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Artificially created scarcity: How AI turns abundance into shortage

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  • Ayoki, Milton

Abstract

The diffusion of general-purpose artificial intelligence (AI) systems is collapsing the marginal cost of cognition, coordination, and capital formation. This abundance of intelligence is simultaneously re-pricing the three residual scarcities that still constrain human welfare: atmospheric carbon space, human labor hours, and irreversible time. Using a unified production–climate–welfare model, we show that (i) AI accelerates decarbonization by driving the cost curve of clean technologies below that of fossil fuels; (ii) labor markets bifurcate into a vanishing low-skill wage sector and an expanding high-skill rent sector, generating a transfer problem that can only be solved by AI dividends; and (iii) the option value of future consumption rises as AI compresses the calendar time needed to unlock large-scale decarbonization, longevity, and existential-risk mitigation. The conjunction of these effects drives the Ramsey rule for optimal climate policy to its mathematical limit: the social discount rate (SDR) must converge to zero. We provide empirical calibration using the latest IPCC scenarios, large-language-model energy-intensity data, and labor-share forecasts through 2100. A zero SDR reconciles inter-generational equity with intra-generational efficiency and unlocks a portfolio of “long-horizon public goods” (LHPGs)—from atmospheric restoration to asteroid defense—that markets at positive discount rates chronically under-supply.

Suggested Citation

  • Ayoki, Milton, 2025. "Artificially created scarcity: How AI turns abundance into shortage," MPRA Paper 126550, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:126550
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    References listed on IDEAS

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    1. Stern,Nicholas, 2007. "The Economics of Climate Change," Cambridge Books, Cambridge University Press, number 9780521700801, November.
    2. Gollier, Christian, 2018. "The cost-efficiency carbon pricing puzzle," TSE Working Papers 18-952, Toulouse School of Economics (TSE), revised May 2024.
    3. Avinash K. Dixit & Robert S. Pindyck, 1994. "Investment under Uncertainty," Economics Books, Princeton University Press, edition 1, number 5474, December.
    4. Daron Acemoglu & Pascual Restrepo, 2022. "Tasks, Automation, and the Rise in U.S. Wage Inequality," Econometrica, Econometric Society, vol. 90(5), pages 1973-2016, September.
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    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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