GEO vs AEO vs LLMO vs AI SEO: The Terminology, Settled

By Cory Maki · Oct 20, 2026

Five acronyms now describe roughly the same work. GEO, AEO, LLMO, AIO, and "AI SEO" all circle the same goal — getting your brand named, cited, and recommended inside AI-generated answers — but they were coined by different people, at different times, emphasizing different parts of the same pipeline. The result is a vocabulary problem that costs real money: two agencies can quote you for "AEO" and "GEO" and sell you the identical deliverable, or sell you two genuinely different things under the same label.

This is a reference page, not a debate. I'm not going to re-argue whether any of this is just SEO wearing a new hat — I've staked out that position elsewhere, and it's further reading if you want the philosophy. Here the job is narrower and more useful: define each term precisely, map it to the platforms and workflows it actually governs, and tell you which overlaps are real and which are marketing. Bookmark it, and define the term before you sign anything.

The one-line answer

If you read nothing else: GEO is the term winning. It's the academic coinage, the one most tools and agencies now use, and the safest default. SEO for AI is most commonly called GEO; you will also see AEO, LLMO, "AI SEO," and "generative search optimization," and these terms mostly describe the same discipline — getting your brand cited and recommended by AI engines — with small differences in emphasis, with GEO gaining the most traction as the safest default. The differences between them are differences of origin and emphasis, not method.

That said, the terms are not perfectly interchangeable, and a few of them carry two meanings depending on who's speaking. The table below is the fast lookup; the sections after it explain where each word came from and when the distinction matters.

The terminology table

Term	Stands for	Primary target	Emphasis	Origin
SEO	Search Engine Optimization	Ranking a page in a list of blue links	Keywords, backlinks, technical health	1990s practitioner craft
GEO	Generative Engine Optimization	Being cited inside AI-generated answers (ChatGPT, Perplexity, Gemini, AI Overviews)	Citable raw material, synthesized multi-source answers	Academic — Princeton et al., Nov 2023
AEO	Answer Engine Optimization	Direct-answer features: featured snippets, voice, People Also Ask, AI answer boxes	The precise, liftable single answer	SEO practitioners, pre-dates GEO
LLMO	Large Language Model Optimization	The model's representation of your brand — in live search and training data	Entity clarity, how the model "knows" you	Practitioner / technical, ~2023–24
AIO	AI Optimization or AI Overviews	Whole AI spectrum or Google's AI Overviews specifically	Umbrella strategy — read in context	Least settled; agency coinage
AI SEO	SEO for AI answers or SEO done with AI tools	Ambiguous — depends entirely on speaker	Continuity with familiar SEO	Generalist marketers

GEO: the academic term that's winning

GEO targets citation inside synthesized AI answers, and it has the only formal definition of the group. The term "generative engine optimization" was introduced in a 2023 paper by Pranjal Aggarwal and co-authors, later presented at the KDD 2024 conference. The term was introduced in a November 2023 paper from Princeton, the Allen Institute, Georgia Tech, and IIT Delhi. That academic provenance is why GEO carries more weight than its rivals — there's a citable paper behind it, with a measurement framework.

The paper did more than name the field. It introduced Generative Engine Optimization as the first novel paradigm to aid content creators in improving their content visibility in generative engine responses through a flexible black-box optimization framework. The authors introduced GEO-bench, a large-scale benchmark of diverse user queries across multiple domains, and demonstrated that GEO can boost visibility by up to 40% in generative engine responses. That 40% figure — specifically, the finding that adding statistics and citations is among the most effective tactics — is the empirical backbone most credible GEO advice still rests on.

Use GEO when you mean the whole practice of earning AI citations across generative surfaces. It's the term I build around, and it's the one that governs the full stack — off-domain presence, entity signals, and citable on-page structure. If you want the plain-English version, start with what generative engine optimization actually is and the GEO pillar guide.

AEO: the older label, narrower in scope

AEO is the oldest term still in active use, and it emphasizes the answer format itself rather than the generative surface. AEO came first, framed around answer boxes and voice; GEO arrived with the 2023 academic research and reframed the same work around generative engines; LLMO is the most recent label and zooms out to include the model's own knowledge of your brand. Traditionally, AEO referred to earning featured snippets, knowledge panel inclusions, and rich results in Google — essentially any format where a search engine provides a direct answer above the traditional blue links.

The practical distinction between AEO and GEO is subtle but real. GEO plays in synthesized multi-source answers, AEO in the direct single-answer situation. AEO's playbook is the "answer-first" structure: take the question verbatim, answer it precisely and concisely right after, and lean on Q&A blocks, definitions, and clean lists. AEO emphasizes the answer format itself — winning the snippet, the voice response, the People Also Ask slot; GEO emphasizes the generative surface — being cited inside an AI Overview or a Perplexity answer right now.

Here's the catch that makes AEO ambiguous in 2026: some people still use it the old way (snippets and voice), while others have repurposed it to mean citation inside AI chat answers — effectively a synonym for GEO. Forrester's Nikhil Lai argued in 2025 that answer engine optimization and related terms are "significantly, but not fundamentally, different from SEO." When someone pitches you "AEO," ask which they mean: the featured-snippet discipline, or the AI-citation discipline. They are not the same budget.

LLMO: the literalist's label, and the one with a split meaning

LLMO means optimizing for the large language models themselves, and it is the term most likely to mean two different things depending on the room. LLMO appears in technical and academic writing and is rare in day-to-day marketing; it points at the models themselves (ChatGPT, Claude, Gemini) rather than any search surface. It's the literalist's label — optimize for large language models — and it rarely survives contact with a marketing meeting.

The split is worth understanding before you hire anyone. In one usage, LLMO is the broadest term of all — an umbrella that includes GEO's live-search surface plus the model's parametric memory of your brand. In this reading, LLMO is the practice of optimizing your brand, content, and entity signals so large language models surface, recommend, and cite you both inside live AI search and in the model's parametric knowledge — the broadest AI-era term because it includes not just the generative search surface where GEO lives but also how your brand is represented in training data and the knowledge graph.

In the other usage, LLMO is the narrowest term — strictly how a model describes you from its training data, with no live web search involved at all. Those are nearly opposite scopes under one acronym. Practically, the training-data side of LLMO is entity work: making your facts consistent everywhere so the model learns them correctly, which is exactly the terrain of entity-building with schema and the Knowledge Graph and why your facts must match everywhere.

AIO: two meanings, read it in context

AIO is the least settled label of the group, and it doubles as shorthand for Google's AI Overviews. AIO — AI optimization — is the least settled label, used by some agencies and consultants, and it doubles as shorthand for Google's AI Overviews, so read it in context. One school treats AIO as the roof over everything: AIO is the umbrella strategy over GEO and AEO, with LLMO as the technical layer underneath — structuring content so language models parse, classify, and attribute it to your brand correctly.

Because "AIO" collides directly with "AI Overviews," it's the term I'd avoid in a brief unless everyone has agreed what it means. If your conversation is about Google specifically, note that Google itself runs two distinct AI surfaces — the Overviews box and the separate conversational AI Mode — which cite the same URLs only about 14% of the time. "AIO" papers over that distinction, which is exactly when it costs you.

AI SEO: the most ambiguous term of all

"AI SEO" is not really an acronym, and it carries two meanings that have nothing to do with each other. One common meaning is SEO for AI answers, covering GEO, AEO, and LLMO; the other meaning is AI tools for SEO work, such as keyword research. The first is about getting into AI answers. The second is about using ChatGPT to write your meta descriptions faster. Same two words, entirely different projects.

The industry is slowly separating these. The tool-assisted kind increasingly goes by AI-assisted SEO or simply SEO, since the deliverable is unchanged; the answer-presence kind is consolidating under GEO and AEO — so AI SEO, when it means optimizing for AI answers, is properly called GEO or AEO. If a vendor leads with "AI SEO," that's your cue to ask the single clarifying question that resolves it: Are you helping AI mention my brand, or helping me produce content faster?

Where the guides actually disagree

The reason this page needs to exist is that the disagreement isn't yours to resolve — the sources themselves conflict. Four names get two meanings each: AEO is snippets and voice or AI chat answers; LLMO is the umbrella term or model memory only; AIO is Google AI Overviews or AI optimization; AI SEO is SEO for AI answers or AI tools for SEO work. This is why you define the term before you sign.

Even the authorities don't align on hierarchy. Some say GEO, LLMO, and AEO are three names for one idea; others put GEO, AEO, and AIO inside LLMO as an umbrella; still others treat LLMO as the narrow case — how a model describes you from training data, with no live search. The honest reading across all of it is simpler than the acronym wars suggest. GEO, AEO, and LLMO are largely converging on the same playbook, and the differences are mostly emphasis, not method.

How to actually use this in a workflow

Stop treating the labels as separate disciplines and treat them as three jobs inside one program. The job of being the direct answer (AEO's emphasis), the job of being cited in synthesized answers (GEO's emphasis), and the job of being correctly understood by the model itself (LLMO's emphasis) are three layers of the same stack — and the same page frequently wins all three. The question is never "should I do GEO or AEO," it's "is my brand named, cited, and recommended across the surfaces my buyers use."

That outcome has its own word, and it's the one worth organizing around. AI visibility is the outcome every label chases — whether AI engines actually mention, cite, and recommend your brand; GEO is something you do, AI visibility is something you have. If you want to turn the vocabulary into a plan, the practical sequence is: audit what AI currently says about you, fix the sources driving it, and make your own content citable — which is the shape of the 90-day GEO roadmap. And before any of that, the fastest way to see where you stand is a free AI visibility report.

The terminology will keep drifting. The work won't. Pick GEO as your default word, define any other term the moment a vendor uses it, and spend your budget on the outcome — not the acronym.