AEO vs SEO vs GEO: what each one is and how they differ

The lines between AEO and GEO are blurry, and many people use the terms as synonyms. A useful way to separate them: AEO thinks in terms of one question and one page (is my page the answer?); GEO thinks in terms of one category and one brand (when someone asks about my category, does AI name me, how does it describe me, and whom does it put ahead of me?).

What works for AEO, based on what Google asks for in its AI features:

• Headings that phrase the real question, with an answer in the first sentence below, including the concrete fact.
• Original, verifiable content: prices, timelines, requirements, conditions. What can be quoted without reinterpreting it.
• A clear structure with headings, lists and tables where the content calls for them, and structured data that matches what is visible on the page.
• No page per variant. One good page for a group of related questions, not ten near-identical pages.

Picture a regional health insurer in Texas and the question “what’s the best health insurance for a family in Austin, Texas?”. The case is invented to illustrate, but it shows something that happens: the three views can give opposite results for the same question.

• The SEO view. The family plans page ranks third on Google for that search. In Search Console everything looks fine: stable impressions, reasonable clicks.
• The AEO view. An AI Overview appears above the results. It summarizes a comparison from a health portal and links to that portal, not to the insurer’s page. The question has a direct answer, but it isn’t yours.
• The GEO view. In ChatGPT, the same question returns three national insurers and no local one. Asked about the company by name, it describes a provider network that changed two years ago. In Meta AI it doesn’t appear. In Gemini it appears in one out of three runs.

All three views are true at the same time, and each one calls for different work: the SEO view says the foundation is in place; the AEO view, that a page answering the comparison with local facts is missing; the GEO view, that you need to be on the portal the engines cite and fix the outdated provider network at its source. If you only look at the first, you think there is no problem.

Google is explicit. In its guide to generative AI features it says that, from Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and so it is still SEO. That makes sense from its side: AI Overviews and AI Mode are built from pages in its index, and the requirement to appear is the same as for any result: being indexed and eligible to be shown with a snippet.

The same guide takes apart several recipes that circulate as “AEO” or “GEO”: you don’t need AI text files or special markup, because Google Search doesn’t use them; you don’t need to break content into tiny chunks or write in a special way for AI; and creating pages for every question variant, to manipulate rankings, violates its scaled content abuse policy. If someone sells you AEO as “add FAQ schema and an llms.txt file”, that is Google’s answer.

The fact that the foundational work is SEO doesn’t mean the result shows up in your SEO metrics. There are five reasons to measure AI answers on their own:

1. Not every engine is Google. ChatGPT has 900 million weekly users (OpenAI, February 2026); its web search shares queries with third-party providers (OpenAI names Bing among them), and when it doesn’t search, it answers from what the model learned. Meta AI answers inside WhatsApp and Instagram. None of that shows up in Search Console.
2. Ranking is not the same as being named. The engine breaks the question into several searches and writes an answer from what it finds, mostly on third-party sites: in Refractia’s data, a brand’s own website accounts for between 0.5% and 2.8% of what AI cites when it talks about that brand. You can rank first for the phrase and not appear in the answer.
3. Answers change by engine and by country. The same question in the United States, Mexico or Spain gets different answers.
4. Answers change from one run to the next. A single attempt tells you nothing; you need to repeat and report every rate with its sample.
5. What it says matters, not just whether you appear. An answer can name you with an old price, a wrong attribute or as the second option behind a competitor. No SEO tool reads that.

That is why Refractia measures answers, not rankings: it asks in the market’s language with the country named in full, queries ChatGPT, Claude, Gemini, Google’s AI Overviews (with each country’s location) and Meta AI, reports every rate as “k of n”, doesn’t count an engine that didn’t answer as a zero, and separates what the model knows from what the web ranks. It doesn’t measure Perplexity, Copilot or Grok, and a person starts each scan.

You don’t have to pick a discipline: you need to know where the bottleneck is. Depending on what you see when you ask AI about your category:

1. 01

   Technical foundation (SEO)

   Pages indexed in Google and in Bing, AI crawlers allowed in robots.txt and by your CDN, and content and structured data in the HTML the server returns.

2. 02

   Answers (AEO)

   One page for each group of real questions in your category and your market, with the answer in the first sentence, local facts and honest comparisons.

3. 03

   Presence and perception (GEO)

   Earned mentions in the sources the engines already cite for your category, a clear and consistent entity, and corrections to what AI gets wrong.

4. 04

   Measurement by engine and by country

   The same questions, repeated, in each engine and each market, with every rate shown next to its sample. Without this, the three previous steps are done blind.