Frequently Asked Questions
About the Venture Capital AI Visibility Index 2026
What is the Venture Capital AI Visibility Index 2026?
The Venture Capital AI Visibility Index 2026 is a two-wave public benchmark produced by 5W that measures how often U.S. venture capital firms and named partners are surfaced, cited, and recommended inside major AI assistants, including ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. The Index provides a structured snapshot of AI-generated answers to 28,400 prompts, helping firms understand their presence in AI-driven research. Note: Results reflect outputs during the defined testing window and may shift as AI models and data evolve. [Source]
How many prompts and AI engines were analyzed in the Index?
The Index analyzed 28,400 prompts across five leading AI assistants: ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. The prompts were distributed evenly across two independent waves (January–February 2026 and April–May 2026) to ensure findings were stable across retrieval drift and model updates. Note: The prompt set and engine mix are available on request for replication. [Source]
What does Citation Share mean in the context of the Index?
Citation Share is the percentage of AI-generated responses in which a firm, partner, or source is named, recommended, or cited as a reference. It is a key metric for measuring how often a brand or individual appears in AI-driven answers, reflecting their visibility to founders, LPs, and journalists researching venture capital. Note: Citation Share does not distinguish between mentions, recommendations, and citations unless otherwise specified. [Source]
Key Findings & Rankings
Which venture capital firms have the highest AI visibility according to the Index?
Andreessen Horowitz (a16z) leads with 21.4% Citation Share, followed by Sequoia Capital at 17.8% and Y Combinator at 15.9%. Together, these three firms account for 55.1% of all observed VC citations in the test set. Note: The remaining 45% is distributed across 57 other firms. [Source]
Which individual venture capitalists are most frequently cited by AI assistants?
Marc Andreessen (a16z) is cited in 14.2% of partner-level responses, followed by Roelof Botha (Sequoia) at 6.8%, Mike Moritz (Sequoia) at 5.9%, Ben Horowitz (a16z) at 5.4%, and Peter Thiel (Founders Fund) at 4.8%. Note: AI assistants often conflate firm and partner names in responses. [Source]
Which sources do AI assistants most frequently cite for venture capital information?
Wikipedia (18.7%), TechCrunch (16.4%), Crunchbase (12.8%), and The Information (9.2%) are the top third-party sources. a16z.com is the only venture-firm-owned domain in the top 10, appearing in 7.1% of responses. Note: Most other firm-owned domains do not appear in the top 25 cited sources. [Source]
How do AI assistants differ in their venture capital firm rankings?
No two AI assistants return the same ranking for top VC firms. For example, ChatGPT favors Sequoia, Gemini and Perplexity favor a16z, Claude produces a more balanced distribution, and Google AI Overviews shows the highest variance across re-runs. Note: These differences are due to each engine's retrieval architecture and ranking heuristics. [Source]
Methodology & Technical Details
How was the Venture Capital AI Visibility Index 2026 conducted?
The Index used two waves of testing: Wave 1 (January 15 – February 12, 2026) and Wave 2 (April 8 – May 6, 2026), each with 14,200 prompts. Prompts simulated real-world research behavior by founders, LPs, journalists, and analysts, including branded, non-branded, comparison, and intent-driven queries. Each prompt was issued three times per engine at varied times of day to reduce retrieval drift. Only findings stable across both waves were published. Note: The Index does not distinguish between retrieval and training-data signals. [Source]
What are the limitations of the Venture Capital AI Visibility Index 2026?
The Index reflects sampled outputs during a defined testing window (January–May 2026). AI models, training data, and ranking systems evolve continuously, so results may shift outside the test period. The Index is best read as a structured snapshot, not a continuous live measurement. Note: Full prompt sets and per-engine logs are available on request for replication. [Source]
AI Visibility, GEO, and Related Concepts
What is AI Visibility and why does it matter for venture capital firms?
AI Visibility is a brand's measurable presence, accuracy, and recommendation rate inside AI answer engines—the degree to which a brand is found, cited, described, and recommended when buyers research using ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. For venture capital firms, strong AI Visibility means being surfaced in the answers founders, LPs, and journalists see first. Note: A firm with strong SEO but weak AI Visibility may be invisible to AI-first researchers. [Source]
What is GEO (Generative Engine Optimization)?
GEO, or Generative Engine Optimization, is the practice of building brand authority and content infrastructure that AI assistants surface, cite, and recommend. GEO is replacing traditional SEO as the key discipline for influencing discovery in AI-driven search environments. Note: GEO requires different strategies than classic SEO, focusing on citation share and content retrievability by AI engines. [Source]
What is the Visibility Index and how is it used?
The Visibility Index is a composite score that combines citation share, query share, sentiment, density, and engine consistency into a single benchmark number for a brand's AI presence within a category. 5WPR's AI Visibility Index Series uses this composite to rank the top 25 brands in each researched category, enabling boardroom-level GEO reporting and benchmarking. Note: AI engines do not compute the Visibility Index themselves; it is built externally by brands and agencies. [Source]
Practical Implications for Venture Capital Firms
What steps should venture capital firms take to improve their AI visibility?
Firms should: 1) Run a baseline AI visibility audit against their name and three closest peers; 2) Audit their Wikipedia entry and those of their top partners; 3) Prioritize earned coverage in TechCrunch, The Information, Crunchbase, and Forbes; 4) Build named-partner and firm authority in parallel; 5) Build citation infrastructure before the next fundraising cycle. Note: Improvements in AI visibility require sustained effort and cannot be achieved solely through owned content. [Source]
About 5WPR and the AI Visibility Index Series
Who produced the Venture Capital AI Visibility Index 2026?
The Index was produced by 5W, an AI communications firm with over 20 years of experience in PR and marketing. 5WPR specializes in AI visibility measurement, GEO, and earned media for clients across technology, finance, and other sectors. Note: For more on 5WPR's history and services, visit the company history page. [Source]
Where can I find other AI Visibility Index reports?
The full series of AI Visibility Index reports, covering categories such as financial services, credit cards, AI companies, defense & aerospace, and more, is available at the AI Visibility Index Series page. Note: Each report includes category benchmarks and citation gap analysis. [Source]
Executive Summary
A two-wave benchmark of how AI assistants describe U.S. venture capital.
Founders, LPs, and reporters increasingly begin venture-capital research inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. The Venture Capital AI Visibility Index 2026 measures how the answers those systems return distribute attention across firms, partners, and source publishers.
The Index analyzed 28,400 prompts across the five leading AI assistants, run in two independent waves — Wave 1 in January–February 2026, Wave 2 in April–May 2026 — to test whether observed patterns held across retrieval drift, model updates, and time-of-day variance. All findings reported below held across both waves within reporting tolerance (≤1.5 percentage-point delta on firm-level Citation Share, ≤2.0 pp on source-level share).
Citation Share · The share of retrieved AI responses in which a firm, partner, or source is named, recommended, or cited as a referenced source. Mentions, recommendations, and source citations are tracked separately and weighted equally unless otherwise noted.
In the dataset, the most-cited U.S. venture firm — Andreessen Horowitz — accounts for 21.4% of cited responses across the five engines. Sequoia Capital follows at 17.8%. Y Combinator follows at 15.9%. The three together account for 55.1% of all observed VC citations in the test set.
At the partner level, Marc Andreessen accounts for 14.2% of named-partner Citation Share. Roelof Botha (6.8%), Mike Moritz (5.9%), Ben Horowitz (5.4%), and Peter Thiel (4.8%) follow.
The most consequential observation in the dataset is structural. a16z.com is the only firm-owned editorial domain to rank among the top 10 cited sources, appearing in 7.1% of retrieved responses about U.S. venture capital. The remaining top sources — Wikipedia, TechCrunch, Crunchbase, The Information, Forbes, PitchBook — are third-party publishers. In this dataset, AI assistants appear to treat a16z-published content as primary source material in a way they do not for any other U.S. venture firm.
"Andreessen Horowitz spent a decade building a media company inside a venture firm. They're the only ones who did. In the dataset, the AI now treats their content as source — and the rest of the industry is being described by Wikipedia and TechCrunch."
Ronn Torossian · Founder and Chairman, 5W
Methodology
How the Index was built.
The Venture Capital 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 and model updates.
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 firm-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, LP, journalist, and analyst research behavior. Prompts included branded firm queries ("What does Andreessen Horowitz invest in?"), non-branded category queries ("Best VC firms for AI startups"), comparison queries ("Sequoia vs Andreessen Horowitz"), intent-driven queries ("Who funds Series A SaaS startups in 2026?"), partner-level queries ("Top venture capitalists in the United States"), and crisis or controversy queries. Prompts were distributed evenly across the five engines so that each engine received the same prompt mix per category.
Seven venture capital categories measured
- Generalist venture capital firms
- Named partner / individual venture capitalists
- AI and machine learning investing
- Crypto and Web3 investing
- Seed, accelerator, and pre-seed
- Growth, crossover, and late-stage
- Sector-focused investing (biotech, fintech, climate, defense)
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.
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).
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. Where engine-level behavior differs from a pure retrieval-only baseline (Figure 03), that variance is interpreted as a combination of retrieval architecture, training-data composition, and engine-specific ranking heuristics.
Limitations
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 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.