Challenges Facing China’s AI Industry Development

Currently, the global competition in artificial intelligence (AI) technology is intensifying. China’s AI industry is at a critical juncture, characterized by application leadership, foundational catch-up, and ecological breakthroughs. Facing external pressures such as computational power restrictions and talent competition, there are still many bottlenecks in areas ranging from high-end chips to foundational algorithms, and from original innovation to industrial transformation.

In recent years, many regions have closely aligned with national strategies, strengthening policy support, computational power backing, scenario openness, and technical breakthroughs to promote deep integration of AI with leading and emerging industries. For instance, on March 16, 2026, in the production workshop of Jiangsu Shalong Electromechanical Technology Co., an automatic detection line for electronic components was conducting inspection operations.

International Competition Squeezing AI Industry Development Space. Research shows that some Western countries have upgraded their policies towards China from single technology restrictions to systemic ecological blockades. Firstly, the “hard” blockade continues to intensify. The United States is tightening controls on AI chip sales to China, causing many domestic innovative teams to slow down the development pace of large models due to “computational power hunger.” Secondly, there are “soft” ecological barriers. NVIDIA’s graphics processing units (GPUs) dominate over 90% of the global market, and its unified computing architecture (CUDA) ecosystem has formed a closed-loop system over more than a decade, encompassing hardware, software, and developer communities. A domestic chip company in Shanghai reported that despite its hardware performance nearing international mainstream levels, clients are primarily concerned with “whether it can be compatible with CUDA.” The crux is that replacing chips is not a simple hardware swap but involves a complete system migration of development frameworks, operator libraries, debugging tools, and development habits. Millions of developers are deeply bound to the CUDA ecosystem, making migration costly and time-consuming. Even if domestic alternatives meet performance standards, large-scale application still faces obstacles. Thirdly, the competition for rule-making power is fierce. Global AI technology standards, governance norms, and cross-border data rules are largely dominated by Western countries. At the beginning of 2025, the DeepSeek large model made waves in the global market due to its technological breakthroughs, prompting several Western countries to issue bans or initiate strict reviews. The reality warns us that technological leadership does not guarantee market access; lacking discourse power can restrict the industry’s overseas expansion.

Large Models Face Reliability Crises in Specialized Scenarios. While large models perform impressively in general dialogue, their capabilities show significant deficiencies when applied to specialized fields such as industrial inspection, medical diagnosis, and financial risk control, where precision and reliability are critical. A manufacturing company reported that its AI visual inspection system misclassified good products as defective due to slight changes in lighting, leading to the release of defective products, which still required manual re-inspection. The phrase “impressive in demonstrations, but failing on the production line” has become a reality for many companies implementing AI. The issue lies in the fact that the generalization ability exhibited by large models in open-domain tasks does not naturally transfer to specialized scenarios where the tolerance for error approaches zero. The gap between being “able to speak” and being “reliably usable” represents a significant engineering challenge. The “hallucination” problem is also concerning. In general scenarios, such errors may be minor flaws, but in critical areas such as medical dosages, legal judgments, and financial risk control, each instance of “seriously misrepresenting facts” can trigger irreparable risks. This exposes a fundamental flaw of large models: they are essentially pattern matchers rather than logical reasoners. Transitioning from “being able to talk” to “speaking the truth,” and from “guessing answers” to “understanding causality” is a threshold that the industry must cross for deeper development.

High-Quality Datasets Still Fail to Meet Model Development Needs. Research indicates that a prevalent issue is the abundance of data “crude oil” but insufficient “refining” capabilities. The scale of globally available private data far exceeds that of public data; however, due to institutional barriers such as non-unified data standards, inadequate authorization mechanisms, and unclear compliance boundaries, a large amount of high-value data remains trapped in “data islands.” Although China possesses vast data resources, there is a severe shortage of data suitable for training large models. In globally applicable datasets of 5 billion scale, Chinese corpus accounts for only 1.3%. Furthermore, the obstruction of data circulation hampers China’s ability to fully leverage its data scale advantage into core competitiveness. Additionally, copyright and legal risks are continuously rising. A company expanding overseas reported that its video generation model faced accusations of unauthorized scraping of overseas platform videos for training, resulting in collective lawsuits abroad. If data sovereignty and copyright barriers evolve into new trade weapons, they could sever domestic companies’ legal access to high-quality international data resources.

AI Industry Application Commercial Loop Yet to Be Established. The AI industry application stands at a crossroads from policy-driven to market-driven, with sustainable business models still being explored. Firstly, there is a misalignment in the industrial chain. The computational power layer is expensive and insufficiently compatible with model layers, which are general but lack industry customization capabilities. The application layer consists mostly of single-point tool products that do not communicate with each other, leading to a lack of effective engagement mechanisms among computational power, models, and applications. Secondly, the profitability model for enterprises is unclear. Domestic user payment habits have yet to form, forcing many application companies to rely on project-based contracts or government subsidies for survival. The transition from “policy subsidies” to “market-driven growth” is crucial for the industry to emerge from its nurturing phase. Thirdly, scaling products for replication is challenging. An industrial AI founder admitted, “Three factory pilots succeeded, but when clients requested to change production lines, the solutions became obsolete. Without standardization, scaling is impossible; without scaling, we will always be burning money.” The difference between a “model room” and a “commercial property” is not merely in individual technologies but in a standardized product system that is configurable, replicable, and maintainable, which requires standardized interfaces across all links in the industrial chain.