这位天桥脑科学研究院创始人在题为《管理学的黄昏与智能的黎明——重写企业的生物学基因》的文章 中提出，未来企业将不再是人领导智能，而是智能扩展人。他强调，传统管理学建立在弥补人类认知缺 陷的基础上，当执行主体变为具备永恒记忆、全息认知的AI智能体时，这套系统的根基将"崩塌"。 陈天桥的观点正值人工智能深刻改变企业运营方式的关键时期。12月1日，咨询公司埃森哲宣布将为数 万名IT专业人员配备ChatGPT Enterprise软件，加快员工AI技能提升。管理咨询公司的研究显示，AI正 在减少对通才分析师的需求，企业架构正转向更依赖经验丰富专业人士的"盒式模型"。 AI智能体成为"新物种"挑战传统认知 陈天桥从"认知解剖学"角度对比了人类员工与AI智能体的根本差异。他指出，智能体具备三大核心优 势：记忆的连续性（永恒记忆vs.瞬时易碎）、认知的全息性（全量对齐vs.层级过滤）、进化的内生性 （奖励模型驱动vs.多巴胺驱动）。 盛大集团创始人陈天桥近日发表文章称，随着AI智能体的崛起，传统管理学将迎来"黄昏"，企业需要从 根本上重塑组织基因，从"人类中心"转向"AI原生"的认知范式。 "这不是更强的员工，而是基于不同 ...

"当管理退出，认知升起。""当新物种遇到旧容器，KPI崩塌了！"这是盛大集团、天桥脑科学研究院创 始人陈天桥最新发表的观点。 日前，陈天桥在媒体上发表了一篇题为《管理学的黄昏与智能的黎明——重写企业的生物学基因》的文 章。他富有创见性地提出：未来的企业，不再是由人领导智能，而是由智能扩展人。 陈天桥同时指出："管理学不会消失，但它将第一次真正建立在智能（Intelligence）的地基之上，而非 生物学（Biology）的废墟之上。" 当新物种遇到旧容器，KPI崩塌了！"我们要KPI，原本是因为人类容易迷路。但对于时刻锁定目标函数 的智能体而言，死板的KPI指标反而限制了它在无限解空间中寻找更优路径的可能性。"陈天桥称，"这 就好比你给自动驾驶汽车画死了一条轨道，却期待它能躲避突发的障碍。" 另一方面，传统的流程与监督体系也在崩塌，从"纠偏"变为了"冗余"。陈天桥对此解释称，这是由于传 统的监督机制本是盯着人别犯错，但在智能体内部，理解即执行，感知即行动，监督不再基于对执行过 程的怀疑，而是基于对目标定义的再校准。 第一财经记者了解到，陈天桥最近对于人工智能有很多新的思考，他还在酝酿更多具有哲学思辨的论 述。 ...

Core Insights - The future of management will be fundamentally based on intelligence rather than biology, marking a shift from human-led to AI-augmented enterprises [1][2] - Traditional management systems are seen as corrective measures for human cognitive limitations, which will collapse as AI agents take over execution roles [2][4] - The emergence of AI-native enterprises will redefine organizational structures and operational paradigms, focusing on cognitive evolution rather than resource management [4][5] Summary by Sections Management Paradigm Shift - Management will transition from being human-centered to AI-native, where AI expands human capabilities rather than being managed by humans [1][2] - The introduction of AI agents will disrupt the biological foundations of traditional management, necessitating a complete rethinking of organizational genetics [1][2] Cognitive Anatomy Comparison - A comparison between human employees and AI agents highlights three key differences: continuous memory (AI's eternal memory vs. human's fragile memory), holistic cognition (AI's full alignment vs. human's hierarchical filtering), and endogenous evolution (AI's self-evolving capabilities vs. human motivation-driven evolution) [3][4] Collapse of Traditional Systems - Traditional KPIs are becoming obsolete as AI agents can navigate complex problem spaces without rigid constraints, unlike human-centric systems that were designed to mitigate cognitive shortcomings [4] - The existing supervisory frameworks are shifting from error correction to recalibrating goals, as AI agents understand and execute tasks without the need for constant oversight [4][5] Definition of AI-native Enterprises - AI-native enterprises will require a new operational framework focused on cognitive evolution, characterized by five aspects: architecture as intelligence, growth as compounding, memory as evolution, execution as training, and humans as meaning-makers [5][6] - The demand for talent is shifting towards mid-career professionals with specialized knowledge, as AI reduces the need for generalist analysts [5][6]

Core Insights - The recent release of the State Council's opinion on implementing "AI+" actions indicates a shift from AI technology being a novelty to becoming practical and essential for businesses [3] - Companies' ability to apply AI technology will be a decisive factor in seizing future development opportunities, while AI technology providers face the challenge of transitioning from selling tools and services to selling outcomes [3][4] - The need for businesses to address challenges such as scenario selection, technology implementation, and ROI measurement will be critical for the commercialization and scalability of enterprise AI applications [3][4] Company Overview - Aozhe, founded during the late information age, initially focused on BPM to help businesses optimize processes and improve operational efficiency [4] - In the digital age, Aozhe shifted towards low-code platforms to enable businesses to respond quickly to market changes, achieving recognition as a leading low-code software vendor [4] - Aozhe's founder, Xu Pingjun, emphasized that understanding business needs is the core value of digital services, a capability that large models currently cannot replace [4][6] Strategic Developments - On October 17, Aozhe announced a strategic upgrade and launched an enterprise-level AI platform, proposing a closed-loop model of "AI + Data + Low-Code" [4][19] - This new strategy aims to address the challenges of integrating single-point capabilities and closing business logic loops in enterprise AI applications [4][19] Industry Insights - Xu Pingjun believes that AI technology will expand market growth by solving past challenges and creating new scenarios, while also enhancing the ability to meet personalized business needs [6][15] - The software industry is shifting towards value delivery and outcome delivery, with a consensus that AI should directly deliver results [15][18] Platform Features - Aozhe's enterprise-level AI platform is designed to facilitate the transition from digitalization to intelligentization, leveraging AI's native development capabilities [19][21] - The platform consists of three layers: AI model integration, a closed-loop business engine, and native AI application development across various industries [21][24] - Aozhe's platform aims to provide a seamless integration of AI, data, and low-code capabilities, allowing businesses to achieve better results at lower costs [27][28] Market Positioning - Aozhe's enterprise-level AI platform is positioned as a core product, with a focus on delivering personalized solutions and integrating various AI capabilities [26][27] - The platform differentiates itself by achieving business closure, supporting high levels of customization, and simplifying the use of AI through model selection and adaptation [27][28] Future Directions - Aozhe aims to become a leader in the enterprise-level AI platform market, building on its previous successes in BPM and low-code [46][47] - The company plans to help businesses transition from digitalization to intelligentization, ultimately enabling them to become AI-native enterprises [47]

Core Insights - The conference on October 17, 2023, marked the launch of the "Aozhe Enterprise AI Platform," showcasing the company's new strategic positioning in the AI era, emphasizing the integration of "AI + Data + Low Code" as a core capability for enterprise digital transformation [1][2] - Aozhe aims to help enterprises transition into "AI-native" companies, highlighting that AI is not just a future trend but is currently replacing existing processes [2][10] Group 1: Company Overview - Aozhe has evolved over 15 years from a BPM product provider to a leader in low-code solutions, recognized as the "No. 1 Low-Code Brand in China" by IDC and other authorities [2] - The company has been actively involved in the formulation of industry standards and has deepened its exploration of AI applications to meet the evolving digital needs of enterprises [2] Group 2: AI Platform Features - The newly launched enterprise-level AI platform combines AI technology with a low-code platform, enabling integrated solutions from AI, data, to applications [3][6] - The platform includes three core AI capabilities: - **AI Designer**: Facilitates the development of AI-native applications by generating business blueprints and structured code, significantly lowering development barriers and enhancing efficiency [4] - **AI Agent**: Automates repetitive tasks, allowing businesses to delegate tasks like IT ticketing and customer service to AI [5] - **Data & AI Discovery**: Empowers business personnel to conduct data analysis and predictions with zero barriers, driving scientific decision-making [6] Group 3: Industry Applications - Companies like Wuhan Guangxun Technology and Beijing Huayuan Real Estate shared their AI transformation experiences, validating the capabilities of Aozhe's enterprise-level AI platform [7][8] - Guangxun Technology implemented a smart contract management system using Aozhe's platform, automating the entire lifecycle of contract management, which was recognized as a leading case in digital transformation [7] - Huayuan Real Estate has integrated AI into its operations, focusing on a phased approach to AI application, enhancing operational efficiency through IT intelligent customer service and AI approval systems [8] Group 4: Future Outlook - Experts at the conference emphasized that AI is a key driver for high-quality enterprise development, predicting that by 2030, around 50% of work content will be automated, necessitating skill upgrades for approximately 200 million workers [8] - Aozhe announced the establishment of the "Enterprise AI Alliance" to promote collaboration and innovation in AI technology across various industries [10] - The company aims to continue its evolution into a leading enterprise-level AI platform provider, focusing on the deep integration of "AI + Data + Low Code" to support enterprises in becoming AI-native [10]

Group 1 - The era of "Artificial Intelligence+" is emerging, transforming work, life, and the future, making everyone a participant in this change [1][3] - The "Artificial Intelligence+" initiative is a significant policy document aimed at integrating AI deeply with the economy and society, following the "Internet+" initiative [3][4] - AI is seen as an upgrade to "Internet+", providing intelligent cores to industries, enhancing efficiency and service experiences [5][6] Group 2 - By 2027, AI is expected to be deeply integrated into key sectors, with over 70% penetration of new intelligent devices, making AI commonplace in daily life [6][7] - By 2030, AI is projected to become a crucial driver of economic growth, with over 90% penetration of intelligent devices [6][10] - The integration of AI with six key areas—science and technology, industrial development, consumer upgrades, public welfare, governance enhancement, and global cooperation—is essential for achieving a smart economy and society by 2035 [6][7] Group 3 - AI can optimize public service management, such as dynamically adjusting traffic lights based on real-time data [7][8] - The concept of "AI-native enterprises" is introduced, where businesses are fundamentally driven by AI from inception, enhancing operational efficiency [8][10] - The transition to an AI-driven economy is seen as a way to improve overall productivity, especially in industries facing labor shortages [10]

Core Viewpoint - The article discusses the emergence of AI-native companies that prioritize artificial intelligence as their core product or service, leading to new technologies, products, and business models in the AI hardware industry [2]. Group 1: AI Consumer Hardware Development Routes - AI consumer hardware has seen significant innovation in 2023, with new categories like AI phones, smart glasses, rings, headphones, and companion robots rapidly emerging [4]. - The development routes can be categorized into three main paths: 1. AI-native devices exploring new interaction paradigms, represented by products like Rabbit R1 and Humane AI Pin, which rely on semantic understanding and task execution driven by large models [5]. 2. Gradual enhancement of existing devices with AI capabilities, exemplified by Apple and Meta, which integrate AI into established hardware like smartphones and wearables [6]. 3. Model-centric empowerment paths led by companies like OpenAI, focusing on providing AI capabilities through APIs and SDKs to third-party devices [7]. Group 2: Emerging Business Models in AI Consumer Hardware - The article identifies the initial emergence of business models corresponding to the three development routes, highlighting their respective core challenges: 1. AI-native exploration models rely on high-priced hardware and subscription services to generate stable revenue streams, but face challenges in proving hardware value and user adoption [10]. 2. Gradual enhancement models focus on hardware sales and value-added subscription services, benefiting from low user recognition barriers and high market acceptance [12]. 3. Model empowerment paths replicate aspects of the Android model, charging for API access and enterprise-level services, but face challenges in cost and adaptation to various hardware [15]. Group 3: Future Trends in AI Consumer Hardware - The integration of upstream and downstream in the industry is becoming tighter, with model vendors collaborating with chip manufacturers to optimize model performance across devices [18]. - The trend towards "unobtrusive" interaction is accelerating hardware paradigm shifts, with AI glasses becoming a focal point for competition among tech giants and emerging brands [21]. - Long-term, AI hardware is expected to evolve towards a model where AI acts as a primary interface, with voice and natural language interactions becoming the norm, potentially replacing traditional graphical user interfaces [27].

Core Viewpoint - The article discusses how Swan AI, an Israeli AI company, is revolutionizing the startup landscape by utilizing a minimalistic team structure combined with AI agents to automate sales processes, aiming for significant revenue growth without traditional hiring practices [5][11][38]. Group 1: Company Overview - Swan AI consists of three founders and over 20 AI agents, aiming to achieve $30 million in annual revenue within a year [5][11]. - The company has successfully acquired 71 B2B clients in just 60 days, showcasing the effectiveness of its AI-driven sales approach [5][31]. - Swan AI's operational philosophy emphasizes leveraging AI to enhance human capabilities rather than replacing them, focusing on maximizing individual contributions [11][12]. Group 2: Business Model and Strategy - Swan AI operates on a unique "Autonomous Business OS" model, where decision-making is streamlined among the three founders, avoiding traditional team expansions [12][14]. - The company has achieved a 30% increase in annual revenue within 30 days of product launch, with a 45% conversion rate from trial to paid users [9][16]. - Swan AI's core product is an AI-driven sales development platform that automates the entire marketing and sales process for small to medium-sized B2B companies [18][30]. Group 3: Marketing and Customer Acquisition - The marketing strategy relies heavily on organic growth through LinkedIn, where the founder shares insights and experiences, generating over $1 million in sales opportunities monthly [33][34]. - Swan AI's customer base primarily consists of B2B companies with high-value products and significant lead conversion potential, particularly in sectors like SaaS and fintech [32][33]. - The company utilizes a dual-platform approach, with LinkedIn for lead generation and Slack for internal operations, ensuring efficient customer engagement and service delivery [34][35]. Group 4: Future Outlook and Industry Impact - Swan AI has not pursued traditional venture capital funding, instead focusing on revenue-driven growth, which the founder believes is a more sustainable model in the AI era [37][38]. - The company's innovative approach to organization and marketing serves as a valuable reference for AI-native startups looking to optimize their operations and growth strategies [38].

Core Insights - Guangyu Mingdao Digital Technology Co., Ltd. launched an industrial AI system at the 2025 World Artificial Intelligence Conference, providing a one-stop solution for manufacturing enterprises to build AI-native capabilities [1][2] - The company aims to integrate AI deeply into business processes, transforming traditional manufacturing paradigms and enhancing efficiency and value [1][2] Group 1: Industrial AI System - The industrial AI system includes an AI application platform and a super intelligent body, designed to support the entire industrial production process from R&D to operations [1] - The system is built on 30 years of manufacturing experience and aims to facilitate intelligent upgrades across various industries, including automotive, new energy batteries, and non-ferrous metals [1][2] Group 2: Core Capabilities - The platform features three core capabilities: efficient industrial data standards to break data silos, closed-loop knowledge encapsulation for reusable modules, and customized intelligent body development tailored to specific business needs [2] - The underlying logic of the system includes a dynamic cycle of AI and industrial knowledge collaboration, ensuring continuous provision of efficient and precise solutions for cost reduction and quality improvement [2] Group 3: Application Scenarios - The Guangyu Mingdao industrial intelligent manufacturing super intelligent body covers the entire business chain, including R&D, production, supply chain, marketing, and service [2] - The platform is currently open to the smart manufacturing sector, targeting industries such as automotive, new energy batteries, and non-ferrous metals [2]

Core Insights - The World Artificial Intelligence Conference showcased Guangyu Mingdao's "Geega Industrial AI Application Platform + Industrial Intelligent Body," highlighting its capability to create "AI-native enterprises" [1] - The implementation of industrial AI faces challenges such as private data accessibility, disconnection between process knowledge and AI technology, and the need for deep integration with existing digital systems [1][2] - The Geega platform offers a comprehensive solution for smart manufacturing, integrating AI technology with industrial know-how, and providing a one-stop solution from AI infrastructure to intelligent application deployment [2] Platform Capabilities - The Geega Industrial AI Application Platform features three core capabilities: efficient industrial data standardization, closed-loop knowledge encapsulation and restoration, and tailored intelligent body development [2] - The platform fosters the creation of an "intelligent brain" for industrial manufacturing, known as the Industrial Intelligent Body Matrix, which covers the entire business process from research and development to sales and service [2] Intelligent Body Functionality - Individual intelligent bodies, such as the production scheduling intelligent body, can recommend optimal constraint combinations in 1-2 minutes and complete validation assessments within 15 minutes, significantly improving operational efficiency [3] - The warehouse intelligent body monitors assembly plans and inventory anomalies in real-time, reducing plan adjustment occurrences by over 50% and improving timely delivery rates to 95% [3] Industry Perspective - The release of the industrial AI "dual engine" is seen as a crucial step in driving China's industrial sector towards an "AI-native" transformation, shaping new competitive advantages [4]