White House accuses Moonshot of violating US export controls using Nvidia hardware

Investing.com -- A top White House official accused Chinese AI startup Moonshot of illicitly accessing cutting-edge US technology, alleging the lab used NVIDIA hardware and proprietary American AI models to build its flagship Kimi K3 system.

Michael Kratsios, Director of the White House Office of Science and Technology Policy, stated on social media that Moonshot acquired servers equipped with NVIDIA GB300 chips and routed workloads through GB300-enabled infrastructure in Thailand to train its models. Kratsios argued that these actions violate both US export regulations and the terms of service set by American AI developers.

The semiconductor systems in question belong to NVIDIA’s Blackwell generation (specifically the GB300 platform), which the US government explicitly bars from export to Chinese companies. While the US administration has granted exemptions for lower-tier processors such as the H200, it maintains a strict ban on high-end Blackwell hardware.

Federal investigators at the Bureau of Industry and Security (BIS)—the agency enforcing technology transfer restrictions—are examining whether Chinese entities have bypassed restrictions by utilizing overseas subsidiaries or cloud infrastructure in third-party countries like Thailand.

NVIDIA has previously maintained that it complies with all export control regulations and actively enforces compliance across its sales channels. The company did not immediately comment on the White House’s latest statement.

The dispute comes on the heels of Moonshot’s Kimi K3 release, an open-weight model that surprised industry analysts by narrowing the performance gap between Chinese labs and US frontier AI companies. Moonshot claims K3 rivals leading American models, asserting it trails only OpenAI’s GPT-5.6 and Anthropic’s Claude Fable 5 in benchmark testing.

According to Kratsios, Moonshot achieved these gains in part by developing an internal platform to conduct model distillation at scale. Distillation is a technique where developers use the outputs of advanced "teacher" models—in this case, proprietary US systems—to rapidly train smaller or open-weight "student" models to replicate their capabilities.

You May Also Be Interested In

• From microchips to missile threats: How the U.S.-China AI race took a dark turn
• Thinking Machines Lab seeks $1b raise at $40b valuation - report
• Citi highlights best Gen AI stocks to own for 2027