Jonathan Bryce, the new CNCF executive director, argues that inference—not model training—will define the next decade of computing. Speaking at KubeCon North America 2025, he emphasized that while the industry obsesses over massive LLM training runs, the real opportunity lies in efficiently serving these models at scale. Cloud-native infrastructure, he says, is uniquely suited to this shift because inference requires real-time deployment, security, scaling, and observability—strengths of the CNCF ecosystem. 

Bryce believes Kubernetes is already central to modern inference stacks, with projects like Ray, KServe, and emerging GPU-oriented tooling enabling teams to deploy and operationalize models. To bring consistency to this fast-moving space, the CNCF launched a Kubernetes AI Conformance Program, ensuring environments support GPU workloads and Dynamic Resource Allocation. With AI agents poised to multiply inference demand by executing parallel, multi-step tasks, efficiency becomes essential. Bryce predicts that smaller, task-specific models and cloud-native routing optimizations will drive major performance gains. Ultimately, he sees CNCF technologies forming the foundation for what he calls “the biggest workload mankind will ever have.” 

Learn more from The New Stack about inference: 

Confronting AI’s Next Big Challenge: Inference Compute 

Deep Infra Is Building an AI Inference Cloud for Developers 

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