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Google’s Vice President of Product, Robby Stein, recently shared insights on AEO and GEO in relation to AI Search, outlining key factors content creators should keep in mind.

In his detailed response, Stein explained how Google’s AI generates answers and concluded with practical advice for creators aiming to optimise their content for these evolving search technologies.

Foundations Of Google AI Search

During a recent discussion, Google’s Vice President of Product, Robby Stein, was asked about AEO (Answer Engine Optimisation) and GEO (Generative Engine Optimisation), described by the host as the next stage of SEO. His response focused on understanding the context behind how AI-generated answers are created.

When asked for his view on the growing importance of AEO and GEO, Stein explained that Google’s AI search still builds upon traditional search mechanics. He shared that when users make a query, the AI performs what’s known as a “query fan-out.” This process involves the system running numerous related searches in the background — sometimes dozens — to collect and analyse information from multiple sources, even requesting real-time data when necessary.

Stein emphasised that, while the process is handled by AI, it still depends on the same foundational elements that drive standard search rankings. In essence, the AI conducts its own scaled version of a typical Google search.

This means that optimising for AI-generated answers isn’t about mastering a new set of rules but about continuing to apply solid SEO principles. The core ranking, indexing, and quality signals remain the same.

For content creators and SEO professionals, this highlights that achieving visibility in AI-generated responses relies on creating high-quality, relevant, and intent-focused content. Rather than trying to manipulate new systems, the goal should be to produce content that directly addresses user needs and stands out as the most valuable answer.

Stein also hinted that originality continues to be an important factor, suggesting that authentic and well-crafted content will perform better as AI systems evolve.

Role Of Traditional Search Signals

A notable part of Robby Stein’s discussion focused on the importance of quality signals — the same principles outlined in Google’s Quality Rater Guidelines. He highlighted that originality, credibility, and user intent remain central to how Google assesses valuable content, even within AI-driven search.

Stein explained that when Google’s AI conducts searches, it pairs each query with web content, assessing how well it aligns with user intent. He encouraged creators to review Google’s human rater guidelines to understand what defines high-quality information. According to Stein, great content demonstrates several key traits: it fulfils the user’s search intent, provides credible sources, cites references properly, and offers original insights rather than repeating widely circulated information.

He noted that these long-standing best practices are still highly relevant, even as AI transforms the search experience. The process may be more automated and data-driven, but the core principles behind what Google considers a “good” result have not changed.

Stein’s comments reinforce that AI-powered search still relies heavily on traditional quality signals — expertise, accuracy, trustworthiness, and originality. While AI technology has altered how people interact with search results and expanded the types of queries users make, the fundamentals of what earns visibility remain consistent. In essence, content that is genuinely useful, well-sourced, and unique continues to perform best, whether evaluated by human raters or AI systems.

More On How Google’s AI Search Works

The podcast host, Lenny, raised another question, asking how Google’s AI Search differs from the typical chatbot model. He pointed out that while other chatbots simply generate responses based on training data, Google’s AI seems to search through multiple web pages in real time to deliver its results.

Robby Stein, Google’s VP of Product, elaborated on this by explaining the deeper mechanics behind AI Search. He discussed the concept of parametric memory, which refers to the knowledge the AI retains from its training—essentially, the information stored within the model itself, rather than data pulled from external sources.

According to Stein, Google’s AI was designed specifically for informational tasks, aligning with Google’s goal of providing the most accurate and reliable information possible. The system not only processes and reasons like any advanced AI model but also incorporates layers that help it evaluate, verify, and cross-check information before presenting it.

Stein highlighted that the AI integrates Google Search signals, such as spam detection and content authority. This allows the model to filter out low-quality or misleading material while prioritising trustworthy, relevant sources. It then references these sources transparently, often linking users to the original website for further reading.

He explained that this integration makes Google’s AI Search distinct—it’s not just an isolated chatbot but a tool built upon Google’s extensive search infrastructure. Its responses are supported by the same evaluation and ranking systems used in traditional Google Search, ensuring that the AI’s answers are grounded in credibility and helpfulness.

Ultimately, Stein’s comments underline that Google’s AI Search isn’t about casual conversation, but about enhancing information accuracy. By merging reasoning capabilities with Google’s established ranking framework, it serves as an extension of the company’s long-standing mission—to provide factual, authoritative, and useful information to users.

Advice For Creators

At one point, Stein recognised that many content creators are eager to understand how to adapt their strategies for AI Search. His key advice was to focus on the questions people are asking, rather than just the keywords they use. In the past, SEO revolved around identifying and targeting specific search terms. However, Stein noted that this approach has evolved, as users now tend to use longer, more conversational queries when interacting with AI systems.

He explained that creators should think carefully about why people are using AI and what types of questions they are asking. According to Stein, users increasingly turn to AI for detailed advice, “how to” content, and solutions to more complex problems rather than simple, fact-based searches. He suggested that creators should focus on developing content that best serves these deeper needs.

“If I were a creator,” he said, “I’d be thinking about what kind of content people are using AI for—and how my content could meet those specific needs in the best way possible.”

While Stein’s guidance doesn’t introduce a completely new concept, it reframes traditional SEO principles for the AI Search era. Rather than concentrating on isolated keywords, he encourages creators to anticipate the broader intent behind users’ questions and to structure their content accordingly.

In essence, modern optimisation now means creating material that provides thorough, context-aware answers to the kinds of complex, conversational queries that users increasingly direct at AI systems. This shift places greater emphasis on understanding intent and crafting content that aligns with how people naturally ask questions today.

Takeaways

AI Search Still Built on Traditional SEO Principles
Google’s AI Search continues to rely on the same fundamental ranking factors as traditional search—meeting user intent, ensuring originality, and citing credible sources.

How Query Fan-Out Operates
When a user submits a query, AI Search performs multiple background searches, drawing from Google’s index to gather real-time information and assess content quality through established ranking signals.

Combining Parametric Memory with Search Data
The system merges its built-in knowledge (parametric memory) with live search information, allowing it to reason through results while maintaining factual accuracy and relevance.

AI Search as an Extension of Traditional Search
Unlike typical chatbots, Google’s AI Search is designed to enhance, not replace, traditional search. It strengthens Google’s focus on delivering authoritative, well-sourced information through its trusted ranking framework.

Guidance for Modern Content Creators
In this new era, optimisation means understanding the intent behind long, conversational queries. Creators should prioritise content that provides in-depth advice and “how-to” guidance, directly addressing users’ complex informational needs.

Overall, Google’s AI Search builds upon the same trusted foundations of retrieval, ranking, and quality assessment. By combining real-time search signals with the model’s own reasoning, it produces informative, well-cited answers. For creators, thriving in this environment means crafting original, authoritative content that fully meets the evolving expectations of AI-driven search.