Research

Buzz, the collective excitement surrounding a technology, draws entrants and capital toward it and makes markets more receptive to the innovations associated with it. Yet before these innovations can advance, they must pass institutional gatekeepers, and how buzz shapes the judgments of these gatekeepers is unknown. I argue that the answer depends on technology expertise. Gatekeepers with expertise in the technology turn the attention that buzz brings into closer scrutiny and may push back against its enthusiasm, which makes their judgments less favorable. I test this argument in patent examination at the United States Patent and Trademark Office, using AlphaGo’s victory over Lee Sedol in March 2016 as a vital event that intensified buzz around artificial intelligence. The applications studied were filed before the event and first examined on either side of it, which holds the innovations fixed. Within examiner, technology experts raised about 14 percent more rejection grounds against AlphaGo-related AI applications after the event, relative to their other applications, while nonexperts showed no comparable change. The increase centered on objections that claims did not clearly define what they covered, as experts looked past the AI label. The excitement that draws market support to a technology can thus tighten the selection its innovations face, so that their fate depends on when and by whom they are judged.

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.

Linking patent and trademark records across four sharply dated buzz episodes—virtual reality, artificial intelligence, augmented reality, and blockchain—I find that US public firms with relevant prior capabilities expand patenting, inventor hiring, and skill demand after buzz onset, yet bring roughly one-quarter fewer products toward market.

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.