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　　Artificial Intelligence (AI), as a general-purpose technology spanning multiple fields and industries, is fueling a new wave of sci-tech revolution and industrial transformation. With support from initiatives such as the National New Generation AI Innovation and Development Pilot Zones and the National AI Innovation and Application Pilot Zones, regions like Beijing, Guangdong, and Zhejiang are accelerating the concentration of innovation resources along the AI industry chain, establishing themselves as leading hubs for China’s AI industry clusters. Recently, the State Council Executive Meeting reviewed and adopted the Opinions on Deepening the Implementation of the “AI Plus” Initiative, stressing the need to promote the deep integration of technological and industrial innovation in AI. Leveraging its strengths, including a comprehensive industrial system, a vast market, and a wealth of application scenarios, China is building AI industry clusters with international competitiveness, a critical step in strengthening its national competitive edge.

　　Five Systems of the AI Industry Cluster

　　An AI industry cluster is a new organizational form that emerges from the geographic and digital concentration of enterprises and stakeholders across the AI innovation and industry chains. An AI industry cluster with international competitiveness should comprise at least five subsystems: an industrial and technological innovation subsystem, an industrial chain and application innovation subsystem, a digital infrastructure subsystem, a talent and capital support subsystem, and a cluster governance and open collaboration subsystem.

　　The industrial and technological innovation subsystem focuses on knowledge creation and application, connecting innovation subjects including universities, research institutions, and corporate R&D centers. It emphasizes fundamental research and cutting-edge breakthroughs in AI, encompassing key areas such as algorithms, models, chip design, operating systems, and software frameworks. This subsystem aims to cultivate forward-looking, original innovation capabilities and ensure the efficient flow of knowledge in key AI technologies.

　　The industrial chain and application innovation subsystem focuses on value creation and distribution, leveraging diverse industry players such as leading enterprises, platform companies, specialized and sophisticated enterprises that produce new and unique products, and digital commerce enterprises. It links the full value chain, from hardware foundations and software platforms to application scenarios, facilitating the broad transformation and diffusion of AI technological achievements and ensuring the continuous release of value across the ecosystem.

　　The digital infrastructure subsystem focuses on positioning data as a critical production factor and unlocking its value. Data centers, cloud and edge computing platforms, data exchange platforms, and computing power service providers collectively form a digital backbone that supports the collection, storage, processing, transmission, governance, and sharing of data, forming a computing power network that covers the entire “cloud–edge–end” spectrum, thus ensuring secure and efficient data flows.

　　The talent and capital support subsystem focuses on the supply of human and financial capital. By combining talent cultivation and mobility mechanisms across universities, research institutes, and enterprises with funding from venture capital, industry funds, and government-guided funds, it provides AI industry clusters with a pipeline of multidisciplinary talent and diversified financial resources, ensuring the efficient allocation and continuous supply of both human and financial resources.

　　The cluster governance and open collaboration subsystem focuses on institutional provision and open cooperation. Government agencies, industry associations, standardization bodies, and international cooperation platforms collaboratively build comprehensive policies, standards, and governance frameworks, fostering a responsible and sustainable institutional environment. They also facilitate cross-regional, cross-industry, and international collaboration, ensuring the seamless alignment and efficient integration of knowledge, value, data, talent, and capital flows both within and beyond the cluster.

　　The five subsystems are guided by knowledge innovation, driven by industrial chain coordination, underpinned by digital infrastructure, supported by talent and capital, and steered through effective governance and collaboration. Together, they enable the deep integration of the innovation, value, data, talent, and capital chains, continuously strengthening the core competitiveness of AI industry clusters.

　　Three Innovation-Driven Development Models of the AI Industry Cluster

　　Building AI industry clusters with international competitiveness requires a three-dimensional approach encompassing “technology, market, and institutions.” This gives rise to three innovation-driven development models: a technology-innovation-driven model, an application-scenario-innovation-driven model, and an institutional-supply-enabled model.

　　The technology-innovation-driven model emphasizes original innovation and cutting-edge breakthroughs in AI. By bringing together innovation subjects, including research institutions, universities, and leading enterprises, it fosters robust R&D capabilities and a vibrant knowledge network, enabling independent breakthroughs in core technologies such as algorithms, models, chips, operating systems, and frameworks. As technological knowledge accumulates and spillover effects intensify, the upstream and downstream segments of the industry chain are gradually strengthened, forming a clustered hub of AI innovation. For example, the AI cluster in Silicon Valley, supported by leading universities such as Stanford University and University of California, Berkeley as well as companies like OpenAI, NVIDIA, and Google DeepMind, has developed a strong network for AI fundamental research and application R&D. It has gradually established a full cycle from venture capital and start-up incubation to technological breakthroughs and industrial application, maintaining a long-standing leadership in AI innovation.

　　The application-scenario-driven model focuses on leveraging application demand and open scenarios to create a market environment driven by real needs through the large-scale deployment of application scenarios, allowing companies to iterate and refine technologies while addressing scenario challenges. This approach fosters clustering and collaboration among upstream and downstream enterprises, establishing a virtuous cycle connecting scenarios, technologies, and the industry. For example, Hangzhou’s AI industry cluster, which has fostered the so-called “Six Tigers,” builds on a strong digital economy foundation and a wide range of application scenarios, including smart cities, intelligent logistics, healthcare, and fintech. The cluster has attracted numerous enterprises, accumulated vast amounts of data, and achieved significant technological breakthroughs, gradually emerging as a leading AI industry hub in China with international influence.

　　The institutional-supply-enabled model focuses on institutional supply and open collaboration. By establishing complete policies, standards, and ethical governance frameworks, it fosters a favorable development environment while promoting cross-regional, cross-industry, and international cooperation, creating a high-level, open innovation network. For example, in 2017, the Canadian government launched the world’s first national AI strategy, providing substantial funding for AI research, talent development, and the establishment of ethical standards. Thanks to its advanced, innovation-driven institutional support, Montreal’s AI industry cluster has attracted top scholars from around the world, established the Global Open Partnership on AI, and fostered a diverse industrial ecosystem alongside a thriving startup environment. As a result, a vibrant hub of AI innovation is gradually taking shape.

　　Four Key Focus Areas for Enhancing International Competitiveness

　　To build an AI industry cluster with international competitiveness, it is crucial to focus on the following four key areas. Efforts should be made to maintain a balance between technology-driven and scenario-driven approaches, properly handle the relationships between an efficient market and a well-functioning government, and ensure both security and development.

　　First, it is essential to strengthen innovation and build an internationally competitive AI sci-tech innovation hub. We should fully leverage the advantages of the new system for mobilizing the resources nationwide, establish resource allocation mechanisms that support breakthroughs in frontier technologies, and ensure the supply of high-quality technologies from the source. The role of enterprises as primary drivers of innovation should be reinforced, promoting deep integration of industry, universities, and research institutes. Enterprise-led AI collaborative innovation networks and platforms should be developed to cultivate a cohort of AI companies that master core technologies.

　　Second, it is essential to strengthen applications and accelerate the deployment of AI to empower the new industrialization. China’s advantages—a complete industrial system, a large market scale, and diverse application scenarios—should be fully leveraged to continuously promote the deep integration of AI technological innovation with industrial development. In key industries, scaled-up applications should drive coordination across the industry chain and foster technological iteration. Meanwhile, efforts should be made to proactively and orderly advance “going global” strategies, aiming to move up the value chain and achieve efficient circulation of value.

　　Third, it is essential to strengthen the foundation by building an advanced digital infrastructure and a robust data flow system. Efforts should be made to further enhance computing power networks, data centers, and cross-domain data platforms to enable controlled data collection, storage, governance, sharing, and security. Data markets, data vouchers, and cross-domain data spaces should be established to ensure the efficient flow of data within and across clusters, supporting the implementation of technological innovation and applications.

　　Fourth, it is essential to strengthen the ecosystem by creating an industrial environment that is internationally attractive and competitive. Guided by the integration of education, technology, and talent, a multi-level system for AI talent development, recruitment, and mobility should be established. The allocation of government-directed funds, venture capital, and industrial investments should be optimized, while policies, standards, intellectual property, and ethical and security governance are enhanced. Efforts should also be made to advance international standards formulation and cross-border cooperation, while reinforcing compliance and risk management. (Authors: Liu Yang, Professor and Deputy Dean, School of Management, Zhejiang University; Wu Wei, Associate Researcher, School of Public Administration, Zhejiang University)