AI Token Futures Market: Commoditization of Compute and Derivatives Contract Design
Yicai Xing

TL;DR
This paper introduces a novel financial market for AI tokens, proposing standardized futures contracts to hedge compute costs, supported by modeling, simulations, and regulatory considerations.
Contribution
It presents a comprehensive design for token futures contracts, including definitions, mechanisms, and market regimes, based on commodity financialization theory and market modeling.
Findings
Futures contracts can significantly reduce compute cost volatility by up to 78%.
The proposed model effectively hedges enterprise compute costs during demand surges.
Feasibility of GPU compute futures is demonstrated through simulations.
Abstract
As large language models (LLMs) and vision-language-action models (VLAs) become widely deployed, the tokens consumed by AI inference are evolving into a new type of commodity. This paper systematically analyzes the commodity attributes of tokens, arguing for their transition from intelligent service outputs to compute infrastructure raw materials, and draws comparisons with established commodities such as electricity, carbon emission allowances, and bandwidth. Building on the historical experience of electricity futures markets and the theory of commodity financialization, we propose a complete design for standardized token futures contracts, including the definition of a Standard Inference Token (SIT), contract specifications, settlement mechanisms, margin systems, and market-maker regimes. By constructing a mean-reverting jump-diffusion stochastic process model and conducting Monte…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Private Equity and Venture Capital · Sustainable Finance and Green Bonds
