People started searching inside ChatGPT, Claude, Perplexity, and Gemini, and a stack of acronyms showed up to describe what to do about it. Most of the confusion is people using AEO, GEO, SEO, and LLMO interchangeably when they describe different work on different surfaces. Here are the definitions, clean enough to lift.

SEO is Search Engine Optimization, the original. You optimize a page to rank in Google's ranked, blue-link results. The unit of success is a ranking position, and the reward is a click.

AEO is Answer Engine Optimization. You structure a page so an engine can extract a direct answer out of it: a featured snippet, a People Also Ask box, a Google AI Overview, a cited line in a chatbot reply. The unit of success is being the extracted answer, not the tenth blue link.

GEO is Generative Engine Optimization. You optimize to be cited inside the long-form responses LLMs generate. ChatGPT, Claude, Perplexity, and Gemini each compose an answer and footnote a handful of sources, and GEO is the work of being one of those footnotes across all of them.

LLMO is LLM Optimization, and it is broader than GEO. It includes whether your brand exists as a recognized entity inside the model's training data at all, which is a longer job than optimizing any single page. GEO gets you cited this quarter; LLMO decides whether the model reaches for you when nobody named you.

"AI SEO" is the umbrella term most teams use for AEO, GEO, and LLMO run as one discipline. I treat them as overlapping layers under a single search-visibility function, not as competing budgets. The whole stack lives in the AI Search and Answer Visibility domain.

SEO vs AEO vs GEO: the comparison table

The fastest way to hold the difference is to put them side by side.

These are not three strategies you choose between. They are three layers, and the top two are growing while the bottom one stops being the whole game.

What is the difference between AEO and GEO?

This is the pair people conflate most. AEO is about extraction. GEO is about citation inside generation. The line between them is the line between a featured snippet and a footnote.

AEO optimizes for systems that lift a clean, direct answer out of your page and show it. The mechanics are the ones that have always won snippets: question-led H2 and H3 headings, a one-sentence answer immediately under each heading, a definitional paragraph at the top of a category page, FAQ and HowTo schema. The same structure that earned a featured snippet on Google in 2022 now earns a citation in ChatGPT.

GEO is the broader job of being cited inside the answer an LLM composes from scratch. The model reads dozens of sources, writes a paragraph, and footnotes a few of them. To be one of those footnotes you need things that are harder to fake than structure: original research, primary statistics, quotable insights, and presence across the wider web that the model already trusts. A perfectly structured page that no other site links to is easy for a model to extract and hard for a model to trust. That trust problem is why GEO bleeds into PR and the brand and entity-presence play.

Do I still need SEO?

Yes. Do not replace SEO with AEO; extend it. You still need to be crawlable, indexed, and authoritative, and AI crawlers read the same web, so SEO is the foundation the other layers sit on.

What trips people up is that the two systems no longer agree on which page wins. Only 38% of Google AI Overview citations come from pages that rank in Google's top 10 for the original query, down from 76% in July 2025 (Ahrefs, March 2026, n=863K SERPs). For non-Google LLMs like ChatGPT, Gemini, and Copilot the gap is wider: roughly 80% of citations come from pages that do not rank in Google's top 100 for the original prompt. Perplexity is the exception, citing top-10 pages 28.6% of the time.

The page that ranks first in Google is often not the page that gets cited in the AI answer. The two systems pull from different signals, and the second one is growing. So the move is not to drop SEO, it is to stop assuming SEO covers you. The teams winning in 2026 run SEO, AEO, and GEO under one function with shared KPIs.

How do you measure AEO and GEO?

By citations instead of clicks, which is the whole point. SEO measurement is rankings and organic clicks: the click economy. AEO and GEO measurement is the citation economy, because a brand can be surfaced, recommended, and influence a purchase inside an AI answer without ever generating a click.

The primary KPI is not a single prompt result, which fluctuates. It is entity presence: whether your brand is the thing the model reaches for when discussing your category, and how that trends. Underneath it sit citation rate (share of target queries where an LLM cites you), LLM share of voice versus competitors, citation diversity across engines, and AI Overview presence.

One warning that costs people real money: tools that track via API systematically under-report what real users see, because API and scraped web responses overlap on only about 24% of brands. If your dashboard is API-only, it answers a different question than the one your buyers ask. Named tools here include Profound and AthenaHQ, and the trustworthy ones lean on scraped UI responses. Bing Webmaster Tools also ships a free AI Performance dashboard that surfaces the actual sub-queries LLMs fan out to.

Where does llms.txt fit?

llms.txt is a technical GEO and LLMO asset, essentially a sitemap written for AI crawlers: a plain-text file that tells a model what your site covers and points it at the content you want pulled into context. Vercel went from under 1% to 10% of new signups coming from ChatGPT in six months after publishing llms-full.txt. That is signups, not a vanity metric, which is why it has become a North Star for AI search in B2B SaaS.

It only works on top of the boring foundation: server-side rendering, valid structured data, sensible robots.txt rules. Many AI crawlers do not execute JavaScript, so a client-rendered page is invisible to them no matter how good the content is. Get the plumbing wrong and the rest of the investment never gets read.

Which one should I invest in first?

Start with AEO, because it has the fastest payback and most of it transfers. Structuring your highest-intent pages for extraction wins snippets, AI Overview citations, and chatbot mentions from the same work, and it makes the pages better for humans too. That structure is what the content production play ships at volume.

Then layer GEO on the pages that matter most, since citation needs more than structure: original data, quotable lines, and corroboration across the web. Run LLMO and entity work in parallel as the long game. Keep SEO running underneath all of it. The order is sequence, not substitution. If you are not sure which layer is your binding constraint right now, run the two-minute diagnostic and it will name the one thing to fix first. The framework that holds it together lives at the Marketing OS.

FAQ

What is the difference between SEO, AEO, and GEO?

SEO is Search Engine Optimization: ranking a page in Google's ranked results to earn a click. AEO is Answer Engine Optimization: structuring a page so an engine can extract a direct answer, such as a featured snippet or a citation in an AI Overview. GEO is Generative Engine Optimization: being cited as a source inside the long-form answers LLMs like ChatGPT, Claude, Perplexity, and Gemini generate. SEO optimizes for rank, AEO for extraction, and GEO for citation. They are layers, not alternatives.

Does SEO still matter in the age of AI search?

Yes. You still need to be crawlable, indexed, and authoritative, and AI crawlers read the same web that Google does, so SEO is the foundation AEO and GEO sit on. What changed is that ranking first in Google no longer guarantees you are cited in AI answers. Only 38% of AI Overview citations come from pages in Google's top 10, and for non-Google LLMs roughly 80% of citations come from pages outside Google's top 100. You extend SEO with AEO and GEO; you do not replace it.

How do you measure AEO and GEO?

By citations instead of clicks. The core metrics are citation rate (the share of target queries where an LLM cites you), LLM share of voice versus competitors, citation diversity across ChatGPT, Claude, Perplexity, and Gemini, and AI Overview presence. The most useful primary KPI is entity presence, meaning whether the model reaches for your brand when discussing your category, since single-prompt results fluctuate. One caution: API-based tracking under-reports reality, because API and scraped responses overlap on only about 24% of brands.

What is LLMO and how does it relate to GEO?

LLMO is LLM Optimization, and it is broader than GEO. GEO is about earning citations inside generated answers, mostly through content and structure. LLMO includes that, but it also covers whether your brand is a recognized entity inside the model's training data at all: Wikipedia and Wikidata presence, Schema.org Organization markup, author credentials, and mentions across the wider web. GEO is the quarter-by-quarter citation work. LLMO is the longer-horizon entity work that decides whether the model knows you exist when no one names you.

Should I publish an llms.txt file?

Yes, especially for content-heavy and developer-tool sites. llms.txt is a plain-text file that tells AI crawlers what your site covers and points them at the content you want in context. The proof point is Vercel going from under 1% to 10% of new signups from ChatGPT in six months after publishing llms-full.txt. It only works on top of server-side rendering and valid structured data, since many AI crawlers cannot execute JavaScript.