Dec 22, 2025
Ours is a remarkable moment in world history. A transformative technology is ascending, and its supporters claim it will forever change the world. To build it requires companies to invest a sum of money unlike anything in living memory. News reports are filled with widespread fears that America's biggest corporations are propping up a bubble that will soon pop.
在这个世界历史上非同寻常的时刻，一项变革性的技术正在崛起，其支持者声称它将永远改变世界。为了构建这项技术，企业需要投入的资金规模在我们的记忆中前所未有。新闻报道中充斥着广泛的担忧，认为美国最大的公司正在支撑一个即将破裂的泡沫。
During my visits to clients in Asia and the Middle East last month, I was often asked about the possibility of a bubble surrounding artificial intelligence, and my discussions gave rise to this memo. I want to start off with my usual caveats: I'm not active in the stock market; I merely watch it as the best barometer of investor psychology. I'm also no techie, and I don't know any more about AI than most generalist investors. But I'll do my best.
在上个月拜访亚洲和中东的客户期间，我经常被问及人工智能领域是否存在泡沫，这些讨论促成了这篇备忘录。我想先重申我一贯的声明：我不活跃于股票市场；我只是将其作为观察投资者心理的最佳晴雨表。我也不是技术专家，对于人工智能的了解并不比大多数通才投资者更多。但我会尽力而为。
One of the most interesting aspects of bubbles is their regularity, not in terms of timing, but rather the progression they follow. Something new and seemingly revolutionary appears and worms its way into people's minds. It captures their imagination, and the excitement is overwhelming. The early participants enjoy huge gains. Those who merely look on feel incredible envy and regret and – motivated by the fear of continuing to miss out – pile in. They do this without knowledge of what the future will bring or concern about whether the price they're paying can possibly be expected to produce a reasonable return with a tolerable amount of risk. The end result for investors is inevitably painful in the short to medium term, although it's possible to end up ahead after enough years have passed.
泡沫最有趣的特征之一在于其规律性，这种规律性并非体现在时间点上，而是体现在其演变过程上。某种新颖且看似革命性的事物出现并逐渐深入人心。它抓住了人们的想象力，引发了压倒性的兴奋。早期的参与者享受了巨大的收益。那些仅仅旁观的人感到难以置信的嫉妒和遗憾，并受制于害怕继续错失机会（FOMO）的心理，蜂拥而入。他们这样做时，既不知道未来会带来什么，也不关心他们支付的价格是否可能在可承受的风险下产生合理的回报。对于投资者而言，最终的结果在短期到中期内不可避免地是痛苦的，尽管在经过足够多年之后，仍有可能最终获利。
I've lived through several bubbles and read about others, and they've all hewed to this description. One might think the losses experienced when past bubbles popped would discourage the next one from forming. But that hasn't happened yet, and I'm sure it never will. Memories are short, and prudence and natural risk aversion are no match for the dream of getting rich on the back of a revolutionary technology that “everyone knows” will change the world.
我亲历过几次泡沫，也读到过其他泡沫的历史，它们都符合这一描述。人们可能会认为，过去泡沫破裂时所经历的损失会阻止下一个泡沫的形成。但这从未发生过，我确信也永远不会发生。记忆是短暂的，审慎和天生的风险厌恶在靠一项“每个人都知道”将改变世界的革命性技术致富的梦想面前，根本不堪一击。
I took the quote that opens this memo from Derek Thompson's November 4 newsletter entitled “AI Could Be the Railroad of the 21st Century. Brace Yourself,” about parallels between what's going on today in AI and the railroad boom of the 1860s. Its word-for-word applicability to both shows clearly what's meant by the phrase widely attributed to Mark Twain: “history rhymes.”
我引用了德里克·汤普森（Derek Thompson）11月4日时事通讯中的一段话作为本备忘录的开篇，该文章题为《人工智能可能是21世纪的铁路。做好准备》，讨论了当今人工智能的发展与19世纪60年代铁路繁荣之间的相似之处。这段话对两者都逐字适用，清楚地展示了广泛归于马克·吐温的那句名言的含义：“历史会押韵”。
Understanding Bubbles
理解泡沫
Before diving into the subject at hand – and having read a great deal about it in preparation – I want to start with a point of clarification. Everyone asks, “Is there a bubble in AI?” I think there's ambiguity even in the question. I've concluded there are two different but interrelated bubble possibilities to think about: one in the behavior of companies within the industry, and the other in how investors are behaving with regard to the industry. I have absolutely no ability to judge whether the AI companies' aggressive behavior is justified, so I'll try to stick primarily to the question of whether there's a bubble around AI in the financial world.
在深入探讨手头的主题之前——为此我已阅读了大量资料——我想先做一点澄清。每个人都在问：“人工智能领域有泡沫吗？”我认为甚至这个问题本身都存在歧义。我的结论是，我们需要考虑两种不同但相互关联的泡沫可能性：一种是行业内公司的行为，另一种是投资者对待该行业的行为。我完全没有能力判断人工智能公司激进的行为是否合理，所以我将尝试主要关注金融界围绕人工智能是否存在泡沫这一问题。
The main job of an investment analyst – especially in the so-called “value” school to which I subscribe – is to (a) study companies and other assets and assess the level of and outlook for their intrinsic value and (b) make investment decisions on the basis of that value. Most of the change the analyst encounters in the short to medium term surrounds the asset's price and its relationship to underlying value. That relationship, in turn, is essentially the result of investor psychology.
投资分析师的主要工作——尤其是在我所信奉的所谓“价值”学派中——是 (a) 研究公司和其他资产，评估其内在价值的水平和前景，以及 (b) 基于该价值做出投资决策。分析师在短期到中期内遇到的大部分变化都围绕着资产的价格及其与潜在价值的关系。这种关系反过来本质上是投资者心理的结果。
Market bubbles aren't caused directly by technological or financial developments. Rather, they result from the application of excessive optimism to those developments. As I wrote in my January memo On Bubble Watch, bubbles are temporary manias in which developments in those areas become the subject of what former U.S. Federal Reserve Chairman Alan Greenspan called “irrational exuberance.''
市场泡沫并非直接由技术或金融发展引起。相反，它们源于对这些发展的过度乐观。正如我在一月份的备忘录《泡沫观察》（On Bubble Watch）中所写，泡沫是暂时的狂热，在这些领域的发展成为了前美联储主席艾伦·格林斯潘所说的“非理性繁荣”的对象。
Bubbles usually coalesce around new financial developments (e.g., the South Sea Company of the early 1700s or sub-prime residential mortgage-backed securities in 2005-06) or technological progress (optical fiber in the late 1990s and the internet in 1998-2000). Newness plays a huge part in this. Because there's no history to restrain the imagination, the future can appear limitless for the new thing. And futures that are perceived to be limitless can justify valuations that go well beyond past norms – leading to asset prices that aren't justified on the basis of predictable earning power.
泡沫通常聚集在新的金融发展（例如18世纪初的南海公司或2005-06年的次级住房抵押贷款支持证券）或技术进步（90年代末的光纤和1998-2000年的互联网）周围。“新颖性”在其中扮演了巨大的角色。因为没有历史来限制想象力，新事物的未来可能显得无限广阔。而被认为无限的未来可以证明远超过去规范的估值是合理的——从而导致资产价格无法基于可预测的盈利能力来证明其合理性。
The role of newness is well described in my favorite passage from a book that greatly influenced me, A Short History of Financial Euphoria by John Kenneth Galbraith. Galbraith wrote about what he called “the extreme brevity of the financial memory” and pointed out that in the financial markets, “past experience, to the extent that it is part of memory at all, is dismissed as the primitive refuge of those who do not have the insight to appreciate the incredible wonders of the present.” In other words, history can impose limits on awe regarding the present and imagination regarding the future. In the absence of history, on the other hand, all things seem possible.
约翰·肯尼斯·加尔布雷思（John Kenneth Galbraith）在《金融狂热简史》（A Short History of Financial Euphoria）中很好地描述了新颖性的作用，这是对我影响极大的一本书。加尔布雷思写到了他所谓的“金融记忆极其短暂”，并指出在金融市场中，“过去的经验，即使它是记忆的一部分，也会被斥为那些没有洞察力去欣赏当下不可思议奇迹之人的原始避难所。”换句话说，历史会对当下的敬畏和未来的想象施加限制。而在缺乏历史的情况下，一切似乎皆有可能。
The key thing to note here is that the new thing understandably inspires great enthusiasm, but bubbles are what happen when the enthusiasm reaches irrational proportions. Who can identify the boundary of rationality? Who can say when an optimistic market has become a bubble? It's just a matter of judgment.
这里需要注意的关键是，新事物理所当然地会激发巨大的热情，但泡沫是在热情达到非理性比例时发生的。谁能确定理性的界限？谁能说乐观的市场何时变成了泡沫？这只是一个判断问题。
Something that occurred to me this past month is that two of my best “calls” came in 2000, when I cautioned about what was going on in the market for tech and internet stocks, and in 2005-07, when I cited the dearth of risk aversion and the resulting ease of doing crazy deals in the pre-Global Financial Crisis world.
上个月我想起的一件事是，我最好的两次“预测”分别发生在2000年，当时我警告了科技股和互联网股市场的状况；以及2005-07年，当时我指出了风险厌恶的匮乏以及由此导致的全球金融危机前世界中疯狂交易的泛滥。
First, in neither case did I possess any expertise regarding the things that turned out to be the subjects of the bubbles: the internet and sub-prime mortgage-backed securities. All I did was render observations regarding the behavior taking place around me.
首先，在这两种情况下，我对后来被证实为泡沫主体的互联网络和次级抵押贷款支持证券都没有任何专业知识。我所做的只是对发生在我周围的行为进行观察。
And second, the value in my calls consisted mostly of describing the folly in that behavior, not in insisting that it had brought on a bubble. Struggling with whether to apply the “bubble” label can bog you down and interfere with proper judgment; we can accomplish a great deal by merely assessing what's going on around us and drawing inferences with regard to proper behavior.
其次，我这些预测的价值主要在于描述了这种行为的愚蠢，而不是坚持认为它已经引发了泡沫。纠结于是否贴上“泡沫”的标签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推断出适当的行为，就能取得很大成就。
What's Good About Bubbles?
泡沫有什么好处？
Before going on to discuss AI and whether it's presently in a bubble, I want to spend a little time on a subject that may seem somewhat academic from the standpoint of investors: the upside of bubbles. You may find the attention I devote to this topic excessive, but I do so because I find it fascinating.
在继续讨论人工智能以及它目前是否处于泡沫中之前，我想花一点时间讨论一个从投资者角度来看似乎有些学术性的话题：泡沫的积极面。你可能会觉得我对这个话题的关注有些过多，但我这样做是因为我觉得它非常迷人。
The November 5 Stratechery newsletter was entitled “The Benefits of Bubbles.” In it, Ben Thompson (no relation to Derek) cites a book titled Boom: Bubbles and the End of Stagnation. It was written by Byrne Hobart and Tobias Huber, who propose that there are two kinds of bubbles:
11月5日的 Stratechery 时事通讯题为《泡沫的好处》。在文中，本·汤普森（Ben Thompson，与德里克无关）引用了一本名为《繁荣：泡沫与停滞的终结》（Boom: Bubbles and the End of Stagnation）的书。这本书由 Byrne Hobart 和 Tobias Huber 撰写，他们提出泡沫有两种：
. . . “Inflection Bubbles” – the good kind of bubbles, as opposed to the much more damaging “Mean-reversion Bubbles” like the 2000's subprime mortgage bubble.
……“拐点型泡沫”（Inflection Bubbles）——好的泡沫，这与破坏性大得多的“均值回归型泡沫”（Mean-reversion Bubbles），如2000年代的次贷泡沫相对立。
I find this a useful dichotomy.
我觉得这种二分法很有用。
The financial fads I've read about or witnessed – the South Sea Company, portfolio insurance, and sub-prime mortgage-backed securities – stirred the imagination based on the promise of returns without risk, but there was no expectation that they would represent overall progress for mankind. There was, for example, no thought that housing would be revolutionized by the sub-prime mortgage movement, merely a feeling that there was money to be made from backing new buyers. Hobart and Huber call these “mean-reverting bubbles,” presumably because there's no expectation that the underlying developments would move the world forward. Fads merely rise and fall.
我读到或目睹的金融狂热——南海公司、投资组合保险和次级抵押贷款支持证券——基于无风险回报的承诺激发了人们的想象力，但人们并没有期望它们代表人类的整体进步。例如，没有人认为次贷运动会彻底改变住房市场，只是觉得支持新购房者有利可图。Hobart 和 Huber 称这些为“均值回归型泡沫”，大概是因为人们并不期望潜在的发展会推动世界前进。狂热只是起起落落。
On the other hand, Hobart and Huber call bubbles based on technological progress – as in the case of the railroads and the internet – “inflection bubbles.” After an inflection-driven bubble, the world will not revert to its prior state. In such a bubble, “investors decide that the future will be meaningfully different from the past and trade accordingly.” As Thompson tells us:
另一方面，Hobart 和 Huber 将基于技术进步的泡沫——如铁路和互联网——称为“拐点型泡沫”。在拐点驱动的泡沫之后，世界不会恢复到之前的状态。在这样的泡沫中，“投资者认定未来将与过去有意义地不同，并据此进行交易。”正如汤普森告诉我们的：
The definitive book on bubbles has long been Carlota Perez's Technological Revolutions and Financial Capital. Bubbles were – are – thought to be something negative and to be avoided, particularly at the time Perez published her book. The year was 2002 and much of the world was in a recession coming off the puncturing of the dot-com bubble.
This distinction is very meaningful for Hobart and Huber, and I agree. They say, “not all bubbles destroy wealth and value. Some can be understood as important catalysts for techno-scientific progress.”
这一区别对 Hobart 和 Huber 来说非常有意义，我也同意。他们说，“并非所有的泡沫都破坏财富和价值。有些可以被理解为技术科学进步的重要催化剂。”
But I would restate as follows: “Mean-reversion bubbles” – in which markets soar on the basis of some new financial miracle and then collapse – destroy wealth. On the other hand, “inflection bubbles” based on revolutionary developments accelerate technological progress and create the foundation for a more prosperous future, and they destroy wealth. The key is to not be one of the investors whose wealth is destroyed in the process of bringing on progress.
但我会重新表述如下：“均值回归型泡沫”——市场基于某种新的金融奇迹而飙升然后崩溃——会破坏财富。另一方面，基于革命性发展的“拐点型泡沫”加速了技术进步，并为更繁荣的未来创造了基础，同时它们也破坏财富。关键在于不要成为那些在推动进步的过程中财富被毁灭的投资者之一。
Hobart and Huber go on to describe in greater depth the process through which bubbles finance the building of the infrastructure required by the new technology and thus accelerate its adoption:
Hobart 和 Huber 继续深入描述了泡沫如何为新技术所需的基础设施建设提供融资，从而加速其采用的过程：
Most novel technology doesn't just appear ex nihilo [i.e., from nothing], entering the world fully formed and all at once. Rather, it builds on previous false starts, failures, iterations, and historical path dependencies. Bubbles create opportunities to deploy the capital necessary to fund and speed up such large-scale experimentation – which includes lots of trial and error done in parallel – thereby accelerating the rate of potentially disruptive technologies and breakthroughs.
In other words, bubbles based on technological progress are good because they excite investors into pouring in money – a good bit of which is thrown away – to carpet-bomb a new area of opportunity and thus jump-start its exploitation.
换句话说，基于技术进步的泡沫之所以是好事，是因为它们刺激投资者涌入资金——其中很大一部分被浪费了——对一个新的机会领域进行地毯式轰炸，从而启动对其的开发。
The key realization seems to be that if people remained patient, prudent, analytical, and value-insistent, novel technologies would take many years and perhaps decades to be built out. Instead, the hysteria of the bubble causes the process to be compressed into a very short period – with some of the money going into life-changing investment in the winners but a lot of it being incinerated.
关键的认识似乎是，如果人们保持耐心、审慎、分析性并坚持价值，新技术的建立可能需要许多年甚至几十年。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内——部分资金进入了赢家手中成为改变生活的投资，但大量资金被焚烧殆尽。
Assessing the Current Landscape
评估当前形势
Now let's get down to what we used to call “brass tacks.” What do we know? First, I haven't met anyone who doesn't believe artificial intelligence has the potential to be one of the biggest technological developments of all time, reshaping both daily life and the global economy.
现在让我们谈谈实际问题。我们知道什么？首先，我遇到的每个人都相信人工智能有潜力成为有史以来最大的技术发展之一，重塑日常生活和全球经济。
We also know that in recent years, economies and markets have become increasingly dependent on AI:
- AI is responsible for a very large portion of companies' total capital expenditures.
- Capital expenditures on AI capacity account for a large share of the growth in U.S. GDP.
- AI stocks have been the source of the vast majority of the gains of the S&P 500.
我们也知道，近年来，经济和市场对人工智能的依赖程度日益增加：
- 人工智能占公司总资本支出的很大一部分。
- 人工智能产能的资本支出占美国GDP增长的很大份额。
- 人工智能股票是标准普尔500指数绝大部分涨幅的来源。
As a Fortune headline put it on October 7:
正如《财富》杂志10月7日的标题所言：
75% of gains, 80% of profits, 90% of capex – AI's grip on the S&P is total and Morgan Stanley's top analyst is 'very concerned'
Further, I think it's important to note that whereas the gains in AI-related stocks account for a disproportionate percentage of the total gains in all stocks, the excitement AI injects into the market must have added a lot to the appreciation of non-AI stocks as well.
此外，我认为值得注意的是，尽管人工智能相关股票的涨幅在所有股票的总涨幅中占据了不成比例的份额，但人工智能注入市场的兴奋感肯定也极大地推动了非人工智能股票的升值。
AI-related stocks have shown astronomical performance, led by Nvidia, the leading developer of computer chips for AI. From its formation in 1993 and its initial public offering in 1999, when its estimated market value was $626 million, Nvidia briefly became the world's first company worth $5 trillion. That's appreciation of around 8,000x, or roughly 40% a year for 26+ years. No wonder imaginations have been fired.
人工智能相关股票表现出了天文数字般的业绩，以人工智能计算机芯片的领先开发商英伟达（Nvidia）为首。自1993年成立及1999年首次公开募股（当时其估计市值为6.26亿美元）以来，英伟达一度成为世界上第一家市值达到5万亿美元的公司。这相当于大约8000倍的增值，或者说在26年多的时间里每年增长约40%。难怪人们的想象力被点燃了。
What Are the Areas of Uncertainty?
不确定性领域在哪里？
I think it's fair to say that while we know AI will be a source of incredible change, most of us have no idea exactly what it will be able to do, how it will be applied commercially, or what the timing will be.
我认为可以公平地说，虽然我们知道人工智能将带来令人难以置信的变化，但我们要么不知道它究竟能做什么，要么不知道它将如何商业化应用，或者时间表会是怎样。
Who will be the winners, and what will they be worth? If a new technology is assumed to be a world changer, it's invariably assumed that the leading companies possessing that technology will be of great value. But how accurate will that assumption prove to be? As Warren Buffett pointed out in 1999, “[The automobile was] the most important invention, probably, of the first half of the 20th century. . . . If you had seen at the time of the first cars how this country would develop in connection with them, you would have said, 'This is the place I must be.' But of the 2,000 companies [that entered the auto business], three survive. . . . So it's not easy to pick winners, and it's not easy to pick winners in a new industry.”
谁将是赢家，他们将值多少钱？如果一项新技术被认为将改变世界，人们总是假设拥有该技术的领先公司将具有巨大的价值。但这个假设会被证明有多准确？正如沃伦·巴菲特在1999年指出的那样，“[汽车是] 20世纪上半叶最重要的发明，可能没有之一……如果你在第一辆汽车出现时看到这个国家将如何围绕它们发展，你会说，‘这是我必须涉足的领域。’但在进入汽车行业的2000家公司中，只有三家幸存下来……所以挑选赢家并不容易，在一个新行业中挑选赢家更不容易。”
Will AI produce profits, and for whom? Two things we know little or nothing about are the profits AI will produce for vendors and its impact on non-AI companies, primarily meaning those who employ it.
人工智能会产生利润吗？为谁产生？我们知之甚少甚至一无所知的两件事是，人工智能将为供应商产生多少利润，以及它对非人工智能公司（主要是那些使用它的公司）有什么影响。
Will AI be a monopoly or duopoly, in which one or two leading companies are able to charge dearly for the capabilities? Or will it be a highly competitive free-for-all in which a number of firms compete on price for users' spending on AI services, making it a commodity? Or, perhaps most likely, will it be a mix of leading companies and specialized players, some of whom compete on price and others through proprietary advantages. It's said that the services currently responding to AI queries, such as ChatGPT and Gemini, lose money on every query they answer (of course, it's not unusual for participants in a new industry to offer “loss leaders” for a while). Will the leading tech firms – used to success in winner-take-all markets – be content to experience losses in their AI businesses for years in order to gain share? Hundreds of billions of dollars are being committed to the race for AI leadership. Who will win, and what will be the result?
人工智能会成为垄断或双寡头垄断，由一两家领先公司对其能力收取高价吗？还是会成为一场高度竞争的混战，许多公司为了争夺用户在人工智能服务上的支出而进行价格竞争，使其成为一种大宗商品？或者，最有可能的是，它会是领先公司和专业参与者的混合体，其中一些在价格上竞争，另一些则通过专有优势竞争。据说目前响应人工智能查询的服务，如 ChatGPT 和 Gemini，每回答一个查询都在亏损（当然，新行业的参与者在一段时间内提供“亏本赚吆喝”的产品并不罕见）。那些习惯了在赢家通吃市场中取得成功的领先科技公司，会满足于在人工智能业务上连年亏损以换取市场份额吗？数千亿美元正被投入到人工智能领导地位的竞赛中。谁会赢，结果会是什么？
Should we worry about so-called “circular deals”? In the telecom boom of the late 1990s, in which optical fiber became overbuilt, fiber-owning companies engaged in transactions with each other that permitted them to report profits. If two companies own fiber, they just have an asset on their books. But if each buys capacity from the other, they can both report profits . . . so they did. In other cases, manufacturers loaned network operators money to buy equipment from them, before the operators had customers to justify the buildout. All this resulted in profits that were illusory.
我们应该担心所谓的“循环交易”吗？在90年代末的光纤过度建设的电信繁荣中，拥有光纤的公司之间进行交易，使它们能够报告利润。如果两家公司拥有光纤，它们的账面上只有资产。但如果它们互相购买对方的容量，它们都可以报告利润……所以它们就这样做了。在其他情况下，制造商借钱给网络运营商购买设备，而当时运营商还没有客户来证明这种建设的合理性。所有这些导致了虚幻的利润。
Nowadays, deals are being announced in which money appears to be round-tripped between AI players. People who believe there's an AI bubble find it easy to view these transactions with suspicion. Is the purpose to achieve legitimate business goals or to exaggerate progress?
如今，宣布的一些交易中，资金似乎在人工智能参与者之间循环流动。那些相信存在人工智能泡沫的人很容易用怀疑的眼光看待这些交易。目的是实现合法的商业目标还是为了夸大进展？
Adding to worries, critics say, some of the deals that OpenAI has made with chipmakers, cloud computing companies and others are oddly circular. OpenAI is set to receive billions from tech companies but also sends billions back to the same companies to pay for computing power and other services. . . .
批评人士称，OpenAI 与芯片制造商、云计算公司等达成的一些交易奇怪地呈现循环状，这加剧了担忧。OpenAI 将从科技公司获得数十亿美元，但也向这些公司回流数十亿美元以支付算力和其他服务费用……
Nvidia has also made some deals that have raised questions about whether the company is paying itself. It announced that it would invest $100 billion in OpenAI. The start-up receives that money as it buys or leases Nvidia's chips. . . .
英伟达也达成了一些引发质疑的交易，人们怀疑该公司是否在自掏腰包。它宣布将向 OpenAI 投资1000亿美元。这家初创公司在购买或租赁英伟达芯片时获得这笔资金……
Goldman Sachs has estimated that Nvidia will make 15 percent of its sales next year from what critics also call circular deals. (The New York Times, November 20)
高盛估计，英伟达明年的销售额中有15%将来自批评者所说的循环交易。（《纽约时报》，11月20日）
Noteworthily, OpenAI has made investment commitments to industry counterparties totaling $1.4 trillion, even though it has yet to turn a profit. The company makes clear that the investments are to be paid out of revenues received from the same parties and that it has ways to back out of these commitments. But all this raises the question of whether the AI industry has developed a perpetual motion machine.
值得注意的是，OpenAI 已向行业交易对手做出了总计1.4万亿美元的投资承诺，尽管它尚未实现盈利。该公司明确表示，这些投资将由从相同方获得的收入支付，并且它有办法退出这些承诺。但所有这些都引发了一个问题：人工智能行业是否开发出了一台永动机。
(On this subject, I've been enjoying articles questioning the ability of people to relate to the word “trillion,” and I think this idea is spot on. A million dollars is a dollar a second for 11.6 days. A billion dollars is a dollar a second for 31.7 years. We get that. But a trillion dollars is a dollar a second for 31,700 years. Who can get their head around the significance of 31,700 years?)
（关于这个话题，我一直在阅读质疑人们理解“万亿”一词能力的文章，我认为这个观点非常到位。一百万美元是每秒一美元，持续11.6天。十亿美元是每秒一美元，持续31.7年。这我们能理解。但一万亿美元是每秒一美元，持续31,700年。谁能理解31,700年的意义？）
What will be the useful life of AI assets? We have to wonder whether the topic of obsolescence is being handled correctly in AI-land. What will be the lifespan of AI chips? How many years of earnings growth should be counted on in assigning p/e ratios for AI-related stocks? Will chips and other aspects of AI infrastructure last long enough to repay the debt undertaken to buy them? Will artificial general intelligence (a machine capable of doing anything the human brain can do) be achieved? Will that be the end of progress, or might there be further revolutions, and what firms will win them? Will firms reach a position where technology is stable and they can extract economic value from it? Or will new technologies continually threaten to supplant older ones as the route to success?
人工智能资产的使用寿命将是多少？我们不得不怀疑，过时这一话题在人工智能领域是否得到了正确的处理。人工智能芯片的寿命有多长？在为人工智能相关股票分配市盈率时，应该指望多少年的盈利增长？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欠的债务？通用人工智能（AGI，一种能够做人脑能做的任何事情的机器）会实现吗？那是进步的终点吗，还是会有进一步的革命，哪些公司会赢得它们？公司会达到技术稳定并能从中提取经济价值的地位吗？还是新技术会不断威胁要取代旧技术成为成功的途径？
In this connection, a single issue of an FT newsletter briefly mentioned two developments that suggest the fluid nature of the competitive landscape:
在这方面，一份《金融时报》时事通讯简要提到了两个事态发展，暗示了竞争格局的流动性：
A study by the Massachusetts Institute of Technology and open-source AI start-up Hugging Face found that the total share of downloads of new Chinese-made open models rose to 17 per cent in the past year. The figure surpasses the 15.8 per cent share of downloads from American developers such as Google, Meta and OpenAI – the first time Chinese groups have beaten their American counterparts. . . .
Dynamic change creates the opportunity for incredible new technologies, but that same dynamism can threaten the leading companies' reign. Amid all these uncertainties, investors must ask whether the assumption of continued success incorporated in the prices they're paying is fully warranted.
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领先公司的统治。在所有这些不确定性中，投资者必须问自己，他们所支付的价格中包含的持续成功的假设是否完全有根据。
Is exuberance leading to speculative behavior? For an extreme example, I'll cite the trend toward venture capital investments in startups via $1 billion “seed rounds.” Here's one vignette:
繁荣是否导致了投机行为？作为一个极端的例子，我将引用通过10亿美元“种子轮”对初创公司进行风险投资的趋势。这里有一个小插曲：
Thinking Machines, an AI startup helmed by former Open AI executive Mira Murati, just raised the largest seed round in history: $2 billion in funding at a $10 billion valuation. The company has not released a product and has refused to tell investors what they're even trying to build. “It was the most absurd pitch meeting,” one investor who met with Murati said. “She was like, 'So we're doing an AI company with the best AI people, but we can't answer any questions.' ” (“The Is How the AI Bubble Will Pop,” Derek Thompson Substack, October 2)
But that's ancient history. . . already two months old. Here's an update:
但这已是古代历史了……都已经过去两个月了。这是一个更新：
Thinking Machines Lab, the artificial intelligence startup founded by former Open AI executive Mira Murati, is in early talks to raise a new funding round at a roughly $50 billion valuation, Bloomberg News reported on Thursday. The startup was last valued at $12 billion in July, after it raised about $2 billion. (Reuters, November 13)
And Thinking Machines Lab isn't alone:
Thinking Machines Lab 并不孤单：
In one of the boldest bets yet in the AI arms race, Safe Superintelligence (SSI), the stealth startup founded by former OpenAI chief scientist Ilya Sutskever, has raised $2 billion in a round that values the company at $32 billion – despite having no publicly released product or service. (CTech by Calcalist, April 13)
What's the end state? Part of the issue with AI includes the unusual nature of this newest thing. This isn't like a business that designs and sells a product, making money if the selling price exceeds the cost of the inputs. Rather, it's companies building an airplane while it's in flight, and once it's built, they'll know what it can do and whether anyone will pay for its services.
最终状态是什么？人工智能的部分问题在于这个新生事物的非同寻常的性质。这不像是一个设计和销售产品的企业，如果售价超过投入成本就能赚钱。相反，这是公司在飞行中制造飞机，一旦建成，他们才会知道它能做什么，以及是否有人会为它的服务付费。
Many companies justify their spending because they're not just building a product, they're creating something that will change the world: artificial general intelligence, or A.G.I. . . . The rub is that none of them quite know how to do it.
许多公司为他们的支出辩护，因为他们不只是在制造产品，他们正在创造将改变世界的东西：通用人工智能（AGI）……问题是他们中没有人完全知道该怎么做。
But Anton Korinek, an economist at the University of Virginia, said the spending would all be justified if Silicon Valley reached its goal. He is optimistic it can be done. “It's a bet on A.G.I. or bust,” Dr. Korinek said. (The New York Times, November 20 – emphasis added)
但弗吉尼亚大学经济学家安东·科里内克（Anton Korinek）表示，如果硅谷实现了目标，这些支出就都是合理的。他对能否实现这一目标持乐观态度。“这是在赌 AGI，不成功便成仁，”科里内克博士说。（《纽约时报》，11月20日——强调为后加）
The yet-to-be-determined nature of the industry under construction is best captured in remarks from Sam Altman, the CEO of OpenAI, that have been paraphrased as follows: “we'll build this sort of generally intelligent system and then ask it to figure out a way to generate an investment return from it.”
OpenAI 首席执行官 Sam Altman 的言论最好地捕捉到了这个正在建设中的行业尚未确定的性质，其大意如下：“我们将建立这种通用智能系统，然后让它想办法从中产生投资回报。”
This should be a source of pause for people who heretofore fully comprehended the nature of the businesses they invested in. Clearly, the value of a technology that equals or surpasses the human brain should be pretty big, but isn't it well beyond calculation?
对于那些迄今为止完全理解他们所投资企业性质的人来说，这应该是一个停顿反思的源头。显然，一项能够匹敌或超越人脑的技术的价值应该相当大，但它难道不是无法计算的吗？
A Word About the Use of Debt
关于债务使用的一点看法
To date, much of the investment in AI and the supporting infrastructure has consisted of equity capital derived from operating cash flow. But now, companies are committing amounts that require debt financing, and for some of those companies, the investments and leverage have to be described as aggressive.
迄今为止，对人工智能及其支持基础设施的大部分投资都来自于经营现金流产生的股本资本。但现在，公司承诺的金额需要债务融资，对于其中一些公司来说，投资和杠杆必须被描述为激进。
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 $5tn (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 $350bn in the bank, collectively, as of the end of the third quarter. (“Unhedged,” Financial Times, November 13)
人工智能数据中心的繁荣绝不可能仅靠现金融资。这个项目太大了，无法自掏腰包支付。摩根大通的分析师在餐巾纸背面，或者可能是桌布上算了一笔账，估计基础设施建设的账单将达到5万亿美元（不包括小费）。谁知道这是否正确，但我们有充分理由预计明年的支出将接近半万亿美元。与此同时，截至第三季度末，最大的支出者（微软、Alphabet、亚马逊、Meta 和甲骨文）银行里的现金总共只有大约3500亿美元。（“Unhedged”，《金融时报》，11月13日）
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.
上述公司从其非常强劲的非人工智能业务中获得健康的现金流。但在人工智能领域大规模的、赢家通吃的军备竞赛正要求一些公司承担债务。事实上，有理由认为他们花费巨额资金的原因之一是为了让较小的公司难以跟上。
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?
甲骨文、Meta 和 Alphabet 发行了30年期债券来资助人工智能投资。就后两者而言，债券收益率仅比同期限国债高出100个基点或更少。接受30年的技术不确定性来进行一项收益率仅略高于无风险债务的固定收益投资，这审慎吗？用债务资助的投资——芯片和数据中心——能否在足够长的时间内保持其生产力水平，以偿还这些30年的债务？
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:
11月14日，Alex Kantrowitz 的 Big Technology 播客刊登了与金融服务公司 D.A. Davidson 技术研究主管 Gil Luria 的对话，主要关于人工智能领域的债务使用。以下是 Luria 的部分观点：
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.”
Among potentially worrisome factors, Luria cites these:
Luria 列举了以下潜在的令人担忧的因素：
- “A speculative asset . . . we don't know how much of it we're really going to need in two to five years.”
“一种投机性资产……我们不知道两到五年后我们到底需要多少。”
- Lender personnel with incentives to make loans but no exposure to long-term consequences
有动力发放贷款但不承担长期后果的贷款人员
- The possibility that the supply of AI capacity catches up with or surpasses the demand
人工智能产能供应赶上或超过需求的可能性
- The chance that future generations of AI chips will be more powerful, obsoleting existing ones or reducing their value as backing for debt
下一代人工智能芯片功能更强大，导致现有芯片过时或降低其作为债务抵押品价值的机会
- Powerful competitors who vie for market share by cutting rental rates and running losses
通过降低租金和亏损运营来争夺市场份额的强大竞争对手
Here are some important paragraphs from Azeem Azhar's Exponential View of October 18:
以下是 Azeem Azhar 10月18日 Exponential View 中的一些重要段落：
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.
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.)
Azhar 提到了通过特殊目的实体（SPV）使用资产负债表外融资，这是导致安然（Enron）岌岌可危并最终倒闭的最大因素之一。一家公司及其合作伙伴为特定目的设立 SPV 并提供股本资本。母公司可能拥有经营控制权，但由于没有多数股权，它不会在财务报表中合并 SPV。SPV 承担债务，但该债务不会出现在母公司的账簿上。母公司可能是投资级借款人，但同样，该债务不是母公司的义务，也不由其担保。今天的债务可能由数据中心租户——有时是股权合作伙伴——承诺的租金支持，但债务也不是股权合作伙伴的直接义务。本质上，SPV 是一种手段，用来让公司看起来没有做 SPV 正在做的事情，也没有 SPV 所拥有的债务。（私募股权基金和私募信贷基金极有可能出现在这些实体的合作伙伴和贷款人中。）
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.
正如我之前引用的，根据佩雷斯（她在互联网泡沫之后写作）的说法，“使部署阶段成为可能的是那些亏损的投资。”早期投资在“明斯基时刻”中损失殆尽，在延长的上升周期中做出的不明智承诺在修正中遭遇价值毁灭。关于债务的使用，有三件事我们可以确定：如果有损失，它会放大损失（就像如果希望的收益实现，它会放大收益一样）；它增加了企业失败的概率。
But I do know something about debt, and it's this:
但我确实知道关于债务的一点，那就是：
- It's okay to supply debt financing for a venture where the outcome is uncertain.
为结果不确定的企业提供债务融资是可以的。
- It's not okay where the outcome is purely a matter of conjecture.
如果结果纯粹是猜测，那是不可行的。
- Those who understand the difference still have to make the distinction correctly.
那些理解这种区别的人仍然必须正确地做出区分。
