AI workloads are exposing the limits of traditional scheduling assumptions in Kubernetes. When GPUs and other specialized accelerators enter the picture, platform design becomes far more complex. This session explains how Kubernetes must evolve to support accelerated computing at scale, with a focus on GPU allocation, Dynamic Resource Allocation (DRA), scheduling behavior, quota design, and capacity planning. The talk connects these technical mechanisms to the broader goal of building reliable and efficient AI platforms. Attendees will understand the operational implications of running accelerated workloads, the common pitfalls teams encounter, and the architectural patterns that can help improve fairness, utilization, and performance.