In May 2026, Google published a new official guide for optimizing websites for generative AI features in Google Search.
The guide is worth reading. It is also worth keeping in its proper place: guidance for Google Search, not the whole operating model for AI discovery.
That distinction matters for marketing teams, SEOs, founders, and CTOs because it changes priorities, staffing, tooling, and budget. If the takeaway becomes “AI search is just SEO,” you may protect the foundation but miss the distribution shift. If the takeaway becomes “SEO is dead, rebuild everything around GEO hacks,” you burn budget on tactics with weak evidence and little durable advantage.
The useful answer is less dramatic: SEO remains the base layer. AI visibility is becoming an additional discovery layer on top of it.
What Google gets right
Google’s strongest point is that AI search visibility still depends on fundamentals. Crawlable pages, clear site structure, helpful content, and real expertise did not become irrelevant because the interface changed.
That is especially useful in a market full of new acronyms. AEO and GEO can describe a real concern, but they can also become packaging for shortcuts. Google’s guide pushes back on that. For Google Search, site owners do not need special AI text files, artificial content chunking, or a campaign of inauthentic mentions to appear in generative AI features.
That is the right default posture. Fix the technical foundation. Publish content that is useful beyond common knowledge. Do not buy a new checklist just because the label changed.
Where the guidance stops
Google’s document is not a complete strategy for AI discovery because Google is not the only surface where buyers now ask questions.
Traditional search still matters enormously. Statcounter reports Google at 90.02% worldwide search-engine market share for April 2026, with Bing at 5.14%. That is enough reason to keep Google SEO as a core operating system.
But AI assistants are now their own behavior pattern. Statcounter’s AI chatbot data for April 2026 shows ChatGPT with the largest share, followed by Gemini, Perplexity, Copilot, Claude, and others.
Those datasets should not be compared as if they measure the same thing. Search-engine share and AI-chatbot share are different metrics. Still, they point to the same strategic reality: discovery is no longer happening in one place.
That means the right question is not “Should we do SEO or AEO?”
The right question is: which systems are using our content, which surfaces matter for our buyers, and what evidence do we have that a change improves visibility, trust, or conversion?
The operating choice behind people-first content
The most dangerous mistake is treating “people-first content” as a soft content slogan.
In practice, people-first content means doing the expensive work competitors avoid: original comparisons, real screenshots, first-party data, field notes, customer language, benchmarks, teardown-style analysis, and opinionated recommendations.
That is not only a writing tactic. It is an operating choice.
It affects who writes, how much time they get, what data they can access, whether product and customer teams contribute, and whether leadership is willing to fund content that is actually differentiated.
For a founder or CTO, this is where the recommendation becomes concrete. A content strategy based on commodity summaries can be cheap, but it gives both search engines and AI systems little reason to retrieve, cite, summarize, or trust you. A strategy based on proprietary evidence costs more, but it creates an asset that can travel across multiple discovery surfaces.
How I would prioritize
I would use Google’s guide as the baseline, not the ceiling.
First, keep the SEO foundation strong: crawlability, indexability, internal linking, schema where it actually clarifies the page, fast pages, durable URLs, and content that matches real user intent.
Second, stop funding hacks that have no evidence. If a tactic only exists because someone renamed SEO into GEO, it should not get budget by default.
Third, invest in content assets that deserve to be retrieved. That means original data, product experience, sharp comparisons, and concrete examples that cannot be produced by rewriting the top ten results.
Fourth, measure AI-assisted discovery separately from classic organic search where possible. Attribution will be imperfect, but imperfect measurement is better than pretending every discovery path still behaves like a blue-link search result.
The winning teams will not be the ones that rename their SEO checklist to GEO. They will be the ones that build content assets worth finding, citing, and trusting across every serious discovery surface their customers use.