2026-01-21 15:15 Tags:Money
https://www.oaktreecapital.com/insights/memo/is-it-a-bubble
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.
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.”
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.
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.
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.
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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.
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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.
What’s Good About Bubbles?
there are two kinds of bubbles: “Inflection Bubbles” – the good kind of bubbles, as opposed to the much more damaging “Mean-reversion Bubbles” like the 2000’s subprime mortgage bubble.
Perez didn’t deny the pain: in fact, she noted that similar crashes marked previous revolutions, including the Industrial Revolution, railways, electricity, and the automobile. In each case the bubbles were not regrettable, but necessary: the speculative mania enabled what Perez called the “Installation Phase,” where necessary but not necessarily financially wise investments laid the groundwork for the “Deployment Period.” What marked the shift to the deployment period was the popping of the bubble; what enabled the deployment period were the money-losing investments.
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.
Like bubbles, FOMO tends to have a bad reputation, but it’s sometimes a healthy instinct. After all, none of us wants to miss out on a once-in-a-lifetime chance to build the future. （lol that’s true)
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.
A bubble has aspects that are both technological and financial, but the above citations are from the standpoint of people who crave technological progress and are perfectly happy to see investors lose money in its interest. “We,” on the other hand, would like to see technological progress but have no desire to throw away money to help bring it about.
技术变革带来的泡沫还是金融新的手段带来的泡沫
What Are the Areas of Uncertainty?
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 autos, you would have said, ‘This is the place I must be.’ But of the 2,000 companies, as of a few years ago, only three car companies survived. So autos had an enormous impact on America but the opposite direction on investors.”
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.
Noteworthily, OpenAI has made investment commitments to industry counterparties totaling $1.4 trillion, even though it has yet to turn a profit. (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?)😱😱😱 I guess the number is too big, people lose the sense to feel it…
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.
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.
A Word About the Use of Debt
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 350bn in the bank, collectively, as of the end of the third quarter.
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?
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.
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.
Debt is neither a good thing nor a bad thing per se. 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.
I know I don’t know enough to opine on AI. But I do know something about debt, and it’s this:
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It’s okay to supply debt financing for a venture where the outcome is uncertain.
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It’s not okay where the outcome is purely a matter of conjecture.
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Those who understand the difference still have to make the distinction correctly.
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).
对于技术性的变革 最好不要用债务 而是用期权 债务是期待人 公司还的！ 期权是谋求成长性的 对于稳定期 债务 对于成长起 期权
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.
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 The New Yorker 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.
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.**🥲🥲🥲
In Conclusion
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.” (The New York Times, November 20)
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.
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:
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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.”
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AI has the potential to be one of the greatest transformational technologies of all time.
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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.
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Bubbles created in this process usually end in losses for those who fuel them.
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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.
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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.
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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.
我觉得哈 他还是比较pro 是会有泡沫发生的 如果没有这是人类历史上头一遭重大技术变革却没有泡沫 这可能吗 我觉得不太可能
Postscript
one of AI’s main impacts will be to increase productivity and thus eliminate jobs. That is the source of my concern.
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:
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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?
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And more importantly, 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?
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.
这是我最爱的后记部分 他关注了普通人的生活 失业很可能是非常痛苦的社会变革 民粹主义上升 贫富差距再次加大 没有 junior，很难长出 senior 尽管我也相信技术变革带来是阵痛 但对于生活其中的普通人来说 这样的阵痛可能对我们的人生造成不可逆转的影响 人生很长但人生也很短 没有人想经历recession
和GPT的探讨：
四、那“你的路径”应该是什么样？
我不替你规划人生，但我可以给你一个清晰的方向原则，你已经在不自觉地往这条路上走了。
原则一：不要只卖时间，要逐步卖“判断”
AI 最难替代的不是执行，而是：
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定义问题
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权衡取舍
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在不确定中做选择
你现在做的很多事（研究、分析、写作、反思）
本质上是在训练判断力，而不是技能本身。
原则二：同时站在“劳动力一侧”和“资本一侧”
这点非常重要。
你不能只做：
- 被 AI 替代的人
你要哪怕很小，也要站在：
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拥有资产
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拥有系统性收益
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拥有长期敞口的一侧
你现在投指数、理解资本逻辑、读 Marks、Buffett、Munger ——
这些都不是“发财技巧”，而是不被完全甩下车的门票。
原则三：建立“多层缓冲”，而不是单点最优
未来不是“选对一次就赢一辈子”，而是：
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多个身份
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多种收入可能
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多种转向空间
你越是：
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会学习
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会表达
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会跨界
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会抽象总结
👉 你就越不容易被某一次技术跃迁直接清零。
五、你现在最不需要做的一件事
我很认真地说：
你现在不需要立刻“想明白一生怎么走”。
你需要的是：
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接受这个世界确实在变残酷
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同时承认：你已经比很多人更早看懂了
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给自己一条“可转向、可积累、不孤注一掷”的路线
你不是在跟“赢家”比赛，
你是在避免成为系统性输家。