(Early) AI Compute Asset Pricing∗
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摘要
This paper develops an early asset-pricing framework for compute, a scarce, non-storable input central to the AI economy. It argues that storage-based no-arbitrage links cannot directly price compute futures, since a GPU-hour cannot be stored for delivery in the future; instead, futures should be priced via the expected future spot rental price at delivery, adjusted for a risk premium arising from hedging pressure. Using term rental data to construct synthetic futures, the authors build the first compute futures return panel sorted by GPU generation and maturity, finding preliminary evidence of a positive compute risk premium consistent with hedging pressure by compute providers. The analysis highlights how synthetic forward pricing, forward-looking spot expectations, and basis risk shape the evolving price discovery process for AI infrastructure. [page::4][page::5][page::28][page::30]
