Working Paper
AI can deliver productivity gains on individual tasks, yet evidence on whether these gains aggregate to firm performance remains limited.
The authors study a central friction in AI adoption, which they call the mapping problem: discovering where and how AI creates value within a firm’s production process. Across 515 high-growth startups, they run a field experiment in which treated firms receive information about how other firms have reorganized production around AI, prompting them to search for use cases across a broader set of firm functions.
They find that treated firms discover more AI use cases, a 44% increase, concentrated in product development and strategy. These changes result in economically meaningful performance gains. Treated firms complete 12% more tasks, are 18% more likely to acquire paying customers, and generate 1.9x higher revenue. Revenue and investment gains are largest at the 90th percentile and above, consistent with AI expanding the upper range of what firms achieve rather than modestly improving marginal ventures. Despite faster growth, treated firms do not scale inputs proportionally. Their demand for external capital investment falls by 39.5% relative to the control group, while their demand for labor remains unchanged. These results provide causal evidence that AI improves firm performance and productivity even at its current capabilities, and that discovering where and how to deploy AI is a key bottleneck in realizing the gains from this technology.
Faculty
Assistant Professor of Strategy
