专题：聚焦2025年第三季度美股财报 11月25日晚间消息，阿里巴巴（NYSE： BABA；HKEX： 9988）今日发布了截至2025年9月30日的 2026财年第二财季财报：营收为2477.95亿元，同比增长5%。若不考虑高鑫零售和银泰的已处置业务的 收入，则同口径收入同比增长将为15%。净利润为206.12亿元，同比下滑53%，主要由于运营利润的下 滑。不按美国通用会计准则，净利润为103.52亿元，同比下滑72%。归属于普通股股东的净利润为 209.90亿元。（注：阿里巴巴财年与自然年不同步，2025年4月1日至2026年9月30日为2026财年）。 详见： 阿里巴巴：2026财年第二财季营收2478亿元 经调净利润104亿元 财报发布后，阿里巴巴集团董事会主席蔡崇信、CEO吴泳铭、CFO徐宏、以及阿里电商事业群CEO蒋凡 出席了随后召开的电话会议，对财报进行了解读，并回答了分析师提问。 以下是分析是问答环节主要内容： 摩根士丹利分析师Gary Yu：我的问题与云业务相关，管理层如何看待云业务未来的增长前景？是否预 期增长会出现加速的情况？另外在需求端，考虑到我们并没有像美国那样的大型人工智能企业，该 ...

阿里云拿下Gartner四项第一，亚太唯一 硬·AI 作者 | 硬 AI 编辑 | 硬 AI 生成式AI的技术周期正在进入一个前所未有的高速区间。 面对日新月异的生成式AI技术，企业正陷入选择难题：如何挑选一个平台，才能跟上快速迭代的创新步伐？ 最近，Gartner发布了最新《生成式AI技术创新指南》系列报告，从云基础设施、模型工程、模型提供商、AI 应用四大维度扫描全球厂商。 结论显示：阿里云是唯一在全部四个维度均被列入领导者象限的亚太公司，并与谷歌、OpenAI并列于全球 前列。 Gartner这次把竞争的底层逻辑摊开了：在技术快速更迭的时代，全栈能力才是制胜关键。 这恰好与阿里云提出的"全栈AI服务商"定位完全契合。 01 全栈能力沉到底层 阿里云是真能打的那家 图说：按照纵轴产品特征（feature）、横轴未来潜力（futurepotential），新兴市场共分为四个象限。阿里云在"面向GenAI的基础设 施"维度位列新兴领导者象限。（报告截图） 02 谁都可以宣布第一 全栈领先者只有一个 Gartner将GenAI能力分为四层，对应阿里云从基础设施到应用的全栈体系。 在底层基础设施维度，重点是训练、 ...

GenAI发展日新月异，客户急需最新的市场洞察与决策建议。为快速反映市场变化，Gartner在该系列报告中持续更 新"新兴市场象限"，自2024年10月首次发布以来，已更新至第8期。在过往的报告中，阿里云持续位于新兴领导者象 限。 按照自下而上的技术栈，最新的系列报告分为四个维度。 在"面向GenAI的基础设施"的维度，报告关注云厂商面向模型训练、推理和服务所提供的基础设施优化。新兴领导者象 限仅有阿里云、微软、谷歌、AWS四家厂商入围，阿里云是唯一入围亚太厂商。华为云和腾讯云位于远见者象限。 在"GenAI工程"维度，报告关注数据准备、模型训练/精调、模型管理、评估、观测等模型全生命周期工具。阿里云依然 位于领导者象限，在纵轴"特征"以及横轴"未来潜力"指标上，均优于AWS、谷歌、微软。 Gartner最新报告：阿里云在生成式AI四大维度均列领导者象限，比肩谷歌、OpenAI 2025年11月中旬，国际权威市场研究机构Gartner发布4篇GenAI（生成式AI）技术创新指南系列报告，公布了GenAI云基 础设施、GenAI工程、GenAI模型以及AI知识管理应用四大维度的新兴市场象限（Emerging M ...

Core Viewpoint - The article discusses how Alibaba Cloud is leveraging the Double 11 shopping festival to showcase its AI capabilities and strengthen its ecosystem partnerships, marking a shift from consumer-focused promotions to B2B applications of AI technology [5][30][34]. Group 1: Alibaba Cloud's Strategy - Alibaba Cloud is positioning itself as a leader in AI by integrating its services with the Double 11 event, which has evolved from a consumer sales event to a platform for businesses to explore AI solutions [6][33]. - The company has defined three stages towards achieving Super AI (ASI): intelligent emergence, autonomous action, and self-iteration, indicating a long-term vision for AI development [5][6]. - The shift in cloud computing sales logic is highlighted, where the focus is moving from transactional partnerships to service-oriented partnerships that can provide comprehensive AI solutions [9][10]. Group 2: Market Response and Ecosystem Development - The first hour of Double 11 saw Alibaba Cloud's orders surpass "tens of millions," indicating a growing confidence in AI solutions among market participants [8][9]. - Alibaba Cloud is restructuring its partner ecosystem to prioritize service capabilities over mere transactional relationships, aiming to enhance the overall AI service delivery [10][11]. - The company is actively inviting AI-native partners who focus on specific industry applications, thereby expanding its ecosystem with both traditional and new partners [14][15]. Group 3: AI Applications and Industry Impact - Real-world applications of Alibaba Cloud's AI capabilities are demonstrated through partnerships in various sectors, such as satellite communication and education, showcasing the practical benefits of AI integration [20][22][23]. - The article emphasizes the importance of localized operations and the "last mile" in AI implementation, where partners play crucial roles in delivering tailored solutions to clients [27][28]. - The Double 11 event serves as a significant moment for businesses to engage with AI technologies, marking a collective movement towards AI adoption across various industries [32][33].

Core Viewpoint - The article discusses the rapid development and challenges of the Agent application market, highlighting the divergence of leading players into two distinct paths: full-stack AI service providers and specialized players focusing on vertical markets [1][4][11]. Group 1: Market Overview - The Agent application market is predicted to reach $27 billion in China by 2028 according to IDC [3]. - The current landscape shows a surge in investment and competition among companies eager to adopt Agent technology [2]. Group 2: Player Strategies - Major players in the Agent space include AI giants and cloud service providers, who are lowering the barriers for enterprises to adopt Agent technology [6][7]. - AI giants like OpenAI leverage their foundational model capabilities to gain a first-mover advantage, while cloud providers like Google and AWS are focusing on comprehensive solutions for enterprise Agent development [8][9]. Group 3: Application Scenarios - The primary application scenarios for Agents in enterprises include processing complex multi-modal content, interactive scenarios like chatbots, and high-value intelligent inspection and risk control [15]. - The consumer electronics industry has been the first to adopt Agent technology, with traditional sectors like agriculture gradually following suit [15]. Group 4: Technical Challenges - There are significant technical challenges in the deployment of Agents, including issues with model hallucination, multi-modal integration, and memory management [16]. - The integration of Agents with existing enterprise systems like ERP and CRM is complex, and the need for multi-Agent collaboration is becoming increasingly important [17][18]. Group 5: Solutions for Implementation - To overcome the challenges of Agent deployment, continuous technological innovation is essential, focusing on enhancing model capabilities and system engineering [22]. - The industry is exploring new development paradigms to improve the breadth and depth of Agent tasks, with protocols like MCP and A2A being tested to facilitate communication between different Agents [23][24]. Group 6: Industry Collaboration - Collaboration between vendors and enterprises is crucial for successful Agent implementation, with a focus on aligning business needs with Agent technology [25]. - The sharing of experiences and best practices among developers is encouraged to address complex scenarios and improve Agent development [26].

Group 1 - Alibaba Cloud's Aegaeon solution has been selected for the top academic conference SOSP2025, significantly reducing GPU usage by 82% and lowering hardware costs [1] - The Aegaeon technology has been applied to the Bailian platform, allowing single GPU services to support multiple models, thereby enhancing throughput [1] - The success of Aegaeon not only represents a technological breakthrough for Alibaba Cloud but also offers new hope and insights for the entire AI industry regarding efficient resource utilization [1] Group 2 - UBTECH has secured a major contract worth 126 million yuan for the procurement and installation of embodied intelligent data collection and testing center equipment, contributing to over 630 million yuan in total orders for the Walker series humanoid robots this year [1][2] - The humanoid robot market is experiencing rapid growth, with increasing demand reflected in UBTECH's rising order volumes across various application scenarios, including industrial production, service industries, education, and home entertainment [2] Group 3 - An semiconductor company in Dongguan plans to implement a "four days on, three days off" work schedule due to supply chain disruptions and product shortages following government intervention [3] - The company is facing pressure from product shortages and price increases, prompting its parent company to initiate measures to stabilize the domestic supply chain to meet customer demands [3] - The complexity of the semiconductor industry necessitates enhanced cooperation among upstream and downstream enterprises to build a more stable and diversified supply chain system to mitigate future risks [3]

Group 1 - The article highlights the growing trend of "intelligent agents" being marketed as a means for ordinary people to achieve financial success, with various eye-catching headlines suggesting significant earning potential [1][2] - Major companies are shifting their focus towards developing intelligent agents, moving from individual applications to multi-agent systems that enhance productivity and efficiency [3][5][9] Group 2 - The concept of "digital employees" is emphasized, where intelligent agents are seen as advanced tools capable of autonomous decision-making and task execution, surpassing traditional AI capabilities [11][13] - The commercial value of intelligent agents is projected to grow significantly, with estimates suggesting the enterprise AI agent market in China could exceed $27 billion by 2028, focusing on task outcomes rather than just technology [14][16] - The competition among major players is expected to intensify as the focus shifts from technological superiority to the ability to deliver practical, monetizable solutions that address real business challenges [16]

Group 1 - The core focus of major companies in the large model field this year is on Agents, driven by the continuous improvement of large model capabilities [1] - Tencent has launched its cloud intelligent agent development platform, integrating its leading RAG technology and comprehensive agent capabilities to help enterprises customize their own intelligent agents [1] - Tencent's large model strategy was fully unveiled at the 2025 Tencent Cloud AI Industry Application Summit, showcasing a comprehensive upgrade of its large model product matrix [1][3] Group 2 - Tencent's senior executives outlined the large model strategy, emphasizing "four accelerations" to enhance innovation, agent application, knowledge base construction, and infrastructure upgrades [3] - Recent structural adjustments have consolidated all AI products and applications related to large models under one business unit, enhancing the importance of Agents within Tencent [3][4] - The launch of the Qbot agent on Tencent's QQ browser signifies Tencent's strategy to improve C-end user retention while competing for B-end clients [4] Group 3 - The Tencent Cloud intelligent agent development platform allows users to enable agents to autonomously decompose tasks and plan paths, significantly lowering the barrier for agent construction [4] - The platform supports a zero-code approach for multi-agent collaboration, catering to various business complexities and knowledge densities [4] - The need for Agents is highlighted across industries with high complexity and knowledge density, suggesting a potential for reengineering business processes using Agents [4]

这位新晋网红并非真人，而是一个 AI 评论机器人，它是微博以通用大模型为基础架构，结合微博的数据训练和 微调出来的模型。 微博 COO、新浪移动 CEO 王巍告诉我们，以 " MBTI 小行家" 为代表的一批 AI 账号上线后，已让微博的互动率 提升了约 10%，这是衡量互联网社区产品的重要指标之一。 从去年到今年，市场焦点是大型科技公司和大模型独角兽的技术、产品与价格竞争。微博等公司的实践是大模型 热潮的另一面：一批公司已在尝试用大模型改造和优化已有业务流程，或寻找新的商业机会。 在教育领域，新东方用大模型智能定制学习计划、实时回复学生问题，学员满意度提升了 3%。营销推广服务商 易点天下基于大模型和自己积累的广告营销数据研发了 AI 数字人，还使用生成式 AI 技术把视频制作时间从 12 小时缩短到了 5 分钟。中国一汽的大模型 GPT-BI 应用能在 5 秒内快速生成财务、质保等环节的多变量报表，该 模型准确率达 92.5%。 "中国发展 AI 的优势是，我们离行业最近。" 今年 3 月，一个名为 " MBTI 小行家" 的账号开始在微博上活跃，微博用户只要 @ 它，它就会根据用户的过往微 博判断其 M ...