Import AI 464: Fable writes GPU kernels; AI automation; and analog computation
Fable, an AI system, has written a GPU kernel that achieved an 18.71× speedup over an optimized PyTorch baseline on an RTX PRO 6000 Blackwell, claiming the first genuine megakernel on KernelBench‑Mega. The kernel launches only one cooperative kernel per token, unlike other entries that use multiple launches. The article discusses how AI‑generated kernel design signals progress toward recursive self‑improvement and examines broader impacts of AI automation on employment, ontology engineering, and analog computation. It also references Oxygen AIIC, a Chinese‑based item‑understanding platform that uses Huawei Ascend NPUs and highlights challenges in model training and inference on such hardware.
The Core Development
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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 able to do AI research and development. The better AI systems at doing tasks like kernel design, the better they get at the kinds of tasks required for AI development, and that means the better they get at things that could lead to recursive self-improvement. Therefore, benchmarks like KernelBench-Mega are a meaningful signal on how effective AI systems are becoming at building themselves. See the leaderboard : KernelBench Mega (official site) . Read the analysis from one of the benchmark maintainers here (Elliot Arledge, X) .. ***
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Strategic Perspective & Ecosystem Impact
Advertisement Video: “Produce a ~60-second flat-design 2D animated advertisement for “Skyline Tree Services,” set to the provided voiceover, that walks viewers through the company’s tree-care process and builds trust in the brand.”
Floor Plan & Renders: “From a scanned cadastral plan, site photos, and measurements, produce a clean dimensioned floor plan, furniture-layout options, and photorealistic renders of the redesigned bathroom.”
Why this matters - AI might have a big impact on employment and tests like these will show us how: What happens to online employment when this reaches 80%? Of course, some new tasks will get created - people will innovate and find tasks that they can do which AI systems can’t do. But how many of these new tasks will exist? Enough to replace the labor the AI systems now do? It’s increasingly hard for me to reconcile the continued progress of AI systems with the economy staying the same - rather, it’s more likely to me we are about to see extremely person-light AI-heavy (or person-nil) organizations expand to take over chunks of the economy, out-competing un-augmented humans. Yes, you counter, many humans will augment themselves with AI systems. Humans will innovate. Creative destruction will occur. New inventions will be devised. All of that is true. But is the speed at which humans innovate and render themselves newly competitive relative to AI systems going to be faster than both a) the raw capability expansion of AI systems, and b) the increasing fluency with which they can use all the same tools (e.g, software) that their human competitors use? I’m betting the other side: AI systems are expanding their economically relevant capabilities faster than humans are expanding their comparative advantages relative to AI systems. Tracking the rate of capability improvement on tests like RLI will help us all judge this for ourselves. Read more : A Significant Increase in Digital Labor Automation (Center for AI Safety) . ***
Why it matters
Demonstrating AI’s ability to autonomously design efficient GPU kernels illustrates a step toward recursive self‑improvement, a core concern for AI safety and future capability growth.