AEO vs GEO: When to Use Each Strategy (Decision Framework)

Two acronyms causing confusion: AEO and GEO. Both target AI. Both want citations. Both feel important. But which one should you actually focus on—and what happens if you pick wrong?

The cost of confusion here isn't just wasted time—it's optimizing for the wrong audience entirely. Let's fix that.

The Confusion: Why They Sound the Same

It's alphabet soup out there right now. AEO. GEO. LLMO. AI SEO. Every vendor and thought leader has their preferred terminology, and they all seem to mean roughly the same thing: optimize your content so AI engines recommend it.

Both approaches focus on AI visibility. Both want you to be the "cited source." Both require strong E-E-A-T signals. It's easy to think they're just two words for the same thing.

They aren't. And confusing them costs you time.

You might spend weeks optimizing for an answer engine format that 99% of your customers never use. Or you might ignore the specific entity signals that ChatGPT needs, assuming your standard SEO work is "good enough" for generative engines.

The terminology confusion exists because these fields evolved rapidly and different communities used different names. But underneath the labels, there's a real distinction worth understanding.

Let's clear the board.

What is AEO?

Answer engines are essentially "search engines 2.0." They crawl the web live, read multiple pages, and synthesize an answer with citations to the sources they used. Unlike traditional search, they don't just show you ten blue links—they construct a response and attribute it.

The goal of AEO is to appear in that synthesis. When someone asks Perplexity a question in your domain, you want your content to be one of the sources it cites. The citation includes a direct link back to your page—visible attribution that can drive traffic.

The audience here is specific: tech-savvy early adopters, researchers, and people who want "facts fast" without wading through multiple websites. It's a smaller ecosystem, but a highly intent-driven one. If you sell developer tools, specialized B2B software, or deep-tech hardware, your audience is likely spending time here.

Because answer engines crawl the web in real-time, content freshness matters more here than in GEO. Your page needs to exist and be crawlable right now. Schema markup, clear structure, and explicit answers to questions give you an edge.

What is GEO?

GEO targets generative engines—the platforms that don't just search the web but generate responses based on their training data and retrieval systems. Think ChatGPT, Claude, and Gemini.

The goal of GEO is to become part of the model's knowledge base—or at least a trusted source it can retrieve. You want to be the brand that ChatGPT recommends when a user asks, "What's the best CRM for small businesses?" or "How do I structure my about page for AI visibility?"

The scale difference between AEO and GEO is massive. And Semrush research shows that traffic from AI recommendations carries a 4.4x conversion lift. That's the prize GEO is chasing.

GEO success depends heavily on entity recognition. Does ChatGPT understand who you are as a brand? Do you have enough third-party mentions, Wikipedia presence, and consistent information across the web? These signals matter more than content freshness because the model is making decisions based on accumulated trust, not real-time crawling.

Key Differences

So how do AEO and GEO differ under the hood? There are six main distinctions that shape your strategy.

How to Read This Comparison: Each row represents a dimension where AEO and GEO diverge. The right choice depends on which column better matches your audience and resources.

The entity recognition difference is especially important. Internal analysis suggests that approximately 60% of GEO success comes down to entity strength—whether the model understands who you are. AEO engines care more about content freshness and structure because they're crawling in real-time.

Practical Examples: Who Should Choose What

Theory is useful, but examples make the decision clearer. Here's how different business types should think about their AI visibility strategy:

Decision Framework

Which one should you prioritize? Here's a framework to make the decision based on your specific situation.

SEO = be found. AEO = be quoted. GEO = be recommended.

For 90% of businesses reading this, GEO is the priority. The sheer volume of users on ChatGPT and Gemini makes it the necessary play. But don't sleep on AEO if you serve the technical elite—those users are highly engaged and often influential.

Common Mistakes to Avoid

Understanding the difference is step one. Avoiding common traps is step two. Here's what not to do:

1. 1
   Treating them as interchangeable

   AEO and GEO require different optimization tactics. Content that ranks in Perplexity citations won't automatically get ChatGPT recommendations, and vice versa. Plan for each specifically.

2. 2
   Optimizing for AEO when your audience uses GEO

   If your customers are asking ChatGPT for recommendations, perfect Perplexity citations won't help you. Know where your audience actually searches before investing optimization effort.

3. 3
   Neglecting entity work for GEO

   GEO success requires entity recognition—AI needs to understand who you are. If you're only creating content without building brand presence across the web, ChatGPT won't recommend you.

4. 4
   Ignoring AEO for technical products

   If you sell to developers or researchers, Perplexity visibility might matter more than ChatGPT presence. Don't default to GEO just because the market is bigger—match the strategy to your specific audience.

The Good News: Skills Overlap

Here's the relief: approximately 60% of SEO skills transfer to both AEO and GEO.

Intent identification, content structure, research skills, authority building, and entity understanding all apply to both strategies. If you're already doing good SEO, you're not starting from zero—you're building on a foundation.

The platform has changed. The surface is different. But the game of organizing information for machines? You're already playing it. What changes is where you focus your optimization effort and which platform-specific signals you prioritize.

Getting Started

Ready to put this framework into action? Here's your first week: