Core Viewpoint - Alibaba plans to invest 380 billion in AI infrastructure over the next three years, positioning itself as a leader in AI development and cloud computing [2][5][23]. Group 1: AI Development Stages - The evolution towards Artificial Superintelligence (ASI) will occur in three stages: "Intelligent Emergence," "Autonomous Action," and "Self-Iteration" [3][9][12]. - The first stage, "Intelligent Emergence," involves AI learning from vast human knowledge to develop generalized intelligence [9][10]. - The second stage, "Autonomous Action," allows AI to assist humans by performing complex tasks in the real world [10][11]. - The final stage, "Self-Iteration," will enable AI to surpass human intelligence through continuous learning and interaction with the physical world [12][15]. Group 2: AI Infrastructure and Investment - Alibaba's investment of 380 billion in AI infrastructure is aimed at meeting the growing demand for AI capabilities and energy requirements, with a projected tenfold increase in energy consumption by 2032 compared to 2022 [5][23]. - The company is building a "super AI cloud" to support the next generation of computing, which will require significant resources and infrastructure [21][22]. - Alibaba's cloud platform, with over 300 open-source models and a global download count exceeding 600 million, is positioned as a leading provider of AI capabilities [4][22][23]. Group 3: Market Position and Strategy - Alibaba aims to create an open-source ecosystem, likening its model to "Android of the AI era," to foster developer engagement and accelerate market demand [4][19]. - The transition from CPU-based to GPU-based AI computing is essential for meeting the demands of modern AI applications, necessitating a shift in cloud computing paradigms [21][22]. - The company anticipates that only a few super cloud computing platforms will dominate the market, with Alibaba aiming to be one of them [21][22]. Group 4: Future Implications of AI - The emergence of ASI is expected to revolutionize human productivity, allowing individuals to achieve results exponentially greater than current capabilities [24][25]. - AI will redefine the relationship between humans and technology, with numerous agents and robots working collaboratively across various sectors [24][25]. - The ongoing AI revolution is projected to reshape the entire infrastructure, software, and application landscape, driving new demands and opportunities [25][26].

当外界还在讨论AGI（通用人工智能）的实现路径时，阿里集团董事兼CEO、阿里云智能集团董事长兼CEO吴泳铭在9月24日提出了一个新概念：超级人工 智能（ASI）。 每年，阿里云都会在云栖大会上描述定位、分享观察、展示能力。2025年的这套组合拳更显强势，目标也更宏大。这种变化很微妙，并不起于当天的演讲和 愿景，而来自于2025年初阿里提出的三年投入3800亿元建设云和AI基础设施的决心、云计算在阿里集团日益提升的地位，以及集团押注未来技术革命承担 的风险。 当天，阿里通义千问大模型七连发，覆盖文本、编程、视觉理解、语音识别等垂直领域；百炼升级了7种企业级Agent（智能体）能力；阿里云发布新一代磐 久128超节点AI服务器。产品层面，阿里云从AI模型、Agent（智能体）开发、AI基础设施三个方面向AI时代的安卓看齐。单论云栖大会第一天的效果，按 当天港股收盘时计算，阿里股价涨了近10%，截至北京商报记者发稿，阿里在美股股价上涨7.71%。 1 . ri i r program CHILACA THE 他还将通往ASI的路线分成三个阶段：智能涌现、自主行动、自我迭代，把当下所处的阶段对标到第二阶段，也就是AI ...

作者 | 周智宇 编辑 | 张晓玲 这套全新的叙事，等于要求资本市场彻底抛弃旧的估值坐标系。阿里试图告诉投资者：不要再用传统 IDC（互联网数据中心）的逻辑来给阿里云定价了，请用一个属于ASI时代的、更高维度的坐标系来重 新审视它。 所谓的ASI，是一个新概念。吴泳铭在会上直接将目标从行业热议的AGI（通用人工智能）拉升至ASI （超级人工智能）。他清晰地描绘了通往ASI的三个阶段：从学习人类知识的"智能涌现"，到辅助人类 的"自主行动"，最终实现自主学习并"超越人类"。 支撑这套叙事的，是饱和式的技术和产品投入。本次大会，阿里云堪称"模型七连发"，一口气推出了旗 舰模型Qwen3-Max、下一代架构Qwen3-Next、视觉编程模型Qwen3-VL等七款重磅新品。其中，旗舰模 型Qwen3-Max的性能已在全球权威榜单LMArena上位居前三，超越了GPT-5等顶尖模型。 在杭州初秋的小雨中，阿里巴巴CEO吴泳铭走上了云栖大会的舞台，并发表了题为《超级人工智能之 路》的演讲。这不仅是他执掌阿里巴巴两年后的年度汇报，更被外界解读为一次决定性的宣言。 此前，市场上就有关于阿里将上调资本开支的传闻，而在云栖大会上 ...

Core Viewpoint - Alibaba Cloud is positioning itself for the future of AI, with a focus on building a robust AI infrastructure and preparing for the arrival of Artificial Super Intelligence (ASI) [1][22]. AI Development Stages - The journey to ASI is defined in three stages: "Intelligent Emergence," "Autonomous Action," and "Self-Iteration" [6][12]. - The first stage involves AI learning from vast human knowledge, achieving capabilities comparable to top human performers in various fields [6][3]. - The second stage focuses on AI's ability to assist humans by performing complex tasks and interacting with the physical world [7][8]. - The third stage will see AI achieving self-learning capabilities, allowing it to optimize and evolve independently [11][12]. Investment Goals - Alibaba is committed to a three-year investment plan of 380 billion yuan in AI infrastructure, anticipating a tenfold increase in energy consumption by 2032 compared to 2022 [22][19]. - The company aims to create a comprehensive AI ecosystem that supports developers and enhances AI applications across industries [20][19]. AI as the Next Operating System - Large models are expected to replace traditional operating systems, serving as the backbone for all tools and applications in the AI era [14][15]. - Natural language will become the programming language of the AI age, enabling users to create applications effortlessly [15][16]. AI Cloud as the Next Computing Paradigm - The future of computing will revolve around AI Cloud, which will require massive computational resources and infrastructure [18][19]. - The shift from CPU-centric to GPU-centric computing is essential for meeting the demands of AI applications [18][20]. Collaboration Between Humans and AI - The emergence of ASI will redefine human-AI collaboration, enhancing productivity and creating new opportunities across various sectors [24][25]. - AI is expected to amplify human capabilities significantly, leading to unprecedented levels of productivity and innovation [24][25].

吴泳铭是一个极少出现在聚光灯之下的CEO。 基于此，他阐述了阿里的两个核心选择：其一，通义千问选择开源路线，希望成为下一代操作系统；其二，阿里云要成为"超级AI云"，因为未来的 商业模式就像电力系统，"Token就是未来的电"。 为支撑这一宏大愿景，吴泳铭表示，阿里巴巴正在积极推进三年3800亿的AI基础设施建设计划，并将会持续追加更大的投入。根据远期规划，为了 迎接ASI时代的到来，对比2022年这个GenAI的元年，2032年阿里云全球数据中心的能耗规模将提升10倍。 这场演讲，是他少有的高调表达，为理解他上任后700多天的所有决策，提供了一个清晰的视角。 1.终极思考：通往ASI的三级火箭 在他看来，实现一个具备人类通用认知能力的AGI，只是AI发展的一个阶段。AI的终极形态，将是能够自我迭代、超越人类智能的ASI。它将可能创 造出一批"超级科学家"和"全栈超级工程师"，以指数级的速度去解决今天人类难以攻克的科学问题，比如新药研发、新材料和可持续能源。 执掌阿里巴巴两年以来，他极少发声，也未接受过任何一家媒体的专访。综合多方信息，可以拼凑出一个更立体的形象：他是阿里"第一个程序 员"，亲手写下了公司早期 ...

Core Viewpoint - Alibaba Group's CEO, Wu Yongming, emphasized the strategic direction of Alibaba Cloud in the AI era, highlighting that large models will replace traditional operating systems and serve as the intermediary layer connecting users, software, and AI computing resources [1][3]. Summary by Sections AI Infrastructure Investment - Alibaba is actively advancing an AI infrastructure investment plan of 380 billion yuan, with intentions for further increases. By 2032, the energy consumption of Alibaba Cloud's global data centers is expected to increase tenfold compared to 2022, indicating exponential growth in computing power to prepare for the era of Super Artificial Intelligence (ASI) [1][10]. AI Model Development - The company launched its largest and most powerful model to date, Qwen3-Max, which ranks third globally on the authoritative LMArena text leaderboard, surpassing GPT-5-Chat. The revenue of Alibaba Cloud's intelligent group grew by 26% year-on-year, marking the highest growth rate in nearly three years, while the overall net profit increased by 76%, indicating the effectiveness of AI investments [3][4]. Market Response - Following these announcements, Alibaba's stock surged by 9.14% on September 24, reaching its highest price since October 2021, with a market capitalization increase of nearly 300 billion yuan in one day. The stock has risen over 40% this month and nearly doubled in value year-to-date, with a total market cap of 3.32 trillion HKD [3][4]. AI Evolution and Stages - The article outlines three stages of AI evolution: emergence of intelligence (learning from humans), autonomous action (assisting humans), and self-iteration (surpassing humans). Currently, the industry is in the autonomous action stage, where AI can perform complex tasks in the real world [5][6]. Future of AI and Computing - Large models are expected to replace current operating systems, becoming the next generation of operating systems. The computing paradigm is shifting from CPU-centric to GPU-centric AI computing, with a prediction that only 5-6 super cloud computing platforms will remain globally [7][8]. Open Source Strategy - Alibaba's open-source model, Tongyi Qianwen, has achieved over 600 million downloads and spawned over 170,000 derivative models, positioning itself as the leading open-source model matrix globally, aiming to create an "Android system" for the AI era [7][8]. Comprehensive AI Services - Alibaba plans to invest 380 billion yuan over three years in AI infrastructure, with a vision to enhance human-machine collaboration in the future [7][10].

（原标题：加大AI基建投资，阿里云要做"AI时代的Android"） 21世纪经济报道记者陶力 杭州报道 从电商到云+AI，再到超级人工智能（ASI），在AI战略的全力投入，让阿里巴巴再次回到巅峰。 9月24日，在2025云栖大会上，阿里巴巴集团CEO、阿里云智能集团董事长兼CEO吴泳铭明确阐述了AI时代下阿里云的核心战略。 他表示大模型是下一代操作系统，而AI云是下一代计算机。"大模型将取代传统OS，成为连接用户、软件与AI计算资源的中间层。" （2025云栖大会现场 本报记者 陶力/摄） 他宣布，公司正积极推进3800亿元的AI基础设施建设，并计划追加更大投入。到2032年，阿里云全球数据中心的能耗规模将比2022年提升10倍。 这一目标预示着阿里云算力投入将指数级增长，为迎接超级人工智能（ASI）时代做准备。此前公布的2025财年第二季度财报显示，阿里云智能 集团收入同比增长26%，创下近三年最高增速。公司整体净利润同比增长76%，表明AI投入已开始产生实效。 受到上述信息的刺激，资本市场也给予了积极回应。截至9月24日下午4点，阿里巴巴港股股价上涨超9%，市值一日增加近3000亿港元。 这意味着，阿里 ...

Core Insights - Alibaba released seven model updates at the 2025 Yunqi Conference, with over 300 models in the Tongyi series [2] - CEO Wu Yongming outlined a three-phase evolution towards Artificial Super Intelligence (ASI), emphasizing that achieving Artificial General Intelligence (AGI) is a certainty but merely a starting point for AI development [3][4][5] Model Updates - Qwen3-MAX: A trillion-parameter model excelling in programming and tool usage, achieving top international rankings [2][12] - Qwen3-Omni: A multimodal model supporting 19 languages for input and 10 for output, capable of processing 30-minute audio recordings [2][18] - Wan2.5-Preview: A video generation model that can create 10-second videos with enhanced quality [2][26] - Tongyi Bailin: An enterprise-level voice model with a significant reduction in hallucination rates [2][28] - Qwen3-VL: A visual understanding model with enhanced capabilities for video and OCR [2][21] - Qwen-Image: An upgraded image editing model with improved consistency in multi-image editing [2][22] - Qwen3-Coder: An intelligent programming model that supports multimodal input and has become popular on the OpenRouter platform [2][24] AI Development Vision - Wu emphasized that the first phase involves AI learning from human knowledge, the second phase focuses on AI assisting humans through tool usage, and the third phase aims for self-learning and surpassing human intelligence [5][6][8] - The ultimate goal is for AI to connect with the physical world and continuously learn from real-time data [6][8] Infrastructure and Strategic Goals - Alibaba is investing 380 billion yuan in AI infrastructure over three years, with plans to increase data center energy consumption tenfold by 2032 [11] - The company aims to establish a "super AI cloud" as the next-generation computing platform, requiring extensive resources and technology [10][11] - Alibaba's Tongyi Qianwen aims to be the "Android of the AI era," with over 300 models already open-sourced and a global download count exceeding 600 million [10][11] Agent Development Framework - The company introduced the Baolian Agent platform, which supports low-code development and aims to enhance the capabilities of intelligent agents [31][36] - The platform allows for dynamic memory updates and multi-modal memory capabilities, facilitating personalized agent experiences [32][35] AI Infrastructure Enhancements - Alibaba launched the Panjiu AI Infra 2.0 server, capable of high power and efficiency, and introduced the HPN 8.0 high-performance network for improved data center connectivity [38][39] - The company also unveiled a multi-modal intelligent data foundation for agent development, enhancing data processing capabilities [39] Conclusion - The Yunqi Conference showcased Alibaba's ambitions in AI, presenting a comprehensive ecosystem from model development to infrastructure, positioning itself for leadership in the next generation of AI operating systems and computing platforms [40]

Core Insights - Alibaba has unveiled its ambitious blueprint for Artificial Super Intelligence (ASI), asserting that the realization of Artificial General Intelligence (AGI) is a certainty, while ASI will elevate human intelligence beyond current limits [6][9][22] - The company aims to liberate humans from 80% of daily tasks through AGI, while ASI is expected to create "super scientists" and "full-stack super engineers" capable of solving complex global issues at unprecedented speeds [11][12][22] Summary by Sections AGI and ASI Vision - The new CEO of Alibaba, Wu Yongming, has shifted focus towards ASI, emphasizing its potential to redefine human capabilities and the future of humanity [8][9] - AGI is described as the beginning of an intelligence revolution, with ASI representing the ultimate goal of surpassing human intelligence [12][22] ASI Roadmap - Alibaba has outlined a three-phase evolution towards ASI: 1. Emergence of intelligence (Learning Man) 2. Autonomous action (Assisting Man) 3. Self-iteration (Surpassing Man) [24][26] - The first phase involves AI learning from vast amounts of digitalized human knowledge, while the second phase focuses on AI assisting humans through tool usage and programming capabilities [25][28] Technological Advancements - The company has introduced several advanced AI models, including Qwen3-Max, which surpasses GPT-5 in performance, and Qwen3-VL, which enhances visual understanding and interaction capabilities [36][38][41] - Qwen3-Omni aims to integrate multiple modalities, allowing AI to process and generate audio, text, and visual content seamlessly [42][43] Future Infrastructure and Strategy - Alibaba positions itself as a full-stack AI service provider, anticipating a future where AI capabilities will be delivered through a few dominant cloud computing platforms [34][35] - The company believes that AI will become the most critical commodity, akin to electricity, with a significant increase in data center energy consumption projected by 2032 [34][35] Market Impact - The advancements in AI technology are expected to lead to a massive transformation in the IT industry, with AI agents and robots becoming ubiquitous in daily life [35][36] - Alibaba's commitment to open-source models aims to democratize access to AI technology, similar to the Android model in the mobile space [34][36]

Core Insights - The article highlights the escalating competition in the AI infrastructure sector, marked by significant investments from major tech companies like Nvidia and Alibaba, indicating a strategic shift towards building powerful computing capabilities for AI development [1][2][5]. Group 1: Major Investments and Strategic Moves - Nvidia and OpenAI recently announced a monumental $100 billion deal to develop next-generation AI supercomputing clusters [1]. - Alibaba has committed to investing 380 billion RMB (approximately $53 billion) in AI infrastructure, joining the ranks of other tech giants like OpenAI, Google, and Meta in the global "computing power war" [2][3]. - The article emphasizes that advanced algorithm models are essential for entering the race towards Artificial General Intelligence (AGI) and Superintelligence (ASI), with robust computing infrastructure being the core battlefield [5]. Group 2: Strategic Challenges in Building Computing Empires - The construction of a successful computing empire requires more than just financial investment; it demands foresight, engineering excellence, innovative system architecture, and a strong developer ecosystem [6]. - The challenges faced by industry players are universal, as they all strive to establish their own "computing barriers" in this competitive landscape [7]. Group 3: Nvidia's Strategic Partnerships - Nvidia's recent $5 billion investment in Intel to co-develop customized data center and PC products has generated significant industry buzz, reflecting a dramatic shift from past rivalries to collaboration [10]. - This partnership is expected to enhance product competitiveness, particularly in the laptop market, while revitalizing Intel's position in the industry [10]. Group 4: GPU Market Dynamics - The GPU market has experienced dramatic fluctuations, likened to a "drug trade," with supply shortages and price wars affecting availability and pricing strategies [12]. - New entrants in the cloud service market have intensified competition, leading to a complex landscape where acquiring GPUs for large-scale deployment remains a significant challenge [12]. Group 5: Oracle's Rise in Cloud Services - Oracle has emerged as a dark horse in the cloud services market, leveraging its substantial balance sheet to support large-scale computing orders for clients like OpenAI [13]. - Its flexible hardware strategy allows Oracle to deploy the most effective technology combinations, enhancing its competitive edge [13]. Group 6: Amazon AWS's Recovery Strategy - Amazon AWS is experiencing a resurgence after a growth slowdown, driven by its vast data center resources and the provision of massive GPU and custom chip capabilities to major clients [14]. - Despite challenges with its custom chip Trainium, AWS is adapting its infrastructure to meet the demands of AI workloads [15]. Group 7: New AI Hardware Opportunities and Challenges - The introduction of Nvidia's Blackwell architecture marks a new era in AI hardware, presenting both performance advancements and new challenges regarding cost, reliability, and system architecture [16]. - The GB200 architecture presents a performance paradox, where its deployment costs are higher, but the performance gains are highly workload-dependent [17]. Group 8: Nvidia's Competitive Edge - Nvidia's success is attributed to its visionary leadership, particularly Jensen Huang's bold decision-making and execution capabilities, which have allowed the company to maintain a significant competitive advantage [22][24]. - The company's ability to deliver new chip designs successfully on the first attempt is a testament to its engineering prowess and operational efficiency [26]. Group 9: Future Considerations for Nvidia - Nvidia faces the challenge of effectively utilizing its substantial cash flow for future investments, with options including infrastructure development and AI factory expansions [27].