The artificial intelligence (AI) hardware cycle initially rewarded designers who could deliver raw training compute at nearly any price. Hyperscalers absorbed heavy infrastructure costs to build foundational models, leaving smaller data centers struggling to participate.
As the industry pivots from training models to running daily inference workloads, AI economics are shifting rapidly. Power constraints, specialized liquid cooling requirements, and ballooning capital expenditures are forcing a reevaluation of how data centers physically scale.
