You’ve probably seen both terms by now. GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) both refer to the work of showing up in AI-generated responses, and they’re mostly describing the same practice with different labels. But there are subtle differences worth understanding, especially when you’re reading vendor pitches or academic research. This post covers both.

The short version

Both describe the work of getting your business, product, or content cited in AI-generated responses. The work overlaps almost entirely.

Where the terms come from

AEO has older roots. The phrase “answer engine” dates back to search engine optimization discussions in the 2010s, when Google started answering some queries directly in the results page rather than just listing links. Early AEO work focused on:

When ChatGPT and similar AI products emerged, the AEO community extended the term to cover them. The underlying logic was similar: optimize content to get pulled into a direct answer rather than just linked to.

GEO emerged more recently and more narrowly. The term gained prominence from an academic paper by Princeton and Allen Institute researchers that studied what kinds of content changes increased visibility in generative AI responses. The paper proposed “GEO” as a framework for optimizing specifically for generative engines.

Since then, GEO has been adopted by some vendors (particularly in enterprise marketing and analytics), some academic researchers, and some consultants who wanted a fresh term to differentiate themselves from the broader SEO/AEO crowd.

What the academic GEO paper actually found

The paper that popularized “GEO” studied how specific content modifications affected visibility in generative AI responses. The key findings:

The headline takeaway: generative AI products reward content that looks like real, authoritative writing, with citations and data, rather than content optimized for keyword matching. This is roughly what AEO practitioners had already concluded empirically.

Where GEO and AEO overlap

Almost completely. The techniques that work for one work for the other.

If you’re running an AEO program, you’re running a GEO program. The terminology doesn’t change the work.

Where the terms diverge slightly

A few subtle differences in how the terms are typically used.

AEO includes non-generative features. Featured snippets, voice assistants, and knowledge panels are part of AEO but not usually part of GEO. If your business depends heavily on those older answer features, AEO is the more complete term.

GEO is more focused on generative outputs. ChatGPT, Claude, Perplexity, Gemini, and other generative products are the exclusive focus of GEO. The term doesn’t usually cover older answer-box features.

GEO has an academic framing. Because GEO emerged from research, the term often appears in contexts that treat the work as empirical and data-driven. AEO has a more practitioner, marketing-focused history.

Vendor positioning differs. Some vendors use GEO to differentiate themselves from SEO agencies who have added “AEO” to their pitch without changing their practices. The distinction is mostly marketing.

Which term should you use

For most businesses and marketers in 2026, AEO is the more widely-used term. It shows up more in industry publications, vendor marketing, client conversations, and general discussion. If you’re building a program, hiring an agency, or explaining the work to a stakeholder, AEO will be the more familiar term.

GEO is worth knowing as a secondary term, especially if you’re reading academic research, enterprise vendor marketing, or anything from the specific research communities that use it. Don’t fight the term, just recognize it describes the same work.

The practical implication

The terminology question matters less than people sometimes make it out to be. What matters is the underlying work: earning credibility signals, creating extractable content, maintaining entity clarity, and measuring results in real AI products.

If an agency tells you GEO and AEO are fundamentally different and requires different strategies, they’re overselling the distinction. If an academic paper uses GEO to describe the work you already know as AEO, it’s the same thing with a different label. Focus on execution, not vocabulary.

The broader pattern

This isn’t the first time the same practice has been called multiple things. SEO itself went through this: early practitioners used terms like “search engine marketing,” “SEM,” and “organic search optimization” before the field settled on SEO. The AI optimization space is in a similar moment where multiple terms compete, and one will eventually dominate.

My guess is AEO wins in the long run because it’s easier to say, has broader historical roots, and is already more widely used. But GEO will persist in specific communities, especially academic and enterprise research contexts.

Don’t pick sides. Know both terms. Do the work.

The bottom line

GEO and AEO are nearly identical concepts with different origins. GEO emerged from academic research with a specific focus on generative AI products; AEO emerged from search marketing with a broader focus on any direct-answer system. The techniques that work for either work for both.

Use AEO as your primary term because it’s more widely understood. Recognize GEO when you see it. Don’t let terminology debates distract you from the actual work, which is the same regardless of what you call it.