GEO vs AEO vs SEO

Three labels, three origins, one body of work

Search engine optimization (SEO) is the long-established practice of making a page findable and rankable in a search index; it has been in documented use since 1997 and predates Google. Generative engine optimization (GEO) comes from a 2023 academic preprint and names the practice of influencing what a generative model says and cites. Answer engine optimization (AEO) is older than GEO, in circulation by early 2018 for voice search and featured snippets, and has since been re-pointed at the same AI surfaces GEO addresses. LLM SEO, AIO and LLMO are further labels for the same activity.

The short version of the honest answer: AEO and GEO are two names for one body of work, and that body of work sits inside search engine optimization rather than beside it — with one genuine, evidenced exception, which is measurement.

The three terms are not equally attributable, and the difference matters more than it sounds. GEO's provenance is clean: Aggarwal and colleagues posted it to arXiv on 16 November 2023 and published at KDD 2024. SEO's is diffuse but old and uncontested. AEO's does not resolve at all. The earliest independently verifiable dated use is Rebecca Sentance in Search Engine Watch on 7 February 2018, writing about voice search, and her phrasing is the passive one — this strategy “has come to be known as AEO” — which is how a writer refers to a term already circulating, not how anyone announces one. The widely repeated attribution of AEO to a 2017 coinage rests on the claimant's own company site, with no dated artifact behind it, so the accurate statement is that the term was in use by early 2018 and its coinage is unattributed.

The parties best placed to draw a line between AEO and GEO decline to draw one. Wikipedia has no standalone answer engine optimization article; the title redirects to the generative engine optimization article, verified 29 August 2026, which is the encyclopedia's own ruling on the question. A vendor selling AEO tooling published on 29 June 2025 that the two terms describe the same goal and that its own preference is about which acronym it owns — a company with every commercial reason to assert a difference, saying in print there is none. And the 2026 academic survey of 45 studies in this area uses GEO as the umbrella term and treats AEO as interchangeable throughout.

Who gains from insisting they are separate

None of these labels arrived as a competing theory of how retrieval works. Each was pushed into general use by a party holding a commercial position in it, and that pattern explains the vocabulary better than any technical account does.

GEO sat as an academic term for eighteen months and became a market category on 28 May 2025, when an investor in GEO tooling published an essay positioning GEO over SEO. It cites no study. It uses the more-than-$80bn size of the existing search optimization market as GEO's addressable market, which is a market-sizing exercise rather than a finding about how anything is retrieved. Nearly every later repetition of “GEO replaces SEO” traces back to it.

The category inherited one citable number from the founding paper, and that number has been carried a long way from what it measured. The paper's headline is a visibility improvement of up to 40%, or 40.9% precisely. The metric behind it is an in-context visibility score — how much of a generated answer is attributable to a source — measured with the retrieval set held fixed at five documents. It is not traffic, not clicks, not rank, and not citations counted in a live product. It is routinely quoted as though it were all four; a number that survives detached from its metric is doing marketing work, not evidentiary work.

The counter-push came from the same kind of place. On 30 September 2025 a Forbes column argued that the field should be called AEO rather than GEO; its author, Joe Toscano, disclosed in the piece that he founded a company selling AEO services. Both sides of the terminology argument are made by parties who sell the terminology.

What is missing from the record is any search operator pushing a label. Google grouped the whole family together — Danny Sullivan at WordCamp US on 28 August 2025: “Good SEO is good GEO, or AEO, AIO, LLM SEO, or LMNOPO,” and on 17 December 2025 he positioned the category as a subset: “SEO has always been about understanding how people look for information and how systems surface it.” OpenAI documents crawler access and no content guidance. Microsoft is the one operator to have adopted any of these words, adding GEO to the Bing webmaster guidelines in February 2026, framing it around content eligibility for grounding in AI responses and stating that GEO guarantees citations no more than SEO guarantees rankings. No operator has ever named AEO in its documentation. A distinction that no platform documents, no study isolates and no encyclopedia recognizes is a distinction in the market, not in the machine.

The two choke points that decide everything

Terminology arguments resolve once you look at where a generative answer can actually be influenced. There are two places, and they are not the same place.

The first is getting into the context window. The system decides whether to search at all, performs query fan-out (decomposing one question into several machine-generated sub-queries before an answer is written), retrieves documents against those sub-queries from a conventional index, reranks them, and allocates a handful into the model's prompt. Google's Search Central documentation for AI features describes fan-out as issuing multiple related searches across subtopics and data sources; the retrieval underneath it is Google's ordinary index. This is a ranking problem, and it is the problem SEO has always addressed.

The second is what happens once you are in the context window. Which retrieved documents the model draws on, what it repeats, whom it cites, and how prominently.

The split is clean. SEO acts on the first. GEO, as originally defined and tested, acts on the second — the founding paper fixed the retrieval set at five documents and rewrote one of them. AEO, as sold in 2026, claims both and tests neither. Every credible dispute in this subject is a dispute about how much of the outcome each choke point controls.

What actually changed about the surface

Concluding from “good SEO is good GEO” that nothing changed is the opposite error to believing GEO replaces SEO, and it is nearly as common. The tactics largely did not change. Four measurable properties of the surface did.

• The unit of retrieval. Classic search returns pages. A generated answer is assembled from passages drawn across several documents, so the retrievable unit is the chunk, not the URL.
• The origin of the query. A classic SERP answers the user's words. A generative answer is built from machine-generated sub-queries the user never typed and cannot see.
• Reproducibility. The same query returns a near-identical SERP. Repeated identical prompts return overlapping source sets only 0.34 to 0.42 of the time, and 57.8% of repeated ChatGPT queries skip web search entirely.
• What a listing means. A search result is the source. A citation is a pointer attached after generation — across four generative search engines, only 51.5% of generated sentences were fully supported by their citations and only 74.5% of citations supported the sentence they were attached to.

The tactical implication of all four is smaller than it looks, and the measurement implication is much larger. That asymmetry is the single most useful thing to hold onto in this subject.

Is classic search engine optimization sufficient?

This is the consequential question, and it deserves a split verdict rather than a slogan.

As the necessary condition, this is documented. Google requires indexation and snippet eligibility: “To be eligible to be shown as a supporting link in AI Overviews or AI Mode, a page must be indexed and eligible to be shown in Google Search with a snippet.” OpenAI requires crawler access. Retrieval runs against conventional indexes. Nothing gets cited that cannot be retrieved, and Google states outright that there are no additional requirements and no special optimizations necessary, as of the documentation's 10 December 2025 update.

As a complete account of citation selection, it is not supported. The correlation between organic ranking and AI citation is real, partial, and apparently loosening. Ahrefs, analyzing 1.9 million citations from a million AI Overviews on 21 July 2025, found 76.10% of cited pages ranked in the top 10, 9.50% ranked 11 to 100, and 14.40% did not rank at all, describing the correlation as positive yet moderate. A later Ahrefs analysis reported in March 2026 across 863,000 keywords and 4 million AI Overview URLs put the top-10 share at 38% — but Ahrefs attributes that partly to changed parsing methodology and partly to query fan-out, so the two numbers are not a trend line and should never be presented as one. A 2026 audit cited in the Martinez survey found 53% of Google AI Overview domains absent from the top 100 organic results.

The residual is undocumented. Something beyond top-10 rank is selecting sources, no operator has said what, and no independent study has isolated it. Anyone who tells you they know what it is, does not — and that vacuum is precisely where unsupported advice fills in.

Measurement is the part that is genuinely new

If there is a new discipline here, it is not optimization. Nothing in classic SEO tells you how to sample a stochastic answer surface, how many repeats you need before a difference is real, or how to report a distribution rather than a rank. That is new work, it is legitimate, and it carries the least marketing of anything in this field.

The native instrumentation is thin. Google Search Console added Generative AI performance reports on 3 June 2026, giving impressions from AI Overviews and AI Mode by page, country, device and date, and withholding queries, clicks, click-through rate, average position, citation placement and the supporting passage. Every other surface is third-party prompt sampling. With repeat-query source overlap at 0.34 to 0.42, a single snapshot is a draw from a distribution, not a position in a ranking.

The evidence also says where to spend effort. C-SEO Bench (Puerto et al., NeurIPS Datasets and Benchmarks 2025) tested ten GEO-style rewrites across two tasks and six domains and found that “making the target document the first one in the LLM context window leads to far greater citation ranking gains in the LLM response than any C-SEO method,” concluding that “traditional SEO strategies remain critical … C-SEO methods must be considered as a complement and not a replacement for traditional SEO.” Three of 54 method-and-domain cases showed a statistically significant improvement. Optimization aimed at the second choke point can also cost you the first: the SAGEO Arena result reported in the Martinez survey describes body-only optimization cutting top-20 retrieval presence by roughly 9% and top-10 by roughly 16%. The benchmark is published in full.

The spam-policy dimension classic SEO did not have

One thing is genuinely new on the risk side, and it arrived in 2026. Google's spam policies, last updated 28 August 2026, describe spam as including attempts to manipulate generative AI responses in Google Search; the 15 May 2026 changelog stated the update was made to make clear that the spam policies apply to all of Google Search, including generative AI responses.

Bing moved first and in the same direction. Its February 2026 guidelines update renamed the keyword-stuffing section to cover artificially engineered language and added a section on prompt injection and AI manipulation. Having named GEO, Microsoft's next act was to extend its abuse policy rather than publish an optimization guide, and no separate ranking system for GEO has followed. Its guidelines page is rendered in JavaScript and could not be read directly for this page; the wording is as reported by Search Engine Journal on 27 February 2026.

Read the two together and the pattern is unmistakable. The tactics most aggressively marketed under GEO and AEO labels — phrasing engineered for models, seeded third-party posts, fabricated supporting statistics — are the tactics both operators have now named in their abuse policies. That is a change in the risk profile of this work that no vendor deck mentions.

Reading a proposal that itemizes SEO, AEO and GEO

The practical test is simple. A proposal that bills SEO, AEO and GEO as three lines is describing its own taxonomy, not a mechanism. The labels do not map to three systems, three indexes or three sets of documentation.

What matters is which choke point a piece of work addresses, and whether the person proposing it can say which. Crawlability, indexation, topical coverage, internal linking and authority act on retrieval, which is the best-evidenced lever available. Clear passage-level structure makes a self-contained answer extractable once retrieved, which is a modest and mechanically sensible aim. Repeated, controlled measurement tells you whether either worked. Everything else on the standard GEO checklist is either restating SEO in new vocabulary or asserting a mechanism no operator documents.

One claim should end the conversation: a specific percentage uplift in traffic, conversions or revenue attributed to GEO or AEO work. No primary source with a published methodology exists for any such figure. Where the trail leads anywhere at all, it ends at the 2023 paper's in-context visibility score, or at an investor's market-sizing essay, neither of which measured a commercial outcome for anybody.

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