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 are among those most exposed, because manufacturing legitimate judgment is their primary product rather than one input among many, 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.

翻译：看似具备胜任力的判断（包括选择、排序、归属与认证）正以趋近于零的边际成本大规模生产，这颠覆了将判断视为廉价预测的稀缺互补物的主流AI经济学解读。科学机构是受影响最深的领域之一，因为制造合法判断是其主要产出而非众多投入之一，故它们不仅需要适应AI，更需与之竞争同一功能角色。由此，四种互补品成为AI增强型科学中稀缺且承重的要素：已验证信号、合法性、真实溯源及整合能力（社群对代理认知的容忍度）。在这四者中，整合能力对科学机构而言发展最不成熟且最具约束性：任何AI工具的改进都无法购得此能力。AI增强型科学的前沿并非加速，而是围绕这些新型稀缺性重新设计认证基础设施。