Can China Overtake the US in the AI Race? A Chart-Based Analysis

Summary

This chart-based analysis argues that the China-US AI race is not a single contest. The United States leads in frontier model capability by roughly eight months and has a large advantage in usable AI compute: about 15 million H100-equivalents versus 2.8 million in China, with much of China’s supply smuggled, rented abroad or made with less capable chips. The article identifies advanced chip manufacturing as China’s most serious bottleneck. Nvidia produces more chips and far more compute than Huawei, while China lacks access to ASML’s EUV lithography machines; its SMIC process technology also trails TSMC. Energy presents a more mixed picture. China added more electricity in 2025, but US data centres consumed 65 TWh of newly added power compared with 15 TWh in China, and US data-centre electricity projections are steeper. China can build power lines and data centres faster, and its western computing hubs offer cheaper electricity, although some facilities remain underused because GPUs are unavailable. Despite less compute, Chinese models are assessed as only about eight months behind US frontier systems, helped partly by engineering ingenuity and distillation. The analysis treats commercial adoption as contested, while China leads in open-weight model diffusion: Alibaba’s Qwen reportedly received about 2 billion Hugging Face downloads from January to July 2026 and had more derivative models than Meta’s models. It also says China had more elite AI researchers working domestically than the US in 2025. The conclusion is conditional: the US leads frontier models and chips, China leads or competes strongly in talent and open weights, and the outcome depends on which capability becomes decisive. The article also discusses cooperation, from an incident hotline and shared safety tests to compute verification and possible joint development, while noting that mutual restraint requires observable actions.