A year after DeepSeek-R1 jolted the AI race, the US/China outlook is complex and challenging to forecast due to many variables at play. Here's what we know today, subject to change – or even another seismic shock like DeepSeek's first release brought. (Which, if it happens, could be on the hardware side.)
DeepSeek-R1 发布一年后，中美 AI 竞争格局变得复杂，难以预测——变量太多。以下是目前掌握的情况，随时可能变化，甚至可能再次出现像 DeepSeek 首发那样的地震级冲击（如果发生，可能会在硬件侧）
The US continues to lead on model capabilities, and US models also have maintained a meaningful lead in science and more complex reasoning. But China has made real progress in deploying models widely and cheaply.
On benchmarks, Chinese systems made up ground quickly in 2024, but despite headlines following DeepSeek-R1's release a year ago, China's progress in 2025 was uneven as US export controls limited access to the computing power needed to train and run frontier models.
What has changed more decisively is depth and deployability: China now has a broad field of near-frontier models, many of them open-weight and aggressively priced, making them easier to deploy across industries and government systems.
Usage data reflects that shift. On OpenRouter, Chinese open-source models grew from 1.2% to nearly 30% of weekly activity at their 2025 peak. Inside China, state-backed firms such as Z.ai (formerly Zhipu AI) are rolling large models into public-sector workflows, and Z.ai's IPO last week in Hong Kong signals Beijing's willingness to turn model builders into national infrastructure.
Yet Chinese constraints are real. Training delays, outages, and delayed next-generation releases point to a persistent bottleneck: compute. Demand for advanced US chips from Chinese AI companies remains high, underscoring how difficult it is to scale frontier models without reliable access to cutting-edge hardware. Meanwhile, export controls on US chips, and Chinese government views on imports of US chips continue to evolve.
但中国的瓶颈确实存在。训练延迟、服务中断、下一代模型发布推迟——都指向一个持续性问题：算力。中国 AI 公司对美国先进芯片的需求依然很高，说明没有可靠的尖端硬件供应，前沿模型的规模化极为困难。与此同时，美国对芯片的出口管制，以及中国政府对进口美国芯片的态度，都在持续演变
Beijing's strategy has solidified around three ideas: 1) build a domestic AI stack; 2) push it everywhere at home (including as a key pillar of the People's Liberation Army's military modernization); and 3) export it abroad. Open-source releases from companies like Alibaba, updated in weeks rather than months, are designed to make Chinese models the default for local developers. At the same time, China is packaging models, cloud infrastructure, and hardware for overseas partners, testing "sovereign AI" stacks from Southeast Asia to the Middle East.
The real contest in 2026 will turn on three questions.
2026 年的真正竞争将围绕三个问题展开：
1. Can US models extend their lead in real-world usefulness? The 2026 "gap" question increasingly turns on which AI ecosystem converts models into economically meaningful work. Western platforms such as OpenAI's Codex still lead in deployed Agentic coding and workflow tools, even as Chinese models continue closing the gap.
2. 美国模型能否扩大在实际应用中的领先优势？ 2026 年的「差距」问题越来越取决于哪个 AI 生态能将模型转化为有经济价值的工作。OpenAI Codex 等西方平台在已部署的智能体编程和工作流工具上仍保持领先，尽管中国模型持续缩小差距
2. Can China build enough compute? Export controls have historically limited China's access to advanced chips and memory as US hyperscalers have continued to expand data-center capacity. Scaling fast enough to close that gap remains a challenge. At the same time, the CCP is reportedly limiting the use of Western chips in new data centers and subsidizing Chinese chips, doubling down on self-reliance for a full AI stack.
3. Can China deploy at scale? Much of Beijing's AI+ agenda and related incentives aim to accelerate diffusion by pushing AI into priority sectors through procurement, standards, and subsidies. Results will hinge on whether AI+ drives productivity advances in manufacturing and other key sectors, while creating a product ecosystem comparable to that in the US. Internationally, China is bundling infrastructure, cloud platforms, models, and domestic hardware to foster reliance on their stacks. – OpenAI's Intelligence & Investigations Team
4. 中国能否实现规模化部署？ 北京「AI+」议程及相关激励措施的目标是通过采购、标准和补贴将 AI 推入优先行业，加速扩散。成效将取决于「AI+」能否推动制造业等关键领域的生产力提升，同时构建出与美国相当的产品生态。在国际上，中国正在捆绑基础设施、云平台、模型和国产硬件，培育对其技术栈的依赖
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