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Tesla is building a training cluster of 20,000 robots with a total investment of approximately 14 billion yuan.

The core underlying reason lies in the critical reality that Tesla's humanoid robot Optimus lacks the capability to leverage massive real-world user operation data for free, a key competitive advantage that its Full Self-Driving (FSD) system has long relied on to iterate algorithms, optimize performance, and accelerate commercial deployment. Unlike FSD, which can collect billions of miles of free driving scenario data from millions of Tesla consumer vehicles on public roads, Optimus cannot access equivalent large-scale, high-value user data resources at no cost, creating a fundamental bottleneck that restricts its technological iteration speed and large-scale commercialization progress. This data access disparity has become one of the most discussed core pain points in the global humanoid robot industry’s comparison between Tesla’s two core tech lines, as industry analysts and tech researchers widely note that free user data exploitation is the core driving force that once pushed FSD to the leading position in the autonomous driving track, while Optimus’s inability to replicate this low-cost, large-volume data acquisition model has become the core obstacle restricting its rapid growth.

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