AI: Mad Maths?

欢迎收听又一期《资本周期》播客。

Welcome to another episode of Capital Cycle Podcast.

我是爱德华·钱塞勒。

This is Edward Chancellor here.

今天请到的是查尔斯·卡特，马拉松资产管理公司的欧洲投资组合经理。

And I have with me Charles Carter, a European Portfolio Manager at Marathon.

很高兴见到你，埃迪。

Good to see you, Eddie.

查尔斯，这期播客我们回到一个主导新闻近三年的投资话题，那就是围绕人工智能的巨大热潮，以及由此引发的资本支出狂潮，而且这个支出规模每个月都在扩大。

So Charles, we're returning in this podcast to the investment topic that has dominated the news for nearly three years, namely the huge excitement around artificial intelligence and the capital spending boom that's been unleashed, a spending boom that gets larger and larger as every month passes.

从资本周期的角度看，这些发展至少可以说是令人不安的，而且在很多方面让人想起上世纪90年代末互联网泡沫时期的投资热潮。

From a capital cycle perspective, these developments are unsettling, to say the least, and reminiscent in many respects to the investment splurge which accompanied the .com mania of the late 1990s.

是的。

Yes.

我觉得，你知道，股市里显然有很多兴奋情绪，目前这种情绪相当狭窄地集中在人工智能这个领域。

I mean, I think, you know, there's obviously a lot of excitement in the stock market, which is quite narrowly confined to this area of AI at the moment.

而且对于资本周期投资者来说，开始担心是很自然的，因为在这种狂热的热情下，可能会出现大规模投资，或者我们正在看到大规模投资，这往往会带来意想不到的过度竞争，最终让投资者失望。

And it's quite natural for capital cycle investors to start worrying because with frenzied enthusiasm, you can get massive investment, or we are seeing massive investment, which often breeds unforeseen and excessive competition and, in the end, disappointment for investors.

那么，你认为投资者应该如何应对当前的狂热呢？

So how do you think investors should respond to the current frenzy?

嗯，我认为对于普通投资者来说，面对像人工智能这样的新投资热潮，有两种做法。

Well, I think for a generalist investor, there are two courses of action when confronted with a new investment boom like AI.

一种是尽快学习新技术，然后试着谈论数万亿的令牌、训练后扩展、强化学习等等，说得头头是道。

One is to bone up as quickly as possible on the new technology and then try and talk plausibly about trillions of tokens and post training scaling and reinforcement learning and so on.

另一种做法不是不懂装懂地参与技术讨论，而是思考基本面。

And the other way is really not to bluff one's way into a technical discussion, but to think about the fundamentals.

从资本周期的角度来看，这意味着要考虑资本流动、行业结构和潜在的投资回报。

And from a capital cycle perspective, this means thinking about capital flows, industry structures and potential return on investment.

这个问题的另一个方面是观察参与者的动机，理解他们为什么这样做，以及这样做是否合理。

And the other aspect of the question is to look at the motives of the participants and to get an understanding of why they are behaving in this way and indeed whether it makes sense.

那么，我们来谈谈目前人工智能领域正在进行的投资规模吧。

So let's discuss the scale of investment that's currently underway in the AI field.

摩根士丹利给出了一些估算，他们预计2025到2028年间，数据中心累计投资将达到3万亿美元，这还不包括能源成本。

So Morgan Stanley have come up with some estimates and they have a figure for cumulative investment in data centers over the period 2025 to 2028 of $3,000,000,000,000 excluding the cost of energy.

麦肯锡的数字是到2030年超过5万亿美元。

McKinsey, they have a figure of over $5,000,000,000,000 up to 2030.

花旗的数据是到2029年，硬件投资2.3万亿美元，研发投资1.4万亿美元。

And Citi have figures of 2,300,000,000,000 for hardware and 1,400,000,000,000 for r and d up to 2029.

所以这些都是非常大的数字。

So these are very big numbers.

确实是很大的数字。

Big numbers indeed.

查尔斯，你还记得二十年前吗？你和我一起写了第一本马拉松的书《资本账户》，里面实时分析了互联网泡沫及其后果。

And Charles, you remember twenty years ago, you and I put together the first Marathon book, Capital Account, which contained real time analysis of the .com boom and its aftermath.

让我印象非常深刻的是，我们在书中收录的90年代末和21世纪初的一些《全球投资评论》中，很明显，正如我当时所说，一些TMT股票中蕴含的收入预期是极其不切实际的。

What struck me very forcibly about some of the Global Investment Reviews of the late 1990s and early 2000s that we included in the book was it was quite clear, as I say, in real time, that the revenue expectations embedded in some of the TMT stocks were wildly unrealistic.

我记得有一篇文章描述了西班牙电信的互联网业务的价值，当时那个业务叫Terra Networks。

And there's one piece I remember which described the value of Telefonica's internet business that at the time was called Terra Networks.

其中一位马拉松的作者指出，按照当时Terra Networks的市值，这家公司需要占据拉美地区消费者总支出中一个难以想象的大份额。

And one of the Marathon authors pointed out that given the then current market cap of Terra Networks, it would require the company to gain an unfathomably large share of Latin American total consumer spending.

但事实并非如此。

And that didn't happen.

最终，Terra Networks的股价在从1999年上市以来翻了近十倍后，从最高点暴跌了98%。

And eventually, Terra Networks share price having climbed nearly 10 times from its '99 IPO, the stock declined 98% from its peak.

那么，你认为目前投入AI领域的资本回报率，是否很难达到市场隐含的预期回报呢？

Now, do you think that the return on capital currently being invested in AI is unlikely to generate the market's implied expected return?

说到投资回报率，市场先生

When it comes to the return on investment, Mr.

似乎认为这笔钱花得很值。据《经济学人》报道，自2022年ChatGPT发布以来，美国十家科技公司的市值增加了1.2万亿美元。

Market seems to believe the money is well spent, given you've seen, I think, a $12,000,000,000,000 increase in the value of 10 technology firms in The US since the release of ChatGPT in 2022 according to The Economist.

要么股市正在用托宾Q比率来奖励这些新投资，看起来在2倍到4倍之间，也就是增量市值除以投资支出。

Either the stock market is rewarding the new investment with what looks like a Tobin's Q ratio of between 2x and 4x, that's the incremental market value divided by the investment outlay.

要么就是对这些企业核心的传统技术业务持更加乐观的看法。

Or it's taking a much rosier view of the core legacy technology franchises in these businesses.

可能两者兼而有之。

And it's probably a combination of both of those.

关于托宾Q，也就是企业的重置成本，我们在两本马拉松的书里都提到过一点：当投资以高托宾Q交易时，也就是说，高于其重置成本时，就会有巨大的动力去继续投资。

With regards to Tobin's Q, the replacement cost of a business, one of the points we make in both of the Marathon books is that when investments are trading at a high Tobin's Q, in other words, at a premium to their replacement cost, there is a huge incentive to carry on investing.

对。

Yes.

我的意思是，绿灯行。

I mean, green for go.

那么告诉我，你认为实际上什么会推动AI资本支出的回报？

So tell me, what do you think will actually drive the returns on AI capital spending in your view?

嗯，我认为在短期，短期到中期，实际回报将取决于，一如既往，资产的寿命和可持续的利润率。

Well, I think in the short term, short to medium term, the actual return will depend, just as it always does, on the lifespans of the assets and the sustainable profit margins.

这取决于对AI解决方案和服务的需求以及竞争程度。

That's a function of the demand for AI solutions and services and the degree of competition.

麦肯锡估计，数据中心投资的60%用于芯片和硬件。

McKinsey estimates that 60% of data center investment goes into chips and hardware.

目前，这些资产被超大规模企业按大约五到六年的期限进行折旧。

Currently, these assets are being depreciated by the hyperscalers over a period of roughly five to six years.

但你认为，考虑到新的技术创新和新芯片的出现，这种折旧安排是否过于乐观？

But you think that this depreciation schedule is optimistic given new technological innovation and new chips that come along?

对。

Yeah.

实际上，尽管新产品的发布节奏似乎在加快，折旧期限却被延长了。

And actually, the time period has been extended despite what appears to be an accelerating cadence of new launches.

所以，英伟达现在每年都会推出一个新的芯片平台。

So, NVIDIA is releasing a new chip platform, if you like, every year now.

尽管如此，亚马逊最近缩短了折旧期限，称这是因为技术发展速度加快。

That said, Amazon has recently reduced its depreciation period, saying that it is due to the increased pace of technological development.

所以，如果我们想计算预期回报，那就按美国人说的，来算算这笔账吧。

So if we're trying to calculate expected returns, let's do the math, as the Americans say.

是的。

Yes.

所以我的意思是，简单来说，如果你假设这些资产在五年半后不再产生经济利润，而且摩根士丹利的那个3数字是后置的，那么仅芯片和硬件投资就需要在2028年产生超过5000亿美元的净现金流，才能满足新设备投资的资本成本。

So I mean, just very simply, if you assume the assets generate no economic profit after five and a half years and that the the 3 figure for Morgan Stanley is back end loaded, then the chips and hardware investment alone would need to generate a net cash flow of over $500,000,000,000 in 2028 just to meet the cost of capital on the new equipment investment.

要产生这样的利润，是否有足够的实际需求？

Is it realistic to expect sufficient demand to generate such profits?

嗯，是的。

Well, yeah.

如果你假设数据中心运营商需要产生20%的自由现金流利润率来证明其股价合理，那就意味着收入需求要达到2.5万亿美元。而如果他们的客户也需要类似的利润率，那就意味着消费者和企业需要在不久的将来为AI服务支付接近或超过3万亿美元。

If you assume the data center operators need to generate 20% free cash flow margin to justify their share prices, that implies a revenue requirement of $2,500,000,000,000 And if their customers in turn need to make similar margins, that implies consumers and businesses would need to pay close or over $3,000,000,000,000 for AI services in the not too distant future.

这相当于美国当前GDP的10%，根据经合组织的数据，也相当于全球劳动力成本的5%。

That's equivalent to 10% of current US GDP and 5% of the global labor costs based on OECD data.

而且再说一遍，这仅仅是为了覆盖设备成本，还不包括能源基础设施和研发等其他投资。

And again, it's just to cover the cost of the equipment and doesn't include other investments like energy infrastructure and r and d.

你知道，我之前提到的花旗的1.4万亿美元数字，以及那些1亿美元的签约奖金或签约工资。

You know, Citi's 1,400,000,000,000 number that I mentioned earlier and all those $100,000,000 sign on bonuses or sign on salaries.

如果能拿到这些，那当然不错。

Which are nice if you can get them.

那这和目前AI的收入相比怎么样？

So how does that compare to current AI revenues?

嗯，目前的AI收入跟这些数字比起来还算是比较小的。

Well, current AI revenues are modest compared to these figures.

OpenAI，它的聊天机器人每周和每日活跃用户数量最多，目前的年化收入大约是130亿美元，据The Information预测，到2029年这个数字会增长到2000亿美元。

OpenAI, which which has the lion's share of weekly daily active users for its chatbot, has a current revenue run rate of about $13,000,000,000 and the information forecasts that to grow to $200,000,000,000 by 2029.

花旗估计，2025年所有应用的年收入总额略高于400亿美元，并且他们预计这个数字会以每年80%的速度增长，到2030年达到7800亿美元。

Citi have an estimate of total application annual revenues of just over $40,000,000,000 in 2025, and they have that rising at 80% a year to reach $780,000,000,000 by 2030.

所以，这跟你之前提到的目标相比，还是有点差距？

So sort of falling short of the target that you mentioned earlier?

对。

Yeah.

那这些收入预期从哪里来呢？

And where are those revenues expected to come from?

嗯，以花旗的数字为例，这主要是基于企业通过应用程序编程接口（API）访问大型语言模型的需求增长，还有企业定制化、战略合作等等。

Well, think in case of the Citi numbers, it's it's largely predicated on rising demand from enterprises through application programming interfaces, APIs, giving access to the large language models, enterprise customizations, strategic partnerships, that kind of thing.

至于瑞银那边，他们对企业采用AI的态度就悲观多了。他们指出，目前上市软件公司的第三方AI产品总收入只有大约25亿英镑，其中微软一家就占了超过80%。

And then for UBS, on the other hand, I mean, they're much more skeptical on the enterprise adoption, noticing in their case that total third party AI product revenue for the listed software companies is only around £2,500,000,000 at the moment, of which Microsoft is more than 80%.

这可能是市场定义的问题。

Probably a problem of market definition.

对。

Yeah.

而且现在AI面临的一个问题，就像当年TMT泡沫时一样，就是不清楚新技术最终能带来哪些盈利的应用。

And one of the problems with AI today, which is what we also had in the TMT boom, is it was unclear what the eventual and profitable applications from the new technology would be.

可以说，到目前为止，早期采用AI的公司所获得的利润提升相当令人失望。

You think that the uplift to profits from the early AI adopters has been pretty underwhelming to date, say the least.

确实，最近有几份报告。其中一份来自麻省理工学院，他们调查了300个公开披露的AI项目，发现其中95%的项目未能带来任何利润增长。

Well, I mean, there've been a couple of reports out recently, one from MIT where they looked at 300 publicly disclosed AI initiatives and found that 95% of the projects failed to deliver any boost to profits.

麦肯锡的另一份报告发现，71%的公司表示使用了生成式AI，但其中超过80%的公司表示这项技术对盈利没有产生实质性影响。

Another report from McKinsey found that 71% of companies reported using generative AI and more than 80% of them reported that the technology had made no tangible impact on earnings.

所以，即使按照花旗的乐观预测，未来五年AI生态系统也极有可能出现巨额亏损。正如你所说，这个数字与证明投资合理性所需的3万亿美元收入之间存在着巨大的差距。

So, with Citi's bullish forecast, huge losses in the AI ecosystem seem highly likely over the next five years, as you say, this huge gap between even their figure and the $3,000,000,000,000 revenue figure that you would need to justify the investment.

当然，你可以说，这只是早期阶段，可能会有J曲线效应。

Of course, you can say, Well, this is just early days and that there may be J curves effects.

毫无疑问，如果应用得当，人工智能将极大地提高生产力。

No doubt when properly applied, AI is going to bring a huge improvement for productivity.

我最近听说一个案例，一家制药公司用人工智能来缩短格式化并分析新药试验数据的时间。

I heard a case recently of pharmaceutical company that was using AI to reduce the time it takes to format and analyze trial data for new pharmaceutical products.

以前，他们采用非常手动的方式，需要三到四个月。

Previously, they'd had a very manual process, which took three to four months.

而有了人工智能，他们可以将时间缩短80%。

And with AI, they could re reduce that time by 80%.

所以你可以说，这些超大规模企业是在为长远投资，马拉松资本应该会欣赏这一点。

So you you could argue that these hyperscalers are investing for the long run, which Marathon should appreciate.

对。

Yeah.

我认为反驳的观点是，投资逻辑是，你不需要在短期内产生利润。

I think the counterargument is that the investment logic is that, you know, you don't need to generate profits in the short term.

他们可以继续承受巨额亏损，因为投资者把当前的投资方式看作未来的一块跳板，未来会有用于视频的智能体AI工具，以及各种未知的超级智能应用。

They can continue to incur huge losses as investors look upon the current way of investment as a sort of stepping stone to a future with, you know, agentic AI tools for video and, you know, yet unknown kind of applications of super intelligence.

对。

Yeah.

我认为这也可以看作是对消费者和企业的一种补贴，让他们开始使用AI，希望以后能向他们收费。

I think it can also be seen as a subsidy to consumers and businesses to start using AI in the hope of of charging them later.

你知道，这有点像几年前风险投资行业补贴外卖配送司机的情况。

You know, it's a bit like what happened a few years ago when the venture capital industry was subsidizing takeaway delivery drivers.

而且我

And I

记得查尔斯·卡特，在互联网泡沫时期，有一家公司成立，他们付钱给人们，给他们电脑，然后期望通过人们上网时的一点广告来获得收入。

remember Charles Carter the .com boom, there was one company that was launched that was paying people, giving them computers and expecting to get its revenue from a bit of advertising when they surfed the web.

这种“先建设，他们就会来”的补贴模式，现在亏损，未来某天利润会出现吗？

Does that same build and they will come subsidize now and profits may turn up sometime in the future?

那我们谈谈这种补贴，你对这种补贴怎么看？

So let's talk about the subsidy, what you think about this subsidy.

你知道，你可以这么看，我是说，人们谈论训练。

You know, you can look at it almost I mean, people talk about training.

这就像接受非常昂贵的大学教育。

It's it's like a very expensive college education.

你有本科、研究生，然后是博士。

You've got your your undergraduate, your postgraduate, and then your PhD.

经过多年的投资，未来几年后，你会得到回报。

And after, you know, years of investing further years in the future, you'll get your payoff.

在技术竞赛中，需求增长且充满不确定性，如果存在长期回报的想法，投资者不会介意没有短期回报。

And in a technology race with growing demand and a lot of uncertainty investors don't mind about not having short term returns if there is this idea of a long term payoff.

而且我认为关键的是，这种耐心得到了赢家通吃经济学的支持，这在美国科技行业过去二十五年里肯定是一个特点。

And I think crucially that patience is supported by the idea that you might have winner take all economics, which has been a feature of the tech industry in The US certainly over the last twenty five years.

是的，过去二十五年，但实际上对于许多参与90年代末、2000年代初TMT热潮的公司来说并非如此。

Yes, over the last twenty five years, but not actually for many of the companies that participated in the TMT boom of the late '90s, early 2000s.

除了亚马逊，其股价下跌了90%，一些公司如谷歌和Facebook是在互联网泡沫之后很久才出现的。

The companies, with the exception of Amazon, whose stock fell 90%, some of them like Google and Facebook appeared long after the dot com bubble

赢家会很少，输家会很多。

You're going to have very few winners and a lot of losers.

对。

Yeah.

所以，正如你所说，最终结果将取决于AI行业未来的结构，而不是需求本身。

So the eventual outcome, as you suggest, will depend on the future structure of the AI industry rather than demand in itself.

是的。

Yes.

从资本周期的角度来看，需求问题其实是次要的。

From a capital cycle perspective, the demand question is is really secondary.

重要的是，未来二十五年这个行业是走向分散还是集中。

What what matters is whether the industry tends to fragmentation or concentration over the next twenty five years.

即使需求超出今天的预期，过度竞争的出现也可能让投资者的希望落空。

Even if demand exceeds today's expectations, investor hopes could still be dashed by the onset of excessive competition.

到目前为止，我们看到了什么？

What have we seen to date?

有一种观点认为，规模经济将推动基础设施和平台领域的集中化。

Well, there's an argument that economies of scale will drive concentration in the infrastructure and the platform segments.

比如芯片、硬件、大型语言模型。

So think chips, hardware, the large language models.

而英伟达目前显然主导着其市场，市场份额超过70%。

And NVIDIA clearly dominates its market at the moment with over 70% market share.

到目前为止，确实有少数几家超大规模企业主导了数据中心行业。

And there have been really a few hyperscalers who've dominated the data center industry so far.

但有迹象表明，数据中心的竞争正在加剧，像甲骨文和CoreWeave这样的参与者正在市场中成长。

But there are signs that competition is rising in data centers with players like Oracle and CoreWeave, etcetera, growing in the market.

超大规模企业之间的竞争很容易在资本密集型的战场上升级，尤其是如果事实证明，由于过度建设或生产率驱动的技术进步而产生的闲置产能。

Competition between hyperscalers could easily escalate in a more capital intensive field of battle, as it were, particularly if it turns out that the spare capacity resulting from either overbuild or productivity driven technological progress.

虽然可能存在资本进入壁垒，但很难看出明显的网络效应，而网络效应一直是这些企业过去和现在护城河的重要特征。

While there may be capital barriers to entry, it's hard to discern major network effects, which have been such an important feature of the moats for these businesses in the past and to date.

数据中心是高固定成本、低边际成本的业务。

Data centers, they're high fixed cost and low marginal cost businesses.

一旦竞争爆发，价格往往会趋向边际成本，有点像互联网泡沫破裂后的航空业或电信业。

When competition breaks out, prices tend to marginal cost, a bit like airlines or the telecom industry after the dot com bust.

所以，这就是所谓的“哑管道”问题。

So, it's the dumb pipe problem.

对于大型语言模型，行业似乎正在分化，而不是整合。

For large language models, the industry does appear to be fragmenting, not consolidating.

已经出现了像Grok和DeepSeq这样的新进入者。

You've had new entrants like Grok and DeepSeq.

公司和企业可能会发现，他们只需使用小型语言模型就能满足需求，而且成本低得多。

Companies, enterprises, may find that they can get by just using small language models that they can adapt to their needs at a much lower cost.

在应用和地理层面，情况确实不太明朗，但很可能会有许多初创公司获得资金，竞争会相当激烈。

And on the application and the geographical level, the picture is is really much less clear, but it's likely that there'll be many start ups that will get funding and and competition will be quite intense.

是的。

Yes.

而且我最近看到一个图表，显示现在大约80%或90%的风险投资都流向了人工智能领域，这并不奇怪。

And I I saw a chart quite recently showing that I think 80% or 90% of VC funding now is going into the AI space, which is not surprising.

但如果所有这些巨额投资的结果仍然不确定，我们怎么解释它还在以如此大的规模进行呢？

But if the outcome from all this incredible investment remains uncertain, How do we explain that it's going on at quite such a scale?

嗯，我觉得部分答案在于行为因素。

Well, I think partly the answer to that is behavioral.

我想引用Alphabet首席执行官在2024年7月财报电话会议上的一句话，他说，我引用一下，对我们来说，投资不足的风险远大于投资过度的风险。

I'd like to quote a comment that was made by the Alphabet CEO on an earnings call back in July 2024 when he said, and I quote, the risk of underinvesting is dramatically greater than the risk of overinvesting for us here.

即使事实证明我们投资过度了，这些基础设施对我们来说也是广泛有用的。

Even in scenarios where it turns out that we are overinvesting, these are infrastructure which are widely useful for us.

它们使用寿命长，我们可以将其应用于各个方面，并且能够应对这种情况。

They have long useful lives, and we can apply it across, and we can work through that.

但我认为，如果不投资以保持领先地位，那肯定会有更大的负面影响。

But I think not investing to be at the front here, I think, definitely has much more significant downsides.

这句话确实暗示了科技领导者面临的创新者困境。

That quotation really hints to the innovator's dilemma facing the tech leaders.

没有人愿意在下一波技术平台变革中被落下，因为害怕错过机会。

Nobody wants to be left behind in the next wave of tech platform change because of the fear of missing out.

而且，微软和英特尔在移动领域的例子，以及更早之前IBM在个人电脑领域的例子，我想，确实深深烙印在行业高管的脑海里。

And the examples of Microsoft and also Intel in mobile and before that, obviously, IBM in PCs, I think, are really scorched on the minds of the industry executives.

换句话说，大型科技公司面临着与许多管理层在不利资本周期中相同的困境。

In other words, big tech faces the same problems that characterises many managements during adverse capital cycles.

正如TD Cohen的分析师去年十月所说，我引用一下：我们认为，人工智能领域的大公司面临着一个以囚徒困境为特征的投资环境。

As the analyst at TD Cohen put it last October, and I quote, We believe the major companies in AI face an investment environment characterized by a prisoner's dilemma.

每家公司都有各自的动机继续投入，因为不这样做的代价可能是毁灭性的。

Each is individually incentivized to continue spending, as the cost of not doing so are potentially devastating.

对。

Yeah.

而且我认为，还存在一种担忧，即智能体人工智能的广泛采用会对这些公司在搜索、生产力、购物和社交媒体等领域的各自优势构成威胁。

And I think there's also this fear that broad adoption of agentic AI poses a threat to the company's respective strongholds in in fields like search, productivity, shopping, and social media.

比尔·盖茨在2023年2月的一个播客中解释了这一困境。

Bill Gates explained this dilemma back in February 2023 on a podcast.

再次，我想引用一下。

And again, I'd like to just quote.

他说，从某种意义上说，个人代理将取代直接访问亚马逊、Siri 或 Outlook，所以谷歌拥有搜索、亚马逊拥有购物、微软拥有生产力、苹果拥有苹果设备上的一切。

He said, in a sense, the personal agent will replace going directly to Amazon or directly to Siri or going to Outlook so that the fact that Google owns search, Amazon owns shopping, Microsoft owns productivity, Apple owns sort of everything on an Apple device.

一旦你有了个人代理，它就会把这些独立的市场整合成一个，嘿，我只需要一个个人代理。

Once you get a personal agent, it kind of collapses those separate markets into, hey, I only want one personal agent.

当然，它可以帮助我购物、规划、写文档，并以这种丰富的方式在我的设备之间工作。

And, of course, it can help me shop and plan and write documents and work across my devices in this rich way.

所以十年后，我们不会认为这些业务是那么独立的。

And so a decade from now, we won't think of those businesses as quite as separate.

这可能是科技市场格局的一次重大洗牌。

It's a pretty dramatic potential reshuffling of how tech markets look.

所以这几乎就像一场希腊悲剧，家庭成员开始互相残杀。

So it's almost like a risk of this becoming like a Greek tragedy when the family members suddenly start killing each other.

这让我想起去年社交媒体上流传的一个可能是杜撰的评论，据说 Alphabet 创始人拉里·佩奇在公司内部说过，他宁愿公司破产，也不愿在通用人工智能竞赛中落败。

It reminds me of a comment possibly apocryphal that circulated on social media last year, in which The Alphabet founder Larry Page is reported as having said internally that he'd prefer for the company to go bust rather than lose the race for artificial general intelligence.

然后就在今天，有人给我发了一条马克·扎克伯格在 Meta 的评论，当被问及 AI 投资时，他说：如果我们误投了几千亿美元，那就这样吧。

And then, only today, someone sent me a comment that Mark Zuckerberg at Meta had made, where he said, asked about the AI investment, If we misinvest a couple of $100,000,000,000, so be it.

这种对巨额资本支出的轻率态度，我们拭目以待。

So the sort of cavalier approach to all incredible capital spending, where we'll see.

那么，查尔斯，最后说点什么？

So a parting thought, Charles?

嗯，我认为这波AI投资热潮可以看作是错失恐惧症和担心被蚕食的结合。

Well, I think the AI investment boom in this sense can be viewed as a combination of of this fear of missing out, the FOMO, and fear of cannibalization.

我们别给那个造个缩写词了。

Let's not generate an acronym for that.

好吧，这又不是直播。

Well, it's not on air.

谢谢你，查尔斯。

So thank you, Charles.

我们还没谈到机构投资者面临的囚徒困境，即是否以当前估值投资于大型科技股，这或许可以成为未来一期《全球投资评论》的话题？

And we haven't even discussed the prisoner's dilemma facing institutional investors with regards to whether to invest in mega cap stocks at current valuations, perhaps the subject for a future Global Investment Review?

嗯，是的，至少对主动型投资者来说是个困境。

Well, yes, at least it's a dilemma for active investors.

对于被动投资者来说，你只是个囚徒。

For passive, you're just a prisoner.

谢谢你，查尔斯。

Thank you, Charles.

感谢你今天抽出时间。

Thank you for your time today.

希望你能收听下一期的《资本周期》。

I hope you will listen to the next edition of the Capital Cycle.

本通讯仅供参考。

This communication is provided for information purposes only.

请访问马拉松网站和《全球投资评论》以获取更多信息，包括重要披露。

Please refer to Marathon's website and the Global Investment Reviews for further information, including important disclosures.