The Development Of AI Technology in China

The development of AI in China can be summarized as follows: from trailing behind to competing on par, and then taking the lead in some areas; strong application capabilities, numerous patents, fast computing power; a leap forward in autonomous intelligent agents and full-stack self-research (as of April 2026).
I. Development Stage: Three-stage Transition
• Before 2015 (in the early stage): Focused on research, computer vision (CV) made the first breakthrough, and applications in security and finance were initially implemented.
2016 - 2022 (Catch-up Period): A series of policies were introduced, and capital flowed in; Baidu, Alibaba, Tencent, and Huawei launched large-scale model development; CV (Computer Vision) and NLP (Natural Language Processing) applications led globally.
• 2023年至今(并列领先/领先)：Generative AI has taken off, with DeepSeek, Wenxin Yiyan, Tongyi Qianwen, Hongyuan, Pan Guan, etc. ranking among the top globally; the open-source ecosystem is active, the computing infrastructure is well-developed, and the industry scale has exceeded one trillion.
II. Core Strengths (Latest as of 2026)
1️⃣ Large Model: World-leading, Chinese-speaking strongest
• Representatives: DeepSeek V4 (open source, with over 1 million contexts), Tongyi Qianwan, Wenxin Yiyan, Tencent Hengyuan, Huawei Pan Gu, Xiaomi Mimo.
• Advantages: Leading in Chinese language understanding; The inference cost is 50% - 70% lower than that of GPT-4; The download volume of the open-source model ranks first globally (Yi Tong Qian Wen series exceeds 10 billion times).
• Scope: The global API usage volume of domestic models accounts for over 65%, surpassing that of American models for the first time.
2️⃣ Computing power and chips: Autonomous and controllable acceleration
• Computing power: 42,000-node artificial intelligence computing clusters, with an intelligent computing capacity exceeding 1,590 EFLOPS, ranking among the top globally.
• Chip: The Huawei Ascend 950PR has a performance that is 2.87 times that of NVIDIA's H20; companies like Huanwu, Haiguang, and Diheng have achieved mass production; by 2026, the share of domestic AI chips will reach 50%.
• Infrastructure: Supercomputers account for 45% of the global total, with 4.395 million 5G base stations. The advantage in data volume supports AI training.
3️⃣ Technological Breakthrough: Multimodal + Embodied Intelligence
• Multimodal: Seedance 2.0 can generate 60-second cinematic-quality videos; its understanding of text, images, and audio-video content is comparable to the top-level international standards.
• Agent (Entity): The world's first universal intelligent being, "Tongtong" 3.0, possesses autonomous planning capabilities and explainable decision-making abilities, with a mental level comparable to that of a five or six-year-old child.
• Robot: Humanoid robots can perform delicate tasks (peeling walnuts, stringing sausages), and are entering commercial testing.
• Research AI: Accelerates discoveries in fields such as materials, biology, and astronomy.
4️⃣ Industry Scale: Trillion-level, Penetrating All Industries
• Core industry: It is estimated to reach 1.2 trillion yuan in 2026, with over 6,000 enterprises involved.
• Application Penetration:
◦ Manufacturing: AI quality inspection, intelligent factories (Midea's global first fully-automated AI water heater lighthouse factory).
◦ Healthcare: AI-assisted diagnosis, drug development.
◦ Transportation: Baidu Apollo has conducted over 70 million kilometers of autonomous driving tests and has launched driverless taxi services.
◦ Government affairs/safety: Leading global in facial recognition and smart city technologies.
5️⃣ Innovation Output: First in the world in terms of patents/academic papers
• Patent: AI patents account for 61.5% of the global total, which is approximately 4 times that of the United States; generative AI patents have surpassed the combined total of all other countries since 2017.
• Paper: In 2023, there were 12,450 generative AI papers, slightly exceeding that of the United States (12,030 papers); the papers with high citation rates contributed the most globally.
III. Policies and Ecology
Top-level design: "14th Five-Year Plan" for AI, "Opinions on the 'Artificial Intelligence+' Initiative", "Special Action Plan for 'Artificial Intelligence+' in Manufacturing", with the goal of 1,000 industrial intelligent agents by 2027.
• Open-source ecosystem: Domestic open-source models (such as DeepSeek, Qwen) are active, reducing the threshold for small and medium-sized enterprises to utilize AI.
• Talents: It has cultivated 47% of the world's top AI researchers (in 2022), up from 26% in 2019.
IV. Gaps and Challenges
• Basic research: Original algorithms and theoretical innovations still lag behind those of the United States.
• High-end chips: High-end chips for training are still relied upon, while mass production and ecosystem development need to be improved.
• Multilingual capabilities: The ability to understand and reason in multiple languages is slightly inferior to that of GPT-5 and other similar models.
V. Trends (2026 - 2030)
From large models to intelligent agents: AI has evolved from "passive response" to "active action", autonomously carrying out complex task chains.
2. Full-stack self-developed: From chips to models, frameworks, and applications, all are fully controllable and independent, reducing external dependencies.
3. Deep integration of AI and physical entities: All industries including industry, healthcare, transportation, and agriculture will achieve full intelligence, giving rise to new forms of industries.