AI交流：电力问题？

欢迎来到高盛交易所。

Welcome to Goldman Sachs Exchanges.

我是艾莉森·内森，今天和我一起的是高盛全球研究院联席主管乔治·李。

I'm Alison Nathan, and I'm here with George Lee, co head of the Goldman Sachs Global Institute.

我们两人将共同主持一系列节目，探讨人工智能的崛起及其对企业、投资者和经济的深远影响。

Together, we're cohosting a series of episodes exploring the rise of AI and everything it could mean for companies, investors, and economies.

乔治，很高兴再次见到你。

George, great to see you again.

很高兴回来。

Good to be back.

我之前因为膝盖手术休养了一段时间，所以暂停了一下，但幸运的是，过去几个月人工智能领域没什么大变化。

I we had a little bit of a hiatus here while I was recovering from knee surgery, but fortunately, not much happened in the world of AI over the past few months.

当然，我在开玩笑。

I'm kidding, of course.

这几个月真是大事不断。

Has been a cavalcade of extraordinary events.

所以和我们的嘉宾聊聊当前推动这一趋势的因素一定会很有趣。

So and be really fun to talk to our guest about what's shaping the movement right now.

是的。

Yes.

很高兴你回来了，乔治。

Well, great to have you back, George.

今天，我们将更深入地探讨一个我们之前在对话中提到过、但真正值得仔细审视的领域。

And today, we are going to take a closer look at an area that we've touched on before in our conversations, you and I, but really deserve a closer look.

那就是人工智能基础设施建设可能需要的资源。

And that is the resources that the AI build out could require.

我们很高兴邀请到Brian Singer，他是高盛可持续发展与高盛研究部的负责人。

We are joined here by Brian Singer, head of GS Sustain and Goldman Sachs Research.

谢谢你邀请我，艾莉森。

Thank you for having me, Alison.

谢谢你，乔治。

Thank you, George.

很高兴你在这里，布莱恩。

Glad to have you here, Brian.

很高兴能和大家在一起。

Great to be with you all.

布莱恩，为了开启对话，我们已经讨论了数据中心将需要的巨大电力消耗一段时间了。

And Brian, just to kick off the conversation, we've been talking about the tremendous amount of power that data centers will require for a while now.

但这些数据中心的支出一直在持续增长、增长、再增长。

But the amount of spending on these data centers have just continued to grow and grow and grow.

我认为，这在过去几个月里，当我们思考人工智能领域时，是一个关键特征。

Think that was a key feature of the last few months when we think about the AI space.

所以，首先请用一些数据来说明这些发展以及它们对电力需求意味着什么。

So first, put some numbers on those developments and what it could mean for power demand.

当然。

Sure.

我的意思是，我们看到超大规模云服务商在2026年和2027年的资本支出和研发预算合计增加了超过3000亿美元。我确信我们之后可以讨论未来增长是否会放缓并出现二阶拐点，但你现在是在一个上调了3000多亿美元的预算基础上继续增长，这将传导至电力需求以及众多其他领域。

I mean, we have seen the hyperscalers' capital budgets and r and d budgets in 2026 and 2027 combined increase by more than $300,000,000,000 And I'm sure we can get into the debate whether there's going to be a second derivative inflection coming for slower growth going forward, but you're growing off of an upwardly revised budget of $300,000,000,000 plus, and that is going to trickle down into power and a whole host of other uses.

因此，我们一直关注这些资金将流向何处，以及为赋能者带来哪些投资机会。

And so we've been focused on where will that money go and what are the investment opportunities for the enablers?

Brian，我注意到你的部分研究指出，尽管这些支出存在滞后效应，但你们的计算表明，我们仍处于严重的供需失衡状态。

And some of your work, Brian, I noticed, all of that spend, and there's obviously a lag effect, but you calculate that we remain in deeply supply demand imbalanced state.

对吧？

Right?

也许你可以谈谈我们目前的状况。

Maybe you could talk a little bit about where we stand there.

是的。

Yeah.

没错。

That's right.

我的意思是，Alison，你和我一起经历过页岩革命。

I mean, Alison, you and I go back to the shale revolution.

是的。

Yeah.

而且可以说，在此之前也是如此，无论是不幸还是幸运。

And arguably before as well, unfortunately or fortunately.

那是一个非常相似的时期，当时我们看到了资本支出的大幅增加，随后是周期性的下滑。

And that was a very similar time period where we saw a massive increase in capital spending followed by a cyclical downturn.

当时推动我们从评估希望与梦想阶段转向执行阶段的，是三个因素。

And what drove the shift then from the appraisal hopes and dreams phase into the execution phase were three factors.

第一，产品出现了供过于求。

One, the product became oversupplied.

第二，你的创新者们发现他们的资本回报率在下降。

Two, your innovators saw their return on capital erode.

第三，创新者们耗尽了财务灵活性。

And third, the innovators ran out of financial flexibility.

你实际上是在问这里第一个因素，即：我们是否真的处于供过于求的状态？

You're asking about really the first of those here, which is, are we actually in oversupply?

但从目前来看，我们还没有到达那样的阶段——高盛等全球大公司还不清楚它们在代币和算力方面究竟需要什么。

And it doesn't look like we're at that point where the Goldman Sachs', the big companies of the world know what they need and are going to get from a token and compute perspective.

但坦率地说，你正身处这一前沿。

But frankly, you're on the front lines of that.

因此，我认为你可能是更好的判断者，来判断我们是否真的正进入一个定义AI需求的时期，或者随着我们持续看到生产率提升，这种需求是否会仅仅被更多对算力和令牌的需求所满足，而资本预算保持不变。

So I actually think you're probably a better arbiter of whether we are actually moving into a period where we're defining the demand for AI or whether, as we see continued productivity gains, whether that's just going to be met with more demand for compute, more demand for tokens for the same capital budgets.

是的。

Yeah.

我的观点是，需求增长将继续让我们感到意外。

Well, my perspective is that demand growth will continue to surprise us.

促成这一点的原因是，正如你在研究报告中观察到的，企业对基础性令牌流的需求正在增加。

And contributing to that, and you observed in your research report, is that there's more demand from enterprises for fundamental sources of of token flow.

其次，如果你观察这些代理系统的工作方式，它们就是令牌的熔炉。

Second, if you just observe the way that these agentic systems work, they are furnaces for tokens.

因此，你有消费者在聊天机器人上进行推理的直接需求，但我看到真正正在打开的是代理机器对机器的通信，我认为这最终将远远超过人类与AI的交互流量。

And so you have the, you know, straightforward demand of consumers performing inference on chatbots, but what I see really opening up and what I think we'll see will ultimately swamp the amount of human AI traffic is agentic machine to machine traffic.

所以我认为，这才是当前供需关系如此紧张的主要驱动力。

So I think that's kind of the runaway demand driver that is keeping it really tight in supply demand today.

这也是我们上调电力需求预测的主要原因之一。

And that's really one of the reasons why we increased our power demand forecasts.

我们现在预计，全球AI及更广泛的非AI数据中心电力需求将增长约220%。

We're now assuming about 220% global growth in AI, in global AI and broader non AI data center power demand.

相比2023年，我们之前的预测是175%。

2030 versus 2023, we were at 175 before.

为了说明这意味什么，这相当于在现有用电国家中新增一个前十名的用电大国。

To put what that means into perspective, that would be like adding another top 10 consuming country to the mix.

这将基本成为全球电力消费排名第六左右的国家。

It would basically be the number six or so on the list of countries consuming power.

这影响之大，由此可见。

It's that significant.

关于上调预测的原因，你提到了其中一点，即我们开始假设推理环节的能耗更高。

The reasons for the upward revisions, you highlighted one of them, which is we're starting to assume a bit more energy intensity in inference.

我认为这还有很大的上升空间，但过去我们通常认为推理阶段每台服务器的能耗要低得多，不过我们正开始看到，由于你提到的一些原因，这一情况正在发生变化。

And I think there's still a long way that could potentially go, but we generally thought of inference as a much lower energy intensive period per server, but we're starting to see that start to change for some of the reasons that you highlighted.

推动这一增长的其他因素包括：第一，出货的服务器数量庞大，我们更广泛的研团队预测这一趋势还会加剧。

Some of the other factors that drove the increase were, number one, just the sheer number of servers that are being shipped, and our broader research team's forecasting more of that.

此外，我们还开始看到你们最新一代服务器的部署增加，同时全球范围内也在建设更多数据中心，这使得服务器能够转化为实际的电力需求。

And then also, we're starting to see a bit more deployment of your latest gen servers as well, in addition to you're building more data centers in the world, and so you have the ability ability to actually have servers turn into power demand.

如果稍微放宽视野，布莱恩，你和乔治为我们提供了一个非常有趣的框架，用于分析在所有这些发展背景下可能推动或制约电力需求增长的因素。

If you zoom out a little bit, Brian, you have a really interesting framework that George and I both take a note for the factors that could drive or constrain power power demand growth amid all of these developments.

请详细介绍一下这个框架，以及你认为它为何有用。

Talk us through that framework and why you think it's useful.

当然。

Sure.

我们认为有六个P因素在推动增长，同时也可能制约增长：人工智能的普及性、模型和服务器的生产率、电力价格、政策、零部件和人才。

We think of the six Ps that are driving both growth and also potentially constraining growth: pervasiveness of AI, productivity of models and servers, price of power, policy, parts, and people.

其中一些因素将是增长的推动力。

And some of these are gonna be drivers of growth.

而另一些则可能成为增长的制约因素。

Some of these could be constraints to growth.

其中一些因素在不同时间段可能同时起到推动和制约作用。

Some of them could actually be both, depending on the time period.

在所有这些因素中，我们从制约角度最担心的是人才方面的问题，我相信我们接下来会深入讨论这一点。

Of all of these, and I'm sure we'll get into it here, we're most worried from a constraint perspective on the people side.

至于我们目前收到很多提问但相对不太担心的方面，是电力供应成本的上涨，以及我们对AI普及程度和生产力提升响应的担忧较少，原因我们刚才已经提到过。

And the ones where we get a lot of questions on that at least for now we are less worried is the rising price supply cost of power, and then we're less worried about the pervasiveness and the response to productivity gains for some of the reasons we just talked about.

这是您工作中非常引人入胜的一部分，它揭示了一个市场尚未充分理解的问题：部署这些解决方案所需的人力密集程度——我们需要多少人来升级电网、建造数据中心、以及多少电工。

This is a fascinating part of your work, which is identifying something that I think is not well understood by the market, which is the human intensity of deploying these solutions, the number of folks we'll need to upgrade grids, the number of people to build data centers, the number of electricians.

能否进一步展开说明，并分享您常引用的一些数据？

Maybe double click on that and offer some of the statistics that you often cite there.

是的。

Yeah.

我们估计，仅在美国，就需要新增50万个岗位，才能满足为数据中心供电所需的发电设施建设所需的人力。

We think we're gonna need to see 500,000 new jobs in The US alone to be able to source the combination of building the generation that's needed to supply power to data centers.

这大约是30万个。

That's about 300,000.

还有电网，即输配电所需的基础设施，这是另外的20万个岗位。

And then also the grid, the infrastructure that's required for transmission and distribution, that's the other 200,000.

这是一个很大的数字。

Now, that's a big number.

我们实际上对这两个数字中更大的那个——30万个岗位及其供给能力——担忧较少。

We are actually less worried about the bigger of those two, the 300,000 and the ability to supply that.

对技能的要求较低。

The skill requirements are less.

我们更担心的是输配电方面，因为在那里，电工需要四年培训。

Where we are more concerned about is on the transmission and distribution side, because there, electricians need four years of skilling.

如果我们看看今天美国现有的能源学徒数量，这个数字需要大幅增加。

And if we look today at the number of energy apprentices that are out there in The US today, and it needs to rise significantly.

目前大约有4.5万名能源学徒。

There's about 45,000 energy apprentices.

我们认为，这个数字还需要再增加2万到2.5万，才能跟上我们预测的某些目标。

We think that needs to rise by another 20,000 to 25,000 to be able to be in the mix here to achieve some of the numbers that we're forecasting.

我们并不是说这不会发生。

We're not saying it's not going to happen.

我们只是说，这是当前我们关注的主要制约因素。

We're just saying that this is the major constraint that we are focused on.

我认为这正在影响我们如何看待超大规模客户和其他方的投资方式。

And I think it is driving some changes in how we're seeing hyperscalers and others invest.

因为如果电网在三到五年内无法建成，那么即使效率稍低一些，你也会看到更多分布式解决方案的部署。

Because if the grid isn't going to show up for three to five years, then even if it's a bit less efficient, you're starting to see greater deployment of behind the meter solutions.

这种状况可能会持续一段时间，但可能不会永远如此，因为电网仍然是优先事项。

And that may last for a period of time, probably not forever, because grid still seems to be the priority.

但归根结底，这都取决于时间、零部件，以及你提到的人力问题——我们是否有足够的熟练劳动力？

But it really all kind of comes down to time, parts, and to your question, people, and do we have enough skilled labor?

那你再详细谈谈分布式解决方案吧。

Well, talk to us a little bit more about behind the meter solutions.

这些方案具体是什么样的？

What do those look like?

因此，场内解决方案主要（但并非全部）由天然气提供。

So behind the meter solutions are largely, but not entirely, supplied with natural gas.

天然气发电机有多种类型。

And there are different types of natural gas generators.

有简单循环发电机和联合循环发电机。

There are simple cycle generators and combined cycle generators.

简单循环发电机每单位电力消耗的天然气比联合循环发电机更多。

The simple cycle generators tend to consume more natural gas per unit of power relative to the combined cycle generators.

这使得它们的效率略低。

That makes them a bit less efficient.

但如果我们必须等到2029年及2030年代天然气联合循环发电机供应增加，那么在那之前，时间至关重要。

But if that's all you can get until we see more supply of natural gas combined cycle generators come on, which we would expect around 2029 and into the 2030s, time to market is important.

对于超大规模企业来说，脱碳也同样重要。

And so is decarbonization for the hyperscalers as well.

但在短期内，如果你能用效率较低的方案让数据中心投入运行，我们已经开始看到这种情况了。

But in the near term, if you can get that data center online with a less efficient solution, we're starting to see that.

你有没有也看到这种情况？

Tell me if you're seeing that as well.

百分之百。

A 100%.

这源于一个较少被了解或理解的维度，即如今实验室和超大规模企业面临的产能限制有多么严重。

And it comes from the fact that one of the lesser known or understood dimensions of this whole phenomenon is just how deeply capacity constrained the labs and hyperscalers are today.

因此，要实际满足这一需求，就会转向表后解决方案。

And so the practicality of meeting that leads you to behind the meter solution.

我认为这是一种持续存在的需求趋势。

So I think it's a persistent demand vector.

正如你所说，这种方案效率较低，同时也受到自身供应链的惯性和引力影响，但我预计它仍会大规模持续存在。

As you say, it's less efficient, and it is also subject to its own supply chain inertial and gravitational forces, but I would expect to continue to see it at scale.

在你的几个P中，我想再增加三个P，也就是政策，你提到的其中一个P。

So on one of your Ps, I would add three more Ps, which is policy, one of your Ps.

我会加上定价、政治和民粹主义。

I would add pricing, politics and populism.

谈谈我们在州一级看到的数据中心暂停建设和州级立法情况，以及消费者电费上涨等方面的担忧。

Talk a little bit about what we're seeing at the state level with data center moratoria and state level legislation, concerns about pricing going up for consumer electricity costs, etcetera.

可负担性是一个重大问题，我们已经在您提到的‘政治’这一P中看到了这一点，未来很可能继续看到这一问题，因为没有人愿意支付更多费用，尤其是当他们的需求未必是直接原因时。

So affordability is a major concern, and we've already seen it in the P that you added, which was politics, and we will likely continue to see that be a concern, because no one wants to have to pay more, especially if their demand isn't necessarily the precise cause of it.

幸运的是，有一些解决办法，我们已经开始看到监管机构、政策制定者、行业和超大规模云服务商之间出现了一些协调，试图将数据中心隔离出来，让云服务商以‘照付不议’的方式承诺建设他们所需的所有设施，这应该能降低对广大客户可负担性的影响风险。

Luckily, there are some ways, and we think we're starting to see some alignment between regulators, policymakers, and industry and the hyperscalers themselves to try to ring fence the data centers where the hyperscalers are committing on a take or pay basis to whatever it is they say they need built, which means that that should reduce the leakage risk to not necessarily have as much of an impact on affordability for broader customers.

但仍有一些步骤可能需要采取，以确保这一点，但至少我们看到了一些朝着这个方向努力的共识。

There are still more steps that likely need to be taken to ensure that, but at least we see some alignment in trying to get that done.

然而，这可能意味着超大规模云服务商需要为电力支付更多费用。

Now, what that could mean, though, is the hyperscalers may be paying more for power.

我们的观点是，这不像‘人’这类其他P那样令人担忧。

Our view is that is not as big of a concern as some of these other Ps like people.

这也涉及到电力来源的类型。

And that also goes to the type of how power could be sourced.

我们做过一项分析，研究了从绿色、可靠的解决方案中获取电力的可能性，比如核能，或者天然气、太阳能、风能与电池储能的组合，这些方案的成本确实更高。

We did an analysis looking at sourcing power from green, reliable solutions that could be nuclear, that could be combinations of natural gas with solar and wind and battery storage together, those do cost more.

例如，与天然气联合循环相比，它们的供电成本更高。

They have a higher supply cost relative to natural gas combined cycle, as an example.

但我们认为它们能够承担得起。

But we think that they can afford it.

这并不是说它们只会选择这些方案，但我们做过一项分析：如果这些解决方案比天然气联合循环每兆瓦时贵40美元，而超大规模客户承担了全部成本，不仅包括美国数据中心及其增长带来的电力需求，还包括全球所有数据中心的增长，这仅会对它们2030年的EBITDA造成约2.5%的影响。

Not to say that that's where they're only going to go, we but did an analysis saying if those solutions cost $40 per megawatt hour more relative to natural gas combined cycle, and the hyperscalers paid all of that, not just for US power demand from data centers and that growth, but all the global growth coming from data centers, that would only have an impact of about 2.5% on their 2030 EBITDA.

这只会使其本已很高的资本回报率和现金回报率降低不到一个百分点。

It would only impact their already high corporate return on capital, cash return on cash invested by less than one percentage point.

因此，我们想表达的重点是：它们并非不在意价格，但对超大规模客户而言，价格并不是主要制约因素，而对广大民众来说却是个重大问题。

And so the point we're trying to make is not that they're not going to care about the price, but that that is less of the constraint for the hyperscalers, but it is a major issue for the broader populace.

但你有没有看到任何迹象表明行业正在朝这个方向发展？

But are you seeing any signs that the industry is moving in that direction?

我的意思是，他们有什么动力真正这样做呢？

I mean, what are the incentives for them to really do that?

我认为，政治层面和‘邻避效应’正变得越来越明显。

Well, I think the political side and the kind of NIMBY side is really starting to become louder.

因此，你已经看到一些公司，包括一些大型超大规模企业，发布了社区声明或其他声明，表示他们将为所消耗的电力付费。

And so you've seen companies, and you've seen some of the big hyperscalers put out community statements saying, or other statements saying, We are going to pay for the power that we consume.

我们不希望我们的数据需求以及数据中心的建设影响普通消费者的电价。

We do not want to let our demand for data and our construction of data centers impact everyday consumer prices.

我认为这印证了刚才的观点：确实存在某种一致性。

And I think that goes to the point on, it does look like there is alignment.

也许这目前还只是停留在口头上，但我们相信它会转化为实际行动，而且这种转化已经开始了。

Maybe it's just in words, but we do think it'll translate, and it already is translating into action.

当你像你在许多研究中那样，展望到2030年及更远的未来时，人工智能数据中心的能源结构会是什么样子？

When you think out, as you have in a lot of your research to 2030 and beyond, what does the mix of energy resources for AI data centers look like?

你提到了天然气解决方案在就近供应和便利性方面的优势，但太阳能和电池技术正呈现出类似摩尔定律的动态发展。

You've talked about the kind of proximate demand and convenience of natural gas solutions, but you've got a sort of Moore's Law like dynamic for solar and batteries.

那么，未来这种能源结构会是什么样的？

What will the mix look like down the road?

是的，我们总体上认为，数据中心的电力来源大约60%来自热力能源，主要是天然气，40%来自可再生能源。

Yeah, we broadly think about power being sourced for data centers as roughly 60% coming from thermal sources, which is largely natural gas, 40% coming from renewable sources.

此外，核能也会占一小部分，这一情况将持续到2030年。

And there's a little bit of extra for nuclear as well, which is going to, and that's through 2030.

2030年代的情况可能会大不相同。

The 2030s could look a lot different.

核能将在其中发挥重要作用，或者至少我们将看到核能被多大程度地接受——不仅限于在美国重启一些停运的核电站，还包括新建小型和大型反应堆。

And nuclear is going to play a major role in that, or at least to what degree will we see nuclear be embraced, not just from demothballing some of the plants in The US, but also building new small and larger scale reactors.

但零部件的供应情况，也就是那个P，将真正影响电力来源的时间安排。

But the parts availability, that P, that's going to really help to drive how power is going to be sourced chronologically.

因为如果你直到2029年、2031年才能拿到天然气联合循环涡轮机，而核能除了重启旧电站外，要到2030年代甚至2030年代中期才能发挥实质性作用，那么在近期的选择就会非常有限。

Because if you can't get that natural gas combined cycle turbine until 2029, 02/3231, and nuclear isn't going to play a meaningful role beyond the demothballings until the 2030s and potentially mid-2030s, you have fewer options in the near term.

这可能意味着户内简单的燃气轮机解决方案。

And that could be that behind the meter simple cycle natural gas solution.

也可能是太阳能和风能搭配电池储能，再辅以天然气简单循环发电，以打造一种绿色且可靠的供电方案。

It could also be solar and wind paired with battery storage, paired with natural gas simple cycle to try to create that green, reliable solution.

当然，这些方案的成本更高，但这并不意味着它们不会被部署。

And, again, those come with higher costs, but that doesn't mean that they're not going to be deployed.

它们成本更高，但同时也带来了在规模、价格和创新曲线上的天然优势。

They come with higher costs, but they also come with very natural leverage on volume and price and innovation curves.

因此，展望2030年代，我完全同意你的观点，核能有望发挥重要作用。

And so I think thinking into the 2030s, I agree with you entirely, nuclear is hopefully gonna play a meaningful role.

此外，我对太阳能和电池技术非常看好。

Also, I'm really bullish on solar and battery.

我认为这两者在未来这一时期内，在效能和成本方面都将取得令人惊讶的进展。

Think they're both on a trajectory to be surprising in terms of their efficacy and cost in that period of time.

时间会给出答案。

Only time will tell.

是的。

Yeah.

有时人们会以为这一切都会集中在一个篮子里，但我们不认同这种看法。

I think sometimes there's the perception that this is all going to be in one basket, and we reject that.

用即兴喜剧的说法，我们这里是一个‘是的，而且’的环境，而不是‘不，或者’的环境。

To use an improv comedy phrase, we are in a yes and environment here, not a no or environment.

让我问你们两位一个问题，因为正如你们所说，成本更高，但这些公司大部分都极其盈利。

Let me just ask a thought to both of you because, as you say, the costs are higher, but these companies are, for the most part, extremely profitable.

但有人担心，它们目前的运营正在消耗大量现金。

But there is some concern about the fact that they are eating through a lot of cash for their operations at this point.

我认为我们都默认认为它们能持续不断地融资下去。

And I think we're all kind of sort of taking it for granted that they're just gonna be able to fund and fund and fund.

当我们展望未来时，这种融资能力有没有什么极限？

Is there some limit to that when we have these views of the future?

我的意思是，是的，肯定存在一个极限。

I mean, yeah, there definitely is a limit.

但我不确定我们是否已经到达那个点。

Not sure we're at that point.

而且，我不认为这必须等同于我们在2020年初讨论页岩油时提到的那个类比。

And not to say it has to equal the analogy that we started talking about with shale back in the 02/2020 period.

但在高峰期，那些公司的资本支出超过了其经营性现金流的120%。

But at peak, those companies were spending more than 120 of their operating cash flow back into CapEx.

在经历了这3000亿美元的上调后，目前对超大规模云服务商而言，2026年的比例约为87%。

The equivalent right now after this $300,000,000,000 upward revision is in 2026 about 87% for the hyperscalers.

他们87%的经营性现金流加上研发支出正被重新投入资本支出和研发。

87% of their operating cash flow plus R and D is being redeployed back into CapEx and R and D.

是的，这一比例一直在稳步上升。

And yes, that's been on a steady move up.

此外，有几家公司比例明显更高，属于异常值，投资者已经注意到并对此作出了反应。

And also, there are a couple that are meaningfully higher than that that are outliers, and investors have responded to that and have noticed that.

但这是自由现金流的情况。

But that's the free cash flow picture.

这并未涉及资产负债表层面的问题。

That doesn't speak to the balance sheet side of the equation.

目前账面上的净债务与EBITDA之比极低。

There is minimal net debt to EBITDA currently on books.

因此，我们确实开始看到债务发行，这相对而言是新现象，因为这些公司过去一直是现金制造机，但这并不意味着今天的资产负债表正在被过度使用。

And so, yes, we're starting to see debt issuance, which is a relatively new thing because these have been free cash machines, But that doesn't mean that balance sheets today are being stretched.

因此，我们不会说这些超大规模云服务商财务上缺乏灵活性，但它们并不是参与这场人工智能投资浪潮的唯一主体。

So we would not call the hyperscalers financially inflexible, but they're not the only ones that are part of the AI investing machine here.

因此，尽管由于其传统业务，它们可能处于最佳位置，但公平地说，我们不仅应关注超大规模云服务商正接近100%的比例，还应注意到有些企业相对于它们而言资本不足。

And so they may be in the best place because of their legacy businesses, but it's fair to not only reflect on the hyperscalers getting closer to 100%, but also that there are some that are undercapitalized relative to them.

此外，关于这一点，我想强调一下你之前提到的一点：实际上，与建设数据中心的其他成本相比，能源和电力成本所占比例相对较低。

Also, to that point, I would underscore something you said earlier, which is the, actually, the amount of investment in energy and electricity cost relative to the other costs of constructing a data center is relatively low.

因此，我认为价格敏感性在这里不会起决定性作用；但你准确地指出了超大规模云服务商和其他行业参与者的财务能力，那么公用事业公司呢？

And so I don't think a price sensitivity there will be hugely determinative, but, and you rightly characterize how much financial capacity the hyperscalers and other industry participants have, what about the utilities?

它们正被要求进行重大投资。

They're being called upon to make major investments.

它们所投入的资本支出大幅增加，增幅几乎与超大规模云服务商的百分比相当。

The amount of CapEx they're deploying has gone up dramatically, almost as much on a percentage basis as the hyperscalers.

所以请谈谈这一动态以及其中存在的能力空间。

So talk through that dynamic and the capacity that exists there.

当然。

Sure.

公用事业公司不具备像超大规模云服务商那样强大的资产负债表实力。

The utilities do not have the extent of the balance sheet strength that the hyperscalers do.

但对于获得监管机构批准投资项目的受监管公用事业公司而言，它们通常需要通过股权和债务来融资。

But for regulated utilities that receive the permission from their regulator to invest in projects, they will likely need to finance that, and that typically comes with equity and debt.

因此，公用事业公司所处的位置大不相同，它们很可能需要进入资本市场融资。

So the utilities are in a much different place where they will likely be needing to tap the capital markets.

因此，投资者会审视并问：这是一个具有可行合同的可行项目吗？

And so the investors will look and say, Is this a viable project with a viable contract?

假设这是不受监管的项目，或者对于已获得批准的受监管公用事业公司而言，关键在于它们的执行能力。

Assuming this is unregulated, or for the regulated utilities that have that approval, it will be their ability to execute.

我知道你非常关注的一个领域，尤其是从公用事业公司角度来看，目前变化较大的是大规模核电，甚至广义上的核能。

The area that I know you are very close to that is, I think, a bit more in flux in particular from the utilities is large scale nuclear or even nuclear broadly.

目前似乎出现了一种异常充裕的骑士精神，每个人都希望为他人敞开大门，让别人的项目先通过。

And there seems to be a bit of a super abundant chivalry going on right now, which is everyone wants to hold the door open for someone else to let those projects go through the door ahead of them.

没有人愿意成为第一个、第二个、第三个或第四个。

No one wants to be first, second, third, or fourth.

其中一部分是技术驱动的，我们能否受益于他人先改进和创新技术，而不是我们必须自己去做？

Some of that is technology driven, of can we actually benefit from others improving and innovating technology before we have to do it ourselves?

部分原因是核能过去并不总是顺利，过去十五年、二十年甚至三十年在美国很少开展。

Some of it is nuclear hasn't necessarily always gone well and hasn't been done a lot in The US over the last fifteen, twenty, thirty years.

因此，人们不愿意将资本投入那些在一段时间内可能无法产生收入的项目。

And so there's a hesitation to be putting capital to work where you wouldn't necessarily get revenues for a period of time.

非常有趣的内容。

Fascinating stuff.

非常感谢你加入我们的讨论，布莱恩。

Thank you so much for joining us, Brian.

谢谢，艾莉森。

Thanks, Alison.

谢谢，乔治。

Thanks, George.

很高兴有你加入。

Great to have you.

而且，艾莉森，我非常钦佩布赖恩的工作，因为这些现象变化非常迅速。

And, you know, Alison, I'm a huge admirer of Brian's work because these are very fast moving phenomena.

正如我们所讨论的，布赖恩扎实的分析框架极具价值。

As we've been talking about, Brian's very grounded analytical frameworks, just think have been extremely valuable.

因此，我赞扬他的工作。

So I commend the work.

感谢你做出这些贡献。

I thank you for it.

也感谢你今天来到这里。

And thank you for being here today.

感谢你们邀请我参加

Thank you for having

高盛交流节目的这一期录制于3月3日星期二。

episode of Goldman Sachs Exchanges was recorded on Tuesday, March 3.

我是艾莉森·纳撒恩。

I'm Allison Nathan.

感谢收听。

Thanks for listening.

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