TY - JOUR AU - Brynjolfsson, Erik AU - Rock, Daniel AU - Syverson, Chad TI - Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics JF - National Bureau of Economic Research Working Paper Series VL - No. 24001 PY - 2017 Y2 - November 2017 DO - 10.3386/w24001 UR - http://www.nber.org/papers/w24001 L1 - http://www.nber.org/papers/w24001.pdf N1 - Author contact info: Erik Brynjolfsson Stanford Digital Economy Laboratory 366 Galvez Street, Room 238 Stanford, CA 94305 E-Mail: erik.brynjolfsson@gmail.com Daniel Rock Wharton School University of Pennsylvania 3730 Walnut Street Philadelphia, PA 19104 E-Mail: rockdi@wharton.upenn.edu Chad Syverson University of Chicago Booth School of Business 5807 S. Woodlawn Ave. Chicago, IL 60637 Tel: 773/702-7815 Fax: 773/702-8490 E-Mail: chad.syverson@chicagobooth.edu M1 - published as Erik Brynjolfsson, Daniel Rock, Chad Syverson. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," in Ajay Agrawal, Joshua Gans, and Avi Goldfarb, editors, "The Economics of Artificial Intelligence: An Agenda" University of Chicago Press (2019) M3 - presented at "Economics of Artificial Intelligence", September 13-14, 2017 AB - We live in an age of paradox. Systems using artificial intelligence match or surpass human level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has declined by half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. We describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each, we argue that lags have likely been the biggest contributor to the paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won’t be realized until waves of complementary innovations are developed and implemented. The required adjustment costs, organizational changes, and new skills can be modeled as a kind of intangible capital. A portion of the value of this intangible capital is already reflected in the market value of firms. However, going forward, national statistics could fail to measure the full benefits of the new technologies and some may even have the wrong sign. ER -