Google Insights For AEO/GEO

Google recently gave insights for AEO/GEO to optimize your local SEO strategy for leveraging data to improve visibility, drive traffic, and boost conversions.

Google’s VP, Robby Stein, recently provided clarification on what creators and SEO professionals should understand about the evolving landscape of search with the introduction of AEO and GEO. His insights emphasize that the core principles of traditional SEO remain vital, even as the interface and technology shift.

Google’s AI Search Foundation

The question was about AEO/GEO, which the podcast host referred to as the evolution of SEO. The answer from Robby Stein at Google was to consider the context in which AI answers.

The following question was asked:

“What’s your take on this whole rise of AEO, GEO, which is kind of this evolution of SEO?

I’m guessing your answer is going to be just create awesome stuff and don’t worry about it, but you know, there’s a whole skill of getting to show up in these answers. Thoughts on what people should be thinking about here?”

Stein replied by describing the foundations of how Google’s AI search works:

“Sure. I mean, I can give you a little bit of under the hood, like how this stuff works, because I do think that helps people understand what to do.

When our AI constructs a response, it’s actually trying to, it does something called query fan-out, where the model uses Google search as a tool to do other querying.

So maybe you’re asking about specific shoes. It’ll add and append all of these other queries, like maybe dozens of queries, and start searching basically in the background. And it’ll make requests to our data kind of backend. So if it needs real-time information, it’ll go do that.

And so at the end of the day, actually something’s searching. It’s not a person, but there’s searches happening.”

Robby Stein demonstrates how Google’s AI still uses traditional search engine retrieval, just at scale and in an automated fashion. It conducts dozens of searches in the background and then assesses the same quality signals that inform regular search rankings.

This basically means that “answer engine optimization” is just SEO again, because the underlying traditional SEO indexing, ranking, and quality factors are still fully exploited for queries issued to the AI as part of the query fan-out process.

So the takeaway for SEOs is this that AI search results are less about gaming a new algorithm and more about producing content that meets intent so well that Google’s algorithm (now augmented by AI) identifies that content as the best possible response. Originality is also a factor, as you will see later in the article.

Traditional Search Signal Roles

An interesting section of this debate is focused on the kinds of quality signals that Google explains in its Quality Raters Guidelines. Stein talks about the originality of the content, for instance,

He said:

“And then each search is paired with content. So if for a given search, your webpage is designed to be extremely helpful.

And then you can look up Google’s human rater guidelines and read… what makes great information? This is something Google has studied more than anyone.

And it’s like:

• Do you satisfy the user intent of what they’re trying to get?
• Do you have sources?
• Do you cite your information?
• Is it original or is it repeating things that have been repeated 500 times?

And there’s these best practices that I think still do largely apply because it’s going to ultimately come down to an AI is doing research and finding information.

And a lot of the core signals, is this a good piece of information for the question, they’re still valid. They’re still extremely valid and extremely useful. And that will produce a response where you’re more likely to show up in those experiences now.”

Stein is talking specifically about AI Search results, but his response indicates that Google’s AI Search differs in how its basic quality factors are similar to the quality factors in traditional search.

Google believes that the qualities that make information “good”, originality, source citations, and satisfying intent, have not changed. While the interface of search has changed in some areas due to AI, and more sophisticated queries can be executed, the fundamentals of ranking remain the same: high-level signals of expertise and authoritativeness.

Google’s AI Search Works Further Insights

Lenny, the podcast host, asked another question about how Google’s AI Search might follow a different approach from a strictly chatbot approach.

He asked:

“It’s interesting your point about how it goes in searches. When you use it, it’s like searching a thousand pages or something like that. Is that a just a different core mechanic to how other popular chatbots work because the others don’t go search a bunch of websites as you’re asking.”

In a follow-up, Stein explained a bit more about how AI search operates. Apart from query fan-outAPreferencing several factors that it prefers to surface what it deems the most relevant answers. He cites a case of parametric memory, for instance.

Parametric memory is the knowledge an AI has as a result of training it. It is essentially the innate knowledge of the model that is not created through building prompts but through everything it has been trained on.

Stein explained:

So that’s how we’ve thought about designing this.”e, well-structured, and visually optimized content.​

“Yeah, this is something that we’ve done uniquely for our AI. It obviously has the ability to use parametric memory and thinking and reasoning and all the things a model does.

But one of the things that makes it unique for designing it specifically for informational tasks, like we want it to be the best at informational needs. That’s what Google’s all about.

• And so how does it find information?
• How does it know if information is right?
• How does it check its work?

These are all things that we built into the model. And so there is a unique access to Google. Obviously, it’s part of Google search.

So it’s Google search signals, everything from spam, like what’s content that could be spam and we don’t want to probably use in a response, all the way to, this is the most authoritative, helpful piece of information.

We’re going link to it and we’re going to explain, hey, according to this website, check out that information and you’re going to probably go see that yourself.

So that’s how we’ve thought about designing this.”

Stein’s explanation makes clear that Google’s AI Search is not intended to act like general chatbots but rather to reaffirm the company’s central objective of providing reliable, authoritative, and useful information.

Therefore, Google’s AI Search bases its AI-generated answers on signals from Google Search, for example, spam detection and helpfulness; thus, the system grounds itself in the evaluation and ranking framework that exists in standard search ranking.

In this framing, AI Search is not so much a search product in its own right as it is a natural offshoot from Google’s information-retrieval ecosystem, where reasoning and ranking collaborate to bring to the top factually correct answers.

Advice for Creators

Stein, at one point, admits that creators are wondering what to do about AI Search. More or less, he is saying, think about what questions people are asking. That would have meant considering what searching individuals searched. He says that is not the case anymore because people are using long conversational queries these days.

He explained:

I mentioned this as an expansionary moment, …that people are asking a lot more questions now, particularly around things like advice or how to, or more complex needs versus maybe more simple things.

And so if I were a creator, I would be thinking, what kind of content is someone using AI for? And then how could my content be the best for that given set of needs now?
And I think that’s a really tangible way of thinking about it.”

While Stein’s further advice doesn’t really add anything we’ve not previously heard, he at least reframes the fundamentals of SEO for the AI Search era. Rather than trying to optimize for standalone keywords, creatives should look to optimize for the more holistic intent and informational path baked into conversational queries.

That means formatting content to directly meet sophisticated informational needs, searchers are increasingly coming to generative AI systems with questions framed like “how to” or advice queries rather than keyword searches.

Final Takeaways

1. Traditional SEO Fundamentals Remain Relevant: Google’s AI Search is based on the same fundamental ranking principles as regular search: satisfying intent, uniqueness, and attribution..
2. How Query Fan-Out Works: On a per-query basis, AI Search issues dozens of background searches, employing Google Search as an instrument to retrieve real-time data and assess quality signals.
3. Parametric Memory and Search Signals: It combines parametric memory for stored knowledge with an augmented Google Search data stream to combine reasoning (via the LLM) with ranking (via traditional search engines) to produce factually accurate outputs.
4. Google AI search is an extension of traditional search: AI Search is not a chatbot but rather a reasoning system apperceived to an information retrieval agency model that draws on Google-style informational trust.
5. The AI Search Era: But while other terms like SEO can benefit from a degree of manipulation, optimization for AI really means understanding the intention of a human user behind something that may be paragraphs-long, conversational queries. This is focused more on landing pages designed around advice- and how-to-style content that best fulfills complex informational needs.

Similar to the core principles that underpin traditional search, Google’s AI Search is based on retrieval, ranking, and quality signals, serving fresh content based on what it believes to be original and trustworthy information.

Google looked to combine live search signals, where the content is up to date, with the model itself, which delivers an explanation of the information and cites the websites providing it.

the questions which people feed into AI systems nowadays are complex, and in ways which background knowledge people are primarily interested within suggests are highly conversational. Thus, achieving success now relies on creating content that can fully answer such questions.

Bottom Line

Stein’s insights reinforce that effective content remains king, even as AI-driven search becomes more sophisticated. Success in this space hinges on quality, trustworthiness, and understanding the evolving ways users seek information.

Watch the podcast segment here: