The GEO vs SEO debate is mostly a naming debate. Agencies have rebranded as “GEO specialists”, tools now report “AI visibility”, and every conference has a talk on optimising for answer engines. Strip the labels off and the question is narrower: does a page need different work to be cited in a generated answer than to rank in a list of links? Sometimes, a little. Mostly, no.
Where the term GEO comes from
Generative engine optimisation has a specific origin. The paper “GEO: Generative Engine Optimization” was submitted on 16 November 2023 and published at KDD 2024. It describes GEO as “the first novel paradigm to aid content creators in improving their content visibility in generative engine responses.”
The authors built GEO-bench, “a large-scale benchmark of diverse user queries across multiple domains”, then tested how changes to source content affected how visible that content was in generated answers. Their headline finding is that GEO methods “can boost visibility by up to 40% in generative engine responses.”
What the 40% figure does and doesn’t mean
That number travels well, and it is usually quoted without its context. It was measured on the paper’s own benchmark and its own generative engine setup. It was not measured on live Google, on ChatGPT, or on any commercial product as it runs today.
Three things follow from that:
- “Up to” is a ceiling. It is the best result reported, not the typical one.
- Lab setups are not production systems. Commercial answer engines retrieve, rank and filter sources in ways that are not public, and they change often.
- The paper is one study. It is a useful starting point and a serious piece of work. It is not a body of evidence, and nobody should sell a 40% uplift on the strength of it.
This is not a criticism of the research. It is a criticism of how the research is marketed. A finding about a controlled benchmark has become a sales line for services that cannot measure the same thing.
GEO vs SEO, side by side
Here is where the two genuinely differ, and where they do not.
| SEO | GEO | |
|---|---|---|
| Output you are aiming for | A position in a list of links | Inclusion or citation in a generated answer |
| How success shows up | Rankings, impressions, clicks | Being named, quoted or linked in the answer |
| Measurement | Mature: Search Console, rank trackers, analytics | Patchy: answers vary by user, wording and session |
| Evidence base | Years of published guidance and testing | One academic paper (2023, KDD 2024) plus vendor claims |
| Where the source pool comes from | The search index | Often a search index, which is why ranking still matters |
| What the content needs | Clear answers, sources, authority | The same |
The last two rows carry the argument. Answer engines that browse the web have to retrieve candidate pages before they can quote them, and retrieval leans on search. A page that nobody can find in search is a poor candidate for citation, however well it is written for a model.
What actually changes for content
The paper tested content changes rather than technical tricks, and that is the useful part. Without leaning on any specific method from it, the general direction holds up against what good SEO already asks for:
- Answer early and plainly. A generated answer lifts a sentence or a fact. If your answer is buried under three paragraphs of preamble, it is harder to lift.
- Make claims citable. Specific, sourced statements are easier to quote than vague ones. That is also what readers and reviewers reward.
- Cover the sub-questions. AI search systems often break a query into parts. Our guide to SEO for Google AI Mode covers how Google describes this.
- Show who wrote it and why they know. The same signals discussed in our post on E-E-A-T examples.
None of that is new. It is what a decent editor would have asked for ten years ago. What has changed is the cost of ignoring it: a padded page can still rank on authority, but it gives an answer engine nothing worth quoting.
The practical test is to read your own page as if you only had room to quote one sentence from it. If no single sentence answers the query on its own, with the specifics a reader would need, the page is weak for both audiences. Fixing that is a rewrite of the opening and the subheadings, not a new strategy.
What genuinely needs separate attention
A few things are specific to AI answers and are worth a dedicated look:
- Crawler access. Each AI company runs its own bots, and blocking the wrong one can remove you from its answers. Our guide to ranking in ChatGPT search walks through OpenAI’s crawlers and what each one does.
- Referral tracking. AI answers send fewer, differently labelled clicks. Check how each platform shows up in your analytics before you judge it.
- Brand mentions. Being named in an answer without a link is a real outcome that traditional reports miss.
Things that do not need separate attention, despite the pitches: special “AI markup”, files that no provider has said it reads (see our post on what llms.txt is), and rewriting every page in a style you think a model prefers.
The limitation: nobody can measure GEO well yet
This is the honest weak point of the whole field. Generated answers differ between users, sessions and small changes in wording. A tool that checks a few hundred prompts and reports your “AI visibility share” is sampling a moving target, and its prompts may not resemble what your audience types. Treat those numbers as directional at best.
That uncertainty cuts both ways. It means nobody can prove a GEO service works, and it also means you cannot prove that ignoring AI answers is safe. The sensible response is the boring one. Keep doing the SEO work that gets pages found and trusted, write content that answers questions directly and backs up its claims, make sure the AI crawlers you care about can reach you, and watch referral data rather than vendor dashboards.
If AI features are already eating into your clicks, that is a traffic problem worth measuring on its own terms; our analysis of AI Overviews and traffic and the post on zero-click searches cover what the data shows. For everything else in this area, the algorithm archive collects the related posts.
Frequently asked questions
What is generative engine optimisation?
It is the practice of shaping content so that it is visible in AI-generated answers. The term comes from the paper “GEO: Generative Engine Optimization”, submitted in November 2023 and published at KDD 2024.
Does GEO boost visibility by 40%?
The paper reports that GEO methods can boost visibility by up to 40%, measured on its own benchmark and generative engine setup. It did not measure live Google or ChatGPT.
Is GEO replacing SEO?
No. Answer engines that browse the web retrieve sources before citing them, and that retrieval leans on search. Most GEO advice overlaps with good SEO practice.
Should I hire a GEO agency?
Ask how they measure results and what evidence they have beyond the original paper. If the answer is a sampled visibility score, treat it as directional.
The takeaway GEO is a useful lens, not a new discipline. The research behind it was done on a benchmark, and the content it rewards is the content SEO already rewards.
