AI-Augmented Science and the New Institutional Scarcities

TL;DR

This paper argues that AI's role in science shifts from prediction to transforming the certifying infrastructure, emphasizing four new scarcities that scientific institutions must develop to effectively integrate AI.

Contribution

It introduces four key complements—verified signal, legitimacy, authentic provenance, and integration capacity—that are essential for AI-augmented scientific institutions.

Findings

Judgment at scale is approaching zero marginal cost, reversing traditional economics.

Scientific institutions compete with AI for legitimacy and certification roles.

Integration capacity is the most critical and underdeveloped scarcity for AI-augmented science.

Abstract

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.