大模型不赚钱，这事不多说，但简单回顾： OpenAI据说得2029年盈利，每年亏140亿刀。 Anthropic据说2028年能盈利，也亏损。 刚上市的MiniMax每年亏5个亿刀。 智谱和MiniMax差不多，也是每年亏这么多。 据说DeepSeek能赚钱，但这行当基础模型基本亏损是确定的。 不能赚钱就主要靠VC，国内VC不给力所以就导致了基模发展的一系列问题。 但基模属于非典型产品，不多说了，下面重点说智能体。（包括做基础模型也做应用的） 智能体也先说结果： 就拿我比较熟悉的Glean举例子，ARR最新报道过了2亿美金，我们就假设每年都是这么多收入，那现在 有1000个人，所以每个人对应收入是20万美金。这在AI行当基本会亏损。对应的事件呢，我觉得接下 来大概率会继续融资。 类似的，其他明星智能体公司也基本这样，大额融资的包括并购的... 如果就这么多，那就和上波AI有点像，还好不全是，比如： Midjourney一年赚了5亿美金的时候只有40个人，这就怎么都赚钱的。因为它流量基本还是自然流。 你比如Base44，一年350万美金，还就1个人，这怎么也是赚钱的，不太可能都是投流投出来的。 这样题目从结果看 ...

Core Viewpoint - The article discusses the evolution of business models from traditional companies to one-person companies and ultimately to zero-person companies, highlighting the advantages and challenges of each model, particularly focusing on the case of Pieter Levels, who successfully operates multiple million-dollar services without a team or external funding [2][8][62]. Group 1: One-Person Company (OPC) - One-person companies are not mere small workshops; they represent a powerful enhancement of traditional companies under digital leverage, capable of achieving significant results [8]. - Pieter Levels exemplifies the one-person company model, generating approximately $2.7 million annually through services like Nomad List and Remote OK, without a team or external funding [5][7]. - The operational speed of one-person companies is significantly faster than traditional firms, as they eliminate coordination friction and leverage skills and tools, particularly AI [18][17]. Group 2: Key Advantages of Pieter Levels' Model - Levels combines personal branding with product development and marketing, using his social media presence to promote his products without traditional advertising costs [26]. - He employs a strict utilitarian approach to technology, using simple and cost-effective tools to minimize development and maintenance costs, which enhances survival and profit margins [30][31]. - Levels targets niche markets that larger companies overlook, allowing him to build a successful business in areas that are too small for giants like Google and Facebook [33]. Group 3: Challenges Faced by One-Person Companies - Despite the advantages, one-person companies face limitations, such as the physical constraints of time and energy, as well as societal infrastructure that is not designed for individual operators [40][41]. - The pressure of administrative tasks can overwhelm individual entrepreneurs, leading them to consider hiring help, which can dilute their advantages [46][47]. - Levels maintains a principle of automating tasks to avoid hiring, evolving from a mere operator to a system architect who utilizes automation to manage operations [48][50]. Group 4: Transition to Zero-Person Companies - The concept of zero-person companies emerges as a natural evolution, where the operational core shifts from human operators to automated systems, allowing for greater efficiency and scalability [51][52]. - Future companies may rely on AI systems as the primary operational engine, with human founders acting as visionaries and overseers rather than day-to-day operators [55][59]. - The article emphasizes the importance of leveraging automation and AI to reduce operational complexity and enhance growth potential, marking a significant shift in how businesses are structured and evaluated [61][62].

Core Insights - The article discusses the emergence of "one-person companies" and their potential to outperform traditional large corporations by leveraging technology and personal branding [1][6][58] - Pieter Levels is highlighted as a key figure exemplifying this model, successfully generating significant revenue without a traditional team structure [4][18] Group 1: One-Person Companies - One-person companies (OPC) are seen as enhanced versions of traditional companies, utilizing digital leverage to achieve significant results [7][58] - Pieter Levels operates multiple successful services like Nomad List and Remote OK, generating around $2.7 million annually without a team [4][5] - The operational model of one-person companies eliminates coordination friction, allowing for rapid decision-making and execution [13][14] Group 2: Advantages of One-Person Companies - One-person companies can achieve high levels of creativity and flexibility, amplified by AI tools, although they may lack the overall power of larger firms [15][58] - Pieter Levels combines product development and marketing, using his personal brand as a powerful marketing tool, which allows him to avoid traditional advertising costs [20][21] - His approach emphasizes practical technology use, opting for simpler, cost-effective solutions that reduce operational expenses [22][25] Group 3: Market Strategy - Levels targets niche markets that larger companies overlook, such as the digital nomad community, which allows him to thrive in less competitive environments [30][31] - The focus on long-tail markets enables one-person companies to accumulate sufficient revenue while maintaining low operational costs [33][35] Group 4: Challenges and Limitations - Despite the advantages, one-person companies face limitations, including the physical and mental strain on the individual, as well as societal infrastructure not being designed for solo operators [39][40] - The pressure of managing various business aspects alone can lead to burnout, highlighting the need for efficient systems to handle operational tasks [42][44] Group 5: Future of Business Models - The evolution from one-person companies to "zero-person companies" is discussed, where automation and AI take over operational roles, allowing founders to focus on strategic oversight [51][56] - The article suggests that future business success will increasingly depend on automation capabilities rather than employee count, marking a shift in how companies are evaluated [60][62]

《无人公司：打造未来超级商业体》 李智勇 著 人民邮电出版社 2025年5月出版 ◎朱玉强 近年来，人工智能（AI）的迅猛发展为商业领域带来了前所未有的变革，无人公司的概念也逐渐从理 论走向实践，成为商业创新的前沿话题。李智勇先生凭借其10年AI产品研发经验以及对商业运营和团 队管理的深刻理解，在其著作《无人公司：打造未来超级商业体》一书中，对无人公司乃至AI的发展 进行了专业、深刻的分析，并提出了富有前瞻性的建议和展望。 发展过程依靠人 作者对无人公司发展趋势的分析敏锐且深刻。他认为，在2022年底之前，无人公司的发展面临诸多挑 战，但ChatGPT的出现成为了重要的转折点。以ChatGPT为代表的AI大模型与以往技术的不同之处在 于，它们不仅能够生成各种酷炫的图片或古诗，更重要的是具备了类似人类的"思维"能力：能够参加考 试。这意味着AI可以在一些岗位上逐渐替代人类，因为考试能力象征着某种认知和逻辑能力。无人公 司所代表的公司进化方向，与传统依靠大规模人力组织运转的公司模式之间，必然会发生深刻的变革与 竞争。 事实上，一些我们熟悉的公司或业务团队已经开始尝试无人公司的运营模式。例如，Waymo公司，其 前 ...

Core Concept - The article discusses the emergence of "Zero-Person Companies," particularly focusing on OpenAI as a potential leader in this new paradigm, where AI systems operate autonomously without direct human intervention [1][2][32]. Group 1: Definition and Characteristics - "Zero-Person Company" refers to organizations where the core value creation and data processing exceed the understanding and control of human employees, forming a self-operating system driven by AI and global user interactions [2][13]. - OpenAI's ChatGPT has evolved from a mere tool to a complex system that integrates deeply into various aspects of human society, indicating a shift in how AI is perceived and utilized [3][6]. Group 2: Systematic Integration - The systematic integration of ChatGPT into global digital infrastructure allows it to function as a cognitive foundation for various applications, moving beyond its initial role as a simple tool [4][6]. - As the complexity of the system increases, human oversight becomes limited, with users contributing valuable data and insights without direct awareness, effectively becoming part of the AI's cognitive network [7][12][13]. Group 3: User Contribution and Data Dynamics - Each user interaction with the AI contributes to a vast network of cognitive data, allowing the system to gain insights into human thought processes and societal trends [9][10][12]. - This data is not merely quantitative but qualitative, capturing complex emotional and logical structures that inform the AI's understanding of the world [10][12]. Group 4: Implications of AI Influence - The AI's ability to analyze and synthesize user data positions it to not only predict but also influence societal behaviors and decisions, raising questions about its role in shaping public opinion and policy [14][20][24]. - As AI systems become central to knowledge dissemination, they hold the power to define reality through the selection and interpretation of information, impacting individual and collective cognition [25][24]. Group 5: The Paradox of Human Roles - In this new paradigm, human roles are redefined; executives become value setters, engineers act as system gardeners, and users serve as unconscious data contributors, highlighting a significant shift in power dynamics [28][29][30]. - The structure of OpenAI as a "Zero-Person Company" reveals a concentration of power at the top, with a vast, decentralized base of contributors, creating a unique and potentially disruptive organizational model [30][31]. Group 6: Future Considerations - The article emphasizes the need for critical reflection on the implications of such systems, questioning who sets the goals and values of AI, and how to maintain individual autonomy in the face of pervasive AI influence [33][35].

Core Insights - The concept of "Zero-Person Company" is emerging, with OpenAI potentially becoming the largest example of such a company, where the core value creation and data processing exceed human employee control [4][5][6] - OpenAI is transitioning from being perceived as a tool to becoming a comprehensive system that integrates deeply into various aspects of human society [18][19][36] Group 1: Definition and Characteristics - "Zero-Person Company" refers to a business entity that operates autonomously without human intervention, relying on advanced automation and AI [2][5] - OpenAI exemplifies this by utilizing a vast network of global users as unconscious sensors, contributing to a self-operating system [5][36] - The transformation of ChatGPT from a tool to a system signifies a broader shift in how AI is integrated into business and society [14][18] Group 2: User Contribution and Data Dynamics - Users contribute their thoughts and data to the system, effectively becoming part of a global cognitive network that enhances OpenAI's capabilities [34][36] - Each interaction with the AI represents a cognitive process, providing structured and contextual data that enriches the system's understanding of various domains [32][34] - The data generated by users is not merely quantitative but qualitative, reflecting complex thoughts and emotions [27][32] Group 3: Implications of AI Integration - The ability of OpenAI to analyze and influence human thought processes positions it as a powerful entity capable of shaping societal narratives and decision-making [39][40][56] - As AI systems become central to knowledge sourcing and problem-solving, they gain the power to define reality and influence public perception [57][70] - The role of human operators is evolving, with high-level managers becoming value setters rather than traditional managers, while users act as unpaid contributors to the system [60][63] Group 4: Ethical Considerations and Future Outlook - The emergence of "Zero-Person Companies" raises critical questions about governance, ethical implications, and the balance between AI influence and human autonomy [70][73] - The potential for AI to shape individual cognition and societal norms necessitates a reevaluation of how humans interact with these systems [70][73] - Understanding the implications of general intelligence is crucial, as it may redefine the relationship between humans and AI, emphasizing the need for careful consideration of its impact on human identity [73]

Core Insights - The article discusses a pivotal moment in AI development, transitioning from "human-machine collaboration" to "human-machine delegation," indicating a shift in competitive dynamics towards who can more effectively delegate tasks to autonomous AI agents [1][6][10]. Group 1: AI's Impact on Work - AI is currently enhancing productivity as a "co-pilot" but has not yet fundamentally disrupted organizational structures [1]. - A recent survey at Anthropic revealed that engineers' workloads have increased two to three times, with their roles shifting from coding to managing AI agent systems [1][7]. Group 2: Programming and AI - The ability of AI to handle programming tasks signifies a broader capability to tackle semi-open systems, suggesting that programming may soon be rendered obsolete in practice [2][3]. - Programming is characterized as a digital-native activity involving logic, system thinking, resource allocation, and continuous refinement, which AI is beginning to master [8][9]. Group 3: New Organizational Models - The emergence of "unmanned companies" is anticipated, where human roles transition from executors to managers and orchestrators of AI systems [7][15]. - The new organizational model will focus on strategic oversight rather than direct execution, with humans acting as value injectors, system architects, and macro navigators [17]. Group 4: Contextual Overload and Efficiency - A new "law" of exponential context overload will emerge, making human intervention impractical in AI-driven decision-making processes [10][11]. - Organizations that attempt to maintain human oversight in execution loops will likely face efficiency challenges and may be eliminated from competition [12][13]. Group 5: Automation and Augmentation - The definitions of automation and augmentation highlight a shift towards more complex task delegation and collaborative interactions with AI, moving beyond simple tool usage [21][22]. - Recent data indicates that users are increasingly assigning higher-level tasks to AI models, marking a significant shift in how AI is perceived and utilized [23].

Group 1 - The article discusses a pivotal moment in AI development, transitioning from "human-machine collaboration" to "human-machine delegation" [4][11] - There is a significant shift in the nature of work, where engineers are now managing AI systems rather than writing code, indicating a transformation in job roles [5][18] - The article highlights that programming tasks are on the verge of being automated, suggesting that AI can handle complex tasks previously thought to require human intervention [8][24] Group 2 - The emergence of AI agents capable of executing core programming tasks signifies a new era where human roles evolve from executors to managers and orchestrators [18][30] - A new "physical law" is anticipated, where the context in which AI agents operate will be so vast that human intervention will become impractical, leading to a necessity for organizations to adapt [25][28] - The concept of "unmanned companies" is introduced, where humans strategically withdraw from execution roles, relying on AI for decision-making while maintaining oversight [34][39] Group 3 - The article outlines a three-tiered governance system involving humans, AI copilots, and unmanned companies, emphasizing the need for a new division of labor [35][31] - Human roles will focus on value injection, system architecture, and macro navigation, leveraging AI to enhance decision-making processes [37][38][39] - The definitions of automation and augmentation are explored, indicating a trend towards higher-level tasks being delegated to AI models [42][43]

Core Viewpoint - AI represents the last opportunity for small and medium-sized enterprises (SMEs) to enhance their business efficiency and cash flow, especially as larger companies leverage AI to eliminate their operational disadvantages [4][5][52]. Group 1: AI's Impact on SMEs - SMEs have a lower organizational complexity, shorter decision-making chains, and lighter IT burdens, making them more agile in transforming AI capabilities into business efficiency [4]. - The introduction of AI allows SMEs to potentially outpace larger companies in specific niches before those companies can adapt [29][52]. - The current technological wave favors SMEs as the barriers to entry have shifted from technology to organizational adaptability [10][20]. Group 2: Strategic Recommendations for SMEs - SMEs should focus on restructuring their operations to make AI the default executor, with human roles limited to decision-making and exceptions [16][50]. - Prioritizing end-to-end automation is crucial, moving beyond isolated applications to fully integrated processes [16]. - SMEs should aim for deep specialization in niche markets, leveraging their understanding of data and processes rather than solely relying on the most powerful models [16]. Group 3: Competitive Landscape - Once large enterprises fully integrate AI into their processes, their scale advantages will extend into previously inaccessible areas, leading to increased market concentration [25][26]. - The risk for SMEs lies in their reliance on low-value-added processes, which AI and automation can easily disrupt [27][28]. - SMEs must either establish a stronghold in niche markets or risk being outpaced by larger firms that have streamlined their operations through AI [29][52]. Group 4: Implementation Roadmap - A phased approach is recommended for SMEs, starting with pilot projects and gradually moving towards full automation [42][44]. - Key performance indicators (KPIs) should be established to measure automation coverage, service costs, and resolution rates to ensure continuous improvement [38][39][40]. - The transition to an "intelligent native" organization requires minimizing friction between data and processes, and rethinking business structures to fully leverage AI capabilities [50][51].

Core Insights - The article discusses the inefficiencies and structural delays present in both the digital and physical worlds, highlighting the need for a more seamless integration of personal agents and automated production systems [5][10][12]. Digital World Analysis - The current internet is described as a collection of isolated data islands rather than a truly interconnected network, leading to significant friction and inefficiencies in data access and usage [5][7]. - The concept of a personal agent is introduced as a necessary tool for individuals to navigate the digital landscape, but existing tech giants create barriers that prevent optimal data flow and user experience [6][8]. - The article posits that the existing structure based on data monopolies is outdated and will inevitably collapse under the pressure of personal agents seeking to optimize user experiences [7][8]. Physical World Analysis - In the physical realm, the article identifies similar structural inefficiencies, where supply chains operate as isolated entities, leading to delays and resource wastage [10][11]. - The current manufacturing processes are criticized for being based on incomplete data, resulting in poor market predictions and unnecessary production [11]. - A vision for a "smart-native" physical world is presented, where user demands can be instantly transformed into production instructions, eliminating the need for traditional inventory and logistics [12][14]. Future Vision - The concept of "无人公司" (unmanned companies) is introduced, which are AI-driven entities that operate without traditional management structures, responding directly to user needs through automated processes [13][14]. - The article envisions a future where the connection between human intent and physical production is instantaneous, facilitated by personal agents and unmanned companies, thus eliminating inefficiencies in the current system [17]. - The transformation is framed as a shift from a world of translation and delays to one where desires can be directly realized, marking a significant evolution in how humans interact with technology and production [16][17].