GEO, AEO and LLMO describe the same work: getting a brand named and cited in answers produced by AI systems. The terms come from different rooms, not from different methods, and choosing between them changes nothing about what you actually do.
Generative Engine Optimization has an academic root, which is unusual for a marketing term.
The paper is "GEO: Generative Engine Optimization" by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande, first posted in November 2023 and published in the proceedings of KDD ’24, held in Barcelona on 25–29 August 2024. It proposed a framework and a benchmark for measuring how content can be adjusted to improve visibility in generative engine responses.
The term spread from there into venture and industry writing, and it is the one most often used when someone wants the practice to sound like a discipline.
Answer Engine Optimization came from practitioners rather than from a paper. It predates the current wave: people were using it when "answer engines" meant featured snippets and voice assistants, and it carried over when the answers started being generated rather than extracted.
It is the everyday term. Agencies sell it, buyers search for it, and it sits more naturally in a sentence than the alternatives.
Large Language Model Optimization is the engineering register. It shows up where the conversation is about model behaviour, retrieval and crawler access rather than about marketing outcomes.
It is the least used of the three commercially and the most precise about the mechanism.
Because a new category needs a name to be sold, and whoever fixes the name gets to define the category. The debate is a positioning contest between vendors, not a technical disagreement.
You can watch this happen in the tooling: the same product will describe itself as a GEO platform on its homepage, an AEO tool in its documentation and an LLM visibility tracker in its changelog, without changing a single feature.
Operationally, nothing.
Under all three labels the work is the same: assemble a basket of real buying questions, run it across the assistants, compare yourself against competitors, find the third-party sources the answers are built from, make your own pages resolvable and liftable, and re-measure. Nobody has proposed a method under one label that would be rejected under another.
Use AEO with buyers, because it is the one they type. Use GEO in writing where you want the academic lineage available. Use LLMO with engineers, who will find the other two vague.
If someone tells you these are different services with different prices, ask them to describe a task that belongs to one and not the others. There isn’t one.
The vocabulary question absorbs attention that belongs elsewhere. The useful questions are which sources your category's answers are assembled from, which engines your buyers actually use, and whether your own pages can be read at all without executing JavaScript. None of those answers change with the label on the invoice.
Whatever you call it, the work starts the same way: measure what the assistants already say about your category, and which sources they read to say it.