Michel Fortin

Tag: EAT 2.0

A senior executive viewing an AI search interface that has cited their named framework in a synthesized answer, with handwritten source material visible beside the laptop. The image illustrates the article's argument that AI search cites content worth citing rather than approximating it, and that named frameworks travel intact through AI search while generic content gets paraphrased and the source link gets dropped.

Why AI Search Stopped Rewarding the Content That Used to Win Google

by Michel Fortin

The shift to AI search did not invent a new set of content rules. It enforced the rules Google has been trying to enforce for the better part of a decade. The expert content that wins AI citation in 2026 is the content that should have been winning Google rankings since the Helpful Content Update in 2022. The operators who built to the principle rather than to the algorithm have a four-year head start. The operators who optimized against the algorithm are rebuilding for the third time.

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A senior executive at a tablet in a premium office, eyes fixed on the screen with subtle skepticism, catching the half-second of recognition someone shows when reading content that looks competent on the surface but feels hollow underneath. The image stages the article's opening argument that the reader can tell, within the first three paragraphs, whether a real person was on the other side of the page.

How EAT 2.0 Builds Authority That AI Cannot Flatten

by Michel Fortin

Google’s EAT 1.0 was the four signals the algorithm could measure: Experience, Expertise, Authoritativeness, Trustworthiness. The problem in 2026 is that AI passes the surface test. EAT 2.0 stacks the human layer the framework was never asked to measure. Empathy, Authenticity, Transparency. These three are what authority now compounds on, and they are the move AI cannot imitate.

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