Google has spoken: 5 myths about AI optimization that the new guide buries (and what you need to stop doing)

A client calls me. He just spent 8,000 dollars on nothing.

The phone rings on a Tuesday morning. An e-commerce merchant, 800 product SKUs, 4,000 organic sessions a month. He just wrapped up an ‘AI optimization’ project with an agency. 8,000 dollars invested. In 4 months, zero improvement in AI snippets. Zero evolution in AI Overviews. Nothing in AI Mode.

His agency had sold him a GEO/AEO package. llms.txt. Content chunking. A schema supposedly ‘compatible with AI Overviews.’ A partial rewrite to ‘match AI tone.’ Result? Same traffic, same visibility, no AI signal. Worse: a few standard organic pages lost 5 positions. Disillusionment.

Then this morning, he discovers Google’s new guide. The one from May 15, 2026, published on Search Central. He sends it to me with a message: ‘I think I got scammed.’ He’s right.

Google’s official guide, cited by Search Engine Journal, is unequivocal. llms.txt? Not needed. Chunking? Not needed. Special AI schema? Not needed. AEO and GEO? Not separate disciplines. Google classifies them as SEO, period.

I hang up. I think back to the dozens of sites I’ve audited over the last 18 months. 94% of them had implemented at least one of these myths. Thousands of euros gone. Time wasted. Energy diluted.

This guide is a guillotine for all those ‘AI SEO’ merchants.

Here are the 5 myths it buries, and what you gain by stopping immediately.

Myth #1: AEO and GEO are separate disciplines (Google says stop)

Since 2023, agencies have built entire offerings around AEO — Answer Engine Optimization — and GEO — Generative Engine Optimization. Proprietary frameworks. Promises of visibility in ChatGPT, Gemini, Perplexity. Costs at 10,000 euros per project.

Google’s guide just settled it. These are semantic variations of SEO. Not new disciplines.

Google states it plainly: its AI features are « rooted in our fundamental ranking and search quality systems. » Translation: the same index, the same signals, the same technical foundation. What works for standard results works for AI Overviews and AI Mode.

Gary Illyes and Cherry Prommawin have been repeating this since 2024 at Search Central conferences. The guide now locks it into official documentation. End of discussion.

What you gain by doing this: treat AI optimization as a natural extension of your SEO. Work on your crawl, your internal linking, your authority. AI snippets follow if the foundation is solid.

Myth #2: the llms.txt file is essential for AI Search

llms.txt, a file at the root of your site that lists URLs relevant to language models. Popularized as a standard for ‘feeding’ generative AI. WordPress plugins sprouted. Consultants sold it as a prerequisite for any visibility in ChatGPT or Gemini.

Google says clearly: not needed. Its RAG system (retrieval-augmented generation) pulls content from the complete index, not from a separate file. The guide names it explicitly in the ‘Mythbusting generative AI search’ section.

I observe 94% of the e-commerce sites I audit in 2026 with an llms.txt in place. Most poorly maintained. Obsolete URLs. Lists of pages with no logic. Syntax errors.

An example: a site with 2,300 products. The technical team had spent 3 days generating and maintaining this file. Internal cost estimated at 1,200 euros. No traffic variation from AI Overviews after deployment. No drop after removal.

Google: « llms.txt is not needed for its generative AI features. »

What you gain by doing this: delete this file and free up technical time for projects that matter: improving load time, cleaning orphan pages, refining crawl budget.

Myth #3: content chunking, the pillar of AI optimization

Chunking involves breaking long content into discrete blocks, supposedly easier for AI to digest. Some recommended creating one page per section, with specific markup, to boost appearance as a snippet in AI Overviews.

Google buries this practice too. The engine doesn’t need pre-processed content in chunks. RAG handles it. Worse, this artificial fragmentation degrades user expérience and hurts page authority.

A concrete case: a site with 800 products, each product page split into 3 ‘micro-pages’ dedicated to specs, reviews, and FAQ. Result: 2,400 thin pages, bounce rate multiplied by 2.3, 19% loss in overall organic traffic. No AI gains.

We merged the content. Refreshed the structure. Rebuilt solid product pages. +47% organic clicks in 6 weeks. And spontaneous appearance in 12 AI Overviews queries, without any ‘chunked’ optimization.

Google: « content chunking isn’t necessary for its generative AI features. »

Lesson: one rich, well-structured page beats 10 fragments.

Myth #4: a special ‘AI’ schema boosts your visibility

Schemas with promising names: AIOverview, AISnippet, GeoSummarization… None are recognized by Google. The guide reminds us: standard schemas (Product, FAQ, Review, Article, etc.) are enough. No secret schema.

I’ve seen product pages stuffed with a detourned ItemList to inject text aimed at AI. Result? No improvement. Sometimes, structured data validation fails, creating GSC errors.

A 300-page brochure site, after adding an ‘AIOptimized’ schema to all key pages, gained no additional visibility in AI Mode. Its Core Web Vitals score deteriorated because the schema’s JS was poorly loaded.

What you gain by doing this: return to schema.org fundamentals. Ensure your products, reviews, FAQs are properly marked up. Google already uses this data to enrich snippets, including those in AI features.

Myth #5: rewrite your site for AI (and why you’ll break everything)

Some advocate for ‘AI tone’ rewriting: shorter sentences, predictable vocabulary, structure designed for models. Google reminds us of the obvious we often forget: write for humans. Content useful to visitors remains the number one signal.

An apparel e-commerce merchant rewrote his 400 product descriptions in this ‘AI friendly’ style. Result: 22% loss in organic traffic in 2 months. Why? The tone became uniform. Descriptions lost their uniqueness. Conversion rate dropped 1.2 points.

Google’s new guide reiterates: « foundational SEO best practices remain relevant. » Produce pages that answer an intent, with expertise, expérience, authority, trust. E-E-A-T doesn’t disappear with AI. The opposite.

Google: « AI-specific rewriting isn’t needed for its generative AI features. »

What you gain by doing this: keep your unique voice and invest in content that solves problems. Product pages that compare, buying guides, tutorials. This depth is what emerges in AI Overviews, not ‘robot-compatible’ uniformity.

What really matters (and you might already be doing it)

Google’s guide closes a chapter. AI optimization isn’t an extra layer. It’s SEO, done right.

I’ve built more than 1,300 semantic silos since 2016. Sites that perform in AI Overviews all share one trait: flawless architecture, intent-driven content, granular internal linking. No AI hacks.

The figures below come from field observations:

• Pages appearing in AI snippets have median load speed 2x faster than their site average.
• AI extraction rate correlates with content depth (number of semantic signals per page).
• None of the sites with active chunking generate more AI snippets than those without.

The real lever remains crawling, indexing, semantics, authority. AI just reveals faster the strengths and weaknesses of a site.

You spent on llms.txt? On a special schema? On an ‘AI tone’ rewrite? Stop. Redirect that budget toward a structural audit, a silo plan, a progressive overhaul of your pillar pages.

An e-commerce site with a clean SEO foundation is naturally readable by AI models. Everything else is distraction.

And you — how many thousands of dollars did you invest in practices Google just buried?

Stéphane Jambu

SEO & AI Engineer

I build growth systems / AI / Neuroscience | 650+ clients · 80 LinkedIn testimonials · 30 years of expertise · 15 years of systems running without me.

Follow on LinkedIn