AEO vs GEO: what's actually different?

AEO stands for answer engine optimization. GEO stands for generative engine optimization. Both describe trying to get your business named, cited or recommended inside an AI-generated answer instead of a list of links. In day-to-day practice they are the same job.

Google addresses this directly in its guidance on AI features and your website, updated in July 2026: both terms describe "work specifically focused on improving visibility in AI search experiences," and from Google Search's perspective, "optimizing for generative AI search is optimizing for the search experience, and thus still SEO." That is the search engine everyone is optimising for telling you the distinction does not change the work.

Independent practitioners land in the same place. Ahrefs published a piece in April 2025 arguing the core mechanism for improving visibility in LLM output is "creating relevant content on topics your brand wants to be associated with, both on and off your website" — and then stated flatly: "That's SEO." Search Engine Land's guide, updated in September 2026, says the acronyms "all mean essentially the same thing" and attributes their spread to positioning: people want to look ahead of the curve, or they are selling a new service.

GEO has an actual academic origin. A paper titled "GEO: Generative Engine Optimization" was submitted to arXiv in November 2023 by researchers from Princeton, Georgia Tech, Allen Institute for AI and IIT Delhi, and published at ACM SIGKDD in 2024. It proposed a benchmark and tested content changes that improved visibility in generative engine responses by up to 40% in their setup. Whatever you think of the acronym, the idea was defined in peer-reviewed work before the marketing industry adopted it.

AEO has no equivalent origin document. It grew out of the older featured-snippet and voice-search world, where the goal was to be the single answer rather than a ranked link, and was repurposed when assistants arrived. LLMO, AIO and AI SEO followed the same path.

This matters less than it sounds. The research paper describes tactics — adding citations, quotations and statistics to content — that any competent SEO would recognise. The origin story explains where the words came from, not why you would run two separate programmes.

If you strip out the marketing, one genuine distinction survives: which index the engine reads from, and therefore which crawler you must allow.

Google's AI surfaces retrieve from Google's index. Its documentation states its generative AI features are "rooted in our core Search ranking and quality systems," describing grounding — pulling relevant pages from the Search index to support an answer — and query fan-out, where the model issues several related searches behind a single question. If Googlebot can reach you and you rank for the cluster of questions around a topic, you are in the running for AI Overviews and AI Mode.

ChatGPT does not use Google's index. OpenAI's crawler documentation is explicit: OAI-SearchBot is the crawler that surfaces sites in ChatGPT's search features, and sites opted out of it "will not be shown in ChatGPT search answers." GPTBot is a separate crawler for model training, and ChatGPT-User handles fetches a person triggers. Blocking GPTBot does not remove you from ChatGPT search; blocking OAI-SearchBot does.

So the practical split is not AEO versus GEO. It is per-engine access and per-engine behaviour: who can crawl you, and which sources each engine tends to cite. Everything above that layer — useful pages, clear answers, consistent facts, third-party mentions — is shared.

Sorted by what an engine has actually published, rather than what the category sells.

The measurement question is where AEO and GEO genuinely differ in tooling, because Google gives you first-party data and the assistants do not.

For Google's AI surfaces, Search Console now reports generative AI performance; Google rolled it out to all websites by 31 August 2026. It is impression-side rather than click-side, but it is free and it is your own data.

For ChatGPT, Claude and Perplexity there is no equivalent report, which is the entire reason the AI visibility tool category exists. The free version is to write the ten questions your customers would ask, put them to each assistant, and record who gets named. Run them twice on different days: the variation between runs is your noise band, and it is usually larger than people expect. Semrush's tracking of 230,000 prompts recorded ChatGPT's citation of Reddit falling from roughly 60% of responses to about 10% within weeks in late 2025.

Keep the scale honest while you do this. Semrush's study of more than 50,000 sites found AI referral traffic grew 66% in 2025 and still made up under 0.15% of total visits. Pew's metered study of 900 US adults found users clicked a result in 8% of visits when an AI summary appeared versus 15% when it did not. The near-term risk is losing clicks on Google, not losing visits to ChatGPT.

Pick whichever word your team will actually use, then ignore the debate and do the work, because the work is the same list either way: be crawlable by the search crawlers of every engine you care about, answer the questions your buyers ask in plain language, keep your business facts consistent everywhere they appear, earn mentions on the sites engines already cite, and measure the answers rather than guessing.

The one place to be deliberate is crawler policy, because it is the only setting where a single mistake makes you invisible on one engine while leaving you fine on another. Check it, then stop thinking about acronyms.