新智元报道 编辑：艾伦 桃子 【新智元导读】马斯克宣布了一个疯狂的计划，将在5年内实现5000万张H100的算力，这是什么概念？这将为人类带来怎样的影响？ASI能否在勇敢者的 孤注一掷下现身？ 世界首富马斯克，这次宣布决定All in AI了。 5年内实现5000万张H100的算力。 要知道，他已经有了全世界最强的Colossus超算集群，AI算力等价于约20万张H100。 他究竟想用这么多GPU做些什么呢？ 十万亿元能创造出怎样的奇迹 目前，每张H100的批发价高达2万美元。 5000万张H100，光是GPU，成本就将高达1万亿美元。 要搭建目前的最先进的超算集群，目前GPU成本只占约50%。 也就是说，最终的成本将超过2万亿美元（逾14万亿元人民币）。 2万亿美元是什么概念？ 美国去年的军费总支出约9970亿美元，而这已经占到了全球军费支出的37%。 这意味着，AI已经成为与传统的军备竞赛分庭抗礼的全新关键领域。 马斯克的身价约4000亿美元。 特斯拉的市值约1.1万亿美元。 加上SpaceX、X和xAI，马斯克旗下的公司市值约1.6万亿美元。 一旦摩尔定律在未来5年不能在GPU上有效，成本将无法产生指数 ...

Core Insights - The recent competition between OpenAI and Meta has escalated into a talent acquisition battle, with OpenAI responding to Meta's aggressive hiring by bringing in top engineers from Tesla, xAI, and Meta itself [1][2]. Group 1: Talent Acquisition - OpenAI has successfully recruited four key engineers for its Scaling team, including Uday Ruddarraju and Mike Dalton, who previously led the development of a powerful AI infrastructure at xAI [2][4]. - David Lau, a former Tesla software engineering vice president, and Angela Fan, a former Meta AI researcher, have also joined OpenAI, enhancing its capabilities in software engineering and model training [2][4]. Group 2: Importance of Scaling - The Scaling team at OpenAI is crucial for building and maintaining the underlying infrastructure that supports AI advancements, including data centers and training platforms [3]. - OpenAI's Stargate project, which aims to invest $500 billion in AI infrastructure, highlights the significance of foundational systems in achieving breakthroughs in artificial general intelligence (AGI) [3]. Group 3: Competitive Landscape - Meta recently established the Meta Superintelligence Labs, hiring 11 core technical personnel from various AI labs, including OpenAI, which prompted OpenAI's swift counteraction [5]. - The ongoing rivalry between OpenAI and Meta reflects the rapid pace of technological advancements and talent movements within the AI sector [5][6].

马斯克旗下人工智能公司xAI正式启动一项总额达3亿美元的股票出售计划。此次交易对该公司的整体 估值定为1130亿美元。知情人士透露，这项股票出售将采用二级市场交易方式进行。 该交易允许xAI内部员工向新的投资者出售其持有股份。据悉，此次股票出售完成后，公司还计划进行 更大规模的融资活动。届时xAI将直接向外部投资者发行新股。 今年3月，马斯克完成了xAI与社交媒体平台X的合并交易。合并后的新公司总估值确定为1130亿美元。 其中xAI部分估值为800亿美元，X平台估值为330亿美元。此次股票出售交易确认了当时达成的估值水 平。 本文源自：金融界 作者：观察君 xAI成立于2023年，旨在挑战目前人工智能领域的领先企业OpenAI。该公司已经推出聊天机器人产品 Grok。与此同时，xAI还建设了名为"Colossus"的超级计算集群。这一设施被认为是美国规模最大的人 工智能数据中心项目之一。 根据马斯克的规划，xAI与X合并后能够实现资源共享。两家公司可以共同利用模型技术、计算能力、 分发渠道以及人才资源。人工智能开发者能够更好地利用社交媒体平台数据来训练模型，并深度挖掘用 户群体。 作为对比，OpenAI在今 ...

Core Insights - The demand for electricity from AI data centers is rapidly approaching the limits of grid capacity, as highlighted by a study from Georgetown University, Epoch AI, and RAND Corporation, which analyzed over 500 AI data center projects from 2019 to the present [1][2]. Group 1: Infrastructure Challenges - The study emphasizes the challenges of building infrastructure to support AI technology development over the next decade, with OpenAI and partners planning to raise up to $500 billion to establish a network of AI data centers [2]. - Major tech companies like Microsoft, Google, and AWS have committed to investing hundreds of millions of dollars this year to expand their data center capacities [2]. Group 2: Power Consumption and Costs - The Colossus AI data center, estimated to cost around $7 billion, is projected to see hardware costs increase by 1.9 times annually from 2019 to 2025, while its power demand is expected to double each year during the same period [2]. - By June 2030, leading AI data centers may be equipped with 2 million AI chips, costing $200 billion and requiring 9 gigawatts of power, equivalent to the output of nine nuclear reactors [6]. Group 3: Energy Efficiency and Environmental Impact - Despite significant improvements in energy efficiency, with computing performance per watt increasing by 1.34 times annually from 2019 to 2025, these advancements are insufficient to offset the growing power demands [5][6]. - A recent analysis by Wells Fargo predicts a 20% increase in data center energy consumption by 2030, which may lead to a reliance on non-renewable energy sources due to the limitations of renewable energy [6]. - AI data centers pose additional environmental threats, including high water consumption, land use, and erosion of state tax bases, with at least 10 states losing over $100 million annually in tax revenue due to excessive tax incentives [6].

人工智能数据中心对电力的需求正迅速逼近电网承载极限，这已非新发现。 乔治城大学、 Epoch AI 和兰德公司研究人员一项新研究的结论，该研究考察了 2019 年至今年全球 AI 数据中心的增长轨迹。合著者汇编并分析了超过 500 个 AI 数据中心项目的数据集，发现虽然数据中心的计算性能每年翻倍以上，电力需求和资本支出同样在成倍增长。 如果当前趋势持续下去，用于训练和运行人工智能的数据中心可能很快将容纳数百万芯片，耗资数千亿美元，并需要相当于大型城市电网的电力供应。 研究结果凸显了未来十年建设支持 AI 技术发展所需基础设施的挑战。 近期宣称全球约 10% 人口使用其 ChatGPT 平台的 OpenAI ，与软银等合作伙伴计划筹集高达 5000 亿美元资金，在美国（可能还包括其他地区）建立 AI 数据中心网络。微软、谷歌和 AWS 等其他科技巨头也已承诺，仅今年就将共同投入数亿美元扩大其数据中心规模。 富国银行最新分析预测，到 2030 年，数据中心的能耗将增长 20% 。这可能迫使依赖不稳定天气的可再生能源达到供应极限，进而刺激化石燃料等不可再 生、破坏环境的电力来源加速扩张。 人工智能数据中心还带来其 ...