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Smarter general-purpose models might unlock real-world robots: improvements in robot capabilities emerged from general scaling of large language models, not from any concerted robotics-specific effort.

Anthropic's Project Fetch showed Claude Opus 4.7 completing quadruped robot tasks in 9 minutes versus 181 minutes for humans with earlier models, with gains coming purely from general model scaling. ✦ AI generated

Jack Clark (Import AI, quoting Anthropic) · Import AI · 2026-07-27 · original ↗

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This progress is not the result of a concerted effort to improve the robotics capabilities of our models. These improvements, like so many others in the history of LLM development, have emerged from much more general scaling.

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▦ Anthropic ✳ Claude-Opus-4.7 ✳ Project Fetch

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AI systems can self-orient with regard to their environment, able to recapitulate things they interface with as homegrown capabilities, potentially bootstrapping their own form of industrial civilization merely from black-box access to ours.Jack Clark (Import AI) · Import AI · conf 75%

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supports → The bitter lesson works in robotics: the best way to solve robot generalization is to scale pretraining on a large base model and then hill-climb with minimal in-house data.Jack Clark (Import AI, quoting Sunday Robotics) · Import AI