AEO vs SEO is a comparison of two targets: ranking in a search engine’s list of links, or being quoted in an AI answer. SEO wins a position in the search results for a query. AEO wins a citation in the answer an AI model composes on ChatGPT, Claude, Gemini, Perplexity and Google’s AI Overviews, where the links arrive as citations.
The difference between AEO and SEO shows up in what decides a win, the unit that competes, and what you measure. Rank and citation diverge, too: in our 91-day refresh study the relationship between Google rank and AI citation was basically zero. Most of the work carries over, so the same content set, the same pages, serves both search surfaces and both disciplines.
Reading the AI half is the job an AI SEO platform does, because an answer has no rank report.
AEO vs SEO at a glance
| SEO | AEO | |
|---|---|---|
| What you’re optimising for | A position in the ranked list of links | A quote inside a composed answer |
| Where your content shows up | The search engine results page | Chat assistants such as ChatGPT, Claude, Gemini and Perplexity, and Google’s AI Overviews |
| What decides whether you win | The ranking algorithm’s scoring of your page against the query | The searches the model runs, the pages it reads, what it can lift from them |
| The unit that competes | The page | The passage |
| What you measure | Impressions, positions, clicks | Whether you’re recommended or cited, and in which answers |
| What you change | Content, technical health, links | The same, plus extractability and third-party presence |
Some of this is familiar ground for anyone who has run SEO. Where the two surfaces genuinely split is what decides a win, the unit that competes and what you measure, and it’s worth slowing down there.
What AEO and SEO actually are
SEO, search engine optimisation, is the work of getting your pages to rank in the list of links a search engine returns for a query. AEO, answer engine optimisation, is the work of getting your content quoted in the answer an AI engine writes in place of that list.
SEO’s levers have been stable for years: relevance to the keyword, a site a crawler can reach, content depth, and authority built through links. Its result is a ranked list, measured in impressions, positions and clicks.
AEO points at the newer surfaces: the chat assistants such as ChatGPT, Claude, Gemini and Perplexity, and Google’s AI Overviews, where a model writes the answer and the links come along as citations. The label has some history, which is worth one paragraph.
It originally meant featured snippets and voice assistants, and some writers now use GEO, generative engine optimisation, for the same target, while a few split the terms and keep AEO for question-and-answer surfaces. This post uses AEO for the whole job, which is the loose common usage.
What decides whether you win
How Google ranks pages hasn’t changed much: an algorithm scores each page against the query and orders the links, so in SEO you win a position. How an AI answer chooses its sources is different: the model runs a series of searches, reads what comes back, composes an answer, and quotes a few of the pages it read, so in AEO you win a place in what it says.
Google describes that first step in its AI features documentation as query fan-out, “issuing multiple related searches across subtopics and data sources”, and says AI Overviews only appear when its systems determine they are “additive to classic Search”. On ChatGPT, Claude and Gemini, the model even decides whether to search at all. That’s what our own monitoring records on every run, and Perplexity is the exception, because its search is always on.
Every recommendation rests on that audit of searches and pages. In our Credibility Layer study, 200 reasoning sessions on ChatGPT ran 5,933 searches and produced 1,982 citations, and every recommendation came out of a credibility audit of roughly 30 searches the model made before answering, mostly across pages the brand didn’t own. Where the business ranked in Google barely touched whether it got recommended.
So the two surfaces don’t agree on who won. Our 10x AI Visibility Refresh study refreshed 98 posts, published nothing new, and saw AI citations rise 10x in 91 days while the relationship between Google rank and citation stayed basically zero (Spearman rho 0.000, p = 0.914). Over the same window, 41 cited pages had no Google impressions at all.
A page can rank first and go uncited, and a page Google barely shows can get quoted. Neither number reports the other, which is the practical reason the two surfaces need their own measurement.
What stays the same when you add AEO
Yes, your SEO still matters for AI answers, almost all of it, and Google says so directly. Its documentation for AI features states there are “no additional requirements” to appear in AI Overviews or AI Mode, beyond a page that’s indexed and eligible to show a snippet. It also rules out the work you might have been sold: “no special schema.org structured data that you need to add”, no machine-readable files, no AI text files.
What Google recommends is what SEO has recommended all along: pages a crawler can reach, internal links, a good page experience, and “important content is available in textual form”. So the fundamentals are the entry ticket for both surfaces rather than a second job the AI side adds.
Off-site authority carries over too, and it works harder on the AI side. The model reads third-party pages before it answers, so the pages other people own are where it goes to check what your brand is worth, and links and mentions were doing that job before AI answers existed. The sources you built for SEO now feed the answers.
There’s no gaming shortcut on either surface. A page that wins answers the question better than the alternatives, with claims a model can attribute, and that’s the same test whether the judge is an algorithm or a model. Making the page better is the whole job, which is why the two disciplines don’t fork into two teams.
What actually changes with AEO
When you optimise for AI answers, three things genuinely change, and each one moves where the effort goes rather than what the work is made of.
The unit that competes is a passage
A search engine ranks your page. An AI scores your page one chunk at a time, lifting the passage that answers the question in front of it, which is what our 3 Numbers study measures. So a section that answers the sub-question on its own beats a page that’s strong overall, because the section is what gets quoted.
That changes how a page is built. The answer has to exist as a passage a model can lift: a stated answer in the first sentence of a section, extractable facts, nothing important buried in a video or implied by the layout. A page that’s strong overall but never says anything plainly has nothing to lift, and it goes uncited for reasons a rank report won’t explain.
What the content is made of
The words matter, but what they carry matters more, and the GEO benchmark, published at KDD 2024, measured the difference. Across its 10,000 queries, the Princeton team found that adding quotations, statistics and source citations to the same text boosted visibility by up to 40% in generative engine responses, evaluated on a BingChat-style engine and on Perplexity. Keyword stuffing did about 10% worse than the baseline on Perplexity in the same tests.
So the content that wins an answer is built from claims a model can attribute: quotes, real numbers, named sources. That’s a brief you can hand a writer, and it’s the same brief that always produced copy people finished reading.
Where you have to show up
Your own pages stop being the only place that matters. The model reads third-party pages before it answers and quotes from what it finds, so being present in the pages it already trusts is part of the work: reviews, comparisons, buyer threads, the sites that show up when it searches your category.
The set of queries widens too. The searches the model runs, fanouts in our tracking, are rarely your keywords: they’re the buyer’s own words, rephrasings and subtopics, so visibility has to extend past the keyword list into the phrases buyers actually type.
How SEO and AEO work together
SEO and AEO work as one content set serving two surfaces, and the same work usually pays twice. The fundamentals are shared: indexed pages, clean crawling, textual content, internal links. On top of those, AEO adds passages that answer on their own and presence on the third-party pages, and neither addition hurts the Google side.
Our own refresh research is the cleanest test of that overlap we have. The work was refreshing pages that already existed, publishing nothing new, and the AI citations followed while the same pages kept serving the search results. One work set, two surfaces, one team.
Sequence the work instead of splitting it. Keep the fundamentals first, because both surfaces stand on them, then make the key pages extractable, then work the third-party sources. And measure each surface separately, because rank won’t report the AI side and citations won’t report rank.
Which should you pick?
Whether you focus on SEO or AEO depends on where your buyers actually ask, and most teams end up doing both on one content set.
Lead with SEO if your buyers still run through Google and the assistants barely touch your category’s questions yet. The demand is there today, and everything you build is reusable if the AI side grows, so nothing is wasted.
Lean into AEO if buyers already ask assistants for recommendations in your category. Those questions get answered either way, and the assistant names a short list of brands while it answers.
And if you’re one operator with one site, there’s no either-or to settle. The same sequence of work covers both surfaces, so the choice is which one you look at first, and the honest answer is the one your buyers are already on.
FAQ
Is AEO replacing SEO?
No, they run on the same pages and the same fundamentals. Google’s documentation for its AI features says a page needs nothing beyond being indexed and eligible for a snippet to appear in AI Overviews or AI Mode, so the work doesn’t fork into two content sets. What does split is the reporting: rank and citation diverge, so your SEO numbers can’t tell you how the answers are going.
Is SEO dead now that AI answers queries?
No. In our research, pages with no Google impressions were getting cited in AI answers while highly ranked pages went uncited, so neither surface reports the other, and buyers still use both. A page that only serves one surface leaves demand on the other.
Where Omnipresence fits
SEO reports on itself. Search Console gives you the queries, the positions, the impressions and the clicks, so the Google side of this comparison is already measurable. The AI side has no rank report, so most teams can’t see which buyer questions got answered without them, whether the assistant put them forward or only named them, or which pages it read before deciding.
Omnipresence covers that half. It tracks the questions your buyers actually ask on ChatGPT, Claude, Gemini and Perplexity, and shows whether each assistant recommends you or a competitor, so a drop turns into a work list: the searches each assistant ran before answering, the pages it read and cited, and yours marked against the pages that won.
The lift is provable, too. You can show it came from your work rather than a model update, because a few questions are held back as control prompts and never worked. And the fix doesn’t sit on a list: the target and the method go to the agent your team already uses, over MCP, for the rewrite, the refresh and the outreach.
The limits, stated plainly: it tracks those four assistants and nothing else, so there’s no AI Overviews or AI Mode reporting. You bring your own model key and pay the providers at list price, and the data on which pick came first is partial, because assistants don’t always return a list at all. It measures recommendations and citations, not traffic or revenue, so you connect the money on your side.
If you want to see which questions the assistants are already deciding in your category, you can point it at one brand for a week.