我们的时刻是世界历史上的一个非凡时刻。一项变革性技术正在崛起，其支持者声称它将永远改变世界。要建造它，公司需要投入一笔与人们记忆中不同的资金。新闻报道中充斥着人们对美国最大的公司正在支撑的泡沫的普遍担忧，而泡沫很快就会破灭。

上个月我拜访亚洲和中东的客户时，经常被问及人工智能泡沫的可能性，我的讨论催生了这份备忘录。我想首先提出我通常的警告：我不活跃于股票市场；我不活跃于股票市场。我只是将其视为投资者心理的最佳晴雨表。我也不是技术人员，而且我对人工智能的了解并不比大多数通才投资者多。但我会尽力而为。

泡沫最有趣的方面之一是它们的规律性，不是在时间上，而是在它们遵循的进程方面。一些看似革命性的新事物出现并逐渐进入人们的脑海。它激发了他们的想象力，令人兴奋不已。早期参与者收获巨大。那些只是袖手旁观的人会感到难以置信的嫉妒和遗憾，并出于对继续错过的恐惧而蜂拥而至。他们这样做时并不知道未来会发生什么，也不担心他们所付出的代价是否可以在可承受的风险下产生合理的回报。短期到中期内，投资者的最终结果不可避免地会带来痛苦，尽管经过足够长的时间后，投资者仍有可能取得领先。

我经历过几次泡沫，也读过其他泡沫，他们都遵循这个描述。人们可能会认为，过去的泡沫破裂时所遭受的损失会阻止下一个泡沫的形成。但这还没有发生，而且我相信它永远不会发生。记忆是短暂的，谨慎和自然的风险规避无法与依靠“众所周知”将改变世界的革命性技术致富的梦想相匹敌。

我引用了德里克·汤普森 (Derek Thompson) 11 月 4 日时事通讯中的引言，题为“人工智能可能成为 21 世纪的铁路”^圣世纪。振作起来，了解当今人工智能领域与 1860 年代铁路繁荣之间的相似之处。它对两者的逐字适用性清楚地表明了人们普遍认为马克·吐温的这句话的含义：“历史押韵”。

了解气泡

在深入讨论手头的主题之前 – 并阅读大量相关内容做好准备 –我想首先澄清一点。每个人都会问，“人工智能是否存在泡沫？”我认为即使这个问题也存在模糊性。我得出的结论是，有两种不同但相互关联的泡沫可能性需要考虑：一是公司的行为之内行业，以及投资者的行为方式关于行业。我完全没有能力判断人工智能公司的侵略行为是否合理，因此我将尝试主要关注金融界人工智能是否存在泡沫的问题。

投资分析师的主要工作——尤其是在我所订阅的所谓“价值”学派中——是（a）研究公司和其他资产并评估其内在价值的水平和前景，以及（b）根据该价值做出投资决策。分析师在中短期内遇到的大部分变化都围绕着资产价格及其与潜在价值的关系。反过来，这种关系本质上是投资者心理的结果。

市场泡沫并不是由技术或金融发展直接造成的。相反，它们是对这些发展过度乐观的结果。正如我在一月份的备忘录中所写关于泡沫手表泡沫是暂时的狂热，这些领域的发展成为美联储前主席艾伦·格林斯潘所说的“非理性繁荣”的主题。

泡沫通常围绕着新的金融发展（例如 1700 年代初期的南海公司或 2005-06 年的次级住宅抵押贷款支持证券）或技术进步（1990 年代末的光纤和 1998-2000 年的互联网）而凝聚。新颖性在其中发挥着重要作用。因为没有历史来限制想象力，所以新事物的未来似乎是无限的。被认为无限的期货可以证明远远超出过去标准的估值是合理的，从而导致资产价格在可预测的盈利能力的基础上不合理。

我最喜欢的一本对我影响很大的书中的段落很好地描述了新鲜感的作用，金融狂热简史作者：约翰·肯尼思·加尔布雷思。加尔布雷思写到了他所谓的“金融记忆的极度短暂”，并指出在金融市场中，“过去的经验，就其根本是记忆的一部分而言，被视为那些没有洞察力来欣赏当前令人难以置信的奇迹的人的原始避难所。”换句话说，历史可以限制对现在的敬畏和对未来的想象。另一方面，在没有历史的情况下，一切似乎都是可能的。

这里需要注意的关键是，新事物会激发巨大的热情，这是可以理解的，但当热情达到非理性的程度时，就会出现泡沫。谁能确定理性的边界？谁能说乐观的市场什么时候就变成了泡沫呢？这只是一个判断问题。

上个月我想到的一件事是，我最好的两次“呼吁”是在 2000 年，当时我对科技和互联网股票市场上正在发生的事情提出了警告，而在 2005-07 年，当时我提到了全球金融危机之前的世界中风险规避的缺乏以及由此导致的疯狂交易的容易性。

• 首先，在这两种情况下，我都不具备有关泡沫主题的任何专业知识：互联网和次级抵押贷款支持证券。我所做的只是对我周围发生的行为进行观察。

• 其次，我的呼吁的价值主要在于描述这种行为的愚蠢之处，而不是坚持认为它带来了泡沫。

纠结是否贴上“泡沫”标签可能会让你陷入困境并干扰正确的判断；我们只需评估周围发生的事情并就正确的行为做出推断，就可以取得很大的成就。

泡沫有什么好处？

在继续讨论人工智能以及它目前是否存在泡沫之前，我想花一点时间讨论一个从投资者的角度来看可能有点学术性的主题：泡沫的好处。您可能会发现我对这个主题的关注过多，但我这样做是因为我发现它很有趣。

11 月 5 日策略时事通讯的标题是“泡沫的好处”。在其中，本·汤普森（与德里克无关）引用了一本名为“泡沫的好处”的书繁荣：泡沫和停滞的结束。它的作者是 Byrne Hobart 和 Tobias Huber，他们提出气泡有两种：。。。

“拐点泡沫”——这是一种好的泡沫，而不是更具破坏性的“均值回归泡沫”，例如 2000 年的次级抵押贷款泡沫。

我发现这是一个有用的二分法。

• 我读过或亲眼目睹的金融时尚——南海公司、投资组合保险和次级抵押贷款支持证券——激发了基于无风险回报承诺的想象力，但没有人指望它们会代表人类的全面进步。例如，人们并不认为次级抵押贷款运动会彻底改变住房业，只是觉得支持新买家可以赚钱。霍巴特和胡贝尔将这些称为“均值回归泡沫”，大概是因为人们并不期望潜在的发展会推动世界前进。时尚只是兴衰而已。

• 另一方面，霍巴特和胡贝尔将基于技术进步的泡沫称为“拐点泡沫”，就像铁路和互联网的例子一样。在拐点驱动的泡沫之后，世界将不会恢复到之前的状态。在这样的泡沫中，“投资者认为未来将与过去发生重大不同，并相应地进行交易。”正如汤普森告诉我们的：

关于泡沫的权威书籍一直是卡洛塔·佩雷斯 (Carlota Perez) 的著作技术革命与金融资本。泡沫过去被认为是负面的东西，应该避免，特别是在佩雷斯出版她的书时。那是 2002 年，世界大部分地区因互联网泡沫破裂而陷入衰退。

佩雷斯并没有否认这种痛苦：事实上，她指出类似的事故标志着以前的革命，包括工业革命、铁路、电力和汽车。在每种情况下，泡沫并不令人遗憾，而是必要的：投机狂热促成了佩雷斯所说的“安装阶段”，必要但不一定在经济上明智的投资为“部署期”奠定了基础。向部署期转变的标志是泡沫的破裂；导致部署期得以实现的是亏损的投资。（所有强调都已添加）

这种区别对于霍巴特和胡贝尔来说非常有意义，我同意。他们说，“并非所有泡沫都会摧毁财富和价值。”有些可以被理解为技术科学进步的重要催化剂。”

但我要重申如下：“均值回归泡沫”——市场在某种新的金融奇迹的基础上飙升，然后崩溃——摧毁财富。另一方面，基于革命性发展的“拐点泡沫”加速了技术进步，为更加繁荣的未来奠定了基础，他们摧毁了财富。关键是不要成为在进步过程中财富被毁掉的投资者之一。

霍巴特和胡贝尔继续更深入地描述了泡沫为新技术所需的基础设施建设提供资金并从而加速其采用的过程：

大多数新技术并不是凭空出现的无中生有[即从无到有]，一次性进入完全成形的世界。相反，它建立在以前的错误启动、失败、迭代和历史路径依赖性的基础上。泡沫创造了部署必要资本的机会，以资助和加速此类大规模实验（其中包括并行进行的大量试验和错误），从而加快潜在颠覆性技术和突破的速度。

通过产生热情和投资的正反馈循环，泡沫可以带来净效益。乐观可以是一种自我实现的预言。投机提供了资助高风险和探索性项目所需的大量资金；短期内看似过度热情或糟糕的投资，结果却对引导社会和技术创新至关重要。。。泡沫可以是集体的错觉，但也可以是集体愿景的表达。这一愿景成为人员和资本协调以及创新并行的场所。突飞猛进的进步不是随着时间的推移而发生的，而是在不同领域同时发生的。并且热情高涨。。。随之而来的是更高的风险承受能力和强大的网络效应。对错过的恐惧（FOMO）吸引了更多的参与者、企业家和投机者，进一步强化了这种积极的反馈循环。就像泡沫一样，“FOMO”往往名声不好，但有时它是一种健康的本能。毕竟，我们谁都不想错过千载难逢的建设未来的机会。

换句话说，基于技术进步的泡沫是好的，因为它们会刺激投资者大量投入资金（其中很大一部分被扔掉），对新的机会领域进行地毯式轰炸，从而启动其开发。

关键的认识似乎是，如果人们保持耐心、谨慎、分析和坚持价值，新技术将需要很多年甚至几十年的时间才能开发出来。相反，泡沫的歇斯底里导致这一过程被压缩到一个非常短的时期——其中一些资金被投入到对胜利者改变生活的投资中，但大部分都被烧毁了。

泡沫既有技术方面的，也有金融方面的，但上述引文是从那些渴望技术进步并非常高兴看到投资者为了利益而赔钱的人的角度出发的。另一方面，“我们”希望看到技术进步，但不想浪费金钱来帮助实现这一进步。

本·汤普森 (Ben Thompson) 在结束这次讨论时说道：“这就是为什么我很高兴谈论新技术，以及新技术的前景。”我不知道……我喜欢这样一个事实：他对未来的可能性感到兴奋，同时承认未来的形态是未知的（在我们的世界中，我们可能会说“风险很大”）。

评估当前形势

现在让我们开始讨论我们过去所说的“黄铜钉”。我们知道什么？首先，我还没有遇到过任何人不相信人工智能有潜力成为有史以来最伟大的技术发展之一，重塑日常生活和全球经济。

我们还知道，近年来，经济和市场对人工智能的依赖程度越来越高：

• 人工智能占公司总资本支出的很大一部分。

• 人工智能能力的资本支出占美国 GDP 增长的很大一部分。

• 人工智能股票是标准普尔 500 指数绝大多数收益的来源。

作为一个财富10 月 7 日的标题是这样的：

75% 的收益、80% 的利润、90% 的资本支出——人工智能对标准普尔指数的控制是彻底的，摩根士丹利的顶级分析师“非常担心”

此外，我认为值得注意的是，虽然人工智能相关股票的涨幅在所有股票总涨幅中所占的比例不成比例，但人工智能给市场注入的兴奋肯定也大大促进了非人工智能股票的升值。

在人工智能计算机芯片领先开发商英伟达的带领下，人工智能相关股票表现出了天文数字。从 1993 年成立到 1999 年首次公开募股（当时估计市值为 6.26 亿美元），英伟达一度成为世界上第一家市值 5 万亿美元的公司。即升值约 8,000 倍，即 26 年以上每年升值约 40%。难怪想象力被激发了。

哪些领域存在不确定性？

我认为可以公平地说，虽然我们知道人工智能将成为令人难以置信的变革的源泉，但我们大多数人并不知道它到底能做什么，如何在商业上应用，或者时机是什么。

谁将成为赢家，他们的身价是多少？如果一项新技术被认为是世界的改变者，那么人们总是会认为拥有该技术的领先公司将具有巨大的价值。但这个假设被证明有多准确呢？正如沃伦·巴菲特在 1999 年指出的那样，“汽车可能是 20 世纪上半叶最重要的发明”th^世纪。。。。如果你在第一批汽车问世时就看到了这个国家将如何与汽车相关的发展，你可能会说，“这就是我必须去的地方。”但是，截至几年前，在这 2,000 家公司中，只有 3 家汽车公司幸存下来。因此，汽车对美国产生了巨大影响，但对投资者却产生了相反的影响。”（时间，2012 年 1 月 23 日）

在人工智能领域，目前有一些非常强大的领导者，包括一些世界上最强大、最富有的公司。但众所周知，新技术具有颠覆性。今天的领导者会占上风还是让位给暴发户？军备竞赛将花费多少成本，谁会获胜？

同样，新贵的份额值多少钱？与价值数万亿美元的领跑者不同，我们可以对一些潜在的挑战者进行投资，其企业价值仅为数十亿甚至是——我可以说吗？– 数百万。2024年6月25日，CNBC报道如下：

一个由大学辍学生创立的团队从 Primary Venture Partners 领导的投资者那里筹集了 1.2 亿美元，用于开发一款新的人工智能芯片来对抗 Nvidia。Etched 首席执行官 Gavin Uberti 表示，该初创公司押注，随着人工智能的发展，该技术的大部分耗电计算需求将由称为 ASIC 的定制硬连线芯片来满足。“如果变形金刚消失了，我们就会死，”乌贝蒂告诉 CNBC。“但如果他们坚持下去，我们就是有史以来最大的公司。”

即使考虑到 Etched 不会成为有史以来最大的公司，如果成功后他们的估值仅为 Nvidia 峰值的五分之一（仅为 1 万亿美元），那么需要多大的成功概率才能证明 1.2 亿美元的投资是合理的？为简单起见，假设投资是为了获得 100% 的所有权，那么您所需要的只是相信，实现万亿美元价值的概率为十分之一，而预期回报则超过您资金的八倍。谁说 Etched 没有这样的机会？既然如此，为什么有人不玩呢？上述就是我所说的“彩票思维”，在这种思维中，巨额回报的梦想证明——不，是强迫——参与一项极有可能失败的努力。

以这种方式计算期望值并没有什么问题。领先的风险投资家每天都在参与其中，并取得了巨大的效果。但关于可能的回报及其概率的假设必须是合理的。考虑万亿美元的支出将超越任何计算的合理性。

人工智能会产生利润吗？为谁产生利润？两件事我们对人工智能将为供应商带来的利润及其对非人工智能公司（主要是那些使用它的公司）的影响知之甚少或一无所知。

人工智能是否会成为垄断或双头垄断，一两家领先公司能够对这些能力收取高昂的费用？或者这会是一场高度竞争的混战，许多公司在价格上展开竞争，争夺用户在人工智能服务上的支出，使其成为一种商品？或者，也许最有可能的是，它将是领先公司和专业参与者的混合体，其中一些公司通过价格竞争，另一些则通过专有优势竞争。据说，目前响应 AI 查询的服务，例如 ChatGPT 和 Gemini，每次回答查询都会赔钱（当然，对于新行业的参与者来说，暂时提供“亏损领导者”的情况并不罕见）。习惯于在赢家通吃的市场中取得成功的领先科技公司是否会满足于在人工智能业务中经历多年亏损以获取份额？数千亿美元正在投入到争夺人工智能领导地位的竞赛中。谁会获胜，结果又会如何呢？

同样，人工智能会对使用它的公司产生什么影响？显然，人工智能将成为提高用户生产力的重要工具，其中包括用计算机提供的劳动力和智能取代工人。但这种削减成本的能力会增加采用该技术的公司的利润率吗？或者它只会引发这些公司之间为了争夺客户而进行的价格战？在这种情况下，节省的成本可能会转嫁给客户，而不是由公司获得。换句话说，人工智能是否有可能在不增加盈利能力的情况下提高企业的效率？

我们应该担心所谓的“循环交易”吗？在 20 世纪 90 年代末的电信繁荣时期，光纤建设过度，拥有光纤的公司相互进行交易，从而可以报告利润。如果两家公司拥有光纤，那么他们的账簿上就只有一项资产。但如果双方从对方购买容量，他们都可以报告利润。。。他们就是这么做的。在其他情况下，制造商借钱给网络运营商购买设备，然后运营商才有客户证明扩建的合理性。这一切都导致了虚幻的利润。

如今，正在宣布的交易中，资金似乎是在人工智能玩家之间往返的。相信人工智能泡沫存在的人很容易对这些交易持怀疑态度。目的是实现合法的业务目标还是夸大进展？

批评人士表示，更令人担忧的是，OpenAI 与芯片制造商、云计算公司和其他公司达成的一些交易是奇怪的循环。OpenAI 将从科技公司获得数十亿美元，但也会向这些公司返还数十亿美元，用于支付计算能力和其他服务的费用。。。。

英伟达还达成了一些交易，引发了人们对该公司是否自行支付费用的质疑。它宣布将向OpenAI投资1000亿美元。这家初创公司通过购买或租赁 Nvidia 芯片来获得这笔资金。。。。

高盛估计，英伟达明年 15% 的销售额将来自批评者所谓的循环交易。（纽约时报，11 月 20 日）

值得注意的是，尽管尚未实现盈利，OpenAI已向行业对手方做出了总计1.4万亿美元的投资承诺。该公司明确表示，投资将从同一方收到的收入中支付，并且它有办法取消这些承诺。但这一切都引发了一个问题：人工智能行业是否已经开发出了永动机。

（在这个主题上，我一直很喜欢质疑人们与“万亿”这个词产生联系的能力的文章，我认为这个想法是正确的。100 万美元相当于每秒 1 美元，持续 11.6 天。10 亿美元相当于每秒 1 美元，持续 31.7 年。我们明白。但是 1 万亿美元相当于每秒 1 美元，持续 31,700 年。谁能理解31,700 年？）

人工智能资产的使用寿命是多少？我们不得不怀疑人工智能领域是否正确处理了过时的话题。AI芯片的寿命是多少？在确定人工智能相关股票的市盈率时，应该考虑多少年的盈利增长？芯片和人工智能基础设施的其他方面能否持续足够长的时间来偿还购买它们所承担的债务？通用人工智能（能够做人类大脑能做的任何事情的机器）会实现吗？这会是进步的终结，还是可能会出现进一步的革命，哪些公司会赢得革命？企业能否达到技术稳定并能从中获取经济价值的地步？或者新技术是否会不断威胁取代旧技术作为成功之路？

在这方面，英国《金融时报》的一期时事通讯简要提到了两项发展，表明竞争格局的流动性：

• 麻省理工学院和开源人工智能初创公司 Hugging Face 的一项研究发现，过去一年中国制造的新开放模型的总下载份额上升至 17%。这一数字超过了谷歌、Meta 和 OpenAI 等美国开发商 15.8% 的下载份额，这是中国企业首次击败美国同行。。。。

• 由于担心谷歌在人工智能领域取得进展，英伟达股价昨天大幅下跌，导致这家人工智能芯片制造商的市值蒸发了 1150 亿美元。（第一FT美洲，11 月 26 日）

动态变化为令人难以置信的新技术创造了机会，但同样的活力也可能威胁到领先公司的统治地位。面对所有这些不确定性，投资者必须问，他们所支付的价格中包含的持续成功的假设是否完全有道理。

繁荣会导致投机行为吗？举一个极端的例子，我将引用通过 10 亿美元种子轮对初创企业进行风险资本投资的趋势。以下是一个小插曲：

由前 Open AI 高管 Mira Murati 领导的人工智能初创公司 Thinking Machines 刚刚筹集了历史上最大的种子轮融资：以 100 亿美元的估值筹集了 20 亿美元的资金。该公司尚未发布产品，也拒绝告诉投资者他们正在尝试开发什么产品。“这是最荒谬的推介会，”一位会见穆拉蒂的投资者说。“她说，“所以我们正在与最优秀的人工智能人员一起创建一家人工智能公司，但我们无法回答任何问题。”“（“人工智能泡沫如何破灭，”Derek Thompson Substack，10 月 2 日）

但这是古老的历史。。。已经两个月大了。以下是更新：

Thinking Machines Lab 是一家由 Open AI 前高管 Mira Murati 创立的人工智能初创公司，目前正处于早期谈判中，以约 500 亿美元的估值筹集新一轮融资。彭博新闻周四报道。这家初创公司最后一次估值为 120 亿美元是在 7 月份，此前该公司筹集了约 20 亿美元资金。（路透社，11 月 13 日）

Thinking Machines Lab 并不孤单：

OpenAI 前首席科学家 Ilya Sutskever 创立的隐形初创公司 Safe Superintelligence (SSI) 在一轮融资中筹集了 20 亿美元，尽管没有公开发布产品或服务，但该公司估值达到 320 亿美元，这是人工智能军备竞赛中迄今为止最大胆的赌注之一。（Calcalist 的 CTech，4 月 13 日）

最终状态是什么？人工智能的部分问题包括这个最新事物的不寻常性质。这与设计和销售产品的企业不同，如果销售价格超过投入成本，企业就能赚钱。相反，它是公司在飞行中建造飞机，一旦飞机建成，他们就会知道它能做什么，以及是否有人会为其服务付费。

许多公司证明他们的支出是合理的，因为他们不仅仅是在制造产品，他们还在创造改变世界的东西：通用人工智能，或 A.G.I.。。。问题是他们都不知道该怎么做。

但弗吉尼亚大学经济学家安东·科里内克表示，如果硅谷实现其目标，这些支出都是合理的。他乐观地认为这是可以做到的。

——这是对 A.G.I. 的赌注。或破产，”科里内克博士说。（纽约时报，11 月 20 日 — 重点已添加）

OpenAI 首席执行官 Sam Altman 的言论最能体现正在建设中的行业尚未确定的性质，他的言论如下：“我们将构建这种普遍智能的系统，然后要求它找到一种方法来从中产生投资回报。”

对于那些迄今为止已经完全了解自己所投资企业本质的人来说，这应该是一个停顿的原因。显然，一项等于或超越人脑的技术的价值应该是相当大的，但它不是远远无法计算吗？

关于债务用途的一句话

迄今为止，人工智能和配套基础设施的大部分投资都是由运营现金流产生的股本资本组成。但现在，公司正在承诺需要债务融资的金额，而对于其中一些公司来说，投资和杠杆必须被描述为激进的。

The AI data centre boom was never going to be financed with cash alone.The project is too big to be paid for out of pocket.JPMorgan analysts have done some sums on the back of a napkin, or possibly a tablecloth, and estimated the bill for the infrastructure build-out would come to 5ドルtn (not including a tip).Who knows if that’s right, but we have good reason to expect close to half a trillion in spending next year.Meanwhile, the biggest spenders (Microsoft, Alphabet, Amazon, Meta and Oracle) had only about 350ドルbn in the bank, collectively, as of the end of the third quarter.(“Unhedged,”金融时报, November 13)

The firms mentioned above derive healthy cash flows from their very strong non-AI businesses.But the massive, winner-take-all arms race in AI is requiring some to take on debt.In fact, it’s reasonable to think one of the reasons they’re spending vast sums is to make it hard for lesser firms to keep up.

Oracle, Meta, and Alphabet have issued 30-year bonds to finance AI investments.In the case of the latter two, the yields on the bonds exceed those on Treasurys of like maturity by 100 basis points or less.Is it prudent to accept 30 years of technological uncertainty to make a fixed-income investment that yields little more than riskless debt?And will the investments funded with debt – in chips and data centers – maintain their level of productivity long enough for these 30-year obligations to be repaid?

On November 14, Alex Kantrowitz’s Big Technology Podcast carried a conversation with Gil Luria, Head of Technology Research at financial services firm D.A.Davidson, primarily regarding the use of debt in the AI sector.Here’s some of what Luria had to say: Healthy behavior is being practiced by “.。。

• reasonable, thoughtful business leaders, like the ones at Microsoft, Amazon, and Google that are making sound investments in growing the capacity to deliver AI.And the reason they can make sound investments is that they have all the customers.。。And so, when they make investments, they’re using cash on their balance sheets;they have tremendous cash flow to back it up;they understand that it’s a risky investment;

• and they balance it out.” Unhealthy behavior – Here he describes “.。。a startup that is borrowing money to build data centers for another startup.

• They’re both losing tremendous amounts of cash, and yet they’re somehow being able to raise this debt capital in order to fund this buildout, again without having the customers or the visibility into those investments paying off.” “So there’s a whole range of behaviors between healthy and unhealthy, and we just need to sort that out so we don’t make the mistakes of the past.” “There are certain things we finance through equity, through ownership, and there are certain things we finance through debt, through an obligation to pay down interest over time.

• And as a society, for the longest time, we’ve had those two pieces in their right place.Debt is when I have a predictable cash flow and/or an asset that can back that loan, and then it makes sense for me to exchange capital now for future cash flows to the lender.。。。We use equity for investing in more speculative things, for when we want to grow and we want to own that growth, but we’re not sure about what the cash flow is going to be.That’s how a normal economy functions.

When you start confusing the two you get yourself in trouble.” Among potentially worrisome factors, Luria cites these: “A speculative asset .

• 。。

• we don’t know how much of it we’re really going to need in two to five years.” Lender personnel with incentives to make loans but no exposure to long-term consequences The possibility that the supply of AI capacity catches up with or surpasses the demand The chance that future generations of AI chips will be more powerful, obsoleting existing ones or reducing their value as backing for debt Powerful competitors who vie for market share by cutting rental rates and running losses Here are some important paragraphs from Azeem Azhar’s Exponential View of October 18: When does an AI boom tip into a bubble?

• [Investor and engineer] Paul Kedrosky points to the Minsky moment – the inflection point when credit expansion exhausts its good projects and starts chasing bad ones, funding marginal deals with vendor financing and questionable coverage ratios.

• For AI infrastructure, that shift may already be underway;

• the telltale signs include hyperscalers’ capex outpacing revenue momentum and lenders sweetening terms to keep the party alive.

Paul makes a compelling case.We’ve entered speculative finance territory – arguably past the tentative stage – and recent deals will set dangerous precedents.As Paul warns, this financing will “create templates for future such transactions,” spurring rapid expansion in junk issuance and SPV proliferation among hyperscalers chasing dominance at any cost.

。。。

For AI infrastructure, the warning signs are flashing: vendor financing proliferates, coverage ratios thin, and hyperscalers leverage balance sheets to maintain capex velocity even as revenue momentum lags.

We see both sides – genuine infrastructure expansion alongside financing gymnastics that recall the 2000 telecom bust.The boom may yet prove productive, but only if revenue catches up before credit tightens.When does healthy strain become systemic risk?That’s the question we must answer before the market does.

(Emphasis added) Azhar references the use of off-balance sheet financing via special-purpose vehicles, or SPVs, which were among the biggest contributors to Enron’s precariousness and eventual collapse.A company and its partners set up an SPV for some specific purpose(s) and supply the equity capital.The parent company may have operating control, but because it doesn’t have majority ownership, it doesn’t consolidate the SPV on its financial statements.The SPV takes on debt, but that debt doesn’t appear on the parent’s books.The parent may be an investment grade borrower, but likewise, the debt isn’t an obligation of the parent or guaranteed by it.Today’s debt may be backed by promised rent from a data center tenant – sometimes an equity partner – but the debt isn’t a direct obligation of the equity partner either.Essentially, an SPV is a way to make it look like a company isn’t doing the things the SPV is doing and doesn’t have the debt the SPV does.

(Private equity funds and private credit funds are highly likely to be found among the partners and lenders in these entities.) As I quoted earlier, according to Perez (who wrote on the heels of the dot-com bubble), “what enabled the deployment period were the money-losing investments.” Early investment is lost in the “Minsky moment,” in which unwise commitments made in an extended up-cycle encounters value destruction in a correction.

• And there are three things we know for sure about the use of debt: it magnifies losses if there are losses (just as it magnifies the hoped-for gains if they materialize), it increases the probability of a venture failing if it encounters a difficult moment, and despite the layer of equity beneath it, it puts lenders’ capital at risk if the difficult moment is bad enough.

• One key risk to consider is the possibility that the boom in data center construction will result in a glut.

• Some data centers may be rendered uneconomic, and some owners may go bankrupt.

In that case, a new generation of owners might buy up centers at pennies on the dollar from lenders who foreclosed on them, reaping profits when the industry stabilizes.This is a process through which “creative destruction” brings markets into equilibrium and reduces costs to levels that make future business profitable.

Debt is neither a good thing nor a bad thing本身。Likewise, the use of leverage in the AI industry shouldn’t be applauded or feared.It all comes down to the proportion of debt in the capital structure;the quality of the assets or cash flows you’re lending against;the borrowers’ alternative sources of liquidity for repayment;and the adequacy of the safety margin obtained by lenders.We’ll see which lenders maintain discipline in today’s heady environment.

It’s worth noting in this connection that Oaktree has made a few investments in data centers, and our parent, Brookfield, is raising a 10ドル billion fund for investment in AI infrastructure.Brookfield is putting up its own money and has equity commitments from sovereign wealth funds and Nvidia, to which it intends to apply “prudent” debt.Brookfield’s investments seem likely to go largely into geographies that are less saturated with data centers and for infrastructure to supply the vast amounts of electric power that data centers will require.Of course, we’re both doing these things on the basis of what we think are prudent decisions.

I know I don’t know enough to opine on AI.

• But I do know something about debt, and it’s this: It’s okay to supply debt financing for a venture where the outcome is uncertain.

• It’s not okay where the outcome is purely a matter of conjecture.

• Those who understand the difference still have to make the distinction correctly.

The FT’s Unhedged quotes Chong Sin, lead analyst for CMBS research at JPMorgan, as saying, “.。。in our conversations with investment grade ABS and CMBS investors, one often-cited concern is whether they want to take on the residual value risk of data centers when the bonds mature.” I’m glad potential lenders are asking the kind of questions they should.

Here’s how to think about the intersection of debt and AI according to Bob O’Leary, Oaktree’s co-CEO and co-portfolio manager of our Opportunities Funds: Most technological advances develop into winner-takes-all or winner-takes-most competitions.

The “right” way to play this dynamic is through equity, not debt.Assuming you can diversify your equity exposures so as to include the eventual winner, the massive gain from the winner will more than compensate for the capital impairment on the losers.That’s the venture capitalist’s time-honored formula for success.

The precise opposite is true of a diversified pool of debt exposures.You’ll only make your coupon on the winner, and that will be grossly insufficient to compensate for the impairments you’ll experience on the debt of the losers.

Of course, if you can’t identify the pool of companies from which the winner will emerge, the difference between debt and equity is irrelevant – you’re a zero either way.I mention this because that’s precisely what happened in search and social media: early leaders (Lycos in search and MySpace in social media) lost out spectacularly to companies that emerged later (Google in search and Facebook in social media).

Trying to Get to a Conclusion There can be no doubt that today’s behavior is “speculative,” defined as based on speculation regarding the future.

There’s also no doubt that no one knows what the future holds, but investors are betting huge sums on that future.

In that connection, I want to say a little about the unique nature of AI.The AI revolution is different from the technological revolutions that preceded it in ways that are bothwonderful和令人担忧的。

It feels to me like a genie has been released from a bottle, and it isn’t going back in: AI may not be a tool for mankind, but rather something of a replacement.It may be capable of taking over cognition, on which humans have thus far had a monopoly.Because of this, it’s likely to be different in kind from prior developments, not just in degree.

(More on this in my postscript.) AI technology is progressing at an incredibly rapid clip , possibly leaving scant time for mankind to adjust.I’ll provide two examples: Coding, which we called “computer programming” 60 years ago, is the canary in the coal mine in terms of the impact of AI.

• In many advanced software teams, developers no longer write the code;they type in what they want, and AI systems generate the code for them.Coding performed by AI is at a world-class level, something that wasn’t so just a year ago.

• According to my guide here, “There is no speculation about whether or not human replacement will take place in that vertical.” In the field of digital advertising, when users log into an app, AI engages in “ad matching,” showing them ads tailored to the preferences displayed by their prior surfing.No humans need apply to do this job.

Perhaps most importantly, the growth of demand for AI seems totally unpredictable.As one of my younger advisers explained, “the speed and scale of improvement mean it’s incredibly hard to forecast demand for AI.Adoption today may have nothing to do with adoption tomorrow, because a year or two from now, AI may be able to do 10x or 100x what it can do today.Thus, how can anyone say how many data centers will be needed?

And how can even successful companies know how much computing capacity to contract for?” With differences like these, how can anyone correctly judge what AI implies for the future?

*           *           * One of the things occupying many observers at this juncture – including me – is the search for parallels to past bubbles.

Here’s some historical perspective from a recent article in有线:

AI’s closest historical analogue here may be not electric lighting but radio.When RCA started broadcasting in 1919, it was immediately clear that it had a powerful information technology on its hands.But less clear was how that would translate into business.“Would radio be a loss-leading marketing for department stores?A public service for broadcasting Sunday sermons?An ad-supported medium for entertainment?” [Brent Goldfarb and David A. Kirsch of the University of Maryland] write.“All were possible.All were subjects of technological narratives.” As a result, radio turned into one of the biggest bubbles in history – peaking in 1929, before losing 97 percent of its value in the crash.This wasn’t an incidental sector;RCA was, along with Ford Motor Company, the most high-traded stock on the market.It was, as纽约客recently wrote, “the Nvidia of its day.” .。。

In 1927, Charles Lindbergh flew the first solo nonstop transatlantic flight from New York to Paris.。。。It was the biggest tech demo of the day, and it became an enormous, ChatGPT-launch-level coordinating event – a signal to investors to pour money into the industry.

“Expert investors appreciated correctly the importance of airplanes and air travel,” Goldfarb and Kirsch write, but “the narrative of inevitability largely drowned out their caution.Technological uncertainty was framed as opportunity, not risk.

The market overestimated how quickly the industry would achieve technological viability and profitability.” As a result, the bubble burst in 1929 – from its peak in May, aviation stocks dropped 96 percent by May 1932. .。。

It’s worth reiterating that two of the closest analogs AI seems to have in tech bubble history are aviation and broadcast radio.Both were wrapped in high degrees of uncertainty and both were hyped with incredibly powerful coordinating narratives.Both were seized on by pure play companies seeking to capitalize on the new game-changing tech, and both were accessible to the retail investors of the day.Both helped inflate a bubble so big that when it burst, in 1929, it left us with the Great Depression.(“AI Is the Bubble to Burst Them All,” Brian Merchant,有线, October 27 – emphasis added.N.b., the Depression had many causes beyond the bursting of the radio/aviation bubble.)

Derek Thompson, who supplied the quote with which I opened this memo, ended his newsletter with some terrific historical perspective:

The railroads were a bubble and they transformed America.Electricity was a bubble, and it transformed America.The broadband build-out of the late-1990s was a bubble that transformed America.I am not rooting for a bubble, and quite the contrary, I hope that the US economy doesn’t experience another recession for many years.But given the amount of debt now flowing into AI data center construction, I think it’s unlikely that AI will be the first transformative technology that isn’t overbuilt and doesn’t incur a brief painful correction.(“AI Could Be the Railroad of the 21^圣世纪。Brace Yourself.” November 4 – emphasis added)

The skeptics readily cite ways in which today’s events are comparable to the internet bubble:

• A change-the-world technology

• Exuberant, speculative behavior

• The role of FOMO

• Suspect, circular deals

• The use of SPVs

• 1ドル billion seed rounds

The supporters have reasons why the comparison isn’t appropriate:

• An existing product for which there is strong demand

• One billion users already (many times the number of internet users at the height of the bubble)

• Well-established main players with revenues, profits, and cash flow

• The absence of an IPO craze with prices doubling in a day

• Reasonable p/e ratios for the established participants

I’ll elaborate regarding the first of the proposed non-comparable factors.Unlike in the internet bubble, AI products already exist at scale, the demand for them is exploding, and they’re producing revenues in rapidly increasing amounts.For example, Anthropic, one of the two leaders in producing models for AI coding as described on page 12, is said to have “10x-ed” its revenues in each of the last two years (for those who didn’t study higher math, that’s 100x in two years).Revenues from Claude Code, a program for coding that Anthropic introduced earlier this year, already are said to be running at an annual rate of 1ドル billion.Revenues for the other leader, Cursor, were 1ドル million in 2023 and 100ドル million in 2024, and they, too, are expected to reach 1ドル billion this year.

As to the final bullet point, see the table below, which comes from Goldman Sachs via Derek Thompson.You’ll notice that during the internet bubble of 1998-2000, the p/e ratios were much higher for Microsoft, Cisco, and Oracle than they are today for the biggest AI players – Nvidia, Microsoft, Alphabet, Amazon, and Meta (OpenAI doesn’t have earnings).In fact, Microsoft’s on a half-off sale relative to its p/e 26 years ago!In the first bubble I witnessed – surrounding the Nifty-Fifty in 1969-72 – the p/e ratios for the leading companies were even higher than those of 1998-2000.

结论

For my final citation, I’ll look to Sam Altman of OpenAI.

His comments seem to me to capture the essence of what’s going on: “When bubbles happen, smart people get overexcited about a kernel of truth,” Mr. Altman told reporters this year.“Are we in a phase where investors as a whole are overexcited about A.I.?My opinion is yes.Is A.I.the most important thing to happen in a very long time?My opinion is also yes.” (纽约时报, November 20)

But do I have a bottom line?是的，我愿意。Alan Greenspan’s phrase, mentioned earlier, serves as an excellent way to sum up a stock market bubble: “irrational exuberance.” There is no doubt that investors are applying exuberance with regard to AI.The question is whether it’s irrational.Given the vast potential of AI but also the large number of enormous unknowns, I think virtually no one can say for sure.We can theorize about whether the current enthusiasm is excessive, but we won’t know until years from now whether it was.Bubbles are best identified in retrospect.

While the parallels to past bubbles are inescapable, believers in the technology will argue that “this time it’s different.” Those four words are heard in virtually every bubble, explaining why the present situation isn’t a bubble, unlike the analogous prior ones.On the other hand, Sir John Templeton, who in 1987 drew my attention to those four words, was quick to point out that 20% of the time things really are different.But on the third hand, it must be borne in mind that behavior based on the belief that it’s different is what causes it to不be different!

Today’s situation calls to mind a comment attributed to American economist Stuart Chase about faith.

I believe it’s also applicable to AI (as well as to gold and cryptocurrencies): For those who believe, no proof is necessary.For those who don't believe, no proof is possible.

Here’s my actual bottom line: There’s a consistent history of transformational technologies generating excessive enthusiasm and investment, resulting in more infrastructure than is needed and asset prices that prove to have been too high.

• The excesses accelerate the adoption of the technology in a way that wouldn’t occur in their absence.

• The common word for these excesses is “bubbles.” AI has the potential to be one of the greatest transformational technologies of all time.

• As I wrote just above, AI is currently the subject of great enthusiasm.If that enthusiasm doesn’t produce a bubble conforming to the historical pattern, that will be a first.

• Bubbles created in this process usually end in losses for those who fuel them.

• The losses stem largely from the fact that the technology’s newness renders the extent and timing of its impact unpredictable.This in turn makes it easy to judge companies too positively amid all the enthusiasm and difficult to know which will emerge as winners when the dust settles.

• There can be no way to participate fully in the potential benefits from the new technology without being exposed to the losses that will arise if the enthusiasm and thus investors’ behavior prove to have been excessive.

• The use of debt in this process – which the high level of uncertainty usually precluded in past technological revolutions – has the potential to magnify all of the above this time.

Since no one can say definitively whether this is a bubble, I’d advise that no one should go all-in without acknowledging that they face the risk of ruin if things go badly.But by the same token, no one should stay all-out and risk missing out on one of the great technological steps forward.A moderate position, applied with selectivity and prudence, seems like the best approach.

Finally, it’s essential to bear in mind that there are no magic words in investing.These days, people promoting real estate funds say, “Office buildings are so yesterday, but we’re investing in the future through data centers,” whereupon everyone nods in agreement.But data centers can be in shortage or in oversupply, and rental rates can surprise to the upside or the downside.As a result, they can be profitable .。。或不。Intelligent investment in data centers, and thus in AI – like everything else – requires sober, insightful judgment and skillful implementation.2025 年 12 月 9 日P.S.: The following has nothing to do with the financial markets or the question of whether AI is the subject of a bubble.

My topic is the impact of AI on society through joblessness and purposelessness.

You needn’t read it – that’s why it’s a postscript – but it’s important to me, and I've been looking for a place to say a few words about it.

On November 18, a research note from Barclays described Fed Governor Christopher Waller as having “highlighted how recent stock market enthusiasm around AI has not yet translated into job creation.” This strikes me as paradoxical given my sense that one of AI’s main impacts will be to increase productivity and thus eliminate jobs.That is the source of my concern.

I view AI primarily as an incredible labor-saving device.Joe Davis, Global Chief Economist and Global Head of the Investment Strategy Group at Vanguard, says, “for most jobs – likely four out of five – AI’s impact will result in a mixture of innovation and automation, and could save about 43% of the time people currently spend on their work tasks.” ( Exponential View , September 3) I find the resulting outlook for employment terrifying.I am enormously concerned about what will happen to the people whose jobs AI renders unnecessary, or who can’t find jobs because of it.

The optimists argue that “new jobs have always materialized after past technological advances.” I hope that’ll hold true in the case of AI, but hope isn’t much to hang one’s hat on, and I have trouble figuring out where those jobs will come from.Of course, I’m not much of a futurist or a financial optimist, and that’s why it’s a good thing I shifted from equities to bonds in 1978. The other thing the optimists say is that “the beneficial impact of AI on productivity will cause a huge acceleration in GDP growth.” Here I have specific quibbles: The change in GDP can be thought of as the change in hours worked times the change in output per hour (aka “productivity”).The role of AI in increasing productivity means it will take fewer hours worked – meaning fewer workers – to produce the goods we need.

Or, viewed from the other direction, maybe the boom in productivity will mean a lot more goods can be produced with the same amount of labor.

• But if a lot of jobs are lost to AI, how will people be able to afford the additional goods AI enables to be produced?

• I find it hard to imagine a world in which AI works shoulder-to-shoulder with all the people who are employed today.

How can employment not decline?AI is likely to replace large numbers of entry-level workers, people who process paper without applying judgment, and junior lawyers who scour the lawbooks for precedents.Maybe even junior investment analysts who create spreadsheets and compile presentation materials.It’s said that AI can read an MRI better than the average doctor.Driving is one of the most populous professions in America, and driverless vehicles are already arriving;where will all the people who currently drive taxis, limos, buses, and trucks find jobs?

I imagine government’s response will be something called “universal basic income.” The government will simply mail checks to the millions for whom there are no jobs.

• But the worrier in me finds problems in this, too: Where will the money come from for those checks?The job losses I foresee imply reduced income tax receipts and increased spending on entitlements.This puts a further burden on the declining segment of the population that is working and implies even greater deficits ahead.In this new world, will governments be able to fund ever-increasing deficits?

• 更重要的是，people get a lot more from jobs than just a paycheck.A job gives them a reason to get up in the morning, imparts structure to their day, gives them a productive role in society and self-respect, and presents them with challenges, the overcoming of which provides satisfaction.How will these things be replaced?I worry about large numbers of people receiving subsistence checks and sitting around idle all day.I worry about the correlation between the loss of jobs in mining and manufacturing in recent decades and the incidence of opioid addiction and shortening of lifespans.

And by the way, if we eliminate large numbers of junior lawyers, analysts, and doctors, where will we get the experienced veterans capable of solving serious problems requiring judgment and pattern recognition honed over decades?

What jobs won’t be eliminated?What careers should our children and grandchildren prepare for?Think about the jobs that machines can’t perform.My list starts with plumbers, electricians, and masseurs –physical tasks.Maybe nurses will earn more than doctors because they deliver hands-on care.And what distinguishes the best artists, athletes, doctors, lawyers, and hopefully investors?I think it’s something called talent or insight, which AI might or might not be able to replicate.But how many people at the top of those professions are needed?A past presidential candidate said he would give laptops to everyone who lost their job to offshoring.How many laptop operators do we need?

Finally, I’m concerned that a small number of highly educated multi-billionaires living on the coasts will be viewed as having created technology that puts millions out of work.This promises even more social and political division than we have now, making the world ripe for populist demagoguery.

I’ve seen incredible progress over the course of my lifetime, but in many ways I miss the simpler world I grew up in. I worry that this will be another big one.I get no pleasure from this recitation.Will the optimists please explain why I’m wrong?

Interestingly in this connection, Vanguard’s Joe Davis points out that more Americans are turning 65 in 2025 than in any preceding year, and that approximately 16 million baby boomers will retire between now and 2035. Could AI merely make up for that?There’s an optimistic take for you.

HM

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