AI Infrastructure Spending Moves Into a New Phase
Capital that once chased model training is shifting toward inference, power, and the unglamorous plumbing that keeps deployed systems running.
Capital that once chased model training is shifting toward inference, power, and the unglamorous plumbing that keeps deployed systems running.
Features that were uneconomical a year ago are becoming defaults, and product teams are redrawing the line between what runs always and what runs on demand.
Rack densities have pushed past what air can carry away, and the retrofit market is scrambling to catch up with the physics.
Packaging capacity, memory, and power components are the constraints that now shape delivery schedules more than leading-edge logic.
For high-volume tasks with narrow scope, engineering teams increasingly route around frontier models, and the routing layer itself is becoming strategic.
Dozens of specialist providers rented scarce accelerators at premium prices; falling scarcity is now sorting operators from arbitrageurs.
Capable local models are shifting latency-sensitive and private workloads onto consumer hardware, with consequences for chipmakers and cloud bills alike.