Research

Scientists and inventors set the direction of their work amidst an evolving landscape of questions, opportunities, and challenges. This paper introduces a measurement framework to quantify how far researchers move from their existing research when producing new works. We apply this framework to millions of scientific publications and patents and uncover a pervasive "pivot penalty", where the impact of new research steeply declines the further a researcher moves from their prior work. The pivot penalty applies nearly universally across scientific publishing and patenting and has been growing in magnitude over the past five decades. While creativity frameworks suggest a benefit to exploratory search by researchers and often emphasize outsider advantages in driving breakthroughs, we find little evidence for such an advantage. The pivot penalty is consistent with increasingly narrow specializations of researchers, and when researchers undertake large pivots, a signature of their work is weak engagement with established mixtures of prior knowledge. Unexpected shocks to the research landscape, which may push researchers away from existing areas or pull them into new ones, further demonstrate substantial pivot penalties. COVID-19 provides a high-scale case study, where many researchers engaged the pandemic, yet the pivot penalty remains severe. The pivot penalty generalizes across fields, career stage, productivity, collaboration, and funding contexts, highlighting both the breadth and depth of the adaptive challenge. Overall, the findings point to large and increasing challenges in adapting to new opportunities and threats. The results have implications for individual researchers, research organizations, science policy, and the capacity of science and society as a whole to confront emergent demands.

Invention disclosure facilitates knowledge spillovers, supporting future progress but potentially limiting appropriability for the inventor. In this paper, I examine invention disclosure behavior by analyzing the readability of patent texts, using both traditional and novel AI-based readability scores. Using two difference-in-differences analyses, I find that following the 1980 Bayh-Dole Act and the establishment of Technology Transfer Offices, university-affiliated inventors reduced the readability of patent detailed descriptions. This decrease in readability does not extend to patent summary texts, suggesting that university inventors strategically limit information on how to make and use the invention. The findings reveal the potential for strategic disclosure behavior not just in the decision of whether to patent or keep inventions as trade secrets, but also in the degree of patent language clarity. Institutional changes lead inventors to selectively adjust the information disclosed in their patents and obfuscate core techniques. Underlying mechanisms and effects on follow-on innovation are further explored.

This paper examines how the loss of personnel impacts the direction of innovation within firms. I introduce three distinct measures to quantify shifts in firms' innovation trajectories. I then analyze changes in the innovation direction of U.S. public firms following personnel deaths in a difference-in-differences framework. The results reveal that the death of upper-tail inventors significantly alters a firm's innovation trajectory, while deaths among lower-tail inventors and management personnel do not. Further analysis suggests that a firm's innovation direction is shaped by the aggregation of individual inventors' expertise and the collaborative dynamics within teams. The findings—characterized by reduced invention in the technology fields of upper-tail inventors, a lack of new inventors in affected firms, and declines in firm performance—highlight the hard-to-replace nature of inventive human capital and underscore its role in firms' dynamic capabilities. Moreover, this paper provides evidence that lower invention output leads to weaker sales and profitability within firms.

This paper explores the contributions of Artificial Intelligence (AI) and Quantum Computing (QC) knowledge capital to the production of knowledge and the production of output within firms. Using the Artificial Intelligence Patent Dataset from the USPTO, the study matches AI and QC patents with firm characteristics data from the CRSP/Compustat Merged database to quantify the AI and QC knowledge adoption and the impact of these forms of knowledge capital on firms. We present five stylized facts about knowledge as an input and as an output. Our regression findings indicate that both AI and QC knowledge stocks are positively associated with their own knowledge production, with AI knowledge stocks positively associated with QC innovation but not vice versa. The impact of the two forms of knowledge capital on firm productivity and growth differs, with AI positively correlating with firm output while QC is an investment that does not impact output immediately. Our study implies that knowledge capital has heterogeneous effects on both knowledge and output production, and with different time scales. This study stands in contrast to common practices that treat knowledge capital as homogeneous, emphasizing the need to distinguish different types of knowledge capital.