The Claude Opus 4.7 neural network has outperformed human engineers in controlling a robot dog: speed is 20 times higher
The artificial intelligence market continues to surprise with its pace of progress. A new phase of the Project Fetch experiment by the Anthropic team showed that AI is capable not just of assisting, but of completely replacing humans in complex engineering tasks. The Claude Opus 4.7 model handled the setup and control of a robot dog 20 times faster than an entire team of human engineers working a year earlier.
For context: in August 2024, Anthropic employees with no robotics experience attempted to program a four-legged robot. At that time, AI only acted as an assistant, speeding up the search for solutions. In the new testing phase, everything changed dramatically.
Autonomous operation without human involvement
Under minimal researcher supervision, Claude Opus 4.7 operated almost entirely autonomously. The neural network independently completed four key stages:
- connected to video sensors and LiDAR;
- wrote a program for manual robot control;
- created a trajectory monitoring system;
- configured an object recognition algorithm.
The numbers are even more impressive: the Opus 4.7 model was 18 times faster than the team using older AI versions, and 37 times faster than humans working without chatbot assistance. Moreover, the neural network generated code that was 10 times smaller in volume than that of human teams — indicating higher efficiency and conciseness of solutions.
Side effect of scaling
Notably, Anthropic did not implement specialized algorithms for controlling the hardware. According to the developers, the progress in robotics became a side effect of the general scaling of language models. This confirms the hypothesis that universal AI systems can adapt to physical tasks without additional tuning.
However, complete superiority over humans has not yet been achieved. Claude successfully guided the robot to the target but could not neatly push a ball to the right spot. Tasks requiring complex real-time feedback remain a weak point for neural networks. Humans are still stronger here.
The era of physical AI agents
Anthropic is confident: the industry is entering the 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. This opens up enormous prospects for automation in industry, logistics, and services.
Let me remind you that on June 13, Anthropic suspended access to the Fable 5 and Mythos 5 models due to a directive from the U.S. government on export controls. However, technology development continues, and Project Fetch is just one vivid example of how quickly the AI landscape is changing.
My comment: The results of Project Fetch are not just a speed record, but a signal of a paradigm shift. If earlier AI was perceived as a tool for data analysis, now it is becoming a full-fledged agent in the physical world. Investors and developers should prepare for the next 2–3 years to bring explosive growth in autonomous robotic systems controlled by neural networks.