AI SEO, GEO and AEO in 2026: What Google, Bing and ChatGPT Actually Say

First: what do SEO, AEO and GEO actually mean?

The labels are useful shorthand, not settled disciplines. SEO is the work of helping useful pages be discovered, understood and selected in conventional search. AEO usually describes making content useful when a system answers a question directly. GEO describes improving how content participates in generative search and AI answers.

All three overlap. A page that is technically inaccessible, vague or interchangeable is unlikely to become more useful because someone gives the work a newer name.

Is GEO replacing SEO?

No. Google says its existing SEO best practices remain relevant to AI Overviews and AI Mode. Pages still need to be crawlable, indexable, relevant and eligible to appear in Search. Bing's AI reporting makes the same point from a different angle: content has to be available before it can be grounded or cited.

AI search changes how visibility is experienced and measured. It does not erase the need for a site that search engines can discover, understand and trust.

What Google actually says about AI search optimisation

Google's guidance is notably unglamorous. Create helpful, reliable, people-first content. Meet the normal technical requirements. Make important content available in text. Use internal links. Ensure structured data matches visible content.

Google also says there are no additional technical requirements, special schema or AI text files needed for its AI features. Its newer generative AI performance reporting gives site owners a dedicated way to assess visibility. Query fan-out also makes a case against producing a forest of near-identical keyword pages. A broader, genuinely useful piece of work has more to contribute.

What Bing tells us about GEO

Bing Webmaster Tools' AI Performance reporting is useful because it moves the conversation from mystical scores to observable activity. It shows citations, cited pages and a sample of grounding queries across supported AI experiences.

The report does not reveal a ranking system, and citation count is not a measure of page importance. It does, however, give content teams a practical feedback loop: see which pages are referenced, inspect the questions around them, then improve depth, clarity, evidence and freshness where the work warrants it.

What about ChatGPT search?

OpenAI says public websites can appear in ChatGPT search. For content to be included in summaries and snippets, publishers should not block OAI-SearchBot. That is an access condition, not a promise of inclusion, placement or traffic.

There is no responsible way to claim a secret ChatGPT ranking formula. The sensible work is still public, crawlable content that answers a real question better than a generic summary can.

So what actually makes content easier to cite?

My practical framework is less exciting than a GEO checklist, which is why it tends to survive contact with reality.

• Answer a real question clearly, then earn the reader's attention with the detail.
• Add non-commodity judgement, examples or evidence.
• Make factual claims verifiable and distinguish them from opinion.
• Use headings, tables and plain language to help people scan the page.
• Build topical depth rather than thin query variants.
• Keep material information current and show who is accountable for it.
• Make sure the page can actually be crawled and indexed.

What I would ignore

I would be wary of magical GEO scores, bulk FAQ stuffing, rewriting every page into tiny fragments, fake third-party mentions and schema that does not describe the visible page. Tools can be useful for research and monitoring. They become a problem when they turn uncertain platform behaviour into a confident to-do list.

llms.txt may have uses in a particular technical environment, but Google explicitly says it is not needed for its AI features. It is not a substitute for useful work.

The sensible 2026 strategy

Get the SEO foundations right. Create material worth retrieving. Make expertise and evidence obvious. Measure conventional and AI visibility separately where the platforms expose useful data. Then adapt as the data improves.

For the operating side of this work, see how I approach AI-enabled content systems. The point is not a separate AI-search circus. It is a better content operation.