AEO basics · Sep 22, 2026 · By Gerald De Oleo

AEO vs GEO: Same Thing? What the Research Actually Shows

The short answer: they overlap heavily, and most people use them interchangeably. But they came from different places, and they ask different questions.

AEO — answer engine optimization — is about being the answer. GEO — generative engine optimization — is about how generative AI engines assemble answers, and how to shape what goes into them.

Put simply: GEO describes the machine. AEO describes the outcome you want from it.

If you run marketing at a B2B software company, you don’t need to pick a side. You need both questions answered. And both are distinct from the SEO question, which still matters but measures something different. The rest of this post explains why — with the data behind it, and with the places where popular claims about both terms fall apart.

Why this is more than a naming debate

Arguing about acronyms would be a waste of time if the underlying shift were small. It isn’t.

G2 surveyed 1,076 B2B software buyers in March 2026. Half of them — 51% — now start software research with an AI chatbot more often than with Google. A year earlier that figure was 29%. Seventy-one percent rely on AI chatbots at some point in their research.

Two more numbers from the same survey matter more than those:

• 69% chose a different vendor than they originally planned, based on guidance from an AI chatbot.
• One in three bought from a vendor they had never heard of before.

That second figure cuts both ways. AI can introduce a buyer to you. It can also route them to a competitor before they know you exist. This is the shift that makes AEO a different discipline from SEO — in SEO, the buyer browses a list and decides. Here, the model decides what the buyer sees.

“My buyers are enterprise teams. Do they actually use ChatGPT for this?”

G2’s survey covers B2B software buyers and decision-makers specifically, not consumers. ChatGPT is the dominant chatbot in that group, used by 63% for software research. And 41% regularly use deep research modes for structured software evaluations.

That last one is the one to watch. A deep research session reads dozens of pages and produces a shortlist with reasons attached. The description of your company inside that shortlist is written before anyone on your team gets a chance to present.

Where “AEO” came from

AEO is the older term, which surprises people.

Its roots are in 2014, when Google started pulling answers out of web pages and displaying them above the results as featured snippets. For the first time, a searcher could get an answer without clicking anything — and the page that supplied it gained visibility without gaining a visit.

Voice assistants pushed the idea further. When Siri or Alexa answers a question, there’s no list of ten results. There’s one answer, read aloud.

The term answer engine optimization was popularized in 2018. Jason Barnard is generally credited with introducing it publicly — first in a white paper with Trustpilot in January 2018, then in a BrightonSEO talk that April. The framing was about winning “position zero” and single-answer voice responses.

So AEO predates ChatGPT by years. As AI answers arrived, the term stretched to cover them, because the goal hadn’t changed: when someone asks a question, be the answer.

Where “GEO” came from

GEO has a precise origin. It was coined in a research paper.

“GEO: Generative Engine Optimization” appeared as a preprint in November 2023 and was published at KDD 2024, one of the major data-mining conferences. The authors came from Princeton, Georgia Tech, the Allen Institute for AI and IIT Delhi.

The paper defined a generative engine as a system that retrieves information from multiple sources and synthesizes it into a single answer, rather than returning a list. ChatGPT, Perplexity, Gemini and Google’s AI Overviews all fit that description.

Then the researchers did something useful: they built a benchmark. GEO-bench contains 10,000 queries across 25 domains. They tested nine different ways of rewriting source content and measured how much each one changed that content’s visibility inside generated answers.

What they found:

• The strongest methods were adding citations, adding quotations and adding statistics. Each improved visibility by roughly 30–40% on the paper’s main metric.
• Improving fluency — simply making the prose clearer, with no new information — also produced meaningful gains.
• Keyword stuffing performed worse than leaving the content alone.
• Effectiveness varied by domain. What worked for one type of query didn’t necessarily work for another.

These findings explain something I cover in AEO vs SEO: content written to be quoted — with direct answers, specific numbers and clear claims — consistently outperforms content written only to rank for a keyword.

“So if I add statistics and quotes, I get 40% more visibility?”

No — and this is probably the most misquoted number in the field.

The 40% is a best case, not an average. It was measured inside the paper’s own benchmark, on the engines it tested, at one point in time. The authors themselves stress that results vary across domains.

What the paper established is directional, and the direction is valuable: content that is verifiable and attributable gets used more, and content written for keyword density gets used less.

Later research adds a useful corrective. A 2026 review of GEO research highlights a 2025 benchmark called C-SEO Bench, which tested a wider range of “conversational SEO” tactics. Most of them didn’t help, several hurt, and plain relevance to the question kept working. Tricks decay. Being genuinely the best source doesn’t.

The difference that actually matters

Here’s the part most comparisons skip.

Look at what the GEO paper measured. Its visibility metrics capture how much of a generated answer can be attributed to your source — how many words, and how prominently placed. That’s a measure of presence inside the answer.

It doesn’t measure whether the answer is right about you.

That gap is where B2B companies lose deals. In the first post on this blog, I described a SaaS company that appears in seven third-party roundups. By any visibility metric it performs well. Every roundup describes it by the category it has since moved away from. High presence, wrong answer.

Here’s a second case, with a different failure. A company selling operations software runs two live domains. Two years of launch posts, directory listings and investor profiles point at the older one. Its current positioning, its security certification and its best customer results live on the newer one. Applying GEO tactics to the new domain — adding statistics, quotations, citations — would be polishing the source the engines trust least.

Neither problem shows up as a visibility problem. Both show up as lost pipeline. And neither would be caught by SEO metrics alone — rankings and click-through rate can look perfectly healthy while the models describe you wrong.

So the distinction worth keeping is this:

• GEO asks: how do generative engines pick and assemble their sources, and how much of the answer comes from mine?
• AEO asks: when my buyer asks the question, is my company the answer — and is it described correctly?

In the four-stage model I use — retrieval, citation, interpretation, expression — GEO research concentrates on the first two stages. AEO, the way I practice it, has to run all the way through the last two. I explain those stages and how they map to SEO in AEO vs SEO: What Actually Changed.

“Isn’t this just you redefining terms to fit your service?”

Fair question. Nobody owns these definitions, and plenty of practitioners draw the line differently. Some treat AEO as the umbrella term, since it’s older and covers featured snippets and voice as well as AI.

I draw the line this way for a practical reason, not a territorial one: the two questions fail in different ways and need different fixes. You can win the first — lots of visibility inside AI answers — and still lose the second, because the answers say the wrong thing about you. If your reporting only covers the first question, you’ll never see the second failure coming.

Side by side

For how both relate to traditional SEO — where they overlap and where SEO’s metrics go blind — see AEO vs SEO: What Actually Changed.

What Google says — and what that doesn’t cover

Google has been unusually direct about its own AI features. Its official documentation states that there are no additional requirements to appear in AI Overviews or AI Mode, and no special optimizations necessary. The same SEO fundamentals apply: a page has to be indexed and eligible to appear in Search with a snippet.

“So Google is saying AEO and GEO aren’t real?”

Not quite. Google is saying there’s no secret switch for its AI features — and that’s worth taking at face value. Anyone selling a hidden Google AI Overviews hack is selling you nothing.

But there are two things that statement doesn’t cover.

First, it describes Google’s surfaces only. ChatGPT, Perplexity and Claude run their own retrieval and draw on different source pools.

Second, eligibility isn’t selection. Google tells you what makes a page eligible to be used. Its own documentation notes that meeting every requirement still doesn’t guarantee a page will be indexed or served. And nothing in that guidance tells you whether you’ll be described accurately once you are. That’s the gap I outline in AEO vs SEO — SEO covers retrieval well, citation partially, and interpretation and expression not at all.

It’s also worth hearing the sharpest skeptic in the room. In August 2025, Google’s John Mueller responded to the flood of new acronyms on Bluesky: “The higher the urgency, and the stronger the push of new acronyms, the more likely they’re just making spam and scamming.”

He’s mostly right, and it’s a useful filter. The work underneath these terms is real. The urgency and the packaging are where the scams live.

“AI search” isn’t one thing

GEO sometimes gets sold as a single discipline for a single channel called “AI search.” The data doesn’t support that.

The figure you’ll see repeated most often is that only around 11% of domains cited by ChatGPT are also cited by Perplexity. I’d treat the exact number loosely — it circulates through dozens of vendor blogs, usually without a clear trail back to the original data. But every study I’ve seen points in the same direction: the engines don’t draw from the same pool.

The split shows up in B2B specifically, too. In AEO vs SEO I covered a study of more than 22,000 answers to B2B prompts, which found ChatGPT leaning heavily on brand websites while Perplexity and Google AI Mode spread their citations across brand sites, comparison lists and editorial coverage.

“Why do these numbers keep changing from one study to the next?”

Three reasons, and they’re worth knowing before you trust any single percentage.

Different methods. Studies disagree about what counts as a citation, which surface they measured and which queries they used. Two studies of “AI citations” can be measuring genuinely different things.

Incentives. Much of the published research comes from companies selling AI visibility tools or services. That doesn’t make it wrong, but it’s a reason to check who’s publishing.

The engines keep changing. Ahrefs’ own figure for how many AI Overview citations come from top-10 results roughly halved between July 2025 and March 2026 — and Ahrefs acknowledged part of that came from measuring better, not only from Google changing. I covered those shifting numbers in more detail in AEO vs SEO.

The practical conclusion: measure your own category directly. Don’t borrow someone else’s percentage.

What a B2B SaaS team actually needs

Strip away the labels, and the work falls into three layers.

1. The GEO layer: be worth using. This is where the research is most useful, because it’s backed by controlled experiments. Make claims verifiable. Attach sources to statistics. Quote credible people by name. Write clear, direct prose, and answer the question at the top of each section rather than three paragraphs in. None of this is exotic — it’s what makes a source easy to use and safe to cite.

2. The AEO layer: be described correctly. This is where most B2B companies actually lose, and the research says less about it. One consistent description of your company across your site, directories, review platforms and profiles. Correcting outdated descriptions after a repositioning, because the old ones don’t expire — they keep competing with the new one. Making sure the lists and reviews in your category agree with what your own site says.

3. Measurement, per engine. Run the questions your buyer actually asks, across ChatGPT, Perplexity, Gemini and Google’s AI results, and track them separately. For each answer, record whether you were retrieved, cited, interpreted correctly and described in the right category. Note who appears when you don’t.

The order matters. If your company’s identity is inconsistent across the web, GEO tactics amplify the confusion rather than fixing it. Resolve the entity first. Then make it worth citing.

And underneath all of it, SEO fundamentals still apply. If your site can’t be crawled, indexed and retrieved, nothing else here matters.

“If I’m hiring someone for this, should I look for AEO or GEO on their site?”

Neither label tells you much. Ask what they measure instead.

If the answer is mentions, citations or share of voice alone, they’re measuring presence — useful, but only half the picture. Ask whether they check how your company is described inside the answers, and what they do when the description is wrong. That second half is the part that affects pipeline.

“What about LLMO, AIO, AI SEO and the rest?”

Same territory, different labels. LLMO (large language model optimization) and “AI SEO” mostly mean what GEO means. AIO is genuinely ambiguous — it’s used for “AI optimization” and for Google’s AI Overviews, which is reason enough to avoid it. Google’s own AI answers were called the Search Generative Experience before they became AI Overviews.

None of these terms has a formal definition. If a proposal leans hard on a new one, apply Mueller’s filter.

Quick answers

What is AEO? Answer engine optimization is the practice of making your content — and your company — the answer when someone asks a question, whether that answer appears in a featured snippet, a voice response or an AI-generated summary.

What is GEO? Generative engine optimization is the practice of making your content more likely to be retrieved and used by AI engines that synthesize answers from multiple sources. The term comes from a 2023 research paper published at KDD 2024.

Are AEO and GEO the same thing? They overlap heavily and are often used interchangeably. The useful distinction is that GEO focuses on how generative engines assemble answers, while AEO focuses on whether you end up as the answer — and are described correctly.

Which came first? AEO. The term was popularized in 2018, around featured snippets and voice search. GEO was coined in 2023.

Which one does a B2B SaaS company need? Both questions answered. GEO research tells you how to make your content worth using. AEO makes sure the resulting answer is right about your company. And both depend on strong SEO fundamentals for retrieval.

Is GEO replacing SEO? No. Google states that its AI features rely on the same SEO fundamentals as the rest of Search. AI engines still need to find your pages first. What changes is what happens after — and that’s what AEO measures.

How we work

The three layers above — be worth using, be described correctly, measure per engine — are the exact order we follow with clients.

First comes the audit: your domain across ChatGPT, Perplexity, Gemini and Google’s AI results. Not just whether you show up, but how you’re described: category, positioning, claims. Before we ever talk, I run a free Shortlist Check — three or four of your buyers’ questions in one engine — so the first call starts with something real; the audit is the full version.

Then we fix the entity layer — your description across directories, review sites and your own pages — before applying any content tactics. The order matters: GEO tactics on an inconsistent identity amplify the confusion.

Once the foundation is clean, we optimize your content to be cited, and track descriptions monthly across all four engines.

If you want to see how this work relates to what you’re already doing with SEO — what changed, what didn’t, and which metrics cover which layer — I lay that out in AEO vs SEO: What Actually Changed.

Book a call — the free Shortlist Check is done before we talk.