AI in Science

Ajay Agrawal, John McHale and Alexander Oettl

No 34953, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We explore the impact of artificial intelligence (AI) on the knowledge production function. We characterize AI as a tool, not for full automation but rather for augmentation through enhanced search over combinatorial spaces. This leads to increased scientific productivity. We decompose knowledge production into a multi-stage process to shed light on the "jagged frontier" of AI in science, revealing differential returns to different tools across domains (e.g., data-rich biology vs. anomaly-sparse physics) and workflow stages (e.g., strong design aids like AlphaFold vs. subtler question generation tools). We treat human judgment as indispensable for tasks involving abductive inference, contextual nuance, and trade-offs, particularly in data-sparse environments. Drawing on a task-based model that distinguishes "ordinary" from AI-expert scientists, we describe how exogenous improvements in AI yield nonlinear productivity gains amplified by the share of scientists that are AI-experts to underscore the role of AI complements like skills training and organizational design.

JEL-codes: I23 O14 O31 O33 O41 (search for similar items in EconPapers)
Date: 2026-03
New Economics Papers: this item is included in nep-ain, nep-eff and nep-lma
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