SEO and AI Search

SEO and AI Search describes how search engine optimization changes when search results are mediated by AI systems such as Google AI Overviews, Google AI Mode, ChatGPT search, and other answer engines. The topic includes traditional search fundamentals, AI-generated answer formats, source citation behavior, crawler controls, paid placements, and the market language around Generative Engine Optimization.

As of June 16, 2026, the strongest official guidance does not support treating AI search visibility as a completely separate discipline from SEO. Google states that optimization for generative AI features in Google Search remains rooted in core Search ranking and quality systems, while OpenAI documents a separate crawler and citation model for ChatGPT search. This makes the current topic less a replacement for SEO than an extension of SEO into answer surfaces where citations, summaries, and source eligibility are more visible and less predictable.

Background

Search engine optimization is the practice of making pages easier for search engines to crawl, understand, index, and present to users. Traditional SEO has included technical accessibility, clear page structure, useful content, links, and signals that help search systems understand what a page is about.

AI-mediated search changes the presentation layer. Instead of only returning a list of links, an AI search surface may synthesize an answer, cite sources, issue related searches in the background, or display links in a source panel. These features make source visibility more complex, but they do not remove the need for pages to be crawlable, understandable, useful, and eligible for search display.

Generative Engine Optimization and Related Terms

Generative Engine Optimization, often shortened to GEO, is a term used for work intended to improve visibility in generative engine responses. Academic work has framed GEO as a response to systems that synthesize answers from multiple sources rather than only ranking links. Market usage of the term also overlaps with Answer Engine Optimization, or AEO.

Google Search Central acknowledges AEO and GEO as terms used for visibility in AI search experiences, but frames optimization for generative AI features on Google Search as still SEO. This creates an important distinction. GEO may be useful as a category label or research term, but claims about GEO methods should be checked against product-specific documentation and separated from agency claims that promise direct control over model outputs.

Google AI Overviews and AI Mode

Google documentation says AI Overviews and AI Mode are rooted in Google Search's core ranking and quality systems. Google also says there are no additional technical requirements for appearing in these AI features beyond normal Search eligibility, snippet eligibility, policy compliance, and foundational SEO practices.

Google describes query fan-out as one technique used by AI Overviews and AI Mode. In query fan-out, the system issues multiple related searches across subtopics and data sources, then uses the results to support an AI response. For publishers and site owners, this makes clear, well-structured pages important because the system may retrieve supporting pages for related sub-questions, not only the exact original query.

Google's current guidance also says that special AI-focused files are not required for visibility in Google Search generative features. In particular, Google says it does not use llms.txt for Google Search, and that adding such files neither helps nor harms Google Search visibility.

ChatGPT Search and Source Inclusion

ChatGPT search has a different documented source model. OpenAI identifies OAI-SearchBot as the crawler used for ChatGPT search and distinguishes it from GPTBot, which is associated with model training. OpenAI says crawler controls for these bots are independent.

OpenAI documentation says sites that block OAI-SearchBot will not be shown in ChatGPT search answers, though they may still appear as navigational links in some cases. OpenAI also describes ChatGPT search responses as potentially including inline citations and a Sources panel with cited sources and related links.

These details support a broader point: AI search visibility is product-specific. Google, OpenAI, and other answer systems may use different crawlers, inclusion rules, citation formats, and source displays. A wiki page about SEO and AI Search should avoid implying that one optimization method applies equally across all systems.

What Carries Over From SEO

Several traditional SEO concerns remain relevant in AI-mediated search:

• Crawlability and indexability: AI search features still depend on systems being able to discover and process pages.
• Clear page structure: headings, navigation, internal links, and page organization help search systems understand topics and relationships.
• Helpful content: Google continues to emphasize content created for people rather than content made primarily to manipulate rankings.
• Clear entity and service descriptions: pages that clearly explain who is responsible for the content, what is offered, and what evidence supports claims are easier for both users and systems to interpret.
• Trust and source quality: AI answer systems may cite or display sources, making provenance and authority more visible to users.

These carryover points align with CJ Miller's fireside observation that credible GEO often looks like good SEO: clear site context, strong content architecture, useful expertise, and source trust rather than magic model-feeding.

What Changes in AI-Mediated Search

AI-mediated search changes how users encounter source material. A user may read a generated summary before clicking any source. A product may display citations inline, in cards, or in a separate source panel. A system may retrieve multiple related queries in the background before assembling an answer.

This changes the practical questions publishers ask. Instead of only asking whether a page ranks, they may ask whether a source is eligible to be cited, whether the page explains an entity clearly enough for answer synthesis, whether robots controls permit inclusion, and whether the page contains evidence that a search system can safely summarize.

The current evidence does not support guaranteed inclusion tactics. It supports careful source hygiene: clear content, technical accessibility, helpful pages, trustworthy sourcing, and product-specific crawler controls where official documentation exists.

Ads and Paid Placement

Paid placement in AI search is changing quickly and should be dated. As of June 16, 2026, Google Ads documentation says ads are eligible to appear above, below, or within AI Overviews on Search. Google also announced tests and pilots for new AI-era Search ad formats in May 2026.

This does not mean every AI answer surface has paid placement, nor does it mean organic AI visibility can be bought directly. It means any current page about SEO and AI Search should separate organic source visibility from ads, and should cite product-specific documentation when describing paid placements.

llms.txt and AI-Specific Files

llms.txt is a proposed file convention intended to help large language models use website information at inference time. It may be useful to discuss as a proposal in the broader AI search ecosystem.

However, Google states that Google Search does not use llms.txt, including for generative AI features. That makes llms.txt a poor primary recommendation for Google AI Overviews or AI Mode. Any claim that llms.txt improves visibility should name the product, cite official support, and state the date of verification.

Risks and Overclaims

The most common risk in GEO discourse is overstating control. Claims that an agency can directly feed, train, or guarantee inclusion in LLM answers should be treated skeptically unless they are tied to a documented product interface or publisher control.

A safer distinction is between controllable site work and uncontrollable answer generation. Site owners can improve clarity, crawlability, source quality, content usefulness, and crawler access. They generally cannot guarantee that an AI system will cite a page, summarize it favorably, or include it for a given query.

Open Questions

Several areas need refresh before publication or after major product changes:

• Which non-Google AI search products provide official publisher inclusion guidance comparable to OpenAI's OAI-SearchBot documentation?
• Do Perplexity, Anthropic, Microsoft/Bing, or other answer engines officially support llms.txt or only conventional crawler controls?
• How should publishers measure AI answer visibility without relying on unstable third-party traffic studies?
• Should Generative Engine Optimization become a separate reference page, or remain a terminology section within SEO and AI Search?
• How will Google AI Mode and AI Overview ads evolve after June 16, 2026?

Related Topics

• Generative Engine Optimization
• Source Trust Signals in AI Search
• llms.txt
• Human-Written Content As A Trust Signal
• AI Content Tells And Editorial Adaptation

Further Reading

• Google Search Central: Optimizing your website for generative AI features on Google Search
• Google Search Central: AI features and your website
• Google Search Central: SEO Starter Guide
• Google Search Central: Creating helpful, reliable, people-first content
• Google Ads Help: About ads and AI Overviews
• OpenAI Developers: Overview of OpenAI Crawlers
• OpenAI Help Center: ChatGPT Search
• arXiv: GEO: Generative Engine Optimization
• llms.txt proposal