China is leveraging state-supported companies to release powerful, open-source AI models at drastically lower prices. The core strategy is not to build the single best model, but to commoditize the market, capture global usage, and undermine the pricing power of Western competitors.

While the US pursues cutting-edge AGI, China is competing aggressively on cost at the application layer. By making LLM tokens and energy dramatically cheaper (e.g., $1.10 vs. $10+ per million tokens), China is fostering mass adoption and rapid commercialization. This strategy aims to win the practical, economic side of the AI race, even with less powerful models.

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.

China is pursuing a low-cost, open-source AI model, similar to Android's market strategy. This contrasts with the US's expensive, high-performance "iPhone" approach. This accessibility and cost-effectiveness could allow Chinese AI to dominate the global market, especially in developing nations.

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.

While a global token shortage suggests rising costs, Chinese AI firms like DeepSeek are employing a counter-strategy: permanent, drastic price cuts. This is not driven by efficiency gains but is a deliberate tactic to lure cost-sensitive global customers away from premium models. This uses price as a geopolitical lever for market penetration.

China is compensating for its deficit in cutting-edge semiconductors by pursuing an asymmetric strategy. It focuses on massive 'superclusters' of less advanced domestic chips and creating hyper-efficient, open-source AI models. This approach prioritizes widespread, low-cost adoption over chasing the absolute peak of performance like the US.

Beyond low electricity costs, Chinese AI models achieve a structural cost advantage through their "mixture of experts" architecture. This technical approach, spurred by US chip restrictions, requires less computing power to generate tokens compared to prevalent US systems.

China is gaining a structural advantage in the global AI race by producing and exporting AI tokens—the computational fuel for LLMs—at a fraction of the cost of US alternatives. This is attracting global startups and creating geopolitical dependency on China's "new oil."