云天励飞-U（688343.SH）深度报告 算法芯片化助力，全产业链发展打造推理 AI 龙头 2025 年 09 月 30 日 ➢ 公司 AI 产品以消费级、企业级、行业级三方向发展。公司以"算法芯片化" 作为底层优势，在消费级、企业级、行业级三种场景部署产品和服务。企业级场 景主要为 AI 推理芯片与相关产品、算力服务及 IP 授权服务等，其中公司自研的 DeepEdge 系列通过"算力积木"架构可提供 8-128T 算力，已经在智慧交通、 机器人、家庭主机、推理服务器等领域与头部企业合作并实现商用，16.07 亿元 的 AI 算力服务订单已经验收并正常回款。消费级场景主要包括噜咔博士 AI 拍学 机、AI 眼镜等 AI 原生产品和 AI 耳机、AI 手表等可穿戴设备模组等 AI 赋能产 品，并实现了快速增长。行业级注重项目质量，考核目标从销售额转向利润端。 ➢ Deepseek 开源模型，推动"算法-芯片协同进化"。Deepseek R1 通过关 键设计的奖励系统、训练模板、自进化现象，以及用于强化学习的核心算法 GRPO，用低算力的 GPU 训练出了媲美 OpenAI o1 的模型，证实了在 AI 行业 ...

Core Viewpoint - The rise of artificial intelligence (AI) is not just a technological shift but a significant transformation in civilization, with implications for investment opportunities and industry dynamics [1][6]. Group 1: AI Industry Developments - The appointment of a digital minister in Albania marks a historic moment in governance, showcasing the integration of AI into political structures [1]. - Elon Musk predicts that by 2026, AI will surpass human intelligence in key metrics, indicating an approaching technological singularity [3]. - China's State Council has issued a strategic document to implement AI, elevating it to a national priority and signaling a shift from isolated advancements to comprehensive integration [1][12]. Group 2: Investment Opportunities - The 华夏上证科创板人工智能ETF (589010) tracks the 上证科创板人工智能指数 (950180.CSI), which has seen a year-to-date increase of nearly 58% and a one-year increase of 167.46% [2][15]. - The ETF serves as a vehicle for investing in core AI assets, capitalizing on the growth of the AI industry amid significant policy and technological support [2][14]. - The index comprises 30 large-cap stocks, focusing on key segments of the AI value chain, enhancing its potential for capturing investment returns [14]. Group 3: Market Dynamics - The global AI industry is experiencing unprecedented growth, with China's AI market projected to exceed 1 trillion yuan by 2029, driven by a compound annual growth rate of 32.1% [7]. - The competition in AI is intensifying, with major tech companies racing to innovate and improve their AI models, leading to a rapid evolution in capabilities [4][5]. - The AI landscape is shifting from a focus on individual technological breakthroughs to a comprehensive ecosystem approach, with significant implications for global competitiveness [5][11]. Group 4: Technological Advancements - The emergence of large models like DeepSeek signifies a breakthrough in AI technology, enabling cost-effective and high-performance solutions that challenge international counterparts [8][10]. - The integration of AI into various sectors is accelerating, with significant advancements in computational power and model iteration rates [9][10]. - The Chinese AI industry is characterized by a unique growth formula that combines industrial foundation, innovation capacity, and talent reserves, positioning it for future success [11].

Investment Rating - Investment recommendation: Outperform the market (maintained) [8] Core Insights - The AI is transitioning from the "human data era" to the "experience era," as highlighted by Richard Sutton, the 2024 ACM Turing Award winner. Current AI large model training relies on human-generated data, but the depletion of high-quality data necessitates a shift towards interaction with the world [5][9] - The evolution of large models is predicted to progress from large language models to native models and eventually to world models, with a distinction between digital and physical worlds in AGI development [10] - The capabilities of large models are continuously improving, with major companies like OpenAI and Google regularly updating their models. However, practical applications in real-world scenarios remain limited, indicating a focus on enhancing AI's problem-solving abilities through interaction with the physical world [11] Summary by Sections AI Technology Progress - AI technology advancements are expected to create investment opportunities across four areas: 1. Infrastructure for computing power, with a focus on domestic GPU ecosystems [12] 2. Software development for edge AI applications, emphasizing the importance of end-user devices [12] 3. Innovations in productivity tools, which could lower professional barriers and reduce repetitive tasks [12] 4. Information technology innovations in industries like finance, law, education, healthcare, and automotive, with key players connecting foundational model providers and industry clients [12]