From Cloud Native to AI Native: Where Are We Going?

At KubeCon + CloudNativeCon 2025 in Atlanta, the panel of experts - Kate Goldenring of Fermyon Technologies, Idit Levine of Solo.io, Shaun O'Meara of Mirantis, Sean O'Dell of Dynatrace and James Harmison of Red Hat - explored whether the cloud native era has evolved into an AI native era — and what that shift means for infrastructure, security and development practices. Jonathan Bryce of the CNCF argued that true AI-native systems depend on robust inference layers, which have been overshadowed by the hype around chatbots and agents. As organizations push AI to the edge and demand faster, more personalized experiences, Fermyon’s Kate Goldenring highlighted WebAssembly as a way to bundle and securely deploy models directly to GPU-equipped hardware, reducing latency while adding sandboxed security.

Dynatrace’s Sean O’Dell noted that AI dramatically increases observability needs: integrating LLM-based intelligence adds value but also expands the challenge of filtering massive data streams to understand user behavior. Meanwhile, Mirantis CTO Shaun O’Meara emphasized a return to deeper infrastructure awareness. Unlike abstracted cloud native workloads, AI workloads running on GPUs require careful attention to hardware performance, orchestration, and energy constraints. Managing power-hungry data centers efficiently, he argued, will be a defining challenge of the AI native era.

Learn more from The New Stack about evolving cloud native ecosystem to an AI native era

Cloud Native and AI: Why Open Source Needs Standards Like MCP

A Decade of Cloud Native: From CNCF, to the Pandemic, to AI

Crossing the AI Chasm: Lessons From the Early Days of Cloud

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