Abstract: Engineers, as implementers of technology, are highly complementary to the intangible knowledge assets that firms accumulate. This paper seeks to address whether technical talent is a source of rents for corporate employers, both in general and in the specific case of the surprising open-source launch of TensorFlow, a deep learning software package, by Google. First, I present a simple model of how employers can use job design as a tool to exercise monopsony power by partially allocating employee time to firm-specific tasks. Then, using over 180 million position records and over 52 million skill records from LinkedIn, I build a panel of firm-level investment in technological human capital (information technology, research, and engineering talent quantities) to measure the market value of technological talent. I find that on average, an additional engineer at a firm is correlated with approximately $854,000 more market value. Firm fixed effects and instrumental variables analyses provide mixed evidence on the marginal causal value of engineers in general. Specifically for AI talent, the value of engineering skills is clearer. AI skills are strongly correlated with market value, though variation in AI skills from 2014-2017 does not explain contemporaneous revenue productivity within firms. AI-intensive companies rapidly gained market value following the launch of TensorFlow, while companies with opportunities to automate relatively larger quantities of labor with machine learning did not. Using a difference-in-differences approach, I show that the TensorFlow launch is associated with an approximate market value increase of 4-7% for AI-using firms. Firms outside the top quintile of AI use (as measured by skill counts on LinkedIn) grow by approximately $3.56 million for a 1% increase in AI skill. AI superstar firms in the top quintile also appear to benefit, but show pre-trends in market value growth.

Bio: Daniel Rock is a postdoctoral associate at the Initiative on the Digital Economy at MIT. His research focuses on the economics of machine learning and artificial intelligence. His recent work has characterized scenarios for the labor market impact of machine learning and measured firm-level returns to investment in AI talent. He will join the Operations, Information, and Decisions department at the Wharton School of the University of Pennsylvania as an Assistant Professor in July 2020.