I talk to Divyansh Kaushik, a Carnegie Mellon machine learning PhD turned national-security advisor at Beacon Global Strategies, about the robotics race between the US and China and why winning the race matters for national security.
We dig into the state of robotic AI models—particularly vision-language-action (VLA) architectures—and why training them is harder than training LLMs. There's no internet-scale dataset of robot manipulation, so some companies are hiring humans in exoskeletons to perform real-world tasks. China has attacked this problem head-on, creating dozens of state-funded data-collection facilities.
Kaushik argues that the Pentagon, which once helped to bootstrap semiconductors and the early internet, could use its procurement and grand-challenge authorities to generate the contact-rich data American startups desperately need. We also explore China's hardware edge; Shenzhen's dense supply chains allow design iteration in a day, compared to weeks in the US.
Kaushik argues there’s an urgent national security case for US leadership in robotics. Unitree robots, which are increasingly used in academia and by law enforcement, have been observed transmitting video, audio, and other data to servers in China without the consent of users. Kaushik argues that the US was too slow to ban drones made by the market-leading Chinese firm DJI. And he worries that the US government will become even more reluctant to act as the next wave of Chinese-made robots enters American homes and factories.
