GEO vs SEO: what actually differs, measured across four engines
Most comparisons of these two things are definitional. This one is built from three published datasets, so every claim below can be checked against a file rather than taken on trust. Broadcastwell sells GEO work, which is a reason to read the method section, and a reason we put the DOIs on the page.
The mechanical difference
That sounds like a phrasing difference and it is not. A ranked list has ten slots and somebody fills all of them. A generated answer names whoever the engine decided to name, and in our own category measurement 120 of the 200 answers came from questions that named no agency at all, while a large share of those answers named nobody the buyer had heard of. Absence is a normal outcome in a generated answer in a way it never is in a ranked list.
| Ranking | Being named | |
|---|---|---|
| What the surface is | An ordered list of links | A written answer that names a few vendors |
| What you occupy | A position, which always exists for someone | A mention, which may exist for nobody |
| How many win | Ten slots, ranked, all visible | Between three and fifteen names, unranked, depending on the engine |
| What the unit is | Rank for a keyword | Named or not named, out of a question set someone chose |
| Who decides | One index per engine, broadly stable between checks | Retrieval plus generation, and the same question can return a different set of firms |
| What a source does | A link earns authority that lifts a page | A source is read to compose an answer that may name someone else |
Answer lengths in the third column are measured, not illustrative. See section 03.
The practical consequence is that the two disciplines optimise against different feedback. Rank moves in small increments and is broadly stable between checks. A named count is a proportion out of a denominator you chose, and it can move for reasons that have nothing to do with anything you did. Reporting the second like the first is where most of this category's overclaiming comes from.
One ranking, four answers
In classical search, checking two engines mostly tells you the same thing twice. In generated answers it does not.
Volume III scored 853 answers and computed vendor-set agreement across every engine pair. The mean pairwise Jaccard was 0.313, with a 95 percent confidence interval of 0.295 to 0.330, over 590 pair observations. On the three-engine subset of 142 questions carrying 2,238 vendor mentions, 63.3 percent of mentions were made by exactly one engine and only 17.5 percent by all three. Of 32 companies testable on two or more engines, 14 were visible on some and invisible on others.
That is the single most consequential difference from SEO for a buyer of these services. A one-engine report is not a category reading. It is a reading of one engine, and the odds are close to even that another engine names a different set of firms entirely.
The 2026 State of Generative Engine Optimization, Volume III. DOI 10.5281/zenodo.21789120.
The caveat that changed our own headline
Volume III was going to say that engines disagree with each other about twice as much as they disagree with themselves. A length control killed half of that claim, and the way it died is worth carrying into how you read anyone else's divergence statistic, including ours.
| Engine | Vendors named per answer |
|---|---|
| Google AI Overviews | 4.8 |
| Claude | 8.8 |
| Perplexity | 10.7 |
| ChatGPT | 14.7 |
ChatGPT's figure rests on 18 answers only, because the collection ran out of credit on that engine. Treat it as indicative. Volume III, DOI 10.5281/zenodo.21789120.
Engines name very different numbers of vendors. An engine compared with itself is length-matched by construction. Two different engines are not, and Jaccard between similar-sized sets runs mechanically higher. So part of any raw within-versus-between gap is an artefact of list length rather than a real disagreement.
Controlling for it separated the two results. Google AI Overviews held: a gap of plus 0.258 on raw Jaccard, and plus 0.193 on an overlap coefficient normalised by the smaller set. Claude did not: plus 0.165 raw fell to plus 0.002 on the overlap coefficient, which is not significant. The honest conclusion is that divergence is engine-specific, and that any cross-engine divergence number published without a length control is suspect. That includes numbers that would have flattered us.
The citation pool is long-tailed
A common instinct carried over from link building is to find the sites the engines cite and get onto them. The measured shape of the citation pool makes that mostly unworkable.
| Measure | Volume I | Volume III |
|---|---|---|
| Share of cited domains appearing exactly once | 56% | 57.2% |
| Share of all citations held by the top ten domains | 12% | 12.2% |
Two independent collections, months apart, different question sets. Volume I DOI 10.5281/zenodo.21537014, Volume III DOI 10.5281/zenodo.21789120.
More than half the domains an engine cites appear exactly once, and the ten most-cited domains between them account for roughly an eighth of all citations. There is no small, stable set of sources to buy into. Two separate collections landing within about a point of each other suggests this is a property of the retrieval, not a quirk of one dataset.
Which sources do matter is category-specific, and that is measurable. In our own category, every top-cited domain was an agency's own website, with no analyst, review platform or trade body near the top, which makes owned pages nearly the whole lever there. A category with a dominant review platform inverts that. The per-stat figures are on the statistics page.
Being cited is not being recommended
In SEO the link and the visit are the same event. In a generated answer they come apart completely.
An engine can read your page, use it to compose the answer, and name a competitor in the sentence. In our own category measurement, rampiq.agency was cited 25 times while Rampiq was named in zero of the 120 answers whose question did not already name an agency. The site was useful to the engine. The company was not the recommendation.
We hit the same distinction on ourselves. Our monthly self-audit moved from 0 of 40 to 1 of 40 in August 2026, and the single mention was Perplexity citing our own measurement page and quoting our figures back. Named as a source, not recommended as an agency. We published it as 1 of 40 with that stated plainly, because the method scores presence and says so. See our visibility.
The practical test for any tool or agency in this category: ask whether it reports a named count beside its citation count. A citation-only dashboard can show a rising line while nothing reaches your pipeline.
What carries over and what does not
Method and limits
Questions
What is the difference between GEO and SEO?
SEO competes for a position in an ordered list of links, and a position always exists for somebody. GEO competes to be named inside a written answer that mentions a handful of vendors and may name nobody at all. The unit of measurement is different: rank for a keyword versus named or not named, out of a question set someone chose. That is why a site can rank respectably and be absent from every answer in its category.
Does GEO replace SEO?
No, and the two are not independent either. Retrieval still runs over indexed pages, so being findable remains a precondition. What changes is that being findable stops being sufficient. In our Volume III collection a Google AI Overview appeared on 92.1 percent of 280 buyer questions, so on most commercial questions a generated answer now sits above the list that SEO competes in.
Can I just get onto the sites AI engines cite?
Usually not, because the citation pool is long-tailed rather than concentrated. In Volume I, 56 percent of cited domains appeared exactly once and the top ten domains held only 12 percent of all citations. Volume III reproduced almost the same shape on a different collection: 57.2 percent cited once, top ten holding 12.2 percent. There is no short list of sites to buy your way onto. Which sources matter is category-specific and has to be measured.
Do all AI engines give the same answer?
No. Across 590 engine-pair observations the mean pairwise Jaccard agreement on vendor sets was 0.313, with a 95 percent confidence interval of 0.295 to 0.330. On the three-engine subset, 63.3 percent of the 2,238 vendor mentions were made by exactly one engine and only 17.5 percent by all three. Of 32 companies testable on two or more engines, 14 were visible on some and invisible on others.
Is being cited the same as being recommended?
No. An engine can read your page to compose an answer and then name a competitor inside it. In our own category measurement rampiq.agency was cited 25 times while Rampiq was named in zero of the 120 answers whose question named no agency. A citation-only dashboard can show a rising line while nothing reaches your pipeline.
How should I read a divergence statistic from any vendor?
Ask whether it was length-controlled. Engines name very different numbers of vendors per answer, from about 4.8 to about 14.7 in our collection, and Jaccard between similar-sized sets runs mechanically higher. Our own Claude result of plus 0.165 raw fell to plus 0.002 and lost significance once normalised by the smaller set. A divergence number published without that control is not yet a finding.
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