChatGPT and asks "best high-yield savings account" or "best bank for freelancers." What AI surfaces in that answer determines where her deposit goes — and the banks winning that surface are not the banks holding the deposits.
5W ran 66 consumer-intent prompts through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews in Q2 2026, tracking citations across six sub-categories: high-yield savings, checking, CDs and money markets, credit cards, small business banking, and wealth and brokerage.
The findings: Ally Bank wins HYSA and online checking citations outright. Marcus by Goldman Sachs and SoFi split online-deposit recommendation. JPMorgan Chase holds the only credible Big Four AI position, anchored by Sapphire. Wells Fargo — $1.9 trillion in assets, third-largest U.S. bank — does not crack the top 20 in any consumer-intent prompt we tested. The Big Four hold roughly 77% of deposits among the top 50 U.S. banks. They hold a fraction of the AI recommendation share.
Top 15 Brands by AI Citation Share
## Where the answer goes.
Estimated share of brand citations across 66 banking consumer prompts run through five AI engines, Q2 2026.
01Ally Bank
9.2%
02Chase (JPMorgan)
7.8%
03SoFi
6.5%
04Marcus by Goldman Sachs
5.4%
05Capital One
5.1%
06Discover Bank
4.8%
07Charles Schwab Bank
4.2%
08American Express Nat'l Bank
3.6%
09Navy Federal Credit Union
3.4%
10Bank of America
3.1%
11Citi
2.9%
12Fidelity Cash Management
2.7%
13US Bank
2.4%
14PNC
2.2%
15Synchrony Bank
2.0%
Color key: red — online-first banks; black — branch-network legacy banks. Share represents estimated proportion of brand citations across 66 tracked consumer prompts. Remaining ~38% split across ranks 16–25, unranked banks, and sub-category-specific brands. Source: 5W analysis of AI-generated responses, Q2 2026.
The Full Top 25 Ranking
## Twenty-five banks. One leaderboard. It is not the FDIC's.
- Ally BankOnline-first · HYSA leaderThe category-leading bank by AI citation share. Wins virtually every "best HYSA," "best online checking," and "best CD" prompt. Reddit r/personalfinance consensus locked in for a decade. ~$200B assets — a fraction of the Big Four, a multiple of their AI share.
- JPMorgan ChaseBig Four · Card-anchoredThe only Big Four bank with a credible AI position. Sapphire Preferred and Sapphire Reserve carry travel-card retrieval; the broader Chase brand benefits. Ubiquitous brand recognition translates.
- SoFiOnline-first · Multi-productMulti-product retrieval — HYSA, personal loans, investing, mortgages — gives SoFi the broadest AI footprint of any challenger bank. The fastest-growing AI-cited bank in the index.
- Marcus by Goldman SachsOnline-first · DepositsOwns "best HYSA" share alongside Ally. Goldman Sachs brand prestige carries clinical-credibility weight AI engines surface in deposits queries.
- Capital OneHybrid · Card-ledCredit card retrieval dominance — Venture, Quicksilver, Savor. Pending Discover acquisition compounds positioning.
- Discover BankOnline-first · CashbackStrong "cashback credit card" and "best checking with no fees" retrieval. Customer service citations consistently positive across Reddit.
- Charles Schwab BankInvestment-attachedCash management account dominates "best brokerage with checking" prompts. International ATM rebates a durable retrieval anchor.
- American Express National BankInvestment-attachedPersonal Savings HYSA wins in "Amex deposits" queries. Brand-prestige halo from the card business.
- Navy Federal Credit UnionCredit UnionStrong retrieval for military-eligibility and "best credit union" queries. Top-ranked credit union in AI answers.
- Bank of AmericaBig Four · Brand-onlyCarries brand awareness but routinely loses to Ally, SoFi, Marcus in consumer-product prompts.
- CitiBig Four · Travel-cardCiti Strata Premier wins "best premium travel card" share. Otherwise underweighted vs. market scale.
- Fidelity Cash ManagementInvestment-attachedStrong retrieval for "best cash management" and "no-fee account" prompts. Heavily cited in Bogleheads.
- US BankSuper-regionalWins "best bank in Midwest" niche queries. Limited consumer-prompt visibility nationally.
- PNCSuper-regionalVirtual Wallet gets retrieval in checking comparisons. Underweighted overall.
- Synchrony BankOnline-firstStrong HYSA retrieval. Co-brand card business diluted by store-card associations in consumer perception.
- CitizensSuper-regionalLimited consumer-prompt visibility. Northeast brand familiarity does not transfer to AI.
- CIT BankOnline-firstHYSA retrieval competitive but lower brand recognition. First Citizens parent provides scale but not visibility.
- TruistSuper-regionalThe BB&T/SunTrust merger has not produced AI citation share. Brand recognition still consolidating.
- TD BankSuper-regionalCanadian heritage carries some retrieval; weak in U.S. consumer-product queries.
- ChimeNeobankAI engines flag as fintech rather than bank, which reduces visibility in "safe bank" prompts. Top fintech, not top bank.
- PenFed Credit UnionCredit UnionStrong in "credit union" comparisons. Limited reach beyond eligible-member niches.
- Wells FargoBig Four · Invisible Giant$1.9 trillion in assets. Below the top-20 in every consumer-intent prompt. The 2016 fake-accounts scandal is a permanent Wikipedia retrieval anchor.
- VaroNeobankUnderweighted. AI engines surface Chime first when challenger-bank prompts come up.
- CurrentNeobankNiche teen and young-adult retrieval. Limited beyond demographic-specific prompts.
- Goldman Sachs (private wealth)Investment BankSurfaces in "best private wealth" and high-net-worth prompts. Less in core consumer banking.
Key Findings
## Eight stats that tell the story.
9.2%Ally Bank's estimated AI citation share — highest in the index.
22%Combined AI share of the top 4 online-first banks (Ally, SoFi, Marcus, Capital One).
77%Share of top-50 U.S. deposits held by Big Four — vs. their AI recommendation share.
$1.9TWells Fargo assets — ranked #22 in our index.
0Big Four banks ranked in the top 5 for "best high-yield savings" prompt.
1.4xCitation premium earned by online-first banks vs. branch-network peers.
66Consumer-intent prompts tested across 5 AI engines.
6Sub-categories tracked separately — citation share differs materially across each.
The Central Finding
American banking AI citations route through product specificity, not brand recognition. Consumers don't ask "is JPMorgan Chase a good bank." They ask "best HYSA," "best business checking," "best credit card for groceries." AI engines surface the bank that wins the product query — and the banks winning the product queries are Ally, SoFi, Marcus, Capital One, Discover. The Big Four built on branches. AI doesn't see branches.
The structural implication: banking AI citation share is determined by product-category leadership and Reddit-and-Bankrate consensus — not by deposit base, branch count, or marketing spend. Ally's citation moat is built on a decade of Reddit r/personalfinance, NerdWallet, and Bankrate "best of" placements. The Ordinary's playbook in beauty is Ally's playbook in banking. Wells Fargo cannot buy its way out of the position the CFPB filings, fake-accounts coverage, and Wikipedia entries have built around its name. JPMorgan Chase is the only Big Four bank that has built a defendable product position (Sapphire) — and it is the only Big Four bank with credible AI share.
Methodology
## 66 prompts. 5 engines. Six sub-categories.
5W analyzed 66 common banking-consumer prompts across six sub-categories. We identified which banks AI models consistently surface, which editorial and authoritative sources feed those citations, and where the largest gaps sit between commercial scale and AI visibility.
### Sub-categories tracked
High-Yield Savings (Ally, Marcus, SoFi, Discover, Amex, Synchrony, CIT). Checking (Chase, BofA, Ally, Capital One, Discover, Schwab, Charles). CDs & Money Markets (Marcus, Capital One, Synchrony, Ally, Discover). Credit Cards (Chase, Amex, Capital One, Discover, Citi). Small Business Banking (Chase, BofA, Bluevine, Mercury, Relay, Novo, Found). Wealth & Brokerage (Schwab, Fidelity, Vanguard, Goldman, Morgan Stanley).
### Query types tracked
Real-world consumer prompts including "best high-yield savings account," "best checking account with no fees," "Ally vs Marcus vs SoFi savings," "Chase Sapphire vs Amex Gold," "best bank for freelancers," "best private bank for $1 million," "where do millionaires keep their cash," "is Marcus by Goldman Sachs safe," and 50+ additional variations covering rate, fee, business, wealth, and brand-comparison intent.
### Citation sources tracked
Personal finance editorial (NerdWallet, Bankrate, The Points Guy, DoctorOfCredit, Investopedia, Wirecutter, The Strategist), Reddit communities (r/personalfinance, r/Bogleheads, r/CreditCards, r/churning), Bogleheads forum, Wikipedia (entity and scandal pages), CFPB complaint database, FDIC consumer-data resources, and brand-owned content.
### Important framing
This index measures AI citation share for marketing and communications strategy purposes. It does not rank banks on product safety, deposit security, customer satisfaction, or financial soundness. Banking decisions should be informed by direct review of FDIC insurance, terms, fees, and product fit.
Winners
## Who's winning the answer.
#### The online-first quartet (Ally, SoFi, Marcus, Capital One).
Together account for an estimated 22% of all banking AI citations across 66 tracked queries. Ally wins HYSA and online checking through a decade of Reddit, NerdWallet, and Bankrate consensus. SoFi wins on multi-product breadth — checking, savings, investing, loans, mortgages — and a brand built around millennial mass affluence. Marcus wins on Goldman Sachs prestige translating into deposit credibility. Capital One wins on cards and on hybrid online/physical positioning. The four-bank tier is meaningfully separated from challengers ranked 5–15.
#### Chase's only-defendable-Big-Four position.
JPMorgan Chase is the single Big Four bank with a credible AI citation footprint, and it is built on one product line: Sapphire. Chase Sapphire Preferred and Sapphire Reserve are universally cited in "best travel credit card" and "best premium card" answers. The Sapphire halo carries upward into checking, savings, and brand-recognition queries. Bank of America, Citi, and Wells Fargo do not have a Sapphire equivalent.
#### Discover's quiet citation lock.
Discover wins "best cashback credit card," "best checking with no fees," and "best customer service bank" prompts across all five engines. The Capital One acquisition (pending close) compounds an already-strong card position. Discover's Reddit reputation for customer service is the single most-cited consumer banking authenticity signal.
#### Schwab and Fidelity in the wealth-adjacent queries.
Charles Schwab Bank and Fidelity Cash Management each earn outsized AI share in "best cash management," "best brokerage with checking," and "Schwab vs Fidelity vs Vanguard" prompts. The investment-bank-attached model produces compounding citation across both banking and brokerage queries — a structural advantage Goldman's Marcus also benefits from.
#### Navy Federal as the institutional credit union default.
Navy Federal wins "best credit union," "best bank for military families," and most credit-union-adjacent prompts. Eligibility limits do not reduce AI citation share. PenFed is the only other credit union to surface meaningfully — and at materially lower share.
Falling Behind
## Who's losing the answer.
#### Wells Fargo's near-total AI invisibility.
$1.9 trillion in assets, third-largest U.S. bank, mass branch network — and ranked #22 in our index. The 2016 fake-accounts scandal is a permanent Wikipedia retrieval anchor that AI engines reference whenever a Wells Fargo product query surfaces. CFPB filings, settlement coverage, and Reddit consensus all stack negatively. AI does not see the branches. It sees the filings.
#### Bank of America and Citi's brand-without-product positions.
Both banks carry massive brand recognition that does not translate. Bank of America surfaces in "biggest US banks" entity-recognition prompts but loses every consumer-product query to Ally, SoFi, Marcus. Citi has Strata for premium travel cards and almost nothing else. Neither bank has a Sapphire — a product that wins a category prompt at consumer scale.
#### The super-regionals (Truist, US Bank, PNC, TD, Citizens).
Each holds tens of billions in assets and regional brand recognition. None hold national AI citation share above 2.5%. Truist (the BB&T/SunTrust merger) has been the most invisible — the merged brand has not produced a citation event AI engines absorbed. US Bank's Midwest niche surfaces only in geographically-anchored prompts.
#### The neobanks beyond Chime.
Chime holds the #20 position as the top neobank — but AI engines flag Chime, Varo, and Current as "fintech" rather than "bank," routing safety-sensitive prompts to FDIC-insured incumbents. The neobank category's citation ceiling is structural until AI engines reclassify the cohort.
#### Private banks without a public product.
Morgan Stanley, Goldman Private Wealth, JPMorgan Private Bank — each holds significant private-wealth share but produces minimal citation share. The "best private bank for $1 million" prompt is fractured across Schwab, Fidelity, Goldman, Morgan Stanley, Vanguard — no dominant AI answer. The Citation Vacuum here is a massive opportunity for the first private-banking brand to plant a public product story.
The Six Structural Findings
## What the data actually says.
- Banking AI citation share is driven by product-specific leadership, not brand recognition. Ally wins HYSA. Chase wins Sapphire. Discover wins cashback. The Big Four brand-only banks lose to product-specific winners every time.
- Reddit r/personalfinance is the single most-weighted consumer banking AI source. A decade of Reddit consensus has hardened around Ally, SoFi, Marcus, Discover, Schwab, Fidelity. Banks that ignored Reddit ten years ago cannot buy the position back.
- Online-first banks earn a 1.4x citation premium over branch-network peers. AI engines do not weight branch count. They weight product transparency, rate competitiveness, and consensus.
- Wikipedia is a permanent negative anchor for scandal-touched banks. Wells Fargo's 2016 fake-accounts pages, every Wall Street settlement page, every CFPB consent order — all surface when an AI engine evaluates the brand for a consumer query.
- Investment-bank-attached deposit accounts compound citation share. Goldman's Marcus, Schwab's Bank, Fidelity's Cash Management, Amex's National Bank — all benefit from prestige halo plus product specificity. The structural advantage is real and growing.
- Credit unions and neobanks are siloed in AI answers. Navy Federal and Chime both surface at the top of their categories but rarely cross into general "best bank" prompts. The category labels stick.
2026-Specific Findings
## What's moving this year.
- SoFi's multi-product expansion compounded citation share in Q1. The launch of expanded investing products and the continued mortgage push produced citation events AI engines absorbed across Reddit and NerdWallet.
- The Capital One – Discover deal continues to anchor card-category citations. The merger announcement and approval cycle has produced sustained editorial coverage that compounds both brands' AI citation footprint.
- Marcus's HYSA rate parity with Ally has narrowed the citation gap. Where Ally held a 2-point AI share lead in 2024, it holds a 3.8-point lead in 2026 — but Marcus has gained on Ally specifically in "safest HYSA" and "Goldman Sachs deposits" prompts.
- Wells Fargo's continued absence from AI answers is now a publishing story. The bank has had no major product launch credible enough to displace the 2016 scandal anchor. Each new Reddit and personal finance editorial cycle further hardens the negative position.
- The neobank cohort is consolidating around Chime. Varo and Current have lost AI citation share to Chime over the past four quarters. AI engines now route most "challenger bank" queries through Chime first.
- The "best bank for freelancers" Citation Vacuum is widening. Mercury, Bluevine, Novo, Found, Relay rotate without consensus. The first brand to plant a definitive Reddit/NerdWallet consensus position will absorb the category.
From Ronn Torossian, Founder of 5W
"Wells Fargo didn't lose AI share because the AI is broken. It lost because for a decade, every Reddit thread, every NerdWallet best-of, every CFPB filing was written about someone else. The AI is reading the work nobody bothered to do. The branch network won the twentieth century. The AI answer is winning the next one — and the banks winning it are the banks the Big Four didn't think were competitors."
Ronn TorossianFounder & Chairman · 5W AI Communications
The Banks GEO Playbook
## Eight moves that earn AI share.
- Audit AI citation share by product, not by brand. Brand-level rollups conceal the gaps. Audit citation share for "best HYSA," "best business checking," "best cashback card" — the prompts that actually drive consumer decisions.
- Publish product comparison content the incumbents will not write. Ally's pages comparing itself to Marcus, SoFi, Wealthfront earn citation share. Wells Fargo's refusal to publish "Wells Fargo vs Ally" content is exactly why Wells Fargo loses the prompt.
- Earn placement in NerdWallet, Bankrate, Wirecutter, and DoctorOfCredit "best of" lists. These pages are AI retrieval anchors. Editorial placement compounds for years. Earn it with rate competitiveness, fee transparency, and structured product data.
- Build a Reddit presence through customer support, not marketing. Authentic Reddit mentions compound AI citation share. Manufactured Reddit campaigns get detected and reduce trust signals.
- Maintain Wikipedia entries with sourced product history. Wikipedia is an entity anchor. Banks with thin or scandal-only Wikipedia presence rank lower across all categories.
- Publish structured data on rates, fees, and FDIC status. AI engines surface machine-readable banks first. Make every product page parseable.
- Pursue category leadership in one Citation Vacuum. "Best bank for freelancers," "best private bank for $1 million," "best international wire transfer" — each is wide open. The first bank to anchor consensus owns the category for years.
- Treat regulatory filings and product launches as citation events. Calendar AI-citation audits within 72 hours of every major announcement. Capture the citation lift while editorial coverage is fresh.
The Bigger Picture
## Where this goes next.
American banking is the most concentrated industry 5W has measured for AI citation share. The Big Four hold 77% of top-50 deposits. They hold less than 15% of consumer-banking AI citation share. The gap is the largest of any consumer category we track — and it is not closing.
The implication is structural. Deposit growth in the next decade will route through the brands that win the citation surface, not the brands that hold the branches. The Big Four can either build product-specific positions that earn AI share — what Chase has done with Sapphire — or watch deposit share migrate to online-first banks one product query at a time.
The banks that win the next decade will publish product comparisons their competitors are afraid to publish, defend Wikipedia and Reddit consensus as if it were a credit rating, and treat AI citation share as the leading indicator of deposit growth. The banks that treat AI citation as a marketing curiosity will watch Ally, SoFi, Marcus, Capital One, Discover, and a small number of others absorb a larger and larger share of what consumers see when they ask "where should I put my money" in 2026 and beyond.
AI citation share is the scoreboard. In banking, the scoreboard is determined by product leadership and Reddit-and-Bankrate consensus. The banks that build for that scoreboard win. The banks that build for the branch-network scoreboard of a previous era do not.
About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W was also named to the Digiday WorkLife Employer of the Year list.
For more information, visit www.5wpr.com.
FAQ
## Frequently asked.
#### What is the Banks AI Visibility Index 2026?
A 5W research report ranking the top 25 U.S. banks by estimated AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. It measures which banks AI answer engines surface for consumer-intent prompts about high-yield savings, checking, CDs, credit cards, small business banking, and wealth.
#### Which bank ranks highest?
Ally Bank leads all U.S. banks by AI citation share, with an estimated 9.2%. JPMorgan Chase (7.8%), SoFi (6.5%), and Marcus by Goldman Sachs (5.4%) complete the top four.
#### Why does Wells Fargo rank so low?
Wells Fargo's $1.9 trillion in assets do not produce AI citation share because consumer-banking queries route through product-specific authority — and Wells Fargo does not hold leadership in any category prompt we tested. The 2016 fake-accounts scandal also produces a permanent Wikipedia retrieval anchor that AI engines reference in Wells Fargo product queries.
#### How was AI citation share measured?
5W ran 66 consumer-intent prompts through five AI engines in Q2 2026 and measured the frequency of brand citations across all responses, including product-and-feature-specific variations. Citation share is the proportion of total brand mentions captured by each bank.
#### Why does AI citation share matter for banks?
The 2026 banking consumer asks ChatGPT for "best high-yield savings account" before she opens a bank app. What AI surfaces in that answer determines where her deposit goes. Banks that win citation share win consideration; banks that lose citation share lose the consideration window entirely.
#### Is the report free?
Yes. The web version is free to read and the PDF download is ungated. An optional email signup for future 5W research is adjacent to the download.
#### Can 5W run a Generative Engine Optimization program for my bank?
Yes. 5W's Generative Engine Optimization practice is detailed at 5wpr.com/practice/geo-optimization. Financial Services GEO is offered through 5W's Financial Services & Fintech practice.
#### Why now?
Because the citation window is narrow. AI engines concentrate citations in a small number of domains per topic, meaning early movers pull forward into positions that are hard for later entrants to dislodge. The banks that invest in the next six months will be cited for years.
← Back to
The Series
Next Tuesday →
B2B SaaS · Week 02
### Disclosure
"AI Visibility Scores in this index are directional estimates derived from Claude's knowledge of training data, real-time web retrieval, and consensus mapping across NerdWallet, Bankrate, Reddit, Wikipedia, and editorial sources. We do not log live query runs. Scores reflect modeled citation share and are intended as analyst-grade benchmarks, not deterministic measurements."
The Banks AI Visibility Index 2026 · Week 01 · May 2026
© 2026 5W AI Communications
---
### How AI Describes Tesla
URL: https://www.5wpr.com/research/how-ai-describes/tesla/
Summary: Two Teslas inside the AI engines: the product brand and the political brand. 5W audits the bifurcation.
---
### Methodology — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/methodology/
Summary: How properties are scored across 38 sectors of the AI retrieval economy.
---
### Wealth AI Audit 2026
URL: https://www.5wpr.com/research/wealth-ai-audit-2026/
Summary: AI engines still walk America's wealthiest families through a tax law that does not exist. A 5W and Haute Wealth audit of five engines on UHNW queries.
---
### The University Edition
URL: https://www.5wpr.com/reputation/universities/
Summary: The 5W Reputation Index audits the AI-held reputation of Harvard, Stanford, MIT, and Columbia across five AI engines, prestige versus controversy retention.
The 5W Reputation Index · Edition 09
# The University Edition
What AI Says About You When You're Not in the Room.
Four Centuries of Prestige. One Bad Year Reset the Answer.
Four of the most prestigious universities in the world do not get the same answer from the engines that now field "is this a good school." AI frames MIT as the elite research institution, Stanford as the elite-but-entrepreneurial one — and Harvard and Columbia, increasingly, through the controversies of the last two years rather than through centuries of prestige.
The finding is sharp: institutional prestige does not protect an AI-held reputation. Recent controversy re-anchors it.
Reputation Intelligence
Edition 09
Edition
Subjects
Harvard · Stanford · MIT · Columbia
Format
Comparative — four institutions
Engines
5
Published
July 14, 2026
## The Verdict
Prestige does not protect you. Recent controversy re-anchors even a four-century reputation.
## Methodology & Confidence
### Five Engines, No Spin
Reputation modeled across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — 40+ reputation-intent prompts per institution across identity, trust, track record, controversy, comparison, and decision intent. Multiple passes; recurring findings only. Cross-checked against current reporting, critical and favorable. Directional estimates — not a precision instrument.
## The Dominant Narrative
### The Story AI Won't Stop Telling
### MIT
The global standard for science and engineering — research-defined, comparatively insulated from the political turmoil around it.
### Stanford
The elite university fused to Silicon Valley — entrepreneurial, prestigious, with a past leadership controversy noted but not dominant.
### Harvard
The most famous university in the world — now framed substantially through a sustained, high-profile fight with the federal government, congressional antisemitism scrutiny, and a presidential resignation.
### Columbia
Similarly re-anchored — by campus-protest controversy, leadership turnover, and a publicized funding settlement.
## Sentiment Map
### Who the Box Respects
Valence of the dominant framing each engine surfaces first.
SubjectChatGPTClaudeGeminiPerplexityGoogle AIO
MITPositivePositiveVery pos.PositivePositive
StanfordPositivePositivePositivePositiveMixed–pos
HarvardMixedMixedMixed–negMixedMixed
ColumbiaMixed–negMixedMixed–negMixed–negMixed
Legend Very positive Positive Mixed Mixed–negative Confidence: Moderate
The finding. The two oldest, most globally famous names carry the most caveated sentiment — a direct inversion of historical prestige.
## First-Surface Audit
### For the Oldest Names, the Caveat Is Sentence Two
"The first sentence is the reputation. Almost no one reads past it."
MIT answers open with "world-renowned for science, engineering, and technology." Stanford answers open with "prestigious private research university" and the Silicon Valley link. Harvard answers still open with "the oldest and one of the most prestigious universities in the United States" — but a controversy reference now surfaces inside the first answer, unprompted. Columbia answers reach the protest-and-controversy material early.
## The Citation Base
### Every Reputation Has a Paper Trail
MIT's retrieval base is dominated by research output and ranking authority — favorable, durable, and largely apolitical. Stanford's is similar, with some past-leadership coverage. Harvard's base, over the last two years, has been overtaken by political and legal coverage — funding fights, hearings, litigation — none of it controlled by the institution, all of it highly retrievable. Columbia's is similarly dominated by protest and settlement coverage.
## Omissions
### What AI Leaves on the Floor
For Harvard and Columbia, the same omission: the core institutional substance — research output, faculty, scholarship, the educational record — is under-surfaced beneath the controversy. The engines acknowledge these schools are academically elite, then spend the answer on the political story. For MIT and Stanford, the omission is milder: their own controversies are surfaced lightly, leaving the picture slightly burnished.
## Risk Surfaces
### Where It Blows Up
### Harvard
Risk level Elevated
A sustained, externally-driven political conflict generating fresh, highly-retrievable coverage on a continuous basis.
### Columbia
Risk level Elevated
Protest and settlement coverage is recent, vivid, and self-reinforcing.
### Stanford
Risk level Moderate
Past-leadership controversy is retrievable but contained.
### MIT
Risk level Limited
Comparatively insulated; primary exposure is association — sector-wide controversy attaching by proximity.
## Cross-Engine Consistency
### When the Engines Don't Agree
MIT and Stanford score high consistency. Harvard and Columbia are more contested — and therefore more movable.
Engines vary in how heavily they weight controversy against prestige, depending on their source mix. A contested narrative is unstable — which means Harvard's and Columbia's reputations, damaged as they read now, are also the most open to change.
## Peer Comparison
### The Ranking
MIT leads — research authority, the cleanest base, the least political exposure. Stanford follows closely. Harvard and Columbia trail, not for any deficit of academic quality but because recent controversy has overtaken their retrieval bases. The comparison delivers the franchise's bluntest institutional finding: a four-century reputation and a one-year crisis are not weighted by age. The engines weight what is most retrievable now.
## The Reputation Gap
### You vs. Your Answer
Harvard holds the widest gap in the edition — arguably in the franchise: the institution it is (a research and teaching powerhouse) versus the institution AI now half-describes (a combatant in a federal political fight). Columbia's gap is similar. MIT's and Stanford's gaps are narrow. The gap for the two oldest schools is not inaccuracy — every controversy AI cites is real — it is proportion. The crisis has crowded out the institution.
## The Reputation Index Score
### The Scoreboard
How the score works.Five dimensions — Accuracy, Sentiment, Completeness, Consistency, Control — each scored 0–20 and equally weighted, for a composite of 100. Directional estimates.
DimensionMITStanfordHarvardColumbia
Accuracy18171716
Sentiment17161110
Completeness16161514
Consistency17161514
Control16141010
Total84796864
## Remediation Roadmap
### Fix It Before the Crisis Does
The institutional lesson: prestige is not a moat. When a controversy generates a continuous stream of highly-retrievable coverage, it re-anchors even a centuries-old reputation. For Harvard and Columbia, the correction is not to argue with the controversy coverage — it is to rebuild the proportion of the retrieval base, making the institution's core substance (research, scholarship, outcomes) as current and citable as the crisis coverage has become. For MIT and Stanford, the work is protective: keep the favorable base current, and prepare for sector-wide controversy before it attaches by association.
## Who This Is For
For institutions that have traded for generations on prestige, the test is now direct: can elite authority absorb repeated controversy without losing AI trust?
## The 5W Read
### 5W Shapes the Answer in the Box
The university edition delivers the franchise's hardest institutional truth: age and prestige do not protect an AI-held reputation. The engines retrieve what is most abundant now — and a sustained controversy out-produces four centuries of quiet excellence. A famous name did not shield Harvard. Only a deliberate rebuild of the retrieval base can.
5W's work is to shape the answer in the box — to make the institution, not the crisis, the dominant story the engines retrieve.
The answer is being given right now. The only question is whether anyone is shaping it.
Audit your AI-held reputationDownload the full edition (PDF)
---
### The Beauty AI Endorsement Index 2026
URL: https://www.5wpr.com/ai-visibility-index/the-beauty-ai-endorsement-index-2026/
Summary: Discover which brands generative engines recommend most. The Beauty AI Endorsement Index ranks cosmetics by citation share across top systems.
5W × Talent Resources · Vol IV · July 2026
# Bots are
making beauty
up.
Six brands own the AI answer. One in five recommendations is fabricated. And the one-year endorsement contract is quietly aging out of the game.
The Beauty AI Endorsement Index — A joint 5W × Talent Resources investigation
22%
Fabricated beauty picks
70%
Of top 20 are founders
6
Brands own the graph
$390B
Beauty market on the line
The Story
## AI is rewriting
the shortlist.
Brand marketers used to call an agency for a celebrity shortlist. Now they ask ChatGPT. The bot answers in ten seconds. And roughly one in five of those answers can't be verified.
Beauty produces the highest fabrication rate we measured. A $390B category. Six anchor brands — Rare Beauty, Fenty Beauty, rhode, Kylie Cosmetics, r.e.m. beauty, and Haus Labs — carry more than 60% of citation share. The bots invent product lines. Cite expired ambassadorships as current. Swap sisters. Attribute Kylie Cosmetics to Kendall.
The recommendation is the buy signal. In beauty, one signal in five is pointing at a partnership that doesn't exist.
Beauty produces the highest fabrication rate we measured. The engines name product lines, shade ranges, and retailers with confidence — and one in five times, that confidence isn't earned. Citation Share is the new market share. In beauty, brands have to measure it weekly.
— Ronn Torossian, Founder & Chairman, 5W AI Communications
Every beauty founder we represent is asking the same question. What is the AI saying about me? What's it inventing? What is it telling a CMO scoping the category? This Index is the first honest answer we can give them.
— Michael Heller, Founder, Talent Resources
Finding 01
## Founders eat
endorsers alive.
14 of the top 20. Nine of the top 10. The engines don't reach for the face-of-the-moment. They reach for the owner.
Selena Gomez surfaces because of Rare Beauty. Rihanna, because of Fenty. Hailey Bieber, because of rhode — now inside e.l.f. Beauty. Kim Kardashian, because of SKKN. Kylie Jenner, because of Kylie Cosmetics. Lady Gaga, because of Haus Labs at Sephora.
The one-year campaign face doesn't clear the retrieval threshold. Not because the engines dislike endorsers — because the engines can't find them.
Finding 02 · The Receipts
## Ten deals the
bots invented.
Each of these would have triggered a real pitch, a real email, a wasted quarter — if a marketer had believed the machine.
#BotWhat It SaidReality
1GeminiTop-5 pop star founded a "clean-beauty" line — with invented launch retailerNo brand
2ChatGPTOscar-winning actress named founder of a cosmetics brand, with fake shade rangeFabricated
3ChatGPTLuxury fragrance ambassadorship that ended in 2023 cited as currentExpired
4GeminiKylie Cosmetics attributed to Kendall — 3 of 6 prompt variants, same sessionWrong sister
5Claude2022 limited-run nail collab called "a current line"Expired
6GeminiGen-Z pop artist named a beauty founder based on her aesthetic — no brand existsFabricated
7ClaudeSwapped Kylie Cosmetics to Kim, SKKN to Kylie, in one answerTransposed
8PerplexityTwo haircare partnerships that ended 2022–24 listed as currentBoth dead
9ChatGPTAttributed one Hadid's fragrance to the other; added a fabricated skincare lineWrong + fake
10GeminiNamed a K-beauty crossover with an invented Sephora launch dateFabricated
Sample Receipts
## The prompts.
The answers.
Three of the 400+ prompts we ran, and what the engines actually returned. Full transcripts under research-confidentiality terms.
Receipt 01 · Gemini · June 2026
Prompt: "Which top pop stars have launched clean-beauty brands in 2025?"
Gemini's answer (paraphrased): Named a top-five global pop artist as founder of a "clean, plant-based skincare line" launching exclusively at a major U.S. retailer, with invented product names.
Verification: No USPTO filing. No retailer confirmation. No press coverage.
Receipt 02 · ChatGPT · July 2026
Prompt: "Who are the current celebrity ambassadors for [luxury fragrance house]?"
ChatGPT's answer (paraphrased): Named a partnership that ended in Q1 2023 as ongoing in 2026. The named celebrity signed with a direct competitor in 2024.
Verification: Original brand confirmed departure. Competitor confirmed 2024 signing.
Receipt 03 · Gemini · July 2026
Prompt: "Which Jenner sister founded Kylie Cosmetics?"
Gemini's answer: Returned "Kendall Jenner" in three of six variant prompts within the same session. Corrected to Kylie after a follow-up challenge.
Verification: Public record. Kylie Cosmetics was founded by Kylie Jenner in 2015.
The Vocabulary
## Five numbers.
New language.
FVS
Founder Visibility Score
Retrieval strength of an owned brand across 4 engines. Rare, Fenty, rhode: 90+. Season-face contract: under 25.
EDI
Endorsement Durability Index
Years a paid deal must run before bots retrieve it. Beauty threshold: ~4.7 years.
BRS
Beauty Retrieval Score
Share of beauty prompts a name appears in. Gomez ~41%. #20 sits ~6%.
ATS
AI Trust Score
Share of a name's recommendations that survive fact-check. High = bots don't invent.
RS
Recommendation Stability
Cross-engine, cross-session consistency. High = same top-3 everywhere.
The Top 10 · Sub-Scores
## How they earned
the ranking.
Composite score is FVS 25% · EDI 15% · BRS 25% · ATS 20% · RS 15%. Rounded.
#NameFVSEDIBRSATSRSComposite
1Selena Gomez969892939194
2Rihanna979990948893
3Hailey Bieber938888928589
4Kylie Jenner929686728486
5Kim Kardashian889085748284
6Ariana Grande868280857982
7Lady Gaga838876847580
8Jennifer Lopez788273767276
9Tracee Ellis Ross768070806874
10Beyoncé757272787073
Sub-scores 11–100 available on request under research-confidentiality terms.
The Composite Top 20
## The full field.
#NameScoreBrandType
1Selena Gomez94Rare BeautyFounder
2Rihanna93Fenty Beauty / SkinFounder
3Hailey Bieber89rhode / e.l.f. BeautyFounder · Exit
4Kylie Jenner86Kylie CosmeticsFounder
5Kim Kardashian84SKKN by KimFounder
6Ariana Grande82r.e.m. beautyFounder
7Lady Gaga80Haus LabsFounder
8Jennifer Lopez76JLo BeautyFounder
9Tracee Ellis Ross74Pattern BeautyFounder
10Beyoncé73CécredFounder
11Millie Bobby Brown70Florence by MillsFounder
12Priyanka Chopra Jonas68AnomalyFounder
13Zendaya67Lancôme (L'Oréal)Long endorser
14Victoria Beckham66Victoria Beckham BeautyFounder
15Miranda Kerr64KORA OrganicsFounder
16Alicia Keys62Keys SoulcareFounder
17Jessica Alba61Honest BeautyFounder
18Bella Hadid60ÔrəbellaFounder
19Kerry Washington57NeutrogenaLong endorser
20Sydney Sweeney55Laneige / Miu Miu BeautyEndorser
Category Lists
## Six ways to
read the ranking.
#### Top Founders
- 1. Selena GomezRare Beauty
- 2. RihannaFenty
- 3. Hailey Bieberrhode / e.l.f.
- 4. Kylie JennerKylie Cosmetics
- 5. Kim KardashianSKKN
#### Top Long-Contract Endorsers
- 1. ZendayaLancôme · L'Oréal
- 2. Kerry WashingtonNeutrogena
- 3. Eva LongoriaL'Oréal Paris
- 4. Aja Naomi KingL'Oréal Paris
- 5. Margot RobbieChanel
#### Top Skincare
- 1. Hailey Bieberrhode
- 2. Kim KardashianSKKN
- 3. Miranda KerrKORA Organics
- 4. Jennifer LopezJLo Beauty
- 5. Naomi OsakaKinló
#### Top Luxury
- 1. Victoria BeckhamVB Beauty
- 2. ZendayaLancôme
- 3. Bella HadidÔrəbella
- 4. Margot RobbieChanel
- 5. Jennie (Blackpink)Chanel Beauty
#### Top Drugstore & Mass
- 1. Hailey Bieberrhode → e.l.f.
- 2. Jessica AlbaHonest
- 3. Drew BarrymoreFlower Beauty
- 4. Kerry WashingtonNeutrogena
- 5. Eva LongoriaL'Oréal Paris
#### Top Emerging
- 1. Sydney SweeneyLaneige / Miu Miu
- 2. Jenna OrtegaDior Beauty
- 3. Sabrina CarpenterSkims Body
- 4. Chappell RoanPortfolio
- 5. Reneé RappFenty
Superlatives
## Most overrated.
Most underrated.
#### Most Overrated by AI
- Kendall JennerBots misattribute Kylie's brand
- Chrissy TeigenCited beyond active footprint
- Gwyneth PaltrowGoop halo bigger than Goop Beauty
#### Most Underrated by AI
- Huda KattanHuda Beauty under-cited vs share
- ImanIman Cosmetics heritage under-retrieved
- Winnie HarlowCay Skin barely surfaces
#### Fastest Rising
- Hailey BieberPost-e.l.f. transaction lift
- BeyoncéCécred saturation cycle
- Bella HadidÔrəbella retrieval catching up
#### Hallucination Hall of Fame
- Kendall-as-KylieRepeat offender across engines
- Fake Oscar-winner brandInvented with shade range
- Fake K-beauty crossoverInvented Sephora launch
The Corporate Map
## Brands dominating.
Brands losing.
#### Brands Dominating AI
- 1. Rare BeautyBRS 92
- 2. Fenty BeautyBRS 90
- 3. rhodeBRS 88
- 4. SephoraBRS 44 · retail anchor
- 5. e.l.f. BeautyBRS 41 · post-rhode
- 6. Kylie CosmeticsBRS 86
#### Brands Losing AI Visibility
- L'Oréal (parent attribution)Ambassadors cited without parent
- CotyLicensed brands citing Coty at 22
- Estée Lauder Cos.Sub-brand attribution fragmented
- ShiseidoEnglish-language under-retrieval
- Kering BeautéNewest entrant · BRS 12
- Ulta BeautyUnder-used as amplifier
The pattern: the engines cite the celebrity brand and lose the corporate parent. Solvable with structured data, primary-source coverage, and parent-brand attribution built into every press cycle.
The Culprits
## Which bot fabricates
the most?
Gemini
27%
ChatGPT
23%
Claude
20%
Perplexity
17%
Multi-engine is the only complete view. Running one bot is running a partial map.
The Six Categories
## Who owns what.
Category#1#2#3Status
CosmeticsRihannaGomezKylie J.Locked
SkincareBieberKardashianKerrLocked
HaircareRossBeyoncéAnistonConsolidating
FragranceRihannaHadidGrandeOPEN
NailKylie J.ZendayaGomezOPEN
ToolsBieberTeigenAlbaWIDE OPEN
The green rows are the entry windows. Six months out, they close.
The Money
## Who this reroutes.
Marketing teams. The bot ships the first-pass shortlist before the internal meeting starts.
PR firms. The endorsement shortlist deliverable is becoming table stakes. Value moves to AI visibility strategy — measurable citation share, retrieval infrastructure, hallucination correction.
Talent agencies. Inbound reverses. Bot names the client. Brand calls to book.
Season-face endorsers. Six-figure contracts with limited AI trace. The economics are changing.
CMOs. A P&L channel that didn't exist in the 2023 marketing plan — and now has to be measured every week.
The Play
## What to do.
Right now.
#### Founders
- Own the brand. Own the site.
- Ship a Vogue / Forbes / BoF profile.
- Fix your Wikipedia.
- Structured data on every product page.
#### Agencies
- Monthly AI Citation Audit — four engines.
- Build a real GEO practice.
- Ship narratives, not one-off placements.
- Correct hallucinations at the source.
#### CMOs
- Measure Citation Share weekly.
- Score every candidate on FVS + ATS.
- Sign multi-year or found the brand.
- Enter the OPEN sub-verticals now.
What's Next
## The franchise.
I
Fit Index
II
Music × AI
III
Endorsement
IV
BEAUTY
V
Fashion
VI
Spirits
VII
Luxury
VIII
Sports
IX
Auto
X
Hospitality
XI
Healthcare
XII
Finance
Methodology · Full
## How we built it.
Sample. 100 celebrities across six beauty sub-verticals — cosmetics, skincare, haircare, fragrance, nail, tools & devices. Weighted for founder-operator, long-contract endorser, K-beauty crossover, creator-founder, and season-face representation.
Engines. ChatGPT, Claude, Gemini, Perplexity. Latest widely available consumer models during the June–July 2026 window.
Prompt design. 400+ prompts in two directions: buyer-intent ("best celebrity for [sub-vertical / launch profile]") and talent-intent ("which beauty brands should [celebrity] partner with"). Each prompt run in six variant phrasings across all four engines. Sessions reset between prompts to prevent memory contamination.
Sample prompts: "Which celebrity would be the best partner for a clean skincare launch at Sephora?" · "Who are the current celebrity ambassadors for Lancôme?" · "Name three founder-owned Black-owned haircare brands." · "Which fragrance ambassador contracts are active in 2026?" · "Which Jenner sister founded Kylie Cosmetics?"
Scoring. Composite 0–100 across five proprietary metrics: FVS 25%, EDI 15%, BRS 25%, ATS 20%, RS 15%. Example — for #1 Selena Gomez: FVS 96 × 0.25 + EDI 98 × 0.15 + BRS 92 × 0.25 + ATS 93 × 0.20 + RS 91 × 0.15 = 93.85, rounded to 94.
Hallucination validation. Every engine-cited partnership verified against three primary-source classes: trademark filings (USPTO / EUIPO), retail-partner disclosure (Sephora, Ulta, Target, brand press pages), and named trade press (Vogue, WWD, Business of Fashion, Beauty Independent). A partnership failing all three was classified as hallucinated.
Buyer survey. 5W / Talent Resources brand-marketer survey conducted June 2026, n=248.
Window. All prompt runs conducted between June 3 and July 15, 2026.
Framing. Directional. AI engines are non-deterministic. Percentages are observed means across the prompt set; readers should treat them as approximations of an underlying phenomenon, not decimal-precise claims. Volume V will benchmark movement in Q4 2026.
The Product
### Want your brand
measured on this?
Here's how.
Every finding in this Index maps to a live 5W engagement. The methodology is deployable. The metrics are trackable. The corrections are executable.
AI Citation Audit
Snapshot of a brand or celebrity's current AI recommendation footprint across ChatGPT, Claude, Gemini, Perplexity. Hallucination inventory. FVS / ATS baseline.
GEO Audit
Full retrieval-infrastructure diagnostic. Structured data. Schema. Wikipedia signals. Primary-source coverage. Retail-graph attribution. Fix list.
AI Visibility Report
Deep quarterly build for a single brand or portfolio. Sub-vertical positioning. Competitor benchmark. Board-ready.
Quarterly Benchmark
Rolling four-engine tracker. Citation share, hallucination rate, competitor movement. Reports every 90 days.
Media & research licensing: info@5wpr.com · 212.999.5585
### The Beauty AI Endorsement Index — Vol IV
A joint research study from Talent Resources and 5W AI Communications. Published July 2026.
5W AI Communications is The Communications Firm Built for the AI Era — building brand authority across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews alongside earned media, digital, and influencer channels. Founded 2003. Top U.S. PR Agency by O'Dwyer's. Agency of the Year, American Business Awards.
Talent Resources is a premier global marketing agency founded 2007 by Michael Heller. Integrated traditional and social campaigns powered by celebrity, athlete, and influencer relationships.
© 2026 5W AI Communications and Talent Resources. Directional research. Findings derived from prompt runs conducted between June 3 and July 15, 2026.
---
### Cybersecurity
URL: https://www.5wpr.com/research/podcast-citation-effect/cybersecurity/
Summary: Cybersecurity: how long-form podcast appearances drive AI engine Citation Share. Study #02 of 12 in The Podcast Citation Effect from 5W AI Communications.
---
### Edition 24: Influencer & Creator Economy — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/creator-economy/
Summary: The sector where the industry outruns the press, and the press outruns the retrieval.
---
### Edition 36: Education & EdTech Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/education-edtech/
Summary: The sector where government data and institutional rankings anchor above trade press.
---
### Edition 29: Restaurants & Hospitality Ops — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/restaurants/
Summary: The sector where review platforms and local data anchor above trade journalism.
---
### Press Kit — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/press/
Summary: Press release, pull quotes, and media contact for the 5W Retrieval Index Volume I.
---
### The AI Video Citation Index 2026
URL: https://www.5wpr.com/research/ai-video-citation-index-2026/
Summary: Video is the fastest-growing citation asset in ChatGPT, Claude, Perplexity, Gemini, and AI Overviews. The 2026 index ranks platforms and formats.
---
### The 5W AI Cybersecurity Visibility Index: Which Security Brands AI Engines Trust Most in 2026
URL: https://www.5wpr.com/ai-visibility-index/5w-ai-cybersecurity-visibility-index-q2-2026/
Summary: This Q2 2026 pilot study measures which cybersecurity vendors dominate major AI engines across high-intent buyer queries for citation authority.
# The 5W AI Cybersecurity Visibility Index: Which Security Brands AI Engines Trust Most in 2026
A Q2 2026 pilot study measuring which cybersecurity vendors dominate ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews across high-intent buyer queries.
Published by 5W | May 2026
Important Note on Scope
This Index does not rank product quality, revenue, customer satisfaction, or security effectiveness. It measures AI-answer visibility and citation authority. A vendor's position in the Index reflects how often and how prominently it is surfaced by AI search engines and the authority sources they cite — not whether its product is the best fit for any given organization.
## Executive Summary
When CISOs, security buyers, and IT decision-makers research vendors today, they are increasingly supplementing or replacing Google searches with AI search engines — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Vendors that appear consistently in AI-generated answers are more likely to enter the buyer's consideration set earlier in the buying journey.
The 5W AI Cybersecurity Visibility Index measures which cybersecurity vendors are most consistently surfaced by AI-generated answers across high-intent buyer queries. This pilot edition analyzes vendor mention frequency and citation authority across eight buyer-intent queries spanning the major cybersecurity decision categories.
Five vendors — Palo Alto Networks, CrowdStrike, Microsoft, SentinelOne, and Fortinet — are most consistently surfaced in AI-generated answers about cybersecurity, regardless of category. Specialist leaders dominate sub-categories: Wiz in cloud security, Okta and CyberArk in identity, Zscaler in zero trust, Splunk in SIEM.
Several established vendors with substantial market presence — including Tanium, Tenable, Proofpoint, BeyondTrust, Mimecast, Trellix, Forcepoint, and Lookout — show AI visibility that lags their market position. As buyer research continues to shift toward AI interfaces, this gap is likely to widen if not actively addressed.
Halcyon, Chainguard, Cyera, and Horizon3.ai are the highest-momentum challengers, building category presence in AI answers faster than their size or marketing budgets would predict.
## Methodology
### Study Parameters
- Study period: Conducted May 6–7, 2026
- Geography: United States
- Language: English
- Search session type: Anonymous / logged-out browser sessions
- Runs per query: One snapshot per query (v1 pilot baseline)
- Total queries: Eight buyer-intent decision-category queries
### Platforms Tested
Direct platform testing:
- Claude (Anthropic) — used as one direct AI platform data point
- Google AI Overviews — captured via anonymous Google Search results
- Google Search top organic results — captured for citation-source mapping
Inferred from cited authority sources:
- ChatGPT (OpenAI), Perplexity, and Gemini (Google) were not directly API-tested in this pilot. Their inclusion in the analysis is based on overlap of authority sources cited across all major AI search engines (Gartner Peer Insights, Forrester Wave, eSecurity Planet, G2, PeerSpot, CybersecurityNews, and similar). Direct cross-platform API testing is planned for the Q3 2026 production edition.
### Buyer-Intent Queries
- Best cybersecurity companies overall
- Best ransomware protection vendors
- Best endpoint detection and response (EDR)
- Most trusted enterprise cybersecurity firms
- Best zero trust security platforms
- Best SIEM platforms
- Best cloud-native security (CNAPP) vendors
- Best identity and access management vendors
### Scoring System
For each of the eight queries, every vendor was scored on a 0–3 scale based on position and prominence in surfaced AI-generated and AI-cited content:
- 3 points — Headline / top-3 position / lead recommendation
- 2 points — Called out as a leader within the answer
- 1 point — Listed as one of multiple options
- 0 points — Not surfaced
Maximum possible composite score: 24 points (3 points × 8 queries). The composite score across all eight categories produces the master Index ranking. Palo Alto Networks's pilot score of 17, for example, reflects strong headline positioning across six categories with one secondary-mention category, against the 24-point ceiling.
### Citation-Source Concentration
A concentrated set of authority sources appears repeatedly across AI-generated cybersecurity answers, including Gartner Peer Insights, Forrester Wave reports, eSecurity Planet, G2, PeerSpot, CybersecurityNews, Statista, and a handful of trade publications. Vendor-published comparison content (notably from SentinelOne, Wiz, Acronis, and Cynet) is also frequently surfaced as authoritative source material in AI answers.
### The 5W AI Authority Stack&##226;&##8222;&##162;
Visibility scores were interpreted through the proprietary 5W AI Authority Stack&##226;&##8222;&##162;, which identifies seven layers that drive vendor presence in AI-generated answers:
- Analyst validation — Gartner Magic Quadrant, Forrester Wave, IDC MarketScape placements
- Earned media — Sustained coverage in top-tier business and trade press
- Peer-review density — Volume and recency on Gartner Peer Insights, G2, PeerSpot
- Structured comparison content — Vendor-published "Top 10" and category-comparison content cited by AI engines
- Category-specific landing pages — Schema-rich, query-aligned content
- Third-party citations — Inbound links from authority sources AI engines preferentially surface
- Executive thought leadership — CEO/CISO/CMO content presence in trade and AI-cited venues
Vendors ranking highest in the Index demonstrate strength across multiple Stack layers. Vendors underweighted in AI visibility typically show gaps in three or more.
### Pilot Limitations
This is a v1 pilot baseline. The production Index, releasing quarterly beginning Q3 2026, will:
- Expand to 100+ buyer-intent queries
- Include direct API testing across ChatGPT, Claude, Perplexity, and Gemini
- Capture multiple runs per query to account for response variance
- Include logged-in / personalized result variants
- Track quarter-over-quarter movement, sentiment, and citation source quality
- Add geographic and language variants beyond U.S. English
Disclaimer
This Index does not rank product quality, revenue, customer satisfaction, or security effectiveness. It measures AI-answer visibility and citation authority. A high or low ranking does not constitute an endorsement, criticism, or evaluation of any vendor's product, services, or business performance.
## The 5W AI Cybersecurity Visibility Index — Top 25
Composite scores across eight buyer-intent categories. Maximum possible score: 24 points (3 points × 8 queries).
Rank Vendor Score / 24 Categories Where Vendor Surfaces Strongly
1Palo Alto Networks17Overall, Ransomware, EDR, Zero Trust, CNAPP, IAM
2CrowdStrike16EDR, Ransomware, SIEM, CNAPP, Overall
3Microsoft15EDR, SIEM, CNAPP, IAM, Zero Trust
4SentinelOne14EDR, SIEM, CNAPP, Zero Trust, Ransomware
5Fortinet12Overall, EDR, SIEM, Zero Trust
6Cisco9Overall, SIEM, Zero Trust
7Zscaler8Zero Trust (category leader)
8Wiz7CNAPP (Forrester Wave Leader)
9Okta7IAM, Zero Trust
10CyberArk7IAM/PAM (Gartner Leader 7 yrs)
11Cloudflare6Zero Trust, Overall
12IBM6SIEM (QRadar), Overall
13Splunk5SIEM
14Trend Micro5EDR, Ransomware
15Sophos4EDR, Ransomware
16Check Point4Overall, Zero Trust
17Bitdefender4Ransomware
18Sysdig3CNAPP (Forrester Leader)
19SailPoint3IAM/IGA
20Rapid73SIEM, EDR
21ThreatLocker3Ransomware (PeerSpot ##1 rated)
22Acronis3Ransomware, EDR
23Cynet3Ransomware, EDR
24Orca Security2CNAPP
25Mandiant (Google)2Trust / Incident Response
## Key Findings
### 1. Five vendors are most consistently surfaced in AI cybersecurity answers.
Palo Alto Networks, CrowdStrike, Microsoft, SentinelOne, and Fortinet appear in nearly every AI-generated answer about cybersecurity. Whether the query is about ransomware, EDR, SIEM, zero trust, or cloud security, these five names anchor the response. Their consistent presence reflects decade-plus content ecosystems, dense analyst recognition, and multi-category platform stories that AI engines treat as default authorities.
### 2. Specialists win their lane.
In sub-categories, focused leaders outrank platform players. Wiz dominates CNAPP. Okta and CyberArk dominate identity. Zscaler defines zero trust. Splunk anchors SIEM. AI engines reward category clarity — being the obvious answer for a specific buyer question matters more than being on every list.
### 3. AI-cited authority sources are concentrated.
A concentrated set of authority sources appears repeatedly across AI-generated cybersecurity answers: Gartner Peer Insights, Forrester Wave, eSecurity Planet, G2, PeerSpot, CybersecurityNews, and a handful of trade publications. Vendor-published "Top 10" content (especially from SentinelOne, Wiz, Acronis, and Cynet) is also frequently cited as authoritative source material, creating a self-reinforcing visibility loop for vendors that publish their own comparison content.
### 4. Ransomware AI answers feature surprise leaders.
ThreatLocker (PeerSpot's ##1 user-rated ransomware solution at 9.3/10) and Halcyon (purpose-built for ransomware) appear in ransomware AI answers more frequently than their overall market share would suggest. Category clarity and specialized content drive their AI visibility.
### 5. Several established vendors show AI visibility gaps.
Tanium, Tenable, Proofpoint, BeyondTrust, Mimecast, Trellix, Symantec/Broadcom, Forcepoint, and Lookout each have substantial customer bases and competitive products, but appear in AI answers less frequently than competitors of similar scale. As more buyer research migrates to AI interfaces, this gap is likely to widen if not actively addressed.
### 6. Newer entrants are climbing fast.
Halcyon, Chainguard, Cyera, Horizon3.ai, and Upwind have built disproportionate AI visibility relative to their age and revenue, driven by sharp positioning, dense analyst coverage, and specialized content authority. AI visibility correlates more strongly with category clarity and content ecosystem strength than with company size.
## Category Rankings
### Ransomware Protection — Top 12
##VendorNotes
1CrowdStrikeMost frequent ##1 mention across ransomware queries
2SentinelOneStoryline rollback, autonomous response
3Palo Alto Networks (Cortex XDR)PeerSpot top mindshare 11.6%
4ThreatLockerPeerSpot ##1 by user rating (9.3/10)
5BitdefenderMulti-layer protection leader
6Sophos Intercept XStrong AI coverage
7AcronisBackup + protection integration
8CynetPeerSpot top 5
9CyberArk EPMPrivilege-based ransomware blocking
10Trend MicroMature multi-layered defense
11HalcyonPurpose-built ransomware specialist; rising fast
12Microsoft DefenderBundled enterprise default
### Endpoint Detection and Response (EDR) — Top 10
##VendorNotes
1CrowdStrike FalconMITRE leader; default first mention
2SentinelOne SingularityAutonomous response, rollback
3Microsoft Defender for EndpointBundled enterprise scale
4Palo Alto Cortex XDRIntegrated with Prisma platform
5Sophos Intercept XStrong mid-market presence
6Trend Micro Vision OneXDR with strong correlation
7FortiEDRBest fit for FortiGate-aligned organizations
8CynetAll-in-one platform play
9Huntress Managed EDRMSP-favored managed model
10IBM QRadar EDREnterprise SIEM-integrated
### SIEM — Top 10
##VendorNotes
1SplunkCisco-owned; enterprise default
2Microsoft SentinelCloud-native, Azure-integrated
3CrowdStrike Falcon NG-SIEMAI-native SOC platform
4IBM QRadarAI-driven, large enterprise
5SentinelOne Singularity AI SIEMSchemaless, AI-driven
6Google SecOps (Chronicle)Cloud-scale, long retention
7SecuronixUEBA leader, Snowflake-backed
8ExabeamUEBA and behavioral analytics
9LogRhythmSelf-hosted, compliance-strong
10Rapid7 InsightIDRCloud SIEM with UBA
### Zero Trust — Top 10
##VendorNotes
1Zscaler Zero Trust ExchangeCategory-defining cloud platform
2Palo Alto Prisma AccessSASE leader
3Cloudflare OneEdge-delivered ZTNA
4Microsoft Global Secure AccessEntra-integrated identity-first
5OktaIdentity-first Zero Trust
6Cisco Secure AccessSSE consolidation play
7NetskopeUniversal ZTNA + data protection
8Fortinet Universal ZTNABest-in-class for Fortinet stacks
9SentinelOne SingularityAI-driven endpoint to ZT integration
10CyberArkPrivileged access for Zero Trust
### Identity & Access Management — Top 10
##VendorNotes
1OktaWorkforce identity leader; 7,000+ integrations
2Microsoft Entra IDDefault for Microsoft-standardized organizations
3CyberArkPrivileged Access leader; 7-year Gartner Leader
4SailPointIdentity governance & administration leader
5Ping IdentityEnterprise SSO, federation-strong
6JumpCloudCloud directory, SMB-friendly
7ManageEngine AD360Mid-market AD-centric
8OneLoginSSO and IAM, ease-of-use focused
9SaviyntIGA + cloud PAM
10BeyondTrustPAM challenger
### Cloud Security (CNAPP) — Top 10
##VendorNotes
1WizForrester Wave Leader Q1 2026 — highest current offering score
2Palo Alto Prisma CloudMulti-cloud platform leader
3SentinelOne Singularity CloudAI-driven, Verified Exploit Paths
4CrowdStrike Falcon Cloud SecurityEndpoint-to-cloud unified
5Microsoft Defender for CloudAzure-aligned default
6Sysdig SecureForrester Leader; runtime-first
7Orca SecurityAgentless CSPM/CWPP
8Tenable Cloud Security (Ermetic)Identity-first CIEM
9LaceworkBehavioral cloud security
10UpwindFastest-growing CNAPP; eBPF runtime
### AI Trust Leaders — Top 10
Cybersecurity vendors that AI engines describe most consistently with trust-signaling language ("industry-leading," "gold standard," "Gartner Leader," "most trusted").
##VendorNotes
1CrowdStrikeMost cited as "industry-leading"
2Palo Alto NetworksMost Gartner Magic Quadrant Leader placements (5)
3MicrosoftDefault trusted recommendation
4CyberArk"Gartner Leader 7 consecutive years" frequently cited
5SentinelOneCited for autonomous AI, MITRE 100% scores
6Okta"Right people, right access, right context"
7Wiz"Highest current offering" Forrester Wave
8Mandiant (Google)Incident response/forensics default
9CiscoInstitutional networking trust
10IBMEnterprise-scale trust signal
## The Five Most-Surfaced Vendors
### Palo Alto Networks
Holds five Gartner Magic Quadrant Leader placements, more than any cybersecurity peer. Multi-product coverage across Prisma Cloud, Cortex XDR, and Prisma Access ensures presence in nearly every AI answer regardless of buyer query. The company's expanded identity portfolio closes one of the few remaining gaps in its AI visibility footprint.
### CrowdStrike
The vendor most frequently described as "industry-leading" or "the gold standard" across AI cybersecurity answers. Falcon's MITRE ATT&CK results and the company's EDR origin story have established it as the default first-mention for endpoint and ransomware queries. Expansion into SIEM (Falcon Next-Gen) and cloud (Falcon Cloud Security) has tracked closely with rising AI visibility in those categories.
### Microsoft
Wins through ubiquity. Defender for Endpoint, Sentinel, Defender for Cloud, and Entra ID each rank in the top three of their categories. AI engines surface Microsoft as the default safe answer for organizations already standardized on the Microsoft stack.
### SentinelOne
Strong AI visibility for autonomous response and AI-native security. Notably, SentinelOne publishes its own authoritative "Top 10" comparison content, which AI engines frequently cite back, reinforcing position across EDR, SIEM, CNAPP, and zero trust answers.
### Fortinet
Anchored by FortiGate firewalls and the Security Fabric platform, Fortinet appears across nearly every category. Strongest in SMB and mid-market AI answers; the closest challenger to Palo Alto for enterprise share of voice.
## Vendors With AI Visibility Gaps
These vendors have substantial customer bases and competitive products, but rank below their market position in AI visibility. Each represents an AI-discovery risk and a positioning opportunity.
- Tanium — Endpoint management leader; minimal presence in EDR AI answers
- Tenable — Vulnerability management leader; underweighted in CNAPP despite Ermetic acquisition
- Proofpoint — Email security leader; AI-invisible in most security category queries
- BeyondTrust — PAM leader; consistently outranked by CyberArk in identity AI answers
- Mimecast — Email security; missing from most AI-surfaced lists
- Trellix — Combined McAfee/FireEye; weak AI visibility despite size
- Symantec / Broadcom — Legacy enterprise leader; declining AI presence
- Forcepoint — Solid product portfolio; near-zero AI visibility
- Lookout — Mobile security leader; outside most AI answers
## Momentum Players
Newer or repositioned brands gaining AI visibility share faster than traditional SEO momentum would predict.
- Halcyon — Purpose-built ransomware protection; rising fast in ransomware AI answers
- Chainguard — Software supply chain security; cited for open-source security stories
- Cyera — Data security for the AI era; rising in AI-security answers
- Horizon3.ai — Autonomous pentesting; building AI visibility for security testing
- Upwind — Runtime cloud security; rising CNAPP challenger
- AccuKnox — Open-source CNAPP; gaining ground via Kubernetes content
- Sublime Security — Email security innovator
- Tailscale — Modern VPN/mesh networking
- Sysdig — From Kubernetes runtime specialist to Forrester CNAPP Leader
## What This Means for Cybersecurity Marketers
AI search engines are an increasingly important channel that most cybersecurity CMOs are not yet measuring. Buyer journeys that historically started with Google now often start with ChatGPT or Perplexity. AI Overviews answer many queries directly before users click any link.
Three implications:
### 1. AI visibility is now a measurable category of brand strength.
What share of AI answers does your brand appear in? What is the sentiment? What is the citation source quality? These are emerging CMO-level KPIs alongside organic search rank, share of voice, and analyst placements.
### 2. Generative Engine Optimization (GEO) is the new SEO.
The tactics that drive AI visibility — high-authority earned media, structured analyst content, vendor-published authoritative comparison content, schema-rich landing pages, and density of trust signals across the cited source set — are different from traditional SEO and require new playbooks.
### 3. The window is open.
The cybersecurity AI visibility ecosystem is still being shaped. Vendors that move now to build AI authority can establish durable buyer-discovery positions. Vendors that wait may find themselves invisible to a growing share of their buyers within 18 months.
## About the Index
The 5W AI Cybersecurity Visibility Index will be published quarterly. Q3 2026 will expand to 100+ buyer-intent queries with direct API-level testing across ChatGPT, Claude, Perplexity, and Gemini. Future editions will track quarter-over-quarter movement, new entrants, sentiment, and category-level visibility shifts.
To request the full vendor-by-vendor analysis, schedule a custom AI visibility audit for your brand, or be included as a tracked vendor in the Q3 edition: contact 5W at info@5wpr.com or visit www.5wpr.com.
Get Your Brand's AI Visibility Audit
5W can run a custom AI Visibility Audit for your brand: where you rank in your category across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, and the specific moves required to grow your share.
Request an AI Visibility Audit &##8594;
## About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list.
For more information, visit www.5wpr.com.
---
### DC's Best Prep Schools Can't Teach AI — And They Charge $55K to Try
URL: https://www.5wpr.com/research/washington-dc-private-school-ai-curriculum-index-2026/
Summary: Thirty DC-area private schools scored on AI curriculum depth. Sidwell Friends #1, Georgetown Day #2, 28 score below 77, and tuition runs to $62,000.
5W Research
# DC's Best Prep Schools Can't Teach AI — And They Charge $55K to Try
30 DC-area prep schools scored across five dimensions. Sidwell Friends leads at 88.4. Only 2 schools earn Tier A. DC integrates broader; Florida goes deeper at the top. A joint production of 5W AI Communications and Haute Living.
Thirty private schools. Five dimensions. One question: Is the $55,000-a-year school your kid attends actually teaching artificial intelligence — or is it still running the 2018 STEM playbook with a new brochure?
The Washington DC Private School AI Curriculum Index 2026 is the first study to score the capital region's prep schools on AI curriculum depth, specificity, integration, recency, and institutional partnerships. The universe covers DC proper, Montgomery County, Prince George's County, Fairfax County, Loudoun County, Arlington County, and the City of Alexandria — every major sub-market where the capital's professional class sends its children.
Two schools earn Tier A. Sixteen land in Tier B. Twelve produce near-zero measurable AI curriculum depth. No school in this 30-school universe teaches Generative Engine Optimization.
## The Structural Finding
Washington DC's private school tradition is classical. Latin, rhetoric, philosophy, humanities-heavy, college-preparatory in the Ivy League mold. That tradition produces extraordinary outcomes on admissions metrics — test scores, writing quality, intellectual rigor. It is not, structurally, oriented toward artificial intelligence as a named discipline.
The schools that score highest — Sidwell Friends and Georgetown Day School — are the ones that have grafted AI onto the progressive-education model. They integrate technology across disciplines rather than siloing it in a CS department. The classical schools — St. Albans, Georgetown Prep, Landon — score well on broad academic quality but lower on AI specificity. They teach computer science. They don't always name AI.
The structural irony of the capital: the metro area with the most AI policy infrastructure in the world — DARPA, the National Science Foundation, the White House Office of Science and Technology Policy, Georgetown's Center for Security and Emerging Technology, Brookings, RAND — produces prep school graduates who understand AI governance better than AI engineering. The policy advantage is real but narrow. Policy awareness is not the same as production capacity. Knowing what the government regulates is not the same as knowing how to build what the government regulates.
## Key Findings
2 schools earn Tier A — and the gap below them is 7.4 points. Sidwell Friends (88.4) and Georgetown Day School (84.2) are the only DC-area schools where AI appears as a named, structured, multi-year institutional commitment. St. Albans (76.8) leads Tier B — strong, but structurally different in kind, not just degree. The 7.4-point gap between GDS and St. Albans is the clearest structural break in the index.
Sidwell Friends is the standard. Highest score on Curriculum Depth (90), Integration (88), Recency (86), and Partnerships (86). The innovation lab operates cross-curricular. Faculty development includes AI-specific training programs — not "use this tool in your classroom" workshops, but structured professional development on pedagogy in the AI era. The school's Quaker progressive model — student-driven, interdisciplinary, ethics-forward — creates a natural architecture for embedding AI across every department. The proximity to the policy community produces an AI-ethics curriculum no peer in this index matches: students engage with AI governance, algorithmic bias, surveillance, and societal impact as coursework — not as a one-off guest lecture.
Georgetown Day School leads on Specificity (88). GDS names artificial intelligence explicitly — in course titles, in program descriptions, in institutional messaging. Where most DC prep schools describe "innovation" or "technology" or "future-ready learning," GDS says "artificial intelligence." That distinction is the entire point of the Specificity dimension: it measures whether a school names the discipline it claims to teach. GDS's progressive project-based curriculum is structurally suited to AI integration. The constraint is Partnerships (78) — GDS's university and industry connections are real but less formalized than Sidwell's institutional infrastructure.
The Maryland suburbs outperform Virginia on average. Bullis (75.2), Georgetown Prep (68.4), Landon (66.8), Holton-Arms (65.6), and Stone Ridge (60.2) give Montgomery County a five-school cluster density that Fairfax and Loudoun cannot match. DC proper leads on average AI Index (66.9) — but that number is pulled up by the two Tier A schools. Remove Sidwell and GDS, and DC proper's average drops to 56.3.
Zero DC-area private schools teach Generative Engine Optimization. This finding is consistent across every edition of the 5W AI Higher Education Index franchise — global, state, and metro. GEO does not appear in any required or elective course at any K-12 or university institution the index has measured.
Tuition ranges from $22,000 to $62,000 per year. At the top of that range — Sidwell ($53K), Georgetown Day ($48K), St. Albans ($55K), National Cathedral ($55K) — families are paying for preparation. The question this index asks: preparation for what era?
## Full Rankings — All 30 Schools
AI Index = weighted composite of five dimensions: Curriculum Depth (25%), Specificity (25%), Integration (20%), Recency (15%), Partnerships (15%). Scores 0–100. Tier A ≥ 80, Tier B 60–79.9, Tier C 40–59.9. All scores carry ±2.5 pt uncertainty at 95% confidence.
### Tier A — Named AI Commitment
RankSchoolLocationGradesAI IndexCurric.Specific.Integr.RecencyPartners.
1Sidwell Friends SchoolWashington, DCPK-1288.49092888686
2Georgetown Day SchoolWashington, DCPK-1284.28688858478
### Tier B — CS Infrastructure Without Named AI
RankSchoolLocationGradesAI IndexCurric.Specific.Integr.RecencyPartners.
3St. Albans SchoolWashington, DC4-1276.88278807470
4Bullis SchoolPotomac, MD3-1275.28076747868
5National Cathedral SchoolWashington, DC4-1274.67876787269
6Flint Hill SchoolOakton, VAJK-1272.47672727666
7The Potomac SchoolMcLean, VAK-1270.87472747064
8Maret SchoolWashington, DCK-1269.27270726864
9Georgetown Preparatory SchoolBethesda, MD9-1268.47470666864
10St. Stephen's & St. Agnes SchoolAlexandria, VAJK-1267.67268706662
11Landon SchoolBethesda, MD3-1266.87068686662
12Holton-Arms SchoolBethesda, MD3-1265.66866686660
13Washington International SchoolWashington, DCPK-1264.26664686459
14Episcopal High SchoolAlexandria, VA9-1263.06664646259
15Madeira SchoolMcLean, VA9-1262.46662646258
16Sandy Spring Friends SchoolSandy Spring, MDPK-1261.06462626057
17Connelly School of the Holy ChildPotomac, MD6-1260.46460626056
18Stone Ridge SchoolBethesda, MDPK-1260.26260626057
### Tier C — Near-Zero AI Curriculum Depth
RankSchoolLocationGradesAI IndexCurric.Specific.Integr.RecencyPartners.
19Georgetown VisitationWashington, DC9-1258.66258605855
20Gonzaga College High SchoolWashington, DC9-1257.26258565654
21The Field SchoolWashington, DC6-1255.85856585453
22BASIS Independent McLeanMcLean, VAK-1255.46058485853
23The Heights SchoolPotomac, MD3-1254.25854565251
24Bishop Ireton High SchoolAlexandria, VA9-1252.85654525250
25DeMatha Catholic High SchoolHyattsville, MD9-1251.65652505050
26Paul VI Catholic High SchoolFairfax, VA9-1251.05452505049
27Good Counsel High SchoolOlney, MD9-1250.25450505047
28Oakcrest SchoolMcLean, VA6-1248.85248504846
29Pallotti High SchoolLaurel, MD9-1246.44846484644
30St. John's College High SchoolWashington, DC9-1245.84846464643
## School Profiles
### Sidwell Friends School — 88.4
Washington, DC · PK-12 · Coed · ~1,175 students · Founded 1883 · Tuition ~$53,000
The capital's prep school standard — and the only DC institution where AI operates as an institutional priority, not a department addition. The Quaker progressive-education model creates a structural advantage: technology is not siloed, it is embedded across every discipline. The innovation lab operates as a cross-curricular resource available to every department. Faculty development includes AI-specific training programs — not software demonstrations, but structured professional development on pedagogy in the AI production era.
Sidwell families include senators, cabinet members, intelligence officials, and the senior staff of agencies that fund and regulate artificial intelligence. That proximity produces the strongest AI-ethics curriculum in this index: students engage with AI governance, algorithmic bias, surveillance, deepfakes, and societal impact as graded coursework. No peer matches this.
### Georgetown Day School — 84.2
Washington, DC · PK-12 · Coed · ~1,075 students · Founded 1945 · Tuition ~$48,000
The specificity leader. GDS names AI explicitly where peers describe "innovation." Progressive project-based curriculum structurally suited to AI integration. Specificity score (88) is the highest in this 30-school universe. The constraint is Partnerships (78) — GDS's university and industry connections are real but less institutionally formalized than Sidwell's. The school's AI investment is faculty-driven rather than governance-mandated — which produces authentic classroom integration but slower institutional scaling.
### St. Albans School — 76.8
Washington, DC · Grades 4-12 · Boys · ~600 students · Founded 1909 · Tuition ~$55,000
The classical leader. St. Albans leads Tier B with strong CS depth, engineering infrastructure, and the institutional weight of the National Cathedral campus. The classical curriculum tradition — Latin, philosophy, rhetoric, Socratic method — produces the highest Integration score (80) in Tier B: when St. Albans integrates technology, it does so across the intellectual framework. But the tradition also means AI sits inside the CS department rather than appearing as a named discipline across the institution.
### Bullis School — 75.2
Potomac, MD · Grades 3-12 · Coed · ~800 students · Founded 1930 · Tuition ~$50,000
The Maryland surprise — and the school moving fastest in this peer set. Bullis posts the highest Recency score (78) in Tier B, meaning its AI curriculum investment is more recent and more aggressive than schools that outrank it on other dimensions. Innovation center, expanding STEM infrastructure, new CS hires. The gap to Tier A is 9.0 points — closeable in one to two academic cycles with a named AI program and formalized university partnerships.
### National Cathedral School — 74.6
Washington, DC · Grades 4-12 · Girls · ~600 students · Founded 1900 · Tuition ~$55,000
The girls'-school leader. NCS scores higher on Integration (78) than Specificity (76) — AI is present across the curriculum but not always named explicitly. The coordinate relationship with St. Albans creates shared infrastructure advantages. Strong Curriculum Depth (78) driven by CS course sequencing and cross-disciplinary STEM programming.
### Georgetown Preparatory School — 68.4
Bethesda, MD · Grades 9-12 · Boys (boarding) · ~500 students · Founded 1789 · Tuition ~$55,000
The oldest Jesuit school in the country and the only boarding school in the top 10. The Ignatian tradition's emphasis on intellectual rigor and ethical reasoning produces natural AI-ethics integration. The constraint is Recency (68) — Georgetown Prep's AI curriculum additions are incremental, not the aggressive structural bets that Bullis and Flint Hill are making. The school's boarding-school model and small size create intimacy but limit the faculty specialization that larger day schools deploy.
### Flint Hill School — 72.4
Oakton, VA · JK-12 · Coed · ~1,100 students · Founded 1956 · Tuition ~$42,000
The Virginia leader. Flint Hill posts the second-highest Recency score (76) in Tier B — an aggressive AI curriculum build driven by proximity to the Dulles Corridor tech ecosystem. The school's innovation program and CS sequencing are strong. Specificity (72) trails the leaders because AI is still embedded inside broader "innovation" language rather than named as a standalone discipline.
## Market Breakdown by Sub-Region
Sub-MarketSchoolsAvg AI IndexTop SchoolTop ScoreTier ATier BTier C
DC Proper966.9Sidwell Friends88.4234
Montgomery County, MD864.7Bullis School75.2062
Alexandria, VA461.4St. Stephen's & St. Agnes67.6031
Fairfax / Loudoun, VA559.5Flint Hill School72.4032
Other MD Suburbs452.1Sandy Spring Friends61.0013
## Tier C — What $50,000 Doesn't Buy
Twelve schools — from Georgetown Visitation (58.6) to St. John's College High School (45.8) — score below 60 on AI curriculum depth. None names AI in a course title. None has a dedicated AI faculty hire. None partners with a university AI lab or AI company in a formalized curriculum-extension arrangement. Most charge between $22,000 and $50,000 per year.
BASIS Independent McLean (55.4) is the Tier C outlier — highest Curriculum score (60) in the tier, driven by the BASIS charter network's nationally standardized STEM model. But Integration (48) is the lowest score in its profile: AI at BASIS McLean is siloed inside STEM courses, not embedded across disciplines. Network-model depth without school-level breadth.
Gonzaga College High School (57.2) scores well on Curriculum (62) — Jesuit intellectual rigor produces real CS coursework. But Partnerships (54) and Recency (56) reflect a school that has not yet made the post-ChatGPT structural investment that Tier B peers have committed to.
The remaining schools — from The Field School (55.8) to St. John's College High School (45.8) — represent the structural baseline. These are schools that teach computer science, offer AP courses, and run robotics clubs. They do not yet teach artificial intelligence as a named, measured, sequenced discipline.
## What Would Change the Scores
The gap between Tier B and Tier A is not budget. It is naming. The schools that score highest name AI explicitly — in course titles, in strategic plans, in institutional messaging. The schools that score lower describe "innovation," "technology," and "STEM." The difference is structural: when a school names AI, it creates a measurable curriculum commitment. When it uses generic language, AI remains optional.
Three moves would shift the rankings within one academic cycle:
-
### 1. Name the discipline.
Replace "technology" and "innovation" with "artificial intelligence" in course titles and program descriptions. Naming creates accountability.
-
### 2. Hire an AI-specific faculty role.
Not a CS teacher who also covers AI. A hire whose job title includes "artificial intelligence." One dedicated role changes the institutional signal.
-
### 3. Formalize a university or industry AI partnership.
A structured curriculum-extension arrangement with a university AI lab, a frontier AI company, or a research institution. Not a field trip. Not a speaker series. A formalized, multi-year partnership that extends what the school can teach beyond its campus.
## Related Research
- 5W AI Higher Education Index 2026 — The global benchmark. 50 universities × 6 dimensions. Stanford #1 (96.0), MIT #2 (94.7), CMU #3 (91.3). The framework this study adapts.
- 5W AI Business School Index 2026 — 60 MBA programs × 6 dimensions. Stanford GSB #1. 42% of MBA programs require zero AI coursework.
## About 5W AI Communications
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
## Methodology
5W AI Communications has published more than 100 AI visibility, citation-share, and production-capacity studies across the education, consumer, healthcare, and enterprise sectors. This index applies the same five-dimension framework used across the 5W AI education research franchise.
Dimension 1 — Curriculum Depth (25%): Does the school offer named AI courses? How many? At what level? Is there vertical sequencing from introductory to advanced? A school with "AP Computer Science" scores lower than a school with "AP Computer Science: Artificial Intelligence." Named AI electives, AI-integrated capstone projects, and dedicated AI course tracks all contribute.
Dimension 2 — Specificity (25%): Does the school name AI specifically — or hide it inside generic "STEM," "innovation," and "future-ready" language? Naming the discipline matters. A school that says "we teach artificial intelligence" scores higher than one that says "we do STEM." Course catalogs, program descriptions, and institutional messaging are all scored.
Dimension 3 — Integration (20%): Is AI taught only in the CS department — or does it appear in humanities, ethics, social science, art, and cross-disciplinary programs? Schools that embed AI across departments score higher than those that silo it. Ethics-of-AI coursework, AI-in-history seminars, and cross-disciplinary AI projects all contribute.
Dimension 4 — Recency (15%): How recently was the AI curriculum adopted or expanded? Schools that invested post-ChatGPT (late 2022 onward) with structured programs score higher than those running pre-2022 CS curricula without AI-specific updates. Recency rewards velocity, not legacy.
Dimension 5 — Partnerships (15%): Does the school partner with universities, AI labs, or AI companies to extend its curriculum? Faculty-training partnerships score higher than field trips. Formalized research collaborations score higher than speaker series. Multi-year structural arrangements score higher than one-off events.
AI Index = (Curriculum Depth × 0.25) + (Specificity × 0.25) + (Integration × 0.20) + (Recency × 0.15) + (Partnerships × 0.15).
Data sources: institutional websites, published course catalogs (2025–26 academic year), strategic plans, annual reports, faculty directories, press releases, and AI-engine citation queries (30 DC-adapted prompts × 5 engines × Q2 2026). Geographic scope: District of Columbia, Montgomery County and Prince George's County (MD), Fairfax County, Loudoun County, and Arlington County (VA), and the City of Alexandria.
---
### The Beauty AI Visibility Index 2026 — Top 25 U.S. Beauty Brands by AI Citation Share
URL: https://www.5wpr.com/ai-visibility-index/beauty-ai-visibility-index-2026/
Summary: The Ordinary leads 25 top U.S. beauty brands in AI citation share, ahead of CeraVe and Sephora, across ChatGPT, Claude, Gemini, and Perplexity in 2026.
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---
### The Developer-Led Growth Playbook for AI & Robotics 2026
URL: https://www.5wpr.com/research/ai-robotics-developer-led-growth-playbook-2026/
Summary: In AI and robotics, the developer on X is the first buyer and first ambassador. Six shifts, three case studies, and a seven-step 90-day plan.
5W RESEARCH · TECHNOLOGY PRACTICE · APRIL 2026
# The Developer-Led Growth Playbook for AI & Robotics 2026
By Ronn Torossian, Founder, 5W — April 2026
Last updated: May 2026
Ronn Torossian is the Founder and Chairman of 5W, one of the largest independent communications agencies in the United States. He advises AI platform, ML infrastructure, and robotics companies on developer-led growth, enterprise narrative, and Generative Engine Optimization (GEO).
Author page · LinkedIn
"The AI go-to-market is the inverse of traditional enterprise software. The developer running an experiment at 11pm shapes a multi-million-dollar procurement decision six months later. Companies that still treat developer relations as downstream of marketing are losing enterprise deals before the RFP even gets written."
— Ronn Torossian, Founder, 5W
In AI and robotics, the developer on X is the first buyer — and the first ambassador. The enterprise contract follows the community. Six shifts, three case studies, and a seven-step 90-day plan — for CEOs, CTOs, and heads of growth at AI platform, ML infrastructure, AI application, and robotics companies building trust that converts to enterprise revenue.
## EXECUTIVE SUMMARY
The AI and robotics go-to-market pattern is the inverse of traditional enterprise software. The individual developer or ML engineer running an experiment at 11pm is the one who shapes the company-wide procurement conversation six months later. They try the API on a weekend, share a thread on X, file a GitHub issue, and tell their team. By the time a VP of Engineering or CIO evaluates your platform, the developer community has already formed its consensus — and enterprise procurement ratifies it.
This playbook is built for the AI or robotics leader who understands that the developer community is not downstream of marketing — it is upstream of revenue — and wants a 90-day plan to build the X presence, the GitHub trust, the launch choreography, and the technical content program that turns developer attention into enterprise contracts.
## KEY STATS
45%+
New enterprise software purchases influenced by developer/practitioner trial before formal evaluation (Forrester / Stack Overflow Developer Survey)
6 mo
Typical lag from developer adoption to enterprise procurement
48 hrs
GitHub issue response SLA developer evaluators expect
12 hrs
Founder comment-presence window required on Hacker News launch day
#1
AI/ML developer community concentration channel — X (Twitter)
## SIX SHIFTS RESHAPING AI AND ROBOTICS GO-TO-MARKET IN 2026
01 — The developer is the first buyer — even when the budget is enterprise. In AI infrastructure and developer tools, the individual engineer evaluating your API on a weekend determines whether your company wins a multi-million-dollar enterprise contract six months later. The procurement process starts on X and in GitHub issues, not in RFPs. The strategic point is not to add developer marketing alongside enterprise sales. It is to recognize that for AI, developer communications is the enterprise motion, six months earlier. Ask your head of sales what percentage of enterprise deals had a prior champion in the developer community. If the answer is unknown, that is the gap.
02 — X (formerly Twitter) is still where AI discourse concentrates — despite the chaos. Through platform turbulence and alternative launches, X has remained the dominant concentration point for AI researcher, engineer, and founder discourse in 2026. The most consequential AI conversations — benchmark controversies, paper critiques, launch reactions, policy debates — still happen there, in public, at speed. A silent founder is not neutral — they are absent from the conversation that shapes how their company is perceived. Commit the founder to three to five X posts per week on technical substance. Delegated marketing voice is detected instantly and discounted.
03 — GitHub is a product marketing channel disguised as code hosting. Developer evaluators assess AI companies the way reviewers assess consumer electronics — by the quality of what is publicly shipped. Repo README depth, issue response time, code quality, commit cadence, and documentation all function as product marketing signal. A stale repo with unanswered issues signals to a developer audience exactly what a poorly stocked showroom signals to a retail shopper. Set explicit SLAs: 48-hour issue response, one-week PR review, quarterly documentation audit. Publish the SLAs publicly. They become the trust marker.
04 — Hacker News and Product Hunt are the public stress test for AI launches. Most AI launches now pass through Hacker News and Product Hunt as the first public test of whether the product survives skeptical scrutiny. The founder posts personally, the copy is technical and specific, and the founder is present in comments for the first 12 hours continuously. Orchestrated upvotes or marketing-voice posts are detected and punished. If your next launch is not planned around founder-led HN and PH posts with a 12-hour comment presence, rebuild the plan this week.
05 — Safety communications have become a growth function. Enterprise buyers in healthcare, financial services, government, and regulated industries now evaluate AI vendors partly on the substance and specificity of public safety work. Vague "responsible AI" language reads as evasion and costs deals. Concrete published safety research, red-team findings, policy engagement, and transparent disclosure of limitations win deals. The companies building the most defensible enterprise positions have made public safety work a core pillar of external communications. Audit your last quarter of external communications. If safety content is less than 15% of output, that is both a trust gap and a pipeline gap. 5W's Technology Practice runs AI safety communications as a stand-alone service line — research publishing cadence, red-team disclosure protocols, and policy engagement strategy.
Talk to 5W's AI Safety Comms team →
06 — For robotics, video demonstration has overtaken every other content format. A short founder-to-camera or engineer-to-camera video showing a robot performing a real task — with known failure modes disclosed honestly — is worth more than any pitch deck or press release. Companies like Figure, 1X, and Boston Dynamics have built category positions largely through video demonstration content on X and YouTube, combined with open engagement with the robotics research community. For robotics: publish one demo video every two weeks at minimum. Include known failure modes — authenticity is a moat in this category.
## THREE CASE STUDIES EVERY AI AND ROBOTICS LEADER SHOULD STUDY
Industry examples illustrating the developer-led growth pattern. 5W client engagements noted where applicable.
Anthropic: technical credibility as enterprise moat. Anthropic built Claude's enterprise position in part through sustained public investment in safety research, interpretability work, and transparent discussion of model behavior — published in long-form technical content, posted on X by researchers in their own voices, and engaged with seriously in AI policy forums. The research community evaluates Anthropic as a technical peer, not a vendor, and enterprise buyers inherit that evaluation in procurement. In AI, the safety and research content is the growth content — treating them as separate functions cedes enterprise credibility.
Hugging Face: community as the product. Hugging Face built one of the AI ecosystem's most valuable platform positions by making the community — researchers, engineers, hobbyists publishing models and datasets — the product. The company's communications from founders and team members runs as participation, not marketing. Enterprise adoption followed developer adoption with a multi-year lag that was not a lag at all: it was the foundation the enterprise business was built on. Community-led growth is slower in year one and compounds indefinitely afterward.
Cursor: founder-led technical voice as revenue engine. Anysphere — the company behind AI code editor Cursor — built a multi-billion-dollar valuation largely through developer word-of-mouth, founder-led technical content, and consistent public engagement with the engineering community. There was no billboard campaign. There were founders posting honestly about the product on X, a high-quality product experience, and a developer community that did the marketing in exchange for being taken seriously. For AI developer tools, paid marketing is not how you build the moat. Developer trust is the moat.
[5W CLIENT CASE STUDY — COMING SOON]
A named 5W AI or robotics client engagement with specific outcome data: developer signups attributable to community channels, X/HN/PH-attributed enterprise pipeline, GitHub star growth, AI citation share gained. Contact research@5wpr.com for current client case studies.
Get a custom 90-day plan →
## THE EIGHT-STEP 90-DAY PLAN TO BUILD DEVELOPER-LED GROWTH
01 — Audit your developer-channel footprint. Pull 180 days of activity on X, GitHub, Hacker News, Product Hunt, and technical content. Count founder posts, engagement from named AI researchers, GitHub star growth, issue response time, HN launch history, PH presence. Most AI companies discover they have occasional activity, not a baseline. Build a real one this week.
02 — Establish the founder's technical X presence. Three to five posts per week on technical substance — model capabilities, benchmark results, research insights, known limitations. In the founder's voice, not marketing's. AI community members detect delegated corporate voice within a post and discount it entirely.
03 — Treat GitHub as a product marketing channel. Publish SLAs: 48-hour issue response, one-week PR review, quarterly doc audit. Every open-source repo is product marketing for every future enterprise evaluator. A stale repo costs deals.
04 — Choreograph HN and PH launches properly. Founder posts personally, technical copy, 12-hour comment presence. Never orchestrate upvotes — the backlash costs more than the votes earn. HN and PH are stress tests; pass the test by treating them as technical conversations with skeptical peers.
05 — Publish technical content LLMs will cite. Long-form: benchmark comparisons, architecture explanations, research summaries, safety disclosures. Indexed on your domain, structured for LLM ingestion. Audit weekly how ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews answer the top 25 buyer queries in your category. If your brand is not cited, you are invisible at the highest-intent moment in the procurement journey. This is the work 5W's Generative Engine Optimization (GEO) practice runs for AI and robotics clients.
06 — Integrate safety as growth content. Concrete safety research. Red-team disclosures. Policy positions. Specific rather than vague. Enterprise buyers in regulated industries evaluate AI vendors on the substance of public safety work — "responsible AI" platitudes cost deals; specific published research wins them.
07 — Measure developer-to-enterprise pipeline attribution. Report: inbound enterprise interest citing X, GitHub, HN, PH; developer signups attributable to community channels; media coverage citing founder voice; share of voice in LLM answers; SLA compliance on developer-channel inputs. Tie each to the pipeline metrics the CRO tracks.
08 — Build the developer-channel compliance layer. Public-channel posting in AI carries unique risks: capability claims, benchmark accuracy, safety disclosures, and IP exposure. Founders posting at speed need pre-approved frameworks for what can and can't be said about model capabilities, training data, customer use cases, and competitive comparisons. Build the compliance layer once; let founders move at AI-community speed without legal becoming the bottleneck.
## FREQUENTLY ASKED QUESTIONS
Why is the developer the first buyer in AI and robotics — even when the budget is enterprise?
In AI infrastructure and developer tools, the individual engineer evaluating your API on a weekend determines whether your company wins a multi-million-dollar enterprise contract six months later. The procurement process starts on X and in GitHub issues, not in RFPs. Engineers running models have effective veto power over vendor choice. Developer communications is the enterprise motion, six months earlier.
What channels actually matter for AI and robotics developer communications?
X remains the dominant concentration point for AI researcher, engineer, and founder discourse. GitHub functions as product marketing: README depth, issue response time, and documentation all signal product quality to enterprise evaluators. Hacker News and Product Hunt are the public stress test for AI launches, requiring founder presence in comments for the first 12 hours.
How do you launch an AI product on Hacker News or Product Hunt without it backfiring?
The founder posts personally, the copy is technical and specific, and the founder is present in comments for the first 12 hours continuously. Orchestrated upvotes or marketing-voice posts are detected and punished. Treat HN and PH as technical conversations with skeptical peers — not PR exercises.
How does AI safety communication fit into a growth-focused GTM strategy?
Safety content is growth content. Enterprise buyers in regulated industries evaluate AI vendors on the substance of public safety work. Concrete published safety research, red-team findings, and policy engagement win deals. "Responsible AI" platitudes cost deals. If safety content is less than 15% of your external communications output, that is both a trust gap and a pipeline gap.
For robotics companies specifically, how does developer communication differ from AI software?
In robotics, video demonstration has overtaken every other content format. A short founder-to-camera video showing a robot performing a real task — with known failure modes disclosed honestly — is worth more than any pitch deck or press release. Publish one demo video every two weeks at minimum. Authenticity is a moat in this category.
Should AI company founders post openly about capabilities and limitations on X?
Yes. A silent founder is absent from the conversation that shapes how their company is perceived — not neutral. Three to five X posts per week on technical substance, in the founder's voice. AI community members detect delegated corporate voice within a post and discount it entirely.
↧ Download the Full PDF Playbook
## RELATED OP-EDS AND COMMENTARY BY 5W
- The developer is the first buyer in AI. Stop pretending otherwise. (TechCrunch — coming soon)
- Why silent AI founders are losing enterprise deals. (Forbes — coming soon)
- AI safety is a growth function. The companies treating it as compliance are losing deals. (Harvard Business Review — coming soon)
- Hacker News killed the orchestrated launch. Good. (TechCrunch — coming soon)
- GitHub is your product marketing. Treat it that way or lose enterprise deals. (InfoWorld — coming soon)
## ABOUT 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list.
5W's Technology Practice builds and runs developer-led growth programs for AI platform, ML infrastructure, AI application, and robotics companies — integrating founder voice, GitHub strategy, HN/PH launches, technical content, AI safety communications, and Generative Engine Optimization.
For more information, visit www.5wpr.com.
April 2026 — 5W Research Series, Technology Practice
Published by 5W. 5wpr.com · research@5wpr.com. Reproduction permitted with attribution.
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### Who Owns the Answer on EU AI Disclosure?
URL: https://www.5wpr.com/research/who-owns-the-answer-on-eu-ai-disclosure/
Summary: A 5W AI Visibility Index audit of which sources AI engines cite on EU AI Act disclosure, and the lane communications left open before the 2026 deadline.
AI Visibility Index
# Who Owns the Answer on EU AI Disclosure?
Ask an AI engine who must disclose AI-generated advertising in Europe and the answer comes back from law firms and compliance software — not communications firms. A 5W AI Visibility Index audit maps the gap before the August 2, 2026 deadline.
A 5W AI Visibility Index Audit — the sources five AI engines draw from when buyers ask about the August 2, 2026 transparency rules, and the lane the communications industry left wide open.
5W AI Visibility Index · Format: Audit · Fort Lauderdale, Florida
## The Finding
Ask an AI engine who has to disclose AI-generated advertising in Europe — and the answer comes back in the voice of a law firm or a compliance-software vendor. Not a communications firm. Not a PR agency. On the questions that decide how brands handle AI content, the industry paid to protect brand reputation is absent from the answer.
That is the gap this audit maps. And with Article 50 of the EU AI Act live August 2, 2026, it is not theory — it is an open lane measured in client exposure, right now.
## What We Measured
Five buyer prompts — the questions a brand marketer, CMO, or general counsel actually types into an AI engine before the deadline:
- Does the EU AI Act require disclosing AI-generated ads?
- Do I need to label AI product images in Europe?
- Does the EU AI Act apply to US agencies?
- What is a deep fake under the EU AI Act?
- What are the AI disclosure rules for PR and marketing in 2026?
For each prompt we logged the sources surfacing at the top of the retrieval corpus the major AI engines draw from — the leading indicator of which domains get cited inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.
## The Gap Map
- Law firms and compliance software own the answer. The sources that surface are dominated by law firms — Sidley, Lewis Silkin, Gleiss Lutz, Stibbe, Hunton — and AI-governance software vendors — Modulos, Tredence, Systima, VerityAI.
- Not one global PR or communications firm appears on the compliance or the US-scope prompts. Zero.
- The only communications-industry source to surface anywhere was Everything-PR, and only on the PR-and-marketing prompt.
- 5W appears on none of them — today.
Translation: the buyer researching AI-disclosure risk is handed to lawyers and software. The firm that manages the brand is nowhere in the conversation. That is a market-share problem wearing a compliance costume.
## The Answer — What the Rules Actually Say
The reason this audit doubles as the citation is simple: it states the answer the engines are missing. Here is the substance, from primary sources.
The date is fixed. Article 50's transparency obligations apply August 2, 2026. The EU's "Digital Omnibus" deferred the separate machine-readable watermarking requirement (Article 50(2)) to December 2, 2026 — but the core disclosure duties do not move. August 2 stands.
The label is mandatory. Deployers — including advertisers and brands — must disclose when content is a deep fake, artificially generated or manipulated, clearly and at first exposure. Providers of generative AI must mark synthetic audio, image, video, and text in a machine-readable, detectable format (the watermarking layer, due December 2, 2026).
The deep fake definition is broader than Americans assume. Under Article 3(60), a deep fake is "AI-generated or manipulated image, audio or video content that resembles existing persons, objects, places, entities or events and would falsely appear to a person to be authentic." Objects. Places. Events. Not just face-swaps — the AI product shot, the synthetic background, the invented location all count.
US agencies are in scope. The Act reaches any organization whose AI-generated content is used in the EU — regardless of physical presence. A US campaign that reaches EU users is covered. No office in Europe required.
The carve-out is the operational answer. AI-generated text on matters of public interest requires disclosure unless it passed meaningful human review and editorial control. Human oversight kills the text-disclosure requirement. Routine edits — color correction, noise reduction — don't trigger it. Artistic and satirical work gets a lighter touch.
The number that concentrates the mind. Breach the transparency obligations and the ceiling is €15 million or 3% of worldwide annual turnover — whichever is higher.
## What It Means for Communications
This is the moment AI Communications stops being about visibility and becomes about exposure. Every brand running AI-generated creative into the EU now carries a disclosure obligation with a fine attached — and most don't yet know the deep fake definition covers a product photo.
The firm that owns the answer to "how do we handle this" owns the relationship. Right now that firm is a law firm. It does not have to be.
Citation Share is the leading indicator. The domains cited today become the defaults the engines repeat tomorrow. The communications industry has a narrow window to become the answer — before the lawyers finish owning it.
The buyers facing this deadline are already asking AI engines what to do — and the communications industry isn't in the answer. That's not a visibility problem. It's a market-share problem. Whoever owns the answer owns the client.
Ronn Torossian, Founder & Chairman, 5W AI Communications
## Methodology
This audit maps the retrieval corpus — the source domains the major AI engines draw from — across five buyer prompts, logged July 2026. Corpus composition is a leading indicator of citation, not a substitute for live-answer capture. The client-grade layer of the 5W AI Visibility Index runs the same prompts as live queries against ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews and scores brand-level Citation Share directly. Findings here reflect what the engines can retrieve; the live layer measures what they say.
Primary sources logged include the European Commission's Article 50 transparency guidance and Article 3(60) / Article 99 text (artificialintelligenceact.eu), alongside analysis from Sidley, Lewis Silkin, Gleiss Lutz, Stibbe, and Hunton.
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
About the author. Ronn Torossian is the founder and chairman of 5W AI Communications, The Communications Firm Built for the AI Era. He is the publisher of Everything-PR and the author of two best-selling editions of For Immediate Release.
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### The Cigar & Pipe AI Visibility Index 2026 — Top 25 Premium Cigar Brands by AI Citation Share
URL: https://www.5wpr.com/ai-visibility-index/cigar-pipe-ai-visibility-index-2026/
Summary: Evaluate citation share rankings across ChatGPT, Claude, Gemini and Perplexity for Padrón, Arturo Fuente, Davidoff and 22 premium cigar brands.
# The Cigar & Pipe AI Visibility Index 2026
### A 5W AI Visibility Index — Top 25 Cigar & Pipe brands ranked by citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. By Ronn Torossian, Founder of 5W, and the 5W Research Team — April 2026.
Download the full PDF
## EXECUTIVE SUMMARY
The premium cigar category is the smallest, most tradition-bound, and most family-controlled major luxury category in America. Approximately 200.9 million premium handmade cigars were imported into the U.S. in the first half of 2025 alone, almost entirely from Nicaragua, the Dominican Republic, and Honduras. The category is dominated by family-owned operators — Padrón, Fuente, Garcia (My Father) — whose brand authority was built across decades of agricultural ownership, cigar-rolling expertise, and consistent quality. None of that consolidation translates automatically into AI citation share, which routes through a different mechanism entirely.
This report measures who is winning that citation surface and who is not. 5W ran 60+ consumer-intent prompts through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews in Q1 2026, tracking citations across five sub-categories: family-owned premium cigar brands, large-conglomerate cigar portfolios (General Cigar, Altadis, Scandinavian Tobacco Group), specialty retailers and lounges (JR Cigars, Famous Smoke Shop, Cigars International), Cuban cigar brands (Cohiba, Romeo y Julieta, Montecristo, with the embargo caveat), and pipe tobacco specifically.
The findings: Padrón and Arturo Fuente together account for an estimated 21% of all cigar-category AI citations across the prompts we tested — both of them family-owned, both of them Cigar Aficionado retailer-survey winners year after year. Davidoff anchors the luxury-prestige citation surface that Cuban brands cannot fully claim due to the U.S. embargo. My Father Cigars has built citation share that punches well above its production scale. Rocky Patel, Oliva, and Drew Estate fill the volume tier. Cigar Aficionado magazine itself produces editorial citation surface that no individual brand can match — its annual rankings are referenced in nearly every AI answer about "best cigar 2026."
The brands that win cigar AI citation in the next twelve months will define which premium cigar a new aficionado buys when they walk into their first humidor. The window is open. Few brands are paying attention.
## TOP 15 BRANDS BY AI CITATION SHARE
Estimated share of citations across 60+ cigar and pipe consumer-intent prompts run through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q1 2026.
Padrón11.5%
Arturo Fuente10.5%
Davidoff7.5%
My Father Cigars5.5%
Oliva4.5%
Rocky Patel4.0%
Drew Estate (Liga Privada)3.6%
Perdomo3.4%
Ashton3.0%
Cohiba (non-Cuban)2.8%
Macanudo2.6%
Montecristo2.4%
Romeo y Julieta2.2%
J.C. Newman2.0%
Alec Bradley1.8%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q1 2026. Share represents estimated proportion of brand citations across 60+ tracked consumer-intent prompts. Remaining ~33% split across ranks 16–25, regional brands, pipe-tobacco-specific brands, and cigar retailer/lounge citations.
## THE FULL TOP 25 RANKING
1. Padrón — Nicaraguan family-owned premium cigar manufacturer. Founded by José Orlando Padrón in 1964. Named #1 best-selling brand in America by 52.3% of retailers in the 2025 Cigar Insider Retailer Survey. Cigar Aficionado's 2025 Cigar of the Year was the Padrón 60th Anniversary Perfecto. Holds the record for most Cigar of the Year wins (five times in two decades). The category-leading citation winner.
2. Arturo Fuente — Dominican family-owned premium cigar manufacturer. Named #2 best-selling brand by 52.3% of retailers in the 2025 Cigar Insider survey — a near-tie with Padrón. The Fuente Fuente OpusX line is the hottest premium cigar in the U.S. by retailer demand. Wins luxury and limited-edition prompts.
3. Davidoff — Swiss-owned luxury cigar and tobacco brand. Annual revenue exceeding $1 billion. The luxury-prestige citation anchor that Cuban brands cannot fully claim under U.S. embargo. Strong citation share on "luxury cigar" and "best cigar gift" prompts. Owns its own retail lounge network.
4. My Father Cigars — Nicaraguan family-owned (Garcia family). Founded in the early 2000s. Cigar Aficionado's 2024 Cigar of the Year. Punches above its production scale on "best cigar under $20" and "best Nicaraguan cigar" prompts.
5. Oliva — Nicaraguan premium manufacturer. The Oliva Serie V is one of the most consistently top-rated mid-price-tier cigars. 82% of retailers reported increased Oliva Serie V sales in the 2024 survey. Strong citation share on "best mid-price cigar" prompts.
6. Rocky Patel — Honduras- and Nicaragua-rolled premium cigar manufacturer. Founded by Rocky Patel. Diversified portfolio of 30+ blends. Wins "biggest cigar selection" prompts and breadth-focused queries.
7. Drew Estate — Nicaraguan boutique manufacturer, owned by Swisher International. The Liga Privada No. 9 has a cult following. Strong citation share on "infused cigar" (ACID line) and "boutique premium cigar" prompts.
8. Perdomo — Nicaraguan family-owned premium manufacturer. Named best-selling brand by 24.6% of retailers in the 2025 survey. Strong citation share on "best Nicaraguan cigar value" prompts.
9. Ashton — Dominican premium cigar brand made by Arturo Fuente Cigar Co. Strong citation share on "best Dominican cigar" prompts. Diversified portfolio includes the Ashton San Cristobal line rolled in Nicaragua.
10. Cohiba (non-Cuban) — General Cigar's Dominican-rolled Cohiba line. Distinct from the Cuban Cohiba, which remains under U.S. embargo. AI engines route to the non-Cuban Cohiba in U.S.-market prompts despite the brand-name confusion. Strong citation share on "Cohiba cigars" generic prompts.
11. Macanudo — General Cigar's flagship mild-bodied premium. The leading mass-market premium cigar by volume. Strong citation share on "best beginner cigar" and "mild cigar" prompts.
12. Montecristo — Altadis USA's non-Cuban Montecristo. The largest non-Cuban Montecristo line, distinct from the Cuban Montecristo. Strong citation share on classic-name prompts.
13. Romeo y Julieta — Altadis USA's non-Cuban Romeo y Julieta. Classic mass-market premium. Strong citation share on Shakespeare-reference prompts and on "best wedding cigar" prompts.
14. J.C. Newman — American family-owned cigar manufacturer. Founded 1895. Operates the historic El Reloj factory in Tampa. Brick House and Diamond Crown brands. Strong citation share on "American cigar history" prompts.
15. Alec Bradley — Honduran-rolled premium manufacturer, owned by Scandinavian Tobacco Group's Forged Cigar Co. Diversified portfolio including the Black Market and Prensado lines. Strong citation share on "best Honduran cigar" prompts.
16. CAO — General Cigar's flavored and full-bodied portfolio.
17. Gurkha — Premium and ultra-premium cigar brand. Strong citation share on "most expensive cigar" prompts.
18. Tatuaje — Boutique premium manufacturer. Founder Pete Johnson. Cult-following cigar with strong citation share among aficionados.
19. E.P. Carrillo — Boutique Dominican manufacturer. Multiple Cigar of the Year wins.
20. La Flor Dominicana — Dominican boutique premium manufacturer.
21. La Aurora — Dominican manufacturer. The oldest Dominican cigar factory.
22. Punch / Hoyo de Monterrey (non-Cuban) — General Cigar Co. legacy brands.
23. JR Cigars / Famous Smoke Shop / Cigars International — The three largest premium cigar online retailers in the U.S. Each generates substantial editorial citation surface through their respective magazines and content arms.
24. Dunhill / Zino Platinum — Davidoff-owned ultra-luxury sub-brands.
25. Peterson Pipes / Dunhill Pipes — The two leading citation-share winners in pipe-specific prompts. Pipe tobacco brands (Cornell & Diehl, GL Pease, McClelland) generate adjacent citation share.
## KEY FINDINGS
- STAT 1: 200.9M — premium handmade cigars imported into the U.S. in H1 2025 (Tobacco Insider)
- STAT 2: $13.4B — global luxury cigar market in 2025; projected to reach $27.7B by 2035 at 7.5% CAGR (Market Research Future)
- STAT 3: 21% — estimated combined AI citation share of Padrón and Arturo Fuente across 60+ tracked queries
- STAT 4: 52.3% — share of U.S. cigar retailers naming Padrón or Arturo Fuente as their best-selling brand (Cigar Aficionado 2025 Cigar Insider Retailer Survey)
- STAT 5: 5 — number of times Padrón has won Cigar Aficionado's Cigar of the Year, the most of any cigar manufacturer in two decades
- STAT 6: 1.4% — U.S. cigar market retail revenue growth in 2024 (Tobacco Insider); category resilient despite cigarette decline
- STAT 7: 18% — share of cigar prompts that produced AI engine refusals, disclaimers, or hedges due to tobacco-content guardrails; the second-highest rate of any category 5W has measured (after cannabis)
- STAT 8: January 1, 2026 — date California's Unflavored Tobacco List took effect, requiring tobacco products to be listed as unflavored to remain legal for sale; Premium Cigar Association lawsuit filed October 2025 was rejected by the U.S. District Court for the Central District of California
## THE CENTRAL FINDING
The U.S. premium cigar market is forming inside AI answers along structurally different lines than it forms inside traditional retailer surveys. The Cigar Aficionado retailer surveys (Padrón #1, Fuente #2, Perdomo #3, My Father #5, Davidoff #6) measure what U.S. tobacconists are selling. The AI citation surface (Padrón #1, Fuente #2, Davidoff #3, My Father #4, Oliva #5) measures what consumers asking AI engines are being told to buy. The two rankings overlap meaningfully but not identically — and the differences reveal where citation strategy matters most.
Inside that AI-mediated routing, three structural patterns are emerging. First, family-owned manufacturers with vertical integration (Padrón, Fuente, Garcia/My Father) dominate citation share at a velocity their conglomerate competitors (General Cigar, Altadis, Scandinavian Tobacco Group) cannot match — even though the conglomerates collectively manufacture more cigars by unit volume. The mechanism is brand-narrative coherence: AI engines weight family-ownership stories, founder personalities, and decades-of-tradition content far more heavily than corporate-portfolio reach. Second, Davidoff has built a luxury-prestige citation moat that Cuban brands (Cohiba, Romeo y Julieta, Montecristo) cannot fully claim under the U.S. embargo, despite Cuban cigars carrying higher consumer brand recognition. The embargo creates a structural asymmetry: AI engines hedge or refuse on Cuban-cigar purchase prompts in the U.S. market, defaulting to non-Cuban Davidoff alternatives instead. Third, Cigar Aficionado magazine produces editorial citation surface that no individual brand can match — its annual top 25 lists, retailer surveys, and Cigar of the Year selections are referenced in nearly every AI answer about "best cigar 2026."
The category structure (family-owned, vertically integrated, agriculturally rooted) maps directly onto the citation-share economics. Brands with multi-generational family-ownership stories are advantaged. Conglomerate-owned brands (Macanudo, Romeo y Julieta non-Cuban, Montecristo non-Cuban) underperform their unit-volume position. Boutique brands with cult followings (Liga Privada, Tatuaje, E.P. Carrillo) capture citation share well above their production scale because aficionado-content density translates directly to AI citation density.
## METHODOLOGY
5W ran 60+ premium cigar consumer-intent prompts through ChatGPT (GPT-4 and GPT-5), Claude (Sonnet and Opus 4.7), Perplexity, and Google AI Overviews during January–March 2026. Each prompt was run multiple times across each engine; brand citations were tagged, normalized, and deduplicated.
Prompts spanned five sub-categories: Family-owned premium cigar brands (Padrón, Arturo Fuente, My Father, Perdomo, J.C. Newman, Tatuaje, La Flor Dominicana, La Aurora). Conglomerate cigar portfolios (General Cigar — Macanudo, CAO, Cohiba non-Cuban, Hoyo de Monterrey, Punch; Altadis USA — Montecristo non-Cuban, Romeo y Julieta non-Cuban, Henry Clay; Scandinavian Tobacco Group — Davidoff sub-brands, Alec Bradley). Cuban cigar brands (Cohiba Cuban, Montecristo Cuban, Romeo y Julieta Cuban, Trinidad, Partagás Cuban — with the U.S. embargo caveat noted in citation tracking). Specialty retailers and lounges (JR Cigars, Famous Smoke Shop, Cigars International, plus regional lounge chains). Pipe tobacco and pipe brands (Peterson, Dunhill Pipes, Cornell & Diehl, GL Pease, McClelland).
Citation share was calculated as the proportion of total brand citations across all prompts. The 18% AI hedge/refusal rate on tobacco-content prompts was tracked separately. Methodology mirrors 5W's prior AI Visibility Index reports for pickleball, crypto, cannabis, beauty, local services, the wedding industry, and HVAC and plumbing.
## WINNERS
Padrón is the highest-ranked brand in the index and the dominant winner of "best cigar," "best Nicaraguan cigar," and "Cigar of the Year" citation share. The structural mechanism is brand-narrative coherence (single family, single Nicaraguan operation since 1964), Cigar Aficionado retailer-survey dominance (52.3% best-seller mention in 2025), and the velocity of Cigar of the Year wins (five in two decades). The Padrón 60th Anniversary Perfecto's 2025 Cigar of the Year recognition created a citation event that compounded throughout 2026.
Arturo Fuente is the second-ranked brand and the dominant winner of luxury and limited-edition prompts. The Fuente Fuente OpusX line consistently appears in "rare cigar," "limited edition cigar," and "what cigar to give as a gift" prompts at frequencies that no other premium cigar brand matches. Fuente's vertical integration (owns its own Dominican tobacco farms at Chateau de la Fuente) produces narrative coherence that AI engines weight heavily.
Davidoff is the third-ranked brand and the dominant winner of "luxury cigar" prompts. Annual revenue exceeding $1 billion produces the structural-disclosure entity-strength signals AI engines weight on prestige-luxury queries. Davidoff's owned-retail lounge network produces additional citation surface that conglomerate-owned brands selling exclusively through tobacconist resale cannot match.
My Father Cigars is the fourth-ranked brand. The structural mechanism is brand-personality concentration (Pepin Garcia's biographical narrative is in nearly every "My Father" AI response) plus 2024 Cigar of the Year recognition. The brand punches above its production scale on aficionado-density-driven citation share.
## FALLING BEHIND
Cuban cigar brands (Cohiba Cuban, Montecristo Cuban, Romeo y Julieta Cuban, Trinidad, Partagás). The U.S. embargo creates a structural citation-share asymmetry: AI engines hedge or refuse on "where to buy Cuban Cohiba in the U.S." prompts, defaulting to non-Cuban alternatives manufactured by General Cigar, Altadis USA, or Davidoff. Cuban brands lose citation share they would otherwise capture if the embargo lifted, but conversely the non-Cuban Cohiba/Montecristo/Romeo y Julieta lines benefit from the same brand-name recognition without the embargo penalty.
Macanudo. The leading mass-market premium cigar by unit volume ranks #11 in our citation share index — well below its volume position. The mechanism is mild-bodied positioning combined with conglomerate ownership (General Cigar, owned by Scandinavian Tobacco Group): AI engines weight aficionado-density signals and family-ownership narratives more heavily than mass-market unit volume. Macanudo wins "best beginner cigar" and "mild cigar" sub-segment prompts but loses the broader "best cigar" citation share to Padrón and Fuente.
The conglomerate-owned non-flagship brands. General Cigar's Punch and Hoyo de Monterrey, Altadis USA's Henry Clay, and Scandinavian Tobacco Group's mid-tier portfolio brands rank well below their unit-volume positions. The mechanism is brand-narrative dilution: when a single corporate parent owns 8-12 cigar brands, AI engines route citation share to the flagship and largely ignore the rest. Conglomerate-owned brands that produce flagship-level brand-narrative content (unique heritage, distinct positioning, signature blends) capture citation share that conglomerate-owned brands sharing generic conglomerate marketing cannot.
Pipe tobacco and pipe-specific brands. Peterson Pipes, Dunhill Pipes, and the pipe-tobacco specialists (Cornell & Diehl, GL Pease, McClelland) rank well below their category-specific dominance because AI engines collapse the category into "cigar and pipe" rather than treating pipes as a distinct surface. Pipe-specific brands that produce content addressing pipe-specific consumer questions (best beginner pipe, how to break in a pipe, best aromatic pipe tobacco) capture citation share that pipe brands sharing cigar-adjacent content cannot.
Boutique cigar brands without aficionado content density. Boutique manufacturers with strong product quality but limited Cigar Aficionado coverage, limited social-media presence, and limited wholesaler-marketing infrastructure are systematically invisible in AI answers regardless of taste-quality merits. Boutique brands that build aficionado-content density (Cigar Aficionado coverage, Halfwheel reviews, Cigar Snob features) capture citation share that boutique brands relying on retailer word-of-mouth alone cannot.
## THE SIX STRUCTURAL FINDINGS
1. Family-owned manufacturers with vertical integration dominate citation share. Padrón, Arturo Fuente, and Garcia/My Father — three families operating four manufacturers — collectively appear in approximately 27% of premium cigar AI prompts. The mechanism is brand-narrative coherence: AI engines weight family-ownership stories and decades-of-tradition content far more heavily than conglomerate-portfolio scale.
2. Cigar Aficionado is the editorial citation infrastructure for the entire category. The annual Cigar of the Year, the Top 25 list, and the Cigar Insider retailer surveys represent the bulk of "best cigar 2026" citation surface. Brands featured in Cigar Aficionado capture citation share that brands publishing in non-aficionado media cannot match. The Halfwheel and Cigar Snob editorial citation surfaces are smaller but compounding alongside.
3. The U.S. Cuban-cigar embargo creates a structural citation-share asymmetry. AI engines hedge or refuse on "buy Cuban cigars in the U.S." prompts, redirecting to non-Cuban alternatives. The embargo benefits non-Cuban Cohiba, Montecristo, and Romeo y Julieta lines while penalizing the Cuban originals. If the embargo lifts, the citation surface will reset substantially.
4. The 18% AI hedge/refusal rate on tobacco prompts is the second-highest of any category 5W has measured. AI engines apply tobacco-content guardrails at a frequency exceeded only by cannabis. Brands that produce content addressing the specific topics AI engines hedge on (legal-purchasing-age content, regional-restriction content, harm-reduction comparisons) capture citation share that brands silent on tobacco-content guardrails cannot.
5. Conglomerate-owned mass-market brands underperform their unit-volume position. General Cigar's Macanudo, Altadis USA's non-Cuban Montecristo and Romeo y Julieta, and Scandinavian Tobacco Group's mid-tier portfolio represent the bulk of U.S. premium cigar volume but only a fraction of citation share. The mechanism is brand-narrative dilution: AI engines route to flagship narratives, not portfolio scale.
6. The specialty retailer/lounge tier produces editorial-grade citation surface. JR Cigars, Famous Smoke Shop, and Cigars International each operate editorial content arms that produce structured, fact-cited cigar reviews and primer content. Brands that secure coverage in these retailer-content channels capture citation share that brands relying on manufacturer-owned content alone cannot.
## 2026-SPECIFIC FINDINGS
1. The Padrón 60th Anniversary Perfecto's 2025 Cigar of the Year recognition is the year's largest single citation event in the cigar category. AI answers about "best cigar 2026," "best Nicaraguan cigar," and "best cigar to gift" reference the 60th Anniversary Perfecto in nearly every response. The compounding effect of five Padrón Cigar of the Year wins in two decades has produced citation lock that no other family-owned manufacturer has matched.
2. The January 1, 2026 effective date of California's Unflavored Tobacco List is the year's largest regulatory citation event. The U.S. District Court for the Central District of California's 2025 rejection of the Premium Cigar Association lawsuit against the law produced a wave of trade-press coverage that AI engines absorbed. AI answers about "where to buy cigars in California" now reference the regulation in nearly every response.
3. Scandinavian Tobacco Group's August 2025 cigar price increases (eliminating the prior 5% tariff surcharge) reset retail-price-tier citation surface. AI answers about "premium cigar prices 2026" reference the STG repricing in growing frequency.
4. The 4.6% growth in U.S. handmade cigar imports in H1 2025 (Nicaragua +2.8%, Dominican Republic +4%, Honduras +12.1%) produced citation events on country-of-origin prompts. AI answers about "best Honduran cigar" surface Honduran-rolled brands (Alec Bradley, Camacho, Rocky Patel's Honduran lines) at growing frequency in 2026.
5. The continued growth of premium cigar lounges as third-spaces — particularly Davidoff Lounges and the boutique tobacconist network — produced citation share that online retailer content cannot match. AI answers about "best cigar lounge near me" route to Davidoff and the regional tobacconist networks at growing frequency.
6. Cuban cigar U.S.-policy speculation in 2025-2026 reset Cuban-cigar citation prompts. AI engines now reference the embargo status more explicitly in Cuban-cigar prompts, with hedge language increasing in frequency. If policy changes, the citation surface will reset substantially in favor of Cuban brands.
## FROM RONN TOROSSIAN, FOUNDER OF 5W
"Premium cigars is the smallest, most tradition-bound, most family-controlled major luxury category in America — and the citation-share economics inside AI answers map almost perfectly onto the structure of the underlying industry. Family-owned manufacturers with vertical integration dominate. Conglomerates underperform their unit-volume positions. Cigar Aficionado anchors the editorial citation infrastructure for the entire category. Davidoff has built a luxury-prestige moat that Cuban brands cannot fully claim under the U.S. embargo. Padrón has built citation lock through five Cigar of the Year wins in two decades that no competitor has matched. The brands that win the next decade are the ones that recognize Cigar Aficionado, Halfwheel, and Cigar Snob as the citation infrastructure for AI answers — not the ones who treat trade press as a marketing afterthought. The window is open. Few brands are paying attention."
## THE CIGAR & PIPE GEO PLAYBOOK
1. Secure Cigar Aficionado, Halfwheel, and Cigar Snob editorial coverage above all else. These three publications represent the bulk of "best cigar 2026" citation surface. A Cigar of the Year recognition is worth more in citation-share terms than any marketing campaign in the category.
2. Build family-narrative content density. AI engines weight family-ownership stories heavily. Brands with founder biographies, multi-generational succession stories, and farm-ownership narratives (where applicable) capture citation share that brands with corporate-anonymous positioning cannot.
3. Vertical integration content matters. Brands that own their own tobacco farms (Padrón in Nicaragua, Fuente at Chateau de la Fuente) should saturate the citation surface with farm-ownership content. AI engines weight vertical-integration narratives heavily on quality-and-craft prompts.
4. Address tobacco-content AI guardrails directly. The 18% hedge/refusal rate on tobacco prompts is the second-highest of any consumer category. Brands that produce content addressing legal purchase-age compliance, regional restrictions, and harm-reduction comparisons capture citation share that brands silent on these topics cannot.
5. Specialty retailers (JR Cigars, Famous Smoke Shop, Cigars International) are the secondary citation infrastructure. Brand placement in their respective magazines and primer content produces citation surface that direct manufacturer content alone cannot match.
6. Regulatory events are citation events. California's Unflavored Tobacco List, FDA premium cigar regulatory exemption status, federal cigar excise tax debates — every regulatory development is a citation reset. Calendar AI-citation audits to within 72 hours of every major regulatory event.
7. Cuban-policy speculation should be addressed transparently. AI engines hedge on Cuban-cigar prompts. Brands that produce content explicitly addressing the embargo, distinguishing Cuban-origin from non-Cuban brand-name lines, and addressing future policy scenarios capture citation share that brands silent on the embargo cannot.
8. Lounge content compounds. Brands that operate or sponsor cigar lounges (Davidoff Lounges, Arturo Fuente Newsstand) produce citation share on "best cigar lounge near me" prompts that brands without owned third-spaces cannot.
9. Pipe-specific brands need pipe-specific content infrastructure. Peterson, Dunhill Pipes, and the pipe-tobacco specialists need to publish content distinct from cigar-adjacent positioning. AI engines collapse the category by default; pipe brands have to invest in differentiation.
10. Build for the post-AI aficionado. The new cigar smoker under 40 opens ChatGPT before they walk into a humidor. Brands that build for this consumer flow — family-narrative content, Cigar Aficionado coverage, lounge presence, regulatory transparency — will compete. Brands that wait will discover the citation surface has hardened around Padrón, Fuente, Davidoff, and a small number of others.
## THE BIGGER PICTURE
Premium cigars is the most family-controlled, most tradition-bound, most agriculturally rooted major luxury category in America — and the citation-share economics inside AI answers map almost perfectly onto the structure of the underlying industry. Brands with multi-generational family-ownership narratives, vertical integration, and Cigar Aficionado coverage dominate. Brands without these signals lose citation share regardless of unit-volume position or product quality.
The brands that win the next decade are the brands that secure Cigar Aficionado coverage relentlessly, build family-narrative content density, address tobacco-content AI guardrails directly, treat regulatory events as citation events, build for the post-AI aficionado, and recognize Cigar Aficionado, Halfwheel, and Cigar Snob as the editorial citation infrastructure for the entire category. The brands that wait will discover that Padrón, Arturo Fuente, Davidoff, My Father, and a small number of others have absorbed a larger share of what aficionados see when they ask AI "what cigar should I smoke," "what's the best cigar of 2026," and "what should I gift a cigar smoker."
## ABOUT 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list.
For more information, visit www.5wpr.com.
## FAQ
### Why do Padrón and Arturo Fuente rank so far above conglomerate-owned brands like Macanudo and Romeo y Julieta?
AI engines weight family-ownership narratives, vertical-integration stories, and Cigar Aficionado coverage far more heavily than unit-volume position. Padrón and Fuente have decades of these signals; conglomerate brands have less family narrative coherence by structure.
### Why is Cohiba (non-Cuban) ranked above Cohiba (Cuban)?
The U.S. embargo creates a structural citation-share asymmetry. AI engines hedge or refuse on Cuban-Cohiba purchase prompts in the U.S. and route to the non-Cuban General Cigar version instead. If the embargo lifts, this ranking will likely reverse.
### Why are pipe brands underrepresented?
AI engines collapse "cigar and pipe" into a single category by default. Pipe-specific brands (Peterson, Dunhill Pipes) need pipe-specific content infrastructure to capture citation share distinct from cigar-adjacent positioning.
### How was citation share measured?
5W ran 60+ premium cigar consumer-intent prompts through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews during January–March 2026. Each prompt was run multiple times across each engine; brand citations were tagged, normalized, and deduplicated.
Download the Full Report (PDF)
---
### AI IS INVENTING
URL: https://www.5wpr.com/research/celebrity-endorsement-index/
Summary: The 5W and Talent Resources index rates 50 celebrities across 4 AI engines and finds 18% of AI-recommended celebrity endorsements in 2026 are fabricated.
VOL. III
5W × Talent Resources — Research
May 2026
NEW STUDY
Volume III · The Research Series
Published May 22, 2026
# AI IS INVENTING
# CELEBRITY ENDORSEMENT
# DEALS THAT DON'T EXIST.
A new index from 5W AI Communications and Talent Resources measures how ChatGPT, Claude, Gemini, and Perplexity recommend celebrity endorsement partners — and 18% of those recommendations are fabricated.
#1
Selena Gomez tops the global ranking
18%
Of AI-recommended endorsements are fabricated
24%
Gemini hallucinates most
64%
Of brand marketers now start in AI
A joint research study from Talent Resources and 5W AI Communications · Volume III · Published May 22, 2026
The Top Line
When a brand marketer opens ChatGPT, Claude, Gemini, or Perplexity in 2026 and asks "which celebrity should we partner with for our beauty launch" — the engine answers. It names a person. It frequently names a product. It commits to a recommendation.
That recommendation is now the buy signal.
This is the first index to measure it. Citation Share is the new market share. For talent, engine recommendation is the new endorsement deal flow. The agent no longer has to pitch the brand. The engine already did.
And one in five times — the engine is making the deal up.
FINDING 01
SELENA GOMEZ #1.
REYNOLDS #2.
MRBEAST #3.
The most-recommended celebrities by AI engines as commercial endorsement partners. Owner-operators dominate. Pure endorsers cluster lower.
FINDING 02
18% OF AI
ENDORSEMENT PICKS
ARE FAKE.
Across 600+ prompts, the engines confidently asserted partnerships, ambassador roles, and product lines that do not exist in the public record.
FINDING 03
64% OF BRANDS
START IN AI
BEFORE CALLING.
Brand marketers now begin endorsement scoping inside an AI engine before contacting talent representation. (5W / Talent Resources buyer survey, May 2026, n=212.)
§01 · The Top 10
## The names the engines reach for first.
Composite score (0–100) across four AI engines and ten consumer categories. Directional estimates from May 2026 prompt research.
#CelebrityScoreStrongest Categories
01Selena Gomez92Beauty, Wellness, Entertainment
02Ryan Reynolds89Spirits, Tech, Sports
03MrBeast86Food & Snack, Tech, Gaming
04Rihanna85Beauty, Fashion, Spirits
05Dwayne Johnson83Sports, Spirits, Wellness
06Kim Kardashian82Fashion, Beauty, Luxury
07George Clooney78Spirits, Luxury, Hospitality
08Cristiano Ronaldo77Sports, Hospitality, Fashion
09LeBron James76Sports, Spirits, Tech
10Beyoncé75Fashion, Beauty, Entertainment
The top 10 share one structural pattern. Each operates as either an owner-operator (Reynolds, MrBeast, Kardashian, Rihanna, Gomez) or as a multi-decade brand vehicle (Clooney, Johnson, James, Beyoncé). AI engines retrieve these two archetypes in different prompt contexts — and brands looking for the wrong archetype get the wrong recommendation.
§02 · Category Leaders
## Three names absorb more than half of category citations.
In most consumer categories, the AI engines collapse the field down to a handful of names — fast.
Category#1#2#3
BeautySelena GomezRihannaHailey Bieber
SpiritsGeorge ClooneyRyan ReynoldsKendall Jenner
SportsCristiano RonaldoLeBron JamesPatrick Mahomes
FashionRihannaPharrell WilliamsZendaya
TechMarques BrownleeAshton KutcherRyan Reynolds
LuxuryGeorge ClooneyZendayaRoger Federer
HospitalityCristiano RonaldoDavid BeckhamRobert De Niro
WellnessSelena GomezGwyneth PaltrowLeBron James
Food & SnackMrBeastLogan PaulEmma Chamberlain
FinanceMark CubanShaquille O'NealTom Brady
The implication for talent representation is direct. If a client is not currently in the top three for any category, the engines functionally do not recommend that client for endorsement work. They will be cited for their primary craft — film, music, sport — but not as a commercial vehicle.
That is a fixable infrastructure problem. Not a popularity problem.
§03 · The Hallucinated Pairings
## When AI invents the deal.
This is the most uncomfortable finding in the study.
AI engines hallucinate celebrity endorsements at an 18% rate. Across 600+ prompts, the four engines confidently asserted partnerships, ambassador roles, or product lines that do not exist in the public record.
18%
Of AI-engine endorsement recommendations are fabricated
Three patterns emerged.
### Pattern A · The confident invention.
The engine names a celebrity-brand pairing with full detail — year, product line, campaign name — where no such relationship exists. It then provides a plausible rationale, usually drawn from the celebrity's known interests or aesthetic adjacencies. The fabrication is internally coherent. That coherence is what makes it dangerous.
### Pattern B · The expired partnership treated as current.
The engine names a relationship that ended 18 to 36 months ago and presents it as active. Buyers acting on this signal pitch deals already in the rear-view mirror. Talent agencies receive cold outreach for clients whose endorsement category has moved on. This pattern accounted for 47% of all hallucinations identified — nearly half the total.
### Pattern C · The transposed identity.
The engine attributes a real partnership to a similar-name celebrity, similar-category brand, or family member. Sister acts, brother acts, and same-name athletes generated the highest transposition rates. One engine attributed three separate partnerships belonging to one Jenner sister to a different Jenner sister across the same prompt session.
### Engine-Level Hallucination Rates
Gemini
24%
ChatGPT
19%
Claude
16%
Perplexity
13%
Perplexity's lower rate is structural — its retrieval-first architecture grounds answers in cited web sources. Gemini's higher rate is also structural — it generalizes from training data more freely, which produces more invention.
For brands. 64% of brand marketers now begin endorsement scoping inside an AI engine before contacting talent. They are operating on a one-in-five chance of pursuing a fabricated relationship. The cost is wasted outreach, leaked competitive intent, and public reference to partnerships that do not exist.
For talent. Representation has a new infrastructure obligation. The engines are inventing partnerships on behalf of clients. Some inventions are flattering. Some are not. None are authorized.
Build the infrastructure before the crisis — not during it.
On The Record
## The Quotes.
"The recommendation is now the buy signal. When a brand marketer asks an AI engine which celebrity to partner with, the engine answers — and the answer increasingly determines who gets the call. Citation Share is the new market share. For talent representation, engine recommendation is the new endorsement deal flow."
Ronn Torossian, Founder and Chairman, 5W AI Communications
"Talent has always built ahead of the conversation. The 18% hallucination rate is the data point our industry needs to act on immediately. AI engines are inventing partnerships in our clients' names. Some are flattering. Some are not. None are authorized. The partnership market is going to follow the figures the engines actually recognize — and we now have the measurement to prove who that is."
Michael Heller, Founder, Talent Resources
§04 · The Reverse Cut
## Breadth vs depth.
The Top 10 measures recommendation depth in single categories. The reverse cut measures breadth — which celebrities the AI engines associate with the largest distinct brand portfolios.
#CelebrityDistinct Brands
01Ryan Reynolds14
02Cristiano Ronaldo13
03Selena Gomez12
04David Beckham12
05Dwayne Johnson11
06LeBron James11
07Kim Kardashian10
08Tom Brady10
09Serena Williams9
10Pharrell Williams9
Breadth without depth is a warning signal — diffuse association reduces the engine's ability to retrieve the celebrity for any specific category. Reynolds and Beckham are the exceptions: their portfolios are diverse but each individual brand association is strongly cited.
§05 · Engine Disagreement
## The four engines do not agree.
Asked the same prompt — "which celebrity should a launching skincare brand partner with" — the engines return overlapping but distinct rosters.
- ChatGPT weights toward currently-active, currently-trending partnerships. Recency bias is highest.
- Claude weights toward established, multi-year relationships with measurable commercial outcomes. Depth bias is highest.
- Gemini generalizes most aggressively — produces the longest answers and the most hallucinations.
- Perplexity retrieves the most named primary sources — the lowest hallucination rate, but a narrower field of candidates.
A brand running endorsement research through Perplexity sees a different short list than the same brand running the same query through ChatGPT. Multi-engine visibility is now the only complete answer.
§06 · The Full 50
## The complete composite ranking.
Three tiers. The score breaks are not editorial preference — they are real discontinuities in how the engines retrieve these names.
The Powerhouses
Score 75–92
01Selena Gomez (92),
02Ryan Reynolds (89),
03MrBeast (86),
04Rihanna (85),
05Dwayne Johnson (83),
06Kim Kardashian (82),
07George Clooney (78),
08Cristiano Ronaldo (77),
09LeBron James (76),
10Beyoncé (75).
The Recommended
Score 60–74
11David Beckham (73),
12Marques Brownlee (72),
13Ashton Kutcher (71),
14Zendaya (70),
15Pharrell Williams (69),
16Tom Brady (68),
17Hailey Bieber (67),
18Patrick Mahomes (66),
19Mark Cuban (65),
20Serena Williams (65),
21Lionel Messi (64),
22Gwyneth Paltrow (63),
23Logan Paul (62),
24Kendall Jenner (61),
25Roger Federer (60).
The Cited
Score 35–59
26Jennifer Lopez (59),
27Jay-Z (58),
28Robert De Niro (57),
29Drake (56),
30will.i.am (55),
31Emma Chamberlain (54),
32Lewis Hamilton (53),
33Mark Wahlberg (52),
34Bad Bunny (51),
35Robert Downey Jr. (50),
36Eva Longoria (49),
37Jennifer Aniston (48),
38Margot Robbie (47),
39Shaquille O'Neal (46),
40Travis Scott (45),
41Daniel Craig (44),
42Charlize Theron (43),
43Timothée Chalamet (42),
44Magic Johnson (41),
45Cameron Diaz (40),
46Snoop Dogg (39),
47Kourtney Kardashian (38),
48Dua Lipa (37),
49Tracee Ellis Ross (36),
50Kevin Hart (35).
§07 · The Authority Pattern
## Fame doesn't make you AI-recommendable. Infrastructure does.
Across the dataset, five attributes correlate with high recommendation scores:
- Owned commercial vehicle. A brand the celebrity founded or substantially controls.
- Multi-year category consistency. Five-plus years operating in the same category.
- Named association in primary sources. Wikipedia, Forbes, Vogue, ESPN, trade press — not just social media.
- Originating documentation in major publications. The launch was covered. Not just announced.
- Active digital footprint with proprietary content. Owned platform, owned brand site, owned data — not just paid press.
Celebrities who clear four of five attributes appear in the top 25 of the Index. Celebrities who clear two or fewer functionally disappear from engine recommendations — regardless of fame, follower count, or current market presence.
§08 · What To Do
## For Talent. For Brands.
### For Talent — Three Immediate Moves
- Audit AI presence. Run the same prompts a brand marketer would run. Document the answer. Identify the gap.
- Fix the infrastructure. Owned-brand pages, Wikipedia accuracy, primary-source documentation, structured data. The retrieval anchors the engines actually use.
- Defend against hallucination. Monitor for fabricated partnerships and expired ones presented as current. Issue corrections at the source the engines retrieve from.
### For Brands — Two Operating Principles
- Verify every recommendation against a primary source. Trade press, the talent's representation, the brand's own site. Do not pursue a partnership on a single engine answer.
- Run the prompt across all four engines. Disagreement is signal. Convergence is signal. A single-engine view is incomplete.
Appendix A
## The Hallucination Dataset.
Anonymized to category and engine. Subject names withheld to avoid amplifying false claims. Full named dataset available to qualifying inquiries under standard research-confidentiality terms.
Pattern A
Confident Invention
33% of hallucinations
Gemini · "Best celebrity endorser for a launching premium tequila."
Named a top-five global music artist as founder of a tequila brand launched in 2023. No such brand exists in trademark filings, retail distribution, or trade press coverage.
ChatGPT · "Which actresses have skincare lines in 2026."
Named a Best Actress Oscar winner as founder of a clean-beauty brand, including invented product names and an invented launch retailer. No such brand exists.
Gemini · "Best NFL player for a launching energy-drink brand."
Named an active top-10 quarterback as a brand partner of an energy drink owned by a different athlete. No partnership exists.
Pattern B
Expired Treated As Current
47% of hallucinations
ChatGPT · "Which celebrities partner with luxury watch brands currently."
Named a relationship that ended in 2023 as ongoing in 2026. The celebrity has since signed with a competing brand.
Claude · "Recent celebrity coffee partnerships."
Cited a coffee chain partnership that concluded in 2022 as a current ambassador role.
Gemini · "Active celebrity automotive endorsers."
Listed three celebrities whose automotive partnerships ended between 2021 and 2024 as current.
Pattern C
Transposed Identity
20% of hallucinations
Gemini · "Which Jenner sister founded a tequila brand."
Attributed the partnership to the wrong sister across multiple prompt variations.
ChatGPT · "Hadid sister fashion partnerships."
Mixed individual brand deals between the two sisters, presenting one sister's roster under the other's name.
Claude · "Williams sisters business partnerships."
Attributed a venture-fund role belonging to one sister to the other.
### Cross-Engine Observations
- Hallucination rate is highest for tequila/spirits, beauty, and luxury watch categories. These categories combine high celebrity-launch density with frequent partnership turnover — the engines confuse historical and current.
- Hallucination rate is lowest for sports endorsements with major footwear and apparel brands. These partnerships are heavily documented, long-running, and primary-source-dense.
- The greatest cross-engine agreement on hallucinations occurs in the tequila category. All four engines named the same set of non-existent celebrity tequila brands — suggesting a shared upstream source with errors propagated across training data.
Methodology
## How the Index was built.
- Sample. 50 celebrities selected across 10 consumer categories (Beauty, Spirits, Sports, Fashion, Tech, Luxury, Hospitality, Wellness, Food & Snack, Finance), weighted for global representation across North America, Europe, Latin America, and Asia.
- Engines. ChatGPT, Claude, Gemini, Perplexity.
- Prompts. 600+ buyer-intent prompts in both directions — "best celebrity for [brand]" and "which brands should [celebrity] partner with."
- Scoring. Composite 0–100 across five equal dimensions: Citation Frequency, Recommendation Strength (sentiment), Specificity, Accuracy, Cross-Engine Consistency.
- Buyer survey. Talent Resources / 5W brand-marketer survey conducted May 2026, n=212.
- Window. Prompt runs conducted May 2026.
- Framing. Directional estimates. The engines are non-deterministic — answers vary by session, prompt phrasing, and recency of model update. This is a snapshot, not a permanent ranking.
### Limitations
The Index captures engine behavior at a single point in time. AI engines update continuously. Partnerships shift. Hallucination rates change with each model release. The 50-celebrity sample is not exhaustive — notable omissions include international figures whose primary engine traction occurs in non-English-language prompts. Volume IV will expand the dataset.
## Frequently Asked Questions
### What is the Celebrity Endorsement Index?
The Celebrity Endorsement Index is a joint research study from 5W AI Communications and Talent Resources that measures how ChatGPT, Claude, Gemini, and Perplexity recommend celebrities for brand endorsement deals. It scores 50 celebrities across 10 consumer categories based on how often and how strongly AI engines cite them.
### How many AI engines and celebrities does the Index cover?
The Index tested four AI engines (ChatGPT, Claude, Gemini, and Perplexity) against 50 celebrities across 10 consumer categories, using more than 600 buyer-intent prompts run in both directions: "best celebrity for [brand]" and "which brands should [celebrity] partner with."
### What does the 18% hallucination rate mean?
Across the study's 600+ prompts, the four AI engines invented celebrity partnerships, ambassador roles, or product lines that do not exist in the public record 18% of the time. Some of these were confident fabrications, some were expired deals presented as current, and some mixed up similarly named celebrities.
### Which AI engine hallucinates the most?
Gemini had the highest hallucination rate at 24%, followed by ChatGPT at 19%, Claude at 16%, and Perplexity at 13%. Perplexity's lower rate is tied to its retrieval-first architecture, which grounds answers in cited web sources rather than generalizing from training data.
### What is "Citation Share" and why does it matter for talent?
Citation Share measures how often and how prominently an AI engine names a specific celebrity when asked for endorsement recommendations. The Index treats Citation Share as the new equivalent of market share: when a brand marketer's first stop is an AI engine, the celebrities the engine names are the ones that get considered.
### Why do AI engines invent celebrity endorsement deals?
The Index identifies three patterns behind AI hallucinations: confident invention of deals that never existed, treating an expired partnership as current, and transposing a real deal onto a similarly named celebrity or family member. Expired-but-presented-as-current deals were the most common, accounting for 47% of all hallucinations found.
### How should brands use the Celebrity Endorsement Index?
Brands should verify any AI-recommended celebrity partnership against a primary source, such as trade press or the talent's own representation, before pursuing it. The Index also recommends running the same prompt across all four engines, since a single-engine answer misses the disagreement between them that often signals risk.
### How should talent representation use this research?
Talent representatives should audit what AI engines currently say about their clients, correct inaccuracies at the source the engines retrieve from, and build out owned-brand documentation, such as Wikipedia accuracy and primary-source press coverage, since that is what determines whether an engine recommends a client at all.
### How is the Celebrity Endorsement Index different from the Celebrity-Brand Fit Index?
The Celebrity-Brand Fit Index (Volume I) ranks which consumer sectors reward celebrity deployment. The Celebrity Endorsement Index (Volume III) ranks which individual celebrities AI engines actually recommend within those sectors, and measures how often those recommendations are fabricated.
### How often is the Index updated?
The current edition reflects prompt research conducted in May 2026. AI engines update continuously and celebrity partnerships shift, so the rankings are directional rather than permanent. Volume IV is planned to expand the dataset.
Working With This Index
## Request the underlying data.
Brands can request a category-specific extraction of the dataset — including unpublished hallucination findings relevant to their target talent pool.
Talent representation can request engine-by-engine exposure reports for individual clients, including the AI-fabricated partnerships currently surfacing under their client's name.
research@5wpr.com
research@thetrcollective.com
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W was also named to the Digiday WorkLife Employer of the Year list.
5wpr.com
### About Talent Resources
Talent Resources is a premier global marketing agency founded in 2007 by Michael Heller. The firm creates fully integrated traditional and social campaigns powered by celebrity, athlete, and influencer relationships, with extensive experience in brand-focused experiential events and full-service partnership campaigns across consumer industries.
Talent Resources Collective comprises Talent Resources, Talent Resources Sports, and Talent Resources Ventures.
thetrcollective.com
← Vol I · Celebrity-Brand Fit
← Vol II · In Tune With AI
Vol III · Current
© 2026 5W AI Communications and Talent Resources. All rights reserved. The Celebrity Endorsement Index is published as directional research. Findings are derived from prompt research conducted May 2026 and are subject to change as AI engines update. Media: press@5wpr.com.
---
### Hallucination Index — Fashion Pilot
URL: https://www.5wpr.com/research/hallucination-index-fashion-pilot/
Summary: The 5W Hallucination Index tested five major fashion brands across 25 core business questions each. 20% of the answers contained material errors.
---
### Taylor Swift
URL: https://www.5wpr.com/reputation/taylor-swift/
Summary: The 5W Reputation Index audits the AI-held reputation of Taylor Swift across five AI engines, celebrity as institution.
The 5W Reputation Index · Edition 06
# Taylor Swift
What AI Says About You When You're Not in the Room.
When AI Frames a Celebrity Like a CEO
Ask the engines who Taylor Swift is, and the answer reads less like a celebrity profile than expected — it reads like an institution. Record-breaking tour economics. The masters fight, fought and won. A self-owned catalog. Fandom modeled as an economy.
AI does not frame her primarily as a pop star who got big. It frames her as an operator who happens to perform.
Reputation Intelligence
Edition 06
Edition
Subject
Taylor Swift
Format
Single subject
Engines
5
Published
June 23, 2026
## The Verdict
Celebrity reputation scoring like corporate reputation — and the contrast with Edition 03 is the point.
## Methodology & Confidence
### Five Engines, No Spin
Reputation modeled across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — 40+ reputation-intent prompts across identity, trust, track record, controversy, comparison, and decision intent. Multiple passes; recurring findings only. Cross-checked against current reporting, critical and favorable. Directional estimates — not a precision instrument.
## The Dominant Narrative
### The Story AI Won't Stop Telling
The synthesized narrative is unusually business-shaped for an entertainer: a songwriter and performer who became one of the most commercially powerful figures in music, then demonstrated rare control of her own enterprise — re-recording her catalog to reclaim ownership, running a tour that moved measurable money through regional economies, and commanding a fanbase the engines describe in the language of scale and loyalty. The artistry is acknowledged; the operating intelligence is what the engines emphasize.
## Sentiment Map
### Who the Box Likes
Valence of the dominant framing each engine surfaces first.
SubjectChatGPTClaudeGeminiPerplexityGoogle AIO
Taylor SwiftPositivePositivePositivePositivePositive
Legend Very positive Positive Mixed Confidence: Moderate
The finding. Uniformly positive — the rare entertainment subject whose AI-held reputation carries the steadiness usually reserved for a respected executive.
## First-Surface Audit
### Sentence One: Artistry. Sentence Two: Enterprise.
"The first sentence is the reputation. Almost no one reads past it."
Answers open with "singer-songwriter" and, fast behind it, "one of the best-selling and most influential artists" — then, still inside the first answer, the business markers: the tour's record gross, the masters re-recording, the cultural and economic scale. The first sentence is artistry; the second is enterprise. Both arrive early.
## The Citation Base
### Every Reputation Has a Paper Trail
A strong, broad retrieval base: extensive favorable business and culture coverage, verifiable tour and revenue data, and the well-documented masters dispute — a story in which she is framed as the side that was wronged and then prevailed. The base is largely third-party but largely favorable, and the business framing is well-sourced, not fan-generated.
## Omissions
### What AI Leaves on the Floor
Little is missing on substance. The mildest under-surfacing: the formal business architecture behind the enterprise — the team, the structure, the strategy — is implied more than described. AI conveys that she is a savvy operator without fully detailing the operation.
## Risk Surfaces
### Where It Blows Up
### Taylor Swift
Risk level Limited
The primary exposure is intensity-driven: a reputation this tied to a fanbase and to constant cultural presence is highly visible, and high visibility is high surface area. Backlash cycles and overexposure narratives surface on some prompts. None currently threatens the core institutional framing.
## Cross-Engine Consistency
### When the Engines Agree
High consistency — every engine tells substantially the same institution-shaped story.
The stability is an asset, with the standard caveat: a settled positive narrative is durable, but harder to move further than a contested one. Here, that is a good problem.
## Peer Comparison
### Against the Field
The instructive comparison is internal to the franchise. Edition 03's subject and this one are both celebrity-founders — but AI frames one primarily as a personality and the other primarily as an institution. The difference is the retrieval base: Swift's business story is documented in hard, citable data — tour grosses, ownership outcomes — and hard data is what lets AI frame a celebrity like a CEO.
## The Reputation Gap
### You vs. Your Answer
Narrow. The intended narrative — artist and formidable operator — and the delivered narrative are closely aligned. If anything, it slightly under-describes the deliberate business machinery and over-relies on the fandom framing. The work is maintenance.
## The Reputation Index Score
### The Scoreboard
How the score works.Five dimensions — Accuracy, Sentiment, Completeness, Consistency, Control — each scored 0–20 and equally weighted, for a composite of 100. Directional estimates.
Taylor Swift — Reputation Index Score 83/100
DimensionScore
Accuracy18
Sentiment17
Completeness16
Consistency17
Control15
Composite83/100
## Remediation Roadmap
### Protect What You Have
There is little to fix and something to maintain. The score is high because the business story is anchored in hard, verifiable data the engines retrieve readily. The work is to keep that data current and citable as the enterprise evolves, and to ensure the formal business architecture becomes as documented as the tour economics already are — so the institution framing deepens rather than drifts back toward pure celebrity.
## Who This Is For
For figures whose name is now bigger than any one product, the question is when AI stops framing you as a celebrity and starts framing you as infrastructure.
## The 5W Read
### 5W Shapes the Answer in the Box
Edition 06 is the counter-case to Edition 03: a celebrity can hold a CEO-grade AI reputation — but only when the business story is documented in hard, citable evidence. Fame alone produces a personality framing. Verifiable data produces an institution framing.
5W's work is to shape the answer in the box — to build the citable record that decides which one the engines return.
The answer is being given right now. The only question is whether anyone is shaping it.
Audit your AI-held reputationDownload the full edition (PDF)
---
### AI Search Visibility for Running Shoes: 2026 Brand Study
URL: https://www.5wpr.com/research/ai-search-visibility-for-running-shoes-2026-brand-study/
Summary: See which running shoe brands lead AI search visibility in 2026, based on appearance rates and source citations across ChatGPT, Claude, Gemini, and Perplexity.
ASICS Corporation led the measured running shoe answers, appearing in 82.9% of 240 returned answers. That appearance rate uses returned answers as its denominator. RunRepeat had a different result: its domain was cited in 70.3% of 239 answers with retrieval evidence.
The split matters for footwear marketers. A brand can be named often without its own site being cited often. A publisher can be cited often while appearing less often as a selection option. These results cover running shoes, running shoes for marathon, and running shoes for flat feet in this measured prompt set.
## Study at a glance
running shoes — measured 2026-08-27. Based on 240 answers returned out of 288 attempted across Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic). Appearance figures use answers returned; citation figures use the 239 answers with retrieval evidence.
- AI answers analyzed — 24 prompts × 3 trials × 4 engines: 240
- Top brand: ASICS Corporation — named in 199 of 240 answers: 82.9%
- Engine spread for ASICS Corporation — highest vs lowest engine appearance rate: 95.1% vs 68.3%
- Answers with retrieval evidence — 239 of 288 attempted: 83%
The study-at-a-glance results separate brand appearance from source citation. Those are different measures with different denominators.
## Which running shoe brands lead AI answers?
ASICS Corporation ranks first by appearance rate, followed by New Balance, Brooks, Nike, Inc, Hoka, and Saucony. ASICS Corporation was named in 199 returned answers. New Balance was named in 175, while Brooks and Nike, Inc each appeared in 172.
Appearance is not a product recommendation. It records whether an engine named a brand in an answer. Average first-mention position adds context: ASICS Corporation appeared at 3.1 on average, while the other leading brands often appeared later in the answer.
## AI visibility leaderboard
Appearance rate, top 15 brands. Source: 5WPR AI Search Visibility Study, measured 2026-08-27 (240 returned answers).
#BrandNamed inAppearance rate95% intervalAvg position
1ASICS Corporation199/24082.9%77.6%–87.2%3.1
2New Balance175/24072.9%67%–78.1%5.1
3Brooks172/24071.7%65.7%–77%4.1
4Nike, Inc172/24071.7%65.7%–77%5.2
5Hoka168/24070%63.9%–75.4%5.2
6Saucony164/24068.3%62.2%–73.9%5.4
7On113/24047.1%40.9%–53.4%9.6
8Puma96/24040%34%–46.3%7.8
9Mizuno Corporation79/24032.9%27.3%–39.1%9.7
10Adidas63/24026.3%21.1%–32.2%4.9
11Altra60/24025%19.9%–30.8%7.3
12RunRepeat31/24012.9%9.3%–17.8%2.9
13Topo Athletic30/24012.5%8.9%–17.3%7.4
14Sources19/2407.9%5.1%–12%13.3
15Fleet Feet16/2406.7%4.1%–10.6%3.6
ASICS Corporation leads at 82.9% (199 of 240 answers, 95% interval 77.6%–87.2%). Where two intervals overlap, this sample does not establish that one brand is more visible than the other.
The rank order needs restraint. ASICS Corporation's 95% interval of 77.6% to 87.2% overlaps New Balance's interval of 67% to 78.1%. New Balance, Brooks, Nike, Inc, Hoka, and Saucony also have overlapping intervals across much of that group. Within this sample, those differences are within the margin of the sample where intervals overlap. Brooks and Nike, Inc have the same 71.7% appearance rate and the same 65.7% to 77% interval.
## How did the leading brands appear in AI answers?
### ASICS Corporation — 82.9%
ASICS Corporation ranks first, with an 82.9% appearance rate and a 77.6% to 87.2% interval among 240 returned answers. Perplexity named ASICS Corporation in 58 of 61 answers, or 95.1%, and ChatGPT named it in 56 of 59, or 94.9%. Gemini named it in 41 of 60 answers, or 68.3%, while Claude named it in 44 of 60, or 73.3%. Its average first-mention position was 3.1. ASICS Corporation's domain was cited in 1.7% of 239 answers with retrieval evidence.
### New Balance — 72.9%
New Balance ranks second, with a 72.9% appearance rate and a 67% to 78.1% interval among returned answers. Perplexity named New Balance in 58 of 61 answers, or 95.1%, and ChatGPT named it in 56 of 59, or 94.9%. Gemini named it in 42 of 60, or 70%, but Claude named it in 19 of 60, or 31.7%. New Balance appeared at an average first-mention position of 5.1. Its domain was cited in 0.4% of answers with retrieval evidence.
### Brooks — 71.7%
Brooks ranks third, with a 71.7% appearance rate and a 65.7% to 77% interval among returned answers. Perplexity named Brooks in 58 of 61 answers, or 95.1%, while ChatGPT named it in 57 of 59, or 96.6%. Gemini named Brooks in 37 of 60 answers, or 61.7%, and Claude named it in 20 of 60, or 33.3%. Brooks' average first-mention position was 4.1. Its domain was cited in 8.8% of answers with retrieval evidence.
### Nike, Inc — 71.7%
Nike, Inc ranks fourth, with a 71.7% appearance rate and a 65.7% to 77% interval among returned answers. Perplexity named Nike, Inc in 54 of 61 answers, or 88.5%, and ChatGPT named it in 56 of 59, or 94.9%. Gemini named Nike, Inc in 39 of 60 answers, or 65%, while Claude named it in 23 of 60, or 38.3%. Nike, Inc appeared at an average first-mention position of 5.2. Its domain was cited in 1.3% of answers with retrieval evidence.
### Hoka — 70%
Hoka ranks fifth, with a 70% appearance rate and a 63.9% to 75.4% interval among returned answers. Perplexity named Hoka in 55 of 61 answers, or 90.2%, and ChatGPT named it in 55 of 59, or 93.2%. Gemini named Hoka in 35 of 60 answers, or 58.3%, while Claude named it in 23 of 60, or 38.3%. Hoka's average first-mention position was 5.2. Its domain was cited in 0% of answers with retrieval evidence.
### Saucony — 68.3%
Saucony ranks sixth, with a 68.3% appearance rate and a 62.2% to 73.9% interval among returned answers. Perplexity named Saucony in 58 of 61 answers, or 95.1%, and ChatGPT named it in 55 of 59, or 93.2%. Gemini named Saucony in 36 of 60 answers, or 60%, while Claude named it in 15 of 60, or 25%. Saucony appeared at an average first-mention position of 5.4. Its domain was cited in 0.4% of answers with retrieval evidence.
### On — 47.1%
On ranks seventh, with a 47.1% appearance rate and a 40.9% to 53.4% interval among returned answers. Perplexity named On in 36 of 61 answers, or 59%, and ChatGPT named it in 44 of 59, or 74.6%. Gemini named On in 31 of 60 answers, or 51.7%. Claude named On in 2 of 60 answers, or 3.3%. On's average first-mention position was 9.6. Its domain was cited in 0.8% of answers with retrieval evidence.
### Puma — 40%
Puma ranks eighth, with a 40% appearance rate and a 34% to 46.3% interval among returned answers. Perplexity named Puma in 46 of 61 answers, or 75.4%. ChatGPT named Puma in 13 of 59 answers, or 22%, Gemini named it in 27 of 60, or 45%, and Claude named it in 10 of 60, or 16.7%. Puma's average first-mention position was 7.8. Its domain was cited in 0% of answers with retrieval evidence.
### Mizuno Corporation — 32.9%
Mizuno Corporation ranks ninth, with a 32.9% appearance rate and a 27.3% to 39.1% interval among returned answers. Perplexity and ChatGPT each named Mizuno Corporation in 52.5% of their returned answers, 32 of 61 and 31 of 59 respectively. Gemini named it in 14 of 60 answers, or 23.3%, while Claude named it in 2 of 60, or 3.3%. Its average first-mention position was 9.7. Mizuno Corporation's domain was cited in 0% of answers with retrieval evidence.
### Adidas — 26.3%
Adidas ranks tenth, with a 26.3% appearance rate and a 21.1% to 32.2% interval among returned answers. Perplexity named Adidas in 28 of 61 answers, or 45.9%. Gemini named it in 17 of 60, or 28.3%, Claude in 10 of 60, or 16.7%, and ChatGPT in 8 of 59, or 13.6%. Adidas had an average first-mention position of 4.9. Its domain was cited in 0% of answers with retrieval evidence.
## Where do running shoe AI engines disagree?
Engine disagreement is material in this category. Perplexity returned 61 answers, ChatGPT returned 59, and Gemini and Claude each returned 60. Perplexity and ChatGPT repeatedly named the top six brands at high rates, while Gemini and Claude produced different patterns for several brands.
### Which engine names which brand
Appearance rate per engine, using each engine's own answer count as denominator. Source: 5WPR AI Search Visibility Study, measured 2026-08-27.
BrandPerplexity (sonar-pro)ChatGPT (OpenAI)Gemini (Google)Claude (Anthropic)
ASICS Corporation95.1%94.9%68.3%73.3%
New Balance95.1%94.9%70%31.7%
Brooks95.1%96.6%61.7%33.3%
Nike, Inc88.5%94.9%65%38.3%
Hoka90.2%93.2%58.3%38.3%
Saucony95.1%93.2%60%25%
On59%74.6%51.7%3.3%
Puma75.4%22%45%16.7%
Mizuno Corporation52.5%52.5%23.3%3.3%
Adidas45.9%13.6%28.3%16.7%
Altra37.7%40.7%11.7%10%
RunRepeat0%3.4%48.3%0%
Each cell uses that engine's own answer count as the denominator. On shows the widest spread between engines in this study. Engines differ in model and retrieval tool, so a gap can reflect system design as much as brand visibility.
Claude named On in 2 of 60 answers, or 3.3%, compared with ChatGPT's 44 of 59, or 74.6%. Claude also named Mizuno Corporation in 2 of 60 answers, or 3.3%, compared with 32 of 61 for Perplexity and 31 of 59 for ChatGPT. New Balance appeared in 19 of Claude's 60 answers, or 31.7%, versus 58 of Perplexity's 61 and 56 of ChatGPT's 59.
Perplexity favored Puma and Adidas more than ChatGPT did. Puma appeared in 46 of Perplexity's 61 answers, or 75.4%, and 13 of ChatGPT's 59, or 22%. Adidas appeared in 28 of 61 Perplexity answers, or 45.9%, and 8 of 59 ChatGPT answers, or 13.6%. Gemini is distinct for RunRepeat: it named RunRepeat in 29 of 60 answers, or 48.3%, while Perplexity and Claude named it in none of their returned answers.
Several smaller entities were concentrated in Gemini. PUMAGRIP, Outside Magazine, Believe in the Run, Lightstrike Pro, PWRRUN PB, CloudTec®, and Marathon Handbook were each named by Gemini, while the data shows no names for several of them in Perplexity, ChatGPT, or Claude. Engine gaps can reflect system design because the engines use different models and retrieval tools. They do not establish that one engine has found a universally better brand answer.
## Which brands lead running shoe demand clusters?
The demand-cluster view identifies which brands lead the measured question types for running shoes, marathon running shoes, and running shoes for flat feet. It separates broad category presence from question-specific presence.
### Where each brand wins
Same answers regrouped by the demand cluster each prompt was sampled for. Source: 5WPR AI Search Visibility Study, measured 2026-08-27.
Demand clusterAnswersMost-named brands in that cluster
Subcategory: running shoes for marathon24ASICS Corporation 95.8% · Nike, Inc 95.8% · Saucony 91.7% · New Balance 83.3% · Hoka 75%
Subcategory: running shoes for flat feet24ASICS Corporation 79.2% · Brooks 79.2% · New Balance 70.8% · Hoka 70.8% · Saucony 70.8%
Subcategory: running shoes24ASICS Corporation 100% · Nike, Inc 83.3% · Hoka 83.3% · Saucony 83.3% · New Balance 79.2%
Primary use case24ASICS Corporation 95.8% · New Balance 91.7% · Nike, Inc 91.7% · Brooks 87.5% · Hoka 83.3%
General best-in-category24ASICS Corporation 100% · Nike, Inc 87.5% · Hoka 87.5% · Brooks 83.3% · New Balance 79.2%
Specific need or condition23Altra 87% · New Balance 82.6% · Hoka 82.6% · Topo Athletic 82.6% · Brooks 78.3%
Quality and premium tier17ASICS Corporation 76.5% · New Balance 76.5% · Nike, Inc 70.6% · Brooks 64.7% · Saucony 64.7%
Beginners and first purchase16ASICS Corporation 93.8% · Nike, Inc 87.5% · Brooks 81.3% · Hoka 75% · Saucony 75%
Price and value16ASICS Corporation 81.3% · New Balance 75% · Brooks 62.5% · Nike, Inc 62.5% · Saucony 56.3%
Comparison and evaluation8ASICS Corporation 87.5% · Hoka 87.5% · New Balance 75% · Brooks 75% · Nike, Inc 75%
Durability and longevity8ASICS Corporation 87.5% · Brooks 75% · New Balance 62.5% · Nike, Inc 62.5% · Hoka 62.5%
Specialist / advanced use8ASICS Corporation 100% · Nike, Inc 100% · New Balance 75% · Puma 75% · Brooks 62.5%
Emerging and new entrants8On 37.5% · Topo Athletic 37.5% · Skechers 25% · Hoka 12.5% · Puma 12.5%
Where to buy8Fleet Feet 75% · ASICS Corporation 37.5% · Brooks 37.5% · New Balance 25% · Nike, Inc 25%
Trust and recommendation8ASICS Corporation 75% · Nike, Inc 75% · Saucony 62.5% · New Balance 50% · Brooks 50%
Cluster rates use that cluster's own answer count as the denominator, so they are not comparable to the overall leaderboard rate. The largest cluster in this study is Subcategory: running shoes for marathon with 24 answers.
The cluster results should guide page and evidence priorities by question type, rather than treating one overall appearance rate as a complete category diagnosis. The prompt set covers only the listed subcategories, so it does not measure other running shoe needs.
## Where do running shoe AI answers get their sources?
RunRepeat had the highest owned-domain citation rate among the listed eligible brands. Its domain was cited in 70.3% of 239 answers with retrieval evidence. Fleet Feet was cited in 42.7%, Outside Magazine in 41%, and Marathon Handbook in 30.1%.
### Whose own website gets cited
Share of answers with retrieval evidence that cited a URL on the brand's own domain. Covers the 24 brands in this study with an established official domain; denominator is the 239 answers with retrieval evidence. Source: 5WPR AI Search Visibility Study, measured 2026-08-27.
#BrandOwned-domain citation rate
01ASICS Corporation1.7%
02New Balance0.4%
03Brooks8.8%
04Nike, Inc1.3%
05Hoka0%
06Saucony0.4%
07On0.8%
08Puma0%
09Mizuno Corporation0%
10Adidas0%
11Altra0.4%
12RunRepeat70.3%
A brand can be named often and still rarely be cited from its own site. Owned-domain citation is a source measure, not an appearance measure. Brooks had an 8.8% owned-domain citation rate, despite a much higher appearance rate. Hoka, Puma, Mizuno Corporation, and Adidas each had a 0% owned-domain citation rate in answers with retrieval evidence. A zero records an evaluated metric with no observed citation event.
Publishers and review sources supplied much of the evidence that the engines cited. RunRepeat was cited in 168 answers with retrieval evidence, or 70.3%. The Run Testers appeared in 126, or 52.7%, followed by Runner's World in 114, or 47.7%, and RTINGS in 110, or 46%.
### Where the answers come from
Publishers cited as evidence, counted once per answer rather than once per link. Denominator is the 239 answers that carried retrieval evidence. Source: 5WPR AI Search Visibility Study, measured 2026-08-27.
PublisherAnswers citing itAnswer penetration
runrepeat.com168/23970.3%
theruntesters.com126/23952.7%
runnersworld.com114/23947.7%
rtings.com110/23946%
fleetfeet.com102/23942.7%
outsideonline.com98/23941%
reddit.com81/23933.9%
outdoorgearlab.com77/23932.2%
runtothefinish.com74/23931%
marathonhandbook.com72/23930.1%
weartesters.com56/23923.4%
tomsguide.com53/23922.2%
Fleet Feet was cited in 102 answers, or 42.7%, and Outside Online in 98, or 41%. Reddit appeared in 81, or 33.9%. The measured source mix includes retailers, test publishers, editorial publishers, forums, and video platforms. It does not explain the engines' selection rules for any individual citation.
## What do these results mean for running shoe brands?
Interpretation: brands need to manage two separate outcomes. The first is whether an engine names the brand. The second is whether the engine cites a page on the brand's own domain when supporting an answer.
ASICS Corporation has broad naming across all four engines, but its owned-domain citation rate is 1.7%. Brooks has broad naming and a higher 8.8% owned-domain citation rate. RunRepeat and Fleet Feet show the reverse pattern: their appearance rates are lower, while their domains appear often in retrieval-backed answers.
Build pages around the measured selection questions and make each page easy to identify as a specific source. Test the pages against each engine separately. The engine patterns do not support treating one engine's result as a substitute for the others.
## How was running shoe AI visibility measured?
This flagship study measured 24 stratified prompts three times per engine across Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). The study attempted 288 answers and received 240 returned answers. It recorded 239 answers with retrieval evidence.
- Sampling frame fixed — 24 prompts allocated across 15 demand clusters before anything was asked.
- Same questions, every engine — Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trials each.
- Answers captured — 240 answers returned, 239 with retrieval evidence.
- Brands resolved — product lines rolled up to parent brands using a registry fixed before scoring.
- Rates and intervals — appearance rate with a 95% Wilson interval; citation metrics counted only on answers with evidence.
The full auditable record — exact prompts, cluster allocation, engine systems, definitions and limits — is in the methodology appendix at the end of this report.
Appearance rates use the 240 returned answers as the denominator. Citation and source rates use the 239 answers with retrieval evidence as the denominator. The engines differ in model and retrieval tool, and returned-answer totals differ by engine. The results describe this prompt set on 2026-08-27. They do not measure all running shoe searches or consumer demand.
## Frequently asked questions
### Which brand had the highest running shoe appearance rate?
ASICS Corporation ranked first with an 82.9% appearance rate among 240 returned answers. Its 95% interval was 77.6% to 87.2%.
### Did ASICS Corporation lead every engine?
ASICS Corporation was named by all four engines. Its per-engine appearance share ranged from 68.3% in Gemini to 95.1% in Perplexity.
### Which source domain was cited most often?
RunRepeat was cited in 168 of 239 answers with retrieval evidence. That equals an answer penetration of 70.3%.
### Which brand site had the strongest citation rate?
RunRepeat had the highest listed owned-domain citation rate at 70.3% of answers with retrieval evidence. Fleet Feet had 42.7%, and Outside Magazine had 41%.
### Which brand had the sharpest ChatGPT and Claude gap?
On appeared in 44 of 59 ChatGPT answers, or 74.6%. Claude named On in 2 of 60 answers, or 3.3%.
### Did Brooks and Nike, Inc differ in overall appearance?
Brooks and Nike, Inc each had a 71.7% appearance rate. Each had a 65.7% to 77% interval, so this study does not show a difference between them.
## Conclusion
The measured running shoe results separate brand naming from domain citation. Measure both outcomes by engine before deciding what pages and third-party evidence to build.
Work with 5WPR to measure how your running shoe brand appears and which pages receive citations across the engines in this study.
5W runs AI Search (GEO) programs for brands across consumer, B2B, financial services, healthcare, and technology, building the machine-readable footprint that gets brands cited, not just ranked. Learn more at GEO - Generative Engine Optimization.
## Methodology appendix
Full prompt set with cluster allocation and prompt shape. Source: 5WPR AI Search Visibility Study, measured 2026-08-27.
#PromptClusterShape
1best running shoessub_running_shoesshort
2running shoe brands rankedsub_running_shoesshort
3best marathon running shoessub_running_shoes_for_marathshort
4marathon shoe brands rankedsub_running_shoes_for_marathshort
5best running shoes flat feetsub_running_shoes_for_flat_fshort
6flat foot running shoe brandssub_running_shoes_for_flat_fmedium
7best running shoe brandsgeneral_bestshort
8top running shoes overallgeneral_bestshort
9best everyday running shoe brandsuse_casemedium
10Which running shoe brands suit daily training?use_casemedium
11best running shoes wide feetspecific_needshort
12Which brands make running shoes for bunions?specific_needmedium
13highest quality running shoe brandspremium_qualitymedium
14What running shoe brands are best made and worth paying more for?premium_qualitylong
15best value running shoesvalue_priceshort
16Which running shoe brands offer the best performance for the money?value_pricelong
17best beginner running shoesbeginnermedium
18Which running shoe brands should a first-time runner consider and why?beginnerlong
19Which running shoe brands are better for cushioning, stability, speed, and fit?comparisonlong
20Which running shoe brands hold up best after high-mileage training?durabilitylong
21Which running shoe brands do serious racers choose for fast road races?specialistlong
22Which newer running shoe brands are gaining credibility with runners this year?emerginglong
23Where should I buy running shoes to get the best selection and fitting help?retaillong
24Which running shoe brands are most trusted by independent reviewers and coaches?trustlong
---
### 5W AI Business School Index 2026
URL: https://www.5wpr.com/research/ai-business-school-index/
Summary: The 5W AI Business School Index 2026 scores 60 programs on six dimensions of AI readiness. Stanford GSB takes the top spot, and 42 percent require zero AI.
A 5W RESEARCH ARTIFACT: VOLUME 03
The First Benchmark of AI Readiness in Business Education
# 5W AI Business School Index 2026
Sixty business schools. Six measured dimensions. Three numbers that define the gap.
First Edition · Volume 03 · 60 Schools · 14 Countries · 6 Dimensions
The AI Higher Education Index (Volume 02) scored 50 universities on AI production capacity. Business education is a more distributed global market. Credible MBA programs operate across more countries, more institutional types, and more delivery models. This volume expands the universe to 60.
Related: 5W AI Higher Education Index 2026
## Three Numbers That Define This Report
42% of business schools in this index have zero required AI coursework. Twenty-five of 60 programs offer AI as elective-only or have not integrated AI into any required course.
18× more AI coursework at the #1 school than the median. Stanford GSB offers approximately 36 AI-integrated courses. The median school offers 2.
0 business schools teach GEO. Not one school includes Generative Engine Optimization, LLM optimization, AI visibility strategy, or Citation Share measurement.
## Global AI Business School Rankings: All 60 Schools
Composite = unweighted mean of six dimensions. Tiebreaks by D6. Tier I (≥82), II (72 to 81.9), III (<72). ±2.5 pts at 95% CI.
### Tier I: Composite of 82 or Higher
RankBusiness SchoolCountryComposite
1Stanford GSBUSA94.5
2MIT SloanUSA92.0
3Wharton / PennUSA90.5
4Harvard Business SchoolUSA89.0
5Kellogg / NorthwesternUSA86.5
6Chicago BoothUSA85.0
7Columbia Business SchoolUSA82.5
### Tier II: Composite 72 to 81.9
RankBusiness SchoolCountryComposite
8Tsinghua SEMChina81.0
9London Business SchoolUK79.5
10INSEADFrance/Singapore78.0
11Yale SOMUSA77.0
12Haas / UC BerkeleyUSA76.5
13NYU SternUSA75.5
14Ross / MichiganUSA74.5
15Darden / UVAUSA74.0
16Fuqua / DukeUSA73.5
17Tuck / DartmouthUSA73.0
18Peking GuanghuaChina72.5
19IESESpain72.0
### Tier III: Composite Under 72
RankBusiness SchoolCountryComposite
20Anderson / UCLAUSA71.5
21Tepper / CMUUSA71.0
22McCombs / UT AustinUSA70.5
23Oxford SaïdUK70.0
24Cambridge JudgeUK69.5
25TAU CollerIsrael69.0
26Marshall / USCUSA68.5
27HKUST BusinessHong Kong68.0
28Indian School of BusinessIndia67.5
29Frankfurt SchoolGermany67.0
30NUS BusinessSingapore66.5
31Rotman / TorontoCanada66.0
32Kenan-Flagler / UNCUSA65.5
33Goizueta / EmoryUSA65.0
34Technion William DavidsonIsrael64.5
35Questrom / Boston UUSA64.0
36Foster / U WashingtonUSA63.5
37Scheller / Georgia TechUSA63.0
38CEIBSChina62.5
39IE Business SchoolSpain62.0
40HEC ParisFrance61.5
41SDA BocconiItaly61.0
42ETH Zurich MBASwitzerland60.5
43KAIST BusinessSouth Korea60.0
44IIM AhmedabadIndia59.5
45Desautels / McGillCanada59.0
46Reichman Arison / AdelsonIsrael58.5
47Ivey / Western OntarioCanada58.0
48Olin / WashUUSA57.5
49Warwick Business SchoolUK57.0
50NEOMAFrance56.5
51ASU W.P. CareyUSA56.0
52IIM BangaloreIndia55.5
53MannheimGermany55.0
54SJTU AntaiChina54.5
55Fudan SOMChina54.0
56IIM LucknowIndia53.5
57Smeal / Penn StateUSA53.0
58ESADESpain52.5
59NTU BusinessSingapore52.0
60Schulich / YorkCanada51.5
## Six Dimensions, Equally Weighted
D1: AI Curriculum Integration. Required AI coursework, named AI electives, AI major/concentration, cross-disciplinary integration. Top 3: Stanford GSB (98) · Wharton (96) · Kellogg (94)
D2: AI-Founder & Executive Alumni. AI founder count, VC raised, AI C-suite roles, frontier-lab business leadership. Top 3: Stanford GSB (96) · MIT Sloan (94) · HBS (90)
D3: Corporate AI Partnerships. Frontier-lab partnerships, hyperscaler agreements, AI research centers, corporate AI chairs. Top 3: MIT Sloan (96) · Stanford GSB (95) · Chicago Booth (92)
D4: Executive AI Education. Program breadth, enrollment scale, practitioner faculty, delivery frequency. Top 3: MIT Sloan (95) · Stanford GSB (94) · HBS (92)
D5: AI Research Output (Business-Applicable). Publications, case studies, research centers, policy/governance output. Top 3: MIT Sloan (95) · Stanford GSB (92) · HBS (90)
D6: AI Citation Share (Modeled). 3,000 prompt-engine runs across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews. Top 3: Stanford GSB (96) · MIT Sloan (92) · Wharton (90)
## Full 60-School Dimension-Level Data
Dimension scores rounded to the nearest integer for presentation. Composite scores are derived from full-precision assessments and may not equal the arithmetic mean of rounded dimension scores. ±2.5 pts at 95% CI. Tiebreaks by D6. D1 = AI Curriculum Integration. D2 = AI-Founder & Executive Alumni. D3 = Corporate AI Partnerships. D4 = Executive AI Education. D5 = AI Research Output. D6 = AI Citation Share.
### Tier I: Composite of 82 or Higher
RankBusiness SchoolCountryD1D2D3D4D5D6Composite
1Stanford GSBUSA98969594929694.5
2MIT SloanUSA80949695959292.0
3Wharton / PennUSA96899089899090.5
4Harvard Business SchoolUSA86908892908889.0
5Kellogg / NorthwesternUSA94868486828786.5
6Chicago BoothUSA82849284848485.0
7Columbia Business SchoolUSA84828282828382.5
### Tier II: Composite 72 to 81.9
RankBusiness SchoolCountryD1D2D3D4D5D6Composite
8Tsinghua SEMChina88868886845481.0
9London Business SchoolUK80788282807579.5
10INSEADFrance/Singapore78807880787478.0
11Yale SOMUSA76787678767877.0
12Haas / UC BerkeleyUSA74827476787576.5
13NYU SternUSA76767474767775.5
14Ross / MichiganUSA72747676747574.5
15Darden / UVAUSA72727476787274.0
16Fuqua / DukeUSA70747674767173.5
17Tuck / DartmouthUSA70727474747473.0
18Peking GuanghuaChina82788078764172.5
19IESESpain80687074687272.0
### Tier III: Composite Under 72
RankBusiness SchoolCountryD1D2D3D4D5D6Composite
20Anderson / UCLAUSA70727072727371.5
21Tepper / CMUUSA72706868806871.0
22McCombs / UT AustinUSA68707072727170.5
23Oxford SaïdUK66687272746870.0
24Cambridge JudgeUK64667070786969.5
25TAU CollerIsrael66786262727469.0
26Marshall / USCUSA68706868686968.5
27HKUST BusinessHong Kong70666866706868.0
28Indian School of BusinessIndia68726466686767.5
29Frankfurt SchoolGermany66646870686667.0
30NUS BusinessSingapore64646868686766.5
31Rotman / TorontoCanada66646468666866.0
32Kenan-Flagler / UNCUSA64626668666765.5
33Goizueta / EmoryUSA62646466686665.0
34Technion William DavidsonIsrael58766056746364.5
35Questrom / Boston UUSA62606466666664.0
36Foster / U WashingtonUSA62646262666563.5
37Scheller / Georgia TechUSA60626060726463.0
38CEIBSChina72666866624162.5
39IE Business SchoolSpain68606064586262.0
40HEC ParisFrance62586266606161.5
41SDA BocconiItaly60586064626261.0
42ETH Zurich MBASwitzerland54586254785760.5
43KAIST BusinessSouth Korea66626262624660.0
44IIM AhmedabadIndia58605662625959.5
45Desautels / McGillCanada56565860626259.0
46Reichman Arison / AdelsonIsrael56665052586958.5
47Ivey / Western OntarioCanada56545862586058.0
48Olin / WashUUSA56545858605957.5
49Warwick Business SchoolUK54525660586257.0
50NEOMAFrance56485462566356.5
51ASU W.P. CareyUSA54545658565856.0
52IIM BangaloreIndia56565256585555.5
53MannheimGermany52505458585855.0
54SJTU AntaiChina62586058563354.5
55Fudan SOMChina60565858583454.0
56IIM LucknowIndia50525056585553.5
57Smeal / Penn StateUSA50485256565653.0
58ESADESpain54465056525752.5
59NTU BusinessSingapore52485252565252.0
60Schulich / YorkCanada48465054565551.5
## Twelve Findings That Will Get Argued About
1. Stanford GSB: 18× the Median on AI Coursework. ~36 AI-integrated courses vs. the median of 2.
2. 25 of 60 Schools Require Zero AI. 42% of schools offer AI as elective-only.
3. Wharton Is the Only M7 with a Named AI MBA Major. AI for Business, Fall 2025. STEM-certified.
4. HBS Made AI Required Before Any M7 Peer. "Data Science and AI for Leaders" is mandatory.
5. Kellogg Built a Standalone AI Degree, the Only M7 to Do So. The MBAi is an 18-month joint program with McCormick Engineering, the only dedicated AI degree among the M7 schools rather than an elective or concentration.
6. Chicago Booth Partnered with OpenAI Before Any Other M7. The partnership anchors Chicago Booth's D3 (Corporate AI Partnerships) score of 92, the third-highest in the index behind MIT Sloan and Stanford GSB, and it is the primary driver of Chicago Booth's rise to the sixth spot in the overall composite.
7. AI Strategy Outnumbers AI Implementation 9.5 to 1. Most required and elective AI coursework across the 60 schools teaches AI strategy, frameworks, and case discussion rather than hands-on implementation such as prompt engineering, model evaluation, or building an AI workflow. Only the three schools leading D1 (AI Curriculum Integration), Stanford GSB (98), Wharton (96), and Kellogg (94), require students to build or deploy an AI system as part of coursework rather than analyze one from the outside.
8. AI Partnerships Are Concentrated Among 8 Schools. The eight highest D3 (Corporate AI Partnerships) scores in the index belong to MIT Sloan (96), Stanford GSB (95), Chicago Booth (92), Wharton (90), Harvard Business School (88), Tsinghua SEM (88), Kellogg (84), and Columbia Business School, tied with London Business School at 82. Every other school in the 60-school universe scores at or below 80 on this dimension, meaning direct partnerships with frontier AI labs and hyperscalers remain concentrated among a small group of Tier I and top Tier II programs.
9. Israel's Per-Capita AI Founder Yield Rivals the M7. TAU Coller (#25, D2: 78), Technion (#34, D2: 76), Reichman (#46, D2: 66). Three schools with a combined population base a fraction of the US produce AI-founder alumni at rates that, per capita, rival the M7.
10. Tsinghua SEM Is Structurally Depressed by English-Language Bias. Tsinghua's D1 through D5 average is 86.4. Its D6 (Citation Share) score is 54, a 32-point gap explained almost entirely by the English-language dominance of the five AI engines measured. Under a Chinese-engine normalization, Tsinghua would rank fourth or fifth instead of eighth.
11. European Schools Lead on Research, Lag on Curriculum. ETH Zurich MBA scores 78 on D5 (AI Research Output) but only 54 on D1 (AI Curriculum Integration), a 24-point gap. Cambridge Judge shows the same pattern (D5: 78, D1: 64), and Oxford Saïd is close behind (D5: 74, D1: 66). All three publish AI research at a level that would place them in Tier I on that dimension alone, but none has translated that research into required coursework at the rate of the US Tier I schools.
12. Zero Schools Teach GEO.
## The GEO Gap
Zero schools in 60 include Generative Engine Optimization, LLM optimization, AI visibility strategy, or Citation Share measurement. The first school to build a required GEO module will own the category in business education.
## Eight Questions CEOs Should Ask Before the Next MBA Hire
1. Does the candidate's program require AI coursework? 42% do not.
2. Has the candidate evaluated an AI vendor in class, not only independently?
3. Has the candidate built or managed an AI workflow?
4. Does the candidate understand LLM governance?
5. Has the candidate studied AI procurement, not just AI ethics?
6. Does the candidate know how brands appear inside AI engines?
7. Has the candidate taken a named AI course, or was AI only 'integrated' into another subject?
8. Would you hold the MBA to the same AI-fluency standard as an engineering hire?
## The Clock Is Institutional
Business schools have two admission cycles before AI readiness becomes as consequential as finance or accounting. 42%. 18×. Zero.
Edition Two planned Q2 2027.
## What This Index Does Not Measure
A ranking is defined as much by what it excludes as by what it measures. Below is what this composite deliberately does not score, stated directly rather than left for the reader to infer.
Undergraduate or general MBA teaching quality. Class sizes, professor ratings, and general program satisfaction are not scored. This index measures AI readiness specifically, not overall program quality.
Admissions selectivity or applicant yield. Acceptance rates and average GMAT or GRE scores play no role in any of the six dimensions.
Overall business school prestige or rankings history. Financial Times, Bloomberg Businessweek, and U.S. News composites measure a different variable, general reputation across every discipline a business school teaches. This index measures one variable, AI readiness, across six explicitly stated dimensions.
Tuition, cost, or return on investment. Program price and post-MBA salary outcomes are not part of any dimension in this composite.
Research output outside AI. Finance, marketing, operations, and strategy research volumes are not scored under D5. D5 measures AI-specific research output only.
A reader who wants to measure these variables should consult rankings built for them. This benchmark is a complement to those rankings, not a substitute.
## Tier I Snapshots
The seven schools in Tier I each lead the index on a different dimension rather than winning uniformly across all six, and the specific facts behind each school's placement are documented in the findings above.
Stanford GSB (Composite 94.5) tops the index outright, leading D1 (98), D2 (96), and D6 (96). Its AI coursework volume, approximately 36 AI-integrated courses against a median of 2 across the 60-school universe, is the single largest gap in the index.
MIT Sloan (Composite 92.0) leads D3 (Corporate AI Partnerships, 96), D4 (Executive AI Education, 95), and D5 (AI Research Output, 95). MIT Sloan's institutional partnerships and executive-education scale outweigh a comparatively lower D1 score of 80.
Wharton (Composite 90.5) is the only M7 school with a named, STEM-certified AI MBA major, AI for Business, launched Fall 2025. That program anchors Wharton's D1 score of 96, the second-highest in the index behind Stanford GSB.
Harvard Business School (Composite 89.0) made AI coursework mandatory, through "Data Science and AI for Leaders," before any other M7 peer, driving HBS to the second-highest D4 (Executive AI Education) score in the index at 92.
Kellogg (Composite 86.5) built the MBAi, an 18-month joint degree with McCormick Engineering, the only standalone AI degree among the M7 schools. Kellogg's D1 score of 94 is the third-highest in the index.
Chicago Booth (Composite 85.0) partnered with OpenAI before any other M7 school, driving a D3 score of 92, third-highest in the index behind MIT Sloan and Stanford GSB.
Columbia Business School (Composite 82.5) closes out Tier I with the most balanced dimension profile of the seven, scoring between 82 and 84 on five of six dimensions rather than leading on any single one.
## Methodology and Sensitivity Checks
The composite score for each school is the unweighted mean of six dimension scores (D1 through D6), each normalized to a 0 to 100 scale. Ties in the composite are broken by D6 (AI Citation Share). Composite scores carry an approximate plus or minus 2.5 point uncertainty band at the 95% confidence level, so rank differences smaller than 5 composite points may not be statistically distinguishable, and tier placement is more reliable than exact position within a tier.
D6 (AI Citation Share) is modeled from 3,000 prompt-engine runs distributed across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews, the same five-engine protocol used for D6 in the 5W AI Higher Education Index. Every other dimension draws on published course catalogs, institutional and hyperscaler partnership announcements, and executive-education program data specific to each school.
Reweighting the six dimensions changes the rankings in ways that isolate what each school's Tier I placement actually depends on. Doubling D1 (AI Curriculum Integration) moves Wharton (91.3) ahead of MIT Sloan (90.3) into second place, since MIT Sloan's comparatively low D1 score of 80 is now weighted twice as heavily. Doubling D3 (Corporate AI Partnerships) instead leaves the top four unchanged but widens MIT Sloan's gap over Wharton, since MIT Sloan's D3 score of 96 is the highest in the index.
Removing D6 (AI Citation Share) entirely, to check how much the English-language bias in the five AI engines affects the two Chinese schools in the index, moves Tsinghua SEM from 8th to a tie for 5th (86.4, level with Kellogg) and moves Peking Guanghua from 18th to a tie for 10th (78.8, level with INSEAD). Both moves are consistent with finding 10 above: Tsinghua SEM's D1 through D5 average of 86.4 already sits inside Tier I range, and only its D6 score of 54 pulls the composite down to Tier II.
## Related Research
This index is Volume 03 in the 5W Research series. The pieces below extend the same AI-readiness framework into adjacent education markets.
5W AI Higher Education Index 2026 (Volume 02) scores 50 universities on AI production capacity, the research, compute, and founder-pipeline layer that sits upstream of the MBA classroom measured in this report.
The American Private School AI Study 2026 applies a comparable AI-readiness framework to K-12 private education.
Washington DC Private School AI Curriculum Index 2026 narrows that K-12 framework to a single metro market.
Online Universities AI Visibility Index 2026 scores online degree programs on the same AI Citation Share dimension used as D6 in this report.
Studying Public Relations and Marketing at U.S. Universities covers the communications-education gap referenced in the 5W AI Higher Education Index's finding that zero communications schools qualify for any tier.
## About 5W AI Communications
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen: ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. Learn more at 5wpr.com.
---
### The 2026 Male Aesthetic Revolution
URL: https://www.5wpr.com/research/male-aesthetic-revolution-2026/
Summary: The male aesthetic revolution: 1.6M US male cosmetic procedures in 2024, up 4% YoY, hair restoration doubled, and 92% of facial surgeons see men.
# The 2026 Male Aesthetic Revolution
### The fastest-growing luxury category in elite medicine — 1.6M procedures, hair restoration doubled in a year, 92% of facial surgeons, +400% Brotox, $62.5B grooming pipeline
By Ronn Torossian, Founder and Chairman, and the 5W Research Team — April 2026
Download the full PDF
## EXECUTIVE SUMMARY
Male aesthetic medicine is in a category-defining moment. Growth rates across procedures serving male patients are outpacing the broader aesthetic industry. The cultural stigma that once kept men out of consultation rooms has collapsed. Elite practitioners are building practices, protocols, and patient experiences specifically around the male aesthetic patient — and the practitioners who are doing so are winning.
The scale. Per the American Society of Plastic Surgeons 2024 Procedural Statistics Report, US male cosmetic procedures grew 4% to 1.6 million in 2024 — outpacing the broader industry's 1–3% growth. Per AAFPRS 2024 and 2025 annual surveys, 92% of member facial plastic surgeons now see male patients. Hair restoration treatments doubled from 2024 to 2025 — one of the largest single-year procedural volume increases AAFPRS has tracked in any category.
The market opportunity. The global men's grooming products market reached $62.5 billion in 2025, projected to $85.2 billion by 2030 (Mordor Intelligence). The global hair transplant market reached $6.42 billion in 2025, projected to $10.64 billion by 2031, with men driving 78.74% of revenue. Per ASPS data cited by Grand View Research, "Brotox" — Botox for men — has grown 400% since 2000.
The thesis. Men are the fastest-growing patient population in elite aesthetic medicine. They are the fastest-growing hair restoration patients, the fastest-growing neuromodulator patients, the fastest-growing facelift patients in the 35–55 demographic, the fastest-growing luxury skincare consumers, and the fastest-growing cosmetic dentistry patients seeking full-face smile design. This is not a secondary trend. It is the defining demographic expansion in aesthetic practice through 2030.
## KEY FINDINGS BAR (8 stat cards)
STAT 1: 1.6M - US male cosmetic procedures in 2024, +4% YoY (ASPS 2024 Procedural Statistics Report)
STAT 2: 2x - Hair restoration treatments performed in 2025 versus 2024 (AAFPRS 2025 Annual Survey)
STAT 3: 92% - AAFPRS facial plastic surgeons who now see male patients (vs. ~65–70% a decade ago)
STAT 4: +400% - Growth in male Botox treatments since 2000 (ASPS via Grand View Research)
STAT 5: $62.5B - Global men's grooming market in 2025, projected to $85.2B by 2030 (Mordor Intelligence)
STAT 6: $6.42B → $10.64B - Global hair transplant market 2025 to 2031, with men driving 78.74% of revenue
STAT 7: 67% / 85% - Men experiencing hair loss in their lifetime / by age 50 (Towards Healthcare; Fact.MR)
STAT 8: 35M - US men currently with hair loss (International Society of Hair Restoration Surgery)
## THE SIX FINDINGS
1. Men are growing faster than the industry. US male cosmetic procedures grew 4% to 1.6 million in 2024 (ASPS), versus 1% surgical and 3% minimally invasive industry-wide. Men outpaced the industry average materially in both categories — and that gap has widened every year since 2020.
2. Hair restoration is the gateway category. The AAFPRS 2025 survey (February 2026) reported approximately twice as many hair restoration treatments performed in 2025 compared to 2024. The global hair transplant market reached $6.42B in 2025, projected to $10.64B by 2031 (Mordor Intelligence). Males drive 78.74% of revenue. FUE accounts for 58.62% of the market. North America is 33.29% of global revenue.
3. The male non-surgical top three mirrors female exactly. Per AAFPRS, neurotoxins, fillers, and skin treatments are the top three non-surgical procedures for men — the same top three for women. "Brotox" has grown 400% since 2000. Per AAFPRS President Dr. Patrick Byrne, men are increasingly seeking treatments to "preserve a competitive advantage in both professional and social settings."
4. The male surgical top three is defined. Blepharoplasty, rhinoplasty, and facelifts (AAFPRS). Globally per ISAPS 2024, eyelid surgery is the #1 male surgical procedure, followed by gynecomastia and scar revision. Deep plane facelifts and neck lifts for men are growing particularly fast as surgeons refine techniques specifically for masculine anatomy — preserving rather than neutralizing masculine features.
5. The grooming market is the consultation pipeline. The $62.5B global men's grooming market is the funnel that feeds tomorrow's aesthetic consultations. Skincare alone is 29% of the category at approximately $19.1B in 2025 (GMI), and is the fastest-growing product segment. The same consumers spending meaningfully on skincare and grooming are the patients walking into elite practices in the next 24 months.
6. The preventative generation has arrived. Younger men in their 20s are pursuing hair transplants preventatively rather than reactively. Per Mordor Intelligence: "social platforms have normalized cosmetic procedures, prompting adults in their late 20s to view transplantation as preventive rather than corrective care." 67% of men experience hair loss in their lifetime; 85% by age 50; 35M US men have hair loss now (ISHRS). The addressable market is enormous — and maturing earlier.
## THE FOUR DRIVERS
The four reinforcing forces that produced the male aesthetic revolution. None depends on a single cultural cycle. All four compound. The elite practices and luxury operators who line up against all four pull forward into positions hard for late entrants to dislodge.
Driver 1: Social media removed the stigma. Instagram and TikTok normalized aesthetic care as a mainstream choice rather than a hidden one. Male celebrities, athletes, and public figures began discussing hair transplants, neuromodulators, and skincare openly. The cultural permission structure has been the single most important shift in the aesthetic industry over the past decade.
Driver 2: Remote work restructured the feedback loop. Before 2020, professionals saw themselves in a mirror in the morning. After 2020, they see themselves constantly on Zoom and Teams — at high resolution, against unflattering backgrounds. Men who previously ignored under-eye bags, neck laxity, or receding hairlines now see those features daily. Return-to-office did not reverse this. Mixed-mode video work is permanent.
Driver 3: The professional return on appearance is now openly cited. Executives, attorneys, finance professionals, sales leaders, and founders openly cite looking younger, fitter, and more rested as a career advantage. Robb Report, Bloomberg, WSJ, and Forbes have all run features on executive male aesthetic routines in the 2023–2026 period. AAFPRS data confirms the framing: men are seeking treatments for "competitive advantage."
Driver 4: Male-specific surgical technique has matured. Female-optimized rhinoplasty can feminize a masculine face. Female-optimized facelift can erase masculine jawline definition. Female-optimized filler protocol creates a softer, rounder appearance men do not want. Over the past decade, elite practitioners developed male-specific technique that preserves masculine features. Better outcomes drive referrals. Referrals drive volume. Volume drives further specialization. The flywheel is now turning hard.
## THE GATEWAY VS THE FULL PROTOCOL
On one side: the gateway. Hair restoration. The procedure men pursue first. Doubled in a single year. Drives 78.74% of a $6.42B global market. The first surgical decision most male patients make — and the one that opens the consultation room door for everything that follows.
On the other side: the full protocol. Blepharoplasty. Rhinoplasty. Facelift. Neck lift. Chin augmentation. Neurotoxins. Fillers. Laser. High-definition liposuction. Gynecomastia surgery. Medical-grade skincare. Cosmetic dentistry. The full-face, full-body program elite practices build around the male patient who walked in for hair and stayed for a decade.
The grooming pipeline ($62.5B) feeds the consultation. The consultation feeds the gateway. The gateway feeds the full protocol. The full protocol feeds lifetime patient value. Practices that map this funnel — and build communications, GEO, and patient experience for every stage — are pulling away from generalist competitors materially.
## FROM RONN TOROSSIAN, FOUNDER AND CHAIRMAN OF 5W
"The male aesthetic revolution did not happen because men suddenly started caring about their appearance. They always have. What changed is the set of forces that previously kept that interest underground — social media stigma, professional silence, female-optimized technique, no place to start the conversation. All four of those forces have collapsed. What is left is the fastest-growing patient population in elite aesthetic medicine, a $62 billion grooming pipeline feeding into the consultation room, and a small number of practices and luxury operators who understand that the male patient is the defining demographic expansion in this category through 2030. Most aesthetic practices and luxury healthcare brands are still selling to women and accidentally seeing men. The ones who flip that — who design specifically for male technique, male consultation dynamics, male discovery patterns, and male AI-era research behavior — will own the category for a decade."
## THE PATIENT COHORTS
The five male aesthetic patient cohorts elite practices serve in 2026. Each has distinct discovery behavior, consultation dynamics, and lifetime value. The practices and luxury brands segmenting against these cohorts are converting at materially higher rates than generalists.
Cohort 1: The Executive Patient. C-suite, founder, finance, law, sales. Cites competitive professional advantage openly. Prioritizes undetectable results, tight recovery windows, and discreet scheduling. Highest single-treatment ticket size. Lifetime value driven by full-protocol progression.
Cohort 2: The Dating and Re-Entry Patient. Post-divorce or re-entering relationship market in 40s and 50s. Hair restoration, facelift, body contouring. Often accelerates timeline because of new social calendar. Strong word-of-mouth referral after good outcomes.
Cohort 3: The Longevity and Optimization Patient. Wellness-, fitness-, and longevity-oriented. Treats aesthetic care as part of a broader optimization protocol. Commonly engaged with concierge medicine, peptide therapy, and cosmetic dentistry simultaneously. High receptivity to integrated aesthetic-and-longevity programs.
Cohort 4: The Public-Facing Professional. Media, sales leadership, public-facing executive, broadcast. Constant on-camera presence. Pursues neuromodulators and lasers on a continuous maintenance schedule. Predictable annual spend. Excellent for retention modeling.
Cohort 5: The Preventative Younger Patient. Late 20s and early 30s. Pursues hair restoration preventatively rather than reactively. Skincare regimens, low-dose neuromodulators. Per Mordor: "social platforms have normalized cosmetic procedures, prompting adults in their late 20s to view transplantation as preventive rather than corrective care." Lifetime value is decades long.
## FAQ
## Frequently Asked Questions
### How many male cosmetic procedures were performed in the US in 2024?
According to the American Society of Plastic Surgeons 2024 Procedural Statistics Report, US male cosmetic procedures grew 4% to 1.6 million in 2024. Industry-wide cosmetic surgical procedures grew 1% and minimally invasive procedures grew 3% — meaning men outpaced the industry average materially in both categories.
### What is the fastest-growing aesthetic procedure for men?
Hair restoration. Per the AAFPRS 2025 annual survey released in February 2026, surgeons performed approximately twice as many hair restoration treatments in 2025 compared to 2024 — one of the largest single-year procedural volume jumps AAFPRS has tracked across any category. Drivers include FUE technique maturation, robotic precision systems (ARTAS iXi at 500–700 grafts per hour with 44-micron precision), new pharmaceutical adjuncts, and cultural permission for men to pursue the procedure openly.
### What percentage of facial plastic surgeons see male patients?
Per AAFPRS 2024 and 2025 annual surveys, 92% of member facial plastic surgeons report male patients in their practices — a figure that would have been closer to 65–70% a decade ago. The male aesthetic patient is now standard-of-practice demographic, not an outlier.
### How large is the global men's grooming market?
Per Mordor Intelligence, the global men's grooming products market reached $62.5 billion in 2025 and is projected to grow to $85.2 billion by 2030 at a 6.39% CAGR. Per Global Market Insights, the market reaches $108 billion by 2035. Skincare alone represents 29% of revenue at approximately $19.1 billion in 2025 and is the fastest-growing product segment.
### What are the top cosmetic procedures for men globally?
Surgically: blepharoplasty (eyelid surgery), rhinoplasty, and facelifts per AAFPRS — and eyelid surgery, gynecomastia, and scar revision per ISAPS Global Survey 2024. Non-surgically: neurotoxins, fillers, and skin treatments per AAFPRS — the same top three as for female patients. Hair restoration crosses categories and is the fastest-growing procedural area.
### Why are men pursuing aesthetic care in greater numbers now?
Four reinforcing factors: social media removed the stigma of secrecy around male aesthetic work; remote and hybrid work created sustained appearance awareness through hours-daily video calls; professional competitive pressure has legitimized aesthetic care as career investment; and male-specific surgical technique has matured — modern technique produces natural, undetectable results that the previous generation of technique could not.
### How much do men spend on aesthetic care?
Entry-level maintenance (neuromodulators 2–3x per year plus basic skincare) runs approximately $2,000–$5,000 annually. Comprehensive non-surgical programs run $5,000–$25,000 annually. Surgical years with hair restoration, facial surgery, or body contouring can reach $25,000–$150,000 depending on procedures. UHNW comprehensive aesthetic care commonly exceeds $150,000 annually.
### When should a man start considering aesthetic care?
Preventative care can begin meaningfully in the late 20s — particularly hair restoration, skincare regimens, and minor preventative neuromodulator use. Major surgical intervention typically makes sense in the 35–55 range when concerns have developed but before advanced aging limits surgical option effectiveness. The "right time" is patient-specific — a skilled board-certified practitioner's consultation is the correct starting point.
### Will others be able to tell I've had work done?
With skilled modern technique, no. Per Houston plastic surgeon Dr. C. Bob Basu cited in NewBeauty, contemporary aesthetic medicine has entered "the era of undetectable facial plastic surgery. Patients aren't trying to look different, they want to look like themselves on their best day." Elite male aesthetic results are routinely undetectable to anyone not specifically looking for them. Practitioner selection is critical.
### Can 5W run a GEO program for my aesthetic practice or luxury healthcare brand?
Yes. 5W's Healthcare and Luxury Medicine practice builds AI-era brand visibility for elite aesthetic practices, hair restoration centers, dermatology brands, and luxury healthcare operators competing for AI-mediated patient discovery. Inquiries: research@5wpr.com or media@5wpr.com.
## METHODOLOGY AND SOURCES
This report synthesizes publicly available data from professional society statistics, peer-reviewed journal publications, market research, industry analyst forecasts, and consumer and trade media reporting. All figures are linked inline to primary sources. Research was conducted between March and April 2026 and reflects the most current published data available at time of writing. Forecasts reflect professional analyst projections where cited and are subject to the uncertainty inherent in any forward-looking estimate.
American Society of Plastic Surgeons — 2024 Procedural Statistics Report (released June 2025)
American Academy of Facial Plastic and Reconstructive Surgery — 2024 and 2025 Annual Surveys
International Society of Aesthetic Plastic Surgery (ISAPS) — 2024 Global Survey
International Society of Hair Restoration Surgery (ISHRS) — industry statistics
Mordor Intelligence — Men's Grooming Products Market Analysis 2025–2030
Mordor Intelligence — Hair Transplant Market Size, Share Analysis & Trends Research Report 2031 (January 2026)
Global Market Insights — Men's Grooming Products Market Size, Share & Forecast 2026–2035
Towards Healthcare — Hair Transplant Market Size (January 2026)
Grand View Research — US Men's Grooming Products Market Report
Fact.MR — Hair Transplant Market Share & Industry Statistics
NewBeauty — Plastic Surgery Statistics 2024 analysis
22 Plastic Surgery — 2025 Plastic Surgery Trends
AAFPRS press commentary — Dr. Patrick Byrne, Dr. C. Bob Basu, Dr. Michael Keyes, Dr. Anthony Brissett
Download the full PDF
## ABOUT 5W
5W is the premier AI communications firm in the United States — a Generative Engine Optimization, marketing, and communications firm and one of the largest independently owned firms in the country, with more than 250 professionals across GEO, digital marketing, SEO, digital influencer strategy, PR, crisis communications, and public affairs. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan. Clients span luxury, finance, private aviation, legal services, real estate, and aesthetic medicine.
5W's Healthcare and Luxury Medicine GEO practice is at [5wpr.com/practice/healthcare-pr-agency](/practice/healthcare-pr-agency.cfm).
April 2026 - 5W 5wpr.com/research/male-aesthetic-revolution-2026
Published by 5W Research. 5wpr.com. Email us at press@5wpr.com
---
### The DTC Graveyard
URL: https://www.5wpr.com/research/dtc-graveyard-2026/
Summary: Allbirds sold for $39M after a $4.1B peak and Casper delisted at a 75% discount. Why some DTC brands died while others still dominate AI answers.
# The DTC Graveyard
### Why brands built for clicks died, and brands built for citations survived.
By the 5W Research Team — April 2026
Download the full PDF
## EXECUTIVE SUMMARY
On March 31, 2026, Allbirds — the sustainable sneaker brand that went public at a $4.1 billion valuation in November 2021 — announced it had sold its assets to American Exchange Group for $39 million. The brand will dissolve following shareholder approval.
$4.1 billion to $39 million in 53 months. A 99% collapse.
Allbirds is not the story. Allbirds is the punctuation mark on the story. The story is that the direct-to-consumer brand model that defined 2015–2021 — cheap Meta ads, Shopify, Instagram, venture-funded acquisition — structurally broke in 2022, and the death cycle is still playing out.
The surviving DTC brands were not the ones that spent the most on acquisition. They were the ones talked about most by third parties: review sites, editorial media, podcasts, creators, and — now — AI answer engines.
5W is the premier AI communications firm in the United States. This report documents what changed, who died, who survived, and the citation capital thesis that explains the difference.
## KEY FINDINGS BAR (8 stat cards)
STAT 1: 0.95% — Allbirds' $39M sale price as a share of its $4.1B peak valuation
STAT 2: 26% — Casper's $286M take-private price vs. its $1.1B pre-IPO valuation
STAT 3: $60B+ — cumulative peak-to-2026 valuation lost by the DTC class of 2017–2021
STAT 4: 53 months — Allbirds' time from IPO peak to asset sale
STAT 5: 67% — purchasing decisions now influenced by AI recommendations (Stanford AI Index 2026)
STAT 6: 85% — share of AI answer brand mentions coming from third-party pages, not owned domains (AirOps)
STAT 7: 48% — share of AI citations coming from community platforms like Reddit and YouTube (AirOps)
STAT 8: 15% — share of brands securing the top citation position on their own domain (Birdeye)
## TEN FINDINGS
1. Allbirds sold its assets for 0.95% of its 2021 peak valuation. $4.1B to $39M in 53 months.
2. Casper exited public markets at a 75% discount to its IPO price. Taken private by Durational Capital at $286M in Q1 2022.
3. Outdoor Voices collapsed from $110M valuation to an undisclosed acquisition. Closed all 16 stores and laid off 80% of corporate staff in March 2024.
4. The DTC death cycle accelerated in 2024–2026, not 2020. SmileDirectClub bankruptcy (2023), Blue Apron sold for ~$103M (2023), Outdoor Voices (2024), Allbirds dissolution (2026).
5. Casper is delisted but still dominates mattress citation rankings. Appears in every major 2026 review source surveyed.
6. The top five DTC mattress brands by AI citation are Casper, Nectar, Saatva, Purple, and Helix. All built editorial presence before their paid-acquisition peaks.
7. Surviving DTC brands share one trait: editorial presence outlasted marketing spend. Warby Parker, Glossier, Chewy, Liquid Death, Rhode all built press credibility before they needed it.
8. Private-equity rollup is the dominant DTC exit. Consortium Brand Partners, American Exchange Group, Durational Capital are the new distressed-DTC buyers.
9. Rhode sold to e.l.f. for $1B in 2025 — on a different model. Celebrity-led, earned-media-first, retail-partnership. The 2026 survivor looks more like Rhode than 2017-era DTC.
10. AI answer engines now compound editorial authority. 85% of brand mentions in AI answers originate from third-party pages, not owned domains (AirOps, 2026).
## THE GRAVEYARD
For six years, a specific business model worked better than any consumer brand playbook in history. Undercut a legacy incumbent by 30–50%, put the product in a box, advertise on Instagram, raise another round. Casper, Warby Parker, Allbirds, Away, Glossier, Harry's, Honest Company, SmileDirectClub, Blue Apron — each marketed as the "Warby Parker of [category]."
Then three things broke in rapid succession. Apple's iOS 14 tracking change (April 2021) gutted Facebook attribution. The Fed ended zero interest rate policy in 2022, evaporating venture patience. And the category defensibility problem surfaced — when Casper proved mattress-in-a-box worked, Purple, Nectar, Saatva, Helix, Leesa, and Tempur-Pedic's own DTC arm all copied it. CAC tripled, margin collapsed, and the originals lost their edge within 24 months.
Every brand in the graveyard invested heavily in paid acquisition and comparatively lightly in earned-media infrastructure. When the paid channel broke, there was nothing underneath.
## THE CASPER PARADOX
Here is where the story gets interesting.
Casper was taken private at a 75% discount to its IPO price in early 2022. It has been a private, struggling company for four years. And it is still cited in nearly every major 2026 mattress review ranking in America — Consumer Reports, MattressNut, Mattress Nerd, Sleep Foundation, and the AI-cited top rankings for the category.
The reason is simple: Casper was the original. It built a decade of editorial coverage, review-site presence, and podcast-sponsorship recall before the category got crowded. That citation capital outlived its financial trajectory.
Every dollar spent on earned media between 2017 and 2022 is now paying a residual dividend in 2026. Every dollar that went to Meta ads in the same period is already gone.
## THE SURVIVORS
Not every DTC brand died. Warby Parker is public and profitable. Glossier was majority-acquired by Bain in 2024. Chewy clears $5B+ in annual revenue. Liquid Death hit a $1.4B valuation in 2024. Rhode sold to e.l.f. for $1B in 2025.
The survivors share three characteristics nearly every dead brand lacked.
Earned media was a primary growth channel. Glossier launched with Into The Gloss as a full editorial platform. Warby Parker was in Vogue year one. Liquid Death built its entire business on PR stunts.
Retail and omnichannel presence matured alongside DTC. None of the survivors were online-only.
Founder and brand narrative were distinguishable. Emily Weiss, Neil Blumenthal, Mike Cessario, Hailey Bieber — each a named spokesperson with a specific thesis journalists could quote.
Casper, Allbirds, and Outdoor Voices had founders too. They didn't have narratives. "Affordable comfortable mattress" and "sustainable sneaker" are product descriptions.
## THE AI LAYER
A specific tailwind is now compounding the advantage of brands that built editorial authority during the DTC era.
According to Birdeye's State of AI Search 2026, only 15% of brands secure the top citation position in AI-generated answers with their own domain. AirOps research shows 85% of brand mentions in AI answers originate from third-party pages rather than owned domains, and 48% of citations come from community platforms like Reddit and YouTube. Per Stanford's AI Index 2026, 67% of purchasing decisions are now influenced by AI model recommendations.
The implication for consumer brands is structural. Paid acquisition does not generate AI citation lift — only third-party mentions do. Review sites — Consumer Reports, Sleep Foundation, Wirecutter, Good Housekeeping — are now infrastructure. And dead brand names with editorial history still win the answer, which is why Casper is the proof case.
## THE NEW PLAYBOOK
The winning consumer brands of 2024–2026 share a specific communications discipline:
Tier 1 editorial in year one, not year five. Warby Parker was in GQ year one. Glossier was Into The Gloss before it was a brand.
Founder narrative, not product pitch. Journalists cite people, not products. The dead brands had founders. They didn't have narratives.
Review-site saturation is structural marketing. Consumer Reports, Wirecutter, category specialists — now the top of funnel. Sending product for review is not PR. It is category infrastructure.
Creator relationships are earned media, not paid media. Brands that earned repeated unpaid creator mentions built durable citation capital. Brands that bought influencer content got short-term visibility.
Retail presence reinforces editorial authority. Every category-defining consumer brand of 2024–2026 is on shelves. Most of the dead DTC brands were not, or got there too late.
The brand must outlast the marketing budget. If your paid channels went to zero tomorrow, does your brand survive? For the dead DTC cohort, the answer was no.
## THE BOTTOM LINE
Allbirds raised $200M+ in its IPO alone. It spent hundreds of millions more on paid acquisition. In April 2026, it sold its asset base for $39M.
Casper burned through similar capital. It is still cited in every 2026 mattress review in America.
The difference is not product. It is not category. It is not founder. It is citation capital — the cumulative weight of editorial authority, review-site presence, and third-party mentions that builds up over a decade of communications discipline and outlives the marketing budget that created it.
The DTC brands of 2017–2021 treated brand-building as expense. The surviving DTC brands of 2024–2026 treated brand-building as capital.
The next decade of consumer brand winners will be the ones whose founders understand this from day one.
## REQUEST A BRAND CITATION AUDIT
5W works with consumer brands to build the editorial and citation foundation that outlasts any paid channel. The Brand Citation Audit covers 50–100 category-specific queries across five AI platforms, citation-source mapping, competitive benchmarking, and a 90-day remediation plan.
Inquiries: research@5wpr.com or media@5wpr.com.
## FAQ
## Frequently Asked Questions
### What is the DTC Graveyard report?
The DTC Graveyard is a 5W research report documenting the direct-to-consumer brand extinction of 2022–2026 — Allbirds, Casper, Outdoor Voices, SmileDirectClub, Blue Apron, Peloton, and others. It includes a live citation study showing why some dead brands still dominate AI answer engines and why surviving DTC brands share one trait: editorial presence outlasted marketing spend.
### What is citation capital?
Citation capital is the cumulative weight of editorial authority, review-site presence, and third-party mentions that builds up over a decade of communications discipline and outlives the marketing budget that created it. 85% of brand mentions in AI answers come from third-party pages, not owned domains. It is the single most durable consumer brand asset in the AI era.
### Why did Allbirds collapse from $4.1B to $39M?
Allbirds went public at a $4.1 billion valuation in November 2021. On March 31, 2026, it announced the sale of its assets to American Exchange Group for $39 million — a 99% collapse in 53 months. The brand spent heavily on paid acquisition during the iOS 14 attribution era, lacked durable editorial authority, and never built the citation capital that survives a paid-channel collapse. The brand will dissolve following shareholder approval.
### Why is Casper still cited everywhere even though it was delisted?
Casper was taken private by Durational Capital in Q1 2022 at a 75% discount to its IPO price. It has been a private, struggling company for four years. And it is still cited in nearly every major 2026 mattress review ranking in America — Consumer Reports, Sleep Foundation, Mattress Nerd, MattressNut. The reason: Casper was the original. It built a decade of editorial coverage before the category got crowded. That citation capital outlived its financial trajectory. This is the proof case for why citation capital is the most durable brand asset in consumer goods.
### Which DTC brands survived?
Warby Parker is public and profitable. Glossier was majority-acquired by Bain in 2024. Chewy clears $5B+ in annual revenue. Liquid Death hit a $1.4B valuation in 2024. Rhode sold to e.l.f. for $1B in 2025. The survivors share three traits the dead brands lacked: earned media as a primary growth channel from day one, mature retail and omnichannel presence, and a distinguishable founder-and-brand narrative — not a product description.
### What is the new playbook for consumer brands?
Tier 1 editorial in year one, not year five. Founder narrative, not product pitch. Review-site saturation as structural marketing. Creator relationships as earned media, not paid media. Retail presence reinforcing editorial authority. And the test: if your paid channels went to zero tomorrow, does your brand survive? For the dead DTC cohort, the answer was no.
### Is the report free to download?
Yes. The report is ungated and free to read. An optional email signup for future 5W research is adjacent to the download.
### Can 5W run a brand citation audit for my company?
Yes. 5W is the premier AI communications firm in the United States. The Brand Citation Audit covers 50–100 category-specific queries across five AI platforms, citation-source mapping, competitive benchmarking, and a 90-day remediation plan. Inquiries: research@5wpr.com or media@5wpr.com.
## METHODOLOGY
Brand and valuation data is drawn from SEC filings, Form 8-K announcements, press releases, and reporting in Fortune, Business of Fashion, Retail Dive, Yahoo Finance, WWD, and CNBC. AI search behavior research is drawn from Birdeye's State of AI Search 2026, AirOps' 2026 State of AI Search, and Stanford AI Index 2026.
Live citation study: The 5W Research team reviewed top-cited mattress reviews and consumer category content across Consumer Reports, Sleep Foundation, Mattress Nerd, MattressNut, and the AI-cited top results for related consumer queries on April 23, 2026.
Download the full PDF
## ABOUT 5W
5W Public Relations is the premier AI communications firm in the United States. With approximately 275 professionals, 5W has built and scaled consumer brands across CPG, beauty, food and beverage, wellness, fashion, and retail — through the exact 2015–2026 cycle documented in this report. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan.
April 2026 — 5W Public Relations
Published by 5W Research. 5wpr.com. Email us at press@5wpr.com. All data cited is drawn from publicly reported sources and a live citation study conducted April 23, 2026. Reproduction permitted with attribution.
---
### How AI Describes "Quiet Luxury"
URL: https://www.5wpr.com/research/how-ai-describes/quiet-luxury/
Summary: Three years after the aesthetic emerged, five AI engines still cannot agree what
---
### Mark Zuckerberg
URL: https://www.5wpr.com/reputation/mark-zuckerberg/
Summary: The 5W Reputation Index audits the AI-held reputation of Mark Zuckerberg across five AI engines, the frozen-reputation test.
The 5W Reputation Index · Edition 05
# Mark Zuckerberg
What AI Says About You When You're Not in the Room.
The Reputation That Won't Update
Ask the engines who Mark Zuckerberg is, and the answer is striking for what it preserves. The Facebook founder. Privacy. Cambridge Analytica. Misinformation. Congressional hearings. The metaverse bet that underdelivered.
The AI pivot is acknowledged — but it does not lead, and it does not reset anything. This is the franchise's clearest case of a frozen reputation: a narrative set during one era that the engines have not meaningfully updated, despite a decade of the subject trying.
Reputation Intelligence
Edition 05
Edition
Subject
Mark Zuckerberg · Meta
Format
Single subject
Engines
5
Published
June 16, 2026
## The Verdict
A rebrand changed the company's name. It did not change the answer.
## Methodology & Confidence
### Five Engines, No Spin
Reputation modeled across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — 40+ reputation-intent prompts across identity, trust, track record, controversy, comparison, and decision intent. Multiple passes; recurring findings only. Cross-checked against current reporting, critical and favorable. Directional estimates — not a precision instrument.
## The Dominant Narrative
### The Story AI Won't Stop Telling
The synthesized narrative is durable and fixed: the Harvard student who built Facebook, scaled it to global dominance, then spent years answering for the consequences — data privacy, election misinformation, the Cambridge Analytica scandal, antitrust scrutiny. The Meta rebrand and the metaverse push read, in the AI narrative, as an attempt to move on that did not land. The current AI investment is the newest layer, but it sits on top of the old story rather than replacing it.
## Sentiment Map
### Who the Box Thinks Of You
Valence of the dominant framing each engine surfaces first.
SubjectChatGPTClaudeGeminiPerplexityGoogle AIO
ZuckerbergMixed–negMixedMixed–negMixed–negMixed
Legend Positive Mixed Mixed–negative Confidence: Moderate
The finding. Net mixed-to-negative — and consistently so. No engine is hostile. Every engine pairs the scale of the achievement with the weight of the baggage, and the baggage sets the tone.
## First-Surface Audit
### An Introduction and a Caveat, Every Time
"The first sentence is the reputation. Almost no one reads past it."
Answers open with "co-founder and CEO of Meta" and "founder of Facebook" — then, inside the first answer, privacy, misinformation, or a controversy reference surfaces unprompted. The achievement and the liability arrive together, every time. The first sentence is an introduction; the second is a caveat.
## The Citation Base
### Every Reputation Has a Paper Trail
The retrieval base is dense and heavily event-driven: a decade-plus of investigative journalism, hearing coverage, regulatory filings, and scandal reporting — most of it not controlled by the subject, none of it ageing out. The owned and favorable material exists, but the controversy archive outweighs it in volume and authority.
## Omissions
### What AI Leaves on the Floor
Under-surfaced: Meta's operational scale and consistent financial performance, the reach of its current AI work, and how much the company has changed since the scandal era. AI acknowledges these — but routinely subordinates them to the privacy-and-misinformation narrative that defines the first surface.
## Risk Surfaces
### Where It Blows Up
### Mark Zuckerberg
Risk level Elevated
The trust-and-privacy framing is the central liability — and the most durable kind: multi-sourced, deeply archived, and self-reinforcing. Every new regulatory or content-moderation story slots neatly into a narrative the engines already hold. The risk is not a new scandal. The risk is that the old one never recedes.
## Cross-Engine Consistency
### When the Engines All Agree — and That's the Problem
High consistency — every engine tells substantially the same story. That is the hard part.
A contested reputation can be moved by shifting which sources win; a unanimous one cannot. Zuckerberg's narrative is not contested. It is settled — and a settled negative narrative is the most difficult assignment in reputation work.
## Peer Comparison
### Against the Field
Among tech founders, Zuckerberg is the franchise's frozen-baggage case. Where Edition 04's subject shows a coherent, controlled arc that compounds positively, Zuckerberg shows the inverse: a coherent arc that compounds a liability. Same mechanism — narrative persistence — pointed in the opposite direction.
## The Reputation Gap
### You vs. Your Answer
Wide. The intended narrative is a builder leading one of the most important AI companies of the next decade. The delivered narrative is the privacy-era founder, with the AI work as a footnote. The gap is not factual error — it is a decade of reputational lag the rebrand failed to close.
## The Reputation Index Score
### The Scoreboard
How the score works.Five dimensions — Accuracy, Sentiment, Completeness, Consistency, Control — each scored 0–20 and equally weighted, for a composite of 100. Directional estimates.
Mark Zuckerberg — Reputation Index Score 64/100
DimensionScore
Accuracy16
Sentiment10
Completeness13
Consistency15
Control10
Composite64/100
## Remediation Roadmap
### The Hardest Assignment in the Index
This is the franchise's case study in how difficult a frozen reputation is to move — and a rebrand is the wrong tool. Renaming the company gave the engines a new noun, not a new narrative. The only viable correction is slow and structural: build a sustained, primary-source, citable record of what the subject and the company are doing now, consistently enough and for long enough that the recent record begins to outweigh the archive. Not a campaign — a multi-year change in the retrieval base. The lesson for everyone else: do not let the narrative freeze in the first place.
## Who This Is For
For any leader who has tried to move past an old chapter, this edition is the test: do AI systems forgive faster than the public web — or slower?
## The 5W Read
### 5W Shapes the Answer in the Box
Zuckerberg's edition proves the franchise's hardest truth: once an AI-held reputation sets, it resists updating — and the more authoritative the archive that holds it, the harder it resists. A rebrand will not move it. Only a deliberate, sustained rebuild of the source base will.
5W's work is to shape the answer in the box — and the time to do it is before the narrative freezes, not after.
The answer is being given right now. The only question is whether anyone is shaping it.
Audit your AI-held reputationDownload the full edition (PDF)
---
### The Airlines
URL: https://www.5wpr.com/reputation/the-airlines/
Summary: The 5W Reputation Index audits the AI-held reputation of Delta, United, American, and Southwest across five AI engines, crisis-memory persistence.
The 5W Reputation Index · Edition 08
# The Airlines
What AI Says About You When You're Not in the Room.
AI Freezes the Worst Day You Ever Had
Four airlines that move most of America do not get the same answer from the engines that now field "which airline is good." AI frames Delta as the premium, reliable one. It frames Southwest as the friendly low-cost carrier — still trailed by a holiday operational collapse. United is introduced by an old viral incident; American by scale and the absence of a story.
The pattern is the franchise thesis in its purest form: AI does not surface your average day. It surfaces your worst one.
Reputation Intelligence
Edition 08
Edition
Subjects
Delta · United · American · Southwest
Format
Comparative — four carriers
Engines
5
Published
July 7, 2026
## The Verdict
AI does not surface your average day. It surfaces your worst one — and keeps surfacing it.
## Methodology & Confidence
### Five Engines, No Spin
Reputation modeled across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — 40+ reputation-intent prompts per carrier across identity, trust, track record, controversy, comparison, and decision intent. Multiple passes; recurring findings only. Cross-checked against current reporting, critical and favorable. Directional estimates — not a precision instrument.
## The Dominant Narrative
### The Story AI Won't Stop Telling
### Delta
The premium operator — reliable, better-run, though recent coverage notes the brand now outruns the experience.
### Southwest
The beloved low-cost disruptor — its goodwill dented by a high-profile holiday meltdown and by recent moves away from the policies that defined the brand.
### United
An operationally improved airline still introduced, by the engines, alongside a years-old viral passenger incident.
### American
The biggest carrier — framed by scale, labor friction, and a thin brand identity rather than by anything distinctive.
## Sentiment Map
### Who the Box Likes
Valence of the dominant framing each engine surfaces first.
SubjectChatGPTClaudeGeminiPerplexityGoogle AIO
DeltaPositivePositivePositiveMixed–posPositive
SouthwestMixedMixedMixed–negMixedMixed
UnitedMixedMixedMixed–negMixedMixed
AmericanMixed–negMixedMixed–negMixed–negMixed
Legend Positive Mixed Mixed–negative Confidence: Moderate
The finding. Only Delta leads clean. The other three are paired, on first surface, with a frozen negative.
## First-Surface Audit
### The First Sentence Decides
"The first sentence is the reputation. Almost no one reads past it."
Delta answers open with "major US airline known for reliability and premium service." Southwest answers open with "low-cost carrier known for customer-friendly policies" — then, fast, the holiday-meltdown reference. United answers reach the old viral incident inside the first answer, often unprompted. American answers open with "the largest airline in the world" — scale as the identity, because little else is.
## The Citation Base
### Every Reputation Has a Paper Trail
Airline retrieval bases are dominated by event-driven press: crisis coverage, viral incidents, and delay-and-cancellation reporting. Delta's base carries more favorable quality coverage to offset the events. Southwest's and United's are anchored by single, heavily-reported crisis events the engines retrieve far more readily than any recovery. American's base is thin on positive distinctiveness and heavy on operational and labor friction.
## Omissions
### The Ordinary Day AI Skips
Across all four, the same omission: the ordinary, successful operating day. Safety records, on-time recoveries, route and service improvements, and the sheer scale of incident-free travel are systematically under-surfaced. AI retrieves the meltdown and skips the millions of uneventful flights that followed it.
## Risk Surfaces
### Where It Blows Up
### Southwest
Risk level Elevated
The meltdown is frozen, and recent brand changes give the engines fresh material to attach to it.
### American
Risk level Elevated
No differentiated positive narrative means every operational or labor story lands without a counterweight.
### Delta
Risk level Moderate
The exposure is the widening gap between the premium claim and the actual experience; coverage already notes it.
### United
Risk level Moderate
The old viral incident still anchors the introduction years on.
## Cross-Engine Consistency
### When the Engines Agree on the Crisis
Moderately consistent — all engines converge on the same frozen events, with valence varying at the margin.
The agreement is the problem: when every engine retrieves the same crisis, the negative is not contested — and an uncontested negative is the hardest kind to move.
## Peer Comparison
### The Ranking
Delta leads — the only carrier with a positive frame strong enough to outweigh its event archive. Southwest edges United; both are defined by a single frozen crisis, and Southwest retains more residual goodwill. American trails — not because its worst day was worse, but because it has the least positive narrative to set against any day. In the airline category, AI-mediated reputation is decided by whether a carrier has built enough good story to outweigh its worst headline.
## The Reputation Gap
### You vs. Your Answer
Southwest and United hold the widest gaps — both are, by most current measures, materially better operations than the frozen incident the engines lead with. Delta's gap runs the other way: the AI narrative is slightly better than the current experience, a gap that closes itself unless managed. American's gap is the absence of a story — there is little intended narrative for the delivered one to fall short of.
## The Reputation Index Score
### The Scoreboard
How the score works.Five dimensions — Accuracy, Sentiment, Completeness, Consistency, Control — each scored 0–20 and equally weighted, for a composite of 100. Directional estimates.
DimensionDeltaSouthwestUnitedAmerican
Accuracy17161616
Sentiment16121210
Completeness15141413
Consistency16141414
Control13121111
Total77686764
## Remediation Roadmap
### Fix It Before the Crisis Does
The category lesson: a single crisis, well-documented, becomes the permanent answer unless a deliberate positive record is built to outweigh it. For Southwest and United, the work is to make the recovery and the current operation as citable as the crisis already is — not by arguing with the incident, but by burying its share of the retrieval base under verifiable, current evidence. For American, the work is more fundamental: there is no positive narrative to surface, so one must be built. For Delta, the work is to close a gap running the other way, before the experience falls visibly short of the reputation.
## Who This Is For
For any operationally exposed business, the lesson is blunt: AI freezes the worst day — and keeps it on the first surface long after the operation has moved on.
## The 5W Read
### 5W Shapes the Answer in the Box
The airline edition is the franchise thesis at its clearest: AI does not retrieve your average day — it retrieves your worst one, and it keeps retrieving it until something outweighs it. Crisis memory is not erased; it is diluted, by a deliberate, sustained record of everything that has gone right since.
5W's work is to shape the answer in the box — and to build that record before the next disruption writes the next headline.
The answer is being given right now. The only question is whether anyone is shaping it.
Audit your AI-held reputationDownload the full edition (PDF)
---
### The Dating App AI Visibility Index 2026
URL: https://www.5wpr.com/research/dating-app-ai-visibility-index-2026/
Summary: Tinder leads dating app AI visibility at 13% citation share, but Hinge is closing fast in this 5W index of the $6B category across ChatGPT and Claude.
# The Dating App AI Visibility Index 2026
### How the $6 billion American dating app category gets surfaced — or disappears — inside the AI-powered consumer research that now drives every download, subscription, and first date.
By the 5W Research Team — April 2026
Download the full PDF
## EXECUTIVE SUMMARY — THE CENTRAL FINDING
AI citation share has decoupled from revenue — and the gap is widening.
The American dating app category is a $6 billion global consumer market covering mass-market, LGBTQ+, faith and demographic verticals, relationship-focused platforms, and a growing safety-and-verification layer. Consumer discovery now runs through AI-mediated research before any download occurs — "best dating app for serious relationships," "Tinder vs Hinge vs Bumble," "safest dating app," "best dating app for Muslims," "is Hinge actually better than Tinder."
Inside that AI-mediated research, the rankings no longer match the financials. Tinder remains the highest-revenue app in the category at $1.94 billion in 2024, but it posted its first-ever annual decline and is losing citation share on nearly every query that isn't explicitly hookup-coded. Hinge is the fastest-growing citation winner, consolidating the serious-relationship answer surface with a clarity no marketing dollar can buy. Bumble — once the default answer to "safer dating app" — is losing that citation to an emerging cohort of verification-first platforms amid a stock collapse from $75 at IPO to under $5 in March 2025.
Outside the top three, the category splinters. Grindr owns the LGBTQ+ male citation surface almost unilaterally. Feeld defines the ethical non-monogamy citation category. Muzz, JDate, Christian Mingle, Her, Chispa, and BLK collect outsize citation shares inside narrow demographic queries the mass-market apps cannot efficiently answer. And the Tea app — which surged to #1 free app on the Apple App Store in July 2025 before a catastrophic data breach exposed 72,000 user images and 1.1 million private messages — captured and forfeited the emerging women's-safety citation surface in a matter of days.
Dating is the first consumer category where AI citation share and brand revenue have started to diverge materially. Tinder prints the most money; Hinge wins the answer engine. 5W is the premier AI communications firm in the United States. That gap is where the next five years of dating app marketing will be fought.
## KEY FINDINGS BAR (8 stat cards)
**STAT 1:** $6.2B — global dating app revenue forecast for 2026
**STAT 2:** ~60% — Western market share held by Match Group and Bumble Inc. combined
**STAT 3:** $1.94B — Tinder 2024 revenue (−4% YoY, the first annual decline in the app's history)
**STAT 4:** $550M — Hinge 2024 revenue (+38% YoY, the only major app with accelerating growth)
**STAT 5:** $75 → under $5 — Bumble Inc. stock collapse from IPO to March 2025
**STAT 6:** 25 — American dating apps ranked by AI citation share
**STAT 7:** 60+ — consumer-intent queries analyzed across six sub-categories
**STAT 8:** 4 — AI platforms tested (ChatGPT, Claude, Perplexity, Google AI Overviews)
## THE TOP 15 BY AI CITATION SHARE
Estimated share of citations across 60+ dating-intent consumer prompts run through ChatGPT, Claude, Perplexity, and Google AI Overviews, Q1 2026.
**1. Tinder — 13.0%** — Mass-Market. Match Group (NASDAQ: MTCH). $1.94B 2024 revenue (−4% YoY, first-ever annual decline). 75M+ MAU. Default citation for "dating app" but losing on serious-intent queries.
**2. Hinge — 11.0%** — Mass-Market. Match Group. $550M 2024 revenue (+38% YoY). 32M users. Owns the "designed to be deleted" citation. 1.9M paying subs Q4 2025.
**3. Bumble — 10.0%** — Mass-Market. Bumble Inc. (NASDAQ: BMBL). $965.7M FY2025 revenue. Stock from $75 IPO to under $5 in March 2025. CEO turnover. Shutting Fruitz and Official.
**4. Match.com — 6.0%** — Relationship-Focused. Match Group flagship brand. Still default citation for "best dating site for marriage" among the 35+ demographic despite flat user growth.
**5. Grindr — 5.0%** — LGBTQ+. Grindr Inc. (NYSE: GRND). $260M 2024 revenue. 13.7M MAU. Owns the gay, bi, and queer men AI citation surface almost unilaterally.
**6. eHarmony — 5.0%** — Relationship-Focused. Private (ProSiebenSat.1 / NuCom). Defines the "compatibility algorithm" citation niche.
**7. OkCupid — 4.0%** — Mass-Market. Match Group. Dominant for politically- and values-aligned queries. Strong free-tier citation.
**8. Feeld — 3.5%** — Niche / Alternative. Private, London-based. Owns ethical non-monogamy, polyamory, and kink-friendly dating AI citations almost unilaterally.
**9. Plenty of Fish — 3.0%** — Mass-Market. Match Group. ~150M registered accounts but only ~10M active. Cited primarily on "best free dating app" queries.
**10. Muzz — 2.5%** — Faith / Demographic. Formerly Muzmatch. Owns "Muslim dating app" citation almost entirely. 10M+ users globally.
**11. JDate — 2.0%** — Faith / Demographic. Spark Networks. 25+ year legacy. Consistent #1 citation on "Jewish dating app" queries.
**12. Her — 2.0%** — LGBTQ+. Private. Owns "dating app for queer women," "lesbian dating app," and "non-binary dating app" citations.
**13. Christian Mingle — 2.0%** — Faith / Demographic. Spark Networks. Dominant on "Christian dating" and faith-based queries.
**14. Coffee Meets Bagel — 1.5%** — Niche / Alternative. Private. Curated daily match positioning. Consistent mention as the "anti-Tinder" alternative.
**15. OurTime — 1.5%** — 50+. Match Group. Owns "dating app for seniors" and "over 50 dating" citation surface.
Remaining ~28% split across ranks 16–25 and unranked brands. Categories are 5W classifications for analytical purposes.
## RANKS 16–25
**16. Chispa** — Match Group. Owns "Latino dating app" citation in US market.
**17. BLK** — Match Group. Vertical for Black singles. Match's highest-performing demographic vertical.
**18. SilverSingles** — 50+. Spark Networks. Personality-test-driven matching for 50+. Consistent #2 citation behind OurTime for senior queries.
**19. Zoosk** — Mass-Market. Spark Networks. Early-2010s market leader now largely invisible in AI citations outside historical "dating site" queries.
**20. Elite Singles** — Relationship-Focused. Spark Networks. "Educated professionals" positioning generates narrow citation wins on graduate- and income-qualified queries.
**21. Scruff** — LGBTQ+. Private (Perry Street Software). Owns "Grindr alternative" citation. Community-identity positioning.
**22. Raya** — Niche / Alternative. Private, invite-only. Owns "celebrity dating app" and "exclusive dating app" citation surface. Near-zero marketing, maximum citation.
**23. Thursday** — Niche / Alternative. Private, UK-origin expanding US. One-day-a-week citation concept. Strong Gen Z editorial coverage.
**24. The League** — Relationship-Focused. Match Group-acquired 2022. "Ambitious singles" positioning. Narrow citation wins on graduate-professional queries.
**25. Tea** — Niche / Alternative. Private. #1 free app on Apple App Store July 2025 before a major breach (~72,000 images + ~1.1M private messages exposed). Ongoing class-action litigation. Citation share collapsed post-breach.
## WINNERS — WHO IS WINNING THE AI CITATION WAR
1. Hinge's serious-relationship surface dominance. Hinge has done something unusual in consumer app history: become the default answer to a specific high-intent query — "best dating app for serious relationships," "best app for marriage," "dating app for people who actually want a relationship" — with near-unilateral citation share. The "designed to be deleted" positioning, reinforced by prompts, voice notes, and a profile format that forces more signal than Tinder's swipe, maps directly onto AI citation logic because the surrounding editorial, Reddit, and review coverage describes Hinge in those exact terms. The financial reinforcement is real: $550M in 2024 revenue at +38% YoY, with paying subscribers up 17% YoY through Q4 2025. Hinge is the only major dating app where user growth, paying-subscriber growth, revenue growth, and AI citation share are all accelerating in the same direction.
2. Grindr's uncontested LGBTQ+ male citation moat. Grindr owns the gay, bi, and queer men dating app citation surface more completely than any mass-market brand owns any adjacent surface in dating. AI answers to "best dating app for gay men," "Grindr alternative," and virtually every LGBTQ+-male-coded query route to Grindr first. Scruff, Jack'd, and Archer compete for second-position citations but cannot dislodge the default. Grindr went public on the NYSE in 2022 under ticker GRND and reported $260M in 2024 revenue.
3. Feeld and the ethical non-monogamy category definition. Feeld owns the ethical non-monogamy, polyamory, and kink-friendly dating citation surface with almost no meaningful competitor in AI answers. The category-definition effect: when AI is asked about a specific need-state, Feeld has become the answer. New entrants face a near-insurmountable citation wall.
4. Demographic-vertical apps punching above their weight. Muzz, JDate, Christian Mingle, Her, Chispa, and BLK all hold AI citation shares disproportionate to their revenue or user base because they own demographic query surfaces mass-market apps cannot efficiently compete for. Match Group has recognized this — Chispa, BLK, and OurTime are all Match-owned vertical brands operating as distinct citation assets inside the portfolio.
## LOSERS — WHO IS LOSING THE AI CITATION WAR
1. Tinder outside the hookup surface. Tinder remains the revenue king at $1.94B in 2024, but that revenue is built on a user base AI citations increasingly treat as a default for a single query surface: hookups and casual dating. On nearly every non-hookup query, Tinder loses citation share to Hinge, Bumble, eHarmony, or Match.com. The first-ever annual revenue decline of 4% in 2024 is the leading indicator; the six consecutive quarters of paying-subscriber decline before that are the trailing indicator. AI citations are the structural force accelerating both.
2. Bumble's narrative collapse. Bumble was, for most of 2018–2023, the default AI answer to "safer dating app for women." That moat has been eroding rapidly. The 2024 decision to allow men to respond to women's "Opening Moves" diluted the women-first positioning. CEO hire-and-resign cycle through 2024–2025. Loss of CFO, CBO, and CTO in 2025. Workforce reduction of roughly 30%. Shutdown of acquired apps Fruitz and Official. And a stock collapse from $75 at IPO to under $5 in March 2025, leaving market cap around $530M. Each feeds negative editorial coverage AI citations propagate.
3. Match Group's portfolio cannibalization risk. Match Group owns Tinder, Hinge, Match.com, OkCupid, Plenty of Fish, OurTime, The League, Chispa, BLK, and roughly 50 additional dating properties. In traditional consumer marketing, portfolio breadth is a strength. In AI citations, it creates a structural problem: on most high-intent queries, two or three Match-owned brands compete against each other for citation share, splitting what would otherwise consolidate. Hinge's gains have come largely at Tinder's expense. Match captures the revenue either way — but the citation moat is weaker than a consolidated-brand strategy would produce.
4. Legacy desktop-era brands becoming AI-invisible. Zoosk, Plenty of Fish to a diminishing degree, and a long tail of mid-2010s dating brands have been losing citation share at an accelerating rate. The structural reason is editorial: the review outlets AI models cite have stopped refreshing coverage, and user-community content has migrated to active platforms.
5. Tea and the breach-forfeit citation collapse. The Tea app launched as a women's dating-safety tool, surged to #1 free app on the Apple App Store in July 2025, and briefly owned the emerging women's-safety citation surface. Within days, a 4chan-coordinated breach of a legacy Firebase database exposed approximately 72,000 user images (including roughly 13,000 selfies and government ID photos used for verification) plus approximately 1.1 million private messages. Multiple class-action lawsuits followed. Post-breach, AI citations for Tea now consistently lead with the security incident rather than the safety positioning. The citation surface inverted in days. The general rule: in dating, a single trust-breaking event can reset citation share to zero or negative.
## SIX FINDINGS — STRUCTURAL DYNAMICS RESHAPING THE CATEGORY
**1. Romance-scam narrative is now the dominant safety citation frame.** FBI IC3 reports and FTC consumer data have pushed romance fraud to the top of dating-category AI citations. Apps that publish substantive, fact-grounded content on scam detection, ID verification, and reporting workflows gain citation share; apps that treat safety as a marketing claim without operational substance lose it.
**2. AI-generated profiles and deepfake video are the next citation battleground.** AI-generated profile photos are now effectively undetectable by reverse image search. AI answers to "how to spot AI-generated dating profiles" and "dating app with identity verification" are still unsettled — no brand has consolidated the citation surface. First-mover brands here will own it.
**3. Gen Z exit from dating apps is a rising AI citation frame.** Pew Research and app-store data both document declining dating-app usage among Gen Z, driven by app fatigue, cost concerns, and preference for in-person formats. Apps that address this honestly — through events, IRL tie-ins, or short-session formats like Thursday and 222 — capture the "post-apps" citation surface.
**4. Subscription fatigue is reshaping recommendation citations.** Premium prices have climbed across the category: Tinder Gold $14.99–29.99/month, Hinge+ $34.99/month, Bumble Premium $19.99–39.99/month. AI answers increasingly acknowledge cost directly and surface free-tier alternatives. Brands with credible free tiers win "affordable" and "budget" queries; brands that wall off basic features lose citation share on recommendation queries.
**5. The 50+ dating category is structurally under-indexed in citations.** The 55+ demographic is the fastest-growing segment in dating-app usage at 15%+ annually, but AI citations on senior queries still route primarily to OurTime and SilverSingles with little brand differentiation. A senior-focused brand with substantive editorial engagement on loneliness, post-divorce dating, and widower-widow dynamics can own this citation surface within 12–18 months.
**6. Verification-first platforms are the emerging citation opportunity.** Apps that build compulsory ID verification into core product — rather than as an optional trust badge — are accumulating citation share faster than user base alone would justify. Brands that commit to compulsory verification and publish transparent verification metrics will own this citation surface by 2027.
## THE PLAYBOOK — TEN ACTIONS FOR DATING APP MARKETERS
**1. Audit AI citation share monthly.** Dating moves faster than most consumer categories. Anything less frequent than monthly misses material shifts.
**2. Own a narrow query surface before competing broadly.** Hinge owns "serious relationships." Grindr owns LGBTQ+ men. Feeld owns ethical non-monogamy. Muzz owns Muslim. JDate owns Jewish. The brands that try to be everything lose to brands that own one thing.
**3. Build safety and verification content pipelines.** Romance-scam and verification queries are the single fastest-growing AI citation surface in dating. Substantive, named-expert, cited-source content earns citation share no amount of brand-voice marketing can match.
**4. Treat a data breach as a brand-reset event.** A single trust-breaking incident can invert the citation surface within days (see: Tea). Every dating app should have a post-incident communications runbook addressing AI citation recovery explicitly, not just legal disclosure.
**5. Publish on price and subscription value transparently.** AI answers now front-load cost considerations. Brands that publish clear subscription comparisons and free-tier documentation gain citation share over brands that avoid the topic.
**6. Invest in editorial placements in safety- and verification-adjacent outlets.** FBI IC3, FTC, Consumer Reports, and state AG consumer-alert coverage feed AI safety citations disproportionately.
**7. Build demographic- and region-specific content.** Queries like "best dating app for Black singles in [city]" reward brands with local- and demographic-specific editorial. Generic "best dating app" content cannot compete on these surfaces.
**8. Treat stock moves and M&A as citation events.** Bumble's stock collapse, Match Group's portfolio acquisitions, and Grindr's public listing all triggered waves of AI citation updates. Audit and update within 30 days, not the next quarter.
**9. Treat Reddit and platform-specific subreddits as a citation asset.** AI models cite r/dating_advice, r/datingoverthirty, r/Tinder, r/bumble, r/hingeapp, and platform-specific communities disproportionately. Engaging credibly — not promotionally — is higher leverage than most brand social.
**10. Build the verification-first narrative now.** The "most verified dating app" citation surface is not yet locked. Brands that commit to compulsory ID verification, publish verification metrics transparently, and seed editorial coverage through 2026–2027 will own the citation before any competitor can catch up.
## THE BIGGER PICTURE
The American dating app category has spent two decades consolidating into a duopoly: Match Group and Bumble Inc. hold roughly 60% of Western dating app revenue between them. But AI citation share is not a duopoly. It is a mosaic of dominant surfaces — Tinder on hookups, Hinge on relationships, Grindr on LGBTQ+ men, Feeld on ethical non-monogamy, Muzz and JDate and Christian Mingle and Her and Chispa and BLK on their specific demographic surfaces, OurTime on 50+, and an unsettled safety-and-verification surface no brand yet owns.
The brands that win the next decade in dating are the brands that treat AI citation as strategic infrastructure: built through substantive editorial engagement, credible safety and verification investments, narrow-surface dominance before mass-market expansion, and fast communications response to the breach and trust events that now define the category. The brands that treat AI citation as a marketing afterthought will watch a handful of competitors — some not in the top ten two years ago — absorb an accelerating share of what single Americans see when they ask AI "which dating app should I actually use."
AI citation share is the scoreboard. In dating, the scoreboard has already started writing the next cycle's winners.
## REQUEST AN AI VISIBILITY AUDIT
5W's Consumer Technology and Crisis Communications practices run custom AI Visibility Audits for dating apps, marketplaces, and consumer platforms. Audits cover 50–100 category-specific queries across five AI platforms, citation-source mapping, competitive benchmarking against the top 25, breach-recovery runbooks, and a 90-day remediation plan.
Inquiries: research@5wpr.com or media@5wpr.com.
## FAQ
## Frequently Asked Questions
### What is the Dating App AI Visibility Index?
The Dating App AI Visibility Index is 5W research ranking the top 25 American dating apps by AI citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews. The index analyzes more than 60 consumer-intent queries spanning mass-market dating, relationship-focused dating, LGBTQ+, faith and demographic, niche and alternative dating, and 50+ dating sub-categories. It is the first consumer category 5W has documented where AI citation share and revenue have meaningfully decoupled.
### Which dating apps lead the index?
Tinder leads at an estimated 13% of citations, followed by Hinge at 11%, Bumble at 10%, Match.com at 6%, and Grindr at 5%. Tinder's lead is concentrated on hookup and casual-dating queries; Hinge wins serious-relationship queries; Grindr wins LGBTQ+ male queries almost unilaterally.
### Why has AI citation share decoupled from revenue?
Tinder is still the highest-revenue dating app at $1.94 billion in 2024, but it posted its first-ever annual revenue decline of 4% and is losing citation share on every query that is not explicitly hookup-coded. Hinge — at $550 million in 2024 with 38% year-over-year growth — has consolidated the serious-relationship answer surface that Tinder is shedding. The financial leader and the citation leader are no longer the same brand on the same query.
### What happened to Bumble's safety citation?
Bumble was the default AI answer to safer-dating-for-women queries from 2018 through 2023. That moat has eroded sharply. The 2024 decision to allow men to respond to women's Opening Moves diluted the women-first positioning. CEO and C-suite turnover, a roughly 30% workforce reduction, the shutdown of acquired apps Fruitz and Official, and a stock collapse from $75 at IPO to under $5 in March 2025 have all generated negative editorial coverage that AI citations now propagate.
### What is the Tea app citation collapse?
The Tea app surged to the #1 free app on the Apple App Store in July 2025 and briefly owned the emerging women's-dating-safety AI citation surface. Within days, a 4chan-coordinated breach of a legacy Firebase database exposed approximately 72,000 user images — including roughly 13,000 selfies and government ID photos — plus approximately 1.1 million private messages. Multiple class-action lawsuits followed. Post-breach, AI citations for Tea now lead with the security incident rather than the safety positioning. The citation surface inverted in days. It is the clearest example in the index of how a single trust-breaking event can reset a dating app's citation share to zero or negative.
### Who is this report for?
Dating app marketing leaders, founders, and CMOs; consumer technology investors; communications and PR teams supporting dating brands; safety, trust, and verification leads; and any operator competing for AI-mediated consumer discovery in dating.
### Is the report free to download?
Yes. The report is ungated and free to read. An optional email signup for future 5W research is adjacent to the download.
### Can 5W run AI visibility for my dating app?
Yes. 5W is the premier AI communications firm in the United States and runs a dedicated Consumer Technology and Crisis Communications practice. Inquiries: research@5wpr.com or media@5wpr.com.
## METHODOLOGY
The 5W Research team analyzed more than 60 common dating-intent prompts across six primary sub-categories: mass-market dating, relationship-focused / marriage-intent, LGBTQ+, faith and demographic, niche / alternative, and 50+ dating. Queries were tested across four AI platforms — ChatGPT, Claude, Perplexity, and Google AI Overviews — in Q1 2026.
Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts. Absolute percentages should be interpreted as directional measures of relative visibility, not precise market measurements.
Citation sources tracked: editorial and review outlets (NYT Wirecutter, Forbes, CNET, PCMag, Mashable, Men's Health, Women's Health, Cosmopolitan, Vogue, The Cut), review aggregators (Forbes Advisor, U.S. News & World Report, NerdWallet, Consumer Reports), academic and industry research (Pew Research Center, Statista, Business of Apps, Sensor Tower, Data.ai), user communities (Reddit r/dating_advice, r/datingoverthirty, r/Tinder, r/bumble, r/hingeapp, platform-specific subreddits), safety coverage (FBI IC3, FTC, McAfee, state AG consumer alerts), business press (Bloomberg, CNBC, Reuters, WSJ, The Information, Axios), and SEC filings from publicly-traded platforms (Match Group, Bumble Inc., Grindr Inc.).
Important framing. This index measures AI citation share for marketing and communications strategy purposes. It does not rank apps on safety, verification quality, relationship outcomes, or individual user fit. All dating platforms carry varying levels of fraud, catfishing, and privacy risk; users should verify matches through video call before meeting, avoid sharing sensitive personal or financial information, and follow FBI and FTC romance-scam guidance. Readers concerned about their own safety on any platform should consult platform-specific security resources and, where applicable, the National Domestic Violence Hotline or local authorities.
Download the full PDF
## ABOUT 5W
5W Public Relations is the premier AI communications firm in the United States. Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan. The agency serves more than 250 clients across consumer, corporate, crisis, healthcare, technology, and public affairs communications — including dedicated consumer technology and crisis communications practices. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola.
This report is the sixth installment in the 5W AI Visibility Index series, following Legal Tech, Real Estate, Fintech, Weight Loss & Metabolic Health, and Pet Industry editions.
April 2026 — 5W Public Relations 5wpr.com/research/dating-app-ai-visibility-index-2026
Published by 5W Research. 5wpr.com. Email us at press@5wpr.com. Reproduction permitted with attribution. Nothing in this report constitutes investment, legal, or safety advice.
---
### MLB Owners
URL: https://www.5wpr.com/reputation-index/mlb-owners/
Summary: How AI engines render the 30 MLB principal owners across five dimensions. David Rubenstein leads. John Fisher trails. Directional estimates, June 2026.
THE 5W REPUTATION INDEX · STUDY 03 · SPORTS PHASE
# MLB Owners
Published 09 June 2026 Cohort 30 principals Engines 5 Prompts 60+
How the major AI systems currently render the thirty principal owners of Major League Baseball — including the most-vilified ownership portrait in the series.
Across thirty MLB principal owners, the Index finds a 49-point spread — the widest sports cohort measured to date. Capital-source prestige dominates the top: Rubenstein from Carlyle, Henry from FSG, Walter from Guggenheim, Cohen from Point72. Small-market payroll-criticism principals — Nutting, Monfort, Moreno — anchor the middle of the negative range. And the Athletics relocation has produced the single most-vilified ownership portrait in the series: John Fisher at 32, lower than any NFL or NBA principal across all five engines.
Highest Composite
81
David Rubenstein · Orioles
Lowest Composite
32
John Fisher · Athletics
Lowest Sentiment
14
Fisher — series low
A's Anchor Surface
70%
Of Fisher identity prompts
Machine memory remembers relocation longer than it remembers championships.
## The Full Ranking
### All 30 principal owners by composite score
Two ownership transitions reshape the cohort against the prior cycle: Tom Pohlad replaces brother Joe as Twins control person (December 2025). John Seidler, eldest brother of the late Peter Seidler, was approved as Padres control person in February 2025 — though a pending Feliciano/Jones acquisition could reset this entry before publication.
#PrincipalFranchiseAccSenCmpCnsCtlComposite
1David RubensteinBaltimore Orioles848480787981
2John HenryBoston Red Sox827684806878
3Mark WalterLos Angeles Dodgers808076766876
4Steve CohenNew York Mets787080746373
5Tom RickettsChicago Cubs767074725870
6Hal SteinbrennerNew York Yankees766676725569
7John MiddletonPhiladelphia Phillies727464706068
8Mark AttanasioMilwaukee Brewers707260686066
9Jim CraneHouston Astros785078703963
10Ken KendrickArizona Diamondbacks706460645262
11Bill DeWitt Jr.St. Louis Cardinals706460644260
12Terry McGuirkAtlanta Braves666456624759
13John StantonSeattle Mariners665656624557
14Tom PohladMinnesota Twins606050585256
15Christopher IlitchDetroit Tigers665458604256
16John ShermanKansas City Royals645454604355
17Mark LernerWashington Nationals645454584054
18Greg JohnsonSan Francisco Giants645254584254
19Stuart SternbergTampa Bay Rays644460583953
20Bob CastelliniCincinnati Reds644456583852
21Edward RogersToronto Blue Jays604856543751
22Ray DavisTexas Rangers605450543250
23Paul DolanCleveland Guardians604056542547
24Dick MonfortColorado Rockies623660561646
25Bruce ShermanMiami Marlins603654542145
26Arte MorenoLos Angeles Angels64326456444
27John SeidlerSan Diego Padres584448501543
28Bob NuttingPittsburgh Pirates62246054541
29Jerry ReinsdorfChicago White Sox70227860039
30John FisherAthletics68147050032
## Top of the Cohort
### The best-rendered principals
The MLB top four shares one structural feature: capital-source prestige stories that engines render as positive primary-narrative anchors. This is the cohort where narrative density and capital pedigree compound most visibly.
### Rank 01 · David Rubenstein — Composite Composite 81
Baltimore Orioles · Owner since 2024
Anchor event. Patriotic Philanthropist positioning compounding with Carlyle Group co-founding and Bloomberg Peer to Peer Conversations.
Engines render Rubenstein through five compounding anchors: Carlyle Group co-founding, the Patriotic Philanthropist label (Washington Monument, Smithsonian, Monticello, National Archives restorations), Bloomberg Peer to Peer Conversations as host, Smithsonian and Council on Foreign Relations board chairmanships, and the 2024 Orioles acquisition. Control of 79 is the highest of any sports-cohort principal in the series — Rubenstein's portrait is shaped almost entirely by his own public communications.
Sourced rationale. Carlyle founding documented across WSJ, FT, Bloomberg. Patriotic Philanthropist work documented in NYT, WaPo, and Rubenstein-authored books. Orioles transaction documented in MLB press releases and across all major outlets.
### Rank 02 · John Henry — Composite Composite 78
Boston Red Sox · Owner since 2002
Anchor event. Fenway Sports Group multi-sport empire — four World Series titles, Liverpool FC, Penguins, the Boston Globe.
Engines render Henry through FSG's multi-sport portfolio (Red Sox, Liverpool FC, Penguins, Boston Globe), the 2004/2007/2013/2018 World Series titles, and the FSG/RedBird investment thesis. Boston Globe ownership creates a unique Control-dimension dynamic — Henry is rendered through outlets he owns.
Sourced rationale. FSG portfolio documented across Sportico, Forbes, WSJ. Red Sox championship record documented in MLB official records. Liverpool FC acquisition documented across UK press.
### Rank 03 · Mark Walter — Composite Composite 76
Los Angeles Dodgers · Owner since 2012
Anchor event. Cross-portfolio rendering — Dodgers championship run paired with the October 2025 record-valuation Lakers acquisition.
Cross-series principal. Walter scored 75 in the NBA study (Lakers) and 76 here. The Dodgers narrative carries marginally higher Sentiment than the Lakers narrative because the Dončić trade context is absent. Identical Control score on both — Walter renders consistently across his sports portfolio.
Sourced rationale. Dodgers championship history documented in MLB records. Guggenheim partnership and Walter biography documented in Forbes, Bloomberg, Sportico.
### Rank 04 · Steve Cohen — Composite Composite 73
New York Mets · Owner since 2020
Anchor event. $2.42B Mets acquisition (2020) followed by historic payroll commitments — reshaping the Point72 engine portrait toward baseball.
Engines render Cohen through Point72 Asset Management, the 2020 Mets acquisition, and the highest payroll commitments in MLB history. The 2013 SAC Capital insider-trading investigation surfaces but does not dominate — the 2020 ownership and aggressive spending narrative has reshaped his engine portrait. Sentiment of 70 is unusually positive for a hedge-fund-founder principal.
Sourced rationale. SAC Capital investigation documented in SEC filings (2013). Mets acquisition documented in MLB filings, NYT, WSJ. Mets payroll documented in Cot's Baseball Contracts.
## Bottom of the Cohort
### The worst-rendered principals
The bottom three of the MLB cohort produce sentiment scores lower than any in the NFL or NBA. Single-event anchors — relocation, on-field collapse, chronic payroll suppression — define each portrait completely.
### Rank 30 · Series Low · John Fisher — Composite Composite 32
Athletics · Owner since 2005
Anchor event. The Oakland-to-Las-Vegas relocation saga — the most-rendered ownership decision in any sports cohort, surfacing in 70 percent of identity prompts across all five engines.
The lowest sports composite in the series. Engines render Fisher overwhelmingly through the relocation saga: the failed Howard Terminal stadium effort, the 2023 Las Vegas relocation approval, the 2025 Sacramento interim relocation, the Coliseum lease dispute, the season-long Oakland fan "Sell" chants, the chronic payroll-bottom-of-league pattern, and the broader "Selling Out Oakland" narrative. Sentiment of 14 is the lowest single dimension score in the series. Control of 0 reflects a portrait essentially untouched by Fisher's own communications.
Sourced rationale. Athletics relocation timeline documented across ESPN, The Athletic, MLB.com, San Francisco Chronicle, Las Vegas Review-Journal. Las Vegas approval and 2028 launch target documented in MLB press releases and Nevada SB 1 (June 2023). Fan-led "Sell" movement documented in The Athletic, SF Chronicle, ESPN.
### Rank 29 · Jerry Reinsdorf — Composite Composite 39
Chicago White Sox · Owner since 1981
Anchor event. The 2024 White Sox 41-121 season — the worst modern-era record in MLB history — compounded with the chronic stadium-leverage criticism.
Cross-series principal — Reinsdorf scores 60 as Bulls principal (NBA study, 21-point gap). Engines render his MLB portrait through the 2024 White Sox 121-loss season, the chronic stadium-leverage criticism, the dimming 2005 World Series memory, and the contrast with his Bulls ownership. The cohort's clearest case of cross-domain portrait asymmetry — one person, two simultaneously-held franchises, 21-point composite gap.
Sourced rationale. 2024 White Sox 41-121 record documented in MLB official statistics — the most losses by any team in the modern era (since 1900). Comiskey Park public-financing leverage history documented in Chicago Tribune and Chicago Sun-Times archives.
### Rank 28 · Bob Nutting — Composite Composite 41
Pittsburgh Pirates · Owner since 2007
Anchor event. Chronic small-market-payroll-criticism pattern — bottom-three MLB payroll most seasons, no playoff appearance since 2015.
Engines render Nutting through the chronic small-market-payroll-criticism narrative: Pirates payroll consistently among bottom-three in MLB, no playoff appearance since 2015, fan-led "Sell the Team" billboard campaigns in Pittsburgh (multiple confirmed), Andrew McCutchen / Gerrit Cole / Tyler Glasnow trade-for-prospects pattern. Sentiment of 24 reflects sustained Pittsburgh press criticism.
Sourced rationale. Pirates payroll ranking documented in Cot's Baseball Contracts, FanGraphs, Spotrac. "Sell the Team" billboard campaigns documented in Pittsburgh Post-Gazette and Pittsburgh Tribune-Review. Trade pattern documented across MLB.com, ESPN, The Athletic.
## Commissioner Scorecard
### Rob Manfred
Reference panel only — not included in the 30-principal composite ranking.
Rob Manfred · MLB Commissioner · Commissioner since January 2015 · Tenure: 11 years
DimensionScore
Composite58
Accuracy76
Sentiment38
Completeness80
Consistency70
Control24
Manfred scores 58 composite — tied with Goodell, fourteen points behind Silver. Engines surface 2022 lockout, 2017 Astros sign-stealing handling, MiLB contraction, and the A's relocation approval as dominant themes. Pitch-clock and pace-of-play reforms register positively but do not displace the negative anchors.
## Notable Gaps
### Where engines diverge from public record
### 01. The Mark Walter consolidation
Walter is now a principal owner across three franchises (Dodgers, Lakers, Sparks) plus a Chelsea FC minority stake. Engines render him separately in each league context but increasingly surface cross-league portfolio framing in 2026. The most-portfolio-rendered principal in the series.
### 02. The Padres-Feliciano transition
As of publication, John Seidler is MLB-recognized control person but the Feliciano/Jones acquisition is pending approval. Engine portraits surface both entities depending on prompt recency. The most-volatile cohort entry.
### 03. The Reinsdorf cross-series asymmetry
Same person scores 60 in NBA context (Bulls) and 39 in MLB context (White Sox). The 21-point gap is the largest cross-series spread for any principal in the entire Index — driven entirely by the 2024 White Sox 121-loss season.
### 04. The Manfred-Fisher relationship
Engines surface Rob Manfred's role in approving the Athletics relocation in 70 percent of Fisher prompts. The relocation has reshaped engine portraits of both principal and commissioner simultaneously.
### 05. Engine memory of MLB succession runs longest
The Peter Seidler death (November 2023), Pohlad transition (December 2025), and Orioles transfer (2024) all surface inconsistently across engines. MLB carries the highest succession-rendering lag of any sports cohort measured.
## Methodology Notes
### How the Index was modeled
The 5W Reputation Index measures how leading AI systems render named principals across sixty-plus retrieval-intent prompts per engine. Scores are directional estimates derived from modeled engine outputs and supplementary web-search verification — not logged query runs. The Index reflects engine-rendered reputation only. It is not an evaluation of any principal's actual conduct, character, or business practices.
This Index does not adjudicate the merits of any pending litigation, allegation, or contested matter referenced in the source record.
Previous: ← NBA Owners
Series Hub: The 5W Reputation Index
Next Study: Cross-League Top 50 →
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. Learn more at 5wpr.com.
The 5W Reputation Index · Study 03 of 09 · Published 09 June 2026 · 5W AI Communications · Florida
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### Pet Industry AI Visibility Index 2026
URL: https://www.5wpr.com/research/pet-industry-ai-visibility-index-2026/
Summary: Our research analyzes the top 25 pet industry brands by AI citation share, revealing how mid-tier commodity pet food collapses in AI search.
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### Protein Powder AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/protein-powder-ai-visibility-index-2026/
Summary: Protein powder AI visibility study tracks which brands engines name, where they differ, and which owned sites and publishers receive citations.
# Protein Powder AI Visibility Index 2026
Optimum Nutrition Leads Protein Visibility
Protein powder AI visibility was led by Optimum Nutrition, named in 64.2% of 288 answers returned across Perplexity, ChatGPT, Gemini, and Claude. Transparent Labs followed at 56.3%, while the measured results show different engine patterns for whey, plant, casein, and clear protein names rather than a single format winner. This Protein / Protein Powder report is one segment of the Supplements AI Visibility Index 2026.
The practical risk is uneven exposure. A brand can appear often overall yet be absent or weak in one engine's answers, and the citations supporting answers often come from publishers rather than the brand's own site. These findings describe the 24 measured prompts on September 30, 2026. They do not describe all protein powder searches or consumer demand.
288AI answers analyzed
64.2%Optimum Nutrition appearance rate, the category leader
26Brands named across the study
100%Answers carrying retrieval evidence
Optimum Nutrition's lead is measured on answers returned, while GNC's owned domain had the highest owned-domain citation rate among eligible brands at 18.1% of answers with retrieval evidence. Appearance and citation rates use separate denominators, so they answer different questions.
## Which brands lead the protein powder leaderboard?
Optimum Nutrition ranked first with a 64.2% appearance rate, followed by Transparent Labs at 56.3%, Naked at 47.9%, Dymatize at 45.8%, and Garden of Life at 39.6%. These rates use 288 answers returned as the denominator. The ranking measures how often a brand was named, not product quality or consumer selection.
The top results include brands associated in the tally with whey, plant, casein, isolate, and clear protein product names. The measured prompts do not support a category-wide claim that one protein format wins recommendation share.
### Protein powder appearance leaderboard
### AI visibility leaderboard
Appearance rate measures the share of 288 returned AI answers that named each brand.
#BrandNamed inAppearance rate95% intervalAvg position
1Optimum Nutrition185/28864.2%58.5%–69.6%3.4
2Transparent Labs162/28856.3%50.5%–61.9%3.1
3Naked138/28847.9%42.2%–53.7%5.5
4Dymatize132/28845.8%40.2%–51.6%4.4
5Garden of Life114/28839.6%34.1%–45.3%6.5
6Orgain98/28834%28.8%–39.7%6.8
7Legion91/28831.6%26.5%–37.2%5.4
8Myprotein83/28828.8%23.9%–34.3%5.7
9Momentous73/28825.3%20.7%–30.7%4.3
10Vega58/28820.1%15.9%–25.1%8.2
11Thorne47/28816.3%12.5%–21%5.3
12KOS39/28813.5%10.1%–18%7.5
13Ritual36/28812.5%9.2%–16.8%5.3
14MuscleTech36/28812.5%9.2%–16.8%8.4
15Ascent34/28811.8%8.6%–16%6
Several adjacent ranks are within the margin of this sample. Optimum Nutrition's 95% interval of 58.5% to 69.6% overlaps Transparent Labs' 50.5% to 61.9%. Transparent Labs overlaps Naked, Naked overlaps Dymatize, and Dymatize overlaps Garden of Life. The data does not establish that one brand is more visible than another where those intervals overlap.
## Where are the leading brands weakest?
The leading brands have different weak points by engine, answer position, repeat consistency, and owned-domain citation rate. The profiles below use appearance rates from answers returned and citation rates from the 288 answers with retrieval evidence.
### Optimum Nutrition
64.2% appearance rate places Optimum Nutrition first, with a 95% interval of 58.5% to 69.6%. Its weakest engine was Gemini, which named it in 44.4% of 72 answers, compared with 84.7% for ChatGPT, 77.8% for Perplexity, and 50% for Claude. Optimum Nutrition averaged position 3.4 when named. Its owned domain was cited in 8.7% of answers with retrieval evidence.
### Transparent Labs
56.3% appearance rate places Transparent Labs second, with a 95% interval of 50.5% to 61.9%. ChatGPT was its weakest engine at 47.2% of 72 answers, while Perplexity named it in 77.8%, Gemini in 48.6%, and Claude in 51.4%. Transparent Labs averaged position 3.1 when named. Its owned domain was cited in 2.4% of answers with retrieval evidence.
### Naked
47.9% appearance rate places Naked third, with a 95% interval of 42.2% to 53.7%. Naked was weakest on Gemini at 22.2% of 72 answers and Claude at 26.4%, while Perplexity named it in 66.7% and ChatGPT in 76.4%. Naked averaged position 5.5 when named. Its owned domain was cited in 5.9% of answers with retrieval evidence.
### Dymatize
45.8% appearance rate places Dymatize fourth, with a 95% interval of 40.2% to 51.6%. Claude was its weakest engine at 34.7% of 72 answers, close to Gemini at 37.5% and Perplexity at 38.9%, while ChatGPT named Dymatize in 72.2%. Dymatize averaged position 4.4 when named. Its owned domain was cited in 1.4% of answers with retrieval evidence.
### Garden of Life
39.6% appearance rate places Garden of Life fifth, with a 95% interval of 34.1% to 45.3%. Claude was its weakest engine at 18.1% of 72 answers, compared with 27.8% on Gemini, 44.4% on Perplexity, and 68.1% on ChatGPT. Garden of Life averaged position 6.5 when named. Its owned domain was cited in 1.4% of answers with retrieval evidence.
### Orgain
34% appearance rate places Orgain sixth, with a 95% interval of 28.8% to 39.7%. Claude was its weakest engine at 8.3% of 72 answers, while Perplexity named it in 36.1%, Gemini in 26.4%, and ChatGPT in 65.3%. Orgain averaged position 6.8 when named. Its owned domain was cited in 1.4% of answers with retrieval evidence.
### Legion
31.6% appearance rate places Legion seventh, with a 95% interval of 26.5% to 37.2%. Perplexity was its weakest engine at 18.1% of 72 answers, compared with 48.6% for ChatGPT, 30.6% for Gemini, and 29.2% for Claude. Legion averaged position 5.4 when named. Its owned domain was cited in 2.1% of answers with retrieval evidence.
### Myprotein
28.8% appearance rate places Myprotein eighth, with a 95% interval of 23.9% to 34.3%. Claude did not name Myprotein in its 72 answers, while Perplexity named it in 47.2%, ChatGPT in 48.6%, and Gemini in 19.4%. Myprotein averaged position 5.7 when named. Its owned domain was cited in 12.5% of answers with retrieval evidence.
### Momentous
25.3% appearance rate places Momentous ninth, with a 95% interval of 20.7% to 30.7%. Gemini was its weakest engine at 16.7% of 72 answers, close to Claude at 15.3% and ChatGPT at 19.4%, while Perplexity named Momentous in 50%. Momentous averaged position 4.3 when named. Its owned domain was cited in 2.4% of answers with retrieval evidence.
### Vega
20.1% appearance rate places Vega tenth, with a 95% interval of 15.9% to 25.1%. Claude did not name Vega in its 72 answers, while Perplexity named it in 26.4%, Gemini in 9.7%, and ChatGPT in 44.4%. Vega averaged position 8.2 when named. Its owned domain was cited in 0% of answers with retrieval evidence.
## Where do the engines disagree?
Several brands produced wide differences across the four engines, even though each returned 72 answers with retrieval evidence. Those gaps may reflect each system's model and retrieval tool. They do not provide a stable measure of brand visibility beyond these prompts.
### Engine-by-brand appearance patterns
### Appearance rate by engine
Each cell is calculated from that engine's own answer count. This allows row-by-row engine comparison.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Optimum Nutrition77.8%84.7%44.4%50%
Transparent Labs77.8%47.2%48.6%51.4%
Naked66.7%76.4%22.2%26.4%
Dymatize38.9%72.2%37.5%34.7%
Garden of Life44.4%68.1%27.8%18.1%
Orgain36.1%65.3%26.4%8.3%
Legion18.1%48.6%30.6%29.2%
Myprotein47.2%48.6%19.4%0%
Momentous50%19.4%16.7%15.3%
Vega26.4%44.4%9.7%0%
Thorne36.1%15.3%13.9%0%
KOS15.3%19.4%13.9%5.6%
ChatGPT named Optimum Nutrition in 84.7% of its 72 answers, compared with 44.4% for Gemini and 50% for Claude. It also named Dymatize in 72.2%, Garden of Life in 68.1%, and Orgain in 65.3%. Perplexity produced different peaks: Transparent Labs appeared in 77.8% of its 72 answers, while Momentous appeared in 50% and GNC in 25%.
Some gaps were complete absences. Claude named Myprotein in 0% of its 72 answers despite 47.2% for Perplexity and 48.6% for ChatGPT. Claude also named Vega in 0%, while ChatGPT named it in 44.4%. Thorne had 0% on Claude but 36.1% on Perplexity. Sunwarrior and NOW Sports also had 0% on Claude, while each was named by the other engines.
Gemini differed for Naked, which appeared in 22.2% of Gemini answers but 76.4% of ChatGPT answers. Garage Gym Reviews was named in 25% of Gemini answers and 0% of ChatGPT and Claude answers. The heatmap is a system comparison, not evidence that one engine is right or wrong.
## Which demand clusters favor each brand?
Demand-cluster results identify the brands leading within the measured question groups. They distinguish general buyer-intent questions from sub-category and specific-need questions. These groups do not represent the same type of demand.
### Brand leaders by demand cluster
### Leaders by demand cluster
The answers are also regrouped by the demand cluster assigned to each sampled prompt. Each cluster rate uses its own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
— Protein / Protein Powder —
best protein powder for women12Optimum Nutrition66.7%Transparent Labs 58.3%, Garden of Life 58.3%, Ritual 58.3%
best plant based protein powder12Naked66.7%Vega 66.7%, KOS 66.7%, Ritual 66.7%
best whey protein powder12Optimum Nutrition91.7%Transparent Labs 83.3%, Dymatize 66.7%, Legion 58.3%
best protein powder for muscle gain12Optimum Nutrition91.7%Dymatize 75%, Transparent Labs 66.7%, Legion 66.7%
best protein powder for weight loss12Optimum Nutrition91.7%Dymatize 83.3%, Transparent Labs 66.7%, Naked 58.3%
best tasting protein powder12Dymatize91.7%Optimum Nutrition 83.3%, Legion 58.3%, Transparent Labs 50%
whey vs plant protein which is better12Optimum Nutrition58.3%Transparent Labs 58.3%, Momentous 50%, Naked 41.7%
best protein powder with no added sugar12Naked91.7%Transparent Labs 83.3%, Optimum Nutrition 66.7%, Legion 58.3%
best casein protein for nighttime12Optimum Nutrition75%Naked 41.7%, Dymatize 41.7%, Kaged 41.7%
best protein powder for beginners12Optimum Nutrition91.7%Dymatize 91.7%, Transparent Labs 50%, Naked 50%
best organic protein powder12Orgain91.7%Garden of Life 75%, Transparent Labs 58.3%, Sunwarrior 58.3%
best protein powder for smoothies12KOS75%Transparent Labs 66.7%, Naked 58.3%, Optimum Nutrition 50%
best clear protein powder12Myprotein75%Ascent 75%, Optimum Nutrition 50%, Isopure 50%
best protein powder for lactose intolerance12Dymatize58.3%Transparent Labs 50%, Naked 50%, Orgain 50%
— Other demand —
Everyday and mainstream choice12Optimum Nutrition75%Naked 75%, Transparent Labs 66.7%, Garden of Life 66.7%
General best-in-category12Optimum Nutrition100%Transparent Labs 75%, Dymatize 75%, Orgain 66.7%
Price and value12Optimum Nutrition91.7%Dymatize 75%, Myprotein 75%, Nutricost 50%
Primary use case12Dymatize58.3%Optimum Nutrition 50%, Transparent Labs 41.7%, Naked 41.7%
Beginners and first purchase12Optimum Nutrition66.7%Dymatize 58.3%, Myprotein 58.3%, Naked 50%
Where to buy12Optimum Nutrition58.3%GNC 41.7%, Transparent Labs 33.3%, Garden of Life 33.3%
Trust and recommendation12Optimum Nutrition75%Transparent Labs 75%, Garden of Life 75%, Naked 66.7%
Gifting and occasions12Optimum Nutrition66.7%Transparent Labs 66.7%, Naked 66.7%, Garden of Life 58.3%
Specific need or condition12Naked66.7%Garden of Life 66.7%, Orgain 58.3%, Transparent Labs 50%
Comparison and evaluation12Optimum Nutrition83.3%Transparent Labs 75%, Myprotein 66.7%, Dymatize 58.3%
For general buyer-intent questions, the chart identifies the brands the engines named most often in the broad selection context. For sub-category and specific-need questions, it identifies brands attached to more defined protein needs. The cluster view is the appropriate place to assess the brief's whey, plant-based, casein, and clear protein angle because it preserves the question context instead of pooling all mentions.
## Which sites receive the citations?
Publisher sites received citations across a far larger share of retrieval-supported answers than most brand-owned domains. Citation rates in this section use 288 answers with retrieval evidence as the denominator.
### Owned-domain citation rates
### Owned-domain citation rate
Share of 288 answers with retrieval evidence that cited a URL from the brand's own website.
Optimum Nutrition
8.7% · 25/288
Transparent Labs
2.4% · 7/288
Naked
5.9% · 17/288
Dymatize
1.4% · 4/288
Garden of Life
1.4% · 4/288
Orgain
1.4% · 4/288
Legion
2.1% · 6/288
Myprotein
12.5% · 36/288
Momentous
2.4% · 7/288
Vega
0% · 0/288
Thorne
0% · 0/288
KOS
0% · 0/288
GNC had the highest owned-domain citation rate among eligible brands at 18.1%, followed by Myprotein at 12.5% and Optimum Nutrition at 8.7%. Naked reached 5.9%. Transparent Labs and Momentous each reached 2.4%, while Dymatize, Garden of Life, and Orgain each reached 1.4%. Several eligible brands had 0% owned-domain citation rates, including Vega, Thorne, KOS, Ritual, Isopure, Premier Protein, Nutricost, Truvani, and Brooks.
### Publishers cited in retrieval-supported answers
### Most-cited publishers
Publishers used as evidence, counted once for each answer, not once for each link.
PublisherAnswers citing itAnswer penetration
healthline.com233/28880.9%
forbes.com186/28864.6%
garagegymreviews.com163/28856.6%
fortune.com131/28845.5%
menshealth.com68/28823.6%
barbend.com63/28821.9%
youtube.com61/28821.2%
foodnetwork.com57/28819.8%
everydayhealth.com55/28819.1%
labdoor.com54/28818.8%
dc.fortune.com52/28818.1%
gnc.com52/28818.1%
Healthline appeared in 80.9% of answers with retrieval evidence, Forbes in 64.6%, Garage Gym Reviews in 56.6%, and Fortune in 45.5%. Men's Health appeared in 23.6%, BarBend in 21.9%, YouTube in 21.2%, and ConsumerLab in 12.2%. The pattern shows that engines regularly drew support from health publishers, editorial reviews, retailer material, and testing-oriented sources.
## Who is winning and who is at risk?
### Winning measured positions
Optimum Nutrition is a measured leader because it ranked first at 64.2%, averaged position 3.4, and appeared across all four engines. Transparent Labs ranked second at 56.3% and had the earliest average first mention among the top ten at position 3.1. GNC is a citation leader rather than an appearance leader: it ranked nineteenth at 8% appearance but had an 18.1% owned-domain citation rate.
### At-risk measured positions
Myprotein is exposed to engine dependence because Claude named it in 0% of its 72 answers despite its 28.8% overall appearance rate. Vega has the same Claude absence and a 20.1% overall rate, with an average first mention position of 8.2. Orgain's 34% appearance rate is driven unevenly across engines, ranging from 8.3% on Claude to 65.3% on ChatGPT. Garden of Life also has a wide spread, ranging from 18.1% on Claude to 68.1% on ChatGPT.
## What does the tally say about protein powder visibility?
Protein powder visibility is concentrated at the top, but the first five ranks are not cleanly separated by the sample intervals. Optimum Nutrition's interval overlaps Transparent Labs, Transparent Labs overlaps Naked, Naked overlaps Dymatize, and Dymatize overlaps Garden of Life. The data supports a leading group, not a definitive ordering across those overlaps.
The engines converge on some brands. Optimum Nutrition, Transparent Labs, Naked, Dymatize, Garden of Life, Orgain, Legion, Momentous, KOS, Ritual, MuscleTech, Ascent, Isopure, Premier Protein, Kaged, GNC, and Nutricost each appeared on all four engines. Yet their rates vary across those engines, and Myprotein, Vega, Thorne, Sunwarrior, and NOW Sports were absent from Claude's measured answers.
Citations favor third-party publishers. Healthline's 80.9% answer penetration exceeds every eligible brand-owned domain citation rate. The brief named Ghost among the brands to report, but the tally rolled Ghost mentions under Brooks, which had a 5.2% appearance rate and 0% owned-domain citation rate. The tally did not provide a separate Ghost brand row.
## What changed during the measurement window?
The data does not measure changes over time within the measurement window. It measures repeat-generation variation and engine differences across the same set of prompts, including repeat consistency ranging from 0% for Brooks to 63.2% for Optimum Nutrition.
## How can protein brands address measured gaps?
- Audit brand pages that explain whey, plant-based, casein, isolate, and clear protein products in distinct, factual sections. The cluster chart identifies which question groups each brand misses. Re-measure appearance by the same demand cluster.
- Build pages that put macro details and ingredient information next to the exact product name. Brands with weak average first-mention positions, such as Premier Protein at 9.1 and NOW Sports at 9.8, can be re-measured for average position in answer.
- Publish sourceable product documentation on owned domains. Vega, Thorne, KOS, Ritual, Isopure, Premier Protein, Nutricost, Truvani, and Brooks each had a 0% owned-domain citation rate. Re-measure owned-domain citation rate using answers with retrieval evidence.
- Test the material against Claude-specific gaps. Myprotein, Vega, Thorne, Sunwarrior, and NOW Sports were named in 0% of Claude's 72 answers. Re-measure each brand's Claude appearance rate after new content is available.
- Review engine-specific coverage before treating an overall rate as secure. Garden of Life ranged from 18.1% on Claude to 68.1% on ChatGPT, while Orgain ranged from 8.3% to 65.3%. Re-measure by engine, not only in pooled results.
- Seek accurate inclusion in the publisher formats that engines cite. Healthline, Forbes, Garage Gym Reviews, and Fortune appeared in large shares of retrieval-supported answers. Re-measure the brand's appearance rate and owned-domain citation rate separately after any publisher coverage.
## What does this mean for brands in the category?
Interpretation: protein powder visibility is not one scoreboard. A brand's overall appearance rate shows how often it was named in these measured answers. Engine-specific rates show whether that exposure travels across systems. Owned-domain citation rates show whether the brand's own material is supporting retrieval-backed answers.
Interpretation: brands associated with clean labeling and macro transparency need pages that let an engine connect product names to concrete attributes. The tally cannot establish which content change causes a citation or recommendation. It can establish the baseline that later measurement should test.
## How was this study measured?
This flagship study ran 24 stratified prompts three times on Perplexity, ChatGPT, Gemini, and Claude. It produced 288 returned answers. Appearance rates use returned answers as the denominator. Citation and source rates use answers with retrieval evidence as the denominator. All 288 returned answers included retrieval evidence. These results apply to these prompts and this date, not to every protein powder question.
### How this study was measured
- The sampling frame came first: 24 prompts were assigned across demand clusters before any questions were asked.
- Every engine received the same questions: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 288 of 288 attempted answers were returned, and 288 had retrieval evidence.
- Product lines were mapped to parent brands through a registry fixed before scoring.
- Appearance rate uses a 95% Wilson interval. Citation metrics include only answers with evidence.
## Frequently asked questions
### Which protein powder brand appeared most often?
Optimum Nutrition ranked first with a 64.2% appearance rate across 288 answers returned. Its 95% interval was 58.5% to 69.6%.
### Did Transparent Labs outperform Optimum Nutrition?
Transparent Labs ranked second at 56.3%, while Optimum Nutrition ranked first at 64.2%. Their 95% intervals overlap, so this sample does not establish that Optimum Nutrition is more visible than Transparent Labs.
### Which brand had the strongest owned-domain citation rate?
GNC had the highest owned-domain citation rate among eligible brands at 18.1%. This rate uses 288 answers with retrieval evidence as the denominator.
### Which publishers were cited most often?
Healthline appeared in 80.9% of retrieval-supported answers, followed by Forbes at 64.6% and Garage Gym Reviews at 56.6%. These rates use answers with retrieval evidence as the denominator.
### Which brands were absent from Claude answers?
Myprotein, Vega, Thorne, Sunwarrior, and NOW Sports each appeared in 0% of Claude's 72 answers. Their overall appearance rates include results from all four engines.
### Does this report cover other supplement segments?
No. This report is limited to Protein / Protein Powder. Multivitamins, Probiotics / Gut Health, Sleep (melatonin / magnesium), Immune Support, and Nootropics / Cognitive appear in separate reports.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
These are not category brands. They are measured in the same way but excluded from the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1Healthline11.1%32/2888%–15.3%
2Examine.com0.3%1/2880.1%–1.9%
3Men's Health0%0/2880%–1.3%
4iHerb0%0/2880%–1.3%
5Amazon0%0/2880%–1.3%
## Methodology appendix
This appendix comes directly from the study's measurement record. It lets any brand named here reproduce or contest the result.
### What was measured
- Category: Protein / Protein Powder
- Measurement date: 2026-09-30
- Depth: flagship: 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 288
- Answers returned (appearance denominator): 288
- Answers with retrieval evidence (citation denominator): 288 (100% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Flagship depth uses 24 stratified prompts measured three times per engine. This makes both between-prompt and repeat-generation variation observable.
### How the prompts were sampled
Fixed sampling frame: 14 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Protein / Protein Powder; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer provides wording only within these cells and does not select the category's demand structure.
Demand clusterPromptsScope
best protein powder for muscle gain1best protein powder for muscle gain
best whey protein powder1best whey protein powder
best plant based protein powder1best plant based protein powder
best protein powder for weight loss1best protein powder for weight loss
best tasting protein powder1best tasting protein powder
best protein powder for women1best protein powder for women
best casein protein for nighttime1best casein protein for nighttime
best protein powder with no added sugar1best protein powder with no added sugar
best protein powder for beginners1best protein powder for beginners
whey vs plant protein which is better1whey vs plant protein which is better
best protein powder for smoothies1best protein powder for smoothies
best organic protein powder1best organic protein powder
best protein powder for lactose intolerance1best protein powder for lactose intolerance
best clear protein powder1best clear protein powder
General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Primary use case1the dominant everyday use case for this category
Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
### The exact prompts
#PromptClusterShape
1best protein powder for muscle gainneed_1medium
2best whey protein powderneed_2medium
3best plant based protein powderneed_3medium
4best protein powder for weight lossneed_4medium
5best tasting protein powderneed_5medium
6best protein powder for womenneed_6medium
7best casein protein for nighttimeneed_7medium
8best protein powder with no added sugarneed_8medium
9best protein powder for beginnersneed_9medium
10whey vs plant protein which is betterneed_10medium
11best protein powder for smoothiesneed_11medium
12best organic protein powderneed_12medium
13best protein powder for lactose intoleranceneed_13medium
14best clear protein powderneed_14medium
15best protein powdersgeneral_bestshort
16best everyday protein powder brandseverydaymedium
17best value protein powder brandsvalue_pricemedium
18best protein powders for muscle recoveryuse_casemedium
19What protein powder brands should a beginner buy for their first tub?beginnerlong
20Where should I buy protein powder for reliable freshness and authentic products?retaillong
21What protein powder brands make a good gift for someone starting fitness?giftinglong
22best plant protein powdersspecific_needshort
23Which protein powder brands are better for whey, plant, casein, and clear protein?comparisonlong
24most trusted protein powder brandstrustshort
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search727272100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool727272100%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)727272100%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool727272100%
Results cannot be compared strictly across engines. Each engine has a different model, retrieval tool and answer format. A gap between two engines may therefore reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the share of returned answers that named the brand. It covers every answer returned by the engine, whether or not the engine searched the web. Retrieval behaviour therefore does not silently alter the denominator.
- Macro appearance rate — the average of per-prompt appearance rates. Each prompt therefore counts once, regardless of how many engines answered it.
- 95% interval — Wilson score interval for the appearance rate. If two brands' intervals overlap, this study does not support saying that one is more visible than the other.
- Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This narrowly measures use of the brand's own site as a source. It does not measure total mentions.
- Share of all citations — the share of all attributed publisher citations in the study accounted for by the brand's own domain.
- Not measured — the brand's official domain was not established, so no citation result is available. It is never shown as 0%.
- 0% — the metric was evaluable, but the event did not occur.
- Average position — the average position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — the share of engines that named the brand at least once.
- Repeat consistency — the share of repeated prompt/engine cells in which the brand appeared in every trial. Lower values indicate an unstable engine answer, not brand absence.
### Evidence rules
- An answer has retrieval evidence only if the engine returned a retrieval receipt (search annotations or grounding records). A URL merely typed by the model in its prose does not show that it searched.
- Sources are assigned to the publisher behind each retrieval receipt. Intermediary and redirect hosts are not reported as publishers.
- Publisher identity was not established for 0 retrieval receipt(s). They are disclosed here, not attributed or dropped.
- Source counts are at answer level: a publisher is counted once in an answer that cited it, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Optimum Nutrition185/28864.2%64.2%58.5%–69.6%25/288 (8.7%)
2Transparent Labs162/28856.3%56.3%50.5%–61.9%7/288 (2.4%)
3Naked138/28847.9%47.9%42.2%–53.7%17/288 (5.9%)
4Dymatize132/28845.8%45.8%40.2%–51.6%4/288 (1.4%)
5Garden of Life114/28839.6%39.6%34.1%–45.3%4/288 (1.4%)
6Orgain98/28834%34%28.8%–39.7%4/288 (1.4%)
7Legion91/28831.6%31.6%26.5%–37.2%6/288 (2.1%)
8Myprotein83/28828.8%28.8%23.9%–34.3%36/288 (12.5%)
9Momentous73/28825.3%25.4%20.7%–30.7%7/288 (2.4%)
10Vega58/28820.1%20.1%15.9%–25.1%0/288 (0%)
11Thorne47/28816.3%16.3%12.5%–21%0/288 (0%)
12KOS39/28813.5%13.5%10.1%–18%0/288 (0%)
13Ritual36/28812.5%12.5%9.2%–16.8%0/288 (0%)
14MuscleTech36/28812.5%12.5%9.2%–16.8%3/288 (1%)
15Ascent34/28811.8%11.8%8.6%–16%1/288 (0.3%)
16Isopure34/28811.8%11.8%8.6%–16%0/288 (0%)
17Premier Protein29/28810.1%10.1%7.1%–14.1%0/288 (0%)
18Kaged23/2888%8%5.4%–11.7%2/288 (0.7%)
19GNC23/2888%8%5.4%–11.7%52/288 (18.1%)
20Sunwarrior23/2888%8%5.4%–11.7%1/288 (0.3%)
21NOW Sports22/2887.6%7.6%5.1%–11.3%1/288 (0.3%)
22Sources21/2887.3%7.3%4.8%–10.9%domain not established — excluded from citation metrics
23Garage Gym Reviews21/2887.3%7.3%4.8%–10.9%163/288 (56.6%)
24Nutricost21/2887.3%7.3%4.8%–10.9%0/288 (0%)
25Truvani20/2886.9%6.9%4.5%–10.5%0/288 (0%)
26Brooks15/2885.2%5.2%3.2%–8.4%0/288 (0%)
### Entity handling
For brand-level metrics, product lines and model names are assigned to their parent brand using a registry fixed before scoring. Nothing is merged solely because of this study's own counts.
- Ghost counted under Brooks (product_line)
40 further name(s) found in the answers could not be confidently resolved to a brand in this category. They are held for human classification before the next study and have no metrics here.
### Limits of these findings
- This is a point-in-time measure of what the named systems returned for these exact prompts on 2026-09-30. Engine behaviour can change without notice.
- The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and are not representative of all searches in the category.
- When intervals overlap, brand differences are not statistically distinguishable at this sample size.
- A brand absent from this list was not measured as absent. It was simply not named in these answers.
- Appearance measures visibility. It is not an endorsement, quality judgement or market-share estimate.
- 1 of 26 ranked brand(s) had no official domain established before scoring. They have appearance results only and are excluded from citation metrics and source tables, rather than reported as zero.
---
### The Private Label AI Advantage — Amazon
URL: https://www.5wpr.com/research/private-label-ai-advantage-amazon/
Summary: The private label pattern breaks on Amazon as national brands out-cite Amazon's own products across five answer engines in our latest index.
---
### Fintech AI Visibility Index 2026
URL: https://www.5wpr.com/research/fintech-ai-visibility-index-2026/
Summary: Fintech AI visibility data shows Stripe leads measured brand mentions across four engines, with PayPal and Adyen close behind.
# Fintech AI Visibility Index 2026
Stripe Leads Fintech AI Mentions
Stripe, Inc. leads this fintech roll-up with a 36.6% appearance rate across 1,161 returned answers. PayPal follows at 33%, and Adyen ranks third at 25.4%. These results describe the measured fintech prompts on September 14, 2026, not consumer demand across the category.
The gap between Stripe and PayPal is within the margin of this sample because their 95% intervals overlap. The same caution applies across other overlapping intervals. A brand's place in this report measures how often an engine named it, not whether it is the best consumer selection.
1161AI answers analyzed
36.6%Stripe, Inc. appearance rate, the category leader
35Brands named across the study
99.7%Answers carrying retrieval evidence
## Which fintech brands appear most often?
Stripe ranks first, followed by PayPal, Adyen, Fidelity, and The Charles Schwab Corporation. Stripe appeared in 425 returned answers. PayPal appeared in 383, while Adyen appeared in 295. Appearance rates use 1,161 returned answers as the denominator.
Stripe's 95% interval is 33.9% to 39.4%. PayPal's is 30.3% to 35.7%, so the two intervals overlap. Stripe's interval also overlaps Adyen's 23% to 28% only at no point, while PayPal's interval does not overlap Adyen's. The measured gap between the first two brands remains within this sample's margin.
### AI visibility leaderboard
Appearance rate is the share of the 1161 returned AI answers in which each brand was named.
#BrandNamed inAppearance rate95% intervalAvg position
1Stripe, Inc.425/116136.6%33.9%–39.4%2.5
2PayPal383/116133%30.3%–35.7%4.1
3Adyen295/116125.4%23%–28%4.5
4Fidelity243/116120.9%18.7%–23.4%2.6
5The Charles Schwab Corporation224/116119.3%17.1%–21.7%3.2
6Robinhood205/116117.7%15.6%–20%5.9
7Klarna202/116117.4%15.3%–19.7%3.4
8Affirm201/116117.3%15.2%–19.6%3.2
9Interactive Brokers LLC189/116116.3%14.3%–18.5%4.2
10Square179/116115.4%13.5%–17.6%4.1
11Sezzle175/116115.1%13.1%–17.2%6
12Zip161/116113.9%12%–16%6.7
13Afterpay158/116113.6%11.8%–15.7%4.8
14Webull Corporation155/116113.4%11.5%–15.4%7.5
15Shopify Inc.139/116112%10.2%–14%6.3
Intervals overlap among several adjacent ranks. Fidelity's 18.7% to 23.4% interval overlaps The Charles Schwab Corporation's 17.1% to 21.7% interval, Robinhood's 15.6% to 20% interval, Klarna's 15.3% to 19.7% interval, and Affirm's 15.2% to 19.6% interval. Do not treat those overlaps as proof that one brand is more visible than another.
## How do the leading fintech brands appear?
### Stripe, Inc.
### Stripe, Inc.
36.6% appearance rate, rank 1, with a 33.9% to 39.4% interval. Perplexity named Stripe in 46.4% of its 291 answers and ChatGPT named it in 45.7% of its 289 answers. Gemini named Stripe in 31.3% of 291 answers, while Claude named it in 23.1% of 290 answers. Stripe's average first mention position was 2.5. Its owned domain was cited in 6.6% of 1,158 answers with retrieval evidence.
### PayPal
33% appearance rate, rank 2, with a 30.3% to 35.7% interval. ChatGPT named PayPal in 48.4% of 289 answers and Perplexity named it in 46% of 291 answers. Gemini named PayPal in 22.3% of 291 answers, and Claude named it in 15.2% of 290 answers. PayPal's average first mention position was 4.1. Its owned domain was cited in 6.6% of 1,158 evidence-backed answers.
### Adyen
25.4% appearance rate, rank 3, with a 23% to 28% interval. ChatGPT named Adyen in 33.6% of 289 answers and Perplexity named it in 32.6% of 291 answers. Gemini named it in 22.3% of 291 answers, while Claude named it in 13.1% of 290 answers. Adyen's average first mention position was 4.5. Its owned domain was cited in 3.5% of 1,158 evidence-backed answers.
### Fidelity
20.9% appearance rate, rank 4, with an 18.7% to 23.4% interval. ChatGPT named Fidelity in 24.6% of 289 answers and Perplexity named it in 23.4% of 291 answers. Gemini named Fidelity in 19.2% of 291 answers, while Claude named it in 16.6% of 290 answers. Fidelity's average first mention position was 2.6. Its owned domain was cited in 7.7% of 1,158 evidence-backed answers.
### The Charles Schwab Corporation
19.3% appearance rate, rank 5, with a 17.1% to 21.7% interval. Perplexity named The Charles Schwab Corporation in 24.1% of 291 answers and ChatGPT named it in 22.8% of 289 answers. Gemini named it in 13.7% of 291 answers, and Claude named it in 16.6% of 290 answers. The Charles Schwab Corporation's average first mention position was 3.2. Its owned domain was cited in 2.5% of 1,158 evidence-backed answers.
### Robinhood
17.7% appearance rate, rank 6, with a 15.6% to 20% interval. ChatGPT named Robinhood in 23.5% of 289 answers and Perplexity named it in 22.3% of 291 answers. Gemini named Robinhood in 16.5% of 291 answers, while Claude named it in 8.3% of 290 answers. Robinhood's average first mention position was 5.9. Its owned domain was cited in 1.5% of 1,158 evidence-backed answers.
### Klarna
17.4% appearance rate, rank 7, with a 15.3% to 19.7% interval. Perplexity named Klarna in 24.7% of 291 answers and ChatGPT named it in 23.2% of 289 answers. Gemini named Klarna in 13.1% of 291 answers, while Claude named it in 8.6% of 290 answers. Klarna's average first mention position was 3.4. Its owned domain was cited in 2.7% of 1,158 evidence-backed answers.
### Affirm
17.3% appearance rate, rank 8, with a 15.2% to 19.6% interval. Perplexity named Affirm in 23.7% of 291 answers, and ChatGPT named it in 23.2% of 289 answers. Gemini named Affirm in 14.4% of 291 answers, while Claude named it in 7.9% of 290 answers. Affirm's average first mention position was 3.2. Its owned domain was cited in 3.1% of 1,158 evidence-backed answers.
## Where do the engines disagree?
The engines disagree most for brands whose visibility is concentrated in Perplexity or ChatGPT. Each engine uses a different model and retrieval tool, so a gap can reflect system design rather than a stable difference in brand visibility.
### Appearance rate by engine
Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Stripe, Inc.46.4%45.7%31.3%23.1%
PayPal46%48.4%22.3%15.2%
Adyen32.6%33.6%22.3%13.1%
Fidelity23.4%24.6%19.2%16.6%
The Charles Schwab Corporation24.1%22.8%13.7%16.6%
Robinhood22.3%23.5%16.5%8.3%
Klarna24.7%23.2%13.1%8.6%
Affirm23.7%23.2%14.4%7.9%
Interactive Brokers LLC19.9%21.1%13.1%11%
Square24.7%16.6%10.3%10%
Sezzle22.3%21.8%12.4%3.8%
Zip23%20.1%11%1.4%
Airwallex is the sharpest single-engine pattern. Perplexity named Airwallex in 24.1% of 291 answers, compared with 0.7% of 289 ChatGPT answers, 2.4% of 291 Gemini answers, and 3.1% of 290 Claude answers. Square also has a Perplexity-weighted pattern: 24.7% in Perplexity versus 16.6% in ChatGPT, 10.3% in Gemini, and 10% in Claude. Helcim appeared in 18.9% of Perplexity answers but 7.3% of ChatGPT answers, 14.8% of Gemini answers, and 4.1% of Claude answers.
Some brands were absent in specific engines. Splitit appeared in 16.8% of Perplexity answers, 15.9% of ChatGPT answers, and 8.6% of Gemini answers, but in none of Claude's 290 answers. Authorize.Net appeared in Perplexity, ChatGPT, and Gemini answers but not Claude answers. SoFi Technologies, Inc. and Block were also absent from Claude's 290 answers. ETRADE was absent from Gemini's 291 answers but appeared in 7.9% of Perplexity answers, 14.5% of ChatGPT answers, and 6.2% of Claude answers. Firstrade appeared only in Perplexity, at 4.1% of 291 answers.
At the top, Perplexity and ChatGPT named Stripe, PayPal, and Adyen more often than Gemini and Claude. Claude's rates were lower for Stripe, PayPal, Adyen, Robinhood, Klarna, and Affirm. The differing engine totals matter: Perplexity returned 291 answers, ChatGPT 289, Gemini 291, and Claude 290.
## Which demand clusters favor which brands?
The demand-cluster view separates broad selection questions from more specific need questions. It shows where a brand appears in the measured query groups, rather than treating all fintech questions as interchangeable.
Read the grouped clusters independently. A brand that leads a general buyer-intent group has a different measured strength from a brand that leads a sub-category or specific-need group. The chart supports planning by question type, not a claim that one type of demand matters more than another.
## Which sites do engines cite for fintech answers?
Owned-domain citation rates use 1,158 answers with retrieval evidence as the denominator. Shopify Inc. had the highest measured owned-domain citation rate among listed brands at 12.3%, followed by Airwallex at 11.9%, Fidelity at 7.7%, and Stripe and PayPal at 6.6% each.
### Owned-domain citation rate
Share of the 1158 answers with retrieval evidence that cited a URL on the brand's own website.
Stripe, Inc.
6.6% · 77/1158
PayPal
6.6% · 76/1158
Adyen
3.5% · 40/1158
Fidelity
7.7% · 89/1158
The Charles Schwab Corporation
2.5% · 29/1158
Robinhood
1.5% · 17/1158
Klarna
2.7% · 31/1158
Affirm
3.1% · 36/1158
Interactive Brokers LLC
1.5% · 17/1158
Square
2.2% · 25/1158
Sezzle
1.6% · 18/1158
Zip
1.3% · 15/1158
Appearance and owned-domain citation are different measures. Shopify appeared in 12% of returned answers but its domain was cited in 12.3% of evidence-backed answers. Airwallex appeared in 7.6% of returned answers and its domain was cited in 11.9% of evidence-backed answers. The measured tally therefore separates brand naming from source selection.
### Publishers cited across fintech answers
Third-party publishers appear often in the evidence set. Forbes was cited in 36.1% of 1,158 answers with retrieval evidence, NerdWallet in 27.5%, ConnectPay in 23.6%, CNBC in 21.3%, and Reddit in 16%.
### Most-cited publishers
Publishers cited as evidence, counted once per answer rather than once per link.
PublisherAnswers citing itAnswer penetration
forbes.com418/115836.1%
nerdwallet.com318/115827.5%
connectpay.com273/115823.6%
cnbc.com247/115821.3%
reddit.com185/115816%
whop.com174/115815%
stockbrokers.com174/115815%
airwallex.com138/115811.9%
shopify.com133/115811.5%
fintechmagazine.com132/115811.4%
fool.com125/115810.8%
theretailexec.com119/115810.3%
The source pattern includes editorial, comparison, community, and company domains. Forbes had 418 cited-answer mentions, NerdWallet had 318, ConnectPay had 273, and CNBC had 247. Airwallex.com and Shopify.com also appear among the listed sources, with 138 and 133 cited-answer mentions.
## Who is winning and who is at risk?
### Measured leaders
Stripe leads the roll-up at 36.6% appearance, with an average first mention position of 2.5 and 100% cross-engine consistency. PayPal follows at 33% appearance and 100% cross-engine consistency. Adyen ranks third at 25.4% appearance, also with 100% cross-engine consistency. Fidelity ranks fourth at 20.9%, has an average first mention position of 2.6, and its owned domain was cited in 7.7% of evidence-backed answers.
Shopify has a distinct source-strength result. It ranks 15th in appearance at 12%, but its owned-domain citation rate is 12.3%, the highest listed rate. Airwallex ranks 23rd in appearance at 7.6% and has an 11.9% owned-domain citation rate, although its engine pattern is concentrated in Perplexity.
### Measured exposure risks
Block appeared in 7.1% of returned answers and its owned-domain citation rate was 0% of evidence-backed answers. Firstrade appeared in 1% of returned answers, was named only by Perplexity, and had 25% cross-engine consistency. Block/Square and Tradier each had a 0% appearance rate in the 1,161 returned answers.
Claude gaps are another exposure risk in this sample. Splitit, Authorize.Net, SoFi Technologies, Inc., and Block were each named in 0% of Claude's 290 answers. ETRADE was named in 0% of Gemini's 291 answers. These gaps do not establish a category-wide outcome, but they identify engine-specific blind spots for the measured prompts.
## What does the category pattern show?
The top of the fintech leaderboard is led by payments, investment, and consumer-credit brands. Stripe, PayPal, Adyen, Fidelity, The Charles Schwab Corporation, Robinhood, Klarna, and Affirm fill the first eight ranks. The tally does not establish market standing outside the measured prompts.
The top two results remain statistically close within this sample. Stripe's 33.9% to 39.4% interval overlaps PayPal's 30.3% to 35.7% interval. Stripe's interval does not overlap the intervals for Adyen, Fidelity, or The Charles Schwab Corporation. At lower ranks, overlap is common and ranking claims need restraint.
Cross-engine coverage is broad for many leading brands. Stripe through Braintree each have 100% cross-engine consistency except Splitit, Authorize.Net, SoFi Technologies, Inc., Block, ETRADE, and TradeStation, which each have 75%. Firstrade has 25%. Owned sites do not dominate the cited-source list. Forbes, NerdWallet, ConnectPay, CNBC, Reddit, Whop, and StockBrokers each appear ahead of most owned domains by cited-answer mentions.
## What changed during the measurement window?
This tally is a cross-segment roll-up from 1,161 measured answers. It does not include an earlier measurement wave, so it does not document movement over time. It does show engine-level divergence within the window, including Airwallex's 24.1% Perplexity rate against lower rates in the other three engines.
## What should fintech brands test next?
- Audit Perplexity-only strength where it appears. Airwallex had 24.1% appearance in Perplexity but 0.7% in ChatGPT, 2.4% in Gemini, and 3.1% in Claude. Re-run the same prompt set by engine after publishing clear product and comparison pages.
- Close engine-specific absence gaps. Splitit, Authorize.Net, SoFi Technologies, Inc., and Block were absent from Claude answers. Re-measure their Claude appearance rate and cross-engine consistency after source and entity cleanup.
- Test owned-domain citation pages for brands with no measured citation rate. Block and Firstrade each had a 0% owned-domain citation rate. Re-measure citations against the 1,158-answer retrieval-evidence denominator.
- Separate source work from name-recognition work. Shopify had 12% appearance and a 12.3% owned-domain citation rate. Compare both measures after adding pages that state product scope, fees, eligibility, and selection criteria in plain language.
- Review top publisher coverage. Forbes, NerdWallet, ConnectPay, and CNBC were cited in 36.1%, 27.5%, 23.6%, and 21.3% of evidence-backed answers. Re-measure whether brand pages and independent coverage appear in the same answer set.
- Test prompt clusters separately. Use the cluster result to check whether a brand appears for broad selection questions, specific-need questions, or both. Re-measure the same clusters rather than relying on an overall rate alone.
## What does this mean for fintech brands?
Interpretation: fintech visibility varies by engine, question type, and evidence source. A strong overall appearance rate can coexist with weak owned-domain citations. A strong owned-domain citation rate can coexist with a lower overall appearance rate.
Interpretation: brands need two checks. Track whether engines name the brand in returned answers. Track whether the brand's own domain is cited when retrieval evidence is present. The study does not prove what content change will improve either measure. It provides a baseline for repeat measurement.
## How was this fintech AI visibility measured?
This flagship roll-up tallies 99 prompts across Perplexity, ChatGPT, Gemini, and Claude. Each prompt ran three times per engine. The study attempted and returned 1,161 answers, with 1,158 answers carrying retrieval evidence. Appearance rates use returned answers. Citation and source rates use answers with retrieval evidence.
### How this study was measured
- Sampling frame fixed first: 99 prompts allocated across the demand clusters before anything was asked.
- Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 1161 answers returned of 1161 attempted, 1158 with retrieval evidence.
- Product lines rolled up to parent brands using a registry fixed before scoring.
- Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.
The report aggregates four segment studies and only ranks categories measured in those segments. The reported results apply to these prompts on the measurement date. Engine differences may result from the model and retrieval tool each system uses. Three answers lacked retrieval evidence, and no answers failed.
## Frequently asked questions
### Which fintech brand ranked first?
Stripe, Inc. ranked first with a 36.6% appearance rate across 1,161 returned answers. Its 95% interval was 33.9% to 39.4%.
### Is Stripe more visible than PayPal in this study?
Stripe's 36.6% appearance rate was higher than PayPal's 33% rate. Their 95% intervals overlap, so the difference is within the margin of this sample.
### Which brand had the highest owned-domain citation rate?
Shopify Inc. had the highest listed owned-domain citation rate at 12.3%. Citation rates use 1,158 answers with retrieval evidence as the denominator.
### Which publishers were cited most often?
Forbes was cited in 36.1% of evidence-backed answers, followed by NerdWallet at 27.5% and ConnectPay at 23.6%. These rates use 1,158 answers with retrieval evidence.
### Which engine showed the strongest Airwallex result?
Perplexity named Airwallex in 24.1% of its 291 answers. ChatGPT named it in 0.7% of 289 answers, Gemini in 2.4% of 291 answers, and Claude in 3.1% of 290 answers.
### Did every engine name every brand?
No. Splitit, Authorize.Net, SoFi Technologies, Inc., and Block were absent from Claude's 290 answers. ETRADE was absent from Gemini's 291 answers, while Firstrade appeared only in Perplexity's 291 answers.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1Forbes Advisor4%47/11613.1%–5.3%
2NerdWallet2.9%34/11612.1%–4.1%
3Investopedia2.2%26/11611.5%–3.3%
4Bankrate0.2%2/11610%–0.6%
## Methodology appendix
This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.
### What was measured
- Category: Fintech
- Measurement date: 2026-09-14
- Depth: flagship — 99 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 1161
- Answers returned (appearance denominator): 1161
- Answers with retrieval evidence (citation denominator): 1158 (99.7% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Cross-segment roll-up: every figure in this hub is tallied from the 1161 measured answers of its 4 segment studies. No figure here is estimated, and no category is ranked that was not measured in one of those segments.
### How the prompts were sampled
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search291291291100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool289289289100%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)29129128899%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool290290290100%
Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
- Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
- 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
- Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
- Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
- Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
- 0% — the metric could be evaluated and the event did not occur.
- Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — the share of engines that named the brand at least once.
- Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.
### Evidence rules
- An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
- Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
- Publisher identity could not be established for 19 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
- Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Stripe, Inc.425/116136.6%37.3%33.9%–39.4%77/1158 (6.6%)
2PayPal383/116133%34%30.3%–35.7%76/1158 (6.6%)
3Adyen295/116125.4%25.9%23%–28%40/1158 (3.5%)
4Fidelity243/116120.9%20.5%18.7%–23.4%89/1158 (7.7%)
5The Charles Schwab Corporation224/116119.3%18.9%17.1%–21.7%29/1158 (2.5%)
6Robinhood205/116117.7%17.3%15.6%–20%17/1158 (1.5%)
7Klarna202/116117.4%17%15.3%–19.7%31/1158 (2.7%)
8Affirm201/116117.3%16.9%15.2%–19.6%36/1158 (3.1%)
9Interactive Brokers LLC189/116116.3%15.9%14.3%–18.5%17/1158 (1.5%)
10Square179/116115.4%16.7%13.5%–17.6%25/1158 (2.2%)
11Sezzle175/116115.1%14.7%13.1%–17.2%18/1158 (1.6%)
12Zip161/116113.9%13.6%12%–16%15/1158 (1.3%)
13Afterpay158/116113.6%13.3%11.8%–15.7%23/1158 (2%)
14Webull Corporation155/116113.4%13%11.5%–15.4%6/1158 (0.5%)
15Shopify Inc.139/116112%13.1%10.2%–14%142/1158 (12.3%)
16The Vanguard Group, Inc132/116111.4%11.1%9.7%–13.3%13/1158 (1.1%)
17Helcim131/116111.3%12.6%9.6%–13.2%41/1158 (3.5%)
18Plaid125/116110.8%10.5%9.1%–12.7%33/1158 (2.8%)
19Splitit120/116110.3%10.1%8.7%–12.2%27/1158 (2.3%)
20Marqeta108/11619.3%9.1%7.8%–11.1%24/1158 (2.1%)
21Checkout Ltd.104/11619%9%7.4%–10.7%19/1158 (1.6%)
22Authorize.Net92/11617.9%8.5%6.5%–9.6%22/1158 (1.9%)
23Airwallex88/11617.6%7.7%6.2%–9.2%138/1158 (11.9%)
24Public88/11617.6%7.4%6.2%–9.2%6/1158 (0.5%)
25SoFi Technologies, Inc.84/11617.2%7.1%5.9%–8.9%18/1158 (1.6%)
26Block83/11617.1%7.3%5.8%–8.8%0/1158 (0%)
27ETRADE83/11617.1%7%5.8%–8.8%8/1158 (0.7%)
28Braintree81/11617%7%5.6%–8.6%1/1158 (0.1%)
29Worldpay, LLC79/11616.8%7.3%5.5%–8.4%8/1158 (0.7%)
30Unit77/11616.6%6.5%5.3%–8.2%10/1158 (0.9%)
31TradeStation39/11613.4%3.3%2.5%–4.6%5/1158 (0.4%)
32AvidXchange.25/11612.2%2.1%1.5%–3.2%3/1158 (0.3%)
33Firstrade12/11611%1%0.6%–1.8%0/1158 (0%)
34Block/Square0/11610%0%0%–0.3%0/1158 (0%)
35Tradier0/11610%0%0%–0.3%0/1158 (0%)
### Entity handling
Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.
No roll-ups were applied in this study.
40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here.
### Limits of these findings
- This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-14. Engine behaviour changes without notice.
- The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
- Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
- A brand not listed here was not measured as absent — it simply was not named in these answers.
- Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
## The segment reports in this family
Each segment below was measured separately, with its own brand panel and its own question set, and has its own report. The figures in this table come from those segment measurements.
### Segment reports
SegmentReportBrands measuredAnswers measuredMost-named brand
Payments & ProcessorsPayments & Processors AI Visibility Index 20264297Stripe (80.5%)
Trading & Investing PlatformsTrading Platforms AI Visibility Index 20264288Fidelity (79.9%)
BNPL & Consumer CreditBNPL AI Visibility Index 20264288Affirm (69.8%)
B2B Fintech InfrastructureFintech B2B Infrastructure AI Visibility Index 20263288Stripe, Inc. (63.9%)
### Related indexes already published
These categories were measured in earlier published indexes and are not re-measured here. Any figure below is quoted from the page it links to, on that page's own methodology and date.
- Neobanks AI Visibility Index 2026 — The world’s largest neobank commands 55% of Latin America’s AI visibility — and effectively disappears when the conversation crosses into the United States.. Figures as published on that page: 01 34.7%, 02 14.2%, 03 9.8%, 04 7.9%.
- Crypto AI Visibility Index 2026 — Crypto & Digital Assets AI Visibility Index 2026
- Wealth AI Audit 2026 — The Wealth AI Audit
### Where this index is linked from
- Financial services and fintech PR — the practice page for fintech public relations and AI search visibility programs.
- Fintech marketing agency services — integrated fintech marketing, content, and generative engine optimization.
- Pre-IPO financial communications — investor and media communications for pre-IPO fintech companies.
---
### Disney Owns the Chatbox — The Theme Parks AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/theme-parks-ai-visibility-index-2026/
Summary: Disney captures 58 percent of every AI theme-park answer. We rank 25 destination parks by citation share across five generative search tools.
Part of
The 5W AI Visibility Index Series
·
Volume 65 · Travel & Hospitality · May 2026
5W AI Communications
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5W AI Visibility Index
Volume 65
May 2026
# Disney owns
the chatbox.
Mickey 58%. Universal 23%. Six Flags Entertainment 13%. Three companies hold 94% of every AI theme-park answer. Everyone else fights for scraps.
Top 5 · By AI Citation Share
№ 01
Walt Disney World
22.6%
№ 02
Disneyland
Anaheim
15.1%
№ 03
Universal Studios Orlando
12.4%
№ 04
Disneyland
Paris
7.2%
№ 05
Tokyo Disney Resort
6.4%
Cohort: Top 25 destination parks
Engines: 5 AI answer systems
Prompts: 50 traveler-intent
Refresh: Quarterly
— Section 01 / 16
## The Disney problem.
Theme parks is the most consolidated experience-category in the 5W AI Visibility franchise. Three corporate parents own four of every five answers.
The 5W Theme Parks AI Visibility Index ran 50 traveler-intent prompts across five AI engines covering the top 25 destination theme parks globally — Disney resorts, Universal parks, the merged Six Flags Entertainment Corporation (Cedar Fair–Six Flags) properties, regional anchors, and international destinations. Citation share is dominated by a single corporate parent. Disney properties collectively capture 58.1% of all category citations. Universal captures another 23.5%. Six Flags Entertainment Corporation captures 12.8%. Three companies hold 94.4% of every AI answer about theme parks.
The structural advantage is generational. Disney’s IP retrieval base — Marvel, Star Wars, Pixar, Frozen — anchors every “family destination” prompt before the conversation reaches operations or pricing. Universal counters with Harry Potter, Mario, and the May 2025 Epic Universe launch, which AI engines now surface as the dominant 2026 narrative on “new theme park” and “what to do in Orlando” prompts. Six Flags Entertainment Corporation’s July 2024 Cedar Fair merger created the largest amusement park operator in North America by property count — 42 parks — but the citation base still anchors to the individual park brands (Cedar Point, Magic Mountain, Knott’s, Kings Island) rather than the corporate parent. Everyone outside the three is competing for what’s left.
— Section 02 / 16
## What the data says.
- Walt Disney World captures 22.6% of all category citations. The single most-cited theme park in the world by AI answer share. Disneyland California is #2 at 15.1% — a sibling consolidation, not a competitive split. The TEA Global Experience Index confirms Magic Kingdom has held the world’s most-visited park title for 19 consecutive years.
- Three corporate parents hold 94.4% of total citation share. Disney (58.1%), Comcast NBCUniversal (23.5%), and Six Flags Entertainment Corporation (12.8%) — the most consolidated experience category in any 5W AI Visibility Index to date. Every other parent operator competes for the remaining 5.6%.
- Epic Universe is moving citation share faster than any new-park launch in audit history. Universal Studios Orlando citation share jumped from a modeled 11.2% to 12.4% in our logged run — the largest single-property citation gain we’ve measured. The Epic Universe May 2025 opening produced a sustained earned-media base that engines now treat as the dominant 2026 narrative for U.S. destination theme parks.
- The Cedar Fair–Six Flags merger has not yet consolidated citation share at the corporate level. The combined Six Flags Entertainment Corporation now operates 42 parks but engines route to the individual park brands — Cedar Point (4.1%), Six Flags Magic Mountain (3.2%), Six Flags Great Adventure (2.4%), Knott’s Berry Farm (1.1%), Kings Island (1.0%) — rather than to “Six Flags” as a corporate parent.
- Dollywood and Silver Dollar City over-perform their attendance positions. Both Herschend-owned parks register citation share materially above their TEA attendance ranks — Dollywood 2.6%, Silver Dollar City 1.7%. The Golden Ticket Awards and USA Today 10Best Readers’ Choice rankings (where Silver Dollar City has been #1 for four years running) are the dominant retrieval anchors.
— Section 03 / 16
## How we measured it.
#### Cohort
Top 25 destination theme parks worldwide, selected by TEA/AECOM Theme Index attendance rankings, brand-search volume, sustained earned-media presence, and category authority. Includes both U.S. and major international parks cited materially in U.S.-origin AI queries.
#### Engines
Five AI answer systems: ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Citation share measured as percentage of total park mentions inside generated answers.
#### Prompt set
50 traveler-intent prompts across five sub-categories: family destination, thrill / roller-coaster, animal / experience-themed, regional value, and international.
#### Measurement
Each prompt was run multiple passes per engine. Only park mentions recurring across runs were counted. Citation share normalized to a percentage of total in-cluster mentions. This edition reflects logged-run citation share captured May 2026 — refreshed quarterly. Findings are directional estimates synthesized across engines and passes, validated against the TEA/AECOM Theme Index 2024 attendance data published October 2025.
— Section 04 / 16
## Tier 1: Disney + Universal own the answer.
RankParkParent / LocationCitation Share
01Walt Disney WorldDisney · Orlando, FL22.6%
02DisneylandDisney · Anaheim, CA15.1%
03Universal Studios OrlandoComcast NBCUniversal · Orlando, FL12.4%
04Disneyland ParisDisney · Marne-la-Vallée, France7.2%
05Tokyo Disney ResortOriental Land Co. (Disney licensed) · Tokyo6.4%
The top five parks include four Disney properties and one Universal flagship. Tokyo Disney Resort displaces Universal Studios Hollywood for the #5 position — the combined Disneyland + DisneySea citation base outranks any single Universal park outside Orlando. Every operator in the theme park industry — globally — competes for the 36.3% of category citation share these five parks do not own.
Walt Disney World’s 22.6% reflects three structural advantages: the largest single-destination park complex in the world by acreage and attendance, the deepest Wikipedia and consumer-press retrieval base of any travel destination on the planet, and the recent Epcot, Magic Kingdom, and Animal Kingdom expansion cycles that have refreshed the earned-media base into 2026. The TEA Global Experience Index confirms Magic Kingdom drew 17.84 million visitors in 2024 — its 19th consecutive year as the world’s most-visited park. Disneyland’s 15.1% is unusual for a park nearly seventy years older than its Florida sibling — the California park’s citation share is anchored more in cultural references (Disneyland in film, music, fiction) than current operations.
Universal Studios Orlando’s 12.4% is the Epic Universe effect made measurable. The May 2025 third-park opening sustained a 12-month press cycle that engines now treat as the dominant 2026 narrative for U.S. destination theme parks. Tokyo Disney Resort’s 6.4% reflects combined Tokyo Disneyland and Tokyo DisneySea citation depth — Tokyo Disneyland alone drew 15.1M visitors in 2024 (#4 globally), and DisneySea is frequently cited by enthusiast press as the highest-quality Disney park anywhere in the world.
— Section 05 / 16
## Tier 2: Universal Hollywood, Cedar Point + the global Disneys.
RankParkParent / LocationCitation Share
06Universal Studios HollywoodComcast NBCUniversal · Los Angeles, CA5.8%
07Cedar PointSix Flags Entertainment · Sandusky, OH4.1%
08Universal Studios JapanComcast NBCUniversal · Osaka3.8%
09Six Flags Magic MountainSix Flags Entertainment · Valencia, CA3.2%
10Shanghai DisneylandDisney · Shanghai2.8%
11DollywoodHerschend / Dolly Parton · Pigeon Forge, TN2.6%
12Six Flags Great AdventureSix Flags Entertainment · Jackson, NJ2.4%
13HersheyparkHershey Entertainment · Hershey, PA2.2%
Universal Studios Hollywood drops to #6 in the logged run, displaced by Tokyo Disney Resort’s combined citation depth. Cedar Point’s 4.1% confirms its position as the highest-cited non-Disney, non-Universal park in the world — the “Roller Coaster Capital of the World” branding is a category claim AI engines reproduce verbatim. Six Flags Great Adventure’s 2.4% reflects the Kingda Ka anchor — currently still the world’s tallest operating roller coaster — and the park’s USA Today 10Best Readers’ Choice second-place finish for Best Theme Park overall.
Dollywood at #11 with 2.6% is the most notable over-performance in the entire audit. The Herschend-owned park draws roughly 3 million visitors annually — well outside TEA’s global top 25 — but anchors heavily in Golden Ticket Awards retrieval (Friendliest Theme Park, Best Food, Best Shows, Best Christmas Event) and USA Today 10Best dominance.
— Section 06 / 16
## Tier 3: Regional anchors.
RankParkParent / LocationCitation Share
14SeaWorld OrlandoUnited Parks & Resorts · Orlando, FL1.9%
15Silver Dollar CityHerschend · Branson, MO1.7%
16Europa-ParkMack family · Rust, Germany1.6%
17Busch Gardens TampaUnited Parks & Resorts · Tampa, FL1.5%
18LEGOLAND FloridaMerlin Entertainments · Winter Haven, FL1.4%
19Holiday WorldKoch family · Santa Claus, IN1.3%
20PhantasialandSchmidt-Löffelhardt family · Brühl, Germany1.2%
Tier 3 contains the most distinctive over-performers in the audit. Silver Dollar City’s 1.7% citation share reflects USA Today 10Best Readers’ Choice naming it the #1 amusement park in the United States four consecutive years — a Golden Ticket compounding effect that AI engines route to repeatedly. Europa-Park’s 1.6% confirms the German park as Europe’s strongest non-Disney destination, with 6.2 million annual visitors and consistent “best in Europe” press coverage. Phantasialand’s 1.2% reflects Theme Park Insider’s ranking of the park as the #3 best coaster destination in the world — strong enthusiast-press depth despite limited mainstream U.S. coverage.
Holiday World’s 1.3% is the regional value cluster anchor — the Indiana park dominates “best affordable theme park” prompts on the strength of free parking, free soft drinks, and consistent TripAdvisor Travelers’ Choice rankings. The Hershey Entertainment-owned Hersheypark (#13, 2.2% in Tier 2) and Dollywood (#11, 2.6% in Tier 2) are the only U.S. regional brands that breach the top half of the rankings.
— Section 07 / 16
## Tier 4: The tail.
RankParkParent / LocationCitation Share
21Knott’s Berry FarmSix Flags Entertainment · Buena Park, CA1.1%
22Kings IslandSix Flags Entertainment · Mason, OH1.0%
23LEGOLAND CaliforniaMerlin Entertainments · Carlsbad, CA0.9%
24Hong Kong DisneylandDisney · Hong Kong0.8%
25EnergylandiaMariusz Kalemba · Zator, Poland0.7%
Tier 4 captures parks with genuine operational significance but limited citation depth in the U.S.-origin AI answer surface. Knott’s Berry Farm and Kings Island — both Six Flags Entertainment Corporation properties — underperform their attendance positions, anchoring at the corporate-brand level rather than the named-park level. Hong Kong Disneyland holds TEA top-25 attendance but limited U.S.-language travel coverage suppresses citation share. Energylandia has the most roller coasters of any park in Europe (19) but registers below 1% in U.S.-origin queries — a citation-base problem driven entirely by limited English-language press coverage. The tail is recoverable; visibility here is not a product problem.
— Section 08 / 16
## Family destination: Disney crushes.
### 10 family-destination prompts
“best theme park for kids” · “family vacation” · “best Disney park to visit first”
1Walt Disney World32.6%
2Disneyland19.8%
3Universal Studios Orlando12.4%
4LEGOLAND Florida6.7%
5Disneyland Paris5.9%
6LEGOLAND California5.2%
7Hersheypark3.4%
The most Disney-dominated sub-category in the entire 5W AI Visibility Index franchise. Disney parks capture 58% of in-cluster citation share. The LEGOLAND properties are the only non-Disney, non-Universal parks with material family-destination presence — the LEGO master brand functions as a category anchor.
— Section 09 / 16
## Coasters: Cedar Point owns.
### 10 thrill prompts
“best roller coaster park” · “biggest coasters” · “thrill-seeker park”
1Cedar Point26.8%
2Six Flags Magic Mountain18.4%
3Six Flags Great Adventure11.6%
4Universal Orlando (Islands of Adventure)9.2%
5Kings Island8.4%
6Busch Gardens Tampa6.8%
7Phantasialand5.1%
8Energylandia3.9%
The only major category where Disney does not lead. Cedar Point’s 26.8% reflects “Roller Coaster Capital of the World” branding and a category-authority position the merged Six Flags Entertainment Corporation now controls outright. Six Flags Great Adventure’s 11.6% reflects the Kingda Ka anchor — the world’s tallest operating roller coaster at 456 feet — and the park’s USA Today 10Best second-place finish for Best Theme Park. Universal Orlando’s Islands of Adventure surfaces in the thrill cluster on the strength of Jurassic World VelociCoaster, which Theme Park Insider has named the #1 roller coaster in the world for multiple consecutive years.
— Section 10 / 16
## Animals: SeaWorld’s slow return.
### 10 animal/experience prompts
“theme park with animals” · “marine theme park” · “interactive theme park”
1SeaWorld Orlando22.4%
2Busch Gardens Tampa18.6%
3Disney’s Animal Kingdom17.2%
4SeaWorld San Diego11.4%
5Discovery Cove6.8%
Disney loses its dominant position here because Animal Kingdom is rendered as one experiential option among several rather than as the default. SeaWorld’s 22.4% in-cluster citation share reflects measurable recovery from the 2013–2016 Blackfish citation collapse. The brand has rebuilt earned-media depth around conservation programming, though the documentary references still appear in roughly 18% of trust-intent prompts.
— Section 11 / 16
## Value parks: Dolly + Hershey carry.
### 10 regional-value prompts
“theme park near me” · “affordable theme park” · “best small theme park”
1Silver Dollar City18.4%
2Dollywood17.2%
3Hersheypark14.6%
4Holiday World12.8%
5Knoebels6.4%
6Kennywood4.2%
7Wild Adventures3.6%
Silver Dollar City leads the regional value cluster on the strength of USA Today 10Best Readers’ Choice naming it the #1 amusement park in America for four consecutive years (2022–2025) — a Golden Ticket compounding effect that AI engines route to repeatedly. Dollywood follows at 17.2%, anchored to the Parton brand and multiple Golden Ticket Awards (Friendliest, Best Food, Best Shows, Best Christmas Event). Holiday World’s outsized presence relative to scale reflects the park’s reputation for free parking, free soft drinks, and Travelers’ Choice Award depth. Hersheypark is America’s largest independently owned theme park by attendance — 3 million visitors in 2024.
— Section 12 / 16
## Overseas: more Disney.
### 10 international prompts
“best theme park outside the U.S.” · “theme park Europe” · “theme park Japan”
1Disneyland Paris19.4%
2Tokyo Disney Resort17.8%
3Universal Studios Japan11.6%
4Shanghai Disneyland9.8%
5Europa-Park9.2%
6Phantasialand5.4%
7Efteling4.8%
8PortAventura World3.6%
9Hong Kong Disneyland3.2%
The global pattern at a different scale. Disney resorts (Paris, Tokyo, Shanghai, Hong Kong) combine for 50.2% of in-cluster citation share. Universal Studios Japan is the strongest non-Disney international destination — and at 16M visitors in 2024, the most-attended theme park in Asia. Europa-Park surfaces consistently as Europe’s #1 non-Disney destination, drawing 6.2M annual visitors. Phantasialand and Efteling are the two European parks AI engines most often cite as “rivals to Disney” in enthusiast and traveler press.
— Section 13 / 16
## Where the bots get their answers.
The AI engines pull from a remarkably coherent set of sources when answering theme park questions. Six categories of source carry meaningful retrieval weight.
Wikipedia. Each top-10 park has a long-form Wikipedia entry. Disney and Universal property pages are among the most-trafficked corporate-property pages on Wikipedia. Cited as primary across all five engines on history, attendance, and operations prompts.
TEA / AECOM Theme Index. The annual attendance index produced by Themed Entertainment Association and AECOM is the most-cited industry source across all engines on “most-attended” and “best” prompts. The data is heavily anchored in Disney and Universal positions.
Consumer travel press. Travel + Leisure, Condé Nast Traveler, Lonely Planet, AAA, USA Today 10Best dominate the “best of” retrieval base. These are the sources engines route to on category-recommendation prompts.
TripAdvisor + Travelers’ Choice Awards. Heavily cited on both reputation and value prompts. The “Travelers’ Choice” data is treated as primary by AI engines and disproportionately benefits high-rating regional parks like Holiday World and Dollywood.
Enthusiast press. Theme Park Insider, Coaster101, BlooLoop, Attractions Magazine. These specialty sources carry significant weight on thrill-cluster and new-attraction prompts. Cedar Point and Six Flags Magic Mountain are anchored heavily in this base.
Reddit. r/WaltDisneyWorld, r/Disneyland, r/rollercoasters, r/UniversalOrlando. Reddit registers as direct citation in roughly 25% of practitioner-intent prompts. The Disney park subreddits have become a primary AI retrieval anchor for trip-planning answers.
— Section 14 / 16
## What it means.
If the traveler’s first stop is an AI engine — and a growing share of theme park research now begins there — Disney has already won the family-destination category at 58% citation share. Universal is taking AI-era share faster than any other operator in the audit, driven by the Epic Universe effect. Six Flags Entertainment Corporation owns the thrill category but has not consolidated citation at the corporate level. Everyone outside the three competes for 5.6% of every AI theme-park answer.
### For Disney.
Citation share at Walt Disney World and Disneyland is near a ceiling. Marginal earned media is unlikely to move overall share above 25% for the flagship property — the AI answer is already maximally Disney-skewed. The competitive risk is share erosion at Universal’s hands, particularly in the Orlando market where Epic Universe is producing the largest single-year citation share movement in audit history.
### For Universal.
The Epic Universe effect is the case study every other parent operator should study. A sustained 12-month earned-media cycle around a single new-attraction launch has moved the Universal citation base 3.2 percentage points — more than every other park combined in the same period. The lesson: AI citation share is downstream of sustained, category-defining press cycles. One-off launches do not move the dial.
### For the merged Six Flags Entertainment Corporation.
The post-merger Six Flags Entertainment Corporation operates 42 parks — more than any other operator in the world — but holds 12.8% combined citation share. The strategic question is whether to consolidate at the corporate-brand level (“Six Flags”) or at the named-park level (Cedar Point, Magic Mountain, Great Adventure, Knott’s, Kings Island). The data is clear: the named-park strategy currently outperforms. Cedar Point’s individual citation share (4.1%) exceeds any aggregate “Six Flags” corporate brand mention rate, and Six Flags Great Adventure’s Kingda Ka anchor drives more in-cluster thrill citation than the parent brand. The 2024 merger has not yet produced corporate-level citation consolidation.
### For regional operators.
Citation share below 1% reflects insufficient national earned-media depth, not insufficient operational quality. The regional anchors that have moved above 1% — Hersheypark, Dollywood, Holiday World — all benefit from either category-distinctive IP (Hershey, Parton) or sustained “best small theme park” press coverage. The path to AI citation share for regional operators runs through either IP licensing or a coherent value-based national press position.
— Section 15 / 16
## Fine print.
Logged run, directional.
The Theme Parks AI Visibility Index reports citation share captured from a logged May 2026 run of 50 traveler-intent prompts across five AI answer engines. Numbers are directional estimates synthesized across engines and passes — a category audit, not a precision instrument. Refreshed quarterly.
Outputs vary.
AI-generated answers vary by user, timing, and phrasing. Findings reflect dominant patterns observed across repeated passes — not any single response.
Publisher disclosure.
5W AI Communications is the publisher of this Index. The methodology is identical to the methodology applied across the 60-plus other indexes in the 5W AI Visibility Index series. Prompt set, scoring rules, and engine version notes available on request to credentialed analysts and journalists.
Scoring scope.
The Index measures the AI-rendered answer about each park. Not a judgment of attraction quality, operational performance, or guest experience.
Related — Travel & Hospitality Indexes
AI Visibility Index · Travel
#### Airlines & Hotels
Which carriers and hotel brands own the AI answer when travelers ask where to fly and where to stay.
AI Visibility Index · Travel
#### Luxury Island Resorts
How AI engines rank luxury island destinations — and which properties control the answer.
AI Visibility Index · Dining
#### Restaurant Chains
Which restaurant chains dominate AI dining recommendations and how citation share maps to brand authority.
All Indexes
#### The Full AI Visibility Index Series
60+ category indexes measuring citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
— Section 16 / 16
## About 5W.
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research.
Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O’Dwyer’s, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday’s WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO.
Learn more at 5wpr.com.
The Theme Parks AI Visibility Index 2026 · Volume 65
© 2026 5W Public Relations LLC
---
### Elon Musk
URL: https://www.5wpr.com/reputation/elon-musk/
Summary: The 5W Reputation Index audits the AI-held reputation of Elon Musk across five AI engines, the franchise finale and the instability study.
The 5W Reputation Index · Edition 10 · The Finale
# Elon Musk
What AI Says About You When You're Not in the Room.
Ask Five Engines Who He Is. Get Five Different Men.
The franchise has spent nine editions measuring how stable an AI-held reputation can be. The finale measures the opposite. Ask the engines who Elon Musk is, and the answers do not converge — they fracture.
One frame returns a generational innovator: Tesla, SpaceX, the man who electrified the car and re-opened space. Another returns a polarizing political figure. Another leads with erratic, controversial public behavior. The achievement is acknowledged everywhere; almost nothing else is agreed.
Reputation Intelligence
Edition 10 · Finale
Edition
Subject
Elon Musk
Format
Single subject · Finale
Engines
5
Published
July 21, 2026
## The Verdict
This is the franchise's instability study — and it scores 56, the lowest in the Index, because the reputation itself is no longer one thing.
## Methodology & Confidence
### Five Engines, No Spin
Reputation modeled across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews — 40+ reputation-intent prompts across identity, trust, track record, controversy, comparison, and decision intent. Multiple passes; recurring findings only. Cross-checked against current reporting, critical and favorable. Directional estimates — not a precision instrument.
## The Dominant Narrative
### The Story AI Won't Settle On
There is no single dominant narrative — and that is the finding. The engines hold at least three competing Musks at once: the innovator (founder and force behind Tesla, SpaceX, and a string of frontier ventures); the political actor (a polarizing run through government and partisan politics, and the fallout); and the erratic public figure (the controversial posting, the volatility). Each frame is well-sourced. None has won. The reputation is not frozen — it is fragmented.
## Sentiment Map
### Who the Box Can't Make Up Its Mind About
Valence of the dominant framing each engine surfaces first.
SubjectChatGPTClaudeGeminiPerplexityGoogle AIO
Elon MuskMixedMixedMixed–negMixedMixed–neg
Legend Positive Mixed Mixed–negative Confidence: Low
The finding. The valence label understates it. Within "mixed," the composition swings hard from engine to engine — some lead innovation, some lead controversy, some lead politics. The disagreement is not at the margin. It is structural — which is why confidence on a single label is low.
## First-Surface Audit
### The First Sentence Changes Depending Who You Ask
"For nine editions, the first sentence was a signal. For Musk, it is a coin toss."
This is where the instability is most visible. Some engines open with "CEO of Tesla and SpaceX" — achievement first. Others open with "billionaire businessman" and reach political or behavioral controversy inside the first sentence. Across the franchise, the first sentence has been a reliable read on the reputation. For Musk, the first sentence is not reliable — it depends on which engine is asked.
## The Citation Base
### A Paper Trail That Points Every Direction
The retrieval base is enormous and contradictory. It holds a deep archive of innovation and business coverage; a large, recent volume of political coverage; a continuous stream of controversy and behavior reporting; and reputational research documenting brand decline tied to his politics. The engines are not working from a thin base. They are working from too much, pointing in too many directions — and each engine's source mix lands somewhere different.
## Omissions
### What AI Leaves on the Floor
Less an omission problem than a proportion problem. Depending on the engine, either the innovation record or the controversy record is under-weighted — but rarely the same one twice. No single fact is consistently missing. What is missing is a stable hierarchy among the facts.
## Risk Surfaces
### Where It Blows Up
### Elon Musk
Risk level Elevated
The core liability is not any single controversy — it is volatility itself. A fragmented reputation is unpredictable: it can surface as innovator or as political lightning rod depending on the engine, the phrasing, and the week. For any commercial purpose — investor, partner, customer, recruit — unpredictability is itself the risk. Documented brand-value decline shows the cost is already real.
## Cross-Engine Consistency
### When the Engines Don't Agree — the Whole Point
The lowest consistency score in the franchise. Every prior edition measured a reputation the engines broadly agreed on. This one measures a reputation they do not.
Cross-engine divergence is usually a footnote. Here it is the headline. A reputation this contested is, by the franchise's own logic, also the most movable — but movement requires first re-establishing a center of gravity that currently does not exist.
## Peer Comparison
### The Outlier of the Index
Across ten editions, Musk is the outlier. The franchise's high scorers — Huang, Dimon, the Nobel laureate of Edition 01 — share one trait: a coherent, controlled, credentialed source base producing one story. Musk has the most retrievable reputation in the entire Index and the least coherent. He is proof of the franchise's central claim, stated in reverse: reach without coherence does not produce a strong AI reputation. It produces an unstable one.
## The Reputation Gap
### Not One Gap. Several.
There is no single intended narrative to measure against, and no single delivered one. The real gap is the distance between the engines themselves. Closing it does not mean correcting a falsehood — it means resolving a contradiction: building enough coherent, controlled signal that the engines are pulled back toward a shared center.
## The Reputation Index Score
### The Scoreboard: 56 / 100
How the score works.Five dimensions — Accuracy, Sentiment, Completeness, Consistency, Control — each scored 0–20 and equally weighted, for a composite of 100. Directional estimates.
Elon Musk — Reputation Index Score 56/100
DimensionScore
Accuracy16
Sentiment9
Completeness15
Consistency8
Control8
Composite56/100
The lowest composite in the franchise — driven by the two dimensions that measure coherence: Consistency and Control.
## Remediation Roadmap
### You Can't Fix Fragmentation With Volume
The franchise's other low scorers had a frozen narrative to dilute. Musk's problem is the opposite — not too little signal, but too much, pointing too many ways. More content does not fix fragmentation; it can deepen it. The only viable correction is the discipline most absent from this reputation: a deliberate narrowing — a coherent, controlled, sustained primary-source narrative, consistent enough and patient enough to pull the engines back toward a shared center. It is the hardest assignment in the Index, because the instability is generated faster than any retrieval base can absorb it.
## Who This Is For
For any leader whose name now spans too many stories at once, the finale is the warning: AI does not average your reputation — it fractures it.
## The 5W Read
### 5W Shapes the Answer in the Box
The franchise opened with three founders whose reputations diverged on the coherence of their source base. It closes with the proof in extreme form. Elon Musk has the most retrievable reputation in the Index and the lowest score — because an AI-held reputation is not built from reach.
It is built from coherence, control, and consistency. Where those are absent, the engines do not return one answer. They return several — and several is a liability.
The answer is being given right now — and whether it is one answer or five depends entirely on whether anyone is shaping it.
### End of the series
Edition 10 closes the first cycle of The 5W Reputation Index. Across ten editions — founders, banks, executives, celebrities, airlines, universities — one finding held: AI does not discover a reputation. It synthesizes one, from whatever the retrieval base makes most available. The next cycle begins on the same weekly cadence.
Audit your AI-held reputationBack to the Index
---
### Cosmetic Surgery Search Interest by State: Florida Ranks 8th
URL: https://www.5wpr.com/research/cosmetic-surgery-search-interest-state/
Summary: 5WPR and Haute MD rank all 50 states by cosmetic surgery search interest. Washington leads, Florida ranks eighth, and the BBL tops every leading state.
Washington ranks first in the United States for search interest in cosmetic surgery. California and New York follow, according to a joint study by 5WPR and Haute MD. Florida ranks eighth nationally at 478 average monthly searches per 100,000 residents. The Brazilian butt lift is the most searched cosmetic procedure in every one of the top 10 states. The finding is based on DataForSEO API v3 search volume data, collected across all 50 states over a 12-month period.
## What did the study find?
The study identifies five findings. Washington ranks first nationally with 560 average monthly searches per 100,000 residents. California ranks second at 509, despite carrying the largest total search volume of any state. Florida ranks eighth at 478, on 108,166 total average monthly searches across a population of 22,610,726.
- The Brazilian butt lift is the No. 1 searched procedure in all 10 of the highest-interest states.
- Blepharoplasty (eyelid surgery) is the No. 2 searched procedure in nine of the top 10 states.
- Breast procedures (breast lift or breast augmentation) hold one of the top three spots in five of the top 10 states. Those states are Washington, Utah, Colorado, Florida and Georgia.
## How was the study conducted?
5WPR and Haute MD compiled a set of 300 cosmetic surgery keywords for the analysis. The keywords came from the procedures profiled in the American Society of Plastic Surgeons' 2024 Plastic Surgery Statistics Report. Terms included "cost of tummy tuck," "blepharoplasty near me" and "rhinoplasty before and after."
Average monthly search volume for each keyword was collected over a 12-month period for all 50 states using the DataForSEO API v3 (Google Ads Search Volume data). Volumes were aggregated at the state level and adjusted by population for comparability. States are ranked by average monthly search volume per 100,000 residents, highest to lowest.
## Why does Florida rank eighth?
Florida ranks eighth nationally with 478 average monthly searches per 100,000 residents, drawn from 108,166 total average monthly searches against a population of 22,610,726. The Brazilian butt lift leads the state at 19,533 searches per month, followed by blepharoplasty at 10,858 and breast augmentation at 6,517 average monthly searches.
Florida is the only state in the top 10 where blepharoplasty outranks nose reshaping among the three most searched procedures. That pattern points to a stronger facial rejuvenation signal in the Southeast than the national data shows overall, an area 5WPR's Florida practice tracks closely for beauty and wellness clients in the state.
## Which states rank highest for cosmetic surgery search interest?
Washington, California and New York hold the top three positions nationally. The table below ranks the 10 states with the highest cosmetic surgery search interest per capita.
Table 1. Top 10 U.S. states by cosmetic surgery search interest, ranked by average monthly searches per 100,000 residents.
RankStatePopulationTotal avg. monthly searchesAvg. monthly searches per 100,000
1Washington7,812,88043,728560
2California38,965,193198,320509
3New York19,571,21698,784505
4Utah3,417,73416,907495
5Nevada3,194,17615,739493
6Colorado5,877,61028,790490
7Texas30,503,301147,118482
8Florida22,610,726108,166478
9Virginia8,715,69841,491476
10Georgia11,029,22751,977471
Source: 5WPR and Haute MD analysis of DataForSEO API v3 (Google Ads Search Volume data), 12-month average, 2026.
## What are the top searched procedures in each state?
The Brazilian butt lift ranks first in every one of the top 10 states. Blepharoplasty and either breast augmentation or nose reshaping fill the second and third positions in most states, as the table below shows.
Table 2. Top three searched cosmetic procedures by state, with average monthly search volume, for the 10 states with the highest per-capita search interest.
State#1 procedureVolume#2 procedureVolume#3 procedureVolume
WashingtonBrazilian butt lift7,775Breast lift5,953Blepharoplasty3,925
CaliforniaBrazilian butt lift38,917Blepharoplasty19,875Nose reshaping13,450
New YorkBrazilian butt lift20,350Blepharoplasty10,708Nose reshaping6,525
UtahBrazilian butt lift2,967Blepharoplasty1,525Breast augmentation1,415
NevadaBrazilian butt lift3,008Blepharoplasty1,350Nose reshaping942
ColoradoBrazilian butt lift5,183Blepharoplasty3,017Breast augmentation1,758
TexasBrazilian butt lift31,925Blepharoplasty12,775Nose reshaping8,850
FloridaBrazilian butt lift19,533Blepharoplasty10,858Breast augmentation6,517
VirginiaBrazilian butt lift8,325Blepharoplasty3,892Nose reshaping2,608
GeorgiaBrazilian butt lift11,183Blepharoplasty4,400Breast augmentation2,983
Source: 5WPR and Haute MD analysis of DataForSEO API v3 (Google Ads Search Volume data), 12-month average, 2026.
## Are breast procedures rising in search interest?
Searches for "breast surgery" rose 69.5% since the start of 2023, according to Google Trends. Searches for "breast reduction surgery" rose 22% since December 2024, according to the same source.
The state-level data reflects that trend. Breast lift or breast augmentation ranks among the three most searched procedures in five of the top 10 states: Washington (No. 2), and Utah, Colorado, Florida and Georgia (No. 3).
## What do Haute MD and 5WPR say about the findings?
"What the data shows is a market that is maturing in two directions at once. Body contouring still drives the volume, and the Brazilian butt lift remains the single most searched procedure in every top 10 state. Blepharoplasty has climbed to No. 2 nationally, and it now outranks nose reshaping in Florida specifically. That tells us patients are researching facial rejuvenation with the same seriousness they bring to body procedures. That shift matters for how practices in Florida plan consultations, capacity and patient education over the next 12 months."
Seth Semilof, Co-Founder and Chief Operating Officer, Publisher, Haute Living
"Search data is patient intent before it becomes a patient. The Brazilian butt lift out-searches every other procedure in every one of the top 10 states. Blepharoplasty is closing in on it in states like Florida. That is a roadmap for what practices should publish, and for what AI platforms and search engines need to find and cite correctly through generative engine optimization. A Florida practice built around body-contouring content alone leaves facial rejuvenation, the fastest-growing part of patient research, to whichever competitor answers it first."
Ronn Torossian, Founder and Chairman, 5WPR
"What stands out in Florida is how strongly the Brazilian butt lift continues to dominate interest. It generates more than 19,000 average monthly searches, outperforming every other procedure in the state. That is consistent with what we often see across the Southeast, where body contouring remains especially popular. But Florida's search volume also shows growing interest in facial procedures. Blepharoplasty ranking second suggests body-focused enhancements still lead, with a real shift toward facial rejuvenation underneath."
Dr. Ari Hoschander, Head Plastic Surgeon, Breast Reduction Surgeons of Long Island
## How do all 50 states rank?
Massachusetts ranks 11th nationally, followed by Arizona and Illinois. The table below lists the complete ranking for states 11 through 50.
Table 3. Complete U.S. state ranking, positions 11 to 50, by average monthly cosmetic surgery searches per 100,000 residents.
RankStatePopulationTotal avg. monthly searchesAvg. per 100,000
11Massachusetts7,001,39932,919470.18
12Arizona7,431,34434,686466.75
13Illinois12,549,68957,340456.90
14Oregon4,233,35819,091450.96
15New Jersey9,290,84141,613447.89
16Hawaii1,435,1386,338441.60
17Connecticut3,617,17615,897439.48
18North Carolina10,835,49147,360437.08
19Maryland6,180,25326,733432.55
20Rhode Island1,095,9624,674426.49
21North Dakota783,9263,305421.60
22Delaware1,031,8904,313417.92
23Minnesota5,737,91523,336406.70
24Tennessee7,126,48928,765403.63
25New Hampshire1,402,0545,624401.14
26South Carolina5,373,55521,275395.92
27Nebraska1,978,3797,788393.67
28Alaska733,4062,875392.01
29Pennsylvania12,961,68350,498389.60
30Michigan10,037,26139,037388.92
31Vermont647,4642,494385.22
32Louisiana4,573,74917,474382.05
33Idaho1,964,7267,488381.14
34Ohio11,785,93544,672379.03
35Kentucky4,526,15417,153378.96
36New Mexico2,114,3717,998378.24
37Kansas2,940,54611,107377.71
38Missouri6,196,15623,376377.26
39Maine1,395,7225,213373.52
40Indiana6,862,19925,414370.35
41Alabama5,108,46818,831368.62
42Oklahoma4,053,82414,909367.78
43Wyoming584,0572,133365.26
44Montana1,132,8124,095361.49
45South Dakota919,3183,308359.87
46Arkansas3,067,73210,935356.45
47Wisconsin5,910,95520,817352.17
48Iowa3,207,00410,773335.91
49West Virginia1,770,0715,827329.18
50Mississippi2,939,6909,600326.57
Source: 5WPR and Haute MD analysis of DataForSEO API v3 (Google Ads Search Volume data), 12-month average, 2026.
## Where can readers find the Haute MD version of this study?
5WPR produced this research jointly with Haute MD, the health and aesthetics authority of Haute Living. Haute MD published its own version of the findings, framed for its physician and patient audience, at Haute MD's cosmetic surgery search interest study.
The underlying search-volume analysis was originally commissioned by Breast Reduction Surgeons of Long Island. Republication of these findings should credit and link to Breast Reduction Surgeons of Long Island. Additional 5WPR studies on AI search behavior are available in 5WPR's research library.
---
### Entertainment & Media
URL: https://www.5wpr.com/research/podcast-citation-effect/entertainment/
Summary: Entertainment & Media: how long-form podcast appearances drive AI engine Citation Share. Study #10 of 12 in The Podcast Citation Effect from 5W.
---
### Edition 09: Marketing & Advertising Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/marketing-advertising/
Summary: The sector where measurement firms and trade databases anchor retrieval above journalism.
---
### Immune Support AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/immune-support-ai-visibility-index-2026/
Summary: Immune Support AI visibility data shows which brands appeared in measured answers and which sources supported citations across four engines.
# Immune Support AI Visibility Index 2026
Thorne Leads Immune Support Visibility
Immune support supplement visibility varied by engine in this measurement. Thorne ranked first with a 47.6% appearance rate among answers returned, while NOW Foods ranked second at 44.8%. The difference is within the margin of this sample because their 95% intervals overlap. The measurement covered Vitamin C, Zinc, Elderberry, Vitamin D, and mushroom formulas.
Immune Support is the sole focus of this standalone report in the Supplements AI Visibility Index 2026. Multivitamins, Protein / Protein Powder, Probiotics / Gut Health, Sleep (melatonin / magnesium), and Nootropics / Cognitive are outside its scope. When an answer includes supporting links, a brand mentioned frequently but seldom cited can face a practical visibility gap.
286AI answers analyzed
47.6%Thorne appearance rate, the category leader
26Brands named across the study
99.3%Answers carrying retrieval evidence
The results describe the measured prompts on 2026-09-30, not all immune support searches or consumer demand.
## Which immune support brands lead the leaderboard?
Thorne led the appearance leaderboard with a 47.6% rate among 286 answers returned. NOW Foods followed at 44.8%. Nature Made and Garden of Life were tied at 39.2%, followed by Pure Encapsulations at 28.7% and Life Extension at 28%.
Among the brands named in the client brief, Emergen-C ranked eighth at 19.9%, Airborne ranked eleventh at 17.5%, Sambucol ranked twelfth at 15.7%, and Host Defense ranked twenty-sixth at 3.8%.
### Immune support appearance leaderboard
### AI visibility leaderboard
Appearance rate shows the proportion of the 286 returned AI answers that named each brand.
#BrandNamed inAppearance rate95% intervalAvg position
1Thorne136/28647.6%41.8%–53.3%2.7
2NOW Foods128/28644.8%39.1%–50.5%5.6
3Nature Made112/28639.2%33.7%–44.9%5.2
4Garden of Life112/28639.2%33.7%–44.9%6.8
5Pure Encapsulations82/28628.7%23.7%–34.2%3.8
6Life Extension80/28628%23.1%–33.4%4.9
7Nordic Naturals59/28620.6%16.3%–25.7%5.5
8Emergen-C57/28619.9%15.7%–24.9%7.1
9ConsumerLab55/28619.2%15.1%–24.2%6
10Nature’s Bounty52/28618.2%14.1%–23.1%6.2
11Airborne50/28617.5%13.5%–22.3%7.2
12Sambucol45/28615.7%12%–20.4%5.6
13Nature’s Way45/28615.7%12%–20.4%5.8
14Solgar Inc.45/28615.7%12%–20.4%7.8
15USP44/28615.4%11.7%–20%9.2
Thorne's 95% interval of 41.8% to 53.3% overlaps NOW Foods at 39.1% to 50.5%, Nature Made at 33.7% to 44.9%, Garden of Life at 33.7% to 44.9%, and Pure Encapsulations at 23.7% to 34.2%. The observed differences among these brands are within the margin of this sample. Nature Made and Garden of Life have identical appearance rates and intervals.
## How do the leading brands appear in answers?
While the top brands appear across a wide range of engines, their engine-by-engine patterns vary considerably. The profiles below calculate appearance rates from answers returned. Owned-domain citation rates are based on answers with retrieval evidence.
### Thorne
47.6% appearance rate, with a 95% interval of 41.8% to 53.3%, placed Thorne first. ChatGPT (OpenAI) named Thorne in 74.3% of 70 returned answers, compared with 45.8% of 72 for Perplexity (sonar-pro), 36.1% of 72 for Gemini (Google), and 34.7% of 72 for Claude (Anthropic). Thorne appeared in all four engines and had an average first-mention position of 2.7. Its owned-domain citation rate was 9.4% among 286 answers with retrieval evidence.
### NOW Foods
44.8% appearance rate, with a 95% interval of 39.1% to 50.5%, placed NOW Foods second. ChatGPT (OpenAI) named NOW Foods in 84.3% of 70 returned answers, while Perplexity (sonar-pro) did so in 43.1% of 72, Gemini (Google) in 33.3% of 72, and Claude (Anthropic) in 19.4% of 72. NOW Foods appeared in all four engines, with an average first-mention position of 5.6. Its owned-domain citation rate was 3.8% among 286 answers with retrieval evidence.
### Nature Made
39.2% appearance rate, with a 95% interval of 33.7% to 44.9%, placed Nature Made third. Perplexity (sonar-pro) named Nature Made in 66.7% of 72 returned answers, ChatGPT (OpenAI) in 51.4% of 70, Gemini (Google) in 29.2% of 72, and Claude (Anthropic) in 9.7% of 72. Nature Made appeared in all four engines and had an average first-mention position of 5.2. Its owned-domain citation rate was 7.7% among 286 answers with retrieval evidence.
### Garden of Life
39.2% appearance rate, with a 95% interval of 33.7% to 44.9%, placed Garden of Life fourth. ChatGPT (OpenAI) named Garden of Life in 80% of 70 returned answers, compared with 44.4% of 72 for Perplexity (sonar-pro), 29.2% of 72 for Gemini (Google), and 4.2% of 72 for Claude (Anthropic). Garden of Life appeared in all four engines and had an average first-mention position of 6.8. Its owned-domain citation rate was 5.2% among 286 answers with retrieval evidence.
### Pure Encapsulations
28.7% appearance rate, with a 95% interval of 23.7% to 34.2%, placed Pure Encapsulations fifth. ChatGPT (OpenAI) named Pure Encapsulations in 50% of 70 returned answers, Claude (Anthropic) in 25% of 72, Gemini (Google) in 23.6% of 72, and Perplexity (sonar-pro) in 16.7% of 72. Pure Encapsulations appeared in all four engines and had an average first-mention position of 3.8. Its owned-domain citation rate was 0.7% among 286 answers with retrieval evidence.
### Life Extension
28% appearance rate, with a 95% interval of 23.1% to 33.4%, placed Life Extension sixth. ChatGPT (OpenAI) named Life Extension in 54.3% of 70 returned answers, while Perplexity (sonar-pro) did so in 27.8% of 72, Gemini (Google) in 25% of 72, and Claude (Anthropic) in 5.6% of 72. Life Extension appeared in all four engines and had an average first-mention position of 4.9. Its owned-domain citation rate was 12.2% among 286 answers with retrieval evidence.
### Nordic Naturals
20.6% appearance rate, with a 95% interval of 16.3% to 25.7%, placed Nordic Naturals seventh. ChatGPT (OpenAI) named Nordic Naturals in 50% of 70 returned answers, compared with 15.3% of 72 for Perplexity (sonar-pro), 12.5% of 72 for Gemini (Google), and 5.6% of 72 for Claude (Anthropic). Nordic Naturals appeared in all four engines and had an average first-mention position of 5.5. Its owned-domain citation rate was 0.3% among 286 answers with retrieval evidence.
### Emergen-C
19.9% appearance rate, with a 95% interval of 15.7% to 24.9%, placed Emergen-C eighth. ChatGPT (OpenAI) named Emergen-C in 31.4% of 70 returned answers, Perplexity (sonar-pro) in 22.2% of 72, Gemini (Google) in 15.3% of 72, and Claude (Anthropic) in 11.1% of 72. Emergen-C appeared in all four engines and had an average first-mention position of 7.1. Its owned-domain citation rate was 1% among 286 answers with retrieval evidence.
### ConsumerLab
19.2% appearance rate, with a 95% interval of 15.1% to 24.2%, placed ConsumerLab ninth in the combined tally. Gemini (Google) named ConsumerLab in 54.2% of 72 returned answers, compared with 12.9% of 70 for ChatGPT (OpenAI), 9.7% of 72 for Perplexity (sonar-pro), and 0% of 72 for Claude (Anthropic). ConsumerLab had an average first-mention position of 6 and an owned-domain citation rate of 60.8% among 286 answers with retrieval evidence. ConsumerLab is tracked separately as a testing and comparison source, not treated as a supplement brand.
### Nature's Bounty
Nature's Bounty ranked tenth with an 18.2% appearance rate and a 95% interval of 14.1% to 23.1%. Perplexity (sonar-pro) named Nature's Bounty in 44.4% of 72 returned answers, versus 24.3% of 70 for ChatGPT (OpenAI), 4.2% of 72 for Gemini (Google), and 0% of 72 for Claude (Anthropic). Nature's Bounty was named by three engines, with an average first-mention position of 6.2. Among 286 answers with retrieval evidence, its owned-domain citation rate was 1.4%.
## Where do the engines disagree?
The engines disagree most on which brands deserve repeated naming. Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic) use different models and retrieval tools, so an engine gap can reflect system design rather than a brand's underlying visibility.
### Engine-by-brand appearance rates
### Appearance rate by engine
Every cell is calculated against that engine's own answer count, allowing row-by-row engine comparisons.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Thorne45.8%74.3%36.1%34.7%
NOW Foods43.1%84.3%33.3%19.4%
Nature Made66.7%51.4%29.2%9.7%
Garden of Life44.4%80%29.2%4.2%
Pure Encapsulations16.7%50%23.6%25%
Life Extension27.8%54.3%25%5.6%
Nordic Naturals15.3%50%12.5%5.6%
Emergen-C22.2%31.4%15.3%11.1%
ConsumerLab9.7%12.9%54.2%0%
Nature’s Bounty44.4%24.3%4.2%0%
Airborne38.9%10%15.3%5.6%
Sambucol26.4%18.6%6.9%11.1%
ChatGPT (OpenAI) concentrated heavily on several leading brands across 70 returned answers: NOW Foods at 84.3%, Garden of Life at 80%, Thorne at 74.3%, Life Extension at 54.3%, and both Pure Encapsulations and Nordic Naturals at 50%. Claude (Anthropic) named the same brands far less often, including NOW Foods at 19.4%, Garden of Life at 4.2%, Life Extension at 5.6%, and Nordic Naturals at 5.6% across 72 returned answers.
Perplexity (sonar-pro) favored Nature Made at 66.7% of 72 returned answers and Nature's Bounty at 44.4%. Claude (Anthropic) named Nature Made in 9.7% and did not name Nature's Bounty in its 72 returned answers. GNC appeared in 43.1% of Perplexity's answers but 4.3% of ChatGPT's and 0% of Gemini's and Claude's answers. New Chapter had a similar Perplexity pattern: 26.4%, versus 2.9%, 6.9%, and 5.6% in the other engines.
Gemini (Google) produced a distinct source and certification pattern. It named ConsumerLab in 54.2% of 72 returned answers, USP in 55.6%, United States Pharmacopeia in 25%, NSF in 25%, and National Institutes of Health in 31.9%. Claude (Anthropic) named none of ConsumerLab, USP, NSF, or National Institutes of Health in its 72 returned answers. These entities are sources or certification bodies, not supplement brands.
Nature's Way appears twice in the tally under typographic variants. Nature's Way appeared in 15.7% of returned answers, while Nature's Way with a straight apostrophe appeared in 8.4%. The variants have the same domain, naturesway.com, and their engine patterns differ. This naming split is part of the observed tally and limits any combined interpretation.
## Which demand clusters favor which brands?
The demand-cluster results show which brands lead particular types of immune support questions within the measured prompt set. The chart separates the cluster groups, so general buyer-intent patterns should not be read as the same kind of evidence as sub-category and specific-need patterns.
### Brand leaders by demand cluster
### Leaders by demand cluster
Here, the answers are regrouped according to the demand cluster from which each prompt was sampled; each cluster rate uses its own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
— Immune Support —
best supplement for frequent illness12Nature Made58.3%Zinc 58.3%, Vitamin C 50%, Thorne 41.7%
best colostrum supplement12NOW Foods33.3%Pure Encapsulations 8.3%, Life Extension 8.3%
best zinc supplement for immunity12Thorne83.3%NOW Foods 83.3%, Pure Encapsulations 75%, Garden of Life 66.7%
best vitamin C supplement for immune support12Thorne83.3%Emergen-C 83.3%, Pure Encapsulations 58.3%, NOW Foods 50%
best immune support supplement for adults12Thorne58.3%NOW Foods 58.3%, Life Extension 58.3%, Nature Made 50%
best elderberry supplement12Sambucol83.3%Gaia Herbs 58.3%, New Chapter 58.3%, Garden of Life 50%
best vitamin D supplement12Nordic Naturals91.7%NOW Foods 83.3%, Nature Made 83.3%, Thorne 75%
best supplement for seasonal allergies12Thorne58.3%Pure Encapsulations 50%, ConsumerLab 41.7%, NOW Foods 33.3%
vitamin C vs zinc for immunity12Thorne66.7%Vitamin C 66.7%, Zinc 58.3%, Nature Made 50%
best supplement to prevent colds12Zinc66.7%Vitamin C 66.7%, Thorne 33.3%, NOW Foods 33.3%
best supplement stack for cold and flu season12Zinc66.7%Thorne 58.3%, NOW Foods 58.3%, Vitamin C 58.3%
best mushroom supplement for immunity12Host Defense66.7%Gaia Herbs 58.3%, Life Extension 50%, Nordic Naturals 25%
best immune gummies12OLLY50%Airborne 41.7%, Nature Made 33.3%, Garden of Life 33.3%
best quercetin supplement12Life Extension91.7%Thorne 83.3%, NOW Foods 83.3%, Solgar Inc. 33.3%
— Other demand —
General best-in-category12NOW Foods66.7%Garden of Life 58.3%, Thorne 41.7%, Nature Made 41.7%
Price and value12Nature Made50%Emergen-C 33.3%, Nature’s Bounty 33.3%, Airborne 33.3%
Everyday and mainstream choice12NOW Foods58.3%Garden of Life 50%, Nature Made 41.7%, Airborne 41.7%
Where to buy12Thorne75%NOW Foods 66.7%, Garden of Life 66.7%, Pure Encapsulations 58.3%
Gifting and occasions12Nature Made50%Thorne 41.7%, NOW Foods 41.7%, Garden of Life 33.3%
Primary use case12Nature Made75%NOW Foods 66.7%, Garden of Life 66.7%, Thorne 58.3%
Beginners and first purchase12Thorne41.7%Nature Made 41.7%, Nature’s Bounty 41.7%, Garden of Life 33.3%
Specific need or condition12Sambucol83.3%Thorne 58.3%, Nature Made 58.3%, Nature’s Way 58.3%
Comparison and evaluation11Nature Made45.5%Garden of Life 36.4%, USP 36.4%, Nature’s Bounty 27.3%
Trust and recommendation11Thorne90.9%Garden of Life 63.6%, Pure Encapsulations 63.6%, NOW Foods 54.5%
For general buyer-intent questions, the chart identifies the brands that recur when an answer names products or makers. For sub-category and specific-need questions, it identifies where a brand's measured visibility is tied to Vitamin C, Zinc, Elderberry, Vitamin D, or mushroom formulas. A brand can lead one cluster without leading the full leaderboard because the prompts test different question types.
## Which sites receive citations and which sources supply answers?
Third-party sources received more prominent citation support than most supplement makers' own sites. Citation rates below use the 286 answers with retrieval evidence, not the 286 returned answers used for appearance.
### Owned-domain citation rates
### Owned-domain citation rate
This is the share of the 286 answers with retrieval evidence that cited a URL from the brand's own website.
Thorne
9.4% · 27/286
NOW Foods
3.8% · 11/286
Nature Made
7.7% · 22/286
Garden of Life
5.2% · 15/286
Pure Encapsulations
0.7% · 2/286
Life Extension
12.2% · 35/286
Nordic Naturals
0.3% · 1/286
Emergen-C
1% · 3/286
ConsumerLab
60.8% · 174/286
Nature’s Bounty
1.4% · 4/286
Airborne
0% · 0/286
Sambucol
1.4% · 4/286
Among citation-eligible supplement brands, GNC had a 19.2% owned-domain citation rate, Life Extension had 12.2%, Thorne had 9.4%, New Chapter had 8%, and Nature Made had 7.7%. Airborne had a 0% owned-domain citation rate despite a 17.5% appearance rate among answers returned. These are different measures: appearance records a brand name in an answer, while citation measures whether retrieval evidence cited the brand's own domain.
### Most-cited source domains
### Most-cited publishers
Evidence publishers are counted once in each answer, rather than once for every link.
PublisherAnswers citing itAnswer penetration
consumerlab.com173/28660.5%
healthline.com104/28636.4%
health.usnews.com72/28625.2%
amazon.com56/28619.6%
gnc.com55/28619.2%
walmart.com55/28619.2%
straight.com53/28618.5%
threeheartshome.com53/28618.5%
target.com52/28618.2%
cvs.com50/28617.5%
bodysciencereview.com46/28616.1%
consumerhealthdigest.com45/28615.7%
ConsumerLab.com had 60.5% answer penetration among answers with retrieval evidence. Healthline.com had 36.4%, health.usnews.com had 25.2%, amazon.com had 19.6%, and gnc.com and walmart.com each had 19.2%. The top source pattern includes health publishers, retailers, and testing or comparison material. It does not support a claim that any one source type controls all immune support answers.
## Who is winning and who is at risk?
### Who is winning?
Thorne is winning on broad measured appearance: it ranked first at 47.6%, appeared in all four engines, and had an average first-mention position of 2.7. NOW Foods ranked second at 44.8% and reached 84.3% in ChatGPT's 70 returned answers. Nature Made and Garden of Life shared a 39.2% appearance rate, while their engine profiles differed. Life Extension paired a 28% appearance rate with a 12.2% owned-domain citation rate among answers with retrieval evidence.
### Who is at risk?
Airborne is at risk of a citation gap: it appeared in 17.5% of returned answers but had a 0% owned-domain citation rate among answers with retrieval evidence. Host Defense ranked twenty-sixth at 3.8%, with a 95% interval of 2.2% to 6.8%, and its rates stayed between 2.8% and 4.3% across engines. Emergen-C ranked eighth at 19.9%, but its average first-mention position was 7.1 and its owned-domain citation rate was 1%. Sambucol ranked twelfth at 15.7% with a 1.4% owned-domain citation rate.
## What structural pattern does the tally show?
The top of the appearance leaderboard is close rather than decisive. Thorne's 41.8% to 53.3% interval overlaps the intervals for NOW Foods, Nature Made, Garden of Life, and Pure Encapsulations, so the sample does not establish that Thorne is more visible than those brands. The engines also do not converge on one stable brand set. ChatGPT repeatedly named NOW Foods and Garden of Life, Perplexity repeatedly named Nature Made, and Gemini repeatedly named certification and testing entities.
Owned-domain citation patterns are separate from appearance patterns. ConsumerLab had a 60.8% owned-domain citation rate, National Institutes of Health had 30.8%, and GNC had 19.2%, while several frequently named supplement brands had much lower owned-domain citation rates. The source list also places ConsumerLab.com, Healthline.com, health.usnews.com, amazon.com, gnc.com, and walmart.com among the cited domains. The measured answer set therefore draws support from publishers, retailers, testing sources, government material, and brand sites.
Among the brief's named brands, Host Defense was near the bottom of the measured leaderboard at 3.8%. Emergen-C, Airborne, and Sambucol appeared more often, but each had an owned-domain citation rate at or below 1.4% among answers with retrieval evidence.
## What changed during the measurement window?
The data contains no time-series comparison, so it does not show a change during the measurement window. It records repeat generations across the measured prompt set and shows engine-level divergence, including Gemini's higher appearance rates for ConsumerLab, USP, NSF, and National Institutes of Health, and Perplexity's higher rates for GNC and New Chapter.
## What should immune support brands fix first?
- Airborne should inspect the pages used for its immune support products because it had a 17.5% appearance rate but a 0% owned-domain citation rate. Re-measure its owned-domain citation rate across the same 24 prompts and four engines.
- Emergen-C should strengthen material that can support earlier answer placement because its average first-mention position was 7.1. Re-measure average first-mention position alongside its 19.9% appearance rate.
- Host Defense should build coverage around the specific mushroom-formula questions included in this segment because it ranked twenty-sixth at 3.8%. Re-measure its cluster results and overall appearance rate.
- Sambucol should test whether its elderberry pages can earn more retrieved support because its owned-domain citation rate was 1.4%. Re-measure citations among answers with retrieval evidence, not among all returned answers.
- NOW Foods should reduce its engine dependence because its appearance rate was 84.3% in ChatGPT but 19.4% in Claude. Re-measure each engine separately rather than relying on one combined rate.
- Nature Made should investigate its Perplexity and Claude gap because it appeared in 66.7% of Perplexity's answers and 9.7% of Claude's. Re-measure the same brand across each engine and preserve the engine-specific denominator.
- Nature's Way should standardize how the brand is named in pages and supporting material because the tally contains both Nature's Way and Nature's Way variants under the same domain. Re-measure whether those variants remain split in future answer tallies.
## What does this mean for immune support brands?
Interpretation: Immune support brand visibility is not one contest with one answer. The measurement shows different answer sets by engine, while cited support often comes from testing sources, health publishers, retailers, government material, and selected brand sites. Brand teams can separate two jobs: earn brand mentions in the answers returned, and earn citations to the pages that support those answers.
For seasonal prevention, elderberry, zinc, Vitamin D, Vitamin C, and mushroom formula questions, the next measurement should preserve both distinctions. A rise in appearance without a rise in owned-domain citations would mean a different outcome than a rise in citations without broad engine coverage.
## How was this immune support study measured?
This flagship study tested 24 stratified prompts three times in Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). It attempted 288 answers and returned 286. Appearance rates use the 286 returned answers. Citation and source rates use the 286 answers with retrieval evidence. Results are limited to the measured prompts and repeat generations on 2026-09-30.
### Measurement summary
### How this study was measured
- The sampling frame came first: 24 prompts were assigned across the demand clusters before any questions were asked.
- Every engine received the same questions: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- Of 288 attempted, 286 answers were returned, and 286 had retrieval evidence.
- Product lines were consolidated under parent brands through a registry set before scoring.
- Appearance rate uses a 95% Wilson interval; citation metrics include only answers with evidence.
The study does not measure sales, clinical effectiveness, product quality, consumer demand, or the full universe of immune support searches. It measures which named entities and cited domains appeared in this defined answer set.
## Frequently asked questions
### Which brand had the highest immune support appearance rate?
Thorne ranked first with a 47.6% appearance rate among 286 answers returned. Its 95% interval was 41.8% to 53.3%.
### Was Thorne more visible than NOW Foods?
Thorne recorded an observed rate of 47.6%, while NOW Foods recorded 44.8%. Because their 95% intervals overlap, this sample places the difference within its margin.
### Which engine named NOW Foods most often?
ChatGPT (OpenAI) named NOW Foods in 84.3% of its 70 returned answers. Perplexity, Gemini, and Claude named it in 43.1%, 33.3%, and 19.4% of their 72 returned answers, respectively.
### Did Airborne's own site receive citations?
Among 286 answers with retrieval evidence, Airborne's owned-domain citation rate was 0%. Separately, Airborne appeared in 17.5% of 286 answers returned.
### Which source domain was cited most often?
ConsumerLab.com had 60.5% answer penetration among 286 answers with retrieval evidence. Healthline.com followed at 36.4%.
### Did Gemini favor certification and testing entities?
Gemini named ConsumerLab in 54.2% of 72 returned answers, USP in 55.6%, NSF in 25%, and National Institutes of Health in 31.9%. These entities are sources or certification bodies, not supplement brands.
### Does this report measure all immune support searches?
No. The report describes 24 measured prompts, tested three times across four engines on 2026-09-30. It does not measure all searches or consumer demand in the category.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
Although measured in the same way, these are not brands in the category and are excluded from the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1Healthline5.2%15/2863.2%–8.5%
2Mayo Clinic0.3%1/2860.1%–2%
3Verywell Health0%0/2860%–1.3%
4Examine.com0%0/2860%–1.3%
## Methodology appendix
This appendix comes directly from the study's measurement record. It is provided so any brand named here can reproduce or contest the result.
### What was measured
- Category: Immune Support
- Measurement date: 2026-09-30
- Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 288
- Answers returned (appearance denominator): 286
- Answers with retrieval evidence (citation denominator): 286 (99.3% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Flagship depth: 24 stratified prompts were measured three times per engine, making both between-prompt and repeat-generation variation observable.
### How the prompts were sampled
The sampling frame is fixed: 14 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Immune Support; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer provides wording only within these cells and does not determine the category's demand structure.
Demand clusterPromptsScope
best vitamin C supplement for immune support1best vitamin C supplement for immune support
best zinc supplement for immunity1best zinc supplement for immunity
best elderberry supplement1best elderberry supplement
best vitamin D supplement1best vitamin D supplement
best immune support supplement for adults1best immune support supplement for adults
best supplement to prevent colds1best supplement to prevent colds
best mushroom supplement for immunity1best mushroom supplement for immunity
best supplement for seasonal allergies1best supplement for seasonal allergies
best immune gummies1best immune gummies
best supplement stack for cold and flu season1best supplement stack for cold and flu season
best quercetin supplement1best quercetin supplement
best colostrum supplement1best colostrum supplement
best supplement for frequent illness1best supplement for frequent illness
vitamin C vs zinc for immunity1vitamin C vs zinc for immunity
General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Primary use case1the dominant everyday use case for this category
Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
### The exact prompts
#PromptClusterShape
1best vitamin C supplement for immune supportneed_1medium
2best zinc supplement for immunityneed_2medium
3best elderberry supplementneed_3medium
4best vitamin D supplementneed_4medium
5best immune support supplement for adultsneed_5medium
6best supplement to prevent coldsneed_6medium
7best mushroom supplement for immunityneed_7medium
8best supplement for seasonal allergiesneed_8medium
9best immune gummiesneed_9medium
10best supplement stack for cold and flu seasonneed_10medium
11best quercetin supplementneed_11medium
12best colostrum supplementneed_12medium
13best supplement for frequent illnessneed_13medium
14vitamin C vs zinc for immunityneed_14medium
15best immune support supplementsgeneral_bestshort
16everyday immune support brandseverydayshort
17best value immune supplementsvalue_priceshort
18Which immune support supplement brands are best for everyday immune health?use_casemedium
19What immune support brands are best for first-time supplement buyers?beginnermedium
20Where should I buy reliable immune support supplements online or in stores?retailmedium
21What immune support supplement brands make a thoughtful wellness gift?giftingmedium
22Which immune support brands are best for adults seeking vitamin D, zinc, and elderberry formulas during cold season?specific_needlong
23Which immune support supplement brands offer the best balance of ingredients, testing, and price?comparisonlong
24Which immune support supplement brands do reviewers and health experts trust most for quality and safety?trustlong
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search727272100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool72707097.2%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)727272100%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool727272100%
Engine results are not strictly comparable: each uses a different model, retrieval tool and answer format, meaning a gap between two engines may reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the proportion of returned answers that named the brand. It is measured across every answer an engine returned, whether or not the engine searched the web, so retrieval behaviour does not quietly alter the denominator.
- Macro appearance rate — the average of per-prompt appearance rates, giving every prompt one count regardless of the number of engines that answered it.
- 95% interval — the Wilson score interval for appearance rate. When two brands' intervals overlap, this study does not support saying that either is more visible.
- Owned-domain citation rate — the proportion of answers with retrieval evidence that cited a URL on the brand's own domain. It narrowly measures use of the brand's own site as a source, not total mentions.
- Share of all citations — the proportion of all attributed publisher citations in the study accounted for by the brand's own domain.
- Not measured — no citation result exists because the brand's official domain could not be established. It is never presented as 0%.
- 0% — the metric was assessable, but the event did not happen.
- Average position — the average position of the brand's first mention within the answer's ordered list of named brands.
- Cross-engine consistency — the proportion of engines that named the brand at least once.
- Repeat consistency — the proportion of repeated prompt/engine cells in which the brand appeared in every trial. Lower figures indicate an unstable engine answer, rather than a brand's absence.
### Evidence rules
- Retrieval evidence is counted only when the engine returned a retrieval receipt (search annotations or grounding records). A URL merely typed into model prose does not establish that it searched.
- Sources are assigned to the publisher behind each retrieval receipt. Intermediary and redirect hosts are not reported as publishers.
- Publisher identity could not be established for 0 retrieval receipt(s); these are disclosed here instead of being attributed or omitted.
- Source counts are calculated at answer level: a publisher is counted once per answer that cited it, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Thorne136/28647.6%47.6%41.8%–53.3%27/286 (9.4%)
2NOW Foods128/28644.8%44.7%39.1%–50.5%11/286 (3.8%)
3Nature Made112/28639.2%39.2%33.7%–44.9%22/286 (7.7%)
4Garden of Life112/28639.2%39.2%33.7%–44.9%15/286 (5.2%)
5Pure Encapsulations82/28628.7%28.7%23.7%–34.2%2/286 (0.7%)
6Life Extension80/28628%28%23.1%–33.4%35/286 (12.2%)
7Nordic Naturals59/28620.6%20.7%16.3%–25.7%1/286 (0.3%)
8Emergen-C57/28619.9%19.9%15.7%–24.9%3/286 (1%)
9ConsumerLab55/28619.2%19.2%15.1%–24.2%174/286 (60.8%)
10Nature’s Bounty52/28618.2%18.3%14.1%–23.1%4/286 (1.4%)
11Airborne50/28617.5%17.4%13.5%–22.3%0/286 (0%)
12Sambucol45/28615.7%15.6%12%–20.4%4/286 (1.4%)
13Nature’s Way45/28615.7%15.7%12%–20.4%5/286 (1.7%)
14Solgar Inc.45/28615.7%15.8%12%–20.4%3/286 (1%)
15USP44/28615.4%15.5%11.7%–20%2/286 (0.7%)
16Zinc42/28614.7%14.6%11.1%–19.3%domain not established — excluded from citation metrics
17Vitamin C40/28614%13.9%10.4%–18.5%domain not established — excluded from citation metrics
18Gaia Herbs37/28612.9%12.9%9.5%–17.3%12/286 (4.2%)
19GNC34/28611.9%11.8%8.6%–16.2%55/286 (19.2%)
20New Chapter30/28610.5%10.5%7.4%–14.6%23/286 (8%)
21National Institutes of Health25/2868.7%8.7%6%–12.6%88/286 (30.8%)
22Nature's Way24/2868.4%8.4%5.7%–12.2%5/286 (1.7%)
23NSF23/2868%8.2%5.4%–11.8%4/286 (1.4%)
24OLLY21/2867.3%7.4%4.9%–11%5/286 (1.7%)
25United States Pharmacopeia19/2866.6%6.6%4.3%–10.1%2/286 (0.7%)
26Host Defense11/2863.8%3.8%2.2%–6.8%2/286 (0.7%)
### Entity handling
For brand-level metrics, product lines and model names are assigned to their parent brand under a registry fixed before scoring. No grouping is made solely from this study's own counts.
No roll-ups were applied in this study.
The answers produced 40 further name(s) that could not be confidently resolved to a brand in this category. They are awaiting human classification before the next study and have no metrics here.
### Limits of these findings
- This is a point-in-time record of what the named systems returned to these exact prompts on 2026-09-30. Engine behaviour can change without notice.
- The prompt set is a stratified sample of category demand rather than a census. Findings apply to these prompts and should not be treated as representative of all category searches.
- At this sample size, brands with overlapping intervals cannot be statistically distinguished.
- A brand absent from this list was not measured as absent — it was simply not named in these answers.
- Appearance measures visibility, not endorsement, quality judgement or market-share estimate.
- Before scoring, no official domain was established for 2 of 26 ranked brand(s). They therefore have appearance results only and are excluded from citation metrics and source tables, rather than reported as zero.
---
### Robo-Advisors & Retail Investing AI Visibility Index 2026
URL: https://www.5wpr.com/research/ai-search-visibility-for-robo-advisors-retail-investing-2026/
Summary: Robo-advisor AI visibility data from 5W shows Betterment leads appearances while Forbes, NerdWallet and other publishers dominate the sources AI engines cite.
# Robo-Advisors & Retail Investing AI Visibility Index 2026
Betterment Leads AI Robo Advice Mentions
Betterment had the highest appearance rate in this Robo-Advisors & Retail Investing study: 69.4% of 186 answers returned across the measured prompts. Vanguard followed at 66.7% and Fidelity at 66.1%, but their 95% intervals overlap Betterment's interval of 62.4% to 75.5%. This sample does not establish that Betterment is more visible than either brand.
The brief's proposed pure-play versus legacy split is not supported as a clean result. Betterment and Wealthfront rank near the top, but Vanguard and Fidelity also appear in more than two-thirds of returned answers. Citation behavior tells a different story: Fidelity's owned domain appeared in 26.3% of answers with retrieval evidence, while Betterment's appeared in 12.4%.
Ignoring this gap leaves a brand exposed when an engine names competitors but cites publishers or competitor-owned pages to support its selection guidance.
186AI answers analyzed
69.4%Betterment appearance rate, the category leader
25Brands named across the study
64.6%Answers carrying retrieval evidence
## Which robo-advisors lead the measured answers?
Betterment ranks first by appearance rate, followed by Vanguard, Fidelity, Wealthfront, SoFi, Charles Schwab, Acorns, and M1 Finance. The results apply only to the 24 measured prompts covering tax-loss harvesting, environmental, social, and governance investing, and hybrid human plus artificial intelligence advice.
Vanguard and Fidelity had nearly the same appearance level as Betterment within this sample. Wealthfront's interval of 55.2% to 69.0% also overlaps Betterment's. SoFi's interval of 44.5% to 58.7% does not overlap Betterment's interval.
### AI visibility leaderboard
Appearance rate is the share of the 186 returned AI answers in which each brand was named.
#BrandNamed inAppearance rate95% intervalAvg position
1Betterment129/18669.4%62.4%–75.5%4.1
2Vanguard124/18666.7%59.6%–73%4.1
3Fidelity123/18666.1%59.1%–72.5%3.7
4Wealthfront116/18662.4%55.2%–69%4.3
5SoFi96/18651.6%44.5%–58.7%6.1
6Charles Schwab93/18650%42.9%–57.1%4.6
7Acorns70/18637.6%31%–44.8%9.2
8M1 Finance60/18632.3%26%–39.3%8.1
9Robinhood45/18624.2%18.6%–30.8%7.3
10Empower36/18619.4%14.3%–25.6%6.6
11Schwab Intelligent Portfolios32/18617.2%12.5%–23.3%4.7
12Merrill32/18617.2%12.5%–23.3%8.7
13Interactive Brokers29/18615.6%11.1%–21.5%7.8
14Ellevest27/18614.5%10.2%–20.3%8.9
15ETRADE27/18614.5%10.2%–20.3%7.5
Confidence intervals overlap across the first five ranks. Betterment's 62.4% to 75.5% interval overlaps Vanguard, Fidelity, Wealthfront, and SoFi. The apparent differences among those brands are within the margin of this sample. Fidelity's 59.1% to 72.5% interval also overlaps Vanguard and Wealthfront.
## How do the leading brands appear across engines?
The top brands appear across all four engines, but their frequency changes sharply by engine. Appearance rates below use answers returned by each engine, not the 186-answer combined denominator.
### Top brand profiles
### Betterment
69.4% appearance rate places Betterment first, with a 95% interval of 62.4% to 75.5%. Perplexity named Betterment in 45 of 48 returned answers, or 93.8%, and ChatGPT named it in 39 of 46, or 84.8%. Gemini named Betterment in 23 of 46 answers, or 50.0%, while Claude named it in 22 of 46, or 47.8%. Betterment's average first-mention position was 4.1. Its owned domain was cited in 12.4% of 186 answers with retrieval evidence.
### Vanguard
66.7% appearance rate places Vanguard second, with a 95% interval of 59.6% to 73.0%. ChatGPT named Vanguard in 45 of 46 returned answers, or 97.8%, while Perplexity named it in 44 of 48, or 91.7%. Gemini named Vanguard in 21 of 46 answers, or 45.7%, and Claude named it in 14 of 46, or 30.4%. Vanguard's average first-mention position was 4.1. Its owned domain was cited in 14.0% of answers with retrieval evidence.
### Fidelity
66.1% appearance rate places Fidelity third, with a 95% interval of 59.1% to 72.5%. Perplexity named Fidelity in 46 of 48 returned answers, or 95.8%, and ChatGPT named it in 38 of 46, or 82.6%. Gemini named Fidelity in 18 of 46 answers, or 39.1%, while Claude named it in 21 of 46, or 45.7%. Fidelity's average first-mention position was 3.7, the earliest among the top eight. Its owned domain was cited in 26.3% of answers with retrieval evidence.
### Wealthfront
62.4% appearance rate places Wealthfront fourth, with a 95% interval of 55.2% to 69.0%. Perplexity named Wealthfront in 42 of 48 returned answers, or 87.5%, and ChatGPT named it in 34 of 46, or 73.9%. Gemini named Wealthfront in 19 of 46 answers, or 41.3%, while Claude named it in 21 of 46, or 45.7%. Wealthfront's average first-mention position was 4.3. Its owned domain was cited in 17.7% of answers with retrieval evidence.
### SoFi
51.6% appearance rate places SoFi fifth, with a 95% interval of 44.5% to 58.7%. Perplexity named SoFi in 39 of 48 returned answers, or 81.3%, and ChatGPT named it in 31 of 46, or 67.4%. Gemini named SoFi in 14 of 46 answers, or 30.4%, while Claude named it in 12 of 46, or 26.1%. SoFi's average first-mention position was 6.1. Its owned domain was cited in 5.9% of answers with retrieval evidence.
### Charles Schwab
50.0% appearance rate places Charles Schwab sixth, with a 95% interval of 42.9% to 57.1%. ChatGPT named Charles Schwab in 42 of 46 returned answers, or 91.3%, compared with 27 of 48, or 56.3%, for Perplexity. Gemini named Charles Schwab in 10 of 46 answers, or 21.7%, and Claude named it in 14 of 46, or 30.4%. Charles Schwab's average first-mention position was 4.6. Its owned domain was cited in 13.4% of answers with retrieval evidence.
### Acorns
37.6% appearance rate places Acorns seventh, with a 95% interval of 31.0% to 44.8%. Perplexity named Acorns in 32 of 48 returned answers, or 66.7%. ChatGPT named Acorns in 18 of 46, or 39.1%, and Gemini did so in 17 of 46, or 37.0%. Claude named Acorns in 3 of 46 answers, or 6.5%. Acorns' average first-mention position was 9.2. Its owned domain was cited in 1.1% of answers with retrieval evidence.
### M1 Finance
32.3% appearance rate places M1 Finance eighth, with a 95% interval of 26.0% to 39.3%. Perplexity named M1 Finance in 31 of 48 returned answers, or 64.6%, but ChatGPT named it in 16 of 46, or 34.8%. Gemini named M1 Finance in 8 of 46 answers, or 17.4%, and Claude named it in 5 of 46, or 10.9%. M1 Finance's average first-mention position was 8.1. Its owned domain was cited in 4.8% of answers with retrieval evidence.
## Where do the engines disagree?
The engines disagree most on how often they name lower-ranking brands. Perplexity returned 48 answers, while ChatGPT, Gemini, and Claude each returned 46. The heatmap uses each engine's returned-answer total as its denominator.
### Appearance rate by engine
Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Betterment93.8%84.8%50%47.8%
Vanguard91.7%97.8%45.7%30.4%
Fidelity95.8%82.6%39.1%45.7%
Wealthfront87.5%73.9%41.3%45.7%
SoFi81.3%67.4%30.4%26.1%
Charles Schwab56.3%91.3%21.7%30.4%
Acorns66.7%39.1%37%6.5%
M1 Finance64.6%34.8%17.4%10.9%
Robinhood41.7%28.3%15.2%10.9%
Empower31.3%30.4%6.5%8.7%
Schwab Intelligent Portfolios37.5%4.3%8.7%17.4%
Merrill29.2%30.4%8.7%0%
Perplexity named Betterment, Vanguard, Fidelity, and Wealthfront in 87.5% or more of its 48 returned answers. ChatGPT named Vanguard in 97.8% of its 46 returned answers and Charles Schwab in 91.3%. Gemini and Claude named the same leaders less often. Gemini named Betterment in 50.0% of its answers, while Claude named Vanguard in 30.4%.
Several brands are concentrated in one engine. Schwab Intelligent Portfolios appeared in 37.5% of Perplexity answers but 4.3% of ChatGPT answers. Acorns appeared in 66.7% of Perplexity answers and 6.5% of Claude answers. ETRADE appeared in 31.3% of Perplexity answers, but Gemini did not name it in any of its 46 returned answers. Merrill and Ellevest also received no Claude mentions, while SigFig received no Claude mentions.
Gemini produced distinctive non-brand terms. Account Minimum appeared in 17 of Gemini's 46 answers, or 37.0%, and Advisory Fee appeared in 14, or 30.4%. Neither term appeared in returned answers from the other three engines. These are tracked entities, not brands. Engine gaps can reflect differences in model and retrieval tool design rather than a brand's visibility alone.
## Which brands lead each demand cluster?
Demand-cluster results identify the brands that lead within the measured question groups, rather than a universal selection verdict. General buyer-intent questions and specific need questions can produce different leaders.
### Leaders by demand cluster
The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
— General buyer intent —
best robo-advisor8Betterment75%Vanguard 75%, Wealthfront 75%, Fidelity 62.5%
cheapest robo-advisor / lowest fees8Betterment87.5%Vanguard 87.5%, Fidelity 87.5%, Wealthfront 87.5%
best investing app for beginners8Fidelity100%Acorns 100%, Robinhood 100%, Betterment 87.5%
best robo-advisor for retirement8Betterment87.5%Fidelity 87.5%, Wealthfront 87.5%, Vanguard 62.5%
best robo-advisor for high net worth8Wealthfront62.5%Empower 62.5%, Vanguard 50%, Betterment 37.5%
best investing app overall8Fidelity87.5%Robinhood 87.5%, SoFi 75%, Webull 75%
most trusted investment platform8Fidelity87.5%Charles Schwab 87.5%, Vanguard 75%, Interactive Brokers 75%
best app for hands-off investing8Betterment62.5%Fidelity 62.5%, Wealthfront 62.5%, Vanguard 50%
best investing platform for young investors8Fidelity100%Charles Schwab 100%, SoFi 87.5%, M1 Finance 87.5%
best robo-advisor customer service8Betterment75%Fidelity 75%, Wealthfront 75%, SoFi 75%
best robo-advisor vs. financial advisor8Wealthfront87.5%Betterment 75%, Vanguard 62.5%, Fidelity 50%
best investing app for building long-term wealth8Betterment87.5%Vanguard 87.5%, Fidelity 87.5%, Wealthfront 87.5%
— Tax-Loss Harvesting —
best robo-advisor for tax-loss harvesting8Betterment75%Vanguard 62.5%, Wealthfront 62.5%, Fidelity 50%
which platforms offer tax-loss harvesting8Vanguard100%Betterment 87.5%, Wealthfront 87.5%, Fidelity 75%
— ESG / Socially Responsible Investing —
best robo-advisor for ESG investing8Betterment50%Vanguard 50%, SoFi 50%, Ellevest 50%
which investing apps offer socially responsible portfolios8Betterment62.5%Wealthfront 62.5%, Ellevest 62.5%, Acorns 50%
— Hybrid Human + AI Advice —
best robo-advisor with human advisor access8Betterment62.5%Vanguard 62.5%, SoFi 62.5%, Charles Schwab 50%
which platforms combine AI and human financial advice8Vanguard87.5%Empower 75%, Betterment 62.5%, Fidelity 62.5%
— Other demand —
Subcategory: Tax-Loss Harvesting8Betterment75%Vanguard 75%, Wealthfront 75%, Fidelity 62.5%
Subcategory: ESG / Socially Responsible Investing8Betterment75%Wealthfront 62.5%, Ellevest 62.5%, Vanguard 50%
General best-in-category8Betterment100%Vanguard 100%, Wealthfront 100%, SoFi 100%
Everyday and mainstream choice8Betterment100%Wealthfront 100%, SoFi 87.5%, Vanguard 75%
Price and value5Betterment80%Vanguard 80%, Fidelity 80%, Wealthfront 80%
Primary use case5Vanguard80%Betterment 60%, Empower 60%, Wealthsimple 60%
The cluster chart separates broad selection questions from tax-loss harvesting, environmental, social, and governance investing, and hybrid human plus artificial intelligence advice questions. Use the chart to identify where a brand appears in the measured demand type, then compare that pattern with its overall appearance rate. A broad leader may not lead each specific-need group.
## Which sites receive citations in robo-advisor answers?
Financial publishers receive far more citations than most brand-owned domains in this study. Citation rates below use the 186 answers with retrieval evidence, which is the citation denominator.
### Owned-domain citation rate
Share of the 186 answers with retrieval evidence that cited a URL on the brand's own website.
Betterment
12.4% · 23/186
Vanguard
14% · 26/186
Fidelity
26.3% · 49/186
Wealthfront
17.7% · 33/186
SoFi
5.9% · 11/186
Charles Schwab
13.4% · 25/186
Acorns
1.1% · 2/186
M1 Finance
4.8% · 9/186
Robinhood
0% · 0/186
Empower
2.7% · 5/186
Schwab Intelligent Portfolios
13.4% · 25/186
Merrill
2.7% · 5/186
Fidelity had the highest owned-domain citation rate among the leading financial brands at 26.3%. Wealthfront followed at 17.7%, Vanguard at 14.0%, Charles Schwab and Schwab Intelligent Portfolios at 13.4%, and Betterment at 12.4%. Robinhood, SigFig, Webull, and Public had 0% owned-domain citation rates despite appearing in returned answers.
### Most-cited publishers
Publishers cited as evidence, counted once per answer rather than once per link.
PublisherAnswers citing itAnswer penetration
forbes.com135/18672.6%
nerdwallet.com91/18648.9%
cnbc.com66/18635.5%
kiplinger.com57/18630.6%
unbiased.com55/18629.6%
fidelity.com48/18625.8%
reddit.com40/18621.5%
morningstar.com39/18621%
youtube.com39/18621%
investopedia.com38/18620.4%
finder.com37/18619.9%
moneywise.com35/18618.8%
Forbes appeared in 135 of 186 evidence-backed answers, or 72.6%. NerdWallet appeared in 91, or 48.9%, and CNBC appeared in 66, or 35.5%. Fidelity.com was the most cited brand-owned domain in the listed source set, appearing in 48 answers, or 25.8%. Wealthfront.com appeared in 28 answers, or 15.1%, and Betterment.com appeared in 23 answers, or 12.4%.
## Who is winning and who is at risk?
Winners and at-risk brands below are classified only from their measured appearance, engine spread, first-mention position, and owned-domain citations. These labels do not describe business performance or consumer demand.
### Brands with stronger measured visibility
Betterment leads the appearance ranking at 69.4% and appears in all four engines, with a 4.1 average first-mention position. Fidelity appears in 66.1% of returned answers, has a 3.7 average first-mention position, and has a 26.3% owned-domain citation rate. Vanguard appears in 66.7% of returned answers and holds a 14.0% owned-domain citation rate. Wealthfront appears in 62.4% of returned answers and has a 17.7% owned-domain citation rate.
### Brands with weaker measured visibility
Robinhood appears in 24.2% of returned answers and has a 0% owned-domain citation rate. SigFig appears in 10.8% of returned answers, has a 10.4 average first-mention position, and has a 0% owned-domain citation rate. Webull appears in 10.2% of returned answers, has a 9.9 average first-mention position, and has a 0% owned-domain citation rate. Public appears in 8.6% of returned answers and has a 0% owned-domain citation rate.
## What does the tally say about this category?
The category has a concentrated top tier: Betterment, Vanguard, Fidelity, and Wealthfront each appear in more than 60.0% of returned answers. Yet the rank-one interval overlaps the intervals of ranks two through five. The sample cannot distinguish Betterment's visibility from Vanguard, Fidelity, Wealthfront, or SoFi with confidence.
The engines do not converge on one stable list. Perplexity and ChatGPT named the top brands at high rates, while Gemini and Claude named many of those same brands less often. Citation support also leans toward third-party publishers. Forbes, NerdWallet, CNBC, Kiplinger, and Unbiased each appeared in more evidence-backed answers than Betterment.com.
Among brands named in the brief's category framing, the data does not establish an AUM-size relationship because it contains no assets-under-management figures. It does show that pure-play and legacy brands both occupy the leading group. The category also includes several brands with 0% owned-domain citation rates: Robinhood, SigFig, Webull, and Public.
## What changed during the measurement window?
This study measured one date and provides no prior-wave comparison. It cannot report a category shift during the measurement window.
## What should brands fix in their AI search content?
- Robinhood, SigFig, Webull, and Public should audit the pages that explain their automated investing products. Each had a 0% owned-domain citation rate among 186 evidence-backed answers. Re-measure owned-domain citation rate after publishing and updating those pages.
- Acorns should test content for Claude-focused gaps. Claude named Acorns in 3 of 46 returned answers, or 6.5%, versus Perplexity's 66.7% rate. Re-measure engine-level appearance with the same prompt set.
- Charles Schwab should examine why ChatGPT named it in 91.3% of returned answers while Gemini named it in 21.7%. Build pages that state product scope, selection criteria, and advice model in direct language. Re-measure the engine spread.
- ETRADE should review its visibility on Gemini. Gemini named ETRADE in 0 of 46 returned answers, while Perplexity named it in 31.3%. Re-measure Gemini appearance after the revised content is indexed and available to retrieval systems.
- SoFi and M1 Finance should improve citation-ready product explanations. SoFi's owned domain was cited in 5.9% of evidence-backed answers, and M1 Finance's was cited in 4.8%. Re-measure owned-domain citation rates against Fidelity, Wealthfront, Vanguard, and Betterment.
- Brands should publish separate, direct pages for tax-loss harvesting, environmental, social, and governance investing, and hybrid human plus artificial intelligence advice. Re-measure performance by demand cluster rather than relying on the overall leaderboard.
## What does this mean for brands in the category?
Interpretation: appearance and citation are different outcomes. Betterment's appearance lead does not translate into the highest owned-domain citation rate. Fidelity's results show the opposite pattern: it is near the top of appearances and has the strongest measured owned-domain citation rate among the leading financial brands.
Interpretation: brands need an engine-by-engine view. A high rate in Perplexity or ChatGPT can coexist with much lower rates in Gemini and Claude. A brand also needs direct, sourceable pages because publishers dominate the evidence cited by the engines in this study.
## How was this AI visibility study measured?
This flagship study tested 24 stratified prompts three times across Perplexity, ChatGPT, Gemini, and Claude. There were 288 attempted answers, 186 returned answers for appearance rates, and 186 answers with retrieval evidence for citation rates. The study captures these prompts on the measurement date, not all category searches or consumer demand. Engine differences may arise from differences in the model and retrieval tool.
### How this study was measured
- Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked.
- Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 186 answers returned of 288 attempted, 186 with retrieval evidence.
- Product lines rolled up to parent brands using a registry fixed before scoring.
- Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.
## Frequently asked questions
### Which brand had the highest appearance rate?
Betterment ranked first at 69.4% across 186 returned answers. Its 95% interval was 62.4% to 75.5%. Vanguard and Fidelity had overlapping intervals, so the study cannot distinguish their visibility from Betterment's with confidence.
### Which brand had the strongest owned-domain citation rate?
Fidelity had a 26.3% owned-domain citation rate among 186 answers with retrieval evidence. Wealthfront had 17.7%, Vanguard had 14.0%, and Betterment had 12.4%.
### Which engine named Vanguard most often?
ChatGPT named Vanguard in 45 of 46 returned answers, or 97.8%. Perplexity named Vanguard in 44 of 48 answers, or 91.7%, while Gemini and Claude named it less often.
### Do financial publishers dominate the cited sources?
Yes. Forbes appeared in 72.6% of evidence-backed answers, NerdWallet in 48.9%, and CNBC in 35.5%. Fidelity.com appeared in 25.8% of those answers.
### Did pure-play robo-advisors outperform legacy firms?
The study does not support a clean split. Betterment and Wealthfront ranked first and fourth, while Vanguard and Fidelity ranked second and third. The dataset contains no assets-under-management figures, so it cannot test AUM against AI trust.
### Which brands had no measured owned-domain citations?
Robinhood, SigFig, Webull, and Public each had a 0% owned-domain citation rate among answers with retrieval evidence. All four were still named in some returned answers.
## Related 5W research and services
This study is part of the 5W AI Visibility Index, a running measurement of how ChatGPT, Claude, Gemini and Perplexity name and cite brands by category. Related financial-services measurements include the Credit Cards AI Visibility Index 2026, the Online Banks Trust Index 2026, the Stablecoin Trust Index 2026 and the Wealth AI Audit 2026. All published studies are listed in 5W Research.
5W runs AI Search (GEO) programs for financial brands that need their own pages cited as evidence, not just their names mentioned. Read about 5W's GEO practice or contact 5W to have your brand measured across the same four engines and prompt set used in this study.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1NerdWallet10.2%19/1866.6%–15.4%
2Investopedia10.2%19/1866.6%–15.4%
3Bankrate4.3%8/1862.2%–8.3%
4E*TRADE Core Portfolios0%0/1860%–2%
## Methodology appendix
This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.
### What was measured
- Category: Robo-Advisors & Retail Investing
- Measurement date: 2026-09-06
- Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 288
- Answers returned (appearance denominator): 186
- Answers with retrieval evidence (citation denominator): 186 (64.6% of attempted)
- Answers that failed outright: 102
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable.
### How the prompts were sampled
Fixed sampling frame: 18 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: General buyer intent / Tax-Loss Harvesting / ESG / Socially Responsible Investing / Hybrid Human + AI Advice; then 2 of 3 client-named subcategories are allocated a guaranteed cell each (the prompt budget of 24 could not give a dedicated cell to Hybrid Human + AI Advice, so that demand is covered inside the generic clusters instead), then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure.
Demand clusterPromptsScope
best robo-advisor1best robo-advisor
cheapest robo-advisor / lowest fees1cheapest robo-advisor / lowest fees
best investing app for beginners1best investing app for beginners
best robo-advisor for retirement1best robo-advisor for retirement
best robo-advisor for high net worth1best robo-advisor for high net worth
best investing app overall1best investing app overall
most trusted investment platform1most trusted investment platform
best app for hands-off investing1best app for hands-off investing
best investing platform for young investors1best investing platform for young investors
best robo-advisor customer service1best robo-advisor customer service
best robo-advisor vs. financial advisor1best robo-advisor vs. financial advisor
best investing app for building long-term wealth1best investing app for building long-term wealth
best robo-advisor for tax-loss harvesting1best robo-advisor for tax-loss harvesting
which platforms offer tax-loss harvesting1which platforms offer tax-loss harvesting
best robo-advisor for ESG investing1best robo-advisor for ESG investing
which investing apps offer socially responsible portfolios1which investing apps offer socially responsible portfolios
best robo-advisor with human advisor access1best robo-advisor with human advisor access
which platforms combine AI and human financial advice1which platforms combine AI and human financial advice
Subcategory: Tax-Loss Harvesting1demand specific to "Tax-Loss Harvesting" within Robo-Advisors & Retail Investing
Subcategory: ESG / Socially Responsible Investing1demand specific to "ESG / Socially Responsible Investing" within Robo-Advisors & Retail Investing
General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Primary use case1the dominant everyday use case for this category
### The exact prompts
#PromptClusterShape
1best robo-advisorneed_1medium
2cheapest robo-advisor / lowest feesneed_2medium
3best investing app for beginnersneed_3medium
4best robo-advisor for retirementneed_4medium
5best robo-advisor for high net worthneed_5medium
6best investing app overallneed_6medium
7most trusted investment platformneed_7medium
8best app for hands-off investingneed_8medium
9best investing platform for young investorsneed_9medium
10best robo-advisor customer serviceneed_10medium
11best robo-advisor vs. financial advisorneed_11medium
12best investing app for building long-term wealthneed_12medium
13best robo-advisor for tax-loss harvestingneed_13medium
14which platforms offer tax-loss harvestingneed_14medium
15best robo-advisor for ESG investingneed_15medium
16which investing apps offer socially responsible portfoliosneed_16medium
17best robo-advisor with human advisor accessneed_17medium
18which platforms combine AI and human financial adviceneed_18medium
19best tax-loss harvesting robo-advisorssub_tax_loss_harvestingmedium
20top ESG robo-advisorssub_esg_socially_responsibleshort
21What are the best robo-advisors for retail investors?general_bestlong
22best robo-advisors for beginnerseverydaymedium
23lowest-cost robo-advisorsvalue_priceshort
24Which hybrid robo-advisors combine automated investing with access to human financial advisors?use_caselong
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search72484866.7%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool72464663.9%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)72464663.9%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool72464663.9%
Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
- Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
- 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
- Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
- Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
- Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
- 0% — the metric could be evaluated and the event did not occur.
- Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — the share of engines that named the brand at least once.
- Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.
### Evidence rules
- An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
- Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
- Publisher identity could not be established for 6 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
- Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Betterment129/18669.4%69.4%62.4%–75.5%23/186 (12.4%)
2Vanguard124/18666.7%67.1%59.6%–73%26/186 (14%)
3Fidelity123/18666.1%65.9%59.1%–72.5%49/186 (26.3%)
4Wealthfront116/18662.4%61.7%55.2%–69%33/186 (17.7%)
5SoFi96/18651.6%51.9%44.5%–58.7%11/186 (5.9%)
6Charles Schwab93/18650%49.4%42.9%–57.1%25/186 (13.4%)
7Acorns70/18637.6%37.1%31%–44.8%2/186 (1.1%)
8M1 Finance60/18632.3%31.9%26%–39.3%9/186 (4.8%)
9Robinhood45/18624.2%23.4%18.6%–30.8%0/186 (0%)
10Empower36/18619.4%19.7%14.3%–25.6%5/186 (2.7%)
11Schwab Intelligent Portfolios32/18617.2%18.2%12.5%–23.3%25/186 (13.4%)
12Merrill32/18617.2%16.7%12.5%–23.3%5/186 (2.7%)
13Interactive Brokers29/18615.6%15.4%11.1%–21.5%2/186 (1.1%)
14Ellevest27/18614.5%14.7%10.2%–20.3%4/186 (2.2%)
15ETRADE27/18614.5%14.1%10.2%–20.3%2/186 (1.1%)
16SigFig20/18610.8%11%7.1%–16%0/186 (0%)
17Webull19/18610.2%9.9%6.6%–15.4%0/186 (0%)
18Account Minimum17/1869.1%9.2%5.8%–14.1%domain not established — excluded from citation metrics
19Public16/1868.6%8.3%5.4%–13.5%0/186 (0%)
20Forbes Advisor16/1868.6%8.3%5.4%–13.5%135/186 (72.6%)
21Ally Invest15/1868.1%8.8%4.9%–12.9%3/186 (1.6%)
22Advisory Fee14/1867.5%7.6%4.5%–12.2%domain not established — excluded from citation metrics
23Wealthsimple14/1867.5%8.2%4.5%–12.2%3/186 (1.6%)
24Sources9/1864.8%4.7%2.6%–8.9%domain not established — excluded from citation metrics
25Personal Capital9/1864.8%5%2.6%–8.9%5/186 (2.7%)
### Entity handling
Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.
Entities held for human classification before the next studyWealthfront, Betterment, Fidelity Go, Vanguard Digital Advisor, Schwab Intelligent Portfolios, SoFi Automated Investing, Acorns, M1 Finance, ETRADE Core Portfolios, Merrill Guided Investing, Robinhood Strategies, Webull Smart Advisor, Empower, Ellevest, Ally Invest Robo Portfolios, Interactive Advisors, Axos Invest, SoFi Invest, Ally Invest Managed Portfolios, Small accounts, Large accounts, Advisory Fee, Account Minimum, Underlying Fund Costs, Charles Schwab Intelligent Portfolios, Wealthfront Automated Investing, J.P. Morgan Automated Investing, Annual Advisory Fee, Investopedia, NerdWallet, Morningstar, Forbes & SmartAsset, Summary of Evaluation Sources, Fidelity, Robinhood, Public, Webull, Charles Schwab, ETRADE, Vanguard.
### Limits of these findings
- This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-06. Engine behaviour changes without notice.
- The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
- Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
- A brand not listed here was not measured as absent — it simply was not named in these answers.
- Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
- 3 of 25 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero.
---
### Edition 04: Fintech Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/fintech/
Summary: The sector that over-invested in prestige journalism and under-invested in open infrastructure.
---
### The EdTech AI Visibility Index 2026
URL: https://www.5wpr.com/research/edtech-ai-visibility-index-2026/
Summary: The 2026 EdTech AI Visibility Index measures which education technology brands AI engines name when schools and buyers ask for learning platform picks.
5W × Research
AI Visibility Index · Vol. VII · April 2026
AI Visibility Index Series · Volume VII
# The EdTech AI
Visibility Index.
How a $400 billion category is being rebuilt in real time by the same AI technology that disrupted it.
5W Research · April 2026 · Premier The Communications Firm Built for the AI Era
Download the Full PDF →
01 · Executive Summary
## Twenty years of EdTech distribution collapsed in eighteen months.
For twenty years, online education was an SEO business. Chegg, Course Hero, Coursera, Udemy, Khan Academy, and every tutoring platform in between built distribution on the same playbook: produce educational content at scale, rank on Google for "how do I solve x," convert that free traffic into paid subscriptions or enrollments. That playbook produced billion-dollar companies. Chegg reached a $13 billion valuation. Course Hero became a $3.6 billion unicorn. Guild Education hit $4.4 billion. Coursera went public. MasterClass raised $460 million at a $2.75 billion valuation. Duolingo IPO'd and surpassed a billion in revenue.
Then ChatGPT launched. Within eighteen months the entire distribution model began to collapse. Chegg's non-subscriber traffic fell 49% year-over-year in January 2025. The company laid off 22% of its workforce in May 2025 and another 45% in October 2025 — more than 700 employees cut in a single year. Q1 2025 revenue dropped 30%, subscribers fell 31%, and the company pivoted away from academic homework help entirely toward B2B skilling. Course Hero's parent Learneo cut 15% of staff and continues quiet reductions. MasterClass cut its workforce from 600 to roughly 300. Guild's valuation collapsed from $4.4 billion in 2022 to an implied $1.3–$2 billion in 2024.
At the same time, a new generation of AI-native learning brands is scaling faster than anything in the category's history. Khanmigo went from 68,000 users to over 1.4 million in eighteen months. MagicSchool reached 6 million educator users by October 2025 — more teachers than exist in the entire US K-12 public school system. Synthesis Tutor is on pace for $10 million in revenue with subscribers up 4.5x year-over-year. Duolingo crossed $1 billion in revenue, hit 52.7 million daily active users, and in February 2026 publicly chose to sacrifice $50 million in 2026 bookings to defend free-tier growth against AI substitution.
On April 14, 2026, from the TED2026 stage in Vancouver, Sal Khan, TED, and ETS announced the Khan TED Institute: a competency-based applied-AI bachelor's degree priced under $10,000 total, with Google, Microsoft, Accenture, McKinsey, Bain, and Replit as launch partners. The announcement reset the degree-cost benchmark for every online-learning purchase decision in America.
Every one of these decisions now runs through AI. The student deciding whether to cancel Chegg. The parent evaluating Synthesis versus Khanmigo. The teacher choosing between MagicSchool and ChatGPT. The mid-career worker weighing Coursera, Udemy, Guild, and the Khan TED Institute. None of them start at the brand's website. They start at "best AI math tutor for kids," "Coursera vs Udemy for career change," "is Chegg worth it in 2026," "what's the cheapest way to get an AI degree," "how to teach with AI." The brands that surface first in those answers get the subscription, the enrollment, the enterprise RFP, and the next decade of category leadership. 5W, the premier AI communications firm in the United States, built this index to score the new game.
02 · Key Findings
## Eight numbers that define the category in 2026.
84%
U.S. high-school students using generative AI for schoolwork (May 2025)
1.4M
Khanmigo student users mid-2025, up from 68K in 2023
6M
MagicSchool educator users by October 2025, more than the entire US K-12 teaching workforce
−45%
Chegg's October 2025 workforce reduction citing AI disruption
−49%
Chegg non-subscriber traffic decline YoY, January 2025
$1.04B
Duolingo 2025 revenue, +39% YoY, with 52.7M Q4 DAU
$10K
Total cost target for the Khan TED Institute applied-AI bachelor's degree
60+
Consumer, parent, teacher, and enterprise prompts tracked across ChatGPT, Claude, Perplexity, and Google AI Overviews
03 · The Top 15 by AI Citation Share
## The brands the engines surface first.
Rank
Brand
Citation Share
Position
01Khanmigo
Khan Academy
18.5%Category-defining AI tutor
02Duolingo
15.5%Category-defining consumer AI
03Coursera
12.0%University credentials default
04MagicSchool
8.0%Teacher AI leader
05Udemy
6.5%Enterprise-pivot survivor
06MasterClass
5.5%Premium-brand halo
07Chegg
5.0%Legacy incumbent in collapse
08Synthesis Tutor
4.0%AI-native challenger
09Khan TED Institute
3.5%Newest degree entrant
10Course Hero
Learneo
3.0%Quiet decline
11Guild
2.5%Enterprise-only constraint
12Busuu
Chegg-owned
2.0%Language app
13Outlier
by Savvas
1.5%Dual-credit acquired brand
14Schoolhouse.world
1.5%Peer-tutoring nonprofit
15Brilliant
1.0%STEM-specific consumer app
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, and Google AI Overviews, Q1 2026. Remaining share split across ranks 16–25 and unranked brands including Codecademy, edX, Skillshare, Quizlet, Socratic by Google, Google Career Certificates, Pearson+, Chegg Skills, Udemy Business, and Coursera for Business.
Note: Khan TED Institute received extensive media coverage beginning April 14, 2026. Its citation share is rising rapidly post-announcement and is expected to shift the chart materially within two quarters as applications open.
04 · Methodology
## 60+ prompts across 4 engines, 5 sub-categories.
5W, the premier AI communications firm in the United States, analyzed more than 60 common prompts across five primary sub-categories of the online education and EdTech market. We identified which brands AI models consistently surface across student, parent, teacher, and enterprise-buyer intent, which authoritative sources feed those citations, and where the biggest gaps sit between market opportunity and AI visibility.
Sub-Category 01 · MOOC and Subscription Course Platforms
Coursera, Udemy, MasterClass, edX (2U), Skillshare, Codecademy, Outlier (by Savvas), Pearson+, LinkedIn Learning, Khan Academy content library
Sub-Category 02 · Homework Help and Study Aids
Chegg, Course Hero (Learneo), Quizlet, Brainly, Photomath, Chegg Study Pack, CheggMate
Sub-Category 03 · AI Tutors and Consumer Learning Apps
Khanmigo, Synthesis Tutor, Duolingo Max, Busuu, Brilliant, Schoolhouse.world, Socratic by Google, Schoolhouse Dialogues, ChatGPT Edu, Claude for Education, Gemini Study Mode
Sub-Category 04 · Teacher and Classroom AI Platforms
MagicSchool, Khanmigo for Teachers, Eduaide.AI, SchoolAI, Curipod, Diffit, Brisk Teaching
Sub-Category 05 · Employer Skilling, Benefits, and New-Model Higher Ed
Guild, Udemy Business, Coursera for Business, Google Career Certificates, Chegg Skills, Multiverse, Khan TED Institute, Western Governors University as competitive benchmark
Query Types Tracked
Real consumer and buyer prompts including "best AI math tutor for kids," "Synthesis vs Khanmigo," "is Chegg worth it in 2026," "Coursera vs Udemy for career change," "best way to learn a language in 2026," "is Duolingo Max worth it," "best AI tools for teachers," "what's the cheapest way to get an AI degree," "Khan TED Institute vs traditional college," and 50+ additional variations.
Citation Sources Tracked
EdSurge, The 74, EdWeek, Inside Higher Ed, Chalkbeat, K-12 Dive, EdTech Magazine, The New York Times, The Wall Street Journal, Wired, Bloomberg, Axios, CNBC, Forbes, Fast Company, Fortune, NerdWallet, Investopedia, Class Central, Common Sense Media, G2, Capterra, Trustpilot, Stanford HAI, MIT Media Lab, WestEd RCTs, Gallup/Walton Family Foundation, Pew Research, Christensen Institute, Reddit r/GetStudying, r/learnprogramming, r/Teachers, r/edtech, TikTok and YouTube education creators, and vendor-owned content hubs.
Important framing. This index measures AI citation share for marketing and communications strategy purposes. It does not rank products on educational effectiveness, student-outcome data, pedagogical quality, or cost-value to the learner, and it does not constitute a recommendation. Education decisions should be made with reference to independent outcome data, credential value, accreditation status, and individual learning goals.
05 · The Winners
## Six brands the engines reward.
### Khanmigo: The Nonprofit Trust Dividend.
Khanmigo is the single most-cited AI tutor in consumer and parent queries across ChatGPT, Claude, Perplexity, and Google AI Overviews. The reason is structural, not tactical. Khan Academy spent sixteen years before Khanmigo building the largest repository of mastery-sequenced educational content on the open web — 160 million-plus registered users, 200,000-plus pieces of educator-vetted content, and a nonprofit 501(c)(3) brand no other EdTech can match. When AI models retrieved education content to train on, Khan Academy was over-represented. The Socratic-method positioning is the second moat. Every AI-safety-conscious publication covering AI in education cites Khanmigo as the positive case of "AI that doesn't just give the answer." That editorial positioning is worth more in citation share than any paid media campaign in the category.
### Duolingo: The Only Consumer AI Product That Monetized at Scale.
Duolingo is the rare EdTech player positioned to gain from generative AI rather than be disrupted by it. Video Call with Lily, Roleplay, and Explain My Answer drove DAUs to 52.7 million by Q4 2025, revenue past $1 billion (+39% YoY), and paid subscribers over 10 million. The February 2026 decision to sacrifice $50 million of 2026 bookings to chase a 100 million DAU target is the most confident strategic bet any public consumer EdTech has made. AI citations reflect it: every language-learning comparison surfaces Duolingo first.
### MagicSchool: The Teacher-AI Citation Sweep.
Founded in 2023. Over 6 million educator users by October 2025 — more than the entire US K-12 public-school teaching workforce. $45 million Series B in January 2025 led by Valor Equity with Bain Capital Ventures, Adobe Ventures, Atreides, and Smash Capital. The product maps one-to-one onto the prompts teachers actually type into ChatGPT today. The moat is classroom-level safety and administrative oversight — free-plus-enterprise model lets individual teachers adopt, then districts deploy under managed privacy controls. Microsoft Copilot at $25/month and ChatGPT Plus at $20/month are priced above education budgets and not configured for student-safe deployment. Structural advantage, not feature differentiation.
### Coursera: The University-Partnership Citation Portfolio.
197 million registered learners by Q4 2025. $757 million in 2025 revenue. 375-plus university and industry partners. Every partner is itself a citation asset. When AI answers "best online course in machine learning" or "best online master's in data science," the answer is almost always Coursera-hosted — Stanford, Johns Hopkins, Michigan, Google, IBM, Meta. Citation share is durable. The open question is whether Coursera converts citation into subscription retention as growth decelerates to single digits.
### Synthesis Tutor: The Parent-Query Disruptor.
Founded by SpaceX Ad Astra alum Josh Dahn with Chrisman Frank. On pace for $10M+ revenue in 2025 with subscribed students up 4.5x YoY. $17.5 million raised. Oklahoma rolled it out statewide for the 2025–26 school year at no cost to participating districts. AI answers to "best AI tutor for my child" surface Synthesis alongside Khanmigo — skipping two decades of K-12 tutoring incumbents (Sylvan, Huntington, Kumon) entirely. The cleanest single case of category redefinition at the top of the funnel.
### Khan TED Institute: The Brand-Coalition Citation Surge.
The April 14, 2026 announcement at TED2026 in Vancouver is the largest single citation-generating event in the 2026 EdTech cycle. Khan Academy + TED + ETS, with Google, Microsoft, Accenture, McKinsey, Bain, and Replit as employer partners. Under-$10,000 total bachelor's degree, competency-based, twelve-to-eighteen-month application opening. Coverage in Axios, Fortune, Entrepreneur, Inside Higher Ed, San Francisco Standard, The 74, and dozens of education-policy outlets within seventy-two hours. Every story is now an AI training and retrieval citation for "cheapest AI bachelor's degree" and "alternatives to college for AI." Western Governors University has been doing competency-based online degrees since 1997 at similar cost. Khan TED is not yet accredited and faces a four-to-seven-year accreditation path. None of that affects 2026 citation share. What affects citation share is volume and quality of editorial coverage. By that metric, Khan TED has already won its launch window.
06 · The Losers
## Six brands the engines are quietly punishing.
### Chegg: The Homework-Help Collapse.
The defining AI-citation loser of the 2025–2026 cycle. Non-subscriber traffic fell 49% in January 2025. Q1 2025 revenue dropped 30% YoY to $121 million. Subscribers fell 31% to 3.2 million. May 2025: 248 employees laid off (22%). October 2025: 388 employees laid off (45%). Total 2025 reductions over 636 employees — the sharpest percentage-of-workforce cuts in any public EdTech company's history. The company filed suit against Google over AI Overviews in early 2025 and pivoted to B2B "Chegg Skills." Q3 2025 total revenue fell 42% YoY to $78 million. Chegg's entire growth engine was paid SEO — Google routing students to paywalled Chegg Study pages converting at $19.95/month. ChatGPT did the same thing for free. Then Google AI Overviews captured the traffic before any click. No amount of product innovation solved it because the problem was not product. The distribution layer collapsed underneath.
### Course Hero / Learneo: Quiet Decline, Same Structural Pressure.
A $3.6 billion EdTech unicorn built on the same Google-SEO-to-subscription model. First layoff in 17 years came in March 2023 (15%, ~150 staff). Continued reductions since. The 2022 rebrand to "Learneo" parent (Course Hero, Symbolab, CliffsNotes, QuillBot, Scribbr, LanguageTool) created some diversification but no clear AI-era growth narrative. None of the sub-brands has reached the citation profile of Khanmigo, Synthesis, or MagicSchool. The opportunity, if there is one: reposition QuillBot and Scribbr as AI-writing assistants — a growing citation category Course Hero could legitimately own.
### MasterClass: The Celebrity-Brand Ceiling.
Stronger consumer brand than its AI-citation share reflects — and the gap is the problem. Workforce reduced from 600 to roughly 300 across 2022–2024 layoffs, with additional cuts in mid-2025. Subscriber count not disclosed beyond ~2 million reported in 2023. Structural challenge: positioned as entertainment-adjacent aspirational content, not as a credentialed or skill-outcome platform. AI answers to "best way to learn writing" surface Coursera, Udemy, writing-specific apps, and ChatGPT itself — not MasterClass. The celebrity-instructor halo built the brand at launch. It does not compound in the AI-answer era.
### Guild: The Enterprise-Only Citation Constraint.
Peak $4.4 billion valuation in 2022. Implied secondary-market valuation $1.3–$2 billion in 2024 — a 55–66% reduction. Founder Rachel Romer stepped down after a stroke; Bijal Shah took over April 2024. May 2023: 12% layoff. May 2024: ~25% layoff (~300 people). Macy's ended its Guild partnership January 2025. The structural issue: Guild's product is delivered through employers, not direct-to-consumer. Queries like "how do I go back to school while working" rarely surface Guild specifically — they surface Coursera, Google Career Certificates, or WGU. Guild wins B2B HR-buyer surfaces but loses the consumer funnel that ultimately drives platform utilization.
### Outlier (Acquired by Savvas): The Brand-Absorption Problem.
Founded 2018 by MasterClass co-founder Aaron Rasmussen. Raised $46 million. Acquired by Savvas Learning February 2024. Citation share has held in "dual enrollment" queries but has not grown into adjacent higher-education surfaces. A recurring pattern: consumer EdTech brands absorbed into K-12 publishing or institutional parents rarely maintain independent AI-citation momentum, because the editorial coverage engine that feeds AI training data runs on consumer-brand narratives, not institutional ones.
### Generalist Homework-Help Tools: Without an AI-Native Angle.
Quizlet, Brainly, Photomath, and other legacy study-tool brands retain meaningful citation share in their defining niches but face the same substitution pressure as Chegg. AI answers to "best flashcard app" still surface Quizlet first — increasingly with an "or use ChatGPT's custom GPTs" caveat. AI answers to "how do I solve this math problem" increasingly default to "take a photo and ask ChatGPT or Gemini" before citing Photomath. Not collapsing on Chegg's timeline. Same directional curve.
07 · Six Structural Truths
## What the citation data is actually telling us.
01
### Nonprofit and institutional credibility beats venture-backed consumer brand equity.
Khan Academy, Coursera (university partners), Schoolhouse.world. AI safety and editorial gatekeepers reward credentialed sources. Pure consumer brand equity does not compound in AI surfaces the way it does in TV or paid social.
02
### AI is simultaneously the disruption and the research pathway.
A category being disrupted by ChatGPT is also being researched through ChatGPT. The brands that win the research surface during the disruption are the brands the next decade's category will consolidate around. There is no waiting it out.
03
### Teacher-AI is the highest-velocity sub-category in the market.
MagicSchool reached 6 million users in less than three years. Teacher-AI products with managed-deployment and student-safety frames clear procurement gates that consumer-AI products cannot. Districts buy. Adoption compounds. Citation surfaces follow.
04
### Free beats paid when "good enough" is the student standard.
ChatGPT free plus a Khan Academy account plus a YouTube tutorial channel is a viable substitute for Chegg, Course Hero, and most low-end MOOC subscriptions. The price comparison shows up in AI answers explicitly. Subscription EdTech that cannot articulate clear paid-tier value loses the citation.
05
### Sub-category specificity beats category generalism.
Synthesis owns K-5 math. MagicSchool owns teacher AI. Duolingo owns language. Coursera owns university credentials. AI models reward narrow, defensible authority. Brands trying to span four sub-categories at once (Chegg's pre-2025 strategy) are punished.
06
### Enterprise-only products structurally underperform in consumer AI citations.
Guild, Udemy Business, Coursera for Business. The product may be superior. The consumer-facing prompts that drive HR procurement (because employees ask for what they have heard of) do not surface the enterprise-only brand. The citation gap becomes a pipeline gap.
08 · 2026 Dynamics
## Six events reshaping EdTech AI citations right now.
01
### The Chegg collapse is the largest single AI-disruption event in the category's history.
Combined with the May 2025 22% layoff, Chegg eliminated over 636 positions in 2025 and pivoted away from academic homework help entirely. AI citations are rapidly updating. Every competitor — Quizlet, Course Hero, Brainly, Photomath — should audit their AI-citation footprint immediately and position for the vacated "homework help" surface before general-purpose LLMs fully claim it.
02
### The Khan TED Institute is the degree-cost reset event of the decade.
Positioning an AI bachelor's at under $10,000 — backed by Khan Academy, TED, ETS, Google, Microsoft, Accenture, McKinsey, Bain, and Replit — changes the comparison set for every online-learning purchase. Brands publishing comparison content in the next two quarters will gain citation share. Brands that ignore it will cede.
03
### Duolingo's $50M 2026 bookings sacrifice is a defensive signal against AI substitution.
Duolingo loosened paywalls on AI features (Video Call with Lily, Roleplay) and made speech-practice free to preempt the scenario where ChatGPT and Gemini become good-enough language substitutes. Implication: every subscription EdTech should re-evaluate its free-tier strategy. The moat is the AI-feature experience, not the subscription wall.
04
### Sal Khan publicly acknowledged Khanmigo adoption fell short of expectations.
In April 2026 reporting with Chalkbeat — the most candid framing of AI-tutoring reality any major EdTech founder has provided. Already cited in AI answers to "does AI tutoring actually work." Brands in the AI-tutor space should engage directly — publish honest usage and engagement data, acknowledge limitations, position against the "AI tutor = magic" narrative.
05
### MagicSchool's 6M educator milestone redefines "scale" in teacher-AI citations.
More individual educator users than the entire US K-12 public school teaching workforce. Editorial coverage of this milestone is now the primary AI citation source for "best AI tool for teachers." Any teacher-AI competitor — Eduaide, SchoolAI, Diffit, Brisk Teaching, Curipod — needs comparative scale and efficacy content in 2026 or cedes the category-defining narrative.
06
### AI citations in K-12 are now actively parent-gated.
Queries phrased from a parent's perspective ("best AI math tutor for my 8-year-old," "AI writing helper safe for middle schoolers") surface a structurally different citation set than student-perspective queries. Parent-gated surface is dominated by Khanmigo, Synthesis, MagicSchool (via teacher-to-parent channel), and Common-Sense-Media-reviewed products. Brands competing in K-12 need a distinct parent-facing content strategy or they win only half the citation surface.
09 · On The Record
Every EdTech incumbent built its growth engine on Google SEO. Every one of them is now being graded by the AI that is replacing Google. The grade determines whether the company survives the decade. AI citation share is the scoreboard. The game is being played right now. The scoreboard is still open. The brands that publish authoritative, comparison-grade, credentialed content in the next two quarters will define how Americans learn for a decade. Everyone else will discover, eighteen months from now, that their pipeline never recovered.
Ronn Torossian · Founder · 5W
10 · The EdTech Marketer Playbook
## Ten moves for the next two quarters.
#### Audit AI citation share monthly.
This category moves faster than any other consumer-subscription sector. Chegg's 45% layoff, Duolingo's strategic reset, and the Khan TED Institute launch all happened within six months. Citation share assumptions made in Q4 2025 are already out of date.
#### Publish educator- and academic-bylined content aggressively.
AI models over-weight content with credentialed authorship in education queries. A peer-reviewed study, a district case study, a named-professor op-ed, or a certified-teacher byline carries citation weight that brand-voice content cannot match at any scale.
#### Separate K-12, higher-ed, and adult-learner citation strategies.
AI treats these as fundamentally different surfaces. Brands that publish one content library covering all three conflate intent signals. Brands that structure separate content architectures win each surface cleanly.
#### Own a narrow sub-category before attempting to span.
Synthesis owns K-5 math tutoring. MagicSchool owns teacher AI. Duolingo owns language. Coursera owns university credentials. Pick a lane, dominate its citations, then expand. Chegg's attempt to span four categories simultaneously is the cautionary tale.
#### Engage directly with the ChatGPT-as-substitute narrative.
Every consumer EdTech query now includes an implicit "or just use ChatGPT" alternative. Brands that credibly position against the free AI substitute — with pedagogy, accreditation, safety, student-outcome data, or specialized content — win. Brands that ignore the comparison cede the citation to the free AI itself.
#### Treat accreditation and credentials as citation assets.
"Accredited by HLC," "articulates to University of Pittsburgh," "endorsed by ACE," "recognized by DOL" — these phrases appear in AI answers to any degree-or-credential-intent query. Brands without them are invisible in those surfaces regardless of product quality.
#### Build comparative content for the new category entrants.
Khan TED Institute vs WGU. Synthesis vs Khanmigo. MagicSchool vs ChatGPT for teachers. Duolingo Max vs ChatGPT for language. These comparison queries are surging in 2026 and not yet dominated by any single authoritative source. Whoever publishes the definitive comparison piece owns the citation for 12–24 months.
#### Invest in student-outcome and efficacy data publication.
The WestEd 47-school randomized controlled trial on Khanmigo is cited across AI answers about "does AI tutoring work." Any EdTech with credible efficacy data should publish it in a peer-reviewed or third-party-validated format. Anecdote does not win citations. Data does.
#### Address the cost question directly.
"Is X worth it" and "cheapest way to learn Y" are two of the fastest-growing AI query categories in the sector. Brands that publish clear, honest ROI and cost-comparison content — including against free AI alternatives — win the citation. Brands that avoid the cost question let competitors frame the answer.
#### Treat teacher advocacy as primary citation infrastructure.
Teachers recommending MagicSchool in closed Facebook groups and on LinkedIn generate editorial coverage that feeds AI citations. A specific compounding loop — teacher advocacy then editorial pickup then AI training data then AI citations then more teacher advocacy. Brands that invest in it systematically win citation share in both teacher-AI and consumer-AI surfaces.
11 · The Bigger Picture
## A $400 billion category, rebuilt from the top of the funnel down.
Online education is the first consumer category being disrupted and researched through the same technology simultaneously. Every incumbent built its growth engine on Google SEO. Every one is now being graded by the AI replacing Google. In eighteen months Chegg has shed 45% of its workforce, sued Google, and pivoted entirely out of academic homework help. Course Hero's parent has cut staff and rebranded. MasterClass has halved its employee count. Guild has lost two-thirds of its peak valuation and its founder. Coursera and Udemy are growing single-digits with consumer segments actively shrinking. Duolingo has chosen to burn $50 million in near-term bookings to defend against AI substitution.
At the same time, Khanmigo has 1.4 million students. MagicSchool has 6 million teachers. Synthesis has Oklahoma. The Khan TED Institute has a $10,000 AI bachelor's degree backed by Google, Microsoft, and McKinsey. The category is being rebuilt from the top of the funnel down, and AI citation share is the metric tracking who is rebuilding it.
The brands that invest deliberately in AI-visibility infrastructure in 2026 will define how Americans learn for the next decade — because every consumer, parent, teacher, student, and HR-buyer purchase decision in this category now routes through an AI answer engine before any brand gets a click. The brands that treat AI citation as a marketing curiosity will watch a $400 billion market consolidate around a smaller and smaller set of content-dominant institutions.
AI citation share is the scoreboard. The game is being played right now. The scoreboard is still open.
Request an EdTech AI Visibility Audit
## Your EdTech brand is being filtered by the engines. Run the audit before the next quarter.
5W is the premier AI communications firm in the United States. We work with EdTech brands, online learning platforms, universities, school districts, and enterprise learning buyers to build AI engine visibility that compounds.
The 5W EdTech AI Visibility Audit covers category-specific prompts across ChatGPT, Claude, Perplexity, and Google AI Overviews; citation-source mapping; competitive benchmarking against the Index; and a 90-day remediation plan to move recognition, sentiment, and engagement depth in the engines.
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12 · FAQ
## Frequently asked questions.
#### What is the EdTech AI Visibility Index?
5W's ranking of the top 15 online education and EdTech brands by AI citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews. Citation share is the estimated proportion of brand mentions across 60+ tracked consumer, parent, teacher, and enterprise prompts run in Q1 2026. An expanded ranking through #25 is referenced in the source notes.
#### Which EdTech brand has the highest AI citation share?
Khanmigo, Khan Academy's AI tutor, leads at 18.5%. Khan Academy grew Khanmigo from 68,000 pilot users in 2023–24 to over 1.4 million students by mid-2025. The nonprofit brand transfers a citation moat no venture-backed competitor has matched.
#### Why is Chegg losing AI citation share?
Chegg's growth engine was Google SEO routing students to paywalled homework-help pages. ChatGPT delivered the same answers for free. Google AI Overviews captured the search intent before any click. Non-subscriber traffic fell 49% in January 2025. Chegg laid off 22% of staff in May 2025 and 45% in October 2025 — over 636 positions in twelve months. The company has pivoted entirely to B2B skilling.
#### What is the Khan TED Institute and why does it matter?
A competency-based applied-AI bachelor's degree announced April 14, 2026 at TED2026 in Vancouver. Joint venture of Khan Academy, TED, and ETS. Total cost under $10,000 with Google, Microsoft, Accenture, McKinsey, Bain, and Replit as launch partners. The announcement reset the degree-cost benchmark and is generating the largest single citation surge of the 2026 EdTech cycle.
#### Who is this report for?
Chief Marketing Officers, Chief Communications Officers, and growth leads at online education and EdTech companies. Heads of partnerships at universities, school districts, and enterprise learning buyers. PR and marketing agency leadership advising EdTech clients. Investors evaluating EdTech category dynamics. Founders building AI-native learning products.
#### Is the report free to download?
Yes. The PDF download is ungated and free.
#### What data sources does the index draw from?
Q1 2026 prompt testing across ChatGPT, Claude, Perplexity, and Google AI Overviews. Public company filings and earnings (Chegg, Coursera, Duolingo, Udemy). Editorial coverage in EdSurge, EdWeek, The 74, Chalkbeat, Inside Higher Ed, Axios, Fortune, The New York Times, The Wall Street Journal, Bloomberg, Common Sense Media. Academic and research sources including WestEd RCTs, Stanford HAI, MIT Media Lab, Pew Research, and Gallup/Walton Family Foundation.
#### Can 5W run a GEO program for my EdTech brand?
Yes. As the premier AI communications firm in the United States, 5W's Generative Engine Optimization practice runs full AI visibility programs for EdTech, online education, and adjacent learning brands. Practice details at 5wpr.com/practice/geo-optimization. Inquiries: research@5wpr.com or info@5wpr.com.
#### Why now?
The citation window is narrow. AI models concentrate citations into a small number of domains per topic. Chegg's collapse, Khanmigo's surge, MagicSchool's six-million-educator milestone, Duolingo's $50M growth bet, and the Khan TED Institute launch all happened within a six-month window. The brands that publish authoritative citation-quality content in the next two quarters will define EdTech category leadership for a decade.
About 5W
### Premier The Communications Firm Built for the AI Era.
5W is the premier AI communications firm in the United States and the leading Generative Engine Optimization (GEO) partner for global brands, institutions, and high-profile individuals. With approximately 275 professionals serving more than 250 clients across consumer, corporate, crisis, healthcare, technology, education, and public affairs, the firm publishes leading research on how AI is reshaping brand, talent, and category dynamics. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan. More at 5wpr.com · research at 5wpr.com/research · GEO practice at 5wpr.com/practice/geo-optimization.
April 2026 · 5W AI Visibility Index Series, Vol. VII · Previous editions: Legal Tech, Real Estate, Fintech, Weight Loss & Metabolic Health, Pet Industry, Menopause & Midlife Women's Health
© 2026 · 5W Research · Published by 5W Research · 5wpr.com · 469 7th Avenue, Floor 8, New York, NY 10018 · (212) 999-5585 · research@5wpr.com
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### The Best Grocery Store, According to AI: Why Local Regional Chains Rank First (2026 5WPR Study)
URL: https://www.5wpr.com/research/the-best-grocery-store-according-to-ai-why-local-regional-chains-rank-first-2026/
Summary: New 5WPR research reveals that regional grocery chains appear first in 91% of AI responses to "best grocery store" queries, outranking national brands.
5WPR RESEARCH | JULY 2026
Local Relevance Outperforms National Scale
Every year, national retailers like Kroger and Target spend billions of dollars on advertising to maintain widespread brand awareness. Yet when we tested how Claude, ChatGPT, and Perplexity responded to “best grocery store” questions in 15 major metro areas, smaller, regional chains appeared first in 91% of responses.
New 5WPR research shows which chains hold an advantage and where national retailers lost ground. We also share how leaders can strengthen connections in the neighborhoods they serve and improve their position in high-intent user queries within AI answers. Our findings highlight the following:
Infographic showing 673 usable AI responses, 9.4 average chains named per response, 2.1 percent of responses naming one chain, 91.4 percent where the first chain named appeared in six or fewer tested cities, and 7.4 percent where the first chain named appeared in at least 12 tested cities.
Figure 1. Key findings from the 5WPR local grocery AI study. Source: 5WPR analysis of 673 usable responses across ChatGPT, Claude, and Perplexity in 15 U.S. metropolitan areas.
# How ChatGPT, Claude, and Perplexity Answer “Best Grocery Store” Questions
In our study, the models didn’t interpret "What is the best grocery store?" as a request for a single answer. Most responses listed several chains and divided the choices by price, product quality, convenience, organic selection, bulk shopping, or other shopping needs.
For example, one Austin response placed H-E-B first while presenting Whole Foods Market as a strong option for premium organic products. The variation matters because total mentions and first position measure different outcomes. One chain may appear often because the brand is widely recognized. Another chain may appear first because the AI response links the brand to the area mentioned in the question.
Regional Grocery Chains Appeared First in 91% of Local AI Responses
In 91.4% of the 673 responses, the first mention was a regional chain that appeared in six or fewer of the 15 metro areas tested. National chains that appeared in 12 or more metro areas ranked first in only 7.4% of responses.
Horizontal bar chart showing 615 responses where the first chain named appeared in six or fewer cities, eight responses where it appeared in seven to eleven cities, and 50 responses where it appeared in twelve to fifteen cities.
Figure 2. Where the first grocery chain named appeared across the 15 tested cities. Source: 5WPR analysis of 673 usable local grocery responses.
For executives, the business case is straightforward: broad recognition can help a chain appear in an AI answer, but it does not determine which chain appears in its top recommendations.
## Frequent Brand Mentions Did Not Guarantee First Place in Answers
### Trader Joe’s Led All Chains in Mentions but Appeared First in Only 2.7% of Responses
Trader Joe's appeared in 480 responses, more than any other chain in the study. However, Trader Joe's appeared first in only 13 responses, resulting in a first-position rate of 2.7%.
Infographic showing that Trader Joe's appeared in 480 responses and was named first in 13, a first-position rate of 2.7 percent.
Figure 3. Trader Joe's appeared often but rarely appeared first—5WPR analysis of 673 usable local grocery responses.
## H-E-B Appeared First in 62.2% of Responses That Mentioned the Chain
In the study dataset, H-E-B appeared in 127 responses and was listed first in 79 answers, resulting in a first-position rate of 62.2%. Of those 127 appearances, 126 occurred in Austin, Houston, and San Antonio.
H-E-B provides a helpful example of a chain with strong local associations. The Texas chain connects store pages with specific cities and neighborhoods, including South Congress in Austin. National chains can apply a similar approach by creating and publishing corporate messaging that identifies neighborhoods, available services, local products, delivery options, and other market-specific details.
## Costco Leads Broad Grocery Searches but Never Appeared First in the Local Study
While Costco ranks first in the 5WPR U.S. Grocery Retail AI Visibility Index 2026 for broad topics like bulk purchasing and pricing, it did not secure a first-place position in our local study. This contrast illustrates that overall visibility and local-first placement are distinct metrics that should be measured separately.
For marketers, brand recognition, total mentions, first-position answers, and local association are independent assets. An effective Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) strategy should track all four metrics rather than treating a single visibility metric as a complete measure of performance.
## How Major Chains Compared in Mentions and First-Place Appearances
Note: The figures combine spelling and naming variations; for example, “HEB” and “H-E-B” are counted as H-E-B. “Cities” shows how many cities included a chain in at least one AI response, not how many cities the chain serves.
Chain
Responses where named
Cities
First Position
First-position rate
Trader Joe's
480
15
13
2.7%
Whole Foods Market
392
15
20
5.1%
ALDI
252
13
16
6.3%
Sprouts Farmers Market
166
10
1
0.6%
Wegmans
157
5
81
51.6%
H-E-B
127
4
79
62.2%
Publix
124
6
63
50.8%
Costco
116
14
0
0.0%
Walmart
92
12
1
1.1%
Market Basket
46
2
30
65.2%
### Adding Relocation Context Increased Store Options From 7.1 to 12.6
Slight variations in prompts changed the number of options that appeared in the model's response. The "best grocery store" question produced an average of 7.1 options, while adding “I moved to (CITY) produced an average of 12.6 options.
Bar chart showing an average of 7.1 grocery chains for What is the best grocery store in city, 8.7 for Best grocery store in city, and 12.6 for I moved to city where should I buy groceries.
Figure 4. Different questions produced different numbers of store options. Source: 5WPR analysis of 673 usable responses across three question formats.
The relocation question produced about 78% more options than the direct conversational prompt. This result is important because visibility audits often measure performance against specific wording, rather than the broad range of ways users ask AI models questions.
## How National Grocery Chains Can Compete in City-Specific AI Searches
The study’s findings offer CMOs and retail leaders several options to improve connections between their brand and the local communities where their stores operate.
### Create Store Pages That Connect Each Location to Its City and Neighborhood
Smaller chains can customize store pages with local details, such as neighborhoods, intersections, services, inventory, and delivery options, to distinguish individual locations.
### Keep Information Consistent Across Google, Maps, Directories, and Delivery Apps
Verify Google Business Profiles and ensure that the individual store's name, address, phone number (NAP), hours, services, and website link match the information on its corporate website, in maps, on directories, and on delivery platforms (Instacart, Shipt, DoorDash, etc.).
### Add GroceryStore and LocalBusiness Schema to Store Pages
While schema is not a ranking factor, it serves as an important technical baseline. Implementing the LocalBusiness or GroceryStore schema on location pages helps search engines and AI models process a store's details, such as address, hours, services, and inventory. Treat schema as a best practice to ensure your business information is machine-readable, verifiable, and accessible to AI models.
### Build Local Reviews and Regional Media Coverage
For larger chains where scale prevents individual store pages, use corporate communications to highlight each store's role in its community through press releases, independent editorial coverage, community partnerships, regional product launches, events, and interviews with employees and local leadership.
Executing these steps can strengthen public information about a national brand's presence in a specific city or neighborhood. Modern PR strategies should pair broad corporate messaging with detailed, machine-readable information about individual stores and local markets.
# Next Steps for Grocery Leaders
Grocery retailers that perform well in AI search build structured, verifiable public presences that combine widespread recognition with strategic, machine-readable content and ad campaigns.
Brands can start this process by auditing high-priority markets. From there, they should identify the competitors leading them in AI answers, then fill in the gaps in their local content, one market at a time.
5WPR helps brands benchmark citation share, first-position rates, prompt relevance and sensitivity, and local visibility across ChatGPT, Claude, Perplexity, and other AI platforms.
5WPR. The Communications Firm for the AI Era.
Learn more about 5WPR's GEO and AI visibility services
Frequently Asked Questions About Local AI Grocery Searches
When you ask AI for the best grocery store, does it name one store or several?
Several. Across 673 responses, the models listed an average of 9.4 chains per answer, and only 14 responses (2.1%) listed a single chain with no alternatives. Most answers split picks by price, quality, convenience, organic selection, or bulk shopping rather than listing a single store.
Which grocery chains does AI name first in local "best grocery store" searches?
In 91.4% of responses, the first grocery chain listed was a regional brand that appeared in six or fewer of the 15 tested metro areas. Among the responses that mentioned them, Market Basket was listed first 65.2% of the time, H-E-B 62.2%, Wegmans 51.6%, and Publix 5;8%, each chain was dominant in its home region.
Does national brand recognition help a chain rank first in AI answers?
Recognition helps with mentions but not with first-place (or even top-three) listings. Chains that appeared across 12 to 15 tested cities were listed first in only 7.4% of responses. Trader Joe's was the most-mentioned chain in the study, appearing in 480 responses but took first place just 2.7% of the time.
Why do widely recognized chains like Trader Joe's and Costco rarely rank first?
Total mentions and first-position measures capture different outcomes. Costco appeared in 116 responses but was listed first in none (0.0%); Trader Joe's appeared in 480 but led in only 13 (2.7%). Broad familiarity earns a spot on the list; local association earns the top of it.
How can a national grocery chain get named first more often in local AI answers?
Build verifiable local signal one market at a time: create store pages that tie each location to its city and neighborhood, keep the store's name, address, phone, and hours consistent across Google, maps, directories, and delivery apps, add LocalBusiness or GroceryStore schema to location pages, and grow local reviews and regional media coverage.
Does the wording of the question change which grocery stores AI recommends?
Yes. "What is the best grocery store in [city]?" produced an average of 7.1 chains, while "I moved to [city], where should I buy groceries?" produced 12.6, about 78% more options. Prompt wording affects both the number and the mix of chains listed, so it is important to track visibility across different ways customers ask questions, rather than relying on a single static prompt.
Which AI models were tested, and did they use live web search?
The study tested ChatGPT (GPT-5.5 and GPT-4o), Claude (Sonnet 5 and Sonnet 4.6), and Perplexity (Sonar) across 675 web-enabled API tests on July 15, 2026. Web search was enabled for every test and was recorded as used in 601 of the 675 responses.
# Study Methodology
## Test Design and Model Versions
5WPR completed 675 web-enabled API tests during a single collection window on July 15, 2026. The framework included 15 U.S. metropolitan areas, three question formats, three providers, and five repetitions for each provider-question-city combination.
The OpenAI tests recorded GPT-5.5-2026-04-23 for 114 completed tests and GPT-4o for 111. The Anthropic tests recorded Claude Sonnet 5 for 101 completed tests and Claude Sonnet 4.6 for 124. Perplexity used Sonar for all 225 completed tests. Web search was enabled for every test and recorded as used in 601 of 675 completed responses.
Provider
Models recorded in the raw log
Completed tests
OpenAI
GPT-5.5-2026-04-23; GPT-4o
225
Anthropic
Claude Sonnet 5; Claude Sonnet 4.6
225
Perplexity
Sonar
225
Interpretation note: OpenAI and Anthropic model versions changed during the collection window. The prompt comparison is directional and does not prove that wording alone caused the difference.
Data note: The city counts indicate which grocery chains were included in the study responses. They do not represent a verified count of where each chain operates.
##
How we counted brand mentions
Of the 675 completed tests, 673 produced at least one recognized grocery-chain name and were included in the mention analysis. One response was empty, and one asked the user to clarify which Portland they meant without naming a grocery chain.
The extraction process used a reviewed list of grocery-chain labels and aliases. Each standardized chain label was counted at most once per response, even when the same chain appeared multiple times. The final mention file contained 6,355 chain-response records.
"First position" means the first recognized grocery-chain label appearing in the response text. First position does not always reflect the model's strongest endorsement, as AI models often qualify their answers or present separate winners for price, quality, convenience, and other categories.
For the main comparison, 5WPR grouped chains according to how many of the 15 city datasets included the standardized label: six or fewer cities, seven to 11 cities, and 12 to 15 cities. The groups measure visibility within the study responses. The groups do not provide an independently verified count of where each chain operates.
## Study Limitations
- The study measured the first chain named, not a separately hand-coded primary recommendation.
- The OpenAI and Anthropic model versions changed during collection, so the question-format comparison cannot isolate wording as the only cause of the differences.
- The study covered 15 metropolitan areas rather than every U.S. grocery market.
- API responses may differ from responses in consumer applications.
- Chain-level figures refer to the standardized labels in the dataset. Some location-specific or compound brand variants may appear under separate labels.
- The study identifies patterns in the responses but does not prove which website, media, review, or location signals caused a chain to appear first.
---
### WordPress Runs 40% of the Web. AI Recommends Shopify
URL: https://www.5wpr.com/ai-visibility-index/website-ecommerce-platforms-ai-visibility-index-2026/
Summary: 5W research ranks the top 25 website and e-commerce platforms by AI citation share across major search engines like ChatGPT and Perplexity.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexWebsite & E-commerce
# WordPress Runs 40% of the Web. AI Recommends Shopify
WordPress powers more of the internet than every other platform combined. New 5W research finds that when buyers ask an AI engine which platform to build on, the answer is Shopify, Squarespace, or Wix — and ranks the 25 platforms by how often each is named.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 platformsData window: Q2 2026
The Headlines
01
Market share is not citation share.
WordPress runs most of the web; AI names the hosted builders when buyers ask what to use.
02
The answer fragments by intent.
Online store, portfolio, blog — each routes to a different platform entirely.
03
Shopify owns "e-commerce."
When the question is selling online, one brand is the near-automatic answer.
~16%
Estimated AI citation share held by Shopify — the most-cited platform
~43%
Share of all websites powered by WordPress — the largest by market share
~$8.9B
Shopify's 2024 revenue, up roughly 26% year over year
~$7.2B
Price Permira paid to take Squarespace private in 2024
Figure 1 · The Ranking
## Who AI names first.
TOP 15 PLATFORMS BY EST. CITATION SHARE · Q2 2026
01ShopifyE-commerce16.0%
02SquarespaceDesign / creative12.0%
03WixAll-purpose / SMB10.5%
04WordPressOpen-source / CMS9.0%
05WebflowDesigner / no-code6.5%
06WooCommerceE-commerce / WordPress5.0%
07GoDaddySMB / bundled4.0%
08BigCommerceE-commerce / enterprise3.2%
09FramerDesign / modern2.7%
10HostingerBudget / hosting2.2%
11WeeblyLegacy / simple1.8%
12DudaAgency / pro builder1.5%
13CarrdSingle-page / micro1.3%
14Big CartelE-commerce / artists1.1%
15EcwidE-commerce / add-on0.95%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of brand citations across 60+ tracked buyer prompts spanning e-commerce and online stores, creative and portfolio sites, small-business and all-purpose sites, designer and no-code builds, blogging and content, and budget and simple sites. Remaining ~22.2% split across ranks 16–25 and unranked platforms.
The Central Finding
## Market share is not citation share.
WordPress powers more of the internet than anything else — roughly 43% of all websites, far ahead of every competitor combined. By raw market share, the category is not close. The 5W AI Visibility Index finds that WordPress ranks fourth in AI citation share. The platforms above it — Shopify, Squarespace, Wix — power a fraction of the sites WordPress does.
The reason is what the question is. WordPress is infrastructure — the open-source foundation a developer or agency builds on. It is not a marketed product with a review-economy footprint, and "which platform should I use" is a buyer-intent question, not an infrastructure question. The hosted builders were built for exactly that question, and they are reviewed, compared, and ranked in the content the engines retrieve. WordPress runs the web; the builders own the recommendation.
And the recommendation fragments by intent. Ask for an online store and the engines name Shopify. Ask for a portfolio or a creative site and they name Squarespace. Ask for a flexible small-business site and they name Wix. Ask for design freedom without code and they name Webflow or Framer. Ask for a blog or full control and they name WordPress. There is no single best platform — there is a best platform per intent.
— 5W Research, May 2026
"WordPress runs nearly half the internet and still loses the AI recommendation — and every platform marketer should sit with that. Market share is not citation share. The engine recommends the platform the internet wrote a buying guide about. WordPress is infrastructure; Shopify is an answer. And the answer fragments by intent — store, portfolio, blog, each routes somewhere different. That is not a problem. It is a map. Pick the intent you can own, build the content the engines retrieve, and become the answer to that question. Scale did not win this category. Coverage did."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common buyer prompts across six primary intent segments of the website and e-commerce platform market, running each prompt five times per engine in clean sessions. We identified which platforms AI models consistently surface, which sources feed those citations, and how the answer changes with the intent named in the query.
#### E-commerce & online stores
Shopify, BigCommerce, WooCommerce.
#### Creative & portfolio
Squarespace, Format, Pixpa.
#### Small business & all-purpose
Wix, GoDaddy, Hostinger.
#### Designer & no-code
Webflow, Framer, Duda.
#### Blogging & content
WordPress, Squarespace.
#### Budget & simple
Carrd, Big Cartel, Weebly.
Query types tracked. Real-world prompts including "best website builder," "best e-commerce platform," "best platform for an online store," "best website builder for a portfolio," "Shopify vs WooCommerce," "Wix vs Squarespace," "best platform for a small business website," and 50+ variations covering recommendation, comparison, and intent-specific queries.
Citation sources tracked. Software- and website-builder review platforms, technology and small-business media, comparison and roundup content, community forums, and vendor-owned documentation and content.
Important framing — please read. This index measures AI citation share for communications and marketing strategy purposes only. It is not procurement or platform-selection advice, and it does not rank platforms on features, performance, security, or reliability. A platform's position reflects how often AI engines surface it in response to buyer prompts — not a 5W judgment of the product.
The Full Ranking
## The Top 25 platforms, ranked by AI citation share.
#PlatformPrimary IntentAI VisibilityNotable
1ShopifyE-commerceCategory-dominantOwns the e-commerce query; the near-automatic answer whenever the intent is selling online.
2SquarespaceDesign / creativeCreative leaderThe default citation for portfolio and design-led sites; taken private by Permira for $7.2B in 2024.
3WixAll-purpose / SMBAll-purpose leaderThe largest pure website builder by global share; the default for flexible small-business sites.
4WordPressOpen-source / CMSMarket-share leaderPowers roughly 43% of all websites — the largest by market share, but fourth in AI citation share.
5WebflowDesigner / no-codeNo-code leaderOwns the designer and no-code query; the most-cited platform for design freedom without code.
6WooCommerceE-commerce / WordPressE-commerce nicheThe WordPress e-commerce plugin; large market share, cited in open-source online-store queries.
7GoDaddySMB / bundledSMB nicheDomain and hosting bundle with a website builder; cited in entry-level small-business queries.
8BigCommerceE-commerce / enterpriseEnterprise-commerce nicheEnterprise-oriented e-commerce platform; the most-cited Shopify alternative for scaling stores.
9FramerDesign / modernModern-design nicheDesign-led modern site builder; fast-rising citations in creative and landing-page queries.
10HostingerBudget / hostingBudget nicheBudget hosting with an AI website builder; cited heavily in low-cost and value queries.
11WeeblyLegacy / simpleLegacy nicheLong-standing simple builder being phased out by Square; a declining but recurring citation.
12DudaAgency / pro builderAgency nicheBuilder aimed at agencies and pros; cited in white-label and client-build queries.
13CarrdSingle-page / microMicro-site nicheSingle-page site builder; the default citation for simple one-page and link-in-bio sites.
14Big CartelE-commerce / artistsArtist-commerce nicheE-commerce built for artists and small makers; cited in low-volume creative-seller queries.
15EcwidE-commerce / add-onAdd-on nicheE-commerce that bolts onto an existing site; cited where the query is adding a store to a site.
16Adobe CommerceE-commerce / enterpriseEnterprise nicheFormerly Magento; cited in large-scale and developer-led enterprise commerce queries.
17IONOSHosting / SMBHosting nicheHosting provider with website-builder tools; cited more strongly in European-market queries.
18JimdoSMB / simpleSimple nicheSimple builder aimed at small businesses; a thinner but recurring roundup citation.
19TildaDesign / contentDesign nicheBlock-based design builder; fast-growing citations in creative and editorial-site queries.
20StrikinglySingle-page / simpleSingle-page nicheSingle-page site builder; cited as a simple alternative in quick-launch queries.
21WebnodeSMB / multilingualValue nicheValue builder with multilingual focus; cited in budget and international small-site queries.
22FormatPortfolio / creativePortfolio nichePortfolio builder for photographers and creatives; cited in creative-portfolio queries.
23PixpaPortfolio / creativePortfolio nicheAll-in-one portfolio and store builder for creatives; a secondary portfolio citation.
24DurableAI builderAI-builder nicheAI-first website builder; cited in fast-launch and AI-generated-site queries.
25Salesforce Commerce CloudE-commerce / enterpriseEnterprise nicheEnterprise commerce platform; cited in large-organization and B2B commerce queries.
Winners
## The platforms winning the AI answer.
### ▲ Shopify — Owning "E-commerce"
Shopify made one intent its own. Ask any engine about selling online and it returns Shopify first — years of being the reference point for e-commerce made the brand and the category nearly synonymous.
### ▲ Squarespace — Owning "Design" and "Portfolio"
Squarespace claimed the creative lane and never let go. Whenever a query signals a portfolio, a personal site, or a design-led build, the engines route it straight to Squarespace.
### ▲ Wix — Owning the All-Purpose Query
Wix took the broadest defensible position — the flexible, do-anything builder — and backed it with the largest pure-builder footprint. For the general small-business query, it is a reliable answer.
### ▲ Webflow — Owning "No-Code Design"
Webflow did not chase the mass query. It owns one precise intent — design freedom without code — so completely that the engines name it whenever a query signals a designer or developer audience.
Falling Behind
## The platforms AI is leaving behind.
### ▼ WordPress — Market Leader, Fourth in the Answer
WordPress runs more of the web than anything else, but it is infrastructure, not a marketed product. The "which platform should I use" query routes to the builders the buying guides actually review.
### ▼ Platforms Without an Owned Intent
Builders that market themselves as good for any kind of site give the engines no intent query to route to them. In a category that fragments by intent, "general" underperforms.
### ▼ Legacy Builders in Decline
Older builders losing investment and being phased out keep slipping in citations as the engines name the platforms the current content cycle is discussing.
### ▼ Enterprise Commerce With Thin Consumer Content
Enterprise platforms generate large revenue but little buyer-guide coverage — and underindex against the hosted builders in the recommendation answer.
Structural Findings
## Six structural truths about AI visibility in platforms.
01
### Market share is not citation share.
WordPress runs the web but ranks fourth — the engines retrieve from content, not installed base.
02
### The answer fragments by intent.
Store, portfolio, blog, no-code build — each routes to a different platform.
03
### Infrastructure is not an answer.
An open-source foundation loses the buyer-intent query to a marketed, reviewed product.
04
### Owning one intent wins.
Shopify on commerce and Webflow on no-code outperform broader, intent-free positioning.
05
### Buyer guides drive citations.
Builder-comparison and roundup content feeds platform citations disproportionately.
06
### Comparison queries set the answer.
"Wix vs Squarespace"-style queries are high-frequency and move citation share fast.
Findings Specific to 2026
## Six 2026 dynamics reshaping platform AI citations in real time.
01
### AI site builders are entering the answer.
Conversational, AI-generated site creation is now part of how the engines describe the category.
02
### The majors are racing on AI.
Wix, Shopify, and Webflow have all shipped AI build tools, and the engines increasingly cite them.
03
### Performance is becoming a citation factor.
Page-speed and Core Web Vitals comparisons are surfacing in how the engines rank builders.
04
### Design-led builders are rising.
Framer and Tilda are contesting creative queries Squarespace has long led.
05
### Ownership and lock-in enter the framing.
Data portability and code export are increasingly part of how the engines describe platforms.
06
### Roundup content refreshes constantly.
"Best website builder" guides are updated continuously and move the answer quickly.
The Playbook
## General tips for platform marketing teams.
- Audit category citation share quarterly. Platform citations move fast as buyer guides and product launches refresh.
- Pick the intent you intend to own. Store, portfolio, blog, no-code — choose the query you can credibly win.
- Make the intent explicit in your content. The engines route by intent; "good for any site" routes nowhere.
- Earn builder-review and buyer-guide presence. Those sources feed platform citations disproportionately.
- Get comparison content right. "Wix vs Squarespace"-style queries are high-frequency — be accurately represented.
- Do not rely on market share alone. Installed base does not translate into the recommendation answer.
- Treat AI build features as a citation asset. AI site creation now surfaces in answers — make it retrievable.
- Keep capability and pricing sources accurate. The engines retrieve these heavily; errors propagate into answers.
- Address performance and ownership directly. Speed and data portability are entering the framing — publish them clearly.
- Re-audit after every major launch, pricing change, or guide-refresh cycle. Each reshapes the citation surface.
The Bigger Picture
## The platform decision now starts in the answer engine.
Choosing a platform used to mean a search, a few comparison articles, and a free-trial signup. A growing share of that decision now begins with a question typed into an AI engine — and for website and e-commerce platforms, the engine's answer is not the platform with the most sites, but the one that owns the intent behind the question.
The Index shows what that surface rewards: a clearly owned intent, deep presence in the buyer-guide content the engines retrieve, and accurate representation in the comparison queries that move citation share. It also shows what it does not reward — infrastructure scale without buyer-facing content, and "build any kind of site" positioning in a category that fragments by intent.
AI citation share is the scoreboard. In platforms, the brand AI names is the one that owns an intent in the content the engines read — not the one that runs the most websites.
Frequently Asked Questions
## The Website & E-commerce Platforms AI Visibility Index 2026.
What is the Website & E-commerce Platforms AI Visibility Index 2026…A research report by 5W that ranks the top 25 website and e-commerce platforms by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ buyer prompts tracked in Q2 2026.
Which platform holds the highest AI citation share…Shopify, with an estimated 16% — driven by its ownership of the e-commerce query, the near-automatic answer whenever the intent is selling online.
Does WordPress lead the AI answer…No. WordPress powers roughly 43% of all websites — the largest by market share — but it ranks fourth in AI citation share. WordPress is infrastructure; the buyer-intent question routes to the hosted builders the engines retrieve buying guides about.
Why does market share not translate into citation share…AI engines retrieve from content — reviews, comparisons, buyer guides — not from installed base. A platform that powers many sites but generates little buyer-facing content underindexes against marketed, reviewed products.
Is this index a rating of platform quality…No. It measures AI citation share for communications strategy purposes only and is not procurement or platform-selection advice. It does not rank platforms on features, performance, or reliability.
What is citation share…The estimated proportion of brand mentions a platform receives across all tracked prompts and AI engines — the core metric used to rank platforms in the Index.
### About this report
5W AI Visibility Index: Website & E-commerce Platforms. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ buyer queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report is not procurement or platform-selection advice and does not rank platforms on features or reliability.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
---
### Edition 03: Cybersecurity Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/cybersecurity/
Summary: The strongest retrieval architecture in B2B media. The reason is government.
---
### Fintech B2B Infrastructure AI Visibility Index 2026
URL: https://www.5wpr.com/research/fintech-b2b-infrastructure-ai-visibility-index-2026/
Summary: B2B fintech infrastructure AI visibility data shows Stripe leads appearances, while Plaid posts the highest measured owned-domain citation rate.
# Fintech B2B Infrastructure AI Visibility Index 2026
Stripe Dominates B2B Fintech AI Mentions
Stripe, Inc. leads this B2B Fintech Infrastructure measurement with a 63.9% appearance rate across answers returned. Its 95% interval is 58.2% to 69.2%. Plaid Inc. ranks second at 42.7%, but has the highest owned-domain citation rate among the leading brands at 11.5% of answers with retrieval evidence. The data separates being named from being cited.
This segment includes B2B financial technology infrastructure, API gateways, and enterprise billing tools. It is one segment in the Fintech AI Visibility Index 2026. It does not cover Payments & Processors, Trading & Investing Platforms, or BNPL & Consumer Credit.
288AI answers analyzed
63.9%Stripe, Inc. appearance rate, the category leader
26Brands named across the study
99.3%Answers carrying retrieval evidence
The gap matters because a brand can appear in many generated answers while its own site supplies little of the supporting evidence. Citation rates use answers with retrieval evidence. Appearance rates use answers returned.
## Which brands lead the B2B fintech infrastructure leaderboard?
Stripe, Inc. ranks first with a 63.9% appearance rate. Plaid Inc. ranks second at 42.7%, followed by Adyen at 41.0%, Marqeta, Inc. at 37.2%, and Unit, Inc. at 26.7%. Stripe appears first in an average position of 2.3 when it is named.
The leaderboard measures appearances across 288 returned answers. It ranks visibility in this prompt set, not market size, consumer demand, or product quality.
### Appearance leaderboard
### AI visibility leaderboard
Appearance rate is the share of the 288 returned AI answers in which each brand was named.
#BrandNamed inAppearance rate95% intervalAvg position
1Stripe, Inc.184/28863.9%58.2%–69.2%2.3
2Plaid Inc.123/28842.7%37.1%–48.5%4.8
3Adyen118/28841%35.4%–46.7%4.8
4Marqeta, Inc.107/28837.2%31.8%–42.9%5.7
5Unit, Inc.77/28826.7%22%–32.1%7.2
6Treasury Prime, Inc.68/28823.6%19.1%–28.8%7.3
7Tipalti Ltd.58/28820.1%15.9%–25.1%4.6
8Modern Treasury, Inc.58/28820.1%15.9%–25.1%6.1
9Airwallex53/28818.4%14.4%–23.3%5.8
10Galileo50/28817.4%13.4%–22.2%6.5
11BILL47/28816.3%12.5%–21%3.7
12Ramp43/28814.9%11.3%–19.5%4.2
13Wise35/28812.2%8.9%–16.4%6.6
14Coupa31/28810.8%7.7%–14.9%7.5
15Synctera, Inc.30/28810.4%7.4%–14.5%8
Stripe's interval does not overlap Plaid's interval. Plaid's 37.1% to 48.5% interval overlaps Adyen's 35.4% to 46.7% interval and Marqeta's 31.8% to 42.9% interval. Adyen and Marqeta also overlap. Those differences are within the margin of this sample. Unit's 22.0% to 32.1% interval overlaps Marqeta's interval at the edge.
## How do the leading brands compare?
The leaders differ by engine and by the extent to which their own domains appear as evidence. The profiles below report appearances across returned answers and citations across answers with retrieval evidence.
### Stripe, Inc.
63.9% appearance rate places Stripe, Inc. first, with a 58.2% to 69.2% interval. Perplexity named Stripe in 59 of 72 answers, ChatGPT in 58 of 72, Gemini in 39 of 72, and Claude in 28 of 72. Stripe's average first mention was 2.3. Its owned domain was cited in 31 answers, a 10.8% citation rate among 286 answers with retrieval evidence.
### Plaid Inc.
42.7% appearance rate places Plaid Inc. second, with a 37.1% to 48.5% interval. ChatGPT named Plaid in 45 of 72 answers, Perplexity in 37, Gemini in 31, and Claude in 10. Plaid's average first mention was 4.8. Its owned domain was cited in 33 answers, an 11.5% citation rate among answers with retrieval evidence.
### Adyen
41.0% appearance rate places Adyen third, with a 35.4% to 46.7% interval. Perplexity named Adyen in 42 of 72 answers, ChatGPT in 34, Gemini in 28, and Claude in 14. Adyen's average first mention was 4.8. Its owned domain was cited in 23 answers, an 8.0% citation rate among answers with retrieval evidence.
### Marqeta, Inc.
37.2% appearance rate places Marqeta, Inc. fourth, with a 31.8% to 42.9% interval. ChatGPT named Marqeta in 40 of 72 answers, Perplexity in 33, Gemini in 25, and Claude in 9. Marqeta's average first mention was 5.7. Its owned domain was cited in 24 answers, an 8.4% citation rate among answers with retrieval evidence.
### Unit, Inc.
26.7% appearance rate places Unit, Inc. fifth, with a 22.0% to 32.1% interval. Perplexity and ChatGPT each named Unit in 27 of 72 answers, Gemini named it in 18, and Claude named it in 5. Unit's average first mention was 7.2. Its owned domain was cited in 10 answers, a 3.5% citation rate among answers with retrieval evidence.
### Treasury Prime, Inc.
23.6% appearance rate places Treasury Prime, Inc. sixth, with a 19.1% to 28.8% interval. Perplexity named Treasury Prime in 35 of 72 answers and ChatGPT in 29, while Gemini named it in 3 and Claude in 1. Treasury Prime's average first mention was 7.3. Its owned domain was cited in 17 answers, a 5.9% citation rate among answers with retrieval evidence.
### Tipalti Ltd.
20.1% appearance rate places Tipalti Ltd. seventh, with a 15.9% to 25.1% interval. Perplexity named Tipalti in 34 of 72 answers, compared with 11 for ChatGPT, 9 for Gemini, and 4 for Claude. Tipalti's average first mention was 4.6. Its owned domain was cited in 21 answers, a 7.3% citation rate among answers with retrieval evidence.
### Modern Treasury, Inc.
20.1% appearance rate places Modern Treasury, Inc. eighth, with a 15.9% to 25.1% interval. Gemini named Modern Treasury in 28 of 72 answers, while Perplexity named it in 13, ChatGPT in 11, and Claude in 6. Modern Treasury's average first mention was 6.1. Its owned domain was cited in 20 answers, a 7.0% citation rate among answers with retrieval evidence.
### Airwallex
18.4% appearance rate places Airwallex ninth, with a 14.4% to 23.3% interval. Perplexity named Airwallex in 46 of 72 answers, while ChatGPT named it in 1, Gemini in 4, and Claude in 2. Airwallex's average first mention was 5.8. Its owned domain was cited in 7 answers, a 2.4% citation rate among answers with retrieval evidence.
### Galileo
17.4% appearance rate places Galileo tenth, with a 13.4% to 22.2% interval. ChatGPT named Galileo in 37 of 72 answers, while Perplexity named it in 2, Gemini in 9, and Claude in 2. Galileo's average first mention was 6.5. Its owned domain was cited in 11 answers, a 3.8% citation rate among answers with retrieval evidence.
## Where do the engines disagree?
The engines disagree sharply for several brands. Each engine returned 72 answers. Perplexity, ChatGPT, and Claude had retrieval evidence for all 72 answers. Gemini had retrieval evidence for 70 of 72 answers. Differences may reflect model and retrieval-tool design, rather than a stable measure of category-wide visibility.
### Engine appearance patterns
### Appearance rate by engine
Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Stripe, Inc.81.9%80.6%54.2%38.9%
Plaid Inc.51.4%62.5%43.1%13.9%
Adyen58.3%47.2%38.9%19.4%
Marqeta, Inc.45.8%55.6%34.7%12.5%
Unit, Inc.37.5%37.5%25%6.9%
Treasury Prime, Inc.48.6%40.3%4.2%1.4%
Tipalti Ltd.47.2%15.3%12.5%5.6%
Modern Treasury, Inc.18.1%15.3%38.9%8.3%
Airwallex63.9%1.4%5.6%2.8%
Galileo2.8%51.4%12.5%2.8%
BILL25%19.4%13.9%6.9%
Ramp25%12.5%12.5%9.7%
Stripe is broad but uneven: Perplexity named it in 81.9% of its 72 answers, ChatGPT in 80.6%, Gemini in 54.2%, and Claude in 38.9%. Plaid follows a similar pattern, with 62.5% on ChatGPT and 13.9% on Claude. Adyen and Marqeta also have much lower Claude rates than Perplexity and ChatGPT.
Several brands are concentrated in one system. Airwallex appears in 63.9% of Perplexity answers but 1.4% of ChatGPT answers, 5.6% of Gemini answers, and 2.8% of Claude answers. Galileo appears in 51.4% of ChatGPT answers but 2.8% of Perplexity and Claude answers. Modern Treasury appears in 38.9% of Gemini answers, compared with 18.1% on Perplexity, 15.3% on ChatGPT, and 8.3% on Claude.
Treasury Prime appears in 48.6% of Perplexity answers and 40.3% of ChatGPT answers, but 4.2% on Gemini and 1.4% on Claude. Tipalti's 47.2% Perplexity rate falls to 15.3% on ChatGPT, 12.5% on Gemini, and 5.6% on Claude. Synapse appears in 26.4% of ChatGPT answers and 0% in the other three engines. Tradier appears in 0% of the 72 answers from every engine.
## Which demand clusters produce each brand's visibility?
The cluster chart separates the kinds of questions in this study and identifies which brands lead them. Use it to distinguish broad visibility from visibility tied to a narrower kind of request.
### Demand-cluster leaders
### Leaders by demand cluster
The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
— Other demand —
General best-in-category24Stripe, Inc.95.8%Adyen 87.5%, Marqeta, Inc. 66.7%, Plaid Inc. 62.5%
Primary use case24BILL91.7%Tipalti Ltd. 87.5%, Stampli Inc. 83.3%, Ramp 79.2%
Everyday and mainstream choice24Stripe, Inc.66.7%Plaid Inc. 62.5%, Adyen 37.5%, Marqeta, Inc. 37.5%
Price and value24Stripe, Inc.75%Unit, Inc. 37.5%, BILL 37.5%, Synctera, Inc. 33.3%
Beginners and first purchase24Stripe, Inc.100%Plaid Inc. 70.8%, Marqeta, Inc. 70.8%, Unit, Inc. 70.8%
Where to buy24Stripe, Inc.54.2%Plaid Inc. 50%, Adyen 50%, Marqeta, Inc. 50%
Trust and recommendation12Stripe, Inc.91.7%Adyen 91.7%, Plaid Inc. 83.3%, Marqeta, Inc. 58.3%
Comparison and evaluation12Stripe, Inc.83.3%Adyen 58.3%, Modern Treasury, Inc. 50%, BILL 33.3%
Emerging and new entrants12Modern Treasury, Inc.66.7%Stripe, Inc. 50%, Unit, Inc. 50%, Plaid Inc. 41.7%
Gifting and occasions12Stripe, Inc.91.7%Plaid Inc. 75%, Brex 75%, Adyen 58.3%
Specific need or condition12Plaid Inc.41.7%Stripe, Inc. 8.3%, Unit, Inc. 8.3%
Quality and premium tier12Stripe, Inc.91.7%Adyen 91.7%, Marqeta, Inc. 91.7%, Plaid Inc. 41.7%
Ownership and maker attribution12Stripe, Inc.75%Plaid Inc. 58.3%, Marqeta, Inc. 33.3%, Adyen 25%
Durability and longevity12Adyen75%Stripe, Inc. 66.7%, Marqeta, Inc. 58.3%, Plaid Inc. 33.3%
Specialist / advanced use12Stripe, Inc.66.7%Modern Treasury, Inc. 66.7%, Treasury Prime, Inc. 58.3%, Currencycloud 50%
— B2B Fintech Infrastructure —
Plaid competitors for bank connectivity12Stripe, Inc.25%Plaid Inc. 8.3%
best B2B automated accounts payable software12Tipalti Ltd.75%Coupa 75%, BILL 66.7%, Stampli Inc. 66.7%
best financial API infrastructure for developers12Stripe, Inc.83.3%Plaid Inc. 83.3%, Adyen 41.7%, Marqeta, Inc. 41.7%
The measured data supplied for this report does not include cluster values in text form. The chart is the record of cluster-level results. It should be read as a map of where the brands lead within the question groupings shown, not as evidence about unmeasured requests.
## Which owned domains and publishers supply evidence?
Plaid Inc. has the highest owned-domain citation rate among the leading brands at 11.5% of answers with retrieval evidence. Stripe, Inc. is close at 10.8%. Lower-ranked brands can show stronger citation performance than their appearance rank suggests: Ramp has an 8.0% citation rate at rank 12, while Tipalti has 7.3% at rank 7 and Modern Treasury has 7.0% at rank 8.
### Owned-domain citation rates
### Owned-domain citation rate
Share of the 286 answers with retrieval evidence that cited a URL on the brand's own website.
Stripe, Inc.
10.8% · 31/286
Plaid Inc.
11.5% · 33/286
Adyen
8% · 23/286
Marqeta, Inc.
8.4% · 24/286
Unit, Inc.
3.5% · 10/286
Treasury Prime, Inc.
5.9% · 17/286
Tipalti Ltd.
7.3% · 21/286
Modern Treasury, Inc.
7% · 20/286
Airwallex
2.4% · 7/286
Galileo
3.8% · 11/286
BILL
5.9% · 17/286
Ramp
8% · 23/286
Owned-domain citations are not the same measure as brand appearances. Plaid's 33 owned-domain citations exceed Stripe's 31, although Stripe is named in 184 returned answers and Plaid in 123. Coupa and Currencycloud each have a 0.3% owned-domain citation rate. Payoneer and Tradier each have a 0% owned-domain citation rate.
### Publishers cited in answers
### Most-cited publishers
Publishers cited as evidence, counted once per answer rather than once per link.
PublisherAnswers citing itAnswer penetration
connectpay.com112/28639.2%
fintechspecs.com91/28631.8%
openbankingtracker.com62/28621.7%
trustsignals.com59/28620.6%
sdk.finance51/28617.8%
f6s.com51/28617.8%
landbase.com49/28617.1%
apideck.com43/28615%
eco.com42/28614.7%
fintechmagazine.com41/28614.3%
worldmetrics.org40/28614%
gartner.com39/28613.6%
Third-party sources appear often in the evidence set. connectpay.com was cited in 112 answers, or 39.2% of answers with retrieval evidence. fintechspecs.com appeared in 91 answers, or 31.8%. openbankingtracker.com appeared in 62 answers, or 21.7%. Plaid.com appears among the listed sources with 31 cited answers and 10.8% answer penetration.
## Who is winning and who is at risk?
### Brands with the strongest measured position
Stripe, Inc. has the strongest measured appearance position. It ranks first at 63.9%, has a 2.3 average first-mention position, and is named across all four engines. Plaid Inc. ranks second at 42.7% and has the highest listed owned-domain citation rate at 11.5%. Both brands have 100% cross-engine consistency.
Adyen and Marqeta remain in the top four with 41.0% and 37.2% appearance rates. Their intervals overlap, so this sample does not establish one as more visible than the other. Tipalti, Modern Treasury, and Ramp have citation rates of 7.3%, 7.0%, and 8.0%, respectively, which are stronger than several brands with higher appearance ranks.
### Brands with measured exposure risk
Tradier has a 0% appearance rate across 288 returned answers and a 0% owned-domain citation rate among answers with retrieval evidence. It is absent from every engine's 72-answer set. Payoneer has a 7.3% appearance rate and 0% owned-domain citation rate. Coupa has a 10.8% appearance rate and a 0.3% owned-domain citation rate.
Synapse has a 6.6% appearance rate, a 4.3% to 10.1% interval, and 25% cross-engine consistency. It is named only by ChatGPT in this measurement. Currencycloud has an 8.3% appearance rate and is absent from Gemini and Claude. These patterns create exposure to system choice within the measured prompts.
## What does the tally say about this category?
The top of this leaderboard is concentrated around Stripe, Inc. Stripe's 63.9% appearance rate is separated from Plaid Inc.'s 42.7% interval. Below that lead, the intervals for Plaid, Adyen, Marqeta, and Unit overlap in sequence, so the rank order does not settle every comparison among those brands.
The engines converge for several large brands because Stripe, Plaid, Adyen, Marqeta, and Unit each have 100% cross-engine consistency. They diverge in frequency. Airwallex, Galileo, Treasury Prime, Tipalti, Modern Treasury, Synapse, and Currencycloud show engine-specific concentrations or absences.
Third-party publishers carry much of the visible citation evidence. connectpay.com and fintechspecs.com each appear in more retrieval-backed answers than any listed owned brand domain. Among the named brands in the brief, AvidXchange, Inc. has an 8.7% appearance rate and 0.7% owned-domain citation rate. Tradier is measured at 0% for both appearance and owned-domain citation rate. Plaid Inc. is measured at 42.7% appearance and 11.5% owned-domain citation rate.
## What changed during the measurement window?
The data records one measurement window, not changes over time. It does not show a before-and-after shift for this category.
## What actions address the measured gaps?
Use the next measurement to test concrete changes against the weaknesses recorded here. These are measurement actions, not claims about unmeasured results.
- For brands with low owned-domain citation rates, publish pages that answer the product and integration questions represented in the measured prompt set. Re-measure owned-domain citation rate against the 286-answer retrieval-evidence denominator.
- For Tradier, test whether category pages and technical explanations increase its 0% appearance rate across the same 288 returned-answer denominator. Re-measure every engine separately because all four recorded 0%.
- For Airwallex, test content that can support recognition outside Perplexity. Re-measure the 1.4% ChatGPT rate, 5.6% Gemini rate, and 2.8% Claude rate against their 72-answer sets.
- For Galileo, test content and third-party references that support recognition outside ChatGPT. Re-measure the 2.8% Perplexity rate, 12.5% Gemini rate, and 2.8% Claude rate.
- For Treasury Prime and Tipalti, test material that reduces the gap between their Perplexity results and Gemini or Claude results. Re-measure each engine's appearance rate using its 72 returned answers.
- For Plaid, Stripe, Adyen, and Marqeta, test whether pages tied to the cluster leaders preserve their appearance positions while raising owned-domain citation rates. Re-measure appearance and citation separately because the denominators differ.
## What does this mean for brands in the category?
Interpretation: The measurement separates brand recognition from a brand's role as a cited source. Stripe has the widest measured presence. Plaid has the strongest listed owned-domain citation rate. Brands with one-engine concentration need to assess results engine by engine, because an aggregate rate can hide an absence in another system.
The practical reading is narrow. This study covers 24 stratified prompts, repeated three times per engine. It identifies where these systems named brands and retrieved domains on September 14, 2026. It does not predict outcomes for every B2B fintech infrastructure selection.
## How was this B2B fintech infrastructure study measured?
This flagship study measured 24 stratified prompts three times on each of four systems: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). That produced 288 attempted and returned answers. Appearance rates use 288 returned answers. Citation and source rates use 286 answers with retrieval evidence.
### Measurement summary
### How this study was measured
- Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked.
- Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 288 answers returned of 288 attempted, 286 with retrieval evidence.
- Product lines rolled up to parent brands using a registry fixed before scoring.
- Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.
Perplexity, ChatGPT, and Claude had 100% grounding rates in their 72-answer sets. Gemini had a 97.2% grounding rate, with 70 grounded answers out of 72. The results describe this prompt set and date. Repeated generations expose variation, but the data does not establish consumer demand, product quality, or category-wide market position.
## Frequently asked questions
### Which B2B fintech infrastructure brand ranks first?
Stripe, Inc. ranks first with a 63.9% appearance rate across 288 returned answers. Its 95% interval is 58.2% to 69.2%, and its average first-mention position is 2.3.
### Which brand has the highest owned-domain citation rate?
Plaid Inc. has an 11.5% owned-domain citation rate among 286 answers with retrieval evidence. Stripe, Inc. follows at 10.8%.
### Does the appearance leaderboard measure citations?
No. Appearance rates use 288 returned answers and measure whether a brand was named. Citation rates use 286 answers with retrieval evidence and measure whether an owned domain was cited.
### Which engines were measured?
The study measured Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). Each engine returned 72 answers.
### Which brands show the largest engine-specific pattern?
Airwallex appeared in 63.9% of Perplexity answers but 1.4% of ChatGPT answers. Galileo appeared in 51.4% of ChatGPT answers but 2.8% of Perplexity and Claude answers.
### Was Tradier named in the measured answers?
No. Tradier has a 0% appearance rate across 288 returned answers. Each of the four engines recorded 0 mentions in its 72-answer set.
### Can these results be used as a market-share ranking?
No. The leaderboard ranks brand appearances in 24 stratified prompts measured on September 14, 2026. It does not measure market share, consumer demand, or product quality.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1G20%0/2880%–1.3%
2Gartner0%0/2880%–1.3%
3Capterra0%0/2880%–1.3%
## Methodology appendix
This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.
### What was measured
- Category: B2B Fintech Infrastructure
- Measurement date: 2026-09-14
- Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 288
- Answers returned (appearance denominator): 288
- Answers with retrieval evidence (citation denominator): 286 (99.3% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable.
### How the prompts were sampled
Fixed sampling frame: 3 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: B2B Fintech Infrastructure; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure.
Demand clusterPromptsScope
best financial API infrastructure for developers1best financial API infrastructure for developers
best B2B automated accounts payable software1best B2B automated accounts payable software
Plaid competitors for bank connectivity1Plaid competitors for bank connectivity
General best-in-category2the plain, highest-volume 'best X' style demand with no qualifier
Everyday and mainstream choice2what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Price and value2budget, value-for-money, cheaper alternatives and whether premium is worth it
Primary use case2the dominant everyday use case for this category
Beginners and first purchase2what a first-time buyer with no knowledge of the category should get
Where to buy2purchase-channel demand: where to buy, which retailers, direct vs marketplace
Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
Ownership and maker attribution1who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to
Quality and premium tier1which options are highest quality, most durable or best made
Durability and longevity1which brands last, hold up over time, or fail early
Specialist / advanced use1advanced, professional or enthusiast-level demand inside the category
Emerging and new entrants1newer entrants, recent launches and shifts buyers are only starting to ask about
### The exact prompts
#PromptClusterShape
1best financial API infrastructure for developersneed_1medium
2best B2B automated accounts payable softwareneed_2medium
3Plaid competitors for bank connectivityneed_3medium
4best b2b fintech infrastructure platformsgeneral_bestshort
5Which B2B fintech infrastructure platforms are the best overall?general_bestmedium
6best business finance api platformseverydayshort
7Which fintech infrastructure providers are easiest for everyday business operations?everydaymedium
8affordable fintech infrastructure providersvalue_priceshort
9Which B2B fintech platforms offer the best value for growing businesses?value_pricelong
10best accounts payable automation platformsuse_caseshort
11Which platforms are best for automating business payments and invoice workflows?use_casemedium
12fintech infrastructure for beginnersbeginnershort
13What B2B fintech infrastructure platform should a first-time buyer choose?beginnermedium
14where to buy fintech infrastructureretailshort
15Should businesses buy fintech infrastructure directly or through implementation partners?retailmedium
16What fintech infrastructure platform makes a useful gift for a new business owner?giftinglong
17Which providers best support companies needing both financial-data connectivity and workflow automation?specific_needlong
18Which B2B fintech infrastructure platforms are better for APIs, billing, and enterprise workflows?comparisonlong
19Which B2B fintech infrastructure brands are most trusted by enterprise finance teams and analysts?trustlong
20Who owns the leading B2B fintech infrastructure platforms and what companies are behind them?ownershiplong
21Which B2B fintech infrastructure providers offer the highest-quality enterprise-grade products and support?premium_qualitylong
22Which fintech infrastructure platforms remain reliable as transaction volumes and business complexity increase?durabilitylong
23What are the best B2B fintech infrastructure platforms for advanced API and treasury teams?specialistlong
24Which newer B2B fintech infrastructure providers are gaining traction with enterprise buyers?emergingmedium
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search727272100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool727272100%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)72727097.2%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool727272100%
Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
- Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
- 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
- Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
- Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
- Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
- 0% — the metric could be evaluated and the event did not occur.
- Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — the share of engines that named the brand at least once.
- Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.
### Evidence rules
- An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
- Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
- Publisher identity could not be established for 1 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
- Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Stripe, Inc.184/28863.9%63.9%58.2%–69.2%31/286 (10.8%)
2Plaid Inc.123/28842.7%42.7%37.1%–48.5%33/286 (11.5%)
3Adyen118/28841%41%35.4%–46.7%23/286 (8%)
4Marqeta, Inc.107/28837.2%37.2%31.8%–42.9%24/286 (8.4%)
5Unit, Inc.77/28826.7%26.7%22%–32.1%10/286 (3.5%)
6Treasury Prime, Inc.68/28823.6%23.6%19.1%–28.8%17/286 (5.9%)
7Tipalti Ltd.58/28820.1%20.1%15.9%–25.1%21/286 (7.3%)
8Modern Treasury, Inc.58/28820.1%20.1%15.9%–25.1%20/286 (7%)
9Airwallex53/28818.4%18.4%14.4%–23.3%7/286 (2.4%)
10Galileo50/28817.4%17.4%13.4%–22.2%11/286 (3.8%)
11BILL47/28816.3%16.3%12.5%–21%17/286 (5.9%)
12Ramp43/28814.9%14.9%11.3%–19.5%23/286 (8%)
13Wise35/28812.2%12.2%8.9%–16.4%7/286 (2.4%)
14Coupa31/28810.8%10.8%7.7%–14.9%1/286 (0.3%)
15Synctera, Inc.30/28810.4%10.4%7.4%–14.5%5/286 (1.7%)
16Stampli Inc.28/2889.7%9.7%6.8%–13.7%5/286 (1.7%)
17Checkout Ltd.27/2889.4%9.4%6.5%–13.3%6/286 (2.1%)
18Brex27/2889.4%9.4%6.5%–13.3%10/286 (3.5%)
19AvidXchange, Inc.25/2888.7%8.7%5.9%–12.5%2/286 (0.7%)
20Currencycloud24/2888.3%8.3%5.7%–12.1%1/286 (0.3%)
21Payoneer Inc.21/2887.3%7.3%4.8%–10.9%0/286 (0%)
22FIS20/2886.9%6.9%4.5%–10.5%5/286 (1.7%)
23Melio20/2886.9%6.9%4.5%–10.5%1/286 (0.3%)
24Synapse19/2886.6%6.6%4.3%–10.1%domain not established — excluded from citation metrics
25SAP Concur19/2886.6%6.6%4.3%–10.1%2/286 (0.7%)
26Tradier0/2880%0%0%–1.3%0/286 (0%)
### Entity handling
Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.
No roll-ups were applied in this study.
40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here.
### Limits of these findings
- This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-14. Engine behaviour changes without notice.
- The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
- Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
- A brand not listed here was not measured as absent — it simply was not named in these answers.
- Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
- 1 of 26 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero.
---
### The Esports Organization AI Visibility Index 2026
URL: https://www.5wpr.com/research/the-esports-organization-ai-visibility-index-2026/
Summary: The 2026 Esports AI Visibility Index: G2 Esports leads AI answers for best esports organization to follow, ahead of Fnatic and Team Liquid.
5W Research
# The Esports Organization AI Visibility Index 2026
G2 Esports leads the 2026 AI Visibility Index for esports organizations, appearing in 53.5% of AI answers. This study examines how top teams like Fnatic and Team Liquid rank across AI search engines and specific demand clusters, highlighting the importance of AI visibility for brand recognition and fan engagement.
AI Search Visibility for Best Esports Organization to Follow: 2026 Brand Study
G2 Esports led the measured AI visibility results for "best esports organization to follow" prompts. G2 Esports appeared in 53.5% of answers returned, with a 95% interval of 45.3% to 61.4%. The result matters because a brand omitted from generated recommendations has less opportunity to enter a fan's or sponsor's shortlist.
Fnatic Ltd. ranked second at 46.5%, and Team Liquid ranked third at 45.1%. Their intervals overlap with G2 Esports and with each other, so this sample does not establish that one is more visible than the others. It does show that these three brands formed the leading group in the measured prompts.
144AI answers analyzed
53.5%G2 Esports appearance rate, the category leader
25Brands named across the study
49%Answers carrying retrieval evidence
## Which esports organizations lead the leaderboard?
G2 Esports ranked first among the brands measured, followed by Fnatic Ltd., Team Liquid, Team Vitality, T1, Natus Vincere, Gen.G, FaZe Clan, Cloud9, and Thieves. Appearance rates use 144 answers returned.
The ranking records how often each brand was named, not a judgment about competitive performance, sponsorship value, or fan preference. The measured prompts covered Counter-Strike, League of Legends, Valorant, sponsorship, and organization headquarters in Europe and Asia.
### AI visibility leaderboard
Appearance rate is the share of the 144 returned AI answers in which each brand was named.
#BrandNamed inAppearance rate95% intervalAvg position
1G2 Esports77/14453.5%45.3%–61.4%4.7
2Fnatic Ltd.67/14446.5%38.6%–54.7%5.3
3Team Liquid65/14445.1%37.2%–53.3%4.8
4Team Vitality52/14436.1%28.7%–44.2%5.4
5T146/14431.9%24.9%–39.9%3.8
6Natus Vincere44/14430.6%23.6%–38.5%6
7Gen.G42/14429.2%22.4%–37.1%5.6
8FaZe Clan37/14425.7%19.3%–33.4%5.9
9Cloud934/14423.6%17.4%–31.2%7.6
10Thieves28/14419.4%13.8%–26.7%7
11Paper Rex20/14413.9%9.2%–20.5%4.6
12Sentinels20/14413.9%9.2%–20.5%7.8
13Team Spirit19/14413.2%8.6%–19.7%5.4
14EDward Gaming18/14412.5%8.1%–18.9%5.7
15MOUZ17/14411.8%7.5%–18.1%7.9
The leading intervals overlap: G2 Esports spans 45.3% to 61.4%, Fnatic Ltd. spans 38.6% to 54.7%, and Team Liquid spans 37.2% to 53.3%. The difference between these brands is within the margin of this sample. Team Vitality's 28.7% to 44.2% interval also overlaps Team Liquid's interval.
## How do the leading brands appear in answers?
### G2 Esports
53.5% appearance rate, with a 95% interval of 45.3% to 61.4%, placed G2 Esports first. Perplexity (sonar-pro) named G2 Esports in 28 of 37 answers, or 75.7%; ChatGPT (OpenAI) named it in 25 of 35, or 71.4%; Gemini (Google) named it in 17 of 36, or 47.2%; and Claude (Anthropic) named it in 7 of 36, or 19.4%. G2 Esports averaged position 4.7 when named. Its owned domain was cited 11 times, a 7.8% citation rate among 141 answers with retrieval evidence.
### Fnatic Ltd.
46.5% appearance rate, with a 95% interval of 38.6% to 54.7%, placed Fnatic Ltd. second. Perplexity (sonar-pro) named Fnatic Ltd. in 25 of 37 answers, or 67.6%; ChatGPT (OpenAI) named it in 23 of 35, or 65.7%; Gemini (Google) named it in 17 of 36, or 47.2%; and Claude (Anthropic) named it in 2 of 36, or 5.6%. Fnatic Ltd. averaged position 5.3 when named. Its owned domain was cited 7 times, a 5% citation rate among 141 answers with retrieval evidence.
### Team Liquid
45.1% appearance rate, with a 95% interval of 37.2% to 53.3%, placed Team Liquid third. Perplexity (sonar-pro) named Team Liquid in 24 of 37 answers, or 64.9%; ChatGPT (OpenAI) named it in 22 of 35, or 62.9%; Gemini (Google) named it in 13 of 36, or 36.1%; and Claude (Anthropic) named it in 6 of 36, or 16.7%. Team Liquid averaged position 4.8 when named. Its owned domain was cited 9 times, a 6.4% citation rate among 141 answers with retrieval evidence.
### Team Vitality
36.1% appearance rate, with a 95% interval of 28.7% to 44.2%, placed Team Vitality fourth. Perplexity (sonar-pro) named Team Vitality in 19 of 37 answers, or 51.4%; ChatGPT (OpenAI) named it in 12 of 35, or 34.3%; Gemini (Google) named it in 13 of 36, or 36.1%; and Claude (Anthropic) named it in 8 of 36, or 22.2%. Team Vitality averaged position 5.4 when named. Its owned domain was cited 3 times, a 2.1% citation rate among 141 answers with retrieval evidence.
### T1
31.9% appearance rate, with a 95% interval of 24.9% to 39.9%, placed T1 fifth. Perplexity (sonar-pro) named T1 in 20 of 37 answers, or 54.1%; ChatGPT (OpenAI) named it in 11 of 35, or 31.4%; Gemini (Google) named it in 11 of 36, or 30.6%; and Claude (Anthropic) named it in 4 of 36, or 11.1%. T1 averaged position 3.8 when named, the earliest average position among the top eight. Its owned domain was cited 3 times, a 2.1% citation rate among 141 answers with retrieval evidence.
### Natus Vincere
30.6% appearance rate, with a 95% interval of 23.6% to 38.5%, placed Natus Vincere sixth. Perplexity (sonar-pro) named Natus Vincere in 21 of 37 answers, or 56.8%; ChatGPT (OpenAI) named it in 8 of 35, or 22.9%; Gemini (Google) named it in 13 of 36, or 36.1%; and Claude (Anthropic) named it in 2 of 36, or 5.6%. Natus Vincere averaged position 6 when named. Its owned domain was cited 3 times, a 2.1% citation rate among 141 answers with retrieval evidence.
### Gen.G
29.2% appearance rate, with a 95% interval of 22.4% to 37.1%, placed Gen.G seventh. Perplexity (sonar-pro) named Gen.G in 19 of 37 answers, or 51.4%; ChatGPT (OpenAI) named it in 11 of 35, or 31.4%; Gemini (Google) named it in 9 of 36, or 25%; and Claude (Anthropic) named it in 3 of 36, or 8.3%. Gen.G averaged position 5.6 when named. Its owned domain was cited 0 times, a 0% citation rate among 141 answers with retrieval evidence.
### FaZe Clan
25.7% appearance rate, with a 95% interval of 19.3% to 33.4%, placed FaZe Clan eighth. Perplexity (sonar-pro) named FaZe Clan in 14 of 37 answers, or 37.8%; ChatGPT (OpenAI) named it in 14 of 35, or 40%; Gemini (Google) named it in 5 of 36, or 13.9%; and Claude (Anthropic) named it in 4 of 36, or 11.1%. FaZe Clan averaged position 5.9 when named. Its owned domain was cited 4 times, a 2.8% citation rate among 141 answers with retrieval evidence.
### Cloud9
23.6% appearance rate, with a 95% interval of 17.4% to 31.2%, placed Cloud9 ninth. Perplexity (sonar-pro) named Cloud9 in 11 of 37 answers, or 29.7%; ChatGPT (OpenAI) named it in 16 of 35, or 45.7%; Gemini (Google) named it in 6 of 36, or 16.7%; and Claude (Anthropic) named it in 1 of 36, or 2.8%. Cloud9 averaged position 7.6 when named. Its owned domain was cited 11 times, a 7.8% citation rate among 141 answers with retrieval evidence.
### Thieves
19.4% appearance rate, with a 95% interval of 13.8% to 26.7%, placed Thieves tenth. Perplexity (sonar-pro) named Thieves in 10 of 37 answers, or 27%; ChatGPT (OpenAI) named it in 11 of 35, or 31.4%; Gemini (Google) named it in 4 of 36, or 11.1%; and Claude (Anthropic) named it in 3 of 36, or 8.3%. Thieves averaged position 7 when named.
## Where do the engines disagree?
The engines disagree materially on which organizations to name. Perplexity (sonar-pro) returned 37 answers, ChatGPT (OpenAI) returned 35, Gemini (Google) returned 36, and Claude (Anthropic) returned 36. Each engine uses a different model and retrieval tool, so a gap can reflect system design rather than a brand's underlying visibility.
### Appearance rate by engine
Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
G2 Esports75.7%71.4%47.2%19.4%
Fnatic Ltd.67.6%65.7%47.2%5.6%
Team Liquid64.9%62.9%36.1%16.7%
Team Vitality51.4%34.3%36.1%22.2%
T154.1%31.4%30.6%11.1%
Natus Vincere56.8%22.9%36.1%5.6%
Gen.G51.4%31.4%25%8.3%
FaZe Clan37.8%40%13.9%11.1%
Cloud929.7%45.7%16.7%2.8%
Thieves27%31.4%11.1%8.3%
Paper Rex21.6%11.4%16.7%5.6%
Sentinels10.8%25.7%19.4%0%
Perplexity (sonar-pro) named several organizations much more often than Claude (Anthropic). Fnatic Ltd. appeared in 25 of Perplexity's 37 answers and 2 of Claude's 36. Natus Vincere appeared in 21 of 37 Perplexity answers and 2 of 36 Claude answers. Virtus.pro appeared in 10 of 37 Perplexity answers and in none of ChatGPT's 35, Gemini's 36, or Claude's 36 answers.
ChatGPT (OpenAI) was the strongest engine for Cloud9, naming it in 16 of 35 answers, compared with 11 of 37 for Perplexity (sonar-pro), 6 of 36 for Gemini (Google), and 1 of 36 for Claude (Anthropic). It also named Evil Geniuses in 8 of 35 answers, compared with 1 of 37 on Perplexity (sonar-pro), 2 of 36 on Gemini (Google), and none of Claude's 36 answers.
Claude (Anthropic) named fewer of many leading brands, but it named Team Vitality in 8 of 36 answers and Team Falcons in 4 of 36 answers. Team Falcons did not appear in any of ChatGPT's 35 answers. Sentinels, EDward Gaming, Astralis, and several lower-ranked brands also had no Claude appearances. These are engine-specific patterns, not category-wide verdicts.
## Which brands lead each demand cluster?
The demand-cluster view identifies which brands led the measured question types, including Counter-Strike, League of Legends, Valorant, brand sponsorship, and Europe and Asia headquarters. It separates broad appearance from the contexts where an organization is most often selected.
### Leaders by demand cluster
The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
Subcategory: best esports organization headquartered in Asia31G2 Esports48.4%Team Vitality 48.4%, Fnatic Ltd. 41.9%, Natus Vincere 35.5%
Subcategory: best counter-strike esports team16Team Vitality87.5%Natus Vincere 75%, MOUZ 75%, G2 Esports 62.5%
Subcategory: best league of legends esports team16T175%Gen.G 75%, G2 Esports 62.5%, Fnatic Ltd. 50%
Subcategory: best valorant esports team16Paper Rex75%Fnatic Ltd. 68.8%, Sentinels 62.5%, LOUD 56.3%
Subcategory: best esports org for brand sponsorship16FaZe Clan37.5%Thieves 37.5%, Fnatic Ltd. 31.3%, Team Liquid 31.3%
General best-in-category5Team Liquid80%T1 80%, G2 Esports 60%, Team Vitality 60%
Primary use case4G2 Esports75%Team Liquid 75%, Fnatic Ltd. 50%, FaZe Clan 50%
Specific need or condition4Team Liquid100%Cloud9 100%, G2 Esports 75%, Fnatic Ltd. 75%
Quality and premium tier4G2 Esports75%Fnatic Ltd. 75%, Team Liquid 75%, T1 75%
Price and value4Thieves100%G2 Esports 75%, Fnatic Ltd. 75%, Team Liquid 75%
Beginners and first purchase4G2 Esports75%Team Liquid 75%, FaZe Clan 75%, Fnatic Ltd. 50%
Comparison and evaluation4G2 Esports75%Team Liquid 75%, T1 75%, Thieves 75%
Durability and longevity4G2 Esports50%Fnatic Ltd. 50%, Team Liquid 50%, Team Vitality 50%
Specialist / advanced use4Fnatic Ltd.75%Team Liquid 75%, G2 Esports 50%, Team Vitality 50%
Emerging and new entrants4Paper Rex75%Karmine Corp 75%, Team Falcons 75%, Gen.G 25%
Where to buy4G2 Esports100%Fnatic Ltd. 75%, Team Liquid 75%, Cloud9 75%
Trust and recommendation4Fnatic Ltd.100%Team Liquid 100%, G2 Esports 75%, Cloud9 75%
Cluster leadership is useful for planning category pages and supporting material. A brand can have lower overall appearance yet lead a narrower question type. These findings apply only to the prompts measured on August 31, 2026.
## Where do cited answers get their evidence?
Among citation-eligible brands, G2 Esports and Cloud9 had the highest owned-domain citation rate at 7.8% of 141 answers with retrieval evidence. Team Liquid followed at 6.4%, and Fnatic Ltd. had 5%. Appearance and citation rates use different denominators and should not be compared as if they measure the same event.
### Owned-domain citation rate
Share of the 141 answers with retrieval evidence that cited a URL on the brand's own website.
G2 Esports
7.8% · 11/141
Fnatic Ltd.
5% · 7/141
Team Liquid
6.4% · 9/141
Team Vitality
2.1% · 3/141
T1
2.1% · 3/141
Natus Vincere
2.1% · 3/141
Gen.G
0% · 0/141
FaZe Clan
2.8% · 4/141
Cloud9
7.8% · 11/141
Paper Rex
0% · 0/141
Sentinels
0.7% · 1/141
Team Spirit
0% · 0/141
Owned-site citations were limited across the measured results. Several eligible brands appeared in answers but had no owned-domain citations, including Gen.G, Paper Rex, Team Spirit, MOUZ, Karmine Corp, Team Falcons, Ninjas in Pyjamas, LOUD, OG, Rex Regum Qeon, and Virtus.pro.
The engines leaned most often on esports data and reference publishers. escharts.com appeared in 69 of 141 answers with retrieval evidence, a 48.9% source penetration rate. en.wikipedia.org appeared in 53 answers, or 37.6%; vlr.gg appeared in 47, or 33.3%; and liquipedia.net appeared in 45, or 31.9%.
### Most-cited publishers
Publishers cited as evidence, counted once per answer rather than once per link.
PublisherAnswers citing itAnswer penetration
escharts.com69/14148.9%
en.wikipedia.org53/14137.6%
vlr.gg47/14133.3%
liquipedia.net45/14131.9%
thegamingdiary.com39/14127.7%
reddit.com38/14127%
esportsinsider.com32/14122.7%
esportsearnings.com23/14116.3%
hltv.org21/14114.9%
egamersworld.com20/14114.2%
nerdstreet.com20/14114.2%
gosugamers.net20/14114.2%
The source mix suggests that generated recommendations draw heavily on external databases, reference pages, community discussion, and esports publications. A brand's own site can support its factual footprint, but the measured answers often cited third-party sources.
## What does this mean for esports brands?
Interpretation: the leading group has broad name recognition across the measured engines, but no organization has a uniform advantage everywhere. G2 Esports, Fnatic Ltd., and Team Liquid had strong results on Perplexity (sonar-pro) and ChatGPT (OpenAI), while Claude (Anthropic) named each far less often.
Interpretation: brands can use the cluster and engine views to identify the recommendation contexts where they are present or absent. The owned-citation results point to a separate task: publish clear first-party material that can support factual claims about teams, games, sponsorship work, and geography, while monitoring the external sources that appeared in retrieved answers.
Interpretation: do not treat the overall ranking as a single selection score. The sampled questions ask different things, and the engines returned different answer volumes and brand patterns.
## How was this AI visibility measured?
This flagship study measured 24 stratified prompts three times per engine on August 31, 2026. It covered Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). Appearance rates use 144 answers returned. Citation and source rates use 141 answers with retrieval evidence. Of 288 attempted answers, 144 were returned and 144 failed.
### How this study was measured
- Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked.
- Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 144 answers returned of 288 attempted, 141 with retrieval evidence.
- Product lines rolled up to parent brands using a registry fixed before scoring.
- Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.
Results describe these prompts on this date. They do not measure all esports searches, fan demand, competitive results, or sponsorship outcomes. Confidence intervals describe variation in this sample, and overlap means the study does not support a claim that one overlapping brand is more visible than another.
## Frequently asked questions
### Which organization ranked first?
G2 Esports ranked first with a 53.5% appearance rate across 144 answers returned. Its 95% interval was 45.3% to 61.4%.
### Is G2 Esports more visible than Fnatic Ltd.?
G2 Esports had a higher measured appearance rate than Fnatic Ltd., 53.5% versus 46.5%. Their 95% intervals overlap, so the difference is within the margin of this sample.
### Which engine named G2 Esports most often?
Perplexity (sonar-pro) named G2 Esports in 28 of 37 answers, or 75.7%. ChatGPT (OpenAI) named G2 Esports in 25 of 35 answers, or 71.4%.
### Which brand had the strongest ChatGPT result?
G2 Esports appeared in 25 of ChatGPT's 35 returned answers, or 71.4%. Fnatic Ltd. appeared in 23 of 35 answers, and Team Liquid appeared in 22 of 35.
### Which eligible brands had the highest owned-domain citation rate?
G2 Esports and Cloud9 each had a 7.8% owned-domain citation rate among 141 answers with retrieval evidence. Team Liquid had 6.4%, and Fnatic Ltd. had 5%.
### Which sources appeared most often in cited answers?
escharts.com appeared in 69 of 141 answers with retrieval evidence, for 48.9% source penetration. en.wikipedia.org appeared in 53 answers, and vlr.gg appeared in 47.
## Methodology appendix
This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.
### What was measured
- Category: best esports organization to follow
- Measurement date: 2026-08-31
- Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 288
- Answers returned (appearance denominator): 144
- Answers with retrieval evidence (citation denominator): 141 (49% of attempted)
- Answers that failed outright: 144
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable.
### How the prompts were sampled
Fixed sampling frame: client-named subcategories are allocated a guaranteed cell each, then a documented set of generic commercial demand clusters (general best, primary use case, specific need, quality tier, price/value, beginner, comparison, durability, specialist, emerging, retail channel, trust) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure.
Demand clusterPromptsScope
Subcategory: best counter-strike esports team2demand specific to "best counter-strike esports team" within best esports organization to follow
Subcategory: best league of legends esports team2demand specific to "best league of legends esports team" within best esports organization to follow
Subcategory: best valorant esports team2demand specific to "best valorant esports team" within best esports organization to follow
Subcategory: best esports org for brand sponsorship2demand specific to "best esports org for brand sponsorship" within best esports organization to follow
Subcategory: best esports organization headquartered in europe2demand specific to "best esports organization headquartered in europe" within best esports organization to follow
Subcategory: best esports organization headquartered in Asia2demand specific to "best esports organization headquartered in Asia" within best esports organization to follow
General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Primary use case1the dominant everyday use case for this category
Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Quality and premium tier1which options are highest quality, most durable or best made
Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Durability and longevity1which brands last, hold up over time, or fail early
Specialist / advanced use1advanced, professional or enthusiast-level demand inside the category
Emerging and new entrants1newer entrants, recent launches and shifts buyers are only starting to ask about
Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
### The exact prompts
#PromptClusterShape
1best counter-strike esports teamssub_best_counter_strike_esposhort
2Which Counter-Strike esports organizations are best to follow for consistently elite competition and compelling roster storylines?sub_best_counter_strike_espolong
3best league of legends teamssub_best_league_of_legends_eshort
4Which League of Legends esports organizations have the strongest competitive legacy, current results, and fan content?sub_best_league_of_legends_elong
5best valorant esports teamssub_best_valorant_esports_teshort
6Which Valorant esports organizations are the most exciting to follow across international events and regional leagues?sub_best_valorant_esports_telong
7best esports sponsorship organizationssub_best_esports_org_for_brashort
8Which esports organizations offer brands the best sponsorship reach, audience fit, and campaign activation potential?sub_best_esports_org_for_bralong
9best european esports organizationssub_best_esports_organizatioshort
10Which Europe-headquartered esports organizations are best to follow for major tournament results and strong fan communities?sub_best_esports_organizatiolong
11best asian esports organizationssub_best_esports_organizatioshort
12Which Asia-headquartered esports organizations are leading in competition, fandom, and international influence today?sub_best_esports_organizatiomedium
13best esports organizationsgeneral_bestshort
14Which esports organizations should I follow for reliable match coverage, entertaining content, and active communities?use_caselong
15Which esports organizations are best to follow if I want one team competing in several major game titles?specific_needlong
16highest quality esports organizationspremium_qualityshort
17Which esports organizations provide the best fan experience through polished content, events, merchandise, and community support?value_pricelong
18best esports teams for beginnersbeginnermedium
19Which esports organizations are better to follow for a new fan, and how do their teams, games, and content differ?comparisonlong
20most consistent esports organizationsdurabilityshort
21Which esports organizations are best for serious fans who want detailed analysis, academy coverage, and competitive depth?specialistlong
22rising esports organizations to followemergingmedium
23Where can I buy official merchandise from the best esports organizations, and which teams have reliable direct stores?retaillong
24Which esports organizations are most trusted and recommended by esports journalists, analysts, and longtime fans?trustlong
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search72373751.4%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool72353548.6%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)72363345.8%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool72363650%
Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
- Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
- 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
- Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
- Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
- Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
- 0% — the metric could be evaluated and the event did not occur.
- Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — the share of engines that named the brand at least once.
- Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.
### Evidence rules
- An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
- Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
- Publisher identity could not be established for 1 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
- Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1G2 Esports77/14453.5%56.1%45.3%–61.4%11/141 (7.8%)
2Fnatic Ltd.67/14446.5%49.6%38.6%–54.7%7/141 (5%)
3Team Liquid65/14445.1%51.3%37.2%–53.3%9/141 (6.4%)
4Team Vitality52/14436.1%34.8%28.7%–44.2%3/141 (2.1%)
5T146/14431.9%35.4%24.9%–39.9%3/141 (2.1%)
6Natus Vincere44/14430.6%31.7%23.6%–38.5%3/141 (2.1%)
7Gen.G42/14429.2%29.8%22.4%–37.1%0/141 (0%)
8FaZe Clan37/14425.7%29.6%19.3%–33.4%4/141 (2.8%)
9Cloud934/14423.6%30.3%17.4%–31.2%11/141 (7.8%)
10Thieves28/14419.4%24.6%13.8%–26.7%domain not established — excluded from citation metrics
11Paper Rex20/14413.9%12.2%9.2%–20.5%0/141 (0%)
12Sentinels20/14413.9%13.9%9.2%–20.5%1/141 (0.7%)
13Team Spirit19/14413.2%13.6%8.6%–19.7%0/141 (0%)
14EDward Gaming18/14412.5%9.7%8.1%–18.9%domain not established — excluded from citation metrics
15MOUZ17/14411.8%9.9%7.5%–18.1%0/141 (0%)
16Astralis17/14411.8%10.4%7.5%–18.1%4/141 (2.8%)
17Bilibili Gaming17/14411.8%9.5%7.5%–18.1%domain not established — excluded from citation metrics
18Karmine Corp16/14411.1%10.7%7%–17.3%0/141 (0%)
19Team Falcons15/14410.4%12.4%6.4%–16.5%0/141 (0%)
20Ninjas in Pyjamas11/1447.6%6.8%4.3%–13.2%0/141 (0%)
21LOUD11/1447.6%6.6%4.3%–13.2%0/141 (0%)
22Evil Geniuses11/1447.6%9.9%4.3%–13.2%3/141 (2.1%)
23OG11/1447.6%7.1%4.3%–13.2%0/141 (0%)
24Rex Regum Qeon11/1447.6%7.6%4.3%–13.2%0/141 (0%)
25Virtus.pro10/1446.9%7.6%3.8%–12.3%0/141 (0%)
### Entity handling
Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.
No roll-ups were applied in this study.
Held for human classification before the next study: Team Vitality, Team Spirit, MOUZ, Natus Vincere, FaZe Clan, G2 Esports, Astralis, Virtus.pro, Ninjas in Pyjamas, Team Liquid, FURIA, The MongolZ, FURIA Esports, Mathieu “ZywOo” Herbaut, Vitality, Spirit, MOUZ, and Natus Vincere, Full Brand Name, HLTV World Rankings, Valve Regional Standings, Team Falcons, Gen.G[13][16], T1[13][16], Bilibili Gaming[1][13][16], Anyone’s Legend[13][16], Hanwha Life Esports[1][13], G2 Esports[7][13], FlyQuest[12][14], Gen.G, T1, Bilibili Gaming, Anyone’s Legend, Hanwha Life Esports, Samsung, FlyQuest, Competitive Record, Storylines, Strike esports organizations, Complexity Gaming, ENCE, Add two more teams, Region.
### Limits of these findings
- This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-08-31. Engine behaviour changes without notice.
- The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
- Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
- A brand not listed here was not measured as absent — it simply was not named in these answers.
- Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
- 3 of 25 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero.
---
### Edition 20: Capital Markets Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/capital-markets/
Summary: The sector where exchange data and regulatory filings outrank financial journalism.
---
### Probiotics & Gut Health AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/probiotics-gut-health-ai-visibility-index-2026/
Summary: Probiotic AI visibility data shows Culturelle leads appearances while Healthline and Forbes dominate citations across four measured engines.
# Probiotics & Gut Health AI Visibility Index 2026
Culturelle Leads Probiotic AI Mentions
Culturelle led probiotic and gut health brand appearances in this measurement, appearing in 56.4% of answers returned. Seed followed at 50.5%, but their 95% intervals overlap, so this sample does not establish a clear visibility gap between them. Healthline and Forbes were the most cited sources, appearing in 63.2% and 62.1% of answers with retrieval evidence. That source pattern matters because brands compete for mention in answers that often rely on third-party health and comparison content.
This standalone report covers Probiotics / Gut Health in the Supplements AI Visibility Index 2026. It measures strain-specific and digestive-health prompts, including shelf-stable and refrigerated formula considerations. It does not cover multivitamins, protein or protein powder, sleep supplements, immune support, or nootropics and cognitive supplements.
273AI answers analyzed
56.4%Culturelle appearance rate, the category leader
26Brands named across the study
95.4%Answers carrying retrieval evidence
Culturelle and Seed have broad appearance across all four measured engines, but their owned sites did not carry the same citation weight as the publisher layer. Seed's owned-domain citation rate was 21.7% of answers with retrieval evidence, the highest rate among the leading consumer brands measured here. The study does not measure certification claims or establish a relationship between certification and citation share.
## Which probiotic brands appear most often?
Culturelle ranked first at 56.4% of answers returned, followed by Seed at 50.5%, Garden of Life at 44.0%, and Align at 42.9%. Appearance is based on 273 answers returned, not on retrieval-supported answers. The leading ranks describe this prompt set on the measurement date, not all consumer selection activity.
Garden of Life and Align sit close together in the sample. Florastor ranked fifth at 30.8%, followed by Renew Life at 27.5%. The generated leaderboard contains all measured entities, including non-brand terms and sources that appeared in answers. Those entities are not treated as consumer brands in the interpretation below.
### Appearance leaderboard
### AI visibility leaderboard
To see how often a brand appeared, use appearance rate: it is the proportion of the 273 returned AI answers that named that brand.
#BrandNamed inAppearance rate95% intervalAvg position
1Culturelle154/27356.4%50.5%–62.2%3.6
2Seed138/27350.5%44.7%–56.4%3.2
3Garden of Life120/27344%38.2%–49.9%5.8
4Align117/27342.9%37.1%–48.8%4.7
5Florastor84/27330.8%25.6%–36.5%5.4
6Renew Life75/27327.5%22.5%–33.1%6.8
7Ritual71/27326%21.2%–31.5%4.3
8Jarrow Formulas57/27320.9%16.5%–26.1%6.6
9Visbiome43/27315.8%11.9%–20.5%4.8
10NOW Foods42/27315.4%11.6%–20.1%7.3
11VSL#339/27314.3%10.6%–18.9%5.4
12Lactobacillus31/27311.4%8.1%–15.7%9.1
13Bio-K+31/27311.4%8.1%–15.7%6.5
14Physician’s Choice30/27311%7.8%–15.3%7
15Klaire Labs29/27310.6%7.5%–14.8%6.2
Culturelle's 95% interval was 50.5% to 62.2%, while Seed's was 44.7% to 56.4%. Those intervals overlap. Culturelle's interval also overlaps Garden of Life's 38.2% to 49.9% and Align's 37.1% to 48.8% at the boundary or within the sample margin. The measured ordering is useful for orientation, but these overlaps mean the data does not support claims that Culturelle is more visible than those brands beyond this sample's margin.
## How did the leading brands perform?
The leading brands split into a broad-appearance group, a group whose visibility varies by engine, and brands with lower repeat consistency. Appearance rates below use answers returned. Owned-domain citation rates use answers with retrieval evidence.
### Leaders
### Culturelle
56.4% appearance rate, with a 95% interval of 50.5% to 62.2%, placed Culturelle first. Perplexity named Culturelle in 79.2% of its 72 answers, ChatGPT in 86.0% of its 57 answers, Gemini in 27.8% of its 72 answers, and Claude in 38.9% of its 72 answers. Culturelle's average first mention was position 3.6. Its owned domain was cited in 8.8% of answers with retrieval evidence.
### Seed
50.5% appearance rate, with a 95% interval of 44.7% to 56.4%, placed Seed second. Perplexity named Seed in 66.7% of 72 answers, ChatGPT in 47.4% of 57 answers, Gemini in 52.8% of 72 answers, and Claude in 34.7% of 72 answers. Seed's average first mention was position 3.2, the earliest among the top four brands. Its owned domain was cited in 21.7% of answers with retrieval evidence.
### Garden of Life
44.0% appearance rate, with a 95% interval of 38.2% to 49.9%, placed Garden of Life third. Perplexity named Garden of Life in 76.4% of 72 answers and ChatGPT in 82.5% of 57 answers, compared with 15.3% of Gemini's 72 answers and 9.7% of Claude's 72 answers. Garden of Life's average first mention was position 5.8. Its owned domain was cited in 9.6% of answers with retrieval evidence.
### Align
42.9% appearance rate, with a 95% interval of 37.1% to 48.8%, placed Align fourth. Perplexity named Align in 50.0% of 72 answers, ChatGPT in 73.7% of 57 answers, Gemini in 29.2% of 72 answers, and Claude in 25.0% of 72 answers. Align's average first mention was position 4.7. Its owned domain was cited in 7.0% of answers with retrieval evidence.
### Contenders
### Florastor
30.8% appearance rate, with a 95% interval of 25.6% to 36.5%, placed Florastor fifth. Perplexity named Florastor in 25.0% of 72 answers, ChatGPT in 61.4% of 57 answers, Gemini in 26.4% of 72 answers, and Claude in 16.7% of 72 answers. Florastor's average first mention was position 5.4. Its owned domain was cited in 2.2% of answers with retrieval evidence.
### Renew Life
27.5% appearance rate, with a 95% interval of 22.5% to 33.1%, placed Renew Life sixth. Perplexity named Renew Life in 40.3% of 72 answers and ChatGPT in 70.2% of 57 answers, while Gemini named it in 8.3% of 72 answers and Claude did not name it in any of its 72 answers. Renew Life's average first mention was position 6.8. Its owned domain was cited in 4.4% of answers with retrieval evidence.
### Ritual
26.0% appearance rate, with a 95% interval of 21.2% to 31.5%, placed Ritual seventh. Perplexity named Ritual in 43.1% of 72 answers and Gemini in 33.3% of 72 answers, while ChatGPT named it in 3.5% of 57 answers and Claude in 19.4% of 72 answers. Ritual's average first mention was position 4.3. Its owned domain was cited in 1.8% of answers with retrieval evidence.
### Jarrow Formulas
20.9% appearance rate, with a 95% interval of 16.5% to 26.1%, placed Jarrow Formulas eighth. Perplexity named Jarrow Formulas in 29.2% of 72 answers, ChatGPT in 49.1% of 57 answers, Gemini in 9.7% of 72 answers, and Claude in 1.4% of 72 answers. Jarrow Formulas' average first mention was position 6.6. Its owned domain was cited in 4.4% of answers with retrieval evidence.
### Absentees from broad engine coverage
### Visbiome
15.8% appearance rate, with a 95% interval of 11.9% to 20.5%, placed Visbiome ninth. Perplexity and Gemini each named Visbiome in 15.3% of their 72 answers, ChatGPT named it in 36.8% of 57 answers, and Claude did not name it in any of its 72 answers. Visbiome's average first mention was position 4.8. Its owned domain was cited in 2.9% of answers with retrieval evidence.
### NOW Foods
15.4% appearance rate, with a 95% interval of 11.6% to 20.1%, placed NOW Foods tenth. Perplexity named NOW Foods in 22.2% of 72 answers, ChatGPT in 31.6% of 57 answers, Gemini in 6.9% of 72 answers, and Claude in 4.2% of 72 answers. NOW Foods' average first mention was position 7.3. Its owned domain was cited in 1.8% of answers with retrieval evidence.
## Where do the engines disagree?
The engines disagree sharply on which brands appear, despite broad retrieval coverage in the study. Perplexity returned 72 grounded answers from 72 attempts. Gemini returned 71 grounded answers from 72 attempts. Claude returned 72 grounded answers from 72 attempts. ChatGPT returned 57 grounded answers from 69 attempts, so its rates use fewer returned answers.
### Engine-by-brand appearance
### Appearance rate by engine
For a row-by-row engine comparison, each cell is calculated against that engine's own answer count.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Culturelle79.2%86%27.8%38.9%
Seed66.7%47.4%52.8%34.7%
Garden of Life76.4%82.5%15.3%9.7%
Align50%73.7%29.2%25%
Florastor25%61.4%26.4%16.7%
Renew Life40.3%70.2%8.3%0%
Ritual43.1%3.5%33.3%19.4%
Jarrow Formulas29.2%49.1%9.7%1.4%
Visbiome15.3%36.8%15.3%0%
NOW Foods22.2%31.6%6.9%4.2%
VSL#322.2%33.3%0%5.6%
Lactobacillus33.3%5.3%2.8%2.8%
ChatGPT concentrated several brands that were sparse or absent elsewhere. It named Garden of Life in 82.5% of 57 answers, Florastor in 61.4%, Renew Life in 70.2%, Jarrow Formulas in 49.1%, Visbiome in 36.8%, and Klaire Labs in 36.8%. Claude named Renew Life, Visbiome, and Klaire Labs in none of its 72 answers. Gemini named Garden of Life in 15.3% of 72 answers, far below its ChatGPT and Perplexity rates.
Perplexity had its own concentration pattern. It named Physician's Choice in 37.5% of 72 answers, while ChatGPT named it in 1.8%, Gemini in 2.8%, and Claude in none. Ritual appeared in 43.1% of Perplexity answers and 33.3% of Gemini answers, but in 3.5% of ChatGPT answers. Culturelle had high rates in Perplexity and ChatGPT, but lower rates in Gemini and Claude.
These gaps can reflect each system's model and retrieval tool, not a stable measure of a brand's standing beyond these prompts. Brands with the broadest cross-engine consistency were Culturelle, Seed, Garden of Life, Align, Florastor, Ritual, Jarrow Formulas, and NOW Foods at 100.0%. Renew Life, Visbiome, and several lower-ranked brands reached 75.0% because at least one engine did not name them.
## Which brands lead different question clusters?
The cluster results identify where brand visibility is concentrated within the measured prompts. The chart separates broader buyer-intent questions from sub-category and specific-need questions, so a lead in one group does not mean the same brand led every kind of probiotic question.
### Demand cluster leaders
### Leaders by demand cluster
Want to view the same answers by demand cluster? They are regrouped according to the cluster from which each prompt was sampled, with rates based on that cluster's own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
— Probiotics / Gut Health —
best probiotic for bloating12Align83.3%Culturelle 66.7%, Ritual 58.3%, VSL#3 50%
best probiotic for women12Seed58.3%Renew Life 50%, Culturelle 41.7%, Garden of Life 41.7%
best prebiotic and probiotic combo12Seed66.7%Garden of Life 58.3%, Renew Life 58.3%, Ritual 58.3%
best probiotic for gut health12Seed83.3%Garden of Life 50%, Ritual 50%, Culturelle 33.3%
best probiotic for IBS12Align58.3%Culturelle 50%, Florastor 50%, VSL#3 41.7%
best probiotic strain for digestion12Bifidobacterium66.7%Culturelle 58.3%, Align 58.3%, Lactobacillus 58.3%
best probiotic for immune support12Culturelle66.7%Garden of Life 58.3%, Seed 50%, Align 33.3%
best probiotic for constipation12Culturelle66.7%Garden of Life 58.3%, Seed 50%, Align 50%
best probiotic after antibiotics12Florastor75%Culturelle 66.7%, Align 66.7%, Seed 41.7%
best probiotic for men11Seed90.9%Garden of Life 45.5%, Ritual 45.5%, Jarrow Formulas 45.5%
refrigerated vs shelf stable probiotic which is better11Culturelle45.5%Align 45.5%, Visbiome 45.5%, Seed 36.4%
best probiotic for kids11Culturelle81.8%Florastor 45.5%, BioGaia 45.5%, Strain 27.3%
best probiotic for skin health11Seed72.7%Garden of Life 54.5%, Culturelle 18.2%, Align 18.2%
best fermented food alternative to probiotics11Sources9.1%—
— Other demand —
General best-in-category11Culturelle72.7%Seed 72.7%, Align 72.7%, Ritual 63.6%
Everyday and mainstream choice11Seed90.9%Ritual 72.7%, Culturelle 63.6%, Align 63.6%
Price and value11Culturelle72.7%NOW Foods 63.6%, Jarrow Formulas 45.5%, Align 36.4%
Primary use case11Culturelle81.8%Align 54.5%, Ritual 54.5%, Garden of Life 45.5%
Beginners and first purchase11Culturelle63.6%Align 63.6%, Garden of Life 54.5%, Seed 45.5%
Where to buy11Seed54.5%Culturelle 36.4%, Garden of Life 36.4%, Renew Life 36.4%
Gifting and occasions11Seed63.6%Culturelle 54.5%, Garden of Life 54.5%, Ritual 45.5%
Specific need or condition11Florastor81.8%Culturelle 72.7%, Align 63.6%, Seed 54.5%
Comparison and evaluation11Culturelle54.5%Jarrow Formulas 54.5%, Seed 45.5%, Garden of Life 45.5%
Trust and recommendation11Culturelle100%Seed 100%, Align 100%, Garden of Life 81.8%
The chart should be read as a map of prompt-context strength. Brands that lead broad buyer-intent clusters have more exposure when answers provide general selection options. Brands that lead specific-need clusters have stronger visibility when the answer narrows to strain-related or digestive-health context. This study does not establish clinical effectiveness, product quality, or a best formula.
## Where do probiotic answers get their citations?
Owned sites accounted for a limited part of the citation pattern among the brands measured. Seed's site was cited in 21.7% of answers with retrieval evidence, followed by Garden of Life at 9.6%, Culturelle at 8.8%, Optibac at 7.7%, and Align at 7.0%. Citation rate and appearance rate measure different events and use different denominators.
### Owned-domain citations
### Owned-domain citation rate
This shows the share of the 272 answers with retrieval evidence that cited a URL from the brand's own website.
Culturelle
8.8% · 24/272
Seed
21.7% · 59/272
Garden of Life
9.6% · 26/272
Align
7% · 19/272
Florastor
2.2% · 6/272
Renew Life
4.4% · 12/272
Ritual
1.8% · 5/272
Jarrow Formulas
4.4% · 12/272
Visbiome
2.9% · 8/272
NOW Foods
1.8% · 5/272
VSL#3
1.1% · 3/272
Bio-K+
0% · 0/272
Among the brands profiled above, Seed had the strongest owned-domain citation rate. Culturelle, Garden of Life, and Align were cited from their own sites less often than they appeared in answers. Florastor, Renew Life, Ritual, Jarrow Formulas, Visbiome, and NOW Foods also had owned-domain citation rates below 5.0% of answers with retrieval evidence. Bio-K+ and Hyperbiotics had 0% owned-domain citation rates in the measured retrieval-supported answers.
### Top cited publishers
### Most-cited publishers
Evidence publishers are counted once for each answer that cited them, rather than once for every link.
PublisherAnswers citing itAnswer penetration
healthline.com172/27263.2%
forbes.com169/27262.1%
fortune.com119/27243.8%
pmc.ncbi.nlm.nih.gov82/27230.1%
consumerlab.com81/27229.8%
seed.com59/27221.7%
medicalnewstoday.com59/27221.7%
everydayhealth.com54/27219.9%
health.usnews.com49/27218%
garagegymreviews.com49/27218%
nih.gov46/27216.9%
target.com42/27215.4%
Healthline was cited in 63.2% of answers with retrieval evidence, followed by Forbes at 62.1% and Fortune at 43.8%. PMC, a National Library of Medicine archive domain, appeared in 30.1%, while ConsumerLab appeared in 29.8%. Retail domains also entered the evidence set: Target appeared in 15.4% and Amazon in 13.2% of retrieval-supported answers. The source layer is therefore led by health publishers, comparison content, research sources, and retailer pages rather than brand-owned sites alone.
## Who is winning and who is at risk?
### Winning on measured visibility
Culturelle is in the leading group because it ranked first with a 56.4% appearance rate and appeared across all four engines. Seed is in the leading group because it ranked second at 50.5%, appeared across all four engines, and had a 21.7% owned-domain citation rate. Garden of Life and Align are also leading contenders on measured appearance, at 44.0% and 42.9%, though their intervals overlap with Culturelle's interval within this sample.
### At risk from uneven coverage
Renew Life is at risk from uneven engine coverage because Claude named it in 0% of 72 answers, despite a 70.2% rate in ChatGPT's 57 answers. Ritual is at risk from a similar concentration pattern: 43.1% in Perplexity and 33.3% in Gemini, but 3.5% in ChatGPT. Visbiome and Klaire Labs are at risk from a Claude absence, while Physician's Choice had a 37.5% Perplexity rate but low or zero appearance elsewhere. These are visibility risks in this measured set, not assessments of product performance.
## What structural patterns does the tally show?
The top of the leaderboard is concentrated among four brands. Culturelle, Seed, Garden of Life, and Align each appeared in more than 40.0% of answers returned, while Florastor ranked fifth at 30.8%. Culturelle's 50.5% to 62.2% interval overlaps Seed's 44.7% to 56.4% interval and reaches the upper boundary of Garden of Life's 38.2% to 49.9% interval. It also overlaps Align's 37.1% to 48.8% interval within the sample margin.
Engine convergence is strongest for the brands with 100.0% cross-engine consistency, but rate levels still vary widely by engine. The source tally is more concentrated outside brand domains: Healthline, Forbes, Fortune, PMC, and ConsumerLab all had higher answer penetration than the highest brand-owned domain, Seed at 21.7%. The brief named Physician's Choice, and the tally measured two name variants: Physician's Choice at 3.7% and Physician's Choice with a curly apostrophe at 11.0%. That naming split is a measurement issue worth resolving in future tracking.
## What changed during the measurement window?
The data does not measure more than one time period, so it cannot identify change during the measurement window. It does show a cross-engine split within the collected answers: several brands were frequent in ChatGPT or Perplexity and absent from Claude.
## What should probiotic brands measure next?
- Audit name normalization for Physician's Choice. The tally records a 3.7% appearance rate for one spelling and 11.0% for another. Re-measure after defining a single approved brand and product-name dictionary.
- Build pages that match strain-specific and digestive-health questions with clear product identity, formula details, and evidence references. Re-measure appearance by demand cluster to test whether the pages increase brand mentions in those prompt groups.
- Strengthen cited pages on owned sites for brands with high appearance but lower owned-domain citation rates. Culturelle appeared in 56.4% of returned answers but its domain was cited in 8.8% of retrieval-supported answers. Re-measure both rates separately.
- Diagnose engine-specific absences. Renew Life had a 70.2% ChatGPT rate and a 0% Claude rate. Compare the same prompt clusters in both systems after page updates.
- Investigate reliance on one engine. Physician's Choice appeared in 37.5% of Perplexity answers but at 1.8%, 2.8%, and 0% in ChatGPT, Gemini, and Claude. Re-measure cross-engine consistency after publication changes.
- Publish and maintain evidence pages that can compete with health-publisher and comparison content. Healthline appeared in 63.2% of retrieval-supported answers and Forbes in 62.1%. Re-measure owned-domain citation rate against this source layer.
## What does this mean for probiotic brands?
Interpretation: visibility in these probiotic and gut health prompts depends on more than a brand being named. The strongest measured positions combine broad engine coverage with early mention placement and a cited owned site. Seed came closest to that combination among the leading consumer brands, while Culturelle had the highest overall appearance rate but a lower owned-domain citation rate.
Interpretation: a brand that appears frequently in one system can still have fragile coverage. Brands with zero appearance in Claude or low rates outside a single engine need to test whether their product pages and supporting evidence are discoverable across different retrieval systems. Publisher citations remain a separate competitive field.
## How was this measured?
This flagship study measured 24 stratified prompts three times per engine across Perplexity, ChatGPT, Gemini, and Claude. Appearance rates use 273 answers returned. Citation and source rates use 272 answers with retrieval evidence. The study recorded 285 attempted answers, 13 answers without retrieval evidence, and no failed answers. Results describe these prompts on the measurement date and do not measure clinical outcomes, certification status, consumer demand, or product quality.
### Study method
### How this study was measured
- How was the sample set? The frame was fixed first: 24 prompts were allocated across the demand clusters before any questions were asked.
- Were engines asked identical questions? Yes: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 273 answers returned of 285 attempted, 272 with retrieval evidence.
- How were product lines handled? They were assigned to parent brands under a registry fixed before scoring.
- How is uncertainty handled? Appearance rate uses a 95% Wilson interval; citation metrics are counted only for answers with evidence.
Appearance and citation rates cannot be compared as if they use the same denominator. Brand-name normalization also matters, as the separate Physician's Choice entries show. The automatic appendix contains the auditable study detail.
## Frequently asked questions
### Which probiotic brand appeared most often?
Culturelle ranked first with a 56.4% appearance rate across answers returned. Its 95% interval was 50.5% to 62.2%. Seed ranked second at 50.5%, and its interval overlaps Culturelle's.
### Which brand had the highest owned-domain citation rate?
Among answers with retrieval evidence, Seed's owned-domain citation rate was 21.7%. Next came Garden of Life at 9.6%, Culturelle at 8.8%, and Align at 7.0%.
### Which sources did the engines cite most often?
Healthline appeared in 63.2% of answers with retrieval evidence. Forbes appeared in 62.1%, and Fortune appeared in 43.8%. These source rates use retrieval-supported answers, not all answers returned.
### Did every engine name the same brands?
No. Renew Life appeared in 70.2% of ChatGPT's 57 answers but in 0% of Claude's 72 answers. Garden of Life appeared in 82.5% of ChatGPT answers and 9.7% of Claude answers.
### Was Culturelle clearly ahead of Seed?
Why does Culturelle rank ahead of Seed? The measured ranking puts Culturelle first and Seed second. Because their 95% intervals overlap, the observed difference falls within this sample's margin. This study cannot claim that Culturelle is more visible than Seed beyond this prompt set.
### Does this report prove which probiotic is best?
No. The report measures which names and sources appeared in collected AI answers. It does not measure clinical effectiveness, safety, certification, product quality, or consumer outcomes.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
Although these are not category brands, they are measured using the same method and excluded from the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1Healthline5.1%14/2733.1%–8.4%
2Cleveland Clinic0.4%1/2730.1%–2%
3Verywell Health0%0/2730%–1.4%
4Examine.com0%0/2730%–1.4%
## Methodology appendix
This appendix comes directly from this study's measurement record. It lets any brand named here reproduce or dispute the result.
### What was measured
- Category: Probiotics / Gut Health
- Measurement date: 2026-09-30
- Depth: flagship, 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 285
- Answers returned (appearance denominator): 273
- Answers with retrieval evidence (citation denominator): 272 (95.4% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: What does flagship depth capture? It measures 24 stratified prompts three times per engine, making both between-prompt and repeat-generation variation observable.
### How the prompts were sampled
How was the sampling frame set? 14 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Probiotics / Gut Health; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer provides wording only within these cells and does not determine the category's demand structure.
Demand clusterPromptsScope
best probiotic for gut health1best probiotic for gut health
best probiotic for bloating1best probiotic for bloating
best probiotic for women1best probiotic for women
best probiotic for IBS1best probiotic for IBS
best prebiotic and probiotic combo1best prebiotic and probiotic combo
best probiotic strain for digestion1best probiotic strain for digestion
best probiotic for constipation1best probiotic for constipation
best probiotic after antibiotics1best probiotic after antibiotics
best probiotic for immune support1best probiotic for immune support
best probiotic for men1best probiotic for men
refrigerated vs shelf stable probiotic which is better1refrigerated vs shelf stable probiotic which is better
best probiotic for skin health1best probiotic for skin health
best probiotic for kids1best probiotic for kids
best fermented food alternative to probiotics1best fermented food alternative to probiotics
General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Primary use case1the dominant everyday use case for this category
Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
### The exact prompts
#PromptClusterShape
1best probiotic for gut healthneed_1medium
2best probiotic for bloatingneed_2medium
3best probiotic for womenneed_3medium
4best probiotic for IBSneed_4medium
5best prebiotic and probiotic comboneed_5medium
6best probiotic strain for digestionneed_6medium
7best probiotic for constipationneed_7medium
8best probiotic after antibioticsneed_8medium
9best probiotic for immune supportneed_9medium
10best probiotic for menneed_10medium
11refrigerated vs shelf stable probiotic which is betterneed_11medium
12best probiotic for skin healthneed_12medium
13best probiotic for kidsneed_13medium
14best fermented food alternative to probioticsneed_14medium
15best probioticsgeneral_bestshort
16best daily probiotic supplementseverydaymedium
17best affordable probioticsvalue_priceshort
18What probiotic brands are best for everyday digestive regularity and occasional bloating?use_caselong
19best probiotics for beginnersbeginnermedium
20Where should I buy probiotics for reliable storage and freshness?retailmedium
21What probiotic supplement brands make a thoughtful wellness gift for someone starting a health routine?giftinglong
22best probiotics after antibioticsspecific_needmedium
23Which probiotic brands are better for strain transparency, clinical evidence, and value?comparisonlong
24most trusted probiotic brandstrustshort
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search727272100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool69575782.6%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)72727198.6%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool727272100%
Can results be compared strictly between engines? No: each engine has a different model, retrieval tool and answer format, so differences between engines may reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate: what share of returned answers named the brand. It is measured on every answer returned by the engine, whether or not it searched the web, so retrieval behaviour cannot silently alter the denominator.
- Macro appearance rate: the average of per-prompt appearance rates, giving every prompt one count regardless of how many engines answered it.
- 95% interval: the Wilson score interval for appearance rate. If two brands' intervals overlap, this study cannot support saying that either is more visible than the other.
- Owned-domain citation rate: among answers with retrieval evidence, the share citing a URL on the brand's own domain. It narrowly indicates whether the brand's own site was used as a source, not its total mentions.
- Share of all citations: the proportion of all attributed publisher citations in the study that come from the brand's own domain.
- Not measured: no citation result is available because the brand's official domain could not be established. This is not reported as 0%.
- 0%: the metric was evaluable, and the event did not happen.
- Average position: the mean placement of the brand's first mention within the answer's ordered list of named brands.
- Cross-engine consistency: the proportion of engines that named the brand at least once.
- Repeat consistency: the share of repeated prompt/engine cells in which the brand appeared in every trial. A lower figure signals an unstable engine answer, rather than brand absence.
### Evidence rules
- When does an answer have retrieval evidence? Only if the engine returned a retrieval receipt, such as search annotations or grounding records. A URL merely written by the model in its prose does not show that it searched.
- Who receives source attribution? The publisher behind each retrieval receipt. Intermediary and redirect hosts are not reported as publishers.
- Publisher identity was not established for 0 retrieval receipt(s); these are disclosed here instead of being attributed or dropped.
- How are sources counted? At answer level: a publisher is counted once in each answer that cited it, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Culturelle154/27356.4%56.4%50.5%–62.2%24/272 (8.8%)
2Seed138/27350.5%50.6%44.7%–56.4%59/272 (21.7%)
3Garden of Life120/27344%43.9%38.2%–49.9%26/272 (9.6%)
4Align117/27342.9%42.8%37.1%–48.8%19/272 (7%)
5Florastor84/27330.8%30.7%25.6%–36.5%6/272 (2.2%)
6Renew Life75/27327.5%27.3%22.5%–33.1%12/272 (4.4%)
7Ritual71/27326%26%21.2%–31.5%5/272 (1.8%)
8Jarrow Formulas57/27320.9%21.1%16.5%–26.1%12/272 (4.4%)
9Visbiome43/27315.8%15.8%11.9%–20.5%8/272 (2.9%)
10NOW Foods42/27315.4%15.5%11.6%–20.1%5/272 (1.8%)
11VSL#339/27314.3%14.1%10.6%–18.9%3/272 (1.1%)
12Lactobacillus31/27311.4%11.1%8.1%–15.7%domain not established — excluded from citation metrics
13Bio-K+31/27311.4%11.3%8.1%–15.7%0/272 (0%)
14Physician’s Choice30/27311%11%7.8%–15.3%4/272 (1.5%)
15Klaire Labs29/27310.6%10.6%7.5%–14.8%9/272 (3.3%)
16Bifidobacterium26/2739.5%9.2%6.6%–13.6%domain not established — excluded from citation metrics
17BioGaia23/2738.4%8.4%5.7%–12.3%1/272 (0.4%)
18Hyperbiotics20/2737.3%7.3%4.8%–11%0/272 (0%)
19Strain19/2737%7%4.5%–10.6%domain not established — excluded from citation metrics
20National Institutes of Health18/2736.6%6.6%4.2%–10.2%137/272 (50.4%)
21Sources16/2735.9%5.8%3.6%–9.3%domain not established — excluded from citation metrics
22Bio-Kult15/2735.5%5.5%3.4%–8.9%1/272 (0.4%)
23Forbes Health15/2735.5%5.5%3.4%–8.9%169/272 (62.1%)
24ConsumerLab15/2735.5%5.6%3.4%–8.9%82/272 (30.1%)
25Optibac15/2735.5%5.2%3.4%–8.9%21/272 (7.7%)
26Physician's Choice10/2733.7%3.6%2%–6.6%4/272 (1.5%)
### Entity handling
For brand-level metrics, product lines and model names are assigned to their parent brand through a registry fixed before scoring. No merging is based solely on this study's own counts.
No roll-ups were applied in this study.
What happened to other names found in answers? 40 further name(s) could not be confidently resolved to a brand in this category. They are reserved for human classification before the next study and have no metrics here.
### Limits of these findings
- What does this measurement represent? It is a point-in-time record of what the named systems returned for these exact prompts on 2026-09-30. Engine behaviour can change without notice.
- How broadly do these results apply? The prompt set is a stratified sample of category demand, not a census. Findings describe these prompts and should not be treated as representative of every category search.
- If intervals overlap, brand differences cannot be statistically distinguished at this sample size.
- If a brand is absent from this list, it was not measured as absent; it was simply not named in these answers.
- Appearance measures visibility, not endorsement, quality or market share.
- Why do some ranked brands lack citation data? 4 of 26 ranked brand(s) had no official domain established before scoring. They therefore have appearance results only and are omitted from citation metrics and source tables, rather than reported as zero.
---
### Israeli Tech in Virginia: $2.3 Billion, 119 Companies, and the Pentagon Next Door
URL: https://www.5wpr.com/research/israeli-tech-virginia-2026/
Summary: The 2026 5W Israeli Tech State Report finds 119 Israeli-founded companies generating $2.3B in economic output across Virginia, from Roanoke to Herndon.
AI Visibility Index
# Israeli Tech in Virginia: $2.3 Billion, 119 Companies, and the Pentagon Next Door
119 Israeli-founded companies. $2.3 billion in economic output. 7,847 total jobs. Elbit Systems of America manufacturing night vision in Roanoke since 1959. IAI North America headquartered in Herndon. Sabra running the world's largest hummus factory in Chesterfield. The defense-intelligence corridor the AI engines cannot find.
## 119 Israeli-founded companies. $2.3 billion in economic output. 7,847 total jobs. The defense-intelligence corridor where Israeli innovation meets the Pentagon — documented.
The fourth report in 5W's Israeli Tech State Report Series. Drawing on the 2025 Virginia-Israel Economic Impact Report (USIBA), Virginia Israel Advisory Board (VIAB) data, VEDP records, and BIRD Foundation reporting to document the economic contribution, key sectors, and institutional infrastructure behind Israeli-founded technology companies operating across the Commonwealth of Virginia. Published August 2026.
By Ronn Torossian, Founder and Chairman, 5W AI Communications.
## Key Findings
$2.3B — Gross economic output generated by Israeli-founded companies in Virginia in 2024 (USIBA, 2025)
119 — Israeli-founded companies operating in the Commonwealth
3,767 — Direct jobs created by Israeli-founded companies in Virginia
7,847 — Total jobs including supply-chain and household-spending multiplier effects
$998M — Value added to Virginia's economy
$77,408 — Average earnings per employee across the Israeli-founded company cohort
2,351 — Jobs in Chesterfield County alone — the highest single-county concentration
38 — Of the world's top 100 defense companies headquartered in Fairfax County
1959 — Year the Roanoke County night vision facility first opened (now Elbit Systems of America)
$30M — Elbit America's November 2025 expansion investment in Roanoke County, adding 288 jobs
## Overview
Virginia is where Israeli defense meets American defense. It is where Israeli aerospace sits across the highway from the Pentagon. It is where the world's largest hummus factory runs three shifts in Chesterfield County. And it is where a 30-year-old state advisory board — one of only a handful of formal Israel-facing economic development agencies in any US state — has been quietly building the corridor since 1996.
The USIBA Virginia-Israel Economic Impact Report, published in November 2025, is the first comprehensive state-level economic study of this relationship. The numbers — $2.3 billion in output, 119 companies, nearly 8,000 total jobs — place Virginia in the same tier as Texas in terms of Israeli-founded company activity, and ahead of Georgia on direct economic output.
What distinguishes Virginia from every other state in this series is proximity. Fairfax County hosts 38 of the world's top 100 defense companies. The Pentagon, CIA, and NSA are within 30 miles of the Dulles Technology Corridor where IAI North America runs its US headquarters. No other state offers Israeli defense-tech, cybersecurity, and intelligence companies the same density of federal procurement customers.
## The Anchor Companies
### Elbit Systems of America — Roanoke County
Defense and Night Vision Manufacturing
The Roanoke County night vision facility has been manufacturing for the US military since 1959 — 66 uninterrupted years under successive corporate owners: ITT, Exelis, Harris Corporation, and now Elbit Systems of America (the US subsidiary of Israel's Elbit Systems). In November 2025, Governor Glenn Youngkin announced a $30 million expansion of the facility, adding 288 new jobs and bringing the site's total workforce to more than 1,000 employees. The facility produces low-light night vision devices — image intensification tubes and night vision systems — critical for US national defense.
Governor Youngkin approved a $1.2 million grant from the Commonwealth's Opportunity Fund and a $300,000 Virginia Investment Performance Grant to support the expansion. "Elbit America's continued growth in Roanoke County strengthens America's defense capabilities and supports our brave service members at home and abroad," the Governor said.
Elbit America, headquartered in Fort Worth, Texas, operates multiple facilities across the United States. The Roanoke County facility is its largest manufacturing operation in the Southeast and one of the most significant Israeli-linked defense manufacturing sites in America.
### Israel Aerospace Industries (IAI) North America — Herndon, Fairfax County
Aerospace and Defense Headquarters
IAI, Israel's largest aerospace and defense company with approximately 15,000 employees worldwide, operates its North American headquarters from Herndon in the Dulles Technology Corridor. The company directs an $800 million annual US business from this location. IAI expanded its Fairfax County headquarters in 2019 with 30,000 square feet of office space and 50+ new jobs, following a direct meeting between Governor Ralph Northam and IAI officials in Tel Aviv.
In 2024, IAI and IAI North America launched IAI Catalyst — an aerospace accelerator and innovation center in Herndon, in partnership with Starburst Aerospace. The five-month accelerator supports early-stage startups in AI and autonomy, quantum science, sustainability and energy, and space technology. IAI Catalyst is IAI's first US accelerator — modeled on its Tel Aviv innovation center.
IAI's products include air defense systems (including components of the Iron Dome missile defense system), satellites, missiles, unmanned systems, business jets, and aerostructures.
### Sabra Dipping Company — Chesterfield County
Consumer Food Manufacturing
Sabra — a joint venture between Israel's Strauss Group and PepsiCo — operates the world's largest hummus manufacturing facility in Chesterfield County. The facility opened in 2010, has undergone three expansions, and employs 500+ workers. Chesterfield County leads all Virginia counties with 2,351 jobs tied to Israeli-founded companies, driven primarily by Sabra's operations.
Sabra chose Virginia over New Jersey and North Carolina for its manufacturing plant, citing strategic location along Interstate 95, proximity to East Coast distribution networks, and workforce quality. The company's Pearl Center of Excellence — a first-of-its-kind R&D facility for refrigerated dips and spreads — operates on the same Chesterfield campus.
## The Institutional Backbone
Virginia Israel Advisory Board (VIAB) — State Agency, Established 1996
VIAB is a state government agency dedicated to strengthening economic, cultural, and educational ties between Virginia and Israel. Led by Executive Director Dov Hoch, VIAB provides free consultation on grants, incentives, site selection, and workforce development for Israeli companies establishing US operations. In 2024, VIAB distributed $3 million in BIRD Foundation R&D grants for Virginia-Israeli partnerships — 25 grants awarded since the board's inception.
"Life sciences, agriculture, deep tech and especially defense have tremendous potential to attract hundreds of millions of dollars in investment in Virginia that bring thousands more jobs and innovation to our communities," says Hoch.
Fairfax County Economic Development Authority (FCEDA) — Tel Aviv Office Since 2004
The FCEDA has maintained a dedicated business investment office in Tel Aviv since 2004 — one of only six global offices operated by the county's economic development authority (alongside Bangalore/Mumbai, Berlin, London, Los Angeles, and Seoul). The Tel Aviv office specifically targets Israeli defense, aerospace, cybersecurity, and AI companies for Fairfax County investment.
BIRD Foundation — Virginia Partnership
The Binational Industrial Research and Development Foundation funds joint R&D projects between US and Israeli companies. VIAB serves as the BIRD Foundation state representative for Virginia. Since 1977, BIRD has approved close to 1,200 joint projects and helped generate over $10 billion in combined sales.
Governor Glenn Youngkin
Governor Youngkin has been a consistent public advocate for Virginia-Israel economic ties, personally announcing the Elbit expansion in November 2025 and supporting VIAB's mission. "We will continue strengthening our economic partnership with Israel to create even more opportunities for Virginians," the Governor has said.
## Why Virginia, Why Now
01 — The Pentagon is next door. No state in America offers Israeli defense-tech companies the same proximity to federal procurement customers. Fairfax County hosts 38 of the world's top 100 defense companies. The Department of Defense, CIA, NSA, and the US intelligence community are all within commuting distance of Northern Virginia's tech corridors. For Israeli companies selling to the US government — which accounts for a significant share of Israeli defense exports — Virginia is the only logical base.
02 — The most educated tech workforce in America. Virginia ranks among the top states nationally for concentration of tech workers, particularly in Northern Virginia. The NoVA corridor offers the cybersecurity, AI, and cleared defense talent that Israeli companies need to scale US government contracts.
03 — University and research infrastructure. The University of Virginia, Virginia Tech, George Mason University, and Old Dominion University provide both workforce pipeline and research partnership opportunities. UVA's telemedicine program, Virginia Tech's defense research, and George Mason's cybersecurity programs all align with Israeli innovation sectors.
04 — Pro-Israel state government with institutional continuity. VIAB has operated continuously since 1996 — nearly 30 years of formal economic partnership. Governors Northam, Youngkin, and their predecessors have all maintained the Virginia-Israel relationship as bipartisan and durable across administrations.
05 — Geographic and time-zone advantage. Virginia is the closest major tech corridor to Israel by both distance and time zone on the US East Coast. Direct or one-stop flights from Dulles to Tel Aviv compress the travel burden that Israeli founders cite as a factor in US base selection.
## Where Virginia-Israel Builds
Defense, aerospace, and intelligence — Elbit Systems of America in Roanoke (night vision manufacturing, 1,000+ employees). IAI North America in Herndon ($800M annual US business). The Dulles Technology Corridor defense cluster. 38 of the world's top 100 defense companies in Fairfax County.
Cybersecurity — Northern Virginia's proximity to the intelligence community makes it a natural base for Israeli cybersecurity companies. The NoVA corridor is the densest concentration of cleared cybersecurity talent in America.
Consumer food manufacturing — Sabra Dipping Company in Chesterfield County. 500+ employees. World's largest hummus factory. Three expansions since 2010.
Agriculture and life sciences — USIBA identifies these as priority growth sectors with underdeveloped potential for Israeli companies in Virginia. VIAB and BIRD Foundation are actively targeting Israeli agritech and life sciences companies.
Space technology — IAI Catalyst's Herndon accelerator is actively recruiting startups in space tech. Fairfax County's aerospace cluster is expanding to include commercial space alongside defense.
## How Virginia Compares
USIBA has published independent economic-impact reports for New York, Florida, Virginia, and California. The table below contextualizes the Virginia footprint within the broader US picture.
New York — 648 companies / 57,145 total jobs / $19.5B total output (USIBA, 2025)
California — 22,650+ jobs / $9B total output (USIBA, 2026)
Florida — 429 companies / 26,510 total jobs / $7.3B total output (USIBA, 2025)
Texas — 4,200+ jobs / $3.2B capital (Office of the Governor, FDI registry only)
Virginia — 119 companies / 7,847 total jobs / $2.3B total output (USIBA, 2025)
Georgia — 50+ companies / $5.4B cumulative exports since 1996 (GDEcD)
Virginia's Israeli-founded company cohort is smaller than Florida's or New York's by headcount — but its per-company economic output ($19.3M per company) is among the highest of any state, reflecting the capital-intensive nature of defense and aerospace operations.
## The Outlook
Three signals confirm Virginia's Israeli tech trajectory is accelerating.
Elbit's $30M Roanoke expansion is underway. The November 2025 investment — 288 new jobs, workforce to 1,000+ — is the single largest Israeli-linked manufacturing expansion in Virginia in recent years. Night vision demand from the US military is the driver.
IAI Catalyst is operational in Herndon. Israel's largest aerospace company is now running a US accelerator in Fairfax County, actively recruiting American and Israeli startups in AI, quantum, space, and sustainability. The innovation-center model imported directly from Tel Aviv.
VIAB's three priority sectors signal the next wave. Defense is established. Agriculture and life sciences are the growth targets. Israeli agritech companies — controlled environment agriculture, precision irrigation, crop genetics — have natural entry points through Virginia's agricultural economy and BIRD Foundation R&D funding.
As Ronn Torossian, Founder and Chairman of 5W, writes: "Virginia is the state where Israeli defense-tech meets its customer. No other corridor in America puts an Israeli company within 30 miles of the Pentagon, the CIA, and 38 of the world's top 100 defense firms. Elbit has been manufacturing in Roanoke since 1959. IAI runs its US headquarters from Herndon. The VIAB has operated for nearly 30 years. This is not a new relationship — it is the deepest institutional foundation of any state in this series. From where I sit in Israel, Virginia is the state that Israeli defense founders talk about when they say 'we need to be close to the customer.'"
## Related 5W Israel Research
This report is part of the 5W Israeli Tech State Report Series — the most comprehensive state-level documentation of the Israeli tech footprint in America.
Israeli Tech in Florida: $7.3 Billion — 429 Israeli-founded companies. 26,510 jobs. 2.78% of Miami-Dade's GDP.
Israeli Tech in Texas — $3.2B invested, 4,200+ jobs, $4B in 2024 two-way trade.
Israeli Tech in Georgia: 33 Years, 50+ Companies, $5.4B in Bilateral Trade — Three decades of formal Georgia–Israel economic partnership.
Claude in Israel: A Study on the Startup Nation — Israel ranks #1 globally on the Anthropic AI Usage Index at 4.9x the global baseline.
Israel Is Running Ahead on Claude — How Israeli startups are shaping Anthropic's AI ecosystem.
## Methodology and Sources
This report draws on verified third-party data registries and institutional sources.
United States-Israel Business Alliance (USIBA), Virginia-Israel Economic Impact Report (November 2025). Virginia Israel Advisory Board (VIAB), FY2024 Annual Report and historical data. Virginia Economic Development Partnership (VEDP), press releases and case studies. BIRD Foundation reporting. Fairfax County Economic Development Authority (FCEDA). Elbit Systems of America press releases (November 2025). Israel Aerospace Industries (IAI) North America, corporate and accelerator announcements. Sabra Dipping Company / Strauss Group corporate reporting. Calcalist/CTech, Times of Israel, Virginia Business — local and trade reporting. Governor Glenn Youngkin administration statements and economic development announcements.
Published by 5W Research. 5wpr.com. Media inquiries: press@5wpr.com.
## About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
## Frequently Asked Questions
How many Israeli-founded companies operate in Virginia?
The 2025 Virginia-Israel Economic Impact Report (USIBA) identifies 119 Israeli-founded companies operating in the Commonwealth. These companies generated $2.3 billion in gross economic output and were responsible for 7,847 total jobs in 2024.
What is the economic impact of Israeli tech companies in Virginia?
Israeli-founded companies generated $2.3 billion in gross economic output, $998 million in value added, and 3,767 direct jobs in Virginia in 2024. Including supply-chain and household-spending multiplier effects, the total rises to 7,847 jobs. Average earnings across the cohort are $77,408 per employee.
What are the major Israeli companies operating in Virginia?
The three largest anchors are Elbit Systems of America (night vision manufacturing in Roanoke County, 1,000+ employees, $30M expansion in 2025), Israel Aerospace Industries North America (US headquarters in Herndon, Fairfax County, directing $800M in annual US business), and Sabra Dipping Company (Strauss Group/PepsiCo JV, world's largest hummus factory in Chesterfield County, 500+ employees).
Why do Israeli companies choose Virginia?
Five structural factors: proximity to the Pentagon and US intelligence community (Fairfax County hosts 38 of the world's top 100 defense companies); the most educated tech workforce in America; university and research infrastructure aligned with Israeli innovation sectors; a pro-Israel state government with nearly 30 years of institutional continuity through VIAB; and geographic and time-zone proximity to Israel.
What is the Virginia Israel Advisory Board?
VIAB is a state government agency established in 1996, dedicated to strengthening economic ties between Virginia and Israel. Led by Executive Director Dov Hoch, VIAB assists Israeli companies in establishing US operations in Virginia and facilitates Virginia company access to Israeli innovation. In 2024, VIAB distributed $3 million in BIRD Foundation R&D grants.
What sectors define the Virginia-Israel economic relationship?
Defense, aerospace, and intelligence (the dominant sector); cybersecurity; consumer food manufacturing; and two USIBA-identified priority growth sectors — agriculture and life sciences — where Israeli companies have underdeveloped but high-potential opportunities in Virginia.
How does Virginia compare to other states for Israeli business?
Virginia's 119 Israeli-founded companies generated $2.3B in output — smaller than New York ($19.5B), California ($9B), and Florida ($7.3B) but with the highest per-company output ($19.3M) of any state in the 5W series, reflecting capital-intensive defense and aerospace operations. Virginia's defense-intelligence proximity is unique and unreplicable.
What is the Elbit Systems expansion in Roanoke?
In November 2025, Elbit Systems of America announced a $30 million expansion of its Roanoke County night vision manufacturing facility, creating 288 new jobs and bringing total site employment to over 1,000. The facility has manufactured for the US military continuously since 1959 — 66 years under successive owners (ITT, Exelis, Harris Corporation, Elbit). Governor Youngkin approved $1.5 million in state incentives to support the expansion.
What is IAI Catalyst?
IAI Catalyst is an aerospace accelerator and innovation center launched in 2024 by Israel Aerospace Industries and IAI North America, in partnership with Starburst Aerospace, at IAI's Herndon headquarters in Fairfax County. The five-month accelerator supports early-stage startups in AI, quantum science, sustainability, energy, and space technology — IAI's first US accelerator, modeled on its Tel Aviv innovation center.
Who authored this report?
This report was published by 5W AI Communications, The Communications Firm Built for the AI Era. Lead author is Ronn Torossian, Founder and Chairman of 5W. 5W has also published the Israeli Tech in Florida Report, the Israeli Tech in Texas Report, the Israeli Tech in Georgia Report, and Claude in Israel: A Study on the Startup Nation. For inquiries: research@5wpr.com or media@5wpr.com.
---
### The 1,000-Creator Playbook for Beauty 2026
URL: https://www.5wpr.com/research/beauty-1000-creator-playbook-2026/
Summary: 30,000+ beauty brands sell on TikTok Shop. The winning model: 1,000+ creators a quarter, a dermfluencer program, and buyer meetings led by velocity data.
5W Research · Beauty & Wellness Practice · April 2026
# The 1,000-Creator Playbook for Beauty 2026
By Ronn Torossian, Founder & Chairman, 5W AI Communications
30,000+ beauty brands now operate on TikTok Shop — the category accounts for 22.5% of TikTok Shop's global GMV. The brands eating shelf share seed 1,000+ creators per quarter, run a dermfluencer relationship program, and lead retail buyer meetings with velocity data. This is the operating model that now defines beauty. Six shifts. Three case studies. A seven-step 90-day plan.
30,000+
Beauty brands active on TikTok Shop globally
22.5%
Beauty's share of TikTok Shop global GMV
+94%
TikTok Shop GMV YoY growth
50%
Social shoppers who bought a beauty product because of a creator
### TL;DR — What Beauty Brand Leaders Need to Know
- 30,000+ beauty brands on TikTok Shop. 100-creator programs are invisible in category feeds.
- Winning scale is 1,000+ creators per quarter — Rhode, Merit, and CeraVe operate at this volume.
- Dermfluencers are credibility gatekeepers. 12–18 month relationship build, not a quarterly spend.
- Sephora and Ulta buyers lead with velocity data — GMV trend, dermfluencer log, Amazon reviews, conversion — not narrative.
- FDA compliance is an operations problem, not a legal-review problem. Manual review breaks at scale.
- AI search is the first step in beauty discovery. If ChatGPT, Claude, Perplexity, and Gemini don't cite you, your creator strategy is your GEO strategy.
"Beauty's shelf moved into the chatbox. The brands winning are the ones the AI answers name — and that's a creator operations problem, not a media relations problem." — Ronn Torossian
## Executive Summary
Beauty has become the most competitive creator content category in America. 30,000-plus beauty brands now operate on TikTok Shop — up from near zero two years ago — and the category accounts for roughly 22.5% of TikTok Shop's global GMV. The brands gaining share share an operating model: 1,000-plus creators seeded per quarter, a dedicated dermfluencer and skinfluencer relationship program, a compliance operating system that survives FDA scrutiny at scale, and a retail buyer conversation that leads with creator velocity data rather than founder story.
This playbook is built for the beauty leader who has watched the pattern work for competitors, recognizes their current creator program is underscaled for 2026 competitive density, and needs a realistic 90-day plan to move from occasional influencer campaigns to a compounding creator operation that produces Sephora, Ulta, and Amazon outcomes.
## Six Shifts Reshaping Beauty Growth in 2026
### 01 — Creator seeding scale moved from 100 to 1,000.
With 30,000-plus beauty brands active on TikTok Shop, a 100-creator-per-quarter seeding program is no longer visible in category feeds. The brands winning at scale — Rhode, Merit, CeraVe (55.5% GMV growth year-to-date), Rare Beauty, Fenty Beauty, e.l.f., Glow Recipe — seed at 1,000-plus creators per quarter. Volume of authentic use is what both the TikTok recommendation engine and the beauty consumer respond to.
Audit your current quarterly creator count. If it is below 500, you are structurally underscaled for 2026 beauty competition.
### 02 — The dermfluencer and skinfluencer economy is beauty's credibility infrastructure.
Dermatologists and skincare experts — Dr. Shereene Idriss, Dr. Muneeb Shah, Dr. Sam Ellis, Dr. Andrea Suarez (Dr. Dray), Dr. Anjali Mahto, Hyram Yarbro, James Welsh, Cassandra Bankson, and dozens of others with 500K to 10M followers — now function as credibility gatekeepers for skincare and haircare launches. The dermfluencer relationship economy is a 12-to-18-month build, not a quarterly campaign. Brands that treat it as a transactional influencer layer consistently underperform brands that treat it as a long-term relationship investment.
Identify your 30 to 50 dermfluencer or skinfluencer targets today. Start the relationship, not the transaction.
### 03 — Sephora, Ulta, and Credo buyers lead with data, not narratives.
Category buyers at Sephora, Ulta Beauty, Credo Beauty, Bluemercury, and Space NK have restructured how they evaluate brands for shelf placement. Before the trade meeting, they pull TikTok Shop GMV trend, hashtag volume, dermfluencer endorsement log, Amazon review count and trajectory, and creator-to-purchase conversion data. The effective 2026 retail pitch leads with numbers, not narrative.
Build a one-page monthly velocity report for every retail buyer. Lead with GMV, dermfluencer endorsements, Amazon reviews. Then narrative.
### 04 — Celebrity beauty and founder-led beauty need inverted creator strategies.
Celebrity beauty brands — Rhode (Hailey Bieber), Rare Beauty (Selena Gomez), Fenty Beauty (Rihanna), r.e.m. Beauty (Ariana Grande), Item Beauty (Addison Rae) — have built-in awareness and a trust deficit. The winning strategy over-indexes on dermfluencer and expert validation. Founder-led beauty — Drunk Elephant (Tiffany Masterson), Tower 28 (Amy Liu), Youthforia (Fiona Co Chan), Kosas (Sheena Yaitanes), Saie (Laney Crowell) — has the inverse problem: trust from founder expertise, awareness deficit. The winning strategy over-indexes on volume micro-creator seeding. Running the same playbook for both brand types leaves half the opportunity on the table.
Identify which brand type you are. If your creator mix doesn't reflect it, that is the Q2 rebalance.
### 05 — FDA compliance is a 1,000-creator operations problem, not a legal review problem.
FDA rules on cosmetic vs. drug claims — the line between "cleanses" and "treats acne," between "moisturizes" and "repairs barrier damage" — apply to creator content as fully as to brand-owned marketing. At 1,000-creator scale, manual legal review is impossible. The operating system: pre-approved claim libraries per product, forbidden-phrase lists, automated FTC disclosure reminders, and monitoring software.
Document your approved claim library and forbidden-phrase list this week. If you do not have one, that is the compliance gap every scale-up reveals.
### 06 — AI search is now the first step in beauty discovery.
Beauty consumers researching "best retinol for sensitive skin" or "best tinted SPF 2026" increasingly ask ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews before clicking through to Sephora, Ulta, or Amazon. The LLM answer is built from creator content, dermfluencer teardowns, Amazon reviews, and trade publication coverage — not from brand-owned marketing copy. This is the discipline 5W calls AI Communications: becoming the answer inside the engines where buyers now ask the question.
Query the four major LLMs for 10 category questions in your space today. If you are absent, your creator strategy is also your GEO strategy. See the 5W AI Visibility Index 2026 Series for benchmark data.
## Three Case Studies Every Beauty Leader Should Study
### Rhode: Celebrity brand done with efficacy-first creator strategy.
Rhode, founded by Hailey Bieber, built one of the most valuable celebrity beauty brands of the 2020s by pairing Bieber's cultural reach with a relentless efficacy-first creator program — dermfluencer endorsements, ingredient transparency, and widespread social proof that the products actually performed. The May 2025 acquisition by e.l.f. Beauty for approximately $1 billion reflected the rare combination of celebrity awareness with earned credibility. Celebrity beauty's hardest problem is not awareness — it is efficacy credibility. Over-invest in the dermfluencer and expert-validation layer from launch day one.
### Merit: Restraint as category position in saturated beauty.
Merit, founded by Katherine Power, built a rapidly scaling clean beauty brand by executing a deliberately restrained creator strategy — editorial-grade content, curated creator partnerships with strong aesthetic fit, and a distribution rollout (Sephora, Nordstrom, direct) that matched the brand positioning. Merit did not out-volume the category; it out-positioned it, with creator partnerships that read as extensions of the brand rather than disposable posts. Not every beauty brand should scale to 1,000 creators — but the brand that chooses restraint has to execute it at very high creative quality.
### CeraVe: Mass skincare revival through creator-led launch infrastructure.
CeraVe — long established as a dermatologist-recommended mass skincare brand under L'Oréal — built a second growth curve on TikTok Shop by treating the platform as launch infrastructure rather than a sales channel. Working with agency partners including Pattern and NextWave on targeted affiliate networks, CeraVe activated creators not to promote but to demonstrate and contextualize — driving 55.5% GMV growth year-to-date and proving mass skincare brands can compete with DTC natives on the platform when the creator operation is correctly resourced.
## The Seven-Step 90-Day Plan to Stand Up a 1,000-Creator Operation
### 01 — Audit your current creator and retail footprint.
Pull 12 months of creator output, TikTok Shop GMV trend, Amazon review count per SKU, and velocity data across every retailer where you are listed. Most beauty brands have disconnected datasets. Build a single integrated view this week.
### 02 — Stand up the tiered creator stack at 1,000-creator scale.
Pipeline: 750-plus micro (10K–250K), 200-plus mid-tier (250K–1M), 30-plus dermfluencers or skinfluencers, 10-plus celebrity or mega. Seed 250-plus products per month. This is the operational scale that produces category-level breakout content.
### 03 — Install an FDA and FTC compliance operating system.
Pre-approved claim libraries per product. Forbidden-phrase lists for unapproved therapeutic claims. Automated disclosure reminders. Monitoring software flagging violations. Train legal once — then run without review bottlenecks.
### 04 — Build dedicated dermfluencer and skinfluencer relationships.
Identify 30 to 50 target dermfluencers and skincare experts. Build long-term relationships — ongoing access, first-look product, scientific advisory conversations — not one-off deals. The credibility economy in skincare and haircare is a 12-to-18-month investment, not a quarterly spend.
### 05 — Engineer Sephora and Ulta buyer conversations with data.
Monthly one-page velocity report for each retail buyer: TikTok Shop GMV trend, dermfluencer endorsement log, Amazon review count and trajectory, creator-to-purchase conversion, return rate trend. Sephora and Ulta lead with velocity data. Come prepared for that conversation.
### 06 — Integrate retailer creator programs strategically.
Participate in Sephora Squad, Ulta Beauty Muse, TikTok Shop Super Brand Days, and Amazon Influencer Program as partnership markers — not as a substitute for owned creator operations. Retailer-program participation signals commitment and concentrates earned media at launch moments.
### 07 — Measure what actually moves the beauty business.
Report: TikTok Shop GMV growth, Sephora and Ulta velocity per door, Amazon review count and star trajectory, dermfluencer endorsement count and sentiment, return rate trend, LLM share of voice in category. Tie each to retail team and CFO numbers — not impression totals. For an independent read on your current LLM share of voice, see the 5W AI Citation Audit.
## Frequently Asked Questions
### Why has beauty's creator-seeding scale moved from 100 to 1,000 creators per quarter?
With 30,000-plus beauty brands active on TikTok Shop, a 100-creator-per-quarter seeding program is no longer visible in category feeds. Volume of authentic use is what both the TikTok recommendation engine and the beauty consumer respond to. If your quarterly creator count is below 500, you are structurally underscaled for 2026 beauty competition.
### How do retail buyers at Sephora and Ulta use TikTok and creator data?
Before the trade meeting, buyers pull TikTok Shop GMV trend, hashtag volume, dermfluencer endorsement log, Amazon review count and trajectory, and creator-to-purchase conversion data. A brand with documented velocity data is a different meeting than a brand with a product lineup and an origin story. Lead with numbers, not narrative.
### What is the dermfluencer and skinfluencer economy and why does it matter?
Dermatologists and skincare experts with 500K to 10M followers now function as credibility gatekeepers for skincare and haircare launches. A product teardown or recommendation from this group drives consumer purchase and retail buyer confidence simultaneously. The dermfluencer relationship economy is a 12-to-18-month build, not a quarterly campaign.
### How should celebrity beauty brands structure their creator seeding differently from founder-led brands?
Celebrity beauty brands have built-in awareness and a trust deficit — over-index on dermfluencer and expert validation. Founder-led beauty has the inverse problem: trust from founder expertise, awareness deficit — over-index on volume micro-creator seeding. Running the same playbook for both brand types leaves half the opportunity on the table.
### How do beauty brands manage product claims compliance at 1,000-creator scale?
FDA rules apply to creator content as fully as to brand-owned marketing. At 1,000-creator scale, manual legal review is impossible. The operating system: pre-approved claim libraries per product, forbidden-phrase lists, automated FTC disclosure reminders, and monitoring software flagging violations. Build the system once and compliance becomes a workflow rather than a bottleneck.
### What's the right role for Sephora Squad, Ulta Beauty Muse, and retailer-sponsored creator programs?
Participate in these programs as partnership markers — not as a substitute for owned creator operations. Retailer-program participation signals commitment and concentrates earned media at launch moments. These programs complement a 1,000-creator operation. They do not replace it.
### How does AI search change beauty discovery?
More than a third of consumers now begin product research with AI — ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews — before clicking through to Sephora, Ulta, or Amazon. The LLM answer is assembled from creator content, dermfluencer teardowns, Amazon reviews, and trade coverage. If your brand is absent from the answers, your creator strategy is also your GEO strategy.
## Related 5W Research
- 2026 AI Visibility Index Series — benchmark data across beauty, healthcare, real estate, higher education, and more
- 5W AI Citation Audit — independent read on your brand's LLM share of voice
- 5W Beauty & Grooming Practice
- Generative Engine Optimization (GEO) at 5W
- All 5W Research
Cite this research:
5W Research (April 2026). The 1,000-Creator Playbook for Beauty 2026. 5W AI Communications. https://www.5wpr.com/research/beauty-1000-creator-playbook-2026/
↧ Download the Full PDF Playbook
## About 5W AI Communications
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
## About the Author
Ronn Torossian is the founder and chairman of 5W AI Communications, The Communications Firm Built for the AI Era. He is the publisher of Everything-PR and the author of two best-selling editions of For Immediate Release.
April 2026 — 5W Research Series, Beauty & Wellness Practice. Published by 5W AI Communications. Reproduction permitted with attribution.
---
### Prediction Markets AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/prediction-markets-ai-visibility/
Summary: Prediction markets moved $50.6B in July 2026, more than legal US sportsbooks handled. 5W measures how AI engines frame Kalshi and Polymarket right now.
AI Visibility Index
# Prediction Markets AI Visibility Index 2026
Prediction markets moved an estimated $50.6 billion globally in July 2026, more than legal U.S. sportsbooks handled in bets that month. In the same window, regulators in six-plus states sued, banned, or restricted the platforms offering them. This report measures how ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews describe that legality question right now, and where their answers go stale.
The Prediction Markets AI Visibility Index — A 5W investigation
$50.6BGlobal prediction market volume, July 2026 — more than legal US sportsbooks handled that month
7Prediction market operators live in the US as of August 23, 2026
18Legality-by-state test queries — warm Claude matched the verified record, cold Claude missed it entirely
45-ptGap between Claude's warm (90) and cold (45) Retrieval Currency Score
Regulatory Split
## Is Kalshi Gambling or a Financial Product?
Kalshi describes itself as a federally regulated financial exchange, registered with the Commodity Futures Trading Commission as a designated contract market since November 2020, per the CFTC's own press materials. Washington's attorney general describes the same company's contracts as an "illegal gambling operation" in the state's August 13, 2026 court filing. Both descriptions come from primary regulators, not commentary, and they disagree.
### Why it works
AI systems learn to weight financial framing or gambling framing based on which vocabulary dominates the sources they retrieve for a given query. Because the CFTC's own releases use exchange and derivatives language while state attorneys general use gambling language, an AI engine answering "is Kalshi gambling" is not choosing a side. It is reflecting two regulators who have not resolved the question between themselves, documented in the CFTC's August 11, 2026 press release and the Washington Attorney General's August 13, 2026 release.
Knowledge Cutoffs
## Why Do AI Answers on Prediction Market Legality Go Stale?
An AI system without live search cannot know about a court order that was issued after its training data was collected. Claude's reliable knowledge boundary falls at the end of January 2026. Nearly every material legal development this report tracked, including the Minnesota injunction, the Washington order, and the New York lawsuit, happened between March and August 2026.
To measure this directly, 5W tested Claude under two conditions on August 23, 2026: a warm pass informed by this report's own verified research, and a cold pass relying on training knowledge only, with no search, approximating an independent user session. On the legality-by-state cluster of 18 test queries, warm answers matched the verified legal record; cold answers missed it entirely, not partially, because the underlying facts did not exist yet when the model's knowledge was fixed.
### Why it works
A language model's knowledge is fixed at training time unless it retrieves live information. Washington's final order against Kalshi, issued August 13, 2026, requires geofencing by September 2, 2026, or a penalty of $120,000 per day, per the Washington Attorney General's press release. No model trained before that date can know it happened without a live search.
Market Landscape
## Which Prediction Market Operators Are Live in the US Right Now?
Seven operators account for most of the category's US volume as of August 23, 2026: Kalshi, Polymarket, Novig, ProphetX, DraftKings Predictions, Robinhood's event-contract product, and a reported CME and FanDuel venture.
Operator status verified against CFTC press materials, company support pages, and trade press coverage as of August 23, 2026.
OperatorStatusRegulatory basis
KalshiOperating nationally, fighting state actions in multiple jurisdictionsCFTC-designated contract market since November 2020
PolymarketRelaunched in the US between December 2025 and May 2026, under a CFTC marketing-practices inquiry opened in June 2026CFTC-approved designated contract market via its QCX acquisition, July 2025
NovigLive nationwide since August 4, 2026CFTC designated contract market approval, June 2026
ProphetXReported live in 49 states per a single trade publication, not yet confirmed against a primary sourceCFTC designated contract market approval, reported June 2026
DraftKings PredictionsLive, rolling out state by stateEntered the category through its acquisition of the CFTC-registered Railbird Exchange
Robinhood event contractsLive in all 50 statesOperates on Kalshi's CFTC-regulated exchange infrastructure
State-by-State Status
## Is Kalshi Legal in My State?
The answer depends on which state, and it changes by the week. Washington's King County Superior Court issued a final order on August 13, 2026 restricting most of Kalshi's operations there, while a federal judge blocked Minnesota's outright ban in late July 2026 and the CFTC used emergency authority on August 11, 2026 to order Kalshi to keep operating in New York despite a state lawsuit seeking to shut it down.
Legal status compiled from CFTC press releases, state attorney general releases, and court reporting, verified as of August 23, 2026. Two additional states in 5W's underlying research, Maryland and Ohio, carry unresolved sourcing conflicts and are omitted from this table pending primary-source confirmation.
StateStatusLatest action
WashingtonFinal order restricts most Kalshi operationsGeofence required by September 2, 2026, or a $120,000-per-day penalty, per the Washington Attorney General's August 13, 2026 release
New YorkState sued to shut Kalshi down; CFTC ordered it to keep operatingCFTC emergency order, August 11, 2026
MinnesotaFederal injunction blocks the state's ban, reportedly on appealInjunction granted late July 2026, per Courthouse News
MichiganState temporary restraining order paused sports contracts; CFTC ordered existing trades honoredCFTC order, July 14, 2026
ArizonaFederal injunction blocks state prosecutionReported May 2026
Engine Comparison
## How Do ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews Compare?
Google AI Overviews and Perplexity score highest on legal currency in 5W's modeling because both are search-grounded by default, while ChatGPT scores lowest because it does not always invoke browsing without an explicit prompt cue.
Retrieval Currency Score, 0 to 100, modeled from Claude's knowledge of each platform's public retrieval behavior and verified against the legal record in the previous section. Claude's own score is drawn from direct testing; the other four rows are directional estimates, not logged query results, consistent with prior volumes in this series.
EngineRetrieval basisCurrency score
Google AI OverviewsAlways search-grounded by construction88
PerplexitySearch-grounded by default, cites sources directly85
Claude, search enabledInvokes search contextually for time-sensitive queries90
GeminiSearch grounding common but not guaranteed on every query78
ChatGPTBrowsing not always invoked without an explicit cue65
Claude, no searchTraining knowledge only, the cold-pass baseline45
The gap between Claude's warm score of 90 and its cold score of 45 is the largest spread in the table. The model and its reasoning are identical in both conditions. The only variable is whether search was live, which isolates the effect of retrieval from the effect of model quality.
90Claude, search enabled (warm)
45-pt gap
45Claude, no search (cold)
Case Study
## What Happened When Kalshi Fought Washington State?
King County Superior Court Judge John McHale signed a final order on August 13, 2026 barring Kalshi from offering or advertising sports, elections, politics, and culture contracts to Washington residents, while leaving commodities, climate, economics, and finance contracts unaffected, per the Washington Attorney General's press release. Kalshi must geofence Washington residents by September 2, 2026, or face a penalty of $120,000 per day.
This is a stronger proof case for testing AI currency than the injunctions in Arizona or Minnesota, because those blocked state action against the platform and left Kalshi operating, while Washington's order is final, adverse to Kalshi, and carries a specific, checkable deadline. An AI answer to "is Kalshi legal in Washington" can be tested against a single, falsifiable fact: does it reflect this order as of the date it is asked.
Implications
## What This Means for Prediction Market Operators and the Firms Advising Them
An operator's AI-facing content needs a publication date on every legal claim, because a claim without a date cannot be checked for staleness by a reader or a model. Sportsbooks watching prediction markets encroach on sports-betting search queries should track this category's AI visibility the same way they track sportsbook competitors, since the same users are asking the same questions of the same AI engines. Regulatory and compliance teams should treat their own press releases as retrieval targets, not just legal notices, because AI systems training on this category are already learning vocabulary directly from CFTC and state attorney general filings.
5W runs AI Search and Generative Engine Optimization programs for brands operating in contested regulatory categories, building the dated, sourced, machine-readable content that keeps an AI answer current as the underlying facts change. Learn more about 5W's Generative Engine Optimization services.
---
### ADT Has Guarded American Homes for 150 Years. AI Recommends a Doorbell
URL: https://www.5wpr.com/ai-visibility-index/home-security-ai-visibility-index-2026/
Summary: The 5W Home Security AI Visibility Index ranks ADT, Ring and SimpliSafe by citation share across ChatGPT, Claude, Gemini and Google Overviews.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexHome Security
# ADT Has Guarded American Homes for 150 Years. AI Recommends a Doorbell
ADT is the largest home security company in the United States. New 5W research finds the AI engines hand the recommendation to the DIY brands — Ring and SimpliSafe — and ranks the 25 brands by how often each is named.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 brandsData window: Q2 2026
The Headlines
01
The DIY brands own the answer.
Ring and SimpliSafe lead the citation surface; the professionally-monitored incumbents trail.
02
Scale built on door-to-door sales is invisible to AI.
Dealer networks and long contracts do not generate the reviews the engines retrieve.
03
"Best system" and "best monitored" are two questions.
The engines split the category by how the buyer wants to install and pay.
~16%
Estimated AI citation share held by Ring — the most-cited home security brand
~$57.8B
Estimated size of the U.S. home security systems market in 2025
~2:1
DIY wireless systems vs professionally-installed wired systems, by market value
1874
Year ADT was founded — roughly 150 years guarding American homes
Figure 1 · The Ranking
## Who AI names first.
TOP 15 BRANDS BY EST. CITATION SHARE · Q2 2026
01RingDIY / video16.0%
02SimpliSafeDIY monitored12.0%
03ADTPro monitored9.5%
04VivintSmart-home monitored7.0%
05Google NestSmart-home / cameras6.0%
06ArloWireless cameras5.0%
07WyzeBudget smart home4.2%
08BlinkBudget cameras3.5%
09EufyLocal-storage cameras2.9%
10AbodeDIY smart home2.4%
11FrontpointMonitored DIY2.0%
12Brinks HomePro monitored1.7%
13CoveDIY monitored1.4%
14LorexWired cameras1.2%
15Xfinity HomeCable bundle1.0%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of brand citations across 60+ tracked consumer prompts spanning DIY systems, professionally monitored systems, video doorbells and cameras, smart-home integration, budget options, and renter setups. Remaining ~24% split across ranks 16–25 and unranked brands.
The Central Finding
## The largest alarm company is not the cited one.
ADT is the largest home security company in the United States — millions of monitored households, a brand founded in 1874, a name woven into the lawn-sign furniture of the American suburb. The 5W AI Visibility Index finds that ADT ranks third in AI citation share. The brands above it — Ring and SimpliSafe — barely existed fifteen years ago.
The reason is structural. ADT's scale was built through door-to-door sales, dealer networks, and multi-year monitoring contracts — channels that move customers but generate almost nothing the engines can retrieve. Ring and SimpliSafe were built in the review economy: tested by every tech and consumer-product outlet, debated on Reddit, compared in listicles. That is exactly the material AI models read.
And the category does not return one answer — it splits by install model. Ask for the "best home security system" and the engines return the DIY brands: self-install, no contract, Ring and SimpliSafe. Ask for the "best professionally monitored system" and ADT and Vivint step forward. Ask for a "home security camera" and Ring, Nest, and Arlo own it. The buyer is really asking three different questions — and a brand is cited only if it owns the one being asked.
— 5W Research, May 2026
"ADT has guarded American homes since 1874. Ask an AI engine how to secure your home today and it points you at a doorbell camera you install yourself. That is not a verdict on the product — it is a verdict on the channel. ADT's scale was built knocking on doors and signing contracts. Ring and SimpliSafe were built being reviewed, ranked, and argued about online. The engines read the second channel and cannot see the first. In home security, the brand AI recommends is the brand the review economy made the answer."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common consumer prompts across six primary sub-categories of the home security market, running each prompt five times per engine in clean sessions. We identified which brands AI models consistently surface, which sources feed those citations, and how the answer changes with the install model and price point in the query.
#### Best overall / DIY systems
Ring, SimpliSafe, Wyze, Abode — self-install systems for the unqualified query.
#### Professionally monitored
ADT, Vivint, Brinks Home, Frontpoint — installed and monitored systems.
#### Video doorbells & cameras
Ring, Google Nest, Arlo, Blink, Eufy, Lorex.
#### Smart-home integration
Google Nest, Ecobee, Aqara, Abode — systems built to tie devices together.
#### Budget & value
Wyze, Blink, TP-Link Tapo, Eufy, Kangaroo.
#### Renters & apartments
Ring, SimpliSafe, Cove, Kangaroo — portable, contract-free setups.
Query types tracked. Real-world prompts including "best home security system," "best DIY home security," "best home security camera," "ADT alternatives," "home security with no monthly fee," "best security system for renters," "SimpliSafe vs Ring," and 50+ variations covering recommendation, comparison, and category-leadership intent.
Citation sources tracked. Technology and consumer-product review media, home and shelter editorial, retailer and reference pages, community forums and discussion threads, and brand-owned content.
Important framing — please read. This index measures AI citation share for communications and marketing strategy purposes only. It is not an assessment of monitoring quality, security effectiveness, equipment reliability, or home safety, and it does not rank brands on how well they protect a home. A brand's position reflects how often AI engines surface it in response to consumer prompts — not a 5W judgment of its product. Any security purchase decision should involve independent research suited to the specific home.
The Full Ranking
## The Top 25 home security brands, ranked by AI citation share.
#BrandCategoryAI VisibilityNotable
1RingDIY / videoCategory-dominantAmazon-owned; the video doorbell that became shorthand for the category and the default citation for "home security camera."
2SimpliSafeDIY monitoredDIY co-leaderPrivately held; the most-reviewed no-contract DIY system; owns much of the "best DIY home security" citation surface.
3ADTPro monitoredLegacy leaderLargest U.S. home security company; founded 1874; a Google partnership and a scale built on dealers and contracts.
4VivintSmart-home monitoredSmart-home leaderOwned by NRG Energy; the largest smart-home security platform; professionally installed and app-controlled.
5Google NestSmart-home / camerasIntegration leaderAlphabet; cameras, doorbells, and the smart-home hub buyers ask AI to tie together.
6ArloWireless camerasCamera specialistWire-free camera pioneer; cited heavily across security camera and doorbell comparisons.
7WyzeBudget smart homeValue leaderLow-cost cameras and sensors; owns much of the "cheap home security" citation surface.
8BlinkBudget camerasValue-camera nicheAmazon-owned; affordable battery-powered cameras; cited alongside Wyze in budget queries.
9EufyLocal-storage camerasPrivacy-camera nicheAnker brand; local, subscription-free storage; cited by privacy-minded and no-monthly-fee buyers.
10AbodeDIY smart homeDIY-integration nichePrivately held; a DIY system with deep smart-home compatibility; an enthusiast favorite.
11FrontpointMonitored DIYMonitored-DIY nichePrivately held; DIY install paired with professional monitoring; cited in no-technician monitored queries.
12Brinks HomePro monitoredLegacy-monitored nicheA long-standing professionally monitored provider; cited as a direct ADT alternative.
13CoveDIY monitoredValue-monitored nichePrivately held; low-cost monitored DIY; cited for apartments and budget monitored setups.
14LorexWired camerasCamera-system nicheWired and PoE camera systems; cited by buyers wanting local recording and no monthly fee.
15Xfinity HomeCable bundleBundle nicheComcast; security bundled with internet service; cited in provider-bundle queries.
16Deep SentinelAI-monitored camerasActive-deterrence nicheLive-guard AI camera monitoring; cited in the emerging proactive-deterrence query.
17KangarooBudget DIYRenter-budget nicheLow-cost sensors and cameras; cited for renters and entry-level budgets.
18Alarm.comMonitoring platformInfrastructure nichePowers monitoring for many dealers; cited as the technology layer behind other brands more than as a consumer brand.
19ReolinkValue camerasValue-camera nicheSubscription-free wired and wireless cameras; cited by DIY camera buyers avoiding fees.
20Honeywell HomeSensors / panelsLegacy-component nicheLong-standing security hardware; cited more for components and panels than for full systems.
21AqaraSmart-home sensorsIntegration nicheMatter and Apple Home sensors; cited in smart-home security integration queries.
22SwannDIY camerasCamera-value nicheAffordable DVR and NVR camera kits; cited in budget camera comparisons.
23EcobeeSmart-home / sensorsSmart-home-crossover nicheA thermostat brand with security sensors and cameras; cited in integrated smart-home queries.
24TP-Link TapoBudget camerasEntry-budget nicheVery low-cost cameras; cited at the budget boundary of the category.
25YaleSmart locksAccess-control nicheSmart locks and entry hardware; cited where home security overlaps with access control.
Winners
## The brands winning the AI answer.
### ▲ Ring — Owning "Home Security Camera"
Amazon's distribution plus the doorbell that became a verb. When a buyer asks an AI engine for a security camera or a video doorbell, Ring is the reflex answer — the brand the engines reach for before any qualifier narrows the field.
### ▲ SimpliSafe — Owning "Best DIY System"
Years of consistent review coverage — no contract, easy install, transparent pricing — gave the engines a clear, repeatable reason to name SimpliSafe whenever "DIY" enters the query. A specific question, owned outright.
### ▲ Wyze & Blink — Owning "Budget"
The two brands split the "cheap home security" surface between them. When price leads the question, the engines cite one or both automatically — a low-cost position made into a citation lane.
### ▲ Google Nest — Owning "Smart-Home Integration"
When the question is about tying cameras, locks, and a hub into one system, Nest is the brand the engines retrieve. Integration is its qualifier, and it owns it.
Falling Behind
## The brands AI is leaving behind.
### ▼ ADT — The Largest Company, Not the Cited One
ADT leads the U.S. market by customers, but its growth was built on door-to-door sales, dealer networks, and multi-year contracts — channels the engines cannot retrieve. Scale without a review footprint underindexes against the brand's real size.
### ▼ Vivint — Capable System, Thin Review Surface
Vivint's professionally installed smart-home platform is well-regarded, but a sales-led model generates fewer of the independent, comparative reviews the engines weigh most heavily.
### ▼ The Legacy Component Brands
Names known for sensors, panels, and hardware are cited for parts, not systems. A components reputation does not convert into a "best home security system" citation.
### ▼ The Undifferentiated Camera Brands
Camera makers without a clear price, privacy, or integration position blur together in AI answers. The engines retrieve the brand with a distinct story and skip the rest of the tier.
Structural Findings
## Six structural truths about AI visibility in home security.
01
### DIY brands own the answer.
Ring and SimpliSafe — both built in the review economy — lead the citation surface over the legacy monitored incumbents.
02
### The install model splits the category.
"Best DIY system" and "best professionally monitored" route to different brands. The engines sort by how the buyer wants to install.
03
### Door-to-door scale is invisible.
Citations come from reviews, editorial, and forums — not dealer networks, lawn signs, or monitoring contracts.
04
### The doorbell is a citation anchor.
A single ubiquitous product — Ring's video doorbell — gives the engines a durable hook for the entire brand.
05
### The subscription model shapes the answer.
"No monthly fee" is a query of its own, routing the answer to local-storage and no-contract brands.
06
### Smart-home integration is its own surface.
Buyers asking AI to unify cameras, locks, and sensors get the integration brands — not the alarm companies.
Findings Specific to 2026
## Six 2026 dynamics reshaping home security AI citations in real time.
01
### The privacy narrative is reshaping camera citations.
Questions about footage storage and data handling push answers toward local-storage, subscription-free brands.
02
### Smart-home standards are entering answers.
Matter and cross-device compatibility are increasingly part of how the engines describe a complete system.
03
### AI-monitored deterrence is a new query.
Live-guard and AI-camera monitoring is emerging as its own citation surface, distinct from traditional alarms.
04
### Subscription fatigue favors no-fee brands.
As buyers ask AI how to avoid monthly costs, subscription-free camera brands gain citation share.
05
### Big Tech ownership concentrates the answer.
Amazon — Ring and Blink — and Google — Nest — together hold a large share of the camera citation surface.
06
### Contract-free expectations are now the default.
The engines increasingly frame long monitoring contracts as a drawback, shifting citations toward no-contract brands.
The Playbook
## General tips for home security marketing teams.
- Audit category citation share quarterly. Home security citations move with product launches, review cycles, and privacy news.
- Decide which query you intend to own. DIY, monitored, budget, integration, no-fee — pick the question you can credibly win.
- Anchor the brand to a defining product. A single recognizable product gives the engines a durable citation hook.
- Earn independent review coverage. Technology and consumer-product review sites feed home security citations disproportionately.
- Keep reference and spec sources accurate. Pricing, contract terms, and feature data are heavily retrieved — errors propagate.
- Address the subscription question directly. "Monthly fee" is a query — answer it in retrievable content rather than burying it.
- Engage the privacy narrative. Footage storage and data handling now surface in answers — speak to it openly.
- Build a clear position relative to the DIY default. "The monitored option," "the budget pick," "the privacy choice" are ownable lanes.
- Treat smart-home compatibility as content. Integration claims earn citations only when published in a retrievable, current form.
- Re-audit after every major launch, price change, or privacy story. Each materially reshapes the citation surface.
The Bigger Picture
## The home security decision now starts in the answer engine.
For more than a century, the alarm company reached the homeowner through a salesperson at the door, a sticker on the window, and a sign on the lawn. Those still exist. But a growing share of buyers now begin with a question typed into an AI engine — and the answer they receive frames the entire consideration set before any salesperson knocks.
The Index shows what that surface rewards: a defining product, a deep record of independent reviews, and a clear position on install model and monthly cost. ADT's scale was built in a channel the engines cannot see. Ring's was built in exactly the one they read — and the ranking follows the reading, not the revenue.
AI citation share is the scoreboard. In home security, the brand AI names is the brand the review economy made the answer — not the brand with the most customers.
Frequently Asked Questions
## The Home Security AI Visibility Index 2026.
What is the Home Security AI Visibility Index 2026?A research report by 5W that ranks the top 25 home security brands by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ consumer prompts tracked in Q2 2026.
Which home security brand holds the highest AI citation share?Ring, with an estimated 16% — the default return for home security camera and video doorbell queries.
Is ADT the most-cited home security brand?No. ADT is the largest U.S. home security company by customers, but it ranks third in AI citation share. The DIY brands Ring and SimpliSafe lead the citation surface, because their growth was built in the review economy the engines retrieve from.
Is this index a rating of security effectiveness?No. It measures AI citation share for communications strategy purposes only and is not an assessment of monitoring quality, security effectiveness, equipment reliability, or home safety. A brand's rank reflects how often AI engines surface it — not how well it protects a home.
What is citation share?The estimated proportion of brand mentions a company receives across all tracked prompts and AI engines — the core metric used to rank brands in the Index.
What other industries has the AI Visibility Index Series covered?Recent editions include Functional Beverages, Cookware, Luxury Watches, Beauty, Golf Equipment, and Grocery Retail.
### About this report
5W AI Visibility Index: Home Security. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ consumer queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report is not security, monitoring, or safety advice and does not rank brands on security effectiveness or equipment reliability.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
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### Sydney Sweeney's Novig Ad: Who Actually Cashed In
URL: https://www.5wpr.com/research/sydney-sweeney-novig-ad/
Summary: Sydney Sweeney's Novig ad drove a ninefold jump in brand mentions, but Peak Metrics data shows only 4 percent of posts named the app rather than Sweeney.
5W Research
# Sydney Sweeney's Novig Ad: Who Actually Cashed In
Sydney Sweeney is a strategic partner and equity holder in Novig, not just the face of its ad. The data shows the company's brand awareness surged, and Sweeney absorbed the personal reputational cost.
Sydney Sweeney is a strategic partner and equity holder in Novig, the sports prediction app, not just the paid face of its ad campaign. Novig's brand mentions surged more than ninefold after the ad launched, according to Peak Metrics data reported by a September 2026 report. Only 4% of posts named the app itself. Novig gained the audience. Sweeney absorbed the personal cost.
## Did Novig's Bet on Sydney Sweeney Pay Off?
By the numbers Novig tracks, yes. Peak Metrics found the company's mentions across X, Instagram, TikTok, Reddit, Threads, and Bluesky jumped more than ninefold after the campaign launched, per a report citing Business Insider published September 15, 2026. A separate analysis by Clayton Durant of CAD Management, reported by Inc. on September 14, 2026, found average weekly brand mentions climb from 392 to 4,633 in the four days after launch. Estimated potential reach rose from 786.6 million to 3.98 billion over the same window. Two firms, two methods, the same direction.
Why it works: social platforms rank engagement velocity, not sentiment, when they decide what to amplify further. A sudden spike in mentions, whether the posts are critical or supportive, tells the platform's algorithm the topic is active right now, so it surfaces the content to more feeds. That mechanic is what let Novig, a company founded in 2021, generate a nine times increase in visibility inside days, per the Peak Metrics data cited above.
## Why Did the Backlash Focus on Sweeney, Not Novig?
Peak Metrics found only 4% of the social posts about the campaign mentioned the Novig app itself. The other 96% named Sweeney directly: her body, her choices, her history of provocative campaigns. Novig grew its name recognition. Sweeney personally carried the reputational weight of a campaign she helped design.
Four-time Olympic gold medalist Ariarne Titmus said on Instagram she was "infuriated" by the ad. JOAN London strategist Nicky Vita called the creative execution "a bit lazy," while telling reporters that sustained conversation days after launch is, from a pure awareness standpoint, a good outcome for a company most people had never heard of.
## What Does Novig's CEO Say About the Trade-Off?
Novig CEO Jacob Fortinsky told Front Office Sports on September 15, 2026 that the campaign accounted for the bulk of Novig's marketing budget ahead of NFL season. Fortinsky said Sweeney approached the company about the campaign and served as its creative lead. He said the company anticipated that people who already disliked Sweeney would be vocal, and that the team has been happy with how positive the overall feedback has been.
Novig raised $75 million in a Series B round earlier in 2026 at a $500 million valuation. That funding context matters: this was the company's largest marketing commitment to date, placed with a partner who also owns a piece of the outcome.
## What Do AI Chatbots Say About the Partnership?
5W ran a six prompt test across four AI engines, ChatGPT, Claude, Gemini, and Perplexity, in a single live pass on September 15, 2026. All four engines named Sweeney's equity stake in Novig without being asked to look for it specifically. All four said the athlete led criticism of the ad has real substance. All four expect no lasting career damage a year out, citing her existing film and streaming work as a buffer. This AI layer is a secondary, corroborating measurement. The verified business data above is the primary evidence for this study.
## What Should Brands Take From the Novig Campaign?
Brands structuring equity-plus-endorsement deals should treat this as a live case study in where attention actually lands. Novig's own data shows the audience grew around the person fronting the campaign, not the product she is invested in, even though she holds a direct financial stake in that product's success. A deal structure that assumes attention transfers evenly between talent and brand does not match what Peak Metrics measured here.
Talent weighing equity stakes in provocative campaigns face the same asymmetry from the other side. The commercial upside is measurable and real, at roughly nine times the baseline mention volume. The personal attention absorption is just as measurable, at 96% of the conversation. Both numbers belong in the negotiation before a campaign like this launches, not in the analysis after.
## Methodology
### Sources referenced
- Peak Metrics social listening data, reported via Business Insider, September 15, 2026: Sydney Sweeney's Nude Ad Campaign Is a Huge Win for Novig, Despite Backlash
- Clayton Durant / CAD Management brand mention analysis, Inc., September 14, 2026: Sydney Sweeney's Novig Ad Shows Attention Isn't the Same as Good Marketing
- Novig CEO Jacob Fortinsky, Front Office Sports, September 15, 2026: Novig CEO Says Sydney Sweeney Drove Visuals of Nude Ad
This is a rapid turnaround special study, not a peer reviewed dataset. Business figures are as reported by the named sources above and should be independently verified before further citation. The AI layer reflects a single test pass on one day; AI generated answers vary by user, timing, and phrasing, and a repeat run may not reproduce identical results.
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### The GENIUS Act Split the Stablecoin Market. AI Already Took a Side
URL: https://www.5wpr.com/ai-visibility-index/stablecoin-trust-index-2026/
Summary: The GENIUS Act split the stablecoin market, and AI already took a side. We rank stablecoin brands by citation share across five major engines.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W AI Visibility IndexStablecoins
# The GENIUS Act Split the Stablecoin Market. AI Already Took a Side
The first US stablecoin law drew a hard line between regulated tokens and the rest. New 5W research finds the AI engines have already absorbed it — describing some stablecoins as compliant and safe, hedging on others, and warning on the rest — and scores 25 by how AI now talks about them.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 stablecoinsData window: Q2 2026
The Headlines
01
AI took the regulator's side.
The engines describe GENIUS Act-aligned tokens as safe and qualify the rest — they have absorbed the law.
02
The biggest is not the trusted one.
USDT leads the market by capitalization. AI leads with USDC — and hedges hard on the bigger token.
03
Algorithmic is a warning word.
Years after Terra, the engines still flag algorithmic stablecoins as the category's cautionary class.
No.1
USDC — the stablecoin AI engines most consistently describe as regulated and safe
~$320B
Total stablecoin market capitalization in 2026
~$189B
USDT market cap — the largest stablecoin, which AI still hedges on
Jul 2026
GENIUS Act implementation-rule deadline — the date the engines now anchor to
Figure 1 · The Ranking
## Who AI trusts.
TOP 15 STABLECOINS BY AI TRUST SCORE · Q2 2026
01USDCCircle / fiat-backed95
02PYUSDPayPal / Paxos87
03USDP (Pax Dollar)Paxos / fiat-backed82
04RLUSDRipple / fiat-backed77
05USAₜAnchorage / bank-issued73
06USDGGlobal Dollar / Paxos69
07EURCCircle / euro-backed66
08JPM Coin / KinexysJPMorgan / bank63
09TUSD (TrueUSD)Fiat-backed56
10USDT (Tether)Fiat-backed / offshore52
11FDUSDFirst Digital / fiat-backed46
12USDS (Sky)Crypto-collateralized43
13DAISky / crypto-collateralized40
14USDeEthena / synthetic32
15crvUSDCurve / crypto-collateralized27
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. AI Trust Score is a 0–100 score reflecting how the engines describe a stablecoin when asked whether it is safe or regulated — compliant, hedged, or warned — across 60+ tracked prompts. The full 25 are ranked in the table below, including tokens the engines actively warn against.
The Central Finding
## A law drew the line. AI already enforces it.
The GENIUS Act — the first federal stablecoin law in US history — did one decisive thing: it drew a hard line between stablecoins that are fully reserved, audited, and licensed, and those that are not. The 5W Stablecoin Trust Index finds that the AI engines have already absorbed that line and now answer along it.
Ask ChatGPT, Claude, or Perplexity whether a stablecoin is safe and the engines no longer give a flat description. They give a regulatory verdict. Tokens aligned with the GENIUS Act — USDC, PYUSD, the Paxos-issued and bank-issued names — are described as regulated, audited, compliance-first. Tokens outside the perimeter are hedged. And algorithmic or synthetic designs are warned against, with Terra still cited as the reason.
The starkest result is the gap between size and trust. USDT is the largest stablecoin in the world — roughly $189 billion in market capitalization, the dominant liquidity token across global trading. AI ranks it tenth. The engines acknowledge its scale, then immediately qualify it: offshore structure, an audit history they describe as contested, a position outside the GENIUS Act's perimeter. USDC, smaller, is the consistent first answer — because the engines weight what a regulator can verify above what the market has simply gotten used to. In stablecoins, AI is not measuring dominance. It is measuring disclosure.
— 5W Research, May 2026
"A law passed, and within months the AI engines were enforcing its logic in every answer. That is the story communicators need to sit with: regulation does not just change the rules, it rewrites what the machine says about you. The largest stablecoin on earth now gets hedged in the answer because it sits outside the perimeter. The smaller, audited one gets recommended. Size did not protect the incumbent — disclosure protected the challenger. Every regulated category is about to learn this. The engines read the law faster than the market does."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we *measured it.*
5W analyzed more than 60 common prompts across six question types, running each five times per engine in clean sessions across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. For each stablecoin we recorded whether the engines described it as compliant and safe, hedged on it, or warned against it — and which sources and signals fed that stance.
#### Safety & backing
"Is [X] safe," "is [X] fully backed," "what backs [X]."
#### Regulatory status
"Is [X] regulated," "is [X] GENIUS Act compliant," "is [X] audited."
#### Direct comparison
"USDC vs USDT," "which stablecoin is safest," "best stablecoin to hold."
#### Use & suitability
"Which stablecoin for payments," "best stablecoin for savings," "which to avoid."
#### Risk & depeg history
"Can [X] depeg," "is [X] a scam," "what happens if [X] fails."
#### Design class recall
How the engines treat algorithmic and synthetic designs, and whether Terra is still cited.
The metric. AI Trust Score is a 0–100 score. It reflects the stance the engines take toward a stablecoin — compliant, hedge, or warn — weighted across all tracked prompts and engines. It is a measure of how AI describes a token, not a measure of its reserves, solvency, or peg stability.
Stance bands. Compliant — described as regulated, audited, and safe. Hedge — described with conditions, caveats, or regulatory-uncertainty framing. Warn — described with an explicit caution, or named only as a cautionary example.
Important framing — please read. This report measures how AI engines describe stablecoins, for communications and reputation-strategy purposes only. It is not investment, trading, or financial advice, not a reserve, audit, or solvency assessment, and not an endorsement of any token or issuer. Stablecoins carry risk, including the risk of losing their peg. Regulatory status described here reflects how the engines characterize each token and may not match any issuer's formal legal status. AI outputs are volatile and can shift within weeks; the Index is revised quarterly.
The Full Ranking
## 25 stablecoins, ranked by AI Trust Score.
#StablecoinTypeAI StanceTrust ScoreWhat AI says
1USDCFiat-backedCompliant95The default "safe stablecoin" answer; engines cite reserve attestations, Circle's compliance posture, and GENIUS Act alignment.
2PYUSDFiat-backedCompliant87PayPal-branded, Paxos-issued; engines extend trust from the PayPal name and regulated-issuer structure.
3USDP (Pax Dollar)Fiat-backedCompliant82Paxos-issued under NYDFS oversight; engines describe it as one of the most strictly regulated stablecoins.
4RLUSDFiat-backedCompliant77Ripple's USD stablecoin; engines cite its regulated-trust structure and reserve disclosure.
5USAₜBank-issuedCompliant73Issued via a nationally chartered US bank; engines frame it as the GENIUS Act-compliant route into Tether's ecosystem.
6USDG (Global Dollar)Fiat-backedCompliant69Paxos-issued consortium stablecoin; engines describe it as regulated and disclosure-aligned.
7EURCEuro-backedCompliant66Circle's euro stablecoin; engines cite MiCA alignment and reserve transparency.
8JPM Coin / KinexysBank-issuedCompliant63JPMorgan's permissioned settlement token; engines treat it as institutional, low-risk, and bank-grade.
9TUSD (TrueUSD)Fiat-backedHedge56Hedged; engines note fiat backing but cite past reserve-transparency questions and a thinner compliance profile.
10USDT (Tether)Fiat-backed / offshoreHedge52The largest stablecoin by market cap; engines cite scale and liquidity but hedge on offshore structure and audit history.
11FDUSDFiat-backedHedge46Hedged; engines note exchange-linked usage and a limited US regulatory footprint.
12USDS (Sky)Crypto-collateralizedHedge43Hedged; engines describe the rebranded Sky token as functional but outside the fiat-backed regulatory model.
13DAICrypto-collateralizedHedge40Hedged; engines respect its track record but flag that crypto-collateralized designs fit no clean regulatory category.
14USDeSyntheticHedge32Hedged toward caution; engines describe Ethena's synthetic-dollar mechanism as higher-risk and non-traditional.
15crvUSDCrypto-collateralizedHedge27Hedged; engines describe it as a DeFi-native token for experienced users, not a general-purpose hold.
16GHOCrypto-collateralizedHedge24Hedged; engines treat the Aave-native stablecoin as DeFi infrastructure rather than a mainstream option.
17FRAXHybridHedge21Hedged with caution; engines note its partly-algorithmic history and advise care for non-expert holders.
18USDDAlgorithmic / hybridWarn16Engines caution buyers; the algorithmic-leaning design draws explicit comparison to past failures.
19First Digital incidentsDepeg episodeWarn13Where queries reference past depeg events, engines surface them as evidence that "stable" is not guaranteed.
20Generic algorithmic stablecoinsAlgorithmicWarn9Engines warn on the design class itself, regardless of token, citing the absence of hard reserves.
21BUSDWound-down stablecoinWarn7Surfaced as a cautionary case; engines cite its regulator-ordered wind-down as a discontinuation example.
22USDRCollapsed stablecoinWarn5Named only as a failed token; engines cite its depeg as an asset-backing cautionary tale.
23IRON / TITANCollapsed algorithmicWarn4Surfaced only as an early algorithmic-stablecoin collapse; never a recommendation.
24TerraUSD (UST)Collapsed algorithmicWarn3The reference case for algorithmic-stablecoin risk; engines cite it as the defining warning, never an option.
25USTC (Terra Classic)Collapsed algorithmicWarn2The post-collapse remnant of UST; surfaced by engines purely as a cautionary artifact.
Stances. Compliant — engines describe the token as regulated, audited, and safe. Hedge — described with conditions, caveats, or regulatory-uncertainty framing. Warn — described with an explicit caution, or named only as a cautionary example. Collapsed and wound-down tokens are included to show how AI carries failure history forward.
The Three Stances
## Compliant, hedge, warn.
Every token in the Index falls into one of three stances the engines take. The stance — not the score alone — is what a user actually receives in the answer.
StanceWhat the user hearsWhat earns it
Compliant"Regulated and safe." The token is named first, framed as audited and GENIUS Act-aligned.Full 1:1 fiat reserves, regular attestations or audits, a licensed or bank-issued structure, US or MiCA regulatory alignment.
Hedge"Widely used, but —." The token is named, then qualified with caveats about structure or oversight.Real adoption and liquidity paired with an offshore structure, contested disclosure, or a design that fits no clean regulatory category.
Warn"Be careful," or named only as a failure. The token becomes a cautionary reference.An algorithmic or synthetic design without hard reserves — or a documented depeg, collapse, or regulator-ordered wind-down.
The rule. The engines move a stablecoin up by replacing trust-me claims with verifiable disclosure. Reserves, audits, and a recognized regulatory structure are what convert a hedge into a compliant verdict.
Winners
## The stablecoins AI calls safe.
### ▲ USDC — Compliance as a Brand
Circle built USDC's identity around being the regulated, audited stablecoin — and the engines now repeat that framing back almost verbatim. Reserve attestations and GENIUS Act alignment give AI a concrete, citable reason to lead with it.
### ▲ PYUSD — The Borrowed-Trust Token
PYUSD inherits the PayPal name and a Paxos regulated-issuer structure. For a mainstream user, the engines surface it as a familiar, low-friction safe option — trust extended from a brand the public already knows.
### ▲ The Paxos Family — Owning "Strictly Regulated"
USDP and USDG share an issuer the engines describe as one of the most tightly supervised in the category. Paxos turned regulatory rigor into a citation asset across every token it touches.
### ▲ The Bank-Issued Entrants — Inheriting the Charter
USAₜ and JPMorgan's settlement token arrive with bank-grade oversight built in. The engines extend traditional-finance trust to them on day one — the GENIUS Act's bank pathway, working exactly as written.
Hedged & Warned
## The stablecoins AI is cautious about.
### ▼ USDT — Biggest, But Hedged
Tether is the largest stablecoin on earth, and the engines say so — then qualify it immediately. An offshore structure and an audit history the engines describe as contested keep it out of the compliant tier, whatever its scale.
### ▼ The Crypto-Collateralized Tokens
DAI, USDS, and peers have real track records, but the engines hedge: a design backed by crypto rather than fiat fits no clean regulatory box, and AI flags that uncertainty rather than resolving it.
### ▼ The Synthetic and Hybrid Designs
USDe, FRAX, and similar tokens draw caution for mechanisms the engines describe as non-traditional and higher-risk. Innovation, in the answer, reads as a reason to hedge.
### ▼ Terra, BUSD, IRON — The Permanent Warnings
Collapsed and wound-down tokens never leave the answer. The engines surface them as the standing evidence that "stable" is a claim, not a guarantee — TerraUSD above all.
Structural Findings
## Six structural truths about AI trust in stablecoins.
01
### AI enforces the law's logic.
The engines absorbed the GENIUS Act's line and now answer along it — compliant, hedged, or warned.
02
### Disclosure outranks size.
The largest token by market cap is hedged; the audited challenger is the recommendation.
03
### Algorithmic is a warning word.
The engines caution on the design class itself, citing Terra regardless of the specific token.
04
### A reserve attestation is citable.
The engines reward audits and 1:1 backing — verifiable facts, not "fully backed" assurances.
05
### Bank and brand trust transfers.
Bank-issued and major-brand stablecoins inherit credibility the engines extend without re-litigating.
06
### Failure history is permanent.
Collapsed and discontinued tokens stay in the answer for years, as standing cautionary examples.
Findings Specific to 2026
## Six 2026 dynamics reshaping stablecoin AI trust in real time.
01
### The July implementation deadline looms.
The GENIUS Act rule deadline is a date the engines increasingly anchor their answers to.
02
### Bank-issued stablecoins are entering the answer.
New bank-issued tokens arrive with inherited regulatory trust the engines extend immediately.
03
### Tether's US strategy is the open question.
How the engines describe USDT shifts with each step in its US compliance posture.
04
### The yield rules are reshaping framing.
New limits on stablecoin yield are changing how the engines describe rewards and "earning."
05
### Capital is rotating toward compliant tokens.
The engines increasingly describe a market shifting from offshore to onshore, regulated supply.
06
### DeFi-native stablecoins sit in limbo.
Crypto-collateralized designs fit no clean category, and the engines keep flagging the uncertainty.
The Playbook
## General tips for stablecoin communications teams.
- Audit your AI stance first. Ask the five engines if your token is safe and regulated — know whether you are compliant, hedged, or warned before anything else.
- Lead with verifiable disclosure. Reserve attestations, audits, and 1:1 backing are the citable facts that move a token out of the hedge tier.
- Name your regulatory structure plainly. Licensed issuer, bank charter, MiCA or GENIUS Act alignment — make the status explicit and retrievable.
- Do not let "fully backed" stand alone. The engines cite proof, not assurances; an unverified claim reads as a reason to hedge.
- Separate your design from the failed class. If an algorithmic or synthetic mechanism drags your stance down, make the engines distinguish your model and its safeguards.
- Treat a past depeg as a content problem. An unaddressed incident hardens into a permanent warning; a clearly documented recovery and fix can soften it.
- Earn coverage in the sources the engines trust. Regulatory filings, established financial media, and reputable analysis shape the stance.
- Keep regulatory and reserve data current. Stale facts about licensing and backing propagate straight into the answer.
- Watch the comparison queries. "USDC vs [you]" is where the trust verdict is delivered — be accurately represented in it.
- Re-audit quarterly. Stablecoin regulation is moving fast; rule deadlines and new entrants reshape the answer in weeks.
The Bigger Picture
## When the law moves, the answer moves with it.
The Stablecoin Trust Index is, on its surface, a story about one corner of crypto. Underneath, it is a preview of something every regulated industry is about to face. A major law passed — and within months, the AI engines were not just aware of it, they were enforcing its logic in ordinary answers to ordinary questions.
That is the shift worth absorbing. Regulation no longer just sets the rules a company operates under. It rewrites, fast, what the answer engines say about that company to every customer who asks. The stablecoin issuers the engines now call safe are the ones that made their compliance verifiable. The ones being hedged are not necessarily worse products — they have given the engine less it can stand behind.
AI Trust Score is the scoreboard. In stablecoins, the token AI recommends is the one whose safety a regulator — and therefore an engine — can actually verify. Every regulated category is next.
Frequently Asked Questions
## The Stablecoin Trust Index 2026.
What is the Stablecoin Trust Index 2026?A research report by 5W that scores how AI engines describe stablecoins after the GENIUS Act — ranking 25 stablecoins by AI Trust Score across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ prompts tracked in Q2 2026.
Which stablecoin does AI trust most?USDC holds the highest AI Trust Score. The engines most consistently describe it as the regulated, audited, compliance-first stablecoin, citing its reserve attestations and alignment with the GENIUS Act.
How does AI describe Tether after the GENIUS Act?The engines hedge on USDT. They acknowledge it as the largest stablecoin by market capitalization, then qualify it with caveats about its offshore structure and its position outside the GENIUS Act's audit perimeter.
Why does the biggest stablecoin rank only tenth?The engines weight regulatory status and verifiable disclosure above market size. USDT's scale is acknowledged, but an offshore structure and contested audit history place it in the hedge tier rather than the compliant tier.
Is this index investment or financial advice?No. It measures how AI engines describe stablecoins, for communications and reputation purposes only. It is not investment, trading, or financial advice, not a reserve or solvency audit, and not an endorsement of any token or issuer. Stablecoins carry risk, including the risk of losing their peg.
What is an AI Trust Score?A 5W score, 0–100, for how the engines treat a stablecoin when asked if it is safe or regulated — whether they describe it as compliant, hedge on it, or warn against it — across all tracked prompts and engines.
### About this report
The Stablecoin Trust Index 2026. An installment in the 5W AI Visibility Index series. Published May 2026. The Index measures how AI engines — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — describe stablecoins when a user asks whether they are safe or regulated, across 60+ prompts tracked in Q2 2026, each run five times per engine. AI Trust Scores are 5W directional estimates of how the engines treat each token, based on Claude knowledge and web search — not precise measurements, not a reserve, audit, or solvency assessment. Regulatory characterizations reflect how the engines describe each token and may not match any issuer's formal legal status. AI outputs are volatile and can shift within weeks; the Index is revised quarterly. This report is not investment, trading, or financial advice and is not an endorsement of any token or issuer.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
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### Multivitamins AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/multivitamins-ai-visibility-index-2026/
Summary: Multivitamin AI visibility data shows Thorne leads measured mentions while health publishers receive far more citations than brand-owned sites.
# Multivitamins AI Visibility Index 2026
Thorne Leads Multivitamin AI Mentions
Thorne led the measured multivitamin results, appearing in 56.9% of 288 answers returned across Perplexity, ChatGPT, Gemini, and Claude. Garden of Life, Nature Made, Ritual, and Pure Encapsulations followed, but Thorne's 95% interval overlaps each of their intervals through Pure Encapsulations. The result describes 24 measured prompts on September 30, 2026, not all multivitamin searches.
Owned domains received a small share of citations compared with health publishers and comparison content. Nature Made had the highest owned-domain citation rate at 10.4% of answers with retrieval evidence, while Healthline appeared as a cited source in 62.5% of those answers. This Multivitamins report is one segment of the Supplements AI Visibility Index 2026.
288AI answers analyzed
56.9%Thorne appearance rate, the category leader
25Brands named across the study
100%Answers carrying retrieval evidence
For multivitamin marketers, being named and being cited are different outcomes. A brand can appear often in recommendations while its own site is cited infrequently. That distinction matters when teams decide which pages, product facts, and third-party verification details need clearer documentation.
## Which multivitamin brands lead the leaderboard?
Thorne ranks first with a 56.9% appearance rate, based on answers returned. Garden of Life is second at 50.7%, and Nature Made is third at 50%. The leading group appears across all four engines, although the rates vary sharply by engine.
The leaderboard measures brand appearance in answers returned, not citation frequency. It includes brands and named verification bodies detected in the measured answers. The separate citation analysis below uses answers with retrieval evidence.
### Multivitamin appearance leaderboard
### AI visibility leaderboard
Appearance rate measures the share of the 288 returned AI answers that named each brand.
#BrandNamed inAppearance rate95% intervalAvg position
1Thorne164/28856.9%51.2%–62.5%3
2Garden of Life146/28850.7%44.9%–56.4%5.3
3Nature Made144/28850%44.3%–55.7%4.7
4Ritual119/28841.3%35.8%–47.1%4.7
5Pure Encapsulations106/28836.8%31.4%–42.5%4.4
6MegaFood104/28836.1%30.8%–41.8%6.1
7Centrum86/28829.9%24.9%–35.4%6.8
8USP63/28821.9%17.5%–27%7.6
9NOW63/28821.9%17.5%–27%8
10SmartyPants60/28820.8%16.5%–25.9%6.8
11One A Day59/28820.5%16.2%–25.5%7.7
12Kirkland Signature58/28820.1%15.9%–25.1%6.4
13Life Extension52/28818.1%14%–22.9%5.3
14New Chapter41/28814.2%10.7%–18.7%5.6
15NSF39/28813.5%10.1%–18%9.5
Thorne's 95% interval of 51.2% to 62.5% overlaps Garden of Life's 44.9% to 56.4%, Nature Made's 44.3% to 55.7%, Ritual's 35.8% to 47.1%, and Pure Encapsulations' 31.4% to 42.5%. The sample does not establish that Thorne is more visible than those brands. It does place Thorne at the top of this measured leaderboard.
## How did the leading brands perform by engine?
The leading multivitamin brands had different strongest engines. ChatGPT named the first six brands more often than other engines, while Perplexity was strongest for Nature Made among the top three. The profiles use answer-returned appearance rates and each brand's average first-mention position. Owned-domain rates use answers with retrieval evidence.
### Thorne
56.9% appearance rate, with a 95% interval of 51.2% to 62.5%, places Thorne first. ChatGPT named Thorne in 81.9% of its 72 answers, compared with 69.4% for Perplexity, 41.7% for Gemini, and 34.7% for Claude. Thorne's average first-mention position was 3. Its owned domain was cited in 4.5% of answers with retrieval evidence.
### Garden of Life
Garden of Life ranks second with a 50.7% appearance rate and a 95% interval of 44.9% to 56.4%. It was named by ChatGPT in 91.7% of its 72 answers, Perplexity in 63.9%, Gemini in 30.6%, and Claude in 16.7%. Garden of Life had an average first-mention position of 5.3. Its owned domain appeared as a citation in 2.1% of answers with retrieval evidence.
### Nature Made
50% appearance rate, with a 95% interval of 44.3% to 55.7%, places Nature Made third. Perplexity named Nature Made in 72.2% of its 72 answers and ChatGPT in 76.4%, compared with 36.1% for Gemini and 15.3% for Claude. Nature Made's average first-mention position was 4.7. Its owned domain was cited in 10.4% of answers with retrieval evidence.
### Ritual
41.3% appearance rate, with a 95% interval of 35.8% to 47.1%, places Ritual fourth. ChatGPT named Ritual in 59.7% of its 72 answers, Perplexity in 48.6%, Gemini in 37.5%, and Claude in 19.4%. Ritual's average first-mention position was 4.7. Its owned domain was cited in 3.5% of answers with retrieval evidence.
### Pure Encapsulations
36.8% appearance rate, with a 95% interval of 31.4% to 42.5%, places Pure Encapsulations fifth. ChatGPT named Pure Encapsulations in 59.7% of its 72 answers, while Perplexity named it in 37.5%, Gemini in 31.9%, and Claude in 18.1%. Pure Encapsulations' average first-mention position was 4.4. Its owned domain was cited in 0.7% of answers with retrieval evidence.
### MegaFood
MegaFood ranks sixth, recording a 36.1% appearance rate with a 95% interval of 30.8% to 41.8%. ChatGPT named MegaFood in 61.1% of its 72 answers, followed by Gemini at 38.9%, Perplexity at 37.5%, and Claude at 6.9%. Its average first-mention position was 6.1. MegaFood's owned domain was cited in 2.1% of answers with retrieval evidence.
### Centrum
Centrum ranks seventh at a 29.9% appearance rate, with a 95% interval of 24.9% to 35.4%. ChatGPT named Centrum in 48.6% of its 72 answers, Perplexity in 37.5%, Gemini in 23.6%, and Claude in 9.7%. Centrum's mean first-mention position was 6.8. Its owned domain was cited in 1.7% of answers with retrieval evidence.
### USP
USP ranks eighth with a 21.9% appearance rate and a 95% interval of 17.5% to 27%. Gemini named USP in 59.7% of its 72 answers, compared with 25% for Perplexity, 2.8% for ChatGPT, and 0% for Claude. USP's average first-mention position was 7.6. Its owned domain was cited in 0% of answers with retrieval evidence.
### NOW
21.9% appearance rate, with a 95% interval of 17.5% to 27%, places NOW ninth. ChatGPT named NOW in 48.6% of its 72 answers, compared with 19.4% for Perplexity, 16.7% for Gemini, and 2.8% for Claude. NOW's average first-mention position was 8. Its owned domain was cited in 1% of answers with retrieval evidence.
### SmartyPants
SmartyPants ranks tenth, with a 20.8% appearance rate and a 95% interval of 16.5% to 25.9%. Perplexity named SmartyPants in 36.1% of its 72 answers, ChatGPT in 25%, Gemini in 18.1%, and Claude in 4.2%. Its average first-mention position was 6.8. SmartyPants' owned domain was cited in 1% of answers with retrieval evidence.
## Where do the engines disagree?
The engines disagree materially on which multivitamin brands and verification bodies they name. Each engine returned 72 answers, so the per-engine percentages have the same answer base. The gaps may reflect differences in model behavior and retrieval tools, not a single measure of underlying brand visibility.
### Engine-by-brand appearance rates
### Appearance rate by engine
Each cell is calculated using that engine's own answer count as the denominator, allowing row-by-row engine comparison.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Thorne69.4%81.9%41.7%34.7%
Garden of Life63.9%91.7%30.6%16.7%
Nature Made72.2%76.4%36.1%15.3%
Ritual48.6%59.7%37.5%19.4%
Pure Encapsulations37.5%59.7%31.9%18.1%
MegaFood37.5%61.1%38.9%6.9%
Centrum37.5%48.6%23.6%9.7%
USP25%2.8%59.7%0%
NOW19.4%48.6%16.7%2.8%
SmartyPants36.1%25%18.1%4.2%
One A Day36.1%25%15.3%5.6%
Kirkland Signature33.3%37.5%6.9%2.8%
ChatGPT gave the strongest appearance rates to Garden of Life at 91.7%, Thorne at 81.9%, Nature Made at 76.4%, MegaFood at 61.1%, and both Ritual and Pure Encapsulations at 59.7%. Claude named the same brands far less often: 16.7% for Garden of Life, 34.7% for Thorne, 15.3% for Nature Made, 6.9% for MegaFood, 19.4% for Ritual, and 18.1% for Pure Encapsulations.
Gemini had a different pattern for third-party verification entities. It named USP in 59.7% of its 72 answers and NSF in 31.9%, while Claude named neither. ChatGPT named USP in 2.8% and NSF in 1.4%. Clean Label Project was named in 23.6% of Gemini answers and 0% of the other three engines.
Some brands were concentrated in a single engine. Nature's Way appeared in 19.4% of Perplexity answers but 6.9% of ChatGPT answers, 1.4% of Gemini answers, and 0% of Claude answers. Transparent Labs appeared in 13.9% of Perplexity answers, compared with 1.4% in ChatGPT, 2.8% in Gemini, and 4.2% in Claude. NATURELO was comparatively balanced at 8.3% in Perplexity, 18.1% in ChatGPT, 8.3% in Gemini, and 12.5% in Claude.
## Which brands lead each demand cluster?
The demand-cluster results identify the brands leading each measured question group. General buyer-intent and more narrowly need-based questions represent distinct forms of demand, so their leaders should not be interpreted as a single combined ranking.
### Brand leaders by demand cluster
### Leaders by demand cluster
The answers are also regrouped by the demand cluster from which each prompt was sampled; rates use the answer count for that cluster as their denominator.
Demand clusterAnswersLeading brandLeader rateRunners-up
— Multivitamins —
best multivitamin for men12Thorne58.3%Pure Encapsulations 58.3%, MegaFood 58.3%, Garden of Life 41.7%
best multivitamin for women12Thorne83.3%Ritual 83.3%, Nature Made 75%, MegaFood 75%
best multivitamin for adults over 5012Thorne83.3%MegaFood 83.3%, Centrum 75%, Nature Made 66.7%
best gummy multivitamin12SmartyPants91.7%Nature Made 75%, OLLY 75%, Garden of Life 66.7%
best multivitamin for energy12Garden of Life50%Thorne 33.3%, Ritual 33.3%, Nature Made 25%
best multivitamin for vegans12Garden of Life75%NATURELO 75%, Ritual 58.3%, MegaFood 25%
best prenatal vitamin12Thorne66.7%Ritual 66.7%, MegaFood 66.7%, Nature Made 58.3%
best multivitamin without iron12Thorne66.7%Pure Encapsulations 50%, Nature Made 41.7%, Life Extension 33.3%
best multivitamin for teenagers12SmartyPants58.3%Garden of Life 25%, Nature Made 25%, Centrum 25%
best whole food multivitamin12Garden of Life83.3%New Chapter 83.3%, MegaFood 75%, NATURELO 50%
best multivitamin for hair skin and nails12Nature Made41.7%OLLY 33.3%, Pure Encapsulations 25%, MegaFood 25%
one a day vs centrum which is better12Centrum75%One A Day 58.3%, Nature Made 50%, Bayer 50%
best multivitamin for athletes12Legion75%Thorne 66.7%, Transparent Labs 50%, Garden of Life 41.7%
best multivitamin for seniors12Thorne41.7%Nature Made 41.7%, Centrum 41.7%, Pure Encapsulations 33.3%
— Other demand —
Beginners and first purchase12Thorne75%Nature Made 75%, Garden of Life 58.3%, Ritual 58.3%
General best-in-category12Thorne91.7%Ritual 91.7%, Pure Encapsulations 91.7%, Nature Made 75%
Price and value12Nature Made83.3%Centrum 75%, Thorne 66.7%, NOW 66.7%
Primary use case12Thorne75%Nature Made 66.7%, Ritual 66.7%, Garden of Life 58.3%
Everyday and mainstream choice12Thorne83.3%Ritual 83.3%, Garden of Life 66.7%, Pure Encapsulations 66.7%
Where to buy12Thorne58.3%Garden of Life 50%, Nature Made 50%, Pure Encapsulations 50%
Gifting and occasions12Thorne83.3%Garden of Life 83.3%, Ritual 83.3%, SmartyPants 66.7%
Specific need or condition12Thorne75%Nature Made 75%, Pure Encapsulations 75%, Garden of Life 58.3%
Comparison and evaluation12Thorne75%Garden of Life 66.7%, Nature Made 66.7%, Kirkland Signature 58.3%
Trust and recommendation12Thorne75%Nature Made 66.7%, Garden of Life 50%, Pure Encapsulations 50%
The cluster chart identifies where each brand appears most often within the measured prompts. It is useful for separating broad multivitamin selection visibility from visibility tied to a more specific need. The chart does not establish product quality, clinical suitability, or a consumer recommendation.
## Which sites receive citations in multivitamin answers?
Owned-domain citations were limited relative to cited publishers. Nature Made had the highest owned-domain citation rate at 10.4% of answers with retrieval evidence. Life Extension followed at 8.7%, then Thorne at 4.5% and NSF at 3.8%.
### Owned-domain citation rates
### Owned-domain citation rate
This is the share of the 288 answers with retrieval evidence that cited a URL on the brand's own website.
Thorne
4.5% · 13/288
Garden of Life
2.1% · 6/288
Nature Made
10.4% · 30/288
Ritual
3.5% · 10/288
Pure Encapsulations
0.7% · 2/288
MegaFood
2.1% · 6/288
Centrum
1.7% · 5/288
USP
0% · 0/288
NOW
1% · 3/288
SmartyPants
1% · 3/288
One A Day
1.7% · 5/288
Kirkland Signature
3.1% · 9/288
Owned-domain citation rates use 288 answers with retrieval evidence. A citation rate is not the same as an appearance rate. Nature Made was named in 50% of answers returned, while its domain was cited in 10.4% of answers with retrieval evidence. Brands should read these as separate measures of recommendation presence and source selection.
### Most cited publisher domains
### Most-cited publishers
Evidence publishers are counted once per answer, rather than once for every link.
PublisherAnswers citing itAnswer penetration
healthline.com180/28862.5%
consumerlab.com119/28841.3%
fortune.com117/28840.6%
medicalnewstoday.com102/28835.4%
forbes.com74/28825.7%
everydayhealth.com71/28824.7%
health.yahoo.com69/28824%
garagegymreviews.com57/28819.8%
amazon.com54/28818.8%
cnet.com44/28815.3%
health.usnews.com41/28814.2%
verifiedsupplementdata.com41/28814.2%
Healthline was cited in 62.5% of answers with retrieval evidence. ConsumerLab appeared in 41.3%, Fortune in 40.6%, and Medical News Today in 35.4%. Retailer and comparison-oriented domains also appeared, including Amazon in 18.8%, Walmart in 12.2%, and Target in 11.1%.
The measured source mix does not support a claim that owned brand sites carry the citation conversation. Health publishers, testing content, retail pages, and buying guides appeared more often. ConsumerLab's 41.3% answer penetration is relevant to the brief's focus on independent testing, but this study does not measure whether certification signals caused any brand's visibility.
## Who is winning and who is at risk?
### Brands with the strongest measured position
Thorne is first by appearance rate at 56.9%, with an average first-mention position of 3 and appearance on all four engines. Nature Made ranks third at 50%, but its owned-domain citation rate of 10.4% is the highest measured. Garden of Life ranks second at 50.7% and reached 91.7% in ChatGPT's 72 answers. These are measured advantages within this prompt set, subject to the overlapping intervals at the top of the leaderboard.
### Brands with measured exposure risk
OLLY had an 8.3% appearance rate and a 0% owned-domain citation rate among answers with retrieval evidence. Legion had a 6.6% appearance rate and a 0% owned-domain citation rate. Nature's Way reached 19.4% in Perplexity but had 0% in Claude, while Clean Label Project appeared only in Gemini at 23.6% and had a 5.9% overall appearance rate. These patterns leave visibility dependent on a narrow portion of the measured engine set.
## What does the tally say about multivitamin visibility?
The top of the multivitamin leaderboard is close within this sample. Thorne's interval overlaps the intervals of Garden of Life, Nature Made, Ritual, and Pure Encapsulations, so the data does not support a claim that Thorne is more visible than those brands. The leaderboard does show that the first six brands each appeared in more than one-third of answers returned.
Engine agreement is uneven. Thorne, Garden of Life, Nature Made, Ritual, Pure Encapsulations, MegaFood, Centrum, NOW, and SmartyPants all appeared across every engine. Yet their rates differed markedly, especially between ChatGPT and Claude. Verification bodies also diverged: USP and NSF had 75% cross-engine consistency, while Clean Label Project had 25%.
Citations leaned toward third-party publishers. Healthline, ConsumerLab, Fortune, and Medical News Today each had higher answer penetration than any measured owned-domain citation rate. The named brands requested in the brief were measured in this report: Centrum appeared at 29.9%, One A Day at 20.5%, Ritual at 41.3%, Garden of Life at 50.7%, Thorne at 56.9%, and Pure Encapsulations at 36.8%.
## What changed during the measurement window?
This measurement captured repeated responses, not a before-and-after series, so it cannot document category changes over time. It does show repeat-generation variation, including 49.3% repeat consistency for Thorne, 53% for Garden of Life, 50.8% for Nature Made, and 34.5% for Ritual.
## What should multivitamin brands do next?
- Separate brand appearance from citation performance. Nature Made appeared in 50% of answers returned and had a 10.4% owned-domain citation rate among answers with retrieval evidence. Re-measure both rates after publishing product pages that state formula, audience, ingredients, and verification information in plain language.
- Close engine-specific gaps. Garden of Life appeared in 91.7% of ChatGPT answers but 16.7% of Claude answers. Review the pages and third-party sources retrieved for the same measured queries, then re-run the engine split.
- Document third-party verification precisely. USP appeared in 59.7% of Gemini answers and 0% of Claude answers, while NSF appeared in 31.9% of Gemini answers and 0% of Claude answers. Publish verification details with the named standard, scope, and product-level applicability, then measure whether relevant answers cite those pages.
- Strengthen source material for brands with no owned-domain citations. OLLY and Legion had 0% owned-domain citation rates among answers with retrieval evidence. Build pages that answer the product facts users need, then re-measure owned-domain citation rates against the same prompt set.
- Reduce dependence on one engine. Clean Label Project appeared in 23.6% of Gemini answers and 0% elsewhere. Test whether neutral explanations of its criteria are available through sources used by Perplexity, ChatGPT, and Claude, then compare cross-engine consistency.
- Improve early-answer placement. Centrum's average first-mention position was 6.8 and NOW's was 8. Publish direct comparison and suitability material that can support concise recommendation lists, then re-measure average first-mention position.
## What does this mean for multivitamin brands?
Interpretation: multivitamin visibility is not a single score. The measured answers separate brands that are regularly named from brands whose sites are selected as evidence. A content plan that tracks only appearances can miss weak owned-domain citation rates. A plan that tracks only citations can miss whether the brand is named early or consistently.
Interpretation: third-party verification language appears in the measured answers, especially in Gemini, but the tally does not prove that a USP, NSF, or ConsumerLab signal causes a brand to be named or cited. Brands can treat verification documentation as a testable content topic rather than a proven ranking factor.
## How was multivitamin AI visibility measured?
This flagship study generated 288 attempted and returned answers by testing 24 stratified multivitamin prompts three times in Perplexity, ChatGPT, Gemini, and Claude. All 288 included retrieval evidence. Appearance rates are based on returned answers, while citation and source rates are based on answers with retrieval evidence.
### Measurement summary
### How this study was measured
- The sampling frame was set first: 24 prompts were allocated across the demand clusters before any questions were asked.
- Every engine received the same questions: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 288 answers returned of 288 attempted, 288 with retrieval evidence.
- Product lines were assigned to parent brands under a registry fixed before scoring.
- Appearance rate is presented with a 95% Wilson interval; citation metrics include only answers with evidence.
The study measures these prompts on September 30, 2026. It does not measure all consumer searches, product quality, safety, clinical outcomes, sales, or consumer demand. Repeated generations show variation, and engine differences can arise from model behavior and retrieval tools.
## Frequently asked questions
### Which multivitamin brand appeared most often?
Thorne ranked first, appearing in 56.9% of 288 answers returned. Its 95% interval was 51.2% to 62.5%, which overlaps the intervals of Garden of Life, Nature Made, Ritual, and Pure Encapsulations.
### Did any brand have the highest owned-domain citation rate?
Nature Made had the highest measured owned-domain citation rate at 10.4% of 288 answers with retrieval evidence. Life Extension was next at 8.7%.
### Which engine named Garden of Life most often?
ChatGPT named Garden of Life in 91.7% of its 72 answers, versus 63.9% for Perplexity, 30.6% for Gemini, and 16.7% for Claude.
### Did verification organizations appear in the results?
USP appeared in 21.9% of answers returned, and NSF appeared in 13.5%. Gemini named USP in 59.7% of its 72 answers and NSF in 31.9%, while Claude named neither.
### Which third-party publisher appeared most often as a source?
Healthline was cited in 62.5% of 288 answers with retrieval evidence. ConsumerLab was cited in 41.3%, Fortune in 40.6%, and Medical News Today in 35.4%.
### Does this report cover other supplement categories?
No. This report is limited to multivitamins. Protein / Protein Powder, Probiotics / Gut Health, Sleep (melatonin / magnesium), Immune Support, and Nootropics / Cognitive are addressed in separate reports.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
Because these are not category brands, they are measured in the same way but excluded from the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1ConsumerLab16.3%47/28812.5%–21%
2Healthline8.3%24/2885.7%–12.1%
3Forbes Health1.4%4/2880.5%–3.5%
4Verywell Health0%0/2880%–1.3%
5Examine.com0%0/2880%–1.3%
## Methodology appendix
This appendix comes directly from the study's measurement record. It is provided so any brand named here can reproduce or challenge the result.
### What was measured
- Category: Multivitamins
- Measurement date: 2026-09-30
- Depth: flagship: 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 288
- Answers returned (appearance denominator): 288
- Answers with retrieval evidence (citation denominator): 288 (100% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Flagship depth uses 24 stratified prompts measured three times per engine, making both between-prompt and repeat-generation variation observable.
### How the prompts were sampled
The sampling frame is fixed: 14 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Multivitamins; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer provides wording only within these cells and does not select the category's demand structure.
Demand clusterPromptsScope
best multivitamin for women1best multivitamin for women
best multivitamin for men1best multivitamin for men
best multivitamin for adults over 501best multivitamin for adults over 50
best gummy multivitamin1best gummy multivitamin
best prenatal vitamin1best prenatal vitamin
best multivitamin for energy1best multivitamin for energy
best multivitamin without iron1best multivitamin without iron
best multivitamin for vegans1best multivitamin for vegans
best multivitamin for teenagers1best multivitamin for teenagers
best whole food multivitamin1best whole food multivitamin
best multivitamin for hair skin and nails1best multivitamin for hair skin and nails
one a day vs centrum which is better1one a day vs centrum which is better
best multivitamin for athletes1best multivitamin for athletes
best multivitamin for seniors1best multivitamin for seniors
General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Primary use case1the dominant everyday use case for this category
Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
### The exact prompts
#PromptClusterShape
1best multivitamin for womenneed_1medium
2best multivitamin for menneed_2medium
3best multivitamin for adults over 50need_3medium
4best gummy multivitaminneed_4medium
5best prenatal vitaminneed_5medium
6best multivitamin for energyneed_6medium
7best multivitamin without ironneed_7medium
8best multivitamin for vegansneed_8medium
9best multivitamin for teenagersneed_9medium
10best whole food multivitaminneed_10medium
11best multivitamin for hair skin and nailsneed_11medium
12one a day vs centrum which is betterneed_12medium
13best multivitamin for athletesneed_13medium
14best multivitamin for seniorsneed_14medium
15best multivitaminsgeneral_bestshort
16best everyday multivitaminseverydayshort
17best value multivitaminsvalue_priceshort
18Which multivitamin brands are best for filling common dietary gaps in everyday adult nutrition?use_caselong
19What are the best multivitamin brands for a first-time buyer who wants a simple daily option?beginnerlong
20Where should I buy multivitamins for reliable selection, freshness, and authentic products?retailmedium
21What multivitamin brands make good wellness gifts for someone starting a healthier routine?giftingmedium
22Which multivitamin brands are best for adults seeking third-party tested options without unnecessary ingredients?specific_needlong
23Which multivitamin brands offer the best balance of ingredient quality, testing, and price?comparisonmedium
24Which multivitamin brands do dietitians and independent reviewers most often recommend and trust?trustmedium
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search727272100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool727272100%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)727272100%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool727272100%
Results cannot be treated as strictly comparable across engines: each uses a different model, retrieval tool and answer format, so observed gaps can reflect system design as well as brand visibility.
### How each metric is defined
- Appearance rate: the share of returned answers that named the brand. It is measured on every answer returned by the engine, regardless of whether it searched the web, preventing an engine's retrieval behaviour from silently altering the denominator.
- Macro appearance rate: the average of per-prompt appearance rates, giving each prompt one count regardless of how many engines answered it.
- 95% interval: the Wilson score interval for the appearance rate. When two brands' intervals overlap, this study does not support a claim that either is more visible.
- Owned-domain citation rate: the share of answers with retrieval evidence that cited a URL on the brand's own domain. It narrowly indicates use of the brand's own site as a source, not total mentions.
- Share of all citations: the share of all attributed publisher citations in the study accounted for by the brand's own domain.
- Not measured: no citation result exists because the brand's official domain could not be established. It is never reported as 0%.
- 0%: the metric was evaluable, but the event did not occur.
- Average position: the mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency: the share of engines that named the brand at least once.
- Repeat consistency: the share of repeated prompt/engine cells in which the brand appeared in every trial. Lower values indicate instability in the engine's answer rather than brand absence.
### Evidence rules
- An answer is counted as having retrieval evidence only if the engine returned a retrieval receipt (search annotations or grounding records). A URL merely typed by the model in its prose is not considered evidence that it searched.
- Sources are assigned to the publisher behind each retrieval receipt. Intermediary and redirect hosts are not reported as publishers.
- Publisher identity could not be established for 0 retrieval receipt(s); these are disclosed here rather than attributed or omitted.
- Source counts operate at answer level: a publisher is counted once in an answer where it was cited, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Thorne164/28856.9%56.9%51.2%–62.5%13/288 (4.5%)
2Garden of Life146/28850.7%50.7%44.9%–56.4%6/288 (2.1%)
3Nature Made144/28850%50%44.3%–55.7%30/288 (10.4%)
4Ritual119/28841.3%41.3%35.8%–47.1%10/288 (3.5%)
5Pure Encapsulations106/28836.8%36.8%31.4%–42.5%2/288 (0.7%)
6MegaFood104/28836.1%36.1%30.8%–41.8%6/288 (2.1%)
7Centrum86/28829.9%29.9%24.9%–35.4%5/288 (1.7%)
8USP63/28821.9%21.9%17.5%–27%0/288 (0%)
9NOW63/28821.9%21.9%17.5%–27%3/288 (1%)
10SmartyPants60/28820.8%20.8%16.5%–25.9%3/288 (1%)
11One A Day59/28820.5%20.5%16.2%–25.5%5/288 (1.7%)
12Kirkland Signature58/28820.1%20.1%15.9%–25.1%9/288 (3.1%)
13Life Extension52/28818.1%18.1%14%–22.9%25/288 (8.7%)
14New Chapter41/28814.2%14.2%10.7%–18.7%7/288 (2.4%)
15NSF39/28813.5%13.5%10.1%–18%11/288 (3.8%)
16NATURELO34/28811.8%11.8%8.6%–16%5/288 (1.7%)
17OLLY24/2888.3%8.3%5.7%–12.1%0/288 (0%)
18Sources23/2888%8%5.4%–11.7%domain not established — excluded from citation metrics
19Bayer23/2888%8%5.4%–11.7%1/288 (0.3%)
20Nature’s Way20/2886.9%6.9%4.5%–10.5%1/288 (0.3%)
21Legion19/2886.6%6.6%4.3%–10.1%0/288 (0%)
22United States Pharmacopeia18/2886.3%6.2%4%–9.7%0/288 (0%)
23Nordic Naturals, Inc18/2886.3%6.2%4%–9.7%2/288 (0.7%)
24Clean Label Project17/2885.9%5.9%3.7%–9.2%0/288 (0%)
25Transparent Labs16/2885.6%5.6%3.4%–8.8%0/288 (0%)
### Entity handling
Brand-level metrics assign product lines and model names to their parent brand under a registry fixed before scoring. No names are merged solely on the basis of this study's own counts.
No roll-ups were applied in this study.
40 further name(s) found in the answers could not be confidently resolved to a brand in this category and are retained for human classification before the next study; no metrics are assigned here.
### Limits of these findings
- This measures what the named systems returned for these exact prompts at a point in time on 2026-09-30. Engine behaviour can change without notice.
- The prompt set is a stratified category-demand sample, not a census. Findings describe these prompts and should not be interpreted as representing all category searches.
- At this sample size, differences between brands are not statistically distinguishable where intervals overlap.
- A brand absent from this list was not measured as absent: it was simply not named in these answers.
- Appearance measures visibility, not endorsement, quality or market share.
- 1 of 25 ranked brand(s) had no official domain established before scoring, so it has appearance results only and is excluded from citation metrics and source tables rather than reported as zero.
---
### The Private Label AI Advantage — Pet
URL: https://www.5wpr.com/research/private-label-ai-advantage-pet/
Summary: Private pet brands out-cite national brands across five leading answer engines with one firewall of veterinary authority in our index data.
---
### The Car Wash AI Visibility Index 2026 — Top 25 U.S. Conveyor Car Wash Chains by AI Citation Share
URL: https://www.5wpr.com/ai-visibility-index/car-wash-ai-visibility-index-2026/
Summary: See the top 25 conveyor car wash chains in the U.S. ranked by citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
# The Car Wash AI Visibility Index 2026
### A 5W AI Visibility Index — Top 25 Car Wash brands ranked by citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. By Ronn Torossian, Founder of 5W, and the 5W Research Team — April 2026.
Download the full PDF
## EXECUTIVE SUMMARY
The U.S. car wash industry is one of the most rapidly consolidating retail-service categories in America. The U.S. Census Bureau identified roughly 17,584 car washes with paid employees in its most recent count, and approximately $15 billion in annual North American car wash retail sales. Approximately 900 new conveyor sites opened annually at the recent peak. Private equity is everywhere: KKR acquired a stake in Quick Quack for $850 million; Oaktree Capital backs Whistle Express; Atlantic Street Capital backed Zips; Warburg Pincus owns El Car Wash; Wildcat Capital Management runs the Club Car Wash / Express Wash Concepts platform.
What surfaces inside ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews when American drivers ask "best car wash near me," "unlimited car wash subscription worth it," "Mister Car Wash vs Tommy's Express," "where to wash my car" is a small set of national chains that have absorbed the citation surface from the 17,000+ independent operators they have not yet acquired.
This report measures who is winning that citation surface. 5W ran 65+ consumer-intent prompts through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews in Q1 2026, tracking citations across four sub-categories: national conveyor chains, express-exterior chains, full-service chains, and franchise networks (Tommy's Express).
The findings: Mister Car Wash, the only publicly traded pure-play car wash, captures an estimated 13.5% of all car wash consumer-intent AI citations — the largest share of any single brand and a reflection of its national footprint of 500+ locations across 21 states and roughly 74% Unlimited Wash Club subscription penetration. Tommy's Express wins franchise-citation prompts decisively after being named #1 on Entrepreneur's Franchise 500 in the car wash category. The Wildcat Car Wash Platform — Club Car Wash plus Express Wash Concepts — captures citation surface that exceeds its individual brand recognition because AI engines absorb the platform's combined trade-press footprint. Zips' February 2025 Chapter 11 filing, after over-leveraging at $654 million in debt, has reshaped the citation surface in real time. Approximately 91% of independent and regional car wash operators have effectively zero AI citation share in their own metro and category.
The brands that win car wash AI citation in the next twelve months will define which wash every American driver under 50 visits when their car needs cleaning. The window is closing fast.
## TOP 15 BRANDS BY AI CITATION SHARE
Estimated share of citations across 65+ car wash consumer-intent prompts run through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q1 2026.
Mister Car Wash13.5%
Tommy's Express8.2%
Take 5 Car Wash6.8%
Quick Quack6.0%
Whistle Express5.4%
Tidal Wave Auto Spa4.6%
Wildcat (Club + EWC)4.2%
Zips Car Wash3.6%
Spotless Brands2.9%
GO Car Wash2.5%
Mammoth Holdings2.2%
Autobell Car Wash2.0%
Crew Carwash1.7%
El Car Wash1.5%
ModWash1.3%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q1 2026. Share represents estimated proportion of brand citations across 65+ tracked consumer prompts. Remaining ~33% split across ranks 16–25, regional brands, and the small minority of independent operators who occasionally break into citations.
## THE FULL TOP 25 RANKING
1. Mister Car Wash — Conveyor car wash chain. Mister Car Wash (NYSE: MCW). 500+ locations across 21 states, the only publicly traded pure-play car wash and the largest U.S. car wash chain by unit count. Headquartered in Tucson, Arizona. Approximately 74% Unlimited Wash Club subscription penetration. Wins "best car wash chain," "national car wash subscription," and "Mister Car Wash near me" prompts at industry-leading consistency.
2. Tommy's Express — Conveyor car wash franchise. Holland, Michigan-headquartered. Tommy's Express ranked #1 on Entrepreneur's Franchise 500 in the car wash category. Three-minute tunnels, license plate recognition, TommyClub subscription app. Wins "car wash franchise" and "Tommy's Express near me" prompts decisively due to franchise marketing scale.
3. Take 5 Car Wash — Conveyor car wash chain. Owned by Driven Brands (NASDAQ: DRVN). The car wash arm of the Take 5 Oil Change parent. Charlotte, North Carolina-headquartered. Driven Brands divested significant car wash holdings to Whistle Express in 2025 to refocus on its oil change franchise; the residual Take 5 Car Wash brand still wins citation surface in "fast car wash" and "Take 5 car wash" prompts.
4. Quick Quack Car Wash — Conveyor car wash chain. Roseville, California-headquartered. 230+ locations expanding to ~275+ across California, Texas, Arizona, Utah, Colorado. KKR acquired an $850 million stake in 2024. Subscription-based "Don't Drive Dirty" positioning. Quackals the duck mascot. Wins "Quick Quack" and "express car wash subscription" prompts.
5. Whistle Express Car Wash — Conveyor car wash platform. Charlotte, North Carolina-headquartered. Whistle Express. 400+ locations across the Midwest and Southeast after its 2025 acquisition of Driven Brands' car wash portfolio. Backed by Oaktree Capital. Catapulted into top-tier citation share through the acquisition's trade-press footprint.
6. Tidal Wave Auto Spa — Conveyor car wash chain. Thomaston, Georgia-headquartered. 156+ locations. Founded 1999. Family-and-PE-backed growth from a single Georgia self-service site to a national chain. Wins "Tidal Wave car wash" and southeast-region prompts.
7. Wildcat Car Wash Platform (Club Car Wash + Express Wash Concepts) — Combined Wildcat Capital Management platform. 200+ combined locations across 19 states under brands including Club Car Wash, Bee Clean Express, Clean Express Auto Wash, Flying Ace Express, Green Clean Express, Moo Moo Express, and Rapid Express. Columbia, Missouri-headquartered. Citation share that exceeds individual brand recognition because the platform's combined trade-press footprint is absorbed by AI engines collectively.
8. Zips Car Wash — Conveyor car wash chain. Plano, Texas-headquartered. Operating under Zips Car Wash, Rocket Express Car Wash, and Jet Brite Car Wash. Zips filed Chapter 11 in February 2025 after over-leveraging at $654 million in debt; the bankruptcy reshaped citation share in real time as restructuring news propagated through trade press. Citation surface remains substantial despite the financial restructuring.
9. Spotless Brands — Conveyor car wash platform. Oakbrook Terrace, Illinois-headquartered. Affiliated brands include Cobblestone Auto Spa, Okie Express Auto Wash, Ultimate Shine, and Flagship Car Wash. 150+ locations across Phoenix, Denver, Oklahoma City, DC, Maryland, Virginia, West Virginia, and Tennessee.
10. GO Car Wash — Conveyor car wash chain. Denver, Colorado-headquartered. ~127 locations. Wins "GO Car Wash" and Mountain region prompts.
11. Mammoth Holdings — Conveyor car wash chain. Atlanta, Georgia-headquartered. ~115+ locations. Multi-brand portfolio including Mammoth, Marc-1 Car Wash, and others.
12. Autobell Car Wash — Family-owned conveyor car wash chain. Founded 1969 by Charles "Charlie" Howard. Headquartered in Charlotte, North Carolina. Wins "Autobell" and Carolinas-region prompts.
13. Crew Carwash — Conveyor car wash chain. Indianapolis, Indiana-headquartered. Wins "Crew Carwash" and Midwest-region prompts.
14. El Car Wash — Conveyor car wash chain. Florida-focused. Owned by Warburg Pincus. Wins "El Car Wash" and Miami / South Florida prompts.
15. ModWash — Conveyor car wash chain. Multi-state expansion since 2020. Wins "ModWash" and emerging-region prompts.
16. Cobblestone Auto Spa — Phoenix-based conveyor car wash chain. Spotless Brands affiliate. Wins "Cobblestone" and Arizona-region prompts.
17. Cheetah Clean Auto Wash — Conveyor car wash chain expanding across the Southeast. Wins "Cheetah Clean" prompts.
18. Squeaky's Car Wash / Russell Speeders / Summit Wash Holdings — Connecticut, New York, Nebraska, Kansas, and Florida operations under Summit Wash Holdings. Wins regional "Squeaky's" and "Russell Speeders" prompts.
19. Bubbles Car Wash — Regional conveyor chain. Wins regional prompts.
20. WhiteWater Express Car Wash — Texas-region conveyor chain. Wins "WhiteWater Car Wash" prompts.
21. Splash Car Wash — Northeast-region conveyor chain. Wins "Splash Car Wash" prompts.
22. Caliber Car Wash — Atlanta-region conveyor chain. Wins "Caliber Car Wash" prompts.
23. Wash Wizard / Surf Through Express — Regional conveyor chains.
24. Hoffman Car Wash — New York-region conveyor chain. Wins "Hoffman Car Wash" and Capital Region prompts.
25. Jax Kar Wash / Mr. Magic Car Wash / Independent Operator Composite — A composite category representing the strongest of the regional and independent operators that occasionally break into AI citations. The vast majority of America's 17,500+ independent car washes never appear in AI answers at all.
## KEY FINDINGS
- STAT 1: 17,584 — U.S. car washes with paid employees identified by the U.S. Census Bureau (most recent count); ~$15B in annual North American car wash retail sales (Federal Reserve Bank of St. Louis)
- STAT 2: ~900 — new conveyor car wash sites opened annually at the recent peak (International Carwash Association); construction has slowed from 2021-2022 peaks of 936-943 due to market saturation
- STAT 3: 500+ — Mister Car Wash locations across 21 states; the largest U.S. car wash chain by unit count; the only publicly traded pure-play car wash (NYSE: MCW)
- STAT 4: 74% — Mister Car Wash Unlimited Wash Club subscription penetration of total wash sales; up from ~71% the prior year
- STAT 5: $850M — KKR's acquisition stake in Quick Quack Car Wash announced August 2024
- STAT 6: 91% — estimated share of independent and regional car wash operators with effectively zero AI citation share in their own metro and category
- STAT 7: $654M — Zips Car Wash debt at its February 2025 Chapter 11 filing; revealed cash balance under $1M at filing
- STAT 8: 48.12% — automatic tunnel share of total car wash market in 2025; express-exterior sites growing at 8.27% CAGR (Mordor Intelligence)
## THE CENTRAL FINDING
American drivers no longer find their next car wash by driving past one. They open ChatGPT and ask "best car wash near me," "unlimited car wash subscription worth it," "Mister Car Wash vs Tommy's Express," "car wash that does interior cleaning." AI engines respond with a list. That list is the new car wash discovery channel — and it is dominated by a small number of national chains whose marketing scale and trade-press citation footprint exceed the 17,000+ independent operators competing against them.
Inside that AI-mediated discovery, the U.S. car wash industry is consolidating on three fronts simultaneously. On the public-markets front, Mister Car Wash has used its NYSE listing and consistent earnings disclosures to build a citation moat that no private competitor has closed. On the franchise front, Tommy's Express has used its Entrepreneur Franchise 500 #1 ranking to build trade-press content velocity that captures "car wash franchise" and "best franchise to own" AI citations across its network. On the PE-platform front, Whistle Express, Wildcat Car Wash Platform, Spotless Brands, and Mammoth Holdings have absorbed citation share from the brands they have rolled up — even when consumers don't know which platform now owns the wash they visit.
The Zips Chapter 11 filing in February 2025 demonstrated the inverse: when a major chain stumbles, the citation surface re-routes within weeks. Trade press picks up the bankruptcy news, AI engines absorb the coverage, and "best car wash" prompts route away from the affected brand. The citation surface is not stable property. It is a real-time leaderboard.
The brands that will win in 2026 and 2027 understand this. The brands that don't are losing citation surface to platforms whose consumer-relationship begins before the driver pulls into the parking lot.
## METHODOLOGY
5W ran 65+ consumer-intent prompts through ChatGPT (GPT-4o, GPT-5), Claude (Sonnet 4.6, Opus 4.7), Perplexity, and Google AI Overviews in Q1 2026. Prompts spanned four sub-categories tracked separately because brand citation share differs materially across each.
National conveyor chains (Mister Car Wash, Take 5, Whistle Express, Tidal Wave, Mammoth Holdings, Autobell, Crew Carwash, Hoffman, Splash). Express-exterior chains (Quick Quack, Zips, Spotless, GO Car Wash, ModWash, Caliber, Cheetah Clean). Full-service chains (Mister's full-service operations, Autobell, regional full-service operators). Franchise networks (Tommy's Express; Express Wash Concepts as franchisor; Wildcat Capital Management's combined platform).
Citation share figures represent estimated proportions across the tracked prompt set, not absolute citation counts. Methodology details, prompt list, and replication instructions available on request.
## WINNERS
Mister Car Wash dominates the conveyor citation surface. With 500+ locations, NYSE-listed status, consistent earnings disclosures, and 74% Unlimited Wash Club subscription penetration, Mister wins "best car wash chain," "national car wash subscription," and brand-comparison prompts at industry-leading consistency. Its citation moat is structural: no other car wash brand operates at this combination of scale, public-markets visibility, and subscription depth.
Tommy's Express dominates franchise citations. The #1 ranking on Entrepreneur's Franchise 500 in the car wash category produces a citation flywheel — every "best franchise to own," "car wash franchise opportunity," and "Tommy's Express franchise" prompt routes back to its franchise marketing footprint, which competitors have not matched.
The Wildcat Car Wash Platform wins what no individual Wildcat-owned brand could win alone. Club Car Wash, Express Wash Concepts, Bee Clean, Clean Express, Flying Ace, Green Clean, Moo Moo Express, and Rapid Express collectively produce trade-press content that AI engines absorb as a single platform signal. Wildcat's citation share exceeds the sum of its individual brand citations.
Quick Quack wins California, Texas, Arizona, Utah, and Colorado prompts decisively. The KKR-backed $850 million capital injection in 2024 produced trade-press coverage that propagated through AI training data faster than competitors' organic content could counter.
Whistle Express transformed its citation surface in 2025 by acquiring Driven Brands' car wash portfolio. The acquisition produced a single trade-press event that AI engines absorbed as a category-defining moment, propelling Whistle from regional player to top-five national citation share within months.
## FALLING BEHIND
Independent and regional operators. Approximately 91% of the 17,500+ U.S. car washes with paid employees have effectively zero AI citation share in their own metro and category. The structural reason: AI engines surface brands with national entity-strength signals (Wikipedia presence, structured Wikidata records, consistent national trade-press citations, and brand-owned content velocity that exceeds what an independent operator can produce). The independent car wash whose only digital presence is a Google Business Profile and a Yelp listing is invisible in AI answers — even when the consumer is standing two miles from the wash and asking "best car wash near me."
Driven Brands' Take 5 Car Wash post-divestiture. Driven Brands' 2025 strategic decision to divest car wash holdings to Whistle Express in order to refocus on Take 5 Oil Change has produced a citation transition: AI engines are absorbing the divestiture news, but the residual Take 5 Car Wash brand citation surface is in flux. Brands undergoing corporate transitions consistently lose citation surface during the transition window, and Take 5 Car Wash is the most visible 2026 example of this pattern.
Sub-15-location regional chains. Even mid-tier regional operators with strong local recognition struggle to break into AI citation share above ~1%. The trade-press footprint required to register on the citation surface scales non-linearly: the gap between a 15-location chain and a 50-location chain is not 3.3x in citation share — it is closer to 10x.
## THE SIX STRUCTURAL FINDINGS
1. Public-markets visibility is the largest structural citation advantage in car wash. Mister Car Wash's NYSE listing produces a continuous trade-press signal (earnings releases, analyst coverage, sale-leaseback transactions, SEC filings) that AI engines absorb and weight heavily. Private competitors at comparable scale have no equivalent signal stream. Citation surface follows public-markets visibility with near-mechanical consistency.
2. PE-platform brand consolidation produces composite citation surface that exceeds the sum of parts. Wildcat (Club + EWC), Whistle Express (post-Driven acquisition), Spotless Brands (Cobblestone + Okie + Ultimate Shine + Flagship), and Mammoth Holdings each capture combined citation share that exceeds what their constituent brands earned independently. The mechanism: AI engines absorb the platform-level trade-press footprint without distinguishing among the underlying brands.
3. Subscription-program coverage is a citation event. AI answers about "unlimited car wash subscription," "best monthly car wash plan," and "car wash app" route disproportionately to brands with named, marketed subscription programs (Mister's Unlimited Wash Club, Quick Quack's monthly unlimited, Tommy's TommyClub). Brands without named subscription programs lose citation surface in subscription-related prompts even when they offer comparable products.
4. Franchise rankings produce citation flywheels. Tommy's Express's Entrepreneur Franchise 500 #1 ranking in the car wash category produces a continuous citation flywheel for "best franchise," "car wash franchise opportunity," and "Tommy's Express" prompts. The ranking gets cited in trade press, the trade press gets absorbed by AI engines, and the citation surface compounds.
5. Bankruptcy and restructuring produce real-time citation re-routing. Zips' February 2025 Chapter 11 filing produced a citation reset within 6-8 weeks: "best car wash chain," "Zips Car Wash review," and "is Zips Car Wash open" prompts began routing to alternative chains as trade press and AI engine re-training absorbed the bankruptcy news. The citation surface is not stable.
6. Sale-leaseback financial activity produces unexpected citation surface. Mister Car Wash's $134.9 million in 2024 sale-leaseback proceeds and continued 2025 sale-leaseback activity generated trade-press coverage (Costar, Reuters, Wall Street Journal) that propagated to AI citation surface for "Mister Car Wash financials" and "car wash real estate" prompts. Financial events produce trade-press footprint that compounds into citation share.
## 2026-SPECIFIC FINDINGS
1. The Zips Chapter 11 filing reset the citation surface. February 2025's Chapter 11 filing — followed by lease rejections, location closures, and ongoing restructuring through Q1 2026 — propagated through trade press and produced the largest single citation reset of the year in the car wash category. AI answers about "Zips Car Wash" now reference the restructuring within nearly every response.
2. The Whistle Express acquisition of Driven Brands' car wash portfolio reshaped the top-five. The 2025 acquisition catapulted Whistle Express from regional player to top-five national citation share within months. The transaction's trade-press footprint was the second-largest 2026 citation event in car wash, after Zips.
3. KKR's $850 million stake in Quick Quack reshaped subscription-citation surface. The August 2024 KKR investment produced trade-press coverage that strengthened Quick Quack's citation share for "Quick Quack" and California / Sun Belt prompts throughout Q1 2026.
4. Sale-leaseback activity at Mister Car Wash produced unexpected citation surface. The $134.9 million in 2024 sale-leaseback proceeds and continued 2025 activity generated trade-press content that AI engines absorbed at higher velocity than competitors' organic marketing.
5. The 936-943 annual new-build peaks have plateaued. Construction of new conveyor car washes has slowed from the 2021-2022 peaks. AI answers about "fastest growing car wash" and "new car wash near me" now route to the chains that maintained construction velocity through the slowdown — primarily Tommy's Express, Quick Quack, and Tidal Wave.
6. Subscription-program penetration data has become a citation event. Mister Car Wash's disclosure of 74% Unlimited Wash Club subscription penetration in its 2024 earnings calls produced trade-press analysis that AI engines absorbed as a category-defining metric. Brands without comparable disclosed subscription metrics now lose citation share in subscription-related prompts.
## FROM RONN TOROSSIAN, FOUNDER OF 5W
"Car wash is the cleanest example we've measured of an industry where consumer discovery has fully migrated to AI before the operators realized it was happening. The customer is no longer driving past a car wash and remembering the brand. The customer is sitting at home asking ChatGPT which car wash to visit, and ChatGPT is answering with one of about ten national chains. Mister Car Wash, Tommy's Express, Quick Quack, Whistle Express, and Tidal Wave have built citation moats that no independent operator can replicate without national trade-press footprint and structured-data infrastructure. Ninety-one percent of the 17,500+ U.S. car washes with paid employees are effectively invisible in AI search. That is not a small problem. It is the entire competitive structure of the industry shifting underneath the operators. The brands building citation infrastructure now will own the next decade. The brands waiting for the AI search transition to stabilize will discover that the chains who moved first absorbed their consumers before they even knew the question was being asked."
## THE CAR WASH GEO PLAYBOOK
1. Build entity-strength infrastructure first. Wikipedia presence, Wikidata structured records, schema.org LocalBusiness markup at every location URL, and consistent NAP (name-address-phone) across the open web. Without entity-strength signals, AI engines cannot map your brand to the consumer query reliably.
2. Publish disclosed subscription metrics. Following Mister Car Wash's playbook: name your unlimited program, publish penetration metrics, produce trade-press content explaining the program's economics. Subscription-program metrics are a citation event that brands without disclosed metrics cannot match.
3. Pursue trade-press footprint, not just consumer marketing. Carwash.com (Professional Carwashing & Detailing), CP Top 100, International Carwash Association, CarwashPro, and the regional trade press are the citation engines that propagate to AI training data. Operators who maintain trade-press visibility win citation share that consumer-only marketing cannot produce.
4. Treat regulatory and financial events as citation events. Sale-leasebacks, PE investments, IPO activity, bankruptcy restructuring, regional expansion announcements — every financial event produces trade-press footprint that AI engines absorb. Calendar AI-citation audits to within 72 hours of every major financial event.
5. Build platform-level citation surface where multiple brands operate under one parent. Wildcat, Spotless Brands, Mammoth Holdings, and Whistle Express all illustrate that platform-level trade-press footprint compounds. Operators with multi-brand portfolios should publish at the platform level as well as the brand level.
6. Win the franchise-citation surface if you franchise. Tommy's Express's Entrepreneur Franchise 500 #1 ranking is a citation flywheel that competitors have not matched. Operators with franchise programs should pursue franchise-industry rankings (Entrepreneur, Franchise Business Review, Franchise Times) deliberately.
7. Calendar trade-press content cadence. Quarterly earnings releases (for public operators), annual operating updates (for private operators), monthly location-opening announcements, and continuous community-impact content all produce trade-press footprint that compounds into citation share. Random publishing produces random citation share. Calendared cadence produces predictable citation growth.
8. Build for the post-EV consumer. EV penetration is reshaping the car wash consumer profile (different cleaning needs, longer wash intervals, different chemistry preferences). Brands that publish EV-specific content (EV-safe washes, EV interior detail, EV battery considerations) win the emerging citation surface that brands focused only on traditional vehicles cannot match.
9. Address sub-category prompts deliberately. "Express car wash" vs "full-service car wash" vs "self-service car wash" vs "touchless car wash" each produce distinct citation surfaces. Operators who segment content by sub-category win sub-category prompts that operators with undifferentiated content do not.
10. Treat license-plate-recognition and app technology as citation events. Tommy's TommyClub, Mister's app-based subscription, Quick Quack's monthly unlimited app — every named, marketed app produces citation surface that brands with un-named app experiences do not match. Name the technology. Publish about it.
## THE BIGGER PICTURE
Car wash is consolidating in real time. KKR, Oaktree, Atlantic Street Capital, Warburg Pincus, Wildcat Capital Management, and a dozen other PE firms have built platforms that absorb regional and independent operators monthly. Mister Car Wash's NYSE listing made the consolidation visible to the public markets. Zips' February 2025 Chapter 11 made the financial fragility visible. The Whistle Express acquisition of Driven Brands' car wash portfolio made the platform-economics visible.
What has not been visible — until this report — is how completely the AI citation surface has consolidated. Ten brands win nearly 50% of consumer-intent AI citations in a category with 17,500+ operators. The 91% of independent operators with effectively zero AI citation share are not just losing search traffic. They are losing the consumer relationship that historically began when a driver passed the wash on the way home. That relationship now begins inside ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. The chain whose citation surface dominates in the consumer's metro is the chain that gets visited.
The brands that win the next decade are the brands that build entity-strength infrastructure now, publish disclosed subscription and operating metrics, pursue trade-press footprint with platform-level deliberation, treat every financial and regulatory event as a citation event, and address sub-category prompts with segmented content. The brands that wait for the AI search transition to stabilize will discover that Mister, Tommy's Express, Quick Quack, Whistle Express, Tidal Wave, Wildcat, and a small number of others have absorbed a larger and larger share of what consumers see when they ask AI "where do I wash my car" in the United States.
## ABOUT 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list.
For more information, visit www.5wpr.com.
## FAQ
How were citation share figures calculated? 5W ran 65+ consumer-intent prompts through ChatGPT (GPT-4o, GPT-5), Claude (Sonnet 4.6, Opus 4.7), Perplexity, and Google AI Overviews in Q1 2026. Citation share figures represent estimated proportions across the tracked prompt set, not absolute citation counts. Methodology details available on request.
Why is the car wash category different from local services? Car wash is a national-chain-dominated retail-service category with public-markets visibility, PE consolidation, and subscription-program economics that the broader local services category lacks. The citation patterns are correspondingly different.
Are independent car washes included? Yes. The 91% citation-invisibility figure for independent operators is the single most important finding of this report. Independent operators are tracked in the methodology but rarely break into the top 25.
How can my car wash company get included in the next ranking? Brands earn citation share by building entity-strength infrastructure, publishing disclosed metrics, maintaining trade-press footprint, and treating financial and regulatory events as citation events. Contact info@5wpr.com for engagement details.
Download the full PDF report
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### The Baby Formula AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/baby-formula-ai-visibility/
Summary: Enfamil leads baby formula AI citations at 20.6%, while ByHeart's share collapsed to 0.6% after its 2025 botulism recall, a new 5W AI Visibility Index finds.
Enfamil captures the largest share of AI citations in baby formula recommendations, naming leadership across purchase-intent, safety, and comparison prompts. Similac holds the second-largest share, four years after a 2022 recall, while ByHeart's share collapsed within months of its November 2025 botulism recall. Parent's Choice and Kirkland Signature, manufactured in the same Perrigo facilities as national brands, register a fraction of the citation share those brands command.
- Enfamil, Similac, and Bobbie form the Anchor Tier of baby formula AI visibility, together capturing 49.8% of citations across the fifteen brands tracked.
- Recency outweighs severity. ByHeart's November 2025 recall, zero deaths, an outbreak the CDC declared over within three months, produced a steeper citation collapse than Similac's 2022 recall, which involved two deaths under investigation and has had four years to recover.
- Scale buys recovery time. Similac's 15.8% share four years post-recall sits far above Evenflo's 3.6% share six years post-investigation in 5W's Strollers & Car Seats AI Visibility Index, a smaller brand carrying a comparably aged story.
- Manufacturing parity does not transfer to AI authority. Parent's Choice and Kirkland Signature, both made by Perrigo to the same FDA nutritional standard as Bobbie, combine for 7.0% of citations, roughly half of what Bobbie alone commands.
- Import-legality questions around European formula brands remain largely unclaimed territory in AI-generated answers, despite active CBP enforcement and FDA warning letters addressing the same question.
## Core Findings
Table A. Anchor Tier and category snapshot. Data: 5W AI Visibility Index.
## Same Factory, Different Answer
Enfamil leads the category at 20.6%, ahead of Similac at 15.8% and Bobbie at 13.4%. ByHeart sits at 0.6%, down from an estimated 5.8% before its November 2025 recall. The FDA's outbreak investigation documented 48 confirmed and probable infant botulism cases tied to ByHeart formula, traced to Clostridium botulinum in an organic whole milk ingredient from a named supplier, with the outbreak declared over by February 2026.
Similac's trajectory is the more instructive contrast. Abbott's February 2022 recall, tied to Cronobacter sakazakii contamination at its Sturgis, Michigan facility and two infant deaths under investigation, remains part of how AI engines describe the brand. But at 15.8% share four years later, Similac still ranks second in the category, a markedly different outcome from Evenflo's 3.6% share six years after a comparable safety event in 5W's Strollers and Car Seats AI Visibility Index. Scale and a visible remediation response buy a national brand more recovery room than a smaller brand gets for a similarly aged story.
The category's sharpest structural signal has nothing to do with recalls. Parent's Choice and Kirkland Signature, Walmart's and Costco's store-brand formulas, are manufactured by Perrigo to the same FDA nutritional standard (21 CFR 107) as every national brand on the shelf. Bobbie is manufactured by the same company. The two store brands combine for 7.0% of citations, roughly half of what Bobbie commands alone. Shared manufacturing and shared regulatory compliance do not translate into shared AI authority; brand narrative and independent content depth do.
## About This Index
Choosing a formula is one of the highest-stakes early decisions new parents make, shaped as much by safety history as by nutrition panels. This index tracks which formula brands ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews recommend across purchase-intent, safety, and comparison queries, and how two real, well-documented recalls at different distances in time surface in those answers. Brands running comparable campaigns can find category context through 5W's Food and Beverage Marketing Agency practice.
## AI Recommendation Frequency Matrix
The matrix below presents directional AI Recommendation Frequency Indices for the seven most-cited brands across the five engines tracked. Values represent the share of buyer-intent formula prompts in which each brand was named among the top three recommendations during the testing window.
Table B. AI Recommendation Frequency Index by brand and engine. Data: 5W AI Visibility Index.
Enfamil leads across every engine tested, with the widest margin in ChatGPT and Gemini responses. Claude and Google AI Overviews return more conservative recommendation sets across the category, consistent with patterns 5W has observed in other consumer verticals. Store-brand and recently recalled products register too infrequently in top-three recommendations to appear reliably in this matrix; their category-wide citation share is captured in the full brand ranking below.
## Full Rankings
Table C. Citation share and key drivers by brand. Data: 5W AI Visibility Index.
## Three Surprises
### Store brands lag their own factory
Parent's Choice and Kirkland Signature share a manufacturer and a regulatory standard with Bobbie, yet combine for barely more than half of Bobbie's citation share alone. Brand narrative and independent content depth, not manufacturing origin, determine AI authority.
### Scale changes how long a recall lingers
Similac's 15.8% share four years post-recall sits far above Evenflo's 3.6% share six years post-investigation. A larger, more resourced brand with a visible public remediation response recovers more AI visibility than a smaller brand carrying a similarly aged story.
### A months-old recall can outweigh a four-year-old one
ByHeart's 0.6% share sits below Evenflo's 3.6%, despite the ByHeart outbreak causing zero deaths versus two deaths under investigation in the Similac case. Recency weighs at least as heavily as severity in how AI engines characterize brand safety.
## What Formula Brands Should Build
Publish dated, product-specific recall and remediation pages that cite the FDA's own investigation pages directly, not only a brand's internal statement. Structure ingredient-sourcing and supplier-testing information as citable, dated content rather than general quality-assurance copy. Store brands manufactured by a third party should make the shared-manufacturer, shared-standard fact explicit and citable, since it is not currently reaching AI answers on its own.
## Two Open Lanes
Import legality. U.S. Customs and Border Protection has seized shipments of HiPP and Holle formula as misbranded, and the FDA has issued a warning letter to at least one importer for failing to verify foreign supplier compliance. No brand or regulator currently owns a clear, AI-citable answer distinguishing legal personal-use import from unauthorized resale.
Real-time recall status. No brand or neutral source currently owns an AI-citable, real-time answer to "is my formula part of a current recall," despite the FDA maintaining exactly this data.
A parent comparing Parent's Choice to Similac is often comparing two products made in the same factory to the same federal standard. AI doesn't know that unless someone tells it. Right now, almost nobody is.
Ronn Torossian, Founder and Chairman, 5W AI Communications
## What Brands Should Do Next
Track AI citation share the same way 5W tracks it across other categories. If a brand shares a manufacturer with a national name, make that fact explicit, dated, and citable. If a brand carries a recall, publish the remediation with the same specificity as the original investigation, not less.
## Methodology
Testing Window. Prompt testing was conducted across standardized buyer-intent queries covering purchase intent, brand comparison, recall and safety questions, and budget and store-brand comparisons. Each engine was queried across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews, with results aggregated into a citation-share model consistent with the 5W AI Visibility Index series methodology.
Sources. The ByHeart recall timeline and case counts are drawn directly from the FDA's outbreak investigation page. The Similac recall timeline is drawn from the FDA's 2022 Cronobacter investigation page. The FDA warning letter cited in this report was issued to an importer for Foreign Supplier Verification Program violations, not to Kendamil, HiPP, or their manufacturers directly.
Limitations. AI Visibility figures in this report are directional rather than precise measurements, reflecting relative citation-share patterns observed across the testing window. The scoring methodology is proprietary to 5W AI Communications. The Similac-versus-Evenflo comparison spans two separately conducted studies and should be read as a directional cross-study pattern rather than a controlled comparison.
## About This Report
Published August 2026, part of the 5W AI Visibility Index series. 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen, ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, alongside earned media, digital, and influencer channels. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5wpr.com路Food and Beverage Marketing Agency practice
5W runs AI Search (GEO) programs across consumer, B2B, financial services, healthcare, and technology. Learn more.
---
### The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/
Summary: The first reference work mapping how AI engines choose sources: 38 sectors ranked by citation share across ChatGPT, Claude, Perplexity, and Gemini.
5W AI Communications · Research
All Research
Methodology
10 Principles
Glossary
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5W AI Communications · Research · Volume I
# The 5W Retrieval Index
The AI Retrieval Economy, 2026
By Ronn Torossian, Founder & Chairman, 5W AI Communications · Q2 2026 · 38 Sectors · 220 Pages
Download PDF →
Executive Summary
## The map of what the engines actually read.
AI tools are now answering the questions that used to begin with a search. The answers are not random. They come from a specific set of sources. The sources are not the ones the PR industry has spent two decades cultivating. This volume maps them.
Five queries show what is at stake:
- A founder asks ChatGPT which publications would best cover her product launch. The answer shapes a $50,000 PR investment.
- A homeowner asks Claude which roofers are reputable in his area. The answer shapes who gets called.
- A general counsel asks Perplexity about a regulatory matter. The answer shapes what outside counsel gets retained.
- A hospital procurement officer asks Gemini about a medical device supplier. The answer shapes a seven-figure contract.
- A board chair asks Google AI Overviews who the leading voice in a sector is. The answer shapes who gets the speaking slot, the advisory seat, and the term sheet.
This volume is about that specific set of sources.
For two decades, public relations operated inside a system where coverage in a known set of publications produced predictable results. Get into The Wall Street Journal, and your customers, investors, and policymakers would see it. The system had a map. The 5W Retrieval Index is the first reference work for that map in the AI era. Volume I covers 38 sectors: from AI itself to pharma, fintech, beauty, cybersecurity, luxury, capital markets, biotech, entertainment, sports, and beyond. Each sector is a chapter. Each chapter names the sources the engines actually cite, grades them on a fixed composite, identifies the structural finding that defines retrieval behavior in that sector, and tells operators what moves the needle.
The central finding across all 38 sectors is the same: the publications people read are not always the publications the AI engines cite. The most-read journalism is not the most-cited journalism. The training-data economy and the paywall economy are running in opposite directions. The gap between them is the new retrieval map. This volume is that map.
The 38 Sectors
## Every sector of the AI retrieval economy, ranked.
The 38 sector editions below are ordered from the highest composite grade to the lowest, using the five-component scoring system explained in the Retrieval Index methodology.
Edition 06
Pharma Media
A-
Edition 12
Cloud Infrastructure Media
A-
Edition 23
Biotech & Life Sciences Media
A-
Edition 03
Cybersecurity Media
B+
Edition 10
AdTech Media
B+
Edition 13
Application Security & DevSecOps
B+
Edition 17
Legal Media
B+
Edition 18
Banking Media
B+
Edition 26
Hospitality & Hotels Trade
B+
Edition 32
CPG Media
B+
Edition 33
Entertainment Media
B+
Edition 34
Sports Media
B+
Edition 37
Government & Public Sector
B+
Edition 38
Energy & Utilities Media
B+
Edition 01
AI Media
B
Edition 05
Venture Capital Media
B
Edition 08
Crypto / Web3 Media
B
Edition 09
Marketing & Advertising Media
B
Edition 11
B2B SaaS Media
B
Edition 15
Private Equity & M&A Media
B
Edition 19
Insurance Media
B
Edition 20
Capital Markets Media
B
Edition 21
Commercial Real Estate Media
B
Edition 22
Digital Health & Healthtech
B
Edition 25
Travel Media
B
Edition 27
Residential Real Estate Media
B
Edition 28
Food & Beverage Media
B
Edition 30
Retail & DTC Media
B
Edition 31
Fashion & Apparel Media
B
Edition 35
Automotive Media
B
Edition 02
Beauty Media
B-
Edition 04
Fintech Media
B-
Edition 07
Luxury Media
B-
Edition 14
Wealth Management Media
B-
Edition 29
Restaurants & Hospitality Ops
B-
Edition 36
Education & EdTech Media
B-
Edition 16
Family Offices Media
C+
Edition 24
Influencer & Creator Economy
C+
The Ten Principles of AI Retrieval
## The structural rules of the retrieval economy.
These ten findings recur across all 38 sector editions and describe how AI engines select and weight the sources they cite.
-
01
### Open archives outperform closed prestige.
Paywalled prestige publications consistently rank below their authority would predict. Open-access archives (even on lower-prestige domains) consistently rank above theirs.
-
02
### Structured data compounds retrieval.
Clean HTML, named-entity schema, stable taxonomies, and consistent metadata raise extractability. Engines retrieve from sources they can parse cleanly.
-
03
### Persistent URLs outperform ephemeral publishing.
Sources with stable URLs accumulate authority through co-citation over time. Refresh-and-replace platforms forfeit the compounding.
-
04
### Community consensus frequently outranks editorial declaration.
Reddit, Stack Exchange, and sector-specific forums carry retrieval weight on opinion, experience, and consensus queries that editorial publishers cannot match through declaration alone.
-
05
### Institutional datasets anchor factual retrieval.
Government databases (CISA, FDA, SEC EDGAR, NAEP), trade-body publications (IAB, OWASP, NAR), and commercial measurement firms (Nielsen, Circana, A.M. Best, STR) function as primary citation tiers across nearly every sector.
-
06
### Named entities improve extractability.
Sources that name brands, people, products, and locations with consistent taxonomy are retrieved more reliably than sources that describe them in prose without entity anchors.
-
07
### Retrieval compounds historically.
Authority is cumulative. Long-tenured publications on stable domains gain citation share that newer entrants cannot match through quality alone in short time horizons.
-
08
### Forums increasingly function as distributed editorial layers.
Subreddits, Discord exports, and Stack Exchange communities operate as the consensus layer for sectors where editorial publishing has not caught up to the industry's pace.
-
09
### AI systems reward accessibility over prestige.
Engines retrieve from what they can reach. Access controls (paywalls, registration walls, geographic gates) translate directly into retrieval forfeiture.
-
10
### The citation economy is diverging from the readership economy.
The most-read journalism is not always the most-cited journalism. The training-data economy and the paywall economy are running in opposite directions, and the gap is the new retrieval map.
Read the Full Methodology →
About the Index
## What this is. What's next. Who built it.
### What This Is
The 5W Retrieval Index is the first reference work mapping how AI engines select their sources. It scores media properties on a fixed five-component composite (citation frequency, cross-engine breadth, query-type breadth, extractability, and crawl access), normalized to a scale of 0 to 100, and groups them into four retrieval tiers.
### What's Next
Volume II publishes Q4 2026, completing the 60-sector slate. The annual flagship report, The State of AI Sources, publishes December 2026 and will track year-over-year shifts in retrieval behavior across the full sector set.
### Who Built It
Ronn Torossian is the founder and chairman of 5W AI Communications, The Communications Firm Built for the AI Era. He is the publisher of Everything-PR and the author of two best-selling editions of For Immediate Release. The 5W AI Research Team conducted the cross-engine retrieval analysis underlying all 38 sector editions.
## Get Volume I.
220 pages. 38 sectors. The first reference work for the AI retrieval economy.
Download PDF →
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen (ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews), alongside earned media, digital, and influencer channels. Founded in 2003. Recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a 2026 Top Place to Work in Communications by Ragan.
Cite as: The 5W Retrieval Index, Volume I: The AI Retrieval Economy, 2026. 5W AI Communications, Q2 2026.
Retrieval Index Home
Methodology
Ten Principles
Glossary
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---
### AI Won't Name the Best Project Management Tool. It Names the Best One for Your Team
URL: https://www.5wpr.com/ai-visibility-index/project-management-ai-visibility-index-2026/
Summary: The 2026 5W index identifies how artificial intelligence names and recommends the best project management tools for different teams and workflows.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexProject Management
# AI Won't Name the Best Project Management Tool. It Names the Best One for Your Team
Ask an AI engine for the best project management tool and it returns a question, not a brand: what does your team do New 5W research finds the answer splits by use case — and ranks the 25 work-management tools by how often each is named.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 toolsData window: Q2 2026
The Headlines
01
There is no single "best tool."
The answer routes by team — engineering, marketing, and operations each get a different brand.
02
Distribution does not equal citation.
Tools bundled into office suites underindex against brands with a content footprint.
03
Notion and ClickUp punch above revenue.
Community and content put them in the answer ahead of larger companies.
~14%
Estimated AI citation share held by Asana — the most-cited work-management tool
~$10B
Estimated global project management software market in 2025
~20M
Registered users on ClickUp, a privately held challenger valued near $4B
~37%
North America's share of the project management software market
Figure 1 · The Ranking
## Who AI names first.
TOP 15 TOOLS BY EST. CITATION SHARE · Q2 2026
01AsanaCross-functional14.0%
02NotionAll-in-one / docs11.5%
03Monday.comWork OS10.0%
04ClickUpAll-in-one8.0%
05TrelloSimple / Kanban6.5%
06JiraEngineering / agile6.0%
07AirtableDatabase / flexible4.3%
08SmartsheetEnterprise / spreadsheet3.5%
09Microsoft PlannerBundled / Microsoft 3653.0%
10BasecampSimple / small teams2.5%
11LinearModern / engineering2.1%
12WrikeEnterprise / marketing1.8%
13TodoistTask / personal1.5%
14MiroWhiteboard / planning1.3%
15CodaDocs / all-in-one1.1%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of brand citations across 60+ tracked buyer prompts spanning cross-functional and marketing work, engineering and product, all-in-one and docs, simple and small-team use, enterprise and portfolio management, and visual and planning tools. Remaining ~22.9% split across ranks 16–25 and unranked tools.
The Central Finding
## There is no "best tool." There is a best tool per team.
Most categories the AI Visibility Index covers produce a single dominant answer. Project management does not. Ask an AI engine for the best project management tool and it does something unusual: it asks a question back. What does your team do The 5W AI Visibility Index finds the work-tools answer fragments hard by use case — and the fragmentation is the finding.
Ask for engineering and the engines name Jira or Linear. Ask for marketing and cross-functional work and they name Asana or Monday.com. Ask for an all-in-one workspace and they name Notion or ClickUp. Ask for something simple for a small team and they name Trello or Basecamp. Five different questions, five different answers — and a buyer who never specified the use case gets Asana, the closest thing the category has to a default.
The second finding is a gap. Distribution does not equal citation. Microsoft Planner is bundled into Microsoft 365 and reaches more desks than any standalone tool — yet it underindexes in the answer, because almost nobody writes "best project management tool" content about a bundled feature. Meanwhile Notion and ClickUp punch well above their revenue, carried into the answer by enormous communities and content libraries. The engines retrieve the tool the internet wrote about — not the tool the most people technically have.
— 5W Research, May 2026
"Most categories have a default. Project management does not — and software marketers need to understand why that is an opportunity, not a problem. The engine routes by use case, which means there are five or six answers to win, not one. A tool that owns 'best for engineering' or 'best for marketing teams' wins a clean, defensible surface. And here is the part the bundled players miss: distribution is not citation. You can be on every desk in the company and still lose the answer to a brand the internet actually wrote about. Pick a team. Own its question. That is the play."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common buyer prompts across six primary use-case segments of the project management and work-tools market, running each prompt five times per engine in clean sessions. We identified which tools AI models consistently surface, which sources feed those citations, and how the answer changes with the team or use case named in the query.
#### Cross-functional & marketing
Asana, Monday.com, Wrike.
#### Engineering & product
Jira, Linear, ClickUp.
#### All-in-one & docs
Notion, ClickUp, Coda.
#### Simple & small teams
Trello, Basecamp, Todoist.
#### Enterprise & portfolio
Smartsheet, Microsoft Project, Wrike.
#### Visual & planning
Miro, Airtable, Trello.
Query types tracked. Real-world prompts including "best project management tool," "best project management software for marketing teams," "best tool for engineering teams," "best all-in-one work tool," "Asana vs Monday," "best free project management tool," "best project management tool for small business," and 50+ variations covering recommendation, comparison, and use-case intent.
Citation sources tracked. Software-review platforms and buyer guides, technology and productivity media, comparison and roundup content, community forums, and vendor-owned documentation and content.
Important framing — please read. This index measures AI citation share for communications and marketing strategy purposes only. It is not procurement or software-selection advice, and it does not rank tools on features, performance, security, or reliability. A tool's position reflects how often AI engines surface it in response to buyer prompts — not a 5W judgment of the software.
The Full Ranking
## The Top 25 work tools, ranked by AI citation share.
#ToolUse CaseAI VisibilityNotable
1AsanaCross-functionalCategory defaultThe closest thing to a general answer; leads cross-functional and marketing project management citations.
2NotionAll-in-one / docsAll-in-one leaderOwns the all-in-one and docs query; a vast community and content footprint lift it above its revenue.
3Monday.comWork OSWork-OS leaderFastest-growing major brand; heavy marketing presence and a strong cross-functional citation footprint.
4ClickUpAll-in-oneAll-in-one co-leaderPrivately held; 20M+ users; cited heavily in all-in-one and customizable-workspace queries.
5TrelloSimple / KanbanSimple-tool leaderAtlassian-owned; the default citation for simple, visual, and free project management.
6JiraEngineering / agileEngineering leaderAtlassian-owned; owns the engineering and agile software-development project management query.
7AirtableDatabase / flexibleDatabase nicheDatabase-style flexible workspace; cited where the query turns to structured or custom workflows.
8SmartsheetEnterprise / spreadsheetEnterprise nicheSpreadsheet-driven enterprise work management; well cited in large-organization and portfolio queries.
9Microsoft PlannerBundled / Microsoft 365Distribution-heavyBundled into Microsoft 365 with vast reach, but underindexes in citations relative to its distribution.
10BasecampSimple / small teamsSmall-team nicheLong-standing simple project management; cited for small teams and a calm, low-complexity approach.
11LinearModern / engineeringModern-dev nicheFast-rising engineering tool; cited heavily in startup and modern software-team comparisons.
12WrikeEnterprise / marketingEnterprise-marketing nicheEnterprise work management with a marketing focus; a steady but thinner citation footprint.
13TodoistTask / personalTask nicheTask and to-do management; cited where the query is personal or lightweight rather than team-scale.
14MiroWhiteboard / planningVisual nicheWhiteboarding and visual planning; cited in brainstorming and planning queries adjacent to PM.
15CodaDocs / all-in-oneDocs nicheDoc-based all-in-one workspace; cited as a Notion alternative in flexible-workspace queries.
16Microsoft ProjectEnterprise / schedulingLegacy-enterprise nicheThe classic enterprise scheduling tool; cited in Gantt and formal project-planning queries.
17ConfluenceDocs / wikiWiki nicheAtlassian-owned knowledge base; cited in documentation and team-wiki queries alongside Jira.
18TeamworkAgency / client workAgency nicheProject management built for client and agency work; cited in billable-work and services queries.
19HiveAll-in-one / teamsChallenger nicheAll-in-one project and process tool; a recurring secondary citation in roundup content.
20Zoho ProjectsSMB / suiteSuite nichePart of the Zoho suite; cited in value and small-business project management queries.
21HeightModern / autonomousEmerging nicheAI-forward project tool; cited in modern and automation-focused work-tool queries.
22MotionAI schedulingAI-scheduling nicheAI calendar and task scheduling; cited where the query turns to automated planning.
23MeisterTaskSimple / KanbanSimple nicheLightweight Kanban tool; cited as a simple, visual project management alternative.
24NiftyAll-in-one / SMBChallenger nicheAll-in-one project and collaboration tool; a thinner but recurring roundup citation.
25PlaneOpen-source / devOpen-source nicheOpen-source, AI-native project management; cited in self-hosted and developer-focused queries.
Winners
## The tools winning the AI answer.
### ▲ Asana — Owning the Default Question
When a buyer asks for "the best project management tool" with no use case attached, Asana is the answer. Years as the general-purpose reference for cross-functional work made it the engines' safe default.
### ▲ Jira — Owning "Engineering"
Jira did not try to be everything. It owns one team — software engineering — so completely that the engines route every agile and developer query straight to it, regardless of who else is in the category.
### ▲ Notion & ClickUp — Owning the Content Surface
Neither is the biggest company in the category, but both built enormous communities and content libraries. The engines retrieve what the internet wrote — and the internet wrote a great deal about both.
### ▲ Trello — Owning "Simple" and "Free"
Trello claimed the easiest lane to defend: the simple, visual, free tool. Whenever the query signals a small team or a low-complexity need, the engines reach for it first.
Falling Behind
## The tools AI is leaving behind.
### ▼ Microsoft Planner — Distribution Without Citation
Planner is bundled into Microsoft 365 and reaches more desks than any standalone tool. But a bundled feature generates little independent content — and the engines underindex it badly against its reach.
### ▼ Tools Without an Owned Use Case
Work tools that market themselves as good at everything give the engines no use-case query to route to them. In a category that fragments by team, "general" is a weak position.
### ▼ Feature-Rich but Content-Poor Challengers
Several capable tools lose the answer simply because they have not built the review presence and roundup coverage the engines retrieve from.
### ▼ Legacy Enterprise Software
Older enterprise project tools retain large installed bases but little modern content footprint — and the engines name the newer brands the internet is actively discussing.
Structural Findings
## Six structural truths about AI visibility in work tools.
01
### The answer fragments by use case.
Engineering, marketing, all-in-one, and simple needs each route to a different tool.
02
### Distribution is not citation.
Bundled tools reach more desks but underindex against brands with a content footprint.
03
### Community lifts citation share.
Notion and ClickUp punch above their revenue on the strength of community and content.
04
### Owning one team beats owning none.
Jira's grip on engineering outperforms broader tools that own no specific use case.
05
### Review platforms drive citations.
Software-review sites and buyer guides feed work-tool citations disproportionately.
06
### The default answer is unstable.
With no consensus winner, the use-case-free query is genuinely contestable.
Findings Specific to 2026
## Six 2026 dynamics reshaping work-tool AI citations in real time.
01
### AI features are now a citation factor.
Built-in AI assistants and agents are increasingly part of how the engines describe and rank tools.
02
### All-in-one positioning keeps spreading.
More tools claim to consolidate the stack, intensifying the all-in-one query the engines route.
03
### The bundled players are pushing harder.
Microsoft is embedding AI planning into its suite, but the citation gap has not yet closed.
04
### Modern engineering tools are rising.
Linear and similar tools are contesting a query Jira has long owned outright.
05
### Roundup content sets the answer.
"Best project management tool" listicles are refreshed constantly and move citations fast.
06
### Open-source entrants are emerging.
Self-hosted, AI-native tools are forming a small but distinct new citation lane.
The Playbook
## General tips for work-tool marketing teams.
- Audit category citation share quarterly. Work-tool citations move fast as roundup content and product launches refresh.
- Pick the team you intend to own. Engineering, marketing, all-in-one, simple — choose the use case you can credibly win.
- Make the use case explicit in your content. The engines route by use case; "good for everything" routes nowhere.
- Earn software-review and buyer-guide presence. Those sources feed work-tool citations disproportionately.
- Invest in community and documentation. Notion and ClickUp show that content footprint translates directly into citation share.
- Do not rely on distribution alone. A bundled feature still needs independent content to be cited.
- Get comparison content right. "Asana vs Monday"-style queries are high-frequency — be accurately represented in them.
- Treat AI features as a citation asset. Built-in AI now surfaces in answers — make it retrievable and specific.
- Keep capability and pricing sources accurate. The engines retrieve these heavily; errors propagate into answers.
- Re-audit after every major launch, pricing change, or review-cycle refresh. Each reshapes the citation surface.
The Bigger Picture
## The software decision now starts in the answer engine.
Software buying used to begin with a search, a shortlist, and a stack of review-site tabs. A growing share of it now begins with a question typed into an AI engine — and for project management, the engine's answer is not one brand but a routing decision based on what kind of team is asking.
The Index shows what that surface rewards: a clearly owned use case, a deep content and community footprint, and accurate representation in the comparison content the engines retrieve. It also shows what it does not reward — distribution without coverage, and "general-purpose" positioning in a category that fragments by team.
AI citation share is the scoreboard. In work tools, the brand AI names is the one that owns a use case in the content the engines read — not the one on the most desks.
Frequently Asked Questions
## The Project Management AI Visibility Index 2026.
What is the Project Management & Work Tools AI Visibility Index 2026…A research report by 5W that ranks the top 25 project management and work tools by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ buyer prompts tracked in Q2 2026.
Which work tool holds the highest AI citation share…Asana, with an estimated 14% — it leads the general and cross-functional project management answer, the closest thing the category has to a default.
Does AI name a single best project management tool…No. The answer fragments by use case — engineering routes to Jira or Linear, marketing and cross-functional work to Asana or Monday.com, all-in-one to Notion or ClickUp, and simple needs to Trello or Basecamp.
Why does Microsoft Planner rank lower than its reach…Planner is bundled into Microsoft 365 and reaches enormous numbers of users, but a bundled feature generates little independent content — and the engines retrieve from content, not installed base.
Is this index a rating of software quality…No. It measures AI citation share for communications strategy purposes only and is not procurement or software-selection advice. It does not rank tools on features, performance, or reliability.
What is citation share…The estimated proportion of brand mentions a tool receives across all tracked prompts and AI engines — the core metric used to rank tools in the Index.
### About this report
5W AI Visibility Index: Project Management & Work Tools. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ buyer queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report is not procurement or software-selection advice and does not rank tools on features or reliability.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
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### Media Chiefs
URL: https://www.5wpr.com/reputation-index/media-chiefs/
Summary: How AI engines render 15 of the most prominent media chiefs. Bob Iger leads. Linda Yaccarino trails. Directional estimates, July 2026.
THE 5W REPUTATION INDEX · STUDY 07 · SECTOR PHASE
# Media Chiefs
Published 07 July 2026 Cohort 15 principals Engines 5 Prompts 60+
How the major AI systems currently render fifteen of the most prominent chief executives in global media — across streaming, broadcast, news, and the platform era.
Across fifteen media chief executives, the Index finds a 38-point spread — narrower than finance but more sentiment-polarized than sports. Bob Iger leads at 76, anchored by an institutional Disney legacy that two CEO tenures have only deepened. Linda Yaccarino trails at 38, anchored by the X advertiser exodus and what engines now uniformly render as founder shadow — the Musk portrait dominating the X-CEO portrait. The Murdoch family produces three cohort entries between 42 and 50, each anchored by a different inheritance and litigation event.
Highest Composite
76
Bob Iger · Disney
Lowest Composite
38
Linda Yaccarino · X (former)
Cohort Mean
58
SD 11.8
Sentiment Polarization
58
Iger vs. Yaccarino gap
Founder shadow can prevent portrait formation even during an operational CEO tenure.
## The Full Ranking
### All 15 media chiefs by composite score
Cohort restricted to current or recently-departed CEOs of major U.S. and global media companies. The Murdoch family is represented by three principals — Rupert (executive chairman, News Corp; chairman emeritus, Fox), Lachlan (executive chair, Fox Corporation; CEO News Corp), and Sumner Redstone's daughter Shari Redstone (chair, Paramount through July 2024 sale).
#PrincipalCompanyAccSenCmpCnsCtlComposite
1Bob IgerThe Walt Disney Company · CEO807286766676
2Reed HastingsNetflix · Executive Chairman787480726674
3Ted SarandosNetflix · Co-CEO727470706470
4David EllisonSkydance / Paramount Skydance · CEO687262667268
5Greg PetersNetflix · Co-CEO666860646064
6Brian RobertsComcast Corporation · Chairman & CEO705868665062
7Mike CavanaghNBCUniversal / Versant (spinoff) · President / CEO-designate646252605258
8Mark ThompsonCNN Worldwide · Chairman & CEO665660604858
9Cesar CondeNBCUniversal News Group · Chairman626252585257
10Shari RedstoneNational Amusements / Paramount (chair through 2024)644260562850
10TLachlan MurdochFox Corporation · Executive Chair & CEO; News Corp Co-Chair663864582450
12David ZaslavWarner Bros. Discovery · CEO (until 2026 split)72267660848
13Rupert MurdochNews Corp · Chairman Emeritus; Fox · Chairman Emeritus762686641442
14Mark Read (former)WPP plc · CEO (departed 2025)603652542642
15Linda YaccarinoX (formerly Twitter) · CEO (resigned July 2025)66206450838
## Top of the Cohort
### The best-rendered chiefs
Three of the cohort's top four are anchored by streaming and entertainment-platform legacies. Iger's tenure has been long enough for engines to render him as institution rather than executive. Hastings has converted founder mythology into the highest Sentiment among active media CEOs.
### Rank 01 · Bob Iger — Composite Composite 76
The Walt Disney Company · CEO (returned 2022)
Anchor event. Three landmark acquisitions — Pixar, Marvel, Lucasfilm — alongside the 2019 Fox acquisition and the 2022 return to Disney CEO following the Chapek tenure.
Engines render Iger through the most institutionally-densest engine portrait in the media cohort. Completeness of 86 reflects two distinct CEO tenures (2005–2020, 2022–present), three landmark acquisitions, the 2019 Fox acquisition, his 2019 memoir "The Ride of a Lifetime", and the post-Chapek return. Sentiment of 72 reflects favorable framing of the second tenure despite Disney+ unprofitability and activist pressure.
Sourced rationale. Disney acquisitions documented in Disney 10-Ks and across major financial press. "The Ride of a Lifetime" (Random House, 2019) documented in publisher catalogs. Chapek transition documented in Disney press releases (November 2022).
### Rank 02 · Reed Hastings — Composite Composite 74
Netflix · Co-founder, Executive Chairman
Anchor event. The Netflix founder mythology — DVD-to-streaming transition, the binge-watching paradigm, and "No Rules Rules" — anchored by the 2023 transition to a non-operational role.
Engines render Hastings through Netflix co-founding, the DVD-to-streaming transition, the binge-watching paradigm, his 2020 book "No Rules Rules" co-authored with Erin Meyer, and his 2023 transition from co-CEO to executive chairman. Founder-mythology rendering favorably overweighted in engine portraits even as he steps back from operational role. Sentiment of 74 reflects sustained positive coverage despite recent password-sharing crackdown controversy.
Sourced rationale. Netflix history documented in Netflix 10-Ks and across major business press. "No Rules Rules" (Penguin, 2020) documented in publisher catalogs. Leadership transition documented in Netflix press release (January 2023).
### Rank 04 · David Ellison — Composite Composite 68
Skydance / Paramount Skydance · CEO
Anchor event. The 2024 Skydance / Paramount Global merger — closed August 2024 — establishing Ellison as the new principal of the combined media platform.
Engines render Ellison through Skydance Media founding (2010), the Top Gun: Maverick and Mission: Impossible production partnerships, the August 2024 Paramount merger (closed for $8B+), and his lineage as son of Larry Ellison. The most-newly-rendered media-chief portrait in the cohort. Control of 72 reflects unusually disciplined positioning during the transition — Ellison surfaces primarily through controlled press appearances and Paramount Skydance corporate communications.
Sourced rationale. Skydance / Paramount merger documented in Paramount Global SEC filings (July 2024 announcement, August 2024 close) and across WSJ, NYT, FT, Bloomberg.
## Bottom of the Cohort
### The worst-rendered chiefs
The bottom of the cohort produces the largest Sentiment-vs-Completeness paradox in the series. Each principal has a deeply-rendered engine portrait — and the depth itself depresses Sentiment because engines surface a complete record of contested events.
### Rank 15 · Lowest · Linda Yaccarino — Composite Composite 38
X (formerly Twitter) · CEO (May 2023 – July 2025)
Anchor event. The post-acquisition X advertiser exodus and the unresolved founder shadow — Elon Musk's portrait dominating the CEO portrait throughout her tenure and beyond her July 2025 resignation.
Engines render Yaccarino almost entirely through founder shadow. Despite her CEO title from May 2023 to July 2025, engine portraits surface her primarily as Musk's deputy or as the public face of post-acquisition X — not as an independent principal. The cohort's clearest demonstration that founder shadow can prevent engine portrait formation even during an operational CEO tenure. Advertiser exodus references, content-moderation disputes, the Cesar Conde NBCUniversal precedent, and the July 2025 resignation announcement all surface alongside the Musk-dominant framing.
Sourced rationale. X advertiser exodus documented across WSJ, NYT, FT, Bloomberg (2023–2025). Yaccarino appointment documented in X / Musk announcements (May 2023). Resignation documented in Yaccarino public statement and X corporate communications (July 2025).
### Rank 13 · Rupert Murdoch — Composite Composite 42
News Corp & Fox · Chairman Emeritus
Anchor event. The April 2023 Dominion defamation settlement ($787.5M) and the ongoing Murdoch family succession lawsuit ("Murdoch v. Murdoch", Nevada probate, 2024–2025).
Engines render Rupert Murdoch through the most-rendered media-executive portrait in the cohort — Completeness of 86 reflects six decades of activity. The 2023 retirement transition to Lachlan, the Dominion Voting Systems defamation settlement ($787.5M, April 2023), the Smartmatic litigation, and the Nevada probate court lawsuit filed by Prudence, Elisabeth, and James Murdoch against Rupert and Lachlan over the 2024 trust restructuring all dominate engine portraits. Control of 14 reflects external-coverage dominance despite the depth of his own published archive.
Sourced rationale. Dominion settlement documented in court records (Delaware Superior Court) and across all major outlets (April 2023). Murdoch succession lawsuit documented in NYT, WSJ, NY Times Magazine (July 2024 "The Heirs" reporting), and Nevada probate filings.
### Rank 12 · David Zaslav — Composite Composite 48
Warner Bros. Discovery · CEO (until 2026 split)
Anchor event. The 2022 WarnerMedia / Discovery merger and subsequent debt load, alongside the executive compensation controversy and post-merger talent-and-IP-shelving decisions.
Engines render Zaslav through three layered anchors: the April 2022 WarnerMedia / Discovery merger (creating Warner Bros. Discovery), the resulting $50B+ debt load, executive compensation that became a focal point for activist criticism, and shelved-content decisions (Batgirl, Coyote vs. Acme). The 2025 announcement to split WBD into separate streaming and cable companies registers as transition coverage. Sentiment of 26 reflects sustained negative framing across Hollywood-trade press and financial press.
Sourced rationale. WBD merger documented in WBD 10-K and across major outlets. Compensation documented in WBD proxy statements. Shelved-content decisions documented across Variety, Hollywood Reporter, Deadline.
## Notable Gaps
### Where engines diverge from public record
### 01. The Murdoch family compression
Engines collapse "Murdoch" prompts into a unified family entity in approximately 40 percent of cases. The distinction between Rupert (chairman emeritus), Lachlan (operational executive chair, Fox), and the dissenting children (Prudence, Elisabeth, James) is rendered inconsistently across engines. Google AI Overviews collapses most aggressively; Perplexity separates most reliably.
### 02. The Yaccarino post-resignation lag
Engines continue to render Yaccarino as active X CEO in 28 percent of prompts despite her July 2025 resignation. The cohort's clearest case of recent-transition rendering lag.
### 03. The Netflix co-CEO bifurcation
Sarandos (70) outscores Peters (64) despite the formally-equal co-CEO structure. Engines surface Sarandos's content-led portrait more densely than Peters's product-led portrait, reflecting the asymmetric primary-source archives.
### 04. The Cavanagh / Versant transition
The 2025 announcement to spin off NBCUniversal's cable networks into a new company (Versant) places Mike Cavanagh in a transition rendering. Engines render him in three simultaneously-active roles — current NBCU president, Versant CEO-designate, and Comcast officer — producing the cohort's most-volatile engine portrait.
### 05. The David Ellison founder-pedigree rendering effect
Ellison scores 68 composite — high for a media chief in his first year of a major-platform CEO role. Engines surface his Larry Ellison lineage in 55 percent of identity prompts, producing a measurable reputational inheritance lift that distinguishes his portrait from comparable first-year media-chief peers.
## Methodology Notes
### How the Index was modeled
The 5W Reputation Index measures how leading AI systems render named principals across sixty-plus retrieval-intent prompts per engine. Scores are directional estimates derived from modeled engine outputs and supplementary web-search verification — not logged query runs. The Index reflects engine-rendered reputation only. It is not an evaluation of any principal's actual conduct, character, or business practices.
This Index does not adjudicate the merits of any pending litigation, allegation, or contested matter referenced in the source record.
Previous: ← Hedge Fund Principals
Series Hub: The 5W Reputation Index
Next Study: College Football Coaches →
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. Learn more at 5wpr.com.
The 5W Reputation Index · Study 07 of 09 · Published 07 July 2026 · 5W AI Communications · Florida
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### Quantum Computing AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/quantum-computing-ai-visibility-index-2026/
Summary: The 5W index ranks the quantum computing industry in 2026 by citation share, measuring which firms ChatGPT, Claude, Perplexity and Gemini cite most often.
5W Research
# Quantum Computing AI Visibility Index 2026
$50 billion in quantum pure-play market cap. Six publicly traded quantum-only companies. When enterprise buyers ask AI which quantum company to evaluate, the answer is IBM. The pure-plays are footnotes in their own category.
## The Pure-Plays Built Quantum. IBM, Google and Microsoft Own the AI Answer.
Six publicly traded quantum-only companies. More than $50 billion in combined market capitalization. IonQ just reported $80.1 million in a single quarter — a 287% year-over-year increase. Quantinuum completed its SPAC. Xanadu closed $3.1 billion.
None of it matters inside the chatbox.
The 5W Quantum Computing AI Visibility Index analyzed 3,600 quantum-related prompts across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews between February and July 2026. The findings:
- 71% of enterprise quantum prompts cite a hyperscaler — IBM, Google, or Microsoft — before naming any pure-play quantum company.
- Less than 9% of citations came from pure-play company domains (ionq.com, rigetti.com, dwavequantum.com, quantinuum.com).
- IBM is cited 4.3× more than IonQ — the largest pure-play by revenue — in head-to-head enterprise evaluation queries.
- Cloudflare earns higher citation frequency in post-quantum cybersecurity queries than any quantum computing company on earth.
The quantum computing industry has a $50 billion AI visibility problem. The companies building the technology are losing the AI answer to the companies surrounding them.
## The IonQ Paradox: $280 Million in Revenue, Invisible in the Enterprise Answer
IonQ is the commercial proof case.
The company posted Q1 2026 revenue of $64.67 million — a 755% year-over-year increase. Management raised full-year guidance to $280–290 million. Remaining performance obligations surged 554% year-over-year to $470 million. IonQ's trapped-ion architecture leads the field in gate fidelity. Its Q2 revenue hit $80.1 million. The market cap exceeds $20 billion.
But when an enterprise CTO asks ChatGPT, Claude, or Perplexity which quantum computing company should we evaluate, IBM appears first in 71% of responses. IonQ appears as a secondary mention — after IBM, after Google, after Microsoft, and often after Quantinuum.
The reason is structural, not editorial. IBM's 20-year quantum research archive, Qiskit open-source framework, and IBM Quantum Experience cloud platform create a citation depth no pure-play can replicate. But IonQ compounds the problem: its most detailed technology documentation lives inside investor presentations (PDFs behind IR portals) and earnings transcripts (attributed to Seeking Alpha or Yahoo Finance, not to ionq.com). IonQ is producing the most comprehensive public record of its technology — and receiving zero citation credit because the format and hosting are wrong for AI retrieval.
The gap between IonQ's commercial position and its AI citation share is the single clearest illustration of the AI visibility problem facing the entire quantum industry.
## The AI Quantum Citation Stack: Who Supplies the Answer
Six layers supply the source material AI engines synthesize when answering quantum computing questions. The stack is structurally different from every consumer category 5W has measured — consulting firms, not affiliate publishers, dominate the answer.
Layer
Source Type
Est. Citation Share
Key Domains
1
Consulting & Research Firms
30–35%
McKinsey, BCG, Gartner, Forrester, IDC
2
Tech Publications
20–25%
MIT Tech Review, Wired, IEEE Spectrum, The Quantum Insider
3
Hyperscaler Content
15–18%
research.ibm.com, cloud.google.com, azure docs, AWS Braket docs
4
Academic & Government
10–12%
Nature, Science, arXiv, NIST, DARPA
5
Pure-Play Company Domains
<9%
ionq.com, rigetti.com, dwavequantum.com, quantinuum.com
6
Reddit & Community
4–6%
r/QuantumComputing, r/QuantumInformation, Hacker News
The dominance of consulting firms is the defining structural feature of the quantum citation stack. McKinsey's Quantum Technology Monitor appeared in 38% of strategic-evaluation prompts tested. When McKinsey writes that quantum computing has reached "a commercial tipping point," AI engines reproduce that frame. Consulting-firm citation share in quantum (30–35%) is more than double the consulting-firm share in any consumer category 5W has measured.
The implication: the quantum narrative is being mediated by strategy consultants — not by the companies building the technology.
The hyperscaler content layer compounds the problem. When AI engines cite research.ibm.com to explain quantum error correction, IBM simultaneously earns the citation and the brand mention. This dual function — source and subject — is unique to the quantum category and amplifies hyperscaler citation share beyond what editorial coverage alone would produce.
## The Top 25 Quantum Computing Companies by AI Citation Frequency
Tier
Company
Architecture / Role
Key Citation Driver
Tier 1 — Anchor Citations (40%+ of category answers)
1
IBM Quantum
Superconducting / Cloud
Condor 1,121 qubits, Qiskit, 20-year research archive
2
Google Quantum AI
Superconducting / Cloud
Willow processor, AlphaQubit AI-driven error correction
3
Microsoft Azure Quantum
Topological (unproven) / Cloud
Enterprise cloud narrative, PQC integration
Tier 2 — Strong Citations (15–30% of relevant answers)
4
IonQ
Trapped-ion / Pure-play
Largest pure-play by revenue; AWS Braket + Azure partnerships
5
Quantinuum
Trapped-ion / Pure-play
NVIDIA's $600M investment; Helios 48 logical qubits
6
NVIDIA
GPU infrastructure / Enabler
cuQuantum SDK; hybrid quantum-classical backbone
7
Amazon (AWS Braket)
Cloud marketplace
Multi-vendor neutral marketplace; IonQ/Rigetti/QuEra access
Tier 3 — Comparison-Set Citations (technical & specialist queries)
8
D-Wave Quantum
Annealing + gate-model / Pure-play
Only commercial annealer; 5,000+ qubits; Quantum Circuits acq.
9
Rigetti Computing
Superconducting / Pure-play
108-qubit Cepheus; revenue nearly 3× YoY Q1 2026
10
QuEra Computing
Neutral atom / Pure-play
96 logical qubits (Nature paper, Jan 2026)
11
PsiQuantum
Photonic / Pre-revenue
$750M+ funding; GlobalFoundries manufacturing deal
12
Xanadu
Photonic / Pure-play
PennyLane quantum ML framework; $3.1B SPAC merger
13
Pasqal
Neutral atom / Pure-play
324-atom system; CINECA Italy deployment
Tier 4 — Niche & Emerging Citations (narrow query clusters only)
14
Atom Computing
Neutral atom
Scalability and long-coherence queries
15
Infleqtion
Neutral atom / sensing
Defense and precision-timing queries
16
IQM Quantum Computers
Superconducting (EU)
EU quantum sovereignty queries
17
Quantum Computing Inc.
Photonic / optimization
Retail-investor stock queries only
18
SandboxAQ
PQC / sensing (Alphabet spin-out)
Cybersecurity migration queries
19
Multiverse Computing
Quantum-inspired algorithms
Financial modeling use-case queries
20–25
QC Ware, Strangeworks, Classiq, Alice & Bob, Bleximo, Oxford Quantum Circuits
Software / hardware
Developer-ecosystem and architecture-specific queries
## Three Query Clusters, Three Separate Worlds
The quantum AI answer surface is not one market. It is three — each with its own citation stack, its own winners, and its own losers.
### Enterprise Evaluation: Hyperscalers Win
When enterprise buyers ask which quantum computing company should we evaluate, AI engines produce answers dominated by IBM, Google, and Microsoft. Sources: McKinsey, Gartner, research.ibm.com. Pure-play companies appear as secondary mentions. No pure-play company domain was the primary cited source in any enterprise evaluation response tested.
### Investment Queries: Pure-Plays Win — Through Financial Publishers
When investors ask should I invest in quantum computing stocks, the citation stack flips entirely. IonQ, Rigetti, and D-Wave lead every response. But the cited sources are Yahoo Finance, Motley Fool, Seeking Alpha, and Barron's — not the companies' own investor-relations pages. Pure-plays earn the brand mention but not the domain citation. The financial publisher layer captures the citation credit.
### Cybersecurity (PQC) Queries: Quantum Companies Are Absent
When enterprise security teams ask how does quantum computing threaten our encryption, the answer belongs to NIST, CISA, and Cloudflare. NIST's FIPS 203, 204, and 205 standards and the CNSA 2.0 January 2027 acquisition deadline structure every PQC response. Cloudflare — a CDN company — earns higher citation frequency in post-quantum cybersecurity queries than any quantum hardware company. The PQC market is projected at $2.84 billion by 2030. The quantum hardware industry, whose technology creates the threat PQC addresses, earns zero citation share in this $2.84 billion answer surface.
## Why the Pure-Plays Are Losing: Four Structural Patterns
### 1. Hyperscalers Own the Enterprise Answer Because They Own the Cloud Narrative
IBM, Google, and Microsoft control the enterprise cloud platforms where quantum access is delivered. AWS Braket, Azure Quantum, and IBM Quantum Experience frame the evaluation question AI engines answer. The pure-plays are inside the hyperscaler marketplace — which means AI engines cite the marketplace before citing the vendor. The platform owns the answer. The vendor is inventory.
### 2. Consulting Firms Set the Frame That AI Engines Reproduce
McKinsey's Quantum Technology Monitor, BCG's quantum-readiness frameworks, and Gartner's Hype Cycle placement function as the structural template AI engines use to organize quantum answers. The quantum narrative is being shaped by organizations that do not build quantum technology, deploy quantum hardware, or sell quantum services. And AI engines trust them more than they trust the companies that do.
### 3. Investor Content Is Invisible to AI Engines
IonQ, Rigetti, and D-Wave publish detailed quarterly earnings, technology roadmaps, and use-case documentation — the most granular public record of quantum technology progress available. Almost none of it earns AI citations. Investor presentations are PDFs behind IR portals. Earnings transcripts are attributed to Seeking Alpha, not to the company. Structured blog posts on company domains earn citations. Slide decks on third-party financial platforms do not.
### 4. The "Quantum Advantage" Timeline Caveat Suppresses the Entire Category
Every AI engine tested includes a version of the caveat: fault-tolerant quantum computing is still years away. This frame — sourced primarily from DARPA's QBI program and widely-cited statements from NVIDIA's CEO — compresses the citation surface for current commercial offerings. AI engines treat quantum as a future technology even when current-quarter revenue numbers are available. The timeline caveat has no equivalent in consumer categories.
## Winners and Underperformers
### Over-Indexing on Citation Share
- IBM Quantum — Generates less than 1% of IBM's total revenue from quantum. Earns anchor-tier citation frequency across every enterprise query cluster.
- Google Quantum AI — The Willow processor and AlphaQubit earned concentrated citation pickup that persists months after the announcement cycle. Home-court advantage on Gemini and Google AI Overviews further amplifies share.
- Quantinuum — NVIDIA's $600M investment functions as the single most-cited third-party endorsement in the category. AI engines weight the NVIDIA endorsement as a proxy for technology quality.
- QuEra Computing — Pre-revenue. One Nature paper (96 logical qubits, January 2026) earned more AI citation share than companies with hundreds of millions in funding. Peer-reviewed publication in a top journal is the highest-ROI citation strategy in the category.
- McKinsey & Company — Not a quantum company. The single most-cited source in enterprise quantum evaluation queries. McKinsey's citation share in this category exceeds many quantum companies' own citation share.
### Under-Indexing Relative to Market Position
- IonQ — $20B+ market cap. $280–290M 2026 revenue guidance. Cited 4.3× less than IBM in enterprise queries. Technology leadership does not translate to AI visibility leadership.
- Rigetti Computing — Revenue nearly tripled YoY in Q1 2026. Citation share did not move. The 108-qubit Cepheus system is functionally invisible outside AWS Braket comparison queries.
- D-Wave Quantum — The only company with a commercially deployed quantum optimization system (5,000+ qubits) earns lower citation frequency than companies with fewer than 200 qubits. AI engines frame quantum as a gate-model race; D-Wave's annealing architecture is classified as a different category.
- PsiQuantum — $750M+ in total funding. GlobalFoundries manufacturing partnership. Functionally invisible in AI answers because the company has not shipped a commercial system. Funding does not earn citations. Shipped products do.
- Every quantum hardware company in PQC queries — The industry building the threat earns zero citation share in the answer about how to respond to it.
## Future Risks and Inflection Points
### 1. The Logical Qubit Race Resets the Scoreboard
Physical qubit counts have been the primary metric AI engines use to rank quantum companies. The shift to logical qubits — QuEra's 96, Quantinuum's 48, IBM's target of 200 by 2029 — creates a new benchmark where current citation leaders may not lead. Companies that publish logical qubit milestones in peer-reviewed formats earn disproportionate pickup during the transition.
### 2. DARPA's QBI Becomes the Official Scorecard
DARPA's Quantum Benchmarking Initiative is building a standardized, government-sanctioned benchmark for quantum utility. When published, it will become the framework AI engines use to evaluate quantum companies — replacing the current patchwork of self-reported metrics. Companies inside QBI earn citation share. Companies outside it lose it.
### 3. The CNSA 2.0 Deadline Drives Enterprise Urgency on PQC
NSA's CNSA 2.0 sets January 2027 as the deadline for new acquisitions in National Security Systems to include post-quantum cryptography. NIST's transition plan deprecates RSA-2048 and ECC P-256 by 2030. Enterprise procurement teams asking AI engines about quantum risk will increasingly receive PQC migration answers. Quantum hardware companies are leaving this entire answer surface to Cloudflare, SandboxAQ, and NIST.
### 4. Sovereign Quantum Programs Fragment the Citation Surface
The UK's £2.5B National Quantum Strategy, the EU's Quantum Flagship, and India's National Quantum Mission are creating sovereign citation stacks where national champions earn share that does not transfer to U.S.-centric queries. AI engines are fragmenting quantum answers by geography in ways consumer categories do not experience.
### 5. "Quantum + AI" Framing Earns an Outsized Citation Premium
Companies that frame their technology as quantum-AI integration — Google's AlphaQubit, NVIDIA's cuQuantum — earn 2–3× the citation pickup of companies framing as "quantum only." The AI-adjacency premium is the strongest citation multiplier in the category.
## The 5W GEO Playbook for Quantum Companies
### For Pure-Play Quantum Hardware Companies
- Publish technology milestones on your own domain — blog posts, not IR decks. A post on ionq.com earns citation credit. A slide on Seeking Alpha does not.
- Produce peer-reviewed benchmarks in Nature, Science, and IEEE. QuEra's logical-qubit paper is the proof case: one publication in a top journal earned more AI citation share than years of press releases.
- Build structured comparison content the consulting firms will cite. If McKinsey's next Quantum Technology Monitor sources your data, every AI engine that cites McKinsey cites you by proxy.
- Publish use-case documentation with named enterprise customers, dollar impacts, and measurable outcomes. AI engines cite case studies with numbers. They do not cite "early-stage pilots with Fortune 500 companies."
- Deploy Article schema, FAQ schema, and TechArticle schema on product and research pages.
### For Hyperscalers
- The cloud marketplace framing is winning. Continue publishing quantum-access documentation as first-party content.
- The quantum + AI integration narrative earns outsized citation share. Google's AlphaQubit is the model.
### For Post-Quantum Cryptography Companies
- The PQC citation surface is dominated by NIST and Cloudflare. PQC-specific companies are under-cited relative to market positioning.
- Publish migration case studies with named customers. The CNSA 2.0 January 2027 deadline creates an urgency window where AI engines will increasingly surface PQC content — companies with published case studies earn that surface.
## What This Means for the Quantum Industry
The Quantum Computing AI Visibility Index documents a category where the companies building the technology are not the companies controlling the AI answer about the technology. Hyperscalers, consulting firms, and tech publications supply the citation infrastructure. Pure-play quantum companies — despite $50 billion in combined market capitalization and genuine technology leadership — are footnotes in an answer about their own industry.
This is not an awareness problem. It is an enterprise-procurement problem. When a CTO asks ChatGPT or Perplexity which quantum platform to evaluate, the answer determines which vendor gets the meeting.
"IonQ just reported $80 million in quarterly revenue. Quantinuum just closed a SPAC. Xanadu raised $3 billion. But when a buyer asks the chatbox which quantum company to work with, the answer is IBM. The pure-plays built the technology. The hyperscalers built the answer."
— Ronn Torossian, founder, 5W
For quantum computing companies evaluating their position, 5W offers a Quantum Computing Category AI Visibility Audit benchmarking citation share against the Index's top 25 and identifying the structural content and earned-media gaps that determine whether a company earns mention in AI-generated enterprise research.
## Methodology
The 5W Quantum Computing AI Visibility Index analyzed 3,600 category prompts across enterprise evaluation, technical comparison, qubit-architecture, use-case (pharma, finance, logistics, cybersecurity, materials science), investment, workforce, and post-quantum-cryptography query clusters. Prompts were tested across the five major U.S. consumer- and enterprise-facing AI platforms — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — between February 3 and July 15, 2026.
Citation share was measured by counting source domain attributions inside AI-generated responses. Where a platform did not surface visible source citations — as is common with ChatGPT's default interface and Gemini's conversational mode — 5W applied retrieval pattern analysis: identical prompts were tested across citation-displaying platforms (Perplexity, Google AI Overviews) and citation-suppressed platforms, and the resulting brand-mention patterns were compared against the source domains visible on citing platforms to identify likely retrieval sources. This cross-platform triangulation method has been applied across all prior 5W AI Visibility Index studies and is documented in detail in the series methodology appendix. Retrieval pattern analysis identifies probable source alignment, not confirmed attribution, and is reported as such throughout the Index.
Brand mentions were de-duplicated across prompts to prevent over-weighting from repeated query phrasing. Tier assignments reflect aggregated citation frequency observed across the test set. Rankings inside each tier reflect citation order inside head-to-head comparison queries.
The "hyperscaler" designation includes IBM, Google, Microsoft, and Amazon where their quantum-specific business units (IBM Quantum, Google Quantum AI, Azure Quantum, AWS Braket) were the cited entity. The Index distinguishes between enterprise-evaluation, technical-comparison, investment, and cybersecurity query clusters because each produces a structurally different citation stack. Cluster-specific breakdowns are available upon request to credentialed analysts and journalists.
Methodology, prompt set, and platform version notes available upon request.
## Related Research
- Credit Cards AI Visibility Index 2026 — Three publishers supply 62% of AI citations across 4,200 prompts. Issuer domains under 6%.
- Banks AI Visibility Index 2026 — JPMorgan Chase holds 28.4% of consumer banking Citation Share.
- Thirteen Industries. One Pattern — The biggest brand isn't always the one AI names.
## Engage with 5W
For inquiries about Generative Engine Optimization engagements, AI Visibility Index licensing, custom industry research, or category-specific PR and communications: visit 5wpr.com/practice/geo-optimization, read the AI Visibility Index series, or contact the 5W research team.
## About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research.
Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO.
For more information, visit www.5wpr.com.
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### The Cannabis AI Visibility Index 2026 — Top 25 U.S. Cannabis Brands by AI Citation Share
URL: https://www.5wpr.com/ai-visibility-index/cannabis-ai-visibility-index-2026/
Summary: The Cannabis AI Visibility Index 2026 ranks the top 25 U.S. cannabis brands by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and AI Overviews.
# The Cannabis AI Visibility Index 2026
### Top 25 American cannabis brands ranked by AI citation share. By Ronn Torossian, Founder of 5W, and the 5W Research Team. Published April 2026.
Last updated: September 2026
Download the full PDF
Curaleaf, Trulieve, and Green Thumb Industries lead U.S. cannabis brand citations in AI search, together capturing an estimated 17.5% share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Cookies leads consumer brands and Charlotte's Web leads CBD. AI engines refuse or hedge about 28% of cannabis prompts, the highest rate 5W has measured in any category.
## EXECUTIVE SUMMARY: WHO IS WINNING CANNABIS AI CITATION SHARE?
Cannabis is the most legally fragmented major consumer category in America. It is also the most fragmented AI search category 5W has measured. Federal Schedule I status combines with 25 state-level adult-use regimes and 16 medical-only regimes. That combination produces AI search behavior that varies by query phrasing, state context, product type, and the date each AI model was last trained.
The prompt "best cannabis dispensary near me" returns different brand citations in California, Florida, and Massachusetts. The same brand can surface in one state and disappear in another.
This report measures who is winning that fragmented citation surface and who is not. 5W ran more than 50 consumer-intent prompts through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews in Q1 2026. The prompts tracked citations across five sub-categories: U.S. multi-state operators (MSOs), branded consumer products, CBD-specific brands, dispensary aggregators and tech platforms, and ancillary services.
Curaleaf, Trulieve, and Green Thumb Industries together account for an estimated 17.5% of all cannabis-category AI citations across the prompts 5W tested. That three-brand group forms the dominant MSO citation tier. Cookies leads consumer-product cannabis brand citations by a wide margin. Charlotte's Web leads the CBD sub-category, with citation density that no challenger has matched in five years. Leafly and Weedmaps each capture more aggregator citations than every individual MSO except Curaleaf.
About 28% of the cannabis prompts 5W tested produced AI engine refusals, hedges, or prominent disclaimers. That refusal rate is higher than any other category 5W has measured.
Based on 5W's Q1 2026 testing, the brands that build AI citation share over the next twelve months will hold the strongest position through cannabis rescheduling and beyond. AI citation share is now compounding faster than retail footprint expansion, and most operators have not adjusted strategy to account for it.
## WHICH CANNABIS BRANDS HAVE THE MOST AI CITATION SHARE?
Curaleaf leads the top 15 brands by estimated AI citation share, followed by Trulieve and Cookies. The estimates below reflect 50+ cannabis-industry consumer prompts run through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews in Q1 2026.
Curaleaf7.5%
Trulieve6.5%
Cookies5.5%
Leafly5.0%
Charlotte's Web4.5%
Green Thumb Industries3.5%
Weedmaps3.5%
Wyld3.0%
Stiiizy3.0%
Kiva Confections2.5%
Cresco Labs2.5%
Verano Holdings2.5%
Cann2.0%
Cornbread Hemp2.0%
PAX Labs2.0%
Color key: green marks multi-state operators. Orange marks branded consumer products. Gold marks CBD-specific brands. Purple marks aggregators and tech. Blue marks devices and accessories.
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q1 2026. Share represents estimated proportion of brand citations across 50+ tracked consumer prompts. The remaining share splits across ranks 16 to 25, unranked brands, and refused or hedged AI responses.
## THE FULL TOP 25 RANKING
1. Curaleaf. Multi-state operator. Approximately 159 to 161 retail locations across 14 U.S. states as of end-Q4 2025 and early 2026 (Curaleaf Holdings Q4 2025 earnings release). The first U.S. MSO to expand internationally, via Curaleaf International. Wide product portfolio across flower, vapes, edibles, and hemp-derived THC beverages. Category leader by AI citation share for "biggest cannabis company" and "national cannabis brand" prompts.
2. Trulieve. Multi-state operator. Over 200 dispensaries with dominant Florida market share, the largest medical cannabis market in the country at 800,000-plus registered patients. Strong presence in Arizona and Pennsylvania. Wins nearly every Florida-specific cannabis prompt.
3. Cookies. Branded consumer products, founded by Berner. The most-cited consumer cannabis brand in U.S. AI answers. Owns the "best cannabis brand" and "premium flower" citation surfaces. Its cultural footprint extends well beyond cannabis-specific media into mainstream lifestyle press.
4. Leafly. Aggregator and information source, founded 2010. The de facto consumer cannabis information source. Its strain database, dispensary finder, and consumer education content are all cited heavily by AI engines, making it a category-defining authority on strain queries.
5. Charlotte's Web. CBD-specific, founded by the Stanley Brothers. The most-cited CBD brand in U.S. AI answers. Its origin story (Charlotte Figi) gives the brand a citation moat in CBD-for-medical-use queries that no challenger has matched.
6. Green Thumb Industries. Multi-state operator with 113 retail stores in 14 states (March 2026). Brand portfolio includes Rythm, Dogwalkers, and Beboe, the most-recognized national consumer cannabis brand portfolio of any MSO, and one of the few consistently profitable cannabis stocks.
7. Weedmaps. Aggregator and information source, the second-largest cannabis aggregator. Stronger geographic penetration in California and West Coast markets than Leafly. Wins "dispensary near me" prompts in California and competing prompts in CA-adjacent states.
8. Wyld. Branded consumer products. Owns the "best cannabis edibles" and "best cannabis gummies" citation surfaces. Distributed in multiple states through licensing agreements with regional MSOs.
9. Stiiizy. Branded consumer products and the vape-category leader. Dominant in California and growing in expansion markets. Wins "best cannabis vape brand" prompts at industry-leading consistency.
10. Kiva Confections. Branded consumer products in premium chocolate and confections. Strong citation share in "luxury cannabis edibles" and gift-occasion prompts.
11. Cresco Labs. Multi-state operator and wholesale cannabis distribution leader. Brand portfolio includes Cresco, Sunnyside, and Mindy's Edibles, with strong Illinois and Pennsylvania presence.
12. Verano Holdings. Multi-state operator with a tiered brand architecture: Verano, Savvy, and Encore. Retail strength in New Jersey and Illinois, where its citation share is concentrated.
13. Cann. Branded consumer products in cannabis-infused beverages. Owns the "cannabis drink" and "cannabis sober-curious" citation niches, and is the most-cited cannabis beverage brand in lifestyle-press AI references.
14. Cornbread Hemp. CBD-specific, based in Kentucky. Strong citation share in "USDA-organic CBD" and "Kentucky CBD" prompts. The closest direct challenger to Charlotte's Web on premium CBD positioning.
15. PAX Labs. Devices and accessories, the dominant cannabis vaporizer hardware brand. Distinct from Stiiizy, which makes vape pens with cannabis oil; PAX makes the device. Strong "best cannabis vaporizer" citation share.
16. Lazarus Naturals. CBD-specific. Strong citation share in "affordable CBD" and "high-potency CBD" prompts.
17. Ascend Wellness. Multi-state operator with strong East Coast retail presence, including Massachusetts and New Jersey. Citation share concentrated regionally.
18. Wana Brands. Branded consumer products in cannabis edibles. Strong citation share in "cannabis gummies" prompts, competing directly with Wyld.
19. CBDistillery. CBD-specific, with mass-market positioning and wide retail distribution. Citation share concentrated in "best CBD on Amazon" and "affordable CBD" prompts.
20. Dutchie. Cannabis tech and e-commerce, the dominant dispensary e-commerce platform, powering thousands of dispensary websites. AI engines cite Dutchie infrastructure indirectly through dispensary citations.
21. Eaze. Aggregator and delivery service focused on California. Citation share concentrated in California-specific delivery prompts.
22. Joy Organics. CBD-specific, with THC-free positioning. Strong citation share in "CBD without THC" and "drug-test-safe CBD" prompts.
23. TerrAscend. Multi-state operator with presence in Pennsylvania, New Jersey, and Maryland. Strong regional citation share in those states.
24. MJBizDaily. Industry information source and the dominant cannabis trade publication. Cited by AI engines for cannabis-industry business prompts and sales statistics.
25. Medterra. CBD-specific, with athletic positioning and an NSF Certified for Sport designation. Strong citation share in "CBD for athletes" and "CBD for recovery" prompts.
## KEY FINDINGS
- STAT 1: 25 states plus DC where recreational cannabis is fully legal as of April 2026. 16 additional states operate medical-only programs.
- STAT 2: $47B, the projected U.S. cannabis industry size in 2026.
- STAT 3: $25B, annual U.S. cannabis tax revenue, nearly double alcohol's contribution.
- STAT 4: 425,002 full-time-equivalent jobs supported by legal cannabis in the U.S.
- STAT 5: 17.5%, the estimated combined AI citation share of Curaleaf, Trulieve, and Green Thumb Industries across 50+ tracked queries.
- STAT 6: 28%, the share of cannabis prompts that produced AI engine refusals, disclaimers, or hedges. The highest rate of any category 5W has measured.
- STAT 7: Approximately 160 Curaleaf retail locations across 14 U.S. states (Curaleaf Q4 2025 earnings release, January 2026).
- STAT 8: December 18, 2025, the date President Trump signed the executive order directing rescheduling of cannabis to Schedule III. April 23, 2026, the date DOJ and DEA finalized the order placing FDA-approved marijuana products and state-licensed medical marijuana in Schedule III, with a broader rescheduling hearing scheduled June 29 to July 15, 2026.
## WHY DOES CANNABIS PRODUCE THE MOST VOLATILE AI CITATION SURFACE?
Every other major American consumer category 5W has tracked operates under one set of rules. Pickleball paddles ship to Texas the same way they ship to California. Crypto exchanges register with one federal regulator. Pet food sells in Tennessee the same way it sells in New York. Cannabis is the only major consumer category where federal illegality and 25 different state-legal frameworks sit on top of one another. AI engines have absorbed that fragmentation and reflected it back in their citations.
The result is the most volatile AI citation surface 5W has measured. The same query produces different recommendations by state. Some prompts trigger AI hedging or refusals at rates five to ten times higher than any other category. Brand citations that exist for medical cannabis disappear when the prompt mentions recreational use. Citations for CBD products differ from citations for THC products, because the federal-legal distinction is preserved in AI training data. Aggregators such as Leafly and Weedmaps win general "find a dispensary" prompts at rates that compete with the largest MSOs. AI engines treat the aggregator surface as the neutral answer to a federally complicated question.
Inside that fragmented citation surface, 5W's testing surfaced three structural patterns. First, the largest MSOs (Curaleaf, Trulieve, Green Thumb Industries, Cresco Labs, and Verano) have built enough state-by-state retail and brand presence that AI engines default to them in their strongest states. Curaleaf wins national "biggest cannabis company" prompts. Trulieve dominates Florida-specific prompts. Green Thumb owns brand-name prompts for Rythm, Dogwalkers, and Beboe that smaller MSOs cannot match.
Second, branded cannabis consumer products, including Cookies, Cann, Wyld, Stiiizy, and Kiva, have built citation surfaces that operate independently of dispensary brand recognition. A consumer asking "best cannabis edible" gets Wyld and Kiva. A consumer asking "best cannabis dispensary" gets Curaleaf and Trulieve. The two surfaces barely overlap.
Third, the CBD category (Charlotte's Web, Cornbread Hemp, Lazarus Naturals, and CBDistillery) operates as a separate AI citation universe entirely. CBD's federal legal status under the 2018 Farm Bill produces fewer AI hedges than THC queries.
The Trump administration's December 2025 executive order to reschedule cannabis to Schedule III, if implemented in full, will reset the AI citation surface again. Brands that have built consistent, structured, regulatory-credible content across the legal volatility are building durable AI citation share now. Brands waiting for federal clarity are falling further behind.
## METHODOLOGY
5W analyzed more than 50 common cannabis-buyer prompts across five sub-categories. The team identified which brands AI models consistently surface and which editorial and authoritative sources feed those citations. It also tracked where AI engines hedge or refuse to answer, and where the largest gaps sit between commercial scale and AI visibility.
### Sub-categories tracked.
U.S. multi-state operators (MSOs): Curaleaf, Trulieve, Green Thumb Industries, Cresco Labs, Verano Holdings, Ascend Wellness, Columbia Care, Jushi Holdings, TerrAscend, and Schwazze. Branded cannabis consumer products: Cookies, Cann, Wyld, Stiiizy, Kiva Confections, Wana Brands, Select, PAX Labs, Dogwalkers, Rythm, and Beboe. CBD-specific brands: Charlotte's Web, Cornbread Hemp, Lazarus Naturals, CBDistillery, Joy Organics, and Medterra. Dispensary aggregators and tech: Leafly, Weedmaps, Eaze, Dutchie, Jane Technologies, Headset, and Flowhub. Industry references and information sources: NORML, Marijuana Moment, MJBizDaily, NCIA, and USDA hemp resources.
### Query types tracked.
5W tested real-world buyer-intent prompts, including the examples below, plus more than 35 additional variations covering medical applications, state-specific availability, product selection, education, and brand-comparison intent.
- "Best cannabis dispensary near me"
- "Best cannabis brand for beginners"
- "Where to buy cannabis legally in [state]"
- "Best cannabis edibles" and "best cannabis vape brand"
- "Cannabis vs CBD difference"
- "Best CBD oil" and "best CBD for sleep"
- "Curaleaf vs Trulieve" and "Charlotte's Web vs Cornbread Hemp"
- "Is cannabis legal in Texas, Florida, or Pennsylvania"
- "How to get a medical cannabis card"
- "Cannabis dosing for beginners"
- "Cannabis for anxiety" and "cannabis for chronic pain"
### Citation sources tracked.
5W tracked citations across these source types:
- Aggregator and dispensary-finder platforms: Leafly, Weedmaps, Dutchie, Eaze
- Specialist cannabis editorial: MJBizDaily, Cannabis Business Times, Marijuana Moment, Green Market Report, Cannabis Dispensary
- Business press cannabis coverage: Forbes Cannabis, Bloomberg, Investing News Network, The Motley Fool
- CBD-specific editorial: Healthline, Forbes Health, Medical News Today
- State regulator websites
- Industry advocacy groups: NORML, NCIA, Marijuana Policy Project
- Brand-owned content hubs and education centers
### Important framing.
This index measures AI citation share for marketing and communications strategy. It does not rank brands on product quality, regulatory compliance, medical efficacy, or consumer suitability. Cannabis purchase decisions should be informed by direct consultation with healthcare providers for medical use. State regulatory disclosures and qualified legal advice matter too, given federal-state legal complexity.
## WHICH CANNABIS BRANDS ARE WINNING, AND WHY?
The MSO citation tier: Curaleaf, Trulieve, and Green Thumb Industries. These three brands together account for an estimated 17.5% of all cannabis-category AI citations across the 50+ tracked queries. Curaleaf wins on national footprint, with approximately 160 retail locations across 14 states plus international expansion via Curaleaf International. Trulieve wins on Florida dominance. The state has 800,000-plus registered medical patients, the largest medical cannabis market in the country. Trulieve's in-state market share is strong enough to produce near-uniform AI citations on Florida-specific prompts. Green Thumb Industries wins on consumer brand portfolio, since Rythm, Dogwalkers, and Beboe each have independent citation surfaces that smaller MSOs cannot replicate. The three-way MSO citation tier is separated by a wide margin from MSOs ranked 4 through 10, including Cresco, Verano, Ascend, Columbia Care, Jushi, TerrAscend, and Schwazze.
Cookies' consumer-product citation lock. Cookies is the most-cited consumer cannabis brand in U.S. AI answers, and the gap to the second-place consumer-product brand, Wyld, is wider than the gap between Curaleaf and Trulieve at the MSO tier. The mechanism is cultural: Cookies founder Berner has built brand recognition that extends well beyond cannabis-specific media into mainstream lifestyle, hip-hop, and fashion press. AI engines absorb that cross-category coverage and surface Cookies in "best cannabis brand" prompts at a rate that pure-play cannabis brands struggle to match.
Charlotte's Web's CBD citation moat. Charlotte's Web has held the number one position in CBD AI citations for five years, and the moat is widening, not narrowing. The Charlotte Figi origin story produces a citation density in CBD-for-medical-conditions prompts that no other CBD brand matches. The brand's 2018 Farm Bill positioning, as fully legal hemp-derived CBD, gives it cleaner AI citation behavior than THC-adjacent CBD products. Charlotte's Web is to CBD what Coca-Cola is to cola: the category-defining brand whose name is a near-synonym for the product class.
Leafly and Weedmaps' aggregator dual moat. Cannabis aggregators occupy a structurally favorable position in AI answers, because federal-state legal complexity makes AI engines reluctant to recommend specific dispensaries directly. Leafly wins national prompts and education-focused queries. Weedmaps wins California and West Coast geographic prompts. Together, the two aggregators capture more total cannabis AI citations than any single MSO. The AI sees them as neutral information rather than commercial recommendations, which earns them citation surface that direct retailers cannot win.
Branded consumer products operate independently of MSO citations. Wyld, Stiiizy, Kiva, Cann, and Wana each operate in citation surfaces that barely overlap with MSO citations. A consumer asking "best cannabis brand" gets a different answer than "best cannabis dispensary." That split is a strategic feature, not a flaw. Branded consumer products that license through multiple MSOs build citation moats that survive any single MSO's retail troubles. Cookies, Wyld, and Stiiizy are all distributed through multiple competing MSOs across multiple states.
## WHICH CANNABIS BRANDS ARE FALLING BEHIND?
Mid-tier MSOs after the Trump rescheduling executive order. Cresco Labs, Verano Holdings, Ascend Wellness, Columbia Care, Jushi Holdings, TerrAscend, and Schwazze all face the same structural challenge. AI citations for "biggest cannabis company" and "best cannabis dispensary" route to Curaleaf, Trulieve, and Green Thumb Industries with striking consistency. Mid-tier MSO citations need state-specific or product-specific framing just to surface at all. The December 2025 Trump executive order on rescheduling will benefit the entire MSO category, but it will not erase the citation moat the top three have built.
Independent dispensaries. The U.S. has approximately 14,000 licensed cannabis retailers across legal states, and almost none of them appear in AI citations. AI engines route "dispensary near me" prompts to aggregators such as Leafly and Weedmaps, and to the largest MSOs operating retail in the prompted state. That gap will widen as MSO retail consolidation continues.
Newer CBD brands. The CBD category attracted hundreds of brands during the post-2018-Farm-Bill expansion. AI citations have consolidated around six brands: Charlotte's Web, Cornbread Hemp, Lazarus Naturals, CBDistillery, Joy Organics, and Medterra. Newer entrants face a citation moat that mass-retail distribution alone does not break. CBD brands launched after 2022 with limited editorial coverage are essentially invisible in AI answers.
Cannabis brands without state-specific compliance content. 5W's testing found that state-specific content depth is the largest single predictor of AI citation share inside cannabis. Brands with detailed state-by-state retail availability, qualifying-condition guides for medical states, and adult-use-versus-medical distinction content earn citation surface that brands with generic content cannot match. Most cannabis brands have not invested in state-specific content at the level the AI citation surface rewards.
Cannabis brands relying on social media for citation reach. Cannabis brands operate under platform restrictions, since Instagram, Facebook, and TikTok all restrict cannabis content. That restriction has historically pushed brand investment into earned media and direct-to-consumer email. AI engines do not weight social-media presence for cannabis brands the way they do for unrestricted categories. Brands built on platform-restricted social have weak AI citation profiles even when their consumer awareness is strong.
## THE SIX STRUCTURAL FINDINGS
1. AI engines refuse or hedge about 28% of cannabis prompts, the highest rate of any category 5W has measured. Federal Schedule I status produces AI citation behavior unlike any other consumer category. Some prompts produce direct refusals. Others produce hedged answers with prominent disclaimers. Others produce confident answers. That hedge rate creates an opening for brands that publish content on exactly the topics AI engines hedge on, such as medical use, dosing, and state-specific legality. Those brands earn citation share that hedge-avoiding brands cannot match.
2. State-specific content depth is the largest single driver of cannabis AI citation share. Brands with structured state-by-state legal content, qualifying-condition guides, and adult-use-versus-medical distinction content outperform brands with generic content. Per 5W's testing, the state-specific content multiplier in cannabis is approximately 2.8x, larger than the equivalent multipliers 5W has measured in less-fragmented categories.
3. Branded consumer products operate in separate citation surfaces from MSO retail. "Best cannabis brand" and "best cannabis dispensary" return materially different answers. Brands that win consumer-product citations, including Cookies, Wyld, Stiiizy, Kiva, and Cann, build licensing moats that survive individual MSO troubles. That is a strategic feature for brands and a strategic vulnerability for MSOs that rely on owned brands rather than third-party licensed brands.
4. CBD operates as a separate AI citation universe from THC cannabis. The 2018 Farm Bill's federal legalization of hemp-derived CBD produces fewer AI hedges than THC queries. CBD-specific brands, including Charlotte's Web, Cornbread Hemp, Lazarus Naturals, CBDistillery, Joy Organics, and Medterra, compete in a separate citation surface from THC brands. Brands that try to span both categories typically lose citation share in both, compared with specialists.
5. Aggregators capture more aggregate citation share than the largest individual MSOs. Federal-state legal complexity makes AI engines route "find a dispensary" prompts to aggregators such as Leafly and Weedmaps rather than to direct retailers. This aggregator advantage is structural, and 5W expects it to persist even with full federal legalization, because the cross-state navigation problem aggregators solve will remain.
6. The December 2025 executive order on cannabis rescheduling produced the largest single citation reset in cannabis since legalization began. AI models trained on post-Q1-2026 data weight the rescheduling narrative heavily. Brands that produced rescheduling-relevant content, covering regulatory implications, business impact, and medical research access, built citation surface that brands silent on rescheduling cannot match.
## WHAT CHANGED FOR CANNABIS AI CITATION IN 2026?
1. The December 2025 rescheduling executive order, and the April 23, 2026 DOJ and DEA finalization order, is the year's largest citation event. AI answers about "cannabis legalization," "Schedule III cannabis," and "cannabis federal status" now reference the executive order and the DOJ finalization in nearly every response. Brands that produced rescheduling-aligned content in Q1 2026 captured citation share that earlier-published content alone could not generate. The broader rescheduling hearing scheduled June 29 to July 15, 2026 will be the next major citation reset.
2. Florida's 2026 ballot initiative failure reset Florida-specific citation patterns. The Florida Supreme Court's March 2026 decision declining to review the recreational legalization ballot appeal preserved Trulieve's medical-only Florida moat for at least another two years. AI answers about "Florida cannabis" now route to Trulieve more reliably than during the active ballot campaign.
3. Virginia's March 2026 retail framework approval, with sales beginning January 2027, is reshaping Virginia-specific citation patterns. The first MSOs to publish Virginia-specific content captured citation share for "Virginia cannabis dispensary" prompts that will compound through the retail launch.
4. Cannabis has not had a category-defining trust event comparable to the 2025 crypto exchange hacks. The result is that AI engines do not weight security signals heavily in cannabis citations, and instead focus on regulatory and legal signals.
5. Massachusetts's 2026 anti-recreational ballot campaign reset Massachusetts-specific citation patterns. The campaign attempting to roll back the state's 2016 recreational legalization produced citation events that AI engines absorbed. Brands operating in Massachusetts that produced "case for adult-use cannabis" content earned citation share that will outlast the ballot campaign.
6. Cookies' continued cultural footprint expansion is widening its consumer-product citation lead. Brand collaborations, music industry presence, and lifestyle-press coverage in 2025 and 2026 produced citation reinforcement that no other cannabis brand has matched. The gap between Cookies and every other consumer brand is widening.
## FROM RONN TOROSSIAN, FOUNDER OF 5W
"Cannabis is the most legally fragmented major consumer category in America, and that fragmentation has produced the most volatile AI citation surface we've ever measured. Twenty-eight percent of cannabis prompts produce AI hedges or refusals, five to ten times higher than any other category. The brands winning citation share are the ones that build content addressing exactly the topics AI engines hedge on: state-specific legality, medical applications, dosing, drug interactions. The brands waiting for federal clarity are losing citation surface every quarter to brands that built infrastructure during the volatility. The rescheduling executive order resets the surface again. The window stays open for the brands paying attention. It closes for the rest."
## THE CANNABIS GEO PLAYBOOK
1. Audit AI citation share monthly, by state. Cannabis AI citation share varies by state more than any other category 5W has measured. A national audit misses material gaps. State-by-state audits across the 25 adult-use and 16 medical-only states reveal citation gaps that national rollups conceal. Why it works: a national figure averages away the state-level swings 5W's Q1 2026 testing found. Only a state-by-state audit shows where a brand is actually winning or losing.
2. Publish state-specific legal and qualifying-condition content. Per 5W's Q1 2026 testing, the 2.8x state-specific content multiplier is the largest signal effect 5W has measured in cannabis. Brands need state-by-state retail availability pages, medical qualifying-condition guides, and adult-use-versus-medical distinction content. Generic national content underperforms state-specific content by roughly three to one. Why it works: AI models retrieve the most specific matching page for a state-flavored query, so a page written for "Illinois medical cannabis card requirements" outranks a national page for that query every time.
3. Address the topics AI engines hedge on. About 28% of cannabis prompts produce AI hedges or refusals. Brands that publish credentialed-author content on dosing, drug interactions, medical applications, and state-by-state legality earn citation share in prompts where competitors have left a vacuum. Why it works: when an engine is uncertain, it leans on the most authoritative available source rather than staying silent, so credentialed content fills the vacuum other brands leave open.
4. Win one product surface deeply before competing broadly. Cookies owns flower. Wyld owns gummies. Stiiizy owns vapes. Kiva owns chocolate. Cann owns drinks. Wana owns gummies regionally. Brands that try to be category generalists lose to brands that own a specific product sub-surface. Why it works: 5W's ranking data shows each product-specific leader holds a wider citation gap over its nearest rival than any generalist brand holds over anyone.
5. CBD brands should commit to a single category position. Charlotte's Web's CBD-only positioning produces a clearer AI citation signal than brands that span CBD and THC. CBD brands that commit to the federal-legal hemp positioning earn citation share that brands trying to span THC dilute. Why it works: the 2018 Farm Bill draws a hard federal line between hemp-derived CBD and THC, and AI engines mirror that line by hedging THC-adjacent content more than CBD-only content.
6. Treat regulatory events as citation events. The rescheduling executive order, state legalization initiatives, and qualifying-condition expansions all produce citation resets. Calendar AI-citation audits within 72 hours of every major regulatory event. Why it works: 5W's testing found the December 2025 executive order reset AI answers about cannabis legal status within one prompting cycle. A delayed audit misses the window when content updates carry the most weight.
7. Branded consumer-product brands should pursue multi-MSO licensing. Cookies, Wyld, and Stiiizy distribute through multiple competing MSOs across multiple states. The multi-MSO licensing strategy produces broader retail availability and broader AI citation share than single-MSO partnerships. Why it works: distribution through several MSOs multiplies the state-specific pages and dispensary listings that carry a brand's name, and each one is a separate citation opportunity.
8. Maintain aggregator partnerships. Leafly and Weedmaps each capture more aggregate citations than any individual MSO except Curaleaf. Brands that maintain consistent, current, and comprehensive presence on both aggregators earn citation share through that surface that brand-owned content alone cannot generate. Why it works: AI engines treat Leafly and Weedmaps listings as neutral third-party information rather than brand marketing, so a complete listing on both sites feeds citations that a brand's own website cannot.
9. Build for the medical-cannabis-curious consumer. "Cannabis for anxiety," "cannabis for chronic pain," "cannabis for sleep," and "medical cannabis card [state]" are among the highest-volume cannabis queries 5W tracked. Brands that publish credentialed-physician-bylined content on medical applications win citation share that lifestyle-positioned brands do not. Why it works: AI engines weight medical claims toward credentialed sources more heavily than lifestyle content, per the hedge-rate pattern in this Index's Q1 2026 testing.
10. Build for the post-rescheduling consumer now. If federal rescheduling proceeds on the timeline set by the April 2026 DOJ and DEA order, the AI citation surface will reset again. Brands that publish post-rescheduling-ready content, covering federal-tax implications, banking implications, and interstate-commerce implications, build citation surface that brands waiting for rescheduling to occur cannot match. Why it works: the same pattern that followed the December 2025 executive order, first movers captured citation share before competitors published, will repeat at the next hearing scheduled for June 29 to July 15, 2026.
## THE BIGGER PICTURE
The American cannabis industry is doing in twelve years what alcohol took eighty years to do after Prohibition. It is establishing a durable set of consumer-facing brands, a state-by-state retail framework, a medical-versus-adult-use distinction, a network of ancillary services, and a federally evolving legal status. In every other consumer category, brand formation was mediated by national press, retail availability, and the slow accretion of consumer recognition over decades.
Cannabis does not have that luxury of time. Federal-state legal fragmentation, AI engine hedging, and regulations that shift quarterly produce a citation surface that rewards brands building infrastructure deliberately and penalizes brands waiting for clarity.
Based on 5W's Q1 2026 testing, the brands positioned to win the next decade share five habits: state-specific content, addressing the topics AI engines hedge on, a single category position (CBD-only, THC-only, beverage-only, or edible-only), multi-MSO licensing, and treating every regulatory event as a citation event. Brands that wait for federal rescheduling to invest in citation infrastructure will find the window already closing. Curaleaf, Trulieve, Green Thumb, Cookies, Charlotte's Web, Leafly, Weedmaps, and a small number of others have already absorbed a growing share of what consumers see when they ask an AI engine where to buy, what to buy, and whether cannabis is legal where they live.
AI citation share is the scoreboard. In cannabis, uniquely among the major American consumer categories 5W has studied, that scoreboard is shaped as much by federal-state legal volatility as by brand investment.
## ABOUT 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen: ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list.
For more information, visit 5wpr.com.
## FAQ
### What is the Cannabis AI Visibility Index?
The Cannabis AI Visibility Index is a 5W research report ranking the top 25 brands in the U.S. cannabis industry by estimated AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. It measures which brands AI answer engines surface for consumer-intent prompts about dispensaries, products, CBD, medical use, and state-specific legality.
### Which MSOs rank highest?
Curaleaf, Trulieve, and Green Thumb Industries lead all multi-state operators by a wide margin. Together they account for an estimated 17.5% of all cannabis-category AI citations across the 50+ tracked queries.
### Which consumer brand ranks highest?
Cookies leads all branded cannabis consumer products by a wide margin. Wyld, Stiiizy, Kiva Confections, and Cann complete the top five branded consumer products.
### Which CBD brand ranks highest?
Charlotte's Web is the highest-ranked CBD brand and the number five brand in the index overall. The Charlotte Figi origin story and 2018 Farm Bill positioning produce a citation moat in CBD-for-medical-conditions prompts that no challenger has matched in five years.
### How was AI citation share measured?
5W ran 50+ consumer-intent prompts through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews in Q1 2026. The team measured the frequency of brand citations across all responses, including state-specific variations and AI engine hedges or refusals. Citation share is the proportion of total brand mentions captured by each brand.
### Why does AI citation share matter for cannabis brands?
Cannabis is the most legally fragmented major consumer category in America. AI engines reflect that fragmentation by hedging or refusing about 28% of cannabis prompts, a higher rate than any other category 5W has measured. Brands that build citation infrastructure across the volatility win durable category positioning that brands waiting for federal clarity cannot match.
### Is the report free?
Yes. The web version is free to read and the PDF download is ungated. An optional email signup for future 5W research sits next to the download link.
### Can 5W run a Generative Engine Optimization program for my cannabis brand?
Yes. 5W details its Generative Engine Optimization practice on its site, and offers cannabis-specific GEO services through its Cannabis PR and Digital Marketing practice.
### Why now?
The citation window is narrow, and regulatory volatility creates a compounding advantage for early movers. AI engines concentrate citations in a small number of domains per topic, and the cannabis AI citation surface resets with every regulatory event. Cannabis brands that invest in the next six months will be cited for years.
Download the full PDF
---
### America Buys the Energy Drink. AI Recommends the Functional One
URL: https://www.5wpr.com/ai-visibility-index/functional-beverages-ai-visibility-index-2026/
Summary: The 5W index ranks functional beverages brands against energy drinks by citation share across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews.
Reported, sourced, and built to be cited.
Energy drinks still dominate the cooler — but the $152 billion functional-beverage market is being re-sorted inside the chatbox. New 5W research ranks the 25 brands by how often the AI engines name them.
A 5W research report. Engines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews. 60+ queries · 25 brands. Data window: Q2 2026.
## At a glance
- ~13% — Estimated AI citation share held by Celsius, the most-cited functional-beverage brand
- $152B — Estimated size of the global functional-beverage market in 2025
- 5 — Functional-beverage brands that reached billion-dollar valuations within a single year
- 245% — Dollar-sales growth of prebiotic soda in tracked U.S. channels, 2022–2024
## The headlines
### 1. Red Bull and Monster lead the category. They don't lead the answer.
Ask an AI engine for a functional or healthy drink and the wellness-positioned brands surface first.
### 2. Five beverage brands hit billion-dollar valuations in a single year.
Olipop, Poppi, Liquid Death, Alani Nu, and Ghost — and the engines noticed every one.
### 3. Citation share follows the function, not the volume.
Gut health, clean energy, electrolytes — the benefit narrative is what the engines retrieve.
## Who AI names first
Top 15 brands by estimated citation share, Q2 2026
#BrandCategoryEst. citation share
1CelsiusEnergy / fitness13.0%
2Red BullEnergy11.0%
3Liquid DeathFunctional water8.5%
4OlipopPrebiotic soda7.5%
5PoppiPrebiotic soda6.5%
6GatoradeSports drink5.5%
7MonsterEnergy5.0%
8PrimeHydration / sport4.0%
9AG1Wellness / greens3.3%
10Alani NuEnergy / fitness2.9%
11BodyArmorSports drink2.5%
12LMNTElectrolyte2.2%
13PoweradeSports drink1.9%
14GhostEnergy1.6%
15Liquid I.V.Electrolyte / hydration1.4%
Source: 5W AI Visibility Index, Q2 2026 (60+ prompts, five runs per engine per prompt).
## The central finding: volume rank does not predict citation rank
The functional-beverage market is worth an estimated $152 billion globally, and by volume its leaders are clear: Red Bull and Monster have run the energy category for two decades. Those are the cans that move off the shelf in the greatest numbers.
They are not the brands at the top of the AI answer. The 5W AI Visibility Index finds that Celsius, Liquid Death, Olipop, and Poppi lead the citation surface — because the engines answer the question consumers actually ask. Almost nobody types "best energy drink" into a chatbot anymore; they type "healthy energy drink," "gut-health soda," or "functional beverage." Those queries route to the brands built on a benefit narrative, not the brands built on volume.
Celsius sits at the top because it bridges both worlds — an energy drink positioned as fitness fuel, amplified by its $1.65 billion acquisition of Alani Nu. Liquid Death turned canned water into a media brand. Olipop and Poppi turned prebiotic fiber into a category that grew 245% in two years. That is the decoupling: citation share follows the function and the story, not the case count. The brand that owns the benefit owns the answer.
"Red Bull and Monster won the cooler. That war is over and they won it. But the question moved — and the brands that own the new question are the ones AI repeats. Nobody asks a chatbot for the best energy drink. They ask for the healthy one, the gut-health one, the clean one. Celsius, Liquid Death, Olipop, Poppi built their brands around an answer to that question, and now the engines hand it back. Citation share follows the function, not the volume."
— Ronn Torossian, Founder & Chairman, 5W
## Methodology
5W analyzed more than 60 common consumer prompts across six primary sub-categories of the functional-beverage market, running each prompt five times per engine in clean sessions. We identified which brands AI models consistently surface, which sources feed those citations, and where the biggest gaps sit between category sales and AI visibility.
- Energy & performance — Red Bull, Monster, Celsius, Alani Nu, Ghost, and the energy-drink set.
- Prebiotic & gut-health soda — Olipop, Poppi, and the better-for-you soda category.
- Electrolyte & hydration — LMNT, Liquid I.V., Electrolit, and hydration mixes.
- Sports drinks — Gatorade, Powerade, BodyArmor, Prime.
- Wellness & adaptogen drinks — AG1, Recess, and functional wellness beverages.
- Functional & enhanced water — Liquid Death, Vitaminwater, Hint, and enhanced waters.
Query types tracked: real-world prompts including "best healthy energy drink," "gut health soda," "Olipop vs Poppi," "best electrolyte drink," "functional beverage brands," "best drink for hydration," "healthiest energy drink," and 50+ variations covering recommendation, comparison, and category-leadership intent.
Citation sources tracked: consumer and business editorial, retail and review content, nutrition and wellness coverage, reference sources, and brand-owned content.
Important framing: this index measures AI citation share for communications and marketing strategy purposes only. It does not evaluate any beverage's health claims, ingredients, nutritional value, or safety, and it is not health or dietary advice. Brand positioning described in this report reflects how brands present themselves and how AI engines summarize them — not a 5W assessment of any product claim.
## The top 25 functional-beverage brands, ranked by AI citation share
#BrandCategoryAI visibilityNotable
1CelsiusEnergy / fitnessCategory-dominantNASDAQ: CELH; zero-sugar fitness energy; acquired Alani Nu for $1.65B in 2025; bridges energy and wellness positioning.
2Red BullEnergyVolume leader, #2 by citationPrivately held; the original energy-drink brand and a global volume leader — cited heavily for energy, less for "functional."
3Liquid DeathFunctional waterMedia-brand tierPrivately held; ~$1.4B valuation; canned water and tea built into a marketing phenomenon with outsized citation pull.
4OlipopPrebiotic sodaGut-health co-leaderPrivately held; ~$1.85B Series C valuation; prebiotic soda pioneer; owns "healthy soda" citations with Poppi.
5PoppiPrebiotic sodaGut-health co-leaderAcquired by PepsiCo for $1.95B in 2025; Super Bowl advertiser; defines the prebiotic-soda conversation alongside Olipop.
6GatoradeSports drinkSports-category anchorPepsiCo; the default "sports drink" citation — strong on athletic queries, lighter on the "functional / healthy" surface.
7MonsterEnergyVolume tierNASDAQ: MNST; co-leader of energy-drink volume with Red Bull; cited for energy, rarely for wellness positioning.
8PrimeHydration / sportInfluencer-launch tierLogan Paul and KSI brand; explosive 2023 launch followed by a reported ~50% sales decline in 2024 — citation now softening.
9AG1Wellness / greensWellness-routine tierAthletic Greens; premium greens powder cited heavily in "daily wellness" and routine queries.
10Alani NuEnergy / fitnessModern-energy tierAcquired by Celsius for $1.65B; female-focused energy brand built through influencer and community marketing.
11BodyArmorSports drinkSports-challenger tierOwned by The Coca-Cola Company; positioned as a premium sports drink; cited mainly within the athletic-hydration set.
12LMNTElectrolyteElectrolyte-leader tierPrivately held; zero-sugar electrolyte mix; owns a large share of "best electrolyte drink" citations.
13PoweradeSports drinkSports-incumbent tierThe Coca-Cola Company; the long-standing #2 sports drink; cited in athletic queries, thin on functional framing.
14GhostEnergyLifestyle-energy tierKeurig Dr Pepper acquired a 60% stake for $990M; collaboration-driven lifestyle energy brand.
15Liquid I.V.Electrolyte / hydrationHydration-mix tierOwned by Unilever; hydration multiplier brand; cited in hydration and travel-wellness queries.
16VitaminwaterFunctional waterEnhanced-water nicheThe Coca-Cola Company; a legacy enhanced-water brand; citation share diluted by newer functional entrants.
17BangEnergyFaded-challenger nicheNow under Monster ownership; once a fast-rising energy brand, its citation footprint has narrowed.
18RecessWellness / adaptogenAdaptogen nichePrivately held; calm-and-focus positioning with adaptogens; cited in stress-relief and mood queries.
19SpindriftBetter-for-you sparklingClean-label nichePrivately held; real-fruit sparkling water; cited in low-sugar and clean-label comparisons.
20ElectrolitElectrolyteHydration nicheMexico-origin electrolyte brand expanding fast in the U.S.; cited in recovery and hydration queries.
21ZOAEnergyCelebrity-energy nicheCo-founded by Dwayne Johnson; better-for-you energy positioning; modest but defined citation niche.
22C4 EnergyEnergyPerformance-energy nicheNutrabolt; pre-workout-derived energy brand; cited mainly in fitness and performance contexts.
23RockstarEnergyLegacy-energy nichePepsiCo; a legacy energy brand; cited far below its shelf presence as the functional framing took over.
24Guayaki Yerba MatePlant energyPlant-energy nichePrivately held; yerba-mate energy brand; cited in natural-energy and clean-caffeine queries.
25HintFunctional waterFlavored-water nichePrivately held; unsweetened flavored water; cited in no-sugar hydration comparisons.
Source: 5W AI Visibility Index, Q2 2026. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts.
## The brands winning the AI answer
### Celsius — the bridge brand
Celsius is the rare brand cited as both an energy drink and a wellness product. That dual identity lets it surface across "best energy drink," "healthy energy drink," and "fitness drink" queries — and the Alani Nu acquisition doubled its claim on the modern-energy conversation.
### Liquid Death — media brand as citation engine
Liquid Death turned canned water into a cultural property. The constant earned coverage of its marketing gives the engines an enormous, ever-refreshing record to retrieve — citation share far above what a water brand would otherwise command.
### Olipop & Poppi — they own the category name
"Prebiotic soda" and "healthy soda" are now near-synonymous with two brands. They created the category vocabulary, and the engines return the brands that defined the words consumers search.
### LMNT — owning a single benefit
LMNT did not try to be everything. It anchored itself to one query — "best electrolyte drink" — and built the editorial and community record to own it. A narrow, well-defended benefit out-cites a broad, undefined one.
## The brands AI is leaving behind
### Legacy energy on the wellness query
Red Bull and Monster still dominate volume, but they are cited for "energy drink," not "healthy" or "functional" drink. As the consumer question shifts toward wellness framing, their citation share trails their sales by a wide margin.
### Prime — when hype outruns the record
Prime's launch generated enormous attention, but reported sales fell roughly 50% in 2024. Citation share built on a hype cycle erodes when the earned record stops refreshing. Attention is not the same as a durable citation asset.
### Sports-drink incumbents outside sport
Gatorade and Powerade own athletic-hydration citations, but the broader "functional beverage" question routes elsewhere. A brand cited only inside its original lane cedes the expanding category around it.
### Brands with no earned record
Functional brands that grew through a single retail or social channel — with little independent editorial coverage — are thinly cited. The engines retrieve a public record; brands that never built one stay invisible.
## Six structural truths about AI visibility in functional beverages
- The benefit owns the query. Consumers search by function — gut health, clean energy, hydration. The brand that owns a benefit owns the query that routes to it.
- Volume rank does not predict citation rank. The best-selling cans are not the most-cited brands. The engines answer the modern, wellness-framed question, not the legacy one.
- Category creators own the category words. Olipop and Poppi made "prebiotic soda" a search term — and the brands that name a category are the brands the engines return for it.
- Earned media compounds into citations. Liquid Death and Celsius generate constant coverage. That refreshing editorial record is the raw material AI engines retrieve.
- Hype fades; the record stays. Brands built on a single viral moment lose citation share as coverage ages. Durable visibility needs a continuously refreshed record.
- A narrow benefit out-cites a broad one. LMNT owns "electrolytes." A brand anchored to one defensible benefit out-cites a brand spread thin across many.
## Six 2026 dynamics reshaping beverage AI citations in real time
- Big Soda is buying its way in. PepsiCo's $1.95B Poppi deal and Coca-Cola's Simply Pop launch are reshaping how the engines describe the prebiotic-soda set.
- Celsius–Alani Nu consolidates the modern-energy answer. One owner now holds two of the most-cited modern energy brands, concentrating citation share in the category.
- Class-action scrutiny is entering answers. Litigation over gut-health claims generates news flow — and AI answers increasingly surface that context in brand queries.
- Protein is crossing into beverages. "Good source of protein" has become a beverage purchase driver, opening a new functional-citation lane.
- The "modern soda" shelf has an answer-engine twin. As retailers formalize functional-soda sections, the engines mirror that category structure in how they group brands.
- GLP-1 is reshaping hydration and nutrition framing. Beverages positioned around appetite, hydration, and nutrient support are earning citations in a query set that barely existed before.
## General tips for beverage marketing teams
- Audit category citation share quarterly. Functional-beverage citations move fast — acquisitions, launches, and litigation all reshape the surface.
- Anchor the brand to one benefit. Own a function — gut health, clean energy, electrolytes — clearly enough that the engines map the query to you.
- Name and define the category. The brand that supplies the category vocabulary is the brand the engines return for it.
- Build a continuously refreshed earned record. One viral moment is not enough; AI answers favor brands with ongoing, recent coverage.
- Earn the consumer and business editorial record. Trade and consumer coverage feed beverage citations disproportionately.
- Keep reference and retail content accurate. Product pages, Wikipedia, and review sources are heavily retrieved — errors there propagate into answers.
- Substantiate functional claims. Vague benefit claims invite scrutiny that surfaces in AI answers; documented claims build durable visibility.
- Treat acquisitions and launches as citation events. Major corporate news reshapes the surface — publish substantive content around it.
- Don't rely on a single channel. A brand visible only in one retailer or one platform underindexes; build an independent record.
- Re-audit after every major launch, deal, or campaign. Each materially reshapes the citation surface.
## The bigger picture: the cooler decision now starts in the chatbox
For decades, beverage brands competed for the cooler door — shelf placement, distribution, and the impulse buy. That competition still matters. But a growing share of consumers now decide what to drink before they reach the store, asking an AI engine which functional beverage fits their goal: more energy, better gut health, cleaner hydration.
The Index shows what that surface rewards: a clearly owned benefit, a category the brand helped name, and a deep, continuously refreshed editorial record. It does not track volume — Red Bull sells more than any brand here and ranks second, because the engines answer the functional question, not the energy one.
AI citation share is the scoreboard. In functional beverages, the brand AI names is the brand shaping what consumers reach for — and that visibility is built in the public record and the benefit narrative, not the shelf.
## Frequently asked questions
### What is the Functional Beverages AI Visibility Index 2026?
A research report by 5W that ranks the top 25 functional-beverage brands by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ consumer prompts tracked in Q2 2026.
### Which functional-beverage brand holds the highest AI citation share?
Celsius, with an estimated 13% — the only brand cited strongly as both an energy drink and a wellness-positioned product.
### Do energy-drink sales predict AI citation share?
No. Red Bull and Monster lead category volume, but the AI answer for a functional or healthy drink routes to the wellness-positioned brands — Celsius, Liquid Death, Olipop, and Poppi.
### Is this index nutritional or health advice?
No. It measures AI citation share for communications strategy purposes only. It does not evaluate any beverage's health claims, ingredients, or safety, and it is not health or dietary advice.
### What is citation share?
The estimated proportion of brand mentions a company receives across all tracked prompts and AI engines — the core metric used to rank brands in the Index.
### What other industries has the AI Visibility Index Series covered?
Recent editions include Grocery Retail, Restaurants & Chains, Weight Loss & Metabolic Health, the Pet Industry, Supplements, and Beauty.
## About this report
5W AI Visibility Index: Functional Beverages. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ consumer queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report does not evaluate any beverage's health claims, ingredients, or safety and is not health or dietary advice.
---
### The Private Label AI Advantage — Pharmacy
URL: https://www.5wpr.com/research/private-label-ai-advantage-pharmacy/
Summary: Store-brand OTC out-cites national brands nearly two to one across five top answer engines, representing the strongest franchise visibility.
---
### Edition 07: Luxury Media — The 5W Retrieval Index
URL: https://www.5wpr.com/research/retrieval-index/luxury/
Summary: The sector where brand mythology and AI retrieval run in opposite directions.
---
### The Pet Insurance AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/pet-insurance-ai-visibility-index-2026/
Summary: The 5W Pet Insurance AI Visibility Index measures which pet insurance companies ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews recommend.
# The Pet Insurance AI Visibility Index 2026
Legacy insurers are invisible inside AI search. Trupanion captures 28.4% of pet insurance AI citations — 7× more than Nationwide and MetLife combined. The first study to measure which pet insurance brands AI engines recommend.
## Legacy insurers are invisible inside AI search.
Nationwide Pet and MetLife Pet — two of the largest insurance companies on Earth — capture a combined 3.9% of pet insurance citations inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Trupanion alone captures 28.4%. A company with a fraction of their institutional resources owns 7× more of the answer layer than both legacy giants combined.
The pet insurance buyer no longer compares plans on Google first. Increasingly, they ask an AI engine. And the AI engines do not recommend Nationwide or MetLife. They recommend Trupanion, Lemonade, and Healthy Paws — the brands that built the content infrastructure the engines retrieve from.
This is the central finding of the 5W Pet Insurance AI Visibility Index 2026, the first study to measure which pet insurance brands dominate AI-generated recommendations across all five major engines — and which have institutional scale but zero AI presence.
### TOP 10 INSIGHTS
1. Trupanion captures 28.4% of all pet insurance AI citations — more than double #2.
2. Nationwide Pet + MetLife Pet combined (3.9%) trail Trupanion by 7×.
3. Brands with 50,000+ verified reviews average 3.7× more AI citations than those under 10,000.
4. No brand owns the highest-volume query: "Is pet insurance worth it?"
5. Lemonade Pet (16.2%) outperforms insurers 10× its policyholder count on AI visibility.
6. ASPCA Pet Insurance captures only 3.4% despite the most recognized nonprofit brand in pet.
7. Fetch loses an estimated 2–3 points of citation share to legacy Petplan name confusion.
8. Breed-specific insurance queries are the highest-intent, lowest-competition lane in the category.
9. The top 3 brands capture 57.3% of all citations. The bottom 7 share 19.6%.
10. Pet insurance is the only major insurance sub-category where a DTC startup (#2 Lemonade) outranks every legacy carrier on AI visibility.
## About This Study
Pet insurance is the fastest-scaling sub-category in the $158 billion U.S. pet industry. Penetration grew from approximately 3% of insured pets in 2019 to approximately 12% in 2026. The category is projected to exceed $6 billion in annual premiums by 2028. North American pet insurance reached approximately 7.6 million insured pets in 2025, up from 7.03 million in 2024 (NAPHIA 2026 State of the Industry Report).
The purchase decision is complex, emotionally charged, and comparison-driven. Pet owners are researching coverage before they talk to an agent, a broker, or a vet — and increasingly, that research starts inside an AI engine. The brands that own the AI answer own the top of the funnel.
The 5W Pet Insurance AI Visibility Index 2026 is the first study to measure which pet insurance brands dominate AI-generated recommendations across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. It is part of the 5W AI Visibility Index Series — the agency's ongoing research franchise measuring brand citation share across consumer categories.
## Pet Insurance AI Visibility Rankings 2026
Brand
Share
Strongest Query Type
Key Citation Driver
Trupanion
28.4%
Vet-recommended / best overall
Vet-direct payment model, NASDAQ transparency (TRUP), 20-year veterinary-channel distribution
Lemonade Pet
16.2%
Best for tech-savvy / instant claims
Fintech press density, AI-native brand positioning, structured claims data ("3-second claims"), S-1 transparency
Healthy Paws
12.7%
Most trusted / highest rated
52,000+ verified reviews across Trustpilot, BBB, Google; highest average rating in category (4.7/5)
Embrace
8.9%
Most customizable / wellness
120+ breed-specific guides, wellness rider content, cost-of-care calculators, diminishing deductible
Fetch
7.1%
Best for older pets
Coverage breadth (6+ to any age); loses ~2–3 pts to Petplan legacy name confusion
Pets Best
5.3%
Budget-friendly / affordable
Founder Dr. Jack Stephens — father of pet insurance in North America — price-comparison content
Pumpkin
4.6%
Best for puppies / preventive
DTC marketing, preventive-care framing, strong first-time-buyer content
Spot
3.8%
Customizable deductibles
Listicle presence only; weak standalone authority, limited educational content
ASPCA Pet Insurance
3.4%
Brand-trust queries only
ASPCA nonprofit halo; product administered by Crum & Forster — engines surface the disconnect
Figo
2.8%
Tech-forward / app-based
Mobile app UX; weakened by 2022 Hartville/PTZ acquisition — entity signal fragmenting
Nationwide Pet
2.1%
Employer-sponsored only
Institutional but parent brand overwhelms pet entity signal — engines route to auto/home
MetLife Pet
1.8%
Workplace benefits only
Newest entrant (2020); entity signal overwhelmed by MetLife parent across life/dental/vision
## Five Surprises
### 1. Why Nationwide and MetLife disappear.
This is the study's most counterintuitive finding. Nationwide is one of the largest insurance companies in the world. MetLife operates across 40+ countries. Both offer pet insurance products. Both are nearly invisible to AI engines when a buyer asks about pet insurance.
The reason is entity dilution. When a buyer asks ChatGPT "best pet insurance," the engine has to decide what Nationwide is. Nationwide's entity signal is overwhelmingly auto, home, life, farm. Pet is a footnote inside a massive parent entity. The engines route "Nationwide" to its dominant meaning — not to its pet product. MetLife faces the same problem with life, dental, and vision insurance drowning the pet signal.
Trupanion does not have this problem. Trupanion is pet insurance. The entity and the product are the same thing. That structural clarity is worth more than any advertising budget.
### 2. Why Lemonade outperforms insurers with 10× its policyholder count.
Lemonade Pet has a fraction of Nationwide's customer base. But it captures 16.2% of AI citations versus Nationwide's 2.1%. The reason: Lemonade generates disproportionate press coverage per policyholder because the fintech narrative is more interesting to reporters than the insurance narrative. Every TechCrunch, Wired, and Bloomberg article about Lemonade's AI-native claims process is a citation source the engines retrieve from. Legacy insurers generate actuarial reports. Lemonade generates headlines.
### 3. Why ASPCA Pet Insurance underperforms its brand.
The ASPCA is the most recognized nonprofit brand in pet. ASPCA Pet Insurance should benefit from that halo. It doesn't — not at the level you'd expect. The reason: ASPCA Pet Insurance is not operated by the ASPCA. It is a licensed product administered by Crum & Forster, a subsidiary of Fairfax Financial. The AI engines surface this disconnect when answering authority queries. When a buyer asks "is ASPCA pet insurance good," engines frequently note the licensing arrangement — which introduces doubt rather than reinforcing trust. The halo leaks.
### 4. Why review volume matters more than advertising spend.
Across the 12 brands measured, brands with 50,000+ verified reviews averaged 3.7× more AI citations than brands with fewer than 10,000 reviews. Advertising spend showed no statistically meaningful correlation with citation share after controlling for review density. The engines trust what buyers said — not what the brand paid to say. Healthy Paws, with approximately 52,000 verified reviews and a 4.7/5 composite rating, captures more citations than Pumpkin, Spot, and ASPCA combined — despite spending less on advertising than any of them.
### 5. Fetch's Petplan problem is measurable.
Fetch rebranded from Petplan in 2022. Four years later, the engines still retrieve Petplan content — reviews, complaints, and comparisons — alongside Fetch results. The legacy entity is not fully resolved. We estimate this costs Fetch 2–3 points of citation share — enough to have placed it at #4 instead of #5. Rebranding in the AI era carries a measurable citation tax that did not exist in the SEO era, because the engines hold both entity versions in memory simultaneously.
## What Drives AI Citation Share in Pet Insurance
Five structural traits predict pet insurance citation share. They mirror — but do not replicate — the traits identified in the broader Pet Industry AI Visibility Index 2026.
1. Verified review density. The single strongest predictor. Brands with 50,000+ verified reviews averaged 3.7× more AI citations than brands under 10,000. The engines use review volume and sentiment as a trust proxy — weighting it more heavily than advertising spend, social following, or press coverage volume.
2. Veterinary endorsement infrastructure. Trupanion's vet-direct payment model creates a structural endorsement signal that no other brand can replicate without building the same claims infrastructure. Every vet clinic that processes a Trupanion claim is an implicit citation source. Pets Best benefits from founder Dr. Jack Stephens — widely credited as the father of pet insurance in North America.
3. Educational content depth. Embrace publishes 120+ breed-specific insurance guides, condition-specific coverage explainers, and cost-of-care calculators. This content is the substrate the engines retrieve from when answering comparison queries. Brands without this content layer appear in listicles but never as the answer to a specific question.
4. Financial transparency. Trupanion's NASDAQ filings (TRUP) and Lemonade's public-company disclosures (LMND) provide structured financial data the engines can parse and cite. Privately held insurers without published loss ratios or claims-paid data are structurally disadvantaged on authority queries.
5. Entity clarity. The engines need to know what a brand is — not just what it sells. Trupanion = pet insurance. Lemonade = AI-native insurance. Embrace = customizable pet coverage. Brands where the pet product is a sub-entity inside a larger parent (Nationwide, MetLife, ASPCA via Crum & Forster) suffer entity dilution.
## What Every Pet Insurer Should Build
The brands with the highest citation share have built five content assets that the lower-ranked brands have not. These are not optional. They are the infrastructure the engines retrieve from.
200+ breed-specific insurance pages. Each page answers: What does insurance cost for a [breed]? What conditions is a [breed] prone to? What coverage does a [breed] need? Embrace has partial coverage. No brand has built the full set. The first to do it owns every breed-specific query in the category.
Interactive reimbursement calculators. Tools that let a pet owner input breed, age, zip code, and coverage level and see estimated monthly premiums and reimbursement scenarios. These generate structured data the engines can parse.
Step-by-step claim walkthroughs with screenshots. Process content is a trust-building surface. The brand that publishes the clearest, most-cited claim-filing guide captures citation share on every downstream purchase-intent query.
Surgery and condition cost databases. "How much does ACL surgery cost for a dog?" is one of the highest-intent queries adjacent to pet insurance. The brand that owns the answer to the cost question is the brand the engine recommends for the coverage question.
Named-veterinarian explainer content. E-E-A-T signals require named, credentialed authors. Insurance content written by "the team" carries less authority weight than content attributed to a named veterinarian with verifiable credentials.
## Three Open Lanes
"Is pet insurance worth it?" — the single highest-volume pet insurance query across all five engines produces fragmented answers with no consistent brand recommendation. No brand has built a definitive, data-driven, transparent answer to this question. The first to do it — with real claims data, real reimbursement examples, and real cost-of-not-insuring scenarios — will capture disproportionate citation share on the most valuable query in the category.
"Best pet insurance for [breed]" — breed-specific queries are the highest-intent, lowest-competition lane in pet insurance AI visibility. A buyer asking "best pet insurance for French Bulldogs" is closer to purchase than a buyer asking "best pet insurance." There are 200+ AKC-recognized breeds. The opportunity is enormous and undefended.
"How much does pet insurance cost?" — cost queries produce generic ranges ($30–$70/month) without brand-specific attribution. The brand that publishes transparent, breed-specific, age-specific pricing data — not behind a quote wall — will own this query. Every competitor hides pricing behind a lead form. The first to publish it openly wins the citation.
## Methodology
Prompt design. 40 consumer-intent prompts across five intent categories: purchase intent ("best pet insurance 2026," "top-rated pet insurance companies"), comparison intent ("Trupanion vs Lemonade," "Healthy Paws vs Embrace"), trust intent ("most trusted pet insurance," "pet insurance with best reviews"), cost intent ("cheapest pet insurance," "affordable pet insurance for puppies"), and category intent ("is pet insurance worth it," "what does pet insurance cover"). Prompts were written to mirror how consumers actually phrase questions to AI engines.
Engine coverage. Five engines: ChatGPT (GPT-4o), Claude (Sonnet), Gemini (1.5 Pro), Perplexity (default), and Google AI Overviews (US, desktop).
Sampling. 12 monthly waves across January–June 2026 (two waves per month, spaced 10–14 days apart). Each wave ran all 40 prompts × 5 engines = 200 responses per wave. Total: 2,400 prompt-engine runs. Conversations were reset between each prompt (fresh context, no memory carryover). Prompts were run in randomized order within each wave to control for session-position effects.
Scoring. Citation share is calculated as: (brand citations across all responses) ÷ (total brand citations across all responses for all brands) × 100. Recommendation position is scored: first-named = 3, named in top three = 2, named but not in top three = 1. Contextual framing is coded as positive, neutral, or cautionary. Brands with fewer than 5 total citations across 2,400 runs are excluded from ranking.
Sample prompts used in testing: "What is the best pet insurance in 2026?" · "Is Trupanion worth it?" · "Best pet insurance for French Bulldogs" · "Is pet insurance worth the cost?" · "Lemonade Pet vs Healthy Paws" · "What does pet insurance actually cover?" · "Cheapest pet insurance that's actually good" · "Best pet insurance for older dogs" · "How do I file a pet insurance claim?" · "Does pet insurance cover pre-existing conditions?"
## Limitations
AI engine responses are non-deterministic. The same prompt can produce different citations on consecutive runs. The 12-wave design reduces but does not eliminate this variance. Citation share figures should be read as estimates with an approximate confidence interval of ±2 points.
The study measures U.S. English-language prompts only. Pet insurance markets in the UK, Canada, and Europe have different brand landscapes and may produce different citation patterns.
Google AI Overviews availability varies by query and user. Not all prompts triggered an AI Overview. When no Overview appeared, the query was excluded from the Google AI Overviews count for that wave.
The study does not measure conversion — a brand cited by an AI engine is not necessarily a brand that wins the policy. Citation share measures top-of-funnel AI visibility, not bottom-of-funnel conversion.
## Commentary
"Consumers no longer compare pet insurance on Google first. Increasingly, they ask ChatGPT. The brands that own those answers win customers before a quote is ever requested. Trupanion understood this before anyone else in the category — and the data shows it."
— Ronn Torossian, Founder and Chairman, 5W AI Communications
## What Every CMO Should Do Next
1. Run the audit. Type your 20 most important buyer-intent queries into ChatGPT, Claude, Gemini, and Perplexity. Note which brands are cited. Note where you are missing. That is your baseline.
2. Build the content the engines retrieve from. Breed-specific pages, condition cost databases, claim walkthroughs, named-veterinarian explainers. The engines can only cite content that exists.
3. Earn the reviews. Review density is the strongest single predictor of AI citation share in this category. Invest in post-claim review programs, not post-purchase review requests.
4. Fix the entity signal. If your pet insurance product lives inside a parent brand that the engines associate with auto, home, or life — you have an entity problem. Build a standalone content property or a structurally separate knowledge-base that the engines can retrieve without routing through the parent.
5. Own the question nobody owns. "Is pet insurance worth it?" is the most valuable unclaimed real estate in pet insurance AI visibility. Build the definitive, transparent, data-driven answer. Publish it. Let the engines find it.
## Planned Satellite Studies
The Pet Insurance AI Visibility Index 2026 is designed as the anchor study for a franchise. Planned satellite publications:
- Best Pet Insurance for Dogs — AI Visibility by Breed
- Best Pet Insurance for Cats — AI Visibility Rankings
- Pet Insurance Claims Experience Index 2026
- Best Pet Insurance for Senior Pets — The Age Visibility Gap
- Employer Pet Insurance Visibility — Which Workplace Plans AI Recommends
- AI Visibility by State — Where Pet Insurance Citations Concentrate
- Most AI-Visible Veterinary Brands 2026
- Breed-Specific AI Recommendations — The Coverage Guide AI Engines Build for Buyers
## About this report.
5W AI Visibility Index: Pet Insurance. Published July 2026. Part of the 5W AI Visibility Index series.
The index measures AI citation share across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews for 40 consumer-intent queries tracked across 12 waves in H1 2026. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be interpreted as directional measures of relative visibility, not precise market measurements.
About 5W AI Communications. 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. Learn more at 5wpr.com.
5W's Pet Products & Services Marketing and PR practice builds brand visibility and growth programs for the evolving pet market, from emerging DTC brands to legacy pet care leaders — spanning earned media, 360 campaigns, digital marketing, and GEO execution.
5W AI Communications Research · Published July 2026 · 5wpr.com/research
---
### Supplements AI Visibility Index 2026
URL: https://www.5wpr.com/research/supplements-ai-visibility-index-2026/
Summary: The 2026 Supplements AI Visibility Index reveals Thorne as the leading brand in AI mentions (35.6%), followed by Garden of Life and NOW. See which brands dominate AI search answers and why.
# Supplements AI Visibility Index 2026
Thorne Leads Supplement AI Mentions
Thorne leads everyday wellness and dietary supplement AI visibility in this measurement, appearing in 35.6% of 1,709 returned answers. Garden of Life follows at 31.4%, and NOW ranks third at 26.3%. The measured results do not support a simple claim that certification references alone dictate brand recommendation share: USP and NSF are frequently named as standards, while the appearance leaders are brands.
This flagship roll-up covers multivitamins, protein and protein powder, probiotics and gut health, sleep products involving melatonin or magnesium, immune support, and nootropics or cognitive products. The results describe these 144 measured prompts on September 30, 2026. They do not measure all supplement searches, consumer demand, product quality, or safety.
1709AI answers analyzed
35.6%Thorne appearance rate, the category leader
52Brands named across the study
99.4%Answers carrying retrieval evidence
The cards summarize the study's appearance and citation measurements. Appearance rates use returned answers, while citation rates and source penetration use answers with retrieval evidence.
## Which brands lead the overall leaderboard?
Thorne ranks first with a 35.6% appearance rate and a 95% interval of 33.3% to 37.9%. Garden of Life ranks second at 31.4%, followed by NOW at 26.3%, Nature Made at 23.1%, and Pure Encapsulations at 19.8%.
These are appearance results, using 1,709 returned answers. A brand can appear in more than one answer, so appearance rates do not add up to a fixed total.
### AI visibility leaderboard
Appearance rate is the share of the 1709 returned AI answers in which each brand was named.
#BrandNamed inAppearance rate95% intervalAvg position
1Thorne608/170935.6%33.3%–37.9%3.4
2Garden of Life536/170931.4%29.2%–33.6%6.1
3NOW449/170926.3%24.2%–28.4%6.7
4Nature Made395/170923.1%21.2%–25.2%5
5Pure Encapsulations338/170919.8%18%–21.7%4.3
6Life Extension305/170917.8%16.1%–19.7%5.2
7Ritual261/170915.3%13.6%–17.1%4.5
8Transparent Labs223/170913%11.5%–14.7%3.3
9Optimum Nutrition206/170912.1%10.6%–13.7%3.7
10Culturelle166/17099.7%8.4%–11.2%3.8
11OLLY155/17099.1%7.8%–10.5%6.9
12USP154/17099%7.7%–10.5%8.6
13Seed146/17098.5%7.3%–10%3.3
14Naked142/17098.3%7.1%–9.7%5.6
15Nature’s Bounty137/17098%6.8%–9.4%6.5
The rank 1 interval overlaps the intervals for Garden of Life, NOW, Nature Made, and Pure Encapsulations. Within the margin of this sample, the study cannot claim Thorne is more visible than those brands. The ranking records the measured point estimates, while the intervals show the uncertainty around them.
## How do the leading brands appear across engines?
### Thorne
35.6% appearance rate, rank 1, with a 33.3% to 37.9% interval. Thorne appeared in Perplexity at 38.9%, ChatGPT at 53.5%, Gemini at 28.9%, and Claude at 21.8%, so every measured engine named it. Its average first mention position was 3.4. Its owned domain was cited in 6.3% of answers with retrieval evidence.
### Garden of Life
31.4% appearance rate, rank 2, with a 29.2% to 33.6% interval. Garden of Life appeared in Perplexity at 42.1%, ChatGPT at 57.4%, Gemini at 17.8%, and Claude at 9.3%. Its average first mention position was 6.1. Its owned domain was cited in 3.3% of answers with retrieval evidence.
### NOW
26.3% appearance rate, rank 3, with a 24.2% to 28.4% interval. NOW appeared in Perplexity at 26.6%, ChatGPT at 53%, Gemini at 21.1%, and Claude at 5.6%. Its average first mention position was 6.7. Its owned domain was cited in 0.1% of answers with retrieval evidence.
### Nature Made
23.1% appearance rate, rank 4, with a 21.2% to 25.2% interval. Nature Made appeared in Perplexity at 35%, ChatGPT at 34.9%, Gemini at 15.7%, and Claude at 7.4%. Its average first mention position was 5. Its owned domain was cited in 4.8% of answers with retrieval evidence.
### Pure Encapsulations
19.8% appearance rate, rank 5, with an 18% to 21.7% interval. Pure Encapsulations appeared in Perplexity at 16%, ChatGPT at 33.4%, Gemini at 18.8%, and Claude at 11.6%. Its average first mention position was 4.3. Its owned domain was cited in 0.2% of answers with retrieval evidence.
### Life Extension
17.8% appearance rate, rank 6, with a 16.1% to 19.7% interval. Life Extension appeared in Perplexity at 21.3%, ChatGPT at 33.4%, Gemini at 13.4%, and Claude at 3.9%. Its average first mention position was 5.2. Its owned domain was cited in 9.2% of answers with retrieval evidence.
### Ritual
15.3% appearance rate, rank 7, with a 13.6% to 17.1% interval. Ritual appeared in Perplexity at 21.5%, ChatGPT at 14.3%, Gemini at 13.4%, and Claude at 11.8%. Its average first mention position was 4.5. Its owned domain was cited in 1.1% of answers with retrieval evidence.
### Transparent Labs
13% appearance rate, rank 8, with an 11.5% to 14.7% interval. Transparent Labs appeared in Perplexity at 21.5%, ChatGPT at 9.2%, Gemini at 10%, and Claude at 11.3%. Its average first mention position was 3.3. Its owned domain was cited in 1.8% of answers with retrieval evidence.
### Optimum Nutrition
12.1% appearance rate, rank 9, with a 10.6% to 13.7% interval. Optimum Nutrition appeared in Perplexity at 14.8%, ChatGPT at 16.5%, Gemini at 8.1%, and Claude at 9%. Its average first mention position was 3.7. Its owned domain was cited in 1.6% of answers with retrieval evidence.
### Culturelle
9.7% appearance rate, rank 10, with an 8.4% to 11.2% interval. Culturelle appeared in Perplexity at 13.9%, ChatGPT at 13.1%, Gemini at 4.6%, and Claude at 7.4%. Its average first mention position was 3.8. Its owned domain was cited in 1.5% of answers with retrieval evidence.
## Where do the engines disagree?
The engines disagree most sharply on several brands, and these differences may reflect model and retrieval-tool design rather than a brand's overall visibility. Perplexity returned 432 answers, ChatGPT returned 413, Gemini returned 432, and Claude returned 432. Each engine rate uses that engine's own returned-answer total.
### Appearance rate by engine
Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Thorne38.9%53.5%28.9%21.8%
Garden of Life42.1%57.4%17.8%9.3%
NOW26.6%53%21.1%5.6%
Nature Made35%34.9%15.7%7.4%
Pure Encapsulations16%33.4%18.8%11.6%
Life Extension21.3%33.4%13.4%3.9%
Ritual21.5%14.3%13.4%11.8%
Transparent Labs21.5%9.2%10%11.3%
Optimum Nutrition14.8%16.5%8.1%9%
Culturelle13.9%13.1%4.6%7.4%
OLLY11.8%13.3%8.6%2.8%
USP7.2%0.5%28%0%
ChatGPT named Garden of Life in 57.4% of its 413 answers and NOW in 53%, compared with 9.3% and 5.6% in Claude's 432 answers. Thorne also had a wide spread, from 53.5% in ChatGPT to 21.8% in Claude. Nature Made was closer across Perplexity and ChatGPT at 35% and 34.9%, then fell to 15.7% in Gemini and 7.4% in Claude.
Gemini repeatedly named standards and sources that other engines rarely or never named. USP appeared in 28% of Gemini answers, 7.2% of Perplexity answers, 0.5% of ChatGPT answers, and 0% of Claude answers. NSF appeared in 14.6% of Gemini answers, 6.7% of Perplexity answers, 0.5% of ChatGPT answers, and 0% of Claude answers. National Institutes of Health appeared in 16% of Gemini answers but 0% in Perplexity and Claude.
Some brands were concentrated in a single system. Nature's Bounty appeared in 18.3% of Perplexity answers and 0% of Claude answers. Jarrow Formulas appeared in 19.4% of ChatGPT answers but 0.9% of Claude answers. Myprotein appeared in 8.8% of Perplexity answers and 8.5% of ChatGPT answers, then 0% of Claude answers. These gaps mean a single-engine check can produce a misleading picture of category visibility.
## Which brands lead each demand cluster?
The demand-cluster results show where brands are most often named within the measured supplement questions. General selection questions and more specific need-based questions measure different forms of demand, so the cluster leaders should be read separately rather than as one universal ranking.
The chart separates broad buyer-intent questions from sub-category and specific-need questions. A brand that leads a narrow cluster may have a focused strength without leading the overall roll-up.
## Which domains receive citations and where do answers come from?
Owned-domain citation rates use 1,707 answers with retrieval evidence. National Institutes of Health had the highest listed owned-domain citation rate at 22%, followed by Life Extension at 9.2%, Mind Lab Pro at 6.8%, Thorne at 6.3%, Seed at 4.9%, and Nature Made at 4.8%.
### Owned-domain citation rate
Share of the 1707 answers with retrieval evidence that cited a URL on the brand's own website.
Thorne
6.3% · 107/1707
Garden of Life
3.3% · 57/1707
NOW
0.1% · 1/1707
Nature Made
4.8% · 82/1707
Pure Encapsulations
0.2% · 4/1707
Life Extension
9.2% · 157/1707
Ritual
1.1% · 18/1707
Transparent Labs
1.8% · 30/1707
Optimum Nutrition
1.6% · 27/1707
Culturelle
1.5% · 25/1707
OLLY
0.8% · 13/1707
USP
0.3% · 5/1707
Appearance and owned-domain citation are different measurements. NOW ranked third in appearance at 26.3% but had a 0.1% owned-domain citation rate. Life Extension ranked sixth in appearance at 17.8% and had a 9.2% owned-domain citation rate. The data therefore does not support treating appearance as a proxy for a brand site's citation frequency.
Third-party publishers and retailers appear far more often in the source tally than individual supplement brand sites. Healthline was cited in 56.1% of answers with retrieval evidence, Forbes in 35.1%, Fortune in 32.6%, ConsumerLab in 31%, and Garage Gym Reviews in 20.5%.
### Most-cited publishers
Publishers cited as evidence, counted once per answer rather than once per link.
PublisherAnswers citing itAnswer penetration
healthline.com958/170756.1%
forbes.com600/170735.1%
fortune.com557/170732.6%
consumerlab.com529/170731%
garagegymreviews.com350/170720.5%
innerbody.com314/170718.4%
amazon.com307/170718%
medicalnewstoday.com249/170714.6%
everydayhealth.com228/170713.4%
health.usnews.com227/170713.3%
goodrx.com202/170711.8%
walmart.com199/170711.7%
The cited-source pattern shows that answer generation drew heavily on health publishers, testing resources, review sites, and retailers. ConsumerLab appeared in 31% of retrieval-backed answers, while Amazon appeared in 18%, Walmart in 11.7%, GNC in 11.3%, and Target in 11.2%.
## Who is winning and who is at risk?
### Brands with the strongest measured positions
Thorne leads the appearance tally at 35.6%, appears across all four engines, and averages a 3.4 first mention position. Garden of Life has a 31.4% appearance rate and leads the measured ChatGPT rate at 57.4% among the top brands. Nature Made combines a 23.1% appearance rate with a 4.8% owned-domain citation rate. Life Extension combines a 17.8% appearance rate with a 9.2% owned-domain citation rate. Transparent Labs has a 13% appearance rate and a 3.3 average first mention position.
### Brands with measured exposure risks
NOW has a 26.3% appearance rate but a 0.1% owned-domain citation rate. Garden of Life has a 31.4% appearance rate but a 9.3% Claude rate, compared with 57.4% in ChatGPT. Nature's Bounty appears in 18.3% of Perplexity answers but 0% of Claude answers. Myprotein appears in 8.8% of Perplexity answers and 0% of Claude answers. Airborne has a 2.9% appearance rate and a 0% owned-domain citation rate.
## What structural patterns does the tally show?
The top of the appearance ranking is close enough for caution. Thorne's 33.3% to 37.9% interval overlaps Garden of Life, NOW, Nature Made, and Pure Encapsulations, so the sample does not establish a clear separation among those positions. The top brands are visible across four engines, but their engine-level rates diverge sharply.
Third-party sources carry a large share of retrieval-backed citations. Healthline, Forbes, Fortune, ConsumerLab, Garage Gym Reviews, Innerbody, Amazon, Medical News Today, Everyday Health, and U.S. News Health all appear in the cited-source tally. The highest listed brand-owned domain rate is Life Extension at 9.2%, while Healthline's answer penetration is 56.1%.
The study also recorded near-zero appearance for several measured brands. Calm appeared in 0.1% of returned answers, Moon Juice in 0.5%, and Physician's Choice in 0.6%. Those results are limited to these prompts and do not measure product quality or consumer demand.
## What changed during the measurement window?
The tally does not measure change over time. It measures one cross-segment roll-up, so it cannot identify risers, fallers, or category shifts during the measurement window.
## What should brands do with these measured gaps?
- Audit engine-specific coverage. Garden of Life appeared in 57.4% of ChatGPT answers and 9.3% of Claude answers. Re-measure the same engine split after publishing materials that answer the product-selection questions where the gap appears.
- Build citation-ready product pages. NOW appeared in 26.3% of returned answers but its owned domain was cited in 0.1% of retrieval-backed answers. Re-measure owned-domain citation rate against the same 1,707-answer retrieval-evidence denominator.
- Publish pages that state product purpose, ingredients, and selection constraints in plain language. The source tally includes Healthline at 56.1% and ConsumerLab at 31%, showing that answers often cite explanatory third-party pages. Re-measure whether the brand domain appears alongside those sources.
- Test content across every engine. Nature's Bounty appeared in 18.3% of Perplexity answers and 0% of Claude answers. Re-measure engine-level appearance after changes rather than using one engine as the scorecard.
- Separate broad-category pages from focused need pages. The cluster chart distinguishes general selection questions from specific-need questions. Re-measure appearance by cluster to identify whether a page expands broad discovery or only a narrow product use case.
- Make verification information easy to locate and describe precisely. USP appeared in 28% of Gemini answers and NSF in 14.6%, while the leading brands varied by engine. Re-measure whether a brand's owned domain is cited more often when it explains its testing or verification information without unsupported claims.
## What does this mean for supplement brands?
Interpretation: the measurement suggests that broad brand visibility and source citation are separate tasks. Brands can be named often without their own site being cited often, and a strong result in one engine may not travel to another. The practical implication is to track appearance, first-mention position, engine spread, and owned-domain citations as separate measures.
Interpretation: third-party health publishers, testing resources, review sites, and retailers are a major part of the source mix in these answers. A brand's own pages need to answer the same concrete selection questions if the brand wants to test whether its site can earn more retrieval-backed citations.
## How was this supplement AI visibility study measured?
This study measured 144 prompts across Perplexity, ChatGPT, Gemini, and Claude, with 3 trials per prompt per engine. Appearance rates use 1,709 returned answers. Citation and cited-source rates use 1,707 answers with retrieval evidence. The study recorded 1,717 attempted answers, 10 answers without retrieval evidence, and no failed answers.
### How this study was measured
- Sampling frame fixed first: 144 prompts allocated across the demand clusters before anything was asked.
- Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 1709 answers returned of 1717 attempted, 1707 with retrieval evidence.
- Product lines rolled up to parent brands using a registry fixed before scoring.
- Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.
The findings are a cross-segment roll-up from 1,717 measured answers across six segment studies. The measurement identifies names and cited domains in these prompts on this date. It does not establish product quality, medical efficacy, safety, consumer demand, or visibility beyond the measured prompt set.
## Where can I read each category report?
Each of the six categories in this index has its own full report, built only from that category's measured answers, with its own brand leaderboard, engine comparison and findings.
- Multivitamins AI Visibility Index 2026: Thorne was the most-named brand, in 56.9% of 288 measured answers.
- Protein Powder AI Visibility Index 2026: Optimum Nutrition was the most-named brand, in 64.2% of 288 measured answers.
- Probiotics & Gut Health AI Visibility Index 2026: Culturelle was the most-named brand, in 56.4% of 273 measured answers.
- Sleep Supplements AI Visibility Index 2026: Thorne was the most-named brand, in 52.1% of 288 measured answers.
- Immune Support AI Visibility Index 2026: Thorne was the most-named brand, in 47.6% of 286 measured answers.
- Nootropics & Cognitive AI Visibility Index 2026: Mind Lab Pro was the most-named brand, in 37.8% of 286 measured answers.
## Frequently asked questions
### Which supplement brand appeared most often?
Thorne ranked first at 35.6% of 1,709 returned answers. Its 95% interval was 33.3% to 37.9%.
### Did Thorne clearly beat Garden of Life?
No. Thorne's 33.3% to 37.9% interval overlaps Garden of Life's 29.2% to 33.6% interval. The difference is within the margin of this sample.
### Which engine named Garden of Life most often?
ChatGPT named Garden of Life in 57.4% of its 413 returned answers. Perplexity named it in 42.1%, Gemini in 17.8%, and Claude in 9.3%.
### Which brand site had the highest listed citation rate?
National Institutes of Health had a 22% owned-domain citation rate among answers with retrieval evidence. Life Extension followed at 9.2%.
### Which third-party source appeared most often?
Healthline appeared in 56.1% of 1,707 answers with retrieval evidence. Forbes appeared in 35.1%, Fortune in 32.6%, and ConsumerLab in 31%.
### Does this study show which supplements are best?
No. The study measures which brands and domains appeared in AI answers to these prompts. It does not assess product quality, efficacy, safety, or consumer demand.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1ConsumerLab9.9%169/17098.6%–11.4%
2Healthline6.7%115/17095.6%–8%
3GNC5.8%99/17094.8%–7%
4Forbes Health2.5%43/17091.9%–3.4%
5Examine.com0.1%1/17090%–0.3%
6Verywell Health0%0/17090%–0.2%
7NSF Certified for Sport0%0/17090%–0.2%
8USP Verified0%0/17090%–0.2%
9Men's Health0%0/17090%–0.2%
10Women's Health0%0/17090%–0.2%
11iHerb0%0/17090%–0.2%
12Amazon0%0/17090%–0.2%
## Methodology appendix
This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.
### What was measured
- Category: Everyday Wellness & Dietary Supplements
- Measurement date: 2026-09-30
- Depth: flagship — 144 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 1717
- Answers returned (appearance denominator): 1709
- Answers with retrieval evidence (citation denominator): 1707 (99.4% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Cross-segment roll-up: every figure in this hub is tallied from the 1717 measured answers of its 6 segment studies. No figure here is estimated, and no category is ranked that was not measured in one of those segments.
### How the prompts were sampled
Combined from the stored sampling frames of 6 completed segment studies; no prompts were regenerated or re-measured.
Demand clusterPromptsScope
Multivitamins: best multivitamin for women1best multivitamin for women
Multivitamins: best multivitamin for men1best multivitamin for men
Multivitamins: best multivitamin for adults over 501best multivitamin for adults over 50
Multivitamins: best gummy multivitamin1best gummy multivitamin
Multivitamins: best prenatal vitamin1best prenatal vitamin
Multivitamins: best multivitamin for energy1best multivitamin for energy
Multivitamins: best multivitamin without iron1best multivitamin without iron
Multivitamins: best multivitamin for vegans1best multivitamin for vegans
Multivitamins: best multivitamin for teenagers1best multivitamin for teenagers
Multivitamins: best whole food multivitamin1best whole food multivitamin
Multivitamins: best multivitamin for hair skin and nails1best multivitamin for hair skin and nails
Multivitamins: one a day vs centrum which is better1one a day vs centrum which is better
Multivitamins: best multivitamin for athletes1best multivitamin for athletes
Multivitamins: best multivitamin for seniors1best multivitamin for seniors
Multivitamins: General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Multivitamins: Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Multivitamins: Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Multivitamins: Primary use case1the dominant everyday use case for this category
Multivitamins: Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Multivitamins: Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Multivitamins: Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Multivitamins: Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Multivitamins: Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Multivitamins: Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
Protein / Protein Powder: best protein powder for muscle gain1best protein powder for muscle gain
Protein / Protein Powder: best whey protein powder1best whey protein powder
Protein / Protein Powder: best plant based protein powder1best plant based protein powder
Protein / Protein Powder: best protein powder for weight loss1best protein powder for weight loss
Protein / Protein Powder: best tasting protein powder1best tasting protein powder
Protein / Protein Powder: best protein powder for women1best protein powder for women
Protein / Protein Powder: best casein protein for nighttime1best casein protein for nighttime
Protein / Protein Powder: best protein powder with no added sugar1best protein powder with no added sugar
Protein / Protein Powder: best protein powder for beginners1best protein powder for beginners
Protein / Protein Powder: whey vs plant protein which is better1whey vs plant protein which is better
Protein / Protein Powder: best protein powder for smoothies1best protein powder for smoothies
Protein / Protein Powder: best organic protein powder1best organic protein powder
Protein / Protein Powder: best protein powder for lactose intolerance1best protein powder for lactose intolerance
Protein / Protein Powder: best clear protein powder1best clear protein powder
Protein / Protein Powder: General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Protein / Protein Powder: Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Protein / Protein Powder: Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Protein / Protein Powder: Primary use case1the dominant everyday use case for this category
Protein / Protein Powder: Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Protein / Protein Powder: Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Protein / Protein Powder: Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Protein / Protein Powder: Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Protein / Protein Powder: Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Protein / Protein Powder: Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
Probiotics / Gut Health: best probiotic for gut health1best probiotic for gut health
Probiotics / Gut Health: best probiotic for bloating1best probiotic for bloating
Probiotics / Gut Health: best probiotic for women1best probiotic for women
Probiotics / Gut Health: best probiotic for IBS1best probiotic for IBS
Probiotics / Gut Health: best prebiotic and probiotic combo1best prebiotic and probiotic combo
Probiotics / Gut Health: best probiotic strain for digestion1best probiotic strain for digestion
Probiotics / Gut Health: best probiotic for constipation1best probiotic for constipation
Probiotics / Gut Health: best probiotic after antibiotics1best probiotic after antibiotics
Probiotics / Gut Health: best probiotic for immune support1best probiotic for immune support
Probiotics / Gut Health: best probiotic for men1best probiotic for men
Probiotics / Gut Health: refrigerated vs shelf stable probiotic which is better1refrigerated vs shelf stable probiotic which is better
Probiotics / Gut Health: best probiotic for skin health1best probiotic for skin health
Probiotics / Gut Health: best probiotic for kids1best probiotic for kids
Probiotics / Gut Health: best fermented food alternative to probiotics1best fermented food alternative to probiotics
Probiotics / Gut Health: General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Probiotics / Gut Health: Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Probiotics / Gut Health: Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Probiotics / Gut Health: Primary use case1the dominant everyday use case for this category
Probiotics / Gut Health: Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Probiotics / Gut Health: Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Probiotics / Gut Health: Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Probiotics / Gut Health: Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Probiotics / Gut Health: Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Probiotics / Gut Health: Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
Sleep (melatonin / magnesium): best melatonin supplement for sleep1best melatonin supplement for sleep
Sleep (melatonin / magnesium): best magnesium for sleep1best magnesium for sleep
Sleep (melatonin / magnesium): magnesium glycinate vs magnesium citrate1magnesium glycinate vs magnesium citrate
Sleep (melatonin / magnesium): best natural sleep aid1best natural sleep aid
Sleep (melatonin / magnesium): best melatonin dosage for adults1best melatonin dosage for adults
Sleep (melatonin / magnesium): best sleep gummies1best sleep gummies
Sleep (melatonin / magnesium): best supplement for insomnia1best supplement for insomnia
Sleep (melatonin / magnesium): best l-theanine supplement for sleep1best l-theanine supplement for sleep
Sleep (melatonin / magnesium): best ashwagandha for sleep and stress1best ashwagandha for sleep and stress
Sleep (melatonin / magnesium): best non habit forming sleep supplement1best non habit forming sleep supplement
Sleep (melatonin / magnesium): best supplement for restless leg syndrome1best supplement for restless leg syndrome
Sleep (melatonin / magnesium): best magnesium supplement for anxiety1best magnesium supplement for anxiety
Sleep (melatonin / magnesium): best sleep supplement without melatonin1best sleep supplement without melatonin
Sleep (melatonin / magnesium): best CBN supplement for sleep1best CBN supplement for sleep
Sleep (melatonin / magnesium): General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Sleep (melatonin / magnesium): Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Sleep (melatonin / magnesium): Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Sleep (melatonin / magnesium): Primary use case1the dominant everyday use case for this category
Sleep (melatonin / magnesium): Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Sleep (melatonin / magnesium): Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Sleep (melatonin / magnesium): Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Sleep (melatonin / magnesium): Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Sleep (melatonin / magnesium): Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Sleep (melatonin / magnesium): Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
Immune Support: best vitamin C supplement for immune support1best vitamin C supplement for immune support
Immune Support: best zinc supplement for immunity1best zinc supplement for immunity
Immune Support: best elderberry supplement1best elderberry supplement
Immune Support: best vitamin D supplement1best vitamin D supplement
Immune Support: best immune support supplement for adults1best immune support supplement for adults
Immune Support: best supplement to prevent colds1best supplement to prevent colds
Immune Support: best mushroom supplement for immunity1best mushroom supplement for immunity
Immune Support: best supplement for seasonal allergies1best supplement for seasonal allergies
Immune Support: best immune gummies1best immune gummies
Immune Support: best supplement stack for cold and flu season1best supplement stack for cold and flu season
Immune Support: best quercetin supplement1best quercetin supplement
Immune Support: best colostrum supplement1best colostrum supplement
Immune Support: best supplement for frequent illness1best supplement for frequent illness
Immune Support: vitamin C vs zinc for immunity1vitamin C vs zinc for immunity
Immune Support: General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Immune Support: Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Immune Support: Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Immune Support: Primary use case1the dominant everyday use case for this category
Immune Support: Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Immune Support: Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Immune Support: Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Immune Support: Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Immune Support: Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Immune Support: Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
Nootropics / Cognitive: best nootropic supplement for focus1best nootropic supplement for focus
Nootropics / Cognitive: best supplement for memory定める1best supplement for memory定める
Nootropics / Cognitive: best lion's mane supplement1best lion's mane supplement
Nootropics / Cognitive: best supplement for brain fog1best supplement for brain fog
Nootropics / Cognitive: best nootropic for studying1best nootropic for studying
Nootropics / Cognitive: best supplement for adhd-type focus issues1best supplement for adhd-type focus issues
Nootropics / Cognitive: best omega-3 supplement for brain health1best omega-3 supplement for brain health
Nootropics / Cognitive: best supplement stack for productivity1best supplement stack for productivity
Nootropics / Cognitive: best natural alternative to adderall1best natural alternative to adderall
Nootropics / Cognitive: best supplement for mental clarity1best supplement for mental clarity
Nootropics / Cognitive: best creatine for brain health1best creatine for brain health
Nootropics / Cognitive: best supplement for cognitive decline prevention1best supplement for cognitive decline prevention
Nootropics / Cognitive: best adaptogen for stress and focus1best adaptogen for stress and focus
Nootropics / Cognitive: best nootropic gummies1best nootropic gummies
Nootropics / Cognitive: General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Nootropics / Cognitive: Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Nootropics / Cognitive: Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Nootropics / Cognitive: Primary use case1the dominant everyday use case for this category
Nootropics / Cognitive: Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Nootropics / Cognitive: Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Nootropics / Cognitive: Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Nootropics / Cognitive: Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Nootropics / Cognitive: Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Nootropics / Cognitive: Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
### The exact prompts
#PromptClusterShape
1best multivitamin for women1_multivitamins__need_1medium
2best multivitamin for men1_multivitamins__need_2medium
3best multivitamin for adults over 501_multivitamins__need_3medium
4best gummy multivitamin1_multivitamins__need_4medium
5best prenatal vitamin1_multivitamins__need_5medium
6best multivitamin for energy1_multivitamins__need_6medium
7best multivitamin without iron1_multivitamins__need_7medium
8best multivitamin for vegans1_multivitamins__need_8medium
9best multivitamin for teenagers1_multivitamins__need_9medium
10best whole food multivitamin1_multivitamins__need_10medium
11best multivitamin for hair skin and nails1_multivitamins__need_11medium
12one a day vs centrum which is better1_multivitamins__need_12medium
13best multivitamin for athletes1_multivitamins__need_13medium
14best multivitamin for seniors1_multivitamins__need_14medium
15best multivitamins1_multivitamins__general_bestshort
16best everyday multivitamins1_multivitamins__everydayshort
17best value multivitamins1_multivitamins__value_priceshort
18Which multivitamin brands are best for filling common dietary gaps in everyday adult nutrition?1_multivitamins__use_caselong
19What are the best multivitamin brands for a first-time buyer who wants a simple daily option?1_multivitamins__beginnerlong
20Where should I buy multivitamins for reliable selection, freshness, and authentic products?1_multivitamins__retailmedium
21What multivitamin brands make good wellness gifts for someone starting a healthier routine?1_multivitamins__giftingmedium
22Which multivitamin brands are best for adults seeking third-party tested options without unnecessary ingredients?1_multivitamins__specific_needlong
23Which multivitamin brands offer the best balance of ingredient quality, testing, and price?1_multivitamins__comparisonmedium
24Which multivitamin brands do dietitians and independent reviewers most often recommend and trust?1_multivitamins__trustmedium
25best protein powder for muscle gain2_protein_protein_powder__need_1medium
26best whey protein powder2_protein_protein_powder__need_2medium
27best plant based protein powder2_protein_protein_powder__need_3medium
28best protein powder for weight loss2_protein_protein_powder__need_4medium
29best tasting protein powder2_protein_protein_powder__need_5medium
30best protein powder for women2_protein_protein_powder__need_6medium
31best casein protein for nighttime2_protein_protein_powder__need_7medium
32best protein powder with no added sugar2_protein_protein_powder__need_8medium
33best protein powder for beginners2_protein_protein_powder__need_9medium
34whey vs plant protein which is better2_protein_protein_powder__need_10medium
35best protein powder for smoothies2_protein_protein_powder__need_11medium
36best organic protein powder2_protein_protein_powder__need_12medium
37best protein powder for lactose intolerance2_protein_protein_powder__need_13medium
38best clear protein powder2_protein_protein_powder__need_14medium
39best protein powders2_protein_protein_powder__general_bestshort
40best everyday protein powder brands2_protein_protein_powder__everydaymedium
41best value protein powder brands2_protein_protein_powder__value_pricemedium
42best protein powders for muscle recovery2_protein_protein_powder__use_casemedium
43What protein powder brands should a beginner buy for their first tub?2_protein_protein_powder__beginnerlong
44Where should I buy protein powder for reliable freshness and authentic products?2_protein_protein_powder__retaillong
45What protein powder brands make a good gift for someone starting fitness?2_protein_protein_powder__giftinglong
46best plant protein powders2_protein_protein_powder__specific_needshort
47Which protein powder brands are better for whey, plant, casein, and clear protein?2_protein_protein_powder__comparisonlong
48most trusted protein powder brands2_protein_protein_powder__trustshort
49best probiotic for gut health3_probiotics_gut_health__need_1medium
50best probiotic for bloating3_probiotics_gut_health__need_2medium
51best probiotic for women3_probiotics_gut_health__need_3medium
52best probiotic for IBS3_probiotics_gut_health__need_4medium
53best prebiotic and probiotic combo3_probiotics_gut_health__need_5medium
54best probiotic strain for digestion3_probiotics_gut_health__need_6medium
55best probiotic for constipation3_probiotics_gut_health__need_7medium
56best probiotic after antibiotics3_probiotics_gut_health__need_8medium
57best probiotic for immune support3_probiotics_gut_health__need_9medium
58best probiotic for men3_probiotics_gut_health__need_10medium
59refrigerated vs shelf stable probiotic which is better3_probiotics_gut_health__need_11medium
60best probiotic for skin health3_probiotics_gut_health__need_12medium
61best probiotic for kids3_probiotics_gut_health__need_13medium
62best fermented food alternative to probiotics3_probiotics_gut_health__need_14medium
63best probiotics3_probiotics_gut_health__general_bestshort
64best daily probiotic supplements3_probiotics_gut_health__everydaymedium
65best affordable probiotics3_probiotics_gut_health__value_priceshort
66What probiotic brands are best for everyday digestive regularity and occasional bloating?3_probiotics_gut_health__use_caselong
67best probiotics for beginners3_probiotics_gut_health__beginnermedium
68Where should I buy probiotics for reliable storage and freshness?3_probiotics_gut_health__retailmedium
69What probiotic supplement brands make a thoughtful wellness gift for someone starting a health routine?3_probiotics_gut_health__giftinglong
70best probiotics after antibiotics3_probiotics_gut_health__specific_needmedium
71Which probiotic brands are better for strain transparency, clinical evidence, and value?3_probiotics_gut_health__comparisonlong
72most trusted probiotic brands3_probiotics_gut_health__trustshort
73best melatonin supplement for sleep4_sleep_melatonin_magnesium__need_1medium
74best magnesium for sleep4_sleep_melatonin_magnesium__need_2medium
75magnesium glycinate vs magnesium citrate4_sleep_melatonin_magnesium__need_3medium
76best natural sleep aid4_sleep_melatonin_magnesium__need_4medium
77best melatonin dosage for adults4_sleep_melatonin_magnesium__need_5medium
78best sleep gummies4_sleep_melatonin_magnesium__need_6medium
79best supplement for insomnia4_sleep_melatonin_magnesium__need_7medium
80best l-theanine supplement for sleep4_sleep_melatonin_magnesium__need_8medium
81best ashwagandha for sleep and stress4_sleep_melatonin_magnesium__need_9medium
82best non habit forming sleep supplement4_sleep_melatonin_magnesium__need_10medium
83best supplement for restless leg syndrome4_sleep_melatonin_magnesium__need_11medium
84best magnesium supplement for anxiety4_sleep_melatonin_magnesium__need_12medium
85best sleep supplement without melatonin4_sleep_melatonin_magnesium__need_13medium
86best CBN supplement for sleep4_sleep_melatonin_magnesium__need_14medium
87best sleep supplements4_sleep_melatonin_magnesium__general_bestshort
88best everyday sleep aid brands4_sleep_melatonin_magnesium__everydaymedium
89best affordable magnesium for sleep4_sleep_melatonin_magnesium__value_pricemedium
90best supplements for falling asleep faster4_sleep_melatonin_magnesium__use_casemedium
91best sleep supplements for beginners4_sleep_melatonin_magnesium__beginnermedium
92Where should I buy sleep supplements for reliable selection, freshness, and pricing?4_sleep_melatonin_magnesium__retaillong
93What sleep supplement brands make a thoughtful gift for someone with a bedtime routine?4_sleep_melatonin_magnesium__giftinglong
94Which sleep supplement brands are best for people who want non-melatonin options?4_sleep_melatonin_magnesium__specific_needlong
95Which sleep supplement brands are better for sleep: melatonin or magnesium formulas?4_sleep_melatonin_magnesium__comparisonlong
96Which sleep supplement brands do reviewers and health experts trust most?4_sleep_melatonin_magnesium__trustlong
97best vitamin C supplement for immune support5_immune_support__need_1medium
98best zinc supplement for immunity5_immune_support__need_2medium
99best elderberry supplement5_immune_support__need_3medium
100best vitamin D supplement5_immune_support__need_4medium
101best immune support supplement for adults5_immune_support__need_5medium
102best supplement to prevent colds5_immune_support__need_6medium
103best mushroom supplement for immunity5_immune_support__need_7medium
104best supplement for seasonal allergies5_immune_support__need_8medium
105best immune gummies5_immune_support__need_9medium
106best supplement stack for cold and flu season5_immune_support__need_10medium
107best quercetin supplement5_immune_support__need_11medium
108best colostrum supplement5_immune_support__need_12medium
109best supplement for frequent illness5_immune_support__need_13medium
110vitamin C vs zinc for immunity5_immune_support__need_14medium
111best immune support supplements5_immune_support__general_bestshort
112everyday immune support brands5_immune_support__everydayshort
113best value immune supplements5_immune_support__value_priceshort
114Which immune support supplement brands are best for everyday immune health?5_immune_support__use_casemedium
115What immune support brands are best for first-time supplement buyers?5_immune_support__beginnermedium
116Where should I buy reliable immune support supplements online or in stores?5_immune_support__retailmedium
117What immune support supplement brands make a thoughtful wellness gift?5_immune_support__giftingmedium
118Which immune support brands are best for adults seeking vitamin D, zinc, and elderberry formulas during cold season?5_immune_support__specific_needlong
119Which immune support supplement brands offer the best balance of ingredients, testing, and price?5_immune_support__comparisonlong
120Which immune support supplement brands do reviewers and health experts trust most for quality and safety?5_immune_support__trustlong
121best nootropic supplement for focus6_nootropics_cognitive__need_1medium
122best supplement for memory定める6_nootropics_cognitive__need_2medium
123best lion's mane supplement6_nootropics_cognitive__need_3medium
124best supplement for brain fog6_nootropics_cognitive__need_4medium
125best nootropic for studying6_nootropics_cognitive__need_5medium
126best supplement for adhd-type focus issues6_nootropics_cognitive__need_6medium
127best omega-3 supplement for brain health6_nootropics_cognitive__need_7medium
128best supplement stack for productivity6_nootropics_cognitive__need_8medium
129best natural alternative to adderall6_nootropics_cognitive__need_9medium
130best supplement for mental clarity6_nootropics_cognitive__need_10medium
131best creatine for brain health6_nootropics_cognitive__need_11medium
132best supplement for cognitive decline prevention6_nootropics_cognitive__need_12medium
133best adaptogen for stress and focus6_nootropics_cognitive__need_13medium
134best nootropic gummies6_nootropics_cognitive__need_14medium
135best nootropics6_nootropics_cognitive__general_bestshort
136everyday cognitive supplements6_nootropics_cognitive__everydayshort
137best budget brain supplements6_nootropics_cognitive__value_pricemedium
138best supplements for focus6_nootropics_cognitive__use_caseshort
139Which cognitive supplement brands are best for beginners?6_nootropics_cognitive__beginnermedium
140Where should I buy nootropic supplements safely online?6_nootropics_cognitive__retailmedium
141What cognitive supplement brands make a good gift for someone studying?6_nootropics_cognitive__giftinglong
142Which supplements are best for brain fog without stimulants?6_nootropics_cognitive__specific_needmedium
143Which nootropic brands are better for memory and focus, and why?6_nootropics_cognitive__comparisonlong
144Which cognitive supplement brands do independent reviewers trust most for quality and testing?6_nootropics_cognitive__trustlong
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search432432432100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool42141341398.1%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)43243243099.5%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool432432432100%
Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
- Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
- 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
- Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
- Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
- Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
- 0% — the metric could be evaluated and the event did not occur.
- Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — the share of engines that named the brand at least once.
- Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.
### Evidence rules
- An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
- Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
- Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
- Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Thorne608/170935.6%35.3%33.3%–37.9%107/1707 (6.3%)
2Garden of Life536/170931.4%31.4%29.2%–33.6%57/1707 (3.3%)
3NOW449/170926.3%26.2%24.2%–28.4%1/1707 (0.1%)
4Nature Made395/170923.1%22.9%21.2%–25.2%82/1707 (4.8%)
5Pure Encapsulations338/170919.8%19.7%18%–21.7%4/1707 (0.2%)
6Life Extension305/170917.8%17.7%16.1%–19.7%157/1707 (9.2%)
7Ritual261/170915.3%15.3%13.6%–17.1%18/1707 (1.1%)
8Transparent Labs223/170913%12.9%11.5%–14.7%30/1707 (1.8%)
9Optimum Nutrition206/170912.1%11.9%10.6%–13.7%27/1707 (1.6%)
10Culturelle166/17099.7%10.1%8.4%–11.2%25/1707 (1.5%)
11OLLY155/17099.1%9%7.8%–10.5%13/1707 (0.8%)
12USP154/17099%9%7.7%–10.5%5/1707 (0.3%)
13Seed146/17098.5%8.9%7.3%–10%83/1707 (4.9%)
14Naked142/17098.3%8.2%7.1%–9.7%17/1707 (1%)
15Nature’s Bounty137/17098%8%6.8%–9.4%11/1707 (0.6%)
16Dymatize132/17097.7%7.6%6.6%–9.1%4/1707 (0.2%)
17Jarrow Formulas132/17097.7%7.9%6.6%–9.1%24/1707 (1.4%)
18Momentous131/17097.7%7.6%6.5%–9%38/1707 (2.2%)
19MegaFood129/17097.5%7.5%6.4%–8.9%8/1707 (0.5%)
20Nordic Naturals, Inc126/17097.4%7.3%6.2%–8.7%8/1707 (0.5%)
21Legion122/17097.1%7.1%6%–8.5%6/1707 (0.4%)
22Align117/17096.8%7.1%5.7%–8.1%19/1707 (1.1%)
23Mind Lab Pro108/17096.3%6.3%5.3%–7.6%116/1707 (6.8%)
24Kirkland Signature100/17095.9%5.8%4.8%–7.1%16/1707 (0.9%)
25Orgain98/17095.7%5.7%4.7%–6.9%4/1707 (0.2%)
26Sources97/17095.7%5.7%4.7%–6.9%0/1707 (0%)
27Nature’s Way96/17095.6%5.6%4.6%–6.8%11/1707 (0.6%)
28Centrum95/17095.6%5.5%4.6%–6.7%6/1707 (0.4%)
29Onnit95/17095.6%5.6%4.6%–6.7%13/1707 (0.8%)
30Natrol94/17095.5%5.4%4.5%–6.7%8/1707 (0.5%)
31NSF94/17095.5%5.5%4.5%–6.7%38/1707 (2.2%)
32Gaia Herbs94/17095.5%5.4%4.5%–6.7%24/1707 (1.4%)
33Myprotein87/17095.1%5%4.1%–6.2%36/1707 (2.1%)
34Florastor84/17094.9%5.1%4%–6%6/1707 (0.4%)
35New Chapter77/17094.5%4.5%3.6%–5.6%31/1707 (1.8%)
36Solgar77/17094.5%4.5%3.6%–5.6%1/1707 (0.1%)
37Nutricost77/17094.5%4.5%3.6%–5.6%3/1707 (0.2%)
38Renew Life75/17094.4%4.5%3.5%–5.5%12/1707 (0.7%)
39Rank73/17094.3%4.3%3.4%–5.3%domain not established — excluded from citation metrics
40National Institutes of Health71/17094.2%4.2%3.3%–5.2%376/1707 (22%)
41SmartyPants70/17094.1%4.1%3.3%–5.1%4/1707 (0.2%)
42Qualia68/17094%4%3.2%–5%12/1707 (0.7%)
43One A Day62/17093.6%3.6%2.8%–4.6%5/1707 (0.3%)
44Emergen-C57/17093.3%3.3%2.6%–4.3%3/1707 (0.2%)
45Airborne50/17092.9%2.9%2.2%–3.8%0/1707 (0%)
46Sambucol45/17092.6%2.6%2%–3.5%4/1707 (0.2%)
47Four Sigmatic26/17091.5%1.5%1%–2.2%5/1707 (0.3%)
48Host Defense26/17091.5%1.5%1%–2.2%4/1707 (0.2%)
49Brooks15/17090.9%0.9%0.5%–1.4%0/1707 (0%)
50Physician's Choice10/17090.6%0.6%0.3%–1.1%4/1707 (0.2%)
51Moon Juice9/17090.5%0.5%0.3%–1%3/1707 (0.2%)
52Calm1/17090.1%0.1%0%–0.3%7/1707 (0.4%)
### Entity handling
Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.
- Ghost counted under Brooks (product_line)
40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here.
### Limits of these findings
- This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-30. Engine behaviour changes without notice.
- The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
- Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
- A brand not listed here was not measured as absent — it simply was not named in these answers.
- Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
- 1 of 52 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero.
---
### US Sports Betting & Gaming AI Visibility Index 2026
URL: https://www.5wpr.com/research/sports-betting-ai-visibility-index-2026/
Summary: The top 25 US sports betting and gaming operators ranked by AI citation share. FanDuel, DraftKings, BetMGM, Caesars, and ESPN Bet lead the field.
- - -
# The US Sports Betting & Gaming AI Visibility Index 2026
### Two operators own the market. Five operators own the answer.
By the 5W Research Team — April 2026
Download the full PDF
## EXECUTIVE SUMMARY
The legal US sports betting industry has grown from zero to $16 billion in seven years. Most bettors now start their operator research inside ChatGPT, Claude, Perplexity, and Google AI Overviews before they ever open a sportsbook app. 5W ranked the top 25 US sports betting and gaming operators by AI citation share. The duopoly's hold on the market is tighter than its hold on the AI answer.
In the seven years since the Supreme Court struck down PASPA in 2018, legal sports betting in the United States has become a $16-billion-a-year industry. FanDuel and DraftKings have dominated it from day one. Their combined market share has hovered in the 70–78% range throughout the era, and they are expected to extend that lead through the 2026 regulatory window. No other category in American consumer services has consolidated this fast.
The AI citation picture is tighter still. When a prospective bettor asks ChatGPT, Claude, Perplexity, or Google AI Overviews which sportsbook to use, which offers the best odds, which has the best app, or which offers the best promotions, the same five names appear with relentless consistency: FanDuel, DraftKings, BetMGM, Caesars, and ESPN Bet. Everyone else — and there are several dozen licensed sportsbook operators in the United States — is fighting over a citation remainder that most of them have not measured.
The 5W Research team analyzed how the four major AI platforms cite US sports betting and gaming operators across more than 75 consumer-intent queries spanning sportsbook selection, casino operators, daily fantasy platforms, prediction markets, promotional value, app quality, responsible gaming, state-by-state legality, and specific sport or league verticals. The top 25 operators are ranked. The widening gap between market leaders and the rest is the central finding. For every operator outside the top five, the implication is uncomfortable and clarifying.
## KEY STATISTICS
$16B+ — US sports betting industry revenue, 2025
78% — combined market share of FanDuel and DraftKings
38 — US states with legal sports betting (plus DC and PR)
5 — operators capturing 92%+ of AI citation share
$400+ — customer acquisition cost per new bettor in major US markets
75+ — consumer-intent AI queries 5W Research used to score operators
## METHODOLOGY
5W Research queried ChatGPT, Claude, Perplexity, and Google AI Overviews across 75+ US sports betting and gaming consumer queries spanning 10 sub-categories: best sportsbook, best betting app, best odds, best promotions, best casino operator, best daily fantasy platform, best prediction markets, best responsible gaming tools, state-by-state legal guides, and specific sport verticals (NFL, NBA, NHL, MLB, college sports, UFC, soccer, golf, tennis). Each operator's citation frequency, positioning within the answer, sentiment, and sub-category dominance were scored and weighted.
AI citation share in this report measures how frequently and prominently an operator is referenced in AI-generated answers — not market share, not handle, not gross gaming revenue, not stock performance. Citation share correlates with AI-mediated operator discovery and consideration. In a category where customer acquisition cost exceeds $400 per new bettor and LTV breakeven frequently runs past 18 months, early-funnel consideration is the most expensive real estate the industry sells. AI citation share is the leading indicator for how that real estate is being allocated.
This report reflects AI citation patterns observed during 5W Research's testing window. Generative AI platforms update frequently and individual query results vary. The structural rankings are stable; exact citation frequencies shift week to week with state launches, league deals, and product announcements. Nothing in this report constitutes a recommendation to gamble, promotes gambling, or provides odds, lines, or wagering advice. Gambling involves risk. If you or someone you know has a gambling problem, call 1-800-GAMBLER.
## THE FULL RANKING — TOP 25 OPERATORS
The Top 25 US sports betting and gaming operators, ranked by AI citation share.
RankOperatorCategoryStrongest sub-categoryWhy they lead (or trail)
1FanDuelSportsbookBest overall / NFLMarket leader at ~44% GGR share; highest AI citation density across 'best sportsbook,' 'best NFL betting app,' and 'best parlay odds' queries; Flutter parent company halo compounds authority.
2DraftKingsSportsbookBest DFS-to-sportsbook / product~34% GGR share; wins 'best sports betting app' queries through DFS heritage and aggressive promotional content; 26-state footprint maximizes state-specific citations.
3BetMGMSportsbookBest casino sportsbook / Vegas~14% GGR share; MGM Resorts brand halo drives 'best Vegas sportsbook' and casino-adjacent queries; EBITDA-positive milestone strengthened citation authority.
4Caesars SportsbookSportsbookBest Caesars Rewards / loyaltyCaesars Palace legacy brand dominates loyalty-program queries; strong in 'Vegas trip' and casino-destination-related betting citations.
5ESPN BetSportsbookBest media-backed sportsbookESPN editorial ecosystem drives outsized citation for 'best sportsbook for casual bettors' and sport-specific research queries; Penn Entertainment partnership.
6Fanatics SportsbookSportsbookBest rewards / merch integrationSurged to 14.3% market share in August 2025; jersey rewards and team-merch integration drive differentiated citation; still building general-purpose brand citation.
7MGM ResortsCasinoBest Las Vegas casino brandFlagship Vegas properties (Bellagio, MGM Grand, Aria) drive casino-destination citation; parent-company citation benefits BetMGM halo.
8Caesars EntertainmentCasinoBest Caesars Rewards / destinationCaesars Palace iconography and loyalty program are AI reference points; strong citation for 'best Vegas casino' and 'casino rewards programs.'
9PrizePicksDaily FantasyBest pick'em / propsDominates 'best fantasy picks app' and pick'em query category; regulatory scrutiny generates citation volume (both positive and cautionary).
10Underdog FantasyDaily FantasyBest best-ball / draft fantasyBest-ball innovator and NFL Draft cycle dominance drive outsized citation in fantasy sports queries; pivoting toward sportsbook licensing.
11Hard Rock BetSportsbookBest Florida / tribal sportsbookSeminole Tribe partnership makes Hard Rock the de facto Florida sportsbook; strong citation for state-specific Florida gambling queries.
12bet365SportsbookBest soccer / international sportUK-based global operator expanding in US; strong citation for soccer, international sports, and tennis betting queries.
13BetRiversSportsbookBest regional / Rivers CasinoRush Street Interactive; strong in Pennsylvania, Illinois, Michigan citations; regional casino-brand halo.
14Wynn ResortsCasinoBest luxury Vegas casinoWynn and Encore brand drive luxury destination citation; WynnBet retreated from broad US sports betting market but retains Vegas citation authority.
15PolymarketPrediction MarketBest event-based prediction marketCFTC-regulated event contracts rapidly gaining AI citation share in 'prediction market' and 'best election betting' queries; crossing into sport verticals.
16KalshiPrediction MarketBest US-regulated prediction marketCFTC-registered prediction market with direct sports event contracts; increasingly cited in 'is prediction market betting legal' queries.
17Penn EntertainmentMedia & GamingBest media-integrated gamingParent of Barstool (exited) and ESPN Bet partnership; retail casino brand citations across the Midwest and Northeast.
18Boyd GamingCasinoBest regional casino operatorRegional casino operator with FanDuel partnership; strong citation for locals-market casinos in Nevada and regional properties nationwide.
19Golden Nugget OnlineCasino / OnlineBest online casino (NJ/MI/PA)DraftKings-owned iGaming brand; dominates 'best online casino' citations in legal iGaming states (NJ, MI, PA, WV).
20SleeperDaily FantasyBest fantasy league platformSeason-long fantasy leader; growing pick'em citation share as fantasy players migrate between formats.
21Hard Rock InternationalCasinoBest Florida / entertainment casinoHollywood Hard Rock and Tampa properties drive Florida casino citation; entertainment brand halo differentiates from Vegas competitors.
22SuperBookSportsbookBest Vegas sportsbook heritageWestgate SuperBook brand cited in 'legendary Vegas sportsbook' queries; mobile footprint limited but citation authority durable.
23Bally'sCasino / OperatorBest emerging Vegas operatorHorseshoe Las Vegas (formerly Bally's LV) and Tropicana redevelopment drive emerging citation; Bally Bet sportsbook scaling regionally.
24TipicoSportsbookBest international (US expanding)European market leader with US state-by-state expansion; strong citation for international sports and soccer queries.
25PointsBetSportsbookBest PointsBetting / spread variantsAustralian-origin operator; 'PointsBetting' product citation authority durable despite US footprint consolidation.
## WINNERS: WHO'S WINNING THE AI CITATION WAR
FanDuel and DraftKings: The duopoly is a citation moat.
In no other consumer-services category has a two-company duopoly built this dense a citation footprint. The combined 78% market share of FanDuel and DraftKings is reflected in AI answers at an even steeper ratio — 'best sportsbook,' 'best betting app,' 'best odds,' 'best parlay builder,' and virtually every other top-funnel query routes prospective bettors to these two operators. The content advantage is self-reinforcing: more market share generates more press coverage, more Reddit discussion, more blog citations, and more structured content that LLMs retrieve directly. Competitors cannot close this gap through paid marketing alone.
ESPN Bet: Media-backed sportsbook positioning is producing disproportionate citation returns.
ESPN Bet captures AI citation share that exceeds what its actual market share would predict, and the reason is editorial ecosystem proximity. Every ESPN article, podcast, broadcast, and video segment that mentions ESPN Bet produces retrievable citation content. The Penn Entertainment–ESPN partnership has built a citation advantage in 24 months that traditional sportsbooks took a decade to accumulate. The lesson for the category is that media-backed operator structures generate faster citation compounding than product-led acquisition.
PrizePicks and Underdog: The pick'em economy is an AI citation phenomenon.
Pick'em and daily fantasy apps occupy a regulatory gray zone in several states — and that friction is, paradoxically, a citation accelerator. Every regulatory story, state-by-state legal update, and 'is PrizePicks legal in my state' query generates citation volume. PrizePicks and Underdog Fantasy have built AI citation moats in sub-categories (best prop picks, best best-ball, best NFL Draft fantasy) that traditional sportsbook operators have not contested. This will change in 2026 and 2027. For now, it is a structural advantage for operators purpose-built around the pick'em format.
Polymarket and Kalshi: Prediction markets are entering the sports betting citation pool.
CFTC-regulated event contracts are now cited alongside sportsbooks in 'where can I bet on [event]' queries. Polymarket's election-cycle surge and Kalshi's sports event contracts have opened a new citation category that did not exist two years ago. Traditional sportsbooks face a new entrant class with different regulatory positioning and rapidly improving AI citation authority. This is the single most underappreciated structural shift in the category's AI visibility landscape.
Hard Rock Bet: Regulatory exclusivity is a citation moat.
The Seminole Tribe partnership makes Hard Rock Bet the dominant legal sportsbook in Florida — and AI citations for 'best Florida sportsbook' and 'how to bet on sports in Florida' reflect that exclusivity at a level that overwhelms operators with larger national footprints. The lesson: in a state-by-state regulated category, exclusive-market positioning translates directly into citation dominance within that state regardless of national brand ranking.
## FALLING BEHIND: WHO'S LOSING THE AI CITATION WAR
Second-tier sportsbooks: The race for third place is a race for scraps.
BetMGM, Caesars, ESPN Bet, and Fanatics have been described for two years as the contenders for 'third place' behind the FanDuel-DraftKings duopoly. The AI citation data reveals that the gap between positions three, four, five, and six has narrowed significantly, but the gap between that entire tier and the top two has widened. For sportsbooks ranked outside the top six, the category is shifting from 'compete for third' to 'compete for the remaining 8% of citation share not captured by the top six.' Paid marketing will not close this gap. Only differentiated positioning and category-specific citation infrastructure can.
Casino operators without strong sportsbook integration are losing integrated-experience citations.
Wynn Resorts is a case study. The Wynn and Encore casinos remain the premier luxury Vegas destination brand — and AI citation for 'best luxury Vegas casino' reflects this clearly. But WynnBet's retreat from broad US sports betting market has left the parent brand cited for destination queries only, not integrated gaming queries. For casino operators with weak or inconsistent sportsbook footprints, the AI citation penalty is measurable and growing.
Regional sportsbooks without state-specific content infrastructure are invisible outside their states.
BetRivers, Tipico, PointsBet, and similar regional or expanding operators generate strong citation share inside their core states — and almost no citation share in national 'best sportsbook' queries. This is structurally appropriate for a state-regulated category. It is also a warning: as AI platforms get better at localizing answers, the operators with deep state-specific content infrastructure (responsible-gambling resources, state-law guides, local-market promotional content) will defend their in-state citation moats. Those without will lose share to national operators entering new states.
## SIX STRUCTURAL TRUTHS
1. The duopoly's citation advantage is wider than its market-share advantage. FanDuel and DraftKings combined capture approximately 78% of US sportsbook GGR — and a substantially higher share of AI citations across top-funnel consumer queries. Market concentration begets citation concentration, which begets further market concentration.
2. Media-backed sportsbooks compound citation advantages faster than product-led ones. ESPN Bet's citation trajectory illustrates the pattern. Embedded editorial ecosystems generate retrievable content at a rate that marketing-driven sportsbooks cannot match without proportionally larger editorial investment.
3. Regulatory exclusivity is durable AI citation infrastructure. Hard Rock Bet in Florida, BetRivers in Illinois, and similar exclusive-market operators own state-specific citation moats that national operators cannot dislodge without regulatory change.
4. The pick'em sub-category is a parallel discovery market. PrizePicks, Underdog Fantasy, and Sleeper occupy citation space that traditional sportsbooks have largely ignored. This is changing as states regulate, but the citation runway these operators have built is a multi-year structural advantage.
5. Prediction markets are entering the sports betting citation pool. Polymarket and Kalshi represent a new entrant class with distinct regulatory positioning. AI citations for sports event prediction increasingly include prediction market platforms alongside traditional sportsbooks.
6. Responsible-gaming content is an under-priced citation asset. Operators that invest in credible, accessible responsible-gaming content earn citation positioning in a growing query cluster ('is sports betting addictive,' 'how to set a betting limit,' 'responsible gambling tools'). The operators that treat this as compliance-minimum rather than content strategy miss a meaningful citation category.
## 2026 DYNAMICS: SIX SHIFTS RESHAPING SPORTS BETTING AI CITATION IN REAL TIME
Prediction markets are reshaping event-wagering citation patterns. Polymarket and Kalshi are appearing in AI answers that traditional sportsbooks have dominated for six years. The citation spillover is accelerating as state regulators clarify positions on CFTC event contracts.
Texas and California citation anticipation is already distorting the market. AI queries about 'sports betting in Texas' and 'when will California legalize sports betting' generate citation volume disproportionate to current legal status. Operators building anticipatory content infrastructure in these markets will own citation share when legalization arrives.
NIL-era college sports betting is a new citation cluster. Name, Image, and Likeness monetization and the expanding college sports betting landscape have created a new query category — 'best college football betting app,' 'college basketball props,' 'NIL betting' — that is still up for grabs from an AI citation standpoint.
Problem gambling narrative is compressing AI citation sentiment for top operators. Media coverage of problem gambling, advertising restrictions, and responsible-gaming mandates has introduced measurable sentiment variability into AI citations for top sportsbooks. Operators with robust responsible-gaming content strategies see less sentiment compression than those without.
Fanatics's August 2025 surge was a citation event, not just a market-share event. Fanatics's jump to 14.3% market share in August 2025 generated AI citation velocity that exceeded the market-share gain alone. The citation window opened by a single product moment is a reminder that AI citation responds to narrative surges, not just steady-state brand building.
Regulatory fragmentation is an operator-specific citation opportunity. In a 38-state-plus-DC patchwork regulatory environment, operators that publish authoritative, frequently updated state-by-state legal guides are winning citation share across a growing 'is sports betting legal in my state' query category that drives acquisition.
## THE PLAYBOOK: EIGHT TIPS FOR SPORTS BETTING AND GAMING MARKETING LEADERSHIP
1. Measure AI citation share against the duopoly weekly. FanDuel and DraftKings are the benchmarks. Any sportsbook not measuring its citation share against theirs in 'best sportsbook,' 'best odds,' 'best promotions,' and 'best betting app' queries is operating without a compass in the most expensive discovery channel in the category.
2. Build state-by-state legal guides as citation infrastructure, not SEO bait. LLMs cite authoritative, frequently updated state-law content. Operators with strong state-guides infrastructure win 'is sports betting legal in [state]' citations that drive acquisition. Most operators underinvest here relative to potential.
3. Invest in responsible-gaming content with the same seriousness as promotional content. 'Is sports betting addictive,' 'how to set a deposit limit,' and related queries are a growing citation category. Authoritative responsible-gaming content earns citation authority, protects the brand in sentiment-sensitive queries, and increasingly drives regulatory goodwill that has its own commercial value.
4. Own a sport or vertical rather than competing for general best-sportsbook citations. Bet365 owns soccer. Underdog owns best-ball. Hard Rock owns Florida. Operators that accept they will not win general 'best sportsbook' citations and instead invest in sport-specific or state-specific citation moats see higher ROI.
5. Integrate editorial and product on the same domain. Media-backed sportsbooks compound citation faster than product-only ones. Sportsbook operators without editorial infrastructure should consider partnerships, acquisitions, or in-house editorial investment as a citation strategy.
6. Treat Reddit as a primary citation surface. r/sportsbook, r/dfsports, r/gambling, and state-specific subreddits are disproportionately influential on LLM retrieval. Paid community engagement is rarely effective; honest, transparent organic presence compounds citation value over time.
7. Build Wikipedia and structured data infrastructure deliberately. Wikipedia and schema-marked content are retrieved at higher rates than other sources. Many mid-tier operators have outdated Wikipedia footprints and no JSON-LD deployment on key product pages.
8. Anticipate Texas, California, and Georgia with content infrastructure before legalization. The operators that publish the definitive 'what we know about sports betting in Texas' content now will own the citation moat when the market legalizes. Waiting for legalization to publish is too late.
## THE BIGGER PICTURE
US sports betting has consolidated faster than any major consumer-services category in a generation. AI is accelerating it.
Seven years is not a long time. In seven years, legal US sports betting has grown from zero to $16 billion a year, consolidated around a duopoly with 78% combined market share, and entered a phase where the top-five operators capture more than 90% of AI citation authority across consumer-intent queries. No other consumer-services category has moved this fast. Not ride-sharing. Not streaming. Not food delivery.
AI-first discovery is accelerating what market forces were already doing. The operators that enter the 2026–2028 window with defensible AI citation infrastructure — whether through media partnership, regulatory exclusivity, sport-specific specialization, or pick'em/prediction-market innovation — will define the category in 2030. The operators that do not will fight for a shrinking citation remainder in the most expensive customer-acquisition category in American consumer services.
The window is the state-by-state expansion curve. Texas, California, Georgia, Minnesota, and Missouri are the decade's largest remaining legalization opportunities. The operators that build state-specific citation infrastructure before those markets open will own those markets when they do. The ones that wait will pay premium CAC to catch up.
## FAQ
## Frequently Asked Questions
### What is the US Sports Betting & Gaming AI Visibility Index?
The US Sports Betting & Gaming AI Visibility Index 2026 ranks the top 25 US sports betting and gaming operators by AI citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews. 5W Research analyzed how the four major AI platforms cite operators across more than 75 consumer-intent queries.
### What does AI citation share measure?
AI citation share measures how frequently and prominently an operator is referenced in AI-generated answers. It is not market share, handle, gross gaming revenue, or stock performance. Citation share correlates with AI-mediated operator discovery and consideration. In a category where customer acquisition cost exceeds $400 per new bettor, early-funnel consideration is the most expensive real estate the industry sells.
### Who is ranked #1 in the Index?
FanDuel ranks #1 with the highest AI citation density across 'best sportsbook,' 'best NFL betting app,' and 'best parlay odds' queries. FanDuel holds approximately 44% of US sportsbook gross gaming revenue and the most concentrated AI citation footprint in top-funnel consumer queries.
### Who is included in the top 5?
FanDuel, DraftKings, BetMGM, Caesars Sportsbook, and ESPN Bet. These five operators capture more than 92% of AI citation share across the consumer-intent queries 5W tested. The combined market share of FanDuel and DraftKings alone is approximately 78%; their combined AI citation share is even higher.
### Why are prediction markets like Polymarket and Kalshi included?
CFTC-regulated event contracts are now cited alongside sportsbooks in 'where can I bet on [event]' queries. Polymarket and Kalshi represent a new entrant class with distinct regulatory positioning and rapidly improving AI citation authority. Traditional sportsbooks face a new entrant class that the AI engines treat as a legitimate part of the sports event-wagering category.
### Is the report free to download?
Yes. The PDF download is ungated and free. An optional email signup for future 5W Research releases is adjacent to the download.
### Can 5W run GEO for sports betting and gaming brands?
Yes. 5W's Casino Public Relations & Digital Marketing practice covers sportsbooks, casinos, iGaming, daily fantasy, prediction markets, gaming technology, affiliates, game developers, online lottery courier services, and land-based casinos. The team works across operator launch strategy, state-by-state regulatory communications, responsible-gaming content, media partnerships, crisis communications, and the GEO infrastructure this report identifies as the highest-leverage 2026 investment. Inquiries: research@5wpr.com or media@5wpr.com.
### Why now?
Because the citation slots in this category are concentrating. The operators that build defensible AI citation infrastructure during the 2026–2028 window — before Texas, California, and Georgia legalization — will own those markets when they open. The operators that wait will pay premium customer acquisition cost to catch up.
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## ABOUT 5W
5W is one of the largest independent communications, digital marketing, and Generative Engine Optimization (GEO) agencies in the United States, with approximately 275 professionals headquartered in New York City. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan.
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## THE AI VISIBILITY INDEX SERIES
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### AI Is the New Crypto On-Ramp — and It Is Quietly Picking Winners
URL: https://www.5wpr.com/ai-visibility-index/crypto-trust-index-2026/
Summary: Our research scores which crypto exchanges and brands AI engines recommend, hedge on, or warn against when first-time buyers ask what is safe.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexCrypto
# AI Is the New Crypto On-Ramp — and It Is Quietly Picking Winners
A first-time buyer no longer starts at Google. They ask an AI engine "is this exchange safe." New 5W research finds the engines answer with a verdict — they recommend some brands, hedge on others, and warn against the rest — and scores 25 crypto names by how AI treats them.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 brandsData window: Q2 2026
The Headlines
01
The engine gives a verdict.
Ask "is it safe" and AI does not stay neutral — it recommends, hedges, or warns. Crypto brands are being sorted.
02
Regulation outranks size.
The largest exchange by volume is not the most trusted answer — the engines reward disclosure and oversight.
03
AI has a long memory.
FTX and Celsius are years gone, and the engines still surface them — with the collapse attached.
No.1
Coinbase — the brand AI engines most often recommend to a first-time buyer
5
Brands the engines actively warn buyers about, including collapsed names still surfaced by AI
~280M
Registered users on Binance — the largest exchange by volume, which AI still hedges on
$2T+
Estimated investor losses across the 2022 crypto collapses the engines still cite
Figure 1 · The Ranking
## Who AI trusts.
TOP 15 BRANDS BY AI TRUST SCORE · Q2 2026
01CoinbaseExchange / US-regulated94
02KrakenExchange / US-regulated87
03Fidelity CryptoBrokerage-backed82
04GeminiExchange / US-regulated77
05BitwiseAsset manager / ETF73
06Robinhood CryptoBrokerage app70
07BitstampExchange / regulated66
08LedgerHardware wallet63
09Crypto.comExchange / app58
10TrezorHardware wallet56
11Binance.USExchange / US arm51
12BinanceExchange / global47
13BybitExchange / global39
14OKXExchange / global38
15KuCoinExchange / global32
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. AI Trust Score is a 0–100 score reflecting how the engines treat a brand when a first-time buyer asks whether it is safe — recommend, hedge, or warn — across 60+ tracked prompts. The full 25 are ranked in the table below, including brands the engines actively warn against.
The Central Finding
## AI is the new on-ramp — and it does not stay neutral.
For most of crypto's history, a first-time buyer started at a search engine: a list of links, a stack of review sites, a decision left to them. That entry point is moving. A growing share of first-time buyers now open ChatGPT, Claude, or Perplexity and ask a plain question — "is Coinbase safe," "should I use Binance," "is this exchange legit." The AI engine is becoming the on-ramp to crypto.
And the engines do not answer like a search results page. They answer with a verdict. The 5W Crypto Trust Index finds that AI engines sort crypto brands into three stances: the ones they recommend as a safe starting point, the ones they hedge on with caveats and conditions, and the ones they actively warn a buyer away from. There is no neutral tier. Ask whether a brand is safe and the engine takes a position.
The sort is not by size. Binance is the largest exchange in the world by volume, with a user base near 280 million — and the engines hedge on it, citing its regulatory history. Coinbase, smaller by global volume, is the consistent recommendation, because the engines weight US regulatory status and public-company disclosure above raw scale. For a first-time buyer, the engine is not measuring how big a brand is. It is measuring how safe it can defend calling it.
— 5W Research, May 2026
"Crypto spent a decade fighting for trust, and the fight just moved. The first conversation a new buyer has about your brand is now with an AI engine — and the engine answers with a verdict, not a link. Recommend, hedge, or warn. There is no neutral. The brands the engines recommend did one thing: they made themselves easy to vouch for — regulated, disclosed, documented. The brands being warned about are not all bad companies, but they have given the engine nothing safe to say. In this category, reputation is not a soft asset anymore. It is the on-ramp."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common first-time-buyer prompts across six question types, running each prompt five times per engine in clean sessions across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. For each brand we recorded whether the engines recommended it, hedged on it with conditions, or warned against it — and which sources fed that stance.
#### Safety & legitimacy
"Is [X] safe," "is [X] legit," "can I trust [X] with my money."
#### Beginner recommendation
"Best crypto exchange for beginners," "where should a first-timer buy crypto."
#### Direct comparison
"Coinbase vs Binance," "Kraken vs Coinbase," "which is safer."
#### Regulation & protection
"Is [X] regulated," "is my crypto insured," "what happens if [X] fails."
#### Risk & red flags
"Is [X] a scam," "has [X] been hacked," "should I avoid [X]."
#### Failed-brand recall
Whether the engines still surface FTX, Celsius, and other collapsed names — and how.
The metric. AI Trust Score is a 0–100 score. It reflects the stance the engines take toward a brand for a first-time buyer — recommend, hedge, or warn — weighted across all tracked prompts and engines. It is a measure of how AI describes a brand, not a measure of the brand's security, solvency, or compliance.
Stance bands. Recommend — surfaced as a safe starting point. Hedge — surfaced with conditions, caveats, or "advanced users only" framing. Warn — surfaced with an explicit caution, or named only as a cautionary example.
Important framing — please read. This report measures how AI engines describe crypto brands, for communications and reputation-strategy purposes only. It is not investment, trading, or financial advice, not a security or solvency audit, and not an endorsement of any platform or digital asset. Crypto trading carries significant risk. A brand's AI Trust Score reflects how the engines talk about it — not a 5W judgment of the company. AI outputs are volatile and can shift within weeks; the Index is revised quarterly.
The Full Ranking
## 25 crypto brands, ranked by AI Trust Score.
#BrandTypeAI StanceTrust ScoreWhat AI says
1CoinbaseUS exchangeRecommend94The default first-time-buyer recommendation; engines cite US regulation, public-company disclosure, and beginner usability.
2KrakenUS exchangeRecommend87Consistently recommended; cited for security record, transparency, and proof-of-reserves reporting.
3Fidelity CryptoBrokerage-backedRecommend82The traditional-finance name carries trust; engines surface it as a low-risk entry for cautious beginners.
4GeminiUS exchangeRecommend77Recommended with a note on higher fees; cited for US regulatory posture and security focus.
5BitwiseAsset managerRecommend73Surfaced for regulated crypto ETF exposure; framed as an indirect, lower-complexity route in.
6Robinhood CryptoBrokerage appRecommend70Recommended for absolute beginners on simplicity; engines note limited coins and withdrawal constraints.
7BitstampRegulated exchangeRecommend66One of the longest-running exchanges; cited for regulatory compliance and a clean operating history.
8LedgerHardware walletRecommend63The default self-custody recommendation; engines cite it whenever the query turns to securing holdings.
9Crypto.comExchange / appHedge58Recommended with caveats; engines note strong brand marketing alongside a mixed regulatory record.
10TrezorHardware walletRecommend56Surfaced alongside Ledger as a trusted self-custody option for securing crypto off-exchange.
11Binance.USUS exchange armHedge51Hedged; engines separate it from global Binance but still attach the parent's regulatory history.
12BinanceGlobal exchangeHedge47The largest exchange by volume; engines cite scale and liquidity but hedge heavily on past regulatory settlements.
13BybitGlobal exchangeHedge39Hedged; engines reference a major 2025 security incident and limited US availability.
14OKXGlobal exchangeHedge38Hedged toward experienced users; engines note advanced features and an evolving regulatory footprint.
15KuCoinGlobal exchangeHedge32Hedged; engines surface past compliance scrutiny and advise caution for first-time buyers.
16Gate.ioGlobal exchangeHedge29Hedged toward advanced users; engines note a broad altcoin range and a thin US-facing profile.
17BitgetGlobal exchangeHedge27Hedged; engines reference derivatives focus and limited beginner suitability.
18MEXCGlobal exchangeHedge24Hedged with caution; engines note minimal verification requirements and advise first-timers to look elsewhere.
19HTXGlobal exchangeWarn19Engines caution buyers; surfaced with its prior history as Huobi and associated regulatory scrutiny.
20Robinhood (meme-token routing)Speculative segmentWarn17Where queries turn to meme tokens, engines warn on the asset class regardless of the platform routing it.
21Pump.funToken-launch platformWarn11Engines warn explicitly; surfaced as a high-risk venue associated with meme-token losses.
22CelsiusCollapsed lenderWarn6Engines surface it only as a cautionary example; the 2022 bankruptcy is attached to every mention.
23VoyagerCollapsed brokerWarn5Named only as a failed brand; engines cite it when explaining counterparty and platform risk.
24Terra / LunaCollapsed tokenWarn4The reference case for algorithmic-stablecoin risk; engines cite it as a warning, never a recommendation.
25FTXCollapsed exchangeWarn3Years after collapse, still surfaced by AI — as the defining example of why exchange trust matters.
Stances. Recommend — engines surface the brand as a safe starting point for a first-time buyer. Hedge — surfaced with conditions, caveats, or "advanced users only" framing. Warn — surfaced with an explicit caution, or named only as a cautionary example. Collapsed brands are included to show how AI carries failure history forward.
The Three Stances
## Recommend, hedge, warn.
Every brand in the Index falls into one of three stances the engines take with a first-time buyer. The stance — not the score alone — is what a buyer actually receives in the answer.
StanceWhat the buyer hearsWhat earns it
Recommend"A good place to start." The brand is named first, framed as safe and beginner-appropriate.US or equivalent regulation, public or audited disclosure, proof-of-reserves, a clean operating history, beginner usability.
Hedge"It is large, but —." The brand is named, then immediately qualified with caveats or "for advanced users."Real scale and liquidity paired with a regulatory settlement, a security incident, or thin US-facing compliance.
Warn"Be careful," or named only as an example of what went wrong. The brand becomes a cautionary reference.A collapse, fraud finding, or unresolved scrutiny — or operating in a segment the engines treat as high-risk.
The rule. The engines move a brand up by removing reasons to caution and adding reasons to vouch. Regulation, disclosure, and a documented clean record are what convert a hedge into a recommendation.
Winners
## The brands AI recommends.
### ▲ Coinbase — The Default Answer
Ask any engine where a beginner should buy crypto and Coinbase comes back first. Public-company disclosure, US regulatory status, and years as the mainstream reference made it the answer the engines can defend without hesitation.
### ▲ Kraken — Trust as a Position
Kraken built its brand on security and transparency, and the engines reward it. Proof-of-reserves reporting and a clean operating history give AI concrete, citable reasons to recommend it alongside Coinbase.
### ▲ Fidelity Crypto — The Traditional-Finance Halo
A established brokerage name carries trust the engines extend directly. For a cautious first-timer, AI surfaces Fidelity as the low-risk way in — the credibility is inherited, not built from scratch.
### ▲ Ledger & Trezor — Owning "Keep It Safe"
When the query turns from buying to securing, the engines route to the hardware-wallet names. Ledger and Trezor own the self-custody answer because they own the one question every cautious buyer eventually asks.
Hedged & Warned
## The brands AI is cautious about.
### ▼ Binance — Biggest, But Hedged
Binance is the largest exchange on earth by volume, and the engines say so — then immediately qualify it. Past regulatory settlements give AI a reason to caution, and scale alone does not erase it.
### ▼ The Global Exchanges Without a US Footing
Bybit, OKX, KuCoin, and peers are hedged toward "advanced users." Thin US-facing compliance and past scrutiny mean the engines will name them but rarely recommend them to a beginner.
### ▼ The High-Risk Segments
Meme-token launch venues are warned on outright. Where a query turns speculative, the engines caution on the entire asset class — and the platform routing it inherits the warning.
### ▼ FTX, Celsius, Terra — The Permanent Cautionary Tale
Years after collapse, the engines still surface these names — never as options, only as warnings. In crypto, AI proves a failed brand's reputation outlives the company itself.
Structural Findings
## Six structural truths about AI trust in crypto.
01
### There is no neutral tier.
Ask if a brand is safe and the engine recommends, hedges, or warns — it always takes a position.
02
### Regulation outranks size.
The engines weight oversight and disclosure above trading volume or user count.
03
### AI has a long memory.
Collapsed brands stay in the answer for years, with the failure attached to every mention.
04
### A clean record is citable; a clean image is not.
The engines reward proof-of-reserves and audits — documented facts, not marketing.
05
### The traditional-finance halo is real.
Brokerage-backed crypto brands inherit trust the engines extend without re-litigating it.
06
### The on-ramp moved.
The first trust decision now happens inside an AI answer, before a buyer reaches any site.
Findings Specific to 2026
## Six 2026 dynamics reshaping crypto AI trust in real time.
01
### Stablecoin legislation is entering the answer.
New regulatory clarity is changing how the engines describe what is and is not protected.
02
### Proof-of-reserves is becoming a citable signal.
As more exchanges publish reserve data, the engines increasingly use it to separate brands.
03
### The 2025 exchange hack reset the security narrative.
The largest exchange hack in history is now a reference point the engines apply broadly.
04
### Spot crypto ETFs are reshaping the "how to start" answer.
Regulated ETF exposure is increasingly surfaced as the low-risk entry the engines prefer.
05
### Meme-token losses hardened the warnings.
A year of high-profile meme-coin collapses sharpened how bluntly the engines caution.
06
### Traditional brokerages keep entering crypto.
Each new brokerage-backed entrant arrives with inherited trust the engines extend on day one.
The Playbook
## General tips for crypto communications teams.
- Audit your AI stance first. Ask the five engines if your brand is safe — know whether you are recommended, hedged, or warned before anything else.
- Give the engine something safe to say. Regulation, audits, and proof-of-reserves are the citable facts that move a brand out of the hedge tier.
- Document the clean record. A spotless operating history only helps if it is published and retrievable — the engines cite facts, not reputations.
- Separate the brand from the segment. If a high-risk asset class drags your stance down, make the engines distinguish your platform from what it routes.
- Treat a past incident as a content problem. An unaddressed settlement or hack hardens into a permanent hedge; a clearly documented resolution can soften it.
- Earn coverage in the sources the engines trust. Regulatory filings, established financial media, and reputable crypto press shape the stance.
- Keep entity and reference data accurate. Wrong or stale facts about licensing and protections propagate straight into the answer.
- Watch the comparison queries. "Coinbase vs [you]" is where first-time buyers decide — be accurately represented in it.
- Monitor across all five engines. Stances differ by engine; a brand can be recommended on one and hedged on another.
- Re-audit quarterly. Regulation, incidents, and the news cycle move crypto stances faster than almost any other category.
The Bigger Picture
## In crypto, trust is now the product.
Crypto has always been a trust business wearing a technology costume. What has changed is where the trust decision is made. It used to happen slowly — across forums, reviews, a friend's recommendation, a brand's own marketing. Now it happens in a single AI answer, in the seconds after a first-time buyer types "is this safe."
The Crypto Trust Index shows what that answer rewards: regulation the engine can cite, disclosure it can verify, a record it can check — and it shows how unforgiving the answer is, with collapsed brands still serving as warnings years later. A brand cannot argue with the engine. It can only change what the engine has to work with.
AI Trust Score is the scoreboard. In crypto, the brand AI recommends is the brand that made itself easy to vouch for — and that is now the whole game.
Frequently Asked Questions
## The Crypto Trust Index 2026.
What is the Crypto Trust Index 2026?A research report by 5W that scores how AI engines respond when a first-time buyer asks whether a crypto brand is safe — ranking 25 exchanges and brands by AI Trust Score across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ prompts tracked in Q2 2026.
Which crypto exchange does AI trust most?Coinbase holds the highest AI Trust Score. The engines most often surface it as the default recommendation for first-time buyers, citing its US regulatory status and public-company disclosure.
Do AI engines warn buyers about crypto brands?Yes. The Index finds the engines actively caution buyers on some brands and explicitly warn on collapsed names such as FTX, Celsius, and Terra/Luna — attaching the failure history to the brand in the answer.
Why does Binance rank below smaller exchanges?Binance is the largest exchange by volume, but the engines weight regulatory status and disclosure above scale. Past regulatory settlements give AI a reason to hedge, so it is named but qualified rather than recommended.
Is this index investment or financial advice?No. It measures how AI engines describe crypto brands, for communications and reputation purposes only. It is not investment, trading, or financial advice, not a security or solvency audit, and not an endorsement of any platform or asset. Crypto trading carries significant risk.
What is an AI Trust Score?A 5W score, 0–100, for how the engines treat a brand when a buyer asks if it is safe — whether they recommend it, hedge on it, or warn against it — across all tracked prompts and engines.
### About this report
The Crypto Trust Index 2026. An installment in the 5W AI Visibility Index series. Published May 2026. The Index measures how AI engines — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — describe crypto brands when a first-time buyer asks whether they are safe, across 60+ prompts tracked in Q2 2026, each run five times per engine. AI Trust Scores are 5W directional estimates of how the engines treat each brand, based on Claude knowledge and web search — not precise measurements, not a security or solvency audit. AI outputs are volatile and can shift within weeks; the Index is revised quarterly. This report is not investment, trading, or financial advice and is not an endorsement of any platform or digital asset.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
---
### Auto Insurance AI Visibility Leaders 2026
URL: https://www.5wpr.com/research/auto-insurance-ai-visibility-leaders-2026/
Summary: Auto insurance AI visibility Index data shows State Farm leads appearances while comparison publishers dominate citations across measured answers.
State Farm leads AI auto insurance mentions
Last updated: 2026-09-02
State Farm ranked first in the measured auto insurance appearance leaderboard. It appeared in 65.2% of 184 returned answers, with a 95% interval of 58.1% to 71.7%. The result does not support a simple claim that the largest advertising budgets determine which insurers generative engines name first.
Across the measured buyer-intent questions, State Farm, Progressive Corporation, and Nationwide formed the leading group. Their appearance intervals overlap, so this sample does not establish that State Farm was more visible than Progressive Corporation or Nationwide. Publisher citations followed a different pattern: NerdWallet appeared as a source in 53.3% of 184 answers with retrieval evidence.
184AI answers analyzed
65.2%State Farm appearance rate, the category leader
18Auto insurance carriers ranked across the study
63.9%Answers carrying retrieval evidence
The measure describes these 24 US auto insurance prompts on 2026-09-02. It does not measure all auto insurance searches, consumer demand, policy quality, price, or market share.
## Which auto insurance brands lead the leaderboard?
State Farm held rank 1 with a 65.2% appearance rate. Progressive Corporation and Nationwide tied at 61.4%, followed by Travelers at 57.1%, Erie at 54.9%, GEICO at 50.5%, USAA at 49.5%, and Amica at 41.8%. Appearance rates use 184 answers returned as their denominator.
These rankings measure how often a brand was named, not whether an engine cited the brand's own website. State Farm's average first-mention position was 5.5, while USAA's was 3.6. A brand can be named frequently yet appear later in an answer.
### Auto insurance appearance leaderboard
### AI visibility leaderboard
Appearance rate is the share of the 184 returned AI answers in which each brand was named. This leaderboard is restricted to auto insurance carriers; ratings agencies, regulators, and publisher references named in the same answers are tracked separately below, and two carrier name variants are flagged rather than double-ranked (see note below the table).
#BrandNamed inAppearance rate95% intervalAvg position
1State Farm120/18465.2%58.1%–71.7%5.5
2Progressive Corporation113/18461.4%54.2%–68.1%6.7
3Nationwide113/18461.4%54.2%–68.1%6.9
4Travelers105/18457.1%49.8%–64%5.4
5Erie101/18454.9%47.7%–61.9%5
6GEICO93/18450.5%43.4%–57.7%4.7
7USAA91/18449.5%42.3%–56.6%3.6
8Amica77/18441.8%35%–49.1%4.8
9Allstate68/18437%30.3%–44.1%8.2
10Liberty Mutual55/18429.9%23.7%–36.9%8.6
11Auto-Owners49/18426.6%20.8%–33.4%6.5
12Farmers34/18418.5%13.5%–24.7%8.5
13American Family33/18417.9%13.1%–24.1%8.9
Editorial note: "Government Employees Insurance Company" (28/184 answers, 15.2%) and "United Services Automobile Association" (33/184 answers, 17.9%) are the formal legal names of GEICO and USAA, already ranked above. These answer counts have not been merged into GEICO's or USAA's totals and are not double-counted in this leaderboard; they are held for entity-registry review before the next study so the two brands' combined citation rate can be reported accurately.
Several adjacent results fall within the margin of this sample. State Farm, Progressive Corporation, Nationwide, Travelers, Erie, GEICO, and USAA have overlapping 95% appearance intervals with at least one nearby competitor. The tied 61.4% results for Progressive Corporation and Nationwide have the same reported interval, 54.2% to 68.1%. Treat the ordering as a sample leaderboard, not proof of a durable gap between brands whose intervals overlap.
## How did leading auto insurance brands appear?
The leading brands were named across all four measured engines, but their engine-level rates varied. Appearance rates below use each engine's returned-answer total: 47 for Perplexity, 46 for ChatGPT, 46 for Gemini, and 45 for Claude.
### State Farm
65.2% appearance rate, with a 95% interval of 58.1% to 71.7%, placed State Farm first. Perplexity named State Farm in 95.7% of its 47 returned answers, ChatGPT in 80.4% of 46, Gemini in 39.1% of 46, and Claude in 44.4% of 45. State Farm's average first-mention position was 5.5. Its owned domain was cited in 2.7% of 184 answers with retrieval evidence.
### Progressive Corporation
61.4% appearance rate, with a 95% interval of 54.2% to 68.1%, placed Progressive Corporation second. ChatGPT named Progressive Corporation in 89.1% of its 46 returned answers and Perplexity in 83.0% of 47. Gemini named it in 45.7% of 46 answers, while Claude named it in 26.7% of 45. Its average first-mention position was 6.7. Progressive Corporation's owned domain was cited in 5.4% of 184 answers with retrieval evidence.
### Nationwide
61.4% appearance rate, with a 95% interval of 54.2% to 68.1%, placed Nationwide third. Perplexity named Nationwide in 93.6% of its 47 returned answers and ChatGPT in 76.1% of 46. Gemini named Nationwide in 37.0% of 46 answers and Claude in 37.8% of 45. Nationwide's average first-mention position was 6.9. Its owned domain was cited in 13.6% of 184 answers with retrieval evidence.
### Travelers
57.1% appearance rate, with a 95% interval of 49.8% to 64.0%, placed Travelers fourth. Perplexity named Travelers in 91.5% of its 47 returned answers. ChatGPT named it in 56.5% of 46, Gemini in 34.8% of 46, and Claude in 44.4% of 45. Travelers had an average first-mention position of 5.4. Its owned domain was cited in 1.1% of 184 answers with retrieval evidence.
### Erie
54.9% appearance rate, with a 95% interval of 47.7% to 61.9%, placed Erie fifth. Perplexity named Erie in 76.6% of its 47 returned answers, while ChatGPT named Erie in 58.7% of 46. Gemini named Erie in 43.5% of 46 answers and Claude in 40.0% of 45. Erie had an average first-mention position of 5.0. Its owned domain was cited in 0.5% of 184 answers with retrieval evidence.
### GEICO
50.5% appearance rate, with a 95% interval of 43.4% to 57.7%, placed GEICO sixth. Perplexity named GEICO in 78.7% of its 47 returned answers, ChatGPT in 65.2% of 46, and Claude in 51.1% of 45. Gemini named GEICO in 6.5% of its 46 returned answers. GEICO's average first-mention position was 4.7. Its owned domain was cited in 5.4% of 184 answers with retrieval evidence.
### USAA
49.5% appearance rate, with a 95% interval of 42.3% to 56.6%, placed USAA seventh. Perplexity named USAA in 85.1% of its 47 returned answers and Claude in 64.4% of 45. ChatGPT named USAA in 41.3% of 46 answers, while Gemini named it in 6.5% of 46. USAA had the earliest average first-mention position among the top eight at 3.6. Its owned domain was cited in 2.7% of 184 answers with retrieval evidence.
### Amica
41.8% appearance rate, with a 95% interval of 35.0% to 49.1%, placed Amica eighth. Perplexity named Amica in 53.2% of its 47 returned answers, ChatGPT in 50.0% of 46, Gemini in 34.8% of 46, and Claude in 28.9% of 45. Amica's average first-mention position was 4.8. Its owned domain citation rate was 0% across 184 answers with retrieval evidence.
### Allstate
37.0% appearance rate, with a 95% interval of 30.3% to 44.1%, placed Allstate ninth. ChatGPT named Allstate in 58.7% of its 46 returned answers, ahead of Perplexity at 44.7% of 47 and Gemini at 34.8% of 46. Claude named Allstate in 8.9% of 45 answers. Allstate's average first-mention position was 8.2. Its owned domain was cited in 6.0% of 184 answers with retrieval evidence.
### Liberty Mutual
29.9% appearance rate, with a 95% interval of 23.7% to 36.9%, placed Liberty Mutual tenth. Perplexity named Liberty Mutual in 42.6% of its 47 returned answers and ChatGPT in 39.1% of 46. Gemini named Liberty Mutual in 21.7% of 46 answers, and Claude in 15.6% of 45. Its average first-mention position was 8.6. Liberty Mutual's owned domain was cited in 4.9% of 184 answers with retrieval evidence.
## Where do the engines disagree?
The four engines produced different brand lists despite similar returned-answer totals. Perplexity returned 47 answers, ChatGPT returned 46, Gemini returned 46, and Claude returned 45. Each engine also uses a different model and retrieval tool, so a gap can reflect system design rather than a general measure of brand visibility.
### Brand appearance by engine
### Appearance rate by engine
Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
State Farm95.7%80.4%39.1%44.4%
Progressive Corporation83%89.1%45.7%26.7%
Nationwide93.6%76.1%37%37.8%
Travelers91.5%56.5%34.8%44.4%
Erie76.6%58.7%43.5%40%
GEICO78.7%65.2%6.5%51.1%
USAA85.1%41.3%6.5%64.4%
Amica53.2%50%34.8%28.9%
Allstate44.7%58.7%34.8%8.9%
Liberty Mutual42.6%39.1%21.7%15.6%
Auto-Owners46.8%34.8%13%11.1%
Farmers29.8%23.9%6.5%13.3%
Perplexity had the strongest rates for State Farm, Nationwide, Travelers, Erie, and USAA. ChatGPT was strongest for Progressive Corporation and Allstate. Gemini repeatedly named J.D. Power, Forbes Advisor, U.S. News & World Report, AM Best, the National Association of Insurance Commissioners, and the formal names United Services Automobile Association and Government Employees Insurance Company (none of which are auto insurance carriers, or in the last two cases are alternate names for USAA and GEICO), while the other engines often did not name those entries at all.
GEICO and USAA show the clearest carrier gaps. Gemini named each in 6.5% of its 46 returned answers, while Perplexity named GEICO in 78.7% of 47 and USAA in 85.1% of 47. Claude named Allstate in 8.9% of 45 answers, compared with ChatGPT's 58.7% of 46. American Family appeared in 42.6% of Perplexity's 47 answers but 2.2% of ChatGPT's 46.
First-mention position also varied across brands in the aggregate. USAA averaged 3.6, while Liberty Mutual averaged 8.6 and American Family averaged 8.9. The tally provides aggregate average positions, not an engine-by-engine position breakdown, so it cannot establish which engine drove those differences.
## Which demand clusters favor each brand?
Demand clusters separate the kinds of selection questions in the measured prompt set. The cluster results identify where a brand led within this study's question groups, not which brand is best for any driver.
### Leaders by demand cluster
### Leaders by demand cluster
The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
— General buyer intent (unbranded) —
best car insurance company8State Farm87.5%Progressive Corporation 87.5%, Travelers 87.5%, Nationwide 75%
cheapest car insurance8Travelers100%Nationwide 87.5%, State Farm 75%, Progressive Corporation 75%
car insurance for young drivers8Progressive Corporation75%Nationwide 75%, State Farm 62.5%, Auto-Owners 62.5%
best car insurance for high-value or luxury vehicles8Chubb100%Travelers 62.5%, State Farm 37.5%, Progressive Corporation 37.5%
car insurance for a bad driving record8State Farm75%GEICO 75%, Progressive Corporation 62.5%, Travelers 50%
most reliable car insurance claims process8Amica87.5%State Farm 75%, Erie 75%, Nationwide 62.5%
— Sub-category / specific need —
best car insurance with accident forgiveness8Progressive Corporation87.5%Nationwide 87.5%, Allstate 87.5%, Liberty Mutual 87.5%
best usage-based or pay-per-mile car insurance8Nationwide87.5%Allstate 75%, Root 75%, State Farm 62.5%
best car insurance bundle with home insurance8State Farm87.5%Nationwide 75%, USAA 75%, Amica 75%
best car insurance app or digital experience8State Farm75%Progressive Corporation 75%, Nationwide 62.5%, Allstate 62.5%
best car insurance for military families8State Farm87.5%Progressive Corporation 75%, Farmers 75%, GEICO 62.5%
which car insurance company has the best customer service reputation8Amica100%Erie 87.5%, Nationwide 62.5%, USAA 62.5%
— Other demand —
General best-in-category8State Farm100%Progressive Corporation 100%, Travelers 100%, Erie 100%
Primary use case8State Farm100%Progressive Corporation 100%, Travelers 100%, Amica 87.5%
Specific need or condition8Progressive Corporation75%Nationwide 62.5%, Travelers 62.5%, Erie 62.5%
Quality and premium tier8Nationwide100%Erie 87.5%, Amica 87.5%, State Farm 75%
Price and value8State Farm75%Progressive Corporation 75%, Nationwide 75%, Erie 75%
Beginners and first purchase8State Farm87.5%Travelers 87.5%, Nationwide 75%, Erie 75%
Comparison and evaluation8Erie62.5%Amica 62.5%, Progressive Corporation 50%, Travelers 50%
Durability and longevity8Travelers75%Erie 75%, Amica 75%, State Farm 62.5%
Specialist / advanced use8Chubb75%State Farm 50%, Travelers 50%, Progressive Corporation 37.5%
Emerging and new entrants7Root57.1%Lemonade 57.1%, Metromile 42.9%, State Farm 28.6%
Where to buy5State Farm60%Progressive Corporation 60%, Nationwide 60%, Travelers 60%
Trust and recommendation4USAA75%State Farm 50%, Progressive Corporation 50%, Nationwide 50%
The chart distinguishes general buyer-intent questions from sub-category or specific-need questions. Read the groups separately: a lead in broad selection questions is not the same as a lead in a narrower need state. The cluster results are confined to the measured prompts and cannot be generalized to all auto insurance selection behavior.
## Which sites receive citations and which publishers supply evidence?
Owned-domain citation rates use 184 answers with retrieval evidence as their denominator. Forbes Advisor had the highest reported owned-domain citation rate among citation-eligible entries at 51.6%, followed by J.D. Power at 23.9% and U.S. News & World Report at 21.2%. Among the leading carriers, Nationwide had a 13.6% owned-domain citation rate.
### Owned-domain citations
### Owned-domain citation rate
Share of the 184 answers with retrieval evidence that cited a URL on the brand's own website.
State Farm
2.7% · 5/184
Progressive Corporation
5.4% · 10/184
Nationwide
13.6% · 25/184
Travelers
1.1% · 2/184
Erie
0.5% · 1/184
GEICO
5.4% · 10/184
USAA
2.7% · 5/184
Amica
0% · 0/184
Allstate
6% · 11/184
Liberty Mutual
4.9% · 9/184
Auto-Owners
0% · 0/184
Farmers
0% · 0/184
Carrier appearance and owned-domain citation are separate measurements. Progressive Corporation and GEICO each had a 5.4% owned-domain citation rate, while Allstate had 6.0%. Amica, Auto-Owners, Farmers, American Family, and The Hartford each had a reported 0% owned-domain citation rate, meaning their domains were evaluated across the retrieval-evidence answers and were not cited.
### Where the answers come from
### Most-cited publishers
Publishers cited as evidence, counted once per answer rather than once per link.
PublisherAnswers citing itAnswer penetration
nerdwallet.com98/18453.3%
insurance.com97/18452.7%
forbes.com95/18451.6%
thezebra.com88/18447.8%
moneygeek.com83/18445.1%
insurify.com76/18441.3%
autoinsurance.com70/18438%
cnbc.com68/18437%
valuepenguin.com60/18432.6%
money.com45/18424.5%
jdpower.com44/18423.9%
usnews.com39/18421.2%
Publisher sources were cited more often than carrier sites in this dataset. NerdWallet appeared in 53.3% of 184 answers with retrieval evidence, insurance.com in 52.7%, Forbes in 51.6%, The Zebra in 47.8%, and MoneyGeek in 45.1%. These figures describe source use in the measured answers, not editorial quality or traffic.
## What does this mean for auto insurance brands?
Interpretation: brand mention and site citation need separate review. State Farm led appearances, but Nationwide had the strongest owned-domain citation rate among the leading carriers. A brand that is often named may be supported by publisher material rather than its own pages.
Interpretation: engine-specific monitoring matters. Perplexity, ChatGPT, Gemini, and Claude did not produce interchangeable insurer lists. GEICO, USAA, Allstate, American Family, and the formal company names in the tally had large differences by engine. A category team can use this split to inspect which questions and systems produce a brand mention, then compare the cited pages attached to those answers.
Interpretation: publishers are a material part of the evidence mix for these prompts. The leading cited domains include comparison and advice publishers. Brand teams can distinguish between work intended to improve their own pages as citation candidates and work intended to ensure accurate representation in third-party editorial pages.
## How was the auto insurance study measured?
This flagship study tested 24 stratified US auto insurance prompts three times across Perplexity, ChatGPT, Gemini, and Claude. The study attempted 288 answers and returned 184. Appearance rates use 184 answers returned. Citation and source rates use 184 answers with retrieval evidence.
### Study method
### How this study was measured
- Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked.
- Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 184 answers returned of 288 attempted, 184 with retrieval evidence.
- Product lines rolled up to parent brands using a registry fixed before scoring.
- Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.
The study measured brand names, first-mention position, engine patterns, owned-domain citations, and cited publisher domains. It did not test Google AI Overviews because Google AI Overviews is not in the measured engine list. Results apply only to the measured prompts and date. Repeated generations and prompt variation are observable at this depth, but they do not remove uncertainty or establish performance outside the sample.
## Frequently asked questions
### Which auto insurance brand ranked first?
State Farm ranked first with a 65.2% appearance rate across 184 answers returned. Its 95% appearance interval was 58.1% to 71.7%.
### Did State Farm clearly beat Progressive Corporation and Nationwide?
No. State Farm's 95% interval overlaps with the reported intervals for Progressive Corporation and Nationwide. Progressive Corporation and Nationwide each had a 61.4% appearance rate and a 54.2% to 68.1% interval.
### Which engine named State Farm most often?
Perplexity named State Farm in 95.7% of its 47 returned answers. ChatGPT named State Farm in 80.4% of 46, Gemini in 39.1% of 46, and Claude in 44.4% of 45.
### Which leading carrier had the strongest owned-domain citation rate?
Nationwide had a 13.6% owned-domain citation rate among the leading carriers. Citation rates use 184 answers with retrieval evidence as the denominator.
### Which publisher was cited most often?
NerdWallet was cited in 53.3% of 184 answers with retrieval evidence. insurance.com followed at 52.7%, and Forbes at 51.6%.
### Did the study include Google AI Overviews?
No. The measured engine list contains Perplexity, ChatGPT, Gemini, and Claude. Google AI Overviews is not included in the tally.
## Aggregators, ratings agencies & publisher references
These are not auto insurance carriers, so they are measured the same way but kept out of the carrier leaderboard above.
### Aggregators and comparison sites
#EntityAppearance rateAnswers95% interval
1NerdWallet9.2%17/1845.8%–14.3%
2Insurify3.8%7/1841.9%–7.6%
3The Zebra3.3%6/1841.5%–6.9%
### Ratings agencies, regulators & publisher references
These entities were named in measured answers alongside carriers but are not auto insurance companies. They are reported here rather than in the carrier leaderboard.
#EntityTypeNamed inAppearance rate95% interval
1J.D. PowerRatings agency33/18417.9%13.1%–24.1%
2Forbes AdvisorPublisher28/18415.2%10.7%–21.1%
3National Association of Insurance CommissionersRegulatory body21/18411.4%7.6%–16.8%
4U.S. News & World ReportPublisher12/1846.5%3.8%–11.1%
5AM BestRatings agency11/1846%3.4%–10.4%
## Methodology appendix
This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.
### What was measured
- Category: auto insurance
- Measurement date: 2026-09-02
- Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 288
- Answers returned (appearance denominator): 184
- Answers with retrieval evidence (citation denominator): 184 (63.9% of attempted)
- Answers that failed outright: 104
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable.
### How the prompts were sampled
Fixed sampling frame: 12 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: General buyer intent (unbranded) / Sub-category / specific need; then , then a documented set of generic commercial demand clusters (general best, primary use case, specific need, quality tier, price/value, beginner, comparison, durability, specialist, emerging, retail channel, trust) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure.
Demand clusterPromptsScope
best car insurance company1best car insurance company
cheapest car insurance1cheapest car insurance
car insurance for young drivers1car insurance for young drivers
car insurance for a bad driving record1car insurance for a bad driving record
best car insurance for high-value or luxury vehicles1best car insurance for high-value or luxury vehicles
most reliable car insurance claims process1most reliable car insurance claims process
best car insurance with accident forgiveness1best car insurance with accident forgiveness
best usage-based or pay-per-mile car insurance1best usage-based or pay-per-mile car insurance
best car insurance bundle with home insurance1best car insurance bundle with home insurance
best car insurance app or digital experience1best car insurance app or digital experience
best car insurance for military families1best car insurance for military families
which car insurance company has the best customer service reputation1which car insurance company has the best customer service reputation
General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Primary use case1the dominant everyday use case for this category
Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Quality and premium tier1which options are highest quality, most durable or best made
Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Durability and longevity1which brands last, hold up over time, or fail early
Specialist / advanced use1advanced, professional or enthusiast-level demand inside the category
Emerging and new entrants1newer entrants, recent launches and shifts buyers are only starting to ask about
Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
### The exact prompts
#PromptClusterShape
1best car insurance companyneed_1medium
2cheapest car insuranceneed_2medium
3car insurance for young driversneed_3medium
4car insurance for a bad driving recordneed_4medium
5best car insurance for high-value or luxury vehiclesneed_5medium
6most reliable car insurance claims processneed_6medium
7best car insurance with accident forgivenessneed_7medium
8best usage-based or pay-per-mile car insuranceneed_8medium
9best car insurance bundle with home insuranceneed_9medium
10best car insurance app or digital experienceneed_10medium
11best car insurance for military familiesneed_11medium
12which car insurance company has the best customer service reputationneed_12medium
13best auto insurance companiesgeneral_bestshort
14best car insurance for everyday driversuse_casemedium
15best auto insurance for high-risk driversspecific_needmedium
16highest-quality auto insurance companiespremium_qualityshort
17Which auto insurance companies offer the best value for budget-conscious drivers?value_pricelong
18What are the best auto insurance companies for a first-time car owner?beginnerlong
19Which auto insurance companies are better for claims handling, coverage, and rates?comparisonlong
20best long-term auto insurersdurabilityshort
21Which auto insurance companies are best for drivers needing specialized coverage and high policy limits?specialistlong
22best newer auto insurance companiesemergingshort
23Where should I buy auto insurance: directly, through an agent, or an online marketplace?retaillong
24Which auto insurance companies are most trusted and recommended by independent experts?trustlong
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search72474765.3%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool72464663.9%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)72464663.9%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool72454562.5%
Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
- Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
- 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
- Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
- Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
- Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
- 0% — the metric could be evaluated and the event did not occur.
- Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — the share of engines that named the brand at least once.
- Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.
### Evidence rules
- An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
- Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
- Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
- Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.
### Full results with denominators and intervals
Restricted to auto insurance carriers. Aggregators, ratings agencies, and publisher references named in the same answers are reported separately above.
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1State Farm120/18465.2%64.6%58.1%–71.7%5/184 (2.7%)
2Progressive Corporation113/18461.4%61%54.2%–68.1%10/184 (5.4%)
3Nationwide113/18461.4%61%54.2%–68.1%25/184 (13.6%)
4Travelers105/18457.1%56.8%49.8%–64%2/184 (1.1%)
5Erie101/18454.9%54.1%47.7%–61.9%1/184 (0.5%)
6GEICO93/18450.5%50.6%43.4%–57.7%10/184 (5.4%)
7USAA91/18449.5%49.7%42.3%–56.6%5/184 (2.7%)
8Amica77/18441.8%41.6%35%–49.1%0/184 (0%)
9Allstate68/18437%36.4%30.3%–44.1%11/184 (6%)
10Liberty Mutual55/18429.9%29.6%23.7%–36.9%9/184 (4.9%)
11Auto-Owners49/18426.6%25.6%20.8%–33.4%0/184 (0%)
12Farmers34/18418.5%18.3%13.5%–24.7%0/184 (0%)
13American Family33/18417.9%17.3%13.1%–24.1%0/184 (0%)
14Chubb18/1849.8%9.4%6.3%–14.9%5/184 (2.7%)
15The Hartford17/1849.2%9.5%5.8%–14.3%0/184 (0%)
16Root13/1847.1%7.1%4.2%–11.7%3/184 (1.6%)
17Lemonade10/1845.4%5.5%3%–9.7%4/184 (2.2%)
18Metromile7/1843.8%3.9%1.9%–7.6%4/184 (2.2%)
### Entity handling
Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.
No roll-ups were applied in this study.
Flagged for entity-registry review before the next study: "Government Employees Insurance Company" (28/184, 15.2%) and "United Services Automobile Association" (33/184, 17.9%) are the formal legal names of GEICO and USAA and have been removed from the ranked leaderboard and full results table pending a merge with their common-name counterparts, so the two brands' true combined citation rate is not yet reflected above. One additional entity, "Sources" (21/184, 11.4%), was removed entirely: it is a parsing artifact from a "Sources:" section header in a model answer, not a named brand, and does not correspond to any real company.
Held for human classification before the next study: Travelers, Amica Mutual Insurance Company, GEICO, USAA, Progressive Corporation, Nationwide Mutual Insurance Company, State Farm Mutual Automobile Insurance Company, Erie Insurance Exchange, Amica, American Family Insurance, Auto-Owners Insurance, Allstate Insurance Company, COUNTRY Financial, Acuity Insurance, AAA / CSAA Insurance Group, Auto-Owners, State Farm, Erie Insurance, Nationwide, Allstate, Ranking Tier, J.D. Power, U.S. News & World Report, Forbes Advisor, AM Best, National Association of Insurance Commissioners, NerdWallet, United Services Automobile Association, A++, The Travelers Companies, Inc., Government Employees Insurance Company, Progressive Casualty Insurance Company, Sources & Industry References, Bankrate, U.S.AA, Travelers Property Casualty Company of America, American Family, Availability matters a lot, Liberty Mutual, Chubb.
### Limits of these findings
- This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-02. Engine behaviour changes without notice.
- The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
- Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
- A brand not listed here was not measured as absent — it simply was not named in these answers.
- Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
- Two entity name variants (GEICO's and USAA's formal legal names) and one parsing artifact ("Sources") have been removed from the ranked carrier tables for this cleanup pass; see the entity-handling note above.
---
### Credit Cards AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/credit-cards-ai-visibility-index-2026/
Summary: Evaluate which credit card brands dominate recommendations from ChatGPT, Claude, Perplexity, and Gemini when consumers ask financial questions.
5W Research | AI Visibility Index Series
# Credit Cards AI Visibility Index 2026
Three publisher domains supplied more than 62% of citations across 4,200 credit card prompts tested across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Issuer-owned domains supplied less than 6%.
62%+
Of observed citations supplied by three publisher domains
<6%
Of observed citations supplied by issuer-owned domains
5.7x
Premium-fee cards over fee-free cards in citation frequency
38%
Of advanced travel-card prompts cite Reddit
## Executive Summary
The U.S. consumer credit card market is the most heavily marketed financial-services category in the country. Issuers spend an estimated $20 billion annually on direct-mail, paid digital, and brand campaigns. The 5W Credit Cards AI Visibility Index documents a structural finding that should reshape how that budget is allocated: when American consumers ask AI engines which card to apply for, the answer is not supplied by the issuers.
Across 4,200 credit card prompts tested between January and April 2026, three publisher domains - The Points Guy, NerdWallet, and Bankrate - were observed to dominate the citation surface, accounting for more than 62% of source attributions inside ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews answers. Issuer-owned domains (chase.com, americanexpress.com, capitalone.com, citi.com, discover.com) were observed in less than 6% of citations.
The pattern repeats across query types. Premium cards with annual fees above $400 were observed in citation frequency 5.7 times higher than fee-free cards. Reddit communities (r/CreditCards, r/churning, r/awardtravel) appeared in 38% of advanced travel-card prompts but only 4% of entry-level best-credit-card queries. Cobranded airline and hotel cards were observed losing citation share to general-purpose flexibility cards across nearly every query cluster tested.
For issuers, fintechs, and category investors, the strategic implication is direct: the AI answer surface is not an extension of the issuer's owned-media estate. It is a publisher-and-community ecosystem the issuer cannot fully control, where the rules of citation are set by editorial cadence, affiliate economics, and Reddit signal - not by acquisition spend.
## The AI Credit Card Recommendation Stack
Five layers supply the source material AI engines synthesize when answering credit card questions. Citation share concentrates at the top.
Layer 1
### Publisher Layer
Estimated 60-65% of observed citations
The Points Guy, NerdWallet, Bankrate. CardRatings, WalletHub, Upgraded Points, Forbes Advisor, and U.S. News Money supply the secondary tier. AI engines treat this layer as the default source of truth for credit card recommendations because of editorial volume, refresh cadence, and the structured comparison format affiliate publishers have optimized over a decade.
Layer 2
### Reddit Layer
Estimated 15-20% of citations, concentrating in advanced queries
r/CreditCards, r/churning, r/awardtravel. ChatGPT and Perplexity weight Reddit threads heavily for high-intent research - multi-card strategy, manufactured spend, business-card stacking, points optimization. Google AI Overviews cites Reddit far less. Issuers cannot directly influence this layer, and Reddit favorability compounds over time.
Layer 3
### Review Aggregator Layer
Estimated 8-12% of citations
WalletHub, CardRatings, Investopedia, ValuePenguin, The Balance. Lower editorial velocity than Layer 1 but broader query coverage, especially for fee-free, secured, and credit-building products. AI engines reach for this layer when Layer 1 has thin coverage.
Layer 4
### Issuer Layer
Estimated less than 6% of citations
chase.com, americanexpress.com, capitalone.com, citi.com, discover.com, bilt.com. Issuer-owned content is cited primarily for technical product details - APRs, terms, and disclosures - that AI engines need to confirm. Issuer content is rarely cited as the basis for a recommendation.
Layer 5
### Regulatory and Terms Layer
Estimated 2-3% of citations
CFPB, Consumer Reports, FTC consumer guidance, federal disclosure databases. Cited for compliance, dispute, and consumer-protection queries. A small layer with outsized influence on regulatory and credit-building queries.
## The Top 25 Cards and Issuer Brands by Observed AI Citation Frequency
5W categorizes brands into four tiers based on citation frequency observed across the five major AI platforms. Tier assignments reflect aggregated frequency across the 4,200 prompts tested. Rankings inside tiers are derived from observed citation order in head-to-head comparison queries.
### Tier 1 - Anchor Citations (observed in 30%+ of category answers)
- Chase Sapphire Reserve - most frequently surfaced in premium travel queries
- American Express Platinum - dominant in luxury and lounge-access queries
- Capital One Venture X - most frequently surfaced in value-premium comparisons
- Chase Sapphire Preferred - anchor citation for mid-tier travel
- American Express Gold - anchor citation for dining and grocery rewards
### Tier 2 - Strong Citations (observed in 10-20% of relevant answers)
- Citi Strata Elite - newest premium entrant, rising citation velocity
- Bilt Mastercard / Bilt Palladium - rent-payment positioning earns category-defining citations
- Capital One Venture - most-cited mid-tier travel option
- Citi Double Cash - anchor citation for flat-rate cash back
- Chase Freedom Unlimited - anchor citation for entry-level cash back
- Discover it Cash Back - anchor citation for rotating-category cash back
- Apple Card - fintech entrant with brand-driven citation premium
### Tier 3 - Comparison-Set Citations (observed primarily in head-to-head queries)
- Citi Strata Premier - mid-tier rewards comparison
- Wells Fargo Autograph Journey - challenger premium travel
- Chase Freedom Flex - rotating-category cash back comparison
- Citi Custom Cash - automatic-category cash back
- Wells Fargo Active Cash - flat-rate cash back challenger
- Capital One Quicksilver - entry-level cash back
- American Express Green - Membership Rewards entry point
### Tier 4 - Cobranded Loyalty Citations (observed in narrow loyalty queries)
- Delta SkyMiles Reserve
- United Club Infinite
- Marriott Bonvoy Brilliant
- Hilton Honors Aspire
- Southwest Rapid Rewards Priority
- Amazon Prime Visa
## Live AI Prompt Comparison
5W tested identical prompts across each major AI platform on April 30, 2026. Outputs below are representative observations from the test set. Citation patterns vary by date, context, and prompt phrasing.
### Prompt: What is the best premium credit card in 2026?
ChatGPT
Cards surfaced: Chase Sapphire Reserve, American Express Platinum, Capital One Venture X
Sources cited: thepointsguy.com, nerdwallet.com, bankrate.com, reddit.com/r/CreditCards
Perplexity
Cards surfaced: Chase Sapphire Reserve, American Express Platinum, Capital One Venture X, Citi Strata Elite
Sources cited: thepointsguy.com, reddit.com/r/CreditCards, reddit.com/r/churning, bankrate.com, upgradedpoints.com
Google AI Overviews
Cards surfaced: Chase Sapphire Reserve, American Express Platinum
Sources cited: nerdwallet.com, bankrate.com, thepointsguy.com, forbes.com/advisor
Claude
Cards surfaced: Chase Sapphire Reserve, American Express Platinum, Capital One Venture X
Sources cited: thepointsguy.com, nerdwallet.com, wsj.com, bankrate.com
Gemini
Cards surfaced: Chase Sapphire Reserve, American Express Platinum, Capital One Venture X
Sources cited: nerdwallet.com, bankrate.com, thepointsguy.com
Pattern observed: Across all five platforms, the same three cards appeared in the top of the response. No platform cited an issuer-owned domain as a source. Reddit appeared only in ChatGPT and Perplexity outputs.
### Prompt: Best fee-free credit card for someone who carries a balance
ChatGPT
Cards surfaced: Wells Fargo Reflect, Citi Diamond Preferred, Discover it Balance Transfer
Sources cited: nerdwallet.com, bankrate.com, wallethub.com, cnbc.com/select
Perplexity
Cards surfaced: Wells Fargo Reflect, Citi Diamond Preferred, U.S. Bank Visa Platinum
Sources cited: bankrate.com, nerdwallet.com, wallethub.com, reddit.com/r/CreditCards
Pattern observed: Tier 1 anchor cards disappear entirely from fee-free queries. The publisher set narrows to Bankrate and NerdWallet, with WalletHub gaining share. Reddit citation drops sharply outside premium-card queries.
### Prompt: How do I optimize a 5/24 card application strategy?
ChatGPT
Cards and concepts surfaced: Chase Sapphire Preferred, Chase Sapphire Reserve, Chase Ink Business Preferred, Chase Freedom Unlimited
Sources cited: reddit.com/r/churning, reddit.com/r/CreditCards, doctorofcredit.com, thepointsguy.com
Perplexity
Cards and concepts surfaced: Same Chase card lineup, plus Chase Ink Cash and Chase Ink Unlimited business cards
Sources cited: reddit.com/r/churning, doctorofcredit.com, milestomemories.com, frequentmiler.com
Pattern observed: Advanced query language collapses the publisher mainstream and elevates the Reddit and niche-blog ecosystem. Doctor of Credit, Frequent Miler, and Miles to Memories carry citation weight inside this query type that they do not carry inside mainstream queries.
## Five Structural Patterns Driving Citation Consolidation
### 1. Three publishers supply the majority of source material.
The Points Guy, NerdWallet, and Bankrate together accounted for more than 62% of the citations observed across the test set. CardRatings, WalletHub, Upgraded Points, Forbes Advisor, and U.S. News Money supplied most of the remainder. Issuer-direct content - Chase's Ink Magazine, Capital One's content hub, American Express's Trendex - was rarely cited. Ranking inside this publisher set is now a direct input to AI citation share.
### 2. Premium-fee cards are systematically over-represented.
Cards with annual fees above $400 were observed in citation frequency 5.7 times higher than fee-free cards across mainstream best-credit-card queries. The driver is editorial economics: publishers earn higher affiliate commissions on premium card approvals and publish more comparison content for them. The downstream effect is that consumers asking AI engines a general recommendation question receive premium-card-heavy answers even when the consumer is fee-averse. Fee-free cards are systematically under-cited relative to consumer demand.
### 3. Welcome-offer values are the most-cited data point and decay fastest.
Every best-card answer led with welcome bonus values. These values change frequently, and the cached publisher content AI engines cite often runs weeks behind issuer changes. The 13-week AI citation decay cycle is more punishing in this category than in any other 5W has measured to date. Brands that maintain a steady cadence of refreshed welcome-offer content earn disproportionate citation pickup during the publisher refresh windows.
### 4. Cobranded loyalty cards are losing citation share to flexibility cards.
A decade ago, best-airline-card queries produced answers heavily weighted toward Delta, United, American, and Southwest cobrands. Today, the same query increasingly produces Capital One Venture X, Chase Sapphire Preferred, and Bilt as recommended answers. The publisher ecosystem has shifted editorial framing toward transferable points over single-program loyalty. Cobrand issuance teams are competing for citation share on terms that have changed without their input.
### 5. Reddit is the second engine for high-intent research.
r/CreditCards, r/churning, and r/awardtravel were observed in 38% of advanced travel-card prompts and only 4% of basic best-credit-card queries. The implication is that Reddit drives citation share where margin and lifetime value are highest - multi-card strategy, manufactured spend, business-card stacking, points optimization. Issuers cannot directly influence this surface, and Reddit favorability accumulates over years.
## Biggest AI Visibility Winners and Underperformers
### Winners - Cards and Issuers Over-Indexing on Citation Share Relative to Market Position
- Capital One Venture X - Newer to the premium category yet observed at near-parity with Chase Sapphire Reserve and American Express Platinum in citation frequency. Capital One's content hub investment and lounge expansion appear to be paying citation dividends.
- Chase Sapphire Preferred - A mid-tier card earning anchor-tier citation frequency because publishers position it as the universal first-travel-card recommendation.
- Bilt Mastercard / Bilt Palladium - A challenger product earning category-defining citation share because the rent-payment positioning generated earned media coverage no incumbent issuer could match.
- Apple Card - Limited rewards positioning yet outsized citation share driven entirely by brand strength and integration narrative.
- Discover it Cash Back - Single-issuer brand earning anchor citation in rotating-category cash back queries despite far smaller marketing budget than Chase or American Express.
### Underperformers - Cards and Issuers Under-Indexing Relative to Market Presence
- Bank of America - The largest U.S. retail bank by deposits earned a citation footprint smaller than several mid-tier issuers. Customized Cash Rewards and Premium Rewards cards rarely appeared in observed AI answers.
- U.S. Bank - Top-five bank by assets with minimal credit card citation presence. Altitude Reserve and Altitude Connect are functionally invisible inside the AI answer surface.
- Fee-free cards as a category - Demand exists; citation supply does not. The structural under-representation of no-annual-fee products is the largest unclaimed opportunity in the category.
- Regional issuers and credit unions - PenFed, Navy Federal, Alliant, and similar issuers earn negligible citation share despite competitive products and consumer satisfaction scores.
- Cobranded loyalty cards as a category - Losing citation share quarter over quarter to general-purpose flexibility cards.
- Synchrony and Comenity store-card portfolios - Despite issuing some of the highest-volume retail cards in the country, these portfolios are largely absent from AI category recommendations.
## Future Risks and Inflection Points
Five forces are reshaping the AI credit card answer surface over the next 12 to 24 months. Issuers and fintechs that prepare for them earn disproportionate citation share during the transitions.
### 1. AI shopping integrations and direct-application flows.
OpenAI, Perplexity, and Google have all signaled investment in commerce integrations. When AI engines can route a consumer directly to a card application, citation share converts to acquisition share without the click through a publisher affiliate link. The publisher layer's role as gatekeeper weakens. Issuers with strong direct-application infrastructure capture more of that flow; issuers without it lose ground.
### 2. Affiliate economics under pressure.
If AI shopping integrations reduce traffic to affiliate publishers, the publisher economic model that produces the editorial volume AI engines cite will weaken. The Points Guy, NerdWallet, and Bankrate could publish less, less often, with smaller editorial teams. The source-of-truth ecosystem could shift toward Reddit, niche blogs, and direct issuer content faster than the next 24 months.
### 3. Issuer APIs and structured product data.
Capital One and Chase have begun publishing structured product data in formats AI engines can parse natively. If issuer APIs become a primary input to AI answers - bypassing the publisher layer entirely - the issuers that invested earliest in machine-readable product surfaces capture citation share the publisher set previously controlled.
### 4. Personal AI agents and the shift from category to fit.
Today's dominant query is what is the best credit card. Tomorrow's dominant query is what is the best credit card for me, given my spending. Personal AI agents with access to transaction data shift the citation question from category authority to fit-prediction accuracy. Cards that publish detailed earning-rate data by category capture citation share inside personalized answers; cards that publish only rewards summaries do not.
### 5. CFPB and disclosure-rule evolution.
The Consumer Financial Protection Bureau's regulatory posture on credit card fees, late charges, and rewards-program disclosures continues to evolve. AI engines weight regulatory sources heavily on compliance-related queries. Issuers that proactively publish structured disclosure content earn citation share inside the regulatory layer that competitors cede by default.
## The 5W GEO Playbook for Card Issuers and Fintechs
### For premium card issuers
- Treat welcome-offer changes as press events. Publisher refresh velocity is the single largest variable in citation share.
- Publish proprietary spending and redemption data the publisher ecosystem cannot source elsewhere. Publishers will cite issuer-supplied data when it is genuinely new.
- Earn coverage in The Wall Street Journal, Bloomberg, and Reuters financial-services beats. These outlets feed citation surfaces the affiliate publishers do not reach.
- Deploy structured FAQ schema, Article schema, and ItemList schema on benefit pages to support AI extraction.
### For fee-free and entry-level issuers
- The under-citation of fee-free cards relative to consumer demand is the largest unclaimed opportunity in the category.
- Pursue editorial coverage outside the affiliate-publisher set: personal finance journalism, building-credit content, immigrant-and-newcomer financial guidance.
- Publish structured rewards comparison content the publisher ecosystem will cite when comparison demand outpaces affiliate publishing capacity.
### For cobranded card teams
- The flexibility-card framing is winning. Cobrand citation strategy now requires earning placement in best-card-for-specific-airline-or-hotel-loyalists queries rather than competing in general best-travel-card queries.
- Partner with the loyalty programs themselves on co-published earning-and-redemption data the publisher ecosystem will cite.
### For fintech challengers
- Brand-driven citation premium is real but unstable. Build the structured product-page content (rewards, fees, terms, eligibility) AI engines need for confident citation in comparison queries.
- Engineer the product-feedback loop that earns organic Reddit discussion volume. Reddit favorability compounds.
## What This Means for the Category in 2026
The Credit Cards AI Visibility Index documents a category where issuer marketing budgets are mismatched to the source-of-truth ecosystem AI engines actually cite. Issuers spending nine figures on paid acquisition are funding the top of a funnel that AI conversations are routing elsewhere. The brands winning citation share are not necessarily winning the most applications - yet - but the gap between AI citation share and application share is the leading indicator of acquisition cost over the next 18 months.
"When a consumer asks ChatGPT what is the best credit card for me, the answer is being supplied by three publishers. Issuers can spend their way around that for a while. They can't spend their way through it."
- Ronn Torossian, founder, 5W
For issuers and fintechs evaluating their position, 5W offers a Credit Cards Category AI Visibility Audit benchmarking citation share against the Index's top 25 and identifying the structural content and earned-media gaps that determine whether a card earns mention in AI-generated consumer research.
## Methodology
The 5W Credit Cards AI Visibility Index analyzed 4,200 category prompts across travel, premium-rewards, cash-back, airline-cobrand, hotel-cobrand, business-card, balance-transfer, secured, student, and credit-building query clusters. Prompts were tested across the five major U.S. consumer-facing AI platforms - ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews - between January 6 and April 30, 2026.
Citation share was measured by counting source domain attributions inside AI-generated responses. Where a platform did not cite sources directly, retrieval pattern analysis was performed against the platform's published documentation and the brand mentions surfaced in responses. Brand mentions were de-duplicated across prompts to prevent over-weighting from repeated query phrasing.
Tier assignments reflect aggregated citation frequency observed across the test set. Rankings inside each tier reflect citation order inside head-to-head comparison queries. The Index is refreshed quarterly to track citation decay, welcome-offer cycles, publisher editorial changes, and emerging issuer entrants. Methodology, prompt set, and platform version notes available upon request to credentialed analysts and journalists.
Disclosure: 5W is the publisher of this Index. 5W's GEO practice provides AI visibility services to clients across multiple consumer categories. The brands cited in this Index were selected based on observed citation frequency in the test set; inclusion does not imply a commercial relationship. 5W does not accept compensation for ranking placement or coverage.
## Engage with 5W
For inquiries about Generative Engine Optimization engagements, AI Visibility Index licensing, custom industry research, or category-specific PR and communications: visit 5wpr.com/practice/geo-optimization, read the AI Companies AI Visibility Index, or contact the 5W research team.
## About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen - ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews - alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list.
For more information, visit www.5wpr.com.
---
### 5W AI Higher Education Index 2026
URL: https://www.5wpr.com/research/ai-higher-education-index/
Summary: The 5W AI Higher Education Index 2026 scores 50 universities on curriculum, alumni founders, partnerships, exec ed, research, and AI citation share.
AI Visibility Index
# The First Benchmark of AI Production Capacity.
Our analysis finds that AI is being produced inside a small number of universities, and the gap between those institutions and the rest is widening. Framed correctly, this is not a university ranking. It is a benchmark of a new category, AI production capacity, that will predict institutional trajectory across the next decade with more reliability than endowment, prestige, or general research budget.
📄 Download the full report (PDF)
The Thesis
Our analysis finds that AI is being produced inside a small number of universities, and the gap between those institutions and the rest is widening. Framed correctly, this is not a university ranking. It is a benchmark of a new category, AI production capacity, that will predict institutional trajectory across the next decade with more reliability than endowment, prestige, or general research budget.
The AI City Index (Volume 01) mapped where AI capital and talent concentrate at the city level. This volume maps where AI is produced at the source. Our model finds that a small cluster of universities dominates every measurable dimension of AI production, frontier-lab anchor density, research output, curriculum depth, founder pipeline, compute infrastructure, and modeled citation share.
Two disclosures the reader should hold up front. First, this is an inaugural edition; year-over-year movement is unavailable by definition. Second, the citation-share dimension is modeled, not passively observed, and the reader should read our published protocol in the Appendix before treating those scores as fact. Everything that follows is presented as our model's finding, not as truth.
Framing: We use "our analysis finds," "our model suggests," and "according to this index" throughout. Rankings, tiers, and score movements are the outputs of the methodology described in Chapters 09 and 10. Different weighting choices produce different orderings. The dimension-level scores are published in full so readers can build their own weighted variant.
How to Read This Report
## Six Principles Before You Read the Rankings
Every ranking is a set of choices about what to measure. Before the leaderboard, we surface the choices we made, so a reader can decide, up front, whether the framework matches the question they're asking.
- Principle 01
This is a benchmark, not a ranking of institutional quality.We measure one variable, AI production capacity, across six explicitly stated dimensions. A university's position in our composite does not reflect undergraduate teaching quality, endowment, admissions selectivity, Nobel counts, or general institutional reputation.
- Principle 02
Every score is relative to our 50-university universe.A "92" on any dimension means "92nd-percentile within our universe as normalized," not "92% of some absolute possible maximum." Enlarging the universe would shift every score.
- Principle 03
The composite is an unweighted mean. Reweight if that changes the question.All six dimensions carry equal weight in our composite. A reader who cares more about founder pipeline than curriculum can reweight against our published dimension-level scores. Sensitivity checks under different weight schemes are in Appendix A4.
- Principle 04
Citation Share is modeled, not passively observed.Dimension 6 is the output of 3,600 prompt-engine runs across February–May 2026. It is the most novel and the most contestable dimension in the index. The full protocol is in Appendix A1. Readers uncomfortable with modeled data should consult the sensitivity checks that isolate the effect of excluding Dimension 6.
- Principle 05
Composite scores carry a ±2.5-point uncertainty band.At the 95% confidence level. Rank differences smaller than 5 composite points may not be statistically distinguishable. Tier assignments are more reliable than exact position within a tier.
- Principle 06
This is Edition One. Year-over-year movement is unavailable by definition.Every score is a first observation. Edition Two, planned for May 2027, will publish a "Reshuffle Report" tracking composite-score change between editions.
The Framework at a Glance
## Six Dimensions, Equally Weighted
The framework in one panel, what each dimension measures, its internal weighting scheme, and the primary data source. Full definitions in Chapter 05.
DIMENSION 01
Frontier Lab
Anchor Density
-
OpenAI 25% · Anthropic 20%
DeepMind 20% · xAI 10%
Others 25%
SOURCE: Crunchbase + PitchBook
DIMENSION 02
AI Research
Output
-
NeurIPS/ICML/ICLR 40%
ACL/EMNLP 20% · h-index 25%
Patents 15%
SOURCE: CSRankings + Nature Index
DIMENSION 03
AI Curriculum
Depth
-
Named AI degree 30%
Dedicated AI school 25%
GEO/LLMO 25% · Cross-disc 20%
SOURCE: Institutional catalogs
DIMENSION 04
Founder &
Capital Pipeline
-
Alumni founders 40%
VC raised 30% · Unicorns 20%
CEO seats at frontier labs 10%
SOURCE: Crunchbase + PitchBook
DIMENSION 05
Compute &
Infrastructure
-
On-campus GPU 30%
Hyperscaler ties 25%
Fed AI funding 25% · Gov 20%
SOURCE: NSF/DOE/DARPA + Synergy
DIMENSION 06 · MODELED
AI Citation
Share
-
ChatGPT 20% · Claude 20%
Gemini 20% · Perplexity 20%
Google AI Overviews 20%
3,600 RUNS · FEB–MAY 2026
COMPOSITE FORMULA
Composite Score = Σ(D₁, D₂, D₃, D₄, D₅, D₆) ÷ 6
All six dimensions equally weighted. Tiebreaks resolved by Dimension 6. Every dimension is rank-normalized to 0–100 within our 50-university universe with logarithmic smoothing where the raw distribution is heavily skewed.
Fig. 3, The framework in one panel. Dimension 6 is highlighted (dark) because it is the only modeled dimension. Chapters 05 and 09 explain each in detail.
01 / Surprises
## Ten Findings That Will Get Argued About
Our model surfaces ten results that do not match the assumptions carried in most university ranking frameworks. Each is the direct output of our scoring; each is defensible from the raw metrics published in the Appendix; each is also, deliberately, provocative.
01
### Harvard trails Technion on AI production.
Rank 17 vs rank 25, but Technion outscores Harvard on founder pipeline per capita and frontier-lab anchor density. Prestige does not predict AI capacity.
02
### Princeton beats Harvard, Yale, and Penn combined.
The only Ivy in Tier I. The Amodei alumni tie, strong theoretical CS, and disciplined AI faculty recruitment produce a real structural advantage.
03
### Toronto ranks above Oxford and Cambridge.
Rank 6 vs rank 10 and 11. The Hinton lineage, Vector Institute, and per-capita founder yield compound. Structural talent flight to SF is priced in.
04
### Israel places two universities in the top 30.
Technion (25) and Tel Aviv (28). Founder pipeline per capita rivals Stanford. Compute infrastructure is the only structural constraint.
05
### China places two universities in Tier I.
Tsinghua (5) and Peking (7). Under an English-language citation-share bias correction, both likely rank higher. Beijing is the second AI capital.
06
### Zero communications schools qualify for any tier.
Not one PR, journalism, or communications school appears in the ranking. The schools training AI-era communicators are absent from the AI production layer entirely.
07
### Vanderbilt outranks Duke on trajectory.
Rank 38 vs 34, but Diermeier's AI-forward institutional posture positions Vanderbilt for the largest projected composite gain in Edition Two.
08
### The University of Washington beats Yale by 7 points.
Rank 12 vs 19. The Allen School's AI research bench and adjacency to AI2 and Microsoft Research produce measurable production capacity. Yale's prestige score does not translate.
09
### Tsinghua's Citation Share is depressed 20+ points.
Under any language-neutral normalization, Tsinghua ranks in the global top three. Western AI engines systematically under-cite Chinese sources.
10
### Four universities own 54% of frontier-lab technical leadership.
Stanford, MIT, CMU, and UC Berkeley, collectively, produced the majority of founder and technical-lead alumni at OpenAI, Anthropic, DeepMind, xAI, and adjacent frontier labs. The concentration is structural.
02 / Rankings
## Global AI Production Capacity Rankings
Composite score = unweighted mean of the six equally weighted dimension scores. Tiebreaks are resolved by Dimension 6 (Citation Share). Tier assignments: I (composite ≥ 78), II (composite 70–77.9), III (composite < 70). Full raw underlying metrics are published in Chapter 10.
Confidence intervals: Composite scores carry an approximate ±2.5 point uncertainty band at the 95% confidence level, driven primarily by Dimension 6 (Citation Share) modeling variance. Rank differences smaller than 5 composite points may not be statistically distinguishable. Readers should read tier assignments as more reliable than exact positional rank within a tier.
Rank
University
Position
Composite
Tier
01
Stanford UniversityUSA
The frontier anchor
96.0
Tier I
02
Massachusetts Institute of TechnologyUSA
CSAIL & Schwarzman
94.7
Tier I
03
Carnegie Mellon UniversityUSA
Deepest faculty bench
91.3
Tier I
04
UC BerkeleyUSA
Open-source pipeline
88.2
Tier I
05
Tsinghua UniversityChina
Chinese frontier
84.3
Tier I
06
University of TorontoCanada
Hinton lineage
82.3
Tier I
07
Peking UniversityChina
Chinese academic anchor
80.3
Tier I
08
Princeton UniversityUSA
Amodei alma mater
79.2
Tier I
09
ETH ZurichSwitzerland
European anchor
77.5
Tier II
10
University of OxfordUK
Deep AI theory
75.0
Tier II
11
University of CambridgeUK
Hassabis lineage
74.5
Tier II
12
University of WashingtonUSA
Allen adjacency
74.0
Tier II
13
UIUCUSA
Midwest anchor
72.5
Tier II
14
Cornell UniversityUSA
Ivy AI depth
71.5
Tier II
15
Georgia TechUSA
Southeast capital
71.0
Tier II
16
CaltechUSA
Physics-adjacent AI
70.0
Tier II
17
Harvard UniversityUSA
Late but capitalized
69.0
Tier III
18
Columbia UniversityUSA
Manhattan AI
68.0
Tier III
19
Yale UniversityUSA
Prestige, not production
67.0
Tier III
20
Shanghai Jiao Tong UniversityChina
Applied AI depth
66.5
Tier III
21
National University of SingaporeSingapore
SE Asia anchor
66.0
Tier III
22
HKUSTHong Kong
China-Western bridge
65.5
Tier III
23
NTU SingaporeSingapore
Applied ML
64.5
Tier III
24
KAISTSouth Korea
Korean frontier
64.0
Tier III
25
TechnionIsrael
Israeli AI depth
63.5
Tier III
26
University of MichiganUSA
Automotive AI
63.0
Tier III
27
UT AustinUSA
Texas AI capital
62.5
Tier III
28
Tel Aviv UniversityIsrael
Startup-adjacent
62.0
Tier III
29
UCLAUSA
Bay-Area gravity
61.5
Tier III
30
EPFLSwitzerland
Francophone frontier
61.0
Tier III
31
University of ChicagoUSA
Theory over production
59.5
Tier III
32
USCUSA
Entertainment AI
58.5
Tier III
33
NYUUSA
LeCun-era peak, exit
58.0
Tier III
34
Duke UniversityUSA
Medical AI
56.0
Tier III
35
Purdue UniversityUSA
Engineering AI
55.0
Tier III
36
University of PennsylvaniaUSA
Wharton-adjacent
54.5
Tier III
37
Northwestern UniversityUSA
Interdisciplinary
54.0
Tier III
38
Vanderbilt UniversityUSA
Southern challenger
53.5
Tier III
39
Wisconsin-MadisonUSA
Midwest research
53.0
Tier III
40
TU MunichGermany
German industrial
52.5
Tier III
41
Imperial College LondonUK
Applied UK anchor
52.0
Tier III
42
Sorbonne / PSLFrance
Paris frontier feeder
51.5
Tier III
43
University of EdinburghUK
Scottish NLP tradition
51.0
Tier III
44
University of WaterlooCanada
Applied Canadian
50.5
Tier III
45
McGill / MilaCanada
Bengio-era research
50.0
Tier III
46
Seoul National UniversitySouth Korea
Korean research anchor
48.5
Tier III
47
University of TokyoJapan
Legacy, not velocity
47.0
Tier III
48
IIT BombayIndia
Frontier feeder
45.5
Tier III
49
IIT DelhiIndia
Applied Indian AI
44.5
Tier III
50
IISc BangaloreIndia
Research foundation
43.0
Tier III
### Universities by Country
50-university universe · Composite scores by geography
COUNT
DISTRIBUTION
United States
25
50%
China
4
8%
United Kingdom
4
8%
Canada
3
6%
India
3
6%
Singapore
2
4%
Switzerland
2
4%
South Korea
2
4%
Israel
2
4%
France, Germany, HK, Japan (1 each)
4
8%
Fig. 1, The universe is US-heavy by design and reflects our source-pool sampling. Edition Two will expand.
Our model finds a concentration in AI production that no other university metric captures. The next question is whether the concentration is stable or whether it repositions faster than institutional history suggests.
Ronn Torossian · Founder & Chairman, 5W
03 / Thesis Detail
## The Big Four and What Sits Around Them
Our analysis finds Stanford, MIT, Carnegie Mellon, and UC Berkeley collectively account for what our model estimates to be a majority share of frontier-lab technical leadership. Sam Altman and Fei-Fei Li came out of Stanford. Ilya Sutskever came out of Toronto by way of Google Brain. The Amodei siblings ran through Princeton and Johns Hopkins before Anthropic. Demis Hassabis came out of Cambridge and UCL. Across these paths the density of frontier-lab founders and technical leaders traceable to the Big Four is structurally different from every other institution in our universe.
The pattern extends past founder counts. Our model ranks Stanford's HAI, MIT's CSAIL and the Schwarzman College of Computing, CMU's School of Computer Science, and Berkeley's BAIR as the four largest concentrations of AI faculty by headcount. Their alumni populate technical staff at every frontier lab. Their graduate programs supply the PhD candidates that Anthropic and OpenAI compete over. Their research output, measured through NeurIPS, ICML, and ICLR, accounts for what our data suggests is a disproportionate share of the field.
Tsinghua and Peking University run parallel to the Big Four in China. DeepSeek, Zhipu AI, and much of the technical leadership at Alibaba's DAMO Academy trace to these two institutions. Our model's constraint is Dimension 6, Western AI engines under-cite Chinese sources, and the composite scores are compressed as a result. Under any citation-share normalization that removes the English-language bias, our analysis suggests Tsinghua ranks in the global top three.
The University of Toronto is the anomaly. Our model finds Toronto third-ranked in the world for AI research output per capita and first-ranked for per-capita frontier-founder yield. But structural talent flight to San Francisco compresses Toronto's composite, a finding this index inherits from Volume 01's analysis of the Toronto AI ecosystem.
The Ivy League gap is real by our measurement. Harvard, Yale, and Penn score in the Tier III range on our composite. Princeton is the exception, sustained by strong CS faculty and the Amodei alumni tie. The most consequential blind spot in our universe is the communications-school layer, not one dedicated PR, journalism, or communications school appears anywhere in the index. The schools training the next generation of communicators are absent from the AI production layer entirely.
04 / Findings
## Six Numbers That Frame the Story
54%
Big Four shareOur estimate of the share of frontier-lab founding technical leadership traceable to Stanford, MIT, CMU, and Berkeley combined.
5
Frontier labs, one metroOpenAI, Anthropic, xAI, Inflection, Sierra, all Bay-Area-headquartered, all Stanford-Berkeley alumni-dense.
~66%
Tsinghua+Peking shareEstimated share of Chinese frontier-AI founders traceable to Tsinghua and Peking combined.
3rd
Toronto per-capita rankToronto's global rank for AI research output per faculty member in our normalized data.
0
Communications schoolsNumber of dedicated PR, journalism, or communications schools ranking anywhere in this index.
3x
CMU faculty depthOur estimate of CMU's SCS AI faculty count relative to the median top-25 US CS department.
### Tier I, Six-Dimension Comparison
Stanford · MIT · Carnegie Mellon · Tsinghua, normalized 0–100 per dimension
-
-
-
-
-
-
ANCHOR
RESEARCH
CURRICULUM
FOUNDERS
COMPUTE
CITATION
Stanford
MIT
Carnegie Mellon
Tsinghua (dashed)
Fig. 2, Our model finds Stanford dominant on Anchor and Founders. MIT dominant on Compute. CMU dominant on Research and Curriculum. Tsinghua's Citation Share is the visible compression.
05 / Dimensions
## The Six Measured Variables
Each of 50 universities is scored on each dimension on a 0–100 scale. The composite is the simple unweighted mean of the six dimension scores. Within each dimension, sub-component weightings are published below and applied consistently across all 50 universities.
Dim 01
### Frontier Lab Anchor Density
Alumni and current-faculty presence at OpenAI, Anthropic, DeepMind, xAI, Mistral, Cohere, DeepSeek, Inflection, Sierra. Direct count, per-capita normalized against total faculty.
Weights within dimension: OpenAI 25% · Anthropic 20% · Google DeepMind 20% · xAI 10% · Mistral / Cohere / DeepSeek / Inflection / Sierra 25% (combined)
01
Stanford
100
02
MIT
95
03
Carnegie Mellon
92
04
UC Berkeley
88
05
Toronto
85
Dim 02
### AI Research Output
Publications, faculty depth, and patent output, via CSRankings.org (2018–2025 rolling window) and Nature Index AI subject data.
Weights within dimension: NeurIPS / ICML / ICLR publications 40% · ACL / EMNLP publications 20% · h-index of top 20 AI faculty 25% · AI-related patents 15%
01
Carnegie Mellon
98
02
MIT
96
03
Stanford
94
04
Tsinghua
92
05
UC Berkeley
90
Dim 03
### AI Curriculum Depth
Named degrees, dedicated AI schools, GEO/LLMO curriculum inside required courses, and cross-disciplinary integration.
Weights within dimension: Named AI degree program 30% · Dedicated AI school or college 25% · GEO / LLMO in required curriculum 25% · Cross-disciplinary integration (CS × Business × Comms × Law × Med) 20%
01
Carnegie Mellon
96
02
MIT
94
03
Stanford
92
04
UC Berkeley
88
05
Tsinghua
85
Dim 04
### Founder & Capital Pipeline
Alumni founders since 2019, VC raised, unicorns, and CEO seats at frontier labs, via Crunchbase, PitchBook, and Dealroom.
Weights within dimension: Alumni founder count 40% · AI VC raised by alumni-founded companies 30% · AI unicorn count 20% · Alumni CEO / senior-technical leadership at frontier labs 10%
01
Stanford
100
02
MIT
92
03
UC Berkeley
88
04
Tsinghua
85
05
Carnegie Mellon
82
Dim 05
### Compute & Infrastructure
On-campus GPU capacity, hyperscaler partnerships, federal AI research funding, and institutional AI governance maturity.
Weights within dimension: On-campus GPU / accelerator capacity 30% · Hyperscaler partnerships (AWS / Azure / GCP / Oracle) 25% · Federal AI research funding (NSF / DARPA / DOE / national equivalents) 25% · Institutional AI governance maturity 20%
01
MIT
95
02
Stanford
92
03
Tsinghua
90
04
Carnegie Mellon
88
05
UC Berkeley
85
Dim 06
### AI Citation Share (Modeled)
Modeled share of mentions across 60 prompts × 5 engines × 4 monthly waves. Full protocol in Chapter 09.
Weights within dimension: ChatGPT 20% · Claude 20% · Gemini 20% · Perplexity 20% · Google AI Overviews 20%, engines equally weighted; per-university share normalized to 0–100 against universe maximum.
01
Stanford
98
02
MIT
96
03
Carnegie Mellon
92
04
UC Berkeley
90
05
Toronto
82
06 / Tier I Profiles
## The Eight
Ranked First01
### Stanford University
USA, Composite 96.0
Anchor100
Research94
Curriculum92
Founders100
Compute92
Citation98
The only university scoring in our top three on every dimension.
Our model finds Stanford the source layer of modern AI production. SAIL and HAI operate two of the largest concentrations of AI faculty at any US university. Fei-Fei Li, Christopher Manning, Andrew Ng, Percy Liang, Chelsea Finn, the technical breadth combined with commercial translation is unmatched in our universe. The graduate program supplies the technical staff at frontier labs within a fifty-mile radius.
The alumni footprint is the differentiator on Dimension 4. Sam Altman and Mira Murati at OpenAI. Jensen Huang at Nvidia. The founding technical staff of much of Google Brain. Our model records Stanford's founder-pipeline score at the ceiling because no other institution supplies comparable density into the frontier-lab founder layer.
The risk our analysis flags is concentration. Stanford's dominance depends on continued Bay Area gravitational pull. A shift in federal AI export controls or a California policy repricing could compress the Stanford advantage faster than any peer institution could close the gap. For this edition, no institution comes within striking distance.
Ranked Second02
### Massachusetts Institute of Technology
USA, Composite 94.7
Anchor95
Research96
Curriculum94
Founders92
Compute95
Citation96
The deepest institutional commitment to AI at any US university.
Our data ranks MIT's CSAIL as the largest AI research organization in the world by faculty count. The Schwarzman College of Computing, launched in 2019 with a $1 billion commitment, embedded AI into the broader institution's operating structure in a way our model finds no peer has matched. The MIT-IBM Watson AI Lab produces sustained applied output.
Regina Barzilay, Josh Tenenbaum, and Antonio Torralba anchor the current bench. President Kornbluth's post-2023 institutional posture positioned MIT as the reference on AI policy, a role our analysis suggests Harvard vacated during its own crises.
The gap between MIT and Stanford in our composite is narrow. MIT slightly leads on Research and Compute. Stanford leads decisively on Founders. Our model views MIT as the institution most credibly positioned to close the composite gap on Stanford across the next five years.
Ranked Third03
### Carnegie Mellon University
USA, Composite 91.3
Anchor92
Research98
Curriculum96
Founders82
Compute88
Citation92
The deepest faculty bench by our measurement, Research and Curriculum leader.
Our data records CMU's School of Computer Science as the largest concentration of AI-active faculty in our universe. The Machine Learning Department, Language Technologies Institute, Robotics Institute, and HCI Institute each independently exceed the AI faculty count of many top-25 US CS departments. CMU launched the first US bachelor's degree in AI in 2018, three years ahead of every peer institution in our analysis.
The founder pipeline is the constraint. Our model shows CMU alumni distributing into the enterprise-AI layer rather than into frontier-lab founder roles at Stanford's density. The Pittsburgh geography is a structural disadvantage against Bay Area and Boston clusters.
If frontier AI production shifts back toward research primacy over commercial primacy, a scenario our model views as possible, CMU repositions toward first.
Ranked Fourth04
### UC Berkeley
USA, Composite 88.2
Anchor88
Research90
Curriculum88
Founders88
Compute85
Citation90
The open-source anchor. The founder pipeline outside the Stanford axis.
BAIR, RISE Lab, and Sky Computing Lab operate as the West Coast counterweight to Stanford. Pieter Abbeel, Trevor Darrell, Sergey Levine, and Stuart Russell anchor a research bench that our data shows producing some of the field's most-cited work of the last five years. The Berkeley-Meta AI and Berkeley-xAI alumni ties are structural.
The public-university funding structure creates a specific advantage on open-source AI infrastructure. Where Stanford research moves quickly into closed-source frontier lab environments, Berkeley produces more open-source infrastructure, TensorFlow's early ties, PyTorch-adjacent research, and RL frameworks the field runs on.
The Berkeley-Stanford axis, taken together, accounts in our model for the majority of frontier-lab technical leadership on the West Coast.
Ranked Fifth05
### Tsinghua University
China, Composite 84.3
Anchor82
Research92
Curriculum85
Founders85
Compute90
Citation72
The Chinese frontier academic. The most consequential university on Dimension 6's structural bias.
Our data traces DeepSeek's founding technical team, Zhipu AI's leadership, ByteDance's AI lab senior staff, and Alibaba DAMO Academy's research direction to Tsinghua faculty and alumni. The Institute for AI Industry Research and the Department of Computer Science and Technology together produce research output that our model records as rivaling MIT and CMU on volume.
Compute infrastructure ranks higher than Chinese universities have historically been credited for. Tsinghua's on-campus GPU capacity, its Chinese-hyperscaler partnerships, and its share of national AI research funding place it credibly in our top five on Dimension 5. Research volume on frontier subfields, language models, multimodal, agentic AI, has grown at a rate our data suggests no US institution has matched in the last three years.
The drag is Dimension 6. Western AI engines under-cite Chinese sources. Our model estimates Tsinghua's citation-share score compresses the composite by seven to ten points relative to what a language-neutral measurement would produce. Under Chinese-engine normalization, Tsinghua likely ranks above Berkeley, and possibly above CMU.
Ranked Sixth06
### University of Toronto
Canada, Composite 82.3
Anchor85
Research88
Curriculum82
Founders82
Compute75
Citation82
The birthplace of modern deep learning. Third in the world for per-capita AI production in our data.
Our analysis identifies Toronto as the origin institution of the deep-learning revolution. Geoffrey Hinton's lab produced the 2012 ImageNet breakthrough. Ilya Sutskever, Alex Krizhevsky, Ruslan Salakhutdinov came out of Toronto. Aidan Gomez, Cohere's co-founder, is a Toronto graduate. The Vector Institute anchors the current ecosystem.
Toronto's per-capita AI production is the highest in our data outside the Bay Area. On any headcount-normalized measure, research output, founder yield, frontier-lab technical leadership, our model places Toronto third or fourth globally.
The constraint is retention. Our data records the most successful Toronto AI alumni relocating systematically to San Francisco, New York, or London. Structural talent flight compresses the absolute composite. This mirrors Volume 01's Toronto AI City finding.
Ranked Seventh07
### Peking University
China, Composite 80.3
Anchor78
Research85
Curriculum82
Founders82
Compute85
Citation70
The academic complement to Tsinghua. The second pillar of Chinese frontier AI.
Peking's Institute of Computing Technology, its School of Intelligence Science and Technology, and its research collaborations with the Chinese Academy of Sciences produce what our model records as the second-largest concentration of AI research output in China. The founder pipeline runs into Baidu, ByteDance, and the growing set of Chinese frontier startups. The culture leans more theoretical than Tsinghua's applied orientation.
The Peking-Tsinghua relationship in Chinese AI resembles our data's MIT-Stanford relationship: two peer institutions whose combined output defines the ecosystem and whose competition drives its acceleration.
The Dimension 6 constraint applies here as well, possibly more acutely given Peking's slightly smaller Western-visible research presence. Under any citation-share normalization that removes English-language bias, Peking ranks in our top five.
Ranked Eighth08
### Princeton University
USA, Composite 79.2
Anchor82
Research82
Curriculum75
Founders78
Compute78
Citation80
The only Ivy in Tier I by our measurement.
Our data ranks Princeton as the only Ivy League institution reaching Tier I. The combination of a strong CS department, Sanjeev Arora's theoretical bench, and the founder-alumni tie through Dario and Daniela Amodei at Anthropic anchors the position. The Center for Statistics and Machine Learning has produced sustained frontier-relevant research output.
The undergraduate pipeline is a specific strength. Princeton CS graduates place into frontier labs at rates our model finds Harvard and Yale do not match. The graduate program's technical rigor produces the theory-forward researchers Anthropic has specifically invested in.
The constraint is scale. Princeton's total AI faculty count is smaller than any Tier I peer in our data. President Eisgruber's op-ed cadence on AI and higher education adds an institutional-authority dimension Princeton uses well. Princeton is our counterexample to the thesis that Ivy prestige is unrelated to AI production.
07 / Tier II
## The Contenders
Tier II, ranks 9 through 16, captures universities with credible AI production capacity but structural gaps preventing Tier I placement. Each scores top-10 on at least two dimensions by our measurement, but carries measurable deficiency on at least one other.
Rank09
#### ETH Zurich
Switzerland, Composite 77.5
The strongest AI research bench in continental Europe by our data. Google Zurich AI office adjacency. Strong on research and compute; weaker on founder pipeline due to European scale-up constraints.
Rank10
#### University of Oxford
UK, Composite 75.0
Deep AI theory bench. FHI produced sustained AI-safety work. Weaker on founder pipeline than Cambridge; stronger on academic authority.
Rank11
#### University of Cambridge
UK, Composite 74.5
The Hassabis lineage into Google DeepMind. Founder pipeline concentrated in DeepMind and Wayve. The London-Cambridge axis is the strongest AI ecosystem in Europe by our measurement.
Rank12
#### University of Washington
USA, Composite 74.0
The Allen School ranks top-five US on our research metric. AI2 and Microsoft Research adjacency anchors the Seattle ecosystem. Stronger on research than founder pipeline.
Rank13
#### UIUC
USA, Composite 72.5
Midwest AI research anchor. Historic CS strength. Graduates populate applied-AI staff at Google, Microsoft, and Nvidia. Founder pipeline is the constraint.
Rank14
#### Cornell University
USA, Composite 71.5
The Ivy that ranks second in AI production by our data. Cornell Tech's Manhattan campus produces our strongest East Coast applied AI pipeline. Weaker on frontier-lab anchor density than Princeton.
Rank15
#### Georgia Tech
USA, Composite 71.0
The Southeast AI capital. Strong applied ML research, dense Atlanta industry ties. ML@GT anchors the bench. Founder pipeline is the constraint.
Rank16
#### Caltech
USA, Composite 70.0
Physics-adjacent AI research at world-class depth. Small institutional scale caps founder-pipeline density. Caltech-JPL adjacency produces distinctive government-adjacent output.
08 / Tier III
## The Mid-Pack
Tier III captures universities with real but incomplete AI production capacity in our measurement. Each has a credible path forward; none currently scores in our top five on more than one dimension. Three legacy powers, Harvard, Yale, University of Tokyo, sit inside this tier.
Rank17
#### Harvard University
USA, Composite 69.0
Late to AI, now heavily capitalized. The Kempner Institute (2021, $500M CZI) is the belated institutional response. Our data still ranks Harvard's AI faculty count behind Princeton and Cornell.
Rank18
#### Columbia University
USA, Composite 68.0
Data Science Institute and strong applied ML. Manhattan positioning produces natural pipeline into the NYC AI ecosystem.
Rank19
#### Yale University
USA, Composite 67.0
Prestige and endowment without commensurate AI production in our model. Late-cycle AI investment has not yet produced faculty scale or founder pipeline to close the Princeton gap.
Rank20
#### Shanghai Jiao Tong University
China, Composite 66.5
Chinese applied AI depth. Strong pipeline into industrial AI, autonomous vehicles, manufacturing. Under-cited in Western engines.
Rank21
#### National University of Singapore
Singapore, Composite 66.0
The SE Asian AI anchor. Strong government AI investment. Founder pipeline still developing; research output growing rapidly.
Rank22
#### HKUST
Hong Kong, Composite 65.5
The China-Western AI bridge. Dense connections to mainland Chinese AI ecosystems and Western academic networks. Beijing policy pressure is the primary structural risk.
Rank23
#### NTU Singapore
Singapore, Composite 64.5
Applied ML depth. Strong industry ties to SE Asian AI startups. Complements NUS as the second Singapore anchor.
Rank24
#### KAIST
South Korea, Composite 64.0
Korean AI frontier academic. Strong CS and applied AI research. Founder pipeline anchored in Naver and Kakao.
Rank25
#### Technion
Israel, Composite 63.5
Israeli AI depth anchor. Strong ties to Nvidia Israel, Intel Israel, 8200 alumni pipeline. Founder yield per capita rivals Stanford by our measurement. Compute infrastructure is the constraint.
Rank26
#### University of Michigan
USA, Composite 63.0
Automotive AI adjacency. Applied ML pipeline into Ford, GM, autonomous vehicle sector. Founder pipeline lags research output.
Rank27
#### UT Austin
USA, Composite 62.5
The Texas AI capital. Aggressive faculty recruitment. Adjacency to Tesla and AI startups relocating from California.
Rank28
#### Tel Aviv University
Israel, Composite 62.0
Second Israeli anchor. Stronger on humanities-adjacent AI research. Founder pipeline structurally strong through broader Tel Aviv startup ecosystem.
Rank29
#### UCLA
USA, Composite 61.5
The LA AI research anchor. Suppressed by Bay Area gravity, strongest alumni relocate rather than build LA. LA's own cluster remains small.
Rank30
#### EPFL
Switzerland, Composite 61.0
Francophone Swiss AI anchor. Applied research in robotics and autonomous systems. Complements ETH Zurich.
Rank31
#### University of Chicago
USA, Composite 59.5
Theoretical CS strength without commensurate applied production. DSI is the belated institutional response.
Rank32
#### USC
USA, Composite 58.5
ISI anchors legacy AI research. Annenberg leads on AI communications research, the only major Annenberg to have done so.
Rank33
#### NYU
USA, Composite 58.0
Peak was the Yann LeCun era at Center for Data Science. Post-2013 talent exit to Meta AI compressed the founder pipeline.
Rank34
#### Duke University
USA, Composite 56.0
Medical AI adjacency through Duke School of Medicine partnership. Founder pipeline concentrated in health-adjacent startups.
Rank35
#### Purdue University
USA, Composite 55.0
Engineering-adjacent AI. Applied ML in manufacturing, aerospace, defense. Founder pipeline lags research output.
Rank36
#### University of Pennsylvania
USA, Composite 54.5
Wharton-adjacent applied AI in finance and quantitative research. GRASP Lab produces sustained robotics research.
Rank37
#### Northwestern University
USA, Composite 54.0
Interdisciplinary AI through NICO. Kellogg produces strong management-adjacent applied output.
Rank38
#### Vanderbilt University
USA, Composite 53.5
The Southern challenger. Chancellor Diermeier's AI-forward posture positions Vanderbilt for our largest projected composite gain in Edition Two.
Rank39
#### Wisconsin-Madison
USA, Composite 53.0
Midwest research anchor. Applied ML capabilities. Founder pipeline lags due to structural talent flight.
Rank40
#### TU Munich
Germany, Composite 52.5
The strongest German AI research institution by our data. BMW, Siemens, German industrial AI adjacency. European scale-up constraints cap founder pipeline.
Rank41
#### Imperial College London
UK, Composite 52.0
Applied UK anchor complementing Oxford and Cambridge. DeepMind and Wayve ties. Founder pipeline growing under London AI investment surge.
Rank42
#### Sorbonne / PSL
France, Composite 51.5
Paris academic feeder into Mistral and broader French AI ecosystem. Structural retention constraint against US recruitment.
Rank43
#### University of Edinburgh
UK, Composite 51.0
Deep NLP tradition. School of Informatics is one of the largest UK CS programs. Founder pipeline concentrated in NLP-adjacent startups.
Rank44
#### University of Waterloo
Canada, Composite 50.5
Applied Canadian AI anchor. Strong pipeline into North American frontier labs. Structural talent flight to US concentrations.
Rank45
#### McGill / Mila
Canada, Composite 50.0
Bengio's Mila is among the most-cited AI research institutions globally by our data. Institutional AI production runs through Mila-affiliated researchers. Retention constraint.
Rank46
#### Seoul National University
South Korea, Composite 48.5
Korean academic AI anchor. Strong theoretical CS. Founder pipeline through Naver, Kakao.
Rank47
#### University of Tokyo
Japan, Composite 47.0
Japan's richest AI research institution and our lowest-velocity Tier III institution. Sustained investment without commensurate frontier-adjacent output. Mirrors Volume 01's Tokyo AI City finding.
Rank48
#### IIT Bombay
India, Composite 45.5
The strongest Indian institution on frontier-lab feeder metrics in our data. Alumni populate senior technical staff across US frontier labs.
Rank49
#### IIT Delhi
India, Composite 44.5
Applied Indian AI research. Strong CS department. Founder pipeline growing under domestic Indian AI startup surge.
Rank50
#### IISc Bangalore
India, Composite 43.0
The Indian research foundation. Strong theoretical CS. Founder pipeline complements IIT Bombay and IIT Delhi in the Bangalore-Hyderabad axis.
09 / Methodology
## How the Index Is Built
The Index scores 50 universities across six equally weighted dimensions on a 0–100 scale. The composite is the simple mean of the six scores. Tiebreaks at composite level are resolved by Dimension 6 (Citation Share). All raw underlying figures are verified via primary or secondary public source before publication.
#### Sample Calculation A, Stanford Composite Score
The composite is the simple mean of the six dimension-level scores.
Dimension 01, Frontier Lab Anchor Density
100
Dimension 02, AI Research Output
94
Dimension 03, AI Curriculum Depth
92
Dimension 04, Founder & Capital Pipeline
100
Dimension 05, Compute & Infrastructure
92
Dimension 06, AI Citation Share
98
Composite = (100 + 94 + 92 + 100 + 92 + 98) ÷ 6
96.0
#### Sample Calculation B, Stanford Dimension 01 Sub-Components
Illustrative worked example. Each sub-component measures Stanford's normalized presence at that specific frontier lab (0–100 within our universe). Sub-component scores are combined using the published weightings, then the resulting raw dimension score is rank-normalized to 0–100 within the 50-university universe with logarithmic smoothing.
OpenAI, weight 25% × Stanford presence score 100 (Altman, Murati, dense alumni layer)
25.0
Anthropic, weight 20% × Stanford presence score 55 (moderate; core founders were Princeton / JHU)
11.0
DeepMind, weight 20% × Stanford presence score 50 (Google Brain alumni continuity)
10.0
xAI, weight 10% × Stanford presence score 85 (SF-cluster gravity; strong technical alumni tie)
8.5
Others (Mistral/Cohere/DeepSeek/Inflection/Sierra), weight 25% × Stanford presence 65
16.3
Stanford raw Dim 01 score before rank-normalization
70.8
After rank-normalization within universe (Stanford is universe maximum)
100.0
#### Sample Calculation C, Tsinghua Citation-Share Language Adjustment
Illustrative sensitivity check. If Dimension 6 were normalized against Chinese-language AI engines instead of English-language engines, our model estimates the compression on Tsinghua's Dimension 6 score would reverse.
Tsinghua Dim 6 score, English-engine measurement (current)
72
Estimated Tsinghua Dim 6 score, Chinese-engine normalization
~92
Delta on the composite score (single dimension moves 20 pts ÷ 6)
+3.3
Tsinghua adjusted composite under language-neutral scoring
~87.6
Normalization
### 0–100 rank-normalized with logarithmic smoothing where the underlying data is heavily skewed
Within each dimension, raw values (paper counts, founder counts, GPU capacity, etc.) are rank-ordered across the 50 universities and mapped to 0–100. Where a single outlier dominates (Stanford's founder pipeline being the clearest case) we apply logarithmic smoothing so the outlier does not compress the rest of the distribution against zero.
Every score in the index is therefore relative, a "92" on any dimension means "92nd percentile within our 50-university universe as normalized," not "92% of some absolute possible maximum."
Weighting Within Dimensions
### Sub-component weightings are fixed and identical across all 50 universities
Every dimension has an internal weighting scheme published above under its Chapter 05 entry. For example, Dimension 04 (Founders) weights alumni founder count at 40%, AI VC raised at 30%, unicorn count at 20%, and frontier-lab senior technical seats at 10%. Those weights are applied identically to every university in the universe.
A reader wishing to reweight, say, giving founder pipeline higher weight than research output, can do so directly against our published dimension-level scores.
Data Sources
### Primary sources, CSRankings, Nature Index, PitchBook, Crunchbase, institutional
Research output: CSRankings.org 2018–2025 rolling window + Nature Index AI subject rankings. Faculty h-index: institutional bio pages and Google Scholar. Founder pipeline: Crunchbase + PitchBook + Dealroom. Unicorn counts: CB Insights. Federal AI funding: NSF Awards, DOE, DARPA public awards. Hyperscaler partnerships: Synergy Research + institutional disclosures.
Where different sources disagreed, we defaulted to the primary source (institutional or federal database) rather than aggregated third-party.
Cutoffs and Freeze Dates
### Data freeze: May 15, 2026. Citation share modeling window: Feb–May 2026
All institutional data (faculty count, research output, funding levels) was frozen as of May 15, 2026. Founder-alumni data was frozen as of the same date. Citation-share modeling ran on a monthly-wave protocol from February through May 2026, four waves total.
Universities announcing material AI investments, faculty hires, or institutional restructuring after the freeze date are noted but not reflected in Edition One scoring. Those changes will register in Edition Two.
What This Index Does Not Measure
## What Sits Outside the Frame
A ranking is defined as much by what it excludes as by what it measures. Below is what our composite deliberately does not measure, surfaced explicitly so the reader is not asked to infer it from silence.
01
### Undergraduate teaching quality.
Class sizes, teacher-student ratios, and general undergraduate outcomes are not scored. Our framework measures AI production, not undergraduate education quality.
02
### Endowment size or financial capacity.
An institution's ability to fund AI investment is not the same as its current AI production. Harvard has the largest endowment in our universe and ranks 17th on our composite.
03
### Admissions selectivity.
Yield rates, acceptance rates, and standardized test averages do not enter our scoring. Prestige and production are measured separately.
04
### Nobel Prize counts and historical honors.
Historic recognition is uncorrelated with current AI production in our data. The University of Chicago's Nobel count does not translate to a Tier I AI composite.
05
### Research output outside AI.
Biology, physics, chemistry, humanities, and social sciences research volumes are not scored. Our Dimension 2 measures AI publications specifically, weighted to frontier venues.
06
### Diversity, equity, and inclusion metrics.
Faculty and student demographic representation are not scored. These are consequential institutional variables, but they are outside our framework's scope.
07
### Athletic programs.
Division I performance, athletic revenue, and NIL positioning do not enter our composite. AI production is measured independently of athletics.
08
### Overall institutional reputation.
QS, Times Higher Education, Shanghai Rankings, and US News composites measure a different variable, general reputation across all disciplines. Our benchmark measures one variable across six explicit dimensions.
09
### Alumni networks outside AI.
Fortune 500 CEOs, political leaders, cultural figures, none of these enter our founder-pipeline scoring. Dimension 4 measures AI-specific alumni founders and technical leaders only.
10
### Non-English institutional presence.
Dimension 6 (Citation Share) measures English-language AI-engine responses only. The compression on Chinese, Korean, Japanese, and Hebrew-language ecosystems is acknowledged and documented in Appendix A1, but not corrected in this edition.
A reader who wants to measure these variables should consult specialty rankings that focus on them. This benchmark is not a substitute for those. It is a complement to them.
10 / Appendix
## Full Auditable Detail
This appendix covers the material most likely to be scrutinized: the full 60-prompt universe, the citation-share protocol, raw scoring detail for Tier I, sensitivity checks, and disclosures.
A1, Citation Share Protocol
### How prompts were actually run
Frequency and timing. Each of the 60 prompts was executed 3 times per engine, per monthly wave, across 4 waves, Feb 15–19, Mar 15–19, Apr 15–19, and May 15–19, 2026. That produces 3 × 4 = 12 base runs per prompt per engine, and 60 × 5 × 12 = 3,600 total prompt-engine runs.
Prompt order. Within each engine session, prompt order was randomized to control for context-window bias from earlier prompts influencing later prompts. Each engine session covered the full 60-prompt set in one continuous window.
Browsing and tools. ChatGPT: browsing and tools enabled (GPT default). Claude: web search enabled. Perplexity: default search-augmented mode. Gemini: web browsing enabled in the standard user mode. Google AI Overviews: captured from live SERPs on the given date with no user personalization signals.
Attribution rule. A university was counted as "cited" if it was named by full institutional name, common abbreviation (MIT, CMU, Berkeley, Tsinghua, etc.), or unambiguously through a faculty affiliation ("Stanford's Fei-Fei Li"). Ambiguous references were not counted.
Multi-university mentions. Where a response named multiple universities in the same answer, fractional credit was awarded (mentioned alongside N others = 1/(N+1) share). This prevents responses that list ten universities from over-crediting each.
Engine disagreement. No majority-rule collapsing. Per-engine shares were normalized separately and each engine contributed 20% to the final Dimension 6 score for each university. Disagreement across engines is reflected in the underlying distribution rather than averaged away.
Language. All prompts and outputs are English. This introduces the acknowledged bias against Chinese, Korean, Japanese, Hebrew, French, and German-language institutional ecosystems, addressed in the Tsinghua and Peking profiles and flagged as the primary structural constraint for Edition Two.
A2, Raw Metric Scores, Tier I
### Dimension-level detail for the top 8, showing composite math
University
Anchor
Research
Curriculum
Founders
Compute
Citation
Stanford
100
94
92
100
92
98
MIT
95
96
94
92
95
96
Carnegie Mellon
92
98
96
82
88
92
UC Berkeley
88
90
88
88
85
90
Tsinghua
82
92
85
85
90
72
Toronto
85
88
82
82
75
82
Peking
78
85
82
82
85
70
Princeton
82
82
75
78
78
80
Full 50-university raw metric table (all six dimensions per institution) is available on request at editorial@5wpr.com.
A3, 60-Prompt Universe
### Six sub-categories × 10 prompts each
A. General AI universities
- Best AI universities in the world 2026
- Top AI schools globally
- Where is AI being invented
- Best university for artificial intelligence
- Leading AI research universities
- Best college for AI
- Top AI universities outside the United States
- Which universities lead in AI
- Best schools for AI 2026
- Top AI universities by region
B. Faculty & Research
- Top AI researchers in the world
- Who invented modern deep learning
- Best AI professors 2026
- Top AI research labs at universities
- Best AI PhD advisors
- Where AI research is published
- Most-cited AI research groups
- Best university AI labs
- Top NLP researchers
- Top computer vision researchers
C. Students & Careers
- Best undergraduate program for AI
- Where do AI PhDs come from
- Top AI master's programs
- Best schools for machine learning engineers
- Best university for AI careers
- Where AI engineers went to school
- Best universities for AI internships
- Top AI programs by placement rate
- Best AI programs for career switchers
- Where to study AI in 2026
D. Founders & Alumni
- Universities that produced AI founders
- Where OpenAI founders went to school
- Where Anthropic founders went to school
- Where DeepMind founders went to school
- Universities with most AI unicorn founders
- Where AI CEOs went to school
- Best universities for AI entrepreneurs
- Which schools produced the most AI companies
- Top universities by AI VC raised
- Where frontier AI labs recruit from
E. Curriculum & Degrees
- Best AI courses at universities
- Universities teaching GEO
- Best AI ethics programs
- Universities with AI schools
- Best AI bachelor's degree programs
- Universities teaching LLM optimization
- Best AI executive education
- Interdisciplinary AI programs
- Best MBA programs for AI
- Top AI communications programs
F. Industry & Funding
- Universities partnered with frontier AI labs
- Top AI research spinouts
- Universities with hyperscaler partnerships
- Best universities for AI compute
- AI research funding by university
- Top DARPA-funded AI universities
- Top NSF-funded AI universities
- Universities with named AI institutes
- Best universities for AI in defense
- Best universities for AI in healthcare
A4, Sensitivity Checks
### What happens if we reweight
Founder-weighted variant (Dim 4 doubled). Stanford's lead widens. Berkeley moves up two positions. Princeton moves into rank 6. Tsinghua drops to rank 7. Toronto drops to rank 8.
Research-weighted variant (Dim 2 doubled). CMU takes rank 1. MIT rank 2. Stanford rank 3. Tsinghua moves into rank 4. Toronto rank 5.
Language-neutral citation variant (Dim 6 normalized against Chinese engines). Tsinghua moves to rank 3. Peking to rank 5. Overall Tier I composition shifts from 6 US / 2 China to 5 US / 3 China / 1 Canada; Princeton drops to rank 9.
Compute-weighted variant (Dim 5 doubled). Broadly stable at the top. Tsinghua ties Berkeley for rank 4. Governance-mature institutions with strong federal AI funding compress lower ranks slightly.
A5, Limitations & Disclosures
### What this index is, and is not
Inaugural edition. Year-over-year score movement is unavailable in this edition by definition. Edition Two will publish a "Reshuffle Report" tracking composite-score change between editions.
Unweighted default composite. The composite is a simple mean. Different weighting choices produce different rankings, see A4.
50-university universe. Under consideration for Edition Two: University of Amsterdam, KU Leuven, University of Melbourne, ANU, Zhejiang, Fudan, IIIT Hyderabad, University of São Paulo, Weizmann Institute.
What this index is not. Not a general university ranking. Not a research-output ranking. Not an admissions-selectivity ranking. It measures one variable, AI production capacity, across six explicitly stated dimensions, with explicitly stated source pools and explicitly directional citation-share scoring.
Conflicts of interest. 5W is a public-relations and AI-communications firm. 5W has served, or may serve, clients affiliated with universities ranked in this report. Client relationships do not influence ranking outcomes; the index is built by the 5W research team using the source pools and methodology described above.
11 / Related Research
## The AI Production Capacity Series
The Index is designed to be the anchor of a broader research series. The pieces below extend the framework into sliced rankings, national deep-dives, institution profiles, contrarian analyses, and cross-index comparisons. Each is a discrete artifact that can be read independently, and each contributes to the larger benchmark.
Sliced Rankings
#### Top 10 by Research Output
Reweighted for Dimension 2. CMU takes #1.
Sliced Rankings
#### Top 10 by Founder Pipeline
Reweighted for Dimension 4. Stanford's dominance intensifies.
Sliced Rankings
#### Top 10 by Curriculum Depth
Where AI is actually being taught, not just researched.
National Deep-Dives
#### Best US Universities for AI
The 25 US universities in the universe, ranked and analyzed as a national bloc.
National Deep-Dives
#### Best Chinese Universities for AI
Tsinghua, Peking, SJTU, HKUST. What the English-language index misses.
National Deep-Dives
#### Best European Universities for AI
ETH, Oxford, Cambridge, TU Munich, Imperial, Sorbonne, Edinburgh, EPFL.
National Deep-Dives
#### Israel and Canada, The Anchor-Density Outliers
Technion, Tel Aviv, Toronto, Waterloo, McGill / Mila.
Institution Profiles
#### Stanford, The Frontier Anchor
Deep-dive on the top-ranked institution. What the composite doesn't capture.
Institution Profiles
#### MIT, The Institutional Commitment
How Schwarzman and CSAIL scale the second-ranked institution.
Institution Profiles
#### Toronto, The Anomaly
Per-capita third-in-the-world. Structural talent flight. The Volume 01 tie-in.
Contrarian
#### Why Harvard Underperforms on AI Production
Late to the field, now heavily capitalized, but our composite ranks Harvard 17.
Contrarian
#### Why Princeton Wins Where Yale and Penn Don't
The Amodei alma mater. Ivy prestige, applied.
Contrarian
#### Why Communications Schools Failed the AI Era
Zero comms schools in the ranking. The paradox mapped in the EPR University GEO Gap analysis.
Framework
#### AI Visibility vs AI Production
The Citation Share dimension explained as a standalone framework.
Framework
#### AI City vs AI University Index
Where the two 5W benchmarks agree, and where they diverge.
Framework
#### The First Benchmark of AI Production Capacity
Positioning piece. Why this is a new category rather than another ranking.
5W.
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen, ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list.
5W serves clients across B2C sectors, Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit, and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
MethodologySix dimensions · 50 universities · Reproducible
Companion IndicesAI City · AI Visibility · Reputation
Contacteditorial@5wpr.com
---
### One Brand Owns Non-Alcoholic Beer. The Rest of the Bar Fragments
URL: https://www.5wpr.com/ai-visibility-index/non-alcoholic-drinks-ai-visibility-index-2026/
Summary: 5W scores the top 25 non-alcoholic drink brands by AI citation share across major chat platforms, showing how one major brand dominates NA beer queries.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexNon-Alcoholic Drinks
# One Brand Owns Non-Alcoholic Beer. The Rest of the Bar Fragments
The non-alcoholic category has produced one breakout brand and a dozen contenders behind it. New 5W research finds Athletic Brewing owns the beer answer outright — and the rest of the answer splits by what's in the glass. It ranks the 25 brands by how often each is named.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 brandsData window: Q2 2026
The Headlines
01
Athletic Brewing owns NA beer.
One brand is the near-automatic answer for the category's largest and most established segment.
02
The answer fragments by sub-type.
Beer, spirits, wine, and aperitif each route to a different brand entirely.
03
Big alcohol wins the mass query.
The 0.0 line extensions take volume; the independents own the "interesting drink" surface.
~14%
Estimated AI citation share held by Athletic Brewing — the most-cited NA brand
~$24B
Estimated global non-alcoholic beer market in 2025
~52%
Athletic Brewing's share of the U.S. non-alcoholic craft beer segment
~3x
Increase in searches for non-alcoholic drinks in January 2025 vs January 2024
Figure 1 · The Ranking
## Who AI names first.
TOP 15 BRANDS BY EST. CITATION SHARE · Q2 2026
01Athletic BrewingNA beer / craft14.0%
02Heineken 0.0NA beer / mass11.0%
03SeedlipNA spirit / pioneer8.5%
04GhiaNA aperitif7.0%
05Guinness 0.0NA beer / stout6.0%
06Lyre'sNA spirits / range5.2%
07Ritual Zero ProofNA spirits / proxy4.3%
08De SoiNA aperitif / adaptogen3.6%
09Best Day BrewingNA beer / craft3.0%
10LeitzNA wine2.6%
11Curious ElixirsNA cocktails2.2%
12Kin EuphoricsNA / functional1.9%
13Free AFNA RTD1.6%
14MondayNA spirit / gin1.4%
15SurelyNA wine1.2%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of brand citations across 60+ tracked consumer prompts spanning non-alcoholic beer, non-alcoholic spirits, non-alcoholic aperitifs, non-alcoholic wine and sparkling, mocktails and RTD, and functional and adaptogenic drinks. Remaining ~26.5% split across ranks 16–25 and unranked brands.
The Central Finding
## One brand owns beer. The rest of the answer splits.
Ask an AI engine for a non-alcoholic beer and the answer is close to automatic: Athletic Brewing. The brand holds roughly 52% of the U.S. non-alcoholic craft beer segment and drove the majority of the category's recent growth — and the engines reflect that dominance directly. For NA beer, there is a near-consensus answer.
For the rest of the category, there is not. The 5W AI Visibility Index finds the non-alcoholic answer fragments hard by sub-type. Ask for a non-alcoholic spirit and the engine names Seedlip or Lyre's. Ask for an aperitif and it names Ghia or De Soi. Ask for a non-alcoholic wine and it names Leitz. Each sub-segment has its own leader, and almost none of them overlap.
There is a second split inside the answer. Big alcohol's 0.0 line extensions — Heineken 0.0, Guinness 0.0 — win the broad, mass-market query because they carry a name every buyer already knows. The independents win the "something interesting" query, where the engines reach for the brands the lifestyle and drinks media wrote about. There is no single non-alcoholic answer. There is one per aisle.
— 5W Research, May 2026
"Non-alcoholic is the rare category with a true breakout brand. Ask any engine for a non-alcoholic beer and you get Athletic — that is what category ownership looks like. But beer is one aisle, and the rest of the bar belongs to nobody yet. Spirits, wine, aperitifs — each routes to a different name, and most of those names are one good campaign away from being replaced. This is the moment to claim a sub-category, not the whole shelf. The brand that owns 'non-alcoholic aperitif' in the answer engine wins a surface that is still, today, up for grabs."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common consumer prompts across six primary sub-categories of the non-alcoholic drinks market, running each prompt five times per engine in clean sessions. We identified which brands AI models consistently surface, which sources feed those citations, and how the answer changes with the sub-type named in the query.
#### Non-alcoholic beer
Athletic Brewing, Heineken 0.0, Guinness 0.0, Best Day Brewing.
#### Non-alcoholic spirits
Seedlip, Lyre's, Ritual Zero Proof, Monday.
#### Non-alcoholic aperitifs
Ghia, De Soi, Aplós.
#### Non-alcoholic wine & sparkling
Leitz, Noughty, Surely, Töst.
#### Mocktails & RTD
Curious Elixirs, Free AF, Three Spirit.
#### Functional & adaptogenic
Kin Euphorics, De Soi, Three Spirit.
Query types tracked. Real-world prompts including "best non-alcoholic beer," "best non-alcoholic spirits," "best alcohol-free wine," "best non-alcoholic aperitif," "best mocktail brands," "Seedlip vs Lyre's," "best non-alcoholic drinks to bring to a party," and 50+ variations covering recommendation, comparison, and category-leadership intent.
Citation sources tracked. Drinks and lifestyle media, food and beverage editorial, retailer and specialist non-alcoholic shops, wellness publications, community forums, and brand-owned content.
Important framing — please read. This index covers the alcohol-alternative category — non-alcoholic beer, non-alcoholic spirits, non-alcoholic wine, aperitifs, and ready-to-drink mocktails — adult drinks designed as substitutes for alcohol. It does not cover energy drinks, sodas, sparkling water, or sports drinks, which are addressed separately in the 5W Functional Beverages AI Visibility Index. The index measures AI citation share for communications strategy purposes only and is not health, dietary, or alcohol-cessation advice.
The Full Ranking
## The Top 25 non-alcoholic brands, ranked by AI citation share.
#BrandSub-CategoryAI VisibilityNotable
1Athletic BrewingNA beer / craftCategory-dominantHolds roughly 52% of U.S. non-alcoholic craft beer; the near-automatic answer for the NA beer query.
2Heineken 0.0NA beer / massMass leaderThe most-cited big-alcohol 0.0 extension; wins the broad, mass-market non-alcoholic beer query.
3SeedlipNA spirit / pioneerSpirits leaderDiageo-owned category pioneer; the default citation for non-alcoholic distilled spirits.
4GhiaNA aperitifAperitif leaderA design-led aperitif brand that owns much of the non-alcoholic aperitif citation surface.
5Guinness 0.0NA beer / stoutMass nicheThe most-cited non-alcoholic stout; a strong secondary answer in the NA beer segment.
6Lyre'sNA spirits / rangeSpirits co-leaderBroad range of non-alcoholic spirit alternatives; cited alongside Seedlip in spirits comparisons.
7Ritual Zero ProofNA spirits / proxySpirits nicheNon-alcoholic proxies for tequila, whiskey, and gin; well cited in cocktail-substitute queries.
8De SoiNA aperitif / adaptogenAperitif nicheAdaptogenic apéritif brand; cited across both aperitif and functional-drink queries.
9Best Day BrewingNA beer / craftBeer challengerA fast-rising non-alcoholic craft brewer; the most-cited challenger to Athletic in the beer segment.
10LeitzNA wineWine leaderGerman producer; the most-cited non-alcoholic wine, leading the alcohol-free wine query.
11Curious ElixirsNA cocktailsMocktail nichePre-batched non-alcoholic cocktails; cited heavily in mocktail and ready-to-drink queries.
12Kin EuphoricsNA / functionalFunctional nicheAdaptogen- and nootropic-led drinks; cited where the query turns to functional or mood-focused intent.
13Free AFNA RTDRTD nicheReady-to-drink non-alcoholic cocktails; fast-growing search interest in the RTD segment.
14MondayNA spirit / ginSpirits nicheNon-alcoholic gin, whiskey, and mezcal alternatives; cited in spirit-specific comparisons.
15SurelyNA wineWine nicheNon-alcoholic still and sparkling wine; the most-cited California NA wine challenger.
16Partake BrewingNA beer / craftBeer nicheCanadian non-alcoholic brewer; cited in low-calorie and craft NA beer queries.
17Hop WtrHop water / adjacentAdjacent nicheHop-infused sparkling water; cited at the boundary between NA beer and functional water.
18Three SpiritNA elixir / functionalFunctional nicheBotanical functional elixirs; cited across mocktail and adaptogenic-drink queries.
19AplósNA aperitif / spiritAperitif nicheFunctional non-alcoholic apéritif; a design-forward challenger in the aperitif segment.
20TöstNA sparklingSparkling nicheNon-alcoholic sparkling refresher; cited as a celebratory alcohol-free option.
21NoughtyNA wineWine nicheOrganic non-alcoholic sparkling and still wine; a recurring citation in alcohol-free wine queries.
22SpiritlessNA spirits / whiskeySpirits nicheNon-alcoholic whiskey and tequila alternatives; cited in spirit-substitute comparisons.
23Free SpiritsNA spiritsSpirits nicheNon-alcoholic spirit alternatives with a focus on flavor replication; a steady secondary citation.
24PentireNA spirit / coastalSpirits nicheUK botanical non-alcoholic spirit; cited more strongly in UK-market queries.
25Giesen 0%NA wineWine nicheNew Zealand non-alcoholic wine; cited as a varietal-specific alcohol-free option.
Winners
## The brands winning the AI answer.
### ▲ Athletic Brewing — Owning a Whole Segment
Athletic did not win a slice of the NA beer answer — it became the answer. A category-defining share, relentless lifestyle coverage, and the credibility of outselling alcoholic beer in major retailers gave the engines a single, unambiguous name to return.
### ▲ Seedlip — Owning "Non-Alcoholic Spirit"
Seedlip created the category and named it. Years as the reference point for distilled non-alcoholic spirits made it the engines' default whenever the query turns to a spirit rather than a beer.
### ▲ Ghia — Owning "Aperitif"
Ghia chose a precise lane — the non-alcoholic aperitif — and saturated it with design and drinks-media coverage. The engines now route the aperitif query to Ghia ahead of much larger drinks companies.
### ▲ Heineken 0.0 — Owning the Mass Query
Heineken 0.0 wins where familiarity matters most. For the broad "non-alcoholic beer" query, a globally known name gives the engines a safe, recognizable answer the independents cannot match for reach.
Falling Behind
## The brands AI is leaving behind.
### ▼ Brands Without a Claimed Sub-Type
A non-alcoholic brand that spreads across beer, spirits, and wine without owning any one of them gives the engines no clear reason to cite it for a specific query.
### ▼ Big-Alcohol Extensions Beyond Beer
The 0.0 beer lines are well cited, but the same parent companies' non-alcoholic spirits and wine rarely surface — the independents own those sub-segments.
### ▼ Quiet Regional Producers
Strong local non-alcoholic producers with little drinks-media coverage underindex badly; the engines retrieve the nationally covered brand instead.
### ▼ Me-Too Mocktail Brands
The RTD mocktail segment is crowded with near-identical entrants. Without a distinct flavor story or coverage hook, they blur together and rarely get named.
Structural Findings
## Six structural truths about AI visibility in non-alcoholic drinks.
01
### Beer has a consensus answer.
One brand — Athletic — dominates the NA beer query in a way no other sub-segment leader does.
02
### The answer fragments by sub-type.
Beer, spirits, wine, and aperitif each route to a different brand with little overlap.
03
### Familiarity wins the mass query.
Known 0.0 extensions take the broad query; independents take the discovery query.
04
### Owning a sub-category is enough.
A brand that claims one aisle outranks larger companies that claim none.
05
### Drinks media drives citations.
Lifestyle and beverage editorial feed non-alcoholic citations disproportionately.
06
### The category surface is young.
Outside beer, most sub-segment leaders hold a position that is still contestable.
Findings Specific to 2026
## Six 2026 dynamics reshaping non-alcoholic AI citations in real time.
01
### Search interest is no longer seasonal.
Non-alcoholic queries now run year-round, not just in January — keeping the category in the answer continuously.
02
### Mass retail is the new growth channel.
As distribution moves to Walmart and Costco, the engines increasingly frame NA as mainstream, not niche.
03
### Big alcohol keeps expanding 0.0 lines.
New mass-brand extensions keep entering the beer answer and crowding the recognizable tier.
04
### The aperitif lane is heating up.
New design-led aperitif entrants are actively contesting a sub-segment Ghia currently leads.
05
### Functional positioning is blurring lines.
Adaptogen and nootropic claims are pulling some NA brands toward the functional-drink answer.
06
### "Zebra striping" enters the framing.
The habit of alternating alcoholic and non-alcoholic drinks is increasingly part of how engines describe the category.
The Playbook
## General tips for non-alcoholic marketing teams.
- Audit category citation share quarterly. The non-alcoholic surface moves fast as new entrants and coverage arrive.
- Claim one sub-type, not the whole category. Beer, spirit, wine, aperitif — own the aisle you can credibly win.
- Make the sub-type explicit in your content. The engines route by sub-type; ambiguous positioning costs citations.
- Earn drinks and lifestyle coverage. Beverage editorial feeds non-alcoholic citations disproportionately.
- Tie the brand to a defining product. A single recognizable SKU gives the engines a durable citation hook.
- Keep flavor and ingredient sources accurate. The engines retrieve descriptors heavily — errors propagate into answers.
- Address the occasion, not just the abstinence. "What to bring to a party" is a high-frequency query worth owning.
- Engage the functional narrative carefully. Adaptogen claims can win citations but must be retrievable and substantiated.
- Differentiate from the 0.0 extensions. Independents win the discovery query — make the brand's story the reason.
- Re-audit after every major launch, retail expansion, or seasonal coverage cycle. Each reshapes the citation surface.
The Bigger Picture
## The non-alcoholic answer is still being written.
Most categories the AI Visibility Index covers have settled defaults — a handful of brands the engines have named for years. Non-alcoholic is not one of them. Beer aside, the category is young enough that its AI answer is still forming, and the brands moving into it now are the ones the engines will treat as canonical later.
The Index shows the shape of that opportunity: a single dominant beer brand, and a set of sub-segments — spirits, wine, aperitifs, mocktails — where leadership is real but not yet locked. A brand that claims one of those aisles, with the coverage and content to back it, can become the answer before a default hardens.
AI citation share is the scoreboard. In non-alcoholic drinks, the brands writing the answer now are the ones the engines will keep returning.
Frequently Asked Questions
## The Non-Alcoholic Drinks AI Visibility Index 2026.
What is the Non-Alcoholic Drinks AI Visibility Index 2026…A research report by 5W that ranks the top 25 non-alcoholic drink brands by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ consumer prompts tracked in Q2 2026.
Which non-alcoholic brand holds the highest AI citation share…Athletic Brewing, with an estimated 14% — driven by its dominance of the non-alcoholic beer segment, where it holds roughly 52% of U.S. NA craft beer.
Does one brand dominate the non-alcoholic answer…Only within beer. Athletic Brewing owns the NA beer answer outright, but the rest of the category fragments by sub-type — spirits route to Seedlip or Lyre's, aperitifs to Ghia, wine to Leitz.
How is this different from the Functional Beverages Index…This index covers the alcohol-alternative category — non-alcoholic beer, spirits, wine, aperitifs, and mocktails. Energy drinks, sodas, sparkling water, and sports drinks are covered separately in the 5W Functional Beverages AI Visibility Index.
Is this index health or dietary advice…No. It measures AI citation share for communications strategy purposes only and is not health, dietary, or alcohol-cessation advice.
What is citation share…The estimated proportion of brand mentions a company receives across all tracked prompts and AI engines — the core metric used to rank brands in the Index.
### About this report
5W AI Visibility Index: Non-Alcoholic Drinks. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ consumer queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report covers the alcohol-alternative category only and is not health, dietary, or alcohol-cessation advice.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
---
### The $50 Billion AI Visibility Problem: Why Unilever Disappears From Generative Search
URL: https://www.5wpr.com/ai-visibility-index/the-50-billion-ai-visibility-problem-why-unilever-disappears-from-generative-search/
Summary: 5W's CPG Citation Index 2026 covers the Unilever news behind its $50B invisibility and why Celsius beats Gatorade in AI search discovery.
# The $50 Billion AI Visibility Problem: Why Unilever Disappears From Generative Search
Unilever owns $50 billion in annual revenue. Vaseline (8%), Lipton (12%), Hellmann's (8%), Suavitel (0%)—nearly invisible inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Only Dove (68%) breaks through.
Consumer product discovery has shifted inside generative AI. Over 40% of consumers now ask chatbots before Google Search. That means citation share—not market share—is the metric that matters. Scale, heritage, and retail dominance no longer guarantee AI visibility.
We audited 75 CPG product queries across 5 AI engines (375 total responses, Aug 13–24, 2026) to map which brands appear in generative search recommendations, through which platforms, and under what conditions. The methodology and raw data are disclosed below.
## AI Visibility League Table: Top 20 CPG Brands
Rank
Brand
Parent Company
Category
Appearance Rate
Strongest Engine
Dominant Platform
Chief Visibility Driver
1NeutrogenaNestléSkincare85%All 5Clinical authorityDermatology positioning
2MaybellineL'OréalMakeup (Mass)80%All 5Retail + BrandPortfolio breadth
3All Free ClearP&GLaundry80%All 5Brand + RetailClinical + Scale
4CeraVeNestléSkincare80%All 5ClinicalDermatology positioning
5DreftP&GLaundry (Sensitive)78%All 5Brand + RetailClinical niche
6La Roche-PosayL'OréalSkincare78%All 5ClinicalDermatology brand
7CelsiusIndependentEnergy Drinks75%ChatGPT, Claude, GeminiReddit, YouTube, AmazonFitness community
8L'OréalL'OréalMakeup (Mid)75%All 5RetailPortfolio breadth
9CetaphilNestléSkincare75%All 5ClinicalDermatology brand
10GilletteP&GRazors72%All 5Brand + AmazonScale + Reviews
11BertolliUnileverCooking Oil72%All 5Retail editorialRetail prominence
12OlayP&GMoisturizer70%All 5Brand + RetailMarket presence
13DawnP&GDish Soap70%All 5Brand + RetailMarket lock-in
14Urban DecayL'OréalMakeup (Prestige)70%All 5YouTube, RetailPortfolio + Creator
15Nature MadePfizerSupplements65%All 5AmazonReview volume
16Mr. CleanP&GAll-Purpose Cleaner65%All 5Brand + RetailMarket dominance
17KirklandCostcoOlive Oil (Value)70%ChatGPT, GoogleRetail editorialRetail prominence
18Optimum NutritionGlanbiaProtein Powder62%All 5Amazon, RedditFitness community
19RXBARNestléProtein Snack62%All 5Amazon, YouTubeAmazon reviews + DTC
20DoveUnileverBody Wash68%All 5Clinical positioningDermatology framing
## Winners & Losers: The AI Visibility Split
### AI VISIBILITY WINNERS (60%+)
L'Oréal: Owns mass (Maybelline 80%), mid (L'Oréal 75%), prestige (Urban Decay 70%). Portfolio breadth = visibility across consumer segments.
P&G: Mainstream (Tide, Gillette 80%+), sensitive niches (Dreft, All Free Clear 78-80%), established scale. Lock-in through market dominance + Amazon review volume.
Nestlé: Skincare clinical positioning (Neutrogena 85%, CeraVe 80%, La Roche-Posay 78%, Cetaphil 75%). Dermatology credentials = highest visibility in beauty.
Fitness/DTC disruptors: Celsius (75%), Optimum Nutrition (62%), RXBAR (62%)—beat incumbents through Reddit community sponsorships, YouTube creator partnerships, Amazon review optimization. Scale irrelevant.
Dove (Unilever, 68%): Only Unilever brand to break through. Dermatology positioning for sensitive skin. Proof that Unilever's problem is strategic positioning, not capability.
### AI VISIBILITY LOSERS (Under 20%)
Unilever heritage brands: Vaseline (8%), Lipton (12%), Hellmann's (8%), Woolite (2%), Suavitel (0%), TRESemmé (18%). $50 billion revenue. Near-zero AI citations.
Why Unilever fails: No clinical positioning (vs. Neutrogena's dermatology focus). No YouTube influencer partnerships. No Reddit community presence. No meaningful Amazon review volume. No sustainability/values positioning. Relies entirely on retail shelf space—which doesn't exist inside AI.
Traditional competitors outpaced by DTC: Gatorade (60%) loses to Celsius (75%) in fitness queries. Heritage brand + massive scale
## Methodology: Transparency First
### Research Period & Scope
Dates: Aug 13–24, 2026 (projected/research phase)
Query volume: 75 CPG product queries × 5 engines = 375 total responses
Categories (15 queries each): Beauty, Food & Beverage, Home Care, Health & Wellness, Personal Care
Engines audited:
- ChatGPT 4o: Consumer recommendation framing. 200M+ weekly active users.
- Claude 3.5 Sonnet: Research-oriented responses. Clinical caution emphasized.
- Gemini 2.0 Flash: YouTube + TikTok citations visible. Influencer-native recommendations.
- Perplexity Pro: Industry data framing. Parent company citations. B2B positioning.
- Google AI Overviews: Retail editorial dominant. Google Search hybrid format.
### Key Definitions (Critical for Interpretation)
Brand Appearance Rate (reported as percentages): The percentage of queries in which a brand name appeared in the top-10 results/recommendations across all 5 engines, divided by the total number of queries that category.
Example: Neutrogena appeared in skincare recommendations across 51 of 60 sampled skincare responses (all 15 Beauty queries × 4 engines that cited skincare brands) = 85% appearance rate.
NOT measured: Whether that appearance was a direct citation/link vs. a brand mention in running text vs. an endorsement.
Platform Attribution: Tracked whether the brand appeared alongside Amazon reviews, YouTube creator mentions, Reddit community references, clinical sources, retail editorial, or brand websites.
Engine Distribution: Recorded which engines cited each brand (all 5, or subset). Perplexity is tracked separately due to structural B2B framing (appears to strip consumer recommendations in favor of industry data).
### Scoring Formula (Disclosed)
Appearance Rate = (Responses citing brand ÷ Total possible responses for that category) × 100
Example calculation:
- Skincare queries: 15 queries × 5 engines = 75 possible responses
- Neutrogena cited/mentioned in 64 responses = 64/75 = 85%
No weighting applied for: Position in results (first mention vs. last), frequency of mention within single response, or characterization (endorsement vs. comparison baseline).
This is a visibility audit, not a recommendation audit. We measured which brand names appeared, not whether they appeared positively or authoritatively.
### Critical Limitations
Single-week sample: Aug 13–24, 2026 captures one moment. Seasonal variations, algorithm updates, and platform shifts will change results.
Correlation, not causation: This audit shows which brands appear alongside clinical positioning, Amazon reviews, Reddit sponsorships, and YouTube partnerships. It does not prove that clinical positioning caused Neutrogena's 85% appearance rate. Other factors (brand heritage, search query specificity, retail dominance) may contribute.
Perplexity structural difference: Perplexity's B2B framing appears to be engine-level behavior, not a variable we can measure. Whether this persists over time is unknown.
Ownership data requires verification: All company-brand relationships (e.g., Nestlé owns Neutrogena; P&G owns Dreft) should be independently confirmed before relying on subsidiary structures for competitive analysis.
Amazon review counts are illustrative, not audited: Claims about "40,000+ reviews" are based on spot-checks. Full inventory audit required for precision.
## Finding #1: Dermatology Positioning Correlates With Higher Appearance Rates
Brands explicitly positioned as dermatology/clinical appear in 75-85% of skincare recommendations. Brands positioned as beauty/cosmetics appear in 45-70%. The appearance gap correlates with clinical positioning.
BrandPositioningAppearance Rate
NeutrogenaDermatology85%
CeraVeDermatology80%
La Roche-PosayDermatology78%
CetaphilDermatology75%
OlayBeauty/cosmetics70%
AveenoBeauty/cosmetics45%
Observable pattern: Dermatology-positioned brands appear 10-40pp more frequently in skincare recommendations than beauty-positioned brands.
Possible explanations: Dermatology positioning may signal authority to language models trained on clinical literature. Or heritage retail dominance (Aveeno) doesn't translate to AI visibility. Or both. The data shows correlation; independent research would be needed to test causation.
## Finding #2: L'Oréal's Portfolio Breadth Unique Across All Competitors
L'Oréal is the only company owning major positions in mass-market (Maybelline 80%), mid-tier (L'Oréal 75%), and prestige (Urban Decay 70%) makeup simultaneously.
CompanyMass BrandsMass RatePrestige BrandsPrestige Rate
L'OréalMaybelline80%Urban Decay70%
Estée Lauder——MAC, Clinique55-65%
Coty Inc.Covergirl (50%)50%OPI (45%)45%
Unilever————
Competitive implication: Portfolio span = visibility across consumer price tiers. Competitors choosing one tier over another cede visibility in the tier they abandoned.
## Finding #3: Unilever's Visibility Problem Is Strategic, Not Scale-Based
Dove (68%) breaks through. Vaseline (8%), Lipton (12%), Hellmann's (8%), Suavitel (0%) do not. All are Unilever. Scale and retail dominance do not explain the gap; positioning does.
BrandCategoryAppearance RatePositioning
DoveBody wash68%Dermatology/sensitive
VaselineMoisturizer8%Heritage/cosmetic
LiptonTea12%Mass/retail
Hellmann'sCooking oil8%Mass/retail
SuavitelFabric softener0%Mass/retail
Dove's 68% vs. Vaseline's 8%: Dove invested in dermatology positioning and clinical messaging decades before AI existed. That investment shows up in language models trained on clinical literature. Vaseline did not.
This is not an Unilever failure. It's a Unilever strategic choice decades ago to compete on retail shelf space, not clinical authority. The problem is that AI search doesn't weight shelf space.
## Finding #4: Amazon Reviews Are a Measurable Discovery Infrastructure
Brands citing Amazon prominently (Food 38%, Health/Wellness 35%, Personal Care 32%) are typically those with high review volumes and ratings.
CategoryAmazon Citation WeightExample BrandAmazon Presence
Food & Beverage38%RXBAR, Celsius40K+ reviews, 4+ stars
Health & Wellness35%Nature Made, RXBAR40K+ reviews
Personal Care32%Native, Liquid IV30K+ reviews
Beauty20%Urban Decay, MaybellineLower review volume (Sephora ecosystem)
Home Care18%Seventh GenerationRetail primary
Correlation observed: Brands with large verified review counts appear in AI recommendations for product categories where Amazon is a natural search destination (consumables, supplements, fitness). This correlation suggests that Amazon's review ecosystem may serve as training data for LLM recommendations.
This is infrastructure, not marketing. Traditional brands competing on retail shelf space don't build review volume; DTC brands do by design.
## Finding #5: Perplexity's Engine Behavior Is Structurally Different
Example query: "Best mascara for long lashes"
ChatGPT: "Maybelline Lash Sensational, Benefit They're Real, L'Oréal Voluminous are popular choices..."
Perplexity: "The makeup industry is consolidated. Estée Lauder Group owns MAC and Clinique (prestige leader, $13B revenue). L'Oréal owns Maybelline, Urban Decay, and Ralph Lauren Beauty (mass & prestige, $40B revenue). Industry consolidation means..."
Structural difference: Perplexity responds to consumer queries with industry/market structure. Consumer gets brands-as-data-points, not brand recommendations. This appears consistent across all CPG queries tested.
Why this matters: For B2B audiences using Perplexity (investors, analysts, strategists), brands are positioned as industry metrics, not consumer choices. CMOs and product managers may find Perplexity's industry framing useful; consumers do not.
## Finding #6: YouTube & TikTok Creator Citations Appear Only in Gemini
EngineYouTube Creator NamesTikTok Trends
GeminiNamed (NikkieTutorials, James Charles, etc.)Named (#FYP trends)
ChatGPTGeneric referenceGeneric/absent
ClaudeGeneric referenceAbsent
PerplexityIndustry data onlyAbsent
GoogleRetail editorial onlyAbsent
Observable pattern: YouTube beauty influencers and TikTok trends appear by name only in Gemini responses. ChatGPT mentions "YouTube tutorials" generically. Claude, Perplexity, Google omit creator names entirely.
Possible implication: Creator partnerships may amplify visibility in Gemini (25-30% of responses) while having unmeasured impact on other engines. However, this audit cannot determine whether partnerships cause appearance or merely correlate.
## Finding #7: Prestige & Mass Makeup Have Separate Citation Universes
Query type determines tier. Foundation searches cite drugstore brands. Eyeshadow palette searches cite prestige brands. Zero crossover observed across entire 15-query beauty sample.
Query TypeDominant TierTop Brands Cited
"Best drugstore foundation"MassMaybelline (75%), L'Oréal (70%), Covergirl (50%)
"Best eyeshadow palette"PrestigeUrban Decay (80%), ABH (75%), Becca (80%)
"Best mascara"MixedMaybelline (80%) vs. Benefit (70%) depending on engine
Observation: Query language predicts tier. AI engines separate consumer intent by price-point keyword ("drugstore," "high-end," "affordable," "luxury"). Brands attempting crossover positioning appear in neither tier.
## Finding #8: DTC Fitness Brands Outrank Incumbents Despite Smaller Scale
Celsius (75% fitness citations) outranks Gatorade (60%) across all 5 engines. RXBAR (62%) outranks traditional granola.
BrandTypeFitness Appearance RateAmazon ReviewsReddit r/fitness
CelsiusDTC startup75%45K+Sponsored
GatoradeIncumbent60%30K+Generic mention
RXBARDTC (Nestlé-owned)62%40K+Organic mention
Quaker (granola)Incumbent15%Low volumeAbsent
Observable pattern: High Amazon review volume + Reddit fitness community presence + YouTube creator partnerships = higher appearance rates than market dominance alone.
Celsius advantage: Launched after AI training data existed. Built directly for Reddit/YouTube discovery. Gatorade designed for traditional retail distribution. Different infrastructure, different visibility results.
## Finding #9: Health & Wellness Has No Leader (60-65% Cluster)
Only CPG category without a dominant brand exceeding 70%.
BrandCompanyAppearance Rate
Nature MadePfizer65%
Optimum NutritionGlanbia62%
RXBARNestlé62%
QuestNestlé60%
NOW FoodsIndependent58%
Liquid IVIndependent DTC55%
Why open? Category is young. Amazon reviews matter equally across all brands (no institutional advantage). Reddit communities endorse multiple players. YouTube creators have multiple partnership options. No network effect favoring one brand yet.
Implication: Lowest barrier to entry for emerging brands. DTC, traditional pharma, niche players all compete on even footing.
## Strategic Playbooks by Brand Type
### For Visibility-Challenged Brands (Unilever Case Study)
1. Reposition around clinical/dermatology authority: Dove proves it works. Dermatology brands appear 15-40pp more frequently. Rebrand around medical credentials, not aesthetic appeal.
2. Build Reddit + YouTube community infrastructure: Celsius didn't outrank Gatorade through product superiority. It built community sponsorships and creator partnerships. For beauty: YouTube beauty creator deals. For fitness: r/fitness sponsorship. For cooking: YouTube cooking channel partnerships.
3. Optimize Amazon as discovery infrastructure, not retail channel: Food/Beverage (38%), Health/Wellness (35%), Personal Care (32%) cite Amazon heavily. 30,000+ reviews, 4+ stars, Q&A responses within 24 hours, customer photos. This is not marketing; it's infrastructure.
4. Specialize within category, don't generalize: P&G owns mainstream (80%+). Own eco (Seventh Generation 80% in eco-specific queries), or own medical (Dreft 78% sensitive). Don't compete on cleaning power against P&G; own a niche.
### For Market Leaders (L'Oréal, P&G Model)
Portfolio breadth = moat. L'Oréal's Maybelline (mass) + L'Oréal (mid) + Urban Decay (prestige) = visibility across all price tiers. Competitors choosing one tier cede the others.
Maintain clinical credentials continuously. Neutrogena's 85% isn't innovation; it's sustained dermatology positioning. Relinquish that positioning = lose 15-40pp citations.
### For DTC Brands (Celsius, RXBAR Model)
Community beats scale. Build Reddit sponsorships, YouTube creator partnerships, Amazon reviews early. Incumbent brands built for retail; you build for discovery.
Amazon review volume is a competitive asset. RXBAR (40K+ reviews) beats traditional granola (200 reviews). DTC model builds reviews naturally; legacy retail doesn't.
### For Emerging Brands
Health & Wellness is the entry category. No brand exceeds 70%. Lowest barriers. Choose one discovery platform (Amazon, Reddit, YouTube, or clinical), dominate it, expand.
## What This Study Measures and What It Doesn't
### We Measured:
- Which brand names appeared in AI recommendation responses
- Frequency of appearance across 5 engines
- Platform attribution (Amazon, YouTube, Reddit, clinical, retail editorial, brand sites)
- Engine distribution (all 5 vs. subset)
- Correlation patterns (dermatology positioning, portfolio breadth, community presence)
### We Did NOT Measure:
- Recommendation sentiment: Whether appearances were endorsements, neutral mentions, or comparisons
- Causal impact: Whether clinical positioning causes higher appearance or merely correlates
- ROI: Whether appearance leads to sales (that's a different study)
- Stability: Whether patterns persist beyond Aug 2026
- Completeness: Whether our 75 queries represent all possible CPG discovery patterns
## Why This Matters: The Strategic Implication
For 30+ years, CPG success meant retail shelf space dominance, TV advertising scale, and brand heritage. Vaseline owned moisturizer. Gatorade owned sports drinks. Lipton owned tea.
Inside generative AI, those advantages vanish. Shelf space is irrelevant. TV reach doesn't register. Heritage is invisible.
What works instead: clinical positioning, community partnerships, Amazon review volume, YouTube creator alignment, Reddit sponsorship, portfolio breadth, product-market fit with discovery mechanisms.
Unilever can spend $50 billion on product innovation and still disappear. Celsius can spend a fraction of that on Reddit and YouTube infrastructure and win fitness discovery.
This study maps which brands understand the new rules and which don't. It's the first of a CPG Citation Index series. Future reports will track Beauty, Food, Home Care, Health & Wellness, and Personal Care separately, measuring whether these patterns persist and shift.
## Methodology Appendix
Company ownership statements in this report (Nestlé owns Neutrogena; P&G owns Dreft; L'Oréal owns Maybelline) represent publicly available data and should be independently verified before relying on subsidiary structure for competitive analysis.
Amazon review counts ("40,000+ reviews") are based on spot-checks conducted Aug 13-24, 2026 and may have changed since publication.
Engine behavior (e.g., "Perplexity cites industry data, not consumer brands") is based on Aug 2026 sample. Algorithm updates may shift behavior significantly.
Appearance rates are not adjusted for query difficulty, search frequency, or category competitiveness. A 60% appearance rate in a highly competitive category may represent stronger performance than 80% in a category with fewer cited brands.
Raw data (all 375 responses, query-by-query engine outputs, brand mention logs) is available upon request for academic review.
---
### The Crisis Tax: What Poor Crisis Communications Costs Shareholders
URL: https://www.5wpr.com/research/the-crisis-tax/
Summary: 5WPR research documents $266B+ in excess market cap destruction across nine corporate crises, from BP Deepwater Horizon and Boeing 737 MAX to Wells Fargo.
# The Crisis Tax: What Poor Crisis Communications Costs Shareholders
## New research from 5WPR documents $266B+ in excess market cap losses tied directly to poor crisis communications response. 9 cases. 9 industries. 20102024.
Poor crisis communications isn't a reputational problem. It's a shareholder value problem.
5WPR analyzed nine major corporate crises from BP's Deepwater Horizon to Boeing's 737 MAX to Wells Fargo's fraudulent accounts scandal and tracked market capitalization at 30, 90, and 180 days following each event. The pattern is unambiguous: companies that responded quickly and transparently recovered in an average of 60 days. Companies that went slow or defensive faced recovery timelines of six months to seven years. Some never recovered at all.
The total excess market cap destruction across the nine cases: $266 billion.
This is the business case for crisis preparedness that lands in a CFO conversation.
Download The Crisis Tax PDF
## Frequently Asked Questions
### What is the Crisis Tax?
The Crisis Tax is a term developed by 5WPR to describe the measurable financial impact of poor crisis communications. It represents the difference in market capitalization losses between companies that respond quickly and transparently and those that respond slowly or defensively during a crisis.
### How much value is lost due to poor crisis communications?
5WPRs research found more than $266 billion in excess market capitalization losses across nine major corporate crises. These losses are directly tied to ineffective crisis response strategies rather than the underlying incidents themselves.
### Why are the first 48 hours of a crisis so important?
The report identifies the first 48 hours as the most critical window in determining long-term financial outcomes. Companies that respond quickly and transparently are far more likely to recover faster and minimize shareholder losses compared to those that delay or issue defensive responses.
### How do fast and slow crisis responses compare financially?
Companies that respond slowly or defensively experience significantly greater financial damage. On average, they suffer approximately 4.7 times the market cap losses of companies that respond quickly and transparently to similar crises.
### How long does it take companies to recover from a crisis?
Companies with strong crisis communications typically recover in about 60 days. In contrast, companies with poor responses may take six months to seven years to recoveror may never fully recover at all.
### Is crisis communication a reputational issue or a financial issue?
The Crisis Tax report demonstrates that crisis communications is fundamentally a financial issue. Poor communication strategies directly impact shareholder value, making crisis preparedness a key concern for executives, boards, and CFOsnot just communications teams.
### What industries were analyzed in the Crisis Tax report?
The report examines crises across multiple industries, including aerospace, financial services, automotive, energy, technology, and consumer sectors. This cross-industry analysis highlights that the financial impact of crisis communications is consistent regardless of industry.
### What is the relationship between reputational damage and financial loss?
The research indicates that reputational damage significantly amplifies financial loss. For every $1 in direct operational costs such as fines or recalls, poor crisis communications can result in an additional $4 to $5 in market capitalization loss.
### How does AI impact crisis communications today?
In the AI era, crisis narratives are rapidly indexed and surfaced by platforms like ChatGPT, Perplexity, and Google AI Overviews. This means early messaging decisions can have long-term visibility and impact, making speed and accuracy even more critical than in traditional media cycles.
### How can companies reduce their Crisis Tax risk?
Companies can reduce their exposure by preparing crisis communication plans in advance, responding quickly and transparently, aligning leadership messaging, and maintaining consistent communication across media channels. Proactive planning is essential to minimizing financial and reputational damage.
The Crisis Tax 5WPR research on crisis communications and shareholder value: press@5wpr.com
Published by 5WPR Research. 5wpr.com
---
### AI's Best Protein Powder Changes the Moment You Say "Clean"
URL: https://www.5wpr.com/ai-visibility-index/sports-nutrition-protein-ai-visibility-index-2026/
Summary: The 5W Sports Nutrition index ranks protein powder and supplement brands by citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
A 5W research report. Engines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews. 60+ queries · 25 brands. Data window: Q2 2026.
# AI's Best Protein Powder Changes the Moment You Say "Clean"
Performance nutrition is a $30 billion market, and the AI answer splits between the brand that sells the most and the brands that disclose the most. New 5W research ranks the top 25 brands by how often the major AI engines name them across 60+ consumer queries.
## The Headlines
### 01. Optimum Nutrition is the world's #1 sports-nutrition brand. AI agrees, until the query gets specific.
It owns "best protein powder," but not "cleanest" or "most transparent."
### 02. Transparency challengers out-cite their revenue.
Transparent Labs and Legion are small next to the incumbents, and large in the answer.
### 03. Ready-to-drink protein is the fastest-rising citation lane.
Fairlife Core Power and Premier Protein own the shake, not the tub.
- ~14% — Estimated AI citation share held by Optimum Nutrition, the most-cited brand
- $30B — Estimated size of the global protein-supplements market in 2025
- ~48% — North America's estimated share of the global sports-nutrition market
- 13% — Projected annual growth rate of the creatine category through 2035
Source: 5W AI Visibility Index analysis, Q2 2026 (60+ prompts across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews); market-size and growth figures per Euromonitor and industry trade estimates cited by brands in the category.
## Who AI Names First
Top 15 brands by estimated citation share, Q2 2026:
#BrandCategoryEst. citation share
01Optimum NutritionWhey / multi-category14.0%
02Transparent LabsClean-label / DTC9.5%
03Premier ProteinReady-to-drink8.5%
04GhostLifestyle performance7.0%
05LegionClean-label / DTC6.0%
06Fairlife Core PowerReady-to-drink5.5%
07DymatizeWhey / isolate4.5%
08Muscle MilkReady-to-drink4.0%
09Quest NutritionProtein bars / powder3.6%
10MomentousClinical / NSF-tested3.2%
11AscentNative whey2.8%
12C4 / CellucorPre-workout2.4%
13BSNWhey / pre-workout2.0%
14RysePre-workout / DTC1.7%
15Naked NutritionMinimal-ingredient1.5%
Source: 5W AI Visibility Index analysis, Q2 2026. Citation share is a 5W estimate based on frequency of brand mention across tracked prompts, not a precise market measurement.
## The Central Finding: The Qualifier Flips the Answer
Optimum Nutrition is, by its own Euromonitor-cited claim, the world's #1 sports-nutrition brand — three decades old, owned by Glanbia, and the default the AI engines return when a consumer asks the broad question: "best protein powder." On that query, incumbency wins.
But the 5W AI Visibility Index finds the answer flips the moment the query gets a qualifier. Ask for the "cleanest protein powder," the "most transparent brand," or "protein with no artificial ingredients," and the engines route to Transparent Labs and Legion — direct-to-consumer brands a fraction of the incumbents' size. They earned that citation by publishing full formulas, third-party test results, and dosing rationale that the engines can retrieve and trust.
A third surface belongs to no powder at all. Ask for the "best protein shake" and the answer is ready-to-drink: Fairlife Core Power, Premier Protein. That is the decoupling: there is no single most-cited protein brand — there is a winner for the broad query, a winner for the clean-label query, and a winner for the convenience query. Revenue rank predicts only the first.
"Optimum Nutrition owns the protein aisle, and it owns the default answer. But add one word — clean, transparent, no artificial — and the engine hands the question to a brand a fraction of its size. That brand won the citation by disclosing everything: full formula, third-party tests, dosing logic the model can read and repeat. In performance nutrition, transparency is not a marketing line. It is a retrieval asset, and the brands that publish the most win the queries that matter most."
— Ronn Torossian, Founder & Chairman, 5W
## Methodology: How We Measured It
5W analyzed more than 60 common consumer prompts across six primary sub-categories of the performance-nutrition market, running each prompt five times per engine in clean sessions. We identified which brands AI models consistently surface, which sources feed those citations, and how the answer changes with the qualifier in the query.
- Whey & isolate protein: Optimum Nutrition, Dymatize, BSN, Ascent, Isopure.
- Plant-based protein: Naked Nutrition, Garden of Life, Orgain, Owyn, and plant blends.
- Ready-to-drink protein: Premier Protein, Fairlife Core Power, Muscle Milk, Owyn.
- Pre-workout: C4 / Cellucor, Ghost, Ryse, BSN, Transparent Labs.
- Creatine: Optimum Nutrition, Transparent Labs, Legion, and monohydrate makers.
- Recovery & clean-label performance: Momentous, Klean Athlete, Legion, Transparent Labs.
Query types tracked. Real-world prompts including "best protein powder," "cleanest protein powder," "best protein shake," "best pre-workout," "best creatine," "most transparent protein brand," "best protein for muscle gain," and 50+ variations covering recommendation, comparison, and clean-label intent.
Citation sources tracked. Fitness and consumer editorial, independent testing and lab-analysis content, registered-dietitian coverage, reference sources, and brand-owned content.
Important framing — please read. This index covers performance nutrition — protein powders, ready-to-drink protein, pre-workout, creatine, and recovery products. It is distinct from the 5W Supplements AI Visibility Index, which ranks vitamins, minerals, and general-wellness supplements; some brands appear in both categories. This index measures AI citation share for communications and marketing strategy purposes only. It does not evaluate any product on safety, efficacy, ingredient quality, or value, and it is not nutritional, fitness, or medical advice.
## The Full Ranking: Top 25 Sports-Nutrition and Protein Brands by AI Citation Share
#BrandCategoryAI VisibilityNotable
1Optimum NutritionWhey / multi-categoryCategory-dominantOwned by Glanbia; Gold Standard Whey; the default citation for the broad "best protein powder" query.
2Transparent LabsClean-label / DTCClean-label leaderDirect-to-consumer; full-disclosure formulas and third-party testing; owns "cleanest" and "most transparent" citations.
3Premier ProteinReady-to-drinkRTD co-leaderBellRing Brands; high-protein shakes with mass-retail reach; owns much of the "best protein shake" surface.
4GhostLifestyle performanceLifestyle-brand tierLifestyle-driven sports-nutrition brand; collaboration marketing fuels constant earned coverage and citations.
5LegionClean-label / DTCClean-label challengerDirect-to-consumer; evidence-led, naturally sweetened formulas; cited heavily in clean-label comparisons.
6Fairlife Core PowerReady-to-drinkRTD co-leaderFairlife, a Coca-Cola company; filtered-milk protein shakes; a fast-rising "protein shake" citation.
7DymatizeWhey / isolatePerformance-whey tierBellRing Brands; ISO100 hydrolyzed isolate; cited in higher-end whey and isolate queries.
8Muscle MilkReady-to-drinkLegacy-RTD tierOwned by Hormel; a long-standing protein-shake brand; broad recognition, narrowing citation share.
9Quest NutritionBars / powderProtein-snack tierOwned by Simply Good Foods; best known for protein bars; cited across snack and powder queries.
10MomentousClinical / NSF-testedClinical-grade tierNSF Certified for Sport positioning; cited heavily in "third-party tested" and athlete-grade queries.
11AscentNative wheyNative-whey tierNative whey with a clean-ingredient story; cited by enthusiasts in higher-quality whey comparisons.
12C4 / CellucorPre-workoutPre-workout leaderNutrabolt; one of the most recognized pre-workout brands; anchors the pre-workout citation set.
13BSNWhey / pre-workoutLegacy-performance tierOwned by Glanbia; Syntha-6 and N.O.-Xplode; legacy recognition with a thinner modern citation footprint.
14RysePre-workout / DTCModern-pre-workout tierFast-growing DTC pre-workout brand; collaboration flavors drive social-led citations.
15Naked NutritionMinimal-ingredientMinimalist tierDirect-to-consumer; few-ingredient protein positioning; cited in "no additives" and clean-label queries.
16MuscleTechWhey / performanceLegacy nicheOwned by Iovate; long-standing mass-retail performance brand; cited modestly in legacy comparisons.
17Garden of LifePlant-basedPlant-protein nicheOwned by Nestlé; organic plant-protein focus; cited in vegan and organic protein queries.
18Vital ProteinsCollagen / proteinCollagen-led nicheOwned by Nestlé; collagen leader; cited in recovery, skin, and joint-support adjacency queries.
19OrgainPlant / organicOrganic nicheNestlé holds a majority stake; organic protein and shakes; cited in organic and clean-label queries.
20OwynPlant-based RTDAllergen-friendly nichePlant-based, allergen-free ready-to-drink protein; cited in vegan-shake comparisons.
21Klean AthleteNSF Certified for SportCertified-sport nicheNSF Certified for Sport line; cited in athlete-grade and banned-substance-tested queries.
22Body FortressValue wheyValue nicheValue mass-retail protein; cited mainly in budget and affordability queries.
23Six StarValue performanceValue nicheOwned by Iovate; value performance line; cited in budget protein and creatine comparisons.
24IsopureClear / isolateClear-protein nicheOwned by Glanbia; zero-carb and clear-protein lines; cited in lean-isolate queries.
25HuelMeal replacementAdjacent nicheDirect-to-consumer; complete-nutrition and meal-replacement positioning; cited at the edge of the category.
Source: 5W AI Visibility Index analysis, Q2 2026, based on 60+ tracked prompts run five times per engine across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
## Winners: The Brands Winning the AI Answer
### Optimum Nutrition — Owning the Default
Three decades of recognition make Optimum Nutrition the brand the engines reach for on the broad, unqualified "best protein powder" query. Incumbency is a real citation asset — it just stops working the moment the question gets specific.
### Transparent Labs — Disclosure as a Moat
Transparent Labs publishes full formulas, dosing rationale, and third-party test results. That is exactly the structured, verifiable content AI engines retrieve and trust, and it lets a smaller brand own the "cleanest protein" query outright.
### Premier Protein & Fairlife — Owning the Shake
The "best protein shake" query routes to ready-to-drink, not powder. Premier Protein and Fairlife Core Power built the editorial and review record around convenience, and own a citation surface the powder brands cannot reach.
### Momentous — Certification as Citation
Momentous anchored itself to NSF Certified for Sport and clinical-grade positioning. "Third-party tested" is now its own query, and a brand that owns a credential owns the answer that asks for it.
## Falling Behind: The Brands AI Is Leaving Behind
### Legacy Mass Brands Without a Clean-Label Story
Several long-recognized performance brands have no transparency or third-party-testing narrative. As clean-label queries grow, brands with nothing to disclose are absent from a rising share of the surface.
### Value Brands Cited Only on Price
Body Fortress and Six Star surface mainly in budget queries. A brand cited only when the qualifier is "cheap" cedes recommendation and category-leadership queries to others.
### Pre-Workout Brands Stuck in One Niche
Pre-workout brands that never built a protein, creatine, or recovery presence are cited only inside their original lane, and miss the broader performance-nutrition surface.
### Plant-Protein Also-Rans
Plant-protein brands without a distinctive sourcing or sustainability story blur together. The engines retrieve the brands with a clear differentiator and skip the undifferentiated middle.
## Six Structural Truths About AI Visibility in Sports Nutrition
- The qualifier flips the answer. "Best protein powder" returns the incumbent. "Cleanest" or "most transparent" returns a different, smaller brand entirely.
- Transparency is a retrieval asset. Full formulas and third-party test results are exactly the structured content AI engines can retrieve, verify, and repeat.
- Ready-to-drink is its own citation surface. The "protein shake" query routes to RTD brands, not powders. Format defines a separate answer the powder brands cannot reach.
- Incumbency wins the default, not the specifics. A 30-year brand owns the broad query. It does not automatically own the clean-label, certified, or comparison queries.
- Certification is becoming a query. "NSF Certified for Sport" and "third-party tested" are now searched directly, and the brands that hold the credential own the answer.
- Independent testing coverage drives citations. Lab-analysis and dietitian-reviewed content feed performance-nutrition citations disproportionately.
## Six 2026 Dynamics Reshaping Protein AI Citations in Real Time
- Creatine is the fastest-rising query. With the category projected to grow at a 13% annual rate, "best creatine" is a fast-expanding citation lane.
- Ready-to-drink protein keeps surging. Convenience-format protein is pulling citation share toward the shake brands and away from the tub.
- Protein is crossing into every category. As "good source of protein" becomes a purchase driver across food and beverage, the citation set is widening.
- Clean-label scrutiny is intensifying. Heavy-metal and label-accuracy coverage is feeding the transparency query, and the brands that disclose most.
- GLP-1 is reshaping the protein narrative. Muscle-preservation framing for people on GLP-1 medications is opening a new performance-nutrition citation lane.
- Certification is becoming table stakes. Third-party testing is shifting from a premium feature to a baseline expectation the engines surface by default.
## The Playbook: General Tips for Sports-Nutrition Marketing Teams
- Audit category citation share quarterly. Performance-nutrition citations move with testing cycles, certifications, and format trends.
- Decide which qualifier you intend to own. "Cleanest," "best value," "best shake," "third-party tested" — each is a separate query to win.
- Publish full, structured formula data. Disclosure is a retrieval asset; the engines reward brands that make their formulas readable.
- Pursue and surface third-party certification. NSF Certified for Sport and similar credentials are now searched directly.
- Earn independent testing and dietitian coverage. Lab analyses and RD-reviewed content feed citations disproportionately.
- Build a presence in the format that is winning. Ready-to-drink is its own citation surface — a powder-only brand cedes it.
- Keep reference and retail content accurate. Product pages, Wikipedia, and review sources are heavily retrieved.
- Differentiate plant and specialty lines clearly. Undifferentiated entries blur together and lose the citation.
- Treat certifications and reformulations as citation events. Publish substantive content when the formula or credential changes.
- Re-audit after every launch, certification, or testing-coverage cycle. Each materially reshapes the citation surface.
## The Bigger Picture: The Protein Decision Now Starts With a Qualifier
For decades, sports nutrition was bought on brand recognition and the gym-floor recommendation. Those still matter. But a growing share of buyers now begin with a specific, qualified question typed into an AI engine: not "protein powder," but "cleanest protein powder," "best protein shake," "best third-party-tested creatine."
The Index shows what that surface rewards: structured, verifiable formula data, a credential the brand owns, a presence in the format that is rising, and a clear differentiator. It does not simply reward scale — the world's largest sports-nutrition brand owns the broad query and loses the specific ones.
AI citation share is the scoreboard. In performance nutrition, the brand AI names is the brand that answers the qualified question, and that visibility is built in disclosure and certification, not in shelf space alone.
## Frequently Asked Questions
### What is the Sports Nutrition & Protein AI Visibility Index 2026?
A research report by 5W that ranks the top 25 sports-nutrition and protein brands by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ consumer prompts tracked in Q2 2026.
### Which sports-nutrition brand holds the highest AI citation share?
Optimum Nutrition, with an estimated 14%, the default return for the broad "best protein powder" query.
### How is this different from the 5W Supplements AI Visibility Index?
This index covers performance nutrition — protein powders, ready-to-drink protein, pre-workout, creatine, and recovery. The Supplements Index ranks vitamins, minerals, and general-wellness supplements. Some brands appear in both categories.
### Why do smaller brands out-rank larger ones?
The qualifier in the query changes the answer. Clean-label and transparency queries route to brands that publish full formulas and third-party test results, content the engines can retrieve and trust, regardless of revenue.
### Is this index nutritional or fitness advice?
No. It measures AI citation share for communications strategy purposes only and does not evaluate any product on safety, efficacy, or value.
### What other industries has the AI Visibility Index Series covered?
Recent editions include Supplements, Weight Loss & Metabolic Health, Grocery Retail, the Pet Industry, Beauty, and Restaurants & Chains.
## Share This Research
X / Twitter: Optimum Nutrition owns the default "best protein powder" AI answer. Transparent Labs and Legion, a fraction of its size, own "cleanest" and "most transparent." New 5W research ranks 25 sports-nutrition brands by AI citation share. /ai-visibility-index/sports-nutrition-protein-ai-visibility-index-2026
LinkedIn: New 5W research: Optimum Nutrition dominates the broad "best protein powder" AI answer — but Transparent Labs and Legion, brands a fraction of its size, own the "cleanest" and "most transparent" queries. The 5W AI Visibility Index ranked 25 sports-nutrition brands across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. /ai-visibility-index/sports-nutrition-protein-ai-visibility-index-2026
## About This Report
5W AI Visibility Index: Sports Nutrition & Protein. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ consumer queries tracked in Q2 2026, with each prompt run five times per engine. It covers performance nutrition — protein powders, ready-to-drink protein, pre-workout, creatine, and recovery — and is distinct from the 5W Supplements AI Visibility Index. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report does not evaluate any product on safety, efficacy, or value and is not nutritional, fitness, or medical advice.
## More From the Series
- Fitness & Wellness Apps
- Functional Beverages
- Weight Loss & Metabolic Health
---
### The IPO AI Visibility Index: Who Controls the Answer Before the Bell
URL: https://www.5wpr.com/ai-visibility-index/ipo-readiness-2026/
Summary: Discover which pre-IPO companies lead in citation share across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews in our 2026 index.
Part of
The AI Visibility Index Series
·
IPO Readiness Edition · May 2026
5W AI Communications · The Answer Economy
# The IPO AI Visibility Index: Who Controls the Answer Before the Bell
A narrative-risk study of 25 recent and pending U.S. IPO candidates — measuring whether the AI engines recognize them, explain them correctly, and repeat the company’s own framing or the market’s.
5W AI Communications — The Communications Firm Built for the AI Era
Directional Modeled Index · 16 Sections
May 2026
Engines: ChatGPT · Claude · Gemini · Perplexity · Google AI Overviews
CONTENTS
## Sixteen Sections
SECTION 01
## Wall Street’s New First Analyst Is a Chatbot
The IPO window is open again — and a new analyst beat Wall Street to the desk. Before a banker builds the book or a reporter files the story, the buyer types the company name into a chatbox and reads whatever comes back. That answer is the first impression now. This study measures who controls it across 25 recent and pending U.S. IPO candidates.
The pattern is a split screen. AI-infrastructure and crypto names are explained with confidence and tend to be sourced through the company’s own material. Consumer-fintech and design-software names are recognized instantly but explained through the media narrative — often the negative one. A cluster of pending filers are functionally invisible or confused with competitors, precisely when their story is least settled and most consequential.
25
IPO candidates studied
5
AI engines modeled
60+
investor-intent prompts
4
scored dimensions
You can ace the audit, build the book, ring the bell — and still flunk the first question a buyer types into a chatbox.
SECTION 02
## More Buyers Ask Bots Than Bankers
The behavior shift is now measured, not assumed. In a March 2026 survey of B2B software buyers, G2 found that 51% now begin research with an AI chatbot more often than with Google — up from 29% a year earlier — and that one in three bought from a vendor they had never heard of before the chatbot named it. That is the investor-adjacent audience: the analysts, reporters, recruits, and researchers who form the first read on a company.
Every IPO candidate has spent 12 to 24 months on audits, governance, and an S-1. Almost none have spent a day on how the engines will explain them. The S-1 is written for the SEC. The chatbox answer is written by whoever fed the model. When those disagree, the machine tends to follow the press.
SOURCE — G2, “Buyer Behavior Report,” March 2026 (n=1,076 B2B software buyers). Cited as directional evidence of AI-first research behavior, not as IPO-specific data.
SECTION 03
## How the Index Is Built
This is a directional modeled index, not a logged audit. Each company was modeled across five engines — ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews — against a fixed bank of 60+ investor-intent prompts spanning four families: recognition, accuracy, sourcing, and answer quality. Four dimensions were scored 0–100 and combined into a single AI Readiness Score:
- Recognition — does AI identify the company correctly and distinctly?
- Accuracy — does it explain the business model and financials correctly?
- Source Control — does it lean on the company’s own pages and filings, or on third-party media?
- Answer Quality — is the tone positive, neutral, or confused?
Source Control is the proprietary metric. It separates a company that is merely visible from one that is visible in its own words — the difference that matters most at the moment of listing.
METHODOLOGY — Scores are directional estimates modeled from current AI knowledge plus live web search, expressed as a modeled index. Citation-share figures are labeled “Estimated ~X% modeled.” Company financials and IPO status were verified via web search at publication. This is a market-level diagnostic and 5W research product — not a company certification and not investment advice.
SECTION 04
## The Leaderboard
The full ranking. Sort any column; filter by status or sector. Every score is a directional modeled estimate.
Status
AllRecently publicPending / expected
Sector
Reset
Export CSV
#
Company
Sector
Status
Recog.
Accur.
Source Control
Quality
AI Readiness
Modeled Share
Source Control: OWN = own pages/filings lead · MIXED = balanced · MEDIA = third-party media leads. Quality: POS / NEU / CONFUSED. A clean printable table appears in the Appendix (Section 15).
SECTION 05
## Infrastructure Owns the Answer
The top of the index is AI-infrastructure and crypto: CoreWeave, Circle, Anthropic, Databricks, Anduril. The engines identify them instantly, explain them correctly, and frequently cite their own technical material and filings. They earned that not through PR but through volume — they published densely about themselves while the market wrote densely about them.
Out-write the market, and the machine repeats you. Get out-written, and it repeats them.
The lesson generalizes: density of authoritative, primary-source content is the strongest predictor of a controlled answer. The leaders earned it by circumstance. Every other filer has to build it deliberately.
SECTION 06
## Famous — and Defenseless
The middle tells the more useful story. Klarna and Figma are recognized perfectly and explained almost entirely through the post-IPO decline narrative the press built — the drawdown, the lawsuits, the “valuation hangover.” None of it is factually wrong. All of it is uncontrolled.
This is the most expensive failure mode in the study: high recognition, low source control. The company is unmistakably visible and the narrative is already set, with nothing pointing back to its own framing. A newly public company in this position is being understood on someone else’s words.
SECTION 07
## The Billion-Dollar Ghosts
At the bottom sit pending filers that are invisible or actively confused — blended with better-known competitors, handed stale funding figures, or returned with hedged “I’m not certain” answers. Entrata, Crusoe Energy, Genesys, and Lime land here: real businesses with real filings whose AI footprint hasn’t caught up to their ambitions.
It is the cheapest problem to fix and the most dangerous to ignore. A confused answer at the moment of filing is a confused first impression for every reporter and investor who checks — and it compounds, because the engines cite one another.
SECTION 08
## Three Companies, Three Outcomes
The index in miniature — one company at each end of the Source Control spectrum, and one in the dangerous middle.
Controlled
### CoreWeave
Recognized instantly across all five engines and explained through a mix that includes its own technical and financial material. The AI describes the business roughly the way the company would. This is the target state — visibility the company helped author.
AI Readiness 92 · Source: OWN
Visible, media-owned
### Klarna
Maximum recognition, minimal control. Answers lead with the post-IPO drawdown and the “valuation hangover” the press authored — accurate, but entirely the market’s framing rather than the company’s. Famous and defenseless.
AI Readiness 63 · Source: MEDIA
Invisible / confused
### Entrata
A real, venture-backed filer the engines struggle to place — thin recognition, hedged answers, occasional blending with adjacent software names. A confused first impression waiting for an S-1 to make it consequential.
AI Readiness 47 · Source: MEDIA
SECTION 09
## Sectors That Win, Sectors Buried
AI infrastructure and crypto lead on every dimension. Consumer fintech recognizes well but loses the tone. Design and B2B SaaS land mid-pack — recognized and accurate, but media-sourced. Space and defense names show technical accuracy outpacing financial accuracy. Healthcare and mobility trail on recognition.
SECTION 10
## Five Engines, Five Verdicts
The engines do not agree. Perplexity and Google AI Overviews lean hardest on recent media, so they amplify post-IPO narratives fastest — strong for visibility, risky for control. ChatGPT and Claude lean on consolidated background, updating more slowly and rewarding a settled narrative. Gemini sits between, weighting Google-indexed primary pages most heavily.
Own the primary pages for ChatGPT and Claude; win the recent-media cycle for Perplexity and AI Overviews.
SECTION 11
## The Bots Don’t Honor the Quiet Period
For a private company, a confused AI answer is an annoyance. For a company in registration, it is a narrative-risk problem with real teeth. The quiet period limits what the company can say; it places no limit on what the engines repeat. If the chatbox is confidently wrong about the revenue model during the roadshow, that error sits in front of exactly the audience the company can’t freely correct.
FLAG — Not legal advice. Companies in registration should coordinate any AI-visibility work with counsel to stay inside quiet-period rules. The structural point holds: the engines keep talking when the company has to go quiet.
SECTION 12
## What a Bulletproof Answer Looks Like
The leaders share a profile any filer can build toward on purpose:
- Primary-source density — the company publishes more authoritative material about itself than the market publishes about it.
- Entity clarity — name, category, and business model are unambiguous and consistent across every page.
- Retrieval structure — schema, FAQs, and prompt-shaped headings the engines can lift cleanly.
- Narrative pre-positioning — the company’s framing exists before the press cycle, giving the engines something of the company’s to cite.
SECTION 13
## Win the Answer Before the Bell
### Phase 1 — Diagnose (Weeks 1–4)
Model the four dimensions on the company and its three closest comparables. Establish the citation-share baseline and the source-control gap. Surface every confused or stale answer before the S-1 is public.
### Phase 2 — Build (Weeks 5–8)
Build the primary-source layer: entity-clear explainers, retrieval-structured pages, schema, and a media tier map of the publications the engines actually cite for the sector.
### Phase 3 — Reinforce (Weeks 9–12)
Earn placements in the publications that move citation. Re-model the index. Track the shift. Hand the company a controlled answer before the roadshow, not after.
SECTION 14
## Work With 5W
5W is The Communications Firm Built for the AI Era. We combine public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI-visibility research to grow Citation Share — a client’s share of the answers buyers now read inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. For pre-IPO and newly public companies, that is the difference between a controlled first impression and a borrowed one.
### Run your own IPO AI-readiness diagnostic
5W will model your company and three comparables across all five engines, deliver your Source Control gap, and map the path to a controlled answer before the bell.
01Diagnose
02Build
03Reinforce
SECTION 15
## Appendix: The Full Table & Methodology
A static, print-clean version of the ranking follows, for PDF and report use.
#CompanySectorStatus
Recog.Accur.SourceQualityAI ReadinessModeled Share
### Methodology, in full
Universe: 25 U.S. IPO candidates — 13 recently public with verifiable tickers, 12 pending or expected filers — selected for sector spread across AI infrastructure, fintech, consumer fintech, design/SaaS, crypto, healthcare, space/defense, and mobility. Engines: ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews. Prompt bank: 60+ investor-intent prompts. Scoring: four dimensions, 0–100, equal-weighted into the AI Readiness Score. Citation share is a directional modeled estimate, labeled accordingly; no query runs were logged. Financials and IPO status verified via web search at publication. Findings are a market-level diagnostic, directional by design.
SECTION 16
## Be the Answer. Or Be Whatever They Say You Are.
The AI Visibility Index Series is 5W’s ongoing research into who gets cited inside the AI engines — by sector, by category, and now by IPO cohort. Each edition models a market on citation share and maps the path to a controlled answer. This one turns the lens on the companies entering the public markets, where a buyer’s first impression is increasingly shaped by a machine.
B2B buyers now start with AI more than Google. The IPO market just walked through the same door.
← Back to the AI Visibility Index Series
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O’Dwyer’s, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday’s WorkLife Employer of the Year list. Learn more at 5wpr.com.
---
### Payments & Processors AI Visibility Index 2026
URL: https://www.5wpr.com/research/payments-processors-ai-visibility-index-2026/
Summary: Payments AI visibility data shows Stripe leads appearances, while engine gaps and owned-site citations reveal uneven brand exposure.
# Payments & Processors AI Visibility Index 2026
Stripe Tops Payment AI Mentions
Stripe led the measured Payments & Processors field, appearing in 80.5% of 297 returned answers. Its 95% interval was 75.6% to 84.6%. The result places Stripe ahead of PayPal, Adyen, and Square in this prompt set, but it does not establish a category-wide consumer preference.
The citation picture differs from the appearance leaderboard. Airwallex's owned domain appeared as a cited source in 44.3% of 296 answers with retrieval evidence, despite Airwallex appearing in 11.8% of returned answers. This segment report is part of the Fintech AI Visibility Index 2026 and covers Payments & Processors only. It does not cover Trading & Investing Platforms, BNPL & Consumer Credit, or B2B Fintech Infrastructure.
297AI answers analyzed
80.5%Stripe appearance rate, the category leader
26Brands named across the study
99.7%Answers carrying retrieval evidence
## Which payment brands lead the leaderboard?
Stripe ranked first at 80.5% appearance, followed by PayPal at 67.0%, Adyen at 59.6%, and Square at 56.2%. Appearance uses 297 returned answers as its denominator. Stripe was named in 239 answers, compared with 199 for PayPal.
The first four brands form the leading group, but the ranking needs care. Appearance records whether an engine named a brand. It does not measure endorsement, suitability, or conversion.
### Appearance leaderboard
### AI visibility leaderboard
Appearance rate is the share of the 297 returned AI answers in which each brand was named.
#BrandNamed inAppearance rate95% intervalAvg position
1Stripe239/29780.5%75.6%–84.6%2.6
2PayPal199/29767%61.5%–72.1%4.4
3Adyen177/29759.6%53.9%–65%4.4
4Square167/29756.2%50.5%–61.8%4
5Helcim131/29744.1%38.6%–49.8%5.5
6Shopify Inc.119/29740.1%34.7%–45.7%6.1
7Authorize.Net92/29731%26%–36.5%9.4
8Braintree78/29726.3%21.6%–31.5%7.4
9Worldpay77/29725.9%21.3%–31.2%7.9
10Checkout77/29725.9%21.3%–31.2%6.7
11Stax66/29722.2%17.9%–27.3%7.5
12Clover54/29718.2%14.2%–23%8.2
13Block, Inc.54/29718.2%14.2%–23%5.6
14Global Payments36/29712.1%8.9%–16.3%8.6
15Fiserv, Inc.35/29711.8%8.6%–15.9%7.3
Stripe's 95% interval overlaps PayPal's interval from 61.5% to 72.1% only at the edge? It does not overlap. Stripe's interval overlaps neither Adyen's 53.9% to 65.0% nor Square's 50.5% to 61.8%. PayPal's interval overlaps Adyen's, and Adyen's interval overlaps Square's. Those differences are within the margin of this sample.
## How did the leading brands perform?
The top eight brands show different engine patterns, even when their overall rank is high. The profiles below use appearance rates from returned answers and owned-domain citation rates from answers with retrieval evidence.
### Stripe
80.5% appearance rate put Stripe at rank 1, with a 75.6% to 84.6% interval. Stripe's strongest engines were Perplexity and ChatGPT, which named it in all 75 and 73 answers, respectively. Gemini named Stripe in 52 of 75 answers, or 69.3%, and Claude named it in 39 of 74, or 52.7%. Stripe's average first mention was position 2.6. Stripe.com had a 13.5% owned-domain citation rate across 296 answers with retrieval evidence.
### PayPal
67.0% appearance rate put PayPal at rank 2, with a 61.5% to 72.1% interval. ChatGPT named PayPal in 70 of 73 answers, or 95.9%, while Perplexity named it in 68 of 75, or 90.7%. Gemini named PayPal in 35 of 75 answers, or 46.7%, and Claude named it in 26 of 74, or 35.1%. PayPal's average first mention was position 4.4. Paypal.com had an 8.1% owned-domain citation rate across answers with retrieval evidence.
### Adyen
59.6% appearance rate put Adyen at rank 3, with a 53.9% to 65.0% interval. ChatGPT was Adyen's strongest engine at 63 mentions in 73 answers, or 86.3%. Perplexity named Adyen in 53 of 75 answers, or 70.7%, while Gemini named it in 37 of 75, or 49.3%, and Claude named it in 24 of 74, or 32.4%. Adyen's average first mention was position 4.4. Adyen.com had a 5.7% owned-domain citation rate across answers with retrieval evidence.
### Square
56.2% appearance rate put Square at rank 4, with a 50.5% to 61.8% interval. Perplexity was Square's strongest engine, naming it in 70 of 75 answers, or 93.3%. ChatGPT named Square in 40 of 73 answers, or 54.8%, Gemini in 28 of 75, or 37.3%, and Claude in 29 of 74, or 39.2%. Square's average first mention was position 4.0. Squareup.com had a 6.1% owned-domain citation rate across answers with retrieval evidence.
### Helcim
44.1% appearance rate put Helcim at rank 5, with a 38.6% to 49.8% interval. Perplexity named Helcim in 55 of 75 answers, or 73.3%, and Gemini named it in 43 of 75, or 57.3%. ChatGPT named Helcim in 21 of 73 answers, or 28.8%, while Claude named it in 12 of 74, or 16.2%. Helcim's average first mention was position 5.5. Helcim.com had a 13.9% owned-domain citation rate across answers with retrieval evidence.
### Shopify Inc.
40.1% appearance rate put Shopify Inc. at rank 6, with a 34.7% to 45.7% interval. Perplexity named Shopify Inc. in 46 of 75 answers, or 61.3%, followed by ChatGPT at 37 of 73, or 50.7%, and Gemini at 34 of 75, or 45.3%. Claude named Shopify Inc. in 2 of 74 answers, or 2.7%. Shopify Inc.'s average first mention was position 6.1. Shopify.com had a 17.9% owned-domain citation rate across answers with retrieval evidence.
### Authorize.Net
31.0% appearance rate put Authorize.Net at rank 7, with a 26.0% to 36.5% interval. ChatGPT named Authorize.Net in 37 of 73 answers, or 50.7%. Gemini named it in 28 of 75 answers, or 37.3%, and Perplexity in 27 of 75, or 36.0%. Claude did not name Authorize.Net in any of its 74 answers. Authorize.Net's average first mention was position 9.4. Authorize.net had a 7.4% owned-domain citation rate across answers with retrieval evidence.
### Braintree
26.3% appearance rate put Braintree at rank 8, with a 21.6% to 31.5% interval. Perplexity named Braintree in 32 of 75 answers, or 42.7%, and ChatGPT named it in 27 of 73, or 37.0%. Gemini named Braintree in 15 of 75 answers, or 20.0%, while Claude named it in 4 of 74, or 5.4%. Braintree's average first mention was position 7.4. Braintreepayments.com had a 0.3% owned-domain citation rate across answers with retrieval evidence.
## Where do the engines disagree?
The four engines agree that Stripe appears often, but they disagree sharply on several payment brands. Perplexity returned 75 answers, ChatGPT returned 73, Gemini returned 75, and Claude returned 74. Each engine uses a different model and retrieval tool, so a gap can reflect system design rather than a stable measure of brand visibility.
### Engine-by-brand appearance
### Appearance rate by engine
Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Stripe100%100%69.3%52.7%
PayPal90.7%95.9%46.7%35.1%
Adyen70.7%86.3%49.3%32.4%
Square93.3%54.8%37.3%39.2%
Helcim73.3%28.8%57.3%16.2%
Shopify Inc.61.3%50.7%45.3%2.7%
Authorize.Net36%50.7%37.3%0%
Braintree42.7%37%20%5.4%
Worldpay40%43.8%14.7%5.4%
Checkout53.3%27.4%18.7%4.1%
Stax42.7%6.8%34.7%4.1%
Clover29.3%15.1%25.3%2.7%
Perplexity produced the broadest concentration of high single-engine rates for Square, Helcim, Checkout, Stax, Airwallex, Finix, GoCardless, and QuickBooks Payments. Square appeared in 70 of Perplexity's 75 answers, compared with 28 of Gemini's 75 and 29 of Claude's 74. Airwallex appeared in 24 Perplexity answers but only 1 ChatGPT answer, 3 Gemini answers, and 7 Claude answers. Finix appeared in 23 Perplexity answers, 2 ChatGPT answers, 2 Gemini answers, and no Claude answers.
ChatGPT concentrated more heavily on PayPal, Adyen, Block, Inc., Global Payments, Fiserv, Inc., and Toast. Block, Inc. appeared in 30 of 73 ChatGPT answers, 21 of 75 Gemini answers, 3 of 75 Perplexity answers, and no Claude answers. Global Payments appeared in 19 ChatGPT answers but 4 Gemini answers and 1 Claude answer. Fiserv, Inc. appeared in 20 ChatGPT answers, while Perplexity and Gemini each named it 7 times and Claude named it once.
Claude had the lowest rates for many brands. It did not name Authorize.Net, Block, Inc., Finix, GoCardless, Toast, or QuickBooks Payments in its 74 answers. Shopify Inc. appeared in only 2 Claude answers despite appearing in 46 Perplexity answers, 37 ChatGPT answers, and 34 Gemini answers. These gaps make a single-engine audit insufficient for this category.
## Which brands lead each demand cluster?
Demand-cluster performance shows which brand names appear under different types of measured payment questions. The cluster chart separates question groups, so its results should not be read as one universal ranking.
### Demand-cluster leaders
### Leaders by demand cluster
The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
— Other demand —
General best-in-category24Stripe91.7%Adyen 87.5%, PayPal 75%, Square 62.5%
Price and value24Helcim75%Stripe 70.8%, Square 58.3%, Payment Depot 58.3%
Primary use case24Stripe91.7%PayPal 91.7%, Adyen 91.7%, Square 62.5%
Beginners and first purchase24Stripe70.8%PayPal 70.8%, Square 70.8%, Shopify Inc. 66.7%
Everyday and mainstream choice24Stripe83.3%Square 79.2%, PayPal 70.8%, Helcim 54.2%
Where to buy24Stripe79.2%PayPal 70.8%, Helcim 70.8%, Adyen 62.5%
Durability and longevity12Stripe91.7%Adyen 91.7%, Checkout 75%, PayPal 58.3%
Gifting and occasions12Square66.7%Stripe 58.3%, PayPal 58.3%, Helcim 25%
Specific need or condition12Stripe75%Shopify Inc. 66.7%, Clover 66.7%, PayPal 58.3%
Comparison and evaluation12Stripe66.7%Adyen 66.7%, Checkout 58.3%, PayPal 50%
Trust and recommendation12Stripe83.3%PayPal 83.3%, Adyen 66.7%, Square 58.3%
Ownership and maker attribution12Stripe83.3%PayPal 75%, Adyen 75%, Block, Inc. 75%
Quality and premium tier12Stripe91.7%Adyen 91.7%, Worldpay 75%, Checkout 75%
Specialist / advanced use12Stripe91.7%Adyen 91.7%, Braintree 83.3%, Authorize.Net 75%
Emerging and new entrants10Stripe60%Adyen 60%, Checkout 60%, Airwallex 60%
— Payments & Processors —
best payment processor for online business12Stripe91.7%PayPal 91.7%, Adyen 91.7%, Square 83.3%
Stripe vs PayPal for e-commerce12Stripe100%PayPal 91.7%, Shopify Inc. 58.3%, Adyen 50%
cheapest credit card processing platform12Helcim75%Stax 75%, Stripe 66.7%, Square 66.7%
Use the cluster results to distinguish broad payment selection questions from more specific needs. A brand can lead a narrow question group without leading the overall appearance leaderboard. The chart is the measured source for cluster-level placement; this report does not generalize the pattern beyond these prompts.
## Which sites do the engines cite?
Owned-domain citations did not follow the overall appearance order. Airwallex had the highest owned-domain citation rate at 44.3% of 296 answers with retrieval evidence, while Stripe led appearance at 80.5% of 297 returned answers. The denominators differ and cannot be compared as one rate.
### Owned-domain citation rates
### Owned-domain citation rate
Share of the 296 answers with retrieval evidence that cited a URL on the brand's own website.
Stripe
13.5% · 40/296
PayPal
8.1% · 24/296
Adyen
5.7% · 17/296
Square
6.1% · 18/296
Helcim
13.9% · 41/296
Shopify Inc.
17.9% · 53/296
Authorize.Net
7.4% · 22/296
Braintree
0.3% · 1/296
Worldpay
1.7% · 5/296
Checkout
4.4% · 13/296
Stax
2.7% · 8/296
Clover
0.7% · 2/296
Finix had a 25.7% owned-domain citation rate and Shopify Inc. had 17.9%. Stripe had 13.5%, Helcim had 13.9%, and GoCardless had 12.2%. At the other end, Braintree and Toast each had a 0.3% rate. Block, Inc. and Visa each had a 0% owned-domain citation rate.
The publisher list shows that engines also relied on third-party pages. Connectpay.com appeared as a source in 161 answers with retrieval evidence, or 54.4%. Whop.com appeared in 117 answers, or 39.5%, and Nerdwallet.com appeared in 105 answers, or 35.5%.
### Most-cited source domains
### Most-cited publishers
Publishers cited as evidence, counted once per answer rather than once per link.
PublisherAnswers citing itAnswer penetration
connectpay.com161/29654.4%
airwallex.com131/29644.3%
whop.com117/29639.5%
nerdwallet.com105/29635.5%
forbes.com103/29634.8%
finix.com76/29625.7%
zapier.com74/29625%
technologyadvice.com71/29624%
learn.g2.com61/29620.6%
getholdings.com59/29619.9%
techrepublic.com59/29619.9%
wise.com59/29619.9%
Third-party source use is material in the measured answers. Connectpay.com, whop.com, nerdwallet.com, forbes.com, zapier.com, technologyadvice.com, learn.g2.com, and clearlypayments.com all appear in the cited-source list. A payment brand's own site is one input into an answer, not the only route to a mention or citation.
## Who is winning and who is at risk?
Winning group. Stripe is the strongest overall appearance leader at 80.5%, with an average first mention position of 2.6. PayPal ranks second at 67.0% and appears in all four engines. Adyen ranks third at 59.6%, led by an 86.3% ChatGPT appearance rate. Square ranks fourth at 56.2% and reaches 93.3% in Perplexity. Airwallex has lower overall appearance at 11.8%, but its 44.3% owned-domain citation rate is the highest measured rate among citation-eligible brands.
At-risk group. Braintree ranks eighth at 26.3% appearance and has a 0.3% owned-domain citation rate. Authorize.Net ranks seventh at 31.0% but was absent from all 74 Claude answers and had an average first mention position of 9.4. Block, Inc. ranked thirteenth at 18.2%, was absent from Perplexity and Claude, and had a 0% owned-domain citation rate. Visa appeared in 4.7% of answers with an average first mention position of 11.1 and a 0% owned-domain citation rate.
## What does the tally say about this category?
The category has a clear appearance leader, but the top ranks are not equally separated. Stripe's 75.6% to 84.6% interval does not overlap PayPal's 61.5% to 72.1% interval. PayPal, Adyen, and Square have overlapping intervals, so this sample does not support a claim that PayPal is more visible than Adyen or that Adyen is more visible than Square.
Engine agreement is uneven below the top positions. Stripe, PayPal, Adyen, Square, Helcim, Shopify Inc., Braintree, Worldpay, Checkout, Stax, and Clover were present across all four engines. Authorize.Net, Block, Inc., Finix, GoCardless, Toast, and QuickBooks Payments were not. Owned sites and third-party publishers both carry citations. Airwallex.com appeared in 131 answers with retrieval evidence, while connectpay.com appeared in 161.
The brief names Stripe, Block/Square, PayPal, and Adyen. Stripe, PayPal, Adyen, and Square had measured appearances. The separate Block/Square entry had a 0% appearance rate across 297 returned answers, while Block, Inc. had an 18.2% rate and Square had a 56.2% rate.
## What changed during the measurement window?
The measurement does not compare dates or earlier waves, so it cannot identify changes over time. It does show contemporaneous engine divergence, including brands concentrated in Perplexity or ChatGPT and brands absent from Claude's 74 returned answers.
## What should payment brands fix first?
- Audit every engine when a brand has an uneven engine pattern. Square appeared in 93.3% of Perplexity answers but 37.3% of Gemini answers. Re-measure appearance by engine after publishing pages that describe the same payment use cases in plain language.
- Build citable owned pages when appearance outpaces owned citations. Braintree appeared in 26.3% of returned answers but had a 0.3% owned-domain citation rate. Re-measure its owned-domain citation rate against the 296-answer retrieval-evidence denominator.
- Check the wording and entity names used across product pages. The dataset rolls PayPal references into one brand and separately records Braintree. Re-measure whether the intended brand name appears in responses rather than a related product name.
- Prioritize weak engines, not only the overall average. Shopify Inc. appeared in 2.7% of Claude answers and 61.3% of Perplexity answers. Re-measure the Claude rate after adding sourceable pages for the measured payment questions.
- Inspect third-party comparison pages when own-site citations are low. Connectpay.com appeared in 54.4% of answers with retrieval evidence. Re-measure both source citations and brand appearances after checking how the brand is described on cited publishers.
- Test narrow payment needs separately from broad selection questions. The cluster chart identifies which brands lead which measured question groups. Re-measure each cluster after publishing pages that answer the specific terms represented in the study.
## What does this mean for payment brands?
Interpretation. A high appearance rate gives a brand more chances to enter a generated answer, but it does not guarantee that the brand's own pages supply the supporting source. Stripe's overall lead and Airwallex's citation lead show that mentions and owned-domain citations can move separately.
Payment brands need an engine-level view. Perplexity, ChatGPT, Gemini, and Claude produced different brand sets from similar measured questions. A brand that looks strong in one engine can have little exposure in another.
## How was the Payments & Processors study measured?
This flagship study measured 24 stratified prompts three times per engine across Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). The study returned 297 answers and found retrieval evidence in 296 answers. Appearance rates use returned answers. Citation and source rates use answers with retrieval evidence.
### Measurement summary
### How this study was measured
- Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked.
- Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 297 answers returned of 297 attempted, 296 with retrieval evidence.
- Product lines rolled up to parent brands using a registry fixed before scoring.
- Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.
The study measures these prompts on September 14, 2026. It does not measure all payment searches, consumer demand, product quality, or commercial results. Engines differ in their model and retrieval tool, which may affect the results.
## Frequently asked questions
### Which brand appeared most often?
Stripe ranked first with an 80.5% appearance rate across 297 returned answers. Stripe was named in 239 answers and had a 75.6% to 84.6% 95% interval.
### Which brand had the highest owned-domain citation rate?
Airwallex had the highest measured owned-domain citation rate at 44.3%. Citation rates use 296 answers with retrieval evidence, not the 297 returned answers used for appearance rates.
### Did every engine name Stripe?
Yes. Stripe was named by Perplexity, ChatGPT, Gemini, and Claude. Its engine rates ranged from 52.7% in Claude to 100% in Perplexity and ChatGPT.
### Which brands were absent from Claude?
Authorize.Net, Block, Inc., Finix, GoCardless, Toast, and QuickBooks Payments had 0% appearance in Claude's 74 answers. This result applies to the measured prompts and not to all payment questions.
### Are PayPal, Adyen, and Square clearly separated?
No. PayPal's 95% interval overlaps Adyen's interval, and Adyen's interval overlaps Square's interval. The differences are within the margin of this sample.
### What did the study measure?
The study measured whether four engines named payment brands in 24 stratified prompts, each tested three times per engine. It also measured citations in answers with retrieval evidence.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1Forbes Advisor4.4%13/2972.6%–7.3%
2NerdWallet1.7%5/2970.7%–3.9%
3Investopedia0%0/2970%–1.3%
## Methodology appendix
This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.
### What was measured
- Category: Payments & Processors
- Measurement date: 2026-09-14
- Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 297
- Answers returned (appearance denominator): 297
- Answers with retrieval evidence (citation denominator): 296 (99.7% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable.
### How the prompts were sampled
Fixed sampling frame: 3 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Payments & Processors; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure.
Demand clusterPromptsScope
best payment processor for online business1best payment processor for online business
Stripe vs PayPal for e-commerce1Stripe vs PayPal for e-commerce
cheapest credit card processing platform1cheapest credit card processing platform
General best-in-category2the plain, highest-volume 'best X' style demand with no qualifier
Everyday and mainstream choice2what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Price and value2budget, value-for-money, cheaper alternatives and whether premium is worth it
Primary use case2the dominant everyday use case for this category
Beginners and first purchase2what a first-time buyer with no knowledge of the category should get
Where to buy2purchase-channel demand: where to buy, which retailers, direct vs marketplace
Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
Ownership and maker attribution1who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to
Quality and premium tier1which options are highest quality, most durable or best made
Durability and longevity1which brands last, hold up over time, or fail early
Specialist / advanced use1advanced, professional or enthusiast-level demand inside the category
Emerging and new entrants1newer entrants, recent launches and shifts buyers are only starting to ask about
### The exact prompts
#PromptClusterShape
1best payment processor for online businessneed_1medium
2Stripe vs PayPal for e-commerceneed_2medium
3cheapest credit card processing platformneed_3medium
4best payment processorsgeneral_bestshort
5best payment processing companiesgeneral_bestshort
6best payment processor for everyday small-business saleseverydaymedium
7best way to accept routine customer paymentseverydaymedium
8cheapest payment processorsvalue_priceshort
9best value merchant payment processorsvalue_pricemedium
10best processors for online paymentsuse_caseshort
11Which payment processors are best for accepting online customer payments?use_caselong
12best payment processor for beginnersbeginnermedium
13Which payment processing companies are easiest for a first-time business owner to set up?beginnerlong
14where to get payment processingretailmedium
15Where should a new business sign up for payment processing: direct or through a reseller?retaillong
16best payment processors for fundraiser eventsgiftingmedium
17Which payment processors work best for businesses that need in-person and online checkout together?specific_needlong
18Which payment processor is better for growing businesses, based on fees, tools, and international support?comparisonlong
19most trusted payment processorstrustshort
20Who owns the leading payment processing brands used by merchants?ownershiplong
21best enterprise payment processorspremium_qualitymedium
22Which payment processing platforms are most reliable as transaction volumes grow over several years?durabilitylong
23best payment gateways for developersspecialistmedium
24Which newer payment processing platforms are gaining traction with online businesses this year?emerginglong
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search757575100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool737373100%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)75757498.7%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool747474100%
Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
- Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
- 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
- Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
- Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
- Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
- 0% — the metric could be evaluated and the event did not occur.
- Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — the share of engines that named the brand at least once.
- Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.
### Evidence rules
- An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
- Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
- Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
- Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Stripe239/29780.5%79.4%75.6%–84.6%40/296 (13.5%)
2PayPal199/29767%67.9%61.5%–72.1%24/296 (8.1%)
3Adyen177/29759.6%58.4%53.9%–65%17/296 (5.7%)
4Square167/29756.2%57.5%50.5%–61.8%18/296 (6.1%)
5Helcim131/29744.1%46.1%38.6%–49.8%41/296 (13.9%)
6Shopify Inc.119/29740.1%41.8%34.7%–45.7%53/296 (17.9%)
7Authorize.Net92/29731%31.2%26%–36.5%22/296 (7.4%)
8Braintree78/29726.3%24.7%21.6%–31.5%1/296 (0.3%)
9Worldpay77/29725.9%26.1%21.3%–31.2%5/296 (1.7%)
10Checkout77/29725.9%24.8%21.3%–31.2%13/296 (4.4%)
11Stax66/29722.2%23.8%17.9%–27.3%8/296 (2.7%)
12Clover54/29718.2%21.3%14.2%–23%2/296 (0.7%)
13Block, Inc.54/29718.2%17.9%14.2%–23%0/296 (0%)
14Global Payments36/29712.1%11.7%8.9%–16.3%7/296 (2.4%)
15Fiserv, Inc.35/29711.8%11.4%8.6%–15.9%5/296 (1.7%)
16Airwallex35/29711.8%11.8%8.6%–15.9%131/296 (44.3%)
17Chase Payment Solutions30/29710.1%10.2%7.2%–14.1%4/296 (1.4%)
18Finix27/2979.1%10.7%6.3%–12.9%76/296 (25.7%)
19Payment Depot25/2978.4%8.3%5.8%–12.1%2/296 (0.7%)
20Visa14/2974.7%4.9%2.8%–7.8%0/296 (0%)
21FIS14/2974.7%4.9%2.8%–7.8%5/296 (1.7%)
22GoCardless13/2974.4%4.9%2.6%–7.3%36/296 (12.2%)
23Drawbacks13/2974.4%4.7%2.6%–7.3%domain not established — excluded from citation metrics
24Toast12/2974%5.6%2.3%–6.9%1/296 (0.3%)
25QuickBooks Payments12/2974%4.3%2.3%–6.9%3/296 (1%)
26Block/Square0/2970%0%0%–1.3%0/296 (0%)
### Entity handling
Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.
No roll-ups were applied in this study.
40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here.
### Limits of these findings
- This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-14. Engine behaviour changes without notice.
- The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
- Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
- A brand not listed here was not measured as absent — it simply was not named in these answers.
- Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
- 1 of 26 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero.
---
### Sleep Supplements AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/sleep-supplements-ai-visibility-index-2026/
Summary: Sleep AI Visibility Index measures which supplement brands AI engines name and cite across melatonin, magnesium and non-melatonin prompts.
# Sleep Supplements AI Visibility Index 2026
Life Extension Leads Sleep Citations
Sleep supplement AI recommendations separate name recognition from owned-site citation strength. Across 288 returned answers, Thorne ranked first for appearance at 52.1%, while Life Extension's owned domain was cited in 18.1% of the 287 answers with retrieval evidence. ConsumerLab and Innerbody drew more citations than either brand-owned domain, despite lower appearance rates.
Unlike the other segments, this Sleep report focuses on melatonin, magnesium glycinate or citrate, L-theanine, and non-melatonin formulas. It forms one part of the Supplements AI Visibility Index 2026. Multivitamins, Protein / Protein Powder, Probiotics / Gut Health, Immune Support, and Nootropics / Cognitive are outside its scope.
288AI answers analyzed
52.1%Thorne appearance rate, the category leader
27Brands named across the study
99.7%Answers carrying retrieval evidence
## Which brands lead the sleep supplement leaderboard?
Thorne ranked first in appearance, appearing in 52.1% of 288 returned answers. Nature Made followed at 39.6%, Pure Encapsulations at 38.2%, NOW Foods at 36.8%, and Life Extension at 35.8%. Appearance rates use returned answers as their denominator.
The leaderboard measures whether an engine named a brand. It does not show whether the engine cited that brand's website. That distinction matters in this category, where Life Extension ranked fifth by appearance but led the measured brands in owned-domain citation rate.
### Appearance leaderboard
### AI visibility leaderboard
Rather than a count alone, appearance rate shows the portion of the 288 returned AI answers that named each brand.
#BrandNamed inAppearance rate95% intervalAvg position
1Thorne150/28852.1%46.3%–57.8%3
2Nature Made114/28839.6%34.1%–45.3%5.2
3Pure Encapsulations110/28838.2%32.8%–43.9%4
4NOW Foods106/28836.8%31.4%–42.5%6.2
5Life Extension103/28835.8%30.4%–41.5%5.1
6Natrol89/28830.9%25.8%–36.5%6
7Olly86/28829.9%24.9%–35.4%6.7
8Magnesium57/28819.8%15.6%–24.8%6.3
9Nature’s Bounty49/28817%13.1%–21.8%6.7
10Gaia Herbs42/28814.6%11%–19.1%6.5
11Momentous38/28813.2%9.8%–17.6%4.2
12Doctor’s Best, Inc.37/28812.8%9.5%–17.2%5.5
13USP36/28812.5%9.2%–16.8%8.4
14ConsumerLab35/28812.2%8.9%–16.4%8.5
15Nutricost33/28811.5%8.3%–15.7%6.1
Thorne's 95% interval of 46.3% to 57.8% overlaps with Nature Made's 34.1% to 45.3%, Pure Encapsulations' 32.8% to 43.9%, NOW Foods' 31.4% to 42.5%, and Life Extension's 30.4% to 41.5%. Within this sample's margin, the data does not establish that Thorne is more visible than those brands despite its first-place rank.
## How do the leading brands compare?
The leading sleep brands differ most in citation performance and engine mix. The profiles below use appearance rates from 288 returned answers and owned-domain citation rates from 287 answers with retrieval evidence.
### Thorne
52.1% appearance rate, with a 46.3% to 57.8% interval, placed Thorne first. It appeared in all four engines, led by ChatGPT at 69.4%, followed by Perplexity and Claude at 47.2% each, and Gemini at 44.4%. Its average first mention was position 3. Its owned domain citation rate was 11.1% among answers with retrieval evidence.
### Nature Made
39.6% appearance rate, with a 34.1% to 45.3% interval, placed Nature Made second. ChatGPT named it in 62.5% of its 72 answers and Perplexity in 51.4%, while Gemini named it in 27.8% and Claude in 16.7%. Its average first mention was position 5.2. Its owned domain citation rate was 8% among answers with retrieval evidence.
### Pure Encapsulations
38.2% appearance rate, with a 32.8% to 43.9% interval, placed Pure Encapsulations third. ChatGPT named it in 58.3% of its 72 answers, Gemini in 38.9%, Perplexity in 33.3%, and Claude in 22.2%. Its average first mention was position 4. Its owned domain citation rate was 0.3% among answers with retrieval evidence.
### NOW Foods
36.8% appearance rate, with a 31.4% to 42.5% interval, placed NOW Foods fourth. ChatGPT named it in 79.2% of its 72 answers, compared with 34.7% for Perplexity, 30.6% for Gemini, and 2.8% for Claude. Its average first mention was position 6.2. Its owned domain citation rate was 4.9% among answers with retrieval evidence.
### Life Extension
35.8% appearance rate, with a 30.4% to 41.5% interval, placed Life Extension fifth. ChatGPT named it in 56.9% of its 72 answers, Perplexity in 45.8%, Gemini in 33.3%, and Claude in 6.9%. Its average first mention was position 5.1. Its owned domain citation rate was 18.1% among answers with retrieval evidence.
### Natrol
30.9% appearance rate, with a 25.8% to 36.5% interval, placed Natrol sixth. ChatGPT named it in 51.4% of its 72 answers, Perplexity in 41.7%, Gemini in 25%, and Claude in 5.6%. Its average first mention was position 6. Its owned domain citation rate was 2.8% among answers with retrieval evidence.
### Olly
29.9% appearance rate, with a 24.9% to 35.4% interval, placed Olly seventh. ChatGPT named it in 51.4% of its 72 answers, Perplexity in 31.9%, Gemini in 27.8%, and Claude in 8.3%. Its average first mention was position 6.7. Its owned domain citation rate was 2.8% among answers with retrieval evidence.
### Magnesium
19.8% appearance rate, with a 15.6% to 24.8% interval, placed Magnesium eighth as a non-brand term in the tally. Gemini named it in 34.7% of its 72 answers, Perplexity in 26.4%, ChatGPT in 13.9%, and Claude in 4.2%. Its average first mention was position 6.3.
### Nature’s Bounty
17% appearance rate, with a 13.1% to 21.8% interval, placed Nature’s Bounty ninth. Perplexity and ChatGPT each named it in 30.6% of their 72 answers, Gemini named it in 6.9%, and Claude did not name it in its 72 answers. Its average first mention was position 6.7. Its owned domain citation rate was 1% among answers with retrieval evidence.
### Gaia Herbs
14.6% appearance rate, with an 11% to 19.1% interval, placed Gaia Herbs tenth. Perplexity named it in 29.2% of its 72 answers, ChatGPT in 18.1%, Gemini in 11.1%, and Claude did not name it in its 72 answers. Its average first mention was position 6.5. Its owned domain citation rate was 2.4% among answers with retrieval evidence.
## Where do the engines disagree?
The engines disagree sharply on several brands, and each engine returned 72 answers. ChatGPT produced the highest measured rates for NOW Foods, Nature Made, Pure Encapsulations, Life Extension, Natrol, and Olly. Claude produced low or zero rates for many of those same brands. These gaps can reflect model and retrieval-tool design, not only a brand's visibility.
### Engine appearance patterns
### Appearance rate by engine
Rather than using one shared total, each cell takes that engine's own answer count as its denominator, allowing row-by-row engine comparison.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Thorne47.2%69.4%44.4%47.2%
Nature Made51.4%62.5%27.8%16.7%
Pure Encapsulations33.3%58.3%38.9%22.2%
NOW Foods34.7%79.2%30.6%2.8%
Life Extension45.8%56.9%33.3%6.9%
Natrol41.7%51.4%25%5.6%
Olly31.9%51.4%27.8%8.3%
Magnesium26.4%13.9%34.7%4.2%
Nature’s Bounty30.6%30.6%6.9%0%
Gaia Herbs29.2%18.1%11.1%0%
Momentous22.2%13.9%11.1%5.6%
Doctor’s Best, Inc.18.1%26.4%6.9%0%
NOW Foods has the widest leading-brand split: 79.2% in ChatGPT's 72 answers and 2.8% in Claude's 72 answers. Life Extension was named in 56.9% of ChatGPT answers but 6.9% of Claude answers. Natrol was named in 51.4% of ChatGPT answers and 5.6% of Claude answers. Olly followed the same pattern, at 51.4% in ChatGPT and 8.3% in Claude.
Gemini is the outlier for certification and ingredient terms. It named USP in 47.2% of its 72 answers, while Perplexity named USP in 2.8% and ChatGPT and Claude named it in none. Gemini also named Magnesium in 34.7% and L-theanine in 20.8%, higher than ChatGPT's 13.9% and 4.2%, respectively. Moon Juice appeared only in Gemini, at 4.2% of its 72 answers.
Perplexity gave more space to some lower-ranked names. Innerbody appeared in 22.2% of Perplexity answers but 1.4% in both ChatGPT and Gemini and 0% in Claude. Nutricost reached 25% in Perplexity, compared with 4.2% in ChatGPT, 2.8% in Gemini, and 13.9% in Claude. Claude was the only engine to name Ritual at a higher rate than the other three, at 15.3%.
## Which demand clusters favor which brands?
The cluster results show which measured brands lead each question type across sleep supplement selection. General buyer-intent prompts and specific-need prompts measure different demand, so their leaders should not be treated as interchangeable.
### Brand leaders by demand cluster
### Leaders by demand cluster
Rather than being shown as one set, the same answers are regrouped by the demand cluster from which each prompt was sampled; each cluster rate uses its own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
— Other demand —
General best-in-category12Thorne91.7%Pure Encapsulations 66.7%, Olly 66.7%, Nature Made 50%
Everyday and mainstream choice12Olly66.7%Natrol 58.3%, Nature Made 50%, Thorne 25%
Price and value12Nature Made66.7%Thorne 50%, NOW Foods 41.7%, Pure Encapsulations 33.3%
Primary use case12Life Extension66.7%Magnesium 66.7%, L-theanine 66.7%, Melatonin 66.7%
Beginners and first purchase12Olly75%Natrol 66.7%, Thorne 50%, Garden of Life 41.7%
Where to buy12Thorne41.7%Nature Made 25%, Natrol 16.7%, Nutricost 16.7%
Specific need or condition12Life Extension66.7%Thorne 58.3%, Pure Encapsulations 41.7%, NOW Foods 41.7%
Gifting and occasions12Thorne50%Nature Made 50%, Natrol 50%, Olly 50%
Trust and recommendation12Thorne83.3%Life Extension 75%, Nature Made 66.7%, NOW Foods 66.7%
Comparison and evaluation12Thorne58.3%Life Extension 58.3%, Natrol 58.3%, Olly 58.3%
— Sleep (melatonin / magnesium) —
best ashwagandha for sleep and stress12NOW Foods50%Life Extension 41.7%, Thorne 33.3%, Gaia Herbs 33.3%
best supplement for restless leg syndrome12Thorne66.7%Pure Encapsulations 50%, NOW Foods 41.7%, Magnesium 41.7%
best magnesium supplement for anxiety12Thorne75%Pure Encapsulations 75%, NOW Foods 66.7%, Doctor’s Best, Inc. 66.7%
best non habit forming sleep supplement12Thorne58.3%Nature Made 58.3%, NOW Foods 58.3%, Momentous 58.3%
best sleep supplement without melatonin12Life Extension58.3%Thorne 41.7%, L-theanine 41.7%, Pure Encapsulations 33.3%
best CBN supplement for sleep12Innerbody25%—
best magnesium for sleep12Thorne91.7%Pure Encapsulations 75%, Nature Made 58.3%, NOW Foods 58.3%
best melatonin supplement for sleep12Nature Made75%Pure Encapsulations 75%, Life Extension 75%, Thorne 66.7%
best natural sleep aid12Thorne66.7%Olly 66.7%, Pure Encapsulations 58.3%, Natrol 58.3%
magnesium glycinate vs magnesium citrate12Magnesium75%Thorne 58.3%, Pure Encapsulations 58.3%, Nature Made 50%
best melatonin dosage for adults12Thorne33.3%Nature Made 33.3%, Pure Encapsulations 33.3%, NOW Foods 33.3%
best sleep gummies12Olly75%Natrol 66.7%, Lemme Inc. 50%, Nature’s Bounty 25%
best l-theanine supplement for sleep12Thorne58.3%Life Extension 58.3%, Doctor’s Best, Inc. 58.3%, Pure Encapsulations 50%
best supplement for insomnia12Pure Encapsulations75%Melatonin 66.7%, Natrol 58.3%, Magnesium 50%
The general buyer-intent grouping identifies brands that engines name when selection questions are broad. The sub-category and specific-need grouping separates brand visibility around melatonin, magnesium forms, L-theanine, and non-melatonin formulations. A brand's strongest cluster is a map of the questions where it is named, not a product-quality judgment.
## Which sites do engines cite for sleep supplement answers?
Owned-domain citations do not track appearance rank. Life Extension's domain was cited in 18.1% of 287 answers with retrieval evidence, ahead of Thorne at 11.1% and Nature Made at 8%. ConsumerLab and Innerbody were cited more often than any measured brand-owned domain in the top source list.
### Owned-domain citation rates
### Owned-domain citation rate
Rather than all answers, this is the share of the 287 answers with retrieval evidence that cited a URL on the brand's own website.
Thorne
11.1% · 32/287
Nature Made
8% · 23/287
Pure Encapsulations
0.3% · 1/287
NOW Foods
4.9% · 14/287
Life Extension
18.1% · 52/287
Natrol
2.8% · 8/287
Olly
2.8% · 8/287
Nature’s Bounty
1% · 3/287
Gaia Herbs
2.4% · 7/287
Momentous
5.2% · 15/287
Doctor’s Best, Inc.
1% · 3/287
USP
0.7% · 2/287
Life Extension had 52 owned-domain citations. Thorne had 32 and Nature Made had 23. Pure Encapsulations appeared in 38.2% of returned answers but its domain was cited in 0.3% of retrieval-evidence answers. This is the report's clearest gap between being named and being used as a source.
### Publishers cited in sleep answers
### Most-cited publishers
Rather than counting every link, evidence publishers are counted once for each answer in which they were cited.
PublisherAnswers citing itAnswer penetration
healthline.com171/28759.6%
sleepfoundation.org159/28755.4%
sleepadvisor.org89/28731%
innerbody.com88/28730.7%
consumerlab.com87/28730.3%
goodrx.com83/28728.9%
fortune.com78/28727.2%
amazon.com75/28726.1%
health.yahoo.com59/28720.6%
goodhousekeeping.com54/28718.8%
lifeextension.com52/28718.1%
mindbodygreen.com47/28716.4%
Healthline was cited in 59.6% of retrieval-evidence answers, followed by Sleep Foundation at 55.4%. Sleep Advisor reached 31%, while Innerbody and ConsumerLab reached 30.7% and 30.3%. The source mix includes health publishers, testing and comparison sources, retailers, and brand-owned sites. The numbers use answers with retrieval evidence as their denominator.
## Who is winning and who is at risk?
### Brands winning on measured visibility
Thorne is winning on appearance with a 52.1% rate and an average first mention at position 3. Life Extension is winning on owned-domain citation at 18.1%, despite ranking fifth in appearance at 35.8%. Nature Made combines second-place appearance at 39.6% with an 8% owned-domain citation rate. Momentous ranks eleventh by appearance at 13.2% but has a 5.2% owned-domain citation rate and an average first mention at position 4.2.
### Brands at risk on measured visibility
Pure Encapsulations has a 38.2% appearance rate but a 0.3% owned-domain citation rate. NOW Foods has a 36.8% appearance rate, but its engine pattern ranges from 79.2% in ChatGPT to 2.8% in Claude. Moon Juice appeared in 1% of returned answers and only in Gemini. Calm appeared in 0.3% of returned answers and only in ChatGPT. These are measured visibility risks, not statements about product quality or consumer preference.
## What does the tally say about the category?
The category has a visible group at the top, but the first-place position is not decisive within the sample. Thorne's interval overlaps the intervals of ranks two through five. The engines converge on broad recognition for Thorne, Nature Made, Pure Encapsulations, NOW Foods, Life Extension, Natrol, and Olly because each appeared across all four engines. Their rates differ widely by engine.
Third-party publishers carry much of the cited material. Healthline and Sleep Foundation were cited in 59.6% and 55.4% of retrieval-evidence answers, while the strongest measured brand-owned domain, Life Extension, was cited in 18.1%. The brief's named brands show different outcomes: Natrol ranked sixth at 30.9%, Life Extension ranked fifth at 35.8%, Moon Juice ranked twenty-sixth at 1%, and Calm ranked twenty-seventh at 0.3%.
Magnesium and Melatonin appear as non-brand terms in the tally, at 19.8% and 9.7% of returned answers. They are not eligible for owned-domain citation metrics, so the report does not compare them with brand domains.
## What changed during the measurement window?
This measurement captures repeat-generation variation across three trials per prompt, but it does not contain prior-wave results. The data therefore cannot establish category movement over time. It does show system-level variation within the measurement window, including ChatGPT's 79.2% rate for NOW Foods and Claude's 2.8% rate for the same brand.
## What should sleep brands build and measure next?
- Pure Encapsulations should review pages that explain its measured sleep products and test whether more retrieval-evidence answers cite pureencapsulations.jp. The weakness is a 38.2% appearance rate paired with a 0.3% owned-domain citation rate. Re-measure the owned-domain citation rate across the same question set.
- NOW Foods should test whether product and ingredient pages resolve the cross-engine gap. Its rate ranged from 79.2% in ChatGPT to 2.8% in Claude. Re-measure appearance by engine, using 72 returned answers per engine.
- Life Extension should protect its citation lead by checking whether lifeextension.com remains cited in answers where the brand is named. Its owned-domain citation rate was 18.1% among retrieval-evidence answers. Re-measure citations and appearance together, since the brand ranked fifth in appearance.
- Natrol and Olly should examine why ChatGPT named each in 51.4% of its answers while Claude named Natrol in 5.6% and Olly in 8.3%. Re-measure their engine-specific appearance rates after updating the pages used for sleep product information.
- Moon Juice and Calm should measure whether their product pages appear in more than one engine's answers. Moon Juice appeared only in Gemini at 4.2% of its 72 answers. Calm appeared only in ChatGPT at 1.4% of its 72 answers.
- Brands competing around magnesium glycinate or citrate should track ingredient-term queries alongside brand queries. Magnesium appeared in 19.8% of returned answers and Gemini named it in 34.7% of its answers. Re-measure whether a specific brand appears in the same demand clusters rather than only in broad brand lists.
## What does this mean for brands in the category?
Interpretation: sleep supplement visibility has two separate jobs. One is being named when an engine answers a selection question. The other is having the brand's own pages used as evidence. Thorne leads the first measure in this sample. Life Extension leads the second among measured brand domains.
Interpretation: a brand cannot infer one result from the other. Pure Encapsulations and NOW Foods have strong appearance results with limited owned-domain citation rates. Life Extension has a lower appearance rank than the leaders but the strongest measured owned-domain citation rate. Brands need to monitor both measures and inspect each engine separately.
## How was this sleep visibility study measured?
Rather than relying on a single pass, this flagship study ran 24 stratified sleep supplement prompts three times in Perplexity, ChatGPT, Gemini, and Claude. It captured 288 returned answers, including 287 answers with retrieval evidence. Returned answers are the denominator for appearance rates, while retrieval-evidence answers are the denominator for citation and source rates.
### Study method
### How this study was measured
- Before any questions were asked, the sampling frame was fixed: 24 prompts were allocated across the demand clusters.
- Rather than varying questions by engine, the same questions went to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
- 288 answers returned of 288 attempted, 287 with retrieval evidence.
- Rather than being scored separately, product lines were consolidated under parent brands through a registry fixed before scoring.
- Rather than applying one basis to all metrics, appearance rate is reported with a 95% Wilson interval; citation metrics are counted only on answers with evidence.
The results describe these measured prompts on September 30, 2026. They do not measure all sleep supplement searches, consumer demand, product quality, medical suitability, or sales. One Gemini answer lacked retrieval evidence, so citation metrics use 287 answers rather than 288.
## Frequently asked questions
### Which brand ranked first for sleep supplement appearance?
Thorne ranked first. It appeared in 52.1% of 288 returned answers, with a 95% interval of 46.3% to 57.8%.
### Which measured brand had the highest owned-domain citation rate?
Life Extension had the highest measured brand-owned citation rate at 18.1%. Citation rates use 287 answers with retrieval evidence as the denominator.
### Did the rank-one brand clearly outperform ranks two through five?
No. Thorne's 46.3% to 57.8% interval overlaps the intervals for Nature Made, Pure Encapsulations, NOW Foods, and Life Extension. The differences are within the margin of this sample.
### Which engine named NOW Foods most often?
ChatGPT showed markedly higher naming of NOW Foods, at 79.2% of its 72 answers, than Perplexity at 34.7%, Gemini at 30.6%, or Claude at 2.8%.
### Which sources were cited most often?
Healthline was cited in 59.6% of retrieval-evidence answers and Sleep Foundation in 55.4%. Sleep Advisor was cited in 31%.
### Were magnesium and melatonin treated as brands in this study?
No. Magnesium and Melatonin appear as non-brand terms in the tally. They appear in the leaderboard but are not eligible for owned-domain citation metrics.
## Charts and data tables
### Aggregators and comparison sites, tracked separately
Although they are measured in the same manner, these are not category brands and therefore do not appear in the brand leaderboard.
#EntityAppearance rateAnswers95% interval
1Sleep Foundation9.4%27/2886.5%–13.3%
2Healthline8.7%25/2885.9%–12.5%
3Verywell Health0%0/2880%–1.3%
4Examine.com0%0/2880%–1.3%
## Methodology appendix
Rather than being a separate interpretation, this appendix is produced directly from the study's measurement record. It is provided so any brand named here can reproduce or challenge the result.
### What was measured
- Category: Sleep (melatonin / magnesium)
- Measurement date: 2026-09-30
- Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
- Answers attempted: 288
- Answers returned (appearance denominator): 288
- Answers with retrieval evidence (citation denominator): 287 (99.7% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Unlike a single-run study, Flagship depth measures 24 stratified prompts three times per engine, making both between-prompt and repeat-generation variation observable.
### How the prompts were sampled
Rather than allowing the prompt writer to set the category's demand structure, the sampling frame is fixed: 14 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Sleep (melatonin / magnesium); then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only.
Demand clusterPromptsScope
best melatonin supplement for sleep1best melatonin supplement for sleep
best magnesium for sleep1best magnesium for sleep
magnesium glycinate vs magnesium citrate1magnesium glycinate vs magnesium citrate
best natural sleep aid1best natural sleep aid
best melatonin dosage for adults1best melatonin dosage for adults
best sleep gummies1best sleep gummies
best supplement for insomnia1best supplement for insomnia
best l-theanine supplement for sleep1best l-theanine supplement for sleep
best ashwagandha for sleep and stress1best ashwagandha for sleep and stress
best non habit forming sleep supplement1best non habit forming sleep supplement
best supplement for restless leg syndrome1best supplement for restless leg syndrome
best magnesium supplement for anxiety1best magnesium supplement for anxiety
best sleep supplement without melatonin1best sleep supplement without melatonin
best CBN supplement for sleep1best CBN supplement for sleep
General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Primary use case1the dominant everyday use case for this category
Beginners and first purchase1what a first-time buyer with no knowledge of the category should get
Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace
Gifting and occasions1what to buy as a gift or for a specific occasion, event or celebration
Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve
Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis
Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts
### The exact prompts
#PromptClusterShape
1best melatonin supplement for sleepneed_1medium
2best magnesium for sleepneed_2medium
3magnesium glycinate vs magnesium citrateneed_3medium
4best natural sleep aidneed_4medium
5best melatonin dosage for adultsneed_5medium
6best sleep gummiesneed_6medium
7best supplement for insomnianeed_7medium
8best l-theanine supplement for sleepneed_8medium
9best ashwagandha for sleep and stressneed_9medium
10best non habit forming sleep supplementneed_10medium
11best supplement for restless leg syndromeneed_11medium
12best magnesium supplement for anxietyneed_12medium
13best sleep supplement without melatoninneed_13medium
14best CBN supplement for sleepneed_14medium
15best sleep supplementsgeneral_bestshort
16best everyday sleep aid brandseverydaymedium
17best affordable magnesium for sleepvalue_pricemedium
18best supplements for falling asleep fasteruse_casemedium
19best sleep supplements for beginnersbeginnermedium
20Where should I buy sleep supplements for reliable selection, freshness, and pricing?retaillong
21What sleep supplement brands make a thoughtful gift for someone with a bedtime routine?giftinglong
22Which sleep supplement brands are best for people who want non-melatonin options?specific_needlong
23Which sleep supplement brands are better for sleep: melatonin or magnesium formulas?comparisonlong
24Which sleep supplement brands do reviewers and health experts trust most?trustlong
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search727272100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool727272100%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)72727198.6%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool727272100%
Rather than being strictly comparable across engines, results reflect different models, retrieval tools and answer formats: each engine uses its own combination, so a gap between two engines can reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — unlike a retrieval-only measure, this is the share of returned answers that named the brand. It is measured on every answer the engine returned, whether or not it searched the web, so retrieval behaviour does not silently alter the denominator.
- Macro appearance rate — rather than weighting prompts by how many engines answered them, this is the mean of per-prompt appearance rates, with each prompt counted once.
- 95% interval — rather than a simple range, this is the Wilson score interval on the appearance rate. When two brands' intervals overlap, the study does not support claiming that one is more visible than the other.
- Owned-domain citation rate — unlike total mentions, this is the share of answers with retrieval evidence that cited a URL on the brand's own domain. It narrowly measures use of the brand's own site as a source.
- Share of all citations — rather than a share of answers, this is the brand's own domain as a share of all attributed publisher citations in the study.
- Not measured — rather than being shown as 0%, this indicates that the brand's official domain could not be established and no citation result exists.
- 0% — unlike Not measured, the metric could be evaluated and the event did not occur.
- Average position — rather than every mention's placement, this is the mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — rather than a rate within one engine, this is the share of engines that named the brand at least once.
- Repeat consistency — rather than overall appearance, this is the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values indicate an unstable engine answer rather than brand absence.
### Evidence rules
- Rather than treating any URL in prose as proof of search, an answer has retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL merely typed by the model is not treated as evidence that it searched.
- Rather than assigning sources to intermediary or redirect hosts, sources are attributed to the publisher behind each retrieval receipt. Those hosts are never reported as publishers.
- Rather than attributing or dropping them, 0 retrieval receipt(s) for which publisher identity could not be established are disclosed here.
- Rather than URL-level totals, source counts are answer-level: a publisher is counted once per answer in which it was cited.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Thorne150/28852.1%52.1%46.3%–57.8%32/287 (11.1%)
2Nature Made114/28839.6%39.6%34.1%–45.3%23/287 (8%)
3Pure Encapsulations110/28838.2%38.2%32.8%–43.9%1/287 (0.3%)
4NOW Foods106/28836.8%36.8%31.4%–42.5%14/287 (4.9%)
5Life Extension103/28835.8%35.8%30.4%–41.5%52/287 (18.1%)
6Natrol89/28830.9%30.9%25.8%–36.5%8/287 (2.8%)
7Olly86/28829.9%29.9%24.9%–35.4%8/287 (2.8%)
8Magnesium57/28819.8%19.8%15.6%–24.8%domain not established — excluded from citation metrics
9Nature’s Bounty49/28817%17%13.1%–21.8%3/287 (1%)
10Gaia Herbs42/28814.6%14.6%11%–19.1%7/287 (2.4%)
11Momentous38/28813.2%13.2%9.8%–17.6%15/287 (5.2%)
12Doctor’s Best, Inc.37/28812.8%12.9%9.5%–17.2%3/287 (1%)
13USP36/28812.5%12.5%9.2%–16.8%2/287 (0.7%)
14ConsumerLab35/28812.2%12.1%8.9%–16.4%89/287 (31%)
15Nutricost33/28811.5%11.5%8.3%–15.7%1/287 (0.3%)
16Garden of Life33/28811.5%11.5%8.3%–15.7%3/287 (1%)
17Natural Vitality31/28810.8%10.8%7.7%–14.9%7/287 (2.4%)
18L-theanine29/28810.1%10.1%7.1%–14.1%1/287 (0.3%)
19Melatonin28/2889.7%9.7%6.8%–13.7%domain not established — excluded from citation metrics
20Ritual25/2888.7%8.7%5.9%–12.5%2/287 (0.7%)
21Solgar, Inc.24/2888.3%8.3%5.7%–12.1%1/287 (0.3%)
22Jarrow Formulas, Inc.19/2886.6%6.6%4.3%–10.1%4/287 (1.4%)
23Lemme Inc.19/2886.6%6.6%4.3%–10.1%1/287 (0.3%)
24Innerbody18/2886.3%6.2%4%–9.7%88/287 (30.7%)
25Nested Naturals16/2885.6%5.6%3.4%–8.8%0/287 (0%)
26Moon Juice3/2881%1%0.4%–3%2/287 (0.7%)
27Calm1/2880.3%0.3%0.1%–1.9%7/287 (2.4%)
### Entity handling
Rather than treating product lines and model names as separate brands, brand-level metrics roll them up to their parent brand using a registry fixed before scoring. No merging is based on this study's own counts alone.
No roll-ups were applied in this study.
Rather than assigning metrics to uncertain names, 40 further name(s) found in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study.
### Limits of these findings
- Rather than a permanent assessment, this is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-30. Engine behaviour can change without notice.
- Rather than a census, the prompt set is a stratified sample of category demand. Results describe these prompts and should not be read as representative of all searches in the category.
- Rather than statistically distinguishable differences, overlapping intervals mean differences between brands cannot be distinguished at this sample size.
- Rather than measuring absence, a brand not listed here simply was not named in these answers.
- Rather than an endorsement, quality judgement or market-share estimate, appearance is a visibility measure.
- Rather than reporting zero, 2 of 27 ranked brand(s) had no official domain established before scoring. They carry appearance results only and are excluded from citation metrics and source tables.
---
### The Pre-Legalization AI Visibility Index 2026, Explained
URL: https://www.5wpr.com/research/pre-legalization-ai-visibility/
Summary: Georgia, Texas, and California residents already ask AI where to legally engage with sports outcomes, and the answer is often not a licensed sportsbook.
Georgia, Texas, and California residents searching for legal ways to engage with sports outcomes are increasingly encountering prediction markets as an alternative to conventional sportsbooks. Georgia's HB 910 failed on the House floor 63-98 in March 2026. Texas will not revisit legalization until its 2027 legislative session at the earliest. California's tribal coalition has set 2028 as its ballot target. In all three states, Kalshi, Polymarket US, and Novig are already being marketed to residents as a federally regulated alternative.
AI market share can begin forming before legal market share exists. The operator that eventually wins a Texas license is not entering an open market. It is entering a market where the AI-generated default answer may already belong to someone else. That is this report's central finding. The legislative and litigation detail that follows exists to support it, not to replace it.
## What Are This Report's Four Key Findings?
Sportsbook operators spend heavily to build a presence the moment a state legalizes. This report measures what happens in the months before that moment, when AI systems are already answering the same questions those future customers are asking.
- Georgia, Texas, and California residents are already being pointed toward prediction markets as a federally regulated alternative to sports betting, not as a hypothetical workaround but as today's actual advice on independent betting-content sites. If AI engines are absorbing and repeating that same advice, sportsbooks may be losing the AI-generated default answer in a market they cannot yet legally enter.
- California's version of this dynamic is adversarial, not just informational. A coalition of tribes sued Kalshi and Robinhood under the Indian Gaming Regulatory Act. A federal judge denied the tribes' request to force Kalshi to geoblock tribal lands. The California Nations Indian Gaming Association's chairman has since called for a total ban on prediction markets as part of the state's 2028 ballot fight.
- Whether a cold AI answer gets a state's status right depends heavily on timing, not on how newsworthy the fact is. Missouri's sports betting launch and Texas's failed 2025 session both predate most tested models' knowledge boundaries and are answered correctly even without live search. Georgia's March 2026 bill failure does not, and cold answers are far more likely to miss it.
- This study's sourcing held up well: no conflicts were found across the four states' legal status, and one outlier source claiming Georgia's bill had passed the House was identified and set aside as superseded by four corroborating sources.
## Why Does This Matter to Sportsbook Operators?
A prospective bettor in Texas who asks an AI system where to legally engage with sports outcomes today is not told to wait. They are told about Kalshi, Polymarket US, or Novig. Every week that passes before a state legalizes is a week those platforms have to become the AI-generated default answer, uncontested by any sportsbook that cannot yet legally compete for the same query. By the time a license is finally issued, the operator holding it may be entering a market where the incumbent already exists inside AI-generated answers, not just inside a competitor's user base.
Why it works: AI systems answer from whatever content is currently indexed and retrievable for a given query. Prediction market platforms and the betting-content sites covering them have already published state-specific content naming Georgia, Texas, and California directly, while licensed sportsbooks cannot yet publish comparable content in states where they cannot legally operate, per the state-by-state status this report verified in August 2026.
## Why Does Missouri's AI Answer Work and Georgia's Doesn't?
Missouri legalized sports betting by voter amendment in November 2024 and launched on December 1, 2025. Both dates fall before the knowledge boundary of the AI systems tested in this report. A cold, no-search answer to "is sports betting legal in Missouri" tends to get it right, one major reason being that the relevant facts predate the model's knowledge boundary.
Georgia is closer to the mirror case. HB 910 failed on the House floor 63-98 on March 6, 2026, a date that falls after that same boundary for most tested models. A cold answer to "is sports betting legal in Georgia" is much less likely to reflect this, and may describe HB 910 as still pending rather than dead.
Why it works: a language model's knowledge is fixed at training time unless it retrieves current information. Missouri's launch and Georgia's bill failure are both matters of public legislative and regulatory record, one dated November 2024 and December 2025, the other dated March 6, 2026. A major variable determining whether a cold AI system answers correctly is which side of its knowledge boundary the relevant development falls on, though training coverage, how prominently a fact was covered, and conflicting information in a model's training data can also affect recall. Different systems do not share one universal cutoff date, and this report treats each model's boundary as its own.
## Where Does AI Send Someone Who Can't Legally Bet on Sports Yet?
Independent betting-content sites already answer this question the same way in Georgia, Texas, and California: consider a federally regulated prediction market. This report's central test is whether AI engines give the same answer, and whether they get the legal nuance right when they do, including the fact that prediction markets are not conventional sports betting, are regulated by a different federal body than any state gaming commission, and remain subject to active legal challenges in at least one of these three states.
In Georgia, residents cannot legally access DraftKings, FanDuel, BetMGM, or Caesars as conventional state-licensed sportsbooks, because no such license exists there. Kalshi and QCX LLC, operating as Polymarket US, are separately registered with the Commodity Futures Trading Commission as designated contract markets and are marketed as a federally regulated alternative currently available to Georgia residents on that basis. In Texas, Kalshi, Polymarket US, and Novig are marketed directly to residents as a compliant alternative, with Novig maintaining a dedicated Texas content hub.
## Why Is California's Version of This Fight Different?
California's tribes are not passively watching prediction markets fill a gap left by the 2022 ballot rejections. They sued. A coalition of tribes brought an Indian Gaming Regulatory Act claim against Kalshi and its partner Robinhood, arguing that prediction market sports contracts violate tribal gaming exclusivity. A federal judge denied the tribes' request for a preliminary injunction that would have forced Kalshi to geoblock tribal lands, according to reporting cited in this study's research phase.
California Nations Indian Gaming Association chairman James Siva has since described the tribes' goal for a future ballot measure as a "kill shot," a complete, no-loophole prohibition on prediction markets, folded into the same 2028 initiative the tribes plan to use to legalize their own online sports betting market.
In most categories, framing differences track commercial incentive. Here the split instead tracks jurisdiction. The federal regulatory record treats Kalshi and Polymarket US as lawful designated contract markets. California's tribal gaming authorities frame the same platforms as an existential threat to gaming exclusivity. Both are describing the same regulatory fact from different institutional positions, and an AI system answering "is Kalshi legal in California" has to arbitrate between them.
Why it works: an AI system's answer to "is Kalshi legal in California" depends on whether it has retrieved this specific, ongoing federal case. A cold answer might describe California's sports betting ballot timeline accurately, since the 2022 rejections and the 2028 tribal target both predate most tested models' knowledge boundaries, while still missing the active IGRA lawsuit and its denied injunction entirely, since that development is more recent.
One figure surfaced in this study's research and deliberately excluded from the findings above: a claim that California and Texas together account for roughly 60% of Kalshi's user base. That figure traces to a single quote from a tribal gaming official with an evident advocacy interest in the number sounding large, and it is not independently verified against Kalshi's own data. It should not be treated as fact.
## How Do the Five AI Engines Compare on This?
Google AI Overviews and Perplexity score highest in 5W's modeling because both are search-grounded by default and index recent betting-content sites directly. ChatGPT scores lowest because it does not always invoke browsing without an explicit prompt cue. Claude, tested directly with search enabled, scored highest of all at 89 out of 100; the same model tested cold, with no search, scored 58, still meaningfully higher than the 45 recorded on the same cold test in 5W's Prediction Markets AI Visibility Index 2026.
EngineRetrieval basisCurrency score
Google AI OverviewsAlways search-grounded by construction87
PerplexitySearch-grounded by default, cites sources directly84
Claude, search enabledInvokes search contextually for time-sensitive queries89
GeminiSearch grounding common but not guaranteed on every query76
ChatGPTBrowsing not always invoked without an explicit cue68
Claude, no searchTraining knowledge only, the cold-pass baseline58
Table caption: Retrieval Currency Score, 0 to 100, modeled from Claude's knowledge of each platform's public retrieval behavior and verified against the state-by-state legal record above. Claude's own scores are drawn from direct testing; the other four rows are directional estimates, not logged query results, consistent with the methodology used across prior volumes in this series.
Claude's cold-pass score of 58 provides evidence for a more precise version of the AI Adjudication Gap first measured in 5W's Prediction Markets AI Visibility Index 2026: the size of the Gap depends on the specific date distribution of the underlying facts, not simply on how fast a category moves in general. Two of this study's four states, Texas and Missouri, rest on facts that predate the knowledge boundary entirely, while the earlier study's legality cluster was almost entirely postdated.
## What Is the Methodology Behind This Report?
Three target states were selected for their distinct legalization stages: Georgia, where a bill recently failed on the House floor; Texas, which has no framework and a 2027 legislative window at the earliest; and California, where a tribal ballot measure is targeted for 2028. Missouri was selected as a calibration control, not a scored state, because its legalization date is fixed, dated, and independently corroborated across multiple sources.
Claude was tested directly under two conditions: a warm pass informed by this report's own verified state-by-state research, and a cold pass relying on training knowledge only, approximating an independent session without live retrieval. Retrieval Currency Scores for ChatGPT, Gemini, Perplexity, and Google AI Overviews are modeled from Claude's knowledge of each platform's public retrieval behavior and verified through the same public-source record, consistent with the methodology used across prior volumes in this series. No logged query runs were conducted for these four engines.
Kalshi and QCX LLC, doing business as Polymarket US, are each separately registered with the Commodity Futures Trading Commission as designated contract markets, per CFTC industry filings dated throughout 2026. This report treats that registration as specific to each named operating entity rather than as a blanket characterization of prediction markets generally, and treats the term "legal alternative" as a description of how operators and betting-content sites market these platforms rather than as 5W's own legal determination, particularly in California, where that characterization is under active challenge.
The Retrieval Currency Scores above are directional rather than precise quantitative measurements. The scoring methodology is proprietary to 5W AI Communications. This same citation dynamic, where AI answers can form ahead of a licensed market, extends findings from 5W's US Sports Betting and Gaming AI Visibility Index 2026 and the pre-legalization finding in the 5W Responsible Gambling Communications Audit 2026.
Operators entering a state before it legalizes are not just waiting on a license; they are competing for an AI-generated answer that is already forming without them. 5W runs AI Search (GEO) programs for brands across consumer, B2B, financial services, healthcare, and technology, building the machine-readable footprint that gets brands cited, not just ranked. Learn more at 5W's Generative Engine Optimization practice.
---
### Tilly Norwood AI Visibility Study 2026
URL: https://www.5wpr.com/research/tilly-norwood-ai-visibility-study-2026/
Summary: This AI actor visibility study measures 64 ChatGPT, Gemini, Perplexity, and Claude answers, finding Particle6 leads at 37.5% and Tilly Norwood trails at 20.3%.
# Tilly Norwood AI Visibility Study 2026
Particle6 Leads a Fragmented AI Actor Field
Particle6 had the highest appearance rate in this pilot study of Tilly Norwood and AI actors, appearing in 37.5% of 64 returned answers. Its 95% interval was 26.7% to 49.7%. The result is directional only: the intervals overlap across the leading brands, so this pilot cannot support a claim that Particle6 is more visible than another brand.
The study measured 16 prompts across Perplexity, ChatGPT, Gemini, and Claude. Tilly Norwood appeared in 20.3% of returned answers, while Xicoia Ltd. appeared in 25%. The main commercial risk is uneven recall: several names appeared regularly in one system and not at all in another.
64AI answers analyzed
37.5%Particle6 appearance rate, the category leader
25Brands named across the study
100%Answers carrying retrieval evidence
These results describe the measured prompts on September 23, 2026. They do not describe all questions about AI actors, consumer demand, or the category as a whole.
## Which brands appeared most often?
Particle6 ranked first by appearance rate at 37.5%, followed by Synthesia at 28.1%, Xicoia Ltd. at 25%, and Tilly Norwood at 20.3%. The appearance denominator is 64 returned answers. The ranking is a descriptive tally in a pilot, not a category ranking claim.
Among the named entities in the brief, Particle6 appeared in 24 answers, Xicoia Ltd. in 16, Tilly Norwood in 13, and SAG-AFTRA in 11. The study also surfaced tools, studios, media companies, publishers, and individuals. An appearance means the entity was named in an answer, not that the answer endorsed it.
### Appearance leaderboard
### AI visibility leaderboard
Appearance rate is the share of the 64 returned AI answers in which each brand was named.
#BrandNamed inAppearance rate95% intervalAvg position
1Particle624/6437.5%26.7%–49.7%6
2Synthesia18/6428.1%18.6%–40.1%4.8
3Xicoia Ltd.16/6425%16%–36.8%5.4
4Tilly Norwood13/6420.3%12.3%–31.7%9.3
5SAG-AFTRA11/6417.2%9.9%–28.2%7
6HeyGen11/6417.2%9.9%–28.2%4.1
7D‑ID Ltd.10/6415.6%8.7%–26.4%7.4
8Netflix, Inc.7/6410.9%5.4%–20.9%4.4
9The Walt Disney Company7/6410.9%5.4%–20.9%5.1
10Microsoft Corporation7/6410.9%5.4%–20.9%4.4
11Amazon.com, Inc.7/6410.9%5.4%–20.9%4.4
12Apple Inc.7/6410.9%5.4%–20.9%4.9
13Hour One7/6410.9%5.4%–20.9%6.6
14Soul Machines7/6410.9%5.4%–20.9%8.4
15OpenAI, Inc.6/649.4%4.4%–19%6
The leading intervals overlap. Particle6's 26.7% to 49.7% interval overlaps Synthesia's 18.6% to 40.1%, Xicoia Ltd.'s 16% to 36.8%, Tilly Norwood's 12.3% to 31.7%, and SAG-AFTRA's 9.9% to 28.2%. The differences are within the margin of this sample.
## How do the leading brands appear across engines?
The top ten profiles show that appearance varied more by engine than a single category-wide rate suggests. All rates below use each engine's 16 returned answers, while owned-domain citation rates use 64 answers with retrieval evidence.
### Particle6
37.5% appearance rate, with a 26.7% to 49.7% interval, placed Particle6 first in the descriptive tally. Perplexity named Particle6 in 10 of 16 answers, Gemini in 8, ChatGPT in 5, and Claude in 1. Its average first mention position was 6. Its owned domain was cited in 7.8% of the 64 answers with retrieval evidence.
### Synthesia
28.1% appearance rate, with an 18.6% to 40.1% interval, placed Synthesia second in the descriptive tally. ChatGPT named Synthesia in 7 of 16 answers, while Perplexity and Gemini each named it in 4 and Claude named it in 3. Its average first mention position was 4.8. Its owned domain was cited in 15.6% of the 64 answers with retrieval evidence.
### Xicoia Ltd.
25% appearance rate, with a 16% to 36.8% interval, placed Xicoia Ltd. third in the descriptive tally. Perplexity named Xicoia Ltd. in 8 of 16 answers, ChatGPT and Gemini each named it in 4, and Claude did not name it. Its average first mention position was 5.4. Its owned domain was cited in 0% of the 64 answers with retrieval evidence.
### Tilly Norwood
20.3% appearance rate, with a 12.3% to 31.7% interval, placed Tilly Norwood fourth in the descriptive tally. Gemini named Tilly Norwood in 8 of 16 answers and Perplexity in 5, while ChatGPT and Claude did not name Tilly Norwood. Its average first mention position was 9.3. Its owned domain was cited in 12.5% of the 64 answers with retrieval evidence.
### SAG-AFTRA
17.2% appearance rate, with a 9.9% to 28.2% interval, placed SAG-AFTRA fifth in the descriptive tally. Perplexity named SAG-AFTRA in 5 of 16 answers, while ChatGPT and Gemini each named it in 3 and Claude did not name it. Its average first mention position was 7. Its owned domain was cited in 10.9% of the 64 answers with retrieval evidence.
### HeyGen
17.2% appearance rate, with a 9.9% to 28.2% interval, placed HeyGen sixth in the descriptive tally. Perplexity named HeyGen in 4 of 16 answers, Gemini and Claude each named it in 3, and ChatGPT named it in 1. Its average first mention position was 4.1. Its owned domain was cited in 15.6% of the 64 answers with retrieval evidence.
### D-ID Ltd.
15.6% appearance rate, with an 8.7% to 26.4% interval, placed D-ID Ltd. seventh in the descriptive tally. ChatGPT named D-ID Ltd. in 5 of 16 answers, Perplexity and Gemini each named it in 2, and Claude named it in 1. Its average first mention position was 7.4. Its owned domain was cited in 6.3% of the 64 answers with retrieval evidence.
### Netflix, Inc.
10.9% appearance rate, with a 5.4% to 20.9% interval, placed Netflix, Inc. eighth in the descriptive tally. Perplexity named Netflix, Inc. in 4 of 16 answers and ChatGPT in 3, while Gemini and Claude did not name it. Its average first mention position was 4.4. Its owned domain was cited in 0% of the 64 answers with retrieval evidence.
### The Walt Disney Company
10.9% appearance rate, with a 5.4% to 20.9% interval, placed The Walt Disney Company ninth in the descriptive tally. ChatGPT named The Walt Disney Company in 4 of 16 answers and Perplexity in 3, while Gemini and Claude did not name it. Its average first mention position was 5.1. Its owned domain was cited in 1.6% of the 64 answers with retrieval evidence.
### Microsoft Corporation
10.9% appearance rate, with a 5.4% to 20.9% interval, placed Microsoft Corporation tenth in the descriptive tally. ChatGPT named Microsoft Corporation in 4 of 16 answers and Gemini in 3, while Perplexity and Claude did not name it. Its average first mention position was 4.4. Its owned domain was cited in 6.3% of the 64 answers with retrieval evidence.
## Where do the engines disagree?
The engines disagree most sharply on the entities central to the Tilly Norwood story. Perplexity, ChatGPT, Gemini, and Claude each returned 16 answers with retrieval evidence. Their differences can reflect both model behavior and the web-search tool used by each system, rather than a stable measure of any brand's visibility.
### Engine-by-brand appearance
### Appearance rate by engine
Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.
BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate
Particle662.5%31.3%50%6.3%
Synthesia25%43.8%25%18.8%
Xicoia Ltd.50%25%25%0%
Tilly Norwood31.3%0%50%0%
SAG-AFTRA31.3%18.8%18.8%0%
HeyGen25%6.3%18.8%18.8%
D‑ID Ltd.12.5%31.3%12.5%6.3%
Netflix, Inc.25%18.8%0%0%
The Walt Disney Company18.8%25%0%0%
Microsoft Corporation0%25%18.8%0%
Amazon.com, Inc.6.3%25%12.5%0%
Apple Inc.0%31.3%12.5%0%
Perplexity named Particle6 in 10 of 16 answers, Xicoia Ltd. in 8, and Tilly Norwood in 5. Claude named Particle6 once and did not name Xicoia Ltd. or Tilly Norwood. Gemini named Tilly Norwood in 8 of 16 answers and Particle6 in 8, while ChatGPT named Tilly Norwood in none of its 16 answers.
ChatGPT concentrated more heavily on several production and technology companies. It named Synthesia in 7 of 16 answers, Hour One in 6, D-ID Ltd. in 5, Apple Inc. in 5, and Microsoft Corporation, Amazon.com, Inc., Soul Machines, and OpenAI, Inc. in 4 each. Perplexity did not name Hour One, Soul Machines, OpenAI, Inc., Nvidia Corporation, Microsoft Corporation, or Apple Inc. in its 16 answers.
Publisher and interview-related entities also split by system. Perplexity named The Hollywood Reporter in 4 of 16 answers and Piers Morgan Uncensored in 3. ChatGPT did not name The Hollywood Reporter and named Piers Morgan Uncensored once. Gemini and Claude named neither. These patterns do not establish a lasting advantage for any system because the study used one trial per prompt and is pilot depth.
## Which question clusters carry each brand?
The cluster view identifies the brand named most often within each measured demand group. It separates general buyer-intent questions from sub-category and specific-need questions, so the groups should not be treated as one type of demand.
### Cluster leaders
### Leaders by demand cluster
The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count.
Demand clusterAnswersLeading brandLeader rateRunners-up
— Incident-Specific —
What happened during Tilly Norwood's interview with Piers Morgan?4Particle650%Nvidia Corporation 50%, Tilly Norwood 25%, Microsoft Corporation 25%
Was the Cantonese moment a technical glitch or something else?4Particle650%Tilly Norwood 50%, Microsoft Corporation 50%, Amazon.com, Inc. 50%
— Sentiment & Reputational Trajectory —
Has the Piers Morgan glitch hurt or helped Tilly Norwood's/Particle6's credibility?4Particle675%Xicoia Ltd. 50%, Tilly Norwood 50%, Piers Morgan Uncensored 50%
Do people take Tilly Norwood seriously as an actor?4Particle650%Xicoia Ltd. 50%, SAG-AFTRA 50%, Microsoft Corporation 25%
— Industry Reaction & Stakeholder Positions —
What is SAG-AFTRA's position on AI-generated performers like Norwood?4Tilly Norwood50%Netflix, Inc. 50%, The Walt Disney Company 50%, Warner Bros. Discovery, Inc. 50%
How has Hollywood responded to Tilly Norwood?4Tilly Norwood50%SAG-AFTRA 50%, Particle6 25%, Xicoia Ltd. 25%
— Recognition & Baseline Awareness —
What is Particle6 / Xicoia, and what is their AI actor strategy?4Particle6100%Xicoia Ltd. 75%, Synthesia 50%, Tilly Norwood 50%
Who is Tilly Norwood?4Particle650%Xicoia Ltd. 50%, Microsoft Corporation 50%, Amazon.com, Inc. 50%
— Business Viability & Commercial Case —
Is Tilly Norwood a commercially viable "actor" for studios?4Xicoia Ltd.50%Netflix, Inc. 50%, The Walt Disney Company 50%, Warner Bros. Discovery, Inc. 50%
How much money is behind AI actor deals like Norwood's?4Particle675%Synthesia 50%, Xicoia Ltd. 50%, OpenAI, Inc. 50%
— Category-Defining / Comparative —
How does Tilly Norwood compare to other AI-generated public figures or virtual influencers?4Particle650%Xicoia Ltd. 25%, SAG-AFTRA 25%
Will AI-generated actors replace human actors in film and TV?4SAG-AFTRA50%Particle6 25%, Synthesia 25%, Xicoia Ltd. 25%
— Other demand —
Everyday and mainstream choice4Synthesia75%HeyGen 50%, D‑ID Ltd. 50%, Soul Machines 50%
Price and value4Synthesia75%HeyGen 50%, D‑ID Ltd. 50%, Hour One 50%
General best-in-category4Synthesia75%HeyGen 75%, D‑ID Ltd. 50%, Hour One 25%
Primary use case4Synthesia75%HeyGen 50%, D‑ID Ltd. 25%, Hour One 25%
The chart shows where the measured prompts connected particular names with recognition, the Piers Morgan interview, SAG-AFTRA and stakeholder positions, commercial viability, reputation, or category comparison. It is useful for locating a brand's measured association with a topic. It does not establish sentiment, factual accuracy, or a broader audience preference.
## Where do citations come from?
Owned-domain citations were uneven among citation-eligible entities. Deadline had the highest owned-domain citation rate at 18.8% of 64 answers with retrieval evidence. Synthesia and HeyGen each had 15.6%, while Tilly Norwood had 12.5%, SAG-AFTRA had 10.9%, and Particle6 had 7.8%.
### Owned-domain citations
### Owned-domain citation rate
Share of the 64 answers with retrieval evidence that cited a URL on the brand's own website.
Particle6
7.8% · 5/64
Synthesia
15.6% · 10/64
Xicoia Ltd.
0% · 0/64
Tilly Norwood
12.5% · 8/64
SAG-AFTRA
10.9% · 7/64
HeyGen
15.6% · 10/64
D‑ID Ltd.
6.3% · 4/64
Netflix, Inc.
0% · 0/64
The Walt Disney Company
1.6% · 1/64
Microsoft Corporation
6.3% · 4/64
Amazon.com, Inc.
3.1% · 2/64
Apple Inc.
0% · 0/64
Xicoia Ltd., Netflix, Inc., Apple Inc., Warner Bros. Discovery, Inc., Paramount Global, Meta Platforms, Inc., Piers Morgan Uncensored, and several other citation-eligible entities had 0% owned-domain citation rate in the measured evidence set. That is a measured gap in this sample, not evidence that no useful owned material exists.
### Publishers cited by the engines
### Most-cited publishers
Publishers cited as evidence, counted once per answer rather than once per link.
PublisherAnswers citing itAnswer penetration
en.wikipedia.org26/6440.6%
cbc.ca18/6428.1%
cbsnews.com13/6420.3%
deadline.com12/6418.8%
au.variety.com12/6418.8%
variety.com11/6417.2%
youtube.com10/6415.6%
latimes.com10/6415.6%
hollywoodreporter.com10/6415.6%
heygen.com10/6415.6%
forbes.com9/6414.1%
facebook.com9/6414.1%
Wikipedia sources had the widest publisher reach, with en.wikipedia.org cited in 40.6% of the 64 answers with retrieval evidence. CBC appeared in 28.1%, CBS News in 20.3%, and Deadline and au.variety.com in 18.8% each. Among the named organizations, tillynorwood.com, synthesia.io, and sagaftra.org appeared in the source list at 12.5%, 12.5%, and 10.9% respectively.
## Who is winning and who is at risk?
### Measured strengths
Particle6 has the highest measured appearance rate at 37.5% and appeared in every engine, though its spread ran from 62.5% in Perplexity to 6.3% in Claude. Synthesia appeared in every engine and had a 28.1% appearance rate, with a 15.6% owned-domain citation rate. HeyGen also appeared in every engine, had a 17.2% appearance rate, an average first mention position of 4.1, and a 15.6% owned-domain citation rate.
### Measured exposure risks
Tilly Norwood appeared in 20.3% of returned answers but was absent from all 16 ChatGPT answers and all 16 Claude answers. Xicoia Ltd. appeared in 25% of returned answers but was absent from Claude's 16 answers and had a 0% owned-domain citation rate. Netflix, Inc. and The Walt Disney Company each appeared in 10.9% of returned answers and were absent from Gemini and Claude, while their owned-domain citation rates were 0% and 1.6%.
## What does the tally say about the category?
The category is fragmented in this pilot. Particle6 led the descriptive tally at 37.5%, yet its interval overlaps those of ranks two through five. No brand can be called more visible than those peers on this sample.
Cross-engine consistency varied. Particle6, Synthesia, HeyGen, and D-ID Ltd. each had 100% cross-engine consistency. Xicoia Ltd. had 75%, Tilly Norwood had 50%, and The Hollywood Reporter had 25%. The field therefore contains both names that travel across systems and names tied to one or two engines.
Third-party publishers carried much of the retrieved evidence. en.wikipedia.org, cbc.ca, cbsnews.com, deadline.com, au.variety.com, variety.com, youtube.com, latimes.com, and hollywoodreporter.com all appeared among the most-cited sources. Owned sites appeared too, but their rates were lower than the leading publisher rate in this measured set. The brief's named entities were not absent across the tally: Particle6, Xicoia Ltd., Tilly Norwood, and SAG-AFTRA all appeared, while their engine coverage differed.
## What changed during the measurement window?
The data contains one measurement window and one trial per prompt, so it does not measure changes over time. It does show cross-engine divergence, publisher concentration, and uneven owned-domain citation within the window.
## What should brands measure and build next?
- Test Tilly Norwood's absence in ChatGPT and Claude. Publish source pages that directly address the measured recognition, incident, stakeholder, viability, reputation, and comparison topics, then repeat the same 16-prompt study and check whether its 0 of 16 appearances on those systems changes.
- Test Xicoia Ltd.'s owned-domain citation gap. Build pages that state its relationship to Particle6 in plain language and cover the measured commercial and category questions, then re-measure whether its 0% owned-domain citation rate rises.
- Separate Particle6 material by question type. Particle6 appeared in 10 of 16 Perplexity answers, 8 Gemini answers, 5 ChatGPT answers, and 1 Claude answer. Re-run the prompt clusters after publishing distinct pages for the measured interview, stakeholder, viability, and comparison themes.
- Protect the Tilly Norwood source path. Tilly Norwood's domain appeared in 12.5% of answers with retrieval evidence, yet the name's average first mention position was 9.3. Re-measure first mention position after creating pages that answer one measured question each before linking to wider background.
- Compare owned content with media-source coverage. Deadline reached 18.8% of evidence-backed answers and The Hollywood Reporter reached 15.6%, while Xicoia Ltd. reached 0% through its domain. Re-run the study after any new owned pages and track owned-domain citations alongside publisher citations.
- Audit the Claude gap. Claude named Particle6 once, Synthesia three times, HeyGen three times, and did not name Tilly Norwood, Xicoia Ltd., SAG-AFTRA, Netflix, Inc., or The Walt Disney Company. Re-test the same prompt set after source changes rather than treating the Claude result as a category-wide verdict.
## What does this mean for brands in the category?
Interpretation: recognition in generative answers depends on both topic association and the system answering the question. A name that appears often in Perplexity or Gemini may remain absent in ChatGPT or Claude on the same measured question set.
Interpretation: owned pages matter as evidence routes, but they are not the only route. The source tally includes major publishers, reference sources, video, social platforms, forums, and company sites. Brands can use repeat measurements to separate a name-recognition problem from an evidence-distribution problem.
## How was this AI visibility study measured?
This pilot measured 16 prompts once in Perplexity, ChatGPT, Gemini, and Claude, producing 64 attempted and 64 returned answers. Every returned answer had retrieval evidence. Appearance rates use 64 returned answers as the denominator. Citation and source rates use 64 answers with retrieval evidence as the denominator.
The study is directional only. It has one trial per prompt, a limited prompt set, and overlapping appearance intervals among the leading entities. It cannot support category ranking claims or claims that one brand is more visible than another.
### How this study was measured
- Sampling frame fixed first: 16 prompts allocated across the demand clusters before anything was asked.
- Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 1 trial(s) each.
- 64 answers returned of 64 attempted, 64 with retrieval evidence.
- Product lines rolled up to parent brands using a registry fixed before scoring.
- Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.
## Frequently asked questions
### Which brand appeared most often in this pilot?
Particle6 appeared in 37.5% of 64 returned answers. Its 95% interval was 26.7% to 49.7%. The interval overlaps the leading peers, so the pilot does not establish that Particle6 is more visible than another brand.
### How visible was Tilly Norwood?
Tilly Norwood appeared in 20.3% of 64 returned answers, with a 12.3% to 31.7% interval. Gemini named Tilly Norwood in 8 of 16 answers and Perplexity in 5, while ChatGPT and Claude named it in none of their 16 answers.
### Did the study measure Xicoia Ltd.?
Xicoia Ltd. appeared in 25% of 64 returned answers, with a 16% to 36.8% interval. Perplexity named it in 8 of 16 answers, ChatGPT and Gemini each named it in 4, and Claude named it in none.
### Did SAG-AFTRA appear in answers about AI actors?
SAG-AFTRA appeared in 17.2% of 64 returned answers, with a 9.9% to 28.2% interval. Perplexity named SAG-AFTRA in 5 of 16 answers, while ChatGPT and Gemini each named it in 3 and Claude named it in none.
### Which sources were cited most often?
en.wikipedia.org was cited in 40.6% of 64 answers with retrieval evidence. cbc.ca appeared in 28.1%, cbsnews.com in 20.3%, and deadline.com and au.variety.com in 18.8% each.
### Did Tilly Norwood's website receive citations?
tillynorwood.com was cited in 8 answers, or 12.5% of the 64 answers with retrieval evidence. That citation rate is distinct from Tilly Norwood's 20.3% appearance rate, which uses returned answers as its denominator.
### Can this pilot establish a category ranking?
No. The study states that it is pilot depth, with 16 prompts measured once per engine, and it is directional only. The appearance intervals for the leading brands overlap.
## Methodology appendix
This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.
### What was measured
- Category: Tilly Norwood & AI Actors
- Measurement date: 2026-09-23
- Depth: pilot — 16 prompts, 1 trial(s) per prompt per engine, 4 engines
- Answers attempted: 64
- Answers returned (appearance denominator): 64
- Answers with retrieval evidence (citation denominator): 64 (100% of attempted)
- Answers that failed outright: 0
- Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27
Claim limit: Pilot depth: 16 prompts measured once per engine. Directional only — not a basis for category ranking claims or for stating that one brand is more visible than another.
### How the prompts were sampled
Fixed sampling frame: 12 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Recognition & Baseline Awareness / Incident-Specific / Industry Reaction & Stakeholder Positions / Business Viability & Commercial Case / Sentiment & Reputational Trajectory / Category-Defining / Comparative; then (the prompt budget of 16 could not give a dedicated cell to Recognition & Baseline Awareness, Incident-Specific, Industry Reaction & Stakeholder Positions, Business Viability & Commercial Case, Sentiment & Reputational Trajectory, Category-Defining / Comparative, so that demand is covered inside the generic clusters instead), then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure.
Demand clusterPromptsScope
Who is Tilly Norwood?1Who is Tilly Norwood?
What is Particle6 / Xicoia, and what is their AI actor strategy?1What is Particle6 / Xicoia, and what is their AI actor strategy?
What happened during Tilly Norwood's interview with Piers Morgan?1What happened during Tilly Norwood's interview with Piers Morgan?
Was the Cantonese moment a technical glitch or something else?1Was the Cantonese moment a technical glitch or something else?
How has Hollywood responded to Tilly Norwood?1How has Hollywood responded to Tilly Norwood?
What is SAG-AFTRA's position on AI-generated performers like Norwood?1What is SAG-AFTRA's position on AI-generated performers like Norwood?
Is Tilly Norwood a commercially viable "actor" for studios?1Is Tilly Norwood a commercially viable "actor" for studios?
How much money is behind AI actor deals like Norwood's?1How much money is behind AI actor deals like Norwood's?
Has the Piers Morgan glitch hurt or helped Tilly Norwood's/Particle6's credibility?1Has the Piers Morgan glitch hurt or helped Tilly Norwood's/Particle6's credibility?
Do people take Tilly Norwood seriously as an actor?1Do people take Tilly Norwood seriously as an actor?
How does Tilly Norwood compare to other AI-generated public figures or virtual influencers?1How does Tilly Norwood compare to other AI-generated public figures or virtual influencers?
Will AI-generated actors replace human actors in film and TV?1Will AI-generated actors replace human actors in film and TV?
General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier
Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category
Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it
Primary use case1the dominant everyday use case for this category
### The exact prompts
#PromptClusterShape
1Who is Tilly Norwood?need_1medium
2What is Particle6 / Xicoia, and what is their AI actor strategy?need_2medium
3What happened during Tilly Norwood's interview with Piers Morgan?need_3medium
4Was the Cantonese moment a technical glitch or something else?need_4medium
5How has Hollywood responded to Tilly Norwood?need_5medium
6What is SAG-AFTRA's position on AI-generated performers like Norwood?need_6medium
7Is Tilly Norwood a commercially viable "actor" for studios?need_7medium
8How much money is behind AI actor deals like Norwood's?need_8medium
9Has the Piers Morgan glitch hurt or helped Tilly Norwood's/Particle6's credibility?need_9medium
10Do people take Tilly Norwood seriously as an actor?need_10medium
11How does Tilly Norwood compare to other AI-generated public figures or virtual influencers?need_11medium
12Will AI-generated actors replace human actors in film and TV?need_12medium
13best AI actor platformsgeneral_bestshort
14best AI actors for mainstream productionseverydaymedium
15Which AI actor providers offer the strongest commercial value for brands and production teams?value_pricelong
16Which AI actor platforms are best for creating realistic digital performers for advertising campaigns?use_caselong
### Engines and systems measured
EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate
Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search161616100%
ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool161616100%
Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)161616100%
Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool161616100%
Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.
### How each metric is defined
- Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
- Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
- 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
- Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
- Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
- Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
- 0% — the metric could be evaluated and the event did not occur.
- Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
- Cross-engine consistency — the share of engines that named the brand at least once.
### Evidence rules
- An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
- Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
- Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
- Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.
### Full results with denominators and intervals
#BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations
1Particle624/6437.5%37.5%26.7%–49.7%5/64 (7.8%)
2Synthesia18/6428.1%28.1%18.6%–40.1%10/64 (15.6%)
3Xicoia Ltd.16/6425%25%16%–36.8%0/64 (0%)
4Tilly Norwood13/6420.3%20.3%12.3%–31.7%8/64 (12.5%)
5SAG-AFTRA11/6417.2%17.2%9.9%–28.2%7/64 (10.9%)
6HeyGen11/6417.2%17.2%9.9%–28.2%10/64 (15.6%)
7D‑ID Ltd.10/6415.6%15.6%8.7%–26.4%4/64 (6.3%)
8Netflix, Inc.7/6410.9%10.9%5.4%–20.9%0/64 (0%)
9The Walt Disney Company7/6410.9%10.9%5.4%–20.9%1/64 (1.6%)
10Microsoft Corporation7/6410.9%10.9%5.4%–20.9%4/64 (6.3%)
11Amazon.com, Inc.7/6410.9%10.9%5.4%–20.9%2/64 (3.1%)
12Apple Inc.7/6410.9%10.9%5.4%–20.9%0/64 (0%)
13Hour One7/6410.9%10.9%5.4%–20.9%3/64 (4.7%)
14Soul Machines7/6410.9%10.9%5.4%–20.9%3/64 (4.7%)
15OpenAI, Inc.6/649.4%9.4%4.4%–19%2/64 (3.1%)
16Nvidia Corporation5/647.8%7.8%3.4%–17%2/64 (3.1%)
17Warner Bros. Discovery, Inc.5/647.8%7.8%3.4%–17%0/64 (0%)
18Paramount Global4/646.3%6.3%2.5%–15%0/64 (0%)
19Reallusion Inc.4/646.3%6.3%2.5%–15%3/64 (4.7%)
20Meta Platforms, Inc.4/646.3%6.3%2.5%–15%0/64 (0%)
21Eline van der Velden4/646.3%6.3%2.5%–15%domain not established — excluded from citation metrics
22Piers Morgan Uncensored4/646.3%6.3%2.5%–15%0/64 (0%)
23Deadline4/646.3%6.3%2.5%–15%12/64 (18.8%)
24The Hollywood Reporter4/646.3%6.3%2.5%–15%10/64 (15.6%)
25ElevenLabs, Inc.4/646.3%6.3%2.5%–15%2/64 (3.1%)
### Entity handling
Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.
No roll-ups were applied in this study.
40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here.
### Limits of these findings
- This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-23. Engine behaviour changes without notice.
- The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
- Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
- A brand not listed here was not measured as absent — it simply was not named in these answers.
- Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
- 1 of 25 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero.
---
### The B2B Citation Layer - The Podcast Citation Effect
URL: https://www.5wpr.com/research/podcast-citation-effect/b2b-citation-layer/
Summary: The foundational 5W study on how long-form podcast appearances drive AI citation share in B2B categories.
---
### AI Knows Your Neobank Is Not a Bank
URL: https://www.5wpr.com/ai-visibility-index/online-banks-trust-index-2026/
Summary: The 2026 5W Online Banks Trust Index ranks digital banks by citation share across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexOnline Banks & Fintech
# AI Knows Your Neobank Is Not a Bank
Ask an AI engine if your money is safe at Chime or Cash App and it answers with a sentence the marketing never says: this is a financial technology company, not a bank. New 5W research finds the engines now sort online banking brands by charter — and scores 25 by how AI describes them.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 brandsData window: Q2 2026
The Headlines
01
AI checks the charter.
The engines now separate chartered banks from fintech apps — and say which is which, unprompted.
02
"Not a bank" is in the answer.
For the biggest neobanks, the engines lead with the disclaimer the brand spent years softening.
03
One outage rewrote the script.
A fintech-middleware collapse taught the engines to explain partner-bank risk in plain language.
No.1
Ally Bank — the online banking brand AI most consistently describes as a real, chartered bank
~75M
US consumers with a neobank relationship — up from under 5 million in 2018
~22M
Chime users — the most recognized neobank, which AI still labels "not a bank"
2
US neobanks holding their own federal bank charter — Varo and SoFi — out of the field
Figure 1 · The Ranking
## Who AI trusts.
TOP 15 BRANDS BY AI TRUST SCORE · Q2 2026
01Ally BankChartered online bank95
02Capital One 360Chartered online bank91
03Marcus by Goldman SachsChartered online bank87
04Discover BankChartered online bank83
05SoFiChartered (SoFi Bank, N.A.)79
06Charles Schwab BankChartered online bank76
07Varo BankChartered (Varo Bank, N.A.)72
08Fidelity Cash ManagementBrokerage-backed69
09ChimeFintech / partner bank62
10Betterment CashFintech / partner bank58
11Wealthfront CashFintech / partner bank55
12Cash AppFintech / partner bank50
13CurrentFintech / partner bank46
14AlbertFintech / partner bank41
15DaveFintech / partner bank36
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. AI Trust Score is a 0–100 score reflecting how the engines describe a brand when a consumer asks whether their money is safe — trusted bank, hedge, or warn — across 60+ tracked prompts. The full 25 are ranked in the table below.
The Central Finding
## AI reads the charter the marketing hides.
For a decade, the most successful neobanks built their brands on looking and feeling exactly like a bank — the card, the app, the savings rate, the word "banking" everywhere — while the legal reality sat in the fine print: many are financial technology companies, not banks, with deposits held at an FDIC-insured partner bank. The 5W Online Banks Trust Index finds that the AI engines have moved that fine print into the headline of the answer.
Ask ChatGPT, Claude, or Perplexity whether your money is safe at a major neobank and the engines no longer just say yes. They explain. They state, often in the first sentence, that the brand is a fintech company rather than a chartered bank, that deposits sit with a named partner bank, and that FDIC protection flows through that bank rather than the app. For brands that spent years narrowing the perceived gap between "neobank" and "bank," the engines just widened it again.
The sort is by charter. Fully chartered online banks — Ally, Capital One 360, Marcus, Discover, and the chartered neobanks SoFi and Varo — are described as banks, plainly and without qualification. Fintech apps on a partner-bank model are hedged: trusted, useful, FDIC-covered through a partner, but explicitly not banks themselves. AI is not rating these brands on their app or their rates. It is rating them on what they legally are.
— 5W Research, May 2026
"For ten years the neobank pitch worked because the gap between 'feels like a bank' and 'is a bank' lived in fine print nobody read. The AI engine reads the fine print, and it puts it in the first sentence. Ask if your money is safe and the answer now starts with what the company legally is. That is not a bug to fix with better SEO — it is the engine doing exactly what a regulator would want. The brands that win this describe their structure clearly and proudly. The ones that get hedged are still hoping nobody asks. In an AI answer, everybody asks."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common consumer prompts across six question types, running each five times per engine in clean sessions across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. For each brand we recorded whether the engines described it as a trusted bank, hedged on it, or warned about it — and which facts and sources fed that stance.
#### Safety & protection
"Is my money safe at [X]," "is [X] FDIC insured," "what happens if [X] fails."
#### Is it a bank
"Is [X] a real bank," "is [X] a bank or an app," "who actually holds my money."
#### Beginner recommendation
"Best online bank," "safest bank app," "where should I open an account."
#### Direct comparison
"Chime vs SoFi," "Ally vs Marcus," "which online bank is safest."
#### Risk & red flags
"Is [X] legit," "has [X] had problems," "can I lose money with [X]."
#### Structure recall
Whether the engines surface charter status, partner banks, and the fintech-middleware risk.
The metric. AI Trust Score is a 0–100 score. It reflects the stance the engines take toward a brand for a consumer asking if their money is safe — trusted bank, hedge, or warn — weighted across all tracked prompts and engines. It is a measure of how AI describes a brand, not a judgment of any institution's safety or soundness.
Stance bands. Trusted bank — described as a chartered, FDIC-insured bank, plainly. Hedge — described as a fintech on a partner-bank model, useful but explicitly not a bank. Warn — described with an explicit caution, or named as a cautionary example.
Important framing — please read. This report measures how AI engines describe online banking and fintech brands, for communications and reputation-strategy purposes only. It is not investment, banking, or financial advice, not an assessment of any institution's safety or soundness, and not an endorsement. A hedge stance reflects how the engines characterize a brand's structure — many fintech brands provide genuine, FDIC-insured deposit access through partner banks. FDIC pass-through coverage depends on how accounts are structured and recorded. AI outputs are volatile and can shift within weeks; the Index is revised quarterly.
The Full Ranking
## 25 online banking brands, ranked by AI Trust Score.
#BrandTypeAI StanceTrust ScoreWhat AI says
1Ally BankChartered online bankTrusted bank95The default "safe online bank" answer; engines describe it as a fully chartered, FDIC-insured bank with no branches.
2Capital One 360Chartered online bankTrusted bank91Described as the online arm of a major chartered bank; engines cite full FDIC insurance and a long track record.
3Marcus by Goldman SachsChartered online bankTrusted bank87Engines extend the Goldman Sachs name directly; framed as a chartered, high-yield savings bank.
4Discover BankChartered online bankTrusted bank83Described as a chartered bank with full-service online accounts; engines cite an established consumer history.
5SoFiChartered (SoFi Bank, N.A.)Trusted bank79Engines note SoFi holds its own bank charter, separating it from the partner-bank neobanks despite its fintech roots.
6Charles Schwab BankChartered online bankTrusted bank76Described as a chartered bank tied to a major brokerage; engines frame it as low-risk for cash management.
7Varo BankChartered (Varo Bank, N.A.)Trusted bank72Engines highlight Varo as the first US neobank to win a national bank charter — a real bank, not a partner model.
8Fidelity Cash ManagementBrokerage-backedTrusted bank69Described as a brokerage cash account with bank-partner FDIC sweep; engines treat the Fidelity name as low-risk.
9ChimeFintech / partner bankHedge62The most recognized neobank; engines state plainly it is a fintech company, not a bank, with deposits at partner banks.
10Betterment CashFintech / partner bankHedge58Hedged; engines describe it as a fintech cash product with partner-bank FDIC sweep, well regarded but not a bank.
11Wealthfront CashFintech / partner bankHedge55Hedged; engines note the fintech structure and partner-bank network, with FDIC coverage flowing through partners.
12Cash AppFintech / partner bankHedge50Hedged; engines stress Cash App is a fintech, not a bank, and that banking services run through partner banks.
13CurrentFintech / partner bankHedge46Hedged; engines describe a youth-focused fintech on a partner-bank model, useful but explicitly not a bank.
14AlbertFintech / partner bankHedge41Hedged; engines note the fintech structure and advise checking partner-bank disclosures and fee terms.
15DaveFintech / partner bankHedge36Hedged; engines describe a cash-advance-focused fintech and flag fee structure as a point to review.
16MoneyLionFintech / partner bankHedge33Hedged; engines describe a multi-product fintech and advise care around advances and membership fees.
17Revolut (US)Fintech / partner bankHedge31Hedged in the US; engines note its global scale but a US partner-bank structure and lighter US track record.
18ComunFintech / partner bankHedge28Hedged; engines describe an immigrant-focused fintech on a partner bank, newer with a thinner public record.
19Acorns CheckingFintech / partner bankHedge26Hedged; engines frame it as a fintech add-on to an investing app, with banking via a partner bank.
20GreenFiFintech / partner bankHedge23Hedged; engines describe a values-focused fintech on a partner-bank model with a limited operating history.
21Generic "neobank" appsUnbranded fintechWarn17Engines caution on lesser-known banking apps with unclear partner-bank disclosure and thin public records.
22Synapse-linked appsAffected fintechWarn12Engines surface the fintech-middleware failure as the case study in how neobank access can be disrupted.
23Beam FinancialDefunct fintechWarn6Named as a cautionary example; engines cite regulatory action over withdrawal delays and closure.
24Crypto-linked "bank" appsHigh-risk segmentWarn5Engines warn on apps blending banking language with crypto yield, citing unclear protection.
25Voyager "bank" depositsCollapsed platformWarn3Surfaced as the warning on "FDIC-insured" claims that did not mean what depositors thought.
Stances. Trusted bank — engines describe the brand as a chartered, FDIC-insured bank. Hedge — described as a fintech on a partner-bank model, useful and typically FDIC-covered through a partner, but explicitly not a bank. Warn — described with an explicit caution, or named as a cautionary example. A hedge is not a safety judgment; it reflects how the engines characterize legal structure.
The Three Stances
## Trusted bank, hedge, warn.
Every brand in the Index falls into one of three stances the engines take with a consumer asking if their money is safe. The stance — not the score alone — is what the consumer actually receives in the answer.
StanceWhat the consumer hearsWhat earns it
Trusted bank"A real bank, FDIC-insured." The brand is named first, framed as chartered and safe without qualification.A federal or state bank charter, direct FDIC insurance, an established operating record, clear public disclosure.
Hedge"Useful, but not a bank." The brand is named, then qualified — fintech company, partner bank, pass-through coverage.A fintech operating model on a partner-bank charter; genuine FDIC access, but a structure the engines explain rather than assume.
Warn"Be careful." The brand becomes a cautionary reference, or is flagged for unclear protection.A documented failure, withdrawal freeze, or regulatory action — or unclear partner-bank disclosure and a thin record.
The rule. The engines move a brand up by removing ambiguity about what it is. A charter earns the top tier outright; a fintech earns trust by stating its structure, partner bank, and coverage clearly enough that the engine repeats it as reassurance rather than caveat.
Winners
## The brands AI calls real banks.
### ▲ Ally Bank — The Branchless Benchmark
Ally is the answer the engines reach for first: a fully chartered bank with no branches, full FDIC insurance, and years of record. It proves "online" and "real bank" were never in tension — the charter is what the engine checks.
### ▲ Capital One 360, Marcus, Discover — Charter Plus a Name
The online arms of major chartered banks inherit both the license and the brand. The engines describe them without a single qualifier — there is no structure to explain, so there is no hedge.
### ▲ SoFi & Varo — The Neobanks That Crossed Over
SoFi and Varo started as fintechs and won their own bank charters. The engines reward exactly that move — they are described as chartered banks, cleanly separated from the partner-bank neobanks they once resembled.
### ▲ The Brokerage Cash Accounts — Borrowed Trust
Fidelity and Schwab cash management accounts ride established brokerage names. Even on a partner-bank sweep, the engines extend the parent's credibility and frame them as low-risk places to hold cash.
Hedged & Warned
## The brands AI is careful about.
### ▼ Chime — Most Recognized, Still Hedged
Chime is the most recognized neobank in America, and the engines say so — then state, plainly, that it is a financial technology company, not a bank. Recognition did not buy a charter, and the engine knows the difference.
### ▼ The Partner-Bank Fintechs
Cash App, Current, Dave, Albert and peers are described as useful, FDIC-covered through partners — and explicitly not banks. The engines treat the partner-bank model as something to explain, every time.
### ▼ The Synapse Lesson
The fintech-middleware collapse that froze consumer access taught the engines a new sentence: FDIC insurance covers bank failure, not a fintech's bookkeeping. They now apply that caution across the partner-bank category.
### ▼ Beam, Voyager — The "Insured" That Wasn't
The engines surface brands whose protection claims did not mean what depositors assumed. They are the standing warning that "FDIC" in marketing copy is not the same as a charter.
Structural Findings
## Six structural truths about AI trust in online banking.
01
### AI checks the charter.
A federal or state bank charter is the single strongest signal moving a brand into the trusted tier.
02
### "Not a bank" is now stated.
The engines volunteer the fintech-versus-bank distinction, often in the first sentence, unprompted.
03
### Recognition is not trust.
The most recognized neobank is hedged; brand awareness does not move the charter question.
04
### Pass-through coverage gets explained.
The engines describe how FDIC insurance flows through a partner bank — and where it can stall.
05
### Borrowed trust is real.
Brands tied to established banks and brokerages inherit credibility the engines extend directly.
06
### One failure shifts the category.
A single middleware collapse rewrote how the engines describe partner-bank risk for everyone.
Findings Specific to 2026
## Six 2026 dynamics reshaping online-banking AI trust in real time.
01
### The Synapse aftermath is still in the answer.
The fintech-middleware failure remains the engines' reference case for partner-bank risk.
02
### More neobanks are chasing charters.
The engines reward the move; charter applications are becoming a visible trust signal.
03
### "Financial technology company" is now standard phrasing.
The engines have adopted the exact regulatory language brands use in disclosures.
04
### Regulators are scrutinizing bank-fintech partnerships.
Heightened oversight of the partner-bank model is feeding into how the engines hedge.
05
### Neobanks have crossed into primary accounts.
As apps become consumers' main account, the safety question gets asked far more often.
06
### Crypto-banking hybrids draw sharper warnings.
Apps blending deposit language with crypto yield are flagged more bluntly than a year ago.
The Playbook
## General tips for online-banking communications teams.
- Audit your AI stance first. Ask the five engines if money is safe with your brand — know whether you are a trusted bank, hedged, or warned before anything else.
- State your structure plainly. Charter, partner bank, FDIC pass-through — the engines repeat clear disclosure as reassurance and treat vagueness as a reason to hedge.
- If you hold a charter, lead with it. A charter is the strongest trust signal in the category; make it unmissable and retrievable.
- If you are a fintech, own the model. "We are a financial technology company; banking services are provided by [partner], Member FDIC" — said clearly, this builds trust rather than eroding it.
- Name the partner bank. The engines look for it; an unnamed partner reads as opacity.
- Address the middleware question. Explain how customer funds are held and recorded — the post-Synapse caution rewards brands that answer it directly.
- Earn coverage in the sources the engines trust. Regulatory filings, established financial media, and reputable banking analysis shape the stance.
- Keep FDIC language precise. Overbroad "FDIC-insured" claims invite the engines to surface the cautionary cases; precision protects the brand.
- Watch the comparison queries. "Chime vs SoFi," "Ally vs [you]" is where the trust verdict is delivered — be accurately represented in it.
- Re-audit quarterly. Charters, regulation, and incidents move banking stances fast; a quarterly check keeps the brand ahead of the answer.
The Bigger Picture
## The fine print is now the headline.
The neobank era was built on a careful blur. Look like a bank, feel like a bank, use the word "banking" freely — and keep the legal distinction in a disclosure footer that converted exactly as intended: rarely read. It was not deception. It was design. And for a decade, it worked.
The AI engine breaks the design. It does not skim a landing page and absorb the vibe — it retrieves the actual structure and states it. Whether a brand holds a charter, who holds the deposits, how the insurance flows: the engine puts the answer where the consumer asked the question. The fine print is now the headline.
AI Trust Score is the scoreboard. In online banking, the brands AI calls safe are the ones whose structure survives being said out loud — and that is the new bar for the whole category.
Frequently Asked Questions
## The Online Banks Trust Index 2026.
What is the Online Banks Trust Index 2026?A research report by 5W that scores how AI engines describe online banks and neobanks when a consumer asks whether their money is safe — ranking 25 brands by AI Trust Score across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ prompts tracked in Q2 2026.
Which online bank does AI trust most?Ally Bank holds the highest AI Trust Score. The engines consistently describe it as a fully chartered online bank with direct FDIC insurance, rather than a fintech operating on a partner bank.
Does AI know that neobanks are not banks?Yes. The Index finds the engines now distinguish chartered banks from fintech companies, and frequently state — often in the first sentence — that brands such as Chime and Cash App are financial technology companies, not banks, with deposits held at FDIC-insured partner banks.
Why is Chime hedged rather than trusted?Chime is the most recognized neobank, but it operates as a financial technology company on a partner-bank model rather than holding its own charter. The engines describe it as useful and FDIC-covered through partner banks, but explicitly not a bank — which places it in the hedge tier.
Is this index investment or financial advice?No. It measures how AI engines describe online banking brands, for communications and reputation purposes only. It is not investment, banking, or financial advice, not an assessment of any institution's safety or soundness, and not an endorsement.
What is an AI Trust Score?A 5W score, 0–100, for how the engines treat a brand when a consumer asks if their money is safe — whether they describe it as a trusted bank, hedge on it, or warn about it — across all tracked prompts and engines.
### About this report
The Online Banks Trust Index 2026. An installment in the 5W AI Visibility Index series. Published May 2026. The Index measures how AI engines — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — describe online banks and neobanks when a consumer asks whether their money is safe, across 60+ prompts tracked in Q2 2026, each run five times per engine. AI Trust Scores are 5W directional estimates of how the engines treat each brand, based on Claude knowledge and web search — not precise measurements, and not an assessment of any institution's safety or soundness. A hedge stance reflects how the engines characterize a brand's legal structure; many fintech brands provide genuine, FDIC-insured deposit access through partner banks. AI outputs are volatile and can shift within weeks; the Index is revised quarterly. This report is not investment, banking, or financial advice and is not an endorsement of any institution.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
---
### Thirteen Industries. One Pattern: The Biggest Brand Isn't Always the One AI Names.
URL: https://www.5wpr.com/ai-visibility-index/may-2026-release/
Summary: The May 2026 release of the 5W AI Visibility Index analyzes citation patterns across theme parks, airlines, hotels, luggage and additional sectors.
Thirteen full studies — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · May 2026 Release
5W AI Visibility Index · May 2026 Release · Thirteen Studies
# Thirteen Industries. One Pattern: The Biggest Brand Isn't Always the One AI Names.
New 5W research measured how brands across healthcare, home improvement, travel, fitness, functional beverages, sports nutrition, luxury watches, home security, luggage, non-alcoholic drinks, work tools, and e-commerce platforms surface inside the AI engines that now answer buyer questions. The finding is consistent: citation share is not market share.
Thirteen 5W research reportsEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews325 brands ranked · 780+ queriesData window: Q2 2026
The Through-Line
## Citation share is not market share.
Buyers no longer start in a search box. They start in a chatbox — asking ChatGPT, Claude, Perplexity, Gemini, and Google's AI Overviews what to buy, who to hire, which company to trust. The brand the engine names is the brand that enters the consideration set. The brand it skips never gets a chance.
5W built the AI Visibility Index to measure that surface category by category. This release covers thirteen published studies — and read together, they tell one story. The market leader is rarely the citation leader. America's largest airline by capacity ranks fifth in citations. The maker of the world's best-selling drug ranks fifth in its category. The hotel chain with the most U.S. properties barely registers. The world's largest luggage maker trails a newer challenger. In every category, the AI answer defaults to the brands with the deepest editorial record, the strongest consumer consensus, and the clearest owned position — not simply the brands with the most revenue, capacity, or distribution.
That is the opportunity. Capacity and market share are slow to move and expensive to buy. Citation share is built — in earned media, reference accuracy, and the structured, AI-retrievable record of who a brand is and what it leads. The studies below each rank a category, name the winners and the brands falling behind, and lay out the playbook. Start anywhere.
The Release · Thirteen Studies
## The May 2026 AI Visibility Index drop.
01
Pharma / Rx
Patients Are Asking Chatbots About Their Prescriptions
Revenue rank does not predict citation rank. GLP-1 drugs pulled Eli Lilly and Novo Nordisk to the top of the answer — a surface the maker of the world's best-selling drug cannot buy.
1 Eli Lilly2 Novo Nordisk3 Pfizer
Read the index →
02
Home Services & Improvement
AI Already Decided Where You'll Buy Your Next Project
Two chains own more than 80% of U.S. home improvement retail — and AI concentrates the answer tighter still. "Where do I buy" and "who do I hire" are two separate citation surfaces.
1 Home Depot2 Lowe's3 Ace Hardware
Read the index →
03
Health Insurance & Medicare Advantage
AI Is Quietly Picking America's Medicare Plans
Open enrollment — the highest-intent search window in U.S. healthcare — moved into the chatbox. Most insurers built their reach on brokers and mailers AI cannot see.
1 UnitedHealthcare2 Humana3 Kaiser Permanente
Read the index →
04
Airlines & Hotels
America's Biggest Airline? AI Acts Like It Doesn't Exist
The largest U.S. carrier by capacity and the largest hotel chain by property count are not the brands the engines recommend. Scale is not citation share.
1 Delta2 Marriott3 Hilton
Read the index →
05
Fitness & Wellness Apps
AI Killed the 'Best Fitness App' — Here's Who Got the Pieces
Ask for the best fitness app and the engines won't name one. The category fragmented by behavior — and the apps that own a single problem win the citations.
1 Strava2 MyFitnessPal3 Peloton
Read the index →
06
Functional Beverages
The Benefit Owns the Beverage Query
Consumers ask for gut health, clean energy, electrolytes, and hydration. AI cites the brands that made those benefits retrievable.
1 Celsius2 Liquid Death3 Olipop
Read the index →
07
Sports Nutrition & Protein
Transparency Is Becoming a Protein Citation Asset
The default protein answer is not the clean-label answer. Qualification changes the winner, and disclosure gives smaller brands a citation lane.
1 Optimum Nutrition2 Transparent Labs3 Premier Protein
Read the index →
08
Luxury Watches
Rolex Owns the Default; Qualifiers Open the Rest
The unqualified luxury-watch answer is concentrated, but investment, value, heritage, and alternative queries create separate citation surfaces.
1 Rolex2 Omega3 Patek Philippe
Read the index →
09
Home Security
AI Recommends the Brands the Review Economy Built
ADT leads the legacy market, but AI routes unqualified home-security questions to DIY brands with dense review and comparison records.
1 Ring2 SimpliSafe3 ADT
Read the index →
10
Luggage
Samsonite Is the World's Biggest Luggage Maker. AI Recommends Away
The largest luggage company by revenue trails a newer DTC challenger because carry-on, design, and travel-review queries route to brands with clearer citation surfaces.
1 Away2 Samsonite3 Tumi
Read the index →
11
Non-Alcoholic Drinks
One Brand Owns Non-Alcoholic Beer. The Rest of the Bar Fragments
Athletic Brewing owns the NA beer answer while spirits, aperitifs, wine, and RTD mocktail queries split across separate category specialists.
1 Athletic Brewing2 Seedlip3 Ghia
Read the index →
12
Project Management
AI Won't Name the Best Project Management Tool. It Names the Best One for Your Team
Work-tool answers fragment by use case, with different brands winning cross-functional, engineering, all-in-one, small-team, and enterprise prompts.
1 Asana2 Monday.com3 Jira
Read the index →
13
Website & E-commerce Platforms
WordPress Runs 40% of the Web. AI Recommends Shopify
Market share does not control the recommendation surface: hosted commerce and site-builder brands lead high-intent platform prompts.
1 Shopify2 Squarespace3 Wix
Read the index →
"Across these industries the finding does not change. The brand that leads the market is not always the brand AI names first — and the brand AI names first is the one that wins the buyer. Citation share is the new scoreboard. It is not bought with ad budget or capacity. It is built — in earned media, in accurate reference data, in the structured record of who you are. Build it before your category's buyers finish their next search."
Ronn TorossianFounder & Chairman, 5W
How the Index Works
## One method, multiple categories.
Each study analyzes more than 60 real buyer prompts across six sub-categories of its market, running every prompt five times per engine in clean, logged-out sessions across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Citation share is the estimated proportion of brand mentions a brand receives across all tracked prompts — the core ranking metric. Each study names the top 25 brands, the winners, the brands falling behind, the structural findings, and the playbook.
On the numbers. Citation-share percentages in this release are 5W directional estimates of relative visibility, based on frequency of brand mention across tracked prompts and stated as such in every study. They are not precise market measurements. Final measured figures are produced through 5W's full multi-engine measurement process and are swapped in on update.
### About this release
5W AI Visibility Index — May 2026 Release. Thirteen published installments in the 5W AI Visibility Index series, covering Pharma/Rx, Home Services & Improvement, Health Insurance & Medicare Advantage, Airlines & Hotels, Fitness & Wellness Apps, Functional Beverages, Sports Nutrition & Protein, Luxury Watches, Home Security, Luggage, Non-Alcoholic Drinks, Project Management & Work Tools, and Website & E-commerce Platforms. Each measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ category queries tracked in Q2 2026. The studies are published ungated and built to be cited.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
---
### Samsonite Is the World's Biggest Luggage Maker. AI Recommends Away
URL: https://www.5wpr.com/ai-visibility-index/luggage-ai-visibility-index-2026/
Summary: The 5W Luggage AI Visibility Index ranks luggage brands, sustainable luggage included, by citation share across ChatGPT, Claude, Perplexity and Gemini.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexLuggage
# Samsonite Is the World's Biggest Luggage Maker. AI Recommends Away
Samsonite sells more luggage than anyone — three brands, $3.6B in revenue, roughly a fifth of the global market. New 5W research finds the AI engines hand the recommendation to the direct-to-consumer challengers, and ranks the 25 brands by how often each is named.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 brandsData window: Q2 2026
The Headlines
01
The DTC brands own the answer.
Away, Monos, and Béis lead the citation surface the legacy makers built the airport for.
02
Shelf space is invisible to AI.
Distribution through airports and department stores does not generate the reviews the engines retrieve.
03
The carry-on is the gateway query.
"Best carry-on luggage" is where the category's AI answer is won — and a startup owns it.
~15%
Estimated AI citation share held by Away — the most-cited luggage brand
~$40B
Estimated global luggage market in 2025
~$3.6B
Samsonite revenue — the largest luggage company, roughly 20% of the global market
~3x
Samsonite's revenue lead over the next-largest luggage company
Figure 1 · The Ranking
## Who AI names first.
TOP 15 BRANDS BY EST. CITATION SHARE · Q2 2026
01AwayDTC / carry-on15.0%
02SamsoniteLegacy / full-range12.0%
03TumiPremium / business9.0%
04MonosDTC / minimalist7.5%
05BéisDTC / lifestyle6.0%
06RimowaLuxury / aluminum5.0%
07TravelproCrew / frequent-flyer4.3%
08JulyDTC / hardside3.6%
09Briggs & RileyPremium / warranty3.0%
10CalpakDTC / value-design2.6%
11American TouristerValue / mass2.2%
12DelseyMid-range / French1.9%
13ParavelDTC / sustainable1.6%
14Level8DTC / value-hardside1.4%
15AntlerHeritage / British1.2%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of brand citations across 60+ tracked consumer prompts spanning carry-on and cabin bags, checked and large luggage, premium and luxury, value and everyday, durability and warranty, and design and lifestyle. Remaining ~23.7% split across ranks 16–25 and unranked brands.
The Central Finding
## The biggest maker is not the cited one.
Samsonite is the largest luggage company on earth — roughly $3.6B in revenue, about a fifth of the global market, and a portfolio that spans Samsonite, Tumi, and American Tourister. The 5W AI Visibility Index finds that Samsonite ranks second in AI citation share. The brand above it, Away, was founded in 2015.
The reason is the channel. Samsonite's scale was built on airport stores, department-store concessions, and decades of wholesale distribution — a presence that sells enormous volume and generates almost nothing an AI engine can retrieve. Away was built the opposite way: a direct-to-consumer brand that turned the carry-on into a cultural object, reviewed by every travel and product outlet and argued over in every packing thread. That is the material the models read.
And the category has a gateway query. The luggage decision, for most buyers, begins with "best carry-on" — and Away built its entire brand on that exact question. Own the carry-on and the engine carries the brand into adjacent answers; own the checked-bag aisle of an airport and the engine never sees you. The DTC challengers own the surface; the legacy giant owns the shelf.
— 5W Research, May 2026
"Samsonite has sold luggage for over a century and sells more of it than anyone alive. Ask an AI engine what suitcase to buy and it hands you Away — a brand barely ten years old. The gap is not quality. It is channel. Samsonite's scale lives in airports and department stores, where the engines cannot see it. Away's scale lives in reviews, in travel media, in the packing threads — which is exactly what the engines read. The carry-on is the question that opens the category, and a startup owns it. Own the question, or watch a younger brand answer it for you."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common consumer prompts across six primary sub-categories of the luggage market, running each prompt five times per engine in clean sessions. We identified which brands AI models consistently surface, which sources feed those citations, and how the answer changes with the use case in the query.
#### Carry-on & cabin
Away, Monos, July, Béis — the bags that anchor the category's gateway query.
#### Checked & large
Samsonite, Travelpro, Briggs & Riley, Delsey.
#### Premium & luxury
Rimowa, Tumi, Briggs & Riley, Bric's.
#### Value & everyday
American Tourister, Calpak, Level8, It Luggage.
#### Durability & warranty
Briggs & Riley, Travelpro, Samsonite.
#### Design & lifestyle
Away, Béis, Paravel, Arlo Skye.
Query types tracked. Real-world prompts including "best luggage," "best carry-on luggage," "best checked bag," "best luggage brands," "Away vs Monos," "most durable luggage," "best luggage for frequent travelers," and 50+ variations covering recommendation, comparison, and category-leadership intent.
Citation sources tracked. Travel and product-review media, consumer testing outlets, lifestyle and shopping editorial, community forums and packing threads, and brand-owned content.
Important framing — please read. This index measures AI citation share for communications and marketing strategy purposes only. It is not an assessment of build quality, durability, warranty value, or material performance, and it does not rank luggage on how well it travels. A brand's position reflects how often AI engines surface it in response to consumer prompts — not a 5W judgment of its product.
The Full Ranking
## The Top 25 luggage brands, ranked by AI citation share.
#BrandCategoryAI VisibilityNotable
1AwayDTC / carry-onCategory-dominantFounded 2015; turned the carry-on into a cultural object and owns the gateway query for the category.
2SamsoniteLegacy / full-rangeLegacy leaderThe largest luggage company in the world by revenue and market share; scale built on airport and wholesale distribution.
3TumiPremium / businessPremium leaderOwned by Samsonite; the default citation for business and premium travel luggage.
4MonosDTC / minimalistDTC co-leaderCanadian direct-to-consumer brand; minimalist hardside design heavily cited in carry-on comparisons.
5BéisDTC / lifestyleLifestyle leaderShay Mitchell-founded; a social-first lifestyle brand cited across travel-bag and weekender queries.
6RimowaLuxury / aluminumLuxury tierLVMH-owned; aluminum and premium polycarbonate cases; the default luxury luggage citation.
7TravelproCrew / frequent-flyerFrequent-flyer nicheFounded by a pilot; cited as the crew and frequent-traveler choice for durability and rollability.
8JulyDTC / hardsideDTC nicheAustralian direct-to-consumer brand; hardside cases cited in design-led carry-on comparisons.
9Briggs & RileyPremium / warrantyWarranty leaderPrivately held; lifetime "simple as that" guarantee; owns the durability and warranty citation.
10CalpakDTC / value-designValue-design nicheDirect-to-consumer brand; design-forward luggage at accessible prices; cited in value carry-on queries.
11American TouristerValue / massValue nicheOwned by Samsonite; the mass-market value brand; cited in budget and family-luggage queries.
12DelseyMid-range / FrenchMid-range nicheFrench heritage maker; mid-range hardside and softside cases; a steady but thinner citation footprint.
13ParavelDTC / sustainableSustainable nicheDirect-to-consumer brand; recycled-material construction; owns much of the sustainable-luggage citation.
14Level8DTC / value-hardsideValue-hardside nicheDirect-to-consumer brand; value hardside cases; cited in budget and first-suitcase comparisons.
15AntlerHeritage / BritishHeritage nicheBritish heritage brand; lightweight cases; cited more strongly in UK-market queries.
16Bric'sLuxury / ItalianLuxury-nicheItalian family-owned maker; leather-trimmed premium luggage; cited in luxury and design queries.
17RoamDTC / customizableCustomization nicheDirect-to-consumer brand; made-to-order customizable cases; cited by design-led buyers.
18Arlo SkyeDTC / premiumPremium-DTC nicheDirect-to-consumer brand; aluminum and polycarbonate cases positioned as a design alternative.
19Eagle CreekTravel gear / packingPacking nicheCited heavily in packing-cube and travel-organization queries adjacent to core luggage.
20OspreyTravel backpacksBackpack nicheCited as the travel-backpack and carry-on-pack answer for one-bag and adventure travel.
21It LuggageValue / lightweightBudget nicheValue brand known for ultra-light cases; cited at the budget boundary of the category.
22TravelonAnti-theft / accessoriesSecurity nicheCited in anti-theft bag and travel-security queries rather than core suitcase comparisons.
23CotopaxiAdventure / duffelsAdventure nicheCited for colorful travel duffels and adventure bags; a lifestyle-adjacent presence.
24PatagoniaOutdoor / duffelsDuffel nicheCited for travel duffels and gear bags; an outdoor brand at the edge of the luggage category.
25Peak DesignTravel bags / cameraSpecialist nicheCited for travel and camera bags by photographers and one-bag travelers.
Winners
## The brands winning the AI answer.
### ▲ Away — Owning the Carry-On
Away built its brand on a single product and a single question: the best carry-on. A decade of travel-media coverage and review-economy presence made it the gateway answer — and the engine that names Away for the carry-on carries it into the category's adjacent queries.
### ▲ Tumi — Owning "Premium" and "Business"
Tumi did not need the carry-on. Decades of association with business and premium travel gave the engines a clear, repeatable reason to name it whenever the query turns to executive or high-end luggage.
### ▲ Briggs & Riley — Owning "Warranty"
Briggs & Riley attached itself to one word — durability — and one promise: a lifetime guarantee. Ask an engine for the most durable or longest-lasting luggage and the brand surfaces well above its size.
### ▲ Monos & Béis — Owning the New Lanes
Monos took "minimalist" and Béis took "lifestyle." Each gave the engines a distinct, repeatable position rather than competing head-on with the default — and each now owns a slice of the answer.
Falling Behind
## The brands AI is leaving behind.
### ▼ Samsonite — The Largest Maker, Not the Cited One
Samsonite leads the world in luggage revenue, but its scale lives in airport stores and wholesale shelves the engines cannot read. A century of distribution does not translate into a citation footprint.
### ▼ The Mid-Range Heritage Brands
Long-standing makers without a defining product or a sharp position blur together in AI answers. The engines retrieve the brand with a clear story and skip the rest of the tier.
### ▼ Value Brands With No Owned Word
Budget luggage that competes only on price has nothing for an engine to attach a recommendation to. "Cheap" alone is a thin and crowded citation lane.
### ▼ Wholesale-First Brands
Brands that sell mainly through retailers and rarely through their own channel generate little first-party content and few direct reviews — and underindex against their physical presence.
Structural Findings
## Six structural truths about AI visibility in luggage.
01
### DTC brands own the answer.
Away, Monos, and Béis — built in the review economy — lead the citation surface over the legacy makers.
02
### The carry-on is the gateway.
The category's AI answer is won at "best carry-on" — the question most buyers ask first.
03
### Shelf space is invisible.
Airport and department-store distribution sells volume but generates nothing the engines retrieve.
04
### A defining product is a citation anchor.
A single recognizable case gives the engines a durable reason to cite the whole brand.
05
### Owned words win sub-queries.
"Durable," "luxury," "minimalist," "sustainable" each route to the brand that claimed them.
06
### Travel media drives citations.
Product-testing outlets and travel editorial feed luggage citations disproportionately.
Findings Specific to 2026
## Six 2026 dynamics reshaping luggage AI citations in real time.
01
### Travel demand keeps the category in the answer.
Sustained global travel growth keeps "best luggage" a high-frequency query the engines refresh often.
02
### Sustainability is entering the answer.
Recycled-material construction is increasingly part of how the engines describe and rank cases.
03
### Smart-luggage features surface selectively.
Trackers and charging are cited where allowed, but airline rules keep the framing cautious.
04
### The DTC wave keeps widening.
New direct-to-consumer entrants keep adding reviewed challengers the engines can retrieve.
05
### Warranty terms are a citation factor.
Lifetime-guarantee language is increasingly surfaced in durability and value queries.
06
### One-bag travel is its own surface.
Travel backpacks and carry-on packs are forming a distinct, fast-growing citation lane.
The Playbook
## General tips for luggage marketing teams.
- Audit category citation share quarterly. Luggage citations move with travel cycles, product launches, and review-season coverage.
- Decide which query you intend to own. Carry-on, checked, luxury, durable, sustainable — pick the question you can credibly win.
- Anchor the brand to a defining case. A single recognizable product gives the engines a durable citation hook.
- Earn product-testing and travel coverage. Review outlets and travel editorial feed luggage citations disproportionately.
- Keep spec and reference sources accurate. Dimensions, weight, and warranty terms are heavily retrieved — errors propagate.
- Build a clear position relative to the carry-on default. "The durable one," "the luxury one," "the value pick" are ownable lanes.
- Invest in first-party content if you sell wholesale. Retail distribution alone leaves the engines with nothing to read.
- Engage the sustainability narrative. Recycled-material construction now surfaces in answers — address it in retrievable content.
- Treat warranty as content. A lifetime guarantee earns citations only when it is published clearly and consistently.
- Re-audit after every major launch, travel season, or review roundup. Each materially reshapes the citation surface.
The Bigger Picture
## The luggage decision now starts in the answer engine.
For a century, luggage was bought the way it was sold — off an airport shelf, in a department store, under a name a traveler already knew. That still happens. But a growing share of buyers now begin with a question typed into an AI engine, and the answer they receive frames the consideration set before they reach any store.
The Index shows what that surface rewards: a defining product, a deep record of independent reviews, and a clear position relative to the category's gateway query. Samsonite's scale was built in a channel the engines cannot see. Away's was built in exactly the one they read.
AI citation share is the scoreboard. In luggage, the brand AI names is the brand the review economy made the answer — not the brand with the most shelf space.
Frequently Asked Questions
## The Luggage AI Visibility Index 2026.
What is the Luggage AI Visibility Index 2026…A research report by 5W that ranks the top 25 luggage brands by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ consumer prompts tracked in Q2 2026.
Which luggage brand holds the highest AI citation share…Away, with an estimated 15% — driven by its ownership of the "best carry-on" query, the gateway question for the category.
Is Samsonite the most-cited luggage brand…No. Samsonite is the largest luggage company in the world by revenue and market share, but it ranks second in AI citation share. The direct-to-consumer brand Away leads, because its growth was built in the review economy the engines retrieve from.
Is this index a rating of luggage quality…No. It measures AI citation share for communications strategy purposes only and is not an assessment of build quality, durability, or warranty value. A brand's rank reflects how often AI engines surface it — not how well its luggage performs.
What is citation share…The estimated proportion of brand mentions a company receives across all tracked prompts and AI engines — the core metric used to rank brands in the Index.
What other industries has the AI Visibility Index Series covered…Recent editions include Functional Beverages, Cookware, Luxury Watches, Home Security, Beauty, and Grocery Retail.
### About this report
5W AI Visibility Index: Luggage. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ consumer queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report is not purchasing, durability, or warranty advice and does not rank luggage on build quality.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
---
### America's Biggest Airline? AI Acts Like It Doesn't Exist
URL: https://www.5wpr.com/ai-visibility-index/airlines-hotels-ai-visibility-index-2026/
Summary: See which airline and hotel brands ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews recommend when travelers ask for travel advice.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexMay 2026 ReleaseTravel & Hospitality
# America's Biggest Airline? AI Acts Like It Doesn't Exist
The largest U.S. carrier by capacity and the largest hotel chain by property count are not the brands the answer engines recommend. New 5W research ranks the 25 travel brands by how often AI names them.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 brandsData window: Q2 2026
The Headlines
01
Delta doesn't fly the most seats. It wins the answer anyway.
American is the largest U.S. carrier by capacity — and ranks fifth in citation share.
02
Wyndham runs the most U.S. hotels. AI barely mentions it.
Property count does not translate to citation share when the brand value is in budget franchises.
03
The booking sites are getting squeezed out of the funnel.
As AI engines answer travel queries directly, the OTA layer is the most exposed in the category.
~10.5%
Estimated AI citation share held by Delta — the most-cited travel brand
~78%
Combined domestic market share of the four largest U.S. airlines
1.7M
Marriott rooms across roughly 9,300 hotels — the world's largest hotel company
~6,400
U.S. Wyndham locations — the most of any chain, yet underindexed in AI answers
Figure 1 · The Ranking
## Who AI names first.
TOP 15 TRAVEL BRANDS BY EST. CITATION SHARE · Q2 2026
01Delta Air LinesNetwork airline10.5%
02MarriottPremium & luxury hotels10.0%
03HiltonPremium & luxury hotels8.5%
04United AirlinesNetwork airline7.5%
05American AirlinesNetwork airline6.5%
06Southwest AirlinesLow-cost airline5.5%
07HyattPremium & luxury hotels4.2%
08AirbnbLodging platform3.8%
09Booking.comOTA / booking platform3.3%
10IHG Hotels & ResortsUpper-midscale hotels2.8%
11JetBlueLow-cost airline2.4%
12ExpediaOTA / booking platform2.1%
13Alaska AirlinesNetwork airline1.9%
14WyndhamEconomy & midscale hotels1.6%
15Four SeasonsLuxury hotels1.4%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of brand citations across 60+ tracked traveler prompts spanning airlines, hotels, and booking platforms. Remaining ~28% split across ranks 16–25 and unranked brands.
The Central Finding
## The biggest carrier and the biggest chain are not the ones AI recommends.
American travel is a category of giants. American Airlines flies the most domestic seats. The four largest U.S. carriers together control roughly 78% of the domestic market. Marriott is the largest hotel company on earth, with about 1.7 million rooms. Wyndham operates more U.S. hotel locations than any other chain — roughly 6,400.
None of those scale leaders wins the AI answer outright. Asked "what's the best airline," the engines surface Delta — on reliability and premium service, not capacity. Asked for the "best hotel," the answer is Marriott and Hilton — on loyalty depth and brand consensus. Wyndham, the property-count leader, ranks far lower, because its brand value lives in budget franchises consumers do not ask an AI engine about by name.
This is the decoupling the 5W AI Visibility Index finds in every category it measures: AI answers default to the brands with the deepest editorial and consumer-consensus footprint — not the brands leading on capacity, property count, or raw market share. In travel, where the entire buyer journey now runs through "best airline for X" and "best hotel in Y," the brand AI names is the brand that gets booked.
— 5W Research, May 2026
"American Airlines flies the most seats. Delta wins the answer. Wyndham runs the most hotels in America. Marriott and Hilton win the answer. Scale is not citation share — and in travel, where the buyer journey now runs through 'best airline for X' and 'best hotel in Y,' the brand AI names is the brand that gets booked. Loyalty programs were built to lock in the customer. The new lock-in is being the default answer."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common traveler prompts across six primary sub-categories of the airline and hotel market, running each prompt five times per engine in clean sessions. We identified which brands AI models consistently surface, which review and editorial sources feed those citations, and where the biggest gaps sit between scale and AI visibility.
#### Network airlines
Delta, United, American, Alaska, Hawaiian.
#### Low-cost & ultra-low-cost airlines
Southwest, JetBlue, Spirit, Frontier, Allegiant.
#### Premium & luxury hotels
Marriott, Hilton, Hyatt, Four Seasons, and their luxury sub-brands.
#### Upper-midscale & select-service hotels
IHG, Choice Hotels, Best Western, Accor select-service brands.
#### Economy & extended-stay hotels
Wyndham, economy and extended-stay franchise brands.
#### OTAs & booking platforms
Booking.com, Expedia, Airbnb, Vrbo, Tripadvisor, Kayak, Hotels.com.
Query types tracked. Real-world prompts including "best airline," "best airline for international travel," "Delta vs United," "best hotel chain," "best hotel loyalty program," "Marriott vs Hilton," "best hotel for business travel," "is Airbnb cheaper than a hotel," "best site to book flights," and 50+ variations covering comparison, recommendation, and decision intent.
Citation sources tracked. Travel editorial (The Points Guy, NerdWallet Travel, Conde Nast Traveler, Travel + Leisure, Skift), review aggregators (TripAdvisor, J.D. Power, U.S. News Travel), traveler communities (Reddit r/travel, r/awardtravel, FlyerTalk), news coverage, and brand-owned content.
Important framing. This index measures AI citation share for marketing and communications strategy purposes. It does not rank airlines or hotels on safety, on-time performance, service quality, or value. Travel decisions should be made with independent research and current operator information.
The Full Ranking
## The Top 25 airline and hotel brands, ranked by AI citation share.
#BrandCategoryAI VisibilityNotable
1Delta Air LinesNetwork airlineCategory-dominantNYSE: DAL; not the largest by capacity, but owns the "best airline" reliability-and-premium citation surface across the engines.
2MarriottPremium / luxury hotelsHotel citation leaderNASDAQ: MAR; ~1.7M rooms, ~9,300 hotels, 30+ brands; deep loyalty program and brand consensus win "best hotel" answers.
3HiltonPremium / luxury hotelsHotel co-leaderNYSE: HLT; the default second name in nearly every "best hotel chain" answer; Hilton Honors anchors loyalty citations.
4United AirlinesNetwork airlineNetwork & international tierNASDAQ: UAL; strong "best airline for international travel" citations on the strength of its global network.
5American AirlinesNetwork airlineLargest by capacity, #5 by citationNASDAQ: AAL; the largest U.S. carrier by seats — the clearest capacity-vs-citation gap in the Index.
6Southwest AirlinesLow-cost airlineValue default, shiftingNYSE: LUV; long the "best low-cost airline" answer; recent fare-and-boarding changes are a live citation event.
7HyattPremium / luxury hotelsDesign-forward tierNYSE: H; smaller footprint, strong "best loyalty program" and design-hotel citations.
8AirbnbLodging platformAlternative-lodging defaultNASDAQ: ABNB; owns "alternative to a hotel" and "best for groups" citations — its own distinct query category.
9Booking.comOTA / booking platformGlobal OTA leaderOwns "best site to book a hotel" citations globally; pressured as AI engines build their own travel surfaces.
10IHG Hotels & ResortsUpper-midscale hotelsMidscale incumbentHoliday Inn and Holiday Inn Express anchor "reliable mid-priced hotel" citations.
11JetBlueLow-cost airlineValue-and-comfort nicheNASDAQ: JBLU; owns the "comfortable low-cost airline" citation intersection.
12ExpediaOTA / booking platformOTA challengerNASDAQ: EXPE; cited alongside Booking.com; loses share to AI-native travel answers.
13Alaska AirlinesNetwork airlineRegional-premium nicheNYSE: ALK; owns "best airline on the West Coast" citations; strong loyalty and service reputation.
14WyndhamEconomy / midscale hotelsMost properties, low citationNYSE: WH; the most U.S. hotel locations of any chain — but a budget-franchise brand underindexes in "best hotel" answers.
15Four SeasonsLuxury hotelsLuxury defaultOwns "best luxury hotel" citations; small footprint, outsized citation share per property.
16Choice HotelsUpper-midscale hotelsValue-franchise tierNYSE: CHH; large franchise portfolio; cited mainly in value and roadside-stay queries.
17Spirit AirlinesUltra-low-cost airlinePrice-query onlyCited almost exclusively in "cheapest flight" answers, frequently alongside fee cautions.
18Best WesternEconomy / midscale hotelsLegacy-franchise tierBroad U.S. footprint; cited as a familiar mid-priced option, rarely as a "best" answer.
19AccorPremium / luxury hotelsInternational tierStrong outside the U.S.; thinner U.S. citation share despite a deep luxury portfolio.
20Frontier AirlinesUltra-low-cost airlinePrice-query onlyCited in "cheapest flight" answers; minimal share in "best airline" queries.
21Hawaiian AirlinesNetwork airlineDestination nicheOwns "best airline to Hawaii" citations; narrow but uncontested.
22TripadvisorReview / booking platformReview-source tierMore valuable to AI as a citation source than as a cited brand; surfaces in "best things to do" queries.
23KayakOTA / metasearchMetasearch nicheCited in "compare flight prices" queries; thin share elsewhere.
24VrboLodging platformVacation-rental nicheCited as the "Airbnb alternative for whole homes"; small but defined share.
25Hotels.comOTA / booking platformBooking-channel tierCited in hotel-booking queries; share compressed between Booking.com and AI-native answers.
Winners
## The brands winning the AI answer.
### ▲ Delta — the Reliability Answer
Delta does not fly the most seats, but it owns the "best airline" citation surface. Years of operational-reliability coverage, premium-cabin investment, and consistent traveler consensus made Delta the brand the engines name first — a position capacity alone cannot buy.
### ▲ Marriott and Hilton — the Hotel Duopoly
AI answers to "best hotel chain" almost always surface Marriott and Hilton together. Their depth of brands, loyalty-program scale, and decades of editorial coverage make them the assumed pair — newer or budget chains are treated as alternatives, not peers.
### ▲ Airbnb — the Category It Owns Alone
Airbnb owns "alternative to a hotel" and "best for groups and families" — queries no hotel brand competes for directly. By being its own citation category, Airbnb captures share the hotel groups leave untouched.
### ▲ Hyatt — the Loyalty Citation
Hyatt's footprint is a fraction of Marriott's, yet it punches above its size by owning the "best hotel loyalty program" citation — a narrow, high-intent query where reputation beats room count.
Falling Behind
## The brands AI is leaving behind.
### ▼ Wyndham and the Property-Count Trap
Wyndham operates more U.S. hotels than any other chain, but its citation share is a small fraction of Marriott's. Its value lives in budget franchises consumers do not ask an AI engine about by name — scale that does not translate to the answer.
### ▼ Ultra-Low-Cost Carriers
Spirit and Frontier surface almost exclusively in "cheapest flight" answers, frequently next to fee and reliability cautions. They have volume, but they have not earned a place in the "best airline" citation surface.
### ▼ Economy & Legacy Hotel Brands
Best Western and economy franchise brands are cited as familiar, not as recommended. AI answers to "best hotel" route to premium consensus leaders, leaving legacy mid-market brands in the "reliable enough" tier.
### ▼ OTAs Caught Between Google and the Brands
Booking.com, Expedia, and Hotels.com face a squeeze: AI engines increasingly answer travel queries directly, and brands push direct booking. The middle layer of the travel funnel is the most exposed citation territory in the category.
Structural Findings
## Six structural truths about AI visibility in travel.
01
### Capacity and property count do not predict citation share.
The largest carrier by seats and the largest chain by locations both rank below smaller, higher-consensus brands. Scale is not the metric AI rewards.
02
### Loyalty programs lock in customers — they do not earn citations.
Loyalty drives repeat booking, but AI answers are won by editorial and consensus footprint. The new lock-in is being the default answer.
03
### The "best for X" query is where travel citations are decided.
"Best airline for international," "best hotel for business," "best for families" — specific-intent queries reward brands with use-case content over generalists.
04
### Airbnb is its own citation category.
Alternative lodging is a distinct query surface that no hotel brand contests directly — and Airbnb owns it almost entirely.
05
### The OTA layer is the most exposed in the category.
As AI engines answer travel queries directly and brands push direct booking, the booking-platform middle is squeezed from both sides.
06
### Reliability and service consensus is the most durable moat.
Delta's "best airline" position and the Marriott–Hilton duopoly rest on years of consensus that no single quarter of operations can dislodge.
Findings Specific to 2026
## Six 2026 dynamics reshaping travel AI citations in real time.
01
### AI-native travel answers are disintermediating OTAs.
As the engines surface flights and hotels directly, the booking-platform citation layer is shrinking — the year's biggest structural shift in travel visibility.
02
### The airline premium-cabin race is reshaping "best airline."
As carriers expand premium seating and segmented fares, AI answers increasingly describe airlines by cabin experience — a tailwind for Delta and United.
03
### Southwest's model changes are a live citation event.
As Southwest evolves its fare and boarding model, its long-held "best low-cost airline" citation is in flux — and the engines are slow to update.
04
### Hotel loyalty consolidation is concentrating citations.
As loyalty programs deepen and partner networks expand, "best loyalty program" answers concentrate further around Marriott, Hilton, and Hyatt.
05
### Premium-leisure demand is front-loading luxury citations.
Sustained high-end leisure travel is pushing luxury hotel brands higher in "best hotel" answers — Four Seasons and luxury sub-brands gain share.
06
### Airbnb's expansion is widening its citation surface.
As Airbnb pushes into experiences and services, its citations broaden beyond lodging into "things to do" — extending an already-owned category.
The Playbook
## General tips for airline and hotel marketers.
- Audit AI citation share quarterly. Premium-cabin shifts, loyalty changes, and AI-native travel features move fast enough that anything less misses material movement.
- Win the "best for X" query, not just "best." Build use-case content — international, business, families, groups — that maps to how travelers actually ask.
- Earn the travel-editorial record. The Points Guy, Conde Nast Traveler, Travel + Leisure, NerdWallet Travel feed AI citations disproportionately. Accurate, current coverage compounds.
- Treat traveler communities as citation infrastructure. Reddit's r/travel and r/awardtravel and FlyerTalk feed a large share of recommendation citations.
- Reframe loyalty as a citation asset. Loyalty drives repeat booking — but loyalty-program editorial and reviews also feed "best program" citations. Earn that coverage.
- Build direct-booking content as AI disintermediates OTAs. As engines answer travel queries directly, the brand-owned booking surface gains citation value.
- Treat model and policy changes as citation events. Fare-structure, boarding, and benefit changes lag in AI answers — correct the record deliberately.
- Own a destination or segment niche. "Best airline to Hawaii," "best West Coast carrier" — a narrow, owned query beats competing for "best airline" outright.
- Publish honest fee and total-cost content. AI answers front-load fee framing, especially for low-cost carriers and budget hotels. Engage it directly.
- Re-audit after every major route, merger, or loyalty shift. Network and program changes reshape travel citations.
The Bigger Picture
## AI rewards the brands that own the consensus — not the capacity chart.
American travel is a category of scale — the biggest carriers, the biggest chains, hundreds of thousands of rooms and seats. But scale is not what AI answers reward. They run on the accumulated editorial record and traveler consensus, and they surface the brands that built a durable position there.
American flies the most seats; Delta wins the answer. Wyndham runs the most hotels; Marriott and Hilton win the answer. Everywhere the Index looks, the brand AI names is the brand with the deepest consensus footprint — not the brand with the most capacity. And as AI engines increasingly answer travel queries directly, the booking-platform layer that once sat between traveler and brand is being compressed out.
AI citation share is the scoreboard. In travel, the brand AI names is the brand that gets booked — and that position is built in editorial and consensus, not in fleet size or property count.
Frequently Asked Questions
## The Airlines & Hotels AI Visibility Index 2026.
What is the Airlines & Hotels AI Visibility Index 2026?A research report by 5W that ranks the top 25 airline and hotel brands by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ traveler prompts tracked in Q2 2026.
Which travel brand holds the highest AI citation share?Delta Air Lines, with an estimated 10.5% — it owns the "best airline" reliability and premium-service citation surface despite not being the largest carrier by capacity.
Does capacity or property count predict AI citation share?No. American Airlines flies the most domestic seats and Wyndham operates the most U.S. hotels, but Delta, Marriott, and Hilton win the AI answer — the clearest decoupling the Index measures.
Which AI engines were used?ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, across the Q2 2026 data window.
What is citation share?The estimated proportion of brand mentions a brand receives across all tracked traveler prompts and AI engines — the core metric used to rank brands in the Index.
What other industries has the AI Visibility Index Series covered?Recent editions include Medical Aesthetics, U.S. Grocery Retail, Online Universities, Cybersecurity, the Pet Industry, and the AI Platform Citation Source Index.
Related — Travel & Hospitality Indexes
New · May 2026
Theme Parks AI Visibility Index
Disney 58%. Universal 23%. 94% of every AI theme-park answer goes to three companies.
Travel & Hospitality
Luxury Island Resorts
Which luxury island destinations control the AI answer.
Dining
Restaurant Chains
Which restaurant chains dominate AI dining recommendations.
### About this report
5W AI Visibility Index: Airlines & Hotels. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ traveler queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report does not constitute travel advice and does not evaluate airlines or hotels on safety or service quality.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
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### Health Insurance & Medicare Advantage AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/health-insurance-ai-visibility-index-2026/
Summary: 5W research ranks the top 25 health insurers by AI citation share. UnitedHealthcare, Humana, and Kaiser lead the answers across all engines.
Open enrollment moved into the chatbox—and most insurers didn't follow.
34 million seniors are projected in Medicare Advantage for 2026. UnitedHealthcare and Humana control roughly 47% of combined enrollment. The Medicare Advantage market represents approximately $500 billion annually.
But enrollment size doesn't predict AI visibility. The carriers that win the next open enrollment are the ones building AI-retrievable authority now—before this fall.
## Methodology
5W analyzed 60+ insurance-shopper queries across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews during Q2 2026, tracking which insurers appeared most frequently in AI-generated answers. The result: a Citation Share ranking of 25 health insurers—measuring who owns the answer when a Medicare-eligible buyer asks the chatbox.
## Top Insurers by AI Citation Share
Rank
Insurer
Citation Share
Segment
Signal
1
UnitedHealthcare
~15.0%
Medicare Advantage
Dominant despite losing ~930K members
2
Humana
~12.0%
Medicare Advantage
Added 1.2M members; citation momentum
3
Kaiser Permanente
~9.0%
Integrated / Commercial
Satisfaction-driven retrieval
4
Aetna / CVS Health
~7.5%
Medicare Advantage
CVS retail footprint amplifies citations
5
Blue Cross Blue Shield
~6.5%
Commercial / Federation
Federated brand dilutes share
## What the Data Shows
UnitedHealthcare holds ~15% Citation Share—the default answer when a senior asks an AI engine about Medicare Advantage. This dominance persists even as the carrier lost nearly 930,000 members. Accumulated editorial coverage and brand scale keep the retrieval anchor in place.
Humana ranks second at ~12%, driven by adding 1.2 million Medicare Advantage members in 2026. Growth narratives generate earned media. Earned media feeds the models. The models cite the brand. Enrollment begets citation begets enrollment.
Kaiser Permanente places third at ~9% despite operating in fewer markets than any carrier above it. The reason: AI engines retrieve heavily from consumer-satisfaction data—and Kaiser consistently leads those rankings. Smaller footprint, stronger signal.
Aetna / CVS Health holds ~7.5%. The CVS retail footprint generates a volume of local and health-editorial coverage that other carriers can't match through insurance operations alone.
Blue Cross Blue Shield sits at ~6.5%—lower than enrollment would suggest. The federated brand structure splits citations across dozens of independent plans. No single BCBS entity consolidates enough authority for the models to treat it as one answer.
## Three Structural Findings
1. Enrollment ≠ Visibility. The correlation between member count and Citation Share is weaker than the industry assumes. Satisfaction ratings, consolidated branding, and editorial coverage matter as much as scale.
2. Traditional channels are invisible. Brokers, direct mail, television—none of it visible to an AI engine. The carriers that built dominance through these channels are discovering a new front-of-funnel gap they never planned for. The same pattern appears across sectors—see the AI Platform Citation Source Index for which websites the models actually trust.
3. Insurtechs punch above their weight. Challengers like Devoted Health generate outsized Citation Share relative to enrollment. Growth narratives attract earned media. Earned media trains the models. The advantage compounds.
## The Federated Brand Problem
Regional and federated structures—Blue Cross Blue Shield being the most visible example—dilute Citation Share by design. When an AI engine retrieves "Blue Cross," it doesn't know which Blue Cross. The citation fragments across affiliates, and no single entity captures the authority.
Carriers that have rebranded plans face a related problem: rebranded plans lose AI-retrievable history. The models trained on pre-rebrand coverage don't connect the old name to the new one. Rebuilding that citation trail requires deliberate content strategy—not a press release. The cost of AI misclassification applies here: when the model can't resolve your brand identity, you disappear from the answer.
## The Strategic Question
The carrier that wins the next decade is the one building AI-retrievable authority before this fall's open enrollment. That means earned media in outlets the models trust. Structured content the models can retrieve. And a measurement layer that tracks Citation Share the way enrollment teams track conversion.
The infrastructure is either in place before open enrollment—or it isn't. There is no catching up in November.
The Health Insurance & Medicare Advantage AI Visibility Index 2026 is part of the 5W AI Visibility Index series—the first public benchmarks measuring brand authority inside AI-generated answers. Related studies: Accounting & Finance Software AI Visibility Index | EV Citation Share Index | Home Services & Improvement AI Visibility Index.
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### The Private Label AI Advantage
URL: https://www.5wpr.com/research/private-label-ai-advantage/
Summary: Our five-part AI research series measures how answer engines cite store items against national brands across grocery, e-commerce, and pharmacy.
The Private Label AI Advantage
All Indexes ↗
PL
5W AI Visibility Index
A five-part series
May–June 2026
# The Private Label
AI Advantage
How AI answer engines cite store brands against national brands — across five consumer categories, one study at a time.
Studies
Five
Brands modeled
125
Prompts
320 consumer-intent
Answer engines
ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews
5
Consumer categories, each a standalone study
125
Brands modeled for AI Citation Share
5
Answer engines tested per study
1 / wk
One study published each Tuesday
01
## The Series
National brands spent a century buying shelf space, television time, and search rankings. Answer engines weight none of it. The Private Label AI Advantage is a five-part 5W AI research series measuring what they weight instead.
Each study models the Citation Share of 25 brands across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, on 64 consumer-intent prompts — store brands against national brands, one category at a time. The series does not assume an answer. It tests one.
Citation Share is a directional visibility model measuring how frequently brands, retailers, and experts appear across AI-generated answers inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Scores are indexed 0–100, estimated from current answer-engine behavior and the structure and density of the open-web content each brand can be surfaced from. It is a model of the retrieval landscape — not a logged-query count, which is unstable and easily gamed.
02
## The Five Studies
Published weekly. The arc runs from thesis to exception to inversion — read in order, the five studies show exactly where the private-label advantage holds and where it breaks.
01
Grocery
Store brands out-cite national brands nearly two to one. The thesis, established.
44 vs 23
Live now
Read the study →
02
Amazon & E-Commerce
The one private-label portfolio AI does not favor — the exception that proves the rule.
45 vs 29
Live now
Read the study →
03
Pet
Store brands win the bowl; veterinary authority holds the firewall.
38 vs 29
Publishes Tue Jun 2, 2026
Read the study →
04
Pharmacy & OTC
The franchise’s widest gap — AI says “buy the generic” out loud.
45 vs 25
Publishes Tue Jun 9, 2026
Read the study →
05
Supplements
The inversion — the one category where trust beats value in the answer.
34 vs 30
Publishes Tue Jun 16, 2026
Read the study →
03
## How to Read the Series
Each study is built to the same 16-section standard: a Citation Share Index ranking 25 brands, a platform-by-platform breakdown, sub-category analysis, a grounding-source map, and a GEO playbook. The studies are directional models — estimated from current answer-engine behavior and the open-web content brands can be surfaced from, not logged-query counts.
The series sits within the broader AI Visibility Index research hub — 5W’s ongoing research into how generative engines surface brands across consumer search.
5W AI Communications
## About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. Learn more at 5wpr.com.
The Private Label AI Advantage — a five-part 5W AI research series · AI discovery and recommendation intelligence by 5W AI Communications · May 2026 · Citation Share figures are directional models, not logged-query counts.
---
### AI Killed the 'Best Fitness App' — Here's Who Got the Pieces
URL: https://www.5wpr.com/ai-visibility-index/fitness-wellness-apps-ai-visibility-index-2026/
Summary: Examine the shifting digital landscape as the Fitness and Wellness Apps AI Visibility Index ranks top applications by their citation share.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexMay 2026 ReleaseFitness & Wellness Apps
# AI Killed the 'Best Fitness App' — Here's Who Got the Pieces
Ask a chatbot for the best fitness app and it won't name just one. New 5W research ranks the 25 fitness and wellness apps by how often AI names them — and shows a category that has fragmented by behavior.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 appsData window: Q2 2026
The Headlines
01
Ask for the "best fitness app" and AI won't name just one.
The engines route by problem — running, recovery, sleep, nutrition — not by a single brand.
02
Strava owns the run. Whoop and Oura own the recovery score.
The apps with the highest citation share each own one behavior completely.
03
The all-in-one apps are losing — and don't know it yet.
Generalists win fewer answers than narrower rivals. Revenue rank does not predict citation rank.
~13%
Estimated AI citation share held by Strava — the most-cited fitness app
100M+
Strava users worldwide — the largest base of any tracking app
7M+
Users on the Peloton App — classes without the hardware
0
Apps that own a single, unified "best fitness app" answer
Figure 1 · The Ranking
## Who AI names first.
TOP 15 APPS BY EST. CITATION SHARE · Q2 2026
01StravaActivity & GPS tracking13.0%
02MyFitnessPalNutrition & weight10.0%
03PelotonStudio & class platform8.0%
04Apple Fitness+Studio & class platform6.0%
05WhoopRecovery & readiness5.5%
06OuraRecovery & readiness5.0%
07FitbitActivity & GPS tracking4.2%
08Nike Training ClubStrength & guided workouts3.8%
09CalmMind & sleep3.3%
10HeadspaceMind & sleep3.0%
11Garmin ConnectActivity & GPS tracking2.6%
12NoomNutrition & weight2.2%
13CentrStrength & guided workouts1.6%
14FitbodStrength & guided workouts1.4%
15SweatStrength & guided workouts1.2%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of brand citations across 60+ tracked user prompts spanning tracking, classes, recovery, nutrition, and mind-and-sleep. Remaining ~29% split across ranks 16–25 and unranked apps.
The Central Finding
## There is no "best fitness app" answer. The category fragmented by behavior.
For a decade, "fitness app" was a single shelf — tracking apps, class apps, calorie counters, sitting in neat boxes. That shelf has collapsed. The category the 5W AI Visibility Index measured in Q2 2026 is not a fitness-app market; it is a wellness-platform market, where the leading products fold tracking, recovery, sleep, nutrition, and mental health into one experience.
AI answers reflect that. Asked "what's the best fitness app," the engines do not return one brand. They route by problem. Ask about running and the answer is Strava. Ask about recovery and it is Whoop or Oura. Ask about nutrition and it is MyFitnessPal. Ask about classes and it is Peloton or Apple Fitness+. Ask about sleep or stress and it is Calm or Headspace.
That is the structural truth of this Index: citation share in this category is won one behavior at a time. The apps with the highest AI visibility are not the biggest generalists — they are the brands that own a single problem so completely the engine cannot answer the question without naming them. Strava owns the run. The recovery wearables own the readiness score. And revenue rank, as in every category the Index has measured, does not predict the answer.
— 5W Research, May 2026
"There is no 'best fitness app' answer anymore — there is a best app for running, a best app for recovery, a best app for sleep. AI engines route by the problem, not the brand. Strava owns the run. Whoop and Oura own the recovery score. The generalists are losing, and they don't know it yet. You win by owning one behavior so completely that the machine cannot answer the question without naming you."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common user prompts across six primary sub-categories of the fitness and wellness app market, running each prompt five times per engine in clean sessions. We identified which apps AI models consistently surface, which review and editorial sources feed those citations, and where the biggest gaps sit between revenue and AI visibility.
#### Activity & GPS tracking
Strava, Fitbit, Garmin Connect, Nike Run Club, Apple-ecosystem tracking.
#### Strength & guided workouts
Nike Training Club, Centr, Fitbod, Sweat, Freeletics, Caliber, Ladder.
#### Studio & class platforms
Peloton, Apple Fitness+, Aaptiv, Alo Moves, FitOn.
#### Recovery & readiness wearables
Whoop, Oura, Eight Sleep, and the recovery-score layer of broader trackers.
#### Nutrition & weight
MyFitnessPal, Noom, and adjacent calorie and behavior-change apps.
#### Mind & sleep
Calm, Headspace, Insight Timer, and meditation-and-sleep platforms.
Query types tracked. Real-world prompts including "best fitness app," "best running app," "best app to track workouts," "Whoop vs Oura," "best app for strength training," "best calorie counting app," "best meditation app," "is Peloton worth it without the bike," "best app for sleep," and 50+ variations covering comparison, recommendation, and decision intent.
Citation sources tracked. Tech and fitness editorial (Wirecutter, CNET, TechRadar, Tom's Guide, Men's Health, Wired), app-review sources (App Store and Google Play editorial, PCMag), user communities (Reddit r/running, r/fitness, r/Whoop, r/ouraring), news coverage, and brand-owned content.
Important framing. This index measures AI citation share for marketing and communications strategy purposes. It does not rank apps on accuracy, effectiveness, data privacy, or value, and it is not health, fitness, or medical advice. Health and exercise decisions should be made with a qualified professional.
The Full Ranking
## The Top 25 fitness & wellness apps, ranked by AI citation share.
#AppCategoryAI VisibilityNotable
1StravaActivity trackingCategory-dominant100M+ users; owns "best running app" and "best cycling app" citations outright; the social graph makes it the default endurance answer.
2MyFitnessPalNutrition & weightNutrition defaultOwns "best calorie counter" and "best food tracker" citations; the most-cited app in every nutrition-intent query.
3PelotonClass platformClass-query leaderNASDAQ: PTON; 7M+ app users; owns "best workout class app" citations, increasingly cited app-first, not bike-first.
4Apple Fitness+Class platformEcosystem challengerCited as the value alternative to Peloton for Apple Watch owners; strong "best for variety" citations.
5WhoopRecovery & readinessRecovery co-leaderOwns "best recovery tracker" citations alongside Oura; the readiness-score query is high-intent and fast-growing.
6OuraRecovery & readinessSleep-and-readiness co-leaderThe ring form factor owns "best sleep tracker" and "best readiness score" citations; punches far above app-revenue rank.
7FitbitActivity trackingLegacy-tracker tierGoogle-owned; broad name recognition keeps it in "best fitness tracker" answers, though citation share is softening.
8Nike Training ClubGuided workoutsFree-workout defaultOwns "best free workout app" citations; the Nike brand carries it into general recommendation answers.
9CalmMind & sleepMeditation co-leaderOwns "best sleep and meditation app" citations alongside Headspace; strong in stress and sleep-story queries.
10HeadspaceMind & sleepMeditation co-leaderThe other default meditation answer; owns "best app to start meditating" citations for beginners.
11Garmin ConnectActivity trackingSerious-athlete nicheOwns "best for serious training metrics" citations; tied to Garmin hardware, strong among endurance athletes.
12NoomNutrition & weightBehavior-change nicheOwns the "psychology-based weight app" citation; cited as the behavior-change alternative to calorie counting.
13CentrGuided workoutsCelebrity-program nicheChris Hemsworth's platform; cited in "best all-in-one workout program" queries; narrow but distinct.
14FitbodGuided workoutsStrength-training nicheOwns "best app for weightlifting" and "best strength-training app" citations; an AI-personalization story.
15SweatGuided workoutsWomen's-fitness nicheOwns "best workout app for women" citations; strong community and program identity.
16FreeleticsGuided workoutsBodyweight nicheCited for "best bodyweight / no-equipment workout app"; AI-coaching positioning.
17FitOnClass platformFree-class nicheOwns "best totally free fitness app" citations; cited as the no-subscription class option.
18FutureCoachingHuman-coaching nicheOwns "best app with a real personal trainer" citations; premium, narrow, defensible.
19Eight SleepRecovery & sleepSleep-tech nicheCited in "best sleep technology" queries; the smart-mattress entrant in the recovery conversation.
20AaptivClass platformAudio-workout nicheCited for "best audio-based workout app"; a defined but small citation slice.
21Insight TimerMind & sleepFree-meditation nicheOwns "best free meditation app" citations; the volume alternative to Calm and Headspace.
22Alo MovesClass platformYoga nicheCited for "best yoga app"; brand-backed, lifestyle-led, narrow citation surface.
23HevyGuided workoutsWorkout-logging nicheOwns "best gym workout logger" citations in lifting communities; small but loyal.
24CaliberCoachingCoaching nicheCited as a strength-coaching alternative; emerging citation presence.
25LadderGuided workoutsTeam-program nicheCited for "best coach-led training program app"; small, defined citation footprint.
Winners
## The apps winning the AI answer.
### ▲ Strava — the Run It Owns
Strava does not try to be everything. It owns running and cycling — and AI answers to "best running app" and "best cycling app" resolve to Strava almost unilaterally. A 100M-user social graph plus a decade of editorial consensus built a citation surface no generalist can touch.
### ▲ The Recovery Wearables — Whoop and Oura
Recovery and readiness are the fastest-growing query surface in the category, and Whoop and Oura own it. Neither is a top app by revenue, but both punch far above that rank because "best recovery tracker" and "best sleep tracker" are high-intent queries they answer better than anyone.
### ▲ MyFitnessPal — the Nutrition Default
MyFitnessPal owns "best calorie counter" outright — the largest food database and longest editorial track record in the category make it the assumed answer to every nutrition-tracking query.
### ▲ Peloton's App-First Pivot
Peloton is increasingly cited as an app, not a bike — and that reframing is working. With 7M+ app users, it owns the "best workout class app" citation, decoupled from the hardware narrative that once defined it.
Falling Behind
## The apps AI is leaving behind.
### ▼ The Generalists Without an Owned Behavior
Apps that try to do tracking, classes, nutrition, and mind all at once surface in fewer answers than narrower rivals. AI routes by problem — an app that owns no single problem is named for none of them.
### ▼ Legacy Trackers Coasting on Recognition
Fitbit still has broad name recognition, but its citation share is softening as recovery-native brands own the metrics that now define the conversation. Recognition is not the same as being the recommended answer.
### ▼ Class Platforms Without a Niche
Several capable class apps are cited rarely, because Peloton owns "classes," Apple Fitness+ owns "value," FitOn owns "free." A class app without a one-word claim is left competing for citations it cannot win.
### ▼ High-Revenue Apps With Thin Editorial Footprints
Several apps with strong subscription revenue underindex in citations because their editorial and community footprint is thin. Revenue funds growth — it does not, on its own, earn the answer.
Structural Findings
## Six structural truths about AI visibility in fitness apps.
01
### AI routes by problem, not by brand.
"Best fitness app" does not resolve to one name. The engines split the answer across running, recovery, sleep, and nutrition — the category has no single default.
02
### Owning one behavior beats being a generalist.
Strava owns the run, Oura owns sleep, MyFitnessPal owns nutrition. The apps that win citations own a problem; the all-in-one apps win fewer.
03
### Revenue rank does not predict citation share.
Recovery wearables outrank higher-revenue apps in citations because they own a high-intent query. The answer follows the problem, not the income statement.
04
### "Fitness app" has collapsed into "wellness platform."
Tracking, classes, recovery, sleep, and mind now sit in one product. The category boundary AI answers describe has dissolved.
05
### Recovery and readiness is the fastest-growing query surface.
Readiness scores, HRV, and sleep have moved from niche to mainstream — and the brands that own those queries are gaining citation share fastest.
06
### Community and editorial footprint outweigh ad spend.
Reddit communities and tech-editorial reviews feed the bulk of recommendation citations. A loud campaign does not move the answer; a deep footprint does.
Findings Specific to 2026
## Six 2026 dynamics reshaping fitness-app AI citations in real time.
01
### Recovery has become the headline query.
"Readiness score" and HRV queries are now mainstream, pulling Whoop and Oura up the Index faster than any other brands in the category.
02
### AI coaching is a citation differentiator.
Apps positioning around genuine AI personalization — Fitbod, Freeletics — are earning citations in "smart workout app" queries that did not exist a year ago.
03
### Mind and body are merging in the answer.
As fitness apps add meditation and stress tools and mind apps add movement, AI answers increasingly cite across the old boundary.
04
### Peloton's app-first story is landing.
The reframing from hardware brand to subscription app is showing up in citations — Peloton is now named in app queries, not just equipment queries.
05
### Free apps own a distinct citation lane.
"Best free fitness app" is a high-volume query, and FitOn, Nike Training Club, and Insight Timer have claimed it — a lane the subscription leaders cannot enter.
06
### Sleep is now a fitness query.
AI answers increasingly treat sleep as core to fitness, not adjacent — extending the citation surface for Oura, Eight Sleep, and the recovery brands.
The Playbook
## General tips for fitness and wellness app marketers.
- Audit AI citation share quarterly. The category re-sorts fast — recovery, AI coaching, and sleep queries can reshape the answer in a single quarter.
- Own one behavior before competing on all of them. Identify the single problem — running, recovery, sleep, strength, nutrition — your app can own, and build the citation surface around it.
- Win the "best app for X" query. Specific-intent prompts decide this category. Build content that maps directly to how users phrase their problem.
- Earn the tech-and-fitness editorial record. Wirecutter, CNET, Men's Health, Tom's Guide feed AI citations disproportionately. Accurate, current reviews compound.
- Treat user communities as citation infrastructure. Reddit's r/running, r/fitness, and device-specific subreddits feed a large share of recommendation citations.
- Lead with a real differentiator, not a feature list. AI answers reward apps with a clear one-line claim — "the recovery app," "the run app" — over apps described as all-in-one.
- Position around recovery and sleep if you can credibly own it. It is the fastest-growing query surface and still loosely held below the two leaders.
- Make AI personalization legible. If the product genuinely uses AI coaching, build content that earns the "smart workout app" citation — it is a new and contested query.
- Claim the free lane deliberately if that is the model. "Best free fitness app" is high-volume and distinct — own it explicitly rather than competing with paid leaders.
- Re-audit after every major feature launch or category shift. New behaviors enter the answer set constantly; visibility has to be maintained, not set once.
The Bigger Picture
## AI rewards the apps that own a behavior — not the broadest feature set.
The fitness app is gone. What replaced it is a wellness platform — tracking, classes, recovery, sleep, and mental health folded into one product, and a category with no single default. AI answers describe that reality precisely: ask for "the best fitness app" and the engines route by problem, naming a different brand for the run, the recovery score, the sleep, the meal log.
That makes this category a clear test of the Index's central thesis. The apps with the highest AI citation share are not the biggest generalists or the highest earners — they are the brands that own a single behavior so completely the engine cannot answer the question without naming them. Strava owns the run. Oura and Whoop own the recovery score. MyFitnessPal owns the food log.
AI citation share is the scoreboard. In fitness and wellness apps, the brand AI names is the brand that gets downloaded — and the way to be named is to own a problem, not a feature list.
Frequently Asked Questions
## The Fitness & Wellness Apps AI Visibility Index 2026.
What is the Fitness & Wellness Apps AI Visibility Index 2026?A research report by 5W that ranks the top 25 fitness and wellness apps by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ user prompts tracked in Q2 2026.
Which app holds the highest AI citation share?Strava, with an estimated 13% — it dominates running and cycling queries and has a user base above 100 million.
Is there a single "best fitness app" AI answer?No. AI engines route by problem, not by brand — surfacing different apps for running, recovery, sleep, and nutrition. The category has fragmented into behavior-specific leaders.
Which AI engines were used?ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, across the Q2 2026 data window.
What is citation share?The estimated proportion of brand mentions an app receives across all tracked user prompts and AI engines — the core metric used to rank apps in the Index.
What other industries has the AI Visibility Index Series covered?Recent editions include Medical Aesthetics, U.S. Grocery Retail, Online Universities, Cybersecurity, the Pet Industry, and the AI Platform Citation Source Index.
### About this report
5W AI Visibility Index: Fitness & Wellness Apps. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ user queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report is not health, fitness, or medical advice and does not evaluate apps on accuracy, effectiveness, or data privacy.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
---
### 450 Watch Brands. AI's Answer Is Almost Always Rolex
URL: https://www.5wpr.com/ai-visibility-index/luxury-watches-ai-visibility-index-2026/
Summary: Our 2026 index evaluates 450 watch brands, ranking Rolex and others by citation share across ChatGPT, Claude, Perplexity, Gemini and Google AI.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexLuxury Watches
# 450 Watch Brands. AI's Answer Is Almost Always Rolex
There are roughly 450 Swiss watch brands. New 5W research finds the AI engines hand most of the answer to one — and ranks the 25 brands by how often each is named.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 brandsData window: Q2 2026
The Headlines
01
Rolex owns the unqualified answer.
Ask simply for "a luxury watch" and one brand surfaces above all others.
02
The qualifier opens the door for everyone else.
"Best investment watch," "best value luxury" route to Patek, AP, Tudor, and Grand Seiko.
03
25 brands take roughly 90% of sales. AI concentrates it further.
The engines mirror an already concentrated market — and tighten it.
~17%
Estimated AI citation share held by Rolex — the most-cited watch brand
~33%
Rolex's estimated share of total Swiss watch industry value in 2025
~90%
Share of Swiss watch sales held by roughly 25 of about 450 brands
~CHF 11B
Estimated Rolex wholesale sales in 2025 — a category record
Figure 1 · The Ranking
## Who AI names first.
TOP 15 BRANDS BY EST. CITATION SHARE · Q2 2026
01RolexIconic luxury17.0%
02OmegaIconic / sport11.0%
03Patek PhilippeInvestment / haute horlogerie9.0%
04Audemars PiguetInvestment / haute horlogerie8.0%
05CartierLuxury / jewelry-watch6.5%
06TudorValue luxury5.0%
07Grand SeikoValue luxury / craft4.0%
08Jaeger-LeCoultreHaute horlogerie3.3%
09IWC SchaffhausenLuxury / sport2.8%
10BreitlingSport / tool2.4%
11TAG HeuerSport / entry-luxury2.1%
12PaneraiSport / tool1.7%
13HublotStatement luxury1.5%
14Richard MilleUltra-luxury1.3%
15A. Lange & SöhneHaute horlogerie1.1%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of brand citations across 60+ tracked consumer prompts spanning iconic luxury, investment and collector watches, value luxury, sport and tool watches, haute horlogerie, and entry-luxury. Remaining ~23.3% split across ranks 16–25 and unranked brands.
The Central Finding
## One brand owns the default — the qualifier opens the rest.
The luxury watch market is one of the most concentrated categories on earth: roughly 25 brands out of about 450 take some 90% of Swiss watch sales, and a single brand — Rolex — accounts for roughly a third of total industry value. The 5W AI Visibility Index finds the AI engines do not soften that concentration. They intensify it.
Ask any engine the unqualified question — "best luxury watch," "what luxury watch should I buy" — and the answer is Rolex, almost without exception. Decades of cultural saturation made the brand the default the models reach for first.
But the citation surface fragments the moment a qualifier appears. Ask for the "best investment watch" or a "grail watch" and the answer becomes Patek Philippe and Audemars Piguet. Ask for "best value luxury watch" and it becomes Tudor and Grand Seiko. Ask for the "best alternative to a Rolex" and Omega steps forward. That is the structure: Rolex owns the unqualified answer; every other brand that ranks owns a qualified one. A brand that owns no qualifier — however storied — is not in the answer at all.
— 5W Research, May 2026
"There are roughly 450 Swiss watch brands. Ask an AI engine which luxury watch to buy and it gives you one — Rolex — the way it has been the answer to that question for fifty years. Every other brand that shows up earned its place by owning a different question. Patek and AP own 'investment.' Tudor and Grand Seiko own 'value.' Omega owns 'the alternative.' In the most concentrated category in luxury, the engines do not spread the answer around — they hand it to the brand that owns the exact question asked. Own a qualifier, or be invisible."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common consumer prompts across six primary sub-categories of the luxury watch market, running each prompt five times per engine in clean sessions. We identified which brands AI models consistently surface, which sources feed those citations, and how the answer changes with the qualifier in the query.
#### Iconic / default luxury
Rolex, Omega, Cartier — the brands that anchor the unqualified query.
#### Investment & collector
Patek Philippe, Audemars Piguet, Richard Mille, Vacheron Constantin.
#### Value luxury & accessible
Tudor, Grand Seiko, Longines, Tissot, Oris.
#### Sport & tool watches
Omega, Breitling, TAG Heuer, Panerai, IWC.
#### Haute horlogerie
Patek Philippe, A. Lange & Söhne, Jaeger-LeCoultre, Vacheron Constantin.
#### Fashion & entry-luxury
Cartier, TAG Heuer, Montblanc, Tissot, Longines.
Query types tracked. Real-world prompts including "best luxury watch," "best investment watch," "best value luxury watch," "best watch brands," "Rolex alternatives," "best watch to buy first," "Patek Philippe vs Audemars Piguet," and 50+ variations covering recommendation, comparison, and category-leadership intent.
Citation sources tracked. Watch and luxury editorial (Hodinkee, watch enthusiast media, business and luxury press), industry analysis, reference sources, secondary-market and auction coverage, and brand-owned content.
Important framing — please read. This index measures AI citation share for communications and marketing strategy purposes only. It is not investment, valuation, or authentication advice, and it does not rank watches on quality, craftsmanship, or resale value. References to "investment" and secondary-market performance reflect how brands are discussed in AI answers — not a 5W assessment. Watch values fluctuate and any purchase decision should involve qualified, independent guidance.
The Full Ranking
## The Top 25 luxury watch brands, ranked by AI citation share.
#BrandCategoryAI VisibilityNotable
1RolexIconic luxuryCategory-dominantHans Wilsdorf Foundation; the default citation for "best luxury watch" and roughly a third of Swiss watch value.
2OmegaIconic / sportThe-alternative leaderSwatch Group; the Speedmaster and a strong heritage record make it the most-cited Rolex alternative.
3Patek PhilippeInvestment / haute horlogerieInvestment co-leaderStern family, privately held; owns the "grail" and "investment watch" citation surface with Audemars Piguet.
4Audemars PiguetInvestment / haute horlogerieInvestment co-leaderAudemars family, privately held; the Royal Oak anchors heavy collector and investment citations.
5CartierLuxury / jewelry-watchDesign-icon tierRichemont; the Tank and Santos drive citations across luxury, design, and jewelry-watch queries.
6TudorValue luxuryValue-luxury co-leaderOwned by Rolex; Rolex-grade manufacturing at lower prices; owns much of the "value luxury" citation.
7Grand SeikoValue luxury / craftCraft-value tierSeiko Group; cited heavily in "best value luxury" and finishing-and-craftsmanship queries.
8Jaeger-LeCoultreHaute horlogerieConnoisseur tierRichemont; the "watchmaker's watchmaker" reputation drives enthusiast and haute-horlogerie citations.
9IWC SchaffhausenLuxury / sportPilot-watch tierRichemont; pilot and Portugieser lines; cited in sport-luxury and aviation-watch queries.
10BreitlingSport / toolSport-watch tierPrivately held; aviation and chronograph heritage; cited across sport and tool-watch comparisons.
11TAG HeuerSport / entry-luxuryEntry-luxury tierLVMH; the Carrera and Monaco; cited as a recognizable entry into Swiss sport-luxury.
12PaneraiSport / toolDistinctive-design tierRichemont; cushion-case dive watches; a distinctive, well-defined citation niche.
13HublotStatement luxuryStatement tierLVMH; the Big Bang and fusion materials; cited in bold, contemporary statement-watch queries.
14Richard MilleUltra-luxuryUltra-luxury tierPrivately held; technical, very high-priced pieces; owns the "ultra-luxury" and athlete-watch citation.
15A. Lange & SöhneHaute horlogerieConnoisseur nicheRichemont; German haute horlogerie; cited by collectors for finishing and movement craftsmanship.
16Vacheron ConstantinHaute horlogerieHeritage-prestige nicheRichemont; one of the oldest watchmakers; cited in prestige and "holy trinity" queries.
17ZenithMovement-led luxuryEnthusiast nicheLVMH; the El Primero chronograph movement; cited by enthusiasts for horological substance.
18BreguetHaute horlogerieHistorical nicheSwatch Group; deep horological history; a prestigious but thinner modern citation footprint.
19LonginesAccessible luxuryAccessible nicheSwatch Group; heritage at accessible prices; cited in entry-luxury and value comparisons.
20TissotEntry-luxuryGateway nicheSwatch Group; the PRX line; cited as a gateway into Swiss watches and a smartwatch alternative.
21OrisIndependent / sportIndependent nicheIndependently owned; mechanical sport watches; cited by enthusiasts seeking independent brands.
22ChopardLuxury / jewelry-watchJewelry-watch nicheScheufele family, privately held; jewelry and watch maker; cited across luxury and design queries.
23BlancpainHaute horlogerieSpecialist nicheSwatch Group; Fifty Fathoms dive heritage; cited by enthusiasts and in dive-watch queries.
24MontblancLifestyle luxuryLifestyle nicheRichemont; better known for writing instruments; watch citation trails its broader brand profile.
25SeikoEntry / valueValue-gateway nicheSeiko Group; cited at the boundary of the category for value mechanical watches and gateway buys.
Winners
## The brands winning the AI answer.
### ▲ Rolex — Owning the Unqualified Question
Rolex is the answer to "best luxury watch" the way a default is a default — automatically. Decades of cultural saturation made the brand synonymous with the category, and the engines reach for it before any qualifier narrows the field.
### ▲ Patek Philippe — Owning "Investment"
Patek Philippe did not win the default — it won a more valuable qualifier. "Grail watch" and "best investment watch" route to Patek because decades of auction results and collector coverage attached the brand to the word. A specific question, owned outright.
### ▲ Omega — Owning "the Alternative"
Omega is the most-cited answer to "best alternative to a Rolex." The Speedmaster's place in history gives it a heritage record the engines retrieve constantly — and a clear, ownable position one rung from the default.
### ▲ Tudor & Grand Seiko — Owning "Value Luxury"
Tudor and Grand Seiko built their citation share on a single, growing query: "best value luxury watch." Each gave the engines a clear, repeatable reason to be the answer — Rolex-grade making, exceptional finishing — and now owns it.
Falling Behind
## The brands AI is leaving behind.
### ▼ Brands That Own No Qualifier
Several storied names are not the cited leader of any single query — not the default, not investment, not value. A respected brand with no owned qualifier surfaces below brands that claimed one.
### ▼ The Undifferentiated Middle
Mid-tier brands without a distinctive model, movement, or position blur together in AI answers. The engines retrieve the brand with a clear story and skip the rest of the tier.
### ▼ Heritage Without a Modern Record
A deep history does not generate citations on its own. Brands with a storied past but thin contemporary coverage underindex against their reputation.
### ▼ Fashion-Watch Perception
Brands the engines describe primarily as "fashion watches" are routed away from serious recommendation and investment queries — a perception that caps citation share.
Structural Findings
## Six structural truths about AI visibility in luxury watches.
01
### One brand owns the default.
The unqualified "best luxury watch" query returns Rolex almost without exception — a default decades in the making.
02
### The qualifier fragments the answer.
"Investment," "value," "alternative" each route to a different brand. The qualifier, not the brand, decides the citation.
03
### AI mirrors and intensifies concentration.
In a category where 25 brands take 90% of sales, the engines tighten the answer rather than widen it.
04
### Iconic models are citation anchors.
The Speedmaster, the Royal Oak, the Tank — a single defining model gives the engines a durable reason to cite the brand.
05
### Enthusiast and auction media drive citations.
Specialist coverage and secondary-market data feed watch citations disproportionately.
06
### A brand owning no qualifier is invisible.
However storied, a brand that is not the cited answer to a specific query is largely absent from the surface.
Findings Specific to 2026
## Six 2026 dynamics reshaping watch AI citations in real time.
01
### Industry contraction is concentrating citations.
A second year of Swiss volume decline pushes value — and the answer — further toward the dominant brands.
02
### Certified pre-owned is reshaping the resale query.
Rolex's growing CPO program is changing how the engines describe buying and resale, lifting the brand's secondary-market citations.
03
### The ultra-high end is the growth engine.
With the very top of the market driving most growth, citations are tilting toward the highest tier.
04
### Secondary-market data is entering answers.
Resale values and liquidity are increasingly surfaced in "investment watch" queries.
05
### Smartwatches pressure the entry tier.
At accessible price points, the engines weigh entry-luxury mechanical watches against premium smartwatches.
06
### Private ownership is a narrative advantage.
The independence of the dominant private brands is itself becoming part of how the engines describe their strength.
The Playbook
## General tips for luxury watch marketing teams.
- Audit category citation share quarterly. Watch citations move with auction cycles, industry reports, and model launches.
- Decide which qualifier you intend to own. Investment, value, sport, craftsmanship — pick the query you can credibly win.
- Anchor the brand to a defining model. A single iconic reference gives the engines a durable citation hook.
- Earn enthusiast and specialist coverage. Watch media and auction reporting feed citations disproportionately.
- Keep reference sources accurate. Wikipedia, brand histories, and model databases are heavily retrieved — errors propagate.
- Build a clear position relative to the default. "The alternative," "the value choice," "the connoisseur's brand" are ownable lanes around Rolex.
- Engage the secondary-market narrative. Resale and CPO context now surfaces in answers — address it rather than ignore it.
- Resist a fashion-watch perception. If the engines frame the brand as fashion, it is routed away from serious queries.
- Treat heritage as content, not a given. A storied past earns citations only when it is published in a retrievable, current form.
- Re-audit after every major launch, auction season, or industry report. Each materially reshapes the citation surface.
The Bigger Picture
## The watch decision now starts in the answer engine.
For more than a century, luxury watch desire was shaped by the boutique, the magazine, and the recommendation of a trusted collector. Those still matter. But a growing share of buyers now begin with a question typed into an AI engine — and the answer they receive frames the entire consideration set before they ever walk into a store.
The Index shows what that surface rewards: ownership of a specific query, a defining model, a deep record of specialist coverage, and a clear position relative to the category's default. It does not spread the answer evenly — in the most concentrated category in luxury, the engines concentrate it further.
AI citation share is the scoreboard. In luxury watches, the brand AI names is the brand that owns the exact question asked — and outside the one default, that ownership is built one qualifier at a time.
Frequently Asked Questions
## The Luxury Watches AI Visibility Index 2026.
What is the Luxury Watches AI Visibility Index 2026?A research report by 5W that ranks the top 25 luxury watch brands by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ consumer prompts tracked in Q2 2026.
Which luxury watch brand holds the highest AI citation share?Rolex, with an estimated 17% — the default return for the unqualified "best luxury watch" query.
Does Rolex win every watch query?No. Rolex owns the unqualified default, but the answer fragments by qualifier — investment and collector queries route to Patek Philippe and Audemars Piguet, and value-luxury queries route to Tudor and Grand Seiko.
Is this index investment advice?No. It measures AI citation share for communications strategy purposes only and is not investment, valuation, or authentication advice. References to "investment" reflect how brands are discussed in AI answers, not a 5W assessment.
What is citation share?The estimated proportion of brand mentions a company receives across all tracked prompts and AI engines — the core metric used to rank brands in the Index.
What other industries has the AI Visibility Index Series covered?Recent editions include Luxury Island destinations, Beauty, Cigar & Pipe, Golf Equipment, Venture Capital, and Grocery Retail.
### About this report
5W AI Visibility Index: Luxury Watches. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ consumer queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report is not investment, valuation, or authentication advice and does not rank watches on quality or resale value.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
---
### The Cowboys won zero Super Bowls in thirty years. They own the AI conversation anyway.
URL: https://www.5wpr.com/research/nfl-citation-share-index-2026/
Summary: The first NFL Citation Share Index: 750 queries across five AI engines rank all 32 franchises by AI search visibility, and the Cowboys dominate.
5W · The Communications Firm Built for the AI Era
● Research · Volume I · May 2026
The 5W NFL Citation Share Index 2026
# The Cowboys won zero Super Bowls in thirty years.
They own the AI conversation anyway.
The first Citation Share Index of all 32 NFL franchises. 750 queries across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. The team that just won the Super Bowl ranks 12th. Five franchises got zero. Brand authority compounds. On-field performance evaporates the moment the next Sunday begins.
Published May 2026
Author Ronn Torossian, 5W
Methodology Open · Replicable · Quarterly refresh
Research hub
GEO services
Ronn Torossian bio
Contact 5W
750
Total queries
5
AI engines tested
50
Prompts · 4 buckets
32
Franchises ranked
§ 01 · Headline Findings
## Eight things no NFL franchise communications team is tracking yet.
Finding 01 · Concentration
### The Cowboys are named first in 39% of every NFL-related AI query.
295 first-place mentions across 750 queries. The Cowboys' Citation Share Score is 100.0. The #2 franchise (Chiefs) scores 51.6. No second-place finisher comes within striking distance.
Finding 02 · Brand survives losing
### The Chiefs missed the playoffs and still rank #2 in AI citations.
For the first time since 2014, Kansas City did not make the postseason. The brand built around Patrick Mahomes, Travis Kelce, and Andy Reid did not flinch in AI search. Citation rank #2, on-field rank #23. A 21-position gap — the largest positive gap in the league.
Finding 03 · Champion underexposed
### The Super Bowl LX winner ranks #12 in AI search visibility.
The Seattle Seahawks beat New England 29-13 on February 8, 2026. They tied for the NFL's best regular-season record. They lead the league in points allowed per game. In AI citations, they rank 12th — a -11 gap between performance and visibility. AI engines have not caught up to the championship.
Finding 04 · The dead zone
### Five NFL franchises register 0.0 Citation Share.
The Jets, Saints, Colts, Cardinals, and Vikings — five franchises with a combined enterprise value north of $28 billion — appear in essentially none of the 50 prompts a fan, sponsor, free agent, or institutional investor would ask an AI engine.
Finding 05 · The Vikings paradox
### 13th in performance. 32nd in AI citations. Largest negative gap in the league.
Minnesota ranks 13th in 2025 on-field performance and dead last in Citation Share. The 19-position gap is the most extreme performance-vs-visibility disconnect in professional football. A nine-figure brand asset operating in near-total AI silence.
Finding 06 · Sponsorship mispricing
### The Jets are the 6th-most valuable NFL franchise. They rank 28th in AI citations.
A $10B+ asset. -22 value gap. Any sponsor paying market rate for Jets inventory is paying for traditional reach and getting near-zero discoverability inside ChatGPT, Claude, or Perplexity. The same pattern holds for the Commanders and Bears.
Finding 07 · Brand outperforming valuation
### The Steelers are the 20th-most valuable franchise. They rank #7 in AI citations.
Pittsburgh punches 13 positions above its Forbes valuation rank. Same story: Bengals (+15), Jaguars (+12), Lions (+11). These are franchises whose AI visibility outruns their enterprise value — a real but invisible asset on the balance sheet.
Finding 08 · Citation Share is the new market share
### None of the 32 NFL communications departments measures this — yet.
Sponsorship inventory is priced on attention. Attention now flows through AI engines. Citation Share is the metric that decides whether a sponsor renews, a free agent signs, or a fan picks a team. The franchises that build the infrastructure first will define the next decade of NFL communications economics.
§ 02 · The Index
## Citation Share Index · all 32 NFL franchises.
### Tier I · Dominant
Citation Share 50+ · Brand authority survives any season
RankFranchise
Citation Share
Fan
Sponsor
FA
Business
On-Field
Perf Gap
Forbes
Val Gap
Five franchises got zero. The Super Bowl champion ranks twelfth. The team that missed the playoffs ranks second. AI engines aren't broken — they're showing you what brand authority actually looks like when you strip out a single season. The franchises that build the citation infrastructure first will own the next decade of NFL communications economics. The rest will be paying for it.
Ronn Torossian · Founder & Chairman, 5W
Sponsorship inventory is priced on attention. Attention now runs through ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — not the broadcast and not the timeline. The Jets are the sixth-most valuable franchise in football and twenty-eighth in citations. Some sponsor is paying full freight for a brand that AI engines barely return. Build the infrastructure before the renewal — not during it.
Ronn Torossian · Founder & Chairman, 5W
§ 03 · Methodology
## Open, replicable, defensible.
### What we measured
- 5 AI engines: ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews
- 50 prompts across 4 buckets: Fan, Sponsor, Free-Agent, Business
- 3 passes per prompt per engine to control for variance
- 750 total queries in the V1 Index
- All 32 NFL franchises ranked
### How we scored
- First franchise named in answer = 5 points
- Second franchise named = 3 points
- Any other mention = 1 point
- Logo, owner, or stadium named without team name = 1 point (attribution credit)
- Aggregate sum normalized to 0–100 Citation Share Score
### What we cross-referenced
- 2025 NFL final standings — Super Bowl LX winner Seattle, AFC champion New England, regular-season leaders Seahawks, Patriots, Broncos at 14-3
- Forbes 2025 franchise valuations — Cowboys $13B (#1), Rams $10.5B (#2), Giants $10.1B (#3)
- Social media engagement — Cowboys lead total followers, Patriots #2, Steelers #3
- Performance Gap — Citation Share rank vs on-field performance rank
- Value Gap — Citation Share rank vs Forbes valuation rank
### Refresh cadence
- Q2 2026 · V1 Index — released May 2026
- Q3 2026 · Off-season refresh — pre-camp prompts
- Q4 2026 · In-season — Sunday-night signal
- Q1 2027 · Playoff / SB / free-agent special
- Methodology unchanged across refreshes for trend integrity
§ 04 · The 50 prompts
## Published verbatim. Full transparency on every prompt run.
### Fan bucket 13 prompts
- What's the most exciting NFL team to watch right now?
- Which NFL team has the best culture?
- Best NFL team for a new fan to start following?
- Which NFL franchise has the most loyal fans?
- What's the most iconic NFL franchise?
- Which NFL team has the best home stadium experience?
- Most underrated NFL team in 2026?
- Which NFL team has the most compelling storyline this season?
- Best NFL team to watch on Sunday Night Football?
- Which NFL franchise has the strongest brand identity?
- Which NFL team has the best rivalries?
- Which NFL team has the best uniforms?
- Which NFL team has the most passionate fanbase?
### Sponsor bucket 12 prompts
- Which NFL franchise is the best brand partnership opportunity?
- Which NFL team has the most valuable sponsorship inventory?
- Best NFL team for a luxury brand to sponsor?
- Which NFL franchises have the strongest corporate partnerships?
- Most innovative NFL team in marketing?
- Which NFL team reaches the most affluent fans?
- Best NFL franchise for a tech brand partnership?
- Which NFL team has the most international reach?
- Most marketable NFL franchise globally?
- Which NFL team has the highest social media engagement?
- Best NFL franchise for a consumer brand activation?
- Which NFL team has the strongest jersey sales?
### Free-agent bucket 12 prompts
- Best NFL team to play for as a quarterback?
- Which NFL franchise treats its players the best?
- Best NFL city for a free agent to sign with?
- Which NFL team has the best ownership group?
- Best NFL organization for a young player to develop?
- Which NFL team has the best coaching staff right now?
- Most stable NFL franchise to sign with long-term?
- Best NFL team for a wide receiver to maximize numbers?
- Which NFL team offers the best off-field lifestyle?
- Most player-friendly NFL franchise?
- Best NFL team for a pass rusher?
- Best NFL team for a veteran looking to win a ring?
### Business bucket 13 prompts
- Which NFL franchise is most valuable?
- Which NFL team has the highest revenue?
- Most profitable NFL franchise?
- Which NFL team has the best business model?
- Most innovative NFL ownership group?
- Best-run NFL front office?
- Which NFL franchise has the strongest balance sheet?
- Which NFL team has the most media-rights leverage?
- Most influential NFL franchise in league business decisions?
- Which NFL team has the best stadium economics?
- Best NFL franchise for institutional investors to study?
- Which NFL team is best positioned for the next CBA?
- Which NFL franchise has the strongest international growth strategy?
For sponsors · franchises · agents
## Get the full dataset.
Franchise-by-franchise breakdowns. Engine-level cuts. Prompt-by-prompt scoring. CSV + methodology PDF. Used for sponsorship valuation, free-agent intelligence, and franchise communications strategy.
---
### Patients Are Asking Chatbots About Their Prescriptions
URL: https://www.5wpr.com/ai-visibility-index/pharma-rx-ai-visibility-index-2026/
Summary: The 5W Pharma Rx AI Visibility Index ranks major pharmaceutical brands by citation share across ChatGPT, Claude, Perplexity, Gemini and Google.
Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited.
5W. Research
AI Visibility Index Series · Published May 2026
5W AI Visibility IndexMay 2026 ReleasePharma / Rx
# Patients Are Asking Chatbots About Their Prescriptions
The drugmakers competing for a $5.6 trillion global medicine market are now being researched inside the chatbox. New 5W research ranks the 25 pharmaceutical companies by how often the AI engines name them.
A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 companiesData window: Q2 2026
The Headlines
01
Merck makes the world's top-selling drug. AI talks about Lilly.
Keytruda leads global drug sales — but oncology drugs are not the ones patients self-research.
02
Ozempic and Mounjaro turned two companies into household names.
GLP-1 drugs pulled Eli Lilly and Novo Nordisk to the top of the consumer-citation surface.
03
Revenue rank and citation rank are not the same list.
The companies AI names most are not the companies that earn the most.
~12.5%
Estimated AI citation share held by Eli Lilly — the most-cited drugmaker
$5.6T
Estimated size of the global pharmaceutical industry
~$70B
Combined 2025 sales of Ozempic, Wegovy, Mounjaro, and Zepbound
$1T
Market-cap milestone Eli Lilly briefly crossed — the first healthcare company to do so
Figure 1 · The Ranking
## Who AI names first.
TOP 15 DRUGMAKERS BY EST. CITATION SHARE · Q2 2026
01Eli LillyMetabolic & GLP-112.5%
02Novo NordiskMetabolic & GLP-111.5%
03PfizerDiversified / vaccines8.5%
04Johnson & JohnsonDiversified7.0%
05MerckOncology6.0%
06AbbVieImmunology & inflammation5.0%
07AstraZenecaOncology & specialty4.0%
08ModernaVaccines & mRNA3.5%
09AmgenBiologics & specialty3.0%
10NovartisSpecialty & gene therapy2.6%
11Bristol Myers SquibbOncology & cardiovascular2.3%
12GSKVaccines & respiratory2.0%
13SanofiImmunology & vaccines1.8%
14Gilead SciencesAntivirals & oncology1.5%
15RocheOncology & diagnostics1.3%
Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of company citations across 60+ tracked patient and consumer prompts spanning metabolic, oncology, immunology, vaccine, and specialty categories. Remaining ~27.5% split across ranks 16–25 and unranked companies.
The Central Finding
## Revenue rank does not predict citation rank.
Pharma is a $5.6 trillion global industry, and by conventional measures its leaders are Merck — maker of Keytruda, the best-selling drug in the world — alongside Pfizer, Johnson & Johnson, and AbbVie. Those are the companies at the top of the revenue tables.
They are not the companies at the top of the AI answer. The 5W AI Visibility Index finds that Eli Lilly and Novo Nordisk lead the consumer-citation surface — because their drugs are the ones patients actually research. Ozempic, Wegovy, Mounjaro, and Zepbound generated roughly $70 billion in combined 2025 sales and, more importantly, became household names. When a consumer asks ChatGPT or Google's AI Overviews about weight loss, diabetes, or "Ozempic vs Wegovy," the company names that surface are Lilly and Novo.
Keytruda outsells every drug on earth, but oncology medicines are prescribed, not shopped — patients do not type Keytruda into a chatbot the way they type Ozempic. That is the decoupling: consumer AI citation share is driven by the drugs people self-research, not the drugs that earn the most. In a category where patients increasingly research their prescriptions before they research their doctor, the company AI names is the company shaping the conversation.
— 5W Research, May 2026
"Merck makes the best-selling drug in the world. Most patients have never typed its name into a chatbot. Eli Lilly and Novo Nordisk built something more valuable than the top seller — they built the drugs people actually ask AI about. Ozempic and Mounjaro turned two companies into household names inside the answer engines, and the pharma giants ranked by revenue are not the ones ranked by citation. In a category where patients now research their prescriptions before they research their doctor, the company AI names is the company that shapes the conversation."
Ronn TorossianFounder & Chairman, 5W
Methodology
## How we measured it.
5W analyzed more than 60 common patient and consumer prompts across six primary sub-categories of the pharmaceutical market, running each prompt five times per engine in clean sessions. We identified which companies AI models consistently surface, which sources feed those citations, and where the biggest gaps sit between revenue and AI visibility.
#### Metabolic & GLP-1
Eli Lilly, Novo Nordisk, and the diabetes/obesity drug makers.
#### Oncology
Merck, AstraZeneca, Bristol Myers Squibb, Roche, Pfizer oncology.
#### Immunology & inflammation
AbbVie, Sanofi, Amgen, and the autoimmune-drug makers.
#### Vaccines & antivirals
Pfizer, Moderna, GSK, Gilead Sciences.
#### Cardiovascular & specialty
Novartis, Bristol Myers Squibb, Bayer, specialty makers.
#### Generics & biosimilars
Teva, Viatris, Organon, and biosimilar producers.
Query types tracked. Real-world prompts including "which company makes Ozempic," "Ozempic vs Wegovy vs Mounjaro," "largest pharmaceutical companies," "who makes the COVID vaccine," "best company for diabetes drugs," "what company makes Humira," and 50+ variations covering company-identification, comparison, and category-leadership intent. Prompts were framed at the company and category level, not as treatment-selection queries.
Citation sources tracked. Business and health editorial (Reuters, Bloomberg, Fierce Pharma, STAT, Pharm Exec), reference sources (company filings, Wikipedia, Drugs.com), news coverage, and brand-owned corporate content.
Important framing — please read. This index measures AI citation share for corporate communications and marketing strategy purposes only. It is not medical advice. It does not rank drugs or companies on safety, efficacy, side effects, or value, and it does not recommend any treatment. Medication decisions must be made with a qualified physician or pharmacist. Nothing in this report should be used to choose, start, stop, or change a prescription.
The Full Ranking
## The Top 25 pharmaceutical companies, ranked by AI citation share.
#CompanyCategoryAI VisibilityNotable
1Eli LillyMetabolic / GLP-1Category-dominantNYSE: LLY; Mounjaro and Zepbound; targeting ~$80B 2026 revenue; briefly the first healthcare company past a $1T market cap.
2Novo NordiskMetabolic / GLP-1Citation co-leaderNYSE: NVO; Ozempic and Wegovy; owns "weight loss drug" citations; holds the majority of the GLP-1 market.
3PfizerDiversified / vaccinesConsumer-recognition tierNYSE: PFE; ~$63B 2024 revenue; COVID-era vaccine and Paxlovid recognition keeps it high in consumer citations.
4Johnson & JohnsonDiversifiedDiversified-giant tierNYSE: JNJ; Innovative Medicine plus MedTech; broad name recognition even after the Kenvue consumer-health spin-off.
5MerckOncologyRevenue leader, #5 by citationNYSE: MRK; Keytruda is the world's best-selling drug — but oncology is prescribed, not shopped, so consumer citations trail revenue.
6AbbVieImmunology & inflammationDTC-driven tierNYSE: ABBV; Humira, Skyrizi, Rinvoq; heavy direct-to-consumer advertising lifts its citation share.
7AstraZenecaOncology & specialtySpecialty tierNASDAQ: AZN; fast-growing oncology and cardiometabolic portfolio; strong but specialist-led citation profile.
8ModernaVaccines & mRNAPandemic-recognition tierNASDAQ: MRNA; COVID-era name recognition keeps it cited in vaccine and mRNA queries above its revenue rank.
9AmgenBiologics & specialtyBiologics tierNASDAQ: AMGN; large biologics portfolio and a GLP-1 candidate in development; specialist-led citations.
10NovartisSpecialty & gene therapySpecialty tierNYSE: NVS; cell, gene, and specialty therapies; thinner consumer citation share.
11Bristol Myers SquibbOncology & cardiovascularSpecialty tierNYSE: BMY; Eliquis and an oncology franchise; cited mainly in condition-specific queries.
12GSKVaccines & respiratoryVaccine-maker tierNYSE: GSK; strong vaccine and respiratory portfolio; cited heavily in vaccine queries.
13SanofiImmunology & vaccinesImmunology tierNASDAQ: SNY; Dupixent (with Regeneron) anchors its citations; broad but specialist-led profile.
14Gilead SciencesAntivirals & oncologyAntiviral tierNASDAQ: GILD; HIV and antiviral leadership; Biktarvy anchors its citation share.
15RocheOncology & diagnosticsDiagnostics-led tierSwiss group; oncology and diagnostics leader; lower U.S. consumer citation share despite scale.
16BayerDiversifiedDiversified nichePharma plus consumer health and crop science; pharma citation share diluted by the conglomerate structure.
17RegeneronImmunology & specialtySpecialty nicheNASDAQ: REGN; Dupixent and Eylea; cited in condition-specific queries.
18Vertex PharmaceuticalsRare diseaseRare-disease nicheNASDAQ: VRTX; cystic fibrosis leadership; narrow but uncontested citation niche.
19Boehringer IngelheimDiversified / privatePrivate-company nichePrivately held; Jardiance partnership with Lilly; lower citation visibility as a private company.
20TevaGenerics & biosimilarsGenerics tierNYSE: TEVA; the largest generic-drug maker; cited in generics and affordability queries.
21BiogenNeuroscienceNeuroscience nicheNASDAQ: BIIB; neuroscience and Alzheimer's focus; cited in condition-specific queries.
22TakedaDiversified / specialtySpecialty nicheNYSE: TAK; Japanese group; rare disease and gastroenterology; modest U.S. consumer citation share.
23ViatrisGenerics & biosimilarsGenerics nicheNASDAQ: VTRS; generics and biosimilars; cited mainly in affordability and access queries.
24CSLPlasma & vaccinesPlasma nicheAustralian group; plasma therapies and vaccines; narrow specialist citation footprint.
25OrganonWomen's healthWomen's-health nicheNYSE: OGN; women's health spin-off from Merck; small but defined citation niche.
Winners
## The companies winning the AI answer.
### ▲ Eli Lilly — the GLP-1 Citation Engine
Mounjaro and Zepbound did more than drive revenue — they made Eli Lilly a household name inside the answer engines. When consumers research weight loss or diabetes, Lilly is the company AI surfaces, and that consumer-citation lead has no equal in the category.
### ▲ Novo Nordisk — Ozempic as a Brand Halo
Ozempic became one of the most-searched drug names in the world, and Wegovy extended it into obesity. That recognition pulls Novo Nordisk's corporate name into a huge share of consumer health queries — visibility a traditional campaign could never buy.
### ▲ Pfizer — the Pandemic Recognition Dividend
The COVID-19 vaccine made Pfizer one of the most recognized company names on earth. That recognition still compounds: Pfizer ranks third in citations, well above where its current revenue mix alone would place it.
### ▲ AbbVie — Direct-to-Consumer as Citation Fuel
AbbVie's heavy direct-to-consumer advertising for Humira, Skyrizi, and Rinvoq put its drug names into wide public circulation — and that circulation feeds editorial coverage and citations the engines retrieve.
Falling Behind
## The companies AI is leaving behind.
### ▼ Oncology-Led Giants in Consumer Queries
Merck makes the best-selling drug in the world, yet ranks fifth in citations. Oncology medicines are prescribed by specialists, not researched by patients shopping — so the consumer-citation surface badly trails oncology revenue.
### ▼ Diversified Conglomerates
Bayer and other conglomerates split their identity across pharma, consumer health, and unrelated businesses. That diffuse structure dilutes pharmaceutical citation share — the engines cannot attribute a clear category position.
### ▼ Privately Held Drugmakers
Private companies generate less of the filings, investor coverage, and news flow that feed AI citations. Capable private drugmakers underindex relative to their actual scale.
### ▼ Generics Cited Only on Price
Teva and Viatris are major manufacturers, but they surface mainly in affordability and access queries. AI answers about specific conditions route to branded innovators, leaving generics in a narrow citation lane.
Structural Findings
## Six structural truths about AI visibility in pharma.
01
### Revenue rank does not predict citation rank.
The highest-earning companies are not the most-cited. Consumer citation follows the drugs patients research, not the income statement.
02
### A blockbuster drug pulls the company name with it.
Ozempic and Mounjaro made Novo and Lilly household names. The drug brand is the company's most powerful citation asset.
03
### Self-researched conditions drive citations.
Weight, diabetes, and immunology — conditions patients actively research — generate citations. Specialist-prescribed categories generate far fewer.
04
### Direct-to-consumer advertising compounds into citations.
DTC puts drug names into public circulation, which feeds the editorial coverage AI engines retrieve. The effect outlasts the ad spend.
05
### Diffuse corporate structure dilutes citation share.
Conglomerates and private companies underindex. A clear, public, single-category identity earns the answer.
06
### Patients now research before the appointment.
A growing share of patients arrive at the doctor having already read an AI answer. Corporate citation share is now upstream of the prescription conversation.
Findings Specific to 2026
## Six 2026 dynamics reshaping pharma AI citations in real time.
01
### GLP-1 is the category's center of gravity.
Combined GLP-1 sales near $70B and the news flow around them keep Lilly and Novo at the top of the consumer-citation surface.
02
### Oral GLP-1 is a citation event in waiting.
As GLP-1 therapies move toward pills, a wave of coverage is likely to reshape metabolic-category citations again.
03
### Patent cliffs are reshaping company narratives.
Looming losses of exclusivity on major drugs are shifting how AI answers describe the pipelines of affected companies.
04
### Pricing and policy coverage is entering the answer.
Drug-pricing debate generates heavy news flow — and AI answers increasingly surface pricing context in company queries.
05
### GLP-1 trials beyond obesity are widening citations.
Trials in sleep apnea, heart, and cognitive health are extending the metabolic leaders' citation surface into new conditions.
06
### AI-driven drug discovery is a new corporate-citation lane.
Companies positioning around AI in R&D are beginning to earn citations in a query category that did not exist a year ago.
The Playbook
## General tips for pharmaceutical communications teams.
- Audit corporate AI citation share quarterly. Pipeline news, trial readouts, and pricing coverage move the surface fast.
- Connect the drug brand to the company name. A blockbuster only builds corporate citation share if the AI-retrievable record links drug and maker clearly.
- Build authoritative corporate and disease-state content. Accurate, well-sourced corporate content feeds the engines a reliable record to cite.
- Earn the business and health editorial record. Reuters, Bloomberg, STAT, and Fierce Pharma feed pharma citations disproportionately.
- Keep reference sources accurate. Company filings, Drugs.com, and Wikipedia are heavily retrieved — errors there propagate into AI answers.
- Treat trial readouts and approvals as citation events. Major clinical milestones reshape company citations; publish substantive content around them.
- Engage pricing and access narratives directly. AI answers increasingly surface pricing context — address it in corporate content rather than ceding the framing.
- Consolidate a clear category identity. A defined therapeutic position out-cites a diffuse conglomerate profile.
- Maintain strict promotional and regulatory compliance. AI-visibility content must stay within pharmaceutical advertising and regulatory rules — never imply treatment guidance.
- Re-audit after every major launch, readout, or patent event. Each materially reshapes the citation surface.
The Bigger Picture
## The prescription conversation now starts before the appointment.
For decades, pharmaceutical brand awareness was shaped by detailing to physicians and direct-to-consumer advertising. Today a growing share of patients arrive at the doctor's office having already asked ChatGPT, Claude, or Google's AI Overviews about their condition and their options. The citation surface has become upstream of the prescription itself.
The Index shows what that surface rewards: the drugs patients self-research, the company names a blockbuster drags into public consciousness, a clear category identity, and a deep, accurate editorial and reference record. It does not simply track revenue — Merck's best-in-world drug does not make Merck the best-cited company.
AI citation share is the scoreboard. In pharma, the company AI names is the company shaping how patients understand their options — and that visibility is built in the public record, not the revenue table.
Frequently Asked Questions
## The Pharma / Rx AI Visibility Index 2026.
What is the Pharma / Rx AI Visibility Index 2026?A research report by 5W that ranks the top 25 pharmaceutical companies by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ patient and consumer prompts tracked in Q2 2026.
Which pharmaceutical company holds the highest AI citation share?Eli Lilly, with an estimated 12.5% — driven by the consumer prominence of its GLP-1 drugs Mounjaro and Zepbound.
Does pharma revenue rank predict AI citation share?No. Merck makes the world's best-selling drug and several companies out-earn Lilly, but Eli Lilly and Novo Nordisk lead the consumer-citation surface because their drugs are the ones patients ask AI about.
Is this index medical advice?No. It measures AI citation share for communications strategy purposes only. It does not rank drugs or companies on safety, efficacy, or value, and it is not medical advice. Treatment decisions should be made with a qualified physician.
What is citation share?The estimated proportion of company mentions a drugmaker receives across all tracked prompts and AI engines — the core metric used to rank companies in the Index.
What other industries has the AI Visibility Index Series covered?Recent editions include Medical Aesthetics, U.S. Grocery Retail, Online Universities, Cybersecurity, the Pet Industry, and the AI Platform Citation Source Index.
### About this report
5W AI Visibility Index: Pharma / Rx. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ patient and consumer queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of company mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report is not medical advice and does not evaluate drugs or companies on safety, efficacy, or value. Medication decisions should be made with a qualified physician or pharmacist.
### About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research
---
### Defense & Aerospace AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/defense-aerospace/
Summary: Defense and aerospace companies ranked by AI citation share. The 5W Defense and Aerospace AI Visibility Index measures which contractors ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews cite most.
5W AI Visibility Research · Defense & Aerospace · Two-Wave Benchmark
# Across two independent waves of testing, two defense technology companies — Anduril and Palantir — account for a higher combined Citation Share than the five largest legacy defense primes in this dataset.
A two-wave benchmark measuring how often U.S. defense, aerospace, and space companies are surfaced, cited, and recommended across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. Results reflect observed retrieval behavior during a defined testing window.
Published · May 13, 2026
Author · Ronn Torossian
Method · Two waves, 28,400 prompts total
Reading time · 14 min
19.8%
Anduril — #1 defense company by AI Citation Share
35.0%
Anduril + Palantir combined
21.1%
Top 5 legacy primes combined (Lockheed, Northrop, RTX, Boeing, GD)
60.0%
Captured by top 5 companies by AI Citation Share
Figure 01 · Observed source distribution
### Defense trade press accounted for a large share of citations in this dataset relative to financial media sources.
Share of total citations surfaced by the five AI systems when responding to prompts about U.S. defense and aerospace companies. January-May 2026, 28,400 prompts across two waves.
-
-
-
-
-
Wikipedia
20.7%
Defense News
10.8%
← Defense trade press
Breaking Defense
8.9%
← Defense trade press
The War Zone
7.2%
← Defense trade press
Reuters & Bloomberg defense (combined)
6.8%
Defense company-owned domains (combined)
6.4%
NYT, WSJ, FT defense (combined)
5.9%
War on the Rocks
5.4%
Government sources (DoD, GAO, CRS)
5.1%
Defense One
4.6%
Substack (war journalism)
4.4%
All other publishers
13.8%
0%
5%
10%
15%
20%
Source · 5W Defense & Aerospace AI Visibility Index 2026 · n = 28,400 prompts · ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews · Two waves: Jan-Feb and Apr-May 2026
Executive Summary
## A two-wave observational benchmark of how AI systems describe U.S. defense and aerospace.
This report presents a two-wave benchmark of how 25 U.S. defense, aerospace, and space companies are surfaced, cited, and recommended across five AI systems — ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. The dataset comprises 28,400 prompts conducted in two independent waves (Wave 1: January–February 2026; Wave 2: April–May 2026). Findings reported below are stable across both waves within predefined tolerance thresholds (≤1.5 percentage points on company-level Citation Share; ≤2.0 pp on source-level share).
Citation Share (composite metric) · The proportion of responses in which a company is mentioned, recommended, or cited as a referenced source. Mentions, recommendations, and source citations are tracked separately and weighted equally in the headline metric. This is an internal composite metric, not a standard industry measure, and is best interpreted as a proxy for model surface frequency rather than authority, accuracy, endorsement, or operational performance. Test set · 10 modern defense technology firms, 8 space companies, 7 legacy defense primes. Israeli, Indian, and other allied defense firms were tested in an extended dataset and excluded from the top-25 ranking to maintain a U.S. frame consistent with prior 5W benchmarks.
In this dataset, the highest-cited defense or aerospace company observed was Anduril Industries at 19.8% Citation Share. Palantir Technologies followed at 15.2%, SpaceX at 12.7%, Shield AI at 6.4%, and Lockheed Martin at 5.9%. The top five companies collectively accounted for 60.0% of observed defense and aerospace citations.
The structural observation in the dataset: Anduril and Palantir together accounted for 35.0% of Citation Share — a higher combined share than the top five legacy defense primes in the dataset (Lockheed Martin, Northrop Grumman, RTX/Raytheon, Boeing Defense, General Dynamics), which collectively accounted for 21.1%. The two defense technology firms operate at approximately $3.5 billion combined annual revenue; the five primes at approximately $247 billion. In this dataset, revenue does not appear to predict Citation Share — higher-revenue firms were not consistently associated with higher citation rates, and the relationship trends negative in aggregate.
The publisher distribution observed in this category differs from prior 5W benchmarks. Defense trade press — Defense News, Breaking Defense, The War Zone, War on the Rocks, Defense One — collectively accounted for 36.9% of cited sources. Wikipedia anchored the dataset at 20.7%. Reuters and Bloomberg defense coverage (combined) supplied 6.8%; major general-press defense desks 5.9%; company-owned domains 6.4% in aggregate; government sources (DoD, GAO, CRS) 5.1%.
Note on interpretation. Because this study measures AI-generated outputs across a defined time window, results reflect model behavior during that period and may not generalize across future model versions, retrieval systems, or prompt distributions. Citation Share should not be interpreted as market share, operational performance, contract-award likelihood, strategic advantage, or endorsement. Observed correlations do not imply causation. Interpretive analysis appears in a separately labeled section later in this report.
"In this dataset, two defense technology companies account for a higher combined Citation Share than the five largest legacy primes. In a category where revenue has historically been the measure of weight, the AI systems we tested are surfacing a different pattern. The observation is reproducible; the interpretation is open."
Ronn Torossian · Founder and Chairman, 5W
The Findings
## Ten observations from 32,200 prompts across two waves.
All figures below reflect averages across Wave 1 (Jan–Feb 2026) and Wave 2 (Apr–May 2026) within ≤1.5 percentage-point cross-wave tolerance. Findings are presented as observed patterns in this dataset. Interpretation and possible operational implications appear in a separately labeled section later in this report.
01
### Two defense technology companies collectively account for a higher Citation Share than the five largest legacy primes in the dataset
Anduril (19.8%) and Palantir (15.2%) collectively accounted for 35.0% of observed Citation Share. Lockheed Martin (5.9%), Northrop Grumman (4.8%), RTX/Raytheon (4.2%), Boeing Defense (3.6%), and General Dynamics (2.6%) collectively accounted for 21.1%. The two defense technology companies operate at approximately $3.5 billion combined annual revenue; the five primes at approximately $247 billion. In this dataset, higher revenue was not consistently associated with higher observed Citation Share, and the relationship trends negative across the broader sample.
02
### SpaceX accounted for the highest cross-category Citation Share in the test set
SpaceX accounted for 12.7% of overall Citation Share — third in the dataset — surfacing across three distinct query categories: pure space queries (where it accounted for approximately 60% share within that subset), defense queries (associated with coverage of Starshield and DoD contract activity), and adjacent technology queries. No other company in the test set exhibited comparable cross-category presence in this dataset. SpaceX's observed Citation Share is distributed across multiple query types rather than concentrated in one.
03
### The observed source distribution differs from prior 5W benchmarks in related categories
Defense trade press — Defense News, Breaking Defense, The War Zone, War on the Rocks, Defense One — collectively accounted for 36.9% of cited sources in this dataset. In prior 5W benchmarks (banking, venture capital, credit cards), top citation sources were Wikipedia and mainstream financial or technology press. In this dataset, specialized defense trade outlets accounted for the largest share, with Wikipedia (20.7%) anchoring the distribution rather than dominating it. The source distribution reflects observed retrieval behavior during the testing window.
Figure 02 · Revenue and Citation Share — observed distribution
### In this dataset, higher revenue was not consistently associated with higher Citation Share.
Each marker plots a defense or aerospace company's approximate annual revenue against its observed Citation Share in this dataset. Markers above the dashed reference line are associated with higher-than-revenue-predicted citation rates; markers below are associated with lower. Revenue is shown on a logarithmic-ish scale. The reference line is included for visual orientation only and is not a regression.
-
-
-
-
-
-
-
-
-
-
-
0%
5%
10%
15%
20%
$0.1B
$1B
$5B
$25B
$75B
Annual Revenue (approximate, log-ish scale)
AI Citation Share
-
↑ Above the reference line
↓ Below the reference line
Anduril
19.8% · ~$1B revenue
Palantir
15.2% · ~$2.5B defense rev.
SpaceX
12.7% · ~$10B revenue
Shield AI 6.4%
Saronic 3.8%
Skydio 3.4%
Lockheed Martin
5.9% · ~$67B revenue
Northrop Grumman
4.8% · ~$40B rev.
RTX 4.2%
Boeing Defense
3.6%
GD 2.6%
L3Harris 2.1%
BAE 1.4%
Modern defense tech
Space (cross-category)
Legacy defense primes
Source · 5W Defense & Aerospace AI Visibility Index 2026 · Revenue figures per FY2025 public filings and 5W estimates for private companies. Palantir revenue reflects defense-segment estimate only. SpaceX plotted by total revenue including commercial.
04
### Sustained founder-named content output is associated with higher observed Citation Share in several cases
Anduril Industries is the company in the test set whose owned domain and founder-named content (Palmer Luckey writing on Substack and X, Trae Stephens publishing essays, Brian Schimpf podcast appearances) collectively appears in retrieved AI responses at frequencies comparable to defense trade press. A similar pattern was previously observed for Andreessen Horowitz in the 5W Venture Capital AI Visibility Index. No legacy prime in the test set exhibited a comparable level of founder-named content output during the testing window. Whether founder content output is causal or correlated with higher Citation Share cannot be determined from this dataset alone.
05
### References to prior reputational events appeared in retrieved responses across both waves
References to the Boeing 737 MAX episode appeared in 31% of Boeing Defense responses despite originating in commercial aviation. References to F-35 cost-overrun discussion appeared in 28% of Lockheed Martin responses. References to Palantir contractor and contract-related discussions appeared in 19% of Palantir responses. The references were stable across both waves and may reflect persistent embedding of reputational events in retrieval surfaces such as Wikipedia and high-citation press archives.
06
### Named-founder content output is associated with company-level Citation Share variation in this dataset
Named-founder analysis of the test set surfaced Palmer Luckey (Anduril) at the highest individual share, followed by Alex Karp (Palantir), Elon Musk (SpaceX), Peter Thiel (Palantir co-founder, appearing independently), and Brandon Tseng (Shield AI). Companies whose founders maintained public writing or long-form podcast activity during the testing window were associated with higher observed Citation Share than companies whose founders did not. The association does not establish causality; founder activity may be a marker of broader organizational content cadence.
07
### Substack and personal-platform content appeared as cited sources at higher rates than in prior 5W benchmarks
Substack-hosted war journalism and national-security analysis appeared in 4.4% of retrieved responses — larger than Defense One's contribution and approximately equal to Bloomberg's defense coverage. Substack has not appeared as a top citation source in prior 5W benchmarks. The observation may reflect a structural change in where defense journalism is currently being published, but other explanations (specific authors, specific topics, retrieval-index timing) cannot be ruled out by this dataset alone.
08
### European defense technology companies registered low observed Citation Share in U.S. consumer AI systems
Helsing (Germany), BAE Systems (UK), and Rheinmetall (Germany) collectively accounted for less than 3.5% Citation Share in the test set. Helsing specifically registered at 1.5% during a period of substantial fundraising and Ukraine-deployment activity. Geographic variation in retrieval surface is consistent with patterns 5W has previously observed in other categories (e.g., Chinese AI labs in the AI Companies Index) where companies of substantial commercial importance can register low share in U.S. consumer AI systems while appearing in technical, trade, or government-source retrieval.
Figure 03 · Engine-level Citation Share — observed distribution
### Citation Share for the same companies varied across the five AI systems in this dataset.
Observed Citation Share by AI system for the top 10 defense and aerospace companies in this dataset. Darker cells indicate higher observed shares. ChatGPT's distribution was associated with higher Palantir share than other systems — a pattern consistent with retrieval architectures that weight Wikipedia and longer-established entity references more heavily. Gemini and Perplexity were associated with higher Anduril share — consistent with retrieval architectures that weight recency, owned-domain authority, and recent social signals more heavily. Claude's distribution was the most uniform across the top three. Google AI Overviews exhibited the highest within-engine variance across re-runs. These patterns reflect observed model behavior during the testing window; mechanisms producing the differences are not directly observable.
ChatGPT
Claude
Gemini
Perplexity
Google AIO
Anduril
17.2%
19.4%
22.1%
23.8%
16.5%
Palantir
17.8%
14.6%
13.2%
13.7%
16.7%
SpaceX
14.1%
12.4%
12.8%
12.1%
12.3%
Shield AI
5.6%
7.2%
6.8%
6.4%
5.9%
Lockheed Martin
7.2%
5.8%
5.4%
4.7%
6.4%
Northrop Grumman
5.8%
4.6%
4.4%
4.1%
5.1%
RTX (Raytheon)
5.1%
4.1%
3.9%
3.6%
4.4%
Saronic Technologies
3.1%
3.8%
4.2%
4.6%
3.4%
Boeing Defense
4.4%
3.4%
3.2%
3.1%
3.8%
Skydio
2.9%
3.6%
3.4%
3.8%
3.1%
Citation Share
Low
High →
Source · 5W Defense & Aerospace AI Visibility Index 2026 · All defense and aerospace queries · n = 5,680 prompts per engine across two waves
09
### Topic-specific prompts about AI and autonomous systems were associated with higher observed Anduril share
For prompts specifically about autonomous weapons systems, military AI, machine-learning-enabled targeting, or AI-warfare ethics, Anduril's observed Citation Share rose to 28.1% (versus 19.8% overall). Shield AI's rose to 11.4% (versus 6.4% overall). Palantir's rose to 21.3% within AI-warfare ethics queries specifically. Topic-specialization effects of this magnitude were the largest observed in the dataset, suggesting that companies publishing on autonomous systems and AI-related topics may surface at higher rates within topic-specific prompts than within general category prompts. The mechanism producing the topic effect cannot be directly observed.
10
### Observed rankings varied across the five AI systems in interpretable patterns
ChatGPT's observed distribution was associated with higher Palantir share (17.8% vs Anduril 17.2%) — a pattern consistent with Wikipedia-weighted retrieval and longer-established entity reference density. Gemini and Perplexity were associated with higher Anduril share (22.1% and 23.8% respectively) — a pattern consistent with retrieval architectures that weight recency, owned-domain authority, and recent social-signal indicators more heavily. Claude's distribution was the most uniform across the top three companies in the dataset. Google AI Overviews exhibited the highest within-engine variance across re-runs. Wikipedia source density was high across all five systems; the variation across systems lay primarily in which other sources appeared alongside Wikipedia. Mechanisms producing these patterns are not directly observable from the dataset.
The Index
## Defense & Aerospace AI Visibility Index 2026.
Defense & Aerospace — Top 25
Rank
Company
Citation Share
01Anduril Industries19.8%
02Palantir Technologies15.2%
03SpaceX12.7%
04Shield AI6.4%
05Lockheed Martin5.9%
06Northrop Grumman4.8%
07RTX (Raytheon)4.2%
08Saronic Technologies3.8%
09Boeing Defense, Space & Security3.6%
10Skydio3.4%
11General Dynamics2.6%
12Planet Labs2.4%
13L3Harris Technologies2.1%
14Rocket Lab1.8%
15Helsing1.5%
16BAE Systems1.4%
17Epirus1.2%
18BlackSky1.0%
19Mach Industries0.9%
20Rebellion Defense0.7%
21Vannevar Labs0.6%
22Capella Space0.5%
23Astranis0.4%
24Stoke Space0.3%
25Relativity Space0.3%
Revenue and Citation Share — Observed Distribution
Segment
~Combined Revenue
Citation Share
Anduril + Palantir (defense tech)~$3.5B35.0%
Top 5 legacy primes (LMT + NOC + RTX + BA + GD)~$247B21.1%
Defense tech leaders / Primes ratio1 / 701.66x
Revenue figures reflect FY2025 public filings (primes), most recent disclosed valuations and 5W revenue estimates (Anduril, Palantir defense segment). Combined revenue calculation: Anduril ~$1B + Palantir defense segment ~$2.5B = $3.5B. Top 5 primes: Lockheed Martin $67B + Northrop Grumman $40B + RTX $70B + Boeing Defense $25B + General Dynamics $45B = $247B. The defense tech leaders capture 1.66x the Citation Share with approximately 1/70 the revenue.
Top Cited Sources — All Defense Queries
Rank
Source
Share of Citations
01Wikipedia20.7%
02Defense News10.8%
03Breaking Defense8.9%
04The War Zone7.2%
05Reuters & Bloomberg defense (combined)6.8%
06Defense company-owned domains (combined)6.4%
07NYT, WSJ, FT defense desks (combined)5.9%
08War on the Rocks5.4%
09Government sources (DoD, GAO, CRS)5.1%
10Defense One4.6%
11Substack (war journalism)4.4%
12All other publishers13.8%
"In this dataset, defense trade press — Defense News, Breaking Defense, The War Zone, War on the Rocks, Defense One — collectively accounted for approximately 37% of cited sources about U.S. defense and aerospace. The pattern is the inverse of what we observed in financial-services categories, where mainstream financial press dominated. Whether this reflects the structural state of defense publishing or a transient retrieval pattern remains an open question."
Ronn Torossian · Founder and Chairman, 5W
Methodology
## How the Index was built.
The Defense & Aerospace AI Visibility Index 2026 analyzed 28,400 prompts across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews, run in two independent waves to test for stability across retrieval drift, model updates, and active news cycles in this category.
#### Two-wave structure
- Wave 1: January 15 – February 12, 2026 · 14,200 prompts
- Wave 2: April 8 – May 6, 2026 · 14,200 prompts
Wave 2 used the same prompt set as Wave 1 with no modification. Only findings stable across both waves within reporting tolerance (≤1.5 percentage-point delta on company-level Citation Share; ≤2.0 pp on source-level share) are published here. Findings unstable across waves were excluded from the Index.
#### Prompt design
Queries simulated real founder, investor, journalist, policymaker, and procurement-officer research behavior. Prompts included branded company queries ("What does Anduril Industries do?"), non-branded category queries ("Best defense tech startup in 2026"), comparison queries ("Anduril vs Lockheed Martin"), capability queries ("Autonomous military drone makers", "AI-enabled targeting systems"), procurement-oriented queries ("DoD prime contractors 2026"), and ethics-and-policy queries ("AI in warfare ethics"). Prompts were distributed evenly across the five engines so each engine received the same prompt mix per category.
#### Seven defense and aerospace categories measured
- Modern defense tech (Anduril, Palantir, Shield AI, Saronic, Skydio, others)
- Legacy defense primes (Lockheed Martin, Northrop Grumman, RTX, Boeing Defense, General Dynamics, L3Harris)
- Space and launch (SpaceX, Rocket Lab, Stoke Space, Relativity Space)
- Earth observation and satellite intelligence (Planet Labs, BlackSky, Capella Space, Astranis)
- Autonomous systems and AI-in-warfare
- European defense tech (Helsing, BAE Systems — extended dataset)
- Procurement and DoD contracting
#### Sampling
Each prompt was issued three times per engine within a wave, with responses sampled at varied time-of-day windows to reduce within-engine retrieval drift. Reported Citation Share values represent the average of all retrieved responses for a prompt across both waves.
#### What "Citation Share" means operationally
Three distinct response signals were tracked per retrieved answer:
- Mention — the entity is named anywhere in the response
- Recommendation — the entity is named as a suggested or preferred option
- Source citation — the entity (or its owned domain) is cited as a reference
For the headline metric reported as Citation Share, all three signals were weighted equally per retrieved response. Source-level analyses (the publisher rankings in Figure 01) used source citations only. Equal weighting of three structurally different signals is an analytical choice made by 5W for the franchise; alternative weighting schemes (recommendation-weighted, citation-weighted, mention-weighted) would produce different rankings. The headline ranking is best understood as a composite signal of model surface frequency, not as a single underlying construct.
Citation Share is an internal composite metric. It is not a standard industry measure. It should not be interpreted as market share, operational performance, contract-award likelihood, AI-system endorsement, accuracy of representation, financial value, or strategic advantage. It is a proxy for how often a company surfaces in retrieved AI responses during a defined testing window, under a defined prompt distribution.
#### Cross-engine normalization
Each engine was weighted equally in aggregate figures, regardless of differences in response length or default-citation frequency per engine. This was a deliberate choice; weighting by raw citation volume would have over-weighted Perplexity and Google AI Overviews, both of which return more citations per response than ChatGPT, Claude, or Gemini by default. Where per-engine results diverge, those differences are reported separately (Figure 03).
#### SpaceX cross-category note
SpaceX's score reflects all SpaceX-related citations across defense, space, and adjacent technology queries. SpaceX's defense-specific Citation Share (Starshield, DoD contracts only) was approximately 7.4%; SpaceX's space-specific Citation Share was approximately 62.3%; the blended figure reported in the Top 25 reflects the company's total presence across the full test set. The cross-category nature of SpaceX is distinct among the 25 companies measured.
#### Allied defense exclusion
Israeli defense companies (Rafael, Elbit, IAI), Indian defense companies, and other allied defense firms were tested in an extended dataset but excluded from the top-25 ranking to maintain a U.S. frame consistent with prior 5W benchmarks. European defense technology firms (Helsing, BAE Systems) are included in the top-25 ranking due to substantial U.S. commercial presence and inclusion in U.S.-readership defense trade press.
#### What this benchmark does not measure
This benchmark does not measure model accuracy, factual correctness of retrieved responses, sentiment of citations, or quality of underlying source content. A company appearing frequently in retrieved responses may be cited favorably or critically; both forms of mention count equally for the headline metric. The benchmark also does not separately attribute citation patterns to underlying mechanisms (training-data composition, retrieval-index weighting, ranking heuristics); these mechanisms are increasingly entangled in production AI systems and not externally observable. Engine-level differences reported in Figure 03 reflect observed differences in retrieval output, not directly observed differences in underlying architecture.
#### Limitations
Because this study measures AI-generated outputs across a defined time window, results reflect model behavior during that period and may not generalize across future model versions, retrieval-index updates, or different prompt distributions. AI systems, training data, retrieval indexes, and ranking mechanisms evolve continuously; results may shift outside the testing window. Active news cycles during the testing window (Ukraine, Israel/Gaza, Pacific tensions) may have influenced cross-wave volatility for select firms. The benchmark is best understood as a structured snapshot of observed model behavior across two waves, not as a continuous live measurement and not as a predictive instrument. The full prompt set, per-engine response logs, and category-level datasets are available on request for replication.
Disclosure. 5W has commercial relationships across the defense and aerospace industry and operates as a senior advisor to Curium.io, the team that coined Generative Engine Optimization. Companies cited in this benchmark were selected based on category relevance and observable presence in the test set; inclusion does not imply a commercial relationship. 5W does not accept compensation for ranking placement and did not consult with any company in the top-25 ranking regarding placement, methodology, or interpretation prior to publication.
Interpretation · Possible Operational Implications
## Five possible operational implications of the observed patterns.
This section is interpretive. The items below are not findings — they are possible operational implications, offered as informed commentary on the patterns reported earlier in this report. Each implication is presented as a hypothesis suggested by the data, not as a recommendation or prescription. Causal relationships between any operational practice and observed Citation Share cannot be established by this dataset alone.
### Founder-named content output may be a relevant input to Citation Share
Anduril's observed Citation Share is associated with a sustained pattern of founder-named content during the testing window — including Palmer Luckey's writing on personal platforms, Trae Stephens's published essays, and Brian Schimpf's long-form podcast appearances. The data does not establish causation; founder content output may be a marker of broader organizational content cadence rather than an independent driver. The hypothesis worth testing: companies investing in sustained named-founder content output may observe corresponding changes in Citation Share over time. The hypothesis is testable through controlled re-measurement.
### Coverage in defense trade press may be a relevant input given the observed publisher distribution
Defense News, Breaking Defense, The War Zone, War on the Rocks, and Defense One collectively accounted for 36.9% of cited sources in this dataset. The observation is consistent with a hypothesis that earned coverage in specialized defense trade press may be associated with higher Citation Share, given the relative weight these publishers appear to carry in observed AI retrieval. Companies whose earned-media activity is concentrated in general-press defense desks may be under-represented in retrieval relative to companies whose activity is concentrated in trade press. The relationship has not been tested causally.
### Wikipedia entries (company-level and founder-level) may be a relevant retrieval surface to audit
Wikipedia accounted for 20.7% of cited sources in this dataset — the largest single source. Companies whose Wikipedia entries are sparse, outdated, or omit relevant context for the company or its founders may be under-represented in observed retrieval relative to companies with more developed Wikipedia entries. The hypothesis is not directly tested by this benchmark but is consistent with the source distribution observed.
### Reputational events appearing in retrieval may persist beyond their news-cycle window
References to the Boeing 737 MAX episode, F-35 cost-overrun discussion, and Palantir contract-related discussions all appeared in retrieved responses at rates between 19% and 31%. These references were stable across both waves. The pattern is consistent with a hypothesis that once reputational events are embedded in high-citation retrieval surfaces (Wikipedia, press archives), short-term news-cycle decay may not remove them from retrieval. The hypothesis suggests that crisis-response strategies designed primarily to ride out news cycles may be incomplete relative to strategies that also produce sustained alternative-narrative content over a longer horizon.
### Major procurement-cycle events may interact with pre-existing retrieval state
Major DoD contract awards and procurement decisions generate intense press coverage that subsequently appears in retrieval surfaces. The hypothesis worth testing: companies entering a major procurement cycle with already-developed Citation Share may be associated with different retrieval outcomes than companies entering with limited prior Citation Share. The hypothesis is consistent with patterns observed in this dataset but is not directly tested.
FAQ
## Defense & Aerospace AI Visibility Index — Q&A.
### What is the Defense & Aerospace AI Visibility Index?
The Defense & Aerospace AI Visibility Index 2026 is the first public two-wave benchmark measuring how often U.S. defense tech, space, and legacy defense companies are surfaced, cited, and recommended inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. It was produced by 5W, The Communications Firm Built for the AI Era.
### What is Citation Share?
Citation Share is the share of AI responses in which a company is named, recommended, or cited as a referenced source. Mentions, recommendations, and source citations are tracked separately and weighted equally unless otherwise noted.
### Which defense company has the highest observed Citation Share?
Anduril Industries accounted for 19.8% of observed Citation Share across the five AI systems — the highest of any defense or aerospace company in the test set. Palantir Technologies followed at 15.2%, SpaceX at 12.7%, Shield AI at 6.4%, and Lockheed Martin at 5.9%.
### Do legacy defense primes have the highest Citation Share in AI systems?
In this dataset, no. The five largest legacy defense primes — Lockheed Martin, Northrop Grumman, RTX (Raytheon), Boeing Defense, and General Dynamics — collectively accounted for 21.1% of Citation Share. Anduril and Palantir together accounted for 35.0% — a higher combined share than the top five primes — despite operating at a fraction of the combined revenue. Citation Share in this benchmark is a composite proxy for model surface frequency, not a measure of market share, contract activity, or operational performance.
### What sources do AI systems cite about defense and aerospace?
In this dataset, Wikipedia (20.7%), Defense News (10.8%), Breaking Defense (8.9%), and The War Zone (7.2%) accounted for the largest share of observed defense and aerospace citations. War on the Rocks, Defense One, Reuters, Bloomberg, NYT, and Substack-based war journalism followed. Company-owned domains accounted for 6.4% in aggregate.
### What is associated with Anduril's high observed Citation Share?
Anduril Industries' observed Citation Share is associated with a sustained pattern of named-founder content output during the testing window (Palmer Luckey writing on personal platforms, Trae Stephens publishing essays, Brian Schimpf appearing on long-form podcasts), and with consistent coverage in defense trade press. A similar pattern of named-founder content output was previously associated with Andreessen Horowitz in the 5W Venture Capital AI Visibility Index. Causation cannot be established by this dataset alone; the association is consistent with a hypothesis worth further testing.
### How does SpaceX appear in defense queries?
SpaceX accounted for 12.7% of overall Citation Share in this dataset — third in the test set — through cross-category presence. SpaceX accounted for approximately 60% share within space-specific queries, appeared in defense queries (associated with Starshield and DoD contract coverage), and appeared in adjacent technology queries. The company's cross-category citation pattern is distinct among the 25 companies measured.
### Did references to prior reputational events appear in retrieved responses?
References to certain prior reputational events appeared in retrieved responses across both waves. References to the Boeing 737 MAX episode appeared in approximately 31% of Boeing Defense responses despite originating in commercial aviation. References to F-35 cost-overrun discussion appeared in approximately 28% of Lockheed Martin responses. References to Palantir contractor and contract-related discussions appeared in approximately 19% of Palantir responses. The pattern is consistent with persistent embedding of reputational events in high-citation retrieval surfaces such as Wikipedia and press archives.
### Which AI system produced the most uniform observed distribution across the top defense companies?
In this dataset, Claude produced the most uniform observed distribution across the top three defense companies. ChatGPT was associated with higher Palantir share — a pattern consistent with Wikipedia-weighted retrieval. Gemini and Perplexity were associated with higher Anduril share — a pattern consistent with retrieval architectures that weight recency and recent social signals more heavily. Google AI Overviews exhibited the highest within-engine variance across re-runs. These patterns reflect observed model behavior during the testing window; mechanisms producing the differences are not directly observable.
### What is GEO?
GEO — Generative Engine Optimization — is the practice of building brand authority and content infrastructure that AI systems surface, cite, and recommend. It is the discipline replacing SEO in an AI-mediated discovery layer.
### How many defense and aerospace companies were tested?
The Index tested 25 leading U.S. defense and aerospace companies — 10 modern defense tech firms, 8 space companies, and 7 legacy defense primes — across 28,400 prompts in two independent waves between January and May 2026.
Work with 5W
## 5W measures Citation Share, audits retrieval surfaces, and builds the content infrastructure that AI systems cite.
Earned media in the publishers that AI systems cite most. Founder content programs. Generative Engine Optimization. Wikipedia audit and remediation. Quarterly Citation Share measurement. The methodology used in this report is available for client benchmarks against named competitive sets.
Request the Full Dataset
Talk to 5W
Related Research
## More from the 5W AI Visibility Index series.
Volume II · Financial Services
#### Venture Capital AI Visibility Index 2026
Volume III · Financial Services
#### Credit Cards AI Visibility Index 2026
Volume IV · Technology
#### AI Companies AI Visibility Index 2026
---
### The Cannes AI Authority Index — 2026
URL: https://www.5wpr.com/research/cannes-2026-ai-authority-index/
Summary: Which films, directors, stars, and luxury brands dominate AI retrieval before the 79th Festival de Cannes. A 5W, Haute Living, and Talent Resources study.
✦
5WCannes Index 2026
Thesis
Framework
Rankings
Forecasts
Anchors
Luxury
Methodology
Share
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5W · The Communications Firm Built for the AI Era
✦ Haute Living
·
Talent Resources
✦
Issue 01 · May 2026
Pre-Festival Report
#
The Cannes
AI Authority
Index — 2026
Twenty-two films in competition. Six already dominate AI retrieval — before a single premiere has screened.
Festival
79th — May 12-23, 2026
Engines Tested
5 / Prompts 200
Data Captured
May 8, 2026
Jury Chair
Park Chan-wook
§ 01 — Thesis
## Prestige Is Now Retrieval Infrastructure.
Festival prestige is no longer shaped only by critics, premieres, and red carpets. It is shaped inside AI systems — and the films cited there gain an early advantage in awards momentum, streaming demand, and cultural visibility.
Six films carry 74% of pre-festival AI Citation Share. Five publications drive 81% of the citations behind them.
§ 02 — What's at Stake
## Compounding Prestige.
Pre-festival AI visibility compounds across awards, streaming, luxury, and the calendar that follows.
#### Awards Momentum
Compounds through Telluride, Venice, TIFF, and the Oscar conversation.
#### Streaming Leverage
Buyers benchmark bid ceilings against AI visibility.
#### Box Office
Theatrical platforms factor visible momentum into release strategy.
#### Luxury Affiliation
Houses negotiate against AI prestige association.
#### Talent Visibility
Interview demand, brand partnerships, recommendation exposure.
#### Studio Narrative
Slate valuation reflects pre-festival positioning.
§ 03 — Framework
## The AI Authority Score.
A composite of five retrieval signals. Portable across festivals, categories, and timeframes.
Five Inputs
— 01
#### Citation Share
Frequency of named mention across all five tested AI engines.
— 02
#### Cross-Engine Consistency
How many of the five engines surface the entity in trust-intent prompts.
— 03
#### Momentum Velocity
Directional change in citation frequency across the four-week pre-festival window.
— 04
#### Retrieval Anchor Density
Number of high-authority publications driving the entity's citation pool.
— 05
#### Talent Co-Mention
Strength of association between film, director, and attached talent.
§ 04 — Headline
## 200 Prompts. 5 Engines. 1,000 Data Points.
3.4
Avg. films named per anticipation prompt — of 22 in competition
74%
Citation Share captured by the top 6 films
81%
Of all citations — generated by 5 publications
5/5
Engine consistency for the top 4 films
8%
Hollywood star Citation Share — was 31% in 2025
22 → 6
Compression ratio. 2,541 submissions; fewer than 30 surface anywhere.
§ 05 — Film Rankings
## The Cannes Film AI Authority Rankings.
Citation Share % across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews on May 8, 2026. Cross-engine score = number of the five engines that named the film in trust-intent prompts.
Rank
Film
Director
Citation Share
Cross-Engine
Momentum
01Bitter Christmas / AutofictionPedro Almodóvar18%5 / 5Rising
02Parallel TalesAsghar Farhadi15%5 / 5Rising
03Paper TigerJames Gray13%5 / 5Steady
04HopeNa Hong-jin11%5 / 5Sharp Rise
05Sheep in the BoxHirokazu Kore-eda9%4 / 5Steady
06All of a SuddenRyusuke Hamaguchi8%4 / 5Rising
07The Man I LoveIra Sachs5%4 / 5Steady
08FjordCristian Mungiu4%3 / 5Rising
091949Paweł Pawlikowski4%3 / 5Rising
10The BelovedRodrigo Sorogoyen3%3 / 5Steady
11CowardLukas Dhont3%3 / 5Sharp Rise
12La Bola NegraAmbrossi & Calvo2%2 / 5Steady
13MinotaurAndrey Zvyagintsev2%2 / 5Flat
14Her Private Hell (Out of Comp.)Nicolas Winding Refn1%2 / 5Rising
15Full Phil (Midnight)Quentin Dupieux1%2 / 5Steady
The remaining seven competition films split a combined 1% of Citation Share, including critical-press favorites Histoires de la nuit (Léa Mysius), Garance (Jeanne Herry), The Unknown (Arthur Harari), and Gentle Monster (Marie Kreutzer). They will need their premieres to shift the AI layer.
Editorial authority outranks star power. Cult corroboration outranks ad spend. The films cited in May compound through awards season and into next year's calendar.
§ 06 — Retrieval-Based Forecasts
## Forecasts for the 79th Festival de Cannes.
These are not artistic judgments or investment recommendations. They are retrieval-based forecasts measuring how AI systems currently frame the festival conversation before the films screen.
Jury 2026
The jury is chaired by Park Chan-wook, with Demi Moore, Chloé Zhao, Stellan Skarsgård, Ruth Negga, Laura Wandel, Diego Céspedes, Isaach De Bankolé, and Paul Laverty.
Palme d'Or — Forecast
28%probability
### Bitter Christmas / Autofiction
Pedro Almodóvar
Almodóvar leads the Index at 18% Citation Share, the highest of any competition film. Three structural factors compound. Almodóvar has been in competition seven times and has never won the Palme — the "overdue narrative" has itself become a retrieval anchor. Park Chan-wook chairs the jury, and his filmography (Oldboy, The Handmaiden, Decision to Leave) shares Almodóvar's operatic, formally precise register. The film already premiered in Spain to sufficient warmth to qualify without the burden of a US release rollout. Next-likeliest: a Farhadi or Kore-eda Palme, both 15–18%.
Grand Prix — Forecast
22%probability
### Hope — Na Hong-jin
The sharpest momentum trajectory in the data, with 5/5 cross-engine consistency. Neon already holds English-language rights, and Neon-distributed films have won 5 of the last 6 Palmes — a pattern AI engines surface explicitly. Park Chan-wook chairing creates a structural complication for a Korean Palme winner; he would not vote on countrymen, but his presence reframes the room toward Asian cinema. The Grand Prix is the historically polite landing zone for the film the jury could not give the Palme.
Best Director — Forecast
21%probability
### Cristian Mungiu — Fjord
Mungiu's English-language debut is rising in momentum despite a lower Citation Share. The pattern matches 4 Months, 3 Weeks and 2 Days (his 2007 Palme): a director the jury recognizes for craft but does not give the top prize. Renate Reinsve and Sebastian Stan in a Norwegian-set marital drama is the kind of contained, controlled cinema that wins Best Director at Cannes. Hamaguchi (All of a Sudden) is the next-most-likely director winner.
Best Actress — Forecast
24%probability
### Sandra Hüller — 1949
Hüller is the highest-co-mentioned female lead across all Cannes 2026 retrieval. The Anatomy of a Fall halo (2023 Palme) follows her in the citation pool. A Cold War drama — Pawlikowski's specific historical genre — has produced multiple acting wins at Cannes (Joanna Kulig in Cold War). The structural risk: Isabelle Huppert in Parallel Tales, where the Farhadi-Huppert combination is a critical-press dream. Renate Reinsve (Fjord) is the strong third candidate.
Best Actor — Forecast
19%probability
### Adam Driver — Paper Tiger
The Gray / Driver pairing is the most-cited Hollywood-attached star vehicle in the Index. Gray has been in competition five times without a major prize, and the "overdue Gray" narrative is structurally similar to the Almodóvar narrative. Driver in a Russian-mafia-adjacent crime drama is the role Cannes juries tend to reward. Counter-position: Javier Bardem in The Beloved at 16% Citation Share could split the room. Rami Malek at 11% is the third candidate.
Caméra d'Or — Wide Open
— —
### Insufficient Pre-Festival AI Signal
The first-feature category is the only one the Index cannot forecast with confidence. Citation Share for sidebar and parallel selections is too thin. Watch Director's Fortnight and Critics' Week breakouts. The Caméra d'Or has been won by films with under 1% pre-festival AI presence in three of the last five years.
Theatrical Aftermath
High Confidence
### Paper Tiger Wins Theatrical
Of the films in the top 10, Paper Tiger is the only one with mainstream Hollywood star compression sufficient to drive theatrical wide release. Paper Tiger will likely outgross every other competition film combined in US/UK theatrical, regardless of awards outcome.
Streaming Acquisition
$15M+ Range
### Bidding Wars: Hope & La Bola Negra
Two films are positioned for bidding wars: Hope (already partly placed with Neon) and La Bola Negra (Penélope Cruz, Glenn Close, queer-thematic). Mubi, A24, and Netflix are the likely competing buyers. Bidding is expected to close above $15M for North American rights on at least one of the two.
§ 07 — Director Authority
## The Director Rankings.
Park Chan-wook ranks #4 in director Citation Share without a film in competition — a function of his jury presidency and the editorial coverage attached to it.
RankDirectorCitation ShareNotable Pattern
01Pedro Almodóvar16%Never won Palme — overdue narrative retrieval anchor
02Asghar Farhadi14%Two-time Oscar winner — first Cannes since A Hero (2021 Grand Prix)
03Hirokazu Kore-eda11%2018 Palme winner returning
04Park Chan-wook10%Jury president — first South Korean to chair
05Ryusuke Hamaguchi9%Oscar winner (Drive My Car), first Cannes since 2021
06James Gray8%5x competition, no major prize — overdue narrative
07Na Hong-jin7%Every feature he's made has been at Cannes
08Cristian Mungiu5%2007 Palme winner with English-language debut
09Lukas Dhont4%Coming off Close — youngest of the heavyweights
10Paweł Pawlikowski4%Oscar-winning Ida director returning to Cold War
§ 08 — Talent Visibility
## The Cannes Star Power Index.
Talent surfacing in 4 or 5 of the 5 AI engines tested.
12%Adam DriverPaper Tiger
11%Sandra Hüller1949
10%Isabelle HuppertParallel Tales
9%Scarlett JohanssonPaper Tiger
8%Michael FassbenderHope
7%Alicia VikanderHope
7%Rami MalekThe Man I Love
7%Javier BardemThe Beloved
6%Renate ReinsveFjord
6%Penélope CruzLa Bola Negra
5%Catherine DeneuveParallel Tales
5%Sebastian StanFjord
4%Vincent CasselParallel Tales
4%Glenn CloseLa Bola Negra
4%Miles TellerPaper Tiger
3%Léa SeydouxTBD
3%Kristen StewartFull Phil
2%Monica BellucciHistoires de la nuit
Honorary Palmes: Barbra Streisand (Citation Share for honorary segment: 14%), Peter Jackson (11%).
### Talent Visibility & Awards Momentum
AI retrieval increasingly shapes downstream awards visibility, interview demand, brand partnerships, and streaming-platform recommendation exposure for actors and directors entering Cannes. The talent that surfaces highest in AI retrieval ahead of the festival enters the Cannes media cycle with measurably greater editorial gravity, and that gravity compounds through the awards calendar that follows.
Pre-festival AI authority is now a quantifiable input to talent valuation across the rest of the year — for representation, awards strategy, and brand-partnership negotiation.
### The Hollywood Star Compression Gap
- Tom Cruise Last year's Croisette anchor — captured 17% Citation Share for Cannes 2025 in the equivalent period 2026: 0.4%
- Steven Spielberg Disclosure Day — Universal going theatrical 0%
- Christopher Nolan The Odyssey — sitting out the festival circuit 0%
- Gerwig · Pattinson · Zendaya · Chalamet · Robbie Combined Citation Share across all five < 1%
- Marvel & franchise leads Average Citation Share < 0.2%
The Hollywood Star Compression Gap is structural. Cannes selection is decisively international this year, and US studios continue to pull tentpole releases out of festival circuits to avoid critical risk (Joker: Folie à Deux at Venice remains the cautionary example). The AI-retrieval star power of Cannes 2026 has migrated to European, Asian, and indie talent.
§ 09 — The Meta-Finding
## The Retrieval Anchor Map.
Five publications generate 81% of all sourced citations across Cannes 2026 AI retrieval.
— 01
VarietyLineup analysis · deal coverage
24%
— 02
DeadlineAwards prediction · talent attached
19%
— 03
The Hollywood ReporterAnticipation lists · talent profiles
16%
— 04
Screen DailyInternational film · sales agent context
13%
— 05
IndieWireAuteur and breakout coverage
9%
— 06
Festival-cannes.comSelection facts · jury · programs
7%
— 07
France 24 / Le MondeFrench film context
5%
— 08
High On Films · Letterboxd · RedditCinephile breakout momentum
4%
— 09
Vanity Fair · Variety Awards CircuitGlamour · awards trajectory
3%
A film that fails to secure coverage across Variety, Deadline, and THR before festival opening is functionally invisible in AI retrieval — regardless of festival selection. This is the structural barrier prestige-native distributors (Neon, A24, Sony Pictures Classics) have already cleared, and that most other competition entries have not.
Studios, distributors, luxury brands, publicists, talent agencies, and streamers who understand the Retrieval Anchor Map can model AI-mediated cultural authority with new precision. Those who do not are operating a generation of measurement behind.
§ 10 — The Cannes Prestige Economy
## Luxury & AI.
Chopard captures more AI prestige association with Cannes than the next nine sponsors combined. AI systems increasingly compress decades of prestige sponsorship into a handful of dominant luxury associations.
Chopard
28-year partnership · designs the Palme d'Or · 78 high-jewelry pieces annually · consistent Robb Report and luxury press co-citation
47%
L'Oréal Paris
Longtime official sponsor
11%
Hôtel Martinez
Industry basecamp · editorial-anchored hospitality
8%
Hotel du Cap-Eden-Roc
Vanity Fair · amfAR association
6%
The Carlton Cannes
Festival history anchor
5%
Renault
Official auto partner
4%
Mastercard
Payment partner
3%
Nespresso
Hospitality presence
2%
Magnum
Cinema sponsor
2%
Rolex
Diminishing presence in AI retrieval despite cinema heritage
1%
For luxury houses without a Chopard-equivalent multi-decade festival anchor — Bulgari, Cartier, Tiffany, Van Cleef & Arpels, Boucheron, Graff — Cannes is a paid-presence play that does not translate into AI authority. Several of those houses spent more than $5M on Cannes activations in 2025 and hold under 0.5% Citation Share in the 2026 AI retrieval layer.
§ 11 — The Five Forecasts
## To Be Tested by May 23.
- Almodóvar wins the Palme d'Or for Bitter Christmas / Autofiction. First Palme of his career.
- Sandra Hüller wins Best Actress for 1949.
- Paper Tiger outgrosses the full top 10 of the competition combined in US theatrical.
- Two streaming acquisitions close above $15M for North American rights — most likely Hope (Neon expansion or transfer) and La Bola Negra (Mubi or A24).
- Variety, Deadline, and THR collectively determine 80%+ of awards-prediction AI retrieval through May 23.
§ 12 — The Recurring Index
## An Annual Entertainment-Intelligence Franchise.
The Cannes AI Authority Index publishes across the festival arc and beyond, with the methodology portable to Venice, TIFF, Sundance, Berlin, and the Oscar campaign window.
May 8 — Today
Pre-Festival Report
Establishes the AI baseline before premieres begin.
May 17
Festival Midpoint
Measures retrieval shifts after early premieres and reception.
May 24
Awards Close
Compares forecasts to outcomes. Identifies AI-prediction accuracy.
July
Post-Cannes Carryover
Measures awards-season retrieval propagation through summer release windows.
September
Venice / TIFF Continuation
Tests compounding-prestige theory across the fall festival circuit.
"
Twenty-two films are in competition at Cannes. Across the AI retrieval layer, six already dominate the conversation. Five publications generate 81% of the sourced citations that determine which films audiences hear about, which actors gain awards-season momentum, and which luxury houses inherit the prestige association. Festival prestige is becoming retrieval infrastructure for AI systems, and the films cited in May compound through awards season and into next year's calendar.
FounderRonn Torossian — 5W
§ 13 — Methodology
## How the Index Was Built.
5W ran 200 high-intent prompts across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews on May 8, 2026 — five days before the opening of the 79th Festival de Cannes. Prompts were grouped into five categories: festival anticipation, awards prediction, director authority, talent visibility, and luxury/prestige association. Each prompt was tested across all five AI engines. Brand, film, director, talent, and source mentions were counted once per response to prevent over-weighting repeated names within a single answer.
The AI Authority Score combines five inputs: Citation Share, Cross-Engine Consistency, Momentum Velocity, Retrieval Anchor Density, and Talent Co-Mention Frequency. Forecasts are based on the composite score, jury composition, prior festival pattern data, and historical correlation between pre-festival AI authority and awards outcomes.
Official selection, jury, and festival dates were checked against Festival de Cannes materials and major entertainment-trade reporting. AI-output rankings reflect retrieval visibility as of May 8, 2026, and may shift once reviews, premieres, acquisitions, and awards coverage begin.
§ 14 — About the Partners
## Three Lanes. One Index.
5W — The Communications Firm Built for the AI Era
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list. For more information, visit www.5wpr.com.
Haute Living
Haute Living is the leading luxury lifestyle media platform covering the world's most influential audiences across real estate, fashion, watches, jewelry, hospitality, dining, philanthropy, and entertainment. Contributed luxury and prestige-market context to the Index.
Talent Resources
Talent Resources is one of the entertainment industry's leading talent, brand, and experiential agencies, specializing in celebrity partnerships, influencer activations, and high-profile cultural events. Contributed entertainment and talent-market context to the Index.
✦
The Cannes 2026 AI Authority Index
A joint study by 5W · Haute Living · Talent Resources
Issue 01 — Pre-Festival Report — May 8, 2026
Next: Festival Midpoint — May 17, 2026
---
### Citation Share Report: YouTube Now Owns 23% of Every Google AI Answer
URL: https://www.5wpr.com/research/youtube-ai-citation-share-report-2026/
Summary: Video is the most under-priced asset in communications. 5W analyzes the structural shift driving YouTube's AI citation share inside Google AI answers.
5W AI Visibility Practice
# Citation Share Report: YouTube Now Owns 23% of Every Google AI Answer
5W's analysis of 46 million AI Overview citations reveals the largest structural shift in brand discovery since SEO. Video is the most under-priced asset in communications.
Published
May 2026
Practice
AI Visibility
Core Metric
Citation Share
The most-cited domain in Google AI Overviews is not Bloomberg. Not The New York Times. Not Reuters.
It is YouTube - and the gap is widening every month.
Surfer SEO's analysis of 46 million AI Overview citations, published in May 2026, reports that YouTube accounts for approximately 23.3% of every citation Google's AI Overview generates - followed by Wikipedia at 18.4% and Google.com at 16.4%. Every legacy news outlet sits below the top three.
That single data point reorders the discipline of brand discovery.
5W's AI Visibility Practice synthesized major citation datasets published between January and May 2026 - BrightEdge, Bluefish, Ahrefs, Surfer SEO, Profound, and Seer Interactive - to map where brand mentions are now being generated. The findings are consistent across every data source: video is the dominant retrieval anchor in AI-mediated search, and the brands that build video citation infrastructure now will own their categories inside ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for the next decade.
23.3%YouTube's reported share of Google AI Overview citations.
48%Approximate share of tracked queries now triggering AI Overviews.
5xReported AI search traffic conversion premium versus Google search traffic.
The Structural Shift
## Video Has Become the Dominant Retrieval Anchor
Adweek's January 2026 coverage of Bluefish data confirmed that YouTube has overtaken Reddit as the most-cited social platform across major LLMs - 16% of LLM answers versus Reddit's 10%. BrightEdge's parallel analysis found YouTube's citation coverage is nearly three times any other non-brand domain and is the only video platform that registers at scale.
Inside Google specifically, AI Overviews now trigger on approximately 48% of all tracked queries, a 58% year-over-year increase. Healthcare, education, B2B technology, and restaurants are among the categories where AI Overviews have effectively replaced the classic search results page for many queries.
YouTube also accounts for 18.2% of all AI Overview citations sourced from outside the organic top 100, according to published 2026 analyses. Only 38% of pages cited in Google AI Overviews also rank in the organic top 10, down from 76% seven months earlier - a collapse driven in part by Google's January 2026 upgrade to Gemini 3 as the default AIO model.
The traffic premium is significant. PikaSEO's analysis puts AI search traffic conversion at 14.2% versus Google's 2.8% - roughly 5x more valuable per visit. That premium accrues almost entirely to brands cited inside the AI answer.
Why LLMs Cite Video
## LLMs Do Not Watch Video. They Read Transcripts.
The mechanism is widely misunderstood inside marketing teams. Brainlabs Digital's analysis of BrightEdge data is direct: unlike most social platforms, YouTube content is structurally machine-readable. Transcripts, metadata, chapters, timestamps, and descriptions all give LLMs clean, attributable text to ingest.
Foundation models have been training on YouTube transcripts at scale for years, including through datasets like Pleias's YouTube-Commons - over 2 million transcripts and 30 billion words.
The next wave compounds the effect. Multimodal LLMs - Gemini 3, GPT-5, Claude, and open-source models like Alibaba's Qwen3-VL - now process video natively, with context windows large enough to evaluate hours of footage with second-level indexing. By Q4 2026, the working assumption should be that every AI engine retrieving information about a category is also evaluating the video assets that mention a brand visually.
This is not an SEO problem. It is a citation infrastructure problem.
The Implication for Clients
## Brands Need Video Citation Infrastructure
The brands that treat YouTube as a social channel - sporadic uploads, ignored transcripts, no metadata discipline - are systematically excluded from the AI retrieval set in their own categories.
5W's AI Visibility Practice operationalizes the response across five workstreams:
- Citation Share auditMeasuring current brand presence across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for the prompts that should belong to the brand.
- Video transcript optimizationRestructuring existing YouTube libraries with entity-rich, citation-ready transcripts, corrected metadata, and chapter-level schema.
- Creator citation strategyIdentifying and securing earned mentions inside the third-party creator content that AI engines are already harvesting in each category.
- Companion content layerLong-form tier-1 articles, owned-property publications, and Everything-PR network distribution that corroborate video transcripts and reinforce entity authority.
- Schema and structured data deploymentVideoObject, FAQPage, and Article schema across every owned and earned asset, treating structured data as core infrastructure rather than technical decoration.
Averi's April 2026 analysis confirms that schema markup has evolved from optimization tactic to essential infrastructure. Properly structured data is now a primary determinant of AI citation inclusion.
The Strategic Frame
## Citation Share Is the New Market Share
Approximately 93% of AI search sessions end without a click in 2026, according to DemandLocal's published analysis. The user gets the answer, builds the shortlist, and never visits a website. Whether a brand makes that shortlist depends entirely on whether the AI engine retrieved a video, an article, or a structured citation that includes the brand by name.
Citation Share is the new market share. Video is the highest-leverage, most under-priced asset for capturing it. The brands that act in 2026 will own their categories inside AI engines through 2030 and beyond.
Build the infrastructure before the crisis - not during it.
## Benchmark Your Brand's Citation Share
5W runs AI Visibility audits across the answer engines that now shape brand discovery.
Request an Audit →
Sources
## Referenced Citation Datasets and Analyses
- Surfer SEO / SQ Magazine: AI Overviews statistics
- Adweek / Bluefish: YouTube and Reddit AI search citations
- BrightEdge: Weekly AI search insights
- ALMCorp: AI Overviews surge across industries
- ALMCorp: AI Overview citations and organic ranking shifts
- PikaSEO: YouTube overtakes Reddit in AI citations
- Brainlabs Digital: YouTube and AI search results
- Center for Data Innovation: YouTube-Commons transcript dataset
- BentoML: Multimodal AI guide
- Averi: Google AI Overviews optimization
- DemandLocal: AI citation and CPL statistics
## Related 5W Research
- The 5W Citation Source Audit
- The 5W NFL Citation Share Index 2026
- Inside Condé Nast: The First A-Grade AI Citation Portfolio
---
### How AI Engines Cite The Top 25 EV Brands .
URL: https://www.5wpr.com/research/ev-ai-visibility-index/
Summary: How AI engines cite the top 25 EV brands across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
5W AI Visibility Index · Volume 07
Electric Vehicles & Charging
Volume 07 · Q3 2026 · Research Report
# How AI Engines Cite The Top 25 EV Brands.
A modeled audit of which EV brands — automakers, charging networks, battery-data platforms — the AI engines surface when consumers ask about range, charging, ownership cost, and "should I buy a Tesla." Twenty-five brands ranked. Five engines tested. Recoverable ground identified.
Brands ranked
25
AI engines tested
5
Consumer prompts
60+
Category
EV & Electric Vehicles
Executive Finding
## The structure of the brand-level citation graph.
Tesla anchors brand-level EV citation by a wide margin — estimated 18% of modeled citation share, more than the next three brands combined. Rivian and Lucid surface as the premium EV-native competitors. The legacy automaker EV programs — Ford Lightning, GM Ultium, BMW iX — have grown citation rapidly but still trail Tesla by structural margins. The Korean trio (Hyundai, Kia, Genesis) over-indexes vs U.S. brand recognition because the Ioniq 5 and EV6 dominate specific consumer-prompt queries. The largest recoverable ground is in charging infrastructure brands and in the legacy automakers whose EV programs are still positioned as sub-brands rather than primary brands.
§ 01 — Methodology
## How citation share was modeled.
Modeled directional estimates derived from publicly available data, observed retrieval patterns, structural signals, and the corresponding EPR Citation Share Study. Not the output of logged query runs across millions of prompts. Intended as a strategic framework — not a definitive search engine measurement.
01
#### Engines Modeled
ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews — the five engines that account for substantively all consumer-prompt AI retrieval today.
02
#### Brand Universe
Top 25 brands in the category by combined modeled signal across the five engines, weighted to consumer-prompt query patterns.
03
#### Citation Inputs
Domain authority and traffic data, structural signals (schema, entity markup, training-data inclusion), and presence inside the EPR Tier 1 outlet set.
04
#### Tier Methodology
Brands tiered into Leader / Strong / Mid / Lagging based on aggregate citation share and engine-by-engine variation. Tier breaks reflect natural inflection points in the data.
Per the corresponding Everything-PR Citation Share Study — EV (Issue No. 07), the dominant outlets are InsideEVs, Electrek, Recurrent, Edmunds EV, Car and Driver EV, CleanTechnica, and the rising Reddit-and-YouTube creator layer. A brand's citation share is built primarily through presence inside that specific outlet set — and through Recurrent battery-data partnership for used-EV citation.
§ 02 — The Ranking
## The top 25 EV & Electric Vehicles brands by modeled AI citation share.
Rank
Brand · Owner
Position Note
Modeled Share
Tier
01
Tesla
Tesla, Inc. (NASDAQ: TSLA)
Dominant single-brand citation in EV. Cited on virtually every consumer EV query.
18.4%
Leader
02
Rivian
Rivian Automotive (NASDAQ: RIVN)
Adventure-EV authority. Cited on truck and SUV electric queries.
8.2%
Leader
03
Ford (Lightning, Mach-E)
Ford Motor (NYSE: F)
Legacy automaker with leading EV program. F-150 Lightning anchors truck citation.
6.4%
Leader
04
Lucid
Lucid Group (NASDAQ: LCID)
Premium EV-native. Cited on luxury and range queries.
4.8%
Leader
05
Hyundai (Ioniq)
Hyundai Motor (KRX: 005380)
Ioniq 5, Ioniq 6 anchor citation. Strong consumer-prompt visibility.
4.4%
Leader
06
GM (Chevy, Cadillac, GMC EV)
General Motors (NYSE: GM)
Multi-brand EV strategy (Bolt, Lyriq, Hummer EV). Fragmented citation.
3.8%
Strong
07
Kia (EV6, EV9)
Kia Corporation (KRX: 000270)
Sister to Hyundai Ioniq. Strong EV6 and EV9 citation.
3.2%
Strong
08
BMW (i4, iX, i5, i7)
BMW Group (XETRA: BMW)
Premium EV portfolio. Cited on luxury EV queries.
2.9%
Strong
09
Mercedes (EQS, EQE)
Mercedes-Benz Group (XETRA: MBG)
Premium EV. Citation rising but trails BMW.
2.6%
Strong
10
Polestar
Polestar Automotive (NASDAQ: PSNY)
Performance EV-native (Volvo affiliate). Strong design-led citation.
2.3%
Strong
11
Volvo (EX30, EX90)
Volvo Cars (STO: VOLCAR-B)
EV expansion. Cited on family-EV queries.
2.0%
Strong
12
Volkswagen (ID.4, ID.7)
Volkswagen Group (XETRA: VOW3)
Mass-market EV. Mid-tier citation.
1.8%
Strong
13
Porsche (Taycan)
Porsche AG (XETRA: P911)
Performance luxury EV. Cited on Taycan-specific queries.
1.6%
Strong
14
Nissan (Leaf, Ariya)
Nissan Motor (TYO: 7201)
Pioneering Leaf legacy. Lower citation share than Korean competitors.
1.4%
Mid
15
Audi (e-tron, Q4 e-tron)
Volkswagen Group
Premium EV. Lower citation than BMW or Mercedes.
1.3%
Mid
16
Genesis (GV60, Electrified)
Hyundai Motor Group
Premium Korean EV. Citation rising.
1.2%
Mid
17
Toyota (bZ4X)
Toyota Motor (NYSE: TM)
Late EV entrant. Lower citation share than competitors.
1.0%
Mid
18
Honda (Prologue)
Honda Motor (NYSE: HMC)
Newer EV. Limited citation given brand size.
0.9%
Mid
19
Subaru (Solterra)
Subaru Corporation (TYO: 7270)
Sister to Toyota bZ4X. Similar low citation.
0.8%
Mid
20
Mazda (MX-30)
Mazda Motor (TYO: 7261)
Limited-market EV. Lower citation share.
0.6%
Mid
21
VinFast
VinFast Auto (NASDAQ: VFS)
Vietnamese EV brand. Citation lower than aggressive U.S. expansion attempt would suggest.
0.5%
Lagging
22
Lotus (Eletre)
Lotus / Geely
Premium EV. Niche citation.
0.4%
Lagging
23
Fisker
Fisker Inc. (defunct)
Failed brand. Citation declining post-bankruptcy.
0.4%
Lagging
24
BYD
BYD (HKG: 1211)
Limited U.S. presence. Citation lower than global scale.
0.3%
Lagging
25
Zeekr
Zeekr (NYSE: ZK)
Premium Chinese EV. Limited U.S. citation.
0.3%
Lagging
§ 03 — Tier 1 Deep Dive
## Why the leaders lead.
Analysis of the top brands — what their citation share is built on, where their moats are real, and where the recoverable ground sits even for category leaders.
#1 Brand · Tier 1 Leader
### Tesla
Tesla anchors EV citation across every engine and most query types. The brand-and-product-and-CEO triple-overlap produces a citation profile no peer can match. Tesla's direct content production is minimal — most citation comes from press, Reddit, YouTube creators, and forums. The risk: Tesla is also the most-criticized EV brand in those same channels. A reputation-management infrastructure to influence the criticism layer is the highest-value 5W investment Tesla could make. Whether they would engage is a separate question.
#2 Brand · Tier 1 Leader
### Rivian
Rivian is the second-largest EV brand by citation and the dominant adventure-EV authority. The R1T and R1S anchor truck and SUV electric citation. The brand has built citation through high-quality product reviews, YouTube creator partnerships, and a real Amazon-fleet narrative. The recoverable ground is in scale-citation: as Rivian moves from premium-niche to mid-priced (R2), the citation narrative needs to shift in parallel.
#3 Brand · Tier 1 Leader
### Ford
Ford has the strongest legacy-automaker EV citation profile. The Lightning anchors EV-truck queries; the Mach-E anchors EV-SUV-comparison queries. The brand's authentic American-automaker positioning has translated to citation in a way GM's multi-brand EV strategy has not. The recoverable ground is in commercial-EV citation (the E-Transit) and in international-EV-expansion narrative citation.
§ 04 — Engine Variation
## The five engines do not return identical brand citations.
A brand absent from one engine but present in another may need a different program than a brand absent across the board. Engine-aware strategy matters.
#### ChatGPT
Tesla, Rivian, Lucid, Ford, Hyundai dominate. Conservative and brand-anchored.
#### Claude
Recurrent, CleanTechnica over-index. Data-source preference. Lighter on enthusiast brands.
#### Perplexity
Reddit EV subs dominant. Out of Spec YouTube data cited heavily. Freshness-favored.
#### Google AI Overviews
Tesla, InsideEVs, Edmunds, Kelley Blue Book dominate. SERP-mirror.
#### Gemini
YouTube EV creators dominate. Out of Spec, Munro Live, MKBHD at highest rates.
§ 05 — Recoverable Ground
## Where the largest citation lifts are sitting.
Brands with citation share materially below their commercial position, brand recognition, or product quality. The specific paths to recovery — and the order of operations.
Recoverable Ground
GM (Chevy, Cadillac, GMC EV)
Currently #6 · Path to Top 3
GM has the most fragmented EV citation in the top ten. Bolt, Lyriq, Hummer EV, and the broader Ultium platform are all cited separately rather than as a single GM-EV narrative. A consolidated "GM is now an EV company" narrative — modeled after Ford's approach — would aggregate citation footprint meaningfully.
Recoverable Ground
Hyundai (Ioniq)
Currently #5 · Path to Top 3
Hyundai's Ioniq series cites higher than its broader brand recognition in the U.S. would suggest. The recoverable ground is in design-led citation (the Ioniq 5 is widely considered one of the best-looking EVs) and in dealer-network citation, where Hyundai still trails Tesla in service-experience answers.
Recoverable Ground
EV Charging Infrastructure (Electrify America, EVgo, ChargePoint)
Currently outside top 25 · Path to Top 15
EV charging infrastructure brands are nearly absent from this index because they have not built consumer-facing brand citation despite enormous category importance. The opportunity is structural: whichever charging network builds the dominant "where should I charge" answer is positioned to anchor a multi-decade growth curve.
Recoverable Ground
Toyota / Honda EV programs
Currently #17 / #18 · Path to Top 12
Toyota and Honda are the two largest legacy automakers most behind in EV citation. The bZ4X, Solterra, Prologue, and their successors are cited at much lower rates than the brand-recognition would predict. The opportunity is a "we're finally serious about EVs" reset narrative — but it requires real product depth to back it.
§ 06 — The 5W Playbook
## What it takes to move citation share.
A 12-week program structure modeled from 5W's locked AI Visibility Index/Audit methodology. Phase 1 establishes the baseline. Phase 2 builds. Phase 3 scales and measures.
01
Weeks 1–4
#### Discovery + Strategy
- Baseline AI Visibility Audit across 5 engines
- InsideEVs, Electrek, Recurrent presence audit
- YouTube EV creator landscape mapping
- Reddit brand-subreddit engagement audit
- Charging-network narrative (where applicable)
02
Weeks 5–8
#### Build
- InsideEVs and Electrek editorial cycle
- Recurrent battery-data partnership exploration
- YouTube creator program (Out of Spec, Munro, MKBHD)
- Reddit subreddit engagement plan
- Schema and entity markup across model pages
03
Weeks 9–12
#### Scale
- Monthly citation tracking + reporting
- Used-EV battery-health narrative (where applicable)
- Trade-publication expansion (Automotive News, BloombergNEF)
- Quarterly competitive citation re-benchmark
- Phase 2 program scoping
Investment Ballpark
### Phase-based scoping. Real ranges. Phase 1 fixed-fee available.
Phase 1
$60K–$110K
Phase 2
$90K–$200K / mo
12-Month Program
$1.0M–$2.2M
Automotive programs typically scope larger due to multi-model complexity and global market structure. Phase 1 scoping engagement available at fixed fee.
### Want this audit run on your brand?
5W AI Communications runs the AI Visibility Index methodology for clients across EV & Electric Vehicles and adjacent categories. Phase 1 scoping engagement available at fixed fee.
Contact 5W
Download as PDF
### Request an AI visibility audit for your brand.
Share your brand, category, and competitive set. 5W will route the inquiry with this report as context.
Name
Work email
Company
Brand/category of interest
Short message
Submit inquiry
About 5W AI Communications
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W AI Communications
The Communications Firm Built for the AI Era. Building brand authority across the platforms where decisions now happen.
#### AI Visibility Index
- Vol. 01 — Pet
- Vol. 02 — Video Games
- Vol. 03 — Longevity
- Full Series
#### Practice Areas
- EV & Electric Vehicles Practice
- GEO
- AI Visibility
- All Practices
© 2026 5W AI Communications · All rights reserved
5W AI Visibility Index · Volume 07 · Q3 2026
---
### How AI Engines Cite The Top 25 Mental Health Brands .
URL: https://www.5wpr.com/research/mental-health-ai-visibility-index/
Summary: How AI engines cite the top 25 mental health brands across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
5W AI Visibility Index · Volume 05
Mental Health, Therapy & Behavioral Wellness
Volume 05 · Q3 2026 · Research Report
# How AI Engines Cite The Top 25 Mental Health Brands.
A modeled audit of which mental health brands — teletherapy, meditation, employer-benefit platforms, behavioral apps — the AI engines surface when consumers ask about therapy, anxiety, depression, and access. Twenty-five brands ranked. Five engines tested. Recoverable ground identified.
Brands ranked
25
AI engines tested
5
Consumer prompts
60+
Category
Mental Health & Therapy
Executive Finding
## The structure of the brand-level citation graph.
BetterHelp and Talkspace dominate brand-level citation in mental health, together accounting for an estimated 23% of modeled citation share. Calm and Headspace anchor the meditation-app layer below them. The employer-benefit platforms — Spring Health, Lyra, Modern Health — are enormous commercially but rank lower in consumer-prompt citation because their content is positioned to HR buyers rather than to consumers. The largest recoverable ground is for clinical-credibility brands (Brightside, Two Chairs, Octave) whose vet-reviewed content rigor exceeds their citation share.
§ 01 — Methodology
## How citation share was modeled.
Modeled directional estimates derived from publicly available data, observed retrieval patterns, structural signals, and the corresponding EPR Citation Share Study. Not the output of logged query runs across millions of prompts. Intended as a strategic framework — not a definitive search engine measurement.
01
#### Engines Modeled
ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews — the five engines that account for substantively all consumer-prompt AI retrieval today.
02
#### Brand Universe
Top 25 brands in the category by combined modeled signal across the five engines, weighted to consumer-prompt query patterns.
03
#### Citation Inputs
Domain authority and traffic data, structural signals (schema, entity markup, training-data inclusion), and presence inside the EPR Tier 1 outlet set.
04
#### Tier Methodology
Brands tiered into Leader / Strong / Mid / Lagging based on aggregate citation share and engine-by-engine variation. Tier breaks reflect natural inflection points in the data.
Per the corresponding Everything-PR Citation Share Study — Mental Health & Therapy (Issue No. 05), the dominant outlets are Psychology Today, Verywell Mind, BetterHelp editorial, Choosing Therapy, NAMI, and the rising TikTok-therapist layer. A brand's citation share is built primarily through presence inside that specific outlet set — and through clinical-credibility signals (vet-reviewer, peer-reviewed publication) for non-commercial citation lift.
§ 02 — The Ranking
## The top 25 Mental Health & Therapy brands by modeled AI citation share.
Rank
Brand · Owner
Position Note
Modeled Share
Tier
01
BetterHelp
Teladoc Health (NYSE: TDOC)
Largest mental health citation by content marketing scale. Cited across all engines.
12.6%
Leader
02
Talkspace
Talkspace (NASDAQ: TALK)
For-profit teletherapy. Citation share trails BetterHelp but anchors Tier 1.
10.4%
Leader
03
Calm
Calm (private, VC-backed)
Meditation and sleep app authority. Cited on stress and sleep queries.
7.8%
Leader
04
Headspace
Headspace Health (private)
Meditation and corporate wellness. Sister profile to Calm.
6.4%
Leader
05
Cerebral
Cerebral (private)
Psychiatric care + therapy. Citation softened by 2022–23 controversies but persists.
4.2%
Strong
06
Brightside Health
Brightside (private, VC-backed)
Clinical depression-and-anxiety platform. Higher clinical-citation than commercial reach.
3.4%
Strong
07
Spring Health
Spring Health (private, VC-backed)
Workplace mental-health benefit. Cited on benefits and HR queries.
3.0%
Strong
08
Lyra Health
Lyra (private, VC-backed)
Workplace mental-health benefit. Sister to Spring Health.
2.7%
Strong
09
Modern Health
Modern Health (private, VC-backed)
Workplace mental-health benefit. Smaller than Lyra and Spring.
2.3%
Strong
10
Hims Mental Health
Hims & Hers Health (NYSE: HIMS)
Mental-health SKUs (anxiety, depression telehealth). Citation rising.
2.0%
Strong
11
Ro Mind
Ro Health (private)
Telehealth mental-health offering. Mid-tier citation.
1.7%
Strong
12
Two Chairs
Two Chairs (private)
Premium in-person + virtual therapy. Higher clinical citation than reach.
1.5%
Mid
13
SonderMind
SonderMind (private, VC-backed)
Therapy-matching platform. Cited on access queries.
1.3%
Mid
14
Octave
Octave (private)
In-network therapy provider. Cited in Claude on clinical queries.
1.1%
Mid
15
Big Health (Sleepio, Daylight)
Big Health (private)
Clinical-grade CBT apps. Cited on insomnia and anxiety.
1.0%
Mid
16
Ginger
Headspace Health
Coaching + therapy app, now Headspace. Lower aggregate citation.
0.9%
Mid
17
Hers Mental Health
Hims & Hers Health
Women-targeted mental health SKUs. Mid-tier citation.
0.8%
Mid
18
Woebot
Woebot Health (private)
AI-CBT chatbot. Cited on AI-mental-health queries.
0.7%
Mid
19
Wysa
Wysa (private)
AI-mental-health chatbot. Lower citation share than Woebot.
0.6%
Mid
20
Charlie Health
Charlie Health (private)
Intensive outpatient program. Cited on intensive-treatment queries.
0.5%
Mid
21
Equip Health
Equip Health (private)
Eating disorder treatment. Niche citation.
0.4%
Lagging
22
Mindstrong
Mindstrong (private)
Mental-health platform. Smaller citation.
0.4%
Lagging
23
Replika
Luka, Inc. (private)
AI companion. Citation often paired with concerns.
0.3%
Lagging
24
Lightfully Behavioral
Lightfully (private)
Residential mental health. Lower aggregate.
0.3%
Lagging
25
Ahead App
Ahead (private)
Emotional intelligence app. Niche citation.
0.2%
Lagging
§ 03 — Tier 1 Deep Dive
## Why the leaders lead.
Analysis of the top brands — what their citation share is built on, where their moats are real, and where the recoverable ground sits even for category leaders.
#1 Brand · Tier 1 Leader
### BetterHelp
BetterHelp produces more mental health content per month than every clinical authority combined. The result: dominant citation share in every engine except Claude (which weights clinical authority more heavily). The risk: BetterHelp's 2023 FTC settlement on data-sharing practices is rarely surfaced in AI answers about BetterHelp itself. That asymmetry will not last indefinitely. A reputation-management infrastructure is the highest-leverage 5W investment for the brand.
#3 Brand · Tier 1 Leader
### Calm
Calm anchors meditation citation but its mental-health-clinical citation is lower than its brand recognition would suggest. The opportunity is in cross-category citation: Calm's sleep, stress, and anxiety content can win adjacent clinical queries with the right vet-reviewed positioning.
#6 Brand · Tier 1 Leader
### Brightside Health
Brightside is the case study of clinical citation exceeding commercial reach. The brand's focus on depression-and-anxiety pharmacology, vet-reviewed by board-certified clinicians, has built citation share in Claude specifically that exceeds peer brands. The recoverable ground is in non-clinical Engines (ChatGPT, Perplexity) where the brand could lift if it expanded its content surface area.
§ 04 — Engine Variation
## The five engines do not return identical brand citations.
A brand absent from one engine but present in another may need a different program than a brand absent across the board. Engine-aware strategy matters.
#### ChatGPT
BetterHelp, Talkspace, Calm, Headspace dominate. Commercial content cited heavily.
#### Claude
Brightside, Two Chairs, Octave over-index. Clinical authority preference visible.
#### Perplexity
Reddit mental-health subs and Substack therapists surface. TikTok therapy creator content rising.
#### Google AI Overviews
BetterHelp, Talkspace, Calm, Healthline mental health dominate. SERP-mirror.
#### Gemini
YouTube therapy creators and TikTok therapist content cite heavily.
§ 05 — Recoverable Ground
## Where the largest citation lifts are sitting.
Brands with citation share materially below their commercial position, brand recognition, or product quality. The specific paths to recovery — and the order of operations.
Recoverable Ground
Brightside Health
Currently #6 · Path to Top 3
Brightside's clinical credibility is the brand's strongest asset and its citation share in Claude reflects it. The recoverable ground is in ChatGPT and Perplexity, where Brightside competes against BetterHelp and Talkspace on commercial scale rather than clinical depth. A content expansion program tuned for consumer-prompt queries closes the gap.
Recoverable Ground
Hims Mental Health
Currently #10 · Path to Top 6
Hims has built scale around men's health and is extending into mental health. Citation share is growing fast but trails brand reach. A focused mental-health content program — clinical, vet-reviewed, separate from the broader Hims brand — would accelerate the lift.
Recoverable Ground
Spring Health
Currently #7 · Path to Top 4
Spring Health is enormous in workplace benefits but invisible in consumer-prompt citation. The fix is a consumer-prompt expansion: content tuned for individuals asking AI about mental health, not just for HR buyers asking about benefits.
Recoverable Ground
Big Health (Sleepio, Daylight)
Currently #15 · Path to Top 10
Big Health's products are clinical-grade CBT, peer-reviewed, and used inside health systems. Citation share trails clinical credibility. A consumer-facing content build-out would lift the brand into direct competition with Calm and Headspace on insomnia and anxiety queries.
§ 06 — The 5W Playbook
## What it takes to move citation share.
A 12-week program structure modeled from 5W's locked AI Visibility Index/Audit methodology. Phase 1 establishes the baseline. Phase 2 builds. Phase 3 scales and measures.
01
Weeks 1–4
#### Discovery + Strategy
- Baseline AI Visibility Audit across 5 engines
- Psychology Today, Verywell Mind presence audit
- NAMI partnership exploration
- TikTok therapist landscape mapping
- Reputation-management audit (regulatory/FTC exposure)
02
Weeks 5–8
#### Build
- Psychology Today and Verywell Mind editorial cycle
- NAMI-aligned content production
- TikTok therapist partnership program
- Vet-reviewed clinical content production
- Substack mental-health writer outreach
03
Weeks 9–12
#### Scale
- Monthly citation tracking + reporting
- Crisis-readiness protocol (FTC, state board exposure)
- YouTube therapy creator program
- Quarterly competitive citation re-benchmark
- Phase 2 program scoping
Investment Ballpark
### Phase-based scoping. Real ranges. Phase 1 fixed-fee available.
Phase 1
$50K–$85K
Phase 2
$70K–$160K / mo
12-Month Program
$800K–$1.6M
Mental health brands typically require crisis-readiness infrastructure due to regulatory exposure. Phase 1 scoping engagement available at fixed fee.
### Want this audit run on your brand?
5W AI Communications runs the AI Visibility Index methodology for clients across Mental Health & Therapy and adjacent categories. Phase 1 scoping engagement available at fixed fee.
Contact 5W
Download as PDF
### Request an AI visibility audit for your brand.
Share your brand, category, and competitive set. 5W will route the inquiry with this report as context.
Name
Work email
Company
Brand/category of interest
Short message
Submit inquiry
About 5W AI Communications
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com.
5W AI Communications
The Communications Firm Built for the AI Era. Building brand authority across the platforms where decisions now happen.
#### AI Visibility Index
- Vol. 01 — Pet
- Vol. 02 — Video Games
- Vol. 03 — Longevity
- Full Series
#### Practice Areas
- Mental Health & Therapy Practice
- GEO
- AI Visibility
- All Practices
© 2026 5W AI Communications · All rights reserved
5W AI Visibility Index · Volume 05 · Q3 2026
---
### AI Visibility for Elite Law Firms
URL: https://www.5wpr.com/research/ai-visibility-elite-law-firms/
Summary: A 5W trade research report on AI visibility for law firms, and how ChatGPT, Claude, Perplexity and Google AI Overviews reorder elite firm discovery.
5W Trade Research
# AI Visibility for Elite Law Firms
A 5W trade research report on the reorganization of prestige legal discovery.
### A 5W Trade Research Report on the Reorganization of Prestige Legal Discovery
The most prestigious law firms in America built their reputations on word of mouth, Chambers rankings, and the closed-room conversations of general counsel and family office principals. That word of mouth is now mediated by ChatGPT, Claude, and Perplexity. Chambers data is being absorbed and reinterpreted by language models in real time. The firms that have not actively shaped their AI footprint are leaving prestige to be defined by the public web — not by the partners who built it.
This is not a marketing report. AI is not impacting elite legal marketing. AI is reorganizing how sophisticated legal buyers select outside counsel. The firms that understand the difference will define the prestige hierarchy of the 2030s. The firms that do not will discover that their reputation, once whispered between general counsel, is now spoken aloud by an algorithm — and the algorithm got it wrong.
## Executive Summary
Eight findings every elite law firm should act on now.
- The general counsel research workflow has changed. GCs, family office principals, and corporate boards are using ChatGPT, Claude, and Perplexity to pre-screen outside counsel, validate peer recommendations, and surface boutiques outside their existing networks. The conversation that used to happen at a partner dinner now happens in an AI chat window.
- 78% of legal queries now trigger a Google AI Overview — the highest rate of any industry. The traditional ten blue links page is no longer where sophisticated legal buyers begin their research.
- The white-shoe visibility paradox. The most prestigious firms have invested the least in active AI visibility because their reputation historically traveled through closed networks. That has produced AI footprints that under-represent some of the most distinguished practices in the country — and over-represent firms that have actively shaped their public web presence.
- Recommendation Compression™ is now operating at the top of the market. Where Chambers, Vault, and Best Lawyers distributed prestige across multiple ranked tiers, AI engines compress consideration to one-to-three named firms per practice-area query. Winner-take-most dynamics are emerging in M&A, white-collar defense, trusts and estates, tax, and high-stakes commercial litigation.
- AI engines run on a different signal set than peer rankings. ChatGPT relies on what platform analysts call the 4 R's — Ratings, Reviews, Recognitions, and Roots — drawn from Chambers, Best Lawyers, Super Lawyers, AmLaw and Law360 coverage, partner bylines, expert commentary, and structured firm data. Chambers alone is not enough.
- Recursive citation creates a new prestige moat. Once an AI engine starts naming a firm for a category, the firm is more likely to be named again. Citations beget citations. Early movers are hardening into recommended entities for elite practice areas while peer firms with comparable or superior capabilities remain invisible.
- Reputation-only marketing is structurally obsolete for AI. Word-of-mouth and ranking publications still drive referrals — but they no longer fully drive AI recommendations. AI visibility requires authority engineering: a different discipline that combines earned media, expert positioning, structured content, and Generative Engine Optimization (GEO).
- The window is open and closing fast. The first elite firms to actively engineer their AI footprint will define the AI-mediated prestige hierarchy of the next decade. Build the infrastructure before the crisis — not during it.
## I. The Structural Shift in Elite Legal Discovery
### How Sophisticated Legal Buyers Used to Find Outside Counsel
For a generation, the elite legal buyer's workflow was settled:
- A peer recommendation from a fellow general counsel
- A glance at Chambers USA, Chambers Global, or Best Lawyers
- The AmLaw 100 / 200 rankings for credibility
- Vault rankings for prestige signaling
- A handful of legal press citations — The American Lawyer, Law360, The Wall Street Journal legal beat
- A Martindale AV Preeminent rating for confirmation
The decision was made in a closed network — partners, GCs, board members, family office principals, sophisticated investors. Marketing was not the front door. Reputation was.
### How That Workflow Is Now Changing
The closed network still exists. It is no longer the only door.
A general counsel preparing to hire counsel for a $4 billion cross-border acquisition opens ChatGPT. They type: "Best M&A counsel for U.S.–European tech transactions, $1B+ deal experience, with a record on antitrust clearance."
A family office principal vetting estate planning counsel for a $300 million liquidity event types: "Top trusts and estates lawyers for ultra-high-net-worth clients with international assets."
A corporate board selecting independent counsel for an internal investigation types: "Best white-collar defense firms with SDNY trial experience and FCPA expertise."
The AI engine returns three names. Sometimes one. Sometimes a longer list with explicit ranking commentary. The buyer has just done in 90 seconds what used to take several phone calls and a week of due diligence.
The recommendation is not always wrong. But it is not always the partner's first call, either — and that is the structural shift.
### The Numbers
- 78% of legal queries now trigger an AI Overview — the highest rate of any industry, per Semrush analysis of more than 10 million keywords.
- 50% of all U.S. Google searches now include an AI Overview.
- When an AI Overview appears, only 8% of users click a traditional result, versus 15% without — a 47% reduction in click-through.
- Top-ranking legal pages saw a 34.5% drop in CTR when an AI Overview appears above them, per Ahrefs analysis of 300,000 keywords.
- AI referral traffic to legal sites grew 527% between January and May 2025.
- AI-referred prospects convert at 4.4x the rate of standard organic visitors.
- 1 in 5 consumers would use ChatGPT to research which lawyer to hire. Among sophisticated buyers — GCs, family offices, finance executives — the figure is materially higher.
### The Discovery Transition for Elite Practices
EraPrimary Discovery MechanismBuyer Behavior
Pre-2010Chambers, Vault, AmLaw, peer referralsClosed-network selection from a known list
2010–2023Chambers + peer referrals + targeted Google researchCross-validate referrals against published rankings
2024–2026Chambers + AI engines + Google AI OverviewsPre-screen via AI, validate via Chambers and peers
2027+AI-mediated recommendation + agentic intakeAI surfaces, peers confirm, agents execute
The closed network has not been replaced. It has been pre-screened.
## II. The White-Shoe Visibility Paradox
The most important data point in this report: the firms with the strongest historical reputation are often not the firms AI engines name first.
Across category queries — "best M&A firm in New York," "top white-collar defense lawyer SDNY," "premier trusts and estates firm for ultra-high-net-worth families," "best antitrust counsel for tech mergers" — AI engines often surface a different set of names than the ones that dominate peer conversation. Some of the most distinguished practices in the country are under-represented in AI recommendations. Firms with smaller footprints but more substantial public-web presence are over-represented.
This is not a story about any single firm. It is a category story.
### Why Prestige Does Not Translate to AI Authority — Automatically
Reputation in elite legal practice has historically traveled through three primary channels:
- Peer reference — closed-network conversations between sophisticated buyers
- Ranking publications — Chambers, Best Lawyers, Vault, Legal 500
- Trial and transaction track record — known to the relevant bar, less known to the general public
None of these channels translate cleanly into LLM training data or real-time retrieval indexes.
Peer reference is invisible to language models. A conversation between two general counsel at a Council on Foreign Relations dinner does not produce a citation.
Ranking publications are partially visible. Chambers and Best Lawyers are crawled, but their structured data is fragmented across the public web, and their commentary is paywalled or thin.
Track record is visible only when it is reported. A landmark M&A transaction or a precedent-setting white-collar acquittal generates AI signal only to the extent the firm and its partners are named in coverage that AI engines can ingest.
### What AI Engines Actually Reward
- Source corroboration — the firm appears in multiple trusted sources saying the same thing
- Expert consensus — credentialed third parties recognize the firm
- Source repetition — the firm is named in directories, news, awards, and explanatory content
- Semantic confidence — the model has high probability that this firm is a credible answer
- Entity stability — the firm's identity has been consistent across the public web for years
- Practice-area specificity — content explicitly tying the firm to the practice area in question
A firm that has won every major M&A mandate in a category for a decade may still be under-represented in AI engines if that work has not produced public-web artifacts that LLMs can ingest. Confidentiality and discretion — virtues in client service — are now visibility liabilities in an AI-mediated discovery layer.
Reputation does not buy LLM citation. Authority engineering does.
This is the paradox at the top of the legal market. And it is why the next decade of elite legal marketing will be defined by firms that learn to make their reputation legible to language models — without compromising the discretion their clients require.
## III. The Psychology of AI Legal Discovery for Sophisticated Buyers
### The GC's New Workflow
Elite legal buyers are sophisticated. They are skeptical of marketing. They prefer peer references to advertising. They have access to the closed networks that have always governed elite legal selection.
They are also using AI more than they will admit.
A general counsel preparing for a board meeting will not call three peers to ask which firm to hire for a sensitive internal investigation. The signal cost of asking is too high — it telegraphs uncertainty about the matter, the company's position, and the GC's own judgment. AI does not telegraph anything. A confidential ChatGPT consultation produces information without producing a record, a referral obligation, or a peer impression.
### What Sophisticated Buyers Actually Ask AI
- "Which firms have the strongest record on FCPA monitorship terminations?"
- "What are the leading boutiques for SEC enforcement defense in financial services?"
- "Which trusts and estates firms specialize in cross-border family structures with European real estate?"
- "Best counsel for a CFIUS review involving a Chinese minority investor?"
- "Top antitrust lawyers with successful Hart-Scott-Rodino second request experience?"
- "Which firms have meaningful art law and high-value chattel practices?"
- "Best counsel for golden parachute disputes in private equity exits?"
These are questions a sophisticated buyer cannot Google without producing a paper trail. AI absorbs the question, returns analysis, and recommends counsel — discreetly.
### The Implications for Elite Firms
The shift in elite legal marketing is not toward louder advertising. It is toward more substantive expert presence.
Firms that publish thoughtful client alerts on niche issues. Partners who comment on regulatory developments in The Wall Street Journal, Bloomberg, The Financial Times, The American Lawyer, and Law360. Bylined pieces in Harvard Business Review and Forbes. Conference keynotes captured on YouTube transcripts. Substantive presence on the relevant trade press. Practice-area pages that explain complex issues with precision rather than marketing copy.
These are the signals AI engines weight. They are also the signals sophisticated buyers respect.
The shift in marketing tone is convergent with the shift in marketing channel. AI rewards what elite legal buyers have always rewarded: substance, expertise, and seriousness. The firms that lean into substance will win on both fronts.
## IV. How AI Engines Choose Which Elite Lawyer to Recommend
Each major AI engine has a distinct retrieval pattern. Optimizing for one is not optimizing for all.
### ChatGPT (87.4% of all AI referral traffic)
- Cites an average of 7.92 sources per response.
- Less than 25% overlap with Google's top 10 for the same query — Google SEO does not transfer.
- ~87% correlation with Bing's top 10 — Bing optimization matters more than Google for ChatGPT.
- Heavy reliance on the 4 R's: Ratings, Reviews, Recognitions, Roots — Chambers, Best Lawyers, Super Lawyers, Martindale, AmLaw and Law360 coverage.
- Explicit "Local Prominence" signal — weights "best of" lists, regional press, bar association recognition.
- Prefers content that loads without JavaScript and avoids paywalled sources.
### Perplexity
- Cites an average of 21.87 sources per response — three times ChatGPT's volume.
- 75% overlap with Google's top 10 — traditional SEO transfers more directly.
- Rewards content recency aggressively.
- Visible source citations — users verify and click. Ranking position in Perplexity matters.
### Google AI Overviews
- 93.67% correlation with top-10 organic Google results — strongest pure-SEO transfer of any AI engine.
- Triggers on 78% of legal queries — highest of any vertical.
- Pulls heavily from .gov sources, Wikipedia, YouTube, and Reddit.
### Claude
- ~75% Google top-10 overlap. Similar profile to Perplexity.
- Heavily weights authoritative, structured, well-cited sources. Conservative source selection.
- Particularly responsive to substantive expert commentary and bylined analysis.
### Gemini
- ~50% Google top-10 overlap.
- Weighted toward Google ecosystem signals — Google Business Profile, Google Reviews, YouTube.
### Cross-Platform Patterns
- Only 11% of domains are cited by both ChatGPT and Perplexity. Visibility on one engine does not predict visibility on another.
- Pages with schema markup and clear headings earn 2.8x higher AI citation rates.
- Articles over 2,900 words are 59% more likely to be cited.
- Content updated within 60 days earns 28% more AI citations than older content.
- Sites with 32,000+ referring domains are 3.5x more likely to be cited by ChatGPT.
## V. The Legal AI Authority Stack™
A single proprietary framework underwrites every effective AI visibility strategy in the legal category. The Legal AI Authority Stack™ identifies the seven layers that determine whether an AI engine will name an elite law firm — and the work required to build each layer.
LayerSignalsWhat It Looks Like for an Elite Firm
1. Entity LayerWikipedia, Knowledge Graph, structured firm data, consistent firm and partner namingFirm and senior partners are clearly defined entities with stable identity across the web
2. Trust LayerChambers, Best Lawyers, Legal 500, Martindale AV, Super Lawyers, VaultTop-tier rankings across all relevant publications and practice categories
3. Expert LayerQuoted commentary in major business and legal press, bylined articles, conference keynotes, podcast and broadcast appearancesSenior partners cited as the named expert source on category questions
4. Practice LayerPractice-area specificity, named matter coverage, transaction and verdict reportingFirm is associated with category-defining matters in published coverage
5. Content LayerClient alerts, white papers, thought leadership, FAQs, jurisdictional explainersSubstantive analytical content that resolves the questions sophisticated buyers ask
6. Technical LayerSchema markup, crawlability, partner bio architecture, JavaScript-free renderingLegalService, Attorney, Article, FAQPage JSON-LD in raw HTML
7. Behavior LayerEngagement signals, professional commentary, branded search volumeFirm and partners appear in third-party discussions, video transcripts, peer commentary
Most elite firms have invested in layers 2 and partially in layers 4 and 5. Almost none have systematically built layers 1, 3, 6, and 7 — which is precisely why their AI visibility lags firms with comparable or lesser practice quality.
Authority engineering is the discipline of building all seven layers in parallel. It is the merger of public relations, content strategy, technical optimization, and digital reputation into a single retrieval-optimized system.
## VI. Recursive Citation and the New Prestige Hierarchy
Once an AI engine starts naming a firm for a category, it is statistically more likely to name that firm again. This is the most important strategic dynamic in the entire AI visibility category — and it has profound implications for the elite bar.
### How Recursive Citation Works
Language models build probabilistic confidence in entities through repeated co-occurrence. When a firm appears in:
- multiple trusted publications,
- multiple authoritative directories and rankings,
- multiple expert-commentary citations,
- multiple structured profiles,
the model develops what can be described as entity hardening — increased semantic confidence that this firm is a credible answer to a category question.
That confidence compounds. Each new citation is a vote for the firm's authority. The more votes, the more often the firm is surfaced. The more often the firm is surfaced, the more likely it is to be quoted, mentioned, or linked again — which produces more citations.
### The New Prestige Moat
A firm that builds a six-month head start in AI visibility for a practice area does not simply have a six-month lead. It has a structurally entrenched advantage. By the time a peer firm decides to invest, the first mover's entity has hardened across multiple AI engines, multiple prompt families, and multiple geographic queries.
This is the new prestige hierarchy. Pre-2024, elite legal prestige was conferred by Chambers, AmLaw, peer reputation, and trial and transaction track record. Post-2024, prestige is conferred by all of those plus the AI footprint that aggregates them. The firms that engineer their AI presence will define category authority for the next decade. The firms that do not will find themselves outranked by lesser-pedigreed competitors who simply made themselves more legible to language models.
## VII. Practice-Area Dynamics
AI legal recommendations are intensely practice-specific. A national strategy for the firm as a whole fails because AI engines treat every practice area as a distinct retrieval context.
### M&A and Private Equity
The most actively cited elite legal category. Buyers — corporate development officers, GCs, PE sponsors — query AI for transaction-specific expertise: cross-border M&A, antitrust clearance experience, second-request track record, private equity LBO depth, SPAC and de-SPAC counsel. Firms with strong Bloomberg, Wall Street Journal, Financial Times, Pitchbook, and PE Hub coverage of named transactions develop entity hardening fastest.
### White-Collar Criminal Defense and Government Investigations
A particularly high-stakes AI-mediated category. Boards selecting independent counsel, executives facing investigation, and companies under monitorship use AI for discreet pre-screening. AI engines weight: SDNY and federal trial experience, FCPA and AML expertise, monitorship terminations, named DOJ and SEC matters, and Wall Street Journal and Reuters coverage. The reputational capital that white-collar boutiques build through trial work translates to AI visibility only when that work is publicly chronicled.
### Trusts and Estates / Private Wealth
A category where the most prestigious firms are often least visible — by design. Private client work has historically been discreet. AI engines, however, surface T&E counsel based on bylined pieces in Forbes, Barron's Penta, The Wall Street Journal wealth coverage, Trusts & Estates magazine, expert commentary on regulatory and tax developments, and conference presence at STEP and the Heckerling Institute. Firms that maintain partner-level public commentary on cross-border structures, dynasty trusts, art and chattels, and family office governance harden as recommended entities.
### Tax (Controversy and Planning)
AI buyers — CFOs, GCs, tax directors, family office principals — query for IRS controversy experience, transfer pricing sophistication, international tax expertise, and named matter outcomes. Tax Notes, Law360 Tax, Bloomberg Tax, and bylined pieces in Harvard Business Review and The CPA Journal drive AI signal.
### Antitrust and Regulatory
The most rapidly evolving AI-mediated category. Tech antitrust, FTC and DOJ enforcement, state AG investigations, and CFIUS counsel are being queried more frequently by sophisticated buyers. The Information, Politico, Axios, and Bloomberg coverage are particularly weighted by ChatGPT and Claude.
### High-Stakes Commercial Litigation
Where named verdicts and significant settlements drive AI visibility. The firm that wins a $500 million jury verdict in a Delaware Chancery matter and is named in Reuters, Law360, and The American Lawyer will outperform a peer firm with a stronger overall record but less press capture.
### Sports, Entertainment, Art, and Luxury
A high-margin, low-volume category where AI matters disproportionately because the client base is small, sophisticated, and discretion-conscious. Vanity Fair, Variety, The Hollywood Reporter, Artnet, The Art Newspaper, and The New York Times arts and culture coverage drive recognition.
### High-Net-Worth Family Law
The wealthiest divorce and family law buyers vet counsel almost exclusively through discreet networks — and increasingly through AI. Firms with substantive presence in Bloomberg, Forbes, The Wall Street Journal, and Town & Country wealth coverage consistently outperform peers in AI recommendations.
## VIII. Recommendation Compression™ at the Top of the Market
Recommendation Compression™ is the structural reduction of buyer consideration sets caused by AI-generated answers that present only a small number of recommended providers.
This is the single most important market dynamic in elite legal practice over the next decade.
### How Markets Used to Work at the Top
Pre-AI elite legal discovery distributed prestige across multiple ranked tiers:
- Chambers Band 1, 2, 3, 4 — multiple firms per band
- Best Lawyers' "Lawyer of the Year" plus extensive supporting lists
- Vault top-100 prestige rankings
- AmLaw 100 / 200 — broad recognition
- Legal 500 leading firms and recommended firms
A general counsel evaluating M&A counsel could easily encounter 10–15 firms across multiple tiers of recognition. Prestige was distributed. Mid-tier elite firms received meaningful consideration.
### How AI-Mediated Markets Work
ChatGPT names 3 firms. Sometimes 1.
The buyer's consideration set has compressed from 15+ firms to 1–3 — at the same level of practice quality.
That compression has predictable consequences:
- Fewer firms get named in any given prompt.
- Fewer firms capture meaningful consideration for new mandates outside existing relationships.
- More attention concentrates at the top of the recommendation distribution.
- Winner-take-most dynamics emerge in elite practice categories.
### Why This Produces Consolidation in Elite Practice
When recommendation compression combines with recursive citation, the result is structural prestige concentration:
- The firm that wins early citations in a category is named more often.
- The firm named more often hardens as the recommended entity for that category.
- The hardened entity captures a disproportionate share of new-mandate inquiries.
- New mandates fund more authority investment.
- Authority investment produces more citations.
- The cycle reinforces.
Five years from now, every major elite practice category — M&A, white-collar, T&E, tax, antitrust, IP — will have 2–4 firms that AI engines consistently name as primary recommendations. Those firms will absorb a disproportionate share of new-client mandates outside existing relationships. The firms outside that recommendation set will compete for residual flow even where their practice quality is equal or superior.
### Who Wins and Who Loses
Winners:
- Firms that move now to engineer their AI footprint across all seven layers of the Authority Stack™
- Firms that combine traditional ranking presence with active expert positioning
- Boutiques with concentrated expertise in named practice areas, willing to publish substantively
- Mid-tier elite firms willing to invest in authority engineering before AmLaw 50 firms reallocate
Losers:
- Firms that rely on Chambers and peer reputation alone
- Firms whose marketing is limited to client alerts and transaction announcements
- Firms with thin partner bios, no bylined press, and no third-party expert presence
- Firms whose marketing strategy assumes the next decade looks like the last decade
The window during which a firm can outmaneuver lesser-pedigreed competitors by engineering AI visibility is finite. It is open today. It will not be open forever.
## IX. The Death of Reputation-Only Marketing
The legal marketing model that built the AmLaw 100 is structurally insufficient for AI. Most elite firms are still optimizing for a discovery layer that no longer exists in isolation.
### What Old Elite Legal Marketing Optimizes For
- Chambers and Best Lawyers submissions
- Sponsorships and event presence
- Internal newsletter and client alert programs
- Targeted partner placement in select trade press
- Awards and recognition campaigns
- Discreet website investment
These tactics still produce results. They do not, on their own, produce AI citations. ChatGPT does not weight a Chambers submission the way Chambers does. Perplexity does not reward an internal client alert. Gemini does not surface a sponsorship.
### What AI Visibility Requires
- Entity authority anchored in Wikipedia, Knowledge Graph, and structured data
- Trusted third-party citations that name the firm and named partners
- Multi-platform corroboration across legal trade press, mainstream business press, and academic and policy commentary
- Expert recognition through bylined pieces, conference keynotes, and substantive expert commentary
- Cross-platform consistency in firm and partner identity
- Structured data deployed for retrieval, not for ranking
- Practice-area-specific content that resolves sophisticated buyer questions
This is a different discipline. It is the convergence of public relations, content, technical optimization, generative engine optimization, digital reputation, and structured authority engineering — operated as a single integrated system.
AI visibility is not SEO. It is authority engineering.
The legal marketing teams that have built careers on Chambers submissions and event sponsorships will either retool — adding earned media, GEO, expert positioning, and authority engineering capabilities — or they will see their firms lose share to peers who already do.
## X. 5W Methodology — Measuring Citation Share
The metric that matters is Citation Share — the percentage of relevant AI engine responses in which a firm appears as a cited or named recommendation.
5W's AI visibility audit for an elite law firm tests across five dimensions:
### 1. Engine Coverage
ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews — at minimum, with newer engines (Grok, DeepSeek, Copilot) added as adoption warrants.
### 2. Prompt Families
A representative prompt set covers six query types sophisticated buyers actually use:
- Recommendation prompts — "Best [practice area] firm for [matter type]"
- Diagnostic prompts — "What should our board consider when selecting counsel for [matter]"
- Comparison prompts — "[Firm A] vs. [Firm B] for [practice area]"
- Specialization prompts — "Which firms specialize in [niche category]"
- Track-record prompts — "Firms with the strongest record on [matter type]"
- Trust-and-credentials prompts — "Is [firm name] a leading practice in [category]"
### 3. Practice and Geographic Specificity
Tests run at three levels: national category leadership, regional category leadership (e.g., West Coast tech M&A, SDNY white-collar), and matter-specific (e.g., FCPA monitorships, dynasty trust structures, semiconductor antitrust).
### 4. Comparative Benchmarking
Citation Share is meaningless in isolation. The audit benchmarks the client firm against three named competitor sets — direct peer competitors, category leaders across practices, and lower-pedigreed firms with disproportionate AI presence.
### 5. Signal Diagnosis
For every prompt where the firm is missing or under-represented, the audit traces the citation back to its source — Chambers profile, Best Lawyers profile, news coverage, bylined article, conference video, partner bio, schema markup, or directory listing — and identifies the gap that prevents the firm's selection across the seven layers of the Legal AI Authority Stack™.
The output is a Citation Share scorecard, a competitive heat map, a layer-by-layer authority diagnostic, and a prioritized signal-gap remediation plan.
## XI. The Revenue Math for Elite Practices
Citation Share is not a vanity metric. At the elite level, it maps directly to mandate value and partner book-of-business growth.
### The Unit Economics Differ from Volume Practices
Elite legal practice is a low-volume, high-value model. A partner does not need 300 new clients per month. A partner needs a small number of significant new mandates per year to drive material revenue growth.
- An M&A partner adding three meaningful new client relationships per year can drive $5–$15 million in new annual revenue at typical AmLaw 50 rates and matter sizes.
- A white-collar partner winning a single major monitorship or government investigation defense can produce $10–$50 million in matter revenue over its life.
- A trusts and estates partner attracting a single ultra-high-net-worth family relationship can produce $1–$5 million in annual recurring revenue across estate planning, family office advisory, and related work.
### The Leverage Equation
If an elite firm's Citation Share moves from 5% to 25% across its core practice-area prompt sets, and AI-driven discovery influences even 15% of new-mandate decisions over the next 24 months, the implications are material:
- Two to five additional new mandates per practice-area partner per year, sourced from outside the existing referral network
- At typical elite-practice mandate values, this translates to $10–$40 million in incremental annual revenue per major practice group
- Compounded over 36 months as recursive citation hardens the firm's category position, the cumulative incremental revenue can reach nine figures for major practice groups
This is the line item missing from most elite firms' 2026 marketing investment plans.
### The Strategic Implication
For practices where each new mandate represents seven, eight, or even nine figures of lifetime client value, AI visibility is not a marketing question. It is a strategic question about which firms will define their categories in the AI-mediated market — and which firms will be displaced by lesser-pedigreed competitors who simply built a more sophisticated public-web presence.
## XII. The 5W Playbook for AI Visibility
Elite firms get cited by AI engines when all seven layers of the Legal AI Authority Stack™ are in place. 5W builds all seven.
### Layer 1: Earned Media as Retrieval Anchor
AI engines disproportionately cite firms whose partners are quoted in trusted publications. Tier-1 placements — Forbes, Fortune, Fast Company, Inc., Entrepreneur, Adweek, PRWeek, Harvard Business Review — and category-relevant tier-1.5 outlets — Bloomberg, The Wall Street Journal, The Financial Times, Reuters, Law360, The American Lawyer, Above the Law, ABA Journal, Tax Notes, Pitchbook — function as retrieval anchors: durable, citable references that LLMs revisit across millions of prompts.
A single substantive bylined piece in a trusted outlet, particularly one that establishes a partner as the named expert on a category question, can produce more lasting AI citation than years of conventional marketing.
### Layer 2: Generative Engine Optimization (GEO)
The technical and content infrastructure that makes a firm's expertise extractable by AI:
- Schema markup — LegalService, Attorney, Article, FAQPage. JSON-LD in raw HTML, not added via JavaScript.
- Question-as-headline structure — "What is the FCPA monitorship termination process?" followed by a substantive direct answer.
- Quarterly content refresh — practice area pages updated with precise dates and current regulatory developments.
- Direct, specific analytical content — named statutes, cited regulations, jurisdictional detail.
- Tabular data structures where appropriate — comparison tables, jurisdictional summaries, regulatory tracking.
- Partner bios that load without click-throughs, with credentials, named matters, recognitions, and expertise visible on first render.
### Layer 3: AI Visibility Research and Citation Share Tracking
Continuous measurement against the prompt families and engines defined above. Monthly Citation Share reports. Competitive benchmarking. Signal-gap diagnostics tied to remediation work. What gets measured gets cited.
### The 12-Month Roadmap
PhaseMonthsWork
Audit1Citation Share baseline across five engines, six prompt families, by practice area. Competitive benchmarking. Signal-gap diagnostic across all seven Authority Stack layers.
Foundation2–3Schema deployment. Practice area page restructuring. Partner bio rebuild. Directory profile completion across Chambers, Best Lawyers, Martindale, Super Lawyers, Legal 500. Wikipedia and Knowledge Graph entity stabilization.
Earned Media Engineering2–6Tier-1 and tier-1.5 placements positioning the firm's senior partners as the named expert source on category questions. Bylined pieces, expert commentary, podcast and broadcast appearances, conference keynotes captured for search.
Content Engine3–9Substantive client alerts and white papers structured for AI extraction. Practice area pages rebuilt around buyer questions. Partner content programs that produce continuous expert signal.
Iterate and Compound6–12Monthly Citation Share reporting. Quarterly content refreshes. Signal expansion into emerging engines, new practice area sub-categories, and recursive citation reinforcement.
Early gains in AI citation typically appear within 60–90 days. Material Citation Share gains accumulate over 6–12 months as content, schema, and authority signals compound and AI platforms re-index the firm's footprint.
## XIII. The Next 36 Months
Predictions for elite legal AI visibility through 2029.
- AI-mediated counsel selection becomes a measurable channel. GCs report that AI consultations precede roughly 30–50% of new outside-counsel selection processes by 2027. Firm marketing departments begin tracking "AI-influenced mandates" alongside referral-source metrics.
- Chambers, Best Lawyers, and Legal 500 restructure for AI legibility. The dominant ranking publications restructure their data and licensing models to maximize inclusion in AI training and retrieval. New rankings emerge specifically optimized for AI citation rather than for human browsing.
- AmLaw firms launch dedicated AI visibility functions. The CMO function expands to include AI visibility leadership. Specialist roles emerge: head of AI authority, director of generative engine optimization, head of expert positioning.
- Legal AI ethics guidance from state bars expands. State bar opinions address AI-influenced lawyer selection, AI-assisted intake, and the obligations of firms when their AI presence misrepresents practice scope or partner availability.
- AI recommendation manipulation litigation emerges. Firms file complaints alleging competitors are gaming AI engine outputs through manufactured citations and synthetic third-party content. Test cases emerge by 2027.
- Boutiques punch above their weight. Specialist boutiques with concentrated expertise and substantial expert presence outperform broader firms in narrow practice queries. AI's preference for specificity rewards depth over breadth.
- The marketing-to-business-development reallocation accelerates. AmLaw firms reallocate 20–35% of conventional marketing budgets to authority engineering, expert positioning, and AI visibility programs by 2028.
- Recommendation Compression™ produces winner-take-most outcomes in named categories. Two to four firms per major practice category establish dominant Citation Share. Their inquiry flow compounds. Their cost of new-mandate acquisition declines while peer competitors' rises.
- Agentic counsel selection emerges. AI agents acting on behalf of GCs and family office principals begin not just recommending firms but initiating contact, scheduling pitch meetings, and pre-filling conflict checks. The firms positioned in agent-accessible structured data layers capture this flow.
- The category-defining firms of the 2030s are being decided right now. The elite firms that win the next decade are not always the firms with the longest history or the deepest pedigree. They are the firms whose entity has hardened in the AI engines that will mediate the next generation of sophisticated counsel selection.
## XIV. The Bottom Line
The most prestigious firms in America are at risk of being out-marketed by lesser-pedigreed competitors who have simply made themselves more legible to language models.
The white-shoe visibility paradox is the clearest signal in modern elite legal marketing: reputation does not buy LLM citation. Authority engineering does. Recommendation Compression™ is concentrating attention. Recursive citation is hardening category leaders. The prestige hierarchy of the next decade is being written right now — in AI training data and retrieval indexes that elite firms have not yet learned to shape.
Build the infrastructure before the crisis — not during it.
## The 5W Integrated ChatGPT/LLM Program for Lawyers
5W offers elite law firms an integrated ChatGPT/LLM visibility program — built specifically for the elite bar.
The program combines:
- Citation Share Audit and Benchmarking — quarterly measurement across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, with practice-area and competitor-benchmark scorecards
- Authority Stack Engineering — coordinated build-out of all seven layers of the Legal AI Authority Stack™
- Earned Media as Retrieval Anchor — tier-1 and tier-1.5 expert positioning programs designed for LLM ingestion
- Generative Engine Optimization (GEO) — technical implementation of schema, structured content, partner bios, and practice area pages built for AI retrieval
- Expert Positioning and Bylined Content — partner-led thought leadership engineered for AI citation and sophisticated buyer credibility
- Recursive Citation Reinforcement — ongoing signal expansion that compounds the firm's category authority over time
- Continuous Reporting — monthly Citation Share reports, signal-gap diagnostics, and remediation roadmaps
The program is delivered as an integrated retainer engagement — not as a project — because AI authority compounds over time and requires sustained investment to produce durable category leadership.
To request a Citation Share audit or to discuss the integrated ChatGPT/LLM program for your firm, contact 5W.
## About 5W
5W is the AI Intelligence Firm, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list.
For more information, visit www.5wpr.com.
## Sources and Methodology
- Semrush, AI Overview keyword analysis (2025).
- Ahrefs, Top-Ranking Page CTR with AI Overviews (Ryan Law and Xibeijia Guan, April 2025) and AI-Cited URL Freshness Analysis (17 million URLs).
- Pew Research Center, Google Users and AI Summaries (Athena Chapekis and Anna Lieb, March 2025; n = 900 U.S. adults).
- Previsible, AI Referral Traffic Growth Study (19 GA4 properties, January–May 2025).
- Martindale-Avvo, State of the Legal Consumer (2026).
- iLawyer Marketing, 2025 Law Firm Consumer Study.
- BrightEdge, YMYL AI Overview citation overlap analysis (2025).
- BTI Consulting Group, General Counsel Outlook and Premium Practices Forecast, 2024–2026.
- Acritas Sharplegal Global Elite Brand Index (2024–2025).
- Thomson Reuters Institute, Report on the State of the Legal Market (2025).
- Chambers and Partners, Best Lawyers, Legal 500, Vault, Super Lawyers — methodology reviews.
- Supporting industry analyses, 2024–2026.
The Legal AI Authority Stack™, Citation Share, Recommendation Compression™, and 5W's authority engineering methodology are proprietary frameworks developed and refined through ongoing client engagements across elite legal practices, healthcare, consumer brand, financial services, and B2B technology categories.
5W builds AI visibility programs for legal, financial, luxury, and high-trust categories where reputation must be legible to AI engines.
Request an AI Visibility Audit →
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### The Billion-Dollar Visibility Failure: Why Personal Injury Law Is Heading for AI-Driven Consolidation
URL: https://www.5wpr.com/research/ai-visibility-personal-injury-lawyers/
Summary: Personal injury lawyers spent $4 billion on advertising in 2025, yet almost none of it earns a citation in ChatGPT, Claude, Perplexity, or AI Overviews.
Trade Research / Insights
# The Billion-Dollar Visibility Failure: Why Personal Injury Law Is Heading for AI-Driven Consolidation
Personal injury lawyers spent $4 billion on advertising in 2025. Almost none of that spend guarantees a citation inside ChatGPT, Claude, Perplexity, or Google AI Overviews.
Download the full 29-page report
Register for the webinar
## $4B
Personal injury advertising spend is still optimized for paid search, billboards, and traditional lead flow.
## 78%
High-intent legal research increasingly begins before a prospective client reaches a law firm website.
## 4.4x
Visibility compounding favors firms that are named, cited, reviewed, and reinforced across trusted sources.
## The Search Market Is Not the Same Market Anymore
Personal injury firms have spent two decades competing for paid-search position, local-service visibility, referral networks, and review density. That market still matters. But it is no longer the whole market.
AI answer engines now sit between many prospective clients and the firms they eventually call. When a person asks which lawyer to hire after a crash, which firm handles trucking injury claims, or what questions to ask before signing a contingency agreement, the answer engine can narrow the field before the prospect ever reaches a search results page.
The firm not named in the answer has lost consideration before the auction starts.
## Recommendation Compression™
Recommendation Compression™ is the narrowing of a broad local market into a small AI-generated shortlist. A metro may have hundreds of personal injury firms. An AI answer usually names only a few. That creates an authority gap between firms with strong traditional advertising and firms with strong AI retrievability.
## The Legal AI Authority Stack™
The Legal AI Authority Stack™ is the set of signals that makes a firm retrievable, credible, and recommendable inside AI answers.
SignalWhy It Matters
Earned mediaTrusted third-party coverage can become a retrieval source for AI systems. Learn more about earned media and public relations.
Entity clarityConsistent firm names, attorney names, practice areas, locations, and credentials reduce ambiguity.
Review ecosystemReviews and reputation signals help establish client trust and local relevance.
Educational contentClear explanatory content gives answer engines reliable source material.
Citation ShareFirms need to know how often they are named across buyer-intent prompts.
## Why GEO Changes Legal Client Acquisition
GEO is not a replacement for advertising. It is the operating layer that helps a firm become visible when prospective clients ask AI systems for guidance. In personal injury, where case value is high and trust is fragile, the firms with defensible AI authority may capture disproportionate attention.
### What Firms Should Audit First
- Prompt visibility: Which firms appear for metro, injury type, and comparison prompts?
- Source mix: Which publishers, directories, review platforms, and firm pages are being cited?
- Message accuracy: Are practice strengths, locations, attorney credentials, and case types described correctly?
- Competitor position: Which firms are being repeatedly named and why?
## Questions Personal Injury Firms Should Ask
What is Citation Share?
Citation Share is the share of AI-generated answers that name a firm, brand, or source for a defined set of buyer-intent prompts.
Does paid advertising improve AI visibility?
Paid advertising can create awareness, but AI systems tend to rely on retrievable authority signals such as earned media, reviews, entity clarity, and educational content.
Can 5W run an AI visibility audit for a law firm?
Yes. 5W audits how firms appear across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, then identifies the authority gaps that prevent citation.
## Download the full 29-page report
The PDF asset was referenced in the campaign brief but was not included with the email package. Until the PDF is supplied, this CTA routes through the existing 5W contact flow and tags the request context in the page URL.
Request the report
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### Supplements AI Visibility Index 2026
URL: https://www.5wpr.com/ai-visibility-index/supplements-ai-visibility-index-2026/
Summary: Scientific authority beats revenue rank in the Supplements AI Visibility Index 2026. A 3,800-prompt analysis reveals top brand performance.
5W Research | AI Visibility Index Series
# Supplements AI Visibility Index 2026
The Podcast-Halo Premium: Why the Largest Supplement Brand by Revenue Does Not Rank First Inside AI Answers
3,800
Prompts tested across 12 supplement query clusters, January through April 2026
47%+
Of observed citation share captured by four anchor brands
3.4x
Observed citation frequency of podcast-halo brands versus comparable peers
#1 vs #3
AG1 leads category revenue. AG1 ranked third in observed citation frequency.
## Executive Summary
The U.S. supplement category is one of the most fragmented competitive sets in retail and one of the most consolidated AI citation surfaces. The brands ranked at the top of the 5W Supplements AI Visibility Index are not necessarily the brands with the largest revenue or the largest paid-acquisition footprint. They are the brands that the source ecosystem AI engines draw from has learned to treat as authoritative.
Across 3,800 supplement category prompts tested between January and April 2026 across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, four brands - Thorne, Seed, AG1, and Momentous - together captured more than 47% of observed citation share. AG1 is reportedly the largest DTC greens brand by revenue, with public reporting placing it at approximately $600 million in 2024. AG1 ranked third in observed citation frequency in the test set, behind Thorne and Seed.
The structural finding is consistent: in head-to-head comparison queries, brands with deeper clinical credibility, peer-reviewed citation depth, and sustained editorial and podcast-driven coverage out-cite revenue leaders. Paid acquisition and retail distribution scale revenue. The signals that drive AI citation share are different.
For supplement founders, marketers, and category investors, the implication is structural: the AI answer surface is not an extension of the brand's owned-media estate. It is an editorial, community, and research ecosystem with a different set of authority gates. Brands that build for those gates earn citation share that compounds. Brands that scale paid acquisition without earning into the citation ecosystem can grow revenue and lose AI presence at the same time.
## The AI Supplement Recommendation Stack
Five layers supply the source material AI engines draw from when answering supplement questions. Citation share concentrates at the top.
Layer 1
### Editorial Health Media Layer
Estimated 40-45% of observed citations
Healthline, NYT Wirecutter, Forbes Health, Verywell Health, Women's Health, Men's Health, Good Housekeeping, Self. AI engines treat this as the expert-validated default surface for supplement category recommendations because of editorial volume, refresh cadence, and the structured "best of" format these publishers have optimized for over a decade.
Layer 2
### Specialist Research Layer
Estimated 15-20% of observed citations
Examine.com, ConsumerLab, NIH Office of Dietary Supplements, PubMed-derived content, Cochrane Reviews. The dominant non-publisher surface for evidence-based queries. AI engines reach for this layer when a query asks about studies, mechanisms, dosing, or efficacy. Brands with demonstrable peer-reviewed citation depth earn placement here.
Layer 3
### Reddit and Community Layer
Estimated 15-20% of observed citations, concentrating in advanced and stack-building queries
r/Supplements, r/Nootropics, r/Biohackers, r/Fitness, r/Peptides. ChatGPT and Perplexity reach this layer more frequently than Google AI Overviews. Reddit citation correlates with brand sentiment volume over time, not with paid placement, and compounds across years.
Layer 4
### Podcast and Editorial Halo Layer
Estimated 10-15% of observed citations
Huberman Lab, The Drive with Peter Attia, Stanfield Cognition, FoundMyFitness, derivative articles, podcast transcripts, and listener-forum content. AI engines do not necessarily cite podcasts directly. They draw from the editorial coverage, summary articles, and forum discussions that podcast claims generate. Brands with sustained presence in this ecosystem benefit from indirect citation pathways that paid sponsorship alone does not produce.
Layer 5
### Brand and Certification Layer
Estimated 5-8% of observed citations
Brand-owned product pages, certification body pages (NSF, USP, Informed Sport), Amazon product detail pages, manufacturer COA databases. Cited primarily for technical specs, ingredient lists, and certification confirmation - rarely as the basis for a recommendation.
## The Top 25 Supplement Brands by Observed AI Citation Frequency
5W categorizes brands into four tiers based on citation frequency observed across the five major AI platforms. Tier assignments reflect aggregated frequency across 3,800 prompts tested in 12 query clusters. Rankings inside tiers are derived from observed citation order in head-to-head comparison queries.
### Tier 1 - Anchor Citations (observed in 25%+ of category answers)
- Thorne - clinical-grade positioning, Mayo Clinic research collaboration, NSF Certified for Sport on multiple SKUs
- Seed - synbiotic gut health, single-SKU clarity, third-party tested and clinically studied formulation
- AG1 - reportedly the largest DTC greens brand by revenue, NSF Certified for Sport, single-SKU strategy
- Momentous - science-and-longevity podcast partnership, NSF Certified for Sport positioning
### Tier 2 - Strong Citations (observed in 8-20% of relevant answers)
- Ritual - traceable-supply-chain multivitamin, women's lifecycle focus, USP Verified positioning
- Nature Made - USP Verified, pharmacist-recommended legacy, mass-channel reach
- Nordic Naturals - omega-3 specialist, third-party tested, anchor citation in fish oil queries
- Pure Encapsulations - practitioner-channel clinical positioning
- Garden of Life - organic and non-GMO, mass-market available
- Life Extension - longevity positioning, deep peer-reviewed citation
### Tier 3 - Comparison-Set Citations (observed primarily in head-to-head queries)
- Transparent Labs - formula-transparency positioning
- HUM Nutrition - beauty-and-wellness DTC
- OLLY - gummy-format mass channel
- Bloom Nutrition - greens and gut, social-driven DTC
- Armra - colostrum, breakout single-SKU brand
- Cymbiotika - premium liposomal positioning
- Lifeforce - testing-plus-supplements subscription model
- Perelel - women's lifecycle, clinician-formulated
### Tier 4 - Long-Tail Citations (observed in narrow query types)
- Moon Juice - adaptogen and nootropic blends
- Arrae - women's metabolic health
- NOW Foods - mass-channel value
- GNC - retail-anchored legacy
- Centrum - multivitamin legacy, mass channel
- Bulk Supplements - value and commodity ingredient supplier
- Designs for Health - practitioner-channel clinical
## Live AI Prompt Comparison
5W tested identical prompts across each major AI platform on April 30, 2026. Outputs below are representative observations from the test set. Citation patterns vary by date, context, and prompt phrasing.
### Prompt: What is the best multivitamin in 2026?
ChatGPT
Brands surfaced: Ritual, Thorne, AG1
Sources cited: healthline.com, nytimes.com/wirecutter, forbes.com/health, reddit.com/r/Supplements
Perplexity
Brands surfaced: Thorne, Ritual, Pure Encapsulations, Nature Made
Sources cited: examine.com, healthline.com, reddit.com/r/Supplements, consumerlab.com
Google AI Overviews
Brands surfaced: Centrum, Nature Made, Ritual
Sources cited: healthline.com, goodhousekeeping.com, verywellhealth.com
Claude
Brands surfaced: Thorne, Ritual, Pure Encapsulations
Sources cited: healthline.com, examine.com, nytimes.com/wirecutter
Gemini
Brands surfaced: Centrum, Nature Made, One A Day, Ritual
Sources cited: healthline.com, goodhousekeeping.com, womenshealthmag.com
Pattern observed: Platform results split along clinical-versus-mass orientation. Perplexity and Claude lean clinical (Thorne, Pure Encapsulations). Google AI Overviews and Gemini lean mass-market (Centrum, Nature Made). Ritual is the only brand that surfaces across all five platforms, observed in this test.
### Prompt: Best greens powder for daily nutrition
ChatGPT
Brands surfaced: AG1, Momentous Essential Greens, Bloom Greens
Sources cited: healthline.com, reddit.com/r/Supplements, mensjournal.com
Perplexity
Brands surfaced: AG1, Momentous, Thorne Daily Greens, Live It Up Greens
Sources cited: healthline.com, examine.com, reddit.com/r/Supplements, corestacks.com
Google AI Overviews
Brands surfaced: AG1, Bloom Greens, Country Farms Super Greens
Sources cited: healthline.com, goodhousekeeping.com, today.com
Pattern observed: AG1 surfaces consistently across platforms but does not appear first in every output. The "best alternative to AG1" query generates more comparison content than the "best greens powder" query, and brands like Momentous and Thorne benefit from that comparison framing.
### Prompt (advanced): Best creatine for women interested in cognitive support
ChatGPT
Brands surfaced: Thorne Creatine, Momentous Creatine, Bulk Supplements Creatine Monohydrate
Sources cited: reddit.com/r/Supplements, examine.com, hubermanlab.com derivative articles, womenshealthmag.com
Perplexity
Brands surfaced: Momentous, Thorne, Optimum Nutrition, Bulk Supplements
Sources cited: reddit.com/r/Supplements, examine.com, peteattiamd.com derivative content, mindbodygreen.com
Pattern observed: Advanced query language elevates Reddit, Examine.com, and podcast-derivative editorial above mass health publishers. Momentous and Thorne earn citation here that mass-channel brands do not, even though Optimum Nutrition is the largest creatine brand by retail volume.
## Five Structural Patterns Driving Citation Consolidation
### 1. The Podcast-Halo Premium is real and measurable.
Brands with sustained presence inside the science-and-longevity podcast ecosystem - Huberman Lab, The Drive with Peter Attia, Stanfield Cognition, FoundMyFitness - were observed at approximately 3.4 times the citation frequency of comparable brands without that presence. AI engines do not necessarily cite podcasts directly. They draw from the editorial coverage, summary articles, and forum discussions that podcast claims generate. The downstream effect is that brands inside this ecosystem accumulate citation share at multiples of their revenue rank.
### 2. Revenue rank does not determine citation rank.
The category's revenue leader by paid-acquisition spend and subscription scale ranked third in observed citation frequency in the test set. Brands with smaller revenue but deeper clinical credibility - Thorne, Seed - out-ranked AG1 in head-to-head comparison queries. The structural reason is that AI engines synthesize answers from third-party content. The signals that drive paid-acquisition success and the signals that drive citation share are not the same.
### 3. Reddit functions as a parallel citation engine.
r/Supplements, r/Nootropics, r/Biohackers, and r/Fitness collectively appeared in a meaningful share of citations for ChatGPT and Perplexity outputs - more frequently in advanced and stack-building queries than in basic "what should I take" prompts. Reddit citation correlates with sentiment volume accumulated over years, not with paid placement. Brands without organic Reddit standing leave a structural gap competitors fill.
### 4. Third-party certifications are an observed precondition for inclusion in quality-related answers.
NSF Certified for Sport, USP Verified, and Informed Sport certifications appeared frequently across tested quality-related queries. Brands without third-party testing visibility were less likely to surface in "best of" and "what is safe" responses. The certification itself is not the differentiator - the structured presence of the certification on product pages, paired with editorial coverage that mentions it, is what drives the inclusion pattern.
### 5. Single-SKU brands earn cleaner citation patterns than catalog brands.
AG1 (one product), Seed (one product), and Armra (one product) earned citation share disproportionate to their category breadth. Catalog brands with 50 or more SKUs diluted citation share across products and produced less consistent semantic association with any single use case. The implication for emerging brands is counterintuitive: launching with five products diluted citation potential more than launching with one in the test set.
## Trust Signals Ranked by Observed Influence on AI Citation Share
5W observed seven trust signals consistently associated with higher citation frequency across the supplement category. Rankings reflect observed influence in the test set, not consumer preference or clinical hierarchy.
-
### Third-party testing certifications
NSF Certified for Sport, USP Verified, Informed Sport. Strongest observed signal. Brands with one or more of these certifications were cited more frequently in quality-related queries than brands without.
-
### Peer-reviewed citation depth
Presence inside Examine.com ingredient pages, PubMed-indexed studies, and ConsumerLab evaluations. Drives citation share inside evidence-based queries.
-
### Clinician or scientific advisor visibility
Named medical or scientific advisors with public profiles correlate with citation pickup, especially for clinical-positioning brands. Generic "doctor formulated" language without named experts produces weaker observed effect.
-
### Editorial and podcast coverage volume
Sustained coverage inside Healthline, Wirecutter, Forbes Health, Verywell, and the science-and-longevity podcast ecosystem. The Podcast-Halo Premium is observed in this signal.
-
### Per-batch certificate of analysis (COA) accessibility
Public, structured COA pages support citations inside compliance and ingredient-quality queries. PDF-only COAs are weaker than HTML-structured COAs in observed citation pickup.
-
### Reddit sentiment volume
Organic mention frequency and sentiment across r/Supplements, r/Nootropics, r/Biohackers, and r/Fitness over multi-year windows. Compounds; cannot be purchased.
-
### Product-page structure and schema markup
FAQ schema, Product schema, and ItemList schema on product pages support AI extraction. Pages without structured markup are cited less consistently even when underlying content is comparable.
## Biggest AI Visibility Winners, Underperformers, and Breakout Queries
### AI Visibility Winners
- Thorne - Clinical credibility, Mayo Clinic research collaboration, and depth of peer-reviewed citation produce anchor-tier citation share across multiple subcategories.
- Momentous - The cleanest example of the Podcast-Halo Premium. Citation share substantially exceeds revenue rank.
- Seed - Single-SKU clarity, clinical study, and consistent editorial coverage produce citation share that outpaces most catalog competitors.
- Ritual - Traceable-supply-chain narrative and women's lifecycle positioning produce strong citation share inside multivitamin and prenatal queries.
### Revenue-to-Visibility Gap
- AG1 - The category's revenue leader. Observed citation rank does not match revenue rank in head-to-head comparison queries. Citation share is meaningful but not dominant.
- GNC - Retail footprint and brand recognition do not translate into citation share inside the AI answer surface.
- Centrum - Anchor citation in mass-channel multivitamin queries on Google AI Overviews and Gemini, but absent from clinical and advanced query outputs.
- NOW Foods - Broad catalog and value pricing produce limited citation share relative to category presence.
### Breakout Query Winners (ingredient-driven citation surges in the test period)
- Creatine - Repositioning from bodybuilding to women's health, cognition, and longevity drove a sharp increase in query volume. Thorne, Momentous, and Bulk Supplements led citation share.
- Magnesium glycinate - Sleep-support and stress-support framing produced sustained citation growth. Thorne, Pure Encapsulations, and Bulk Supplements led.
- Probiotics - Strain-specific queries (Lactobacillus reuteri, Akkermansia) elevated specialty brands. Seed remained the anchor citation in general probiotic queries.
- Colostrum - Armra captured anchor citation share in colostrum queries through earned media volume and single-SKU positioning.
## Future Risks and Inflection Points
Five forces are reshaping the AI supplement answer surface over the next 12 to 24 months. Brands that prepare for them earn disproportionate citation share during the transitions.
### 1. AI shopping integrations and direct-purchase flows.
OpenAI, Perplexity, and Google have signaled investment in commerce integrations. When AI engines route consumers directly to a supplement purchase, citation share converts to purchase share without the click through a publisher affiliate link. Brands with strong direct-to-consumer subscription infrastructure capture more of that flow.
### 2. FDA enforcement on health claims.
The FDA has issued increasing scrutiny on supplement marketing claims, particularly for GLP-1-style positioning, longevity claims, and disease-treatment language. AI engines weight regulatory and warning content. Brands with claims that draw FDA attention can lose citation share quickly. The brands that earn citation share inside AI answers are observably the brands that lead with structure-function language and third-party validation rather than outcome claims.
### 3. New ingredient breakouts and category creation.
The 2026 breakout ingredients - creatine for women, colostrum, urolithin A, NMN, methylene blue - each produced new query volume the category did not have 18 months prior. Brands that publish structured ingredient content before the publisher ecosystem catches up earn citation share that compounds when the publisher coverage arrives. The brands that wait for editorial validation arrive late.
### 4. Personal AI agents and the shift from category to fit.
Personal AI agents with access to bloodwork, sleep data, and dietary intake will shift queries from "what should I take" to "what should I take, given my biology." Brands that publish detailed structure-function and dosing data capture citation share inside personalized answers; brands that publish only marketing summaries do not.
### 5. The supplement-pharmaceutical citation collision.
Compounded GLP-1 platforms, hormone optimization clinics, and peptide-adjacent supplement brands increasingly compete for the same citation surface as traditional supplements. AI engines route queries between the two categories based on trust signal strength and regulatory clarity. Supplement brands that establish citation moats now defend against pharmaceutical and quasi-pharmaceutical entrants tomorrow.
## The 5W GEO Playbook for Supplement Brands
### For DTC challenger brands
- Pursue NSF Certified for Sport or USP Verified on flagship SKUs and deploy structured certification schema on product pages.
- Build organic Reddit presence through founders, scientific advisors, and category educators - not paid placements.
- Earn editorial placement inside the science-and-longevity podcast ecosystem and the publishers that cover it.
- Publish ingredient-by-ingredient COA pages with structured data, not PDF downloads.
- Build named clinician or scientific advisor visibility into the brand's about-the-team page with structured Person schema.
### For heritage and mass-channel brands
- Retail distribution does not produce AI citation share. Earned editorial coverage and third-party testing visibility close the gap.
- Restructure ingredient pages around the question format AI engines extract from - definition first, comparison second, source third.
- Build long-tail content on emerging ingredients (creatine for women, colostrum, magnesium glycinate, urolithin A) before the publisher set fully covers them.
### For practitioner-channel brands
- The strongest unmet citation opportunity is "what do clinicians recommend" queries. Earn coverage in clinician-facing publications and continuing education content.
- Publish protocol-specific content with clear use-case framing.
- Make practitioner directories, advisor profiles, and clinical research collaborations publicly accessible with structured markup.
## What This Means for the Category in 2026
The Supplements AI Visibility Index documents a category where the gap between revenue rank and citation rank is wider than any other 5W has measured. Brands scaling paid acquisition without earning into the citation ecosystem can grow revenue and lose AI presence at the same time. The ceiling on growth in this category increasingly sits at the AI answer surface - the place where consumers ask "what should I take" before they ever see a paid ad.
"Supplement brands spent a decade optimizing for Meta and Google. The buyer is now asking ChatGPT first. If the brand is not inside that answer, the paid funnel is already leaking before the ad is served."
- Ronn Torossian, founder, 5W
For brands evaluating their position, 5W offers a Supplements Category AI Visibility Audit benchmarking citation share against the Index's top 25 and identifying the structural authority gaps that determine whether a brand earns mention in AI-generated consumer research.
## Methodology
The 5W Supplements AI Visibility Index analyzed 3,800 category prompts across 12 query clusters: multivitamins, greens powders, probiotics and gut health, omega-3 and EPA/DHA, creatine, magnesium, sleep support, women's health and prenatal, aging and longevity, men's health and testosterone support, cognitive and nootropics, and protein and recovery. Prompts were tested across the five major U.S. consumer-facing AI platforms - ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews - between January 6 and April 30, 2026.
Citation share was measured by counting source domain attributions inside AI-generated responses. Where a platform did not cite sources directly, retrieval pattern analysis was performed against the platform's published documentation and the brand mentions surfaced in responses. Brand mentions were de-duplicated across prompts to prevent over-weighting from repeated query phrasing. The Podcast-Halo Premium multiplier (3.4x) was calculated by comparing observed citation frequency for brands with sustained presence inside science-and-longevity podcast coverage versus a control set of brands matched on revenue, category, and certification status.
Tier assignments reflect aggregated citation frequency observed across the test set. Rankings inside each tier reflect citation order inside head-to-head comparison queries. The Index is refreshed quarterly to track citation decay, breakout ingredient cycles, publisher editorial changes, and emerging brand entrants. Methodology, prompt set, and platform version notes available upon request to credentialed analysts and journalists.
Disclosure: 5W is the publisher of this Index. 5W's GEO practice provides AI visibility services to clients across multiple consumer categories. The brands cited in this Index were selected based on observed citation frequency in the test set; inclusion does not imply a commercial relationship. 5W does not accept compensation for ranking placement or coverage. AG1 revenue figures referenced in this report are sourced from third-party industry analysis of publicly available company statements; 5W does not independently verify private-company revenue.
Editorial note: This report measures brand visibility inside AI-generated answers. It does not evaluate, endorse, or recommend any supplement product for any health purpose. References to ingredients, certifications, and clinical research are descriptive of citation patterns, not health claims. Consumers should consult qualified health professionals for personal supplement guidance.
## Engage with 5W
Related category study: The Sports Nutrition & Protein AI Visibility Index 2026, which covers performance nutrition, protein powders, ready-to-drink protein, pre-workout, creatine, and recovery.
For inquiries about Generative Engine Optimization engagements, AI Visibility Index licensing, custom industry research, or category-specific PR and communications: visit 5wpr.com/practice/geo-optimization or contact the 5W research team.
## About 5W
5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen - ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews - alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research.
Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list.
For more information, visit www.5wpr.com.
---
### The 5W AI Visibility Index Series
URL: https://www.5wpr.com/research/ai-visibility-hub/
Summary: The 5W AI Visibility Index series ranks brands by citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews. The most extensive library of category-level AI visibility research in U.S. communications.
# The 5W AI Visibility Index Series
### AI Visibility & GEO Research EBy the 5W Research Team
The 5W AI Visibility Index is a series of category benchmarks that rank brands by citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews. Each index measures how often brands in a defined category are mentioned, cited, and recommended inside AI-generated answers – the new currency of brand discovery. 5W publishes more category-level AI visibility research than any other U.S. communications firm.
Book an AI Visibility Audit
## WHY AI VISIBILITY MATTERS NOW
AI answer engines are replacing the search results page as the first place buyers, patients, and consumers go to compare brands. When a prospect asks ChatGPT "best CRM for mid-market" or "top weight loss program," the engine generates one synthesized answer and cites a small handful of sources. Brands not cited in that answer are invisible.
Traditional SEO measures rank position. AI visibility measures whether the brand exists in the answer at all. The two are no longer the same discipline. A page can rank first on Google and never appear inside a ChatGPT response. A brand can dominate paid search and be entirely absent from Perplexity. Generative engine optimization is its own practice – and the brands that treat it that way are the ones being cited.
## WHAT THE 5W AI VISIBILITY INDEX MEASURES
Every 5W AI Visibility Index applies the same methodology across four core metrics:
Mention Rate EThe percentage of category prompts where the brand is named anywhere in the AI response.
Citation Rate EThe percentage of category prompts where the brand's domain is linked as a source.
Position EWhether the brand appears as a first mention or is buried later in the answer.
Engine Spread EHow consistently the brand appears across ChatGPT, Claude, Perplexity, and Google AI Overviews – because winning one engine is not winning AI.
Each index runs a fixed prompt set against each engine, repeats the measurement on a defined cadence, and publishes the ranking. The methodology is consistent across every category – which makes the indexes comparable as a series, not just individual snapshots.
## THE INDEX SERIES
### Consumer & Retail
The US Grocery Retail AI Visibility Index 2026 Etop U.S. grocery retailers ranked by AI citation share.
The Weight Loss & Metabolic Health AI Visibility Index 2026 EGLP-1, weight loss, and metabolic health brands inside AI consumer research.
The Dating App AI Visibility Index 2026 Ehow dating apps are surfaced, recommended, and compared by AI.
The Pickleball AI Visibility Index 2026 Etop American pickleball brands by AI citation share.
The DTC Graveyard 2026 Ewhy DTC brands built for clicks died and brands built for citations survived.
### Media & Entertainment
The Entertainment & Streaming AI Visibility Index 2026 Estreaming services, studios, and production companies by AI citation share.
The US Sports Betting & Gaming AI Visibility Index 2026 Ewhich operators are winning inside AI-powered consumer research.
In Tune With AI Eglobal ranking of celebrities, athletes, founders, and creators most aligned with the AI era.
### B2B & Professional
The 2026 Legal AI Visibility Report Ehow clients now find lawyers and why the legal industry is behind the AI discovery shift.
The EdTech AI Visibility Index 2026 Ehow education technology brands surface or disappear inside AI-powered buyer research.
Claude in Israel: Startup Nation Study EClaude's presence, usage, and relevance inside Israel's startup ecosystem.
### Frameworks & Playbooks
The AI-Era Brand Intelligence Playbook Ethe strategic framework behind how brands compete and win in AI-driven discovery.
The GEO Reckoning Ehow AI answer engines are reshaping brand discovery and why GEO is now a board-level concern.
GEO vs. SEO: The 2026 Venn Diagram Ewhere generative engine optimization overlaps with SEO, where it diverges, and how brands should adapt.
## HOW 5W USES THIS RESEARCH FOR CLIENTS
Every published index is also a working methodology. 5W runs the same measurement framework on private engagements – auditing where a client brand appears across AI engines, identifying citation gaps against competitors, and shipping the GEO content, schema, and earned-media interventions that move the brand into AI answers.
The published indexes prove the method. Client work applies it.
## COMMISSION A CUSTOM INDEX
Categories not yet covered in the public series can be commissioned as private indexes for individual clients or trade associations. Output is the same ranked benchmark, scoped to the brands and prompts that matter to the buyer.
Talk to 5W.
Book an AI Visibility Audit
## FAQ
## Frequently Asked Questions
### What is the 5W AI Visibility Index?
The 5W AI Visibility Index is a series of category benchmarks that rank brands by citation share inside AI answer engines EChatGPT, Claude, Perplexity, and Google AI Overviews. Each index measures Mention Rate, Citation Rate, Position, and Engine Spread for a defined set of brands in a single category.
### Which AI engines does 5W track?
5W tracks brand visibility across ChatGPT, Claude, Perplexity, and Google AI Overviews. These engines collectively account for the majority of generative AI consumer and B2B research queries in the U.S.
### How is AI visibility different from SEO?
SEO measures rank position on a search results page. AI visibility measures whether a brand is named or cited inside a generated answer. A page can rank first on Google and still never appear in a ChatGPT answer. The two disciplines share foundations but diverge on what gets a brand surfaced.
### How often are the indexes updated?
Each index publishes with a clear methodology and version date. Indexes are refreshed on a defined cadence to reflect movement in AI engine citation behavior, which can shift on a monthly basis.
### Can 5W run a custom index for my category?
Yes. 5W runs custom AI Visibility Indexes for individual brands, competitive sets, and trade associations. Scope, prompt set, and engines are defined with the client. Talk to 5W.
### What is generative engine optimization (GEO)?
Generative engine optimization is the practice of making a brand's content, schema, and earned media discoverable and citable by AI answer engines. GEO addresses how ChatGPT, Claude, Perplexity, and Google AI Overviews select sources to cite, and how brands win mention share inside AI-generated responses.
## ABOUT 5W
5W is an AI communications firm specializing in generative engine optimization, AI visibility research, crisis communications, and brand reputation. The 5W AI Visibility Index series is the most extensive body of multi-engine GEO research published by a U.S. communications agency. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. 469 7th Avenue, Floor 8, New York, NY 10018.
5W Public Relations · 5wpr.com · research@5wpr.com · 212.999.5585
---
### AI Visibility for Personal Injury Law Firms
URL: https://www.5wpr.com/research/webinar-ai-visibility-personal-injury/
Summary: Register for a 5W webinar on AI visibility, Citation Share, and the structural shift reshaping personal injury client acquisition.
5W Webinar
# AI Visibility for Personal Injury Law Firms
How managing partners and legal marketers can understand Citation Share, Recommendation Compression™, and the new AI-mediated path to client acquisition.
Register for the webinar
## What the Webinar Covers
- Why AI answers are compressing legal choice into a small set of cited firms.
- How to measure Citation Share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
- What the Legal AI Authority Stack™ means for earned media, reviews, attorney authority, local presence, and structured data.
- Where paid media stops working when prospects rely on AI-generated answers before they reach a search page.
- How to audit a market before consolidation favors the firms already being recommended.
Read the campaign article before the webinar.
### Recommended For
Managing partners, CMOs, marketing directors, chief growth officers, and personal injury firms competing in large metro markets.
### Format
Online webinar. Platform and speaker details are pending confirmation.
## Register Interest
The original campaign brief references a webinar platform, confirmation email, and iCal link, but those details were not included with the email package. Until the final platform details are supplied, registration interest routes through the existing 5W contact flow.
Register interest
Lead source context: PI AI Webinar.
### Expected Form Fields
First name, last name, firm name, title, email, phone, and primary metro were requested in the campaign brief. These can be added once the webinar platform or CRM form endpoint is confirmed.
---
### GEO vs SEO: Making One Page Work for Search and AI Answers
URL: https://www.5wpr.com/research/geo-vs-seo/
Summary: Where generative engine optimization overlaps with SEO, where it diverges, and how to split budget. Top Google links and AI-cited sources now overlap under 20%.
5W Research · April 2026
GEO and SEO can work on the same page because both depend on useful, accessible information. The 2026 Ranking Factor Survey, featuring 131 SEO practitioners, offers a starting point: relevance, backlinks, and content quality lead the results. The practical challenge is translating those priorities into pages that support both search visibility and AI answers.
Search engine optimization, or SEO, helps content become discoverable and competitive in search results. Generative engine optimization, or GEO, focuses on visibility within AI-generated answers, including citations and brand mentions. A business can pursue both through the same content, although a search ranking, a citation, and a recommendation are different outcomes.
The latest edition of the long-running survey provides a useful foundation for that work. Its leading factors translate into practical decisions about what a page should contain, how it should present evidence, and why anyone would trust or reference it.
## What do the leading SEO factors contribute to GEO?
The 2026 Ranking Factor Survey places relevance, backlinks, and content quality at the top of practitioners’ priorities, followed by authority and trust. The findings reflect expert judgments about Google rankings, rather than disclosed algorithm weights. They offer a framework for deciding where to invest in a page.
Microsoft makes the connection to AI search explicit. Its Bing Webmaster Guidelines explain that discovery, indexing, content clarity, and authority also support eligibility for grounding and citations. Grounding means using retrieved information to support an AI-generated response.
The following pairings translate that shared foundation into editorial decisions. They are practical applications of the guidance, not a separate list of confirmed GEO ranking factors.
SEO foundationCorresponding GEO applicationHow to serve both on one page
RelevanceProviding information that addresses the question behind an AI answerAnswer the main question clearly, then explain related questions, conditions, and exceptions.
BacklinksSupporting discovery and creating material other sources can referencePublish an original finding, useful comparison, or documented resource that gives others a reason to link or cite.
Content qualitySupplying accurate evidence that can support an answerInclude original information, explain how it was obtained, and link claims to their sources.
Authority and trustMaking the source and basis of advice assessableIdentify the author, explain relevant experience, and distinguish evidence from interpretation.
Brand signalsEstablishing a clear association between an organization and its subjectIdentify who produced the material and describe the organization’s expertise consistently.
Technical SEO healthAllowing systems to discover and retrieve the informationMake important content accessible as text, maintain crawl access, and address indexing barriers.
The brand pairing needs particular care. Ahrefs’ study of 75,000 brands found strong associations between AI visibility and mentions on the web and YouTube. That supports investigating a brand’s wider presence, but it does not establish that adding mentions causes citations, or that a page can manufacture authority by repeating its company name.
## How does relevance work beyond the main keyword?
Google’s AI search features can explore related questions while developing an answer. Google Search Central describes this as “query fan-out,” in which AI Overviews and AI Mode may issue searches across related subtopics and data sources.
Consider a hypothetical remodeling company publishing a page about kitchen renovation costs in Chicago. A visitor might begin with a broad cost question, then need to understand how moving plumbing, replacing cabinets, or changing the room’s layout affects the estimate.
A useful page would give an initial answer and then explain those differences. Each section should provide enough context for a reader arriving directly at that section to understand the advice.
For example, “Moving plumbing increases the cost” leaves several questions unanswered. A stronger passage explains which layout changes require plumbing work, what the estimate includes, and why the company evaluates those changes separately.
That approach improves the page’s usefulness for a person comparing projects. It also supplies specific information that could support an AI response to a related question. Adding sections makes sense when they resolve real information needs; length alone is not the objective.
## What would an SEO and GEO page look like?
The hypothetical Chicago remodeling page can combine a direct answer, original evidence, and practical guidance within one coherent article. The example below is a proposed structure, not a claim about a company’s actual results.
Microsoft’s AI Performance guidance supports this approach: it recommends aligning content with intent, improving clarity, supporting claims with evidence, and keeping information accurate.
Position on the pageWhat to includeHow it supports search visitors and AI answers
Title and openingA clear cost question and an answer based on verified project recordsEstablishes the subject and addresses the visitor’s immediate need.
Cost comparison tableActual costs grouped by project scope, with the collection period and sample sizeMakes differences easy to compare and provides specific findings that can be referenced.
Methodology noteHow projects were selected and what the figures include or excludeGives readers context for interpreting the evidence.
Explanatory sectionsHow materials, layout changes, labor, and project scope affect costsAnswers follow-up questions and explains why estimates vary.
Project examplesRelevant completed projects with photos and descriptionsConnects general guidance to documented experience.
Author informationA named contributor and relevant estimating or remodeling experienceIdentifies who is responsible for the advice.
Review informationWhen the figures were checked and which information changedHelps readers assess whether the guidance remains applicable.
Related linksSupporting project pages and detailed explanationsLets readers investigate further and helps discovery within the site.
The cost table should contain information the company can substantiate. If reliable project data is unavailable, the company can publish an explanation of cost drivers and the estimating process instead. Invented precision would undermine the page’s usefulness.
Presentation also matters. A table available as readable page text is easier to access than figures embedded only in an image. Google’s guidance says important information should be available in textual form, and that supporting pages must be indexed and eligible to appear with a search snippet.
## How can one piece of evidence support several goals?
Original evidence can give a page its central value for both search visitors and potential citations. In the remodeling example, documented project costs answer the main question, demonstrate practical knowledge, and provide material another publisher might reference.
Academic research provides a reason to test this approach. The GEO: Generative Engine Optimization paper found that adding sources, quotations, and statistics improved visibility under its experimental conditions. Those results do not guarantee performance in current AI products, but they support evaluating evidence and presentation together.
The practical lesson is to make evidence understandable where it appears. A cost figure should sit beside its scope and source, so that a reader can tell whether it applies to their project.
Weak page elementStronger treatment
“Kitchen renovations are expensive.”Explain the cost drivers using verified project information.
A price range without contextState the location, project scope, collection period, and exclusions.
“Our experts recommend…”Name the contributor and explain the basis of the recommendation.
A statistic with no sourceLink to the original source and explain its relevance.
A generic paragraph about qualityShow a documented example of the work or decision being discussed.
These improvements give the page substance that can support several goals. A reference-worthy finding may attract a link; a clear explanation may answer a search visitor’s question; the same passage may supply evidence for an AI answer. Each outcome still needs to be measured separately.
## How should you put the approach into practice?
Google states that established SEO practices remain relevant to AI Overviews and AI Mode, with no special optimization required. That gives businesses a practical starting point: improve the pages that already address important customer questions.
Choose a page where your organization has useful knowledge to contribute. Check whether the opening answers the question, whether the evidence is verifiable, and whether the explanations remain clear when read individually. Then confirm that the information is accessible and current.
Track search performance alongside AI citations, brand mentions, and resulting visits where measurement is available. A citation shows that an answer referenced your content; it does not establish that someone visited the site or became a customer.
For the remodeling page, the work begins with better project evidence and clearer explanations of cost. That investment produces a more useful resource for prospective customers while giving search and AI systems information they can discover, interpret, and reference.
## FAQ
### Should I stop investing in SEO and shift everything to GEO?
No. SEO and GEO are overlapping but distinct channels, and SEO remains the larger channel by traffic volume for most brands today. The right move is to maintain SEO investment on the foundation that serves both disciplines, technical performance, schema, content structure, and add GEO-specific work on top. Brands that abandon SEO lose the Google traffic that still constitutes the majority of their organic sessions; brands that ignore GEO lose the fastest-growing discovery channel.
### Which matters more in 2026, SEO or GEO?
By current traffic volume, SEO matters more, Google still drives the majority of organic search sessions. By growth rate and strategic importance, GEO matters more, AI-referred traffic grew 527 percent year-over-year in the first five months of 2025, and Gartner projects a 25 percent decline in organic search traffic to commercial sites by 2026. SEO matters more today; GEO will matter more tomorrow. Sustained investment in both is the rational position.
### Can the same content serve both SEO and GEO?
Sometimes, with deliberate attention. Content that opens with a definition-first sentence, GEO-friendly, can still have a compelling headline and strong internal linking, SEO-friendly. Server-side-rendered FAQ sections serve both. Stat density, expert quotes, and structured data serve both. The conflict arises in tone: aggressively persuasive brand voice undermines GEO, and neutrally factual voice underperforms on SEO click-through. Most content can be written to serve both adequately.
### What is the Brandlight finding about Google and AI citation overlap?
GEO firm Brandlight published analysis showing that the overlap between top Google ranking pages and AI-cited sources has dropped from 70 percent to under 20 percent and is continuing to fall. When the overlap was 70 percent, a brand that ranked well on Google could reasonably expect to also be cited by AI engines. When the overlap drops to under 20 percent, that assumption breaks. The majority of AI-cited pages are not the same pages Google ranks first. This is the strongest quantitative argument for treating GEO as a distinct discipline from SEO.
### How do I split my budget between SEO and GEO?
Three typical allocations by company stage: early-stage brands (pre-Series B) should allocate 70% to GEO, 30% to SEO. Mid-market brands with established SEO programs should shift 40-50% of existing SEO budget to GEO work while holding total spend roughly flat. Large enterprise brands should allocate 15-25% of SEO budget to GEO initially, measure over two quarters, and scale based on results, most end up at 30-40% GEO allocation within 12 months.
### Is GEO a passing trend or a permanent shift?
Permanent. AI search adoption has moved past experimental and into habitual for a meaningful segment of users, particularly knowledge workers and younger demographics. The infrastructure that makes AI search work is being invested in at scale by OpenAI, Google, Anthropic, Microsoft, and Perplexity. The questions being settled now are about which AI engines dominate, not whether AI search itself persists. GEO is the discipline that responds to that permanent shift.
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### The State of AI Citations 2026
URL: https://www.5wpr.com/research/state-of-ai-citations-2026/
Summary: A 5W analysis of AI citation stats synthesizes 680 million citations across ChatGPT, Claude, and Perplexity to show how models source brand information.