Artificial Intelligence and Its Implications .
The Economics ofArtiﬁcial Intelligence
National Bureau ofEconomic ResearchConference Report
The Economics ofArtiﬁcial Intelligence:An AgendaEdited byAjay Agrawal, Joshua Gans,and Avi GoldfarbThe University of Chicago PressChicago and London
The University of Chicago Press, Chicago 60637The University of Chicago Press, Ltd., London 2019 by the National Bureau of Economic Research, Inc.All rights reserved. No part of this book may be used or reproducedin any manner whatsoever without written permission, except in thecase of brief quotations in critical articles and reviews. For moreinformation, contact the University of Chicago Press, 1427 E. 60th St.,Chicago, IL 60637.Published 2019Printed in the United States of America28 27 26 25 24 23 22 21 20 191 2 3 4 5ISBN-13: 978-0-226-61333-8 (cloth)ISBN-13: 978-0-226-61347-5 (e-book)DOI: 0001Library of Congress Cataloging-in-Publication DataNames: Agrawal, Ajay, editor. Gans, Joshua, 1968– editor. Goldfarb,Avi, editor.Title: The economics of artiﬁcial intelligence : an agenda / AjayAgrawal, Joshua Gans, and Avi Goldfarb, editors.Other titles: National Bureau of Economic Research conference report.Description: Chicago ; London : The University of Chicago Press,2019. Series: National Bureau of Economic Research conferencereport Includes bibliographical references and index.Identiﬁers: LCCN 2018037552 ISBN 9780226613338 (cloth : alk.paper) ISBN 9780226613475 (ebook)Subjects: LCSH: Artiﬁcial intelligence—Economic aspects.Classiﬁcation: LCC TA347.A78 E365 2019 DDC 338.4/70063—dc23LC record available at https://lccn.loc.gov/2018037552 This paper meets the requirements of ANSI/NISO Z39.48-1992(Permanence of Paper).DOI: 10.7208/chicago/9780226613475.003.0014
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ContentsI.AcknowledgmentsxiIntroductionAjay Agrawal, Joshua Gans, and Avi Goldfarb1AI as a GPT1. Artiﬁcial Intelligence and the ModernProductivity Paradox: A Clash ofExpectations and StatisticsErik Brynjolfsson, Daniel Rock, andChad SyversonComment: Rebecca Henderson232. The Technological Elements ofArtiﬁcial IntelligenceMatt Taddy613. Prediction, Judgment, and Complexity:A Theory of Decision-Making andArtiﬁcial IntelligenceAjay Agrawal, Joshua Gans, and Avi GoldfarbComment: Andrea Prat894. The Impact of Artiﬁcial Intelligenceon Innovation: An Exploratory AnalysisIain M. Cockburn, Rebecca Henderson,and Scott SternComment: Matthew Mitchell115vii
viiiContents5. Finding Needles in Haystacks: ArtiﬁcialIntelligence and Recombinant GrowthAjay Agrawal, John McHale,and Alexander Oettl6. Artiﬁcial Intelligence as the Next GPT:A Political-Economy PerspectiveManuel TrajtenbergII.149175Growth, Jobs, and Inequality7. Artiﬁcial Intelligence, Income, Employment,and MeaningBetsey Stevenson1898. Artiﬁcial Intelligence, Automation, and WorkDaron Acemoglu and Pascual Restrepo1979. Artiﬁcial Intelligence and Economic GrowthPhilippe Aghion, Benjamin F. Jones, andCharles I. JonesComment: Patrick Francois23710. Artiﬁcial Intelligence and Jobs:The Role of DemandJames Bessen11. Public Policy in an AI EconomyAustan Goolsbee12. Should We Be Reassured If Automationin the Future Looks Like Automationin the Past?Jason Furman29130931713. R&D, Structural Transformation,and the Distribution of IncomeJeffrey D. Sachs32914. Artiﬁcial Intelligence and Its Implicationsfor Income Distribution and UnemploymentAnton Korinek and Joseph E. Stiglitz34915. Neglected Open Questions in theEconomics of Artiﬁcial IntelligenceTyler Cowen391
ContentsIII.Machine Learning and Regulation16. Artiﬁcial Intelligence, Economics, andIndustrial OrganizationHal VarianComment: Judith Chevalier39917. Privacy, Algorithms, and Artiﬁcial IntelligenceCatherine Tucker42318. Artiﬁcial Intelligence and Consumer PrivacyGinger Zhe Jin43919. Artiﬁcial Intelligence and International TradeAvi Goldfarb and Daniel Treﬂer46320. Punishing Robots: Issues in the Economicsof Tort Liability and Innovation inArtiﬁcial IntelligenceAlberto Galasso and Hong LuoIV.ix493Machine Learning and Economics21. The Impact of Machine Learningon EconomicsSusan AtheyComment: Mara Lederman50722. Artiﬁcial Intelligence, Labor, Productivity,and the Need for Firm-Level DataManav Raj and Robert Seamans55323. How Artiﬁcial Intelligence and MachineLearning Can Impact Market DesignPaul R. Milgrom and Steven Tadelis56724. Artiﬁcial Intelligence andBehavioral EconomicsColin F. CamererComment: Daniel KahnemanContributorsAuthor IndexSubject Index587611615625
AcknowledgmentsThis volume contains chapters and ideas discussed at the ﬁrst NBER Conference on the Economics of Artiﬁcial Intelligence, held in September 2017in Toronto. We thank all the authors and discussants for their contributions.Funds for the conference and book project were provided by the Sloan Foundation, the Canadian Institute for Advanced Research, and the CreativeDestruction Lab at the University of Toronto. At the Sloan Foundation,Danny Goroff provided guidance that improved the overall agenda. TheNBER digitization initiative, under the leadership of Shane Greenstein, wasa key early supporter. We thank our dean, Tiff Macklem. In addition, JimPoterba at the NBER has been generous, giving us the ﬂexibility needed tobring this project together. Special thanks are due to Rob Shannon, DenisHealy, Carl Beck, and Dawn Bloomﬁeld for managing the conference andlogistics and to Helena Fitz-Patrick for guiding the book through the editorial process. Finally we thank our families, Gina, Natalie, Rachel, Amelia,Andreas, Belanna, Ariel, Annika, Anna, Sam, and Ben.xi
14Artiﬁcial Intelligence and ItsImplications for IncomeDistribution and UnemploymentAnton Korinek and Joseph E. Stiglitz14.1IntroductionThe introduction of artiﬁcial intelligence (AI) is the continuation of along process of automation. Advances in mechanization in the late nineteenth and early twentieth centuries automated much of the physical laborperformed by humans. Advances in information technology in the mid- tolate twentieth century automated much of the standardized data processingthat used to be performed by humans. However, each of these past episodesof automation left large areas of work that could only be performed byhumans.Some propose that advances in AI are merely the latest wave in this longprocess of automation, and may in fact generate less economic growth thanpast technological advances (see, e.g., Gordon 2016). Others, by contrast,emphasize that AI critically differs from past inventions: as artiﬁcial intelligence draws closer and closer to human general intelligence, much of humanlabor runs the risk of becoming obsolete and being replaced by AI in alldomains. In this view, progress in artiﬁcial intelligence is not only a continuaAnton Korinek is associate professor of economics and business administration at theUniversity of Virginia and Darden GSB and a research associate of the National Bureau ofEconomic Research. Joseph E. Stiglitz is University Professor at Columbia University and aresearch associate of the National Bureau of Economic Research.This chapter was prepared as a background paper for the NBER conference The Economicsof Artiﬁcial Intelligence. We would like to thank our discussant Tyler Cowan as well as JayantRay and participants at the NBER conference for helpful comments. We also acknowledgeresearch assistance from Haaris Mateen as well as ﬁnancial support from the Institute for NewEconomic Thinking (INET) and the Rewriting the Rules project at the Roosevelt Institute,supported by the Ford, Open Society, and the Bernard and Irene Schwartz Foundations. Foracknowledgments, sources of research support, and disclosure of the authors’ material ﬁnancial relationships, if any, please see http://www.nber.org/chapters/c14018.ack.349
350Anton Korinek and Joseph E. Stiglitztion, but the culmination of technological progress; it could lead to a courseof history that is markedly different from the implications of previous wavesof innovation, and may even represent what James Barrat (2013) has termed“Our Final Invention.”No matter what the long- run implications of AI are, it is clear that it hasthe potential to disrupt labor markets in a major way, even in the short andmedium run, affecting workers across many professions and skill levels.1 Themagnitude of these disruptions will depend on two important factors: thespeed and the factor bias of progress in AI.On the ﬁrst factor, measured productivity has increased rather slowlyin recent years, even as the world seems to be captured by AI fever.2 If AIrelated innovations enter the economy at the same slow pace as suggestedby recent productivity statistics, then the transition will be slower than, forexample, the wave of mechanization in the 1950– 1970s, and the resulting disruptions may not be very signiﬁcant. However, there are three possible alternatives: First, some suggest that productivity is signiﬁcantly undermeasured,for example, because quality improvements are not accurately captured. Thebest available estimates suggest that this problem is limited to a few tenthsof a percentage point (see, e.g., the discussion in Groshen et al. ).Furthermore, there are also unmeasured deteriorations in productivity, forexample, declines in service quality as customer service is increasingly automated. Second, the aggregate implications of progress in AI may follow adelayed pattern, similar to what happened after the introduction of computers in the 1980s. Robert Solow (1987) famously quipped that “you can see thecomputer age everywhere but in the productivity statistics.” It was not untilthe 1990s that a signiﬁcant rise in aggregate productivity could be detected,after sustained investment in computers and a reorganization of businesspractices had taken place. Third, it is of course possible that a signiﬁcantdiscontinuity in productivity growth occurs, as suggested, for example, byproponents of a technological singularity (see, e.g., Kurzweil 2005).On the second factor, the disruptions generated by AI- related innovations depend on whether they are labor- saving, using the terminology ofHicks (1932), that is, whether at a given wage the innovations lead to lessdemand for labor. Some suggest that artiﬁcial intelligence will mainly assisthumans in being more productive, and refer to such new technologies asintelligence-assisting innovation (IA), rather than AI. Although we agreethat most AI- related innovations are likely to be complementary to at leastsome jobs, we believe that in taking a broader perspective, progress in AI1. For example, Frey and Osborne (2017) warn that 47 percent of jobs in the US economy areat risk of being automated by advances in AI- related ﬁelds. Areas in which human intelligencehas recently become inferior to artiﬁcial intelligence include many applications of radiology,trading in ﬁnancial markets, paralegal work, underwriting, driving, and so forth.2. For example, Google Trends reveals that search interest in the topic “artiﬁcial intelligence”has quadrupled over the past four years.
AI and Its Implications for Income Distribution and Unemployment351is more likely to substitute for human labor, or even to replace workersoutright, as we will assume in some of our formal models below.We believe that the primary economic challenge posed by the proliferationof AI will be one of income distribution. We economists set ourselves tooeasy a goal if we just say that technological progress can make everybodybetter off —we also have to say how we can make this happen. This chapter isan attempt to do so by discussing some of the key economic research issuesthat this raises.3In section 14.2 of this chapter, we provide a general taxonomy of therelationship between technological progress and welfare. We ﬁrst observethat in a truly ﬁrst- best economy—in which complete risk markets are available before a veil of ignorance about innovations is lifted—all individualswill share in the beneﬁts of technological progress. However, since the realworld does not correspond to this ideal, redistribution is generally needed toensure that technological progress generates Pareto improvements. If markets are perfect and redistribution is costless, it can always be ensured thattechnological progress makes everybody better off. The same result holdsif the costs of redistribution are sufficiently low. In all these cases, therecan be political unanimity about the desirability of technological progress.However, if redistribution is too costly, it may be impossible to compensate the losers of technological progress, and they will rationally opposeprogress. Even worse, if the economy suffers from market imperfections,technological progress may actually move the Pareto frontier inwards, thatis, some individuals may necessarily be worse off. Finally, we observe thatthe ﬁrst welfare theorem does not apply to the process of innovation, and asa result, privately optimal innovation choices may move the Pareto frontierinward.In section 14.3, we decompose the mechanisms through which innovationleads to inequality into two channels. First, inequality rises because innovators earn a surplus. Unless markets for innovation are fully contestable, thesurplus earned by innovators is generally in excess of the costs of innovationand includes what we call innovator rents. We discuss policies that affect thesharing of such rents, such as antitrust policies and changes in intellectualproperty rights. The second channel is that innovations affect market prices;they change the demand for factors such as different types of labor andcapital, which affects their prices and generates redistributions. For example,AI may reduce a wide range of human wages and generate a redistributionto entrepreneurs. From the perspective of our ﬁrst- best benchmark withcomplete insurance markets, these factor price changes represent pecuniary externalities. We discuss policies to counter the effects of the resultingfactor price changes.3. An important, and maybe even more difficult, complementary question, which is beyondthe scope of this chapter, is to analyze the political issues involved.
352Anton Korinek and Joseph E. StiglitzIn section 14.4, we develop a simple formal model of worker- rep
Artificial Intelligence and Its Implications . The Economics of . Artiﬁ cial Intelligence, Economics, and Industrial Organization 399 Hal Varian Comment: Judith Chevalier 17. . ference on the Economics of Artiﬁ cial Intelligence, held in September 2017 in Toronto.
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