发表机构
Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人工智能计算资产定价问题，构建早期资产定价框架，通过讨论计算租赁市场及定价方式，利用合成期货构建收益面板，得出初步证据显示存在正计算风险溢价及计算提供商有套期保值压力的结论。
AI 中文摘要
计算（算力）是人工智能经济核心中稀缺且资本密集型的投入。计算资本支出和服务流量已超过美国国内生产总值的1%且增长迅速。计算价格反映了人工智能采用的不确定性。计算期货的推出将这种不确定性转化为可交易风险，引发了对新资产类别定价的问题。我们提供了一个早期的计算资产定价框架。先讨论了基础计算租赁市场及其指数化，然后转向定价：1）由于计算的不可存储性，期货价格与当前现货价格之间的直接无套利联系失效；2）现有定期租赁合同的合成期货价格可能是真实期货价格的上限；3）金融化后，期货价格将是投资者对到期现货价格扣除风险溢价后的预期。在计算期货市场推出之前，我们使用合成期货作为替身，构建了第一个按GPU代和期限分类的计算期货收益面板。我们的初步证据与正的计算风险溢价一致，表明计算提供商存在套期保值压力。
英文摘要
Compute (computing power) is a scarce, capital-intensive input at the center of the AI economy. Compute capital expenditure and service flow already exceed 1% of U.S. GDP and are growing rapidly. The price of compute reflects uncertainty over AI adoption. The announced launch of compute futures turns this uncertainty into a tradable risk, raising questions on the pricing of a new asset class. We provide an early asset-pricing framework for compute. We begin by discussing the underlying compute rental market and its indexation. We then turn to pricing: 1) direct no-arbitrage links between futures prices and current spot prices fail due to the non-storable nature of compute, 2) synthetic futures prices from existing term rental contracts are likely upper bounds on true futures prices and, 3) upon financialization, futures prices will be investors' expectations of spot prices at expiration net of a risk premium. Using synthetic futures as stand-ins before the compute futures market launches, we construct the first compute futures return panel sorted by GPU generation and maturity. Our preliminary evidence is consistent with a positive compute risk premium, suggesting hedging pressure on the part of compute providers.
