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<title>EAT 2.0 – Michel Fortin</title>
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<title>EAT 2.0 – Michel Fortin</title>
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<title>Why AI Search Stopped Rewarding the Content That Used to Win Google</title>
<link>https://michelfortin.com/ai-search-not-google/</link>
<dc:creator><![CDATA[Michel Fortin]]></dc:creator>
<pubDate>Tue, 23 Jun 2026 15:27:31 +0000</pubDate>
<category><![CDATA[SEO Strategy]]></category>
<category><![CDATA[AEO]]></category>
<category><![CDATA[AI marketing]]></category>
<category><![CDATA[AI Search]]></category>
<category><![CDATA[Content Strategy]]></category>
<category><![CDATA[EAT 2.0]]></category>
<category><![CDATA[GEO]]></category>
<guid isPermaLink="false">https://michelfortin.com/?p=14354</guid>
<description><![CDATA[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.]]></description>
<content:encoded><![CDATA[
<div class="wp-block-group article-summary"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained">
<h2 id="article-summary" class="wp-block-heading">Article Summary</h2>
<p class="wp-block-paragraph">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. Operators whose content strategy is still optimized for the old playbook are watching their visibility decline. AI search will not approximate content it can cite by name, so the operator who has built content worth citing earns the new visibility layer.</p>
</div></div>
<div role="navigation" aria-label="Table of Contents" class="simpletoc toc wp-block-simpletoc-toc"><h2 class="simpletoc-title">Table of Contents</h2>
<ul class="simpletoc-list">
<li><a href="#article-summary">Article Summary</a>
</li>
<li><a href="#the-moment-the-playbook-stopped-working">The moment the playbook stopped working</a>
</li>
<li><a href="#google-was-already-trying-to-be-ai-search">Google was already trying to be AI search</a>
</li>
<li><a href="#what-ai-search-actually-rewards">What AI search actually rewards</a>
</li>
<li><a href="#what-this-looked-like-at-consulting-success%25c2%25ae">What this looked like at Consulting Success®</a>
</li>
<li><a href="#the-principle-to-internalize">The principle to internalize</a>
</li></ul></div>
<h2 id="the-moment-the-playbook-stopped-working" class="wp-block-heading">The moment the playbook stopped working</h2>
<p class="wp-block-paragraph">In 2025 I watched the same scene repeat across multiple content teams. The team had spent two or three years building authority content optimized for Google. Keyword research disciplined. Internal linking deliberate. Schema markup clean. Topic clusters tight. The work was good by every measure the team had been trained to use.</p>
<p class="wp-block-paragraph">The rankings still slipped. AI Overviews started showing up for the queries the team had been ranking on. Buyers began telling sales they had found the firm through ChatGPT or Perplexity, not through the search results the content was built to win. The visible traffic dropped while the inbound conversations got better.</p>
<p class="wp-block-paragraph">The team’s diagnosis was wrong almost every time. They believed AI search was breaking their old playbook. The accurate diagnosis was that AI search was enforcing the rules Google has been trying to enforce since 2022, and the team’s old playbook had been quietly aging out for years.</p>
<h2 id="google-was-already-trying-to-be-ai-search" class="wp-block-heading">Google was already trying to be AI search</h2>
<p class="wp-block-paragraph">When I wrote the <a href="https://www.wix.com/seo/learn/resource/user-first-seo" data-type="link" data-id="https://www.wix.com/seo/learn/resource/user-first-seo">User First SEO article for Wix</a> in July 2022, the thesis was that Google and the operator are serving the same end user. The reader who lands on a search result is the same buyer the operator is trying to reach. The engine and the operator are aligned in purpose. Gaming the algorithm becomes the losing move once you see that alignment.</p>
<p class="wp-block-paragraph">The Helpful Content Update rolled out in September of the same year. The update penalized content written for search engines rather than for humans. The thesis predicted the update because both were built on the same principle Google had been working toward.</p>
<p class="wp-block-paragraph">What changed in 2024 and 2025 was not the principle. The consequences changed. The Helpful Content Update penalized the worst offenders. AI search demoted the rest. Content that was tactically optimized but substantively thin had nowhere to hide once the AI engines became the front door to the buyer’s journey.</p>
<p class="wp-block-paragraph">The operators who built to the principle rather than to the algorithm did not have to rebuild anything. Their content was already aligned with what the engines were trying to surface. The operators who built to the algorithm rebuilt twice. Once for the Helpful Content Update. Now for AI search.</p>
<h2 id="what-ai-search-actually-rewards" class="wp-block-heading">What AI search actually rewards</h2>
<p class="wp-block-paragraph">AI search engines do not return ten blue links. They return synthesized answers. The synthesis pulls from sources the engine is willing to attribute the answer to. The expert content that wins AI search is content the engine can cite by name without losing accuracy in the citation.</p>
<p class="wp-block-paragraph">Three properties matter.</p>
<p class="wp-block-paragraph">The content has to demonstrate lived experience rather than describe a topic from the outside. AI engines were trained on enough generic explanations of every topic that another well-structured generic explanation adds nothing the engine cannot already average. Content with specific cases, specific numbers, and specific scars the engine has not seen elsewhere is the content the engine treats as worth citing.</p>
<p class="wp-block-paragraph">The content has to carry named frameworks the engine can reference. Generic content gets paraphrased into the engine’s own language. Named frameworks travel intact. A piece that explains “the four-pillar framework I call FAME, which stands for Focus, Aim, Multiply, Engage” gets cited as that. A piece that explains “the four pillars of positioning” gets paraphrased and the source link gets dropped. This is why <a href="https://michelfortin.com/brandifying-not-branding/">brandifying</a> the move you teach is itself an AI search move.</p>
<p class="wp-block-paragraph">The content has to be source-worthy. Original research. Original analysis. Original synthesis the engine cannot produce on its own. The engine is not looking for what it can write itself. It is looking for what it needs to point at because it cannot otherwise approximate the same answer.</p>
<h2 id="what-this-looked-like-at-consulting-success%25c2%25ae" class="wp-block-heading">What this looked like at Consulting Success®</h2>
<p class="wp-block-paragraph">When I joined Consulting Success as Head of Growth in early 2025, Michael Zipursky had spent more than a decade building authority in the consulting space. Books, podcasts, named frameworks, more than two hundred articles. The library was deep. The rankings were starting to slip because the discovery layer was shifting.</p>
<p class="wp-block-paragraph">The brief was upstream. Make the existing authority visible on the surfaces buyers were now using. I rewrote and restructured the content engine on top of Michael’s existing foundation. One hundred core articles became the spine of the AI retrieval architecture. Roughly a hundred and ninety-two pieces were rewritten or consolidated across the full tenure. I merged related articles for comprehensiveness, restructured pages for AI retrieval, added schema, layered in signal amplification, and tuned the voice for <a href="https://michelfortin.com/eat-2-0/">humanization</a>.</p>
<p class="wp-block-paragraph">AI search visibility lifted 924% year over year. New inbound leads started telling the sales team they had found the firm through ChatGPT and Gemini. The lift compounded across the revenue system underneath.</p>
<p class="wp-block-paragraph">Nothing changed in the funnel, the attribution stack, or the CRM. The discovery architecture changed. The position became visible on the surfaces buyers were now using. The revenue system inherited the lift.</p>
<h2 id="the-principle-to-internalize" class="wp-block-heading">The principle to internalize</h2>
<p class="wp-block-paragraph">The instinct to call AI search a new game is the instinct that produces the wrong fix. The game is the one Google has been trying to play since 2022. The AI engines made the consequences sharper, faster, and harder to ignore.</p>
<p class="wp-block-paragraph">The operator whose content is built on lived experience, named frameworks, and source-worthy material has a head start that compounds every quarter as the AI layer absorbs more of the discovery traffic. The operator whose content is built on keyword density, link velocity, and the optimization layer of 2018 is rebuilding for the third time, against a deadline they did not set, in a market that is no longer asking for what their playbook produces.</p>
<p class="wp-block-paragraph">The principle is the one I have been teaching since 2022. The engine and the operator are serving the same buyer. Stop optimizing against the algorithm and start operating in alignment with what the engine is trying to do. The buyer rewards the same content the engine does, because both are reading for the same signal.</p>
<p class="wp-block-paragraph">In 2026 the signal is named. AI search cites the content that earns the citation, and the operators who build content worth citing are the ones the new discovery layer carries forward.</p>
<div class="wp-block-wpseopress-faq-block-v2 is-layout-flow wp-block-wpseopress-faq-block-v2-is-layout-flow">
<details id="what-is-ai-search-and-how-is-it-different-from-google" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>What is AI search and how is it different from Google?</summary>
<p class="wp-block-paragraph">AI search refers to engines like ChatGPT, Claude, Perplexity, Gemini, and Google’s own AI Overviews that synthesize answers rather than return ten blue links. The engine reads multiple sources and produces a single response that may or may not cite the sources by name. Traditional Google rewards content that ranks against a query. AI search rewards content the synthesizing engine wants to point at because it cannot otherwise approximate the same answer.</p>
</details>
<details id="why-has-my-seo-optimized-content-stopped-performing-in-ai-search" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>Why has my SEO-optimized content stopped performing in AI search?</summary>
<p class="wp-block-paragraph">The content was probably optimized for the algorithm rules of 2018, when keyword density, internal linking, and schema were the discipline. Those signals still matter to AI search engines as part of the surface check, but they are no longer sufficient. AI search additionally requires lived experience the engine has not seen elsewhere, named frameworks it can cite intact, and source-worthy material the engine cannot produce on its own. SEO-optimized content that lacks those properties is content the engine can paraphrase without citing.</p>
</details>
<details id="how-do-i-get-my-expert-content-cited-by-chatgpt-claude-or-perplexity" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>How do I get my expert content cited by ChatGPT, Claude, or Perplexity?</summary>
<p class="wp-block-paragraph">Three properties matter most. First, content has to demonstrate lived experience with specific cases, numbers, and detail the engine has not been trained on. Second, content has to carry named frameworks (such as FAME, OATH, FORCEPS, or any framework the operator has brandified) so the engine cites the name rather than paraphrasing the concept. Third, content has to be source-worthy through original research, original analysis, or original synthesis. The engine is not looking for content it can write itself. It is looking for content it needs to point at.</p>
</details>
<details id="is-traditional-seo-dead" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>Is traditional SEO dead?</summary>
<p class="wp-block-paragraph">No. The technical and structural signals Google’s quality raters score still matter to AI search engines as part of the surface check. The change is that the technical signals are necessary but no longer sufficient. An operator running only the 2018 SEO playbook will see visibility decline. An operator running the technical layer alongside content built on lived experience, named frameworks, and source-worthy material will see compounding lift.</p>
</details>
<details id="how-does-this-connect-to-eat-2-0" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>How does this connect to EAT 2.0?</summary>
<p class="wp-block-paragraph"><a href="https://michelfortin.com/eat-2-0/">EAT 2.0</a> names the human layer that AI search engines are increasingly rewarding under the surface signals of EAT 1.0. Empathy, Authenticity, and Transparency are the three components AI cannot fake at scale. Content that carries those three is the content AI search engines treat as worth citing rather than paraphrasing. EAT 2.0 is the principle. AI search citation is the measurable visibility result.</p>
</details>
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]]></content:encoded>
</item>
<item>
<title>How EAT 2.0 Builds Authority That AI Cannot Flatten</title>
<link>https://michelfortin.com/eat-2-0/</link>
<dc:creator><![CDATA[Michel Fortin]]></dc:creator>
<pubDate>Tue, 09 Jun 2026 19:08:06 +0000</pubDate>
<category><![CDATA[Marketing Strategy]]></category>
<category><![CDATA[AI marketing]]></category>
<category><![CDATA[Authority Building]]></category>
<category><![CDATA[EAT 2.0]]></category>
<category><![CDATA[Power Positioning]]></category>
<category><![CDATA[Thought Leadership]]></category>
<guid isPermaLink="false">https://michelfortin.com/?p=13734</guid>
<description><![CDATA[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.]]></description>
<content:encoded><![CDATA[
<div class="wp-block-group article-summary"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained">
<h2 id="article-summary" class="wp-block-heading">Article Summary</h2>
<p class="wp-block-paragraph">A buyer who has been reading well-written content for two decades can tell, inside the first three paragraphs, whether a real person was on the other side of the page or whether the page was generated to look like one. In 2026, that recognition matters more than the four quality signals Google’s EAT framework taught its raters to score. 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 fake at scale.</p>
</div></div>
<div role="navigation" aria-label="Table of Contents" class="simpletoc toc wp-block-simpletoc-toc"><h2 class="simpletoc-title">Table of Contents</h2>
<ul class="simpletoc-list">
<li><a href="#article-summary">Article Summary</a>
</li>
<li><a href="#what-the-reader-detects-under-the-surface">What the reader detects under the surface</a>
</li>
<li><a href="#how-eat-got-here">How EAT got here</a>
</li>
<li><a href="#empathy">Empathy</a>
</li>
<li><a href="#authenticity">Authenticity</a>
</li>
<li><a href="#transparency">Transparency</a>
</li>
<li><a href="#true-thought-leadership">True thought leadership</a>
</li>
<li><a href="#the-ai-irony">The AI irony</a>
</li>
<li><a href="#frequently-asked-questions">Frequently Asked Questions</a>
</li></ul></div>
<h2 id="what-the-reader-detects-under-the-surface" class="wp-block-heading">What the reader detects under the surface</h2>
<p class="wp-block-paragraph">A buyer who has been reading well-written content for two decades can tell, inside the first three paragraphs, whether a real person was on the other side of the page or whether the page was generated to look like a person was. The tell varies by reader. The recognition is universal.</p>
<p class="wp-block-paragraph">In 2026, that recognition matters more than the four signals Google has spent the last seven years teaching its raters to score. Surface credentials. Structured authority. Citation networks. Trust markers. A modern AI model passes all four at near-zero cost. The reader does not. The reader detects the absence under the surface even when they cannot name what is missing.</p>
<p class="wp-block-paragraph">What they are detecting is the human layer Google’s original EAT framework was never asked to measure. Three components, none of them fakeable at scale: empathy, authenticity, transparency. EAT 2.0 is the operator’s response to a buyer who can now tell.</p>
<h2 id="how-eat-got-here" class="wp-block-heading">How EAT got here</h2>
<p class="wp-block-paragraph">In 2018, Google rolled out the Medic Update. The update penalized health and medical sites whose content could not be tied to qualified expertise, after Google had been watching too many pages publish health advice no qualified clinician would have signed off on. After Medic, the engine stopped pretending the surface of a page could be evaluated independently of who wrote it.</p>
<p class="wp-block-paragraph">Out of Medic came EAT. Expertise, Authoritativeness, Trustworthiness. Three quality signals named in Google’s Search Quality Rater Guidelines, the published rubric Google’s human raters use to spot-check whether the algorithms are surfacing the right kinds of results. EAT is not a direct ranking factor. The algorithm does not measure it line by line. The algorithm learns from rater evaluations and surfaces results that match what the raters scored highly. The practical effect on visibility is the same.</p>
<p class="wp-block-paragraph">Then EAT became E-E-A-T. Google added a second E for Experience, because some of the most helpful content on the web was being written by people who had lived a situation without holding a credential for it. A cancer survivor writing about treatment side effects. A parent writing about a specific developmental disorder. The lived experience was its own kind of authority, and the four-letter version of the framework named it explicitly.</p>
<p class="wp-block-paragraph">After Medic, the doctors started calling. Plastic surgeons, dentists, specialists across the medical world wanted help with their EAT signals, and that work became a meaningful slice of my consulting practice for a few years. The mechanics of how I rebuilt their credibility surfaces sit in the <a href="https://michelfortin.com/forceps-framework/">FORCEPS framework</a>. What matters here is what the doctors signaled. Authority had become a layer Google’s raters were grading on, and the algorithm followed. Operators who took it seriously earned the citations and the clicks. Operators who did not lost them.</p>
<p class="wp-block-paragraph">The problem with EAT 1.0 in 2026 is not that the four signals are wrong. They are still what the raters score and what the algorithm follows. The problem is that AI now produces content that passes the EAT 1.0 surface test at near-zero cost. The credentials look right. The references look right. The structure looks right. The bibliography looks right. The reader still feels the absence. EAT 2.0 names what is missing.</p>
<h2 id="empathy" class="wp-block-heading">Empathy</h2>
<p class="wp-block-paragraph">Empathy on the page is the reader catching the operator’s prior recognition of a situation the reader is currently inside. Not a “we understand” sentence. The recognition that makes the reader stop reading for half a beat and say, this person has sat where I am sitting.</p>
<p class="wp-block-paragraph">This is the surface of the move the <a href="https://michelfortin.com/quest-formula/">QUEST formula</a> names as Understand. The reader who feels read stays. The reader who feels misread leaves, and the leaving is permanent in that moment, because nothing the page says after the misread will reach that reader again.</p>
<p class="wp-block-paragraph">You cannot fake empathy at scale. It either lives in the work or it does not, and the binary is the part the model cannot manufacture. An operator who has sat across from the buyer carries the language in their tissue. An operator who has not, has nothing to imitate. The model can mimic the surface of empathy. The recognition empathy is built on has to come from somewhere outside the model’s training corpus, which is to say, from someone who was in the room.</p>
<p class="wp-block-paragraph">The reader who feels recognized stays for reasons unrelated to the conscious decision to keep reading. Recognition lowers the resistance to the rest of the page, because the page has told the reader, accurately, who is on the other side. The work that follows gets evaluated on whether it earns the recognition, rather than on whether it deserves the attention. Attention has already been granted. The work decides what to do with it.</p>
<h2 id="authenticity" class="wp-block-heading">Authenticity</h2>
<p class="wp-block-paragraph">Authenticity is showing up on the page as a recognizably real person rather than as a brand-shaped surface.</p>
<p class="wp-block-paragraph">Jessica Jensen, the CMO of LinkedIn, said it on the <em>Uncensored CMO</em> podcast. The posts performing best on the platform read as human, personal, sometimes vulnerable, sometimes whimsical. The platform’s own data points at what the framework points at. Surfaces written as a person outperform surfaces written as a brand. The reader can tell.</p>
<p class="wp-block-paragraph">My own LinkedIn is the authenticity practice live. I write about powerlifting. I write about drumming for Nelson Colt, the country band I sit behind the kit for. I wrote about a recent emergency surgery for a bowel obstruction and turned the experience into business lessons about diagnosis, risk, and the things that get ignored until they cannot be ignored. None of those posts began as marketing. All of them did marketing’s work, because the surface was unmistakably mine.</p>
<p class="wp-block-paragraph">The fractional buyer is reading the work to decide whether the operator is real before deciding whether the operator is right. Authenticity answers the first question. The frameworks answer the second. The order is not negotiable. A buyer who does not believe the operator is real never reads the frameworks.</p>
<h2 id="transparency" class="wp-block-heading">Transparency</h2>
<p class="wp-block-paragraph">Transparency is naming what others will not. The industry whispers about pricing, and the operator publishes the range. The peer firm hedges on limitations, and the operator admits them inside the proposal. The category avoids declining engagements out loud, and the operator says no in public when the fit is wrong. The pattern is the same in each case. The thing the buyer wonders about and the operator could hide is the thing the operator names anyway.</p>
<p class="wp-block-paragraph">That principle has a cost, and I learned the size of the cost early. In 2008, my late wife Sylvie wrote a report called <em>Internet Marketing Sins: A Manifesto</em>. The recession had pushed too many operators in our community toward selling things they should not have been selling, and she had been watching the damage from the customer support seat. She was going through chemotherapy at the time. The verbal fight with bad actors had gotten too costly, so she wrote the fight down and sent it into the same community we both made our living inside.</p>
<p class="wp-block-paragraph">The bill arrived fast. We got blacklisted from events. Clients dropped us. Some of the pushback came from people we had worked with for years. The currency we earned back was the one that compounds. Respect from operators who had been waiting for someone to say it. New relationships with buyers who had been looking for someone they could trust. Sylvie’s line, which I still carry, was simple. Make money at the service of others, not at the expense of others. The transparency principle that anchors one third of EAT 2.0 was lived before it was named. The manifesto was 2008. The framework arrived later. The principle was already in the room.</p>
<p class="wp-block-paragraph">In a fractional or expert practice, the same principle compounds through small repeated acts. The case study published with the parts that did not work alongside the parts that did. The result reported with the methodology underneath it, not just the headline number. The credit shared with the team or the predecessor whose work made the result possible. The buyer reading the pattern across a year of those acts is the buyer who decides to call. Each act looks small in isolation. The pattern is what the reader is reading.</p>
<h2 id="true-thought-leadership" class="wp-block-heading">True thought leadership</h2>
<p class="wp-block-paragraph">Most operators use the term thought leadership to describe a thinner version of it. How-to content with mild opinion attached. The operator pulls from the same conventional wisdom every peer pulls from, adds a personal anecdote, and publishes the result under the leadership label.</p>
<p class="wp-block-paragraph">That is leadership of thoughts the field already had. Real thought leadership produces something the field did not have before the operator brought it. Three forms it can take.</p>
<p class="wp-block-paragraph"><strong>Unique research.</strong> The operator surveys their own list. Runs an original poll. Publishes the data with their own interpretation rather than citing someone else’s. Google’s Quality Rater Guidelines specifically reward unique research, because the engine is trying to elevate sources that produce the material the field is citing rather than sources that are doing the citing. The operator who runs the research earns the citation tail behind it.</p>
<p class="wp-block-paragraph"><strong>A unique point of view.</strong> A perspective that differs from the consensus and is defended on the merits. Sylvie’s manifesto was a unique point of view, defended in plain language, at cost. Cost is what tells the reader the position is real. A free opinion is an opinion no one is paying for. A position the operator can name a price for has weight no free opinion carries.</p>
<p class="wp-block-paragraph"><strong>Named frameworks.</strong> Power Positioning. FAME. OATH. QUEST. FORCEPS. The Bullseye Method. Revenue Architecture. EAT 2.0 itself. Each one began as a private way I made sense of work I was doing, and turned into a unit of authority other people quote, teach, and pass on. The framework becomes a carrier of authority once it has a name the field can repeat, and the act of giving it a name is what <a href="https://michelfortin.com/brandifying-not-branding/">brandifying</a> produces. The framework gets to do the spreading the operator’s own time cannot.</p>
<p class="wp-block-paragraph">The three forms compound on each other. Unique research is the kind of thing readers cite. A unique point of view is the kind of thing readers defend. A coined framework is the kind of thing readers teach. Each act of citation, defense, and teaching pushes the operator’s authority into rooms the operator’s calendar never reaches.</p>
<h2 id="the-ai-irony" class="wp-block-heading">The AI irony</h2>
<p class="wp-block-paragraph">The era of AI-generated content is also the era of the highest-value human signal underneath the content. The machine is closing the gap on every part of the work it can imitate. The parts a person has to bring are the parts the market is now paying a premium for.</p>
<p class="wp-block-paragraph">The reader, the buyer, and the algorithm itself are converging on the same demand. Prove there is a person here. Prove the experience under the page is lived experience. Prove the position is one a real human will defend at cost. Three audiences asking the same question in three different voices, and the operator who answers compounds.</p>
<p class="wp-block-paragraph">EAT 1.0 measures the surface. EAT 2.0 carries the human layer underneath. The operators who treat the two as a stack rather than a substitution are the operators whose authority compounds across the AI era. The framework I <a href="https://michelfortin.com/high-tech-high-touch/">first wrote about as the humanization strategy</a> has a sharper name now, and the name is the move EAT 1.0 was never asked to make.</p>
<p class="wp-block-paragraph">Empathy lives in the work or it does not. Authenticity is visible before the reader reaches the first framework. Transparency costs what it costs, and the cost is the currency the relationship is built in.</p>
<p class="wp-block-paragraph">Authority compounds on the layer AI cannot flatten. That layer is EAT 2.0.</p>
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<h2 id="frequently-asked-questions" class="wp-block-heading">Frequently Asked Questions</h2>
<div class="wp-block-wpseopress-faq-block-v2 is-layout-flow wp-block-wpseopress-faq-block-v2-is-layout-flow">
<details id="what-is-eat-2-0" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>What is EAT 2.0?</summary>
<p class="wp-block-paragraph">EAT 2.0 is the three-component framework I use to extend Google’s original E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) into the AI era. It stacks Empathy, Authenticity, and Transparency on top of the four quality signals Google’s raters score. EAT 1.0 evaluates the surface of a page. EAT 2.0 carries the human layer underneath, the layer AI cannot fake at scale.</p>
</details>
<details id="how-is-eat-2-0-different-from-googles-e-e-a-t" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>How is EAT 2.0 different from Google’s E-E-A-T?</summary>
<p class="wp-block-paragraph">E-E-A-T is Google’s framework for evaluating page quality through four signals Google’s human raters are trained to score from the Search Quality Rater Guidelines. The algorithm learns from those evaluations rather than measuring E-E-A-T directly. EAT 2.0 is the operator’s response to those signals in 2026, when AI can pass the surface test at near-zero cost. The two stack rather than compete. E-E-A-T is what the raters score and the engine learns. EAT 2.0 is what makes the reader stay on the page after the engine sends them there.</p>
</details>
<details id="why-does-eat-2-0-matter-in-the-ai-era" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>Why does EAT 2.0 matter in the AI era?</summary>
<p class="wp-block-paragraph">Because AI now produces content that looks competent, structured, sourced, and credentialed without a human ever being on the other side of it. Readers feel the absence even when they cannot name it. The credibility surface that EAT 1.0 measures is no longer a reliable proxy for the human depth underneath. EAT 2.0 names what readers, buyers, and increasingly the algorithm itself are looking for under the surface.</p>
</details>
<details id="what-are-the-three-components-of-eat-2-0" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>What are the three components of EAT 2.0?</summary>
<p class="wp-block-paragraph">Empathy, Authenticity, and Transparency. Empathy is showing the reader you have read their situation accurately, not with platitudes but with the kind of recognition that comes from having been in the room. Authenticity is showing up as a recognizably real person rather than a polished brand surface. Transparency is naming the things others in your industry will not, including pricing, limitations, methodology, and engagements declined when the fit is wrong.</p>
</details>
<details id="how-does-eat-2-0-connect-to-thought-leadership" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>How does EAT 2.0 connect to thought leadership?</summary>
<p class="wp-block-paragraph">True thought leadership is what gives EAT 2.0 something durable to carry. Three forms qualify: unique research the operator produces themselves, a unique point of view defended on the merits at cost, and named frameworks the field can repeat. EAT 2.0 makes the surfaces human enough that the work lands. Thought leadership gives the human layer something specific to land on.</p>
</details>
<details id="can-ai-help-with-eat-2-0-at-all" class="wp-block-details is-layout-flow wp-block-details-is-layout-flow"><summary>Can AI help with EAT 2.0 at all?</summary>
<p class="wp-block-paragraph">AI can support the surrounding work. It can draft, research, structure, and edit. What it cannot do is supply the original recognition empathy is built on, the lived experience authenticity carries, or the position transparency is willing to defend at cost. The operator is the source of the human layer. AI is the amplifier. Treating AI as a replacement collapses the layer the framework was built to protect.</p>
</details>
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