黄仁勋新年首场采访，谈了做CEO的秘诀

Core Insights - The article discusses the increasing demand for computing power in the AI sector, with predictions that global computing capacity needs to increase by 100 times in the coming years [3][4] - NVIDIA's CEO Jensen Huang emphasizes the necessity for significant advancements in chip performance and energy efficiency to meet this demand, indicating a shift from traditional semiconductor improvements to a more holistic approach involving entire computing systems [4][8] Group 1: AI Demand and Chip Performance - AMD and NVIDIA executives highlight the exponential growth in model sizes and inference outputs, with NVIDIA's chips achieving 10 times the throughput of previous generations [3][4] - Huang mentions that the performance improvements are becoming increasingly difficult to achieve solely through chip manufacturing processes, necessitating a focus on system-level optimizations [4][8] - The introduction of new architectures like Blackwell and Rubin aims to enhance throughput while reducing costs, with Huang stating that each generation should ideally see a 10-fold increase in throughput and a 10-fold decrease in costs [6][8] Group 2: Energy Efficiency and System Design - Huang points out that energy efficiency is critical for supporting AI development, with a need for sustainable energy sources to power the growing demand [6][7] - The concept of a new "Moore's Law" is introduced, where improved energy efficiency leads to higher revenue through increased token generation without additional power consumption [7][8] - NVIDIA is focusing on collaborative designs that encompass the entire data center, including CPUs, GPUs, and storage systems, to ensure scalability and efficiency [9][10] Group 3: Storage and Ecosystem Investments - Huang discusses the revolutionary changes needed in storage systems to accommodate AI workloads, indicating that NVIDIA may become a leading storage company through partnerships rather than direct manufacturing [11][14] - The company is actively investing in its supply chain, including memory suppliers and ecosystem partners, to ensure a robust infrastructure for AI applications [14][15] - NVIDIA's strategy includes investing in both foundational technologies and emerging startups to enhance its ecosystem and maintain a competitive edge [14][15] Group 4: AI Applications and Future Outlook - The article highlights NVIDIA's expansion into various sectors, including autonomous driving and robotics, with expectations for significant advancements in these areas within the next few years [18][19] - Huang predicts that robots will achieve human-like capabilities this year, addressing labor shortages and driving economic growth through increased automation [20] - The potential for AI to transform gaming is also discussed, with expectations for more realistic character interactions and enhanced gaming experiences [19][20]