Kubernetes has become the standard platform for deploying containerized applications. Its default scheduler excels at placing stateless microservices: it finds a node with sufficient CPU and memory, starts the pod, and moves on. If a pod fails, the system restarts it. If demand increases, horizontal autoscaling adds more pods. This model works well for web servers, APIs, and similar workloads. AI workloads break these assumptions. Understanding why requires examining the AI/ML lifecycle in detail.