这是泡沫吗？

这是霍华德·马克斯的备忘录。

This is the memo by Howard Marks.

这是一场泡沫吗？

Is it a bubble?

我们正处在一个非凡的历史时刻。

Ours is a remarkable moment in world history.

一种变革性技术正在崛起，其支持者声称它将永远改变世界。

A transformative technology is ascending, and its supporters claim it will forever change the world.

要构建它，企业需要投入一笔前所未有的巨额资金。

To build it requires companies to invest a sum of money unlike anything in living memory.

新闻报道中充斥着广泛的担忧，认为美国最大的企业正在支撑一个即将破裂的泡沫。

News reports are filled with widespread fears that America's biggest corporations are propping up a bubble that will soon pop.

上个月，我在亚洲和中东拜访客户时，经常被问及人工智能是否正在形成泡沫，我的这些讨论促成了这份备忘录。

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.

我想先谈谈我一贯的保留意见。

I wanna start off with my usual caveats.

我不参与股票市场。

I'm not active in the stock market.

我只是将其视为投资者心理的最佳晴雨表。

I merely watch it as the best barometer of investor psychology.

我也不是技术专家，对人工智能的了解并不比大多数普通投资者多，但我还是会尽力而为。

I'm also no techie, and I don't know any more about AI than most generalist investors, but I'll do my best.

泡沫最有趣的特点之一是其规律性，不是体现在时间上，而是体现在它们的发展进程上。

One of the most interesting aspects of bubbles is their regularity, not in terms of timing, but rather the progression they follow.

某种新颖且看似革命性的事物出现，并逐渐渗透到人们的思维中。

Something new and seemingly revolutionary appears and worms its way into people's minds.

它激发了人们的想象力，兴奋之情难以抗拒。

It captures their imagination, and the excitement is overwhelming.

早期参与者获得了巨大收益，而旁观者则感到无比嫉妒和懊悔，出于害怕继续错失机会，纷纷涌入。

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.

他们这样做时，并不了解未来会怎样，也不关心他们所支付的价格是否有可能带来合理的回报并承受可接受的风险。

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.

对投资者而言，最终结果在短期到中期不可避免地是痛苦的，尽管经过足够多年后，有可能最终获利。

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.

我亲历过几次泡沫，也读过其他泡沫的历史，它们都遵循了这一模式。

I've lived through several bubbles and read about others, and they've all hewed to this description.

人们或许会认为，过去泡沫破裂带来的损失会阻止下一次泡沫的形成，但这种情况从未发生，我相信也永远不会发生。

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.

记忆是短暂的，谨慎和与生俱来的风险规避无法与那种通过革命性技术一夜暴富的梦想相抗衡，而每个人都相信这项技术将改变世界。

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.

我引用的这句开场引语来自德里克·汤普森于11月4日发布的通讯，标题为《人工智能可能是21世纪的铁路》。

I took the quote that opens this memo from Derek Thompson's November 4 newsletter entitled AI could be the railroad of the twenty first century.

做好心理准备，看看当今人工智能与19世纪60年代铁路热潮之间的相似之处。

Brace yourself about parallels between what's going on today in AI and the railroad boom of the eighteen sixties.

这段话一字不差地适用于两者，清晰地说明了那句常被归于马克·吐温的话的含义：历史会押韵。

Its word for word applicability to both shows clearly what's meant by the phrase widely attributed to Mark Twain, history rhymes.

理解泡沫。

Understanding bubbles.

在深入探讨当前主题之前，鉴于我已为此阅读了大量资料，我想先澄清一点。

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.

每个人都问：AI领域是否存在泡沫？

Everyone asks, is there a bubble in AI?

我认为这个问题本身就有歧义。

I think there's ambiguity even in the question.

我得出结论，有两类不同但相互关联的泡沫可能性需要考虑：一类是行业内公司的行为，另一类是投资者对该行业的投资行为。

I've concluded there are two different but interrelated bubble possibilities to think about: one in the behaviour of companies within the industry, and the other in how investors are behaving with regard to the industry.

我完全没有能力判断AI公司激进行为的合理性，因此我将主要聚焦于金融领域是否存在AI泡沫这一问题。

I have absolutely no ability to judge whether the AI company's 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.

投资分析师的主要工作，尤其是我所归属的价值投资学派，一是研究公司和其他资产，评估其内在价值的水平和前景；二是基于这种价值做出投资决策。

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.

分析师在短期至中期遇到的大部分变化，都围绕着资产价格与其内在价值之间的关系。

Most of the change the analyst encounters in the short to medium term surrounds the asset's price and its relationship to underlying value.

而这种关系本质上是投资者心理的结果。

That relationship, in turn, is essentially the result of investor psychology.

市场泡沫并非直接由技术或金融发展引起。

Market bubbles aren't caused directly by technological or financial developments.

相反，它们源于对这些发展过度乐观的解读。

Rather, they result from the application of excessive optimism to those developments.

正如我在一月份的《泡沫观察》备忘录中所写，泡沫是一种暂时性的狂热，其中这些领域的进展成为前美国联邦储备委员会主席艾伦·格林斯潘所称的‘非理性繁荣’的对象。

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 US Federal Reserve chairman Alan Greenspan called irrational exuberance.

泡沫通常围绕新的金融发展形成，例如18世纪早期的南海公司，或2006年2月的次级住宅抵押贷款支持证券；或技术进步，如20世纪90年代末的光纤，以及1998年至2000年的互联网。

Bubbles usually coalesce around new financial developments, for example, the South Sea Company of the early seventeen hundreds, or subprime residential mortgage backed securities in 02/2006, or technological progress, optical fiber in the late nineteen nineties, and the Internet in 1998 to 2000.

新颖性在其中扮演了至关重要的角色。

Newness plays a huge part in this.

由于缺乏历史经验来约束想象力，新事物的未来似乎无限广阔，而被视为无限的未来前景，会使得估值远远超越过去的常态，导致资产价格脱离了可预测盈利能力的基础。

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.

新颖性的作用在约翰·肯尼斯·加尔布雷斯所著、对我影响深远的《金融狂热简史》中得到了精彩阐述。

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.

加尔布雷斯谈到他所说的金融记忆的极端短暂性，并指出，在金融市场中，过去的经验——即使存在记忆之中——也被视为缺乏洞察力者逃避现实的原始避难所，他们无法欣赏当今令人难以置信的奇迹。

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.

换句话说，历史可以对当下令人敬畏的事物和对未来的想象施加限制。

In other words, history can impose limits on awe regarding the present and imagination regarding the future.

另一方面，如果没有历史，一切似乎都可能实现。

In the absence of history, on the other hand, all things seem possible.

这里需要注意的关键是，新事物自然会引发极大的热情，但当这种热情达到非理性的程度时，就会形成泡沫。

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.

谁能界定理性的边界？

Who can identify the boundary of rationality?

谁能说清楚一个乐观的市场何时变成了泡沫？

Who can say when an optimistic market has become a bubble?

这仅仅是一个判断问题。

It's just a matter of judgment.

我上个月想到的一件事是，我最成功的两次预测都发生在2000年，当时我警示了科技和互联网股票市场的状况，以及2005年至2007年间，我指出风险规避的缺失以及全球金融危机前疯狂交易盛行的现象。

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 to o seven, when I cited the dearth of risk aversion and the resulting ease of doing crazy deals in the pre global financial crisis world.

首先，在这两种情况下，我都对最终成为泡沫主题的事物——互联网和次级抵押贷款支持证券——没有任何专业知识。

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 subprime mortgage backed securities.

我所做的只是对周围发生的行为进行观察。

All I did was render observations regarding the behavior taking place around me.

其次，我的观点的价值主要在于描述这种行为中的荒谬之处，而非坚持认为它已经引发了泡沫。

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.

纠结于是否使用‘泡沫’这一标签，可能会让你陷入困境，妨碍正确的判断。

Struggling with whether to apply the bubble label can bog you down and interfere with proper judgment.

我们只需评估周围发生的事情，并据此推断出适当的行为，就能取得很大成就。

We can accomplish a great deal by merely assessing what's going on around us and drawing inferences with regard to proper behavior.

泡沫有什么好处？

What's good about bubbles?

在继续讨论人工智能以及它是否目前处于泡沫中之前，我想花一点时间谈谈一个可能对投资者而言显得有些学术的话题——泡沫的正面作用。

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.

你可能会觉得我对此话题的关注过度了，但我这么做是因为我觉得它非常有趣。

You may find the attention I devote to this topic excessive, but I do so because I find it fascinating.

11月5日的Strathecari通讯文章标题为《泡沫的好处》。

The November 5 Strathecari newsletter was entitled The Benefits of Bubbles.

其中，本·汤普森（与德里克无关）引用了一本名为《泡沫与停滞的终结》的书。

In it, Ben Thompson, no relation to Derek, cites a book titled Bubbles and the End of Stagnation.

该书由伯恩·霍伯特和托比亚斯·许伯撰写，他们提出泡沫分为两类：有益的转折型泡沫，以及更具破坏性的均值回归型泡沫，比如2000年代的次级抵押贷款泡沫。

It was written by Bern Hobart and Tobias Huber, who proposed that there were two kinds of bubbles inflection bubbles, the good kind of bubbles, as opposed to the much more damaging mean reversion bubbles, like the two thousands subprime mortgage bubble.

我认为这种二分法很有用。

I find this a useful dichotomy.

我所读过或亲历的金融潮流——南海公司、投资组合保险和次级抵押贷款支持证券——都基于无风险回报的承诺激发了人们的想象力。

The financial fads I've read about or witnessed, the South Sea Company, Portfolio Insurance, and subprime mortgage backed securities, stirred the imagination based on the promise of returns without risk.

但人们并不认为它们会为人类整体带来进步。

But there was no expectation that they would represent overall progress for mankind.

例如，没有人认为次级抵押贷款运动会彻底改变住房领域，人们只是觉得支持新购房者能赚钱。

There was, for example, no thought that housing would be revolutionized by the subprime mortgage movement, merely a feeling that there was money to be made from backing new buyers.

霍伯特和许伯称这些为均值回归型泡沫，大概是因为人们并不期待这些底层发展能推动世界前进。

Hobart and Huber call these mean reverting bubbles, presumably because there's no expectation that the underlying developments would move the world forward.

潮流只是兴起又消退。

Fads merely rise and fall.

另一方面，霍伯特和休伯将基于技术进步的泡沫称为转折点泡沫，例如铁路和互联网的情况。

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.

在由转折点驱动的泡沫之后，世界不会恢复到之前的状态。

After an inflection driven bubble, the world will not revert to its prior state.

在这种泡沫中，投资者认为未来将与过去有显著不同，并据此进行交易。

In such a bubble, investors decide that the future will be meaningfully different from the past and trade accordingly.

正如汤普森告诉我们，关于泡沫的权威著作长期以来是卡洛塔·佩雷斯的《技术革命与金融资本》。

As Thompson tells us, the definitive book on bubbles has long been Carlotta Perez's technological revolutions and financial capital.

在佩雷斯出版她的书时，泡沫被认为是一种负面现象，应当避免。

Bubbles were, are, thought to be something negative and to be avoided, particularly at the time Perez published her book.

那一年是2002年，全球大部分地区正经历着互联网泡沫破裂后的经济衰退。

The year was 2002, and much of the world was in a recession coming off the puncturing of the .com bubble.

佩雷斯并未否认其中的痛苦。

Perez didn't deny the pain.

事实上，她指出，类似的崩溃也标志着以往的革命，包括工业革命、铁路、电力和汽车。

In fact, she noted that similar crashes marked previous revolutions, including the industrial revolution, railways, electricity, and the automobile.

在每种情况下，这些泡沫并非令人遗憾，而是必要的。

In each case, the bubbles were not regrettable but necessary.

投机狂热促成了佩雷斯所称的‘安装阶段’，在此阶段，那些必要但未必财务上明智的投资为部署阶段奠定了基础。

The speculative mania enabled what Perez called the installation phase, where necessary but not necessarily financially wise investments laid the groundwork for deployment period.

标志着向部署阶段转变的，正是泡沫的破裂。

What marked the shift to the deployment period was the popping of the bubble.

推动部署阶段的是那些亏损的投资。

What enabled the deployment period were the money losing investments.

这一区别对霍巴特和休伯而言意义重大，我同意他们的观点。

This distinction is very meaningful for Hobart and Huber, and I agree.

他们指出，并非所有泡沫都会摧毁财富和价值。

They say not all bubbles destroy wealth and value.

有些泡沫可以被理解为推动科技进步的重要催化剂。

Some can be understood as important catalysts for technoscientific progress.

但我想重新表述如下：均值回归型泡沫，即市场基于某种新的金融奇迹飙升后又崩溃的泡沫，会摧毁财富。

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.

另一方面，基于革命性发展的转折点泡沫会加速技术进步，为更繁荣的未来奠定基础，同时也会摧毁财富。

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.

关键在于不要成为在推动进步过程中财富被摧毁的投资者之一。

The key is to not be one of the investors whose wealth is destroyed in the process of bringing on progress.

霍巴特和休伯进一步详细描述了泡沫如何融资建设新技术所需的基础设施，从而加速其普及。

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.

大多数新兴技术并非凭空出现，即并非从无到有、一次性完全成型地进入世界。

Most novel technology doesn't just appear ex nihilo, that is from nothing, entering the world fully formed and all at once.

相反，它们建立在以往的失败尝试、失败、迭代和历史路径依赖之上。

Rather, it builds on previous false starts, failures, iterations, and historical path dependencies.

泡沫创造了机会，使资本得以投入并加速这种大规模的实验，其中包括大量并行进行的试错，从而加快了潜在颠覆性技术和突破的进程。

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.

通过产生热情与投资的正向反馈循环，泡沫可以带来净收益。

By generating positive feedback cycles of enthusiasm and investment, bubbles can be net beneficial.

乐观主义可能成为一种自我实现的预言。

Optimism can be a self fulfilling prophecy.

投机行为提供了资助高风险、探索性项目所需的巨额资金。

Speculation provides the massive financing needed to fund highly risky and exploratory projects.

从短期来看，这似乎只是过度热情或糟糕的投资，但实际上却是推动社会和技术创新的关键动力。

What appears in the short term to be excessive enthusiasm, or just bad investing, turns out to be essential for bootstrapping social and technological innovations.

泡沫可能是一种集体幻觉，但也可能是一种集体愿景的体现。

A bubble can be a collective delusion, but it can also be an expression of collective vision.

这种愿景成为人们与资本协调、并行推进创新的平台。

That vision becomes a site of coordination for people and capital and for the parallelization of innovation.

进步并非按部就班地发生，而是同时在不同领域爆发，随着热情高涨，风险承受能力增强，网络效应也日益显著。

Instead of happening over time, bursts of progress happen simultaneously across different domains, and with mounting enthusiasm comes increased risk tolerance and strong network effects.

错失恐惧症（FOMO）吸引了更多参与者、企业家和投机者，进一步强化了这一正向反馈循环。

The fear of missing out, or FOMO, attracts even more participants, entrepreneurs, and speculators, further reinforcing this positive feedback loop.

和泡沫一样，错失恐惧症往往名声不佳，但有时却是一种健康的本能。

Like bubbles, FOMO tends to have a bad reputation, but it's sometimes a healthy instinct.

毕竟，我们谁都不想错过这一生中唯一一次塑造未来的机会。

After all, none of us wants to miss out on a once in a lifetime chance to build the future.

换句话说，基于技术进步的泡沫是好的，因为它们激发了投资者投入资金，尽管其中很大一部分被浪费了，但这种地毯式轰炸的方式能够开辟新的机遇领域，从而加速其开发。

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.

关键的洞察似乎是，如果人们保持耐心、谨慎、理性并坚持价值导向，新兴技术的成熟可能需要多年甚至数十年的时间。

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.

然而，泡沫时期的狂热将这一过程压缩到极短的时间内，部分资金投入到了赢家身上，带来了改变生活的影响，但大量资金却被烧毁了。

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.

泡沫既具有技术层面的特征，也具有金融层面的特征，但前面提到的例子都是从渴望技术进步、并乐于看到投资者为技术进步而亏损的人的角度出发的。

A bubble has aspects that are both technological and financial, but the examples just mentioned are from the standpoint of people who crave technological progress and are perfectly happy to see investors lose money in its interest.

另一方面，我们希望看到技术进步，却不希望浪费金钱来推动它实现。

We, on the other hand, would like to see technological progress but have no desire to throw away money to help bring it about.

本·汤普森在讨论结束时说：这就是为什么我对谈论新技术感到兴奋，因为我不知道它们的前景如何。

Ben Thompson ends this discussion by saying, this is why I'm excited to talk about new technologies, the prospect for which I don't know.

我喜欢他既对未来的可能性充满热情，又坦承未来形态尚不明确的态度。

I love the fact that he's excited by future possibilities and at the same time admits that the shape of the future is unknown.

在我们的世界里，我们可能会说这非常危险。

In our world, we might say very risky.

评估当前的格局。

Assessing the current landscape.

现在让我们谈谈我们过去所谓的实质问题。

Now let's get down to what we used to call brass tacks.

我们都知道些什么？

What do we know?

首先，我还没有遇到过任何人不认为人工智能有潜力成为有史以来最重要的技术发展之一，重塑日常生活和全球经济。

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.

我们还知道，近年来，经济和市场对人工智能的依赖日益增加。

We also know that in recent years, economies and markets have become increasingly dependent on AI.

人工智能占了企业总资本支出的很大一部分。

AI is responsible for a very large portion of companies' total capital expenditures.

人工智能能力的资本支出占美国GDP增长的很大一部分。

Capital expenditures on AI capacity account for a large share of the growth in US GDP.

人工智能股票是标普500指数大部分涨幅的来源。

AI stocks have been the source of the vast majority of the gains of the S and P 500.

正如《财富》杂志在10月7日的标题所言：75%的收益、80%的利润、90%的资本支出。

As a Fortune headline put it on October 7, 75% of gains, 80% of profits, 90% of CapEx.

AI对标普指数的掌控是全面的，摩根士丹利的首席分析师对此深感担忧。

AI's grip on the S and P is total, and Morgan Stanley's top analyst is very concerned.

此外，我认为重要的是要注意，尽管与AI相关的股票收益占所有股票总收益的不成比例的份额，但AI为市场注入的兴奋情绪也必然大幅推高了非AI股票的价值。

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.

与AI相关的股票表现惊人，主要由AI计算机芯片领域的领军企业英伟达引领。

AI related stocks have shown astronomical performance led by NVIDIA, the leading developer of computer chips for AI.

自1993年成立、1999年首次公开募股（当时估值约为6.26亿美元）以来，英伟达曾短暂成为全球首家市值达到5万亿美元的公司。

From its formation in 1993 and its initial public offering in 1999, when its estimated market value was $626,000,000, NVIDIA briefly became the world's first company worth $5,000,000,000,000.

这相当于约8000倍的增长，或者说二十多年来年均约40%的涨幅。

That's appreciation of around 8,000 x or roughly 40% a year for twenty six plus years.

难怪人们的想象力被彻底点燃了。

No wonder imaginations have been fired.

哪些领域存在不确定性？

What are the areas of uncertainty?

我认为可以说，尽管我们知道人工智能将带来巨大的变革，但大多数人并不清楚它具体能做什么、如何商业化应用，以及何时会实现。

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.

谁会成为赢家？他们又会值多少钱？

Who will be the winners, and what will they be worth?

如果一种新技术被认为能改变世界，人们通常会假设拥有这项技术的领先公司将具有巨大价值。

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.

但这一假设最终会有多准确呢？

But how accurate will that assumption prove to be?

正如沃伦·巴菲特在1999年指出的，汽车可能是二十世纪上半叶最重要的发明。

As Warren Buffett pointed out in 1999, the automobile was the most important invention probably of the first half of the twentieth century.

如果你在汽车刚出现时就看到这个国家将如何与汽车发展紧密相连，你一定会说：‘这正是我必须投身的地方。’

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.

但据几年前的数据，两千多家汽车公司中，只有三家幸存下来。

But of the 2,000 companies, as of a few years ago, only three car companies survived.

因此，汽车对美国产生了巨大影响，但对投资者而言，结果却恰恰相反。

So autos had an enormous impact on America, but the opposite direction on investors.

时间，2012年1月23日。

Time, 01/23/2012.

目前，人工智能领域有一些非常强大的领军企业，包括世界上一些最强壮和最富有的公司。

In AI, there are some very strong leaders at present, including some of the world's strongest and richest companies.

但新技术历来具有颠覆性。

But new technology is notoriously disruptive.

今天的领军者会胜出，还是会输给新兴企业？

Will today's leaders prevail or give way to upstarts?

这场军备竞赛将花费多少？

How much will the arms race cost?

谁会获胜？

And who will win?

同样，一家新兴企业的股份价值几何？

Similarly, what's a share in an upstart worth?

与价值数万亿美元的领先者不同，我们有可能以仅数十亿甚至——恕我直言——数百万美元的企业估值投资一些潜在挑战者。

Unlike front runners worth trillions, it's possible to invest in some would be challengers at enterprise values in mere billions or even, might I say, millions?

2024年6月25日，CNBC报道称：

On 06/25/2024, CNBC reported as follows.

一家由大学辍学者创立的团队已从以主要风险合作伙伴为首的投资者处筹集了1.2亿美元，用于开发一款新的AI芯片以挑战英伟达。

A team founded by College Dropouts has raised $120,000,000 from investors led by primary venture partners to build a new AI chip to take on NVIDIA.

Etched公司首席执行官加文·乌伯蒂表示，这家初创公司相信，随着人工智能的发展，该技术大部分耗能的计算需求将由定制的专用集成电路（ASIC）满足。

Etched CEO Gavin Uberti said the startup is betting that as AI develops, most of the technology's power hungry computing requirements will be filled by customized hardwired chips called ASICs.

乌伯蒂对CNBC说：‘如果Transformer被淘汰，我们就完了。’

If transformers go away, we'll die, Uberti told CNBC.

但如果它们继续存在，我们将成为有史以来最大的公司。

But if they stick around, we're the biggest company of all time.

即使假设Etched即使成功也无法成为有史以来最大的公司，只要其估值能达到英伟达峰值的五分之一——仅仅1万亿美元——那么要使1.2亿美元的投资合理，需要多高的成功概率？

Even granting the possibility that Etched won't become the biggest company of all time if success could give them a valuation just one fifth of NVIDIA's peak, a mere $1,000,000,000,000, what probability of success would be required to justify an investment of $120,000,000?

为简化起见，假设这笔投资获得了100%的股权，你只需相信实现万亿估值的概率为千分之一，就能获得超过八倍的预期回报。

Assuming for simplicity's sake that the investment was for a 100% ownership stake, All you need is a belief that achieving the trillion dollar value has a probability of one tenth of a percent for an expected return of over eight times your money.

谁又能说Etched没有这个机会呢？

Who's to say Etched doesn't have that chance?

在这种情况下，为什么有人不参与呢？

And in that case, why would anyone not play?

上述内容正是我所说的彩票思维，即对巨额回报的幻想，使得人们即使面对极高的失败概率，也觉得参与是必然的。

The foregoing is what I call lottery ticket thinking, in which the dream of an enormous payoff justifies, no compels participation in an endeavor with an overwhelming probability of failing.

以这种方式计算期望值并没有错。

There's nothing wrong with calculating expected values this way.

领先的风投每天都在这样做，并取得了巨大成效。

Leading venture capitalists engage in it every day to great effect.

但关于潜在回报及其概率的假设必须是合理的。

But assumptions regarding the possible payoffs and their probabilities must be reasonable.

只要一想到万亿美金的回报，任何计算中的合理性都会被淹没。

Thinking about a trillion dollar payout will override reasonableness in any calculation.

人工智能会带来利润吗？利润归谁？

Will AI produce profits and for whom?

我们对人工智能为供应商带来的利润，以及对非人工智能公司（主要是使用人工智能的公司）的影响，知之甚少或完全不了解。

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.

AI会形成垄断或寡头格局，由一两家领先公司对AI能力收取高额费用吗？

Will AI be a monopoly or duopoly in which one or two leading companies are able to charge dearly for the capabilities?

还是会成为一个高度竞争的自由市场，众多公司通过价格竞争争夺用户在AI服务上的支出，使其成为一种商品？

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?

或者更有可能的是，它将是一个由领先公司和专业玩家混合而成的格局，一些公司通过价格竞争，另一些则依靠专有优势？

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?

据说，目前响应AI查询的服务，如ChatGPT和Gemini，每回答一次查询都会亏损。

It's said that the services currently responding to AI queries, such as ChatGPT and Gemini, lose money on every query they answer.

当然，新行业中的参与者短期内提供亏损产品作为引流手段并不罕见。

Of course, it's not unusual for participants in a new industry to offer loss leaders for a while.

习惯了赢家通吃市场的大型科技公司，会甘愿在AI业务中多年亏损以获取市场份额吗？

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?

数百亿美元正被投入到AI领导权的竞争中。

Hundreds of billions of dollars are being committed to the race for AI leadership.

谁会获胜，结果又会如何？

Who will win, and what will be the result?

同样，人工智能将对使用它的公司产生什么影响？

Likewise, what will be AI's impact on the companies that use it?

显然，人工智能将通过用计算机驱动的劳动力和智能取代工人等方式，极大地提升用户的生产力。

Clearly, AI will be a great tool for enhancing users' productivity by, among other things, replacing workers with computer sourced labor and intelligence.

但这种降低成本的能力，是会增加使用它的公司的利润率，还是会仅仅促使这些公司为了争夺客户而展开价格战？

But will this ability to cut costs add to the profit margins of the companies that employ it, or will it simply enable price wars among those companies in the pursuit of customers?

在这种情况下，节省的成本可能会传递给客户，而不是被公司所获取。

In that case, the savings might be passed on to the customers rather than garnered by the companies.

换句话说，人工智能是否有可能提高企业的效率，却并未提升其盈利能力？

In other words, is it possible AI will increase the efficiency of businesses without increasing their profitability?

我们是否应该担心所谓的循环交易？

Should we worry about so called circular deals?

在二十世纪九十年代末的电信热潮中，光纤网络过度建设，拥有光纤的公司之间相互进行交易，从而能够报告利润。

In the telecom boom of the late nineteen nineties, in which optical fiber became overbuilt, fiber owning companies engaged in transactions with each other that permitted them to report profits.

利润。

Profits.

如果两家公司都拥有光纤，它们只是在账面上拥有资产。

If two companies own fiber, they just have an asset on their books.

但如果每家公司都从对方购买容量，它们就可以都报告利润，于是它们就这么做了。

But if each buys capacity from the other, they can both report profits, so they did.

在其他情况下，制造商在运营商尚未拥有客户来证明建设合理性之前，就向网络运营商提供贷款以购买设备。

In other cases, manufacturers loaned network operators money to buy equipment from them, before the operators had customers to justify the build out.

所有这些都导致了虚幻的利润。

All this resulted in profits that were illusory.

如今，正在宣布一些交易，其中资金似乎在人工智能公司之间循环流动。

Nowadays, deals are being announced in which money appears to be round tripped between AI players.

相信人工智能存在泡沫的人，很容易对这些交易持怀疑态度。

People who believe there's an AI bubble find it easy to view these transactions with suspicion.

这些交易的目的是实现合法的商业目标，还是夸大进展？

Is the purpose to achieve legitimate business goals or to exaggerate progress?

更令人担忧的是，批评者称，OpenAI与芯片制造商、云计算公司等达成的一些交易异常循环。

Adding to worries, critics say some of the deals that OpenAI has made with chipmakers, cloud computing companies, and others are oddly circular.

OpenAI 将从科技公司获得数十亿美元，但同时也会将数十亿美元回流给这些公司，用于支付计算能力和其他服务费用。

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.

英伟达也达成了一些引发质疑的交易，人们怀疑这家公司是否在向自己付款。

NVIDIA has also made some deals that have raised questions about whether the company is paying itself.

它宣布将投资1000亿美元用于OpenAI。

It announced that it would invest $100,000,000,000 in OpenAI.

这家初创公司收到这笔资金后，会用于购买或租赁英伟达的芯片。

The startup receives that money as it buys or leases NVIDIA's chips.

高盛估计，英伟达明年15%的销售额将来自批评者所称的循环交易。

Goldman Sachs has estimated that NVIDIA will make 15% of its sales next year from what critics also call circular deals.

《纽约时报》，11月20日。

The New York Times, November 20.

值得注意的是，尽管OpenAI尚未实现盈利，但它已向行业合作伙伴承诺了1.4万亿美元的投资。

Noteworthy, OpenAI has made investment commitments to industry counterparties totaling $1,400,000,000,000, even though it has yet to turn a profit.

该公司明确表示，这些投资将来自从同一方获得的收入，并且它拥有退出这些承诺的途径。

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.

但所有这些都引发了一个问题：人工智能行业是否已经发展出了一台永动机。

But all this raises the question of whether the AI industry has developed a perpetual motion machine.

关于这一点，我最近读到一些文章，质疑人们是否能真正理解‘万亿’这个概念，我认为这个观点非常到位。

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.

一百万美元相当于每秒一美元，持续11.6天。

A million dollars is a dollar a second for eleven point six days.

十亿美元相当于每秒一美元，持续31.7年。

A billion dollars is a dollar a second for thirty one point seven years.

我们能理解这一点。

We get that.

但一万亿美金相当于每秒一美元，持续三万一千七百年。

But a trillion dollars is a dollar a second for thirty one thousand seven hundred years.

谁能理解三万一千七百年所代表的意义？

Who can get their head around the significance of thirty one thousand seven hundred years?

人工智能资产的使用寿命会有多长？

What will be the useful life of AI assets?

我们不得不怀疑，人工智能领域是否正确地处理了过时性这一问题。

We have to wonder whether the topic of obsolescence is being handled correctly in AI land.

AI芯片的使用寿命会有多长？

What will be the lifespan of AI chips?

在为人工智能相关股票确定市盈率时，应该预期多少年的收益增长？

How many years of earnings growth should be counted on in assigning PE ratios for AI related stocks?

芯片及其他AI基础设施的寿命是否足够长，以偿还为购买它们而承担的债务？

Will chips and other aspects of AI infrastructure last long enough to repay the debt undertaken to buy them?

通用人工智能——一种能够完成人类大脑所能做任何事情的机器——会被实现吗？

Will artificial general intelligence, a machine capable of doing anything the human brain can do, be achieved?

这会是进步的终点吗？还是会有进一步的革命？哪些公司会赢得这些革命？

Will that be the end of progress, or might there be further revolutions, and what firms will win them?

企业是否会达到技术稳定的阶段，从而从中提取经济价值，还是新技术会不断威胁并取代旧技术，成为成功的途径？

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?

与此相关的是，一份《Feet》通讯的一期内容曾简要提及两项发展，表明竞争格局的流动性。

In this connection, a single issue of an Feet newsletter briefly mentioned two developments that suggest the fluid nature of the competitive landscape.

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，中国自主研发的开源模型下载总量占比上升至17%。

A study by the Massachusetts Institute of Technology and open source AI startup Hugging Face found that the total share of downloads of new Chinese made open models rose to 17% in the past year.

这一数字超过了谷歌、Meta和OpenAI等美国开发者的15.8%下载份额，这是中国团队首次超越其美国竞争对手。

The figure surpasses the 15.8% share of downloads from American developers such as Google, Meta, and OpenAI, the first time Chinese groups have beaten their American counterparts.

由于担心谷歌在人工智能领域正在迎头赶上，英伟达股价昨日大幅下跌，导致这家人工智能芯片制造商市值蒸发了1150亿美元。

NVIDIA shares fell sharply yesterday on fears that Google is gaining ground in artificial intelligence, erasing $115,000,000,000 in market value from the AI chipmaker.

《First Feet Americas》，11月26日。

First Feet Americas, November 26.

动态变化带来了令人惊叹的新技术机遇，但这种动态性也可能威胁到领先企业的统治地位。

Dynamic change creates the opportunity for incredible new technologies, but that same dynamism can threaten the leading company's reign.

在所有这些不确定性中，投资者必须质疑：他们所支付价格中所隐含的持续成功假设是否完全合理。

Amid all these uncertainties, investors must ask whether the assumption of continued success incorporated in the prices they're paying is fully warranted.

狂热是否正导致投机行为？

Is exuberance leading to speculative behavior?

举一个极端的例子，我引用一下风险投资通过十亿美元种子轮投资初创企业的趋势。

For an extreme example, I'll cite the trend toward venture capital investments in startups via $1,000,000,000 seed rounds.

这里有一个小故事。

Here's one vignette.

由前OpenAI高管米拉·马拉蒂领导的AI初创公司Thinking Machines刚刚完成了历史上最大的种子轮融资。

Thinking Machines, an AI startup helmed by former OpenAI executive Meera Marathi, just raised the largest seed round in history.

融资额达20亿美元，估值为100亿美元。

$2,000,000,000 in funding at a $10,000,000,000 valuation.

该公司尚未发布任何产品，也拒绝向投资者透露他们究竟在开发什么。

The company has not released a product and has refused to tell investors what they're even trying to build.

一位与马拉蒂会面的投资者说：‘这是最荒谬的路演会议。’

It was the most absurd pitch meeting, one investor who met with Murati said.

她当时说：‘我们是一家由顶尖AI人才组成的AI公司，但我们不能回答任何问题。’

She was like, so we're doing an AI company with the best AI people, but we can't answer any questions.

这就是AI泡沫破裂的方式。

This is how the AI bubble will pop.

德里克·汤普森，Substack，10月2日。

Derek Thompson, Substack, October 2.

但那已经是陈年旧事了，足足两个月前的事了。

But that's ancient history, already two months old.

这是最新进展。

Here's an update.

据彭博社周四报道，由前OpenAI高管米拉·马拉蒂创立的人工智能初创公司Thinking Machines Lab正在就新一轮融资进行早期洽谈，估值约为500亿美元。

Thinking Machines Lab, the artificial intelligence startup founded by former OpenAI executive, Meera Marathi, is in early talks to raise a new funding round at a roughly $50,000,000,000 valuation, Bloomberg News reported on Thursday.

该公司在7月筹集了约20亿美元后，估值为120亿美元。（路透社，11月13日）

The startup was last valued at $12,000,000,000 in July after it raised about $2,000,000,000 Reuters, November 13.

而Thinking Machines Lab并非孤例。

And Thinking Machines Lab isn't alone.

在人工智能军备竞赛中最具魄力的投资之一，由前OpenAI首席科学家伊利亚·苏茨克弗创立的保密初创公司Safe Superintelligence（SSI）已筹集20亿美元，公司估值达320亿美元，尽管尚未发布任何公开的产品或服务。

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 Sutskover has raised $2,000,000,000 in a round that values the company at $32,000,000,000 despite having no publicly released product or service.

C Tech by Calculist，4月13日。

C Tech by Calculist, April 13.

最终目标是什么？

What's the end state?

人工智能面临的问题之一是，这种最新事物的性质非同寻常。

Part of the issue with AI includes the unusual nature of this newest thing.

这不像一家设计并销售产品、当售价高于投入成本时就能盈利的企业。

This isn't like a business that designs and sells a product, making money if the selling price exceeds the cost of the inputs.

相反，这些公司是在飞行中建造一架飞机。

Rather, it's companies building an airplane while it's in flight.

一旦飞机建成，他们才会知道它能做什么，以及是否有人愿意为其服务付费。

And once it's built, they'll know what it can do and whether anyone will pay for its services.

许多公司为自己的支出辩解，称他们不仅仅是在打造一个产品，而是在创造一种将改变世界的事物。

Many companies justify their spending because they're not just building a product, they're creating something that will change the world.

人工通用智能，即AGI。

Artificial general intelligence or AGI.

问题是，他们中没有人确切知道该如何实现它。

The rub is that none of them quite know how to do it.

但弗吉尼亚大学的经济学家安东·科拉内克表示，如果硅谷实现了其目标，这些支出都将得到合理化。

But Anton Koranek, an economist at the University of Virginia, said the spending would all be justified if Silicon Valley reached its goal.

他乐观地认为这是可以实现的。

He is optimistic it can be done.

科里内克博士说，这是孤注一掷押注AGI。

It's a bet on AGI or bust, doctor Korinek said.

《纽约时报》，11月20日。

The New York Times, November 20.

尚未确定的这个行业正在建设中的本质，最好通过OpenAI首席执行官萨姆·阿尔特曼的言论来体现，这些言论被概括如下。

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.

我们将构建这种通用智能系统，然后让它自己找出如何从中产生投资回报。

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.

这应该让那些此前完全理解自己投资业务性质的人停下来思考。

This should be a source of pause for people who heretofore fully comprehended the nature of the business they invested in.

显然，一种等于或超越人脑的技术的价值应该非常巨大，但这难道不是远远超出了计算范围吗？

Clearly, the value of a technology that equals or surpasses the human brain should be pretty big, but isn't it well beyond calculation?

关于债务使用的一点说明。

A word about the use of debt.

到目前为止，人工智能及其支持基础设施的大部分投资都来自运营现金流产生的股权资本。

To date, much of the investment in AI and the supporting infrastructure has consisted of equity capital derived from operating cash flow.

但现在，企业投入的资金规模已需要债务融资，对某些公司而言，这些投资和杠杆水平必须被视为激进。

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.

AI数据中心的繁荣从来不可能仅靠现金来融资。

The AI data center boom was never going to be financed with cash alone.

这个项目规模太大，无法自掏腰包支付。

The project is too big to be paid for out of pocket.

摩根大通的分析师在餐巾纸或桌布上做了一些计算，估计基础设施建设的费用将达到5万亿美元，还不包括小费。

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 $5,000,000,000,000, not including a tip.

谁知道这是否准确？

Who knows if that's right?

但我们有充分理由预期明年支出将接近5000亿美元。

But we have good reason to expect close to half 1,000,000,000,000 in spending next year.

与此同时，截至第三季度末，最大的支出方——微软、谷歌、亚马逊、Meta和甲骨文——合计现金储备仅为3500亿美元，来源：未对冲的《金融时报》，11月13日。

Meanwhile, the biggest spenders, Microsoft, Alphabet, Amazon, Meta, and Oracle, had only about $350,000,000,000 in the bank collectively as of the end of the third quarter, unhedged Financial Times November 13.

上述公司从其强大的非人工智能业务中获得了健康的现金流，但人工智能领域激烈的赢家通吃竞赛迫使一些公司举债融资。

The firms just mentioned 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.

事实上，可以合理地认为，它们花费巨额资金的部分原因是为了让规模较小的公司难以跟上步伐。

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.

甲骨文、Meta 和 Alphabet 已发行了三十年期债券以资助人工智能投资。

Oracle, Meta, and Alphabet have issued thirty year bonds to finance AI investments.

对于后两家公司而言，这些债券的收益率仅比同期美国国债高出100个基点或更少。

In the case of the latter two, the yields on the bonds exceed those on treasuries of like maturity by 100 basis points or less.

为了获得仅略高于无风险债务收益的固定收益投资，接受三十年的技术不确定性是否明智？

Is it prudent to accept thirty years of technological uncertainty to make a fixed income investment that yields little more than riskless debt?

通过债务融资建设的船舶和数据中心，其生产力能否维持足够长的时间，以偿还这些三十年期的债务义务？

And will the investments funded with debt in ships and data centers maintain their level of productivity long enough for these thirty year obligations to be repaid?

11月14日，亚历克斯·坎特罗维奇的大型科技播客播出了一段与金融服务公司DA Davidson科技研究主管吉尔·卢里亚的对话，主要讨论人工智能领域中的债务使用问题。

On November 14, Alex Kantrowicz's big technology podcast carried a conversation with Gil Luria, head of technology research at financial services firm DA Davidson, primarily regarding the use of debt in the AI sector.

以下是卢里亚的一些观点。

Here's some of what Luria had to say.

像微软、亚马逊和谷歌这样的理性、深思熟虑的企业领袖正在践行健康的行为，他们正明智地投资以提升交付AI的能力。

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.

他们之所以能做出明智的投资，是因为他们拥有所有客户。

And the reason they can make sound investments is that they have all the customers.

因此，当他们进行投资时，使用的是资产负债表上的现金。

And so when they make investments, they're using cash on their balance sheets.

他们拥有雄厚的现金流作为支撑。

They have tremendous cash flow to back it up.

他们明白这是一项高风险的投资，并且会加以平衡。

They understand that it's a risky investment, and they balance it out.

不健康的行为体现在他描述的一家初创公司，这家公司借钱为另一家初创公司建设数据中心。

Unhealthy behavior, here he describes a startup that is borrowing money to build data centers for another startup.

两家公司都在大量亏损现金，却仍能筹集到债务资本来支持这一建设，而它们既没有客户，也无法预见这些投资能否带来回报。

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 build out, again, without having the customers or the visibility into those investments paying off.

因此，在健康与不健康行为之间存在着一系列表现，我们需要厘清这些差异，以免重蹈过去的覆辙。

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.

有些事情我们通过股权融资，有些事情则通过债务融资，即承诺在未来一段时间内偿还利息。

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.

长期以来，作为社会，我们一直将这两者放在正确的位置上。

And as a society, for the longest time, we've had those two pieces in their right place.

债务是指当我有可预测的现金流或可以作为贷款担保的资产时，将现在的资本与未来向贷款人支付的现金流进行交换是合理的。

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.

我们用股权来投资更具投机性的项目，当我们希望成长并拥有这份成长时使用。

We use equity for investing in more speculative things, for when we want to grow, and we want to own that growth.

但我们不确定未来的现金流会是多少。

But we're not sure about what the cash flow is going to be.

这就是正常经济的运作方式。

That's how a normal economy functions.

当你开始混淆这两者时，就会陷入麻烦。

When you start confusing the two, you get yourself in trouble.

在可能令人担忧的因素中，卢里亚提到了以下几点。

Among potentially worrisome factors, Luria cites these.

一种投机性资产。

A speculative asset.

我们不知道在未来两到五年内究竟需要多少。

We don't know how much of it we're really going to need in two to five years.

贷款人员有发放贷款的激励，却无需承担长期后果。

Lender personnel with incentives to make loans, but no exposure to long term consequences.

AI产能可能赶上甚至超过需求的可能性。

The possibility that the supply of AI capacity catches up with or surpasses the demand.

未来一代AI芯片可能更强大，使现有芯片过时或降低其作为债务抵押品的价值。

The chance that future generations of AI chips will be more powerful, obsoleting existing ones or reducing their value as backing for debt.

强大的竞争对手通过降低租金、亏损运营来争夺市场份额。

Powerful competitors who vie for market share by cutting rental rates and running losses.

以下是阿齐姆·阿扎尔在10月18日的指数视角中的几段重要文字。

Here are some important paragraphs from Azim Azar's exponential view of October 18.

AI繁荣何时会演变为泡沫？

When does an AI boom tip into a bubble?

投资者兼工程师保罗·凯德罗斯基指出明斯基时刻，即信贷扩张耗尽优质项目、转而追逐劣质项目，用供应商融资和可疑的覆盖比率资助边缘交易的转折点。

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.

对于人工智能基础设施而言，这种转变可能已经 underway。

For AI infrastructure, that shift may already be underway.

明显的迹象包括超大规模企业资本支出增长超过收入增长势头，以及贷款方为维持局面而放宽条款。

The telltale signs include hyperscalers' CapEx outpacing revenue momentum and lenders' sweetening terms to keep the party alive.

保罗提出了一个令人信服的论点。

Paul makes a compelling case.

我们已经进入了投机性金融领域， arguably 已经超越了初步阶段，最近的交易将树立危险的先例。

We've entered speculative finance territory, arguably past the tentative stage, and recent deals will set dangerous precedents.

正如保罗警告的那样，这种融资将为未来类似交易建立模板，推动垃圾债券发行和超大规模企业为追求主导地位而不惜一切代价大量设立特殊目的载体（SPV）。

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.

对于人工智能基础设施而言，预警信号正在闪现。

For AI infrastructure, the warning signs are flashing.

供应商融资泛滥，覆盖比率下降，超大规模企业利用资产负债表维持资本支出速度，即使收入增长势头滞后。

Vendor financing proliferates, coverage ratios thin, and hyperscalers leverage balance sheets to maintain CapEx velocity even as revenue momentum lags.

我们看到了两个方面：一方面是真正的基础设施扩张，另一方面是 reminiscent 2000 年电信泡沫的融资操作。

We see both sides, genuine infrastructure expansion alongside financing gymnastics that recall the two thousand telecom bust.

这场繁荣可能最终证明是富有成效的，但前提是收入在信贷收紧之前赶上。

The boom may yet prove productive, but only if revenue catches up before credit tightens.

健康的压力何时会演变为系统性风险？

When does healthy strain become systemic risk?

在市场给出答案之前，我们必须回答这个问题。

That's the question we must answer before the market does.

阿扎尔提到了通过特殊目的载体（SPV）进行表外融资的做法，这种做法曾是安然公司财务脆弱乃至最终崩溃的主要原因之一。

Azar 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.

一家公司及其合作伙伴为特定目的设立一个SPV，并提供股权资本。

A company and its partners set up an SPV for some specific purpose or purposes and supply the equity capital.

母公司可能拥有运营控制权，但由于没有多数所有权，因此不会在财务报表中合并该SPV。

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.

SPV承担债务，但这些债务不会出现在母公司的账面上。

The SPV takes on debt, but that debt doesn't appear on the parent's books.

母公司可能是投资级借款人，但同样，这笔债务并非母公司的义务，也不由其担保。

The parent may be an investment grade borrower, but likewise, the debt isn't an obligation of the parent or guaranteed by it.

今天的债务可能由数据中心租户承诺的租金支持，有时是股权合作伙伴，但该债务也不是股权合作伙伴的直接义务。

Today's debt may be backed by a promised rent from a data center tenant, sometimes an equity partner, but the debt isn't a direct obligation of the equity partner either.

本质上，特殊目的实体是一种让公司看起来没有从事特殊目的实体所做之事、也没有承担其债务的方式。

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.

私募股权基金和私募信贷基金极有可能是这些实体中的合作伙伴和贷款方。

Private equity funds and private credit funds are highly likely to be found among the partners and lenders in these entities.

正如我前面引用的，佩雷斯在互联网泡沫之后写道，推动部署期的是那些亏损的投资。

As I quoted earlier, according to Perez, who wrote on the heels of the .com bubble, what enabled the deployment period were the money losing investments.

早期投资在明斯基时刻中化为乌有，即在长期繁荣周期中做出的不明智承诺，在市场调整时遭遇价值毁灭。

Early investment is lost in the Minsky moment in which unwise commitments made in an extended up cycle encounters value destruction in a correction.

关于债务的使用，我们有三件事可以确定。

And there are three things we know for sure about the use of debt.

如果出现亏损，债务会放大亏损；如果预期收益实现，它也会放大收益。

It magnifies losses if there are losses, just as it magnifies the hoped for gains if they materialize.

如果企业遭遇困境，债务会增加其失败的可能性，尽管其下方有股权缓冲，但如果困境足够严重，仍会使贷款人的资本面临风险。

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.

需要考虑的一个关键风险是，数据中心建设的热潮可能导致供应过剩。

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

一些数据中心可能变得无利可图，一些业主可能破产。

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

在这种情况下，新一代业主可能以极低的价格从已行使抵押权的贷款人手中收购这些数据中心，并在行业稳定后获利。

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.

这是一个通过创造性破坏使市场达到均衡、并将成本降至使未来业务盈利水平的过程。

This is a process through which creative destruction destruction brings brings markets markets into into equilibrium equilibrium and reduces costs to levels that make future business profitable.

债务本身既不是好事，也不是坏事。

Debt is neither a good thing nor a bad thing per se.

同样，人工智能行业使用杠杆也不应被盲目推崇或恐惧。

Likewise, the use of leverage in the AI industry shouldn't be applauded or feared.

这一切都取决于资本结构中债务的比例、你所贷款抵押的资产或现金流的质量、借款人偿还债务的其他流动性来源，以及贷款人获得的安全边际是否充足。

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 borrower's alternative sources of liquidity for repayment, and the adequacy of the safety margin obtained by lenders.

我们将看到在当今这股热潮中，哪些贷款方能保持纪律。

We'll see which lenders maintain discipline in today's heady environment.

在此需要指出的是，橡树资本已对一些数据中心进行了投资，我们的母公司布鲁克菲尔德正在筹集一笔100亿美元的基金，用于投资人工智能基础设施。

It's worth noting in this connection that Oaktree has made a few investments in data centers, and our parent, Brookfield, is raising a $10,000,000,000 fund for investment in AI infrastructure.

布鲁克菲尔德正在投入自有资金，并获得了主权财富基金和英伟达的股权承诺，计划为这些项目审慎地运用债务融资。

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.

布鲁克菲尔德的投资似乎主要会流向数据中心饱和度较低的地区，并用于建设为数据中心提供大量电力所需的基础设施。

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.

当然，我们做这些事都是基于我们认为审慎的决策。

Of course, we're both doing these things on the basis of what we think are prudent decisions.

我知道自己对人工智能了解不足，无法妄加评论，但我对债务确实有所了解，以下是我的看法。

I know I don't know enough to opine on AI, but I do know something about debt, and it's this.

为结果不确定的项目提供债务融资是可以接受的。

It's okay to supply debt financing for a venture where the outcome is uncertain.

但如果结果纯粹是臆测，那就不可接受了。

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