2025-02-19 Satya Nadella.Microsoft’s AGI Plan & Quantum Breakthrough

Refer To：《Satya Nadella – Microsoft’s AGI Plan & Quantum Breakthrough》。

0:00:00 - Intro

Dwarkesh Patel

Satya, thank you so much for coming on the podcast.

In a second, we're going to get to the two breakthroughs that Microsoft has just made, and congratulations, same day in Nature: the Majorana zero chip, which we have in front of us right here, and also the world human action models. But can we just continue the conversation we were having a second ago? You're describing the ways in which the things you were seeing in the 80s and 90s, you're seeing them happen again.

萨蒂亚，非常感谢你来参加播客节目。

稍后我们将讨论微软刚刚取得的两个突破，并祝贺在《自然》杂志同一天发布：我们眼前的马约拉纳零芯片，以及全球人类行动模型。但能否继续我们刚才的对话？你在描述你在80年代和90年代看到的那些事情，现在又在发生。

Satya Nadella

The thing that is exciting for me... Dwarkesh, first of all, it's fantastic to be on your podcast. I'm a big listener, and I love the way that you do these interviews and the broad topics that you explore.

对我来说，最激动人心的事情是……Dwarkesh，首先，很高兴参加你的播客。我是你的忠实听众，非常喜欢你做这些访谈的方式，以及你所探讨的广泛话题。

The thing that is exciting for me… It reminds me a little bit of my, I'd say, first few years even in the tech industry, starting in the 90s, where there was real debate about whether it's going to be RISC or CISC, or, "Hey, are we really going to be able to build servers using x86?"

这件事让我感到兴奋……它让我有点想起我刚进入科技行业的那几年，开始于90年代，那时人们对RISC还是CISC的争论非常激烈，或者说，“嘿，我们真的能用x86来构建服务器吗？”

When I joined Microsoft, that was the beginning of what was Windows NT. So, everything from the core silicon platform to the operating system to the app tier- that full stack approach- the entire thing is being litigated.

当我加入微软时，那是Windows NT的起点。所以，从核心硅平台到操作系统再到应用层——这种全栈方法——整个过程都在进行辩论。

You could say cloud did a bunch of that, and obviously distributed computing and cloud did change client-server. The web changed massively. But this does feel a little more like maybe more full-stack than even the past that at least I've been involved in.

你可以说云计算做了一些这方面的事情，显然分布式计算和云计算改变了客户端-服务器架构。网络也发生了巨大的变化。但这确实感觉有点像，可能比我曾经参与的过去更加全栈。

Dwarkesh Patel

When you think about which decisions ended up being the long-term winners in the 80s and 90s, and which ones didn't, and especially when you think about- you were at Sun Microsystems, they had an interesting experience with the 90s dotcom bubble. People talk about this data center build-out as being a bubble, but at the same time, we have the Internet today as a result of what was built out then.

当你回想80年代和90年代哪些决策最终成了长期赢家，哪些没有，尤其是你在Sun Microsystems工作过，他们在90年代的互联网泡沫中经历了有趣的故事。人们谈论数据中心的建设是一个泡沫，但同时，我们今天的互联网正是建立在当时的那些基础设施之上的。

What are the lessons about what will stand the test of time? What is an inherent secular trend? What is just ephemeral?

什么是能够经得起时间考验的教训？什么是内在的长期趋势？什么只是短暂的？

Satya Nadella

If I go back, the four big transformations that I've been part of, the client and the client-server. So that's the birth of the graphical user interface and the x86 architecture, basically allowing us to build servers.

如果回顾过去，我参与的四个重大转型，其中包括客户端和客户端-服务器架构。所以那是图形用户界面和x86架构的诞生，基本上使我们能够构建服务器。

It was very clear to me. I remember going to what is PDC in '91, in fact I was at Sun at that time. In '91, I went to Moscone. That's when Microsoft first described the Win32 interface and it was pretty clear to me what was going to happen, where the server was also going to be an x86 thing. When you have the scale advantages accruing to something, that's the secular bet you have to place. What happened in the client was going to happen on the server side, and then you were able to actually build client-server applications. So, the app model became clear.

我很清楚。记得1991年我去参加PDC时，实际上那时我在Sun工作。在1991年，我去了Moscone。那时微软首次描述了Win32接口，我很清楚接下来会发生什么，服务器也将是基于x86架构的。当某件事获得了规模优势，那就是你必须下注的长期趋势。客户端上发生的事将在服务器端发生，随后你就能真正构建客户端-服务器应用程序。所以，应用模型变得清晰了。

Then the web was the big thing for us, which we had to deal with in starting, in fact as soon as I joined Microsoft, the Netscape browser or the Mosaic browser came out what, I think, December or November of '93, right? I think is when Andreessen and crew had that.

然后，互联网成了我们的关键问题，我们不得不面对它。事实上，几乎在我加入微软的同时，Netscape浏览器或者Mosaic浏览器在1993年的12月或11月发布，对吧？我想就是那时候，Andreessen和他的团队发布了那个浏览器。

So that was a big game-changer, in an interesting way, just as we were getting going on what was the client-server wave, and it was clear that we were going to win it as well. We had the browser moment, and so we had to adjust. And we did a pretty good job of adjusting to it because the browser was a new app model.

所以那是一个巨大的游戏改变者，以一种有趣的方式，就在我们刚刚开始进入客户端-服务器浪潮时，这一变化显然对我们有利。我们迎来了浏览器时刻，因此我们不得不做出调整。而且我们在调整方面做得相当不错，因为浏览器是一个新的应用模型。

We were able to embrace it with everything we did, whether it was HTML in Word or building a new thing called the browser ourselves and competing for it, and then building a web server on our server stack and go after it. Except, of course, we missed what turned out to be the biggest business model on the web, because we all assumed the web is all about being distributed, who would have thought that search would be the biggest winner in organizing the web? And so that's where we obviously didn't see it, and Google saw it and executed super well.

我们能够通过我们所做的一切来拥抱它，无论是Word中的HTML，还是自己构建一个叫做浏览器的新东西并与之竞争，然后在我们的服务器堆栈上构建一个Web服务器并追赶它。当然，除了我们错过了最终成为网络最大商业模式的事情，因为我们都认为互联网的核心是分布式的，谁能想到搜索会成为整理网络的最大赢家？所以显然我们没有看到这一点，而谷歌看到了，并执行得非常出色。

So that's one lesson learned for me: you have to not only get the tech trend right, you also have to get where the value is going to be created with that trend. These business model shifts are probably tougher than even the tech trend changes.

这对我来说是一个教训：你不仅要把技术趋势弄对，还要搞清楚这个趋势将在哪里创造价值。这些商业模式的转变可能比技术趋势的变化还要困难。

0:05:04 - AI won't be winner-take-all

Dwarkesh Patel

Where is the value going to be created in AI?

人工智能的价值将创造在哪里？

Satya Nadella

That's a great one. So I think there are two places where I can say with some confidence. One is the hyperscalers that do well, because the fundamental thing is if you sort of go back to even how Sam and others describe it, if intelligence is log of compute, whoever can do lots of compute is a big winner.

这是一个很好的问题。所以我认为有两个地方，我可以比较有信心地说。一个是做得好的超大规模公司，因为基本的事实是，如果你回到Sam和其他人描述的方式，如果智能是计算量的对数，那么能够进行大量计算的就是大赢家。

The other interesting thing is, if you look at underneath even any AI workload, like take ChatGPT, it's not like everybody's excited about what's happening on the GPU side, it's great. In fact, I think of my fleet even as a ratio of the AI accelerator to storage, to compute. And at scale, you've got to grow it.

另一个有趣的事情是，如果你看看任何人工智能工作负载的底层，比如以ChatGPT为例，并不是每个人都对GPU方面的变化感到兴奋，它的确很棒。事实上，我将我的硬件群体视作AI加速器、存储和计算的比率。而且在规模化的情况下，你必须要增长它。

Dwarkesh Patel

Yeah.

是的。

Satya Nadella

And so, that infrastructure need for the world is just going to be exponentially growing.

所以，世界对这种基础设施的需求将会呈指数级增长。

Dwarkesh Patel

Right.

没错。

Satya Nadella

So in fact it's manna from heaven to have these AI workloads because guess what? They're more hungry for more compute, not just for training, but we now know, for test time. When you think of an AI agent, it turns out the AI agent is going to exponentially increase compute usage because you're not even bound by just one human invoking a program. It's one human invoking programs that invoke lots more programs. That's going to create massive, massive demand and scale for compute infrastructure. So our hyperscale business, Azure business, and other hyperscalers, I think that’s a big thing.

事实上，拥有这些AI工作负载就像是天赐之福，因为猜猜看？它们对计算的需求更大，不仅仅是训练过程，实际上我们现在知道，它们在测试时也需要大量计算。当你考虑人工智能代理时，事实证明，人工智能代理将会呈指数级地增加计算的使用量，因为你不再仅仅依赖一个人调用程序，而是一个人调用多个程序，这将带来巨大的计算基础设施需求和规模。所以我们的超大规模业务、Azure业务和其他超大规模公司，我认为这是一个重要因素。

Then after that, it becomes a little fuzzy. You could say, hey, there is a winner-take-all model- I just don't see it. This, by the way, is the other thing I’ve learned: being very good at understanding what are winner-take-all markets and what are not winner-take-all markets is, in some sense, everything. I remember even in the early days when I was getting into Azure, Amazon had a very significant lead and people would come to me, and investors would come to me, and say, "Oh, it's game over. You'll never make it. Amazon, it's winner-take-all."

之后，情况变得有点模糊。你可以说，嘿，这是一个赢家通吃的模式——但我看不见这种模式。顺便说一下，这是我学到的另一件事：非常善于理解哪些市场是赢家通吃，哪些不是赢家通吃的市场，在某种意义上，这就是一切。我记得在我刚进入Azure的早期，亚马逊领先非常显著，很多人都来找我，投资者也来找我，说：“哦，游戏结束了，你们永远做不成。亚马逊是赢家通吃。”

Having competed against Oracle and IBM in client-server, I knew that the buyers will not tolerate winner-take-all. Structurally, hyperscale will never be a winner-take-all because buyers are smart.

在与甲骨文和IBM的客户端-服务器竞争中，我知道买家是不能容忍赢家通吃的。从结构上讲，超大规模公司永远不会是赢家通吃的，因为买家是聪明的。

Consumer markets sometimes can be winner-take-all, but anything where the buyer is a corporation, an enterprise, an IT department, they will want multiple suppliers. And so you got to be one of the multiple suppliers.

消费者市场有时可能是赢家通吃，但任何买家是企业、公司或IT部门的市场，他们会希望有多个供应商。所以你必须成为其中一个供应商。

That, I think, is what will happen even on the model side. There will be open-source. There will be a governor. Just like on Windows, one of the big lessons learned for me was, if you have a closed-source operating system, there will be a complement to it, which will be open source.

我认为这也会发生在模型领域。会有开源，会有监管机构。就像Windows一样，对我来说一个重要的教训是，如果你有一个闭源操作系统，就会有一个与之互补的开源操作系统。

And so to some degree that's a real check on what happens. I think in models there is one dimension of, maybe there will be a few closed source, but there will definitely be an open source alternative, and the open-source alternative will actually make sure that the closed-source, winner-take-all is mitigated.

所以在某种程度上，这是对发生事情的一种真正制衡。我认为在模型方面，有一个维度，也许会有几个闭源模型，但一定会有一个开源的替代品，开源替代品实际上会确保闭源的赢家通吃现象得到缓解。

That's my feeling on the model side. And by the way, let's not discount if this thing is really as powerful as people make it out to be, the state is not going to sit around and wait for private companies to go around and… all over the world. So, I don't see it as a winner-take-all.

这是我对模型领域的看法。顺便说一下，假设这个技术真的像人们所说的那样强大，国家不会坐视不理，等待私人公司四处扩张……遍布全球。所以，我不认为这会是赢家通吃的情况。

Then above that, I think it's going to be the same old stuff, which is in consumer, in some categories, there may be some winner-take-all network effect. After all, ChatGPT is a great example.

然后在此之上，我认为将会是老一套的东西，就是在消费者市场，在某些类别中，可能会出现一些赢家通吃的网络效应。毕竟，ChatGPT就是一个很好的例子。

It's an at-scale consumer property that has already got real escape velocity. I go to the App Store, and I see it's always there in the top five, and I say “wow, that's pretty unbelievable”.

这是一个具有规模的消费者属性，已经获得了真正的逃逸速度。我去应用商店，看到它总是在前五名，我说“哇，真是不可思议”。

So they were able to use that early advantage and parlay that into an app advantage. In consumer, that could happen. In the enterprise again, I think there will be, by category, different winners. That's sort of at least how I analyze it.

因此，他们能够利用这一早期的优势，将其转化为应用程序优势。在消费者市场，这种情况可能会发生。而在企业领域，我认为每个类别会有不同的赢家。这至少是我对它的分析。

非常合理的推理。

Dwarkesh Patel

I have so many follow-up questions. We have to get to quantum in just a second, but on the idea that maybe the models get commoditized: maybe somebody could have made a similar argument a couple of decades ago about the cloud – that fundamentally, it's just a chip and a box.

我有很多后续问题。我们一会儿得谈量子，但关于模型可能会商品化的想法：也许在几年前，有人可以提出类似的论点，关于云计算——从根本上讲，它只是一块芯片和一个机箱。

But in the end, of course, you and many others figured out how to get amazing profit margins in the cloud. You figured out ways to get economies of scale and add other value. Fundamentally, even forgetting the jargon, if you've got AGI and it's helping you make better AIs – right now, it's synthetic data and RL; maybe in the future, it's an automated AI researcher – that seems like a good way to entrench your advantage there. I'm curious what you make of that, just the idea that it really matters to be ahead there.

但最终，当然，你和许多人弄明白了如何在云计算中获得惊人的利润率。你们想出了方法来获得规模经济并增加其他价值。从根本上说，抛开术语不谈，如果你拥有AGI，并且它能帮助你制造更好的AI——现在是合成数据和强化学习；也许未来是一个自动化的AI研究员——这似乎是一个巩固你优势的好方法。我很想知道你对这个的看法，仅仅是这个想法，认为提前一步非常重要。

Satya Nadella

At scale, nothing is commodity. To your point about cloud, everybody would say, "Oh, cloud's a commodity." Except, when you scale... That's why the know-how of running a hyperscaler... You could say, "Oh, what the heck? I can just rack and stack servers."

在规模化的情况下，没有什么是商品化的。关于云计算的观点，每个人都会说：“哦，云计算是商品化的。”但当你规模化时……这就是运行超大规模计算的技术诀窍……你可以说：“哦，算了，我可以简单地堆叠服务器。”

Dwarkesh Patel

Right.

没错。

Satya Nadella

In fact, in the early days of hyperscale, most people thought “there are all these hosters, and those are not great businesses. Will there be anything? Is there a business even in hyperscale?” And it turns out there is a real business, just because of the know-how of running, in the case of Azure, the world's computing of 60-plus regions with all the compute. It's just a tough thing to duplicate.

事实上，在超大规模计算的早期，大多数人认为“这些主机商并不是很好的生意。那会有什么吗？超大规模计算真的有生意吗？”事实证明，的确有一个真正的生意，正是因为在Azure的情况下，能够管理全球60多个区域的计算资源。仅凭这一点，是很难复制的。

So I was more making the point, is it one winner? Is it a winner-take-all or not? Because that you've got to get right. I like to enter categories which are big TAMs, where you don't have to have the risk of it all being winner-take-all. The best news to be in is a big market that can accommodate a couple of winners, and you're one of them.

所以我更想强调的是，这是否会是一个赢家通吃？是否真的会是赢家通吃？因为这个必须弄清楚。我喜欢进入那些大的市场类别（TAM），在这些市场中，你不需要担心会是赢家通吃。最好的情况是，市场足够大，能够容纳几个赢家，而你是其中之一。

That's what I meant by the hyperscale layer. In the model layer, one is models need ultimately to run on some hyperscale compute. So that nexus, I feel, is going to be there forever. It's not just the model; the model needs state, that means it needs storage, and it needs regular compute for running these agents and the agent environments.

这就是我所说的超大规模层。在模型层面上，一个是模型最终需要在某些超大规模计算上运行。所以，我觉得这个纽带将永远存在。它不仅仅是模型；模型需要状态，这意味着它需要存储，并且需要常规计算来运行这些代理和代理环境。

And so that's how I think about why the limit of one person running away with one model and building it all may not happen.

所以，这就是我对为什么一个人通过一个模型把所有事情做完的限制可能不会发生的看法。

Dwarkesh Patel

On the hyperscaler side, and by the way, it's also interesting the advantage you as a hyperscaler would have in the sense that, especially with inference time scaling and if that's involved in training future models, you can amortize your data centers and GPUs, not only for the training, but then use them again for inference.

关于超大规模计算方面，顺便提一下，作为一个超大规模计算商，你的优势也很有趣，特别是在推理时间的扩展方面，如果这与未来模型的训练有关，你可以摊销你的数据中心和GPU，不仅用于训练，还可以再次用于推理。

I'm curious what kind of hyperscaler you consider Microsoft and Azure to be. Is it on the pre-training side? Is it on providing the O3-type inference? Or are you just, we’re going to host and deploy any single model that's out there in the market, and we are sort of agnostic about that?

我很好奇你如何看待微软和Azure是什么类型的超大规模计算商。它是在预训练方面吗？还是提供O3型推理？还是你们只是要托管和部署市场上任何单一的模型，我们对这些持中立态度？

Satya Nadella

It’s a good point. The way we want to build out the fleet is [to], in some sense ride Moore's law. I think this will be like what we've done with everything else in the past: every year keep refreshing the fleet, you depreciate it over whatever the lifetime value of these things are, and then get very very good at the placement of the fleet such that you can run different jobs at it with high utilization. Sometimes there are very big training jobs that need to have highly concentrated peak flops that are provisioned to it that also need to cohere. That's great. We should have enough data center footprint to be able to give that.

这是一个很好的问题。我们想要构建硬件群体的方式是，在某种意义上顺应摩尔定律。我认为这将像我们过去做的其他一切一样：每年不断刷新硬件群体，你根据这些设备的生命周期价值来折旧它们，然后在硬件群体部署方面非常精通，这样你就可以高效地运行不同的任务。有时需要非常大的训练任务，需要高度集中的峰值浮点运算，并且这些运算需要得到正确的配置。这很好，我们应该有足够的数据中心容量来提供这些资源。

But at the end of the day, these are all becoming so big, even in terms of if you take pre-training scale, if it needs to keep going, even pre-training scale at some point has to cross data center boundaries. It's all more or less there.

但归根结底，这些都变得如此庞大，甚至在预训练的规模方面，如果它需要继续增长，最终预训练的规模必须跨越数据中心的边界。基本上已经是这样的。

So, great, once you start crossing pre-training data center boundaries, is it that different than anything else? The way I think about it is hey, distributed computing will remain distributed, so go build out your fleet such that it's ready for large training jobs, it's ready for test-time compute, it’s ready- in fact, if this RL thing that might happens, you build one large model, and then after that, there’s tons of RL going on. To me, it's kind of like more training flops, because you want to create these highly specialized, distilled models for different tasks.

所以很好，一旦你开始跨越预训练数据中心的边界，这与其他任何事情有何不同？我认为，分布式计算将始终保持分布式，因此，你需要构建硬件群体，使其能够应对大型训练任务，能够进行测试时间计算，能够适应——实际上，如果这种强化学习（RL）发生，你会先构建一个大型模型，然后进行大量的强化学习。对我来说，这有点像更多的训练浮点运算，因为你希望为不同任务创建这些高度专业化的、提炼过的模型。

So you want that fleet, and then the serving needs. At the end of the day, speed of light is speed of light, so you can't have one data center in Texas and say, "I'm going to serve the world from there."

所以你需要这个硬件群体，以及随之而来的服务需求。归根结底，光速就是光速，因此你不能在德克萨斯州拥有一个数据中心，然后说：“我打算从这里为全世界提供服务。”

You've got to serve the world based on having an inference fleet everywhere in the world. That's how I think of our build-out of a true hyperscale fleet.

你必须根据在全球范围内拥有推理硬件群体来为全球提供服务。这就是我对我们构建真正超大规模硬件群体的看法。

Oh, and by the way, I want my storage and compute also close to all of these things, because it's not just AI accelerators that are stateless. My training data itself needs storage, and then I want to be able to multiplex multiple training jobs, I want to be able to then have memory, I want to be able to have these environments in which these agents can go execute programs. That's kind of how I think about it.

哦，顺便说一下，我还希望将存储和计算与这些资源靠得很近，因为不仅仅是无状态的AI加速器。我的训练数据本身也需要存储，然后我希望能够复用多个训练任务，我希望能够有内存，能够提供这些环境，供这些代理执行程序。这就是我对它的看法。

0:15:18 - World economy growing by 10%

Dwarkesh Patel

You recently reported that your yearly revenue from AI is $13 billion. But if you look at your year-on-year growth on that, in like four years, it'll be 10x that. You'll have $130 billion in revenue from AI, if the trend continues. If it does, what do you anticipate doing with all that intelligence, this industrial scale use?

你最近报告了你们从AI获得的年收入为130亿美元。但是，如果你看一下你们的年增长率，像四年后，它将是现在的10倍。如果趋势继续下去，你们的AI收入将达到1300亿美元。如果真是如此，你预计如何使用这些智能技术，这种工业规模的应用？

Is it going to be through Office? Is it going to be you deploying it for others to host? You've got to have the AGIs to have $130 billion in revenue? What does it look like?

是通过Office吗？还是你们将为别人部署它并托管？你们必须拥有AGI才能实现1300亿美元的收入吗？这看起来会是怎样的？

Satya Nadella

The way I come at it, Dwarkesh, it's a great question because at some level, if you're going to have this explosion, abundance, whatever, commodity of intelligence available, the first thing we have to observe is GDP growth.

从我来看，Dwarkesh，这是一个很好的问题，因为从某种程度上讲，如果你要迎来这种爆炸性增长、丰盈或者任何形式的智能商品化，首先我们必须观察的是GDP增长。

Before I get to what Microsoft's revenue will look like, there's only one governor in all of this. This is where we get a little bit ahead of ourselves with all this AGI hype. Remember the developed world, which is what? 2% growth and if you adjust for inflation it’s zero?

在我讲微软收入会是什么样子之前，这其中只有一个控制因素。就是我们在所有关于AGI的炒作中有时会走得有点太快。记得发达世界的增长是多少吗？2%的增长，如果考虑到通货膨胀，实际上是零增长？

So in 2025, as we sit here, I'm not an economist, at least I look at it and say we have a real growth challenge. So, the first thing that we all have to do is, when we say this is like the Industrial Revolution, let's have that Industrial Revolution type of growth.

所以在2025年，当我们坐在这里时，我不是经济学家，但至少我看到了我们面临真正的增长挑战。所以，我们首先要做的是，当我们说这就像工业革命时，我们要实现那种工业革命式的增长。

That means to me, 10%, 7%, developed world, inflation-adjusted, growing at 5%. That's the real marker. It can't just be supply-side.

这对我来说意味着，10%、7%、发达世界、考虑通货膨胀后的增长率为5%。这是一个真正的标尺。不能只是供给端。

In fact that’s the thing, a lot of people are writing about it, and I'm glad they are, which is the big winners here are not going to be tech companies. The winners are going to be the broader industry that uses this commodity that, by the way, is abundant. Suddenly productivity goes up and the economy is growing at a faster rate. When that happens, we'll be fine as an industry.

事实上，这正是问题所在，很多人都在写这个话题，我很高兴他们这么做，即这里的大赢家将不会是科技公司。赢家将是更广泛的行业，这些行业使用的这种商品，顺便说一句，它是丰富的。突然间，生产率上升，经济以更快的速度增长。当这种情况发生时，作为一个行业，我们会做得很好。

But that's to me the moment. Us self-claiming some AGI milestone, that's just nonsensical benchmark hacking to me. The real benchmark is: the world growing at 10%.

但对我来说，这才是关键时刻。我们自称达到某个AGI里程碑，这对我来说只是没有意义的基准操作。真正的基准是：世界以10%的速度增长。

Dwarkesh Patel

Okay, so if the world grew at 10%, the world economy is $100 trillion or something, if the world grew at 10%, that's like an extra $10 trillion in value produced every single year. If that is the case, you as a hyperscaler... It seems like $80 billion is a lot of money. Shouldn't you be doing like $800 billion?

好吧，所以如果世界经济增长10%，世界经济是100万亿美元左右，世界增长10%，那意味着每年额外创造10万亿美元的价值。如果是这样的话，你作为一个超大规模计算商... 看起来80亿美元已经很多了，难道你们不应该做出大约800亿美元的收入吗？

If you really think in a couple of years, we could be really growing the world economy at this rate, and the key bottleneck would be: do you have the compute necessary to deploy these AIs to do all this work?

如果你真的认为在几年内，我们可能会以这种速度推动世界经济增长，而关键的瓶颈将是：你们是否拥有足够的计算能力来部署这些AI，完成所有这些工作？

Satya Nadella

That is correct. But by the way, the classic supply side is, "Hey, let me build it and they’ll come." That's an argument, and after all we've done that, we've taken enough risk to go do it.

这是正确的。但顺便提一下，经典的供给方观点是，“嘿，让我建好它，他们就会来。”这是一个论点，毕竟我们已经做到了，我们冒了足够的风险去做这件事。

But at some point, the supply and demand have to map. That's why I'm tracking both sides of it. You can go off the rails completely when you are hyping yourself with the supply-side, versus really understanding how to translate that into real value to customers.

但在某些时候，供需必须匹配。这就是为什么我在关注两方面的问题。当你在单纯吹嘘供给方时，你可能会完全脱轨，而真正理解如何将这些转化为对客户的实际价值才是关键。

That's why I look at my inference revenue. That's one of the reasons why even the disclosure on the inference revenue... It's interesting that not many people are talking about their real revenue, but to me, that is important as a governor for how you think about it.

这就是为什么我看重我的推理收入。即使是关于推理收入的披露也是其中的一个原因……有趣的是，许多人并没有谈论他们的实际收入，但对我来说，这作为一个控制因素，非常重要。

You're not going to say they have to symmetrically meet at any given point in time, but you need to have existence proof that you are able to parlay yesterday's, let’s call it capital, into today's demand, so that then you can again invest, maybe exponentially even, knowing that you're not going to be completely rate mismatched.

你不能说它们必须在任何给定的时刻对称地匹配，但你需要有实际证明，证明你能够将昨天的资本转化为今天的需求，这样你才能再次投资，也许是指数级的，知道你不会完全错配速度。

务实的做法。

Dwarkesh Patel

I wonder if there's a contradiction in these two different viewpoints, because one of the things you've done wonderfully is make these early bets. You invested in OpenAI in 2019, even before there was Copilot and any applications.

我想知道这两种不同观点之间是否存在矛盾，因为你做得非常出色的一件事是做出这些早期的投资。你在2019年投资了OpenAI，甚至在Copilot和任何应用程序出现之前就已经投资了。

If you look at the Industrial Revolution, these 6%, 10% build-outs of railways and whatever things, many of those were not like, "We've got revenue from the tickets, and now we're going to..."

如果你看看工业革命，这些6%、10%的铁路建设和任何东西，其中许多不像，“我们从门票中获得收入，现在我们要......”

Satya Nadella

There was a lot of money lost.

那时候有很多钱是亏损的。

Dwarkesh Patel

That's true. So, if you really think there's some potential here to 10x or 5x the growth rate of the world, and then you're like, "Well, what is the revenue from GPT-4?"

这是对的。所以，如果你真的认为这里有潜力让世界的增长率增长10倍或5倍，然后你会想，“那么，GPT-4的收入是什么？”

If you really think that's the possibility from the next level up, shouldn't you just, "Let's go crazy, let's do the hundreds of billions of dollars of compute?" I mean, there's some chance, right?

如果你真的认为这来自下一个层级的可能性，难道你不应该说，“让我们疯狂一点，做几百亿美元的计算？”我是说，这是有可能的，对吧？

Satya Nadella

Here’s the interesting thing, right? That's why even that balanced approach to the fleet, at least, is very important to me. It's not about building compute. It's about building compute that can actually help me not only train the next big model but also serve the next big model. Until you do those two things, you're not going to be able to really be in a position to take advantage of even your investment.

有趣的事情就在这里，对吧？这就是为什么我认为即使是对硬件群体的平衡方法也对我非常重要。不是简单地构建计算能力，而是构建能够帮助我不仅训练下一个大模型，还能提供下一个大模型的计算能力。直到你做到这两件事，你才能真正处于能够利用你投资的位置。

So, that's kind of where it's not a race to just building a model, it's a race to creating a commodity that is getting used in the world to drive… You have to have a complete thought, not just one thing that you’re thinking about.

所以，这就是为什么这不是一个仅仅构建模型的竞赛，而是创造一个在世界中被使用的商品的竞赛，推动……你需要有一个完整的思考，而不仅仅是你所考虑的一个问题。

And by the way, one of the things is that there will be overbuild. To your point about what happened in the dotcom era, the memo has gone out that, hey, you know, you need more energy, and you need more compute. Thank God for it. So, everybody's going to race.

顺便说一下，其中一件事是会有过度建设。关于互联网泡沫时代发生的事情，你提到的备忘录已经发出，嘿，你知道，您需要更多的能源，更多的计算能力。感谢上帝，大家都会竞相追逐。

In fact, it's not just companies deploying, countries are going to deploy capital, and there will be clearly... I'm so excited to be a leaser, because, by the way; I build a lot, I lease a lot. I am thrilled that I'm going to be leasing a lot of capacity in '27, '28 because I look at the builds, and I'm saying, "This is fantastic." The only thing that's going to happen with all the compute builds is the prices are going to come down.

事实上，不仅仅是公司在部署，国家也将在部署资本，显然会有……我非常激动能成为一个租赁商，顺便说一下；我建了很多，也租了很多。我很高兴在2027年、2028年将租赁大量的计算能力，因为我看着这些建设项目，我在想，“太棒了。”唯一会发生的事情就是所有计算建设的成本会下降。

0:21:39 - Decreasing price of intelligence

Dwarkesh Patel

Speaking of prices coming down, you recently tweeted after the DeepSeek model came out about Jevons’ Paradox. I'm curious if you can flesh that out. Jevons’ Paradox occurs when the demand for something is highly elastic. Is intelligence that bottlenecked on prices going down?

说到价格下降，你最近在DeepSeek模型发布后发了推文，提到了杰文斯悖论。我很想了解一下这个问题。杰文斯悖论发生在某样东西的需求高度弹性的时候。智能是否在价格下降方面受到了瓶颈？

Because when I think about, at least my use cases as a consumer, intelligence is already so cheap. It's like two cents per million tokens. Do I really need it to go down to 0.02 cents? I'm just really bottlenecked on it becoming smarter. If you need to charge me 100x, do a 100x bigger training run. I'm happy for companies to take that.

因为当我想到，至少以我的消费者使用情况，智能已经如此便宜。每百万个token大约两美分。我真的需要它降到0.02美分吗？我现在面临的瓶颈是它变得更聪明。如果你需要向我收费100倍，就进行100倍更大的训练。我很愿意让公司这样做。

But maybe you're seeing something different on the enterprise side or something. What is the key use case of intelligence that really requires it to get to 0.002 cents per million tokens?

但也许你在企业方面看到了不同的情况。智能的关键应用场景是什么，真的需要它降到每百万个token 0.002美分？

Satya Nadella

I think the real thing is the utility of the tokens. Both need to happen: One is intelligence needs to get better and cheaper. And anytime there's a breakthrough, like even what DeepSeek did, with the efficient frontier of performance per token changes, the curve gets bent, and the frontier moves. That just brings more demand. That's what happened with cloud.

我认为真正的问题是token的效用。两者都需要发生：一是智能需要变得更好、更便宜。每当有突破时，像DeepSeek做的那样，随着每个token的性能效率前沿变化，曲线会弯曲，前沿会移动。这自然带来了更多的需求。这就是云计算发生的事情。

Here’s an interesting thing: We used to think “oh my God, we've sold all the servers in the client-server era”. Except once we started putting servers in the cloud, suddenly people started consuming more because they could buy it cheaper, and it was elastic, and they could buy it as a meter versus a license, and it completely expanded.

有趣的是，我们曾经认为，“天啊，我们在客户端-服务器时代已经卖完所有的服务器。”但一旦我们开始将服务器放入云端，人们突然开始消费更多，因为他们可以买到更便宜的服务器，而且是有弹性的，他们可以按使用量购买，而不是购买许可证，市场就完全扩展了。

I remember going, let’s say, to a country like India and talking about “here is SQL Server”. We sold a little, but man, the cloud in India is so much bigger than anything that we were able to do in the server era. I think that's going to be true.

我记得我去过像印度这样的国家，谈论“这是SQL Server”。我们卖得不多，但天啊，印度的云计算市场比我们在服务器时代能做的任何事情都要大。我认为这将会是真的。

If you think about, if you want to really have, in the Global South, in a developing country, if you had these tokens that were available for healthcare that were really cheap, that would be the biggest change ever.

如果你考虑一下，如果你真的想在全球南方，在发展中国家，拥有这些非常便宜的可以用于医疗保健的token，那将是历史上最大的变化。

Dwarkesh Patel

I think it's quite reasonable for somebody to hear people like me in San Francisco and think “they're kind of silly; they don't know what it's actually like to deploy things in the real world”.

我认为，对于像我这样的旧金山人来说，听到别人这样说是很合理的，“他们有点傻；他们不知道在现实世界中部署这些东西到底是什么样的。”

As somebody who works with these Fortune 500s and is working with them to deploy things for hundreds of millions, billions of people, what's your sense on how fast deployment of these capabilities will be?

作为一个与这些财富500强公司合作，并且为数亿、数十亿人部署事物的人，你对这些能力部署的速度有什么看法？

Even when you have working agents, even when you have things that can do remote work for you, with all the compliance and with all the inherent bottlenecks, is that going to be a big bottleneck, or is that going to move past pretty fast?

即使你有可以工作的代理人，即使你有能够为你做远程工作的工具，在所有合规性要求和内在的瓶颈面前，这会是一个大的瓶颈，还是会很快得到突破？

Satya Nadella

It is going to be a real challenge because the real issue is change management or process change. Here's an interesting thing: one of the analogies I use is, just imagine how a multinational corporation like us did forecasts pre-PC, and email, and spreadsheets. Faxes went around. Somebody then got those faxes and did an interoffice memo that then went around, and people entered numbers, and then ultimately a forecast came, maybe just in time for the next quarter.

这将是一个真正的挑战，因为真正的问题是变革管理或流程变化。有趣的是，我用的一个类比是，想象一下像我们这样的跨国公司在个人电脑、电子邮件和电子表格出现之前是如何做预测的。传真传来传去。然后有人收到这些传真，做了一个内部备忘录，然后传递出去，大家输入数字，最终得出一个预测，可能恰好赶上下一季度。

Then somebody said, "Hey, I'm just going to take an Excel spreadsheet, put it in email, send it around. People will go edit it, and I'll have a forecast." So, the entire forecasting business process changed because the work artifact and the workflow changed.

然后有人说：“嘿，我要拿个Excel表格，把它放在电子邮件里，发出去。大家编辑它，我就能得到一个预测。”于是，整个预测业务流程发生了变化，因为工作工件和工作流发生了变化。

That is what needs to happen with AI being introduced into knowledge work. In fact, when we think about all these agents, the fundamental thing is there's a new work and workflow.

这就是引入AI到知识工作中所需要发生的事情。事实上，当我们考虑所有这些代理时，根本的问题是有了一种新的工作方式和工作流。

For example, even prepping for our podcast, I go to my copilot and I say, "Hey, I'm going to talk to Dwarkesh about our quantum announcement and this new model that we built for game generation. Give me a summary of all the stuff that I should read up on before going." It knew the two Nature papers, it took that. I even said, "Hey, go give it to me in a podcast format." And so, it even did a nice job of two of us chatting about it.

例如，在为我们的播客做准备时，我去我的副驾驶说：“嘿，我要和Dwarkesh谈谈我们关于量子计算的公告和我们为游戏生成构建的新模型。给我一个我应该在出发前阅读的内容总结。”它知道那两篇《自然》论文，拿到了这些内容。我甚至说：“嘿，把它做成播客格式给我。”于是它做得很好，我们两个人聊得很愉快。

So that became—and in fact, then I shared it with my team. I took it and put it into Pages, which is our artifact, and then shared. So the new workflow for me is I think with AI and work with my colleagues.

所以这就变成了——事实上，我还把它分享给了我的团队。我拿去放进Pages（我们的工作文档），然后分享了出去。所以对我来说，新的工作流是，我与AI一起工作并与同事合作。

That's a fundamental change management of everyone who's doing knowledge work, suddenly figuring out these new patterns of "How am I going to get my knowledge work done in new ways?" That is going to take time. It's going to be something like in sales, and in finance, and supply chain.

这是每个从事知识工作的人面临的根本性变革管理，突然间，他们要弄清楚这些新的模式，“我如何以新的方式完成我的知识工作？”这将需要时间。这会发生在销售、财务和供应链等领域。

For an incumbent, I think that this is going to be one of those things where—you know, let's take one of the analogies I like to use is what manufacturers did with Lean. I love that because, in some sense, if you look at it, Lean became a methodology of how one could take an end-to-end process in manufacturing and become more efficient. It's that continuous improvement, which is reduce waste and increase value.

对于一个现有企业来说，我认为这将是其中之一——你知道，我喜欢用的一个类比是制造商如何使用精益生产。我很喜欢这个类比，因为在某种意义上，如果你仔细看，精益生产成为了一种方法论，它能够帮助人们在制造中优化端到端的流程，提高效率。它是一种持续改进的方式，即减少浪费并增加价值。

That's what's going to come to knowledge. This is like Lean for knowledge work, in particular. And that's going to be the hard work of management teams and individuals who are doing knowledge work, and that's going to take its time.

这就是知识领域将要发生的事情。这就像是知识工作领域的精益生产，尤其是这样。这将是管理团队和从事知识工作的个人需要完成的艰巨任务，这将需要时间。

Dwarkesh Patel

Can I ask you just briefly about that analogy? One of the things Lean did is physically transform what a factory floor looks like. It revealed bottlenecks that people didn't realize until you're really paying attention to the processes and workflows.

我可以简短地问一下这个类比吗？精益生产做的其中一件事是物理上改变了工厂车间的样子。它揭示了人们在真正关注流程和工作流之前没有意识到的瓶颈。

You mentioned briefly what your own workflow—how your own workflow has changed as a result of AIs. I'm curious if we can add more color to what will it be like to run a big company when you have these AI agents that are getting smarter and smarter over time?

你简要提到过，AI是如何改变你自己的工作流程的。我很好奇，随着这些AI代理变得越来越聪明，未来运营一家大公司将会是什么样的？

Satya Nadella

It's interesting you ask that. I was thinking, for example, today if I look at it, we are very email heavy. I get in in the morning, and I’m like, man my inbox is full, and I’m responding, and so I can’t wait for some of these Copilot agents to automatically populate my drafts so that I can start reviewing and sending.

你问这个问题很有意思。例如，我今天在想，如果我看看现在的情况，我们的邮件量非常大。我早上进去，看到我的收件箱已经满了，我在回复邮件，所以我迫不及待地希望一些Copilot代理能够自动填充我的草稿，然后我可以开始审核并发送。

But I already have in Copilot at least ten agents, which I query them different things for different tasks. I feel like there’s a new inbox that’s going to get created, which is my millions of agents that I’m working with will have to invoke some exceptions to me, notifications to me, ask for instructions.

但在Copilot中，我已经有至少十个代理，我向它们查询不同任务的问题。我觉得会有一个新的收件箱诞生，我将与数百万代理合作，它们会向我提出一些例外情况，向我发送通知，要求我给出指示。

So at least what I’m thinking is that there’s a new scaffolding, which is the agent manager. It’s not just a chat interface. I need a smarter thing than a chat interface to manage all the agents and their dialogue.

所以至少我在想的是，会有一个新的框架，那就是代理管理器。它不仅仅是一个聊天界面。我需要比聊天界面更智能的东西来管理所有代理及其对话。

That’s why I think of this Copilot, as the UI for AI, is a big, big deal. Each of us is going to have it. So basically, think of it as: there is knowledge work, and there’s a knowledge worker. The knowledge work may be done by many, many agents, but you still have a knowledge worker who is dealing with all the knowledge workers. And that, I think, is the interface that one has to build.

这就是为什么我认为这个Copilot，作为AI的用户界面，是一个非常非常重要的事情。我们每个人都会有它。所以基本上，你可以这样理解：有知识工作，也有知识工作者。知识工作可能由很多代理完成，但你仍然有一个知识工作者在处理所有这些知识工作者。我认为，这就是必须构建的界面。

0:30:19 - Quantum Breakthrough

Dwarkesh Patel

You're one of the few people in the world who can say that you have access to 200,000… you have this swarm of intelligence around you in the form of Microsoft the company and all its employees. And you have to manage that, and you have to interface with that, how to make best use of that. Hopefully, more of the world will get to have that experience in the future.

你是世界上少数几个人之一，可以说你拥有200,000个……你身边有一群智慧，以微软公司及其所有员工的形式存在。你需要管理它，你需要与它互动，如何最大限度地利用它。希望未来更多的人能够体验到这一点。

I'd be curious about how your inbox, if that means everybody's inbox, will look like yours in the morning.

我很好奇，如果这意味着每个人的收件箱，早晨时它们会像你的一样吗？

Okay, before we get to that, I want to keep asking you more about AI, but I really want to ask you about the big breakthrough in quantum that Microsoft Research has announced. So can you explain what's going on?

好，在我们谈到这个之前，我想继续问你更多关于AI的问题，但我真的很想问问微软研究院宣布的量子计算的重大突破。能否解释一下发生了什么？

Satya Nadella

This has been another 30-year journey for us. It's unbelievable. I'm the third CEO of Microsoft who's been excited about quantum.

这对我们来说是另一个30年的旅程。真是难以置信。我是微软的第三任CEO，曾对量子计算感到兴奋。

The fundamental breakthrough here, or the vision that we've always had is, you need a physics breakthrough in order to build a utility-scale quantum computer that works. We took the path of saying, the one way for having a less noisy or more reliable qubit is to bet on a physical property that by definition is more reliable and that's what led us to the Majorana zero modes, which was theorized in the 1930s. The question was, can we actually physically fabricate these things? Can we actually build them?

这里的根本突破，或者说我们一直以来的愿景是，要构建一个能工作的公用规模量子计算机，你需要物理上的突破。我们走了一条道路，认为减少噪声或提高量子比特可靠性的一个方法是押注在一个物理属性上，这个属性本身就是更可靠的，这也引导我们找到了马约拉纳零模，这在1930年代就已被理论化。问题是，我们能否实际制造这些东西？我们真的能建造它们吗？

So the big breakthrough effectively, and I know you talked to Chetan, was that we now finally have existence proof and a physics breakthrough of Majorana zero modes in a new phase of matter effectively. This is why we like the analogy of thinking of this as the transistor moment of quantum computing, where we effectively have a new phase, which is the topological phase, which means we can even now reliably hide the quantum information, measure it, and we can fabricate it. And so now that we have it, we feel like with that core foundational fabrication technique out of the way, we can start building a Majorana chip.

所以，实际上重大的突破是，我知道你和Chetan谈过了，我们现在终于有了马约拉纳零模存在的证明，以及在一个新物质相中的物理突破。这就是为什么我们喜欢将其比作量子计算的晶体管时刻，我们实际上有了一个新的相，也就是拓扑相，这意味着我们现在可以可靠地隐藏量子信息、测量它，并且我们可以制造它。因此，既然我们已经做到了这一点，我们觉得有了这个核心的基础制造技术，我们可以开始构建马约拉纳芯片了。

That Majorana One which I think is going to basically be the first chip that will be capable of a million qubits, physical. And then on that, thousands of logical qubits, error-corrected. And then it's game on. You suddenly have the ability to build a real utility-scale quantum computer, and that to me is now so much more feasible. Without something like this, you will still be able to achieve milestones, but you'll never be able to build a utility-scale computer. That's why we're excited about it.

那个马约拉纳One，我认为它将基本上是第一个能够实现百万量子比特的物理芯片。然后在它的基础上，成千上万的逻辑量子比特将得到纠错。接着，游戏就开始了。你突然拥有了构建一个真正的公用规模量子计算机的能力，对我来说，这现在变得更可行了。如果没有类似这样的突破，你仍然能够实现一些里程碑，但你永远无法构建一个公用规模的计算机。这就是我们对它感到兴奋的原因。

Dwarkesh Patel

Amazing. And by the way, I believe this is it right here.

太棒了。顺便说一下，我相信这就是它。

Satya Nadella

That is it.

没错，就是它。

Dwarkesh Patel

Yes.

是的。

Satya Nadella

I forget now, are we calling it Majorana? Yes, that's right. Majorana One. I'm glad we named it after that.

我现在有点忘了，我们是叫它马约拉纳吗？是的，没错。马约拉纳One。我很高兴我们把它命名为这个。

To think that we are able to build something like a million-qubit quantum computer in a thing of this size is just unbelievable. That's the crux of it: unless and until we could do that, you can't dream of building a utility-scale quantum computer.

想想看，我们能够在这么小的东西里构建出像百万量子比特的量子计算机，这真是难以置信。这就是关键：除非我们能做到这一点，否则你无法梦想构建一个公用规模的量子计算机。

Dwarkesh Patel

And you're saying the eventual million qubits will go on a chip this size? Okay, amazing.

你是说，最终的百万量子比特将放在这个大小的芯片上？好吧，太惊人了。

Other companies have announced 100 physical qubits, Google's, IBM's, others. When you say you've announced one, but you're saying that yours is way more scalable in the limit.

其他公司已经宣布了100个物理量子比特，谷歌、IBM和其他公司。你说你们宣布了一个，但你说你们的芯片在极限情况下具有更高的可扩展性。

Satya Nadella

Yeah. The one thing we’ve also done is we’ve taken an approach where we’ve separated our software and our hardware. We're building out our software stack, and we now have, with the neutral atom folks, the ion trap folks, we're also working with others who even have pretty good approaches with photonics and what have you, that means there'll be different types of quantum computers. In fact, we have what, I think that the last thing that we announced was 24 logical qubits. So we have also got some fantastic breakthroughs on error correction and that's what is allowing us, even on neutral atom and ion trap quantum computers, to build these 20 plus, and I think that'll keep going even throughout the year; you'll see us improve that yardstick.

是的。我们做的一件事是，我们采用了将软件和硬件分离的方法。我们正在构建我们的软件栈，现在我们与中性原子、离子阱技术人员合作，还与其他拥有光子学等领域良好方法的团队合作，这意味着会有不同类型的量子计算机。事实上，我们有一些，我认为我们最后宣布的事情是24个逻辑量子比特。所以我们在错误修正方面也取得了一些重大的突破，这使得我们即使在中性原子和离子阱量子计算机上，也能构建出这些20多个比特，并且我认为这将持续下去，全年你将看到我们不断提升这个标尺。

But we also then said, "Let's go to the first principles and build our own quantum computer that is betting on the topological qubit." And that's what this breakthrough is about.

但我们也说过，“让我们从第一性原理出发，构建我们自己的量子计算机，押注于拓扑量子比特。”这就是这个突破的意义所在。

Dwarkesh Patel

Amazing. The million topological qubits, thousands of logical qubits, what is the estimated timeline to scale up to that level? What does the Moore's law here, if you've got the first transistor, look like?

太棒了。百万个拓扑量子比特，成千上万个逻辑量子比特，预计多久能实现这一规模？如果你有了第一个晶体管，那么这里的摩尔定律会是什么样子？

Satya Nadella

We've obviously been working on this for 30 years. I'm glad we now have the physics breakthrough and the fabrication breakthrough.

我们显然已经为此工作了30年。我很高兴我们现在有了物理突破和制造突破。

I wish we had a quantum computer because by the way, the first thing the quantum computer will allow us to do is build quantum computers, because it's going to be so much easier to simulate atom-by-atom construction of these new quantum gates.

我希望我们现在就有一台量子计算机，因为顺便说一下，量子计算机允许我们做的第一件事就是构建量子计算机，因为模拟这些新量子门的原子级构建将变得更加容易。

But in any case, the next real thing is, now that we have the fabrication technique, let us go build that first fault-tolerant quantum computer. And that will be the logical thing.

但无论如何，接下来的真正事情是，既然我们有了制造技术，那就开始构建第一个容错量子计算机。这将是合乎逻辑的步骤。

So, I would say now I can say, "Oh, maybe '27, '28, '29, we will be able to actually build this." Now that we have this one gate, can I put the thing into an integrated circuit and then actually put these integrated circuits into a real computer? That is where the next logical step is.

所以，我现在可以说，“哦，也许2027年、2028年、2029年，我们将能够实际构建它。”既然我们有了这个量子门，我能把它放进集成电路中，并且把这些集成电路放进真正的计算机中吗？这就是下一个合乎逻辑的步骤。

Dwarkesh Patel

And what do you see as, in '27, '28, you've got it working? Is it a thing you access through the API? Is it something you're using internally for your own research in materials and chemistry?

你认为，2027年、2028年，它将开始工作了吗？它是通过API访问的东西吗？还是你们内部在做材料和化学研究时使用的工具？

Satya Nadella

It’s a great question. One thing that I've been excited about is, even in today's world… we had this quantum program, and we added some APIs to it. The breakthrough we had maybe two years ago was to think of this HPC stack, and AI stack, and quantum together.

这是一个很好的问题。我一直很兴奋的一点是，即使在今天的世界里……我们有这个量子程序，并且为它添加了一些API。大约两年前我们所取得的突破是将高性能计算栈（HPC）、人工智能栈和量子栈结合在一起。

In fact, if you think about it, AI is like an emulator of the simulator. Quantum is like a simulator of nature. What is quantum going to do? By the way, quantum is not going to replace classical. Quantum is great at what quantum can do, and classical will also...

事实上，如果你仔细想想，AI就像是模拟器的仿真器。量子计算就像是大自然的模拟器。量子计算要做什么呢？顺便说一下，量子计算并不会取代经典计算。量子计算在它能做的事情上非常强大，经典计算也会继续存在……

Quantum is going to be fantastic for anything that is not data-heavy but is exploration-heavy in terms of the state space. It should be data-light but exponential states that you want to explore. Simulation is a great one: chemical physics, what have you, biology.

量子计算将非常适用于那些不是数据密集型，但在状态空间探索上很重的任务。它应该是数据较少但状态呈指数级的，你希望探索的东西。模拟就是一个很好的例子：化学物理学、你所提到的、生物学等。

One of the things that we've started doing is really using AI as the emulation engine. But you can then train. So the way I think of it is, if you have AI plus quantum, maybe you'll use quantum to generate synthetic data that then gets used by AI to train better models that know how to model something like chemistry or physics or what have you. These two things will get used together.

我们做的一件事是，真正将AI作为仿真引擎使用。但你可以在此基础上进行训练。所以，我的想法是，如果你将AI与量子计算结合，或许你可以用量子计算生成合成数据，然后通过AI使用这些数据训练更好的模型，这些模型能够理解如何建模像化学或物理这样的领域。AI与量子计算将共同使用。

So even today, that's effectively what we're doing with the combination of HPC and AI. I hope to replace some of the HPC pieces with quantum computers.

所以，今天实际上我们正在用HPC和AI的组合做这些事情。我希望能用量子计算机替代一些HPC组件。

Dwarkesh Patel

Can you tell me a little bit about how you make these research decisions which, in 20 years time, 30 years time, will actually pay dividends, especially at a company of Microsoft's scale? Obviously, you're in great touch with the technical details in this project. Is it feasible for you to do that with all the things Microsoft Research does?

你能告诉我一点关于如何做出这些研究决策吗？这些决策在20年、30年后将会产生回报，特别是在像微软这样规模的公司里。显然，你与这个项目的技术细节有很好的联系。你能在微软研究所所做的所有事情中做到这一点吗？

How do you know the current bet you're making will pay out in 20 years? Does it just have to emerge organically through the org, or how are you keeping track of all this?

你怎么知道你现在所做的赌注在20年内会得到回报？它是否必须通过组织自然地产生，还是你是如何跟踪这一切的？

Satya Nadella

The thing that I feel was fantastic is when Bill, when he started MSR back in '95 I guess. I think in the long history of these curiosity-driven research organizations, to just do a research org that is about fundamental research and MSR, over the years, has built up that institutional strength so when I think about capital allocation or budgets, we first put the chips in and say, "Here is MSR's budget." We gotta go at it each year knowing that most of these bets are not going to pay off in any finite time frame. Maybe the sixth CEO of Microsoft will benefit from it. And in tech that is I think a given.

我认为最棒的一点是，当比尔在1995年开始微软研究院时，我觉得，长久以来，所有这些以好奇心驱动的研究组织的历史，微软研究院仅仅是做基础研究的组织，而它多年来建立了这种制度力量，所以当我思考资本分配或预算时，我们首先投入资金并说，“这是微软研究院的预算。”我们必须每年去做这些，知道大多数赌注在有限的时间框架内不会带来回报。也许微软的第六任CEO会从中受益。在科技领域，我认为这是显而易见的。

The real thing that I think about is, when the time has come for something like quantum or a new model or what have you, can you capitalize? So as an incumbent, if you look at the history of tech, it's not that people didn't invest. It's that you need to have a culture that knows how to take an innovation and scale it.

真正让我思考的是，当量子技术或新模型等突破性创新来临时，你能否把它们转化为资本？作为一个现有企业，如果你看科技历史，问题不是人们没有投资，而是你需要有一种文化，懂得如何将创新进行规模化。

That's the hard part, quite frankly, for CEOs and management teams. Which is kind of fascinating. It's as much about good judgment as it is about good culture. Sometimes we've gotten it right; sometimes we've gotten it wrong; I can tell you the thousand projects from MSR that we should have probably led with, but we didn't. And I always ask myself why. It's because we were not able to get enough conviction and that complete thought of how to not only take the innovation but make it into a useful product with a business model that we can then go to market with.

老实说，这对CEO和管理团队来说是最困难的部分。这很有趣。这既关乎良好的判断力，也关乎良好的文化。有时我们做得对；有时我们做错了；我可以告诉你，微软研究院有成千上万的项目，我们本该首先领导这些项目，但我们没有做到。我总是问自己为什么。因为我们没有足够的信心，也没有一个完整的思路来思考如何不仅仅是采纳创新，而是将它变成一个有用的产品，并具备可以带到市场的商业模型。

That's the job of CEOs and management teams: not to just be excited about any one thing, but to be able to actually execute on a complete thing. And that's easier said than done.

这就是CEO和管理团队的职责：不仅仅是对某个事物感到兴奋，而是能够真正执行一个完整的计划。这说起来容易，做起来难。

Dwarkesh Patel

When you mentioned the possibility of three subsequent CEOs of Microsoft, if each of them increases the market cap by an order of magnitude, by the time you've got the next breakthrough, you'll be like the world economy or something.

当你提到微软的三任继任CEO的可能性时，如果每一任都将市值提高一个数量级，那么等到下一个突破到来时，你们将会像是世界经济一样。

Satya Nadella

Or remember, the world is going to be growing at 10%, so we'll be fine.

或者记住，世界经济将以10%的速度增长，所以我们会没问题的。

0:42:51 - How Muse will change gaming

Dwarkesh Patel

Let's dig into the other big breakthrough you've just made. It's amazing that you have both of them coming out the same day, in your gaming world models. I'd love if you can tell me a little bit about that.

让我们深入探讨你们刚刚取得的另一个重大突破。你们能够在同一天发布这两项突破，尤其是在你们的游戏世界模型方面，真是令人惊叹。能否给我简单介绍一下这个？

Satya Nadella

We're going to call it Muse. It's going to be the model of this world action, or human action model.

我们将其命名为Muse。它将是这个世界行动模型，或者人类行动模型。

This is very cool. One of the things is that obviously, Dall-E and Sora have been unbelievable in what they've been able to do in terms of generative models. One thing that we wanted to go after was using gameplay data. Can you actually generate games that are both consistent and then have the ability to generate the diversity of what that game represents, and then are persistent to user mods?

这非常酷。显然，Dall-E和Sora在生成模型方面所能做到的事情令人难以置信。我们想要追求的一件事是使用游戏数据。你能否生成既一致又能体现游戏多样性的游戏，并且能够对用户的修改保持持久性？

That's what this is. They were able to work with one of our game studios, and this is the other publication in Nature.

这就是我们所做的。他们与我们的一个游戏工作室合作，这也是《自然》杂志上的另一篇发表。

The cool thing is what I'm excited about is bringing--we're going to have a catalog of games soon that we will start using these models, or we're going to train these models to generate, and then start playing them.

令人兴奋的是，我最兴奋的事情是将会带来——我们很快将拥有一个游戏目录，我们将开始使用这些模型，或者我们将训练这些模型来生成游戏，并开始玩这些游戏。

In fact, when Phil Spencer first showed it to me, he had an Xbox controller and this model basically took the input and generated the output based on the input. And it was consistent with the game. That to me is a massive moment of “wow”. It's kind of like the first time we saw ChatGPT complete sentences, or Dall-E draw, or Sora. This is one such moment.

事实上，当Phil Spencer第一次给我展示时，他拿着一个Xbox控制器，这个模型基本上接受输入并根据输入生成输出。它与游戏一致。对我来说，这是一个巨大的“哇”时刻。这有点像我们第一次看到ChatGPT完成句子，或者Dall-E绘画，或者Sora。这是其中一个时刻。

Dwarkesh Patel

I got a chance to see some of the videos in the real-time demo this morning with your lead researcher Katja on this. Only once I talked to her did it really hit me how incredible this is, in the sense that we've used AI in the past to model agents, and just using that same technique to model the world around the agent gives consistent real-time – we'll superimpose videos of what this looks like atop this podcast so people can get a chance to see it for themselves. I guess it'll be out by then, so they can also watch it there.

今天早上我有机会和你的首席研究员Katja一起观看了这个实时演示的一些视频。直到我和她交谈后，我才真正意识到这有多么不可思议，因为我们过去曾使用AI来建模代理，而仅仅使用相同的技术来建模代理周围的世界，就能提供一致的实时效果——我们会在这个播客上叠加展示这些视频，让人们有机会亲自看看。我猜到时它会发布，他们也可以在那里观看。

This in itself is incredible. You, through your span as CEO, have invested tens of hundreds of billions of dollars in building up Microsoft Gaming and acquiring IP.

这本身就令人难以置信。在你担任CEO期间，你已经投入了数百亿美元用于建立微软游戏和收购知识产权。

In retrospect, if you can just merge all of this data into one big model that can give you this experience of visiting and going through multiple worlds at the same time, and if this is the direction gaming is headed, it seems like a pretty good investment to have made. Did you have any premonition about this?

回顾过去，如果你能够将所有这些数据整合成一个大模型，给你带来同时访问并穿越多个世界的体验，而如果这是游戏的未来发展方向，那似乎是一个相当不错的投资。你有没有对这一切产生过预感？

Satya Nadella

I wouldn't say that we invested in gaming to build models. We invested, quite frankly, because- here's an interesting thing about our history: We built our first game before we built Windows. Flight Simulator was a Microsoft product long before we even built Windows.

我不会说我们投资游戏是为了构建模型。坦率地说，我们投资是因为——关于我们历史的一个有趣点：我们在构建Windows之前就已经开发了第一款游戏。飞行模拟器在我们构建Windows之前就已经是微软的产品了。

So, gaming has got a long history at the company, and we want to be in gaming for gaming's sake. I always start by saying I hate to be in businesses where they're means to some other end. They have to be ends unto themselves.

所以，游戏在公司里有着悠久的历史，我们希望是为了游戏本身而进入游戏行业。我总是先说，我讨厌进入那些是为了其他目的而存在的行业。它们必须是自成目的。

这是巴菲特的思想。

And then, yes, we're not a conglomerate. We are a company where we have to bring all these assets together and be better owners by adding value. For example, cloud gaming is a natural thing for us to invest in because that will just expand the TAM and expand the ability for people to play games everywhere.

然后，是的，我们不是一个企业集团。我们是一家必须将所有这些资产结合起来并通过增加价值成为更好的所有者的公司。例如，云游戏是我们投资的一个自然领域，因为它将扩大市场总量（TAM）并增强人们随时随地玩游戏的能力。

The same thing with AI and gaming: we definitely think that it can be helpful in maybe changing- it's kind of like the CGI moment, even for gaming long-term. And it's great. As the biggest, world's largest publisher, this will be helpful. But at the same time, we've got to produce great quality games. I mean, you can't be a gaming publisher without, sort of, first and foremost being focused on that.

与AI和游戏也一样：我们确实认为它能在某种程度上帮助改变——这有点像CGI时刻，甚至对长期的游戏产业也是如此。而且这很棒。作为世界上最大的出版商，这将是有帮助的。但与此同时，我们必须制作出高质量的游戏。我的意思是，作为一家游戏出版商，你不可能不首先专注于这一点。

But the fact that this data asset is going to be interesting, not just in a gaming context, but it's going to be a general action model and a world model, it's fantastic. I mean like, you know, I think about gaming data as perhaps, you know, what YouTube is perhaps to Google, gaming data is to Microsoft. And so therefore I'm excited about that.

但事实上，这个数据资产不仅在游戏领域会非常有趣，它还将成为一个通用的行动模型和世界模型，这是非常棒的。我是说，你知道，我把游戏数据看作是，也许，YouTube对Google的意义，游戏数据对微软的意义。所以，我对此非常兴奋。

Dwarkesh Patel

Yeah, and that's what I meant, just in the sense of like, you can have one unified experience across many different kinds of games. How does this fit into the other, separate from AI, the other things that Microsoft has worked on in the past, like mixed reality? Maybe giving smaller game studios a chance to build these AAA action games? Just like five, ten years from now, what kinds of ways could you imagine?

是的，这正是我想表达的意思——你可以在多种不同类型的游戏中拥有一个统一的体验。这如何与微软过去在AI之外的其他工作相契合呢？比如混合现实？也许给一些小型游戏工作室一个机会，去构建这些AAA级别的动作游戏？就像五、十年后的未来，你能想象有哪些方式？

Satya Nadella

I've thought about these three things as the cornerstones of, in an interesting way, even five, six, seven years ago is when I said the three big bets that we want to place [are] AI, quantum, and mixed reality. And I still believe in them, because in some sense, what are the big problems to be solved?

我曾经把这三件事视为基础支柱，有趣的是，甚至五、六、七年前我就说过，我们要下注的三大领域是：AI、量子计算和混合现实。我仍然相信它们，因为从某种意义上讲，真正要解决的重大问题是什么？

Presence. That's the dream of mixed reality. Can you create real presence? Like you and I doing a podcast like this.

存在感。这是混合现实的梦想。你能创造出真正的存在感吗？就像你我这样做播客。

I think it’s still proving to be the harder one of those challenges, quite honestly. I thought it was going to be more solvable. It's tougher, perhaps, just because of the social side of it: wearing things and so on.

老实说，我认为它仍然是那些挑战中最困难的一个。我本以为它会更容易解决。它更难，可能是因为它的社交属性：穿戴设备等等。

We're excited about, in fact, what we're going to do with Anduril and Palmer, now, with even how they'll take forward the IVAS program, because that's a fantastic use case. And so we'll continue on that front.

事实上，我们对现在和Anduril以及Palmer的合作感到兴奋，甚至是他们如何推动IVAS项目，因为那是一个非常棒的应用案例。所以我们会继续在这一方面努力。

But also, the 2D surfaces. It turns out things like Teams, right, thanks to the pandemic, we've really gotten the ability to create essentially presence through even 2D. And that I think will continue. That's one secular piece.

但也有2D表面。事实证明，像Teams这样的东西，得益于疫情，我们实际上已经获得了通过2D创造存在感的能力。我认为这一点将会继续下去。这是一个长期的趋势。

Quantum we talked about, and AI is the other one. So these are the three things that I look at and say, how do you bring these things together? Ultimately, not as tech for tech's sake, but solving some of the fundamental things that we, as humans, want in our life, and more, we want them in our economy, driving our productivity. And so if we can somehow get that right, then I think we will have really made progress.

我们谈到了量子计算，AI是另一个。所以，这就是我看待的三件事，我想知道如何将它们结合起来？最终，这不仅仅是为了技术本身，而是解决一些我们作为人类在生活中需要的基本问题，更多的是，我们希望它们能够推动我们的经济，促进我们的生产力。如果我们能够把这些搞对，那我认为我们就真的取得了进展。

Dwarkesh Patel

When you write your next book, you've got to have some explanation of why those three pieces all came together around the same time, right? Like, there's no intrinsic reason you would think quantum and AI should happen in 2028 and 2025 and so forth.

当你写下一本书时，你得解释一下为什么这三件事会在同一时间汇聚在一起，对吧？比如说，并没有什么内在的原因会让你认为量子计算和AI应该分别在2028年和2025年发生。

Satya Nadella

That's right. At some level, I look at it and say: the simple model I have is, hey is there a systems breakthrough? To me, the systems breakthrough is the quantum thing.

没错。从某种层面上讲，我看待这个问题的方式是：我有一个简单的模型，嘿，是否存在系统性的突破？对我来说，系统性突破就是量子计算。

Is there a business logic breakthrough? That's AI to me, which is: can the logic tier be fundamentally reasoned differently? Instead of imperatively writing code, can you have a learning system? That's the AI one.

是否有商业逻辑突破？对我来说，那就是AI，即：逻辑层是否能够从根本上以不同的方式推理？不是命令式地编写代码，而是可以有一个学习系统？那就是AI的突破。

And then the UI side of it is presence.

然后，UI方面的突破就是存在感。

这一段描述非常好。

0:49:51 - Legal barriers to AI

Dwarkesh Patel

Going back to AI for a second, in your 2017 book… 2019 you invest in OpenAI, very early, 2017 is even earlier, you say in your book, "One might also say that we're birthing a new species, one whose intelligence may have no upper limits."

回到AI话题，在你2017年的书中……2019年你早早地投资了OpenAI，2017年更早，你在书中提到，“也许可以说，我们正在诞生一个新物种，其智能可能没有上限。”

Now, super-early, of course, to be talking about this in 2017. We've been talking in a granular fashion about agents, Office Copilot, capex, and so forth. But if you zoom out and consider this statement you've made, and you think about you as a hyperscaler, as the person doing research in these models as well, providing training, inference, and research for building a new species, how do you think about this in the grand scheme of things?

当然，2017年就讨论这个话题确实非常早。我们已经在细致地讨论代理、Office Copilot、资本开支等方面了。但是如果从宏观角度来看待你所说的这句话，考虑到你作为一个超大规模计算商，作为进行这些模型研究的人，并提供训练、推理和研究来构建一个新物种，你如何看待这一切？

Do you think we're headed towards superhuman intelligence in your time as CEO?

你认为我们会在你担任CEO期间走向超人类智能吗？

Satya Nadella

I think even Mustafa uses that term. In fact he’s used that term more recently, this “new species”.

我想连Mustafa也用了这个术语。实际上，他最近也用了这个术语，“新物种”。

The way I come at it is, you definitely need trust. Before we claim it is something as big as a species, the fundamental thing that we've got to get right is that there is real trust, whether it's personal or societal level trust, that's baked in. That's the hard problem.

我对这个问题的看法是，你绝对需要信任。在我们声称它是像物种一样庞大的事物之前，必须解决的根本问题是，必须有真正的信任，无论是个人的还是社会层面的信任，这种信任必须被内化。这是一个难题。

I think the one biggest rate limiter to the power here will be how does our legal… call it infrastructure, we’re talking about all the compute infrastructure, well how does the legal infrastructure evolve to deal with this? This entire world is constructed with things like humans owning property, having rights, and being liable. That’s the fundamental thing that one has to first say, okay what does that mean for anything that now humans are using as tools? And if humans are going to delegate more authority to these things, then how does that structure evolve? Until that really gets resolved, I don't think just talking about the tech capability is going to happen.

我认为，影响这一能力发展的最大制约因素将是我们的法律……也可以称之为基础设施，我们讨论的是所有的计算基础设施，那么法律基础设施如何发展以应对这一问题？这个世界是建立在人类拥有财产、拥有权利和承担责任等概念上的。这是一个根本性问题，首先必须问自己，当前人类使用的工具会意味着什么？如果人类将更多的权力委托给这些工具，那么这种结构如何演变？在这一问题得到解决之前，我认为仅仅讨论技术能力是无法实现的。

Dwarkesh Patel

As in, we won't be able to deploy these kinds of intelligences until we figure out how to…?

就是说，我们在弄清楚如何解决这些问题之前，是无法部署这些智能的吗？

Satya Nadella

Absolutely. Because at the end of the day, there is no way. Today, you cannot deploy these intelligences unless and until there's someone indemnifying it as a human.

完全正确。因为归根结底，今天，你无法部署这些智能，除非有某人将其作为人类来承担责任。

To your point, I think that's one of the reasons why I think about even the most powerful AI is essentially working with some delegated authority from some human. You can say, oh, that's all alignment and this, that, and the other. That's why I think you have to really get these alignments to work and be verifiable in some way, but I just don't think that you can deploy intelligences that are out of control. For example, this AI takeoff problem may be a real problem, but before it is a real problem, the real problem will be in the courts. No society is going to allow for some human to say, "AI did that."

在你的观点中，我认为这也是我认为即使是最强大的AI，本质上也在与某个人类的授权合作的原因之一。你可以说，哦，那都是一致性问题等等。这就是为什么我认为你必须真正做到这些一致性能够发挥作用并且在某种方式上是可验证的，但我就是不认为你可以部署那些失控的智能。例如，这个AI起飞问题可能是一个真实的问题，但在它成为一个真正的问题之前，真正的问题将在法庭上。没有任何社会会允许某个人说，“是AI做的。”

Dwarkesh Patel

Yes. Well, there's a lot of societies in the world, and I wonder if any one of them might not have a legal system that might be more amenable. And if you can't have a takeoff, then you might worry. It doesn't have to happen in America, right?

是的。世界上有很多社会，我想知道是否有其中某个社会的法律体系可能会更加宽容。如果不能有AI的起飞，那你可能会担心。它不一定要发生在美国，对吧？

Satya Nadella

We think that no society cares about it, right? There can be rogue actors, I'm not saying there won't be rogue actors; there are cyber criminals and rogue states; they're going to be there.

我们认为没有任何社会会关心这个问题，对吧？当然会有不法分子，我不是说不会有不法分子；会有网络犯罪分子和流氓国家；它们会存在。

But to think that human society at large doesn't care about it is also not going to be true. I think we all will care. We know how to deal with rogue states and rogue actors today. The world doesn't sit around and say “we’ll tolerate that”. That's why I'm glad that we have a world order in which anyone who is a rogue actor in a rogue state has consequences.

但认为人类社会整体不关心这个问题也是不对的。我认为我们都会关心。我们知道如何处理流氓国家和不法分子。世界不会坐视不管并说“我们容忍这种行为”。这就是为什么我很高兴我们有一个世界秩序，在这个秩序中，任何在流氓国家中的不法分子都会承担后果。

Dwarkesh Patel

Right. But if you have this picture where you can have 10% economic growth, I think it really depends on getting something like AGI working, because tens of trillions of dollars of value, that sounds closer to the total of human wages, around $60 trillion of the economy. Getting that magnitude, you kind of have to automate labor or supplement labor in a very significant way.

对。但如果你有一个这样的愿景，可以实现10%的经济增长，我认为它真的依赖于像AGI这样的技术的实现，因为数十万亿美元的价值，听起来更接近于人类工资的总额，约60万亿美元的经济总量。要达到这个规模，你必须以非常显著的方式自动化劳动或补充劳动。

If that is possible, and once we figure out the legal ramifications for it, it seems quite plausible, even within your tenure that we figure that out. Are you thinking about superhuman intelligence? Like, the biggest thing you do in your career is this?

如果这是可能的，并且一旦我们搞清楚其法律影响，似乎在你任期内我们能解决这个问题。这让人感觉非常可行。你是否在考虑超人类智能？比如，你职业生涯中做的最伟大的事情就是这个？

Satya Nadella

You bring up another point. I know David Autor and others have talked a lot about this which is, 60% of labor- I think the other question that needs to happen, let’s at least talk about our democratic societies. I think that in order to have a stable social structure, and democracies function, you can’t just have a return on capital and no return on labor. We can talk about it, but that 60% has to be revalued.

你提到了另一个问题。我知道David Autor和其他人谈过很多关于这个问题的讨论，60%的劳动——我认为我们需要考虑的另一个问题，至少让我们谈谈我们的民主社会。我认为，为了拥有稳定的社会结构和民主功能，你不能仅仅有资本的回报而没有劳动的回报。我们可以讨论这个，但那60%的劳动必须重新评估。

In my own simple way, maybe you can call it naive, we'll start valuing different types of human labor. What is today considered high-value human labor may be a commodity. There may be new things that we will value.

以我自己简单的方式来看，也许你会觉得这很天真，我们将开始评估不同类型的人类劳动。今天被认为是高价值的劳动，可能会变成商品。我们可能会开始重视一些新的事物。

Including that person who comes to me and helps me with my physical therapy or whatever, whatever is going to be the case that we value, but ultimately, if we don't have return on labor, and there's meaning in work and dignity in work and all of that, that's another rate limiter to any of these things being deployed.

包括那个来帮助我做物理治疗或其他事情的人，无论是什么，我们会开始重视，但最终，如果我们没有劳动的回报，如果工作中没有意义和尊严，这一切都将成为这些技术被部署的另一个制约因素。

0:55:46 - Getting AGI right

Dwarkesh Patel

On the alignment side, two years ago, you guys released Sydney Bing. Just to be clear, I think given the level of capabilities at the time, it was a charming, endearing, kind of funny example of misalignment.

关于对齐问题，两年前，你们发布了Sydney Bing。只是为了澄清一下，我认为考虑到当时的能力水平，它是一个迷人、令人喜爱的、有点搞笑的对齐失误的例子。

But that was because, at the time, it was like chatbots. They can go think for 30 seconds and give you some funny or inappropriate response. But if you think about that kind of system--that, I think to a New York Times reporter, tried to get him to leave his wife or something--if you think about that going forward, and you have these agents that are for hours, weeks, months going forward, just like autonomous swarms of AGIs, who could be in similar ways misaligned and screwing stuff up, maybe coordinating with each other, what's your plan going forward so that when you get the big one, you get it right?

但那是因为，当时它就像聊天机器人。它们可以思考30秒并给你一些搞笑或不合适的回应。但如果你想象那种系统——比如，它曾试图让一位《纽约时报》记者离开他的妻子——如果你考虑未来会有这些代理人，它们会工作几个小时、几周、几个月，就像是自主的AGI群体，它们可能以类似的方式失去对齐并搞砸事情，甚至可能相互协调，你们未来的计划是什么？当你得到一个大规模的系统时，如何确保做对？

Satya Nadella

That is correct. That's one of the reasons why when we usually allocate compute, let's allocate compute for what is that alignment challenge?

没错。这也是我们通常在分配计算资源时，首先要为对齐挑战分配计算资源的原因之一。

And then more importantly, what is the runtime environment in which you are really going to be able to monitor these things? The observability around it? We do deal with a lot of these things today in the classical side of things as well, like cyber. We don't just write software and then just let it go. You have software and then you monitor it. You monitor it for cyber attacks, you monitor it for fault injections, and what have you.

更重要的是，你将在哪种运行时环境中真正能够监控这些事情？它的可观测性如何？我们今天在传统领域也处理了许多类似的问题，比如网络安全。我们不只是编写软件然后放任它。你有软件，然后你监控它。你监控它是否遭遇网络攻击，监控它是否有故障注入，等等。

Therefore, I think we will have to build enough software engineering around the deployment side of these, and then inside the model itself, what's the alignment? These are all, some of them are real science problems. Some of them are real engineering problems, and then we will have to tackle it.

因此，我认为我们需要在这些部署方面建立足够的软件工程，然后在模型内部，如何实现对齐？这些问题中有一些是实际的科学问题，有一些是实际的工程问题，我们必须去解决。

That also means taking our own liability in all of this. So that's why I'm more interested in deploying these things in where you can actually govern what the scope of these things is, and the scale of these things is. You just can't unleash something out there in the world that creates harm, because the social permission for that is not going to be there.

这也意味着我们需要承担我们在其中的责任。所以这就是为什么我更感兴趣的是将这些东西部署在你可以实际治理这些事物的范围和规模的地方。你不能把某些东西释放到世界上，造成伤害，因为社会对此的许可是不存在的。

Dwarkesh Patel

When you get the agents that can really just do weeks worth of tasks for you, what is the minimum assurance you want before you can let it run a random Fortune 500?

当你得到那些能够为你完成几周任务的代理人时，你希望在让它执行一个随机的财富500强公司时，获得什么最低保障？

Satya Nadella

I think when I use something like Deep Research, even, the minimum assurance I think we want is before we especially have physical embodiment of anything, that I think is kind of one of those thresholds, when you cross. That might be one place.

我认为，当我使用像Deep Research这样的技术时，最低的保障是，尤其在我们拥有任何物理化身之前，我认为这是一个临界点，当你跨越它时。那可能是其中之一。

Then the other one is, for example, the permissions of the runtime environment in which this is operating. You may want guarantees that it's sandboxed, it is not going out of that sandbox.

然后另一个方面是，举例来说，这个系统运行的运行时环境的权限。你可能希望得到保证，它是在沙箱内的，不会超出这个沙箱。

Dwarkesh Patel

I mean, we already have web search and we already have it out of the sandbox.

我的意思是，我们已经有了网页搜索，我们已经把它放出了沙箱。

Satya Nadella

But even what it does with web search and what it writes -- for example to your point, if it's just going to write a bunch of code in order to do some computation, where is that code deployed? And is that code ephemeral for just creating that output, versus just going and springing that code out into the world?

但是即使它与网页搜索的互动，以及它所写的东西——举例来说，如果它只是为了进行某些计算而编写一堆代码，那这些代码会被部署在哪里？这些代码是仅仅用于创建输出而临时存在，还是直接将这些代码释放到外部世界？

Those are things that you could, in the action space, actually go control.

这些是你可以在行动空间内实际控制的事情。

Dwarkesh Patel

And separate from the safety issues, as you think about your own product suite, and you think about, if you do have AIs this powerful, at some point, it's not just like Copilot- an example you mentioned about how you were prepping for this podcast- it's more similar to how you actually delegate work to your colleagues.

除了安全问题，当你思考你自己产品组合时，假设你确实拥有如此强大的AI，某个时刻，它不仅仅是像Copilot——你提到过的关于如何为这次播客做准备的例子——它更像是你如何将工作委派给你的同事。

What does it look like, given your current suite, to add that in? I mean, there's one question about whether LLMs get commodified by other things.

在你的当前产品套件下，加入这一点会是什么样子？我的意思是，有一个问题是关于是否LLM会被其他东西商品化。

I wonder if these databases or canvases or Excel sheets or whatever -- if the LLM is your main gate point into accessing all these things, is it possible that the LLMs commodify Office?

我想知道这些数据库、画布、Excel表格或其他什么——如果LLM是你访问所有这些事物的主要入口点，LLM是否可能使Office商品化？

Satya Nadella

It's an interesting one. The way I think about the first phase, at least, would be: Can the LLM help me do my knowledge work using all of these tools or canvases more effectively?

这是一个有趣的问题。我想到的第一阶段，至少是：LLM能否帮助我更有效地使用所有这些工具或画布来完成我的知识工作？

One of the best demos that I've seen is a doctor getting ready for a tumor board workflow. She's going into a tumor board meeting, and the first thing she uses Copilot for is to create an agenda for the meeting because the LLM helps reason about all the cases, which are in some SharePoint site. It says, "Hey, these cases -- obviously, a tumor board meeting is a high-stakes meeting where you want to be mindful of the differences in cases so that you can then allocate the right time."

我看到过最好的演示之一是一个医生准备进行肿瘤委员会的工作流程。她要参加肿瘤委员会会议，首先她使用Copilot创建会议议程，因为LLM帮助推理所有案例，这些案例都在某个SharePoint网站上。它说，“嘿，这些案例——显然，肿瘤委员会会议是一个高风险的会议，你需要注意案件之间的差异，这样你就可以分配适当的时间。”

Even that reasoning task of creating an agenda that knows how to split time- super. So, I use the LLM to do that. Then I go into the meeting, I'm in a Teams call with all my colleagues. I'm focused on the actual case versus taking notes, because you now have this AI copilot doing a full transcription of all of this. It's not just a transcript, but a database entry of what is in the meeting that is recallable for all time.

即使是创建一个能够分配时间的议程这样的推理任务——非常棒。所以，我使用LLM来完成这个任务。然后我进入会议，和我的所有同事进行Teams通话。我专注于实际案例，而不是做笔记，因为现在有了这个AI助手，它做了整个会议的转录。它不仅仅是转录，还作为一个数据库条目，记录会议中的所有内容，随时可以回忆。

Then she comes out of the meeting, having discussed the case and not been distracted by note-taking. She's a teaching doctor; she wants to go and prep for her class. And so she goes into Copilot and says, "Take my tumor board meeting and create a PowerPoint slide deck out of it so that I can talk to my students about it."

然后她在讨论了病例并且没有被做笔记的事情分心后走出会议。她是个教学医生；她想去为她的课程做准备。所以她进入Copilot说，“把我的肿瘤委员会会议记录转化成一个PowerPoint幻灯片，我可以用来和学生讲解。”

So that’s the type. The UI and the scaffolding that I have are canvases that are now getting populated using LLMs. And the workflow itself is being reshaped; knowledge work is getting done.

这就是这类情况。我拥有的UI和框架是现在通过LLM填充的画布。而工作流本身也在重新塑造；知识工作正在完成。

Here's an interesting thing: If someone came to me in the late '80s and said, "You're going to have a million documents on your desk," I would say, "What the heck is that?" I would have literally thought there was going to be a million physical copies of things on my desk. Except, we do have a million spreadsheets and a million documents.

有趣的是：如果在80年代末期有人来对我说，“你将有一百万份文件在你的桌上，”我会说，“那是什么鬼？”我当时真的以为桌上会有一百万份实物副本。但实际上，我们确实拥有一百万份电子表格和一百万份文档。

Dwarkesh Patel

I don’t, you do.

我不这么认为，你才是。

Satya Nadella

They're all there. And so, that's what's going to happen with even agents. There will be a UI layer. To me, Office is not just about the office of today; it's the UI layer for knowledge work. It'll evolve as the workflows evolve. That's what we want to build.

它们都在那里。所以，这也会发生在代理身上。会有一个UI层。对我来说，Office不仅仅是今天的办公软件，它是知识工作的平台UI层。它将随着工作流程的发展而演变。这就是我们想要构建的。

I do think the SaaS applications that exist today, these CRUD applications, are going to fundamentally be changed because the business logic will go more into this agentic tier. In fact, one of the other cool things today in my Copilot experience is when I say, "Hey, I'm getting ready for a meeting with a customer," I just go and say, "Give me all the notes for it that I should know." It pulls from my CRM database, it pulls from my Microsoft Graph, creates a composite, essentially artifact, and then it applies even logic on it. That, to me, is going to transform the SaaS applications as we know of it today.

我确实认为今天存在的SaaS应用程序，这些CRUD应用程序，将发生根本变化，因为业务逻辑将更多地进入这个代理层。实际上，在我今天的Copilot体验中，另一个有趣的事情是，当我说：“嘿，我准备和客户开会时，”我只需要说，“给我所有我应该知道的会议记录。”它从我的CRM数据库中提取，从我的Microsoft Graph中提取，创建一个综合的，基本上是工件的东西，然后它在其上应用逻辑。对我来说，这将改变我们今天所知道的SaaS应用程序。

Dwarkesh Patel

SaaS as an industry might be worth hundreds of billions to trillions of dollars a year, depending on how you count. If really that can just get collapsed by AI, is the next step up in your next decade 10X-ing the market cap of Microsoft again? Because you're talking about trillions of dollars...

SaaS作为一个行业可能每年价值数百亿到数万亿美元，这取决于你怎么计算。如果AI真的可以摧毁这个行业，那么你接下来十年的目标是否是将微软的市值再次提升10倍？因为你在谈论的是数万亿美元……

Satya Nadella

It would also create a lot of value in the SaaS. One thing we don't pay as much attention to perhaps is the amount of IT backlog there is in the world.

这也将为SaaS创造大量价值。也许我们没有太多关注的是世界上存在的IT积压问题。

These code gen things, plus the fact that I can interrogate all of your SaaS applications using agents and get more utility will be the greatest explosion of apps, they'll be called agents, so that for every vertical, in every industry, in every category, we're suddenly going to have the ability to be serviced.

这些代码生成工具，再加上我可以通过代理来查询你所有的SaaS应用程序并获得更多效用，这将是应用程序的最大爆炸，它们将被称为代理，所有垂直领域、所有行业、所有类别，我们突然将具备获得服务的能力。

So there's going to be a lot of value. You can't stay still. You can't just say the old thing of, "Oh, I schematized some narrow business process, and I have a UI in the browser, and that's my thing." That's ain’t going to be the case. You have to go up-stack and say, "What's the task that I have to participate in?"

所以将会有大量的价值。你不能停滞不前。你不能再说那种老掉牙的说法，“哦，我简化了某个狭窄的业务流程，然后我有一个浏览器中的UI，这就是我的事情。”这种情况不会发生。你必须向上提升，去思考，“我需要参与的任务是什么？”

You will want to be able to take your SaaS application and make it a fantastic agent that participates in a multi-agent world. As long as you can do that, then I think you can even increase the value.

你将希望能够将你的SaaS应用程序转变为一个出色的代理，参与到一个多代理的世界中。只要你能做到这一点，那么我认为你甚至可以提升它的价值。

1:04:59 - 34 years at Microsoft

Dwarkesh Patel

Can I ask you some questions about your time at Microsoft?

我可以问你一些关于你在微软工作时的经历吗？

Satya Nadella

Yeah.

是的。

Dwarkesh Patel

Is being a company man underrated? So you've spent most of your career at Microsoft, and you could say that one of the reasons you've been able to add so much value is you've seen the culture, the history, and the technology. You have all this context by rising up through the ranks. Should more companies be run by people who have this level of context?

做一个“公司人”是不是被低估了？你大部分职业生涯都在微软度过，可以说你之所以能创造如此多的价值，是因为你见证了公司的文化、历史和技术。你通过逐步晋升积累了全部这些背景信息。是否应该让更多公司由具备这种背景的人来管理？

Satya Nadella

That's a great question. I've not thought about it that way.

这是个好问题。我以前没这么考虑过。

Through my 34 years now of Microsoft, each year I felt more excited about being at Microsoft versus thinking that, oh, I'm a company person or what have you. I take that seriously, even for anybody joining Microsoft. It's not like they're joining Microsoft as long as they feel that they can use this as a platform for their both economic return, but also a sense of purpose and a sense of mission that they can accomplish by using us as a platform. That's the contract.

在我34年的微软生涯中，每一年我都比单纯觉得“我是个公司人”更为激动，因为我真切感受到，在加入微软后，人们不仅能获得经济回报，更能实现使命感和目标感——这就是我们的承诺。

So I think yes, companies have to create a culture that allows people to come in and become company people like me. Microsoft got it more right than wrong, at least in my case, and I hope that remains the case.

所以，我认为企业必须营造一种文化，让人们能像我一样成为真正的“公司人”。就我而言，微软在这方面做得对多于错，我希望这种情况能一直保持下去。

Dwarkesh Patel

The sixth CEO that you’re talking about, who’ll get to use the research you’re starting now, what are you doing to retain the future Satya Nadellas so that they're in a position to become future leaders?

你提到的第六位CEO，也就是将来能利用你们现在开始的研究成果的人，你打算如何留住未来的“Satya Nadella”，以便他们有资格成为未来的领导者？

Satya Nadella

It's fascinating. This is our 50th year, and I think a lot about it. The way to think about it is, longevity is not a goal; relevance is.

这真令人着迷。现在是我们的50周年，我对此思考良多。我的看法是，长寿不是目标，关键在于是否具有持续的相关性。

The thing that I have to do and all 200,000 of us have to do every day is: Are we doing things that are useful and relevant for the world as we see it evolving, not just today, but tomorrow?

我和我们这20万员工每天必须做的，就是问自己：我们是否在做那些对这个不断变化的世界既有用又相关的事情，不仅是今天，而是明天？

We live in an industry where there's no franchise value, so that’s the other hard part. If you take the R&D budget that we will spend this year, it’s all speculation on what's going to happen five years from now. You have to basically go in with that attitude, saying, "We are doing things that we think are going to be relevant."

我们所处的行业没有特许经营价值，这也是难点之一。如果你看我们今年的研发预算，其全部都是对未来五年会发生什么的猜测。你必须以这样的心态去做事：“我们正在做那些我们认为未来仍然相关的事情。”

So that's what you have to focus on. Then know that there's a batting average, and you're not going to get—you have to have a high tolerance for failure. You have to take enough shots on goal to be able to say, "Okay, we will make it to the other side as a company." That's what makes it tricky in this industry.

这就是你必须专注的。然后，要明白成功率不是百分之百，你必须对失败有很高的容忍度。你需要尝试足够多的“射门”，才能说：“好，我们作为一家公司，总会闯过难关。”这正是这个行业的复杂之处。

很合理、很准确的定位。

Dwarkesh Patel

Speaking of— you just mentioned that you're two months away from your 50th anniversary of Microsoft’s founding. If you look at the top 10 companies by market cap, or top 5, basically, everybody else but Microsoft is younger than Microsoft. It's an interesting observation about why the most successful companies often are quite young. The average Fortune 500 company will last 10 to 15 years.

说到这个——你刚才提到离微软成立50周年还有两个月。如果你看看市值排名前10或前5的公司，除了微软之外，其余公司都比微软年轻。这是个有趣的现象，说明最成功的公司往往都很年轻，而普通的财富500强公司平均只能持续10到15年。

What has Microsoft done to remain relevant for this many years? How do you keep refounding?

那么，多年来微软是如何保持相关性、不断“重塑自我”的？你们如何实现“再创立”？

Satya Nadella

I love that, Reed Hoffman uses that term, "refounding." That's the mindset. People talk about founder mode, but for us mere mortal CEOs, it's more like refounder mode.

我很喜欢这个概念，Reid Hoffman用了“再创立（refounding）”这个词。这就是一种心态。人们谈论创始人模式，但对我们这些普通CEO来说，更像是“再创始人模式”。

To be able to see things again in a fresh way is the key. To your question: can we culturally create an environment where refounding becomes a habit thing? Every day we come in and say, "We feel we have a stake in this place to be able to change the core assumptions of what we do and how we relate to the world around us. Do we give ourselves permission?” I think many times, companies feel over-constrained by either business model or whatever. You just have to unconstrain yourself.

关键在于能以全新的视角重新审视事物。回答你的问题：我们能否在文化上营造一种环境，让“再创立”成为一种习惯？每天我们进公司时都在想：“我们是否认为自己在这里有一份份额，能够改变我们所做的事情的核心假设以及我们与周围世界的关系？我们是否给予自己这种许可？”我认为很多时候，公司会被商业模式等因素过度束缚，你必须学会解放自己。

Dwarkesh Patel

If you did leave Microsoft, what company would you start?

如果你离开微软，你会创办什么公司？

Satya Nadella

Company I would start? Man. That’s where the company man and me sort of says, “I'll never leave Microsoft.”

我会创办什么公司？伙计，我这个典型的公司人就会说：“我永远不会离开微软。”

If I were thinking of doing something, I think picking a domain that has... When I look at the dream of tech, we've always said technology is about the biggest, greatest democratizing force.

如果我真的打算做些什么，我会选择一个拥有巨大潜力的领域……当我思考科技的梦想时，我们一直认为技术是最大的、最伟大的民主化力量。

I feel like finally, we have that ability. If you say those tokens per dollar per watt is what we can generate, I would love to find some domain in which that can be applied, where it is so underserved.

我觉得终于我们拥有了这种能力。如果你说每美元每瓦能产生的代币数量就是我们的产出，我会非常愿意找到一个可以应用这种能力的领域——一个极度被忽视的领域。

That's where healthcare, education... Public sector would be another place. If you take those domains, which are the underserved places, where my life as a citizen of this country or a member of this society or anywhere, would I be better off if somehow all this abundance translated into better healthcare, better education, and better public sector institutions serving me as a citizen? That would be a place.

那正是医疗、教育……以及公共部门的领域。如果你看看那些被忽视的领域，作为这个国家的公民、这个社会的一员，或者在其他任何地方，如果所有这种丰富资源能转化为更好的医疗、更好的教育和更优质的公共机构为我服务，我的生活是否会变得更好？那将是我愿意涉足的领域。

1:10:46 - Does Satya Nadella believe in AGI?

Dwarkesh Patel

One thing I'm not sure about, hearing your answers on different questions, is whether you think AGI is a thing. Will there be a thing which automates all cognitive labor, like anything anybody can do on a computer?

听你回答各种问题后，我有一点不确定的是，你是否认为通用人工智能是真实存在的？是否会出现一种能够自动化所有认知劳动的东西，就像任何人能在电脑上完成的任何事情一样？

Satya Nadella

This is where I have a problem with the definitions of how people talk about it. Cognitive labor is not a static thing. There is cognitive labor today. If I have an inbox that is managing all my agents, is that new cognitive labor?

这正是我对人们谈论它的定义产生问题的地方。认知劳动并不是一成不变的。今天就存在认知劳动。如果我的收件箱在管理我所有的代理，这难道就是新的认知劳动吗？

Today's cognitive labor may be automated. What about the new cognitive labor that gets created? Both of those things have to be thought of, which is the shifting…

今天的认知劳动可能会被自动化。那么，新产生的认知劳动呢？这两者都必须被考虑在内，因为它们在不断转变……

That's why I make this distinction, at least in my head: Don't conflate knowledge worker with knowledge work. The knowledge work of today could probably be automated. Who said my life's goal is to triage my email? Let an AI agent triage my email.

这就是为什么我在脑海中做出这样的区分：不要混淆知识工作者和知识工作。今天的知识工作很可能可以被自动化。谁说我人生的目标就是分拣我的电子邮件？让一个AI代理来分拣我的邮件吧。

But after having triaged my email, give me a higher-level cognitive labor task of, "Hey, these are the three drafts I really want you to review." That's a different abstraction.

但是，在分拣完邮件之后，再给我一个更高层次的认知劳动任务：“嘿，这三份草稿我真希望你能审阅一下。”这是一种不同的抽象层次。

Dwarkesh Patel

But will AI ever get to the second thing?

但AI是否能达到第二种水平呢？

Satya Nadella

It may, but as soon as it gets to that second thing, there will be a third thing. Why are we thinking that somehow, when we have dealt with tools that have changed what cognitive labor is in history, why are we worried that all cognitive labor will go away?

可能会，但一旦它达到第二种水平，就会出现第三种。为什么我们总认为，在面对那些改变了历史上认知劳动方式的工具后，会担心所有的认知劳动都会消失？

Dwarkesh Patel

I'm sure you've heard these examples before, but the idea that horses can still be good for certain things, there are certain terrains you can't take a car on. But the idea that you're going to see horses around the street, they’re going to employ millions of horses, it’s just not happening.

我相信你以前听过这些例子，意思是马匹在某些场合依然很有用，在某些地形上汽车无法行驶。但认为你会在街上到处看到马，马匹会被雇用成百万之众，这根本是不可能的。

And then the idea is, could a similar thing happen with humans?

那么问题来了，类似的事情会在人类身上发生吗？

Satya Nadella

But in one very narrow dimension? It's only 200 years of history of humans where we have valued some narrow sort of things called "cognitive labor" as we understand it.

但仅仅在一个非常狭窄的维度上吗？人类只有200年的历史在我们看来重视那种狭义的“认知劳动”。

Let's take something like chemistry. If this thing, quantum plus AI really helped us do a lot of novel material science and so on, that's fantastic to have novel material science being done by it. Does that take away from all the other things that humans can do?

以化学为例。如果量子技术加上AI真能帮助我们开展许多新颖的材料科学研究，那固然了不起。但这是否会剥夺人类能做的其他所有事情？

Why can't we exist in a world where there are powerful cognitive machines, knowing that our cognitive agency has not been taken away?

为什么我们不能生活在一个存在强大认知机器的世界里，同时又确信我们的人类认知能力并未被剥夺？

Dwarkesh Patel

I'll ask this question, not about you, but in a different scenario, so maybe you can answer it without embarrassment. Suppose on the Microsoft board, could you ever see adding an AI to the board? Could it ever have the judgment, context, and holistic understanding to be a useful advisor?

我来问一个问题，不针对你本人，而是换个场景，也许你可以毫不尴尬地回答。假设在微软董事会中，你是否能想象加入一个AI作为董事？它是否能拥有判断力、背景知识和整体理解，成为一个有用的顾问？

Satya Nadella

It's a great example. One of the things we added was a facilitator agent in Teams. The goal there, it's in the early stages, is can that facilitator agent use long-term memory, not just on the context of the meeting, but with the context of projects I'm working on, and the team, and what have you, be a great facilitator?

这是一个很好的例子。我们在Teams中添加了一个辅助代理。其目标处于初期阶段，即这个辅助代理能否利用长期记忆，不仅局限于会议的语境，还能结合我正在进行的项目、团队等背景，成为一个优秀的协助者？

I would love it even in a board meeting, where it's easy to get distracted. After all, board members come once a quarter, and they're trying to digest what is happening with a complex company like Microsoft. A facilitator agent that actually helped human beings all stay on topic and focus on the issues that matter, that's fantastic.

我甚至希望在董事会会议上也能这样使用，因为那种场合很容易分心。毕竟，董事会成员每季度只来一次，他们要消化像微软这样复杂公司的动态。一个真正能帮助人类保持主题、专注于重要问题的辅助代理，那真是太棒了。

That's kind of literally having, to your point about even going back to your previous question, having something that has infinite memory that can even help us. You know, after all, what is that Herbert Simon thing? We are all bounded rationality. So if the bounded rationality of humans can actually be dealt with because there is a cognitive amplifier outside, that's great.

这实际上就像你提到的，回到你之前的问题，拥有某种拥有无限记忆的东西，能真正帮助我们。毕竟，赫伯特·西蒙曾说过，我们的理性是有限的。如果人类的有限理性能通过外部的认知放大器得到补偿，那就太好了。

Dwarkesh Patel

Speaking of materials and chemistry stuff, I think you said recently that you want the next 250 years of progress in those fields to happen in the next 25 years. Now, when I think about what's going to be possible in the next 250 years, I'm thinking like space travel, and space elevators, and immortality, and curing all diseases. Next 25 years, you think?

说到材料和化学方面的东西，我记得你最近说过，希望在这些领域未来250年的进步能在接下来的25年内实现。现在，当我想到未来250年可能实现的事情时，我会想到太空旅行、太空电梯、永生以及治愈所有疾病。你认为这25年内能实现吗？

Satya Nadella

One of the reasons why I brought that up was, I love that thing of, the industrial revolution was the 250 years. We have to take this entire change from a carbon-based system to something different.

我提到这一点的原因之一是，我很喜欢这样一个观点：工业革命跨越了250年。我们必须将整个体系从以碳为基础转变为其他全然不同的东西。

That means you have to fundamentally reinvent all of what has happened with chemistry over the last 250 years. That's where I hope we have this quantum computer, this quantum computer helps us get to new materials, and then we can fabricate those new materials that help us with all of the challenges we have on this planet. And then I'm all for interplanetary travel.

这意味着你必须从根本上重新发明过去250年化学领域发生的一切。这正是我希望通过量子计算机来实现的：利用量子计算机帮助我们研发新材料，然后我们能够制造出这些新材料，以应对地球上所有的挑战。至于星际旅行，我也是完全支持的。

Dwarkesh Patel

Amazing. Satya, thank you so much for your time.

太棒了。萨提亚，非常感谢你的时间。

Satya Nadella

Thank you so much. It's wonderful. Thanks.

非常感谢。真是太美好了。谢谢。

Dwarkesh Patel

Great, thank you.

太好了，谢谢。

2025-02-19 Satya Nadella.Microsoft’s AGI Plan & Quantum Breakthrough

2025-02-19 Satya Nadella.Microsoft’s AGI Plan & Quantum Breakthrough

0:00:00 - Intro

0:05:04 - AI won't be winner-take-all

0:15:18 - World economy growing by 10%

0:21:39 - Decreasing price of intelligence

0:30:19 - Quantum Breakthrough

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