An AI infrastructure failure does not always begin with a complete service outage. A training cluster can lose performance because of network instability, cooling faults, repeated GPU errors, or storage problems. These conditions can affect individual workloads while the remaining infrastructure continues operating normally. Customers may continue paying for reserved capacity while their workloads make little progress, depending on billing arrangements and contractual terms. This creates a commercial problem because customers need timely information to decide whether to pause workloads, investigate performance, or move operations. A provider’s ability to explain an incident can become almost as important as its ability to restore the affected infrastructure.