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.
Highlights
Artificial intelligence (AI) refers to any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals (Russell and Norvig 2016)
While it is advantageous for artificial intelligence (AI) start-ups to have patents that are technologically broad in nature in order the provide broad patent protection for their innovation, investors may be aware of this strategy such that they may not put much weight behind the number of technology classes assigned to a patent alone
We found that knowledge coupling at both the patent-level and firm-level was positively associated with an increased likelihood of receiving venture capital (VC) investment
Summary
Artificial intelligence (AI) refers to any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals (Russell and Norvig 2016). The defeat of chess master Garry Kasparov by DEEP BLUE in 1997, ushered in a new wave of optimism and interest towards AI This spurred technological advances in data-driven AI, language analysis and facial recognition algorithms, and machine learning, especially deep learning and neural networks. While basic and applied research into AI continues to increase, the focus appears to be shifting from theoretical research towards the commercialization of AI-enabled products, such as Apple’s Siri and Amazon’s Alexa digital assistants, with autonomous vehicles on the horizon These advances are accompanied by increasing levels of interconnectivity (i.e., knowledge coupling) and interaction between AI-enabled products and their environment that form the genesis of an emerging AI ecosystem that spans multiple disciplines and cuts across many technological domains
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