GPU Shortage: CEOs Split Capacity Personally, Startups Sign Multi-Year Contracts

Compute capacity in Silicon Valley has become a scarce commodity, and the competition for it is fierce. As 3DNews reports, citing industry observations, company heads now allocate resources personally. Direct competitors are forced to join forces to get access to additional capacity. The most painful blow falls on startups: they are offered GPU access in exchange for multi-year financial commitments.

The original source does not disclose specific amounts or deal names, so what follows covers the structure of the phenomenon and its context, not the figures of individual contracts.

Who decides who gets the GPUs

A couple of years ago, buying servers was routine work for an infrastructure team. Now the question of how many accelerators a given project gets goes up to the top executives. The reason is simple. The amount of compute directly determines which models a company can train, how quickly it can ship a new version, and how many users it can serve without degrading quality.

Key observations from the report:

• company executives allocate capacity personally, not just their CTOs;
• competitors form alliances to gain access to additional resources;
• the shortage hits startups hardest;
• the price of access is often expressed as multi-year commitments rather than ordinary pay-as-you-go billing.

This matches what we saw in 2025. At that time, OpenAI reported contracts worth hundreds of billions of dollars with Oracle, as well as partnerships with Nvidia, AMD and Broadcom. Anthropic announced an expansion of its use of Google TPUs, involving tens of thousands of accelerators and capacity on the order of a gigawatt. If executives are still dividing up resources by hand in 2026, supply is evidently still not keeping up with demand.

Alliances with the companies you compete against

The most unusual thing about the situation is how the market is behaving. Companies that fight over the same customers and engineers are agreeing to share infrastructure: they split clusters, rent capacity from each other, and enter joint data center construction projects. The logic is pragmatic. Building your own data center of tens or hundreds of megawatts alone is slow and expensive. The constraints are not only money but also the supply of chips, cooling equipment, and power grid connections.

Such an alliance also benefits the party that owns the capacity. Utilization rises, and in return it gets a stake in the partner's model, data, or strategic loyalty. The line between supplier and competitor blurs, and that creates new dependencies. Whoever controls the clusters can see how the partner's load is growing and holds leverage in negotiations.

Startups pay with time and freedom

Large players can take out loans or issue bonds to fund data center construction. A young company has no such option. Its only choice is to sign a contract several years ahead, often with a mandatory minimum purchase. That turns compute from a flexible operating expense into fixed debt. If the market shifts and customers turn out not to need the model, the company still has to pay.

There is also a hidden cost. A long-term contract with a single cloud ties the team to a specific chip architecture and software stack. Switching to another provider or to different accelerators later is difficult and expensive.

What teams should do

For a startup building a product on AI, this points to several practical steps.

• Calculate inference cost per unit of value (a request, a task, a document) and build it into your unit economics before signing a contract.
• Do not lock in your entire capacity for a long term: keep part of it on a flexible plan or with a second provider.
• Check how portable your stack is: the less your code is tied to one type of accelerator, the stronger your negotiating position.
• Treat optimization (quantization, distillation, caching) as a way to reduce your GPU needs, not as a secondary task.
• Build into your financial model a scenario in which compute prices fall while long-term commitments stay the same.

Investors and corporate customers should ask AI service providers what capacity they run on and on what terms they obtained it. That determines whether the provider survives the next spike in demand.