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.

The emergence of high-quality open-source models from China drastically shortens the innovation window of closed-source leaders. This competition is healthy for startups, providing them with a broader array of cheaper, powerful models to build on and preventing a single company from becoming a chokepoint.

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.

China is predicted to flood the market with low-cost, high-performance open-weight AI models. This competitive pressure will challenge the dominance and rich valuations of US AI giants like OpenAI, leading to a significant downturn in their related stocks.

While the U.S. AI strategy pursues a 'winner-take-all' model leading to high profits, China's state-backed approach aims to commoditize AI. By spreading resources across many players to create a low-cost, replicable model for export, it structurally limits the potential for monopoly profits to accrue to shareholders.

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.

Z.AI and other Chinese labs recognize Western enterprises won't use their APIs due to trust and data concerns. By open-sourcing models, they bypass this barrier to gain developer adoption, global mindshare, and brand credibility, viewing it as a pragmatic go-to-market tactic rather than an ideological stance.

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.