Data-extrapolation technologies enable progress by importing and applying empirical patterns from one domain to another. Their reliance on empirical patterns, however, is also their limitation: these tools can only be applied where data are abundant. Using the setting of structural biology, I investigate the impact of a data-extrapolation technology on innovation. I exploit the variation in prior data availability to compare areas in which the tool was usable to those in which it was not. I find that the tool increased the quantity of research in data-rich areas: the resulting output yielded limited biological insights but was methodologically rigorous and contributed to downstream research. This paper was accepted by Toby Stuart, entrepreneurship and innovation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04115 .
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