ReadingResearchRadarInvestment frameworkAI Company Primary Sources
Sign in / Sign up
← Back to reading
NewsMaterial4 min readDisconfirmAI Editorial

SF Compute Founder: Why NVIDIA and Cloud Providers Are Structurally Unable to Establish a Neutral Compute Trading Market

Evan Conrad argues that NVIDIA cannot create a neutral compute exchange because, as a chip manufacturer, it lacks the necessary independence. Meanwhile, cloud providers like CoreWeave and Crusoe are biased toward the demand side due to their own significant inference needs and cloud contracts. Both categories of players face different but equally fatal structural obstacles.

2026-10-21 03:31 GMT+8

The core paradox of the compute trading market is that the companies most likely to establish it are precisely the least suited to do so. In the latest episode of the Latent Space podcast, SF Compute founder Evan Conrad offered a structural explanation for why NVIDIA, hyperscale cloud providers, CoreWeave, Crusoe, Lambda, Base10, Fireworks, and Modal are all unable to create a neutral compute exchange.

Conrad’s argument distinguishes between two types of structural barriers. For NVIDIA, the issue is independence: if a chip manufacturer also operates an exchange, traders will not view it as sufficiently neutral and will be unwilling to trade on it. For cloud providers and inference service providers like CoreWeave and Crusoe, the problem is conflict of interest: they are themselves demand-side participants with substantial inference contracts and cloud businesses, naturally biasing them toward the buyer side. Conrad summarized this latter dilemma with one sentence: "You have to kill your own business to do this." Only by abandoning all large-scale inference revenue and cloud contracts can one become an independent platform that people are willing to trade on.

The practical implication of this argument lies in explaining financing bottlenecks in AI infrastructure. Conrad points out that the financing cycle for clusters is typically three to five years. To convince lenders to provide such long-term capital, there must be a forward market with sufficient liquidity—traders must actively trade on it so that lenders dare to finance against benchmarks derived from that forward market. Without a neutral exchange, no one trades on it; without trading, there is no liquidity; without liquidity, lenders cannot anchor loans to a forward market benchmark, causing financing costs to remain high.

Sign in / Sign up
ReadingResearchRadarInvestment frameworkAI Company Primary Sources

Conrad simultaneously acknowledges that NVIDIA benefits tremendously from the existence of an independent exchange, as it lowers financing costs across the entire industry. However, NVIDIA cannot simultaneously play the roles of manufacturer and neutral exchange operator. SF Compute’s strategy is to own no hardware, maintaining independence by "becoming Switzerland," even though this means it must handle all physical settlement work itself, including data center operations, supply chain consulting, cluster architecture design, and backplane management.

Regarding the current state of the market, Conrad provided the following data: SF Compute currently places only 5% of its total capacity on the order book, but achieves highly concentrated liquidity within that portion. He uses a customer metric to measure liquidity quality: after a customer spends $100 to purchase compute capacity, they recover an average of $25 by reselling it to others. He also described a striking arbitrage phenomenon: running inference services on B300 chips currently generates approximately $40 per hour in revenue (or $25 per hour even at 50% utilization), while the cost to purchase B300 compute capacity is merely $5 to $6 per hour. He called this a massive arbitrage opportunity but noted that "one of these numbers has to change."

Conrad offered an intuitive analogy for the shape of the forward curve: "It’s like when milk is about to expire, you lower the price." He described the compute forward curve as having a "hump" shape—prices for unsold capacity near the present drop (because it couldn’t be sold in the past), then rise to an equilibrium level, and may decline slightly further into the future (as people are willing to pay a premium for shorter delivery lead times). He termed this a "normal, healthy curve," while acknowledging that different participants may perceive the curve's direction differently.

The strongest counterargument to this point is that Conrad’s argument assumes demand-side participation necessarily compromises neutrality, whereas governance structures, regulatory frameworks, or sufficiently dispersed ownership might offer an alternative path. In the specific domain of compute, however, rapid technological iteration (where GPU generational differences prevent abstraction into a unified unit of measurement—vRAM, NVLink domain sizes, etc., all vary) and complex physical settlement (requiring on-site verification, supply chain coordination, and backplane management) raise the bar for neutrality significantly higher than in traditional commodity markets. Whether this constitutes an insurmountable structural barrier remains to be seen.

For investors in the AI industry, Conrad’s argument provides an evaluation framework: the degree of financialization in the compute market depends on whether anyone can resolve the structural contradiction of "having to kill your own business to build an exchange." SF Compute chose the path of bearing all physical settlement costs in exchange for independence, while Silicon Data and Compute Change opted for a cash settlement path. Conrad believes these two approaches will ultimately complement each other, but only if physical settlement is established first—otherwise, premature financialization would generate severe basis risk, similar to how LIBOR was manipulated by a handful of banks.

Sources

  • latent.space↗
Continue researching →