mlsanityData to 7 Oct 2026

Computational Challenges in Token Economics: Bridging Economic Theory and AI System Design

arXiv 2605.17410 ↗First posted 17 May 2026cs.AIcited by 0

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Abstract (arXiv)

Token economics has emerged as a useful lens for understanding resource allocation, value creation, and pricing in large language model systems. While recent work has increasingly treated tokens as economic primitives, there remains a substantial gap between high-level economic theory and the computational realities of modern AI infrastructure. This paper identifies and analyzes the key computational challenges that arise when token-economic principles are implemented in real-time inference systems. We argue that computational feasibility is not merely one dimension of token economics, but its governing constraint: these challenges are driven by fundamental tensions among fine-grained valuation, low-latency execution, and allocation optimality under uncertainty. To structure this problem space, we introduce the notion of Computational Token Economics and propose the Token Economics Trilemma -- a conditional no-free-lunch principle that captures the inherent trade-offs among granularity, real-time performance, and optimality. We further categorize the main technical challenges into three areas: real-time value accounting, constrained resource allocation, and economic-aware system architecture. Rather than presenting a complete solution, this paper aims to define a research agenda for bridging token economics and AI system design, highlighting open problems at the intersection of computational economics, machine learning systems, and AI infrastructure.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 45 of its 74 references that are in the map. First posted on arXiv 17 May 2026.

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References · 45

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