AEO vs GEO vs SEO: What’s Actually Different

Search has three new job titles and one old one, and the market is selling them as if they were three different professions. SEO, AEO and GEO: one promises rankings, one promises answers, one promises citations inside AI-generated replies. The honest answer, the one Google’s own documentation now gives in plain language, is less exciting and more useful. Most of the work is the same work. A smaller part is genuinely new. And several of the “hacks” sold under the new labels are things Google has explicitly said do not work.

That is the short version of this whole article. The long version is worth your time, because the differences that are real — how answers get built, who crawls what, and what you can actually measure — change where a site should spend its effort. The differences that are not real change nothing except the invoice.

Checked 9 November 2026.

The short answer: mostly the same work, three different labels

SEO is the foundation. It is the discipline of helping search engines understand your content and helping users find your site through a search engine. That is Google’s own definition, from its SEO Starter Guide, and it has not changed because the results page changed shape.

AEO, answer engine optimisation, is the label for what happens when the search engine stops showing you a list and gives you one extracted answer instead: the featured snippet at the top of the page, the “People Also Ask” box, or the single spoken reply from a voice assistant. The work is about making a page the obvious source for that one answer.

GEO, generative engine optimisation, is the label for what happens when the answer is not extracted from one page but synthesised from many: an AI Overview at the top of Google, an answer in ChatGPT search, a sourced reply in Perplexity. The work is about being one of the sources the model cites when it writes its answer.

Put side by side, the three labels describe three surfaces — the link list, the single answer, the synthesised answer — not three separate crafts. The pages that win on all three surfaces are, overwhelmingly, the same pages: indexed, crawlable, well structured, original, and written for people first. Google says so directly in its guide to optimising for generative AI features: “Is SEO still relevant for generative AI search? In short, yes! The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems.”

That sentence is the spine of this article. Everything that follows is a test of it: where it holds, where the new surfaces genuinely add something, and where the industry has invented work that the platforms themselves say is pointless.

The rest of this guide works through the comparison in order. It defines each term from primary sources, puts the three surfaces next to each other, maps the overlap, separates the genuinely new from the hype, explains how each one is measured, and ends with a decision table for where to invest and a single workflow that covers all three. If you want the practical, step-by-step side — how to actually earn citations from ChatGPT, Perplexity and AI Overviews — that is a different article, and it is the companion to this one: our GEO Explained guide walks through the mechanics engine by engine.

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SEO first: what search engine optimisation actually is

Before judging the new labels, it helps to be precise about the old one. Google’s SEO Starter Guide defines the discipline in a single sentence: “SEO — short for search engine optimization — is about helping search engines understand your content, and helping users find your site and make a decision about whether they should visit your site through a search engine.”

Notice what the definition does not promise. It does not promise a ranking. It describes a job: make content understandable to a machine, and useful enough that a person who finds it decides to visit. The guide is equally blunt about shortcuts: “There are no secrets here that’ll automatically rank your site first in Google.” That line was written for the classic results page, and Google repeats its twin in the generative AI guide: “Creating content that people find unique, compelling, and useful will likely influence your website’s presence in generative AI search in the long run more than any of the other suggestions.” The same advice, in both guides, sits at the top of the list. That is not a coincidence; it is the point.

Under the definition, the work of SEO breaks into a few durable parts. Crawling: can the search engine’s bots reach and read the page? Indexing: is the page stored in the index and eligible to appear? Ranking: when a query matches, does the page’s quality, relevance and reputation earn it a position? And around those, the supporting work: technical structure, page experience, links from other sites — Google notes that links are how it finds most new pages — and content written in the language real people use, since Google’s language-matching systems “can understand how your page relates to many queries, even if you don’t explicitly use the exact terms in them.”

Two features of classic SEO matter for everything that follows. First, the output is comparatively stable: a page holds a ranking for a query, you can watch it move in Search Console, and two people searching the same words usually see a similar list. Second, the feedback loop is public and measurable: rankings, impressions and clicks are all visible to the site owner. Both of those features weaken as we move toward generated answers, and that weakening — not any new bag of tricks — is the real story of AEO and GEO.

SEO is also the only one of the three terms that has an official definition from a platform. Nobody at Google has published a definition of AEO or GEO, and Google’s guide treats both words as marketing labels rather than technical terms. That asymmetry is worth remembering whenever someone sells you a service named after one of them.

AEO: the answer-extraction era

AEO grew up around a different shape of result. Instead of ten blue links, the search engine started showing one extracted answer: the featured snippet, a box at the top of the results page where “the format of a regular search result is reversed, showing the descriptive snippet first,” in Google’s words. The same extracted answers feed the “People Also Ask” boxes and, when a voice assistant reads out a single reply, the spoken answer. The term AEO emerged among SEO practitioners in the late 2010s, as featured snippets and voice search made the single answer a visible prize. There is no single primary source naming who coined the term or exactly when — secondary accounts disagree — so this article does not attribute it to any one person or year. What matters is the idea, not the byline.

The idea was simple: if the search engine is going to lift one answer out of one page, make your page the easiest one to lift from. In practice that meant question-style headings that match how people actually ask things, followed by a short, self-contained answer block that states the answer directly before going deeper. It meant clear structure, so the extraction system could find the answer without guessing. And it meant understanding the controls Google gives you over snippets: nosnippet blocks all snippets for a page, max-snippet limits their length — shorter limits make featured snippets less likely — and data-nosnippet excludes part of a page. These are real levers, documented by Google, and they are the closest thing AEO has to a technical toolkit.

But there is a hard limit that AEO sellers rarely lead with. Google’s own featured-snippet documentation says, in plain words: “How can I mark my page as a featured snippet? You can’t. Google systems determine whether a page would make a good featured snippet for a user’s search request, and if so, elevates it.” You can make a page eligible and easy to extract from. You cannot claim the box. There is also no published minimum length for a snippet, and no formula for which page gets picked. The work is real; the outcome is not yours to command.

Voice search added a sharper edge to the same idea. When an assistant reads one answer aloud, there is no second place. The contest is winner-takes-all in a way the ten-link list never was, which is why voice became such a fertile ground for AEO claims — and why it is also where the least verifiable statistics in the field circulate. Vendor studies quote voice-search shares that were never traced to a primary source, and this article does not repeat them. Qualitatively, though, the point stands: a growing share of searches ends without a click, because the answer was given on the results page itself. That shift is the commercial reason AEO exists. A site that only counts clicks will undervalue the answers it gives away for free, and a site that only chases snippets may forget that the snippet is supposed to earn the click, not replace it.

AEO, then, is best understood as a layer on top of SEO, not a replacement for it. A page must be indexed and ranking before it can be elevated into a snippet, and the snippet controls only make sense on a page that already deserves to be found. The genuinely new part — the single extracted answer as a distinct outcome, with its own controls — is real, and it is covered in detail later in this guide. The rest is SEO with a question mark at the front.

GEO: the term with a paper behind it

GEO is unusual among marketing terms: it has a paper behind it. In November 2023, researchers Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande published “GEO: Generative Engine Optimization” (arXiv:2311.09735, revised June 2024, presented at KDD 2024). The paper formalises “generative engines” as search engines that use generative models to gather and summarise information, and introduces GEO as “the first novel paradigm to aid content creators in improving their content visibility in generative engine responses.” It also introduces GEO-bench, a large-scale benchmark of diverse queries used to test which content changes improve visibility inside generated answers.

The headline finding, as stated in the paper’s abstract, is that GEO can “boost visibility by up to 40% in generative engine responses,” with effectiveness varying across domains. That figure needs careful handling, and this article handles it the way the brief requires: it is a benchmark result from the paper’s abstract, not a real-world guarantee and not an average gain. It describes what happened to visibility scores on the authors’ benchmark when their methods were applied, in the domains they tested. It does not mean your site will gain 40% of anything. The paper’s per-method breakdowns — which techniques helped how much — were not verified in this run, since only the abstract was read in full, so they are not cited here.

The origin matters, because it makes GEO different from AEO in a way that is often missed. AEO is practitioner slang that grew out of a feature: featured snippets existed, and someone needed a word for optimising toward them. GEO is an academic coinage that grew out of a model of the future: the authors named a discipline before the products it describes had fully arrived, and defined it around a measurable outcome — visibility inside a generated response. That gives GEO a sharper definition than AEO has, and a benchmark to argue about. It does not give it more authority over how the platforms actually behave. The paper proposes methods; Google, OpenAI and Perplexity decide what their systems do, and none of them has endorsed the paper’s methods or published a citation-selection formula of its own.

The distinction also explains why the two terms attract different kinds of hype. AEO hype tends to be about winning a box: tricks to force a snippet, tools that promise a featured position. GEO hype tends to be about gaming a model: files, formats and mentions that supposedly make a language model cite you. Both kinds of hype fail the same test — what the platforms actually say about how their systems work — and the hype audit later in this article runs that test claim by claim. For the definition itself, the short companion piece What Is GEO? Generative Engine Optimisation, Explained gives the one-paragraph version; this article is the comparison that sits on top of it.

One more note on the paper: its framing has aged well in one respect and poorly in another. Well, because generative answers did become a major surface, exactly as the authors anticipated. Poorly, because the term escaped the paper. “GEO” is now used to mean everything from “getting cited by ChatGPT” to “any SEO done after 2023,” and most of that usage has nothing to do with the paper’s methods. When a vendor says “GEO,” ask which one they mean: the paper’s benchmark discipline, or the relabelled SEO on the invoice. The answer tells you most of what you need to know.

The three surfaces side by side

The cleanest way to see the difference between SEO, AEO and GEO is to look at what the user actually sees. There are three surfaces, and each one gives the site something different.

The first surface is the classic blue-link results page: ten links, titles and descriptions, ranked. The user chooses where to go. The site gets a click, or it does not, and the ranking is comparatively stable — the same query tomorrow will usually show a similar list. This is SEO’s home ground, and it is still the surface that produces the most measurable traffic.

The second surface is the single extracted answer: the featured snippet, the “People Also Ask” expansion, the voice reply. Google’s description of the featured snippet is precise — a box where the format of a regular result is reversed, snippet first, link second. The user reads the answer without opening anything. The site gets visibility and, sometimes, a click from a user who wants more than the extract. The outcome is binary and unstable in a way rankings are not: a page either is the snippet for a query or it is not, and the decision is made afresh by Google’s systems each time. You cannot mark your page as the snippet, and there is no published rule for why one page is elevated over another.

The third surface is the synthesised multi-source answer: Google’s AI Overview, which appears “when our systems determine that generative AI can be especially helpful — for example, when you want to quickly understand information from a range of sources”; Google’s AI Mode, which Google calls “its most powerful AI search experience,” where you “can ask anything and get an AI-powered response, with the ability to go deeper through follow-up questions and helpful links to the web”; ChatGPT’s search answers, which carry a Sources panel of citations; and Perplexity’s answers, with numbered citations throughout. Here the user reads a paragraph the model wrote, assembled from several pages. The site gets a citation — a link inside or beside the generated text — and a click only if the reader decides to check the source.

The difference between the second and third surfaces is the one that matters most, and it is a difference of probability. A ranking is a position: your page is number three, and it stays number three until something changes. A featured snippet is an elevation: your page is the answer, until it is not. A citation is a draw: the model retrieves a set of pages, writes an answer, and cites some of them, and the same question asked twice can produce different cited sources. OpenAI says this plainly about ChatGPT search: responses that use web search “may include citations,” and “search results and citations can be incomplete, outdated, or incorrect.” Google says the same about its own features: “AI responses may include mistakes” in AI Overviews, and “as with any early-stage AI product, AI Mode doesn’t always get it right.” No platform publishes a formula for which pages get cited, and none guarantees placement. OpenAI is explicit: “ChatGPT ranks search results using multiple factors intended to help users find relevant, reliable information. Placement is not guaranteed.”

That probabilistic character changes what “optimisation” can honestly mean. For a ranking, you can measure a position and improve it. For a citation, you can only improve your odds: be retrievable, be quotable, be trustworthy, and accept that the model may still pick someone else today. Anyone selling guaranteed citations is selling something no platform offers. Our guide to Google’s AI search features goes deeper into how AI Overviews and AI Mode differ from classic results on the reader’s side; the point here is the structural one. Three surfaces, three kinds of reward — a click, an extraction, a citation — and the third is the least predictable of the three.

There is also a reader-side counterpart worth knowing. If the generated answer is not what a user wants, they can push back: our guide on how to turn off AI on Google Search covers what users do about AI answers. Publishers should not assume the AI surface is the only surface their readers see.

What genuinely changed when answers replaced lists

If the overlap between the three labels is large, the change underneath them is still real. It is not a new discipline. It is a new pipeline, and Google’s own guide describes it in two technical terms that are worth understanding properly: RAG and query fan-out.

RAG — retrieval-augmented generation, which Google also calls grounding — is the technique that makes generated answers different from a chatbot’s free-form reply. Instead of answering from what the model memorised during training, the system first retrieves relevant, up-to-date web pages using its core search ranking systems, then writes an answer grounded in those pages. Google’s definition is worth quoting whole: RAG is “a technique (also known as grounding) used to improve the quality, accuracy, and freshness of AI responses by relying on our core Search ranking systems to retrieve relevant, up-to-date web pages.” The retrieval step is why the same content-quality advice tops both of Google’s guides, and why the sentence “our generative AI features on Google Search are rooted in our core Search ranking and quality systems” is doing so much work. The answer is only as good as the retrieval, and the retrieval is search.

Query fan-out is the second term, and it is the one that most changes how a query is understood. Instead of running your question as one search, the model generates “a set of concurrent, related queries” — Google’s example is a question about lawn weeds fanning out to “best herbicides for lawns,” “remove weeds without chemicals,” and “how to prevent weeds in lawn” — and retrieves pages for all of them before writing. The answer you see is synthesised from a broader net than your words alone would have caught. This is a genuine break from classic SEO, where one query maps to one ranked list. Under fan-out, a page can be cited for a question it does not directly answer, because it answered one of the model’s sub-questions. It also means the set of pages in play for any answer is wider and less predictable than the set competing for a single ranking.

ChatGPT’s search works on a related principle, described in OpenAI’s own help pages: when a question would benefit from current information, ChatGPT “typically rewrites the query into one or more targeted queries sent to search providers” — the example given is a question about a CCR8 cancer drug rewritten to “CCR8 immunotherapy drug development 2025” — and the response “may include citations” with a Sources panel. The rewrite is the model’s judgment about what to look for, not yours. Your page is found because the model’s version of the question matched it, which is a different game from matching the user’s exact words. Google’s language-matching systems already understood that a page “can relate to many queries, even if you don’t explicitly use the exact terms,” but fan-out and query rewriting make the distance between the user’s words and the retrieved pages longer still.

Put together, the pipeline is: retrieve, then write. The retrieval is search — rankings, quality, relevance. The writing is generation — synthesis, summarising, citation. The first half is SEO’s territory, and it is the half that determines whether your page is in the room at all. The second half is where the new uncertainty lives: the model decides how to combine what it retrieved, which sources to name, and how to phrase the answer. That is why the same question can produce different cited sources on different runs, and why no honest practitioner can promise a citation for a given query. The retrieval is deterministic enough to optimise for. The writing is not.

For a fuller picture of how the models behind these answers work, our pillar article How Does AI Work explains the machinery underneath. What matters for this comparison is the consequence: the genuinely new thing is not a new kind of content. It is that your content is now read by a system that retrieves first and writes second, and that the writing step introduces a roll of the dice that classic rankings never had.

The overlap map: the work all three share

Draw the three labels as circles and the picture is lopsided: the shared middle is most of the diagram, and the separate edges are thin. That is the overlap map, and it is the most practical section of this article, because it tells you what to do on Monday morning regardless of which label your agency uses.

Start with the entry ticket, which is identical for all three surfaces. A page must be indexed, crawlable and eligible to be shown with a snippet. Google’s generative AI guide states the eligibility condition plainly, and its May 2025 blog post on succeeding in AI search adds the operational version: a page must work (return HTTP 200), have indexable content, and Googlebot must not be blocked. If any of those fail, the page is invisible to the link list, the snippet and the generated answer alike. Technical SEO is not a separate chore from AEO and GEO. It is the same chore, and it gates all three.

Next, the content itself. Both of Google’s guides — the SEO Starter Guide and the generative AI guide — put the same advice first: create content that people find unique, compelling and useful. The starter guide says it “will likely influence your website’s presence in search results more than any of the other suggestions.” The AI guide says the same about presence in generative AI search. When two guides from the same company, written for two different eras of search, lead with the identical sentence, the message is that the advice did not change. Original evidence helps most of all: first-hand testing, original data, a quote from a real expert, a photograph you took yourself. These are the things a retrieval system can recognise as distinctive and a generation system can quote without sounding like every other page.

Structure is the third shared item. Clear headings, a direct answer near the top of a section, short paragraphs that can stand alone — these help a ranking system understand the page, help an extraction system lift an answer, and help a generation system find a quotable passage. The same is true of the snippet controls: nosnippet, max-snippet and data-nosnippet set display preferences that apply across classic results and AI features, so one decision covers both.

Finally, authority and trust. A page that other reputable sites link to, that is written by someone with visible expertise, that is accurate and up to date, will be retrieved more often, extracted more often and cited more often. No platform publishes a trust formula, and none should be invented here — but the direction of travel is the same on all three surfaces, because the retrieval underneath them is the same system. Google says its AI features are rooted in its core ranking and quality systems. Take that sentence seriously and the overlap map draws itself: do the SEO, write the answers, and the labels take care of themselves.

The overlap is also a warning about budgets. If a vendor proposes three separate workstreams — an SEO retainer, an AEO package and a GEO programme — and the three workstreams list the same tasks, you are being charged three times for one job. The honest version of the offer is one programme with three measurement views, and the measurement section later in this article explains what each view actually shows.

What is genuinely new in GEO

The overlap is large, but the genuinely new parts of GEO are real, and they cluster around four things: the crawler split, the citation as the unit of visibility, multi-source synthesis, and conversational follow-ups.

The crawler split is the most concrete. For classic SEO, the crawler that matters is Googlebot, and for ChatGPT and Perplexity there are now separate search crawlers with their own rules. OpenAI distinguishes OAI-SearchBot, which crawls for ChatGPT search, from GPTBot, which crawls for training. To be eligible for ChatGPT search, a site must allow OAI-SearchBot to crawl and confirm that its host or content delivery network allows traffic from OpenAI’s published searchbot IP addresses. Blocking GPTBot does not block OAI-SearchBot, and allowing one does not allow the other. Perplexity makes the same split: PerplexityBot “is designed to surface and link websites in search results on Perplexity” and “is not used to crawl content for AI foundation models,” while Perplexity-User supports user actions — it may visit a page when a user asks a question and include a link, is not used for crawling or training, and “generally ignores robots.txt rules” because a user requested the fetch. Perplexity recommends allowing PerplexityBot in robots.txt and permitting its published IP ranges, and notes that robots.txt changes may take up to 24 hours to reflect.

This is new work, and it is the kind of work that can be checked rather than believed: read the robots.txt, read the server logs, confirm the bot can reach the page. It is also work that only matters if the audience uses those engines. A site whose readers never touch ChatGPT or Perplexity gains nothing from tuning their crawlers, and a site that blocks all bots in the name of keeping AI out should understand exactly which door it is closing — the training crawler and the search crawler are different doors.

The second new thing is the citation as the unit of visibility. In classic SEO the unit is the ranking: a position, a click-through rate, a stable number you can track. In GEO the unit is the citation, and it behaves differently. It is probabilistic, it varies by run and wording, it is not a position you hold, and it is not measured by any official tool. A page can be cited for a query it ranks tenth for, or not cited for a query it ranks first for. That is not a failure of the method; it is the nature of a system that retrieves and then writes.

The third is multi-source synthesis. A generated answer is assembled from several pages at once, which means your content is no longer competing to be the one answer — it is competing to be one of the ingredients. The qualities that win change subtly: a distinctive fact, a precise number with a source, a clear definition, a quotable sentence. Generic passages that could have come from anywhere are easy to synthesise around and hard to cite. Original evidence, again, is what gets named.

The fourth is conversational follow-up. In AI Mode, a user can ask a question, get an answer, and then go deeper with follow-up questions, and the system keeps the thread. A page that answers the follow-up question as well as the first one has more chances to be retrieved across the conversation. That rewards depth and breadth on a topic in a way that a single-query ranking never did. For the full how-to — the crawler settings, the quotability checklist, the engine-by-engine steps — see our companion guide, GEO Explained: How to Get Your Content Cited by ChatGPT, Perplexity and AI Overviews. This article is the comparison; that one is the manual.

What is genuinely new in AEO

AEO’s genuinely new contribution is smaller than GEO’s, because its era came earlier and its surface is simpler — but it is still real, and it is worth naming precisely so it does not get lost in the overlap.

First, the direct-answer extraction as a distinct outcome. Before featured snippets, every optimisation goal in search was a ranking. The snippet created a second goal: be the answer, not just a link. That changed what a good page looks like at the level of the paragraph. A section that opens with a direct, self-contained answer — one or two sentences that make sense on their own, with the detail following underneath — serves both goals at once, but it serves the snippet goal first. Pages written as walls of undifferentiated prose are hard to extract from, and the fix is structural, not cosmetic: ask the question as a heading, answer it immediately, then explain.

Second, the snippet controls. Nosnippet, max-snippet and data-nosnippet are the only levers in this whole field that are entirely in the site owner’s hands, and they are AEO’s toolkit. They let a publisher decide what can be shown: block snippets entirely, shorten them, or exclude a part of the page. Used well, they are a way to protect content that should not be given away in an extract — a paywalled conclusion, a passage whose value depends on its context. Used carelessly, they can remove a page from the snippet surface altogether. The controls also illustrate the asymmetry of the field: you can opt out of being an answer, but you cannot opt in and demand to be one.

Third, “People Also Ask” as a surface. The expandable question boxes are a map of what users ask next, and they reward the same structure — one question, one direct answer — repeated across a page. They are also a research tool: the questions Google shows are the questions real users asked, and a page that answers them in order is a page that understands its topic the way its audience does.

Fourth, the voice answer as a one-answer contest. When an assistant reads a single reply, second place does not exist. That is a genuinely different competitive shape from the ten-link list, and it raises the stakes of being the extracted answer rather than merely ranking well. It is also the reason AEO attracted the most inflated promises in the field, including vendor statistics about voice search that were never verified from a primary source and are not repeated in this article. The honest version of the claim is qualitative and sufficient: a growing share of searches is answered on the results page, and the sites that provide those answers are the ones that get named.

What AEO does not add is a separate discipline. The snippet is won by the page that already ranks, is already well structured, and already answers the question directly. AEO’s new parts are a new outcome to aim for and a small set of controls to manage it — and that is enough to justify the term, without justifying a separate retainer.

The hype audit: what Google and OpenAI have already answered

This is the sceptical core of the article, and it is the section that justifies the comparison format. A large share of what is sold as AEO and GEO has already been answered — not by critics, but by the platforms themselves, in their own documentation. Google’s guide to optimising for generative AI features contains a mythbusting section that names the terms directly: “While terms like Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) are common online, many suggested ‘hacks’ aren’t effective or supported by how Google Search actually works.” Each claim below is tested against that guide and against OpenAI’s documentation. The verdicts are theirs, not this article’s.

Claim one: you need an llms.txt file. Google’s answer is no. Llms.txt files and other special machine-readable files, markup and Markdown are “not needed for Google Search,” because “Google Search itself doesn’t use them.” The file is a proposal from outside Google, and no platform has adopted it as a signal. Creating one is harmless; selling it as optimisation is not honest. Your crawl budget and your time are better spent on the page itself.

Claim two: you must break content into tiny pieces — “chunking.” Google’s answer is that “there’s no requirement” to do this. A well-structured page with clear headings is already machine-readable. Chopping an article into fragments does not make it more retrievable, and it usually makes it worse to read. Structure helps; shredding does not.

Claim three: you need special schema.org markup for AI. Google’s answer is that “structured data isn’t required for generative AI search, and there’s no special schema.org markup you need to add.” Ordinary structured data — the kind that supports rich results — is still worth having where it applies. But there is no secret AI schema, and anyone selling one is inventing it.

Claim four: rewrite your content just for AI systems. Google’s answer is to write for people and let the systems follow. The guide’s advice is to focus on what users want, and its warning is sharper: creating pages for every fan-out query “primarily to manipulate rankings” violates the scaled content abuse spam policy. A page written for a model’s imagined sub-questions, rather than for a reader’s real one, is the wrong kind of content — and in the worst case it is a spam policy problem, not an optimisation strategy.

Claim five: chase inauthentic “mentions.” Google’s answer is that seeking inauthentic mentions — manufactured references designed to make a brand look cited and discussed — is not a supported tactic. The guide lists it among the hacks to ignore. Real mentions, from real coverage, still matter; they are part of authority. Manufactured ones are not a shortcut to them.

Claim six: guaranteed citations, guaranteed snippets, guaranteed rankings. Both platforms’ answer is that placement is not guaranteed. OpenAI says it about ChatGPT search in so many words. Google says it about featured snippets — “You can’t” mark a page as one — and about generative features generally, where the same content-quality advice that governs rankings governs presence in answers. Any service that guarantees a citation is guaranteeing an outcome no platform offers. Treat the guarantee as the tell: it is the clearest signal that the seller is not describing how the systems work.

The audit leaves a short list of things that do work, and it is the same list that appears in the overlap map: indexed and crawlable pages, unique people-first content, clear structure, original evidence, authority. The guide’s closing takeaway says it directly: “Prioritize effective SEO strategies over ‘AEO/GEO hacks’: For Google Search, you can ignore tactics like ‘chunking’ content, creating unnecessary AI text files (like llms.txt), or pursuing inauthentic mentions.” That is not a dismissal of AEO and GEO. It is a definition of them, given by the platform that matters most: the labels describe real surfaces, and the work that wins on those surfaces is, with a few genuine exceptions, the work of SEO.

How the three are measured differently

If the work overlaps, the measurement does not — and that is where the three labels earn their keep as a vocabulary. Each surface has its own numbers, its own tools, and its own honest limits.

SEO is the best measured of the three. Rankings, impressions and clicks are visible in Search Console, and the feedback loop is direct: a page moves, you see it move. The metrics are imperfect — a ranking is not a business outcome, and a click is not a customer — but they are real, standardised and comparable over time. This is the baseline the other two are judged against, and it is why SEO remains the only one of the three with a complete measurement story.

AEO is measured by snippet wins: how often a page is the featured snippet or appears in “People Also Ask” for its target questions. There is no official tool for this. Search Console will show the impressions and clicks a page earns, and rank trackers can report whether a page holds a snippet for a query, but no platform publishes a snippet dashboard, and the outcome is binary and changeable. A useful way to think about it is as a supplement to SEO measurement rather than a replacement: the page ranks, and sometimes it is also the answer. Tracking the “sometimes” is worthwhile — it tells you which of your answers are being lifted — but it should not be confused with a ranking report.

GEO is the least measurable of the three, and honesty about that is part of the discipline. There is no official citation dashboard from OpenAI or Perplexity. What exists is a mixture: the Search Console Generative AI performance report, which Google provides for its own AI features and which shows how a site performs in generative search; third-party tools that sample prompts and count how often a brand is cited; and manual checks, asking the engines the questions that matter and recording what they cite. Each of these is partial. The Search Console report covers Google’s surfaces only, and it is still in beta. Sampling tools measure a set of prompts you chose, not the questions your audience actually asked. Manual checks are unrepresentative by construction, since answers vary by run, wording, user and date.

The most common measurement mistake in GEO is treating impressions as clicks. A citation is not a visit, and an AI Overview impression is not traffic. Google’s own reporting distinguishes the two, and its May 2025 blog post notes that clicks from AI Overviews pages are higher quality — users are more likely to spend more time on the site — which is a claim about the clicks that do happen, not about the impressions that do not convert. A reporting habit that counts citations as traffic will overstate GEO’s value and mislead the next budget decision.

For the detail, two companion pieces are worth reading: our explanation of Search Console’s Generative AI features report (beta), and our guide to how brands track their visibility inside ChatGPT and Gemini answers. Between them they cover the official report and the third-party sampling approach, including their limits. The summary for this article is simpler: SEO can be measured well, AEO can be measured adequately, GEO can be measured only partially — and any GEO report that looks as precise as an SEO report is probably measuring the wrong thing.

Where to invest: the decision table

The question this article has been building toward is a budget question: given that most of the work overlaps, where should a site actually spend its effort? The table below is editorial judgment, labelled as such — it is not derived from a study, and the splits are recommendations, not measurements. No invented percentages are used; the splits are given as relative emphasis, because the honest answer for most sites is a direction, not a number.

Three patterns run through the table. First, SEO is the largest share for every site type, because it is the entry ticket and the measurement baseline. Second, AEO is cheap to add: a direct answer at the top of a section costs nothing once the page exists, so its share is limited mainly by how many of your pages answer questions. Third, GEO earns a large share only where the audience actually uses generative engines for that kind of question, and where being cited is valuable even without a click — brand questions, research questions, comparison questions. Everywhere else, GEO is a by-product of doing the other two well.

One more rule for reading the table: if a vendor’s proposal gives GEO the largest share for a local business or a new blog, ask them to show the audience evidence. The burden of proof is on the person selling the newest label, not on the site being asked to pay for it.

One workflow that covers all three

The comparison ends where it started: with one playbook, not three. The workflow below covers SEO, AEO and GEO in a single pass over a page, and every step in it is drawn from the primary sources this article relies on. Run it for each important page, and the labels take care of themselves.

1. Technical eligibility. The page returns HTTP 200, its content is indexable, and Googlebot is not blocked. Check the search crawlers too: OAI-SearchBot allowed if ChatGPT search matters to your audience, PerplexityBot allowed if Perplexity does. Confirm the host or CDN permits traffic from the search bots’ published IP ranges. This is the gate; nothing else matters if it fails.

2. One clear answer per question. Give each section a question-style heading and open with a direct, self-contained answer — one or two sentences that make sense on their own — before going deeper. This is the single change that helps rankings, snippets and citations at the same time, and it costs nothing but discipline.

3. Original evidence. Add something no other page has: a test you ran, data you collected, a quote from a real expert, a photograph you took. Google’s guide says unique, compelling content influences presence in generative search more than any other suggestion; the same advice tops the SEO Starter Guide. Original evidence is what gets quoted, and what gets cited is what gets named.

4. Structure machines can parse. Clear headings, short paragraphs, lists where a list is the honest shape of the information. Do not chop the page into fragments — Google says there is no requirement to — and do not add special AI schema or an llms.txt file, which Google says its search does not use. Structure is for readers first; machines follow readers.

5. Authority beyond the page. Links from reputable sites, visible authorship, accurate and current information, a site that is plainly maintained. No platform publishes a trust formula, so this step is a direction rather than a checklist item — but the direction is the same on all three surfaces, because the retrieval underneath them is the same system.

6. Measurement. Track rankings and clicks in Search Console for SEO. Track snippet wins for your target questions for AEO, knowing there is no official tool. Track citation share by sampling for GEO, using the Search Console Generative AI performance report for Google’s surfaces and manual or third-party sampling for the rest — and never count an impression as a click.

That is the whole programme. It is deliberately boring, and that is the point: the durable work in search has always been boring, and the new surfaces have not changed its shape. They have changed the reward — a citation instead of a click, an answer instead of a link — and the odds, which are now partly a model’s decision rather than a ranking’s. Everything else is execution.

The verdict: when to use each word — and when to stop

So: are AEO, GEO and SEO three different disciplines, or one discipline with three labels? The evidence in this article points to a middle answer, and it is worth stating plainly.

SEO is the discipline. It is the only one of the three with an official definition, a complete measurement story, and a feedback loop you can watch. Everything else in this article is a layer on top of it.

AEO is a useful word for a real outcome: the single extracted answer. Use it when the conversation is about winning the featured snippet, the “People Also Ask” box, or the spoken reply — and use it knowing that you can make a page eligible for the box but cannot claim it. Stop using it when it starts to sound like a separate service: if the “AEO package” is the same work as the SEO retainer with question marks added, it is the SEO retainer.

GEO is a useful word for a real surface: the synthesised, cited answer. Use it when the conversation is about being cited by ChatGPT, Perplexity or AI Overviews, about the crawler split, or about the probabilistic nature of citations. Stop using it when it starts to promise what no platform offers: guaranteed citations, a citation formula, or a set of hacks that Google has already said do not work. The paper that coined the term is real and worth reading; most of what is sold under its name is not the paper.

The test for any “AEO” or “GEO” service is simple, and it is the test this whole article has been running. Ask the seller three questions. First, what will you do that is not already in the SEO work? The honest answer is a short list: direct-answer structure, snippet controls, the search-crawler settings, quotable evidence. Second, what can you measure, and with what tool? The honest answer names Search Console, the Generative AI performance report, and sampling — and admits what cannot be measured. Third, what do you guarantee? The honest answer is nothing, because no platform guarantees placement. A seller who passes all three is selling real work, whatever they call it. A seller who fails the third is selling the label.

For a reader deciding where to spend the next quarter, the closing position is the one Google’s guide states and this article has tested from every angle: do the SEO, write the answers, and let the labels catch up. Build pages that are indexed, crawlable and eligible. Write content that is unique, compelling and useful, with direct answers and original evidence. Check the crawlers that matter to your audience. Measure what can be measured, and be honest about the rest. The link list, the single answer and the synthesised answer will all draw from the same pages — because, underneath the three labels, they are the same search.

Sources

Primary sources read in full for this article. Checked 9 November 2026.

• Google Search Central, “Google’s Guide to Optimizing for Generative AI Features on Google Search” (last updated 10 July 2026) — developers.google.com/search/docs/fundamentals/ai-optimization-guide
• Google Search Central, “SEO Starter Guide: The Basics” — developers.google.com/search/docs/fundamentals/seo-starter-guide
• OpenAI Help, “Searching the web with ChatGPT” — help.openai.com/en/articles/9237897-searching-the-web-with-chatgpt
• Perplexity, “Perplexity Crawlers” (updated 29 October 2026) — docs.perplexity.ai/docs/resources/perplexity-crawlers
• Aggarwal et al., “GEO: Generative Engine Optimization”, arXiv:2311.09735 (submitted 16 November 2023, revised 28 June 2024, KDD 2024) — arxiv.org/abs/2311.09735
• Google Search Help, “Find information in faster & easier ways with AI Overviews in Google Search” — support.google.com/websearch/answer/14901683
• Google Search Help, “Get AI-powered responses with AI Mode in Google Search” — support.google.com/websearch/answer/16011537
• Google Search Central, “Featured Snippets and Your Website” — developers.google.com/search/docs/appearance/featured-snippets
• Google Search Central Blog, “Top ways to ensure your content performs well in Google’s AI experiences on Search” (21 May 2025) — developers.google.com/search/blog/2025/05/succeeding-in-ai-search