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Joshua Gans is a Professor of Strategic Management and holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management, the University of Toronto (with a cross-appointment in the Department of Economics). Joshua is also Chief Economist of the University of Toronto's Creative Destruction Lab and a Distinguished Fellow of the Luohan Academy. While Joshua's research interests are varied, he has developed specialities in the nature of technological competition and innovation, economic growth, publishing economics, industrial organisation and regulatory economics. Joshua serves as an associate editor at the Journal of Industrial Economics and is on the editorial board of Economic Analysis and Policy.
Abstract
This paper examines how the introduction of artificial intelligence (AI), particularly generative and large language models capable of interpolating precisely between known data points, reshapes scientists’ incentives for pursuing novel versus incremental research. Extending the theoretical framework of Carnehl and Schneider (2025), we analyse how decision-makers leverage AI to improve precision within well-defined knowledge domains. We identify conditions under which the availability of AI tools encourages scientists to choose more socially valuable, highly novel research projects, contrasting sharply with traditional patterns of incremental knowledge growth. Our model demonstrates a critical complementarity: scientists strategically align their research novelty choices to maximise the domain where AI can reliably inform decision-making. This dynamic fundamentally transforms the evolution of scientific knowledge, leading either to systematic “stepping stone” expansions or endogenous research cycles of strategic knowledge deepening. We discuss the broader implications for science policy, highlighting how sufficiently capable AI tools could mitigate traditional inefficiencies in scientific innovation, aligning private research incentives closely with the social optimum.
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