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 resources driven by the digital transformation of industries and households, highlighting the importance of all-optical capacity as a foundational support for connecting users and computing resources [3][4] - It identifies key application scenarios in various sectors such as smart transportation, industrial simulation, digital cultural tourism, and smart home entertainment, while also exploring distributed model training scenarios [3][4] - The report outlines critical technologies required to meet the quality computing needs of enterprises and users, including ultra-large bandwidth, determinism, high reliability, business awareness, and collaborative computing [3][4] Summary by Sections Overview - The report discusses the emergence of various computing applications that create new demands and challenges for networks, driven by the growth of consumer internet and industrial internet applications [7][8] - It notes that the construction of all-optical networks has made significant progress, with China having the largest optical fiber communication network globally, and highlights the rapid increase in the number of users with gigabit and above internet access [7][8] All-Optical Capacity Application Demand - **Smart Transportation**: The report details the upgrade of traffic cameras to smart cameras, which necessitates increased bandwidth from 9 Mbps for traditional cameras to 103 Mbps for high-definition smart cameras [9][10] - **Industrial Simulation**: It highlights the integration of cloud technology and AI in industrial simulation, emphasizing the need for high-performance computing and the ability to dynamically adjust bandwidth for data transmission [16][20] - **Digital Cultural Tourism**: The report discusses the trend of internet cafes moving to cloud services, with expectations of cloud adoption increasing from 6% in 2023 to 15% in 2024, requiring stable and high bandwidth for seamless user experience [22][26] - **Smart Home Entertainment**: It outlines the requirements for cloud gaming and VR applications, emphasizing the need for low latency and high bandwidth to ensure a smooth user experience [38][40] Key Technologies for All-Optical Capacity - The report identifies essential technologies such as ultra-large bandwidth, deterministic carrying, high reliability, business awareness scheduling, and collaborative computing control as critical for supporting industry applications [3][4] Industry Application Cases - The report presents various innovative application cases across sectors, including smart transportation, digital cloud internet cafes, smart home computers, and remote computing [3][4] Conclusion and Outlook - The report concludes by emphasizing the need for collaborative efforts in advancing all-optical capacity technologies and applications to support the digital economy's high-quality development [3][4]

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 to support this demand [3][7] - It identifies four key demands for the all-optical network in the computing era: high-quality cloud access, urban computing interconnectivity, interconnectivity between hubs, and intelligent network scheduling [3][9] - The report outlines the target architecture and key technology systems for the all-optical network, aiming to provide flexible and high-quality access to computing resources [30][31] 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 to enhance optical network infrastructure as a foundation for digital economy development [7][8] 2. Four Key Demand Characteristics of the All-Optical Network - The report identifies four characteristics: stable high bandwidth, high reliability and security, deterministic low latency, and intelligent services [9][10] - It emphasizes the need for the all-optical network to evolve towards ultra-large bandwidth and ultra-low latency to meet the demands of various applications [9][10] 3. Target Architecture and Key Technologies of the All-Optical Network - The target architecture consists of four components: computing access network (DCA), interconnect network (DCI), data center network (DCN), and unified scheduling system [30][31] - The report outlines the importance of high-quality access and interconnectivity to enhance the efficiency of computing resource utilization [30][31] 4. High-Quality Cloud Access - The report discusses the global shift towards 10Gbps optical access networks, with various countries implementing plans to enhance broadband connectivity [11][12] - It highlights the increasing demand for high-bandwidth home networks driven by diverse applications and devices [12][16] 5. Urban Computing Interconnectivity - The report emphasizes the critical need for low-latency networks in sectors like finance, where microsecond-level latency savings are significant [22][23] - It discusses the requirements for cloud service providers to maintain low latency between availability zones [22][23] 6. Interconnectivity Between Hubs - The report notes the surge in demand for distributed computing clusters due to the training of large AI models, necessitating high-capacity interconnects between data centers [24][25] - It highlights the need for high reliability in data center interconnect networks to minimize downtime during model training [27] 7. Intelligent Network Scheduling - The report discusses the integration of AI and big data technologies to enhance the efficiency of network operations and resource management [28][29] - It emphasizes the importance of intelligent scheduling for optimizing computing resource allocation and service delivery [28][29]

Core Insights - The report emphasizes the importance of data as a fundamental resource and innovation engine, highlighting the need for a unified data factor market in China to enhance economic growth and industrial development [2][3] - It categorizes data factor markets into four types based on competition levels and transaction costs, which influences their development focus and value release pathways [2][3][10] - The report indicates that the contribution of the data economy to China's GDP reached 2.05% in 2023, reflecting a growth of 0.99 percentage points from 2022, showcasing the increasing role of data in driving economic growth [2][3][30] Group 1: New Theories - The report identifies four types of data factor markets: low transaction cost competitive markets, low transaction cost monopoly markets, high transaction cost monopoly markets, and high transaction cost competitive markets, each with distinct characteristics and development focuses [2][3][10] - It discusses the characteristics of data factors, emphasizing their scarcity due to high externalities and competition, which complicates their pricing and trading [7][10][19] - The marketization of data factors is seen as essential for maximizing efficiency and productivity, aligning with the principles of a socialist market economy [11][20] Group 2: New Developments - The report notes that data value realization is accelerating, with significant advancements in data resource management, asset registration, and market activity [2][3][29] - It highlights the establishment of a data management system and the formation of data management institutions at various levels to enhance data governance and utilization [29][30] - The report mentions the development of data infrastructure, which supports the entire lifecycle of data from collection to application, thereby improving data supply quality [30][31] Group 3: New Value - Data is recognized for its ability to enhance overall productivity and drive economic growth, with specific contributions from various industries such as agriculture, manufacturing, and healthcare [2][3][30] - The report outlines how data applications are fostering innovation across multiple sectors, indicating a clear path for data empowerment in achieving high-quality development [2][3][30] Group 4: New Strategies - Recommendations include improving data property rights, market access, fair competition, and data security governance to stimulate market vitality and unlock data value [2][3][30] - The report suggests a tailored approach to developing data factor markets based on their unique characteristics, advocating for support in data service industries to facilitate value release [2][3][30] - It emphasizes the importance of encouraging practical applications of data factors to stimulate corporate innovation and market engagement [2][3][30]

Global Quantum Computing Landscape - Quantum computing is entering a rapid development phase, with over 30 countries actively investing in quantum information technologies, including quantum computing [7] - The US has invested $39 39 billion in quantum information from 2019 to 2023, exceeding its initial $12 75 billion plan, with quantum computing receiving the highest share of $14 billion [8] - The EU has launched a new Quantum Flagship Program with short-term (2027) and mid-term (2030) goals to achieve leadership in quantum technology and industry [9] - China has integrated quantum computing into its national development strategy, with over 20 provinces including quantum computing in their local development plans [10] Technological Advancements and Challenges - Quantum computing research has seen a 4x increase in global publications over the past decade, with the US and China leading in both publication volume and patent applications [13][16] - Superconducting quantum computing remains the most prominent technology route, with 9380 patent applications and 3976 granted patents globally [18] - Quantum error correction research is advancing, with IBM achieving a 0 7% error threshold using 288 physical qubits to protect 12 logical qubits [30] - Multiple hardware routes (superconducting, ion trap, neutral atom, photonic, silicon-based) are competing, with no clear convergence in the near term [23][28] Industry Ecosystem and Investment Trends - Global quantum computing companies reached 329 by July 2024, with a significant slowdown in new company formation (only 1 new company in H1 2024) [19] - The US leads in quantum computing companies (93), followed by China (36), with superconducting technology being the most pursued route (25 companies) [20] - Quantum computing investments showed signs of recovery in H1 2024, with $1 2 billion raised, following a dip to $1 4 billion in 2023 from $2 billion in 2022 [21][22] - Venture capital remains the dominant funding source, accounting for over 60% of the 143 investment deals in 2023 [22] Application Exploration and Cloud Platforms - Quantum computing applications are being explored in finance, chemistry, biology, transportation, and AI, with potential market value estimated at $1 trillion by 2035 [40][41] - Quantum cloud platforms are emerging as a key service model, with IBM, IonQ, and Chinese companies like Origin Quantum offering access to various quantum processors [46][48] - China has launched several quantum cloud platforms, including Quafu (100+ qubits) and Tianyan (504 qubits), aiming to expand quantum computing applications [48][49] - Quantum-classical hybrid computing is gaining traction, with NVIDIA and Microsoft developing systems to integrate quantum and classical computing resources [56] Benchmarking and Standardization - Quantum computing benchmarking is evolving, with IBM introducing new metrics like EPLG and CLOPSh to better assess hardware performance [52] - The QED-C updated its application-oriented benchmarking suite in 2024, expanding evaluation criteria for quantum algorithms like HHL and VQE [53] - Standardization is becoming a focus area, with international efforts to establish evaluation systems and interoperability standards for quantum computing [54]

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].