In-depth Research

Seizing the High Ground in the Intelligent Era: A Survey on the Development of China’s Artificial Intelligence Industry

In recent years, artificial intelligence (AI) has become the core engine of a new round of technological revolution, penetrating deeply into the fabric of human production and life, profoundly reshaping the global economic structure, innovation paradigms, and social governance logic. Currently, China has entered the first tier of global AI development and is at a critical opportunity period for transitioning from a follower to a leader. In the face of increasingly fierce international competition and the endogenous demand for high-quality development, we conducted field research to understand the current state of China’s AI industry, its development momentum, and existing shortcomings.

Current Development Trends of China’s AI Industry

General Secretary Xi Jinping has pointed out that “artificial intelligence is a strategic technology leading this round of technological revolution and industrial transformation, with a strong ’leading goose’ effect.” AI is not merely a linear iteration of a single technology or a partial upgrade of an industry; it represents a comprehensive and disruptive reconstruction of the underlying logic of economic and social operations. To assess its development level and trends, we must break away from traditional technical evaluation and industrial analysis frameworks, and conduct a comprehensive analysis from dimensions such as technical capabilities, industrial scale, element support, and integrated applications.

From a technical capability perspective, AI technologies led by open-source initiatives have achieved collective breakthroughs, forging new standards within the global developer network. During our research at a laboratory, we observed a research team implementing an AI self-criticism mechanism that required no human intervention. After multiple rounds of self-play, the model’s accuracy in solving complex programming problems significantly improved. AI has progressed from “being able to listen and see” to “thinking, reasoning, planning,” and even “mastering how to learn.” Overall, the gap between China and international top levels in key indicators such as model performance, training efficiency, and multi-modal integration continues to narrow, with some fields already achieving parity or leading. By 2025, China’s share of global downloads of open-source models is expected to reach 17.1%. Recent statistics show that among the top 10 open-source models globally, eight are from China. The DeepSeek—V4 model’s performance rivals that of the world’s top models, with API prices dropping to below 1% of the GPT—5.5 model. This has profound implications, breaking the technological monopoly of a few tech giants and enabling millions of global developers to conduct secondary development based on Chinese open-source models. Open-source not only provides benefits but also harnesses collective strength, as knowledge accelerates its flow and spillover in an open ecosystem, continuously forging self-evolution capabilities in China’s AI technology.

From an industrial scale perspective, the AI industry has experienced nonlinear explosive growth, with significant value spillover effects behind the trillion-dollar blue ocean. By 2025, the global AI market size is expected to reach $757.58 billion, with China’s core AI industry scale surpassing 1.2 trillion yuan. The significance of this 1.2 trillion yuan lies not only in the number itself but also in the growth logic behind it. Traditional industries follow the iron law of linear input and diminishing marginal returns, while AI breaks this curse, with technological breakthroughs and application diffusion mutually reinforcing, forming a positive feedback loop of “the more it is used, the stronger it becomes.” Our research found that Beijing, as an innovation source, is projected to reach a core AI industry scale of 450 billion yuan by 2025, with a batch of mature algorithm models acting as “digital technology pumps,” continuously delivering intellectual momentum to factories in Hebei, ports in Tianjin, and pastures in Inner Mongolia. Shanghai is leveraging the “Mold City” initiative to construct an ecological attraction through “Mold Speed Space,” while Shenzhen aims to create a highly concentrated enterprise ecosystem that precisely serves the real economy. Ultimately, the AI industry exhibits a clear multiplier effect of “investing one yuan to leverage several yuan,” with the trillion-scale being supported by a full industrial chain from underlying computing power to upper-level applications, from core algorithms to intelligent terminals, giving rise to new services, new divisions of labor, and new markets.

On April 24, 2026, DeepSeek officially released the preview version of its new series model DeepSeek—V4, which is also open-sourced. This model adopts the MoE architecture and natively supports ultra-long contexts of 1 million tokens. The model achieves domestic and open-source field leadership in agent capabilities, world knowledge, and reasoning performance. Visual China provided the image.

From the perspective of element support, China’s core AI resources have achieved a strategic leap, with institutional innovation accelerating the release of element vitality. The later stages of AI competition depend not only on how fast models run but also on how solidly the computing power foundation is built and how smoothly data flows. In these two core resources, China has established significant scale advantages. In terms of computing power, 42 intelligent computing clusters have been built, and as of the first quarter of this year, the scale of intelligent computing power reached 188.2 quintillion floating-point operations per second, ranking among the top globally. Regarding data, there are over 100,000 high-quality datasets nationwide, with a total volume exceeding 890 petabytes, equivalent to 310 times the total digital resources of the National Library of China. Moreover, institutional advantages are gradually becoming evident. In the data foundation pilot area of Beijing, a “regulatory sandbox” mechanism has been established to effectively break the deadlock, allowing companies to conduct integrated training in a protected “experimental field” without transferring data ownership. A technology manager from a company stated, “Previously, training with our small data led to increasingly biased models; now, the sandbox gathers real data from over 10 industries, significantly improving accuracy, making data more valuable with use.”

From the perspective of integrated applications, China’s AI is accelerating its penetration into various industries, with the breadth of applications and depth of integration establishing new global competitive advantages. By the end of 2025, the CNC rate of key processes in major industries in China is expected to reach 68.6%, with AI integrated applications transitioning from “point blooming” to “full-chain intelligence.” First, the penetration fields continue to expand, covering most major industries in the national economy, and forming a batch of benchmark applications in manufacturing, healthcare, transportation, finance, and energy. Second, the enabling level has significantly improved, advancing from auxiliary links to core links such as R&D design, production manufacturing, and operation management. In a heavy equipment manufacturing company in Shandong, we observed that a comprehensive industrial large model system took over the entire process from blueprint analysis, process planning to quality inspection, compressing the time for new process design from several weeks by multiple senior engineers to less than 72 hours, with a five percentage point increase in yield rate. Third, new business formats and models are emerging rapidly, with intelligent connected vehicles, AI pharmaceuticals, and embodied intelligent robots flourishing, continuously forming new trillion-level industrial tracks. Throughout the research, it became evident that in this global intelligent competition, whoever has the richest application scenarios, the closest integration, and the densest industrial feedback holds the standards and paradigms for defining how AI is used, where it is applied, and how deeply it is integrated, thus seizing the initiative in the intelligent era.

Problems and Challenges Facing China’s AI Industry Development

Currently, the global AI technology competition is becoming increasingly intense, and China’s AI industry development is at a critical juncture of application leadership, foundational catch-up, and ecological breakthroughs. In the face of external pressures such as computing power blockades and talent competition, there are still many “bottleneck” links and points of obstruction, from high-end chips to basic algorithms, from original innovation to industrial transformation.

International competition is squeezing the development space of the AI industry. Our research found that some Western countries have upgraded their policies towards China from single technology restrictions to systematic ecological blockades. First, “hard” blockades are intensifying. The U.S. has continuously increased restrictions on the sale of AI chips to China, forcing many domestic innovation teams to slow down their large model development due to “computing power hunger.” Second, “soft” ecological barriers are being constructed. NVIDIA’s GPUs dominate over 90% of the global market share, and its unified computing device architecture (CUDA) ecosystem, built over more than a decade, has formed a closed-loop system of “hardware + software + developer community.” We learned from a domestic chip company in Shanghai that although its hardware computing power indicators are close to international mainstream levels, customers are primarily concerned with whether it can be compatible with CUDA. The crux is that chip replacement 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, and even if domestic alternatives meet performance standards, large-scale applications still face obstacles. Third, the competition for discourse power over rules is fierce. Global AI technology standards, governance norms, and cross-border data rules are largely dominated by Western countries. In early 2025, the DeepSeek large model shook the global market with its technological breakthrough, 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 hinder the international expansion of industries.

Large models face reliability crises in specialized scenarios. While large models perform impressively in general dialogue, their capability deficiencies become apparent when entering fields that require precision and reliability, such as industrial inspection, medical diagnosis, and financial risk control. A manufacturing company reported that its AI visual inspection system misjudged good products as waste due to slight changes in lighting, allowing waste products to pass through, necessitating manual re-inspection. “Stunning during demonstrations, but failing on the production line” has become a true reflection of AI implementation in many companies. The issue lies in the fact that the generalization capabilities exhibited by large models in open-domain tasks do not naturally transfer to specialized scenarios with near-zero tolerance for errors. The gap between “being able to talk” and “being reliable” is significant. The “hallucination” problem cannot be overlooked either. In general scenarios, such errors may be minor flaws, but in contexts like medical dosages, legal judgments, and financial risk control, each instance of “seriously speaking nonsense” could 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 speak” to “speaking the truth,” from “guessing answers” to “understanding causality” is a threshold that the industry must cross for deeper development.

High-quality datasets still struggle to meet model development needs. Our research found a common issue: while there is an abundance of “raw oil” data, the “refining” capabilities are insufficient. The scale of globally available private data far exceeds that of public data, but due to institutional barriers such as non-unified data standards, inadequate authorization mechanisms, and unclear compliance boundaries, a large amount of high-value data is trapped in “islands.” Although China possesses vast data resources, the data truly usable for large model training is severely lacking. In globally common datasets of 5 billion scale, the proportion of Chinese corpus is only 1.3%. Furthermore, the bottlenecks in data circulation hinder the full transformation of China’s data scale advantage into core competitiveness. Additionally, copyright and legal risks are continuously rising. An overseas enterprise informed us that its video generation model was accused of unauthorized scraping of overseas platform videos for training, leading to a collective lawsuit abroad. If data sovereignty and copyright barriers evolve into new trade weapons, they could cut off domestic companies’ legal access to international high-quality data resources.

The commercial closed loop of AI industry applications has yet to be established. The AI industry application is at a crossroads from policy-driven to market-driven, and sustainable business models are still being explored. First, the “gear misalignment” in the industrial chain. The computing power layer is expensive and insufficiently compatible with the model, the model layer is general but lacks industry customization capabilities, and the application layer mainly consists of single-point tool-type products that do not communicate with each other, leading to a lack of effective engagement mechanisms among computing power, models, and applications. Second, the profit models of enterprises are unclear. Domestic user payment habits have yet to form, and many application companies can only rely on project-based contracts or government subsidies for sustenance. The transition from “policy blood transfusion” to “market blood production” is key to whether the industry can emerge from its nurturing phase. Third, scaling product replication is challenging. An industrial AI founder remarked, “Three factory pilot projects succeeded, but when the client requested a different production line, the solution became obsolete. Without standardization, there can be no scalability; without scalability, we will always be burning money.” The difference between a “showroom” and a “commercial property” is not in individual technologies but in a standardized product system that is configurable, replicable, and maintainable, which in turn requires standardized interfaces among all links in the industrial chain.

Accelerating the Development of China’s AI Industry Requires a Systematic Collaborative Battle

AI is a highly unique general-purpose technology, distinctly different from any frontier technology in historical technological revolutions. Firstly, it has a strong path dependency and ecological lock-in effect. The underlying chips define the form of computing power, the middle frameworks determine the development paradigm, and the upper applications deeply rely on the interface standards of the previous two layers—this highly coupled technical architecture means that once a first mover gains dominance at any layer, it can penetrate upwards and downwards, locking the entire industrial chain into its ecological system. Secondly, competition has evolved into a systematic game of interlocking links. Traditional technological competition focuses on single technologies, which can be broken through; however, AI competition encompasses a full-dimensional contest covering chips, frameworks, data, applications, and rules, where any shortcoming in one dimension could become the “Achilles’ heel” of the entire system. Thirdly, the diffusion cycle has been extremely compressed. The electrical revolution took a century to fully permeate society, and information technology took half a century to reshape business forms; however, AI is rewriting the underlying logic of industries at an instantaneous emergence, penetration, and transformation speed, greatly accelerating the conversion of first-mover advantages into lock-in advantages, leaving diminishing response time for followers. In this global competition that determines the future of AI, we are facing not just a “bottleneck” in a specific technology but a full-stack competition from underlying hardware to upper-level ecology, from technical standards to governance rules. To break the deadlock and seize the initiative, a singular breakthrough is insufficient; we must engage in a “full-element + full-ecology” systematic collaborative battle. It is essential to ensure that various elements such as computing power, data, algorithms, and scenarios flow freely, stimulate the innovative vitality of diverse entities such as enterprises, universities, research institutions, and developer communities, and align all forces under a national strategy to form a collective strength.

In recent years, Hebei has become an important node in the national computing power industry layout, accelerating the construction of a leading national computing power industry ecosystem with policies as guidance, infrastructure as the foundation, integrated development as the goal, and regional collaboration as the path. The “2025 Comprehensive Computing Power Index” shows that Hebei’s comprehensive computing power index remains first in the country. The image shows the Qinhuai Big Data Industrial Park in Huailai County, Zhangjiakou City, Hebei Province, taken on September 7, 2025. People’s Picture, Chen Xiaodong/Photographer.

Strengthening Core Technology Breakthroughs to Build a Solid Foundation for Autonomous and Controllable Development. The core technology breakthrough must upgrade the goal from chasing single indicators to a systematic operation driven by ecosystem building. First, it is essential to root in basic principles. If source innovation only focuses on the application and engineering layers, it will forever remain in others’ theoretical frameworks. More resources must be directed toward foundational research in areas like algorithm interpretability, causal reasoning, and brain-like computing to grasp the underlying logic that defines technological routes, fundamentally breaking away from path dependency. Second, targeted breakthroughs and large-scale iterations must be balanced. Focus on core links in the AI chip, development framework, and basic software industrial chain, implementing a “challenge and reward” mechanism for breakthroughs, concentrating efforts to overcome key bottlenecks. More importantly, technological breakthroughs must form a closed loop with market applications; only by investing domestic software and hardware on a large scale in real training scenarios and continuously iterating and optimizing through large-scale trial and error can market feedback nurture technological maturity, gradually forming an ecological attractiveness that can compete with first movers.

Optimizing Data Element Supply to Unblock High-Quality Supply Bottlenecks. China has obvious advantages in data resources, but it must address the two bottlenecks of “refinability” and “circulation.” First, build high-quality “data oilfields.” Relying on national-level data annotation bases, prioritize establishing standardized dataset systems in mature fields such as industry, healthcare, and finance, while increasing investment in data synthesis and intelligent enhancement technologies. Only by processing raw data into high-quality data directly usable for model training can data elements truly enter the production function. Second, use institutional innovation to unblock circulation bottlenecks. Accelerate the supply of foundational systems around property rights definition, revenue distribution, and safety compliance, promoting innovative models like “data sandboxes” and “regulatory sandboxes” to achieve multi-source data fusion training under the premise of ensuring ownership and safety, allowing data to truly realize value multiplication through flow.

Accelerating the Promotion of Scaled Applications to Build a Sustainable Commercial Closed Loop. Application scenarios are the ultimate battlefield for testing the quality of AI. The core challenge facing the current development of the AI industry is not the lack of good pilot projects but the inability to replicate good pilots in bulk. It is essential to deeply implement the “AI +” initiative. First, deeply embed AI into core business processes, pushing it from auxiliary scenarios into high-value links such as R&D design, production scheduling, and risk control, thereby significantly reducing costs and increasing efficiency to stimulate companies’ willingness to pay. Second, construct an industrial chain collaboration engagement mechanism. Promote deep coupling among computing power providers, model vendors, and industry users, forming a collaborative network that supplies computing power on demand, adapts models as needed, and rapidly implements scenarios, breaking the “each managing their own” situation with standardized interfaces. Third, firmly advance productization transformation. Transition from customized project-based solutions to configurable, replicable, and maintainable standardized solutions, diluting R&D and computing power costs through scaling, driving the industry from a money-burning cycle into a profit cycle.

Enhancing Safety Governance Capabilities to Establish a Secure Bottom Line for Industry Development. The black-box nature of AI, its self-evolving capabilities, and generalization abilities extend risk sources from external attacks to the “genetic defects” of the models themselves. Safety governance must upgrade from static compliance checks to dynamic protection throughout the entire lifecycle. First, establish a layered and categorized agile governance framework. Emphasize transparency and traceability for general foundational models, while implementing differentiated regulation based on risk levels for vertical application scenarios, such as strict certification and robustness assessments for high-risk fields like healthcare and finance, and lighter regulation for other low-risk scenarios, achieving a precise balance between safety and development. Second, strengthen internal security barriers of technology. Increase R&D investment in safety technologies such as algorithm interpretability, privacy computing, and adversarial training, and establish a routine model safety inspection mechanism to preemptively address risks with a “technical firewall,” making safety capabilities a “factory setting” of the model rather than an afterthought. Third, proactively lead the construction of global rules. Promote the transformation of China’s practical experiences in data classification, algorithm filing, and safety assessment into international governance solutions, seizing the initiative in rule-making within multilateral frameworks to avoid being locked in from behind.

Strengthening Multi-Dimensional Collaborative Guarantees to Build a Full-Element and Full-Ecology Support System. Systematic breakthroughs require matching institutional supply and element support. In terms of funding, it is necessary to cultivate truly patient capital that adapts to innovation. Leverage national funds to lead and form a patient capital matrix with local support, ensuring long-term investments in foundational breakthroughs and infrastructure construction. Simultaneously promote inclusive tools like “computing power vouchers” to lower the threshold for SMEs to participate in innovation. In terms of talent, focus on cultivating “dual-skilled talents” who understand both algorithm logic and industry pain points. Such composite talents cannot be mass-produced in classrooms but must be nurtured through long-term immersion in real industrial scenarios via partnerships between leading enterprises and universities. Accelerate the establishment of a composite talent training system with scale effects, forming a tiered supply from top scientists to large-scale application talents. In terms of open cooperation, it is essential to root in China while connecting with the world. Rely on mechanisms like the “Belt and Road Initiative” to support enterprises in deeply embedding themselves in the global innovation network through open-source collaboration and joint R&D, breaking non-commercial barriers under compliance, and enhancing competitiveness in open competition, thus seizing strategic initiatives in the new round of technological revolution and industrial transformation.

Research Notes:

From the perspective of the grand historical coordinates of human civilization evolution, the profound significance of AI may far exceed our current cognitive boundaries. It is not only a technological iteration or industrial upgrade but a systemic reshaping of human cognition and social organization forms. As machines begin to learn to learn, reason, and create, we face not only a technological competition but also a re-examination of humanity’s own position. Throughout the research journey, from the computing power artery woven by “East Data West Computing” to the data vitality activated by “regulatory sandboxes,” from the ecological wave stirred by open-source large models among global developers to humanoid robots working alongside humans on production lines, we deeply felt a vigorous upward force. This indicates that in this wave of technological innovation, we are no longer latecomers, followers, or catch-up players, but competitors in the same arena, and even leaders in certain fields. As global AI development and governance remain in a chaotic contest, our path choice is opening up a new possibility—replacing closure with openness, replacing monopoly with collaboration, and replacing control with empowerment in a new paradigm of intelligent civilization. Years later, when people look back at the starting point of this AI transformation, they may evaluate it this way: at the historical juncture of the new era, China did not hesitate or miss the opportunity but stepped forward decisively.