AI analyst surpasses humans: Claude Opus 4.7 controls a robodog 20 times faster
The world of robotics has received a clear confirmation of the exponential growth of artificial intelligence capabilities. Anthropic has presented the results of the second phase of its Project Fetch experiment, and the data is impressive. The Claude Opus 4.7 model fully autonomously completed tasks for configuring and controlling a four-legged robot, outperforming teams of human engineers by a factor of 20.
For context: in August 2024, Anthropic employees with no experience in robotics attempted to program a robot dog. At that time, the AI only acted as an assistant, accelerating the search for solutions. Today's picture is radically different. Claude Opus 4.7 worked virtually without human involvement, under minimal supervision from a researcher. The neural network independently connected to video sensors and LiDAR, wrote a program for manual control, created a path monitoring system, and configured an object recognition algorithm.
The key metric is speed. Opus 4.7 turned out to be 18 times faster than a team using previous versions of AI, and 37 times faster than people working without chatbot assistance. Moreover, the neural network generated much more efficient code: its volume turned out to be 10 times smaller than that of human teams. This suggests that AI does not simply copy human actions, but finds fundamentally more optimal solutions.
It is important to emphasize that Anthropic did not develop specialized algorithms for controlling the hardware. The progress in robotics became a side effect of the general scaling of language models. This confirms the thesis that improving fundamental AI architectures automatically expands their applicability in the physical world.
However, one should not rush to conclusions about the complete replacement of humans. Claude still experiences difficulties 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 exact spot. This requires complex real-time feedback—an area where humans still maintain an advantage. Nevertheless, Anthropic rightly believes that the industry is entering an era of "physical AI agents." In the future, neural networks will work with standard tools and equipment as effectively as they do today with software code.
Analytical commentary: The performance gap between AI and humans in tasks requiring both cognitive and motor skills is closing faster than many anticipated. The fact that Claude Opus 4.7 completed robot programming 37 times faster than inexperienced people and 18 times faster than teams with AI assistants is not just a test. It is a signal to the market: automation of physical labor using AI is ceasing to be a futuristic concept. Investors should closely monitor companies that integrate LLMs into robotics, as this could become the next productivity multiplier.