打造智能革命的「发电机」

非常感谢你，Jensen。我们正处于一场大规模的人工智能革命之中。它可能比工业革命还要宏大和迅速，而你曾指出，当前正在发生的是人类历史上最大的基础设施建设。这一建设的核心是 AI 工厂，而实现这一切的公司是 Nvidia。你能告诉我们什么是 AI 工厂，以及为什么它是未来十年任何企业的最佳投资吗？

Thank you so much, Jensen. So, we are in the middle of a massive AI revolution. It is probably bigger and faster than even the industrial revolution and you have called out what's happening right now as the largest infrastructure buildout in human history. At the center of that buildout is the AI factory and the company enabling all of that is Nvidia. Can you tell us what is an AI factory and why is it the best investment for any enterprise in the next decade?

好的，你可以从多个角度理解 AI。最可能的方式是通过聊天机器人或网页浏览器。你与它互动，给它提示，它回复你。即使你已经使用 AI 一段时间，也会看到过去两三年里 AI 能力的显著进化。两年前，你听说过 ChatGPT。ChatGPT 基本上是一种计算机软件，能理解你给出的输入。它能感知和理解信息，并将信息转换和生成为其他内容。你可以给它提示，比如「这是我给你的 PDF，请总结一下」，这是从文本到文本。你也可以说「这是我给你的 PDF，请生成这个故事的一张图片」，这是从文本到图像。你还可以从图像到文本，比如给一张图片，问图片里发生了什么，这是从图像到文本。明白吗？任何内容到任何其他内容。两年前，AI 主要能完成这种转换，我们称之为生成，生成式模型，生成式 AI。但「生成式 AI」这个词里非常重要的一点是，为了做比生成更有价值的事情，理解和生成之后是思考。如果不生成词语，你就无法思考。生成式 AI 的基础给了我们生成内部思想、思考、推理、逐步推理和解决问题的能力。它还让我们能做另一件现在非常重要的事情：生成智能来控制其他东西，生成控制来使用工具。明白吗？使用浏览器、电子表格、Photoshop、PowerPoint、AutoCAD 或其他工具。现在这些工具是数字化的，但未来会是机械化的。如果我向机械系统生成命令，那就是机器人技术。如果我向带方向盘的机器生成命令，那就是自动驾驶汽车。明白吗？两年前，你看到了基础，我们称之为 ChatGPT。每个人都说，「啊，这很有趣，很傻，或者它产生了一大堆疯狂的幻觉文本。」这都是真的，但它是导致这一切的基础技术。两年后，我们现在有了智能体系统。这是 AI 的一个视角。我刚才描述的是 AI 能做什么。现在你们都意识到，从 ChatGPT、Codex、Claude 中看到，它现在不仅能理解，还能工作、推理和执行任务。两年前，当 AI 能理解你并生成信息时，那很有趣、新颖、有点可爱。需要写诗时，这是个好方法。谁不想写首乡村歌曲呢？那是两年前。但现在，因为它能工作，AI 变得有价值。有价值意味着它能生成信息，能生成有用的工作，并且可以为此付费。因为我们为工作的人付费。我们喜欢无所不知的人，但我们不为此付费。我们为工作的人付费。明白吗？这就是过去两年发生的事情。AI 从拥有这种能力到变得智能体化，从不太有价值到产生有用的工作。如此多有用的工作，以至于你和我每天都在做。我们按小时支付 AI 费用。我们可能每小时付 30 美元或 20 美元让它工作。今天我们基本上付给 AI 很多钱。这是人类历史上增长最快的软件业务，因为它现在在做有用的工作，我们可以付钱让它做。这是 AI 的一个视角，即它能做什么。但另一个非常重要的视角有助于理解 Constantine 所说的。例如，为什么一些公司和个人能够建立伟大的企业并进入大型产业的核心？因为他们看到这种能力时，觉得非常有趣。一个有趣的想法是，如果我们能做到这一点，对下游产业意味着什么？这是一个值得讨论的话题。既然 AI 能做到这些，那么医疗、金融服务、生命科学、制造、物流、运输、零售、广告、未来娱乐等所有行业会发生什么？这个列表很长。这是一个有趣的话题，但你应该向上游思考，从产业角度这意味着什么？首先你会意识到这一点。回到第一性原理。我刚才告诉你 AI 是软件，由计算机生产。是什么让计算机能够做到这一点？关键想法是，我们今天所知的计算机大约在 64 年前出现。IBM System 360 是计算领域最重要的发布，64 年前 IBM 是世界上最值钱的公司。他们创造了现代计算机的理解。我们对计算机的所有描述实际上在 1964 年就已经确定。40 年来基本保持不变。这种计算形式被称为预记录。你写下故事，保存到文件。你手写程序，保存到文件。你拍照，保存到文件。你录制音乐，保存到文件。你制作视频。现在我们正在直播，但有人会录制它。你会保存到文件。当你想稍后使用时，从磁盘驱动器中检索。明白吗？检索过程是智能完成的。这就是为什么每个人对新闻故事的检索略有不同。这被称为推荐系统。

Okay, so you could understand AI in a number of ways. The way that you understand AI probably most is through a chatbot through a web browser. You interact with it. You give it a prompt. It says something back to you. And even those of you who have been using AI for some time have seen in the last two or three years a very significant evolution in the capabilities of AI. Two years ago, you heard about ChatGPT. ChatGPT is basically a computer software that understands the input you give it. It can perceive and understand information, and it can translate and generate the information into something else. You can give it a prompt. You can say, "Here's this PDF I gave you. I would like you now to summarize it." It went from text to text. You could also tell it, "Here's a PDF I gave you. I would like you to now generate an image of that story." It goes text to image. You could go from image to text, meaning you could give it a picture and ask what is happening inside this picture. It goes image to text. Does that make sense? Anything to anything else. And AI in the last two years, two years ago was largely able to do this translation. We called it generation, generative models. Generative AI. But the thing that is a very big deal inside that word generative AI is in order to do something even more valuable than generation, understanding and generating is thinking. Well, you can't think if you don't generate words. The foundation of generative AI gave us the ability to generate internal thoughts, thinking, reasoning, step-by-step reasoning, problem solving. It also allowed us to do another thing that is now very important, which is generate intelligence to control something else, to generate control to use a tool. Does it make sense? To use a browser, use a spreadsheet, use Photoshop, use PowerPoint, use AutoCAD, use another tool. Now that tool today is digital, but someday that tool will be mechanical. So if I generate a command to a mechanical system, that would be called robotics. If I generate commands for a machine with a steering wheel, that would be called self-driving cars. Does that make sense? Two years ago, you saw the foundations. We call it ChatGPT. And everybody said, "Ah, it's fun. It's silly or it produced a whole bunch of crazy hallucinated text." That's all true, but it was the foundational technology that led to all of this. Two years later, we now have agentic systems. That's one view of AI. I just described the view which is what can AI do. And now all of you realize you see it from ChatGPT, you see it from Codex, you see it from Claude, you see that it's now able to not just understand but to do work, reason, and do work. Two years ago when AI was able to understand you and generate information, that was interesting, novel, a little cute. Whenever you need a poem written, great way to do it. Who doesn't want to write a country song? That was two years ago. But now, because it's able to do work, AI is valuable. Valuable meaning it can generate information. It can generate useful work, and it could be paid for. Because we pay for people who do work. We love people who are know-it-alls, but we don't pay them for it. We pay for people who do work. Does that make sense? Which is what happened in the last two years. AI went from having this capability to now agentic, went from not very valuable to now producing useful work. So much useful work that you and I are doing this every day. We're paying AI by the hour. We might pay them $30 an hour to do the work, $20 an hour to do the work. We're basically paying AI a lot of money today. The fastest growing software business in the history of mankind because now it's doing useful work and we can pay them to do it. Now that's one view of AI which is what it can do. But another view of AI that's really important to help reason through what Constantine is saying. For example, the reason why some companies, some people are able to build great businesses and could maneuver themselves into the center of very large industries is because when they see this capability, this is very interesting. One interesting thought is if we're able to do this, what is the implication to downstream industries? That's an interesting conversation we should have. So now that AI can do this, what happens to all the industries like healthcare, financial services, life sciences, manufacturing, logistics, transportation, retail, advertising, future entertainment, the list goes on. That's an interesting conversation, but you should go upstream meaning industrially what does that mean? The first thing you realize is this. Go back to first principles. I told you just now that AI is software and it's being produced by a computer. What happened to the computer that made it possible to do this? The big idea is about if you think about the computer as we know it today really emerged about 64 years ago. IBM System 360 was the biggest announcement of computing, and 64 years ago IBM was the most valuable company in the world. They created the modern understanding of computers. Everything that we can describe about computers was really described in 1964. For 40 years largely has remained the same. What happened was that in that form of computing is called pre-recording. You write down your story, you save it to a file. You write a program by hand, you save it to a file. You take a picture, you save it to a file. You record music, you save it to a file. You make a video. Right now, we're in streaming right now, but somebody's going to record it. You're going to save it to a file. And when you want to use it later, you retrieve it from the disc drive. Does that make sense? And the retrieval process is done intelligently. That's why everybody's retrieval of a news story is a little bit different. It's called a recommender system.

但基本上，我们今天所知的计算机是一个基于检索的系统，这就是为什么这些建筑被称为数据中心。它们存储数据。注意，它们不叫计算机中心，因为你并没有做太多计算。它们只是存储数据，你根据在手机上触摸的内容来检索。那么，现在发生了什么？如果你看看我刚才描述的，为了让这个 AI 像我说的一样工作，每次我对它说话，我都必须给它新信息，我们称之为上下文，我给它一个新提示，称为查询。在上下文和查询之间，它会先理解，推理，然后根据那个上下文和查询，根据情况产生输出。到目前为止说得通吗？好，点个头。好。所以如果是这样，每次你使用 AI，内容都是全新生成的。我现在对你们说的每一句话都是实时生成的。这是因为我的解释基于这样一个事实：我意识到你们来自 60 个不同的国家，128 个不同的家庭。你们都有很多不同的背景。你们中有些人可能来自计算机行业。大多数人可能不是。所以我以一种足够深入的方式向你们解释信息。但最终我的目标是这个。这样你们就知道如何进行下一次投资。这就是我要说的。所以我会给你们足够的信息，让你们自己能够推理，这样下次你们看到某样东西时，就知道那值得投资。那将是一个大行业。现在它有一千亿美元，看起来很大，但和它将来的规模相比，这不算什么。我会给你们直觉来解决这个问题。好吗？所以，我们从 60 年来主要基于检索的计算机行业，突然有一天完全变成了实时生成。我们称之为智能。这就是我现在为你们做的。我在展示智能、上下文感知。他给了我一个提示，然后我的答案就来了。说得通吗？

But basically computers as we know it today is a retrieval-based system, which is the reason why these buildings are called data centers. They store data. Notice they don't call them computer centers because you're not doing much computing. They just store data that you retrieve based on what you touch on your phone. Well, what happened now? If you look at what I just described, in order for this AI to work as I described it, every time I say something to it, I have to give it new information, we call it context, I give it a new prompt that's called a query. Between the context and the query, it will understand it first, reason about it, and it will produce an output based on that context in that query, based on the circumstance. Does that make sense so far? Okay, give me one nod. Okay. And so if that is the case, every time you use the AI, the content is produced originally every single time. Everything I'm saying to you right now is being produced in real time. And it's because my explanation is based on the fact that I realize all of you come from 60 different countries, 128 different families. You all have many different backgrounds. Some of you probably came from the computer industry. Most of you probably did not. And so I'm explaining the information to you in a way that is sufficiently deep. But ultimately my goal is this. So that you know how to make your next investment. That's what I'm leading to. And so I'm going to give you enough sufficient information that you can reason about it for yourself so that when you see something in the next time you go, that's worth investing in. That's going to be a big industry. That's a hundred billion dollars right now and it looks really big, but that's nothing compared to how big it's going to be. I'm going to give you the intuition to solve that problem. Okay? And so, here we went from a computer industry that was largely based on retrieval for 60 years and all of a sudden one day it's completely generated in real time. We call it intelligence. This is what I'm doing right now for you. I'm demonstrating intelligence, contextual awareness. He gave me a prompt and here comes my answer. Does it make sense?

极端智能。极其人工的智能。

Extreme intelligence. Extremely artificial intelligence.

好的。所以现在的情况是这样的。我刚才给了你们一个词。它叫生成式 AI。今天的计算机已经被彻底重新发明了。它现在是生成式的。每一个脚趾，你看到的每一个字母，未来你看到的每一个单词，每一个视频，每一张图片，每一个广告，每一个电视广告，每一次你读一个故事，一个新闻故事，每一个都会不同。康斯坦丁看到的，我看到的，你看到的会完全不同，因为它是为你生成的。因为你的兴趣、你的上下文、你是谁、你为什么问、你怎么问，都是完全不同的。说得通吗？所以，未来你看到的每一个像素，你听到的每一个声音，你看到的每一个视频，都将是原始生成的，而不是检索的。这意味着未来我们需要更多的生成器。而这些生成器就是我们建造的。这就是我们谋生的手段。这些是大型计算机，它们在生成智能。现在，下一个问题是：它可能有多大？它能有多大？事实证明，信息量、智能生成量，我们今天为大约十亿人做这件事。现在我已经告诉你们，AI 已经变得具有智能体性，意味着它可以自己工作。如果它可以自己工作，那么一个智能体可以和另一个智能体通信，说：我有一些工作要做。我们组队吧。我们一起做点工作。现在你有了所有这些不同的智能体，它们一起工作来解决你公司内部的问题。所以在我们公司内部，康斯坦丁知道我们是智能体式 AI 的巨大用户。我们现在可能有成千上万的智能体在四处运行，它们在做工作，互相交谈，解决问题。都有护栏，都在沙盒中，但它们都在互相合作。这意味着未来很可能我们今天为十亿人使用的互联网，将主要被几十亿，甚至一千亿个智能体全天候使用，它们使用互联网互相交谈，它们在说什么？例如，会有公司与公司合作，员工智能体与其他员工智能体合作。会有自动驾驶汽车，它们是智能体式的。会有机器人，它们是智能体式的。所有的制造系统，每一栋建筑都将是智能体式的。到处都会有智能体，它们会使用互联网，它们互相产生的所有命令都将是生成的。它们需要理解的所有想法都是生成的。说得通吗？所以基本上，世界将会有这一层计算，它将包裹地球，并且一直在生成智能。

Okay. And so here's what's going on now. I just gave you one word earlier. It's called generative AI. The computer of today has been completely reinvented. It's now generative. And every single toe, every single letter you see, every single word you see in the future, every video, every image, every ad, every TV commercial, every single time you read a story, a news story, every one of them will be different. What Constantine sees, what I see, what you see will be completely different because it'll be generated for you. Because your interest, your context, who you are, for what reason you asked, how you asked is completely different. Does it make sense? And so therefore every single pixel that you see, every single sound that you hear in the future, every video you see in the future will be originally generated, not retrieved. Which means in the future we need a lot more generators. And these generators is what we built. That's what we build for a living. These are large computers and they're generating intelligence. Now, the next question is this. Well, how big could it be? How big can it be? And so, it turns out the amount of information, the amount of generation of intelligence is we do it for about a billion people in the world today. Now that I told you that AI has become agentic, meaning that it can actually do work by itself. Well, if it can do work by itself, then one agent can communicate with another agent and say, I have some work to do. Let's team up together. Let's do some work. And now you have all these different agents and they're all working together to solve problems inside your company. So inside our company, Constantine knows that we're huge users of agentic AI. We have hundreds of thousands of agents probably running around right now that are doing work and they're talking to each other and they're solving problems. All guardrailed, all sandboxed, but they're all working with each other. Which means in the future it is very likely that the internet that we use today for a billion people will likely mostly be several billion, call it a hundred billion agents working around the clock and they're using the internet and talking to each other and what are they saying? So for example, there'll be companies working with companies, employees agents working with other employees agents. There'll be self-driving cars which are agentic. There will be robots which are agentic. All the manufacturing systems, every building will be agentic. There'll be agents all over the place and they'll be using the internet and all of those commands that they generate to each other will be generated. All of the thoughts that they have to understand all generated. Does that make sense? And so basically the world is going to be this layer of computing that's going to cocoon the earth and it's going to be generating intelligence all the time.

我刚才说了一些听起来很荒谬的话，但事实上它已经发生过两次。300 年前，德国一家叫西门子的公司生产了一台机器，这台机器非常有趣。你走到这台机器前，点燃它，然后另一端就会产生一种不可思议的无形力量。没有人明白那是什么。我们现在知道那是电。世界上有多少发电机？我们称之为电网。发电包裹了地球。我们称之为电网。然后当然，20 多年前，更早一点，35 年前，这个网络方案、网络矩阵结构在美国被创造出来，最终变成了互联网。它在哪里？它包裹了世界。所以现在你有能源、通信、智能，它将包裹世界。我们会用它，你知道，它将成为一种商品，我们到处都会使用它。所以英伟达谋生的手段就是这台新机器。300 年前发明的机器叫做发电机。那个发电机，任何运动的东西都可以进入。可以是瀑布，可以是风、火、蒸汽。把它从运动原子转换成电子原子到电子。然后我们把电子带入我们的机器，叫做英伟达。电子现在进入我们的机器，进入这个工厂，出来的是数字。这些数字根据你如何组合它们，变成语言、数学。它也可以变成一种新语言。我们学会了蛋白质。我们学会了人类生物学的语言。我们学会了物理世界的语言，物理学、气候、天气。

Now I just said something that sounds ridiculous except in fact it's already happened twice. So 300 years ago a company in Germany called Siemens produced a machine and this machine is really interesting. You go up to this machine you light it on fire and then this incredible invisible force comes out the other end. Nobody understood what it was. We understand it now as electricity. How many power generators are there in the world? We call it a grid. Power generation cocoons the planet. We call it the grid. And then of course 20 some odd years ago, earlier than that, 35 years ago, this networking scheme, networking matrix fabric was created here in the United States eventually became the internet. And where is it? It cocoons the world. And so now you have energy, communications, intelligence, and it will cocoon the world. And we'll use it for, you know, it'll just be a commodity and we'll use it all over the place. And so what Nvidia does for a living is this new machine. The machine that was invented 300 years ago is called the dynamo. That dynamo, anything that moves comes in. Could be waterfalls, it could be wind, fire, steam. Transfer it from motion atoms right to electrons atoms to electrons. We then take the electrons into our machine called Nvidia. Electrons now comes into our machine comes in this factory and what comes out are numbers. These numbers depending on how you combine them turns into language math. It can also turn into a new language. We've learned proteins. We learned the language of human biology. We learned the language of the physical world, physics, climate, weather.

我们学会了 3D 世界、机器人、自动驾驶汽车的语言。我们学会了各种不同智能形式的语言。但关键在于，现在这两台机器相隔 300 年，原子进，电子出；电子进，数字出。而这些数字可以被重新调整、重新组合成各种不同的智能。这就是我们建造的东西。这就是我们的谋生之道。这就是为什么我称之为工厂，因为它正在生产。我们称它们为 token，但它们只是数字、token。而这些 token 就是智能。就是这样。这就是我们所做的。这并不难。

We learned the language of the 3D world, robotics, self-driving cars. We learned the language of all kinds of different forms of intelligence. But the point being now these two machines 300 years apart, atoms in, electrons out, electrons in, numbers out. And those numbers could be rejiggered, reformulated into all kinds of different intelligence. That's what we built. That's what we do for a living. And that's why I call it a factory because it's producing. We call them tokens, but they're just numbers, tokens. And these tokens are intelligence. That's it. That's what we do. It's not that hard.

太棒了。现在你知道 AI 是用来做什么的了。现在你也知道 AI 是如何构建的，以及它将变得多么庞大。感谢大家今天的参与。

Brilliant. Now you know what AI is for. And now you know how AI is built and how big it's going to be. Thank you all for joining today.

谢谢。

Thank you.

做得好。非常好的问题。

Good job. Excellent question.

我真的觉得那个问题引导得很好。你知道那个提示。你为我铺好了路。

I really felt that carried it. You know the prompt. You laid it up for me.

好的。所以这是一场巨大的革命。你阐述了三个变革：能源变革，它影响着今天的每个人，而观众中的许多人正是全球制造业和能源生产商的一部分；电信，它连接了我们所有人；以及现在的智能。在能源方面，你谈到了发电机；电信方面，我想类似物可能是交换机或类似的东西，用于全球通信路由；而在智能革命中，核心是 GPU 和 AI 工厂，比如 H100 或任何将所需一切整合在同一机箱下的新系统。Vera Rubin，等等。

Okay. So this is a massive revolution. And you laid out three transformations: energy transformation which touches everyone today and a lot of the people in the audience are part of these manufacturing and energy producers globally, telecommunications which connects all of us, and now intelligence. And in energy you talked about the generator; telecommunications I guess the comparable would be the switch or something along those lines for routing communications globally; and now in the intelligence revolution it's the GPU at the core and the AI factory like the H100 or whatever new systems that bring everything you need under the same hood. Vera Rubin, what have you.

而这些工厂，你知道，这些发电机，我们的每个单元，我们称之为一个机架。里面有 72 个芯片。我们今年生产了大约 800 万个芯片，但其中 72 个组成一个机架。那个机架重两吨，价值 400 万美元，有 150 万个零件，是世界上最昂贵的设备。我们像生产手机一样生产它们。我的意思是，我们大量生产它们，它们被运往世界各地的数据中心。我们批量制造这些机器。它们是大型设备。这就是你锻炼力量的方式。

And these factories, you know, these generators, each one of our units, we call it a rack. There's 72 chips inside. We manufacture, call it 8 million of them this year, but 72 of them go into a rack. That rack weighs two tons. It is $4 million, has 1.5 million parts, and it's the most expensive piece of equipment in the world. And we manufacture them like phones. I mean we crank them out and they go into data centers all over the world. We build these machines in volume. They are big devices. This is how you do your weightlifting.

是的，我明白。没有批量折扣。

Yes, I understand. No volume discounts.

好的。所以，你描绘了一个非常令人兴奋的世界，我们正身处其中。我们正处于这场革命之中。可以说是几十年，也可以说是几年。当然，现在正处于智能革命的主流。我们如何参与？我相信这里的每个人都想参与这场革命。

Okay. So, you laid out this picture of a very exciting world that we are in. We're in the middle of this revolution. You could say decades in. You could say years in. Certainly now in the mainstream of the intelligence revolution. How do we participate? I'm sure everybody here wants to participate in this revolution.

是的，非常好的问题。让我们从大型企业开始，然后再谈到个人。人们如何加入这场运动？现在我已经给了你两个思维模型。我将再给你一个思维模型。所以一个思维模型是，我们可以谈论这个故事，希望我们涵盖所有四个阶段。我刚才谈到了 AI 能做什么。我谈到了 AI 是如何制造的，这些东西都在这些工厂里。这些工厂，每个吉瓦大约 500 亿美元。所以如果你见过，它是世界上最昂贵的工厂，但那个 500 亿美元的工厂也产生了 3000-4000 亿美元的智能。所以生产价值令人难以置信。投资回报非常快。所以那是工厂部分。我现在要告诉你的部分，当你考虑投资时非常重要，就是 AI 的产业布局是什么样的。思考产业布局的方式是把它看作一个五层蛋糕。现在我说过底层是能源。底层是，记得我说过的发电机。现在我们当然有不同的交流发电机和类似的东西用于发电。所以最底层是能源。这是几代以来能源行业增长的最大机会。可能是一百年来第一次，许多国家的电网可以投资。这是投资可持续能源的最佳机会。如果你关心可持续能源，核能、空气能、风能、太阳能，无论什么形式，氢能，无论什么形式，只要它产生能源，就会得到资金。这告诉你这是一个多么伟大的时代，因为我们有一万亿美元。想想看，仅今年一年，我们将从市场投入一万亿美元到我即将描述的整个五层蛋糕中。所以第一层是能源。这就是为什么西门子做得这么好。三菱也做得非常好。GE Vernova，我是说每个人。蛋糕的第一层是能源。蛋糕的第二层是芯片、计算机、网络、交换机和硅光子学。这有意义吗？都是计算机。蛋糕的第三层，我们称之为基础设施：土地、电力、外壳、资金、数据中心运营，每一个今天都供应短缺。所以下一层是基础设施层。然后每个人都看到、每个人都认为是 AI 的那一层是模型层。这有意义吗？那是下一层。它位于计算机、云基础设施之上。这是人类历史上最大的机会，我知道如此多的市场驱动投资自然流入生态系统。这是一个建设的好时机。所以那是模型层。模型层是 OpenAI、Anthropic，但这是你不能忽视的部分。这非常重要。所以你有两个你知道的公司。然而，不要忘记，正如我之前解释的，AI 已经学会了语言，它学习任何有结构的东西的语言和含义。所以那一层，真正重要的是我们听到我们谈论所有语言，但不要忘记我们可以学习任何有结构的东西。所以让我给你一个关于有结构的东西的例子。今天当我走进房间时，我预料到会有很多人，你们出现的方式并不意外。但如果你们中的一些人吊在天花板上，漂浮在半空中，而你们中的一些人，一个人但身体部位在 17 个不同的地方，而且我能看穿你们中的一些人，那么学习起来就很难，因为每次都不一样，因为量子事物很难学习。然而，有结构的东西我们可以学习，对吧？人有眼睛，所以你可以学习这些东西。所以 3D，我学习了物理定律。我自信地坐了下来。

Yeah, excellent question. Let's start with big enterprises and then let's get to individuals as well. How do people join this movement? And so now I gave you two mental models. I'm going to give you one more mental model. So one mental model there really, you know, we could talk about this story and hopefully we cover all four phases. I just talked about what AI can do. I talked about how AI is made and these things are in these factories. These factories are, you know, each gigawatt is about $50 billion. So if you've ever seen, it's the most expensive factory in the world, but that one $50 billion factory also generates $300-400 billion in intelligence. And so the production value is incredible. The return on investment is extremely fast. So that's the factory part. The part that I'm going to tell you now and this is very important when you think about investment is what does the industrial layout look like for AI. And the way to think about the industrial view is think of it as a five layer cake. Now I told you on the bottom is energy. The bottom is remember I said the dynamo. Now we of course different AC generators and things like that power generation. And so on the lowest layer is energy. This is the single greatest opportunity in several generations for the energy industries to grow. The very first time in probably I don't know a hundred years since the energy grid in many countries could be invested in. This is the best opportunity to invest in sustainable energy. If you care about sustainable energy, nuclear, air, you know, or wind, solar, you name it, whatever form, hydrogen, whatever form, so long as it produces energy, it's going to get funded. And so that tells you something about how great of a time this is because we have a trillion dollars. Just think this one year, this year alone, we're going to put a trillion dollars from the market into this entire five layer cake I'm about to describe to you. So the first layer is energy. That's the reason why Siemens is doing so well. That's Mitsubishi is doing fantastically. GE Vernova, I mean everybody. The first layer of the cake is energy. The second layer of the cake is chips and computers and networking and switches and silicon photonics. Does that make sense? It's all the computers. The third layer of the cake, we call it infrastructure: land, power, shell, money, data center operations, every one of them in scarce supply today. And so that's the next layer is infrastructure layer. And then the layer that everybody sees that everybody thinks is AI is the model layer. Does it make sense? That's the next layer. It sits on top of the computers, the cloud infrastructure. And this is the greatest opportunity in recent human history that I know that so much market-driven investment is naturally coming into the ecosystem. This is a great time to build. So now that's the model layer. The model layer is OpenAI, it's Anthropic, but this is the part that you can't overlook. This is very important. So you have two companies that you know of that you hear about. However, don't forget AI as I was explaining earlier has learned the language, it learns the language and the meaning of anything that is structural. So that layer, what's really important is we hear we talk about all the language, but don't forget we can learn anything with structure. And so let me give you an example of something with structure. Today when I walked into room I was expecting a lot of people and it wasn't unexpected the way you appeared. Now if some of you were hanging off the ceiling and floating in midair and some of you, you know, one human but body parts in 17 different places, and I could see through some of you, then it's hard to learn that because every time it's different, because it's hard to learn quantum things. However, things with structure we can learn, right? People have eyes and so you can learn these things. And so 3D, I learned the laws of physics. I sat down with confidence.

我并不是……53%的情况下我安全地坐到了椅子上。另外 47%的情况我直接穿过去了。所以我不能信任它，但 100%的情况下……你们明白吗？如果事情是可预测的，那么就有结构可以学习，你可以学习它的含义。对吧？所以我们学习了蛋白质的含义。我们学习了含义。我们正在学习基因的含义。不仅仅是测序，不仅仅是 CRISPR 编辑，而是那个基因的含义是什么？细胞的含义是什么？为什么细胞会做它做的事情？两个细胞结合在一起会发生什么？所以这没什么不同，想象一下我学习细胞含义的方式就像我学习单词的含义一样。当我拿两个单词放在一起会发生什么？这两个单词互相激活，变成另一个含义的东西。对吧？所以从计算机的角度来看，它不在乎这是一个细胞、一个蛋白质、一个单词、一张图片还是一辆车。这有意义吗？它们只是 token。所以作为计算机科学家，我们必须弄清楚如何以所有这些不同的方式表示世界的信息，以便计算机能够理解它、推理它、制定计划、生成行动。智能循环：蛋白质也一样，细胞也一样，人体解剖学也一样。它必须是可预测的。它是可预测的，因为明天早上我基本上还是老样子。它必须是可预测的。对吧？所以我们正在学习所有这些不同的……我的意思是，你们知道两种语言模型，但 AI 是一个巨大的产业。其他所有物理产业的规模大约是 80 万亿美元。这实际上是最重要的前沿，我们还没有谈论的部分。然后在此基础上，这个模型、这项技术又反馈到 Constantine 现在看到的所有东西中，也就是所有那些在金融服务、法律、会计、交通、物流等领域提出革命性想法的初创公司。是不是这样？

I wasn't 53% of the time I landed safely on the chair. The other 47% of the time I went right through it. And so I can't trust it, but 100% of the time. Do you guys understand? And so if things are predictable, then there's structure you can learn from it and you can learn the meaning of it. Okay? And so we learned the meaning of protein. We learned the meaning. We're learning the meaning of genes. Not just sequencing it, not just CRISPR editing it, but what is the meaning of that gene? What is the meaning of a cell? Why does a cell do what the cell does? What happens when two cells come together? And so this is no different than imagine I learned the meaning of a cell the way I learned the meaning of a word. And what happens when I take two words, put them together? These two words activate each other, turn into something else of another meaning. Okay? And so from a computer's perspective, it doesn't care if it's a cell, a protein, a word, an image, a car. Does that make sense? It's just tokens. And so we have to figure out as computer scientists, we have to figure out how to represent the world's information in all these different ways so that the computer can understand it, reason about it, come up with a plan, generate an action. The intelligence loop: proteins the same way, cells the same way, the human anatomy is the same way. It must be predictable. It's predictable because tomorrow morning I'm largely the same. It must be predictable. Okay? And so we're learning all these different... My point is there are two language models that you guys know about, but AI is a giant industry. The industry of everything else physical is about $80 trillion. It is actually the most important frontier, the one that we're not talking about. And then on top of that, this model, this technology then feeds into all the stuff that Constantine gets to see these days, which is all of these startups that are coming up with revolutionary ideas in financial services, in legal, in accounting, in transportation, logistics. Isn't that right?

所以在那层之上，去年有 1000 亿美元的风险投资，这是人类历史上风险投资额最大的一年。所有这些钱都流向了第五层，也就是顶层，即提升人类状况的应用层。所以当你思考 AI 并想投资这个未来时，有五个层次。我向你们保证，这个未来将是巨大的，因为两年前还是零。我们大约要投入 1 万亿美元，但这只是……我们可能会投入 AI 产业，我猜一下，大概每年 20 万亿美元。我们投入了 1 万亿美元，而这是一个每年 20 万亿美元的生态系统，因为智能的生产——你只需要问问自己智能有多重要，谁需要它，你想要多少？所以这些是基本问题，所有这些智能，无论是用于蛋白质、汽车、机器人、语言、数学、科学还是其他任何东西，都必须由这些机器生成。所以这个五层蛋糕是工业版本，我认为这是一个思考在哪里进行大规模投资的好方法。

And so what that layer above this last year, a hundred billion dollars of venture capital investment, the single largest year of VC investment in the history of humanity. All of that money is going into that fifth layer, the top layer, which is the layer of applications to enhance human condition. And so there are five layers when you think about AI and you want to invest in this future. And I promise you this future is going to be gigantic because two years ago zero. It's approximately we're about to put $1 trillion in, but that's 1 trillion out of the... we're going to be putting in probably the AI industry, I'm going to guess for a second, probably something along the lines of $20 trillion a year. We're one trillion dollars in of a $20 trillion a year ecosystem because the production of intelligence, you just got to ask yourself how important is intelligence and who needs it and how much of it do you want? And so those are kind of the basic questions and all of that intelligence, whether it's for proteins or cars or robots or language or math, science, whatever it is, has to be generated by these machines. And so this five layer cake is the industrial version and I think that's a good way to think about where to invest hugely.

所以你描述了一个数万亿美元的机会，成为这场革命的一部分，这包括硬件和设施。如果每吉瓦 500 亿美元，未来几年有 100 多个项目上线，那就是数万亿美元，再加上应用层，那是更多的数万亿美元，这意味着为从事实际建设的人提供了真正的就业机会。

So you've described what is a multi-trillion dollar opportunity to become part of this revolution and that includes the hardware and the facilities. If it's $50 billion for a gigawatt and there's 100 plus coming online in the next several years, that is trillions plus the application layer where that is many many more trillions plus plus plus and that means real jobs for people doing the hands-on building.

正是如此。我们必须强调这一点，这对你非常重要。如今每个国家对 AI 的态度都不同。你们同意吗？每个国家，因为每个国家的文化都有点不同。好的。这是我的建议。小心那些类比和科幻故事，比如这是《终结者》，以及像奇点这样的词，还有那些说 20%概率这将是人类文明的终结的想法。那些关于 AI 的表述完全是胡说八道。完全是胡说八道。哦，我们不知道它是怎么工作的。这太神秘了。我们甚至不知道它是怎么工作的。它可能明天早上就站起来走出去了。我毫不怀疑它是计算机和软件。我毫不怀疑他们知道它是怎么工作的。你知道我怎么知道他们知道它是怎么工作的吗？因为每年它显然都在变得更好。如果你不知道某样东西是怎么工作的，你怎么让它变得更好？我不知道它是怎么工作的，但我知道怎么让它变得更好。那是胡说八道。所以他们为什么说这些话？这是个有趣的问题。然而，不要让它吓到你。你必须参与其中。你可能会也可能不会因为 AI 而失去工作，但你绝对会因为使用 AI 的人而失去工作。你同意吗？

Right now exactly. And we have to really emphasize this and this is very important to you. Every country has a different attitude about AI today. Would you guys agree with that? Every country because everybody's culture is a little different. Okay. And here's my recommendation. Be careful with the analogies and the science fiction stories that this is Terminator and words like singularity and ideas that somebody says there's a 20% chance this will be the end of humanity as we know it. Okay, those kinds of articulations of AI is just nonsense. It is complete nonsense. Oh, we have no idea how it works. This is so mysterious. We don't even know how it works. It might just get up out of its seat and walk out tomorrow morning. There's no question in my mind it's computer and software. And there's no question in my mind they know how it works. And do you know how I know they know how it works? Because every single year apparently it's getting better. If you don't know how something works, how do you make it better? I have no idea how it works. But I know how to make it better. That's nonsense. So why are they saying these things? That's an interesting question. However, don't let it scare you. You must engage it. You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI. Would you agree with that?

是的。

Yes.

好的。所以，我们不要担心那些不确定的事情，专注于你确定的事情。我绝对确定我会因为使用 AI 的人而失去工作。所以，在我担心 AI 之前，让我们先确保我使用 AI。那么，为什么这个常识对我来说如此重要？因为你们中有些人有孩子。你们在建议他们什么？逃跑，还是确保无论这项技术是什么，它赋予人们超能力，你要确保使用它。所以我希望我们做两件事。第一，我们正在尽一切努力安全地为世界构建这项技术。我向你们保证，有大量的计算机科学、大量的投资、大量的热情致力于让这项技术对每个人都安全。我可以证明。两年前使用 ChatGPT，现在再用一次。幻觉的数量完全减少到几乎为零，以至于它产生的知识不仅准确，而且与当下上下文相关。如果它不知道答案，它会做研究。当它得出答案时，它甚至在告诉你答案之前质疑自己，反思它。它想出两三个不同的答案，并在给你答案之前反思这些答案。安全、护栏、真理基础的数量，这项技术已经进步得如此之快，以确保安全。我可以肯定地告诉你们，这是事实。我更喜欢今天的汽车而不是 100 年前的汽车。技术好多了，但也安全多了。

Okay. So, let's not worry about the things you're not sure about and focus on the things that you are sure about. I am absolutely certain I will lose my job to someone who uses AI. So, before I worry about AI, let's just go make sure I use AI. And so the part that now why is that why is that common sense so important for me to tell you? Because some of you have children. What are you advising them? Run away or make sure whatever this technology is that gives people superpowers that you go make sure you use it. So I'm hoping that we do two things. One, we are and we're doing everything we can to build this technology safely for the world. I promise you so much computer science, so much investment, so much passion dedicated to making this technology safe for everybody to use. And I can prove it. Use ChatGPT two years ago and use it again. The amount of hallucination completely reduced to almost nothing to the point where it's producing knowledge not only accurately contextually relevant to the moment. And if it doesn't know the answer, it does research. And when it comes up with an answer, it even questions itself before it tells you an answer, it reflects on it. And it comes up with two or three different answers and it reflects on those before it produces the answer for you. The amount of safety and guardrailing, grounding of truth, the technology has advanced so fast to make it safe. I am certain I can tell you this completely with fact. I prefer my car today than the car that was 100 years ago. The technology is a lot better, but it's a lot safer.

而且需要发明很多技术才能让它安全。所以我可以告诉你，这是我们的工作，是科技行业的责任，是科学家和工程师的责任，让我们安全地构建 AI。第二，你有责任确保你告诉你所爱的人，无论是你的家人、孩子、孙子，还是你工作的公司，或者你所在的国家，无论我们做什么，都要参与 AI。如果我们认为它是一种超能力，就参与它。因为如果我们不参与，别人会。我们不会因为 AI 而失去生命，我们会因为使用 AI 的人而失去生命。所以这就是我的……好吧，这太严肃了。

And it takes a lot of technology to be invented in order for it to be safe. And so I can tell you that it is our job, it is the responsibility of the technology industry, it is the responsibility of scientists and engineers for us to build AI safely. Two, it is your responsibility to make sure that you tell the people that you love, whether it's your family, your kids, your grandkids, or the company you work for, or the country that you're in, whatever we do, engage AI. If we think it's a superpower, engage it. Because if we don't engage it, somebody else will. We're not going to lose our lives to AI. We're going to lose our lives to somebody who uses AI. And so that's my... Well, that's too serious.

这太严肃了。那是因为他说了「工作」这个词。

That's the... that's too serious. That was because he said the word job.

所以我有一个触发点，那就是一堆人在编造关于工作的谣言。

So I've got a trigger and the trigger is a bunch of people making stuff up about jobs.

我们今年向全球生态系统投入了一万亿美元，不是吗？它在做什么？创造就业。现在，能源部门的就业比以往任何时候都多。芯片部门，就业比以往任何时候都多。基础设施层，就业比以往任何时候都多。从土地、电力、外壳、金融到 AI 模型层，就业比以往任何时候都多。我们刚刚说去年有 1000 亿美元进入了上层，就业比以往任何时候都多。我们创造了更多的就业机会。现在，有人可能会说，那传统工作呢？所以，让我给你举个例子。

We put a trillion dollars into the world's ecosystem this year, did we not? What's it doing? Making jobs. Right now, the energy sector, more jobs than ever. The chip sector, more jobs than ever. Infrastructure layer, more jobs than ever. Everything from land, power, shell, finances, AI model layer, more jobs than ever. And we just said a hundred billion dollars last year went into the upper layer, more jobs than ever. We're creating so many more jobs. Now, somebody might say, well, what about the traditional jobs? So, let me give you the example.

你知道每个人的工作和他们的任务是有关系但不相同的。工作和你在工作中做的任务是有关系但不相同的。例如，我的工作是 CEO，领导公司。大部分时间，我今天花了很多时间，我的大部分任务是打字和说话。所以你可以说 CEO 等于打字和说话。这两者 AI 都能以超人的方式完成，而我比以往更忙。当然，那是个很可爱的例子，但让我给你一个更深刻的例子，你现在可以应用它。所以 10 年前，甚至更久，一位世界领先的计算机科学家想警告大家 AI 的力量。他说，康斯坦丁可能知道是谁。他说 AI 将摧毁和消除的第一个工作，我建议没有人进入这个领域，因为这个领域将被消灭……放射学。计算机视觉在 12 年前就已经是超人的了。计算机视觉。计算机可以识别图像，以超人的能力检测异常。从不疲倦，从不遗漏细节。12 年前它就能做到。他预测结果放射学将被消灭。好吧，他完全正确。放射学被计算机视觉完全渗透。计算机视觉扩散到每一种放射学形式和每一个放射学堆栈，今天每个放射科医生都通过计算机视觉增强。然而，有趣的是这一点。放射学需求上升了。世界上的放射科医生数量增加了。为什么？请观众参与。为什么？

You know that everybody's job and their task is related not the same. A job and the task you do in the job is related not the same. So for example my job is to be the CEO to lead the company. Most of the time, and I spend a lot of it today, most of my time my task is typing and talking. And so you could say CEO equals typing and talking. Both of them AI does in a superhuman way and I'm busier than ever. Then give you that of course that's a really cute example but let me give you a more deep example and you can now apply it. So 10 years ago, more than that, one of the world's leading computer scientists wanted to warn everybody about the power of AI. And so he said, and Constantine probably knows who it is. He said the first job that AI will destroy and eliminate and I advise nobody goes into this field because this field will be wiped out... radiology. Computer vision is superhuman already 12 years ago. Computer vision. A computer can recognize images, detect anomalies with superhuman capability. Never gets tired, never miss a detail. 12 years ago it was able to do that. And he predicted as a result of that radiology is going to be wiped out. Well, he was absolutely right. Radiology was completely penetrated by computer vision. Computer vision proliferated through every single form of radiology and every radiology stack and every radiologist today is augmented by computer vision. However, the interesting thing is this. Radiology demand went up. The number of radiologists in the world went up. Why? Audience participation please. Why?

我听到了一些东西。都是真的。原来放射学花很多时间研究扫描。但放射学的目的，放射科医生的目的是与医生合作诊断疾病。

I heard some of the things. It's all true. It turns out radiology spends a lot of time studying scans. But the purpose of the radiology, the purpose of the radiologist is to work with doctors to diagnose disease.

与医生合作诊断疾病，而且因为现在自动化了，他们更高效。所以发生了两件事。更多病人入院。他们做更多扫描。放射科变得更盈利。当他们意识到更盈利并且接收更多病人时，他们雇佣了更多放射科医生，以便接收更多病人，赚更多钱，照顾更多人，因为事实证明有很多人在受苦，在等待入院。所以现在让我们假装一下。你理解计算机科学家告诉你这对放射科医生来说是世界末日吗？我的观点是，我们必须对我们编造的东西负责，因为我们可能造成伤害。事实证明，在他的演讲之后，因为渗透到一切，想成为放射科医生的人数开始下降。但我们需要更多放射科医生。

To work with doctors to diagnose disease and because it's now automated, they are more productive. So two things happen. More patients are admitted into the hospital. They do more scans. The radiology department became more profitable. When they realized they were more profitable and they were admitting more patients, they hired more radiologists so that they could admit more patients so that they could make more money, take care of more people because as it turns out, there are a lot of people who are suffering and they're waiting to get into the hospital. So now let's pretend for a second. Do you appreciate the computer scientists tell you it's going to be the end of the world for radiologists? My point is we have to be responsible about what we make up because we could have done harm. And it turns out the number of people who want to be radiologists after his speech because it permeated through everything the number of radiologists started to decline. But we need more radiologists.

最近有人说 90%的软件编码将消失。因此我们不需要软件工程师。与此同时，我们雇佣的软件工程师比以往任何时候都多。原因是软件工程师的工作是解决问题，并想出要解决的问题以创新。我从未雇佣过一个人说：「嘿，你知道吗？你是软件工程师。听着，这是键盘。让我看看你每秒能打多少字。」打字不是软件工程师的工作。编码不是他们的工作。解决问题是他们的工作。所以，我刚刚给了你两个例子。任务与目的。这有道理吗？

Now somebody recently said 90% of software coding will be gone. And therefore we don't need software engineers. Meanwhile, we're hiring more software engineers than ever. And the reason for that is because a software engineer's job is to solve problems and dream up problems to solve innovate. I never hired somebody and said, 'Hey, guess what? You're a software engineer. Listen, here's a keyboard. Show me how many words a second you can type.' Typing is not the job of a software engineer. Coding is not their job. Solving problems is their job. And so, I just gave you two examples. Task versus purpose. Does that make sense?

事实证明这个例子到处都在发生。但因为我们对任务有如此做作、如此天真的理解，计算机科学家可以说像 50%的工作将消失这样的话。软件编码完全无关。放射学将被消灭，因为我们从任务角度思考。我们忘记了工作的目的。

It turns out this example happens all over the place. But because we have such a contrived such a naive understanding that computer scientists could say things like 50% of the jobs will be gone. Software coding is completely irrelevant. Radiology is going to be wiped out because we think about it from the task perspective. We forgot the purpose of the job.

在工作站出现之前就有放射科医生。AI 之后也会有放射科医生。在软件编码之前就有工程师。我向你保证之后也会有工程师。这有道理吗？所以这就是思考工作的方式。我现在已经涵盖了两件事。第一，如果你的国家不投资 AI，你会错过大量的就业机会。如果你的国家或公司不投资 AI，你会错过提升你员工的机会。AI 不会消除工作。AI 会提升你的工作。如果我现在是水管工，我通常会得到一份工作任务表或示意图。然而，如果我明天是水管工，我很可能也是设计师。这有道理吗？因为你我都知道，我们可以用 AI 生成这些令人惊叹的厨房设计。如果我是木匠，我可以变成……如果我是家具销售员，我肯定会成为室内设计师。所以我提升了自己的手艺。从卖家具的人变成了可以建议你家可以多漂亮的人。我从一个木匠，你期望我来只是把木头拼在一起，现在我是你的家居设计师。我提升了自己的手艺。我给了你这么多例子，但这就是我的观点。我认为关于 AI 的叙述是完全错误的。目标是吓跑所有人，以便一些人能从中受益。

There were radiologists before there were workstations. There are going to be radiologists after AI. There were engineers before software coding. I promise you that there will be engineers after. Does that make sense? And so that's the way to think about jobs. And I've now covered two things. One, if your country is not investing in AI, there's a massive boom of jobs you're missing out on. If your country or your company is not investing in AI, there's a level of elevation of your people that you're missing out on. AI is not going to eliminate jobs. AI is going to elevate your job. If I were a plumber today, largely I get a job task sheet or a schematic. However, if I'm a plumber tomorrow, it is very likely I'm a designer as well. Does that make sense? Because you and I both know that we could just use AI to generate these incredible designs of a kitchen. If I'm a carpenter, I can turn... if I were a salesperson of furniture. I'm going to be an interior designer for sure. And so I've elevated my craft. Went from somebody who sells furniture to somebody who could advise you on how beautiful your home could be. I went from somebody who's a carpenter, you expected me to come and just put some wood together and now I'm your home designer. I've elevated my craft. I've given you so many examples, but that's my point. I think the narrative about AI is absolutely wrong. And the goal is to scare everybody out of it so that some people could benefit from it.

但正如你所知，AI 是我整个职业生涯中消除技术鸿沟的最强大力量。

But AI, as you know, is the greatest force for eliminating the technology divide in my entire career.

我花了四十多年，我的一生都在从事计算机设计。这四十多年来，我们创造的技术变得越来越复杂，能够编程的人占总人口的比例却在下降。这个房间里有多少人懂 C++？得了吧，别装了，你们这些怪人。这一排简直是个创业公司。好吧，所以大概只有 2%。这是个非常奇怪的房间。社会上只有 2% 的人懂 C++。有多少人懂人类语言？超过 2%。所以现在每个人都能编程计算机了，而过去只有 2% 的人能做到。我们已经消除了技术鸿沟。

I spent 40 some odd years, my entire life has been in computer design. I spent 40 some odd years and this entire time the technology we created became more and more complex and the number of people who could program these computers as a percentage of population declined. Who in this room knows C++? Come on, cut it out, you weirdos. This row is just a startup company. Okay. So we're looking at 2%. And this is a very strange room. And so 2% of society knows C++. How many people know human? More than 2%. And so everybody now can program a computer. And yet in the past only 2% could. We have closed the technology divide.

我们必须让每个人都参与进来。这说得通吗？好吧。总之，周五晚上就聊这些。这太严肃了。这是极其乐观的看法，我同意。而且很高兴听到来自比世界上任何人都更接近实际构建驱动一切的技术的人的声音。所以 Jensen，你谈到了未来我们将从检索（我们一生都遵循的范式）转向生成，一切都被定制，知识为个人定制。一个智能生成的时代，与能源革命、电信革命并行，现在是智能革命。你谈到了计算机能说的语言，不仅仅是英语或德语，甚至包括蛋白质。你谈到了五个参与层次，为这个房间里的每个人以及所有听众提供了丰富的参与机会。你还谈到了这场变革将带来真正的后果。真正的后果让人们从仅仅执行任务转变为构想问题和解决方案。甚至可能是一种有目标的生活，从木匠转变为建筑师。谢谢你，Jensen。请和我一起感谢 Jensen Swang，这位让这一切成为现实的人。谢谢。非常感谢。你太棒了。真的很感激。谢谢。太棒了。

We got to bring everybody with us. Does that make sense? Okay. Anyways, that's it on a Friday night. That's too serious. That is extremely optimistic and I agree. And it's great to hear from someone who is closer to actual building of the actual technology that powers everything than anyone else in the world. So Jensen, you talked about a future where we move from retrieval, this paradigm that we've had our entire lives, to generation, where everything is customized, knowledge is customized for the individual. A world where we have the generation of intelligence paralleling from the energy to the telecommunications revolution to now the intelligence revolution. You talked about these languages that the computer can speak, not just English or German, but even protein. You talked about five layers of participation, an abundant opportunity to participate in this revolution for everyone in this room and everyone listening. And you talked about how this transformation is going to be something that has real consequences. Real consequences that allow people to move from just doing the task to dreaming the problems and the solutions. Maybe even a life of purpose and a life where we move from carpenters to architects. Thank you, Jensen. Please join me in thanking Jensen Swang, the man who made this all happen. Thank you. Thank you so much. You're awesome. Really appreciate it. Thank you. That was great.