Erik Brynjolfsson
Jerry Yang and Akiko Yamazaki Professor, Senior Fellow at Stanford Institute for Human-Centered Artificial Intelligence, at SIEPR & Professor, by courtesy, of Economics & of Operations, Information & Technology & of Economics at the GSB
Institute for Human-Centered Artificial Intelligence (HAI)
Web page: http://brynjolfsson.com
Bio
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
-
Senior Fellow, Institute for Human-Centered Artificial Intelligence (HAI)
-
Senior Fellow, Stanford Institute for Economic Policy Research (SIEPR)
-
Professor (By courtesy), Economics
-
Professor (By courtesy), Operations, Information & Technology
-
Professor (By courtesy), Economics
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)
2024-25 Courses
- The AI Awakening: Implications for the Economy and Society
ECON 295 (Spr) -
Independent Studies (4)
- Directed Reading
ECON 139D (Spr) - Directed Reading
ECON 239D (Spr) - Honors Thesis Research
ECON 199D (Spr) - Practical Training
ECON 299 (Spr)
- Directed Reading
-
Prior Year Courses
2023-24 Courses
- The AI Awakening: Implications for the Economy and Society
CS 323 (Spr) - The AI Awakening: Implications for the Economy and Society
ECON 295 (Spr)
2022-23 Courses
- The AI Awakening: Implications for the Economy and Society
Stanford Advisees
-
Postdoctoral Faculty Sponsor
Ruyu Chen, Zivvy Epstein, Basil Halperin, Christina Langer, Gabriel Unger -
Doctoral Dissertation Advisor (AC)
Wajeeha Ahmad
All Publications
-
Companies inadvertently fund online misinformation despite consumer backlash.
Nature
2024; 630 (8015): 123-131
Abstract
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
2023
Abstract
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
2023; 101 (11-12): 42-+
View details for Web of Science ID 001085108200016
-
The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence
DAEDALUS
2022; 151 (2): 272-287
View details for DOI 10.1162/daed_a_01915
View details for Web of Science ID 000786702600019
-
The Productivity J-Curve: How Intangibles Complement General Purpose Technologies
AMERICAN ECONOMIC JOURNAL-MACROECONOMICS
2021; 13 (1): 333–72
View details for DOI 10.1257/mac.20180386
View details for Web of Science ID 000604613800010
-
Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
MANAGEMENT SCIENCE
2019; 65 (12): 5449–60
View details for DOI 10.1287/mnsc.2019.3388
View details for Web of Science ID 000500924400001
-
Using massive online choice experiments to measure changes in well-being
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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
-
Consumer Protection in an Online World: An Analysis of Occupational Licensing t
AMERICAN ECONOMIC JOURNAL-APPLIED ECONOMICS
2024; 16 (3): 549-579
View details for DOI 10.1257/app.20210716
View details for Web of Science ID 001258881200016
-
AI adoption in America: Who, what, and where
JOURNAL OF ECONOMICS & MANAGEMENT STRATEGY
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
2024
View details for DOI 10.1287/mnsc.2021.02026
View details for Web of Science ID 001140977100001
-
The Attention Economy: Measuring the Value of Free Goods on the Internet
INFORMATION SYSTEMS RESEARCH
2023
View details for DOI 10.1287/isre.2021.0153
View details for Web of Science ID 001062100900001
-
A causal test of the strength of weak ties.
Science (New York, N.Y.)
2022; 377 (6612): 1304-1310
Abstract
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
2022; 39 (1): 41-67
View details for DOI 10.1080/07421222.2021.2023406
View details for Web of Science ID 000780515800003
-
Working With Robots in a Post-Pandemic World
MIT SLOAN MANAGEMENT REVIEW
2021; 62 (2)
View details for Web of Science ID 000799862200002
-
Do Digital Platforms Reduce Moral Hazard? The Case of Uber and Taxis
MANAGEMENT SCIENCE
2021; 67 (8): 4665-4685
View details for DOI 10.1287/mnsc.2020.3721
View details for Web of Science ID 000697828200002
-
THE ECONOMICS OF IT AND DIGITIZATION: EIGHT QUESTIONS FOR RESEARCH
MIS QUARTERLY
2021; 45 (1): 473–77
View details for DOI 10.25300/MISQ/2021/15434.1.4
View details for Web of Science ID 000628690000020
-
Social Advertising Effectiveness Across Products: A Large-Scale Field Experiment
MARKETING SCIENCE
2020; 39 (6): 1142–65
View details for DOI 10.1287/mksc.2020.1240
View details for Web of Science ID 000587868800008
-
Measuring the Impact of Free Goods on Real Household Consumption
AMER ECONOMIC ASSOC. 2020: 25–30
View details for DOI 10.1257/pandp.20201054
View details for Web of Science ID 000534590600004
-
How Should We Measure the Digital Economy?
HARVARD BUSINESS REVIEW
2019; 97 (6): 140-+
View details for Web of Science ID 000493019200024
-
What Drives Differences in Management Practices?
AMERICAN ECONOMIC REVIEW
2019; 109 (5): 1648–83
View details for DOI 10.1257/aer.20170491
View details for Web of Science ID 000466609600003
-
Toward understanding the impact of artificial intelligence on labor
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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
AMER ECONOMIC ASSOC. 2018: 473–76
View details for DOI 10.1257/pandp.20181035
View details for Web of Science ID 000434468600091
-
What Can Machines Learn and What Does It Mean for Occupations and the Economy?
AMER ECONOMIC ASSOC. 2018: 43–47
View details for DOI 10.1257/pandp.20181019
View details for Web of Science ID 000434468600008
-
Information Technology, Repeated Contracts, and the Number of Suppliers
MANAGEMENT SCIENCE
2018; 64 (2): 592–612
View details for DOI 10.1287/mnsc.2016.2631
View details for Web of Science ID 000426191500008
-
Profound change is coming, but roles for humans remain
SCIENCE
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
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
-
CROWD-SQUARED: AMPLIFYING THE PREDICTIVE POWER OF SEARCH TREND DATA
MIS QUARTERLY
2016; 40 (4): 941-+
View details for DOI 10.25300/MISQ/2016/40.4.07
View details for Web of Science ID 000397046800008
-
Human Work in the Robotic Future Policy for the Age of Automation
FOREIGN AFFAIRS
2016; 95 (4): 139–50
View details for Web of Science ID 000377637500016
-
The Rapid Adoption of Data-Driven Decision-Making
AMER ECONOMIC ASSOC. 2016: 133–39
View details for DOI 10.1257/aer.p20161016
View details for Web of Science ID 000379341300025
-
VALUING INFORMATION TECHNOLOGY RELATED INTANGIBLE ASSETS
MIS QUARTERLY
2016; 40 (1): 83–110
View details for DOI 10.25300/MISQ/2016/40.1.04
View details for Web of Science ID 000370447400004
-
OR Forum-Tenure Analytics: Models for Predicting Research Impact
OPERATIONS RESEARCH
2015; 63 (6): 1246–61
View details for DOI 10.1287/opre.2015.1447
View details for Web of Science ID 000367833500002
-
Open Letter on the Digital Economy
TECHNOLOGY REVIEW
2015; 118 (4): 11–12
View details for Web of Science ID 000369555100016
-
Will Humans Go the Way of Horses? Labor in the Second Machine Age
FOREIGN AFFAIRS
2015; 94 (4): 8–14
View details for Web of Science ID 000356128900003
-
New World Order Labor, Capital, and Ideas in the Power Law Economy
FOREIGN AFFAIRS
2014; 93 (4): 44-+
View details for Web of Science ID 000337260400005
-
Competing in the Age of Omnichannel Retailing
MIT SLOAN MANAGEMENT REVIEW
2013; 54 (4): 23–29
View details for Web of Science ID 000209280100007
-
STRATEGY & COMPETITION Big Data: The Management Revolution
HARVARD BUSINESS REVIEW
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
2012; 23 (3): 849–67
View details for DOI 10.1287/isre.1110.0408
View details for Web of Science ID 000309091200001
-
Three-Way Complementarities: Performance Pay, Human Resource Analytics, and Information Technology
MANAGEMENT SCIENCE
2012; 58 (5): 913–31
View details for DOI 10.1287/mnsc.1110.1460
View details for Web of Science ID 000304043500005
-
The Extroverted Firm: How External Information Practices Affect Innovation and Productivity
MANAGEMENT SCIENCE
2012; 58 (5): 843–59
View details for DOI 10.1287/mnsc.1110.1446
View details for Web of Science ID 000304043500001
-
Thriving in the Automated Economy
FUTURIST
2012; 46 (2): 27–31
View details for Web of Science ID 000300132400010
-
Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales
MANAGEMENT SCIENCE
2011; 57 (8): 1373–86
View details for DOI 10.1287/mnsc.1110.1371
View details for Web of Science ID 000293506000002
-
Long Tails vs. Superstars: The Effect of Information Technology on Product Variety and Sales Concentration Patterns
INFORMATION SYSTEMS RESEARCH
2010; 21 (4): 736–47
View details for DOI 10.1287/isre.1100.0325
View details for Web of Science ID 000285383200007
-
Cloud Computing and Electricity: Beyond the Utility Model
COMMUNICATIONS OF THE ACM
2010; 53 (5): 32–34
View details for DOI 10.1145/1735223.1735234
View details for Web of Science ID 000277063000013
-
A nearly perfect market?
QME-QUANTITATIVE MARKETING AND ECONOMICS
2010; 8 (1): 1–33
View details for DOI 10.1007/s11129-009-9079-7
View details for Web of Science ID 000273327900001
-
Battle of the Retail Channels: How Product Selection and Geography Drive Cross-Channel Competition
MANAGEMENT SCIENCE
2009; 55 (11): 1755–65
View details for DOI 10.1287/mnsc.1090.1062
View details for Web of Science ID 000271524000001
-
DYNAMICS OF RETAIL ADVERTISING: EVIDENCE FROM A FIELD EXPERIMENT
ECONOMIC INQUIRY
2009; 47 (3): 482–99
View details for DOI 10.1111/j.1465-7295.2008.00161.x
View details for Web of Science ID 000268168500005
-
Investing in the IT that makes a competitive difference
HARVARD BUSINESS REVIEW
2008; 86 (7-8): 98-+
View details for Web of Science ID 000257047500024
-
Bundling and competition on the Internet
INTERNET AND DIGITAL ECONOMICS
2007: 313–44
View details for DOI 10.1017/CBO9780511493201.011
View details for Web of Science ID 000296789900011
-
From niches to riches: Anatomy of the long tail
MIT SLOAN MANAGEMENT REVIEW
2006; 47 (4): 67-+
View details for Web of Science ID 000239175900013
-
Innovation incentives for information goods
MIT PRESS. 2006: 99-+
View details for DOI 10.1086/ipe.7.25056191
View details for Web of Science ID 000244626800004
-
Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence
QUARTERLY JOURNAL OF ECONOMICS
2002; 117 (1): 339–76
View details for DOI 10.1162/003355302753399526
View details for Web of Science ID 000173476500010