What we measured, stated before the findings
Forty buyer questions, frozen word for word — sixteen "best X" prompts, eight "X alternatives", eight head-to-head comparisons and eight how-do-I-buy questions. Seven engines: ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, Google AI Mode, and Bing as a proxy for Copilot. Two runs, 3 and 7 September 2026. Coverage was 40 of 40 questions on every engine in both weeks, with zero failed checks — which matters, because a citation rate computed over answers that never arrived is a rate with the wrong denominator.
Every number below is reproducible: the panel publishes its results as JSON, including the prompt count, the per-engine coverage and the instrument fingerprint that says whether two weeks are comparable at all. If a prompt or an engine changes, the comparison is marked incomparable rather than being quietly presented as a trend. You can read the week yourself on the public index.
And the limits, up front. Two weeks is two weeks: where we say a finding held, we mean these two runs agreed, not that it is stable. One question set, one market, one moment. The shapes below are worth your attention; the decimals are not load-bearing.
There is no shared canon — each engine eats a different diet
The single most useful result is also the most inconvenient one for anybody selling "AI optimisation" as one job. Ask the same question of seven engines and you get seven different source lists, concentrated differently.
- Gemini cited reddit.com for 22 of 40 questions and youtube.com for 20 — more than half its answers lean on community and video.
- ChatGPT's heaviest sources were youtube.com (10 questions), techradar.com (9) and forbes.com (6): review publishers and video, almost no forums.
- Perplexity spread thinner than any other engine — its top domain, forbes.com, appeared in 4 of 40, with reddit.com level at 4.
- Claude cited review publications and almost nothing else; it reached community content least of all seven.
- Google AI Overviews and AI Mode were the two most video- and forum-heavy surfaces, and the two least reliant on the long tail.
The practical consequence: "we got cited by AI" is not a fact about your brand, it is a fact about one engine. A tactic that earns you Gemini citations through community presence can be worth nothing on Claude, which in this panel cited community content the least of all seven. If you are going to work on this, measure per engine or you will be optimising for an average nobody experiences.
Four out of five cited domains fit no category at all
We classify every cited domain into media, community, social, directory or reference — and we keep an explicit "unknown" bucket rather than forcing a guess. In the published week, 1,414 engine-and-domain pairs produced 1,142 unknowns. That is 80.8%, against 80.3% the week before.
Read that carefully, because it is easy to over-claim. "Unknown" is our classifier's blind spot, not a property of the web. What is in there is not mysterious: vendor pages, product documentation, niche blogs, retailer listings, the specific page that happened to answer the specific question. The finding is not "AI cites unknown things", it is that the named categories everybody argues about — Reddit, media, directories — describe a minority of what actually gets cited, and the majority is ordinary pages doing an ordinary job.
One more caveat we owe you: the category is assigned per DOMAIN, not per page. A directory listing and an editorial article on the same domain are one row in our data. That is a real limitation and it is why we do not report a media-versus-marketing split at all.
Review directories are about half a percent — and we got the reason wrong once
"Get listed on G2 and Capterra" is standard advice, and on this panel the directories are a rounding error: 0.4% of distinct cited domains in the published week, 0.5% the week before. Five directory domains out of 1,414 pairs.
We originally published a sharper claim than the data supported, and this is the correction. We said directory citations tracked query SHAPE on the answer engines — that "alternatives" prompts pulled them in while "best" prompts almost never did. X produced directory citations in 4 of 96 and 1 of 96 prompt-engine cells, and "alternatives" in 2 of 48 and 2 of 48. That is the same rate inside the noise.
What survives is the level, not the mechanism: directories are a very small share of what answer engines cite, whatever shape the question takes. The rule we took from it is the one worth passing on — re-query a number before you repeat it in public, especially a number that made a satisfying story.
Being cited and being named are two different things
There is a gap most dashboards collapse. An engine can cite your page as a source without ever saying your name in the answer — and a buyer reads the prose, not the footnotes.
On five of seven engines, the source that gets cited is almost never the source that gets named. Those are close to disjoint outcomes, which has a direct consequence for how you read any AI-visibility report: a rising citation count and a falling mention count are not a contradiction, and a tool that reports one number for both is hiding the more useful of the two.
This figure is a FLOOR. We score the visible answer excerpt, which is truncated, so the true rate is higher than 10.9% — by how much we cannot say without storing whole answers, which is a different instrument and would have no back-data. We would rather publish a floor and label it than publish an estimate and imply it.
What we would actually do with this
- Pick the engines your buyers use and measure those. The average across seven is a number nobody experiences.
- Stop treating directory listings as an AI-visibility tactic. They may be worth it for other reasons; this is not one of them.
- Look at the pages in the long tail for your own category — the vendor pages and docs that got cited are the closest thing to a template you will find. Score one of your own against the signals that decide whether a page can be quoted.
- Track mention and citation as separate outcomes, and say which one moved.
- Freeze your question set before you measure anything, and re-freeze it deliberately. A changed prompt makes last week incomparable, and a comparison nobody flagged as broken is worse than no comparison.
We publish this panel weekly and the raw week is public. If it disagrees with what you measure, we would rather hear it than not: the correction above exists because somebody asked us how we had counted.
Frequently asked questions
Which AI engine cites the most sources?
In this panel, ChatGPT and Gemini drew on the widest range of domains and Claude the narrowest — Claude cited community content least of all seven engines, and had the highest share of long-tail domains outside every named category (92.6%). Breadth varies enough between engines that a single "AI visibility" score averages away the thing you needed to know.
Do AI answer engines cite G2, Capterra and other review directories?
Rarely. Across two weeks of 40 buyer questions on seven engines, directories were 0.4–0.5% of distinct cited domains. The one surface where they show up often is Bing, which is a classic search results page rather than an AI answer — so evidence from Bing should not be generalised to ChatGPT or Perplexity.
If an AI engine cites my page, will it say my brand name?
Usually not. In our measurement the cited source's name appeared in the visible answer text in only 10.9% of cases, and on Gemini, Perplexity and Claude it was under 4%. That figure is a floor, since we score a truncated excerpt. The practical point stands: citation and mention are separate outcomes and should be tracked separately.
Is two weeks of data enough to act on?
It is enough to see structure and not enough to see trend. The engine-by-engine concentration and the size of the long tail held across both runs; anything we describe as movement between two runs would be noise. We publish a new week every week and mark any pair as incomparable when the instrument changed.