以下文章来源于并购最前线 ，作者杨继云 并购最前线 . 投资界（PEdaily.cn）旗下，专注并购动态 欢迎加入投资界读者群 新革命。 作者 I 杨继云 报道 I 投资界-并购最前线 一笔超级并购诞生。 事 情 想 必 大 家 已 看 到 —— 瑞 士 ABB 集 团 日 前 宣 布 以 5 3. 75 亿 美 元 （ 约 383 亿 人 民 币 ） 估 值，将其机器人业务单元整体剥离给软银集团，该交易预计在2 02 6年中后期完成。 这被看作全球工业自动化领域的"地震级"交易。ABB机器人业务是全球工业机器人"四大 家族"之一，底蕴深厚，今年上半年曾谋求独立IPO，不料如今高价卖给了孙正义。 至此，全球机器人产业格局被重塑。硝烟再起，大家都在争夺短暂的窗口期。 全球机器人霸主，被卖了 根 据 相 关 协 议 ， ABB 将 先 把 机 器 人 事 业 部 注 入 一 家 新 设 控 股 公 司 ， 接 着 再 由 软 银 集 团 以"全现金"方式收购该公司100%股权。 交割后，软银集团一举接手ABB机器人业务的700 0人工程师团队、5 0万台存量装机与全 球服务网络和全部知识产权。2 0 24年，AB ...

一笔超级并购诞生。 事情想必大家已看到——瑞士ABB集团日前宣布以53.75亿美元（约383亿人民币）估值，将其机器人业 务单元整体剥离给软银集团，该交易预计在2026年中后期完成。 这被看作全球工业自动化领域的"地震级"交易。ABB机器人业务是全球工业机器人"四大家族"之一，底 蕴深厚，今年上半年曾谋求独立IPO，不料如今高价卖给了孙正义。 至此，全球机器人产业格局被重塑。硝烟再起，大家都在争夺短暂的窗口期。 全球机器人霸主，被卖了 根据相关协议，ABB将先把机器人事业部注入一家新设控股公司，接着再由软银集团以"全现金"方式收 购该公司100%股权。 交割后，软银集团一举接手ABB机器人业务的7000人工程师团队、50万台存量装机与全球服务网络和 全部知识产权。2024年，ABB机器人业务年收入23亿美元，占集团总营收的7%。 ABB则看起来"稳赚"。公告显示，此次交易金额约53亿美元，扣除与本次剥离相关的约2亿美元成本及 4-5亿美元的税费支出后，ABB预计净落袋约47亿美元，并预计将产生约24亿美元（约171亿元）的非运 营性税前账面收益。 至此，原定的机器人业务分拆上市计划告吹。 今年4月，ABB发布 ...

2025.10. 08 本文字数：1124，阅读时长大约2分钟 作者 | 第一财经 钱童心 10月8日，软银集团宣布重磅投资。公司拟以54亿美元收购瑞士工业巨头ABB的机器人业务部门。此 举是日本商业大亨孙正义掌控的这家大型科技集团押注通用人工智能（AGI）的最新举措。 人工智能正在重塑机器人领域，这也是软银商业版图中的重要投资板块。孙正义对机器人投资充满热 情，孙正义提出人工超级智能（ASI）的理念，他认为未来十年内，人工智能将比人类聪明一万倍。 他曾在与英伟达CEO黄仁勋进行的一场炉边对话中表示："未来每个人都会有自己的AI智能体，它就 像人的第二个身体，能看到你，可以跟你对话，管理你的生活日常。" 通过投资ABB机器人，软银将进一步充实机器人板块的布局。此前，该公司已经拥有包括Agile Robots以及AutoStore在内的机器人相关企业的投资。 不过，软银对机器人的投资也有失败的经历。该公司曾于2012年收购法国机器人公司Aldebaran多 数股权。两年后，两家公司推出了一款Pepper的人形机器人，但最终未能赢得市场的认可。 相比于人形机器人而言，工业机器人的商业化路径更为清晰。工业机器人也是 ...

通过投资ABB机器人，软银将进一步充实机器人板块的布局。此前，该公司已经拥有包括Agile Robots 以及AutoStore在内的机器人相关企业的投资。 2025.10.08 本文字数：1124，阅读时长大约2分钟 作者 |第一财经 钱童心 10月8日，软银集团宣布重磅投资。公司拟以54亿美元收购瑞士工业巨头ABB的机器人业务部门。此举 是日本商业大亨孙正义掌控的这家大型科技集团押注通用人工智能（AGI）的最新举措。 软银集团董事长孙正义表示，软银的下一个前沿领域是物理人工智能（physical AI）。"我们将与ABB 机器人一起，将世界一流的技术和人才结合起来，融合人工智能和机器人技术，推动突破性的进化，推 动人类前进。" 孙正义近年来将软银的使命定义为"推动人类进化"。他看好人工智能与机器人的结合，并认为这是通往 AGI的重要路径。孙正义预测，通用人工智能将在未来2-3年内率先由大企业实现，并在未来十年内全 面实现。"要实现这一目标，需要巨额的资金，而这些资金目前只有大型企业才具备。"他表示。 人工智能正在重塑机器人领域，这也是软银商业版图中的重要投资板块。孙正义对机器人投资充满热 情，孙正义提出人 ...

人工智能正在重塑机器人领域，这也是软银商业版图中的重要投资板块。孙正义对机器人投资充满热情，孙正义提出人工超级智能（ASI）的理念，他认为 未来十年内，人工智能将比人类聪明一万倍。 通过投资ABB机器人，软银将进一步充实机器人板块的布局。此前，该公司已经拥有包括Agile Robots以及AutoStore在内的机器人相关企业的投资。 不过，软银对机器人的投资也有失败的经历。该公司曾于2012年收购法国机器人公司Aldebaran多数股权。两年后，两家公司推出了一款Pepper的人形机器 人，但最终未能赢得市场的认可。 相比于人形机器人而言，工业机器人的商业化路径更为清晰。工业机器人也是目前包括英伟达在内的科技巨头看好的领域。今年早些时候，英伟达推出全球 首个生成式世界基础模型Cosmos，为机器人制造商提供底层模型，部署用于训练人工智能机器人的软件层到内置芯片的所有环节。 包括黄仁勋在内的很多科技行业领导者都认为，机器人技术的ChatGPT时刻即将到来。"人工智能的下一波浪潮是物理人工智能——也就是能够理解物理定 律的人工智能，可以在我们之中工作的人工智能。"黄仁勋表示。 10月8日，软银集团宣布重磅投资。 ...

Core Insights - The article discusses the transition from static large language models (LLMs) to self-evolving agents capable of continuous learning and adaptation in dynamic environments, paving the way towards artificial superintelligence (ASI) [3][4][46] - It emphasizes the need for a structured framework to understand and design self-evolving agents, focusing on three fundamental questions: what to evolve, when to evolve, and how to evolve [6][46] Group 1: What to Evolve - Self-evolving agents can improve various components such as models, memory, tools, and architecture over time to enhance performance and adaptability [19][20] - The evolution of these components is crucial for the agent's ability to handle complex tasks and environments effectively [19][20] Group 2: When to Evolve - The article categorizes self-evolution into two time modes: intra-test-time self-evolution, which occurs during task execution, and inter-test-time self-evolution, which happens between tasks [22][23] - Intra-test-time self-evolution allows agents to adapt in real-time to specific challenges, while inter-test-time self-evolution leverages accumulated experiences for future performance improvements [22][23] Group 3: How to Evolve - Self-evolution emphasizes a continuous learning process where agents learn from real-world interactions, seek feedback, and adjust strategies dynamically [26][27] - Various methodologies for self-evolution include reward-based evolution, imitation learning, and population-based approaches, each with distinct feedback types and data sources [29][30] Group 4: Applications and Evaluation - Self-evolving agents have significant potential in various fields, including programming, education, and healthcare, where continuous adaptation is essential [6][34] - Evaluating self-evolving agents presents unique challenges, requiring metrics that capture adaptability, knowledge retention, and long-term generalization capabilities [34][36] Group 5: Future Directions - The article highlights the importance of addressing challenges such as catastrophic forgetting, knowledge transfer, and ensuring safety and controllability in self-evolving agents [40][43] - Future research should focus on developing scalable architectures, dynamic evaluation methods, and personalized agents that can adapt to individual user preferences [38][44]

Core Viewpoint - SoftBank, under the leadership of Masayoshi Son, is strategically positioning itself to become the world's leading provider of Artificial Super Intelligence (ASI) by investing heavily across the AI and semiconductor value chain, from IP to application layers [5][10][37]. Group 1: Historical Context and Vision - Masayoshi Son's journey began in 1975 when he was inspired by a microcomputer chip photo, which ignited his lifelong commitment to technology and innovation [6][9]. - In the 2025 fiscal year report, Son articulated a new strategic goal for SoftBank: to become the foremost ASI platform provider, emphasizing the belief in the eventual emergence of intelligence surpassing human capabilities [9][10]. Group 2: Strategic Investments - SoftBank has made significant investments in various companies to build a comprehensive AI and semiconductor ecosystem, including a $20 billion investment in Intel, becoming one of its top shareholders [13]. - The Stargate project, in collaboration with OpenAI and Oracle, aims to construct large-scale data centers for AI infrastructure, with an estimated investment of up to $500 billion [14]. - SoftBank led a $40 billion financing round for OpenAI, indicating its commitment to both infrastructure and application layers in the AI stack [16][19]. - The acquisition of Ampere for $6.5 billion aims to fill gaps in SoftBank's CPU capabilities, enhancing its position in the cloud computing and AI inference markets [20]. - The purchase of Graphcore, a struggling AI chip company, allows SoftBank to diversify its AI accelerator technology portfolio [21]. Group 3: Capital Map and Ecosystem Integration - SoftBank is constructing a capital map that integrates various components of the AI and semiconductor ecosystem, from IP (Arm) to CPUs (Ampere) to AI accelerators (Graphcore) and manufacturing (Intel Foundry) [23]. - The strategy involves creating a closed-loop system that connects upstream IP with downstream applications, thereby enhancing SoftBank's influence in the AI sector [27][28]. Group 4: Arm's Role and Future Prospects - Arm remains a crucial asset for SoftBank, with the company holding approximately 90% of Arm's shares post-IPO, which is pivotal for revenue generation through licensing and royalties [26][30]. - Arm's business model, characterized by long-term benefits from initial licensing, positions it well for sustained revenue growth, particularly in emerging markets like AI and cloud computing [30][31]. - The potential development of proprietary chips by Arm could further solidify its position in the data center market, although it presents challenges and risks [31][32]. Group 5: Competitive Landscape - SoftBank's approach contrasts with Nvidia's vertical integration strategy, as it seeks to leverage capital to control various segments of the AI and semiconductor landscape without focusing solely on in-house development [34][35]. - Unlike cloud giants like Microsoft and Amazon, which emphasize self-developed chips and infrastructure, SoftBank aims to reorganize production factors across the ecosystem, culminating in applications like OpenAI [35][36].

Core Insights - Meta's CEO Mark Zuckerberg is shifting the company's focus from the "metaverse" to "Artificial Super Intelligence" (ASI), aiming to create an AI that surpasses human intelligence and provides each user with a "personal superintelligence" [1][3][5] - The company is investing hundreds of billions of dollars into AI infrastructure, with projected capital expenditures reaching between $66 billion to $72 billion by 2025, primarily for building AI capabilities [6][7] - Meta's AI strategy is built on four pillars: model ecosystem, commercialization, infrastructure, and ecosystem extension, with varying degrees of success across these areas [15] Investment and Infrastructure - Meta is engaged in a significant arms race for computational power, with substantial investments in data centers named "Prometheus" and "Hyperion" to support AI research [6][7] - The company faces operational challenges, as over 66% of training interruptions are due to hardware failures, highlighting the need for excellent execution in addition to financial resources [8] Competitive Strategy - Meta promotes an "open" strategy with its Llama series models, aiming to democratize AI technology and stimulate innovation, contrasting with competitors like OpenAI and Google [9][10] - The open model is intended to lower development costs for AI applications, indirectly increasing demand for Meta's infrastructure and advertising services [11][12] Advertising Success - Meta's AI-driven advertising tools have significantly improved ad effectiveness, with reported increases in return on ad spend (ROAS) by 12% in Q1 2025 [16][18][19] - The integration of AI has enhanced user experience, leading to over 20% growth in video viewing time on Facebook and Instagram [18] Consumer Products and Market Position - Meta's AI assistant has over 400 million monthly active users, but it lags behind competitors like ChatGPT and Google Gemini in market share [20][21] - Users have criticized the AI assistant for lacking personalization and cross-application memory, indicating challenges in user retention and experience [21] Metaverse and Hardware Integration - AI capabilities are being integrated into Meta's metaverse platform, Horizon Worlds, but user engagement remains low compared to competitors [22] - The company is also embedding AI in its smart hardware products, such as Ray-Ban Meta smart glasses, to enhance user interaction [22] Internal Challenges - Meta's aggressive talent acquisition strategy has led to internal morale issues, as existing employees feel undervalued [24][25] - Frequent organizational restructuring has raised concerns about project continuity and employee retention [26][27] Structural Limitations - Meta lacks its own operating system, which limits its ability to deeply integrate AI and collect comprehensive user data compared to competitors like Google and Apple [28][29] Privacy and Trust Issues - Meta faces significant privacy challenges, including incidents where sensitive user queries were inadvertently made public, damaging user trust [30][31] - The lack of end-to-end encryption in certain platforms raises concerns about data security and has attracted regulatory scrutiny [32][33] Future Outlook - Meta's AI strategy is characterized by high stakes and uncertainty, with challenges in talent integration, organizational dynamics, and trust potentially hindering its path to achieving ASI [34]

Core Insights - The article discusses the limitations of static large language models (LLMs) and introduces the concept of self-evolving agents as a new paradigm in artificial intelligence [2] - A comprehensive review has been published by researchers from Princeton University and other top institutions to establish a unified theoretical framework for self-evolving agents, aiming to pave the way for artificial general intelligence (AGI) and artificial superintelligence (ASI) [2][32] Definition and Framework - The review provides a formal definition of self-evolving agents, laying a mathematical foundation for research and discussion in the field [5] - It constructs a complete framework for analyzing and designing self-evolving agents based on four dimensions: What, When, How, and Where [8] What to Evolve? - The four core pillars for self-improvement within the agent system are identified: Models, Context, Tools, and Architecture [11] - Evolution can occur at two levels for models: optimizing decision policies and accumulating experience through interaction with the environment [13] - Context evolution involves dynamic management of memory and automated optimization of prompts [13] - Tools evolution includes the creation of new tools, mastery of existing tools, and efficient management of tool selection [13] - Architecture evolution can target both single-agent and multi-agent systems to optimize workflows and collaboration [14] When to Evolve? - Evolution timing determines the relationship between learning and task execution, categorized into two main modes: intra-test-time and inter-test-time self-evolution [17] How to Evolve? - Intra-test-time self-evolution occurs during task execution, allowing agents to adapt in real-time [20] - Inter-test-time self-evolution happens after task completion, where agents iterate on their capabilities based on accumulated experiences [20] - Evolution can be driven by various methodologies, including reward-based evolution, imitation learning, and population-based methods [21][22] Where to Evolve? - Self-evolving agents can evolve in general domains to enhance versatility or specialize in specific domains such as coding, GUI interaction, finance, medical applications, and education [25] Evaluation and Future Directions - The review emphasizes the need for dynamic evaluation metrics for self-evolving agents, focusing on adaptability, knowledge retention, generalization, efficiency, and safety [28] - Future challenges include developing personalized AI agents, enhancing generalization and cross-domain adaptability, ensuring safety and controllability, and exploring multi-agent ecosystems [32]

Group 1 - The article discusses Kevin Kelly's vision of a future shaped by AI, particularly in his new book "2049: The Possible 10,000 Days" [1][2] - Kelly emphasizes the concept of "alien intelligence" rather than AGI, suggesting that AI will coexist with humans as a different form of intelligence rather than a superior one [6][7][10] - The idea of a "mirror world" is introduced, which is a virtual dimension layered over reality, allowing for interaction and collaboration between humans and AI [16][17][18] Group 2 - Kelly expresses skepticism about the current AI models achieving AGI, arguing that intelligence is a complex compound requiring more than just scaling existing models [4][5][13] - He believes that the future will see a few dominant companies in the AI space due to network effects, leading to a natural monopoly [26][27] - The article highlights the potential for AI to enhance human creativity and collaboration, suggesting that human value will increase as AI takes over routine tasks [29][35][36] Group 3 - The discussion includes the importance of learning how to learn in an AI-driven future, emphasizing the need for lifelong learning and adaptability [41][42] - Kelly argues that human interaction will remain valuable, even in a world with advanced AI, as the essence of human presence becomes a rare commodity [35][38] - The article raises concerns about privacy and trust in AI systems, suggesting that people may willingly trade privacy for personalized services [38][39]