If you are trying to work out whether you need “GEO” or “AEO” or both, the short answer is that the distinction matters less than the people selling it suggest, and the reason it matters at all is worth ten minutes.
The one-sentence version
AEO is about being the answer a machine reads back to someone. GEO is about being a source a machine uses when it writes its own answer.
Answer Engine Optimization, in practice
AEO predates ChatGPT by years. The target is the extracted answer: Google’s featured snippet, the People Also Ask box, the response a voice assistant reads aloud.
The mechanic is extraction. Something on your page is lifted, largely intact, and presented as the answer. That rewards a particular kind of writing: answer the question directly, in clean structured language, near the top, with no ambiguity about what is being answered.
Success is legible. You either hold the snippet or you do not, and you can check.
Generative Engine Optimization, in practice
GEO targets what happens when someone asks ChatGPT, Claude, Perplexity or Google’s AI Overviews a question. The model does not read one snippet back. It retrieves from several sources, synthesises an original answer, and may cite a handful of them.
Three things follow, and they are what actually make GEO different:
There is no position to hold. You are named or not named, described well or badly, cited or uncited. “Where do I rank” has no answer.
The output is unstable. Ask the same engine the same question twice and you can get different companies named. Anything measured once has been measured wrong. This is not a flaw you can optimise away; it is how the systems work.
Most of the deciding content is not yours. The model assembles its shortlist largely from third-party pages: listicles, review sites, Reddit threads, roundups. In AEO you improved your page and could win the box. In GEO you can have the best page in the category and still be absent from the answer, because the pages the model actually read do not mention you.
That third point is the one that changes what you do on Monday.
Where the work overlaps
Most of it. Both reward:
- Answering the question directly rather than warming up to it
- Clear headings that match how people actually ask
- Short factual claims that survive being quoted on their own
- Tables for comparisons, lists for options
- Structured data (FAQ, Product, Organization schema)
- One clear topic per page instead of one page attempting everything
- Genuine topical depth
If you write for extraction, you serve both. That is why “do I need two programmes” has a clean answer: no.
Where it genuinely does not overlap
| AEO | GEO | |
|---|---|---|
| Target | One answer box on one engine | Being named and cited across several models |
| Main surface | Your own pages | Third-party pages you do not control |
| Measurement | Do you hold the snippet | What share of answers name you, across repeated samples |
| Stability | Deterministic enough to check once | Varies run to run; requires repeated sampling |
| Fastest lever | Restructure your page | Get onto the sources the engines cite |
The two rows that cost people the most are the last two. Companies apply AEO habits to GEO, check once, see themselves mentioned, and conclude they are fine. Or they spend a year improving their own pages while their competitor spends a month getting onto four listicles and wins the answer.
Which of these acronyms are real
Worth knowing before someone sells you a fifth one.
- GEO has an academic origin. It was coined in a 2024 KDD paper, Aggarwal et al., “GEO: Generative Engine Optimization”. Real term, real literature, indexed in our research library alongside the work that contests it.
- AEO is industry vocabulary that grew out of the featured-snippet era. Widely used, no formal definition.
- AI SEO, LLM SEO, SEO for AI, AI visibility optimisation, GSO and AIO are mostly the same idea rebranded. Some are vendor coinages designed to sound like a new discipline.
Google’s own stated position is that optimising for AI search “is still SEO”. That is worth holding alongside everything above: a company selling GEO has an obvious interest in the distinction being large.
Our honest read is that the techniques are largely continuous with good SEO, and the measurement genuinely is not. You cannot use a rank tracker on something with no ranks. That is the part that needed new tooling, and it is the part where the new vocabulary earns its keep. We grade all of this in our evidence register, including the claims that cut against our own commercial interest, and define the terms in the glossary with their provenance marked.
What to actually do
Do the GEO work. The AEO wins come along with it, because structured quotable content serves both.
Then add the two things AEO never asked for. Presence on the third-party sources the models pull from, which for most categories is the fastest available lever and the one companies neglect. And measurement that counts how often you are named across engines and repeated runs, rather than checking one box on one engine once.
None of this shows up in your analytics. When a model names three vendors and yours is not among them, nothing is recorded anywhere you can see. You have to go and ask the engines, repeatedly, and count. We wrote up exactly how to do that yourself, free, in an afternoon.
Or run the free visibility check and we will show you how often ChatGPT, Claude and Perplexity name you against your competitors, and which sources they cite in your category.
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