🤖 AI Summary
This study addresses the sustainability challenges faced by operators due to cost-benefit mismatches and price volatility in AI services. To manage such uncertainty, it proposes a structured contract infrastructure that decouples service consumption rights from payment collection rights. Methodologically, this work pioneers the introduction of financial derivatives logic into the AI computing market, enabling standardized commitments and risk hedging across heterogeneous services. Furthermore, it leverages smart contracts to formalize participant roles, ownership structures, and settlement rules, validating system logic through API replay. Experimental results demonstrate that forward contracts effectively reduce average expenditures, while financing mechanisms significantly enhance user contributions under low-capital conditions.
📝 Abstract
AI services incur inference costs during execution, while revenue may arrive later. Changing API prices, limited upfront capital, and service failures can limit operators' ability to sustain or expand their services. Beyond reducing per-request costs, operators need to plan future spending, fund execution before revenue arrives, and obtain compensation for specified losses. This requires clear agreements across services with different pricing and execution conditions. These agreements must distinguish rights to consume services from rights to receive payments, define obligations under uncertain costs and income, and specify which failures qualify for compensation and how much can be paid. We present TokenBank, a financial infrastructure that represents these commitments through structured contracts. It supports service-consumption rights, agreements that settle API-price differences in cash (forwards), financing through limited rights to future service revenue, and protection claims for specified service failures. Contracts specify participants, covered services, validity, ownership, fulfillment conditions, and settlement rules. Evaluation combines replay of 899,441 API requests, real model-driven agent execution, and contract API tests. In a zero-discount rising-price resampling scenario, forwards reduce mean expenditure by USD 304.88 but increase its standard deviation from USD 1,152.45 to USD 1,190.82. A controlled replication with five portfolios per capital condition finds mean contribution differences between financing and self-funding of +1.0635, -0.1406, and -0.2962 experimental USD under low, baseline, and ample capital, respectively. The evaluation distinguishes contract correctness from economic effectiveness under declared economic and failure assumptions; supplier invoices and commercial revenue are unavailable.