SEO vs AEO vs GEO vs LLMO: Only One of These Distinctions Is Real

An earlier version of this article drew the same distinction almost every guide draws, and it was wrong. It told you GEO was about getting your brand into a model's training data while AEO was about live query-time answers. That framing is widespread, intuitive, and contradicted by the paper the term comes from. This version corrects it, adds LLMO, and argues something more useful than a four-way comparison: that the proliferation of acronyms has outrun the proliferation of actual practices, and knowing which boundaries are real saves you from buying the same service three times.

The boundary that holds is between competing for a position and competing for a citation.

Classic SEO optimises for placement in a ranked list. Success is measurable, binary at any given position, and degrades gracefully: rank eight still earns something. The user sees your title, your URL, and decides whether to click.

Optimising for a generated answer is a different shape. There is no list. A handful of sources, typically around four or five, get folded into synthesised text, and the words shown are usually not your words. Success means being one of those sources and being represented accurately. It degrades badly rather than gracefully, because being sixth-best on a query that produces one answer usually means being absent.

Those are genuinely different objectives, and they justify different measurement. That is the real distinction, and it sits between SEO and everything else, not between the three newer acronyms.

They come from three different places, which is the only reason they feel distinct.

GEO has an academic origin. It was coined in "GEO: Generative Engine Optimization" by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande, presented at ACM SIGKDD in 2024. The paper defines generative engines as systems that retrieve documents and synthesize an answer from them, then shows that modifying a source document can raise its visibility in the generated response.

AEO has no citable origin. It emerged from practitioner vocabulary and originally described optimising for featured snippets and voice assistants, well before generative AI. It was retrofitted onto AI answers once those became the dominant surface.

LLMO emerged from the same practitioner vocabulary across 2023 and 2024, with no single author, in parallel with the academic term.

Three origins, one practice. No consensus definition separating them exists in the research literature, and the techniques sold under each name are the same techniques. Google, for its part, published guidance in May 2026 stating that from its perspective, optimising for generative AI search is optimising for the search experience, and therefore still SEO. That is correct about Google, whose AI Overviews and AI Mode run on its core ranking and quality systems. It understates the case elsewhere, because ChatGPT and Perplexity draw on noticeably different source pools, but it is a reasonable position rather than a defensive one.

The most common way people distinguish GEO from AEO is to say GEO shapes what a model absorbed during training. This is the part worth unlearning.

Almost every surface people mean when they say AI search is a retrieval-augmented system. Google's AI Overviews and AI Mode, ChatGPT with browsing, Perplexity, Gemini with grounding, Claude with web access: each one searches for relevant documents when the question arrives, then writes an answer from what it fetched. The model is not reciting from memory. It is reading, at answer time.

Two consequences follow. First, the fixation on training data is misplaced, and it is not actionable anyway: training runs are periodic and closed, and you cannot retroactively edit what a model absorbed. Second, retrieval is the gate. If your page is not fetched, the quality of its writing is irrelevant, because the model never sees it. Crawlability and being in the candidate pool are floor conditions.

Training data has not become irrelevant. When a model answers with no retrieval in play, it draws on what it absorbed, and that shapes baseline brand recall. But that is one layer among two, not the definition of a separate discipline.

Less than most writing on the subject implies, including writing that agrees with this article.

That last row is the one to sit with. A July 2026 survey of 45 studies in this field concluded that no reviewed technique demonstrates a stable, longitudinal, cross-platform causal effect on discoverability. Nearly everything the field asserts is retrieval-stage correlation. Anyone selling you certainty here is selling something.

Stop buying the distinction and start using a better one. The split that earns its keep is on-domain versus off-domain.

On-domain is making your own pages retrievable and quotable: be crawlable, answer directly and early, make each passage true on its own, make it obvious which organisation is answering. Off-domain is being accurately represented everywhere else: directories, review platforms, industry press, the third-party roundups that generative engines lean on heavily.

Both matter. Neither is GEO or AEO specifically, and any vendor proposing three separate programmes with three separate retainers should be asked what a practitioner would actually do differently under each name.

A note on one tactic that keeps appearing in these guides: FAQPage schema is no longer a lever. Google deprecated FAQ rich results on 7 May 2026, removed the reporting in June 2026, and ended Search Console API support in August 2026. Its own guidance also states no special structured data is required for generative AI features. Unused markup causes no harm, so there is no rush to remove it, but do not count it as a reason anything wins.