AEO vs SEO: what is the difference?

Treat AEO as an emphasis within SEO. It rests on the same foundations, pages that can be crawled, indexed and understood, and adds attention to how cleanly each page answers a specific question, so a system assembling an answer can quote it or name you.

On this page we label three kinds of claim: what a platform documents, what a research study found, and what we recommend. Most pages on this topic blur them together.

What people mean by AEO

AEO stands for answer engine optimisation. There is no formal standard behind it and no specification that any platform maintains. Google’s own guide to generative AI search does mention the term, describing AEO and GEO as “terms you may see used to describe work specifically focused on improving visibility in AI search experiences.” Google is describing how the words get used; it does not set rules for them.

In our observation, usage varies between writers. Some include voice assistants and featured snippets. Some use AEO interchangeably with GEO. Others mean something narrower: content shaped so a chatbot can lift a clean answer from it. If someone offers you “AEO”, ask which of these they mean and what exactly they will change on your site.

What SEO covers

SEO is the broader discipline. It covers technical access, so search engines can fetch and index your pages; content that answers real queries; site structure and internal linking; page experience; and the signals that make a source look credible. It predates answer engines by decades, and the core logic holds: be findable, be useful, be credible. We set out the principles we work from in our GEO principles.

Where AEO and SEO overlap

Most of the work. An answer engine cannot quote a page it cannot reach, and a page that is not indexed will not surface in a search feature. Clear writing helps a human reader and a summarising system alike. Consistent facts help everywhere.

Google says this directly about its own AI features (documented behaviour). Its Search Central page on AI features states that “The best practices for SEO remain relevant for AI features in Google Search” and that “There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary.” To be eligible as a supporting link, “a page must be indexed and eligible to be shown in Google Search with a snippet.”

So if your SEO foundations are weak, an AEO layer on top will not rescue them. For the closely related comparison, see GEO vs SEO.

Where they differ in practice

The differences are mostly about emphasis and about who is doing the reading.

What success looks like

Classic SEO usually aims at a click from a ranked result. AEO, as people use the term, aims at being named or quoted inside an answer, which may or may not send anyone to your site. That changes what you measure (see below).

How you write

Question-led headings with the direct answer first suit a skimming reader and a system pulling out a passage. That is our recommendation and nothing more. Google’s guide also says “You don’t need to write in a specific way just for generative AI search”, so think of it as good writing practice that Google does not require.

Which crawlers you manage

AI products use several user agents, each with its own job, and robots.txt can treat each one separately. This is what each company documents:

Company	User agent	What the company says it is for
OpenAI	OAI-SearchBot	Surfacing websites in ChatGPT’s search features
OpenAI	GPTBot	Training; disallowing it signals content should not be used to train its foundation models
OpenAI	ChatGPT-User	Fetches a user asks for; not automatic crawling, and robots.txt rules may not apply
Perplexity	PerplexityBot	Surfacing and linking websites in Perplexity search results; not used to train foundation models
Perplexity	Perplexity-User	Fetches for user actions; generally ignores robots.txt because a user requested the fetch
Anthropic	ClaudeBot	Collecting web content that could contribute to training
Anthropic	Claude-User	Retrieving pages when people ask Claude questions
Anthropic	Claude-SearchBot	Indexing to improve search result quality

OpenAI says its settings are independent: a site can allow OAI-SearchBot to appear in search results while disallowing GPTBot. The practical risk is a blanket rule aimed at training crawlers that also shuts out a search crawler you wanted. More on this in crawler access.

What the platforms themselves say

Google’s guide to optimising for generative AI features on Google Search is the most direct statement we have found from any platform on this subject. Among other things, it says:

• You do not need new machine readable files, “AI text files, markup, or Markdown” to appear in Google Search, and Google Search itself does not use them.
• “Structured data isn’t required for generative AI search, and there’s no special schema.org markup you need to add.” Google still recommends structured data for rich result eligibility, and its AI features page asks that structured data matches the visible text on the page.
• “There’s no requirement to break your content into tiny pieces for AI to better understand it.”
• Prioritise “effective SEO strategies” over what it calls “AEO/GEO hacks”, and “Be wary of third-party tools that promise ranking success or claim to use ‘internal’ Google metrics.”

OpenAI, Perplexity and Anthropic each publish crawler documentation. It covers how their bots identify themselves and how you can allow or restrict them.

robots.txt itself is defined in RFC 9309, the Robots Exclusion Protocol. The RFC describes rules that crawlers “are requested to honor” and states that “These rules are not a form of access authorization.” So it works only as far as a crawler chooses to follow it. Anthropic says its bots honour robots.txt; OpenAI and Perplexity both say their user-requested fetchers may not apply it.

None of this documentation offers a method that guarantees inclusion in an answer. Our stance is that no such guarantee exists, from any platform or any provider.

What research says, and its limits

The most cited study is Aggarwal et al., “GEO: Generative Engine Optimization”, accepted at KDD 2024. The authors tested nine ways of rewriting a source: Authoritative, Statistics Addition, Keyword Stuffing, Cite Sources, Quotation Addition, Easy-to-Understand, Fluency Optimization, Unique Words and Technical Terms. They measured how visible each rewritten source became in generated answers, using a word count weighted by position in the answer and a model-scored “subjective impression”.

The conditions matter. Their benchmark, GEO-bench, holds about 10,000 queries drawn from nine sources, mostly informational. The main experiments ran on a simulated engine that took the top five Google results for each query and wrote answers with gpt-3.5-turbo, with a smaller test on Perplexity.ai. The authors report that the effective methods varied by domain.

They also state their limits: the methods “may need to adapt over time” as engines change, they did not evaluate how the methods affect search rankings, the benchmark needs continuous updates as queries shift, and some labels may reflect subjective judgement. Read the paper as useful evidence about one controlled setup. We do not quote its headline figures here, because a number stripped of those conditions misleads more than it informs.

What to do if you have limited time

This checklist is our recommendation, with no guarantee attached.

1. Check your key pages are indexed and can show a snippet, which is Google’s stated condition. Look for stray noindex or nosnippet tags.
2. Read your robots.txt and confirm it is not blocking crawlers you want, such as OAI-SearchBot, PerplexityBot or Claude-SearchBot.
3. Rewrite the headings on your most important pages as the questions buyers ask, with a direct answer in the first sentence or two beneath.
4. Keep facts consistent across your site and your external profiles: names, descriptions, locations, what you offer.
5. If you use structured data, make sure it matches what the page visibly says.
6. Start tracking whether you are named or cited for your buyers’ questions.

Most small teams can work through the first five in a sitting. The sixth is ongoing.

How to tell if it is working

This is harder than rank tracking. Answers vary by phrasing, by product and over time, so a single check proves little.

For Google, its guide points to the Generative AI performance report in Search Console as the way to see how your content performs in its generative AI features. For other assistants, list the questions your buyers really ask, run them on a regular schedule, and record whether you are named or cited and whether the details are right. Over time that gives you a trend rather than a snapshot. Our measurement section sets out a simple routine.

If nothing moves, the work is still useful: the same groundwork supports ordinary search.

Where GEO fits in

GEO, generative engine optimisation, is the name the Aggarwal paper gave its framework, and Google’s guide mentions it alongside AEO. In our observation the two are often used interchangeably, though some writers draw a line between them. For the longer treatment, see what GEO is. For practical purposes the labels matter less than the work underneath.