Artificial intelligence now produces convincing-looking scientific judgment (reviews, rankings, attributions, verifications) at almost no marginal cost. An influential reading of AI economics holds that prediction becomes cheap while human judgment stays scarce. For science, that reading understates the problem: what has become cheap is a counterfeit of judgment itself. This matters most for institutions whose product is trusted judgment, which is what journals, universities, funders, and learned societies exist to manufacture. They do not merely adapt to the technology; they compete with it for the same functional role. Four things become scarce instead: verified signal, legitimacy, authentic provenance, and integration capacity. Integration capacity means how much AI-delegated judgment a scientific community will accept before it stops trusting the journals, panels, and conferences that admitted it. It is the least developed of the four and the most binding: better tooling cannot buy it. The way forward for AI-augmented science is not acceleration but the redesign of its certifying infrastructure around these new scarcities.

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