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AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models

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  • Quanyan Zhu

Abstract

Tokens have become the practical accounting unit for modern foundation model services, linking information processing, computation, memory use, energy expenditure, pricing, and economic value. This paper develops a framework for AI tokenomics: the study of how tokens are generated, consumed, priced, allocated, and optimized across AI systems. We connect token-level technical costs to workflow-level production functions, enterprise resource allocation, measurement and instrumentation methods, and emerging market-design questions. The framework shows that token expenditure and economic value are distinct: value depends on marginal productivity, workflow position, hidden reasoning activity, risk, and downstream propagation effects. The paper concludes by identifying open research directions in hidden-token measurement, empirical calibration, token productivity, dynamic allocation, and token-based markets.

Suggested Citation

  • Quanyan Zhu, 2026. "AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models," Papers 2606.24616, arXiv.org.
    • Handle: RePEc:arx:papers:2606.24616
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