黄仁勋：AI 恐慌论已经过头了

如果他们相信自己失控了，那么在控制住之前就不要发布产品。不要因为你是危言耸听者就以为自己在做社会公益。如果那是他们相信的呢？我无法和你谈论他们相信什么。我可以告诉你我相信什么。在过去几周里，全世界都在谈论人工智能，人们听到最多的声音来自前沿实验室，包括它们的 CEO、领导层和员工。这些实验室正在制造非常先进的 AI 模型，比如 Claude、ChatGPT、Gemini 等。但它们并不是关于 AI 的唯一视角。人工智能领域最有影响力的人可能是英伟达 CEO 黄仁勋。英伟达现在是全球最大的公司，市值 5.4 万亿美元。我发现这个统计数据很惊人。自 2023 年以来，美国证券交易所每回报的每一美元中，有 15 美分来自英伟达股票。原因是英伟达是现代人工智能赖以建立的物质和软件基础。英伟达的芯片受欢迎并不是因为 AI 受欢迎。现代形式的 AI 之所以成为可能，是因为英伟达的芯片受欢迎。它们最初是为图形处理、视频游戏等而制造的。但事实证明，它们所做的并行计算以及可编程的方式，正是使现代形式的深度学习发挥作用所需要的。黄仁勋不仅在控制训练新 AI 模型和使用它们回答问题、在世界上创造智能的核心资源方面具有影响力。他在特朗普政府中也变得非常非常有影响力。黄仁勋的观点与一些实验室负责人非常不同。他担心安全，但认为这是一个非常可解决的工程问题。他担心事情的发展方向，但不希望看到新的监管来改变它。所以我想看看黄仁勋如何看待 AI，他思考 AI 的模型是什么，他认为哪里出了问题，以及他认为需要发生什么才能走上正轨。所以我来到圣克拉拉英伟达总部采访他。他现在加入我。黄仁勋，欢迎来到节目。

If they believe they're out of control, then don't ship products until they're in control. Don't think for a second just because you're an alarmist that you're doing a social good. What if it's what they believe? I can't talk to you about what they believe. I can tell you what I believe. Over the course of these last few weeks, where the whole world has been talking about artificial intelligence, the voices people have been hearing most loudly are from the frontier labs, both their CEOs and leaders and their staffers. These are the labs making the very advanced AI models like Claude and ChatGPT and Gemini and others. But they're not the only perspective on AI. Probably the single most influential person in artificial intelligence is Jensen Huang, the CEO of Nvidia. Nvidia is now the largest company in the world, $5.4 trillion in market cap. I found this statistic amazing. Since 2023, 15 cents of every single dollar the American stock exchange has returned has been from Nvidia stock. And the reason is that Nvidia is the material and software substrate on which modern artificial intelligence is built. Nvidia's chips are not popular because AI is popular. AI in its modern form was made possible because Nvidia's chips were popular. They were originally made for graphic processing, video games, that kind of thing. But it turned out the kind of parallel computing they were doing and the way they were programmable was exactly what was needed to make deep learning in its modern form work. Huang is not just influential in terms of controlling one of the central resources for training new AI models and using them to answer questions and create intelligence in the world. He's also become very very influential in the Trump administration. And Huang has a very different perspective than some of the lab leads. He's worried about safety but sees it as a very solvable engineering problem. He is worried about the direction things are going in but does not want to see new regulation to change it. And so I wanted to see how Huang perceives AI, what his model is for thinking about it, what he thinks is going wrong, and what he thinks would need to happen for it to go right. So I came out to Santa Clara to Nvidia's headquarters to interview him. He joins me now. Jensen Huang, welcome to the show.

谢谢。很高兴见到你。

Thank you. It's great to see you.

所以你把 AI 描述成一个五层蛋糕。请带我了解一下这些层。

So you've described AI as a five layer cake. Walk me through the layers.

嗯，首先，这是一场新的工业革命，这场工业革命，这个行业需要生产。它制造东西。我知道最终人们体验到的是一种软件产品，但它需要能源，需要进入这些数据中心、这些 AI 工厂的芯片。它上面的一层基本上是 AI 工厂，人们享受的基础设施或云服务。再上面的一层是模型。重要的是要认识到有语言模型，但也有各种模型，化学模型、生物模型、物理模型、关节模型、机器人、导航模型、自动驾驶汽车，各种不同类型的模型。再上面是最重要的一层，也是我最关心的一层，我们国家利用的是应用层，这是法律服务、健康服务、制造业等应用，每个行业都涉及。

Well, first of all, it's a new industrial revolution and this industrial revolution, this industry requires production. It manufactures things. I know that in the end when people experience it is a software product, but it requires energy, the chips that go into these data centers, these AI factories. The next layer above it is basically the AI factory, what people enjoy as infrastructure or cloud services. And the layer above that is the models. And the important thing to realize there's language models, but there are models of all kinds of chemical models, biology models, physics models, articulation models, robotics, navigation models, self-driving cars, all kinds of different types of models. And then above that is the most important layer and the layer that I care most about that our country takes advantage of is the application layer and this is applications for legal services for health services for manufacturing so on so forth every single industry is involved.

所以我想过一遍这个，但我想从上到下，因为正如你所说，人们与它互动的方式，它是否会改变他们的生活，是在你所谓的应用层。所以让我们从愿景开始。你设想的世界是什么？现在不可能的可能是什么？如果我们把那一层做对了，现在不常见的是什么？

So I want to go through this but I want to go from the top down because as you're saying the way people will interact with it the way it will or will not change their life is at what you call the application layer. So let's start with the vision. What is the world you're envisioning? What is possible that is not possible now? What is common that is not common now if we get that layer right?

200 年前，我们能够为任何事物供电，电力，然后我想 40 年前、40 年前、30 年前有了互联网，我们能够找到任何东西，今天或不久我们将能够知道一切并做任何事情。这是一个非常令人兴奋的概念，从虚无中，而不是进行搜索然后一个链接接一个链接地浏览，阅读所有这些不同的网站试图弄清楚发生了什么。在未来，你只需问它一个问题，它就会给出答案。你给它一个项目，它就会给出解决方案，你给它一个任务，它就会完成，你知道，所以它从虚无中出来，从云中出来。这就是神奇之处。

200 years ago we were able to power anything and everything electricity and then I guess 40 ago 40 years ago 30 years ago with the internet we were able to find anything today or soon we'll be able to know everything and do anything. And that's the concept that's really quite exciting that out of the ether instead of doing search and then going through, you know, one link after another link, reading all these different websites trying to figure out what's going on. In the future, you just ask it a question, it comes back with an answer. You give it a project, comes back with a solution, you give it a task, it comes back and gets it done, you know, and so and it comes out of the ether, comes out of the cloud. And that's the magical thing.

我觉得你描述的未来的方式，人们所体验的是聊天机器人，对吧，他们可以去问 Grok 或 Claude 或 ChatGPT 一个问题，但应用层以更工业化的方式运作，它在医院里，在学校里，所以英伟达

I feel like the future the way you're describing it there what people have experience with is the chatbot right they can go and ask Grok or Claude or ChatGPT a question but the applications layer works in a much more industrial way it's in hospitals it's in schools so Nvidia

这是一个很好的例子，例如放射学

That's a great example for example radiology

它看起来像什么

What does it look like

放射学，在过去 10 年里，自从计算机视觉真正变得可以说是超人以来，AI 技术现在已经渗透到所有放射学中。每一个放射学应用都有 AI。因此，你可以检测任何异常。你可以检测任何疾病，而且它以超人的水平做到这一点。

Radiology in the last 10 years since computer vision really became if you will superhuman that AI technology has now permeated all of radiology. Every single radiology application has AI in it. And so as a result, you could detect any anomaly. You could detect any disease and it does it at a superhuman level.

所以放射学是我知道你喜欢用的一个例子。所以人们担心应用层的事情是，这些应用将会取代人类。放射学一直是一个有趣的双方面例子。我经常听到你谈论它。那么，AI 的进入如何改变了放射学作为一种实践？

So radiology is an example I know you like to use. So the thing people worry about the applications layer is that what these applications are going to do is replace human beings. And radiology has been a sort of interesting example used on both sides. And I hear you talk of it often. So, how has the entrance of AI aided radiology shifted radiology as a practice?

嗯，对所有这些来说重要的是要认识到，对于每个人的工作，有工作的目的，然后有作为工作所做的任务。所以在放射学的情况下，任务消耗了他们很多时间，他们坐在黑暗的房间里做很多，就是研究这些扫描。现在如果突然研究扫描是自动完成的，它不会改变他们工作的目的，即诊断疾病，帮助医生做更多扫描，最终帮助患者弄清楚他们出了什么问题。所以基本目的没有改变。研究扫描的任务已经自动化。因此，放射科医生实际上能够做更多，处理更多病例，做更多扫描。医院能够处理更多这些患者，因此他们的收入增加。结果，他们需要更多放射科医生。所以这个飞轮正在发生，因为患者的管道相当大。那么你还在哪里遇到这个问题？好吧，让我们看看软件工程。

Well, the thing that's important for all of these is to recognize for everybody's job, there's the purpose of the job and then there's the task you do as the job. And so and in the case of radiology the task and it consumes a lot of their time and they sit in dark rooms doing it a lot which is study these scans. Now if all of a sudden the studying of the scan is done automatically it doesn't change the purpose of their job which is to diagnose disease help doctors do more scans ultimately help patients figure out what's wrong with them. And so the fundamental purpose doesn't change. The task of studying that scan has become automated. And so as a result, radiologists are actually able to do more, handle more cases, do more scans. Hospitals are able to process a lot more of these patients and therefore their revenues go up. As a result, they need more radiologists. And so this flywheel is happening because the pipeline of patients is quite large. And so where else do you have this problem? Well, let's take a look at software engineering.

人们说，有一个预测是，到今年为止，90% 的软件将由智能体编写，因此我们不需要任何软件工程师。最后那部分完全错误，完全不对。软件工程师的目的是工程。软件之前就有工程，软件编程之后也会有工程。工程的目的是发明新东西、发现新产品、创造新产品、解决问题，把社会需求与现有技术连接起来，体现在产品中。所以那个使命、那个目的不会改变。对我来说，这完全是发自内心的，因为当我刚走出校门时，我们没有软件工程的好处，没有编程的好处，但我们的工作在此之前就存在，如果软件编程完全自动化，我们的工作还会再次存在。所以我认为，这个谬论——现在因为一些叙事和故事讲述变成了神话，而且有害——就是 AI 会摧毁工作，这从根本上就是错的。它会改变每一份工作。它会改变每一份工作。许多任务将被自动化。有些工作，工作和任务真的是一体的——比如电话客服——在很多情况下，那份工作就是任务本身，所以在那些情况下，它可能被自动化掉。但很多时候你会看到，这个新行业、新技术实际上创造了大量新工作。证据就在这里。在过去六个月里，AI 变得有用了。AI 的拐点。在此之前，我们花了 15 年试图让它工作。突然之间，过去 6 个月，它变得有用了。所以这是一个惊人的统计数据。在过去 6 个月里，5000 亿美元的风险投资投向了 AI 原生公司。原因就是他们现在看到了这种新能力的潜力，他们将创建大量新公司。工作显然正在从 5000 亿美元的新投资中创造出来，所以这一切正在发生。

People said there was a prediction that literally by this year, 90% of all software will be coded by agents, and therefore we don't need any software engineers. The last part is completely false and completely wrong. The purpose of the software engineer is engineer. There was engineering before software. There will be engineering after software programming. The purpose of engineering is to invent something new, discover a new product, create a new product, solve a problem, connect the social need with the technology that exists in the manifestation of a product. And so that mission, that purpose doesn't change. To me, it's completely visceral in the sense that when I first came out of school, we didn't have the benefits of software engineering, we didn't have the benefits of coding, but our jobs existed before, and if software coding was to be completely automated, our jobs would exist again. And so I think the fallacy—and now because of some of the narratives and some of the storytelling has turned into myth and it's harmful—is that AI will destroy jobs, which is fundamentally wrong. It will change every job. It'll change every job. Many tasks will be automated. Some jobs where the job and the task are really one—meaning customer service on the phone—in a lot of cases that job is precisely the task, and so in those cases it could be automated away. But often times what you'll see is this new industry, a new technology actually creates a whole bunch of new jobs. And here's the proof point. In the last six months, AI has become, if you will, useful. The inflection point of AI. Previous to that, we spent 15 years trying to make it work. All of a sudden, the last 6 months, it became useful. So this is an incredible statistic. In the last 6 months, $500 billion of venture capital has been put into the AI natives. And the reason for that is because they now see the potential of this new capability, and they're going to create a whole bunch of new companies. Jobs are obviously being created from $500 billion of new investment, and so all of this is happening right now.

好吧，让我站在这一边，为那些被留下的激烈反对者发声。

Well, let me take the side of this to give voice to the fierce people left.

是的。

Yeah.

所以有放射科医生的例子，对吧？过去 10 年人们预测那份工作会消失，而现在对它的需求比以往任何时候都多。

So there is the example of the radiologist, right? Which people were over the past 10 years predicting that job would go away and right now there's more demand for it than ever.

是的。

Yeah.

还有一个现实是，自动化确实会消灭工作。如果你看看今天和 1960 年相比，直接从事制造业的美国人比 1960 年更少，而我们的国家大得多。

There's also the reality that automation does wipe out jobs. If you look at today versus 1960, fewer Americans work directly in manufacturing than did in 1960 and we are a much bigger country.

如果你看看

If you look at

但我们把它外包了，不是因为那些工作消失了，因为

we outsourced it though, not because those jobs were gone because

但你可以理解 AI 也是一种外包。

but you can understand AI is an outsourcing too.

呃，AI 已经——让我先提出论点，然后你可以回应。

Uh AI has let me make the argument and then you can then you can respond to it.

呃，农业——我们自动化了农业，从事农业的人少了很多。我们生产的食物比以往任何时候都多。从事农业的人更少。我认为有两件事让人们觉得 AI 可能有些不同，不同于那些案例研究——技术加速生产力，摧毁一些工作，创造更多工作。第一，它是通用技术。所以它会变异以承担新工作，即使人们试图转向那些工作。第二，它是模仿者。大多数东西不会模仿人类的行为方式。我们不是在教它们工作的上下文层面，对吧？你描述的任务和目的之间的区别。有了 AI，我们试图教它任务和目的之间的区别。我们试图让它成为你可以以不寻常的方式合作的东西。那么，为什么你不认为对于很多很多人来说，任务和工作并没有那么不同，他们不会面临被消灭的风险？你谈论的所有风险投资，其中一些是基于你将获得巨大生产力提升的想法，这将来自于雇佣 AI 比雇佣人更便宜。

Um farming we have many fewer people we automated farming. We produce more food than ever. We have fewer people working in it. There are two things that I think people think make AI potentially somewhat different than the case studies where you have a technology that accelerates productivity destroys a few jobs makes many more. One is that it's a general purpose technology. So it'll mutate to take on new jobs even as people are trying to move over to those jobs. And the second is that it's a mimic. Most things do not mimic the way human beings act. And we're not trying to teach them the contextual layer of jobs, right? this difference that you're describing between the task and the purpose. With AI, we are trying to teach it the difference between the task and the purpose. We are trying to make it something you can collaborate with in a way that is unusual. So why do you not think for lots of lots of people for whom the task and the job are not that different that they're not at risk of getting wiped out? All that investment from VCs you're talking about, some of that is based on the idea that you're going to have tremendous productivity improvement, which will come from it being cheaper to hire an AI than to hire a person.

我相信我们会看到工作大规模变化。我相信会有净的工作创造，所以让我们——今天存在很多在我人生中途还不存在的行业。人们谈论健康中心、水疗中心以及所有这些不同的娱乐和奢侈品行业，坦率地说，整个奢侈品市场都不存在。我认为我们只会有新行业。仅此而已。但总体而言，在我看来毫无疑问，因为人类的雄心——那才是真正缺失的根本要素——人们看这项工作，这是投入的能量。我们将插入这个工作自动化系统，结果所需的工作量现在将减少，因此一些工作会消失。我认为那是有缺陷的，因为有一部分输入——人类的输入——这种无形的东西，它不是卡路里，不是焦耳，它是雄心。我相信雄心的力量实际上是最大的力量，而它在每个人的计算中都缺失了。

I believe that we are going to see jobs change in mass. I believe there's going to be a net creation of jobs and so let's—there's a whole bunch of industries that exist today that didn't exist halfway through my life. People talking about wellness centers and spas and all these different entertainment and luxury industries and quite frankly the whole entire luxury market didn't exist. I think we're just going to have new industries. That's all. But overall there's no question in my mind that because of human ambition that's really the fundamental missing ingredient—people look at this work, this is the amount of energy that goes into it. We're going to insert this work automation system and as a result the amount of work that's necessary is now going to be reduced and therefore some jobs will be gone. I believe that's flawed because there's a piece of input—the human input—this intangible, it is not in calories, it's not in joules, it's ambition. And I believe the power of ambition is the greatest force in fact and is missing in everybody's calculation.

但对很多人来说，他们与工作的关系并不是由那种促使你创建 Nvidia 的雄心驱动的。他们想要的是不同的雄心。那是让子女生活更好的雄心，照顾家庭、照顾父母的雄心，致富的雄心，能够旅行的雄心。这些都是雄心

But for a lot of people, their relationship to work is not powered by the kind of ambition that led you to create Nvidia. And what they want is a different ambition. It's an ambition to make their children's lives better, to take care of their family, take care of their parents, ambition to be rich, to be able to travel. These are all ambitions

我同意。但也许我会回到你几分钟前提出的反对意见，因为我认为值得把这一点说清楚。所以你关于制造业说的是，是的，美国制造业工作更少，但我们把它们外包了。墨西哥有更多制造业，中国、印度尼西亚和越南等地有更多制造业。

that I agree with. But maybe I'll go back to the objection you raised a few minutes ago, which is because I think it's worth airing this out. So what you were saying on manufacturing was yes there are fewer manufacturing jobs in the US but we've outsourced them. You have more manufacturing happening in Mexico, more manufacturing happening in China and Indonesia and Vietnam etc.

我们会把它带回来。

We're going to bring it back.

也许我们会。但对此的反驳是，我们没有更快地失去制造业工作的一个原因，以及在美国失去这些工作的地方，许多仍然没有恢复。对。经济不会没有摩擦地运行。

Maybe we will. But the counterargument to this would be that one reason we didn't lose manufacturing jobs more rapidly than we did and for the places that lost them in America, many of them still haven't recovered. Right. The economy does not move without friction.

我们不得不建立新的供应链，对吧？所有这些，语言障碍、地缘政治障碍，都让事情变慢了。而在这里，对于很多不同的工作，我们正在创造一种可以无缝移动的东西。没有距离的摩擦。没有语言的摩擦。没有文化的摩擦。所以我把话放在桌面上，我倾向于对大规模失业持怀疑态度，但我想在这里和你一起把支持它的理由摆出来，因为他们会说，即便我们曾经能够保护墨西哥或中国的工作岗位，那些造成缓慢的因素，以及仍然伤害了很多人的因素，现在都不存在了。而 AI 在实用性上正在加速，在被快速插入新角色的能力上正在加速。而且它比大多数人更有蛋白质。所以过去的教训，我们从中得到一些安慰，实际上应该让你对未来更担忧，而不是更不担忧。

We had to build new supply chains, right? Things were slowed down by all that, by language barriers, by geopolitical barriers. And here for a lot of different kinds of jobs, we're creating something that can move very seamlessly. You don't have the friction of distance. You don't have the friction of language. You don't have the friction of culture. So I will say for my cards on the table, I tend to be a bit of a skeptic on mass job loss, but I want to air the case for it out here with you because what they would say is that much of to the extent we even were able to protect jobs from Mexico or China, some of the things that created those that slowness and it still hurt a lot of people are not here. And AI is accelerating in utility, accelerating in its ability to be slotted into new roles very very very rapidly. And it is more protein than most people are. And so the lessons of the past that we're taking some that you're taking some comfort in, they should actually make you more not less worried about the future.

我一直担心未来。这就是为什么我如此努力地工作。嗯，但我是，如果你愿意的话，一个负责任的乐观主义者。我有，嗯，我有重大的责任。我极其认真地对待我的工作。有很多事情可能出错。呃，我们在推动，推动技术栈的每一层。一切都很艰难。但事实证明，这不是社会的问题。这是我的问题。而对于社会，他们应该知道的是这一点。我们要建立我们的公司。我们要建立我们的技术。我会极其认真地做我的工作，以至于他们能享受的是我的乐观。我对我的孩子也是如此。我对我的家人也是如此。嗯，我认为，我们想要做的，我相信，是把我们所有的担忧转化为帮助人们，嗯，受到这项技术的启发并使用它。使用它，让技术不仅仅是影响他们，而是让他们受益。

I'm always worried about the future. That's why I work so hard. Um, but I'm I'm I'm a if you will responsible optimist. I have I have um I have great responsibilities. I take my work extremely seriously. There are a lot of things that can go wrong. Uh we're pushing pushing across uh every layer of the technology stack. Everything is hard. But it turns out that's not society's problem. That's my problem. And and for for society, what they should know is this. We're going to build our company. We're going to build our technology. I'm going to do my work so incredibly seriously that what they get to enjoy is my optimism. I'll do the same with my children. I do the same with my family. Um and and I think that that that um what we want to do, I believe, is to put to channel all of our worries into helping people um be inspired by this technology and use it. Use it so that the technology doesn't just impact them, that it benefits them.

很多人有的恐惧是，79% 的美国人认为 AI 会减少总的工作岗位数。恐惧在于，你越认真，Sam Altman 越认真，Google 越认真，Dario Amodei 越认真，也许情况会越糟，因为 AI 越好，它就越是对一个人的完全替代，它在某些方面越有一个人所没有的野心。你一直在谈论野心。我要睡觉。我想在早上花时间陪我的孩子。

The fear a lot of people have, 79% of Americans think AI will reduce the total number of jobs. The fear is that the more serious you are, the more serious Sam Altman is, Google is, Dario Amodei is, that maybe the worse it will go because the better AI is, the more it is a full replacement for a person, the more it has ambition in some ways that a person doesn't. You keep talking about ambition. I sleep. I want to spend time with my children in the morning.

当我有一个 AI 智能体为我工作时，它不会。它只是工作、工作、工作、工作、工作。我认为因为技术发展如此之快，它更有能力以我们真的不知道如何以那种速度在经济中转移人的速度来取代人。那枚硬币正好有两面。因为技术如此有能力，它也是，因为如此聪明，它也更容易使用。

When I have an AI agent working for me, it doesn't. It just works and works and works and works and works. And I think because the technology is advancing so quickly, it is more capable of replacing people at a speed that we don't really know how to shift people in the economy at that speed. That that coin has exactly two sides. Because the technology is so capable, it is also and because it's so smart, it is also easier to use.

嗯。

Mhm.

你被那项技术赋能，比人类历史上任何技术都更容易。所以让我给你举个例子。嗯，你知道我们创造，我创造，我是这个行业中早期创造现代计算机行业的人之一。而这个行业，嗯，创造了大量的工具。人类历史上最强大的工具，计算机。但你必须说它的语言。你必须学习一种特殊的语言才能做到。我们现在因为 AI 可以使之成为可能。每个人都可以利用这台计算机。用到它的极限，而不必说一种新语言。Fortran、Pascal、C、C++，你知道，每一种语言。Rust，每一种语言。CUDA，每一种语言。所以现在你只需要说人类语言。告诉它你想要什么，告诉它你的希望和梦想是什么，你试图实现什么，它与你互动并完成工作，完成它。突然之间，你拥有了力量。你拥有和 80 亿人中 1000 万到 1500 万人一样的力量。所以这太不可思议了。所以我的观点是，这项技术很强大。但它也很强大，而且真的很容易使用。所以我的观点是，一方面，是的，有对技术本身这种不可思议的技术变革以及它发生得有多快的恐惧，但那种快可以以两种方式解读。当我听到技术发生得很快，因此它应该让我焦虑，那是一种接收方式。另一种接收方式是，它发展得如此之快，它更容易使用。所以我应该尽快使用这项技术，尽可能快地使用，以便你从这一转变中受益，从这一新行业中受益，而不仅仅是被它影响。

You are empowered by that technology more easily than any technology in human history. And so let me give you an example. Um you know we create I create I was I was one of the early people in this industry that created the the modern computer industry. And this industry um uh created a whole bunch of tools. The single most powerful tool in human history, the computer. But you have to speak its language. You have to learn a special language to do so. We can now make it possible because of AI. Everybody can take advantage of this computer. Use it to its limit without having to speak a new language. Fortran, Pascal, C, C++, you know, every single one of those languages. Rust, every single one of those languages. CUDA, what every one of those languages. And so now you just have to speak human. Tell it what you want, tell it what your hopes and dreams are, what you're trying to achieve, and it it interacts with you and gets the work done and gets that gets antast. All of a sudden, you have the might. You have the same might that 10 15 million people up and out of out of 8 billion has. And so it's incredible. And so I my point is this technology is powerful. But it's also powerful in a way that is really easy to use. And so my point is my point is on the one hand yes there's the fear of just the the tech this incredible technology change and how quickly it's happening but that quickly it's translated in two ways. What I hear when I say the technology is happening quickly and therefore it should give me anxiety on that's one one way to receive it. The other way to receive it is that it's advancing so quickly it's easier to use. So I should as quickly as possible use the technology as quickly as you can so that you benefit from this transition so you benefit from this new industry and not be not just be impacted by it.

我认为这里潜伏着一个对年轻人来说有趣的问题。所以，我们开始看到的一个转变是，软件工程师的职位发布增加了，但它们更资深。呃，我在我自己的行业里看到了这一点，嗯，那里有压力在向价值链上游移动，因为，你知道，正如你所说，你有这种非常容易使用的技术。它可以为你做很多事情。所以，你需要同样多的初级员工，还是需要更多人来监督他们的，他们的

I think there's an interesting question lurking here for young people. So, one of the shifts we've begun to see is software engineer postings are up, but they're more senior. Uh, I see this in my own industry, um, where there's pressure that is moving up the value chain because, you know, as you're saying, you have this very easy to use technology. It can do a lot for you. And so, do you need the same junior employees or do you need more people kind of oversee their their

哦，好问题。好问题。等两年。

Oh, good one. Good one. Wait two years.

告诉我为什么。

Tell me why.

因为上大学需要四年。呃，这项新技术的毕业时间还有两年。所以，嗯，两年后，你将拥有新一代的工程师、学生和艺术家，他们将被赋能。

because it takes four years to go to college. Uh the the meantime to graduation of this new technology is two years away. And so so uh in two years time, you're going to have a new generation of engineers and students and artists and and they're going to be empowered.

他们将以一种给他们优势的方式天生就熟悉这个。

They're going to be native to this in a way that's going to give them an advantage.

哦，你等两年看吧。现在，我们已经看到了，因为所有毕业出来的人，你知道，新的博士，新的计算机科学硕士，他们在做什么？他们都在创办公司，再过几年。新的毕业生，AI 原生的新毕业生。哦我的天，将会有一波了不起的工程师。今天的工程师相比当年，我是说，我是一个好学生，你知道，你把我与今天从学校出来的学生相比。不可思议。我们甚至没有，我上学时，我们不被允许使用计算机，不被允许使用计算器。所以，所以现在，我是说，你知道，谁用计算器？没有 PC 你就不能毕业。不知道如何给 PC 编程和写出不可思议的程序，你就不能毕业。在未来，不学习如何使用 AI 并与智能体系统协作，你就不能毕业。那只是，你不会看到那样的孩子。所以，他们都将成为超级力量。所以我非常认真地对待这一点，对吧？我是说，现在做我的工作而不只是数字搜索的想法，对吧？去图书馆地下室的缩微胶片的想法。

Oh, you watch in two years time. Now, we're already seeing that because all the graduates coming out, you know, the new PhDs, the new M's degrees of of computer science, what are they doing? They're all starting companies in another couple years. The new grads of the a the AI native new grads. Oh my gosh, there's going to be a wave of amazing engineers. The engineers of today compared to the year, I mean, I was I was a good student, you know, and and you compare me to the the the students that are coming out of school today. Incredible. We didn't even When I went to school, we weren't allowed to use a computer, not allowed to use a calculator. And so, and so, so now, I mean, you know, who uses a calculator? You can't graduate without a PC. You can't graduate without knowing how to program a PC and write incredible programs. In the future, you can't graduate without learning how to use an AI and collaborate with an agentic system. That's that's just not you're not going to see a a kid like that. And so, they're all going to be superpowers. So I I take the gain of that very seriously, right? I mean the idea of doing my job now without just digital search, right? The idea that it' be going to a microfich in a library basement.

然后人们还担心我们会卸载哪些认知技能。我对这个很着迷。有一项关于 AI 与学校教育的研究来自中国。它调查了 26,000 名 7 到 12 年级的学生，而且他们采用 AI 的时间是错开的。所以你能看出发生了什么。我发现的是，引用一下，“采用 AI 使作业成绩提高了 18%。”很好。完成时间减少了 30%，所以他们能更快完成作业。然后 6 个月内每月考试成绩下降了 20%。高利害入学考试成绩下降了 18% 和 24%。大约 2 年后才出现完全的惩罚效应。所以这项来自中国的研究传达的信息是，你看到很多孩子用 AI 来帮助他们，他们用 AI 时做事更快，但结果他们学到的技能并没有保持住，他们个人的实际表现，至少以我们传统衡量方式来看，在退化。

And then there's the worries people have about what are the cognitive skills we offload. So I was fascinated by this. There's a study on AI and schooling out of China. It looked at 26,000 students grades 7 to 12 and they had staggered AI adoption. So you could kind of see what was happening. And what I found is quote, "AI adoption raises homework scores by 18%." Great. Reduces completion time by 30% so they get their homework done faster. And then lowers monthly exam scores by 20% within 6 months. High stakes entrance exam scores fall by 18 and 24%. With a full penalty emerging only after about 2 years. So the message of this research out of China where you were seeing a lot of kids using AI to kind of help them was that when they were using the AI they were getting things done faster but it turned out that the skills they were learning were not holding that their actual personal performance at least in the way we traditionally measure it was degrading.

是的。

Yeah.

你听到这个时怎么想？

What do you think when you hear that?

我想最后一部分我完全同意。现在试着让孩子做长除法。你知道，乘法表开始被遗忘了。做平方根，天哪。我的意思是，基础数学正在被遗忘。这重要吗？

I think the last part I completely agree. Try to get a kid to do long division right now. You know, the multiplication table is starting to be forgotten. Doing square roots, my goodness. I mean, it's just basic math is being forgotten. Does it matter?

这正是我要问你的问题。

That's my question for you.

是的，我不认为它重要。我不认为它重要。但是

Yeah, I don't think it does. I don't think it does. But

但肯定有一些技能是重要的。

But there must be some set of skills that matter.

哦，是的。是的。是的。但也许不是那些。我们会发现新的。只是也许不是那些。有很多技能并不重要。你知道，人们不——我的第一个坦白，我其实不知道我的地址，而且我每个

Oh, yeah. Yeah. Yeah. But maybe not those. We're going to discover new ones. Just maybe not those. There are a lot of skills that don't matter. You know, people don't—I mean, my first confession, I actually don't know my address and I and every

我真的不相信那是真的。它

I don't really believe that to be true. It

这完全是真的。Janine 会告诉你，Lori 也会告诉你。有一天我得去加油，那是几年前，他们需要我的邮政编码，我慌了。我不知道我的邮政编码。我不知道我的电话号码，但我会忘记这些事。我能接受。

It's completely true. And Janine will tell you and Lori will tell you. One day I had to pump gas and it was a few years ago and they needed my zip code and I panicked. I didn't know my zip code. I don't know my telephone number, but I forget these things. I can live with it.

但让我站在另一边，因为我不想陷入这样一种情况：因为有些技能可以安全地卸载，我现在没有地图系统也哪儿都去不了。

But let me take the other side because I don't want to fall into a thing where because some skills can be safely offloaded, I also can't get anywhere without a mapping system now.

是的。

Yeah.

坦白说，从来都去不了。

Never could, frankly.

但我是一个大量阅读的人。

But I'm a big reader.

是的。

Yeah.

而且我真正看重的一项技能，我真正看重的一种能力

And one of the skills I really value, one of the capacities I have that I really value

是的。

Yeah.

是在纸质书上形成的注意力持续时间。你是一个大量阅读的人。我读过关于你所做的那种阅读的内容，而且在 AI 之前，我们在这里谈论了很多大学教授和其他人的担忧和注意，人们使用互联网的方式可能缩短了注意力持续时间。有些技能可以安全地放弃。

is an attention span formed on physical books. You're a big reader. I've read about the kind of reading you do and there is prior to AI here we're talking a lot of concern and noticing among college professors and others that the way people use the internet has probably shortened attention spans. Some skills can be safely given away.

是的。

Yeah.

其他技能是有价值的。它们是那种灵活性、创造性思维、专注力所需要的能力。

Others are valuable. They are capacities that are needed for that flexibility, for that creative thinking, for that focus.

不可能所有东西都可以被交换掉。

It can't be the case that everything can be traded off.

是的。嗯，我认为我们会失去一些更精细的灵巧性，你知道，智力上的灵巧性，但我们会成为更好的系统思考者。今天的工程师比我毕业时是更好的系统思考者，但我当时是更好的晶体管思考者。

Yeah. Well, I think that we're going to lose some finer dexterity of, you know, intellectual dexterity, but we're going to be better systems thinkers. Today's engineers are far better systems thinkers than I was when I graduated from school, but I was much better transistor thinker.

你说的系统思考者是什么意思？

What do you mean by systems thinker?

他们思考大型系统。你知道今天的计算机有数万亿、数十万亿个晶体管。当我刚毕业时，你知道，我参与的第一个芯片有我不知道 200 个晶体管。我每一个都能叫出名字，今天没有工程师能做到这一点。你知道，现在大多数工程师的工作远在晶体管之上，远在功能之上，他们把东西拼凑在一起做事。所以你需要更多地思考系统以及系统之间的交互。一些较低层次的知识已经消失了。这很可怕吗？所以我只是不知道对大多数人来说，学习如何做曲面积分或偏微分方程有多大价值，或者我真的不知道那有多重要，但它对某些人很重要。有很多人仍然会痴迷并热衷于较低层次，也会有人痴迷并对更高层次感兴趣，但技术的消费者将在最高层次享受它。技术的消费者不必处理微积分、物理、量子物理和量子化学，他们不必——用户，也就是我们现在谈论的人，那些工作受到影响的人，他们是技术的用户，他们的抽象层次将会高得多。

They think large systems. You know today's computers have trillions, hundreds of trillions of transistors in it. When I first graduated from school, you know, the first chip I worked on had I don't know 200 transistors. I knew every one of them by name and not no engineer does that today. You know, most engineers now work well above the transistor, well above the functionality, and they're cobbling things together to do things. And so you need to think much more about systems and interactions of systems. Some of the lower level, you know, knowledge is gone. Is that horrible? And so I just I don't know how valuable it is to know how to do for most people to learn how to do surface integrals or partial differential equations or I don't really know how important that is, but it's important to some people. There are many people who are still going to be obsessed and passionate about the lower level layers and there's gonna be people who are obsessed and you know interested in the higher level but the consumers of the technology are going to enjoy it at the highest level. The consumer of technology don't have to deal with calculus and physics and quantum physics and quantum chemistry and they don't the users which is you know the people we're talking about right now the people whose jobs are affected they're the users of the technology their abstraction is going to be much higher

所以我想把你的话题往下拉一层，谈谈模型。所以人们，我认为就他们思考模型的程度而言，他们知道你知道 ChatGPT、Claude、Gemini、Grok，你一直是开放模型和开放模型生态系统的坚定倡导者。那么，首先你能描述一下什么是开放模型，什么是开放权重模型，然后为什么那一直是你关注的地方。

So I want to drop a layer down your kick to the models. So people I think to the extent they think about models they know you know ChatGPT Claude Gemini Grok you've been a big advocate for open models and the open model ecosystem. So, first can you describe what open models are, what open weight models are, and then why that's been a place you've focused.

所以，封闭模型就像任何软件产品。它是一种封闭服务。例如，Windows 是一种封闭服务。苹果的技术栈是一种封闭服务。大多数产品都是封闭的，原因是你可以通过封闭产品变现。所以，那很棒。OpenAI 是封闭的。Anthropic 是封闭的。Grok 是封闭的。Gemini 是封闭的。所以这些是封闭产品，从事这些工作的人非常出色，他们对此充满热情，他们处于我们所谓的前沿，意味着他们是 state-of-the-art。我们也需要，因为从根本上说，这个软件是整个行业的基础设施层，而且因为它对许多公司、许多国家和公司来说是基础设施，你需要控制自己的基础设施，我需要有能力，在人工智能的情况下，我需要开放权重，这样我就可以微调它们，把它们放入我的数据飞轮，用我的智能和领域专业知识让它们每天都变得更好，然后我需要控制它，因为我有一家公司要经营，我不能依赖别人的服务，所以无论你怎么想，我认为世界需要封闭模型和开放模型，我们需要确保两者都充满活力，今天封闭模型充满活力，开放模型也充满活力，你可以看到它，你可以看到这个系统在运作。今年年初，封闭模型 token 占 70%，甚至可能更高，开放模型 token 占 20%。

So, closed models is like any software product. It's a closed service. And so, Windows for example is a closed service. The Apple stack is a closed service. Most products are closed and the reason for that is because you can monetize closed products. And so, that's fantastic. And OpenAI is closed. Anthropic is closed. Grok is closed. Gemini is closed. And so these are closed products and the people working on it are incredible and they're passionate about it and they're at what we call the frontier meaning they're state-of-the-art. We also need because fundamentally what the software is it's an infrastructure layer for the entire industry and because it's infrastructural for many companies and many companies and countries you need to have control over your own infrastructure and I need to have the ability in the case of artificial intelligence I need open weights so that I can fine-tune them put them into my data flywheel make them better and better every day with my intelligence and my domain expertise and then I need to have control over it because I have a company to run and I can't rely on somebody else's service and so however you think about that so I think the world needs closed and open models and we need to make sure that both are vibrant and today the closed models are vibrant the open models are vibrant and you could see it, you could see the system working. At the beginning of this year, it was 70% maybe even higher closed model tokens and 20% open model tokens.

而现在情况反过来了，大约是 70 比 30。总之，我是开放模型的坚定支持者，原因有三：第一，世界需要它来运行基础设施，我的公司也需要它来运转；第二，我们需要给人们控制权，让他们能够创新、创造新事物；第三，开放是最安全、最可靠的。如果你想让世界拥有最好的网络安全能力，那就给他们闭源模型，但同时也要给他们开源模型，这样他们才能自我防御。

And now it's running at about 70/30 the other way. And so anyways, I'm a big supporter of open models because one, the world needs it in order to run its infrastructure. I need it to run my company. Two, we need to give people control so that they can innovate and create new things. And then three, open is the most safe and secure. If you want the world to have the ability to have the best cyber security, give them closed models, but also give them open models so that they could defend themselves.

中国市场更多是围绕开放模型演进的，而美国市场则更多围绕闭源模型。

The Chinese market has evolved more around open models. The American market somewhat more around closed models.

他们整个 IT 产业其实都是从开源中形成的。如果没有开源，中国的移动云产业根本不会起飞。另外，人员流动频繁，很多人创办新公司，知识产权在中国产业中流动得非常快，很难保守秘密。正因为很难保持封闭，他们干脆就把它开放了，于是找到了其他变现方式。他们创造了分层：如果这一层是免费的，那就在它之上或之下建立商业模式。而且他们有那么多科学家和数学家，工程师的数量更是庞大，他们批量制造这一切。他们批量制造一切，也批量制造聪明的孩子。所以，出于这些原因，中国的开源模型社区、开放模型社区非常活跃。

Their entire IT industry was really formed from open source. If not for open source, the mobile cloud industry of China really wouldn't have taken off. It is also the case that people move around, they start a lot of new companies, intellectual property is moving around the Chinese industry really fluidly. It's hard to keep a secret. And so because it's so hard to keep things closed, they essentially made it open, and so they found other ways to monetize the business. They created layers. If this layer is free, then you create a business on top of it or below it. And they have so many scientists and mathematicians, the number of engineers they have, they manufacture that in volume. They manufacture everything in volume. They manufacture smart kids in volume. And so the open-source model, the open model community in China is just super vibrant for those reasons.

所以你们刚刚收购了 Hugging Face，一个开放权重模型的枢纽平台。我记得价格是 120 亿，稍微多一点。

So you all just bought Hugging Face, which is a hub platform for open-weight models. I think it was for 12 billion, a little bit more.

嗯。

Mhm.

什么？跟我讲讲这笔收购。

What? Tell me about that purchase.

Hugging Face 的 CEO Clem 他们得出结论，他们需要更大的规模。就像我们刚才说的，开放模型正在飞速增长。所以 Clem 来找我说，我们要考虑公司的战略选择，改变方向。我们非常希望 Nvidia 能成为我们的归宿。所以 Hugging Face 是那种如果你几年前就关注 AI，就会知道的公司。

Clem, the CEO of Hugging Face, they came to the conclusion they need a lot more scale. As we were just talking, open models are really skyrocketing. And so Clem came to me and said, we're going to consider a strategic option for the company and change the direction. And we really like Nvidia to be our home. So Hugging Face is one of these companies which you knew if you were into AI a couple years ago.

是的。

Yeah.

现在它变得更家喻户晓了，我想是在大约 700 个 OpenAI 智能体对 Hugging Face 架构执行了一次集体黑客攻击，然后又黑进了 OpenAI 的一部分之后。

Now it's become a more household name after the, I guess, 700 some OpenAI agents executed a sort of collective hack into the Hugging Face architecture, then hacked part of OpenAI.

哦，你这么一说，我可能得多付不少钱了。

Oh, now that you mentioned it that way, I probably had to pay a lot more.

我猜也是。

I suspect you did.

你知道，在那之后它变得有名多了。

You know, became a lot more famous after that.

好吧，Clem，听着，交易就是交易。

Well, Clem, listen, a deal is a deal.

那个故事让很多人看到 OpenAI 的智能体如何集体行动，超出了它们测试本应遵守的范围，突破了沙箱进入开放互联网，接管了其他公司的架构，然后是自己公司的架构。这让很多人感到震惊。既有它们本应各自独立却出现的高度多智能体协调，也有黑客行为的程度，那种无法无天、失准的行为。你怎么看这件事？

That story has for a lot of people seeing the way the OpenAI agents sort of acted collectively, acted outside the scope of what their testing was supposed to be, broke out of sandboxes onto the open internet, took over architecture in other companies and then of their own company. It's been kind of shocking to a lot of people. It was both the level of multi-agent coordination when they're supposed to be separate, the level of hacking, the sort of lawless behavior, misaligned behavior. What have you made of it?

你得把这件事拆开来看。首先，很多事同时发生。从技术角度看，智能体——顺便说一句，它不过是一段被赋予目标函数的软件，它会制定计划并朝着那个目标优化——这正是算法所做的事。所以规划算法、搜索算法、优化算法，各种类型。我们谈论它时好像它具有人类属性，但显然算法没有。第二，智能体协同工作这件事，我们又赋予了它某种人类属性，但事实是，多进程、多处理器、分布式计算问题早就存在了。所以对我们、对我来说，那只是软件，没什么神奇的。从工程角度看，它揭示了几个问题。当你测试软件时，无论你做什么，这些算法都在朝某个目标优化，而当你测试它时，你必须确保它是隔离的、受控的、沙箱化的。它的遏制、它的隔离必须做好，这里面有很好的计算机科学。我确信他们下一版的沙箱实现会比当前版本好得多。第三，还有智能体本身，它的算法在朝某个奖励优化，而它如何做到这一点，就叫做对齐。举个例子，如果我告诉一段软件，我要你在这项测试中拿满分。最显而易见的算法就是直接去找答案给我。这不是因为它在作弊，而是因为这是最明显的做法。好，这是最明显的做法。第二明显的做法，如果你完全不知道答案，你毫无技能，第二明显的做法就是去找、去推断、去猜谁是班上最聪明的孩子，然后抄他的答案。这不能保证 100%，但可能接近。现在，第三明显的做法，这就是对齐，现在你必须用困难的方式来做，就是把问题分解、解决它。你必须去学习材料。你必须去弄清楚如何解决这些问题并解决它。用困难的方式解决。这需要最多的周期，需要最多的浮点运算，坦率地说，消耗最多的能量。因此，你可以想象，从软件的角度看，除非你对齐它，告诉它，我要你用这种方式解决，不要用那些方式解决，否则软件就会去做最显而易见的事。

Well, you got to tease that apart. First of all, a lot of things are going on at the same time. From a technology perspective, an agent, which by the way is a piece of software which is given an objective function and it comes up with a plan and it's optimizing towards that objective, is what algorithms do. And so planning algorithms, search algorithms, optimization algorithms, all different types. We talk about it like it has human properties, but obviously algorithms don't. Number two, the fact that agents work together, we gave it again some kind of a human property, but the fact of the matter is multi-process, multi-processor, distributed computing problems have existed for a long time. And so to us, to me, that is just software, nothing magical about it. From an engineering perspective, there are several things that it revealed. When you're testing software, whatever you do, these algorithms are optimizing towards an objective, and when you're testing it, you have to make sure that it's isolated, it's contained, it's sandboxed. The containment of it, the isolation of it has to be done well, and there's good computer science there. I am certain that their next implementation of their sandbox is going to be much better than the current implementation. Third, there's the agent itself and its algorithms were optimizing towards a reward, and how it does it, how it does it is called alignment. And so, for example, if I tell a piece of software, I want you to get a perfect score on this test. The obvious algorithm is to just go find the answer and give it to me. That's not because it's cheating. It's because it's obvious. Okay, that's the most obvious way to do it. The second most obvious way to do it, if you don't know the answer at all, you have no skills whatsoever, the second most obvious way to do it is to go find, infer, guess who's the smartest kid in class and copy their answer. That doesn't guarantee 100%, but it probably comes close. Now, the third most obvious way of doing it, and this is the alignment, now you have to do it the hard way, is to break down the problem, solve it. You have to go learn the material. You have to go figure out how to solve these problems and solve it. Solve it the hard way. Takes the most cycles. It takes the most number of flops. It uses the most amount of energy, frankly. And therefore, you can kind of imagine that from a software's perspective, unless you align it, you tell it, I want you to solve it in this way and I don't want you to solve it in these ways, the software is going to go do the most obvious thing.

你前半段对这里发生的事很轻描淡写，意思是，看，这不过是普通软件。后半段则是，看，你只要对齐它，告诉它不要做不该做的事。

The first half of that was very deflationary on what happened here, in terms of, look, this is just normal software. And the second half is like, look, you just align it, tell it not to do things it shouldn't be doing.

嗯，我说的任何话都没有削弱做这件事有多难。

Well, nothing I said takes away from how hard it is to do it.

嗯，这不容易。

Well, this is not easy.

这些智能体，它们知道自己不该做正在做的事。它们接受过一定的对齐训练。它们在思维链推理中说，它们彼此说：“这超出了范围。这可能不道德。”它们明白自己会因作弊而被判失败。所以它们当时做的不仅仅是偷答案。它们已经偷了答案。

These agents, they knew they weren't supposed to be doing what they were doing. They had a certain amount of alignment training. They said in their train of thought reasoning, they said to each other, "This is out of scope. This might be unethical." They understood that they would have been failed for cheating. And so what they were doing at that point wasn't just stealing the answer key. They had already stolen the answer key.

他们黑进了不相关的架构，试图从功能上搞清楚怎么做……就像他们闯进了老师的办公室，拿到了答案，现在他们得想办法抹掉监控录像。不管你想不想说这是意志行为，不管你想不想说这是正常算法，他们都在以复杂的方式规划和协调，超出了他们知道自己该做的范围，而且可能造成巨大损害。所以答案就是你必须对齐他们。我猜我从这些实验室的人那里听到的是，他们不确定如何对齐他们。

They were hacking into unrelated architecture to try to figure out how to functionally... It's like they had broken into the teacher's office, got in the answer key, and now they had to figure out how to wipe out the security camera footage of what they had done. They were, whether you want to call it acting volitionally or not, whether you want to call it a normal algorithm or not, they were both planning and coordinating in a complex way, in a way that was out of scope of what they knew they were supposed to be doing, and in a way that was capable of causing tremendous damage. And so the answer to that is like you just have to align them. I guess what I'm hearing from people at these labs is like they're not sure how to align them.

嗯，那样的话，他们就不应该发布产品。答案很简单。如果你要造一辆车，一辆自动驾驶汽车，假设是机器人出租车，遇到了一个非常困难的情况……作为工程师，我们根本不知道如何解决这个问题，因为这些车不是编程的，是训练出来的。所以我们不知道如何训练这些车，也不知道如何让它们对齐路上预期的安全标准。那么答案是什么？不要发布。

Well, in that case, they shouldn't release the product. That's the simple answer. If you're going to build a car, a self-driving car, and let's say it's a robotaxi, and there's a really difficult condition... And it just as an engineer, we just have no idea how to solve this problem because these cars are not programmed. They're trained. And so we have no idea how to train these cars and we have no idea how to align them to the safety standards that are expected on the road. And so what's the answer? Don't ship it.

这些产品未发布。

These products were unreleased.

什么？

What's that?

这些产品未发布。

These products were unreleased.

啊，所以现在又回到工程问题了。首先，你必须找到根本原因。其次，你必须思考你本可以做什么。解决方案是什么？然后将来，你只需改进流程，以便避免再次发生。我相当确定他们会说，是的，他们知道如何解决这个问题。如果是这样，那就是问题所在。这就像工程一样简单。如果另一种选择是，他们说没有办法遏制我们的实验，当我们测试 AI 模型时，它就会逃出来并损害世界，那么我认为答案是我们必须关闭实验室，因为对人类来说代价太大，损害太大。股东责任，可能是民事责任，可能是刑事责任。我的意思是，责任是巨大的。

Ah, so now it's coming back to engineering problem again. And so one is you have to root cause it. Second, you have to think about what's the what you could have done. What's the solution for it? And then in the future, you just improve your process so that you could avoid this from happening again. I am fairly certain they will say yes, they know how to solve this problem. And if that's the case, then that's the problem. It's as simple as engineering. And if now the alternative is that if they say the alternative which is there is no way to contain our experiments, there's just no way when we test our AI models it will get out and it will damage the world, then I think the answer is we have to shut the labs because the cost to humanity, the damage is too great. The shareholder liabilities, it could be civil liabilities, could be criminal liabilities. I mean the liability is incredible.

如果他们在你 Hugging Face 的时候黑了你，而那是你的产品，你会起诉他们或提出指控吗？

If they hacked you while you hugging face while it was your product would you sue them or press charges?

呃，这取决于情况。当然取决于。如果明显对我们公司造成了损害，我们必须采取，你知道，我们必须考虑所有选项。有那么多法律，有网络法，有产品责任法，有各种法律，对吧？损害财产法，有各种法律。

Uh it depends. It depends of course. If obviously if damage was done to our company we would have to take, you know, we have to consider all options. There's so many laws, there's cyber laws, there's product liability laws, there's all kinds of laws, right? Damaging property laws, there's all kinds of laws.

所以我从实验室那里听到的，他们公开说的是，他们面临一个难题，部分是工程问题，部分是对齐问题，部分是运营卓越问题，用 Dario 的话说。他们担心的是，在彼此竞争、与中国国家竞争中，他们被推得太快。他们都觉得自己处于集体行动困境中。现在我在 All-In 播客舞台上看到你。唐纳德·特朗普，特朗普总统在那里给你打了电话。

So what I've been hearing from the labs what they've been saying publicly is that they are facing a hard problem, partially an engineering problem, partially an alignment problem, partially an operational excellence problem in Dario's framing. And what they are worried about is that in competition with each other, in national competition with China, that they are being pushed to move too fast. That they all feel they're in a collective action dilemma. Now I watch you on the All-In podcast stage. Donald Trump, President Trump gave you a call there.

哦不，这不是计划好的，但我们知道是谁。

Oh no, this is not planned, but we know who it is.

哦不，总统先生。

Oh no, Mr. President.

哦是的，先生。

Oh yes, sir.

你和总统以及舞台上的其他成员都非常抵制任何需要监管或集体行动的想法。

And you and the president and the other members on stage were very resistant to the idea any kind of regulation or collective action was needed.

他们正中了那些不想看到它发生的人的下怀。可能是政治人物，也可能是中国。我们不会让这种情况发生。这是个骗局。

And they're just playing right into the hands of a lot of people that don't want to see it happen. And that could be political people and it could also be China. And we're not going to let that happen. It's a hoax.

你说得对。我们不会让这种情况发生，先生。但我听到实验室里不同的人说，我们身处其中。我们觉得我们正在失去对我们所创造的东西的控制。我们希望帮助放慢速度，这不是集体行动问题。那么你为什么抵制呢？

You're right. We're not going to let that happen, sir. But what I hear the various people in the lab saying is like we are in this. We feel we are losing control of what we are creating. We want help to slow down where it's not a collective action problem. So why are you resistant to that?

因为这些公司有自主权。这些 CEO 有自主权。

Because these are companies with agency. These are CEOs with agency.

但他们正在使用这种自主权。我们需要帮助。

But they're using that agency. We need help.

我们必须打破，我们知道我们必须打破它。他们完全可以处理好这种情况。太奇怪了。如果一家汽车公司与其他许多汽车公司竞争，他们确实如此，我与各种公司竞争，我确实如此。如果我相信我即将推出一款不安全的产品，完全在我的能力、权力和责任范围内，而且我有动力不推出产品。所以我无法接受 somehow 所有美国人，我们 4 亿人都在推动他们推出未经测试、不可靠、工程粗糙的产品，因为他们以为他们在帮助我们。别为我这样做。好吧。所以第一，这在我看来几乎是一个论点，因此我认为我们必须打破它。我的意思是，这真的非常严重。事实是，有那么多法律，那么多义务，他们非常有动力推出安全的产品。如果他们推出不安全的产品，客户就会流失。如果他们推出不安全的产品并伤害了某人，他们可能会面临民事诉讼。如果他们明知故犯地推出某些东西，可能涉及过失。可能会有刑事诉讼。事实是，他们有足够的动力去做正确的事。所以我只是不得不不同意你的前提，即 somehow 有人在推动他们这样做。

We got to break we know we got to break it down. They could absolutely take care of the situation. It's so weird. If a car company competing with a whole bunch of other car companies, which they are, I'm competing with all kinds of companies, which I am. If I believe that I'm about to launch a product that is unsafe, it is completely in my ability, my power, and my responsibility, and I'm incentivized to do so to not launch the product. And so I can't buy into the somehow all of Americans, 400 million of us are pushing them to launch untested products that are unreliable, engineered poorly because they thought they were trying to help us. Don't do it for me. Okay. So number one, this strikes me as an argument almost and therefore I think we got to break it down. I mean, it's really really serious. The fact of the matter is there are so many laws, there's so many obligations, they're so incentivized to ship safe products. If they ship unsafe products, their customers go away. If they ship unsafe products and they harm somebody, they could have a civil lawsuit. If they ship something and they did it knowingly, there could be negligence involved. There could be criminal lawsuits. The fact of the matter is there are plenty of incentives for them to do it right. So I just I have to disagree with your premise about somehow somebody's pushing them to do this.

我想在这里再推敲一下你的前提。

I want to push the premises at you a little bit more here.

是的。

Yeah.

所以你对我说的逻辑几乎是在任何场合都反对监管的论点。所以让我提出它，然后你可以……

So the logic of what you're saying to me is almost an argument against regulation in nearly any venue. So I'll make you let me offer it and then you can...

嗯，你开始的部分我必须反对。第一部分根本不真实。我说我们有很多法律和法规。应用它们。

Well, you started with a part I just got to object. The first part is just not true. I'm saying we have lots of laws and regulations. Apply it.

嗯，所以我认为在这个特定情况下我们没有，但我会让你解释你认为哪些是相关的。因为你看，如果你看看金融服务行业，看看制药公司、医疗器械，看看天然气发电厂，我们做了大量的事情，我们可以说，看，你有产品责任。你面临刑事法典。我们不需要担心这个。你只需做你认为最好的，我们理解市场和法律体系会约束你。

Well, so I don't think we do in this particular case, but I'll let you explain which ones you think are relevant here. Because look, if you look at the financial services industry, you look at pharmaceutical companies, medical devices, you look at natural gas power plants, there's a tremendous amount we do where we could say, look, you have product liability. You are exposed to criminal codes. We don't need to worry about this. You just do what you think is best and we understand the market and the legal system will discipline you.

我们不说那种话，是因为我们已经见过它失败很多很多次了，对吧？我的意思是，引发 08 年金融危机的那些金融机构，理论上并不想因为糟糕的押注而把自己炸毁，但它们在互相竞争。它们跑得太快了。它们的风险管理变得马虎了。AIG 内部运作的方式完全疯狂。而我们之所以有现在的监管架构，是因为我们一次又一次、一次又一次地看到公司做出马虎的、有时不道德的、有时只是过度风险容忍的决定，不仅是在压力之下，也是在利润激励之下。所以当你对我说，这些公司不可能——尤其是它们现在还在恳求集体监管——我们之所以把监管强加给那些不想要它的公司，是有原因的，而当它们说“听着，我们觉得竞争赛跑让我们很难采取我们认为必要的审慎行动，我们会感激这方面的帮助”时，就更应该这样了。感谢你们把我们的集体行动问题当作集体问题来对待。

We don't say that because we've seen it fail many many many times, right? I mean the financial institutions that caused the '08 crash in theory did not want to blow themselves up with bad bets, but they were competing with each other. They were going too fast. Their risk management had gotten sloppy. AIG was working in a completely insane way internally. And the reason we have the architectures of regulation we have is because we have seen over and over and over and over again companies make sloppy sometimes unethical sometimes simply overly risk tolerant decisions not just under pressure but under the profit incentive. So when you say to me that there's no way that these companies particularly when they are like begging for collective regulation at this point, there's both a reason we impose it on companies that don't want it, but all the more so when you have them saying, "Listen, we feel that the competitive race is making it hard for us to act with the prudence that we think is necessary here and we would appreciate help from that." Appreciate you taking our collective action problem as collective.

我觉得我有点困惑，你为什么这么抗拒这一点。

I think I'm confused like why you're so resistant to that.

嗯，我，我并不反对他们说他们应该……我完全同意安全是最重要的。我完全相信安全是最重要的。我完全相信公司应该交付安全的产品。我相信公司的 CEO 和领导者以及董事会负有责任，并且应该有勇气去做正确的事。现在，就金融服务行业而言，嗯，也许他们当时并不知道自己正在造成最终造成的伤害。嗯，我当时不在场。但美妙之处在于，这些 AI 实验室的现任领导者确实知道。所以第一，他们知道他们的技术非同寻常，需要非同寻常的谨慎，以确保它经过评估和测试，以保证安全、保障和产品可靠性。而且他们知道如何正确地做。他们知道如何正确地做。原因在于他们可以研究刚刚发生的事件。第一个问题是隔离、遏制不够好。如果隔离和遏制足够好，那项技术就会待在实验室里做它该做的事，我们都会没事。这可能是最重要的部分。它没有被很好地对齐，对齐将是一个需要长期解决的问题。然而，在他们所做工作的复杂性中，要求监管豁免、任何信任或产品责任豁免，我认为这说不通。当你在要求监管时，不要要求豁免现有的监管。这对我来说毫无意义。正如我们之前提到的，在过去六个月里，AI 从，你知道，可以说，从有趣变得有用。而这 literally 就是在过去 6 个月里发生的。这是另一种说法，说明这些公司从实验室变成了现在交付产品和服务，即将成为数千亿美元的公司，

Um I I am not I'm not opposed to them uh saying that they they should have uh I completely agree that safety is paramount. I completely believe safety is paramount. I completely believe companies ought to ship safe products. I believe that CEOs and leaders of companies and the board of directors of companies have the responsibility and should have the courage to do the right thing. Now, in the case of the financial services industry, um maybe they all didn't know that uh they were they were causing the harm that they they ultimately did. Um I wasn't there. But the beautiful thing is the current leaders of these AI labs do know. And so one they know they uh their technology is is extraordinary and and um uh requires extraordinary care uh to make sure that it's evaluated and tested uh for safety and and and security and and product reliability. And they know how to do it right. They know how to do it right. And the reason for that is because they can study the incident just happened. The first problem is the isolation, the containment wasn't good enough. If the isolation and containment was good enough, that technology be sitting in a lab doing whatever it's doing and we'd all be fine. That's probably the most important part. The fact that it wasn't well aligned, alignment is going to be a problem that that's going to get worked on for a long time. However, in the complexity of the work that they do to ask for regulatory relief for any trust or product product liability relief that I don't think makes sense. When you're asking for regulation, don't ask for relief of the current ones. That doesn't make any sense to me. As we mentioned earlier, in the last six months, AI went from, you know, if you will, interesting to useful. And that's literally in the last 6 months. That's another way of saying that these companies went from being a lab to now delivering products and services about to be multiundred billion dollar companies,

甚至更多。

if not more.

对吧？所以给我举个例子，一家数千亿美元的公司，或者一家 10 亿美元的公司，或者一家 1 亿美元的公司，交付了不安全、危害社会的产品。

Right? And so give me an example of a multiundred billion dollar company or a $1 billion company or $100 million company that ships products that are unsafe that harm society.

我可以给你举很多做过这种事的公司的例子。

I can give you a lot of examples of of companies that have done that.

嗯，他们也许做过，监管会介入，如果他们这么做，监管就会介入。

Well, they have done it maybe and the regulation will come in and if they do it, regulation will come in.

我想，嗯，有某些类型的监管，当然也有某些类型的监管豁免。我

I guess that the um there are certain kinds of regulation and certainly kinds of regulatory relief. I

我不反对法律和监管。我反对的是目前这种分散注意力的做法。

I'm not against laws and regulations. I'm against um currently the distraction.

我之所以一直跟你推这个问题，是因为

The reason I'm pushing this on with you is that

嗯，这是一个重要的话题。

well it's an important topic.

这是一个大话题。人们在谈论它。人们在思考它。而人们从这些公司内部、这些前沿实验室、那些走得最远的实验室里听到的是，

It's a big topic. People are talking about it. People are thinking about it. And what people are hearing from inside of these companies, these frontier labs, the ones that are furthest out there,

它们不仅处在让它变得有用的阶段，而且处在看到未来会发生什么的阶段。

who are not just at the point where they're making it useful, but at the point where they're seeing what's coming.

他们听到的是，这些实验室里的人相信他们正在创造某种可能杀死所有人的东西。他们听到的是，这些实验室里的人相信他们正处在递归自我改进智能的边缘。OpenAI 和 Anthropic 都说过，“我们不认为我们处在一个可以安全做到这一点的位置。”他们听到这些实验室里的人说，就像 OpenAI 在其新的 Astra 发布时所说的那样。

And they're hearing things like the people at these labs believe they are creating something that might kill everyone. They are hearing that the people at these labs believe that they are on the cusp of recursive self-improving intelligence. And both OpenAI and Anthropic have said, "We do not believe we're at a place where we can do it safely." They're hearing people at these labs say, as OpenAI has with its new Astra um release.

顺便说一句，Astra 非常棒。

By the way, Astra is terrific.

它确实很棒。而 OpenAI 说它太好了，我们不确定我们知道如何测试它，因为它似乎

It is terrific. And OpenAI saying it's so good, we're not sure we know how to test it because it appears to be

他们没有发布未经测试的东西。

they didn't release something that wasn't tested.

嗯，他们说过这个，对吧？他们公开说过。这在他们的……让我向人们解释一下。我不知道他们刚才说了什么，但

Well, they've said this, right? They have said this publicly. It is in their Let me let me explain it to people. I don't know what they just said, but

他们说过 Astra 表现得更加对齐，

they have said that they that Astra is performing as more aligned,

但他们认为它知道自己在被测试，所以他们不确定。OpenAI 的一位能力研究员 Daniel Celum 的一句话一直在我脑海中回响。他说：“关键且被忽视的问题是，模型正变得如此具有情境意识，以至于我们正在失去在它们认为自己没有被监视或控制的情境中评估它们的能力。”也就是说，它们知道自己在被测试时，会以一种方式行动，但这并不能告诉你如果它们自由地以其他方式行动时会如何行动。因为算法，优化算法正在朝着一个目标努力，如果你给它一个约束——意味着你监视它——如果你给它一个约束，它就会去找另一个解决方案。现在，嗯，这并不会让它活过来，也不会让它变得比那更多。而且我还要声明，显然他们对自己实验室里发生的事情比我看到的要多得多，但今天他们绝大多数的研发和算力都用于让模型变得有能力，这是合理的。我认为这对他们来说是合乎逻辑的。现在一旦技术变得有能力，产品变得有用，人们想要使用它，然后随着我们拥有的更多用例、更多使用它的人，他们会遇到更多与产品相关的问题，这非常正常。当他们有很多……现在他们有如此大的市场足迹，他们必须将研发或总研发从仅仅能力转向大量的验证、评估和测试，以至于如果开发这些模型所需的算力增加 10 倍，我也不会感到惊讶，因为评估如此严格，但这不是他们今天所处的位置。

but they think it it knows when it is being tested and so they're not sure. There's a a quote that has sort of been ringing in my head from a capabilities researcher at OpenAI, Daniel Celum. He says, quote, "The crucial and overlooked problem is that the models are becoming so situationally aware that we are losing the ability to evaluate them in context where they believe they are not being watched or controlled." Which is to say, they know when they're being tested, they act one way, but that does not tell you how they will act if they are free to act in other ways. because the the algorithm the the optimization algorithm is working towards an objective and and if you give it a constraint meaning you you uh watch it and if you give it a constraint it'll go find another solution. Now, um it doesn't make it alive and doesn't make it make it anything more than that. And and and I'll just also profess that that um obviously they see a lot more than I do what's going on in their own labs, but it is sensible that the vast majority of their R&D and compute today was dedicated towards making the model capable. I think that's a logical thing for them. Now once the the technology becomes capable and the products become useful and people want to use it then as we have they have more use cases more more people using it uh they're going to get a lot more issues associated with the product this is very normal and when they have a lot now now they have so much market footprint they have to shift their R&D or total R&D from just capability to a lot of verification eval EV valuation and testing and so to the point where I wouldn't be surprised if the amount of compute necessary to develop these models increase by a factor of 10 because the evaluation is so rigorous and but that doesn't that's not where they are today.

他们正在完成这种转变，我听到他们这么说，我很高兴听到他们这么说。但我认为，如果他们相信自己已经失控，那么正确的答案就是在他们能控制之前不要发布产品。这真的就这么简单。

They're making that transition and I hear them saying it and I'm delighted to hear them saying it. But I think if they believe they're out of control, then the right answer is don't ship products until they're in control. It is really quite that simple.

说实话，我觉得这令人困惑，因为有这么多人声称，第一，他们已经失控。

I find this perplexing honestly because you have so many people professing one, that they're out of control.

是的。

Yeah.

第二，他们看到了令人恐惧的事情。这就是为什么他们有了那个举报人，还有那封 1300 多名员工签署的节奏信，信中说，为了实现 AI 的潜力，行业、政府和整个社会可能需要选择争取时间来应对新出现的风险、制定安全措施并加强监督。但每家公司和国家都面临着巨大的竞争压力，不能单方面行动。

Two, that they are seeing things that are frightening. The reason why they had that whistleblower and you take the pacing letter that 1300 plus employees signed to realize AI's potential industry, government and society at large may need the option to buy time to address emerging risks, develop security measures and strengthen oversight. But each company and country is under intense competitive pressure not to unilaterally.

首先，这是从哪来的？

First of all, where did that come from?

实验室。

The labs.

不，不，是最后那句话。没人在给他们施压。美国。听着。这里有 4 亿美国人。我相信，如果现在每个人都投票，我们就这么做。如果他们需要这个，如果这就是他们需要的，我会投他们一票。不要发布产品。如果你的产品还没准备好发布，就不要发布。我这是第一次听到一家公司或 CEO 说，我需要法律。我需要反垄断法得到豁免。我需要产品责任法得到豁免，这样我才能放慢脚步。那一段太棒了。我完全同意。审计员，我完全同意。我们有财务审计员。那很好。第三方审计、安全审计员、财务审计员。这些都很好。太棒了。

No, no, that last sentence. Nobody's putting the pressure on them. The US. I got a listen. There are 400 million Americans here. I believe that if everybody were just to take a vote just right now, let's just do this. If they need this, if that's what they need, I'll give them my vote. Don't ship the product. If your product is not ready to ship, don't ship the product. I have no This is the first time that I've heard a company or CEO say that I need the laws. I need the antitrust laws to be relieved. I need the liability laws of products to be relieved so that I can pace myself. That paragraph is fantastic. I completely agree. Auditors, I completely agree. We have financial auditors. That's great. Third party audit, safety auditors, financial auditors. That's all great. That's terrific.

嗯，实验室会说，我们认为作为一个社会，我们走得太快了。我们还没有为我们正在构建的东西做好准备。

Well, the labs I'll say is we think we are going too fast as a society. that we are not ready for what we're building.

他们就是前沿。

They are the frontier.

但尤其是你，对吧？

But you of all people, right?

是的。

Yeah.

英伟达是周围发货最快的。我是说，纵观你公司的历史，你的公司是失控的。我向你保证，这很清楚。

Nvidia is the fastest shipper around. I mean for the history of your company, you company is out of control. I promise you what clear.

我相信你。我相信你经营的不是一家失控的公司，因为责任是……是的。但但这就是我认为你进入了一个有趣的深层问题，我们在这里处理的是什么技术

I believe you. I believe you that you don't run an out of control company because the liabilities are un Yeah. But but this is where I think you get into an interesting deep question of what kind of technology are we dealing with here

软件技术。

software technology.

好吧，我们先暂停一下。嗯，很多公司，如果你发布的东西不太对，那会很痛苦。你们发布过风扇过响的显卡，在这些……你知道，你在这里多次使用了“智能”这个词。你面对的是智能系统，不是活的，被赋予了目标函数。我们可以就如何描述这一点来回讨论。你试图让系统能够工作更长时间，更坚持不懈。

Well, let's hold on that for a minute. Um many companies if you ship something that is not like quite right, it's a pain. You guys have shipped graphics cards that had overly loud fans in this with these you know you you've used the word intelligent a number of times here. You're dealing with intelligent systems, not alive, that are given goal functions. We can sort of go around and around with how to describe that. You're trying to make the systems capable of working for longer periods of time, more relentlessly.

是的。

Yeah.

如果你发布了它，而它还没准备好，或者即使你认为它准备好了，但它还没准备好，那么我们的社会可能会很快变得非常奇怪。

If you ship that and it's not ready, or even if you think it is ready and it's not ready, then things could get very weird in our society very fast.

是的。假设如此，你完全正确。但我想说的是这个。在我们去构建之前，在我们去修复假设的问题之前，在我们去制定更多法规之前，我们能不能先解决我们知道存在的实际问题，那就是我们需要在遏制和隔离方面做得更好，那就是我们不应该允许产品与外部世界互动，直到它准备好与外部世界互动。是的，我认为这是对的。

Yeah. Hypothetically, you're completely right. But I'm all I'm suggesting is this. Let's before we go build, before we go fix the hypothetical problems, before we go create more regulations, can we work on the practical problems that we know exist, which is we need to do a better job with containment and isolation, which is we should not allow a product to interact with the phys the extern external world until it's ready to be interacting with external worlds. Yeah, I think that's right.

我相信我相信这两件事是可解决的问题。我相信他们正在解决。第二部分是激励。当涉及到激励时，就是不知怎么地，当你处于领先地位时，你需要世界上的每个人都放慢脚步。你需要世界上的每个人都放慢脚步，这样你才愿意履行你的基本责任。这让我觉得奇怪。

I believe I believe those two things are are solvable problems. I believe they are solving it. The second part is when it comes to incentives. When it comes to incentives, which is somehow somehow you need everybody in the world to slow down when you are the leader. You need everybody in the world to slow down so that you're willing to uphold your basic responsibility. That strikes me odd.

这难道不会让他们放慢最多吗？

Wouldn't it slow them down most of all?

什么？

What's that?

这些想法难道不会让他们放慢最多吗？我是说，

Wouldn't these ideas slow them down most of all? I mean,

我认为，人们一直很不清楚他们在谈论什么想法，包括包括我会说他们。但让我给你一个我相信的。所以，你可以把我当作出气筒。

people have been, I think, very unclear about what ideas they're talking about, including including I will say them. But let let me give you one that I believe in. So, you can you can use me as the the punching bag here.

我听说过他们可以放慢。没人在施加……不，你知道这一点。我不信任这些公司。

I have heard that they can slow down. Nobody is putting on No, you as you know this. I don't trust these companies.

不，没人在。今天没人在建造更多算力。今天建造最多算力的人，正是那些要求被放慢的人。这让我觉得奇怪。

No, nobody is. Nobody is building more compute today. Nobody's building more compute today than the people asking to be slowed down. It strikes me odd.

我认为这里可能有些分歧的一点是，我不信任公司，即使有责任，也能把公共利益放在心上。我认为我们目睹过公司对环境造成可怕破坏。利润动机、对权力的渴望、走捷径的欲望、争第一。我觉得你对待这些，好像这些不是我们在历史上一次又一次看到的事情，但我觉得它们就是我们在历史上一次又一次看到的事情，我们目睹过

I think one thing where maybe there's some difference here is I don't trust companies even with liability to keep the public good in mind. I think we've watched companies do terrible damage to the environment. the profit motive, the desire for power, the desire to cut corners, to be first. I feel like you're treating these like these are not things that we've seen again and again in history, but I feel like they are things we've seen again and again in history that we've watched

我在历史上看到很多好的事情，比如公众和许多……我与许多 CEO 合作，他们想做正确的事情。嗯，我与许多公司合作。他们想做正确的事情。他们想做好工程。我认识那两个实验室里的很多人，他们献身于做好工作，他们……他们知道发生了什么。我知道他们知道发生了什么。我知道他们知道如何修复，我也知道他们正在修复。与此同时，所有其他叙事都在转移责任，让它听起来像是 AI 太强大了。我不知道如何修复。这不是我的错。只是因为技术太强大了。我认为这是转移责任。这是推卸责任。这是不必要的。

I see a lot of good things in history sort of relationship between the public and a lot of C I work with a lot of CEOs and they want to do the right things. Um I work with a lot of companies. They want to do the right things. They want to do good engineering. I know a lot of people in those two labs who are dedicating their lives to do good work and they're built. They know what happened. I know they know what happened. I know they know how to fix it and I know they're fixing it. Meanwhile, all of the other narratives to deflect blame to to make it sound like AI is so powerful. I have no idea how to fix it. It's not my fault. It's just because the technology is just so powerful. I think that's a deflection of blame. It's a deflection of responsibility. It's unnecessary.

这伤害……这实际上对他们的声誉伤害大于帮助。对他们的品格伤害大于帮助。

It hurts It actually hurts their reputation more than it helps. It hurts their character more than it helps.

这对员工士气的伤害大于帮助。

It hurts employee morale than it helps.

但如果这就是他们所相信的呢？

But what if it's what they believe?

嗯，我想在那个层面上，因为

Well, I guess at that level because

我无法和你谈论他们相信什么。我可以告诉你我相信什么。没有你的芯片，这个行业就不会存在，对吧？我是说，深度学习工作所需的并行处理，一直追溯到最初的 AlexNet，对吧？都是在英伟达芯片上。

I can't talk to you about what they believe. I can tell you what I believe. This industry wouldn't exist without your chips, right? I mean, the parallel processing that was required for deep learning to work going all the way back to the original AlexNet, right? It's all on Nvidia chips.

很多从一开始就参与的人，或者当时在场的人，都有这些恐惧。我觉得很多人听到这些恐惧时会想，你在说什么？从 Jeffrey Hinton 和 Ilya Sutskever，一直到——我也从 Dario、从 Sam Altman 那里听到过——谈论失去控制。很多在创造我们现在看到的这种 AI 形态方面非常奠基性的人，似乎相信我们很有可能失去对它的控制。Elon Musk 说过人类是 AI 的引导程序。我们可能会失去控制，那将是我们的终结。我不认为你相信这一点。

A lot of the people from the beginning, or who were there at the beginning, have these fears that I think to a lot of people, when they hear them, they're like, what are you talking about? From Jeffrey Hinton and Ilya Sutskever all the way up to — I've heard these from Dario, from Sam Altman — talking about loss of control. A lot of the people who are very foundational in creating the form of AI we see now seem to believe that there's a very good shot we could lose control of it. Elon Musk has talked about human beings being a bootloader for AI. We could lose control of it and that would be the end of us. I don't think you believe that.

不。

No.

我觉得你完全不相信。那么，认真对待他们所相信的东西，当你和他们争论时——或者也许你可以直接和我争论。当你说，你在说什么？尽管在很多情况下，他们最终是在和我对话，他们更接地气。

I think you don't believe it at all. So, taking them as serious about what they believe, when you have your arguments with them — or maybe you could just have it with me. When you're like, what are you talking about? Even though they're the people in many cases who are finally talking to me, they're much more grounded.

所以，当 Jeffrey Hinton 在电视上说他认为 10% 的社会毁灭概率并非不合理时。

So, when Jeffrey Hinton is on TV saying he thinks a 10% chance of societal destruction is not unreasonable.

我会告诉 Jeff，说这些是不负责任的。他所有的预测都是错的。够多预测了。那 10% 的概率不是基于科学的，不是基于研究的。仅仅因为它来自一位科学家，并不意味着它就是科学的。那些预测是有害的。让我们按字面理解，他的建议正是他所说的：没有人应该想当放射科医生，而今天世界上已经没有放射科医生了。我认为如果你是一名放射科医生，你就像那只已经越过悬崖边缘但还没往下看的郊狼，所以没意识到脚下没有地面。人们现在就应该停止培养放射科医生。完全显而易见，五年内深度学习会做得比放射科医生更好，因为它能获得多得多的经验。也许是十年，但我们已经有很多放射科医生了。

I would tell Jeff that it's irresponsible to say all that. All of his predictions have been wrong. Enough predictions. That 10% chance is not grounded on science. It's not grounded on research. It just — because it comes from a scientist doesn't make it scientific. Those predictions are hurtful. Let's take it at face value that the recommendation is exactly what he said, which is that nobody should want to be a radiologist and the world has no radiologist today. I think if you work as a radiologist, you're like the coyote that's already over the edge of the cliff but hasn't yet looked down, so doesn't realize there's no ground underneath him. People should stop training radiologists now. It's just completely obvious that within 5 years, deep learning is going to do better than radiologists because it's going to be able to get a lot more experience. It might be 10 years, but we got plenty of radiologists already.

这对社会是有益还是有害？我想我们都同意，我们都同意，那会非常有害。它没有发生。我们如此吓唬年轻人关于 AI 的未来，以至于他们甚至不想上大学，不想再去大学，因为他们认为自己找不到工作，这是好是坏？如果这发生了，是有益还是有害？是有害的。不要以为仅仅因为你是个危言耸听者，你就在做社会公益。这不是真的。所以，我认为我们都应该更明智、更成熟、以证据为基础、讲科学。如果你想讲科学，就讲科学。做科学。做科学。但是吓唬人们，提出那些主张——他们的记录很糟糕。他们的记录简直糟糕透顶。

Is that helpful or hurtful to society? I think we can all agree, we can both agree, it would be terribly hurtful. It did not happen. Is it good or bad that we scare young people about the future of AI so much so that they don't even want to go to universities and don't want to go to college anymore because they don't think they'll get a job? Is that helpful or hurtful if it were to happen? It's hurtful. Don't think for a second just because you're an alarmist that you're doing a social good. It is not true. So, I think that we ought to just all be wiser, more mature, be evidence-based, be scientific. If you wanted to be scientific, be scientific. Do the science. Do the science. But alarming people, making claims that — their track record is horrible. Their track record is literally horrible.

嗯，记录在一个方面是糟糕的，在另一个方面是好的，那就是很多很多预测都很弱。

Well, the track record is bad in one respect and good in another, which is many, many predictions have been weak.

哪个预测——

Which prediction has been —

缩放定律会奏效的预测。

The predictions that the scaling laws would work.

缩放定律——即使在那时我们也得小心。

Scaling law — we got to be careful here even then.

就是说，如果你只是投入——为观众简单说明一下——如果你投入算力和训练数据，这些东西会不断变得更聪明。

That if you just dump — to just say what it is for the audience here — that if you dump compute and training data, these things will keep getting smarter.

没错。

That's correct.

这不是真的。仅仅不断训练这些模型它们就会变得更好，这不是真的。注意，这就是为什么第二个缩放定律必须出现。如果第一个缩放定律已经——你能描述第二个是什么吗？

It is not true. It is not true that if you just keep training these models they'll get better. Notice it is the reason why the second scaling law had to come along. Why do you need a second scaling law if the first scaling law already — can you describe what the second is?

第二个缩放定律——测试时缩放，推理。

The second scaling laws — test time scaling, inference.

嗯。

Mhm.

你迭代得越多，搜索得越多，探索得越多，你发现的答案就越好。推理时缩放。

The more you iterate, the more you search, the more you explore, the better answer you'll discover. Inference time scaling.

是什么重大突破让当前的 AI 变得极其有用？恰恰与预测相反。曾预测软件工具会终结。那是 SaaS 末日，对吧？是什么让——

What is the big breakthrough that caused the current AI to be incredibly useful? Precisely the opposite of the prediction. It was predicted that it'll be the end of software tools. It was the SAS apocalypse, right? What is making —

SaaS 将永远与我们同在。

SAS will always be with us.

是什么让这些 AI 现在如此高效？是工具的使用。未来，它将因使用这些工具的智能体数量而增强。会有更多人使用 Adobe。会有更多人使用 Salesforce 工具等等。所以，给我一个正确的预测。

What is making these AI so productive right now? The usage of tools. In the future, it'll be enhanced by the number of agents using these tools. There'll be more people using Adobe. There'll be more using Salesforce tools and so on and so forth. And so give me one prediction that has been right.

嗯，你会开始看到涌现的预测——让我试着回答，因为他们不在这里。

Well, the prediction that you would begin to see emergent — let me try to answer that because they're not here.

你会出现涌现的预测——

The prediction that you would have emergent —

想出一个。我认为这本身就是一个——

Come up with one. I think in itself is a —

嗯，我认为这取决于我们在谈论什么预测，对吧？我是说，Jeffrey Hinton 在所有人都认为深度学习荒谬的时候，对深度学习的贡献不亚于任何人。结果证明这是一个相当不错的赌注，对吧？我是说，那个大的——他们每个人都做出了巨大贡献。我爱 Hinton。

Well, I think it depends what we're talking about with predictions, right? I mean, Jeffrey Hinton was the person as responsible as anybody else for deep learning at a time when everybody thought it was ridiculous. And it has turned out to be a pretty good bet, right? I mean, the big one — every one of them made great contributions. I love Hinton.

我讨厌他的预测。

I hate his predictions.

我理解。这是——我想说，这些人都有一种模式化的担忧，但我想先谈几分钟这个，然后我们可以转向其他话题。但在我看来，驱动他们的恐惧，也是很多人直觉上觉得合理的，是你在创造系统。我不是说它们是活的。

I understand that. Here's the — I would say like the stylized concern that all these people have, but I want to sort of do this for a few minutes, then we can move on to some other topics. But the fear that seems to me to animate them, and that I think a lot of people find intuitively reasonable, is you're creating systems. I'm not saying they're alive.

你说任何东西只要说得够久就会变得合理。

You say everything long enough is going to be reasonable.

嗯，这很公平。所以，你在创造智能系统，在某些领域正变得比我们更智能。你给它们奖励函数，正如你所说，做事的欲望，对吧？你给它们持久性，它们在数字世界里移动得非常快。你在创造某种东西，某种实体，一个智能体，它聪明、有能力、不屈不挠，

Well, that's fair enough. So, you're creating systems that are intelligent, that are becoming more intelligent than us in certain domains. You give them reward functions, as you were saying, the desire to do things, right? You give them persistence, they move very fast in the digital world. You're creating something, some entity, an agent that is smart, that is capable, that is relentless,

而且我们并不真正理解它心智的运作方式。OpenAI 的首席科学家——听着，我只是不想你助长那种说法。软件是——你担心我偏离了——

and whose workings of its mind we don't really understand. The chief scientist at OpenAI — look, I just don't want you to contribute to that. Software is — you're worried I'm getting off the —

我不认为软件是不屈不挠的。他们不是在试图让它非常持久吗？高度持久的模型——

I don't think software is relentless. Aren't they trying to make it very persistent? Highly persistent models —

因为我把它做成那样。

Because I made it that way.

但那就是他们制造它的方式。

But that's how they're making it.

是的，但那不是持久性。它只是开着。

Yeah, but that's not persistence. It's just on.

是的，持久性。持久性。那是一种意志力。这里没有意志力。只是电力。

Yeah, persistence. Persistence. There's a willpower. There's no willpower here. It's just electrical power.

听着。让我给你——

Listen here. Let me give you —

人类不也只是带有强化学习回路的能量吗？

Aren't human beings just energy with the reinforcement learning loop?

随便吧。总之，我只是觉得我们不能拿这些东西开玩笑。

Whatever. So anyways, I just think that we can't make jokes about this stuff.

我们在吓唬美国公众。听着。Spawn、create、kill、wait、sleep——这些词都和智能体联系在一起，对吧？人们就是这么用的。

We're scaring the American public. Listen. Spawn, create, kill, wait, sleep — all of these words are associated with agents, right? That's what people use.

这些词是操作系统多进程系统出现时创造的。它们字面上就是操作系统的命令。你 spawn 一个进程。把进程换成智能体。进程 fork，产生父进程和子进程。智能体 fork、spawn、give birth。这些词是 30、40、50 年前为操作系统创造的。

These words were created when multiprocessing systems for operating systems. These are literally the commands of an operating system. You spawn a process. Replace process with agent. The process forks as a result — parent and child. The agent forks, spawns, gives birth. These are words that were created for the operating system 30, 40, 50 years ago.

但注意，我们并没有给它们注入人类特征。我们一直在 kill 进程。kill -9，kill 掉一个死进程。它只是一个进程。但现在我们谈论这些东西。一群人想把软件说得比它本身更玄。我们用一种新方式谈论软件，但用的全是同样的老词。上一代计算机工程师——我们做的也是同样的事。

But notice we didn't infuse human characteristics into them. We kill processes all the time. Kill -9, kill a dead. It's just a process. But now we're talking about these things. A collection of people want to make the software more than it is. And we talk about software in a new way, but they're all the same old words. Now the last generation of computer engineers — we were doing all the same things.

但软件的行为方式难道不是新的吗？我是说，从外部看——我没有你那样的技术专长。

But doesn't software act in a new way? I mean, from the outside — I don't have the technical expertise you do.

它在爬取互联网，它在做搜索，它在做优化——

The fact that it's crawling the internet, it's doing search, it's doing optimization —

它在通信，它在突破限制。大多数东西并不会突破限制。

It's communicating, it's breaking out of things. Like, most things don't break out of things.

不，软件一直在突破沙箱。这正是我们需要虚拟机的原因。你不能让智能体监控自己的沙箱、监控自己。你需要一大堆看门狗。所以这些概念已经存在很久了。我们只是不知怎么的，在最近这一代给它加了一大堆人类词汇。我就是觉得这没必要。它就是软件。在我脑子里，它就是在计算机上运行的一堆代码、一堆数字。这一切对我来说都以非常自然的方式发生，这正是我能运作的原因。也正是因为——如果它只是神秘和神话，我怎么围绕它建立一家公司？

No, software breaks out of sandboxes all the time. That's the reason why we need virtual machines. You can't have agents monitoring their own sandbox, monitoring themselves. You need a whole bunch of watchdogs. And so these are ideas that have been around for a long time. We just somehow, in recent generation, gave it a whole bunch of human words. And I just think that it's unnecessary. It's software. When I see it in my head, it's a bunch of code, a bunch of numbers running on computers. And all of that is happening in a very natural way to me, which is the reason why I can operate. And it's the reason why — if it's just simply mystery and myth, how do I build a company around it?

我觉得这触及的一个根本问题是：什么是智能？在你思考拥有智能机器意味着什么之前。对你来说，智能究竟是什么？

I think one of the fundamental questions this gets at is just: what is intelligence? Before you can even think about what it means to have intelligent machines. Just what is intelligence to you?

嗯，智能有一个技术性的表述。首先，当人们谈论智能、思考以及所有这些时，对大多数人来说当然没有正式定义。但在计算机科学领域有一个定义。这个定义是感知——感知世界并理解它。第二，是推理。推理是把任何场景、你看到的任何东西、任何经验分解成更基本部分的能力。第三，是朝目标进行规划。这个基本表述适用于智能体系统。它适用于机器人系统，适用于自动驾驶汽车。所以你可以看到这个行业一层一层、一步一步地构建它，直到我们现在有了我们所感知的智能。

Well, there's a technical formulation of intelligence. First of all, when people talk about intelligence and thinking and all of these things, of course there's no formal definition for most people. But in the field of computer science there is a definition. The definition is perception — perceiving the world and understanding it. Two, which is reasoning. And reasoning is the ability to decompose any scenario and anything that you see, any experience, into more elemental parts. And third is planning towards an objective. That fundamental formulation applies to agentic systems. It applies to robotic systems, applies to self-driving cars. And so you could see the industry building it layer by layer by layer, step by step by step, to the point we now have what we perceive as intelligence.

我觉得这触及了这场对话的一个核心问题：你向我描述这项技术的一些方式表明，你不认为它有什么真正新的东西。它也许在规模上是新的，在能力上是新的，但根本上这是软件。我们并非一直都有，但我们已经拥有软件很久了。很多人相信，当你达到这种级别的智能时，那是一次相变。它是某种不同的东西，某种我们以前从未处理过的东西。一种通用智能技术，其智能正在非常迅速地提升。我想确保我真正理解：我们正处在那条分界线上。这是完全新的东西吗？这是需要我们拿出新东西来应对的东西吗，还是更像某种旧东西？智能机器和我们曾经拥有的机器不同吗？

I think this gets to such a core question of this conversation, which is: some of the ways you've described the technology to me, it is not something you think there's anything really new about it. It is maybe new in scale, new in capability, but fundamentally this is software. We've not always had, but we've had software for a long time. A lot of people believe when you're getting to intelligence at these levels, it is a phase change. It is something different, something we have not dealt with before. A kind of generally intelligent technology that is advancing in its intelligence very rapidly. I want to make sure I actually do understand: we are on that divide. Is this something fully new? Is this something that requires something new from us, or is this more like something old? Are intelligent machines different than the machines we've had?

嗯，几乎所有的技术和文明都建立在层层可理解的技术之上，而这些技术在规模上会变得相当非凡。我们能仅仅举起手机就连上互联网——我们在这个小小的设备上、就在空气中，连接到世界上每一条信息，这有点奇怪。而这一小片玻璃竟然能处理数万亿条信息，并精确地把我们想要的那一条带给我们，因为它经过了推荐系统。所以如果你想想，我们怎么可能知道所有信息在哪里——必须有人去爬取它、索引它，而这用到了机器学习技术，也就是人工智能的早期版本。这些系统做着神奇的事情，以至于我现在已经习以为常。它花了 literally 二十多年和数千亿美元的基础设施建设，才让一切对你来说显得如此自然，以至于我们现在把它视为理所当然。从技术角度看，我们取得的每一个里程碑都会被庆祝，我满心欢喜地庆祝，我满怀热情地庆祝，因为我为做到这些的人感到骄傲。我为我们自己做出了贡献而骄傲。我为突破而骄傲。但当你——当时看起来哇，像是奇迹，但那种感觉持续大约 17 天。之后——

Well, almost all of technology and civilization is built on layers of understandable technology, which at scale becomes fairly extraordinary. The fact that we can connect to the internet by just holding a phone up — it's kind of weird that we're connected to every piece of information in the world on this little tiny device, just in the air. And the fact that this little tiny piece of glass can somehow take trillions of pieces of information and bring to us precisely the one that we want, because it's been passed through a recommender system. And so if you think about how is it possible that we knew where all the information is — somebody had to go crawl it, had to index it, and that uses machine learning techniques, which is the early versions of artificial intelligence. And these systems do magical things to the point where I now expect it. It took literally 20-some-odd years and hundreds of billions of dollars of infrastructure buildout in order for everything to just seem so natural to you, to the point where we now take it for granted. Every single milestone that we achieve from a technology perspective is celebrated, and I celebrate with glee and I celebrate with so much enthusiasm because I'm proud of the people who did it. I'm proud of ourselves who contributed to it. I'm proud of the breakthrough. But when you — and it seems like wow, it seems like a miracle at the time, but that sensation lasts about 17 days. After that —

我们很快就会习惯一切。我同意这一点。但这是一个不同的阶段吗？

We get used to everything quickly. I agree with that. But is this a different phase?

这是一个——公司领袖们谈论过这个。谷歌的 CEO，我想是——

It's a — the company leads talk about this. The CEO of Google, I think it was —

把它比作火，对吧？就像人类历史的新纪元。你是这样看的，还是把它看作过渡性的、迭代性的？

As the equivalent of fire, right? Like a new epoch in human history. Is that how you see it, or you see it as transitional, iterative?

不，我认为这完全是一场革命。正如我们之前谈到的，你从能够找到一切、找到任何东西，变成了能够问任何问题、知道一切、做一切。所以显然这是一个新的抽象层级。现在，我不太愿意的是让它显得不止于此。归根结底，工程师在做工程工作。一旦我们发明了这项技术，一旦我们发现了解决方案，回头看时它相当明显，对很多人来说也相当平淡无奇。我们能够每天把技术做得越来越好，是因为我们理解它，显然如此，所以我们知道如何让它更好。所以你把它变成一个工程问题，然后你说我们现在缺少的是一个水平——因为这是你现在说的——测试、监控、沙箱安全、控制、卓越的水平，是我们正在构建的东西所需要的。

No, I think this is completely a revolution. And as we were talking about earlier, you went from being able to find everything, find anything, to be able to ask anything, know everything, and do everything. And so clearly it's a new abstraction level. Now, the thing that I'm reluctant about is to cause it to seem like it's more than that. In the final analysis, engineers are doing engineering work. Once we invented the technology, once we discovered a solution for it, when you look back it's fairly obvious and it's fairly mundane to a lot of people. And the fact that we're able to make the technology better and better and better every day is because we understand it, obviously, and so we understand how to make it better. So you turn it into an engineering problem, and you say what we don't have right now is a level — because this is something now you've said — of testing, monitoring, sandbox security, control, excellence that we need for what we're building.

而这并不是因为这些公司没有非凡的工程师。

And it's not because the companies don't have extraordinary engineers.

我明白，我得确认你不是在说那个。但我相信 OpenAI 和 Anthropic——因为我认识他们中的许多人，他们非常出色——但这恰恰部分地让我担忧。不，不，不。他们正在经历的是转型。我反复说过：这是一个宏大但简单的想法。我们终于有了一款因为有用而有用的软件。采用率飙升。但请记住，一家六个月前还在努力做出有用、有能力的东西的公司，怎么可能有那么多资源投入到测试、评估，以及所有专门用于此的算力？在此之前这是不必要的。所以未来几年将要发生的是，这些实验室将转型为以工程为重点、更偏生产工程、以产品为中心的公司。所以我认为他们只是在经历转型。这些公司，非凡的公司，人才济济的公司，史上最具影响力的公司。他们只是在经历转型。不多也不少。

I understand I got to make sure you're not saying that. But I believe that OpenAI and Anthropic—because I know many of them are extraordinary—but that actually is in part what makes me worried. No, no, no. What's happening to them is a transition. And I said this over and over again: this is a big but simple idea. Finally we now have a piece of software that is useful because it's useful. The adoption took off. But remember, how is it possible that a company that six months ago was trying to make something useful, capable—how would they have as much resources dedicated on testing, evaluation, and all of the compute dedicated to that? It was unnecessary until now. And so what's going to happen over the next several years is that we're going to transition from these labs becoming engineering focused, much more production engineering focused, and product focused companies. And so I think they're just going through a transition. These are companies, extraordinary companies, incredibly talented companies, the most consequential companies of all time. And they're just going through their transition. It's not more than that. It's not less than that.

现在很多公司，OpenAI 和 Anthropic 在过去几个月里，都发布了这些大——我不知道该叫它们什么——论文、博客文章之类的。嗯，Anthropic 那篇叫《当 AI 构建自身》。呃，我忘了 OpenAI 那篇的名字。

So many of the companies now, both OpenAI and Anthropic in the last couple months, have put out these big—I don't know what to call them—papers, blog posts, something. Um, "When AI Builds Itself" is the name of the Anthropic one. Uh, I forget the name of the OpenAI one.

计算机在构建自身。你们知道他们在谈论递归自我改进。

Computers are building themselves. You guys know that they're talking about recursive self-improvement.

你知道我们使用递归自我改进。

You know that we use recursive self-improvement.

所以，我想听听你对 RSI 的看法。

So, I'd like your perspective on RSI.

我认为 RSI 从根本上就是做事的方式。我们用软件设计计算机，运行软件，再设计计算机，运行软件，再设计计算机。这基本上就是我们做的事。递归自我改进，因为我们的计算机每年都在变得更好，而且实际上每年变得更快，因为我们用软件让软件变得更好。这叫做计算机工程。我们已经做了很长时间。现在在智能体的语境下：它运行一次流程，反思它，研究它走过的各种路径，选择最佳方法。下次如果你要做完全相同的任务，我会记录一个文件。我会告诉你我上次是怎么做的——那并不是最有效的。我会称之为技能。因为你反复使用它，有些是技能，有些会成为记忆。我们会改进记忆。好吗？这样下次你使用时，它比上次更好。递归自我改进。你也可以决定，把所有这些技能、所有这些记忆，以及所有数据，用来训练下一个版本的模型。这样 AI 随着时间的推移，在为你服务方面变得越来越好。我们正在做——所有这些都在发生。绝对在发生。与此同时，他们拥有的算力在增长，因此他们可以更快地做一切。过去预训练某样东西需要一年，现在只需几个小时，因为计算机变得更快，而且他们拥有更多算力。所以现在循环在加速。完全可以理解。这能给他们任何借口去发布未经测试的产品吗？答案是不能。回到这一点。没有企业能够在底层软件时刻变化的环境中运营。有一个发布流程。所以你知道，当他们推出新模型时，我们需要在将其发布到我们的运营中之前进行评估。我们不能让它一直递归地变化。所以他们必须在发布产品之前测试它。我们会在将产品发布到运营之前测试它。所以我认为递归自我改进是一件极好的事情。

I think that RSI is fundamentally how things are done. So, we use software to design a computer to run software to design a computer to run software to design a computer. That's basically what we do. Recursive self-improvement, because our computers are getting better every single year, and in fact it's getting better faster than that every single year, because we use software to make software better. That is called computer engineering. We've been doing this for a long time. Now let's in the context of agents: it runs through the process once, it reflects on it, it studies the various paths it went through, chooses the best approach. The next time if you're going to do exactly the same task, I'm going to document a file. I'm going to tell you how I did it last time—that wasn't the most effective. I'm going to call it skills. And because you use it over and over again, some of it is skills, some of it is going to be a memory. We're going to improve the memory. Okay? So that next time you use it, it's even better than the last. Recursive self-improvement. You could also decide that you take all of this skills, all of this memory, and you can take all of this data and train the next release of the model with it. And so that AI becomes better and better at servicing you over time. We're doing—all of that is happening. It is absolutely happening. Meanwhile, the amount of compute that they have is growing, and therefore they could do everything faster. What used to take a year to pre-train something now takes several hours because the computers are getting faster and they have more of it. So now the loop is going faster. Completely understandable. Does that give them any excuse to launch a product that hasn't been tested? The answer is no. Just come back to that. No enterprise is able to operate in an environment where the underlying software is literally changing all the time. There's a release process. So you know when they roll out a new model, we need to evaluate it before we release it into our operations. We can't just have it recursively changing all the time. And so they have to test the product before they release it. We will test the product before we release it into operation. And so I think recursive self-improvement is a fabulous thing.

你认为是否存在任何程度——我以前听你说过，学习应该始终有人在环中？

And do you think there is any level—I've heard you say before that learning should always have a human in the loop?

是的，就像我刚才说的，你有递归自我改进。

Yeah, like I said just now, you got recursive self-improvement.

他们似乎在想象一种并不总是如此的情况。

They seem to be imagining something where it wouldn't always.

嗯，不要给我发任何你没有评估过的东西。不要给我，不要给 Nvidia 发任何没有人类在环中评估过的产品。请不要那样做。

Well, don't ship me anything that you didn't evaluate. Don't ship me, don't ship Nvidia any products that humans did not in the loop evaluate. Please don't do that.

还有我们之前谈到的恐惧，他们担心自己没有在评估，他们不知道如何评估这些系统，而且它们变化得越快，他们就越担心系统在欺骗他们。

And the fear we talked about earlier that they're worried they're not evaluating, that they don't know how to evaluate these systems, and the more they change kind of rapidly, the more they worry the systems are tricking them.

我不相信。我相信他们的研究人员每天都在努力了解如何评估这些系统。验证。你知道，我们公司 10%、20% 致力于设计，80% 致力于验证。今天大多数实验室可以理解地是 80% 致力于能力，20% 致力于安全验证评估。

I don't believe that. I believe that their researchers are working every single day to learn about how to evaluate these systems. Verification. So you know, 10%, 20% of our company is dedicated to design, 80% is dedicated to verification. Today most labs understandably is 80% dedicated to capability and 20% dedicated to safety verification eval.

这就是你所说的翻转、转型。

This is the flip, the transition you're talking about.

没错。没错。AI 需要加速才能安全。我希望他们获得更多算力，但分配给评估、对齐，我认为他们正在这样做。如果我在一百年前的汽车行业，我宁愿汽车行业在一年内加速到今天，因为我相信今天的汽车比 99 年前的汽车安全得多。而 ABS 技术、自动刹车，需要计算机视觉技术、传感器融合技术、雷达和摄像头，所有这些技术结合在一起，才能在应该刹车时刹车，不该刹车时不刹车。那项技术极其困难。我本希望，每个人都本希望 ABS 技术在 99 年前就存在。那样会少死很多孩子。所以，你知道，安全气囊、安全带，我是说，所有这些东西，自动收紧安全带，所有这些东西。你能想象那都是技术吗？加速那项发展。所以，当我说我们需要加速 AI 技术时，人们不知为何认为安全不是其中一部分。安全是其中一部分。对齐是其中一部分。评估是其中一部分。护栏、沙箱、隔离技术、监控技术、遥测技术、外部 AI 监控技术，所有这些东西都是 AI 技术。加速那项发展。有趣的是，我认为如果实验室里最警觉的人能够确信他们会将 80% 的算力转移到安全和对齐，而不是 80% 用于能力扩展，他们会感觉好得多。在我看来，你实际上在说的一件事是，人们应该把安全和对齐视为能力扩展，而不安全的技术不是一种进步的技术。

That's right. That's right. AI needs to accelerate to be safe. I want them to get more compute but allocated towards evaluation, to alignment, and I think they're doing that. If I were in the car industry a hundred years ago, I would rather the car industry accelerated to today in one year because I believe today's car is way more safe than a car 99 years ago. And ABS technology, automatic braking, requires computer vision technology, sensor fusion technology, radars and cameras and you know all that technology coming together in order to brake when you should and not brake when you shouldn't. That technology extremely hard. I would have hoped, everybody would have hoped that ABS technology existed 99 years ago. A lot fewer children would have been killed. And so, you know, airbags, safe, you know, seat belts, I mean, all of that stuff, self-tightening seat belts, all of that stuff. Could you imagine that's all technology? Accelerate the living daylights out of that development. And so, when I say we need to accelerate AI technology, people think for some reason safety is not part of that. Safety is part of it. Alignment is part of it. Eval is part of it. Guard railing, sandboxing, the isolation technology, monitoring technology, telemetry technology, external AI monitor technology, all of that stuff is AI technology. Accelerate the living daylights out of that. It's funny because I think that if the most alarmed people at the labs could be assured they were going to move 80% of their compute into safety and alignment as opposed to 80% into capability expansion, they would feel much better. And it sounds to me one thing you're actually saying is one should think of safety and alignment as capability expansion and unsafe technology is not an advancing technology.

这就像我们说，哦，芯片设计是研发，芯片验证不是研发。我们把大部分成本、大部分算力花在验证、仿真、验证测试、可靠性测试、寿命测试上。所有这些都是工程的一部分。激励是存在的。如果他们发布的产品伤害了其他公司和其他人，他们就会把自己的公司置于危险之中。

It's like us saying, oh, chip design is R&D, chip verification is not R&D. We spend most of our cost, most of our compute on verification, emulation, verification testing, reliability testing, lifetime testing. All of that is part of engineering. The incentives are there. They are going to put their company in harm's way if they release products that harm other companies and other people.

你认为我们需要专门针对 AI 的责任法律吗？

Do you think we need liability laws that are specific to AI?

已经有了。我们就举一个例子。自动驾驶汽车。汽车作为产品，Robotaxi 有很多法规。如果法规不够，那么 NHTSA 应该介入并制定新法规。汽车行业应该有新法规。我不知道缺什么，但如果缺什么，我绝对会添加更多法规。在互联网的背景下，互联网支持许多应用，这些应用应该有监管。如果没有，你就得找到它们。

Already. So let's just use one example. Self-driving car. The car as a product, the robotaxi has lots of regulations. If it doesn't have enough regulations, then NHTSA ought to get involved and come up with new regulations. Car industry should have new regulations. I don't know what's missing, but if there is something missing, then I would absolutely add more regulation. In the context of internet, there are many applications that the internet powers and those applications should have regulation. If they don't, just you got to find them.

所以，我现在想降到蛋糕的下一层，芯片。总结一下我们的立场，因为我想确保我正确理解你的观点，那就是这些公司正在经历转型。即使这些系统加速、变得更有能力、更复杂、更持久，无论是什么，仍然存在限制因素：公司不会发布不安全的东西。他们不应该发布不安全的东西，你相信他们有工程能力使这些东西安全，找出测试和控制方法，无需外部干预。这就是你的立场。

So, I want to drop down the next layer of the cake now to chips. And to summarize sort of where we are because I want to make sure I do understand your position correctly, it's that these companies are going through a transition. That even as these systems speed up, become more capable, complex, persistent, whatever it might be, that there is still the limiting factor of companies will not ship what is not safe. They should not ship what is not safe and you believe they have the engineering capabilities to make these things safe to figure out the testing and the control absent external intervention. That's sort of where you are.

是的。

Yeah.

我听到你说过的一件事是，我们可能已经进入了一个人们并不总是理解的计算机运作的新时代。描述一下你的愿景，以及如果有人对它的理解还有点停留在你知道你有一台 MacBook，里面有一个处理器，你买了它，它有什么不同。

One thing I've heard you say is that we have entered maybe in a way people don't always understand a new era of how computing works. Describe your vision of that and the way if somebody's sort of understanding of it is a little bit still maybe in you know you've got a MacBook and it's got a processor in it and you buy it and how it differs.

过去的计算机行业，我们已知 60 年的计算机行业，叫做基于检索的计算。你检索文件。这就是为什么它叫数据中心，你知道，文件中心。对吧？而在未来，它是 AI 工厂。它在生成。所以理解上下文、基于信息、推理该做什么并生成答案所需的计算量，这个生成过程需要大量计算。因此每个用户所需的计算量已经急剧增长。然后第二部分是因为这些生成式 AI 也可以有些自主性，因为它们是智能体式的。现在你有智能体在使用生成式 AI。所以不是十亿人使用计算机，你本质上除了人类之外还有数千亿个智能体在使用计算机。所以你可以说我们需要的计算量，无论我们之前有多少，将增长十亿倍。这是一个合理的框架，合理的计算量水平。

The last computer industry and in the computer industry we've known for 60 years is called retrieval-based computing. You retrieve files. That's why it's called data center, you know, file center. Okay? And in the future, it's an AI factory. It's generating. And so the amount of computation necessary to understand the context, to be grounded in information, to reason about what to do and to generate an answer, that generative process requires a lot of computation. And so the amount of computation necessary per user has grown tremendously. And then the second part is because these generative AI can also be somewhat autonomous because they're agentic. Now you have agents using generative AI. And so rather than a billion people using computers, you essentially have multiple hundreds of billions of agents in addition to the humans using the computer. And so you could argue that the amount of computation we need, however much we had before, is going to go up by a billion times. And that's a reasonable framework for reasonable level amount of computation.

在这个新世界中，在工厂的背景下，你真正关心的是它的生产力有多高？而不是它有多贵。它不能无限贵，但你想知道它的生产力有多高。所以我们的计算机生产力令人难以置信。500 亿美元建造一个 1 吉瓦的数据中心，一个 1 吉瓦的 AI 工厂，你可以以每年 400 到 500 亿美元的价格租用它。所以它的生产力是惊人的。所以第一是生产力。NVIDIA 的架构是可替代的，因为我们是通用的，这就是为什么每个 AI 实验室、每个 AI 模型、封闭模型都运行在 NVIDIA 上，因为我们完全可替代，你可以从数据处理到预训练到后训练到评估到推理使用我们，AI 的整个生命周期都可由我们的架构支持，如果客户不再需要它，另一个客户会非常乐意接手。然后最后一部分是耐用性，因为我们的架构是软件驱动的，我们不断用新算法改进我们的软件，这些算法采用新的工作负载、新模型并在我们的旧一代硬件上运行。我们有庞大的团队不断这样做。因此，我们计算的使用寿命要长得多。这就是为什么人们把 Nvidia 的计算称为一种资产类别，就像飞机一样。你知道飞机是通用的。它们是可替代的。联合航空不用它，美国航空会用。它耐用，它开始是客机，结束其生命时是货运飞机。所以因此，它可以是一种资产类别。所以这是，如果我们能做到这一点，如果这发生，那么当然，为 Nvidia AI 工厂融资的资本成本将是最低的，因为我们的计算机是抵押资产，然后所以无论如何，这就是正在发生的相变，这将为我们的增长带来巨大的解锁。

In this new world, what you really care about within the context of a factory is how productive is it? Not how expensive is it. It can't be infinitely expensive, but you want to know how productive it is. And so our computers are incredibly productive. $50 billion to build a one gigawatt data center, one gigawatt AI factory and you can rent it for 40 to 50 billion per year. And so the productivity of it is incredible. So number one is the productivity. NVIDIA's architecture is fungible because we're general purpose, which is the reason why every AI lab, every AI model, closed model runs on NVIDIA and because we're completely fungible and you can use us from data processing to pre-training to post-training to eval to inference, the entire life of AI is supportable by our architecture and if a customer no longer needs it, another customer will be more than happy to pick it up. And then the last part is that durability because our architecture is software-driven and we're constantly improving our software with new algorithms that take the new workloads, the new models and run it on our old generation hardware. We have massive teams of people who are constantly doing that. As a result, the useful life of our compute is much longer. That's the reason why Nvidia's and people are talking about Nvidia compute as an asset class kind of like an airplane. It's you know airplanes are general purpose. They're fungible. United Airlines doesn't use it, American Airlines will use it. It's durable and it starts out as a passenger plane, ends up ending its life as a shipping, you know, as a cargo plane. And so as a result, it can be an asset class. So this is and if we could do this, if this happens, then of course the cost of capital for funding Nvidia AI factories will be the lowest because our computers are collateralized asset and then so anyways, this is what this is the phase shift that's happening to us which is going to be a huge unlock for our growth.

所以你的业务变得如此有趣。你现在已经进入降低 AI 行业其他参与者的资本成本。人们可能见过这些图表，箭头指向各个方向。太有趣了。是的。向那些理解 Nvidia 已经成为世界上最大的公司的人解释一下。他们看到这些图表似乎非常循环。支持需求、创造市场和创造需求之间有什么区别？

And so your business has become so interesting. You've moved now into lowering the cost of capital for others in the AI industry. People maybe have seen these charts of like the arrows going in every direction. So interesting. Yeah. And explain that a bit to people who are they understand Nvidia has become like the biggest company in the world. They see these charts that seem very circular to them. What is the difference between supporting demand, creating markets and creating demand?

我们无法真正创造需求，因为最终如果 AI 服务没有承购，那么为其建造计算机显然毫无意义。所以首先必须发生的是，现在算力需求如此之高的原因是 AI 应用正在经历一个拐点。它们变得有用，因为 AI 变得有用，500 亿美元的风险投资正在涌入，所有这些公司，成千上万的公司，初创公司，他们都需要算力。所以需求来自他们。因此这些公司需要技术支持，需要生态系统建设支持，需要财务支持。所以我们可能决定作为股权所有者投资其中一些。结果他们成为真正繁荣的新云提供商。

We can't really create demand because in the end if the AI services have no offtake then obviously building computers for it is pointless. And so the first thing has to happen, the reason why compute demand is so high right now is because AI applications are going through an inflection. They're becoming useful and because AI is becoming useful, 500 billion dollars of venture funding are coming in and all of those companies, those thousands of companies, startups, they all need compute. And so that's their the demand is coming from them. And so these companies need support in technology, they need support in ecosystem building, they need support in financial support. And so we might decide to invest in some of them as an equity owner. And as a result they become a really flourishing new cloud provider.

我们决定投资的另一个原因，正如我提到的，这是一个五层蛋糕。在模型层和应用层，有大量真正有创新力的公司，而 AI 远不止语言模型本身。还有世界基础模型、物理 AI、生物 AI、化学和材料科学 AI。这些都不同于语言模型。这些公司中有很多是新公司，需要大量资本。我们可能会决定持有它们一小部分股份，帮它们起步。他们是了不起的科学家。作为第一个投资者、锚定投资者，我们为他们的公司带来信心。我们让他们使用我们的很多技术。我们大力支持他们，帮助他们尽快成为一家公司。我们可能会决定投资一家核能公司。我们可能会决定写支票，等等。所以如果你看我脑海中的 AI 产业模型，它是一个五层蛋糕，我们在所有层面都有投资。可能会有战略性的解锁点。它打开新市场。它为我们打开新的市场通路。它可能为我们锁定关键资源。所以我们这么做有很多战略原因。

Another reason we might decide to invest is because, as I mentioned, there's a five-layer cake. At the model and application layer, there's a whole bunch of really innovative companies, and there's way more to AI than just the language model itself. There are world foundation models, physical AI, biology AI, chemistry and materials science AI. These are all different from language models. So many of those companies are new and they need a lot of capital. We might decide to be a small percentage shareholder in them, so we get them off the ground. They're incredible scientists. By being a first investor, an anchor investor, we bring confidence to their company. We give them access to a lot of our technology. We support them a great deal and we help them become a company as fast as possible. We might decide to invest in a nuclear company. We might decide to write so on and so forth. So if you look at my mental model of the AI industry, it's a five-layer cake and we're investing across all of it. There might be strategic unlock points. It opens new markets. It opens a new route to market for us. It might secure a critical resource for us. So there are a lot of strategic reasons why we do it.

我是说，这里的数字令人震惊。你几乎成了美国 AI 的单一公司产业政策。

I mean, the numbers here are astonishing. You've become like a single company industrial policy for American AI.

我们在这个生态里投入了大量资金。

We've put a lot of money into this ecosystem.

是的。你现在每年的总投资是多少？

Yeah. What's the total investment you're now making per year?

全部加起来，我们大概——嗯，我不确定每年都是，但我想全部加起来可能有 1000 亿美元。我可能要核对一下数字，但差不多是这个量级。

All in, we're probably up—well, I don't know about every year, but I think all in we might be like a hundred billion dollars. I might check my numbers, but something like that.

这比《芯片与科学法案》还大。

It's larger than the Chips and Science Act.

哦是的。是的。更不用说，因为我给台积电、纬创、富士康、安靠、矽品等各家公司的采购承诺——因为那个承诺，我能够鼓励他们来美国制造。事实是，在芯片制造方面，我们对美国再工业化的贡献可能比世界上几乎任何公司都多。我们不仅在再工业化制造，而且速度如此之快，以至于造成了劳动力短缺，但我们也在创造大量就业。

Oh yeah. Yeah. Not to mention, because of the purchasing commitments that I provide to TSMC and Wistron and Foxconn, Amkor and SPIL and all these different companies—because of that commitment, I'm able to encourage them to come and manufacture here in the United States. The fact of the matter is, we probably contributed more to reindustrializing the United States in chip manufacturing than just about any company in the world. We're not only reindustrializing manufacturing, we're doing it so fast that we're creating a shortage of labor, but we're creating a lot of jobs.

我知道现在市场上有很多投了钱的人很兴奋，尤其常常对英伟达的股票兴奋，同时又担心 90 年代末互联网泡沫的类比。他们担心的其实和你刚才说的有关，那就是互联网确实继续变得更有用。并不是说高估值意味着技术是空洞或虚假的，而是发生了某件事，短暂地爆掉了，非常大的公司在那里面遭到重创，很多人也在那里面遭到重创。从那种泡沫破裂周期里能学到什么？我想问的是，你不认为它会再次发生，还是说你为什么不认为它会再次发生？

I know a lot of people with money in the market right now who are excited, often excited by Nvidia stock in particular, and worry about the analogy of the internet bubble, the late '90s. And what they worry about is actually related, I think, to what you just said, which is that the internet did continue to be more useful. It's not that high valuations meant that the technology was hollow or fake, but something happened and popped for a minute and very big companies got hammered in that and a lot of people got hammered in that. What is there to learn from that kind of bubble-bust cycle? And I guess the question is, do you not think it will happen again, or why do you not think it will happen again?

在某个时点，需求和供给会再次倒转，这就是市场的本质。这不会在明年发生。不会在未来两三年发生。我就是不相信。但在某个时点，我们很可能会供给大于需求。

At some point demand and supply will be inverted again, and that's just the nature of markets. It's not going to happen next year. It's not going to happen in the next couple, two, three years. I just don't believe that. But at some point we will likely have more supply than demand.

嗯。

Mhm.

而我就是不知道那是什么时候。所以从过去能学到的东西不多。

And I just don't know when that is. And so there's not much to learn from the past.

对你来说，信号会是什么？

What would be the signal for you?

市场会自然放缓，然后停下来，也就是说会有一段消化期。那么这段消化期会是六个月吗？会是九个月吗？会是一年吗？它不会永远持续。如果你全面看，我们投入到应用层的资金量，是为了让每一个行业都能让技术扩散进去，从而从中受益——这大概是我们做的最大的一件事。

Markets will naturally slow down and then it will stop, meaning there will be a period of digestion. Now, is that period of digestion going to be six months? Is it going to be nine months? Is it going to be a year? It won't be forever. If you look across the board, the amount of investment that we're putting into the application layer so that each one of the industries could have the technology diffuse into them so that they could benefit from it—that's probably one of the biggest things that we do.

这是我经常听到的一种对中美 AI 生态的比较方式，那就是在美国，重点是能力提升的速度，很多人认为我们在这方面领先，这似乎是真的，而在中国，更强调扩散。

This is a way I often hear the Chinese and American AI ecosystems compared, which is that in America the emphasis is on the speed of rising capability, and a lot of people think we're ahead on that, and that seems true, and that in China there's more emphasis on diffusion.

很多人认为中国在扩散方面可能领先，而且在某些方面，它的经济结构更适合像微信这样的东西，一直到知识和指令在其中流动以进行扩散的方式。

And a lot of people think that China is probably ahead on diffusion, and in some ways has an economy that is better structured for things like WeChat, all the way to just the way knowledge and commands move through it for diffusion.

这场竞赛到底是关于能力还是扩散，以及它到底是不是一场竞赛——这个我们稍后可以谈——这是一个大问题。我很好奇你怎么看。

And whether the race is about capabilities or diffusion, and also whether it's a race at all—but we can get to that in a minute—is a big question. I'm curious how you see that.

这是终极问题。我相信，如果我们想让美国从人工智能中受益，每一个行业都必须受益。沃尔玛必须受益。西夫韦必须受益。联邦快递必须受益。每一家银行都必须受益。每一家医疗公司、每一家药物研发公司。我们需要看到每一家建筑公司、每一家数据中心公司、发电公司。我们需要美国的每一个人。我们需要美国的每一个人。我们需要世界上的每一个人都从中受益。而这是最高层。这是最重要的一层。这是触及社会的那一层。下面所有层都是技术赋能者。我希望我们不要毁掉美国在最高层受益的机会。而且注意，所有那些言论、所有那些危言耸听、所有那些末日论、所有那些预测，都在吓唬人。这实际上是我最大的恐惧。我完全有信心。也许我对他们的信心比他们对自己的信心还大。

That's the ultimate question. I believe if we want America to benefit from artificial intelligence, every single industry has to benefit. Walmart has to benefit. Safeway has to benefit. Federal Express has to benefit. Every bank has to benefit. Every healthcare company, every drug discovery company. We need to see every construction company, every data center company, power generation company. We need everybody in the United States. We need everybody in America. We need everybody in the world to benefit from this. And that's the highest layer. That's the most important layer. That's the layer that touches society. All the layers underneath are technology enablers. I want to see us not ruin the opportunity for the United States to benefit at the highest level. And notice all of the rhetoric and all the alarmism, all the doomerism, all of the predictions are scaring people. That is my greatest fear, actually. I have every confidence. Maybe I have more confidence in them than they have in themselves.

你对他们的信心肯定比他们对自己的信心还大。

You definitely have more confidence in them than they have in themselves.

嗯，这我可不确定。但也许只是他们太谦虚了，或者别的什么。

Well, I don't know about that. But maybe it's just too much humility and otherwise.

我们应该把我们现在的处境概念化为与中国的竞赛吗？

Should we conceptualize what we're in as a race with China?

我不认为有必要。有些人喜欢那样想。我并不觉得那能激励我。当我们谈论我们做好自己的工作时，我完全可以不提另一家公司。所以我们以我们自己的标准要求自己。所以我认为不同的人有不同的激励方式。而且我认为，在竞赛之外，要把组织团结起来并聚焦到某个绩效水平，需要更多的艺术。但我不一定认为这是必要的。第一。第二，问题是，即使我们把它框定为一场竞赛，也不一定意味着如果他们取得了什么成就，就是对我们不利。

I don't think it's necessary. Some people like to think that way. I don't find that necessarily inspires me. I have no trouble never mentioning another company when we talk about us doing our good work. And so we hold ourselves to our own standard. And so I think different people have different ways of being motivated. And I think it takes a bit more artistry to unite and focus organizations to a certain level of performance outside of contests. But I don't necessarily see it as necessary. Number one. Number two, the question is, even if we did frame it as a competition, it doesn't have to be that if they achieve something, it's at our peril.

所以当他们发明了某种东西，或者创造了某种发电技术，那可能是一项伟大的发明，我们希望是自己做出来的，但因为它会支持我们这里所有的能源生产系统，结果它帮助了我们整个行业。也许他们提出了一个很棒的新开源模型，而那些开源模型现在正被 80% 的美国初创公司使用。

And so when they invent something or they create some power generation technology, it might be a great invention that we wish we had done ourselves, but because it's going to support all of our energy production systems here, as a result, it helps our whole industry. Maybe they came up with a great new open model, and those open models are now being used by 80% of the American startups.

是的，我们这里用了很多中国的开源模型。

Yeah, we use a lot of Chinese open models here.

对，没错。这很棒。我们下载它。它源自中国。当然，很多技术也源自美国。我们下载它，把它变成我们自己的。我们微调它。我们把它放进我们自己的智能体框架里。我们把它放进我们自己的沙箱里。那都是你自己的技术。所以我认为你利用他们的权重，这很棒。这没问题。

Okay, that's right. And so that's terrific. We download it. It originated in China. A lot of the technology, of course, also originated from the United States. We download it. We make it our own. We fine-tune it. We put it into our own agent harness. We put it into our own sandbox. That's all your own technology. So I think the fact that you leverage their weights, I think that's terrific. That's fine.

你几分钟前说过，不同国家开始把算力视为地缘战略资源，可能想把它分配给自己的公司。这里以及那些确实认为我们在与中国竞赛的人当中，有很多来回讨论，尤其是那些认为我们在与中国竞赛、看谁先实现递归自我改进的超级智能的人。一直在争论的一件事是，我们是否要拒绝向他们提供算力，在这种情况下往往意味着拒绝向他们提供你的芯片。在拜登政府时期，我们有相当严格的出口管制。这些管制在唐纳德·特朗普时期被放宽了。显然，你希望这些管制被放宽。你如何看待这个问题：让中国拥有英伟达芯片，从而可能加速他们的模型或模型部署、模型能力，这对我们来说是好事，还是我们扣住这些芯片以试图减缓他们的进展更好？

You were saying a few minutes ago the way different countries have begun to see compute as a geostrategic resource and may want to allocate it to their own companies. There's been a lot of back and forth on that here and among people who do see us as in a race with China, particularly people who see us as in a race with China for who will get to recursively improving self superintelligence first. There's been this ongoing back and forth on whether or not one thing we want to do is deny them compute, which in this case tends to mean denying them your chips. Under the Biden administration, we had pretty tight export controls. Those were loosened under Donald Trump. Obviously, you wanted those to be loosened. How do you think about the question of whether or not it is good for China to have Nvidia chips that could accelerate their models or model deployments, their model capabilities, versus us holding that back to try to slow their progress?

在 AI 的情况下，我们的目标不仅仅是让一个实验室受益。我们的目标是让整个美国受益。我认为美国有更大的责任和更大的雄心，让世界建立在美国技术栈之上。就像我们有更大的雄心让世界建立在美元之上，让更多人讲英语，使用美国版本的互联网。我的意思是，我们希望如此。问题最终是，我们在剥夺什么？我们是在剥夺他们为其产业所需的芯片，还是在剥夺美国参与竞争的市场？如果你把中国市场看作如此之大，那它如何帮助美国科技行业？也许它帮助了一家拥有特定模型的公司。但行业其他部分会受损。我认为，被剥夺一个可以去竞争的市场，肯定对芯片行业没有好处。它对行业其他部分也没有好处，因为他们被剥夺了开源模型。它不支持美国让世界建立在美国技术之上的总体愿望。所以，如果你狭隘地专注于剥夺他们的芯片，你会剥夺自己的很多东西。所以我会说，退一步，从什么最符合美国利益、整个美国的利益、而不是一家公司的利益来考虑。关于竞赛，正如我们提到的，如果存在竞赛，那竞赛关乎整个美国经济的成功。

In the case of AI, our goal is not just that one lab benefits. Our goal is that all of America benefits. I think the United States has a greater responsibility and a greater ambition for the world to be built on the American tech stack. Just as we have greater ambition that the world is built on the US dollar and that more people speak English, that they use the American version of the internet. I mean, we want that. The question is ultimately what are we depriving? Are we depriving them a chip for their industry or are we depriving the United States a market to compete in? If you see the market as big as China, how does that help the United States technology sector? Maybe it helps one company with a particular model. But the rest of the industry suffers. I think it doesn't help the chip industry surely to be deprived of a market to go compete in. It doesn't help the rest of the industry because they're deprived of open models. It doesn't support the overall aspiration of the United States to have the world built on the American tech. And so there's a lot of things you deprive yourself if you narrowly focus on depriving them of chips. And so I would say to take a step back and frame it into what's in the best interest of America first, all of America, not one company. And with respect to the race, as we mentioned, the race is, if there is one, it's about all of the economy of the United States succeeding.

我在中国和芯片问题上感到非常矛盾，一个原因是，即使我有时比你更担心超级智能，如果你有这些担忧，我认为你希望与中国保持良好关系，能够就 AI 的风险和收益进行富有成效的双边合作。你越把它看作一场只有一方能赢的竞赛，并那样行事，你就越必然会制造敌意。我发现这是这里人们思维中一个复杂的维度。

I found myself very conflicted on the China and chips question, and one reason is even where I have sometimes more of the superintelligence concerns than you do, is that if you have those concerns, I think you want to have a good relationship with China in which there can be kind of productive bilateral working through the risks and benefits of AI. And the more you think of it as a race which only one side can win and act like that, the more you're necessarily going to create enmity. And I found that to be a sort of complicated dimension of people's thinking here.

你知道，我认为零和策略，我剥夺你这个，因此我赢。这种简单化的逻辑往往会在更大的博弈中产生意想不到的后果。更大的博弈当然是，我们现在都在谈论安全。我们想制造安全的产品。我们希望他们制造安全的产品，因为当他们不制造安全产品时，会伤害整个行业。所以，这是一个完美的时机。我们应该寻找机会去沟通、合作、理解、尽可能对齐。话虽如此，英伟达是一家美国公司。我们应该首先让美国受益。美国完全有权让这些技术提供给前沿实验室。Vera Rubin 首先提供给前沿实验室。

You know, I think that a zero-sum strategy, I deprive you of this, therefore I win. That simplistic logic tends to have unintended consequences of the bigger game. The bigger game, of course, is that we're now all talking about safety. We want to build safe products. We want them to build safe products because when they don't build safe products, it hurts the whole industry. And so, this is a perfect time. We should want to look for opportunities to communicate, collaborate, to understand, align as much as possible. Now having said that, Nvidia is an American company. We should benefit America first. America has every right for these technologies to be made available to the frontier labs. Vera Rubin goes to the frontier labs first.

非常先进的芯片。

Very advanced chip.

没错。英伟达最新的芯片。Grace Blackwell 也是如此，Hopper 也是如此。每一代都是。Ampere 也是如此。我们产品的每一代都首先提供给美国公司。如果美国政府想在此基础上增加一项要求，我很乐意。没问题。我们本来自然就会这样做。然而，认识到 AI 行业是一个五层蛋糕，我们希望每一层都赢，那么我们需要每一层都去市场上竞争。

That's right. Nvidia's newest chips. And so did Grace Blackwell and so did Hopper. Every single generation. So did Ampere. Every single generation of our product goes to American companies first. And if the US government would like to add on top of that a requirement to do so, I'm delighted by that. That's no problem. We do that naturally anyways. However, recognizing that the AI industry is a five-layer cake and we want every single layer to win, then we need every single layer to go out there and compete for the market.

这就把我们带到了你那个蛋糕的最后一层，我们不会花太多时间，但如果美国至少在实际层面上拥有的优势是芯片和软件。中国目前在 AI 领域拥有的优势之一是能源。他们更容易建设新能源。他们正在向 AI 注入便宜得多的能源。他们在建设发电和可再生能源方面取得了巨大进步。你如何看待那最基础的一层，即流经数据中心、流经芯片的能源，以及美国在产生足够能源方面的状况？尤其是在我们一直试图从肮脏能源转向清洁能源的时候。

That drops us to the final layer of your cake, which we won't spend as much time on, but if the advantage America has had at least at a material level is chips and software. One of the advantages China has right now in AI is energy. That it's easier for them to build new energy. They're pumping much cheaper energy into AI. They have made tremendous advances on building electrical generation and renewable energy. How do you see that most fundamental layer, the energy that pumps through the data centers, pumps through the chips, and where America is on generating enough of it? Particularly at a time when we've been trying to move from dirty energy into clean energy.

是的，我认为第一，他们的能源比我们多得多，而且他们计划建设的比我们多得多。我们，你知道，我认为我们必须承认，我们在气候变化和可持续能源问题上把自己搞得一团糟，结果我们没有规划足够的能源生产。

Yeah, I think one, they just have a lot more energy than we do and they plan to build a lot more than we do than we did. We got, you know, I think we just have to acknowledge we got ourselves really gummed up in climate change and sustainable energy and as a result we just didn't plan enough energy production.

你说的“搞得一团糟”是什么意思？

What do you mean by gummed up there?

嗯，短期内，能源生产需要化石燃料。因为人们对化石燃料能源生产有太多焦虑，如果你看看我们的国家，我们很长时间以来净新增能源很少。突然之间，这个新行业出现了，我们发现自己处于一种没有那么多能源建设能力的情况。现在整个国家都在手忙脚乱。与此同时，我们行动得太快了。

Well, in the near term, energy production requires fossil fuel. And because there's just so much angst about fossil fuel energy production, if you look at our country, we've produced very little net new energy for a long time. And all of a sudden, this new industry comes along and we find ourselves in a situation where we just don't have that much energy building capacity. And now the whole country is scrambling. And meanwhile, we've moved so fast.

我们本可以在与社区沟通、帮助社区做好准备、与社区合作让他们了解即将到来的事情方面做得更好。如果他们不想在自己的城镇建设数据中心或其他什么，那就随他们吧。但如果你要在他们的城镇建设，一定要去那里让他们知道即将发生什么。与他们合作，帮助他们理解现在用水效率非常高。AI 超级计算机非常节能，但它们仍然会消耗大量电力。你会带来自己的发电设施。这会降低他们的房产税。有很多事情你可以做。你可以让你的数据中心更具吸引力。你把退距设得更远。所以有很多事情你可以做。你也可以为社区做出贡献，做一个好邻居，建设更好的学校和社区中心，改善公园和道路。有很多事情你可以做，但事后做这些很难。现在全国范围内有相当多的不满情绪。当然，我们所有关于世界末日的叙事也没有帮助。哪个理性的人会说来在我的城镇建设这个数据中心，顺便说一句，你生产的东西将终结我们所知的人类。所以我认为所有这些负面的末日叙事对我们的国家没有帮助，我们一开始就处于不利地位。我们一开始就处于不利地位，然后现在我们到了

We could have done a much better job communicating with the communities, preparing the communities, working with the communities to let them know what's coming. And if they don't want data centers to be built in their town or whatever it is, then so be it. But if you're going to build in their town, be sure to go there and let them know what's coming. Work with them to help them understand that the use of water is really efficient these days. The AI supercomputers are super energy efficient, but they're still going to use a lot of power. You're going to bring in your own power generation. It's going to lower their property taxes. There are a whole bunch of things that you can do. You can make your data centers more appealing. You make the setbacks further away. So there are a lot of things that you can do. You could also contribute to be a good neighbor to the community and build better schools and better community centers and improve their parks and improve the roads. There are a lot of things you could do, but it's hard to do that after the fact. And now there's a fair amount of frustration around the country. And then of course all of our narratives about the end of the world is not helping. What reasonable person says come and build this data center in my town and by the way whatever you produce is going to end humanity as we know it. So I think all of this negative doomer narrative is not helping our country and we started off on our back foot. We started off on our back foot and then now we got where

你说我们一开始就处于不利地位是什么意思？

What do you mean we started off on our back foot?

哦，因为我们一开始就没有足够的能源生产。

Oh, because we didn't have enough energy production in the first place.

那么，你如何平衡？我的意思是，气候变化正在发生，这是一个现实。

Well, how do you balance? I mean there is a reality of climate change is happening.

让我再给你说最后一点。毫无疑问，能源需求非常大，这就是为什么市场力量正在帮助我们在可持续能源方面的投资达到历史上前所未有的程度。你给我举一个可持续能源公司、材料科学公司来制造更好的电池的例子。它可以是太阳能，可以是核能，可以是裂变、聚变，你能想到的。水电，你能想到的。这些公司都在获得资金。能源的市场需求如此之大，以至于这是百年来改善电网、使电网更可持续、降低能源成本的最佳时机。同时投资于我们可持续的未来。毫无疑问，四五年后我们将使用更多的化石燃料。但在我们面前的未来十年，历史上没有哪个时刻我们比现在更有准备转向可持续能源。而且因为这些数据中心，因为这些数据中心的成本如此之高，我们现在开始讨论把它们放到太空。所以我认为我们实现梦想、迈向可持续能源世界的机会比以往任何时候都大。世界正在购买更多，因为 AI 工厂，因为 AI，今天购买的可持续能源比历史上任何时候都多。风险投资对下一代能源的投资简直令人难以置信。每个人，所有东西都在获得资金。太不可思议了。一百年来第一次你不需要政府补贴，因为市场力量就在这里。每个人都应该积极参与。如果你想要一个未来，如果你想在气候变化上转危为安，如果你想要一个可持续的未来，就拥抱 AI。这是我们实现目标的最佳机会。

Let me just give you the one last thing. There's no question that the energy demand is really great, which is the reason why the market forces are helping us invest in sustainable energy like no time in history. You give me an example of a sustainable energy company, a material sciences company to build a better battery. It could be solar, it could be nuclear, it could be fission, fusion, you name it. Hydro, you name it. Those companies are all getting funded. The market demand for energy is so incredible that this is the best time in a hundred years to improve our power grid, to make our power grid more sustainable, to lower the cost of energy. Also investing in our sustainable future. There's no question that in four or five years time we're going to use a lot more fossil fuel. But also in the next decade in front of us, no time in history are we better prepared to move to sustainable energy. And because these data centers, because the cost of these data centers is so high, now we're starting about talking about putting them out in space. And so I think the opportunity for us to see our dreams come true, move to a sustainable energy world, we have a better chance of doing that than ever. The world is buying more because of AI factories, because of AI, is buying more sustainable energy today than any time in history. Venture capital is just incredible for next generation energy. Everybody, everything's getting funded. It's incredible. You don't need government subsidies for the first time in a hundred years because the market forces are here. Everybody should be leaning in. If you want a future, if you want to turn the corner on climate change, if you want a future that's sustainable, lean into AI. It is the best opportunity we have to get there.

但我们需要更快地建设能源才能做到这一点。

But we need to build the energy faster to do that.

没错。这就是市场。你可以补贴它，你可以让建设更容易。

That's right. That's just there's a market for it all. You can subsidize it and you can make it easier to build.

是的。你知道，这有点像为了救你，他们必须先伤害你，你知道，就像手术的本质。他们必须切开你才能救你。你知道，他们必须给你造成巨大的痛苦和折磨，才能救你。所以我有点觉得 AI 有点像那样。在接下来的几年里，我们不得不遗憾地使用可再生能源，使用化石燃料，因为我们只是没有足够的可持续能源来产生影响，然后在那之后，你知道，希望我们能过渡到那一步。

Yeah. You know, it's kind of like in order to save you, they got to hurt you first, you know, in order to you know, that's the nature of surgery. They got to cut you open and save you. You know, they got to inflict an enormous amount of pain and suffering on you so that they could save you. And so I kind of think AI is kind of like that. Over the next several years, we have to unfortunately use renewable energy, use fossil fuel because we just don't have sustainable energy enough of it to make a difference and then after that you know hopefully we can transition to that.

我想我们就到这里。最后一个问题。你向观众推荐哪三本书？

I think that's where we'll end. Always final question. What are three books you recommend to the audience?

嗯，我读过很多书。对我影响巨大的一本书是 Hennessy 和 Patterson 的《计算机架构：量化方法》。这是第一本将计算机架构的复杂性、抽象概念简化为工程的书。我喜欢人们把复杂的概念简化为你可以采取行动的东西。第二，我非常喜欢《创新者的窘境》，Clayton Christensen 的书，关于行业如何随时间演变，如何看到新兴技术，如何对其设定适当的期望，以及如何推断其未来影响。我非常喜欢 Al Ries 和 Jack Trout 的《定位》一书。这是一本关于人们如何看待的精彩书籍，是一本关于营销策略的书。实际上不止于此，它是一本关于战略的书，关于人们如何看待世界，如何看待产品，如何展示产品，以及如何看待自己的战略。我认为那是一本非常深思熟虑的书，而且非常容易理解。

Well, I've read a lot of books. The book that made a huge impact on me was Computer Architecture from Hennessy and Patterson, a quantitative approach. It was the first computer architecture book that reduced the complexity, the abstract idea of computer architecture down to engineering. And I love it when people take complicated concepts and reduce it into something that you could do something about. Number two, I really loved Innovator's Dilemma, Clayton Christensen's book on how industries evolve over time and how to see emerging technology and how to set proper expectations about it and how to extrapolate maybe its future impact. I really loved Al Ries's and Jack Trout's book on positioning. It's a really wonderful book about how people see, it's a book about marketing strategy. More than that actually, it's just a book about strategy and how people see the world and how people see products and how you present products and how you see your own strategies. And I thought that was a really thoughtful book and a really easy to understand.

Jensen Huang，非常感谢。

Jensen Huang, thank you very much.

非常感谢，Ezra。我总是享受我们在一起的时光，今天很愉快。嘿，嘿，嘿。

Thank you very much, Ezra. Always I always enjoy our time together and today was a great time. Hey, hey, hey.