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Banning general-purpose computation in favor of specialized analAI agents' success rate on end-to-end, economically valuable freSlowing or banning the open-source AI model ecosystem is an effeFrontier AI agents' reliability on long-horizon, multi-step, mul

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Claim◆Article · 16:30 · 2m

Because Fable-class models are so much more capable, tradeoffs between good, fast, and cheap are no longer necessary constraints — you can demand ambition on all fronts — though building being easy doesn't mean generating real value has gotten any easier.

Thariq contends that with more capable models like Fable, builders should reject the assumption that quality, speed, and cost must be traded off — even though building is now easy, actually generating value remains hard.

Thariq · Latent Space
Data◆Article

Claude has a global-workspace-like internal structure — a small subset of activations called 'J-space' — that acts as a privileged representational substrate available for report, modulation, and flexible reasoning, distinct from simple chain-of-thought extraction.

Anthropic's interpretability research identifies 'J-space,' a small subset of Claude's internal activations that appears to function like a global workspace, usable for reasoning, reporting, and modulation.

Anthropic · Latent SpaceRebuttals · 1
Prediction◆Article

AI systems are expanding their economically relevant capabilities faster than humans are expanding their comparative advantage relative to AI, meaning the economy is likely headed toward person-light or person-nil, AI-heavy organizations outcompeting unaugmented humans rather than staying fundamentally the same.

The author argues that despite human innovation and augmentation, AI capability growth is outpacing humans' ability to stay competitive, predicting a shift toward extremely person-light, AI-heavy organizations taking over parts of the economy.

Jack Clark (Import AI) · Import AISupports · 1Extends · 1
Prediction◆Article

AI systems are expanding their economically relevant capabilities faster than humans are expanding their comparative advantages, so person-light, AI-heavy organizations will increasingly out-compete unaugmented humans.

Citing the Remote Labor Index's jump from 2.5% to 16.1% success in nine months, Jack Clark argues AI capability growth is outpacing human adaptation, predicting AI-heavy, person-light organizations will take over parts of the economy.

Jack Clark · Import AISupports · 2
Prediction◆Article

AI systems are expanding their economically relevant capabilities faster than humans are expanding their comparative advantages relative to AI, so person-light, AI-heavy organizations will increasingly out-compete unaugmented humans and take over parts of the economy.

Jack Clark argues that AI capability growth is outpacing humans' ability to develop new comparative advantages, predicting that person-light, AI-heavy organizations will increasingly dominate the economy despite human innovation and augmentation.

Jack Clark · Import AIExtends · 1Supports · 2
Fact◆Article

Fable's AI system autonomously wrote a CUDA megakernel that achieves an 18.71x speedup over an optimized PyTorch baseline using a single cooperative kernel launch per token, beating every other frontier model's multi-kernel approach on KernelBench-Mega.

Fable wrote the first megakernel submitted to KernelBench-Mega, achieving an 18.71x speedup with a single kernel launch per token, versus 4-14 launches for competing models, signaling rapid AI progress on tasks central to automating AI R&D itself.

Jack Clark · Import AISupports · 1Extends · 1
Data◆Article

Fable's AI system wrote the first genuine and fastest megakernel ever submitted to KernelBench-Mega, hitting an 18.71X speedup with just one cooperative kernel launch per decoded token, versus rivals that needed 4 to 14 separate launches.

Fable's AI-written CUDA megakernel achieved an 18.71x speedup on KernelBench-Mega using only one kernel launch per token, beating attempts by Claude Opus 4.8, GLM-5.2, and GPT-5.5, signaling progress toward AI automating its own R&D.

Jack Clark (Import AI) · Import AI
Data◆Article

Fable's AI-written megakernel is the fastest and most efficient solution ever submitted to KernelBench-Mega, using a single cooperative kernel launch per token where every other high-scoring entry needed 4 to 14 — a sign AI is closing in on the R&D tasks that underlie recursive self-improvement.

Fable's Cuda-written megakernel hit an 18.71X speedup over an optimized PyTorch baseline, beating Claude Opus 4.8, GLM-5.2, and GPT-5.5 on the same benchmark, and did so with a single kernel launch per token versus competitors' 4-14.

Jack Clark (Import AI) · Import AIExtends · 1