巨头砸钱6500亿加剧担忧，黄仁勋发声“灭火”

Core Viewpoint - The surge in AI infrastructure capital expenditure in the tech industry is deemed reasonable, appropriate, and sustainable, driven by extremely high demand for computing power, as stated by Jensen Huang [1][5]. Group 1: AI Infrastructure Investment - Huang indicated that the capital expenditure from key clients like Meta, Amazon, Google, and Microsoft is projected to reach approximately $650 billion by 2026, representing a 60% increase from 2025 [3][7]. - This spending level significantly exceeds the GDP of many medium-sized economies, with most funds allocated for purchasing NVIDIA chips [3][7]. - The anticipated capital expenditures for these companies will be close to or exceed their total spending over the past three years, setting records for individual company annual capital expenditures [7]. Group 2: Market Reactions and Concerns - Recent financial reports and guidance have led to severe sell-offs in tech stocks, with a cumulative market value loss of about $1.35 trillion among major tech firms [3][10]. - Despite some companies like Meta and Alphabet seeing stock price increases, others like Amazon and Microsoft faced significant declines, resulting in a total market value drop exceeding $950 billion since the latest earnings reports [10]. - Concerns about investment efficiency and potential overcapacity have created a negative sentiment in the market, with analysts highlighting the structural issues surrounding the massive capital expenditures required for AI development [11][12]. Group 3: Profitability and Future Outlook - Huang emphasized that as long as companies continue to pay for AI, they will generate profits, leading to exponential growth in revenue [6][5]. - AI companies are already becoming profitable, with NVIDIA's clients leveraging AI to enhance their operations, such as Meta transitioning its recommendation systems to generative AI [6][5]. - The ongoing demand for AI computing power is reflected in the rental of all previously sold GPUs, indicating a robust and sustained need for AI infrastructure [6][5].