AEO (answer engine optimisation) and GEO (generative engine optimisation) are two labels for the same shift in search: from ranking in a list of links to being the source an AI answer actually uses. AEO is the older and looser label, the one that grew out of featured snippets and voice assistants. GEO is the newer one, coined in a 2023 paper for visibility in generative engine responses.
The AEO vs GEO debate is mostly about labels and history, because the work under the two terms converges on the same levers. What the labels don’t settle is whether any of it is working, and that’s what an AI visibility platform measures: whether the assistants recommend you when buyers ask.
AEO vs. GEO at a glance
| AEO | GEO | |
|---|---|---|
| What the term names | Getting your content into the direct answers engines give | Improving content visibility in generative engine responses |
| Where it came from | Practitioner usage, no single owner; vendor glossaries each draw the edges a little differently | Coined in a paper, “GEO: Generative Engine Optimization”, first posted in November 2023, accepted at KDD 2024 |
| Which surfaces it points at | Any engine that answers directly: featured snippets, People Also Ask, voice assistants, AI chat | Generative engines: the assistants and AI search modes that build an answer from retrieved pages |
| What the reader sees | An answer box, or a spoken answer, with a citation | A generated answer with inline citations |
| What you measure | Whether you appear in the answer, and where | Whether the answer cites you, and whether it recommends you |
| What you change | Page structure and wording so an engine can lift your passage into an answer | Page structure and wording so a model can quote you, plus the third-party sources it keeps reading |
The rows that carry real differences are origin, surfaces and what you measure. What you change is close to the same list under both labels, and that’s the claim the rest of this post tests.
What AEO means
AEO is the practice of optimising your content to appear in the answers an engine gives directly: featured snippets, People Also Ask boxes, voice assistants, and now AI chat. The term came out of practitioner usage rather than one defining paper or vendor, so nobody owns the definition. Search “answer engine optimization” today and you’ll mostly find vendor glossaries, each with its own edge drawn around the term: some start at featured snippets, some at voice search, some at anything that isn’t a list of links.
The common core is narrow enough to state in one line. An answer engine takes a question and returns an answer, built from content it lifted from somewhere. AEO is the work of being that somewhere, and that’s the usage this post follows, because picking any one glossary as the authority would be arbitrary.
What GEO means
GEO has a paper behind it, which is why its definition is tighter than AEO’s. The paper defines GEO as improving content visibility in generative engine responses. Generative engines, in its framing, are the systems that build an answer from retrieved pages: chat assistants, and the AI search modes that generate a reply instead of listing links.
“GEO: Generative Engine Optimization” first went up on arXiv in November 2023 and was accepted at KDD 2024, and it tested its methods on a benchmark of its own. On GEO-bench, the optimisation methods it describes improved visibility in generative engine responses by up to 40%, which is the paper’s own claim. One of those methods, adding quotations to the content, raised visibility by 40.9% in the GEO benchmark’s results.
The paper is careful with its numbers: how well each method worked varied by domain, so a result on one set of pages doesn’t promise the same on yours. It’s one benchmark result, not a rule about what always works.
Where the terms actually differ
The difference between AEO and GEO is origin and emphasis. AEO is older and looser: it grew from practitioner usage, and it covers every engine that answers directly, including featured snippets and voice assistants, which aren’t generative at all. GEO arrived with a paper and points at generative engines specifically, the ones that build an answer from the pages they retrieve.
Then there’s what the industry does with the two words, which is mostly to pick one. Profound’s AEO guide uses AEO as its umbrella term throughout and never defines GEO separately, and the one quoted expert in its first chapter who mentions GEO uses the two interchangeably.
That’s a silence about the distinction rather than an argument that the terms mean the same thing. Either way, the line between the terms isn’t doing much work in how vendors actually write.
Do AEO and GEO need different work?
Mostly no. In the work each term describes, AEO is much the same as GEO.
Both terms assume one mechanism: an assistant takes a question, runs searches, reads pages, and builds an answer from what it found. Our Credibility Layer study recorded 200 reasoning sessions in detail, and they came to 5,933 searches and 1,982 citations. The model reads a lot before it says anything, and the pages it reads are the lever.
So the work converges. You need passages an assistant can quote, claims it can attribute to you, and third-party sources it already trusts. That’s the task list under both labels, and it doesn’t change depending on which word you use.
Neither label tells you whether the work paid off, and the old rank metrics don’t follow AI answers either. Our 10x AI Visibility Refresh is the cleanest test we have of that: 98 posts refreshed, nothing new published, citations up 10x in 91 days. Over that window the correlation between Google rank and AI citation was basically zero (Spearman rho 0.000, p = 0.914), and 41 cited pages had no Google impressions at all.
So rank reporting won’t manage this work, whichever label is on the deck.
Which should you pick?
Which one you should do, AEO or GEO, comes down to who you’re talking to. Use AEO if you’re explaining the practice to executives or clients who remember featured snippets: it’s the more familiar label, and it covers the direct answers that aren’t generative. Use GEO if you’re hiring, writing for a technical audience, or citing research, because it’s the term with a paper behind it and the tighter definition travels better in those rooms.
And if you’re the one doing the work, the label won’t change your task list, so pick one and hold it. Client conversations go badly when the same deck carries two words for one practice.
Where Omnipresence fits
Where Omnipresence fits is the measurement gap both terms leave. You still need to know which queries you’re losing and which pages decided them, and neither AEO nor GEO gives you that.
Omnipresence shows whether ChatGPT, Claude, Gemini and Perplexity recommend you or a competitor for the questions your buyers actually ask, plus the searches each assistant ran before answering, so the pages to work are named rather than guessed. Then the fix goes to the agent your team already uses, with the playbook for the rewrite, the refresh and the outreach, and every change is logged with its date and target.
It has limits, and they’re worth knowing up front. It tracks ChatGPT, Claude, Gemini and Perplexity, and nothing else yet, and you bring your own model key and pay the providers at list price.
And it measures recommendations and citations, not pipeline or revenue, so you connect the money on your side.
If you want to see which queries decide your category, you can point it at one brand for a week.