The writer is a professor emeritus at New York University and author of ‘Taming Silicon Valley: How We Can Ensure That AI Works for Us’
本文作者是纽约大学(New York University)荣誉退休教授，著有《驯服硅谷：如何确保AI为我们服务》(Taming Silicon Valley: How We Can Ensure That AI Works for Us)
Between now and 2030, US hyperscalers like Meta, Microsoft, Alphabet and Amazon are expected to spend over $5tn on compute. It is a huge bet on technology — one that has already led some tech companies to reduce buybacks and issue new debt and stock. What is it that they hope to get for their money?
从现在到2030年，Meta、微软(Microsoft)、Alphabet和亚马逊(Amazon)等美国超大规模云服务商预计将在算力上投入逾5万亿美元。这是一场对技术的豪赌——它已促使一些科技公司减少股票回购，同时发行新的债务和股票。他们究竟希望通过这些投入获得什么呢？
“Scaling compute” is AI industry terminology for spending more on data centres and the chips that go inside them — GPUs made by Nvidia, for example, TPUs in the case of Alphabet. These power the large language models used in generative systems such as chatbots like ChatGPT, Gemini and Claude. Compute is shorthand for how much computation a given system can do. More compute means adding more chips better able to compute at high speeds in parallel, allowing for the training and use (known as inference) of ever larger neural networks.
“扩展算力”(scaling compute)是AI行业的一个术语，指的是向数据中心及其所用芯片——例如英伟达(Nvidia)研发的GPU和Alphabet研发的TPU——加大投入。这些芯片为生成式系统（如ChatGPT、Gemini和Claude等聊天机器人）所用的大型语言模型(LLM)提供算力支持。“算力”一词是对给定系统计算能力的简称。增加算力意味着部署更多高速并行计算性能更强的芯片，以训练和使用（称为“推理”）越来越大的神经网络。