Wedbush Securities Managing Director Dan Ives delivered a highly bullish forecast for the technology sector, declaring that the artificial intelligence (AI) revolution is firmly cemented in Nvidia Corp.‘s (NASDAQ:NVDA) growth. He bluntly stated that “At the end of the day, it’s Nvidia’s world, everyone else is paying rent.”Check out NVDA’s stock price here.Demand Outstrips Supply 12-to-1Speaking on Schwab Network’s ‘Watch List,’ Ives predicted that the current tech bull market has significant momentum remai ...

有接近双方的知情人士表示，目前双方合作更倾向于技术生态共建。"中公教育可能通过联合行业伙 伴，共同构建基于全球AI算力巨头技术的教育算力平台。" 天风证券认为，布局AI算力有助于中公教育突破传统业务天花板，但需关注其实际落地能力。技术转 型需要长期投入，关键要看合资公司或合作项目的具体进展，若算力平台能成功商业化，中公教育的估 值体系有望重构。 教育AI基础设施的构建是一个系统性工程，需要技术、内容、市场多轮驱动。中公教育能否借此开启 新业务板块的增长曲线，取决于其技术整合能力与市场开拓效率。从当前布局来看，新希纪元作为独立 运营的AI业务载体，既要依托中公教育现有的渠道资源与教学内容优势，又需突破传统教培思维，建 立符合技术服务和平台运营需求的商业模式。若能在未来1—2年内实现标杆案例的规模化验证，并形成 可持续的营收模式，则有望成为中公教育超越周期波动的新增长引擎。（CIS） 11月25日，北京中关村互联网教育中心一场以"共筑AI教育新基座"为主题的闭门研讨会悄然举行。这场 由中公教育与新希纪元联合主办的会议，因英伟达高管的现身引发市场关注。 据了解，研讨会主题直指"教育云算力重构"与"AI人才培养"，议 ...

BLOOMBERG TECH ASIA IS LIVE WITH SHERY AHN IN TOKYO AND ANNABELLE DROULERS IN HONG KONG. SHERY: WE ARE FOCUSED ON THE MULTIBILLION-DOLLAR CIRCULAR DEALS FUELING CONCERNS OF A BUBBLE IN THE GLOBAL AI INDUSTRY. WHERE DO ASIA'S TECH TITANS FIT INTO THE AI MONEYMAKING MACHINE.ANABEL: WE HEAR FROM THE CHAIRMAN OF NVIDIA AND OPENAI PARTNER FOX ON ON WHETHER HE IS WORRIED ABOUT THE FUTURE OF MONETIZATION OF AI. MORE OF OUR INTERVIEW, COMING UP. SHERY: WHILE SOME OF ASIA'S TECH PLAYERS STAND TO BENEFIT FROM THE AI ...

作者：林更 在内蒙古乌兰察布，冬季夜晚气温一般在-25℃，极寒时-35℃。阿里巴巴崭新的数据中心坐落于此，察 哈尔右翼前旗200多亩的园区可容纳数十万台服务器。这里的寒风降低了能耗成本，延迟低于2毫秒的光 纤通道可直连北京，承接京津冀海量数据存算需求。 不只是阿里巴巴，苹果、快手、华为等50多家企业在乌兰察布签约落地了67个数据中心，总投资2600亿 元。这里的算力运营规模达10.87万P，智能算力占比超90%。 在转型成为数据中心基地之前，乌兰察布盛产土豆、煤炭、石墨、铜、铅。 整个2025年，全球科技巨头的资本开支持续爆发式增长。自2022年11月ChatGPT发布以来，"美股七 雄"市值大幅上涨。财富带动支出，它们向AI和云基础设施投入巨额资金。截至2025年第三季度财报， 巨头们年度资本开支超过4000亿美元，较2024年增长30%-50%，主要受AI训练、推理需求、数据中心 扩容驱动。 巨额投资直接拉动AI服务器和数据中心硬件需求，硬件巨头如英伟达、联想、戴尔、Supermicro等从中 获益显著，AI相关营收占比飙升，预计2025年全球AI服务器市场规模达2520亿美元，同比增长55%。 即便市场 ...

目前英伟达在AI芯片市场占据超过90%的份额。与此同时，以谷歌为代表的科技巨头正加速自研芯片步 伐。AWS持续更迭Graviton、Trainium、Inferentia系列芯片，微软发布Maia系列后也在推进新芯片计 划。 这种趋势在Anthropic的采购策略中得到体现。该公司一方面与英伟达签订围绕Blackwell、Rubin系统的 长期基础设施协议，另一方面也采购谷歌最新的TPU。这种"多路线并行"的采购方式表明，大型AI公司 不再愿意将未来完全押注在单一芯片架构上。 整个AI基础设施行业正从单一硬件竞争转向系统级竞争。随着软件框架、模型体系、能效要求的变 化，AI芯片格局仍在持续演变。市场正重新评估GPU在未来AI基建中的份额与利润率，这也触动了投 资者对英伟达峰值时刻的敏感神经。 声明：市场有风险，投资需谨慎。本文为AI基于第三方数据生成，仅供参考，不构成个人投资建议。 英伟达正遭受前所未有的市场冲击。自10月29日以来，这家AI芯片巨头的市值从5.03万亿美元暴跌至11 月25日收盘的4.32万亿美元。不到一个月时间内，市值缩水超过7000亿美元，约合人民币5万亿元。 冲击波的源头来自谷歌加速推 ...

雪球有大佬总结了Michael Burry的一系列操作： 只是现在过了两天，订阅人数增加到了88k，已经超过3300万美元的订阅费了，当然我给他贡献了 379美元的订阅费.. 目前Michael Burry总共写了三个文章，我们来看下他的观点。 1、泡沫的主要标志：供给侧的贪婪 第一篇内容《The Cardinal Sign of a Bubble: Supply-Side Gluttony》主要是在回顾历史了。 核心观点是： 愚蠢（狂热的创新尝试）是美国成为世界创新中心的重要原因 ，但愚行过度会引发泡 沫；核心标志是供给侧过度扩张，而非需求不足或盈利缺失。 支撑这个观点的核心理论是 资本周期理论 ，即狂热驱动下的资本过度投资会导致供需失衡，最终引 发行业洗牌与市场崩盘，这一理论是贯穿全文的分析框架。 说到历史上的辉煌的愚蠢行为，90 年代的互联网泡沫是绕不开的案例，但大众对它的记忆大多是错 的。美联储主席鲍威尔曾说：当下公司有商业模式和盈利，与当年不同。但事实并非如此。当年的 泡沫根本不是无利可图的".com 公司" 驱动的，而是一场数据传输基础设施建设狂潮。那时候的口号 是互联网流量每 100 天翻一番， ...

Group 1 - The article discusses the gradual improvement of China-US relations, highlighted by three significant events [1] - China's central bank has increased its gold reserves for 12 consecutive months, reaching 74.09 million ounces, which is still below the global average of 15% [3][27] - The increase in gold reserves aims to optimize foreign exchange reserves and reduce risks associated with excessive dollar assets, acting as a "safety cushion" for the economy [5] Group 2 - China has resumed large-scale purchases of US soybeans, with 3 million tons valued at approximately $1.5 billion, marking a significant trade development since May [10][12] - This soybean purchase is strategically timed ahead of the US midterm elections, benefiting agricultural states that are crucial for the Republican Party [12][14] - The US is considering the export of Nvidia's H200 AI chips to China, which could significantly impact the AI chip market and reflects ongoing negotiations between the two countries [15][19] Group 3 - The article suggests that these developments indicate a pragmatic approach to trade, with both countries seeking mutual benefits, contrasting with the tensions seen during the 2018 trade war [24][26] - Despite the positive signals, underlying differences remain, particularly regarding chip exports, which are still under intense debate in the US [26] - The overall economic interdependence of China and the US, accounting for over 40% of global GDP, emphasizes the need for cooperation rather than confrontation [29]

Core Insights - Nvidia's GPUs dominate the AI chip market with a 90% share, but competition is increasing as tech giants develop custom ASICs, threatening Nvidia's leadership [1][3] - The shift from "training" to "inference" in AI development favors more energy-efficient chips like TPUs and NPUs over traditional GPUs [5][6] Group 1: Nvidia's Market Position - Nvidia's GPUs are priced between $30,000 to $40,000, making them expensive and contributing to Nvidia becoming the highest-valued company globally [1] - Major tech companies are moving towards developing their own chips, indicating a potential decline in Nvidia's dominance in the AI sector [1][3] Group 2: Custom AI Chips - Google's TPU, designed specifically for AI, outperforms GPUs in certain tasks and is more energy-efficient, leading to lower operational costs [3][5] - Companies like OpenAI and Meta are investing in custom chips, with OpenAI planning to produce its own chips in collaboration with Broadcom [3][5] Group 3: Economic Factors - The cost of installing Nvidia's latest GPUs is significantly higher than that of Google's TPUs, with estimates of $852 million for 24,000 Nvidia GPUs compared to $99 million for the same number of TPUs [5] - The emergence of cheaper custom chips is expected to alleviate concerns about an AI investment bubble [5] Group 4: AI Ecosystem Changes - The AI ecosystem centered around Nvidia is likely to change as large tech companies collaborate with chip design firms, creating new competitors [6] - The current manufacturing landscape, dominated by TSMC for Nvidia chips, may shift as companies develop their own semiconductor solutions [6] Group 5: Chip Types - CPUs serve as the main processing units but are slower compared to GPUs, which can handle multiple tasks simultaneously [8] - TPUs are specialized for AI tasks, while NPUs are designed to mimic brain functions, offering high efficiency for mobile and home devices [8]

Core Viewpoint - The company is aggressively promoting a comprehensive "AI transformation" across all levels, with CEO Jensen Huang emphasizing the necessity of automating tasks using AI and reassuring employees about job security despite industry layoffs [1][5][8]. Group 1: AI Implementation - Jensen Huang expressed strong dissatisfaction with managers advising employees to reduce AI usage, insisting that every task that can be automated should be [2][3]. - Huang encouraged employees to use AI tools even if they are not fully capable yet, stating that they should contribute to improving these tools [4][6]. Group 2: Recruitment and Expansion - Despite widespread concerns about job losses due to AI, the company has been actively hiring thousands of employees, leading to a shortage of parking spaces at their offices [4][6]. - The workforce has grown from 29,600 at the end of fiscal year 2024 to 36,000 by the end of fiscal year 2025, indicating a significant expansion [6]. Group 3: Financial Performance - The company's aggressive strategy is supported by strong financial performance, with a reported revenue of $57.01 billion for the last quarter, a 62% increase year-over-year [8]. - The company has become the highest-valued firm globally, with a market capitalization exceeding $4 trillion [8]. Group 4: Industry Context - The company's approach reflects a broader trend among tech giants, with competitors like Microsoft and Meta also integrating AI into their operations and performance evaluations [2][8]. - There are ongoing debates about the sustainability of the AI boom, with some investors expressing skepticism about the long-term viability of the current AI trends [8].

Core Insights - Google is positioning itself as a strong competitor to Nvidia by securing significant partnerships and expanding its TPU offerings, potentially disrupting Nvidia's dominance in the AI chip market [1][3] - The shift towards Google's TPU is driven by its system-level cost efficiency and scalability, which appeals to major AI companies like Meta and Anthropic [5][10] - The emergence of a "Google Chain" signifies a structural change in the AI computing landscape, allowing for a more diversified supply chain beyond Nvidia [22][25] Google’s Strategic Moves - Google is negotiating multi-billion dollar TPU purchases with Meta, which may lead to a shift of some of Meta's computing power from Nvidia to Google [1] - A partnership with Anthropic aims to expand TPU capacity significantly, indicating a strong demand for Google's AI infrastructure [1] - Google's TPU is designed to optimize cost and efficiency, with the latest generation showing a performance-to-cost ratio improvement of up to 2.1 times compared to previous models [5][7] Performance Comparison - Nvidia's Blackwell architecture remains the industry benchmark for single-chip performance, but Google is focusing on system-level efficiency rather than direct competition on chip performance [4][5] - Google’s TPU v5e can achieve a performance-to-cost ratio that is 2-4 times better than traditional high-end GPU solutions, making it an attractive option for large model training [7][10] - The cost of using Google’s TPU v5e is significantly lower than Nvidia's H100, with TPU priced at $0.24 per hour compared to H100's $2.25 [8][9] Market Dynamics - The increasing adoption of Google’s TPU by major AI firms indicates a shift in the AI computing market, where companies are looking for alternatives to Nvidia to mitigate risks and reduce costs [10][13] - The competition between "Nvidia Chain" and "Google Chain" is not a zero-sum game; rather, it represents a broader expansion of AI computing resources [22][27] - The structural change allows companies to choose from a diversified set of computing resources based on their specific needs, enhancing flexibility and cost-effectiveness [25][26] Beneficiaries of Google’s Strategy - AVGO is identified as a key player benefiting from Google's TPU ecosystem, providing essential communication and networking components [15][16] - The manufacturing partners, including TSMC, Amkor, and ASE, are crucial for the production of Google's TPU, ensuring the scalability of its offerings [18] - Companies like VRT, Lumentum, and Coherent are positioned to benefit from the increased demand for high-performance cooling and optical communication solutions as TPU deployments expand [20][19] Future Implications - The rise of Google’s TPU could lead to a more balanced and resilient AI infrastructure, reducing the industry's over-reliance on Nvidia [22][25] - The dual-engine approach of Google, combining cloud and edge computing, is expected to reshape the AI landscape, making it more accessible and efficient for various applications [20][21] - The ongoing competition will likely drive further innovation and investment in AI computing, benefiting the entire industry [27]