AEO vs GEO vs SEO: what the three terms actually mean

The short answer: SEO is the whole discipline of being findable in search. AEO, answer engine optimisation, is the slice of it aimed at systems that write an answer instead of listing links. GEO, generative engine optimisation, is a research term for that same goal, coined in a 2024 academic paper. One discipline, then, wearing two newer labels for its newest surface. The question worth arguing about is not which word wins. It is which stage of the retrieval pipeline you are losing at.

Where each word came from

SEO is thirty years old and means everything: crawling, indexing, markup, speed, links, content, the lot. It never had a tight definition and it does not need one.

GEO has the cleanest origin of the three. A paper titled *GEO: Generative Engine Optimization*, by Aggarwal and colleagues, was published at KDD 2024 and introduced both the term and a benchmark for testing content edits against generative answers. That paper is the reason the acronym exists at all.

AEO grew the other way round, out of practice rather than research. It started as a name for optimising toward featured snippets and voice assistants, then got reused for assistants that compose answers. The word is older than the current wave, which is why people use it loosely.

So the three terms are not three methods. They are one discipline described at three moments, and most arguments about them are arguments about vocabulary. What answer engine optimisation actually covers is the substance underneath the label.

The part that genuinely diverges

Here is the real split, and it has nothing to do with naming.

Classic search returns a ranked list, and your page is the unit being judged. Answer surfaces run a longer chain. Google describes part of it in its own AI features guidance: AI Overviews and AI Mode may use a "query fan-out" technique, issuing multiple related searches across subtopics and data sources before composing a response. The rest of the chain, cutting documents into passages, scoring those passages and writing prose from the survivors, is how we model the behaviour we observe from outside. The engines publish no specification for it. Treat the stage names below as a working model.

That changes what a loss looks like. A page can rank well, be retrieved, and still lose the citation because a competing passage answered the question in fewer words with less hedging around it. Page-level metrics cannot see that failure, which is one reason a team can watch its rankings hold steady while its presence in answers drops. We have seen that split inside client audits; we cannot say how common it is across the web, because we only see the sites that hire us.

Three stages, three different fixes:

Never retrieved. An access or coverage problem. Bot rules, client-side rendering, thin coverage of the topic, an origin too slow to finish inside the fetch budget.

Retrieved, not selected. A passage problem. The answer exists on the page but is buried, split across two sections, or written as narrative rather than as a claim.

Selected, cited, wrong. An identity and corroboration problem. The engine named you and attached a stale price, an old founding year, or someone else's product description.

Fix the stage that failed. Rewriting paragraphs for a page nobody ever fetched is wasted work.

The failure mode the naming argument hides

Across the audits we have run, the most common reason a site is absent from AI answers is not a lost ranking contest. It is an access rule the business did not intend. That is a pattern in our own casework, not a measured share of the web, and the sites that come to us are already selected for having a visibility problem.

Take OpenAI, which documents this clearly enough to check. Its crawler page, read on 12 October 2026, lists four user agents and says the settings are independent of each other. OAI-SearchBot surfaces sites in ChatGPT's search features, and the page says sites opted out of it will not be shown in ChatGPT search answers. GPTBot crawls content that may be used in training. ChatGPT-User fetches a page when a person asks for one, and OpenAI warns that a user-triggered fetch of this kind is not necessarily governed by your robots.txt. The fourth, OAI-AdsBot, visits only landing pages submitted as ads. So the robots.txt lever covers search and training separately, which is the distinction a blanket "block OpenAI" rule erases. Other engines document their agents differently, and we are describing OpenAI's published behaviour here, not a rule that holds across all of them.

This is checkable in about ninety seconds, and it is the first thing worth checking:

Read the result against what the business actually decided. For OpenAI's agents, training opt-out and search opt-out are different decisions, and only one of them costs you a place in ChatGPT's search answers. Bot-management rules at a CDN are enforced outside robots.txt, so a clean robots.txt does not tell you the request was allowed through; Google's own AI features guidance lists allowing crawling "by any CDN or hosting infrastructure" alongside robots.txt for that reason. Check the edge separately. The ten-point self-check walks through the rest of the access layer.

What the research supports, and what it does not

The GEO paper's headline finding is that content edits aimed at generative engines boosted visibility by up to forty per cent in generative engine responses, measured on the authors' own GEO-bench benchmark. The abstract also says the effect of these strategies varies across domains, which is the part usually dropped when the forty per cent figure gets quoted. The paper was accepted at KDD 2024 and its first preprint went up in November 2023, so the systems it measured are not the systems answering queries today.

Set it next to what Google publishes. Its AI features guidance, which carried a last-updated date of 10 December 2025 when we read it on 12 October 2026, says there are no additional requirements to appear in AI Overviews or AI Mode, that you do not need to create new machine readable files, AI text files or markup, and that there is no special schema.org structured data to add. That is Google's statement about Google's surfaces. Other engines have published nothing equivalent, so it is not a cross-engine rule.

Both things are true at once, and the tension between them is the honest state of the field. One platform says there is no separate checklist for its own surfaces. One benchmark says passage-level edits moved visibility on the engines it tested in 2023. Our reading of the two is that the edits which work are not tricks, they are clarity: a claim stated once, early, with a number and a source attached, in a passage that survives being lifted out of context. That reading is an interpretation, not a finding either source states.

That is also why budget conversations go sideways. Teams expect a new channel with a new toolchain, and what the work mostly buys is unglamorous repair. What an AEO budget actually buys is worth reading before anyone signs a retainer on the strength of a three-letter acronym.

Which word should you use

Internally, pick one and move on. Externally, there is a mild argument for AEO, because "answer" names the surface a buyer can picture, while "generative" names a technology. Neither acronym changes the work.

What does change the work is sector. A regulated lender, a hospital group and a developer tools company fail at different stages, for different reasons, and the fix order is not the same. Where the work differs by industry covers that.

The other split that matters more than vocabulary is ownership. Some of your visibility lives on pages you control and some lives inside third-party listicles and review sites. In the prompt runs we do for audits, third-party pages show up among the cited sources often enough that we plan for them, though we cannot put a share on it that would hold outside our own prompt set. One is an asset, the other is weather. Rented visibility versus owned visibility draws the line.

Honest limits

This post establishes where three words came from and what two named sources say. It does not establish that the three-stage model above matches how the systems are built. The engines do not publish their retrieval architecture, and we inferred the stages from the outside.

The claim that accidental access rules are the commonest cause of absence comes from our own audit casework, which is a self-selected sample of sites that already suspected a problem. It is not a measured share of the web and we have not published a count.

We cannot tell you how long a fix lasts. Engines re-rank, re-crawl and change retrieval without notice, and we re-run prompt sets for clients rather than relying on an earlier run. We have not published a longitudinal study of decay, so treat durability as untested here.

Attribution is the last gap. In our own analytics, most visits we can trace to answer surfaces arrive with little or no referrer, which means a mention that drove a sale is usually not provable from logs alone. That is what our instrumentation can see, not a claim about how other answer surfaces behave. Anyone quoting a clean revenue figure from AI answers is estimating, including us.

FAQ

Is AEO just SEO with a new name? Mostly, at the technical layer. Crawlability, clean markup and real content serve both. The difference is the unit of success: a liftable passage and a brand identity that resolves, rather than a position in a list.

Should I write content differently for GEO than for SEO? The same edit usually helps both. State the answer in the first two sentences of a section, attach a number with a source, and keep each claim inside one passage rather than spreading it across three paragraphs.

Does Google want special markup for AI Mode? Its published guidance says no. There is no AI-specific schema type and no required text file. Structured data still earns rich results in classic search, which is a separate and still good reason to keep it.

Which acronym do clients use? It varies by team and by year. We answer to all three, and the audit report uses whichever word the client's own documents use, because the argument is not worth a page of anyone's budget.

Sources

All four were opened and checked against the sentences that cite them on 12 October 2026.

Aggarwal et al., GEO: Generative Engine Optimization, accepted at KDD 2024, preprint first

posted November 2023: arxiv.org/abs/2311.09735

Google Search Central, AI features and your website, page last updated 10 December 2025:

developers.google.com/search/docs/appearance/ai-features

OpenAI, overview of OpenAI crawlers, which lists OAI-SearchBot, GPTBot, ChatGPT-User and

OAI-AdsBot: developers.openai.com/api/docs/bots

Schema.org type definitions: schema.org/docs/full.html