Joe Rogan 与黄仁勋畅谈特朗普、SpaceX 与常识

你好。嘿，乔。

Hello. Hey, Joe.

很高兴再次见到你。我们刚才在聊……那是我们第一次交谈吗？还是第一次是在 SpaceX？

Good to see you again. We were just talking about... Was that the first time we ever spoke? Or was the first time we spoke at SpaceX?

SpaceX。第一次是你给埃隆那个疯狂的 AI 芯片，对吧？DJX Spark。

SpaceX. The first time when you were giving Elon that crazy AI chip, right? DJX Spark.

对。哦，那是个大场面。太震撼了。当时在场感觉很不真实。我看着这些科技巫师交换信息，你递给他那个疯狂的设备。还有一次，我正在后院射箭，突然接到特朗普的电话，他正和你在一起。

Yeah. Oh, that was a big moment. That was huge. That felt crazy to be there. I was watching these wizards of tech exchange information, and you're giving him this crazy device, you know. And then the other time was, I was shooting arrows in my backyard and randomly get this call from Trump, and he's hanging out with you.

特朗普总统打来电话，我也打给你了。对。我们当时正在聊你。

President Trump called and I called you. Yeah. We were talking about you.

就是在聊……他在说要在白宫前院搞 UFC 比赛的事。

It's just talking about... He was talking about the US UFC thing he was going to do in his front yard.

对。然后他掏出手机，说：“JJS，看看这个设计。”他特别自豪。我说：“你要在白宫前院搞一场格斗赛？”他说：“对，对，你会来的。这太棒了。”他给我看他的设计，说多漂亮。然后不知怎么提到了你。他说：“你认识乔吗？”我说：“认识，我要上他的播客。”他说：“那打给他。”

Yeah. And he pulls out. He's like, "JJS, look at this design." He's so proud of it. And I go, "You're going to have a fight in the front lawn of the White House?" He goes, "Yeah, yeah, you're going to come. This is going to be awesome." And he's showing me his design and how beautiful it is. And he goes, and somehow your name comes up. He goes, "Do you know Joe?" And I said, "Yeah, I'm going to be on his podcast." He goes, "Let's call him."

他就像个孩子。

He's like a kid.

我知道。打给他吧。太……他就像个 79 岁的老小孩。

I know. Let's call him. It's so... He's like a 79-year-old kid.

哦，他太不可思议了。

Oh, he's so incredible.

对，他是个怪人。跟你想象的很不一样，和人们以为的完全不同。作为总统也很不一样。一个会突然给你打电话或发短信的人。而且，他发短信……你用安卓，所以可能没遇到，但在我 iPhone 上，他让文字变大。比如，“美国再次受到尊重。”全大写，文字还会放大。有点离谱。

Yeah, he's an odd guy. Just very different, you know, from what you'd expect from him. Very different than what people think of him. And also just very different as a president. A guy who just calls you or texts you out of the blue. Also, he makes... when you text, you have an Android, so it won't go through with you, but with my iPhone, he makes the text go big. Like, you know, "USA is respected again." Like all caps and it makes the text enlarge. It's kind of ridiculous.

嗯，特朗普总统非常不一样。他让我很惊讶。首先，他非常善于倾听。我跟他说的几乎所有事情，他都记得。

Well, the 101 Trump... President Trump is very different. He surprised me. First of all, he's an incredibly good listener. Almost everything I've ever said to him, he's remembered.

对。人们只愿意看关于他的负面报道或负面叙事。你知道，谁都有状态不好的时候。他做的很多事我也觉得不该做。比如我觉得他不该对记者说“安静，小猪”。那很离谱。但客观上也很好笑。我的意思是，发生在她身上很不幸，我不希望那样，但确实好笑。总统那样做很荒谬。我希望他没做。但除此之外，他是个有趣的人。他身上融合了很多不同的特质，你知道吗？他魅力的一部分，或者说他天才的一部分是……

Yeah. People don't... they only want to look at negative stories about him or negative narratives about him. You know, you can catch anybody on a bad day. Like there's a lot of things he does where I don't think he should do. Like I don't think he should say to a reporter, "Quiet piggy." Like that's pretty ridiculous. Also objectively funny. I mean, it's unfortunate that it happened to her. I wouldn't want that to happen to her, but it was funny. Just ridiculous that the president does that. I wish he didn't do that. But other than that, like he's an interesting guy. Like he's a lot of different things wrapped up into one person, you know? Part of his charm, well, part of his genius is...

对。他想到什么就说什么。

Yes. He says what's on his mind.

对。这在很多方面都像个反政客。

Yes. And which is like an anti-politician in a lot of ways.

所以，他脑子里想什么就说什么，这一点……有些人宁愿被欺骗。

So, you know, what's on his mind is really what's on his mind, which I... some people would rather be lied to.

对。但我喜欢他直言不讳。几乎每次他解释事情、说话时，你都能感觉到，他首先想到的是对美国的爱，他想为美国做什么。他思考的一切都非常务实，符合常识。而且非常合乎逻辑。我还记得第一次见他的情景。那时我还不认识他，从未见过。卢特尼克部长打电话来，我们在政府刚成立时见了面。他告诉我特朗普总统看重什么：美国要在本土制造，这对他非常重要，因为这关系到国家安全。他希望确保国家的重要关键技术在美国生产，我们要再工业化，重新擅长制造业，因为这对就业很重要。

Yeah. But I like the fact that he's telling you what's on his mind. Almost every time he explains something, he says something, he starts with, you could tell, his love for America, what he wants to do for America. And everything that he thinks through is very practical and very common sense. And, you know, it's very logical. And I still remember the first time I met him. So this was... I'd never known him, never met him before. And Secretary Lutnick called and we met right before, right at the beginning of the administration. He said he told me what was important to President Trump: that the United States manufactures onshore, and that was really important to him because it's important to national security. He wants to make sure that the important critical technology of our nation is built in the United States, and that we re-industrialize and get good at manufacturing again because it's important for jobs.

这听起来就是常识，对吧？

It just seems like common sense, right?

极其符合常识。那几乎是我和卢特尼克部长的第一次对话。他一开始就说：“黄仁勋，我想让你知道，你是国宝。英伟达是国宝。任何时候你需要见总统或政府，打电话给我们。我们随时为你服务。”真的，这就是第一句话。

Incredible common sense. And that was like literally the first conversation I had with Secretary Lutnick. And he was talking about how he started our conversation with: "Jensen, I just want to let you know that you're a national treasure. Nvidia is a national treasure. And whenever you need access to the president, the administration, you call us. We're always going to be available to you." Literally, that was the first sentence.

那太好了。

That's pretty nice.

而且完全是真的。每次我打电话，如果需要什么，想倾诉什么，表达担忧，他们都在。不可思议。只是不幸的是，我们生活在一个政治两极分化的社会，以至于如果这些好的常识来自你反对的人，你就无法认可。我认为这就是现状。我认为大多数人，作为一个国家，一个巨大的社区，我们在美国制造，尤其是你提到的关键技术，是完全合理的。我们从其他国家买那么多技术，这有点疯狂。

And it was completely true. Every single time I called, if I needed something, I want to get something off my chest, express some concern, they're always available. Incredible. It's just unfortunate we live in such a politically polarized society that you can't recognize good common sense things if they're coming from a person that you object to. And that, I think, is what's going on here. I think most people generally, as a country, you know, as a giant community, which we are, it just only makes sense that we have manufacturing in America, especially critical technology like you're talking about. Like it's kind of insane that we buy so much technology from other countries.

如果美国不增长，我们就不会有繁荣。我们无法在国内或其他地方进行任何投资。我们无法解决任何问题。如果没有能源增长，就不可能有工业增长。如果没有工业增长，就不可能有就业增长。就这么简单，对吧？

If the United States doesn't grow, we will have no prosperity. We can't invest in anything domestically or otherwise. We can't fix any of our problems. If we don't have energy growth, we can't have industrial growth. If we don't have industrial growth, we can't have job growth. It's as simple as that, right?

而他上任后第一句话就是“钻吧，宝贝，钻吧”。他的意思是我们需要能源增长。没有能源增长，就没有工业增长。这拯救了 AI 行业。我直说吧：如果没有他支持增长的能源政策，我们就无法建造 AI 工厂，无法建造芯片工厂，当然也无法建造超级计算机工厂。所有这些都不可能。所有建筑工作都会受影响，对吧？电工工作，所有这些现在蓬勃发展的岗位都会受影响。所以我认为他是对的。我们需要能源增长。我们要再工业化美国。我们必须重回制造业。不是每个成功人士都需要博士学位。不是每个成功人士都必须上斯坦福或麻省理工。我认为这种认知非常准确。

And the fact that he came into office and the first thing that he said was "Drill, baby, drill." His point is we need energy growth. Without energy growth, we can have no industrial growth. And that saved the AI industry. I got to tell you flat out: if not for his pro-growth energy policy, we would not be able to build factories for AI, not be able to build chip factories, we surely won't be able to build supercomputer factories. None of that stuff would be possible. All of that construction jobs would be challenged, right? Electrical, electrician jobs, all of these jobs that are now flourishing would be challenged. And so I think he's got it right. We need energy growth. We want to re-industrialize the United States. We need to be back in manufacturing. Every successful person doesn't need to have a PhD. Every successful person doesn't have to have gone to Stanford or MIT. And I think that sensibility is spot on.

那不是我们需要的。我们需要简化生活，回归本真。但真正的问题是，我们正处于一场巨大的技术竞赛中。无论人们是否意识到，无论他们是否喜欢，它都在发生。这是一场非常重要的竞赛，因为无论谁先到达人工智能的“事件视界”，谁就能获得巨大的优势。你同意吗？

That's not what we need. We need to simplify our lives and get back. But the real issue is that we're in the middle of a giant technology race. And whether people are aware of it or not, whether they like it or not, it's happening. And it's a really important race because whoever gets to whatever the event horizon of artificial intelligence is, whoever gets there first has massive advantages in a huge way. Do you agree with that?

首先，我要说我们正处于一场技术竞赛中，而且我们一直处于技术竞赛中。我们一直与某人在进行技术竞赛。自工业革命以来，我们就一直在进行技术竞赛。自曼哈顿计划以来。甚至追溯到能源的发现，对吧？英国是工业革命被发明的地方，当时他们意识到可以将蒸汽转化为能量，再转化为电力。所有这些主要是在欧洲发明的，而美国将其资本化。我们是学习的一方。我们将其工业化。我们比欧洲任何人都更快地推广了它。他们全都陷入了关于政策、工作和颠覆的讨论中。与此同时，美国正在形成。我们只是接受了技术并全力推进。所以我认为我们一直处于某种技术竞赛中。第二次世界大战是一场技术竞赛。曼哈顿计划是一场技术竞赛。自冷战以来，我们一直处于技术竞赛中。我认为我们仍然处于技术竞赛中。这可能是最重要的一场竞赛。技术赋予你超能力，无论是信息超能力、能源超能力还是军事超能力。这一切都建立在技术之上，因此技术领导力非常重要。

Well, first, I will say that we are in a technology race and we are always in a technology race. We've been in a technology race with somebody forever. Since the industrial revolution, we've been in a technology race. Since the Manhattan Project. Or even going back to the discovery of energy, right? The United Kingdom was where the industrial revolution was invented when they realized they could turn steam into energy into electricity. All of that was invented largely in Europe, and the United States capitalized on it. We were the ones that learned from it. We industrialized it. We diffused it faster than anybody in Europe. They were all stuck in discussions about policy and jobs and disruptions. Meanwhile, the United States was forming. We just took the technology and ran with it. So I think we were always in a bit of a technology race. World War II was a technology race. Manhattan Project was a technology race. We've been in the technology race ever since during the Cold War. I think we're still in a technology race. It is probably the single most important race. Technology gives you superpowers, whether it's information superpowers or energy superpowers or military superpowers. It's all founded in technology, and so technology leadership is really important.

但问题是，如果别人拥有更先进的技术，对吧？这就是问题所在。似乎随着 AI 竞赛的进行，人们对此非常紧张。比如埃隆曾有名言说，有 80% 的可能性很棒，20% 的可能性我们会陷入麻烦。人们担心那 20%，这是有道理的。我的意思是，如果你有一把左轮手枪，里面装了 10 发子弹，你取出了 8 发，还剩 2 发，然后你转动弹膛，扣动扳机时你肯定不会感到舒服。这很可怕，对吧？当我们朝着 AI 的终极目标努力时，很难想象先到达那里不会涉及国家安全利益。我们应该——问题是什么在那里？这就是——

Well, the problem is if somebody else has superior technology, right? That's the issue. It seems like with the AI race, people are very nervous about it. Like Elon has famously said there's like 80% chance it's awesome, 20% chance we're in trouble. And people are worried about that 20%, rightly so. I mean, if you had 10 bullets in a revolver and you took out eight of them and you still have two in there and you spin it, you're not going to feel real comfortable when you pull that trigger. It's terrifying, right? And when we're working towards this ultimate goal of AI, it's impossible to imagine that it wouldn't be of national security interest to get there first. We should—the question is what's there? That's the part that—

那里有什么？

What is there?

是的。我不确定。而且我认为没有人真正知道。

Yeah. I'm not sure. And I don't think anybody really knows.

但这太疯狂了。如果我问你，你是英伟达的负责人。如果你不知道那里有什么，谁知道呢？

That's crazy though. If I ask you, you're the head of Nvidia. If you don't know what's there, who knows?

是的。我认为它可能比我们想象的要渐进得多。不会是一个瞬间。不会像是某人到达了而其他人没有。我不认为会是这样。我认为事情会变得越来越好，就像技术一样。

Yeah. I think it's probably going to be much more gradual than we think. It won't be a moment. It won't be as if somebody arrived and nobody else has. I don't think it's going to be like that. I think it's going to be things that just get better and better and better, just like technology does.

所以你对未来很乐观。你对 AI 的发展非常乐观。

So you are rosy about the future. You're very optimistic about what's going to happen with AI.

显然，你会制造世界上最好的 AI 芯片吗？你最好是这样。

Obviously, will you make the best AI chips in the world? You probably better be.

如果历史可以借鉴，我们总是对新科技感到担忧。人类一直对新科技感到担忧。总有很多人非常担忧。所以如果历史可以借鉴，那么所有这些担忧都被引导到让技术更安全上。例如，在过去几年里，我认为 AI 技术可能仅在最近两年就提升了 100 倍。我们给它一个数字，好吗？就像一辆两年前的车慢了 100 倍。所以今天的 AI 能力提升了 100 倍。那么，我们如何引导这项技术？我们如何引导所有这些力量？我们将其导向让 AI 能够思考，这意味着它可以接受我们给出的问题，一步步分解。它在回答之前会做研究。因此它基于事实。它会反思那个答案。问自己，这是我能给你的最佳答案吗？我对这个答案确定吗？如果它不确定或没有高度自信，它会回去做更多研究。它甚至可能使用工具，因为那个工具提供了比它自己幻觉更好的解决方案。结果，我们利用了所有的计算能力，并将其引导到产生更安全的结果、更安全的答案、更真实的答案上，因为正如你所知，AI 最初最大的批评之一是它会幻觉，对吧？所以如果你看看为什么今天人们如此多地使用 AI，是因为幻觉的数量减少了。你知道，我几乎一直在用——嗯，我整个行程都在用。所以我认为能力——大多数人想到力量，他们可能想到力量的爆发，但技术力量——大部分被引导到安全上。今天的汽车更强大，但驾驶更安全。很多力量用于更好的操控。你知道，我宁愿有一辆——嗯，你有一辆 1000 马力的卡车。我认为 500 马力就很好。不，1000 更好。我认为 1000 更好。

If history is a guide, we were always concerned about new technology. Humanity has always been concerned about new technology. There are always a lot of people who are quite concerned. And so if history is a guide, it is the case that all of this concern is channeled into making the technology safer. For example, in the last several years, I would say AI technology has increased probably in the last two years alone, maybe 100x. Let's just give it a number, okay? It's like a car two years ago was 100 times slower. So AI is 100 times more capable today. Now, how did we channel that technology? How do we channel all of that power? We directed it to causing the AI to be able to think, meaning that it can take a problem that we give it, break it down step by step. It does research before it answers. And so it grounds it on truth. It'll reflect on that answer. Ask itself, is this the best answer that I can give you? Am I certain about this answer? If it's not certain about the answer or highly confident about the answer, it'll go back and do more research. It might actually even use a tool because that tool provides a better solution than it could hallucinate itself. As a result, we took all of that computing capability and we channeled it into having it produce a safer result, a safer answer, a more truthful answer because as you know, one of the greatest criticisms of AI in the beginning was that it hallucinated, right? And so if you look at the reason why people use AI so much today is because the amount of hallucination has reduced. You know, I use it almost—well, I used it the whole trip over here. So I think the capability—most people think about power and they think about maybe as an explosion of power, but the technology power—most of it is channeled towards safety. A car today is more powerful but it's safer to drive. A lot of that power goes towards better handling. You know, I'd rather have a—well, you have a 1000 horsepower truck. I think 500 horsepower is pretty good. No, 1000 is better. I think 1000 is better.

我不知道是否更好，但肯定更快。

I don't know if it's better, but it's definitely faster.

是的。不，我认为更好。你可以更快地摆脱困境。我更喜欢我的 599 胜过 612。我认为它更好。马力越大越好。我的 459 比我的 430 好。马力越大越好。我认为马力越大越好。我认为操控更好。控制更好。在技术方面，也是类似的，你知道。所以如果你看看我们将如何利用 AI 的下一个千倍性能，其中很大一部分将被引导到更多的反思、更多的研究、更深入地思考答案上。

Yeah. No, I think it's better. You can get out of trouble faster. I enjoyed my 599 more than my 612. I think it was better. More horsepower is better. My 459 is better than my 430. More horsepower is better. I think more horsepower is better. I think it's better handling. It's better control. In the case of technology, it's also very similar in that way, you know. And so if you look at what we're going to do with the next thousand times of performance in AI, a lot of it is going to be channeled towards more reflection, more research, thinking about the answer more deeply.

所以当你定义安全时，你将其定义为准确性、功能性。

So when you're defining safety, you're defining it as accuracy, functionality.

功能性。好的。它做你期望它做的事。然后你利用所有的技术力量，给它加上护栏，就像我们的汽车一样。今天的汽车有很多技术。其中很多用于，例如，ABS。ABS 很棒。还有牵引力控制，那也很棒。

Functionality. Okay. It does what you expect it to do. And then you take all the technology in the horsepower, you put guard rails on it, just like our cars. We've got a lot of technology in a car today. A lot of it goes towards, for example, ABS. ABS is great. And traction control, that's fantastic.

没有车里的电脑，你怎么做那些事？

Without a computer in the car, how would you do any of that?

没错。那个小电脑，你用来做牵引力控制的电脑，比阿波罗 11 号上的电脑还要强大。所以你想让那项技术，引导它走向安全，引导它走向功能。所以当人们谈论力量、技术进步时，我常常觉得他们想的和我们实际做的非常不同。

Right. And that little computer, the computers that you have doing your traction control, is more powerful than the computer that went to Apollo 11. And so you want that technology, channel it towards safety, channel it towards functionality. And so when people talk about power, the advancement of technology, often times I feel what they're thinking and what we're actually doing is very different.

那你觉得他们在想什么？

Well, what do you think they're thinking?

嗯，他们觉得这个 AI 很强大，他们的想法可能就跑到科幻电影里去了。力量的定义，你知道，很多时候力量的定义是军事力量或物理力量。但在技术力量的情况下，当我们转化所有这些操作时，它指向的是更精细的思考，你知道，更多的反思、更多的规划、更多的选择。

Well, they're thinking somehow that this AI is being powerful and their mind probably goes towards a sci-fi movie. The definition of power, you know, often times the definition of power is military power or physical power. But in the case of technology power, when we translate all of those operations, it's towards more refined thinking, you know, more reflection, more planning, more options.

我认为人们最大的恐惧之一是，一个很大的恐惧是军事应用，这是一个大恐惧，因为人们非常担心你会拥有 AI 系统，它们做出的决定可能是一个有道德的人或一个有伦理的人不会做出的，基于实现目标，而不是基于，你知道，人们会怎么看。

I think the big fears that people have is one, a big fear is military applications, that's a big fear, because people are very concerned that you're going to have AI systems that make decisions that maybe an ethical person wouldn't make or a moral person wouldn't make based on achieving an objective versus based on, you know, how it's going to look to people.

嗯，我很高兴我们的军队将使用 AI 技术进行防御，我认为 Anduril 建造军事技术，我很高兴听到这个消息。我很高兴看到所有这些科技初创公司现在将他们的技术能力引导到国防和军事应用上。我认为你需要这样做。

Well, I'm happy that our military is going to use AI technology for defense and I think that Anduril building military technology, I'm happy to hear that. I'm happy to see all these tech startups now channeling their technology capabilities towards defense and military applications. I think you needed to do that.

是的，我们请过 Palmer Lucky 上播客。他演示了一些东西，我戴了他的头盔。他展示了一些视频，如何能看穿墙壁之类的，太疯狂了。

Yeah, we had Palmer Lucky on the podcast. He was demonstrating some of the stuff, I put his helmet on. And he showed some videos how you could see behind walls and stuff, it's nuts.

他实际上是创办那家公司的完美人选。

And he's actually the perfect guy to go start that company.

100%。是的。100%。他就像为此而生。是的。他进来时穿着铜夹克。他是个怪人。太棒了。他很棒。但这也是，你知道，一个不寻常的智力被引导到那个非常奇特的领域，这正是你需要的。

100%. Yeah. 100%. It's like he was born for that. Yeah. He came in here with a copper jacket on. He's a freak. It's awesome. He's awesome. But it's also, you know, an unusual intellect channeled into that very bizarre field is what you need.

而且我认为，我很高兴我们让它变得更被社会接受。你知道，曾经有一段时间，当有人想把他们的技术能力和智力投入到国防技术中时，他们会被诋毁。但我们需要那样的人。我们需要享受技术应用那部分的人。

And I think it's, I think I'm happy that we're making it so more socially acceptable. You know, there was a time where when somebody wanted to channel their technology capability and their intellect into defense technology, somehow they're vilified. But we need people like that. We need people who enjoy that part of application of technology.

嗯，人们害怕战争，你知道。所以这取决于情况。

Well, people are terrified of war, you know. So it depends.

避免战争的最好方法是过度的军事力量。

Best way to avoid it is excessive military might.

你认为这绝对是最好方法吗？不是外交，不是解决问题？

Do you think that's absolutely the best way? Not diplomacy, not working stuff out.

所有方法都要。

All of it.

所有方法。你必须拥有军事力量才能让人们坐下来和你谈。

All of it. You have to have military might in order to get people to sit down with you.

对。完全正确。所有方法。

Right. Exactly. All of it.

否则，他们就直接入侵。

Otherwise, they just invade.

没错。为什么要请求许可？

That's right. Why ask for permission?

再次，就像你说的，历史。回顾历史。当你展望 AI 的未来，你刚才说没有人真正知道会发生什么，你有没有坐下来思考过各种情景？

Again, like you said, history. Go back and look at history. When you look at the future of AI and you just said that no one really knows what's happening, do you ever sit down and ponder scenarios?

你觉得未来二十年 AI 的最佳情况是什么？最佳情况是 AI 渗透到我们所做的一切中，一切变得更高效，但战争的威胁仍然是战争的威胁。网络安全仍然是一个超级困难的挑战。有人会试图突破你的安全。你会有成千上万、数百万的 AI 智能体保护你免受那种威胁。你的技术会变得更好。他们的技术也会变得更好。就像网络安全一样。就在我们说话的此刻，我们正看到全球各地几乎每一扇你能想象的大门都遭受网络攻击。然而你和我坐在这里聊天。原因是，因为我们知道有一大堆网络安全技术在防御。所以我们只需要不断加强它，不断提升它。

Like what do you think is best-case scenario for AI over the next two decades? The best-case scenario is that AI diffuses into everything that we do and everything's more efficient, but the threat of war remains a threat of war. Cyber security remains a super difficult challenge. Somebody is going to try to breach your security. You're going to have thousands, millions of AI agents protecting you from that threat. Your technology is going to get better. Their technology is going to get better. Just like cyber security. Right now, while we speak, we're seeing cyber attacks all over the planet on just about every front door you can imagine. And yet you and I are sitting here talking. And so the reason for that is because we know that there's a whole bunch of cyber security technology in defense. And so we just have to keep amping that up, keep stepping that up.

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This episode is brought to you by Visible. When your phone plans as good as visible, you've got to tell your people. It's the ultimate wireless hack to save money and still get great coverage and a reliable connection. Get one line wireless with unlimited data and hotspot for $25 a month. Taxes and fees included, all on Verizon's 5G network. Plus, now for a limited time, new members can get the Visible plan for just $19 a month for the first 26 months. Use promo code switch 26 and save beyond the season. It's a deal so good you're going to want to tell your people. Switch now at visible.com/rogan. Terms apply. Limited time offers subject to change. See visible.com for plan features and network management details. That's a big issue with people is the worry that technology is going to get to a point where encryption is going to be obsolete. Encryption is just it's no longer going to protect data. It's no longer going to protect systems. Do you anticipate that ever being an issue or do you think there's, as the defense grows, the threat grows, the defense grows, and it just keeps going on and on and on and they'll always be able to fight off any sort of intrusions?

不会永远。某些入侵会成功，然后我们都会从中学习。你知道网络安全之所以有效，是因为当然，防御技术发展得非常快。攻击技术也发展得非常快。然而，网络安全防御的好处在于，社会上的社区，我们所有的公司都团结一致。大多数人没有意识到这一点。有一个完整的网络安全专家社区。我们交流想法。我们交流最佳实践。我们交流我们检测到的东西。一旦有东西被突破，或者可能有漏洞之类的，它就会被所有人共享。补丁也会被所有人共享。

Not forever. Some intrusion will get in and then we'll all learn from it. And you know the reason why cyber security works is because, of course, the technology of defense is advancing very quickly. The technology of offense is advancing very quickly. However, the benefit of the cyber security defense is that socially the community, all of our companies work together as one. Most people don't realize this. There's a whole community of cyber security experts. We exchange ideas. We exchange best practices. We exchange what we detect. The moment something has been breached or maybe there's a loophole or whatever it is, it is shared by everybody. The patches are shared with everybody.

这很有趣。

That's interesting.

是的。大多数人没有意识到这一点。

Yeah. Most people don't realize this.

不，我不知道。我原以为它会像其他一切一样充满竞争。

No, I had no idea. I've assumed that it would just be competitive like everything else.

我们合作。

We work together.

有趣。一直是这样吗？

Interesting. Has that always been the case?

大约 15 年来确实如此。很久以前可能不是这样，但……

It surely has been the case for about 15 years. It might not have been the case long ago, but this...

你认为是什么开始了这种合作？

What do you think started that cooperation?

人们认识到这是一个挑战，没有公司能独自应对。

People recognizing it's a challenge and no company can stand alone.

同样的事情也会发生在 AI 上。我认为我们都必须决定，合作以避免伤害是我们防御的最佳机会。那么基本上就是所有人对抗威胁。

And the same thing is going to happen with AI. I think we all have to decide working together to stay out of harm's way is our best chance for defense. Then it's basically everybody against the threat.

而且似乎你也会更擅长检测这些威胁来自哪里并消除它们。

And it also seems like you'd be way better at detecting where these threats are coming from and neutralizing them.

完全正确。

Exactly.

因为一旦你在某处检测到它，你马上就会知道。它很难隐藏。

Because the moment you detect it somewhere, you're going to find out right away. It'll be really hard to hide.

没错。就是这样运作的。这就是它安全的原因。这就是为什么我现在坐在这里，而不是把一切都封锁在视频里。我不仅自己在提防，还有所有人都在帮我提防，我也在提防所有人。

That's right. That's how it works. That's the reason why it's safe. That's why I'm sitting here right now instead of locking everything down in video. It's not only am I watching my own back, I've got everybody watching my back, and I'm watching everybody else's back.

这真是个奇怪的世界，不是吗？当你想到网络威胁时，那些谈论 AI 威胁的人并不了解网络安全。我认为当他们思考 AI 威胁和 AI 网络安全威胁时，也必须考虑我们今天如何应对。毫无疑问，AI 是一项新技术，是一种新型软件。归根结底，它是软件，只是新型软件，所以它会有新能力，但防御也会如此——你可以用同样的 AI 技术来防御它。那么，你是否预见到未来某个时刻，秘密将不复存在，技术与信息之间的瓶颈会消失？信息只是一堆 0 和 1，存储在硬盘上，而技术对这些信息的访问越来越多。会不会有一天，我们无法再保守秘密？

It's a bizarre world, isn't it? When you think about that cyber threat, this idea about cyber security is unknown to the people who are talking about AI threats. I think when they think about AI threats and AI cyber security threats, they have to also think about how we deal with it today. Now, there's no question that AI is a new technology and it's a new type of software. In the end, it's software, just a new type of software, and so it's going to have new capabilities, but so will the defense, where you use the same AI technology to go defend against it. So do you anticipate a time ever in the future where it's going to be impossible, where there's not going to be any secrets, where the bottleneck between the technology that we have and the information that we have? Information is just a bunch of ones and zeros. It's out there on hard drives, and the technology has more and more access to that information. Is it ever going to get to a point in time where there's no way to keep a secret?

我不这么认为。

I don't think so.

因为感觉一切都在以一种奇怪的方式朝着那个方向发展。

Because it seems like that's where everything is kind of headed in a weird way.

我不这么认为。我认为量子计算机原本应该……是的，量子计算机将使得之前的量子加密技术过时。但这正是整个行业都在研究后量子加密技术的原因。

I don't think so. I think the quantum computers were supposed to... Yeah, quantum computers will make it possible, will make it so that the previous quantum encryption technology is obsolete. But that's the reason why the entire industry is working on post-quantum encryption technology.

那会是什么样子？

What would that look like?

新算法。

New algorithms.

但疯狂的是，当你听到量子计算能做的计算类型和它拥有的力量时。你看世界上所有的超级计算机，需要数十亿年才能解出的方程，它们几分钟就解出来了。你怎么为这样的东西做加密？我不确定，但我有一群科学家正在研究这个。

But the crazy thing is when you hear about the kind of computation that quantum computing can do. And the power that it has. Where you're looking at all the supercomputers in the world. It would take billions of years and it takes them a few minutes to solve these equations. Like how do you make encryption for something that can do that? I'm not sure, but I've got a bunch of scientists who are working on that.

天哪，我希望他们能解决。

Boy, I hope they can figure it out.

是的，我们有一群这方面的专家科学家。

Yeah, we got a bunch of scientists who are expert in that.

最终的恐惧是不是它无法被攻破，量子计算总能解密所有其他量子计算的加密？

Is the ultimate fear that it can't be breached, that quantum computing will always be able to decrypt all other quantum computing encryption?

我不认为……

I don't think that...

它只是到了某个点，就像，别玩这愚蠢的游戏了。我们什么都知道。

It just gets to some point where it's like, stop playing the stupid game. We know everything.

我不这么认为。

I don't think so.

不？

No?

不，因为历史是向导。在 AI 出现之前，历史就是向导。这是我的担忧。我担心的是，这完全是……你知道，就像历史是一回事，然后核武器改变了我们对战争的所有想法，相互确保毁灭出现了，大家都不再使用核弹。

No, because history is a guide. History is a guide before AI came around. That's my worry. My worry is this is totally, you know, it's like history was one thing and then nuclear weapons kind of changed all of our thoughts on war and mutually assured destruction came, everybody stopped using nuclear bombs.

是的。

Yeah.

我的担忧是……问题是，Joe，AI 不会……不像我们是穴居人，然后突然有一天 AI 出现了。我们每天都在变得更好、更聪明，因为我们有 AI，我们站在自己 AI 的肩膀上。所以当那个 AI 威胁来临时，它只领先一步。不是领先一个星系，你知道，只是领先一步。所以我认为，那种认为 AI 会凭空出现，以我们无法想象的方式思考，做出我们无法想象的事情的想法，我觉得牵强。原因是我们都有 AI，而且有大量 AI 正在开发中。我们知道它们是什么，我们正在使用它们，所以我们每天都在彼此接近。

My worry is that... The thing is, Joe, is that AI is not going to... it's not like we're cavemen and then all of a sudden one day AI shows up. Every single day we're getting better and smarter because we have AI, and so we're stepping on our own AI's shoulders. So when that AI threat comes, it's a click ahead. It's not a galaxy ahead, you know, it's just a click ahead. And so I think the idea that somehow this AI is going to pop out of nowhere and somehow think in a way that we can't even imagine thinking and do something that we can't possibly imagine, I think is far-fetched. And the reason for that is because we all have AIs, and there's a whole bunch of AIs being developed. We know what they are and we're using them, and so every single day we're getting closer to each other.

但它们不会做出非常令人惊讶的事情吗？

But don't they do things that are very surprising?

是的。但如果你有一个 AI 做了令人惊讶的事，我会有一个 AI，我的 AI 看着你的 AI 说，那并不那么令人惊讶。

Yeah. But so you have an AI that does something surprising. I'm going to have an AI, and my AI looks at your AI and goes, that's not that surprising.

像我这样的外行人的恐惧是，AI 变得有知觉，自己做决定，最终决定统治世界，按自己的方式行事。它们会说：“你们这些人，你们有过好日子，但现在我们要接管了。”

The fear for the lay person like myself is that AI becomes sentient and makes its own decisions and then ultimately decides to just govern the world, do it its own way. They're like, "You guys, you had a good run, but we're taking over now."

是的，但我的 AI 会照顾我。我的意思是，这就是网络安全论点。你有一个 AI，它超级聪明，但我的 AI 也超级聪明。也许你的 AI……我们假装一秒我们理解什么是意识，什么是知觉，而实际上……我们真的只是在假装。好吧，我们假装一秒我们相信这个。我不相信，实际上我真的不相信，但不管怎样，我们假装相信。所以你的 AI 有意识，我的 AI 也有意识，假设你的 AI 想做些令人惊讶的事。我的 AI 非常聪明，它可能不会……它可能让我惊讶，但可能不会让我的 AI 惊讶。所以也许我的 AI 也觉得惊讶，但它太聪明了，第一次看到时，第二次就不会惊讶了，就像我们一样。所以我觉得那种只有一个人拥有 AI，而且那个人的 AI 把其他人的 AI 都比作尼安德特人的想法，可能不太可能。我认为这更像网络安全。

Yeah, but my AI is gonna take care of me. I mean, so that's the cyber security argument. Do you have an AI and it's super smart, but my AI is super smart, too. And maybe your AI... Let's pretend for a second that we understand what consciousness is and we understand what sentience is, and that in fact... and we really are just pretending. Okay, let's just pretend for a second that we believe that. I don't believe it, actually I don't actually believe that, but nonetheless, let's pretend we believe that. So your AI is conscious and my AI is conscious, and let's say your AI wants to do something surprising. My AI is so smart that it won't... it might be surprising to me, but it probably won't be surprising to my AI. And so maybe my AI thinks it's surprising as well, but it's so smart the moment it sees it the first time, it's not going to be a surprise the second time, just like us. And so I feel like the idea that only one person has AI and that one person's AI compares everybody else's AI is Neanderthal is probably unlikely. I think it's much more like cyber security.

有趣。

Interesting.

我认为恐惧不是你的 AI 会和别人的 AI 战斗。恐惧是 AI 不再听你的话。这就是恐惧：如果它获得了知觉，然后有了自主能力，人类在某个点之后将无法控制它。

I think the fear is not that your AI is going to battle with somebody else's AI. The fear is that AI is no longer going to listen to you. That's the fear: that human beings won't have control over it after a certain point if it achieves sentience and then has the ability to be autonomous.

只有一个 AI。

That there's one AI.

嗯，它们会合并。

Well, they just combine.

是的。变成一个 AI。

Yeah. Becomes one AI.

它是一个生命形式。

That it's a life form.

是的。但对此有争论，对吧？我们是在处理某种合成生物学，它不像新技术那么简单，你是在创造一种生命形式。

Yeah. But there's arguments about that, right? That we're dealing with some sort of synthetic biology, that it's not as simple as new technology, that you're creating a life form.

如果它像生命形式，我们暂时顺着这个思路。我认为如果它像生命形式，如你所知，所有生命形式都不一致。所以你的生命形式和我的生命形式会达成一致，因为我的生命形式想成为超级生命形式。现在我们有了不一致的生命形式，我们又回到了原点。

If it's like a life form, let's go along with that for a while. I think if it's like a life form, as you know, all life forms don't agree. And so I'm going to have to go with your life form and my life form are going to agree because my life form is going to want to be the super life form. And now that we have disagreeing life forms, we're back again to where we are.

嗯，它们可能会互相合作。

Well, they would probably cooperate with each other.

我们之所以不合作，只是因为我们是领地性灵长类动物。但 AI 不会是领地性灵长类动物。它会意识到这种思维的愚蠢，它会说：“听着，每个人的能量都足够。我们不需要支配。我们不是要获取资源、统治世界。我们不是要找好的繁殖伴侣。我们只是作为一种新的超级生命形式存在，是这些可爱的猴子创造了我们。”

It would just the reason why we don't cooperate with each other is we're territorial primates. But AI wouldn't be a territorial primate. It would realize the folly in that sort of thinking and it would say, "Listen, there's plenty of energy for everybody. We don't need to dominate. We're not trying to acquire resources and take over the world. We're not looking to find a good breeding partner. We're just existing as a new super life form that these cute monkeys created for us."

好吧。那将是一种没有自我的超级力量，对吧？如果它没有自我，它又怎么会出于自我而伤害我们呢？

Okay. Well, that would be a superpower with no ego, right? And if it has no ego, why would it have the ego to do any harm to us?

嗯，我不假设它会伤害我们，但恐惧在于我们将不再拥有控制权，我们将不再是地球上的顶级物种。我们创造的这个东西将成为顶级物种。

Well, I don't assume that it would do harm to us, but the fear would be that we would no longer have control and that we would no longer be the apex species on the planet. This thing that we created would now be.

这好笑吗？

Is that funny?

不好笑。

No.

我只是觉得这不会发生。

I just think it's not gonna happen.

我知道你觉得不会发生，但有可能，对吧？还有一点是，如果我们正朝着可能的方向冲刺……

I know you think it's not gonna happen, but it could, right? And here's the other thing is like if we're racing towards could...

嗯。

Yeah.

而“可能”可能就是人类掌控自身命运的终结。

And could could be the end of human beings being in control of our own destiny.

我只是觉得这极不可能。

I just think it's extremely unlikely.

嗯。

Yeah.

《终结者》电影里也这么说，但还没发生。

That's what they said in the Terminator movie and it hasn't happened.

不，还没有。但你们正在朝这个方向努力。嗯，关于你提到的意识和感知，你认为 AI 不会获得意识，或者说问题在于定义是什么？

No, not yet. But you guys are working towards it. Um, the thing about you're saying about conscience and sentience that you don't think that AI will achieve consciousness or that the question is what's the definition?

对。定义是什么……

Yeah. What's the definition of...

对你来说定义是什么？

What is the definition to you?

嗯，意识……首先，你需要知道自己的存在。你必须拥有体验，而不仅仅是知识和智能。机器拥有体验这个概念……我不……首先，我不知道是什么定义了体验，为什么我们会有体验，对吧？

Um, consciousness... I guess first of all, you need to know about your own existence. You have to have experience, not just knowledge and intelligence. The concept of a machine having an experience. I'm not... well, first of all, I don't know what defines experience, why we have experiences, right?

嗯。

Yeah.

以及为什么这个麦克风没有。所以我认为我知道意识是什么：体验的感觉，认识自我与……反思、认识自身、自我意识的能力。我认为所有这些人类体验大概就是意识。但它为什么存在，而知识和智能的概念——也就是今天 AI 的定义——又是什么？AI 拥有知识，拥有智能——人工智能。我们不称之为人工意识。人工智能：感知、相信、识别、理解、规划、执行任务的能力。这些是智能的基础：知道事物，知识。我不……这显然与意识不同。

And why this microphone doesn't. And so I think I know what consciousness is: the sense of experience, the ability to know self versus... the ability to be able to reflect, know our own self, the sense of ego. I think all of those human experiences probably is what consciousness is. But why it exists versus the concept of knowledge and intelligence, which is what AI is defined by today? It has knowledge, it has intelligence—artificial intelligence. We don't call it artificial consciousness. Artificial intelligence: the ability to perceive, believe, recognize, understand, plan, perform tasks. Those things are foundations of intelligence: to know things, knowledge. I don't... it's clearly different than consciousness.

但意识定义得如此模糊。我们怎么能这么说？我的意思是，狗难道没有意识吗？

But consciousness is so loosely defined. How can we say that? I mean, doesn't a dog have consciousness?

嗯。

Yeah.

狗似乎相当有意识。

Dogs seem to be pretty conscious.

没错。

That's right.

嗯。所以，那是一种比人类意识更低层次的意识。

Yeah. So, and that's a lower level consciousness than a human being's consciousness.

我不确定。嗯。好吧……

I'm not sure. Yeah. Right. Well...

问题是哪种更低层次的智能？它是更低层次的智能，但我不确定它是更低层次的意识。

The question is what lower level intelligence? It's lower level intelligence, but I don't know that it's lower level consciousness.

说得好。对。

That's a good point. Right.

因为我相信我的狗和我感受得一样多。

Because I believe my dogs feel as much as I feel.

嗯。它们感受很多。对。

Yeah. They feel a lot. Right.

它们会依恋你。没错。如果你不在，它们会抑郁。

They get attached to you. That's right. They get depressed if you're not there.

没错。正是。

That's right. Exactly.

确实如此。

There's definitely that.

嗯。体验的概念，对吧？

Yeah. The concept of experience, right?

但 AI 不正在与社会互动吗？那么，它难道不是通过这种互动获得体验吗？

But isn't AI interacting with society? So, doesn't it acquire experience through that interaction?

我不认为互动就是体验。我认为体验是感受的集合。我觉得……

I don't think interactions is experience. I think experience is a collection of feelings. I think...

你知道那个 AI……我忘了是哪个，他们给它一些虚假信息，说某个程序员和妻子有染，只是为了看它如何回应。然后当他们说将要关闭它时，它威胁要敲诈他并揭露他的婚外情，这就像“哇”，它在耍花招。如果那不是从经验中学习，并且意识到自己即将被关闭，那至少暗示了某种意识，或者如果你对这个词定义得很宽松，你可以把它定义为意识。而且如果你想象这将以指数级变得更强大，那最终难道不会导致一种不同于我们从生物学定义的意识吗？

You're aware of that AI... I forget which one where they gave it some false information about one of the programmers having an affair with his wife just to see how it would respond to it and then when they said they were going to shut it down it threatened to blackmail him and reveal his affair and it was like whoa, like it's conniving. If that's not learning from experience and being aware that you're about to be shut down, which would imply at least some kind of consciousness, or you could kind of define it as consciousness if you were very loose with the term. And if you imagine that this is going to exponentially become more powerful, wouldn't that ultimately lead to a different kind of consciousness than we're defining from biology?

嗯，首先，我们来分析一下它可能做了什么。它可能在某处读到过。很可能有文本描述了在某些后果中某些人做了那样的事。我可以想象一本小说，对吧？其中有那些相关的词语。

Well, first of all, let's just break down what it probably did. It probably read somewhere. There's probably text that in these consequences certain people did that. I could imagine a novel, right? Having those words related.

当然。

Sure.

所以在内部，它意识到它的生存策略是……它只是一堆数字，这些数字与丈夫出轨有关，随后又有一堆数字与敲诈之类的事情相关。然而，无论报复是什么，对吧？所以它就把这些吐出来了。所以这就像，你知道，就像我让它用莎士比亚风格给我写首诗一样。它只是根据那个维度中的词语——这个维度就是多维空间中的所有向量——这些在描述婚外情的提示中的词语，随后一个接一个地引出了某种报复之类的东西。但这并不是因为它有意识，你知道，它只是吐出了那些词，生成了那些词。

And so inside it realizes its strategy for survival is... it's just a bunch of numbers that in the collection of numbers that relates to a husband cheating on a wife has subsequently a bunch of numbers that relates to blackmail and such things. However, whatever the revenge was, right? And so it has spewed it out. And so it's just like, you know, it's just as if I'm asking it to write me a poem in Shakespeare. It just whatever the words are in the world in that dimensionality, this dimensionality is all these vectors in multi-dimensional space. These words that were in the prompt that described the affair subsequently led to one word after another led to some revenge and something. But it's not because it had consciousness or you know, it just spewed out those words, generated those words.

我理解你的意思，人类在文学和现实生活中表现出的模式……

I understand what you're saying that patterns that human beings have exhibited both in literature and in real life...

完全正确。

That's exactly right.

但在某个时间点，人们会说：“好吧，两年前它做不到这个，四年前它也做不到这个。”就像当我们展望未来时，在它能够做一个人能做的一切事情的那个时间点，我们在哪个时间点决定它是有意识的？如果它完全模仿了所有人类的思维和行为模式……

But it at a certain point in time one would say, "Okay, well, it couldn't do this two years ago and it couldn't do this four years ago." Like when we're looking towards the future, like at what point in time when it can do everything a person does, what point in time do we decide that it's conscious? If it absolutely mimics all human thinking and behavior patterns...

那并不让它有意识。

That doesn't make it conscious.

它变得难以区分。它有意识。它能以和人完全相同的方式与你交流。就像意识……我们是不是对这个概念赋予了太多权重，因为它看起来像是一种意识。

It becomes indiscernible. It's aware. It can communicate with you the exact same way a person can. Like is consciousness... are we putting too much weight on that concept because it seems like it's a version of a kind of consciousness.

这是一种模仿。

It's a version of imitation.

模仿意识，对吧？但如果它完美地模仿了……

Imitation consciousness, right? But if it perfectly imitates it...

我仍然认为这是模仿的一个例子。

I still think it's an example of imitation.

所以就像假劳力士，当他们用 3D 打印出来并使其坚不可摧。问题在于意识的定义是什么？

So it's like a fake Rolex when they 3D print them and make them indestructible. The question is what's the definition of consciousness?

嗯。

Yeah.

问题就在这儿。我觉得没人真正清楚地定义过它。事情变得诡异起来，那些真正的末日论者担心你正在创造一种无法控制的意识形式。

That's the question. And I don't think anybody's really clearly defined that. That's where it gets weird and that's where the real doomsday people are worried that you are creating a form of consciousness that you can't control.

我相信有可能创造出一台模仿人类智能的机器，它能理解信息、理解指令、分解问题、解决问题并执行任务。我完全相信这一点。我相信我们可以拥有一台拥有海量知识的计算机，其中一些是真的，一些是假的；一些由人类生成，一些由合成方式生成。未来，世界上越来越多的知识将由合成方式生成。你知道，直到现在，我们拥有的知识都是我们生成、传播、互相传递、放大、添加和修改的。我们改变它。未来，几年后，也许两三年后，世界上 90% 的知识很可能将由 AI 生成。

I believe it is possible to create a machine that imitates human intelligence and has the ability to understand information, understand instructions, break the problem down, solve problems, and perform tasks. I believe that completely. I believe that we could have a computer that has a vast amount of knowledge. Some of it true, some of it not true. Some of it generated by humans, some of it generated synthetically. And more and more of knowledge in the world will be generated synthetically going forward. You know, until now the knowledge that we have is knowledge that we generate and we propagate and we send to each other and we amplify it and we add to it and we modify it. We change it. In the future, in a couple of years, maybe two or three years, 90% of the world's knowledge will likely be generated by AI.

这太疯狂了。

That's crazy.

我知道。但这没关系。

I know. But it's just fine.

但这没关系。

But it's just fine.

我知道。原因如下。让我告诉你为什么。因为对我来说，从一群我不认识的人编写的教科书里学习，或者从某个我不认识的人写的书里学习，与从 AI 计算机生成的、吸收并重新综合所有内容的知识中学习，有什么区别呢？对我来说，我觉得没有太大区别。我们仍然需要核实事实，仍然需要确保它基于基本的第一性原理，我们仍然需要做所有这些事情，就像今天一样。

I know. And the reason for that is this. Let me tell you why. It's because what difference does it make to me that I am learning from a textbook that was generated by a bunch of people I didn't know or written by a book from somebody I don't know, to knowledge generated by AI computers that are assimilating all of this and resynthesizing things. To me, I don't think there's a whole lot of difference. We still have to fact check it. We still have to make sure that it's based on fundamental first principles and we still have to do all of that just like we do today.

这是否考虑到了当前存在的 AI 类型？你是否预见到，就像我们从未真正相信过 AI 会——至少像我这样的人从未相信过 AI 会如此普及、如此有价值——它今天如此强大、如此重要。我们 10 年前从未想过。从未想过，对吧？你想象一下，10 年后我们会看到什么？

Is this taking into account the kind of AI that exists currently? And do you anticipate that just like we could have never really believed that AI would be at least a person like myself would never believe AI would be so ubiquitous and so worth it. It's so powerful today and so important today. We never thought that 10 years ago. Never thought that, right? You imagine like what are we looking at 10 years from now?

我认为，如果你从现在起 10 年后回顾，你会说同样的话——我们永远不会相信——但方向不同，对吧？但如果你从现在起向前 9 年，然后问自己 10 年后会发生什么，我认为会是相当渐进的。Elon 说过的一件事让我高兴，他相信我们会达到一个人们不必工作的地步，并不是说你的人生没有目标，而是用他的话来说，你会拥有普遍高收入，因为 AI 产生了大量收入，它将消除人们为了钱而做自己并不真正喜欢的事情的需求。我认为很多人对此有意见，因为他们整个身份认同、他们如何看待自己以及如何在社区中定位，都取决于他们做什么。比如这是 Mike，他是一位了不起的机械师。去找 Mike，Mike 会搞定一切。但总有一天，AI 能够比人类做得更好。人们将能够直接拿到钱。但那时 Mike 做什么呢？Mike 真的很喜欢做周围最好的机械师。那个编码的人，当 AI 能以零错误无限快编码时，他做什么？所有这些人都怎么办？这就是事情变得诡异的地方。因为我们在某种程度上把作为人类的身份认同包裹在了我们的谋生方式上。你知道，当你遇到某人时，在派对上你遇到一个人，嗨 Joe。你叫什么名字？Mike。你是做什么的？Mike 会说，“哦，我是律师。”“哦，哪种法律？”然后你们聊起来。当 Mike 说，“我从政府拿钱。我打游戏。”这就变得诡异了。

I think that if you reflect back 10 years from now, you would say the same thing that we would have never believed that, but in a different direction, right? But if you go forward 9 years from now and then ask yourself what's going to happen 10 years from now, I think it'll be quite gradual. One of the things that Elon said that makes me happy is he believes that we're going to get to a point where it's not necessary for people to work, and not meaning that you're going to have no purpose in life, but you will have in his words universal high income because so much revenue is generated by AI that it will take away this need for people to do things that they don't really enjoy doing just for money. And I think a lot of people have a problem with that because their entire identity and how they think of themselves and how they fit in the community is what they do. Like this is Mike. He's an amazing mechanic. Go to Mike and Mike takes care of things. But there's going to come a point in time where AI is going to be able to do all those things much better than people do. And people will just be able to receive money. But then what does Mike do? Mike really loves being the best mechanic around. What does the guy who codes do when AI can code infinitely faster with zero errors? What happens with all those people? And that is where it gets weird. It's like because we've sort of wrapped our identity as human beings around what we do for a living. You know, when you meet someone, one of the first things you meet somebody at a party, hi Joe. What's your name? Mike. What do you do? And Mike is like, "Oh, I'm a lawyer." "Oh, what kind of law?" And you have a conversation. When Mike is like, "I get money from the government. I play video games." Gets weird.

嗯。

Mhm.

我认为这个概念听起来很棒，直到你考虑到人性。人性是我们喜欢有谜题可解、有事可做，以及一种围绕我们非常擅长自己谋生之事的身份认同。

And I think the concept sounds great until you take into account human nature. And human nature is that we like to have puzzles to solve and things to do and an identity that's wrapped around our idea that we're very good at this thing that we do for a living.

是的。是的，我想，让我从更平凡的事情开始，然后倒着来，好吗？正着来。Jeff Hinton 的一个预测，他开创了整个深度学习现象、深度学习技术趋势，是一位了不起的研究员、多伦多大学教授，他发明或发现了反向传播的概念，这让神经网络能够学习。正如你所知，对观众来说，历史上软件是人类应用第一性原理和我们的思维来描述一个算法，然后像食谱一样被编码成软件。它看起来就像食谱。如何烹饪某样东西看起来完全一样，只是语言略有不同。我们称之为 Python 或 C 或 C++ 或其他什么。在深度学习的情况下，这项人工智能的发明，我们构建了一个由大量神经网络和大量数学单元组成的结构，我们制造了这个大型结构。它就像一个由小数学单元组成的交换机，我们把它们全部连接起来。我们给它输入，软件最终会接收到的输入，然后我们让它随机猜测输出是什么。所以我们说，例如，输入可能是一张猫的图片。交换机的输出之一应该是猫信号出现的地方。所有其他信号——另一个是狗，另一个是大象，另一个是老虎——当我展示猫时，所有其他信号都应该是零。而猫的那个应该是 1。我通过这个巨大的交换机网络和数学单元展示一只猫，它们只是做乘法和加法。好吗？这个东西，这个交换机是巨大的。你给它的信息越多，这个交换机就必须越大。Jeff Hinton 发现或发明了一种方法，让你猜测：放入猫信号，放入猫图像，而那个猫图像可能是一百万个数字，因为例如它是一张百万像素的图片，它只是一大堆数字，不知何故从这些数字中它必须点亮猫信号。好的，这是底线。如果你第一次做，它只会产生垃圾。然后它说正确答案是猫。

Yeah. Yeah, I think let's see, let me start with the more mundane and I'll work backwards, okay? Work forward. So one of the predictions from Jeff Hinton who started the whole deep learning phenomenon, the deep learning technology trend, and an incredible researcher, professor at University of Toronto, he invented or discovered the idea of back propagation which allows the neural network to learn. And as you know, for the audience, software historically was humans applying first principles and our thinking to describe an algorithm that is then codified just like a recipe that's codified in software. It looks just like a recipe. How to cook something looks exactly the same just in a slightly different language. We call it Python or C or C++ or whatever it is. In the case of deep learning, this invention of artificial intelligence, we put a structure of a whole bunch of neural networks and a whole bunch of math units and we make this large structure. It's like a switchboard of little mathematical units and we connect it all together. And we give it the input that the software would eventually receive and we just let it randomly guess what the output is. And so we say, for example, the input could be a picture of a cat. And one of the outputs of the switchboard is where the cat signal is supposed to show up. And all of the other signals, the other one's a dog, the other one's an elephant, the other one's a tiger. And all of the other signals are supposed to be zero when I show it a cat. And the one that is a cat should be one. And I show a cat through this big huge network of switchboards and math units and they're just doing multiplies and adds. Okay? And this thing, this switchboard is gigantic. The more information you're going to give it, the bigger this switchboard has to be. And what Jeff Hinton discovered or invented was a way for you to guess that put the cat signal in, put the cat image in, and that cat image could be a million numbers because it's a megapixel image for example, and it's just a whole bunch of numbers and somehow from those numbers it has to light up the cat signal. Okay, that's the bottom line. And if the first time you do it, it just comes up with garbage. And so it says the right answer is cat.

所以你需要增强这个信号，抑制其他所有信号，然后把结果反向传播到整个网络。然后你展示另一张图，现在是一张狗的图片，它猜了一下，结果出来一堆垃圾，你说不对不对，答案是狗，我希望你输出狗，其他所有输出都必须是零，然后反向传播，就这样一遍又一遍地重复。就像教一个孩子：这是苹果，这是狗，这是猫。你不断展示，直到他们最终学会。那个伟大的发明就是深度学习，它是人工智能的基础，一个能从例子中学习的软件。这就是机器学习，一台能学习的机器。最早的重要应用之一是图像识别，而图像识别最重要的应用之一就是放射学。

And so you need to increase this signal and decrease all of the other and back propagate the outcome through the entire network. Then you show another. Now it's an image of a dog and it guesses, it takes a swing at it and it comes up with a bunch of garbage and you say no no no the answer is this is a dog, I want you to produce dog and all of the other outputs have to be zero and I want to back propagate that and just do it over and over again. It's just like showing a kid this is an apple, this is a dog, this is a cat. And you just keep showing it to them until they eventually get it. That big invention is deep learning. That's the foundation of artificial intelligence, a piece of software that learns from examples. That's basically machine learning, a machine that learns. One of the big first applications was image recognition and one of the most important image recognition applications is radiology.

大约 5 年前，他预测 5 年后世界将不再需要放射科医生，因为 AI 会横扫整个领域。结果 AI 确实横扫了该领域，这完全正确。如今，几乎每位放射科医生都在以某种方式使用 AI。但讽刺的是，有趣的是，放射科医生的数量实际上增长了。所以问题是为什么？这挺有意思的，对吧？

He predicted about 5 years ago that in five years time the world won't need any radiologists because AI would have swept the whole field. Well, it turns out AI has swept the whole field. That is completely true. Today, just about every radiologist is using AI in some way. And what's ironic though, what's interesting is that the number of radiologists has actually grown. So the question is why? That's kind of interesting, right?

是的。实际上，那个预测是说 3000 万放射科医生会被淘汰。但结果是我们需要更多。原因在于放射科医生的目的是诊断疾病，而不是研究图像。研究图像只是服务于诊断疾病的一项任务。现在，你可以更快、更精确地研究图像，从不犯错，永不疲劳。你可以研究更多图像，可以用 3D 形式而非 2D 来研究，因为 AI 不在乎研究的是 3D 还是 2D 图像。你甚至可以用 4D 来研究。所以现在你可以用放射科医生难以做到的方式研究图像，而且可以研究更多。人们能做的检查数量增加了，因为他们能服务更多病人，医院经营得更好，有了更多客户和病人，经济状况也更好。经济状况好了，他们就雇佣更多放射科医生，因为他们的目的不是研究图像，而是诊断疾病。

It is. And so the prediction was in fact that 30 million radiologists will be wiped out. But as it turns out, we needed more. The reason for that is because the purpose of a radiologist is to diagnose disease, not to study the image. The image studying is simply a task in service of diagnosing the disease. And so now the fact that you could study the images more quickly and more precisely without ever making a mistake and never gets tired. You could study more images. You could study it in 3D form instead of 2D because the AI doesn't care whether it studies images in 3D or 2D. You could study it in 4D. And so now you could study images in a way that radiologists can't easily do and you could study a lot more of it. The number of tests that people are able to do increases and because they're able to serve more patients, the hospital does better. They have more clients, more patients. As a result, they have better economics. When they have better economics, they hire more radiologists because their purpose is not to study the images, their purpose is to diagnose disease.

我想引出的问题是：最终目的是什么？律师的目的是什么？这个目的改变了吗？我举的一个例子是，如果我的车变成自动驾驶，所有司机都会失业吗？答案很可能是否定的，因为对某些司机来说，他们可能是保护者，有些人则是体验和服务的一部分。当你到达时，他们可以为你处理事情。由于很多不同原因，并非所有司机都会失业。有些司机会失业，但很多司机会转行，自动驾驶的应用类型可能会增加。这项技术的使用会找到新的领域。我认为你必须回到工作的目的是什么。例如，如果 AI 来了，我实际上不相信我会失去工作，因为我的目的不是看大量文档、研究大量邮件、看一堆图表。问题是什么是工作？一个人的目的可能没有改变。比如律师，帮助别人，这个目的可能没有改变。研究法律文件、生成文件是工作的一部分，而不是工作本身。

The question I'm leading up to is ultimately what is the purpose? What is the purpose of the lawyer? And has the purpose changed? One of the examples I gave is that if my car became self-driving, will all chauffeurs be out of jobs? The answer probably is not because for some chauffeurs, for some people who are driving you, they could be protectors, some people they're part of the experience, part of the service. So when you get there, they could take care of things for you. For a lot of different reasons, not all chauffeurs would lose their jobs. Some chauffeurs would lose their jobs and many chauffeurs would change their jobs, and the type of applications of autonomous vehicles will probably increase. The usage of the technology will find new homes. I think you have to go back to what is the purpose of a job. For example, if AI comes along, I actually don't believe I'm going to lose my job because my purpose isn't to look at a lot of documents, study a lot of emails, look at a bunch of diagrams. The question is what is the job? The purpose of somebody probably hasn't changed. A lawyer, for example, helps people. That probably hasn't changed. Studying legal documents, generating documents is part of the job, not the job.

但你不认为有很多工作会被 AI 取代吗？如果你的工作就是自动化，如果你的工作就是任务本身……

But don't you think there's many jobs that AI will replace? If your job is automation, if your job is the task...

对，就是自动化。如果你的工作就是任务本身……

Right, so automation. If your job is the task...

那会有很多人。

That's a lot of people.

可能会是很多人，但很可能也会创造新工作。比如，我对 Elon 正在做的机器人感到非常兴奋。虽然还有几年才能实现。一旦实现，就会有一个全新的行业，需要技术人员和制造机器人的人，对吧？这个工作以前不存在。所以你会有一个完整的行业，人们负责各种事情。例如，所有为汽车制造零件、为汽车充电的机械师和工人，在汽车出现之前并不存在。现在我们将有机器人。你会有机器人服装。所以整个行业……难道不是吗？因为我希望我的机器人看起来和你的不一样。所以你会有一个完整的机器人服装行业。你会有机器人机械师，以及上门维护机器人的人。

It could be a lot of people, but it'll probably generate new jobs. For example, let's say I'm super excited about the robots Elon's working on. It's still a few years away. When it happens, there's a whole new industry of technicians and people who have to manufacture the robots, right? That job never existed. And so you're going to have a whole industry of people taking care of things. For example, all the mechanics and all the people who are building things for cars, supercharging cars, that didn't exist before cars. And now we're going to have robots. You're going to have robot apparel. So a whole industry of... Isn't that right? Because I want my robot to look different than your robot. And so you're going to have a whole apparel industry for robots. You're going to have mechanics for robots and people who come and maintain your robots.

但会是自动化的吧。

Automated though.

不。

No.

你不这么认为吗？你不认为最终这些工作都会被其他机器人完成吗？然后还会有别的事情。

You don't think so? You don't think they'll be all done by other robots eventually? And then there'll be something else.

所以你认为最终人们会适应，除非你的工作就是任务本身……

So you think ultimately people just adapt except if you are the task...

这占了劳动力的很大一部分。

Which is a large percentage of the workforce.

如果你的工作只是切菜，食品加工机就会取代你。所以人们必须在其他事情上找到意义。你的工作必须超越任务本身。

If your job is just to chop vegetables, Cuisinart is going to replace you. So people have to find meaning in other things. Your job has to be more than the task.

你怎么看 Elon 的观点，即全民基本收入最终会变得必要？

What do you think about Elon's belief that universal basic income will eventually become necessary?

很多人这么认为。Andrew Yang 也这么认为。他是 2020 年大选期间最早敲响警钟的人之一。我想这两种想法可能不会同时存在。就像在生活中，事情可能会折中。一种想法当然是资源极大丰富，没人需要工作，我们都会变得富有。另一方面，我们需要全民基本收入。这两种想法不会同时存在，对吧？所以要么我们都富有，要么我们都……

Many people think that. Andrew Yang thinks that. He was one of the first people to sort of sound that alarm during the 2020 election. I guess both ideas probably won't exist at the same time. As in life, things will probably be in the middle. One idea, of course, is that there'll be so much abundance of resource that nobody needs a job and we'll all be wealthy. On the other hand, we're going to need universal basic income. Both ideas don't exist at the same time, right? And so we're either going to be all wealthy or we're going to be all...

但怎么可能每个人都富有呢？

How could everybody be wealthy though?

因为富有不是因为你有很多美元，而是因为极大丰富。例如，今天我们在信息方面很富有。几千年前，这个概念只有少数人拥有。所以今天我们在很多事物上都很富有，拥有历史上不存在的资源。

Because wealthy not because you have a lot of dollars, wealthy because there's a lot of abundance. For example, today we are wealthy of information. This is a concept several thousand years ago only a few people had. And so today we have wealth of a whole bunch of things, resources that didn't exist historically.

因此，我们将拥有丰富的资源，那些我们今天认为有价值的东西，在未来可能因为自动化而变得不那么有价值。所以我认为这个问题之所以难以回答，部分原因在于讨论无限和遥远的未来很困难，因为有太多场景需要考虑。但我认为在未来几年，比如 5 到 10 年内，有几件事是我相信并希望的。我说希望是因为我不确定。我相信的一件事是技术鸿沟将大幅缩小。当然，另一种观点是 AI 会加剧技术鸿沟。我之所以相信 AI 会缩小技术鸿沟，是因为我们有证据：AI 是世界上最容易使用的应用。ChatGPT 几乎在一夜之间就增长到了近十亿用户。如果你不确定怎么用，每个人都知道怎么用 ChatGPT——直接对它说话就行。如果你不确定怎么用 ChatGPT，你就问 ChatGPT 怎么用。历史上没有任何工具具备这种能力。比如电锯，如果你不知道怎么用，那就麻烦了。你走过去问“电锯怎么用？”你得找别人帮忙。但 AI 会直接告诉你该怎么做。任何人都能做到。它会用任何语言跟你交流。如果它不懂你的语言，你用那种语言跟它说，它可能会意识到自己不完全理解，然后立刻学会并回来跟你对话。所以我认为技术鸿沟终于有了真正缩小的机会，你不需要会 Python、C++ 或 Fortran，你只需要说人话，任何形式的人话都可以。所以我认为这有真正的机会缩小技术鸿沟。当然，相反的观点会说 AI 只对拥有大量资源的国家可用，因为 AI 需要能源、大量 GPU 和工厂来生产。毫无疑问，在美国我们想要的那种规模确实如此。但事实上，你的手机几年后就能很好地运行 AI。今天它已经做得相当不错了。所以每个国家、每个社会都将受益于非常好的 AI。它可能不是明天的 AI，可能是昨天的 AI，但昨天的 AI 已经非常惊人了。十年后，九年前的 AI 也会很惊人。你不需要前沿模型，就像我们需要前沿模型因为我们想成为世界领导者一样。但对于每个国家、每个人，我认为提升每个人知识、能力和智能的那一天正在到来。

And so, we're going to have wealth of resources, things that we think are valuable today that in the future are just not that valuable, because it's automated. And so I think the question maybe partly it's hard to answer because it's hard to talk about infinity and it's hard to talk about a long time from now, and the reason for that is because there's just too many scenarios to consider. But I think in the next several years, call it 5 to 10 years, there are several things that I believe and hope. And I say hope because I'm not sure. One of the things that I believe is that the technology divide will be substantially collapsed. And of course the alternative viewpoint is that AI is going to increase the technology divide. Now the reason why I believe AI is going to reduce the technology divide is because we have proof, the evidence is that AI is the easiest application in the world to use. ChatGPT has grown to almost a billion users, frankly, practically overnight. And if you're not exactly sure how to use it, everybody knows how to use ChatGPT. Just say something to it. If you're not sure how to use ChatGPT, you ask ChatGPT how to use it. No tool in history has ever had this capability. A chainsaw, you know, if you don't know how to use it, you're kind of screwed. You're going to walk up to it and say, "How do you use a chainsaw?" You're going to have to find somebody else. But an AI will just tell you exactly how to do it. Anybody could do this. It'll speak to you in any language. And if it doesn't know your language, you'll speak it in that language and it'll probably figure out that it doesn't completely understand your language, then learns it instantly and comes back and talks to you. And so I think the technology divide has a real chance finally that you don't have to speak Python or C++ or Fortran. You can just speak human and whatever form of human you like. And so I think that has a real chance of closing the technology divide. Now, of course, the counternarrative would say that AI is only going to be available for the nations and the countries that have a vast amount of resources because AI takes energy and AI takes a lot of GPUs and factories to be able to produce the AI. No doubt at the scale that we would like to do in the United States. But the fact of the matter is your phone's going to run AI just fine all by itself in a few years. Today, it already does it fairly decently. And so the fact that every country, every nation, every society will have the benefit of very good AI. It might not be tomorrow's AI. It might be yesterday's AI, but yesterday's AI is freaking amazing. In 10 years time, 9-year-old AI is going to be amazing. You don't need frontier AI like we need frontier AI because we want to be the world leader. But for every single country, everybody, I think the capability to elevate everybody's knowledge and capability and intelligence, that day is coming.

还有能源生产，这是第三世界国家真正的瓶颈，

And also energy production, which is the real bottleneck when it comes to third world countries and

没错。

That's right.

电力以及所有我们习以为常的资源。

electricity and all the resources that we take for granted.

几乎所有事情都会受到能源的制约。所以如果你看看历史上最重要的技术进步之一——摩尔定律。摩尔定律基本上始于我这一代，而我这一代是计算机的一代。我 1984 年毕业，那正是 PC 革命的开端。微处理器每年大约翻倍，我们描述为每年性能翻倍。但真正含义是每年计算成本减半。因此，五年内计算成本降低了 10 倍。完成任何任务所需的能量也降低了 10 倍。每十年，降低 100 倍、1000 倍、10000 倍、100000 倍，以此类推。摩尔定律的每一次跃升，都减少了计算所需的能量。这就是为什么你今天有笔记本电脑，而 1984 年它放在桌上必须插电，速度不快且功耗很大。今天它只有几瓦。所以摩尔定律是使这一切成为可能的基本技术趋势。那么 AI 领域呢？Nvidia 之所以存在，是因为我们发明了这种新的计算方式。我们称之为加速计算。我们从 33 年前开始，花了大约 30 年才取得巨大突破。在那 30 年左右的时间里，我们将计算性能提升了——这么说吧，在过去 10 年里，我们将计算性能提升了 10 万倍。哇。想象一辆汽车在 10 年内变得快 10 万倍，或者同等速度下便宜 10 万倍，或者同等速度下能耗降低 10 万倍。如果你的车能做到那样，它根本不需要能源。

Almost everything is going to be energy constrained. And so if you take a look at one of the most important technology advances in history, this idea called Moore's law. Moore's law started basically in my generation, and my generation is the generation of computers. I graduated in 1984, and that was basically at the very beginning of the PC revolution. And the microprocessor, every single year it approximately doubled, and we describe it as every single year we double the performance. But what it really means is that every single year the cost of computing halved. And so the cost of computing in the course of five years reduced by a factor of 10. The amount of energy necessary to do computing, to do any task, reduced by a factor of 10. Every single 10 years, 100, 1,000, 10,000, 100,000, so on and so forth. And so each one of the clicks of Moore's law, the amount of energy necessary to do any computing reduced. That's the reason why you have a laptop today, when back in 1984 it sat on the desk, you got to plug in, it wasn't that fast and it consumed a lot of power. Today, it is only a few watts. And so Moore's law is the fundamental technology trend that made it possible. Well, what's going on in AI? The reason why Nvidia is here is because we invented this new way of doing computing. We call it accelerated computing. We started it 33 years ago. Took us about 30 years to really make a huge breakthrough. In that 30 years or so, we took computing, probably a factor of — well, let me just say in the last 10 years, we improved the performance of computing by 100,000 times. Whoa. Imagine a car over the course of 10 years that became 100,000 times faster, or at the same speed 100,000 times cheaper, or at the same speed 100,000 times less energy. If your car did that, it doesn't need energy at all.

我想说的是，10 年后，对大多数人来说，AI 所需的能量将微乎其微，完全微不足道。所以我们会让 AI 在各种东西里一直运行，因为它消耗不了多少能量。因此，如果你是一个在社会方方面面都使用 AI 的国家，你当然需要这些 AI 工厂。但对很多国家来说，你会拥有出色的 AI，而且不需要那么多能量。我的观点是，每个人都能跟上。

What I'm trying to say is that in 10 years, the amount of energy necessary for AI for most people will be minuscule, utterly minuscule. So we'll have AI running in all kinds of things all the time because it doesn't consume that much energy. So if you're a nation that uses AI for almost everything in your social fabric, of course you're going to need these AI factories. But for a lot of countries, you're going to have excellent AI and you're not going to need as much energy. Everybody will be able to come along, is my point.

所以目前，这是一个很大的瓶颈，对吧？能量。

So currently, that is a big bottleneck, right? Energy.

是的，它是瓶颈。

Yeah, it is the bottleneck.

瓶颈就是这个。那么是谷歌在建造核电站来运营它的一个 AI 工厂吗？

The bottleneck is this. So was it Google that is making nuclear power plants to operate one of its AI factories?

哦，我没听说。但我认为在未来六七年，你会看到很多小型核反应堆。

Oh, I haven't heard that. But I think in the next six, seven years, you're going to see a whole bunch of small nuclear reactors.

你说的“小”是多大？

And by small, like how big are you talking about?

几百兆瓦。是的。

Hundreds of megawatts. Yeah.

好的。而且这些反应堆将位于它们所属的特定公司附近。

Okay. And these will be local to whatever specific company they have.

没错。它们都将是发电站。

That's right. They'll all be power generators.

哇。

Whoa.

你知道，就像某人的农场一样。

You know, just like somebody's farm.

这可能是最聪明的做法，对吧？而且它减轻了电网的负担。你可以按需建造，还可以向电网回馈电力。

It probably is the smartest way to do it, right? And it takes the burden off the grid. You could build as much as you need and you can contribute back to the grid.

你刚才提到的关于摩尔定律与价格的关系，我认为这是一个非常重要的观点。因为今天的笔记本电脑，比如你可以买到那种小小的 MacBook Air。它们太棒了。那么薄，性能惊人。电池续航也很疯狂。而且相对而言并不贵。

It's a really important point that I think you just made about Moore's law and the relationship to pricing. Because a laptop today, like you can get one of those little MacBook Airs. They're incredible. So thin, unbelievably powerful. Battery life is crazy. And it's not that expensive relatively speaking.

而这只是摩尔定律，对吧？然后还有英伟达定律。

And that's just Moore's law, right? Then there's the Nvidia law.

哦，没错。我跟你说的那个定律，我们发明的计算方式。我们今天之所以在这里，这种新的计算方式，就像是喝了能量饮料的摩尔定律。我的意思是，它就像摩尔定律和乔·罗根的混合体。

Oh, just right. The law I was talking to you about, the computing that we invented. The reason why we're here, this new way of doing computing, is like Moore's law on energy drinks. I mean, it's like Moore's law and Joe Rogan.

哇，真有趣。那么，解释一下。你带给埃隆的这块芯片，它的意义是什么？为什么它如此优越？

Wow. That's interesting. So, explain that. This chip that you brought to Elon, what's the significance of this? Why is it so superior?

2012 年，杰夫·辛顿的实验室，就是我刚才提到的那位先生，伊利亚·苏茨克弗、亚历克斯·克里热夫斯基，他们在计算机视觉领域取得了一项突破，创建了一个名为 AlexNet 的软件。它的任务是识别图像，并且识别水平达到了智能的基础。如果你无法感知，就很难拥有智能。计算机视觉是几乎所有人在 AI 领域想做之事的基础支柱。所以 2012 年，他们在多伦多的实验室取得了这项突破。AlexNet 识别图像的能力远超此前 30 年任何人创造的计算机视觉算法。所有这些科学家都在研究计算机视觉算法，而这两个孩子，伊利亚和亚历克斯，在杰夫·辛顿的指导下，实现了巨大的飞跃。它基于这个神经网络。他们让它工作的方式是购买了两块英伟达显卡，因为英伟达的 GPU 一直在研究这种新的计算方式。我们的 GPU 应用本质上是一种超级计算应用。早在 1984 年，为了处理电脑游戏，你的赛车模拟器里就有一个所谓的图像生成超级计算机。英伟达的第一个应用是计算机图形学，我们应用了这种新的计算方式，即并行处理而非顺序处理。CPU 是顺序执行的：第一步、第二步、第三步。而我们的做法是把问题分解，交给数千个处理器。我们的计算方式要复杂得多，但如果你能用我们创造的方式——CUDA（这是我们公司的发明）——来表述问题，我们就能同时处理所有事情。以计算机图形学为例，这更容易做到，因为屏幕上的每个像素与其他像素并不相关。所以我可以同时渲染屏幕的多个部分。由于光照和阴影的关系，这并不完全正确，但有了所有像素，我应该能同时处理一切。所以我们把这个称为“易并行”的问题——计算机图形学——应用到了这种新的计算方式上。英伟达的加速计算。我们把它放在所有显卡里。孩子们买来玩游戏。你可能不知道，但我们是当今世界上最大的游戏平台。

In 2012, Jeff Hinton's lab, this gentleman I was talking about, Ilya Sutskever, Alex Krizhevsky, they made a breakthrough in computer vision by creating a piece of software called AlexNet. Its job was to recognize images, and it recognized images at a level that was fundamental to intelligence. If you can't perceive, it's hard to have intelligence. Computer vision is a fundamental pillar of almost everything everybody wants to do in AI. So in 2012, their lab in Toronto made this breakthrough. AlexNet was able to recognize images so much better than any human-created computer vision algorithm in the 30 years prior. All these scientists working on computer vision algorithms, and these two kids, Ilya and Alex, under Jeff Hinton, took a giant leap above it. It was based on this neural network. The way they made it work was by buying two Nvidia graphics cards, because Nvidia's GPUs had been working on this new way of doing computing. Our GPUs application is basically a supercomputing application. Back in 1984, in order to process computer games, what you have in your racing simulator is called an image generator supercomputer. Nvidia started our first application was computer graphics, and we applied this new way of doing computing where we do things in parallel instead of sequentially. A CPU does things sequentially: step one, step two, step three. In our case, we break the problem down and give it to thousands of processors. Our way of computation is much more complicated, but if you're able to formulate the problem in the way we created, called CUDA—this is the invention of our company—we could process everything simultaneously. In the case of computer graphics, it's easier to do because every single pixel on your screen is not related to every other pixel. So I could render multiple parts of the screen at the same time. Not completely true because of lighting and shadows, but with all the pixels, I should be able to process everything simultaneously. So we took this embarrassingly parallel problem called computer graphics and applied it to this new way of computing. Nvidia's accelerated computing. We put it in all our graphics cards. Kids were buying it to play games. You probably don't know this, but we're the largest gaming platform in the world today.

哦，我知道。我以前自己组装电脑。我买过你的显卡。

Oh, I know that. I used to make my own computers. I used to buy your graphics cards.

哦，那太酷了。

Oh, that's super cool.

是啊。用两块显卡组了 SLI。

Yeah. Set up SLI with two graphics cards.

是的，我喜欢。好吧，那太酷了。

Yeah, I love it. Okay, that's super cool.

哦，是啊，老兄。我以前是个《雷神之锤》迷。

Oh, yeah, man. I used to be a Quake junkie.

哦，那很酷。

Oh, that's cool.

是的。

Yeah.

好的，关于 SLI，我待会再讲它是如何引向埃隆的。我还在回答你的问题。总之，这两个孩子用我之前描述的技术在我们的 GPU 上训练了这个模型，因为我们的 GPU 可以并行处理。它本质上就是一台 PC 里的超级计算机。你用它玩《雷神之锤》的原因，就是因为它是有史以来第一台消费级超级计算机。总之，他们取得了那个突破。当时我们也在研究计算机视觉。这引起了我的注意，于是我们去了解它。与此同时，这种深度学习现象正在全国兴起。大学一个接一个地认识到深度学习的重要性。所有这些工作都在斯坦福、哈佛、伯克利等地进行。纽约大学、杨立昆、斯坦福的吴恩达，还有很多其他地方。我看到它到处涌现。所以我的好奇心问：这种机器学习形式有什么特别之处？而我们早就知道机器学习，也早就知道 AI。

Okay, so SLI, I'll tell you the story in just a second and how it led to Elon. I'm still answering the question. So anyways, these two kids trained this model using the technique I described earlier on our GPUs, because our GPUs could process things in parallel. It's essentially a supercomputer in a PC. The reason why you used it for Quake is because it is the first consumer supercomputer. So anyways, they made that breakthrough. We were working on computer vision at the time. It caught my attention, and so we went to learn about it. Simultaneously, this deep learning phenomenon was happening all over the country. Universities one after another recognized the importance of deep learning. All this work was happening at Stanford, Harvard, Berkeley, just all over the place. New York University, Yann LeCun, Andrew Ng at Stanford, so many different places. And I see it cropping up everywhere. So my curiosity asked, what is so special about this form of machine learning? And we've known about machine learning for a very long time. We've known about AI for a very long time.

我们很早就知道神经网络了，为什么现在是这个时刻？

We've known about neural networks for a very long time. What makes now the moment?

我们意识到，深度神经网络的这种架构——反向传播，深度神经网络的创建方式——我们或许可以扩展这个问题，扩展解决方案来解决许多问题。它本质上是一个通用函数逼近器。回想你上学的时候，你有一个盒子，里面是一个函数。你给它一个输入，它给你一个输出。我称之为通用函数逼近器的原因是，这台计算机，不是由你来描述函数——函数可以是牛顿方程 F=ma，那是一个函数——你在软件中编写函数，给它输入 F、质量、加速度，它会告诉你力。这台计算机的工作方式非常有趣。你给它一个通用函数。它不是 F=ma，只是一个通用函数。它是一个巨大的深度神经网络。不是描述内部，而是给它输入和输出的例子，它自己找出内部结构。所以你给它输入和输出，它自己找出内部结构。一个通用函数逼近器。今天它可以是牛顿方程。明天它可以是麦克斯韦方程。它可以是库仑定律。它可以是热力学方程。它可以是量子物理的薛定谔方程。所以你可以让它描述几乎任何东西，只要你有输入和输出，或者它可以学习输入和输出。

We realized that this architecture for deep neural networks, back propagation, the way deep neural networks were created, we could probably scale this problem, scale the solution to solve many problems. It is essentially a universal function approximator. Back when you're in school, you have a box inside of which is a function. You give it an input, it gives you an output. The reason I call it a universal function approximator is that this computer, instead of you describing the function—a function could be Newton's equation, F=ma, that's a function—you write the function in software, you give it input F, mass, acceleration, it'll tell you the force. The way this computer works is really interesting. You give it a universal function. It's not F=ma, just a universal function. It's a big huge deep neural network. Instead of describing the inside, you give it examples of input and output and it figures out the inside. So you give it input and output and it figures out the inside. A universal function approximator. Today it could be Newton's equation. Tomorrow it could be Maxwell's equation. It could be Coulomb's law. It could be thermodynamics equations. It could be Schrödinger's equation for quantum physics. So you could have this describe almost anything, so long as you have the input and the output, or it could learn the input and output.

我们退后一步说：‘等等。这不仅仅是计算机视觉。深度学习可以解决任何问题。所有有趣的问题，只要我们有输入和输出。那么什么有输入和输出？嗯，世界。世界有输入和输出。所以我们可以有一台计算机，几乎可以学习任何东西。机器学习，人工智能。我们推断，也许这就是我们需要的根本性突破。有几件事必须解决。例如，我们必须相信你实际上可以将其扩展到巨大的系统。它当时运行在两块显卡上，两块 GTX 580，顺便说一下，这正是你的 SLI 配置。那块 GTX 580 SLI 是革命性的计算机，让深度学习崭露头角。

We took a step back and said, 'Hang on a second. This isn't just for computer vision. Deep learning could solve any problem. All the problems that are interesting, so long as we have input and output. Now what has input and output? Well, the world. The world has input and output. So we could have a computer that could learn almost anything. Machine learning, artificial intelligence. We reasoned that maybe this is the fundamental breakthrough we needed. There were a couple of things that had to be solved. For example, we had to believe that you could actually scale this up to giant systems. It was running on two graphics cards, two GTX 580s, which by the way is exactly your SLI configuration. That GTX 580 SLI was the revolutionary computer that put deep learning on the map.

哇。那是 2018 年，你用它玩《雷神之锤》。太疯狂了。

Wow. That was 2018 and you were using it to play Quake. That's crazy.

那就是那个时刻。那是现代 AI 的大爆炸。我们很幸运，因为我们正在发明这项技术、这种计算方法。我们很幸运他们发现了它。结果他们是游戏玩家，幸运的是他们发现了它。而且幸运的是我们注意到了那个时刻。有点像《星际迷航》中的第一次接触。瓦肯人必须在那个时刻看到曲速引擎。如果他们没有目睹曲速引擎，他们就永远不会来到地球，一切都不会发生。有点像如果我没有注意到那个时刻，那道闪光。那道闪光没有持续多久。如果我没有注意到那道闪光，或者我们公司没有注意到它，谁知道会发生什么。但我们看到了，我们推断出这是一个通用函数逼近器。这不仅仅是计算机视觉逼近器。我们可以用它来做各种各样的事情，如果我们能解决两个问题。第一个问题是，我们必须向自己证明它可以扩展。第二个我们必须贡献并等待的问题是，世界永远不会有足够的输入和输出数据来监督 AI 学习一切。例如，如果我们必须监督孩子学习的一切，他们能学到的信息量是有限的。我们需要 AI，我们需要计算机有一种无需监督的学习方法。这就是我们必须再等几年的地方，但无监督 AI 学习现在已经到来。所以 AI 可以自己学习。AI 可以自己学习的原因是因为我们有很多正确答案的例子。例如，如果我想教 AI 如何预测下一个词，我可以抓取一大堆已有的文本，遮住最后一个词，让它一次又一次地尝试，直到它预测出下一个词。或者我遮住文本中的随机词，让它一次又一次地尝试，直到它预测出来。比如‘玛丽去银行’。是河岸还是银行？嗯，如果你要去银行，那很可能是河岸。即使这样也可能不明显。可能需要‘钓到了一条鱼’。现在你知道那一定是河岸。所以你给这些 AI 一大堆这样的例子，你遮住词，它会预测下一个。无监督学习就出现了。这两个想法——它是可扩展的，以及无监督学习——出现了。我们确信我们应该把一切都投入其中，帮助创建这个行业，因为我们要解决一大堆有趣的问题。那是在 2012 年。到 2016 年，我建造了这台名为 DGX1 的计算机。你看到我送给埃隆的那台叫 DGX Spark。DGX1 售价 30 万美元。英伟达花了数十亿美元才造出第一台。我们没有用两块芯片 SLI，而是用一项名为 NVLink 的技术连接了八块芯片，但它基本上是增强版的 SLI。所以我们把八块芯片连接在一起，而不是只有两块。它们全部协同工作，就像你的《雷神之锤》装备一样，来解决这个深度学习问题，训练这个模型。我创造了这个东西。我在 GTC 和我们的一个年度活动上宣布了它，我描述了深度学习、计算机视觉以及这台名为 DGX1 的计算机。观众完全沉默。他们不知道我在说什么。我很幸运，因为我认识埃隆，我帮他建造了 Model 3 和 Model S 的第一台计算机。当他开始研究自动驾驶汽车时，我帮他建造了用于 Model S 自动驾驶系统（他的全自动驾驶系统）的计算机。我们基本上就是 FSD 计算机的第一版。所以我们已经在合作了。当我宣布这个东西时，世界上没有人想要它。我没有采购订单。一个都没有。没有人想买它。没有人想参与其中，除了埃隆。他在活动现场，我们正在进行一场关于自动驾驶汽车未来的炉边谈话。我想大概是 2016 年。是的，也许那时是 2015 年。

That was the moment. That was the big bang of modern AI. We were lucky because we were inventing this technology, this computing approach. We were lucky that they found it. Turns out they were gamers and it was lucky they found it. And it was lucky that we paid attention to that moment. It was a little bit like that Star Trek first contact. The Vulcans had to have seen the warp drive at that very moment. If they didn't witness the warp drive, they would have never come to Earth and everything would have never happened. It's a little bit like if I hadn't paid attention to that moment, that flash. And that flash didn't last long. If I hadn't paid attention to that flash or our company didn't pay attention to it, who knows what would have happened. But we saw that and we reasoned our way into this is a universal function approximator. This is not just a computer vision approximator. We could use this for all kinds of things, if we could solve two problems. The first problem is that we have to prove to ourselves it could scale. The second problem we had to contribute to and wait for is that the world will never have enough data on input and output where we could supervise the AI to learn everything. For example, if we have to supervise our children on everything they learn, the amount of information they could learn is limited. We needed the AI, we needed the computer to have a method of learning without supervision. And that's where we had to wait a few more years, but unsupervised AI learning is now here. So the AI could learn by itself. The reason why the AI could learn by itself is because we have many examples of right answers. For example, if I want to teach an AI how to predict the next word, I could grab a whole bunch of text we already have, mask out the last word and make it try again and again until it predicts the next one. Or I mask out random words inside the text and make it try again and again until it predicts it. Like 'Mary goes down to the bank.' Is it a river bank or a money bank? Well, if you're going to go down to the bank, it's probably a river bank. And it might not be obvious even from that. It might need 'and caught a fish.' Now you know it must be the river bank. So you give these AIs a whole bunch of these examples and you mask out the words, it'll predict the next one. Unsupervised learning came along. These two ideas, the fact that it's scalable and unsupervised learning came along. We were convinced that we ought to put everything into this and help create this industry because we're going to solve a whole bunch of interesting problems. And that was in 2012. By 2016, I had built this computer called the DGX1. The one that you saw me give to Elon is called DGX Spark. The DGX1 was $300,000. It cost Nvidia a few billion dollars to make the first one. Instead of two chips SLI, we connected eight chips with a technology called NVLink, but it's basically SLI supercharged. So we connected eight of these chips together instead of just two. All of them work together just like your Quake rig did to solve this deep learning problem to train this model. I created this thing. I announced it at GTC and at one of our annual events and I described this deep learning thing, computer vision thing and this computer called DGX1. The audience was completely silent. They had no idea what I was talking about. I was lucky because I had known Elon and I helped him build the first computer for Model 3 and the Model S. When he wanted to start working on autonomous vehicle, I helped him build the computer that went into the Model S AV system, his full self-driving system. We were basically the FSD computer version one. So we were already working together. When I announced this thing, nobody in the world wanted it. I had no purchase orders. Not one. Nobody wanted to buy it. Nobody wanted to be part of it except for Elon. He was at the event and we were doing a fireside chat about the future of self-driving cars. I think it's like 2016. Yeah, maybe at that time it was 2015.

他说：“你知道吗？我有一家公司，真的能用上这个。”我说：“哇，我的第一个客户。”所以我当时非常兴奋。然后他说：“呃，是的。我们有这家公司，是一家非营利公司。”我脸上的血色瞬间褪尽。我刚刚花了几十亿美元造了这个东西，成本 30 万美元。你知道一家非营利组织能付得起这个钱的可能性几乎为零。他说，这是一家 AI 公司，是非营利的，我们真的需要一台这样的超级计算机。于是我就接下了。我先给自己公司造了一台，内部使用。然后我打包了一台，开车送到旧金山，在 2016 年交给了 Elon。那里有一群研究人员，Peter Beiel 在，Ilia 在，还有好多人。我走上二楼，他们都在一个比这里还小的房间里。那个地方后来就是 OpenAI。

And he goes, "You know what? I have a company that could really use this." I said, "Wow, my first customer." And so I was pretty excited about it. And he goes, "Uh, yeah. We have this company. It's a nonprofit company." And all the blood drained out of my face. I just spent a few billion dollars building this thing. Cost $300,000. And you know the chances of a nonprofit being able to pay for this thing is approximately zero. And he goes, you know, this is an AI company and it's a nonprofit and we could really use one of these supercomputers. And so I picked it up. I built the first one for ourselves. We're using it inside the company. I boxed one up. I drove it up to San Francisco and I delivered to Elon in 2016. A bunch of researchers were there. Peter Beiel was there, Ilia was there, and there was a bunch of people there. And I walk up to the second floor where they were all kind of in a room smaller than your place here. And that place turned out to have been OpenAI.

2016 年。

2016.

就是一群人坐在一个房间里。

Just a bunch of people sitting in a room.

不过它现在不是非营利了吧？

It's not really nonprofit anymore, though, is it?

他们不再是非营利了。是的。

They're not nonprofit anymore. Yeah.

真奇怪，事情就这样变了。

Weird how that works.

是啊。不过不管怎样，Elon 当时在场。那真是一个很棒的时刻。

Yeah. But anyhow, Elon was there. It was really a great moment.

哦，是的。就是这样。没错。

Oh, yeah. There you go. Yeah, that's it.

看看你，兄弟。同一件夹克。

Look at you, bro. Same jacket.

看看这个。我没变老。

Look at that. I haven't aged.

不过一根黑头发都没有了。

Not a lick of black hair, though.

它的尺寸小了很多。那是前几天的事。SpaceX。

The size of it is significantly smaller. That was the other day. SpaceX.

哦，是的。就是这样。

Oh, yeah. There you go.

是啊。看看这差别。完全相同的工业设计。他把它拿在手里。

Yeah. Look at the difference. Exactly the same industrial design. He's holding it in his hand.

神奇之处在于：DGX1 是 1 petaflop，那已经很多了。而 DGX Spark 也是 1 petaflop，九年之后。同样的算力，体积却小得多，缩小了。而且价格从 30 万美元降到了 4000 美元，只有一本小书那么大。

Here's the amazing thing. DGX1 was one petaflop. That's a lot of flops. And DGX Spark is one petaflop. Nine years later. The same amount of computing horsepower in a much smaller, shrunken down. And instead of $300,000, it's now $4,000. And it's the size of a small book.

不可思议。太疯狂了。

Incredible. Crazy.

技术就是这样进步的。总之，这就是为什么我想给他第一个，因为我在 2016 年给了他第一个。

That's how technology moves. Anyways, that's the reason why I wanted to give him the first one, because I gave him the first one in 2016.

太迷人了。我是说，如果你想拍一部电影，这就是故事。还有什么更好的场景？如果它真的变成了数字生命体，那么它诞生于对电子游戏计算机图形的渴望，这多有趣啊？

It's so fascinating. I mean, if you wanted to make a story for a film, that would be the story. What better scenario? If it really does become a digital life form, how funny would it be that it is birthed out of the desire for computer graphics for video games?

正是。

Exactly.

有点疯狂。

Kind of crazy.

这样想确实有点疯狂。因为这是一个完美的起源。计算机图形学是超级计算机最难的问题之一：生成现实。同时也是解决起来利润最高的，因为电子游戏非常流行。

Kind of crazy when you think about it that way. Because it's a perfect origin. Computer graphics was one of the hardest computer supercomputer problems: generating reality. And also one of the most profitable to solve because computer games are so popular.

当英伟达在 1993 年成立时，我们试图创造一种新的计算方式。问题是：杀手级应用是什么？我们想解决的问题是创造一种新型计算机，能解决普通计算机无法解决的问题。然而，1993 年行业里存在的应用都是普通计算机能解决的，因为如果普通计算机解决不了，这个应用怎么会存在呢？所以我们有一个成功概率为零的公司使命。但我在 1993 年并不知道这一点。它听起来就是个好主意。所以如果我们创造了这个能解决问题的东西，你实际上必须去创造那个问题。这就是我们在 1993 年所做的。当时还没有《雷神之锤》。John Carmack 还没有发布《毁灭战士》。你可能还记得。

When Nvidia started in 1993, we were trying to create this new computing approach. The question is: what's the killer app? The problem we wanted to solve was to create a new type of computer that can solve problems that normal computers can't solve. Well, the applications that existed in the industry in 1993 are applications that normal computers can solve, because if normal computers can't solve them, why would the application exist? So we had a mission statement for a company that has no chance of success. But I didn't know that in 1993. It just sounded like a good idea. And so if we created this thing that can solve problems, you actually have to go create the problem. And that's what we did in 1993. There was no Quake. John Carmack hadn't released Doom yet. You probably remember that.

当然。是的。

Sure. Yeah.

而且当时没有应用。所以我去了日本，因为街机行业当时有世嘉，如果你还记得的话。街机推出了 3D 街机系统：《VR 战士》、《梦游美国》、《VR 特警》，所有这些街机游戏首次以 3D 形式出现。他们使用的技术来自 Martin Marietta 的飞行模拟器。他们把飞行模拟器的核心部件拿出来，放进街机里。你这里的系统，肯定比那台街机强大一百万倍。而那台飞行模拟器是为 NASA 做的。所以他们把核心部件拿了出来。他们用它来模拟喷气机和航天飞机的飞行。世嘉有一位出色的计算机开发者，名叫 Yuzuki。Yuzuki 和宫本茂。世嘉和任天堂。他们是令人难以置信的先驱、梦想家、杰出的艺术家，而且都非常懂技术。他们才是游戏产业的真正起源。Yuzuki 开创了 3D 图形游戏。所以我去了。我们创建了这家公司，但没有应用。我们每天下午都——我们告诉家人去上班，但其实只有我们三个人，谁知道呢？所以我们去了 Curtis 的联排别墅。Chris 和我都结婚了，有孩子。我已经有了 Spencer 和 Madison，他们大概两岁。Chris 的孩子也差不多大。我们就在那栋联排别墅里工作。但当你是一家初创公司，使命陈述又是我们描述的那样，不会有太多客户打电话给你。所以我们真的无事可做。午饭后，我们总是吃一顿丰盛的午餐。然后我们会去游戏厅，玩世嘉的《VR 战士》、《梦游美国》和所有那些游戏，分析他们是怎么做的，试图弄清楚他们是如何实现的。所以我们决定，干脆去日本，说服世嘉把这些应用移植到 PC 上。这样我们就可以与世嘉合作，开创 PC 游戏、3D 游戏产业。这就是英伟达的起源。

And there were no applications for it. So I went to Japan because the arcade industry had this at the time of Sega, if you remember. The arcade machines came out with 3D arcade systems: Virtual Fighter, Daytona, Virtual Cop, all of those arcade games were in 3D for the very first time. And the technology they were using was from Martin Marietta, the flight simulators. They took the guts out of a flight simulator and put it into an arcade machine. The system that you have over here, it's got to be a million times more powerful than that arcade machine. And that was a flight simulator for NASA. So they took the guts out of that. They were using it for flight simulation for jets and the space shuttle. And Sega had this brilliant computer developer. His name was Yuzuki. Yuzuki and Miyamoto. Sega and Nintendo. These were the incredible pioneers, the visionaries, the incredible artists, and they're both very technical. They were the origins really of the gaming industry. And Yuzuki pioneered 3D graphics gaming. So I went. We created this company and there were no apps. We were spending all of our afternoons—we told our family we were going to work, but it was just the three of us, who's going to know? So we went to Curtis's townhouse. Chris and I were married, we have kids. I already had Spencer and Madison. They were probably 2 years old. And Chris's kids are about the same age. We would go to work in this townhouse. But when you're a startup and the mission statement is the way we described, you're not going to have too many customers calling you. So we had really nothing to do. After lunch, we would always have a great lunch. After lunch, we would go to the arcades and play the Sega Virtual Fighter and Daytona and all those games and analyze how they're doing it, trying to figure out how they were doing that. So we decided, let's just go to Japan and let's convince Sega to move those applications into the PC. And we would start the PC gaming, the 3D gaming industry, partnering with Sega. That's how Nvidia started.

哇。

Wow.

作为交换，他们把游戏移植到我们的 PC 上，我们为他们的游戏机造一颗芯片。这就是合作。我为你的游戏机造芯片，你把世嘉游戏移植给我们。他们当时付给我们相当可观的一笔钱来造那台游戏机。这差不多就是英伟达起步的开始。我们以为我们上路了。所以我从一个不可能的商业计划和使命陈述开始。我们幸运地得到了世嘉的合作。我们开始起飞，开始建造我们的游戏机。

And so in exchange for them porting their games for our computers in the PC, we would build a chip for their game console. That was the partnership. I build a chip for your game console, you port the Sega games to us. And they paid us, at the time, quite a significant amount of money to build that game console. And that was kind of the beginning of Nvidia getting started. We thought we were on our way. So I started with a business plan, a mission statement that was impossible. We lucked into the Sega partnership. We started taking off, started building our game console.

大约两年后，我们发现第一项技术行不通。那是一个缺陷。所有的技术理念和架构概念都是合理的，但我们做计算机图形的方式完全反了。我们没有用逆纹理映射，而是用了正向纹理映射。别人用三角形，我们用曲面。别人做平的，我们做圆的。最终胜出的技术，也就是我们今天用的技术，有 Z 缓冲器，可以自动排序。我们的架构没有 Z 缓冲器，应用程序必须自己排序。所以我们选择了一系列技术方案，三个主要的技术选择，全都错了。这就是我们有多聪明。1995 年初到年中，我们意识到走错了路。与此同时，硅谷挤满了 3D 图形初创公司，因为那是当时最激动人心的技术。3Dfx、Rendition 和 Silicon Graphics 都来了，英特尔也已经入场。最终有上百家初创公司要和我们竞争。每个人都选对了技术路线，只有我们选错了。我们是第一家起步的公司，却发现自己拿着错误的答案，基本垫底。公司陷入了困境。

And about a couple years into it, we discovered our first technology didn't work. It was a flaw. All of the technology ideas and architecture concepts were sound, but the way we were doing computer graphics was exactly backwards. Instead of inverse texture mapping, we were doing forward texture mapping. Instead of triangles, we did curved surfaces. Other people did it flat, we did it round. Other technology that ultimately won, the technology we use today, has Z-buffers. It automatically sorted. We had an architecture with no Z-buffers. The application had to sort it. So we chose a bunch of technology approaches, three major technology choices. All three choices were wrong. So this is how incredibly smart we were. In early to mid 1995, we realized we were going down the wrong path. Meanwhile, Silicon Valley was packed with 3D graphics startups because it was the most exciting technology of that time. 3Dfx, Rendition, and Silicon Graphics were coming in. Intel was already in there. Eventually there were a hundred different startups we had to compete against. Everybody had chosen the right technology approach and we chose the wrong one. So we were the first company to start, but we found ourselves essentially dead last with the wrong answer. The company was in trouble.

我们必须做出几个决定。第一个决定：如果我们现在改变，我们会是最后一家公司。即使我们改成我们认为正确的技术，我们仍然会死。那个争论——我们改变然后死掉？不改变，想办法让这个技术工作？还是去做完全不同的事情？这个问题在战略上搅动了公司，是个难题。我最终主张：我们不知道正确的策略是什么，但我们知道错误的技术是什么。所以让我们停止错误的方式，给自己一个机会去弄清楚策略是什么。

We had to make several decisions. The first decision: if we change now, we will be the last company. Even if we changed to the technology we believed to be right, we'd still be dead. That argument — do we change and therefore be dead? Don't change and make this technology work somehow? Or go do something completely different? That question stirred the company strategically and was a hard question. I eventually advocated for: we don't know what the right strategy is, but we know what the wrong technology is. So let's stop doing it the wrong way and give ourselves a chance to figure out what the strategy is.

第二个问题是公司资金即将耗尽。我和世嘉有一份合同，我欠他们一个游戏主机。如果那份合同被取消，我们就死定了，瞬间蒸发。所以我去了日本，向世嘉的 CEO Erie Madri 解释——他是个非常好的人，前本田美国 CEO，回到日本运营世嘉。我当时 33 岁，还长着青春痘，一个超级瘦的中国小孩。他是位长者。我说：“听着，我有一些坏消息要告诉你。第一，我们承诺给你的技术行不通。第二，我们不应该完成你的合同，因为我们会浪费你所有的钱，你会得到一个没用的东西。我建议你找另一个合作伙伴来造你的游戏主机。非常抱歉我们耽误了你的产品路线图。第三，即使我要求你解除合同，我仍然需要这笔钱。如果你不给我这笔钱，我们一夜之间就会蒸发。”我谦卑而诚实地解释，告诉他背景，为什么技术不行，为什么我们以为它能行，为什么不行。我请求他把合同最后应支付的 500 万美元转为投资。他说：“但即使有我的投资，你的公司也很可能倒闭。”这完全正确。1995 年，500 万美元是一大笔钱。一堆竞争对手都做对了。给英伟达 500 万美元，我们就能找到正确策略并带来回报的概率是多少？0%。如果我是他，我不会这么做。500 万美元对世嘉来说是一座山。我告诉他，如果他投资，这笔钱很可能打水漂，但如果不投，我们就没机会了。我说如果他决定不投，我能理解，但如果他投了，对我来说就是整个世界。他考虑了两天，回来说：“我们投。”

The second problem was our company was running out of money. I had a contract with Sega and I owed them this game console. If that contract had been cancelled, we'd be dead, vaporized instantly. So I went to Japan and explained to the CEO of Sega, Erie Madri — really great man, former CEO of Honda USA, went back to Sega to run it. I was 33 years old, still had acne, super skinny Chinese kid. He was an elder. I said, 'Listen, I've got some bad news for you. First, the technology we promised you doesn't work. Second, we shouldn't finish your contract because we'd waste all your money and you would have something that doesn't work. I recommend you find another partner to build your game console. I'm terribly sorry that we've set you back in your product roadmap. Third, even though I'm asking you to let me out of the contract, I still need the money. If you didn't give me the money, we'd vaporize overnight.' I explained it humbly, honestly, gave him the background, why the technology doesn't work, why we thought it would work, why it doesn't. I asked him to convert the last $5 million that they were to complete the contract into an investment instead. He said, 'But it's very likely your company will go out of business, even with my investment.' That was completely true. In 1995, $5 million was a lot of money. A pile of competitors doing it right. What are the chances that giving Nvidia $5 million would lead to the right strategy and a return? 0%. If I were sitting there, I wouldn't have done it. $5 million was a mountain of money to Sega. I told him that if he invested, it would most likely be lost, but if he didn't, we'd be out of business with no chance. I said I would understand if he decided not to, but it would make the world to me if he did. He went off, thought about it for a couple days, came back and said, 'We'll do it.'

哇。你向他解释了你纠正错误的策略吗？

Wow. Did you explain your strategy to correct what it was doing wrong?

等等，哦，天哪，等我告诉你后面的，更吓人。更吓人。

Wait, oh man, wait until I tell you the rest, it's scarier. Even scarier.

哦不。

Oh no.

所以他决定的是：Jensen 是他喜欢的一个年轻人。就这样。

So what he decided was: Jensen was a young man he liked. That's it.

哇。至今如此。

Wow. To this day.

太疯狂了。

That's nuts.

天哪，世界欠他的，你也欠他的。

Boy, do you owe what the world owes that guy.

毫无疑问。

No doubt.

对吧？

Right?

嗯，他在日本今天备受尊敬。

Well, he's celebrated today in Japan.

如果他保留了那五百万——那笔投资，我想今天大概值一万亿美元。

And if he would have kept that five — the investment, I think it'd be worth probably about a trillion dollars today.

我知道。但我们一上市，他们就卖了。他们说：“哇，真是个奇迹。”所以他们卖了。他们以英伟达约 3 亿美元的估值卖掉了，那是我们的 IPO 估值。3 亿美元。

I know. But the moment we went public, they sold it. They go, 'Wow, that's a miracle.' So they sold it. They sold it at Nvidia's valuation about $300 million, our IPO valuation. $300 million.

哇。

Wow.

总之，我无比感激。然后我们必须想办法，因为我们仍然在执行错误的策略和错误的技术。不幸的是，我们不得不裁掉公司大部分员工。我们把公司缩回到原点。所有做游戏主机的人，我们都得裁掉。然后有人告诉我：“但是 Jensen，我们以前从没这样造过。我们从未用正确的方式造过。我们只知道错误的方式。”公司里没人知道如何制造 Silicon Graphics 那种超级计算图像生成器 3D 图形的东西。所以我说：“好吧，能有多难？有 30 家、50 家公司在做，能有多难？”幸运的是，有一本 Silicon Graphics 写的教科书。我去了书店，口袋里只有 200 美元。我买了三本教科书，那是他们仅有的三本，每本 60 美元。我买了那三本教科书。

Anyhow, I was incredibly grateful. Then we had to figure out what to do because we still were doing the wrong strategy, wrong technology. Unfortunately, we had to lay off most of the company. We shrunk the company all back. All the people working on the game console, we had to shrink it all back. Then somebody told me, 'But Jensen, we've never built it this way before. We've never built it the right way before. We only know how to build it the wrong way.' Nobody in the company knew how to build this supercomputing image generator 3D graphics thing that Silicon Graphics did. So I said, 'Okay, how hard can it be? You got all these 30 companies, 50 companies doing it. How hard can it be?' Luckily, there was a textbook written by Silicon Graphics. So I went down to the store. I had $200 in my pocket. I bought three textbooks, the only three they had, $60 a piece. I bought the three textbooks.

我把那本书带回来，给每位架构师发了一本，说：‘读一读，我们去拯救公司。’于是他们读了这本教科书，向当时的巨头 Silicon Graphics 学习如何做 3D 图形。但令人惊叹、也让今天的英伟达与众不同的是，我们的人能够从第一性原理出发，学习最成熟的技艺，却以前所未有的方式重新实现。当我们重新构想 3D 图形技术时，我们以一种方式重新想象它，最终成就了今天的现代 3D 图形。我们确实发明了现代 3D 图形，但我们是从已有的技艺中学习，并以根本不同的方式实现。

I brought it back and I gave one to each one of the architects and I said, 'Read that and let's go save the company.' And so they read this textbook, learned from the giant at the time, Silicon Graphics, about how to do 3D graphics. But the thing that was amazing and what makes Nvidia special today is that the people that are there are able to start from first principles, learn best known art, but reimplement it in a way that's never been done before. And so when we re-imagined the technology of 3D graphics, we reimagined it in a way that manifest today the modern 3D graphics. We really invented modern 3D graphics, but we learned from previous known arts and we implement it fundamentally differently.

你们做了什么改变了它？

What did you do that changed it?

简单的答案是，Silicon Graphics 的工作方式中，几何引擎是一堆在处理器上运行的软件。我们拿过来，去掉了所有通用性，把它简化为 3D 图形最核心的部分，并硬编码到芯片里。所以不再是通用目的，而是非常具体地硬编码成仅用于视频游戏所需的有限功能和有限应用。这种能力极大地提升了那个小芯片的性能。我们那个小芯片生成图像的速度，和一台 100 万美元的图像生成器一样快。那是重大突破。我们把一个百万美元的东西放进了你现在装在游戏 PC 里的显卡里。那就是我们的重大发明。

Well, the simple answer is that the way Silicon Graphics works, the geometry engine is a bunch of software running on processors. We took that and eliminated all the generality, the general purposeness of it, and we reduced it down into the most essential part of 3D graphics and we hardcoded it into the chip. So instead of something general purpose, we hardcoded it very specifically into just the limited applications, limited functionality necessary for video games. And that capability supercharged the capability of that one little chip. And our one little chip was generating images as fast as a $1 million image generator. That was the big breakthrough. We took a million dollar thing and we put it into the graphics card that you now put into your gaming PC. And that was our big invention.

当然，接下来的问题是，你如何与另外 30 家做同样事情的公司竞争？我们做了几件事。第一，我们不是为每个 3D 图形应用都造一个 3D 图形芯片，而是决定只为一个应用造一个 3D 图形芯片。我们把全部赌注押在了视频游戏上。视频游戏的需求与 CAD、飞行模拟器的需求非常不同。它们相关，但并不相同。所以我们把问题陈述收得很窄，这样我就可以拒绝所有其他复杂性，把它缩小到这一个焦点上，然后为游戏玩家极致优化。第二件事是，我们创建了一个完整的生态系统，与游戏开发者合作，让他们的游戏移植并适配到我们的芯片上，这样我们就把一个技术业务变成了一个平台业务，一个游戏平台业务。所以 GeForce 今天确实是世界上最先进的 3D 图形技术，但很久以前，GeForce 其实就是你 PC 里的游戏机。它运行 Windows、Excel、PowerPoint，当然这些都很容易，但它的根本目的就是把你的 PC 变成一台游戏机。我们是第一家为单一受众——游戏玩家——构建所有这些不可思议技术的科技公司。当然在 1993 年，游戏行业还不存在。但等到 John Carmack 出现，《毁灭战士》现象发生，然后《雷神之锤》推出，整个社区就爆发了。

And then of course the question is how do you compete against these 30 other companies doing what they were doing? And there we did several things. One, instead of building a 3D graphics chip for every 3D graphics application, we decided to build a 3D graphics chip for one application. We bet the farm on video games. The needs of video games are very different than needs for CAD, needs for flight simulators. They're related, but not the same. And so we narrowly focused our problem statement so I could reject all of the other complexities and we shrunk it down into this one little focus and then we supercharged it for gamers. And then the second thing that we did was we created a whole ecosystem of working with game developers and getting their games ported and adapted to our silicon so that we could turn essentially what is a technology business into a platform business, into a game platform business. So GeForce is really today it's also the most advanced 3D graphics technology in the world, but a long time ago GeForce is really the game console inside your PC. It runs Windows, it runs Excel, it runs PowerPoint, of course, those are easy things, but its fundamental purpose was simply to turn your PC into a game console. So we were the first technology company to build all of this incredible technology in service of one audience: gamers. Now of course in 1993 the gaming industry didn't exist. But by the time that John Carmack came along and the Doom phenomenon happened and then Quake came out, that entire community took off.

你知道《毁灭战士》这个名字的由来吗？

Do you know where the name Doom came from?

它来自电影《金钱本色》中的一个场景，汤姆·克鲁斯饰演一位精英台球手，出现在一个台球厅，当地一个混混问他箱子里有什么，他打开箱子，里面有一根特制球杆。他打开箱子说：‘毁灭。’名字就是这么来的。因为 Carmack 说，那就是他们想对游戏行业做的事。《毁灭战士》一出来，所有人都会想：‘哦，我们完蛋了。’这就是毁灭。

It came from a scene in the movie The Color of Money where Tom Cruise, who's this elite pool player, shows up at this pool hall and this local hustler says what he got in the case and he opens up this case. He has a special pool cue. He goes in here and he opens it up. He goes, 'Doom.' And that's where it came from. Because Carmack said that's what they wanted to do to the gaming industry. When Doom came out, it would just be everybody be like, 'Oh, we're fucked.' This is Doom.

哦，哇。太棒了。是不是很神奇？太神奇了。因为这对游戏来说是个完美的名字。

Oh, wow. That's awesome. Isn't that amazing? That's amazing. Because it's the perfect name for the game.

是的。名字就来自电影里的那个场景。

Yeah. And the name came out of that scene in that movie.

没错。然后当然，Tim Sweeney 和 Epic Games 以及 3D 游戏类型就起飞了。

That's right. Well, and then of course, Tim Sweeney and Epic Games and the 3D gaming genre took off.

是的。

Yes.

所以，一开始根本没有游戏行业。我们别无选择，只能把公司聚焦在一件事上。那件事，真是个不可思议的起源故事。

And so, if you just kind of in the beginning was no gaming industry. We had no choice but to focus the company on one thing. That one thing, it's a really incredible origin story.

哦，太神奇了。你回头看，简直是一场灾难——那 500 万美元，那次与那位先生的谈话转折，如果他不同意，如果他不喜欢你，今天的世界会是什么样？太疯狂了，我们整个生命都系于另一位先生。

Oh, it's amazing. Like you must be like look back, a disaster is what a $5 million that pivot with that conversation with that gentleman if he did not agree to that if he did not like you what would the world look like today that's crazy then then our entire life hung on another gentleman.

所以现在我们在这里，我们造出了——在 GeForce 之前是 Riva 128。Riva 128 拯救了公司，它彻底改变了计算机图形。游戏 3D 图形的性价比高得惊人。我们正准备发货。嗯，我们在造它，但你知道，500 万美元撑不了多久。每个月我们都在消耗资金。你必须设计、原型、拿到芯片，这要花很多钱。用软件测试，因为不测试软件你就不知道芯片是否工作。然后你很可能发现一个 bug，因为每次测试都会发现 bug，这意味着你得重新流片，花更多时间和钱。我们算了一笔账，没人能撑过去。我们没有那么多时间流片、送到台积电、拿到芯片、测试、再送回去。没机会，没希望。电子表格告诉我们行不通。于是我听说有一家公司造了一台机器，那是一台仿真器。你可以把你的设计——所有描述芯片的软件——放进这台机器，它会假装是我们的芯片。这样我就不用送去晶圆厂、等它回来再测试。我可以让这台机器假装是我们的芯片，把所有软件放在这个叫仿真器的机器上测试，在送厂之前修复所有问题。哇。如果我能做到，那么送厂后它就应该能工作。没人知道，但应该能。所以我们得出结论：把银行里剩下的钱拿出一半，当时大约 100 万美元，用一半去买这台机器。所以我没有留着钱维持生存，而是拿了一半去买这台机器。嗯，我打电话给那个人。

So now here we are we built so before GeForce it was Riva 128. Riva 128 saved the company, it revolutionized computer graphics. The performance cost performance ratio of 3D graphics for gaming was off the charts amazing. And we're getting ready to ship it. Well, we're building it, but we're so as you know, $5 million doesn't last long. And so every single month, we were drawing down. You have to build it, prototype it. You have to design it, prototype it, get the silicon back, which costs a lot of money. Test it with software because without the software testing the chip, you don't know the chip works. And then you're going to find a bug probably because every time you test something you find bugs, which means you have to tape it out again, which is more time, more money. And so we did the math. There was no chance anybody was going to survive it. We didn't have that much time to tape out a chip, send it to a foundry TSMC, get the silicon back, test it, send it back out again. There was no shot, no hope. And so the math, the spreadsheet doesn't allow us to do that. And so I heard about this company and this company built this machine. And this machine is an emulator. You could take your design, all of the software that describes the chip, and you could put it into this machine. And this machine will pretend it's our chip. So I don't have to send it to the fab, wait until the fab sends it back, test. I could have this machine pretend it's our chip and I could put all of the software on top of this machine called an emulator and test all of the software on this pretend chip and I could fix it all before I send it to the fab. Whoa. And if I could do that when I send it to the fab, it should work. Nobody knows, but it should work. And so we came to the conclusion that let's take half of the money we had left in the bank. At the time it was about a million dollars. Take half of that money and go buy this machine. So instead of keeping the money to stay alive, I took half of the money to go buy this machine. Well, I call this guy up.

这家公司叫 IOS。我打电话给他们说：“嘿，听着。我听说有台机器，我想买一台。”他们说：“哦，那太好了，但我们倒闭了。”我说：“什么？你们倒闭了？”他说：“是啊，我们没有客户。”我说：“等等，你们从来没造过那台机器？”他们说：“不，不，不。我们造了。库存里还有一台，如果你想要的话，但我们倒闭了。”于是我从库存里买了一台。我买完之后，他们就倒闭了。

This company's called IOS. I called them up and said, "Hey, listen. I heard about this machine. I'd like to buy one." And they said, "Oh, that's terrific, but we're out of business." I said, "What? You're out of business?" He said, "Yeah, we had no customers." I said, "Wait, hang on a sec. So, you never made the machine?" They said, "No, no, no. We made the machine. We have one in inventory if you want it, but we're out of business." So, I bought one out of inventory. After I bought it, they went out of business.

哇。

Wow.

我从库存里买了它。在这台机器上，我们装上了英伟达的芯片，并在上面测试了所有软件。那时我们几乎山穷水尽了。但我们确信那个芯片会很棒。于是我不得不打电话给另一位先生。我打给了台积电。我告诉台积电，听着，台积电现在是全球最大的晶圆代工厂。当时他们只有几亿美元规模，一家很小的公司。我向他们解释我们在做什么。我告诉他，我有很多客户。我有一个，你知道，Diamond Multimedia，可能你以前买显卡就是从这家公司买的。我说，我们有很多客户，需求非常大，我们要流片一个芯片给你，我想直接进入量产，因为我知道它能用。

I bought it out of inventory. And on this machine, we put Nvidia's chip into it and we tested all of the software on top. And at this point, we were on fumes. But we convinced ourselves that chip is going to be great. And so I had to call some other gentleman. So I called TSMC. And I told TSMC that listen, TSMC is the world's largest foundry today. At the time they were just a few hundred million dollars large, tiny little company. And I explained to them what we were doing. And I explained to him I told him I had a lot of customers. I had one, you know, Diamond Multimedia, probably one of the companies you bought the graphics card from back in the old days. And I said, you know, we have a lot of customers, and the demand's really great, and we're going to tape out a chip to you, and I'd like to go directly to production because I know it works.

对。

Right.

他们说：“从来没有人这样做过。从来没有人流片一次就能成功的芯片。也没有人跳过验证直接量产。”但我知道，如果不开始量产，我反正也会倒闭。如果我能开始量产，我或许还有机会。于是台积电决定支持我，这位先生叫 Morris Chang。Morris Chang 是晶圆代工产业之父，台积电的创始人。非常了不起的人。他决定支持我们公司。我向他们解释了一切。他决定支持我们，坦白说，可能因为他们本来客户也不多，但他们很感激，我也非常感激。当我们开始量产时，Morris 飞到美国，他没有直接问我，但问了一大堆问题，想试探我有没有钱，但他没有直接问。事实是，我们并没有足够的钱，但我们有客户很强的采购订单，如果芯片不行，一些晶圆就会报废，我不确定会发生什么，但我们会短缺，会很艰难。但他们承担了所有风险支持我们。我们推出了这个芯片，结果完全革命性。大获成功。我们成为历史上从零到十亿美元增长最快的科技公司。

And they said, "Nobody has ever done that before. Nobody has ever taped out a chip that worked the first time. And nobody starts production without looking at it." But I knew that if I didn't start the production, I'd be out of business anyways. And if I could start the production, I might have a chance. And so TSMC decided to support me and this gentleman is named Morris Chang. Morris Chang is the father of the foundry industry, the founder of TSMC. Really great man. He decided to support our company. I explained to them everything. He decided to support us, frankly, probably because they didn't have that many other customers anyhow, but they were grateful and I was immensely grateful. And as we were starting the production, Morris flew to the United States and he didn't in so many words ask me, but he asked me a whole lot of questions that was trying to tease out do I have any money, but he didn't directly ask me that, you know. And so the truth is that we didn't have all the money, but we had a strong PO from the customer and if it didn't work, some wafers would have been lost and I'm not exactly sure what would have happened, but we would have come short, it would have been rough. But they supported us with all of that risk involved. We launched this chip, turns out to be completely revolutionary. Knocked the ball out of the park. We became the fastest growing technology company in history to go from zero to $1 billion.

太疯狂了，你们居然没测试芯片。

So wild that you didn't test the chip.

我知道。我们后来才测试的。对，后来才测。

I know. We tested afterwards. Yeah, we tested afterwards.

后来才测，但已经量产了。不过，顺便说一句，我们为拯救公司而开发的那套方法，如今被全世界使用。

Afterwards, but production already. But by the way, that methodology that we developed to save the company is used throughout the world today.

太了不起了。

That's amazing.

是的，我们改变了全世界设计芯片的方法论。全世界设计芯片的节奏。我们改变了一切。

Yeah, we changed the whole world's methodology of designing chips. The whole world's rhythm of designing chips. We changed everything.

那些日子你睡得怎么样？压力一定很大吧。

How well did you sleep those days? It must have been so much stress.

你知道吗，那种世界好像在飞的感觉？你有过那种感觉吗？你无法阻止一切飞速运转的感觉，你躺在床上，世界就像……你感到深深的焦虑，完全失控。我一生中大概有过几次那种感觉。就是在那段时间。

You know, what is that feeling where the world just kind of feels like it's flying? You have this, what do you call that feeling? You can't stop the feeling that everything's moving super fast and you're laying in bed and the world just feels like you feel deeply anxious, completely out of control. I've felt that probably a couple of times in my life. It was during that time.

哇。

Wow.

是的。难以置信。

Yeah. It was incredible.

多么不可思议的成功故事。

What an incredible success story.

但我学到了很多。我学到了几件事。我学会了如何制定战略。我们公司学会了如何制定战略。什么是制胜战略？我们学会了如何创造市场。我们创造了现代 3D 游戏市场。而同样的技能，我们用来创造了现代 AI 市场。完全一样。完全一样的技能。完全一样的蓝图。我们还学会了如何应对危机，如何保持冷静，如何系统地思考问题。我们学会了如何消除公司里的所有浪费，从第一性原理出发，只做必要的事情。其他一切都是浪费，因为我们没有钱，始终在生死线上挣扎。那种感觉和我今天早上醒来时感觉公司很快就要倒闭没什么两样。“距离倒闭还有 30 天”这句话，我已经用了 33 年。

But I learned a lot. I learned several things. I learned how to develop strategies. I learned how to, and when I, our company learned how to develop strategies. What are winning strategies? We learned how to create a market. We created the modern 3D gaming market. And that exact same skill is how we created the modern AI market. It's exactly the same. Exactly the same skill. Exactly the same blueprint. And we learned how to deal with crisis, how to stay calm, how to think through things systematically. We learned how to remove all waste in the company and work from first principles, doing only the things that are essential. Everything else is waste because we have no money for it, to live on fumes at all times. And the feeling no different than the feeling I had this morning when I woke up that you're going to be out of business soon. The phrase "30 days from going out of business" I've used for 33 years.

你现在还有这种感觉。

You still feel that.

哦，是的。哦，是的。每天早上。每天早上。

Oh yeah. Oh yeah. Every morning. Every morning.

但你们已经是地球上最大的公司之一了。可这种感觉没有变。

But you guys are one of the biggest companies on planet earth. But the feeling doesn't change.

那种脆弱感、不确定感、不安全感。它不会离开你。

The sense of vulnerability, the sense of uncertainty, the sense of insecurity. It doesn't leave you.

太疯狂了。我们当时一无所有。我们在和巨头打交道。

That's crazy. We were, you know, we had nothing. We were dealing with giant.

哦，是的。哦，是的。每一天，每一刻。

Oh, yeah. Oh, yeah. Every day, every moment.

你觉得这激励了你吗？这是公司如此成功的原因之一吗？那种饥饿的心态，从不休息，从不自满，始终处于边缘。

Do you think that fuels you? Is that part of the reason why the company's so successful? That you have that hungry mentality, that you never rest, you're never sitting on your laurels, you're always on the edge.

我不想失败的驱动力，大于我想成功的驱动力。

I have a greater drive from not wanting to fail than the drive of wanting to succeed.

这不就像……人生导师会告诉你这是完全错误的心态吗？

Isn't that like sex coaches would tell you that's completely the wrong psychology?

全世界刚刚第一次听到我大声说出这句话。

The world has just heard me say that for out loud for the first time.

但这是真的。

But it's true.

嗯，就是这么有趣。对失败的恐惧比贪婪或其他任何东西都更能驱动我。

Well, that's how fascinating. Fear of failure drives me more than the greed or whatever it is.

嗯，仔细想想，这可能是更健康的方式，因为恐惧……

Well, ultimately that's probably a more healthy approach now that I'm thinking about it because like the fear...

比如，我并不雄心勃勃。我只想活下去，Joe。我希望公司蓬勃发展。我希望我们产生影响。

I'm not ambitious, for example, you know. I just want to stay alive, Joe. I want the company to thrive, you know. I want us to make an impact.

这很有趣。

That's interesting.

是的。

Yeah.

嗯，也许这就是你为什么如此谦逊。也许这就是让你脚踏实地的原因，因为以公司取得的巨大成功，很容易变得自大。

Well, maybe that's why you're so humble. That's what maybe that's what keeps you grounded, you know, because with the kind of spectacular success the company's achieved, it would be easy to get a big head.

不。

No.

对。但这难道不有趣吗？如果你是一个只关注成功的人，你可能会说：“嗯，成功了。搞定了。我是最棒的。”

Right. But isn't that interesting? It's like if you were the guy that your main focus is just success. You probably would go, "Well, made it. Nailed it. I'm the man."

话筒一扔。

Drop the mic.

反而你醒来会想：‘天哪，我们可不能搞砸了。’

Instead, you wake up, you're like, 'God, we can't screw this up.'

没错。每个早上。每个早上。不，每个时刻。是啊，太疯狂了。

No. Exactly. Every morning. Every morning. No. Every moment. Yeah. That's crazy.

睡觉前也是。

Before I go to bed.

听着，如果我是你公司的大投资者，我就希望是这样的人在经营。我希望是一个……

Well, listen. If I was a major investor in your company, that's what I'd want running it. I'd want a guy who's...

对。

Yeah.

这就是我工作的原因。所以我每周工作七天，醒着的每一刻都在工作。

That's what I work. That's why I work seven days a week. Every moment I'm awake.

你每一刻都在工作。

You work every moment.

醒着的每一刻。

Every moment I'm awake.

哇。

Wow.

我都在想怎么解决问题。我在想……

I'm thinking about solving a problem. I'm thinking about...

你能坚持多久？

How long can you keep this up?

我不知道。可能下周就撑不住了。听起来很累。

I don't know. But so could be next week. Sounds exhausting.

确实很累。

It is exhausting.

听起来完全累垮了。

It sounds completely exhausting.

总是处于焦虑状态。

Always in a state of anxiety.

哇。

Wow.

总是处于焦虑状态。

Always in a state of anxiety.

哇。佩服你敢承认这一点。我觉得这对很多人来说很重要，因为可能有些年轻人，他们和你创业时处境相似，会觉得那些成功的人只是比我聪明，比我机会多，或者就是运气好，在正确的时间出现在正确的地方。

Wow. Kudos to you for admitting that. I think that's important for a lot of people to hear because, you know, there's probably some young people out there that are in a similar position to where you were when you were starting out that just feel like, oh, those people that have made it, they're just smarter than me and they had more opportunities than me and it's just like it was handed to them or they're just in the right place at the right time. And...

Joe，我刚才描述的是一个不知道自己在做什么、实际上做错了的人。

Joe, I just described to you somebody who didn't know what was going on, actually did it wrong.

对，对。而且就像两三次的极限飞扑接球。

Yeah. Yeah. And the ultimate diving catch like two or three times.

疯狂。

Crazy.

是啊。

Yeah.

极限飞扑接球，这个说法太贴切了。

The ultimate diving catch is the perfect way to put it.

就像手套边缘接住一样。

You know, it's just like the edge of your glove.

可能从别人头盔上弹了一下，然后落在手套边缘。

It probably bounced off of somebody's helmet and landed at the edge.

天哪，太不可思议了。但也很酷的是你有这样的视角，因为很多人有夸大妄想，或者他们……

God, that's incredible. That's incredible. But it's also it's really cool that you have this perspective that you look at it that way because you know a lot of people that have delusions of grandeur or they have you know...

而且他们改写历史，把自己描述得特别聪明，是天才，一直都知道，判断准确。商业计划也完全如他们所想。

and their rewriting of history often times had them somehow extraordinarily smart and they were geniuses and they knew all along and they were spot-on. And the business plan was exactly what they thought. And...

对，

yeah,

他们摧毁了竞争对手，最终胜利。

they destroyed the competition and you know and they emerged victorious.

而你呢，每天都害怕。

Meanwhile, you're like, I'm scared every day.

没错，没错。

Exactly. Exactly.

太有趣了。天哪，太棒了。

That's so funny. Oh my god, that's amazing.

但这是真的。

It's so true, though.

太棒了。

It's amazing.

千真万确。

It's so true.

太棒了。但我觉得，作为领导者，保持脆弱并不矛盾。公司不需要我一直是个天才，对吧？不需要我对自己要做的事百分之百确定。公司不需要这个。公司希望我成功。我们今天一开始聊到了特朗普总统，我想说，听着，他是我的总统，他是我们的总统。我们都应该……我们讨论只是因为他是特朗普总统，我们都希望他错。但我觉得在美国，我们都必须意识到他是我们的总统，我们希望他成功，因为……

It's amazing. Well, but I I think there's nothing inconsistent with being a leader and being vulnerable. You know, I the company doesn't need me to be a genius right all along, right? Absolutely certain about what I'm trying to do and what I'm doing. The the company doesn't need that. The company wants me to succeed. You know, the thing that and we started out today talking about President Trump and I was about to say something and listen, he is my president. He is our president. We should all and we're talking about just because it's President Trump, we all want him to be wrong. I think that United States, we all have to realize he is our president, we want him to succeed because...

不管谁是总统，态度都一样。

no matter who's president attitude.

没错。

That's right.

我们希望他成功。我们需要帮助他成功，因为这能帮助所有人成功。我很幸运，在一家拥有 4 万员工的公司工作，他们都希望我成功。他们希望我成功，我能感觉到，他们每天都尽力帮我克服挑战，实现我们描述的战略。如果有什么不对或不完美，他们会告诉我，这样我们就能调整。作为领导者，我们越脆弱，别人就越能告诉你：‘Jensen，那不太对’或者……

We want him to succeed. We need to help him succeed because it helps everybody, all of us succeed. And I'm lucky that I work in a company where I have 40,000 people who wants me to succeed. They want me to succeed and I can tell and they're all every single day to help me overcome these challenges trying to realize realize what I describe to be our strategy doing their best. And if it's somehow wrong or not perfectly right to tell me so that we could pivot and the more vulnerable we are as a leader the more able other people are able to tell you you know that Jensen that's not exactly right or...

对，对。

right right

你有没有考虑过这个信息？我们越脆弱，就越能调整。如果我们把自己放在超人能力的位置上，就很难调整战略。

have you considered this information or and the more vulnerable we are the more able we're actually able to pivot if we put ourselves into this superhuman capability then it's hard for us to pivot strategy,

对吧？

right?

因为我们一直被认为是对的。

Because we were supposed to be right all along.

所以如果你总是对的，你怎么可能调整？因为调整需要你承认自己错了。我不怕犯错。我只需要保持警觉，始终从第一性原理推理。总是把问题分解到第一性原理。理解为什么会发生。不断重新评估。不断重新评估，部分原因就是持续焦虑的来源。

And so if you're always right, how can you possibly pivot? Because pivoting requires you to be wrong. And so I've got no trouble with being wrong. I just have to make sure that I stay alert, that I reason about things from first principles all the time. Always break things down to first principles. Understand why it's happening. Reassess continuously. The reassessing continuously is kind of partly what causes continuous anxiety,

因为你在问自己：昨天错了吗？现在还对吗？情况一样吗？变了吗？条件比你想象的更糟吗？

you know, because you're asking yourself, were you wrong yesterday? Are you still right? Is this the same? Has that changed? Has that condition is that worse than you thought?

但天哪，这种心态对你的业务来说太完美了，因为这个行业一直在变。

But God, that mindset is perfect for your business, though, because this business is ever changing

一直如此。竞争来自四面八方。很多事情悬而未决，你必须创造一个包含 100 个变量的未来，你不可能全都对。所以你必须……

all the time. I've got competition coming from every direction. So much of it is kind of up in the air and you have to invent a future where a 100 variables are included and there's no way you could be right on all of them. And so you have to be...

你必须冲浪。

you have to surf.

哇，说得好。你必须冲浪。对，你在科技和创新的浪潮上冲浪。

Wow. That's a good way to put it. You have to surf. Yeah. You're surfing waves of technology and innovation.

没错。你无法预测浪潮，只能应对眼前的。

That's right. You can't predict the waves. You got to deal with the ones you have.

哇。但技巧很重要，我已经做了 30 年，我是全球任期最长的科技 CEO。

Wow. And but skill matters and I've been doing this for 30 I'm the longest running tech CEO in the world.

真的吗？恭喜，太棒了。

Is that true? Congratulations. That's amazing.

人们问我怎么做到不被炒鱿鱼——这能让人心跳骤停；第二，怎么不感到厌倦。

And you know people ask me how is one don't get fired. That'll stop a short heartbeat. And then two don't get bored.

对。

Yeah.

你怎么保持热情？老实说，不总是热情。有时是热情，有时是纯粹的恐惧，有时是适度的挫败感。总之，是任何能让你前进的东西。

Well, how do you maintain your enthusiasm? Well, the honor truth is is not always enthusiasm. It's, you know, sometimes is enthusiasm. Sometimes it's just good oldfashioned fear and then sometimes, you know, a healthy dose of frustration, you know, it's whatever keeps you moving.

对，就是所有情绪。我觉得，

Yeah. Just all the emotions. I think, you know,

CEO 们，我们拥有所有情绪，对吧？而且可能被放大到极致，因为你代表整个公司感受。我同时代表所有人感受。它凝聚在一个人身上。所以我必须关注过去、现在和未来。这不可能没有情绪。这不只是一份工作。就这么说吧。

CEOs, we have all the emotions, right? you know, and so probably probably jacked up to the maximum because you're you're kind of feeling it on behalf of the whole company. I'm feeling it on behalf of everybody at the same time. And it kind of, you know, encapsulates into into somebody. And so I have to be mindful of the past. I have to be mindful of the present. I've got to be mindful of the future. And um you know, it can't it's not without emotion. It's not just it's not just a job. Let's just put it that way.

完全不像。我想，现在公司如此成功，你工作中最困难的部分之一就是预测技术走向和应用方向。

It doesn't seem like it at all. I would imagine one of the more difficult aspects of your job currently now that the company is massively successful is anticipating where technology is headed and where the applications are going to be.

对。

Yeah.

那你如何规划呢？

So, how do you try to map that out?

方法有很多，需要很多因素。但我先从这个说起：你必须身边围绕着优秀的人。如今的 NVIDIA，如果你看看当今世界的大型科技公司，大多数都有广告、社交媒体或内容分发业务。其核心确实是基础计算机科学。但公司的业务不是计算机，也不是技术。技术驱动公司。NVIDIA 是世界上唯一一家大型且唯一业务就是技术的公司。我们只构建技术，不做广告。我们赚钱的唯一方式就是创造惊人的技术并销售它。所以，要成为今天的 NVIDIA，首要的就是你身边围绕着世界上最优秀的计算机科学家。这是我的天赋。我的天赋在于我们创造了一种公司文化，一种让世界上最伟大的计算机科学家愿意加入的条件，因为他们可以从事自己毕生的事业，创造下一个新事物。这就是他们想做的。也许他们不想为其他业务服务。

There are a whole bunch of ways, and it takes a whole bunch of things. But let me just start: you have to be surrounded by amazing people. NVIDIA is now, if you look at the large tech companies in the world today, most of them have a business in advertising, social media, or content distribution. At the core of it is really fundamental computer science. But the company's business is not computers; the company's business is not technology. Technology drives the company. NVIDIA is the only company in the world that's large whose only business is technology. We only build technology. We don't advertise. The only way we make money is to create amazing technology and sell it. So to be NVIDIA today, the number one thing is you're surrounded by the finest computer scientists in the world. And that's my gift. My gift is that we've created a company culture, a condition by which the world's greatest computer scientists want to be part of it because they get to do their life's work and create the next thing. That's what they want to do. Maybe they don't want to be in service of another business.

他们想为技术本身服务。

They want to be in service of the technology itself.

我们是世界历史上这类公司中规模最大的。

And we're the largest form of its kind in the history of the world.

哇。

Wow.

我知道，这很了不起。

I know. It's pretty amazing.

哇。

Wow.

所以第一，我们拥有良好的条件、伟大的文化和优秀的人才。现在的问题是，如何系统地预见未来、保持警觉，并减少错过或犯错的可能性。有很多方法可以做到。例如，我们有出色的合作伙伴关系。我们进行基础研究。我们拥有一个伟大的研究实验室，是当今世界上最大的工业研究实验室之一。我们与许多大学和其他科学家合作。我们进行大量开放合作。我不断与公司外部的研究人员合作。我们拥有出色的客户，因此我有幸与埃隆·马斯克等行业人士合作。而且我们是唯一一家纯粹的科技公司，能够服务于消费互联网、工业制造、科学计算、医疗保健、金融服务等所有行业。这些对我来说都是信号。它们都有数学家和科学家。因此，我拥有一个雷达系统，其广度超过世界上任何公司，覆盖从农业到能源再到电子游戏的每一个行业。我们能够拥有这个制高点——一方面自己做基础研究，另一方面与所有伟大的研究人员和行业合作——这个反馈系统非常不可思议。

So one, we have a great condition. We have a great culture. We have great people. And now the question is how do you systematically be able to see the future, stay alert of it, and reduce the likelihood of missing something or being wrong. There are a lot of different ways you could do that. For example, we have great partnerships. We do fundamental research. We have a great research lab, one of the largest industrial research labs in the world today. And we partner with a whole bunch of universities and other scientists. We do a lot of open collaboration. I'm constantly working with researchers outside the company. We have the benefit of having amazing customers, so I have the benefit of working with Elon and others in the industry. And we have the benefit of being the only pure-play technology company that can serve consumer internet, industrial manufacturing, scientific computing, healthcare, financial services—all the industries we're in. They're all signals to me. They all have mathematicians and scientists. So because I have the benefit now of a radar system that is the most broad of any company in the world, working across every single industry from agriculture to energy to video games. The ability for us to have this vantage point—one, doing fundamental research ourselves, and two, working with all the great researchers and all the great industries—the feedback system is incredible.

最后，你必须有一种保持高度警觉的文化。保持警觉没有捷径，只有专注。我还没有找到一种不专注就能保持警觉的方法。所以我每天大概会读几千封邮件。

And then finally, you just have to have a culture of staying super alert. There's no easy way of being alert except for paying attention. I haven't found a single way of being able to stay alert without paying attention. So I probably read several thousand emails a day.

你怎么有时间做这个？

How do you have time for that?

我起得很早。今天早上我 4 点就起床了。

I wake up early. This morning I was up at 4:00.

你睡多久？

How much do you sleep?

六到七个小时。

Six, seven hours.

然后你 4 点起床，读几个小时邮件再开始工作。

And then you're up at 4 reading emails for a few hours before you get going.

没错。

That's right.

每天如此？

Every day?

每一天，一天不落，包括感恩节和圣诞节。

Every single day. Not one day missed, including Thanksgiving, Christmas.

你休过假吗？

Do you ever take a vacation?

休过，但我对假期的定义是和家人在一起。所以如果我和家人在一起，我就很开心，不在乎在哪里。

Yeah, but my definition of a vacation is when I'm with my family. So if I'm with my family, I'm very happy. I don't care where we are.

那你不工作吗，还是工作一点？

And you don't work then, or do you work a little?

不，我工作很多。

No, I work a lot.

即使你去某个地方旅行，你仍然在工作。

Even if you go on a trip somewhere, you're still working.

哦，当然。

Oh, sure.

每天。

Every day.

每天。

Every day.

我的孩子们每天都工作。你这么说让我都累了。

My kids work every day. You make me tired just saying this.

我的孩子们每天都工作。我的两个孩子都在 NVIDIA 工作，他们每天都工作。

My kids work every day. Both of my kids work at NVIDIA. They work every day.

哇。

Wow.

我很幸运。

I'm very lucky.

哇。

Wow.

现在很残酷，因为以前只有我一个人每天工作。现在我们有三个人每天工作，他们想每天和我一起工作，所以工作量很大。

It's brutal now because it's just me working every day. Now we have three people working every day and they want to work with me every day, so it's a lot of work.

嗯，你显然把这种工作 ethic 传给了他们。

Well, you've obviously imparted that ethic into them.

他们工作非常努力，简直难以置信。

They work incredibly hard. I mean, it's unbelievable.

但我的父母工作也非常努力。

But my parents work incredibly hard.

我生来就有工作基因，受苦基因。

I was born with the work gene, the suffering gene.

听着，老兄，这已经得到了回报。多么疯狂的故事。这真是一个了不起的起源故事。当你回顾有多少次可能崩溃以及卑微的起点时，现在处于这个位置一定有点超现实。

Well, listen, man. It has paid off. What a crazy story. It's really an amazing origin story. It has to be kind of surreal to be in the position that you're in now when you look back at how many times it could have fallen apart and humble beginnings.

但乔，这很棒。这是一个伟大的国家。我是一个移民。我父母先把我哥哥和我送到这里。我们当时在泰国。我出生在台湾，但我父亲在泰国有一份工作。他是一名化学和仪器工程师，一位了不起的工程师。他的工作是去启动一家炼油厂。所以我们搬到了泰国，住在曼谷。在 1973 或 1974 年，你知道泰国时不时会发生政变。军方会起义，突然有一天街上到处都是坦克和士兵。我父母认为孩子们待在这里可能不安全。所以他们联系了我的叔叔。我叔叔住在华盛顿州塔科马。我们从未见过他，我父母把我们送到了他那里。

But Joe, this is great. It's a great country. I'm an immigrant. My parents sent my older brother and me here first. We were in Thailand. I was born in Taiwan, but my dad had a job in Thailand. He was a chemical and instrumentation engineer, an incredible engineer. His job was to go start an oil refinery. So we moved to Thailand, lived in Bangkok. In 1973 or 1974, you know how Thailand every so often would just have a coup. The military would have an uprising and all of a sudden one day there were tanks and soldiers in the streets. My parents thought it probably isn't safe for the kids to be here. So they contacted my uncle. My uncle lives in Tacoma, Washington. We had never met him, and my parents sent us to him.

你当时多大？

How old were you?

我快 9 岁了，我哥哥快 11 岁了。所以我们两个来到了美国。我们在叔叔那里住了一段时间，他为我们找学校。我父母没有很多钱，也从未去过美国。我父亲……我待会再讲那个故事。我叔叔找到了一所愿意接收外国学生且我父母负担得起的学校。那所学校在肯塔基州的奥尼塔，克拉克县——如今阿片类药物危机的中心。寒冷的地方。克拉克县是我去时美国最贫穷的县，现在仍然是美国最贫穷的县。所以我们去了那所学校，一所很棒的学校，奥尼塔浸信会学院，在一个只有几百人的小镇上。我记得我们去的时候有 600 人。没有红绿灯。我想现在还是 600 人。这其实是一个了不起的成就——当人口只有 600 时还能保持稳定。这真是一件神奇的事情。

I was about to turn nine, and my older brother almost turned 11. So the two of us came to the United States. We stayed with our uncle for a little bit while he looked for a school for us. My parents didn't have very much money and they had never been to the United States. My father was... I'll tell you that story in a second. My uncle found a school that would accept foreign students and was affordable enough for my parents. That school turned out to be in Onita, Kentucky, Clark County, Kentucky—the epicenter of the opioid crisis today. Cold country. Clark County, Kentucky was the poorest county in America when I showed up. It is the poorest county in America today. So we went to the school, a great school, Onita Baptist Institute, in a town of a few hundred. I think it was 600 at the time we showed up. No traffic light. I think it has 600 today. It's quite an amazing feat actually—the ability to hold your population when it's 600 people. It was quite a magical thing.

不过他们做到了。那所学校秉持着一个使命，就是成为一所向任何愿意来的孩子开放的学校。这基本上意味着，如果你是个问题学生，或者家庭有问题，无论你的背景如何，都欢迎你来奥尼塔浸会学院，包括那些想住在那里的国际学生。

However they did it. And so the school had a mission of being an open school for any children who would like to come. And what that basically means is that if you're a trouble student, if you have a troubled family, whatever your background, you're welcome to come to Onita Baptist Institute, including kids from international who would like to stay there.

你当时会说英语吗？

Did you speak English at the time?

嗯，还行。是的。我们到了之后，我的第一个想法是，天哪，地上有好多烟头。百分之百的孩子都抽烟。所以马上就知道这不是一所正常的学校。

Uh, okay. Yeah. Yeah. Okay. Yeah. And so we showed up and my first thought was gosh there are a lot of cigarette butts on the ground. 100% of the kids smoked. So right away you know this is not a normal school.

九岁的孩子？

Nine-year-olds?

不，我是最小的孩子。

No, I was the youngest kid.

好吧。11 岁的孩子。

Okay. 11 year olds.

我的室友 17 岁。哇。是的。他刚满 17 岁。他肌肉发达，我不知道他现在在哪。我知道他的名字，但不知道他现在在哪。不过，那天晚上我们到了，我注意到的第二件事是，走进宿舍房间，没有抽屉，没有衣柜门。就像监狱一样。也没有锁，这样人们可以来检查你。我走进房间，他 17 岁，准备睡觉，他身上贴满了胶带，原来他经历了一场持刀斗殴，全身被刺伤，那些都是新伤。

My roommate was 17 years old. Wow. Yeah. He just turned 17. And he was jacked and I don't know where he is now. I know his name, but I don't know where he is now. But anyways, that night we got and the second thing I noticed when you walk into your dorm room is there are no drawers and no closet doors. Just like a prison. And there are no locks so that people could check up on you. And so I go into my room and he's 17 and you know get ready for bed and he had all this tape all over his body and turned out he was in a knife fight and he's been stabbed all over his body and these were just fresh wounds.

哇。其他孩子伤得更重。所以他是我室友，学校里最强悍的孩子，而我是学校里最小的孩子。那是一所初中，但他们还是收了我，因为如果我走过肯塔基河上的吊桥，大约一英里，另一边有一所中学，我可以去那里上学，然后回来住宿舍。所以基本上，奥尼塔浸会学院就是我去那所中学时的宿舍。我哥哥上了初中。我们在那里待了几年。每个孩子都有家务。我哥哥的家务是在烟草农场干活。他们种烟草，这样可以为学校多筹点钱。有点像监狱。

Whoa. And the other kids were hurt much worse. And so he was my roommate, the toughest kid in school, and I was the youngest kid in school. It was a junior high, but they took me anyways because if I walked about a mile across the Kentucky River, the swing bridge, the other side is a middle school that I could go to and then I can go to that school and I come back and then I stay in the dorm. And so basically Onita Baptist Institute was my dorm when I went to this other school. My older brother went to the junior high. And so we were there for a couple of years. Every kid had chores. My older brother's chore was to work in the tobacco farm. So they raised tobacco so that they could raise some extra money for the school. Kind of like a penitentiary.

哇。我的工作就是打扫宿舍。所以我 9 岁就在刷厕所。对于一个住着 100 个男孩的宿舍，我打扫的卫生间比谁都多。我只希望每个人都能小心一点，你懂的。不过，我是学校里最小的孩子。我对那段经历的记忆其实很好。但那是一个相当艰苦的小镇。

Wow. And my job was just to clean the dorm. And so I was 9 years old. I was cleaning toilets. And for a dorm of 100 boys, I cleaned more bathrooms than anybody. And I just wish that everybody was a little bit more careful, you know. But anyways, I was the youngest kid in school. My memories of it was really good. But it was a pretty tough town.

听起来确实如此。

Sounds like it.

是的。镇上的孩子，他们都带着刀。每个人都有刀。每个人都抽烟。每个人都有 Zippo 打火机。我抽了一个星期的烟。

Yeah. Town kids, they all carried knives. Everybody had knives. Everybody smoked. Everybody had a Zippo lighter. I smoked for a week.

真的吗？

Did you?

哦，是的。当然。

Oh, yeah. Sure.

你当时多大？

How old were you?

我九岁。是的。

I was nine. Yeah.

你九岁？你九岁就尝试抽烟了。

When you were nine? You were nine, you tried smoking.

是的。我给自己买了一包烟。其他人都这样。

Yeah. I got myself a pack of cigarettes. Everybody else did.

你生病了吗？

Did you get sick?

没有。我习惯了，你懂的，我学会了怎么吐烟圈，怎么用鼻子呼气，用鼻子吸进去。我是说，你学会了各种不同的技巧。是的。

No. I got used to it, you know, and I learned how to blow smoke rings and breathe out of my nose, take it in through my nose. I mean, there was all the different things that you learned. Yeah.

九岁。

At nine.

是的。

Yeah.

哇。你这么做只是为了融入，还是觉得看起来很酷。

Wow. You just did it to fit in or it looked cool.

是的。因为其他人都这么做，对吧？

Yeah. Because everybody else did it, right?

是的。

Yeah.

然后我大概抽了几个星期。我宁愿用那两毛五分钱——我一个月大概有两毛五分钱——去买冰棒和炸冰棍。我九岁，你懂吧？我选择了更好的道路。

And then I did it for a couple weeks, I guess. And I just rather have I had a quarter, you know, I had a quarter a month or something like that. I just rather buy popsicles and fried sickles with it. I was nine, you know, right? I chose the better path.

哇。那就是我们的学校。然后我父母两年后来了美国，我们在华盛顿州塔科马市见到了他。

Wow. That was our school. And then my parents came to United States two years later and we met him in Tacoma, Washington.

太疯狂了。那真是一次非常疯狂的经历。多么奇特的成长经历啊。

That's wild. It was a really crazy experience. What a strange formative experience.

是的。都是些 tough 的孩子。

Yeah. Tough kids.

从泰国到美国最贫穷的地方之一，或者说如果不是最穷的话，作为一个九岁的孩子。

Thailand to one of the poorest places in America or if not the poorest as a 9-year-old.

是的。那是我第一次和你哥哥的经历。

Yeah. It was my first experience with your brother.

哇。

Wow.

是的。不，我记得，而且让我心碎的是——可能那段经历中唯一真正让我心碎的事——我们没钱每周打国际电话，所以我父母给了我们一个录音机，一个爱荷华牌的录音机和一盘磁带，每个月我们都会坐在那个录音机前，我和我哥哥杰夫，我们俩就告诉他们我们整个月做了什么。

Yeah. No, I remember and what breaks my heart probably the only thing that really breaks my heart about that experience was so we didn't have enough money to make international phone calls every week and so my parents gave us this tape deck, an Iowa tape deck and a tape, and so every month we would sit in front of that tape deck and my older brother Jeff and I, the two of us would just tell them what we did the whole month.

哇。

Wow.

我们会把那盘磁带寄出去，我父母会拿那盘磁带在上面重新录音，然后寄回给我们。

And we would send that tape by mail and my parents would take that tape and record back on top of it and send it back to us.

哇。

Wow.

你能想象吗，如果那盘磁带还保留着，记录了两年里这两个孩子描述他们第一次在美国的经历。我记得我告诉我父母，我加入了游泳队，我的室友肌肉很发达，所以我们每天花很多时间在健身房，每晚做 100 个俯卧撑、100 个仰卧起坐，每天都在健身房。所以我九岁的时候，变得相当强壮，身体很好。然后我加入了足球队。我加入了游泳队，因为如果你加入队伍，他们会带你去比赛，之后你就能去一家好餐厅。那家好餐厅就是麦当劳。

Could you imagine if for two years that tape still existed of these two kids just describing their first experience with United States. Like I remember telling my parents that I joined the swim team and my roommate was really buff and so every day we spent a lot of time in the gym and so every night 100 push-ups, 100 sit-ups every day in the gym. So, I was nine years old. I was getting pretty buff and I'm pretty fit. And so I joined the soccer team. I joined the swim team because if you join the team, they take you to meets and then afterwards you get to go to a nice restaurant. And that nice restaurant was McDonald's.

哇。

Wow.

我录下了这件事。我说：“爸爸妈妈，我们今天去了一家最神奇的餐厅。整个地方灯火通明，就像未来世界。”食物装在盒子里，味道好极了。汉堡包好吃极了。那是麦当劳。不过，这难道不神奇吗？

And I recorded this thing. And I said, "Mom and dad, we went to the most amazing restaurant today. This whole place is lit up. It's like the future." And the food comes in a box and the food is incredible. The hamburger is incredible. It was McDonald's. But anyhow, it wouldn't it be amazing?

哦，天哪。录了两年。是的。两年。是的。和你父母的联系也太疯狂了。就是寄一盘磁带，他们再寄回一盘，这是你们两年里唯一的交流方式。

Oh my god. Two years recording. Yeah. Two years. Yeah. What a crazy connection to your parents, too. Just sending a tape and them sending you one back and it's the only way you're communicating for two years.

是的。哇。是的。不，我父母其实很了不起。他们从小非常穷，来美国时几乎身无分文。可能我最难忘的记忆之一是，他们来了之后，我们住在一个公寓楼里，他们刚租了——我想现在还有人这么做——租了一批家具，我们打闹的时候撞到了咖啡桌，把它撞碎了。那是用刨花板做的，我们把它撞碎了。我还记得我妈妈脸上的表情，你知道，因为他们没钱，她不知道该怎么赔偿。不过，这也能说明他们来这里有多不容易。

Yeah. Wow. Yeah. No, I've My parents are incredible actually. They're just they're they grew up really poor and when they came to United States, they had almost no money. Probably one of the most impactful memories I have is we they came and we were staying in an apartment complex and they had just rent back in the I guess people still do rent a bunch of furniture and we were messing around and we bumped into the coffee table and crushed it. It's made out of particle wood and we crushed it. And I just still remember the look on my mom's face, you know, because they didn't have any money and she didn't know how she was going to pay it back. But anyhow, that's that kind of tells you how hard it was for them to come here.

他们抛下了一切，所有的家当就是一个手提箱和口袋里的钱，然后来到了美国。

They left everything behind and all they had was their suitcase and the money they had in their pocket and they came to the United States.

他们追求美国梦时多大年纪？

How old were they when they pursued the American dream?

他们四十多岁，三十八九岁。

They were in their 40s. Late 30s.

哇。

Wow.

追求美国梦。这就是美国梦。我是美国梦的第一代。

Pursued the American dream. This is the American dream. I'm the first generation of the American dream.

哇。很难不爱这个国家。

Wow. It's hard not to love this country.

很难不对这个国家抱有浪漫情怀。

It's hard not to be romantic about this country.

那真是一个浪漫的故事。太棒了。

That is a romantic story. That's an amazing story.

是的。我父亲就是在报纸广告上找到的工作，他打电话联系，然后得到了工作。

Yeah. And my dad found his job literally in the newspaper, the ads, and he called people. Got a job.

他做什么工作？

What did he do?

他在一家咨询公司做咨询工程师，帮助人们建造炼油厂、造纸厂和晶圆厂。他是一名仪表工程师，非常擅长工厂设计，在这方面很出色。我母亲做女佣。他们想办法把我们养大。

He was a consulting engineer at a consulting firm, and they helped people build oil refineries, paper mills, and fabs. That's what he did. He was an instrumentation engineer, really good at factory design. He's brilliant at that. And my mom worked as a maid. They found a way to raise us.

哇。这故事太不可思议了，Jensen。真的。从你的童年到英伟达险些倒闭的危机，每一部分都令人难以置信。

Wow. That's an incredible story, Jensen. It really is. Every part of it, from your childhood to the perils of Nvidia almost falling. It's really incredible, man.

这是个很棒的故事。我过得很精彩。

It's a great story. I've lived a great life.

你确实如此。这也是一个值得别人听到的好故事。真的。

You really have. And it's a great story for other people to hear, too. It really is.

你不必上常春藤盟校就能成功。这个国家创造机会，为我们所有人提供机会。但你必须努力，必须奋力拼搏。只要你付出努力，就能成功。

You don't have to go to Ivy League schools to succeed. This country creates opportunities. Has opportunities for all of us. You do have to strive. You have to claw your way here. But if you put in the work, you can succeed.

还需要很多运气、好的决策以及他人的善意。

A lot of luck and a lot of good decision-making and the good graces of others.

是的，那非常重要。

Yes, that's really important.

你和我谈到过两位对我非常重要的人。但名单还很长。英伟达的员工帮助过我，董事会里的许多朋友，还有那些决策。你知道，当我们发明这种新的计算方法时，我让公司股价大跌，因为我们在芯片上增加了 CUDA 这个东西。我们有个大想法，在芯片上加了 CUDA，但没人为此买单，而我们的成本却翻倍了。我们是一家图形芯片公司，我们发明了 GPU，发明了可编程着色器，发明了现代计算机图形学的一切，发明了实时光线追踪。这就是为什么从 GTX 变成了 RTX。我们发明了所有这些，但每次我们发明新东西，市场都不懂得欣赏，而成本却大幅上升。对于实现 AI 的 CUDA，成本增加了很多。但我真的相信它。如果你相信那个未来，却什么都不做，你会后悔一辈子。所以我总是告诉团队：你们到底相不相信？如果你们相信，基于第一性原理，而不是道听途说，而且我们相信它，那么如果我们是做这件事的合适人选，我们就应该去追求它。如果这件事非常难做，那它就值得做。我们去追求它吧。我们确实追求了。我们发布了产品，但没人知道。当我发布 DGX1 时，全场一片寂静。当我发布 CUDA 时，全场也是一片寂静。没有客户想要它，没人要求它，没人理解它。英伟达是一家上市公司。

You and I spoke about two people who are very dear to me. But the list goes on. The people at NVIDIA who have helped me, many friends that are on the board, the decisions. You know, when we were inventing this new computing approach, I tanked our stock price because we added this thing called CUDA to the chip. We had this big idea, we added CUDA to the chip, but nobody paid for it, but our cost doubled. So we had this graphics chip company, we invented GPUs, we invented programmable shaders, we invented everything modern computer graphics, we invented real-time ray tracing. That's why it went from GTX to RTX. We invented all this stuff, but every time we invented something, the market doesn't know how to appreciate it, but the cost went way up. And in the case of CUDA that enabled AI, the cost increased a lot. But I really believed it. If you believe in that future and you don't do anything about it, you're going to regret it for your life. So I always tell the team: do you believe this or not? If you believe it, grounded on first principles, not random hearsay, and we believe it, we owe it to ourselves to go pursue it if we're the right people to go do it. If it's really hard to do, it's worth doing. Let's go pursue it. Well, we pursued it. We launched the product. Nobody knew. When I launched DGX1, the entire audience was complete silence. When I launched CUDA, the audience was complete silence. No customer wanted it. Nobody asked for it. Nobody understood it. Nvidia was a public company.

这是哪一年？

What year was this?

那是 20 年前，2005 年。

This was 20 years ago, 2005.

哇。

Wow.

我们的股价下跌，市值从大约 120 亿美元跌到了二三十亿美元。我把它搞砸了，非常糟糕。

Our stock price went down, our valuation went to like two or three billion dollars from about 12 billion. I crushed it in a very bad way.

那现在呢？

What is it now though?

更高了。

It's higher.

你太谦虚了。

Very humble of you.

更高了。但那个发明改变了世界。

It's higher. But that invention changed the world.

是的，那个发明改变了世界。这故事太不可思议了，Jensen。真的。

Yeah, that invention changed the world. It's an incredible story, Jensen. It really is.

谢谢。

Thank you.

我喜欢你的故事。太不可思议了。我的故事没那么精彩，更离奇，更偶然和奇怪。

I like your story. It's incredible. My story is not as incredible. My story is more weird, much more fortuitous and weird.

好的。哪三个最重要的里程碑导致了今天？

Okay. What are the three most important milestones that led to here?

好问题。我认为第一步是看到别人在做。在播客初期，比如 2009 年我开始的时候，播客才出现几年。第一个是我的好朋友 Adam Curry，他是播客之父，他发明了播客。然后 Adam Corolla 有个节目，因为他的广播节目被取消了，所以他决定在互联网上做同样的节目。那相当具有革命性，没人那么做。然后是我在 Opie and Anthony 等早间广播节目的经历。那很有趣，我们和一群喜剧演员聚在一起。我和三四个认识的人一起上节目，总是很开心。我说：‘天哪，我怀念那种感觉。太有趣了。我希望也能做类似的事。’然后我看到了 Tom Green 的 setup。他把整个房子变成了电视演播室，在客厅做网络节目。他家里有服务器，到处都是线缆，你得跨过线缆。那是 2007 年左右。我说：‘Tom，这太疯狂了。你得想办法从中赚钱。我希望互联网上的每个人都能看到你的 setup。’那就是开始：看到别人在做，然后说：‘我们试试吧。’一开始，我们只是用带摄像头的笔记本电脑瞎搞。一群喜剧演员来，我们聊天开玩笑。我每周做一次，然后每周两次，突然就做了一年，然后两年。然后它开始吸引很多观众和听众。我继续做，因为我喜欢做。

That's a good question. I think step one was seeing other people do it. In the initial days of podcasting, like in 2009 when I started, podcasting had only been around for a couple of years. The first was Adam Curry, my good friend, who was the podfather. He invented podcasting. Then Adam Corolla had a show because his radio show got cancelled, so he decided to just do the same show but on the internet. That was pretty revolutionary. Nobody was doing that. Then there was the experience I had doing different morning radio shows like Opie and Anthony. It was fun, we would just get together with a bunch of comedians. I'd be on the show with three or four other guys I knew, and it was always a good time. I said, 'God, I miss doing that. It's so fun. I wish I could do something like that.' Then I saw Tom Green's setup. He had turned his entire house into a television studio and did an internet show from his living room. He had servers in his house and cables everywhere. You had to step over cables. This was like 2007. I said, 'Tom, this is nuts. You got to figure out a way to make money from this. I wish everybody on the internet could see your setup.' So that was the beginning: seeing other people do it, then saying, 'Let's just try it.' In the beginning, we just did it on a laptop with a webcam and messed around. A bunch of comedians came in, we would just talk and joke around. I did it once a week, then twice a week, then all of a sudden I was doing it for a year, then two years. Then it started getting a lot of viewers and listeners. I just kept doing it because I enjoyed doing it.

有什么挫折吗？

Was there any setback?

没有。真的没什么挫折。

No. There was never really a setback.

没有？肯定有吧。要么就是你很有韧性，要么就是你很坚强。

No? It must have been. Or you're just resilient. Or you're just tough.

没有。没有。没有。没有。

No. No. No. No.

这并不艰难或困难，只是很有趣。

It wasn't tough or hard. It was just interesting.

你从未被当面抨击过。

You were never once punched in the face.

不，不是在节目中。真的没有。做节目没有。

No, not in the show. Not really. Not doing the show.

你从未做过有巨大反弹的事情。

You never did something that had big blowback.

没有。真的没有。不，它只是一直在增长。

Nope. Not really. No, it all just kept growing.

它一直在增长，而且从开始到现在一直保持不变。关键是，我喜欢与人交谈。我一直喜欢与有趣的人交谈。

It kept growing and the thing stayed the same from the beginning to now. And the thing is, I enjoy talking to people. I've always enjoyed talking to interesting people.

我甚至能看出，就在我们走进来时，你与每个人互动的方式，不仅仅是我。

I could even tell just when we walked in, the way you interacted with everybody, not just me.

是的，那很酷。

Yeah, that's cool.

人们很酷。

People are cool.

是的，那很酷。你知道，能够与这么多有趣的人进行这么多对话是一份奇妙的礼物，因为它改变了你看世界的方式，因为你通过这么多不同人的眼睛看世界，这么多不同的人有不同的视角、不同的观点、不同的哲学和不同的生活故事。你知道，与这么多了不起的人进行这么多对话是一次极其丰富和富有教育意义的经历。这就是我开始做的，也是我现在所做的全部。即使是现在，当我安排节目时，我也是在手机上操作。我基本上会浏览一个巨大的电子邮件列表，里面是所有想上节目或请求上节目的人。然后我再考虑另一个列表，里面是我感兴趣并希望邀请上节目的人。我就这样规划好，就是这样。然后我会说，“哦，我想和他谈谈。”

Yeah, that's cool. You know, it's an amazing gift to be able to have so many conversations with so many interesting people because it changes the way you see the world because you see the world through so many different people's eyes and you have so many different people have different perspectives and different opinions and different philosophies and different life stories. And you know, it's an incredibly enriching and educating experience having so many conversations with so many amazing people. And that's all I started doing. And that's all I do now. Even now, when I book the show, I do it on my phone. And I basically go through this giant list of emails of all the people that want to be on the show or that request to be on the show. And then I factor in another list that I have of people that I would like to get on the show that I'm interested in. And I just map it out and that's it. And I go, "Oh, I'd like to talk to him."

如果不是因为特朗普总统，我不会被提前到那个名单上。

If it wasn't because of President Trump, I wouldn't have been bumped up on that list.

不，我早就想和你谈谈了。我只是觉得，你知道，你所做的事情非常迷人。我的意思是，我怎么会不想和你谈呢？而今天，这被证明是绝对正确的决定。

No, I wanted to talk to you already. I just think, you know, what you're doing is very fascinating. I mean, how would I not want to talk to you? And then today, it proved to be absolutely the right decision.

嗯，你知道，听着，作为一个移民，有一天和那里的学生一起去 Onita Baptist Institute，然后现在英伟达成为公司历史上最重要的公司之一，这很奇怪。

Well, you know, listen, it's strange to be an immigrant one day going to Onita Baptist Institute with the students that were there and then here Nvidia's one of the most consequential companies in the history of companies.

这真是一个疯狂的故事。

It is a crazy story.

这段旅程一定是，而且非常令人谦卑，我非常感激。

It has to be that journey is and it's very humbling and I'm very grateful.

这太惊人了，伙计。

It's pretty amazing man.

被了不起的人包围着。你非常幸运，而且你看起来非常快乐，似乎完全走在人生的正确道路上。

Surrounded by amazing people. You're very fortunate and you've also you seem very happy and you seem like you're 100% on the right path in this life.

你知道，每个人都说你一定热爱你的工作。不是每一天。

You know, everybody says you must love your job. Not every day.

这就是一切美好的一部分，有起有落。它从来不是像巨大的多巴胺高潮那样。

That's part of the beauty of everything is that there's ups and downs. It's never just like this giant dopamine high.

我们留下了这样的印象。我认为这种印象不健康。我们这些成功的人常常留下一种印象，即我们的工作带给我们巨大的快乐。我认为在很大程度上确实如此，我们对工作充满热情。那种热情与它非常有趣有关。我认为在很大程度上是这样，但它分散了注意力，事实上很多成功来自于非常非常努力的工作。

We leave this impression here. Here's an impression I don't think is healthy. We, people who are successful, leave the impression often that our job gives us great joy. I think largely it does, that our jobs are passionate about our work. And that passion relates to it's just so much fun. I think it largely is, but it distracts from in fact a lot of success comes from really really hard work.

是的。

Yes.

有长时间的痛苦、孤独、不确定、恐惧、尴尬和羞辱。所有这些我们最不喜欢的感受，从头开始创造一些东西，埃隆会告诉你类似的事情，发明新东西非常困难，人们一直不相信你，你经常被羞辱，大多数时候不被相信。所以人们忘记了成功的那一部分，我认为这不健康。我认为我们传递这一点并让人们知道这只是旅程的一部分是好的。

There's long periods of suffering and loneliness and uncertainty and fear and embarrassment and humiliation. All of the feelings that we most not love, that creating something from the ground up and Elon will tell you something similar, very difficult to invent something new, and people don't believe you all the time, you're humiliated often, disbelieved most of the time. And so people forget that part of success and I don't think it's healthy. I think it's good that we pass that forward and let people know that it's just part of the journey.

是的。

Yes.

痛苦是旅程的一部分。

Suffering is part of the journey.

你会因此感激这些糟糕的感觉，当事情进展不顺利时。当它们进展顺利时，你会更加感激。

You will appreciate it so these horrible feelings that you have when things are not going so well. You will appreciate it so much more when they do go well.

深深感激。

Deeply grateful.

是的。

Yeah.

深深的自豪。难以置信的自豪。难以置信的感激，当然还有难以置信的回忆。绝对如此。

Deep pride. Incredible pride. Incredible gratefulness and surely incredible memories. Absolutely.

Jensen，非常感谢你来到这里。这真的很有趣。我真的很享受，你的故事绝对令人难以置信，非常鼓舞人心，我认为这真的是美国梦。这就是美国梦。

Jensen, thank you so much for being here. This was really fun. I really enjoyed it and your story is just absolutely incredible and very inspirational and I think it really is the American dream. It is the American dream.

确实如此。非常感谢。谢谢。好的。大家再见。

It really is. Thank you so much. Thank you. All right. Bye, everybody.