Both things can be true: The market is constrained, and the headlines are overheated.

What enterprises should do now

First, separate workload types before talking about capacity. Do not say “we need AI infrastructure” as if that is one thing. Break it down into training, fine-tuning, inference, development, testing, batch processing, real-time applications, and data movement. Each has a different capacity profile, cost model, and architectural pattern. Once you know the workload, you can have a real capacity conversation.

Second, validate availability directly with providers and partners. Do not base architecture decisions on public statements, analyst chatter, or executive humble-brags. Ask specific questions about instance types, regions, service quotas, reservation options, pricing, lead times, and fallback paths. Get answers in writing when the workload matters.