GEO, AEO, LLMO, AI SEO: four perspectives on the same problem

A CMO put four vendor proposals on the conference table last week and asked a straightforward question: which one should we buy? All four described essentially the same thing, just with different acronyms and different price sheets. She understood marketing. The terminology simply confused her.

Here is a working definition: GEO, AEO, LLMO, and AI SEO are four different ways of describing the same goal: making your content retrievable, understandable, and citable in AI-generated answers. The distinction lies not in what you do, but in which layer you optimize, which engine you target, and which metric you measure. To understand how they differ, the starting point is this: they're not four competing products to choose between. They're four perspectives on the same problem. Once that clicks, those four proposals shift from pick-one-of-four to four points on the same spectrum.

How GEO, AEO, and LLMO actually differ: a comparison chart

Four terms on the same spectrum

An AI engine answering a question follows three layers: it retrieves relevant content, synthesizes it into an answer, and relies on a layer of base knowledge baked into the model during training. These four terms target different points along that same spectrum.

AI SEO is the broadest umbrella. It covers almost everything, which is exactly why vendors use it as a catch-all term. It sounds comprehensive while specifying nothing. When someone pitches AI SEO work, the question to ask is this: Are you optimizing for clicks or citations?

AEO sits one layer deeper, focused on the answer itself. A user asks a question, the answer engine delivers a single response, and AEO means claiming that one spot. It evolved from the featured snippet era and now extends into voice search and chatbots. The measure is unforgiving: you're either selected or you're not.

GEO is what's genuinely new in recent years. Generative engines stopped picking one link from a list of ten and started weaving multiple sources into a single answer with citations. GEO doesn't optimize for ranking. It optimizes for being woven in and receiving attribution for it. We measure this one way: citation share, the percentage of the time your content gets cited when the same question comes up.

LLMO reaches deepest and remains most uncertain. It attempts to influence what's embedded in the model's core knowledge. If ChatGPT is asked about enterprise AI adoption in Taiwan, would it mention you without searching? At this level, you can mostly influence but not guarantee outcomes. Anyone claiming they can guarantee the model will remember you isn't being trustworthy.

Which one should you actually buy

In practice, these four layers aren't pick-one-of-four. They're different outputs from a single content operation. A well-structured piece with high fact density and clear definitions feeds all four: it retrieves well (AI SEO), extracts cleanly as an answer (AEO), deserves citation (GEO), and over time might register in the model (LLMO). The real question has never been which acronym to buy. It's whether your content is substantial enough for AI to cite it.

The difference comes down to which layer is broken right now. Your brand doesn't appear on Perplexity? Start with GEO. Voice search keeps surfacing your competitors? Start with AEO. These terms identify where the work is needed. They don't solve it.

How we see it

At Tenten, we frame this as FDM, or front-line deployment marketing. The approach fits one sentence: instead of chasing acronyms, write every piece of content as a citable source. Clear definitions, verifiable numbers, clean structure, paragraphs that AI can copy straight into its response. Acronyms change every year. LLMO may carry a different name next year. What drives content selection as an AI source? Those principles remain constant. This comparison table follows that standard.