到时候进入5 4 3现在Harry我们等于还等个5到10秒开始开始好 大家好欢迎来到每周一的大摩宏观策略谈上周因为我们好多同事都是在机场去新加坡的路上出了一些技术故障这周回到办公室跟大家正好商量一下全球市场好像也是多事之秋出现了巨大的震荡在上个礼拜我们但是还是给日后的全球互联网革命带来的生产力的进步奠定了基础 所以这个是对AI是否革命从市场的角度和从经济的角度看到的不一样之处不过我觉得对中国这块的担忧会小一点因为中国走出了一个独特的路径你比如说中国的这些我们说的大厂未来两年投资额大概只有美国的十分之一这里面部分原因是中国在 基础设施比如说发电、能源、数据中心以及人才以及数据这三个领域成本要比美国低得多所以虽然我们也得投芯片但是在另外三个领域可以补中国的短板那就是基础设施我们是过剩的比美国便宜人才中国也是AI方面的人才比较多也比美国便宜最后就是数据中国从企业到老百姓拥抱这些super app各种各样的海量数据也比美国便宜 这三大优势有助于降低中国AI投资的成本还不需要像美国这样大干快上 所以本质上现在两国的路径是不一样的美国还是重资产豪赌要实现AGI就是对高精尖的人工智能中国还是轻量化主要关注的是铺开产业生态这 ...

8 月以来的科技股回调，让笼罩美股的 AI 狂热开始降温。当英伟达 4 万亿美元市值超过欧洲前 20 大公司总和，当标普 500 席勒市盈率攀升至 40.95 倍、 直逼 2000 年互联网泡沫时的 44.19 倍，"AI 泡沫" 的担忧已不再是危言耸听。作为投资者，我们必须穿透市值狂欢的表象，看清高估值、高集中度与高杠 杆下的三重风险。 市场结构的失衡已创下历史纪录。当前美股市值前十大公司中，英伟达、微软等 AI 科技股占据 8 席，而 1999 年互联网泡沫巅峰时仅 4 家科技公司入 围。这种极致集中直接体现在指数权重上：7 家科技巨头贡献纳斯达克 54%、标普 500 36% 的权重，一旦龙头回调，整个市场将剧烈震荡。更值得警惕的 是估值水位 —— 席勒市盈率超过 35 倍时，标普 500 未来 1 年至 10 年回报均为负值，这一历史规律让 "大空头" 迈克尔・伯里果断做空英伟达，软银也套 现 58.3 亿美元英伟达股票兑现浮盈。 资本狂欢的隐忧正在累积。为抢占 AI 赛道，科技巨头掀起资本开支竞赛：微软三季度资本支出 349 亿美元，同比增速飙升至 74%；Meta 单季投入 193.7 亿美元，全 ...

Core Insights - The future of AI may hinge on understanding the evolutionary codes of the human brain, as highlighted by Yann LeCun's departure from Meta to focus on "World Models" [1] - Fei-Fei Li emphasizes that the advancement of AI should pivot from merely expanding model parameters to embedding "Spatial Intelligence," a fundamental cognitive ability that humans possess from infancy [1][3] - The launch of Marble by World Labs, which utilizes multimodal world models to create persistent 3D digital twin spaces, marks a significant step towards achieving spatial intelligence in AI [1] Group 1: AI Development Perspectives - Yann LeCun's vision diverges from Meta's focus on large language models (LLMs), arguing that LLMs cannot replicate human reasoning capabilities [3] - LLMs are constrained by data quality and scale, leading to cognitive limitations that hinder their ability to model the physical world and perform dynamic causal reasoning [3][4] - The reliance on text data restricts AI's ability to break free from "symbolic cages," necessitating a shift towards a structured understanding of the world for true AI evolution [4] Group 2: World Models vs. Large Language Models - World models are seen as a solution to the fundamental limitations of LLMs, focusing on high-dimensional perceptual data to model the physical world directly [4][5] - The key characteristics of world models include internal representation and prediction, physical cognition, and counterfactual reasoning capabilities [11] - A complete world model consists of state representation, dynamic models, and decision-making models, enabling AI to simulate and plan actions in a virtual environment [12][13] Group 3: Industry Trends and Innovations - Recent advancements in world models have been made by major tech companies, with Google DeepMind's Genie series and Meta's Code World Model leading the charge [16] - The concept of "physical AI" is gaining traction, with Nvidia's CEO asserting that the next growth phase will stem from these new models, which will revolutionize robotics [16] - The application of world models is already influencing various sectors, including autonomous driving and robotics, as companies like Tesla integrate these models for real-world learning and validation [17] Group 4: Challenges and Future Directions - The development of world models faces technical challenges, including the need for extensive multimodal data and the lack of standardized training datasets [20] - Cognitive challenges arise from the complexity of decision-making processes within world models, raising concerns about transparency and alignment with human values [20][21] - Despite the challenges, the global competition in the world model space is intensifying, with the potential to redefine industries and enhance human-AI collaboration [21][22]

Investment Rating - The report maintains a positive outlook on the AI industry, indicating that the AI bubble is still in its early stages, closer to the year 1997 of the internet era rather than 1999 [3]. Core Insights - The report addresses key questions regarding the potential risks of a bubble in the US AI industry, methods to measure the extent of the AI bubble, and how these indicators compare to the internet era [3]. - Five dimensions are used to monitor the AI bubble's degree, including Capex/GDP, Capex/revenue, revenue growth rate, valuation, and funding quality [3]. - The AI industry is experiencing a shift from a "cash flow battle" to a "financing battle," with increased competition and a focus on efficiency [5]. Summary by Sections Five Dimensions to Monitor AI Bubble Degree - **Capex/GDP**: Approaching or exceeding levels seen during the internet bubble, with AI technology's adoption and its impact on GDP growth occurring at a faster pace than in the past [3]. - **Capex/Revenue**: High Capex relative to AI-related revenue, but still manageable compared to free cash flow [3]. - **Revenue Growth Rate**: AI-related revenue growth is on par with Capex growth, with large AI tech companies showing stronger financial health than their internet bubble counterparts [3]. - **Valuation**: Valuations are nearing internet bubble levels, but strong profit support and high market concentration among tech giants enhance their market influence [3]. - **Funding Quality**: Remains healthy, although there are concerns that funding quality may decline due to rising interest rates and the influence of new players in cloud computing [3]. Credit Cycle Positioning - A new round of the US corporate credit cycle has begun, primarily driven by the AI industry, while the US consumer credit cycle is still in a downward trend [5][9]. AI Industry Changes - The AI industry is facing intensified competition, with a shift in focus from cash flow to financing, leading to a decline in revenue quality due to cyclical trading [5]. AI Industry Core Issues - The primary challenge in the AI industry is enhancing efficiency, with limited new productivity and a reliance on existing ToB clients for orders [5].

Group 1 - The global economy shows significant resilience despite rising tariffs, geopolitical tensions, and fiscal pressures, driven by AI transformation, flexible trade adjustments, and moderate monetary and fiscal policies [9] - China aims for resilient, stable, and inclusive economic growth from 2026 to 2030, focusing on self-reliance in technology, particularly in semiconductors and AI, while facing challenges such as demand fluctuations and a declining real estate market [12] - Japan's economic growth is expected to slow due to tariff impacts, but it can avoid recession, with core CPI inflation projected to drop below 2% by 2026 [15] Group 2 - The Asian economy (excluding Japan) presents mixed growth prospects, with a strong performance in the tech sector but challenges in non-tech sectors due to high tariffs on labor-intensive industries [19] - The Chinese internet sector's focus will remain on AI strategies and competition in the instant retail space, with expectations of reduced competitive intensity in the fourth quarter [22][23] - China is increasingly developing a self-sufficient AI supply chain, with significant investments in AI infrastructure and a focus on enhancing operational efficiency through large language models [26] Group 3 - Market attention is shifting towards fiscal stimulus policies, inflation trends, and real estate market support, with stable performance in the onshore stock market and steel-related commodities [30] - The A-share market's future growth will be driven by policy support, liquidity, and industrial upgrades, despite high valuations and the need for confirmed improvements in fundamentals [35]

野村最新观点来了。 野村亚洲（除日本外）及印度首席经济学家Sonal Varma则认为，从中期来看，亚洲（除日本外）地区在科技 板块持续表现优异的同时，正面临着充满挑战的外部环境。预计科技与非科技板块的显著分化将持续下去。科 技板块仍保持韧性，这得益于人工智能需求的持续旺盛以及存储芯片超级周期的支撑。然而，非科技板块仍然 疲软，原因在于人工智能相关的 溢出效应 有限，且美国对劳动密集型行业加征了更高关税。 11月12日，"2025年野村中国投资年会"在深圳开幕，吸引了超过40家企业和400多位投资者参与。 "我们见证中国取得了令人瞩目的成就，最新公布的'十五五'规划建议也让我们备受鼓舞。"野村国际（香港） 有限公司社长川前顺平表示，中国将专注于在2026—2030年实现有韧性、平稳和包容的经济增长。政府将继续 通过大力投资和产业政策推进科技自立自强，特别是在半导体和人工智能领域。 野村东方国际证券研究部总经理、首席策略分析师高挺认为，政策支持、流动性和产业升级是驱动A股后续上 涨的核心动力。"AI、自主可控和高附加值出海将是明年A股的核心主线。"高挺说。 全球经济仍表现出显著韧性 川前顺平在致辞时表示，野村进 ...

Core Insights - Nomura emphasizes China's focus on resilient, stable, and inclusive economic growth from 2026 to 2030, driven by significant investments and industrial policies, particularly in semiconductors and artificial intelligence [1][5] - The core drivers for the A-share market's future growth are policy support, liquidity, and industrial upgrades, with AI and high-value exports identified as key themes for the upcoming year [4][1] Group 1: Global Economic Resilience - Despite rising tariffs, geopolitical tensions, and fiscal pressures, the global economy shows significant resilience, supported by the AI revolution, flexible trade adjustments, and moderate monetary and fiscal policies [2] - Economic inequality is becoming more pronounced, with low-income families and small businesses struggling, posing challenges for policymakers to maintain global economic stability [2] Group 2: Asian Economic Outlook - The technology sector in Asia (excluding Japan) is expected to perform well, driven by strong demand for AI and a supercycle in storage chips, while non-tech sectors face challenges due to limited spillover effects from AI and increased tariffs on labor-intensive industries [3] - The region's solid economic fundamentals and new growth drivers, such as supply chain shifts and increased AI investment, position India, the Philippines, and Malaysia as some of the fastest-growing economies in the next decade [3] Group 3: A-share Market Dynamics - A-share valuations have expanded over the past year but remain reasonable when considering the equity risk premium in a declining risk-free rate environment [4] - The "14th Five-Year Plan" emphasizes long-term productivity upgrades and technological transformation, which will catalyze structural market trends, although improvements in earnings fundamentals are still needed [4][7] Group 4: AI and Technology Sector Developments - The trend towards a self-sufficient AI supply chain in China is becoming more evident, with significant investments in AI infrastructure and a focus on developing large language models and generative AI applications [8] - The competitive landscape in the instant retail sector is expected to stabilize, potentially alleviating losses for companies expanding in this area [8] Group 5: Entertainment Sector Insights - The online entertainment sector, particularly online gaming and music services, is expected to remain resilient, while long-form video content may continue to lag due to shifts in consumer preferences towards short videos [9]

Core Insights - Meta is undergoing significant changes in its AI strategy, with key personnel departures including Yann LeCun, the Chief AI Scientist, who plans to start a new AI startup focused on "world models" [1][3] - Mark Zuckerberg is shifting the company's focus from foundational research to practical applications, as evidenced by the hiring of Alexandr Wang to lead the new Meta Superintelligence Labs with a substantial investment of $14.3 billion [1][2] - Internal policies at Meta have restricted academic freedom within the FAIR lab, leading to dissatisfaction among members and contributing to LeCun's potential departure [2][3] Group 1 - Yann LeCun's departure is part of a broader trend of leadership changes in Meta's AI division, which is facing challenges from competitors like OpenAI and Google [1][3] - The company has initiated layoffs affecting around 600 employees, particularly in the FAIR lab, while the newly formed TBD Lab remains unaffected [3] - LeCun's vision for AI emphasizes "world models" that understand the physical world through video and spatial data, contrasting with Meta's current focus on large language models (LLMs) [3][4] Group 2 - Meta's strategic pivot includes a new policy requiring additional scrutiny of research outputs from the FAIR lab, which has been perceived as a limitation on academic freedom [2] - Competitors like Google DeepMind and NVIDIA are also investing in "world models," indicating a growing interest in this area within the AI industry [4] - Stanford's Fei-Fei Li has raised approximately $230 million for her startup World Labs, which aims to enhance AI's "spatial intelligence," further highlighting the competitive landscape [4]

Core Viewpoint - The ongoing debate about the "AI bubble" is intensifying, with prominent figures in the AI field expressing differing opinions on the sustainability and future of AI investments [1][3][8]. Group 1: AI Industry Perspectives - Jensen Huang argues that unlike the internet bubble of the early 2000s, where much infrastructure was unused, today's GPU resources are fully utilized, indicating a robust foundation for a new trillion-dollar industry [1]. - Fei-Fei Li emphasizes that AI is still a nascent field with vast unexplored areas, particularly in "spatial intelligence" beyond language [1]. - Yann LeCun expresses skepticism about the current large language models achieving human-level intelligence, suggesting that fundamental breakthroughs are necessary [1]. Group 2: Market Reactions - Following Meta's Q3 2025 earnings report, its stock plummeted over 11%, primarily due to investor fears regarding its substantial AI capital expenditures [2]. - The stock market's volatility has amplified discussions around the "AI bubble," with comparisons being drawn to the 2000 internet bubble [3]. Group 3: Financial Metrics and Valuations - OpenAI's valuation has reportedly soared to $1 trillion, while its projected annual revenue is only $13 billion, raising concerns about a valuation-revenue imbalance reminiscent of the dot-com era [5]. - NVIDIA's CEO revealed that the company has accumulated $500 billion in orders for its upcoming chips, pushing its market capitalization to over $5 trillion [5]. - Tesla's ambitious compensation plan for Elon Musk requires the company's market value to increase to $8.5 trillion, with a target annual profit of $400 billion, highlighting the aggressive growth expectations in the AI sector [5]. Group 4: Institutional Concerns - Michael Burry has taken significant short positions against NVIDIA and Palantir, reflecting institutional concerns about excessive AI investments [6][7]. - The divergence in opinions about the AI bubble is stark, with some experts warning of an impending collapse while others believe the revolution is just beginning [8]. Group 5: Long-term Viability and Infrastructure - Amazon plans to increase its capital expenditures to approximately $125 billion in 2025 for AI-related infrastructure, despite announcing significant layoffs, indicating a complex relationship between AI investment and workforce management [11]. - Bezos suggests that even if an AI bubble bursts, the infrastructure built will remain valuable for future advancements in artificial general intelligence (AGI) [11]. - The timeline for achieving AGI remains uncertain, posing a challenge for many companies to survive until that potential future [12].

Core Insights - The Long Triangle region is projected to achieve a record high in foreign trade scale in 2024, with a total import and export trade volume of 16,014.8 billion RMB, reflecting a growth of 5.6% compared to the previous year [1] - The region's foreign trade accounts for 36.5% of the national total, an increase of 0.2 percentage points from the previous year [1] Group 1: Trade and Economic Performance - The Long Triangle region's overall business operation is expected to remain stable and show progress, contributing positively to economic recovery [1] - The total retail sales of consumer goods in the Long Triangle region is projected to reach 12,354.2 billion RMB, marking a growth of 3.3% year-on-year [1] - The per capita retail sales of consumer goods in the region is 51,910 RMB, which is 17,588 RMB higher than the national average of 34,322 RMB [1] Group 2: Business Development Characteristics - The Long Triangle Business Development Report outlines eight key characteristics, including the strengthening of consumption's foundational role and the improvement of the modern commercial circulation system [2] - The region demonstrates strong resilience in foreign trade and significant results from diversified foreign investment strategies [2] - The construction of free trade zone networks is advancing at a high standard, and the achievements of national-level economic development zones are substantial [2] Group 3: Technological Integration and Future Outlook - The forum highlighted the integration of artificial intelligence and big data as new drivers and opportunities for regional business development [2] - The Shanghai Commercial Development Report (2025) indicates that the AI industry scale has surpassed 450 billion RMB, with significant applications of large language models in retail scenarios [2] - Shanghai is enhancing its position as an international consumption center through optimized commercial space layout and improved community commercial quality [2]