Evaluating China’s Development in Artificial Intelligence

In the realm of artificial intelligence (AI), China has carved out a distinct path that diverges from the performance-driven approach of Silicon Valley and the regulatory-first stance of Europe. To truly understand the state of AI development in China, it is essential to compare it against the two major benchmarks: the United States and Europe. Together, these three regions form the core triangle of the global AI competition, each possessing substantial economic power, technological foundations, and clear national strategies.

Despite facing the same technological wave, these regions have made markedly different choices in four dimensions: model ecology, application direction, cost control, and global governance, leading to a clear tri-polar structure in AI development.

The United States: Elite Path of Technical Performance and Challenges in Real-World Integration

When comparing Chinese AI to that of the US, a common question arises: “Is core technology still lagging?” The answer to this question reveals fundamental differences in the developmental paths of the two countries.

The US has opted for an elite route focused on “technical performance and commercial value,” gathering top global talent and capital to maintain a lead in the absolute performance of general large models. According to Stanford University’s 2026 AI Index Report, the performance gap between top models in China and the US has narrowed to about 2.7%.

Models like Claude and the GPT series in the US still represent the pinnacle of performance in complex reasoning and creative generation tasks. This model aims to quickly achieve high commercial returns by replacing white-collar jobs in high-value professional scenarios such as law, finance, and research.

However, the Achilles’ heel of this approach lies in its integration with the real economy. While AI applications thrive in offices, courts, and trading floors, they struggle to penetrate factories, farms, and ports due to high computational costs, aging energy infrastructure, and strict labor regulations.

The development logic can be summarized as: Using extreme “brain” performance to prioritize conquering the highest commercial value in the “digital world”.

Europe: Regulatory Power and Systemic Innovation Shortcomings

Turning to Europe, we see a different imbalance. Europe leads the world in establishing ethical frameworks and standards for AI, with the EU’s AI Act being the first comprehensive AI regulatory law globally. However, this strong regulatory output has not translated into equivalent industrial competitiveness.

A report from Germany’s patent office econsight reveals a harsh reality: in nearly all frontier technology fields, except biotechnology, Europe has been significantly outpaced by China and the US. The systemic challenges include:

• Innovation Bottlenecks: Strict GDPR data privacy regulations and the AI Act, while protecting citizen rights, have significantly increased compliance and trial-and-error costs for businesses.
• Computational Shortcomings: Europe’s computational capacity accounts for less than 5% of the global total, insufficient to support large-scale AI research and training needs.
• Capital and Talent Drain: A conservative capital market that favors short-term profits struggles to support long-term, high-risk foundational AI research; top talent continues to flow to China and the US.

Europe’s path can be summarized as: Using stringent “traffic rules” (regulations and standards) to regulate a race it lacks the “top racing cars” (industry and computational power) for.

China: A Closed Loop of Technology, Industry, and Energy with Cost Advantages

In contrast, China’s core features and key differentiators in AI development become clear. China has not chosen to confront the US on the “absolute performance” track nor has it prioritized building a complete regulatory framework like Europe. Instead, it has leveraged its unique endowments to create an enhanced closed loop of technology, industry, and energy.

Key Differentiator One: Depth and Breadth of Application Scenarios. China possesses the world’s most complete and largest manufacturing system (41 industrial categories), providing an unparalleled training ground for AI. Research indicates that by 2025, the AI application penetration rate in China will reach 88%.

• In Guangdong, the “Yue Medical Intelligence” AI system has covered 2,146 public hospitals across the province, achieving a diagnostic accuracy rate of 98%.
• In Midea’s Jingzhou washing machine AI smart factory, AI has improved production scheduling response speed by 90%.

This capability of “entity AI” that integrates with sectors like steel, healthcare, and agriculture is difficult for Silicon Valley and Europe to replicate.

Key Differentiator Two: Extreme Cost Control Optimization. China has built significant cost advantages through a combination of “green power from the West + algorithm innovation.” The US Treasury Secretary has acknowledged that its lead in AI is now only 3 to 6 months, while the cost gap is an order of magnitude.

For instance, the API call costs for Chinese large models like DeepSeek can be as low as 1/700 of comparable US products; overall AI inference costs have dropped to 1/30 of those in the US. Low costs enable technology to rapidly penetrate and democratize, forming a “data flywheel” that strengthens with increased usage.

Key Differentiator Three: Open Source Ecosystem and Android-style Breakthroughs. Unlike the US’s closed elite model, leading Chinese AI companies generally adopt a strategy of “first open source, then build an ecosystem.” By 2025, China’s share of global downloads for open-source models will reach 17.1%, surpassing the US for the first time. Eight out of the top ten open-source models globally are from China.

This strategy quickly attracts global developers, particularly in emerging markets like Southeast Asia and the Middle East, building a strong ecological barrier.

Insights from Benchmarking: The Gains and Losses of the Chinese Model and Future Competitive Dynamics

Through horizontal benchmarking, we can more calmly evaluate China’s AI development:

• Advantages are Not Accidental: Its large-scale application capability, extreme cost control, and influence of the open-source ecosystem are deeply linked to China’s status as a global manufacturing hub, a unified market, and its energy infrastructure and engineering talent advantages, creating high barriers.
• Challenges Remain Clear: In areas like fundamental original algorithms, high-end AI chips (such as GPUs), and top research talent, there is still a need for continuous catch-up. Reports from Germany also indicate that the US maintains a lead in biotechnology and high-end semiconductors.
• The Global Landscape Has Changed: AI competition has shifted from a mere “technological race” to a stage of “systemic confrontation” supported by a complete industrial ecosystem and national computational energy foundation. A “bipolar” structure between China and the US has formed, while Europe faces the risk of marginalization.

Ultimately, this benchmarking reveals that there is no universally applicable path for AI development. The US relies on global talent and financial hegemony, Europe depends on regulatory traditions, while China’s path is rooted in its vast physical industry and system engineering capabilities.

In the future, the global AI ecosystem is likely to exhibit a form of “excellence in segmented scenarios”: while we may still look to Silicon Valley’s lighthouse in exploring the frontiers of science, China’s scalable, low-cost, and deeply integrated model has already provided a highly competitive “Chinese solution” for transforming AI technology into productivity across various industries and empowering global sustainable development.

Competition and cooperation will coexist for the long term, and the ultimate discourse power will depend on which model can more broadly benefit the overall progress of human society.