Group 1 - The commercialization of AI applications is approaching a critical turning point, driven by the maturation of underlying technology and the validation of business models [8][23][24] - The efficiency and cost-effectiveness of AI computing power have significantly improved, with the cost of inference for AI models dropping dramatically, providing economic support for commercialization [9][20] - The capabilities of large models have reached commercial standards, with advancements in multi-turn dialogue, long-term memory, and multi-modal perception, exemplified by models like GPT-5 [19][11][12] Group 2 - The demand for AI applications is rapidly expanding, as indicated by the significant increase in token consumption, which reflects the growing commercial value of AI across various sectors [24][26][28] - AI programming has emerged as a leading area for commercialization, with notable advancements and user growth in tools like GitHub Copilot and Cursor, indicating a clear path to revenue generation [28][29][49] - AI multi-modal applications, particularly in visual content generation, are becoming commercially viable, with companies like Kuaishou and Meitu demonstrating significant revenue growth [33][35] Group 3 - AI is reshaping the advertising value chain, enhancing return on investment (ROI) through improved user insights and targeted advertising strategies [39][40] - AI-driven enterprise services are catalyzing performance across various sectors, with companies like Salesforce and Palantir reporting substantial revenue growth attributed to AI integration [44][45] - The education sector is experiencing a transformation through AI, with personalized learning solutions driving user engagement and revenue growth for platforms like Duolingo [46][49] Group 4 - The healthcare industry is leveraging AI for precise diagnostics and accelerated drug development, with companies like Tempus achieving significant revenue increases through AI applications [51][52] - The current AI application market is influenced by overseas business models, with a focus on sectors that have demonstrated clear revenue validation, such as AI programming and AI healthcare [53][54]

Investment Rating - The report maintains a "Buy" rating for the domestic intelligent computing chip sector, highlighting strong demand and advancements in performance and ecosystem [2]. Core Insights - The global demand for intelligent computing continues to surge, driven by large-scale AI model training and inference needs, with significant capital expenditures and supportive policies enhancing the market landscape [6][7]. - The domestic AI chip market is projected to grow at a CAGR of 53.7% from 2025 to 2029, with GPU market share expected to rise from 69.9% in 2024 to 77.3% by 2029 [18][20]. - The report emphasizes the importance of hardware-software synergy, showcasing advancements in chip performance and the development of independent software ecosystems to break the CUDA monopoly [6][7]. Summary by Sections 1. High Demand for Intelligent Computing - Global AI computing infrastructure investments are experiencing explosive growth, with major tech companies planning substantial investments in AI clusters, such as OpenAI's $500 billion "Star Gate" project [10][11]. - The daily token consumption in China has surged from 100 billion to 10 trillion within a year, indicating rapid adoption of generative AI across various sectors [13][15]. - Domestic capital expenditures in AI computing are being driven by major players like ByteDance, Alibaba, and Tencent, with significant investments planned for 2025 [23][24]. 2. Hardware Performance Breakthroughs - Domestic chip manufacturers are rapidly closing the performance gap with international competitors, particularly in advanced process nodes and single-card performance [6][7]. - Innovations in architecture, such as Huawei's CloudMatrix, demonstrate competitive capabilities against leading international solutions [6][7]. 3. Software Ecosystem Development - The report outlines the shift from compatibility adaptation to the establishment of independent standards in the software ecosystem, enabling domestic chips to compete effectively [6][7]. - Domestic companies are developing their own software stacks to reduce reliance on NVIDIA's CUDA, enhancing the overall ecosystem for AI applications [6][7]. 4. Investment Recommendations - The report suggests focusing on various segments within the intelligent computing industry, including chip manufacturers like Cambricon and Haiguang, server providers like Sugon and Inspur, and data center operators like GDS and Kuaishou [6][7].

Investment Rating - The industry investment rating is "Outperform the Market," indicating that the industry index is expected to perform better than the benchmark index over the next 6-12 months [11]. Core Insights - The report highlights the sustained high demand for domestic computing power driven by the ongoing restrictions on advanced chip imports from the US, which accelerates the domestic computing power substitution process. Domestic cloud vendors are increasing capital expenditures and gradually releasing industrial demand, while the iteration of domestic AI models and applications is boosting computing power demand [1][3]. Summary by Sections Investment Recommendations - It is recommended to prioritize attention on the construction and application of domestic AI computing power network infrastructure, including operators such as China Mobile, China Telecom, and China Unicom, as well as server and switch equipment manufacturers like ZTE, Unisoc, Inspur, Ruijie Networks, and Shengke Communication. Additionally, focus on optical modules and optical devices from companies like NewEase, Zhongji Xuchuang, Yuanjie Technology, Huagong Technology, Guangxun Technology, Shijia Photonics, and Huafeng Technology [3]. Industry Trends - The report notes that Huawei's Ascend 910C has begun mass shipments, marking a new phase in the commercialization of domestic computing power. A recent tender announcement indicated that a smart computing center project plans to use 4,500 Ascend 910C-2 servers, with an expected capacity of 20,000 P computing power. The Ascend 910C features a single-chip computing power of 320 TFLOPS (FP16), making it suitable for AI tasks such as natural language processing and computer vision [1][3]. - Domestic cloud vendors and operators are increasing capital expenditures on computing power, with Alibaba planning to invest 380 billion RMB in cloud construction and AI hardware infrastructure over the next three years, averaging over 120 billion RMB annually. Tencent's capital expenditure in Q1 2025 reached 27.5 billion RMB, a year-on-year increase of 91%, with a focus on resources for large model training and inference [1][3]. - The demand for computing power is expected to grow due to breakthroughs in application-side inference technology, which significantly lowers barriers to entry. The report cites Alphabet's inference volume reaching approximately 634 trillion tokens in Q1 2025, a 50-fold increase from the previous year [1][3].

Investment Rating - The report maintains a "Buy" rating for several key companies in the semiconductor and AI sectors, including 中芯国际 (SMIC), 海光信息 (Haiguang), and others, indicating strong growth potential in the domestic AI and computing landscape [5][6]. Core Insights - The domestic AI landscape is witnessing significant advancements with the emergence of models like 豆包 (Doubao) and DeepSeek, which are leading the charge in multi-modal and lightweight AI model development, respectively [1][2]. - The report highlights a shift towards domestic computing power solutions, with chip manufacturers rapidly adapting to the evolving AI ecosystem, particularly through advancements in semiconductor processes and AI training capabilities [2][3]. - There is a notable increase in capital expenditure among cloud computing firms, driven by the rising demand for AI computing infrastructure, which is expected to lead to a "volume and price rise" scenario in the cloud computing market [3][4]. Summary by Sections Section 1: Breakthroughs in Domestic AI Models - 豆包 has emerged as a leading multi-modal model, enhancing capabilities in speech, image, and code processing, with a significant release of its visual understanding model in December 2024 [1][11]. - DeepSeek focuses on lightweight model upgrades, achieving a remarkable cost-performance ratio with its DeepSeek-V3 model, which has 671 billion total parameters and costs only 557.6 million USD, positioning it among the world's top models [1][12]. - The rapid iteration of domestic models, including updates from 通义千问 and others, reflects a competitive landscape that is accelerating the development of AI applications [1][34]. Section 2: Advancements in Domestic Computing Power - 中芯国际 is advancing its semiconductor processes, with N+1 and N+2 technologies being developed to support the growing demand for AI chips, achieving significant performance improvements [2][56]. - The report notes that the domestic chip industry is evolving, with companies like 昇腾 (Ascend) and others making strides in AI training and inference capabilities, thereby reducing reliance on international competitors [2][59]. - The cloud computing sector is experiencing a capital expenditure boom, with companies like 华勤 and 浪潮 rapidly deploying servers that are compatible with domestic computing power solutions [3][4]. Section 3: Infrastructure and Supply Chain Developments - The report emphasizes the need for enhanced computing infrastructure to meet the surging demand for AI applications, with significant investments being made in server and power supply innovations [3][4]. - Innovations in power supply and cooling systems, particularly the shift from traditional air cooling to liquid cooling, are becoming essential to support the increasing power density in data centers [4]. - The report identifies key players in the supply chain, including companies in power supply, cooling, and server manufacturing, that are poised to benefit from the growth of the AI and computing sectors [5].