Import AI 439: AI kernels; decentralized training; and universal representations

Meta says KernelEvolve cut kernel development from weeks to hours and delivered up to 17x over PyTorch baselines in production tests. The system uses Llama, GPT, and Claude to generate kernels, validates them, and feeds results into a knowledge base across NVIDIA, AMD, and MTIA; the post also says decentralized training is growing 20x per year but still uses about 1000x less compute than frontier runs. The real signal is continuous self-optimizing infra in production, while decentralized training matters if that 1000x gap keeps shrinking.

Why it matters: HKR-H/K/R all pass: the kernel-writing angle is novel, the post includes concrete numbers and mechanism, and the decentralization thread hits cost and power-concentration nerves. I stop at 80 because this is a newsletter synthesis of technical work, not a single industry-defining