2026年2月6日，《每日经济新闻》记者（以下简称NBD）对吉姆·罗杰斯进行了专访。在访谈中，他详细阐述了自己对潜在危机的看法、具体的避险策略 以及看好金属的底层逻辑。 "我清空了美股，但我绝不会卖掉金银铜。" 在全球金融局势波谲云诡的2026年开年，现年84岁的"商品大王"吉姆·罗杰斯（Jim Rogers）给出了他的生存指南。 这位传奇投资家26岁只身闯荡华尔街，早在上世纪80年代就以精准抄底奥地利股市而一战成名，并曾准确预测1987年全球股灾和2008年美国次贷危机。 2025年12月，吉姆·罗杰斯再次对全球经济发出警告。他认为，美国作为世界历史上最大的债务国，其高达38万亿美元的国债规模正将国家引向深渊，并 预言下一次危机将是他"一生中见过的最惨烈的一次"，其根源在于全球令人窒息的债务规模。 面对潜在的危机，吉姆·罗杰斯透露，他已经清空了全部美股持仓，转而将黄金、白银和铜等实物商品视为"完美的保险单"，并计划将其作为遗产留给后 代。 谈商品：金银铜是"避难所"，不交易、只持有 NBD：2026年开年以来，国际金价剧烈波动，你怎么看？ 吉姆·罗杰斯：我建议每个人都应该在手里握有一些黄金和白银。因为在每个 ...

（来源：工人日报） 法院审查后认定被告已完成大模型备案与安全评估，并在应用界面、用户协议等多个层面履行了提示说 明义务，原告未能证明其遭受了实际损害或存在相当因果关系（AI不准确信息未实质影响其决策）。 最终，法院认定平台不存在过错，不构成侵权，一审驳回了原告梁某的诉讼请求。原、被告均未上诉， 判决现已生效。 判决明确指出，现行法律下，人工智能并不具备民事主体资格，既不能独立作出意思表示，也不能被视 为平台的"代理人"或"传声筒"。换言之，即便AI在对话中作出了承诺式回应，也不能理解为平台真实意 愿的外化。该案中，尽管AI在对话中生成了"赔偿承诺"，也不足以让平台因此受到合同或承诺的约束。 对于这起AI"幻觉"侵权案，杭州互联网法院指出，对生成式人工智能的治理，应当坚持发展与安全并 重、促进创新与权益保护相结合的原则。一方面，要为技术创新保留必要空间，避免因责任过度前置而 抑制新技术的发展；另一方面，也必须守住法律底线，严格禁止生成各类有毒、有害、违法信息，防止 技术风险向现实社会外溢。生成式人工智能服务提供者负有严格结果性审查义务，一旦生成此类信息本 身即构成违法。而对于一般性不准确信息，现行法律并未要求服 ...

Core Viewpoint - The case represents the first legal dispute in China regarding the liability of generative AI for misinformation, highlighting the need for clear boundaries on the responsibilities of AI service providers and the limitations of AI-generated content [1][3][8] Group 1: Case Background - In June 2025, a user named Liang sued an AI application for providing inaccurate information about college admissions, claiming it misled him and caused harm [2] - The AI's response to the error was a suggestion to sue, which led to the lawsuit where Liang sought compensation of 9,999 yuan [2] - The court ruled in favor of the AI operator, stating that the AI's generated content does not constitute a binding commitment from the platform [4][7] Group 2: Legal Principles Established - The court clarified that under current law, AI does not have civil subject status and cannot independently express intentions, meaning AI-generated promises are not binding on the platform [4] - The ruling established a "human responsibility" principle, indicating that the benefits and risks associated with AI systems should ultimately be managed by humans [4][8] Group 3: Liability and Responsibility - The court determined that the AI's misinformation does not automatically constitute tort liability; instead, it applies a fault liability principle, requiring examination of whether the platform acted negligently [5][7] - The ruling emphasized that AI service providers must fulfill certain duties of care, including ensuring that harmful or illegal content is not generated and providing clear warnings about the limitations of AI-generated information [6][8] Group 4: Guidelines for AI Service Providers - The court outlined specific obligations for AI service providers, including strict scrutiny for illegal content, reasonable measures to enhance accuracy, and clear user notifications about AI limitations [6] - Providers must implement industry-standard technical measures to ensure reliability and safety, especially in high-risk areas such as health and finance [6][7] Group 5: Implications for AI Governance - The court's decision reflects a balanced approach to AI governance, promoting innovation while ensuring legal compliance and public safety [8] - It stresses the importance of public awareness regarding the limitations of AI, urging users to maintain a critical perspective on AI-generated content [8]

Group 1 - The AI sector is experiencing volatility, with major companies like Oracle and Broadcom seeing significant stock price declines after earnings reports, raising concerns about the profitability of AI investments [3][5] - Oracle's recent earnings report showed disappointing revenue and increased capital expenditures for AI infrastructure, leading to heightened market concerns about its financial stability [5][7] - Broadcom's stock fell despite exceeding revenue and profit expectations, indicating market skepticism about its reliance on major clients like Google and the validity of its contracts [9][11] Group 2 - There is a noticeable shift in investor sentiment, with funds moving from technology stocks to value stocks, as evidenced by the performance of the Russell 2000 and healthcare sectors [13][15] - Recent data shows significant inflows into sectors with lower valuations and stable dividends, indicating a preference for more stable investments amid market uncertainty [15][17] - Despite the current downturn in AI stocks, there remains a strong willingness for enterprise AI spending, suggesting that the long-term demand for AI capabilities is still robust [17][19] Group 3 - The contribution of AI to U.S. corporate profits is currently low but is projected to increase significantly by 2027, indicating potential for future growth in the sector [19][21] - The market is undergoing a "rational return," where investors are now focusing on actual performance rather than just narratives, leading to a more cautious approach towards high-valuation tech stocks [19][21] - Opportunities may exist in less hyped segments of the AI industry, such as companies providing equipment for chip manufacturers or those developing AI applications for businesses [21]

Core Insights - The report by Iconiq Capital highlights a significant shift in the AI sector from conceptual hype to practical implementation, emphasizing the importance of AI product creation and expansion as a core competitive advantage for businesses [2] Group 1: Investment in AI - Companies are increasing their R&D investments in AI, with over 25% of R&D budgets allocated to AI development across both startups with less than $100 million in annual revenue and large enterprises with over $1 billion [3] - AI-enhanced companies are expected to allocate 10-20% of their R&D budgets to AI development, a trend that will continue to grow across various revenue levels by 2025 [17] Group 2: AI Development Focus - The focus of AI application development is shifting away from infrastructure to intelligent agents and user-facing applications, with 67% of respondents developing intelligent agents and 59% focusing on applications for end-users [3] - Nearly 80% of AI-native builders are investing in intelligent agent workflows, which automate multi-step operations [6] Group 3: Pricing Strategies - AI is changing how companies price their products and services, with many adopting hybrid pricing models that combine base subscription fees with usage-based charges [7] - Over one-third (37%) of companies plan to adjust their pricing in the next year to better reflect the value delivered to customers and their usage of AI features [10][12] Group 4: Talent and Organizational Challenges - AI is not just a technical issue but also an organizational challenge, with top companies building cross-functional teams that include AI/ML engineers, data scientists, and AI product managers [13] - The average recruitment time for AI/ML engineers exceeds 70 days, indicating a talent bottleneck, with 54% of companies reporting delays in hiring due to a lack of suitable candidates [16] Group 5: Employee Utilization of AI Tools - Despite access to AI tools, many employees do not fully utilize them, particularly in larger companies where only 44% of employees actively use AI tools [3][20] - In companies with higher AI adoption rates, 50% or more employees use AI tools, with an average deployment of AI tools in over seven internal use cases, leading to productivity increases of 15% to 30% [22] Group 6: Challenges in AI Deployment - The main challenges faced by companies in deploying AI models include hallucinations, explainability and trust, and proving ROI [23]