AI-Augmented Science and the New Institutional Scarcities
For scientific institutions, this paper reframes the challenge of AI integration from a technical to an institutional design problem, but it is a conceptual argument without empirical validation.
The paper argues that AI's ability to produce competent judgment at near-zero marginal cost inverts the traditional economics of AI, creating new scarcities in science: verified signal, legitimacy, authentic provenance, and integration capacity. It identifies integration capacity as the most binding constraint, requiring redesign of certifying infrastructure rather than acceleration.
Competent-looking judgment, including selecting, ranking, attributing, and certifying, is now produced at scale at marginal cost approaching zero, inverting the dominant economics-of-AI reading that treats judgment as the scarce complement to cheap prediction. Scientific institutions, distinctively, manufacture legitimate judgment, so they do not merely adapt to AI; they compete with it for the same functional role. Four complements then become scarce and load-bearing for AI-augmented science: verified signal, legitimacy, authentic provenance, and integration capacity (the community's tolerance for delegated cognition). Of these four, integration capacity is the least developed for scientific institutions and the most binding: no improvement in AI tooling can buy it. The frontier for AI-augmented science is not acceleration; it is the redesign of the certifying infrastructure around these new scarcities.