Cryptalist AI Analyst: Claude Opus 4.7 Rewrites the Rules of the Game in Robotics — Humans Left Far Behind
We have witnessed another tectonic shift at the intersection of artificial intelligence and robotics. Anthropic's Project Fetch experiment has reached a fundamentally new level, and the results are impressive even to the most skeptical analysts.
The Claude Opus 4.7 model demonstrated the ability to fully autonomously control a robot dog. This is not just about assisting a human, but about radical superiority: the AI completed setup and control tasks 20 times faster than a team of professional engineers. This is not evolution—it is a revolution.
Boundless Autonomy
Unlike the first phase of the experiment in 2024, where the AI served only as an auxiliary tool, Claude Opus 4.7 operated with virtually no human involvement. The neural network independently performed a set of highly complex operations:
- connected to video sensors and LiDAR;
- wrote a full-fledged program for manual robot control;
- created a trajectory monitoring system;
- configured an object recognition algorithm.
Key metric: Opus 4.7 was 18 times faster than teams using previous AI versions, and 37 times faster than humans working without chatbot assistance. At the same time, the generated code was 10 times more compact than human-written code—indicating a qualitatively different level of optimization.
The Physical Barrier Remains
However, one should not think that AI has already completely replaced humans. Claude Opus 4.7 excelled at logistics and navigation but failed at the final stage—gently nudging a ball. This task requires fine real-time feedback, where humans still maintain an advantage. Fine physical motor skills remain the last bastion of human superiority.
The most important takeaway: progress in robotics has become a side effect of the general scaling of language models. Anthropic did not create specialized algorithms for controlling hardware—this is pure magic of large language models.
My analytical commentary: Anthropic is absolutely right—we are entering the era of "physical AI agents." If the current pace continues, by the end of this year we will see neural networks capable of managing industrial equipment with efficiency unattainable by humans. Investors should pay close attention to startups working in the AI robotics segment—this is the next big bubble that will burst in our favor.