In a notable post published on October 22, 2026, titled “The end of startups”, Evan Conrad, founder of the SF Compute platform, offered a bleak forecast: within the next six months, the vast majority of AI startups will no longer be able to purchase compute. The cause lies not only in an overloaded global chip supply, but also stems from an invisible yet decisive bottleneck: a credit crunch in the GPU supply chain.
This reality unfolds alongside infrastructure pressure on cloud providers as compute cluster lease contracts swell beyond what balance sheets can bear.
The credit rating spiral and 100% upfront requirements
According to Conrad’s analysis, virtually all available compute capacity in the world is currently sold out. To secure additional resources, companies are forced to sign contracts to deploy new server clusters. However, financing institutions and cloud providers are refusing to take on further credit risk from non-investment grade customers.
The core problem is that most tech startups lack investment-grade credit ratings. To access hardware, they are required to make increasingly large upfront deposits, with some contracts even demanding prepayment of up to 100% of the contract value.
Meanwhile, investment-grade enterprise clients are also growing scarce. These corporations face repeated credit downgrades from financial institutions for taking on too much debt to hoard chips, only to resell token output to end-buyers who also lack credit backing. As profit margins on inference services are eroded by competition, the pressure for massive cash deposits will back small startups into a corner. At this point, optimizing token costs is no longer just a competitive edge—it has become a battle for survival.
The overwhelming advantage of Fortune 500 giants
Another dangerous consequence is deep polarization at the application layer. A Fortune 500 corporation can deploy a $1 billion budget to purchase tokens in just a few days, giving it an overwhelming advantage over a startup still struggling to raise capital before its product even takes shape.
Under current market dynamics: those with strong credit scores secure tokens; those with tokens build products faster, make more money, and further strengthen their credit lines. Conversely, underfunded founding teams are knocked out of the game right from the starting line.
Turning compute hours into liquid collateral
To break this impasse, Conrad proposes turning the unit of compute measurement into highly liquid collateral for lenders. Currently, lenders are forced to assess risk based entirely on the buyer’s creditworthiness (typically AI labs). As capital requirements scale up, banks’ capacity to absorb credit risk hits a ceiling.
The model SF Compute pursues is building a physically settled forwards market for compute power. By using each GPU hour as collateral on a liquid order book, lenders will no longer need to worry about the bankruptcy risk of a two-person startup in San Francisco: if the borrower defaults, the collateral—the compute hours—can be liquidated immediately on the open market.
While this concept offers a theoretical way out, it also underscores a harsh reality: the era when founders needed only a great idea and a laptop to achieve AI breakthroughs may be over, giving way to a pure financial game of leverage and credit lines.