Artificial Intelligence, Scientific Discovery, and Product Innovation
Description of Resource
This paper studies the impact of artificial intelligence on innovation, exploiting the
randomized introduction of a new materials discovery technology to 1,018 scientists in
the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials,
resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more
radical inventions. However, the technology has strikingly disparate effects across
the productivity distribution: while the bottom third of scientists see little benefit,
the output of top researchers nearly doubles. Investigating the mechanisms behind
these results, I show that AI automates 57% of “idea-generation” tasks, reallocating
researchers to the new task of evaluating model-produced candidate materials. Top
scientists leverage their domain knowledge to prioritize promising AI suggestions,
while others waste significant resources testing false positives. Together, these findings
demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence
reveals that these gains come at a cost, however, as 82% of scientists report reduced
satisfaction with their work due to decreased creativity and skill underutilization.