#494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution

发布时间 2026-03-24 00:28:42 来源

The following is a conversation with Jensen Huang, CEO of NVIDIA, one of the most important and influential companies in the history of human civilization. NVIDIA is the engine powering the AI revolution, and a lot of its success can be directly attributed to Jensen's sheer force of will and as many brilliant bets and decisions as a leader, engineer, and innovator.

以下是一段与黄仁勋的对话。黄仁勋是NVIDIA公司的首席执行官，NVIDIA是人类文明历史上最重要和最具影响力的公司之一。NVIDIA是推动人工智能革命的核心力量，其成功很大程度上可以直接归功于黄仁勋的坚定意志以及他作为领导者、工程师和创新者所做出的许多明智决策与押注。

And now a quick few second mention of eSponsor. Check them out in the description or at lexfreedman.com slash sponsors. It is in fact the best way to support this podcast. We got Shopify for selling stuff online, Element for electrolytes, Finn for customer service AI agents, Quote for a phone system like call text contacts for your business, and Perplexity for curiosity-driven knowledge exploration. Choose what's in my friends.

现在快速提一下我们的赞助商。可以在描述中查看他们或者访问 lexfreedman.com/sponsors。这确实是支持这个播客的最佳方式。我们有用于在线销售商品的 Shopify，提供电解质的 Element，客服AI代理 Finn，适合企业的电话系统 Quote，可以进行拨打、短信、联系人管理，以及针对好奇心驱动知识探索的 Perplexity。选择你感兴趣的吧，我的朋友们。

And now onto the full ad reads. I try to make them interesting, but if you skip, please still check out our sponsors. I enjoy their stuff, maybe you will too. To get in touch with me for whatever reason, go to lexfreedman.com slash contact. Alright, let's go.

现在进入完整的广告宣传。我会尽量让它们变得有趣，但如果你跳过了，仍然请查看我们的赞助商。我很喜欢他们的产品，也许你也会喜欢。如需与我联系，请访问 lexfreedman.com/contact。好了，我们开始吧。

This episode is brought to you by Shopify, a platform designed for anyone to sell anywhere with a great looking online store. Now I know it's an incredible platform for selling stuff. It's a mechanism by which you can buy stuff on the internet. The thing I like to celebrate is engineering. They just recently tweeted about SIMGEM, which runs simulated shopping sessions by the hundreds of thousands daily.

本期节目由Shopify赞助。Shopify是一个平台，为任何人提供了随时随地开设漂亮的在线商店的可能。我知道它是一个非常棒的商品销售平台，你可以通过它在互联网上购买商品。我想特别提到的是他们的工程技术。他们最近在推特上谈到SIMGEM，这是一个每天运行数十万次模拟购物会话的系统。

I personally love the idea that things at scale, especially now the LLM models, can be simulated. You basically want to be simulating human behavior, human decision making, human choice, in this particular context of course, the shopping. It's really fascinating and they describe in their blog posts how they're leveraging and video stack to accomplish this task. But you should know, in general, you can send out for a $1 per month trial period at shopify.com slash lex. That's all the more case. Go to shopify.com slash lex to take your business to the next level today.

我个人非常喜欢这样一个想法：大规模的事物，尤其是现在的大型语言模型，可以被模拟。你实际上是想要模拟人类行为、人类决策、人类选择，在这个特定的背景下，当然就是购物。这真的很吸引人。他们在博客文章中描述了他们如何利用视频技术来完成这个任务。不过你应该知道，总的来说，你可以花每月1美元在 shopify.com/lex 注册一个试用期。在 shopify.com/lex，让你的业务更上一层楼。

This episode was also brought to you by Element. My daily zero sugar delicious electrolyte mix that as far as I know, has very little to do with the artificial intelligence and GPUs and CPUs and the revolution that we're experiencing in the tech sector. And I think that's beautiful because I got in the chance to train a bunch of world class fighters, wrestlers, grapplers recently.

这一期节目也由Element赞助。Element是我每天饮用的一种零糖美味电解质混合饮料。据我所知，它与人工智能、GPU、CPU以及我们在科技领域经历的革命几乎没有什么关系。我觉得这很棒，因为最近我有机会训练一群世界级的拳手、摔跤手和搏击术选手。

I'm going to be traveling towards the world that doesn't really have much. And I think in those parts of the world is where the mind can reconnect with the things that are truly important, that are truly timeless. Anyway, in those parts of the world, I often get really out there in terms of physical strain and diet and dehydration and so on as well. Element's one of the crucial things in my bag, really water and salt and really nice, delicious, well balanced salt, meaning sodium potassium magnesium electrolytes. Element is my go to watermelon salt. My favorite flavor.

我要去一个资源不多的地方旅行。我认为，在这样的地方，我们的心灵可以重新连接那些真正重要和永恒的事物。无论如何，在这些地方旅行时，我经常会面临身体的极限，比如饮食不规律和脱水等等。在我的旅行包里，水和盐是至关重要的，特别是那种味道好、营养均衡的盐，包含钠、钾、镁等电解质。Element 是我旅行必备的西瓜味盐，是我最喜欢的口味。

Get a free eight-count sample pack with any purchase. Try it to drinkelement.com slash lex. This episode was also brought to you by Finn, a powerful AI system that focuses on customer service. It's trusted by over 6000 companies. It has a 65 average resolution rate and is built to handle complex multi-step queries like returns, exchanges, and disputes.

购买任何商品即可获得一份免费的八件装样品。你可以访问drinkelement.com/lex试用。本集节目还由Finn赞助，它是一个强大的人工智能系统，专注于客户服务，深受6000多家公司的信赖。它的平均解决率为65%，能够处理复杂的多步骤问题，如退货、换货和争议。

This is such a fascinating problem because customer problems, the bulk of them fall into very specific set of categories, but there's nuanced details within those categories that make all the difference. It can be an incredibly frustrating thing for a human being like myself. I swear, I promise. Definitely not a robot wouldn't tell you if I was, but it's frustrating for a human to come to the customer service process and to know that your problem kind of is like this problem.

这真是一个令人着迷的问题，因为大多数客户问题都属于非常特定的类别，但这些类别中存在的细节差异往往决定了问题的解决方式。这对像我这样的普通人来说可能是非常让人沮丧的。我发誓，我保证，如果我是机器人的话也不会告诉你，但对于人类来说，参与客户服务过程并发现自己的问题与其他问题有些相似，却又有细微不同，这确实会让人感到挫败。

All these details that you can provide about the system you're operating on, those specifics of the puzzle you're trying to solve. But there's details that you just know in your gut that this is important, especially if you kind of thought through the problem. I've been there for quite a bit. You want to have some level of personalization that can get to the tricky aspect, the perspective on the problem that really would lead you down the road to a solution.

你可以提供关于你正在操作的系统的所有细节，以及你试图解决的难题的具体信息。但是，有些细节就是凭直觉知道很重要，尤其是在你已经深入思考过这个问题的时候。我有过不少这样的经历。你需要有一定程度的个性化，才能触及棘手的方面，从而找到真正引导你走向解决方案的视角。

Anyway, love this problem. Really glad Finn is focusing on it. Go to fin.ai-slash-lex to learn more about transforming your customer service and scaling your support team that's fin.ai-slash-lex. This episode is also brought to you by Quo, spelled Q-U-O. Also known as a company with just three letters will win you a game of Scrabble.

无论如何，我非常喜欢这个问题。很高兴Finn正在关注它。访问 fin.ai/lex 以了解更多关于如何改变您的客户服务并扩展您的支持团队的信息，即 fin.ai/lex。本集节目的赞助商还有Quo，拼写为Q-U-O。这个名字仅三个字母，在拼字游戏中也会助你获胜。

That is not a joke. It feels like a joke I have made before, but let's run with it. It's a bad joke. It's a bad joke. The only thing worse than a bad joke is a bad joke. But here we go, spelled Q-U-O, a business phone platform for calling and messaging. Basically, you have a bunch of people trying to help a larger group of people and you want to orchestrate how they communicate with each other. This is just the system that does it extremely well, period. Quo integrates AI into the whole, Chabang, organizing everything, generating summaries, highlighting the next steps, all that kind of stuff. It just does as well. The interface on top of the AI is also really strong.

这不是一个玩笑。虽然感觉像是我以前讲过的笑话，但我们还是先这样吧。这是个糟糕的笑话。唯一比糟糕笑话更糟糕的就是再次讲糟糕笑话。不过我们继续，说的是 Quo，一个用于拨打电话和发送信息的商务通讯平台。简单来说，就是有一群人试图帮助更大群体的人，而你想要协调他们之间的交流。这就是一个非常好的系统。Quo 将人工智能整合到整个过程中，组织一切，生成摘要，突出下一步等。这些都能很好地完成。针对人工智能的界面也非常出色。

So try Quo for free. Plus get 20% off your first six months when you go to Quo.com-slash-lex. That's Q-U-O.com-slash-lex. This is the Lex Freeman podcast and now, dear friends, here's Johnson, Quo. You've propelled Nvidia into a new era in AI. You can be on this focus on chip scale design to now rack scale design. I think it's fair to say that winning for Nvidia for a long time used to be about building the best GPU possible. You still do, but now you've expanded that extreme code design of GPU, CPU, memory, networking, storage, power, cooling, software, the rack itself, the pod that you've announced, and even the data center.

试用Quo，免费体验。此外，在您首次访问Quo.com/lex时，可以享受前六个月的费用八折优惠。网址为Q-U-O.com/lex。这是Lex Freeman的播客节目，现在，亲爱的朋友们，这里是Johnson，来自Quo。你们已经推动Nvidia进入AI的新时代。从专注于芯片级设计到现在的机架级设计。我认为可以公平地说，长期以来，Nvidia的成功主要在于打造最好的GPU。当然，你们仍在这样做，但现在你们已经将这种极致的共同设计扩展到GPU、CPU、内存、网络、存储、电力、冷却、软件、本身的机架、你们宣布的模块，甚至包括整个数据中心。

Let's talk about extreme code design. But is the hardest part of a code designing system with that many complex components and design variables? Yeah, thanks for that question. So first of all, the reason why extreme code design is necessary is because the problem no longer fits inside one computer to be accelerated by one GPU. The problem that you're trying to solve is you would like to go faster than the number of computers that you add. So you added 10,000 computers, but you would like to go a million times faster. Then all of a sudden, you have to take the algorithm, you have to break up the algorithm, you have to refactor it, you have to shard the pipeline, you have to shard the data, you have to shard the model.

让我们谈谈极限代码设计。最困难的部分是否是在拥有众多复杂组件和设计变量的系统中进行设计呢？谢谢你提出这个问题。首先，之所以需要进行极限代码设计，是因为问题的规模已经超出了单台计算机使用一个GPU加速的能力。你想要解决的问题是，即便增加了10,000台计算机，你也希望能达到快一百万倍的速度。因此，你必须对算法进行拆解和重构，管道、数据和模型都需要进行分片处理。

Now all of a sudden, when you distribute the problem this way, not just scaling up the problem, but you're distributing the problem, then everything gets in the way. This is the Amdol's law problem, where the amount of speed up you have for something depends on how much of the total workload it is. And so if computation represents 50% of the problem, and I sped up computation infinitely, like a million times, I only sped up the total workload by a factor of two.

现在，当你以这种方式分配问题时，突然之间，一切都会变得复杂。不仅仅是把问题扩大规模，而是要分配这个问题，就会遇到很多阻碍。这就是阿姆达尔定律的问题，其意思是你加速完成任务的效果取决于这个任务在总工作量中所占的比例。举个例子，如果计算部分占整个问题的50%，即便我让计算速度提升了一百万倍，也只能把总工作量的速度提高到两倍。

Now all of a sudden, not only do you have to distribute the computation, you have to shard the pipeline somehow, you also have to solve the networking problem, because you've got all of these computers are all connected together. And so distributed computing at the scale that we do, the CPU is a problem, the GPU is a problem, the networking is a problem, the switching is a problem, and distributing the workload across all these computers are a problem. It's just a massively complex computer science problem. So we just got to bring every technology to bear. Otherwise we scale up linearly, or we scale up based on the capabilities of Moore's law, which has largely slowed, because Dynard's scaling has slowed.

现在突然之间，不仅需要分布计算，还得想办法对管道进行分片，同时也得解决网络问题，因为所有计算机都连接在一起。因此，在我们这种规模的分布式计算中，CPU是个问题，GPU是个问题，网络是个问题，切换也是个问题，将工作负载分配到所有这些计算机上更是个问题。这真是一个极其复杂的计算机科学难题。所以我们必须利用所有技术，否则我们只能线性扩展，或者依赖摩尔定律的能力进行扩展，而摩尔定律因为Dynard缩放的减缓，其发展速度已明显放慢。

I'm sure there's trade-offs there. Plus you have a complete disparate disciplines here. I'm sure you have specialists in each one of these, high bandwidth memory, the network and the NV link, the nix, the optics and the copper that you're doing, the power delivery, the cooling, all that. I mean, there's like world experts in each of those. How do you get them in a room together to figure out my staff is so large? What's the process? You take me through the process of the specialists and the journalists, like how do you put together the rack when you know the set of things you have to shove into a rack together?

我相信这里面肯定有一些取舍。而且，你这里涉及到的是完全不同的学科。我相信在其中的每一个领域，比如高带宽内存、网络、NV链接、网络接口卡、光学和铜线、供电系统、冷却系统等，都有各自的专家。我的意思是，这些领域都有世界级的专家。你们是怎么让他们齐聚一室来解决“我的团队如此庞大”的问题的？这个过程是怎样的？请你带我了解一下这些专家和一般人员是如何合作的，比如当需要把所有这些设备整合到一个机架里时，该怎么做？

What does that process look like of designing it all together? There's the first question, which is what is extreme co-design? You're optimizing across the entire stack of software, from architectures to chips to systems, the system software to the algorithms to the applications. That's one layer. The second thing that you and I just talked about is, goes beyond CPUs and GPUs and networking chips and scale up switches and scale out switches. And then of course you got to include power and cooling and all of that because all these computers are extremely, extremely power hungry.

这个过程看起来是怎样的——一起设计所有东西？首先要问的是，什么是极致共同设计？这意味着你需要在整个软件堆栈上进行优化，从体系结构到芯片再到系统，从系统软件到算法再到应用程序。这是一个层面。第二点，我们刚刚谈到的要超越传统的CPU和GPU，以及网络芯片、纵向扩展交换机和横向扩展交换机。当然，还必须考虑到电力和散热，因为所有这些计算机都极其耗电。

They do a lot of work and they're very energy efficient, but these in aggregate still consume a lot of power. And so that's one, the first question is what is it? The second question is why is it? And we just spoke about the reason you want to distribute the workloads so that you can exceed the benefit of just increasing the number of computers. And then the third question is how is it? How do you do it? That's kind of the miracle of this company. When you're designing a computer, you have to have an operating system of computers. When you're designing a company, you should first think about what is it that you want the company to produce? I see a lot of companies' organization charts and they all look the same. Burger organization charts, software organization charts and car company organization charts, they all look the same. And it doesn't make any sense to me.

他们做了很多工作并且非常节能，但总体上仍然消耗大量电力。所以，第一个问题是什么？第二个问题是为什么？我们刚刚谈到了一个原因，那就是你希望分配工作负载，以便能够获得超越仅仅增加计算机数量的好处。接下来第三个问题是如何做到这一点？这正是这家公司的神奇之处。在设计计算机时，你需要有一个计算机操作系统。在设计公司时，你首先应该考虑公司想要生产什么。我见过很多公司的组织结构图，它们看起来都一样。无论是汉堡公司、软件公司还是汽车公司的组织结构图，它们都看起来一样。这让我觉得不合理。

You know, the goal of a company is to be the machinery, the mechanism, the system that produces the output. And that output is the product that we like to create. It is also designed, the architecture of the company should reflect the environment by which it exists. It almost directly says what you should do with the organization. My direct staff is 60 people. You know, I don't have one on one with them because it's impossible. You can't have 60 people on your staff if you're going to get work done. So you still have 60 reports? You still have a course. Yeah. And most stars at least have a foot in engineering, almost all of them. There's experts in memory, there's experts in CPUs, there's experts in optical, all of them.

你知道，公司目标就是成为生产成果的机器、机制或系统。而这个成果就是我们想要创造的产品。此外，公司设计的架构应该反映其所处的环境。这几乎直接告诉我们应该如何管理公司。我的直属员工有60人，你知道，和每个人一对一沟通是不可能的。如果想完成工作，不可能直接管理60个人。所以，你有60个下属吗？你还有责任要承担。当然是的。且大多数核心成员至少有工程背景，几乎所有人都是。他们中有内存方面的专家，有CPU专家，还有光学的专家，各个领域都有。

Yeah, GPUs and architecture, algorithms, design. So you constantly have an eye on the entire stack and you're having to do like intense discussions about the design of the entire stack. And no conversation is ever one person. That's why I don't do one-on-ones. We present a problem and all of us attack it. You know, because we're doing a stream code design. Literally the company is doing a stream code design all the time. So even if you're talking about a particular component, like cooling, networking, everybody's listening in. Yeah. And it can contribute. Well, this doesn't work for the power distribution. This doesn't work for the memory. This doesn't work for this. Exactly. And whoever wants to tune out, tune out. You know what I'm saying?

是的，GPU、架构、算法、设计。这些你都需要一直关注整个技术栈，并且必须就整个栈的设计进行深入讨论。谈话从来不是一个人的事情，这就是我不进行一对一谈话的原因。我们会提出一个问题，然后我们所有人一起攻克它。因为我们一直在进行一种流水线式的代码设计。即使我们在讨论一个特定的组件，比如散热、网络，所有人都会在听，并且都可以作出贡献，比如有人会说：这对电源分配不合适，这对内存不合适，这个或那个不合适，等等。完全没问题。如果有人想走神就走神。你懂我意思吧？

Yeah. And the reason for that is because the people who are on the staff, they know when to pay attention. They're supposed to, you know, something they could have contributed to. They didn't contribute to. I'm going to call them out. You know, and so, hey, come on. Let's get in here. So as you mentioned, Nvidia is this company that's adapting to the environment. So which point can you say did the environment change and began adapting sort of secretly in the early days from GPU for gaming, maybe the early deep learning revolution to we're not going to start thinking of it as an AI factory. What does Nvidia do is produce AI? Let's build a factory.

好的。原因在于，团队中的人知道什么时候需要注意。他们本应该参与其中，但他们没有。我会指出他们的问题，然后鼓励他们参与进来。正如你提到的，英伟达是一家适应环境的公司。那么，你可以指出环境在哪一点开始改变，从最初的用于游戏的GPU逐渐适应到早期的深度学习革命，开始以AI工厂的思路进行构建。英伟达究竟做了什么来生产AI产品？让我们来建造一个“工厂”。

Oh, yeah. I could reason through it just systematically. We started out as an accelerator company. But the problem with accelerators is that the application domains to narrow. It has the benefit of being incredibly optimized for the job. You know, any specialist has that benefit. The problem with intent specialization is that, of course, your market reach is narrower. But that's even fine. The problem is the market size also dictates your R&D capacity. And your R&D capacity ultimately dictates the influence and impact that you can possibly have in computing.

哦，是的。我可以系统地推理出来。我们一开始是一家加速器公司。但加速器的问题在于其应用领域过于狭窄。虽然它在特定工作上效率极高，这是一大优势。正如所有专家一样，专注的好处很明显。不过，问题在于专注意味着市场覆盖面也会变得狭窄。不过这还算可以接受。但更大的问题是，市场规模也决定了你的研发能力。而你的研发能力最终影响着你在计算领域可能产生的影响和作用。

And so when we first started out as an accelerator, a very specific accelerator, we always knew that that was going to be our first step. We had to find a way to become accelerated computing. But the problem is when you become a computing company, it's two general purpose and it takes away from your specialization. I connected two words that actually have fundamental tension. The better computing company we become, the worse we became as a specialist. The more of a specialist, the less capacity we have to do overall computing. And so, and I connected those two words together on purpose, that the company has defined that really narrow path, step by step by step, to expand our aperture of computing, but not give up on the most important specialization that we had.

当我们作为一个加速器开始项目时，我们明确知道那是我们的第一步。我们必须找到方法成为一个加速计算的企业。但是，问题在于当你成为一个计算公司时，你会变得更通用，反而削弱了你的专业性。我将两个本质上存在紧张关系的词连接在一起：我们越成为一个优秀的计算公司，就越失去了作为专业公司的优势。越专注于专业领域，我们在整体计算方面的能力就越有限。因此，我故意把这两个词结合在一起，强调公司正在一步步地通过一个狭窄的路径来扩展我们的计算能力视野，同时不放弃我们最重要的专业领域。

Okay, so the first step that we took beyond acceleration was we invented the programmable pixel shader. So that was the first step towards programmability. You know, it was our first journey towards moving into the world of computing. The second thing that we did was we created, we put FP32 into our shaders. That FP32 step, I triply compatible FP32, was a huge step in the direction of computing. It was the reason why all of the people who were working on stream processors and other types of data flow processors discovered us. And they said, hey, all of a sudden, you know, we might be able to use this GPU as incredibly computationally intensive. And it's now compliant with I triply. I can take my software that I was writing previously on CPUs, and I can see about, you know, using the GPU for that.

好的，第一个步骤超越加速的是我们发明了可编程像素着色器。这是迈向可编程性的一大步，也是我们迈向计算领域的初次尝试。接下来，我们在着色器中引入了FP32。FP32这一步，也就是符合IEEE标准的FP32，是通往计算方向的巨大进步。这正是为什么那些从事流处理器和其它类型数据流处理器的人们发现了我们的原因。于是他们说，突然之间，我们或许可以用这个GPU进行非常密集的计算。而且它现在遵循IEEE标准。我可以尝试将以前在CPU上编写的软件，迁移到GPU上运行。

And which led us to create, put C on top of FP32, was called, we call CG. That CG path took us to eventually CUDA, CUDA, step by step by step. Well, putting CUDA on G-Force, that was a strategic decision that was very, very hard to do because it cost the company enormous amounts of our profits. And we couldn't afford it at the time. But we did it anyways because we wanted to be a computing company. A computing company has a computing architecture, a computing architecture has to be compatible across all of the chips that we built. Can you take me to that decision? So putting CUDA on G-Force could not afford to do. Can you explain that decision? Why boldly choose to do that anyway?

这个决定促使我们创建了将C放在FP32之上的技术，我们称之为CG。这个CG路径最终一步一步引导我们走向了CUDA。把CUDA应用在G-Force上是一个非常重要的战略决策，但这是个非常艰难的决定，因为它消耗了公司大量的利润，而当时我们并不具备这样的承受能力。但我们还是毅然决然地这么做了，因为我们希望成为一家计算公司。计算公司需要有一个计算架构，而这个计算架构必须与我们制造的所有芯片相兼容。可以分享一下做出这个决定的过程吗？选择将CUDA应用于G-Force虽然是公司难以承受的，但还是勇敢地做出了这样的决定，能解释一下为什么我们做了这样的决定吗？

Can you explain that decision? That was the first, I would say that that was the first strategic decision that is as close to an existential threat. For people who don't know, it turned out to be spoiler alert. One of the most incredibly brilliant decisions ever made by a company. So CUDA turned out to be an incredible foundation for computation in this AI infrastructure world. So you're just setting the context. It turned out to be a good decision. Yeah, it turned out to have been a good decision. So here's the way it went. So we invented this thing called CUDA. And it expanded the aperture of applications that we can accelerate with our accelerator.

你能解释一下这个决定吗？我想说，这是第一个接近于存在威胁的战略决策。对于不熟悉情况的人来说，剧透一下，这被证明是公司做过的最聪明的决定之一。因此，CUDA 成为这个 AI 基础设施世界中计算的一个了不起的基础。你只是简要地说明了背景，事实证明这是个不错的决定。是的，最后证明这是个不错的决定。事情是这样的：我们发明了一种叫做 CUDA 的东西，它拓宽了我们可以用加速器加速的应用程序的范围。

The question is how do we attract developers to CUDA? Because a computing platform is all about developers. And developers don't come to a computing platform just because it could perform something interesting. They come to a computing platform because the install base is large. Because a developer like anybody else wants to develop software that reaches a lot of people. The install base is in fact the single most important part of an architecture. The architecture could attract enormous amounts of criticism. For example, no architecture has ever attracted more criticism than the X86. And you know, as as a less than less than elegant architecture. But yet, it is the defining architecture of today.

问题在于我们如何吸引开发者来使用CUDA？因为一个计算平台的成功关键在于开发者。开发者不会仅仅因为一个平台可以做一些有趣的事情就去使用它。他们选择一个计算平台是因为它的用户基础庞大。因为和其他人一样，开发者希望他们开发的软件能够接触到大量用户。实际上，用户基础是一个架构最重要的部分。一个架构可能会受到大量批评。例如，没有哪个架构像X86那样受到如此多的批评，被认为是一个不够优雅的架构。但它仍然是当今最具代表性的架构。

It gives you an example that in fact so many risk architectures, which were beautifully architected, incredibly well designed by some of the brightest computer scientists in the world largely failed. And so I've gave you two examples where one is elegant, the other one is barely aesthetic. And so yet X86 survived. And the install base is everything. Install base defines an architecture. Everything else is secondary. And so there were other architectures at the time. Kuda came out, OpenCL was here. There were several other competing architectures. But the thing that the decision that we made that was good was we said, hey, look, ultimately it's about install base.

这段文字传达的意思是，即使许多由顶尖计算机科学家精心设计的风险架构失败了，x86架构依然幸存下来。这是因为安装基础（install base）至关重要，它定义了一个架构的成败，而其他因素都相对次要。当时，还有其他架构如Kuda和OpenCL等在竞争。但我们做出的正确决策是认识到安装基础才是最关键的。

And what is the best way we could get a new computing architecture into the world? By that timeframe, G-Force had become successful. We were already selling millions and millions of G-Force GPUs a year. And we said, you know, we had to put Kuda on G-Force and put it into every single PC where their customers use it or not. And use it as a starting point of cultivating our install base. Meanwhile, we'll go and attract developers and go into universities and wrote books and taught classes and put Kuda everywhere. And eventually people discover, and at the time the PC was the primary computing vehicle.

我们如何才能将一种新的计算架构推向世界？到那个时候，G-Force 已经取得了成功。我们每年已经在销售数以百万计的 G-Force GPU。我们决定将 Kuda 集成到 G-Force 中，并安装在每一台 PC 上，无论用户是否使用它。这就是我们培养用户群的起点。同时，我们去吸引开发人员，进入大学，编写书籍，教授课程，把 Kuda 推广到各个地方。最终，人们会发现这个工具，而那时 PC 是主要的计算设备。

There was no cloud. And we could put a supercomputer in the hands of every researcher in school, every scientist, every engineering school, every student in school. And eventually something amazing will happen. Well, the problem was, Kuda increased our cost of that GPU, which is a consumer product. So tremendously, it completely consumed all of the company's gross profit dollars. And so at the time, the company was probably, you know, worth, I don't know, at the time, $8 billion or something, $67 billion or something like that. After we launched Kuda, I recognized that it was going to add so much cost, but it was something we believed in.

没有云计算。我们设想给每个学校的研究员、每位科学家、每个工程学校、每位学生都配备一台超级计算机。最终，一定会有惊人的事情发生。然而，问题在于，Kuda大幅增加了显卡的成本，显卡本来是一种面向消费者的产品。这个成本增加得如此之高，以至于公司所有的毛利都被吃掉了。当时，公司大概值80亿美元或者类似的数字，在我们推出Kuda之后，我意识到这将带来巨大的成本增加，但这仍然是我们所信仰的事情。

You know, our market cap went down to like one and a half billion dollars. And so we were down, we were down there for a while. And we clawed our way back slowly. But we carried Kuda on GeForce. I always say that Nvidia is the house that GeForce built because it was GeForce that took Kuda out to everybody. Researchers, scientists, they discovered Kuda on GeForce because they were all, you know, many of them were gamers, many of them built their own PCs anyways. In a university lab, many of them built clusters themselves, you know, using PC components. And so that, you know, that's kind of how we got going. And then that became the platform of the foundation for the deep learning revolution. That was also another great, great observation, yeah.

你知道，我们的市值曾经跌到大约15亿美元。而且我们在低点停留了一段时间。然而，我们慢慢地重整旗鼓。我们把Kuda放到了GeForce上，我总是说Nvidia是由GeForce打造的，因为正是GeForce让Kuda被大家所认识。很多研究人员、科学家在GeForce上发现了Kuda，因为他们中许多人本来就是游戏玩家，很多人自己组装电脑。在大学的实验室里，他们中的很多人使用PC组件自己搭建了计算集群。所以，这就是我们开始的方式，这也成为了深度学习革命的基础平台。这个观察也非常出色。

That existential moment, do you remember, like, what were those meetings like? Were those discussions like deciding as a company, risking everything? Well, I had to make it clear to the board what we're trying to do. And the management team knew our gross margins were going to get crushed. So you could imagine a world where GeForce would carry the burden of Kuda and none of the gamers would appreciate it and none of the gamers would pay for it. You know, they only pay certain price and it doesn't matter what your cost is. And so we increased our cost by 50% and that consumed and we were a 35% gross margin company. And so it was quite a difficult decision to make. But you could imagine that someday, this could go into work stations and it would go into super computers and in those segments, maybe we can capture more margin. You could reason your way into being able to afford this, but it still took a decade.

你还记得那个关乎生死的时刻吗？那些会议是什么样的？那些讨论是在公司层面作出冒险一搏的决定吗？我是需要向董事会明确我们正在努力实现的目标。而管理团队也清楚，我们的毛利率将会大大受损。所以可以想象这样的情况：GeForce这款产品需要承担Kuda项目的重担，但却没有玩家会意识到这一点，并且没有玩家愿意为此买单。他们只愿意支付一定的价格，而不管我们的成本是多少。于是我们的成本增加了50%，而我们当时的毛利率只有35%。这的确是一个艰难的决定。但你可以想象，有一天这个项目可以进入工作站甚至超级计算机领域，在那些领域，或许我们可以获取更高的利润。尽管我们可以通过理性思考来支撑这个决策，但它确实花费了整整十年的时间。

But that's more of a conversation with the board convincing them, but you psychologically. And VDS continued to make bold bets that predict the future and in part, especially now, define the future. So I'm almost looking for wisdom about how you're able to make those decisions, to make leaps like that as a company. Well, first of all, I'm informed by a lot of curiosity. At some point, there's a reasoning system that convinces me so clearly this outcome will happen, that this will happen. So I believe in my mind and when I believe in my mind, you know how it is, you manifest a future and that future is so convincing. There's no way it won't happen. There's a lot of suffering in between, but you've got to believe what you believe.

这更多是与董事会的对话，说服他们，同时也是对自己心理上的说服。VDS不断做出大胆的决策，预测未来，尤其是在现在，甚至决定了未来。因此，我几乎是在寻求智慧，以了解你们作为公司是如何能够做出这样的决定、进行这样的飞跃的。首先，我受到极大的好奇心驱动。在某个时候，我的推理系统让我确信某个结果会发生，相信这个结果一定会发生。因此，我在头脑中相信，而当我在头脑中相信时，你知道，那就好像是在打造一个未来，这个未来是如此令人信服，根本不可能不实现。虽然其间有许多艰辛，但你必须相信自己的信念。

So you envision the future. And you essentially from a sort of engineering perspective manifest it. And you reason about how to get there. You reason about why it must exist. And I reasoned, we all reasoned here, the management team we reasoned about it, all the people that we spent a lot of time reasoning about it. The thing that the next part of this is probably a skill thing, which is oftentimes in leadership, the leadership stays quiet or they learn about something and then they do some manifesto. And it's a brand new year and somehow at the end of the year, next year we're going to have a brand new plan, big huge layoff this way, big huge organization change this way, new mission statement, brand new logos, you know, that kind of stuff.

所以你对未来有一个设想。从工程学的角度来看，你实际上是在实现这个设想。你在思考如何实现这个目标以及为什么它必须存在。我思考过，我们在这里的所有人都思考过，管理团队也曾花费大量时间来思考这个问题。接下来的部分可能涉及到一种技能，它常常出现在领导力中。领导者通常保持沉默，或者他们了解一些事情后发表宣言。新的年份开始了，某种程度上，在年底，我们将有一个全新的计划，也许会有大量裁员，组织结构发生重大变革，全新的使命宣言，新的标志，诸如此类的事情。

We just never, I never do things that way. When I learned about something and it's starting to influence how I think I'll make it very clear to everybody near me that, you know, this, this is interesting. This is going to make a difference. This is going to impact that. And I reason about things step by step by step, oftentimes I've already made up my mind, but I'll take every possible opportunity, external information, new insights, new discoveries, new engineering, you know, revelations, new milestones developed. I'll take those opportunities and I'll use it to shape everybody else's belief system.

我们就是不那样做事，我个人从来不那样做。每当我了解到一些新东西，并开始影响我的思维时，我会让周围的人非常清楚地知道这点。我会说，“你看，这个很有趣，这会带来改变，这会产生影响。” 我会逐步推理事情，很多时候我可能已经有了定论，但我仍会抓住所有可能的机会，获取外部信息、新的见解、新的发现、新的工程进展和新的里程碑。我会利用这些机会来塑造其他人的信念体系。

And I'm doing that literally every single day. I'm doing that with my board. I'm doing that with my management team. I'm doing that with my employees. I'm trying to shape their belief system such that when I come the day I say, hey, let's buy Melanox. It's completely obvious to everybody that we absolutely should. On the day that I said, hey guys, let's go all in on deep learning and let me tell you why. I've already been laying down the bricks to different organizations inside the company. Every organization and everybody, many of the people might have heard everything. Most of the company heard, here's of course, pieces of it.

我每天都在这样做。我和我的董事会一起做，我和我的管理团队一起做，我和我的员工一起做。我努力去塑造他们的信念体系，这样当有一天我说“嘿，我们去收购Melanox吧”时，每个人都能认为这是理所当然的。当我说“大家，我们全力投入深度学习，让我告诉你们原因”时，我已经在公司内部的不同组织中铺好了基础。每个组织和每个人都可能已经听到过这些，大部分公司已经听过其中的一部分了。

And on the day that I announced it, everybody's kind of bought it into many pieces of it. In a lot of ways, I like to announce these things and I imagine that the employees are kind of saying, you know, Jensen, what took you so long? And in fact, I've been shaping their belief system for some time and therefore leadership. Sometimes it looks like you're leading from behind, but you've been shaping there, you know, to the point where on the day that I declared it, 100% buy it. But that's what you want. You want to bring everybody along. You know, otherwise we announced something about deep learning and everybody goes, what are you talking about? You know, you announced something about, let's go all in on this thing and your management team, your board, your employees, your customers, they're kind of like, where's this coming from? You know, this is insane.

在我宣布这件事情的那天，几乎所有人都立刻认可了它的每一个部分。很多时候，我喜欢宣布这些事情，我想象员工们会心想：“Jensen，你怎么现在才说？”其实，我已经为他们塑造这个信念体系有段时间了，尽管看来像是我在“幕后领导”，但实际上一直在影响他们，以至于到我宣布那天，所有人都百分之百支持。这就是你所希望的效果——带领所有人一起前进。否则，当你宣布一些关于深度学习的事情时，大家会一头雾水：“你在说什么？”宣布我们要全面投入这件事情时，你的管理团队、董事会、员工、客户可能会质疑：“这是什么情况？”“这简直疯了。”

And so, so GTC, in fact, if you go back in time, you look at the keynotes, I'm also shaping the belief system of my partners in the industry and I'm using that to shape, you know, the belief system of my own employees. And so by the time that I announced something, like for example, we just announced GROC, we've been late. I've been talking about the stepping stones for two and a half years. You guys just go back and you know, oh my gosh, they've been talking about it for two and a half years. And so I've been laying the foundation step by step by step. So when the time comes, you announce it, everybody's, you know, what took you so long? But it's not just inside the company, you're shaping the landscape, the broader global landscape of innovation. Like putting those ideas out there, you really are manifesting reality.

因此，在GTC（GPU技术大会）上，实际上，如果你回顾过去的主题演讲，会发现我也在塑造行业内合作伙伴的信念系统，并利用这一点来塑造自己员工的信念系统。因此，当我宣布某个新事物时，比如我们刚刚宣布的GROC，其实已经有些迟了。在过去两年半的时间里，我一直在谈论实现这一目标的每一步。大家回想一下，就会发现我们已经谈论了两年半。因此，我一步一步地奠定了基础，以便在宣布时，大家会问：“怎么花了这么久？” 但这不仅仅是在公司内部，你实际上是在塑造更广泛的全球创新格局。通过把这些想法和理念公开，你确实是在将其变为现实。

We don't build computers. We actually don't build clouds. We don't, as it turns out, we're a computing platform company. And so nobody can buy anything from us. That's the weird thing. You know, we vertically design, vertically integrate to design and optimize. But then we open up the entire platform at every single layer to be integrated into other companies, products and services and clouds and super computers and OEM computers. And so the amazing thing is I can't do what I do without having convinced them first. And so most of GTC is about manifesting a future that by the time that we, my product is ready, they're going, what took you so long?

我们不制造计算机。实际上，我们也不构建云服务。事实上，我想说的是，我们是一家计算平台公司。所以，没有人能从我们这里直接购买任何东西。这就是比较奇怪的地方。你知道，我们在垂直设计和优化方面都进行自我整合。但是，我们会在每一层级开放整个平台，以便能与其他公司的产品、服务、云服务、超级计算机和OEM计算机进行整合。所以，令人惊讶的是，我必须首先说服他们，才能做到我所做的事情。因此，大部分的GTC（GPU技术大会）内容是关于展示一个未来，到那个时候，当我的产品准备好的时候，他们会说，你怎么花了这么长时间才到这个地步？

So one of the things you've been a believer for a long time is scaling laws broadly defined. So are you still a believer in the scaling laws? Yeah, we have more scaling laws now. So I think you've outlined four of them with pre-training, post-training, test time and agentic scaling. What do you think, when you think about the future, deep future and the near term future? What are the blockers that you're most concerned about to keep you up at night? They have to overcome in order to keep scaling? Well, we can go back and reflect on what people thought were blockers. So in the beginning, the first pre-training scaling law, people thought, well, rightfully so that the amount of data that we have, high quality data that we have, will limit the intelligence that we achieve.

你一直以来都相信广义的"扩展规律"，你现在仍然相信这些规律吗？是的，我们现在有更多的扩展规律。我想你提到了四种规律：预训练、后训练、测试阶段和代理性扩展。当你思考未来时，无论是远期还是近期，你最担心哪些障碍会让你夜不能寐？为了继续扩展，我们需要克服哪些障碍呢？我们可以回顾一下人们曾经认为的障碍。最初的预训练扩展规律中，人们认为——也有一定的道理——我们拥有的高质量数据量会限制我们所能达到的智能水平。

And that scaling law was an important, very important scaling law. The larger the model, the correspondingly more data results in a better result in a smarter AI. And so that was pre-training. And Ilias, Susquefer Ilias said, we're out of data or something, that pre-training is over or something like that. The industry panicked. You know that this is the end of AI. And of course, that's obviously not true. We're going to keep on scaling the amount of data that we have to train with. A lot of that data is probably going to be synthetic. And that also confuses people. You know, and what people don't realize is they've kind of forgotten that most of the data that we are training, that we teach each other with and form each other with, is synthetic.

这种缩放定律是一个非常重要的定律。模型越大，相应需要的数据越多，这样可以得到更好的、更聪明的AI。这就是所谓的预训练。然后，Ilias，或者说Susquefer Ilias，他说我们已经没有数据了，或者预训练已经结束了，类似这样的意思。这时行业内一片恐慌，认为这是AI的终结。当然，这显然不是真的。我们会继续扩大用来训练的数据量，其中很大一部分数据可能是合成的。这也让一些人感到困惑。人们没有意识到的是，他们似乎忘记了，大部分用来训练和互相学习的数据本身就是合成的。

You know, it's synthetic because it didn't come out of nature. You created it. I'm consuming it. I modify it, augment it. I regenerate it. Somebody else consumes it. And so we've now reached a level where AI is able to take ground truth, augment it, enhance it, synthetically generate an enormous amount of data. And that part of post-training continues to scale. And so the amount of data that we could use that is human generated will be smaller, smaller, smaller, the amount of data that we use to train model is going to continue to scale. To the point where we're no longer limited, training is no longer limited by data is now limited by compute. And the reason for that is most of the data is synthetic.

你知道，它是合成的，因为它不是从自然界中产生的。你创造了它，我在使用它。我修改它，增强它，再生它。然后，其他人也在使用它。现在我们已经达到一个水平：人工智能能够获取真实数据，对其进行扩充和增强，合成生成大量数据。而这样的后期训练还在不断扩展。所以，我们用于训练模型的人类生成数据将越来越少，而整体用于模型训练的数据量则会继续扩增。我们再也不会因为数据限制而对训练感到束手无策，现在的限制是计算能力。这是因为大多数数据都是合成的。

Then the next phase is a test time. And I still remember people telling me that inference, oh yeah, that's easy. Pre-training, that's hard. These are giant systems that people are talking about. Infraints must be easy. So inference chips are going to be little tiny chips. They're not like Nvidia's chips. Those are going to be complicated and expensive. We could make, and the future inference is going to be the biggest market. It's going to be easy. We're going to commoditize it. Everybody can build their own chips. That was always illogical to me because inference is thinking. And I think thinking is hard. Thinking is way harder than reading. Pre-training is just memorization and generalization, and looking for patterns and relationships. You're reading versus thinking, reasoning, solving problems, taking unexplored experiences, new experiences, and breaking it down into decomposing it into solvable pieces that we then go off either through first principle reasoning or through previous examples, prior experiences, or just exploration and search and trying different things.

接下来阶段是测试时期。我还记得有人告诉我，推理很简单，预训练很难。他们讨论的是大型系统，因此认为推理应该很简单。所以，他们认为推理芯片会很小，和Nvidia的芯片不一样，后者又复杂又昂贵。有人说，我们可以做到，而且未来推理市场会是最大的市场，推理会简单化，大家都能制造自己的芯片。这对我来说一直不太合逻辑，因为推理其实是思考。我认为思考是困难的，比阅读要难多了。预训练只是记忆化、泛化，寻找模式和关系，这类似于阅读。而推理涉及思考、推理、解决问题，需将未经历过的新体验分解为可以解决的小部分，这可能通过基础原理推理，或借鉴以前的例子与经验，甚至是通过探索与尝试不同的方式来完成。

That whole process of test time scaling inference is really about thinking. It's about reasoning, planning, search, and so how could that possibly be compute-like? We were absolutely right about that. Test time scaling is intensely compute-intensive. Then the question is, now we're at inference and we're at test time scaling. What's beyond that? Obviously, we have now created one agentic person. But one agentic person has a large language model that we've now developed. But during test time that agentic system goes off and does research and bangs on databases and it uses tools. One of the most important things that does is spins off and spawns off a whole bunch of subagents, which means we're now creating large teams. It's so much easier to scale and video by hiring more employees than it is to scale myself.

整个测试时间扩展推理过程其实就是关于思考的。它涉及到推理、计划、搜索等，那怎么可能像计算一样简单呢？我们完全正确地认识到了这一点。测试时间扩展非常依赖计算资源。然后问题是，现在我们已经进入了推理阶段和测试时间扩展阶段，那之后会有什么呢？显然，我们现在已经创造了一个有自主能力的个体。但是这个有自主能力的个体由一个我们开发的大型语言模型驱动。在测试时间期间，这个自主系统会自己展开研究，查询数据库，并使用各种工具。最重要的是，它会分裂出很多子代理，这意味着我们现在在创建大型团队。通过雇佣更多员工来扩展团队，比单靠我自己扩展要容易得多。

The next scaling law is the agentic scaling law. It's kind of like multiplying AI. Multiplying AI, we could spin off agents as fast as you want to spin off agents. I have four scaling laws. As we use the agentic systems, they're going to create a lot more data. They're going to create a lot of experiences. Some of it we're going to say, wow, this is really good. We are at a memorizes. That data set then comes all the way back to pre-training. We memorize and generalize it. We then refine it and fine tune it back into post-training. Then we enhance it even more with test time in the agentic systems. Put it onto the industry. This loop, the cycle, is going to go on and on and on.

下一个扩展法则是代理扩展法则。它有点像是在复制AI。通过复制AI，我们可以以您想要的速度衍生出多个代理。我有四个扩展法则。在使用代理系统时，它们会产生更多的数据和经验。有些经验会让我们感叹：哇，这真的很棒。我们会将这些经验铭记并存入数据集，然后用于预训练。我们对这些经验进行记忆和泛化，然后精炼和微调到后续训练中。接着，在代理系统的测试阶段，我们会进一步增强这些经验。然后将其应用到行业中。这个循环将持续不断地进行。

It kind of gums down to basically intelligence is going to scale by one thing in this compute. There's a tricky thing there that you have to anticipate and predict, which is some of these components requires different kind of hardware to really do it optimally. You have to anticipate where the AI innovation is going to lead. For example, make sure it's perfect. Perfix with sparsity. Perfect. With hardware, you can't just pivot on a week's notice. You have to anticipate what that's going to look like. That's so scary and difficult to do. For example, these AI model architectures are being invented about once every six months. System architectures and hardware architectures every three years. You need to anticipate what likely is going to happen two, three years from now.

这段话的意思是：基本上，智能的提升主要依赖于计算能力。然而，这里面有一件复杂的事情，就是某些组件需要不同类型的硬件才能实现最佳性能。你需要预见人工智能创新的方向。例如，要确保其完美地结合稀疏性和硬件。你不能在一周的时间内快速转变方向。你必须预见将来的变化，这确实很困难而且令人感到恐惧。举个例子，这些AI模型的架构大约每六个月就有新的发明，而系统架构和硬件架构每三年才会有一次大的更新。你需要预见在未来两到三年可能会发生的事情。

There's a couple of ways that you could do that. First of all, we could do research internally ourselves. That's one of the reasons why we have basic research. We have applied research. We create our own models. We have hands-on life experience right here. This is part of the code design that I'm talking about. We're also the only AI company in the world that works with literally every AI company in the world. To the extent that we can, we try to get a sense of what are the challenges that people are experiencing. You're listening to the whispers across the industry, the ad labs. That's right. You're going to listen and learn from everybody.

有几种方法可以做到这一点。首先，我们可以自己进行内部研究。这就是为什么我们会进行基础研究的原因之一。我们还进行应用研究。我们创建自己的模型。我们这里有实实在在的生活经验。这就是我所说的代码设计的一部分。我们还是全球唯一一家与世界上所有AI公司合作的AI公司。在我们能力范围内，我们尽量了解人们面临的挑战。你要倾听整个行业的细微动向，就像广告实验室一样。对，就是这样。你会倾听并向所有人学习。

The last part is to have an architecture that's flexible, that can adapt and move with the wind. One of the benefits of CUDA is that it's on the one hand an incredible accelerator. On the other hand, it's really flexible. That balance, incredible balance between specialization, otherwise we can't accelerate to CPU versus generalization so that we can adapt with changing algorithms. That's really, really important. That's the reason why CUDA has been so resilient on the one hand. We continue to enhance it where CUDA 13.2. We're evolving the architecture so fast that we can stay with the modern algorithms.

最后一部分是要有一个灵活的架构，能够适应和随风而动。CUDA的一个好处是，它一方面是一个强大的加速器，另一方面，它又非常灵活。这种平衡，就是在专用性和通用性之间的令人难以置信的平衡，非常重要。因为如果没有专用性，我们无法加速处理器，如果没有通用性，我们就无法适应变化的算法。这就是CUDA一直以来如此持久耐用的原因。一方面，我们不断改进它，现在已经发展到CUDA 13.2版本。我们的架构在快速发展，以便能够跟上现代算法的步伐。

For example, when mixtures experts came out, that's the reason why we had NVLink 72 instead of NVLink 8. We could now take an entire 4 trillion, 10 trillion parameter model and put it in one computing domain as if it's running on one GPU. People probably didn't notice. I said it, but if you look at the architecture of the Grace Blackwall Racks, it was completely focused on doing one thing, processing the LLM. All of a sudden, one year later, you're looking at a Vera Rubin Rack, it has storage accelerators.

例如，当混合模型专家出现时，这就是为什么我们拥有 NVLink 72 而不是 NVLink 8 的原因。我们现在可以将整个 4 万亿或 10 万亿参数的模型放入一个计算域中，就好像它运行在一个 GPU 上一样。人们可能没有注意到这一点，我已经提到过了，但如果你看看 Grace Blackwall 机架的架构，它完全专注于做一件事情，就是处理大型语言模型（LLM）。突然之间，一年后，你会看到一个 Vera Rubin 机架，它配备了存储加速器。

It has this incredible new CPU called Vera. It has Vera Rubin and NVLink 72 to run the LLMs. It also has this new additional rack called GROC. This entire rack system is completely different than the previous one. It's got all these new components in it. The reason for that is because the last one was designed to run MOE large language models, inference, and this one is to run agents and agents bang on tools. Obviously, the design of the system had to have been done before, cloud code, codecs, open claw, so you were anticipating the future essentially.

它有一个叫做Vera的出色新型CPU。这个CPU配有Vera Rubin和NVLink 72，用来运行大型语言模型（LLMs）。此外，它还有一个名为GROC的新机架。这整个机架系统与之前的完全不同，因为它包含了许多新的组件。之所以做出这样的改变，是因为之前的系统是为运行MOE大型语言模型的推理而设计的，而这个新的系统则是为了运行代理并让代理使用工具。显然，这个系统的设计是在云代码、编解码器和开放爪出现之前就已经完成的，所以你基本上是在预测未来的发展。

That comes from the whispers, from the understanding what all the state of the artist is. It's easier than that. You just reason about it. First of all, it's just reason. No matter what happens at some point in order for that large language model to be a digital worker, let's just use that metaphor. Let's say that we want the LLM to be a digital worker. What does it have to do? It has to access ground truth. That's our file system. It has to be able to do research. It doesn't know everything.

这源于一些低声细语，来自对艺术家状态的理解。其实，这要简单得多。你只需对其进行理性分析。首先，这就是理性的过程。无论发生什么，某个阶段为了让大型语言模型成为一名数字工人，不妨用这个比喻。假设我们希望这个大型语言模型成为数字工人，它需要做什么？它需要访问真实世界的信息，这就是我们的文件系统。它也需要能够进行研究，因为它并不是全知的。

I don't want to wait until this AI becomes universally smart about everything past, present, and future before I make it useful. Therefore, I might as well let it go to research. It's obviously, if it wants to help me, it's got to use my tools. A lot of people would say, AI is going to completely destroy software. We don't need software anymore. We don't need tools anymore. That's ridiculous. Let's use a thought experiment. You could just sit there and you were a glass of whiskey and think about all these things and it would come completely obvious.

我不想等到人工智能对过去、现在和未来的所有事情都变得无所不知之后，才让它发挥作用。因此，我不妨让它进行研究。显然，如果它想帮助我，就必须使用我的工具。很多人可能会说，人工智能将彻底颠覆软件，我们不再需要软件和工具了。但这是不切实际的。我们可以做个思想实验：想象一下，你坐在那里，面前有一杯威士忌，思考这些问题，一切都会变得显而易见。

If I were to create the most amazing agent that we can imagine in the next 10 years, let's say be a human or robot. If that human or robot were to be created, is it more likely that the human or robot comes into my house and uses the tools that I have to do the work that it needs to do? Or does this hand turn into a 10-pound hammer in one instance, turns into a scalpel in another instance, and in order to boil water, it beams microwaves out of its fingers? Or is it more likely just to use the microwave?

如果我在未来10年内创造出一位我们能够想象到的最厉害的助手，不论是人类还是机器人，那这位助手更有可能进入我家，利用我现有的工具来完成它需要做的工作？还是说它的手能瞬间变成10磅重的锤子、手术刀，或者在需要烧水时能从指尖发射微波？又或者它其实更有可能只是用微波炉来加热水呢？

The first time it goes up to the microwave, it probably doesn't know how to use it. That's okay. It's connected to the internet. It reads the manual of this microwave, reads it instantly, becomes an expert, and so it uses it. I think the, I just described in fact, almost all of the properties of open claw. That it's going to use tools, that it's going to access files, it's going to be able to do research. It has IO subsystem. When you're done reasoning about it through it in that way, then you say, oh my gosh, the impact to the future computing is deeply profound.

第一次把它放进微波炉时，它可能不知道如何使用。没关系，它连接了互联网。它可以立即读取微波炉的使用手册，瞬间成为专家，然后就能够使用它了。我认为，我刚刚描述的几乎涵盖了开放爪的所有特点。它会使用工具，能访问文件，还能进行研究。它有输入输出子系统。当你以这种方式思考它之后，你会发现，对未来计算的影响是多么深远。

The reason for that is, I think we've just reinvented the computer. Now you say, okay, when did we reason about that? When did we reason about open claw? If you take the open claw schematic that I used at GTC, you will find it two years ago. Nearly two years ago at GTC, I was talking about agentic systems that exactly reflect open claw today. Of course, the confluence of many things had to happen. First of all, we needed clawed in and GPT and all of these models to reach a level of capability.

我认为，我们刚刚重新发明了计算机。这是为什么呢？你可能会问，我们是什么时候开始考虑这个问题的？我们是什么时候开始思考"开放爪"这个概念的呢？如果你查看我在GTC（图形技术大会）上使用的开放爪原理图，你会发现它是两年前的。将近两年前，我在GTC上谈到了与今天的开放爪完全契合的智能系统。当然，很多因素需要共同作用。首先，我们需要像Clawed In和GPT这样的模型达到一定的能力水平。

Their innovation and their breakthroughs and their continued advances was really important. Of course, somebody had to create an open source project that was sufficiently robust, and sufficiently complete. We can all put to work. I think open claw did for agentic systems, what ChatGPT did for generative systems. I just think it's a very big deal. It's a really special moment. I'm not exactly sure why it captured so much of the world's attention, but it did more than clawed code and codex and so on, because consumers could reach it. Sure.

他们的创新和突破，以及他们不断的进步，都是非常重要的。当然，必须有人创建一个足够健全、足够完整的开源项目，让我们大家都能使用。我认为Open Claw对代理系统的贡献，就像ChatGPT对生成系统的贡献一样。我认为这是一个非常了不起的成就，是一个非常特殊的时刻。我不太确定为什么它吸引了这么多世界的目光，但它的影响超过了Clawed Code和Codex等，因为普通消费者也能够接触到它。

There's also so much of this as vibes and Peter had a podcast with him, a wonderful human being. Part of it is also the humans that represent the thing, and part of it as memes. We're all trying to figure it out. There's really serious and complicated security concerns about when you have such powerful technology, how do you handle your data so they can do useful stuff, but then there are scary things associated with that. We're a civilization, as individual people, and as a civilization figuring out how to find that right balance.

这其中有很多是关于“氛围”的，彼得和他一起录制了一期播客，他是一个很棒的人。部分原因在于代表这件事物的人，也有一部分是因为网络上的流行文化。我们都在努力弄清楚其中的道理。这里有一些非常严肃且复杂的安全问题，当你拥有如此强大的技术时，该如何处理你的数据，使它们能干有用的事情，但同时还有一些与之相关的令人害怕的问题。作为个人和一个文明，我们正在努力寻找一种合适的平衡。

We jumped on it right away and we sent a bunch of security experts this way, and we did this thing called Open Shell. It's already been integrated into open claw. It's an innovative but forward Nimo claw. Exactly. It installs super easy. It makes sure that it's secure. We give you two out of three rights. Agentex systems can access sensitive information. It can execute code, and it can communicate externally. We could keep things safe if we give you two out of those three capabilities at any time, but not all three.

我们立刻采取行动，派遣了一群安全专家来这里，并开展了一个叫做“Open Shell”的项目。这已经被整合进了“Open Claw”系统。这是一种创新但前瞻性的Nimo Claw装置。没错，它安装非常简单，并确保安全性。我们为你提供三种权限中的两种：Agentex系统可以访问敏感信息，执行代码，以及进行外部通信。通过在任何时候仅提供其中的两种能力，而不是全部三种，我们可以保证安全性。

Out of those two out of three capabilities, we also give you access control based on whatever rights that you're given by enterprise. We connect it to a policy engine that all these enterprises already have. We're going to try to do our best to help open claw become a better claw. You eloquently explained how we have a long history of blockers that we thought we're going to be blockers and we overcame them. We're now looking into the future. What do you think might be the blockers?

在这三项功能中的两项之外，我们还为您提供了基于企业所赋予的权限进行访问控制的功能。我们将其与所有这些企业已经拥有的策略引擎连接起来。我们将尽最大努力帮助“开放爪”成为一个更好的“爪”。您很有见地地解释了我们曾认为会构成阻碍但已经克服的障碍的悠久历史。现在我们展望未来，您觉得还可能存在哪些阻碍呢？

Now that it's clear that agents will be everywhere. Obviously, we're going to need compute. What is going to be the blocker for that scaling? Power is a concern, but it's not the only concern. But that's the reason why we're pushing so hard on extreme code design so that we can improve the tokens per second, per watt, orders of magnitude every single year. In the last ten years, Moore's Law would have progressed computing about a hundred times in the last ten years. We progressed and scaled up computing by a million times in the last ten years.

现在很明显，智能代理会无处不在。显然，我们需要计算能力来支持这一点。那么，究竟是什么阻碍了这种扩展呢？电力问题值得关注，但并不是唯一的问题。这也是我们为什么如此努力推动极限代码设计，以便每年都能大幅度提高每秒每瓦特的处理能力。在过去的十年中，摩尔定律本可以使计算能力提升大约一百倍，但实际上，我们在过去十年间已经将计算能力提升了一百万倍。

We're going to keep on doing that through extreme code design. Energy efficiency per watt completely affects the revenues of a company. It affects the revenues of a factory and we're just going to push that to a limit so that we can keep on driving token costs down as fast as we can. Our computer price is going up, but our token generation effectiveness is going up so much faster that token cost is coming down. It's coming down an order of magnitude every year. So power, that's an interesting one.

我们将通过极端的代码设计继续这样做。每瓦的能效完全影响着公司的收入，这也影响着工厂的收入。我们将尽力推动这一点，以便尽可能快地降低代币成本。虽然我们的电脑价格在上涨，但我们的代币生成效率提高得更快，以至于代币成本在下降。每年的代币成本都在数量级地下降。所以，电力方面，这是一个有趣的问题。

The way to try to get around the power blocker is to try to with the tokens per second per watt, try to make it more and more efficient. Of course, there's the question of how do we get more power. We should also get more power. That's a really complicated one. We've talked about small module nuclear power plants. There's all kinds of ideas for energy. How much does it keep you up at night? The bottlenecks in the supply chain of AI, like ASMR with EUV, lithography machines, DSMC with advanced packaging, like COAS and SK Hennings with the high bandwidth memory. All the time and we're working on all the time.

要绕过电力瓶颈的方法是通过每瓦特每秒处理的令牌数量来提高效率。当然，我们也要考虑如何获得更多电力。这是一个非常复杂的问题。我们讨论过小型模块化核电站，也有各种各样的能源想法。这些问题让你夜不能寐吗？AI供应链中的瓶颈，比如具有极紫外光刻技术（EUV）的ASMR、使用先进封装技术（如COAS）的TSMC和拥有高带宽内存的SK海力士。我们一直在努力解决这些问题。

No company in history has ever grown at a scale that we're growing while accelerating that growth. It's incredible. And it's hard for people to even understand this. In the overall world of AI computing, we're increasing share. So supply chain, upstream and downstream are really important to us. I spent a lot of time informing all the CEOs that I work with. What are the dynamics that's going to cause the growth to continue or even accelerate?

历史上没有一家公司能在我们这样的规模上成长，并且同时加速增长。这真是不可思议，人们甚至很难理解这种情况。在整个AI计算领域，我们的市场份额正在增加。因此，供应链的上下游对我们来说非常重要。我花了很多时间向与我合作的所有CEO传达信息，哪些因素会导致这种增长持续甚至加速。

It's part of the reasons why to the right-hand side of me were CEOs of practically the entire IT industry upstream and practically the entire infrastructure industry downstream. And there were several hundred CEOs. And I don't think there's ever been keynotes where several hundred CEOs show up. Part of it is I'm telling them about our business condition now. I'm telling them about the growth drivers in the very near future and what's happening.

这就是为什么在我右手边坐着几乎整个IT行业上游和整个基础设施行业下游的CEO们。其中有好几百位CEO。我认为从来没有哪场主题演讲能吸引到这么多的CEO来参加。部分原因是我正在向他们介绍我们目前的业务状况。我还在告诉他们我们在不久的将来的增长动力以及目前正在发生的事情。

And I'm also describing where are we going to go next so that they could use all of this information and all of the dynamics that are here to inform how they want to invest. And so I informed them that way like I informed my own employees. And then of course, then I make trips out to them and make sure that hey listen, I want you to know this quarter, this coming year, this next year, these things are going to happen.

我还在描述我们接下来要去的地方，以便他们可以利用这些信息和这里的各种动态来指导他们的投资决策。我以同样的方式通知他们，就像我通知自己的员工一样。当然，我还会亲自拜访他们，确保他们知道这个季度、即将到来的一年、以及下一年会发生哪些事情。

And if you look at the CEOs of the DRAM industry, the number one DRAM in the world was DDR memory for CPUs and data centers. About three years ago, I was able to convince several of the CEOs that even though at the time, HBM memory was used quite scarcely and barely by supercomputers, that this was going to be a mainstream memory for data centers in the future. And at first, it sounded ridiculous. But several of the CEOs believed me and decided to invest in building HBM memories. Another memory was rather odd to put into a data center is the low power memories that we use for cell phones. And we wanted them to adapt them for supercomputers in the data center. And they go cell phone memory for supercomputers and I explained to them why.

如果你看看DRAM行业的CEO们，你会发现当时全球排名第一的DRAM是用于CPU和数据中心的DDR内存。大约三年前，我说服了几位CEO，尽管那时HBM内存几乎只在超级计算机中少量使用，但它在未来将成为数据中心的主流内存。起初，这种想法听起来荒谬，但几个CEO相信了我，并决定投资于HBM内存的开发。另一个比较奇怪的想法是，将我们用于手机的低功耗内存应用于数据中心的超级计算机。有人质疑为什么要把手机内存用于超级计算机，而我向他们解释了原因。

Well look at these two memories, LPDDR5, HBM4, the volumes are so incredible. All three of them had record years in history and these are 45 year companies. And so, you know, that's part of my job is to inform and shape, inspire, you know. So you're not just manifesting the future and maybe inspiring in VIA, the different engineers of the company, you're manifesting the supply chain of the future. So you're having conversations with the SML upstream downstream downstream downstream.

好的，看看这两种内存，LPDDR5 和 HBM4，它们的销量都是令人难以置信的。这三家公司历史上都创下了纪录，而且它们都有45年的历史。所以，你知道的，我的工作之一就是去传达信息、塑造未来、激励人心。这样一来，你不仅是在塑造未来，并可能在 VIA 中激励公司里的不同工程师，你还在塑造未来的供应链。因此，你需要与供应链上下游的合作伙伴进行对话。

So that's the same EV caterpillar. That's downstream from us. Yeah, yeah. Yeah, the whole thing. I mean, but that's so, there's so much incredibly difficult engineering that happens in the entire semiconductor industry and it just feels scary how intricate the supply chain is, how many components there are, but it works somehow. Exactly, the deep science, the deep engineering, the incredible manufacturing and so much of the manufacturing is already robotics, but we have a couple of hundred suppliers that contribute. The technology that goes into our 1.3 million component rack, each rack is 1.3 1.5 million components.

所以，这就是同一个电动车的毛毛虫。这是在我们下游的部分。是的，是的，整个过程。我是说，整个半导体行业涉及到很多极其复杂的工程挑战，供应链的复杂程度让人感到害怕，有那么多的组件，但却 somehow 能够运作成功。没错，深邃的科学、深奥的工程、令人惊叹的制造，还有很多制造工序已经由机器人完成，但我们仍然有数百个供应商在贡献技术。每个设备架都包含130万到150万个组件。

There are 200 suppliers across the Vera Ruben rack. So it's interesting that you don't list that as the thing that keeps you up at night and list the blockers, but I'm doing all the things necessary to see. I can go to sleep because I checked it off. I said, okay, I go, I can go to sleep, but I go, well, let's see. Let's reason about this. What's important for us? Because, okay, let's reason about this. Because we changed the system architecture from the original DGX1 that you remembered to Enveiling 72 Rack Scale Computing.

在Vera Ruben机架上有200个供应商。所以很有趣的是，你没有把这列为让你失眠的原因，而是列出了其他障碍。但我正在做所有必要的事情，以确保我能够安然入睡，因为我已经确认了一切。我告诉自己，好吧，我可以去睡觉了，但又想，好吧，让我们来分析一下。对我们来说什么是重要的？因为，我们改变了系统架构，从你记得的原始DGX1变成了Enveiling 72机架规模计算。

What does that mean? What does that mean to software? What does that mean to engineering? What does that mean to how we design and test? What does that mean to the supply chain? Well, one of the things that it meant was we moved super computer, supercomputer integration at the data center into super computer manufacturing in the supply chain. If you're doing that, you also have to recognize you're going to move and if you're in a total footprint of whatever data center you're going to build, let's say you would like to have 50 gigawatts of supercomputers that are running simultaneously.

这是什么意思？这对软件意味着什么？这对工程意味着什么？这对我们的设计和测试方法意味着什么？这对供应链意味着什么？其中一个意义在于，我们将超级计算机和数据中心的整合转移到了供应链中的超级计算机制造中。如果这样做，你还需要意识到你将会移动，并且如果你正在建设一个数据中心，不妨设想你希望有50千兆瓦的超级计算机同时运行。

It takes one week to manufacture that 50 gigawatts of supercomputers, then each week in the supply chain, the supercomputers are going to need a gigawatts of power. So we're going to need the supply chain to increase the amount of power it has to build test, to build and test the supercomputers in the supply chain before I ship it. Well, Enveiling 72 literally builds supercomputers in the supply chain and ships them two, three tons at a time per rack. It used to come in parts and we used to assemble them inside data center.

制造50吉瓦的超级计算机需要一周时间，然后在供应链中的每一周，这些超级计算机需要一吉瓦的电力。因此，我们需要供应链增加电力供应，以便在最终出货之前能够在供应链中建造和测试这些超级计算机。而Enveiling 72能够在供应链中直接制造超级计算机，并每次以每个机架两到三吨的方式出货。以前，超级计算机是以零件的形式送达，我们需要在数据中心内部进行组装。

But that's impossible now because Enveiling 72 is so dense. That's an example and I would have to go into, I've fly into the supply chain, go meet my partners and say, hey, I said, guess what? Here's what I'm going to do with this is the way we used to build our DJXs. We're going to build them this way. This is going to be so much better because we're going to need them for inference, the market for inferences. Coming, the inflection point for inference is coming. It's going to be a big market.

但现在这几乎是不可能的，因为Enveiling 72过于复杂。这只是一个例子，我可能需要深入供应链，亲自去见我的合作伙伴，并说，嘿，你知道吗？我要这样做，这是我们以前制造DJXs的方式。我们将以这样的方式制造它们。这会更好，因为我们需要它们用于推理，推理市场即将到来，推理的拐点即将到来。这将是一个巨大的市场。

So I first explained to them what's going on, why it's going to happen. Then I ask them to make several billion dollars of capital investments each. Because they trust me and I'm very respectful of them and I give them every opportunity to question me and I spend time to explain things to people and I reason about it. I draw on pictures and I reason about it in first principles and by the time I'm done with them, there's no what to do.

所以，我首先向他们解释了事情的来龙去脉，以及为什么会发生这种情况。然后，我请他们每个人投入数十亿美元的资金。因为他们信任我，我对他们也非常尊重，并给他们提供了质疑我的各种机会。我花时间向他们解释，并与他们讨论其中的道理。我画图来辅助说明，并从基本原则出发进行推理。等我解释完之后，他们就没有疑问该怎么做了。

So a lot of us about relationships and building a shared view of the future. But do you worry about certain bottlenecks? I mean, what are the biggest bottlenecks in the supply chain? Are you worried about it? ASMRZ, VTooling, are you worried about the packaging and co-host packaging of TSMC about how fast it could scale? Like you said, you're not only growing incredibly fast, you're accelerating in growth. It feels like everybody in the supply chain and those are certainly bottlenecks would have to scale up. Are you having conversations with them? How can you scale up all the time? Do you worry about it? No, I'm okay. Because I told them what I needed, they understood what I need, they told me what they're going to go do and I believe they're what they're going to do.

这段话的大意是关于关系和未来愿景的建立。你是否担心某些瓶颈问题？比如供应链中的主要瓶颈是什么？你对此感到忧虑吗？有关 ASMRZ 和 VTooling，还有 TSMC 的包装和共同包装能力能否快速扩张的问题。正如你所说，你不仅在快速增长，还在加速发展。整个供应链中的企业都需要扩大规模，确保没有瓶颈。你是否与他们进行过沟通？你是否为此感到担忧？不，我没有担心。因为我已经告诉他们我的需求，他们也理解这些需求，并告知了他们的计划，我相信他们会履行承诺。

Interesting. Yeah, that's great to hear. So maybe if we can just link in the power for a little bit, what are your hopes for how to solve the energy problem? One of the areas that I would love us to talk about and just get the message out. Our power grid is designed for the worst case condition with some margin. Well, 99% of the time, we're nowhere near the worst case condition because the worst case condition is a few days in the winter, a few days in the summer and extreme weather. Most of the time, we're nowhere near the worst case condition and we're probably running around, call it 60% of peak. And so 99% of the time, our power grid has excess power and they're just sitting idle.

有意思。是的，听到这些消息真是太好了。那么，也许我们可以暂时把话题转到电力上来，您希望如何解决能源问题？这是一个我很想和大家讨论并传达的信息。我们的电网是为应对最严峻的情况而设计的，并留有一定的余量。然而，在99%的时间里，我们都远没有达到这种最严峻的情况，因为这种情况只会在冬天的几天、夏天的几天以及极端天气时才会出现。大多数时候，我们远未达到这种最严峻的情况，电网的使用大概只有峰值的60%。所以，在99%的时间里，我们的电网都有多余的电力，这些电力就只是闲置着。

But they have to be there sitting idle because just in case, when the time comes, hospitals have to be powered and infrastructure has to be powered and airports have to run and so on and so forth. And so the question that I have is whether we could go and help them understand and create contractual agreements and design computer architecture systems, data centers such that when they need the maximum power for infrastructure in society, that the data centers would get less. That's in a very rare instance anyways. And during that time, we could have backup generators for that little part of it or we just have our computers shift to workload somewhere else or we have the computers just run slower.

但它们必须闲置在那里，因为万一有需要，医院必须供电，基础设施必须运转，机场也需要运转，等等。所以我的问题是，我们是否可以帮助他们理解并制定合同协议，设计计算机架构系统和数据中心，以便在社会基础设施需要最大功率的时候，数据中心能够减少一些用电。这种情况非常罕见。在此期间，我们可以为这一小部分配备备用发电机，或者将计算任务转移到其他地方，或者让计算机运行得慢一些。

You know, we could degrade our performance, reduce our power consumption and provide for a slightly longer latency response when somebody asks for an answer. And so I think that that way of using computers of building data centers instead of expecting a 100% up time and these contracts that are really, really quite rigorous, it's putting a lot of pressure on the grid to be able to now they're going to have to increase from their maximum. I just want to use their excess. I just sit in there.

你知道，我们可以降低性能，减少能耗，并在有人请求答案时提供稍微长一点的延迟响应。我认为，这种使用计算机和建设数据中心的方式，与其追求100%不间断运行和签订非常严格的合同，不如利用电网的剩余能力。这样的话，电网就不必总是运行在最大负荷下，而只是利用它们的多余容量。我只是希望这样。

Yeah, that's not what I talked about enough. So what's what's this, what's stopping there is a regulation? Is it bureaucracy? I think it's a through a problem. It starts with the end customer. The end customer puts requirements on the data centers that they can never not be available. So that the end customer expects perfection. Now in order to deliver that perfection, you need a combination of backup generators and your grid power supplier to deliver on perfection. So everybody's got to have six nines.

是的，我之前没有太充分地谈论过这一点。那么这是什么问题，是法规吗？是官僚作风吗？我认为这是一个全面的问题。问题始于终端客户。终端客户要求数据中心必须始终保持可用状态，他们期待完美无缺。为了实现这种完美，你需要结合备用发电机与电网供应商的电力供应来达到这个目标。因此，每个人都必须做到 "六个九"（即99.9999% 的可靠性）。

Well, I think first of all, right now we have to have everybody understand that when the customer asks for these things, you have somebody, you have somebody in your data center operations team disconnected from the CEO. I bet the CEO doesn't know this. I'm going to talk to all the CEOs. The CEOs are probably not paying any attention to the contracts that are being signed. And so everybody wants to sign the best contract, of course. And they go down to the cloud service providers and the contract, the two contract negotiators that are, I could just see them now, negotiating these multi-year contracts. Both sides want the best contract as a result.

好吧，我认为首先，现在我们必须让每个人明白，当客户提出这些要求时，你的数据中心运营团队中有人与CEO脱节。我敢打赌，CEO对此毫不知情。我打算和所有CEO谈谈。CEO可能并没有关注正在签署的合同。因此，每个人都想签订最好的合同，当然。他们会去找到云服务提供商，然后可以想象到两边的合同谈判者正在商谈这些多年的合同。结果，双方都想要达成最好的合同。

The CSPs then have to go down to the utilities and they expect the nine, the six nines. And so I think the first thing is just make sure that all of the customers, the CEOs of the customers, realize what they're asking for. Now the second thing is we have to build data centers that gracefully degrade. And so if the power, if the utility, if the grid tells us, listen, we're going to have to back you down to about 80%. We're going to say that's no problem at all. We're just going to move our workload around when I make sure that data is never lost.

云服务提供商（CSPs）需要与电力公司沟通，并且他们期望获得 "九个九" 或 "六个九" 的高可靠性。因此，我认为首先要确保所有客户，尤其是客户的CEO，明白他们的需求是什么。其次，我们需要建设能够优雅降级的数据中心。如果电力公司或电网通知我们说电力供应要降到约80%，我们会说这完全没有问题。我们会调整工作负载，以确保数据不丢失。

But we can reduce the computing rate and use less energy. The quality of service degrades a little bit. For the critical workloads, I shift that somewhere else right away. So I don't have that problem. And so, you know, whoever whichever data center still has a 100% uptime. And so how difficult of an engineering problem is that the smart dynamic allocation of power in the data center as soon as you could specify you could engineer it. So long as it obeys the laws of physics on first principles, I think we're good.

我们可以降低计算速度，从而减少能耗。服务质量可能会稍微下降一点。对于关键的工作负载，我会立刻将其转移到其他地方处理。所以我不会遇到这个问题。因此，任何数据中心都能保持 100% 的正常运行时间。那么，在数据中心实现智能动态电力分配有多难呢？只要我们能指定并设计出来，并且遵循物理定律的基本原则，我认为我们就可以做到。

What was the third thing you were mentioning? So the second thing is the data centers. And the third thing is we need utilities to also recognize that this is an opportunity. Instead of saying, look, it's going to take me five years to increase my grid capability. If you're willing to take power of this level of guarantee, I can make them available for you next month and at this price. And so if utilities also offered more segments of power delivery promises, then I think everybody will figure out what to do with it. But there's just way too much waste in the grid right now. We should go after it.

你刚才提到的第三点是什么？第二点是关于数据中心。第三点是我们需要公用事业公司也能看到这个机会。与其说提升电网能力要花五年时间，不如说，如果你愿意接受这种等级的电力保障，我下个月就能以这个价格为你提供这些电力。如果公用事业公司能提供更多的电力供应选项，我认为大家都会找到利用这些电力的方法。但现在电网中有太多的浪费，我们应该努力减少这种浪费。

You've highly lauded Elon and XA has accomplished in Memphis in building colossus, super computer, probably in record time in just four months. It's now at 200,000 GPUs and growing very quickly. Is there something that you could speak to the understand about his approach that's instructive to the broadly to all the data center creators that's that enable that kind of accomplishment? His approach is engineering. His approach to the whole management of construction, everything. First of all, Elon is deep in so many different topics.

你对伊隆和XA在孟菲斯建造超级计算机的成就给予了高度赞扬，他们可能仅在四个月的时间里就创造了一项记录。这台超级计算机现在有20万块GPU，并且还在快速增长。能否谈谈他的做法有什么值得所有数据中心创建者学习的地方？无论是他的工程方法还是对整个施工管理的方式。他首先在许多不同的话题上都有深入研究。

Yet he's also a really good systems thinker. And so he's able to think through multiple disciplines. He obviously pushes things, questions everything, where they're number one, is it necessary? Number two, does it have to be done this way? And it doesn't have to take this long. He has the ability to question everything to the point where everything is down to its minimal amount that's necessary. You can't take anything else out. The necessary capabilities of the product retains. And so he is as minimalist as you could possibly imagine. And he does it at a system scale.

同时，他也是一个非常出色的系统思考者。他能够跨多个学科进行思考。他显然敢于挑战事物，对所有事情提出质疑。首先，这件事是否必要？其次，这种做法是否必须？最后，这样做是否需要这么长时间？他有能力质疑一切，直到把事情精简到仅保留最基本、最必要的部分，没有任何多余。产品的必要功能得以保留。因此，他是你所能想象到的极简主义者，而且他是在系统层面上做到这一点的。

I also love the fact that he has represented. He has present at the point of action. He'll just go there. There's a problem. He'll just go there and show me the problem. When you do all of this in combination, you overcome a lot of previous, this is just the way we do it. I'm waiting for them. Everybody has a lot of excuses. And then the last thing is when you act personally with so much urgency, it causes everybody else to act with urgency. And every supplier has a lot of customers going on.

我也很喜欢他在行动中的代表性。他会亲自到现场去解决问题。有问题时，他会直接去现场给我展示问题。通过这种方式，你可以克服很多过去 "我们就是这么做的" 这种固定思维。很多人都在等待，总有各种借口。而当你自己以极大的紧迫感采取行动时，会促使所有人也带着这种紧迫感去行动。而且每个供应商都有很多客户需要应对。

Every supplier has a lot of projects going on. He makes it his business that he's the top priority of everybody else's. In a project. He does that by demonstrating it. Yeah, I've been in a bunch of those meetings. It's fun to watch because really, not enough people ask the question like, okay, so can this be done a lot faster? And how? Why does it have to take this long? Yeah, right. And then that becomes an engineering question often. And yes, I think when you get the ground truth of actually, I remember one of the times I was hanging out with him, he literally is going through the entire process of how to plug in cables into a rack.

每个供应商都有很多项目在进行。他总是让自己成为其他人心中的头等大事。在一个项目中，他通过实际行动证明了这一点。是的，我参加过很多这样的会议。这很有趣，因为确实没有足够的人会问这样的问题：好了，那么这能快很多完成吗？怎么样才能做到这一点？为什么需要花这么长时间？没错，然后这通常就变成一个工程问题。我记得有一次和他在一起，他真的亲自展示了如何将电缆插入机架的整个过程。

He's working with an engineer on the ground that's doing that task. And he's just trying to understand what does that process look like so it can be less error-prone. And just building up that intuition from every single task involved in putting together a data center, you start to immediately get a sense at the detailed scale and at the broad system scale of where the inefficiencies are. And so you can make it more and more and more efficient. Plus you have the big hammer of being able to say, let's do it totally different.

他正在与一位现场工程师合作完成这个任务。他只是想了解这个过程是什么样的，以便减少出错的可能性。通过积累完成数据中心每个任务的直觉，你会立刻在细节和整体系统的层面上察觉到效率低下的地方。这样你就可以不断提高效率。同时，你还有一个强有力的手段，那就是可以说，让我们彻底改变做事的方式。

Yeah. And remove all possible blockers. That's right. Is there parallels in the Nvidia Extreme Systems Co-design approach that you've seen in the way Elon approaches systems engineering? Well, first of all, Co-design is a ultimate systems engineering problem. And so we approach the work that we do from that principle. The other thing that we do, and this is a philosophy that I thought a state of mind, I guess, a method that I started 30 years ago and it's called the speed of light.

好的，并移除所有可能的障碍。没错。在你所见的Nvidia极端系统共同设计方法中，有没有什么与Elon在系统工程上的方法类似的地方？首先，共同设计是一个终极的系统工程问题，因此我们从这个原则出发进行工作。我们还采用另外一种理念——我认为这是一种心态吧，一种我30年前就开始的方法，叫做“光速”。

The speed of light is not just about the speed of light, it might shorthand for what's the limit of what physics can do. And so everything that we do is compared it against the speed of light. Memory speed, math speed, power, cost, time, effort, number of people, manufacturing cycle time. And when you think about latency versus throughput, when you think about cost versus throughput, cost versus capacity, all of these things, you test against the speed of light to achieve all of these different constraints separately.

光速不仅仅是光的速度，它可能代表了物理学所能达到的极限。因此，我们在做任何事情时，都会拿它与光速进行比较。比如，记忆速度、计算速度、功率、成本、时间、努力程度、人员数量、制造周期等等。当你考虑延迟与吞吐量、成本与吞吐量、成本与容量之间的关系时，你会把这些因素与光速进行比较，以便在不同的限制条件下达成目标。

And then when you consider it together, you know you have to make compromises because a system that achieves extremely low latency versus a system that achieves very high throughput or architected fundamentally differently. But you want to know what's the speed of light of a system that achieves high throughput, what's the speed of light of a system that achieves low latency. And then when you think about the total system, you could make trade-offs. And so I force everybody to think about what's the first principles, the limits, the physical limits for everything before we do anything. And we test everything against that. And so that's a good frame of mind.

当你把这些因素放在一起考虑时，你会意识到需要做出一些妥协，因为一个实现极低延迟的系统与一个实现极高吞吐量的系统在架构上是根本不同的。但是，你想知道一个实现高吞吐量的系统的"速度极限"是什么，一个实现低延迟的系统的"速度极限"又是什么。然后，当你考虑整个系统时，你可以做出一些权衡。因此，我要求所有人首先考虑最基本的原则、限制和所有事情的物理极限，然后再行动。我们会用这些标准来检验一切，因此这是一种很好的思维方式。

I don't love the other methods, which is continuous improvement. The problem with continuous improvement, first of all, I usually engineer something from first principles with speed of light thinking, limited only by physical limits and physics limits. And after that, of course, you would improve it over time. But I don't like going into a problem and somebody says, hey, it takes 74 days to do this today right now. And we can do it for you in 72 days. You know, I rather strip it all back to zero. And so first of all, I explain to me why it's 74 days in the first place.

我不喜欢其他的方法，尤其是持续改进。这种持续改进的方式有一个问题，就是在一开始，我通常会从最基本的原则出发，快速思考，受到的限制仅来自物理极限和自然规律。之后，当然会逐步改进。但是，我不喜欢处理问题时有人告诉我，"我们现在需要74天才能完成这个任务，但可以帮你改进到72天。" 我更倾向于从头开始，从零开始思考。首先，我需要知道为什么一开始需要74天。

And let's think about what's possible today. And if I were to build it completely from scratch, you know, how long would it take? Oftentimes, you'd be surprised. And it might come to six days. Now the rest of the six days to 74 could be very well-reasoned and compromises and cost reductions and all kinds of different things. But at least you know what they are. And now that you know that six days possible, then the conversation from 74 to 6, is surprisingly much more effective.

让我们考虑一下今天可以实现什么。如果我完全从头开始构建，你知道，需要多长时间？很多时候，你会感到惊讶。可能只需要六天。接下来的六天到七十四天中，可能包含很多合理的理由、妥协、成本削减等各种因素。但至少你知道这些因素是什么。现在你知道六天是可能的，那么这段从七十四天到六天的对话会令人惊讶地更有效。

And such incredible complex systems that you're working with. This simplicity sometimes a good heuristic to reach for. I mean, if I can just. I mean, the pod, the very Ruben Pade analysis is just incredible. We're talking about seven chips, seven chip types, five purpose built rack types, 40 racks, 1.2 quadrillion transistors, nearly 20,000 video dies. And that is over 1,100 Ruben GPUs, 60 XFLOPS, 10 petabytes per second of scale bandwidth. That's all just one. That's just one pod. That's just one pod.

你正在处理的是如此复杂的系统。有时候，简单的方法是一个不错的启发性策略。我的意思是，虽然我只是……我指的是Rubén Pade分析，这实在是难以置信。我们说的是七种芯片类型、五种专用机架类型、40个机架、1.2千万亿个晶体管、近20,000个视频芯片。这些都包括了超过1,100个Rubén GPUs、60 XFLOPS处理能力、每秒10PB的规模带宽。而这仅仅是一个单元。这只是一个单元而已。

Yeah, that's just one pod. I mean, so you have the. And then even the NVL72 rack alone is 1.3 million components. 1,300 chips, 4,000 pounds crammed it to a single 19-inch wide rack. And Lex, we're probably kind of crank out about 200 of these pods a week. Just to put in perspective. The amount of different components, I suppose, simplicity is impossible. But is that a metric that you kind of reach for and trying to design things?

是的，那只是一台机架。我是说，你有那个。同时，仅仅NVL72这一款机架就有130万个组件，1300个芯片，重4000磅，全部挤在一个19英寸宽的机架中。Lex，我们每周大概能生产出200个这样的机架。这样的话，从这个角度来看，组件的数量太多，我想，实现简单化是不可能的。但这种数量是否是你设计时努力追求的指标呢？

The phrase that I use most often is we need things to be as complex as necessary, but as simple as possible. And so the question is, is all that complexity there necessary? And we had a test for that. And we had a challenge that. And then after that, everything else above it, you know, is gratuitous. But it's some of the most incredible. Semican ductile industry broadly, but what Nvidia is doing. Some of the greatest engineering in history. So these systems are just truly, truly marvels of engineering.

我常用的一句话是：我们需要让事情保持必要的复杂，但尽可能简单。那么问题来了，所有这些复杂性都真的是必要的吗？我们对此进行了测试和挑战。然后，除了那些必要的，其他的一切都是多余的。但在半导体行业，尤其是Nvidia正在做的事情，一些最令人惊叹的工程奇迹。这些系统确实是工程领域中真正的奇观。

It is the most complex computer the world has ever made. Yeah, the engineering teams, I mean, it's not a competition, but I don't know. If it was like an Olympics of engineering teams, it means TSMC does incredible engineering. Like I said, ASMR at every scale, but Nvidia is going to give them around for their money. Yeah, just incredible. This is incredible teams. Gold medalists in every single sport, all assembled right here, and have to work together and report directly to you.

这是一台世界上最复杂的计算机。工程团队...我并不是在说这是个竞争，但如果真有工程团队的奥运会，TSMC（台积电）的工程水平确实令人惊叹。就像我说的，那种每个层面的技术精细程度，但英伟达也要做到不相上下。这真的是令人难以置信的团队。就像每项运动中的金牌得主都聚集在一起，必须一起合作并直接向你汇报。

This is wonderful. You've recently traveled to China. So it's interesting to ask you, China has been incredibly successful in building up a technology sector. What do you understand about how China is able to, over the past 10 years, build so many incredible world-class companies, world-class engineering teams, and just this technology ecosystem that produces so many incredible products? A whole bunch of reasons. Well, first of all, let's start with some facts.

这真是太棒了。你最近去了中国旅行，所以问你这个问题很有趣：中国在发展科技行业方面取得了非凡的成功。在过去的十年里，中国是如何打造出如此多的世界级公司、世界级工程团队以及能够生产出这么多令人赞叹的产品的科技生态系统的？原因有很多。首先，让我们从一些事实开始说起。

50% of the world's AI researchers are Chinese. Plus or minus. And they're mostly in China. Still, we have many of them here, but there's amazing researchers still in China. They're tech industry showed up at precisely the right time. At the time of the mobile cloud era, their way of contributing with software. And so this is a country's incredible science and math, really well educated kids. Their tech industry was created during the era of software.

世界上50%的人工智能研究人员是中国人，大约如此。大多数都在中国。当然，我们这儿也有很多中国研究人员，但在中国仍有很多了不起的研究人员。中国的科技产业恰逢其时地发展起来，正值移动云时代，他们通过软件做出贡献。这个国家在科学和数学方面有着令人难以置信的水平，孩子们受到了良好的教育。他们的科技产业是在软件时代兴起的。

They're very comfortable with modern software. China is not one giant economic country. It's got many provinces and cities with mayors all competing with each other. That's the reason why there's so many EV companies. That's the reason why there's so many AI companies. That's the reason why there's so many, every company you could imagine. They all create some of them. And as a result, they have insane competition internally. And what remains is an incredible company. They also have a social culture where it's family first, friend second, and company third. And so the amount of conversation that goes back and forth between they're essentially open source all the time.

他们非常熟悉现代软件。中国不是一个统一的经济体，而是由多个省份和城市组成，各个城市的市长之间相互竞争。这就是为什么中国有那么多电动汽车公司、人工智能公司，以及其他各种类型公司的原因。这种竞争非常激烈，最终留下的都是非常优秀的公司。他们的社会文化注重家庭第一、朋友第二、公司第三。因此，在他们之间的对话中，信息交流几乎是开放共享的。

So the fact that they contribute more to open source is so sensible because they're probably, what are we protecting? Mageners, the brothers are in that company. Their friends are in that company and they're all schoolmates. You know, the schoolmate concept. It's a, you know, one schoolmate, your brother for life. And so they share knowledge very, very quickly. And so there's no sense keeping technology hidden. You're minus what put it on open source.

因此，他们更多地参与开源项目是非常合理的，因为他们可能会想，我们到底在保护什么呢？公司的管理者、兄弟，还有他们的朋友都在这家公司里，而且他们都是校友。你知道，校友这个概念是这样的：一个校友，就像终身兄弟。因此他们知识共享的速度非常快，所以没有必要把技术藏着掖着。你为什么不把它放到开源平台上呢？

And so the open source community then amplifies, accelerates the innovation process. So you get this rapid, incredible great talent, rapid innovation because of open source and just, you know, the nature of friends. And, and insane competition among the company, what emerges is incredible stuff. And so this is the fastest innovating country in the world today. And this is something that has everything that everything that I just said is fundamental to just how the kids were grown. The fact that they have excellent education, the fact that they, parents, want them to do well in school, the fact that they, their culture is that way.

因此，开源社区加速并放大了创新过程。因为开源的存在，以及朋友之间的互动，你会看到快速且不可思议的人才涌现和创新。另外，由于公司之间激烈的竞争，产生了许多令人惊叹的成果。因此，这个国家今天成为世界上创新最快的国家。这一切的基础在于这里的孩子们是如何成长的：他们拥有良好的教育，家长希望他们在学业上取得好成绩，并且文化也支持这种趋势。

These are, you know, these are just the thing about their country. And they showed up at a precisely the time when technology is going through that exponential. Plus culturally, it's pretty cool to be an engineer. It connects to all the components that you're mentioning. It's a, it's a builder nation. It's a builder nation. Yeah, it's a builder nation. Our country's leaders, incredible, but they're mostly lawyers. They're country's leaders because we're, they're trying to keep us safe, rule of law governing.

这些就是关于他们国家的一些特点。你知道，现在正处于技术呈指数增长的时期，同时，在文化上，成为一名工程师也很酷。这跟你提到的各个方面都有联系。这是一个建设者的国家，是一个建设者的国家。是的，这是一个建设者的国家。他们国家的领袖很了不起，但他们大多是律师。他们负责领导国家，是因为他们在努力确保我们的安全，维护法治。

Their country was built out of poverty. And so most of their leaders are incredible engineers. Some of the brightest minds. To take a small tangent because you mentioned open source, I have to go to Proplexity here who you have been a fan of a long time. I love it. Yeah. And thank you for releasing open source Neematron 3 Super, which you can also use inside Proplexity. It looks stuff up now, which is 120 billion parameter open weight, M O E model.

他们的国家是在贫困中建立起来的，因此大多数领导者都是了不起的工程师，一些最聪明的头脑。稍微偏离话题，因为你提到了开源，我必须提到你一直以来都很喜欢的Proplexity。我很喜欢，谢谢你们发布了开源的Neematron 3 Super，它现在可以在Proplexity中使用。这是一个可以搜索东西的模型，具有1200亿参数的开放权重M O E模型。

What's your vision with open source? So you mentioned China with deep sea, good minimax with all these companies, really pushing forward the open source AI movement. And Nvidia is really leading the way in close to state of the art, open source, elements. What's your vision there? First, if we're going to be a great AI computing company, we have to understand how AI models are evolving. One of the things that I love about Neematron 3 is it's not a just a pure transformer model. It's transformer and SSMs.

你对开源的愿景是什么？你提到了中国在深海领域，以及与多家公司合作推进开源AI运动的优良表现。而Nvidia在接近最先进的开源元素方面处于领先地位。你对此有何看法？首先，如果我们想成为一家出色的AI计算公司，我们必须了解AI模型的发展趋势。我特别喜爱Neematron 3的一个原因是，它不仅仅是一个纯粹的Transformer模型，而是结合了Transformer和SSM（状态空间模型）的优点。

And we were early in developing the conditional GANS, which that progressive GANS, which led step by step to diffusion. And so the fact that we're doing basic research in model architecture and in different domains gives us visibility into what kind of computing systems would do a good job for future models. And so it is part of our extreme co-design strategy. Second, I think we rightfully recognize that on the one hand, we want world-class models as products and they should be proprietary.

我们很早就开始开发条件生成对抗网络（GANS），然后逐步发展出渐进式GANS，这一步步引导我们走向扩散技术。因此，我们对模型架构和不同领域进行基础研究，这让我们能够预见什么样的计算系统能够很好地支持未来的模型。这是我们极限协同设计策略的一部分。其次，我认为，我们确实意识到了，一方面，我们希望拥有世界一流的模型作为产品，这些模型应该是专有的。

On the other hand, we also want AI to diffuse into every industry and every country, every researcher, every student. And if everything is proprietary, it's hard to do research and it's hard to innovate on top of around with. And so open source is fundamentally necessary for many industries to join the AI revolution. And Vitya has the scale and we have the motives to not only skills, scale and motivation to build and continue to build these AI models for as long as we shall live.

另一方面，我们也希望人工智能能渗透到每一个行业和每一个国家，惠及每位研究人员和每位学生。如果所有东西都是专有的，那么研究和在其基础上进行创新就会变得困难。因此，开源对于许多行业参与AI革命是至关重要的。而且，Vitya具备规模，我们也有将这些AI模型持续开发下去的技能、规模和动力，并将一直坚持下去。

And so therefore we ought to do that. We can open up, we can activate every industry, every researcher, every country to be able to join the AI revolution. There's the third reason, which is that recognizing that AI is not just language. These AI will likely use tools and models and subagents that were trained on other modalities of information. Maybe it's biology or chemistry or laws of physics or fluids and thermodynamics and not all of it is in language structure.

因此，我们应该这样做。我们可以开放和激活每个行业、每位研究人员、每个国家，让他们能够加入人工智能革命。第三个原因是，要认识到人工智能不仅仅是语言。这些人工智能可能会使用工具、模型和子代理，它们是在其他信息模式上训练出来的。也许是生物学、化学、物理定律、流体和热力学等等，并不是所有的东西都在语言结构中。

And so somebody has to go make sure that whether prediction, biology, AI, AI for biology, physical AI, all of that stuff stays, can be pushed to the limits and pushed to the frontier. We don't build cars, but we want to make sure every car company has access to great models. We don't, we don't discover drugs, but I want to make sure that Lilly has the world's best biology, AI systems so that they can go use it for discovering drugs. So these three fundamental reasons, both in recognizing that AI is not just language, that AI is really broad, that we want to engage everybody into the world of AI and then also code design of AI.

因此，有人必须去确保无论是预测、生物学、人工智能、生物学用的人工智能、物理人工智能，所有这些领域都能被推动到极致和前沿。我们不造汽车，但我们希望确保每个汽车公司都能获得出色的模型。我们不研发药物，但我希望确保制药公司拥有世界上最佳的生物学人工智能系统，以便他们可以用这些来研发药物。因此，出于这三个基本原因，我们要认识到人工智能不仅仅是语言，在认知上，人工智能的应用非常广泛。我们希望让每个人都能够参与到人工智能的世界中，并共同设计和完善人工智能。

Well, I have to say once again, thank you for open sourcing. It's really, truly open sourcing in Evertron 3. I appreciate you for saying that. We open sourced the models, we open sourced the weights, we open sourced the data, we open sourced how we created it. Yeah, it's amazing. It's really incredible. You're originally from Taiwan and have a close relationship with TSMC. So I have to ask TSMC, I think also is a legendary company in terms of the engineering teams, in terms of the incredible engineering work that they do. What do you understand about TSMC culture and their approach that explains how they're able to achieve this singular unmatched success in everything they're doing with semiconductor.

好的，我得再次说一声感谢你们开源。这次在Evertron 3中，你们真的彻底实现了开源。我很感激你这样说。我们开源了模型、权重、数据，还开源了我们如何创造它们的方法。真是太棒了，令人难以置信。你来自台湾，并与台积电有密切关系。所以我不得不提到台积电，我认为在工程团队和非凡的工程成就方面，台积电也是一个传奇公司。你是如何理解台积电的文化及其方法，使得它们在半导体领域能够如此成功，没有对手？

You know, first of all, the deepest misunderstanding about TSMC is that their technology is all they have. That somehow they have a really great transistor. If somebody shows up another transistor game over, it's the technology. Of course, I don't mean just the transistor and metalization systems, the three D packaging, the silicon photonics, all of the technology that they have. That technology is really what makes the company special. Their technology makes the company special. But their ability to orchestrate the demands, the dynamic demands of hundreds of companies in the world as they're moving up, shifting out, increasing, decreasing, pushing out, pulling in, changing from customer to customer.

你知道，首先，关于台积电最大的误解就是以为他们只是靠技术吃饭。很多人认为他们只是拥有很棒的晶体管技术。如果有人开发了更好的晶体管，他们就完蛋了。但实际上，台积电的特别之处不仅仅在于晶体管，还有金属化系统、3D封装、硅光子学等所有相关技术。这些技术确实让公司与众不同。然而，更重要的是他们有能力协调全球数百家公司的动态需求。这些公司需求瞬息万变，包括发展、变动、增加、减少、推进和转移客户等多方面的变化。台积电能够在这种复杂的环境中游刃有余，才是他们真正的优势所在。

Way for starting, way for stopping, emergency, way for starts. All of this dynamics of the world's complexity as the world is shifting all the time. Somehow they're running a factory with high throughput, high yield, really great cost, excellent customer service. They take their work seriously. When you're wafer, because they know that they're helping you run your company, when the waifers were promised to show up, the waifers show up so that you could run your company appropriately. So their manufacturing system is completely miraculous. I would say then the second thing is their culture. This culture is simultaneously technology focused on one hand, advancing technology.

启动方式、停止方式、紧急情况、启动流程。所有这些都反映了世界的复杂动态，因为世界一直在变化。他们以高效率、高产量、出色的成本效益和优质的客户服务来运营工厂。他们对自己的工作非常认真。对于你的半导体晶圆，他们深知这能帮助你管理公司，因此当承诺的晶圆到货时，它们就会如期而至，从而确保你的公司能够正常运作。因此，他们的制造系统堪称奇迹。我想说第二点就是他们的企业文化。这种文化一方面专注于技术，推动技术进步。

Some will tangiously customer service oriented on the other hand. A lot of companies are very customer service oriented, but they're not very technology excellent. They're not at the bleeding edge of technology or a lot of companies who are at the bleeding edge of technology, but they're not the best customer service oriented company. It just depends on somehow they've balanced these two and their world class of both. Then probably the third thing is the technology that I most value in them, that they created this intangible call trust. I trust them to put my company on top of them.

有些公司在客户服务方面非常出色，但在技术上可能不那么先进。很多公司专注于客户服务，但在技术上并没有处于前沿。与此同时，也有一些公司在技术上非常先进，但在客户服务方面不是顶尖。关键在于这些公司如何平衡这两者，从而在这两方面都成为世界一流。第三个重要的方面是，这些公司创造了一种无形的资产——信任。我信任他们，因为我相信，他们能够让我的公司在他们的基础上蓬勃发展。

That's a very big deal. Well, they trust. I mean, there's a really close relationship there. The of established and that trust is established based on many years of performance, but there's human relationships involved there as well. Three decades. I don't know how many tens, hundreds of billions of dollars of business we've done through them. We don't have a contract. That's pretty great. That's amazing. Okay, there's the story that in 2013, the founders of TSMC and Morse Chang offered you the chance to become TSMC's chief executive. And you said you already had a job. This story, true.

这是个非常重大的事情。他们非常信任对方。我是说，他们之间有着非常紧密的关系。这种信任是建立在多年的合作表现上，但同时也涉及到了人际关系。三十年来，我不知道我们通过他们达成了多少十亿、上百亿美元的业务。我们之间没有合同。这真的很了不起。好吧，有个说法是，在2013年，台积电的创始人张忠谋给过你一次机会，让你成为台积电的首席执行官，而你当时说你已经有了一份工作。这个故事，是真的。

Story is true. I didn't dismiss it. Yeah. But I was deeply honored. Of course, I knew then as I know now, TSMC is one of the most consequential companies in history. Morse is one of the highest regarded executive and business and personal friend that I've had in my life. And for him to ask is I was humbled and really honored. But the work that I'm doing here is really important. And I've seen in my mind in any ways, in my mind's eye, what in video was going to be and what the impact that we could have. And it was really important work.

这个故事是真的。我没有忽视它。是的，但我感到非常荣幸。当然，我当时就知道，就像现在一样，台积电是历史上最有影响力的公司之一。莫尔斯是我人生中最受尊敬的高管和商业及个人朋友之一。他的邀请让我感到谦卑且非常荣幸。然而，我在这里所做的工作是非常重要的。我心里一直描绘着英伟达（NVIDIA）的未来及其可能带来的影响，这项工作真的非常重要。

And it's my responsibility. My sole responsibility to make this happen. And so I declined it. Not because it wasn't an incredible offer. It's an unbelievable offer. But I simply couldn't take it. I think in both in video and TSMC are two of the greatest companies in the history of human civilization. And running either one, I'm sure is incredibly complicated effort. And takes you have to truly be all in everybody at every scale, not just at the CEO level. Everybody is really truly all in. Yeah. To accomplish this kind of complexity. So now I can help both companies. Exactly.

这是我的责任，我的唯一责任是让这件事情发生。因此，我拒绝了。不是因为这个提议不够好，而是我实在不能接受。我认为英伟达和台积电都是人类文明历史上最伟大的公司之一，管理其中任何一家都无比复杂。为了达成这样的复杂性，必须要求每个人、在各个层面都全力以赴，不仅仅是CEO。每个人都必须真正全身心投入。是的。所以现在我可以帮助这两家公司。就是这样。

So Nvidia is now the most valuable company in the world. I have to ask, what is the Nvidia's biggest moat as the folks in the tech sector say? The edge you have that protects you from the competition. Our single most important property as a company is the install base of our computing platform. Our single most important thing is the end of today is the install base of CUDA. Now the reason why 20, 20 years ago, of course, there was no install base. But what makes, and if somebody, if somebody came up with CUDA or a CUDA, it wouldn't make any difference at all.

所以现在英伟达是世界上最有价值的公司。我想问一下，正如科技行业的人所说，英伟达最大的“护城河”是什么？你们在竞争中保持领先的关键是什么？我们作为一家公司的最重要资产是我们计算平台的安装基数。对我们来说，最关键的是截至今天的CUDA安装基数。回到20年前，当然完全没有这样的安装基数。如果有人现在研发出CUDA或者类似CUDA的东西，那基本上也不会产生任何影响。

And the reason for that is because it's never been just about the technology, the technology, of course, was incredible visionary. But it's the fact that the company was dedicated to it, stuck with it, expanded its reach. It wasn't three people that made CUDA successful. It was 43,000 people that made CUDA successful. And the several million developers that believed in us, that trusted that we were going to continue to make CUDA 1, 2, 3, 13, that they decided to port and dedicate their software on top of it. Their mountain of software on top of it. And so the install base is the number one most important advantage.

原因在于，这不仅仅关乎技术。技术当然是具有非凡前瞻性的，但关键在于，我们公司对它的坚持与投入，并且不断扩大其影响力。让CUDA成功的，不是三个人，而是整个团队的4.3万人，以及信任并支持我们的数百万开发者。他们相信我们会不断改进CUDA，从1发展到2、3、甚至13，并决心将他们的软件建立在此基础之上。因此，庞大的安装基数成为最重要的优势。

That install base when you amplified with the velocity of our execution at the scale that we're talking about, no company in history had ever built systems of this complexity period. And then to build it once a year is impossible. And that velocity combined with the install base in the developer's mind, is just going to now take a developer's mind from the developer's perspective. If I support CUDA, tomorrow it will be 10 times better. I just have to wait six months on average. Not only that, if I develop it on CUDA, I reach a few hundred million people, computers, I'm in every cloud, I'm in every computer company, I'm in every single industry, I'm in every single country.

当你将我们的执行速度与我们所谈论的规模结合在一起时，这样的安装基础，没有任何公司在历史上曾经建立过如此复杂的系统。而且，要一年内完成这样的构建几乎是不可能的。这种速度加上安装基础，在开发者看来，如果我支持CUDA，那么明天效果就会好10倍，我只需要平均等待六个月。不仅如此，如果我在CUDA上开发，我能够触达数亿人、电脑，我在每一个云平台、每一个电脑公司、每一个行业、每一个国家都能被使用。

So if I decorate an open-source package and I put it on CUDA first, I get these built attributes simultaneously. And not only that, I trust 100% that Nvidia is going to keep CUDA around and maintain it and improve it and keep optimizing the libraries for as long as they shall live. You could take that to the bank and that last part, trust. You put all that stuff together. If I were a developer today, I would target CUDA first. I would target CUDA most. And that's the reason that I think in the final analysis is our first, that's even our first core advantage. Our second one is our ecosystem.

因此，如果我为一个开源包添加修饰，并且首先将其放在CUDA上，我会同时获得这些构建属性。不仅如此，我百分之百相信Nvidia会持续支持CUDA，对其进行维护、改进，并优化这些库。这点我很有信心。综合这些因素，如果我今天是一名开发者，我会优先选择CUDA。我会主要关注CUDA。这也是我认为最终分析中CUDA是首选的原因，这甚至是我们的首要核心优势。我们的第二个核心优势是我们的生态系统。

The fact that we vertically integrated this incredibly complex system, but we integrated horizontally into every single, every single company's computers. We're in the Google Cloud, we're in Amazon, we're in Azure, we're ramping up AWS like Crazy right now, we're in new companies like Core Weave and NScale, we're in supercomputers that lily, we're in enterprise computers, we're at the edge in radio base stations. It's just crazy. One architecture isn't all these different systems, we're in cars, we're in robots, we're in satellites, we're out in space.

我们已经将这个极其复杂的系统实现了纵向整合，而且横向整合到每一个公司的计算机中。我们的系统存在于谷歌云、亚马逊云、Azure中，并且目前正在迅速增加AWS的使用。我们还进入了像Core Weave和NScale这样的新公司，甚至在超级计算机中都有使用。我们的系统被应用于企业计算机、无线电基站的边缘设备，这是相当疯狂的。我们的架构已经覆盖到各种不同的体系中，包括汽车、机器人、卫星，甚至是在太空中。

And so the fact that you have this one architecture and the ecosystem is so broad, it basically covers every single industry in the world. Well, how does the CUDA install base evolve into the future with AI factories as a moat? Do you think it's possible that in the video, the future is all about the AI factory? Well, the unit of computing used to be GPU to us, then it became a computer, then it became a cluster, now it's an entire AI factory. When I see a computer, when I see what Nvidia builds, in the old days, I visualized the chip, and then when I announced a new product, a new generation, like Ladies and Gentlemen, we're announcing Ampere today, I pick up the chip.

这个段落可以翻译为中文如下：所以，你拥有一种架构，而生态系统又如此广泛，基本涵盖了全球的每一个行业。那么，CUDA的装机基数如何在未来随着AI工厂的发展演变？你认为在视频中，未来将完全围绕AI工厂展开吗？对我们来说，计算单元曾经是GPU，然后是计算机，再后来是集群，现在则是一个完整的AI工厂。当我看到一台计算机或Nvidia的产品时，以前我会想象芯片；当我宣布一款新产品或新一代产品时，比如说，女士们先生们，今天我们发布的是Ampere，我会拿起芯片来展示。

Yeah. That was my mental model, what I was building. Today, picking up the chip is still adorable, but it's adorable. It's not my mental model, what I'm doing, my mental model is this giant gigawatt thing that has power generations connected to the grid. It's got cooling systems and networking of incredible monstrosity. 10,000 people are in there trying to install it, hundreds of networking engineers in there, thousands of engineers behind it trying to power it up. Powering up one of those factories, as you know, it's not somebody going, it's on now. It takes thousands of people to bring it up.

好的。这是我的思维模型，我以前正在构建的东西。今天，拿起芯片仍然很迷人，但这只是可爱。这不再是我现在的思维模型，我现在的思维模型是一个庞大的千兆瓦项目，拥有与电网相连的发电系统。它还配有极其庞大的冷却系统和网络。为了安装它，那里有1万人在努力工作，上百名网络工程师在那里，还有成千上万的工程师在背后努力启动它。启动这样一个工厂，并不是某个人随便按下开关就能完成的。这需要成千上万的人共同努力才能成功。

So mentally, you're actually, when you're thinking about a single unit of compute, you're like literally, when you go to bed at night, you're thinking now about collection of racks, so pods, not individual chips. Entire infrastructure. And I'm hoping my next click is when I'm thinking about building computers, it's planetary scale. That'll be the next click.

所以，当你在心理上考虑单个计算单元时，你实际上是在想着一整组机架，而不仅仅是单个芯片。就像你晚上睡觉时，你脑海中想到的是整个基础设施，而不是某个具体的部分。而我希望下一步，当我在思考构建计算机时，我能想到的是行星级的规模。这将是我思维的下一次转变。

What do you think about the space angle that Elon has talked about doing compute in space? For solving some of the, it makes some of the energy issues in terms of scaling energy easier. Cooling issues is not easy. Cooling, well, there's a large number of engineering complexities in all of that. So what, you know, Nvidia has also announced that you're already thinking about that. Yeah, we're already there. Nvidia GPUs are the first GPUs in space. And I didn't realize it was so interesting to it. I would have declared it maybe. We're in space. Little astronaut suit on one of our GPUs. But we've been in space.

你觉得埃隆提到的“在太空中进行计算”的观点怎么样？这种方式在解决一些能量问题方面能够使能源扩展更容易一些。不过，冷却问题并不简单。冷却涉及很多工程上的复杂性。而且，你知道吗，Nvidia也宣布他们已经开始考虑这个问题了。是的，我们已经在这方面有所行动。Nvidia的GPU是首批进入太空的GPU。我之前都没意识到这事这么有趣，否则我可能已经对外宣布了。我们的GPU已经在太空中，就好像穿上了一件小宇航服一样。不过，我们已经在太空中开展一些工作了。

It's the right place to do a lot of imaging, because those satellites have really high resolution imaging systems. And they're sweeping the earth, you know, continuously now. And you want, you know, centimeter scale, you know, imaging that is done continuously for the world. So though, you know, you'll basically have real time telemetry of everything. You don't want to beam that back down to earth. It's just, you know, petabytes and petabytes of data. You got to just do AI right there at the edge, throw away everything you don't need. You've seen before. It didn't change. And then just keep the stuff that you need.

这是进行大量成像的理想场所，因为那些卫星具有非常高分辨率的成像系统。现在它们正在不停地环绕地球扫描。你希望能够获得厘米级的成像，这种成像是对全球进行连续监测。因此，基本上，你将拥有所有事物的实时数据传输。你不希望将这些数据全都传输回地球，因为这些数据量非常大，以太字节计。你需要在边缘计算上使用人工智能，删掉所有你不需要的数据，那些你以前见过且没有变化的数据，然后只保留你需要的部分。

And so AI had to be done at the edge. Obviously, we have, we have 24, seven solar if we put it at the pollers. And, um, but, you know, there's no conduction, no convection. And so, you know, you're pretty much just radiation. And, um, but, you know, space is big. I guess we're just going to put big giant radiators out there. How crazy an idea I do think it is. Like, is this, is this five years out, 10 years out, 20 years out?

因此，人工智能必须在边缘完成。显然，如果我们把它放在极地，我们有24小时的太阳能供应。但是，你知道，这里没有传导和对流，所以基本上只有辐射。此外，你知道，太空很大。我想我们只能在那里放置大型的散热器。这听起来有多疯狂？我认为这个想法是在五年后实现，还是十年甚至二十年后？

So, uh, we're talking about blockers for AI scaling. You know, I'm just so much more practical. I, I look for where, where, um, uh, my next, next bucket of opportunities are first, uh, meanwhile, I'm cultivating space. And so I sent, I sent engineers to go work on the problem where we're starting, we're learning a lot about it. Um, how do we do a radiation? How do we do a degrading performance?

关于限制AI扩展的问题，我比较注重实用性。我先去找下一个机会，另一方面，我也在为未来的空间做准备。我派了一些工程师去研究我们现在遇到的问题，我们正在对此进行大量学习，比如我们如何应对辐射、如何处理性能下降的问题。

How do we deal with, um, uh, continuous testing and attestation of, of, um, defects and, and, um, you know, how do we deal with redundancy and, uh, how do we degrade, uh, gracefully and things like that? And so we could, we could do, uh, what, what about software? How do you think about software and, and redundancy and performance out in space? Uh, make it so that, so that the computer never breaks. It's just gets slower, you know, and, um, I, so we could start doing a lot of engineer exploration up front.

我们如何处理持续测试和确认缺陷的问题？我们又如何应对系统冗余、以及优雅降级等问题呢？对于这些，我们可以做些什么呢？对于软件而言，你如何考虑其冗余性和性能，特别是在太空中？能否使计算机永远不会完全崩溃，而只是变慢呢？所以，我们可以在前期开始进行大量的工程探索。

But in the meantime, my, my favorite answer is, eliminate waste. You know, we, we've got all that idle power. I want to evacuate it as fast as possible. Yeah, there, there, there's a lot of low hanging food here on earth. Yeah. Uh, the, we can utilize, uh, for the AI scaling, uh, quick pause, quick 30 second. Thank you to our sponsors. Check them out in the description.

在此期间，我最喜欢的答案是消除浪费。我们知道有很多闲置的能量资源。我想要尽快利用这些能量。是的，地球上有很多容易获得的资源。我们可以利用这些资源来扩大人工智能的规模。感谢我们的赞助商，请在描述中查看他们的信息。

It really is the best way to support this podcast. Go to lexfreedman.com slash sponsors. We got perplexity for curiosity driven knowledge exploration, Shopify for selling stuff online, element for electrolytes, thin for customer service AI agents, and quote for a phone system like calls, text, contacts for your business. Choose wise and my friends.

支持这个播客的最佳方式就是去访问 lexfreedman.com/sponsors。我们有 Perplexity，用于好奇心驱动的知识探索；Shopify，用于网上销售商品；Element，用于电解质补充；Thin，用于客户服务的人工智能助手；还有 Quote，用于企业的电话系统，包括通话、短信和联系人管理。朋友们，请明智选择。

And now back to my conversation with jensen qua. Do you think Nvidia may be worth 10 trillion at some point? Let's, let's ask it this way. What is the future of the world look like where that, where that's true? I think that Nvidia's growth is, is, um, uh, extremely likely. And in my mind, inevitable. And let me explain why.

现在我们回到我与Jensen Qua的对话。你觉得在某个时候英伟达的市值可能达到10万亿美元吗？我们换种方式来问这个问题：如果这样的情况成真，未来的世界会是什么样子？我认为英伟达的增长是非常有可能的，并且在我看来是不可避免的。让我来解释原因。

What are the largest computer company in history? That alone should beg the question why? And the reason for, of course, uh, two reasons. First, two foundational technical reasons. The first reason is that computing went from being a retrieval based file retrieval system. Almost everything is a far, we pre, prewrite something, we pre-record something, you know, we, we draw something, we put it on the web, we put it in a file and we, we use a recommender system, some smart filter to figure out what to retrieve for you.

历史上最大的计算机公司有哪些？这一点本身就引发了一个问题：为什么？原因有两个，归根结底是两个技术基础上的原因。第一个原因是计算从一个基于检索的文件检索系统演变而来。几乎所有东西都是文件，我们预先撰写、记录或绘制一些内容，然后将其放到网络上或存入文件中，并使用推荐系统或智能过滤器来为你找出需要检索的内容。

And so we were a pre-recording, human pre-recording, and file retrieving system. That's what a computer is largely. To now AI computers are contescially aware, which means that it has the process and generate tokens in real time. So we went from a retrieval based computing system to a generative based computing system. We're going to need a lot more processing in this new world than in the old world. We need a lot of storage in the old world. We need a lot of computation in this new world. And so, so that's, that's the first part of it. We fundamentally changed computing in the way how computing is done. The only thing that would cause it to go back is if this way of computation, this way of computing, generating information that's contextually relevant, situationally aware, that is grounded on new insight before it generates information.

我们从前是一个预先录制、人工存储和文件检索系统，计算机在很大程度上就是这样一个系统。而现在，人工智能计算机具有上下文感知能力，这意味着它能够实时处理和生成信息。我们从基于检索的计算系统转变为基于生成的计算系统。在这个新世界中，我们将需要比旧世界更多的计算处理能力。在旧的计算模式中，我们需要大量的存储，而在新的模式中，我们需要大量的计算。可以说，我们从根本上改变了计算的方式。除非这种计算方式，即生成具有上下文相关性和情境感知的信息、在生成信息前依据新的洞察来进行运算的方式发生变化，否则这一转变不会逆转。

This computation intensive way of doing computing would only go back if it's not effective. So if, for the last 10, 15 years, while working on deep learning, if at any single moment, I would have come to the conclusion that, that, you know what, this is not going to work out. I think this is a dead end, or it's not going to scale, it's not going to solve this modality, not going to be used in this application. Then, of course, I would feel very differently about it. But I think the last five years has given me more confidence than the last 10 year, the previous 10 years.

这种计算密集型的方法，只有在它无效的情况下才会被抛弃。所以，在过去的10到15年里，我一直在研究深度学习。如果在任何一个时刻，我得出结论说，这些努力不会有结果，认为这是一条走不通的路，无法扩展，无法解决这个模式，也不会在这个应用中使用。那么，我的感觉当然会大不相同。但我认为，过去五年给我的信心比之前的十年更强。

The second idea. It's computers, because it was a storage system. It was largely a warehouse. We're now building factories. Warehouses don't make much money. Factories directly correlates with the company's revenues. And so, the computer did two things. Not only did it change the way it did it, its purpose in the world changed. It's no longer a computer, it's a factory. It's a factory is used for a generation of revenues. We're now seeing not only is this factory generating products, commodities that people want to consume. We're seeing that the commodities are so interesting, so valuable, so to so many different audiences, that the tokens are starting to segment, like iPhones.

第二个想法是关于计算机的。起初，计算机被当作一个存储系统，主要用作仓库。但现在，我们正在打造工厂。仓库本身并不怎么赚钱，而工厂则直接与公司的收入挂钩。因此，计算机实现了两个转变：不仅改变了它的运作方式，还改变了它在世界上的使命。它不再仅仅是计算机，而是一个创收的工厂。我们现在看到，这个工厂不仅在生产人们想要消费的产品和商品，还看到这些商品在各个受众中都非常受欢迎且有价值，开始像iPhone一样细分市场。

You have free tokens. You have premium tokens. You have several tokens in the middle. Intelligence, as it turns out, is a scalable product. There's extremely high intelligence products, tokens that you could use for specialized things. People will be willing to pay the idea that somebody's willing to pay $1,000 per million tokens. It's just around the corner. It's not if it's only when. Now we're seeing that the commodity that this factory makes is actually valuable and is revenue generating and profit generating.

你有免费代币，也有高级代币，还有几种中档代币。结果表明，智能是一种可扩展的产品。有些产品智能程度极高，可以用于专业用途。人们会愿意为此付费，比如有人愿意为每百万个代币支付1000美元的想法，即将成为现实。这不是有没有可能的问题，而是什么时候实现的问题。现在我们看到，这个工厂生产的"商品"实际上是有价值的，并且能够产生收入和利润。

Now the question is how many of these factories can does the world need? How much how many tokens does the world need? How much is society willing to pay for these tokens? What would happen to the world's economy if the productivity were to improve so substantially? What would happen? Are we going to discover new drugs, new products, new services? When you take these things in combination, I am absolutely certain that the world's GDP is going to accelerate in growth. I'm absolutely certain the percentage of that GDP that will be used for computation will be 100 times more than the past because it's no longer storage unit. It's a product generation unit.

现在的问题是，世界需要这些工厂中的多少？世界需要多少代币？社会愿意为这些代币支付多少费用？如果生产力大幅提升，世界经济会发生什么变化？会发生哪些变化？我们会发现新的药物、新的产品、新的服务吗？综合考虑这些因素，我完全确信世界的GDP将加速增长。我也非常确定，用于计算的GDP比例将是过去的100倍，因为它不仅仅是一个存储单元，更是一个产品生成单元。

When you look at it in that context, and then you back into what is Nvidia's, what does Nvidia do and how much of that new economics, new industry would we have to benefit to address? I think we're going to be a lot lot bigger. Then the rest of it, to me, is it possible for Nvidia to be a $3 trillion revenues company in the near future? The answer is, of course, yes. The reason for that is because it's not limited by any physical limits. There's nothing that I see that says, gosh, $3 trillion is not possible. As it turns out, Nvidia supply chain is the burden is shared by 200 companies.

当你从这个角度来看，然后再回过头看看英伟达的情况：英伟达是做什么的？在这新的经济和产业中，我们能从中获得多少利益？我认为，我们会变得更大。至于其他的，我认为，英伟达在不久的将来成为一家营收3万亿美元的公司有可能吗？答案当然是肯定的。原因是因为这一目标没有任何物理限制。我没有看到任何因素认为3万亿美元是不可能实现的。事实是，英伟达的供应链是由200家公司共同承担的。

The fact that we scale out on the backs of with the partnership of this ecosystem, the question is, do we have the energy to do so? Surely we will have the energy to do so. All of these things combined, that number is just a number. I still remember, Nvidia was the first time we crossed a billion dollars. I was reminded of a CEO who told me, you know, Jensen is theoretically impossible for a fabulous semiconductor company to exceed a billion dollars. I won't bore you with why, but of course, it's illogical and there's a lot of evidence. We're not.

我们在这个生态系统的合作支持下不断扩展，问题是，我们有足够的精力去实现这一目标吗？当然，我们肯定有足够的精力来做到这一点。所有这些因素结合在一起，那些数字不过是个数字。我还记得，Nvidia第一次突破十亿美元时，我想起了一位告诉我的CEO，他说，从理论上讲，没有工厂的半导体公司要超过十亿美元是不可思议的。我就不赘述其中的原因了，但当然，这种说法是不合逻辑的，有很多证据表明我们可以做到。

Then somebody told me, you know, Jensen, you'll never be more than $25 billion because of some other company. Somebody told me that you'll never be, you know, because, and then so those aren't principle, first principle, reason thinking, and the simple way to think about that is, what is it that we make and how large is the opportunity that we can create? Now, Nvidia is not in the market share business. Almost everything that I just talked about don't exist. That's the part that's hard. You know, if Nvidia was a 10 billion dollar company trying to take Nvidia share, then it's easy to see for shareholders that, oh yeah, if they could just take 10% share, they could be this much larger. But it's hard for people to imagine how large we could be because there's nobody I could take share from. You know, and so, I think that that's one of the challenges for the world is the imagination of the future.

然后有人告诉我，说，知道吗，Jensen，你永远不会超过250亿美元，因为有其他公司。有人告诉我你永远不会，因为这些都不是基于基本原理的思考。简单来说就是，我们创造了什么，以及我们能创造多大的机会。现在，Nvidia（英伟达）并不是做市场份额的生意。我刚刚谈到的几乎所有东西都不存在。这就是难点所在。你知道，如果Nvidia是一家市值100亿美元的公司，并试图抢占市占率，那么股东们很容易看出，如果他们能拿下10%的市场份额，市值就能增长到这么多。但人们很难想象我们能变得多大，因为没有现成的市场可供我们拿下。我认为，这就是世界面临的一个挑战——对未来的想象。

But I got plenty of time and I'll keep reasoning about it and I'll keep talking about it and every single GTCO become more and more real, you know, and then more and more people talk about it in one of these days, you know, we'll get there. But I'm 100% we'll get there. Yeah, this view of, you know, token factories essentially, this token per second per watt and every token having value. Like it's an actual thing that brings value and it brings different kinds of value, different amounts of value to different people was value. That's the actual product. This really could be a loosely thought of as the token. And so you have a bunch of token factories and it's very easy. First principle is to imagine a future given all the potential things that AI can solve, that you're going to need an exponential number more of token factories.

我有很多时间，我会继续思考和讨论这个问题，每一个GTCO（假设指某种代币）变得越来越真实。你知道，总有一天会有越来越多的人谈论它，我们会实现这个目标。我百分之百相信，我们一定能做到。这种对代币工厂的看法，可以理解为每秒每瓦都有代币产生，并且每个代币都有其价值。它是真正带来价值的东西，为不同的人带来不同类型和数量的价值。这就是实际的产品。这可以被宽泛地视作代币。因此，你会有很多代币工厂。考虑到人工智能可以解决的所有潜在问题，想象一个未来需要指数级增长的代币工厂，这是很容易的。

Yeah. And what's really interesting, the reason why I was so excited about it, the iPhone of tokens arrived. What do you call it? Are you saying awful clause iPhone? Yeah. That's interesting. Agents. Yeah. Agents. True. Agents in general. The iPhone of tokens arrived. It is the fastest growing application in history. It went straight up. Yeah. Went straight up. That says something. Yep. There's no question. Open claw is the iPhone of tokens. If there's something truly as you know, something truly special happening from about December, where people really woke up to the power of cloud code of codex of open claw. I mean, I have embarrassed to admit that in the way here in the airport, I'm this first time I've done this in public. I was programming quote unquote by talking and I was embarrassed because I was pretending like I'm talking to a human colleague.

好的。这段话中提到的意思是：最近出现了一种被称为"代币的iPhone"的新技术，这让我感到非常兴奋。有人问这是什么意思，你是否说的是令人厌恶的iPhone？不，我指的是"代理"。是的，代理。在代理方面，代币的iPhone已经问世。它是历史上增长最快的应用，发展势头迅猛。这说明了一些问题，毋庸置疑，Open Claw 就是代币的iPhone。从大约12月开始，这件事变得非常特别，人们真正意识到了Open Claw编码云的强大。我有些不好意思地承认，在机场的路上，我第一次在公共场合这样做，我通过说话“编程”，感到有些尴尬，因为我假装自己是在和一位同事聊天。

I'm not sure how I feel about the future where everybody is walking around talking to their AI, but it's such an efficient way to get stuff done. And it's more likely that your AI is bothering you all the time. And the reason for that is because it's getting stuff done so fast. Yeah. It's reporting back to you. I got that done. You know, what do you want me to do next? You know, that's the part that I think most people don't realize is the person who's going to be chatting with texting them most is their claws or lobster. What an incredible future. I read the eutribute a lot of your success to your ability to work harder than anyone and withstand much suffering than anyone. So we can list many of the things that entails.

我对未来每个人都在与他们的人工智能交谈的情况感到有些不确定，但这确实是一个高效处理事情的方式。而且，你的人工智能很可能会一直打扰你，因为它完成事情的速度太快了。它会不断地向你报告："我完成了这个，那下一步你希望我做什么呢？"我认为大多数人没有意识到的是，将来和你聊天最多的可能就是你的个人智能助手。多么不可思议的未来啊。我了解到你的很多成功都归功于你比别人更努力工作和承受更多痛苦的能力。我们可以列举出这其中包含的许多事情。

I mean, dealing with failure, the cost of engineering problems we've talked about, the human problems on certainty, responsibility, exhaustion, embarrassment, the near-death company moments that you've mentioned, but also the pressure. Now is the CEO of this company that economies and nations strategize around, plan their financial allocations around, plan their AI infrastructure around. How do you deal with this much pressure? What gives you strength given how many nations and peoples depend on you? I'm conscious about the fact that and video success is very important to your United States. We generate enormous amounts of tax tax revenues. We establish technology leadership for our nation. Technology leadership is important for national security. National security not just in one aspect of national security, all aspects of national security.

我指的是，处理失败，包括我们谈到的工程问题的代价、人类问题的不确定性、责任、耗尽、尴尬，以及你提到的公司濒临倒闭的瞬间，但还有压力。现在作为这家公司的CEO，各国经济和国家都围绕这家公司进行战略规划，安排他们的财政预算和AI基础设施建设。你是如何处理如此大的压力的？考虑到有那么多国家和人民依赖你，是什么给你力量？我意识到，英伟达的成功对美国来说非常重要。我们创造了大量的税收收入，并为我们的国家确立了技术领先地位。技术领导力对国家安全很重要，不仅仅是国家安全的某一个方面，而是所有方面。

When our country is more prosperous, we could do a better job with domestic policies and helping social benefits because we're generating so much reindustrialization in the United States. We're creating mountains of jobs. We're helping shift how we build things back to the United States in so many different plants, chips, computers, and of course, these AI factories. I'm completely aware that I have the benefit and this is a real real gift with mainstream investors, teachers, policemen who have somehow, for whatever reason, invested in Nvidia or because they watched Jim Kramer, bought some stock in now or millionaires. I am completely aware of that circumstance. I'm aware of the circumstance that that Nvidia is central to a very large network of ecosystem partners behind us and downstream from us.

当我们的国家更加繁荣时，我们可以在国内政策和社会福利方面做得更好，因为我们在美国促进了大规模的再工业化。我们正在创造大量的工作岗位，帮助将制造业重新转移回美国，包括在许多不同的工厂、芯片、计算机，当然还有这些人工智能工厂。我完全清楚，我受益于这一切，这对主流投资者、教师、警察来讲是真正的礼物，他们由于某种原因投资了Nvidia，或是因为看了吉姆·克莱默的节目而购买了股票，现在变成了百万富翁。这种情况我是完全了解的。我也意识到Nvidia在我们背后和我们的下游，与一个非常大的生态系统合作伙伴网络息息相关。

And so the way I deal with that is exactly what I just did. I reason about what is it that we're doing? What's the impact that has on other people, benefit positively or even through Great Britain, for example, to supply chain? The question is, therefore, what are you going to do about it? In almost everything that I feel, I break it down, I reason about, okay, what's the circumstance? What's changed? What's hard? And what am I going to do about it? I break it down, decompose the problem. And the decomposition of these circumstances turns it into manageable things that I can do. And the only thing that I, after that I could do is, did you do it? Did you either do it or did you get somebody else to do it? And if you didn't do it, you reason that you need to do it and you didn't do it and you didn't get anybody else to do it, then stop crying about it.

所以，我处理这种情况的方式正是我刚才所做的。我会思考我们在做什么？这对其他人有什么影响？是积极的还是通过像英国这样的供应链产生了影响？问题是，你打算怎么处理它？在几乎所有的情况下，我会把问题分解开，想想现在的情况是什么？有什么变化？什么事情是困难的？然后我打算怎么办？我把问题分解成可以处理的小部分。分解这些情况使它们变得可以管理。我之后能做的唯一一件事情就是：你做了吗？你是自己做了还是找别人做了？如果你没去做，也没让别人去做，那就别再抱怨了。

And so I'm fairly tough on myself. But I also break things down so that I don't panic. I can go to sleep because I've made the list of things that needed to be done. And I've made sure that everything that could put our company in harm's way, could put my partners in harm's way, put our industry in harm's way, I've told somebody, everything that I feel could put anybody in harm's way, I've told someone. And I've told that someone who could do something about it. And so I've gotten it off my chest or I'm doing something about it. And so after that, flex, what else can you do?

因此，我对自己要求比较严格。但我也会把事情分解开来，以免自己感到恐慌。我能够安稳入睡，因为我列出了需要完成的任务清单。同时，我确保所有可能会对我们的公司、合作伙伴或整个行业造成危害的事情都已经告诉了某个可以采取措施的人。在这一切之后，我也尽力而为，那还有什么可以做的呢？

So given all the insane, intense amount of suffering on the journey of building up in video, you have you hit low-point psychologically? Oh yeah. Oh yeah. Sure. All the time. All the time. And there's just breakdown the problem into pieces. Yeah. See what you could do about it. And part of it is forgetting. One of the most important attributes of AI learning, as you know, is right, systematic forgetting. You need to know when to forget something. You can't memorize everything. You can't keep everything. And you don't want to carry everything. One of the things that I do very quickly is I decompose the problem. I reason about the problem. And I share the load with it. When I say I tell everybody, I'm essentially sharing that burden as quickly as possible. Whatever worries me, tell somebody else.

所以，在视频创作的过程中，经历了如此疯狂和剧烈的痛苦，你有没有心理上的低谷？哦，有的，有的，当然有，经常有。每次我都会把问题拆解成小部分，看看能做些什么。其中一部分是遗忘。正如你所知，AI学习最重要的特性之一就是系统性遗忘。你需要知道什么时候该忘记一些事情，不能记住所有东西，不能保留所有东西，也不想背负所有东西。我很快就会把问题分解，分析问题，并分担负担。当我说我会和大家分享时，我实际上是在尽快分担这种负担。无论是什么让我担忧，我都会告诉其他人。

Don't just keep it. You know, decompose, don't freak them out. Decompose the problem into smaller parts and get people to, and inspire them to be able to go do something about it. But part of it is just forgetting. You know, a lot of it is you've got to be tough on yourself. You know, just come on. Stop crying about it. Let's get going. You know, and then you get out of bed. And then the other part is, is you're attracted to the next shiny light, the next future. You know, the next opportunity, the next, okay, that's behind us. Let's what's next. It's a lot. I think, you know, you watch this with great athletes, they, they just worry about the next point. The last point is behind them, the embarrassment, the, you know, the sad back.

不要只是保持现状。你知道的，要分解问题，不要让他们感到害怕。把问题分解成小部分，然后激励人们采取行动，解决它。但部分关键在于遗忘。很多时候你需要对自己严厉一些。来吧，别再为此哭泣了。振作起来，然后从床上爬起来。与此同时，我们常常被下一个闪亮的目标所吸引，被未来的机会所吸引。过去的就让它过去，看看接下来会有什么。你看那些伟大的运动员，他们总是专注于下一个得分点。过去的分数、尴尬和失败就让它们过去吧。

You know, and then, and because I do so much of my job publicly, you know, it likes you to do a fair amount of your job publicly too. And so, so I do a lot of my job publicly. And so, you know, I, I say a lot of things that, that seems sensible at the time, or funny at the, mostly it's just because it's funny to me at the time. And then, you know, you reflect on his less money. But, but yeah, trust me, I know. But you basically allow yourself to be pulled by the light of the future. You forget the past and just keep, that's right. Keep working towards that.

你知道，由于我的工作很大一部分是在公众面前进行的，所以这也要求你在公众前完成很多工作。因此，我经常公开地完成我的工作。我常常说一些当时看来合理或有趣的话，主要是因为我当时觉得有趣。但事后想想可能就没那么有趣了。不过，相信我，我确实明白。不过，总的来说，你就是让自己被未来的光芒所吸引，忘记过去，不断朝着那个方向努力。这就是正确的方向。

I mean, you did say there's this kind of famous thing you said that, if you knew how hard it would be to build an video, it turned out to be, what is it, a million times more hard than you anticipated, that you wouldn't do it. Yeah. But it isn't, you know, when I hear that, that's probably true about everything worth doing, right? Exactly. That is, by the way, what I was trying to explain is that there's a, there's a incredible superpower of being, being, being a, have a, the mind of a child. Yeah. You know, and I say to myself, oftentimes, when I look at something and, and almost, almost everything, my first thought is how hard can it be? You know, and so, and so you get yourself into that mode, how hard could it be? And, and nobody's ever done it. It looks gigantic. It's going to cost hundreds of billions dollars. It's going to take, you know, all this, and you just go, yeah, but how hard could it be?

我想说的是，你曾经提到过一句非常有名的话：如果你知道制作一个视频会有多难，结果发现这比你预想的要难一百万倍的话，你可能不会去做这件事。是的。但是，我听到这句话的时候，想到的是，任何值得去做的事情大概都是这样，对吧？没错。另外，我想表达的是，拥有孩子般纯真的心态其实是一种绝对的超能力。常常当我在观察一件事情的时候，我会对自己说：“这能有多难呢？”几乎每件事情都是如此。所以，当你调动自己进入这种思维模式时：这能有多难呢？即便从未有人做到过，尽管这件事看起来庞大无比，可能需要花费数千亿美元，需要投入巨大的精力，你还是会问自己：“这能有多难呢？”

You know, yeah, how hard could it be? And so, so you got to get yourself into that state of mind. You don't want to, you don't want to actually over simulate everything and all the setbacks and all the trials and tribulations and all the disappointments. You don't want to simulate all that in advance. You don't want to know that. You don't, you don't, you want to go into a new experience thinking it's going to be perfect. It's going to be great. It's going to be incredibly fun. And then while you're there, you know, you need to have, you need to have endurance. You need to have grit so that when the setbacks actually happened and those setbacks are going to surprise you, the disappointments, disappointments are going to surprise you. You know, the embarrassment is going to surprise you. The humiliations are going to surprise you. You just can't let, now you just got to turn on the other bit, which is just forget about it. Move on. Keep, keep moving.

你知道的，感觉没那么难，对吧？所以，你需要让自己进入这种心态。你不想过度预想所有的困难、挫折和失望。你不需要提前知道这些。你应该以为即将面对的新体验会很完美、很棒、非常有趣。然后在过程中，你需要有毅力和勇气。当挫折发生时，它们会让你措手不及；失望、尴尬和羞辱也会让你意外。你要做的就是忘掉它们，继续前进，不要停下。

And, and to the extent that, to the extent that, my assumptions about the future and why the future is going to manifest so long as those assumptions and that input doesn't change or didn't change materially, then I should expect that the output won't change. And so my simulated output of the future is still going to happen. And if it's still going to happen, I'm still going to go after it. I believe it's going to, you know, and so there's a combination of two or three human characteristics. The ability to go into an experience, fresh-minded, the ability to forget the setbacks, the ability to believe in yourself, you know, to believe what you believe and stay true to that belief, but you're constantly reevaluating. This combination of three, four, five things, I think is really important for resilience.

而且，只要我的关于未来的假设和这些假设背后的原因在没有发生重大变化的情况下，我预计结果也不会改变。因此，我对于未来的预测依然有效。如果未来仍然如我所预期，那我就会继续追求它。我相信它会实现。因此，这是几个特质的结合：以开放的心态进入新的经历，忘记挫折，相信自己，相信自己的信念并始终坚持，但同时不断重新评估。这三四五个特质的结合，我认为对增强适应能力非常重要。

And, and, and, and, you know, I'm fortunate that that whatever, whatever life experiences led to this, I've got kind of those four or five things, you know, I'm always curious, always learning, always learning from everybody, you know, I'm always asking, and because I'm humble about, about, about everything, I'm always thinking, gosh, they did that so nicely, they did that so wonderfully, you know, I wonder what they're thinking through. How do they, you know, so I'm simulating everybody in a lot of ways, you know, emulating almost everybody I watch, right? Your, your empathetic towards towards everything that they do that you're observing and respect, and it's so you're constantly learning and, you know, you're now one of the wealthiest people on earth, one of the most successful humans on earth, is it harder to be humble and to be able to, do you feel the effect of money and power and fame in making it harder for you to, sort of, be wrong in your own head, enough to hear out and opinion of somebody else when it disagrees with you and learn from them, those kinds of things.

这个段落可以翻译成中文如下：而且，而且，而且，而且，你知道，我很幸运，无论是怎样的生活经历导致了这一点，我具备了那四五种品质。你知道，我总是充满好奇，总是学习，总是向每个人学习。你知道，我总是在提问，因为我对一切都很谦逊，总是想着，哇，他们做得真好，他们做得真精彩。我常常在想，他们是怎么思考的呢？他们是怎么做到的呢？所以在很多方面，我是在模仿几乎我所观察到的每一个人，对他们所做的一切抱有同理心和尊重，这样你就能不断学习。你现在是地球上最富有、最成功的人之一，是否更难保持谦逊？你是否觉得金钱、权力和名声让你更难在心里承认自己错了，难以倾听那些和你意见相左的人的意见并向他们学习？

Um, surprisingly no, and I would, I would actually go the other way, because I do so much of my work publicly, when I'm wrong, pretty much everybody sees it, you get how I'm old, yeah, and, and, and when I'm wrong, when I'm wrong, or it didn't turn out that way, or, um, you know, I mean, most of the things that that I say outside, um, I'm fairly certain about, and the reason for that is because, because it's going to impact somebody else, and I want to be quite concerned about that, and quite, quite circumspect about that.

嗯，出乎意料的是，并不是那样，其实正好相反，因为我很多工作都是公开进行的，所以当我出错时，几乎所有人都能看到，你明白我老了吗，是的，当我错了或者事情没按预期发展时，嗯，我在外面说的大部分事情，我都是相当确定的，原因是因为这些事情会影响到别人，我对此非常关注和谨慎。

Um, for stuff that, that I'm reasoning about inside of meeting, you know, a lot of things could turn out differently. And so, but it doesn't ever stop me from reasoning. The way that, the way that I imagine lead, you know, I'm constantly reasoning in front of people, and even when I'm talking to you, you can kind of see me kind of reasoning through things. And I want to make sure that you understand what I'm saying, not because I told you, because I'm so humble about what I'm about to tell you, I kind of show you the steps that I got there, and then you could decide whether you believe what I said in the end. And so I'm doing that all day long in meetings with all of my employees. I'm constantly reasoning through, let me tell you, let me tell you what, how I see it. And I reason through it. It gives everybody the opportunity to intercept and say, I disagree with that part.

嗯，在会议中讨论的时候，有很多事情可能会有不同的结果。但这并不会阻止我去思考。我总是在大家面前推理，即使和你交谈的时候，你也可以看到我在推理。我希望你能理解我所说的，不是因为我告诉你，而是因为我谦逊地展示了我是如何得出结论的，这样你可以决定是否相信我最后说的话。在会议上，我整天都在做这件事，和我的所有员工一起推理。我会说，"让我告诉你，我是怎么看这个问题的。" 通过这种推理方式，给大家一个机会来插话，比如说“我不同意那一点”。

The nice thing about reasoning through things and letting, and letting people interact with it is that they don't have to disagree with your outcome. They can disagree with your reasoning steps. And they could pull me in different directions. And then we can reason forward. And so we're, we're kind of, you know, collective path searching method. And it's really fantastic. Yeah, you have this way about you of when you're explaining stuff, I can feel you actually reasoning on the spot about it with a constant open mind in this, where you could, I could feel like I could steer your thinking. Yeah. And that's a, that's really beautiful. That you've been able to maintain that after so many years of success and pain. I think sometimes pain makes you close, close you down a bit.

通过事物进行推理，并让人们参与其中的好处在于，他们不必同意你的结论。他们可以不同意你的推理步骤，并从不同角度引导我。这样我们就能一起通过推理前进。可以说这是一种集体寻路的方法，真的很棒。你在解释事情时有一种独特的方式，我能感受到你在当场进行推理，并始终保持开放的心态，我甚至觉得我可以影响你的思维。这真是太美妙了，尤其是在经过多年成功与痛苦的历练之后，你还能保持这种开放。我认为有时候，经历痛苦会让一个人变得封闭。

Yeah. And I think to maintain tolerance for embarrassment, because that's the tolerance. I mean, that's a real thing. Yeah. There's many years of embarrassing yourself, even those meetings, knowing that there's people wrong, you were you declared one idea and it was shown that that idea was wrong and be able to admit that and to grow from that. That's not that's very difficult on the human level. Yeah. Well, you know, they knew I was, they knew that recently my first job was, was, you know, cleaning toilets. So I'm glad you maintain that same spirit of Denny's, the work. I mean, that was beautiful. Your whole journey from starting from Denny's is a beautiful one.

是的。我认为要保持对尴尬的宽容，因为这就是宽容。我是说，这是真的。这需要多年面对尴尬的时光，即便在那些会议中，你知道有人是错的，而你曾经提出来的某个想法被证明是错误的，能够承认并从中成长，这在人类层面上是很困难的。是的。你知道，他们知道我是什么样的人，他们知道我刚开始的工作是清洁厕所。所以我很高兴你还保持着在Denny's工作时的那种精神。你的整个旅程，从Denny's起步，就是一段美好的经历。

Let me ask you about video games. Some of big gaming fan. Yeah. So I have to say thank you to Nvidia for many years of incredible graphics. By the way, it is GeForce is our still to this day. Yeah. Our number one marketing strategy. Right. People learn about Nvidia while they're in their teenage years. And then they go to college and they know who Nvidia is. And then in the beginning, it's just, you know, playing Call of Duty, you know, Fortnite. And then later they're using CUDA. And then later they're using Nvidia and, you know, Blender and the so and auto test.

让我来问你一些关于电子游戏的事情。我是一个超级游戏迷。是的。所以我必须感谢英伟达多年来提供的出色图形。顺便说一下，GeForce 至今仍然是我们的首要营销策略。对，人们在青少年时期就了解英伟达，然后他们上大学后也知道英伟达是谁。一开始，他们只是玩《使命召唤》或《堡垒之夜》。然后，后来他们使用 CUDA，再后来他们会在使用英伟达进行 Blender 和其他自动化测试。

Yeah. I mean, I should say I mentioned to a friend that I'm talking with you. He said, Oh, they made great gaming GPUs. Yeah, exactly. Exactly. You know, there's more to it. But yeah, yeah, people really love the, it really brought a lot of joy to a lot of people. The hardware really brings these worlds to life. There was some controversy around this with the DLSS five. Yeah. Can you explain to me the drama around this? I guess people, gamers online will concern that it makes games look like AI Slop.

好的。我提到我正在和你交流的时候，告诉了一个朋友。他说：“哦，他们做的游戏显卡很棒。”的确是这样。当然，不仅仅如此。确实，这些显卡给很多人带来了快乐，硬件让虚拟世界更栩栩如生。不过，围绕着DLSS 5有一些争议。你能给我解释一下这方面的争议吗？我猜是有些玩家在线上担心这会让游戏画面看起来像是由AI生成的劣质图像。

Yeah. What do you think of this drama? Yeah. I think their perspective makes sense. And I can see where they're coming from. Because I don't love AI Slop myself. You know, all of the AI generated content increasingly looks similar and they're all beautiful. And so I can, I'm empathetic towards where they're thinking. That's just not what DLSS five is trying to do. I showed several examples of it. But DLSS five is 3D condition, 3D guided. It's ground truth structure data guided. And so, so the artist determined the geometry.

好的。你怎么看这部剧？我觉得他们的观点是合理的，我能理解他们的出发点。因为我自己也不太喜欢AI生成的东西。你知道，现在越来越多的AI生成内容看起来都很相似，但又都很美。所以我能理解他们的想法。不过，DLSS五代并不是这样做的。我展示了一些例子。DLSS五代是3D条件和3D引导的，并以真实结构数据为指导。因此，艺术家决定了几何形态。

We are completely truthful to the geometry, maintain so in every single frame. It's conditioned by the textures, the artistry of the artist. And so every single frame, it enhances, but it doesn't change anything. Now, the question is, the question about enhancing. DLSS five also lets, because the system is open, you could train your own models, determine, and you could even, in the future, prompt it. I wanted to be a tune shader. I wanted to look like this kind of, so you can give it even an example.

我们完全忠实于几何形状，并在每一帧中保持这种忠实。这是由纹理和艺术家的艺术性所决定的。因此，每一帧都得到了增强，但并没有改变任何东西。现在，问题在于如何进行增强。DLSS五代也让系统保持开放性，你可以训练自己的模型，并决定其表现形式。将来，你甚至可以提示它，比如我希望它是卡通着色风格，希望它看起来像这样，你可以提供一个示例。

And it would generate in the style of that all consistent with the artistry, the style, the intent of the artist. And so all of that is done 40 artists so that they can create something that is more beautiful, but still in the style that they want. I think that they got the impression that the games are going to come out the way the games are, ship the way they do. And then we're going to post process it. That's not what DLSS is intended to do. DLSS is integrated with the artist. So it's about giving the artist the tool of AI, the tool of generative AI. I think it's the sign of the user. I think people are very sensitive to human faces. And we're now living in this moment, which I think is a beautiful one, which is people are sensitive to AI slop. Yeah. It puts a mirror to ourselves to help us realize that what we seek is imperfections, what we seek is sometimes not perfect graphics. It helps us understand what we find compelling in the world we create. That's beautiful. As long as it's tools that help us create those worlds.

这段文字的大意是指，他们的目标是创造出既美丽又符合艺术家风格和意图的作品。DLSS的作用不是在游戏发布后再做处理，而是与艺术家紧密集成，通过为他们提供生成式AI的工具，使他们能在创作过程中使用这些技术。人们对AI生成的内容非常敏感，尤其是对人脸的处理。我们生活在一个关注AI细节的时代，这促使我们意识到我们所追求的有时是那些不完美的图像。这种意识帮助我们理解我们所创造的世界中令人着迷的是什么。只要这些工具能够帮助我们创造这些世界，那将是美好的。

Yeah, that's right. It's wonderful. It's yet another tool and they want the generative models to generate the opposite of photo real. Yeah, you'll do that too. And so it's just yet another tool. I think the gamers might also appreciate that in the last couple of years, we introduced skin shaders to the game developers. And many of those games have skin shaders that include subsurface scattering that may skin look more skin like. And so the industries, the game developers are looking for more and more and more tools to express their art. And so this is just yet more and more tool. They get to that with you. Ridiculous question. What are the things the greatest or most influential game ever made, maybe from an Nvidia's perspective? Doom. Doom. Unquestionably. That was the start of the 3D. I would say Doom from the intersection of the cultural implication as well as the industry turning a PC into a gaming device. That was a very important moment.

是的，没错。这非常棒。这又是一种工具，他们希望生成模型能够生成与照片真实感相反的内容。是的，你也可以做到这一点。所以这只是另一个工具。我认为玩家们可能也会欣赏，在过去几年中，我们为游戏开发者引入了皮肤着色器。许多游戏中使用的皮肤着色器包含次表面散射，使皮肤看起来更像真实的皮肤。因此，游戏开发者们一直在寻找越来越多的工具来表达他们的艺术。这只是越来越多工具中的一个。他们可以和你一起使用这个工具。至于一个荒谬的问题：从英伟达的角度来看，有史以来最伟大或最具影响力的游戏是什么？《毁灭战士》。毫无疑问。《毁灭战士》是3D游戏的开端。从文化影响以及将个人电脑转变为游戏设备的角度来说，我认为《毁灭战士》是一个非常重要的时刻。

Now of course, flight simulation companies were before it. But they just didn't have the popularity that Doom did to have made the industry turn the PC from an office automation tool into a personal computer for families and gamers and things like that. And so Doom was really impactful there. From an actual game technology perspective, I would say virtual fighter. And so we were great friends both of them. And then there's games more recently. I mean, Cyberpunk 2077. Really nice GPU accelerated graphics like fully ray traced, fully ray traced. Also, I like at first, I'm a huge fan of Skyrim, Ella Scrolls. And it's been released a long, long time ago, but people have released mods and they create these. It's like a different game. And it just allows me to replay it a game over and over and it makes you realize that you can re-experience in a totally new way. The world you already love.

当然，在此之前已经有飞行模拟公司了。但它们并没有像《毁灭战士》那样火爆，无法让业界将个人电脑从办公自动化工具转变为家庭用户和游戏玩家的个人电脑。因此，《毁灭战士》在这方面真的非常有影响力。从真正的游戏技术角度来看，我认为《VR战士》（Virtual Fighter）同样具有重要意义。我们和这两个游戏都成为了很好的朋友。最近还有一些游戏，比如《赛博朋克2077》。具备了非常棒的GPU加速图形，完全的光线追踪。我最初就特别喜欢《上古卷轴》系列的《天际》（Skyrim），尽管它发行已经很久了，但人们发布了各种模组，把游戏变得像一个新游戏。这让我可以一次又一次地重玩这个游戏，让我意识到我们可以用全新方式重新体验我们已经热爱的世界。

Yeah, let's do that all the time. One of my favorite things is just walk across Skyrim. We created this thing called RTX mod. Yeah, it's a modding tool. And it allows it allows the community to inject the latest technology into an old game. Of course, what makes a great video game is not just graphics. It's also story and character development, but that's right. Beautiful graphics can add to the immersion, the feeling like it's another place of your trash border to. What's you said, I think accurately, that the AGI timeline question rests on your definition of AGI. So let's let me ask you about possible timelines here.

好的，我们一直这样做吧。我最喜欢的事情之一就是在《上古卷轴：天际》中四处漫步。我们制作了一个叫做RTX mod的东西。这是一个修改工具，它允许玩家社区将最新技术注入到一款老游戏中。当然，一款伟大游戏的优秀之处不仅仅在于画面效果，还在于其故事和角色发展。不过，漂亮的画面确实可以增强沉浸感，让你觉得仿佛身处另一个世界。你提到过AGI（人工通用智能）的时间表问题在于如何定义AGI，所以让我问问你对时间表的看法。

Let's this ridiculous definition, perhaps, of what AGI is, but an AI system that's able to essentially do your job. So run, no, start, grow, and run a successful technology company. That's worth a good one or a one. No, it has to be worth more than a billion, more than a billion dollars. So, you know, how hard it is to do all those components. So how far are we away from that? So we're talking about open claw that does all the incredibly complex stuff that are required to, to, first of all, innovate, to find customers, to self-to-them, to manage, to build a team of some agents, some humans, all that kind of stuff. Is this 5, 10, 15, 20 years away?

让我们来谈谈这个也许有些荒谬的定义：AGI（通用人工智能）实际上是一个能够基本上做你的工作的AI系统。也就是说，创办、发展并经营一家成功的科技公司。这样的公司需要价值超过十亿美金。你知道，实现所有这些方面有多困难。那么我们离实现这一目标还有多远呢？我们在讨论一个开放的人工智能系统，它能完成所有极其复杂的任务，包括首先进行创新、找到客户、向他们推销、管理以及建立一个由不同AI代理和人类组成的团队等等。这样一个系统是否还需要5年、10年、15年或者20年才能实现？

I think it's now. I think we've achieved AGI. You think you can have a company run by an AI system like this? Possibly. And the reason for that is you said a billion and you didn't say forever. And so for example, it is not out of the question that a claw was able to create a web service, some interesting little app that all of a sudden, you know, a few billion people used for 50 cents. And then it went out of business again shortly after. Now we saw a whole bunch of those type of companies during the internet era. And most of those websites were not anything more sophisticated than what open claw could generate today. I achieved virality and monetized virality.

我认为就是现在。我觉得我们已经实现了通用人工智能（AGI）。你认为可以用这样的AI系统来运营一家公司吗？有可能。原因是你提到的是一个十亿的目标，而不是永远的目标。因此，例如，一个"爪"（假设是AI系统）有可能创建了一个网络服务，一个有趣的小应用，突然之间就有几十亿人以50美分的价格使用它。然后，这个服务很快就关闭了。在互联网时代，我们见过很多这种类型的公司。而在那个时候，大多数网站并没有比今天的"Open Claw"更复杂。我实现了病毒式传播并从中获利。

Yeah. It's just that I don't know what it is. But I couldn't have predicted any of those companies at the time either. You know, you're going to get a lot of people excited with that statement. Yeah. I know. They're like, what do you mean? I can just launch an agent and make a lot of money. Well, by the way, it's happening right now, right? You know that when you go to China, you're going to see, you're going to see a whole bunch of people teaching their getting their claws to try to go out and look for jobs and, you know, do work, make money. And I'm not actually, I wouldn't be surprised if some social thing happened or somebody created a digital influencer super, super cute or some social application that, you know, feed your little tomagotchi or something like that. And it become out of the blue and instant success. A lot of people use it for a couple of months and it kind of dies away.

是的。只是我不知道那是什么。但是我当时也无法预测那些公司的出现。你知道，你这番话会让很多人感到兴奋。是的，我知道。他们会想，你的意思是我可以推出一个代理人然后赚很多钱？顺便提一下，这种事情现在正在发生。你知道，当你去中国时，你会看到很多人正在努力提升自己的技能以寻找工作、赚钱。而且，如果某种社交现象发生或有人创建了一个超级可爱的数字影响者或某种社交应用，比如养一个虚拟宠物之类的，突然之间取得成功，我也不会感到惊讶。很多人可能会用上几个月，但随后渐渐就没那么流行了。

Now, the odds of, of, of, you know, a hundred thousand of those agents building in video zero percent. And then, and then the one part that I won't do, and I want to make sure we all do, is to recognize that people are really worried about their jobs. And, and, um, I just want to remind them that the purpose of your job and the tasks and the tools that you use to do your job are related not the same. I've been doing my job for 33 years. I'm the longest running text of you in the world. 34 years. And the tools that I've used to do my job has changed continuously in the last 34 years. And sometimes quite dramatically, you know, over the course of a couple of two, three years.

现在，要让十万个这样的代理零风险地进行视频制作，这是不可能的。我不想逃避，也想确保我们都认识到人们对他们工作的担心。我想提醒大家，工作的目的以及你在工作中使用的工具和任务是相关的，但并不相同。我已经做了33年的工作，我是世界上在这个领域持续工作最长的人，做到第34年了。在过去的34年里，我用来工作的工具一直在变化，有时甚至在短短两三年内发生了很大的改变。

And, and the one story that I really want to make sure that everybody hears is the story, the, the first job that, that computer scientist said, AI researcher said, was going to go away, was radiology. Because computer vision was going to achieve superhuman levels. And it did see the computer vision was superhuman in 2019, 20 maybe maybe a little bit later, 20, okay. And so it's been a long time since computer vision has been superhuman. And so the prediction was radiologists would go away. Because studying radiology scans was the thing of the past AI will do that. Well, they were absolutely right. Computer vision is completely superhuman. Every radiology platform and package today is driven by AI. And yet, the number of radiologists grew.

这个故事，我非常希望每个人都能听到。最早就有计算机科学家和人工智能研究专家预测，放射科医生的工作会消失，因为计算机视觉将达到超越人类的水平。而事实上，计算机视觉在2019年或可能稍晚的时间确实达到了这种超人水平。所以，计算机视觉已经超越人类很长一段时间了。这个预测因此认为放射科医生将被淘汰，因为研究放射扫描这一工作将会成为过去，人工智能会接管。然而，令人惊讶的是，计算机视觉虽然已经完全超越人类，如今每个平台和软件包都由人工智能驱动，但放射科医生的数量却在增加。

And so the question is why, and we now have a shortage of radiologists in the world. And so one, the alarmist warning went too far and it scared people from doing this profession that is so important to society. And so it did harm. Now, why was it wrong? The reason why is because the purpose of a radiologist, the purpose is to diagnose disease and help patients and doctors diagnose disease. And because we're able to study scans so much faster now, you could study more scans. You could diagnose better. You could you could, um, impatient faster. We can see people more. The hospitals are making more money. You have more patients in the hospital. You need more radiologists. I mean, the amazing thing is it's so obvious.

那么问题来了，为什么现在世界上会有放射科医生短缺的现象。一个原因是过于夸张的警告让大家感到害怕，因此减少了进入这一对社会非常重要的职业的人数，这带来了不良影响。那么，为什么这些警告是错误的呢？原因在于，放射科医生的职责是诊断疾病，协助患者和医生进行疾病诊断。由于我们现在能够更快地研究扫描图像，所以可以研究更多的图像，更准确地进行诊断，可以更快地接诊病人。医院能接待更多患者，从而赚更多钱，因此需要更多的放射科医生。这一切其实显而易见得令人惊叹。

This was going to happen. The number of software engineers that Nvidia is going to grow, not decline. And the reason for that is because the purpose of a software engineer and the task of a software engineer recording are related, not the same. I wanted my software engineers to solve problems. I didn't care how many lines of code they wrote. You know, but their job, their purpose of their job didn't change. Solving problems, working as a team, diagnosing problems, evaluating the result, looking for new problems to solve, innovation, connecting dots. You know, none of that stuff is going to go away.

这本就是预料之中的事情。英伟达的软件工程师数量将会增长，而不是减少。原因在于，软件工程师的目的和他们记录任务的工作是相关的，但却并不一样。我希望我的软件工程师能够解决问题，而不是在乎他们写了多少行代码。他们的工作、工作的目的并没有改变。解决问题、团队合作、诊断问题、评估结果、寻找新问题、创新、联系点滴，这些任务都不会消失。

So you think it's possible that let's even take coding. You think the number of programmers in the world might increase? Not decrease. Yes. And the reason for that is this, what is the definition of coding? I believe that the definition coding of today is simply specifying, specification, and maybe if you want to be rather directive, you could even give an architecture of the software the year you wanted to write. So the question is, how many people could do that? Describe a specification for a computer to go, telling the computer what to go build. How many people? I think we just went from 30 million to probably one billion.

所以你认为这是可能的，我们甚至以编程为例。你认为全球程序员的数量可能会增加，而不是减少。对的。原因在于，什么是编程的定义呢？我相信今天的编程定义只是指定和规范化，如果你想更具体一点，你甚至可以给出一套你想要编写的软件的架构。那么问题来了，有多少人可以做到这一点？就是描述出一个规范，让电脑知道要去建造什么。有多少人能做到呢？我认为这个数量可能从3千万增加到10亿。

And so every carpenter in the future will be a coder, except a carpenter with AI is also an architect. They just increase the value that they could deliver to the customer. They're their artistry, just elevated tremendously. I believe that every accountant is, you know, also your financial analyst, also your financial advisor. So all of these professions have just been elevated. And if I were a carpenter, I see AI, I would just completely go berserk. You know, the services that I can bring to my clients, if I were a plumber, completely go berserk. And the people that are currently programmers in software engineers, I think they're at the cutting edge of understanding intuitively how to communicate with the agents using natural language in order to design the best kind of software.

未来，每一位木匠都会成为程序员，只不过使用人工智能的木匠同时也是一位建筑师。他们能够为客户提供的价值大大提升，他们的技艺水平得到了极大提高。我认为，每位会计师不仅仅是会计，他们也是你的财务分析师和财务顾问。因此，所有这些职业都被提升到了更高的层次。如果我是木匠，看到人工智能，我会非常激动。我可以为客户提供的服务，如果我是水管工，同样会非常激动。而目前作为程序员的软件工程师，我认为他们正处于理解如何使用自然语言与智能代理沟通以设计出最佳软件的前沿。

That's right. So over time, they'll converge, but I think there's still value in getting, I think, learning how to program, like learning what programming languages are, the old kind of programming, what are good practices for programming languages, what are design principles for programming, that's for languages for large software systems. And the reason for that, Lex, you know, I just say for the audience, I think the goal of the goal of specification, the artistry of specification, the goal and the artistry of it, is going to depend on what problem you're trying to solve.

没错。随着时间推移，它们会逐渐趋同，但我认为了解编程、了解编程语言是什么，学习传统编程的价值仍然存在，比如什么是编程语言的良好实践、软件设计原则等，尤其是针对大型软件系统的语言设计。Lex，我想向听众解释一下，规格的目标和艺术性，很大程度上取决于你要解决什么问题。

When I'm thinking about giving the company strategies and formulating corporate directions and things that we should do, I describe it at a level that is sufficiently specific, that people generally understand the direction and it's actionable, they it's so specific enough that they can take action on it, but I under specify it on purpose so that enable 43,000 amazing people to make it even better than I imagined. And so when I'm working with engineers and when I'm working with people, I think about who what problem am I trying to solve, who am I working with, and the level of specification, the level of architecture definition relates to that.

当我在为公司制定战略和规划企业方向，以及确定我们应该做的事情时，我会将这些描述得足够具体，以便大家能够理解方向并采取行动。同时，我故意不做过多的细化，这样可以让公司内部43,000名出色的员工有空间去改进和完善这些想法，比我想象的还要好。因此，当我与工程师和团队合作时，我会思考我想要解决什么问题，和谁在合作，具体细化和架构定义的程度会根据这些因素来决定。

And so everybody's going to have to learn how we're in the spectrum of coding they want to be, writing a specification is coding. And so you might decide to be quite prescriptive, because there's a very specific outcome you're looking for. You might decide that, you know, this is an area you want to be much more exploratory. And so you might under specify and enable you to go back and forth with the AI to even push your own boundaries of creativity. And so this artistry of where you are in the spectrum, this is the future of coding.

所以，每个人都需要学习自己希望在编码的哪个范围内工作。编写规范其实也是编码的一部分。因此，你可能会决定制定得非常详细，因为你在寻找一个非常具体的结果。你也可能觉得某个领域值得进行更为探索性的尝试，于是可能会减少具体的规范，这样可以让你与人工智能来回交流，甚至挑战自己的创造力极限。所以，在这个光谱中的艺术掌握，就是编码的未来。

But just the link on it outside of coding, I think a lot of people, rightfully so, are worried about their jobs, have a lot of anxiety about their jobs, especially in the white collar sector. I don't think anyone knows what to do with tumultuous times that always come when automations and new technology arrives. And I just, first of all, I think we all need to have compassion and the responsibility to feel sort of the burden of what the actual suffering feels like for individual people and families that lose their job.

除了在编程之外的联系，我认为很多人都对他们的工作感到担忧，尤其是在白领行业，这是完全可以理解的。在自动化和新技术出现时，总会带来动荡，没有人真正知道该如何应对这种情况。首先，我认为我们都需要有同情心和责任感，去理解那些失去工作的人和家庭所承受的真实痛苦。

I think whatever you have, transformative technology, like that's coming with artificial intelligence, there's going to be a lot of pain. And I don't know what to do about that pain. Hopefully it creates much more opportunities for those same people. For the same kind of job as the tooling evolves and makes them more productive and makes them more fun. Hopefully as it does in the programming, I've been having so much fun programming I have to say, like I've never had this much fun.

我认为无论你拥有何种变革性技术，比如人工智能带来的那种，都会伴随很多痛苦。而且我不知道该如何应对这些痛苦。但希望这会为同样的人创造更多的机会。随着工具的不断进化，这些工作能让他们更高效、更有趣。希望就像在编程中一样，我不得不说，我从未像现在这样享受编程的乐趣。

So hopefully makes their job automates the boring parts and makes the creative parts, the ones that the human beings are responsible for. But still there's going to be a lot of pain and suffering. So my first recommendation before and this is now how I deal with anxiety. In fact, we just talked about it earlier. And norm is anxiety about the future and norm is anxiety about the pressure and norm is anxiety about uncertainty. I first break it down and then I'm going to tell myself, okay, there are some things you can do something about.

希望能够通过自动化来处理那些枯燥的部分，让人类能够专注于创意性的任务。然而，这个过程中仍然会伴随很多痛苦和挑战。所以，我的第一个建议是，这也是我应对焦虑的方法。实际上，我们刚才已经讨论过了。对于未来的焦虑是正常的，对于压力的焦虑是正常的，对于不确定性的焦虑也是正常的。我首先将这些焦虑分解开来，告诉自己，有一些事情是我可以采取行动去解决的。

There's some things you can't do anything about. But for the stuff that you can do something about, let's reason the reason about it and let's go do it. If we were to hire a new college graduate today and I have a choice between two, one that has that is no clue what AI is and one that is expert in using AI. I would hire the one who's expert in using AI. If I had an accountant, a marketing person, the one that is expert in using AI, supply chain, customer service, a sales person, business development, a lawyer, I would hire the one who is expert in using AI.

有些事情你无法改变，但对于那些你能做些什么的事，我们应该找出原因并付诸行动。如果今天我打算招聘一位新毕业的大学生，并且我有两个选择：一个对人工智能一窍不通的人和一个精通使用人工智能的人，我会选择那个精通使用人工智能的人。同样，如果我要请一位会计师、市场营销人员、供应链管理人员、客服人员、销售人员、业务拓展人员或律师，我都会优先考虑那个精通人工智能的人。

And so I would advise that every college student, every teacher should encourage their student to go use AI, every college student should graduate and be an expert in AI and everybody, if you're a carpenter, if you're a electrician, go use AI. Go see what it can do to transform your current job, elevate yourself. If I were a farmer, I would absolutely use AI. If I were a pharmacist, I would use AI. I want to see what it could do to elevate my job so that I could be the, I could be the innovator to revolutionize this industry myself. And so that would be the first thing that I would do.

因此，我建议每一位大学生和每一位老师都应该鼓励他们的学生去使用人工智能。每个大学生都应该在毕业时精通人工智能。不管你是木工还是电工，都应该去使用人工智能。去看看它能如何改变你的工作，提高自己的能力。如果我是农民，我绝对会使用人工智能。如果我是药剂师，我也会使用人工智能。我想要看看人工智能能够如何提高我的工作，这样我就能够成为革新这个行业的创新者。因此，这将是我会做的第一件事。

And then I would also, I would also help them. It is the case that the technology will dislocate and will eliminate many tasks. And because it will automate it, if your job is the task, if your job is the task, then you're very highly going to be disrupted. If your job's purpose includes certain tasks, then it's vital that you go learn how to use AI to automate those tasks. And then there's the world of spectrum in between.

然后，我也会帮助他们。事实上，技术会导致许多任务的变化或消失。因为技术会自动化这些任务，如果你的工作本身就是这些任务，那么你很可能会受到冲击。如果你的工作目的是包含某些任务，那么你必须去学习如何使用人工智能来自动化这些任务。在这两者之间，还有一个广阔的范围。

And by the way, the beautiful thing about AI, so the chatbot versions, is you can break down, you have anxiety and you can break down the problem by talking to it. I've recently, it's really just incredible how much you can think through your life's problems and through, and I don't mean like therapy problems. I mean like very practically, okay, I'm worried about what, literally I'm worried about my job, what are the skills, what are the steps I need to take? How do I get better at the day?

顺便说一下，AI 的美妙之处在于，像聊天机器人这样的版本，可以通过与它交流来分析你所焦虑的问题。我最近发现，这真的非常神奇，你可以通过与它对话来思考生活中的问题。我不是指治疗方面的问题，而是一些非常实际的问题。比如说，我担心的是什么？字面上来说，我担心的是我的工作，那我需要哪些技能？需要采取哪些步骤？我如何才能让自己每天做得更好？

I, everything you just said, you can literally ask and it's going to give you a point by point of plan. I mean, it's just a great life coach period. This, I don't know how to use AI and the AI goes, well, let me show you exactly. It's very matter, but it's, it's kind of incredible. So people definitely should. You can't walk up to excel and say, I don't know how to use Excel. You're done.

我听懂了你刚才所说的一切，你可以直接提问，它将为你提供一个逐步的计划。我是说，这真的是一个很棒的生活导师。我不知道如何使用人工智能，而人工智能会说：好吧，让我来告诉你怎么做。这非常直接，但也很令人惊讶。所以人们绝对应该尝试。你不能直接走到Excel面前说：我不知道怎么用Excel，然后就停步不前了。

I mean, that's really what AI has done for me in all walks of life. Is that initial friction of being a beginner, of using a thing for the first time? I can literally ask about any single thing. What are the first steps I need to take? That's right. And at that hand holding that it does, removing the friction of all the experiences that the world offers is, you know, like I mentioned to you offline, you mentioned, I'm going to China and Taiwan. So awesome. So excited for you.

我的意思是，这正是人工智能在我生活的各个方面所做的事情。就是最开始作为新手，第一次使用某样东西时的那种阻力。现在我可以随便问任何事情，我需要采取的第一步是什么？没错。它通过手把手的引导，消除了世界所提供的所有体验中的阻碍。就像我之前私下和你提到的，你提到我要去中国和台湾，太棒了，我为你感到兴奋。

Where do I, what do I, how do I, all those questions immediately answered? It is beautiful. Well, when you, when you go to Taiwan, just ask AI, what are Jensen's favorite restaurants in Taiwan? Yeah. And it will actually tell you, oh yeah, is it accurate?

在哪里、做什么、怎么做，所有这些问题立刻就有答案？这真是太美好了。当你去台湾的时候，只要问 AI：Jensen 在台湾最喜欢的餐厅有哪些？它会告诉你，哦，是的，准确吗？

Yeah. All right. It's all over, all over Taiwan. Well, you're a rock star over there. And like we also mentioned, I'm glad it made our path across, which would be really wonderful in computer techs and video GTC Taiwan. Do you think there are some things about human nature, about human consciousness that is fundamental non-computational, maybe something a chip, no matter how powerful, it can never replicate?

好的。嗯，对，没错。这一切都在台湾传开了。你在那边就像个摇滚明星。就像我们也提到的，我很高兴我们的道路有交集，这将会在台湾的计算机技术和视频方面非常美好。你认为在人性和人类意识中是否有一些根本上是无法计算的东西，也许再强大的芯片也无法复制？

I don't know if the chip will ever get nervous. And that's the, you know, of course, the conditions by which that causes anxiety or nervousness or whatever emotion, I believe that AI will be able to recognize those and understand those. I don't think my chips will feel those.

我不知道芯片是否会感到紧张。当然，引起焦虑或紧张等情绪的条件，我相信AI能够识别和理解这些。我不认为我的芯片会真正感受到这些。

And therefore, the how, how that anxiety, how that feeling, how that excitement, how that, how that, you know, all of those feelings manifest in human performance, for example, extremely amazing human performance, athletic performance, you know, average or lesser than average, that entire spectrum of human performance that comes out of exactly the same circumstances for different people, manifesting in different outcome, manifesting in different performance, I don't think there's anything about anything that we're building that would suggest that two different computers being presented with all of exactly the same context would perform, of course, it would produce statistically different outcomes, but it's not because it felt different.

因此，这种情感，无论是焦虑、感受、兴奋等，如何在人的表现中显现出来，比如极为出色的表现、运动表现、普通的或者低于平均水平的表现，这些不同的人在相同环境下表现出的整个人类表现的光谱，会产生不同的结果和表现。我不认为我们构建的任何东西会表明两个在完全相同背景下的计算机会表现出同样的结果。当然，它们会产生在统计上不同的结果，但这并不是因为它们感受不同。

Yeah, the subjective boy, there's something truly special about the subjective experience that we humans feel. Like I mentioned to you, I was, I was pretty nervous talking to you, like I mentioned to you that the hope to fear the anxiety and just life itself, the richness of life, how amazing everything is, how deeply we fall in love, how deeply a heart gets broken, how afraid we are of death and how much pain we feel when our loved ones pass away all of that, the whole thing.

是的，那个主观的男孩，我们人类所感受到的主观体验真的有一些特别之处。就像我跟你提到的，我在和你交谈时感到很紧张，就像我跟你提到的，从满怀希望到充满恐惧的焦虑，以及生命本身，生活的丰富多彩，一切是多么令人惊叹，我们是如何深深爱上一个人，心又是如何被深深伤透，我们对死亡有多么害怕，当我们爱的人离世时我们感到多么痛苦，所有这些，整个过程。

I know it's very hard to think AI being able to, a computational device being able to do that, but there's so many mysteries about this whole thing that we're yet to uncover that I am open to be surprised. Yeah, I've been surprised a lot of the past few months and few years, scaling can create some incredible miracles in the space of intelligence has been truly marvelous to watch. So I'm open to surprise.

我知道很难想象人工智能或计算设备能够做到这一点，但在这个领域还有许多未解之谜等待我们去发现，所以我愿意保持开放的态度，随时准备接受惊喜。是的，在过去的几个月和几年里，我已经经历了很多惊喜。规模化在智能领域创造了一些令人惊叹的奇迹，真是令人赞叹。所以我愿意接受惊喜。

And it's just really important to break down what is intelligence. The word, that word, we use all the time, it's not a mysterious word. Intelligence has a meaning. And it's a system that, it's something that we do that includes perception and understanding and reasoning and the ability to do plan. And that loop, that loop is fundamentally what intelligence is. Intelligence is not one word that is exactly equal to humanity.

理解“智能”这个词的含义非常重要。我们经常使用这个词，它并不神秘。智能是有特定意义的，它是一种我们用来感知、理解、推理和规划的系统。这个循环过程就是智能的本质。智能这个词不仅仅等同于人类。

And that's, I think, really important to separate the two. We have two words for that. I'm not, I don't over fantasize about, and I don't over romanticize about intelligence. Intelligence is, and people have heard me say it before, I actually think intelligence is a commodity. I'm surrounded by intelligent people. And I'm surrounded by intelligent people more intelligent than I am in each one of the spaces that they're in.

我认为，把这两者分开是非常重要的。我们有两个词来表达这个。我不会过度幻想，也不会过度浪漫化地看待智力。智力，我以前也说过，我实际上觉得智力是一种普通的东西。我身边有很多聪明的人，他们在各自领域都比我更聪明。

And yet, I have a role in that circle. It's actually kind of interesting. They're more educated than I am. They went to better schools than I did. They're deeper than any, in the fields that they're in, all of them. I have 60 of them. They're all superhuman to me. And somehow, I'm sitting in the middle orchestrating all 60 of them. And so you got to ask yourself, what is, what is it about a dishwasher that allows that dishwasher to sit in the middle of superhumans? Does that make sense?

尽管如此，我在这个圈子里有一个角色。这其实挺有趣的。他们比我受过更好的教育，上了比我更好的学校。在各自的领域里，他们都非常深刻。我有60个人，他们对我来说都是超凡脱俗的人。然而，不知怎么的，我坐在中间指挥这60人。所以你得问自己，一个洗碗工有什么特别之处能让他坐在一群超凡脱俗的人中间？这有道理吗？

And so, but that's my point. My point is intelligence is a functional thing. Humanity is not, not specified functionally. It's a much, much bigger word. And, and our life experience, our tolerance for pain, our determination, those are, those are different words in intelligence. And so the thing that I want to help the audience understand, if I could give them one thing is, intelligence is a word that we've elevated to very high form over time.

所以，这就是我的观点。我的观点是，智力是一种功能性的东西。而人性不是，它不是功能上规定的。人性是一个更大、更广泛的概念。我们的生活经历、对痛苦的忍耐能力、我们的决心，这些都是与智力不同的词。因此，我想帮助观众理解的一点是，如果我能给他们传达一个信息，那就是：智力是一个我们随着时间推移而赋予很高地位的词。

The word should really elevate its humanity. Character, humanity, all of those things, compassion, generosity, all of the things that you say, just now, I believe those are superhuman powers. And that now intelligence is going to be commoditized because we've spoken about it, the most important things are your education. The most, now even, even when they said the most important things are your education, when you went to school, there's more than just knowledge that you gained.

这句话应该真正提升人性的价值。品格、人性，这些东西，比如同情心、慷慨等等，我相信这些都是超凡的力量。如今，智慧正在被商品化，因为我们已经谈论过它，但最重要的还是你的教育。即使在他们说教育是最重要的时候，在学校里你获得的不仅仅是知识。

And so, but unfortunately, our society had put everything into one single word. And life is more than one word. And I'm just telling you, my life would suggest that being lower on the intelligence curve, than everybody around me doesn't change the fact I'm the most successful. And so, and I think, I think that that kind of is, I'm trying to hopefully inspire everybody else that don't let this democratization of intelligence, this commoditization of intelligence, you know, cause your anxiety, you should be inspired by that.

我们的社会把一切都简化成一个词，但生活远不止一个词。我想说的是，我的生活经历表明，即使我的智力水平比周围的人低，也不妨碍我成为最成功的人。我希望能激励大家，不要因为智力的普遍化和商品化而感到焦虑，相反，你应该从中获得启发。

Yeah, I think AI will help us celebrate humans more. And I'm certainly the humanity and human first. And I think what makes this world incredible is humans forever will be so. And just AI is this incredible tool that makes us. That's exactly right. Humans more powerful. That's exactly right. So much of the success of Nvidia and the lives of millions of people that I mentioned, depend on you.

是的，我认为人工智能会帮助我们更好地赞美人类。我绝对认为人性和人类是最重要的。我觉得这个世界之所以如此不可思议，是因为人类，而这一点永恒不变。人工智能只是一个让我们更强大的工具。没错，让人类更强大。正因为如此，像英伟达这样的公司的成功以及我提到的数百万人的生活，都与你息息相关。

But you're just one human, like we mentioned, mortal like all of us. Do you think about your mortality? Are you afraid of death? I really don't want to die. I have a great life, I've a great family. I have really important work. This is, this is not a once in a, once in a lifetime experience suggests that it has been experienced by many people, just not one person. This is a once in a humanity experience what I'm going through. Nvidia is one of the most consequential technology companies in history, which only very important work I take it very seriously. And so some of the, some of the things that, that of course are, are practical things. Like how do we think about succession planning? And, and I, I'm famous in saying that I don't believe in succession planning. And, and the reason, the reason for that, the reason for that isn't because I'm immortal. The reason for that is because if you're worried about succession planning, if you're worried, all that anxiety of succession planning, then what should you do about it?

但是你我都是普通人，如我们提到的，和大家一样都是凡人。你会思考自己的生命有限吗？你害怕死亡吗？我真的不想死。我有很棒的生活，有美满的家庭，并且我从事非常重要的工作。这不是一种偶然的经历，而是一种人类独有的经历。英伟达是历史上最有影响力的科技公司之一，我对自己的工作非常认真严肃。当然，这其中有一些实际问题需要考虑，比如如何进行接班计划。我出名的一点是，我不相信接班计划。原因并不是因为我认为自己不朽，而是因为假如你总是担心接班问题，这种忧虑该如何解决呢？

Then you break it all the way back down. The most important thing you should do today, if you care about the future of your company, post you, is to pass on knowledge, information, insight, skills, experience as often and continuously as you can, which is the reason why continuously reason about everything in front of my team. Every single meeting is about a reasoning meeting. Every moment I spend inside a company, outside a company is about passing on knowledge to people as fast as I can. Nothing I learn ever since it sits on my desk, longer than, you know, a fraction of a second. I'm passing that information that now, oh my gosh, this is cool. Before I even finish learning all of it myself, I've already pointing it to somebody else. Get on this. This is so cool. You're going to want to, you're going to want to learn this. And so I'm constantly passing knowledge, empowering people, elevating the capability of everybody around me.

然后你再把所有的事情一步步深入分析。如果你关心你公司在你离开后的未来，今天最重要的事情就是尽可能频繁和连续地传授知识、信息、见解、技能和经验。这就是为什么我在团队面前要不断地讲解所有事情的原因。每次会议都是一次推理的会议。每一个我在公司内外度过的时刻，都是为了尽快地传递知识。自从我学到新东西后，它从来没有在我的桌子上停留超过一瞬间。我都会立刻分享那些信息，感叹，“天哪，这真是太棒了。”即便我自己还没完全学会，我已经在把它介绍给其他人了。快看看这个，实在太棒了，你会想学习的。所以我不断地传播知识，赋予他人力量，提高周围每个人的能力。

So that, the outcome that I seek that I hope for is that I die on the job. And hopefully I die on the job instantaneously. And there's no long fears of suffering, it's a well from a fan perspective. Given your extremely, your enormous positive impact on civilization, of course, I hope you keep going, but also it's just fun to watch. What is it you're doing? You're, you know, it's just the rate of innovation. And I'm a huge fan of engineering. It's so much incredible. Engineering is continuously being done by Nvidia. It's just fun to watch. It's a celebration of humanity, celebration of great build. There's a celebration of great engineering. So it represents something special. So I hope you and Nvidia keep going.

我希望的结果是我能在工作中去世，而且最好是瞬间去世，这样就不用长期忍受痛苦。作为一个粉丝，从你的贡献对文明的极大积极影响来看，我当然希望你能继续走下去，但同时也觉得看到你所做的事情很有趣。你的创新速度实在令人惊叹，我对工程学非常着迷。Nvidia不断进行着如此多令人难以置信的工程创新，看着这些是一种享受。这是对人类、伟大建造和工程的庆祝，具有特殊的意义。所以我希望你和Nvidia能够继续前行。

What gives you hope about this whole thing we got going on? About humanity, about the future of humanity. When you look out, and you think about the future quite a bit, when you look out at 10, 20, 50, 100 years from now, what gives you hope? I've always had great confidence in the kindness, the generosity, the compassion, the human capacity. I've always been extremely confident of that. Sometimes more so than I should. And I get taken advantage of, but it doesn't, it doesn't ever cause me not to. I start with always that that people want, want to do good. People want to help others. And vastly I am proven right. Constantly proven right. And often, exceeds my expectations. And so I have complete confidence in the human capacity.

是什么让你对我们所做的一切抱有希望？关于人类，关于人类的未来。当你展望未来，思考未来10年、20年、50年、甚至100年时，什么给了你希望？我一直对人类的善良、慷慨、同情和能力充满信心。我一直对此极为自信。有时候我可能过于信任，以至于会被利用，但这从未让我放弃这种信任。我始终相信，人们是希望行善的，是想帮助他人的。而且，我的这种信念往往被事实证明是正确的，甚至超出我的预期。所以，我对人类的能力有完全的信心。

I think the thing that the things that give me incredible hope is what I see as I extrapolate, as I what I see now is possible. And as I extrapolate based on the things that we're doing, what will very likely happen. And that there's so many things that we want to solve. There's so many problems we want to solve. There's so many things that we want to build. There's so many good things that we want to do that are now within our reach and within the reach of my lifetime, you just can't possibly not be romantic about that. You know what I'm saying? Yeah, what an exciting time to be alive. Yeah, like truly, truly so. How can you not be romantic about that? The fact that there is a reasonable thing to expect at the end of disease. It's a reasonable thing to expect. It's a reasonable thing to expect that pollution will be drastically reduced. It's a reasonable thing to expect that traveling at the speed of light is actually in our future.

我觉得让我充满希望的是，我看到的一切都有可能实现。而我根据我们正在做的事情进行推测时，我认为很可能会发生的事情。我们想要解决的问题太多了，想要建造的事物太多了，想要完成的美好事物也太多了，而这些都在我们的掌握之中，并且在我有生之年可能实现。你不可能对此不充满向往，对吧？活在这个时代真是令人兴奋。是啊，真的就是这样。你怎么能不浪漫呢？想想看，疾病的终结是可以合理期待的，污染大幅减少也是可以合理期待的，光速旅行实际上在我们的未来也是可以期待的。这些想法本身就让人充满期待，你明白我的意思吗？

And then, you know, not for a long distances, but short distances. People ask me how? And it was first of all, very soon, I'm going to put a humanoid on a spaceship. And it's going to be my humanoid. And we're going to send it out as soon as possible. And it's going to keep improving and enhancing along the flight. And then when it's time, all of my consciousness has already been so much of my life. It's been uploaded on the internet, take all my inbox, take everything I've done, everything I've said. It's been collected and becoming my AI. And I'm just, you know, when the time comes, you know, we'll just send that out to speed light, catch up with my robot. I mean, but for me, that's sort of application-focused.

然后，你知道，不是长距离，而是短距离。有人问我是怎么做到的。首先，很快我就要把一个类人机器人送上飞船，而且这是我自己的机器人。我们会尽快将它发射出去。在飞行过程中，它会不断改进和增强。到时，我的大部分意识已经在互联网上进行了记录。把我所有的邮件、所有做过的事情、说过的话都收集起来，形成我的人工智能。当时机成熟时，我们就会以光速发送这些信息，让它去追赶我的机器人。对我来说，这就是一种实用导向的应用。

But also for me, the curiosity, a maxing perspective. I just all of those mysteries is so much fascinating scientific questions there. Understanding the biological machine is right around the corner. It's not 10 years. This five years probably. And the neurobiological machine, the human mind and cracking physics, theoretical physics open. It's so exciting. Explaining consciousness. That one would be awesome. And it's all within our reach.

对我来说，好奇心带来了一个极好的视角。这些令人着迷的神秘问题中有许多科学问题。理解生物机器的奥秘近在咫尺，这可能不是十年的事情，而是五年内就能实现。而神经生物机器，即人类大脑，以及揭开理论物理的奥秘，也令人无比兴奋。解释意识的问题将是非常棒的。这一切都是在我们的能力范围之内的。

Yeah. Jessen, thank you so much for everything you've done over the years. Thank you for everything you're doing for the world. Thank you for being who you are. I can tell you're a great human being. And I wish you incredible success this year. I can't wait. As a fan, I can't wait to see what you do next. And hopefully I'll see you in Taiwan. And thank you so much for talking today. Thank you, Lex. I had a great time.

是的。Jessen，非常感谢你多年来所做的一切。感谢你为这个世界所做的一切。感谢你做你自己。我能看出你是一个伟大的人。祝你今年取得巨大的成功。我迫不及待地想知道你接下来会做些什么，作为一个粉丝，我非常期待。希望能在台湾见到你。非常感谢你今天的交流。谢谢你，Lex。我度过了一段美好的时光。

And also, if I could just say one more thing. Yes. And thank you for all the interviews that you do, the depth, the respect that you go through with and the research that you do to reveal for all of us, the amazing people that you've interviewed over the years. I've enjoyed them immensely. And as an innovator, to have created this long form, unbelievable. And yet, it's captivating. So anyways, thank you for everything you do. It means the world.

如果可以，我想再补充一点。首先，非常感谢您所做的所有采访，您在采访中展现出的深入、尊重和细致的研究，向我们揭示了多年来那些令人惊叹的人物。我非常享受这些采访。作为一个创新者，您创造了这种长篇的形式，真是令人难以置信并且引人入胜。不管怎样，非常感谢您所做的一切，这对我意义重大。

Thank you, Jessen. Thank you, Lexen. Thank you for listening to this conversation with Jessen Huang. To support this podcast, please check out our sponsor in the description, where you can also find links to contact me, ask questions, give feedback, and so on. And now, let me leave you with some words from Alan Kay. The best way to predict the future is to invent it. Thank you for listening. And I hope to see you next time.

感谢你，Jessen。感谢你，Lexen。感谢你收听我与Jessen Huang的对话。为了支持这个播客，请查看描述中的赞助商信息，你还可以在那找到联系我、提问、反馈等的链接。接下来，让我引用Alan Kay的一句话：预测未来的最好方法就是去创造未来。感谢你的收听，希望下次再见。