Automation, AI & Work
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We characterize artificial intelligence as “routine-biased technological change on steroids,” adding intelligence to automation tools that substitute for humans in physical tasks and substituting for humans in routine and increasingly nonroutine cognitive tasks. We predict how AI will displace humans from existing tasks while increasing demand for humans in new tasks in both manufacturing and services. We also examine the effects of AI-enabled digital platforms on labor. Our conjecture is that AI will continue, even intensify, automation's adverse effects on labor, including the polarization of employment, stagnant wage growth for middle- and low-skill workers, growing inequality, and a lack of good jobs. Though there likely will be enough jobs to keep pace with the slow growth of the labor supply in the advanced economies, we are skeptical that AI and ongoing automation will support the creation of enough good jobs. We doubt that the anticipated productivity and growth benefits of AI will be widely shared, predicting instead that they will fuel more inequality. Yet we are optimistic that interventions can mitigate or offset AI's adverse effects on labor. Ultimately, how the benefits of intelligent automation tools are realized and shared depends not simply on their technological design but on the design of intelligent policies.
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Manually crafted artifacts are often replaced by learnable and optimizable ones.
“manually crafted artifacts are often replaced by learnable and optimizable ones”
From EvoFlow: Evolving Diverse Agentic Workflows On The Fly (2025) · cites this paper as Tyson & Zysman, 2022
The discussion on p. 257 describes machine learning and AI automating tasks, while the References explain that algorithms can improve through training. Neither passage addresses whether manually crafted artifacts are often replaced by learnable, optimizable ones; searches for that phrase and its key terms found no passage that does. (LLM, conf=0.72)
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- For a comprehensive analysis of automation's effects, see David Autor, David Mindell, and Elisabeth Reynolds, The Work of the Future: Building Better Jobs in an Age of Intelligent Ma- chines (Cambridge, Mass.: MIT Press, 2020);
- Thomas W. Malone, Daniela Rus, and Robert Laubacher, "Artificial Intelligence and the Future of Work" (Cambridge, Mass.: MIT Future of Work, 2020);
- Gary Marcus, "The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence," arXiv (2020), https://arxiv.org/abs/2002.06177; · arXiv
- While not dismissive of the possibility of AGI, many scholars agree that the focus of policy and regulation should be on the impacts of narrow AI. See, for example, Gary Marcus and Ernest Davis, Rebooting AI: Building Artificial Intelligence We Can Trust (New York: Pan- theon Books, 2019); and Erik Brynjolfsson, Tom Mitchell, and Daniel Rock, "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?" AEA Papers and Proceedings 108 (2018): 43-47.
- Erik Brynjolfsson, Daniel Rock, and Chad Syverson, "Understanding and Addressing the Modern Productivity Paradox" (Cambridge, Mass.: MIT Future of Work, 2020).
- Maarten Goos, Alan Manning, and Anna Salomons, "Explaining Job Polarization: Rou- tine-Biased Technological Change and Offshoring," American Economic Review 104 (2) (2014): 2509-2526.
- Laura D. Tyson and Michael Spence, "Exploring the Effects of Technology on Income and Wealth Inequality," in After Piketty: The Agenda for Economics and Inequality, ed. Heath- er Boushey, J. Bradford DeLong, and Marshall Steinbaum (Cambridge, Mass.: Harvard University Press, 2017), 170-208.
- Anke Hassel and Bruno Palier, eds., Growth and Welfare in Advanced Capitalist Economies: How Have Growth Regimes Evolved? (Oxford: Oxford University Press, 2021); Autor et al., The Work of the Future; and Christian Dustmann, "Trade, Labor Markets, and the China Shock: What Can be Learned from the German Experience?" in Combating Inequality: Rethinking Government's Role, ed. Olivier Blanchard and Dani Rodrik (Cambridge, Mass.: MIT Press, 2021), 117-124.
- Daron Acemoglu and Pascual Restrepo, "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives 33 (2) (2019): 3-30. The 151 (2) Spring 2022 269
- Daron Acemoglu, "Tasks, Automation and Labor Market," presentation and lecture at Gorman Conference & Lectures 2020, virtual event, October 12, 2020; and Daron Acemoglu, "New Tasks, Good Automation and Bad Automation: Implications for the Future of Work," presentation and lecture at Gorman Conference & Lectures 2020, virtual event, October 13, 2020.
- This is well documented in Carl Benedikt Frey, The Technology Trap: Capital, Labor, and Power in the Age of Automation (Princeton, N.J.: Princeton University Press, 2019); and Barry Eichengreen, The Populist Temptation: Economic Grievance and Political Reaction in the Modern Era (New York: Oxford University Press, 2020).
- Daron Acemoglu, "Could We and Should We Reverse (Excessive) Automation?" in Combating Inequality: Rethinking Government's Role, ed. Olivier Blanchard and Dani Rodrik (Cambridge, Mass.: MIT Press, 2021), 163-170.
- Erik Brynjolfsson, Tom Mitchell, and Dan- iel Rock, "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?" AEA Papers and Proceedings 108 (2018): 43-47; and Edward W. Felten, Manav Raj, and Robert Seamans, "A Method to Link Advances in Artificial Intelligence to Oc- cupational Abilities," AEA Papers and Proceedings 108 (2018): 54-57.
- Kai-Fu Lee and Chen Qiufan, AI 2041: Ten Visions for Our Future (New York: Currency, 2021); Autor et al., The Work of the Future; and Ajay Agrawal, Josh- ua Gans, and Avi Goldfarb, eds., The Economics of Artificial Intelligence: An Agenda (Chicago: University of Chicago Press, 2019).
- The share of middle-skill occupations in total employment declined (11 percent), high- skill occupations grew (9 percent), and low-skill occupations grew (3 percent) be- tween the mid-1990s to the late 2010s across OECD countries. See Organisation for Economic Co-operation and Development, OECD Employment Outlook 2020: Worker Secu- rity and the COVID-19 Crisis (Paris: OECD Publishing, 2020), Figure 4.1, https://doi.org/ 10.1787/1686c758-en. · DOI
- Florian Hoffmann, David S. Lee, and Thomas Lemieux, "Growing Income Inequality in the United States and Other Advanced Economies," Journal of Economic Perspectives 344 (4) (2020): 52-78; and Autor et al., The Work of the Future. Dædalus, the Journal of the American Academy of Arts & Sciences Automation, AI & Work 19 David Autor and Anna Salomons,"Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share," Working Paper 24871 (Cambridge, Mass.: Nation- al Bureau of Economic Research, 2018); Mathilde Pak and Cyrille Schwellnus, "Labour Share Developments Over the Past Two Decades: The Role of Public Policies," OECD Economics Department Working Papers No. 1541 (Paris: OECD Publishing, 2019);
- Lucas Chancel, "Ten Facts about Inequality in Advanced Economies," in Combating Inequality: Rethinking Government's Role, ed. Olivier Blanchard and Dani Rodrik (Cam- bridge, Mass.: MIT Press, 2021), 3-30.
- Laura Tyson, "Automation and the Future of Work in Germany: A Summary of Re- search and Policy Recommendations?" Governing Work in the Digital Age Project Working Paper (Berlin: Hertie School, 2020), https://digitalage.berlin/wp-content/ uploads/2021/12/2021_Tyson-WP1-Paper-FINAL-1.pdf; and Laura Tyson, "The Future of Work in Germany," The Berlin Journal 33 (2019).
- Tyson and Spence, "Exploring the Effects of Technology on Income and Wealth In- equality"; and Anton Korinek and Joseph E. Stiglitz, Artificial Intelligence and Its Implications for Income Distribution and Unemployment (Chicago: University of Chicago Press, 2019).
- Dafna Bearson, Martin Kenney, and John Zysman, "Measuring the Impacts of Labor in the Platform Economy: New Work Created, Old Work Reorganized, and Value Creation Reconfigured," Industrial and Corporate Change 30 (3) (2021), https://doi.org/10.1093/icc/ dtaa046; and Martin Kenney, Dafna Bearson, and John Zysman, "The Platform Econo- my Matures: Measuring Pervasiveness and Exploring Power," Socio-Economic Review 19 (4) (2021), https://doi.org/10.1093/ser/mwab014. · DOI
- Jonathan I. Dingel and Brent Neiman, "How Many Jobs Can be Done at Home?" Journal of Public Economics 189 (2) (2020).
- Libby Reder, Shelly Steward, and Natalie Foster, Designing Portable Benefits: A Resource Guide for Policymakers (Washington, D.C.: Aspen Institute, 2019); and Steven Hill, Raw Deal: How the "Uber Economy" and Runaway Capitalism Are Screwing American Workers (New York: St. Martin's Press, 2015).
- For other recent studies predicting the future effects of AI on work, see Jason Furman and Robert Seamans, "AI and the Economy," NBER Working Paper 24689 (Cambridge, Mass.: National Bureau of Economic Research, 2018);
- Marguerita Lane and Anne Saint-Martin, "The Impact of Artificial Intelligence on the Labour Market: What Do We Know So Far?" OECD Social, Employment and Migration Working Papers No. 256 (Paris: OECD Publishing, 2021); and Michael Chui, Artificial Intelligence-The Next Digital Frontier? (New York: McKinsey and Company Global Institute, 2017), 3-6. · DOI
- Thomas Davenport and Ravi Kalakota, "The Potential for Artificial Intelligence in Healthcare," Future Healthcare Journal 6 (2) (2019): 94; and Ari Bronsoler, Joseph Doyle, and John Van Reenen, "The Impact of New Technology on the Healthcare Workforce" (Cambridge, Mass.: MIT Work of the Future, 2020).
- Daron Acemoglu and Pascual Restrepo, "The Wrong Kind of AI? Artificial Intelligence and the Future of Labour Demand," Cambridge Journal of Regions, Economy and Society 13 (1) (2019): 25-35.
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“Although AI contributes to productivity and economic growth, it increases inequality, reduces good jobs and negatively impacts wages for workers with low or average skills (Tyson & Zysman, 2022).”
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“The progress of machine learning consistently reveals a recurring pattern: manually crafted artifacts are often replaced by learnable and optimizable ones (Tyson & Zysman, 2022; Clune, 2019).”
- Authors
- Tyson, L. D., & Zysman, J.
- ORCID
- Zysman, John
- Venue
- Daedalus
- Publisher
- MIT Press
- Published
- 2022
- Volume
- 151
- Issue
- 2
- Pages
- 256-271
- ISSN
- 0011-5266 (ISSN-L)
- 1548-6192
- OpenAlex
- W4226283890
- Cited by
- 96
- References
- 0
- Citations by year
- 2026: 33 · 2025: 31 · 2024: 23 · 2023: 8 · 2022: 1
- FWCI
- 12.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Daedalus (publishedVersion, cc-by-nc)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence, automation, labor market outcomes, technological change, job polarization, wage stagnation, income inequality, job quality, digital platforms, manufacturing employment, service sector employment, labor supply, productivity growth, policy interventions
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