Core Viewpoint - Gold is projected to be the biggest winner among commodities and safe-haven assets in 2025, with a significant increase in its price, while traditional safe-haven assets have underperformed compared to the S&P 500 index [1][4]. Group 1: Precious Metals Performance - Precious metals, including gold, silver, and platinum, have collectively surged in 2025, outperforming the MSCI global stock index, with gold prices rising over 70%, marking the largest increase since the 1979 oil crisis [1][4]. - Silver and platinum have also seen their prices more than double, indicating a strong performance across the precious metals sector [1][4]. Group 2: Market Dynamics - The unexpected resilience of the global economy, coupled with rising geopolitical tensions and concerns over an "AI bubble," has contributed to the extreme pricing dynamics where traditional safe-haven assets have failed [1][7]. - The defense and military sector has emerged as an alternative safe haven, with U.S. aerospace and defense stocks rising by 36% and European counterparts increasing by 55% due to rearmament efforts in Europe [4][8]. Group 3: Traditional Safe-Haven Assets - Traditional safe-haven assets, such as government bonds and defensive stocks, have underperformed significantly, with global government bond prices unexpectedly declining by about 1% [15][17]. - The performance of traditional defensive sectors, including utilities and consumer staples, has lagged behind the S&P 500 index, with consumer staples being the worst-performing sector, rising only about 2% [17][18]. Group 4: Currency Performance - The Japanese yen and Swiss franc, typically viewed as safe-haven currencies, have also disappointed in 2025, with the yen declining approximately 4% against major trading partners [20][21]. - The U.S. dollar index (DXY) experienced a significant drop of 12% during periods of geopolitical turmoil, indicating a lack of safe-haven characteristics for both the dollar and yen in 2025 [23][21]. Group 5: Volatility Strategies - Volatility strategies, often used as hedging tools, have failed to deliver returns in 2025, with the VIX index reflecting lower volatility levels by year-end compared to the beginning of the year [24][26].

Group 1 - The core concern is that inflation in the U.S. may spiral out of control, exceeding the Federal Reserve's ability to manage it [1][2] - The stock market is currently overvalued, indicating a potential crash back to reality [2] - The M2 money supply has surged by $3.5 trillion over the past five years, which is a critical indicator for inflation outlook [2][3] Group 2 - The Federal Reserve has initiated a rate-cutting cycle, which may lead to an acceleration in inflation despite not fully controlling it [2][3] - The end of quantitative tightening by the Federal Reserve is expected to loosen financial conditions, potentially increasing inflationary pressures [3] - Relaxation of credit rules in early next year will enhance banks' ability to expand the money supply, further exacerbating inflation [3] Group 3 - Increased issuance of short-term government bonds by the U.S. Treasury to finance deficits will also contribute to rising money supply and inflation [3] - The technology sector, particularly driven by the AI boom, is facing significant overvaluation risks, with warnings of a potential market correction [4] - Historical parallels are drawn to the internet bubble, suggesting that AI companies may face similar challenges if growth expectations are not met [4]

Group 1: Market Overview - In 2025, precious metals emerged as the biggest winners, while traditional "safe-haven" investments performed poorly amid market turmoil, conflicts, and concerns over an AI bubble [1] - The global economy showed strong growth, with politicians advocating for loose monetary policies, leading to a decline in recession fears and a surge in AI enthusiasm, alongside escalating geopolitical tensions [1] - The commodity index performed poorly due to an oversupply of crude oil, with oil prices dropping by 20% year-on-year, currently at about half of the previous highs [1] Group 2: Defense Sector Performance - For investors concerned about global conflicts, the best investment option was the defense sector, with U.S. aerospace and defense stocks rising by 36% and European counterparts increasing by 55% as Germany and Europe accelerated military rearmament [1] Group 3: Bond and Defensive Asset Performance - Most traditional hedging tools and safe assets underperformed this year, with global "risk-free" government bond indices declining by approximately 1% and total returns slightly exceeding 6% [2] - The Bloomberg Multiverse index, which includes government, supranational, agency, and corporate bonds, saw a price increase of about 1% and total returns close to 7% [2] Group 4: Stock Market Insights - The MSCI All-Country Stock Index's performance was more than double that of government bonds, indicating a strong recovery in the stock market [4] - The S&P 500 index rose by 15% due to the boost from large tech stocks and AI themes, with growth stocks outperforming value stocks by more than double [4] - Defensive sectors like utilities, healthcare, and financials saw gains over 10%, but still lagged behind major indices, while the consumer staples sector had a meager increase of about 2% [4] Group 5: Currency Performance - Traditionally safe-haven currencies like the yen and Swiss franc underperformed, with the yen dropping approximately 4% against its major trading partners despite initial gains [7] - The Swiss franc maintained its early-year gains, becoming one of the few standout safe-haven assets alongside gold and silver [7] - The U.S. dollar index fell by 12% during the year's most turbulent months, raising questions about its status as a safe-haven asset [7] Group 6: Volatility and Investment Strategies - Strategies involving options and volatility indices failed to yield profits in 2025, with the VIX closing down 2 points from the beginning of the year [9] - The MOVE index for bond market volatility was less than two-thirds of its initial level, indicating a decline in market volatility [9] - Overall, overly cautious investment strategies did not prove profitable this year [10]

她续提到，预期明年初可能仍然由增长型股份带动，但年中若重燃通胀担忧，抗跌力会较佳。在市况波 动下，成为增长板块以外的一个避风港。 临近年底，港股交投转淡，上周日均成交续低于2,000亿港元，市场预期资金或留待明年首季才再度大 举涌入。摩根资产管理环球市场策略师周奂彤分析，有两大因素支撑，中长线继续看好内地和香港市 场，人工智能(AI )板块短期或仍有忧虑，建议可趁市况波动吸纳高息股。 她指出，关税战不确定性已延至明年第四季，近期出口数据显示内地市场变得更多元化，相关忧虑的迫 切性已有所减少；内地居民存款率高企，但明年会有更多中长期存款到期，"存款搬家"或带动资金流入 内地和香港市场，利好股市。 她续指，香港多只早前上市的IPO禁售期即将届满，或令资金面短期受压，但近期有不少受欢迎的人工 智能(AI)相关股份准备上市，有机会抵消相关影响。 另一方面，周奂彤亦密切注视生物科技板块，"内地创新药在全球创新药份额已飙升至约3成，行业已非 再由欧美等成熟市场主导。"她补充，市场短期对人工智能泡沫(AI Bubble)仍有忧虑，令投资气氛受到 影响，料明年AI板块不确定性会较今年高。 ...

Group 1 - The core argument of the article is that while current AI models are becoming more powerful, the realization of Artificial General Intelligence (AGI) remains distant due to physical and resource limitations [3][22][24] - Tim Dettmers' blog post titled "Why AGI Will Not Happen" argues that due to physical constraints, meaningful superintelligence cannot be achieved [3][6][22] - The article discusses the limitations of hardware improvements and the challenges in achieving efficient computation, emphasizing that the current AI architectures are bound by physical realities [8][10][11] Group 2 - The blog highlights that the efficiency of current AI systems is far from optimal, with significant room for improvement in both training and inference processes [35][37][56] - It points out that the current models are lagging indicators of hardware development, suggesting that advancements in hardware will lead to better model performance [43][57] - The article proposes multiple pathways for enhancing AI capabilities, including better model-hardware co-design and exploring new hardware features [40][46][55] Group 3 - The article contrasts the AI development philosophies of the US and China, noting that the US focuses on achieving superintelligence while China emphasizes practical applications and productivity improvements [20][21] - It suggests that the pursuit of superintelligence may lead to difficulties, as organizations focusing solely on this goal may be outpaced by those driving practical AI applications [26][28] - The discussion includes the potential for smaller players in the AI space to innovate beyond scale, leveraging efficiency and practical applications [17][18][19]

Core Viewpoint - The article discusses the limitations of achieving Artificial General Intelligence (AGI) due to physical and resource constraints, emphasizing that scaling alone is not sufficient for significant advancements in AI [3][20][32]. Group 1: Limitations of AGI - Tim Dettmers argues that AGI will not happen because computation is fundamentally physical, and there are inherent limitations in hardware improvements and scaling laws [8][10][12]. - The article highlights that as transistor sizes shrink, while computation becomes cheaper, memory access becomes increasingly expensive, leading to inefficiencies in processing power [11][17]. - The concept of "superintelligence" is critiqued as a flawed notion, suggesting that improvements in intelligence require substantial resources, and thus, any advancements will be gradual rather than explosive [28][29][30]. Group 2: Hardware and Scaling Challenges - The article points out that GPU advancements have plateaued, with significant improvements in performance per cost ceasing around 2018, leading to diminishing returns on hardware investments [16][17]. - Scaling AI models has become increasingly costly, with the need for linear improvements requiring exponential resource investments, indicating a nearing physical limit to scaling benefits [20][22]. - The efficiency of current AI infrastructure is heavily reliant on large user bases to justify the costs of deployment, which poses risks for smaller players in the market [21][22]. Group 3: Divergent Approaches in AI Development - The article contrasts the U.S. approach of "winner-takes-all" in AI development with China's focus on practical applications and productivity enhancements, suggesting that the latter may be more sustainable in the long run [23][24]. - It emphasizes that the core value of AI lies in its utility and productivity enhancement rather than merely achieving higher model capabilities [24][25]. Group 4: Future Directions and Opportunities - Despite the challenges, the article suggests that there are still significant opportunities for improvement in AI systems through better hardware utilization and innovative model designs [39][45][67]. - It highlights the potential for advancements in training efficiency and inference optimization, indicating that current models are not yet fully optimized for existing hardware capabilities [41][43][46]. - The article concludes that the path to more capable AI systems is not singular, and multiple avenues exist for achieving substantial improvements in performance and utility [66][69].

Core Viewpoint - Michael Burry, a well-known short-seller, is seeking evidence of Nvidia's GPUs being hoarded by customers, particularly photographs, in light of doubts raised about the company's reported sales figures and data center capacity [1][3]. Group 1: Nvidia's GPU Sales and Demand - Nvidia's CEO Jensen Huang claimed that the demand for Nvidia chips remains strong, stating that the company has shipped 6 million Blackwell chips over the past four quarters, with expectations of generating $500 billion in total sales from the Blackwell and upcoming Rubin series products [1]. - An analysis on social media questions whether the reported $111 billion in data center revenue aligns with the claimed shipment volume of Blackwell chips, suggesting a potential shortfall of hundreds of thousands to millions of GPUs [1][2]. Group 2: Energy Requirements and Capacity Concerns - The operation of 6 million Blackwell chips would require between 8.5 GW to 11 GW of power, approximately equivalent to Singapore's total electricity generation capacity, while the U.S. is only expected to add about 8.5 GW of power capacity for data centers between 2024 and 2025 [2]. - This power supply is barely sufficient to match Nvidia's claimed GPU shipment volume, raising concerns about the feasibility of such high deployment levels [2][3]. Group 3: Burry's Scrutiny and Market Concerns - Burry has intensified his scrutiny of Nvidia, focusing on issues such as "circular investments" among U.S. AI companies, revenue recognition methods, and how tech giants depreciate computing equipment [3]. - He has also raised alarms about the sustainability of Nvidia's AI infrastructure spending and has warned about a potential stock market bubble in AI, referencing historical patterns of market downturns following similar wealth distribution scenarios [3].

格隆汇12月19日｜凯投宏观(Capital Economics)在一份报告中称，新兴市场股票的AI泡沫尚未破裂，且 明年可能进一步膨胀。市场经济学家Elias Hilmer称："虽然今年新兴市场股票的估值普遍上涨，但仍普 遍低于美国。"他补充称，新兴市场与美国AI股票之间的巨大估值差距预计明年不会大幅收窄。然而， 如果该AI泡沫像凯投宏观(CE)预测的那样在2027年破裂，较低的新兴市场估值不太可能提供太多缓冲。 在这种情况下，台湾和韩国将面临大幅回调。他称，如果在该泡沫破裂后全球市场出现持续回调，不断 上升的风险溢价将对亚洲以科技股为主的新兴市场构成压力。 ...

Core Insights - The article discusses the challenges and bubbles in the artificial intelligence (AI) industry, highlighting that 95% of AI projects are failing, with only 5% achieving success, according to a MIT survey [2][15] - The discussion emphasizes the need for realistic expectations, system integration, and data availability as critical factors for successful AI implementation [6][16][18] Group 1: Challenges in AI Industry - The AI industry faces three main challenges: expectation management, system integration, and data availability [6][16][18] - High expectations from business leaders, driven by media hype, lead to unrealistic goals and potential industry collapse [16][26] - System integration issues arise when AI technologies do not align with existing traditional systems, causing operational inefficiencies [17][18] - Data limitations hinder AI's ability to function effectively, as many applications rely solely on language models without sufficient diverse data [18][29] Group 2: Bubbles in AI - Two significant bubbles identified are in the computing power sector and the AI application sector, where many resources are underutilized or overly reliant on human input [8][30] - The computing power bubble is characterized by excessive investment in inference capabilities while lacking sufficient training infrastructure [29][30] - The AI application bubble is marked by a high degree of similarity among products, with many applications not achieving true AI capabilities [8][30] Group 3: Future Opportunities - Potential areas for investment include small models in specialized fields, which could be integrated to create comprehensive solutions [39][45] - The healthcare sector presents opportunities for AI, particularly in developing models that can work with limited data while ensuring privacy [39][42] - Safety and control in AI applications are crucial for future development, especially in sensitive industries like healthcare and finance [42][45]

《金融时报》周四报道，即将离任的元宇宙平台公司（Meta）人工智能首席科学家杨立昆正就新创公 司融资事宜展开初步洽谈，拟募资 5 亿欧元（约合 5.86 亿美元）。在这家人工智能公司正式成立前， 其估值预计将达到 30 亿欧元。 该报道援引知情人士的话称，杨立昆已邀请法国医疗科技初创企业纳布拉（Nabla）创始人亚历山大・ 勒布伦担任新公司首席执行官。 杨立昆在推动 Meta 的人工智能发展蓝图中发挥了关键作用。他于上月宣布，将于年底离开这家社交媒 体巨头，专注于打造一家新创企业，目标是开发新一代超级智能人工智能系统。 此前已有行业领军者警示，市场对人工智能的狂热可能已脱离商业基本面。而这家初创公司在成立前就 获得巨额融资承诺并拥有高估值，或进一步加剧外界对人工智能泡沫的担忧。 杨立昆在推动 Meta 的人工智能发展蓝图中发挥了关键作用。他于上月宣布，将于年底离开这家社交媒 体巨头，专注于打造一家新创企业，目标是开发新一代超级智能人工智能系统。 此前已有行业领军者警示，市场对人工智能的狂热可能已脱离商业基本面。而这家初创公司在成立前就 获得巨额融资承诺并拥有高估值，或进一步加剧外界对人工智能泡沫的担忧。 该新 ...