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

TL;DR

This paper explores the computational challenges of implementing token economics in AI systems, emphasizing the trade-offs among valuation granularity, latency, and optimality.

Contribution

It introduces the concept of Computational Token Economics and the Token Economics Trilemma to formalize the inherent trade-offs in real-time AI infrastructure.

Findings

Identifies key computational challenges in token-based AI systems.

Proposes the Token Economics Trilemma as a framework for understanding trade-offs.

Categorizes technical challenges into value accounting, resource allocation, and system architecture.

Abstract

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 \textbf{Computational Token Economics} and propose the \textbf{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.