Can You Trust AI Citations? A Third Don't Support the Claim

On the same day, two independent research groups published audits of what Perplexity actually cites — and both found that a citation is a weaker signal than any dashboard treats it as. Haus Research checked 1,826 citations attached to numeric claims about technology companies and found 34.7% pointed at a page that would not open or did not contain a single figure from the sentence. Trellner Research mapped the sources behind software recommendations and found a three-site network that had generated 215,128 buying guides — some for categories that don't exist — sitting inside Perplexity's retrieval.

Key takeaways

The two studies asked different questions of the same engine and arrived at the same place from opposite ends.

Haus measured support: fetch every cited URL, check whether the page contains the figures in the sentence it's attached to. Trellner measured provenance: cross-reference every cited domain against Tranco rankings and the Wayback Machine, then look at who registered the sites that keep showing up. One found the citations often don't hold up; the other found the sources often shouldn't have been trusted in the first place.

Neither publisher sells AI visibility tooling, which matters. Most numbers in this space come from vendors measuring their own product's shadow; these are third-party audits with published methods and, in Haus's case, per-question-type breakdowns you can check.

The Haus headline — a third of citations don't contain the number they're cited for — is a per-citation figure. Perplexity typically attaches several citations to one sentence, so a claim can survive one bad citation if another supports it. Counted per claim, 14.4% of the 872 numeric claims had no supporting source at all.

Both numbers are real; they answer different questions. Per citation tells you how much of the citation display is decoration. Per claim tells you how often the answer is actually ungrounded. If you have read our piece on ghost citations, this will feel familiar — published figures in this field disagree mostly because they divide by different things, and the honest move is to state the denominator rather than pick the scarier number.

The accessibility breakdown adds texture: 78.7% of cited pages were live and readable, 16.1% were gated behind paywalls or logins, 1.3% were dead, and 1.4% unreachable after retries. A gated citation isn't necessarily wrong — the page may well contain the figure — but it is unverifiable by the reader the citation is supposed to reassure.

The per-question-type pass rates are the most actionable table in the study:

The pattern: the worse a fact ages, the worse the citations. CEOs change; founding dates don't. And the sources filling the gap are aggregators — 23.1% of citations pointed at B2B directories like PitchBook and Crunchbase, essentially tied with company domains at 23.4%. When your own page doesn't state a fact plainly, the engine cites a directory's stale copy of it instead.

Trellner's audit looked at the queries marketers actually care about — "best [software category]" — and the source picture is starker. The median cited domain that had a Tranco rank at all ranked 71,611th. Nearly a quarter of cited domains had no rank. Wikipedia, the most-cited domain in several studies of where AI citations come from, earned three citations across all 760 queries. A vendor's own marketing blog was the third most-cited domain in the entire study, at 194 citations.

Into that vacuum, someone built supply. The three-site network — worldmetrics.org, gitnux.org, wifitalents.com — was registered between December 2023 and May 2024, shares DNS infrastructure and page templates, and had published 215,128 generated buying guides at the time of the audit, including guides for software categories that do not exist. The pages carry HTML titles reading "Facts & Grounding Page" — a phrase with no meaning to a human visitor and a very specific meaning to a retrieval system deciding what grounds an answer.

We looked at worldmetrics.org ourselves on the day the report published. It presents as a market research firm — "proprietary research & verified data," named researchers, an editorial process — and its homepage advertises 72,716 "best lists" across 96 software categories. Ownership is not disclosed. It is, in short, dressed as exactly the kind of source an engine should want to cite, at a volume no research organization produces.

This is the supply-side answer to a question our GEO detection piece raised from the demand side: if 9% of the web already shows GEO optimization, what does the fully optimized endpoint look like? It looks like this — content produced for no reader at all.

Set these findings next to the academic audit published two days earlier and you get an apparent contradiction worth resolving. The Sources of Truth study recorded 15,942 citations across 1,140 responses to mental health questions on ChatGPT, Perplexity, and Google AI Overviews, and found heavy concentration: the ten most-cited domains carried 43.6% of English citations, with government, commercial health, and academic sources at roughly 22% each.

So which is it — a handful of authoritative domains, or an unranked long tail? Both, and the difference is supply. For a health question, an authoritative page almost certainly exists: a government agency, a medical body, a university has written it. Retrieval finds it and citations pile up on it. For "best subscription-box analytics software," no institution has written anything, so retrieval falls to whatever matches the shape of the question — and shape is cheap to manufacture.

That makes the useful diagnostic category-level, not engine-level: does your category have authoritative citation supply? If the top cited domains for your prompts are recognizable institutions, the shortlist is stable and hard to enter. If they're domains ranked 70,000th or nowhere, the shortlist is contestable — by you, and by a content farm. Our own 42-day volatility study found the set of cited domains for a prompt turning over 60–70% day to day, and it is exactly the thin-supply prompts where that churn creates openings.

The instinct this research should correct is treating a citation as a unit of credibility. It is a unit of retrieval. Perplexity's own models scored identically in the Haus audit — sonar at 65.9%, sonar-pro at 64.7% — so paying for the better model doesn't buy better-grounded citations, and there is no evidence the engine verifies that a cited page supports the sentence it decorates.

Three practical consequences:

What this research does not license is imitation. The farms work because a supply gap exists, and the direction of travel on the platform side — laid out in the mechanism-design work on citation wars — is toward rewarding claims that verify against a source and stripping the rest. The durable version of the farms' play is publishing the genuinely grounded page their templates fake: real numbers, stated method, a brand willing to put its name on it.

Both audits are single-day snapshots of one engine, taken through the API rather than the consumer product, and neither tested whether removing a source changes the recommendations — so "cited by" is established and "decisive for" is not. Haus's sample is hand-selected English-language technology companies; gated pages were scored as failures for verifiability even though some surely contain the figure; JavaScript-rendered content wasn't evaluated. Trellner's provenance findings are strong for the named network and suggestive beyond it. And the Sources of Truth concentration figures come from one topic — health — chosen precisely because authoritative supply exists there. Treat the direction as solid and the decimals as September 2, 2026.

Every number above describes someone else's prompt set. Whether your category runs on institutions or on an unranked tail — and whether the citations attached to claims about your brand hold up — is checkable from your own tracking data in an afternoon.

Elmo is an open-source, self-hosted AI visibility platform that runs your prompt sets across ChatGPT, Claude, Gemini, Perplexity, and Google's AI surfaces, recording every cited domain and the answer text on every run. Because the data is yours to query, ranking the domains cited in your category — including the runs where you were absent — is a query over your own results, and the cited URLs are right there to open and verify.

For the fundamentals, start with AI citations and where AI citations come from, then how to track your brand in AI search. For how the source landscape shifts under optimization pressure, see citation wars, GEO detection, and do GEO tactics stop working. For the vocabulary, see the AI search glossary.