Entrepreneurs
1 Curated Dialogues
Evan Conrad
Key Views & Dialogues
The Programmable Data Center — Evan Conrad, SF Compute
SF Compute’s bet is that GPU trading requires cloud-like operations: VM configurations can differ by 40%, leaving financial contracts exposed to manipulation and basis risk before reliable delivery. On the 5% of capacity listed, customers recover $25 per $100 spent through resale; Conrad reports several hundred million dollars in volume, but order-book volume remains small, leaving lender-financeable forward-curve liquidity unproven.
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- SF Compute’s core bet is that a tradable GPU market requires operating the infrastructure that delivers the compute. Conrad says the company spent three years learning why physical settlement matters: “these things aren’t as fungible as you think.” Even two VM configurations can differ by 40% in performance; financial contracts built ahead of reliable delivery risk manipulation and hedges disconnected from executable prices.
- SF Compute reports useful resale liquidity on the 5% of capacity currently listed in its order book. Customers spending $100 recover $25 on average by selling capacity to someone else, a practical measure of whether liquidity helps commercial users. Conrad separately reports closing several hundred million dollars of volume, while describing order-book volume as small.
- Swyx’s B300 inference estimates expose the episode’s sharpest pricing tension. Swyx estimates roughly $40 an hour of inference revenue at 100% utilization, or around $25 at roughly 50%, against rental costs of $5–$6 an hour. Conrad agrees it is a “huge overcharge”; buyers expecting higher compute prices may pay more to secure future supply.
- SF Compute wants a liquid forward curve that prices customers’ ability to surrender capacity. Buyers can purchase from an hour through three years; batch jobs might tolerate minutes of notice, while live inference may need one to three months. Swyx describes a “hump” in prices: unsold imminent capacity gets discounted like expiring milk, while shorter lead times can command a premium over distant delivery. Conrad also notes that some buyers may pay more for future capacity if they expect prices to rise.
- Conrad argues that independence is the condition for turning compute trading into cheaper financing. His impression is that NVIDIA, clouds and major inference buyers are not independent enough for people to trade on their venue. SF Compute avoids hardware ownership: “we have to be Switzerland,” enabling liquidity that lenders can finance against.
- The programmable data center gives asset owners an operating layer for their GPU investments. Customers can subdivide their InfiniBand fabric themselves as capacity needs change, while SF Compute operates the infrastructure and owns the SLA. Its sovereign-compute pitch combines local clusters with San Francisco demand and expertise: “We’re going to make it financeable.”