关于AI的一切，嘉宾包括@altcap、@sama和@satyanadella。万圣节特辑。🎃🔥BG2与布拉德·格斯特纳

我认为这确实是一段贯穿每个阶段的非凡合作关系。正如萨提亚所说，我们刚开始时完全不知道会走向何方，但我认为这是有史以来最伟大的科技合作之一。如果没有微软，尤其是萨塔早期的坚定支持，我们不可能做到这一切。

I think this is really an amazing partnership through every phase. We had kinda no idea where it was all gonna go when we started, as Satya said, but I I don't think I think this is one of the great tech partnerships ever. And without certainly without Microsoft, and particularly Sarta's early conviction, we would not have been able to do it.

多么精彩的一周啊。真高兴见到你们两位。山姆，宝宝怎么样？

What a week. What a week. Great to see you both. Sam, how's the baby?

宝宝很棒。那真是世界上最美好的事情，老兄。所有陈词滥调都是真的，这确实是人生最棒的体验。

Baby is great. That's the best thing ever, man. Every every cliche is true, and it is the best thing ever.

嘿，萨提亚，你所有的时间

Hey, Satya. With all your time

每当山姆谈起他的宝宝时脸上的笑容——那感觉完全不同。我猜他谈论计算技术时也是这种神情，当他说起计算和他的宝宝时。

smile on Sam's face whenever he talks about it's just it's his baby. It's just so different. It's he do it with compute, I guess, when he talks about compute and his baby.

瓦萨奇，你们相处这么久，有没有给他传授些当爸爸的建议？

Wasatchi, have you given him any dad tips with all this time you guys have spent together?

我说就好好享受吧。我的意思是，我们在这么年轻时就拥有了自己的孩子，这实在太美好了，我多希望能重来一次。从某种意义上说，那是最珍贵的时光。看着他们成长是如此美妙。我真为山姆感到高兴

I said just enjoy it. I mean, it's so awesome that, you know, we had our babies, or our children, so young, and I wish I could redo it. So, in some sense, it's just the most precious time. As they grow, it's just so wonderful. I'm so glad Sam is

我很高兴能在年纪大些时做这些事，但有时确实会想，伙计，真希望我能有25岁时的精力。这部分确实更难。

I'm happy to be doing it older, but I do think sometimes, man, I wish I had the energy of when I was like 25. That part's harder.

毫无疑问。OpenAI的平均年龄是多少，Sam？你知道吗？很年轻。

No doubt about it. What's the average age at OpenAI, Sam? Any idea? It's young.

也不算特别年轻。就像大多数硅谷初创公司一样。我不确定，可能平均年龄在30岁出头。

It's not crazy young. Like most Silicon Valley startups. I don't know, maybe low 30s average.

婴儿出生率是上升趋势还是下降趋势？上升趋势。哦，那很好。那很好。你们这周真是大事不断啊。

Are babies trending positively or negatively? Babies trending positively. Oh, that's good. That's good. Well, you guys, such a big week.

你知道，我在想我从NVIDIA的GTC大会开始关注，谷歌市值刚突破5万亿美元，Meta、微软、Satya，你们昨天公布了财报，我们不断听到算力不足、算力不足、算力不足。周三我们迎来了降息。GDP增速接近4%。我刚才还在和Sam说，总统在马来西亚、韩国、日本达成了巨额协议，听说还有中国，这些协议确实为美国再工业化提供了惊人的财力支持。800亿美元用于新型核裂变，这些都是你们需要用来增加算力的。

You know, I was thinking about I started at NVIDIA's GTC, you know, just hit $5,000,000,000,000 Google, Meta, Microsoft, Satya, you had your earnings yesterday, you know, and we heard consistently not enough compute, not enough compute, not enough compute. We got rate cuts on Wednesday. The GDP is tracking near 4%. And then I was just saying to Sam, you know, the President's cut these massive deals in Malaysia, South Korea, Japan, sounds like with China, you know, deals that really incredibly provide the financial firepower to re industrialize America. 80,000,000,000 for new nuclear fission, all the things that you guys need to build more compute.

但在这所有事情中，最不容忽视的是你们周二发布的重要公告，明确了合作关系。恭喜你们。我想我们就从这里开始。我真的很想用最简单直白的语言解析这笔交易，确保我和其他人都能理解。不过我们就先从Satya你们的投资说起吧。

But certainly wasn't what wasn't lost in all of this was you guys had a big announcement on Tuesday that clarified your partnership. Congrats on that. And I thought we'd just start there. I really want to just break down the deal in really simple plain language to make sure I understand it and others. But you know, we'll just start with your investment Satya.

微软从2019年开始投资，已经向OpenAI投入了大约1.314万亿美元。作为回报，你们获得了公司27%的所有权（完全稀释后）。我记得原本是三分之一左右，去年经过多轮投资后有所稀释。这个所有权比例听起来对吗？

You know, Microsoft started investing in 2019, has invested in the ballpark of $1,314,000,000,000 dollars into OpenAI. And for that, you get 27% of the business ownership in the business on a fully diluted basis. I think it was about a third, and you took some dilution over the course of last year with all the investment. So does that sound about right in terms of ownership?

确实如此。但我想说的是，在我们投资之前，布拉德，OpenAI独特之处在于其重组过程中创建了最大的非营利组织之一。别忘了，从某种意义上说，微软很自豪能与两大非营利机构——盖茨基金会和现在的OpenAI基金会——建立联系。我认为这才是重大新闻。我们当然非常激动。

Yeah, it does. But I would say before even our stake in it, Brad, I think what's pretty unique about OpenAI is the fact that as part of OpenAI's process of restructuring, one of the largest nonprofit gets created. Mean, let's not forget that, in some sense, say at Microsoft, we are very proud of the fact that we associated with the two of the largest nonprofits, the Gates Foundation and now the OpenAI Foundation. So, that's, I think, the big news. We obviously are thrilled.

这与我们最初的设想不同。正如我对某人说的，最初投资十亿美元时，我们也没想到这会成为我向风投们津津乐道的百倍回报项目。但事实如此。作为早期投资者，我们非常兴奋。坦率地说，这真正证明了山姆及其团队的成就。

It's not what we thought. And as I said to somebody, it's not like when we first invested our billion dollars that, oh, this is going to be the 100 bagger that I'm going to be talking about to VCs about. But here we are. But we are very thrilled to be an investor and an early backer. It's really a testament to what Sam and team have done, quite frankly.

他们显然很早就预见到这项技术的潜力，并全力以赴，以高超的执行力将其实现。

Mean, they obviously had the vision early about what this technology could do, and they ran with it and just executed in a masterful way.

我认为这确实是一段贯穿每个阶段的惊人合作关系。正如萨提亚所说，开始时我们完全不知道未来会如何发展。但我想这是史上最伟大的科技合作之一。没有微软，尤其是萨提亚早期的坚定信念，我们不可能做到。在当时的世界环境下，我认为没有多少人愿意下这样的赌注。

I think this is really an amazing partnership through every phase. We had kind of no idea where it was all going to go when we started, as Satya said. But I I don't think I think this is one of the great tech partnerships ever. And without certainly without Microsoft, and particularly Satya's early conviction, we would not have been able to do this. I don't think there were a lot of other people that would have been willing to take that kind of a bet, given what the world looked like at the time.

我们并不完全清楚技术会如何发展——准确说是完全不清楚。我们只是坚信深度学习这个方向，相信只要坚持下去，就能找到创造优秀产品、实现巨大价值的方法。正如萨提亚所说，还能创建我们相信将成为史上最大的非营利组织。

We didn't know exactly how the tech was going to go. Well, not exactly. We didn't know at all how the tech was going to go. We just had a lot of conviction in this one idea of pushing on deep learning, and trusting that if we could do that, we'd figure out ways to make wonderful products and create a lot of value. And also, as Satya said, create what we believe will be the largest nonprofit ever.

我认为它将会取得惊人的成就。我特别喜欢这个架构设计：非营利组织能增值的同时，公益公司也能获得持续扩张所需的资金。如果没有这个架构，没有合作伙伴们对这种运作方式的热情支持，非营利组织不可能实现如此价值。这段合作已持续六年多，取得的成就令人惊叹。我真心希望萨提亚的投资能赚到万亿而非千亿——无论具体数字是多少。

And I think it's going to do amazingly great things. It was I really like the structure because it lets the nonprofit grow in value while the PBC is able to get the capital that it needs to keep scaling. I don't think the nonprofit would be able to be this valuable if we didn't come up with the structure and if we didn't have partners around the table that were excited for it to work this way. But you know, I think it's been six more than six years since we first started this partnership, and, a pretty crazy amount of achievement for six years, and I think much, I much more to hope that Satya makes a trillion dollars on the investment, not a 100,000,000,000, you know, whatever it is.

作为重组的一部分，你们讨论过这个架构：顶层是非营利组织，下层是公益公司。这非常惊人——非营利组织初始资本就达到1300亿美元的OpenAI股票，一成立就跻身全球最大非营利机构之列。

Well, as part of the restructuring, you guys talked about it. You have this nonprofit on top and a public benefit corp below. It's pretty insane. The nonprofit is already capitalized with a $130,000,000,000, a $130,000,000,000 of OpenAI stock. It's one of the largest in the world out of the gates.

最终规模可能会大得多。加州总检察长表示他们不会反对。你们已经投入1300亿美元确保AGI造福全人类，并宣布将首批250亿美元用于健康、AI安全和韧性研究。Sam，首先我想说，作为这个生态系统的参与者，我要为你们两位点赞。

It could end up being much, much larger. The California attorney general said they're not going to object to it. You already have this $130,000,000,000 dedicated to making sure that AGI benefits all of humanity. You announced that you're going to direct the first 25,000,000,000 to health and AI security and resilience, Sam. First, let me just say, you know, as somebody who participates in the ecosystem, kudos to you both.

这对AI未来的贡献令人惊叹。但Sam，请谈谈健康与韧性领域选择权的重要性。然后帮我们理解，如何确保获得最大效益而不被拖累——就像我们看到许多非营利组织因政治偏见而陷入困境那样？

It's incredible, this contribution to the future of AI. But Sam, talk to us a bit about the importance of choice around health and resilience. And then help us understand how do we make sure that you get maximal benefit without it getting weighted down as we've seen with so many nonprofits with its own political biases?

是的。首先，为世界创造最大价值的最佳方式，希望就是我们一直在做的：打造这些神奇工具并让人们自由使用。我认为资本主义很棒，公司很棒，人们正在做惊人工作——让先进AI被众多人群和企业掌握，创造出不可思议的成果。

Yeah. First of all, the the best way to create a bunch of value for the world is hopefully what we're we've already been doing, which is to make these amazing tools and just let people use them. And I think capitalism is great. I think companies are great. I think people are doing amazing work getting advanced AI into the hands of a lot of people and companies that are doing incredible things.

在某些领域，我认为市场机制并不能完全符合人类最佳利益，这时就需要不同方式。这项技术也带来前所未有的新事物，比如用AI快速推进科研，实现真正自动化发现。当我们考虑首批重点领域时，显然若能攻克大量疾病并使相关数据广泛可用，这对世界将是莫大贡献。关于AI韧性这点，我认为某些情况可能会变得棘手，单靠企业运作无法解决所有问题。在全球应对这场转型时，如果我们能资助相关研究——无论是网络防御、航空安全研究还是经济分析——所有这些都能帮助社会平稳过渡。

There are some areas where the, I think, market forces don't quite work for what's in the best interest of people, and you do need to do things in a different way. There are also some new things with this technology that just haven't existed before, like the potential to use AI to do science at a rapid clip, like really truly automated discovery. And when we thought about the areas we wanted to first focus on, clearly, if we can cure a lot of disease and make the data and information for that broadly available, that would that would be a wonderful thing to do for the world. And then on this point of AI resilience, I do think some things may get a little strange, and they won't all be addressed by companies doing their thing. So as the world has to navigate through this transition, if we can fund some work to help with that, and that could be, you know, cyber defense, that could be air safety research, that could be economic studies, all of these things, helping society get through this transition smoothly.

我们对转型后的美好前景充满信心，但过程中难免会有波折。

We're very confident about how great it can be on the other side, but, you know, I'm sure there will be some choppiness along the way.

让我们继续剖析协议细节。关于模型和排他性：Sam，OpenAI可以在Azure上分发其领先模型，但在2032年前（即七年内）不能在其他主要云平台分发——除非AGI提前获得验证。这点我们稍后再讨论。

Let's keep busting through the deal. So models and exclusivity. Sam, OpenAI can distribute its models, its leading models on Azure, but I don't think you can distribute them on any other leading or the big clouds for seven years until 2032. But that would end earlier if AGI is verified. We can come back to that.

但你们可以开源模型，Sora、智能体、编解码器、可穿戴设备等其他产品都能登陆其他平台。所以Sam，这是否意味着ChatGPT六代不会登陆亚马逊或谷歌？

But you can distribute your open source models, Sora, agents, codecs, wearables, everything else on other platforms. So Sam, I assume this means no ChatGPT six on Amazon or Google.

不。首先我们有一只猫，我们想一起做很多事情来帮助，你知道的，为微软创造价值。我们希望他们做很多事情来为我们创造价值。在那个类别中将会发生许多许多事情。我们正在保留萨提亚曾经提到的一个很好的说法，即在2030年之前，Azure上只提供无状态API，其他所有内容我们都会分发到其他地方。

No. So we have a cat first of all, we want to do lots of things together to help, you know, create value for Microsoft. We want them to do lots of things to create value for us. There are many, many things that will happen in that category. We are keeping what Satya termed once, and I think it's a great phrase, of stateless APIs on Azure exclusively through 2030 and everything else we're going to, you know, distribute elsewhere.

这显然也符合微软的利益。所以我们会把很多产品放在很多地方，然后这件事我们会在Azure上做，人们可以在那里或通过我们获取。我认为这很棒。

That's obviously in Microsoft's interest, too. So we'll put lots of products, lots of places, and then this thing we'll do on Azure and people can get it there or via us. I think that's great.

然后是收入分成，OpenAI仍需向微软支付你们所有收入的收入分成，这一安排将持续到2032年，或直到AGI被验证。为了讨论方便，我知道这有点老生常谈，但很重要，假设收入分成是15%。那么如果你有200亿美元的收入，就要支付30亿美元给微软，这算作Azure的收入。萨提亚，这样说对吗？

And then the rev share, there's still a rev share that gets paid by OpenAI to Microsoft on all your revenues that also runs until 2032, or until AGI is verified. So let's just assume for the sake of argument, I know this is pedestrian, but it's important, that the rev share is 15%. So that would mean if you had $20,000,000,000 in revenue, that you're paying $3,000,000,000 to Microsoft, and that counts as revenue to Azure. Satya, does that sound about right?

是的，我们有收入分成，我认为正如你所描述的，它要么持续到AGI出现，要么持续到协议结束。老实说，我不太清楚我们具体把它算在哪里，是算在Azure还是其他地方。这是个好问题，应该问艾米。

Yeah, we have a rev share, and I think as you characterized it, it's either going to AGI or till the end of the term. And I actually don't know exactly where we count it, quite honestly, whether it goes into Azure or somewhere else. That's a good question. It's a good question for Amy.

鉴于在AGI被验证的情况下，独占权和收入分成都会提前结束，这使得AGI显得非常重要。据我理解，如果OpenAI声称实现了AGI，似乎会提交给一个专家小组，你们基本上会选出一个评审团，他们需要相对快速地决定是否达到了AGI。萨提亚，你在昨天的财报电话会议上说，目前没有人接近实现AGI，也不认为它会在短期内实现。你谈到了这种尖峰和锯齿状的智能。山姆，我听说你对我们何时可能实现AGI的看法可能更乐观一些。

Given that both exclusivity and the rev share end early in the case AGI is verified, it seems to make AGI a pretty big deal. And as I understand it, you know, if OpenAI claimed AGI, it sounds like it goes to an expert panel and you guys basically select a jury who's got to make a relatively quick decision whether or not AGI has been reached. Satya, you said on yesterday's earnings call that nobody's even close to getting to AGI and you don't expect it to happen anytime soon. You talked about this spiky and jagged intelligence. Sam, I've heard you perhaps sound a little bit more bullish on, you know, when we might get to AGI.

所以我想问你们两位的问题是，你们是否担心在未来两三年内，我们将不得不召集评审团来实际判断我们是否已经实现了AGI？

So I guess the question is to you both. Do you worry that over the next two or three years, we're gonna end up having to call in the jury to effectively make a call on whether or not we've hit AGI?

我知道你想在我们之间制造一些戏剧性。我认为为此制定一个流程是件好事。我预计技术会有几次出人意料的转折，我们将继续成为彼此的好伙伴，并找出合理的解决方案。

I realize you've got to try to make some drama between us here. I think putting a process in place for this is a good thing to do. I expect that the technology will take several surprising twists and turns, and we will continue to be good partners to each other and figure out what makes sense.

说得好。我认为，这也是为什么我觉得我们制定的这个流程很合理。归根结底，我坚信智能能力将持续进步。坦率地说，我们的真正目标是如何让这些能力触达个人和组织，使他们获得最大收益。这也正是当初吸引我加入OpenAI与山姆团队的最初使命，也是我们计划继续推进的方向。

That's well said. I think And that's one of the reasons why I think this process we put in place is a good one. And at the end of the day, I'm a big believer in the fact that intelligence capability wise is going to continue to improve. Our real goal, quite frankly, is that, Rich, is how do you put that in the hands of people and organizations so that they can get the maximum benefits? And that was the original mission of OpenAI that attracted me to OpenAI and Sam and team, and that's kind of what we plan to continue on.

布拉德，说句大实话——就算明天就实现了超级智能，我们依然需要微软协助将产品推向市场，我们确实需要他们，没错。

Brad, to say the obvious, if we had superintelligence tomorrow, we would still want Microsoft's help getting this product out into people's hands, and we want them like, yeah.

当然，当然。我再次提出这些问题是知道大家心里都有这些疑惑，这对我来说完全合理。显然微软是全球最大的分发平台之一。你们长期以来都是出色的合作伙伴，我认为这破除了一些外界误解。

Of course. Of course. Yeah, no, again, I'm asking the questions I know that are on people's minds, and that makes a ton of sense to me. Obviously Microsoft is one of the largest distribution platforms in the world. You guys have been great partners for a long time, I think it dispels some of the myths that are out there.

不过让我们稍微转换下话题。OpenAI无疑是史上增长最快的公司之一。萨提亚，你一年前在这个播客里说过，每次技术范式转变都会催生新的谷歌，而这次范式转变中的谷歌已经明确就是OpenAI。如果没有你们当初的重磅押注，这一切都不可能实现。话说回来，据报告OpenAI 2025年营收仍将达到130亿美元。

But let's shift gears a little bit. Obviously OpenAI is one of the fastest growing companies in history. Satya, you said on the pod a year ago, this pod, that every new phase shift creates a new Google, and the Google of this phase shift is already known and it's OpenAI. And none of this would have been possible had you guys not made these huge bets. With all that said, know, OpenAI's revenues are still a reported $13,000,000,000 in 2025.

山姆，你在本周直播中谈到对算力的巨额投入对吧？未来四五年将投入1.4万亿美元，包括向英伟达承诺5亿，AMD和甲骨文各3亿，Azure 2500亿。所以本周市场上最大的疑问就是：一家年收入130亿美元的公司如何能承诺1.4万亿美元的支出？你也听到那些质疑声了——

And Sam, on your live stream this week, you talked about this massive commitment to compute, right? 1,400,000,000,000.0 over the next four or five years with, you know, big commitments, 500,000,000 to Nvidia, 300,000,000 to AMD and Oracle, $250,000,000,000 to Azure. So I think the single biggest question I've heard all week and hanging over the market is, you know, how can a company with 13,000,000,000 in revenues make $1,400,000,000,000 of spend commitments? You know? And and you've heard the criticism,

首先我们实际收入远不止这个数。其次布拉德，如果你想抛售股份，我可以帮你找接盘方——如果你

We're doing well more revenue than that. Second of all, Brad, if you wanna sell your shares, I'll find you a buyer if you

不想的话。

don't feel like.

我只是觉得，你知道，有很多人应该会很想买OpenAI的股票。我不认为你会想要

I just enough. Like, you know, people are I think there's a lot of people who would love to buy OpenAI shares. I don't I don't think you want

包括我自己。那些

to myself. Including myself. People who

用近乎夸张的担忧语气讨论我们算力问题的人，其实他们自己会抢着买股票。所以我觉得我们可以很快把你的股份或其他人的股份卖给在推特上对此事叫得最响的那些人。我们确实预计收入会大幅增长。收入正在快速增长。我们押注这种增长会持续，不仅ChatGPT会继续壮大，我们还将成为重要的AI云服务商，我们的消费级设备业务将成为重要板块，能自动化科研的AI将创造巨大价值。

talk with a lot of, like, breathless concern about our compute stuff or whatever that would be thrilled to buy shares. So I think we could sell, you know, your shares or anybody else's to some of the people who are making the most noise on Twitter, whatever, about this very quickly. We do plan for revenue to grow steeply. Revenue is growing steeply. We are taking a forward bet that it's going to continue to grow and that not only will Chachapiti keep growing, but we will be able to become one of the important AI clouds, that our consumer device business will be a significant and important thing, that AI that can automate science will create huge value.

说实话，我很少想上市，但少数让我心动的情况之一，就是当那些人写那些荒谬的公开信说OpenAI快倒闭的时候。我特想告诉他们可以直接做空股票，更想看到他们因此血本无归。但我们精心规划过，我们清楚技术路线和发展方向，知道能围绕它开发什么产品、创造多少收入——当然也可能搞砸。这就是我们的赌注，我们也在承担相应风险。

So, you know, there are not many times that I want to be a public company, but one of the rare times it's appealing is when those people are writing these ridiculous open AIs about to go out of business and, you know, whatever. I would love to tell them they could just short the stock, and I would love to see them get burned on that. But, you know, I we carefully plan. We understand where the technology, where the capability is going to grow, go and how the products we can build around that and the revenue we can generate, we might screw it up. Like, this is the bet that we're making, and we're taking a risk along with that.

一个特定风险是：如果缺乏算力，我们就无法实现这种规模的收入或模型训练。让我

A certain risk is if we don't have the compute, we will not be able to generate the revenue or make the models at this kind of scale. Let me

作为合作伙伴兼投资人，我直说一点布拉德——OpenAI提出的每一个商业计划，他们不仅实现了，还都超额完成。从增长速度和商业表现来看，他们的执行力简直令人难以置信。虽然大家都知道OpenAI的成功和用户量，但整体而言，他们的商业落地能力确实超乎想象。

just say one thing, Brad, as both a partner and an investor. There has not been a single business plan that I've seen from OpenAI that they're putting and not beating it. In some sense, this is the one place where in terms of their growth and just even the business, it's been unbelievable execution, quite frankly. I mean, obviously, OpenAI, everyone talks about all the success and the usage and what have you. But even, I would say all up, the business execution has been just pretty unbelievable.

几周前我听Greg Brockman在CNBC上说：如果我们算力增长十倍，收入未必翻十倍，但肯定会多赚很多。

I heard Greg Brockman say on CNBC a couple weeks ago, right, if we could 10x our compute, we might not have 10 x more revenue, but we'd certainly have a lot more revenue.

仅仅因为计算能力的不足。确实如此。当我看到我们被限制了多少时，感觉真的很疯狂。从很多方面来说，你知道，过去一年我们的计算能力可能已经扩展了10倍。但如果我们有10倍的计算能力，我不确定收入是否也会增长10倍，但差距应该不会太大。

Simply because of lack of compute power. Things like yeah. It's just it's really wild when I just look at how much we are held back. And in many ways, we have you you know, we've scaled our compute probably 10 x over the past year. But if we had 10 x more compute, I don't know if we'd have 10 x more revenue, but I don't think it'd be that far.

昨晚我们也从你那里听到了类似的观点，萨提亚，你们受限于计算能力，如果增加更多计算资源，增长会更高。所以萨姆，帮我们理解一下，你现在感觉计算能力有多受限？展望未来两到三年的建设，你认为会达到不再受限于计算能力的阶段吗？

And we heard this from you as well last night, Satya, that you were compute constrained and growth would have been higher even if you add more compute. So help us contextualize, Sam, maybe like how compute constrained do you feel today? Do you, when you look at the build out over the course of the next two to three years, do you think you'll ever get to the point where you're not compute constrained?

我们经常讨论‘计算能力是否永远足够’这个问题。我认为最好的思考方式就像能源一样。你可以讨论特定价格下的能源需求，但不能脱离不同价格水平来谈需求。如果以智能单位计算的计算成本明天下降100倍，使用量增长的幅度会远超100倍。人们会想做许多在当前成本下不经济的事情，但会催生新的需求。

We talk about this question of is there ever enough compute a lot. I think the answer is the only the best way to think about this is like energy or something. You can talk about demand for energy at a certain price point, but you can't talk about demand for energy without talking about at different, you know, different demand at different price levels. If the price of compute per like unit of intelligence or whatever, however you want to think about it, fell by a factor of 100 tomorrow, you would see usage go up by much more than 100. And there'd be a lot of things that people would love to do with that compute that just make no economic sense at the current cost, but there would be new kind of demand.

另一方面，随着模型变得更智能，可以用它们来治愈癌症、发现新物理定律、驱动人形机器人建造空间站或任何疯狂想法时，人们可能愿意为更高水平的智能支付更高的单位成本。这尚不确定，但我敢打赌会出现这种情况。所以讨论产能时，要同时考虑单位成本和单位能力，没有这些曲线支撑，问题就难以准确定义。

So I think the now, on the other hand, as the models get even smarter and you can use these models to cure cancer or discover novel physics or drive a bunch of humanoid robots to construct a space station or whatever crazy thing you want, then maybe there's huge willingness to pay a much higher rate cost per unit of intelligence for a much higher level of intelligence. That we don't know yet, but I would bet there will be. So I think when you talk about capacity, it's like a cost per unit and capability per unit. And you have to kind of without those curves, it's sort of a made up it's not a super well specified problem.

是的。萨姆你提到的观点很正确——如果把智能视为计算的对数函数，那么关键就是持续提升效率。这意味着我们要最大化每美元每瓦特产生的token数和社会获得的经济价值，同时降低成本。这正是杰文斯悖论所指的方向：通过不断降低和商品化智能，使其成为全球GDP增长的真实驱动力。

Yeah. I mean, I think the one thing that, Sam, you've talked about, which I think is the right way to think about is that if intelligence is one of a log of compute, then you try and really make sure you keep getting efficient. And so that means the tokens per dollar per watt and the economic value that the society gets out of it is what we should maximize and reduce the costs. And so that's where, if you sort of where the Jevan's paradox point is that, which is you keep reducing it, commoditizing, in some sense, intelligence, so that it becomes the real driver of GDP growth all around.

遗憾的是，实际情况更接近‘智能对数等于计算对数’，但我们可能会发现更好的扩展规律，找到突破方法。

Unfortunately, it's something closer to log of intelligence equals log of compute, but we may figure out better scaling laws and we may figure out how beat this. Yeah.

昨天微软和谷歌都表示，如果有更多GPU，他们的云业务增长会更快。我在节目里问过黄仁勋，未来五年出现计算过剩的可能性。他说未来两到三年几乎不存在这种可能。我猜你们两位都同意黄仁勋的观点——虽然看不清五到七年后，但基于刚讨论的原因，未来两到三年几乎不可能出现计算资源过剩的情况。

We heard from both Microsoft and Google yesterday, both said their cloud businesses would have been growing faster if they have more GPUs. You know, I asked Jensen on this pod if there was any chance over the course of the next five years, we would have a compute glut. And he said it's virtually nonexistent chance in the next two to three years. And I assume you guys would both agree with Jensen that while we can't see out five, six, seven years, certainly over the course of the next two to three years for the reasons we just discussed, that it's almost a nonexistent chance that you have excess compute.

嗯，我是说，在这种特定情况下，供需周期真的很难预测，对吧？关键在于长期趋势是什么？正如Sam所说，归根结底，我们目前最大的问题不是算力过剩，而是电力不足。以及能否在靠近电源的地方快速完成设备部署。如果做不到这点，我们可能会积压大量无法通电的芯片库存。

Well, mean, I think the cycles of demand and supply in this particular case, you can't really predict, right? I mean, the point is, what's the secular trend? The secular trend is what Sam said, which is at the end of the day, because quite frankly, the biggest issue we are now having is not a compute glut, but it's a power. And it's the ability to get the builds done fast enough, close to power. So if you can't do that, you may actually have a bunch of chips sitting in inventory that I can't plug in.

事实上，这正是我当前面临的问题，明白吗？不是芯片供应问题，而是缺乏可供接入的现成电力设施。某些供应链瓶颈的出现难以预测，因为需求本身就难以预测。我和Sam坐在这里可不是为了说'天啊我们算力短缺是因为需求预测能力太差'。顺便说一句，全球范围内，讨论单个国家的某个领域是一回事，但真正要实现全球覆盖又是另一回事。所以瓶颈肯定会出现，而如何应对才是关键。

And in fact, that is my problem today, right? It's not a supply issue of chips. It's actually the fact that I don't have warm shelves to plug into. And so how some supply chain constraints emerge, tough to predict because the demand is tough to predict, It's not like Sam and I would want to be sitting here saying, Oh my God, we're less short on computers because we just were not that good at being able to project out what the demand would really look And by the way, the worldwide side, it's one thing to sort of talk about one segment in one country, but it's about really getting it out to everywhere in the world. And so there will be constraints and how we work through them is going to be the most important thing.

这绝对不会是线性发展。

It won't be a linear path for sure.

产能过剩迟早会出现。究竟是两三年还是五六年，我和Satya都无法断言。但这必定会发生，可能还会出现多次。这背后涉及人类心理和泡沫经济的深层机制。正如Satya所说，这是个极其复杂的供应链体系。

There will come a glut, for sure. And whether that's in two to three years or five to six, Satya and I can't tell you. But it's going to happen at some point, probably several points along the way. There is something deep about human psychology here and bubbles. Also, as Satya said, like, there's it's such a complex supply chain.

各种奇怪的技术会被创造出来。技术格局会发生重大转变。如果某种超低成本能源突然大规模上市，很多签了现有合同的人会损失惨重。如果我们继续保持这种单位智能成本的惊人降幅——假设每年平均40倍——从基础设施建设角度来看，这个指数级增长确实可怕。我们押注价格下降会刺激需求，但我担心的是：当突破性进展不断涌现，人人都能在笔记本上运行个人AGI时，我们可能正在制造一场灾难。就像历次技术基础设施周期中总有人会付出惨痛代价那样。

Weird stuff gets built. The technological landscape shifts in big ways. So, you know, if a very cheap form of energy comes online soon at mass scale and a lot of people are going to be extremely burned with existing contracts they've signed, if we can continue this unbelievable reduction in cost per unit of intelligence, let's say it's been averaging like 40x for a given level per year, you know, that's like a very scary exponent from an infrastructure build out standpoint. Now, again, we're taking the bet that there will be a lot more demand as that gets cheaper, but I have some fear that it's just like, man, we keep going with these breakthroughs and everybody can run like a personal AGI on their laptop, and we just did an insane thing here. Some people are gonna get really burned like has happened in every other tech infrastructure cycle at some points along the way.

说得非常好。必须同时把握这两个并行的事实。2000-2001年我们就经历过这种情况，但后来互联网的规模和社会效益远超当时任何人的预估。

I think that's really well said. And you have to hold those two simultaneous truths. We had that happen in 2000, 2001, and yet the Internet became much bigger and produced much greater outcomes for society than anybody estimated in that period of time.

是的，但我觉得Sam有件事说得不够多——比如OpenAI在GPU推理堆栈上做的优化。我们总在讨论摩尔定律的硬件进步，但软件优化的指数级效应其实更惊人。

Yeah, but I think that the one thing that Sam said is not talked about enough, which is, for example, the optimizations that OpenAI has done on the inference stack for a given GPU. I mean, it's kind of like, we talk about the Moore's Law improvement on one end, but the software improvements are much more exponential than that.

总有一天，我们会打造出一款惊人的消费设备，能够完全在本地运行GPT-5或GPT-6级别的模型，且功耗极低。这简直让人难以置信。

Someday we will make an incredible consumer device that can run a GPT-five or GPT-six capable model completely locally at a low power draw. And this is like so hard to wrap my head around.

那将是非凡的成就。要知道，我认为这类技术会让那些构建大型集中式计算堆栈的人感到恐慌。萨提亚，你经常谈到分布式计算，既包括边缘计算，也包括全球分布的推理能力。

That will be incredible. And you know, that's the type of thing I think that scares some of the people who are building obviously these large centralized compute stacks. And Satya, you've talked a lot about the distribution, both to the edge as well as having inference capability distributed around the world.

是的，至少我是这么想的，关键在于打造一支可互换的计算舰队。在云计算基础设施领域，关键要做到两点：一是建立高效的生产机制——比如在这个语境下就是高效的token工厂，二是实现高利用率。就这么简单。

Yeah, mean, way, at least I've thought about it, is more about really building a fungible fleet. I mean, when I look at sort of in the cloud infrastructure business, one of the key things you have to do is have two things. One is an efficient, like in this context, a very efficient token factory, and then high utilization. That's it. There are two simple things that you need to achieve.

而要实现高利用率，就必须能调度多种工作负载。即便是训练任务也不例外。看看AI工作流就知道——有预训练、中期训练、后训练，还有强化学习。你需要能处理所有这些环节。所以对云服务商来说，计算舰队的可互换性就是一切。

And in order to have a high utilization, you have to have multiple workloads that can be scheduled. Even on the training. Mean, you look at the AI pipelines, there's pre training, there's mid training, there's post training, there's RL. You want to be able to do all of those things. So thinking about fungibility of the fleet is everything for a cloud provider.

好的。山姆，你提到...路透社昨天报道称OpenAI可能计划在2026年底或2027年上市。

Okay. So, Sam, you referenced, you know, and and Reuters was reporting yesterday that OpenAI may be planning to go public late twenty six or in '27.

不，不，不。我们...我们没有任何具体计划。我是个现实主义者。

No. No. No. We we don't we don't have anything that specific. I I'm a realist.

我猜总有一天会上市，但那些报道...真不知道他们为什么写这些。我们根本没有设定任何日期。至于是否要做这类决定，那还远着呢。

I assume it'll happen someday, but that was, I don't know why people write these reports. We don't have, like, a date in mind. Great. Great. Well decision to do this or anything like that.

我只是假设事情最终会发展到那一步。

I just assume it's where things will eventually go.

但在我看来，如果你们在2028或2029年实现超过1000亿美元的收入，那你们至少应该处于什么位置？

But it does seem to me if you guys were you know, are are are doing in excess of a $100,000,000,000 of revenue in 'twenty eight or 'twenty nine, that you at least would be in What?

那2027年呢？

How about 'twenty seven?

对，2027年更好。你们完全有条件进行IPO，传闻中的万亿美元估值——再给听众们补充下背景——如果你们以1000亿美元收入的10倍估值上市（这个倍数比Facebook上市时还低，比很多大型消费公司上市时的倍数都低），那估值就是一万亿美元。如果你们出让10%到20%的股份，就能融资1000到2000亿美元，这看起来是支撑未来发展和我们刚讨论事项的理想路径。所以你们并不反对，只是...我也说过，我认为这是家极其重要的公司，包括我孩子在内的很多人都在用ChatGPT操作自己的小账户。

Yeah, 'twenty seven. Even better. You are in position to do an IPO and the rumored trillion dollars, again, just to contextualize for listeners, if you guys went public at 10 times $100,000,000,000 in revenue, right, which would be I think a lower multiple than Facebook went public at, a lower multiple than a lot of other big consumer companies went public at, That would put you at a trillion dollars. If you floated 10 to 20% of the company, that raises a 100 to $200,000,000,000, which seems like that would be a good path to fund a lot of the growth and a lot of the stuff that we just talked about. So you're not opposed to it, you're not but you guys are making Well, I've also said I think that this is such an important company, and, you know, there are so many people, including my kids, who like to trade their little accounts, and they use ChatGPT.

说实话，我认为让散户有机会投资最重要的大型企业之一...

And I think having retail investors have an opportunity to buy one of the most important and largest companies. Honestly,

这对我来说可能是最具吸引力的一点。那确实会非常棒。

that that is probably the single most appealing thing about it to me. That would be really nice.

我之前和你们两位聊过的另一个话题（再次切换方向）是《美丽大法案》的部分内容——克鲁兹参议员曾提议联邦优先立法，以避免各州零散立法形成50种不同法律，让行业陷入无谓的合规泥潭。但不幸的是，布莱克本参议员在最后一刻否决了该条款，因为坦白说，我认为华盛顿对AI的认知非常有限，而且末日论调在那里很有市场。所以现在我们面临像《科罗拉多AI法案》这样的州法律（该法案将于明年二月全面生效），它创造了一类新型诉讼主体：任何声称遭受聊天机器人算法歧视造成不公影响的人都可以提起诉讼，索赔理由可能五花八门。山姆，你有多担心这种各州AI立法零散化的局面会给我们持续加速发展和全球竞争带来实质挑战？

One of the things I've talked to you both about, shifting gears again, is part of the big beautiful bill, you know, Senator Cruz had included federal preemption so that we wouldn't have this state patchwork, 50 different laws that mires the industry down in kind of needless compliance and regulation. Unfortunately, it got killed at the last second by Senator Blackburn because frankly, I think AI is pretty poorly understood in Washington and there's a lot of doomerism, I think, that has gained traction in Washington. So now we have state laws like the Colorado AI Act that goes into full effect in February, I believe, that creates this whole new class of litigants. Anybody who claims any unfair impact from an algorithmic discrimination in a chatbot, so somebody could claim harm for countless reasons. Sam, how worried are you that, you know, having this state patchwork of AI, you know, poses real challenges to, you know, our ability to continue to accelerate and compete around the world?

我不知道我们该如何遵守那个——抱歉是科罗拉多州的法律。我希望他们能明确告诉我们具体怎么做。但从我读到的内容来看，我完全不知道我们该做什么。我非常担心各州各自为政的局面，我认为这是个重大错误。

I don't know how we're supposed to comply with that California sorry, Colorado law. I would love them to tell us, you know, we'd like to be able to do it. But that's just from what I've read of that, that's like a I literally don't know what we're supposed to do. I'm very worried about a 50 state patchwork. I think it's a big mistake.

我觉得我们通常不这么处理这类事情是有原因的。这样做会很糟糕。

I think there's a reason we don't usually do that for these sorts of things. I think it'd be bad.

是的。坦率地说，我认为这种拼凑式监管的根本问题在于——虽然OpenAI和微软之间总能找到应对方法，但初创公司就完全束手无策了。这完全违背了监管初衷——当然安全很重要，要解决人们的核心关切，但应该在联邦层面统一解决。如果我们不这样做，欧盟就会抢先行动，那又会引发新问题。

Yeah. I mean, I think the fundamental problem of this patchwork approach is, quite frankly, between OpenAI and Microsoft, we'll figure out a way to navigate this. We can figure this out. The problem is anyone starting a startup and trying to It just goes to the exact opposite of, I think, what the intent here is, which obviously safety is very important, making sure that the fundamental concerns people have are addressed, but there's a way to do that at the federal level. I think if we don't do this, again, EU will do it and then that'll cause its own issues.

所以我认为如果由美国主导，建立统一的监管框架会更好。

So I think if The US leads, it's better as one regulatory framework.

确实。需要澄清的是，我们并非反对监管，而是主张在联邦层面制定统一法规，而不是50个州各自为政——这绝对会摧毁AI初创企业。即便对你们这样有能力应对诉讼的大公司也极具挑战性。

For sure. And to be clear, it's not that one is advocating for no regulation. It's simply saying let's have agreed upon regulation at the federal level as opposed to 50 competing state laws, which certainly firebombs the AI startup industry. I think it makes super challenging even for companies like yours who can afford to defend all these cases.

坦白说，我真心希望这次欧盟和美国能达成共识——这对欧洲初创企业来说本该是理想状态。

Yeah, I would just say, quite frankly, my hope is that this time around, even across EU and The United States, that that will be the dream, right? Quite frankly, for any European startup.

萨提亚，我觉得这不可能实现。

I don't think that's going to happen, Satya.

那是什么？

What is that?

那太好了。不过我不会对此抱太大期望。那确实很棒。

That would be great. I wouldn't hold your breath for that one. That would be great.

不，但我真心认为，如果你仔细想想，欧洲的任何企业若在思考如何参与这场AI经济浪潮？这应该也是他们最关心的问题。因此，我希望对此能有一些开明的应对方式，不过我同意你的观点——目前我不会押注于此。

No, but I really think that if you think about it, right, if anyone in Europe is thinking about how can they participate in this AI economy with their companies? This should be the main concern there as well. So therefore, I hope there is some enlightened approach to it, but I agree with you that today I wouldn't bet on that.

我认为既然由SACS担任AI事务总负责人，至少我们拥有一位可能会为协调AI政策而战的总统，将贸易作为杠杆来确保欧洲政策不会过度受限。但还需观望。我认为当务之急是美国联邦优先权法案至关重要。山姆，我们刚才讨论得有些琐碎，现在我想把视野放宽些。

I do think that with SACS as the AI czar, you at least have a president that I think might fight for that in terms of coordination of AI policy, trade as a lever to make sure that, you know, we don't end up with overly restricted European policy. But we shall see. I think first things first, federal preemption in The United States is pretty critical. You know, we've been down in the weeds a little bit here, Sam. So I want to telescope out a little bit.

我听到你团队成员讨论过许多即将到来的突破。当你们开始构想近乎无限的计算能力、ChatGPT六代及后续版本、机器人技术、实体设备与科研突破时——展望2026年，你认为最让我们惊讶的会是什么？你对规划中的哪些项目最感到兴奋？

You know, I've heard people on your team talk about all the great things coming up. And as you start thinking about much more unlimited compute, ChatGPT six and beyond, robotics, physical devices, scientific research. As you look forward to 2026, what do you think surprises us the most? What are you most excited about in terms of what's on the drawing board?

你刚才提到的都是关键点。我认为今年Codecs的发展非常引人注目。当这些技术从处理多小时任务进阶到处理多日任务时（我预计明年就会实现），人们将能以空前速度创造软件，并且是以根本性的新方式。对此我非常期待，其他行业也会见证这种变革。

You just hit on a lot of the key points there. I think Codecs has been a very cool thing to watch this year. And as these go from multi hour tasks to multi day tasks, which I expect to happen next year, what people will be able to do to create software at an unprecedented rate, and really in fundamentally new ways. I'm very excited for that. I think we'll see that in other industries, too.

我对编程领域有些偏爱，因为更熟悉这个领域。但我认为我们将看到它真正开始改变人类的能力边界。我期待2026年能有些微小科学发现，若能实现这些微小突破，未来几年就会有更大成果。这么说可能很疯狂——AI将在2026年取得突破性科学发现。

I have like a bias towards coding. I understand that one better. But I think we'll see that really start to transform what people are capable of. I hope for very small scientific discoveries in 2026, but if we can get those very small ones, we'll get bigger ones in future years. That's a really crazy thing to say, is that like, AI is going to make a novel scientific discovery in 2026.

即便是非常微小的一个。这就像是讨论一个极其重要的话题。所以我对此感到兴奋。当然，未来几年里机器人和计算机以及新型计算机将会变得非常重要。但我的个人倾向是，如果我们真的能让AI在这里进行科学研究，那在某种意义上就是超级智能。

Even a very small one. This is like this is a wildly important thing to be talking about. So I'm excited for that. Certainly robotics and computer and new kind of computers in future years, that'll be that'll be very important. But yeah, my personal bias is if we can really get AI to do science here, that is I mean, that is super intelligence in some sense.

比如，如果这能扩展人类知识的总和，那将是件极其重大的事。

Like, if this is expanding the total sum of human knowledge, that is a crazy big deal.

是的，以你的Codex为例，我认为模型能力的结合——想想ChatGPT那个神奇时刻，就是当用户界面遇上智能时突然爆发的场景。那种形态简直难以置信。部分原因还在于模型已具备遵循指令进行对话的能力。我认为Codex和这些编程代理即将帮助我们实现的是：让编程代理长时间运行后返回结果，然后我再介入进行微调。我们都在追求的一个隐喻就是‘宏观委托与微观调控’。

Yeah, I mean, I think one of the things, to use your Codex example, I think the combination of the model capability, I mean, you think about the magical moment that happened with Chad GPT was the UI that met intelligence that just took off. There's just unbelievable right form factor. Some of it was also the instruction following piece of model capability was ready for chat. I think that that's what the Codex and these coding agents are about to help us, which is what's that coding agent goes off for a long period of time, comes back, and then I'm then dropped into what I should steer. One of the metaphors I think we're all working towards is I do this macro delegation and micro steering.

当用户界面遇上这种新型智能能力会怎样？你可以在Codex上看到雏形，对吧？至少我在GitHub Copilot中使用时，它完全不同于聊天界面。坦白说，我认为这将开创人机交互的新方式，其意义可能远超现有模式——甚至可能成为分水岭。

What is that UI meets this new intelligence capability? And you can see the beginnings of that with Codex, right? The way at least I use it inside of GitHub Copilot is it's just a different way than the chat interface. That, I think, would be a new way for the human computer interface, quite frankly. It's probably bigger than That might be the departure.

这正是我对开发新型计算设备形态感到兴奋的原因——传统计算机本就不适合这种工作流。像ChatGPT这样的界面显然不对路。但设想有这样一个设备：它常伴你左右，能自主处理事务并在需要时接受你的微调，同时对你整个生活流拥有绝佳的上下文感知能力——这将会非常酷。

That's one reason I'm very excited that we're doing new form factors of computing devices, because computers were not built for that kind of workflow very well. Certainly, a UI like ChatGPT is wrong for it. But this idea that you can have a device that is sort of always with you, but able to go off and do things and get micro steer from you when it needs and have like really good contextual awareness of your whole life and flow, I think that'll be cool.

你们都没谈到消费者用例。我经常思考：我们依然要埋头在各种设备里，穿梭于上百个应用间填写网页表单——这些二十年来毫无变化。但如果能拥有一个我们认为理所当然的个人助理（实际上我们确实需要），还能几乎免费地为全球数十亿人提供这种服务来改善生活——无论是为孩子订购尿布、预订酒店还是调整日程——有时最平凡的改变反而最具冲击力。当我们从答案走向记忆与行动，再通过耳塞等设备进行交互（而不必总盯着那块矩形玻璃），这...

And what neither of you have talked about is the consumer use case. I think a lot about, you know, again, we go under this device and we have to hunt and peck through a 100 different applications and fill out little web forms, things that really haven't changed in twenty years. But to just have, you know, a personal assistant that we take for granted perhaps that we actually have a personal assistant, but to give a personal assistant for virtually free to billions of people around the world to improve their lives, whether it's ordering diapers for their kid or whether it's booking their hotel or making changes in their calendar. I think sometimes it's the pedestrian that's the most impactful. As we move from answers to memory and actions, and then the ability to interface with that through an earbud or some other device that doesn't require me to constantly be staring at this rectangular piece of glass.

我认为这相当了不起。

I think it's pretty extraordinary.

我想那就是Sam在暗示的事情。

I think that that's what Sam was teasing.

希望你是对的。可惜我得先走了。

I hope you're right. I got to drop off, unfortunately.

Sam，见到你真是太好了。感谢你的参与。再次祝贺你迈出这一大步，我们很快会再聊的。

Sam, it was great to see you. Thanks for joining us. Congrats again on on this big step forward, we'll talk soon.

谢谢让我临时加入。再见，Sam。保重。再见。

Thanks for letting me crash. See you, Sam. Take care. See you.

正如Sam很清楚的那样，我们绝对是买家而非卖家。但有时候，我认为这很重要，因为世界很小。我们整天都在思考这些事情，对吧？所以信念来自于我们投入的一万小时思考。

As Sam well knows, we're certainly a buyer, not a seller. But sometimes, you know, I think it's important because the world, you know, we're pretty small. We spend all day long thinking about this stuff. Right? And so conviction, it comes from the ten thousand hours we've spent thinking about it.

但现实是，我们必须让世界其他部分跟上节奏，而其他人并没有花一万小时思考这些。坦白说，他们看到一些看起来过于雄心勃勃的事情时，会担心我们能否实现。所以你在2019年向董事会提出投资10亿美元给OpenAI时，这在董事会是理所当然的吗？你需要动用政治资本才能促成这件事吗？

But the reality is we have to bring along the rest of the world, and the rest of the world doesn't spend 10,000 hours thinking about this. And frankly, they look at some things that appear overly ambitious, right, and get worried about whether or not we can pull those things off. So you took this idea to the board in 2019 to invest a billion dollars into OpenAI. Was it a no brainer in the board room? Did you have to expend any political capital to get it done?

给我讲讲那个时刻是怎样的，因为我认为那不仅对微软、对国家，而且对全世界来说都是一个关键转折点。

Dish for me a little bit like what that moment was like, because I think it was such a pivotal moment, not just for Microsoft, not just for the country, but I really do think for the world.

是的，我是说，回顾起来很有趣。当我审视这段历程时，我们早在2016年OpenAI成立之初就参与其中。实际上，Azure应该是第一个赞助商。那时他们做了更多强化学习相关的工作，我记得Dota 2比赛就是在Azure上进行的，后来他们转向了其他方向。

Yeah, I mean, it's interesting when you look back. The journey, when I look at it, it's been We were involved even in 2016 when initially OpenAI started. In fact, Azure was even the first sponsor, I think. Then they were doing a lot more reinforcement learning at that time. I remember the Dota two competition, I think, happened on Azure, and then they moved on to other things.

我对强化学习很感兴趣，但坦白说，这需要一万小时定律或充分准备。微软自1995年起就痴迷于此，比尔对公司的执念就是自然语言处理。毕竟我们本质上是一家编码公司，

I was interested in RL, but quite frankly, it speaks a little bit to your ten thousand hours or the prepared mind. Microsoft since 1995 was obsessed. Bill's obsession for the company was natural language, natural language. After all, are a coding company,

我们是

we are

一家信息处理公司。所以当2019年山姆开始讨论文本、自然语言、Transformer模型和规模化时，我就觉得——哇，这太有意思了。这个团队的发展方向突然变得清晰，与我们的兴趣高度契合。从这点看，投资决定根本不用犹豫。当然要去董事会说：嘿，我有个动用十亿美元投资这个我们都不太理解的疯狂架构的想法。

an information work company. So, it's when Sam in 2019 started talking about text and natural language and That's transformers and scaling when I said, Wow, this is an interesting I mean, this was a team that was going in the direction or the direction of travel was now clear. It had a lot more overlap with our interest. So in that sense, it was a no brainer. Obviously, go to the board and say, Hey, I have an idea of taking a billion dollars and giving it to this crazy structure, which we don't even kind of understand.

它是什么？是个非营利组织之类的...然后说干就干。当时有争议，比尔持合理怀疑态度很正常。但当他看到GPT-4演示后——比尔公开说过——那就像当年查尔斯·西蒙尼在施乐帕洛阿尔托研究中心给他看的演示一样震撼。说实话，我们所有人都...

What is it? It's a nonprofit, blah, blah, blah, and saying, go for it. There was a debate. Bill was kind of rightfully so skeptical because And then he became like, once he saw the GPD four demo, that was the thing that Bill's talked about publicly, where when he saw it, he said it's the best demo he saw after what Charles Simone showed him at Xerox PARC. Quite honestly, none of us could.

所以对我来说那个转折点就是：放手一试吧。后来看到GitHub Copilot内部的早期编解码器，看着代码自动补全功能运作，那时我才觉得可以从1做到10。最初投资决定虽有争议，但1到10的跨越才真正开创了这个时代。当然还有团队出色的执行力和产品化能力——想想GitHub Copilot、ChatGPT、Microsoft 365 Copilot和Copilot这四大产品的商业化规模，这就是全球最强大的AI产品矩阵了。

So the moment for me was that, let's go give it a shot. Then seeing the early codecs inside of Copilot, inside of GitHub Copilot, and seeing just the code completions and seeing it work, that's when I would say I felt like I can go from one to 10 because that was the one big call, quite was controversial, but the one to 10 was what really made this entire era possible. And then obviously, the great execution by the team and the productization on their part, our part. I mean, I think about it, the collective monetization reach of GitHub Copilot, ChatGPT, Microsoft three sixty five Copilot and Copilot, you add those four things, that is it, right? That's the biggest sort of AI set of products out there on the planet.

这显然是我们能持续发展的根基。

And that's what obviously has let us sustain all of this.

我认为很多人不知道你们的CTO凯文·斯科特——前谷歌员工——就住在硅谷这里。要理解背景，微软确实错过了搜索和移动领域的机遇。你成为CEO时，几乎又要错过云计算。对吧？你自己也说过，是赶上了末班车才抓住了云计算的机遇。

And I think not many people know that your CTO, Kevin Scott, an ex Googler, lives down here in Silicon Valley. And to contextualize it, right, Microsoft had missed out on search, had missed out on mobile. You become CEO, almost had missed out on the cloud. Right? You've described it, caught the last train out of town to capture the cloud.

我觉得你非常坚定要在这里安插耳目，以免错过下一个大趋势。所以我猜想凯文在DeepSeek和OpenAI方面也为你发挥了重要作用。

And I think you were pretty determined to have eyes and ears down here so you didn't miss the next big thing. So I assume that Kevin played a good role for you as well I find DeepSeek and OpenAI.

是的，实际上我想说的是凯文的信念。而且凯文最初也是持怀疑态度的。关键就在这里——要留意那些持怀疑态度但最终改变观点的人，因为对我来说这是个信号。所以我总是在寻找那些原本不信某件事、突然转变态度并对此充满热情的人。

Yeah, mean, in fact, I would say Kevin's conviction. And Kevin was also skeptical. That was the thing. Always watch for people who are skeptical who change their opinion because to me that's a signal. So, I'm always looking for someone who's a non believer in something and then suddenly changes and then they get excited about it.

我永远关注这类转变，因为我会好奇为什么改变、改变了什么。凯文最初和我们一样持怀疑态度。从某种角度说，这很反常。就像上学时突然说'天啊，肯定存在破解这个问题的算法'，而不是只依赖规模定律和算力堆砌。但坦率说，正是凯文'这事值得追求'的信念推动了整个进程。

I have all the time for that because I'm then curious why, what. So, Kevin started with, of us were skeptical. In some sense, it defies that. Having gone to school and said, God, there must be an algorithm to crack this versus just scaling laws and throw compute. But quite frankly, Kevin's conviction that this is worth going after is one of the big things that drove this.

我们现在讨论的这项投资估值已达1300亿美元，按萨姆的说法未来可能达到万亿规模。但这其实在很多方面低估了合作关系的价值，对吧？你们通过收入分成每年从微软获得数十亿，还有来自OpenAI 2500亿美元Azure算力承诺的利润。当然，还有独家API分销带来的巨额销售。

Well, we talk about that investment that's now worth $130,000,000,000 I suppose could be worth a trillion someday, as Sam says. But it really in many ways understates the value of the partnership, right? So you have the value in the rev share, billions per year going to Microsoft. You have the profit you make off the $250,000,000,000 of the Azure compute commitment from OpenAI. And of course, you get huge sales from the exclusive distribution of the API.

请谈谈你如何看待这些领域的价值，特别是这种排他性如何让许多原本使用AWS的客户转向了Azure。

So talk to us how you think about the value across those domains, especially how this exclusivity has brought a lot of customers who may have been on AWS to Azure.

没错，完全正确。对我们来说，抛开股权部分，真正具有战略意义且将持续产生价值的是Azure中无状态API的独家服务。坦白说，这帮助了OpenAI、我们和客户三方。因为当企业用户构建应用时，他们需要无状态API，需要将其与计算存储混合使用，建立底层数据库来捕获状态，构建完整工作负载——而这正是Azure与该API结合的价值所在。

Yeah, no, absolutely. I mean, so to us, if I look at it, aside from all the equity parts, the real strategic thing that comes together and that remains going forward is that stateless API exclusive video in Azure. That helps, quite frankly, both OpenAI and us and our customers. Because when somebody in the enterprise is trying to build an application, they want an API that's stateless, they want to mix it up with compute and storage, put a database underneath it to capture state, and build a full workload. And that's where Azure coming together with this API.

那么我们在Azure Foundry上的做法是什么呢？因为从某种意义上说，假设你想构建一个AI应用，但关键在于如何确保AI评估效果出色？这就需要Foundry中完整的应用服务器支持。这正是我们已经实现的。因此我认为这将是我们基础设施业务的上市路径。

And so what we're doing with even Azure Foundry, right? Because in some sense, let's say you want to build an AI application, but the key thing is how do you make sure that the evals of what you're doing with AI are great? So that's where you need even a full app server in Foundry. That's what we have done. And so therefore, I feel that that is the way we will go to market in our infrastructure business.

对我们而言价值捕获的另一面在于整合所有这些知识产权。我们不仅在Azure上拥有模型的独家使用权，还能获取相关IP。我的意思是，拥有免版税使用权——暂且不谈技术诀窍和知识层面——仅就未来七年免费使用这一点，就为商业模式提供了灵活性。某种程度上这就像免费拥有一个前沿模型。作为微软股东，你应该从这个角度思考：我们拥有可部署的前沿模型，无论是在GitHub、M365还是消费者Copilot中，都能叠加自有数据进行后期训练。

The other side of the value capture for us is going to be incorporating all this IP. Not only we have the exclusivity of the model in Azure, but we have access to the IP. I mean, having a royalty free, let's even forgetting all the know how and the knowledge side of it, but having royalty free access all the way till seven more years gives flexibility business model wise. It's kind of like having a frontier model for free in some sense. If you're an MSFT shareholder, that's kind of where you should start from, is to think about, we have a frontier model that we can then deploy, whether it's in GitHub, whether it's in M365, whether it's in our consumer copilot, then add to it our own data, post train it.

这意味着我们可以将其嵌入权重中。因此，我们对Azure和基础设施领域的价值创造感到兴奋，同样也期待在医疗、知识工作、编程或安全等高价值领域的发展。

So, that means we can have it embedded in the weights there. And so, therefore, we are excited about the value creation on both the Azure and the infrastructure side, as well as in our high value domains, whether it is in health, whether it's in knowledge work, whether it's in coding or security.

你们一直在合并OpenAI的亏损。我记得昨天刚发布的财报显示，本季度合并了40亿美元的亏损。你认为投资者是否可能因此赋予负估值？毕竟他们用盈利倍数来评估，萨提亚。而我听到这些时想到的是我们刚描述的所有好处，更不用说你们持有的这家公司本身可能价值万亿美元的穿透股权价值。你觉得市场是否低估了OpenAI作为微软组成部分的价值？

You've been consolidating the losses from OpenAI. You I think you just reported earnings yesterday, I think you consolidated 4,000,000,000 of losses in the quarter. Do you think that investors are, I mean, may even be attributing negative value, right, because of the losses, you know, they apply their multiple of earnings Satya, whereas I hear this and I think about all of those benefits we just described, not to mention the look through equity value that you own in a company that could be worth a trillion unto itself. You know, do you think that the market is kind of misunderstanding the value of OpenAI as a component of Microsoft?

这是个好问题。我认为艾米将采取完全透明的处理方式，毕竟从某种程度上说，我不是会计专家，所以最佳做法就是保持完全透明。这次我们也通过非GAAP准则呈现，至少让人们看清每股收益数据。布拉德，我的常识性看法很简单：如果你投资了135亿美元，当然可能亏损135亿，但不可能超过这个数字——至少据我所知，这就是风险上限。

Yeah, that's a good one. So, I think the approach that Amy is going to take is full transparency because at some level, I'm no accounting expert, so therefore the best thing to do is to give all transparency. I think this time around as well, I think that's why the non GAAP gap, so that at least people can see the EPS numbers. Because the common sense way I look at it, Brad, is simple. If you've invested, let's call it $13,500,000,000 you can, of course, lose $13,500,000,000 But you can't lose more than $13,500,000,000 At least the last time I checked, that's what you have at risk.

你也可以说，如今我们1350亿美元的股权 stake 流动性不足。我们并不打算出售，因此存在相关风险。但真正的故事在于其他正在发生的事：Azure的增长如何？

You could also say, Hey, the $135,000,000,000 that is today our equity stake is sort of illiquid, what have you. We don't plan to sell it. Therefore, it's got risk associated with it. But the real story I think you are pulling is all the other things that are happening. What's happening with Azure growth?

如果没有OpenAI的合作，Azure还能保持增长吗？正如你所说，首次从其他云平台迁移而来的客户数量才是我们真正的收益来源。微软365的情况如何？事实上，关于微软三月的关键问题是：E5之后的下一个重大突破是什么？你猜怎么着？

Would Azure be growing if we had not sort of had the OpenAI partnership? To your point, the number of customers who came from other clouds for the first time, this is the thing that really we benefited from. What's happening with Microsoft three sixty five? In fact, one of the things about Microsoft March was what was the next big thing after E5? Guess what?

我们在Copilot中发现了它。它比任何套件都要庞大。我们讨论渗透率、使用率和增长速度。它比我们几十年来在信息工作中所做的任何事情都要宏大。因此，我们对为股东创造价值的机会感到非常非常乐观。

We found it in Copilot. It's bigger than any suite. We talk about penetration and usage and the pace. It's bigger than anything we have done in our information work, which we have been at it for decades. And so, we feel very, very good about the opportunity to create value for our shareholders.

与此同时，保持完全透明，让人们能看清亏损情况？我是说，谁知道会计准则是什么，但我们会做任何必要的事，让人们能看清现状。

Then at the same time, fully transparent so that people can look through what are the losses? I mean, who knows what the accounting rules are, but we will do whatever is needed and people will then be able to see what's happening.

但一年前，萨提亚，有大量头条新闻说微软在撤回AI基础设施投入，对吧？无论公平与否，这些声音都存在。你们当时或许更保守些，对局势更持怀疑态度。不过艾米昨晚在电话会上说，你们已连续多个季度面临电力和基础设施短缺，她原以为你们能赶上，但需求持续增长导致始终未能补足。所以问题在于，以现在所知，你们是否过于保守了？接下来的发展路线是什么？

But a year ago, Satya, there were a bunch of headlines that Microsoft was pulling back on AI infrastructure, right? Fair or unfair, were out there. You know, and perhaps you guys were a little more conservative, a little more skeptical of what was going on. Amy said on the call last night though that you've been short power and infrastructure for many quarters, and she thought that you would catch up, but you haven't caught up because demand keeps increasing. So I guess the question is, were you too conservative, you know, knowing what you know now, and what's the roadmap from here?

这是个好问题。你看，我们意识到——我很庆幸我们意识到了——关键在于打造一支真正可置换的舰队：能适应AI生命周期所有环节、跨地域通用、跨世代兼容。就像黄仁勋团队正在做的，他们保持着惊人的节奏。实际上我很欣赏这种光速般的进展——我们正在部署GB300系列。

Yeah, it's a great question because see, the thing that we realized, and I'm glad we did, is that the concept of building a fleet that truly was fungible, fungible for all the parts of the life cycle of AI, fungible across geographies, and fungible across generations. So because one of the key things is when you have, let's take even what Jensen and team are doing, right? I mean, they're at a pace. In fact, one of the things I like is the speed of light, right? We now have GB300s that we're bringing up.

你肯定不希望刚部署完大批GB200就发现GB300已全面投产。所以必须确保持续现代化改造，让舰队全球分布，真正实现工作负载层面的可置换性，再加上我们讨论过的软件优化。这就是我们的决策核心。有时你必须拒绝部分需求，包括OpenAI的需求——比如当Sam要求在某地建造专用于训练的多吉瓦级数据中心时。

So you don't want to have ordered a bunch of GB200s that are getting plugged in only to find the GB300s are in full production. So you kind of have to make sure you're continuously modernizing, you're spreading the fleet all over, you are really truly fungible by workload, and you're adding to that the software optimizations we talked about. So to me, that is the decision we made. And we said, look, sometimes you may have to say no to some of the demand, including some of the OpenAI demand, right? Because sometimes Sam may say, hey, build me a dedicated, big, whatever, multi gigawatt data center in one location for training.

从OpenAI角度看这很合理，但对Azure的长期基础设施建设而言并不明智。因此我们选择给予他们从别处采购的灵活性，同时保持与OpenAI的大量业务往来，更重要的是为我们自己和其他客户（包括第一方业务）保留弹性。记住，我们最不愿看到的就是Azure资源短缺。

Makes sense from an OpenAI perspective. Doesn't make sense from a long term infrastructure build out for Azure. And that's where I thought we did the right thing to give them flexibility to go procure that from others while maintaining, again, a significant book of business from OpenAI, but more importantly, giving ourselves the flexibility with other customers, our own 1P. Remember, one of the things that we don't want to do is be short on. We talk about Azure.

实际上投资者有时过度关注Azure数据。但对我来说，高利润业务是Copilot系列：安全Copilot、GitHub Copilot、医疗Copilot。

In fact, sometimes our investors are overly fixated on the Azure number. But remember, for me, the high margin business for me is Copilot. It is security Copilot. It's GitHub Copilot. It's the healthcare Copilot.

因此，我们希望能以平衡的方式处理投资者的回报问题。这可能是我们投资者群体中另一个被误解的地方，我觉得这既奇怪又有趣——他们明明是因为微软的整体业务组合而持股，却偏偏对Azure这个小业务板块的增长率如此执着。

So, we want to make sure we have a balanced way to approach the returns that the investors have. And so, that's kind of one of the other misunderstood, perhaps, in our investor base in particular, which I find pretty strange and funny because I think they want to hold Microsoft because of the portfolio we have. But man, are they fixated on the growth number of one little thing called Azure?

说到这点，Azure本季度以惊人的930亿美元年化运行速率实现了39%的增长。相比之下，GCP增长率为32%，AWS接近20%。但Azure本可以增长41%-42%——如果你们能为ROT（研发运营转型）提供更多计算资源的话，毕竟你们确实把部分算力分配给了第一方业务和研究部门。

On that point, Azure grew 39% in the quarter on a staggering $93,000,000,000 run rate. And, you know, I think that compares to GCP that grew at 32% and AWS closer to 20%. But could Azure, because you did give compute to 1P and because you did give compute to research, it sounds like Azure could have grown 41%, 42% had you had more compute to ROT.

毫无疑问。正因如此，我们认为内部需要平衡各方考量——既要符合股东的长期利益，也要为客户提供优质服务。另一个常被提及的是集中风险问题：我们当然重视OpenAI这样的客户，但同样需要发展多元化客户群。

Absolutely. There's no question. So that's why I think the internal thing is to balance out what we think, again, is in the long term interests of our shareholders also to serve our customers well. And also, of the other things was people talk about concentration risk. We obviously want a lot of OpenAI, but we also want other customers.

因此我们正在引导需求端。当前并非需求不足，而是供给受限。我们正以长期视角优化需求配置，使其与供给能力达到最佳匹配。

And so we're shaping the demand here. We're not demand constrained, we're supply constrained. So we are shaping the demand such that it matches the supply in the optimal way with the long term view.

关于这点，萨提亚，你昨晚提到4000亿美元的未履行绩效义务——这个数字令人震撼。你说这是目前已签约的业务量，随着销售持续进行明天肯定还会增长。你还表示需要大幅扩建基础设施才能消化这些积压订单。那么按你的说法，这些订单的行业分布有多多元化？

To that point, Satya, you talked about $400,000,000,000 it's an incredible number, of remaining performance obligations last night. You said that, you know, that's your booked business today. It'll surely go up tomorrow as sales continue to come in. And you said you're gonna, you know, your need to build out capacity just to serve that backlog is very high. You know, how diversified is that backlog to your point?

你有多大把握这4000亿美元能在未来几年内转化为实际收入？

And how confident are you that that 400,000,000,000 does turn into revenue over the course of the next couple of years?

是的，正如艾米解释的，这4000亿美元合约的平均周期很短——只有两年。这正是我们的目标，也是我们确信需要投入资本支出以消化积压订单的原因之一。而且正如你所说，这些订单在第一方和第三方业务中都相当多元化。

Yeah, that $400,000,000,000 has a very short duration, as Amy explained. It's the two year duration on average. So that's definitely our intent. That's one of the reasons why we are spending the capital outlay with high certainty that we just need to clear the backlog. And to your point, it's pretty diversified, both on the 1P and the 3P.

坦率地说，我们自身对第一方业务的需求相当高。即使在第三方领域，我们现在看到的一个趋势是其他公司正在构建可扩展的实际工作负载。鉴于这一点，我认为我们感觉非常好。显然，RPO最棒的一点就是可以进行规划，坦白讲。因此，我们对建设充满信心。

Our own demand is quite frankly pretty high for our one first party. And even amongst third party, one of the things we now are seeing is the rise of all the other companies building real workloads that are scaling. And so given that, I think we feel very good. I mean, obviously, that's one of the best things about RPO is you can be planned for, quite frankly. And so therefore, we feel very, very good about building.

这显然还不包括我们已经开始看到的额外需求，包括那250项目——它的周期会更长，我们将相应地进行建设。

And then this doesn't include, obviously, the additional demand that we're already going to start seeing, including the two fifty, which will have a longer duration and we'll build accordingly.

没错。现在有大量新进入者参与这场算力建设竞赛：甲骨文、CoreWeave、Crusoe等等。通常我们认为这会压缩利润空间。但你们却能在Azure保持健康运营利润率的同时完成所有这些建设。所以我想问微软的问题是：在这个人们都在加杠杆、接受更低利润空间的世界里，你们如何平衡利润与风险参与竞争？

Right. So there are a lot of new entrants in this race to build out compute: Oracle, CoreWeave, Crusoe, etc. And normally we think that will compete away margins. But you've somehow managed to build all this out while maintaining healthy operating margins at Azure. So I guess the question is for Microsoft, how do you compete in this world that is where people are levering up, taking lower margins while balancing that profit and risk?

你是否看到某些竞争对手的交易让你挠头感叹：'我们又要重蹈繁荣与萧条的覆辙了'？

And do you see any of those competitors doing deals that cause you to scratch your head and say, Oh, we're just setting ourselves up for another boom and bust cycle?

某种程度上，我认为对我们来说好消息是即便作为超大规模云服务商，我们每天都在竞争。我们与亚马逊、谷歌在所有领域都存在激烈竞争。这其实很有趣——表面上所有东西都成了大宗商品对吧？算力、存储。我记得大家都说'哇，他们怎么可能做到'。但实际上规模化后，没有什么是真正的大宗商品。

I mean, I'd say at some level, the good news for us has been competing even as a hyperscaler every day. There's a lot of competition between us and Amazon and Google on all of these. I mean, it's sort of one of those interesting things, which is everything is a commodity, right? Compute, storage. I remember everybody saying, Wow, how can they have Except a at scale, nothing is a commodity.

是的，我们必须建立成本结构，我们的供应链效率、软件效率必须持续叠加才能确保利润空间。但规模才是关键。说到这个，我特别喜欢与OpenAI合作的一点就是它让我们实现了规模效应。这是个规模游戏。当全球最大规模的工作负载运行在你的云上时，不仅意味着我们能更快掌握规模化运营经验，更意味着你的成本结构会以最快速度下降。

Yes, we have to have a cost structure, our supply chain efficiency, our software efficiencies, have to continue to compound in order to make sure that there's margins. But scale. And to your point, one of the things that I really love about the OpenAI partnership is it's gotten us to scale, right? This is a scale game. When you have the biggest workload there is running on your cloud, that means not only are we going to learn faster on what it means to operate with scale, that means your cost structure is going to come down faster than anything else.

知道这意味着什么吗？这将使我们的价格更具竞争力。因此，我对保持利润率的能力相当有信心。这就是业务组合的优势所在。我一直说——我是被迫公布Azure数据的对吧？

And guess what? That'll make us price competitive. And so, I feel pretty confident about our ability to have margins. This is where the portfolio helps. I've always said, I've been forced into giving the Azure numbers, right?

因为在某种程度上，我从未考虑过分配问题。我的意思是，我们的资本配置完全围绕云业务展开——无论是Xbox云游戏、Microsoft 365还是Azure，这都是统一的资本支出。在我看来，从微软（MSFT）的角度来说，关键在于如何让混合平均值匹配公司所需的运营利润率。毕竟若非如此，我们就不是集团企业，而是一家拥有统一平台逻辑的公司。

Because at some level, I never thought of allocating. I mean, my capital allocation is for the cloud from whether it is Xbox Cloud gaming or Microsoft three sixty five or for Azure, it's one capital outlay. Then everything is a meter, as far as I'm concerned, from an MSFT It's a question of, hey, the blended average of that should match the operating margins we need as a company. Because after all, otherwise, we're not a conglomerate. We're one company with one platform logic.

我们并非在运营五六个独立业务。涉足这些领域只是为了放大对云和AI投资的回报。

It's not running five, six different businesses. We're in these five, six different businesses only to compound the returns on the cloud and AI investment.

是的，我很认同这个观点。规模化之下没有商品化业务。要知道，甚至在这个播客里，我和搭档Bill Gurley就花了很多时间讨论循环收入问题，包括微软授予OpenAI的Azure信用额度被记为收入这件事。您是否看到类似AMD交易的情况？就是那种用10%股权换取合作协议的模式，或者英伟达的交易？重申一下，我不想过度聚焦于担忧，但确实需要直面CNBC和彭博社每天都在讨论的话题。

Yeah, I love that line. Nothing is a commodity at scale. You know, there's been a lot of ink and time spent even on this podcast with my partner, Bill Gurley, talking about circular revenues, including Microsoft's Azure credits right to OpenAI that were booked as revenue. Do you see anything going on like the AMD deal, you know, where they traded 10% of their equity for a deal or the Nvidia deal? Again, I don't want to be overly fixated on concern, I do want to address head on what is being talked about every day on CNBC and Bloomberg.

而且目前市场上存在大量这类重叠交易。当您从微软的角度思考这个问题时，是否会对当前全球AI收入的可持续性和持久性再次感到担忧？

And there are a lot of these overlapping deals that are going on out there. Do you When you think about that in the context of Microsoft, does any of that worry you again as to the sustainability or durability of the AI revenues that we see in the world?

首先，我们那130.5亿的训练投资——这部分完全没有记为收入——正是我们获得股权比例的原因，也是我们持有27%（即1350亿）的由来。这部分资金绝不可能转化为Azure收入。实际上，Azure收入纯粹来自ChatGPT及其他产品的使用费，以及他们发布的变现API。

Yeah, mean, first of all, our investment of, let's say, that 13 0.5, which was all the training investment, that was not booked as revenue. Is the reason why we have the equity percentage. That's the reason why we have the 27% or 135,000,000,000. So that was not something that somehow that made it into Azure revenue. In fact, if anything, the Azure revenue was purely the consumption revenue of ChatGPT and anything else, and the APIs that are put out that they monetized and be monetized.

关于其他厂商的情况，某种程度上这始终存在于供应商融资领域，对吧？当建设者遇到同样在建设但需要融资的客户时，采取些非常规形式并不新鲜——当然这些需要投资界仔细审视。但话说回来，我们微软从未需要这样做。我们投资OpenAI本质上是用算力换取股权，或者通过优惠算力价格来扶持他们起步。

To your aspect of others, to some degree, it's always been there in terms of vendor financing, right? So it's not like a new concept that when someone's building something and they have a customer who is also building something, but they need financing, you know, for whether it is, you know, it's sort of some, they're taking some exotic forms, which obviously need to be scrutinized by the investment community. But that said, vendor financing is not a new concept. Interestingly enough, we have not had to do any of that, right? I mean, we may have really either invested in OpenAI and essentially got an equity stake in it for return for compute or essentially sold them great pricing of compute in order to be able to sort of bootstrap them.

但其他公司选择了不同路径。我认为循环模式最终会由市场需求验证——只要终端产品存在需求，这套模式就能持续。而迄今为止，事实正是如此。

But others choose to do so differently. And I think circularity ultimately will be tested by demand because all this will work as long as there is demand for the final output of it. And up to now, that has been the case.

当然，当然。嗯，我想转变一下话题，正如你所说，你公司过半业务是软件应用。我想谈谈软件和智能体。去年在这个播客上，你曾引发不小轰动，说大多数应用软件不过是覆盖在CRUD数据库上的一层薄壳。

Certainly, certainly. Well, I want to shift, as you said, over half your business is software applications. I want to think about software and agents. Last year on this pod, you made a bit of a stir by saying that much of application software you know, was this thin layer that sat on top of a CRUD database.

商业应用存在的理念——在智能体时代这些很可能会全面崩塌，对吧？因为仔细想想，它们本质上就是带着一堆业务逻辑的CRUD数据库。而这些业务逻辑都将被智能体接管。

The notion that business applications exist, that's probably where they'll all collapse, right, in the agent era. Because if you think about it, right, they are essentially CRUD databases with a bunch of business logic. The business logic is all going to these agents.

目前上市软件公司的远期市销率约为5.2倍，低于十年平均的7倍水平——尽管股市正处于历史高位。很多人担心SaaS订阅和利润率可能受到AI威胁。那么当前AI如何影响你们核心软件产品的增长率？特别是数据库、数据架构、安全系统和Office 365这些产品线？

Public software companies are now trading at about 5.2 times forward revenue. So that's below their ten year average of seven times despite the markets being at all time highs. And there's lots of concern that SaaS subscriptions and margins may be put at risk by AI. So how today is AI affecting the growth rates of your software products, of, you know, those core products? And specifically, as you think about database, fabric, security, Office three sixty.

第二个问题：你们如何确保软件不被颠覆，而是被AI赋能？

And then second question, I guess, is what are you doing to make sure that software is not disrupted, but is instead super powered by AI?

确实如此。上次讨论时我就强调过，SaaS应用的架构正在变革——智能体层正在取代传统业务逻辑层。因为传统SaaS开发模式中数据、逻辑层和UI是紧耦合的，而AI本质上要求解耦。不过上下文工程会变得至关重要。

Yeah, I think that's right. So the last time we talked about this, my point really there was the architecture of SaaS applications is changing because this agent tier is replacing the old business logic tier. So because if you think about it, the way we build SaaS applications in the past was you had the data, the logic tier, the UI all tightly coupled. And AI, quite frankly, doesn't respect that coupling because it requires you to be able to decouple. And yet the context engineering is going to be very important.

以Office 365为例，我们产品最让我欣赏的就是低ARPU（每用户平均收入）高使用率。无论是Outlook、Teams、SharePoint还是Word/Excel，用户持续生成海量数据注入图谱，而ARPU却很低。这让我坚信：通过开放数据接口，我们完全能对接AI层。事实上GitHub和Microsoft 365都因AI迎来了数据和代码库提交量的历史峰值。

I mean, take something like Office three sixty five. One of the things I love about our Microsoft three sixty five offering is it's low ARPU, high usage. I mean, if you think about it, Outlook or Teams or SharePoint, you pick Word or Excel, people are using it all the time, creating lots and lots of data which is going into the graph, and our ARPU is low. So, that's sort of what gives me real confidence that this AI tier, I can meet it by exposing all my data. In fact, one of the fascinating things that's happened, Brad, with both GitHub and Microsoft three sixty five is thanks to AI, we are seeing all time highs in terms of data that's going into the graph or the repo.

想想看：无论是Codex还是Claude生成的代码，最终流向哪里？GitHub。还有不断产生的PPT、Excel模型、各类工作成果和聊天记录——这些对话本身就是新型文档。

I mean, think about it. More code that gets generated, whether it is Codex or Claude or wherever, where is it going? GitHub. More PowerPoints that get created, Excel models that get created, all these artifacts and chat conversations. Chat conversations are new docs.

它们都将进入图表，而所有这些又是实现基础所需的。因此，这就是你将其转化为前向索引、转化为嵌入的方式。本质上，这种语义就是你真正用来支撑任何代理请求的基础。所以我认为下一代SaaS应用将不得不——如果你的ARPU（每用户平均收入）高但使用率低，那么你会有点问题。但我们恰恰相反。

They're all going in to the graph and all that is needed again for grounding. So that's what you turn it into a forward index, into an embedding. And basically that semantics is what you really go ground any agent request. And so I think the next generation of SaaS applications will have to sort of if you are high ARPU, low usage, then you have a little bit of a problem. But we are the exact opposite.

我们是低ARPU、高使用率。我认为任何能构建这种模式并利用AI作为加速剂的人——实际上，如果你看看M365 Copilot的价格，它比我们销售的任何其他产品都高。然而，它的部署速度更快，使用率更高。所以我对编程感觉非常好。谁能想到呢？

We are low ARPU, high usage. And I think that anyone who can structure that and then use this AI as, in fact, an accelerant, because if you look at the M365 Copilot price, it's higher than any other thing that we sell. Yet, it's getting deployed faster and with more usage. And so I feel very good at coding. Who would have thought?

以GitHub为例。GitHub在其存在的前十五年或十年里所做的事情，基本上在去年一年就完成了。仅仅因为编程不再是一个工具，它在某种程度上替代了某些地方的工资。所以这甚至是一种非常不同的商业模式。

In fact, take GitHub. What GitHub did in the first fifteen years of its existence or ten years of its existence, it was basically done in the last year. Just because coding is no longer a tool, it's more a substitute for wages somewhere. So it's a very different type of business model even.

我在思考技术栈以及价值分配的问题。直到最近，云主要运行预编译软件。你不需要很多GPU，大部分价值积累在软件层、数据库以及像CRM和Excel这样的应用中。但未来这些界面似乎只有在智能化时才有价值。如果是预编译的，它们就显得有些‘笨’。

Kind of thinking about the stack and where value gets distributed. So until very recently, right, clouds largely ran precompiled software. You didn't need a lot of GPUs and most of the value accrued to the software layer, to the database, to the applications like CRM and Excel. But it does seem in the future that these interfaces will only be valuable if they're intelligent. If they're pre compiled, they're kind of dumb.

软件必须能够思考、行动和提供建议。这需要生产这些标记（tokens），处理不断变化的上下文。因此，在那个世界里，似乎更多的价值将积累到‘AI工厂’——比如Jensen以最低成本生产这些标记和模型。而代理或软件未来积累的价值可能会比过去少一些。请为我论证这种观点为什么是错误的。

The software's got to be able to think and to act and to advise. And that requires, you know, the production of these tokens, you know, dealing with the ever changing context. And so in that world, it does seem like much more of the value will accrue to the AI factory, if you will, to, you know, Jensen producing, you know, helping to produce these tokens at the lowest cost and to the models. And maybe that the agents or the software will accrue a little bit less of the value in the future than they've accrued in the past. Steelman for me why that's wrong.

是的，我认为推动AI价值需要两件事。一是你首先描述的‘标记工厂’。即使拆解标记工厂，它包含硬件硅系统，但更重要的是通过系统软件以最高效率运行，实现最大程度的可替代性和利用率。这就是超大规模运营商的作用所在，对吧？什么是超大规模运营商？

Yeah, so I think there are two things that are necessary to try and to drive the value of AI. One is what you described first, which is the token factory. And even if you unpack the token factory, it's the hardware silicon system, but then it is about running it most efficiently with the system software, with all the fungibility, max utilization. That's where the hyperscalers role is, right? What is a hyperscaler?

超大规模运营商就像大家说的那样，如果你说‘嘿，我想运营一个超大规模平台’，有人可能会说‘哦，这很简单，买一堆服务器连起来运行就行’。但并非如此，对吧？如果真那么简单，现在就不会只有三家超大规模运营商了。

Is hyperscaler Like everybody says, if you sort of said, Hey, I want to run a hyperscaler. Yeah, you could say, Oh, it's simple. I'll buy a bunch of servers and wire them up and running. It's not that, right? I mean, if it was that simple, then there would have been more than three hyperscalers by now.

因此，超大规模服务商（hyperscaler）的核心能力在于如何最大化利用率和运行token工厂。顺便说一句，这将是异构的。显然，Jensen极具竞争力。Lisa即将加入。Hawk将从博通生产产品。

So the hyperscaler is the know how of running that max util and token factories. By the way, it's going be heterogeneous. Obviously, Jensen's super competitive. Lisa is going to come. Hawk's going to produce things from Broadcom.

我们都会自主运营。因此，未来会出现组合模式。最终你需要运营一个异构集群，以实现token吞吐量和效率的最大化等等。这算是第一项任务。接下来就是我所说的智能体工厂。

We will all do our own. So, there's going to be a combination. So you want to run ultimately a heterogeneous fleet that is maximized for token throughput and efficiency and so on. So that's kind of one job. The next thing is what I call the agent factory.

记住，现代世界的SaaS应用是为了推动业务成果。它知道如何最高效地使用token来创造商业价值。事实上GitHub Copilot就是绝佳案例——如果你仔细想想，它的自动模式是我们做过最聪明的设计。它能根据提示自动选择用哪个模型来完成代码补全或任务交接。这种选择不是简单的轮询机制，而是基于你拥有的反馈循环。

Remember that a SaaS application in the modern world is driving a business outcome. It knows how to most efficiently use the tokens to create some business value. In fact, GitHub Copilot is a great example of it, which is, if you think about it, the auto mode of GitHub Copilot is the smartest thing we've done. So it chooses based on the prompt which model to use for a code completion or a task handoff. And you do that not just by choosing in some round robin fashion, you do it because of the feedback cycle you have.

你拥有评估体系、数据闭环等机制。正如你正确指出的，新型SaaS应用都是智能应用，它们针对特定评估标准和业务结果进行优化，从而最有效地利用token工厂的输出。有时延迟很重要，有时性能很关键。而懂得如何进行智能权衡，正是SaaS应用的价值所在。

You have the evals, the data loops, and so on. So the new SaaS applications, as you rightfully said, are intelligent applications that are optimized for a set of evals and a set of outcomes that then know how to use the token factory's output most efficiently. Sometimes latency matters. Sometimes performance matters. And knowing how to do that trade in a smart way is where the SaaS application value is.

但总体而言，这次软件确实存在真实的边际成本。云时代也是如此。当我们做CD-ROM时边际成本很低，云计算时代出现了边际成本，而这次边际成本要高得多。

But overall, it is going to be true that there is a real marginal cost to software this time around. It was there in the cloud era too. When we were doing CD ROMs, there wasn't much of a marginal cost. With the cloud, there was. And this time around, it's a lot more.

因此，商业模式必须调整，你需要分别针对智能体工厂和token工厂进行这些优化。

And so therefore, the business models have to adjust and you have to do these optimizations for the agent factory and the token factory separately.

你们有一项大多数人都不知道的大型搜索业务，但事实证明这可能是人类历史上最赚钱的生意之一。因为人们进行大量搜索，数十亿次搜索，而微软完成每次搜索的成本只有几分钱的零头，对吧？完成搜索的成本很低。但如今使用聊天机器人时，可比查询或提示堆栈的成本结构就不同了。那么问题在于：假设未来这两项业务的收入水平相似，对吧？

You have a big search business that most people don't know about, you know, but it turns out that that's probably one of the most profitable businesses in the history of the world because people are running lots of searches, billions of searches, and the cost of completing a search if you're Microsoft is many fractions of a penny, right? It doesn't cost very much to complete a search. But the comparable query or prompt stack today when you use a chatbot looks different. Right? So I guess the question is, assume similar levels of revenue in the future for those two businesses, right?

你是否曾遇到过这样的情况：聊天互动的单位经济效益与搜索一样有利可图？

Do you ever get to a point where kind of that chat interaction has unit economics that are as profitable as search?

我认为这个观点很到位，因为你看，搜索在广告单元和成本经济性方面相当神奇，因为有索引这个固定成本，可以更高效地分摊。而聊天每次互动，正如你所说，需要消耗更多GPU周期，包括意图理解和检索。所以经济模式不同。因此我认为确实如此，这也是为什么早期聊天经济多采用免费增值模式，甚至在消费者端也采用订阅制。

I think that's a great point because, see, search was pretty magical in terms of its ad unit and its cost economics because there was the index, which was a fixed cost that you could then amortize in a much more efficient way. Whereas this one, each chat, to your point, you have to burn a lot more GPU cycles, both with the intent and the retrieval. So the economics are different. So I think you do. That's why I think a lot of the early sort of economics of chat have been the freemium model and subscription even on the consumer side.

所以我们仍需探索这究竟是代理型商务还是其他广告单元形式，以及它将如何被规范。但与此同时，事实上我现在很清楚——我只用搜索处理非常具体的导航类查询。过去常说我会大量用于购物，但现在这部分需求也逐渐转向Copilot。看看Edge和Bing里的Copilot模式，它们正在融合。

So we are yet to discover whether it's agentic commerce or whatever is the ad unit, how it's going to be litigated. But at the same time, the fact that at this point, I kind of know, in fact, I use search for very, very specific navigational queries. Used to say I use it a lot for commerce, but that's also shifting to my Copilot. Look at the Copilot mode in Edge and Bing or Copilot. Now they're blending in.

所以我认为，是的，未来确实会出现真正的行业变革，就像我们讨论过的SaaS颠覆一样——初期这类消费经济模式会经历奶酪被轻微挪动的阶段。

So I think that, yes, I think there is going to be a real litigation, just like we talked about the SaaS disruption, where in the beginning of the cheese being a little moved in consumer economics of that category.

我的意思是，考虑到这是数万亿美元的产业，正是它驱动了整个互联网经济，对吧？当你改变了你和谷歌双方的搜索经济模式，使其趋近于更像个人代理、个人助理聊天的形态，最终在给人类带来的总价值方面可能会大得多。但从单位经济效益来看，你不仅仅是在分摊这一次性的固定索引成本。没错。所以消费者，

Mean, and given that it's the multi trillion dollar, this is the thing that's driven all the economics of the internet, right? When you move the economics of search for both you and Google, and it converges on something that looks more like a personal agent, a personal assistant chat, that could end up being much, much bigger in terms of the total value delivered to humanity. But the unit economics, you're not just amortizing this one time fixed index. That's right. So the consumer,

这个类别，因为你触及了一个我经常思考的问题，对吧？就是在这些颠覆性变革中，你必须真正理解什么是类别经济？是赢家通吃吗？这两者都很重要，对吧？消费领域的问题始终在于时间总量是有限的。

category, because you are putting a thread on something that I think a lot about, right? Which is during these disruptions, you kind of have to have a real sense of what is the category economics? Is it winner take all? And both matter, right? The problem on consumer space always is that there's a finite amount of time.

所以如果我不做这件事，就会做另一件事。如果你的盈利模式依赖于特定的人际互动，尤其是在消费领域出现真正具有代理性的功能时，情况可能会不同。而在企业领域，首先这不是赢家通吃的局面，其次它会更适合代理性互动。所以这不像按席位收费与按使用量收费那样的区别。

And so if I'm not doing one thing, I'm doing something else. And if your monetization is predicated on some human interaction in particular, if there was truly agentic stuff even on consumer, that could be different. Whereas in the enterprise, one is it's not winner take all. And two, it is going to be a lot more friendly for agentic interaction. So it's not like, for example, the per seat versus consumption.

现实情况是，智能体已成为新的席位。因此你可以理解为，企业变现路径更加清晰，而我认为消费者变现则略显模糊。

The reality is agents are the new seats. And so you can think of it as the enterprise monetization is much clearer. The consumer monetization, I think, is a little more murky.

你知道，最近我们看到一波裁员潮，亚马逊本周就宣布了大裁员。尽管营收表现强劲，但过去三年MAG七巨头的就业增长微乎其微。你们从24年到25年员工人数基本持平，维持在22.5万人左右。很多人将此归因于疫情后企业优化增效，我认为这确实有很大道理。

You know, we've seen a spate of layoffs recently with Amazon announcing some big layoffs this week. You know, the MAG-seven has had little job growth over the last three years despite really robust top lines. You know, you didn't grow your headcount really from 24 to 25. It's around 225,000. You know, many attribute this to normal getting fit, know, just getting more efficient coming out of COVID, and I think there's a lot of truth to that.

但你认为其中部分原因是否与AI有关？你认为AI会成为净就业创造者吗？你如何看待这对微软生产力的长期积极影响？在我看来，市场规模在扩大，但借助AI可以更高效完成所有工作，这意味着要么利润率提升，要么将节省的利润再投资以实现更持久的增长——我称之为利润率扩张的黄金时代。

But do you think part of this is due to AI? Do you think that AI is going to be a net job creator? And do you see this being a long term positive for Microsoft productivity? Like, it feels to me like the pie grows, but you can do all these things much more efficiently, which either means your margins expand or it means you reinvest those margin dollars and you grow faster for longer. I call it the golden age of margin expansion.

我坚信生产力曲线正在并终将发生转折，具体表现为我们将看到工作内容尤其是工作流程的改变。借助这些强大的工具，你在任务层面将获得更多自主权来完成工作。这就是为什么我们甚至在内部——比如谈到令牌分配时——要确保每位微软员工都标配Microsoft 365的完整功能和无限制使用权，并配备GitHub Copilot来真正提升效率。但布拉德，我们还发现个有趣现象：现在连学习方式都在革新，即如何与智能体协作。

I'm a firm believer that the productivity curve does and will bend, in the sense that we will start seeing some of what is the work and the work flow in particular change, right? There's going to be more agency for you at a task level to get to job complete because of the power of these tools, in your hand. And that, I think, is going to be the case. So that's why I think we are even internally, for example, when you talked about even our allocation of tokens, we want to make sure that everybody at Microsoft, standard issue, right, all of them have Microsoft three sixty five to the tilt in the most unlimited way and have GitHub Copilot so that they can really be more productive. But here is the other interesting thing, Brad, we're learning is there's a new way to even learn, which is how to work with agents.

这就像当年Word、Excel、PowerPoint首次集成到Office时，我们重新构思了预测流程。想想80年代的预测全靠办公室备忘录和传真，突然有人提议用Excel表格通过邮件传阅收集数据。同理现在，任何计划执行都始于AI——用AI研究、思考、与同事共享协作。

So that's kind of like when the first, when Word, Excel, PowerPoint all showed up in Office, we learned how to rethink, let's say, how we did a forecast. Mean, think about it, right? In the '80s, the forecasts were interoffice memos and faxes and what have you. Then suddenly somebody said, Oh, here's an Excel spreadsheet, let's put in an e mail, send it around, people enter numbers, and there was a forecast. Similarly, right now, any planning, any execution starts with AI, you research with AI, you think with AI, you share with your colleagues, and what have you.

因此正在形成新的工作产物和流程。当业务流程变革速度与AI能力相匹配时，就能释放生产力红利。无论是科技行业还是现实世界，能驾驭这种变革的将成为最大受益者。

So there's a new artifact being created and a new workflow being created. That is the pace of change of the business process that matches the capability of AI, that's where the productivity efficiencies come. So, that can master that are going to be the biggest beneficiaries, whether it's in our industry or quite frankly, the real world.

那么微软是否从中获益？假设按当前增速——可能更快——但就说五年后，萨提亚，当你们营收翻倍时，员工数量会增加多少？

And so is Microsoft benefiting from that? So let's think about a couple of years from now, five years from now at the current growth rate will be sooner, but let's call it five years from now, your top line is twice as big as what it is today, Satya. How many more employees will you have if grow revenue by?

目前最棒的事情之一就是微软员工每天带给我的这些案例。比如我们网络运营的负责人，你知道吗？想想我们为费尔沃特新建的两千兆瓦数据中心铺设的光纤量，那里的光纤规模、我们能实现的AI应用等等，简直疯狂。事实证明这是实实在在的资产——全球范围内我们合作的光纤运营商就有400家。

Like one of the best things right now is these examples that I'm hit with every day from the employees of Microsoft. There was this person who leads our network operations, right? I mean, if you think about the amount of fiber we have had to put for this two gigawatt data center we just built out in Fairwater, and the amount of fiber there, the AI we can, and what have you, it's just crazy. It turns out this is a real world asset. There are, I think, 400 different fiber operators we are dealing with worldwide.

每次出现问题，我们都要处理所有这些DevOps流程。但那位负责人对我说：'知道吗？我永远不可能招到足够人手来处理这些。别说预算批不批，就算批了也招不到这么多人。'于是她采取了次优方案——

Every time something happens, we are literally going and dealing with all these DevOps pipelines. But the person who leads it, she basically said to me, You know what? There's no way I'll ever get the headcount to go do all this. Not forget, even if I even approve the budget, I can't hire all these folks. So she did the next best thing.

她直接开发了一整套智能代理来自动化运维流程中的DevOps管道。这就是你提到的典型案例：配备AI工具的团队如何提升效能。所以如果问我的观点，我会说我们会增加编制。但在我看来，这些新增编制将比AI时代前的编制具备更高的杠杆效应。这就是当前最显著的结构性调整，对吧？

She just built herself a whole bunch of agents to automate the DevOps pipeline of how to deal with the maintenance. That is an example of, to your point, a team with AI tools being able to get more productivity. So if you have a question, I will say we will grow a headcount. The way I look at it is that headcount we grow will grow with a lot more leverage than the headcount we had pre AI. And that's the adjustment I think structurally you're seeing first, right?

你称之为'健身'，我更倾向于认为这是让所有人重新思考工作方式的过程——重点不在于'做什么'，而在于'怎么做'。即使工作内容不变，执行方式也需要重新学习。这种'遗忘旧方法-学习新方法'的过程预计还要持续一年左右。

Which is one, you called it getting fit. I think of it as more getting to a place where everybody is really not learning how to rethink how they work. And it's the how, not even the what. Even if the what remains the constant, how you go about it has to be relearned. And it's the unlearning and learning process that I think will take the next year or so.

届时人员增长将伴随最大化的杠杆效应。

Then the headcount growth will come with max leverage.

是的，我认为我们正处在惊人经济生产力增长的前夜。但就像我和迈克尔·戴尔交流时的感受，大多数公司甚至还没进入重新设计工作流的第一局比赛，可能只是第一局的首位打者，远未充分发挥这些智能代理的杠杆作用。但未来两三年内，这必将成为主要增长点。我始终持乐观态度——整体上这会带来就业净增长，不过对企业而言，其利润增速会快于员工数量增速。

Yeah, no, I think we're on the verge of incredible economic productivity growth. It does feel like when I talk to you or Michael Dell that most companies aren't even really in the first inning, maybe the first batter in the first inning in reworking those workflows to get maximum leverage from these agents. But it sure feels like over the course of the next two to three years that's where a lot of gains are going to start coming from. And again, I certainly am an optimist. I think we're going to have net job gains from all of this, but I think for those companies, they'll just be able to grow their bottom line, their number of employees, slower than their top line.

这就是企业获得的生产力提升。聚沙成塔后就是整个经济的生产力飞跃，届时我们就能将这种消费者剩余投资于创造前所未有的新事物。

That is the productivity gain to the company. Aggregate all that up, that's the productivity gain to the economy, and then we'll just take that consumer surplus and invest it in creating a lot of things that didn't exist before.

百分之百。即便在软件开发领域，我观察到的一个现象是：没人会说我们将在让更多软件工程师为社会做贡献方面遇到挑战。因为现实是，你去看任何组织的IT待办清单。所以问题在于，所有这些软件代理有望帮助我们解决积压的IT任务。

100%. 100%. Even in software development, one of the things I look at is no one would say we're going to have a challenge in having more software engineers contribute to our society. Because the reality is, you look at the IT backlog in any organization. And so the question is, all these software agents are hopefully going to help us go and take a whack at all of the IT backlog we have.

想想那个关于常青软件的梦想即将成真。再考虑下软件需求。所以我认为，正如你所说，知识工作的抽象层级将发生变化。我们会适应这种变化，包括工作本身和工作流程。这甚至将反过来调整对本行业产品的需求。

And think of that dream of evergreen software that's going to be true. And then think about the demand for software. So I think that to your point, it's the levels of abstraction at which knowledge work happens will change. We will adjust to that, the work and the workflow. That will then adjust itself even in terms of the demand for the products of this industry.

我要用这个话题收尾——关于美国的再工业化。如果把你们和其他美国科技巨头未来四五年计划投入的4万亿美元资本支出加起来，经通胀或GDP调整后，这相当于曼哈顿计划规模的十倍。对美国来说这是项浩大工程。

I'm going to end on this, which is really around the reindustrialization of America. Now, I've said if you add up the $4,000,000,000,000 of CapEx that you and so many of the big large U. S. Tech companies are investing over the course of the next four or five years, it's about 10 times the size of the Manhattan Project on an inflation adjusted or GDP adjusted basis. So it's a massive undertaking for America.

总统已将重新制定贸易协议列为政府要务，目前我们似乎已获得数万亿美元投资。就在今天，韩国承诺向美国投资3500亿美元。当你审视美国电力领域正在发生的变革——无论是生产端还是电网等基础设施，以及这场再工业化进程，你如何看待当前进展？能否谈谈你对未来几年的着陆预期和乐观程度？

The President has made it a real priority of his administration to recut the trade deals, and it looks like we now have trillions of dollars. South Koreans committed $350,000,000,000 of investments just today into The United States. And when you think about what you see going on in power in The United States, both production, the grid, etc, what you see going on in terms of this re industrialization, How do you think this is all going? And maybe just reflect on where we're landing the plane here and your level of optimism for the few years ahead.

是的，我非常乐观。布拉德·史密斯曾向我讲述威斯康星数据中心周边经济生态，这很有趣。多数人认为数据中心就是个大型自动化仓库——这种认知部分正确。但首先要考虑：数据中心的建设投入及其本地供应链意味着什么？

Yeah, no, I feel very, very optimistic because in some sense, Brad Smith was telling me about sort of the economy around our Wisconsin data center. It's fascinating. Most people think, oh, data center that is sort of like, yeah, it's going to be one big warehouse and there is fully automated. A lot of it is true. But first of all, what went into the construction of that data center and the local supply chain of the data center?

从某种意义说，这也是美国的再工业化。更不用说台积电在亚利桑那的工厂、美光在存储领域的投资、英特尔的晶圆厂等等。我们需要启动建设的项目很多。这并不意味着我们不会与其他国家签订有利的贸易协议。但正如你所说，为新经济再工业化，确保从电力到各环节的技能与产能，这对我们至关重要。

That is, in some sense, the re industrialization of The United States as well. Even before you get to what is happening in Arizona with the TSMC plants or what was happening with Micron and their investments in memory or Intel and their fabs and what have you, right? There's a lot of stuff that we will want to start building. Doesn't mean we won't have trade deals that make sense for The United States with other countries. But to your point, the re industrialization for the new economy and making sure that all the skills and all that capacity from power on down, I think is very important for us.

布拉德，还有一点很重要——这也是我曾向特朗普总统和卢特尼克部长等人阐述过的：必须认识到我们这些美国超大规模企业同时也在全球投资。换句话说，美国是全球算力工厂和代币工厂的最大投资方。我们不仅吸引外资助力本国再工业化，还通过在欧洲、亚洲、拉美和非洲的资本投入，将最先进的美国技术输向全球，供各地创新和信赖。这两点都将长期利好美国。

The other thing that I also say, Brad, it's important, and this is something that I've had a chance to talk to President Trump as well as Secretary Lutnick and others, is it's important to recognize that we as hyperscalers of The United States are also investing around the world. So in other words, The United States is the biggest investor of compute factories or token factories around the world. Not only are we attracting foreign capital to invest in our country so that we can re industrialize, We are helping, whether it's in Europe or in Asia or elsewhere, in Latin America and in Africa, with our capital investments, bringing the best American tech to the world that they can then innovate on and trust. And so both of those, I think, bode well for The United States long term.

我感谢你的领导。山姆确实在引领OpenAI为美国冲锋陷阵。我认为这是一个展望未来的时刻，你知道，你可以看到4%的GDP增长就在眼前。我们会遇到挑战，会有起伏。这更像是阶梯式上升，而非直线向右上方攀升。

I'm grateful for your leadership. Sam is really helping lead the charge at OpenAI for America. I think this is a moment where I look ahead, you know, you can see 4% GDP growth on the horizon. We'll have our challenges, we'll have our ups and downs. These tend to be stairs up rather than a line straight up and to the right.

但就我个人而言，我看到华盛顿与硅谷之间、大型科技公司与美国再工业化之间正在进行的协调合作，这让我充满希望。看到总统及其团队本周在亚洲的成果，再看看这里的进展，实在令人振奋。所以感谢你抽空参加。我们非常感谢你，萨提亚。

But I, for one, see a level of coordination going on between Washington and Silicon Valley, between Big Tech and the re industrialization of America that gives me cause for incredible hope. Watching what happened this week in Asia led by the President and his team, and then watching what's happening here is super exciting. So thanks for making the time. We're big Thanks, Satya.

非常感谢，布拉德。谢谢。

Thanks so much, Brad. Thank you.

提醒大家，这只是我们的观点，不构成投资建议。

As a reminder to everybody, just our opinions, not investment advice.