SEO. AEO. GEO. LLMO. AIO. SXO. GXO. If you have sat through a vendor pitch lately, you have probably been told that at least two of these are the future, that the others are dead, and that your current agency does not understand the difference. It is worth knowing, before you spend a budget on any of it, that the experts do not agree either. As of early 2026 there was still no consensus definition separating these terms in the academic literature, and trade publications use them more or less interchangeably.
So here is the plain-English version, without the alphabet-soup theatre. There is one shift and four labels for it. People increasingly get answers from AI systems instead of clicking through a list of links, and every one of these acronyms is an attempt to name some slice of "make sure your brand shows up in that answer." Below: what each term genuinely means, how much they overlap, the roughly 20% that is actually different, and how to decide what your team should do about it.
Terminology in this area is unsettled and changing fast. The definitions below reflect the most common industry usage in late 2026; treat any vendor's stricter definition as a preference, not a standard.
The four terms, defined
Each term came from a different moment and carries the fingerprints of that origin, which is most of why they sound different.
| Term | The job it names | Where it came from |
|---|---|---|
| SEO Search engine optimization |
Rank in results and earn the click | The original discipline |
| AEO Answer engine optimization |
Be the answer that gets extracted and quoted | Featured snippets and voice search |
| GEO Generative engine optimization |
Be cited as a source inside an AI-generated answer | Academic research, 2023 |
| LLMO Large language model optimization |
Be understood and described accurately as a brand | Practitioners, as ChatGPT scaled |
The shorthand worth remembering: SEO gets you ranked, AEO gets you quoted, GEO gets you cited, LLMO gets you known. The others you will see are mostly umbrella or experience terms. AIO and "AI SEO" are used as catch-alls for the whole area. SXO and GXO shift the emphasis to the experience someone has after they arrive, which matters more as AI sends people to you pre-informed rather than at the start of their research.
How much of this is actually different?
Less than the marketing suggests. Industry analysis commonly puts the functional overlap between GEO and LLMO at around 80%, with the main distinction being origin and scope rather than tactics. AEO sits close behind, since being formatted as a clean, extractable answer is also what makes you easy to cite.
Google's own position is blunter still. Its 2026 documentation on optimizing for generative AI features says that optimizing for generative AI search is optimizing for the search experience, and thus still SEO. That does not mean nothing has changed, but it does mean the foundations are unchanged: crawlable, fast pages, clear structure, genuine expertise, accurate content that answers real questions. If those are broken, no acronym saves you, which is why our guide to technical SEO fundamentals and the on-page SEO checklist are still the right starting point.
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The short answer There is one shift and four labels for it. SEO gets you ranked, AEO gets you quoted, GEO gets you cited, LLMO gets you known. Roughly 80% of the work is shared, and Google says optimizing for AI search is still SEO.
The 20% that genuinely changes your work
The overlap is large, but the remainder is real, and it is where the new thinking lives. Four things behave differently once an AI is assembling the answer.
You get cited from pages you do not own. AI systems draw heavily on community sites, review platforms, listicles and video. Analysis of the most-referenced domains has repeatedly found sites like Reddit, LinkedIn and YouTube near the top. That makes digital PR, review presence and third-party mentions part of search work, not a separate brand activity, which is a genuine departure from classic on-site SEO.
Content has to survive being chopped up. Models extract passages, not pages. A paragraph that only makes sense after three paragraphs of preamble will rarely be quoted. Self-contained sections, direct answers placed near the question, and clear headings matter more than they did, a point we covered in designing websites for AI search.
Your brand becomes an entity, not a page. LLMO's real contribution is the reminder that models hold a compressed representation of who you are, assembled from everything they have read. If that picture is wrong or outdated, you have a problem no landing page fixes. Consistency in how you describe yourself across the web is the lever.
Measurement changes shape. There is no position to track. You track whether you are mentioned, how often, in what company, and whether what the AI says about you is accurate. That is a different reporting job, and it follows the same logic as the shift we described in zero-click content and success metrics.
Which lens should you actually prioritise?
Rather than adopting all four as separate workstreams, pick the emphasis that matches the questions your buyers ask.
→ They ask factual, specific questions ("how long does X take", "what is the fee") → lean AEO. Format crisp, self-contained answers near the top of the page.
→ They ask comparison or recommendation questions ("best X for Y", "X vs Z") → lean GEO. Earn presence on the third-party lists, reviews and communities AI tools quote from.
→ They ask about you by name ("is X any good", "who is X") → lean LLMO. Make sure your brand facts are consistent, current and verifiable everywhere.
→ They still click through and buy on your site → keep SEO the priority, and treat the rest as an extension.
The rule: most businesses need all four eventually, but in one plan with one owner, not four teams and four retainers.
For most teams the honest sequence is: fix the SEO foundations, make your best pages quotable, then work on off-site presence and brand accuracy. The tactical detail for the citation half is in our GEO playbook, and the broader SERP context is in how AI Overviews are reshaping SEO.
Why the names keep multiplying
It is worth naming the commercial dynamic. A new acronym is a marketing asset: it implies a new discipline, which implies new expertise, which justifies a new line item. That is not automatically cynical, since genuinely new practices do need names, but it does explain why the list keeps growing faster than the underlying practice changes.
The practical defence is to judge any proposal on what it will change on your site and how it will be measured, not on the label. If a pitch leans on owning a three-letter term rather than on specific work, treat that as a signal. Internally, the useful move is to choose one term, write down what you mean by it, and use it consistently in briefs and reports. GEO has become the most widely used umbrella in practice; what matters is that your team stops debating vocabulary and starts shipping.
What to do this quarter
Start with an honest audit: ask the ten questions your buyers actually ask across a few AI tools, and record whether you appear, what is said, and who is cited instead. That single exercise usually settles the strategy debate faster than any framework. Then fix the foundations before chasing citations, since crawlability, structure and clarity are prerequisites for everything else.
From there, make your most valuable pages extractable, with direct answers to specific questions rather than long wind-ups. Build the off-site presence that AI systems quote, including reviews, credible lists and communities where your buyers already are, and support it with formats that travel, using approaches from our guide to content distribution. Keep your brand facts consistent so models describe you correctly, and treat original data, which is hard for anyone to summarise away, as a citation magnet worth investing in, the same principle behind a content strategy that compounds.
Finally, set expectations on measurement. Reporting suggests that between 40% and 60% of cited sources change month to month across major AI surfaces, so visibility here is far less stable than rankings, and attribution is the single biggest complaint marketers raise about this area. Track a consistent set of prompts over time, watch the trend rather than the week, and pair it with the wider picture from attribution in a privacy-first world and an honest funnel audit. Owned channels such as a newsletter remain the steadiest counterweight to all this volatility.
The short version
SEO, AEO, GEO and LLMO are four labels for one shift: people increasingly get answers from AI systems rather than clicking a list of links. SEO gets you ranked, AEO gets you quoted, GEO gets you cited, LLMO gets you known, and AIO, SXO and GXO are umbrella or experience variants of the same idea. There is no agreed taxonomy, the terms are used interchangeably, GEO and LLMO overlap by roughly 80%, and Google says optimizing for generative AI search is still SEO. The 20% that genuinely differs is worth your attention: citations often come from pages you do not own, content must work when extracted in pieces, your brand is treated as an entity whose facts need to be consistent, and measurement moves from rankings to mentions, share of voice and accuracy. Choose your emphasis by the questions your buyers ask, run one plan with one owner rather than four retainers, judge vendors on the work rather than the acronym, and expect volatile results that only make sense as a trend.
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Explore SEO →Frequently asked questions
What is the difference between SEO, AEO, GEO and LLMO?
They describe overlapping work with different emphasis. SEO, search engine optimization, is about ranking in classic search results and earning the click. AEO, answer engine optimization, is about being formatted so precisely that your content becomes the answer that gets extracted and quoted, a term that grew out of featured snippets and voice search. GEO, generative engine optimization, is about being cited as a source inside an AI-generated answer in tools like ChatGPT, Perplexity and Google's AI features, and it came from academic research in 2023. LLMO, large language model optimization, is about how models understand and represent your brand as an entity, so they describe you accurately when someone asks. A useful shorthand: SEO gets you ranked, AEO gets you quoted, GEO gets you cited, LLMO gets you known. In practice the work overlaps heavily, commonly estimated at around 80% between GEO and LLMO, and no agreed taxonomy exists, so different vendors use the terms differently.
Is AI search optimization just SEO with a new name?
Mostly, yes, and Google says so directly. Its 2026 documentation on optimizing for generative AI features states that optimizing for generative AI search is optimizing for the search experience, and is therefore still SEO. The foundations do not change: crawlable pages, clear structure, genuine expertise, accurate information and content that answers real questions. What does change is emphasis. AI systems pull from a wider spread of sources, including community sites and video, so mentions on pages you do not own matter more. Content needs to be extractable in self-contained chunks rather than requiring the whole page for context. Your brand needs to be a recognisable entity described consistently across the web. And measurement shifts from rankings and clicks toward citations, mentions and share of voice. So treat AI search optimization as an extension of SEO with new emphases and new metrics, not as a separate discipline that replaces it or requires a separate team.
Which acronym should my team actually use?
Pick one term, define it internally and move on, because the label matters far less than the work. There is no consensus taxonomy in the industry or in academic literature, and the terms are used interchangeably across trade publications and vendors, so arguing about which is correct wastes time you could spend on the actual optimization. GEO has become the most widely used term for AI citation work, AEO is the clearest when your focus is being the extracted answer, and LLMO is the most precise when your concern is how models describe your brand. AIO, AI SEO, SXO and GXO circulate too, usually as umbrella or experience-focused variants. What matters for a marketing team is agreeing on internal vocabulary so briefs and reports are consistent, and evaluating vendors on their methods and measurement rather than on the acronym they sell. If a supplier's pitch rests on owning a new three-letter term rather than on what they will change on your site, that is a signal in itself.
How do you measure AI search visibility?
You measure presence rather than position, because there is no ranking to track. The practical metrics are citation or mention rate, meaning how often your brand appears when relevant prompts are asked, share of voice against competitors in those answers, the accuracy of what the AI says about you, coverage across different platforms since each behaves differently, and any referral traffic that arrives from AI tools. Expect volatility: reporting suggests that between 40% and 60% of cited sources change month to month across major AI surfaces, so a single week's snapshot tells you very little and trends matter more than individual results. Attribution is genuinely hard, and a majority of marketers name it as their biggest challenge in this area, partly because much AI influence produces no click at all. The sensible approach is to track a consistent set of prompts over time, watch the trend rather than the daily noise, and pair it with brand-level signals such as branded search and direct traffic.
