GEO versus SEO, explained through measured answers
Most comparisons of these two things are definitional. This comparison draws on three published datasets. Sources and verification limits are stated beside the findings. 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 can be cited in an answer that names 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. Vendor-mention shares: 1,416 of 2,238 from one engine and 392 of 2,238 from all three, across 142 questions. Interval not reported in the published source.
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.
A length control changes the conclusion
Volume III initially suggested that engines disagree with each other about twice as much as they disagree with themselves. After controlling for answer length, the conclusion held for Google AI Overviews but not for Claude. That distinction belongs in any responsible reading of cross-engine divergence.
| 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. 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 conclusions that look stronger before the control is applied.
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: historical page value |
|---|---|---|
| Share of cited domains appearing exactly once | 56% | 57.2% (historical page value) |
| Share of all citations held by the top ten domains | 12% | 12.2% (historical page value) |
Volume I was collected 18 to 23 July 2026; Volume III was collected 4 August 2026, with a different question set. Volume I DOI 10.5281/zenodo.21537014; Volume III DOI 10.5281/zenodo.21789120. The Volume III percentages shown here are historical values from this page. They differ from the deposited results and are not used as evidence of a replicated result. Their original numerator, denominator and interval have not been verified. Volume I uses 1,753 cited domains for the singleton share and 609 of 5,160 citations for the top-ten share; its exact singleton numerator and intervals are not reported in the published tables.
The historical Volume III values above do not match the deposited citation-concentration results. They therefore do not support a claim that Volume III replicated the Volume I percentages. The source remains available for inspection. Read the deposited results.
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
A search-result link and a visit are separate events. Likewise, a citation in a generated answer does not establish that a user or engine visited the source.
An answer can cite a vendor domain without naming that vendor. In our 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 citation and naming counts describe different observed outcomes.
The current measurement protocol is on the methodology page, with its scope and limitations. This commercial page uses the general finding: source inclusion and vendor recommendation must be scored separately.
The practical test for any tool or agency in this category: ask whether it reports a named count beside its citation count. A citation count alone does not establish whether a vendor was named, or whether leads or revenue changed.
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, an observed 258 of 280 questions in this sample. Interval not reported in the published source.
Can I just get onto the sites AI engines cite?
In Volume I, 56 percent of cited domains appeared exactly once and the top ten domains held 12 percent of all citations. The bases are 1,753 cited domains and 609 of 5,160 citations, respectively; the exact singleton numerator and intervals are not reported in the published tables. This page previously listed Volume III values of 57.2 percent cited once and 12.2 percent held by the top ten. Those historical page values differ from the deposited results and do not establish replication. Which sources matter remains category-specific.
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. Vendor-mention shares: 1,416 of 2,238 from one engine and 392 of 2,238 from all three, across 142 questions. Interval not reported in the published source.
Is being cited the same as being recommended?
No. An answer can cite your domain while naming a competitor. 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 count alone does not establish whether a vendor was named, or whether leads or revenue changed.
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. The 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.
Start with a measured baseline
See which buyer questions leave you out, the sources visible in those answers, and the three changes worth testing first. The Category Audit covers ten questions; the Diagnostic extends to 35 questions and 525 scheduled observed answers, adaptive runs extra. AI answers vary, so repeated observations and disagreements are reported.
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