The AI model Claude Opus 4.7 programmed a robot dog 20 times faster than humans — a breakthrough in physical robotics
Anthropic has presented impressive results from the second phase of the Project Fetch experiment. The Claude Opus 4.7 model demonstrated the ability to independently configure and control a four-legged robot, completing tasks 20 times faster than teams of human engineers. This is not just a statistic — it is a turning point in the evolution of AI agents.
To recall, in August 2024, Anthropic employees with no experience in robotics attempted to program a robot dog, and at that time, the AI only acted as an assistant. Now, the Claude Opus 4.7 model worked almost autonomously, under minimal researcher supervision. The neural network independently connected to video sensors and LiDAR, wrote a program for manual control, created a robot path monitoring system, and configured an object recognition algorithm.
The results are impressive: Opus 4.7 turned out to be 18 times faster than a team using older AI versions and 37 times faster than people working without chatbot assistance. Moreover, the neural network wrote code that was 10 times smaller in volume than that of human teams. This indicates much higher efficiency and cleaner solutions generated by AI.
Notably, the progress in robotics became a side effect of the general scaling of language models. Anthropic did not introduce specialized algorithms for controlling hardware — this is a pure victory for fundamental research.
However, one should not rush to conclusions. Despite success in navigation, Claude still struggles with precise physical actions. The model managed to guide the robot to the target but failed at the task of gently pushing a ball to the right spot. This requires complex real-time feedback, where humans still maintain superiority.
Anthropic believes the industry is entering an era of "physical AI agents." In the future, neural networks will be able to use standard tools and equipment as effectively as they currently work with software code.
My analysis: This is a key signal for the market. We are witnessing a transition from virtual tasks to real physical actions. If AI can control robots faster and more efficiently than humans, it will change logistics, manufacturing, and even household robotics. However, the problem of fine motor skills remains — and for now, it is what holds back full automation. Investors should closely monitor the development of models capable of real interaction with the physical world.