A marketing leader researching AI search currently meets three acronyms presented as three different things: SEO, which they know; AEO, which sounds new; and GEO, which sounds newer. Vendors attach different price tags to each. The obvious question — are these actually different disciplines, or one discipline with three names — rarely gets a straight answer, because straight answers are bad for selling three programs.
Here is the straight answer. AEO and GEO are two angles on the same discipline, and both are built on the foundation SEO already laid. Most of the work overlaps. A few things genuinely change. Knowing which is which is the difference between buying a coherent program and buying the same work twice.
The three terms, defined plainly
Search engine optimisation (SEO) is the practice of making a website visible in ranked search results — technically crawlable, relevant to the queries that matter, and authoritative enough to earn a position in the list of links a search engine returns.
Answer engine optimisation (AEO) is the practice of structuring a brand’s content, entities and authority signals so that answer engines — Google AI Overviews, ChatGPT, Perplexity and Copilot — cite that brand when answering the questions its buyers ask.
Generative engine optimisation (GEO) is the practice of making a brand’s facts, claims and content easy for generative AI models to retrieve, trust and reproduce accurately in generated answers.
Read those last two definitions carefully and the relationship becomes visible. AEO is defined by its output: the brand is cited in the answer. GEO is defined by its input: the brand’s facts are fit to be retrieved and repeated. One names the outcome, the other names the supply chain behind it. They are not competing methodologies — they are the demand side and the supply side of the same result.
SEO sits underneath both. The list of links has not disappeared; it has been joined by a layer that reads the same web and answers instead of listing. Which is why the honest comparison is not SEO versus its successors. It is one discipline acquiring a second scoreboard.
What each optimises for
The clearest way to separate the three is to ask what each one is trying to win, and what signals move the result.
SEO optimises for position. The unit of competition is the page, the contest is the ranked list, and the classic signals decide it: crawlability, indexation, relevance to the query, site experience, and authority — historically expressed through links. Success is observable in rank trackers and analytics.
AEO optimises for citation. The contest is the generated answer, which names a handful of sources and discards everyone else. The signals that decide it operate at a finer grain: whether a passage answers the buyer’s question completely on its own, whether the brand is a clearly defined entity the model recognises, whether its claims are corroborated by sources the engine already trusts. Success is observable only by querying the engines and reading the answers — there is no rank tracker for a sentence.
GEO optimises for accurate reproduction. Models restate rather than quote, so the contest is whether a brand’s facts survive paraphrase intact — the right name, the right services, the right locations, repeated confidently rather than hedged or garbled. The signals are declarative content, entity markup, deliberate crawler access, and a public record that agrees with itself across the site, directories, profiles and press.
Put side by side: SEO asks “do we rank?”, AEO asks “are we cited?”, and GEO asks “when the machine describes us, is it right?”. Three questions, one underlying system.
Where the work overlaps — which is most of it
Strip the labels off a competent program in any of the three and the task lists converge.
All three require a crawlable, technically sound site — AI engines retrieve from the same indexed web that traditional search does, so a page that cannot be crawled cannot be cited any more than it could rank. All three reward content that genuinely answers the questions buyers ask, written by people who know the subject. All three reward authority that exists off the brand’s own site: coverage, corroboration, third-party evidence that the brand is what it claims to be. All three punish thin content, and no acronym changes that.
Structured content is the clearest example of one task serving every scoreboard. A page that answers one question per section, states its claim in the first sentence and supports it after, tends to rank better, gets extracted into answers more often, and gives a model a fact clean enough to repeat without distortion. That is one piece of work producing three results — which is the practical argument for running one program rather than three.
This is also the test to put to any proposal: if the “GEO deliverables” and the “SEO deliverables” are listed as separate line items, ask which tasks are actually different. Usually the honest answer is a short list.
What genuinely changes
The overlap is most of the work. It is not all of it. Four things are genuinely new, and they are where the AI-specific effort concentrates.
The unit of competition shrinks. SEO judges pages; answer engines lift passages. A page can hold position one and contribute nothing to the answer, because no section of it states anything cleanly enough to extract. Writing for extraction — one question, one passage, a liftable claim — is a discipline classic SEO never demanded. The mechanics of why ranking does not guarantee citation are covered in how AI Overviews choose which brands to cite.
Authority changes shape. Classic search leans on links as its trust signal. Generative systems lean on corroboration — whether independent sources state the same facts. A claim that lives only on the brand’s own site is an assertion; the same claim echoed across the third-party record becomes a fact a model will repeat. Reconciling that record is real work, and it appears on no traditional SEO checklist.
Machine-readability gains new artefacts. Schema markup existed in SEO but was often decorative; for entity-reasoning systems it is closer to structural. Alongside it sit newer conventions such as llms.txt and deliberate AI-crawler access policies — covered in detail in llms.txt, schema and the machine-readable brand.
Measurement moves inside the engines. Rankings can no longer proxy for visibility, because the answer layer does not publish a ranking. The only reliable read is systematic: put the buying questions to the engines on a schedule, record who is cited and how the brand is described, and track it as a share over time. That is a new measurement discipline with new tooling, and it is where AI search programs are most visibly held to account — or not.
The label wars, without the cynicism
It is tempting to read three acronyms for one discipline as marketing mischief. The duller truth is that young categories always name themselves several times at once, and the names track how different buyers arrived.
AEO descends from the answer-box and featured-snippet lineage — practitioners who were already optimising for extracted answers before the answers were generated. GEO arrived through the research literature on generative engines and reads more naturally to technical audiences. “AI SEO” is what people type when they know the problem but not the jargon. Each label is the same discipline described from a different starting point, and vendors adopt the term their buyers search — which is not deception, it is how search demand works. This site maintains separate pages for AEO and GEO for exactly that reason, while being explicit that they run as one program.
The market will eventually settle on one name, the way “search engine optimisation” beat its early rivals. Until then, the label on the invoice matters far less than the work items under it.
When the distinction matters — and when it is noise
The distinction earns its keep in two places.
The first is scoping. Framing the citation outcome (AEO) separately from the fact-supply work (GEO) produces a cleaner brief: one workstream is measured in citation share, the other in whether the engines describe the brand accurately. Different failure modes, different fixes — a brand that is cited but misdescribed has a GEO problem; a brand that is invisible has an AEO problem upstream of everything else.
The second is internal communication. Boards and executives fund outcomes, not acronyms. “We are tracked in the answers buyers read, and the machines describe us correctly” is a fundable sentence in a way that a taxonomy debate never will be.
Everywhere else, treat the distinction as noise. Warning signs are consistent: AEO and GEO sold as separate retainers with separate teams; any pitch that positions either as a replacement for SEO rather than an extension of it; tools that rebadge rank tracking as “GEO scoring” without ever querying an engine; and guarantees of citation, which nobody controlling neither the models nor the retrieval can honestly give.
A simpler filter works in every conversation: ignore the acronym and ask what will be measured. If the answer is citations, descriptions and competitors recorded from the engines themselves against a baseline, the discipline is real regardless of what it is called. A sensible way to establish that baseline yourself is set out in how to audit your own AI search visibility.
One discipline, two scoreboards
The summary fits in a paragraph. SEO built the foundation and still owns it — crawlability, content quality and authority decide who is in contention, exactly as they did before. AEO and GEO extend that foundation toward a second scoreboard: not where a page ranks, but whether the brand is named in the answer and described correctly when it is. Most of the work serves both scoreboards at once, a defined slice is genuinely new, and none of it rewards buying the same program under two names.
For organisations ready to compete on the second scoreboard deliberately, the citation side of the discipline — how answers are assembled, what the work involves and how citation share is tracked — is set out on the answer engine optimisation page.