Different Strokes for Different Folks: Experimental Evidence on Complementarities Between Human Capital and Machine Learning

by Prithwiraj Choudhury, Evan Starr, and Rajshree Agarwal

Overview — This study contributes to scholarship on how adoption of machine learning tools will shape knowledge worker productivity. Among its implications for managers, it suggests that complementarities between prior skills and technology will determine the productivity of workers using AI tools.

Author Abstract

The advent of artificial intelligence in the form of machine learning technologies ushers new questions regarding the pace at which it may substitute both older technology vintages and human capital. Rather than assuming superior productivity of one vintage over another, we examine contingency effects on productivity by heterogeneity in prior specialized knowledge bases in complementary human capital and provision of concurrent expert advice. Within the research context of patent examination by the U.S. Patent and Trademark Office, which has developed both machine learning and Boolean search technologies, we hypothesize that absorptive capacity from skill-technology matches will result in higher productivity, and these effects will be stronger in the presence of expert advice constituting technology and task specific information. We test and find support for our hypotheses using experimental methods that permit causal inferences and examination of underlying mechanisms. Our study contributes to literature streams on artificial intelligence, endogenous technological change, and strategic management of the pace of technological substitution by providing insights on complementarities between technologies and horizontally differentiated human capital.