黄仁勋谈末日论骗局、超级智能，以及为什么赢得 AI 就赢得未来（特朗普中途来电）

有些人称之为愿景。愿景对我来说是个很大的词，因为我相信首先愿景很重要。

Some people call it vision. Vision is an awfully big word to me because I believe first of all vision matters.

我们中断了每周节目。我们只为三个人中断节目：特朗普总统、耶稣和 Jensen。

We preempted the weekly show. And there's only three people we preempt the show for: President Trump, Jesus, and Jensen.

世界排名第一的播客。

The number one podcast in the world.

那就是 Jensen 黄。他是 Nvidia 的创始人、总裁兼 CEO。

That's Jensen Huang. He's the founder, president, CEO of Nvidia.

无论你是否意识到，他的决策正在塑造你的未来。

Whether you know it or not, his decisions are shaping your future.

Nvidia 是这个市场上最重要的股票。Jensen 可以说是历史上最好的高管。

Nvidia is the most important stock in this market. Jensen is arguably the best executive in history.

营收同比增长了 97%。

Revenue exploded 97% year-over-year.

需求不仅已经强劲，实际上还在加速。Nvidia 是唯一一个全栈 AI 工厂的计算平台。GPU 就像一台时间机器，因为它让你更早看到未来。如果我们能看到未来并预测未来，那么我们就有更好的机会让那个未来成为最好的版本。

Not only is demand already strong, it's actually accelerating. Nvidia is the only computing platform that is a full stack AI factory. A GPU is like a time machine because it lets you see the future sooner. And if we could see the future and we can predict the future, then we have a better chance of making that future the best version of it.

请欢迎 Jensen 黄。

Please welcome Jensen Huang.

哦，我们进场时得到了起立鼓掌。

Oh, we got a standing O on the way in.

哦，来吧。

Oh, come on.

起立鼓掌。

Standing O.

进场时起立鼓掌。

Standing O on the way in.

我们的人来了。

There's our guy.

女士们先生们，GPU 耶稣。

Ladies and gentlemen, GPU Jesus.

他们爱你。

They love you.

谢谢。我也爱你们。世界排名第一的播客。

Thank you. I love you back. Number one podcast in the world.

在世界上。

In the world.

绝对。

Absolutely.

哇。我们喜欢新夹克。

Wow. We like the new jacket.

嗯，你知道，你拍卖了开场。

Well, you know, you auctioned the open.

我只是觉得你们需要一些能量。

I just felt you guys needed some energy.

是的。这是

Yes. This is the

我知道我们在这里谈论严肃的事情，但我们需要带着能量来谈。

I know we're talking about serious stuff here, but we need to talk about it with energy.

是的。

Yes.

让我们从这个周末的这篇文章开始。

Let's start with this essay from this weekend.

哪一篇？

Which one?

让我们从 Dario 的文章开始，因为

Let's start with Dario's essay because

海明威参与了吗？

was Hemingway involved?

实际上，有人用 Pangram 检查过吗？我甚至不知道有多少是 AI 帮助的，但那是一件相当不可思议的事情。然后我认为很多人惊讶的是前沿实验室围绕这篇文章本身的联合。Jensen，请解释发生了什么，你如何阅读它，你如何解释它，然后我们会深入一些细节。但也许只是高层次的思考来开始。

Actually, did anybody run it through Pangram? I don't even know how much of it was AI helped, but that was a pretty incredible thing. And then I think what a lot of people were surprised by was the coalescing of the frontier labs around the essay itself. Just Jensen unpack what happened, how you read it, how you interpreted it, and then we'll get into some details that were inside of it. But maybe just the high-level thoughts to kick it off.

嗯，首先，里面有很多东西。

Well, first of all, there were a lot of stuff in there.

是的。

Yeah.

首先，有一部分是关于安全的，我们必须非常认真地对待。安全是最重要的。显然，安全和领导力不是错误的。它们是错误的选择。你能够快速创新。你能够快速执行，美国能够领导并安全地做到这一点。我认为那些是错误的选择，但安全显然很重要。有一个内部控制的问题，我认为他是在谈论这个。显然，Coxin 举报者是非常严重的事情。每当你有举报者，你必须非常认真地对待。我认为 Coxin 有极大的勇气提出他的担忧。即使如此，还有一些问题被混为一谈。我认为举报是好的。我认为关于未来的科学预测不太一致，因为它显然不是基于科学的，虽然是由一位科学家表达的，但显然不是基于科学的，所以我对此有异议，但显然举报的部分，你知道，我认为有一大堆东西，暂停、节奏，这些都是他们可以自愿做的事情，如果他们觉得他们的公司失控了。如果 Coxin 看到了什么，你知道，显然我们不知道 Coxin 看到了什么，

And first there is a part about safety which we have to take very seriously. Safety is paramount. Obviously safety and leadership are not false. They're false choices. You're able to innovate quickly. You're able to execute quickly and America's able to lead and to do it safely. I think those are false choices but safety is obviously important. There's a matter of internal control that I think he was speaking to. Obviously the Coxin whistleblower is very serious matter. Whenever you have a whistleblower, you got to take it very seriously. I thought Coxin had great courage to put out what his concerns were. And even then there were some issues that were kind of conflated within that. I think the whistleblowing is fine. I think the scientific prediction about the future is less aligned because it's not grounded on science obviously and it was expressed by a scientist but it was obviously not grounded on science and so I take issue with that but obviously the whistleblower part of it, you know, I think there's just a whole bunch of stuff pausing, pacing those are all the voluntary things that they could do if they feel that their company is out of control. If Coxin saw something, you know, obviously we don't know what Coxin saw,

但如果他看到公司失控了

but he if he saw that the company was out of control

也许这是从研究到工程的过渡。如你所知，这些实验室正在从研究过渡到工程。非凡的人才，非凡的工程。但显然工程不同于研究。也许那个过渡是笨拙的。你知道，我们不知道他看到了什么，最终只有他知道。但如果存在缺乏控制的问题，那是另一个话题。政府应该如何应对？现在突然之间，监管和监管，我的意思是它在一篇博客中涵盖了一切。

and maybe it's a transition from research to engineering. As you know, these labs are transitioning from research to engineering. Extraordinary talent, extraordinary engineering. But obviously engineering is different than research. Maybe that transition is clumsy. You know, we don't know what he saw and ultimately only he knows. But if there was a matter of lack of control, that's a different topic. How should the government deal with it? Now all of a sudden, regulation and reg I mean it just covers everything in one blog.

你能帮我们解释一下吗？我们本周在播客上试图玩这个游戏，但很难，就是你怎么描述，你知道我妈妈打电话给我，她说 Jimoth，这整个文明死亡的事情是什么？我不知道怎么向她解释。所以当你有非常聪明的人像那样量化它，我认为这可能是一些人感到不安的原因。他们说，“那是什么意思，10% 的灭绝？”没有人知道如何向普通人解释那怎么可能。

Can you just help us sort of unpack? We tried to play this game actually this week on the pod and it was difficult which is how do you describe like you know my mom calls me and she's like Jimoth what is this whole civilizational death thing? I don't know how to explain it to her. So when you have very smart people like that quantize it and quantify it, I think that's probably what's perturbing to some people. They're like, "What does that mean, 10% of extinction?" Nobody knows how to explain that to the average person how that's even possible.

嗯，首先，我们不应该，因为它是编造的。首先我认为我们不应该，因为它是编造的，这些是受过良好教育的人，他们被称为研究人员，显然他们在实验室工作，所以这些词的汇合，然后预测是令人震惊和不安的，不应该这样做。这是不负责任的。现在事实是，让我们回去看看真正的事实。事实是，有一个预测说在 5 年内，放射学将完全被人工智能接管，世界上将没有放射科医生。事实证明恰恰相反。我们需要比以往更多的放射科医生。然而，AI 已经完全接管了放射学，这很好，是自动扫描阅读，这很好。有一个预测说在 6 到 12 个月内，不是去年吗？在 6 到 12 个月内，90% 的代码将已经由 AI 生成。结果证明是错误的。在 6 到 9 个月内，那是去年预测的，50% 的入门工作将被消灭。结果证明是错误的。让我们看看还有什么。还有什么被证明是错误的？我的意思是，所有这些预测都是错误的，

Well, first of all, we shouldn't because it's made up. First of all I think that we shouldn't because it's made up and these are well educated they're called researchers obviously they're working in a lab and so the confluence of these words and then the prediction is alarming and troubling and it shouldn't be done. It's irresponsible. Now the fact of the matter is let's go back and look at the real facts. The facts are there was a prediction that in 5 years time radiology will be completely taken over by artificial intelligence and there'll be no radiologists in the world. That has proven to be exactly the opposite. We need more radiologists than ever in the world. However, AI has taken over radiology completely which is great is automated scan reading which is great. There was a prediction that within 6 to 12 months, wasn't it just last year? Within 6 to 12 months, 90% of code would already be generated by AI. That has turned out to be wrong. Within 6 to 9 months, that was predicted last year, 50% of entry jobs will be wiped out. That has proven to be wrong. Let's see what else. What else has proven to be wrong? I mean, all of these predictions have been wrong,

对吧？

right?

嗯，GPT2 太不安全不能发布。Llama 3 太不安全不能发布。

Well, that GPT2 would be too unsafe to release. That llama 3 would be too unsafe to release.

哦，一个。

Oh, one.

是的，我们听说过

Yeah, we've heard the

一半的白领工作明年将消失。

half of white collar jobs would be gone next year.

工作末日。是的。

The jobs apocalypse. Yeah.

我们必须承担责任。我们必须对所有做出的愚蠢预测负责，

We have to take accountability. We have to take account for all of the stupid predictions that were made,

对吧？

right?

有人必须，有人必须承担，是的。

Somebody has somebody has to take Yeah.

所以我们应该记录所有这些。当然人们会这样做，并提醒我们那些预测与最终美国赢得 AI 竞赛不一致。

And so we ought to just keep track of all that. And of course people do and remind us that those predictions are inconsistent with ultimately America winning the AI race.

简而言之，有些人说，你知道，他们说“相信专家”，他们用新冠疫情作类比，而新冠疫情最初也是从研究人员、受过教育的人开始的，他们对事情的了解不对称，而我们其他人并不了解，他们说的话最终被事实揭穿，我们发现并不是真的。嗯，所以现在正在发生一场战争，一边是“相信专家”运动，另一边是，你知道，好吧，让我们看看这些预测的实际历史，让我们更系统地思考。这是从哪里来的？因为它来自实际制造它的那些地方内部。比如你觉得心理构成是什么，或者真正的动机是什么？也许是商业动机，也许是政治动机。你能猜一猜吗，或者你如何看待他们为什么这样做？

The short form for that is some people are saying, you know, they say trust the experts, and they used the analog of COVID, which again started with people that were researchers, educated people that had an asymmetric awareness of the thing that the rest of us did not, saying things that ultimately turned out, we find out in facts, uh, not to be true. Um, and so there's this war that's happening right now between the trust the experts movement and the, you know, well, let's just look at the actual history of these predictions and let's just think more methodically. Where is this coming from? Because it's coming from inside the places that's actually making it. Like what do you think is the psychological makeup or what is the real incentive? Maybe it's a business incentive, maybe it's a political incentive. Can you just maybe guess or how do you how do you think about what's why they're doing this?

嗯，首先，我得告诉你，这些是历史上一些最重要的公司。呃，呃，非凡的工程师，非凡的研究人员，呃，真正出色的工作。嗯，呃，一方面，呃，我作为公司对公司与他们密切合作。呃，另一方面，呃，我们不得不在公开场合进行这样的对话。这真的很不幸。而且我我我认为这些这些公司嗯真的应该像我们过去建立公司那样建立，那就是在沉默中，

Well, first of all, I got to tell you these are some of the most consequential companies in history. Uh, uh, extraordinary engineers, extraordinary researchers, uh, really fantastic work. Um, uh, on the one hand, uh, I work very closely with them as companies to companies. Uh, on the other hand, uh, we have to have conversations like this in public. And it's really unfortunate. And I I think that that these these companies um really ought to be built the way that we used to build companies, which is in silence,

对吧？你知道，所以等等，等等，Jensen，你不允许组织中的任何人代表整个组织发言，尤其是当他们像度过糟糕的周末或愤怒辞职时。他们他们不允许代表你和组织发推文。

right? You know, and so wait, wait, Jensen, you don't allow anybody in your organization to speak for the entire organization, especially when they're having like a bad weekend or they rage quit. They're they're not allowed to tweet on your behalf and the organization's behalf.

不，因为嗯，那是他们来为我们工作时决定的，我们告诉他们，呃，这些是你在我们公司工作时应该表现的方式，呃，如果你愿意，如果你喜欢我们公司的文化，嗯，呃，如你所知，NVIDIA 的文化和 NVIDIA 的员工队伍，呃，非常快乐。是的。呃，他们喜欢公司是一致的，我们是稳定的，我们的核心价值观与照顾家庭和创造他们能够做他们一生工作的条件是一致的。呃，我们做有意义的工作，我们尽可能安静地做，呃，我们为每个人的成功做出贡献，我们为此感到非常自豪。所以那种核心价值观吸引人们。嗯，但是当你来我们公司工作时，也有一些我们不欣赏你做的事情。例如，我们不欢迎呃，在我们公司内部进行政治讨论。带回家去。你们在公司外谈论政治。嗯，我们

No, because well that's that's what they decided when they came to work for us and we told them uh these are this is the way you behave when you work in our company and and uh if you would like if you like the culture of our company um uh which as you know the NVIDIA culture and the NVIDIA employee base uh incredibly happy. Yeah. uh they like the fact that the company is consistent, that we're stable, that our core values are consistent with taking care of the families and creating the conditions by which they can do their life's work. Uh that we do meaningful work, we do it we do it as quietly as we can and uh we contribute to everybody else's success, which we're very proud of. And so those kind of core values people are attracted to. Um but when you come and work in our company, there are also some things that we don't appreciate that you do. Like for example, we don't welcome uh political discourse in our inside our company. Take it home. You guys talk about politics outside the company. Um we

是的。

Yeah.

呃，我们我们是嗯，公司是一个非政治性公司。你知道，我们是两党合作的。我们希望美国成功，嗯，呃，我们希望，我们希望，呃，无论哪个政府执政，呃，我们都会尽我们所能帮助美国成功。所以所以关于种族、宗教、政治以及所有这些东西的讨论，我们告诉人们在公司外进行。这不是不是为我们。

Uh we we are um the company is an a-political company. You know, we're bipartisan. We want America to succeed and and um uh we want we want uh whatever uh government is in place uh we'll do everything in our power to help America succeed. And so so the the discourse about about uh about race and religion and politics and all of that stuff we tell people do it outside the company. It's not not for us.

就嗯，也许更狭义地谈 AI 监管。嗯，Satya 今天早上在这里，他说的是，你知道，在我们谈论可能真正阻碍事情的监管之前，为什么我们不先把一些基本的事情做好？为什么我们不把测量做好？为什么我们不把标准化做好？对吧。嗯，你对此怎么看

In terms of u maybe AI regulation then more narrowly. Um Satya was here this morning and what he said is you know before we talk about regulation that could really styy things why don't we just get some basics right? Why don't we get measurement right? Why don't we get standardization right? Right. Um where do you land on

把工程做好？

get engineering right?

把工程做好。对。以更可预测的方式转化研究，这样我们就不会散布恐惧。在准备好公开之前保持内部。嗯，你认为正确的回应是什么？你知道 Demis 有一个提议，有点像更类似 FINRA 的组织。不清楚 Daria 想要什么。这个跨国变异的东西有某种控制。你对此怎么看？那种我们需要什么现在的视角？

Get the engineering right. Right. Translate the research in a more predictable way so that we're not fear-mongering. Keep it inside until we're ready to expose it. Um what do you think the right response is? You know Demis had a proposal which was sort of this more FINRA like organization. It's not clear what Daria wants. This transnational mutated thing that has some sort of control. Where do you land on this? The sort of perspective of what what do we need right now?

你知道，监管应该解决实际问题。所以问题是我们享受了哪些实际问题，

You know, regulation should solve actual problems. And so the question is what actual problems have we enjoyed,

对吧？

right?

而且嗯，如果你看看实际问题，嗯，到目前为止所有实际问题都来自实验室。原因，原因，而且为他们辩护，原因是他们拥有最多的算力，

And and um if you look at look at the actual problems um all of the actual problems so far have come from the labs. And the reason for that, the reason for that and and just in their defense, the reason for that is because they have the most compute,

对吧？

right?

原因是因为他们试图解决呃前沿问题。所以为他们辩护，所以合理的是，嗯，实验室，前沿实验室将是最大危险来源。不太可能是一个高中生呃做了某事，因为他们根本没有足够的算力，

And the reason for that is because they're trying to solve uh the frontier problems. And so in their defense and so it's sensible that that um the labs, the frontier labs will be where the most danger come from. It is unlikely that a high school student uh did something because they just simply won't have enough compute,

对吧？

right?

所以呃，不太可能是一个初创公司成为原因，因为他们没有足够的算力。这是这是呃，他们你知道，事实上你可以看看整个星球，除了前沿实验室，每个人都没有足够的算力。所以现在问题是，如果你看看实际发生了什么，嗯，他们在做开创性工作。这真的非常难。嗯，他们正在从研究过渡到工程。嗯，我可以想象，他们他们显然在构建世界上一些最重要的技术和公司。呃，他们在构建公司，构建文化，构建技术，构建工程，构建产品，同时进行。所以我我能理解这有点火烧眉毛。嗯，呃，但尽管如此，一个实验室的四起事件，另一个实验室的一起巨大事件，嗯，你首先要做的就是从工程角度根本原因分析问题。发生了什么，

And so uh it's unlikely that a startup will be the reason because they won't have enough compute. It's it's uh they you know in fact you could look across the planet and everybody won't have enough compute with the exception of the frontier labs. And so so now the question is if you look at what actually happened um and they're doing pioneering work. It's really very hard. Um they're transitioning from research to engineering. Um, I could imagine and they're they're they're they're obviously building some of the most consequential technology and companies in the world. Uh, they're building their company, they're building their culture, they're building the technology, they're building engineering, they're building products all at the same time. And so I I can understand it's a little bit hair on fire. Um, uh, but nonetheless, the four incidents from one lab, the one giant incident from the other lab, um, the first thing that you have to do is just root cause the problem from an engineering perspective. what happened,

我们本可以做什么不同，我们将要实施和制度化什么，无论是技术、方法还是流程，并确保我们不让它再次发生。现在，我敢跟你打赌，在每一个案例中，未来都在他们的控制范围内防止它，因为另一种选择，如果不在他们的控制范围内，我确信他们，我确信我我确信我确信那四起事件不会再次发生。嗯，他们我确信他们根本原因分析并修复了。我确信呃，他们现在有技术，你知道，沙箱和运行时和监视器以及连续监视器，你知道，所以我我确信他们现在有更好得多的技术，另一种选择也不太可能，那就是他们说，看，我们发生了这些事件，分析完后，我们得出结论，我们不知道发生了什么，我们不知道如何控制它，我们请求社会帮助。

what could we have done differently and what are we going to in to implement and institutionalize whether it's technology or methods or processes and make sure that we don't let it happen again. Now, I would bet you money that in every single one of those cases is within their control in the future to prevent it because the alternative if it's not in their control and I'm sure that they are I'm sure I'm I'm sure that I'm sure those four four incidents won't happen again. Um they I'm sure they root caused it and fixed it. I'm sure uh they have now technology for you know sandboxes and run times and monitors and continuous in continuous monitors and you know and so I'm I'm certain they have much much better technology now the alternative is also unlikely which is for them to say look we had these incidents after we're done analyzing it we came to the conclusion we don't know anything that happened and we have no idea how to control it and we're asking society for help.

是的。

Yeah.

现在，如果是那样，那么我们应该，你知道，一堆一堆有工程师的公司应该派工程师进去。我的意思是，我们应该尽可能建议他们，但我怀疑。

Now, if that's the case, then we ought to, you know, a bunch of bunch of companies with engineers ought to send engineers in. I mean, and we should advise them if we can, but I doubt it.

我认为他们有非凡的人才。他们能处理好这件事。但我们并不是在真空中运作。David，昨晚你告诉我，有一家中国实验室，就是 GLM 的开发者，将投入 30 亿用于递归自我改进的尝试。所以，也许你可以为 J 铺垫一下。

I think they have extraordinary people. They got this handled. But we're not operating in a vacuum. David, last night you informed me that there is a Chinese lab, the makers of GLM, who are going to put three billion towards a recursive self-improvement run. So, maybe you could tee that up for J.

嗯，这就是宣布的内容。是的。zpoo.com 的创始人刚刚筹集了 50 亿，并表示他们的优先事项之一将是尝试实现递归，你知道，就是让 AI 训练下一个 AI，并尽可能多地自动化这个过程。嗯，是的，我认为，我的意思是

Well, that's what was announced. Yeah. zpoo.com the founder just raised 5 billion and said that one of their priorities is going to be trying to get to recursive, you know, AI that trains the next AI and to try and automate as much of that as possible. Um yeah I think that I mean

嗯，这是个新的时髦词，但正如你们所知，RSI 是一套系统性的理念组合。它从上下文相关的东西开始，从技能开始，从反思开始，从强化学习和合成数据生成开始。这些都是非常合理的想法，能让 AI 随着时间的推移更擅长解决问题。你还可以有低秩适应，你知道，所有这些都不涉及权重。你实际上可以改进权重，这叫做 LoRA。LoRA 可以通过合成数据生成和强化学习来改进，增强它，而无需训练基础模型本身，然后随着时间的推移，你可以用所有这些经验再次训练基础模型。所以我认为，利用技术来提高各种任务的生产力，包括构建 AI，是一件合理的事情。我认为这是一个非常合乎逻辑的想法，我确信每个人都在某种程度上使用它。只是这个短语现在被用来以某种方式将技术武器化，也许是为了让

well this is the new sexy phrase but as you guys know RSI is a combination of a system of ideas. It's um it starts everything with in context stuff. It starts with skills. It starts with reflection. It starts with, you know, reinforcement learning and synthetic data generation. And these are all very sensible ideas that causes AI to get better at solving a problem, you know, over time. And you could also have uh low rank, you know, all of that stuff doesn't include the weights. Uh you could actually improve the weights and it's called Laura. uh Laura could be could be improved in synthet synthetic data generation reinforcement learning enhance it without training the the base model itself and then over time uh you could train the base model again with all of that experience and and so I I think I think it's a sensible thing that that you're going to use the technology uh to enhance productivity of all kinds of tasks including building AI. I think that's a very logical idea and and I'm I'm certain that everybody is using it in some degree. It's just this phrase is now being used um to weaponize the technology in some way and maybe to turn the

给人一种它将要失控的印象。但你不相信那是真的？

as if it's going to spiral out of control is the impression they're trying to give. But you don't believe that's real?

不，不，当然不是。原因在于，你可以在公司内部整天进行 RSI，但当你发布产品时，你必须评估它，不是吗？你必须再次测试它，不是吗？你必须确保没有性能退化，对吧？所以基本的控制过程。这些实验室，随着它们从实验室走向工程化，将会有更好得多的控制，

No. No, of course not. And the reason for that is because you could RSI all day long inside your company, but when you release a product, you've got to evaluate it, don't you? You have to test it again, don't you? You have to make sure that there's no regression, right? And so the basic process of control. These labs are going to as they move from labs to engineering, they will have much much better control,

对吧？当他们有更好的控制时，控制来自于方法、知识、实践、工具和技术，所有这些都能带来更好的控制、验证和评估

right? And when they have much better control that and control comes from methods and knowledge and practice and tools and technology all of those things that leads to better control verification and evals

这将使 RSI 能够在公司内部完成，并让好的产品对外发布。

it's going to enable RSI to be done inside the company and for good products to be released outside.

让我们谈谈开源。我的意思是，这个 Hugging Face，我们之前交流过，我说这将是影响最重大的收购之一。我甚至不想称之为交易，因为我认为它比交易更重要。嗯，请给我们讲讲你对开源、闭源和开放权重的第一性原理的解释，以及生态系统应如何随着时间的推移而融合。

Let's talk about uh open source for a second. I mean this hugging face we we were communicating about this and I said it's going to be one of the most consequential um acquisitions. I don't even want to call it a transaction because I think it's more important than that. Um, give us your first principles explanation of open source versus closed source versus open weights and how the ecosystem should fit together over time.

世界需要闭源模型和开源模型。嗯，你想用，我尽可能多地使用闭源模型。这个周末我用了四个，它们工作得非常出色。它们是前沿的。体验很棒。它们工作得难以置信地好。它们一直在变得更好。嗯，我对闭源模型的看法有点像瓶装水。你们知道，水是免费的，伙计们。我不知道我是否告诉过你们，但水是免费的。我不想打破大家的幻想，但水是免费的。今天早上，我洗澡用了很多免费的水。所以，在合适的地方使用合适的水。这和电没什么不同。这和我们在世界上使用的各种商品没什么不同。两者都需要。现在，在开源的情况下，你需要它的原因可能是出于主权原因、隐私原因、专有技术原因。看看事实。事实是，在过去 6 个月里，有 4000 亿美元的风险投资流向了 AI 原生公司。其中 80% 使用开源模型。如果没有开源模型，他们怎么能实现梦想，

The world needs both closed models and open models. Um, you want you want to use I use as much closed models as I can. This weekend I I used four of them and and uh they work terrifically. They're frontier. They're great experience. They right they work incredibly well. They're getting better all the time. Uh, and and the way I think about closed closed closed models is kind of like bottled water. You know, water is free, you guys. I don't know if I've told you guys, but water is free. I I don't want to, you know, burst everybody's bubble, but water's free. And this morning, I used a lot of free water taking a shower. And so, you use the right water in the right places. And this is no different than electricity. This is, you know, this is no different than all kinds of commodities that we use in the world. You need both. Now in the case of open the reason why that you need it is because it could be for sovereignty reasons, privacy reasons, um proprietary technology reasons. Look at the facts. The facts are in the last 6 months $400 billion of venture funding went into AI native companies. 80% of them use open models. If not for open models, how could they build their dream,

对吧？

right?

因为他们的梦想可能不同。显然，它会不同于实验室，前沿实验室的梦想。美国有如此多不同的创新方式。这是我们核心优势之一。伟大的想法就像从喷泉中涌出。所以开源模型使这成为可能。开源模型使每一个，如果我们想赢得 AI 竞赛。这不是关于少数科技公司赢得 AI 竞赛。这是关于美国的每一家公司。每一家公司，每一个行业，每一位研究人员，每一位教师，每一位学生，每一家初创公司，每个人都赢。有些人会使用闭源模型。很多人会使用开源模型。有 1000 万

Because their dream could be different. Obviously, it'll be different than the labs, the frontier labs dreams. And there's America has so many different ways to innovate. That's one of our core strengths. Great ideas just coming out of the fountain. And and so open models enables that. Open models enables every single if we want to win the AI race. It's not about a few technology companies winning the AI race. It's about every company in America. Every comp, every company, every industry, every researcher, every teacher, every student, every startup, everybody wins. Some of them will use closed models. A lot of them will use open models. There's 10 million

这重要吗？

Does it matter?

嗯，让我问问，如果开源模型来自中国或美国，这重要吗？

Well, let me just ask, does it matter if the model the open models come from China or the US?

嗯，我们正在尽一切努力为开源模型做出贡献。然而，当你下载的那一刻，比如，今天世界上对开源的大部分贡献可能来自中国。他们只是有更多的工程师。他们大规模生产一切，因为这是一个更大的国家。所以他们大量培养科学和数学学生，

Well, we're doing everything we can um to make a contribution in open models. However, the moment you download, like for example, probably the vast majority of the world's contribution to open source today is coming from China. They just have a lot more engineers. They produce everything in large scale because it's a larger country. And so they produce science and math students in volume,

对吧？

right?

这是我们的劣势之一，对吧？他们通过像清华大学这样的优秀大学大量培养。嗯，他们今天为开源做出贡献。我们下载 Linux。我们下载 Kubernetes。我们下载所有软件。很多都经过中国人之手。一旦你下载了它，它就是你的。我们分叉它。我们改进它。我们把它变成我们的。所以，当你下载这些中国模型之一时，它恰好是由中国一些非常出色的研究人员制作的，但现在它是你的了。你想用它做什么就做什么。

That's one of our disadvantages, right? They're manufacturing them through amazing universities like Chinua University in high volume. Well, they contribute to open source today. We download Linux. We download Kubernetes. We download all the software. A lot of it has been touched by Chinese. And once you download it, it's yours. We fork it. We improve it. We make it ours. And so we when you download one of these Chinese models, it just happens to be made by some really great researchers in China, but it's now yours. Whatever you want to do with it.

那么，这场竞赛到底是什么？这场竞赛。

So what exactly is the race? the race.

是的，我认为这是一个非常好的观点。我的观点是，这场竞赛真正关乎谁最能利用技术。你知道，上一次工业革命，所有的发明家都是麦克斯韦、伏特、安培。他们都不是美国人。没错。上一次工业革命来自欧洲。但我们利用了它。我们在社会上比世界上任何人都更好地利用了它。看看结果对我们如何。我想确保下一代就像这样发生。

Yeah, I think that's that's a really good point. My point is the race is really about who exploits the technology best. You know, the last industrial revolution, all of the inventors were Maxwell, Volulta, Ampier. None of them were American. They were right. The last industrial revolution came from Europe. But we exploited it. We took advantage of it socially better than anybody else in the world. Look how it turned out for us. I want to make sure that this next generation happens just like this.

是的。是的。

Yeah. Yeah.

那么，为什么共产主义者现在在这里如此成功地传播他们的信息？

So why why are the communists getting their message out so successfully here right now?

你知道，我认为首先，叙事更加务实。叙事更加务实。

You know I I think first of all the narrative is much more practical. The narrative is much more practical.

中国没有人说这是这个的终结、那个的终结，灾难性的这个、末日那个。他们对此务实得多。他们把 AI 视为一种能推动经济、推动社会进步的技术，他们没有那些基本上在说它会终结文明的团体。

Nobody in China is saying that there's an end to this and an end to that, and cataclysmic this and doom or that. They're much more pragmatic about it. They see AI as a technology that's going to advance their economy, advance their society, and they don't have these groups who are basically saying it's going to end civilization.

而我们是在编造。

And we're making it up.

令人沮丧的部分是，如果这是真的，如果这是真的，那我们就该讨论它，并去做点什么，对吧？即使这是真的，我们也该花更多时间去做点什么，而不是去吓唬一群对此无能为力的人。把它造出来是我们的工作，对吧？

The part that is frustrating is if it was true, if it was true, then we ought to talk about it and go do something about it, right? Even if it's true, we ought to spend more time doing something about it than worrying a bunch of people who can't do anything about it. It's our job to build it, right?

历史上有没有过这样的时刻：这么多人如此激烈地说着如此不真实的事情？

Has there ever been a point in history where so many people have so vehemently said something that is so untrue?

而且它们可衡量地、实际上可证明地是不真实的，而且它们不真实这件事本身也说得通。它不是基于科学，不是基于研究。所有基于科学和研究的东西都证明恰恰相反。这是对前沿的恐惧吗？人类从未到过那里。我们从未见过它。因此我们害怕它，因此很容易让所有人都害怕它。

And they're measurably, they're actually demonstrably untrue, and it actually makes sense as untrue. It's not based on science. It's not based on research. Everything that's based on science and research proves otherwise. Is it a fear of the frontier? Humans have never been there. We've never seen it. Therefore, we're scared of it, and therefore it's easy to tell everyone to be scared of it.

这也可能是人生经历，David。让我举个例子。我刚毕业时是一名工程师，我打字并不多。原因是我属于软件普及之前的第一代。我们得去造计算机，才能让软件成为可能。你能想象在这一代，每一个进入工程世界的工程师都把全部时间花在打字上吗？真的，那就是你做的事。你找到一份工作，他们给你一台笔记本电脑，给你一把椅子，你就开始打字。你整天都在打字。你从醒来的那一刻一直打到……

It could be life experience as well, David. So let me give you an example. When I first graduated from school, I was an engineer and I didn't do that much typing. And the reason for that is because I was the first generation before software became popular. We had to go build the computers to make software possible. Could you imagine in this generation every single engineer who came into the world of engineering spends all your time typing? Literally, that's what you do. When you get a job, they give you a laptop, they give you a chair, and you start typing. You type all day long. You type from the moment you wake up to the m—

嗯，在打字之前就有工程了。

Well, there was engineering before typing.

对。

Right.

所以你能想象吗，世界上有堆积如山的工程工作要做，而其中大部分不再是打字了？当然，在打字之前我们也有忙碌的工程师。我认为在打字之后，我们会做很多伟大的工程。

And so can you imagine that the world has a mountain of engineering work to do where most of it is not typing anymore? Sure, we had busy engineers before typing. I think we're going to do a lot of great engineering after typing.

是的。

Yeah.

我说打字，指的是写代码。所以即使在 NVIDIA，当软件工程师跟我说话时，我告诉他们，你们只是在打字。这话我一直在说，但显然是为了好玩。我告诉他们，我最喜欢的键是退格键。原因是，最好的软件是最小的软件。所以我希望你们用退格键写软件。

When I say typing, I mean coding. And so even at NVIDIA when software engineers talk to me, I tell them, you're just typing. I've been saying that forever, but obviously for fun. And I tell them, my favorite key is backspace. And the reason for that is because the best software is the smallest software. So I want you to use backspace software.

我们来谈谈 NVIDIA 吧。我们来稍微拆解一下 NVIDIA。拆解的意思是解释各个部分，因为其中有很多战略在起作用。我们从最底层开始。那么——

Let's actually talk about Nvidia. Let's do a little tear down of Nvidia. So tear down meaning just explain the pieces because there's a lot of strategy at play. Let's start at the absolute bottom. So—

哦不。

Oh no.

这不是计划好的，但我们知道是谁。

This is not planned, but we know who it is.

哦不。不。总统先生。哦，是的，先生。我得告诉你一件事。要不是你打电话来，我——我正在台上和好兄弟们在一起。我正在台上和好兄弟们在一起。我正在台上和好兄弟们在一起。我正在台上和 Sachs 在一起。

Oh no. No. Mr. President. Oh, yes, sir. I gotta tell you something. If it wasn't because of you calling, I would— I'm on stage with the besties. I'm on stage with the besties. I'm on stage with the besties. I'm on stage with Sachs.

是的。

Yeah.

你知道，整个团队。是的。Jason 在这儿。Chamath 在这儿。David 和 David 也在这儿。是的。我坐在几千人面前，我们正在聊，结果我们正在聊你。干得好，先生。干得好。你看穿了这一切，我的意思是，其中有很多复杂性，而事实是你看穿了这一切，我——你知道，我们都非常感激。

You know, the whole group. Yeah. Jason's here. Chamath's here. David and David is here. Yeah. I'm sitting in front of a few thousand people and we're talking as it turned out we were talking about you. Good job, sir. Good job. The fact that you saw through all of that, I mean, there's a lot of complexity and the fact of the matter is you saw through all of that and I— you know, we're all just really grateful.

替我向他们问好。

Tell them I said hi.

你想向人群问好吗？Jason 想——Jason 想把你放到——

Do you want to say hi to the crowd? Jason would like— Jason would like to put you on the—

甚至 Jason 的扬声器模式。

Even Jason speaker mode.

我们怎么——我们怎么打开扬声器？

How do we put— How do we put on speaker?

把他放到扬声器上。

Put him on speaker.

扬声器。是的。

Speaker. Yeah.

直接对着麦克风。

Right into the microphone.

这里我们要拿个麦克风。

Here we're going to get a mic.

等一下。

Hang on a second.

稍等，先生。我们正在拿麦克风。

Hold on, sir. We're getting a microphone.

总统先生，

Mr. President,

你现在正在对全世界讲话。

you're now talking to the planet.

你看，生活美妙的地方在于，Jensen 能开发出世界上最复杂的计算机芯片，十年内没人能复制。但他却搞不定怎么把我放到扬声器上。我们得记住这件事。AI 真有意思。这几乎就像阴谋，而最开心的群体是中国，中国非常开心。我甚至可以说，在这个国家里，很多州本来什么都得不到，现在却很开心，因为他们正被想来的人淹没。但现在突然之间，你看他们在芬兰建。他们想建一个。谷歌想在芬兰建一个大的，我对此不高兴，因为他们拿不到许可。我告诉你，这全是骗局。数据中心很棒，它们让人富有，让州富有，它们是未来 20、25 年的石油。它比互联网更大，而 AI，你知道，更是如此。而他们正中了那些不想看到这件事发生的人的下怀。那可能是政界人士，也可能是中国。我们不会让这种事发生。这是骗局。而且——

You see, the great thing about life is that Jensen can develop the most complex computer chip in the world that nobody can copy for 10 years. But he can't figure out how to put me on speaker thing. We have to remember this one. So interesting the AI. It's almost as conspiracy and the happiest group is China and China is very happy. And I could even say in the country a lot of states are happy that weren't going to get anything because they're being inundated by people that want to be there. But now all of a sudden you see they're building in Finland. They want to build one. Google wants to build a big one in Finland, which I'm not happy about because they were unable to get permitting. And I'm telling you, it's all a hoax. The data centers are great and they make people wealthy and they make states wealthy and it's the oil of the next 20, 25 years. It's bigger than the internet and the AI, you know, much more so. 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. It could also be China. And we're not going to let that happen. It's a hoax. And—

你说得对。我们不会让这种事发生，先生。

You're right. We're not going to let that happen, sir.

不，我们不会让它发生。机器人不会接管世界。那不会发生。你知道，我叔叔是麻省理工学院的顶级教授——坦白说，也许是有史以来最好的——在那里待了 41、42 年，被认为是最聪明的人之一，他在那里 41 年，是顶级的，他就像在梯子的顶端，顶端——做了很多事情，Jensen 全都知道，但做了很多事情。所以我有一些基因上的——如果你相信资源理论的话，有一些基因上的优势，但我相信，我有基因上的——

No, we're not going to let it happen. The robots are not going to be taking over the world. And that's not going to happen. You know, my uncle was a top— probably maybe the best of all time, frankly— professor at MIT for 41, 42 years and known as being one of the most brilliant men and he was there for 41 years as the top, he was like at the top of the ladder, top of— did many things, Jensen knows all about it, but did many things. So I have a little genetic— a little genetic strength if you believe in the resource theory, but I do, I have genetic—

这就解释了为什么你对 AI 懂得这么多。

That explains why you know so much about AI.

是的。嗯，我懂 AI。我懂——我对 AI 也有常识。机器人不会接管。AI 不会接管世界其他地方。整件事都是骗局。话虽如此，我们得小心一点。我们得，你知道，我们得做事，而且得谨慎地做。但这不意味着我们要停止产业，因为，你知道，我们还要在未来 10 年研究如何摧毁它。所以，我完全支持你。我甚至不知道你对此怎么看。我以为你和我感觉一样。

Yeah. Well, I know about AI. I know— I also have common sense about AI. The robots will not be taking over. The AI will not be taking over the rest of the world. The whole thing is a hoax. Now, with that, we have to be a little bit careful. We have to be, you know, we have to do things and we have to do them prudently. But that doesn't mean we're going to stop industry because, you know, as we work on the next 10 years about how to destroy it. So, I'm with you all the way. I didn't even know how you felt about it. And I assumed you felt the same way as me.

是的，先生。

Yes, sir.

而我们——如果我们要领导，我有一句话：谁赢得 AI，谁就赢得一切。它就有这么大。它比互联网更大。谁赢得 AI，谁就赢得一切。我们不能让这种事发生。这非常包括数据中心。有些社区本来正在消亡，现在有了数据中心。现在它们是富裕的社区。非常富裕的社区。

And we— if we're going to lead and I have an expression, it's whoever wins AI wins. That's how big it is. It's bigger than the internet. And whoever wins AI wins. And we can't let this kind of stuff happen. And that includes very much includes data centers. There are communities that were dying that have data centers right now. And now they're wealthy communities. Really wealthy communities.

我们要确保在美国的 AI 竞赛中，每个人都是赢家。每个行业、每家公司、每个州、每个人。

We're going to make sure that everybody wins in the AI race in America. Every industry, every company, every state, every people.

好。我对这件事感受很强烈，而且我有能力做点什么。我们不会让那种事情发生。所以，我不知道谁在开会。我根本不知道我在跟谁说话，但我会看看。

Good. Well, I feel strongly about it and I have the position that can do something about it. We're not going to let that stuff happen. So, I have no idea who's at the meeting. I have no idea who the hell I'm talking to, but I'll see.

你听到了吗？你听到了吗？成千上万的人在为你鼓掌，先生。

Did you hear that? Did you hear that? Thousands of people are clapping for you, sir.

我只知道你们是来听 Jensen 的，但他做得非常出色，David 也做得非常出色，祝大家好运，我们要面向未来。这个国家从未像现在这样好。我们有 20 万亿美元的投资流入这个国家，而对比之下，睡不醒的乔·拜登执政四年还不到 1 万亿美元。这是一年之内的事。所以，你知道，这个国家从未见过这样的景象，我们会继续保持下去。非常感谢大家。

All I know if you're there to listen to Jensen, but uh he's done an amazing job and David has done an amazing job and good luck to everybody and uh we're going to stay with the future. The country has never done better. We have 20 trillion dollars of investment coming into the country and that's as opposed to much less than 1 trillion under sleepy Joe Biden and that was for four years. This is in one year. So, you know, it's it's really the country is there's ne the country has never seen anything like it and we're going to keep it going. And so, thank you all very much.

谢谢您，总统先生。

Thank you, Mr. President.

总统先生，谢谢。我稍后给您回电话。谢谢您，总统先生。谢谢。

Mr. President, thank you. I'll call you back later. Thank you, Mr. President. Thank you.

呃，我当时很独特。我以为是在开玩笑。你知道那会发生吗？

Um I was unique. I thought it was a bit. Did you know that was happening?

我以为是在开玩笑。是的，那是——不，是真的。一开始我以为是在开玩笑，当时我说，把他放到免提上。

I thought it was a bit. Yeah, that was it was No, it was real. I thought it was a bit at first when I was like, put him on speakerphone.

哇。他打电话给你。你觉得他怎么会半夜任何时候都打电话给你，对吧？

Wow. and he calls you. How do you how do you think he calls you any hour of the night, right?

嗯，我们当时在椭圆形办公室，他打电话给你的时候你在睡觉。

Well, we we were we were in the uh we were in the oval that time when he called you sleeping.

你在睡觉，他就说：“把他叫醒。”

You were asleep and he like said, "Wake him up."

我觉得很过意不去，因为他说：“谁来参加这个晚宴？”我们过了一遍名单。他说：“那 Jensen 呢？”我说：“不行，先生。我们——他在度假。”因为他这个假期已经推迟了 5 年。

I felt I felt so bad because he's like, "Who's coming to this dinner?" And we go through the list. He's like, "Well, what about Jensen?" I said, "No, sir. We I He's on vacation." Cuz he he had to postpone this vacation for 5 years.

他就说：“让他接电话。”

And he's like, "Get him on the phone."

度假算什么？

What's vacation?

但你觉得他为什么能看穿这个骗局？这就是——这是一件相当了不起的事。

But what why do you think he sees through the hoax? It's it's this is the thing quite an extraordinary thing.

它的民调支持率是负 80。

It was it's polling minus 80.

所以对任何坐在椭圆形办公室的人来说，你会做受欢迎的事。你代表人民。这是所有人想要的。他们想关掉数据中心和 AI。这似乎是当下受欢迎的事。但他说这是骗局，他点破了它。他是怎么做到的？

So for anyone else that's sitting in the Oval Office. You're going to do what's popular. You're representing the people. This is what everyone wants. They want to shut down the data centers and AI. It seems to be the popular thing in the moment. But he says it's a hoax and he calls it. How does he do that?

我得告诉你，我不确定。原因是很多人都上当了。所以事实是，这很复杂。你知道，一开始，如果你看这个故事，如果你看这些报道，它都锚定在两件事上。第一件是国家安全。而最近，那已经被彻底炸得粉碎，对吧？

I got to tell you, I'm not sure. And the reason for that is because a lot of people are falling for it. And so the fact of the matter is it's complicated. You know, at first, I mean, if you look at the story, if you look at the stories, it's all anchored on two things. The first thing that it was anchored on was national security. And recently, that was all blown blown to bits, right?

所以，不，那个说法不再锚定在国家安全上了。现在，它锚定在安全上。现在，如果你想让 AI 安全，呃，第一件事是我们需要确保构建它的实验室处于掌控之中，确保有好的测试。呃，如果我们希望有第三方，呃，确保有第三方评估者可用，这跟财务控制没什么不同。你们知道我们有审计师。

And so, no, that story is no longer anchored on national security. Now, it's anchored on safety. Now, if you want AI to be safe, um the first thing is we need to make sure that the the labs that are building it are in control, that they're they're good tests for them. uh if we would like to have third parties uh uh to to um uh make sure that a third party evaluator third party evaluators are available that's no different than financial control. You guys know we have auditors

而审计师相当——呃，他们不必像我们一样精通我们的业务，但他们只需要问对问题。呃，我想我听有人说，有独立的审计师或评估者是好事，但必须有多家。我也同意。就像有多家评估者和审计师一样，这能确保没有一家公司变得，你知道，被带偏或出于某种原因受到影响。所以，你知道，有很多不同的方法可以解决这个问题。呃，所以我认为最重要的一件事是，让我们安全地构建这项技术。让我们确保对它的测试是安全的。我完全认识到，正在构建的东西是非凡的。呃，但这些是非凡的公司，我们应该用非凡的标准来要求它们。呃，它们想成为——它们想成为。

and the auditors are quite quite um they don't have to be as expert as we are in our business but they just have to ask the right questions and um I I think I heard somebody say that it's good to have uh independent auditors or evaluators but they just have to have multiple. I agree with that too. Just as there's multiple evaluated and auditors, it makes sure that one company doesn't become, you know, pilled or somehow influenced um for for whatever reason. And so you, you know, there's a lot of different ways that you could solve this. Um and so I think the number one thing is let's build the technology safely. Let's make sure that the testing of it is safe. And I I recognize completely that that what what is being built is extraordinary. Um but these are extraordinary companies and and we ought to hold them to to extraordinary standards. Um and they want to be and they want to be

我想回到开源话题一下。呃，一年前我们没太把它当回事。它落后两年、18 个月。

I wanted to go back to open source for a second. Um a year ago we weren't taking it very seriously. It was two years 18 months behind.

有一件事，你知道，你们知道，跟特朗普总统通电话时的挑战之一就是很难插上话。呃，我会因此惹上麻烦。我肯定他会为此打电话给我。但不管怎样，呃，我本来要告诉他以及你们所有人的是，AI 正在创造大量就业。他在政府初期最想要的，也是我第一次跟他通电话、第一次见到他时他说的，就是他想在美国创造就业。他想让美国再工业化。他想确保美国有能源来支撑下一次工业革命。没有能源，就没有工业增长。所以他想确保能源增长、就业增长，确保供应链再工业化。看看我们现在做的一切。一切都在我们说话的同时发生着。我们创造的就业比以往任何时候都多。我们在创造软件就业。我们刚才还在聊。最近有 4000 亿美元的风险融资进入了 AI 行业。是的，6 个月。嗯，那创造了大量就业。那创造了大量就业。呃，它创造了，你知道，显然对算力的巨大需求，我对此很高兴。呃，这也对数据中心产生了大量需求，我们应该谈谈这个。我想我刚才在跟得克萨斯州州长 Abbott 交谈，他——他想呼吁业界，确保我们在全美各地建设数据中心时，对小社区有同理心，只是要更好地倾听。我们实际上来谈谈这个。那——

The one thing that you know one of the as you guys know one of the challenges when you're on the call with President Trump is hard to say something. Um I'm going to get in trouble for that. I'm sure he's going to call me up up on that. But anyhow, uh what I was going to tell him and and and all of you is that AI is creating an enormous number of jobs. The the the thing that he wanted more than anything at the beginning of the the administration and that my first phone call with him, my first time I met him is that he wants to create jobs in America. He wants to re-industrialize the United States. He wants to make sure that United States has the energy to support the next industrial revolution. Without energy, there's no industrial growth. And so he wants to make sure that there's energy growth, that there's job growth, that they're re-industrializing the supply chain. Look at everything that we're doing right now. All of it is happening right now as we speak. We're creating more jobs than ever. We're creating software jobs. We were just talking about earlier. $400 billion dollar of venture financing went into the AI industry just recently. Yeah. 6 months. Well, that's created a ton of jobs. That's created a ton of jobs. Um it's created you know obviously enormous amount of demand for compute which we're I'm happy about. Um which is also which is also creating a lot of demand for data centers and we ought to talk about that. I think I was just I was talking to um uh Governor Abbott uh uh of uh Texas and he was he was uh he wants to appeal to the industry to make sure that we are we are empathetic to the small communities as we're building data centers all of all across America just to be better listeners. Let's actually talk about that for a second. That's

Nvidia 令人难以置信的地方在于，如果你拆解各个组成部分，你实际上不得不成为 AI 的银行来推动生态系统运转，而且你不得不在各个层面都这样做。你知道，你刚跟 Cloverleaf 做了这件事，做土地和电力。你跟 BlackRock、Goldman 以及所有这些机构做了这件了不起的事，基本上是在创造融资能力。给我们讲讲你的资本配置策略，比如需要发生什么才能让更广泛的生态系统参与者能够进来，为下一阶段承保。

what's incredible about Nvidia if you if you break down the component parts is you've effectively had to become the bank of AI to get the ecosystem going and you've had to do it at all the levels. You know, you just did this thing with Cloverleaf where you're doing land powers shell. You did this great thing with Black Rockck and Goldman and all these folks to to essentially create the financing capability. walk us through your capital allocation strategy like what has to happen to get a broader ecosystem folks to be able to come in and underwrite this next phase.

嗯，正如你们所知，我们正在创造一场新的工业革命，它的方方面面都是真实的。这个新产业需要制造业，就像电力、互联网，现在还有 AI。我们为一切供电，我们能找到一切。现在有了 AI，我们可以询问并知道一切。对不对？所以这就是我们的未来。我们接入以太，可以向它询问任何我们想要的东西，它就能向我们解释。现在，为了实现这一点，它必须产生智能。所以这是一个生产过程，这就是为什么必须建设这些基础设施。但一旦基础设施建成，问题就是美国各地的其他层面怎么办？这个产业不仅仅是关于模型。不仅仅是关于芯片。它主要是关于上层的应用。主要是关于基础设施层、数据中心和所有基础设施、建设、电力、发电，所有这些都涉及其中。所以我审视整个生态系统，寻找瓶颈，看看哪些地方正在诞生非凡的公司。

Well, we're creating, as you guys know, this is a new industrial revolution and every aspect of it is true. This new industry requires manufacturing just as electricity, internet and now AI. We power anything, we can find anything. Now with AI, we can ask and know anything. Isn't that right? And so that's our future. We tap into the ether and we can ask it of anything we want and it could explain it to us. Now, in order for that to happen, it's got to produce the intelligence. And so that's a production process which is the reason why this infrastructure has to get built. But once you get the infrastructure built, the question is what about all of the other layers across the United States? This industry isn't just about the model. It's not just about the chips. It's mostly about the applications on top. It's mostly about the infrastructure layer, the data centers and all the infrastructure, the construction, the electricity, the power generation that all of that is involved. And so I look across the entire ecosystem and look for bottlenecks and if there are places where extraordinary companies are being built.

制约因素。

Constraints.

制约因素，非凡的公司正在被建立。也许是供应链需要扩大规模，以便当我们准备部署算力时，他们能为我们准备好——土地、电力、厂房。所以这就像审视上游供应链一样。你知道，我可能比大多数人更考虑长期供应链，因为我们公司真的很大，为了让我们成功，一大堆公司必须支持我。你知道，康宁必须——康宁的温德尔必须支持我，Lumentum，当然还有台积电和内存公司，所以我们在革命之前、增长到来之前就开始与所有这些公司合作，以便增长能够发生。现在我在做下游。

Constraints, extraordinary companies being built. Maybe it's supply chain that has to get scaled up so that when we're ready to deploy compute that they'll be ready for us—land, power, shell. And so this is no different than looking at the supply chain upstream. You know, I probably think about the long-term supply chain more than most because our company's really large and in order for us to succeed, a whole bunch of companies has to support me. You know, it's got to—Corning has to, you know, Wendell at Corning has to support me, Lumentum, and you know, TSMC of course and memory companies and so we started working with all of these companies long before the revolution, before the growth came so that the growth could happen. Now I'm doing a downstream.

不过，竞争周期往往是，长期来看，盈利会沿着技术栈向上移动，移向应用层，在那里你可以在更长的时期内获得超额收益。我的意思是，你收购了 Hugging Face，现在你算是积极进入了服务业务。像 Open Router 这样的产品似乎很自然就很有意义。很明显，你知道，有更好的方式来构建像 Bedrock 这样的东西。我相信你考虑过。自然的结论是什么？因为看起来在座各位对于向下游移动毫无顾虑。

The compet cycle tends to be though that the earnings over time over long stretches of time tends to move up the stack right towards the application layer where you can over earn for larger periods of time. I mean you bought Hugging Face now you're sort of actively in the serving business. I mean it seems pretty natural that products like Open Router make a lot of sense. It seems pretty obvious that you know there are better versions of ways to build things like Bedrock. I'm sure you think about it. What's the natural conclusion? Because it seems like the folks up here have no issue trying to move down.

嗯。

Mhm.

而且你有最好的资产负债表，这些了不起的工程师，你有经过验证的经验来把它做对，设计产品并推出。那么，你如何看待向上看并说：“我大概也能做那个。”

And you have the best balance sheet, these incredible engineers, and you have the proven experience to make it right and engineer the product and get it out. So, how do you think about looking up and saying, "I could probably do that."

英伟达运行世界上每一个模型的原因，我们是唯一——这很不可思议。去年，大约一年半前，我们唯一运行的是 OpenAI。

The reason why Nvidia runs every single model in the world, we were the only—it's incredible. Last year about a year and a half ago the only thing we ran was OpenAI.

是的。

Yeah.

现在看看，有那么多惊人的模型可用。Meta 的 Muse 可用。Grok 可用。Grok bots 很不可思议。我们现在运行 Gemini。Anthropic 也在我们的平台上扩大规模。从一年半前开始，所有这些前沿 AI 模型现在都开放可用了。所以模型的数量在增长。还有一大堆我不会提及的公司也在构建前沿模型。AI 实验室的数量在增长。是的。那些不可言说的、反思的，名单还在继续。物理智能，名单还在继续。好了。所以所有这些实验室都在英伟达上构建。原因在于，作为一家公司，我更愿意帮助每个人成功，而不是从中分一杯羹。所以我们会向上走到我们需要的高度，但尽可能保持低姿态。

And now look at amazing models are available. The Meta Muse is available. You got Grok is available. Grok bots is incredible. We now run Gemini. And Anthropic is scaling up on our platform as well. Since a year and a half ago, you got all these frontier AI models that are now open that are available. So the number of models that are growing. There's a whole bunch of companies that I won't mention that are building frontier models as well. And the number of AI labs are growing. Yeah. The ineffables, the reflections, the list goes on. The physical intelligence, the list goes on. Okay. And so all of these labs are building on NVIDIA. And the reason for that is because as a company, I rather for us to help everybody succeed instead of taking a slice out. And so we would go up as far as we need to but as low as possible.

我们的策略是向上走到我们需要的高度，同时尽可能保持低姿态。原因在于，如果我这样做，如果我解决了——如果不是英伟达创造了 cuDNN，所有的框架都不会存在。如果不是我们创造了 Megatron、Megatron Core，那么所有的大规模训练都不会发生。

Our strategy is go up as far as we need to and as low as possible. And the reason for that is because if I do that, if I solved the—if not for Nvidia creating cuDNN, all of the frameworks wouldn't exist. If not for us creating Megatron, Megatron Core, then all of the large scale training wouldn't have happened.

不会存在。

Wouldn't exist.

嗯，所以我们去发明所有必要的技术，达到我们需要的高度，然后我们让百花齐放。

Um, so we go and we invent all the technology necessary as far as we need to and then we let a thousand flowers bloom.

所以这种姿态让我们坦率地说成为唯一——

And so that posture allows us to be quite frankly the only—

嗯，看，老实说，我同意你的看法。反驳意见是，在超大规模云层有更多竞争确实会很好。我认为你在支持 NeoClouds 方面做得很好。有一些。顺便说一句，我想你介绍我认识了 NBS。太棒了，很好，一切。他们很了不起。但我们需要大约 50 个这样的家伙。我们需要一百个。我们需要一千个。这可能还需要一些——

Well, look, let's be honest that I agree with you. The push back would be that it really would be great to have more competition at the hyperscaler layer. And I think you've done a great job supporting the NeoClouds. There are some. And by the way, I think you introduced me to NBS. Superb, great, everything. They're amazing. But we need like 50 of these guys. We need a hundred of them. We need a thousand of them. And it just may take some—

是的。

Yeah.

你知道，只是我出奇地不具竞争性。

You know, it's just I'm surprisingly uncompetitive.

真的。

Really.

是的。那不是我的风格。你知道，我的风格有点像，比如说，我会很乐意有五家超大规模云。然而，我注意到所有 Neoclouds、所有我们称之为 NCP 的早期客户，所有早期客户都是超大规模云。

Yeah. That's not my thing. You know, my thing is kind of like for example, I'd be more than happy with five hyperscalers. However, the reason I noticed the early customers of all the Neoclouds, all the what we call NCPs, all the early customers were the hyperscalers.

正是。

Exactly.

原因在于，超大规模云每年只规划一次，但市场动态现在如此波动，以至于他们几乎总是错的。所以有了所有这些区域云，他们很敏捷，能快速行动，他们了解自己的州或自己的国家，了解自己的地区，他们正在以坐在西雅图或帕洛阿尔托的人难以看到全球的方式获取土地、电力和厂房。所以我们现在基本上有一个大规模的分布式公司网络，为我们建设、获取土地、电力和厂房。现在各国意识到这是战略性的。

And the reason for that is because the hyperscalers plan once a year, but the market dynamics is so volatile right now that they're always almost wrong. And so with all these regional clouds who are agile and they can move fast, they know their state or they know their country, they know their region, they're securing land, power and shell in a way that's hard for somebody who sits in Seattle or sits in Palo Alto to be able to see the planet. And so we now have basically a large-scale distributed network of companies that are building, securing land, power, shell for us. And now countries realize it's strategic.

是的。

Yeah.

很多国家说我要把我的电力只给我自己的公司，

So many countries are saying I'm going to take my power and only give it to my own companies,

对吧？

Right?

嗯，英伟达也在那个国家，我们可以帮助那个国家的 Neoclouds 成长，所以无论是澳大利亚的 Fermas，我们刚在澳大利亚做了一大堆事情。增加了两个吉瓦。东南亚当然 IOH 和其他公司增加了几吉瓦。所以我们在建设吉瓦。所以我们在建设，你知道，我们在扩大规模。你知道，这——

Well, Nvidia is in that country as well and we could help the Neoclouds in that country grow and so whether it's Fermas and Australia, we just did a whole bunch of stuff in Australia. Brought on two more gigawatts. Southeast Asia of course IOH and others bring on a few gigawatts. And so we're building gigawatts. So, we're building, you know, we're scaling up. You know, it's—

不过很清楚。我只想把这个说清楚。很明显你正在向上走得很高，并且非常专注于开源。显然，你的 Neotrons 做得非常好。我经常用它们。Hugging Face、Poolside 和 Laguna——非常非常扎实的产品，你现在正在 aqua-hiring，招聘，不管是什么。

Pretty clear, though. I just want to get this one thing in. It's pretty clear that you're going pretty high up and getting very focused on open-source. Obviously, you have your Neotrons doing exceptionally well. I use them often. Hugging Face, Poolside and Laguna—very very solid product that you're now aqua-hiring, hiring, whatever it is.

然后你还有用于自动驾驶的开源技术栈，也非常具有颠覆性。

And then you have your open source stack for self-driving also very disruptive.

我们在五个领域都是前沿模型。

We are the frontier model in five domains.

是的。所以你似乎不是那种只拿银牌的产品，你似乎是要拿金牌。所以你是要拿金牌吗？你会拥有最好的开源模型吗？然后第二部分是，开源能追上前沿模型吗？你是那个能做到的人吗？

Yeah. So you don't seem to build products to get the silver medal. You seem to go for the gold. So are you going for the gold? And will you have the best hands-down open-source model? And then part B to that is can open source catch up to frontier models and are you the person to do it?

所以逻辑是，Jason，我们会构建它，因为第一，我们有技能去做，第二，我们的客户需要我们去做。

So the logic, Jason, is that we will build it because one, we have the skills to do it, and because our customers need us to do it.

对。

Right.

所以，举个例子，Alpamo 是世界上第一个会思考的自动驾驶汽车。通过思考、通过推理，你不需要那么多数据，你不必在几十亿小时的道路数据上训练，因为你可以推理。把问题分解成：我见过这个。它不完全一样，但大体上和那个一样。明白吗？那么，Alpamo 为什么必要？嗯，有很多汽车公司。世界上的每辆车都将实现自动驾驶，但除此之外，每个农业科技、每辆卡车、每辆货车，它们大多数规模不够大，无法构建整个技术栈。所以，我会为它们构建一个非凡的技术栈。它们为各自的应用做最后一公里的适配。现在，未来所有移动的东西都可以是自动驾驶的。如果不是我们构建了一些生物学模型，世界就不会有这些。我们创建的 ESM2 蛋白质语言模型，那个 ESM fold、open fold、alpha fold 2，所有与 coup equavariant 相关的东西，如果我们没有构建，所有这些技术都不会存在。我最喜欢的一个，proteina complexa，是合成下一代蛋白质，它正在结合，这是开创性的东西。我们构建了它，所以我们会构建它，因为 Lily 需要它，Merc 需要它，其他人也需要它，他们没有能力去做，或者他们还没到那一步，所以我们可以做出真正的贡献。所以我做一切都是出于需要。我不是试图颠覆。我的意思是，我们不会早上醒来就试图颠覆任何人。

So, for example, Alpamo is the world's first thinking self-driving car. And by thinking, by reasoning, you don't need as much data as, you know, you don't have to train on a few billion hours of road data because you could reason about it. Break down the problem into I've seen this before. It's not exactly the same, but it's largely the same as that. Okay? And so, so Alpamo, why is it necessary? Well, there's a whole bunch of car companies. Every car in the world is going to be autonomous, but beyond that, every ag tech, every truck, every van, and most of them aren't big enough in scale to be able to build that whole stack. So, I'll build an extraordinary stack for them. They do last mile adapting for their application. Now, everything that moves in the future could be autonomous. If not for us building some of the biology models, the world wouldn't have it. The ESM2 protein found language model we created, that ESM fold, open fold, alpha fold 2, all the stuff with coup equavariant, all of that stuff technology wouldn't have existed if we didn't build it. One of my favorites, proteina complexa, is you know synthesizing next generation proteins and it's binding, it's groundbreaking stuff. We built that and so we'll build that because Lily needs it and Merc needs it and others need it and they don't have the capability to do it or they're not yet there and so we can make a real contribution. So I do everything out of need. I'm not trying to disrupt. I mean we don't wake up in the morning try to disrupt anybody.

我们只是早上醒来试图帮助每个人。

We just wake up in the morning try to help everybody.

Jensen，那可能对你核心业务出现的竞争威胁呢？你能评论一下吗？

Jensen, what about competitive threats that might be emerging to your core business? Can you just comment?

太好了。

Just so nice.

是的。嗯，我知道这是，嗯，我实际上想，我想得到你的，我们就叫它看法吧。你对 Elon 宣布的 Terraab 1 亿平方英尺设施有什么看法？

Yes. Well, I know this is Well, I actually want I want to just get your let's just call it a take. What's your take on Terraab 100 million square foot facility Elon's announced and um

如果有人能做到，他就能，我们俩一起坐飞机去一个国家，嗯

If anybody could do it he can and the two of us were on a flight together to a country and um

和一个有时给你打电话的人。那是一架不错的飞机，我们就像你知道的，Elon 喜欢谈论这些事情，所以我们花了很多时间讨论它。

with a person who sometimes calls you on the phone. It was it was a nice plane and and and we had like you know and you know Elon likes to talk about these things and and so uh we spent a lot of time talking about it.

我，我是说任何人都能做到，因为我的意思是你设计芯片，但你不制造它们。你的芯片能在那里制造吗？还是

I I is that anybody could do it because I mean you could you design chips you don't fab them. Could your chips be fab there or is it

嗯，我们知道我们很了解工艺技术，因为我们在推动一切的极限，

Well, we know we we know a lot about process technology because we're pushing the limits of everything,

对吧？你知道，因为我们以如此大的规模扩展，嗯，我们在公司内部有令人难以置信的内存技术。我们是世界上最好的 sis 公司。你知道我们有很多惊人的。

right? And you know because we scale at such large scale uh we have incredible memory technology inside the company. We're the world's best sis company. You know we got lots of amazing.

所以你的看法是你已经谈了很多。

So your take is you've talked a lot about it.

所以我们可以只是，是的，我们可以谈论它，嗯，你无法阻止 Elon 去做，这是他不可思议的，这是他的超能力，一旦他决定去做某事，很难阻止他。所以我

So we could just yeah we could talk about it and and um you can't discourage Elon from doing it which is one of his incred that's his superpower and once he decides to go do something it's hard to stop him. And so I

你能给我们谈谈中国在先进光刻系统方面的进展吗？嗯，本土生长的。

And can can you give us your take on where China is with advanced lithography systems? Um native grown.

他们会在 2030 年前实现。

They're going to get there by 2030.

到 2030 年。

By 2030.

是的。2030 年就在眼前。

Yeah. And 2030 is just around the corner.

是的。

Yeah.

而且，到那时我们可能都死了。所以，

Also, that's how long will all be dead at that time. So,

对中国来说，这是否意味着开关一开，然后一切都会进入大陆晶圆厂

and does and for China, does that mean the switch is flipped and then that's all going to go into um mainland fabs

几乎立即？你知道，看待中国的方式是它非常擅长大批量生产。这只是时间问题。

almost immediately? You know, the the way to think about China is really good at high volume production. And this is just matter of time.

是的。

Yeah.

而且我，你知道，我认为在几十年内也是如此，你知道我在这行很久了，对 Nvidia 来说，我必须考虑下一个十年和再下一个十年会发生什么。所以两三年只是一瞬间。没什么。

And I you know I I think in I I think in decades as well you know I've been around a long time and you know for Nvidia I've got to think about what happens next decade and decade after that. So two or three years is it's just a click. It's nothing.

所以对他们来说，他们已经在那里了。

And so as far as they're concerned they're already there.

他们已经在那里了。

They're already there.

是的。

Yeah.

Jensen、Elon 和 Gwen，我们必须运行美国。我们必须运行。

Jensen Elon uh and Gwen we've got to run America. We got to run.

是的。加速。

Yeah. speedun.

所以，我们加速。

So, we got speedun.

减速绝对是错误的策略。

Slowing down is definitely the wrong strategy.

嗯，我的意思是，我认为对我们行业的大多数人来说，我们正处于 AGI 时刻，这很明显。它的定义显然是和任何其他人一样聪明。

Well, I mean it it feels apparent, I think, to most of us in the industry that we're kind of in the AGI moment. And it's a definition obviously just as smart as any other human.

我认为我们已经在那里了。

I think we're already there.

我们到了，对吧？那么根据你所看到的，根据你的客户群，根据你在这里的历史，超级智能是下一个航点。

We're there, right? And so then super intelligence is the next way point based on what you see, based on your customer base, based on your history here.

但 Jason，我认为我们也到了。

But Jason, I think we're there, too.

你认为我们处于超级智能？

You think we're at super intelligence?

是的。是的。当你，当你，当你取一个狭窄的领域，一个狭窄的领域，我的意思是我的自动驾驶汽车，我不想让你给我做煎蛋卷，我只想让你开车

Yeah. Yeah. When you when you when you take a narrow segment a narrow segment I mean my my self-driving car I don't want you to make me an omelette I just want you to drive the car

对

right

那就是超级智能

that is super intelligent

超级，它更好，它比人类更好

super it's better it's better than a human

是的，是的

yeah yeah

事故率只有十分之一

onetenth the the accident rate

没错

exactly

嗯，合成蛋白质，你知道，嗯，做蛋白质的虚拟筛选，我们已经在那里了

uh synthesizing proteins you know uh doing virtual screening of proteins we're already there

你享受处于人类前沿吗？

are you having fun being on the frontier of humanity

我喜欢，是的，

I like Yeah,

女士们，先生们。

ladies and gentlemen.

女士们，先生们，

Ladies and gentlemen,

我喜欢。我喜欢。伙计们，伙计们，那里很棒。未来很棒，我们想去那里。听着，骑上自行车。我们很多人不必工作。但我得告诉你，太好了，不能不去。

I like it. I like it. And guys, guys, it's it's it's great there. The future is great and we want to get there. Listen, ride the bike. A lot of us don't have to work. But I got to tell you, it's too good not to be.

太有趣了，

So fun,

对吧？所以，所以我想每个，我想在那里。我想你们所有人都和我一起在那里。我们都会在那里。作为人类，我们将一起取得巨大成功。嗯，与此同时，嗯，我们必须鼓励他们，敦促他们。正如你们所知，他们正在做非常非常重要的工作。我希望他们成功。嗯，我也希望我们能够淡化戏剧性，最重要的是，我们需要所有美国人与我们同行。这就是我们成功的方式。

right? And so, so I want every we I want to be there. I want all of you guys there with me. We're all going to be there. we're going to be enormously successful together as a humanity. And um and in the meantime, uh we got to encourage them, urge them on. They're doing really, really important work as you guys know. And I want them to succeed. Um I also would love for us to tone down the the the the drama and most importantly, we need all of America to come with us. That's how we make it.

女士们，先生们，Jensen Long。

Ladies and gentlemen, Jensen Long.

谢谢，伙计。感谢你。谢谢。

Thanks, man. Appreciate you. Thank you.

谢谢。

Thank you.

太棒了。只有你的部分。

That was awesome. Only your part.

太棒了。太好了。

That was awesome. That was great.

谢谢，伙计们。太棒了，是吧？很愉快。

Thanks, guys. That was great, huh? Great time.