Zhong Guo Yi Dong·2025-12-28 06:16Investment Rating - The report does not explicitly provide an investment rating for the industry. Core Insights - The report emphasizes the rise of AI-native infrastructure as a critical foundation for the digital transformation of industries, driven by the integration of AI technologies across various sectors. This infrastructure is essential for supporting AI-native applications and is seen as a key driver of economic and social transformation in China [8][9]. Summary by Sections 1. Background of AI-Native Infrastructure - The report outlines the increasing demand for AI-native infrastructure due to the ongoing digital transformation and the government's supportive policies. The AI industry in China is projected to exceed 900 billion yuan by 2024, with a year-on-year growth of 24% [23][24]. - The report highlights the shift in AI's role from a mere efficiency tool to a foundational infrastructure akin to electricity, reshaping industry dynamics [19][20]. 2. Development Path and Architecture of AI-Native Infrastructure - The concept of AI-native infrastructure has evolved through three stages: the emergence phase (1950-2009), the exploration phase (2010-2022), and the development phase (2023-present) [31][34]. - AI-native infrastructure is defined as a system designed from the outset to support AI applications, integrating hardware, software, and data to provide comprehensive lifecycle support for AI applications [35][36]. 3. Construction Ideas for AI-Native Infrastructure - The report details various components of AI-native infrastructure, including: - Intelligent Computing Resources: A combination of GPU, NPU, and traditional computing resources to support AI model training and inference [45]. - Unified Scheduling Engine: A system for dynamic allocation of computing, network, and storage resources tailored to different application scenarios [46]. - Sandbox: A secure environment for AI agents to interact with external tools while ensuring system stability [51]. - Model Development and Production: A comprehensive toolchain for model tuning, deployment, evaluation, and management [58]. - Data Supply: A robust data infrastructure that encompasses data collection, storage, processing, and quality assessment [60][61]. 4. Industry Practice Cases - The report includes various case studies across different sectors, such as telecommunications, government, manufacturing, finance, energy, transportation, and healthcare, showcasing the practical applications of AI-native infrastructure [12][12][12]. 5. Conclusion and Outlook - The report concludes that AI-native infrastructure is poised to become a cornerstone of future economic development, enabling new business models and operational efficiencies across industries [36][37].