Anthropic shocks the market: AI Claude controls a robodog 20 times faster than humans
The AI robotics market has just received a powerful signal: the Claude Opus 4.7 language model from Anthropic has demonstrated results that call into question the very need for human engineers in a number of tasks. As part of the updated Project Fetch experiment, the neural network completed the full cycle of configuring and controlling a four-legged robot 20 times faster than the best teams of human engineers working with previous versions of AI.
Autonomy Without Limits
The key difference in the new testing phase is near-complete autonomy. While in August 2024, AI acted only as an assistant for people without experience in robotics, now Claude Opus 4.7 operated under minimal researcher supervision. The model independently:
- connected to video sensors and LiDAR;
- wrote a program for manual control;
- created a robot path monitoring system;
- configured an object recognition algorithm.
The performance is impressive: Opus 4.7 turned out to be 18 times faster than the team using previous versions of AI, and 37 times faster than people working without chatbot assistance. Moreover, the amount of code written by the neural network was 10 times less than that of human teams — this indicates a qualitatively different level of efficiency.
A Side Effect of Scaling
Particularly noteworthy is the fact that Anthropic did not introduce specialized algorithms for controlling hardware. The progress in robotics has become a direct side effect of the general scaling of language models. This means that with each new version of the base LLM, we can expect an automatic improvement in its ability to control physical objects.
However, there were limitations. Claude still faces serious difficulties with precise physical actions in real time. The model successfully guided the robot to the target but could not neatly push a ball to the right spot. This task requires complex feedback, where humans still maintain an advantage.
My Analysis
This experiment is not just another tech record. It is a marker that we are entering the era of "physical AI agents," as Anthropic rightly notes. Investors and developers should prepare for the fact that in the next 12–18 months, neural networks will begin to effectively use standard industrial tools and equipment as easily as they now write code. The only question is how quickly regulators and the labor market will adapt to this new reality.