Group 1 - The "Huacai Cup" computing power competition concluded with the Suzhou Industrial Park Public Computing Power Service Platform winning the first prize among 150 projects [1] - The competition, organized by multiple associations, attracted over 10,000 projects from various industries, highlighting its significance in the computing power sector in China [1] - The event emphasizes the growing importance of computing power as a key driver of new productive forces amid rapid technological and industrial changes [1] Group 2 - The "Xirang" platform, part of China Telecom's Tianyi Cloud 4.0, will support the new computing power scheduling center in Wujiang, set to launch in June 2024 [2] - The infrastructure aims to create a collaborative network system that integrates cloud, edge, and terminal computing, facilitating access to various computing resources [2] - A comprehensive AI security protection system is being developed to ensure the safe and stable operation of AI applications [2] Group 3 - The "Xirang" platform has successfully integrated computing resources from nine diverse manufacturers, achieving a resource utilization rate improvement of over 17% [3] - The platform has established a sustainable mechanism benefiting government, enterprises, and users, serving over 40 industry users with more than 600 online demands [3] - The successful resource scheduling rate remains stable at 99.99%, demonstrating the platform's effectiveness in addressing computing power interconnectivity challenges [3]

Core Insights - Gartner's Vice President, Gao Ting, presented ten strategic technology trends for 2026, focusing on themes of "architects, coordinators, and sentinels," covering areas such as AI-native development, multi-agent systems, physical AI, and cybersecurity [1] Group 1: AI Native Development - AI-native development platforms are seen as the core of next-generation software engineering, utilizing "ambient programming" to generate complete applications or assist developers in coding [2] - Currently, 20%-40% of code in some tech companies is generated by AI, indicating a shift in software development from efficiency tools to a new development paradigm [2] Group 2: AI Supercomputing Platforms - The demand for computing power in AI is growing exponentially, with AI supercomputing platforms characterized by hybrid AI computing and scheduling capabilities [3][7] - Technologies like NVIDIA's NVQLink and CUDA-Q enable the integration of quantum computing with classical supercomputing, enhancing task scheduling across architectures [3] Group 3: Multi-Agent Systems - Multi-agent systems improve reliability in executing complex tasks by breaking down tasks and allowing different agents to collaborate, addressing the limitations of single-agent systems [8][9] - This approach represents a key step in AI evolving from a "tool" to a "collaborator," reflecting a management mindset of "AI teamwork" [9] Group 4: Domain-Specific Language Models - The high failure rate of enterprise AI projects (95%) is attributed to general models lacking business understanding, which domain-specific language models aim to address through retraining with industry data [10] - Companies must invest in data governance and domain training to effectively utilize AI, avoiding the pitfall of having "models without intelligence" [10] Group 5: Physical AI - Physical AI refers to AI systems that interact with the real world, primarily in applications like autonomous driving and robotics, utilizing VLA models and "world models" [11] - This technology serves as a bridge between AI and the real economy, gradually replacing repetitive labor in sectors like manufacturing and logistics [11] Group 6: Proactive Cybersecurity - AI-driven attacks are lowering the barriers for hackers, necessitating the development of proactive cybersecurity systems that include predictive threat intelligence and automated defenses [12][14] - Companies must transition from static defenses to a proactive security framework that integrates prediction, response, and deception [14] Group 7: Digital Traceability - Digital traceability is becoming essential for building trustworthy digital supply chains, especially in light of frequent software supply chain attacks [15][16] - Establishing software SBOM and model MLBOM lists allows companies to track component origins and security, while watermarking and identification technologies for AI-generated content are being standardized [15][16] Group 8: Geopolitical Migration - Geopolitical risks are prompting companies to migrate data and applications from global public clouds to local "sovereign clouds," with European firms being the most affected [17] - Chinese companies are balancing self-sufficiency and global collaboration to avoid becoming "technology islands" [17] Group 9: Confidential Computing and AI Security Platforms - Although not the main focus, "confidential computing" and "AI security platforms" are ongoing trends that protect data and prevent new types of attacks [18] - The emphasis is on embedding AI into business processes and ensuring ecological collaboration rather than chasing technology fads [18]

Core Viewpoint - Musk predicts that in five years, there will be no more smartphones and apps, emphasizing a shift towards intention-driven human-computer interaction rather than manual input [2] Investment Perspective - The value of Musk's predictions lies not in the timeline but in highlighting the irreversible trend towards advanced human-computer interaction [2] - Investors are encouraged to focus on sectors such as neural interaction and computational scheduling to capitalize on this evolving trend [2]

Investment Rating - The report does not explicitly provide an investment rating for the computing power scheduling platform industry. Core Insights - The rapid development of artificial intelligence technology has led to an exponential increase in global demand for computing power, necessitating computing power scheduling for resource integration and optimization across regions and platforms [2]. Summary by Sections Overview of the Computing Power Scheduling Industry - Computing power is defined as the ability of computer devices or data centers to process information, categorized into general computing power, intelligent computing power, and supercomputing power [15][18]. - China's computing power scale has grown rapidly, reaching 280 EFLOPS by 2024, with intelligent computing power accounting for 32% [20][23]. Challenges in Heterogeneous Computing Power Scheduling - Heterogeneous computing power scheduling faces multiple core challenges, including increased scheduling complexity due to resource heterogeneity and software environment fragmentation, high migration costs for cross-architecture tasks, and a lack of unified scheduling standards leading to resource mismatch and low utilization [4][43]. Major Domestic Computing Power Scheduling Platforms - National-level computing power scheduling platforms are primarily government-led or constructed by major operators, emphasizing cross-regional collaboration and market-oriented transactions [5][48]. - Provincial platforms cover key regions like the Yangtze River Delta and Chengdu-Chongqing, while municipal platforms focus on local AI and smart manufacturing scenarios [48]. Mainstream Open Source Computing Power Scheduling Technologies - Domestic computing power scheduling platforms are often built on open-source technologies, with openFuyao emerging as a versatile scheduling platform with advantages in domestic adaptation, while Kubernetes and Slurm have strong foundations in cloud-native and HPC fields [6][51].

Data Center - Nvidia has ordered 300,000 H20 chips from TSMC due to strong demand in China, shifting from a reliance on inventory to actual supply [1] - Major internet companies like ByteDance, Tencent, and Alibaba are accelerating their data center construction demands, leading to potential increases in capital expenditures [1] - ByteDance plans to start its modular data center procurement project by July 30, 2025, which may provide guidance for the related sectors [1] - There is a forecasted increase in demand for H20 chips, with ByteDance potentially purchasing an additional 100,000 units and Tencent needing between 20,000 to 50,000 units [1] AI Infrastructure - ByteDance's procurement plan for modular data centers is a key part of its AI-driven data center strategy, aimed at enhancing computing power for AI model training and applications [2] - The company is expected to spend 160 billion yuan in capital expenditures by 2025, with approximately 90 billion yuan allocated for AI computing power procurement [2] - The procurement plan aligns with the H20 chip release, likely leading to explosive growth in demand across the liquid cooling, power generation, and server industries [2] Computing Power Scheduling - The first intelligent storage scheduling platform in China has been launched, addressing key challenges in data storage [3] - This platform integrates heterogeneous resources and dynamic scheduling, enhancing the foundation for nationwide data center construction and AI technology implementation [3] - The platform aims to optimize resource allocation and improve network stability, facilitating the efficient circulation and value extraction of data elements [3] Huawei Harmony - Huawei's Cangjie programming language will be open-sourced on July 30, featuring a compiler and standard library aimed at native intelligence and high performance [4] - Cangjie is designed to support all-scenario application development within the Harmony ecosystem, playing a crucial role in its infrastructure [4] Macro and Industry News - The Central Organization Department allocated 140 million yuan to support flood relief efforts in Beijing and other areas [5] - Chinese stock ETFs listed overseas have seen significant capital inflows since July [5] - The Beijing Stock Exchange is conducting self-inspections focusing on the technical and business preparations for batch switching of securities codes [5]

Investment Rating - The report indicates a positive investment outlook for the computing power scheduling platform industry, driven by the rapid growth in AI model applications and the increasing demand for high-performance computing resources [10][14]. Core Insights - The demand for intelligent computing power in China is experiencing unprecedented growth, particularly in large model applications, which account for nearly 60% of the demand, highlighting the significant potential of the future computing power market [14][30]. - The profitability of computing power scheduling centers heavily relies on government subsidies, which are designed to ensure local resource utilization and risk control. Successful engagement with government decision-making is crucial for companies to secure these subsidies [22][30]. - The core value of computing power scheduling platforms lies in their ability to efficiently integrate and schedule heterogeneous computing resources, significantly improving resource utilization and reducing costs for users [17][20]. Summary by Sections Computing Power Scheduling Platform's Scaling Path - The rapid growth of intelligent computing demand in China is driven by AI large models, leading to accelerated development of large-scale, high-performance computing centers [11][10]. - The profitability of computing power scheduling centers is highly dependent on government subsidies, which aim to ensure local resource utilization and risk management [22][30]. - Platforms must possess efficient integration and scheduling capabilities for heterogeneous computing resources to achieve low-cost, scalable, and marketable monetization [31][39]. Value of Computing Power Scheduling Platforms - The platforms enhance resource utilization, lower user costs, and simplify management processes, providing efficient and convenient computing power services [17][20]. - The core technologies required include resource virtualization, fine-grained slicing, real-time monitoring, and tidal scheduling, which enable low-cost and efficient utilization of resources [31][38]. Government Subsidies as Core Source - Government subsidies are essential for the profitability of computing power scheduling centers, with a structured mechanism to ensure local resource utilization and risk control [22][30]. - Companies must strategically engage early in government decision-making to influence standards and secure contracts [23][30]. Technical Features of Quality Computing Power Scheduling Platforms - Platforms need to have capabilities for fine-grained resource slicing, heterogeneous compatibility, cross-regional scheduling, real-time monitoring, and dynamic scheduling to achieve efficient resource reuse and low-cost monetization [31][38]. Core of Scaling Computing Power Centers - The core of monetizing the value of computing power platforms lies in a large and diverse customer base, which determines profitability speed and pricing potential [39][42]. - A diverse customer base allows for tiered pricing strategies to maximize revenue, while partnerships can help focus on high-margin computing power sales [42][39].

Group 1 - The State Intellectual Property Office of China has published a patent application by China Southern Power Grid Co., Ltd. for a method and device related to a computing power scheduling mechanism based on a container orchestration platform, with publication number CN120029785A and application date of February 2025 [1] - The patent describes a method that involves extracting key attribute information from configuration files of heterogeneous computing resources, adjusting component information in the container orchestration platform, and deploying heterogeneous computing resources on the adjusted platform [1] - The method aims to determine target computing resources based on the processing requirements of tasks and the status information of candidate computing resources, allowing for efficient task allocation [1] Group 2 - China Southern Power Grid Co., Ltd. was established in 2004 and is located in Guangzhou, primarily engaged in the production and supply of electricity and heat [2] - The company has a registered capital of 9,020 million RMB and has made investments in 35 enterprises, participated in 5,000 bidding projects, and holds 3,520 trademark records and 4,985 patent records [2] - Additionally, the company possesses 65 administrative licenses [2]