China vs US AI Regulation: Two Wrong Answers, One Lesson

I’ve been整理AI监管资料 lately, and spotted an interesting pattern: China and the US are regulating AI in completely different ways now.

China’s approach: register first, operate later. LLMs need algorithm filings and security assessments before launch. Content needs review mechanisms. Users need real-name verification. The goal is clear — control content and data.

The US approach? Totally different. They focus on compute and open source. Chip export controls, foundation model open-source policies, research collaboration reviews… they care about who has the resources, who’s training the models.

Both approaches have logic. Both have problems.

China’s system: high compliance costs, heavy burden on companies. But content risks are relatively contained. When something goes wrong, you can find who’s responsible.

America’s system: innovation-friendly, more space to experiment. But lacks effective content controls. Deepfakes and disinformation are getting out of hand.

Honestly? Both countries are at extremes right now.

China’s problem: too granular, too high entry barriers. Innovative small companies can’t afford the compliance costs. Only giants survive. Less competition means a weaker ecosystem.

America’s problem: too loose, let things run too freely. Security incidents and deepfakes are rising, but the regulatory toolbox is almost empty.

My prediction: the two sides will converge eventually. China will gradually open up to smaller players. The US will gradually tighten content rules. That’s probably 3-5 years out.

Until then, building AI products means navigating two completely different rulebooks. That’s the real challenge.

Which approach do you think makes more sense?