OpenAI 与微软：历史性的科技合作伙伴关系

是的，我认为这确实是一段在每个阶段都令人惊叹的合作关系。正如萨蒂亚所说，我们开始时完全不知道它会走向何方。但我认为这是有史以来最伟大的科技合作伙伴关系之一。没有微软，尤其是萨蒂亚早期的坚定信念，我们不可能做到这一点。

Yeah, I think this has really been an amazing partnership through every phase. We had no idea where it was all going to go when we started, as Satya said. But I think this is one of the great tech partnerships ever. Without Microsoft, and particularly Satya's early conviction, we would not have been able to do this.

真是精彩的一周。很高兴见到你们两位。山姆，宝宝怎么样？

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

宝宝很好。这是最棒的事情，老兄。所有那些老生常谈都是真的，这真的是最棒的事情。

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

嘿，萨蒂亚，你花了这么多时间……

Hey Satya, with all your time...

山姆每次提到他宝宝时脸上的笑容就是不一样。大概就是爸爸和算力，当他谈论算力和他的宝宝时。

That smile on Sam's face whenever he talks about his baby is just so different. It's dad and compute, I guess, when he talks about compute and his baby.

那么，萨蒂亚，你们在一起这么久，你有没有给他一些当爸爸的建议？

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

我说就好好享受吧。这太棒了。我们生孩子的时候还很年轻，我真希望能重来一次。所以从某种意义上说，这是最珍贵的时光，随着他们长大，一切都很美好。我很高兴山姆……

I said just enjoy it. It's so awesome. We had our children so young, and I wish I could redo it. So in some sense it's the most precious time, and as they grow it's just so wonderful. I'm so glad Sam is...

我很高兴年纪大一些才做这件事，但我有时确实想，老兄，我真希望自己 25 岁时有这种精力。那部分更难。

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

毫无疑问。

No doubt about it.

山姆，OpenAI 的平均年龄是多少？有概念吗？挺年轻的。

What's the average age at OpenAI, Sam? Any idea? It's young.

不算特别年轻。不像大多数硅谷初创公司。我不知道，平均大概 30 出头吧。

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

宝宝是积极趋势还是消极趋势？

Are babies trending positively or negatively?

宝宝是积极趋势。

Babies trending positively.

哦，那很好。很好。是的。

Oh, that's good. That's good. Yeah.

好了，各位，真是重要的一周。我在想，我从英伟达 GTC 开始，市值刚达到 5 万亿美元。谷歌、Meta、微软，萨蒂亚，你昨天发布了财报，我们一直听到算力不够、算力不够。周三我们降息了。GDP 接近 4%。然后我刚刚对山姆说，总统在马来西亚、韩国、日本签了这些大规模协议，听起来还有中国。这些协议确实提供了再工业化美国的金融火力。800 亿美元用于新的核聚变，所有你们需要建设更多算力的东西。但所有这些中不容忽视的是，你们周二发布了一个重大公告，明确了你们的合作关系。恭喜。我想就从这里开始。我真的想用非常简单直白的语言来解读这笔交易，确保我理解正确。我们就从你的投资开始，萨蒂亚。微软从 2019 年开始投资，已经向 OpenAI 投资了大约 1340 亿美元，为此你获得了完全稀释基础上 27% 的业务所有权。我记得大概是三分之一，去年随着所有投资你被稀释了一些。这个所有权比例听起来对吗？

Well, you guys, such a big week. I was thinking about I started at Nvidia's GTC, just hit $5 trillion. Google, Meta, Microsoft, Satya, you had your earnings yesterday, and we heard consistently not enough compute, not enough compute. We got rate cuts on Wednesday. The GDP's tracking near 4%. And then I was just saying to Sam, the president's cut these massive deals in Malaysia, South Korea, Japan, sounds like with China. Deals that really provide the financial firepower to re-industrialize America. $80 billion for new nuclear fusion, all the things that you guys need to build more compute. But certainly what wasn't lost in all of this was you guys had a big announcement on Tuesday that clarified your partnership. Congrats on that. I thought we'd just start there. I really want to break down the deal in really simple plain language to make sure I understand it. We'll just start with your investment, Satya. Microsoft started investing in 2019, has invested in the ballpark of $134 billion into OpenAI, and for that you get 27% of the business ownership 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. Does that sound about right in terms of ownership?

是的，没错。但布拉德，在我们讨论持股之前，我认为 OpenAI 非常独特的一点是，作为 OpenAI 重组过程的一部分，会诞生一个最大的非营利组织。别忘了这一点。从某种意义上说，在微软，我们非常自豪能与两个最大的非营利组织相关联：盖茨基金会和现在的 OpenAI 基金会。所以这才是大新闻。我们显然很激动。这并非我们当初所想。就像我对某人说的，这不像我们最初投资十亿美元时想的那样，“哦，这将会是我跟风投们谈论的百倍回报”，但我们现在就在这里。我们非常高兴能成为投资者和早期支持者。坦率地说，这很好地证明了山姆和团队所做的一切。他们显然很早就看到了这项技术的潜力，并全力以赴，以精湛的方式执行。

Yeah, it does. But 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 nonprofits gets created. Let's not forget that. In some sense, at Microsoft, we are very proud of the fact that we are associated with two of the largest nonprofits: the Gates Foundation and now the OpenAI Foundation. So that's the big news. We obviously are thrilled. It's not what we thought. 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 a great testament to what Sam and team have done quite frankly. They obviously had the vision early about what this technology could do and they ran with it and just executed in a masterful way.

是的。我认为这确实是一段在每个阶段都令人惊叹的合作关系。正如萨蒂亚所说，我们开始时完全不知道它会走向何方。但我认为这是有史以来最伟大的科技合作伙伴关系之一。没有微软，尤其是萨蒂亚早期的坚定信念，我们不可能做到这一点。我认为在当时的世界环境下，没有多少人愿意下这样的赌注。我们并不确切知道技术会如何发展。嗯，不确切。我们完全不知道技术会如何发展。我们只是对这个推动深度学习的想法充满信心，并相信如果我们能做到，我们就能找到方法制造出精彩的产品，创造大量价值，并且，正如萨蒂亚所说，创造出我们认为将有史以来最大的非营利组织。我认为它会做出非常了不起的事情。我真的很喜欢这个结构，因为它让非营利组织价值增长，同时公益公司能够获得继续扩张所需的资本。我认为如果我们没有想出这个结构，如果没有合作伙伴在桌边兴奋地希望它这样运作，非营利组织不可能有这么大的价值。但自从我们首次建立合作关系以来已经超过六年了，六年里取得了相当惊人的成就，我认为未来还会有更多。我希望萨蒂亚能从这笔投资中赚到一万亿美元，而不是一千亿，你知道，不管多少。

Yeah. I think this has really been an amazing partnership through every phase. We had no idea where it was all going to go when we started, as Satya said. But I think this is one of the great tech partnerships ever. 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. 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. I think it's going to do amazingly great things. 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 it's been more than six years since we first started this partnership and a pretty crazy amount of achievement for six years, and I think much more to come. I hope that Satya makes a trillion dollars on the investment, not a hundred billion, you know, whatever it is.

那么，作为重组的一部分，你们谈到了这一点。你们上面有一个非营利组织，下面有一个公益公司。这相当疯狂。这个非营利组织已经拥有 1300 亿美元的资本。1300 亿美元的 OpenAI 股票。它一成立就是世界上最大的非营利组织之一。最终可能会变得更大。加州总检察长表示他们不会反对。你们已经有这 1300 亿美元专门用于确保 AGI 惠及全人类。你们宣布将把最初的 250 亿美元用于健康、AI 安全和韧性。山姆，首先让我说，作为生态系统的一员，向你们两位致敬。这对 AI 未来的贡献令人难以置信。但是山姆，跟我们谈谈选择健康和安全韧性的重要性。然后帮助我们理解，如何确保你获得最大收益，而不会像我们看到许多非营利组织那样被自身的政治偏见所拖累。

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 $130 billion. $130 billion of OpenAI stock. It's one of the largest in the world out of the gates. It could end up being much larger. The California Attorney General said they're not going to object to it. You already have this $130 billion dedicated to making sure that AGI benefits all of humanity. You announced that you're going to direct the first $25 billion to health and AI security and resilience. Sam, first let me just say, as somebody who participates in the ecosystem, kudos to you both. It's incredible this contribution to the future of AI. But Sam, talk to us a bit about the importance of the choice around health and resilience. And then help us understand how do we make sure that you get maximal benefit without it getting weighed down as we've seen with so many nonprofits with its own political biases.

是的。

Yeah.

首先，为世界创造大量价值的最佳方式，希望是我们已经在做的事情，那就是打造这些神奇的工具，让人们使用它们。我认为资本主义很棒，公司也很棒。人们在将先进 AI 交到大量个人和公司手中方面做着出色的工作，他们正在创造不可思议的成果。但在某些领域，市场力量并不完全符合人们的最佳利益，你需要以不同的方式行事。这项技术还带来了一些前所未有的新事物，比如利用 AI 快速进行科学研究的潜力，真正实现自动化发现。当我们思考最初要聚焦的领域时，显然，如果我们能治愈许多疾病，并让相关数据和信息广泛可用，那将是为世界做的一件好事。关于 AI 韧性这一点，我确实认为有些事情可能会变得有点奇怪，而公司单打独斗无法解决所有问题。因此，当世界需要度过这一转型期时，如果我们能资助一些工作来提供帮助，那可能是网络防御、AI 安全研究、经济研究等等，帮助社会平稳度过这一转型。我们非常确信转型后的世界会多么美好，但我也知道过程中会有一些波折。

First of all, the best way to create a bunch of value for the world is hopefully what 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. They're doing incredible things. There are some areas where 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 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 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 cyber defense, that could be AI safety research, that could be economic studies, all of these things, helping society get through this transition smoothly. We're very confident about how great it can be on the other side, but I'm sure there will be some choppiness along the way.

我们继续深入剖析这笔交易。关于模型和排他性：Sam，OpenAI 可以在 Azure 上分发其领先模型，但我认为在 2032 年之前的七年内，你们不能在其它主要大型云平台上分发这些模型，但如果 AGI 被验证，这一限制会提前结束。我们可以稍后再谈这个，但你们可以在其他平台上分发开源模型、Sora、智能体、编解码器、可穿戴设备等所有其他产品。所以 Sam，我猜这意味着 ChatGPT 或 GPT-6 不会出现在亚马逊或谷歌上。

Let's keep busting through the deal. So models and exclusivity: Sam, OpenAI can distribute its leading models on Azure, but I don't think you can distribute them on any other leading big clouds for seven years until 2032, but that would end earlier if AGI is verified. We can come back to that, but you can distribute your open source models, Sora, agents, Codex, wearables, everything else on other platforms. So Sam, I assume this means no ChatGPT or GPT-6 on Amazon or Google.

不。首先，我们希望一起做很多事情，为微软创造价值。我们也希望他们做很多事情为我们创造价值。这方面会有很多很多合作。我们保留 Satya 曾经称之为——我认为这是个很棒的短语——无状态 API 在 Azure 上的独家使用权，直到 2030 年。其他所有东西我们都会在其他地方分发，这显然也符合微软的利益。所以，我们会在很多地方推出很多产品，而这件事我们会在 Azure 上做，人们可以通过 Azure 或直接通过我们获取。我认为这很棒。

No. So, first of all, we want to do lots of things together to help create value for Microsoft. We want them to do lots of things to create value for us. And there are many, many things that'll 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 distribute elsewhere, and that's obviously in Microsoft's interest too. So, we'll put lots of products in lots of places, and then this thing we'll do on Azure, and people can get it there or via us. And I think that's great.

然后是收入分成，OpenAI 仍需就所有收入向微软支付分成，同样持续到 2032 年或 AGI 被验证。为了讨论方便，我知道这有点琐碎，但重要的是分成比例是 15%。这意味着如果你们有 200 亿美元收入，就要向微软支付 30 亿美元，这算作 Azure 的收入。Satya，这听起来差不多吗？

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 billion in revenue, you're paying $3 billion to Microsoft, and that counts as revenue to Azure. Satya, does that sound about right?

是的，我们有收入分成，正如你所说，要么到 AGI 实现，要么到协议期满。老实说，我确实不太清楚我们具体把它记在哪里，是算作 Azure 收入还是其他地方。这是个好问题，得问 Amy。

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.

鉴于排他性和收入分成都在 AGI 被验证的情况下提前终止，这似乎让 AGI 变得非常重要。据我了解，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做出是否达到 AGI 的决定。Satya，你在昨天的财报电话会议上说，没有人接近实现 AGI，你预计短期内不会发生。你提到了这种尖刺状、锯齿状的智能。Sam，我听说你对于何时可能实现 AGI 似乎更乐观一些。所以，我想问你们两位：你们是否担心在未来两三年内，我们最终不得不召集评审团来有效判断是否达到了 AGI？

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

我明白你想在我们之间制造一些戏剧性。我认为为此建立一个流程是件好事。我预计这项技术会经历一些令人惊讶的曲折，我们将继续成为彼此的好伙伴，共同找出合理的方案。

I realize you 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。我认为这就是为什么我们建立的这个流程是好的原因之一。归根结底，我坚信智能能力会持续提升，而我们的真正目标，坦率地说，就是如何将这种能力交到个人和组织手中，让他们获得最大收益。这正是 OpenAI 最初的使命，也是吸引我加入 OpenAI、与 Sam 和团队合作的原因，我们计划继续沿着这条路走下去。

That's well said. I think 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, and our real goal, quite frankly, is that: 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.

Brad，说句显而易见的话，即使我们明天就有了超级智能，我们仍然需要微软的帮助将产品交到人们手中，我们非常需要他们。

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。是的。不，我再次问这些我知道大家关心的问题，这对我来说非常有意义。显然，微软是世界上最大的分发平台之一。你们长期以来一直是很好的合作伙伴。但我认为这澄清了一些误解。我们换个话题吧。显然，OpenAI 是历史上增长最快的公司之一。Satya，一年前你在播客上说，每一次新的范式转变都会催生一个新的谷歌，而这次范式转变的谷歌已经众所周知，就是 OpenAI。如果没有你们下的这些巨大赌注，这一切都不可能发生。尽管如此，据报道 OpenAI 2025 年的收入仍是 130 亿美元。Sam，你在本周的直播中谈到了对算力的大规模投入，对吧？未来四五年投入 1.44 万亿美元，其中包括对 Nvidia 的 5 亿美元、对 AMD 的 3 亿美元，以及对 Oracle 和 Azure 的 2500 亿美元。所以我认为本周我听到的市场最大的疑问是：一家收入 130 亿美元的公司如何能做出 1.44 万亿美元的支出承诺？Sam，你听到了这些批评。

Of course. Of course. Yeah. No, it 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. But I think it dispels some of the myths that are out there. But let's shift gears a little bit. You know, obviously OpenAI is one of the fastest growing companies in history. Satya, you said on the pod a year ago 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, OpenAI's revenues are still a reported $13 billion in 2025. And Sam, on your livestream this week, you talked about this massive commitment to compute, right? $1.44 trillion over the next four or five years, with big commitments: $500 million to Nvidia, $300 million to AMD, and Oracle $250 billion to Azure. So I think the single biggest question I've heard all week hanging over the market is: how can a company with $13 billion in revenues make $1.44 trillion of spend commitments? And you've heard the criticism, Sam.

首先，我们的收入远不止这些。其次，Brad，如果你想卖股票，我可以帮你找买家。

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

我只是想说，你知道，人们……我认为有很多人愿意购买 OpenAI 的股票。

I just enough like, you know, people are... I think there's a lot of people who would love to buy OpenAI shares.

我不认为你——包括我自己，那些对算力问题忧心忡忡的人——会急着买股票。所以我觉得我们可以把你们的股票或任何人的股票卖给那些在 Twitter 上嚷嚷得最凶的人，很快就能卖掉。我们的确计划让营收大幅增长，营收也确实在快速增长。我们押注它会继续增长，不仅 ChatGPT 会持续增长，我们还能成为重要的 AI 云服务商，我们的消费设备业务会变得举足轻重，能自动化科学的 AI 将创造巨大价值。所以，我很少想成为一家上市公司，但少数有吸引力的时刻就是当那些人写那些荒谬的‘OpenAI 要倒闭了’之类的帖子时。我很想告诉他们可以卖空股票，然后看着他们被烧死。但我们精心规划，我们了解技术和能力的发展方向，以及我们能围绕它们构建的产品和能产生的营收。我们可能会搞砸，这是我们正在下的赌注，我们也在承担风险。一个确定的风险是，如果我们没有算力，就无法产生营收，也无法以这种规模制造模型。

I don't think you, including myself, people who talk with a lot of breathless concern about our compute stuff or whatever, would be thrilled to buy shares. So I think we could sell your shares or anybody else's to some of the people who are making the most noise on Twitter 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 ChatGPT 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. So, 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 'OpenAI is about to go out of business' and 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 we carefully plan, we understand where the technology and capability are going to go, and how the products we can build around that and the revenue we can generate. We might screw it up; this is the bet that we're making and we're taking a risk along with that. 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 scale.

完全正确。让我说一句，Brad，作为合伙人和投资者，我见过的 OpenAI 的商业计划，没有一个他们没有超额完成的。所以从某种意义上说，这是唯一一个在增长和业务方面执行得令人难以置信的地方。坦白说，显然 OpenAI，每个人都在谈论使用量等方面的成功，但总体而言，业务执行也相当惊人。几周前我听到 Greg Brockman 在 CBC 上说：如果我们能把算力提升 10 倍，营收可能不会增长 10 倍，但肯定会多很多，仅仅是因为算力不足。确实，当我看到我们被拖累了多少时，真的很疯狂。在很多方面，我们过去一年可能已经把算力扩大了 10 倍，但如果再有 10 倍的算力，我不知道营收是否会增长 10 倍，但我认为不会差太远。昨晚我们也从你那里听到了，Satya，你说你受算力限制，如果有更多算力，增长会更快。所以 Sam，帮我们理解一下，你今天感觉算力受限有多严重？展望未来两到三年的建设，你认为会达到不再受算力限制的地步吗？

Exactly. And let me 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 have put in and not beaten it. So in some sense, this is the one place where in terms of their growth and even the business, it's been unbelievable execution, quite frankly. I mean, obviously OpenAI, everyone talks about all the success in usage and what have you, but even all up, the business execution has been pretty unbelievable. I heard Greg Brockman say on CBC a couple weeks ago, right? If we could 10x our compute, we might not have 10x more revenue, but we'd certainly have a lot more revenue simply because of lack of compute power. Things like, yeah, it's just really wild when I just look at how much we are held back. And in many ways, we have scaled our compute probably 10x over the past year, but if we had 10x more compute, I don't know if we'd have 10x more revenue, but I don't think it'd be that far. 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 had more compute. So help us contextualize, Sam, maybe like how compute constrained do you feel today, and when you look at the buildout over the next two to three years, do you think you'll ever get to the point where you're not compute constrained?

我们经常讨论算力是否永远够用这个问题。我认为最好的思考方式就是把它看作能源之类的东西。你可以谈论某个价格点上的能源需求，但你不能不谈论不同价格水平上的不同需求。如果每单位智能的算力价格明天下降 100 倍，你会看到使用量增长远超 100 倍，人们会想用这些算力做很多在当前成本下没有经济意义的事情，但会有新的需求出现。所以另一方面，随着模型变得更智能，你可以用这些模型来治愈癌症、发现新物理、驱动一堆人形机器人建造空间站，或者任何疯狂的事情，那么也许人们愿意为更高水平的智能支付高得多的每单位成本，我们目前还不知道，但我打赌会有的。所以我认为，当你谈论容量时，它就像每单位成本和每单位能力，如果没有这些曲线，这个问题就有点虚构，不是一个定义得很好的问题。

We talk about this question of is there ever enough compute a lot. I think the answer is the only 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 different demand at different price levels. If the price of compute per unit of intelligence, or 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 would 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. So I think 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 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。我认为你 Sam 提到过的一个正确思路是，如果智能是算力的对数，那么你要确保不断提高效率，这意味着每美元每瓦特的 token 数，以及社会从中获得的经济价值是我们应该最大化的，同时降低成本。这就是杰文斯悖论的点：你不断降低它，在某种意义上将智能商品化，使其成为全球 GDP 增长的真实驱动力。

Yeah. I mean, I think the one thing that you know Sam you've talked about which I think is the right way is to think about is that if intelligence is a log of compute, then you try and really make sure you keep getting efficient, and 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 like the Jevons paradox point is that right, which is you keep reducing it, commoditizing in some sense intelligence, so that it becomes the real driver of GDP growth all around.

不幸的是，它更接近智能的对数等于算力的对数。但我们可能会找到更好的缩放定律，并找到如何超越这一点。

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 to beat this.

我们昨天从微软和谷歌那里都听到了。两家公司都说，如果有更多 GPU，他们的云业务增长会更快。我在这个播客上问过 Jensen，未来 5 年内是否可能出现算力过剩，他说未来 2 到 3 年内几乎不可能。我想你们两位都同意 Jensen 的看法，虽然我们看不到 5、6、7 年后，但基于我们刚才讨论的原因，未来 2 到 3 年内几乎不可能出现算力过剩。

We heard from both Microsoft and Google yesterday. Both said their cloud businesses would have been growing faster if they had more GPUs. I asked Jensen on this pod if there was any chance over the next 5 years we would have a compute glut, and he said it's virtually non-existent chance in the next 2 to 3 years. I assume you guys would both agree with Jensen that while we can't see out 5, 6, 7 years, certainly over the next 2 to 3 years for the reasons we just discussed, it's almost a non-existent chance that you have excess compute.

嗯，我的意思是，在这种特定情况下，需求和供应的周期确实无法预测。关键是，长期趋势是什么？长期趋势就是 Sam 说的，归根结底，坦白说，我们现在最大的问题不是算力过剩，而是电力，以及能否在靠近电源的地方快速完成建设。如果你做不到，你可能实际上有一堆芯片躺在库存里无法插电。事实上，这就是我今天的问题，对吧？不是芯片供应问题，而是我没有可以插电的机壳。所以一些供应链约束如何出现很难预测，因为需求本身就在增长，很难预测。我的意思是，我和 Sam 不会坐在这里说‘天哪，我们算力不那么短缺了’，因为我们就是不太擅长预测实际需求会是什么样子。

Well, I mean, I think the cycles of demand and supply in this particular case you can't really predict. I mean, even 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 power and it's sort of 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. 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 shells to plug into. And so how some supply chain constraints emerge tough to predict, because the demand is just going, you know, it's tough to predict. I mean, I wouldn't, it's not like Sam and I would want to be sitting here saying 'oh my god, we're less short on compute' because it's just we were not that good at being able to project out what the demand would really look like.

所以我认为这是全球层面的问题。谈论某个国家的某个细分市场是一回事，但真正重要的是把它推广到全世界。会有各种制约因素，我们如何应对这些制约将是最重要的事情。这肯定不会是一条直线路径。肯定会出现供过于求的情况，无论是两三年后还是五六年，我无法告诉你确切时间，但这种情况迟早会发生，而且很可能在过程中出现多次。这里面涉及人类心理和泡沫的深层问题。另外，正如萨蒂亚所说，供应链非常复杂，奇怪的东西会被造出来，技术格局会发生巨大变化。如果某种非常廉价的能源很快大规模上线，那么很多人会因为已经签署的现有合同而遭受巨大损失。如果我们能继续实现每单位智能成本令人难以置信的降低——比如说每年特定水平平均降低 40 倍——从基础设施建设角度来看，这是一个非常可怕的指数。再次强调，我们押注的是随着成本降低，需求会大幅增长，但我有些担心，随着这些突破不断出现，每个人都能在笔记本电脑上运行个人 AGI，我们在这里做了一件疯狂的事情。有些人会像以往每个技术基础设施周期中那样，在某个阶段遭受重创。

So I think that's the worldwide side. It's one thing to talk about one segment in one country, but it's about getting it out to everywhere in the world. There will be constraints, and how we work through them is going to be the most important thing. It won't be a linear path for sure. There will come a glut for sure, whether that's in two to three years or five to six, I can't tell you, but it's going to happen at some point, probably several points along the way. There's something deep about human psychology here, and bubbles. Also, as Satya said, it's such a complex supply chain; weird stuff gets built, the technological landscape shifts in big ways. If a very cheap form of energy comes online soon at mass scale, then 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—that's a very scary exponent from an infrastructure buildout standpoint. Again, we're taking the bet that there will be a lot more demand as that gets cheaper, but I have some fear that we keep going with these breakthroughs and everybody can run a personal AGI on their laptop, and we just did an insane thing here. Some people are going to get really burned, as has happened in every other tech infrastructure cycle at some points along the way.

我觉得说得非常好，你必须同时接受这两个事实。我们在 2020 年就经历过这种情况，但互联网变得比当时任何人预期的都要大得多，为社会带来了更大的成果。

I think that's really well said, and you have to hold those two simultaneous truths. We had that happen in 2020, and yet the internet became much bigger and produced much greater outcomes for society than anybody estimated in that period of time.

是的。但我认为山姆提到的一点没有被充分讨论，那就是 OpenAI 目前对特定 GPU 推理栈所做的优化。我的意思是，我们一方面在谈论摩尔定律的改进，但软件改进的指数级增长远超于此。总有一天，我们会制造出令人难以置信的消费设备，能够在低功耗下完全本地运行 GPT-5 或 GPT-6 级别的模型。这实在太难理解了。

Yeah. But I think that the one thing that Sam said is not talked about enough is the current 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. Someday we will make an incredible consumer device that can run a GPT-5 or GPT-6 capable model completely locally at a low power draw. This is so hard to wrap my head around.

那将是不可思议的。我认为这正是让那些建造大型集中式计算堆栈的人感到害怕的事情。萨蒂亚，你谈了很多关于将推理能力分布到边缘和全球各地的想法。

That will be incredible. And that's the type of thing I think that scares some of the people who are building 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，我的思考方式更多是关于构建一个可互换的算力集群。在云基础设施业务中，你必须做到两件事：一是非常高效的 Token 工厂，二是高利用率。就这些。你需要实现这两个简单的目标。为了实现高利用率，你必须能够调度多种工作负载，甚至包括训练。如果你看 AI 流水线，有预训练、中期训练、后训练和强化学习。你希望能够完成所有这些任务。因此，对于云提供商来说，考虑算力集群的可互换性就是一切。

Yeah, I mean, the way I've thought about it is more about building a fungible fleet. In the cloud infrastructure business, one of the key things you have to do is have two things: one is a very efficient token factory, and then high utilization. That's it. There are two simple things you need to achieve. In order to have high utilization, you have to have multiple workloads that can be scheduled, even on the training. If you look at the AI pipelines, there's pre-training, mid-training, post-training, and 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说 OpenAI 可能计划在 2026 年底或 2027 年上市。

Okay. So, Sam, you referenced that Reuters was reporting yesterday that OpenAI may be planning to go public late 2026 or in 2027.

不不不，我们没有那么具体的计划。我是个现实主义者。我假设它总有一天会发生，但那是——我不知道为什么人们写这些报道。我们没有考虑具体日期，也没有做出任何决定。我只是假设事情最终会朝那个方向发展。

No, no, no. We don't have anything that specific. I'm a realist. I assume it will happen someday, but that was—I don't know why people write these reports. We don't have a date in mind or a decision to do this or anything like that. I just assume it's where things will eventually go.

但在我看来，如果你们在 2028 年或 2029 年营收超过 1000 亿美元，你们至少会有能力——

But it does seem to me if you guys were doing in excess of a hundred billion dollars of revenue in 2028 or 2029, you at least would be in position—

什么？

What?

那 2027 年呢？

How about 2027?

是的，2027 年更好。你们有能力进行 IPO，并且传闻中的万亿美元估值。再次为听众说明一下：如果你们以营收 1000 亿的 10 倍上市，这比 Facebook 上市时的倍数低，也比许多其他大型消费公司上市时的倍数低，那将使你们达到万亿美元。如果你们发行 10% 到 20% 的股份，就能筹集 1000 亿到 2000 亿美元，这似乎是为我们刚才讨论的增长和项目提供资金的好途径。所以你们并不反对。你们不是——但你们正在用营收增长来为公司提供资金，这也是我希望我们做的。

Yeah, 2027 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 billion in revenue, which would be 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 hundred to two hundred billion dollars, which seems like a good path to fund a lot of the growth and the stuff we just talked about. So you're not opposed to it. You're not—but you guys are funding the company with revenue growth, which is what I would like us to do.

毫无疑问。嗯，我也说过，我认为这是一家非常重要的公司，有很多人，包括我的孩子，喜欢用他们的小账户交易，他们使用 ChatGPT。我认为让散户投资者有机会购买最重要、最大的公司之一——

No doubt about it. Well, I've also said I think that this is such an important company, and there are so many people including my kids who like to trade their little accounts and they use ChatGPT. I think having retail investors have an opportunity to buy one of the most important and largest—

老实说，这可能是对我个人来说最吸引人的一点。那真的很好。

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

我和你们两位都谈过的一件事，换个话题，是那个宏伟法案的一部分。克鲁兹参议员曾加入了联邦优先权条款，这样我们就不会有 50 个州各自为政的法律，使行业陷入不必要的合规和监管泥潭。不幸的是，它在最后一刻被布莱克本参议员否决了，因为坦率地说，我认为华盛顿对 AI 的理解非常有限，而且很多末日论在华盛顿获得了关注。所以现在我们有了像科罗拉多州 AI 法案这样的州法律，据我所知该法案将于 2 月全面生效，这创造了一个全新的诉讼群体：任何声称因聊天机器人中的算法歧视而受到不公平影响的人。所以有人可能出于无数理由声称受到伤害。山姆，你有多担心这种各州各自为政的 AI 法规对我们继续加速和在全球竞争的能力构成真正挑战？

One of the things I've talked to you both about, shifting gears again, is part of the big beautiful bill. Senator Cruz had included federal preemption so that we wouldn't have this state patchwork of 50 different laws that mires the industry down in 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 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, which creates this whole new class of litigants: anybody who claims any unfair impact from algorithmic discrimination in a chatbot. So somebody could claim harm for countless reasons. Sam, how worried are you that having this state patchwork of AI poses real challenges to our ability to continue to accelerate and compete around the world?

我不知道我们该如何遵守那项加州——抱歉，科罗拉多州的法律。我希望他们能告诉我们，我们也希望能够做到，但根据我读到的内容，这就像——我真的不知道我们该做什么。我非常担心 50 个州各自为政的局面。我认为这是一个大错误。我认为我们通常不会为这类事情这样做是有原因的。我认为这会很糟糕。

I don't know how we're supposed to comply with that California—sorry, Colorado law. I would love them to tell us, and we'd like to be able to do it, but from what I've read of that, it's like—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. 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，我的意思是，我认为这种各自为政方法的根本问题是，坦率地说，在 OpenAI 和微软之间，我们会想办法应对，对吧？我们能解决这个问题。

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, right? We can figure this out.

问题在于，任何初创公司试图应对这种情况时，结果都与初衷完全相反。显然安全非常重要，要确保人们的基本关切得到解决。但有一种方式可以在联邦层面做到这一点。所以我认为如果美国不这样做，欧盟就会做，那会带来它自己的问题。因此我认为如果美国带头，会更好，作为一个统一的监管框架。

The problem is anyone starting a startup and trying to navigate this, it goes to the exact opposite of what the intent here is. 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. So I think if the US doesn't do this, the EU will, and that'll cause its own issues. So I think if the US leads, it's better, as one regulatory framework.

当然。

For sure.

需要明确的是，并不是主张完全没有监管。只是说让我们在联邦层面有统一的监管，而不是 50 个相互竞争的州法律，这无疑会摧毁 AI 初创行业。我认为即使对于像你们这样有能力应对所有这些诉讼的公司来说，这也极具挑战性。

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. And I think it makes it super challenging even for companies like yours who can afford to defend all these cases.

是的。我只想说，坦白讲，我希望这次，甚至欧盟和美国之间也能达成一致，那将是梦想，对吧？坦白讲，对任何欧洲初创公司来说都是如此。

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

我认为这不会发生。

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

什么不会发生？

What is that?

那会很棒。我不会……我不会对此抱太大希望。那会很棒。不，但我真的认为，如果你仔细想想，如果欧洲的任何人正在考虑他们的公司如何参与这个 AI 经济，这也应该成为他们那里的主要关切。因此，我希望会有一些开明的做法。但我同意你的看法，今天我不会押注于此。我确实认为，有了 Sachs 作为 AIS，你至少有一位总统，我认为他可能会为此努力，在 AI 政策协调方面，利用贸易作为杠杆，确保我们不会最终陷入过度限制的欧洲政策。但我们会看到的。我认为首要任务是，美国的联邦优先权非常关键。

That would be great. I don't... I wouldn't hold your breath for that one. That would be great. No, but I really think that if you think about it, if anyone in Europe is thinking about how they can 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. I do think that with Sachs as the AIS are, you at least have a president that I think might fight for that in terms of coordination of AI policy, using trade as a lever to make sure that 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.

你知道，我们刚才有点陷入细节了，Sam。所以我想把视野拉远一点。我听到你团队里的人谈到即将到来的所有伟大事物。当你开始思考更无限的算力、ChatGPT-6 及以后、机器人技术、物理设备、科学研究，展望 2026 年，你认为什么最让我们惊讶？在规划中的事情里，你最兴奋的是什么？

You know, we've been down in the weeds a little bit here, Sam. So, I want to telescope out a little bit. 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-6 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?

你刚才提到了很多关键点。我认为 Codex 今年是非常酷的事情。随着这些任务从几小时扩展到几天——我预计明年就会实现——人们将能够以前所未有的速度和全新的方式创建软件。我对此非常兴奋。我认为我们也会在其他行业看到这一点。我对编程有偏爱，我更理解那个领域。我认为这将真正开始改变人们的能力。我希望在 2026 年能看到非常小的科学发现。但如果我们能获得这些非常小的发现，未来几年我们就会获得更大的发现。说 AI 将在 2026 年做出新颖的科学发现，即使是非常小的一个，这听起来很疯狂。但这是一个极其重要的话题。所以我对此很兴奋。当然，还有机器人技术和未来几年新型计算机。那将非常重要。但就我个人偏好而言，如果我们真的能让 AI 做科学，那在某种意义上就是超级智能。如果这能扩展人类知识的总和，那将是一件极其重大的事情。

You just hit on a lot of the key points there. I think Codex 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 in fundamentally new ways. I'm very excited for that. I think we'll see that in other industries too. I have a bias towards coding. I understand that one better. 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, that AI is going to make a novel scientific discovery in 2026, even a very small one. This is a wildly important thing to be talking about. So, I'm excited for that. Certainly, robotics and new kinds of computers in future years. That'll be very important. But yeah, my personal bias is if we can really get AI to do science here, that is superintelligence in some sense. If this is expanding the total sum of human knowledge, that is a crazy big deal.

是的。我的意思是，我认为其中一件事，以你的 Codex 为例，我认为模型能力的结合……如果你想想 ChatGPT 发生的那个神奇时刻，是 UI 遇到了智能，然后一飞冲天，对吧？简直难以置信。合适的形态因素。其中一部分也是模型遵循指令的能力已经为聊天做好了准备。我认为这正是 Codex 和这些编码智能体将要帮助我们的。也就是说，编码智能体长时间运行，然后返回，然后我介入并指导它该怎么做。就像我们都在努力的一个比喻：我做宏观委托和微观指导。这个 UI 如何与这种新的智能能力结合？你可以在 Codex 中看到它的雏形，对吧？至少我在 GitHub Copilot 中使用它的方式是，它与聊天界面完全不同。我认为这将是人机交互的一种新方式。坦白讲，这可能比……

Yeah. I mean, I think one of the things, to use your Codex example, I think the combination of the model capability... I mean, if you think about the magical moment that happened with ChatGPT, it was the UI that met intelligence that just took off, right? It's just unbelievable. Right form factor. And 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 with. Which is, what's that? Coding agent goes off for a long period of time, comes back, and then I'm dropped into what I should steer. Like one of the metaphors I think we're all sort of working towards is I do this macro delegation and micro steering. 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 GitHub Copilot is, it's just a different way than the chat interface. And I think that that would be a new way for the human-computer interface. Quite frankly, it's probably bigger than...

那可能是一个转折点。

That might be the departure.

这就是为什么我非常兴奋我们正在开发新形态的计算设备，因为计算机并不是为那种工作流而设计的。当然，像聊天这样的 UI 不适合它。但你可以拥有一个始终伴随你的设备，能够去执行任务，在需要时获得你的微观指导，并对你的整个生活和流程有很好的上下文感知。我认为那会很酷。

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 chat 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 really good contextual awareness of your whole life and flow. And I think that'll be cool.

你们俩都没有谈到的是消费者用例。我经常想，我们埋头于这个设备，不得不在上百个应用程序中翻找，填写各种小网页表单，这些东西 20 年来几乎没有变化。但如果我们能拥有一个个人助理——也许我们觉得理所当然，实际上我们确实有个人助理——但将个人助理几乎免费地提供给全球数十亿人，改善他们的生活，无论是为他们的孩子订购尿布，还是预订酒店，或修改日历。我认为有时正是这些平凡的事情影响最大。随着我们从答案走向记忆和行动，然后通过耳机或其他不需要我 constantly 盯着这块矩形玻璃的设备与之交互的能力，我认为这非常了不起。

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 hundred different applications and fill out little web forms, things that really haven't changed in 20 years. But to just have 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 booking their hotel or making changes in their calendar. I think sometimes it's the pedestrian that's the most impactful. And 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. I think it's pretty extraordinary.

我想这就是 Sam 在暗示的。

I think that that's what Sam was teasing.

是的。是的。

Yeah. Yeah.

希望我们能做对。不幸的是，我得下线了。

Hope we get it right. I got to drop off unfortunately.

Sam，很高兴见到你。感谢你加入我们。再次祝贺你迈出这一大步，我们很快再聊。

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

谢谢你让我加入。

Thanks for letting me crash.

再见 Sam。保重。再见。

See you Sam. Take care. See you.

正如 Samwell 所知，我们当然是买家，而不是卖家。

As Samwell knows, we're certainly a buyer, not a seller.

但有时候，我觉得这很重要，因为世界上的其他人并没有花一万小时思考这些事情。他们看到一些看似过于雄心勃勃的事情，就会担心我们能否实现。你在 2019 年把这个想法带到董事会，投资 10 亿美元给 OpenAI。这在董事会上是理所当然的吗？你需要动用政治资本才能让它通过吗？跟我聊聊那个时刻是什么样的，因为我认为那不仅对微软、对这个国家，而且对世界来说都是一个关键时刻。

But sometimes, I think it's important because the world doesn't spend 10,000 hours thinking about this stuff. They look at some things that appear overly ambitious and get worried about whether we can pull them off. You took this idea to the board in 2019 to invest a billion dollars into OpenAI. Was it a no-brainer in the boardroom? Did you have to expend political capital to get it done? Dish for me a little bit about what that moment was like, because I think it was such a pivotal moment not just for Microsoft, not just for the country, but for the world.

是的，回顾这段历程很有意思。我们在 2016 年 OpenAI 刚起步时就参与了。事实上，Azure 是第一个赞助商，我记得。他们当时做了很多强化学习。我记得 Dota 2 比赛就是在 Azure 上进行的。后来他们转向了其他事情。我对强化学习很感兴趣，但老实说，这体现了有准备的头脑。微软从 1995 年起就对自然语言着迷。比尔对公司的执念就是自然语言。毕竟，我们是一家编码公司，一家信息工作公司。所以当 Sam 在 2019 年开始谈论文本、自然语言、Transformer 和缩放定律时，我就说：“哇，这很有趣。”这个团队的方向与我们的兴趣有了更多重叠。从这个意义上说，这是理所当然的。显然，你去董事会说：“嘿，我有一个想法，拿出 10 亿美元给这个疯狂的结构，我们甚至都不理解它是什么。它是一个非营利组织，等等等等，然后说放手去做。”当时有争论。比尔理所当然地持怀疑态度，但他在看了 GPT-4 演示后变得信服了。他说那是他自 Charles Simony 在 Xerox PARC 给他演示以来看到的最好的演示。但老实说，我们谁都不能……所以对我来说，那一刻就是，让我们试试看。然后看到 GitHub Copilot 中早期的 Codex，看到代码补全起作用了——那时我觉得我可以从 1 走到 10。那才是关键。1 是有争议的，但从 1 到 10 才真正让整个时代成为可能。然后显然是团队出色的执行以及他们和我们双方的产品化。如果我想想，GitHub Copilot、ChatGPT、Microsoft 365 Copilot 和 Copilot 这四个产品的集体变现能力——把它们加起来，那就是地球上最大的 AI 产品组合。这让我们能够维持这一切。

Yeah, it's interesting when you look back at the journey. We were involved even in 2016 when OpenAI initially started. In fact, Azure was the first sponsor, I think. They were doing a lot of reinforcement learning at that time. I remember the Dota 2 competition happened on Azure. Then they moved on to other things. I was interested in RL, but quite frankly, it speaks to the prepared mind. Microsoft since 1995 was obsessed with natural language. Bill's obsession for the company was natural language. After all, we're a coding company, an information work company. So when Sam in 2019 started talking about text, natural language, transformers, and scaling laws, that's when I said, 'Wow, this is interesting.' This team was going in a direction that had a lot more overlap with our interest. So in that sense, it was a no-brainer. Obviously, you 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 understand. It's a nonprofit, blah blah blah, and saying go for it.' There was a debate. Bill was rightfully skeptical, but then he became convinced after he saw the GPT-4 demo. He said it was the best demo he saw after what Charles Simony showed him at Xerox PARC. But honestly, none of us could... So the moment for me was, let's go give it a shot. Then seeing the early Codex inside GitHub Copilot and seeing the code completions work — that's when I felt I could go from 1 to 10. That was the big call. One was controversial, but the 1 to 10 was what really made this entire era possible. Then obviously the great execution by the team and the productization on their part and our part. If I think about it, the collective monetization reach of GitHub Copilot, ChatGPT, Microsoft 365 Copilot, and Copilot — add those four things, that's the biggest set of AI products out there on the planet. That has let us sustain all of this.

没有多少人知道你的 CTO Kevin Scott，一位前谷歌员工，就住在硅谷这里。背景是，微软错过了搜索，错过了移动。你成为 CEO 后，差点错过了云。你赶上了最后一班车抓住了云。我认为你非常决心在这里有耳目，以免错过下一件大事。所以我猜 Kevin 也为你发挥了很好的作用。

Not many people know that your CTO Kevin Scott, an ex-Googler, lives down here in Silicon Valley. To contextualize, Microsoft had missed out on search, missed out on mobile. You became CEO, almost missed out on the cloud. You caught the last train out of town to capture the cloud. 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 Kevin played a good role for you as well.

当然。事实上，我想说 Kevin 的信念——Kevin 也曾持怀疑态度，就像那样。我总是在观察那些持怀疑态度但后来改变看法的人，因为对我来说那是一个信号。所以我一直在寻找那些对某事不信任，然后突然改变并变得兴奋的人。我对这样的人很有耐心，因为我好奇原因和内容。所以 Kevin 一开始和我们一样持怀疑态度。在某种意义上，这违背了我们上学时学到的：一定有一种算法可以破解这个，而不是仅仅靠缩放定律和投入算力。但老实说，Kevin 认为这值得追求的信念是推动这一切的重要因素之一。

Absolutely. In fact, I would say Kevin's conviction — and Kevin was also skeptical, like that was the thing. I 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 gets excited about it. I have all the time for that because I'm curious why and what. So Kevin started with all of us being kind of skeptical. In some sense, it defies what we all learned in school: that there must be an algorithm to crack this, versus just scaling laws and throwing compute. But quite frankly, Kevin's conviction that this is worth going after is one of the big things that drove this.

我们谈到那笔投资现在价值 1300 亿美元，正如 Sam 所说，有一天可能价值一万亿美元，但这实际上低估了合作伙伴关系的价值。你有收入分成，每年数十亿美元流向微软。你有来自 OpenAI 的 2500 亿美元 Azure 算力承诺的利润。而且你从 API 的独家分销中获得了巨大的销售额。那么跟我们谈谈你如何看待这些领域的价值，尤其是这种排他性如何吸引了许多可能原本在 AWS 上的客户迁移到 Azure。

We talk about that investment that's now worth $130 billion, could be worth a trillion someday as Sam says, but it really understates the value of the partnership. You have the value in the revshare, billions per year going to Microsoft. You have the profit you make off the $250 billion Azure compute commitment from OpenAI. And you get huge sales from the exclusive distribution of the API. 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.

是的，当然。所以对我们来说，除了所有股权部分，真正具有战略意义且未来持续的是 Azure 上的无状态 API 排他性。这既帮助了 OpenAI，也帮助了我们和我们的客户。因为当企业中的某人试图构建一个应用程序时，他们想要一个无状态的 API。他们希望将其与计算和存储混合，在下面放一个数据库来捕获状态，并构建一个完整的工作负载。这就是 Azure 与这个 API 结合发挥作用的地方。我们甚至通过 Azure AI Foundry 所做的——因为在某种意义上，如果你想构建一个 AI 应用程序，关键是如何确保评估很好。所以这就是为什么你需要在 Foundry 中有一个完整的应用服务器。这就是我们所做的。所以我觉得这就是我们在基础设施业务中走向市场的方式。对我们来说，价值捕获的另一面是整合所有这些知识产权。我们不仅在 Azure 上拥有模型的排他性，而且我们还能访问知识产权。拥有免版税的使用权，甚至忘记专有技术和知识方面，但拥有长达七年的免版税使用权，给了我们在商业模式上很大的灵活性。在某种意义上，如果你是微软股东，这就像免费拥有一个前沿模型。

Yeah, absolutely. So to us, aside from all the equity parts, the real strategic thing that comes together and remains going forward is that stateless API exclusivity on Azure. That helps 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. That's where Azure coming together with this API works. And what we're doing with even Azure AI Foundry — because in some sense, if you want to build an AI application, the key thing is how do you make sure the evals are great. So that's where you need even a full app server in Foundry. That's what we've done. So I feel that is the way we will go to market in our infrastructure business. The other side of the value capture for us is incorporating all this IP. Not only do we have the exclusivity of the model in Azure, but we have access to the IP. Having royalty-free access, let's even forget the knowhow and knowledge side, but having royalty-free access all the way till seven more years gives us a lot of flexibility business model wise. It's kind of like having a frontier model for free, in some sense, if you're an MSFT shareholder.

你一直在合并 OpenAI 的亏损。我想你昨天刚发布了财报，本季度合并了 40 亿美元的亏损。你认为投资者是否——我的意思是，他们可能甚至因为亏损而给出负估值，你知道，当他们应用市盈率倍数时。Satya，而我听到这个时，想到的是我们刚才描述的所有那些好处，更不用说你在 OpenAI 中拥有的穿透股权价值，这家公司本身可能价值一万亿美元。你知道，你认为市场是否有点误解了 OpenAI 作为微软一部分的价值？

You've been consolidating the losses from OpenAI. I think you just reported earnings yesterday. I think you consolidated $4 billion of losses in the quarter. Do you think that investors are, I mean they may even be attributing negative value right because of the losses, you know, as 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?

嗯，这是个好问题。我认为 Amy 会采取完全透明的做法，因为在某种程度上我不是会计专家。所以最好的做法是这次也提供完全的透明度。我想这就是非 GAAP 差距的原因，这样至少人们能看到每股收益数字，因为我看待这件事的常识很简单，Brad。如果你投资了，比如说 135 亿美元，你当然可能损失 135 亿美元，但你不能损失超过 135 亿美元。至少我上次查的时候，这就是你面临的风险。你也可以说，嘿，那 1350 亿美元，你知道，今天我们的股权，有点缺乏流动性，我们不打算出售，所以它确实有风险。但我认为你真正想说的是所有其他正在发生的事情：Azure 的增长怎么样？如果没有 OpenAI 的合作，Azure 还会增长吗？正如你所说，第一次从其他云迁移过来的客户数量，对吧，这才是我们真正受益的地方。Microsoft 365 怎么样？事实上，关于 Microsoft 365 的一个问题是，E5 之后的下一个大事件是什么？你猜怎么着？我们在 Copilot 中找到了。它比任何套件都大。你知道，我们谈论渗透率、使用率和速度。它比我们在信息工作领域几十年来所做的任何事情都大。所以我们对于为股东创造价值的机会感到非常非常满意。同时保持完全透明，这样人们就能看穿亏损。我的意思是，谁知道会计规则是什么，但我们会做任何必要的事情，然后人们就能看到发生了什么。

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 the 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 $13.5 billion, you can of course lose $13.5 billion but you can't lose more than $13.5 billion. At least the last time I checked, that's what you have at risk. You could also say hey, the $135 billion that has, you know, today our equity stake, you know, is sort of illiquid, what have you, we don't plan to sell it, so therefore it's got risk associated with it. But the real story I think you were pulling is all the other things that are happening: what's happening with Azure growth? 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, right, this is the thing that really we benefited from. What's happening with Microsoft 365? In fact, one of the things about Microsoft 365 was what was the next big thing after E5? Guess what? We found it in Copilot. It's bigger than any suite. Like you know, we talk about penetration and usage and the pace. It's bigger than anything we've done in our information work which we've been adding for decades. And so we feel very, very good about the opportunity to create value for our shareholders. And then at the same time be fully transparent so that people can look through the 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.

一年前，Satya，有很多头条新闻说微软在缩减 AI 基础设施，对吧？公平与否，它们就在那里，你知道，也许你们当时有点保守，对正在发生的事情有点怀疑。但 Amy 昨晚在电话会议上说，你们已经多个季度面临电力和基础设施短缺，她以为你们会赶上，但你们没有赶上，因为需求一直在增长。所以我想问题是，你们是不是太保守了，你知道，以你们现在所知，以及接下来的路线图是什么？

A year ago, Satya, there were a bunch of headlines that Microsoft was pulling back on AI infrastructure, right? Fair or unfair, they 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 road map from here?

嗯，这是个好问题。因为你看，我们意识到并且很高兴我们做到了的一点是，构建一个真正可互换的集群的概念——在 AI 生命周期的所有部分可互换，跨地域可互换，跨代际可互换。对吧？因为关键之一是，就拿 Jensen 和他的团队正在做的事情来说，他们的节奏很快。事实上，我喜欢的一点是光速。对吧，我们现在有 GB300 正在上线。所以你不想订购了一堆 GB200 正在安装，却发现 GB300 已经全面投产。所以你必须确保持续现代化，把集群分散到各处，真正按工作负载可互换，再加上我们谈到的软件优化。所以对我来说，这就是我们做出的决定，我们说，看，有时你可能不得不对某些需求说不，包括一些 OpenAI 的需求，对吧？因为有时 Sam 可能会说，嘿，给我建一个专用的、大型的、多吉瓦的数据中心，放在一个地方用于训练。从 OpenAI 的角度看合理，但从 Azure 的长期基础设施扩建看不合理。而我认为他们做了正确的事情，给了他们灵活性，让他们可以从别处采购，同时保持与 OpenAI 的大量业务，但更重要的是，给了我们自己与其他客户以及我们自己的第一方业务的灵活性。记住，我们不想做的事情之一就是短缺，你知道，我们谈论 Azure。事实上，有时我们的投资者过于关注 Azure 的数字，但记住，对我来说，高利润业务是 Copilot，是 Security Copilot，是 GitHub Copilot，是 Healthcare Copilot。所以我们希望确保有一种平衡的方式来为投资者创造回报。这可能是投资者群体中另一个被误解的地方，我觉得这很奇怪也有趣，因为我认为他们持有微软是因为我们的投资组合。但天哪，他们却只盯着一个叫 Azure 的小东西的增长数字。

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 for all the parts of the life cycle of AI, fungible across geographies, and fungible across generations. Right? 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 bringing up, you know, that we're bringing up. So you don't want to have ordered a bunch of GB200s that are getting plugged in only to find that 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 you know Sam may say hey, build me a dedicated, you know, big, you know, whatever multi-gigawatt data center in one location for training. Makes sense from an OpenAI perspective, doesn't make sense from a long-term infrastructure buildout for Azure. And that's where I thought they 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, like one of the things that we don't want to do is be short on, you know, we talk about Azure. 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. 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.

关于这一点，Azure 本季度增长了 39%，年化收入高达 930 亿美元。你知道，我认为相比之下，GCP 增长了 32%，AWS 接近 20%。但是，Azure 是否——因为你把算力给了第一方业务，也给了研究，听起来如果 Azure 有更多算力可用，它本可以增长 41% 或 42%。

On that point, Azure grew 39% in the quarter on a staggering $93 billion 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 or 42% had you had more compute to offer.

绝对。绝对。毫无疑问。毫无疑问。所以这就是为什么我认为内部的事情是平衡我们认为符合股东长期利益的东西，同时也要服务好客户，并且不要，你知道，另一件事是，人们谈论集中度风险，对吧？我们显然想要很多 OpenAI 的业务，但我们也想要其他客户。所以我们在这里塑造需求。你知道，我们处于供应——我们不是需求受限，我们是供应受限。所以我们正在塑造需求，使其以最优方式匹配供应，并着眼于长期。

Absolutely. Absolutely. There's no question. There is 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 and also to serve our customers well and also not to kind of, you know, one of the other things was, you know, people talk about concentration risk, right? We obviously want a lot of OpenAI but we also want other customers. And so we're shaping the demand here. You know, we are in a supply, you know, 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.

说到这一点，Satcha，你提到了 4000 亿。这是一个惊人的剩余履约义务数字。昨晚你说，这是你今天已签约的业务，明天随着销售继续进来，它肯定会上升。你还说你需要建设产能来服务这个积压订单，需求非常高。这个积压订单有多多元化？你对这 4000 亿在未来几年内转化为收入有多大信心？

To that point, Satcha, you talked about 400 billion. It's an incredible number of remaining performance obligations. Last night, you said that's your booked business today. It'll surely go up tomorrow as sales continue to come in. And you said you need to build out capacity just to serve that backlog is very high. How diversified is that backlog? And how confident are you that that 400 billion does turn into revenue over the next couple of years?

是的，那 4000 亿的持续时间非常短，正如 Amy 解释的，平均是两年。所以这绝对是我们的意图。这也是我们为什么以高确定性投入资本支出的原因之一——我们只需要清理这个积压订单。而且，正如你所说，它在第一方和第三方之间相当多元化。坦率地说，我们自己的第一方需求相当高，甚至在第三方中，我们现在看到的一个趋势是其他公司都在构建真实的工作负载，并且正在 Scaling。因此，我认为我们感觉非常好。显然，RPO 的最大好处之一就是你可以进行规划。所以我们对建设感到非常非常乐观。这还不包括我们即将看到的额外需求，包括那 250，它的持续时间会更长，我们会相应地进行建设。

Yeah, that 400 billion has a very short duration, as Amy explained. It's a 2-year duration on average. So that's definitely our intent. That's one of the reasons why we're spending the capital outlay with high certainty that we just need to clear this backlog. And to your point, it's pretty diversified both on the 1P and the 3P. Our own demand is quite frankly pretty high for our 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. 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 planful, quite frankly. So we feel very, very good about building. And this doesn't include obviously the additional demand that we're already going to start seeing, including the 250, which will have a longer duration, and we'll build accordingly.

对，所以在这场建设算力的竞赛中有很多新进入者：Oracle、CoreWeave、Crusoe 等等。通常我们认为这会侵蚀利润率，但你却设法在 Azure 保持健康运营利润率的同时完成了所有这些建设。所以问题是，对微软来说，你如何在这个人们加杠杆、接受更低利润率的世界中竞争，同时平衡利润和风险？你有没有看到任何竞争对手在做一些让你挠头并说“哦，我们只是在为另一个繁荣-萧条周期做准备”的交易？

Right, so there are a lot of new entrants in this race to build out compute: Oracle, CoreWeave, Crusoe, etc. 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 the question is for Microsoft: how do you compete in this world where people are levering up, taking lower margins, while balancing that profit and risk? 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.'

我的意思是，从某种程度上说，我们的好消息是即使作为超大规模云服务商，我们每天都在竞争。我们和亚马逊、谷歌之间有很多竞争。有趣的是：一切都是商品——算力、存储。我记得每个人都说过，“哇，除了规模，怎么可能有利润率？”没有什么是商品。所以我们必须让我们的成本结构、供应链效率、软件效率持续复合增长，以确保规模化的利润率。而且，正如你所说，我非常喜欢 OpenAI 合作的一点是它让我们达到了规模。这是一场规模游戏。当你在你的云上运行最大的工作负载时，这不仅意味着我们能更快地学习如何规模化运营，还意味着你的成本结构会比任何其他方式下降得更快。猜怎么着？这会让我们在价格上具有竞争力。所以我对我们保持利润率的能力相当有信心。这就是投资组合发挥作用的地方。我一直说……

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, right, between us and Amazon and Google on all of these. It's one of those interesting things: everything is a commodity—compute, storage. I remember everybody saying, 'Wow, how can there be a margin except at scale?' Nothing is a commodity. So we have to have our cost structure, our supply chain efficiency, our software efficiencies all have to continue to compound in order to make sure that there are margins at scale. And to your point, one of the things that I really love about the OpenAI partnership is it's gotten us to scale. 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. And guess what? That'll make us price competitive. So I feel pretty confident about our ability to have margins. And this is where the portfolio helps. I've always said...

你知道，我被迫给出 Azure 的数字，因为在某种程度上我从未考虑过分配。我的意思是，我的资本配置是针对云的，无论是 Xbox 云游戏、Microsoft 365 还是 Azure——都是一笔资本支出。然后，从 MSF 的角度来看，一切都是按计量收费的。问题在于：这些的混合平均值应该匹配我们公司所需的运营利润率。因为毕竟，否则我们为什么不成为一个集团？我们是一家拥有单一平台逻辑的公司。我们不是在运营五六个不同的业务。我们涉足这五六个不同的业务，只是为了复合云和 AI 投资的回报。

You know, I've been forced into giving the Azure numbers, 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 365 or for Azure—it's one capital outlay. And then everything is a meter as far as I'm concerned from an MSF perspective. It's a question of: the blended average of that should match the operating margins we need as a company. Because after all, otherwise, why are we not a conglomerate? We're one company with one platform logic. It's not running five or six different businesses. We're in these five or six different businesses only to compound the returns on the cloud and AI investment.

是的，我喜欢那句话：没有什么是规模化的商品。有很多笔墨和时间被花在讨论循环收入上，包括在这个播客里我和我的合伙人 Bill Gurley 也讨论过，比如微软给 OpenAI 的信用额度被记为收入。你有没有看到像 AMD 的交易那样，他们用 10% 的股权换取交易，或者 Nvidia 的交易？我不想过度关注担忧，但我确实想正面回应每天在 CNBC 和 Bloomberg 上讨论的内容。有很多这种重叠的交易正在进行。当你从微软的角度考虑这个问题时，这些会不会让你担心我们所看到的 AI 收入的可持续性或持久性？

Yeah, I love that line: nothing is a commodity at scale. 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 credits to OpenAI that were booked as revenue. Do you see anything going on like the AMD deal, 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, but I do want to address head-on what is being talked about every day on CNBC and Bloomberg. There are a lot of these overlapping deals going on. When you think about that in the context of Microsoft, does any of that worry you as to the sustainability or durability of the AI revenues we see in the world?

是的。我的意思是，首先，我们的投资，比如说那 135 亿，全部是培训投资，没有记为收入。这就是我们拥有股权比例的原因。这就是我们拥有 27% 或 1350 亿的原因。所以那并不是以某种方式计入 Azure 收入的。事实上，如果说有什么不同的话，Azure 收入纯粹是 ChatGPT 和其他任何东西的消费收入，以及他们推出并变现的 API，我们也从中变现。至于其他方面，从某种程度上说，供应商融资一直存在。所以这并不是一个新概念，当有人正在构建某样东西，而他们有一个客户也在构建某样东西但需要融资时，就会发生。它采取了一些奇特的形式，显然需要投资界仔细审视。但话虽如此，供应商融资并不是一个新概念。有趣的是，我们不需要做任何这类事情。我的意思是，我们可能要么投资了 OpenAI，并因此获得了股权作为算力的回报，要么以极好的价格向他们出售算力，以便能够帮助他们启动。但其他人选择不同的方式。我认为循环性最终将由需求来检验，因为只要最终产出有需求，这一切就都能运转。到目前为止，情况确实如此。

Yeah. I mean, first of all, our investment of, let's say, that 13 and a half, which was all the training investment that was not booked as revenue. That is the reason why we have the equity percentage. That's the reason why we have the 27% or 135 billion. So that was not something that somehow 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 they put out that they monetized and we monetized. To your aspect of others, to some degree, it's always been there in terms of vendor financing. 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. Whether it is, it's sort of 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. I mean, we may have either invested in OpenAI and essentially got an equity stake in it in return for compute, or essentially sold them great pricing of compute in order to be able to sort of bootstrap them. 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。好吧，我想转换话题。正如你所说，你一半以上的业务是软件应用。我想谈谈软件和智能体。去年在这个播客上，你说大部分应用软件只是 CRUD 数据库之上的一层薄薄的层，这引起了一些轰动。商业应用存在的概念，在智能体时代，它们可能都会崩溃。

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 was this thin layer that sat on top of a CRUD database. The notion that business applications exist, that's probably where they'll all collapse in the agent era.

因为仔细想想，它们本质上就是一堆带有业务逻辑的众包数据库。业务逻辑全都转移到了这些智能体上。现在上市软件公司的交易价格大约是远期收入的 5.2 倍，低于 10 年平均的 7 倍，尽管市场处于历史高位。很多人担心 AI 可能会危及 SaaS 订阅和利润率。那么，如今 AI 如何影响你们软件产品（比如核心产品，以及数据库、Fabric、安全、Office 360）的增长率？第二个问题是，你们在做什么来确保软件不会被颠覆，而是被 AI 赋能？

Because if you think about it right, they are essentially crowd databases with a bunch of business logic. The business logic is all going to these agents. Public software companies are now trading at about 5.2 times forward revenue. So that's below their 10-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 those core products, and specifically as you think about database fabric security office 360? And then second question I guess is what are you doing to make sure that software is not disrupted but is instead superpowered by AI?

是的，我认为没错。上次我们讨论时，我的观点是 SaaS 应用的架构正在改变，因为智能体层正在取代旧的业务逻辑层。因为你想，过去我们构建 SaaS 应用的方式是数据层、逻辑层和 UI 紧密耦合。而 AI 坦率地说不尊重这种耦合，因为它要求你能够解耦。但上下文工程将变得非常重要。比如 Office 365。我喜欢 Microsoft 365 的一点是它的低 ARPU、高使用率，对吧？想想看，Outlook、Teams、SharePoint，你选 Word 或 Excel，人们一直在用，产生大量数据进入图谱，而我们的 ARPU 很低。这让我非常有信心，这个 AI 层可以通过暴露我所有的数据来满足它。事实上，GitHub 和 Microsoft 365 都发生了一件有趣的事：由于 AI，我们看到进入图谱或仓库的数据量达到了历史最高水平。

Yeah, I think that's right. So, the last time we talked about this, my point really 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 built SaaS applications in the past was you had the data, the logic tier, and 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. I mean take something like Office 365. One of the things I love about our Microsoft 365 offering is it's low ARPU, high usage, right? 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 what gives me real confidence that this AI tier with I can meet it by exposing all my data. In fact, one of the fascinating things that's happened with both GitHub and Microsoft 365 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，生成的代码越多，无论是 Codex 还是云端或其他地方，它们都去了哪里？GitHub。创建的 PowerPoint 越多，Excel 模型越多，所有这些工件和聊天对话——聊天对话就是新文档——都进入图谱，而这些正是 grounding 所需要的。

I mean think about it. The more code that gets generated, whether it is Codex or cloud 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, they're all going into the graph, and all that is needed again for grounding.

所以你把它变成一个前向索引，嵌入到向量中，基本上这些语义就是任何智能体请求的 grounding 基础。所以我认为下一代 SaaS 应用必须这样：如果你是高 ARPU、低使用率，那你就有点问题。但如果你正好相反，我们是低 ARPU、高使用率，我认为任何能够构建这种结构并利用 AI 作为加速器的人——因为你看 M365 Copilot 的价格，比我们卖的任何其他产品都高，但它的部署速度更快，使用率更高——所以我感觉非常好。或者编程，对吧？谁能想到？事实上，看看 GitHub。GitHub 在成立的头 15 年或 10 年里所做的事情，基本上在去年一年就完成了，只是因为编码不再是一种工具，它更像是工资的替代品，所以这是一种非常不同的商业模式，甚至需要重新思考技术栈和价值分配。

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 if you are the exact opposite, we are low ARPU, high usage, and I think that anyone who can structure that and then use this AI as an accelerant because I mean like if you look at the M365 Copilot price, it's higher than any other thing that we sell, and yet it's getting deployed faster and with more usage, and so I feel very good. Or coding, right? Who would have thought? In fact, take GitHub. What GitHub did in the first 15 years of its existence or 10 years of its existence was basically done in the last year just because coding is no longer a tool. It's more a substitute for wages, and so it's a very different type of business model even kind of thinking about the stack and where value gets distributed.

所以直到最近，云主要运行预编译软件。你不需要大量 GPU，大部分价值归于软件层、数据库、像 CRM 和 Excel 这样的应用。但未来似乎这些界面只有在智能的情况下才有价值，对吧？如果是预编译的，它们就有点笨。软件必须能够思考、行动和建议。这需要生产这些 token，处理不断变化的上下文。所以在那个世界里，似乎更多的价值将归于 AI 工厂——如果愿意这么说的话——归于 Jensen 以最低成本生产这些 token，归于模型，而智能体或软件未来获得的价值可能比过去少。那么，请你为我做一个强有力辩护。为什么这是错的？

So until very recently, clouds largely ran pre-compiled 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, right? If they're pre-compiled, they're kind of dumb. The software's got to be able to think and to act and to advise. And that requires the production of these tokens, 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 Jensen producing 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. Well, steelman for me. Why that's wrong?

是的。所以我认为要驱动 AI 的价值，有两件事是必要的。第一件是你刚才描述的 token 工厂。即使拆解 token 工厂，它也是硬件芯片系统，但关键在于用系统软件以最高效的方式运行它，实现所有可互换性和最大利用率。这就是超大规模云服务商的作用，对吧？什么是超大规模云服务商？就像大家说的，如果你想运营一个超大规模云，你可能会说很简单：买一堆服务器，连起来运行。但事实并非如此。如果那么简单，现在就不止三家超大规模云服务商了。所以超大规模云服务商是运行最大利用率和 token 工厂的 know-how。而且，它将是异构的。显然 Jensen 非常有竞争力，Lisa 也会加入，Hawk 会从 Broadcom 生产东西，我们都会自己做自己的。所以会是一个组合。你最终要运行一个异构集群，最大化 token 吞吐量和效率等等。这是第一项工作。第二项是我所说的智能体工厂。记住，现代世界的 SaaS 应用驱动的是业务成果。它知道如何最高效地使用 token 来创造商业价值。事实上，GitHub Copilot 就是一个很好的例子，对吧？想想看，GitHub Copilot 的自动模式是我们做过的最聪明的事情。它根据提示选择用哪个模型来完成代码补全或任务交接。这不是通过轮询方式选择的，而是基于反馈循环。你有评估、数据循环等等。所以新的 SaaS 应用，正如你所说，是智能应用，针对一组评估和结果进行了优化，知道如何最高效地利用 token 工厂的输出。有时延迟重要，有时性能重要，知道如何聪明地权衡这些，就是 SaaS 应用的价值所在。但总体而言，这次软件确实有真正的边际成本。云时代也有，CDROM 时代边际成本不高，云时代有，这次更多，因此商业模式必须调整，你必须分别为智能体工厂和 token 工厂进行优化。

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 hyperscaler's role is, right? What is a hyperscaler? 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 run it. It's not that right. I mean it was that simple then there would have been more than three hyperscalers by now. So the hyperscaler is the knowhow of running that max util and the token factories. And it's not and by the way it's going to be heterogeneous. Obviously Jensen's super competitive. Lisa is going to come, you know, Hawk's going to produce things from Broadcom. 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. 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, right? Which is, if you think about it, the auto mode of GitHub Copilot is the smartest thing we've done, right? So, it chooses based on the prompt which model to use for a code completion or a task handoff, right? That's what you do, and you do that not just by choosing in some round-robin fashion. You do it because of the feedback cycle. You have the eval, 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-off in a smart way is where the SaaS application value is. 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 you know CDROMs there wasn't much of a marginal cost, with the cloud there was, and this time around it's a lot more, 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.

你有一个大多数人不知道的大型搜索业务。但事实证明，这可能是世界历史上最赚钱的业务之一，因为人们在进行大量搜索，数十亿次搜索，而如果你是微软，完成一次搜索的成本只有几分之一美分。完成一次搜索的成本非常低。但如今使用聊天机器人时，类似的查询或提示堆栈看起来却不同。所以我想问题是：假设未来这两项业务的收入水平相似。聊天交互的单位经济性能否达到与搜索一样盈利的程度？

You have a big search business that most people don't know about. But it turns out 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. 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. So I guess the question is: assume similar levels of revenue in the future for those two businesses. Do you ever get to a point where that chat interaction has unit economics that are as profitable as search?

我认为这是一个很好的观点，因为搜索在广告单元和成本经济性方面非常神奇，因为索引是固定成本，你可以更有效地摊销它。而聊天，正如你所说，每次对话都需要消耗更多的 GPU 周期，无论是意图理解还是检索，所以经济性不同。这就是为什么早期聊天的经济模式主要是高级模式和订阅，即使在消费者端也是如此。所以我们尚未发现是智能体式商务还是其他什么作为广告单元，以及它将如何被裁定。但与此同时，事实上，我现在确实知道——实际上，我只在非常具体的导航查询中使用搜索。我以前说我经常用它进行商务查询，但现在这也转向了我的 Copilot。我看看 Edge 和 Bing 中的 Copilot 模式，或者现在 Copilot 正在融合。所以我认为是的，将会有一场重新裁定，就像我们讨论过的 SaaS 颠覆一样。我们正处于该类别消费者经济性开始发生变化的初期。

I think that's a great point because 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 that's why a lot of the early economics of chat have been the premium model and subscription, even on the consumer side. 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 specific navigational queries. I used to say I use it a lot for commerce, but that's also shifting to my copilot. I look at the copilot mode in Edge and Bing, or Copilot now they're blending in. So I think that yes, there is going to be a relitigation, just like we talked about the SaaS disruption. We're in the beginning of the cheese being a little moved in consumer economics of that category.

没错。考虑到这是推动互联网所有经济学的数万亿美元的东西，当你改变你和谷歌的搜索经济学，使其趋近于更像个人智能体、个人助理聊天的东西时，从为人类带来的总价值来看，这可能会大得多，但单位经济性——你不再只是为一次性固定索引做广告。

Right. And given that it's the multi-trillion dollar thing that's driven all the economics of the internet, 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 bigger in terms of the total value delivered to humanity, but the unit economics—you're not just advertising this one-time fixed index.

没错。我认为消费者端可能会更糟。是的，消费者类别，因为你在探讨我经常思考的一个问题：在这些颠覆中，你必须真正了解类别经济性——它是赢家通吃的吗？两者都很重要。消费者领域的问题始终是时间有限。所以如果我不做一件事，我就会做另一件事。如果你的货币化依赖于某种人类交互，特别是如果消费者端真的有智能体式的东西，那可能会不同。而在企业端，第一，它不是赢家通吃；第二，它对智能体式交互要友好得多。所以它不像按席位与按消耗的模式。现实是智能体就是新的席位。所以你可以认为企业货币化要清晰得多。消费者货币化，我认为，则更加模糊。

That's right. I think that the consumer could be worse. Yeah, the consumer category because you are pulling a thread on something that I think a lot about: what during these disruptions, you kind of have to have a real sense of where the category economics is—is it winner-take-all? And both matter. The problem in consumer space always is that there's finite amount of time. 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, 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. 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，我们最近看到了一波裁员潮，亚马逊本周宣布了一些大规模裁员。七大科技公司过去三年的就业增长微乎其微，尽管营收非常强劲。你知道，你的员工人数从 24 财年到 25 财年并没有增长，大约在 22.5 万左右。许多人将其归因于正常的瘦身，即从疫情中走出来后变得更高效，我认为这很有道理。但你认为部分原因是 AI 吗？你认为 AI 会成为净就业创造者吗？你认为这对微软的生产力是长期利好？在我看来，蛋糕变大了，但你可以更高效地完成所有这些事情，这意味着要么你的利润率扩大，要么你将这些利润再投资，从而更长时间地更快增长。我称之为利润率扩张的黄金时代。

You know, we've seen a spate of layoffs recently with Amazon announcing some big layoffs this week. The Mag 7 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. Many attribute this to normal getting fit, just getting more efficient coming out of COVID, and I think there's a lot of truth to that. 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.

我坚信生产力曲线确实会并且将会弯曲，因为我们将开始看到工作本身，特别是工作流程的变化。由于你手中这些工具的力量，你在任务层面将有更多的自主权来完成工作，我认为这将会成为现实。这就是为什么我认为即使在内部——例如，当你谈到我们的 token 分配时，我们希望确保微软的每一位员工，标配，都拥有最无限制的 Microsoft 365 和 GitHub Copilot，这样他们才能真正提高生产力。但还有另一件有趣的事，Brad：我们正在学习一种新的学习方式，对吧？那就是如何与智能体协作。这有点像当 Word、Excel、PowerPoint 首次出现在 Office 中时，我们学会了如何重新思考，比如说，我们如何做预测。我的意思是，想想看：在 80 年代，预测是通过内部备忘录和传真等方式进行的。然后突然有人说：“哦，这里有一个 Excel 电子表格。我们把它放在电子邮件里，发给大家。人们输入数字，就有了预测。”同样地，现在，任何规划、任何执行都从 AI 开始。你用 AI 进行研究。你用 AI 进行思考。你与同事分享等等。所以正在创造一种新的工件和一种新的工作流程。这就是业务流程变化的速度与 AI 能力相匹配的地方。生产力效率就来自这里。因此，能够掌握这一点的组织将成为最大的受益者，无论是在我们的行业，还是坦率地说，在现实世界中。

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 workflow in particular change. 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 365 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 there is a new way to even learn, right? Which is how to work with agents. So that's kind of like when the first Word, Excel, PowerPoint all showed up in Office, we kind of learned how to rethink, let's say, how we did a forecast. I mean, think about it: in the 80s, the forecasts were inter-office memos and faxes and what have you. And then suddenly somebody said, "Oh, here's an Excel spreadsheet. Let's put it in an email. 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. So there's a new artifact being created and a new workflow being created. And that is the rate of the pace of change of the business process that matches the capability of AI. That's where the productivity efficiencies come. And so organizations that can master that are going to be the biggest beneficiaries, whether it's in our industry or, quite frankly, in the real world.

那么微软是否从中受益？你知道，让我们想想几年后。按照目前的增长率，五年后会更快，但我们就说五年后吧，你的营收是现在的两倍。Satya，如果你的收入增长……你会增加多少员工？

And so is Microsoft benefiting from that? You know, so let's think about a couple 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 you grow revenue by...

就像现在最好的事情之一就是每天从微软员工那里看到的这些例子。

Like one of the best things right now is these examples that I'm hit with every day from the employees of Microsoft.

我们有一位负责网络运营的同事。想想看，为了我们在 Fairwater 刚建成的这个 2 吉瓦数据中心，我们铺设了多少光纤，还有 AI 等等，简直疯狂。而且这是实实在在的资产。我们在全球大概要跟 400 家不同的光纤运营商打交道。每次出问题，我们都要去处理所有这些 DevOps 流水线。那位负责人跟我说：‘我根本不可能招到足够的人手来做这些。就算我批了预算，也招不到这么多人。’所以她做了次优选择：自己建了一堆智能体，来自动化处理维护相关的 DevOps 流水线。这就是一个团队利用 AI 工具提升生产力的例子。

There was this person who leads our network operations. If you think about the amount of fiber we had to put for this 2-gigawatt data center we just built out in Fairwater, and the amount of fiber there, the AI and what have you, it's just crazy. And it turns out this is a real-world asset. There are, I think, 400 different fiber operators we're dealing with worldwide. Every time something happens, we are literally going and dealing with all these DevOps pipelines. The person who leads it basically said to me, 'There's no way I'll ever get the headcount to go do all this. Not to mention even if I approve the budget, I can't hire all these folks.' So she did the next best thing. 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 a team with AI tools being able to get more productivity.

所以回到你的问题，我会说我们会增加员工人数，但在我看来，新增的员工将比 AI 之前的员工拥有更大的杠杆效应。我认为这就是你首先看到的结构性调整。你称之为‘瘦身’，我更倾向于认为这是让每个人真正学会重新思考工作方式。关键是‘怎么做’，甚至不是‘做什么’。即使‘做什么’保持不变，‘怎么做’也必须重新学习。这个‘去学习’和‘再学习’的过程大概需要一年左右，之后员工增长才会带来最大杠杆。

So if your question is, I will say we will grow headcount, but 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? Which is one you called 'getting fit.' I think of it as more getting to a place where everybody is really learning how to rethink how they work. And it's the 'how,' not even the 'what.' Even if the 'what' remains constant, the '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, then the headcount growth will come with max leverage.

没错。我认为我们正处于经济生产力惊人增长的边缘。当我和您或 Michael Dell 交谈时，感觉大多数公司甚至还没进入第一局，可能只是第一局的第一个击球手，在重新设计工作流程以从这些智能体中获得最大杠杆方面。但未来两三年，很多收益肯定会开始显现。我当然是乐观派。我认为这一切将带来净就业增长。但对那些公司来说，它们将能够以比营收增长更慢的速度增加员工人数，从而提升利润。这就是公司的生产力收益。汇总起来，就是整个经济的生产力收益。然后我们会把这些消费者剩余投资到创造许多前所未有的事物上。

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, 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. 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.

100%。100%。即使在软件开发领域也是如此。我观察到的一点是，没人会说我们很难让更多软件工程师为社会做贡献，因为现实是，任何组织都有 IT 积压任务。所以问题是，所有这些软件智能体有望帮助我们解决 IT 积压问题，实现永续软件的梦想。这将成为现实。然后再想想软件的需求。所以我认为，正如你所说，知识工作发生的抽象层次将会改变。我们会适应这一点。工作和流程本身也会调整，甚至包括对这个行业产品的需求。

100%. 100%. Even in software development, right? 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 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, the levels of abstraction at which knowledge work happens will change. We will adjust to that. The work and the workflow will then adjust itself even in terms of the demand for the products of this industry.

最后我想谈谈美国的再工业化。我说过，如果把您和众多美国大型科技公司未来四五年投资的 4 万亿美元资本支出加起来，按通胀或 GDP 调整后，大约是曼哈顿计划的 10 倍。所以这对美国来说是一项巨大的工程。总统已将重新谈判贸易协定作为其政府的优先事项，现在看来我们有了数万亿美元。仅今天，韩国就承诺向美国投资 3500 亿美元。当你看到美国在电力生产、电网等方面的情况，以及再工业化的进展时，你觉得这一切进展如何？也许可以反思一下我们目前的处境，以及你对未来几年的乐观程度。

I'm going to end on this, which is really around the reindustrialization of America. I've said if you add up the $4 trillion of capex that you and these and so many of the big large US 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. 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 billion 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。我感到非常非常乐观。因为从某种意义上说，Brad Smith 跟我讲过威斯康星州一个数据中心周边的经济情况，非常有趣。大多数人认为，‘哦，数据中心，就是一个大仓库，全自动的。’很多确实如此。但首先，建设那个数据中心及其本地供应链，在某种意义上就是美国的再工业化，甚至早于亚利桑那州台积电工厂、美光在内存上的投资、英特尔晶圆厂等等。对吧？我们有很多东西要开始建设。这并不意味着我们不会与其他国家达成对美国有利的贸易协定。但正如你所说，新经济的再工业化，以及确保从电力往下所有技能和产能，我认为对我们非常重要。还有一点也很重要，Brad，我有机会跟特朗普总统以及卢特尼克部长等人谈过，那就是要认识到，我们作为美国的超大规模云服务商，也在全球投资。换句话说，美国是全球算力工厂或代币工厂的最大投资者。但我们不仅吸引外国资本来我国投资以实现再工业化，我们还在欧洲、亚洲、拉丁美洲和非洲等地通过资本投资，将美国最好的技术带给世界，让他们在此基础上创新并信任。我认为这两点对美国长期发展都是好兆头。

Yeah. No, I feel very, very optimistic. Because in some sense, Brad Smith was telling me about the economy around a Wisconsin data center. It's fascinating. Most people think, 'Oh, a data center, that's like one big warehouse and 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 is in some sense the reindustrialization 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. It doesn't mean we won't have trade deals that make sense for the United States with other countries. But to your point, the reindustrialization 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. And 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 Lutnik 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. But 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 really bode well for the United States long term.

感谢你的领导，Sam。你确实在 OpenAI 为美国引领潮流。我认为展望未来，你可以看到 4% 的 GDP 增长在地平线上。我们会遇到挑战，会有起伏。这更像是台阶，向上的台阶，而不是一条直线向右上。但我个人看到华盛顿和硅谷之间、大型科技公司与美国再工业化之间的协调程度，这让我感到无比希望。看到本周总统及其团队在亚洲的成果，再看看这里发生的事情，真是令人兴奋。

I'm grateful for your leadership, Sam. You're 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, you know, stairs up rather than a line straight up and to the right. 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.

感谢你抽出时间。我们是你的忠实粉丝。

So thanks for making the time. We're big fans.

谢谢。谢谢 Satcha。

Thanks. Thanks Satcha.

非常感谢 Brad。谢谢。提醒大家，这只是我们的观点，不构成投资建议。

Thanks so much Brad. Thank you. As a reminder to everybody, just our opinions, not investment advice.