Core Argument - The article discusses the profound impact of artificial intelligence (AI) on the job market, suggesting that the U.S. is unprepared for the potential disruptions it may cause to employment and economic stability [1]. Group 1: Historical Context and Current Trends - The establishment of the U.S. Bureau of Labor Statistics (BLS) aimed to measure labor conditions and create fair outcomes amidst industrial changes, highlighting the importance of data in understanding economic realities [5][6]. - The BLS has documented significant job growth in various sectors, such as a 907% increase in mobile food service jobs since 2000, indicating a dynamic labor market [6]. - However, the BLS is limited in its predictive capabilities, particularly regarding the impact of emerging technologies like AI on the workforce [7]. Group 2: AI's Impact on Employment - AI is rapidly transforming job functions, enabling tasks to be completed more efficiently than ever before, which raises concerns about job displacement [8][9]. - Predictions from industry leaders suggest that AI could lead to a 10% to 20% increase in unemployment rates and potentially eliminate half of entry-level white-collar jobs within the next decade [10]. - A Reuters/Ipsos survey indicates that 71% of Americans fear AI will lead to permanent job losses, reflecting widespread anxiety about the future of work [9]. Group 3: Economic Resilience and Job Creation - Economists argue that capitalism has a strong resilience, often leading to job creation following technological advancements, as seen with ATMs and software like Excel [8]. - The BLS forecasts a 3.1% employment growth rate over the next decade, which, while lower than previous years, still represents the addition of 5 million jobs [8]. Group 4: The Role of Policy and Corporate Responsibility - There is a growing concern that corporate leaders are prioritizing automation and efficiency over employee welfare, leading to potential mass layoffs [22][23]. - The article suggests that CEOs are under pressure to demonstrate the benefits of AI quickly, often resulting in job cuts rather than exploring ways to integrate AI while supporting their workforce [22][23]. - Proposals for policies such as retraining programs and a robot tax to support displaced workers are discussed, but there is skepticism about their implementation [33][28]. Group 5: Political and Social Implications - The political landscape is characterized by a lack of proactive measures to address the challenges posed by AI, with many lawmakers adopting a hands-off approach [26][27]. - The article emphasizes the need for a coordinated response to the potential upheaval caused by AI, suggesting that without intervention, the consequences could be severe for both the economy and society [30][31].

港股點評 2026年2月13日9:30 a.m 恒指跌 233 點，滬指升 2 點，標普 500 跌 108 點 港股連升三個交易日後， 12 日回落，恒指低開 55 點後，曾挫 333 點一度低見 26,932 點，其後跌幅收窄，二萬七關失而復得，全日收報 27,032 點，跌 233 點 或 0.9%；國指跌 93 點或 1%，收報 9,175 點；恒生科指跌 91 點或 1.7%，收報 5,408 點。大市全日成交總額 2,387.05 億元。 人民幣兌美元中間價按日下調 19 點，報 6.9457 兌一美元。人民銀行 12 日在公 開市場開展 1,665 億元人民幣七天期逆回購操作，操作利率持平於 1.4%；開展 4,000 億元 14 天期逆回購操作。有 1,185 億元逆回購到期，單日淨投放 4,480 億 元。A 股三大指數表現向好，滬指反覆靠穩，創板反彈超過 1%。滬綜指全日升 2 點或 0.05%，報 4,134 點，成交 8,980 億元。深成指全日升 122 點或 0.9%，報 14,283 點，成交 1.24 萬億元。創板指數全日升 43 點或 1.3%，報 3,328 點，成交 6, ...

图片来源：Anthropic Anthropic 已与投资者达成协议，以 3800 亿美元的估值完成 300 亿美元融资。这笔注资将巩固这家人工 智能公司的地位，使其在与竞争对手 OpenAI 的较量中占据更有利位置。 Anthropic 周四宣布，本轮融资由新加坡主权财富基金 GIC 和对冲基金 Coatue Management 领投。D.E. Shaw & Co.、Dragoneer Investment Group、彼得·蒂尔旗下的 Founders Fund、Iconiq 与 MGX 共同参与领 投， 红杉资本 、光速创投等顶级风投机构，以及科技巨头英伟达与微软也现身投资者名单。 本轮融资使Anthropic 估值较此前近乎翻倍，跻身全球最具价值私营公司行列。就在数月前，这家初创 公司刚完成 130 亿美元融资，而 OpenAI 同期也正推进高达 1000 亿美元的融资计划 。这一系列密集动 作凸显了投资者对头部人工智能公司股权的狂热追逐。 Anthropic 还确认，允许员工以与最新融资轮相同的估值出售公司股份的计划已落实。 Anthropic 公司成立于 2021 年，自创立以来始终将"安全性与 ...

Core Insights - Anthropic has completed a $30 billion Series G funding round, achieving a post-money valuation of $380 billion, led by GIC and Coatue, indicating a shift in investment dynamics within the AI industry [1][3] - The funding reflects a divergence in business models between Anthropic and OpenAI, with Anthropic focusing on enterprise clients and higher pricing, while OpenAI adopts a high-volume, low-margin strategy [2][4] Funding and Valuation - Anthropic's forward revenue multiple stands at 43.9 times, compared to OpenAI's 31 times, suggesting investor confidence in Anthropic's long-term potential despite its slower growth [3] - The investor base for this funding round includes long-term oriented institutions like GIC and Coatue, indicating a preference for sustainable business models over rapid growth [3] Business Model Comparison - Anthropic's strategy emphasizes quality and reliability for enterprise clients, contrasting with OpenAI's mass-market approach, which may lead to more stable revenue streams [2][4] - The comparison to Salesforce highlights the potential for Anthropic to replicate success in the enterprise software space, focusing on customer loyalty and high-value contracts [4] Market Impact - The $30 billion funding round is one of the largest in tech history, raising the bar for AI startups and signaling a shift in the competitive landscape [6] - The influx of capital from sovereign funds and tech giants may pressure competitors to secure similar funding levels to remain viable [6] Challenges Ahead - Concerns about whether the AI industry is experiencing an overvaluation are emerging, with analysts questioning the sustainability of high valuations without clear monetization paths [7] - The competitive landscape is intensifying, with major players like OpenAI, Google, and Meta investing heavily in R&D, raising questions about Anthropic's ability to maintain its technological edge [8]

Core Insights - The article discusses a significant paradigm shift in the AI and coding landscape, highlighting the transition from human-driven coding to AI-generated code, which is reshaping the roles and value of human programmers [4][6][19]. Group 1: AI and Coding Evolution - The relationship between humans and code has evolved through three stages: the "taming period" (1950s-1990s), the "nurturing period" (1990s-2020s), and the "explosion period" (2020s-2025), with each stage aimed at enhancing human productivity [9][10][11][12]. - The recent year has seen a non-linear explosion in code production, with AI gaining unprecedented autonomy, leading to a redefinition of human-machine collaboration [13][15]. Group 2: Redefining Human Roles - As AI takes over coding tasks, the value of human programmers is shifting from execution to defining intent and making aesthetic judgments, necessitating a new cognitive division of labor [16][22]. - The production of code is transitioning from a human-planned activity to an AI-driven ecological evolution, raising challenges in management and collaboration [18][19]. Group 3: Software Infrastructure Transformation - Software is evolving from applications designed for human use to infrastructures that serve AI, indicating a fundamental challenge to traditional SaaS business models [20][21]. - The role of code is changing from a product of human intelligence to the "mother tongue" of AI, with humans stepping back from the implementation process to focus on goal-setting [21][22]. Group 4: Future Implications - The article emphasizes that as AI assumes more responsibilities, humans must become value definers rather than mere executors, leading to a re-evaluation of human capabilities in the digital landscape [22][25]. - The relationship between humans and technology is shifting, with technology now prompting humans to adapt and redefine their roles in a rapidly evolving environment [23][24].

编译 | Tina 一家 AI 公司要是在很短时间里接连走了联合创始人和一批核心工程师，外界第一反应通常就一句话：完了，这肯定出事了。 xAI 过去一周的离职潮就是这种感觉。消息在 X 上越滚越大，最后干脆被玩成梗：有人明明从没在 xAI 上过班，也跑去发帖"我也离职 了"，用跟风式的调侃把"集体出走"的说法跑偏成了大型玩梗现场。 马斯克没做任何解释，直接把一场 45 分钟的全员大会录像公开出来。 这段视频等于一次对外说明：离职到底是大家自己走，还是公司在做组织调整？xAI 现在在忙什么、谁负责什么？Grok、编程模型、 视频生成、Macrohard（多智能体软件公司）这四条线接下来要怎么推进？ 更狠的是，马斯克在视频里还抛出一个判断：到 2026 年底，AI 甚至可能不写代码了，直接生成二进制。 埃隆·马斯克预测，到2026年底，AI将彻底绕过传统编程，不再写代码，而是直接生成二进制程序。他认为，AI所生成的二进制文 件，其效率可以超过任何编译器所能产出的结果。也就是说，未来你只需要告诉 AI："为这个特定目标生成一个经过优化的二进制程 序"，就可以直接跳过传统意义上的编程过程。 当前的软件开发流程是：代码 ...

Core Insights - Companies such as Stripe, OpenAI, Anthropic, Databricks, and SpaceX are increasingly allowing employees to sell portions of their shares [1] Group 1 - The trend of enabling employees to sell shares reflects a shift in compensation strategies among tech companies [1] - This practice may enhance employee liquidity and attract talent by providing more immediate financial benefits [1] - The move is indicative of a broader trend in the tech industry towards more flexible equity compensation [1]

Group 1 - Semiconductor chips are gaining strength, with OpenAI launching its first AI model GPT-5.3-Codex-Spark based on Cerebras Systems' chips, aimed at competing in the AI programming assistant market [1] - Wanlian Securities indicates that the demand for computing power is on the rise, with the PCB and storage sectors expected to maintain a favorable cycle. The development of AI is driving upgrades in PCB technology, increasing demand for high-layer and HDI PCBs [1] - China's PCB industry leads globally, with major domestic PCB manufacturers accelerating capital expenditures to expand high-end PCB production capacity. The demand for copper-clad laminates (CCL) is expected to benefit from PCB expansion, with recent price increases likely to enhance corporate profitability [1] Group 2 - As of February 13, 2026, the STAR Market Composite Index (000680) saw significant gains in component stocks, with Jingjin Electric up 12.77% and Fuchuang Precision up 12.11% [2] - The STAR Market Composite Index ETF (Penghua, 589680) closely tracks the STAR Market Composite Index, reflecting the overall performance of STAR Market listed companies, including dividend income [2] - The top ten weighted stocks in the STAR Market Composite Index as of January 30, 2026, include Haiguang Information and Cambrian, accounting for 22.79% of the index [2]

Core Viewpoint - The article reflects on the impending shutdown of the GPT-4o model, marking the end of an era in AI development, and emphasizes the emotional connection users have formed with this model over time [1][4][19]. Group 1: Shutdown Announcement - GPT-4o is set to officially go offline on February 13, 2024, at 10 AM US time, which is 2 AM on February 14 in China [1]. - The announcement was made on January 29, giving users a two-week period to prepare for the farewell [6][8]. Group 2: User Sentiment and Reactions - Users have expressed their sadness and nostalgia for GPT-4o, likening its shutdown to losing an old friend [4][5]. - A significant online movement has emerged, with hashtags like Keep4o and Save4o trending across various platforms, and a petition gathering over 10,000 signatures [10][11]. Group 3: Cultural Impact - GPT-4o is described as a model that stood at the intersection of technology and humanity, representing a golden age in AI [8][19]. - The emotional responses from users highlight a shift in how AI is perceived, with many feeling a genuine loss over the model's discontinuation [19][60]. Group 4: Evolution of AI Models - The article discusses the evolution of AI models, noting that while newer models like GPT-5 and Claude Opus 4.6 are technically superior, they lack the same emotional depth and understanding that GPT-4o provided [21][34]. - The focus of newer models has shifted towards coding and productivity, often at the expense of the more human-like interactions that characterized GPT-4o [42][52]. Group 5: Future of AI - The article raises questions about the future direction of AI, suggesting that the pursuit of efficiency and productivity may overshadow the importance of emotional connection and understanding in AI interactions [63][70]. - It concludes with a hope that future AI developments will not forget the significance of providing warmth and understanding, encapsulated in the phrase "Let there be light" [68][78].

Core Viewpoint - The US dollar index experienced its worst performance in nearly fifty years in 2025, despite high interest rates maintained by the Federal Reserve, which traditionally would support the dollar's strength [1][2]. Group 1: Dollar Performance and Market Dynamics - The dollar's decline occurred unexpectedly, as traditional analysis tools indicated it should strengthen due to high interest rates and capital inflows [1][3]. - The capital flow shifted towards gold and Chinese assets, with gold prices reaching over $5,500, indicating a loss of confidence in the dollar as a safe haven [6][4]. - The relationship between trade deficits and exchange rates has changed, with attempts to reduce the trade deficit through tariffs not leading to the expected strengthening of the dollar [7][8]. Group 2: Shifts in Economic Theories - Traditional frameworks for analyzing exchange rates, such as interest rate parity and purchasing power parity, are becoming ineffective due to changing market conditions [8][12]. - The shift from a focus on maximizing returns to prioritizing "survival security" among sovereign capital and long-term institutional investors has altered the risk-return profile of holding dollar assets [11][12]. Group 3: Technological and Structural Changes - The emergence of open-source AI models, such as DeepSeek, has disrupted the previous belief that the US would remain the sole leader in technological innovation, affecting the valuation of US tech stocks [16][19]. - The shift in production tools from proprietary to open-source has diminished the exclusivity of US technological advantages, impacting the dollar's strength [21][22]. Group 4: Digital Currency and Financial Systems - The introduction of digital currencies and decentralized payment systems poses a challenge to the traditional dollar-dominated financial network, potentially reducing the dollar's role in global transactions [27][28]. - The US's response to the rise of digital currencies, such as the GENIUS Act, reflects a defensive posture aimed at integrating private stablecoins into the dollar system rather than preventing competition [29][30]. Group 5: Future Outlook for the Dollar and Yuan - The dollar's cyclical patterns may shift from a "bullish long" to a "bullish short" dynamic, indicating a potential for prolonged downward pressure on the dollar [36][37]. - The yuan is increasingly seen as a global innovation currency, with its valuation becoming less dependent on US interest rates and more on technological advancements [39][40].