AI SEO vs GEO vs AEO vs LLMO: which term means what

AI SEO, GEO, AEO and LLMO describe overlapping work on one problem, and only two distinctions between them change any decision. Use GEO for whether a brand gets named in AI answers, AEO for whether a page can supply a liftable answer, and treat AI SEO and LLMO as umbrella labels. The two distinctions that matter are passage versus brand, and training versus retrieval.

This guide is about vocabulary: what each label usefully means, which differences are real, and how to keep a team from arguing about terms instead of evidence. For practical comparisons, see GEO vs SEO at programme level and AEO vs SEO at page level.

AI SEO vs GEO vs AEO vs LLMO, side by side

GEO is about outcomes in the answer: whether your brand is named, whether it is recommended, and whether your pages are cited as sources. Those three are different results and should be recorded separately, because a page can be cited while the answer recommends a competitor.

Of the four, GEO is the one with a clear origin. A research paper by Aggarwal and colleagues, accepted to KDD 2024, used generative engine optimization for methods that change how content appears in generative engine responses. On its benchmark it reported visibility gains of up to 40%, with citing sources, adding quotations and adding statistics working best and keyword stuffing offering little or no improvement (arXiv). The other three labels grew out of industry use and have no single definition to point to.

AEO is about one page's fitness for extraction: the answer is in the HTML, the heading states the question, the answer comes first, facts are sentences. The AEO working guide covers this in full.

LLMO is the widest label. In principle it covers both what a model absorbed during training and what it retrieves when answering. In practice most advice published under it is about the retrieval half, because that is the half a site owner can act on directly. See the LLMO glossary entry for the short definition.

AI SEO usually means existing SEO practice pointed at AI surfaces. It is the least precise of the four. That is not a criticism of the work, only a reason to ask what exactly is being measured whenever you see it.

Laid out on the two distinctions explained below, the labels stop competing and start to look like regions of one map.

Distinction one: passage versus brand

Making a page extractable and getting a brand recommended are different projects with different owners and different limits.

• The passage project is editing and markup on your own domain. You can finish it, and you can verify it yourself by fetching the page and reading the first sentences under each heading.
• The brand project depends on what other sites say about you. A model needs the same fact from independent places before it will state it confidently, and a claim that exists only on your own domain is the weakest evidence you can offer. You can influence this, but not finish it. Third-party corroboration covers where to start.

Most confusion between AEO and GEO comes from blending these two. A team that has fixed every page and still is not named has done AEO well and has not yet done GEO.

Distinction two: training versus retrieval

An assistant can know about you in two ways: from what its model absorbed in training, or from pages it fetches when composing an answer. Vendors separate these two at the crawler level, which is the clearest evidence that the distinction is real.

• OpenAI documents GPTBot as crawling content that may be used to train its models, and OAI-SearchBot as building the index behind ChatGPT's search features. ChatGPT-User fetches a page because a person asked about it. See OpenAI's bot documentation.
• Anthropic separates ClaudeBot for training, Claude-SearchBot for search and Claude-User for user-requested fetches.
• Google documents Google-Extended as a usage control rather than a crawler. It governs whether already-crawled content may be used to train Gemini models and to ground Gemini answers, while Google Search inclusion is unaffected.
• Apple draws the same line: Applebot powers search across Spotlight, Siri and Safari, while disallowing Applebot-Extended opts content out of training Apple's foundation models, and pages that disallow it can still appear in search results.

The practical consequence is in measurement. Results with and without web search are different measurements. In our own product, enabling web search once moved a site from 3% to 13% while the site itself did nothing. If you compare a run with retrieval against one without, you are measuring the change in method, not the brand. Label which mode each run used, and compare like with like (Figure 3).

It also tells you how fast work can pay off. If an assistant names you only when it retrieves live pages, what you publish and fix now can affect the next answer. If it names you without retrieval, that reflects what the model already absorbed, which you cannot update by publishing this month.

What stays the same under every label

Whichever term a vendor or a consultant uses, the prerequisites do not change:

1. The engine's search and user agents can fetch your pages, tested with the real user agent rather than inferred from robots.txt.
2. The content is in the HTML, because most AI crawlers do not execute JavaScript.
3. Facts are written as sentences that name their subject.
4. Structured data agrees with the visible page.
5. Pages carry real published and modified dates.
6. The brand is described the same way everywhere you control.

If a method sold under any of these labels sounds entirely new, check whether it is one of these fundamentals renamed, or something that cannot be verified.

Questions that cut through any label

When a tool or an agency reports a "GEO score", an "AI SEO score" or an "LLM visibility" number, the label tells you little. These questions tell you whether the number means anything:

• Does the question set contain your brand name? A question naming your brand will be answered with your brand. In one of our own runs, 13 of 15 generated questions contained the brand name, and the score read 13% when the honest value was zero.
• Are results reported per engine before any average? In our own benchmark one model named 3.5 providers per answer and another 2.1.
• Are citations counted from the answer text, or from everything retrieved? Counting the retrieval pool overstated our own figures by two to four times.
• Does every run carry a methodology version? Otherwise a change in method appears as a change in the brand.

GEO, AEO or AI SEO: which term to use

Inside a team, name the thing you measure rather than the discipline. "Mention rate on our fixed question set, per engine" survives any change of vocabulary; "our GEO score" does not. Then split the work by the first distinction: AEO to whoever owns the pages, GEO to whoever owns demand and third-party presence, and both measured against the same fixed question set so the discussion is about evidence. The GEO measurement framework lays out that shared measurement.

The same habit helps when you brief an agency or write a request for a tool. A label in a brief invites the supplier to define success for you; a measurable outcome does not.

None of the right-hand column depends on which term wins. That is the point: the vocabulary will keep shifting, and a brief written in measurable outcomes stays valid when it does.

Sources

• OpenAI, Overview of OpenAI crawlers.
• Anthropic, Does Anthropic crawl data from the web.
• Google, Google's common crawlers (Google-Extended).
• Apple, About Applebot.
• Aggarwal et al., GEO: Generative Engine Optimization, KDD 2024.