从集体止血到 AI 碾压，通过这 8 个转折点，看懂 ToB 终局。 曾经，中国 ToB 行业习惯了躲在"默认增长"的幻觉里，以为终局可以被无限期推迟。但 2025 年，最后一块遮羞布被扯掉了。 这一年，有人在纳斯达克敲钟，也有人在破产重整的边缘绝望求生；腾讯不再满足于只做财务投资，而是直接下场接手销售易；当老牌软件公司还在为回 款焦头烂额时，横空出世的 Manus 已经带着 1 亿美元的 ARR 奔向新加坡。 这不是一次温和的迭代，而是一场残酷的"分化与定型"。 资本不再奖励虚无缥缈的"故事"，"活着"从默认状态变成了一种需要拼命证明的能力。只有那些能自我造血、能探索到更多的退出路径选择的企业，才有 继续下注的资格。 以下，是 2025 年中国企业软件行业在血与火中完成的生存复盘。 01 告别"故事溢价"： ToB 开启质量驱动的港股上市潮 这类企业已完成发行，正式挂牌交易，如下 | 企业名称 | 上市日期 | 上市地点/代码 | 发行价 | 募资净额 | 核心亮点/业绩 | | --- | --- | --- | --- | --- | --- | | 一直田 | 2025/8/19 | 纳斯达克(YMT ...

AI硬件行业的躁动，在2025年最后一个月还没有冷却的迹象。 2025年以来，AI行业的新闻一波接一波刺激着投资人、用户，甚至是普通人的认知 。Open AI砸下65亿美元，将苹果前首席设计师Jony Ive创立的硬件公 司io Products收入麾下。影石创新年中登陆A股，被称为"智能影像第一股"最高市值突破千亿。有影石创新早期一级市场投资机构，回报达到千倍。 相信这是DingTalk A1团队员工在今后回望自己职业生涯时难忘的里程碑瞬间，这也可能是阿里进军AI硬件赛道的冲锋号角。 过去十年，互联网大厂在硬件赛道的尝试，往往折戟沙场。在海外，微软收购诺基亚成为商业史上被反复鞭笞的失败案例。国内大厂对硬件业务的收编， 也多以关停散场。在行业里甚至流传一个迷思：互联网公司天生缺乏做硬件的基因。 然而巨头们从未放下这一执念，因为硬件永远是抢占流量最关键、转换门槛最高的入口。复盘它们的失败，可以发现某些相似的原因：硬件与既有软件生 态貌合神离，很难建立和稳固大规模落地场景。 这是一波堪称烈火烹油般的巨潮。36氪统计，截至2025年上半年，中国具身智能与AI硬件投融资达114起，融资总额超145亿元。仅在2025年 ...

Core Insights - Cheniere Energy Partners (CQP) is a partnership formed by Cheniere Energy, focusing primarily on the assets at Sabine Pass, which is a strategic LNG facility [1] Company Overview - Cheniere Energy is recognized as one of the largest energy companies in the United States [1] - The partnership CQP is specifically involved with the operations and assets related to liquefied natural gas (LNG) [1] Analyst Background - The analyst has over a decade of experience in financial markets, primarily in hedge funds, with a focus on sectors like technology, particularly SaaS and cloud businesses [1] - The analyst emphasizes rigorous standards in investment decisions and conducts independent research [1]

Core Viewpoint - The article discusses the shift from traditional SaaS models to RaaS (Result as a Service) in the AI industry, highlighting the challenges and opportunities in deploying AI solutions for enterprises [2][35]. Group 1: Challenges in SaaS and AI Deployment - Service providers are struggling with high inference costs and inconsistent delivery quality, leading to a decline in the attractiveness of SaaS in the AI era [2][8]. - Traditional paths for deploying AI involve high upfront costs and significant trial-and-error expenses, which deter many potential customers from adopting AI solutions [11][15]. - The complexity of integrating new AI systems with existing infrastructure adds to the challenges faced by enterprises [12][17]. Group 2: Emergence of RaaS - RaaS is seen as a promising alternative to SaaS, focusing on paying for results rather than just tools, which aligns better with customer needs [39][40]. - The Results Cloud by BaiRongYunChuang offers a comprehensive solution that includes infrastructure, an operating system, and an application store, addressing the pain points of traditional AI deployment [16][34]. - RaaS encourages a collaborative relationship between service providers and clients, transforming the dynamic from a client-vendor relationship to a partnership [42][44]. Group 3: Results Cloud Architecture - The Results Cloud is structured in three layers: BaiJi (infrastructure), BaiGong (operating system), and BaiHui (application store), each serving a specific purpose in the AI deployment process [19][29]. - BaiJi provides a marketplace for AI infrastructure, offering pre-packaged models and computing power without exposing the underlying complexity to clients [20][21]. - BaiGong acts as a central hub that filters and optimizes the combination of models and computing resources, significantly reducing decision-making costs for clients [25][26]. Group 4: Performance Measurement and Compensation - The Results Cloud aligns the performance metrics of AI employees with human employees, allowing for a more straightforward evaluation of effectiveness [46]. - Compensation models for AI employees can include task-based pricing, value-sharing agreements, or fixed salaries, ensuring that clients only pay for actual results [48][49]. - This approach mitigates concerns about upfront costs, encouraging clients to trial AI solutions without financial risk [52]. Group 5: Ecosystem Development - BaiRongYunChuang emphasizes the importance of building an ecosystem for AI solutions, inviting third-party developers to contribute to the platform [57][59]. - The company aims to create a "Silicon-based Productivity Alliance" to foster collaboration and innovation in the AI space [59][60]. - By leveraging its established technology and client base, BaiRongYunChuang seeks to facilitate market opportunities for developers and enhance the overall AI ecosystem [62][63].

Company Overview - Clearwater Analytics is a leading provider of cloud-based investment accounting and analytics software, serving a diverse institutional client base [6] - The company leverages a scalable SaaS platform to deliver automated, real-time investment data solutions that streamline compliance, performance measurement, and risk management [6] - Clearwater Analytics operates a subscription-based business model, generating recurring revenue from cloud-based software offerings tailored for investment data management and analytics [9] Financial Performance - For the third quarter, Clearwater Analytics reported revenue of $205.1 million, up 77% year over year, and adjusted EBITDA increased 84% to $70.7 million, with margins expanding to 34.5% [10] - Annualized recurring revenue reached $807.5 million, up 77%, while net revenue retention held at a solid 108% [10] - The company has strong cash flow, allowing for $40 million in debt repayment during the quarter, and management reiterated full-year guidance of approximately $730 million in revenue and $247 million in adjusted EBITDA [10] Market Position - Clearwater Analytics' stock price as of the last market close was $22.25, reflecting a 20% decline over the past year, underperforming the S&P 500, which is up 16.5% in the same period [3][4] - The new position taken by TFJ Management in Clearwater Analytics, acquiring 357,043 shares valued at $6.43 million, represents 4.3% of the fund's reportable assets at quarter-end [2][3] - The investment by TFJ Management suggests a focus on the quality of the business rather than a short-term stock price movement, indicating confidence in Clearwater Analytics' long-term growth potential [7]

智通财经APP获悉，摩根大通发布研报称，首次覆盖聚水潭(06687)，予"增持"评级，目标价37港元。该 行指，聚水潭在中国日益分散的电商行业中脱颖而出，使其增长速度超越大多数软件同业及其所处的垂 直领域，同时保持强劲的盈利能力。作为中国少数具备高客户留存率的SaaS供应商之一，该行相信公司 的收入增长及盈利可持续，料2024至27年的收入年均复合年增长率为23%，2027年非国际财务报告准则 净利润率达29%。同时，该行认为公司估值吸引，将其列为行业首选股份之一。 ...

The Williams Companies, Inc. ( WMB ) does get mentioned sometimes when talking about midstream companies, but not enough. It is an industry that is already characterized as being very reliable in terms of revenuesMy name is Andres Veurink and I have been in the financial markets for over a decade at this point, spending the majority of that in a hedge fund here in Rotterdam, working my way up as an analyst. My work relfect rigourious standards as I myself have a very high standard as to what I invest my mon ...

The Williams Companies, Inc. ( WMB ) does get mentioned sometimes when talking about midstream companies, but not enough. It is an industry that is already characterized as being very reliable in terms of revenuesMy name is Andres Veurink and I have been in the financial markets for over a decade at this point, spending the majority of that in a hedge fund here in Rotterdam, working my way up as an analyst. My work relfect rigourious standards as I myself have a very high standard as to what I invest my mon ...

We recently published 10 Best SaaS Stocks Trading at a Discount.  Block, Inc. (NYSE:XYZ) is one of the best SaaS stocks trading at a discount. Block, Inc. (NYSE:XYZ) is a well-known payments processing and financial technology company. Over the past couple of weeks, the firm has been at the center of attention of several analysts. For instance, on December 8th, TD Cowen kept a Buy rating on the stock and kept a $91 share price target for the firm. It called Block, Inc. (NYSE:XYZ) a “Best Idea for 2026,” d ...

Core Viewpoint - Traditional SaaS applications like CRM and ERP systems will not be replaced but will evolve to serve as the infrastructure for AI Agents, which will enhance the importance of data definition and interpretation within enterprises [2][10][15] Group 1: The Role of AI Agents - AI Agents will not eliminate traditional software systems; instead, they will necessitate a clearer separation between how tasks are performed and the sources of facts [2][10] - The effectiveness of AI Agents is contingent upon their ability to access and understand the correct data from various systems, highlighting the need for accurate and structured input data [2][9] - The emergence of AI Agents creates significant entrepreneurial opportunities for companies that can help businesses manage and structure their unstructured data [3][10] Group 2: Data Management Challenges - A significant portion of enterprise knowledge (80%) exists in unstructured data, which is becoming increasingly difficult to manage [2] - The complexity of data definitions within organizations leads to discrepancies in key metrics like Annual Recurring Revenue (ARR), complicating the role of AI Agents in providing accurate information [7][11] - The traditional approach of consolidating data into warehouses has only partially succeeded, as operational teams still rely on individual systems for real-time transactions [8][10] Group 3: Evolution of Systems - CRM and ERP systems will transition from user-centric interfaces to machine-oriented APIs, allowing AI Agents to interact with these systems programmatically [12][15] - The core value of enterprise systems lies in their ability to encapsulate chaotic data, which will remain essential despite changes in interface and interaction methods [13][15] - The demand for a clear, authoritative source of truth will only increase as AI Agents become more prevalent in business processes [14][15] Group 4: Future of Data Infrastructure - The combination of data warehouses, semantic layers, and governance tools will form the foundation for AI Agent workflows, evolving beyond traditional reporting systems [10][12] - The valuation of AI platforms will increasingly depend on their ability to define and manage facts, rather than just their user interfaces [14][15] - Companies that can create exceptional AI Agent experiences based on reliable data sources will have a competitive advantage in the evolving landscape [15]