SEO vs GEO vs AEO: What’s Actually Different in AI Search Optimization

SEO, GEO and AEO are three names for overlapping work, not three separate systems you can optimize for independently. SEO (search engine optimization) is the established practice of making pages discoverable, indexable and useful in search results. AEO (answer engine optimization) is a label for shaping content so it can serve as a direct answer, from featured snippets to voice assistants. GEO (generative engine optimization) is a label, first formalized in a 2023 research paper, for improving how content appears inside AI-generated responses. None of the three is a Google ranking algorithm, and Google states that no special optimization is required to appear in its AI features. What actually differs is where the answer is shown, how sources are retrieved, and what can be measured.

Where each term comes from

SEO has the clearest reference point: search engines publish documentation about crawling, indexing, ranking systems and policies, and the practice is built around that documentation.

AEO has no single defining publication. It is an industry term used for work aimed at answer formats: featured snippets, “People also ask” style questions, voice responses and, more recently, AI answers. Because it has no formal definition, two practitioners can mean different things by it.

GEO has a specific origin. A paper titled “GEO: Generative Engine Optimization”, submitted to arXiv in November 2023 by researchers including Pranjal Aggarwal and Karthik Narasimhan, defines generative engines as systems that synthesize information from multiple sources using large language models, and proposes GEO as a framework for improving content visibility in their responses (the GEO paper on arXiv). The authors report visibility gains of up to 40 percent on their own benchmark, GEO-bench, and add that the efficacy of strategies “varies across domains”. That result describes a research benchmark, not a guarantee about any commercial product’s behavior.

The comparison matrix

The last row is the important one. There is no “AEO algorithm” and no “GEO algorithm” in Google Search. In Google’s case the AI features are part of Search.

What Google says about its AI features

Google’s guide to AI features addresses the question. Site owners can “apply the same foundational SEO best practices for AI features as you do for Google Search overall”, and the page states: “There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary” (AI features and your website). The same page says no new machine-readable files, AI text files or special schema.org markup are needed.

For Google, then, “GEO for AI Overviews” collapses into SEO: be indexable, be snippet-eligible, follow the policies and publish useful content. The differences are in control and measurement. Search Console has a Generative AI performance report showing impressions in AI Overviews and AI Mode since 2026, and a Search generative AI control that lets a site include or exclude itself from those features. Neither exists for classic results.

Where AI search genuinely differs

Treating the labels as the same work does not mean nothing has changed. Four differences are real and documented.

1. More than one platform, each with its own crawler. Google’s AI features use Googlebot and Google’s index. OpenAI’s ChatGPT search uses OAI-SearchBot (OpenAI’s crawler documentation); Anthropic documents Claude-SearchBot (Anthropic’s crawler documentation); Perplexity documents PerplexityBot (Perplexity’s crawler documentation). Each provider publishes its own robots.txt tokens and their effects. A site can be visible in one AI product and absent from another because of a single robots.txt line.

2. Answers are synthesized from retrieved information, not presented as a single ranked list. Google describes a “query fan-out” technique in AI Overviews and AI Mode that issues related searches across subtopics before composing a response, and shows supporting links with it. Retrieval and the choice of supporting pages still involve Google’s systems; Google does not publish how those pages are selected. A page may be used to support one sentence of an answer rather than as a ranked destination. Content that states a clear, self-contained fact can serve that role; a page whose key information is spread across tabs and images is harder to use.

3. Visibility without a visit. A user can read an AI answer that draws on a page and never click. Classic search had this with featured snippets; AI answers extend it. This is why AI visibility and AI referral traffic must be reported separately.

4. Measurement is partial and platform-specific. Google reports AI feature impressions without clicks or queries. Microsoft reports citation activity in the AI Performance report in Bing Webmaster Tools, announced in February 2026 as a public preview (Microsoft’s AI Performance announcement). Where a provider offers no report, analytics referrals and server logs are what remain.

What does not change

• Crawlability and indexability. A page that cannot be fetched cannot be used, whatever the label.
• Accuracy and clarity. An AI system that retrieves a wrong or ambiguous statement can repeat it. The case for content built on real expertise holds under every label.
• Policies. Google’s spam policies govern Google Search, and Google’s AI features are part of Search. Scaled content abuse, for example, is defined by purpose and value, “no matter how it’s created” (Google’s spam policies).
• No guarantees. No method can guarantee a citation in an AI answer, a featured snippet or a ranking.

Practical implications for a content team

A team that wants to cover AI search without chasing labels can organize the work by question, not by acronym:

Worked example: one page, three framings

Consider a hypothetical page explaining how a small business deducts a home office in the United States.

• As SEO, the work is making sure the page is indexed, answers the query in its title and opening, cites the IRS rules and earns links.
• As AEO, the same page needs a concise, correct answer near the top that could stand alone as a snippet, followed by the conditions that qualify it.
• As GEO, the page needs the same concise answer plus clearly stated conditions and figures, so that an AI response drawing on it carries the conditions instead of a bare rule, and crawler access for each AI provider the publisher wants to appear in.

The tasks overlap almost completely. The difference is the attention to conditions and quotable facts, and to platform access and measurement.

How to use the terms

Use “SEO” for the discipline, and name the platform and the outcome when the work is specific: “visibility in Google AI Overviews”, “appearance in ChatGPT search”, “featured snippet eligibility”. Those phrases point to documentation, controls and reports that exist. “GEO” and “AEO” are useful as shorthand in conversation, but a plan built on them should still say which platform it targets and how success will be measured, because neither term names a system that can be optimized on its own.