文｜邓咏仪 王铭说，在钉钉近5年，看过那么多To B产品和模式，甚至一个很大的感受是，趋势大于选择，选择大 于努力。 原因在于，中国用户普遍缺乏为工具付费的习惯；而对于大客户，To B业务的迁移成本极高，决策周期 漫长——这在AI时代下，对创业者而言，并不是最适合的路。 编辑｜苏建勋 不过，在选择做什么细分赛道方向时，王铭给自己定了个前提：拒绝只做工具型产品，以及从第一天 起，就要做海外全球化。 2023年，王铭在钉钉已经成为最年轻的副总裁，接触了上千家中国ToB SaaS软件和企业客户，但在第五 年，他做出了一个反向决定——创业去做一款To C的AI原生应用。 在成立攀峰智能仅一个月，王铭就带着团队完成了新产品Moras AI的第一阶段构建，也拿到了来自云 时资本的数千万元融资。 王铭有过多次职业转向——毕业开始，创业做"闪购"零售，被并购，去58集团负责到家等业务，一直面 向的是最To C的市场——然后，在2020年疫情期间加入钉钉，负责SaaS生态、大模型与AI生态、产业 生态和AI创新业务。 "这才是重塑了供给。AI不仅仅是工具，现在最重要的是要直接交付结果。"王铭对《智能涌现》抛出了 一个笃定的判断 ...

专题：未竟之约：张小珺访谈录 近日在《未竟之约》栏目中，智谱AI CEO张鹏在与张小珺对话中，被问到"是否想过要参与豆包、Kimi 的竞争"时表示，那不是我们的风格。 张小珺提问表示：To C永远比To B对于投资人来说，更有吸引力一些，对吧？ 专题：未竟之约：张小珺访谈录 近日在《未竟之约》栏目中，智谱AI CEO张鹏在与张小珺对话中，被问到"是否想过要参与豆包、Kimi 的竞争"时表示，那不是我们的风格。 张小珺提问表示：To C永远比To B对于投资人来说，更有吸引力一些，对吧？ 对此张鹏回答称：不太理解这个吸引力从哪来。后来他猜测了一个原因是在于模型比较好算，一个用户 值多少钱，比较简单。"但To B太复杂了，一个客户值多少钱这个事情，千奇百怪，而且各种各样的因 素都有，所以投资人想要把这个事情算得很明白，比较困难。"他说。 新浪声明：所有会议实录均为现场速记整理，未经演讲者审阅，新浪网登载此文出于传递更多信息之目 的，并不意味着赞同其观点或证实其描述。 责任编辑：李思阳 对此张鹏回答称：不太理解这个吸引力从哪来。后来他猜测了一个原因是在于模型比较好算，一个用户 值多少钱，比较简单。"但To B太复杂 ...

Core Viewpoint - The only correct strategy for To B platform companies is to focus on a core track, invest sufficient resources to achieve excellence, and rely on ecosystem collaboration for other parts [2]. Group 1: AI and Intelligent Agents - The essence of large models is to address the issues of intelligent and scalable service processes, but not all problems require intelligent agents [3][6]. - The application of intelligent agents is focused on enhancing collaboration and productivity in marketing, customer service, and sales [6][9]. - The commercial model for AI agents is still in a state of exploration, with various billing methods being tested, but the priority should be on delivering value to clients [11][12]. Group 2: Industry Positioning and Strategy - The core mission of the company is to integrate past experiences and product matrices with new AI technologies to find a unique ecological position in the industry [4][26]. - The company emphasizes the importance of deep integration with existing business processes and the need for continuous effect tuning based on industry experience [7][8]. - The company has launched the "Super Connection" global ecosystem partnership plan, collaborating with major cloud and computing companies to enhance its service offerings [15][26]. Group 3: Market Trends and Client Needs - The demand from clients varies by sector, with state-owned enterprises focusing on efficiency and innovation, while private enterprises prioritize revenue growth and cost reduction [14][20]. - The future trend is expected to shift towards "everyone having an agent," facilitating direct communication and collaboration between personal and business agents [9][10]. - The company aims to create a comprehensive marketing service platform that integrates various customer interaction points, enhancing the overall customer experience [23][24][25]. Group 4: Product Development and Ecosystem - The company has developed a product matrix that includes various platforms and applications to support the deployment of intelligent agents across different industries [26][27]. - The focus is on ensuring that the intelligent agent platforms can effectively support business scenarios and provide measurable value [29]. - The company believes that the biggest opportunity in the AI industry lies in the chip sector, anticipating that chips will become standardized products, thus enabling broader application opportunities [28].

Core Viewpoint - The article discusses the return of Wu Zhao to DingTalk and his vision of leveraging AI to redefine work processes, emphasizing the necessity of capital and technological strength in the AI era [4][5][11]. Group 1: AI Integration and Product Development - DingTalk has initiated the AI 1.0 version, which includes features like AI interaction, AI inquiry, AI note-taking, and AI forms, showcasing a significant product innovation push [6][12]. - Within four months of Wu Zhao's return, DingTalk addressed 1,850 user needs and optimized over 20 product lines, indicating a proactive approach to user feedback and product enhancement [5][6]. - The company has established over ten product innovation teams that report directly to Wu Zhao, highlighting a structured approach to innovation [5]. Group 2: Market Position and Competition - Wu Zhao does not view competitors as threats in the AI context, stating that everyone is starting from scratch with AI DingTalk 1.0 [9][111]. - The focus is on creating new solutions for the B2B market in the AI era, rather than merely competing with existing players [9][12]. - DingTalk aims to become a global representative of work processes, leveraging the opportunities presented by the AI wave [54]. Group 3: Future Vision and Strategy - The company envisions a future where AI surpasses human capabilities in understanding and decision-making, fundamentally transforming work processes [11][51]. - Wu Zhao believes that the integration of AI into work should help AI understand the physical world, rather than just enhancing human efficiency [11][45]. - DingTalk's strategy includes building its own models for various industries, enabling companies to develop their own AI capabilities [66][72]. Group 4: User Engagement and Feedback - Customer feedback has been more positive than expected, with many users appreciating the improvements made during Wu Zhao's absence [41]. - The company emphasizes the importance of understanding customer needs and ensuring that user demands are met promptly [168]. - Wu Zhao stresses that the primary goal is to enhance user experience and ensure that DingTalk effectively supports businesses [148][150]. Group 5: Organizational Culture and Team Dynamics - Wu Zhao acknowledges the challenges of aligning the team with the new AI vision, indicating a need for effective communication and shared understanding [160]. - The company aims to maintain a startup mentality within a large organization, which can be challenging but is essential for innovation [167]. - There is a recognition that different team members have varying motivations, which can impact the overall drive for innovation [86].

Core Insights - The article discusses the emergence of a new market segment called "To P" (To Professional), driven by employees' anxiety about being replaced by AI, leading them to self-fund AI tools for personal productivity [1][7][12]. Group 1: AI Adoption Trends - A significant 90% of employees are reportedly using personal AI tools, often without company support, indicating a trend of "shadow AI economy" [2][4][5]. - The frequency of AI tool usage among employees is more than double that of corporate adoption rates, highlighting a disconnect between individual and organizational strategies [5]. Group 2: The "To P" Market - The "To P" market is characterized by professionals purchasing AI tools to enhance their work efficiency, distinguishing it from traditional B2B and B2C models [12][18]. - Cursor, an example of a successful company in this space, achieved $1 billion in revenue in 2024, a significant increase from $1 million in 2023, and is projected to exceed $500 million in ARR by mid-2025 [12][13]. Group 3: Economic Justification for AI Tools - The return on investment (ROI) for AI tools is substantial; for instance, a programmer spending $20 monthly on AI tools can potentially double their income, leading to a 500-fold ROI [15]. - The ease of calculating ROI for AI tools contributes to the rapid growth of the "To P" market, as professionals can directly link their investment to increased productivity [15]. Group 4: Comparison with B2B and B2C Models - The slow adoption of AI in B2B settings is attributed to lengthy decision-making processes and the need for consensus among multiple stakeholders [16]. - In contrast, the "To P" model allows for quicker individual purchasing decisions, similar to B2C, but with a focus on professional productivity rather than personal enjoyment [18]. Group 5: Future Outlook - The article suggests that while the "To P" market is currently thriving, both B2B and B2C markets will eventually develop as AI's value becomes more evident in organizational contexts [25]. - The potential for B2C growth hinges on the reduction of token costs associated with AI applications, which could make these services more accessible to consumers [29][31].