Fable 编写 GPU 内核，AI 自动化能力提升

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. Subscribe now 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).. *** AI systems are getting better at pricey online work tasks – what does that mean for the economy?…AI capability expansion versus human comparative advantage expansion…Researchers with the Center for AI Safety (CAIS) and Scale Labs have detected a significant improvement in the ability for AI systems to automate online freelance projects. Specifically, a rise in the success rate of AI systems from 2.5% at launch in October 2025 to 16.1% in July 2026 on the “Remote Labor Index“. What RLI is: The Remote Labor Index tests out how well AI systems can perform economically valuable projects online in a fully end-to-end way. Assessed tasks include 3D & CAD, architecture, graphic design, video and animation, audio, data analysis, web applications, and more. Rising automation: In a July update, the authors publish results from evaluating three recent frontier models – GPT-5.5, Opus 4.8, and Fable 5, which get 6.3%, 8.3%, and 16.1% respectively. “The frontier has more than quadrupled in under eight months, a concrete signal of how quickly economically capable AI agents are advancing,” they write. Types of tasks: Some of the assessed tasks include: Ring design: “Re-create the client’s existing engagement ring with its emerald-cut center stone swapped for a marquise cut, delivering an updated 3D model plus photorealistic rose- and yellow-gold renders.” 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). *** OSWORLD 2.0 shows we’re in the era of multi-hour computer-using robots:…A challenging benchmark highlights the recent progress on AI systems becoming increasingly competent at using computers…Researchers with the University of Hong Kong, the University of California at San Diego, Columbia University, the University of California at Santa Barbara, Mila, Snorkel AI, the University of Wisconsin, Alibaba Qwen, The Ohio State University, Simular, and NeoCognition have released OSWORLD 2.0, a benchmark for evaluating how well AI systems can carry out multi-step multi-program tasks on computers. The tasks in OSWORLD 2.0 are far more complicated than in its 1.0 predecessor, with the median task taking a person approximately 1.6 hours, about 48x longer than the 2-minute median in OSWORLD 1.0. What it consists of: OSWorld 2.0 contains 108 long-horizon tasks including 31 self-hosted websites. “Each task in OSWORLD 2.0 is defined as a self-contained end-to-end workflow that an agent must complete given a high-level user goal, realistic artifacts, a stateful computer environment, and a scoreable final state. A retained task must satisfy two design criteria,” they write. “69.6% of tasks are estimated to take a skilled human user more than one hour.” Broader software: OSWORLD 1.0 shipped with some inbuilt software to support some of its tasks, including LibreOffice, GIMP, VLC, Thunderbird, VS Code, and Chrome. OSWORLD 2.0 ships with a massively expanded set, including: Slack, LinkedIn, Shortcut, REAPER, MuseScore, WPS, GitLab, Overleaf, LabPlot, Zotero, AWS, as well as websites meant to mimic professional services like insurance claim, visa application, and conference management portals. The categories of tasks people need to complete include: document prep, software & database work, finance/ops analysis, admin support, sales and customer support, graphic presentation, and more. Poor performance (for now): “Our experiments show that current agents remain far from reliable computer use: the strongest setting, Claude Opus 4.8 with maximum thinking and batched tool calls, reaches only 20.6% binary accuracy and 54.8% partial-score accuracy,” they write. “Performance drops sharply as tasks grow longer, and agents struggle most when they must recover hidden state, track many items, resolve conflicting information, or adapt to changing requirements”. We should expect performance to rise here, just as happened with OSWORLD 1.0; in July 2025 the highest scoring models got ~30%, and recent models have scored more like ~75% (MiniMax M3; June 2026). We should expect the same ramp with OSWORLD 2.0. Why this matters – this is how AI gets into the broader economy: Computer use is a fundamental skill for AI being able to perform a wide variety of economically valuable tasks, and also for it being