What is the difference between GEO and AEO?

The difference between GEO and AEO is one of origin rather than of practice. Answer engine optimization, AEO, was named for surfaces that return a direct answer: featured snippets, voice assistants, knowledge panels and now Google AI Overviews. Generative engine optimization, GEO, was named in a research paper about getting content used by systems that write a fresh answer, such as ChatGPT, Claude and Perplexity.

Both names point at the same failure. A buyer asks a question, a machine answers it, and either your page is one of the sources behind that answer or it is not. The work that follows is also the same: make the page reachable by the crawler, make the answer extractable, make the claims verifiable, and use the words the buyer actually typed.

Where the two labels still pull apart is in emphasis. AEO leans toward extraction, so it puts weight on a clean answer in the first sentence under a heading, on tables and on structured data. GEO leans toward synthesis, so it puts weight on being one of the handful of sources an engine gathers, corroborated elsewhere, and quotable in a sentence the model can attribute.

In practice suppliers use whichever term their market uses. Profound argues the two are the same thing and says it prefers AEO, while Jasper treats GEO as the broader discipline with AEO inside it. Two vendors, opposite hierarchies, identical deliverables. Treat the taxonomy as marketing and judge the measurement instead.

Term	Where it came from	What it was originally about	What it means in 2026
AEO	Search marketing, in use before generative answers	Featured snippets, voice answers, knowledge panels, AI Overviews	Getting cited in AI answers
GEO	Academic research, Aggarwal et al., ACM SIGKDD 2024	Content changes that raise visibility inside a generated answer	Getting cited in AI answers
LLM SEO	Agency and tool vocabulary	SEO work aimed at language models rather than result pages	Getting cited in AI answers
AI search optimization	Plain description, used by buyers	Being found when someone searches with an AI tool	Getting cited in AI answers
AI citation SEO	Measurement vocabulary	The citation itself as the unit of success	Getting cited in AI answers

Where did the term GEO come from?

The term GEO came from a paper. Aggarwal et al., "GEO: Generative Engine Optimization", published at ACM SIGKDD 2024, introduced the term along with GEO-Bench, a benchmark of 10,000 queries, and ran a controlled test of content modifications to see which ones changed how visible a source was inside a generated answer.

The paper reported visibility gains of up to roughly 40% for the strongest methods, which were adding verifiable statistics, adding credible quotations and citing reliable sources. Improving fluency and readability produced smaller gains. Keyword stuffing, the tactic the term SEO still carries the smell of, produced negligible or negative effects.

That result is the reason the academic name stuck to the commercial practice. A paper with a benchmark and a control group gave the field something to cite, and vendors adopted the label because it came with evidence attached rather than with a logo. Our methodology page lists the same paper as one of four studies the SIGNALS weights are derived from.

AEO has no comparable origin document. The term grew out of search marketing while Google was expanding featured snippets and voice answers, and it was already in use before generative engines existed, which is why some practitioners treat it as the older, wider word and others treat it as the narrower one. Neither reading is wrong, and neither tells you anything about whether a given supplier can move a citation.

Is LLM SEO the same thing as GEO and AEO?

LLM SEO is the same work under a name that travels better in a marketing team. The variants multiply quickly: AI SEO, AIO, AI search optimization, AI citation SEO, AI visibility. Every one of them describes making a page likely to be retrieved and quoted when a language model answers a question, and no published definition separates them in a way that changes what you would do to the page.

The naming matters commercially rather than technically, because buyers search with the words they know. Our own search data shows the same idea arriving in several vocabularies at once, and a page written in only one of them is invisible to the rest. That is a measurable cost rather than a stylistic one, since vocabulary match is the single heaviest dimension in the SIGNALS score at 35%, a weight taken from the Discovered Labs 2026 citation study reported on our methodology page.

One caveat is worth keeping. "LLM SEO" invites the assumption that you are optimising the model, which nobody outside a training lab can do. What you influence is retrieval: which pages the system fetches when it needs evidence, and whether your page survives the ranking step once fetched. Our explainer on the RAG pipeline walks through where that decision is actually made.

So use whichever term your buyer uses, and be suspicious of any supplier whose pitch depends on their term being the correct one. A vendor who spends the first call explaining why GEO is not AEO is spending it on taxonomy rather than on your citation data.

Does the label change what you actually do to the page?

The label changes nothing about the page. Whatever the service is called, the same five things decide whether an answer engine can use your content, and all five are observable without buying anything.

The crawler has to reach the page. AI crawlers are not Googlebot, they mostly do not execute JavaScript, and a page that renders its content on the client can be blank to them, which our guide on whether AI crawlers read JavaScript covers with the fetch data. The answer has to be extractable, which means a heading worded as the question and the answer in the first sentence under it rather than three paragraphs later.

The claims have to be checkable. The Princeton GEO experiment measured its largest gains from adding statistics, quotations and cited sources, which in practice means naming the source in the same paragraph as the number rather than collecting references at the foot of the page. The vocabulary has to match the question as asked, not as your category writes it internally.

And the brand has to exist somewhere other than its own website. The ConvertMate GEO Benchmark 2026, which looked at 8,000 domains, found brands mentioned on third-party domains receiving 6.5 times more citations than brands present only on their own site, a finding summarised on our methodology page. None of those five items has a GEO version and an AEO version. They are the job.

Which term should you use when you buy this work?

Use the term your own buyers type, and ignore the supplier who insists on a different one. For a procurement conversation the name is a poor filter, because it tells you which conference the supplier attended rather than whether they can show a citation moving. A better filter is what they propose to measure.

Ask which engines will be sampled and how often. An answer that covers only ChatGPT is a partial answer, since published citation overlap between engines is low: an analysis of 680 million citations reported by Averi in 2026 and an independent study of 118,000 responses by Whitehat SEO both put domain overlap between ChatGPT and Perplexity at roughly 11%. Winning one engine does not carry to the next, as our comparison of which engine to optimise for first sets out.

Ask what gets recorded from each answer. Prompt, engine, date, every URL cited and whether the brand was named without a link. Anything less cannot show movement, and a screenshot of a good answer is not evidence, because the same prompt returns a different answer on the next run.

Then ask what happens when the honest answer is that a page cannot be fixed. Pages too thin to support a claim should be reported as thin rather than padded, and a supplier who never returns that answer is telling you something about the rest of their reporting. Our notes on choosing an AEO agency and on agency against tool against in-house go through the rest of the diligence.

What do the studies agree on, whatever you call it?

The studies agree on four things, and none of them depends on the name. First, content changes move citation measurably: the Princeton GEO study found that across 10,000 benchmark queries the strongest methods produced gains of up to roughly 40% from adding statistics, quotations and cited sources, and next to nothing from keyword stuffing.

Second, classic ranking is a weak proxy for citation. Ahrefs found only 12% of AI-cited URLs ranking in Google's top 10 for the original prompt, and its later study of 863,000 keywords and 4 million AI Overview URLs put the share of cited pages that also rank in the top 10 at 38%, down from 76% in the earlier run. Page one is neither necessary nor sufficient.

Third, vocabulary is the dominant page-level lever. The Discovered Labs 2026 analysis of more than 2 million citations found that alignment between page vocabulary and buyer phrasing was the only page-level signal to survive domain fixed-effects controls, at an effect size of 0.37, which is why it carries 35% of the SIGNALS weighting.

Fourth, a single measurement means nothing. Citations decay and answers vary between identical runs, so any claim about your visibility has to come from a repeated prompt set rather than from one good screenshot. Our pages on measuring citation decay and why answers change every time cover what that costs in practice.

What else do people ask about the difference between GEO and AEO?