KubeCon 2025 highlighted the growing importance of Kubernetes as the primary platform for AI workloads, with a strong emphasis on AI, machine learning, and data management. The event showcased the shift from proof-of-concept to production-level deployments of data-intensive AI/ML tasks, including Large Language Model inference, which are transforming infrastructure operations. Kubernetes' scalability, flexibility, and cost-effectiveness make it ideal for handling the complexities of modern AI models that require advanced GPU and multi-node architectures. The emergence of projects like llm-d and LanceDB signifies the integration of distributed inference and scalable AI data lakes with Kubernetes, while Dynamic Resource Allocation enhances GPU and accelerator management. As AI demands evolve, platform teams face challenges in providing self-service capabilities for data scientists and ML engineers without imposing the intricacies of Kubernetes. This necessitates a focus on developing user-friendly platform abstractions, automating operations with AI-powered tools like Model Context Protocol (MCP) and Agentic AI, and ensuring efficient incident management. The shift towards proactive, AI-driven operations aims to reduce Mean Time to Resolve (MTTR) for critical issues, exemplified by Salesforce's implementation of an AIOps system for over 1,000 Kubernetes clusters. The conference underscored the need for platform teams to treat infrastructure as a product, balancing diverse user needs with operational control and cost management, as AI workloads expand and the demand for scalable, resilient platforms grows.