（原标题：两部门拟明确"守门人"认定标准，AI新贵们也入围了？） 21世纪经济报道记者 王俊 章驰 一批平台即将成为你的个人信息"守门人"。 11月22日，国家互联网信息办公室、公安部发布《大型网络平台个人信息保护规定（征求意见稿）》（以下简称"征求意见稿"），明确了对大型 网络平台的认定标准，以及应履行的个人信息保护义务。 根据征求意见稿，除却阿里、腾讯、蚂蚁、字节跳动、百度、微博、小红书等互联网平台，DeepSeek、MiniMax、Kimi等迅速增长的AI公司，以 及OPPO、vivo、荣耀等智能终端厂商，不少用户规模也满足征求意见稿中的"用户超5千万或月活超1千万"等条件，同样可能进入大型网络平台 的序列。 "能力越大、责任越大"，这一原则贯穿了数字经济监管始终。该征求意见稿与9月份发布的《大型网络平台设立个人信息保护监督委员会规定》一 脉相承，均可被视为个人信息保护法第58条"守门人条款"以及《网络数据安全管理条例》对大型平台规定的配套文件。 配套文件的规定，待正式版本发布后，将对上述平台的个人信息保护合规带来重要影响。 AI新贵进入大型平台监管射程？ 4年前，个人信息保护法正式实施。其中第58条创 ...

Group 1 - Vietnam has identified artificial intelligence (AI) as a priority technology for national development, playing a key role in promoting knowledge economy and digital transformation [1] - The draft of the "Artificial Intelligence Law" is currently under discussion in the National Assembly, which, if passed, will be Vietnam's first AI law, providing a legal foundation for the development and governance of AI in the country [1][2] - The law aims to establish a legislative framework for AI, incorporating international trends and practices while ensuring alignment with Vietnam's context and international treaties [1][2] Group 2 - There is a significant shortage of high-quality AI talent in Vietnam, particularly in the intersection of technology, law, and ethics, presenting an opportunity to build high-standard AI systems from scratch [2] - Approximately 170,000 enterprises in Vietnam are currently applying AI technology, accounting for about 18% of the total number of enterprises [2] - The draft law consists of 8 chapters and 36 articles, outlining the basic principles for AI activities in Vietnam, balancing effective regulation with healthy development [2][3] Group 3 - The law emphasizes a "human-centered" principle, ensuring that AI development serves human welfare and retains human oversight in critical decision-making [2][3] - It requires AI systems to possess transparency, reliability, and security, implementing classified management based on risk levels [2] - The law aims to encourage domestic AI technology development and innovation, positioning AI as a vital force for rapid and sustainable economic growth and safeguarding national digital sovereignty [2][3]

Core Insights - The World Health Organization (WHO) warns about the rapid adoption of artificial intelligence (AI) in healthcare, highlighting significant gaps in legal and ethical safeguards [1][2] Group 1: AI Adoption in Healthcare - Nearly all countries recognize the potential of AI in diagnosis, disease monitoring, and personalized medicine [1] - 32 out of 50 surveyed European countries have adopted AI-assisted diagnostics, and half have introduced chatbots for patient support [1] - Over half of the countries have identified priority application areas for AI in healthcare, driven by the need to improve patient care quality, alleviate workforce pressure, and enhance efficiency and productivity [1] Group 2: Challenges and Barriers - 86% of the surveyed countries view "legal uncertainty" as the primary barrier to AI application in healthcare, while 78% cite "insufficient funding" as a major issue [1] - Only 25% of the countries provide dedicated funding for healthcare AI, and less than 8% have established "responsibility standards" for AI-related errors or harm [1] Group 3: Recommendations for Policy and Strategy - The report emphasizes the need for countries to develop national strategies for healthcare AI that align with public health goals [2] - It calls for investments in capacity building, strengthening legal and ethical safeguards, and improving cross-border data governance [2]

在此情境下，共情指的是顾客认为公司及其代表真诚地试图理解并回应其情绪状态，尤其是在顾客脆弱 的时刻。对于保险客户而言，这可能意味着保险代表不仅处理理赔事宜，还认可客户正在经历的困难， 或者公司事后跟进了解情况。这是一种从客户视角看待问题，并将这种认知转化为关怀和积极回应的能 力。 曾几何时，共情被认为过于温情柔弱，不适用于职场环境。但数十年的研究已打破这一误解。共情包含 三个要素：分享他人经历、尝试理解他人眼中的世界，以及关心他人的福祉。当人们表达共情时，会建 立起更深层次、更具滋养性的关系；当他们感受到共情时，其信任度、士气和幸福感也会随之提升。 职场亦是如此。富有共情力的领导者能够打造出员工敬业度更高、忠诚度更强的团队，在这样的团队 中，员工不仅感觉更良好（体验到更多的快乐、更强的韧性和更高的幸福感），而且工作表现也更出色 （协作更高效、创新能力更强、工作产出更高）。如今，任何一家希望以数据驱动企业文化的公司，都 应确保领导者能够给予共情，员工也能感受到共情。 但企业的顾客又如何呢？在苏黎世保险集团赞助的一项全新全球调查中，我们对11个国家近1.2万人进 行了民意调查，结果发现，大多数顾客希望从与之打交道 ...

Core Viewpoint - The article discusses the challenges and potential of AI in healthcare, emphasizing that while AI shows promise in controlled environments, its integration into real clinical settings faces significant hurdles [1][2][4]. Group 1: AI Performance and Clinical Integration - Microsoft's AI diagnostic system scored four times higher than human doctors in a complex case test, highlighting AI's potential in medical diagnostics [1]. - A study from Harvard indicates that simply providing doctors with AI tools like ChatGPT does not improve diagnostic outcomes; optimal results occur when AI analyzes cases first, followed by human input [4]. - The disconnect between AI's technical capabilities and clinical needs is a core issue, as medicine requires both scientific and artistic approaches, which AI struggles to replicate [5]. Group 2: Challenges in Implementation - The integration of AI into clinical workflows is hindered by established practices among doctors, who may find AI tools burdensome rather than helpful [6]. - Medical data infrastructure is crucial for AI's effectiveness; for instance, Yidu Tech invested over $100 million over four years to build a data foundation necessary for AI applications [6]. - Patient trust remains paramount, as evidenced by a survey where none of the 3,000 patients chose hospitals based on AI tools, indicating that patients prefer human doctors over algorithms [6]. Group 3: Democratization of Healthcare - AI's ultimate goal is to democratize access to quality healthcare, as illustrated by a new insurance model in Beijing and Shenzhen that offers affordable premiums and high coverage [7]. - The use of AI in non-critical care settings is being explored to enhance service delivery, particularly in underserved areas [7]. - AI can reduce administrative burdens on doctors, allowing them to spend more time with patients, thus improving overall healthcare delivery [7]. Group 4: Future Collaboration Between Doctors and AI - There is a consensus that AI will not replace doctors; however, those who do not learn to utilize AI effectively may be outpaced by their peers [8]. - Medical education is evolving to include AI collaboration skills, ensuring future doctors can use AI tools while understanding their limitations [9]. - Continuous monitoring and optimization of AI tools are necessary to ensure they are user-friendly and effective for busy healthcare professionals [9].

贝索表示，2026年很可能成为两大技术深度融合的一年。 回望即将结束的2025年，智能体应用爆发，AI开始从"听令行事"走向"主动服务"，人形机器人产业链也 开始提前规划产能。但整体而言，AI在物理世界的部署仍面临诸多挑战，在追求高精度和高效率的工 业场景中，这一问题则更为突出。李飞飞、杨立昆等顶尖人工智能科学家近日均发声强调AI学习理解 物理世界的重要性。 为何AI在物理世界走得更慢？AI在2026年又迎来怎样的发展？ 世界经济论坛人工智能卓越中心的人工智能应用与影响负责⼈玛丽亚·贝索（Maria Basso）近日在接受 第一财经记者专访时解释称，将机器人集成至工业场景的难度远高于部署聊天机器人。她认为，让机器 人能够理解物理世界的"世界模型"技术尚未完善，而实际部署时需要考虑更多因素、调试更多参数。另 外，机器人部署还需要解决安全问题和劳动力适配等问题。 但她也观察到，在工业特别是制造业场景中，越来越多AI、传感器与机器人技术相融合的案例涌现出 来。展望未来，她期待世界模型技术取得更大进展，同时AI智能体可以实现从技术概念和零星应用， 到被企业规模化应用的跨越。"2026年很可能成为两大技术深度融合的一年 ...

Core Viewpoint - The article draws a parallel between OpenAI and Enron, questioning whether OpenAI's current trajectory could lead to a similar downfall due to financial and operational challenges in the AI industry [1][2][41]. Group 1: Financial and Operational Challenges - OpenAI is projected to require $650 billion in new revenue annually to justify its investments, which is significantly higher than its current revenue of approximately $20 billion [11][37]. - The AI industry is expected to invest $5 trillion by 2030, but this investment is constrained by physical limitations such as the availability of critical components like transformers and power supply [25][36]. - Major tech companies are increasingly relying on debt to finance their AI infrastructure investments, raising concerns about sustainability and financial health [25][28]. Group 2: Infrastructure Limitations - The construction of data centers is facing significant delays due to the need for physical infrastructure, including power grid connections and fiber optic installations [20][21]. - There is a shortage of essential components, such as transformers, which are crucial for connecting data centers to the power grid, leading to potential project delays [28][33]. - The CEO of GE Vernova indicated that their production capacity for transformers is fully booked until 2028, highlighting the supply chain constraints in the industry [28]. Group 3: Market Demand and Revenue Generation - Analysts predict that AI products must generate substantial revenue to meet the high expectations set by investors, with a need for continuous growth in consumer and enterprise spending on AI services [39][40]. - The article suggests that while there are various monetization avenues for AI, the fundamental challenge remains in aligning production capabilities with market demand [40][41]. - The potential for AI services to evolve into more sophisticated offerings could drive revenue growth, but this is contingent on overcoming existing operational hurdles [36][41].

Core Insights - The article discusses the impending risks associated with AI advancements, highlighting concerns from AI pioneer Geoffrey Hinton about potential mass unemployment and existential threats posed by superintelligent AI [2][12][18]. Group 1: AI Investment and Financial Implications - Major tech companies, including Microsoft, Meta, Google, and Amazon, are projected to spend $420 billion on AI in the coming year, up from $360 billion this year [5]. - OpenAI has signed contracts exceeding $1.4 trillion for computing power, indicating a significant financial commitment to AI development [5]. - Nvidia is identified as the biggest winner in the AI boom, with its market value soaring to $5 trillion and predictions suggesting it could exceed $8.5 trillion in the future [8]. Group 2: Employment and Labor Market Impact - Hinton warns that to achieve profitability, companies must replace human labor with AI, leading to increased risks of job displacement, particularly for ordinary workers [9][21]. - Since the launch of ChatGPT, job vacancies have reportedly decreased by approximately 30%, while the stock market has risen by 70% [21]. - Amazon's recent announcement of a 4% workforce reduction, affecting 14,000 employees, exemplifies the trend of job losses driven by AI investments [23]. Group 3: AI Safety and Ethical Concerns - Hinton criticizes tech giants for prioritizing commercial competition over safety, suggesting that their focus is more on winning the AI race than on ensuring human survival [17]. - He emphasizes the need for a serious discussion on how to coexist with superintelligent AI, likening the situation to an impending alien invasion [15][28]. - Hinton's perspective is that the current approach to AI development is flawed, as executives mistakenly believe they can control AI as a subordinate [28]. Group 4: Future of AI and Economic Growth - The article suggests that the current AI investment bubble could lead to significant economic repercussions, with AI and data center investments contributing to 92% of GDP growth in the first half of 2025 [35]. - OpenAI's revenue is estimated at $13 billion, with an IPO valuation around $1 trillion, indicating a potentially unsustainable bubble in the AI sector [37]. - Despite the massive influx of capital into AI, a study indicates that 95% of enterprises applying generative AI have failed, highlighting the challenges in finding effective applications [45].

Core Insights - The rapid growth of AI Token consumption since 2025, particularly by companies like Google, has led some investors to overly optimistic projections regarding AI computing power investments and monetization potential [1] Group 1: AI Token Consumption Growth - Current growth in AI Token consumption is primarily driven by applications in AI search and chatbots, but without new commercially viable scenarios, this growth trend may quickly slow down in the short term [1] Group 2: Economic Investment in AI Hardware - The actual economic investment in AI hardware systems is expected to be significantly lower than the surface-level data suggests, due to the combined improvements in chips, hardware systems, and software [1] Group 3: Commercial Viability of AI Applications - High-value commercial scenarios for AI remain limited, and companies may not continue to increase investments solely based on Token growth; further breakthroughs in application scenarios and more efficient monetization models are necessary for sustained growth [1] - Short-term focus should be on advancements in cutting-edge models like Gemini 3, progress in AI monetization (AI Overview, AI Mode, Gemini), and the scaling of AI+software value through AI Agents [1]

Core Insights - Silicon Valley is experiencing a complex mix of anxiety, envy, and self-reflection in response to China's rapid technological advancements, particularly in infrastructure, AI applications, and manufacturing [1][2] - The admiration for China's efficiency and execution is prompting discussions among Silicon Valley elites about work models and industrial policies, reflecting a struggle with national identity and self-confidence in the U.S. [1][4] Group 1: Technological Advancements - Chinese companies, once viewed as mere imitators, are now seen as benchmarks for efficiency and scale in research and manufacturing [4] - The integration of AI and hardware is crucial, with American companies racing to develop smarter machines, while Chinese firms focus on practical applications of AI in services and manufacturing [8][10] Group 2: Identity and Perception - The admiration for China highlights a deeper psychological state in the U.S., where the realization that other countries are leading in technological progress is difficult for American elites to accept [4][5] - The narrative surrounding China often oversimplifies the complexities of its technological landscape, which includes both innovation and challenges [8][10] Group 3: Policy and Economic Implications - The U.S. innovation-manufacturing-export model has collapsed due to extensive outsourcing, with China increasingly taking on the manufacturing role [5][10] - Silicon Valley's discussions about China often overlook domestic contradictions, such as policies that undermine the revival of American manufacturing [10][11] Group 4: Competitive Landscape - The ongoing tech competition between the U.S. and China is characterized as a continuous race, with significant milestones prompting accelerated development in both countries [11] - The emergence of cost-effective and high-performance AI solutions from China serves as a wake-up call for the U.S. tech industry, emphasizing the urgency of innovation [11]