GPU Kernel Generation Races Ahead of Production Validation

The rapid advancement in automated CUDA kernel generation tools (TileGym, AutoKernel, KernelEvolve) has created a critical blind spot: while these frameworks excel at demonstrating kernels work in test environments, they lack post-launch validation in production workloads. A kernel that passes numerical tests can still regress p99 latency or cause synchronization stalls in real inference traffic—issues only visible through production telemetry like eBPF traces. As generation capabilities continue to outpace observability infrastructure, this validation gap threatens to undermine the actual performance gains that automated kernel generation promises.