Erik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Director of the Stanford Digital Economy Lab at HAI. He is also the Ralph Landau Senior Fellow at SIEPR, and a Professor, by courtesy, at the Stanford Graduate School of Business and at the Department of Economics. Prof. Brynjolfsson is a Research Associate at the National Bureau of Economic Research and co-author of six books, including The Second Machine Age. His research, teaching and speaking focus on the effects of digital technologies, including AI, on the economy and business.

Academic Appointments

Administrative Appointments

  • Jerry Yang and Akiko Yamazaki Professor, HAI (2020 - Present)
  • 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)

Stanford Advisees

All Publications

  • Companies inadvertently fund online misinformation despite consumer backlash. Nature Ahmad, W., Sen, A., Eesley, C., Brynjolfsson, E. 2024; 630 (8015): 123-131


    The financial motivation to earn advertising revenue has been widely conjectured to be pivotal for the production of online misinformation1-4. Research aimed at mitigating misinformation has so far focused on interventions at the user level5-8, with little emphasis on how the supply of misinformation can itself be countered. Here we show how online misinformation is largely financed by advertising, examine how financing misinformation affects the companies involved, and outline interventions for reducing the financing of misinformation. First, we find that advertising on websites that publish misinformation is pervasive for companies across several industries and is amplified by digital advertising platforms that algorithmically distribute advertising across the web. Using an information-provision experiment9, we find that companies that advertise on websites that publish misinformation can face substantial backlash from their consumers. To examine why misinformation continues to be monetized despite the potential backlash for the advertisers involved, we survey decision-makers at companies. We find that most decision-makers are unaware that their companies' advertising appears on misinformation websites but have a strong preference to avoid doing so. Moreover, those who are unaware and uncertain about their company's role in financing misinformation increase their demand for a platform-based solution to reduce monetizing misinformation when informed about how platforms amplify advertising placement on misinformation websites. We identify low-cost, scalable information-based interventions to reduce the financial incentive to misinform and counter the supply of misinformation online.

    View details for DOI 10.1038/s41586-024-07404-1

    View details for PubMedID 38840014

    View details for PubMedCentralID 6377495

  • Will Generative Artificial Intelligence Deliver on Its Promise in Health Care? JAMA Wachter, R. M., Brynjolfsson, E. 2023


    Since the introduction of ChatGPT in late 2022, generative artificial intelligence (genAI) has elicited enormous enthusiasm and serious concerns.History has shown that general purpose technologies often fail to deliver their promised benefits for many years ("the productivity paradox of information technology"). Health care has several attributes that make the successful deployment of new technologies even more difficult than in other industries; these have challenged prior efforts to implement AI and electronic health records. However, genAI has unique properties that may shorten the usual lag between implementation and productivity and/or quality gains in health care. Moreover, the health care ecosystem has evolved to make it more receptive to genAI, and many health care organizations are poised to implement the complementary innovations in culture, leadership, workforce, and workflow often needed for digital innovations to flourish.The ability of genAI to rapidly improve and the capacity of organizations to implement complementary innovations that allow IT tools to reach their potential are more advanced than in the past; thus, genAI is capable of delivering meaningful improvements in health care more rapidly than was the case with previous technologies.

    View details for DOI 10.1001/jama.2023.25054

    View details for PubMedID 38032660

  • How to Capitalize on Generative Al A guice to realizing its benefits while limiting its risks HARVARD BUSINESS REVIEW McAfee, A., Rock, D., Brynjolfsson, E. 2023; 101 (11-12): 42-+
  • The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence DAEDALUS Brynjolfsson, E. 2022; 151 (2): 272-287
  • The Productivity J-Curve: How Intangibles Complement General Purpose Technologies AMERICAN ECONOMIC JOURNAL-MACROECONOMICS Brynjolfsson, E., Rock, D., Syverson, C. 2021; 13 (1): 333–72
  • 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


    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

  • Consumer Protection in an Online World: An Analysis of Occupational Licensing t AMERICAN ECONOMIC JOURNAL-APPLIED ECONOMICS Farronato, C., Fradkin, A., Larsen, B. J., Brynjolfsson, E. 2024; 16 (3): 549-579
  • AI adoption in America: Who, what, and where JOURNAL OF ECONOMICS & MANAGEMENT STRATEGY Mcelheran, K., Li, J., Brynjolfsson, E., Kroff, Z., Dinlersoz, E., Foster, L., Zolas, N. 2024

    View details for DOI 10.1111/jems.12576

    View details for Web of Science ID 001147380100001

  • Helping Small Businesses Become More Data-Driven: A Field Experiment on eBay MANAGEMENT SCIENCE Bar-Gill, S., Brynjolfsson, E., Hak, N. 2024
  • The Attention Economy: Measuring the Value of Free Goods on the Internet INFORMATION SYSTEMS RESEARCH Brynjolfsson, E., Kim, S., Oh, J. 2023
  • A causal test of the strength of weak ties. Science (New York, N.Y.) Rajkumar, K., Saint-Jacques, G., Bojinov, I., Brynjolfsson, E., Aral, S. 2022; 377 (6612): 1304-1310


    The authors analyzed data from multiple large-scale randomized experiments on LinkedIn's People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world's largest professional social network. The experiments randomly varied the prevalence of weak ties in the networks of over 20 million people over a 5-year period, during which 2 billion new ties and 600,000 new jobs were created. The results provided experimental causal evidence supporting the strength of weak ties and suggested three revisions to the theory. First, the strength of weak ties was nonlinear. Statistical analysis found an inverted U-shaped relationship between tie strength and job transmission such that weaker ties increased job transmission but only to a point, after which there were diminishing marginal returns to tie weakness. Second, weak ties measured by interaction intensity and the number of mutual connections displayed varying effects. Moderately weak ties (measured by mutual connections) and the weakest ties (measured by interaction intensity) created the most job mobility. Third, the strength of weak ties varied by industry. Whereas weak ties increased job mobility in more digital industries, strong ties increased job mobility in less digital industries.

    View details for DOI 10.1126/science.abl4476

    View details for PubMedID 36107999

  • Do Computers Reduce the Value of Worker Persistence? JOURNAL OF MANAGEMENT INFORMATION SYSTEMS Brynjolfsson, E., Liu, M., Westerman, G. 2022; 39 (1): 41-67
  • Working With Robots in a Post-Pandemic World MIT SLOAN MANAGEMENT REVIEW Beane, M., Brynjolfsson, E. 2021; 62 (2)
  • Do Digital Platforms Reduce Moral Hazard? The Case of Uber and Taxis MANAGEMENT SCIENCE Liu, M., Brynjolfsson, E., Dowlatabadi, J. 2021; 67 (8): 4665-4685
  • Social Advertising Effectiveness Across Products: A Large-Scale Field Experiment MARKETING SCIENCE Huang, S., Aral, S., Hu, Y., Brynjolfsson, E. 2020; 39 (6): 1142–65
  • 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


    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

  • 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

  • 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
  • OR Forum-Tenure Analytics: Models for Predicting Research Impact OPERATIONS RESEARCH Bertsimas, D., Brynjolfsson, E., Reichman, S., Silberholz, J. 2015; 63 (6): 1246–61
  • Will Humans Go the Way of Horses? Labor in the Second Machine Age FOREIGN AFFAIRS Brynjolfsson, E., McAfee, A. 2015; 94 (4): 8–14
  • 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
  • New World Order Labor, Capital, and Ideas in the Power Law Economy FOREIGN AFFAIRS Brynjolfsson, E., McAfee, A., Spence, M. 2014; 93 (4): 44-+
  • 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-+


    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