Week 2 · the discipline

AEO, GEO, SEO: what's actually different

Three acronyms, one real question — are you in the answer? Here's how AI visibility differs from search, and how AEO and GEO fit together.

Makefield · Berlin

One umbrella, two labels

AI visibility, and the two names for it

Three acronyms get thrown around for this work, and the alphabet soup hides a simple picture.

The umbrella term — the clearest one for anyone outside the field — is AI visibility: how a company is recognised, described, and recommended when someone asks an AI assistant. Under that umbrella sit two labels you'll see used almost interchangeably: AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization). As of 2026 there's no settled definition separating them, and practitioners swap the terms freely. So don't let the two acronyms convince you there are two disciplines. There's one.

The outcome and the machinery

Where AEO and GEO actually differ

When it's worth drawing a line between them, the cleanest one is outcome vs machinery.

AEO is the outcome — the answer itself. Are you named? Described correctly? Recommended over the alternatives? That's what a buyer experiences, and it's what you ultimately care about.

GEO is the machinery — being retrievable and citable inside the model's synthesised answer. Clear content, corroborating sources, structured data, an unambiguous identity: the things that make a model able to pull you in and quote you.

The honest one-liner: GEO is largely how you earn AEO. Same goal — visibility inside AI answers — seen from two angles. One is the result; the other is the work that produces it.

Why it isn't SEO with a new name

Ranking gives you links; AI gives a conclusion

The most common mistake is treating this as search engine optimization wearing a costume. It isn't, and the difference is structural.

SEO answers one question: do we rank for this query? It earns you a spot in a list of ten links, and the buyer chooses. AI visibility answers three different questions:

• Recognition — does the model know you exist, as the right kind of entity?
• Accuracy — when it describes you, is what it says true?
• Citation — when it answers, whose sources is it drawing on: yours, or someone else's account of you?

And the output is different in kind. A search engine hands over links and lets the reader decide. An assistant hands over a conclusion. A page can rank at the top of Google and still be missing, misdescribed, or uncited inside the AI answer — because being findable is not the same as being understood and repeated correctly. Different surface, different rules.

The map

Both halves, in one method

Because AEO and GEO are two views of one discipline, measuring AI visibility properly means covering both — the outcome and the machinery. The way I split it:

• The outcome (the AEO half): whether you're recognised as an entity, described accurately, framed fairly against competitors, and actually recommended.
• The machinery (the GEO half): whether your information is documented clearly, structured so a machine can read it, and present in the sources AI engines trust.

Measure only the outcome and you know you have a problem but not why. Measure only the machinery and you're optimising parts without knowing if the answer changed. You need both halves to connect a fix to a result.

Method vs field

ACE is how you work it, not what it is

One last distinction, because it's where a lot of writing in this space gets sloppy. AEO and GEO name the field — the terrain. They don't name a method. The method I use to work that terrain is ACE: Audit, Correct, Engineer — then Measure and Monitor to keep it honest. The field is the same one everyone is in; the method is how the work actually gets done. Keep them separate and the rest of this course stays clear: every lesson ahead is about one part of the terrain, worked with that method.

Next, the first dimension of the outcome — Recognition: does AI know you exist at all? For the short definitions, the glossary has them.

Common questions

Questions this lesson answers

What's the difference between AEO, GEO, and SEO?

SEO earns you a rank in a list of links, and the reader still chooses. AEO and GEO are about the AI answer itself — whether an assistant names you, describes you correctly, and recommends you inside its synthesised reply. Of the two AI terms, AEO (Answer Engine Optimization) points at the outcome a buyer sees, and GEO (Generative Engine Optimization) points at the machinery that earns it: being retrievable, citable, and clearly documented. As of 2026 practitioners use AEO and GEO almost interchangeably — the honest summary is that they name one discipline, AI visibility, while SEO is a separate, older one.

Are AEO and GEO two different things?

Not in practice. There is no settled definition separating them in 2026, and practitioners swap the terms freely. When a distinction is useful, the cleanest one is outcome versus machinery: AEO is the answer itself — are you named and described correctly? — and GEO is what makes that possible: clear content, corroborating sources, structured data, an unambiguous identity. Same goal seen from two angles. Treat them as one discipline, not two.

I already rank #1 on Google — isn't that enough for AI to recommend me?

No. A top rank governs a list of links; it does not govern whether an assistant names you in its answer, describes you accurately, or recommends you over a competitor. A page can rank first and still be missing, misdescribed, or uncited inside the AI reply, because being findable is not the same as being understood and repeated correctly. Those are separate outcomes, and this course measures them separately.

Is AI visibility just SEO with a new name?

No — the difference is structural, not cosmetic. A search engine hands over links and lets the reader decide; an assistant hands over a conclusion. That changes the questions you have to ask: not only "do we rank?" but "does the model know we exist, does it describe us accurately, and whose sources is it drawing on?" The skills overlap with SEO in places, but the surface and the rules differ enough that treating it as search with a costume is the most common way to get this work wrong.