"在AI时代，美图要成为一个什么样的组织？我的答案是，我们既要敏捷，又要有体系，并且要形成具 有强大内驱力的美图文化"。 公司情报专家《财经涂鸦》获悉，10月10日，美图创始人吴欣鸿在美图17周年生日会上发表内部讲话， 对未来机遇和当前挑战进行了全面表述。 他透露，今年观察到，海外市场由于面临很高的人力成本，对高效AI工具的解决方案有强劲需求。 两天前，美图秀秀曾凭借AI合照功能，荣获欧洲14个国家App Store总榜第一，28个国家分类榜第 一。"这也是美图的产品首次在欧洲国家大放异彩，非常非常令人振奋"。吴欣鸿表示。 于《美图在AI时代的组织进化》主题演讲中，吴欣鸿首先提到了来自外部和内部的挑战。 外部环境方面，在AI浪潮下，影像赛道已经成了行业的风口。一方面，影像产品从过去不太被看好， 到现在成为大厂标配，市场竞争越来越激烈；另一方面，全球涌现出大量的影像 AI 创业团队，不到 10 个人，就能做出千万美元的 ARR（年度经常性收入）。 "所以，在当前的市场环境里，我们既要与大厂竞争，也要和灵活的创业公司赛跑"。吴欣鸿坦言。 从内部看，他认为，美图需要打破17年的认知惯性和路径依赖，比如固化的工作流程 ...

机器之心发布 机器之心编辑部 「统一认知、智能执行、决策中枢、记忆进化、智能体工厂、 AI 治理」六大维度，让企业 彻底跳出「用 AI 」的工具思维，成为 「 AI 原生组织」。 走进任何一家大中型企业，「系统横跳」已成为日常：员工每天要在 5 个以上业务系统间切换完成工 作， 80% 的生产数据沉睡在 ERP 、 CRM 、 OA 的孤岛中无法调用， AI 工具仍停留在「问答式辅 助」而非「全流程执行」 ...... 本该驱动业务迭代的核心资产，成了看得见、用不上的「数据孤岛」， 企业数字化落地早已陷入「工具堆砌而非价值重构」的困境。 曾经一家企业 CTO 的感慨颇具代表 性：「每个系统都很专业，可当处理复杂业务时，却连一份完整的分析报告都凑不出来。」 2025 云栖大会上，在多数玩家还在聚焦「智能体」时，浩鲸科技正式推出的「鲸智百应」，以「企业 AI 操作系统」的定位撕开了差异化缺口。 据 浩鲸科技董事、云智能总裁杨名 介绍，鲸智百应并非简单的功能叠加，而是从「统一认知、智能执 行、决策中枢、记忆进化、智能体工厂、 AI 治理」六大维度，让企业彻底跳出「用 AI 」的工具思 维，成为 具备感知、思考、行动 ...

Core Viewpoint - The article discusses the challenges and opportunities in the AI startup landscape, emphasizing the need for a shift from traditional metrics like Product-Market Fit (PMF) to a focus on continuous value delivery and user outcomes in the AI tools sector [4][5][9]. Group 1: Product-Market Fit and Value Creation - The concept of PMF is being misused in the AI tools market, where subscription models do not equate to actual value realization for users [5][6]. - Many AI tools are currently catering to early adopters, leading to a potential revenue decline as user budgets stabilize [6]. - A new model of value creation is emerging, where continuous value delivery is essential for long-term user retention and growth [7]. Group 2: Outcome vs. Output - The traditional B2B model focuses on selling products, while the new paradigm emphasizes creating outcomes for customers [9]. - AI products should not just provide capabilities but should ensure users achieve tangible results, integrating customer success mechanisms into the product [9][10]. Group 3: AI Evaluation Systems - Finding PMF is just the beginning; the real challenge lies in building effective AI evaluation systems that understand user behavior and measure performance [10]. - The shift from a waterfall model to a discovery-based approach allows for rapid iteration and testing, enhancing collaboration and reducing development time [12][13]. Group 4: AI-Native Organizations - AI-native organizations are reshaping management paradigms, reducing the need for middle management and promoting a flatter organizational structure [14]. - The traditional management theories are becoming obsolete as AI tools enhance decision-making and execution efficiency [14]. Group 5: Human-AI Collaboration - The "1+N" model promotes collaboration between humans and multiple AI agents, enhancing productivity and efficiency [17]. - New roles are emerging within teams, such as "Product Owners" and "Infrastructure Builders," to better leverage AI capabilities [18]. Group 6: Lifelong Learning in the AI Era - The future of education is shifting from content delivery to feedback-driven learning, emphasizing continuous improvement and personal growth [22][25]. - The design of effective feedback mechanisms is crucial for creating a closed-loop learning system that fosters individual development [25]. Group 7: The Unique Value of Humans - In a world where AI can replicate knowledge and skills, the unique human perspective and creativity become invaluable [26]. - The ultimate goal of education should be to help individuals become unique and irreplaceable, leveraging their personal experiences and insights [26].

Group 1 - The consumption finance industry is facing new opportunities and challenges amid a backdrop of increased consumer spending and regulatory policies [2] - The industry is experiencing a "Matthew effect," where leading companies gain more advantages, prompting smaller institutions to seek ways to break through [2] - The focus is on balancing compliance operations with innovative development to unlock a new landscape in the consumption finance ecosystem [2] Group 2 - The chairman of the leading consumption finance company, Ma Shang Consumption, emphasizes the need for product innovation and technology to expand service scenarios and accurately identify user needs [3][4] - The company plans to increase AI investments to better identify user demands and enhance service precision, particularly in the context of building an international consumption finance center in Chongqing [3][4] - Ma Shang Consumption has a significant portion of its workforce in R&D, with nearly 3,000 out of 4,100 employees dedicated to technology [3] Group 3 - The current economic environment is leading to structural changes in consumer credit demand, with an increase in rigid demand and a decline in improvement demand due to changing consumption habits among high-income groups [4] - The company has applied for over 2,500 patents and established more than 100 standards, positioning itself as a leader in industry standards and institutional innovation [4] Group 4 - Ma Shang Consumption is addressing industry challenges such as high service costs and low data decision efficiency through digital intelligence [4][7] - The company is focusing on three main areas for technology investment: improving service efficiency, enhancing data decision-making capabilities, and building an open platform for various consumption scenarios [7] - The deployment of a retail finance large model has significantly improved customer service response times and risk identification accuracy [7][8] Group 5 - The company emphasizes the importance of multi-dimensional collaboration in building competitive advantages in digital intelligence, integrating strategy, talent, organization, management, and technology [8] - The AI platform developed by the company has lowered the barriers to using large models, allowing business personnel to independently access AI capabilities [8]

Core Insights - The rise of AI-native organizations represents a dual revolution in technology and organizational structure, posing significant challenges to traditional internet giants [1][2] - The competition is not only about technological capabilities but also about organizational forms, cultural genes, and talent strategies [2][3] Group 1: Characteristics of AI-native Organizations - AI-native organizations integrate AI as a core driver of products, services, and business processes, rather than as an added feature [2] - They possess self-developed core technologies, with rapid iteration speeds that outpace traditional companies, exemplified by OpenAI's swift transition from GPT-3 to GPT-4 within two years [2] - Product design inherently relies on AI capabilities, making it impossible for products to exist independently of AI [3] - The focus has shifted from "data and computing power" to "algorithms and community," emphasizing algorithm breakthroughs and scenario innovations as keys to market recognition [4] - Organizational structures are fluid, with flat, self-organizing teams that enable rapid decision-making and resource responsiveness [5] - A geek culture and strong founder cohesion drive these organizations, emphasizing technical idealism and long-term value [6] Group 2: Challenges for Traditional Internet Giants - Traditional tech giants face a core issue: how to evolve their organizations to maintain competitiveness in the AI-native wave [2][9] - Despite having significantly more resources, traditional companies struggle to replicate the technical sharpness of AI-native organizations like DeepSeek [1][9] - The lack of visionary leadership and a clear pursuit of algorithmic efficiency hampers traditional firms' ability to compete effectively [9] - The user engagement battle is intensifying, with AI-native applications rapidly gaining traction and threatening traditional applications' user time [10] Group 3: Strategic Responses from Major Companies - Major companies are attempting to integrate AI-native capabilities into their core businesses, recognizing the potential for scalable applications [11][21] - ByteDance is restructuring its AI organization to enhance agility and innovation, with a focus on AI-native talent [19][20] - Tencent is migrating its AI product lines to a more integrated structure, emphasizing collaboration with AI-native models [21] - Alibaba plans to invest over 380 billion yuan in AI infrastructure and aims for a comprehensive transformation across its core businesses [22] Group 4: Future Directions and Organizational Evolution - The evolution of organizational forms will be crucial as companies transition from traditional data-algorithm-traffic models to a model-data-agent framework [27] - Companies must focus on enhancing their organizational learning speed to convert technological breakthroughs into business cycles effectively [27] - The historical challenges of organizational inertia must be addressed to facilitate meaningful transformation in response to AI-native competition [25][26]