Import AI 464: Fable Writes GPU Kernels, AI Automation, and Analog Computing

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.”

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.”

Ontology engineering driven by efficient human-AI collaboration. “Experts focus on distilling industry knowledge, while algorithms learn from it to scale ontology construction and drive continuous evolution”.

“Semantic Search then Discrimination”: “In the semantic search stage, the dynamically evolving ontology is externalized as a separate ontology knowledge base, enabling continuous ontology updates without model retraining,” they write. “. In the discrimination stage, the model only determines whether the item matches the retrieved ontology entries. This formulation substantially reduces task complexity, mitigates model hallucination, and enhances generalization to ontology evolution”.

Self-evolving item-understanding LLMs/VLMs : “Through incremental learning and model self-evolution, the system fills targeted knowledge gaps and mitigates catastrophic forgetting”, they write. “The core method is to build on the robust multi-task foundation, develop lightweight “expert modules” for incremental requirements, and dynamically integrate them into the expert pool, enabling agile capability expansion”.

“Unified item tunnel”: The main interface between Oxygen AIIC and other business applications. “it supports daily-, minute-, and second-level production and distribution pipelines while preserving data consistency”.

To work on these problems, you study the specific type of analog computation needed to work on them. Weather requires a vast computer with geographical features such as mountains implemented as fixed impedance structures in the hardware; flooding demands physically accurate models of floodplains and rivers where electronics are woven into the landscape allowing the utilization of physics and computation to create better answers; utility grids are toy boxes of the electrical system that must be painstakingly rebuilt and rebalanced as new power stations are added and transmissions changed. For every problem, there is a computational solution, and for every problem of sufficient civilizational importance, a computer will be built. In the past, we had general computers. But they were deemed eventually too dangerous - too unpredictable. The more powerful they became and the more diffuse the knowledge about them grew, the more they tickled at the tails of various dragons. Synthetic minds that might rip the world apart. Ethereal Pandora’s boxes to spit out poisons keyed to individuals or races. Minds that might whisper to human minds and drive them to insanity or acts of malice. So the great restructuring took place. General computation was banned - walled off as a forbidden technology. We moved the world to analog at the cost of untold billions of harmed human lives and trillions in economic damages. But we had obtained a kind of safety. Now, the guild supervises the construction of the earth’s ‘world computers’ and academia has found a new mission in life, pairing expertise in specific subjects with customized engineering schools to help build the analog computers that let each specialism work. There is troubling talk that for a trillion dollars it may be possible to implement in analog a general-purpose mind. Things that inspired this story: Thinking about analog computation and how far it could be taken if budgets were $10 billion to $20 billion; taking to its logical conclusion the implication of AI being existentially dangerous; the Difference Engine; steampunk; the fact a neural network can be implemented via a series of containers and pipes and a liquid for weights. Thanks for reading!