Read three articles about optimizing for AI search and you'll walk away with four acronyms for it: AEO, GEO, LLM SEO, AI SEO.
Some writers treat them as the same thing. Others invent razor-sharp distinctions to sound like they coined a category that, frankly, doesn't have settled borders yet.
So let me give you the honest starting point: the terminology is unsettled, and anyone who tells you otherwise is selling certainty that doesn't exist yet.
The acronyms point at a real, overlapping set of practices. But the field is young. No industry body or dominant player has locked the definitions in place.
What I can do is tell you what each term actually means in practice today, where the lines genuinely fall, and which one is worth your attention.
We run this work for clients and on our own properties. So this is the distinction we use day to day, not a thesaurus exercise.
The acronym soup, sorted#
Before the comparison, here's the one-line version of each term as people actually use it.
SEO (search engine optimization) is the original. You optimize a page to rank in a list of links on Google or Bing, you win a position, you collect a click. Everything below grows out of it.
AEO (answer engine optimization) is optimizing so a search engine lifts your content as the direct answer to a question, instead of as one link among ten. Think featured snippets, the answer box at the top of a Google result, the spoken reply a voice assistant reads back. AEO predates generative AI. It grew up around Google's answer features and voice search.
GEO (generative engine optimization) is optimizing to be named and cited inside the answers generative AI engines write from scratch, like ChatGPT, Perplexity, and Google AI Overviews. The engine doesn't hand you a position. It writes an original answer and either quotes you as a source or leaves you out.
LLM SEO and AI SEO are the loose umbrella phrases. Different people use them to mean GEO, or GEO plus AEO, or "whatever it takes to show up when AI is involved." Handy as conversational shorthand. Useless as precise categories.
So don't agonize over which label is "right." Agonize over the work.
The terminology is unsettled, and anyone who tells you otherwise is selling certainty that does not exist yet.
What answer engine optimization (AEO) is#
AEO is about winning the direct-answer surfaces a search engine assembles, mostly by pulling text straight off a page.
Ask Google "how long does a cold email sequence take," and it shows a boxed answer at the top lifted verbatim from someone's page. That's the AEO surface. Same mechanism feeds voice assistants: when a smart speaker reads back a single answer, it's usually reading an extracted snippet.
Here's the defining trait. The engine is selecting and displaying your existing text, not writing something new. Your sentence shows up more or less as you wrote it.
That makes AEO an extension of classic SEO. The page still has to rank well, and on top of that it has to be structured so the engine can lift a clean, self-contained answer out of it.
AEO has been a named practice for years, which is why it feels more settled than GEO. It was the first label that caught on for "optimize for the answer, not just the ranking."
What generative engine optimization (GEO) is#
GEO is about being cited inside an answer the engine writes itself.
Ask ChatGPT or Perplexity "which agency should I hire for B2B SEO," and the engine doesn't return a list of pages. It composes an answer in its own words and footnotes the sources it leaned on. You're either named in that answer, with a link, or you're invisible.
There's no position to climb, because there's no list. For the tactics that win the most-used surface, see how to rank in ChatGPT.
That's the real break from everything before it. AEO lifts your sentence and shows it. GEO reads your page, reasons over it, blends it with other sources, and decides whether you're credible and clear enough to quote.
The work changes accordingly. You're not just structuring a quotable answer anymore. You're building enough topical authority that a model treats you as a source worth trusting.
GEO is also broader in surface. It spans live retrieval (ChatGPT Search, Perplexity reading the web in real time) and the model's training data (what it "knows" from being written about across the web over months). The deep how on each lives in our complete guide to generative engine optimization. Here we're drawing the boundary, not walking the full playbook.
AEO vs GEO, side by side#
The cleanest way to see the difference is across a few questions: what you're optimizing for, where it shows up, what signals move it, and how you measure it.
ChatGPT Search vs training data, at a glance
- Goal — AEO (answer engine optimization): Win the direct-answer slot a search engine extracts; GEO (generative engine optimization): Get cited as a source inside an AI-written answer
- Example surfaces — AEO (answer engine optimization): Featured snippets, Google answer box, voice assistant replies; GEO (generative engine optimization): ChatGPT and ChatGPT Search, Perplexity, Google AI Overviews, Gemini
- How you appear — AEO (answer engine optimization): Your text is lifted and displayed largely as written; GEO (generative engine optimization): The engine writes an original answer and names you as a source
- Core signals — AEO (answer engine optimization): Page ranks well, content structured for clean extraction; GEO (generative engine optimization): Extractable content, topical authority, references across the web
- How you measure it — AEO (answer engine optimization): Snippet wins, answer-box presence in the SERP; GEO (generative engine optimization): Whether AI engines cite you, tracked by running real queries
- Maturity of the term — AEO (answer engine optimization): Established for years; GEO (generative engine optimization): New and still forming
That table doubles as the answer to most "aren't these the same thing" questions. They share a backbone (be structured, be extractable) and split on the surface and the trust mechanics.
Do they overlap?#
Yes. Heavily. And pretending otherwise is where most of the confusion comes from.
The shared foundation is real. Both reward content that leads with the answer, uses headings that mirror real questions, and keeps facts in short, standalone sentences a machine can lift cleanly. Both depend on a crawlable, fast, well-structured page.
Do the work to win a featured snippet and you've already done a chunk of the work to be citable in an AI answer.
AEO and GEO share a backbone and split on the surface and the trust mechanics.
Where they genuinely differ is the target surface and the trust model. AEO targets answer features inside a traditional search engine, and mostly cares whether your page ranks and extracts cleanly. GEO targets generative engines, which don't just extract your text. They reason over it and weigh whether you're a credible enough source to quote, blended with everyone else they read.
That trust-and-citation layer is the new work. It's the reason GEO isn't just AEO with a longer acronym.
So the lines are blurry by design. The structural work overlaps. The surface and the citation mechanics are where the practices actually split.
Which should you focus on?#
For most B2B companies, the honest answer is GEO is the bigger near-term opportunity - with one caveat.
The caveat first. AEO isn't dead, and the structural work it asks for is the same work GEO needs. Optimize a page to be extractable enough to win a snippet, and you've made it more citable in an AI answer too. You rarely have to choose between the foundations.
So why point your attention at GEO? Because of where the field stands.
Generative AI search behavior is net-new, and the corpus the engines pull from is still young. On a lot of topics, no source owns the answer yet.
That's the opposite of fighting for a featured snippet on a query with a decade-old incumbent. The seat is often empty. And citations earned early compound into a position that's expensive to dislodge later.
Then there's where your buyers are going. More and more of the high-intent research, the "which tool, which partner, which approach" questions, is happening inside generative engines instead of a ten-link results page. Being the cited answer at that moment is about as warm as discovery gets.
None of this is hype. It's a read on timing. The structural foundations serve both, and the open, compounding opportunity is on the generative side right now.
How to start#
Good news: you don't need to resolve the terminology to start the work. The first moves are the same no matter what you call them.
Make your most important pages extractable. Lead each section with the answer, break walls of text into quotable sentences, add structured data.
Confirm the engines can actually reach you, with clean HTML and the right crawlers allowed.
Then run your real buyer questions through ChatGPT, Perplexity, and Google, and log where you're cited and where you're absent. That baseline tells you exactly where the authority work needs to go next.
The deeper how lives in the full playbook and the printable GEO checklist.
One note on which term we use, since we have a side. We use GEO as the umbrella. It's the broadest of the four, it names the surface where the opportunity actually is right now, and it covers the citation-and-trust work that the older "answer optimization" framing leaves out.
AEO is a real, narrower piece of the picture. LLM SEO and AI SEO are fine as casual shorthand. But when we scope and report this work, we call it GEO, and that's the discipline our service is built around.
If you want this done for your business, that's our job. Book a consultation and we'll run your real buyer questions across the AI engines, show you where you stand today, and map what it'll take to own those answers. You can see the full range of what we build on our services page.
