Import AI 464: Fables writes GPU kernels; AI automation; and analog computation

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Fable writes a decent GPU kernel, hinting at broader AI R&D automation:…The start of an RSI loop…Fable has written “the first genuine (and fastest) megakernel ever submitted to KernelBench-Mega, according to one of the benchmarks maintainers as well as its official leaderboard. This is a sign of how AI systems are getting better at doing some tasks that are fundamental to AI research and development, like kernel design. The results: Fable achieved an 18.71X speedup by writing Cuda code on an RTX PRO 6000 Blackwell, compared against an optimized PyTorch baseline. For calibration, other attempts at this get 14.4X (Claude Opus 4.8, writing Triton), 11.14X (GLM-5.2, Triton), and 4.34X (GPT 5.5, Triton). Here’s where it gets complicated: This solution is particularly impressive because “torch.profiler shows exactly ONE cooperative kernel launch per decoded token”. By comparison, every other high-scoring entry decomposed the problem into anywhere from 4 to 14 separate kernel launches per token. Why this matters: Being able to autonomously develop and improve kernels is one of the fundamental input tasks for being…