Industry Investment Rating - The report does not explicitly provide an investment rating for the industry [1] Core Viewpoints - The report emphasizes the importance of high-quality computing power in driving the development of artificial intelligence (AI) and the digital economy [1] - It highlights the transition from quantity to quality in computing power development, focusing on efficiency, intelligence, and sustainability [1][13] - The report identifies key challenges such as insufficient computing power supply, low intelligence levels, and energy consumption issues [8][9][10] Development Status and Challenges Development Status - **Policy**: Governments worldwide are accelerating the construction of computing power competitiveness through policy support and strategic planning The US plans to invest over $2511 billion in AI-related fields, while China is promoting high-quality development of computing power infrastructure [5] - **Technology**: Generative AI technologies, such as ChatGPT, are rapidly advancing, driving the need for more powerful computing resources [7] - **Market**: Investments in computing power are increasing globally, with the US planning to invest $2800 billion in chip technology and China's "East Data West Computing" project attracting over 435 billion yuan in direct investment [8] - **Scale**: Global computing power is expanding, with intelligent computing power growing by 136% year-on-year, reaching 335 EFLOPS by the end of 2023 [12][13] - **Development Level**: China's computing power industry is shifting from scale expansion to quality improvement, focusing on application-driven development and green energy efficiency [13][14] Challenges - **Challenge 1**: Insufficient computing power supply and mismatched demand, with AI model training requiring massive computing resources [14] - **Challenge 2**: Low intelligence levels of computing power, making it difficult to meet diverse application scenarios [15] - **Challenge 3**: Energy consumption and carbon emissions are significant issues, with computing centers consuming 1500 billion kWh of electricity in 2023 [16] - **Challenge 4**: Rising demand for diverse computing power, but the level of universal accessibility and affordability remains low [17] - **Challenge 5**: Incomplete supply chains and underdeveloped ecosystems, with compatibility issues between different hardware and software platforms [18] - **Challenge 6**: Simple performance evaluation systems, lacking comprehensive assessment of actual computing power performance [19] Definition, Connotation, and Characteristics Definition - High-quality computing power is defined as advanced computing capability based on the latest AI theories, combined with algorithms and data, driving productivity and economic development [20][21] Connotation - **Technological Innovation**: High-quality computing power serves as the main engine for AI model training and application, reducing the threshold for AI adoption [23] - **Optimization of Production Factors**: It optimizes the allocation of data and resources, enhancing the efficiency of production, distribution, and consumption [24] - **Industrial Transformation**: It drives the integration of advanced technologies with traditional industries, fostering new business models and services [24] Characteristics - **High Computational Efficiency**: Focuses on both theoretical and practical performance, with an average computational efficiency of 118 GFLOPS/W in China [25][26] - **High Intelligence Efficiency**: Combines efficient AI processing with intelligent optimization capabilities [27] - **High Carbon Efficiency**: Aims to maximize computing output with minimal carbon emissions, emphasizing lifecycle carbon management [28] - **Accessibility**: Ensures computing power is widely available and affordable, supporting diverse application scenarios [29] - **Sustainability**: Emphasizes technical compatibility, complete supply chains, and open industrial ecosystems [30] - **Evaluability**: Requires a comprehensive evaluation system to reflect the actual performance of computing power [31] Development Path and Outlook Development Path - **System Design**: Focuses on improving computational efficiency through optimized system architecture and resource management [32] - **Collaborative Drive**: Enhances intelligence efficiency through the integration of computing power, algorithms, and data [33] - **Lifecycle Management**: Promotes carbon efficiency through green procurement, design, and operation [34] - **Infrastructure First**: Advances universal accessibility and affordability by building intelligent computing centers [36] - **Ecosystem Prosperity**: Encourages sustainable development through open and standardized industrial ecosystems [38][39] - **Diverse Evaluation**: Accelerates the standardization of computing power development through comprehensive evaluation systems [42][44] Outlook - **Market Environment**: China's vast application market and supportive policies provide a strong foundation for the computing power industry [46] - **Universal Accessibility**: The trend towards universal accessibility will unlock the potential of AI across various fields [47] - **Intelligent Upgrade**: The integration of AI and computing power will drive deep economic and social development [48] Evaluation System Exploration Background - The evaluation system for computing power is transitioning from hardware-focused assessments to comprehensive evaluations that consider application performance and lifecycle carbon management [51][52] Principles - The evaluation system should be policy-aligned, systematic, targeted, comprehensive, operable, and adaptable to future changes [58] Practice - The "Five-in-One" evaluation system assesses computing power quality across five dimensions: computational efficiency, intelligence efficiency, carbon efficiency, accessibility, and sustainability [60][70] Significance - The evaluation system standardizes and accelerates the high-quality development of the computing power industry, providing guidance for technological innovation and infrastructure construction [70] Application Recommendations - Accelerate the development of supporting standards and tools to ensure the effective implementation of the evaluation system [71] - Conduct evaluation tests in typical AI application scenarios and expand theoretical research to support the industry's high-quality development [73]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The global competition in foundation model technology, spurred by ChatGPT, is driving a significant shift from narrow AI to general AI, indicating a transformative change in human-computer interaction and application development [4] - The commercialization of foundation models is accelerating, but it also introduces new security risks such as model "hallucinations," instruction injection attacks, and the democratization of cyberattacks [4] - The report emphasizes the need for a comprehensive security framework for foundation models, focusing on both inherent security and the security enabled by these models [4][28] Summary by Sections 1. Foundation Model Evolution - The evolution of foundation models has gone through three phases: exploration (2017-2021), explosion (2022-2023), and enhancement (2024-present) [17][19][20] - The exploration phase saw the introduction of pre-trained language models like GPT-3, which marked a shift towards large-scale pre-trained deep neural networks [17][18] - The explosion phase was characterized by the release of various language models, leading to a competitive landscape [19] - The enhancement phase is witnessing the rise of multimodal models capable of processing diverse types of data, improving understanding and interaction with the physical world [20] 2. Security Challenges Faced by Foundation Models - Foundation models face significant security challenges due to their integration into various sectors, which can lead to unintended security impacts [21] - The report categorizes security risks into four components: training data, algorithm models, system platforms, and business applications [21] - Key risks include data leakage, model robustness issues, and the potential for malicious use of models [21][22][23][24][25] 3. New Security Opportunities Presented by Foundation Models - Foundation models offer new solutions to existing cybersecurity challenges, enhancing threat detection and response capabilities [27] - They can improve the accuracy and timeliness of threat identification and response through advanced data analysis and automated processes [27] - The models' self-learning capabilities can enhance data security technologies, making them more accessible and effective [27] 4. Scope of Foundation Model Security Research - The security of foundation models encompasses both their inherent security and the security they can provide to other systems [28] - The report outlines a security framework that includes security goals, attributes, protection targets, and measures [28][29] - The framework aims to ensure the reliability, compliance, and robustness of foundation models while protecting systems, data, users, and behaviors [29]

Investment Rating - The report does not explicitly provide an investment rating for the telecommunications industry Core Insights - The telecommunications industry in China is experiencing a critical period of transformation and upgrade, driven by the need for digitalization and the integration of artificial intelligence (AI) technologies [3][8][10] - The report highlights that the growth of the telecommunications sector is facing cyclical slowdowns, necessitating the cultivation of new growth drivers [10][12] - AI is identified as a key engine for the transformation of the telecommunications industry, enhancing operational efficiency and enabling new business models [18][21][25] Summary by Sections 1. Telecommunications Industry Transformation - The telecommunications industry has made significant progress in digital transformation, but is now facing a bottleneck in growth, with revenue growth rates declining [8][10] - The industry has achieved a compound annual growth rate (CAGR) of 3.86% during the 13th Five-Year Plan and 7.30% since the 14th Five-Year Plan, with a net profit margin above 9.5% [9][10] - The integration of AI technologies is seen as a potential catalyst for the next phase of transformation, with the government emphasizing the importance of AI in driving new productive forces [3][15] 2. AI as a Driving Force - The report outlines three aspects of the AI-driven transformation in the telecommunications industry: AI as a core value addition, a key growth driver for core services, and an enhancer of operational efficiency [20][21] - AI technologies are expected to penetrate various operational aspects, leading to improved service delivery and operational management [19][21] - The report notes that the application of AI can create significant value, with estimates suggesting a potential value creation of $600-1000 billion for the telecommunications sector [17] 3. Global and Domestic AI Strategies - Global telecommunications companies are pursuing diverse AI strategies, with a focus on innovation and resource allocation to support AI integration [26][28] - Chinese telecommunications companies are systematically advancing their AI development, achieving notable progress in product-level and scenario-level applications [30][32] - The report emphasizes the need for a robust ecosystem to support AI development, including partnerships and collaborative efforts to enhance service delivery [30][31] 4. Challenges and Recommendations - The telecommunications industry faces several challenges in AI implementation, including the complexity of large model applications and the need for a robust infrastructure [4][10] - Recommendations for telecommunications companies include strategic planning for AI integration, enhancing core capabilities, and fostering an environment conducive to innovation [4][10][30] - The report suggests that the industry should focus on optimizing the policy environment for AI and telecommunications integration to facilitate growth [4][10]

Industry Overview - Data has become a new type of production factor, recognized globally as a strategic resource, with countries competing to harness its value [3] - China has taken a leading role in the global data element market by pioneering data trading platforms and data asset accounting [3] - The integration of new technologies, models, and infrastructure is driving the development of the data element sector, with AI playing a significant role in accelerating data value release [3] - Data assetization has introduced new models like "data credit" and "data trusts," expanding the application scenarios of data elements [3] - Data infrastructure, including advanced storage, is critical for the lifecycle management of data, ensuring its availability, flow, and security [3][4] Data Elements and AI Synergy - AI accelerates the transformation of cold data into warm and hot data, with large models like GPT requiring massive datasets for training [7][18] - AI-driven applications generate vast amounts of hot data, increasing the demand for high-frequency data storage solutions [19] - High-quality datasets are crucial for AI model training, with dataset size and quality directly impacting model performance [21] - Advanced storage technologies, such as all-flash arrays, are essential for supporting AI's high-performance data processing needs [19][24] Data Assetization and Storage - Data assetization has led to significant breakthroughs in data asset valuation and registration, with China pioneering data asset accounting [25][27] - Data asset circulation and trading have increased the volume of data copies, necessitating robust storage solutions [31] - Advanced storage facilities act as secure "vaults" for data assets, ensuring their safety, reliability, and scalability [32][33] - The integration of storage with other data infrastructure components, such as networks and computing, is vital for efficient data management [37] Data Infrastructure Development - Data infrastructure, including storage, is a key enabler of data element utilization, supporting data collection, processing, and circulation [34][35] - Storage facilities are one of the "six foundations" of data infrastructure, alongside networks, computing, data collection, data circulation, and data security [35][37] - The construction of data infrastructure is accelerating, with advanced storage capacity expected to reach 30% of total storage by 2025 [12][40] - Regional storage centers, such as those in Guizhou and Chongqing, are emerging as models for integrating storage and computing, enhancing data value realization [42][43] Recommendations for Future Development - Promote the construction of high-quality datasets and advance the application of AI-driven storage technologies [45] - Improve the data asset valuation system by incorporating storage security and risk management metrics [46] - Accelerate the construction of advanced storage facilities and optimize the layout of data infrastructure to support regional and industrial needs [48]

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The industrial internet ecosystem in China has begun to take shape, with significant advancements in platform applications and a thriving ecosystem since the issuance of the "Three-Year Action Plan for the Innovative Development of the Industrial Internet (2018-2020)" by the Ministry of Industry and Information Technology [2][8] - The report emphasizes the importance of network infrastructure as the foundation of the industrial internet, highlighting the need for flexible networking solutions to achieve seamless connectivity between IT and OT [8][10] - The report outlines the current state of industrial equipment networking, indicating that approximately 80% of equipment remains unconnected, with a digitalization rate of only 50% in 2020 [12][11] - The report identifies three key characteristics of advanced industrial networks: equipment networking, IP connectivity, and network intelligence, which are essential for driving innovation in the industrial internet [2][8] Summary by Sections Industrial Equipment Networking Status and Trends - The report details the current state of industrial equipment networking, noting that only 23% of industrial devices are connected, significantly lower than the 70% connectivity rate in the consumer internet sector [11][12] - It highlights the challenges faced by manufacturers, including a lack of standardized communication protocols and the prevalence of "dumb terminals" that require significant retrofitting to connect to networks [12][13] - The report discusses the trend towards full connectivity in factories, necessitating comprehensive management solutions for equipment, networks, and business operations [12][13] Technical Development Trends - The report outlines the technical development trends in industrial equipment networking, driven by business applications and the need for cloud connectivity [13][14] - It describes the evolution of communication protocols from industrial buses to Ethernet and IP-based solutions, emphasizing the importance of integrating IT and OT networks [15][18] - The report also discusses the shift from wired to wireless connections, driven by the need for flexibility and efficiency in manufacturing processes [22][24] Networking Solutions - The report presents a comprehensive networking solution framework for industrial equipment, emphasizing the importance of data collection as the foundation of the industrial internet [28][32] - It outlines various data collection methods, including manual data entry, non-intrusive modifications, and automated data collection through networked devices [33][34] - The report emphasizes the need for robust security measures in industrial networking, including identity authentication and secure data transmission protocols [44][46] Industry Practices - The report highlights successful case studies, such as SAIC's Ningde factory, which has implemented a fully connected smart factory using Wi-Fi 6 technology to enhance operational efficiency and reduce downtime [48][49] - It also discusses Huawei's Southern factory, which has adopted a wireless industrial network to improve flexibility and automation in production processes [51][51]

Investment Rating - The report indicates a positive outlook for the domestic smartphone market, with a significant year-on-year growth in both overall shipments and 5G smartphone shipments [1][3]. Core Insights - In August 2024, the domestic smartphone shipment reached 24.047 million units, representing a year-on-year increase of 26.7%, with 5G smartphones accounting for 82.1% of total shipments [1]. - From January to August 2024, the total smartphone shipments amounted to 195 million units, up 16.6% year-on-year, with 5G smartphones making up 84.5% of the total [1]. - The number of new smartphone models launched in August 2024 was 47, a decrease of 14.5% year-on-year, with 5G models comprising 61.7% of the new launches [1][3]. - For the first eight months of 2024, 281 new smartphone models were introduced, down 4.1% year-on-year, while 5G models increased by 13.5%, representing 53.7% of the new launches [1][3]. Domestic Smartphone Market Overview - In August 2024, domestic brand smartphone shipments reached 22.178 million units, a year-on-year increase of 31.7%, accounting for 92.2% of total shipments [3]. - From January to August 2024, domestic brand smartphone shipments totaled 168 million units, up 21.3% year-on-year, representing 86.0% of total shipments [3]. - The number of new models from domestic brands in August 2024 was 40, down 14.9% year-on-year, making up 85.1% of the new launches [3]. Smart Phone Development - In August 2024, smart phone shipments were 21.098 million units, reflecting a year-on-year growth of 17.7%, which constituted 87.7% of total smartphone shipments [4]. - For the first eight months of 2024, smart phone shipments reached 182 million units, a 14.1% increase year-on-year, accounting for 93.4% of total shipments [4]. - The number of new smart phone models launched in August 2024 was 33, a decline of 28.3% year-on-year, representing 70.2% of the total new launches [4].

CAICT 中国信通院 ［幸运 트题报告 ■ | --- | --- | --- | --- | --- | --- | --- | |-------|-----------|-------|-------|-------|--------------|-------| | | | | | | | | | | | | | | | | | | 全球 5G | | | | 标准必要专利 | | 中国信息通信研究院知识产权与创新发展中心 2024年9月 | --- | --- | |-------------------------------------------------------------------------------------------------------|-------| | 版权声明 | | | | | | 本报告版权属于中国信息通信研究院，并受法律保护。 转载、摘编或利用其它方式使用本报告文字或者观点的，应 | | | 注明"来源：中国信息通信研究院"。违反上述声明者，本 | | 前 言 自 2021 年 3GPP 立项通过 5G 标准 Rel-18 版本的首批项目以来， 5G ...

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The report emphasizes the growing demand for computing power driven by the digital transformation and AI advancements, highlighting the importance of all-optical networks as a foundational infrastructure for supporting computing resources [3][7] - It identifies four key demands for the development of all-optical networks in the computing era: high-quality cloud access, urban computing interconnectivity, interconnectivity between hubs, and intelligent network scheduling [9][30] - The report outlines the target architecture and key technology systems for all-optical networks, aiming to provide flexible and high-quality access to computing resources [32][50] Summary by Sections 1. High-Quality Innovation Development in the Computing Era - The report discusses the increasing requirements for computing and network integration services due to AI advancements and the need for high-quality communication networks to support distributed computing clusters [7][8] - It highlights global initiatives and policies aimed at enhancing optical network infrastructure to support digital economy growth [8] 2. Four Key Demand Characteristics of All-Optical Networks - Stability in bandwidth to support increasing data flow demands [9] - High reliability and security to ensure seamless operation of intelligent computing services [10] - Deterministic low latency to support distributed computing innovations [10] - Intelligent services for automated resource scheduling and management [10] 3. Target Architecture and Key Technologies for All-Optical Networks - The target architecture consists of four components: computing access networks (DCA), interconnect networks (DCI), data center networks (DCN), and unified scheduling systems [32] - The report emphasizes the need for high-capacity interconnects between data centers to meet the demands of large-scale AI models [27][50] 4. High-Quality Cloud Access - The report outlines the global shift towards 10Gbps optical access networks, with various countries implementing plans to enhance broadband connectivity [11][12] - It details the increasing bandwidth demands from smart homes and enterprises, driven by diverse applications and devices [12][16] 5. Urban Computing Interconnectivity - The report highlights the critical need for low-latency networks in sectors like finance, where microsecond-level latency savings are essential [23][24] - It discusses the construction of urban networks with a target latency of under 1ms to enhance real-time computing services [24] 6. Interconnectivity Between Hubs - The report notes the significant bandwidth requirements for distributed AI model training, necessitating high-capacity interconnects between data centers [25][27] - It emphasizes the importance of reliability in data center interconnect networks to minimize downtime during critical operations [29] 7. Intelligent Network Scheduling - The report discusses the integration of AI and big data technologies to enhance the operational efficiency of all-optical networks [30][31] - It outlines the goal of achieving automated and intelligent management of network resources to support diverse computing applications [31]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Digital trade is increasingly recognized as a vital component of global trade, with its share in total exports rising to 13.8% in 2023, up 1.4 percentage points from 2022 [11] - The global digital trade rules are evolving, with "pure digital" agreements leading the way, reflecting a shift towards more flexible and efficient negotiation frameworks [4][25] - Emerging economies are beginning to play a significant role in shaping digital trade rules, with countries like Singapore, China, and India actively participating in the development of new regulations [4][39] Summary by Sections 1. New Situations Facing Digital Trade Rule Formulation - The importance of digital trade is growing, with geographical shifts in trade patterns, particularly towards Asia [10][12] - New digital technologies are driving changes in international trade methods, although disparities in global development remain pronounced [15] - The regulatory environment for digital trade is becoming increasingly stringent, impacting the pace of digital trade growth [19] 2. Overall Progress of Digital Trade Rules - A new multi-layered structure of global digital trade rules is emerging, characterized by multilateral, bilateral, and regional agreements [24][25] - There is a noticeable divergence in the progress of digital trade facilitation and liberalization topics, with consumer trust rules advancing more rapidly [34][35] - Emerging economies are starting to play a crucial role in rule-making, with a significant increase in the number of digital trade agreements signed in Asia [37][38] 3. Latest Trends in Key Rules and Issues - New technology rules are beginning to emerge, enhancing innovation cooperation and supply chain resilience [55] - Artificial intelligence has become a focal point, prompting discussions on new and existing regulatory frameworks [59] - Rules governing cross-border data flow are entering a new adjustment phase, with ongoing evolution in rule templates [65] 4. Outlook for Digital Trade Rules - The importance of digital inclusivity is expected to rise, with a focus on bridging the digital divide and enhancing access to digital technologies [74] - Environmental sustainability in the digital economy is becoming a new focal point, with international organizations emphasizing the need for sustainable practices [77] - The development of rules related to artificial intelligence and emerging technologies is gaining momentum, with various countries actively pursuing regulatory frameworks [79]

Industry Overview - The 2024 Government Work Report introduced the "AI+" strategic action, aiming to empower various industries with AI, particularly through large models that drive intelligent transformation in software engineering [3] - Intelligent software development tools, leveraging large models, significantly reduce technical barriers for developers and enhance development efficiency and quality [3] - Despite advancements, challenges remain in areas such as model selection, tool integration, and scenario-specific implementation [3] Intelligent Development Evolution - Software engineering has evolved through three stages: Software Engineering 1.0 (structured methods), 2.0 (agile development), and 3.0 (intelligent software engineering driven by large models) [6][7][8] - Software Engineering 3.0 focuses on AI-driven tools that enhance the entire software lifecycle, including development, testing, and operations, with core characteristics like intelligence, data-driven processes, and adaptability [10][11][12] Market and Tool Landscape - The intelligent development tool market is rapidly growing, with GitHub Copilot leading with 1.8 million paid subscribers and a 64.5% market share as of April 2024 [14] - Domestic tools, such as those from Huawei, Alibaba, and Baidu, are also emerging, with over 40 tools available, though performance and user experience vary [14] - Tools are increasingly adopted across industries, including tech, finance, telecom, and manufacturing, with significant efficiency gains reported [14][15] Core Capabilities of Intelligent Development - Key capabilities include code generation, code completion, unit test generation, code conversion, code explanation, and code inspection [32][33][37][42][46] - These capabilities aim to improve coding efficiency, code quality, and developer productivity, with tools like GitHub Copilot showing a 55% increase in coding speed and 46% more code written [15][16] Challenges in Intelligent Development - Organizations face challenges in cultural transformation, talent acquisition, and integrating AI tools with existing workflows [19][20] - Technical challenges include model selection, tool integration, and ensuring security across data, models, and tools [20][21] Case Studies - **Cloud Services**: A major cloud service provider implemented intelligent development tools, achieving a 57% unit test coverage rate and generating over 2.2 million lines of AI-generated code [81][82] - **Software Services**: A software service company developed an AI-powered platform, reducing project communication time from weeks to days and improving development efficiency by 30% [84][85] - **Power Industry**: A state grid company used AI tools to improve code quality and reduce project delivery time by 40%, with code quality scores exceeding 90% [87][89] - **Finance**: A state-owned bank leveraged AI tools to convert legacy Flex code to React, achieving a 40% accuracy rate and significantly improving unit test coverage [91][93] - **Manufacturing**: A leading home appliance manufacturer adopted AI tools to enhance code readability and development efficiency, addressing challenges in code maintenance and innovation [95]