2023-10-15 Jensen Huang.ACQUIRED Interview with NVIDIA CEO Jensen Huang

Refer To：《ACQUIRED Interview with NVIDIA CEO Jensen Huang》。

Transcript: (disclaimer: may contain unintentionally confusing, inaccurate and/or amusing transcription errors)

对话记录：（免责声明：可能包含无意中令人困惑、不准确和/或有趣的转录错误）

Ben: 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.

本: 我得说，David，我真希望每一集都有Nvidia的完整制作团队。这样就不需要担心自己开关摄像机、确保录制过程中不会出什么问题，感觉轻松多了。

David: Yeah, just the gear. The drives that came out of the camera.

David: 对，就是那些设备。摄像机里出来的硬盘。

Ben: All right. Red cameras for the home studio starting next episode.

本: 好的，下集开始用红色摄像机录制家用工作室。

David: Yeah. Great.

David: 好的，太好了。

Ben: All right, let’s do it. Welcome to this episode of Acquired, the podcast about great technology companies and the stories and playbooks behind them. I’m Ben Gilbert.

本: 好的，开始吧。欢迎收听这一集的《Acquired》，这是一个关于伟大科技公司及其背后故事和策略的播客。我是本·吉尔伯特。

David: I’m David Rosenthal.

David: 我是David Rosenthal。

Ben: And we are your hosts. Listeners, just so we don’t bury the lead, this episode was insanely cool for David and I. After researching Nvidia for something like 500 hours over the last two years, we flew down to Nvidia headquarters to sit down with Jensen himself.

本: 我们是你们的主持人。听众们，直接告诉大家，这一集对David和我来说非常酷。经过两年多大约500小时的Nvidia研究后，我们飞到Nvidia总部，亲自与Jensen坐下来交流。

Jensen is the founder and CEO of Nvidia, the company powering this whole AI explosion. At the time of recording, Nvidia is worth $1.1 trillion and is the sixth most valuable company in the entire world. Right now is a crucible moment for the company. Expectations are set sky high. They have about the most impressive strategic position and lead against their competitors of any company that we’ve ever studied.

Jensen是Nvidia的创始人兼CEO，Nvidia是推动这场AI爆炸的公司。在录制时，Nvidia的市值为1.1万亿美元，是全球第六大最有价值的公司。现在对公司来说是一个关键时刻，预期极高。与我们研究过的任何公司相比，它在战略位置和领先优势方面都无与伦比。

But here’s the question that everyone is wondering. Will Nvidia’s insane prosperity continue for years to come? Is AI going to 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?

但大家都在问的问题是：Nvidia的疯狂繁荣会持续多年吗？AI会成为下一个万亿美元的技术浪潮吗？我们有多确定这一点？如果是的话，Nvidia是否能够在这个市场逐渐成形的过程中保持其令人难以置信的主导地位？

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.

Jensen带我们回顾了他们从图形技术到数据中心，再到AI的故事，以及他们如何从多次濒临死亡的经历中生还。他还给创始人们提供了许多建议，并在节目的最后分享了他作为创始人的一些情感历程。

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

David: 通过这次经历，我对公司以及他作为创始人和领导者有了全新的认识，尽管我们以为在来之前就已经了解一切，结果发现我们并没有。

Ben: Turns out the protagonist actually knows more.

本: 结果发现，主角实际上知道得更多。

All right. 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. That is acquired.fm/slack. We would love to see you.

好的。听众们，加入我们的Slack。那里正在进行关于这家公司、AI、整个生态系统以及我们最近做过的其他节目的精彩讨论。网址是acquired.fm/slack，我们很期待见到你们。

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. Onto Jensen.

不再赘言，本节目不是投资建议。David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用途。接下来是Jensen。

Jensen, this is Acquired. We want to start with story time. We want to wind the clock all the way back to, I believe it was 1997. You’re getting ready to ship the RIVA 128, which is one of the largest graphics chips ever created in the history of computing. It is the first fully 3D-accelerated graphics pipeline for a computer. And you guys have about six months of cash left.

Jensen，欢迎来到《Acquired》。我们想从讲故事开始。我们想把时间倒回到1997年左右。你们正准备发布RIVA 128，这款芯片是计算机历史上最庞大的图形芯片之一，也是首个完全3D加速的计算机图形管道。而你们大约只剩下六个月的现金了。

You decide to do the entire testing in simulation, rather than ever receiving a physical prototype. You commission the production run site unseen with the rest of the company’s money. You’re betting it all right here on the RIVA 128. It comes back, and of the 32 DirectX blend modes, it supports 8 of them. You have to convince the market to buy it, and you have to convince developers not to use anything but those eight blend modes. Walk us through what that felt like.

你决定全部通过模拟进行测试，而不是接收任何物理原型。你用公司剩余的所有资金委托生产，而你把所有的赌注都押在了RIVA 128上。生产结果回来后，32个DirectX混合模式中，它只支持其中的8个。你必须说服市场购买它，同时还得说服开发者只使用这8个混合模式。请给我们讲讲那时候的感受。

Jensen: The other 24 weren’t that important.

Jensen: 其他24个其实没那么重要。

David: Okay, so wait. First question. Was that the plan all along? When did you realize that—

David: 好的，等一下。第一个问题，那是你们一开始的计划吗？你什么时候意识到—

Jensen: I realized I didn’t learn about it until it was too late. We should have implemented all 32. But we built what we built, so we had to make the best of it. That was really an extraordinary time.

Jensen: 我直到太晚才意识到这一点。我们本应该实现所有32个，但我们做出来的就是这些，所以我们只能尽力做到最好。那真是一个非凡的时刻。

Remember, RIVA 128 was NV3. NV1 and NV2 were based on forward texture mapping, no triangles but curves, and tessellated the curves. Because we were rendering higher-level objects, we essentially avoided using Z buffers. We thought that that was going to be a good rendering approach, and turns out to have been completely the wrong answer. What RIVA 128 was, was a reset of our company.

记住，RIVA 128是NV3。NV1和NV2基于前向纹理映射，使用的是曲线而不是三角形，并对曲线进行了细分。因为我们渲染的是更高层次的物体，所以基本上避免使用Z缓冲区。我们认为那是一种好的渲染方法，结果证明完全是错误的。RIVA 128的出现，实际上是我们公司的一次重启。

Now remember, at the time that we started the company in 1993, we were the only consumer 3D graphics company ever created. We were focused on transforming the PC into an accelerated PC because at the time, Windows was really a software-rendered system.

再想想，在我们1993年创办公司时，我们是唯一的一家消费级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的系统。

Anyway, RIVA 128 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. Thirty competitors have already shown up even though we were the first company at the time that we were founded, so the world was a completely different place.

总之，RIVA 128是我们公司的重启，因为在我们意识到自己走错了路时，微软已经推出了DirectX。这与Nvidia的架构根本不兼容。尽管我们是公司创立时的第一家，但此时已有30家竞争对手出现，世界变得完全不同。

The question about what to do as a company strategy at that point, I would’ve said that we made a whole bunch of wrong decisions. But on that day that mattered, we made a sequence of extraordinarily good decisions.

那时关于公司战略该怎么办的问题，我会说我们做了一堆错误的决策。但在那个至关重要的时刻，我们做了一系列非常正确的决策。

That time—1997—was probably Nvidia’s best moment. The reason for that was our backs were up against the wall. We were running out of time, we’re running out of money, and for a lot of employees, running out of hope. The question is, what do we do?

那段时间——1997年——可能是Nvidia最好的时刻。原因是我们已经背水一战。我们时间不多了，资金也快耗尽，很多员工也失去了希望。问题是，我们该怎么办？

Well, the first thing that we did was we decided that look, DirectX is now here. We’re not going to fight it. Let’s go figure out a way to build the best thing in the world for it.

首先，我们决定：DirectX已经来了，我们不再与之对抗。我们要想办法为它打造世界上最好的东西。

RIVA 128 is the world’s first fully accelerated hardware-accelerated pipeline for rendering 3D. The transform, the projection, every single element, all the way down to the frame buffer was completely hardware-accelerated.

RIVA 128是世界上第一个完全加速的硬件加速管线，用于渲染3D图形。变换、投影、每一个元素，直到帧缓冲，全部都是完全硬件加速的。

We implemented a texture cache. 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.

我们实现了一个纹理缓存。我们将总线限制、帧缓冲区限制提升到当时物理上能承受的最大值。我们制造了一个任何人都无法想象过的最大芯片。我们使用了最快的内存。基本上，如果我们建造了那个芯片，就没有什么能比它更快。

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

我们还选择了一个成本点，远高于我们认为任何竞争对手愿意接受的最高价格。如果我们做对了，我们加速了所有的功能，实施了我们知道的所有DirectX，并尽可能地将其做大，那么显然没有人能够建造比这更快的东西。

好汉。

David: Today, in a way you do that here at Nvidia, too. You were a consumer products company back then, right? It was the end consumers who were going to have to pay the money to buy that.

David: 今天，在某种程度上，你们在Nvidia也做了类似的事情。那时你们是消费品公司，对吧？最终消费者需要为购买这些产品支付费用。

Jensen: That’s right. But we observed that there was a segment of the market. At the time, the PC industry was still coming up and it wasn’t good enough. Everybody was clamoring for the next fastest thing. 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’ve 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.

Jensen: 没错。但我们观察到市场中有一个细分群体。当时，PC行业还在发展，且性能还不够好。每个人都在争相追求下一代更快的产品。如果今年你的性能比现有产品高出10倍，我们相信有一大群发烧友会争相购买。而我们完全正确，PC行业确实拥有一个庞大的发烧友市场，他们愿意购买最好的产品。

To this day, it remains true. For certain segments of a market where the technology is never good enough like 3D graphics, and we chose the right technology, 3D graphics is never good enough. We call it back then 3D gives us sustainable technology opportunity because it’s never good enough, so your technology can keep getting better. We chose that.

直到今天，这依然成立。对于某些市场的细分领域，技术永远不够好，比如3D图形，我们选择了正确的技术，3D图形永远不够好。我们当时称之为“3D为我们提供了可持续的技术机会，因为它永远不够好，因此你的技术可以不断改进”。我们选择了这一点。

聪明的假设。

We also made the decision to use this technology called emulation. There was a company called ICOS. On the day that I called them, they were just shutting the company down because they had no customers. I said, hey, look. I’ll buy what you have inventory. No promises are necessary.

我们还决定使用一种叫做仿真技术的技术。有家公司叫ICOS。我打电话给他们那天，他们正准备关闭公司，因为没有客户。我说，嘿，看看，我会买你们的库存，不需要任何承诺。

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. We would’ve been out of business already.

我们需要那个仿真器的原因是，如果你算一下我们当时的资金情况，如果我们进行了芯片生产，拿到芯片并开始开发软件，到我们发现所有的漏洞，然后再次进行芯片生产时，我们早就破产了。

David: And your competitors would’ve caught up.

David: 而且你的竞争对手会追赶上来。

Jensen: Well, not to mention we would’ve been out of business.

Jensen: 更别提我们会倒闭。

David: Who cares?

David: 谁在乎？

Jensen: Exactly. If you’re going to be out of business anyway, that plan obviously wasn’t the plan. The plan that companies normally go through—build a chip, write the software, fix the bugs, tape out a new chip, so on and so forth—that method wasn’t going to work. The question is, if we only had six months and you get to tape out just one time, then obviously you’re going to tape out a perfect chip.

Jensen: 没错。如果你最终注定会倒闭，那么这个计划显然不是正确的计划。公司通常的流程是——制造芯片、编写软件、修复漏洞、再次生产新的芯片，依此类推——这种方法显然行不通。问题是，如果我们只有六个月的时间，并且只能进行一次芯片生产，那显然你会尽力制造出一颗完美的芯片。

I remember having a conversation with our leaders and they said, but Jensen, how do you know it’s going to be perfect? I said, I know it’s going to be perfect, because if it’s not, we’ll be out of business. So let’s make it perfect. We get one shot.

我记得和我们的领导们进行过一次对话，他们问我，但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，我们就会倒闭。所以我们必须做到完美。我们只有一次机会。

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

我们本质上通过购买这个仿真器虚拟原型了芯片。Dwight和软件团队编写了我们的软件，整个堆栈，并在这个仿真器上运行，只是在实验室里等待Windows绘制。

David: It was like 60 seconds for a frame or something like that.

David: 每帧大约需要60秒，差不多是这样。

Jensen: Oh, easily. I actually think that it was an hour per frame, something like that. We would just sit there and watch it paint. On the day that we decided to tape out, I assumed that the chip was perfect. Everything that we could have tested, we tested in advance, and told everybody this is it. We’re going to tape out the chip. It’s going to be perfect.

Jensen: 哦，轻松搞定。我实际上认为每帧大约需要一个小时，差不多是那样。我们就坐在那里看它渲染。当我们决定进行芯片生产的那一天，我以为芯片已经完美无缺。我们能测试的都提前测试过了，并告诉大家，这就是最终版。我们要进行芯片生产，它将是完美的。

Well, if you’re going to tape out a chip and you know it’s perfect, then what else would you do? That’s actually a good question. If you knew that you hit enter, you tape out a chip, and you knew it was going to be perfect, then what else would you do? Well, the answer, obviously, go to production.

如果你要进行芯片生产，并且知道它是完美的，那么你还会做什么呢？这是个好问题。如果你知道按下回车键后，芯片生产完成，并且你知道它会是完美的，那么你还会做什么呢？答案显然是，进入生产阶段。

Ben: And marketing blitz. And developer relations.

本: 还有市场推广。以及开发者关系。

Jensen: Kick everything off because you got a perfect chip. We got in our head that we have a perfect chip.

Jensen: 一切开始，因为我们有了一颗完美的芯片。我们都认为我们有了一颗完美的芯片。

David: How much of this was you and how much of this was your co-founders, the rest of the company, the board? Was everybody telling you you were crazy?

David: 这其中有多少是你做的，多少是你的联合创始人、公司其他人、董事会的决定？大家都在告诉你你疯了吗？

Jensen: No. Everybody was clear we had no shot. Not doing it would be crazy.

Jensen: 没有。大家都很清楚我们没有机会。不去做才是疯狂的。

David: Otherwise, you might as well go home.

David: 否则，你们也许直接回家算了。

Jensen: Yeah, you’re going to be out of business anyway, so anything aside from that is crazy. It seemed like a fairly logical thing. Quite frankly, right now as I’m describing it, you’re probably thinking yeah, it’s pretty sensible.

Jensen: 是的，反正你最终会倒闭，所以除了这个计划外，任何其他计划都是疯狂的。看起来这似乎是一个相当合乎逻辑的决定。坦率地说，现在我描述这些时，你可能会觉得，是的，确实很有道理。

David: Well, it worked.

David: 好吧，结果是成功的。

Jensen: Yeah, so we taped that out and went directly to production.

Jensen: 是的，所以我们进行了芯片生产并直接进入生产阶段。

Ben: So is the lesson for founders out there when you have conviction on something like the RIVA 128 or CUDA, go bet the company on it. This keeps working for you. 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?

本: 那么对创始人来说，当你对像RIVA 128或CUDA这样的东西有信心时，是时候把公司全部押上去吗？这对你来说一直有效。看起来你从中学到的教训是：是的，继续押上所有赌注，因为到目前为止，它每次都奏效。你怎么想的？

Jensen: No, no. When you push your chips in I know it’s going to work. Notice we assumed 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. 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.

Jensen: 不，不是的。当你把赌注全部押上时，我知道它一定会成功。注意，我们假设我们生产了一颗完美的芯片。我们生产完美芯片的原因是，在生产之前我们已经对整个芯片进行了仿真。我们开发了完整的软件堆栈。我们对所有驱动程序和软件进行了质量保证（QA）。我们运行了所有的游戏，运行了我们拥有的每一个VGA应用程序。

When you push your chips in, what you’re really doing is, when you bet the farm you’re saying, I’m going to take everything in the future, all the risky things, and I pull in in advance. That is probably the lesson. To this day, everything that we can prefetch, everything in the future that we can simulate today, we prefetch it.

当你押上所有赌注时，实际上你是在说，我要提前将未来所有的风险因素都纳入考虑。这大概是我们的经验教训。直到今天，我们所有可以预取的东西，所有可以今天模拟的未来事物，我们都提前进行预取。

David: We talk about this a lot. We were just talking about this on our Costco episode. You want to push your chips in when you know it’s going to work.

David: 我们经常讨论这个问题。我们刚刚在Costco的那一集里也讨论了这个。你要在知道它会成功时才全力以赴。

Ben: Every time we see you make that company move, you’ve already simulated it. Do you feel like that was the case with CUDA?

本: 每次我们看到你做出公司的决策时，你已经提前模拟过了。你觉得CUDA也是这样吗？

Jensen: Yeah. In fact, before that was CUDA, there was a CG. 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 a higher level expression? And how can we use our GPU for things like CT reconstruction, image processing? We were already down that path.

Jensen: 是的。事实上，在CUDA之前，我们有一个CG。我们已经在探索如何创建一个位于我们芯片之上的抽象层，可以用更高层次的语言和表达方式来表示？以及如何将我们的GPU用于CT重建、图像处理等应用？我们已经在这条路上走得很远了。

There was some positive feedback and some intuitive positive feedback that we think that general-purpose computing could be possible. If you just looked at the pipeline of a programmable shader, it is a processor. It is highly parallel, it is massively threaded, and it is the only processor in the world that does that. There were a lot of characteristics about programmable shading that would suggest that CUDA has a great opportunity to succeed.

有一些积极的反馈和直观的正向反馈，我们认为通用计算是可能的。如果你只看一下可编程着色器的流水线，它就是一个处理器。它具有高度的并行性，拥有大量线程，它是世界上唯一能够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。

Ben: And that is true if there was a large market of machine learning practitioners who would eventually show up and want to do all this great scientific computing and accelerated computing.

本: 如果未来会有一个庞大的机器学习从业者市场，他们最终会出现在那里并希望做所有这些伟大的科学计算和加速计算，那么这一点是正确的。

But at the time when you were starting to invest what is now something like 10,000 person years in building that platform, did you ever feel like, oh man, we might’ve invested ahead of the demand for machine learning, since we’re a decade before the whole world is realizing it?

但在你开始投资建立这个平台（现在大约投入了10,000人年）的那个时候，你有没有想过，天啊，我们可能投资过早了，机器学习的需求还没到来，因为我们比整个世界意识到它的需求要早十年？

Jensen: I guess yes and no. When we saw deep learning, when we saw AlexNet and realized its incredible effectiveness and 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?

Jensen: 我想是有和没有吧。当我们看到深度学习，看到AlexNet并意识到它在计算机视觉上的巨大有效性时，我们有了足够的智慧，可以回到最基本的原理，问问自己，这个东西究竟有什么让它如此成功？

When a new software technology or a new algorithm comes along and somehow leapfrogs 30 years of computer vision work, you have to take a step back and ask yourself, but why? Fundamentally, is it scalable? And if it’s scalable, what other problems can it solve?

当一种新的软件技术或新算法出现，并且以某种方式跨越了30年的计算机视觉工作时，你必须停下来问自己，为什么会这样？从根本上讲，它是否具有可扩展性？如果它具有可扩展性，它还能解决哪些其他问题？

There were several observations that we made. The first observation is that if you have a whole lot of example data, you could teach this function to make predictions.

我们做了几个观察。第一个观察是，如果你有大量的示例数据，你可以教这个函数进行预测。

What we’ve basically done is discovered a universal function approximator, because the dimensionality could be as high as you wanted 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.

我们实际上做的是发现了一个通用的函数逼近器，因为其维度可以高得任意大。因为每一层都是逐层训练的，所以没有理由不能做出非常非常深的神经网络。

Okay, now you just reason your way through. Now I go back to 12 years ago. You could just imagine the reasoning I’m going through in my head that we’ve discovered a universal function approximator. In fact, we might have discovered with a couple of more technologies, a universal computer that you can—

好，现在你可以推理一下。让我回想一下12年前。你可以想象我脑海中的推理过程：我们发现了一个通用的函数逼近器。事实上，我们可能已经通过几项技术发现了一个通用计算机，你可以—

David: Have you been paying attention to the ImageNet competition every year leading up to this?

David: 你有没有一直关注每年到这个时刻的ImageNet竞赛？

Jensen: Yeah, and the reason for that is because we were already working on computer vision at the time. 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.

Jensen: 是的，原因是我们当时已经在做计算机视觉的工作。我们试图让CUDA成为一个好的计算机视觉系统，或者说大多数为计算机视觉创建的算法并不适合CUDA。

We were sitting there trying to figure it out. All of a sudden, AlexNet shows up. That was incredibly intriguing. It’s so effective that it makes you take a step back and ask yourself, why is that happening?

我们当时坐在那里试图弄明白。突然，AlexNet出现了。它非常引人注目。它如此有效，以至于你不得不回过头来问自己，为什么会这样？

By the time that you reason your way through this, you go, well, what are the problems in a world where a universal function approximator can solve? We know that most of our algorithms start from principal sciences. You want to understand the causality. And from the causality, you create a simulation algorithm that allows us to scale.

当你理清了这一点后，你会发现，嗯，一个通用函数逼近器可以解决哪些问题？我们知道，大多数算法都源于基本科学。你想要理解因果关系。从因果关系出发，你创建一个模拟算法，使我们能够进行扩展。

Well, for a lot of problems, we don’t care about the causality. We just care about the predictability of it. Like do I really care for what reason you prefer this toothpaste over that? I don’t really care the causality. I just want to know that this is the one you would’ve predicted. Do I really care that the fundamental cause of somebody who buys a hot dog buys ketchup and mustard? It doesn’t really matter. It only matters that I can predict it.

对于很多问题，我们并不关心因果关系。我们只关心它的可预测性。比如，我真的关心你为什么偏好这个牙膏而不是那个牙膏吗？我其实并不关心因果关系。我只想知道这是你会预测到的那一个。我真的关心买热狗的人为什么会买番茄酱和芥末吗？其实不重要。重要的是我能预测到它。

It applies to predicting movies, predicting music. It applies to predicting, quite frankly, weather. We understand thermal dynamics, radiation from the sun, cloud effects, oceanic effects. We understand all these different things. We just want to know whether we should wear a sweater or not, isn’t that right? Causality for a lot of problems in the world doesn’t matter. We just want to emulate the system and predict the outcome.

它适用于预测电影、预测音乐。坦白说，它也适用于预测天气。我们理解热力学、太阳辐射、云效应、海洋效应。我们理解所有这些不同的因素。我们只想知道我们是否应该穿上毛衣，不是吗？对很多世界上的问题来说，因果关系并不重要。我们只想模拟系统并预测结果。

Ben: 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 hugely valuable market.

本: 而且这可能是一个极具盈利潜力的市场。如果你能预测下一个表现最好的社交媒体推送内容，结果证明这是一个极其有价值的市场。

David: I love the examples you pulled—toothpaste, ketchup, music, movies.

David: 我喜欢你举的例子——牙膏、番茄酱、音乐、电影。

Jensen: When you realize this, you realize, hang on a second. A universal function approximator, a machine learning system, something that learns from examples could have tremendous opportunities because just the number of applications is quite enormous.

Jensen: 当你意识到这一点时，你会明白，等等。一个通用的函数逼近器，一个机器学习系统，一个从示例中学习的系统，可能会有巨大的机会，因为应用的数量是非常庞大的。

Everything from, obviously, we just are talking about commerce all the way to science. 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. If that’s the case, then how you build a computer and how you build a chip (in fact) can be completely changed. Realizing that, the rest of it just comes with, do you have the courage to put your chips behind it?

从商业到科学，显然，所有这些领域。你意识到，也许这可以影响世界上很大一部分行业。几乎所有的世界软件最终都将以这种方式编程。如果是这样，那么你如何构建计算机，如何构建芯片（实际上）可以完全改变。意识到这一点，剩下的就是，你是否有勇气将赌注押在它上面？

David: That’s where we are today. That’s where Nvidia is today. This is a couple of years after AlexNet, and this is when Ben and I were getting into the technology industry and the venture industry ourselves.

David: 这就是我们今天所处的位置。这就是Nvidia今天所处的位置。这是在AlexNet发布几年的时候，这也是我和Ben自己开始进入科技行业和风险投资行业的时候。

Ben: I started at Microsoft in 2012, so right after AlexNet but before anyone was talking about machine learning, even the mainstream engineering community.

本: 我2012年开始在微软工作，所以是在AlexNet发布之后，但在任何人讨论机器学习之前，甚至主流工程社区也还没有谈论到机器学习。

David: There were those couple of years there where to a lot of the rest of the world, these looked like science projects. The technology companies here in Silicon Valley, particularly the social media companies, were just realizing huge economic value out of this—the Googles, the Facebooks, the Netflixes, et cetera.

David: 那些年里，对世界上很多人来说，这些看起来像是科学项目。硅谷的科技公司，特别是社交媒体公司，刚刚意识到这一技术带来了巨大的经济价值——谷歌、Facebook、Netflix等。

Obviously, that led to lots of things including OpenAI a couple of years later. But during those couple of years, when you saw just that huge economic value unlock here in Silicon Valley, how are you feeling during those times?

显然，这导致了许多事情，包括几年后成立的OpenAI。但是在那几年的时候，当你看到硅谷释放出巨大的经济价值时，你当时的感受是什么？

Jensen: The first thought was reasoning about how 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?

Jensen: 第一个想法是考虑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使用可能性？如果我们要去构建这台计算机，人们会用它做什么？

We were fortunate that working with the world’s universities and researchers was innate in our company. We were already working on CUDA, and CUDA’s early adopters were researchers because we democratized supercomputing.

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一部分。我们已经在开发CUDA，而CUDA的早期用户是研究人员，因为我们使超级计算成为大众化。

CUDA is not just used (as you know) for AI. CUDA is used for almost all fields of science. Everything from molecular dynamics to imaging, CT reconstruction to seismic processing, to weather simulations, quantum chemistry. The list goes on. The number of applications of CUDA in research was very high.

CUDA不仅仅用于AI（你知道的）。CUDA被用于几乎所有科学领域。从分子动力学到成像，CT重建到地震处理，天气模拟到量子化学。这个列表还在继续。CUDA在研究中的应用数量非常高。

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, find every single AI researcher on the planet, and say how can we help you advance your work?

当时机成熟，我们意识到深度学习可能非常有趣时，我们自然而然地回到研究人员那里，找遍地球上的每一个AI研究员，问我们如何帮助你推动你的工作？

That included Yann LeCun, Andrew Ng, and Jeff Hinton. That’s how I met all these people. I used to go to all the AI conferences, and that’s where I met Ilya Sutskever there for the first time.

这包括Yann LeCun、Andrew Ng和Jeff Hinton。这就是我认识所有这些人的方式。我曾经参加所有的AI会议，这也是我第一次在那儿遇见Ilya Sutskever。

It was really about at that point, what are the systems that we can build and the software stacks that we can build to help you be more successful to advance the research? Because at the time, it looked like a toy. 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? So we believed in it.

当时真正的问题是，我们能构建什么样的系统，能构建哪些软件堆栈，帮助你更成功地推动研究？因为当时，这看起来像是一个玩具。我第一次见到Goodfellow时，GAN的分辨率是32x32，只是一个模糊的猫的图像。但是它能走多远呢？所以我们相信它。

We believed that you could scale deep learning because obviously it’s trained layer by layer. You could make the data sets larger and you could make the models larger. We believe that if you made that larger and larger, we get better and better. Kind of sensible.

我们相信你可以扩展深度学习，因为显然它是逐层训练的。你可以让数据集更大，模型也可以做得更大。我们相信，如果你把它做得更大，我们会变得更好。这很有道理。

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. That’s where it all happened.

我认为，与研究人员的讨论和互动正是我们所需要的积极反馈系统。我会回到研究中去。那就是一切发生的地方。

David: When OpenAI was founded in 2015, that was such an important moment. That’s obvious today now, but at the time, I think most people, even people in tech were like, what is this? Were you involved in it at all?

David: 2015年OpenAI成立时，那是一个非常重要的时刻。现在看来这是显而易见的，但当时我认为大多数人，甚至是科技界的人都会问，这是什么？你参与其中了吗？

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

因为你和研究人员关系密切，和Ilya一起，从谷歌和Facebook带走那些人才，直白点说，重新培养研究社区并开放它是一个非常重要的时刻。你参与其中了吗？

Jensen: I wasn’t involved in the founding of it, but I knew a lot of the people there. Elon, of course, I knew. Peter Beal was there and Ilya was there. We have some great employees today that were there in the beginning.

Jensen: 我没有参与它的创立，但我认识很多那里的人员。当然，我认识Elon。Peter Beal和Ilya也在那儿。今天我们公司也有一些伟大的员工，他们在开始时就在那儿。

I knew that they needed this amazing computer that we were building, and we’re building the first version of the DGX, which 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.

我知道他们需要我们正在建设的这台令人惊叹的计算机，我们正在构建DGX的第一个版本，今天当你看到Hopper时，它有70磅重，35,000个零件，10,000安培。但我们构建的第一个版本的DGX是内部使用的，我将第一个交给了OpenAI。那是个有趣的日子。

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 it could be really remarkable. That belief system came from the interactions with all these amazing researchers. It came from just seeing the incremental progress.

我们的大部分成功一开始就集中在帮助研究人员达到下一个层次上。我知道它在当时并不非常有用，但我也相信，在几次点击后，它可以变得非常出色。这个信念系统来自与所有这些了不起的研究人员的互动。它来源于看到逐步进展。

At first, the papers were coming out every three months. Then papers today are coming out every day. You could just monitor the archive papers. I took an interest in learning about the progress of deep learning, and to the best of my ability read these papers. You could just see the progress happening exponentially in real time.

一开始，论文每三个月才发布一次。现在，论文几乎每天都有发布。你可以随时监控这些论文。我对学习深度学习的进展产生了兴趣，尽我所能阅读这些论文。你可以看到进展以指数级速度实时发生。

Ben: 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 billion parameter mark, and certainly once you cross the hundred billion, 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?

本: 甚至在业内，从我们和一些研究人员的交流中看，似乎没有人预测到当你只是增加模型的大小时，语言模型会变得如此有用。他们认为，哦，必须发生某种算法的变化。但一旦你突破了100亿参数的门槛，特别是当你突破了1000亿时，它们就神奇地变得更准确、更有用、更接近真实。你第一次看到真正的大型语言模型时感到震惊吗？你记得那个感觉吗？

Jensen: 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. I know what the answer is. I’ll just make you guess it. My first impression of BERT was really how clever it was. Now the question is how can you scale that?

Jensen: 我对语言模型的第一感觉是它如何巧妙地遮蔽掉单词并让模型预测下一个单词。这是最好的自监督学习。我们拥有所有这些文本。我知道答案是什么，我只是让你猜。我的第一印象是BERT真的很聪明。现在的问题是，如何扩展它？

The first observation on almost everything is interesting, and then try to understand intuitively why it works. Then the next step is from first principles. How would you extrapolate that? Obviously, we knew that BERT was going to be a lot larger.

几乎对所有事物的第一次观察都是有趣的，然后试着直观地理解它为什么有效。接下来的步骤是从最基本的原理出发。你如何推测它的未来？显然，我们知道BERT会变得更大。

Now, one of the things about these language models is it’s encoding information. It’s compressing information. Within the world’s languages and text, there’s a fair amount of reasoning that’s encoded in it. We describe a lot of reasoning things. if you were to say that a few step reasoning is somehow learnable from just reading things, I wouldn’t be surprised.

现在，这些语言模型的一个特点是它正在编码信息。它在压缩信息。在世界的语言和文本中，有相当多的推理被编码在其中。我们描述了很多推理的事情。如果你说几步推理是通过阅读来学习的，我一点也不会惊讶。

For a lot of us, we get our common sense and reasoning ability by reading. Why wouldn’t a machine learning model also learn some of the reasoning capabilities from that? From reasoning capabilities, you could have emergent capabilities.

对我们很多人来说，我们通过阅读获得常识和推理能力。为什么机器学习模型也不能从中学习一些推理能力呢？从推理能力中，你可能会获得涌现能力。

Emergent abilities are consistent with intuitively from reasoning. 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. I could visualize literally the entire computer and all the modules in a self-driving car. The fact that it’s still keeping lanes makes me insanely happy.

涌现能力与推理的直觉是一致的。有些东西是可以预测的，但它仍然令人惊叹。它合乎逻辑并不意味着它不惊人。我能直观地看到整个计算机和自动驾驶车中的所有模块。它仍然能保持车道让我非常高兴。

Ben: I even remember that from my first operating systems class in 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.

本: 我甚至记得在大学时上第一次操作系统课的情景，当我终于从编程语言到电气工程课，一路理解过来，期间通过那门操作系统课的桥梁。我当时想，哦，我想我明白了冯·诺依曼计算机是如何从头到尾工作的，但它仍然是一个奇迹。

Jensen: Exactly. When you put it all together, it’s still a miracle.

Jensen: 没错。当你把一切结合起来，它仍然是一个奇迹。

Ben: We have some questions we want to ask you. Some are cultural about Nvidia, but others are generalizable to company-building broadly. The first one that we wanted to ask is that we’ve heard that you have 40+ direct reports, and that this org chart works a lot differently than a traditional company org chart.

本: 我们有一些问题想问你。有些问题是关于Nvidia的文化，另外一些是关于公司建设的普适问题。我们想问的第一个问题是，我们听说你有40多个直接下属，而你的组织结构与传统公司结构有很大不同。

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 fricking best work and the most important thing in the world. Now go. (a) Is that correct? and (b) is there something special about Nvidia that enables that?

你认为Nvidia有什么特别之处，使得你能够有这么多直接下属，不需要过多关注呵护或聚焦于高管的职业成长，而是你们的目标就是做出最好的工作，为世界上最重要的事去努力。(a) 这是正确的吗？(b) 是否有某些特别之处使得Nvidia能够做到这一点？

Jensen: I don’t think it’s something special in Nvidia. I think that we had the courage to build a system like this. Nvidia’s not built like a military. It’s not built like the armed forces, where you have generals and colonels. We’re not set up like that. We’re not set up in a command and control and information distribution system from the top down.

Jensen: 我不认为这在Nvidia是特别的。我认为我们有勇气建立这样一个系统。Nvidia不像军队那样建立。它不像军队那样有将军和上校。我们不是那样设置的。我们也不是按照自上而下的指挥控制和信息分发系统来设立的。

We’re really built much more like a computing stack. The lowest layer is our architecture, then there’s our chip, 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.

我们更像是建立一个计算堆栈。最底层是我们的架构，然后是我们的芯片，再到我们的软件，最后在上面是这些不同的模块。每一层模块都是人。

The architecture of the company (to me) is a computer with a computing stack, with people managing different parts of the system. 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, is in charge. That person is the pilot in command. That’s one characteristic.

我认为公司架构就像是一台计算机，拥有一个计算堆栈，由人来管理系统的不同部分。谁向谁汇报，职位与你在堆栈中的位置无关。关键在于谁最擅长运行那一层的模块和功能，谁就负责。那个人就是指挥官。这是一个特点。

David: Have you always thought about the company this way, even from the earliest days?

David: 你从一开始就一直以这种方式思考公司吗？

Jensen: Yeah, pretty much. The reason for that is because your organization should be the architecture of the machinery of building the product. That’s what a company is. And yet, everybody’s company looks exactly the same, but they all build different things. How does that make any sense? Do you see what I’m saying?

Jensen: 是的，差不多。从一开始就是这样的。原因是因为你的组织结构应该是构建产品的机器架构。这就是公司本身。尽管如此，每个公司的组织架构看起来都差不多，但他们建造的东西却各不相同。这怎么能说得通呢？你明白我的意思吗？

How you make fried chicken versus how you flip burgers versus how you make Chinese fried rice is different. Why would the machinery, why would the process be exactly the same?

炸鸡的做法、煎汉堡的方式、做中式炒饭的方式都不同。为什么这些过程的机器和方式必须完全相同？

It’s not sensible to me that if you look at the org charts of most companies, it all looks like this. 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 supposedly autonomous.

我认为，如果你看大多数公司的组织结构图，它们看起来都是这样的。然后你会看到一个团队负责一个业务，另一个团队负责另一个业务，还有一个团队负责另一个业务，而且他们都应该是独立的。

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? That’s number one.

这些对我来说都没有任何意义。关键是我们想要构建什么，以及公司架构如何最适合去构建它？这是第一要点。

Jensen: In terms of information systems and how you enable collaboration, we’re wired up like a neural network. The way that we say this is that there’s a phrase in the company called ‘mission is the boss.’ We figure out what is the mission of what is the mission, and we go wire up the best skills, the best teams, and the best resources to achieve that mission. It cuts across the entire organization in a way that doesn’t make any sense, but it looks a little bit like a neural network.

Jensen: 就信息系统和如何促进协作而言，我们就像神经网络一样连接起来。我们公司有一个说法叫做“使命为主”。我们首先确定使命是什么，然后将最好的技能、最好的团队和最好的资源调动起来，去实现这一使命。这种方式贯穿了整个组织，虽然乍看之下没有任何意义，但它有点像神经网络。

David: And when you say mission, do you mean Nvidia’s mission is…

David: 你说的使命是指Nvidia的使命吗……

Jensen: Build Hopper.

Jensen: 构建Hopper。

David: Okay, so it’s not like further accelerated computing? It’s like we’re shipping DGX Cloud.

David: 好的，所以并不是像进一步加速计算这样的使命？比如我们要发布DGX Cloud？

Jensen: No. Build Hopper or somebody else’s build a system for Hopper. Somebody has built CUDA for Hopper. Somebody’s job is to build cuDNN for CUDA for Hopper. Somebody’s job is the mission. Your mission is to do something.

Jensen: 不是的。构建Hopper，或者是别人构建一个Hopper的系统。有人为Hopper构建了CUDA。有人为Hopper构建了CUDA的cuDNN。某个人的工作就是完成这些任务。你的使命就是做某件事。

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

本: 这种方式与传统结构相比有什么权衡？

Jensen: The downside is the pressure on the leaders is fairly high. The reason for that is because in a command and control system, the person who you report to has more power than you. The reason why they have more power than you is because they’re closer to the source of information than you are.

Jensen: 缺点是领导者的压力相当大。原因是，在指挥控制系统中，你汇报的上级比你更有权力。之所以他们有更多权力，是因为他们比你更接近信息的来源。

In our company, the information is disseminated fairly quickly to a lot of different people. It’s usually at a team level. For example, just now I was in our robotics meeting. We’re talking about certain things and we’re making some decisions.

在我们公司，信息传播得相当快，涉及到很多不同的人。通常是在团队层面。例如，刚才我参加了我们的机器人会议，我们在讨论一些事情，并做出了一些决策。

There are new college grads in the room. There are three vice-presidents in the room, there are two e-staff in the room. At the moment that we decided together, we reasoned through some stuff, we made a decision, everybody heard it exactly the same time. Nobody has more power than anybody else. Does that make sense? The new college grad learned at exactly the same time as the e-staff.

房间里有刚毕业的大学生，也有三位副总裁和两位执行团队成员。当我们一起做出决定时，我们共同推理了一些事情，做出了决定，每个人在同一时刻听到了。没有人比其他人更有权力。明白吗？新毕业的大学生和执行团队成员是在同一时刻学到这些的。

The executive staff, the leaders that work for me, and myself, you earned the right to have your job based on your ability to reason through problems and help other people succeed. It’s not because you have some privileged information that I knew the answer was 3.7, and only I knew. Everybody knew.

执行团队和为我工作的大领导们，以及我自己，你之所以拥有这个职位，是因为你能通过推理解决问题，并帮助其他人成功。并不是因为你有某些特权信息，比如我知道答案是3.7，只有我知道，其他人都不知道。每个人都知道。

David: When we did our most recent episode, Nvidia part three, that we just released, we did this thought exercise especially over the last couple of years. 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 said, could you imagine Apple shipping two iPhones a year?

David: 在我们最近发布的Nvidia三部曲节目中，我们进行了这个思维练习，特别是在过去几年。你的产品发布周期令人印象深刻，尤其是考虑到你们所涉及的技术水平以及这一切的难度。我们说，你能想象苹果每年发布两款iPhone吗？

Ben: And we said that for illustrative purposes.

本: 我们这么说只是为了举例。

David: For illustrative purposes, not to pick on Apple or whatnot.

David: 只是为了举例，并不是要挑苹果的毛病。

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

本: 一家大型科技公司每年发布两款旗舰产品，或者两次发布其旗舰产品。

David: Or two WWDCs a year.

David: 或者每年举行两次WWDC大会。

Ben: There seems to be something unique.

本: 这里似乎有某种独特性。

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

David: 你真的无法想象那样的情况，而这种情况在这里发生了。是否有其他公司，不论是现在的还是历史上的，你非常钦佩，可能也从中获得了一些灵感？

Jensen: In the last 30 years I’ve read my fair share of business books. As in everything you read, you’re supposed to first of all 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.

Jensen: 在过去的30年里，我读了不少商业书籍。就像你读的所有书籍一样，首先是享受它，从中获得灵感，但并不是要完全采纳它们。这些书籍的目的并不是让你照搬，而是分享他们的经验。

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 and the environment that I’m in? What does this mean to me in what I’m trying to achieve? What does this mean to Nvidia and the age of our company and the capability of our company?

你应该问自己，这对我来说意味着什么，在我所处的世界中，意味着什么？在我正在经历的背景下，它对我有什么意义？这对我正在努力实现的目标有什么意义？这对Nvidia及我们公司的时代和能力意味着什么？

You’re supposed to ask yourself, what does it mean to you? From that point, being informed by all these different things that we’re learning, we’re supposed to come up with our own strategies.

你应该问问自己，这对你意味着什么？从这一点出发，通过我们所学到的不同知识，我们应该制定出自己的战略。

What I just described is how I go about everything. You’re supposed to be inspired and learn from everybody else. The education’s free. When somebody talks about a new product, you’re supposed to go listen to it. You’re not supposed to ignore it. You’re supposed to go learn from it.

我刚刚描述的就是我处理一切事务的方式。你应该从其他人身上汲取灵感和学习。教育是免费的。当有人谈论一款新产品时，你应该去听，而不是忽视它。你应该从中学习。

It could be a competitor, it could be an adjacent industry, it could be nothing to do with us. 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, what does this mean to us?

它可以是竞争对手，也可以是相关行业，甚至可能与我们无关。我们从外界发生的事情中学到的越多，就越好。但之后，你应该回头问问自己，这对我们意味着什么？

David: You don’t just want to imitate them.

David: 你不只是想模仿他们。

Jensen: That’s right.

Jensen: 没错。

David: I love this tee-up of learning but not imitating, and learning from a wide array of sources. There’s this unbelievable third element, I think, to what Nvidia has become today. That’s the data center.

David: 我喜欢这种“学习但不模仿”的思路，并且从广泛的来源中学习。我认为Nvidia今天成为的另一个令人难以置信的第三个元素，就是数据中心。

It’s certainly not obvious. I can’t reason from AlexNet and your engagement with the research community, and social media feed [...]. You deciding and the company deciding we’re going to go on a five-year all-in journey on the data center. How did that happen?

这显然并不明显。我无法从AlexNet和你与研究社区的互动，以及社交媒体流推测出……你和公司决定要在数据中心投入五年的时间。是怎么发生的？

Jensen: Our journey to the data center happened, I would say almost 17 years ago. I’m always being asked, what are the challenges that the company could see someday?

Jensen: 我们的数据中心之旅，差不多发生在17年前。我总是被问到，公司未来可能面临的挑战是什么？

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. There are only so many CRTs and (at the time) LCDs that we could possibly drive.

我一直觉得，Nvidia的技术必须连接到一台计算机，而这台计算机必须靠近你，因为它需要连接到显示器，这会在某一天限制我们的机会，因为能插入GPU的桌面PC有限。我们能驱动的CRT和（当时的）LCD的数量也有限。

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.

问题是，如果我们的计算机不需要连接到显示设备，岂不是很棒吗？它与显示设备的分离使得我们可以在其他地方进行计算。

One of our engineers came and showed it to me one day. It was really capturing the frame buffer, encoding it into video, and streaming it to a receiver device, separating computing from the viewing.

一天，我们的一位工程师展示给我看。实际上就是捕捉帧缓冲区，将其编码为视频，并将其流式传输到接收设备，将计算和显示分开。

Ben: In many ways, that’s cloud gaming.

本: 在很多方面，那就是云游戏。

Jensen: In 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 and—

Jensen: 事实上，那时我们开始了GFN。我们知道GFN将是一个需要很长时间的旅程，因为我们将面临各种各样的问题，包括光速问题——

Ben: Latency everywhere you look.

本: 到处都是延迟。

Jensen: That’s right.

Jensen: 没错。

David: To our listeners, GFN GeForce NOW.

David: 向我们的听众介绍，GFN就是GeForce NOW。

Jensen: Yeah. GeForce NOW.

Jensen: 是的，GeForce NOW。

David: It all makes sense. Your first cloud product.

David: 一切都说得通了，你们的第一个云产品。

Jensen: That’s right. Look at GeForce NOW. It was Nvidia’s first data center product.

Jensen: 没错，看看GeForce NOW。它是Nvidia的第一个数据中心产品。

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 our GPU, which became a supercomputer. Which then worked towards more and more and more.

我们的第二个数据中心产品是远程图形，将我们的GPU放入全球企业数据中心。这也促成了我们的第三个产品，将CUDA与GPU结合，成为了超级计算机，接着不断向更多方向发展。

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.

之所以这非常重要，是因为Nvidia计算的发生地和享受地之间的断开，如果你能够将其分开，你的市场机会将会爆炸式增长。

And it was completely true, so we’re no longer limited by the physical constraints of the desktop PC sitting by your desk. We’re not limited by one GPU per person. It doesn’t matter where it is anymore. That was really the great observation.

这完全是真的，我们不再受到桌面PC摆放在你桌旁的物理限制。我们不再受限于每人一个GPU。现在，它不再受地点的限制。这真的是一个伟大的观察。

Ben: It’s a good reminder. The data center segment of Nvidia’s business (to me) has become synonymous with how is AI going. And that’s a false equivalence. It’s interesting that you were only this ready to explode in AI in the data center because you had three-plus previous products where you learned how to build data center computers. Even though those markets weren’t these gigantic world-changing technology shifts the way that AI is. That’s how you learned.

本: 这是一个很好的提醒。在我看来，Nvidia的业务中的数据中心板块已经与AI的进展划上等号。但这其实是错误的等式。很有趣的是，你们在数据中心准备好迎接AI的爆发，正是因为你们已经有了三个以上的先前产品，学习了如何构建数据中心计算机。尽管那些市场不像AI那样发生巨大且改变世界的技术变革，但你们就是这样学到的。

Jensen: That’s right. You want to 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, so you have to anticipate.

Jensen: 没错，你要为未来的机会铺平道路。你不能等到机会摆在你面前再去抓住它，因此你必须要提前预见。

Our job as CEO is to look around corners and to anticipate where will opportunities be someday. Even if I’m not exactly sure what and when, how do I position the company to be near it, to be just standing near under the tree, and we can do a diving catch when the apple falls. You guys know what I’m saying? But you’ve got to be close enough to do the diving catch.

作为CEO，我们的工作就是环顾四周，预见未来的机会在哪里。即使我不完全确定是什么和何时发生，我也要将公司定位在机会的附近，站在树下，当苹果掉下来时，能够快速接住。你们知道我的意思吧？但你必须要足够接近，才能做出快速接住的动作。

David: Rewind to 2015 and OpenAI. If you hadn’t been laying this groundwork in the data center, you wouldn’t be powering OpenAI right now.

David: 回到2015年和OpenAI。如果你们没有在数据中心打下这块基础，你们现在不会为OpenAI提供支持。

Jensen: Yeah. But the idea that computing will be mostly done away from the viewing device, that the vast majority of computing will be done away from the computer itself, that insight was good.

Jensen: 是的。计算将主要不再依赖于显示设备，绝大多数计算将不再依赖于计算机本身，这个洞察是对的。

In fact, cloud computing, everything about today’s computing is about separation of that. By putting it in a data center, we can overcome this latency problem. You’re not going to overcome the speed of light. Speed of light end-to-end is only 120 milliseconds or something like that. It’s not that long.

实际上，云计算，今天的一切计算，都是关于这种分离的。通过将计算放入数据中心，我们可以克服延迟问题。你无法克服光速。光速的端到端延迟只有120毫秒左右，并不是很长。

Ben: From a data center to—

本: 从数据中心到—

Jensen: Anywhere on the planet.

Jensen: 地球上的任何地方。

Ben: Oh, I see. Literally across the planet.

本: 哦，我明白了。字面上来说，跨越整个地球。

Jensen: Right. If you could solve that problem, approximately something like—I forget the number—70 milliseconds, 100 milliseconds, but it’s not that long.

Jensen: 对。如果你能解决这个问题，大约是70毫秒、100毫秒，我忘了具体的数字，但也不会太长。

My point is, if you could remove the obstacles everywhere else, then the speed of light should be perfectly fine. You could build data centers as large as you like, and you could do amazing things. This little, tiny device that we use as a computer, or your TV as a computer, whatever computer, they can all instantly become amazing. That insight 15 years ago was a good one.

我的意思是，如果你能消除其他地方的障碍，那么光速就完全没有问题了。你可以建造任何你想要的巨大数据中心，做出令人惊叹的事情。我们用作计算机的小小设备，或者你的电视作为计算机，任何计算机，它们都可以立刻变得惊人。15年前的这个洞察是非常正确的。

Ben: Speaking of the speed of light—David’s begging me to go here—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?

本: 说到光速——David一直催我提这个——你完全看到了InfiniBand会比任何人预期的更早变得更加有用。收购Mellanox，我认为你独特地看到了这对训练大型语言模型是必需的，而你在收购这家公司时非常果断。为什么是你在其他人看不到的时候就看到了这一点？

Jensen: There were several reasons for that. First, if you want to be a data center company, building 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.

Jensen: 这背后有几个原因。首先，如果你想成为一家数据中心公司，单纯靠处理芯片并不是解决之道。数据中心与桌面计算机或手机的区别，不在于其中的处理器。

A desktop computer in a data center uses the same CPUs, uses the same GPUs, apparently. Very close. 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, and so on and so forth. Those characteristics are associated with Mellanox, not Nvidia.

在数据中心中，桌面计算机使用的CPU和GPU显然是相同的，非常接近。描述数据中心的并不是处理芯片，而是它的网络，它的基础设施。它是如何分配计算的，如何提供安全，如何进行网络连接，等等。这些特点与Mellanox相关，而非Nvidia。

The day that I concluded that really Nvidia wants to build computers of the future, and computers of the future are going to be data centers, embodied in data centers, then if we want to be a data center–oriented company, then we really need to get into networking. That was one.

那天，我得出结论，Nvidia真正想要构建的是未来的计算机，而未来的计算机将会是数据中心，并且体现于数据中心中。所以，如果我们想成为一家数据中心导向的公司，我们就需要进入网络领域。这是其中之一。

The second thing is observation that, whereas cloud computing started in 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 training job is orchestrated across millions of processors.

第二个观察是，虽然云计算从超大规模开始，它是通过使用商品化组件、许多用户，并将许多用户虚拟化到一台计算机上，而AI实际上是关于分布式计算，其中一个训练任务是在数百万个处理器之间协调进行的。

It’s the inverse of hyperscale, almost. 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—

这几乎是与超大规模的完全相反。设计超大规模计算机时，你使用的是现成的商品化以太网，这对Hadoop来说完全没问题，对搜索查询也没问题，所有这些都没问题—

Ben: But not when you’re sharding a model across.

本: 但当你将模型拆分时就不行了。

Jensen: Not when you’re sharding a model across, right. That observation says that the type of networking you want to do is not exactly ethernet. The way that we do networking for supercomputing is really quite ideal.

Jensen: 当你拆分模型时，不行。这个观察表明，您所需要的网络类型并不是传统的以太网。我们为超级计算机做的网络连接方式实际上非常理想。

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. We worked with them in so many different areas in high performance computing already. Plus, I really like the people. The Israel team is world class. We have some 3200 people there now, and it was one of the best strategic decisions I’ve ever made.

这两个想法的结合让我确信Mellanox绝对是正确的公司，因为它是全球领先的高性能网络公司。我们已经在高性能计算的多个领域与他们合作。而且，我非常喜欢他们的人。以色列团队是世界级的。现在我们有3200人在那里，这也是我做出的最好的战略决策之一。

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

David: 在我们研究Nvidia三部曲的过程中，我们与许多人进行了交流。许多人告诉我们，收购Mellanox是所有技术公司中最好的收购之一，甚至是有史以来最好的收购。

Jensen: I think so, too. It’s so disconnected from the work that we normally do, it was surprising to everybody.

Jensen: 我也这么认为。这与我们通常做的工作完全脱节，令每个人都感到惊讶。

Ben: But framed this way, you were standing near where the action was, so you could figure out as soon as that apple becomes available to purchase, like, oh, LLMs are about to blow up, I’m going to need that. Everyone’s going to need that. I think I know that before anyone else does.

本: 从这个角度看，你站在了事态发展的前沿，这样你就能在那个苹果准备好被摘取的时候意识到，哦，LLM（大型语言模型）就要爆发了，我需要那个，大家都会需要。我觉得我比其他人更早知道这个。

Jensen: You want to position yourself near opportunities. You don’t have to be that perfect. You want to position yourself near the tree. 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 want to position yourself close to the opportunities.

Jensen: 你想要将自己放在靠近机会的位置。你不必做到完美，只需要站在树下。即使你没能在苹果掉到地上之前抓住它，只要你是第一个捡起来的人就行。你要让自己靠近那些机会。

That’s kind of a lot of my work, is positioning the company near opportunities, and the company having the skills to monetize each one of the steps along the way so that we can be sustainable.

这就是我工作的一个核心，站在公司靠近机会的地方，并让公司具备能力去盈利化每一步的过程，从而确保公司的可持续发展。

Ben: 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.

本: 你刚才说的让我想起了巴菲特和芒格的一个伟大格言，那就是，做事“差不多对”总比“完全错”要好。

Jensen: There you go. Yeah, that’s a good one.

Jensen: 没错。是的，那是一个很好的格言。

Ben: It’s a good one to live by.

本: 这是一个值得遵循的格言。

I want to 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, some advice for company building.

本: 如果你没问题的话，我想离开Nvidia的话题，问你一些问题，因为我们节目有很多创始人听众，我想请你提供一些关于公司建设的建议。

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. Your company’s likely to die just because people don’t actually care as much as you do about [...].

本: 第一个问题是，在最初创办初创公司时，你最大的竞争对手是你做不出人们想要的东西。你的公司很可能会因为人们并不像你一样在乎而失败。

In the later days, you actually have to be very thoughtful about competitive strategy. 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?

本: 在后期，你实际上需要非常深思熟虑地考虑竞争策略。我很好奇，你对那些已经找到产品/市场契合，开始成长的公司有何建议？它们应该在哪里寻找竞争，并如何应对？

Jensen: 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.

Jensen: 你可以从各种角度思考竞争。我们更倾向于将自己定位于满足一个通常还没有出现的需求。

David: I’ve heard you or others in Nvidia (I think) used the phrase zero billion dollar—

David: 我听说你或Nvidia的其他人（我想）用过“零十亿美元”这样的说法—

Jensen: That’s exactly right. It’s our way of saying there’s no market yet, but we believe there will be one. Usually when you’re positioned there, everybody’s trying to figure out why are you here. When we first got into automotive, because we believe that in the future, the car is going to be largely software. If it’s going to be largely software, a really incredible computer is necessary.

Jensen: 没错。这是我们说还没有市场，但我们相信会有市场的一种方式。当你定位在那个位置时，通常每个人都在试图弄清楚你为什么在这里。当我们第一次进入汽车行业时，我们相信未来汽车将会是一个以软件为主的设备。如果它是以软件为主，那么就需要一台非常强大的计算机。

When we positioned ourselves there, I still remember one of the CTOs told me, you know what? Cars cannot tolerate the blue screen of death. I said, I don’t think anybody can tolerate that, but that doesn’t change the fact that someday every car will be a software-defined car. I think 15 years later we’re largely right.

当我们定位在那个位置时，我仍然记得其中一位CTO告诉我，“你知道吗？汽车不能容忍蓝屏死机。”我说，“我觉得没有人能容忍这个，但这并不改变未来每辆车都会成为软件定义汽车的事实。”我想15年后我们基本是对的。

Oftentimes there’s non-consumption, and we like to navigate our company there. By doing that, by the time that the market emerges, it’s very likely there aren’t that many competitors shaped that way.

经常会有非消费市场，我们喜欢让公司进入这些市场。通过这样做，当市场出现时，很可能不会有太多竞争者拥有相同的定位。

We were early in PC gaming, and today Nvidia’s very large in PC gaming. 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. And look today, Nvidia’s in accelerated computing is quite large.

我们在PC游戏领域走得很早，而今天Nvidia在PC游戏中占据了很大份额。我们重新定义了设计工作站的样子。如今，几乎每台工作站都在使用Nvidia的技术。我们重新定义了超级计算应该如何进行，并且谁应该从中受益，我们将其普及化。如今，Nvidia在加速计算领域也占有一席之地。

We reimagine how software would be done, and today it’s called machine learning, and how computing would be done, we call it AI. We reimagine these things, try to do that about a decade in advance. We spent about a decade in zero billion dollar markets, and today I spent a lot of time on omniverse. Omniverse is a classic example of a zero billion dollar business.

我们重新定义了软件应该如何开发，今天这叫做机器学习；计算应该如何进行，我们称之为人工智能。我们重新定义这些领域，尝试提前十年开始行动。我们在零十亿美元市场中投入了大约十年，今天我花了很多时间在Omniverse上。Omniverse就是一个典型的零十亿美元市场的例子。

Ben: There are like 40 customers now? Something like that?

本: 现在大概有40个客户？差不多吧？

David: Amazon, BMW.

David: 亚马逊，宝马。

Jensen: Yeah, I know. It’s cool.

Jensen: 对，我知道。很酷。

Ben: Let’s say you do get this great 10-year lead. But then other people figure it out, and you’ve got people nipping at your heels. What are some structural things that someone who’s building a business can do to stay ahead? You can just keep your pedal to the metal and say, we’re going to outwork them and we’re going to be smarter. That works to some extent, but those are tactics. What strategically can you do to make sure that you can maintain that lead?

本: 假设你获得了这个十年的领先优势，但随后其他人也找到了机会，你又有了紧随其后的竞争者。对于正在建立公司的人来说，有什么结构性措施可以帮助你保持领先？你可以一直保持高速度，告诉自己，我们会比他们更努力、更聪明。这样做在某种程度上有效，但那是战术。战略上你能做些什么，确保能够维持领先优势？

Jensen: Oftentimes, if you created the market, you ended up having 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.

Jensen: 通常，如果你创建了市场，你最终会拥有所谓的护城河，因为如果你正确地构建了你的产品，并且它使周围的整个生态系统得以支持这个最终市场，那么你就实际上创造了一个平台。

Sometimes it’s a product-based platform. Sometimes it’s a service-based platform. Sometimes it’s a technology-based platform. But if you were early there and you were mindful about helping the ecosystem succeed with you, you ended up having this network of networks, and all these developers and customers who are built around you. That network is essentially your moat.

有时它是基于产品的平台，有时是基于服务的平台，也有时是基于技术的平台。但如果你早早进入并且注意到与生态系统一起成功，你最终会拥有一个网络中的网络，所有围绕你的开发者和客户。这个网络本质上就是你的护城河。

I don’t love thinking about it in the context of a moat. The reason for that is because you’re now focused on building stuff around your castle. I tend to like thinking about things in the context of building a network. That network is about enabling other people to enjoy the success of the final market. That you’re not the only company that enjoys it, but you’re enjoying it with a whole bunch of other people.

我不太喜欢在护城河的框架中思考这个问题。原因是你现在专注于围绕你的城堡建造东西。我更倾向于从建立网络的角度来看待事情。这个网络的核心是使其他人也能分享最终市场的成功。你不是唯一享受它的公司，而是和很多其他人一起享受它。

大型科技企业的特征。

David: I’m so glad you brought this up because I wanted to ask you. In my mind, at least, and it sounds like in yours, too, Nvidia is absolutely a platform company of which there are very few meaningful platform companies in the world.

David: 我很高兴你提到了这一点，因为我一直想问你。至少在我看来，听起来在你看来也是，Nvidia绝对是一家平台公司，而世界上有意义的平台公司是非常少的。

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. Every example I can think of, of a company that tried to start as a platform company, fails. You got to start as a technology first.

我也认为可以公平地说，当你们刚开始时，最初几年你们是科技公司，而不是平台公司。我能想到的所有试图作为平台公司起步的公司，都失败了。你必须首先作为技术公司开始。

When did you think about making that transition to being a platform? Your first graphics cards were technology. There was no CUDA, there was no platform.

你什么时候开始考虑从技术公司转变为平台公司？你们的第一代显卡是技术产品，并没有CUDA，也没有平台。

Jensen: What you observed is not wrong. However, inside our company, we were always a platform company. 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.

Jensen: 你观察到的并没有错。然而，在我们公司内部，我们一直都是平台公司。原因是从公司成立的第一天起，我们就有一个架构叫做UDA，它就是CUDA的UDA。

David: CUDA is Compute Unified Device Architecture?

David: CUDA是计算统一设备架构，对吧？

Jensen: That’s right. The reason for that is because what we’ve done, what we essentially did in the beginning, even though RIVA 128 only had computer graphics, the architecture described accelerators of all kinds. We would take that architecture and developers would program to it.

Jensen: 没错。原因在于我们所做的，实际上从一开始，我们虽然RIVA 128只有计算机图形，但这个架构描述了各种加速器。我们采用这个架构，开发者则会根据它来编程。

In fact, Nvidia’s first business strategy was we were going to be a game console inside the PC. 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. It’s the reason why we knew all the game developers and all the 3D developers.

实际上，Nvidia的第一商业战略是我们要成为PC内的游戏主机。游戏主机需要开发者，这就是为什么Nvidia很早就有了一位开发者关系负责人。这也就是我们为什么认识所有的游戏开发者和3D开发者的原因。

David: Wow. Wait, so was the original business plan to…

David: 哇，等等，那么最初的商业计划是……

Ben: Sort of like to build DirectX.

本: 有点像构建DirectX。

David: Yeah, compete with Nintendo and Sega as with PCs?

David: 是的，与任天堂和世嘉竞争，类似于PC吗？

Jensen: In fact, the original Nvidia architecture was called Direct NV (Direct Nvidia). DirectX was an API that made it possible for the operating system to directly connect with the hardware.

Jensen: 事实上，Nvidia最初的架构被称为Direct NV（Direct Nvidia）。DirectX是一个API，它使得操作系统能够直接连接到硬件。

David: But DirectX didn’t exist when you started Nvidia, and that’s what made your strategy wrong for the first couple of years.

David: 但在你们创办Nvidia时，DirectX并不存在，这也使得你们的战略在最初几年是错误的。

Jensen: In 1993, we had Direct Nvidia, which in 1995 became DirectX.

Jensen: 在1993年，我们有Direct Nvidia，1995年它变成了DirectX。

Ben: This is an important lesson. You—

本: 这是一个重要的教训。你—

Jensen: We were always a developer-oriented company.

Jensen: 我们一直是一家开发者导向的公司。

Ben: Right. The initial attempt was we will get the developers to build on Direct NV, then they’ll build for our chips, and then we’ll have a platform. What played out is Microsoft already had all these developer relationships, so you learned the lesson the hard way of—

本: 对。最初的尝试是我们将让开发者在Direct NV上开发，然后他们会为我们的芯片开发，接着我们就有了平台。结果是微软已经拥有了所有这些开发者关系，所以你们吃了苦头，才学到这个教训——

David: [...] did back in the day. They’re like, oh, that could be a developer platform. We’ll take that. Thank you.

David: [...] 他们早就这样做了。他们会说，哦，那可以是一个开发平台。我们接受，谢谢。

Jensen: They did it very differently and did a lot of things right. We did a lot of things wrong.

Jensen: 他们做得很不一样，做了很多对的事情，而我们做了很多错的事情。

David: You were competing against Microsoft in the nineties.

David: 你们在九十年代与微软竞争。

Ben: It’s like [...] Nvidia today.

本: 就像今天的Nvidia。

Jensen: It’s a lot different, but I appreciate that. We were nowhere near competing with them. If you look now, when CUDA came along and there was OpenGL, there was DirectX, but there’s still another extension, if you will. That extension is CUDA. That CUDA extension allows a chip that got paid for running DirectX and OpenGL to create an install base for CUDA.

Jensen: 现在完全不同，但我很感激你这么说。我们远没有与他们竞争。如果你现在看，当CUDA出现时，OpenGL和DirectX也存在，但它仍然是一个扩展，可以说那个扩展就是CUDA。这个CUDA扩展使得通过运行DirectX和OpenGL获得收入的芯片，可以为CUDA创建一个安装基础。

David: That’s why you were so militant. I think from our research, it really was you being militant that every Nvidia chip will run CUDA.

David: 这就是为什么你如此坚定。我想从我们的研究中可以看到，正是你坚定地要求每一款Nvidia芯片都支持CUDA。

Jensen: Yeah. If you’re a computing platform, everything’s got to be compatible. We are the only accelerator on the planet where every single accelerator is architecturally compatible with the others. None has ever existed.

Jensen: 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所有加速器在架构上都与其他加速器兼容的平台。以前从未有过这样的存在。

There are literally a couple of hundred million—250 million, 300 million—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 are all different? At 30 years, it’s all completely compatible. That’s the only unnegotiable rule in our company. Everything else is negotiable.

目前世界上有几亿个活跃使用中的CUDA GPU，大约是2.5亿到3亿个，它们都是架构兼容的。如果NV30、NV35、NV39和NV40都不同，怎么能称得上一个计算平台？而且经过30年，它们都是完全兼容的。这是我们公司唯一不可妥协的规则，其他一切都是可以商量的。

David: I guess CUDA was a rebirth of UDA, but understanding this now, UDA going all the way back, it really is all the way back to all the chips you’ve ever made.

David: 我猜CUDA是UDA的重生，但现在理解这一点，UDA一直延续至今，实际上它从所有你们生产的芯片开始就一直存在。

Jensen: Yeah. In fact, UDA goes all the way back to all of our chips today. For the record, I didn’t help any of the founding CEOs that are listening. I got to tell you while you were asking that question what lessons would I impart? I don’t know.

Jensen: 是的。事实上，UDA一直延续到我们今天所有的芯片。顺便说一下，所有听到的创始CEO们，我没有帮助过你们。我在你们问这个问题时，我得告诉你们，能给出的教训是什么？我不知道。

The characteristics of successful companies and successful CEOs (I think) are fairly well-described. There are a whole bunch of them. I just think starting successful companies is insanely hard. It’s just insanely hard. When I see these amazing companies getting built I have nothing but admiration and respect because I just know that it’s insanely hard.

我认为，成功公司的特征和成功CEO的特征是相当明确的。它们有很多。我只是觉得创办成功公司是非常困难的，简直是难以置信的困难。当我看到这些伟大的公司被建立起来时，我只有钦佩和尊敬，因为我知道这真的是非常难。

I think that everybody did many similar things. There are some good, smart things that people do. There are some dumb things that you can do. But you could do all the right smart things and still fail. You could do a whole bunch of dumb things—I did many of them—and still succeed.

我认为每个人都做了很多相似的事情。有些事情是聪明且正确的，也有一些傻事可以做。但你可以做所有正确的聪明事情，还是失败。你也可以做很多傻事——我做了很多——但仍然成功。

Obviously, that’s not exactly right. I think skills are the things that you can learn along the way, but at important moments, certain circumstances have to come together. I do think that the market has to be one of the agents to help you succeed. It’s not enough, obviously, because a lot of people still fail.

显然，这不完全正确。我认为技能是你在过程中学到的东西，但在关键时刻，某些环境因素必须结合在一起。我确实认为，市场必须是帮助你成功的因素之一。显然这还不够，因为很多人仍然失败。

Ben: 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 it takes the sum of all the luck and all the skill in order to succeed.

本: 你记得Nvidia历史上有任何时刻，觉得，“哦，我们做了一堆错决策，但 somehow 我们被救了？” 因为成功需要好运和技能的总和。

Jensen: I actually thought that you started with RIVA 128 was spot on. RIVA 128, 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. We pulled every trick in the book in a desperation because we had no other choice.

Jensen: 我其实认为你从RIVA 128开始的看法非常正确。正如我提到的，RIVA 128上我们做了很多聪明的决策，至今依然聪明，今天我们设计芯片的方式与当时完全相同，因为天哪，那时没有人做过这种事。我们在绝望中用尽了所有技巧，因为别无选择。

Well, guess what? That’s the way things are ought to be done. And now everybody does it that way. 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?

那么，猜猜怎么了？那就是应该做事的方式。而现在每个人都这么做。每个人都这么做，因为如果可以一次做完，为什么要做两次？如果一次就能完成，为什么要做七次芯片生产？

The most efficient, the most cost effective, the most competitive speed is technology. Speed is performance. Time to market is performance. All of those things apply. So why do things twice if you could do it once?

最高效、最具成本效益、最具竞争力的速度就是技术。速度就是表现。上市时间就是表现。所有这些都适用。那么，如果你可以一次做完，为什么要做两次呢？

RIVA 128 made a lot of great decisions in how we spec products, how we think about market needs and lack of, how we judge markets, all of this. Man, we made some amazingly good decisions. Yeah, we were back against the wall. We only had one more shot to do it, but—

RIVA 128做出了很多正确的决策，关于如何规范产品、如何看待市场需求和不足、如何判断市场，所有这一切。伙计，我们做了一些令人惊讶的正确决策。是的，我们曾被逼到了墙角，只剩下最后一次机会，但——

Ben: Once you pull out all the stops and you see what you’re capable of, why would you put stops in—

本: 一旦你放开所有的限制，看到自己能做什么，为什么还要设置限制呢——

Jensen: Exactly.

Jensen: 正是如此。

Ben: Let’s keep stops out all the time, every time.

本: 我们每次都不设限制，一直保持。

Jensen: That’s right.

Jensen: 没错。

David: 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 3D graphical performance in games?

David: 但从运气的角度来说，回想1997年，那是否是消费者开始真正、深刻重视游戏中的3D图形性能的时刻？

Jensen: Oh yeah. For example, luck. Let’s talk about luck. If Carmack had decided to use acceleration, because remember, Doom was completely software-rendered.

Jensen: 哦，对。比如运气，我们来谈谈运气。如果Carmack决定使用加速，因为记得《毁灭战士》是完全通过软件渲染的。

The Nvidia philosophy was that although general-purpose computing is a fabulous thing and it’s going to enable software and IT and everything, we felt that there were applications that wouldn’t be possible or it would be costly if it wasn’t accelerated. It should be accelerated. 3D graphics was one of them, but it wasn’t the only one. It just happens to be the first one and a really great one.

Nvidia的理念是，虽然通用计算是一个极好的事情，它将促进软件、IT等领域的发展，但我们认为有些应用如果没有加速，就无法实现，或者成本太高。它们应该被加速。3D图形是其中之一，但不是唯一一个。它只是第一个，而且是一个非常棒的应用。

I still remember the first times we met John. He was quite emphatic about using CPUs and his software render was really good. Quite frankly, if you look at Doom, the performance of Doom was really hard to achieve even with accelerators at the time. If you didn’t have to do bilinear filtering, it did a pretty good job.

我仍然记得第一次遇见John时，他非常坚持使用CPU，他的软件渲染做得非常好。坦白说，如果你看《毁灭战士》，即便有加速器，那个时期的性能也很难达到。如果不需要进行双线性过滤，它已经做得相当不错了。

David: The problem with Doom, though, was you needed Carmac to program it.

David: 然而，《毁灭战士》的问题是，你需要Carmack来编程。

Jensen: Exactly. It was a genius piece of code, but nonetheless, software renders did a really good job. If he hadn’t decided to go to OpenGL and accelerate for Quake, frankly what would be the killer app that put us here? Carmack and Sweeney, both between Unreal and Quake, created the first two killer applications for consumer 3D, so I owe them a great deal.

Jensen: 没错。那是一段天才的代码，但尽管如此，软件渲染做得非常好。如果他没有决定去支持OpenGL并为《半条命》加速，说实话，是什么让我们站在了这里呢？Carmack和Sweeney，在《虚幻》和《毁灭战士》之间，创造了前两个杀手级应用，给消费者3D图形带来了革命，所以我欠他们很多。

David: I want to come back real quick to you told these stories and you’re like, well, I don’t know what founders can take from that. I actually do think 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.

David: 我想回到你刚刚讲的故事，你说，“我不知道创始人能从中学到什么。” 但我确实认为，如果你看今天所有的大型科技公司，可能除了谷歌，它们都开始——现在了解你后——通过关注开发者，计划构建平台和为开发者提供工具。

All of them—Apple, not Amazon. [...] That’s how AWS started. I think that actually is a lesson to your point of, that won’t guarantee success by any means, but that’ll get you hanging around a tree if the apple falls.

所有公司——苹果，除了亚马逊……[AWS]就是这样开始的。我认为这实际上是你说的一个教训，虽然这不能保证成功，但如果苹果掉下来，你至少能站在树下等着。

Jensen: As many good ideas as we have. You don’t have all the world’s good ideas and the benefit of having developers is you get to see a lot of good ideas.

Jensen: 我们有很多好主意，但你并不是拥有所有世界上好主意的人，拥有开发者的好处是你能看到很多好主意。

Ben: Well, as we start to drift toward the end here, we spent a lot of time on the past. I want to 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.

本: 好的，随着我们逐渐接近尾声，我们花了很多时间讨论过去。我想稍微谈谈未来。我确信你作为AI前沿的参与者，肯定花了很多时间思考这个问题。

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. In the short term, it’s going to be inevitably bumpy as we figure out what that means.

我们正在进入一个时代，软件在一个人使用时所能实现的生产力，将大大放大他们所创造的影响力和价值，从长远来看，这对人类来说将是惊人的。从短期来看，我们将不可避免地经历一段波动期，直到我们搞清楚这意味着什么。

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 going to come from it?

随着AI变得越来越强大，并更好地加速生产力提升，那么对于所有将会受到影响的工作岗位，你认为一些解决方案是什么？

Jensen: First of all, we have to keep AI safe. There are a couple of different areas of AI safety that’s really important. Obviously, in robotics and self-driving car, there’s a whole field of AI safety. We’ve dedicated ourselves to functional and active safety, and all kinds of different areas of safety. When to apply human in the loop? When is it okay for a human not to be in the loop? How do you get to a point where increasingly human doesn’t have to be in the loop, but human largely in the loop?

Jensen: 首先，我们必须保持AI的安全。有几个不同的AI安全领域非常重要。显然，在机器人和自动驾驶汽车领域，有一个完整的AI安全领域。我们已经投入了大量精力来确保功能性和主动安全，并涵盖了各种不同的安全领域。何时需要将人类纳入其中？何时可以不需要人类参与？如何才能做到越来越多的情况下，AI可以不依赖人类，但依然让人类主导？

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.

在信息安全方面，显然包括偏见、虚假信息，并且尊重艺术家和创作者的权利，这一整块领域值得我们高度关注。

You’ve seen some of the work that we’ve done, instead of scraping the Internet we, we partnered with Getty and Shutterstock to create commercially fair way of applying artificial intelligence, generative AI.

你们已经看到了我们所做的一些工作，我们并没有直接抓取互联网数据，而是与Getty和Shutterstock合作，创造了在商业上公平应用人工智能和生成式AI的方式。

In the area of large language models in 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 human in the loop. The ability for an AI to self-learn, 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 in the wild again. So human is in the loop.

在大型语言模型和未来更强大的代理AI领域，显然答案是：只要它仍然合理——我认为这会在相当长的时间内是合理的——就应该让人类参与其中。避免让AI自己学习、改进并在野外以数字形式自行变化。我们应该收集数据，处理数据，训练模型，在模型被重新放到现实中之前，我们应该测试和验证模型。所以人类参与其中。

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, two-pilot system, then air traffic control, 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. I think there are a lot of models of creating safe AI, and I think we need to apply them.

很多行业已经展示了如何构建安全且有益于人类的系统。显然，飞机的自动驾驶系统就是如此，双驾驶员系统，再加上空中交通控制、冗余性和多样性，所有这些设计安全系统的基本理念也适用于自动驾驶汽车等等。我认为有很多创建安全AI的模型，我们需要应用它们。

With respect to automation, my feeling is that—and we’ll see—it is more likely that AI is going to create more jobs in the near term. The question is what’s the definition of near term? And the reason for that is the first thing that happens with productivity is prosperity. When the companies get more successful, they hire more people because they want to expand into more areas.

关于自动化，我的感觉是——我们将拭目以待——在短期内AI更有可能创造更多的工作岗位。问题是，短期的定义是什么？原因在于，生产力提升后，首先带来的是繁荣。当公司变得更加成功时，它们会雇佣更多人，因为它们希望扩展到更多领域。

So the question is, if you think about a company and say, okay, if we improve the productivity, then 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.

所以问题是，如果你考虑一个公司并说，好的，如果我们提高生产力，那么就需要更少的人。这是因为公司没有新的想法了。但对于大多数公司来说并非如此。如果你变得更高效，公司变得更有利润，通常他们会雇佣更多人来拓展新的领域。

So long as we believe that they’re more areas to expand into, there are more ideas in drugs, there’s 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.

只要我们相信还有更多的领域可以扩展，在制药领域，药物发现，交通运输，零售，娱乐，技术领域都有更多的想法，只要我们相信有更多的想法，行业的繁荣就会来自生产力提升，这将带来更多的就业机会和更多的创意。

Now you go back in history. We can fairly say that today’s industry is larger than the world’s industry a thousand years ago. The reason for that is because obviously, humans have a lot of ideas. I think that there are plenty of ideas yet for prosperity and plenty of ideas that can be begat from productivity improvements, but my sense is that it’s likely to generate jobs.

现在回顾历史，我们可以公正地说，今天的产业规模比一千年前的世界产业要大。原因显然是人类有很多想法。我认为，繁荣的想法还有很多，生产力提升也会带来很多新想法，但我的感觉是，这可能会创造更多的工作。

Now obviously, net generation of jobs doesn’t guarantee that any one human doesn’t get fired. That’s obviously true. It’s more likely that someone will lose a job to someone else, some other human that uses an AI. Not likely to an AI, but to some other human that uses an AI.

显然，工作的净增长并不能保证某个人不会被解雇。这是显而易见的。更可能的情况是，有人会失去工作，被另一个使用AI的人成为替代。不是被AI取代，而是被另一个使用AI的人取代。

I think the first thing that everybody should do is learn how to use AI, so that they can augment their own productivity. Every company should augment their own productivity to be more productive, so that they can have more prosperity, hire more people.

我认为每个人应该做的第一件事就是学习如何使用AI，以便他们能够增强自己的生产力。每个公司都应该增强自己的生产力，提高效率，从而拥有更多的繁荣，雇佣更多的人。

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 of labor, workforce is likely to use AI to get themselves off their feet and get back to growth and prosperity. I see it a little bit differently, but I do think that jobs will be affected, and I’d encourage everybody just to learn AI.

我认为工作会发生变化。我的猜测是，我们实际上会有更高的就业率，创造更多的工作。我认为各行业会变得更加高效。许多目前因劳动力短缺而受困的行业，很可能会利用AI重新振作并恢复增长与繁荣。我从不同的角度看待这个问题，但我确实认为工作会受到影响，我鼓励每个人都去学习AI。

David: This is appropriate. There’s a version of something we talked about a lot on Acquired, we call it the Moritz corollary to Moore’s law, after Mike Moritz from Sequoia.

David: 这很合适。我们在《Acquired》节目中讨论过一个观点，我们称之为摩里茨定理，这是根据Sequoia的Mike Moritz命名的。

Jensen: Sequoia was the first investor in our company.

Jensen: Sequoia是我们公司的第一个投资者。

David: Of course, 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. 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 going to top that? Don’s going to have us beat. We’re never going to beat that.

David: 当然。背后的精彩故事是，当Mike接替Doug和Don Valentine时，他坐在那里看着Sequoia的回报。他看到的是第三或第四期基金，我想应该是第四期，里面有思科。他想，怎么可能超过那个呢？Don一定会超过我们，我们永远也无法超越那个。

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 it get adopted more widely, well then the markets that we can address should get bigger. Your argument is basically AI will do the same thing. The cycle will continue.

他思考了一下，意识到，随着计算变得更便宜，它能够进入经济的更多领域，且可以被更广泛地采用，那么我们能够触及的市场应该会变得更大。你的论点基本上是AI将做同样的事情。这个循环将继续。

Jensen: Exactly. I 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. Because we have infinite ambition. The world has infinite ambition. If a company is more profitable, they tend to hire more people to do more.

Jensen: 没错。我刚才给了你一个完全相同的例子，实际上，生产力提升并不会让我们做得更少。生产力通常让我们做得更多。我们做的每件事都会变得更容易，但我们最终会做得更多。因为我们有无限的雄心。这个世界有无限的雄心。如果一家公司更有利润，它们通常会雇佣更多人去做更多的事。

Ben: That’s true. Technology is a lever, and the place where the idea falls down is that we would be satisfied.

本: 这是真的。技术是一个杠杆，而这个想法的缺点是我们会满足于现状。

David: Humans have a never-ending ambition.

David: 人类有着永无止境的雄心。

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

本: 不，人类总是会扩展，消耗更多的能量，并尝试追求更多的想法。这一直是我们物种在时间的推移中一直存在的特点。

David: We have a few lightning round questions we want to ask you, and then we have a very fun—

David: 我们有几个快速问答的问题想问你，然后我们有一个非常有趣的问题—

Jensen: Oh dear. I can’t think that fast.

Jensen: 哦天哪，我想不出这么快。

Ben: We’ll open up an easy one based on all these conference rooms we see named around here. Favorite sci-fi book?

本: 我们先来一个简单的问题，基于这些会议室名字，你最喜欢的科幻书籍是什么？

Jensen: I’ve never read a sci-fi book before.

Jensen: 我从来没有读过科幻书籍。

Ben: No.

本: 真的？

David: Oh, come on.

David: 哎，来吧。

Jensen: Yeah.

Jensen: 是的。

David: You’re missing out.

David: 你真是错过了。

Ben: What with the obsession with Star Trek and…

本: 你不是很痴迷《星际迷航》吗？

Jensen: Well, it’s easy. I just watch the TV show.

Jensen: 其实很简单，我就看电视节目。

Ben: Okay. Favorite sci-fi TV show?

本: 好的。你最喜欢的科幻电视节目是什么？

Jensen: Well, Star Trek’s my favorite. Yeah, Star Trek’s my favorite.

Jensen: 哦，《星际迷航》是我最喜欢的。是的，《星际迷航》是我最喜欢的。

Ben: I saw VGER out there on the way in. It’s a good conference room name.

本: 我在进来的路上看到VGER，这是个很棒的会议室名字。

Jensen: VGER’s an excellent one, yeah.

Jensen: VGER真的是个很棒的名字，没错。

David: What car is your daily driver these days? And related question, do you still have the Supra?

David: 你现在的日常驾驶车是什么？相关问题，你还保留那辆Supra吗？

Jensen: Oh, it’s one of my favorite cars, and also favorite memories. You guys might not know this, but Lori and I got engaged Christmas one year, and we drove back in my brand new Supra, and we totaled it. We were this close to the end.

Jensen: 哦，它是我最喜欢的车之一，也是我最喜欢的回忆之一。你们可能不知道，Lori和我在某年圣诞节订婚，我们开着我全新的Supra回家，结果车撞毁了。我们离结束就差一点。

Ben: Thank God you didn’t.

本: 谢天谢地你们没事。

Jensen: Yeah. But nonetheless, it wasn’t my fault. It wasn’t the Supra’s fault, but I love that car.

Jensen: 是的。不过，无论如何，不是我的错，也不是Supra的错，但我喜欢那辆车。

David: The one time when it wasn’t the Supra’s fault.

David: 就是唯一一次，车不是Supra的错。

Jensen: Yeah. I love that car. For security reasons and others, I’m driven in the Mercedes EQS. It’s a great car.

Jensen: 是的。我喜欢那辆车。由于安全原因等，我现在开的是奔驰EQS，这是一辆很棒的车。

David: Using Nvidia technology?

David: 使用Nvidia技术吗？

Jensen: Yeah, it has. We’re the central computer.

Jensen: 是的，它用了。我们是中央计算机。

Ben: Sweet. I know we already talked a little bit about business books, but one or two favorites that you’ve taken something from.

本: 很酷。我知道我们已经聊了一些商业书籍，但能否说一下你从中学到的其中一两本？

Jensen: Clay Christensen, I think the series is the best. There’s just no two ways about it. The reason for that is because it’s so intuitive and so sensible, it’s approachable. But I read a whole bunch of them, and I read just about all of them. I really enjoyed Andrew Grove’s books. They’re all really good.

Jensen: 我认为Clay Christensen的系列书籍是最棒的，真的没有第二种选择。原因是它非常直观，非常合理，很容易理解。不过我读了很多其他书，几乎所有的都读过。我也非常喜欢Andrew Grove的书，都很好。

Ben: Awesome. Favorite characteristic of Don Valentine.

本: 太棒了。Don Valentine最喜欢的特点是什么？

Jensen: Grumpy, but endearing. What he said to me the last time as he decided to invest in our company, he says, if you lose my money, I’ll kill you.

Jensen: 很脾气暴躁，但又令人喜欢。他最后一次决定投资我们公司时对我说：“如果你亏了我的钱，我就杀了你。”

David: Of course he did.

David: 当然，他会这么说。

Jensen: And then over the course of the decades, the years that followed, when something is nicely written about us in Mercury News, it seems like he wrote it in a crayon, he’ll say, ‘Good job, Don.’ Just write over the newspaper, just, ‘Good job, Don,’ and he mails it to me. I hope I’ve kept them, but anyway, you could tell he’s a real sweetheart, but he cares about the companies.

Jensen: 随着岁月流逝，几年后，当《水星新闻》上写到我们时，他似乎用蜡笔写的，他会写“好工作，Don”。就这样在报纸上写，“好工作，Don”，然后寄给我。我希望我保留了这些，但不管怎样，你可以看得出，他真的是一个非常可爱的人，但他很关心公司。

David: I bet he’s a special character.

David: 我敢打赌他是个特别的人物。

Jensen: Yeah, he’s incredible.

Jensen: 是的，他真了不起。

David: What is something that you believe today that 40-year-old Jensen would’ve pushed back on and said, no, I disagree.

David: 今天你相信的有什么东西是40岁的Jensen曾经会反驳的并说：“不，我不同意。”

Jensen: There’s plenty of time. If you prioritize yourself properly and you make sure that you don’t let Outlook be the controller of your time, there’s plenty of time.

Jensen: 时间是足够的。如果你合理地优先安排自己的事情，并确保不让Outlook控制你的时间，时间是足够的。

David: Plenty of time in the day? Plenty of time to achieve this thing?

David: 一天有足够的时间吗？有足够的时间去实现这一切吗？

Jensen: To do anything. Just don’t do everything. Prioritize your life. Make sacrifices. Don’t let Outlook control what you do every day.

Jensen: 做任何事情都是可以的。只是不做所有事情。优先安排你的生活，做出牺牲，不要让Outlook决定你每天要做什么。

Notice I was late to our meeting, and the reason for that, by the time I looked up, oh my gosh. Ben and David are waiting.

注意到我迟到了我们的会议，原因是等到我抬头时，哦天哪，Ben和David在等。

David: We have time.

David: 我们有时间。

Jensen: Exactly.

Jensen: 没错。

David: Didn’t stop this from being your day job.

David: 这并没有阻止你把这当作你的日常工作。

Jensen: No, but you have to prioritize your time really carefully, and don’t let Outlook determine that.

Jensen: 不，但你必须非常小心地优先安排你的时间，不要让Outlook来决定这些。

David: Love that. What are you afraid of, if anything?

David: 喜欢这个。如果有的话，你怕什么？

Jensen: I’m afraid of the same things today that I was in the very beginning of this company, which is letting the employees down. You have a lot of people who joined your company because they believe in your hopes and dreams, and they’ve adopted it as their hopes and dreams.

Jensen: 今天我害怕的事情和公司刚起步时一样，那就是让员工失望。你有很多人加入公司，是因为他们相信你的希望和梦想，并且他们把这些也当做自己的希望和梦想。

You want to be right for them. You want to be successful for them. You want them to be able to build a great life as well as help you build a great company, and be able to build a great career. You want them to have to enjoy all of that.

你希望为他们做对的事，帮助他们取得成功。你希望他们能建立一个伟大的生活，同时帮助你建立一个伟大的公司，拥有伟大的事业。你希望他们能够享受这一切。

These days, I want them to be able to enjoy the things I’ve had, the benefit of enjoying, and all the great success I’ve enjoyed. I want them to be able to enjoy all of that. So I think the greatest fear is that you let them down.

如今，我希望他们能够享受我曾拥有的那些事物，享受那些好处和我获得的伟大成功。我希望他们能够享受这一切。所以我认为最大的恐惧是让他们失望。

David: What point did you realize that you weren’t going to have another job, like this was it.

David: 你在什么时刻意识到你不会再换工作了？这就是你一生的工作。

Jensen: I don’t change jobs. If it wasn’t because of Chris and Curtis convincing me to do Nvidia, I would still be at LSI Logic today. I’m certain of it.

Jensen: 我不换工作。如果不是因为Chris和Curtis说服我做Nvidia，我今天还会在LSI Logic工作。我确信这一点。

Ben: Wow. Really?

本: 哇，真的吗？

Jensen: Yeah, I’m certain of it. I would keep doing what I’m doing. At the time that I was there, I was completely dedicated and focused on helping LSI Logic be the best company it could be. I was LSI Logic’s best ambassador. I’ve got great friends that to this day that I’ve known from LSI Logic. It’s a company I loved then, I love dearly today.

Jensen: 是的，我很确定。我会继续做我在做的事情。当时我在那里，我全身心投入并专注于帮助LSI Logic成为它能成为的最佳公司。我是LSI Logic最好的代言人。我有很多从LSI Logic认识的好朋友，直到今天。那是我曾深爱的公司，今天我依然深深热爱它。

I know exactly the revolutionary impact it had on chip, system, and computer design. In my estimation, one of the most important companies that ever came to Silicon Valley and changed everything about how computers were made. It put me in the epicenter of some of the most important events in computer industry.

我非常清楚它在芯片、系统和计算机设计上所产生的革命性影响。在我看来，它是有史以来进入硅谷并改变计算机制造方式的最重要公司之一。它将我置于计算机行业一些最重要事件的核心。

It led me to meeting Chris, Curtis, Andy Bechtolsheim, and Jon Rubinstein, some of the most important people in the world. Frank, I was with the other day. The list goes on. LSI Logic was really important to me, and I would still be there. Who knows what LSI Logic would’ve become if I were still there. That’s how my mind works.

它让我遇到了Chris、Curtis、Andy Bechtolsheim和Jon Rubinstein，这些是世界上一些最重要的人。Frank，我前几天还和他在一起。名单还在继续。LSI Logic对我来说真的很重要，如果我还在那里，不知道LSI Logic会变成什么样子。这就是我的思维方式。

David: Powering the AI of the world.

David: 推动世界的AI。

Jensen: Exactly. I might be doing the same thing that I’m doing today.

Jensen: 没错，我可能会做我今天做的同样的事情。

David: I got the sense from remembering back to part one of our series on Nvidia.

David: 我从回顾我们关于Nvidia系列的第一部分中得到了一个感觉。

Jensen: Until I’m fired, this is my last job. This is it.

Jensen: 只要我没被解雇，这就是我最后的工作。就这样。

David: I got the sense that LSI Logic might have also changed your perspective and philosophy about computing, too. A sense we got from the research was that when right out of school and when you first went to AMD first, you believed a version of Jerry Sanders’ real men have fabs. You need to do the whole stack, you got to do everything, and that LSI Logic changed you.

David: 我有一种感觉，LSI Logic也改变了你对计算机的看法和哲学。我们从研究中得到的感觉是，刚从学校毕业并且刚开始在AMD工作时，你相信Jerry Sanders的“真正的男人拥有晶圆厂”那种观点。你必须做完整的技术栈，做所有事情，而LSI Logic改变了你。

一条好汉。

Jensen: What LSI Logic did was realize that you can express transistors, logic gates, and chip functionality in high-level languages. That by raising the level of abstraction in what is now called high-level design—it was coined by Harvey Jones who’s on Nvidia’s board and I met him way back in the early days of Synopsys—during that time, there was this belief that you can express chip design in high level languages. And by doing so, you could take advantage of optimizing compilers, optimization logic, and tools, and be a lot more productive.

Jensen: LSI Logic所做的事情是意识到你可以用高级语言来表达晶体管、逻辑门和芯片功能。通过提高抽象级别，在现在称为高级设计的领域——这是Harvey Jones提出的，他是Nvidia董事会成员，我很早就在Synopsys的初期与他相识——当时人们相信你可以用高级语言来表达芯片设计。通过这样做，你可以利用优化编译器、优化逻辑和工具，提高生产力。

That logic was so sensible to me. I was 21 years old at the time, and I wanted to pursue that vision. Frankly, that idea happened in machine learning. It happened in software programming. I want to see it happen in digital biology, so that we can think about biology in a much higher level language, probably a large language model would be the way to make it representable.

那个逻辑对我来说非常合理。当时我21岁，我想追求那个愿景。坦率地说，这个想法发生在机器学习中，发生在软件编程中。我希望看到它在数字生物学中发生，这样我们就可以用更高层次的语言来思考生物学，可能通过大语言模型来使它能够表示出来。

That transition was so revolutionary, I thought that was the best thing ever happened to the industry. I was really happy to be part of it, and I was at ground zero. I saw one industry change revolutionize another industry. If not for LSI Logic doing the work that it did, Synopsys shortly after, then why would the computer industry be where it is today? It’s really, really terrific. I was at the right place at the right time to see all that.

那一转变是如此革命性，我认为这是行业中发生的最好的事情。我非常高兴能成为其中的一部分，而且我处于最前线。我亲眼看到了一个行业的变革如何颠覆另一个行业。如果没有LSI Logic做它所做的工作，随后是Synopsys，那么今天的计算机行业怎么可能是现在这个样子？这真的非常了不起。我很幸运，在正确的时间，处在正确的地方，看到了这一切。

David: That’s super cool. It sounded like the CEO of LSI Logic put a good word in for you with Don Valentine, too.

David: 这太酷了。听起来LSI Logic的CEO也为你在Don Valentine那里说了好话。

Jensen: I didn’t know how to write a business plan.

Jensen: 我不知道怎么写商业计划书。

Ben: Which it turns out is not actually important.

本: 结果证明，这其实并不重要。

Jensen: No. It turns out that making a financial forecast that nobody knows is going to be right or wrong, turns out not to be that important. But the important things that a business plan probably could have teased out, I think that the art of writing a business plan ought to be much, much shorter.

Jensen: 不。事实证明，做一个没人知道是对还是错的财务预测，其实并不是那么重要。但商业计划书可能揭示出来的重要问题，我认为写商业计划书的艺术应该更简短。

It forces you to condense what is the true problem you’re trying to solve? What is the unmet need that you believe will emerge? And what is it that you’re going to do that is sufficiently hard, that when everybody else finds out is a good idea, they’re not going to swarm it and make you obsolete? It has to be sufficiently hard to do.

它迫使你简明扼要地表达你真正想要解决的问题是什么？你认为会出现的未满足的需求是什么？你要做的事情必须足够困难，足够困难到当别人发现这是个好主意时，他们不会蜂拥而至让你过时。它必须足够难做。

There are a whole bunch of other skills that are involved in just product positioning, pricing, go to market and all that stuff. But those are skills, and you can learn those things easily. The stuff that is really, really hard is the essence of what I described.

还有很多其他技能涉及产品定位、定价、市场推广等等。但那些都是技能，你可以很容易学到。真正难的东西是我所描述的本质。

I did that okay, but I had no idea how to write the business plan. I was fortunate that Wilf Corrigan was so pleased with me in the work that I did when I was at LSI Logic, he called up Don Valentine and told Don, invest in this kid. He’s going to come your way. I was set up for success from that moment and got us off the ground.

我做得还行，但我根本不知道怎么写商业计划书。我很幸运，Wilf Corrigan对我在LSI Logic做的工作非常满意，他打电话给Don Valentine，告诉Don，投资这个孩子，他会走到你这儿。我从那一刻起就注定会成功，并帮助我们起步。

David: As long as you don’t lose the money.

David: 只要你不亏钱。

Jensen: I think Sequoia did okay. I think we probably are one of the best investments they’ve ever made.

Jensen: 我觉得Sequoia做得不错。我认为我们可能是他们做过的最好的投资之一。

Ben: Have they held through today?

本: 他们一直持有到今天吗？

Jensen: The VC partner is still on the board, Mark Stevens. All these years. The two founding VCs are still on the board.

Jensen: 风投伙伴Mark Stevens仍然在董事会里。这么多年了，两位创始风投仍然在董事会里。

Ben: Sutter Hill and Sequoia?

本: Sutter Hill和Sequoia？

Jensen: Yeah. Tench Coxe and Mark Stevens. I don’t think that ever happens. We are singular in that circumstance, I believe. They’ve added value this whole time, been inspiring this whole time, gave great wisdom and great support. But they also were so—

Jensen: 是的。Tench Coxe和Mark Stevens。我认为这种情况几乎从未发生过。我相信我们在这种情况下是独一无二的。他们一直在增加价值，一直在激励我们，给予智慧和支持。但他们也非常——

David: Haven’t killed you yet?

David: 还没有让你倒闭吧？

Jensen: No, not yet. But they’ve been entertained by the company, inspired by the company, and enriched by the company, so they stayed with it and I’m really grateful.

Jensen: 还没有。但他们一直被公司所吸引，受到了公司激励，并从公司中受益，因此他们一直支持我们，我非常感激。

David: Well, and that being our final question for you. It’s 2023, 30 years anniversary of the founding of Nvidia. If you were magically 30 years old again today in 2023, and you were going to Denny’s with your two best friends who are the two smartest people you know, and you’re talking about starting a company, what are you talking about starting?

David: 好的，这也是我们最后的问题。2023年，Nvidia成立30周年。如果你今天变回30岁，并和你认识的两个最聪明的朋友一起去Denny’s，讨论开创一家公司，你们会讨论什么创业？

Jensen: I wouldn’t do it. I know. The reason for that is really quite simple. Ignoring the company that we would start, first of all, I’m not exactly sure. The reason why I wouldn’t do it, and it goes back to why it’s so hard, is building a company and building Nvidia turned out to have been a million times harder than I expected it to be, any of us expected it to be.

Jensen: 我不会做的。我知道。原因其实非常简单。先不说我们要创办的公司，首先，我不确定。之所以我不会做，是因为它回到为什么它如此困难的问题，创业和建立Nvidia证明比我预期的困难百倍，甚至千倍。

At that time, if we realized the pain and suffering, just how vulnerable you’re going to feel, and the challenges that you’re going to endure, the embarrassment and the shame, and the list of all the things that go wrong, I don’t think anybody would start a company. Nobody in their right mind would do it.

如果那时我们意识到那种痛苦和折磨，意识到你会感到多么脆弱，面对的挑战、尴尬和羞耻，以及一切错误的事情，我认为没有人会创办公司。没有人会做这件事。

I think that that’s the superpower of an entrepreneur. They don’t know how hard it is, and they only ask themselves how hard can it be? To this day, I trick my brain into thinking, how hard can it be? Because you have to.

我认为那就是创业者的超级力量。他们不知道这有多难，只会问自己：这能有多难？直到今天，我依然骗自己，想：这有多难？因为你必须这么做。

Ben: Still, when you wake up in the morning.

本: 依然如此，早上醒来时。

Jensen: Yup. How hard can it be? Everything that we’re doing, how hard can it be? Omniverse, how hard can it be?

Jensen: 是的，能有多难？我们正在做的所有事情，能有多难？Omniverse，能有多难？

David: I don’t get the sense that you’re planning to retire anytime soon, though. You could choose to say like, whoa, this is too hard.

David: 不过，我感觉你并不打算很快退休。你可以选择说，“哇，这太难了。”

Ben: The trick is still working.

本: 这个窍门仍然有效。

David: Yeah, the trick is still working.

David: 是的，这个窍门仍然有效。

Jensen: I’m still enjoying myself immensely and I’m adding a little bit of value, but that’s really the trick of an entrepreneur. You have to get yourself to believe that it’s not that hard, because it’s way harder than you think. If I go taking all of my knowledge now and I go back, and I said, I’m going to endure that whole journey again, I think it’s too much. It is just too much.

Jensen: 我仍然非常享受自己，并且为公司带来一些价值，但这真的是创业者的窍门。你必须让自己相信这并不难，因为它比你想的要难得多。如果我现在带着所有的知识回到过去，我说我要再次经历整个过程，我认为这太难了。简直太难了。

Ben: Do you have any suggestions on any support system or a way to get through the emotional trauma that comes with building something like this?

本: 你有什么建议，如何通过支持系统来度过创业过程中所带来的情感创伤吗？

Jensen: Family, friends, and all the colleagues we have here. I’m surrounded by people who’ve been here for 30 years. Chris has been here for 30 years. Jeff Fisher’s been here 30 years, Dwight’s been here 30 years. Jonah and Brian have been here 25-some years, and probably longer than that. Joe Greco’s been here 30 years.

Jensen: 家人、朋友和我们这里的所有同事。我被那些在这里待了30年的人包围。Chris在这里已经30年，Jeff Fisher也在这里待了30年，Dwight在这里也待了30年。Jonah和Brian待了25年以上，可能还更长。Joe Greco也在这里待了30年。

I’m surrounded by these people that never one time gave up, and they never one time gave up on me. That’s the entire ball of wax. To be able to go home and have your family be fully committed to everything that you’re trying to do, thick or thin they’re proud of you and proud of the company, you need that. You need the unwavering support of people around you.

我被这些人包围，他们从来没有一次放弃过，而且他们从来没有一次放弃过我。这就是一切。能够回家，看到你的家人全心全意支持你所做的一切，无论顺境还是逆境，他们都为你和公司感到骄傲，你需要这种支持。你需要周围人坚定不移的支持。

Jim Gaithers and the Tench Coxes, the Mark Stevens, the Harvey Jones, and all the early people of our company, the Bill Millers, they not one time gave up on the company and us. You need that. I’m pretty sure that almost every successful company and entrepreneurs that have gone through some difficult challenges, had that support system around them.

Jim Gaithers、Tench Coxe、Mark Stevens、Harvey Jones以及我们公司早期的所有人，Bill Millers，他们从来没有一次放弃过公司和我们。你需要这种支持。我敢肯定，几乎所有经历过挑战的成功公司和创业者，都有这样的支持系统。

David: I know how meaningful that is in any company, but for you, I feel like the Nvidia journey is particularly amplified on these dimensions. You went through two, if not three, 80%-plus drawdowns in the public markets, and to have investors who’ve stuck with you from day one through that, must be just so much support.

David: 我知道这种支持在任何公司中都是如此重要，但对你来说，我觉得Nvidia的历程在这些维度上尤为突出。你经历了两次，甚至三次80%以上的股价回撤，而投资者从一开始就支持你，经历这一切，这必须是巨大的支持。

Jensen: It is incredible. You hate that any of that stuff happened. Most of it is out of your control, but 80% fall, it’s an extraordinary thing no matter how you look at it.

Jensen: 这真的很不可思议。你会讨厌这些事情发生。大部分都不在你的控制范围内，但股价下跌80%，无论怎么看，这都是一件非同寻常的事。

I forget exactly, but we traded down at about a couple of $2–$3 billion in market value for a while because of the decision we made in going into CUDA and all that work. Your belief system has to be really, really strong. You have to really, really believe it and really, really want it.

我不记得确切的时间了，但因为我们决定进入CUDA并做了所有那些工作，我们的市值曾一度下降到大约20到30亿美元。你的信念体系必须非常非常强大。你必须真正相信它，真正渴望它。

Otherwise, it’s just too much to endure because everybody’s questioning you. Employees aren’t questioning you, but employees have questions. People outside are questioning you, and it’s a little embarrassing.

否则，承受这一切就太难了，因为每个人都在质疑你。员工并不会质疑你，但员工有疑问。外部的人在质疑你，这有点让人尴尬。

It’s like when your stock price gets hit, it’s embarrassing no matter how you think about it. It’s hard to explain. There are no good answers to any of that stuff. The CEOs are humans and companies are built of humans. These challenges are hard to endure.

就像股价下跌时，无论你怎么想，这都让人尴尬。很难解释。没有什么好的答案。CEO是人，公司是由人组成的。这些挑战很难承受。

David: Ben had an appropriate comment on our most recent episode on you all, where we were talking about the current situation in Nvidia. I think you said, for any other company this would be a precarious spot to be in, but for Nvidia…

David: Ben在我们最近一期关于你们的节目中说了一个恰当的评论，我们讨论了Nvidia当前的状况。我记得你说，任何其他公司处于这个位置都将是一个危险的境地，但对于Nvidia来说……

Ben: This is kind of an old hat. You guys are familiar with these large swings in amplitude.

本: 这对你们来说就像家常便饭。你们习惯了这种大幅度的波动。

Jensen: Yeah. The thing to keep in mind is, at all times what is the market opportunity that you’re engaging in? That informs your size. I was told a long time ago that Nvidia can never be larger than a billion dollars. Obviously, it’s an underestimation, under imagination of the size of the opportunity. It is the case that no chip company can ever be so big. But if you’re not a chip company, then why does that apply to you?

Jensen: 是的，始终要记住的是，你所从事的市场机会是什么？这决定了你的规模。很久以前有人告诉我，Nvidia永远不会大于十亿美元。显然，这是对机会规模的低估，是对其潜力的想象不足。确实，没有芯片公司能够如此庞大。但如果你不是一家芯片公司，那么这为什么会适用于你呢？

This is the extraordinary thing about technology right now. Technology is a tool and it’s only so large. What’s unique about our current circumstance today is that we’re in the manufacturing of intelligence. We’re in the manufacturing of work world. That’s AI. The world of tasks doing work—productive, generative AI work, generative intelligent work—that market size is enormous. It’s measured in trillions.

Jensen: 现在技术的非凡之处在于，技术本身是一种工具，它的规模是有限的。我们目前所处的独特情况是，我们正在制造智能。我们正在制造工作世界。这就是AI。任务处理工作——生产性、生成性的AI工作、生成性的智能工作——这个市场规模是巨大的，按万亿来衡量。

One way to think about that is if you built a chip for a car, how many cars are there and how many chips would they consume? That’s one way to think about that. However, if you build a system that, whenever needed, assisted in the driving of the car, what’s the value of an autonomous chauffeur every now and then?

Jensen: 一种思考方式是，如果你为一辆车制造了一颗芯片，市场上有多少辆车，它们会消耗多少芯片？这是一种思考方式。然而，如果你构建了一个系统，在需要时辅助驾驶这辆车，那么偶尔出现的自动驾驶员的价值又是什么呢？

Obviously, the problem becomes much larger, the opportunity becomes larger. What would it be like if we were to magically conjure up a chauffeur for everybody who has a car, and how big is that market? Obviously, that’s a much, much larger market.

Jensen: 显然，问题变得更大了，机会也变得更大了。如果我们能为每一辆车的车主神奇地召唤出一位司机，那将是怎样的情况？那市场的规模又有多大呢？显然，这是一个更大、更庞大的市场。

The technology industry is that what we discovered, what Nvidia has discovered, and what some of the discovered, is that by separating ourselves from being a chip company but building on top of a chip and you’re now an AI company, the market opportunity has grown by probably a thousand times.

Jensen: 技术行业的特点是，我们所发现的，Nvidia所发现的，以及一些人所发现的，就是通过从一个芯片公司转型为构建芯片并建立在其基础上的AI公司，市场机会已经可能增长了千倍。

Don’t be surprised if technology companies become much larger in the future because what you produce is something very different. That’s the way to think about how large can your opportunity, how large can you be? It has everything to do with the size of the opportunity.

Jensen: 不要惊讶于未来技术公司变得更大，因为你生产的是非常不同的东西。这就是思考你的机会有多大，你能做多大的方法。这一切都与机会的规模有关。

Ben: Yup. Well, Jensen, thank you so much.

本: 是的。那么，Jensen，非常感谢你。

David: Thank you.

David: 谢谢。

Ben: Ooh, David, that was awesome.

本: 哦，David，那太棒了。

David: So fun.

David: 太有趣了。

Ben: Listeners, we want to tell you that you should totally sign up for our email list. Of course, it is notifications when we drop a new email, but we’ve added something new. We’re including little tidbits that we learn after releasing the episode, including listener corrections.

本: 各位听众，我们想告诉你们，应该完全加入我们的邮件列表。当然，它是关于我们发布新邮件时的通知，但我们添加了一些新内容。我们会包括一些我们在发布节目后学到的小信息，包括听众的更正。

We also have been teasing what the next episode will be. If you want to play the little guessing game along with the rest of the Acquired community, sign up at acquired.fm/email.

本: 我们还在预告下一个节目是什么。如果你想和其他Acquired社区的成员一起玩这个小猜测游戏，欢迎注册acquired.fm/email。

You should check out ACQ2, which is available at any podcast player. As these main Acquired episodes get longer and come out once a month instead of once every couple of weeks, it’s a little bit more of a rarity these days.

本: 你应该去看看ACQ2，它可以在任何播客播放器上收听。由于这些主要的Acquired节目越来越长，且改为每月发布一次而不是每两周一次，现在它变得有些稀有了。

David: We’ve been upleveling our production process, and that takes time.

David: 我们一直在提升我们的制作过程，这需要时间。

Ben: Yes. ACQ2 has become the place to get more from David and I, and we’ve just got some awesome episodes coming up that we are excited about.

本: 是的，ACQ2已经成为我们与大家分享更多内容的地方，我们即将推出一些非常棒的节目，我们对此感到非常兴奋。

If you want to come deeper into the Acquired kitchen, become an LP, acquired.fm/lp. Once every couple of months or so, we’ll be doing a call with all of you on Zoom just for LPs to get the inside scoop of what’s going on in Acquired land and get to know David and I a little bit better. Once a season, you’ll get to help us pick a future episode. That’s acquired.fm/lp.

本: 如果你想更深入了解Acquired的幕后工作，成为LP，请访问acquired.fm/lp。每隔几个月，我们会和所有LP进行Zoom电话会议，分享Acquired的最新动态，让你更好地了解David和我。每个季度，你将有机会帮助我们选择未来的节目主题。网址是acquired.fm/lp。

Anyone should join the Slack, acquired.fm/slack. God, we’ve got a lot of things now, David.

本: 大家应该加入我们的Slack，acquired.fm/slack。天啊，David，我们现在有很多事情了。

David: I know. The hamburger bar on our website is expanding.

David: 我知道，我们网站上的汉堡菜单在扩展。

Ben: That’s how you know we’re becoming enterprise. Wait until we have a mega menu, a menu of menus, if you will.

本: 这就是你知道我们在变得越来越像企业的方式。等到我们有了超级菜单，一个菜单的菜单，你就会知道了。

David: What is the Acquired solution that we can sell?

David: 我们能卖的Acquired解决方案是什么？

Ben: That’s true.

本: 确实。

David: We got to find that.

David: 我们得找到那个。

Ben: All right. With that, listeners, acquired.fm/slack to join the Slack and discuss this episode, acquired.fm/store to get some of that sweet merch that everyone is talking about. And with that, listeners, we will see you next time.

本: 好的，听众们，加入Slack并讨论这一集请访问acquired.fm/slack，想买大家都在谈论的优质商品请访问acquired.fm/store。听众们，我们下次见。

David: We’ll see you next time.

David: 下次见。

Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.

注：Acquired的主持人和嘉宾可能持有本集讨论的资产。本播客不是投资建议，仅供信息和娱乐目的。考虑任何财务交易时，您应自行进行研究并做出独立决策。

2023-10-15 Jensen Huang.ACQUIRED Interview with NVIDIA CEO Jensen Huang

2023-10-15 Jensen Huang.ACQUIRED Interview with NVIDIA CEO Jensen Huang

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