The Foundation
AI Search Still Runs on the Same Systems
Google published a guide to optimizing for generative AI search features. It is publicly available, written in plain language, and says something the SEO industry has been overcomplicating for two years: generative AI features in Google Search are built on the same core ranking and quality systems that have always determined what shows up.
Not separate systems. Not a parallel algorithm for AI answers. The same infrastructure. Google describes retrieval-augmented generation as a technique that improves AI responses by pulling from pages in its existing search index, ranked by the same signals, surfaced through the same quality filters. The pages that perform well in traditional search are the pages AI features draw from.
The last article on this blogargued that semantic SEO gives small business websites a stronger foundation for search, AI answers, and Google Ads. Google's guide confirms that argument line by line. If a website ranks because it is clearly structured, well-organized, and semantically coherent, those are the same qualities AI features will pull from. No new playbook required.
The Naming Problem
AEO, GEO, and the Acronym Industry
Answer engine optimization. Generative engine optimization. Search experience optimization. The consulting industry has been productive this year, coining new acronyms for what amounts to the same underlying work.
Google's guide does not describe AEO as a distinct discipline. It does not mention GEO. It does not introduce a separate optimization path for AI features. The recommendation is straightforward: apply foundational SEO best practices. The systems are the same.
If AEO were a genuinely different discipline, Google would not have built its AI search features on top of its existing ranking systems. The work has not changed. The acronym has. The semantic SEO articlemade this point already: AEO is not a new trick. It is structured data and clear content applied to a well-organized website. Google's guide says the same thing, without the rebrand.
How AI Searches
Query Fan-Out Makes Structure More Important
Google's guide describes a mechanism called query fan-out. When someone types a question, the AI system does not only process that single query. It generates a set of concurrent, related queries to fetch additional relevant results. One question becomes five.
Google's own example: a search for “how to fix a lawn full of weeds” triggers related queries about herbicide recommendations, chemical-free removal methods, and weed prevention. For a small business, a search for logo design cost might fan out into pricing ranges, design process, portfolio examples, and turnaround.
If a website has one vague service page and a contact form, fan-out has nothing to find. If a website has clear service pages, relevant blog content, portfolio work connected to specific services, FAQ content that answers real questions, and strong internal linking between all of it, every fan-out query has somewhere to land. That is the structural advantage semantic SEO builds. Not a trick. An architecture that matches how AI actually retrieves information.
The Warning
Stop Chasing Keyword Variations. Build Meaning Instead.
Google's guide explicitly warns against creating separate content for every possible variation of how someone might search. The language is direct: this behavior violates Google's scaled content abuse spam policy.
This aligns with what semantic SEO already teaches. Structure matters more than volume. A website does not need 200 thin pages covering every long-tail keyword. It needs enough well-organized pages to clearly communicate what the business does, where it operates, who it serves, and what makes it credible.
Google also confirms that AI systems understand synonyms and general meanings. A business does not need separate pages for “web designer Seattle” and “website designer in Seattle” and “Seattle web design services.” The system already knows these mean the same thing. The investment is in a website built around clarity.
Schema
Structured Data Is Not a Substitute for Substance
Google's guide says structured data is not required for generative AI search. No special schema.org markup needed.
That correction matters. The SEO industry has spent the past two years suggesting that structured data is the key to showing up in AI answers. Google says it helps with rich results in traditional search but is not a requirement for AI features.
This echoes what the semantic SEO articleargued: schema clarifies meaning that already exists on the page. It does not create meaning from nothing. Adding FAQ schema to a thin, disorganized website is not strategy. It is markup on top of a problem. Google's recommendation is clear: create unique, valuable content first. Schema follows substance.
Content
Non-Commodity Content Is the Real Advantage
Google draws a distinction that matters. Commodity content restates what everyone already knows. Non-commodity content provides a unique perspective based on actual experience. Their example of commodity: “7 Tips for First-Time Homebuyers.” Their example of non-commodity: a first-person account of a specific real estate decision with details only someone who lived it could provide. The first is a template. The second is a story only one person can tell.
For a small business, this is the dividing line. A contractor who writes generic advice about choosing the right contractor is producing content any AI model can generate on its own. A contractor who writes about a specific project, a specific problem solved on a specific job site in a specific neighborhood, is producing something search systems value: experience that cannot be replicated from a public dataset.
This is also why portfolio work, case studies, and project documentation are competitive assets, not decoration. A design studio that explains the strategic decisions behind a specific rebrand is producing pages that AI systems and traditional search alike will prefer over content that reads like it was assembled from the same public knowledge base every other site already drew from.
From the Guide
AI Search Hacks Google Explicitly Debunks
- Special AI Text Files
- Google says you do not need to create new machine-readable files, AI text files, markup, or Markdown to appear in generative AI search. Files like llms.txt do not receive special treatment.
- Content Chunking
- There is no requirement to break content into tiny pieces for AI to understand it. Google's systems can process the nuance of multiple topics on a single page and surface the relevant piece.
- Rewriting for AI Systems
- Google confirms that AI systems already understand synonyms and general meanings. Businesses do not need to rewrite content to match every possible phrasing of a query. Writing clearly for people is the optimization.
- Inauthentic Mentions
- Buying or manufacturing brand mentions across the web is not a meaningful signal. Google's core ranking systems focus on high-quality content while spam systems block manipulation.
- Ideal Page Length
- There is no ideal page length for AI search. Google says to make pages for your audience, not for generative AI. The obsession with hitting a specific word count is a distraction from the actual work: writing content that answers the question.
The Confirmation
Google's AI Guidance Confirms the Point
The semantic SEO article argued that the future of search rewards websites that are easier to understand. Clear services. Clear locations. Clear proof. Clear hierarchy. Clear relationships between pages.
Google's AI optimization guide confirms that argument point by point. AI features pull from the same ranking systems. Query fan-out rewards websites with semantic depth and internal structure. Creating pages for every keyword variation is spam, not strategy. AI systems already understand meaning, synonyms, and context. Structured data is helpful but not the point. Non-commodity, experience-driven content is the competitive advantage.
The stronger thesis is simple: the best optimization for AI search is a website that was built to communicate clearly in the first place. Not tricks. Not hacks. Not a separate layer of AI-specific optimization bolted onto a weak foundation. A website that communicates what the business does, organizes that information with intention, and backs it up with real work. That is semantic SEO. Google published a guide confirming it.
The Point
The Work Has Not Changed
The businesses that will show up in AI search are the same businesses that already show up in traditional search: the ones whose websites clearly communicate meaning, not just keywords. There is no AI optimization shortcut. There is a well-built website, or there is not.
If the website underneath your search strategy needs that kind of structure, that is the work Green Lake Digital does.
Questions
Frequently Asked Questions
Does Google's AI search require special SEO optimization?
No. Google's own AI optimization guide states that generative AI features in Google Search are rooted in the same core ranking and quality systems that power traditional search results. There is no separate algorithm for AI answers. The same foundational SEO best practices that improve organic visibility also improve a website's presence in AI search features.
What is query fan-out in Google AI search?
Query fan-out is a mechanism Google uses in its AI search features. When a user types a single question, the AI system generates a set of concurrent, related queries to gather more information and fetch additional relevant results. Websites with clear internal structure and semantic depth across multiple related pages benefit because each fan-out query has a relevant page to land on.
Does structured data help with AI search results?
Google's AI optimization guide says structured data is not required for generative AI search and there is no special schema.org markup to add for AI features. Structured data remains useful for rich results in traditional search, but it is not the strategy for AI visibility. Schema should clarify meaning that already exists on the page, not substitute for substance the page lacks.
What kind of content does Google's AI search prefer?
Google distinguishes between commodity content and non-commodity content. Commodity content restates common knowledge anyone could compile. Non-commodity content provides unique, experience-based perspectives that go beyond what is already widely available. For small businesses, this means case studies, specific project documentation, and industry-specific expertise outperform generic advice articles.
Is AEO (answer engine optimization) different from SEO?
Not in practice. AEO, GEO (generative engine optimization), and similar acronyms describe optimization for AI answer systems, but Google's AI search features are built on the same core ranking systems as traditional search. Google's guide does not describe AEO as a distinct discipline or introduce a separate optimization path for AI features.