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A New Theory of Value for Post-AGI Economics

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  • Keyun Ruan

Abstract

Artificial general intelligence (AGI) may weaken scarcities in labour, expertise, information, and productive capability that underpin established theories of economic value. If cognitive work becomes widely automatable, market price, labour input, revealed preference, profit, and gross output may diverge sharply from human and societal benefit. This paper develops Flourishing Value Theory (FVT) as a foundation for post-AGI economics. FVT defines value as the counterfactual, distribution-sensitive contribution of a system, institution, asset, or intervention to the durable capabilities of persons and communities to flourish within social and planetary constraints. It treats societal value as multidimensional, agency-preserving, regenerative, and non-compensatory at critical thresholds. Drawing on the economics of AI, welfare and capability theory, automation, digital valuation, and ecological economics, the paper distinguishes value creation from value capture and retains price, profit, productivity, and GDP as partial signals rather than final measures of progress. It develops the shifts from scarcity to governed abundance, transaction to transformation, and zero-sum rivalry to positive-sum and infinite-game dynamics, with collective expansion of consciousness as an agency-preserving form of regenerative value. Building on Flourishing Metrics and Return on Flourishing (RoF), it proposes a layered architecture for firms, governments, work transitions, AI governance, and national accounting. The central post-AGI economic problem is not how to maximise output, but how to convert abundant intelligence into durable, fairly distributed human, societal, and planetary flourishing.

Suggested Citation

  • Keyun Ruan, 2026. "A New Theory of Value for Post-AGI Economics," Papers 2608.01432, arXiv.org, revised Aug 2026.
  • Handle: RePEc:arx:papers:2608.01432
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    References listed on IDEAS

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    1. Benjamin Moll & Lukasz Rachel & Pascual Restrepo, 2022. "Uneven Growth: Automation's Impact on Income and Wealth Inequality," Econometrica, Econometric Society, vol. 90(6), pages 2645-2683, November.
    2. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2021. "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 333-372, January.
    3. Gmyrek, Pawel, & Berg, Janine, & Kaminski, Karol, & Konopczyński, Filip, & Ładna, Agnieszka, & Nafradi, Balint, & Rosłaniec, Konrad, & Troszyński, Marek,, 2025. "Generative AI and jobs a refined global index of occupational exposure," ILO Working Papers 995653377802676, International Labour Organization.
    4. Keyun Ruan & Jonathan D. Teubner & John M. Bremen, 2026. "Optimising for Flourishing: Flourishing Metrics and Return on Flourishing as Success Criteria for Artificial Intelligence and Post-AGI Economic Systems," Papers 2608.00151, arXiv.org, revised Aug 2026.
    5. Edward Felten & Manav Raj & Robert Seamans, 2021. "Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses," Strategic Management Journal, Wiley Blackwell, vol. 42(12), pages 2195-2217, December.
    6. Mauro Cazzaniga & Ms. Florence Jaumotte & Longji Li & Mr. Giovanni Melina & Augustus J Panton & Carlo Pizzinelli & Emma J Rockall & Ms. Marina Mendes Tavares, 2024. "Gen-AI: Artificial Intelligence and the Future of Work," IMF Staff Discussion Notes 2024/001, International Monetary Fund.
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