Framing the US-China AI dynamic as a zero-sum race is inaccurate. The reality is a complex 'coopetition' where both sides compete, cooperate on research, and actively co-opt each other's open-weight models to accelerate their own development, creating deep interdependencies.
Related Insights
The justification for accelerating AI development to beat China is logically flawed. It assumes the victor wields a controllable tool. In reality, both nations are racing to build the same uncontrollable AI, making the race itself, not the competitor, the primary existential threat.
By limiting access to top-tier proprietary models, U.S. policy may have ironically forced China to develop more efficient, open-source alternatives. This strategy is more effective for global adoption, as other countries can freely adapt these models without API limits or vendor lock-in.
While US firms lead in cutting-edge AI, the impressive quality of open-source models from China is compressing the market. As these free models improve, more tasks become "good enough" for open source, creating significant pricing pressure on premium, closed-source foundation models from companies like OpenAI and Google.
Joe Tsai reframes the US-China 'AI race' as a marathon won by adoption speed, not model size. He notes China’s focus on open source and smaller, specialized models (e.g., for mobile devices) is designed for faster proliferation and practical application. The goal is to diffuse technology throughout the economy quickly, rather than simply building the single most powerful model.
Counterintuitively, China leads in open-source AI models as a deliberate strategy. This approach allows them to attract global developer talent to accelerate their progress. It also serves to commoditize software, which complements their national strength in hardware manufacturing, a classic competitive tactic.
Unable to compete globally on inference-as-a-service due to US chip sanctions, China has pivoted to releasing top-tier open-source models. This serves as a powerful soft power play, appealing to other nations and building a technological sphere of influence independent of the US.
A nation's advantage is its "intelligent capital stock": its total GPU compute power multiplied by the quality of its AI models. This explains the US restricting GPU sales to China, which counters by excelling in open-source models to close the gap.
The emergence of high-quality, open-source AI models from China (like Kimi and DeepSeek) has shifted the conversation in Washington D.C. It reframes AI development from a domestic regulatory risk to a geopolitical foot race, reducing the appetite for restrictive legislation that could cede leadership to China.
A common misconception is that Chinese AI is fully open-source. The reality is they are often "open-weight," meaning training parameters (weights) are shared, but the underlying code and proprietary datasets are not. This provides a competitive advantage by enabling adoption while maintaining some control.
While the U.S. leads in closed, proprietary AI models like OpenAI's, Chinese companies now dominate the leaderboards for open-source models. Because they are cheaper and easier to deploy, these Chinese models are seeing rapid global uptake, challenging the U.S.'s perceived lead in AI through wider diffusion and application.
