解析大模型中令牌的经济逻辑，揭示其成本与价值的内在差异。
AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models
- 构建令牌经济学框架，连接技术成本与实际应用价值
- 发现令牌支出不等于经济价值，后者受多因素影响
- 适合关注模型定价、资源分配与市场设计的研究者
令牌已成为现代大模型服务的实际计费单位，关联信息处理、计算、内存使用、能耗、定价与经济价值。本文提出人工智能令牌经济学（AI Tokenomics）框架，研究令牌在生成、消耗、定价、分配与优化中的机制。该框架将令牌级技术成本与工作流级生产函数、企业资源配置、测量方法及新兴市场设计问题相连接。研究表明，令牌支出与经济价值是不同概念：价值取决于边际生产力、工作流位置、隐藏推理活动、风险及下游传播效应。论文最后指出隐式令牌测量、实证校准、令牌生产率、动态分配与基于令牌的市场等开放研究方向。
原文摘要 · Abstract (English)
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.
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