Core Insights - The importance of both large and small AI models is emphasized, with a warning that without small models, the implementation of artificial intelligence becomes challenging [2][3] - Knowledge engineering is identified as a core technology for achieving artificial intelligence, alongside models, computing power, and algorithms [4] Group 1 - The need to focus on large models while not neglecting small models is highlighted, indicating a balanced approach is necessary for AI development [2][3] - Knowledge is categorized into qualitative and quantitative types, with models representing quantitative knowledge [4] - Large models require significant computing power for training on diverse data, underscoring the necessity of substantial computational resources behind big data and models [4]

作者 ｜ 木子、高允毅 2023 年 4 月，在很多人刚听说"大模型"这个词的时候， 智谱团队 开始研究一个听上去不太现实的目标： 让 AI 真正学会"使用手机" ，也就是像真人一样，对智能手机等设备具有使用能力。 32 个月后，智谱把阶段性 重要成果、核心 AI Agent 模型：AutoGLM 给 完整开源 了出来，并放话："每台手机，都可以成为 AI 手机。" 目前，AutoGLM 已支持上百个主流 APP，包括以下这些： 另外，AutoGLM 还能同时在上千台云端虚拟手机里"练功"，通过强化学习等极大地扩展了 Agent 的准确性和泛化能力。而且它被严格关在虚拟设备的安 全沙箱里，既能自由试错，又不会碰到用户真实手机上的隐私数据。 智谱今天开源的是一整套可以"拿来就用"的能力，具体包括： 模型会以 MIT 开源许可证 的形式开放，而所有代码会以 Apache-2.0 开源许可证 的形式，托管在 GitHub 仓库中：github.com/zai-org/Open- AutoGLM。 为什么选择开源？ "从产品的角度，AutoGLM 已经可以支撑起很多真实场景；从工程的角度，AutoGLM 的积累足够写 ...

Core Insights - The article discusses the transformation of search, advertising, and recommendation systems through the integration of large models, emphasizing the challenges and solutions for implementing generative recommendations in practical scenarios [2][4]. Group 1: Key Changes in Search and Recommendation Systems - The most significant change brought by large models is in feature engineering, where traditional methods are being enhanced by the capabilities of large language models to extract richer features from vast amounts of data [6]. - The industry is still far from achieving a fully unified end-to-end pipeline, with most efforts focused on integrating large models into specific points of the pipeline rather than complete reconstruction [12][4]. - The scaling law remains applicable in recommendation systems, indicating that the marginal benefits of model scaling have not yet reached their limits, particularly due to the vast amount of user behavior data available [13][17]. Group 2: Challenges and Solutions in Model Implementation - A major challenge in deploying large models is the need for extensive foundational work, such as data cleaning and sample construction, which can consume significant time and resources [8]. - The transition from traditional feature engineering to a more systematic approach to data and sample construction is crucial for realizing the potential of large models [8][9]. - Balancing model size, performance, and computational costs is essential, with smaller models being preferred in low-value scenarios while larger models are pursued for high-value applications [19][20]. Group 3: Future Directions and Innovations - The future of recommendation systems may see a shift from feature engineering to knowledge engineering, where models learn directly from raw user behavior data supplemented by incremental knowledge [30]. - The development of intelligent agents capable of autonomous planning and execution of complex tasks is anticipated, moving beyond predefined workflows [30]. - The industry is encouraged to focus on maximizing the utility of existing models by improving the quality of training data and optimizing the model's effective parameters [20][38].

Investment Rating - The report suggests a "barbell" investment strategy focusing on companies with stable cash flows supporting AI-related investments and those with strong operational capabilities for overseas expansion [1][3]. Core Insights - 2026 is viewed as a pivotal year for capturing user attention in the AI era, emphasizing the importance of lowering usage barriers, enhancing decision-making efficiency, and creating real value [1]. - Companies like Tencent, Alibaba, and Kuaishou are highlighted for their potential to benefit from AI-driven growth in advertising and cloud services, while firms like NetEase and Trip.com are noted for their stable earnings growth and reasonable valuations [1][3]. Summary by Sections AI Theme - Companies to watch include Tencent, which is expected to benefit from AI-driven advertising and cloud growth, Alibaba, which has a lower valuation compared to peers, and Kuaishou, which is making progress in monetizing AI applications [1][3]. Profit Growth Certainty - Focus on companies with reasonable valuations and strong performance, such as NetEase and Trip.com, which are expected to maintain stable profit growth [1][3]. E-commerce and Online Retail - The online retail sector is anticipated to see growth in experiential consumption, while competition in physical goods retail may normalize due to reduced government subsidies [3][15]. - Instant retail is expected to maintain high GMV growth, but the overall e-commerce sector may face challenges in revenue and profit growth due to competitive pressures [3][15]. Online Gaming and Music - The online gaming industry is projected to grow by around 10% in revenue, with profit growth expected to be between 10-15% [15]. - The online music sector is expected to see revenue and profit growth of 10-15%, although competition from ByteDance's music platform poses challenges [15][18]. Cloud and Advertising - Cloud and advertising are expected to remain the main growth drivers for companies leveraging AI, with a focus on investment returns [3][39]. Overseas Expansion - The report highlights the potential for overseas expansion in cloud services and OTA, with companies that have strong cash flow and profit margins likely to perform better in the long term [3][15]. Investment Recommendations - Specific companies recommended for investment include Tencent, Alibaba, Kuaishou, Trip.com, and NetEase, each with unique strengths and growth prospects in the AI and digital landscape [37][38].

Investment Rating - The report maintains a "Buy" rating for Microsoft (MSFT US), Google (GOOG US), Amazon (AMZN US), and Palo Alto Networks (PANW US) [22][23]. Core Insights - The competition in the large model industry is expected to intensify, with AI applications continuing to monetize effectively. The report highlights the potential for revenue growth driven by enhanced capabilities in AI models, particularly in image editing and video generation [1][25]. - Cloud service providers are experiencing accelerated revenue growth, with capital expenditures increasing significantly, indicating strong demand and a healthy outlook for profitability [3][9]. - AI monetization is driving growth in core business areas while exploring new revenue opportunities, with a focus on cost reduction and efficiency improvements [3][22]. Summary by Sections Industry Outlook - The large model industry is witnessing intensified competition, with continuous improvements in model capabilities and decreasing costs for model invocation. Key trends include a focus on agentic capabilities, faster iteration of open-source models, and the feasibility of large-scale deployment of end-to-end voice interaction models [3][25]. Cloud Business Performance - Revenue growth for U.S. internet companies' cloud businesses has accelerated, with a year-on-year increase in capital expenditures reaching $93.1 billion (+71% YoY). Operating profit for cloud businesses grew by 24.1% YoY in Q3 2025 [3][9]. AI Monetization - AI is driving growth beyond traditional cloud and advertising sectors, creating new revenue opportunities and enhancing operational efficiency. The report notes that enterprise AI applications are commercializing more slowly than AI cloud and advertising but still show promise for profit margin improvements [3][22]. Investment Opportunities - The report suggests focusing on two main scenarios for AI monetization: applications where large models excel, such as programming and creative generation, and high-value AI applications in traditional verticals like enterprise intelligence and education [3][22]. Financial Projections - The report projects that global AI spending will grow by 37% year-on-year to reach $2.0 trillion by 2026, with significant growth expected in AI application software and AI infrastructure software [8][22].

Core Points - Liang Wenfeng has been recognized as one of the top ten scientists of 2025 by the prestigious journal Nature for his significant contributions to the AI field through the DeepSeek model [1][3] - DeepSeek's model has disrupted the AI industry by achieving remarkable cost-effectiveness and enhancing the global visibility of domestic large models [9][10] - The recent release of DeepSeek's V3.2 model has set a new benchmark in the Agent evaluation, marking a significant advancement in open-source models [11][12] Group 1: Recognition and Impact - Liang Wenfeng is described as a "Tech disruptor" by Nature, highlighting his dual identity as a financial expert and a pioneer in AI [4][5] - The introduction of DeepSeek has been a game-changer for the AI sector, proving that high-performance models can be developed without excessive data or resources [10][21] - The model's cost efficiency has positioned it as a competitive player in the global AI landscape [9] Group 2: Background of Liang Wenfeng - Liang Wenfeng was born in 1985 in Guangdong and excelled academically, earning a place at Zhejiang University [14][15] - He transitioned into quantitative investment in 2008, capitalizing on the emerging trend of quantitative trading in China [17][18] - In 2021, his firm became one of the largest quantitative private equity firms in China, prompting him to explore opportunities in large models [19][20] Group 3: Other Recognized Scientists - Mengran Du, another Chinese researcher, was also recognized for her groundbreaking work in deep-sea ecology [6][22] - Du's research led to the discovery of the deepest known animal ecosystems, challenging existing models of extreme life and carbon cycling [25][26] - Her academic journey includes significant contributions to deep-sea science and technology, with multiple publications in prestigious journals [33]

人民财讯12月9日电，华泰证券发布传媒行业2026年度展望：聚焦游戏产品向上周期，寻找AI应用突破 性机会。展望2026年，看好传媒行业三大配置方向：1)游戏板块自下而上选择有业绩α的公司，行业规 模稳健增长，2026年部分现运营产品流水或仍攀升，多款重点新品有望上线；2)AI应用有望涌现及加速 商业化，大模型能力跃迁、推理成本下降带动AI应用渗透率提升，Agent或迎来奇点，重点看好AI+广 告、AI+电商等；3)影视行业在政策利好的驱动下，持续优化内容供给。AI技术已深度赋能制作环节， AI漫剧商业化落地迅速。 ...

Core Viewpoint - The article highlights the significant market enthusiasm surrounding domestic GPU companies, particularly focusing on the successful IPO of Moer Thread and the subsequent interest in Muxi Co., which is positioned as the "second domestic GPU stock" [4][5][9]. Group 1: Market Performance - Moer Thread, the first domestic GPU stock, debuted at an issue price of 114.28 yuan per share and opened at 650 yuan, peaking at 688 yuan, resulting in a market capitalization of approximately 2700 to 3000 billion yuan [4][5]. - Muxi Co. launched its IPO on the same day with an issue price of 104.66 yuan per share, aiming to raise 4.197 billion yuan, which would give it a market capitalization of around 418.74 billion yuan [5][9]. - Muxi's offline subscription saw a staggering 2227.6 times oversubscription, surpassing Moer Thread's previous record of 1572 times, marking it as the "king of new share subscriptions" [9][10]. Group 2: Investor Sentiment - The excitement from Moer Thread's IPO translated seamlessly to Muxi, with retail investors expressing a strong desire to participate in the latter's offering, driven by the potential for significant profits [6][7][12]. - Social media discussions reflected a prevalent "lottery mentality" among retail investors, focusing more on potential profits rather than understanding the underlying technology [6][12]. Group 3: Company Background and Financials - Muxi Co., established in September 2020, has a founding team with experience from leading chip companies like AMD and ARM, and has developed a product line that includes GPUs for general computing and AI applications [14]. - The company reported revenues of 42.64 million yuan in 2022, projected to grow to 743 million yuan by 2024, indicating a rapid growth trajectory [14]. - Despite revenue growth, Muxi has faced significant losses, with net losses of 7.77 billion yuan in 2022 and an expected continuation of losses into 2025, highlighting the challenges of high R&D costs [15][22]. Group 4: Investment Landscape - Muxi has attracted substantial investment, with total financing exceeding 10 billion yuan, supported by a diverse range of investors including state-owned funds and private equity [16][17]. - The company's shareholder structure reflects a mix of domestic and international capital, indicating strong backing but also potential pressure from early investors looking to realize gains [18]. Group 5: Future Considerations - The article emphasizes the need for Muxi to establish a sustainable business model beyond project-based revenues, aiming for recurring income streams from cloud services and software licensing [24]. - The success of Muxi will depend on its ability to validate its technology, secure long-term contracts, and maintain investor confidence amidst market volatility [23][26].

作者 | 雍 军 （ 阿 维 塔 科 技 副 总裁 ） 加入轩辕同学，成就新汽车人！ 一直以来，安全都是最大的豪华，作为长安汽车打造的高端品牌，阿维塔将安全提升到了一个新高度。 "安全另外一个层面就是健康，阿维塔在乘员舱里，包括各种有害物质都是按照国标低两个数量级，也就 1% 的要求在做车，但即使低两个数量级 依然有有害物质，对基础材料、基础工艺的研究，我相信今天供应链也有解决方案，如果有解决方案告诉我，我们一定会用。" 2022 年开始交付后，阿维塔展现出了大厂新势力的实力。 2025 年 11 月，阿维塔销量达 14057 辆，创单月历史新高，并连续 9 个月销量破万； 1-11 月累计销量达 118302 辆，品牌成立以来总销量已突破 22 万辆。 随着品牌的热销，阿维塔科技宣布正式向香港联合交易所递交 IPO 申请，成为首家向港交所提交 IPO 申请的央企新能源车企。 在雍军看来，阿维塔目前的发展方向正处于 0 到 1 阶段，接近" 1 "的位置，但还没有到，"我觉得是 0.8 的样子。"他说。这番话，也是对世界新 汽车生态协会理事长、轩辕同学校长贾可在开幕式致辞中的回应。 设计 | 甄 尤 美 编辑 ...

Core Viewpoint - The establishment of the data science joint laboratory, led by the United Nations Global Center for Statistical Big Data and Data Science, aims to integrate technological advantages in large models and artificial intelligence to address key technical bottlenecks in statistical big data and data science research [1][6]. Group 1 - The joint laboratory is a collaboration between the United Nations Global Center for Statistical Big Data and Data Science, Zhejiang University of Technology, and the Zhijiang Laboratory, focusing on global data governance and sharing [3][9]. - The laboratory will leverage the innovative resources of research institutions to provide global services based on mature practical experiences in the field of big data [4][8]. - The initiative is expected to share China's experiences in digital economy research and development with international experts and scholars [6][9].