CHINA'SS AI NATIONAL STRATEGY CHINA'S AI NATIONAL STRATEGY

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1. China’s National AI Strategy Muhammad Aufa Cholil Fayyadl Nishat Naoal Oishee Putri Santika Mayangsari Porto Mauritio Hartley

2. Introduction China’s fast-growing $70 billion AI industry sees soaring optimism, yet collective effort is key to unlocking scalable impact. Today, China’s artificial intelligence (AI) industry is large and growing fast: it now exceeds $70 billion and has cultivated over 4,300 companies that have contributed to a continuous stream of breakthroughs. This transformation is propelled by a dynamic interplay between market forces and government initiatives, all operating within a comprehensive framework designed to promote innovation.

3. China’s national strategy and governance approach China’s three-tiered AI strategy: a strategic roadmap, adaptive regulations and multilevel implementation. 1.1 Strategic roadmap for AI development China has demonstrated a clear commitment to long-term goals in the AI sector through top-level planning. The Next Generation AI Development Plan (2017)6 details a three-phase strategy for advancing AI and its applications in the country.

4. China’s AI standards framework (2024) Overarching standards Technical foundations Key technologies Intelligent product and service Industry applications Intelligence process in manufacturing and other key sectors Security and ethics

5. 1.2 Adaptive regulations balancing development, safety and governance China's AI governance integrates strict regulations with government oversight to balance innovation and responsibility. Key policies include the AI Governance Principles (2019), AI Code of Ethics (2021), and Ethical Review Measures (2023). Laws like the Deep Synthesis Measures (2022) regulate deepfakes, while the AI Safety Governance Framework (2024) classifies AI risks. The Interim Measures for Generative AI (2023) establish a tiered approach, allowing supervised market testing of new AI technologies. 1.3 Multi-level policy design to accelerate AI implementation China's AI policy follows a multi-tiered approach, with the central government setting strategic direction while local governments implement policies and support industry growth. This coordination fosters regional AI clusters, leveraging local strengths. Provinces tailor policies to their development stages, such as Shanghai’s industrial AI regulation and Guangzhou’s smart transport initiatives. Despite efforts to create a cohesive AI ecosystem, regional disparities persist due to uneven economic development. The central government provides the overarching strategic direction for AI development, while local governments focus on implementing these strategies and supporting industry growth. “

6. Key enablers in the AI ecosystem Five key enablers : 1. Infrastructure 2. Data 3. Technology 4 . Energy 5. Talent Development. 1. Infrastruture  Including extensive 5G networks, high-capacity data centres and robust cloud computing facilities. e.g = China Mobile’s Baichuan Platform-building a unified intelligent computing power network. 2. Data  China has unveiled a comprehensive data strategy that positions data as a cornerstone for national development and technological innovation. Central to this strategy is the launch of the National Data Administration.

7. Key enablers in the AI ecosystem Five key enablers : 1. Infrastructure 2. Data 3. Technology 4 . Energy 5. Talent Development. 3. Technology  Maximize the impact of domain-specifics LLMs (Large Language Models) with industry partners. 4. Energy  Prioritizing sustainable energy solutions to power AI while minimizing its environmental impact . e.g = Dongjiang Lake Big Data Centre – sustainable cooling and renewable energy innovation. 5. Talent Development  535 universities in china currently offer AI-related majors.

8. Scaling AI innovation in industries AI-Driven Industrial Transformation in China Sector-Specific AI Innovations: AI is deeply integrated into industries like manufacturing, automotive, retail, healthcare, finance, and public services. Cross-Disciplinary AI Integration: AI is combined with 5G, robotics, and digital twins to enhance productivity and efficiency. Industrial AI Growth: China leads in AI-powered robotics, with 1.7 million industrial robots in operation (51% of global demand in 2023). Example: Haier COSMOPlat : AI-powered industrial internet platform optimizing factory efficiency and reducing order-to-delivery time by 50%.

9. Scaling AI innovation in industries AI in Manufacturing: Predictive Maintenance & Quality Control: AI-driven systems detect defects and optimize production. Smart Manufacturing: AI enables flexible, demand-driven production instead of rigid assembly lines. Case Study: GAC Honda – AI-powered quality inspection improved data utilization by 80% and analysis efficiency by 10x. AI in Autonomous Transport: Vehicle-Road-Cloud Collaboration: AI integrates vehicles with road infrastructure and cloud computing for better decision-making. Autonomous Vehicles: Over 50 cities piloting AI-powered autonomous taxis. Case Study: Baidu Apollo Go – Achieved 7M+ driverless rides, with full operation in Wuhan.

10. Scaling AI innovation in industries Retail & AI : Hyper-Personalization: AI-driven recommendations and virtual live hosts enhance customer engagement. Case Study: JD’s Digital Humans – AI-powered hosts reduce live-streaming costs while increasing efficiency. Healthcare & AI : AI-Assisted Diagnosis: AI is used in 76% of clinical decision-making in China, improving diagnostic accuracy. Case Study: GE Healthcare – AI-powered deep learning enhances CT imaging for better patient outcomes. Public Services & AI : Smart Cities: AI optimizes urban management, traffic, and public safety. Case Study: Alibaba’s City Brain – AI-managed traffic systems reduced congestion and improved emergency response.

11. Key challenges in China’s AI development Infrastructure and computing power : Improving network connectivity to facilitate seamless communication between distributed computing centres . Managing the diversity of computing resources. Optimizing compatibility across diverse chip architectures and instruction sets. Promoting greater collaboration among ecosystem stakeholders. Data Use : Problem in data quality, interoperability and accessibility that prevents effective AI model training and limit insights across sectors .

12. Key challenges in China’s AI development Algorithms and Model Sophistication : Further attempts to continue innovation in core algorithmic capabilities through encouraging closer partnerships between industry and academic institutions . AI Proficiency and Talent : Shortages of talented AI researchers caused by the sheer demand for said talent .