We're seeing this massive wave where everyone's trying to run AI workloads on Kubernetes, but honestly, most teams are figuring this out as they go.

Between the three of us, we've had conversations with dozens of teams at KubeCons, through OpenSSF, and across the platform engineering community. This panel is basically us connecting those dots and having an honest conversation about what patterns are actually emerging versus what's still a mess.

We'll talk through the technical challenges teams are hitting, how to not blow your cloud budget on GPUs, why ML engineers and Kubernetes don't naturally get along, and what the new standards like Kubernetes AI Conformance actually mean for people building platforms.

It's not a "here's how we solved everything" talk—it's more like "here's what we're seeing work, here's where people are still struggling, and here's the stuff nobody's figured out yet."