A Quest for AI Knowledge

This paper examines how AI tools that excel at interpolating existing knowledge affect scientific research directions. Using a model where AI assists both scientists (S-AI) and decision-makers (DM-AI), it is shown that AI’s impact on research novelty is non-monotonic and depends critically on capability thresholds. While S-AI predictably encourages knowledge consolidation by reducing costs within established domains—potentially creating distinct pockets of deepening—DM-AI generates surprising effects. With limited capabilities, scientists ignore DM-AI. In a moderate regime, scientists “work to the AI,” constraining novelty to match AI’s operational range. Only with sufficiently advanced DM-AI do scientists unambiguously pursue more novel research. The strong complementarity between AI capabilities and knowledge gaps means that moderate AI may reduce research ambition. These findings challenge the conventional wisdom that interpolative AI uniformly pushes science toward consolidation, revealing a nuanced relationship between AI capabilities and scientific progress instead.