Research

Why do promising technologies that dominate public attention so often end in disappointment? I propose that media buzz makes a technology strategically urgent before firms have equally clear knowledge of where it can profitably be applied. I test this idea around four sharply dated buzz episodes, in virtual reality, artificial intelligence, augmented reality, and blockchain, linking patent and trademark records to observe invention and commercialization within the same US public firms. Following buzz onset, firms with relevant prior capabilities expand patenting, inventor hiring, and skill demand in the focal technology relative to comparable firms. Yet the number of products they bring toward market falls by roughly one-quarter. The technological advance is real; the trap of buzz sits downstream of invention, in commercialization.

Using novel data on federal regulations and the patents of 1,242 firms from 1994 to 2013, we find that regulatory restrictiveness can have both a negative and positive relationship with innovation output depending on the level of regulatory uncertainty and the innovation type in question.

Applying machine learning to five decades of U.S. patent applications, we show that the rise of organizational software turns organizational innovations, long assumed unpatentable, into patentable technological ones, identifying more than 200,000 such applications.

The first systematic, large-scale comparison of DARPA with the NSF and NIH. DARPA-funded research is more disruptive and more likely to be linked to technological discoveries: DARPA disproportionately selects investigators with prior histories of disruptive research and work in Pasteur’s Quadrant.

Using novel household data, we show that firms led by CEOs with broader nonwork interests were more likely to form cross-industry alliances in response to the COVID-19 pandemic.

Exploiting the 2016 AlphaGo victory as an exogenous shock to AI buzz, we find that patent applications with AI buzzwords faced significantly higher rejection rates on disclosure grounds after the shock. Expert gatekeepers resist buzz rather than absorb it.

Using large-scale bibliometric data and epidemic shocks as quasi-natural experiments, we find that the emergence of hot research topics widens the gender gap in publication output, driven by unequal capacity to pivot into newly salient areas.

A natural-language-processing crosswalk between 439,000 USPTO green patents and 4.25 million pages of the Code of Federal Regulations (1998–2022), using generative AI to classify command-and-control versus market-based regulation and citation network analysis to trace regulatory dynamics.

Using the launch of Amazon Web Services as a shock to VC screening, we examine how a shift in the criteria investors use to select startups propagates into the direction of scientific research, including its disruptiveness and the coupling between science and technology.