NVIDIA首席执行官黄仁勋

我得说，David，我真希望每期节目都能有英伟达的完整制作团队。这次录制时不用操心开关摄像机，也不用担心录制过程中出什么差错，感觉真好。

I will say, David, I would love to have NVIDIA's full production team every episode. It was nice not having to worry about turning the cameras on and off and making sure that nothing bad happened myself while we were recording this.

是啊，光是设备就够厉害了。我是说，从摄像机里出来的那些硬盘。

Yeah. Just the gear. I mean, the drives that came out of the camera.

好吧，从下期开始家庭工作室也要用Red摄像机了。

Alright. Red cameras for the home studio starting next episode.

嗯，太棒了。

Yeah. Great.

好了，我们开始吧。

Alright. Let's do it.

是你吗？是你吗？是你吗？现在谁掌握了真相？是你吗？

Is it you? Is it you? Is it you? Who got the truth now? Is it you?

是你吗？是你吗？让我坐下。直说吧。又一个故事正在路上。

Is it you? Is it you? Sit me down. Say it straight. Another story on the way.

谁掌握了真相？

Who got the truth?

欢迎收听本期《Acquired》，这是一档关于伟大科技公司及其背后故事与运营策略的播客节目。我是本·吉尔伯特。

Welcome to this episode of Acquired, the podcast about great technology companies and the stories and playbooks behind them. I'm Ben Gilbert.

我是大卫·罗森塔尔。

I'm David Rosenthal.

我们是您的主持人。听众朋友们，先说重点——这期节目对我和大卫来说简直太酷了。没错，在花费近两年时间研究英伟达约500小时后，我们专程飞往英伟达总部与黄仁勋本人进行了面对面访谈。黄仁勋正是英伟达的创始人兼CEO，这家公司正推动着整个人工智能革命的浪潮。

And we are your hosts. Listeners, just so we don't bury the lead, this episode was insanely cool for David and I. Yeah. After researching NVIDIA for something like five hundred hours over the last two years, we flew down to NVIDIA headquarters to sit down with Jensen himself. And Jensen, of course, is the founder and CEO of NVIDIA, the company powering this whole AI explosion.

录制本期节目时，英伟达市值已达1.1万亿美元，是全球第六大市值公司。此刻正是该公司的关键转折点——市场预期极高，甚至堪称天花板级别。在我们研究过的所有企业中，英伟达拥有令人惊叹的战略地位，且对竞争对手保持着前所未有的领先优势。

At the time of recording, NVIDIA is worth $1,100,000,000,000 and is the sixth most valuable company in the entire world. And right now is a crucible moment for the company. Expectations are set high. I mean, sky high. They have about the most impressive strategic position and lead against their competitors of any company that we've ever studied.

但所有人都在思考：英伟达的惊人繁荣能否持续？人工智能是否会成为下一个万亿美元级别的技术浪潮？我们对此有多大把握？如果答案是肯定的，随着市场格局逐渐成形，英伟达真能维持其近乎垄断的主导地位吗？

But here's the question that everyone is wondering. Will NVIDIA's insane prosperity continue for years to come? Is AI gonna be the next trillion dollar technology wave? How sure are we of that? And if so, can NVIDIA actually maintain their ridiculous dominance as this market comes to take shape?

黄仁勋带我们重温了企业发展历程：从图形处理到数据中心再到人工智能，如何多次绝处逢生。他还为创业者提供了诸多建议，并在节目尾声动情地分享了创始人之路的情感体验。

So Jensen takes us down memory lane with stories of how they went from graphics to the data center to AI, how they survived multiple near death experiences. He also has plenty of advice for founders, and he shared an emotional side to the founder journey toward the end of the episode.

是的。通过这次经历，我对公司以及他作为创始人和领导者有了新的认识，尽管我们原以为事先已经了解一切，但事实证明我们并不完全了解。

Yeah. I got new perspective on the company and on him as a founder and a leader just from doing this despite you know, we thought we knew everything before we came in advance, and, it turned out we didn't.

结果发现主角其实知道得更多。

Turns out the protagonist actually knows more.

没错。

Yes.

好的。那么，听众们，请加入我们的Slack频道。那里正在进行关于这家AI公司、整个生态系统以及我们最近制作的其他多期节目的精彩讨论。链接是acquired.fm/slack。我们期待您的加入。

Alright. Well, listeners, join the Slack. There is incredible discussion of everything about this company AI, the whole ecosystem, and a bunch of other episodes that we've done recently going on in there right now. So that is acquired.fm/slack. We would love to see you.

闲话少说，本节目不构成投资建议。大卫和我可能在我们讨论的公司中有投资，本节目仅供信息和娱乐目的。现在来聊聊Jensen。Jensen，这里是Acquired节目，我们想从故事时间开始。我们要把时钟拨回到1997年。

And without further ado, this show is not investment advice. David and I may have investments in the companies we discuss, and this show is for informational and entertainment purposes only. On to Jensen. So, Jensen, this is Acquired, so we wanna start with story time. We wanna wind the clock all the way back to I believe it was 1997.

你们正准备发布Riva 128，这是计算史上最大的图形芯片之一，也是首个完全支持3D加速的计算机图形管线。是的，而且你们当时

You're getting ready to ship the Riva one twenty eight, which is one of the largest graphics chips ever created in the history of computing. It is the first fully three d accelerated graphics pipeline for a computer. Yeah. And you guys have

你们快没钱了。

You're running out of money.

剩下的资金。于是你决定完全在模拟环境中进行测试，而不是接收实体原型，你动用公司剩余的资金，在未见实物的情况下就委托了生产运行。所以你在这里把所有赌注都押在了Riva 128上。是的。结果出来了。

Of cash left. And so you decide to do the entire testing in simulation rather than ever receiving a physical prototype, you commission the production run sight unseen with the rest of the company's money. So you're betting it all right here on the Riva one twenty eight. Yeah. It comes back.

在32种DirectX混合模式中，它支持其中的8种。你必须说服市场购买它，还必须说服开发者只使用这8种混合模式。带我们了解一下

And of the 32 DirectX blend modes, it supports eight of them. And you have to convince the market to buy it, and you gotta convince developers not to use anything but those eight blend modes. Walk us through what that

另外24种没那么重要。

The other 24 weren't that important.

好的，等等，第一个问题。这是一开始就计划好的吗？你是什么时候意识到

Okay, so wait, wait, first question. Was that the plan all along? Like when did you realize that

我们...我本应该更早意识到，但直到为时已晚我才知道。我们本应该实现全部32种模式。是的。但我们已经建成了现有的架构，所以只能尽力而为。那真是一段非凡的时期。

we I should have even gone for realized I didn't learn about it until it was too late. We should have implemented all 32. Yeah. But we built what we built and so we had to make the best of it. That was really an extraordinary time.

记得Vivo 01/2020是MV3。MV1和MV2基于正向纹理映射，没有三角形，而是使用曲线并对曲线进行细分。因为我们渲染的是更高级的对象，基本上避免了使用Z缓冲区。我们以为那会是一种很好的渲染方法，结果证明完全错了。所以Riva 128实际上是我们公司的一次重置。

Remember Vivo 01/2020 was MV3. MV1 and MV2 were based on forward texture mapping, no triangles, but curves and it tessellated the curves. And because we were rendering higher level objects, we essentially avoided using Z buffers. And we thought that that was going to be a good rendering approach and turns out to have been completely the wrong answer. And so what Revo Run '28 was, was a reset of our company.

要记得，我们在1993年创立公司时，是当时唯一一家面向消费者的3D图形公司。我们专注于将PC转变为加速PC，因为当时Windows实际上是一个软件渲染系统。总之，Riva 128是我们公司的一次重置，因为当我们意识到走错了路时，微软已经推出了DirectX。它与NVIDIA的架构根本不相容。尽管我们是最早创立的公司，但当时已经出现了30家竞争对手。

Now remember at the time that we started the company in 1993, we were the only consumer three d graphics company ever created. And we were focused on transforming the PC into an accelerated PC because at the time Windows was really a software rendered system. And so anyways, Riva 01/1928 was a reset of our company because by the time that we realized we had gone down the wrong road, Microsoft had already rolled out DirectX. It was fundamentally incompatible with NVIDIA's architecture. 30 competitors have already shown up, even though we were the first company at the time that we were founded.

所以当时的世界完全不同。关于那时公司战略该如何制定，我会说我们做了很多错误的决定。但在那个关键的日子，我们做出了一系列非凡的正确决策。1997年可能是英伟达最辉煌的时刻，原因在于我们当时已无路可退。

So the world was a completely different place. The question about what to do as a company strategy at that point, I would have said that we made a whole bunch of wrong decisions. But on that day that mattered, we made a sequence of extraordinarily good decisions. And that time, 1997, was probably NVIDIA's best moment. And the reason for that was our backs were up against the wall.

我们的时间所剩无几，资金即将耗尽。对许多员工而言，希望也在消逝。问题是我们该怎么办？我们做的第一件事是决定接受DirectX已成为现实。

We were running out of time. We're running out of money. And for a lot of employees, running out of hope. And the question is, what do we do? Well, the first thing that we did was we decided that, look, DirectX is now here.

我们不打算与之对抗，而是要想办法为它打造世界上最好的产品。Rivo 128是世界上首个完全硬件加速的3D渲染管线——从变换、投影到帧缓冲器的每个环节都实现了全硬件加速。我们还实现了纹理缓存。

We're not gonna fight it. Let's go figure out a way to build the best thing in the world for it. Rivo one twenty eight is the world's first fully accelerated hardware accelerated pipeline for rendering three d. And so the transform, the projection, every single element all the way down to the frame buffer was completely hardware accelerated. We implemented a texture cache.

我们将总线限制和帧缓冲限制推到当时物理条件允许的最大值。我们制造了前所未有的巨型芯片，采用最快的存储器。基本上，只要造出这颗芯片，就不可能存在更快的产品。我们还选择了远高于竞争对手心理价位的成本定位。

We took the bus limit, the frame buffer limit to as big as physics could afford at the time. We made the biggest chip that anybody had ever imagined building. We used the fastest memories. Basically, if we built that chip, there could be nothing that could be faster. And we also chose a cost point that is substantially higher than the highest price that we think that any of our competitors would be willing to go.

如果我们正确构建它，加速所有环节，实现已知的所有DirectX功能，并尽可能将其做大，那么显然没有人能造出更快的产品。

If we built it right, we accelerated everything, we implement everything in DirectX that we knew of, and we build it as larger as we possibly could, then obviously nobody can build something faster than that.

如今英伟达在某种程度上仍在这样做。当时你们是消费产品公司对吧？最终是需要终端消费者付费购买的。

Today, in a way, you kind of do that here at NVIDIA too. You were a consumer products company back then, right? It was end consumers who were gonna have to pay the money to buy them.

没错。但我们观察到当时PC产业仍在发展且不够成熟，市场上存在一个细分群体——所有人都在渴求更快的产品。如果你的性能比现有产品高十倍，我们相信会有大量发烧友追捧。我们完全正确判断了PC产业存在庞大的发烧友市场，他们会购买最好的产品。

That's right. But we observed that there was a segment of the market where people were because at the time the PC industry was still coming up and it wasn't good enough. Everybody was clamoring for the next fastest thing. And so if your performance was 10 times higher this year than what was available, there's a whole large market of enthusiasts who we believe would have gone after it. And we were absolutely right that the PC industry had a substantially large enthusiast market that would buy the best of everything.

直到今天，这仍然基本正确。对于市场中某些技术永远不够好的领域，比如三维图形，当我们选择了正确的技术时，三维图形就永远不够好。我们当时称之为三维技术给我们带来了可持续的技术机遇，因为它永远不够好。所以你的技术可以不断进步。我们选择了这个方向。

To this day, kind of remains true. And for certain segments of the market where the technology is never good enough, three d graphics, when we chose the right technology, three d graphics is never good enough. And we call it back then, three d gives us sustainable technology opportunity because it's never good enough. And so your technology can keep getting better. We chose that.

我们还决定使用一种叫做模拟的技术。有一家公司叫IQOS。在我打电话给他们的那天，他们正准备关闭公司，因为他们没有客户。我说，嘿，听着，我会买下你们所有的库存，不需要任何承诺。我们需要那个模拟器的原因是，如果你算算我们有多少钱，如果我们流片了一个芯片，从晶圆厂拿回来，然后开始开发我们的软件，等到我们发现所有漏洞（因为我们做了软件），然后再重新流片芯片。

We also made the decision to use this technology called emulation. There was a company called IQOS. And on the day that I called them, they were just shutting the company down because they had no customers. And I said, hey, look, I'll buy what you have in inventory and no promises are necessary. The reason why we needed that emulator is because if you figure out how much money that we have, if we taped out a chip and we got it back from the fab and we started working on our software, By the time that we found all the bugs, because we did the software, then we taped out the chip again.

那样的话，我们早就破产了。而且你的竞争对手也会

Well, we would have been out of business already. And I And your competitors would have

追上来了。

caught up.

嗯，更不用说我们早就

Well, not to mention we would have been out

破产了。谁

of business. Who

在乎呢？没错。所以如果你反正都要破产，那个计划显然不是我们的计划。公司通常采用的计划——制造芯片、编写软件、修复漏洞、重新流片新芯片，如此循环——那种方法是行不通的。

cares? Exactly. So if you're gonna be out of business anyways, that plan obviously wasn't the plan. The plan that companies normally go through, which is build the chip, write the software, fix the bugs, tape out the new chip, so on and so forth. That method wasn't gonna work.

所以问题是，如果我们只有六个月时间，而且你只有一次流片机会，那么显然你会流片出一颗完美芯片。我记得和领导们讨论时他们说，但是Jensen，你怎么知道它会完美？我说，我知道它会完美，因为如果不完美，我们就得倒闭。所以让我们把它做到完美。我们只有一次机会。

And so the question is, if we only had six months and you get the tape out just one time, then obviously you're gonna tape out a perfect chip. So I remember having conversation with our leaders and they said, but Jensen, how do you know it's gonna be perfect? And I said, I know it's gonna be perfect because if it's not, we'll be out of business. And so let's make it perfect. We get one shot.

我们基本上通过购买这台仿真器来虚拟原型化芯片。Dwight和软件团队编写了我们的软件，整个软件栈，并在仿真器上运行，就坐在实验室里等待窗口渲染。

We essentially virtually prototype the chip by buying this emulator. And Dwight and the software team wrote our software, the entire stack and ran it on this emulator and just sat in the lab waiting for windows to paint.

大概是每帧60秒左右，或者类似的时间，很容易计算。

It was like sixty seconds per frame or Oh, something like easily.

我其实觉得是每小时一帧，差不多这样。所以我们就是坐在那里看渲染。在我们决定流片的那天，我假设芯片是完美的，所有能测试的我们都提前测试过了，然后告诉大家就是现在了。我们要流片了，它会完美的。那么，如果你要流片一个芯片，而且你知道它是完美的，那你还会做什么？

I actually think that it was an hour per frame, something like that. And so we just sit there and watch a paint. And so on the day that we decided to tape out, I assumed that the chip was perfect and everything that we could have tested, we tested in advance and told everybody this is it. We're gonna tape out the chip, it's gonna be perfect. Well, if you're gonna tape out a chip and you know it's perfect, then what else would you do?

这其实是个好问题。如果你知道按下回车键，流片了芯片，而且你知道它会完美，那你还会做什么？答案显然是投入生产。

That's actually the good question. If you knew that you hit enter, you taped out a chip and you knew it was gonna be perfect, then what else would you do? Well, the answer obviously go to production.

还有营销闪电战。

And marketing blitz.

是的。没错。

Yeah. Yeah.

以及开发者关系。

And developer relations.

关闭是因为Kick你得到了一个完美的芯片。所以我们脑子里就认为我们有了一个完美的芯片。

Off because Kick you got a perfect chip. And so we got in our head that we have a perfect chip.

这其中有多少是你的主意，有多少是你的联合创始人、公司其他成员或董事会的？是不是所有人都觉得你疯了？

How much of this was you and how much of this was, like, your cofounders, the rest of the company, the board? Was everybody telling you you were crazy?

不，大家都很清楚我们毫无胜算。不这么做才是疯了，因为否则

No, everybody was clear we had no shot. Not doing it would be crazy because otherwise

你可能

you might

反正都要倒闭了。所以除此之外的任何做法都是疯狂的。所以这看起来是相当合乎逻辑的事情。坦白说，现在我只是在描述它，你们可能都在想，是的，这很合理。嗯，它确实成功了。

as You're well go gonna be out of business anyways. So anything aside from that is crazy. So it seemed like a fairly logical thing. And quite frankly, right now, just I'm describing it, everybody's you're probably thinking, yeah, it's pretty sensible. Well, it worked.

是的。所以我们把它拿出来直接投入生产。

Yeah. And so we take that out and went directly to production.

所以对于创业者来说，教训就是当你对某件事有坚定信念时，比如Revo 128或CUDA，就应该把整个公司押上去。而且这种方法一直对你有效。所以看起来你从中得到的教训是：是的，继续全押筹码，因为到目前为止每次都成功了。你怎么看待这一点？

So is the lesson for founders out there when you have conviction on something like the Revo one twenty eight or, CUDA, go bet the company on it. And this keeps working for you. So it seems like your lesson learned from this is, yes, keep pushing all the chips in because so far it's worked every time. How do you think about that?

不，不。当你全押筹码时，我知道它会成功。请注意，我们假设我们流片的是一个完美芯片。我们之所以能流片完美芯片，是因为在流片前我们模拟了整个芯片。

No. No. When you push your chips in, I know it's gonna work. Notice, we assume that we taped out a perfect chip. The reason why we taped out a perfect chip is because we emulated the whole chip before we taped it out.

我们开发了完整的软件栈。我们对所有驱动程序和所有软件进行了质量测试。我们运行了所有已有的游戏。我们运行了每一个VGA应用程序。所以当你全押筹码时，你实际上是在说：我要把未来所有有风险的事情都提前完成。

We developed the entire software stack. We ran QA on all the drivers and all the software. We ran all the games we had. We ran every VGA application we had. And so when you push your chips in, what you're really doing is when you bet the farm, you're saying, I'm gonna take everything in the future, all the risky things and I pull it in advance.

这可能才是真正的教训。直到今天，所有我们能预取的东西，所有未来能在今天模拟的东西，我们都提前预取了。

And that is probably the lesson. And to this day, everything that we can prefetch, everything in the future that we can simulate today, we prefetch it.

我们经常讨论这个。我们刚刚在Costco那期节目中还谈到这个。你只有在知道会成功的时候才应该全押筹码。

And we talk about this a lot. We were just talking about this on our Costco episode. You wanna push your chips in when you know it's gonna work.

所以每次我们看到你做出'押上整个公司'的举动时，其实你已经模拟过了。你知道吗？

So every time we see you make a Yeah. Bet the company move you've already simulated it. You know?

是的。是的。是的。

Yeah. Yeah. Yeah.

你觉得CUDA的情况是这样的吗？

Do you feel like that was the case with CUDA?

是的。事实上，在CUDA出现之前，还有CG。对吧。所以我们当时已经在探索如何在我们芯片之上创建一个可通过高级语言和高级表达式表达的抽象层。我们如何将GPU用于CT重建、图像处理等应用。

Yeah. In fact, before there was CUDA, there was CG. Right. And so we were already playing with the concept of how do we create an abstraction layer above our chip that is expressible in a higher level language and higher level expression. How can we use our GPU for things like CT reconstruction, image processing.

我们当时已经沿着这条道路前进了。因此有一些积极的反馈和一些直觉上的积极反馈让我们认为通用计算是可能实现的。如果你仔细观察可编程着色器的流水线，它就是一个处理器，具有高度并行性，大规模多线程，而且是世界上唯一能做到这一点的处理器。可编程着色器的许多特性都表明CUDA有很大的成功机会。

We were already down that path. And so there were some positive feedback and some intuitive positive feedback that we think that general purpose computing could be possible. And if you just looked at the pipeline of a programmable shader, it is a processor and is highly parallel and it is massively threaded and it is the only processor in the world that does that. And so there were a lot of characteristics about programmable shading that would suggest that CUDA has a great opportunity to succeed.

如果存在一个庞大的机器学习从业者市场，他们最终会出现并想要进行所有这些伟大的科学计算和加速计算，那么这一点是成立的。但是当你开始投入现在相当于大约一万个人年的资源来构建这个平台时，你是否曾觉得，天啊，我们可能是在机器学习需求出现之前就进行了投资，因为我们比全世界意识到这一点早了差不多十年？

And that is true if there was a large market of machine learning practitioners who would eventually show up and wanna do all this great scientific computing and accelerated computing. But at the time when you were starting to invest what is now something like ten thousand person years Yeah. In building that platform Yeah. Did you ever feel like, oh, man. We might have invested ahead of the demand for machine learning since we're like a decade before the whole world is realizing it?

我想说是也不是。你知道，当我们看到深度学习，当我们看到AlexNet并意识到它在计算机视觉中的惊人效果时，我们有足够的智慧，如果你愿意这么说的话，回到第一性原理并问：是什么让这个东西如此成功？当一个新的软件技术、新算法出现并以某种方式跨越了三十年的计算机视觉工作时，你必须退后一步问自己：但是为什么？从根本上说，它具有可扩展性吗？如果它具有可扩展性，它还能解决哪些其他问题？

I guess yes and no. You know, when we saw deep learning, when we saw AlexNet and realized its incredible effectiveness in computer vision, we had the good sense, if you will, to go back to first principles and ask, what is it about this thing that made it so successful? When a new software technology, a new algorithm comes along and somehow leapfrogs thirty years of computer vision work, you have to take a step back and ask yourself, but why? And fundamentally, is it scalable? And if it's scalable, what other problems can it solve?

我们得出了几个观察结果。第一个观察结果当然是，如果你有大量示例数据，你可以教会这个函数进行预测。我们基本上发现了一个通用函数逼近器，因为维度可以任意高。由于每一层都是一层一层训练的，没有理由不能构建非常非常深的神经网络。好吧。

And there were several observations that we made. The first observation, of course, is that if you have a whole lot of example data, you could teach this function to make predictions. Well, what we've basically done is discovered a universal function approximator because the dimensionality could be as high as you want it to be. Because each layer is trained one layer at a time, there's no reason why you can't make very, very deep neural networks. Okay.

所以现在你只需要通过推理来理解。对吧？好吧。那么现在我回到十二年前。你可以想象我当时在脑海中进行的推理：我们发现了一个通用函数逼近器。

So now you just reason your way through. Right? Okay. So now I go back to twelve years ago. You could just imagine the reasoning I'm going through in my head that we've discovered a universal function approximator.

事实上，我们或许已经通过几项技术发现了一种通用计算机，你和你能够

In fact, we might've discovered with a couple more technologies, a universal computer that you And you've can

关注ImageNet竞赛。是的，你正在引出这个话题。

paying attention to the ImageNet competition. Yeah. You're leading up to this.

是的，是的。原因在于我们当时已经在研究计算机视觉，并且试图让CUDA成为一个优秀的计算机视觉系统。但大多数为计算机视觉创建的算法并不适合CUDA。所以我们当时正坐在那里试图解决这个问题。

Yeah. Yeah. And the reason for that is because we were already working on computer vision at the time, and we were trying to get CUDA to be a good computer vision system. Or most of the algorithms that were created for computer vision aren't a good fit for CUDA. And so we're sitting there trying to figure it out.

突然之间AlexNet出现了。这非常引人入胜。它如此有效，以至于让你退后一步问自己：为什么会这样？当你通过推理想明白时，你会思考：在一个存在通用函数逼近器的世界里，哪些问题

All of a sudden AlexNet shows up. And so that was incredibly intriguing. It's so effective that it makes you take a step back and ask yourself, why is that happening? So by the time that you reason your way through this, you go, well, what are the kind of problems in a world where a universal function approximator

能够 嗯，

can Well,

我们知道大多数算法都源于原理性科学。好吧，你想要理解因果关系，然后基于因果关系创建一个能让我们扩展的模拟算法。但对于很多问题，我们其实不太关心因果关系，只关心其可预测性。

we know that most of our algorithms start from principled sciences. Okay. You wanna understand the causality and from the causality, you create a simulation algorithm that allows us to scale. Well, for a lot of problems, we kind of don't care about the causality. We just care about the predictability of it.

比如，我真的在乎你为什么更喜欢这个牙膏而不是那个吗？我并不真正关心因果关系，我只想知道这是你会预测选择的那一款。我真的在乎某人买热狗时买番茄酱和芥末的根本原因吗？其实并不重要。

Like, do I really care for what reason you prefer this toothpaste over that? I don't really care the causality. I just wanna know that this is the one you would have predicted. Do I really care that the fundamental cause of somebody who buys a hotdog, buys ketchup and mustard? It doesn't really matter.

重要的是我能预测它。这适用于预测电影、预测音乐。坦率地说，也适用于预测天气。我们理解热力学。我们理解太阳辐射。

It only matters that I can predict it. It applies to predicting movies, predicting music. It applies to predicting, quite frankly, weather. We understand thermodynamics. We understand radiation from the sun.

我们理解云层效应。我们理解海洋效应。我们理解所有这些不同的因素。我们只想知道是否该穿汗衫。对吧？

We understand cloud effects. We understand oceanic effects. We understand all these different things. We just want to know whether we should wear sweat or not. Isn't that right?

没错。所以对于世界上很多问题来说，因果关系并不重要。我们只想模拟系统并预测结果。

Yep. And so causality for a lot of problems in the world doesn't matter. We just wanna emulate the system and predict the outcome.

这可能是一个利润极其丰厚的市场。如果你能预测下一个在社交媒体信息流中表现最好的内容是什么，事实证明这是

And it can be an incredibly lucrative market. If you can predict what the next best performing feed item to serve into a social media feed, turns out that's a

极具价值的东西。

hugely valuable thing.

说到这个。我很喜欢你举的例子，你知道的。牙膏、番茄酱、音乐、电影。

With that. I love the examples you pulled, you know. Toothpaste, ketchup, music, movies.

当你意识到这一点时，你会突然明白——等一下。一个通用函数逼近器，一个机器学习系统，一个从示例中学习的东西，可能拥有巨大的机会，因为应用的数量相当庞大。从我们刚刚开始讨论的商业一直到科学领域。于是你意识到，这可能会影响世界上很大一部分产业。世界上几乎每一款软件最终都会以这种方式编程。

And when you realize this, you realize, hang hang on a second. A universal functional approximator, a machine learning system, you know, something that learns from examples could have tremendous opportunities because it's just the number of applications is quite enormous. And everything from obviously we just started talking about commerce all the way to science. And so you realize that maybe this could affect a very large part of the world's industries. Almost every piece of software in the world would eventually be programmed this way.

如果是这样的话，那么计算机和芯片的构建方式实际上可以完全改变。意识到剩下的只是，你知道，是否有勇气把筹码押在这上面？

And if that's the case, then how you build a computer and how you build a chip, in fact, can be completely changed. And realizing that the rest of it is just comes with, you know, do you have the courage to put your chips behind it?

这就是我们今天的处境，也是英伟达今天的处境。嗯。但我很好奇，在AlexNet之后的几年里。嗯。这正是本和我自己进入科技行业和风险投资行业的时候。

So that's where we are today, and that's where NVIDIA is today. Mhmm. But I'm curious in the know, there's a couple years after AlexNet. Mhmm. And this is when Ben and I were getting into the technology industry and the venture industry ourselves.

我2012年加入了微软。

I started at Microsoft in 2012.

是的。

Yeah.

没错。就在AlexNet之后，但在任何人谈论机器学习甚至主流工程界之前。

Yes. So right after Alex Knapp, but before anyone was talking about machine learning and even the mainstream engineering community.

有那么几年，对世界上其他地方很多人来说，这些看起来像是科学项目。是的。硅谷这里的科技公司，特别是社交媒体公司，他们刚刚从中意识到巨大的经济价值，比如谷歌、Facebook、Netflix等等。显然，这导致了包括几年后的OpenAI在内的许多事情。但在那几年里，当你看到硅谷这里释放出如此巨大的经济价值时，你当时感觉如何？

There were those couple of gears there where to a lot of the rest of the world, these looked like science projects. Yeah. The technology companies here in Silicon Valley, particularly the social media companies, they were just realizing huge economic value out of this, the Googles, Facebooks, the Netflixs, etcetera. And obviously that led to lots of things, including OpenAI a couple years later. But during those couple years, when you saw just that huge economic value unlock here in Silicon Valley, How are you feeling during those times?

第一个想法当然是思考我们应该如何改变我们的计算堆栈。第二个想法是我们在哪里可以找到最早的应用可能性？如果我们去建造这台计算机，人们会用它来做什么？我们很幸运，与世界各地的大学和研究人员合作是我们公司的天性，因为我们已经在研究CUDA，而CUDA的早期采用者是研究人员，因为我们 democratize 了超级计算。CUDA不仅仅用于人工智能，你知道的。

The first thought was, of course, reasoning about how we we should change our computing stack. The second thought is where can we find earliest possibilities of use? If we were to go build this computer, what would people use it to do? And we were fortunate that working with the world's universities and researchers was innate in our company because we were already working on CUDA and CUDA's early adopters were researchers because we democratize supercomputing. CUDA is not just used as you know, for AI.

CUDA几乎应用于所有科学领域。从分子动力学到成像、CTB构建、地震处理、天气模拟、量子化学，应用范围不胜枚举。因此CUDA在科研中的应用数量非常庞大。当时代来临，我们意识到深度学习可能极具前景时，很自然地我们就回到研究人员中间，寻找全球每一位AI研究者并询问：我们如何帮助您推进工作？这其中就包括Yan LeCun、Andrew Ng和Jeff Hinton。

CUDA is used for almost all fields of science. Everything from molecular dynamics to imaging, CTB construction, to seismic processing, to weather simulations, quantum chemistry, the list goes on. So the number of applications of CUDA in research was very high. And so when the time came and we realized that deep learning could be really interesting, it was natural for us to go back to the researchers and find every single AI researcher on the planet and say, how can we help you advance your work? And that included Yan LeCun and Andrew Ng and Jeff Hinton.

我就是这样结识了所有这些人。我过去经常参加所有AI会议，正是在那里我第一次见到了Iliya Suskabur。所以当时的核心问题是：我们可以构建什么样的系统和软件栈来帮助您更成功地推进研究？因为当时它看起来像个玩具，但我们有信心——即使GAN，我第一次见到Goodfellow时，GAN还是32x32的分辨率。那只是一张模糊的猫图像，但它能走多远？

And that's how I met all these people. And I used to go to all the AI conferences and that's where I met Iliya Suskabur there for the first time. And so it was really about at that point, what are the systems that we can build and the software stacks we can build to help you be more successful to advance the research? Because at the time it looked like a toy, but we had confidence that even GAN, the first time I met Goodfellow, the GAN was like 32 by 32. And it was just a blurry image of a cat, but how far can it go?

因此我们深信不疑。我们相信一方面可以扩展深度学习，因为它显然是逐层训练的，你可以扩大数据集，也可以扩大模型规模。我们相信如果不断放大，效果就会越来越好。是的，这很合理。

And so we believed in it. We believed that one, you could scale deep learning because obviously it's trained layer by layer and you could make the datasets larger and you could make the models larger. And we believe that if you made that larger and larger, it would get better and better. Yep. Kind of sensible.

我认为与研究人员的讨论和互动正是我们需要的正向反馈系统。我会回归研究领域。一切正是从那里开始的。

And I think the discussions and the engagements with the researchers was the exact positive feedback system that we needed. I would go back to research. It was that's where it all happened.

当OpenAI在20年成立时——

When OpenAI was founded in '20 Yeah.

15年？

'15?

是的。我的意思是，那显然是个重要时刻，现在大家都明白了。但当时，我觉得大多数人，甚至科技界的人都在问：这是什么？

Yeah. I mean, that was such an important moment that's obvious today now. But at the time, I I think most people, even people in tech, were like, what is this?

是的。是的。是的。我们当时

Yeah. Yeah. Yeah. Were were

你是否参与其中？因为，你知道，你与研究人员联系如此紧密，与Ilya也是，直白地说就是把人才从谷歌和Facebook带出来，但重新培育了研究社区。是的。并且开放它确实是如此重要的时刻。你是否参与其中？

you involved in it at all? Like, you know, because you were so connected to the researchers, to Ilia, taking that talent out of Google and Facebook to be blunt, but reseeding the research community Yeah. And opening it up was such an important moment. Were you involved in it at all?

我没有参与它的创立，但我认识那里的很多人。当然，我也认识Elon。Peter Beale在那里，Ilya也在那里。而且我们现在有一些优秀的员工是最初就在那里的。我知道他们需要这台我们正在建造的惊人计算机。

I wasn't involved in the founding of it, but I knew a lot of the people there. And Elon, of course, I knew. And Peter Beale was there, and Ilya was there. And we have we have some great employees today that were there in the beginning. And I knew that they needed this amazing computer that we were building.

我们当时正在建造第一版DGX，你知道，今天当你看到Hopper时，它有70磅重，35000个零件，10000安培。但我们建造的第一版DGX是内部使用的，我把第一台送到了OpenAI。那是很有趣的一天。但我们最初的大部分成功都围绕着帮助研究人员达到下一个水平。我知道它在当时的状态下不太有用，但我也相信通过几次点击，它可能会变得非常了不起。

And we're building the first version of the DGX, which, you know, today when you see a hopper, it's 70 pounds, 35,000 parts, 10,000 amps. But DGX, the first version that we built was used internally and I delivered the first one to OpenAI. That was a fun day. But most of our success was aligned around in the beginning just about helping the researchers get to the next level. I knew it wasn't very useful in its current state, but I also believe that in a few clicks, could be really remarkable.

这种信念来自于与所有这些惊人研究人员的互动，也来自于看到渐进的进步。起初，论文每三个月发表一次，而今天的论文每天都在发表。所以你可以监控存档论文，我对了解深度学习的进展产生了兴趣，并尽我所能阅读这些论文。你可以实时看到进步的发生，你知道，指数级地实时发生。

And that belief system came from the interactions with all these amazing researchers and it came from just seeing the incremental progress. At first, the papers were coming out every three months, and then papers today are coming out every day. So you could just monitor the archive papers, I took an interest in learning about the progress of deep learning, and and to the best of my ability, read these papers. And you could just see the progress happening, you know, in real time, exponentially in real time.

甚至在行业内，根据我们交谈过的一些研究人员，似乎没有人预测到当你只是增加模型大小时，语言模型会变得多么有用。他们认为，哦，必须有一些算法上的改变需要发生。但一旦你跨过那个100亿参数的标志，当然一旦你跨过1000亿，它们就神奇地变得更加准确、更有用、更逼真。当你第一次看到真正的大型语言模型时，你感到震惊吗？你还记得那种感觉吗？

It even seems like within the industry, from some researchers we spoke with, it seemed like no one predicted how useful language models would become when you just increase the size of the models. They thought, oh, there has to be some algorithmic change that needs to happen. But once you cross that 10,000,000,000 parameter mark and certainly once you cross the 100,000,000,000, they just magically got much more accurate, much more useful, much more lifelike. Were you shocked by that the first time you saw a truly large language model? And do you remember that feeling?

嗯，我对语言模型的第一个感觉是，屏蔽单词并让它预测下一个单词是多么聪明。这是自我监督学习的最佳体现。我们有所有这些文本。你知道，我知道答案是什么。我就让你猜它。

Well, my first feeling about the language model was how clever it was to just mask out words and make it predict the next word. It's self supervised learning at its best. We have all this text. You know, I know what the answer is. I'll just make you guess it.

因此我对BERT的第一印象确实是觉得它非常巧妙。现在的问题是，如何将其规模化？对几乎所有事物的第一观察都很有趣，然后尝试直观地理解它为何有效。当然，下一步就是从第一性原理出发，思考如何将其外推。所以很明显，我们知道BERT将会变得庞大得多。

And so my first impression of BERT was really how clever it was. And now the question is how can you scale that? The first observation on almost everything is interesting, and then try to understand intuitively why it works. And then the next step, of course, is from first principles, how would you extrapolate that? And so obviously we knew that BERT was gonna be a lot larger.

这些语言模型的特点之一是它们正在编码信息，对吧？它们是在压缩信息。在世界上的语言和文本中，编码了大量的推理内容。我们描述了很多推理相关的事情。

Now, one of the things about these language models is it's encoding information. Isn't that right? It's compressing information. And so within the world's languages and texts, there's a fair amount of reasoning that's encoded in it. We describe a lot of reasoning things.

因此，如果你说通过阅读就能学到一些多步推理能力，我一点也不会感到惊讶。我们很多人都是通过阅读来获得常识和推理能力的。那么为什么机器学习模型不能从中学习一些推理能力呢？从推理能力中，你可以涌现出新的能力。沉浸式能力与直觉推理是一致的。

And so if you were to say that few step reasoning is somehow learnable from just reading things, I wouldn't be surprised. A lot of us, we get our common sense and we get our reasoning ability by reading. And so why wouldn't a machine learning model also learn some of the reasoning capabilities from that? And from reasoning capabilities, you could have emergent capabilities. Immersion abilities are consistent with intuitively from reasoning.

所以其中一些可能是可预测的，但这仍然令人惊叹。它合乎情理这一事实并不会让它减少任何神奇之处。

And so some of it could be predictable, but still, it's still amazing. The fact that it's sensible doesn't make it any less amazing.

没错。

Right.

我可以清晰地想象出整个计算机以及自动驾驶汽车中的所有模块。而它仍然能保持车道行驶这一事实让我无比开心。所以

I could visualize literally the entire computer and and all the modules in a self driving car. And the fact that it's still keeping lanes makes me insanely happy. And so

我甚至还记得大学第一次上操作系统课时，当我终于弄明白了从编程语言到电气工程课程之间的所有环节，中间由那门操作系统课连接起来。我当时就想，哦，我想我彻底理解了冯·诺依曼计算机是如何工作的，但这仍然是一个奇迹。

I even remember that from my first operating systems class college when I finally figured out all the way from programming language to the electrical engineering classes bridged in the middle by that OS class. I'm like, oh, I think I understand how the Von Neumann computer works soup to nuts, and it's still a miracle.

是的。是的。是的。是的。完全正确。

Yeah. Yeah. Yeah. Yeah. Exactly.

是的。是的。当你把所有因素结合起来，这仍然是个奇迹。是的。

Yeah. Yeah. When you put it all together, it's still a miracle. Yeah.

好了，听众们。现在正是感谢节目新朋友Koyfin的好时机。有趣的是，他们虽然是新赞助商，但实际上我使用他们的产品已经好几年了。每一期新收购案例的研究项目都离不开Koyfin。

Alright, listeners. Now is a great time to thank a new friend of the show, Koyfin. And it's funny. They're new, but, actually, I've been using their product for years. My research project for every single new acquired episode involves Koyfin.

所以当他们联系赞助节目时，我觉得，这真是太方便了。确实如此。Koyfin是一款深受个人投资者和财务顾问喜爱的金融研究工具。个人用户用它进行股票研究、绘制财务图表和投资组合跟踪，财务顾问则用它构建模型组合和创建客户提案。他们提供实时市场数据和强大的分析工具。

So when they reached out to sponsor the show, I thought, well, this is convenient. Indeed. So Koyfin is a financial research tool loved by both individual investors and financial advisers. Individuals use it for stock research, graphing financials, and portfolio tracking, and financial advisers use it to build model portfolios and create client proposals. They have live market data and powerful analytics tools.

所以这有点像彭博终端，但没有那么高昂的价格标签。对吧？

So it's kinda like a Bloomberg terminal except without the huge price tag. Right?

是的。本质上就是这样。它是一个网页应用，完全自助服务。实际上在我使用它的头几年里，我从未与公司任何人交流过。所以Koyfin是更广泛市场——比如所有《收购》听众——都会使用的产品，而不仅仅是华尔街投资银行家。

Yes. Essentially. It's a web app, and it's totally self serve. I've actually not talked to anyone at the company for the first few years that I used it. So Koyfin is a product that the broader market, like all acquired listeners, would use, not just Wall Street investment bankers.

我就是在这里获取我们研究的每家公司的增长率、毛利率、市盈率或收入倍数等数据，你可以通过历史图表查看这些数据的时序变化或与其他公司进行对比。在研究私营公司时我也经常使用它，比如劳力士、玛氏或宜家，通过查看可比公司来估算这些公司如果上市会值多少钱。他们还有一个筛选器，可以让你从数千只股票中过滤，快速发现投资机会。

It's where I pull things like growth rate or gross margins or the PE ratio or revenue multiples for every company we study, and you can compare these things over time with historical graphs or against other companies. It's often what I use when we're studying private companies too, like Rolex or Mars or IKEA, to look at the comparables to estimate what these companies would be worth if they were public. They also have a screener that lets you filter across thousands of stocks so you can quickly surface investment ideas.

是的。所以总体思路是，如果你习惯了与数据打交道，那么在考虑投资时，你应该能够随时掌握这些数据。

Yep. So the general idea is if you're someone who's used to living in data, you should have that at your fingertips as you think about investing.

没错。它拥有这些出色的数据可视化图表，围绕机构级数据构建。所以如果你想了解当前股价中隐含了哪些假设，Koyfin就是为你准备的。

Exactly. It's got these great graphs for data visualization wrapped around institutional grade data. So if you wanna understand what assumptions are baked into the stock price today, Koyfin is for you.

我正要说Acquired的听众有个很棒的优惠，但Koyfin的免费产品其实已经非常强大了。

I was about to say that acquired listeners have a great offer, but KoiFin's free product is actually already really robust.

这就是我多年来一直在用的。

Is what I was using for years.

我知道，我知道。但确实，对于Acquired的听众以及你本人Ben来说，如果你访问koifin.com/acquired并最终升级到付费版，第一年将享受20%的折扣。

I know. I know. But indeed, for acquired listeners and also for you, Ben, if you go to koifin.com/acquired and you end up upgrading to paid, you'll get 20% off your first year.

感谢Koyfin。网址是k0yfin.com/acquired，或者点击节目说明中的链接。我们有些问题想问你们。有些是关于英伟达的文化问题，但其他问题普遍适用于公司建设。我们想问的第一个问题是，我们听说你有40多个直接下属，而且这种组织架构图与传统公司的运作方式大不相同。

Our thanks to Koyfin. That's k0yfin.com/acquired, or click the link in the show notes. We have some questions we wanna ask you. Some are cultural about NVIDIA, but, others are generalizable to company building broadly. And the first one that we wanted to ask is, we've heard that you have 40 plus direct reports and that this org chart works a lot differently than a traditional company org chart.

你是否认为英伟达有什么特别之处，使你能拥有这么多直接下属，不必担心溺爱或专注于高管的职业发展，而是就像说：不，你在这里就是要做到最好，完成世界上最重要的工作。现在就去吧。嗯，A，这个说法正确吗？

Do you think there's something special about NVIDIA that makes you able to have so many direct reports, not worry about coddling or focusing on career growth of your executives, and you're like, no. You're just here to do your freaking best work and the most important thing in the world. Now go. Mhmm. A, is that correct?

那么b，英伟达有什么特别之处使其能够做到这一点吗？

And b, is there something special about NVIDIA that enables that?

我不认为这是英伟达的特别之处。我认为我们有勇气构建这样的系统。英伟达的架构不像军队，不像武装部队那样有将军和上校之类的层级。我们的组织方式不是那样的。

I don't think it's something special about NVIDIA. I think that we had the courage to build a system like this. NVIDIA is not built like a military. It's not built like a like the armed forces where you have, you know, generals and colonels. You know, we just we're not set up like that.

我们没有建立自上而下的命令控制与信息分发系统。我们的架构更像一个计算堆栈。计算堆栈的最底层是我们的架构，然后是芯片，再是软件。在这之上有各种不同的模块。这些模块的每一层都是由人组成的。

We're not set up in a command and control and information distribution system from the top down. We're really built much more like a computing stack. And a computing stack, the lowest layer is our architecture, and then there's our chip, and then there's our software. And on top of it, there are all these different modules. Each one of these layers of modules are people.

所以对我来说，公司的架构就像一台计算机，拥有一个计算堆栈，由不同的人管理系统的各个部分。谁向谁汇报、你的头衔与你在堆栈中的位置无关。只是碰巧谁最擅长运行那个层级上那个功能的模块，就由谁负责，那个人就是指挥飞行员。这就是一个特点。

And so the architecture of the company to me is a computer with a computing stack with people managing different parts of the system. And who reports to whom your title is not related to anywhere you are in the stack. It just happens to be who is the best at running that module on that function on that layer. It is in charge, and that person is the pilot in command. And so that's one characteristic.

你是否

Have you

一直这样看待公司？即使从最早期的日子开始？

always thought about the company this way? Even from the earliest days?

是的。基本上是这样。没错。原因在于，你的组织架构应该反映构建产品的机器架构。对吧？

Yes. Pretty much. Yeah. And the reason for that is because your organization should be the architecture of the machinery of building the product. Right?

是的。公司就是这样的。没错。然而每家公司看起来都一模一样，但它们却打造不同的产品。这怎么说得通呢？

Yep. That's what a company is. Yep. And yet everybody's company look exactly the same, but they all build different things. How does that make any sense?

你明白我的意思吗？是的。制作炸鸡的方法、翻转汉堡的方法、以及制作中式炒饭的方法都是不同的。那么为什么机器设备、为什么流程会完全相同呢？所以在我看来，如果你观察大多数公司的组织结构图，它们看起来都差不多是这样，这并不合理。

Do you see what I'm saying? Yeah. How you make fried chicken versus how you flip burgers versus how you make Chinese fried rice is different. And so why would the machinery, why would the process be exactly the same? And so it's not sensible to me that if you look at the org charts of most companies, it all kind of looks like this.

然后你有一个团队负责一项业务，另一个团队负责另一项业务，再有一个负责其他业务，它们都应该是自主的。所以这些对我来说都不合理。这完全取决于我们要构建什么，以及最适合构建它的公司架构是什么。这是第一点。在信息系统和如何促进协作方面，我们有点像搭建了一个神经网络。

And then you have one group that's for a business and you have another for another business, you have another for another business, and they're all kind of supposedly autonomous. And so none of that stuff makes any sense to me. It just depends on what is it that we're trying to build and what is the architecture of the company that best suits to go build it. So that's number one. In terms of information system and how do you enable collaboration, we kind of wired up like a neural network.

我们的说法是，公司里有句话叫'使命就是老板'。所以我们弄清楚使命是什么？然后我们调动最优秀的技能、最出色的团队和最佳的资源来实现那个使命。这种方式贯穿整个组织，虽然看起来不太合理，但确实有点像神经网络。

And the way that we say is that there's a phrase in the company called mission is the boss. And so we figure out what is the mission of what is the mission? And we go wire up the best skills and the best teams and the best resources to achieve that mission. And it cuts across the entire organization in a way that doesn't make any sense, but it looks like a little bit like a neural network.

你知道吗？当你说使命时，你是指像英伟达的使命那样的吗？

You know? And when you say mission, do you mean mission like NVIDIA's mission is

打造Hopper。

Build Hopper.

是的。好吧。所以不是更广泛的加速计算。不。更像是我们要推出DGX Cloud。

Yeah. Okay. So it's not like further accelerated computing. No. It's like we're shipping DGX Cloud.

构建Hopper或者其他人构建一个Hopper系统。有人为Hopper构建了CUDA。有人的工作是构建Hopper的CUDA。有人的工作就是使命。对吧？

Build Hopper or somebody else's build a system for Hopper. Somebody has build CUDA for Hopper. Somebody's job is build CUDA for CUDA for Hopper. Somebody's job is the mission. Right?

就是这样，你知道，你的使命就是去做某件事。

Is is so, you know, your mission is to do something.

与传统结构相比，这样做有哪些权衡取舍？

What are the trade offs associated with that versus the traditional structure?

缺点在于领导者的压力相当大。原因在于，在命令与控制系统中，你汇报的人比你拥有更多权力。他们比你更有权力的原因是他们比你更接近信息来源。在我们公司，信息会相当快速地传播给许多不同的人，通常是在团队层面。例如，刚才我参加了我们的机器人会议，我们在讨论某些事情并做出一些决定，会议室里还有新毕业的大学生。

The downside is the pressure on the leaders is fairly high. And the reason for that is because in a command and control system, the person who you reports to has more power than you. And the reason why they have more power than you is because they're closer to the source of information than you are. In our company, the information is disseminated fairly quickly to a lot of different people, it's usually at a team level. So for example, just now I was in our robotics meeting and we're talking about certain things and we're making some decisions and there are new college grads in the room.

会议室里有三位副总裁，两位高管团队成员。当我们一起做出决定时，我们推理了一些事情，做出了决策。每个人都在同一时间听到了完全相同的信息。所以没有人比其他人拥有更多权力。

There's three vice presidents in the room. There's two E staffs in the room. And at the moment that we decided together, we reasoned through some stuff, we made a decision. Everybody heard it exactly the same time. So nobody has more power than anybody else.

这说得通吗？新毕业的大学生与高管团队成员在同一时间了解到信息。因此，高管团队、为我工作的领导者以及我自己，你们获得工作的权利是基于你们解决问题的能力以及帮助他人成功的能力。而不是因为你拥有某些特权信息，比如只有我知道答案是3.7。所有人都知道。

Does that make sense? The new college grad learned at exactly the same time as the E staff. And so the executive staff and the leaders that work for me and myself, you earn the right to have your job based on your ability to reason through problems and helping other people succeed. And it's not because you have some privileged information that I knew the answer was 3.7 and only I knew. Everybody knew.

当我们制作最近刚发布的NVIDIA第三集节目时，我们进行了这种思维练习，尤其是在过去几年里。嗯。你们的产品发布周期非常令人印象深刻，特别是考虑到你们正在研发的技术水平和这一切的难度。我们当时说，你能想象苹果一年发布两款iPhone吗？

When we did our most recent episode of NVIDIA part three that we we just released, we sort of did this thought exercise, especially over the last couple years. Mhmm. Your product shipping cycle has been very impressive, especially given the level of technology that you are working with and the difficulty of this all. We sort of said, like, could you imagine Apple shipping two iPhones a year?

我们说那是为了说明目的。为了说明目的，不是

And we said that for illustrative purposes. For illustrative purposes, not

针对苹果或其他什么。

to pick on Apple or whatever.

一家大型科技公司每年推出两次旗舰产品或他们的旗舰产品。

A large tech company shipping two flagship products or their flagship product twice per year.

是的。或者，你知道，一年两次WWDC。

Yeah. Or, you know, two WWDCs a year.

是的。似乎有些事情你

Yeah. There seems to be something You

很难想象这种情况，而在这里却发生了。还有其他公司，无论是现在的还是历史上的，嗯。你钦佩、敬仰，或许从中获得了一些灵感？

can't really imagine that whereas that happens here. Are there other companies, either current or historically Mhmm. That you look up to, admire, maybe took some of this inspiration from?

在过去的30年里，我读了不少商业书籍。就像你读的所有东西一样，首先应该享受它，对吧？享受它，从中获得灵感，但不是全盘接受。这些书的目的不是这个。这些书的全部意义在于分享他们的经验。

In the last 30, I've read my fair share of business books. And as in everything you read, you're supposed to, first of all, enjoy it, right? Enjoy it, be inspired by it, but not to adopt it. That's not the whole point of these books. The whole point of these books is to share their experiences.

你应该问自己，这在我的世界里意味着什么？在我正在经历的事情背景下意味着什么？在我所处的环境中意味着什么？对我以及我试图实现的目标意味着什么？在我们公司的时代和能力背景下，这对英伟达又意味着什么？

And you're supposed to ask, what does it mean to me in my world? And what does it mean to me in the context of what I'm going through? What does this mean to me in the environment that I'm in? And what does this mean to me and what I'm trying to achieve? And what does this mean to NVIDIA in the age of our company and the capability of our company?

所以你应该问自己，这对你意味着什么？然后基于我们正在学习的这些不同信息，我们应该制定出自己的策略。我刚才描述的就是我处理一切事情的方式。你应该受到他人启发并向他们学习，而且这种教育是免费的。当有人谈论新产品时，你应该去听听。

And so you're supposed to ask yourself, what does it mean to you? And then from that point, being informed by all these different things that we're learning, we're supposed to come up with our own strategies. What I just described is kind of how I go about everything. You're supposed to be inspired and learn from everybody else, and the education is free. When somebody talks about a new product, you're supposed to go listen to it.

你不应该忽视它。你应该去从中学习。它可能是竞争对手，可能是相邻行业，也可能与我们毫无关系。

You're not supposed to ignore it. You're supposed to go learn from it. And it could be a competitor. It could be adjacent industry. It could be nothing to do with us.

我们从世界上发生的事情中学得越多越好。但之后你应该回来问自己，你知道，这对我们意味着什么？

The more we learn from what's happening out in the world, the better. But then you're supposed to come back and ask yourself, you know, what does this mean to us?

是的。不过你不仅仅是想模仿。

Yeah. You don't just wanna imitate, though.

没错。

That's right.

是的，是的。我很喜欢这种既要学习但不模仿、并且从广泛来源中学习的理念。我认为英伟达能取得今天的成就，还有一个难以置信的第三要素，那就是数据中心。这显然不是显而易见的。

Yeah. Yeah. I love this tee up of learning but not imitating and learning from a wide array of sources. There's this sort of unbelievable third element, I think, to what NVIDIA has become today, and that's the data center. It's certainly not obvious.

我无法从AlexNet以及你们与研究社区和社交媒体反馈的互动中推断出，你们这些供应商和公司是如何决定要开启一段为期五年全力投入数据中心之旅的。这是怎么发生的？

I can't reason from AlexNet and your engagement with the research community and social media feedback You vendors deciding and the company deciding we're gonna go on a five year all in journey on the data center. How did that happen?

是的。我们进军数据中心的旅程，我想说差不多始于十七年前。我总是被问到，公司未来可能会面临哪些挑战？我一直认为，英伟达的技术是插在电脑里的，而那台电脑必须放在你身边，因为它需要连接显示器，这终将限制我们的发展机会，因为能插入GPU的台式电脑数量有限，我们能驱动的CRT显示器和后来的LCD显示器也只有那么多。

Yeah. Our journey to the data center happened, I would say almost seventeen years ago. I'm always being asked, what are the challenges that the company could see someday? And I've always felt that the fact that NVIDIA's technology is plugged into a computer and that computer has to sit next to you because it has to be connected to a monitor, that will limit our opportunity someday because there are only so many desktop PCs that plug a GPU into. And there's only so many CRTs and the time LCDs that we could possibly drive.

所以问题是，如果我们的计算机不必连接显示设备，那岂不是太棒了？这种分离使得我们可以在别处进行计算。有一天，我们的一位工程师向我展示了这个技术，它实际上是捕获帧缓冲区，将其编码成视频并流式传输到接收设备，实现了计算与观看的分离。

So the question is, wouldn't it be amazing if our computer doesn't have to be connected to the viewing device? That the separation of it made it possible for us to compute somewhere else. And one of our engineers came and showed it to me one day, and it was really capturing the frame buffer and coding it into video and streaming it to a receiver device, separating computing from the viewing.

从很多方面看，这就是云游戏。云游戏。

Many ways, that's cloud gaming. Cloud gaming. In

事实上，那正是我们启动GeForce NOW（GFN）的时候。我们知道GFN将是一段漫长的旅程，因为你需要克服各种各样的问题，包括光速的限制。

fact, that was when we started GFN. We knew that GFN was going to be a journey that would take a long time because you're fighting all kinds of problems, including the speed of light.

放眼望去，到处都是延迟问题。

Latency everywhere you look.

没错。

That's right.

对于听众来说，GFN，GeForce NOW。

For listeners, GFN, GeForce NOW.

GeForce NOW。是的。是的。GeForce NOW。我们一直在研究GeForce，它使得

GeForce NOW. Yeah. Yeah. GeForce NOW. And we've been working on GeForce It makes

那是你们的首个云产品。

that your first cloud product.

没错。你看，GeForce Now是英伟达的首个数据中心产品。我们的第二个数据中心产品是远程图形技术，将我们的GPU部署在全球的企业数据中心中，这进而引领我们开发了第三个产品，它结合了CUDA和RGPU，形成了一个超级计算机，然后不断演进和完善。之所以如此重要，是因为英伟达的计算执行地点与享受地点之间的分离——如果能实现这种分离，市场机会就会爆炸性增长。

That's right. And look GeForce Now was NVIDIA's first data center product. And our second data center product was remote graphics, putting our GPUs in the world's enterprise data centers, which then led us to our third product, which combined CUDA plus RGPU, which became a supercomputer, which then worked towards more and more and more. And the reason why it's so important is because the disconnection between where NVIDIA's computing is done versus where it's enjoyed, if you can separate that, your market opportunity explodes.

是的。是的。

Yeah. Yeah.

这完全正确。因此，我们不再受限于放在你桌面的台式PC的物理限制。你知道吗？我们也不再受限于每人一块GPU。所以，它在哪里已经不再重要了。

And it was completely true. And so we're no longer limited by the physical constraints of the desktop PC sitting by your desk. You know? And we're not limited by one GPU per person. And so it doesn't matter where it is anymore.

因此，这确实是一个伟大的洞察。

And so that was really the great observation.

这是个很好的提醒。你知道，对我来说，英伟达业务中的数据中心部分已经等同于AI将如何发展。嗯。但这是一种错误的等同。有趣的是，你之所以能在AI和数据中心领域如此迅速地爆发，是因为你之前有过三个以上的产品，从中学会了如何构建数据中心计算机。

It's a good reminder. You know, the data center segment of NVIDIA's business to me has become synonymous with how is AI going. Mhmm. And that's a false equivalence. And it's interesting that you were only this ready to sort of explode in AI and the data center because you had three plus previous products where you learned how to build data center computers.

没错。尽管那些市场不像AI这样是改变世界的巨大技术变革。是的。你就是这么学习过来的。

Exactly. Even though those markets weren't these like gigantic world changing technology shifts the way that AI is. Yeah. That's how you learned.

是的，没错。你要为未来的机会铺路。不能等到机会摆在面前才伸手去抓。所以你必须预见，作为CEO，我们的工作就是预见未来，预测机会将在何处出现。

Yeah. That's right. You wanna pave the way to future opportunities. You can't wait until the opportunity is sitting in front of you for you to reach out for it. And so you have to anticipate, our job as CEO is to look around corners and anticipate where will opportunities be someday.

即使我不完全确定具体是什么、何时出现，我该如何让公司靠近它，就像站在树下，当苹果落下时我们能一个飞身接住。你们明白我的意思吗？是的。但你必须足够接近才能完成那个飞身接球。

And even if I'm not exactly sure what and when, how do I position the company to be near it, to be just standing kind of near under the tree and we can do a diving catch when the apple falls. You guys know what I'm saying? Yeah. But you've gotta be close enough to do the diving catch.

回到2015年和OpenAI。如果你没有在数据中心打下这些基础，你现在就不可能为OpenAI提供支持。是的。没错

Rewind to 2015 and OpenAI. If you hadn't been laying this groundwork in the data center, you wouldn't be powering OpenAI Yeah. Right

但计算将主要在观看设备之外完成的想法，即绝大部分计算将在计算机本身之外进行。这个洞察很好。事实上，云计算，当今计算的一切都是关于这种分离。通过将其放在数据中心，我们可以克服延迟问题，意思是说你无法克服光速。端到端的光速只有120毫秒左右。

But the idea that computing will be mostly done away from the viewing device, That the vast majority of computing would be done away from the computer itself. That insight was good. In fact, cloud computing, everything about today's computing is about separation of that. And by putting it in a data center, we can overcome this latency problem, meaning you're not gonna overcome speed of light. Speed of light end to end is only a hundred and twenty milliseconds or something like that.

时间并不长。

It's not that long.

从数据中心到全球任何地方的互联网

From a data center to an Internet Anywhere on the

是的。所以我们可以哦，

planet. Yeah. And so we can Oh,

我明白了。确实是遍布全球。是的。

I see. And literally across the planet. Yeah.

没错。所以如果你能解决这个问题，大约是类似这样的数值，我记不清具体数字了，大概是70毫秒，100毫秒，但时间并不长。我的观点是，如果你能消除所有其他障碍，那么光速应该完全足够。你可以建造超大规模的数据中心，实现惊人的功能。而我们用作计算机的这个小设备，或是你的电视作为计算机，任何计算机，它们都能瞬间变得强大无比。

Right. So if you could solve that problem, approximately something like that, I forget the number, it's seventy milliseconds, a hundred milliseconds, but it's not that long. And so my point is, if you could remove the obstacles everywhere else, then speed of light should be perfectly fine. You could build data centers as large like, and you could do amazing things. And this little tiny device that we use as a computer or your TV as a computer, whatever computer, they can all instantly become amazing.

所以这个洞察，你知道，十五年前就是个很好的想法。

So that insight, you know, fifteen years ago was a good one.

说到光速，InfiniBand。是的。就像戴夫·大卫一直在恳求我去那里。嗯哼。

So speaking of the speed of light, InfiniBand. Yeah. Like, Dave David's, like, begging me to go here. Uh-huh.

我当时也在场。

I was at the same time.

你完全预见到了InfiniBand会比任何人意识到的都更有用、更早发挥作用。收购Mellanox，我认为你独具慧眼地认识到这是训练大语言模型所必需的，并且你在收购那家公司时表现得极为果断。为什么别人都没看到这一点，而你看到了？

You totally saw that InfiniBand would be way more useful, way sooner than anyone else realized. Acquiring Mellanox, I think you uniquely saw that this was required to train large language models, and you were super aggressive in acquiring that company. Why did you see that when no one else saw that?

嗯，这有几个原因。首先，如果你想成为一家数据中心公司，处理芯片并不是实现这一目标的途径。数据中心与台式电脑或手机的区别，不在于其中的处理器。数据中心里的台式电脑使用的CPU相同，显然也使用相同的GPU，对吧？非常接近。

Well, there were several reasons for that. First, if you wanna be a data center company, the processing chip isn't the way to do it. A data center is distinguished from a desktop computer versus a cell phone, not by the processor in it. A desktop computer in a data center uses the same CPUs, uses the same GPUs apparently, right? Very close.

所以描述它的不是芯片，不是处理芯片，而是它的网络连接，是它的基础设施。是计算如何分布、安全如何提供、网络如何构建等等。而这些特性与Mellanox相关，而非Nvidia。因此，当我得出结论，Nvidia真正想要成为并构建未来的计算机，而未来的计算机将以数据中心的形式体现时，我们就需要成为一家以数据中心为导向的公司，我们真的需要进入网络领域。

And so it's not the chip, it's not the processing chip that describes it, but it's the networking of it, it's the infrastructure of it. It's how the computing is distributed, how security is provided, how networking is done, you know, so on and so forth. And so those characteristics are associated with Mellanox, not Nvidia. And so the day that I concluded that really Nvidia wants to be, build computers of the future and computers of the future are going to be data centers, embodied in data centers. And we want to be data center oriented company, we really need to get into networking.

这是其一。第二点观察是，虽然云计算始于超大规模计算，即采用商用组件、大量用户，并在一台计算机上虚拟化多个用户，但AI实际上是关于分布式计算，其中一个任务、一个训练任务在数百万处理器之间协调进行。所以这几乎是超大规模计算的逆过程。而用现成的商用以太网设计超大规模计算机的方式，对于Hadoop、搜索查询等所有那些应用来说完全没问题。

And so that was one. The second thing is observation that whereas cloud computing started in the hyperscale, which is about taking commodity components, a lot of users, and virtualizing many users on top of one computer. AI is really about distributed computing where one job, one training job is orchestrated across millions of processors. And so it's the inverse of hyperscale almost. And the way that you design a hyperscale computer with off the shelf commodity ethernet, which is just fine for Hadoop, it's just fine for search queries, it's just fine for all of those things.

但现在当你将一个模型分片 across

But now when you're sharding a model across

不是多个，当你将一个模型分片 across。对吧？因此这一观察表明，你想要做的网络类型并不完全是以太网。而我们为超级计算所做的网络方式实际上非常理想。所以这两个想法的结合让我确信Mellanox绝对是正确的公司，因为他们是世界领先的高性能网络公司。

Not multiple when you're sharding a model across. Right? And so that observation says that the type of networking you wanna do is not exactly ethernet. And the way that we do networking for supercomputing is really quite ideal. And so the combination of those two ideas convinced me that Mellanox is absolutely the right company because they're the world's leading high performance networking company.

而且我们在高性能计算的许多不同领域已经与他们合作过。加上我真的很喜欢那里的人。以色列团队是世界级的。我们现在那里有大约3200人。这是我做过的最好的战略决策之一。

And we worked with them in so many different areas in high performance computing already. Plus I really liked the people. The Israel team is world class. We have some 3,200 people there now. And it was one of the best strategic decisions I'd ever made.

在我们做研究时，特别是英伟达系列第三部分，我们与很多人交流过，许多人都告诉我们，迈络思收购案是有史以来所有科技公司中最好（如果不是最好的话）的收购之一。

When we were researching, particularly part three of our NVIDIA series, we talked to a lot of people and many people told us the Mellanox acquisition is one of, if not the best of all time by any technology company.

我也这么认为。是的。而且这与我们平时做的工作完全脱节。这让每个人都感到惊讶。

I think so too. Yeah. And it's so disconnected from the work that we normally do. It was surprising to everybody.

但换个角度想，你当时就站在行动发生地的附近。是的。所以你能在苹果产品刚上市时就意识到，哦，大语言模型即将爆发。我需要那个。每个人都会需要那个。

But frame this way, you were you were standing near where the action was. Yeah. So you could figure out as soon as that Apple sort of becomes available to purchase, like, oh, LLMs are about to blow up. I'm gonna need that. Everyone's gonna need that.

我觉得我比任何人都更早知道这一点。

I think I know that before anyone else does.

是的。你要把自己定位在机会附近。你不必做到完美。懂吗？你要把自己定位在那棵树旁边。

Yeah. You wanna position yourself near opportunities. You don't have to be that perfect. You know? You you wanna position yourself near the tree.

即使你没有在苹果落地前接住它，只要你是第一个捡起它的人就行。你要让自己靠近机会。所以我很多工作就是让公司定位在机会附近，并让公司具备将每一步变现的能力，这样我们才能持续发展。

And even if you don't catch the apple before it hits the ground, so long as you're the first one to pick it up. You wanna position yourself close to the opportunities. And so that's kind of a lot of my work is positioning the company near opportunities and having the company having the skills to monetize each one of the steps along the way so that we can be sustainable.

你刚才说的让我想起了巴菲特和芒格的一句名言：大致正确比精确错误要好。

What you just said reminds me of a great, aphorism from, Buffett and Munger, which is it's better to be approximately right than exactly wrong.

是的。就是这样。没错。这个不错。确实不错。

Yeah. There you go. Yeah. That's a good one. It's good one.

拜。是的。

By. Yeah.

好了，听众们。是时候聊聊我们最喜爱的另一家公司Statsig了。自从我们上次介绍Statsig以来，他们有了一个非常令人兴奋的更新。他们完成了C轮融资，估值达到了11亿美元。

Alright, listeners. It's time to talk about another one of our favorite companies, Statsig. Since you last heard from us about Statsig, they have a very exciting update. They raised their series c, valuing them at $1,100,000,000.

是的。巨大的里程碑。恭喜团队。时机也很有趣，因为实验领域确实正在升温。

Yeah. Huge milestone. Congrats to the team. And timing is interesting because the experimentation space is, really heating up.

没错。那么为什么投资者将Statsig估值超过十亿美元？这是因为实验已成为世界上最佳产品团队产品技术栈的关键组成部分。

Yes. So why do investors value stat seg at over a billion dollars? It's because experimentation has become a critical part of the product stack for the world's best product teams.

是的。这一趋势始于Web 2.0公司，如Facebook、Netflix和Airbnb。这些公司面临一个问题：如何在扩展到数千名员工的同时，保持快速、去中心化的产品和工程文化？实验系统是这个答案的重要组成部分。

Yep. This trend started with web two dot o companies like Facebook and Netflix and Airbnb. Those companies faced a problem. How do you maintain a fast, decentralized product and engineering culture while also scaling up to thousands of employees? Experimentation systems were a huge part of that answer.

这些系统让这些公司的每个人都能访问一套全球产品指标，从页面浏览量到观看时长再到性能。然后，每当团队发布新功能或产品时，他们都可以衡量该功能对这些指标的影响。

These systems gave everyone at those companies access to a global set of product metrics, from page views to watch time to performance. And then every time a team released a new feature or product, they could measure the impact of that feature on those metrics.

所以Facebook可以设定一个公司范围内的目标，比如增加应用内使用时间，然后让各个团队去想办法实现。将这种方法扩展到数千名工程师和产品经理身上，砰，你就获得了指数级增长。难怪实验现在被视为必不可少的基础设施。

So Facebook could set a company wide goal like increasing time in app and let individual teams go and figure out how to achieve it. Multiply this across thousands of engineers and PMs, and boom, you get exponential growth. It's no wonder that experimentation is now seen as essential infrastructure.

是的。如今最好的产品团队，如Notion、OpenAI、Rippling和Figma，同样依赖实验。但他们不是内部自建，而是直接使用Statsig。而且他们不仅仅用Statsig做实验。过去几年里，Statsig已经添加了快速产品团队所需的所有工具，比如功能开关、产品分析、会话回放等等。

Yep. Today's best product teams like Notion, OpenAI, Rippling, and Figma are equally reliant on experimentation. But instead of building it in house, they just use Statsig. And they don't just use Statsig for experimentation. Over the last few years, Statsig has added all the tools that fast product teams need, like feature flags, product analytics, session replays, and more.

所以如果你想帮助团队的工程师和产品经理找到更快构建和做出更明智决策的方法，请访问statsig.com/acquired，或点击节目说明中的链接。他们提供非常慷慨的免费套餐、5万美元的创业计划，以及适合大公司的实惠企业合同。只要告诉他们是本和大卫推荐你的。我想暂时离开NVIDIA的话题，如果你同意的话，问你一些问题，因为我们有很多创始人听众，算是关于公司建设的建议。第一个问题是，在创业初期，你最大的竞争对手是——你没有做出人们真正想要的东西。

So if you would like to help your team's engineers and PMs figure out how to build faster and make smarter decisions, go to statsig.com/acquired, or click the link in the show notes. They have a super generous free tier, a $50,000 startup program, and affordable enterprise contracts for large companies. Just tell them that Ben and David sent you. I wanna move away from NVIDIA, if you're okay with it, and ask you some questions since we have a lot of founders that listen to this show, sort of advice for company building. The first one is when you're starting a startup in the earliest days, your biggest competitor is, you don't make anything people want.

就像，你的公司很可能会死掉

Like, your company is likely to die

不是因为消费。

Not consumption.

仅仅因为人们对你所做的事情实际上没有你那么在乎。是的。到了后期，你必须非常谨慎地考虑竞争策略。我很好奇，对于那些已经实现产品市场匹配、开始增长、处于有趣增长市场的公司，你会给出什么建议？他们应该在哪里寻找竞争，又该如何应对？

Just because people don't actually care as much as you do about what you're doing. Yeah. In the later days, you actually have to be very thoughtful about competitive strategy. And I'm curious, what would be your advice to companies that, have product market fit, that are starting to grow, they're in interesting growing markets? Where should they look for competition, and how should they handle it?

嗯，思考竞争的方式多种多样。我们更喜欢将自己定位在满足通常尚未出现的需求上。

Well, there are all kinds of ways to think about competition. We prefer to position ourselves in a way that serves a need that usually hasn't emerged.

我听说，你或英伟达的其他人，我想你们用了'零美元'这个说法

I've heard, you or others in NVIDIA, I think you used the phrase $0

是的，完全正确。这是我们表达'市场尚未形成但我们相信终将存在'的方式。通常当你定位在那里时，所有人都在试图理解你为何在此布局。

Yeah. That's exactly right. Yeah. It's our way of saying there's no market yet, but we believe there will be one. And usually when you're positioned there, everybody's trying to figure out why are you here.

对吧？当我们最初进入汽车领域时，因为我们相信未来的汽车将主要由软件定义。如果主要由软件驱动，就必须配备非凡的计算机系统。我记得当时有位CTO对我说：'汽车可受不了蓝屏死机'。

Right? Because when we first got into automotive, because we believe that in the future, the car is gonna be largely software. And if it's gonna be largely software, a really incredible computer is necessary. And so when we positioned ourselves there, most people I I still remember one one of the one of the CTOs told me, you know what? Cars cannot tolerate the blue screen of death.

我回应说：'没人能接受蓝屏，但这改变不了未来每辆车都将是软件定义汽车的事实'。十五年后证明我们基本正确。我们常选择非消费领域布局，这样当市场兴起时，就很难有同等形态的竞争者。我们在PC游戏领域早期介入，如今英伟达已是该领域巨头。

And I said, I don't think anybody can tolerate that, but it doesn't change the fact that someday every car will be a software defined car. I think, you know, fifteen years later, we're largely right. And so oftentimes there's non consumption and we like to navigate our company there. And by doing that, by the time that you that the market emerges, it's very it's very likely there aren't that many competitors shaped that way. And so we were early in PC gaming, and today, NVIDIA is very large in PC gaming.

我们重新构想了设计工作站形态，如今全球几乎所有工作站都采用英伟达技术。我们重塑了超级计算的发展方向与服务对象，使其民主化——如今加速计算已规模巨大。我们还重新定义了软件开发范式。

We reimagined what a design workstation would be like. Today, just about every workstation on the planet uses NVIDIA's technology. We reimagine how supercomputing ought to be done and who should benefit from supercomputing, that we would democratize it. Look today, NVIDIA's and accelerated computing is quite large. And we reimagine how software would be done.

现在这被称为机器学习，而计算方式的变革我们称之为人工智能。我们提前十年进行这类重构，因此在零美元市场耕耘了约十年。如今我重点投入Omniverse项目。

And today it's called machine learning and how computing would be done, we call it AI. And so we reimagine these kind of things, try to try to do that about a decade in advance. And so we spent about a decade in $0 markets. Mhmm. And today, I spent a lot of time on Omniverse.

Omniverse正是零美元业务的典型范例。目前...

And Omniverse is a, you know, classic example of a $0 business. And There's,

大概，现在有40个客户，差不多这样

like, 40 customers now, something like

对。亚马逊，宝马。是的。

that. Amazon, BMW. Yeah.

不。这很酷。

No. It's cool.

这很

It's

酷。假设你真的获得了这十年的巨大领先优势，但随后其他人也搞明白了，有人开始紧追不舍。创业者可以做哪些结构性的事情来保持领先？你可以一直全力以赴，说我们会比他们更努力、更聪明。这在某种程度上确实有效，但这些只是战术。

cool. So let's say you do get this great ten year lead, but then other people figure it out, and you got people nipping at your heels. What are some structural things that someone who's building a business can do to sort of stay ahead? And you can just keep your pedal to the metal and say, we're gonna outwork them, we're gonna be smarter. And, like, that works to some extent, but those are tactics.

在战略上，你能做些什么来确保维持这种领先优势？

What strategically can you do to sort of make sure that you can maintain that lead?

通常，如果你创造了这个市场，最终你会拥有人们所说的护城河。因为如果你的产品做得对，并且围绕你建立了一个完整的生态系统来服务最终市场，你实际上就创建了一个平台。可能是基于产品的平台，有时是基于服务的平台，有时是基于技术的平台。

Oftentimes, if you created the market, you ended up having, you know, what what people describe as moats. Because if you build your product right and it's enabled an entire ecosystem around you to help serve that end market, you've essentially created a platform. It's a product based platform. Sometimes it's a service based platform. Sometimes it's a technology based platform.

但如果你早期进入并用心帮助生态系统与你一同成功，最终你会拥有这个网络中的网络，以及围绕你建立的所有这些开发者和客户。是的。而这个网络本质上就是你的护城河。所以我不喜欢在护城河的语境下思考这个问题，原因在于你现在专注于围绕城堡建造东西。

But if you were early there and you you were mindful about helping the ecosystem succeed with you, you ended up having this network of networks and all these developers and all these customers who are who are built around you. Yep. And that network is essentially your moat. And so, I don't love thinking about it in the context of a moat. And the reason for that is because you're now focused on building stuff around your castle.

我倾向于在构建网络的语境下思考问题，这个网络是关于让其他人也能享受最终市场的成功。你知道，你不是唯一享受成功的公司，而是与包括我在内的一大群人共同享受。

I tend to like thinking about things in the context of building a network, and that network is about enabling other people to enjoy the success of the final market. You know, that you're not the only company that enjoys it, but you're enjoying it with a whole bunch of other people, including me.

我很高兴你提到这一点，因为我想问你。在我看来，至少听起来你也这么认为——嗯。英伟达绝对是一家平台公司——嗯。世界上有意义的平台公司寥寥无几。我认为也可以说，在最初的几年里，你们是一家技术公司，而不是平台公司。

I'm so glad you brought this up because I wanted to ask you. In my mind, least, and sounds like in yours too Mhmm. NVIDIA is absolutely a platform company Mhmm. Of which there are very few meaningful platform companies in the world. I think it's also fair to say that when you started for the first few years, you were a technology company and not a platform company.

我能想到的每一家试图从一开始就作为平台公司的例子都失败了。你必须先以技术起家。你是什么时候考虑转型成为

Every example I can think of of a company that tried to start as a platform company fails. You gotta start as a technology first. When did you think about making that transition to being

一个平台的？

a platform?

比如你们最初的显卡是技术产品。那时没有CUDA，也没有平台。

Like your first graphics cards were technology. They weren't there was no CUDA. There was no platform.

是的。你的观察没错。然而，在我们公司内部，我们一直是一家平台公司。原因是从公司成立的第一天起，我们就有一个叫做UDA的架构。它就是CUDA的前身UDA。

Yeah. What you observed is not wrong. However, inside our company, we were always a platform company. And the reason for that is because from the very first day of our company, we had this architecture called UDA. It's the UDA of CUDA.

CUDA是计算统一设备架构？

CUDA is compute unified Yeah. Device architecture?

没错。原因在于我们最初所做的，尽管Riva 128只有计算机图形功能，但其架构描述了各种类型的加速器。我们会采用这种架构，开发者可以基于它进行编程。实际上，英伟达最初的商业策略是成为PC内的游戏主机。而游戏主机需要开发者，这就是为什么英伟达很早以前，我们的首批员工中就有一位开发者关系专员。

That's right. And the reason for that is because what we've done, what we what we essentially did in the beginning, even though Reva one twenty eight only had computer graphics, the architecture described accelerators of all kinds. And we would take that architecture and developers would program to it. In fact, NVIDIA's first strategy, business strategy, was we were going to be a game console inside the PC. And a game console needs developers, which is the reason why NVIDIA, a long time ago, one of our first employees was a developer relations person.

因此这就是为什么我们认识所有游戏开发者和所有3D开发者，我们当时就觉得'哇'。

And so it's the reason why we knew all the game developers and all the three d developers, and we knew Wow.

所以最初的商业计划是，像是

So so was the original business plan to, like

有点像是要构建DirectX。

Sort of like to build DirectX.

是的。与世嘉竞争，在PC领域。

Yeah. Compete with And and and Sega as, like, with PCs.

英伟达最初的架构叫做DirectNV。直接英伟达。没错。而DirectX是一个API，使得操作系统能够直接访问硬件。

Original NVIDIA architecture was called DirectNV. Direct NVIDIA. Yeah. And DirectX was an API that made it possible for operating system to directly Address the But hardware.

你创办英伟达时DirectX还不存在，对吧？这就是你最初战略出错的原因

DirectX didn't exist when you started NVIDIA. Right? And that's what made your strategy wrong for

93年时我们已经有了DirectX

the '93, we had DirectX.

是的

Yeah.

然后在1995年，你知道的，DirectX正式发布了

And which in 1995 became, you know, well, DirectX came out.

所以这是一个

So this is an

重要的教训。我们始终是一家以开发者为导向的公司

important lesson. You We were always a developer oriented company.

最初的尝试是让开发者基于DirectNV开发，然后他们就会为我们的芯片开发，这样我们就能建立一个平台。是的，完全正确。但实际情况是微软早已拥有所有这些开发者关系。所以你通过艰难的方式学到了这个教训，就像这样

The initial attempt was we will get the developers to build on DirectNV, and then they'll build for our chips, and then we'll have a platform. And Yeah. Exactly. What played out is Microsoft already had all these developer relationships. So you learned the lesson the hard way of like Yeah.

哎呀。我们只需要 那就是

Yikes. We just gotta That's

微软当年就是这么做的。他们觉得，哦，那可以成为一个开发者平台。我们就收下了。谢谢。

what Microsoft did back in the day. They're like, oh, that could be a developer platform. We'll take that. Thank you.

你知道吗？不。但他们有很多优势。他们的做法完全不同，而且他们很多事情都做对了。我们很多事情都做错了。

You know? No. But they had a lot. They did it very differently, and and they did a lot of things right. We did a lot of things wrong.

但是 但是拥有

But but having

在九十年代说过反对微软的话。我的意思是，那是

said against Microsoft in the nineties. I mean, that's

是的。就像你今天在跟英伟达竞争一样。

Yeah. It's like you're competing against NVIDIA today.

是的。不。情况很不一样，但我很感激这个比喻。但我们当时远远谈不上与他们竞争。如果你现在看，当CUDA出现时，有OpenGL，有DirectX，但还有另一个扩展，如果你愿意这么说的话，那个扩展就是CUDA。

Yeah. No. It's a lot different, but I appreciate that. But but we were we were nowhere near near competing with them. If you look now, when CUDA came along and there was OpenGL, there was DirectX, but there's there's still another extension, if you will, and that extension is CUDA.

而那个CUDA扩展使得一个原本为运行DirectX和OpenGL而付费的芯片能够为CUDA建立一个装机基础。这就是最终结果

And that CUDA extension allows a chip that got paid for running DirectX and OpenGL to create an installed base for CUDA. And so that's the end

的原因，也是你为什么如此坚持的原因。根据我们的研究，确实是你坚持要让每一块NVIDIA芯片都能运行CUDA。

of And it is why you were so militant. And I think from our research, it really was you being militant that every NVIDIA chip will run CUDA.

是的。如果你是一个计算平台，所有东西都必须兼容。我们是地球上唯一一个每块加速器在架构上都与其他加速器兼容的加速器平台。从未有过这样的存在。现在实际上有数亿块。

Yeah. If you're a computing platform, everything's gotta be compatible. We are the only accelerator on the planet where every single accelerator is architecturally compatible with the others. None that has ever existed. There are literally a couple of 100,000,000.

对吧？全球有2.5亿、3亿活跃的CUDA GPU装机量，它们全部都是架构兼容的。如果NV30、NV35、NV39和NV40都各不相同，你怎么可能形成一个计算平台呢？经过三十年发展，它们仍然完全兼容。

Right? 250,000,000, 300,000,000 installed base of active CUDA GPUs being used in the world today, and they're all architecturally compatible. How would you have a computing platform if NV30 and NV35 and NV39 and NV40, they're all different? Right? At thirty years, it's all completely compatible.

所以这是我们公司唯一不可协商的规则。其他一切都可以商量。

And so that's the only unnegotiable rule in our company. Everything else is negotiable.

我想，Kudo算是UDA的重生，但现在明白了这一点，UDA其实可以一直追溯回去

I mean, and I guess Kudo was a rebirth of UDA, but understanding this now, UDA going all the way back

没错。

Yeah.

这确实要追溯到所有你听说过的芯片。

It really is all the way back to all the chips you've ever heard.

是的。是的。是的。事实上，UDA可以追溯到我们今天所有的芯片。哇。

Yeah. Yeah. Yeah. In fact, UDA goes all the way back to all of our chips today. Wow.

需要说明的是，我没有帮助过任何正在收听的创始CEO们。我得告诉你，当你问那个问题时，我会传授什么经验教训？我不知道。我的意思是，成功公司和成功CEO的特征，我认为已经被很好地描述了。有很多这样的特征。

For the record, I didn't help any of the founding CEOs that are listening. I gotta tell you, while you were asking that question, what lessons would I impart? I don't know. I mean, the characteristics of successful companies and successful CEOs, I think are fairly well described. There are a whole bunch of them.

我只是认为创办成功公司极其困难。就是极其困难。当我看到这些了不起的公司被建立起来时，我只有钦佩和尊重，因为我知道这极其困难。而且我认为每个人都做了许多类似的事情。人们会做一些明智的好事情。

I just think starting successful companies are insanely hard. It's just insanely hard. And when I see these amazing companies getting built, have nothing but admiration and respect because I just know that it's insanely hard. And I think that everybody did many similar things. There are some good, smart things that people do.

也有一些你可以做的愚蠢事情。但即使你做对了所有事情，仍然可能失败。你可能做了一堆蠢事，我就做过很多，但仍然成功了。所以显然那并不完全正确。我认为技能是你可以一路学习的东西，但在重要时刻，某些条件必须汇聚在一起。

There are some dumb things that you can do. But you could do all the right, things and still fail. You could do a whole bunch of dumb things, and I did many of them, and still succeed. So obviously that's not exactly right. Think skills are the things that you can learn along the way, but at an important moment, certain circumstances have to come together.

而且我确实认为市场必须，你知道，成为帮助你成功的因素之一。是的。但这显然不够，因为很多人仍然会失败。

And and I do think that that the market has to, you know, be one of the agents Yeah. To help you succeed. It's not enough, obviously, because a lot of people still fail.

你还记得英伟达历史上的任何时刻吗？比如，哦，我们做了一堆错误决定，但不知怎么得救了，因为你知道，成功需要所有运气和所有技能的总和。是的。你还记得任何这样的时刻吗？

Do you remember any moments in NVIDIA's history where you're like, oh, we made a bunch of wrong decisions, but somehow we got saved because, you know, it takes the sum of all the luck and all the skill Yeah. In order to succeed. Do you remember any moments where

实际上，我认为你们从Rebo 128开始就做得非常精准。正如我提到的，REVO 128，我们当时做出了许多明智的决策，这些决策至今仍然非常明智。我们设计芯片的方式至今完全一样，因为天哪，那时候根本没人这么做过。我们当时是出于绝望，用尽了所有能想到的办法，因为我们别无选择。但你知道吗？

you I actually thought that you started with Rebo one twenty eight was spot on. REVO one hundred twenty eight, as I mentioned, the number of smart decisions we made, which are smart to this day. How we design chips is exactly the same to this day, because gosh, nobody's ever done it back then. And we pulled every trick in the book in a desperation because we had no other choice. Well, guess what?

这才是做事应有的方式。现在每个人都是这么做的，对吧？大家都这么做是因为，如果能一次完成，为什么要做两次呢？如果能一次流片成功，为什么要流片七次呢？

That's the way things ought to be done. And now everybody does it that way. Right? Everybody does it because why should you do things twice if you can do it once? Why tape out a chip seven times if you could tape it out one time?

对吧？所以最有效率、最具成本效益、最具竞争力的就是速度技术。对吧？速度就是性能。上市时间就是性能。

Right? And so the most efficient, the most cost effective, the most competitive speed is technology. Right? Speed is performance. Time to market is performance.

所有这些都适用。所以如果我们能一次完成，为什么要做两次呢？REBA 128在产品规格制定、市场需求的思考与不足之处的判断，以及如何评估市场等方面做出了许多伟大的决策。天啊，我们做出了一些非常非常棒的决定。是的。

All of those things apply. So why do things twice if we could do it once? And so REBA one hundred twenty eight made a lot of great decisions in how we spec products, how we think about market needs and lack of, and how do we judge markets and all of this. Man, we made some amazing amazingly good decisions. Yeah.

我们当时，你知道，背水一战。我们只剩下最后一次机会了。但是

We were, you know, back against the wall. We only had one more shot to do it. But

一旦你全力以赴，看到了自己的能力，为什么下次还要有所保留呢？

Once you pull out all the stops and you see what you're capable of, why would you put stops in

下次？

next time?

没错。就像，让我们始终保持停止

Exactly. Like, let's keep stops out all the

时间 说得对。

time That's right.

每一次都是。

Every time.

说得对。

That's right.

不过，从运气这个角度来说，回顾1997年，是否可以说那是消费者真正开始极度重视游戏中3D图形性能的时刻？

Is it fair to say though maybe on the luck side of the equation, thinking back to 1997, that that was the moment where consumers tipped to really, really valuing three d graphical performance in games.

哦，是的。举个例子，运气。我们来谈谈运气。如果卡马克没有决定使用硬件加速——记得吗，《毁灭战士》完全是软件渲染的。NVIDIA的理念是，虽然通用计算非常了不起，它能推动软件和IT等一切发展。

Oh, yeah. So for example, luck. Let's let's talk about luck. If Carmack hadn't decided to use acceleration because remember, Doom was completely software rendered. The NVIDIA philosophy was that although general purpose computing is a fabulous thing, it's gonna enable software and IT and everything.

但我们认为，有些应用如果没有硬件加速，要么无法实现，要么成本高昂。它应该被加速。3D图形是其中之一，但不是唯一。它恰好是第一个，而且是一个非常棒的应用。我至今还记得我们第一次见到约翰时，他非常坚持使用CPU，认为软件渲染器已经非常出色了。

We felt that there were there were applications that wouldn't be possible or would be costly if it wasn't accelerated. It should be accelerated. And three d graphics was one of them, but it wasn't the only one. And it was just happens to be the first one and a really great one. And I still remember the first times we met John, he was quite emphatic about using CPUs and the software render was really good.

坦率地说，如果你看《毁灭战士》，这款游戏的性能在当时即使有加速器也很难实现。你知道，如果不进行过滤，不需要做双线性过滤，它的表现其实相当不错。

I mean, quite frankly, if you look Doom, the performance of Doom was really hard to achieve even with accelerators at the time. You know, if you didn't filter, if you didn't have to do bilinear filtering, it did a pretty good job.

不过《毁灭战士》的问题在于你需要卡马克来编程。

The problem with Doom though was you needed Carmack to program it.

是的。你需要卡马克来编程。完全正确。那是一段天才代码。但尽管如此，软件渲染器确实做得非常出色。

Yeah. You needed Carmack to program it. Exactly. It a genius piece of code. But nonetheless, software renderers did a really good job.

如果他当初没有决定转向OpenGL并为《雷神之锤》做加速加速，说实话，你知道，哪款杀手级应用会把我们带到今天这个位置？没错。所以卡马克和斯维尼，通过《虚幻》和《雷神之锤》，共同创造了消费级3D领域最初的两个杀手级应用。是的。因此我非常感激他们。

And if he hadn't decided to go to OpenGL and accelerate accelerate for Quake, frankly, you know, what would be the killer app that put us here? Right. And so Carmack and Sweeney, both between Unreal and Quake, created the first two killer applications for for consumer three d. Yeah. And so I I owe owe them a great deal.

我也想很快地再回到一点。你知道，你说你讲过这些故事，然后你觉得不知道创始人能从中学到什么。我其实认为，如果你看看今天所有的大型科技公司，可能除了谷歌之外，它们确实都是起步于，并且我现在了解你的背景了，通过面向开发者，计划为开发者构建平台和工具。你知道，所有公司都是这样。苹果，不是亚马逊。

I wanna come back real quick too. You know, you said you told these stories and you're like, well, I don't know what founders can take from that. I I actually do think, you know, if you look at all the big tech companies today, perhaps with the exception of Google, they did all start, and understanding this now about you, by addressing developers, planning to build a platform and tools for developers. You know, all of them. Apple, not Amazon.

嗯，想想AWS，AWS就是这样起步的。所以我认为这实际上印证了你的观点，这绝不意味着能保证成功。但这会让你待在树下，万一苹果掉下来呢。

Well, guess with AWS, that's how AWS started. So I think that actually is a lesson to your point of like that won't guarantee success by any means. But that'll get you hanging around a tree if the Apple Falls.

是的。尽管我们有很多好主意，但你不可能拥有世界上所有好主意。而拥有开发者的好处是，你能看到很多好主意。

Yeah. As many good ideas as we have, you don't have all the world's good ideas. And and the benefit of having developers is you get to see a lot of good ideas.

是的。嗯。当我们逐渐接近尾声时，我们花了大量时间讨论过去，我想稍微思考一下未来。我相信作为人工智能前沿的从业者，你在这方面投入了很多时间。要知道，我们正在进入一个时代，当人们使用软件时，软件所能实现的生产力能够极大地放大他们创造的影响和价值，从长远来看，这对人类来说必定是惊人的。

Yep. Yeah. Well, as we we start to drift toward the end here, we spent a lot of time on the past, and I wanna think about the future a little bit. I'm sure you spend a lot of time on this being on the cutting edge of AI. You know, we're moving into an era where the productivity that software can accomplish when a person is using software can massively amplify the impact and the value that they're creating, which has to be amazing for humanity in the long run.

短期来看，在我们弄清楚这意味着什么的过程中，不可避免地会有些颠簸。随着人工智能变得越来越强大，越来越擅长加速生产力，对于那些因此而被取代的工作，你认为有哪些解决方案？首先，

In the short term, it's gonna be inevitably bumpy as we sort of figure out what that means. What do you think some of the solutions are as AI gets more and more powerful and better at accelerating productivity for all the displaced jobs that are gonna come from it? Well, first of

我们必须确保人工智能的安全。人工智能安全有几个不同的重要领域。显然，在机器人和自动驾驶汽车领域，存在一整套人工智能安全范畴，我们致力于在各种安全领域实现功能安全和主动安全。何时需要人工介入？何时可以不需要人工介入？

all, we have to keep AI safe. And there's a couple of different areas of AI safety that's really important. Obviously, robotics and self driving car, there's a whole field of AI safety and we've dedicated ourselves to functional safety and active safety in all kinds of different areas of safety. When to apply human in the loop? When is it okay for human not to be in the loop?

如何逐步实现越来越少需要人工介入，但总体上仍以人工为主的状态？在信息安全方面，显然要关注偏见、虚假信息，并尊重艺术家和创作者的权益。这个领域值得高度重视。你已经看到我们做的一些工作——我们没有抓取网络内容，而是与Getty和Shutterstock合作，以商业上公平的方式应用人工智能生成式AI。

How do you get to a point where increasingly human doesn't have to be in the loop, but human largely in the loop? In the case of information safety, obviously bias, false information and appreciating the rights of artists and creators. That whole area deserves a lot of attention. You've seen some of the work that we've done. Instead of scraping the internet, we partnered with Getty and Shutterstock to create commercially a fair way of applying artificial intelligence generative AI.

在大语言模型和未来具有更强自主性的人工智能领域，明确的答案是：在合理的情况下——我认为在很长一段时间内都将是合理的——保持人工介入。应该避免人工智能在数字环境中自我学习、改进和改变的能力。我们应该收集数据，处理数据，训练模型，测试模型，在再次发布前验证模型。始终保持人工介入。是的。

In the area of large language models and the future of increasingly greater agency AI, clearly the answer is for as long as it's sensible, and I think it's going to be sensible for a long time, is a human in the loop. The ability for an AI to self learn and improve and change out in the wild in a digital form should be avoided. We should collect data, we should carry the data, we should train the model, we should test the model, validate the model before we release it on the wild again. Human is in the loop. Yep.

许多不同行业已经展示了如何构建对人类安全有益的体系。显然，飞机自动驾驶的工作方式、双飞行员系统、空中交通管制、冗余性和多样性，以及所有设计安全系统的基本理念，同样适用于自动驾驶汽车等领域。因此我认为有很多创建安全人工智能的模式可供借鉴，我们需要应用这些模式。关于自动化，我的感觉是——我们拭目以待——但人工智能更可能会创造更多工作岗位。近期的问题是：'近期'的定义是什么？

There are a lot of different industries that have already demonstrated how to build systems that are safe and good for humanity. Obviously the way autopilot works for a plane and two pilot system and then air traffic control and redundancy and diversity and all of the basic philosophies of designing safe systems apply as well in self driving cars and so on and so forth. So I think there's a lot of models of creating safe AI, and I think we need to apply them. With respect to automation, my feeling is that, and we'll see, but it is more likely that AI is gonna create more jobs. And in the near term, the question is what's the definition of near term?

原因在于，生产力提升首先带来的是繁荣。当公司变得更加成功时，由于希望拓展更多领域，它们会雇佣更多人。所以问题是，如果你考虑一家公司时说：好吧，如果我们提高了生产力，就需要更少的人。那是因为公司没有更多想法了，但对大多数公司来说并非如此。如果你变得更有生产力，公司变得更有盈利能力，通常他们会雇佣更多人以拓展新领域。

And the reason for that is the first thing that happens with productivity is prosperity. And prosperity, when the companies get more successful, they hire more people because they want to expand into more areas. And so the question is, if you think about a company and say, okay, if we improve the productivity, need fewer people. Well, that's because the company has no more ideas, but that's not true for most companies. If you become more productive and the company becomes more profitable, usually they hire more people to expand into new areas.

只要我们相信还有更多领域可以拓展，药物研发中还有更多创意，交通运输中还有更多创意，零售业中还有更多创意，娱乐业中还有更多创意，科技领域还有更多创意。只要我们相信还有更多创意，行业繁荣——源于生产力的提升——就会带来更多就业机会和更多创意。回顾历史，我们可以公允地说今天的产业规模远大于一千年前的世界产业。原因显然是人类拥有无穷的创意。我认为未来仍有大量促进繁荣的创意，以及通过生产力提升催生的创意。

And so long as we believe that there are more areas to expand into, that there are more ideas in drugs, this drug discovery, there are more ideas in transportation, there are more ideas in retail, there are more ideas in entertainment, that there are more ideas in technology. So long as we believe that there are more ideas, the prosperity of the industry, which comes from improved productivity, results in hiring more people, more ideas. Now, you go back in history, we can fairly say that today's industry is larger than the industry, world's industries a thousand years ago. And the reason for that is because obviously humans have a lot of ideas. And I think that there's plenty of ideas yet for prosperity and plenty of ideas that can be begat from productivity improvements.

但我的直觉是这很可能会创造就业。显然，净就业增长并不能保证任何个体不被解雇——这是显而易见的。更可能的情况是某人会输给另一个使用AI的人类，而不是输给AI本身。因此我认为所有人首先应该学习如何使用AI来提升自身生产力。

But my sense is that it's likely to generate jobs. Now, obviously, net generation of jobs doesn't guarantee that any one human doesn't get fired. I mean, that's obviously true. It's more likely that someone will lose a job to someone else, some other human that uses an AI, and not likely to an AI, but to some other human that uses an AI. And so I think the first thing that everybody should do is learn how to use AI so that they can augment their own productivity.

每家公司都应通过提升生产力来实现更高效率，从而获得更大繁荣并雇佣更多员工。我认为工作岗位将发生变革。我的预测是实际就业率会更高，我们将创造更多就业机会。产业生产力将提升，当前许多受劳动力短缺困扰的行业很可能会借助AI重新站稳脚跟，恢复增长与繁荣。

And every company should augment their own productivity to be more productive so that they can have more prosperity, hire more people. And so I think jobs will change. My guess is that we'll actually have higher employment, We'll create more jobs. I think industries will be more productive. Many of the industries that are currently suffering from lack labor, workforce is likely to use AI to get themselves off their feet and get back to growth and prosperity.

因此我的看法略有不同，但我确实认为就业将受到影响。我鼓励每个人都去学习AI。

So I see it a little bit differently, but I do think that jobs will be affected. And I I'd encourage everybody just to learn AI.

这个观点很恰当。这其实是我们常在《Acquired》节目中讨论的一个观点的变体——我们称之为莫里茨对摩尔定律的推论，以红杉的迈克·莫里茨命名。

This is appropriate. There's a version of something we talk about a lot on Acquired. We call it the Moritz corollary to Moore's law after Mike Moritz from Uh-huh.

我知道。是的。红杉是我们公司的首轮投资者。

I know. Yeah. Sequoia was the first investor in our company. Yeah.

当然。没错。这背后有个精彩的故事：当迈克接替唐·瓦伦丁与道格共事时，他查看红杉的回报数据，正在研究第三或第四期基金（我记得是包含思科的那期第四基金），当时他就想：我们怎么可能超越这个成就？

Of course. Yeah. Yeah. The great story behind it is that when Mike was taking over for Don Valentine with Doug, he was sitting and looking at Sequoia's returns and he was looking at fund three or four, I think it was four maybe that had Cisco in it. He was like, how are we ever gonna top that?

不行，唐会把我们打败，我们永远赢不了。他思考后意识到，随着计算成本降低，它能触及经济中更多领域，因为成本降低使其能被更广泛采用，那么我们能够进入的市场应该会变得更大。是的。而人工智能，你的论点基本上是人工智能也会做同样的事情。没错。

Can't, Don's gonna have us beat, we're never gonna beat that. And he thought about it and he realized that, well, as compute gets cheaper and it can access more areas of the economy because it gets cheaper and can get adopted more widely, well, then the markets that we can address should get bigger. Yeah. And AI, your argument is basically AI will do the same thing. Exactly.

我

I

刚才给你举了完全相同的例子，实际上，生产力提高并不会导致我们做得更少。生产力提高通常会导致我们做得更多。我们做的一切都会变得更容易，但最终我们会做得更多。是的。因为我们有无限的雄心。

just gave you exactly the same example that in fact, productivity doesn't result in us doing less. Productivity usually results in us doing more. Everything we do will be easier, but we'll end up doing more. Yep. Because we have infinite ambition.

这个世界有无限的雄心。所以如果公司利润更高，他们往往会雇佣更多人来做更多事情。是的。

The world has infinite ambition. And so if a company is more profitable, they tend to hire more people to do more. Yep.

是的。确实如此。技术是一个杠杆，而这个想法站不住脚的地方在于，好像我们会因此满足。

Yeah. That's true. Technology is a lever, and the the place where the idea kinda falls down is that, like, that we would be satisfied.

是的。就像...是的。人类有着永不满足的雄心。

Yeah. Like Yeah. Humans have never ending ambition.

不。人类总是会扩张，消耗更多能源，并试图追求更多想法。我们物种的每一个版本都是如此。

No. Humans will always expand and consume more energy and attempt to pursue more ideas. That has always been true of every version of our species

是的。

Yeah.

随着时间的推移。

Over time.

现在是感谢我们节目的好朋友ServiceNow的好时机。我们曾向听众介绍过ServiceNow惊人的创业故事，以及他们如何成为过去十年表现最佳的公司之一，但有些听众对ServiceNow实际做什么提出了疑问。所以今天，我们将回答这个问题。

Now is a great time to thank good friend of the show, ServiceNow. We have talked to listeners about ServiceNow's amazing origin story and how they've been one of the best performing companies the last decade, but we've gotten some questions from listeners about what ServiceNow actually does. So today, we are gonna answer that question.

首先，最近媒体经常使用的一个说法是，ServiceNow是企业的

Well, to start, a phrase that has been used often here recently in the press is that ServiceNow is the, quote, unquote, AI operating system for the enterprise. But to make that more concrete, ServiceNow started twenty two years ago focused simply on automation. They turned physical paperwork into software workflows initially for the IT department within enterprises. That was it. And over time, they built on this platform going to more powerful and complex tasks.

他们从仅服务IT扩展到其他部门，如人力资源、财务、客户服务、现场运营等。在过去二十年的过程中，ServiceNow已经奠定了所有必要的繁琐基础工作，以连接企业的每一个角落，并实现自动化。

They were expanding from serving just IT to other departments like HR, finance, customer service, field operations, and more. And in the process over the last two decades, ServiceNow has laid all the tedious groundwork necessary to connect every corner of the enterprise and enable automation to happen.