Bio


Prof. Erik Brynjolfsson is Director of the Stanford HAI Digital Economy Lab. He also holds appointments at SIEPR, the Stanford Graduate School of Business and the Department of Economics and the National Bureau of Economic Research. His research and speaking focus on the effects of IT on strategy, productivity, performance, digital commerce, and intangible assets.

Academic Appointments


Administrative Appointments


  • Director, Stanford Digital Economy Lab (2020 - Present)
  • Senior Fellow, Stanford Institute for Human-centered AI (2020 - Present)
  • Ralph Landau Senior Fellow, SIEPR (2020 - Present)
  • Professor, by Courtesy, Stanford Graduate School of Business (2020 - Present)
  • Professor, by Courtesy, Stanford Department of Economics (2020 - Present)
  • Research Associate, National Bureau of Economic Research (1995 - Present)

All Publications


  • Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform MANAGEMENT SCIENCE Brynjolfsson, E., Hui, X., Liu, M. 2019; 65 (12): 5449–60
  • Using massive online choice experiments to measure changes in well-being PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Brynjolfsson, E., Collis, A., Eggers, F. 2019; 116 (15): 7250–55

    Abstract

    Gross domestic product (GDP) and derived metrics such as productivity have been central to our understanding of economic progress and well-being. In principle, changes in consumer surplus provide a superior, and more direct, measure of changes in well-being, especially for digital goods. In practice, these alternatives have been difficult to quantify. We explore the potential of massive online choice experiments to measure consumer surplus. We illustrate this technique via several empirical examples which quantify the valuations of popular digital goods and categories. Our examples include incentive-compatible discrete-choice experiments where online and laboratory participants receive monetary compensation if and only if they forgo goods for predefined periods. For example, the median user needed a compensation of about $48 to forgo Facebook for 1 mo. Our overall analyses reveal that digital goods have created large gains in well-being that are not reflected in conventional measures of GDP and productivity. By periodically querying a large, representative sample of goods and services, including those which are not priced in existing markets, changes in consumer surplus and other new measures of well-being derived from these online choice experiments have the potential for providing cost-effective supplements to the existing national income and product accounts.

    View details for DOI 10.1073/pnas.1815663116

    View details for Web of Science ID 000463936900020

    View details for PubMedID 30914458

    View details for PubMedCentralID PMC6462102

  • Track how technology is transforming work NATURE Mitchell, T., Brynjolfsson, E. 2017; 544 (7650): 290–92

    View details for DOI 10.1038/544290a

    View details for Web of Science ID 000399524400015

    View details for PubMedID 28426011

  • Will Humans Go the Way of Horses? Labor in the Second Machine Age FOREIGN AFFAIRS Brynjolfsson, E., McAfee, A. 2015; 94 (4): 8–14
  • New World Order Labor, Capital, and Ideas in the Power Law Economy FOREIGN AFFAIRS Brynjolfsson, E., McAfee, A., Spence, M. 2014; 93 (4): 44-+
  • Measuring the Impact of Free Goods on Real Household Consumption Brynjolfsson, E., Collis, A., Diewert, W., Eggers, F., Fox, K. J. AMER ECONOMIC ASSOC. 2020: 25–30
  • How Should We Measure the Digital Economy? HARVARD BUSINESS REVIEW Brynjolfsson, E., Collis, A. 2019; 97 (6): 140-+
  • What Drives Differences in Management Practices? AMERICAN ECONOMIC REVIEW Bloom, N., Brynjolfsson, E., Foster, L., Jarmin, R., Patnaik, M., Saporta-Eksten, I., Van Reenen, J. 2019; 109 (5): 1648–83
  • Toward understanding the impact of artificial intelligence on labor PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., Feldman, M., Groh, M., Lobo, J., Moro, E., Wang, D., Youn, H., Rahwan, I. 2019; 116 (14): 6531–39

    Abstract

    Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human-machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

    View details for DOI 10.1073/pnas.1900949116

    View details for Web of Science ID 000463069900008

    View details for PubMedID 30910965

    View details for PubMedCentralID PMC6452673

  • Measuring Welfare with Massive Online Choice Experiments: A Brief Introduction Brynjolfsson, E., Eggers, F., Gannamaneni, A. AMER ECONOMIC ASSOC. 2018: 473–76
  • What Can Machines Learn and What Does It Mean for Occupations and the Economy? Brynjolfsson, E., Mitchell, T., Rock, D. AMER ECONOMIC ASSOC. 2018: 43–47
  • Information Technology, Repeated Contracts, and the Number of Suppliers MANAGEMENT SCIENCE Aral, S., Bakos, Y., Brynjolfsson, E. 2018; 64 (2): 592–612
  • Profound change is coming, but roles for humans remain SCIENCE Brynjolfsson, E., Mitchell, T. 2017; 358 (6370): 1530–34

    View details for Web of Science ID 000418448000033

    View details for PubMedID 29269459

  • CROWD-SQUARED: AMPLIFYING THE PREDICTIVE POWER OF SEARCH TREND DATA MIS QUARTERLY Brynjolfsson, E., Geva, T., Reichman, S. 2016; 40 (4): 941-+
  • Human Work in the Robotic Future Policy for the Age of Automation FOREIGN AFFAIRS McAfee, A., Brynjolfsson, E. 2016; 95 (4): 139–50
  • The Rapid Adoption of Data-Driven Decision-Making Brynjolfsson, E., McElheran, K. AMER ECONOMIC ASSOC. 2016: 133–39
  • VALUING INFORMATION TECHNOLOGY RELATED INTANGIBLE ASSETS MIS QUARTERLY Saunders, A., Brynjolfsson, E. 2016; 40 (1): 83–110
  • OR Forum-Tenure Analytics: Models for Predicting Research Impact OPERATIONS RESEARCH Bertsimas, D., Brynjolfsson, E., Reichman, S., Silberholz, J. 2015; 63 (6): 1246–61
  • Open Letter on the Digital Economy TECHNOLOGY REVIEW Brynjolfsson, E., McAfee, A., Jurvetson, S., O'Reilly, T., Manyika, J., Tyson, L., Benioff, M., Bass, C., Schoendorf, J., Bresnahan, T., Khosla, V., Howard, J., Spence, M., Suleyman, M., Stern, S., Kirkpatrick, D. 2015; 118 (4): 11–12
  • Competing in the Age of Omnichannel Retailing MIT SLOAN MANAGEMENT REVIEW Brynjolfsson, E., Hu, Y., Rahman, M. S. 2013; 54 (4): 23–29
  • STRATEGY & COMPETITION Big Data: The Management Revolution HARVARD BUSINESS REVIEW McAfee, A., Brynjolfsson, E. 2012; 90 (10): 60-+

    Abstract

    Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources. The managerial challenges, however, are very real. Senior decision makers have to learn to ask the right questions and embrace evidence-based decision making. Organizations must hire scientists who can find patterns in very large data sets and translate them into useful business information. IT departments have to work hard to integrate all the relevant internal and external sources of data. The authors offer two success stories to illustrate how companies are using big data: PASSUR Aerospace enables airlines to match their actual and estimated arrival times. Sears Holdings directly analyzes its incoming store data to make promotions much more precise and faster.

    View details for Web of Science ID 000309093300026

    View details for PubMedID 23074865

  • Information, Technology, and Information Worker Productivity INFORMATION SYSTEMS RESEARCH Aral, S., Brynjolfsson, E., Van Alstyne, M. 2012; 23 (3): 849–67
  • Three-Way Complementarities: Performance Pay, Human Resource Analytics, and Information Technology MANAGEMENT SCIENCE Aral, S., Brynjolfsson, E., Wu, L. 2012; 58 (5): 913–31
  • The Extroverted Firm: How External Information Practices Affect Innovation and Productivity MANAGEMENT SCIENCE Tambe, P., Hitt, L. M., Brynjolfsson, E. 2012; 58 (5): 843–59
  • Thriving in the Automated Economy FUTURIST Brynjolfsson, E., McAfee, A. 2012; 46 (2): 27–31
  • Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales MANAGEMENT SCIENCE Brynjolfsson, E., Hu, Y., Simester, D. 2011; 57 (8): 1373–86
  • Long Tails vs. Superstars: The Effect of Information Technology on Product Variety and Sales Concentration Patterns INFORMATION SYSTEMS RESEARCH Brynjolfsson, E., Hu, Y., Smith, M. D. 2010; 21 (4): 736–47
  • Cloud Computing and Electricity: Beyond the Utility Model COMMUNICATIONS OF THE ACM Brynjolfsson, E., Hofmann, P., Jordan, J. 2010; 53 (5): 32–34
  • A nearly perfect market? QME-QUANTITATIVE MARKETING AND ECONOMICS Brynjolfsson, E., Dick, A. A., Smith, M. D. 2010; 8 (1): 1–33
  • Battle of the Retail Channels: How Product Selection and Geography Drive Cross-Channel Competition MANAGEMENT SCIENCE Brynjolfsson, E., Hu, Y., Rahman, M. S. 2009; 55 (11): 1755–65
  • DYNAMICS OF RETAIL ADVERTISING: EVIDENCE FROM A FIELD EXPERIMENT ECONOMIC INQUIRY Simester, D., Hu, Y., Brynjolfsson, E., Anderson, E. T. 2009; 47 (3): 482–99
  • Investing in the IT that makes a competitive difference HARVARD BUSINESS REVIEW McAfee, A., Brynjolfsson, E. 2008; 86 (7-8): 98-+
  • Bundling and competition on the Internet INTERNET AND DIGITAL ECONOMICS Bakos, Y., Brynjolfsson, E., Brousseau, E., Curien, N. 2007: 313–44
  • From niches to riches: Anatomy of the long tail MIT SLOAN MANAGEMENT REVIEW Brynjolfsson, E., Hu, Y., Smith, M. D. 2006; 47 (4): 67-+
  • Innovation incentives for information goods Brynjolfsson, E., Zhang, X., Jaffe, A. B., Lerner, J., Stern, S. MIT PRESS. 2006: 99-+
  • Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence QUARTERLY JOURNAL OF ECONOMICS Bresnahan, T. F., Brynjolfsson, E., Hitt, L. M. 2002; 117 (1): 339–76