GEO / AEO

GEO stands for Generative Engine Optimization. AEO stands for Answer Engine Optimization. Both name the practice of preparing content so that AI systems can find it, understand it, and quote or cite it when they compose an answer for a user. In everyday use they are close to synonyms, and most people reaching for either one mean the same set of tactics. The residual differences are differences of history and emphasis, and they matter less than the volume of writing about them suggests. For the architecture underneath both — the six properties that decide whether an assistant can retrieve, parse, and attribute a site at all — see Your CMS is a GEO decision.

What each acronym stands for

	GEO	AEO
Expands to	Generative Engine Optimization	Answer Engine Optimization
Documented from	November 2023, in an academic paper	February 2018, in SEO trade press
Systems it was coined for	Large language models composing an answer from several retrieved sources	Featured snippets, the Knowledge Graph, and voice assistants returning one definitive answer
Usual emphasis today	Being one of the sources a generative system draws on, and being represented accurately in what it writes	Being the answer a system returns to a direct question
Common objection to the term	”GEO” already means geography and geo-targeting in a search context	The 2018 meaning predates language models, so the term carries older baggage

Those rows describe emphasis. Every one of the distinctions in them is broken somewhere in current usage.

Where the terms came from

Generative Engine Optimization entered circulation through a paper of that name by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, posted to arXiv on 16 November 2023 and published at KDD 2024. The paper treats generative engines as systems that answer a query by synthesising information from multiple sources, proposes a black-box framework for optimising against a defined visibility metric, and reports gains of up to 40% on GEO-bench, a benchmark the authors constructed. Marketing usage followed the paper and has drifted a long way from it. The paper measures one metric under controlled conditions; the industry term covers whatever a practitioner does in the hope of being quoted.

Answer Engine Optimization is older and has no comparable founding document. It appears in trade press by 7 February 2018, in a Search Engine Watch article by Rebecca Sentance covering a webinar with Chee Lo of Trustpilot and Jason Barnard of Kalicube. The framing there is voice search and Google’s shift toward returning a single definitive answer through featured snippets and the Knowledge Graph, with smart speakers reading that answer aloud. Language models are absent from the account, because consumer products built on them were years away. Claims about who coined the term circulate, most often naming Barnard, and they rest largely on self-description. What is checkable is that the term was in trade use by early 2018 and meant something narrower then.

So the two acronyms describe technologies that converged. AEO was named for answer engines that existed before language models, and GEO for the systems that arrived afterwards. Both are now applied to both.

Where they overlap

The overlap in practice is close to total. The work described under either heading is the same work: content that is reachable by an automated reader, clearly structured, accurate, and specific enough to be worth quoting.

Vendors selling into the category say as much. Profound, which sells AI-visibility tracking, published a post in June 2025 arguing that the two terms describe one strategy, and stating a preference for AEO on the grounds that GEO collides with geography and geo-targeting in search results. That is a naming argument from an interested party. The useful part is that a company with a commercial stake in the distinction declines to claim a methodological one.

What Google says about both terms

Google addresses the acronyms directly in its guide Optimizing your website for generative AI features on Google Search, last updated 10 July 2026. It records that AEO and GEO are terms used for work aimed at visibility in AI search experiences, and states that from Google’s perspective, optimizing for generative AI search “is optimizing for the search experience, and thus still SEO”.

The mechanism Google documents explains why it takes that position. Its generative features are grounded in the core Search ranking systems through retrieval-augmented generation, which retrieves pages from the Search index and generates a response from them. Alongside the query a user types, Google’s systems perform query fan-out, issuing concurrent related queries to gather more results. Eligibility follows from ordinary Search eligibility: a page has to be indexed and eligible to be shown with a snippet, and the site has to be included in Search generative AI features in Search Console.

Google’s guide also lists what it says can be ignored for its surfaces:

• Special files and markup. No new machine-readable files, AI text files, markup or Markdown are needed, and Google states that Search ignores llms.txt files, so maintaining one neither helps nor harms Google visibility.
• Chunking. There is no requirement to break content into small pieces, and Google states there is no ideal page length.
• Rewriting for machines. Google’s systems handle synonyms and general meaning, so writing in a special register for AI is unnecessary.
• Manufactured mentions. Seeking inauthentic mentions across the web is called out as less useful than it appears, and scaled content produced to cover every query variation falls under Google’s scaled content abuse spam policy.
• AI-specific structured data. Structured data is not required for generative AI search and there is no special schema.org markup to add, though it remains worth using for rich results.

Two qualifications belong beside all of that. Google speaks for Google, and the assistants that account for a large share of AI answers publish nothing equivalent about how they select sources. And a search engine’s guidance describes the outcome it wants from publishers, which is a legitimate source and a partial one.

What the measurements show

The best-evidenced claim in the field is unglamorous: conventional search visibility predicts AI visibility better than any AI-specific tactic yet measured.

Seer Interactive ran the study most often cited for this. Published on 7 January 2025, it started from more than 300,000 finance and SaaS keywords and roughly 600,000 People Also Ask questions across Google and Bing, narrowed to about 10,000 questions, and put those through the GPT-4o API to see which brands were mentioned. Brands ranking on page one of Google showed a correlation of about 0.65 with mentions in the model’s answers. Bing rankings correlated less strongly, around 0.5 to 0.6. Backlink counts, expected to matter, came out weak or neutral. Filtering out forums, aggregators and social platforms strengthened the ranking correlation for the remaining sites.

Ahrefs has published measurements that complicate the picture at the level of individual pages. In August 2025 it took 15,000 long-tail queries across ChatGPT, Gemini, Copilot and Perplexity, and found that 12% of cited links appeared in Google’s top 10 for the same prompt, with Perplexity higher at 28.6% and the others near 8%; 80% of citations did not rank anywhere in Google for the original query. In March 2026 it analysed 863,000 keyword SERPs and 4 million AI Overview URLs, and found 37.9% of cited URLs ranking in the top 10, 31.2% at positions 11 to 100, and 31.0% beyond position 100. Its own earlier analysis from July 2025 had put the top-10 share near 76%.

Why those findings are less contradictory than they look

They measure different objects. Seer measured whether a brand gets named across a set of questions. Ahrefs measured whether a specific cited URL ranks for the exact query that produced the citation. A brand can be mentioned reliably while the particular page cited each time varies.

Query fan-out accounts for more of the gap. Because Google generates related queries alongside the one typed, a page can be retrieved for a fan-out query it ranks well on and appear in an answer to a question it does not rank for at all. Checking the cited URL against the original query understates the role of ranking by design.

What survives both readings is modest and worth stating plainly. A page that is indexed and ranks well for the questions around its topic is in the best-evidenced position to be cited. No published measurement has identified a tactic that produces AI visibility independently of that.

The llms.txt question

llms.txt is the file most often proposed as an AI-specific lever, which makes it the clearest test of whether such levers work.

Ahrefs studied it across 137,210 domains that received traffic in May 2026. Some 28% of them published an llms.txt file, roughly 38,000 sites. Of those files, 97% received no requests at all during the month. Among the requests that did arrive, 96% came from bots, and the largest single category was SEO audit tools at 21.7%, followed by general web crawlers, tech profiling tools, and tools studying llms.txt adoption itself. Named AI tools of all kinds accounted for 19.5% of fetches, and AI retrieval bots specifically, the ones tied to live answering in ChatGPT and Perplexity, for 1.1%.

Set that beside Google’s statement that Search ignores the file, and the position is clear enough: publishing one is cheap, harmless, and currently does close to nothing for retrieval. The 28% adoption figure is informative in its own right. A large share of sites maintain the file with no published evidence that the systems it addresses read it.

What is still unsettled

The vocabulary. GEO, AEO, LLMO and “AI SEO” are all in circulation for overlapping territory, no standards body defines any of them, and Google’s own documentation avoids all four in favour of describing generative AI features in Search. With no definitional owner, each vendor sets the scope of the term to fit what it sells, which is why two published definitions rarely line up.

The measurement. First-party data for Google arrived on 3 June 2026, when Search Console added Generative AI performance reports showing impressions, pages, countries, devices and dates for AI features in Search and Discover. Google rolled them out to a subset of sites first, so coverage is incomplete, and it cautions that no third-party tool has access to its internal ranking or AI systems. Outside Google there is no first-party reporting at all. Visibility in ChatGPT, Claude or Perplexity is estimated by running sample prompts and counting mentions, which produces a number that moves with the prompt set, the model version and the day.

The stability of any specific finding. Ahrefs’ top-10 overlap figure moved from about 76% to 37.9% in under a year, on a larger sample and with methods that are not strictly comparable. Whatever the cause, tactical advice in this field ages quickly, so any confident claim about what AI systems reward is worth checking against its publication date.

Where it connects

Content an automated reader can parse and reuse is the substrate under all of this; see structured content. For the bots that fetch pages on behalf of these systems, and the difference between fetching for a live answer and fetching to train a model, see AI crawlers. For the file most often proposed as an AI-specific lever, see llms.txt.