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Risk Capital and Emerging Technologies: Innovation and Investment Patterns Based on Artificial Intelligence Patent Data Analysis

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  • Roberto S. Santos

    (Robert J. Manning School of Business, University of Massachusetts Lowell, One University Avenue, Pulichino-Tong Business Center Suite 228, Lowell, MA 01854, USA)

  • Lingling Qin

    (School of Management, University of Science and Technology of China, No. 96 Jinzhai Road, Hefei 230026, China)

Abstract

The promise of artificial intelligence (AI) to drive economic growth and improve quality of life has ushered in a new AI arms race. Investments of risk capital fuel this emerging technology. We examine the role that venture capital (VC) and corporate investments of risk capital play in the emergence of AI-related technologies. Drawing upon a dataset of 29,954 U.S. patents from 1970 to 2018, including 1484 U.S. patents granted to 224 VC-backed start-ups, we identify AI-related innovation and investment characteristics. Furthermore, we develop a new measure of knowledge coupling at the firm-level and use this to explore how knowledge coupling influences VC risk capital decisions in emerging AI technologies. Our findings show that knowledge coupling is a better predictor of VC investment in emerging technologies than the breadth of a patent’s technological domains. Furthermore, our results show that there are differences in knowledge coupling between private start-ups and public corporations. These findings enhance our understanding of what types of AI innovations are more likely to be selected by VCs and have important implications for our understanding of how risk capital induces the emergence of new technologies.

Suggested Citation

  • Roberto S. Santos & Lingling Qin, 2019. "Risk Capital and Emerging Technologies: Innovation and Investment Patterns Based on Artificial Intelligence Patent Data Analysis," JRFM, MDPI, vol. 12(4), pages 1-24, December.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:4:p:189-:d:297960
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