——@李善友，混沌创办人 当 AI 成为商业的「操作系统」 " 技术无所谓颠覆，市场无所谓颠覆。只有 新技术×新市场组合的原生价值网 ，才具备重构商业生态的颠覆性。" 大模型一日千里，算力成本指数级下降，算法正从工具进化为基础设施。 所有行业都值得用 AI 重做一遍，但 " 重做 " 不等于 " 叠加 " ，而是 " 重构 "—— 重构价值网络、重构组织流程、重构增长曲线 。 今天的商业竞争，早已不是「用不用 AI 」的选择题，而是「如何用 AI 解决真问题」的生存题： • 是让 AI 成为客服岗的效率工具，还是让智能体成为客户增值的核心能力？ • 是用算法优化现有产品，还是基于 AI 重构产品的「原生逻辑」？ • 是零散试点 AI 应用，还是让 AI 贯穿战略、组织、运营的全链条？ 混沌，正在尝试与你共创答案。 过去，我们问 " 你的企业数字化了吗？ " 今天，我们必须问 " 你的企业 AI 化了吗？ " 混沌十年： 与创新创业者共踏每一个时代浪头 2015 年，混沌第一次把 " 哲科思维 " 带进中国创业者的课堂； 2025 年，我们将把 "AI 创新思维 " 升级为每一位商业决策者的必修课。 十年来，我 ...

Core Insights - The article emphasizes that many companies face growth challenges in a rapidly changing business environment, often struggling to find breakthroughs against industry giants [1] - It highlights that nearly two-thirds of companies fail due to rigid adherence to mainstream value networks, while those that choose "emerging value networks" have a success rate of 37%, significantly higher than the average startup success rate of 10% [1] - The article introduces a new course titled "Modeling for Competition," which combines insights from ten years of innovation research, suggesting that true disruption arises from the effective combination of "native technology" and "native markets" [1] Group 1 - The concept of competition is redefined as a struggle between different value networks rather than just products or technologies [1] - The "Innovation Three-Step Method" is introduced, which involves building models first, identifying single points of focus, and then refining concepts [2][24] - The importance of modeling as a cognitive tool is emphasized, stating that cognition is essentially about building models to understand the world [7][12] Group 2 - The article discusses the significance of finding "single points" of focus, which are the smallest identifiable units of a product that can lead to breakthroughs [28] - It stresses that true innovation should come from identifying opportunities in emerging markets and not just from creating new products [30] - The article notes that entering emerging value networks can yield a success rate of 37%, compared to just 6% when competing in established markets [33] Group 3 - The third step of the innovation process involves a "mindset leap," moving beyond structured thinking to a higher level of consciousness that drives mission and purpose [34][39] - This stage is described as a qualitative shift, where the focus is on a deeper understanding of one's mission rather than just operational methods [40][46] - The article concludes that the ultimate victory lies in creating a new value network that can replace the old order, driven by a significant cognitive leap [57][59]

本周AI商业焦点必读 （2025.7.24-7.31） 本周核心趋势 2025年7月31日 1、 「国产开源」 中国AI霸榜Hugging Face前十！开源狂潮颠覆全球AI格局！ 中国AI巨头智谱、Qwen、腾讯混元等集体发力，在Hugging Face榜单包揽前10名，全部为开源模型，近 一个月密集发布超10款创新模型如GLM-4.5登顶、Qwen占5席。此开源浪潮推动全球AI生态向中国倾 斜，对比海外闭源涨价趋势，重塑产业竞争规则，加速创新普惠化。 原文链接： 整个HuggingFace榜，已经被中国AI模型一统江湖了 2025年7月30日 2、 「功能上新」 OpenAI深夜引爆学习革命！Study Mode免费上线，10亿用户AI导师时代开启！ OpenAI推出ChatGPT学习模式，通过交互式提示和个性化支持，引导学生主动探索知识而非直接获取答 案。该功能免费开放所有用户，或将重塑教育科技竞争格局，加速AI在教育领域的渗透和用户粘性提升。 AI从"炫技"变"实干"： WAIC现场投资额150亿、35万人潮凸显AI从参数竞赛转向实用价值，具身 智能与智能体成新维度，预示产业重心向生产力迁移。 Age ...

据路透社报道，这家公司正启动首轮融资，目标募资10亿美元，估值或达150亿美元 （约合1000亿元人民币） 。 这听起来像个天方夜谭，但它真实发生了。 在今天这个AI的"淘金热"时代，所有人都坚信着"大力出奇迹"的"规模法则"（Scaling Law）——更大的模型、更多的数据、更强的算力，就能换来更聪 明的AI。然而，就在所有巨头都在疯狂堆人、烧钱、扩大规模时，一个"异类"悄然崛起。 这家公司仅有110名正式员工，却在2024年创造了超过10亿美元（约70亿人民币）的年营收，甚至反超了拥有上千员工、背靠Meta这棵大树的行业霸主 Scale AI。 故事的主角叫Surge AI，一个在AI"军备竞赛"的后勤线上掀起风暴的"隐形帝国"。它的创始人，37岁的华人理工男Edwin Chen，面对外界对竞品Scale AI的热捧，只是淡淡地回应： "他们在追逐资本时，我们在打磨数据纯度。真正的AGI（通用人工智能），需要人类智慧的精粹，而非廉价标签。" 这句话，几乎点明了Surge AI逆袭的所有秘密，它在告诉世界： 在通往AGAI的路上，高质量的"人性"，远比海量的"人数"更重要。 风口上的"数据民工" 喂不饱真 ...

AI 热潮下，你的企业是否正面临这些困境？ 只见投入，不见产出： 部署了 AI 工具，为何团队效率不升反降？ 你是不是用 AI ，结果效果难以复制？ 精心调教的提示词，换个场景、换个人就失灵，无法规模化应用？ 你是不是做了大量培训，结果培训流于形式？ 员工参加了无数 AI 课程，回到工作中却依旧 " 不会用 " 、 " 用不好 " ？ 你是不是发现管理成为了瓶颈？ 传统的管理模式，反而限制了 AI 潜力的发挥，成为创新的最大阻碍？ 我们发现，多数企业 AI 落地失败，并非技术问题，而是陷入了思维、流程与组织管理的误区。 8 月 2 日（本周六），工信部 AI 内容创作师认证 主讲师、元一畅想科技 联合创始人 & COO、 混沌 AI 创新导师李桢将带来新课《企业 AI 落地交付 400 场后的心得：从凑热闹到有结果的三大误区与解法》， 将彻底跳出 " 技术工具 " 的单一视角，带你从企业战略与组织肌理出发，探寻 AI 落地的根本解法。 扫码报名 观看本课程和 600 + 主题课 为什么是李桢老师来讲？ 李桢是国内最早一批 投身 AI 企业落地实战 的专家，曾交付 数百场企业级培训与咨询项目 ，横跨 法务、 财 ...

Core Viewpoint - The article emphasizes the importance of utilizing AI in businesses to reduce costs and enhance efficiency, presenting eight practical techniques for entrepreneurs to implement AI effectively in their operations [2][36]. Cost Reduction Techniques - **Automating Repetitive Tasks**: AI can handle tasks such as data entry and invoice processing, significantly increasing efficiency. For instance, a Shanghai accounting firm improved its invoice processing from 800 to 2000 invoices in two hours using AI [6][7]. - **Optimizing Operations and Supply Chain**: AI can analyze historical data to optimize inventory and logistics. A merchant in Yiwu saved on storage costs by using AI to predict demand accurately, reducing umbrella stock from 300,000 to 180,000 units [10][11]. - **Enhancing Customer Service Efficiency**: AI can manage routine customer inquiries, allowing human staff to focus on complex issues. A hotel in Shenzhen reduced its customer service costs by 40% while improving response times through AI [12][13]. - **Optimizing Human Resources**: AI can streamline the hiring process, reducing the average hiring time from 28 days to 7 days and halving the turnover rate during the probation period [15][16]. Efficiency Enhancement Techniques - **Enhancing Decision-Making Capabilities**: AI analyzes vast amounts of market data to provide actionable insights for strategic decisions, transforming vague feelings into clear data [21][22]. - **Accelerating Innovation and R&D**: AI can significantly shorten the research and development cycle in industries like pharmaceuticals by simulating molecular structures and predicting compound properties [23][24]. - **Improving Marketing and Sales Efficiency**: AI enables targeted advertising by analyzing customer profiles, leading to a threefold increase in conversion rates while optimizing marketing spend [26][28]. - **Increasing Production and Manufacturing Efficiency**: AI visual systems can enhance defect detection and optimize production parameters, improving efficiency by at least 50 times [31][32]. Conclusion - The article concludes that the integration of AI into business processes is a gradual but essential journey, requiring a strategic approach to harness its full potential for cost reduction and efficiency enhancement [36][39].

Core Viewpoint - The article discusses Geoffrey Hinton's insights on the relationship between AI and human intelligence, emphasizing the evolution of AI from symbolic reasoning to large language models (LLMs) and the implications of AI surpassing human intelligence [1][10]. Group 1: Evolution of AI Understanding - For over 60 years, there have been two distinct paradigms in AI: the logical inference paradigm, which views intelligence as symbolic reasoning, and the biological paradigm, which sees intelligence as rooted in understanding and learning through neural networks [1]. - In 1985, Hinton created a small model to explore how humans understand vocabulary by linking features of words to predict the next word without storing entire sentences [2]. - The development of LLMs is seen as a continuation of Hinton's early work, processing more input words and utilizing complex neural structures to build richer interactions [3]. Group 2: Mechanism of Language Understanding - LLMs and human language understanding mechanisms are highly similar, transforming language into features and integrating these features across neural network layers for semantic understanding [4]. - Each word in language is likened to a multi-dimensional Lego block, which can flexibly combine to form complex semantic structures, with the shape of words adapting based on context [6]. - Understanding a sentence is compared to deconstructing a protein molecule rather than converting it into a clear, unambiguous logical expression [5]. Group 3: Knowledge Transfer in AI - The human brain operates at 300,000 watts but cannot easily transfer knowledge to another person, relying instead on explanation [11]. - In contrast, digital intelligence allows for efficient knowledge transfer, directly copying parameters and structures without intermediary language, sharing trillions of bits of information during synchronization [13][14]. - Current technology enables the same model to be deployed across different hardware, facilitating efficient knowledge migration and collaborative learning [15]. Group 4: The Dangers of Advanced AI - There is a concern that AI could surpass human intelligence, leading to scenarios where AI becomes an active system with its own goals, potentially manipulating humans [18][19]. - Hinton warns that developing AI is akin to raising a tiger; once it grows powerful, losing control could be fatal [20]. - Despite the risks, AI holds significant value in various fields, and eliminating it is not feasible; instead, a method must be found to ensure AI does not threaten humanity [21]. Group 5: Global Cooperation for AI Safety - No single country desires AI to dominate the world, and if one country discovers a method to prevent AI from going rogue, others will likely follow suit [22][23]. - Hinton proposes the establishment of an international AI safety organization to research technology and create standards to ensure AI develops positively [24]. - The long-term challenge is to ensure that AI remains a supportive tool for humanity rather than a ruler, which is a critical issue for global collaboration [25].

Core Viewpoint - DeepSeek, once hailed as a "national-level" project, has seen a significant decline in its monthly downloads, dropping from 81.13 million to 22.59 million, a decrease of 72.2% within six months [3][4]. Group 1: User Feedback and Issues - Users have expressed frustration over DeepSeek's tendency to generate nonsensical or fabricated content, leading to a growing movement to "remove the AI flavor" from its outputs [4][5]. - Specific examples include users receiving absurd suggestions or completely fictitious information, such as non-existent restaurants or fabricated academic references [6][11][13]. - The phenomenon of "AI flavor" has become a common complaint, with users noting that the writing style resembles "robotic assembly" rather than genuine human expression [19]. Group 2: Underlying Causes of Decline - The decline in DeepSeek's performance is attributed to its over-reliance on logical connectors and formulaic phrases, which detract from narrative flow and coherence [22]. - The model's training data is heavily skewed, with over 90% being English content, leading to a lack of quality Chinese language resources, which further hampers its effectiveness [28]. - The "data metabolism disease" in AI models is exacerbated by the recycling of AI-generated content, which diminishes linguistic diversity and quality [22][23]. Group 3: Recommendations for Improvement - To combat the decline in quality, users are encouraged to develop skills to identify AI-generated hallucinations, cross-check data, and apply critical thinking to AI outputs [30]. - Users should also test the logic of AI responses by seeking counterexamples and identifying contradictions, which can help break the cycle of logical rigidity [30]. - Finally, users should cultivate an awareness of AI's output characteristics, treating AI-generated content as drafts that require further scrutiny and verification [30]. Group 4: Conclusion - The challenges faced by DeepSeek reflect broader issues in the AI industry regarding the expectations placed on technology and the importance of maintaining human creativity and critical thinking in the face of automation [33].

Core Viewpoint - The AI social application sector, which experienced explosive growth in 2023, faces a severe survival crisis in 2025 due to intense competition and challenges in monetization [1][2][6]. Group 1: Market Dynamics - In 2024, leading AI social applications in China, such as Byte's Cat Box and MiniMax's Starry Sky, saw their daily downloads plummet from over 20,000 to below 7,000, a decline of more than 65% [2]. - Character.AI, a leading overseas AI social application, boasts 200 million monthly active users but generates only $16.7 million in annual revenue, indicating significant challenges in commercialization [4]. Group 2: Challenges Faced by AI Social Applications - The low technical barrier and lack of competitive moat allow personal developers and small teams to quickly launch new applications, leading to fierce competition [8]. - The threat of substitutes is high, as large models like ChatGPT already provide chat functionalities, reducing the need for users to download additional AI social applications [9]. - The industry suffers from severe homogenization, with many applications competing on price and lacking unique user experiences, leading to a vicious cycle of low-end competition [9]. - High computational costs and low user willingness to pay hinder profitability, as evidenced by Character.AI's struggle despite its large user base [10]. Group 3: Potential Paths for AI Social Applications - AI social applications must decide whether to continue focusing on virtual character chat apps or to pivot towards content transformation and service expansion to find new value [14]. - Transitioning from a consumer-focused (ToC) model to a business-focused (ToB) model could provide better ROI by addressing enterprise needs such as language practice and employee mental health support [15]. - Emphasizing refined content and creating high-quality, engaging narratives can enhance user retention, as seen in successful otome games [16]. - The future of AI social applications lies in their ability to address diverse emotional needs of individuals, suggesting a potential for niche and vertical market opportunities [18].

Core Viewpoint - The article emphasizes the establishment of AI innovation bases in various cities, aiming to transform local economies through collaborative efforts and localized solutions in the AI sector [5][12][19]. Group 1: AI Innovation and Collaboration - A total of 14 cities have gathered to form the first batch of co-builders for AI innovation centers, with an invitation extended to 24 new cities to join the initiative [1][6]. - The initiative focuses on creating localized AI transformation scenarios, utilizing real business cases and chaotic AI tools to drive regional economic development [2][4]. Group 2: Key Principles and Strategies - The three key principles being validated include: 1. Opportunities arise from gaps in the market 2. Localization is essential for success 3. Ecosystem collaboration is more valuable than individual efforts [12]. - The article outlines a structured approach for co-builders, including a candidate selection process, systematic training, and resource allocation to ensure effective participation [18]. Group 3: Economic Impact and Future Vision - The initiative aims to embed innovation genes into local economies, with the potential to rewrite industrial logic and create significant economic impacts by 2025 [19]. - The article highlights the importance of creating a network of regional innovation communities to facilitate knowledge sharing and collaborative growth [16].