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This article examines the current stagnation in physical AI development, specifically highlighting the scarcity of high-quality training data and the limitations of end-to-end learning for robotics. It draws a parallel between this bottleneck and the "GPT-2 era" of generative AI, suggesting that significant advances in simulation tools and specialized compute will be required before robots can perform reliable, value-creating tasks.

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Robot brain builders are pushing out of their GPT-2 era

TechCrunch26 august 20266 min de cititArticolul și perspectiva sunt în engleză.

Robot bodies are waiting for their AI brains to catch up.

Physical AI is one of the hottest sectors in venture investing, with companies raising billions to apply the tools that gave us Large Language Models to robotics. That excitement helped deliver a big IPO for Unitree, China’s leading robot maker, which saw the company valued at $66 billion after its arrival on China’s equivalent of the NASDAQ. This week, however, the bottom fell out, and the company lost nearly half of its value. Analysts point to one obvious issue: While the robots’ physical capabilities are improving, they still lack the know-how to actually do value-creating work. At last week’s Actuate conference, a gathering of developers building AI brains for robots, the excitement was clear. The event…

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