Core Viewpoint - Morgan Stanley indicates that despite a short-term slowdown in core e-commerce growth, Alibaba's investment logic is supported by robust growth in Alibaba Cloud [1][4]. Alibaba Cloud Performance - Alibaba Cloud is experiencing strong industry demand, with management stating that current demand exceeds supply. The three-year capital expenditure guidance of 380 billion RMB may be insufficient to meet current customer needs [1][5]. - The third fiscal quarter (Q4) revenue for Alibaba Cloud is expected to grow by 35%, with a further increase to 36% in the fourth fiscal quarter (next Q1) [1][5]. - New AI applications, such as Quark AI Assistant and Tongyi Qianwen, are anticipated to further enhance user adoption rates [1][5]. - Alibaba Cloud's revenue for the second fiscal quarter reached 39.824 billion RMB, a year-on-year increase of 34.5%, with an adjusted EBITA of 3.604 billion RMB and a profit margin of 9.0%, exceeding Morgan Stanley's previous expectations [5]. E-commerce Business Outlook - The core e-commerce business is expected to see a slowdown in growth to 7.5% due to a weak macro environment [1][6]. - Online retail sales growth further slowed to 5% in October, with package volume decreasing from low double-digit growth in September to 8% in October, which is expected to negatively impact GMV and core e-commerce revenue [6]. - The adjusted EBITA for the Chinese e-commerce group in the second fiscal quarter was 10.497 billion RMB, a year-on-year decline of 76.3%, primarily due to rapid business investments [6]. - The losses in the Cainiao business are expected to narrow to 25 billion RMB in the third fiscal quarter, aligning with market expectations [1][6]. - Morgan Stanley has revised its expectations for Cainiao's losses down from 37 billion RMB to 25 billion RMB, indicating better-than-expected performance [6].

Core Insights - Alibaba's Qwen models have achieved top rankings in the latest SpatialBench benchmark for spatial reasoning, surpassing leading international models like Gemini 3 and GPT-5.1 [1] Model Performance - Qwen3-VL scored 13.5 points and Qwen2.5-VL scored 12.9 points, significantly outperforming Gemini 3.0 Pro Preview (9.6), GPT-5.1 (7.5), and Claude Sonnet 4.5 [1] - Despite the strong performance of AI models, there remains a notable gap compared to human capabilities, with the human benchmark around 80 points for complex spatial reasoning tasks [1] Model Capabilities - Qwen3-VL excels in recognition, multi-target grounding, and understanding spatial relations, indicating advanced capabilities in visual understanding [4]

北京商报讯（记者 魏蔚）11月26日，阿里宣布阿里千问与全新的夸克AI浏览器深度融合，用户在首页 搜索框和侧边栏可直接调用千问，无需切换标签页便可完成"边浏览边对话、边看边总结、即问即答"的 交互。用户也可将千问"常驻桌面"，在不打开夸克AI浏览器的情况下，直接唤起六项"千问智能套件"能 力——读屏、快捷框、侧边栏、悬浮球、划词和截屏。 ...

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 从2月宣布投入3800亿元建设AI基础设施，到9月宣布向超级人工智能（ASI）进发至上周开启千问APP 正式公测，今年以来阿里巴巴在AI领域的密集布局与多线叙事，以及持续投入的进取姿态，正推动其 业绩增长与资本市场估值重回巅峰。 11月25日阿里发布的2026财年第二季度财报，阿里巴巴集团收入2477.95亿元，剔除已出售业务影响， 收入同比增长15%，超市场预期。 AI+云、消费两大战略领域业务强劲增长。阿里云季度收入同比加速增长34%，AI相关产品收入连续第 九个季度实现三位数增长；大消费平台协同效应显著，即时零售带动淘宝App月活跃消费者快速增长。 随着阿里AI战略在今年全面铺开，一条从AI算力、云平台、大模型再到应用层面的完整布局的全栈AI 能力已清晰浮现，阿里正向B端与C端全面发力。 在财报分析师电话会上，阿里巴巴集团CEO吴泳铭分享了AI战略进展，阿里正在AI to B 和AI to C两大 方向齐发力——在AI to B领域，做世界领先的全栈AI服务商，服务千行百业不断增长的AI需求；在AI to C领域，基于性能领先的A ...

文/陈归辞 从2月宣布投入3800亿元建设AI基础设施，到9月宣布向超级人工智能（ASI）进发至上周开启千问APP正式公测，今年以来阿里巴巴在AI领域的密集布局与 多线叙事，以及持续投入的进取姿态，正推动其业绩增长与资本市场估值重回巅峰。 11月2 5日阿里发布的2 0 2 6财年第二季度财报，阿里巴巴集团收入2 4 7 7 . 9 5亿元，剔除已出售业务影响，收入同比增长1 5%，超市场预期。 AI+云、消费两大战略领域业务强劲增长。阿里云季度收入同比加速增长3 4%，AI相关产品收入连续第九个季度实现三位数增长；大消费平 台协同效应显著，即时零售带动淘宝Ap p月活跃消费者快速增长。 随着阿里AI战略在今年全面铺开，一条从AI算力、云平台、大模型再到应用层面的完整布局的全栈AI能力已清晰浮现，阿里正向B端与C端 全面发力。 在财报分析师电话会上，阿里巴巴集团CEO吴泳铭分享了AI战略进展，阿里正在AI t o B 和AI t o C两大方向齐发力——在AI t o B领域，做 世界领先的全栈AI服务商，服务千行百业不断增长的AI需求；在AI t o C领域，基于性能领先的AI模型和阿里生态优势，打造面向C ...

Core Insights - Alibaba reported a solid FY2Q26 performance with total revenue of RMB247.8 billion, a 5% year-over-year increase, slightly above market expectations [1] - The company's Cloud Intelligence Group revenue grew 34% year-over-year, exceeding consensus estimates, while international digital commerce showed a 10% increase [1][2] - Adjusted EBITA dropped significantly by 77.6% year-over-year, primarily due to increased investments in quick commerce, although the overall outcome was better than feared due to strong cloud and AIDC performance [1][2] Revenue Performance - Alibaba's total revenue reached RMB247.8 billion, surpassing the consensus of RMB245.2 billion [1] - The Alibaba China E-commerce Group revenue grew 16% year-over-year to RMB132.6 billion, with customer management revenue rising 10% to RMB78.9 billion [1] - Cloud revenue increased by 34% year-over-year to RMB39.8 billion, ahead of the consensus of RMB37.93 billion [1][2] - International Digital Commerce revenue grew 10% year-over-year to RMB34.8 billion, below the consensus of RMB37.2 billion [1] Profitability and Margins - Non-GAAP net income to ordinary shareholders fell 71.3% year-over-year to RMB10.5 billion, which was 23% below consensus [1] - Adjusted EBITA decreased by 77.6% year-over-year to RMB9.07 billion, exceeding the consensus estimate of RMB6.87 billion [1] Cloud Business Insights - Cloud revenue growth accelerated to 34% year-over-year, with external customer revenue up 29% and internal customer revenue up 53% [2] - AI revenue now accounts for approximately 20% of external cloud revenue, marking the ninth consecutive quarter of triple-digit AI revenue growth [2] - The EBITA margin remained stable at 9%, reflecting ongoing strategic investments in AI [2] Capital Expenditure and Strategy - Alibaba's quarterly capital expenditure rose 80% year-over-year to RMB32 billion, contrasting with Tencent's decline [3] - The company aims to strengthen its AI infrastructure and full-stack capabilities, similar to global leaders like Google [3] E-Commerce and Quick Commerce - Customer management revenue in e-commerce grew 10% year-over-year, but management noted potential deceleration in growth due to competition and user investment intensity [4] - Quick commerce recorded an EBITA loss of approximately RMB36.4 billion in FY2Q26, but improvements in unit economics are expected to narrow losses in FY3Q26 [5] Valuation Insights - Alibaba's current trading price implies an enterprise value of US$356 billion, with consensus EBITDA forecasts suggesting potential upside [6] - The valuation gap compared to peers indicates meaningful upside potential if Alibaba can stabilize e-commerce profitability and improve visibility on quick-commerce losses [6]

Core Insights - Alibaba's latest financial report indicates a strategic shift from pursuing scale in its instant retail business "Flash Purchase" to controlling losses, which may support future profit recovery for the group [1][2] Financial Performance - For Q3, Alibaba reported a revenue increase of 4.8% year-on-year to 247.8 billion yuan, but Non-GAAP net profit plummeted 71.7% to 10.35 billion yuan, primarily due to significant investments in the Flash Purchase business [1] - The adjusted EBITA for Alibaba's Chinese e-commerce group fell sharply by 76.3% to 10.5 billion yuan, with the strategic investment in Flash Purchase being the main drag [2] Strategic Focus - Alibaba's management confirmed that the current quarter represents a peak in investment for the Flash Purchase business, with expectations of a significant reduction in investment in the next quarter as the focus shifts to loss reduction [2] - Analysts from CITIC Securities believe that the investment in Flash Purchase may have peaked, indicating a strategic shift towards enhancing profitability [1][2] Operational Efficiency - There are positive signs of improved operational efficiency in the Flash Purchase business, with average losses per order halving since July-August, while maintaining stable order share [3] - The unit economic model (UE) for Flash Purchase has shown significant improvement since September, suggesting that prior investments are beginning to yield operational returns [3] Cloud Business Performance - Alibaba Cloud's revenue grew by 34.5% year-on-year to 39.82 billion yuan, becoming a highlight of the financial report, with AI-related revenue experiencing triple-digit growth for nine consecutive quarters [4][5] - The management noted strong demand for AI, with capital expenditures reaching 31.5 billion yuan to enhance AI computing power and cloud infrastructure [4][5] Market Outlook - Investment banks have adjusted their short-term profit forecasts for Alibaba, with Huatai Research raising its FY26 Non-GAAP net profit estimate by 10.1% to 105.8 billion yuan, citing better-than-expected loss reduction in the Flash Purchase business [6] - CITIC Securities forecasts a Non-GAAP net profit of 114.2 billion yuan for FY26, with a strong rebound of 40% expected in FY27, reaching nearly 160 billion yuan [6]

Core Insights - The article discusses the launch of ROCK, a new open-source project by Alibaba that addresses the challenge of scaling AI training in real environments [2][5]. - ROCK, in conjunction with the existing ROLL framework, creates a complete training loop for AI agents, enabling developers to deploy standardized environments for training without the need for complex setups [3][4][5]. Group 1: AI Training Environment - The current evolution of large language models (LLMs) into Agentic models requires them to interact deeply with external environments, moving beyond mere text generation to executing actions [6][7]. - A stable and efficient training environment is crucial for the scaling potential of Agentic models, as it directly impacts the performance and learning capabilities of the AI [9][10]. - The performance bottleneck in training processes often stems from the limitations of the training environment, necessitating a dual approach to develop both high-performance RL frameworks and efficient environment management systems [10]. Group 2: ROLL Framework - ROLL is built on Ray and is designed specifically for large-scale reinforcement learning, covering the entire RL optimization process from small-scale research to production environments with billions of parameters [12]. - ROLL enhances training speed through asynchronous interactions and redundancy sampling, utilizing a simplified standard interface called GEM [13][14]. - The design of ROLL allows for quick adaptation to new applications, enabling seamless integration of various tasks from simple games to complex tool interactions [15]. Group 3: ROCK's Features - ROCK aims to facilitate the scaling of AI training by allowing concurrent processing of thousands of instances, addressing the resource limitations of traditional training environments [22][24]. - It provides a unified environment resource pool, enabling rapid deployment and management of training environments, significantly reducing setup time from days to minutes [25][26]. - ROCK offers unprecedented flexibility, allowing both homogeneous and heterogeneous environments to run simultaneously within the same cluster, enhancing the generalization capabilities of agents [27][28]. Group 4: Debugging and Stability - ROCK addresses the common issue of "black box" environments by providing developers with a comprehensive debugging interface, allowing for deep interaction with multiple remote sandboxes [30][33]. - The system is designed for enterprise-level stability, featuring fault isolation and precise resource scheduling to ensure high-quality data collection and model convergence [41][44]. - Quick state management ensures that any environment failures can be rapidly reset, maintaining the continuity of the training pipeline [45]. Group 5: ModelService Integration - ROCK introduces ModelService as an intermediary that decouples the agent's business logic from the training framework, allowing for smoother collaboration between the two [50][51]. - This architecture reduces maintenance complexity and enhances cost efficiency by concentrating GPU resources on centralized inference services while running large-scale environments on lower-cost CPU instances [57]. - The design promotes compatibility and flexibility, enabling support for custom agent logic while maintaining robust training capabilities [58].

Core Insights - Alibaba's Qianwen project is accelerating its comprehensive coverage of C-end scenarios through deep integration with the new Quark AI browser, positioning the browser as a core carrier for Qianwen's capabilities [1][3] - The Quark AI browser has reached an installation volume of 110 million on computers, indicating a significant user base for potential AI integration [1] - The global AI browser market is competitive but lacks a clear leader, with Google's Chrome holding about 70% market share but adopting a conservative approach to AI [3] Summary by Sections - **Integration and Functionality** - Qianwen has evolved from a web browsing assistant to a system-level task assistant through its deep integration with the Quark AI browser, enhancing user experience beyond the traditional AI plugin model [3] - Users can access Qianwen's capabilities directly from the Quark interface without switching tabs, allowing for seamless interactions such as browsing while conversing and summarizing [3] - **User Engagement and Adoption** - The Qianwen app has surpassed 10 million downloads within a week of public testing, indicating strong initial user engagement [3] - Users have the option to keep Qianwen "always on" on their desktops, enabling direct access to six key AI capabilities across various high-frequency tasks [3] - **Strategic Importance** - Alibaba views the Qianwen project as a critical component in the "future battle of the AI era," aiming to reshape the underlying capabilities of its products systematically [3]

Core Viewpoint - Jefferies has raised Alibaba's target price from $230 to $231 [1] Group 1 - The adjustment in target price reflects a positive outlook on Alibaba's performance [1]