Mapping the Landscape of the Economics of AI Literature: Gaps and Opportunities for Research and Policy

Abstract

This paper investigates the evolving landscape of research on the economics of artificial intelligence (AI). Using topic modeling on a corpus of over 4,600 academic papers, we identify the dominant themes that have shaped the literature to date and highlight areas that remain relatively underexplored. In addition, we assess the extent to which current research trajectories align with policy priorities. Our findings show significant research concentration on AI applications in supply chains, labor markets, and large language models (LLMs), especially following ChatGPT's release. However, comparing these topics to national AI strategies and legislation across regions, we identify notable gaps, particularly in measuring AI-driven economic growth, effective industrial policy, and the economic impacts of risk-based AI regulations. Global AI governance, regulatory fragmentation, and the effects of privacy laws on market competition in this context are also under-studied. By mapping these trends and gaps, the study offers guidance for future research and for policymakers navigating AI's economic and regulatory landscape.