Why Generative Engine Optimization (GEO) Is the Right Term
Profound argues AEO is better than GEO. At SearchSeal, we're sticking with Generative Engine Optimization.
The AI visibility industry has a naming problem, and Generative Engine Optimization is the answer.
There's an ongoing debate in the AI search optimization space: should we call it GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization)?
Our competitor Profound recently published a blog arguing AEO is the better term. They're wrong. Here's why GEO is the industry standard and why it should be.
The a16z Endorsement Changed Everything
In May 2025, Andreessen Horowitz published their thesis: "GEO Over SEO."
That wasn't just an article but a signal to the entire industry. a16z didn't pick AEO. They didn't pick LLMO. They picked GEO. When a firm with that track record says "the foundation of the $80 billion+ SEO market just cracked," people listen. GEO became the dominant term almost overnight.
The Data: GEO Is Winning
According to Search Engine Land's research in late 2025, GEO leads in awareness at 84%, leads in usage at 42%, and grew 121% quarter-over-quarter in search interest. AEO trails behind on all metrics. The market has clearly made its choice.
Profound's Arguments (And Why They Don't Hold Up)
Let's address Profound's specific claims:
"GEO conflicts with geography and geo-targeting"
This is Profound's strongest argument, and yes, "geo" has other meanings. But context matters.
When someone in digital marketing says "GEO strategy," no one thinks they're talking about geology. Just like when someone says "SEO," no one thinks they mean "seniority" or "CEO." Terms derive meaning from context, and in AI visibility conversations, GEO means one thing.
"AEO is more distinct and ownable"
AEO has its own problem: American Eagle Outfitters uses AEO as their stock ticker. Good luck owning that acronym.
More importantly, "ownable" isn't the goal because accuracy is what matters.
"Answer Engine Optimization is clearer"
Here's where Profound fundamentally misunderstands the technology.
ChatGPT and Claude and Gemini and Perplexity are not "answer engines." They're generative engines.
Traditional answer engines like Google's featured snippets or Alexa extract answers from existing content. They find a paragraph that answers your question and display it.
Generative engines do something fundamentally different. They synthesize responses from multiple sources. They don't find answers because they create them.
That's not a subtle distinction but the entire paradigm shift. Calling ChatGPT an "answer engine" is like calling a chef a vending machine. Both provide food, but the process is entirely different.
"AEO builds naturally on existing SEO knowledge"
This argument actually works against AEO.
AEO came from the featured snippet era when the goal was formatting content so Google could extract quick answers. That's 2018 thinking.
GEO represents something new. It's about getting cited by AI models rather than just displayed. It's about building entity authority across the web. It's about creating content that AI systems trust enough to reference. It's about optimizing for a world where users never click through to your site at all.
GEO isn't an extension of SEO but a new discipline, and using a new term signals that to stakeholders.
"Answer engines will remain relevant; generative engines might feel outdated"
This argument has it backwards.
"Generative AI" is the defining technology of our era. GPT and Claude and Gemini are generative models. The technology is generative, and the optimization should reference that.
If anything, "answer engine" sounds like a relic because it evokes the featured snippet days when we optimized for position zero.
GEO Has Academic Legitimacy
The term "Generative Engine Optimization" has academic backing. Princeton University research introduced GEO methodology in a peer-reviewed paper. Wikipedia has a dedicated page for "Generative Engine Optimization" but not for AEO. The Princeton GEO-BENCH study found that adding citations and quotes boosted AI-sourced mentions by over 40%.
When academia and venture capital and industry practitioners all converge on the same term, that's not coincidence but consensus.
The Real Difference: Format vs. Substance
Here's the core issue:
AEO is about formatting answers. Structure your FAQ, add schema markup, make your content extractable.
GEO is about earning citations. Build authority, get mentioned across the web, create content that AI systems recognize as trustworthy.
As Power Digital's Alyssa Smith put it: "Where AEO is about formatting answers, GEO is about earning them."
That distinction matters because AEO tactics like structured data and Q&A formatting are table stakes. They're necessary but not sufficient.
GEO encompasses a broader strategy. It includes entity building across platforms, third-party mentions and citations, digital PR specifically for AI visibility, and creating content that AI systems actively want to reference. AEO is a subset of GEO. It's the formatting layer, but the larger discipline of becoming AI-visible is what GEO represents.
Why This Matters for SearchSeal
At SearchSeal, we track brand visibility across ChatGPT and Claude and Gemini and Perplexity and DeepSeek.
We don't call this "answer engine monitoring." We call it AI visibility tracking.
These platforms aren't answering questions in the traditional sense. They're generating responses and synthesizing information and creating new content that references (or doesn't reference) your brand. Understanding that distinction is the first step to optimizing for it.
The Bottom Line
Profound prefers AEO, and that's their choice.
But the industry has chosen GEO. Andreessen Horowitz used GEO in their thesis. Princeton researchers built their methodology around GEO. Wikipedia documents GEO. Search volume favors GEO. Industry adoption favors GEO.
More importantly, GEO is the accurate term because these engines generate rather than just answer.
Call it what it is: Generative Engine Optimization.
