新华财经上海2月25日电（葛佳明）当地时间2月25日（周三）美股盘后，AI芯片巨头英伟达将公布 2026财年第四财季（2025年11月1日至2026年1月31日）业绩。目前分析师普遍对英伟达财报表达了乐观 预期。 分析人士对新华财经表示，在AI泡沫争议升温之际，英伟达的财报或成为科技板块的"压力测试"。当其 业绩超预期成为市场惯性认知时，焦点正从"是否强劲"转向"能否持续"。英伟达CEO黄仁勋对AI需求强 度、供给约束以及谷歌等巨头资本开支回报率等问题的答案将成为市场判断的重点。 数据中心业务或再加速 新华财经统计发现，华尔街投行普遍预计英伟达能够在2026财年第四季度再次实现强劲的增长，并发布 乐观的业绩指引，但在近期"AI担忧"引发美股软件股集体大跌的风暴中，英伟达这份财报能否力挽狂 澜，仍是一个未知数。 分析师认为，人工智能基础设施建设需求的持续释放、下一代芯片放量出货，以及全球数据中心支出保 持扩张，共同印证了公司业绩增长动能的稳固与延续性。 从上一季度的财报看，英伟达实现季度总营收570亿美元，其中数据中心收入达到512亿美元，占比 90%，同比增长 66%。数据中心再度成为英伟达的增长来源。 转自 ...

智通财经APP获悉，随着有"地球最重要股票"称号的"AI芯片霸主"英伟达(NVDA.US)将在美东时间周三美股收盘后(北 京时间周四晨间)公布季度业绩，一场关于AI算力投资主题的"压力测试"也随之到来。聚焦于AI算力基础设施以及更广 泛AI基建狂潮的全球投资者们正寻求证据，证明这家全球最高市值芯片巨头的利润正在紧密依托高达6500亿美元至 7000亿美元的美国四大科技巨头(hyperscalers)巨额AI资本支出预算大趋势而同步实现强劲增长预期。 与此同时，来自hyperscalers近期密集宣布将推出基于自研模式的性价比更高AI ASIC芯片的动向，也正显现出对英伟达 在全球AI基建最核心领域——AI芯片领域长期绝对主导地位构成风险的迹象。 在过去三年推动美国股市迈向超级牛市轨迹之后，在纳斯达克100指数以及标普500指数中占据高权重的英伟达股价在 2026年迄今仅上涨约2%，主要因Anthropic一系列AI代理产品引发的"AI末日叙事"重创软件股以及高估值的科技巨头 们，加之超大规模云厂商们加速自研性价比更高的替代AI ASIC芯片(比如TPU)并推动多供应商策略，叠加AMD等竞争 加剧。下图为英伟达 ...

约 23% 的投资级受访者将 AI 泡沫风险视为首要担忧，高于去年 12 月美银上一次调查中的 9%。 IT之家 2 月 25 日消息，据彭博社报道，美国银行（Bank of America）对其客户开展的一项调查显示，"AI 泡沫"有史以来首次成为信贷投资者最担忧的问 题。 包括巴纳比 · 马丁在内的美银策略师在当地时间周二的一份报告中写道："很少有人担心地缘政治或央行政策失误。" 调查显示，对 AI 企业投资与估值可能不可持续飙升的担忧，已超过"信贷泡沫"成为头号顾虑。 在 2025 年，市场最担心的风险曾是贸易紧张局势与全球经济衰退。 投资级投资者将今年超大规模云服务商的债券发行预期上调至 2850 亿美元（IT之家注：现汇率约合 1.96 万亿元人民币），较去年 12 月调查时的 2100 亿美 元预期"大幅跃升"。 策略师指出："不过，投资者对未来最终的科技颠覆性反而更为乐观：仅 10% 的人表示，AI 导致企业被淘汰是他们的主要担忧。" 他们同时表示，基金资金流入是决定信贷利差水平的主要因素，足以抵消由 AI 风险引发的债券走弱。 本次调查共有 54 家美银的高评级及高收益客户参与，其中包括保险公 ...

分析师 1: 各位投资朋友们，大家晚上好。欢迎收听我们开门红系列的第二期，AI 革命和泡沫分析框 架。这一期，我们核心想讨论的是本轮的 AI 泡沫到了什么样的阶段。我们给出的清晰的 对照是类似于科网泡沫的 1998 年，又是泡沫加速期，没有到泡沫破灭期。在这个时候， 我们坚定，依然对 AI 整个的科技革命抱有很大的信心。那么接下来，我们通过我们策略 的框架。包括对比本轮 AI 跟 2000 年的科网泡沫做各全方位的一个对比，然后找出里面的 相似点和不同点。 以及找出我们现在所处的，面临的问题，我们都做一一的这个解答。那么第一个，我们去 判断一个事物，特别是一个金融资产是否出现泡沫？需要一套相对完整的一个框架去界定 那么我们会从宏观层面，是不是有一个大的，就是从宽松环境，让一切的金融资产都进行 重估，或者是估值给予。过分的一种溢价，是不是有一种巨大的叙事，能够持续的催化， 这是从宏观产业层面，宏观层面。那么第二个，从产业层面上。那么该项事物是否具备一 定的故事性？ 能不能吸引很多投资者去抱团它？它的商业模式未来是否能盈利？应用场景是否是我们熟 悉的？特别是能承载很大的资金容量的。那未来竞争格局是否出现了裂缝？这 ...

Core Insights - The global investment wave in AI is reshaping the technology industry and capital markets, characterized by significant capital accumulation since 2008, driven by large models, computing infrastructure, and data center construction [1] - The current AI investment cycle is marked by larger scales, faster paces, and shorter depreciation cycles compared to traditional tech cycles, creating a feedback loop that may lead to systemic risks [1] - The AI industry is experiencing a dual-track development between profit potential and cost realities, leading to market fluctuations between prosperity and bubbles [1] AI Investment Historical Progression - The early exploration phase (1950s-1980s) focused on academic research with limited investment, primarily funded by government grants [2] - The AI winter (1980s-1990s) saw a significant reduction in investment due to unmet market expectations and technological limitations [2] - The revival phase (2000s-2010s) was driven by the internet and big data, leading to renewed investment interest, particularly in data-driven algorithms [3] - The rapid development of generative AI since 2021 has sparked a new investment frenzy, with significant stock price increases for major companies like NVIDIA (up 964%) and Google (up 211%) [4] Industry Structure and Participants - The AI industry is advancing across three levels: infrastructure, platforms, and applications, with various stakeholders driving capital flow and technology implementation [5] - Major tech companies and cloud providers are the primary drivers of infrastructure and platform capabilities, while smaller cloud service providers and private equity are facilitating access to AI services for SMEs [7] - The financing structure for AI infrastructure is becoming more diversified, involving private credit and various forms of debt financing, which introduces complexities in risk management [8] Financing Forms and Cycle Characteristics - AI hardware, particularly GPUs and AI-optimized servers, has a short update cycle, leading to intensive capital expenditures and rapid depreciation [10] - In large AI data center projects, GPUs account for approximately 40-50% of total capital expenditures, significantly impacting financial pressures [10] Similarities and Differences with the Dot-Com Bubble - The current AI investment trend shares similarities with the 1999 internet bubble, including market enthusiasm and overvaluation of companies [11] - However, the technological foundation of AI is more robust, with established applications across various industries, unlike the immature internet technologies of the late 1990s [12] - The AI investment landscape is more diverse, involving various financing methods and a stronger connection to global infrastructure, which provides long-term value [12] Potential for AI Bubble and Transmission Paths - The potential for an AI bubble to burst is linked to valuation logic, macroeconomic policies, and global capital flows, with a likelihood of gradual structural adjustments rather than a sudden collapse [15] - Key triggers for a potential bubble burst include slower-than-expected commercialization of AI models and rising refinancing costs due to tightening monetary policies [16] Cross-Border Risk Transmission - The global nature of AI investments means that market adjustments could have cross-border impacts, particularly in emerging markets reliant on foreign currency financing [18] - Macroeconomic policies from major central banks will significantly influence the risk landscape, affecting debt burdens and risk premiums across the AI investment spectrum [19]

Core Insights - Google executives, including CEO Sundar Pichai, emphasized that the current AI wave is akin to a "10 times faster industrial revolution" during an AI summit in India, addressing concerns over massive capital expenditures and AI investment returns [3][5][6] Group 1: AI Investment and Business Growth - Pichai revealed that Google's cloud business backlog has doubled year-over-year, reaching $240 billion, indicating significant return potential from AI investments [6] - The current AI investment is compared to historical infrastructure projects like the U.S. railway and highway systems, which are expected to drive substantial growth and value [5][6] Group 2: AGI Development Timeline - Demis Hassabis, CEO of Google DeepMind, set a timeline for Artificial General Intelligence (AGI) development, estimating it will take at least 5 to 10 years to achieve the necessary cognitive capabilities [7] Group 3: Employment and Economic Impact - James Manyika introduced a framework distinguishing between "tasks" and "jobs," suggesting that while some jobs may decrease, many will grow or change due to technological advancements [7] - Manyika noted that AI provides unprecedented capabilities to small businesses, enabling them to build technical systems without needing to be tech experts [7] Group 4: Strategic Positioning in India - Pichai redefined India's role from merely a large user market to a "full-stack player" in the AI field, highlighting the potential for comprehensive growth in AI infrastructure, applications, and innovation [8]

Core Insights - Google executives, including CEO Sundar Pichai, emphasized that the current AI wave is akin to a "10x faster industrial revolution" during the AI summit in India, addressing concerns about massive capital expenditures and AI investment returns [3][5][26] - Pichai revealed that Google's cloud business backlog has doubled year-over-year, reaching $240 billion, indicating significant growth potential and justifying ongoing investments in AI [5][26] Group 1: AI Investment and Economic Impact - Pichai compared current AI investments to historical infrastructure projects like the U.S. railway system, highlighting their potential for high leverage and substantial growth [5][26] - The executives discussed the importance of investing in foundational elements such as research and infrastructure to ensure AI benefits reach the general population, including farmers and students [17][26] Group 2: AGI Development Timeline - Demis Hassabis, CEO of Google DeepMind, set a timeline of at least 5 to 10 years for achieving Artificial General Intelligence (AGI), emphasizing the need for systems to exhibit human-like cognitive abilities [6][7][29] - Hassabis noted that over 3 million researchers globally are using AlphaFold, with more than 200,000 in India, showcasing AI's impact on scientific discovery [21][29] Group 3: Employment and Task Transformation - James Manyika introduced a framework separating "tasks" from "jobs," suggesting that while some jobs may decline, many will grow or transform due to AI [8][24] - Manyika highlighted the potential for AI to empower small and medium enterprises (SMEs) by enabling them to leverage technology without needing to be tech experts [33] Group 4: India's Strategic Positioning - Pichai redefined India's role in the AI landscape from merely a large user market to a "full-stack player," recognizing its potential in AI infrastructure and innovation [9][16] - The executives expressed optimism about India's unique advantages in AI, driven by a vibrant developer ecosystem and local AI model development [37]

Core Viewpoint - A record number of investors believe corporate spending is excessive, with 35% warning of over-investment, the highest level in two decades [1] Group 1: Investor Sentiment - Investor bullish sentiment has reached its highest level since June 2021 [1] - Despite the optimism, a significant portion of investors are concerned about over-investment in corporations [1] Group 2: Capital Expenditure - Capital expenditures are expected to reach record levels this year, with the four major U.S. tech companies projected to spend approximately $650 billion by 2026 [1]

这项调查覆盖 162 名管理着4400 亿美元资产的基金经理。结果显示，现金持仓比例从 1 月份创纪录低 点的 3.2% 升至 3.4%；投资者仍大幅超配大宗商品与股票，同时低配债券。 宏观经济乐观情绪进一步升温，预期全球将进入 "繁荣期" 的比例升至 2022 年 2 月以来最高水平，企业 盈利增长预期超过 10%，为 2021 年以来最强。 但创纪录比例的受访者认为，企业支出过于激进，投资总监们目前更倾向于改善资产负债表，而非扩大 资本开支。 美国银行月度基金经理调查周二显示，尽管市场情绪仍处于 "极度乐观"状态、资产进一步上涨难度加 大，但全球投资者愈发担忧企业正在过度投资 。 这项调查覆盖 162 名管理着4400 亿美元资产的基金经理。结果显示，现金持仓比例从 1 月份创纪录低 点的 3.2% 升至 3.4%；投资者仍大幅超配大宗商品与股票，同时低配债券。 宏观经济乐观情绪进一步升温，预期全球将进入 "繁荣期" 的比例升至 2022 年 2 月以来最高水平，企业 盈利增长预期超过 10%，为 2021 年以来最强。 但创纪录比例的受访者认为，企业支出过于激进，投资总监们目前更倾向于改善资产负债表，而 ...

Group 1 - The article discusses the impact of Trump's imposition of tariffs exceeding 160% on Chinese graphite products, which was intended to pressure China but ultimately harmed U.S. industries, particularly Tesla [1][9][22] - The U.S. market experienced a significant downturn, with the Dow Jones dropping 669 points and the Nasdaq falling by 2.03%, attributed to fears surrounding the AI bubble and ambiguous signals from the Federal Reserve [3][24] - The tariffs imposed by the U.S. Department of Commerce resulted in anti-dumping duties ranging from 93.5% to 102.72%, along with additional countervailing duties, leading to a total tax rate that far exceeded the value of the goods [9][11] Group 2 - The U.S. is heavily reliant on Chinese graphite, importing nearly 180,000 tons annually, with 59% dependency on natural graphite and 68% on synthetic graphite, indicating a lack of domestic alternatives [20][22] - The tariffs created a paradox where U.S. companies, like Tesla, faced skyrocketing raw material costs or potential production halts due to the absence of local supply chains, leading to a significant drop in Tesla's stock price [16][22] - The article highlights China's dominance in the graphite market, producing 127,000 tons in 2024, which accounted for 78% of global production, and controlling 90% of battery-grade refining capacity, making it difficult for the U.S. to establish a competitive supply chain [26][28][30]