China's AI Self-Reliance: The Full-Stack Strategy and Industry Competition from Chips to Large Language Models

China's AI Self-Reliance: The Full-Stack Strategy and Industry Competition from Chips to Large Language Models

Industry Context

Currently, artificial intelligence has become a core frontier of Sino-US geopolitical competition. Driven by the wave of generative AI, AI is seen as a key technology for reshaping social and military power. Faced with the US technological restrictions and export controls in the AI field, China has elevated the "self-reliance" AI ecosystem to the level of national security and economic sovereignty. This strategy is not just about catching up technologically; it is a determination to build an integrated "AI technology stack" covering everything from computing hardware and software frameworks to the final application layer.

The shift in China's AI strategy marks a transition from merely pursuing technological catch-up to a systematic layout of a complete and controllable AI industry chain. This requires China to adopt differentiated strategic deployments at three key levels: AI infrastructure, model R&D, and application landing.

Market Impact

Impact on Enterprises: Enterprises are facing a reality of "dual tracks." On one hand, competition is extremely fierce at the AI application layer (such as large model applications and Agent development), requiring enterprises to rapidly adopt and implement technologies to seize market share. On the other hand, the supply of AI infrastructure (especially high-end GPUs) is constrained by external factors. Enterprises must pay close attention to supply chain resilience and the potential for domestic substitution in terms of computing power acquisition, model procurement, and technology selection.

Impact on Investors and Capital: The focus of capital flow is shifting from simply "implementing AI applications" to "breaking through core AI underlying technologies." State guidance on capital has provided strategic support at the national level for R&D investment in semiconductors, AI chip design, and AI foundational software frameworks. This foreshadows that future investments in the AI field will be more policy-driven, favoring hard technology solutions that solve "bottleneck" problems.

Competitive Landscape: Competition has shifted from a simple race for model parameters to competition for "technology stack completeness." China's deep integration into AI applications and the open-source community is balanced by the US's lead in foundational model top-tier technology. Regions like Europe are exploring paths for "sovereign AI," trying to find a balance between technological independence and international cooperation.

Competitive Landscape

The layout of China's AI technology stack shows a clear hierarchical division and different focuses of various vendors:

1. Bottom of the stack: Chips and Computing Power. This is the foundation for all AI computation. The Chinese government views AI chips as one of the most core strategic areas. Although China has made progress in the domestic design and manufacturing of AI chips, their performance still lags behind international leaders like NVIDIA. At this level, the state provides heavy capital support through mechanisms like the "Big Fund" to accelerate vertical integration from design to manufacturing.2. Middle of stack：Machine Learning Frameworks. This layer is mainly composed of open-source frameworks and is currently a participant in the global open-source ecosystem. Chinese tech giants are investing heavily in the research and development of domestic machine learning frameworks, but the user base's acceptance of international mainstream frameworks remains high. This indicates that China employs a strategy of "local optimization" and "global compatibility" in its technology selection.

3. Top of the stack：Large Models and Applications. This is the most fiercely competitive area. At the model application level, China is deeply involved in the global open-source community through domestic LLM developers like DeepSeek, achieving rapid innovation and iteration. However, in training large-scale foundation models, China still faces external dependency challenges regarding data, computing power, and high-end hardware.

Enterprise Implications

Business decision-makers should pay attention to the following points:

• Infrastructure Resilience: Assess the enterprise's dependency on external supplies (especially GPUs and advanced computing power) and formulate risk contingency plans for multi-source procurement or domestic substitution. Cost-benefit analysis for AI deployment must incorporate strategic considerations for obtaining computing power.
• Technology Stack Synergy: Focus on how enterprises can effectively couple upper-layer applications (such as AI Agents) with lower-layer infrastructure (models, frameworks) to achieve true business value transformation, rather than remaining at the tool level.
• Compliance and Security: As AI regulation gradually takes effect, enterprises must establish data governance and model security systems in advance to ensure AI applications comply with domestic regulations and to avoid potential legal and technical risks.

Outlook

Next 12 Months: Competition will further focus on the deep implementation of "full-stack self-reliance and control" for AI. China's pace of implementation in chips and specific fields (such as vertical industry AI applications) will be a key indicator of strategic success or failure. Enterprises will accelerate the shift from "AI experimentation" to "AI engineering and落地 (implementation)".

Next 24 Months: As the performance of domestic AI chips gradually improves and the open-source ecosystem matures further, China will demonstrate stronger technological confidence and innovation speed at the model application layer. Europe and North America will continue to compete in "sovereign AI" and technology standard setting, forming new benchmarks for China's technological roadmap.

Next 3 Years: The industry landscape will become clearer: China will build a relatively independent, internally driven AI ecosystem and achieve rapid scaling in specific application scenarios. The global AI market will show a trend of technological standard divergence and regionalization, and the differences in Sino-US technological roadmaps will continue to affect the boundaries of global cooperation.

SEO & Key Takeaways

Core Insight: China's self-reliance strategy for AI is a systemic project; its success or failure depends on the precision of national resource allocation and the effectiveness of industrial synergy at different technology stack levels.## SEO & Key Takeaways

Core Insight: China's AI self-reliance strategy is a systemic project, and its success or failure depends on the precision of national resource allocation across different technological stacks and the effectiveness of industrial synergy. This is not just a technological competition; it is an industrial reshaping at the national strategic level.

Key Focus Areas: The process of domesticating AI infrastructure, the openness of the foundation model ecosystem, and how enterprises can transform cutting-edge AI technologies into quantifiable business ROI.

Keywords: Enterprise AI, OpenAI, Anthropic, NVIDIA, Microsoft AI, NVIDIA, industry, enterprise AI, AI infrastructure, generative AI, AI adoption, AI governance, AI agents, AI ecosystem, investment, AI investment, AI market trends, AI startups