AI SEO

AI SEO is the practice of applying search-optimization work — crawl access, indexation, page structure, and clear on-page answers — to the AI surfaces that read and cite web pages, rather than only to ranked search results. It is the part of AI visibility that happens on a domain you own and can edit.

That boundary is the whole value of the term, and it is also its limit. AI SEO is necessary: an answer engine cannot cite a page it cannot fetch, parse, or trust. It is also bounded by arithmetic. An AI answer is assembled from many sources, and only one of them is yours.

Is AI SEO a separate discipline, or SEO renamed?

Mostly it is SEO applied to a new surface, with one genuinely new requirement: crawler access is now split between search and training. Google is explicit that its AI features run on core Search systems.

Google's AI features documentation states that a page must be indexed and eligible to appear with a snippet before it can be shown as a supporting link in AI Overviews or AI Mode, and that there are no additional technical requirements beyond that. Its generative AI optimization guide calls AEO, GEO, and AI SEO common industry terms, says sites do not need special AI files or markup for its search features, and says it does not treat llms.txt as a visibility signal.

The one control that is genuinely new is access separation. OpenAI documents OAI-SearchBot as the crawler that surfaces sites in ChatGPT search, and GPTBot as the crawler governing model-training use. Its publisher FAQ says public sites can appear in ChatGPT search when OAI-SearchBot is allowed. A robots rule written to keep content out of model training will quietly remove a site from a live answer surface if it blocks both.

So the honest answer: if a team already does technical SEO well, AI SEO adds a small number of decisions rather than a new function.

What does citation data say about the ceiling on your own domain?

Across the sources AI engines actually cite, vendor-owned domains are a small minority of the population — 823 of 23,978. That ratio is the single most useful number for sizing an AI SEO program.

The Machine Relations Index observes six answer engines against 1,010 monitored prompts and records which root domains they cite. In release mri_score_v2.0+2026-09-26+2b779408cfda, covering May 10 to September 26, 2026 — 133 observed days, 16,925 answer runs, 132,514 citation events — the engines cited 23,978 distinct source domains. The Index classifies those domains by the role the source plays, and publishes, for each class, how many domains it contains and how many answer runs cited a domain of that class.

Source class	Cited domains	Runs cited	Runs cited per domain
Search or media platform	9	1,515	168.3
Community and social platform	27	4,468	165.5
Wire and press-release distribution	10	248	24.8
Vendor-owned source	823	11,450	13.9
Editorial publication	1,294	13,993	10.8
Analyst and consulting research	364	3,443	9.5
Academic and government source	428	4,006	9.4
Market and company database	700	6,256	8.9
Other observed source	20,323	72,589	3.6

(Counts read from the Index's published Source Role Summary at the release above. The per-domain column divides the two published columns within a class. The Index publishes run counts per class, not a share of citations by class, so no percentage of total citations is derived here.)

Two findings sit in that table, and they cut in opposite directions.

A vendor-owned domain is not a weak source. At 13.9 cited runs per domain, vendor-owned sources are cited slightly more per domain than editorial publications at 10.8, and more than analyst research, academic sources, or market databases. This is the case for doing AI SEO at all: a company's own site is a legitimate, frequently cited source, and the work that makes it fetchable and extractable is not wasted.

The heaviest-cited individual domains are platforms, not company sites. Nine search and media platform domains average 168.3 cited runs each, and 27 community and social platform domains average 165.5 — roughly twelve times a vendor-owned domain. A brand reaches domains like those by being discussed on them, which is a different program from editing its own pages.

An AI SEO program optimizes one domain among 823 vendor-owned domains, inside a cited universe of 23,978. That is a real contribution with a hard ceiling, and knowing the ceiling is what stops a team from spending a year of budget on the wrong layer.

What is in scope for AI SEO, and what is not?

In scope: everything on a domain you control. Out of scope: every other source in the answer.

In scope for AI SEO	Out of scope, and reached another way
Crawl access for search and AI crawlers	What third parties publish about the brand
Index and snippet eligibility	Whether community platforms discuss the product
One clear answer near the top of the page	Whether analysts and databases list the company
Headings that match real buyer questions	Whether the brand's facts are consistent off-site
Structured data that matches visible text	Which sources an engine already trusts in the category
Original evidence on the page	Corroboration of the brand's claims by anyone else

The right-hand column is not a list of things to ignore. It is the list that decides most answers, and it is the subject of Machine Relations — the discipline of making a brand legible, retrievable, and credible across AI-mediated discovery, coined by Jaxon Parrott, founder of AuthorityTech, in 2024. AI SEO sits at Layer 4 of the Machine Relations stack, alongside AEO and GEO, and depends on the earned authority, entity clarity, and citation architecture beneath it.

How do AI SEO, AEO, GEO, and Machine Relations differ?

They are nested, not competing. Each names a different scope of the same problem, and the names are often used loosely in the market.

Term	Scope	Success condition
SEO	Ranked search results	A position on the results page
AI SEO	Your own domain, on AI surfaces	The engine can fetch, parse, and use your page
AEO	Direct-answer surfaces	Your source is selected inside the answer
GEO	Generative responses broadly	Visibility across generated answers
Machine Relations	Every source the engine reads	Cited and recommended across engines, from sources you do not own

The research paper that formalized generative engine optimization defines GEO as improving content visibility in generative-engine responses and reports visibility gains of up to 40% from its evaluated methods, varying by domain. That work is about the content itself. It does not address the part of the answer supplied by sources outside the publisher's control, which the table above puts at the majority of the cited universe.

How should a brand run AI SEO?

Run it as a bounded technical program with a fixed scope, then measure what it cannot reach.

1. Confirm the page is indexed and snippet-eligible, which Google names as the technical requirement for its AI features.
2. Confirm Googlebot and OAI-SearchBot can fetch it, and check that any training-related block does not also block search.
3. Put one direct answer to one buyer question in the first paragraph.
4. Give each follow-up question its own descriptive heading, so a retrieval system gets clean passages.
5. Match structured data to the visible text rather than adding claims the page does not make.
6. Put original evidence on the page — data, measurement, or first-hand results — so the source is worth selecting over a summary.
7. Measure citations per engine, not only Google rankings, and record which cited sources in your category are not yours.

Step seven is where the program's limit becomes visible. When the answer for a buying question is assembled largely from sources you do not own, the next move is not another on-page revision.

Frequently asked questions

What is AI SEO in simple terms?

AI SEO is making your own website easy for AI systems to reach, read, and use. It covers crawl access, indexation, clear answers, clean structure, and honest evidence on pages you control.

Is AI SEO different from AEO?

Yes, by scope. AI SEO describes the optimization work on your own domain for AI surfaces. AEO describes competing to be the source selected inside a direct answer, which depends on more than your own pages.

Does AI SEO require a special file like llms.txt?

No. Google's generative AI optimization guide states that sites do not need special AI files or markup for its search features and that it does not use llms.txt as a visibility signal. Ordinary crawl access and index eligibility remain the requirement.

Can AI SEO alone get a brand cited in AI answers?

Sometimes, and within a limit. Vendor-owned domains are cited at 13.9 runs per domain in the Machine Relations Index, so an owned page is a plausible source. But 823 of 23,978 cited source domains are vendor-owned, and the most-cited individual domains are search, media, and community platforms that on-page work cannot reach.

How is AI SEO measured?

Per engine, on citations rather than rank. OpenAI appends utm_source=chatgpt.com to ChatGPT search referral links, and Google reports AI-feature traffic inside the Web search type in Search Console. Those two signals report referral traffic. The cited set itself — which sources appeared beside you inside the answer — is what the Machine Relations Index measures.

Is AI SEO part of Machine Relations?

Yes. AI SEO is a Layer 4 distribution tactic inside the Machine Relations stack. It exposes the authority, entity clarity, and citation architecture built in the layers below it, and it cannot substitute for them.