Growth in AI Knowledge

Building on recent advances in the literature on knowledge creation and innovation (notably Carnehl and Schneider (2025), we propose a novel general equilibrium model that explicitly incorporates artificial intelligence (AI) as a decision-enhancing technology capable of interpolating between known points of knowledge. Our framework formalises the trade-off between AI’s coverage— its ability to span wider knowledge gaps—and its accuracy, and reveals the surprising result that, beyond producing immediate productivity gains, AI fundamentally alters the novelty of research. Specifically, when AI systems offer sufficiently broad coverage, they incentivise exploratory research that taps into novel, distant areas of knowledge and accelerates long-run growth; conversely, limited coverage promotes incremental research that may boost short-term efficiency while dampening the overall advancement of new ideas. Moreover, our analysis uncovers that the type of knowledge—whether novel or dense—plays a critical role in determining both the growth and welfare implications of AI, charting a new path for understanding how knowledge influences research strategies. By also examining the roles of market structure, licensing arrangements, and regulatory frameworks, our work contributes new, policy-relevant insights that reconcile the immediate benefits of AI adoption with the demands of sustainable long-term economic expansion.