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Journal of Economic Perspectives
Winter 2023
Landings, Soft and Hard: The Federal Reserve, 1965–2022
Journal of Econometrics
August 2021
Semiparametric Estimation of Dynamic Discrete Choice Models
Journal of Business and Economic Statistics
Constrained Polynomial Likelihood
Journal of Financial and Quantitative Analysis
High Frequency Tail Risk Premium and Stock Return Predictability
Nonparametric option pricing with generalized entropic estimators, can a machine correct option pricing models.
The Economic Journal
September 2023
The Mortality Effects of Winter Heating Prices
American Economic Review: Insights
Forthcoming
Detecting Mother-Father Differences in Spending on Children: A New Approach Using Willingness-to-Pay Elicitation
Does biology drive child penalties evidence from biological and adoptive families.
American Economic Journal: Economic Policy
Do Family Policies Reduce Gender Inequality? Evidence from 60 Years of Policy Experimentation
Journal of Political Economy
Plants in Space
Journal of Political Economy Microeconomics
Econographics
Journal of Health Economics
Mothers as Insurance: Family Spillovers in WIC
Quarterly Journal of Economics
Wealth of Two Nations: The U.S. Racial Wealth Gap, 1860–2020
Economic Development and Cultural Change
The Unintended Consequences of Deportations: Evidence from Firm Behavior in El Salvador
Review of Economic Studies
Market Structure and Extortion: Evidence from 50,000 Extortion Payments
Journal of Public Economics
Collective Progress: Dynamics of Exit Waves
Understanding the ownership structure of corporate bonds.
August 2023
Which Investors Matter for Equity Valuations and Expected Returns?
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 Empirical EconomicsJournal of the Institute for Advanced Studies, Vienna, Austria
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Societies and partnerships Latest issueVolume 66, Issue 6 Latest articlesUncertainty and financial asset return spillovers: are they related empirical evidence from three continents.
 Combining cluster analysis with synthetic control for evaluating economic impacts of the dam breach in Mariana, Brazil
 Unconditional cash transfer programs and women’s empowerment: evidence from Pakistan
 The impact of diversification on the profitability and risk of Chinese banks: evidence from a semiparametric approach
 From bits to emissions: how FinTech benefits climate resilience?
 Journal updatesLawrence r. klein award 2021/2022. This biannual prize is awarded for the best paper published in the journal Empirical Economics. The Empirical Economics prize was awarded for the fi rst time by Springer in 2006, and was renamed in honor of the Nobel prize winner Lawrence R. Klein in 2013. Journal information
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 Search IDEAS All Articles Papers Chapters Books Software In: Whole record Abstract Keywords Title Author Sort by: new options Relevance Oldest Most recent Most cited Title alphabet Recently added Recent & relevant Relevant & cited Recent & cited From: Any Year 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1985 1980 1975 1970 1960 1950 1940 1930 1900 1800 1700 To: Any Year 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1985 1980 1975 1970 1960 1950 1940 1930 1900 1800 1700 More advanced search New: sort by citation count and by recently added --> What is IDEAS? IDEAS is the largest bibliographic database dedicated to Economics and available freely on the Internet. Based on RePEc , it indexes over 4,700,000 items of research, including over 4,200,000 that can be downloaded in full text. RePEc is a large volunteer effort to enhance the free dissemination of research in Economics which includes bibliographic metadata from over 2,000 participating archives , including all the major publishers and research outlets. IDEAS is just one of several services that use RePEc data. For some statistics about the holdings on this site, see here . Authors are invited to register with RePEc to create an online profile. Then, anyone finding some of their research here can find your latest contact details and a listing of their other research. They will also receive a monthly mailing about the popularity of their works, their ranking and newly found citations. How do I find on IDEAS what I am looking for?More services and features. Follow serials, authors, keywords & more Public profiles for Economics researchers Various research rankings in Economics RePEc GenealogyWho was a student of whom, using RePEc Curated articles & papers on economics topics Upload your paper to be listed on RePEc and IDEAS New papers by emailSubscribe to new additions to RePEc EconAcademicsBlog aggregator for economics research Cases of plagiarism in Economics About RePEcInitiative for open bibliographies in Economics News about RePEc Questions about IDEAS and RePEc RePEc volunteers Participating archivesPublishers indexing in RePEc Privacy statementCorrections. Found an error or omission? Opportunities to help RePEc Get papers listedHave your research listed on RePEc Open a RePEc archiveHave your institution's/publisher's output listed on RePEc Get RePEc dataUse data assembled by RePEc
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Job Search Advice for a Tough Market: Think Broadly and Stay FlexibleSome employers have pared staff and reduced hiring amid mixed economic signals. What does it mean for job seekers? Paul Gompers, Letian Zhang, and David Fubini offer advice for overcoming search challenges to score that all-important offer. 
What the Rise of Far-Right Politics Says About the Economy in an Election YearWith voters taking to the polls in dozens of countries this year, could election outcomes lean conservative? Paula Rettl says a lack of social mobility and a sense of economic insecurity are some of the factors fueling far-right movements around the world. 
Why Progress on Immigration Might Soften Labor PainsLong-term labor shortages continue to stoke debates about immigration policy in the United States. We asked Harvard Business School faculty members to discuss what's at stake for companies facing talent needs, and the potential scenarios on the horizon. 
Navigating the Mood of Customers Weary of Price HikesPrice increases might be tempering after historic surges, but companies continue to wrestle with pinched consumers. Alexander MacKay, Chiara Farronato, and Emily Williams make sense of the economic whiplash of inflation and offer insights for business leaders trying to find equilibrium. 
Do Disasters Rally Support for Climate Action? It's Complicated.Reactions to devastating wildfires in the Amazon show the contrasting realities for people living in areas vulnerable to climate change. Research by Paula Rettl illustrates the political ramifications that arise as people weigh the economic tradeoffs of natural disasters. 
Technology and COVID Upended Tipping Norms. Will Consumers Keep Paying?When COVID pushed service-based businesses to the brink, tipping became a way for customers to show their appreciation. Now that the pandemic is over, new technologies have enabled companies to maintain and expand the use of digital payment nudges, says Jill Avery. 
‘Not a Bunch of Weirdos’: Why Mainstream Investors Buy CryptoBitcoin might seem like the preferred tender of conspiracy theorists and criminals, but everyday investors are increasingly embracing crypto. A study of 59 million consumers by Marco Di Maggio and colleagues paints a shockingly ordinary picture of today's cryptocurrency buyer. What do they stand to gain? 
Why Giving to Others Makes Us HappyGiving to others is also good for the giver. A research paper by Ashley Whillans and colleagues identifies three circumstances in which spending money on other people can boost happiness. 
What Would It Take to Unlock Microfinance's Full Potential?Microfinance has been seen as a vehicle for economic mobility in developing countries, but the results have been mixed. Research by Natalia Rigol and Ben Roth probes how different lending approaches might serve entrepreneurs better. 
After High-Profile Failures, Can Investors Still Trust Credit Ratings?Rating agencies, such as Standard & Poor’s and Moody's, have been criticized for not warning investors of risks that led to major financial catastrophes. But an analysis of thousands of ratings by Anywhere Sikochi and colleagues suggests that agencies have learned from past mistakes. 
How Much More Would Holiday Shoppers Pay to Wear Something Rare?Economic worries will make pricing strategy even more critical this holiday season. Research by Chiara Farronato reveals the value that hip consumers see in hard-to-find products. Are companies simply making too many goods? 
Buy Now, Pay Later: How Retail's Hot Feature Hurts Low-Income ShoppersMore consumers may opt to "buy now, pay later" this holiday season, but what happens if they can't make that last payment? Research by Marco Di Maggio and Emily Williams highlights the risks of these financing services, especially for lower-income shoppers. 
Is It Time to Consider Lifting Tariffs on Chinese Imports?Many of the tariffs levied by the Trump administration on Chinese goods remain in place. James Heskett weighs whether the US should prioritize renegotiating trade agreements with China, and what it would take to move on from the trade war. Open for comment; 0 Comments. 
Have We Seen the Peak of Just-in-Time Inventory Management?Toyota and other companies have harnessed just-in-time inventory management to cut logistics costs and boost service. That is, until COVID-19 roiled global supply chains. Will we ever get back to the days of tighter inventory control? asks James Heskett. Open for comment; 0 Comments. 
War in Ukraine: Soaring Gas Prices and the Return of Stagflation?With nothing left to lose, Russia's invasion of Ukraine will likely intensify, roiling energy markets further and raising questions about the future of globalization, says Rawi Abdelal. Open for comment; 0 Comments. 
Why Are Prices So High Right Now—and Will They Ever Return to Normal?And when will sold-out products return to store shelves? The answers aren't so straightforward. Research by Alberto Cavallo probes the complex interplay of product shortages, prices, and inflation. Open for comment; 0 Comments. 
Can Entrepreneurs and Governments Team Up to Solve Big Problems?In 2017, Shield AI’s quadcopter, with no pilot and no flight plan, could clear a building and outpace human warfighters by almost five minutes. It was evidence that autonomous robots could help protect civilian and service member lives. But was it also evidence that Shield AI—a startup barely two years past founding—could ask their newest potential customer, the US government, for a large contract for a system of coordinated, exploring robots? Or would it scare them away? Harvard Business School professor Mitch Weiss and Brandon Tseng, Shield AI’s CGO and co-founder, discuss these and other challenges entrepreneurs face when working with the public sector, and how investing in new ideas can enable entrepreneurs and governments to join forces and solve big problems in the case, “Shield AI.” Open for comment; 0 Comments. 
How Four Women Made Miami More Equitable for StartupsA case study by Rosabeth Moss Kanter examines what it takes to break gender barriers and build thriving businesses in an emerging startup hub. Open for comment; 0 Comments. 
The Emergence of Mafia-like Business Systems in ChinaThis study sheds light on the political pathology of fraudulent, illegal, and corrupt business practices. Features of the Chinese system—including regulatory gaps, a lack of formal means of property protection, and pervasive uncertainty—seem to facilitate the rise of mafia systems.
Nonprofits in Good Times and Bad TimesTax returns from millions of US nonprofits reveal that charities do not expand during bad times, when need is the greatest. Although they are able to smooth the swings of their activities more than for-profit organizations, nonprofits exhibit substantial sensitivity to economic cycles. Economics JournalsWe publish a wide variety of open access journals in Economics . We invite you to learn more about the journals, view their metrics, explore our research articles, get to know our excellent author's services, and submit your research. Read top open access articles from our collection
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Diane Sredl  Data Reference Librarian, Lamont Library James Adams  Manager of Research and Data Services, HKS Library Schedule meeting online Econlit (Harvard Login) Indexes international economic literature. Includes major journals, articles in collective volumes (essays, proceedings, etc.), books, full-text book reviews, dissertations, and working papers. Annual Review of Economics (Harvard Login) Comprehensive review of the literature in economics. Can help researchers identify major trends in the field as well as find general overviews of research in specific subject areas of economics. Academic Search Premier (Harvard Login) A multi-disciplinary database that includes citations and abstracts from over 8,500 scholarly publications (journals, magazines and newspapers). ABI/Proquest (Harvard Login) Articles from over 1,000 academic management, marketing, and general business journals. Business Source Complete (Harvard Login) Articles from academic journals, magazines, and trade publications, as well as current company, industry and region reports. Fed in Print Provides access to online research and publications from across the U.S. Federal Reserve System. JSTOR Economics (Harvard Login) Full-text journal articles with focus on economics literature. National Bureau of Economic Research (NBER) Working Papers (Harvard Login) Full-text of NBER Working Papers from 1973 to the present. PAIS (Public Affairs Information System) (Harvard Login) Full-text journal articles covering economics, environmental science, political science, government and policy. RePec Open Access repository with links to full-text working papers and journal articles on economics. SSRN A large collection of working papers mostly authored by prominent university faculty, both pre- and post-publication. These papers cover a range of academic subjects including law, economics, business and public policy topics. Sage Research Methods (Harvard Login) A research methods tool providing material to guide users through every step of the research process. It contains a methods library with more than 1000 books, journal articles, and instructional videos by academics from across the social sciences.
Except where otherwise noted, this work is subject to a Creative Commons Attribution 4.0 International License , which allows anyone to share and adapt our material as long as proper attribution is given. For details and exceptions, see the Harvard Library Copyright Policy ©2021 Presidents and Fellows of Harvard College. Publications & PapersInterest rate cuts vs. stimulus payments: an equivalence result, can deficits finance themselves, automation and rent dissipation: implications for wages, inequality, and productivity, financial conditions targeting, learning, diversity and adaptation in changing environments: the role of weak links, the simple macroeconomics of ai, monetary policy and asset price overshooting: a rationale for the wall/main street disconnect, learning from ricardo and thompson: machinery and labor in the early industrial revolution - and in the age of ai, the unexpected compression: competition at work in the low wage labor market, doubly robust inference in causal latent factor models, the textbook case for industrial policy: theory meets data, incentive-compatible unemployment reinsurance for the euro area, the macroeconomics of supply chain disruptions, the local scarring effect of negative trade shocks: evidence from the collapse of finnish-soviet trade, public ownership and anti-preemption, methodological background for “the miracle weight-loss drug is also a major budgetary threat”.  Equilibrium Analysis in Behavioral One-Sector Growth ModelsGlobalization and the ladder of development: pushed to the top or held at the bottom.
Research papersWe advance principles for the construction of a stable and broadly beneficial world order that does not require significant commonality in interests and values among states. In particular, we propose a ‘meta-regime’ as a device for structuring a conversation around the relevant issues, and facilitating either agreement or accommodation. Participating in this meta-regime would impose few constraints on states, yet in favourable circumstances could facilitate significant cooperation. It could also encourage increased cooperation over time even among adversaries, as participation in the meta-regime builds trust. We apply these ideas to several issue areas, including US–China competition. We discuss the considerable literature that has developed in recent years providing rigorous evidence on how industrial policies work. This literature is a significant improvement over the earlier generation of empirical work, which was largely correlational and marred by interpretational problems. On the whole, the recent crop of papers offers a more positive take on industrial policy. We review the standard rationales and critiques of industrial policy and provide a broad overview of new empirical approaches to measurement. We discuss how the recent literature, paying close attention to measurement, causal inference, and economic structure, is offering a nuanced and contextual understanding of the effects of industrial policy. We re-evaluate the East Asian experience with industrial policy in light of recent results. Finally, we conclude by reviewing how industrial policy is being reshaped by a new understanding of governance, a richer set of policy instruments beyond subsidies, and the reality of de-industrialization. We distinguish between ideational and interest-based appeals to voters on the supply side of politics, integrating the Keynes-Hayek perspective on the importance of ideas with the Stigler-Becker approach emphasizing vested interests. In our model, political entrepreneurs discover identity and worldview “memes” (narratives, cues, frames) that invoke voters’ identity concerns or shift their views of how the world works. We identify a complementarity between worldview politics and identity politics and illustrate how they may reinforce each other. Furthermore, we show how adverse economic shocks (increasing inequality) lead to greater incidence of ideational politics. We use these results to analyze data on 60,000 televised political ads in U.S. localities over the years 2000 through 2018. Our empirical work quantifies ideational politics and provides support for key model implications, including the impact of higher inequality on the supply of both identity and worldview politics. Using Fontana et al.’s (2019) database, we analyze levels and trends in the global distribution of authorship in economics journals, disaggregating by country/region, quality of journal, and fields of specialization. We document striking imbalances. While Western and Northern European authors have made substantial gains, the representation of authors based in low-income countries remains extremely low -- an order of magnitude lower than the weight of their countries or regions in the global economy. Developing country representation has risen fastest at journals rated 100 th or lower, while it has barely increased in journals rated 25 th or higher. Fields such as international or development where global diversification may have been expected have not experienced much increase in developing country authorship. These results are consistent with a general increase in the relative supply of research in the rest of the world. But they also indicate authors from developing countries remain excluded from the profession’s top-rated journals. Conventional welfare state policies that center on education, training, progressive taxation, and social insurance are inadequate to address labor market polarization, which is capitalism’s most pressing inclusion challenge at present. We propose a strategy aimed directly at the productive sphere of the economy and targeting an increase in the supply of ‘good jobs. The main elements of this strategy are: (i) active labour market policies linked to employers; (ii) industrial and regional policies directly targeting the creation of good jobs; (iii) innovation policies that incentivize labour-friendly technologies; (iv) international economic policies that facilitate the maintenance of high domestic labour/social standards. These elements are connected both by their objective—expanding the number of good jobs—and by a new approach to regulation that is collaborative and iterative rather than top-down and prescriptive. We emphasize the importance of new institutional arrangements that enable strategic long-term information exchange and cooperation between governments and firms. Dani Rodrik Ford Foundation Professor of International Political Economy John F. Kennedy School of Government at Harvard University 79 J.F. Kennedy Street Cambridge, MA 02138 [email protected] Mastodon: @[email protected] Blog Faculty Assistant: Jessica De Simone [email protected] 617-495-1415  Study at CambridgeAbout the university, research at cambridge.
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University of MissouriCollege of Arts and Science Published ResearchOur faculty and graduate students (* PhD year ) regularly publish in top journals . Find our latest working papers on RePEc . Michael Pesko (with S. M. Farin and L. Hoehn-Velasco). The Impact of Legal Abortion on Maternal Mortality . American Economic Journal: Economic Policy . J. Isaac Miller and William Brock. Beyond RCP8.5: Marginal Mitigation Using Quasi-Representative Concentration Pathways . Journal of Econometrics. Liisa Laine (with P. Böckerman, M. Kortelainen, M. Nurminen, and T. Saxell). Information Technology, Access, and Use of Prescription Drugs. Journal of the European Economic Association . Alyssa Carlson (with R. Joshi). Sample Selection in Linear Panel Data Models with Heterogeneous Coefficients . Journal of Applied Econometrics . Chao Gu (with G. Menzio, R. Wright, and Y. Zhu). Market Freezes. Journal of Money, Credit and Banking . David M. Kaplan. Inference on Consensus Ranking of Distributions . Journal of Business and Economic Statistics . Xin Liu (*21). Averaging Estimation for Instrumental Variables Quantile Regression . Oxford Bulletin of Economics and Statistics . Cory Koedel and Eric Parsons (with I. Fazlul). Using Predicted Academic Performance to Identify At-Risk Students in Public Schools . Educational Evaluation and Policy Analysis . J. Isaac Miller (with F. Pretis). Introduction to the Themed Issue on Climate Econometrics . Journal of Econometrics . Alyssa Carlson (with D. Brewer). Addressing Sample Selection Bias for Machine Learning Methods . Journal of Applied Econometrics . David M. Kaplan and Xin Liu (*21). Confidence Intervals for Intentionally Biased Estimators . Econometric Reviews . Wei Zhao (*24) and David M. Kaplan. Conditions for extrapolating differences in consumption to differences in welfare. Economic Inquiry . Yong Bian (*21) and Wei Kong (*18) (with Q. Zhang). Salary inequality in young professors: evidence from public U.S. economic departments . Applied Economics . Cheng Qian (*21) and Cory Koedel. Could Shifting the Margin Between Community College and University Enrollment Expand and Diversify University Degree Production in STEM Fields? Research in Higher Education . Xin Liu (*21). Testing in smoothed GMM quantile models with an application to quantile Euler equation. Econometrics and Statistics . Michael Pesko (with M. Cooper). The effect of E-cigarette indoor vaping restrictions on infant mortality . Southern Economic Journal . Cory Koedel (with N. Hwang). Helping or Hurting: The Effects of Retention in the Third Grade on Student Outcomes . Educational Evaluation and Policy Analysis . David M. Kaplan and Xin Liu (*21). k -Class instrumental variables quantile regression . Empirical Economics . Brittany Street (with K. Finlay and M. Mueller-Smith). Children's Indirect Exposure to the U.S. Justice System: Evidence from Longitudinal Links between Survey and Administrative Data . Quarterly Journal of Economics. Alyssa Carlson. Relaxing conditional independence in an endogenous binary response model . Journal of Econometrics. Liisa Laine (with P. Böckerman, M. Nurminen, and T. Saxell). Information Integration, Coordination Failures, and Quality of Prescribing . Journal of Hum an Resources . Chao Gu (with C. Monnet, E. Nosal, and R. Wright). Diamond-Dybvig and Beyond: on the Instability of Banking . European Economic Review . Cory Koedel (with A. Bacher-Hicks). Estimation and Interpretation of Teacher Value Added in Research Applications . Handbook of the Economics of Education . Michael Pesko (with K. Callison, S. Phillips, and J. A. Sosa). Cancer Screening after the Adoption of Paid-Sick-Leave Mandates . New England Journal of Medicine . Michael Pesko (with D. Dave, Y. Liang, S. Phillips, and J. J. Sabia). Have recreational marijuana laws undermined public health progress on adult tobacco use? Journal of Health Economics . David M. Kaplan and Wei Zhao (*24). Comparing latent inequality with ordinal data . Econometrics Journal . Chao Gu. Endogenous Cycles in a Competitive Search Credit Market . Economics Letters . J. Isaac Miller. Local Climate Sensitivity: What Can Time Series of Distributions Reveal about Spatial Heterogeneity of Climate Change? Advances in Econometrics . Oksana Loginova and X. H. Wang. Complementarity on Software as a Service Platforms . Managerial and Decision Economics . Brittany Street (with K. Finlay and M. Mueller-Smith). Criminal Justice Involvement, Self-Employment, and Barriers in Recent Public Policy . Journal of Policy Analysis and Management. Saku Aura, William Brock, and Shawn Ni. A Chinese mayor problem . Journal of Government and Economics . Xiqian Wang (*19) and Yong Bian (*21) (with Qin Zhang). The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods . Energy Economics . Cory Koedel and Eric Parsons (with I. Fazlul). Free and Reduced-Price Meal Eligibility does not Measure Student Poverty: Evidence and Policy Significance . Economics of Education Review . Michael Pesko (with W. Austin, S. Carattini, and J. Gomez-Mahecha). The effects of contemporaneous air pollution on COVID-19 morbidity and mortality . Journal of Environmental Economics and Management . Michael Pesko (with R. Abouk, C. Courtemanche, D. Dave, B. Feng, A. S. Friedman, J. C. Maclean, J. J. Sabia, and S. Safford). Intended and unintended effects of e-cigarette taxes on youth tobacco use . Journal of Health Economics . Michael Pesko (with R. Abouk, S. Adams, B. Feng, and J. C. Maclean). The effect of e-cigarette taxes on prepregnancy and prenatal smoking . Journal of Policy Analysis and Management . Sanguk Kwon (*21) and Shawn Ni. Has Consumption Inequality Mirrored Income Inequality: New Evidence from a Korean Household Panel . Applied Economics Letters . Wei Kong (*18) and Shawn Ni. A Structural Econometric Approach to Analyzing the Impact of Teacher Pension Reform . Educational Researcher . J. Isaac Miller (with Y. Chang and S. Lee). Introduction to "Essays in Honor of Joon Y. Park: Econometric Theory." Advances in Econometrics . J. Isaac Miller (with Y. Chang and S. Lee). Introduction to "Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications." Advances in Econometrics . Michael Pesko (with L. Hoehn-Velasco and S. Phillips). The Long-term Impact of In-Utero Cigarette Taxes on Adult Prenatal Smoking . American Journal of Health Economics . Michael Pesko. Effects of e-cigarette minimum legal sales ages on youth tobacco use in the United States . Journal of Risk and Uncertainty . Michael Pesko (with J. C. Maclean, C. McClellan, D. Polsky). Medicaid reimbursement rates for primary care services and behavioral health outcomes . Health Economics . Cory Koedel and Eric Parsons (with many others). Academic Mobility in U.S. Public Schools: Evidence from Nearly 3 Million Students. Journal of Public Economics . Michael Pesko (with M. Andersen, J.C. Maclean, K. Simon). Does paid sick leave encourage staying at home? Evidence from the United States during a pandemic . Health Economics . David M. Kaplan and Lonnie Hofmann (*21). High-order Coverage of Smoothed Bayesian Bootstrap Intervals for Population Quantiles . Austrian Journal of Statistics . David M. Kaplan (with A.N. Kaser, W. Goette, and Andrew M. Kiselica ). The impact of conventional versus robust norming on cognitive characterization and clinical classification of MCI and dementia . Journal of Neuropsychology . Michael Pesko (with B.J. Lipton). State Medicaid and private telemedicine coverage requirements and telemedicine use, 2013–2019 . Health Services Research . Michael Pesko (with J.C. Maclean, T. Khan, S. Tsipas). The effect of cigarette and e-cigarette taxes on prescriptions for smoking cessation medications . Health Services Research . Liisa Laine (with K.E.M. Miller, J. Zhao, and N.B. Coe). Growth of Private Pay Senior Housing Communities in Metropolitan Statistical Areas in the United States: 2015–2019 . Medical Care Research and Review . Cory Koedel and Eric Parsons (with I. Fazlul). A Poor Poverty Measure: To Identify Children in Need, We Need to Look Beyond Free Lunch Data . Education Next . Cory Koedel (with T. Pham). The Narrowing Gender Wage Gap Among Faculty at Public Universities in the U.S. SAGE Open . Cory Koedel and Eric Parsons (with Shannon Breske ). Field Interest and the Choice of College Major . Economics Bulletin . Michael Pesko (with K. M. Cummings, C. E. Douglas, J. Foulds, T. Miller, N. A. Rigotti, K. E. Warner). United States public health officials need to correct e-cigarette health misinformation . Addiction . Michael Pesko (with A. C. Liber, C. Cadham, M. Cummings, and D. T. Levy). Poland is not replicating the HTP experience in Japan: a cautionary note . Tobacco Control . Michael Pesko (with C. Cotti, E. Nesson, S. Phillips, and N. Tefft). Standardising the measurement of e-cigarette taxes in the USA, 2010–2020 . Tobacco Control . Shawn Ni, Michael Podgursky, and Fangda Wang (*22). How Much Are Public School Teachers Willing to Pay for Their Retirement Benefits? Comment . American Economic Journal: Economic Policy. Shawn Ni, Michael Podgursky, and Xiqian Wang (*19). Teacher Pension Plan Incentives, Retirement Decisions, and Workforce Quality . Journal of Human Resources . J. Isaac Miller and Kyungsik Nam (*18). Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions . Energy Economics . J. Isaac Miller (with B. A. Eroğlu and T. Yiğit). Time-varying cointegration and the Kalman filter . Econometric Reviews . Oksana Loginova. Branded Websites and Marketplace Selling: Competing during COVID-19 . Journal of Economic Behavior and Organization. Oksana Loginova. Price Competition Online: Platforms vs. Branded Websites . Journal of Economics and Management Strategy. X. H. Wang (with L. Liu and H. Yu). Sequential Search with Partial Depth . Economics Letters. Oksana Loginova and X. H. Wang (with Q. Liu). The Impact of Multi-Homing in a Ride-Sharing Market . Annals of Regional Science. X. H. Wang (with J. Zhao). Merger effects in asymmetric and differentiated Bertrand oligopolies . Mathematical Social Sciences. Tai Lee (*19). The Impact of Employment Protection on the Probability of Job Separation: Evidence from Job Duration Data in South Korea . Journal of Labor Research . Liisa Laine (with G. David, C. Gunnarsson, M. Ryan, S. Clancy, G. Gunnarsson, K. Moore, and W. Irish). The Unintended Consequences of Medicare's Wage Index Adjustment on Device Intensive Hospital Procedures: The Case of Transcatheter Aortic Valve Replacement (TAVR) versus Surgical Aortic Valve Replacement (SAVR) . The American Journal of Managed Care . Shawn Ni, Michael Podgursky, and Xiqian Wang (*19). Teacher Pension Enhancements and Staffing in an Urban School District . Journal of Pension Economics and Finance . David M. Kaplan. Smoothed instrumental variables quantile regression . Stata Journal . William Brock and Joseph Haslag. Two Approaches to Modeling Uncertainty: How Did Uncertainty Affect the Economy During and After the Great Recession . Singapore Economic Review. Liisa Laine (with H. M. Ollila, M. Partinen, J. Koskela, J. Borghi, R. Savolainen, and A. Rotkirch). Face masks to prevent transmission of respiratory diseases: Systematic review and meta-analysis of randomized controlled trials on face mask use . PLoS ONE . Cory Koedel (with J. Eagan, N. Hwang, H. F. Ladd, and L. C. Sorensen). Teacher Attrition and the Business Cycle . Teachers College Record . Cory Koedel and Eric Parsons (with D. Goldhaber and U. Ozek). Using Longitudinal Student Mobility to Identify At-Risk Students . AERA Open . David M. Kaplan and Longhao Zhuo (*17). Frequentist Properties of Bayesian Inequality Tests . Journal of Econometrics . Saroj Dhital (*19) and Joseph Haslag (with Pedro Gomis-Porqueras). Monetary and Fiscal Policy Interactions in a Frictional Model of Fiat Money, Nominal Public Debt and Banking . European Economic Review . Li Tan (*18). Imputing Top-Coded Income Data in Longitudinal Surveys . Oxford Bulletin of Economics and Statistics . Brittany Street (with M. Hoekstra). The Effect of Own-Gender Jurors on Convictions . Journal of Law and Economics . Kyungsik Nam (*18). Investigating the effect of climate uncertainty on global commodity markets . Energy Economics . Dongwoo Kim (post-doc), Cory Koedel, Wei Kong (*18), Shawn Ni, Michael Podgursky, and Weiwei Wu (*17). Pensions and Late Career Teacher Retention . Education Finance and Policy . Joseph Haslag and Xue Li (*15). On Phase Shifts in a New Keynesian Model Economy . Macroeconomic Dynamics . Cory Koedel (with N. Hwang and B. Kisida). 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Parametric Identification of Multiplicative Exponential Heteroscedasticity . Oxford Bulletin of Economics and Statistics . Li Tan (*18) and Cory Koedel. The Effects of Differential Income Replacement and Mortality on U.S. Social Security Redistribution . Southern Economic Journal . Kuo-Hsuan Chin (*15). New Keynesian Phillips Curve with time-varying parameters . Empirical Economics . Brittany Street (with A. Packham). The Effects of Physical Education on Student Fitness, Achievement, and Behavior . Economics of Education Review . Sanha Noh (*19). Posterior Inference on Parameters in a Nonlinear DSGE Model via Gaussian-Based Filters . Computational Economics . J. Isaac Miller. Testing Cointegrating Relationships Using Irregular and Non-Contemporaneous Series with an Application to Paleoclimate Data . Journal of Time Series Analysis . Aaron Hedlund. Pork-Barrel Politics and Polarization . Federal Reserve Bank of St. Louis Review . Cory Koedel, Jiaxi Li (*14), and Li Tan (*18) (with M. 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Taehwan Kim (*18). Price Competition and Market Segmentation in Retail Gasoline: New Evidence from South Korea . Review of Industrial Organization . Aaron Hedlund. Estate Taxation and Human Capital with Information Externalities . Macroeconomic Dynamics . David Mandy. Leading Principal Minors and Semidefiniteness . Economic Inquiry . David M. Kaplan (with M. Goldman). Comparing Distributions by Multiple Testing Across Quantiles or CDF Values . Journal of Econometrics . Aaron Hedlund. Credit Constraints, House Prices, and the Impact of Life Cycle Dynamics . Economics Letters . Cory Koedel (with B. Backes, J. Cowan, D. Goldhaber, L. Miller, and Z. Xu). The Common Core Conundrum: To What Extent Should We Worry that Changes to Assessments Will Affect Test-Based Measures of Teacher Performance? Economics of Education Review . Peter Mueser (with M. Michaelides). Are Reemployment Services Effective? Experimental Evidence from the Great Recession . Journal of Policy Analysis and Management . Cory Koedel (with R. Darolia). High Schools and Students' Initial Colleges and Majors . Contemporary Economic Policy. J. Isaac Miller (with J. Zhao and W. Thompson). Modeling and Extrapolating Wheat Producer Support Using Income and Other Factors . Journal of Agricultural Economics . X. H. Wang (with A. Tao and B. Z. Yang). Duopoly Models with a Joint Capacity Constraint . Journal of Economics . X. H. Wang (with Y.-H. Chen, S.-J. Huang, and A. K. Mishra). Effects of Input Capacity Constraints on Food Quality and Regulation Mechanism Design for Food Safety Management . Ecological Modelling . J. Isaac Miller. Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series . Econometrics and Statistics . William A. Brock and Joseph Haslag. Two Approaches to Modeling Uncertainty: How Did Uncertainty Affect the Economy During and After the Great Recession? Singapore Economic Review . David M. Kaplan (with M. Goldman). Nonparametric Inference on (Conditional) Quantile Differences and Linear Combinations, Using L-statistics . Econometrics Journal . Peter Mueser (with C. Jepsen and K. Troske). Second Chance for High School Dropouts? A Regression Discontinuity Analysis of Postsecondary Educational Returns to the GED . Journal of Labor Economics , 2017. Chao Gu (with F. Mattesini and R. Wright). Money and Credit Redux . Econometrica , 2016. Peter Mueser (with C. Jepsen and K. Troske). Labor-Market Returns to the GED Using Regression Discontinuity Analysis . Journal of Political Economy , 2016. Aaron Hedlund. The Cyclical Dynamics of Illiquid Housing, Debt, and Foreclosures . Quantitative Economics , 2016 . Christopher Otrok (with G. Benigno, H. Chen, A. Rebucci, and E. R. Young). Optimal Capital Controls and Real Exchange Rate Policy: A Pecuniary Externality Perspective . Journal of Monetary Economics , 2016. Shawn Ni and Michael Podgursky. How Teachers Respond to Pension System Incentives: New Estimates and Policy Applications . Journal of Labor Economics , 2016. X. H. Wang and Seoung Joun Won (*10) (with H. Warren). Climate Normals and Weather Normalization for Utility Regulation . Energy Economics , 2016. William A. Brock and Joseph Haslag. A Tale of Two Correlations: Evidence and Theory Regarding the Phase Shift between the Price Level and Output . Journal of Economic Dynamics and Control , 2016. David Mandy (with J. W. Mayo and D. E. M. Sappington). Targeting efforts to raise rivals' costs: Moving from "Whether" to "Whom." International Journal of Industrial Organization , 2016. Aaron Hedlund. Illiquidity and its Discontents: Trading Delays and Foreclosures in the Housing Market . Journal of Monetary Economics , 2016. David M. Kaplan. Improved quantile inference via fixed-smoothing asymptotics and Edgeworth expansion . Journal of Econometrics , 2015. Shawn Ni and Youn Seol (*08). 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 One Federal Reserve Bank Plaza St. Louis, MO 63102 Information for Visitors  The economic potential of generative AI: The next productivity frontier AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. As a result, its progress has been almost imperceptible. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness. Generative AI applications such as ChatGPT, GitHub Copilot, Stable Diffusion, and others have captured the imagination of people around the world in a way AlphaGo did not, thanks to their broad utility—almost anyone can use them to communicate and create—and preternatural ability to have a conversation with a user. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. About the authorsThis article is a collaborative effort by Michael Chui , Eric Hazan , Roger Roberts , Alex Singla , Kate Smaje , Alex Sukharevsky , Lareina Yee , and Rodney Zemmel , representing views from QuantumBlack, AI by McKinsey; McKinsey Digital; the McKinsey Technology Council; the McKinsey Global Institute; and McKinsey’s Growth, Marketing & Sales Practice. The speed at which generative AI technology is developing isn’t making this task any easier. ChatGPT was released in November 2022. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities. 1 “Introducing ChatGPT,” OpenAI, November 30, 2022; “GPT-4 is OpenAI’s most advanced system, producing safer and more useful responses,” OpenAI, accessed June 1, 2023. Similarly, by May 2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000 words in a minute—the length of the average novel—compared with roughly 9,000 tokens when it was introduced in March 2023. 2 “Introducing Claude,” Anthropic PBC, March 14, 2023; “Introducing 100K Context Windows,” Anthropic PBC, May 11, 2023. And in May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that will power its Bard chatbot, among other Google products. 3 Emma Roth, “The nine biggest announcements from Google I/O 2023,” The Verge , May 10, 2023. To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. For the purposes of this report, we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task.  Creating value beyond the hypeLet’s deliver on the promise of technology from strategy to scale. Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks. All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. The following sections share our initial findings. For the full version of this report, download the PDF . Key insightsGenerative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. This would increase the impact of all artificial intelligence by 15 to 40 percent. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases. About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D. Across 16 business functions, we examined 63 use cases in which the technology can address specific business challenges in ways that produce one or more measurable outcomes. Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales, and draft computer code based on natural-language prompts, among many other tasks. Generative AI will have a significant impact across all industry sectors. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI. Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year. Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities. Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today. In contrast, we previously estimated that technology has the potential to automate half of the time employees spend working. 4 “ Harnessing automation for a future that works ,” McKinsey Global Institute, January 12, 2017. The acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time. Thus, generative AI has more impact on knowledge work associated with occupations that have higher wages and educational requirements than on other types of work. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. Our updated adoption scenarios, including technology development, economic feasibility, and diffusion timelines, lead to estimates that half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates. Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities. Combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world. The era of generative AI is just beginning. Excitement over this technology is palpable, and early pilots are compelling. But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills. Where business value liesGenerative AI is a step change in the evolution of artificial intelligence. As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions. We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be (Exhibit 1). The first lens scans use cases for generative AI that organizations could adopt. We define a “use case” as a targeted application of generative AI to a specific business challenge, resulting in one or more measurable outcomes. For example, a use case in marketing is the application of generative AI to generate creative content such as personalized emails, the measurable outcomes of which potentially include reductions in the cost of generating such content and increases in revenue from the enhanced effectiveness of higher-quality content at scale. We identified 63 generative AI use cases spanning 16 business functions that could deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefits annually when applied across industries. That would add 15 to 40 percent to the $11 trillion to $17.7 trillion of economic value that we now estimate nongenerative artificial intelligence and analytics could unlock. (Our previous estimate from 2017 was that AI could deliver $9.5 trillion to $15.4 trillion in economic value.) Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. We modeled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities”—such as “communicating with others about operational plans or activities”—that make up those occupations across the world economy. This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce. Some of this impact will overlap with cost reductions in the use case analysis described above, which we assume are the result of improved labor productivity. Netting out this overlap, the total economic benefits of generative AI —including the major use cases we explored and the myriad increases in productivity that are likely to materialize when the technology is applied across knowledge workers’ activities—amounts to $6.1 trillion to $7.9 trillion annually (Exhibit 2). How we estimated the value potential of generative AI use casesTo assess the potential value of generative AI, we updated a proprietary McKinsey database of potential AI use cases and drew on the experience of more than 100 experts in industries and their business functions. 1 ” Notes from the AI frontier: Applications and value of deep learning ,” McKinsey Global Institute, April 17, 2018. Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies. We analyzed only use cases for which generative AI could deliver a significant improvement in the outputs that drive key value. In particular, our estimates of the primary value the technology could unlock do not include use cases for which the sole benefit would be its ability to use natural language. For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis. We then estimated the potential annual value of these generative AI use cases if they were adopted across the entire economy. For use cases aimed at increasing revenue, such as some of those in sales and marketing, we estimated the economy-wide value generative AI could deliver by increasing the productivity of sales and marketing expenditures. Our estimates are based on the structure of the global economy in 2022 and do not consider the value generative AI could create if it produced entirely new product or service categories. While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”). In this section, we highlight the value potential of generative AI across business functions. Generative AI could have an impact on most business functions; however, a few stand out when measured by the technology’s impact as a share of functional cost (Exhibit 3). Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases. Notably, the potential value of using generative AI for several functions that were prominent in our previous sizing of AI use cases, including manufacturing and supply chain functions, is now much lower. 5 Pitchbook. This is largely explained by the nature of generative AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI. In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue. This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies. In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information. If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. Such virtual expertise could rapidly “read” vast libraries of corporate information stored in natural language and quickly scan source material in dialogue with a human who helps fine-tune and tailor its research, a more scalable solution than hiring a team of human experts for the task. In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks. Following are four examples of how generative AI could produce operational benefits in a handful of use cases across the business functions that could deliver a majority of the potential value we identified in our analysis of 63 generative AI use cases. In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. Customer operations: Improving customer and agent experiencesGenerative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. Research found that at one company with 5,000 customer service agents, the application of generative AI increased issue resolution by 14 percent an hour and reduced the time spent handling an issue by 9 percent. 1 Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, Generative AI at work , National Bureau of Economic Research working paper number 31161, April 2023. It also reduced agent attrition and requests to speak to a manager by 25 percent. Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents. This is because AI assistance helped less-experienced agents communicate using techniques similar to those of their higher-skilled counterparts. The following are examples of the operational improvements generative AI can have for specific use cases:
We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs. Our analysis captures only the direct impact generative AI might have on the productivity of customer operations. It does not account for potential knock-on effects the technology may have on customer satisfaction and retention arising from an improved experience, including better understanding of the customer’s context that can assist human agents in providing more personalized help and recommendations. Marketing and sales: Boosting personalization, content creation, and sales productivityGenerative AI has taken hold rapidly in marketing and sales functions, in which text-based communications and personalization at scale are driving forces. The technology can create personalized messages tailored to individual customer interests, preferences, and behaviors, as well as do tasks such as producing first drafts of brand advertising, headlines, slogans, social media posts, and product descriptions. Introducing generative AI to marketing functions requires careful consideration. For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data. Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs. Potential operational benefits from using generative AI for marketing include the following:
We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending. Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments. Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies. Generative AI could also change the way both B2B and B2C companies approach sales. The following are two use cases for sales:
Our analysis suggests that implementing generative AI could increase sales productivity by approximately 3 to 5 percent of current global sales expenditures. This analysis may not fully account for additional revenue that generative AI could bring to sales functions. For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover new leads and facilitate more effective outreach that would bring in additional revenue. Also, the time saved by sales representatives due to generative AI’s capabilities could be invested in higher-quality customer interactions, resulting in increased sales success. Software engineering: Speeding developer work as a coding assistantTreating computer languages as just another language opens new possibilities for software engineering. Software engineers can use generative AI in pair programming and to do augmented coding and train LLMs to develop applications that generate code when given a natural-language prompt describing what that code should do. Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity. According to our analysis, the direct impact of AI on the productivity of software engineering could range from 20 to 45 percent of current annual spending on the function. This value would arise primarily from reducing time spent on certain activities, such as generating initial code drafts, code correction and refactoring, root-cause analysis, and generating new system designs. By accelerating the coding process, generative AI could push the skill sets and capabilities needed in software engineering toward code and architecture design. One study found that software developers using Microsoft’s GitHub Copilot completed tasks 56 percent faster than those not using the tool. 1 Peter Cihon et al., The impact of AI on developer productivity: Evidence from GitHub Copilot , Cornell University arXiv software engineering working paper, arXiv:2302.06590, February 13, 2023. An internal McKinsey empirical study of software engineering teams found those who were trained to use generative AI tools rapidly reduced the time needed to generate and refactor code—and engineers also reported a better work experience, citing improvements in happiness, flow, and fulfillment. Our analysis did not account for the increase in application quality and the resulting boost in productivity that generative AI could bring by improving code or enhancing IT architecture—which can improve productivity across the IT value chain. However, the quality of IT architecture still largely depends on software architects, rather than on initial drafts that generative AI’s current capabilities allow it to produce. Large technology companies are already selling generative AI for software engineering, including GitHub Copilot, which is now integrated with OpenAI’s GPT-4, and Replit, used by more than 20 million coders. 2 Michael Nuñez, “Google and Replit join forces to challenge Microsoft in coding tools,” VentureBeat, March 28, 2023. Product R&D: Reducing research and design time, improving simulation and testingGenerative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs. For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design. Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others. While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit their application. Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, have much broader areas of application than models optimized for a single task. They can therefore accelerate time to market and broaden the types of products to which generative design can be applied. For now, however, foundation models lack the capabilities to help design products across all industries. In addition to the productivity gains that result from being able to quickly produce candidate designs, generative design can also enable improvements in the designs themselves, as in the following examples of the operational improvements generative AI could bring:
We also identified a new R&D use case for nongenerative AI: deep learning surrogates, the use of which has grown since our earlier research, can be paired with generative AI to produce even greater benefits. To be sure, integration will require the development of specific solutions, but the value could be significant because deep learning surrogates have the potential to accelerate the testing of designs proposed by generative AI. While we have estimated the potential direct impacts of generative AI on the R&D function, we did not attempt to estimate the technology’s potential to create entirely novel product categories. These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall. Industry impactsAcross the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on a variety of factors, such as the mix and importance of different functions, as well as the scale of an industry’s revenue (Exhibit 4). For example, our analysis estimates generative AI could contribute roughly $310 billion in additional value for the retail industry (including auto dealerships) by boosting performance in functions such as marketing and customer interactions. By comparison, the bulk of potential value in high tech comes from generative AI’s ability to increase the speed and efficiency of software development (Exhibit 5). In the banking industry, generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data. In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development. We share our detailed analysis of these industries below. Generative AI supports key value drivers in retail and consumer packaged goodsThe technology could generate value for the retail and consumer packaged goods (CPG) industry by increasing productivity by 1.2 to 2.0 percent of annual revenues, or an additional $400 billion to $660 billion. 1 Vehicular retail is included as part of our overall retail analysis. To streamline processes, generative AI could automate key functions such as customer service, marketing and sales, and inventory and supply chain management. Technology has played an essential role in the retail and CPG industries for decades. Traditional AI and advanced analytics solutions have helped companies manage vast pools of data across large numbers of SKUs, expansive supply chain and warehousing networks, and complex product categories such as consumables. In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence. For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. Generative AI at work in retail and CPGReinvention of the customer interaction pattern. Consumers increasingly seek customization in everything from clothing and cosmetics to curated shopping experiences, personalized outreach, and food—and generative AI can improve that experience. Generative AI can aggregate market data to test concepts, ideas, and models. Stitch Fix, which uses algorithms to suggest style choices to its customers, has experimented with DALL·E to visualize products based on customer preferences regarding color, fabric, and style. Using text-to-image generation, the company’s stylists can visualize an article of clothing based on a consumer’s preferences and then identify a similar article among Stitch Fix’s inventory. Retailers can create applications that give shoppers a next-generation experience, creating a significant competitive advantage in an era when customers expect to have a single natural-language interface help them select products. For example, generative AI can improve the process of choosing and ordering ingredients for a meal or preparing food—imagine a chatbot that could pull up the most popular tips from the comments attached to a recipe. There is also a big opportunity to enhance customer value management by delivering personalized marketing campaigns through a chatbot. Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. Accelerating the creation of value in key areasGenerative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation. The potential improvement in writing and visuals can increase awareness and improve sales conversion rates. Rapid resolution and enhanced insights in customer careThe growth of e-commerce also elevates the importance of effective consumer interactions. Retailers can combine existing AI tools with generative AI to enhance the capabilities of chatbots, enabling them to better mimic the interaction style of human agents—for example, by responding directly to a customer’s query, tracking or canceling an order, offering discounts, and upselling. Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information. Disruptive and creative innovationGenerative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly. A designer can generate packaging designs from scratch or generate variations on an existing design. This technology is developing rapidly and has the potential to add text-to-video generation. Factors for retail and CPG organizations to considerAs retail and CPG executives explore how to integrate generative AI in their operations, they should keep in mind several factors that could affect their ability to capture value from the technology:
To address these concerns, retail and CPG companies will need to strategically keep humans in the loop and ensure security and privacy are top considerations for any implementation. Companies will need to institute new quality checks for processes previously handled by humans, such as emails written by customer reps, and perform more-detailed quality checks on AI-assisted processes such as product design. Why banks could realize significant valueGenerative AI could have a significant impact on the banking industry , generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion. On top of that impact, the use of generative AI tools could also enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk. Banking, a knowledge and technology-enabled industry, has already benefited significantly from previously existing applications of artificial intelligence in areas such as marketing and customer operations. 1 “ Building the AI bank of the future ,” McKinsey, May 2021. Generative AI applications could deliver additional benefits, especially because text modalities are prevalent in areas such as regulations and programming language, and the industry is customer facing, with many B2C and small-business customers. 2 McKinsey’s Global Banking Annual Review , December 1, 2022. Several characteristics position the industry for the integration of generative AI applications:
Generative AI at work in bankingBanks have started to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications. Three uses demonstrate its value potential to the industry. A virtual expert to augment employee performanceA generative AI bot trained on proprietary knowledge such as policies, research, and customer interaction could provide always-on, deep technical support. Today, frontline spending is dedicated mostly to validating offers and interacting with clients, but giving frontline workers access to data as well could improve the customer experience. The technology could also monitor industries and clients and send alerts on semantic queries from public sources. For example, Morgan Stanley is building an AI assistant using GPT-4, with the aim of helping tens of thousands of wealth managers quickly find and synthesize answers from a massive internal knowledge base. 4 Hugh Son, “Morgan Stanley is testing an OpenAI-powered chatbot for its 16,000 financial advisors,” CNBC, March 14, 2023. The model combines search and content creation so wealth managers can find and tailor information for any client at any moment. One European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert by synthesizing and extracting from long documents with unstructured information. The model answers complex questions based on a prompt, identifying the source of each answer and extracting information from pictures and tables. Generative AI could reduce the significant costs associated with back-office operations. Such customer-facing chatbots could assess user requests and select the best service expert to address them based on characteristics such as topic, level of difficulty, and type of customer. Through generative AI assistants, service professionals could rapidly access all relevant information such as product guides and policies to instantaneously address customer requests. Code acceleration to reduce tech debt and deliver software fasterGenerative AI tools are useful for software development in four broad categories. First, they can draft code based on context via input code or natural language, helping developers code more quickly and with reduced friction while enabling automatic translations and no- and low-code tools. Second, such tools can automatically generate, prioritize, run, and review different code tests, accelerating testing and increasing coverage and effectiveness. Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. Last, the tools can review code to identify defects and inefficiencies in computing. The result is more robust, effective code. Production of tailored content at scaleGenerative AI tools can draw on existing documents and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing. In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts. Factors for banks to considerWhen exploring how to integrate generative AI into operations, banks can be mindful of a number of factors:
Pharmaceuticals and medical products could see benefits across the entire value chainOur analysis finds that generative AI could have a significant impact on the pharmaceutical and medical-product industries—from 2.6 to 4.5 percent of annual revenues across the pharmaceutical and medical-product industries, or $60 billion to $110 billion annually. This big potential reflects the resource-intensive process of discovering new drug compounds. Pharma companies typically spend approximately 20 percent of revenues on R&D, 1 Research and development in the pharmaceutical industry , Congressional Budget Office, April 2021. and the development of a new drug takes an average of ten to 15 years. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value. For example, lead identification—a step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drug—can take several months even with “traditional” deep learning techniques. Foundation models and generative AI can enable organizations to complete this step in a matter of weeks. Generative AI at work in pharmaceuticals and medical productsDrug discovery involves narrowing the universe of possible compounds to those that could effectively treat specific conditions. Generative AI’s ability to process massive amounts of data and model options can accelerate output across several use cases: Improve automation of preliminary screeningIn the lead identification stage of drug development, scientists can use foundation models to automate the preliminary screening of chemicals in the search for those that will produce specific effects on drug targets. To start, thousands of cell cultures are tested and paired with images of the corresponding experiment. Using an off-the-shelf foundation model, researchers can cluster similar images more precisely than they can with traditional models, enabling them to select the most promising chemicals for further analysis during lead optimization. Enhance indication findingAn important phase of drug discovery involves the identification and prioritization of new indications—that is, diseases, symptoms, or circumstances that justify the use of a specific medication or other treatment, such as a test, procedure, or surgery. Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications. Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data. Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications. The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups. Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug. This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process. Factors for pharmaceuticals and medical products organizations to considerBefore integrating generative AI into operations, pharma executives should be aware of some factors that could limit their ability to capture its benefits:
Work and productivity implicationsTechnology has been changing the anatomy of work for decades. Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies. More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually. These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves. At a conceptual level, the application of generative AI may follow the same pattern in the modern workplace, although as we show later in this chapter, the types of activities that generative AI could affect, and the types of occupations with activities that could change, will likely be different as a result of this technology than for older technologies. The McKinsey Global Institute began analyzing the impact of technological automation of work activities and modeling scenarios of adoption in 2017. At that time, we estimated that workers spent half of their time on activities that had the potential to be automated by adapting technology that existed at that time, or what we call technical automation potential. We also modeled a range of potential scenarios for the pace at which these technologies could be adopted and affect work activities throughout the global economy. Technology adoption at scale does not occur overnight. The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific work activity—developing such solutions takes time. Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy. Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time. About the researchThis analysis builds on the methodology we established in 2017. We began by examining the US Bureau of Labor Statistics O*Net breakdown of about 850 occupations into roughly 2,100 detailed work activities. For each of these activities, we scored the level of capability necessary to successfully perform the activity against a set of 18 capabilities that have the potential for automation. We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time. Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts. Based on these assessments of the technical automation potential of each detailed work activity at each point in time, we modeled potential scenarios for the adoption of work automation around the world. First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development. Second, we estimated a range of potential costs for this technology when it is first introduced, and then declining over time, based on historical precedents. We modeled the beginning of adoption for a specific detailed work activity in a particular occupation in a country (for 47 countries, accounting for more than 80 percent of the global workforce) when the cost of the automation technology reaches parity with the cost of human labor in that occupation. Based on a historical analysis of various technologies, we modeled a range of adoption timelines from eight to 27 years between the beginning of adoption and its plateau, using sigmoidal curves (S-curves). This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms. The modeled scenarios create a time range for the potential pace of automating current work activities. The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction. The reality is likely to fall somewhere between the two. The analyses in this paper incorporate the potential impact of generative AI on today’s work activities. The new capabilities of generative AI, combined with previous technologies and integrated into corporate operations around the world, could accelerate the potential for technical automation of individual activities and the adoption of technologies that augment the capabilities of the workforce. They could also have an impact on knowledge workers whose activities were not expected to shift as a result of these technologies until later in the future (see sidebar “About the research”). Automation potential has accelerated, but adoption to lagBased on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6). For example, MGI previously identified 2027 as the earliest year when median human performance for natural-language understanding might be achieved in technology, but in this new analysis, the corresponding point is 2023. As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities. Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7). Our analysis of adoption scenarios accounts for the time required to integrate technological capabilities into solutions that can automate individual work activities; the cost of these technologies compared with that of human labor in different occupations and countries around the world; and the time it has taken for technologies to diffuse across the economy. With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8). As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work. With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance. Our previously modeled adoption scenarios suggested that 50 percent of time spent on 2016 work activities would be automated sometime between 2035 and 2070, with a midpoint scenario around 2053. Our updated adoption scenarios, which account for developments in generative AI, models the time spent on 2023 work activities reaching 50 percent automation between 2030 and 2060, with a midpoint of 2045—an acceleration of roughly a decade compared with the previous estimate. 6 The comparison is not exact because the composition of work activities between 2016 and 2023 has changed; for example, some automation has occurred during that time period. Adoption is also likely to be faster in developed countries, where wages are higher and thus the economic feasibility of adopting automation occurs earlier. Even if the potential for technology to automate a particular work activity is high, the costs required to do so have to be compared with the cost of human wages. In countries such as China, India, and Mexico, where wage rates are lower, automation adoption is modeled to arrive more slowly than in higher-wage countries (Exhibit 9). Generative AI’s potential impact on knowledge workPrevious generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks. As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10). Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023. Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply. Some 40 percent of the activities that workers perform in the economy require at least a median level of human understanding of natural language. As a result, many of the work activities that involve communication, supervision, documentation, and interacting with people in general have the potential to be automated by generative AI, accelerating the transformation of work in occupations such as education and technology, for which automation potential was previously expected to emerge later (Exhibit 11). Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12). Another way to interpret this result is that generative AI will challenge the attainment of multiyear degree credentials as an indicator of skills, and others have advocated for taking a more skills-based approach to workforce development in order to create more equitable, efficient workforce training and matching systems. 7 A more skills-based approach to workforce development predates the emergence of generative AI. Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do. Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor. Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles. However, generative AI’s impact is likely to most transform the work of higher-wage knowledge workers because of advances in the technical automation potential of their activities, which were previously considered to be relatively immune from automation (Exhibit 13). Generative AI could propel higher productivity growthGlobal economic growth was slower from 2012 to 2022 than in the two preceding decades. 8 Global economic prospects , World Bank, January 2023. Although the COVID-19 pandemic was a significant factor, long-term structural challenges—including declining birth rates and aging populations—are ongoing obstacles to growth. Declining employment is among those obstacles. Compound annual growth in the total number of workers worldwide slowed from 2.5 percent in 1972–82 to just 0.8 percent in 2012–22, largely because of aging. In many large countries, the size of the workforce is already declining. 9 Yaron Shamir, “Three factors contributing to fewer people in the workforce,” Forbes , April 7, 2022. Productivity, which measures output relative to input, or the value of goods and services produced divided by the amount of labor, capital, and other resources required to produce them, was the main engine of economic growth in the three decades from 1992 to 2022 (Exhibit 14). However, since then, productivity growth has slowed in tandem with slowing employment growth, confounding economists and policy makers. 10 “The U.S. productivity slowdown: an economy-wide and industry-level analysis,” Monthly Labor Review, US Bureau of Labor Statistics, April 2021; Kweilin Ellingrud, “ Turning around the productivity slowdown ,” McKinsey Global Institute, September 13, 2022. The deployment of generative AI and other technologies could help accelerate productivity growth, partially compensating for declining employment growth and enabling overall economic growth. Based on our estimates, the automation of individual work activities enabled by these technologies could provide the global economy with an annual productivity boost of 0.5 to 3.4 percent from 2023 to 2040, depending on the rate of automation adoption—with generative AI contributing 0.1 to 0.6 percentage points of that growth—but only if individuals affected by the technology were to shift to other work activities that at least match their 2022 productivity levels (Exhibit 15). In some cases, workers will stay in the same occupations, but their mix of activities will shift; in others, workers will need to shift occupations. Considerations for business and societyHistory has shown that new technologies have the potential to reshape societies. Artificial intelligence has already changed the way we live and work—for example, it can help our phones (mostly) understand what we say, or draft emails. Mostly, however, AI has remained behind the scenes, optimizing business processes or making recommendations about the next product to buy. The rapid development of generative AI is likely to significantly augment the impact of AI overall, generating trillions of dollars of additional value each year and transforming the nature of work. But the technology could also deliver new and significant challenges. Stakeholders must act—and quickly, given the pace at which generative AI could be adopted—to prepare to address both the opportunities and the risks. Risks have already surfaced, including concerns about the content that generative AI systems produce: Will they infringe upon intellectual property due to “plagiarism” in the training data used to create foundation models? Will the answers that LLMs produce when questioned be accurate, and can they be explained? Will the content generative AI creates be fair or biased in ways that users do not want by, say, producing content that reflects harmful stereotypes? Using generative AI responsiblyGenerative AI poses a variety of risks. Stakeholders will want to address these risks from the start. Fairness: Models may generate algorithmic bias due to imperfect training data or decisions made by the engineers developing the models. Intellectual property (IP): Training data and model outputs can generate significant IP risks, including infringing on copyrighted, trademarked, patented, or otherwise legally protected materials. Even when using a provider’s generative AI tool, organizations will need to understand what data went into training and how it’s used in tool outputs. Privacy: Privacy concerns could arise if users input information that later ends up in model outputs in a form that makes individuals identifiable. Generative AI could also be used to create and disseminate malicious content such as disinformation, deepfakes, and hate speech. Security: Generative AI may be used by bad actors to accelerate the sophistication and speed of cyberattacks. It also can be manipulated to provide malicious outputs. For example, through a technique called prompt injection, a third party gives a model new instructions that trick the model into delivering an output unintended by the model producer and end user. Explainability: Generative AI relies on neural networks with billions of parameters, challenging our ability to explain how any given answer is produced. Reliability: Models can produce different answers to the same prompts, impeding the user’s ability to assess the accuracy and reliability of outputs. Organizational impact: Generative AI may significantly affect the workforce, and the impact on specific groups and local communities could be disproportionately negative. Social and environmental impact: The development and training of foundation models may lead to detrimental social and environmental consequences, including an increase in carbon emissions (for example, training one large language model can emit about 315 tons of carbon dioxide). 1 Ananya Ganesh, Andrew McCallum, and Emma Strubell, “Energy and policy considerations for deep learning in NLP,” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , June 5, 2019. There are economic challenges too: the scale and the scope of the workforce transitions described in this report are considerable. In the midpoint adoption scenario, about a quarter to a third of work activities could change in the coming decade. The task before us is to manage the potential positives and negatives of the technology simultaneously (see sidebar “Using generative AI responsibly”). Here are some of the critical questions we will need to address while balancing our enthusiasm for the potential benefits of the technology with the new challenges it can introduce. Companies and business leadersHow can companies move quickly to capture the potential value at stake highlighted in this report, while managing the risks that generative AI presents? How will the mix of occupations and skills needed across a company’s workforce be transformed by generative AI and other artificial intelligence over the coming years? How will a company enable these transitions in its hiring plans, retraining programs, and other aspects of human resources? Do companies have a role to play in ensuring the technology is not deployed in “negative use cases” that could harm society? How can businesses transparently share their experiences with scaling the use of generative AI within and across industries—and also with governments and society? Policy makersWhat will the future of work look like at the level of an economy in terms of occupations and skills? What does this mean for workforce planning? How can workers be supported as their activities shift over time? What retraining programs can be put in place? What incentives are needed to support private companies as they invest in human capital? Are there earn-while-you-learn programs such as apprenticeships that could enable people to retrain while continuing to support themselves and their families? What steps can policy makers take to prevent generative AI from being used in ways that harm society or vulnerable populations? Can new policies be developed and existing policies amended to ensure human-centric AI development and deployment that includes human oversight and diverse perspectives and accounts for societal values? Individuals as workers, consumers, and citizensHow concerned should individuals be about the advent of generative AI? While companies can assess how the technology will affect their bottom lines, where can citizens turn for accurate, unbiased information about how it will affect their lives and livelihoods? How can individuals as workers and consumers balance the conveniences generative AI delivers with its impact in their workplaces? Can citizens have a voice in the decisions that will shape the deployment and integration of generative AI into the fabric of their lives? Technological innovation can inspire equal parts awe and concern. When that innovation seems to materialize fully formed and becomes widespread seemingly overnight, both responses can be amplified. The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it. All of us are at the beginning of a journey to understand this technology’s power, reach, and capabilities. If the past eight months are any guide, the next several years will take us on a roller-coaster ride featuring fast-paced innovation and technological breakthroughs that force us to recalibrate our understanding of AI’s impact on our work and our lives. It is important to properly understand this phenomenon and anticipate its impact. Given the speed of generative AI’s deployment so far, the need to accelerate digital transformation and reskill labor forces is great. These tools have the potential to create enormous value for the global economy at a time when it is pondering the huge costs of adapting and mitigating climate change. At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence. They are capable of that most human of abilities, language, which is a fundamental requirement of most work activities linked to expertise and knowledge as well as a skill that can be used to hurt feelings, create misunderstandings, obscure truth, and incite violence and even wars. We hope this research has contributed to a better understanding of generative AI’s capacity to add value to company operations and fuel economic growth and prosperity as well as its potential to dramatically transform how we work and our purpose in society. Companies, policy makers, consumers, and citizens can work together to ensure that generative AI delivers on its promise to create significant value while limiting its potential to upset lives and livelihoods. The time to act is now. 11 The research, analysis, and writing in this report was entirely done by humans. Michael Chui is a partner in McKinsey’s Bay Area office, where Roger Roberts is a partner and Lareina Yee is a senior partner; Eric Hazan is a senior partner in McKinsey’s Paris office; Alex Singla is a senior partner in the Chicago office; Kate Smaje and Alex Sukharevsky are senior partners in the London office; and Rodney Zemmel is a senior partner in the New York office. The authors wish to thank Pedro Abreu, Rohit Agarwal, Steven Aronowitz, Arun Arora, Charles Atkins, Elia Berteletti, Onno Boer, Albert Bollard, Xavier Bosquet, Benjamin Braverman, Charles Carcenac, Sebastien Chaigne, Peter Crispeels, Santiago Comella-Dorda, Eleonore Depardon, Kweilin Ellingrud, Thierry Ethevenin, Dmitry Gafarov, Neel Gandhi, Eric Goldberg, Liz Grennan, Shivani Gupta, Vinay Gupta, Dan Hababou, Bryan Hancock, Lisa Harkness, Leila Harouchi, Jake Hart, Heiko Heimes, Jeff Jacobs, Begum Karaci Deniz, Tarun Khurana, Malgorzata Kmicinska, Jan-Christoph Köstring, Andreas Kremer, Kathryn Kuhn, Jessica Lamb, Maxim Lampe, John Larson, Swan Leroi, Damian Lewandowski, Richard Li, Sonja Lindberg, Kerin Lo, Guillaume Lurenbaum, Matej Macak, Dana Maor, Julien Mauhourat, Marco Piccitto, Carolyn Pierce, Olivier Plantefeve, Alexandre Pons, Kathryn Rathje, Emily Reasor, Werner Rehm, Steve Reis, Kelsey Robinson, Martin Rosendahl, Christoph Sandler, Saurab Sanghvi, Boudhayan Sen, Joanna Si, Alok Singh, Gurneet Singh Dandona, François Soubien, Eli Stein, Stephanie Strom, Michele Tam, Robert Tas, Maribel Tejada, Wilbur Wang, Georg Winkler, Jane Wong, and Romain Zilahi for their contributions to this report. For the full list of acknowledgments, see the downloadable PDF . Explore a career with usRelated articles.  What every CEO should know about generative AI Exploring opportunities in the generative AI value chain What is generative AI?How Does Concealed Carrying of Weapons Affect Violent Crime?15 Pages Posted: 31 May 2024 John R. LottCrime Prevention Research Center Carlisle E. MoodyCollege of William and Mary - Department of Economics; Crime Prevention Research Center Date Written: May 31, 2024 Several articles have been published finding that carry concealed weapons (CCW) laws allowing citizens with a concealed carry permit to go about with a firearm concealed on their person, cause violent crime to increase. While most of the research finds that right-to-carry laws reduce crime, let’s assume that these laws do increase violent crime. There are two possible explanations, though few studies have examined them. Permit holders, despite the fact that they must pass a background check and satisfy the legal requirements to obtain a permit, become violent criminals after issuance. For example, one suggestion is that permit holders might be more susceptible to road rage than others. The second explanation is that permit holders reduce the effectiveness of police. Using a unique new data set on the number of stolen guns and the number of concealed handgun permits, we find no evidence for either explanation. Keywords: Concealed Carry, Constitutional Carry, Stolen Guns, Crime, Policing JEL Classification: K14 Suggested Citation: Suggested Citation John R. Lott (Contact Author)Crime prevention research center ( email ). PO Box 2293 1100 W Kent Ave Missoula, MT 59801 United States College of William and Mary - Department of Economics ( email )Williamsburg, VA 23187-8795 United States 757-221-2373 (Phone) 757-221-1175 (Fax) Missoula, MT United States Do you have a job opening that you would like to promote on SSRN?Paper statistics, related ejournals, criminal law ejournal. Subscribe to this fee journal for more curated articles on this topic Correlates of Crime eJournalHelp | Advanced Search Computer Science > Computation and LanguageTitle: gpt-4 technical report. Abstract: We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.
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Several articles have been published finding that carry concealed weapons (CCW) laws allowing citizens with a concealed carry permit to go about with a firearm concealed on their person, cause violent crime to increase. While most of the research finds that right-to-carry laws reduce crime, let's assume that these laws do increase violent crime.
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer ...