Guide 04 · Definitions
What is AEO, and how is it different from GEO and SEO?
In practice, no. The two terms describe the same work and most sources use them interchangeably. One has an academic origin and a published benchmark behind it; the other does not. Neither distinction changes what you would actually do on a Tuesday.
What is measurably different, and worth the attention the acronym argument is absorbing, is retrieval versus attribution — and one engine versus another. Those two splits are real, they are large, and you can check both yourself.
What is AEO?
AEO, answer engine optimization, is the work of getting your content picked as the answer: the featured snippet, the voice reply, the AI Overview, the sentence an AI chat quotes back. GEO, generative engine optimization, names the same goal from the side of the engines that write answers, such as ChatGPT, Perplexity and Google’s AI Mode. SEO is the base both stand on: a page that is not crawled, indexed and trusted gets nowhere under either label.
| Aims for | Main surfaces | Measured by | |
|---|---|---|---|
| SEO | A ranked link | Google and Bing results | Rankings, clicks |
| AEO | Being the answer | Featured snippets, voice, AI Overviews | Share of answers |
| GEO | Being cited or named in a generated answer | ChatGPT, Perplexity, AI Mode | Citations, mentions |
Where does each term come from?
Generative Engine Optimization comes from a paper. Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande presented GEO: Generative Engine Optimization at KDD 2024, defining it as a black-box optimisation framework for improving content visibility in generative engine responses, and reporting visibility gains of up to 40% on GEO-bench, the benchmark they built alongside it.
Answer Engine Optimization has no equivalent. It is an industry coinage, older in ordinary usage, generally meaning optimisation for anything that returns an answer rather than a list of links — featured snippets and voice assistants as much as chat interfaces.
| GEO | AEO | |
|---|---|---|
| Origin | KDD 2024 paper, six authors | Industry coinage, no source paper |
| Published benchmark | GEO-bench | None |
| Usually said to cover | Generative answers — ChatGPT, Perplexity, AI Overviews | Anything answering directly, snippets and voice included |
| What it asks you to do | The same things | |
That last row is the whole finding. Read a GEO checklist and an AEO checklist side by side and you get the same items: be retrievable, answer the question early, structure for extraction, keep it current, get mentioned elsewhere. The provenance differs. The advice does not.
Does the distinction survive contact with a real answer?
Not cleanly. The tidy version says AEO covers extractive answers — the engine lifts a passage — and GEO covers generative ones, where the engine writes prose from several sources. Real answers do both in the same paragraph, citing one source for a claim it paraphrases and another for a figure it lifts verbatim.
You cannot optimise for one half of a sentence. And because you cannot see which mode produced which clause, the distinction is not something you could act on even if it were clean.
Buying, not doing. If someone is selling you AEO and someone else is selling you GEO, the label tells you nothing about which is better. Ask both what they measure and how often the measurement changes. That question separates them; the acronym does not.
What is actually different, then?
Two splits, both measured, both larger than anything the terminology captures.
Retrieval is not attribution
Being fetched into the source set and being pointed at by a citation mark in the answer are different outcomes, and the gap between them is wide. In one Perplexity corpus a single domain carried 67 attribution marks — about one citation mark in ten across the whole corpus — while appearing in only five of the ten categories tested. Presence in the source list is the entry ticket; share of the marks is the result.
Any tool reporting a single “visibility” number is collapsing those two. Ask which one it counts.
One engine is not another
Asked the identical question in ten categories, Perplexity and Google AI Mode cited a median of only 15% of the same domains, and in one category shared no source at all — while naming 53% of the same products. Two largely disjoint sets of pages, roughly the same shortlist.
| What varies | Median source overlap | What it measures |
|---|---|---|
| Nothing — identical query string | 100% | A cache |
| One character of wording | 79% | Churn inside one engine |
| The engine | 12% | A genuinely different index |
The engine gap is roughly four times the noise inside either engine. So “am I visible in AI search” is not a question with one answer — it has one answer per engine, and being absent from one of them is a real and separately fixable problem. That is a far more consequential distinction than GEO against AEO, and nobody is arguing about it.
Which term should you use?
Whichever your reader already uses. If your buyers say AEO, say AEO. If your industry has settled on GEO, say GEO. There is no accuracy cost either way, because the referent is the same.
For what it is worth, this site says GEO — the term has a citable origin and a benchmark attached to it, which is a mild tiebreak and nothing more. If the industry settles on the other one next year, the work does not change.
What this page cannot tell you
“Nobody uses these terms differently in practice” is a claim about current usage, which is soft evidence by nature. If a standards body or a major engine gave the two terms distinct technical meanings tomorrow, this page would be wrong.
The 40% figure from the GEO paper is a result on the authors’ own benchmark, not a field measurement of what happens to a real site. Treat it as evidence that the problem is tractable, not as an expected return.
The 15% and 12% overlap figures come from ten and five categories respectively, one day, two engines. Small samples. The direction is not in doubt; the decimals are.
Definition: what is generative engine optimization (GEO).
How this was made: figures come from our own runs in the engines’ public interfaces or from the studies named above. Drafted with an AI assistant (Claude) and reviewed by Alauddin Chowdhury. Method and AI use.
Which engine can see you?
Send me your wordpress.org slug. I will run your category on both engines, pull the full source lists, and tell you whether you are missing from one or from both — which are different problems with different fixes. Free, no call, usually within a day.