The hidden risk of AI compute - by Dave Friedman

Most people who talk about the market for AI compute think of it in terms of cloud computing: wrap scarce hardware in an API, meter usage, send invoices, add a sprinkle of scheduling magic, raise a round. That’s the Silicon Valley reflex. It’s also the wrong mental model. Frontier AI compute is not a product category. It’s a risk exposure. It’s a volatile, time-sensitive capacity constraint that needs to be priced, hedged, and made liquid. The right analogy is not AWS or Snowflake. It’s CME, power markets, and derivatives desks in Chicago and New York. Cloud infrastructure was built on a few assumptions: Supply is elastic. Demand is smooth. Cost curves are predictable. The right abstraction is service consumption. Frontier Ai violates all of that. AI compute is: Spiky: driven by discontinuous training runs, not steady web traffic. Scarce: bound by wafer cycles, export controls, and multi-year power buildouts. Time-critical: missing a training window can mean losing a whole product cycl

Most people who talk about the market for AI compute think of it in terms of cloud computing: wrap scarce hardware in an API, meter usage, send invoices, add a sprinkle of scheduling magic, raise a round. That’s the Silicon Valley reflex. It’s also the wrong mental model. Frontier AI compute is not a product category. It’s a risk exposure. It’s a volatile, time-sensitive capacity constraint that needs to be priced, hedged, and made liquid. The right analogy is not AWS or Snowflake. It’s CME, power markets, and derivatives desks in Chicago and New York. Cloud infrastructure was built on a few a