The Name Mess: SEO, GEO, AEO, LLMO, and What This Book Calls Things

The industry produced a pile of overlapping acronyms for substantially the same work: getting brands and content into AI-generated answers. GEO, AEO, LLMO, AI SEO, and several others all name that work, each from a slightly different angle, and none of them has won. This chapter defines every term in circulation, says where each one helps and where it misleads, and then commits to the vocabulary the rest of this book uses. Read it once and you can decode any article, sales deck, or job posting in the space.

The terms, one by one

GEO: generative engine optimization

GEO has the most respectable origin of the group: a November 2023 academic paper by Aggarwal and coauthors that coined the term, built a benchmark of user queries, and measured which content changes made sources more visible in generated answers, reporting visibility gains of up to 40% from tactics like adding citations, quotations, and statistics."GEO: Generative Engine Optimization" (Aggarwal et al.), the paper that introduced the term and the GEO-bench evaluation set. The 40% figure is the paper's best case, and gains varied widely by domain. In practice the term now covers all optimization aimed at AI answers, well beyond the paper's content-editing experiments.

Two problems. "Geo" already meant geography to every search marketer; twenty years of "geo-targeting" and "geo-modifiers" guarantee permanent collision, and a search for GEO advice returns local-SEO content mixed into the results. And "generative engine" describes only the writing step, while much of the real work targets the retrieval and training layers underneath it.

AEO: answer engine optimization

AEO frames the target as answer engines: systems that respond with a direct answer rather than a list of links. The framing predates LLMs, since featured snippets and voice assistants were already answer engines, and its partisans emphasize being the quoted, cited answer to specific questions.Semrush's "What Is Answer Engine Optimization" is representative of how the industry defines the term and its citation emphasis. That emphasis is useful, and this book covers the same ground as citation and prominence work. The term misleads by scope: brand visibility in AI answers depends heavily on model memory and entity presence, which "answering questions well" does not describe.

LLMO: large language model optimization

LLMO names the model as the target: influence what an LLM says. The framing is too narrow in the opposite direction from AEO. People do not use raw LLMs; they use products wrapped around LLMs, and the product layer decides whether to search, which search index to use, and what enters the model's context before it writes. Optimizing "for the LLM" while ignoring the retrieval layer misses roughly half the system. The term appears mostly in technical writing and tool marketing, rarely from buyers.

AI SEO, and the rest

"AI SEO" carries a double meaning that makes it nearly useless: it names both using AI tools to do classic SEO and optimizing for AI search platforms. When you see it, work out which one the writer means before trusting anything that follows. The remaining variants are self-describing: "AI search optimization" and "SEO for AI platforms" say the work plainly, "generative AI optimization" (GAIO) is GEO respelled, and "agentic search optimization" extends the idea to AI agents that browse and act on your site rather than just answering questions.

What the field actually calls itself

The naming contest has data. In a Search Engine Land survey of practitioners, 36% said their clients and managers call the discipline "AI search optimization", 27% stick with SEO applied to AI platforms, and 18% lead with GEO.Search Engine Land's practitioner survey on what the discipline is called. A separate Search Engine Land study found 84% of surveyed marketers recognize GEO, with 42% personally using it.Search Engine Land's follow-up naming research on recognition, usage, and sentiment across the candidate terms. So GEO is widely recognized inside the trade and still is rarely how the people paying for the work describe it.

Demand data points the same direction. Keyword tracking across the category shows "geo vs seo" at 2,900 monthly US searches, up 510% year over year, and "ai search visibility" as the fastest-growing measurement term at roughly 4,900% year over year.Tracemetry's "State of AI search demand 2026", using DataForSEO keyword volumes captured May 2026. Volumes are US, Google Ads index, and will have moved since. People are learning the acronyms and, at the same time, converging on plain language for the thing they actually want: visibility.

The stack this book uses

Three terms, three layers, never interchanged:

• AI search is the channel: every surface where a model composes the answer, from ChatGPT to Google's AI Overviews.
• AI search visibility is the outcome: how often, and how prominently, your brand and pages appear in that channel's answers. It is what you measure.
• AI Search Optimization is the practice: the work you do to change the outcome. It is the umbrella over everything the acronyms name, and it includes classic SEO and paid placement, because the retrieval layer of AI answers runs on classic search indexes and the platforms are building ad programs on top.

Optimization is what you do; visibility is what you get. Keeping the outcome separate from the practice keeps reporting honest, because a deliverable ("we shipped the entity work") is not a result ("mention rate rose").

The translation table

When you read the rest of the industry, translate on the fly:

One boundary note: this chapter sorts the names; it does not argue over "GEO versus SEO" as disciplines. That comparison, what carries over from SEO and what breaks, has its own chapter: What Carries Over from SEO, and What Breaks. And every term defined here also has a one-line entry in the Glossary.