The Economics of Artificial Intelligence Read online

  The Economics of

  Artifi cial Intelligence

  National Bureau of

  Economic Research

  Conference Report

  The Economics of

  Artifi cial Intelligence:

  An Agenda

  Edited by

  Ajay Agrawal, Joshua Gans,

  and Avi Goldfarb

  The University of Chicago Press

  Chicago and London

  The University of Chicago Press, Chicago 60637

  The 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 reproduced

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  information, contact the University of Chicago Press, 1427 E. 60th St.,

  Chicago, IL 60637.

  Published 2019

  Printed in the United States of America

  28 27 26 25 24 23 22 21 20 19 1 2 3 4 5

  ISBN-13: 978-0-226-61333-8 (cloth)

  ISBN-13: 978-0-226-61347-5 (e-book)

  DOI: https:// doi .org / 10 .7208 / chicago / 9780226613475 .001 .0001

  Library of Congress Cataloging-in-Publication Data

  Names: Agrawal, Ajay, editor. | Gans, Joshua, 1968– editor. | Goldfarb,

  Avi, editor.

  Title: The economics of artifi cial intelligence : an agenda / Ajay

  Agrawal, 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 conference

  report | Includes bibliographical references and index.

  Identifi ers: LCCN 2018037552 | ISBN 9780226613338 (cloth : alk.

  paper) | ISBN 9780226613475 (ebook)

  Subjects: LCSH: Artifi cial intelligence—Economic aspects.

  Classifi cation: LCC TA347.A78 E365 2019 | DDC 338.4/ 70063—dc23

  LC record available at https:// lccn .loc .gov / 2018037552

  ♾ This paper meets the requirements of ANSI/ NISO Z39.48-1992

  (Permanence of Paper).

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  Contents

  Acknowledgments xi

  Introduction

  1

  Ajay Agrawal, Joshua Gans, and Avi Goldfarb

  I.

  AI as a GPT

  1. Artifi cial Intelligence and the Modern

  Productivity Paradox: A Clash of

  Expectations and Statistics

  23

  Erik Brynjolfsson, Daniel Rock, and

  Chad Syverson

  Comment: Rebecca Henderson

  2. The Technological Elements of

  Artifi cial Intelligence

  61

  Matt Taddy

  3. Prediction, Judgment, and Complexity:

  A Theory of Decision-Making and

  Artifi cial Intelligence

  89

  Ajay Agrawal, Joshua Gans, and Avi Goldfarb

  Comment: Andrea Prat

  4. The Impact of Artifi cial Intelligence

  on Innovation: An Exploratory Analysis

  115

  Iain M. Cockburn, Rebecca Henderson,

  and Scott Stern

  Comment: Matthew Mitchell

  vii

  viii

  Contents

  5. Finding Needles in Haystacks: Artifi cial

  Intelligence and Recombinant Growth

  149

  Ajay Agrawal, John McHale,

  and Alexander Oettl

  6. Artifi cial Intelligence as the Next GPT:

  A Political-Economy Perspective

  175

  Manuel Trajtenberg

  II.

  Growth, Jobs, and Inequality

  7. Artifi cial Intelligence, Income, Employment,

  and Meaning

  189

  Betsey Stevenson

  8. Artifi cial Intelligence, Automation, and Work

  197

  Daron Acemoglu and Pascual Restrepo

  9. Artifi cial Intelligence and Economic Growth

  237

  Philippe Aghion, Benjamin F. Jones, and

  Charles I. Jones

  Comment: Patrick Francois

  10. Artifi cial Intelligence and Jobs:

  The Role of Demand

  291

  James Bessen

  11. Public Policy in an AI Economy

  309

  Austan Goolsbee

  12. Should We Be Reassured If Automation

  in the Future Looks Like Automation

  in the Past?

  317

  Jason Furman

  13. R&D, Structural Transformation,

  and the Distribution of Income

  329

  Jeff rey D. Sachs

  14. Artifi cial Intelligence and Its Implications

  for Income Distribution and Unemployment

  349

  Anton Korinek and Joseph E. Stiglitz

  15. Neglected Open Questions in the

  Economics of Artifi cial Intelligence

  391

  Tyler Cowen

  Contents ix

  III.

  Machine Learning and Regulation

  16. Artifi cial Intelligence, Economics, and

  Industrial Organization

  399

  Hal Varian

  Comment: Judith Chevalier

  17. Privacy, Algorithms, and Artifi cial Intelligence

  423

  Catherine Tucker

  18. Artifi cial Intelligence and Consumer Privacy

  439

  Ginger Zhe Jin

  19. Artifi cial Intelligence and International Trade

  463

  Avi Goldfarb and Daniel Trefl er

  20. Punishing Robots: Issues in the Economics

  of Tort Liability and Innovation in

  Artifi cial Intelligence

  493

  Alberto Galasso and Hong Luo

  IV.

  Machine Learning and Economics

  21. The Impact of Machine Learning

  on Economics

  507

  Susan Athey

  Comment: Mara Lederman

  22. Artifi cial Intelligence, Labor, Productivity,

  and the Need for Firm-Level Data

  553

  Manav Raj and Robert Seamans

  23. How Artifi cial Intelligence and Machine

  Learning Can Impact Market Design

  567

  Paul R. Milgrom and Steven Tadelis

  24. Artifi cial Intelligence and

  Behavioral Economics

  587

  Colin F. Camerer

  Comment: Daniel Kahneman

  Contributors 611

  Author Index

  615

  Subject Index

  625

  Acknowledgments

  This volume contains chapters and ideas discussed at the fi rst NBER Con-

  ference on the Economics of Artifi cial Intelligence, held in September 2017

  in Toronto. We thank all the authors and discussants for their contributions.

  Funds for the conference and book project were provided by the Sloan Foun-

  dation, the Canadian Institute for Advanced Research, and the Creative

  Destruction Lab at the University of Toronto. At the Sloan Foundation,

  Danny Goroff provided guidance that improved the overall agenda. The

  NBER digitization initiative, under the leadership of Shane Greenstein, was

  a key early supporter. We thank our dean, Tiff Macklem. In addition, Jim

  Poterba at the NBER has been generous, giving us the fl exibility needed to

  bring this project together. Special thanks are due to Rob Shannon, Denis

  Healy, Carl Beck, and Dawn Bloomfi eld for managing the conference and

  logistics and to Helena Fitz-Patrick for guiding the book through the edito-

  rial process. Finally we thank our families, Gina, Natalie, Rachel, Amelia,

  Andreas, Belanna, Ariel, Annika, Anna, Sam, and Ben.

  xi

  Introduction

  Ajay Agrawal, Joshua Gans, and Avi Goldfarb

  Artifi cial intelligence (AI) technologies have advanced rapidly over the last

  several years. As the technology continues to improve, it may have a substan-

  tial impact on the economy with respect to productivity, growth, inequality,

  market power, innovation, and employ
ment. In 2016, the White House put

  out several reports emphasizing this potential impact. Despite its impor-

  tance, there is little economics research on the topic. The research that exists

  is derived from past technologies (such as factory robots) that capture only

  part of the economic reach of AI. Without a better understanding of how

  AI might impact the economy, we cannot design policy to prepare for these

  changes.

  To address these challenges, the National Bureau of Economic Research

  held its fi rst conference on the Economics of Artifi cial Intelligence in Sep-

  tember 2017 in Toronto, with support from the NBER Economics Digitiza-

  tion Initiative, the Sloan Foundation, the Canadian Institute for Advanced

  Research, and the University of Toronto’s Creative Destruction Lab. The

  purpose of the conference was to set the research agenda for economists

  working on AI. The invitation emphasized these points as follows:

  Ajay Agrawal is the Peter Munk Professor of Entrepreneurship at the Rotman School of

  Management, University of Toronto, and a research associate of the National Bureau of Economic Research. Joshua Gans is professor of strategic management and holder of the Jeff rey S.

  Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management, University of Toronto (with a cross appointment in the Department of Economics), and a research associate of the National Bureau of Economic Research. Avi Goldfarb holds the Rotman Chair in Artifi cial Intelligence and Healthcare and is professor of marketing at the Rotman School of Management, University of Toronto, and a research associate of the National Bureau of Economic Research.

  For acknowledgments, sources of research support, and disclosure of the authors’ material fi nancial relationships, if any, please see http:// www .nber .org / chapters / c14005 .ack.

  1

  2 Ajay Agrawal, Joshua Gans, and Avi Goldfarb

  The context is this: imagine back to 1995 when the internet was about to

  begin transforming industries. What would have happened to economic

  research into that revolution had the leading economists gathered to scope

  out a research agenda at that time? Today, we are facing the same oppor-

  tunity with regard to AI. This time around we are convening a group of

  30 leading economists to scope out the research agenda for the next 20

  years into the economics of AI.

  Scholars who accepted the invitation were asked to write up and pre-

  sent ideas around a specifi c topic related to their expertise. For each paper,

  a discussant was assigned. Throughout the conference, in presentations,