#compute
5 articles
The AI Infrastructure Boom Has a Utilization Problem
The industry is financing compute as if demand were both limitless and predictable. The harder business question is whether expensive, power-constrained infrastructure can stay productively occupied as models, workloads, and hardware economics keep changing.
Open-Weight Models Just Became a Procurement Decision, Not a Research One
Running your own model used to be a research project with an uncertain payoff. With serious open-weight releases now clustering close to proprietary frontier performance on many tasks, the conversation inside regulated companies has shifted from "can we" to "should we," and that is a very different, much faster conversation.
AI’s Most Expensive Bet Is Not the Model. It Is Idle Capacity.
The infrastructure race is usually framed as a contest to secure more compute. The harder business problem is deciding how much irreversible capacity to build before demand, hardware, and model economics change again.
AI Is Becoming an Infrastructure Business With a Software Front End
The industry still talks as if model intelligence alone decides the winners. Increasingly, the harder contest is over power, cooling, utilization, and the financial discipline required to turn compute into a product.
AI Competition Is Starting to Look Less Like Software and More Like Infrastructure Finance
The loudest AI stories are about models and products, but the underlying contest is increasingly about capital structure, distribution, and who can afford to stay in the race long enough to matter. The industry may be entering an era where balance sheet strategy shapes technical destiny.