Within the next 6 months, most AI startups will not be able to buy compute. The world’s compute is sold out. To get capacity, you’ll need to sign up for a new cluster. However, it’s becoming increasingly difficult to get capacity online from folks who are not investment grade.
Startups are not investment grade. So, to get capacity, they are asked to pay larger and larger upfront payments. We’ve seen it go as high as 100% of the contract paid up front, which was required because neither the lender nor the cloud could take on any additional credit risk.
Meanwhile, investment grade tenants are rare. The few investment grade tenants that remain are getting increasingly down rated as they take on more and more debt and then turn around to sell to buyers of tokens who themselves are not investment grade.
This is the credit bottleneck.
We expect the investment grade buyers of GPUs to increasingly ask that the buyers of tokens be either investment grade themselves or pay, up front, for a large deposit. Today, inference companies are able to serve at high margins, which means their cashflows may allow them to be seen as credit worthy even when their customers are not.
However, it’s not clear that those margins are sustainable. As of writing, you can buy a B300 for $6/hr and then make about $30/hr selling inference on it. That’s a healthy margin, but there’s little to stop others from arbitraging that margin away. We expect GPU prices to rise, which means that margins will fall for the inference layer. Declining margins means it will become harder to secure a better rating, which means there’s a stronger incentive to secure investment grade customers for tokens.
When that happens, it becomes structurally harder to build a startup, in any industry. If you have credit, you get tokens, and if you get tokens, you build better products faster, make more money, and get better credit. Those tokens will become harder to acquire for customers with poor credit. Meanwhile, competition in the application layer will require you to spend more tokens faster; a Fortune 500 that can deploy $1b in tokens in a few days has an overwhelming advantage over a startup that first must raise $1b prior to their product existing.
To solve this problem, we must get lenders comfortable with the GPU cluster as valuable collateral. Today, lenders are forced to underwrite solely on the credit risk of the offtaker (the AI lab). As AI scales, they exhaust their ability to stomach credit risk, which pushes the whole industry to seek more and more credit worthy counterparties.
This is the problem that SF Compute solves, by building a physically settled forwards market for compute. We make it possible for lenders to use the GPU hour as collateral, on a liquid book, rather than the GPU cluster, which is non-fungible and requires a servicer that might blow up right at the moment they need it the most. If a lender gets a GPU hour as collateral, the credit risk of the customer matters less; if the two person AI lab in a San Francisco Victorian fails to make their payments, the lender has a real path to selling the collateral. That makes startups possible at all, a path that seems increasingly difficult in AI.
If you think from first principles and care deeply about this problem, we’re hiring at San Francisco Compute.