A marketing leader researching AI search meets three acronyms presented as three different things: SEO, which they know; AEO, which sounds new; and GEO, which sounds newer. Vendors attach a different price tag to each. The obvious question is whether these are separate disciplines or one discipline with three names, and it rarely gets a straight answer.
Here is the 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, and a few things genuinely change. Knowing which is which is what separates buying a coherent program from 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.
The relationship between the last two is visible in their definitions. 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 and the other names the supply chain behind it. They are the demand side and the supply side of the same result.
SEO is underneath both. The list of links has not disappeared. It has been joined by a layer that reads the same web and answers the question directly. So the useful way to read this is that one discipline now has 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 and the contest is the ranked list. The classic signals decide it: crawlability, indexation, relevance to the query, site experience, and authority, which has historically been expressed through links. Success is visible 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 work 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, and whether its claims are corroborated by sources the engine already trusts. The only way to see the result is to query the engines and read the answers, because no rank tracker measures a sentence.
GEO optimises for accurate reproduction. Models restate a brand’s facts in their own words, so the contest is whether those facts survive paraphrase: the right name, the right services, the right locations, repeated with confidence and without hedging or distortion. 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.
The three questions are: do we rank, are we cited, and does the machine describe us correctly. All three are asked of the same underlying system.
Where the work overlaps
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, just as it could never rank. Each of the three also rewards content that genuinely answers the questions buyers ask, written by people who know the subject, and authority that exists off the brand’s own site: coverage, corroboration, and third-party evidence that the brand is what it claims to be. Thin content is a problem under any of the labels.
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. It also gets extracted into answers more often, and it gives a model a fact clean enough to repeat without distortion. That is one piece of work producing three results, and it is the practical argument for running a single program.
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 answer is a short list.
What genuinely changes
The overlap covers most of the work, though not all of it. Four things are genuinely new, and the AI-specific effort concentrates there.
The unit of competition shrinks. SEO judges pages, while 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 means one question, one passage and a claim that can be lifted whole, which classic SEO never demanded. How AI Overviews choose which brands to cite covers why ranking does not guarantee citation.
Authority changes shape. Classic search leans on links as its trust signal. Generative systems lean on corroboration, meaning whether independent sources state the same facts. A claim that lives only on the brand’s own site is an assertion. The same claim repeated across the third-party record becomes a fact a model will pass on. Reconciling that record is real work, and no traditional SEO checklist includes it.
Machine-readability gains new artefacts. Schema markup existed in SEO but was often decorative. For entity-reasoning systems it is closer to structural. There are newer conventions alongside it, 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 stand in for visibility, because the answer layer does not publish a ranking. The 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 an AI search program is held to account.
Why there are three labels
It is tempting to read three acronyms for one discipline as marketing mischief. The duller explanation is that young categories name themselves several times at once, and the names track how different buyers arrived.
AEO comes from the answer-box and featured-snippet lineage, where practitioners were already optimising for extracted answers before the answers were generated. GEO comes from the research literature on generative engines, and it reads more naturally to technical audiences. “AI SEO” is what people type when they know the problem but not the jargon. Each label describes the same discipline from a different starting point, and vendors adopt the term their buyers search for, which is how search demand works. This site keeps separate pages for AEO and GEO for that reason, and says plainly 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, what matters on a proposal is the list of work items under the label.
When the distinction matters
The distinction is useful 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, and the other in whether the engines describe the brand accurately. The failure modes differ, so the fixes differ. 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. “We are tracked in the answers buyers read, and the machines describe us correctly” is a sentence a board can fund. The acronym behind it is a detail.
Everywhere else, treat the distinction as noise. The warning signs are consistent: AEO and GEO sold as separate retainers with separate teams; any pitch that calls either one a replacement for SEO; tools that rebadge rank tracking as “GEO scoring” without ever querying an engine; and guarantees of citation, which no agency can honestly give, because none of them control the models or the retrieval.
One 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 work is real whatever it is called. How to audit your own AI search visibility sets out a sensible way to establish that baseline yourself.
One discipline, two scoreboards
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 to a second scoreboard, which measures whether the brand is named in the answer and described correctly when it is. Most of the work serves both scoreboards at once, and a defined slice of it is genuinely new. Buying the same program twice under two names adds nothing.
The answer engine optimisation page sets out the citation side of the discipline: how answers are assembled, what the work involves, and how citation share is tracked.