Sacks Rejects Using Regulatory Uncertainty Against Chinese AI
- •David Sacks criticized using regulatory uncertainty as a strategic tool to suppress Chinese AI model adoption.
- •Chinese AI models reached a record 58% of tokens processed by U.S. firms, nearly tripling since January.
- •Sacks warned that undermining open-source competitors through regulatory fear could compromise institutional trust and favor proprietary firms.
David Sacks, formerly a White House AI and crypto advisor, publicly denounced using regulatory uncertainty to curb the adoption of Chinese artificial intelligence models. Responding on X to researcher Dean W. Ball, who proposed creating 'soft law' to generate fear, uncertainty, and doubt (FUD) around foreign systems, Sacks argued that regulatory policy must remain grounded in evidence rather than strategic manipulation. He warned that such tactics threaten the integrity of the regulatory process and could unfairly benefit dominant firms with proprietary models by eliminating open-source competition.
The debate follows a significant shift in model usage among U.S. developers. According to OpenRouter, Chinese AI models reached a record 58% of tokens processed by U.S. firms, a usage share that has nearly tripled since January. During this period, the adoption of U.S. models declined significantly. Systems from companies like DeepSeek have driven this trend, while the Qwen family from Alibaba Group Holding Ltd. has surpassed 700 million downloads on Hugging Face. Amid this growth, Chinese President Xi Jinping recently called for international cooperation on AI at the 2026 World AI Conference, emphasizing that the technology should be treated as a shared global effort.