The consultants selling AEO, GEO, and AIO packages are not going to love this one. Google has made their position clear when they say there is no separate discipline for optimizing generative AI features. From Google’s perspective, it is all search. It has always been search.
And far from making the conversation less interesting, makes it more honest.
Google’s Official Stance
Google’s documentation states plainly that AEO, GEO, and similar terms may be circulating across the industry, but from a technical standpoint, optimizing for Google’s AI-powered features means optimizing for the search experience as a whole. Every generative AI feature Google ships, from AI Overviews to conversational search responses, runs on the same core ranking and quality systems that have always powered organic results.
There is no hidden layer or o separate algorithm to crack. And there is no new signal class to chase, because the systems determining which content surfaces in an AI Overview are the same systems that determine what ranks on page one.
Google is being precise about what actually drives outcomes inside their ecosystem.
How It Actually Works Under the Hood
Understanding the technical reality here matters, as it explains why most “AI SEO tactics” floating around the industry do nothing.
Google’s generative features rely primarily on two mechanisms.
The first is Retrieval-Augmented Generation, called RAG or grounding. When a query triggers a generative response, Google does not hallucinate an answer from a frozen model. It uses its core ranking systems to pull relevant, freshly indexed pages from its search index and grounds the AI response in that real content. This is why page quality, authority, and indexation still matter. The AI is only as good as what the index surfaces.
The second is query fan-out. Before generating a response, the model automatically expands the original prompt into multiple related sub-queries. It looks at the topic from different angles, then synthesizes a coherent answer. This happens internally. You cannot see it or reverse-engineer it into a content brief.
Both mechanisms point to the same conclusion. Strong, well-structured, authoritative content that ranks well in traditional search is exactly what gets picked up. Nothing more exotic is required.
The Tactics to Drop Right Now
This is where the article gets practical, and “some people” will not like it.
If an agency or consultant is selling any of the following, the deliverable has no measurable basis in how Google’s systems actually work.
LLMS.txt files and AI-specific markup serve no indexing or ranking function inside Google Search. Google crawls via standard bots and does not use third-party AI instruction files as a signal. Chunking content into small pieces to be “more AI-friendly” misunderstands RAG entirely. Google retrieves whole pages and interprets them in context. Rewriting existing content specifically for AI with forced long-tail variants or unnatural phrasing degrades content quality and helps nothing. Chasing inauthentic mentions through fake forum posts, paid placements, or manufactured brand signals is a spam risk, so if you think it’s an optimization strategy… be aware.
None of these provide an advantage. Several of them introduce real downside.
A Fair Word to Both Sides
There is a version of this debate that gets unproductive fast. On one side, practitioners building real frameworks around AI search visibility. On the other, skeptics who dismiss the entire conversation as vendor noise.
Both sides have a point, and both are missing something.
The skeptics are right that most branded AEO/GEO deliverables are thin repackaging of existing SEO work. If the tactics would not hold up in a traditional search audit, they will not hold up here either.
But the enthusiasts are right that something has changed. The interface is different. The user behavior is different. Understanding how AI features select and present content is genuinely useful, even if the underlying optimization levers are the same ones SEOs have always had. The framing matters for client communication and the analysis matters for content strategy. Where it goes wrong is when new vocabulary is used to justify inflated retainers for tactics that do not work.
The honest middle ground here is that the discipline is the same, the stakes are higher, complexity may be the same as well, and doing it well requires more depth, but there are no crazy AI secret optimizations behind this.
For a closer look at where the confusion tends to start, the piece on common AI search myths breaks down several of the most persistent ones still circulating.
What Actually Works
Google is consistent on this point. Non-commodity content wins. Generic listicles, surface-level guides, and thinly differentiated blog posts are not getting cited in AI Overviews because they add nothing distinct to the conversation.
What gets surfaced is content with a real point of view. First-hand experience. Specific insight that cannot be scraped from five other tabs. Instead of publishing another “7 Tips for Homebuyers” article, a mortgage broker writing about how rate lock timing played out for a specific client scenario in a specific market creates something genuinely useful and genuinely different. That is the content Google’s systems are trying to find.
Beyond the content itself, technical fundamentals still apply in full, your site needs to be crawlable by standard bots and pages need to be indexable. Core Search Essentials are not optional just because the output format has changed.
The Spam Warning Worth Taking Seriously
One more thing, and this one carries real risk.
Creating large volumes of content specifically to target every possible query fan-out variation is considered scaled content abuse under Google’s spam policies. The intent to game AI responses by saturating query space it’s a violation, and Google has both the documentation and the enforcement history to back that up.
Build for the reader. The AI will follow.
