What AI Says
About You When
You're Not in the Room.

The three people who built the engines that now answer the world's questions do not receive the same answer about themselves. AI-generated answers frame Demis Hassabis as a Nobel laureate and a scientist. They frame Dario Amodei as a measured, safety-first operator who walked away and was vindicated. They frame Sam Altman — the most famous of the three — as a visionary trailed by a recurring question of trust.

Same industry. Similar prominence. Three materially different AI-held reputations.

Every finding in this report was built to survive scrutiny. Reputation was modeled across five AI engines using more than 40 reputation-intent prompts per subject, spanning six intent categories. Across three subjects, that is over 600 prompt-level observations.

Each subject was audited across multiple passes. The findings reported here are those that recurred consistently across runs — not single outputs. Every read was triangulated against current independent web sources, and deliberately checked against critical and contrarian coverage, not only favorable, so no finding is favorable by omission. Every factual and financial claim was independently verified.

Across engines, one dominant synthesized narrative tends to harden around each subject. Below — that narrative, and where it came from.

Valence of the dominant framing each engine surfaces first.

Reading the map. Valence reflects the dominant framing across 40+ prompts per subject and multiple passes — a directional estimate, not a sentiment score. The Altman row is the finding: no engine is hostile, but no engine leads clean. Every engine pairs the achievement with the caveat.

"The first sentence is the reputation. Almost no one reads past it."

Reputation is downstream of retrieval. The sources that hold each narrative in place are the lever — and they differ sharply across the three.

What is true and material, but consistently under-surfaced.

Amodei and Hassabis show high consistency — engines tell substantially the same story, which makes their reputations stable, but also harder to move.

Altman's reputation is more contested across engines: the achievement is unanimous, the trust framing varies in prominence. A contested narrative is an unstable one — and an unstable narrative is the one most open to being moved.

Measured against one another, the order is counterintuitive. The most publicly famous founder holds the weakest AI reputation. The Nobel laureate holds the strongest. Amodei sits between — less famous than Altman, less decorated than Hassabis, but with the cleanest controlled-source base of the three. Reputation in AI-mediated discovery does not track fame. It tracks what your sources are, and who controls them.

The distance between the narrative each subject would write and the one AI returns.

The pattern across all three subjects is the lesson. The two high scorers built their AI reputations on sources they shaped, or earned through credentialed third parties — essays, peer-reviewed work, a serious biography, the Nobel record. The lowest score belongs to the founder whose narrative is anchored most heavily by event-driven press he does not control.

The correction is never to argue with the model. It is to change the retrieval base it draws from: publish primary-source material, earn credentialed third-party validation, and build the structured, citable record before the next crisis sets the narrative for you — not during it.