AI has changed data architecture, but storage hasn't caught up

Your GPU dashboard says 70% utilization. On paper, the cluster is busy. In practice, a large chunk of that time is spent with your $40,000 accelerators sitting idle, waiting on a file that lives three network hops away on a NAS box. The compute queue is empty, and the pipeline is fine. The problem is that data is just somewhere else. This is the awkward truth underneath most stalled AI projects. The constraint in modern AI infrastructure stopped being storage capacity years ago. Now, it's more a