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Scenarios for the Transition to AGI

Author

Listed:
  • Anton Korinek
  • Donghyun Suh

Abstract

We analyze how output and wages behave under different scenarios for technological progress that may culminate in Artificial General Intelligence (AGI), defined as the ability of AI systems to perform all tasks that humans can perform. We assume that human work can be decomposed into atomistic tasks that differ in their complexity. Advances in technology make ever more complex tasks amenable to automation. The effects on wages depend on a race between automation and capital accumulation. If automation proceeds sufficiently slowly, then there is always enough work for humans, and wages may rise forever. By contrast, if the complexity of tasks that humans can perform is bounded and full automation is reached, then wages collapse. But declines may occur even before if large-scale automation outpaces capital accumulation and makes labor too abundant. Automating productivity growth may lead to broad-based gains in the returns to all factors. By contrast, bottlenecks to growth from irreproducible scarce factors may exacerbate the decline in wages.

Suggested Citation

  • Anton Korinek & Donghyun Suh, 2024. "Scenarios for the Transition to AGI," NBER Working Papers 32255, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:32255
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    Cited by:

    1. Casey O. Barkan, 2024. "Can an increase in productivity cause a decrease in production? Insights from a model economy with AI automation," Papers 2411.15718, arXiv.org.
    2. Quitz'e Valenzuela-Stookey, 2025. "Automation and Task Allocation Under Asymmetric Information," Papers 2511.02675, arXiv.org.
    3. Maydell, Richard & Firth, John, 2025. "Tilting the Race Between Humans and AI: The Role of Education and Unemployment Policies," VfS Annual Conference 2025 (Cologne): Revival of Industrial Policy 325460, Verein für Socialpolitik / German Economic Association.
    4. Gillian K. Hadfield & Andrew Koh, 2025. "An Economy of AI Agents," Papers 2509.01063, arXiv.org.
    5. Bekkers, Eddy & Humphreys, Lee & Kalachyhin, Hryhorii & Wilczynska, Karolina & Zhao, Danchen, 2025. "Through the looking glass: Artificial intelligence, international trade, and economic growth in the long run," WTO Staff Working Papers ERSD-2025-09, World Trade Organization (WTO), Economic Research and Statistics Division.
    6. Pascal Stiefenhofer, 2025. "Artificial General Intelligence and the Social Contract: A Dynamic Political Economy Model," Journal of Economic Analysis, Anser Press, vol. 4(3), pages 142-183, September.
    7. Michael R. Douglas & Sergiy Verstyuk, 2025. "Progress in Artificial Intelligence and its Determinants," Papers 2501.17894, arXiv.org.
    8. Mark Fisher & John Severini, 2025. "Making AI Inevitable: Historical Perspective and the Problems of Predicting Long-Term Technological Change," Papers 2508.16692, arXiv.org.
    9. Anton Korinek & Lee M. Lockwood, 2026. "Public Finance in the Age of AI: A Primer," NBER Chapters, in: The Economics of Transformative AI, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

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