(Early) AI Compute Asset Pricing

Battle Count: 69

The paper is highly relevant for quantitative trading in the emerging compute futures market. It provides the theoretical foundation for pricing compute futures, identifies that standard cash-and-carry arbitrage fails, establishes synthetic futures as upper bounds, and documents positive risk premia that could inform trading strategies. However, it does not propose specific trading algorithms or backtest strategies. The findings are most relevant for futures market-making, basis trading, and hedging strategies in the prospective compute futures market.

The theoretical framework is conceptually straightforward (no-arbitrage pricing, risk premium decomposition). Empirical implementation requires access to term rental curve data from Silicon Data, construction of synthetic futures via marginal differentiation of rental strips, and computation of hold-to-maturity and rolling constant-maturity returns. The main complexity lies in data acquisition and handling of basis risk, not in computational methods.

The paper uses data from Silicon Data and Ornn, which are start-up companies providing compute market data. Some indexes are publicly available on Bloomberg (SDA100RT, SDH100RT, SDB200RT, Ornn Compute Price Index). However, the full term rental curve data and proprietary index construction methods are not fully documented. The theoretical framework is transparent and reproducible, but empirical replication requires access to the specific Silicon Data forward curve data.