GEO vs SEO: what changes and what carries over.

1. GEO means two different things. Sort that out first.

Search for geo SEO and you get two unrelated topics stacked in one result page. For twenty years, geo in an agency context meant geographic: local rankings, map packs, service-area pages, a plumber trying to own one suburb. Since late 2023 the same three letters have been reassigned to generative engine optimization, the practice of getting your content used as a source inside AI-generated answers. Same acronym, completely different job.

The test is simple. If your question is which city you rank in, you want local SEO, and nothing else on this page applies to you. If your question is why an assistant summarised your category without mentioning you, keep reading. Everything below uses GEO in the generative sense, and so does the rest of our AI search optimization material.

The confusion is not harmless. It is the reason so much GEO advice reads like it was written by someone who has never run either discipline: the keyword pulls two audiences, and generic content tries to serve both. It is also why the tooling market is noisy, which we worked through separately in our breakdown of the GEO tools worth paying for.

2. What generative engine optimization actually is.

The term comes from a 2024 research paper, not a marketing department. Aggarwal and colleagues formalised GEO at the KDD conference, built a benchmark of 10,000 queries, and tested which content edits changed how often a source was used inside a generated answer. Adding quotations, citations, and statistics moved visibility by up to 40%. Rewriting for keyword density did roughly nothing. That result is the whole thesis of GEO in one line: generative engines reward evidence, not optimisation theatre.

Mechanically, a generative engine answers a question by issuing several searches behind the scenes, pulling candidate passages from an index, and synthesising them into prose with links back to a handful of sources. You are not competing for a position in a list. You are competing to be one of the four or five passages the model decides it needs. That is a different objective function, and it is why the sibling discipline of answer engine optimization overlaps with GEO so heavily that most teams should treat them as one workstream.

Worth naming the stakes plainly. The Pew Research Center tracked the browsing of 900 US adults and found that users clicked a traditional result on 8% of visits where an AI summary appeared, against 15% where none did, with clicks on links inside the summary itself running at about 1%. The click was already scarce. GEO is the response to it becoming scarcer. The mechanics of that response, engine by engine, are laid out in our generative engine optimization guide.

3. What carries over from SEO, unchanged.

This is the part the category vendors skip, because there is nothing to sell in it. Generative engines do not maintain a separate web. They read the same index, built by the same crawlers, from the same HTML. Google says so in its own words: optimizing for generative AI search is still SEO. The same page tells site owners to skip the popular hacks. Google Search does not use llms.txt files, there is no requirement to chop your content into tiny pieces, and you do not need a special writing style for machines.

Concretely, five things transfer with no modification. Crawlable server-rendered HTML, because a page a crawler cannot read is invisible to both systems. Indexation hygiene: canonicals, a clean sitemap, no duplicate near-identical pages. Genuine topical depth, which is what makes a site a plausible source on a subject at all. Internal linking, which is still how authority moves around a site and how crawlers find new pages. And publishing cadence, because a stale site loses to a fresh one in both places. Every one of those is the classic checklist, and we run the same checklist inside our SEO automation pipeline whether the target is a ranked result or a citation.

The practical implication is a sequencing rule rather than a philosophy. If your technical foundation is broken, GEO work is wasted spend: you are tuning the quote on a page nothing can read. Fix the substrate, then layer the answer work on top. Teams that invert that order buy a visibility tracker before they fix their render path, and then wonder why the dashboard stays empty.

4. What genuinely changes.

Four real differences, and they are all downstream of one fact: the retrieval unit is a passage, not a page. A classic SERP hands the whole document to the reader and lets them scroll. A generative engine lifts the two or three sentences that answer the question and discards the rest. So the answer has to be near the top, self-contained, and phrased so it survives being cut out of context. A page whose best paragraph sits under the fourth H2, and only makes sense after the preamble, is a page that ranks fine and never gets quoted.

Second, evidence density becomes a ranking input rather than a nicety. The research is blunt on this: statistics, quotations, and citations were the edits that moved visibility, and the size of the effect varied by topic rather than by cleverness. In practice that means every claim on a page should carry either a number or a source. We build that into generation itself, which is why our fact-check pass attaches citations before a draft is allowed through the quality gate rather than after.

Third, entity naming matters more than keyword phrasing. Models resolve entities. They need to know which company, product, or category a passage is about, and a page that says the leading platform in the space gives them nothing to resolve. Write the names out. Use the same name every time. This is unglamorous and it is most of what LLM SEO comes down to at page level.

Fourth, the traffic profile changes shape. A citation sends fewer visitors than a first-place ranking used to, and it sends them further down the buying process, because the assistant has already done the comparison and the reader arrives to verify a recommendation rather than to browse. Fewer sessions, higher intent per session. Any measurement plan built on raw session counts will read that as a failure, which brings us to the awkward part.

5. The part that breaks: measurement.

SEO has one clean scalar: position. GEO does not have an equivalent, and pretending otherwise is how tool budgets get wasted. Answers are generated per prompt, per user, per session, and they vary between runs of the same question. There is no stable thing to rank. The closest usable proxy is citation share, meaning how often you appear as a source across a fixed set of prompts a buyer would plausibly type, which is what the AI visibility trackers are all approximating with different sampling methods.

The link between rankings and citations is also weakening fast, which undercuts the comforting assumption that GEO is a free by-product of good SEO. An analysis of 863,000 keywords and roughly 4 million AI Overview URLs, reported in March 2026, found 38% of cited pages ranking in the top 10 for the same query, down from 76% when the same study ran in July 2025. About 31% of citations came from positions 11 to 100 and another 31% from beyond position 100. Ranking is one door in. It is no longer the only one, and it is no longer most of them.

A workable measurement set, in the absence of a single number: citation share across twenty fixed buyer prompts, checked monthly rather than daily, because the noise between runs will otherwise eat you. Referral traffic from assistant hosts in your analytics, which is small but unusually well-qualified. Search Console impressions on answer-heavy queries, since impressions persist even when the click does not. And branded search volume, which is the honest lagging indicator that being quoted is doing something. We keep the underlying numbers current in our AI search statistics reference rather than restating them page by page.

6. Why the answer is both, from one pipeline.

The framing of GEO vs SEO implies a budget decision, and there is not one to make. They consume the same input. One article, correctly built, competes for the ranked result and supplies the quotable passage. Splitting them into two teams with two calendars produces two thin programs and doubles your review overhead for no additional coverage.

What actually changes is the brief. Every page gets three extra acceptance criteria on top of the classic ones: a direct answer in the first screen, at least one specific figure with a real source, and entities named in full. Those are cheap to add at drafting time and expensive to retrofit across two hundred published pages, which is the argument for putting them in the template now rather than treating GEO as a later migration. Our GEO agent encodes exactly those criteria in the draft prompt and the quality gate, and the AEO agent does the same work aimed at the direct-answer surfaces.

If you would rather assemble it yourself, the honest sequence is: fix the substrate, rewrite the top of your twenty highest-intent pages, then publish weekly with the new criteria baked into the brief. If you would rather it just ran, that is the job the AI SEO agent does end to end for a flat $99 a month, research through to native publish. Either way the work is the same work. Only the labour source differs.

A worked example: one page, both systems.

Take a fictional payroll product, Paywell, targeting the query how long does payroll processing take. Here is the same page opening written twice. The first version is what a competent SEO writer produces. The second is the GEO edit. Neither is longer than the other.

Three things changed. The answer moved to the first sentence and is complete without the paragraph around it, so a model can lift it whole. Specific numbers replaced a promise of numbers later. And the mechanism is named, which gives the passage something to be about beyond the keyword. The before version ranks; a crawler will index it and Google may well put it on page one for a low-competition query. It gives a generative engine nothing to use, because there is no claim in it.

Note also what did not change. The URL, the title tag, the internal links, the schema, the publishing cadence. This is a two-hundred-word edit to the top of a page, repeated across the twenty pages that matter, and it is most of what a small team needs to do about GEO this quarter. The rest is the same publishing discipline that answer-engine tooling is built to monitor rather than replace.

Common mistakes in GEO vs SEO thinking.

1. Treating GEO as a replacement. Budget gets moved off technical and editorial work into visibility tooling, the foundation degrades, and both channels fall together. Generative retrieval reads the same index as ranked search. Starving the index starves both.
2. Buying the tracker before fixing the page. A citation dashboard measures a problem it cannot solve. If the pages have no quotable answers and no sourced claims, the dashboard will faithfully report zero for six months.
3. Chasing hacks the platforms have disowned. Google states plainly that it does not use llms.txt and that chunking content into tiny pieces is not required. Any strategy whose core mechanic is a file the search engine ignores is not a strategy.
4. Burying the answer. The single most common failure. The page contains the answer, in paragraph nine, phrased as a continuation of paragraph eight. It cannot be lifted, so it never is.
5. Writing claims with no numbers. Vague authority language reads as filler to a retrieval system and to a human. A page with one specific figure and a source beats a page with five confident adjectives.
6. Judging GEO on session volume. Citations send fewer, later-stage visitors than a first-place ranking did. Measured on raw sessions, a working GEO program looks like a failing SEO program.