The Wharton School, U. of Pennsylvania, United States
This study examines how firms’ search strategies influence innovation outcomes in artificial intelligence (AI). In the context of emerging general-purpose technologies (GPTs), AI firms must balance searches for technological advancements (supply-side) and discoveries of new use cases (demand-side). These search focuses face contrasting competitive pressures, requiring firms to dynamically manage tradeoffs. Using a sample of 135 AI firms and 26,397 papers (1990–2024), this study finds that knowledge for technological advancements exhibits lower initial performance due to higher competitive pressures but sustains performance over time. In contrast, knowledge for new use cases achieves higher initial performance due to reduced pressures but this advantage decays more rapidly due to imitability. At the firm level, transitioning from technological advancements to new use cases enhances the likelihood of commercialization. This research contributes to the literature on search and technology innovation by exploring the interplay between supply-side and demand-side capabilities, offering broader implications for firms navigating similar dual challenges in other emerging technologies.
