2024-04-25 Jensen Huang.NVDA.How to Use First-Principles Thinking to Drive Decisions

“You can learn how something can be done and then go back to first principles and ask yourself, ‘Given the conditions today, given my motivation, given the instruments, the tools, given how things have changed, how would I redo this? How would I reinvent this whole thing?’”

“你可以学习如何做某事，然后回到最基本的原则，问自己：‘考虑到今天的条件，考虑到我的动机，考虑到工具和仪器，考虑到事物的变化，我会如何重新做这件事？我会如何重新发明整个事情？’”

Jensen Huang, founder and CEO of NVIDIA, started his career washing dishes at Denny’s. He then worked his way to busboy and eventually founded what is one of today’s most valuable companies. In this interview at Stanford GSB’s View From The Top event, founder and CEO Jensen Huang shares the stage with Shantam Jain, MBA ’24, to detail his experience founding NVIDIA, funding it, and finally, his views on AI.

英伟达创始人兼首席执行官黄仁勋（Jensen Huang）在 Denny's 餐厅洗碗开始他的职业生涯。随后，他逐步晋升为餐厅服务员，最终创立了如今价值连城的公司之一。在斯坦福大学商学院“顶峰之景”活动的采访中，创始人兼首席执行官黄仁勋与 MBA '24 的 Shantam Jain 共同登台，详细介绍了他创立英伟达的经历、为其筹资，以及最终对人工智能的看法。

Full Transcript 完整记录

Note: Transcripts are generated by machine and lightly edited by humans. They may contain errors.

注意：文字记录是由机器生成并经人类轻微编辑。它们可能包含错误。

Jensen Huang: If you send me something and you want my input on it and I can be of service to you and in my review of it, share with you how I reasoned through it, I’ve made a contribution to you. I’ve made it possible to see how I reason through something. And by reasoning, as you know, how someone reasons through something empowers you. You go, “Oh my gosh. That’s how you reason through something like this.” It’s not as complicated as it seems. This is how you reason through something that’s super ambiguous. This is how you reason through something that’s incalculable. This is how you reason through something that seems to be very scary. Do you understand? So, I show people how to reason through things all the time.

黄仁勋：如果你给我发送了一些东西，并且希望我对此提出建议，并且我可以为你提供服务，并在我审查过后，与你分享我是如何推理的，那么我已经为你做出了贡献。我已经让你看到了我是如何推理的。而你知道，了解某人如何推理某事会让你更有能力。你会说，“天哪。原来是这样推理这样的事情。”这并不像看起来那么复杂。这就是你如何推理一些非常模糊的事情。这就是你如何推理一些无法计算的事情。这就是你如何推理一些看起来非常可怕的事情。你明白吗？所以，我总是向人们展示如何推理事物。

Shantam Jain: That was Jensen Huang, the CEO of NVIDIA. Jensen visited Stanford Graduate School of Business, as part of View From The Top, a speaker series where students, like me, sit down to interview leaders from around the world. I’m Shantam Jain, an MBA student of the class of 2024. In our conversation, we discussed the key pillars of Jensen’s leadership philosophy and how he breaks down generative AI using first-principles thinking.

那位是 NVIDIA 的首席执行官黄仁勋（Jensen Huang）。黄仁勋访问了斯坦福大学商学院，作为“鸟瞰高峰”系列活动的一部分，这是一个学生们（包括我）与来自世界各地领导者进行访谈的演讲系列。我是 2024 级的工商管理硕士学生 Shantam Jain。在我们的对话中，我们讨论了黄仁勋领导哲学的关键支柱以及他如何通过首要原理思维来解构生成式人工智能。

You’re listening to View From The Top, the podcast.

您正在收听《高层视角》播客。

Shantam Jain: Jensen, this is such an honor. Thank you for being here.

Shantam Jain: Jensen，这真是一种荣幸。感谢你能在这里。

Jensen Huang: I’m delighted to be here. Thank you.

黄仁勋：很高兴能在这里。谢谢。

Shantam Jain: In honor of your return to Stanford, I decided we’d start talking about the time when you first left. You joined LSI Logic, and that was one of the most exciting companies at the time. You’re building a phenomenal reputation with some of the biggest names in tech, and yet you decided to leave to become a founder. What motivated you?

Shantam Jain：为了纪念您重返斯坦福，我决定我们开始谈论您第一次离开的时候。您加入了 LSI Logic，那是当时最令人兴奋的公司之一。您正在与一些科技界最大的名字建立卓越的声誉，然而您决定离开成为一名创始人。是什么激励了您？

Jensen Huang: Chris and Curtis. I was an engineer at LSI Logic, and Chris and Curtis were at Sun. And I was working with some of the brightest minds in computer science at the time, of all time, including [unintelligible] and others building workstations and graphics workstations and so on and so forth. And Chris and Curtis one day said that they’d like to leave Sun, and they’d like me to go figure out where they’re going to go leave for.

黄仁勋：克里斯和柯蒂斯。我当时在 LSI Logic 担任工程师，克里斯和柯蒂斯在 Sun 工作。当时，我与一些计算机科学领域最聪明的人才合作，包括[无法辨识的名字]等，一起建造工作站、图形工作站等设备。有一天，克里斯和柯蒂斯说他们想离开 Sun，希望我去找出他们将要去的地方。

I had a great job, but they insisted that I figure out with them how to build a company. So, we hung out at Denny’s whenever they dropped by, which was, by the way, my alma mater, my first company. My first job before CEO was a dishwasher, and I did that very well.

我原本有一份很棒的工作，但他们坚持要我和他们一起想办法创办一家公司。所以，每当他们过来时，我们就在丹尼餐厅聚会，顺便说一句，那是我的母校，也是我的第一家公司。在成为首席执行官之前，我做过洗碗工，而且做得非常出色。

[Laughter] 【笑声】

Jensen Huang: So, anyways, we got together, and it was during the microprocessor revolution. This was 1993 and 1992 when we were getting together. The PC revolution was just getting going. You know that Windows ’95, obviously, which is the revolutionary version of Windows, didn’t even come to the market yet, and Pentium wasn’t even announced yet. This was all right before the PC revolution, and it was pretty clear that the microprocessor was going to be very important. And we thought, “Why don’t we build a company to go solve problems that a normal computer that is powered by general purpose computing can’t?” And so that became the company’s mission, to go build a computer, the type of computers that solve problems that normal computers can’t. And to this day, we’re focused on that.

黄仁勋：总之，我们聚在一起，那时正值微处理器革命。这是 1993 年和 1992 年我们聚在一起的时候。PC 革命刚刚兴起。你知道，Windows '95 显然是 Windows 的革命性版本，甚至还没有上市，Pentium 甚至还没有宣布。这都发生在 PC 革命之前，很明显微处理器将会非常重要。我们想，“为什么不建立一家公司来解决一般计算机无法解决的问题呢？”于是，公司的使命就成为了建立一种计算机，解决一般计算机无法解决的问题。直到今天，我们仍专注于这一点。

And if you look at all the problems in the markets that we opened up as resolved, it’s things like computational drug design, weather simulation, materials’ design. These are all things that we’re really, really proud of — robotics, self-driving cars, autonomous software we call artificial intelligence. And then, of course, we drove the technology so hard that eventually the computational cost went to approximately zero, and it enabled a whole new way of developing software, where the computer wrote the software itself, artificial intelligence as we know it today. So, that was it; that was the journey.

如果您看待我们开发的市场中所有问题都已解决，那些问题包括计算药物设计、天气模拟、材料设计等。这些都是我们非常自豪的事情——机器人技术、自动驾驶汽车、我们称之为人工智能的自主软件。当然，我们推动技术发展到极致，最终计算成本几乎为零，这使得软件开发出现了全新的方式，计算机自己编写软件，这就是我们今天所知的人工智能。这就是我们的旅程。

Shantam Jain: Yeah. Thank you all for coming.

尚坦姆·詹恩：是的。谢谢大家的到来。

Well, these applications are on all of our minds today. Back then, the CEO of LSI Logic convinced his biggest investor, Don Valentine, to meet with you. He is obviously the founder of Sequoia. Now I can see a lot of founders here edging forward in anticipation. But how did you convince the most sought-after investor in Silicon Valley to invest in a team of first-time founders building a new product for a market that doesn’t even exist?

嗯，这些应用程序如今都在我们的脑海中。当时，LSI Logic 的首席执行官说服了他最大的投资者唐·瓦伦丁（Don Valentine）与您会面。显然，他是红杉资本的创始人。现在我可以看到很多创始人在这里怀着期待向前迈进。但是，您是如何说服硅谷最抢手的投资者投资一支由首次创始人组成、为一个甚至不存在的市场开发新产品的团队的呢？

Jensen Huang: I didn’t know how to write a business plan. So I went to a bookstore, and back then, there were bookstores. And in the business book section, there was this book. And it was written by somebody I knew, Gordon Bell. And this book, I should go find it again, but it’s a very large book, and the book says, “How to Write a Business Plan.” That was a highly specific title for a very niche market. And it seems like he wrote it for 14 people, and I was one of them.

黄仁勋：我不知道如何写商业计划。所以我去了一家书店，那时候还有书店。在商业书籍区，有一本书。这本书是由我认识的人戈登·贝尔写的。这本书，我应该再去找找，但是这是一本非常厚的书，书上写着“如何写商业计划”。这是一个非常具体的标题，针对一个非常小众的市场。看起来他是为 14 个人写的，而我是其中之一。

So, I bought the book. I should have known right away that it was a bad idea because Gordon is super smart. And super-smart people have a lot to say. I’m pretty sure Gordon wants to teach me how to write a business plan completely. So, I picked up this book, and it’s like 450 pages long.

所以，我买了这本书。我应该立刻意识到这是个坏主意，因为戈登非常聪明。而非常聪明的人有很多话要说。我很确定戈登想要教我如何完全撰写一份商业计划。所以，我拿起了这本书，它有大约 450 页。

Well, I never got through it, not even close. I flipped through it, a few pages. And I go, “You know what? By the time I’m done reading this thing, I’ll be out of business. I’ll be out of money. And Laurie and I only had about six months in the bank. And we had already Spencer, Madison and a dog. So, the five of us had to live off of whatever money we had in the bank, so I didn’t have much time.

嗯，我从来没有读完它，甚至差得远。我只是随便翻了几页。我心想，“你知道吗？等我读完这个东西，我就要破产了。我会没钱了。劳瑞和我账户里只有大约六个月的钱。我们已经有了斯宾塞、麦迪逊和一只狗。所以，我们五口人只能靠我们账户里的钱生活，所以我没有太多时间。

So, instead of writing the business plan, I just went to talk to [Wilf Corey]. He called me one day, and he said, “Hey, you left the company. You didn’t even tell me what you were doing. I want you to come back and explain it to me.” And so, I went back and explained it to Wilf. And Wilf at the end of it said, “I have no idea what you said. That’s one of the worst elevator pitches I’ve ever heard.”

所以，我没有写商业计划，而是去找[威尔夫·科里]谈了一下。有一天他给我打电话，他说：“嘿，你离开公司了。你甚至都没告诉我你在做什么。我希望你回来给我解释一下。”于是，我回去给威尔夫解释了一番。最后，威尔夫说：“我完全不明白你在说什么。这是我听过的最糟糕的电梯演讲之一。”

[Laughter] 【笑声】

Jensen Huang: And then he picked up the phone, and he called Don Valentine. He called Don, and he said, “Don, I’m going to send a kid over. I want you to give him money.” He’s one of the best employees LSI Logic ever had. And so, the thing I learned is you can make up a great interview. You can even have a bad interview. But you can’t run away from your past, and so have a good past. Try to have a good past.

黄仁勋：然后他拿起电话，打给了唐·瓦伦丁。他打电话给唐，说：“唐，我要派一个孩子过去。我希望你给他钱。”他是 LSI Logic 有史以来最优秀的员工之一。所以，我学到的是你可以编造一个很棒的面试。你甚至可以有一个糟糕的面试。但你无法逃避你的过去，所以要有一个好过去。努力拥有一个好过去。

这是信用背书，但为什么要讲的这么复杂？

And in a lot of ways, I was serious when I said I was a good dishwasher. I was probably Denny’s best dishwasher. I planned my work, I was organized, I was mise-en-place, and then I washed the living daylights out of the dishes, and then they promoted me to busboy. I was certain I’m the best busboy Denny’s ever had. I never left a station emptyhanded. I never came back emptyhanded. I was very efficient.

在很多方面，当我说我是一个优秀的洗碗工时，我是认真的。我可能是丹尼最优秀的洗碗工。我计划我的工作，我有条理，我有准备工作，然后我把盘子洗得干干净净，然后他们提拔我做了清洁工。我确信我是丹尼有史以来最优秀的清洁工。我从不让工作台空着手。我从不空手而归。我非常高效。

So, anyways, eventually I became a CEO. I’m still working on being a good CEO.

所以，无论如何，最终我成为了一名首席执行官。我仍在努力成为一名优秀的首席执行官。

Shantam Jain: Talking about being the best, you needed to be the best among 89 other companies that were funded after you build the same thing. And then with six to nine months of runway left, you realize that the initial vision was just not going to work. How did you decide what to do next to save the company when the cards were so stacked against you?

Shantam Jain：谈到成为最好，你需要成为其他 89 家公司中最好的一家，在你构建相同产品之后获得资金。然后，在只剩下六到九个月的时间时，你意识到最初的愿景根本行不通。当情况对你极为不利时，你是如何决定下一步该怎么做来拯救公司的？

Jensen Huang: Well, we started this company called [unintelligible] Computing. And the question is, what is it for? What’s the killer app? That became our first great decision. And this is what Sequoia funded. The first great decision was the first killer app was going to be 3D graphics. And the technology was going to be 3D graphics. And the application was going to be videogames. At the time, 3D graphics was impossible to make cheap. It was million-dollar image generators from silicon graphics. And so, it was a million dollars, and it’s hard to make cheap. And the videogame market was [zero billion dollars]. So, you had this incredible technology that’s hard to commoditize and commercialize. And then you have this market that doesn’t exist. That intersection was the founding of our company. And I still remember when Don, at the end of my presentation, one of the things he said to me, which made a lot of sense back then; it makes a lot of sense today, he said, “Startups don’t invest in startups or startups don’t partner with startups.” And his point is that in order for NVIDIA to succeed, we needed another startup to succeed, and that other startup was electronic arts.

黄仁勋：嗯，我们创立了这家名为[无法辨识] Computing 的公司。问题是，它是用来做什么的？什么是杀手级应用？这成为了我们的第一个重要决定。这也是 Sequoia 资助的。第一个重要决定是第一个杀手级应用将是 3D 图形。技术将是 3D 图形。应用将是视频游戏。当时，制作廉价的 3D 图形是不可能的。那时是硅图形公司的百万美元图像生成器。所以，它价值百万美元，而且很难做到廉价。而视频游戏市场是[零十亿美元]。所以，你有这种难以商品化和商业化的令人难以置信的技术。然后你有这个不存在的市场。这个交集是我们公司的创立。我仍然记得当唐在我演示结束时对我说的一件事，那时候这句话很有道理；今天这句话仍然很有道理，他说，“初创公司不会投资于初创公司，或者初创公司不会与初创公司合作。”他的观点是，为了让英伟达成功，我们需要另一家初创公司成功，而那家初创公司就是艺电。

杀手级应用的意思是一开始就想成为自己，而不是别人。

And then on the way out, he reminded me that electronic arts is CTO, is 14 years old and had to be driven to work by his mom. He just wanted to remind me that that’s who I’m relying on.

然后在离开的路上，他提醒我说，电子艺界的首席技术官是 14 岁，需要他妈妈开车送他上班。他只是想提醒我，这就是我所依赖的人。

Jensen Huang: And then after that, he said, “If you lose my money, I’ll kill you.” And that was kind of my memories of that first meeting. But nonetheless, we created something. We went on the next several years to go create the gaming market for PCs. It took a long time to do so. We’re still doing it today. We realized that not only do you have to create the technology and invent a new way of doing computer graphics so that what was a million dollars is now 3, 400, 500 dollars that fits in the computer, and you have to go create this new market. So, we had to create technology, create markets.

黄仁勋：然后在那之后，他说，“如果你亏了我的钱，我会杀了你。”那是我对那次第一次会议的记忆。但尽管如此，我们创造了一些东西。在接下来的几年里，我们继续为个人电脑创造游戏市场。这需要很长时间。我们今天仍在继续。我们意识到，不仅需要创造技术并发明一种新的计算机图形处理方式，使原本价值百万美元的东西现在只需 3、4、500 美元就能放入计算机，还需要开创这个新市场。因此，我们必须创造技术，创造市场。

The idea that company would create technology, create markets defines NVIDIA today. Almost everything we do, we create technology, we create markets. That’s the reason people call it a stack, an ecosystem, words like that, but that’s basically it — a décor for 30 years when NVIDIA realized we had to do is in order to create the conditions by which somebody could buy our products, we had to go invent this new market, and it’s the reason why we’re early in autonomous driving. It was the reason why we were early in deep learning. It’s the reason why we’re early in just about all these things including computational drug design and discovery. All these different areas we’re trying to create the market while we’re creating the technology.

公司创造技术、创造市场的理念定义了今天的英伟达。我们几乎所有的事情都是如此，我们创造技术，我们创造市场。这就是人们称之为堆栈、生态系统等词语的原因，但基本上就是这样——30 年来，当英伟达意识到我们必须做的是创造购买我们产品的条件时，我们不得不发明这个新市场，这就是为什么我们在自动驾驶领域走在前列的原因。这就是我们在深度学习领域走在前列的原因。这就是我们在包括计算药物设计和发现在内的几乎所有这些领域走在前列的原因。在我们创造技术的同时，我们也在努力创造市场。

这是自我意识的另一种表达方式，表达方式过于奇怪可能不利于企业文化的形成。

Okay. Then we got going, and then Microsoft introduced a standard called Direct 3D, and that spawned off hundreds of companies. And we found ourselves a couple of years later competing with just about everybody. The thing that we invented the company, the technology we invented 3D graphics with, that consumerized 3D with turns out to be incompatible with Direct 3D.

好的。然后我们开始行动，微软推出了一个名为 Direct 3D 的标准，这催生了数百家公司。几年后，我们发现自己几乎与所有人竞争。我们创立公司时发明的技术，我们用来消费化 3D 图形的技术，结果与 Direct 3D 不兼容。

So, we started this company. We had this 3D graphics thing, a million-dollar thing. We’re trying to make it consumerized, and so we invented all this technology. And then shortly after, it became incompatible, so we had to reset the company or go out of business. But we didn’t know how to build it the way that Microsoft had defined it. I remember a meeting on a weekend, and the conversation was, “We now have 89 competitors. I understand the way we do it is not right, but we don’t know how to do it the right way.”

所以，我们开始了这家公司。我们有这个 3D 图形的东西，一个价值百万美元的东西。我们试图让它变得更适合消费者，所以我们发明了所有这些技术。然后不久之后，它变得不兼容，所以我们不得不重置公司或倒闭。但我们不知道如何按照微软定义的方式构建它。我记得一个周末的会议，谈话是，“我们现在有 89 个竞争对手。我明白我们的做法不对，但我们不知道如何以正确的方式做。”

Thankfully, there was another bookstore, and the bookstore was called [Fry’s Electronics]. I don’t know if it’s still here. I think I drove Madison, my daughter, on the weekend to Fry’s, and it was sitting right there, the open GL manual, which would define how silicon graphics did computer graphics. So, it was right there; it was like $68.00 a book. I had a couple hundred dollars. I bought three books. I took it back to the office, and I said, “Guys, I found it. Our future.” I had the three versions of it. I handed it out. It had a big, nice centerfold. The centerfold is the open GL pipeline, which is the computer graphics pipeline. And I handed it to the same geniuses that I founded the company with. And we implemented the open GL pipeline like nobody had ever implemented the open GL pipeline, and we built something the world had never seen.

幸运的是，还有另一家书店，这家书店叫做[Fry's Electronics]。我不知道它是否还在这里。我记得周末我开车带着我的女儿麦迪逊去了 Fry's，那本开放 GL 手册就在那里，它定义了硅图形如何制作计算机图形。所以，就在那里；每本书大约 68.00 美元。我带了几百美元。我买了三本书。我把它带回办公室，然后说：“伙计们，我找到了。我们的未来。”我有三个版本。我把它们分发出去。它有一个很大很好的中心折页。中心折页就是开放 GL 管线，也就是计算机图形管线。我把它交给了和我一起创立公司的天才们。我们实现了开放 GL 管线，没有人像我们这样实现过，我们创造了世界从未见过的东西。

有自我的总会想方设法为自己的自我找到一席之地。

So, a lot of lessons are right there. That moment in time for our company gave us so much confidence. And the reason for that is you can succeed in doing something, inventing a future, even if you were not informed about it at all. And that’s kind of my attitude about everything now. When somebody tells me about something and I’ve never heard of it before, or if I’ve heard of it and don’t understand how it works at all, my first thought is always, “How hard can it be? And it’s probably just a textbook away. You’re probably one archive paper away from figuring this out.”

所以，很多教训就在那里。对我们公司来说，那一刻给了我们很多信心。原因是你可以成功地做某事，创造未来，即使你对此一无所知。这就是我现在对所有事情的态度。当有人告诉我某件事，而我以前从未听说过，或者我听说过但完全不明白它是如何运作的，我的第一个想法总是，“这有多难呢？也许只是一本教科书的距离。你可能只差一篇归档文件就能弄清楚这个问题。”

So, I spent a lot of time reading archive papers. And it’s true. Now, of course, you can’t learn how somebody else does something and do it exactly the same way and hope to have a different outcome. But you can learn how something can be done and then go back to first principles and ask yourself, “Given the conditions today, given my motivation, given the instruments, the tools, given how things have changed, how would I redo this? How would I reinvent this whole thing? How would I design it? How would I build a car today? Would I build it incrementally from 1950’s and 1900’s? How would I build a computer today? How would I write software today” Does that make sense?

所以，我花了很多时间阅读档案文件。这是真的。当然，你不能学习别人如何做某事，然后完全照搬，希望得到不同的结果。但你可以学习如何做某事，然后回到最基本的原则，问自己，“考虑到今天的条件，考虑到我的动机，考虑到工具、设备，考虑到事物的变化，我会如何重新做这件事？我会如何重新发明整个事情？我会如何设计它？我会如何建造一辆汽车？我会逐步从上世纪 50 年代和 19 世纪开始建造吗？我会如何建造一台计算机？我会如何编写软件？”这样说得通吗？

So, I go back to first principles all the time, even in the company today, and just reset ourselves, because the world has changed. The way we wrote software in the past, it was monolithic, and it’s designed for supercomputers, but now it’s this aggregated so on and so forth. How we think about software today, how we think about computers today, just always cause your company, always cause yourself to go back to first principles, and it creates lots and lots of opportunities.

所以，我一直回到第一原则，即使在今天的公司里，只是重新定位自己，因为世界已经改变了。我们过去编写软件的方式是单片式的，它是为超级计算机设计的，但现在它是这种聚合等等。我们如何思考软件，如何思考计算机，总是让你的公司，总是让你自己回到第一原则，这创造了许多机会。

这一段关于第一性原理的解释一般，第一性原理要求回到事物的源头思考问题，关键是如何回到源头？首要前提是排除心理上的干扰，排除各种信息上的噪音。

Shantam Jain: The way you apply this technology tends to be revolutionary. You get all the momentum that you need to IPO and then some more, because you grow your revenue nine times in the next four years. But in the middle of all of this success, you decide to [pip] it a little bit, the focus of innovation happening at NVIDIA based on a phone call you have with this chemistry professor. Can you tell us about that phone call and how you connected the dots from what you heard to where you went?

Shantam Jain：您应用这项技术的方式往往是革命性的。您获得了上市所需的所有动力，甚至更多，因为在接下来的四年里，您的收入增长了九倍。但在所有这些成功的中间，您决定稍微调整一下，关注创新的重点发生在 NVIDIA，这是基于您与这位化学教授的一次电话交谈。您能告诉我们关于那通电话的情况以及您是如何从所听到的内容中连接到您所走的道路的吗？

Jensen Huang: I remember at the core, the company was pioneering a new way of doing computing. Computer graphics was the first application. But we always knew that there would be other applications, so image processing came, particle physics came, fluids came, so on and so forth, all kinds of interesting things that we wanted to do.

黄仁勋：我记得，公司最初是开创了一种新的计算方式。计算机图形学是第一个应用。但我们始终知道会有其他应用，所以图像处理、粒子物理、流体等等都出现了，我们想做各种有趣的事情。

We made the processor more programmable so that we could express more algorithms, if you will. And then one day, we invented programmable shaders, which made all forms of imaging and computer graphics programmable. That was a great breakthrough, so we invented that.

我们使处理器更具可编程性，以便能够表达更多的算法。然后有一天，我们发明了可编程着色器，使所有形式的图像和计算机图形都具有可编程性。这是一次重大突破，所以我们发明了它。

On top of that, we tried to look for ways to express more sophisticated algorithms that could be computed on our processor, which is very different than a CPU. So, we created this thing called a CG. I think it was 2003 or so. C for GPUs. It predated [CUDA] by about three years.

除此之外，我们尝试寻找一种能在我们的处理器上计算的更复杂算法的表达方式，这与 CPU 大不相同。因此，我们创造了一个叫做 CG 的东西。我记得大概是在 2003 年左右。C 代表 GPU。它比[CUDA]早大约三年。

The same person who wrote the textbook that saved the company, Mark [Kilgard], wrote that textbook. And so, CG was super cool. We wrote textbooks about it. We started teaching people how to use it. We developed tools and such. And then several researchers discovered it. Many of the researchers here, students here at Stanford were using it. Many of the engineers that then became engineers at NVIDIA were playing with it.

同一个人写了拯救公司的教科书，马克[Kilgard]，也写了那本教科书。因此，CG 非常酷。我们写了关于它的教科书。我们开始教人们如何使用它。我们开发了工具等等。然后几位研究人员发现了它。斯坦福大学的许多研究人员和学生在使用它。随后许多成为 NVIDIA 工程师的工程师们也在使用它。

A couple of doctors at Mass General picked it up and used it for CT reconstruction. So, I flew out and saw them and said, “What are you guys doing with this thing?” And they told me about that. Then a computational, quantum chemist used it to express his algorithms.

麻省总医院的几位医生发现了它，并将其用于 CT 重建。所以，我飞出去见了他们，问道：“你们用这个东西做什么？”他们告诉了我。然后，一位计算机量子化学家用它来表达他的算法。

So, I realized that there’s some evidence that people might want to use this. And it gave us incrementally more confidence that we ought to go do this, that this form of computing could solve problems that normal computers really can’t and reinforced our belief and kept us going.

所以，我意识到有一些证据表明人们可能想要使用这个。这让我们逐渐更有信心应该去做这件事，这种计算形式可以解决普通计算机无法解决的问题，并加强了我们的信念，让我们继续前行。

这种事目前只能发生在硅谷。

Shantam Jain: Every time you heard something new, you really savored that surprise, and that seems to be a theme throughout your leadership at NVIDIA. It feels like you make these bets so far in advance of technology inflections that when the apple finally falls from the tree, you’re standing right there in your black leather jacket waiting to catch it.

Shantam Jain：每次你听到新事物时，你都会真正品味那份惊喜，这似乎贯穿在你在英伟达的领导中。感觉你在技术转折之前做出这些赌注，当苹果最终从树上掉下来时，你就站在那里，穿着黑色皮夹克，等着接住它。

[Laughter] 【笑声】

Shantam Jain: How do you find the [conviction]?

Shantam Jain：你是如何找到[信念]的？

Jensen Huang: It always seems like a diving catch. You do things based on core beliefs. We deeply believe that we could create a computer that solves problems that normal processing can’t do. There are limits to what a CPU can do. There are limits to what general purpose computing can do. And then there are interesting problems that we can go solve. The question is always — are those interesting problems only or can they also be interesting markets? Because if they’re not interesting markets, it’s not sustainable. And NVIDIA went through about a decade where we were investing in this future and the markets didn’t exist. There was only one market at the time; it was computer graphics.

黄仁勋：这总让人感觉像是一次扑救。你的行动基于核心信念。我们深信我们可以创造一台能够解决普通处理无法完成的问题的计算机。CPU 的能力是有限的。通用计算的能力也是有限的。然后就有一些有趣的问题等着我们去解决。问题始终是 — 这些有趣的问题只是有趣，还是它们也可能是有趣的市场？因为如果它们不是有趣的市场，那就不具备可持续性。NVIDIA 经历了大约十年的投资于这个未来，而当时市场并不存在。当时只有一个市场；那就是计算机图形。

CUP擅长串行任务，GPU擅长并行任务，明显有区别，奇怪的是为什么只有NVIDIA去做了这个暂时没有市场的项目，不需要奇怪、夸张的描述，说到底是安全感，安全感充沛、能延迟满足的团队才愿意这么干。

For 10, 15 years, the markets that fuel NVIDIA today just didn’t exist. So how do you continue with all of the people around you, our company and NVIDIA’s management team and all of the amazing engineers that were there creating this future with me — all of your shareholders, your board of directors, your partners, you’re taking everybody with you, and there’s no evidence of a market. That is really, really challenging. The fact that the technology can solve problems, and the fact that you have research papers that are used, that are made possible because of it are interesting. But you’re always looking for that market. But nonetheless, before a market exists, you still need early indicators of future success.

在今天推动英伟达的市场在过去的 10 年、15 年里根本不存在。那么，你要如何继续前行，身边有这么多人，我们的公司和英伟达的管理团队，以及所有那些与我一起创造未来的优秀工程师们——所有的股东、董事会成员、合作伙伴，你要带着所有人一起前行，而市场却毫无迹象。这真的非常具有挑战性。技术可以解决问题，你有研究论文可以利用，这些都很有趣。但你总是在寻找市场。尽管如此，在市场出现之前，你仍然需要未来成功的早期指标。

We have this phrase in the company. There’s a phrase called “key performance indicators.” Unfortunately, KPIs are hard to understand. I find KPIs hard to understand. What’s a good KPI? A lot of people, when we look for KPIs, they go, “Gross margins.” That’s not a KPI; that’s a result. You’re looking for something that’s early indicators of future positive results and as early as possible. The reason for that is because you want that early sign that you’re going in the right direction.

在公司中我们有这样一句话。有一句话叫做“关键绩效指标”。不幸的是，KPI 很难理解。我发现 KPI 很难理解。什么是一个好的 KPI？很多人，在寻找 KPI 时，会说，“毛利率”。那不是 KPI；那是一个结果。你要找的是未来积极结果的早期指标，尽可能早。原因是因为你想要早期迹象表明你正在朝着正确的方向前进。

So, we have this phrase that’s called, “EOIFS,” early indicators to EOIFS, early indicators of future success. And it helps people, because I was using it all the time, to give the company hope that, “Hey, look, we solved this problem, we solved that problem, we solved this problem.” The markets didn’t exist, but there were important problems, and that’s what the company’s about, to solve these problems. We want to be sustainable, and therefore, the markets have to exist at some point.

所以，我们有一个短语叫做“EOIFS”，即未来成功的早期指标。它帮助人们，因为我一直在使用它，给公司带来希望，“嘿，看，我们解决了这个问题，我们解决了那个问题，我们解决了这个问题。”市场并不存在，但存在着重要的问题，这就是公司的使命，解决这些问题。我们希望能够持续发展，因此，市场最终必须存在。

But you want to decouple the result from evidence that you’re doing the right thing, okay? So that’s how you kind of solve this problem of investing into something that’s very, very far away and having the conviction to stay on the road is to find as early as possible the indicators that you’re doing the right things. So, start with a core belief. Unless something changes your mind, you continue to believe in it. And look for early indicators of future success.

但是你想要将结果与证据分开，证明你正在做正确的事情，好吗？这样你就可以解决投资于遥远事物并坚定地走下去的问题，尽早找到表明你正在做正确事情的指标。所以，从一个核心信念开始。除非有什么改变了你的想法，你就继续相信它。寻找未来成功的早期指标。

NVIDIA的成功看着是风险投资的成功，皮衣黄的这个解释不能自洽。

Shantam Jain: What are some of those early indicators that have been used by product teams at NVIDIA?

Shantam Jain：NVIDIA 的产品团队使用过哪些早期指标？

Jensen Huang: All kinds. I saw a paper. Long before I saw the paper, I met some people that needed my help on this thing called deep learning. At the time, I didn’t know what deep learning was. And they needed us to create a domain-specific language so that all of their algorithms could be expressed easily on our processors. And we created this thing called [Ku-DNN]. And it’s essentially the [SQL]. SQL is in-storage computing. This is neural-network computing, and we created a language, if you will, domain-specific language from that, kind of like the open GL of deep learning.

黄仁勋：各种各样。我看到了一篇论文。在看到这篇论文之前很久，我遇到了一些需要我帮助的人，他们在做一种叫做深度学习的东西。当时，我不知道深度学习是什么。他们需要我们创建一种领域特定语言，以便他们所有的算法可以在我们的处理器上轻松表达。于是我们创建了这个叫做[Ku-DNN]的东西。它本质上就是[SQL]。SQL 是存储计算。这是神经网络计算，我们从中创建了一种语言，可以说是领域特定语言，有点像深度学习的 OpenGL。

So, they needed us to do that so that they could express their mathematics. And they didn’t understand KUDO, but they understood the deep learning. So, we created this thing in the middle for them. And the reason why we did it was because these researchers had no money. This is kind of one of the great skills of our company, that you’re willing to do something even though the financial returns are completely non-existent or maybe very, very far out, even if you believed in it.

所以，他们需要我们这样做，这样他们就可以表达他们的数学。他们不理解 KUDO，但他们理解深度学习。所以，我们为他们创建了这个中间的东西。我们这样做的原因是因为这些研究人员没有钱。这是我们公司的一项伟大技能之一，即使财务回报完全不存在，甚至可能非常遥远，即使你相信它，你也愿意去做一些事情。

We ask ourselves, “Is this worthy work to do? Does this advance a field of science somewhere that matters?” Notice, this is something that I’ve been talking about since the very beginning of time. We find inspiration, not from the size of a market, but from the importance of the work, because the importance of the work is the early indicators of a future market. Nobody has to do a business case on it. Nobody has to show me a [PNL]. Nobody has to show me a financial forecast. Th only question is, “Is this important work?” And if we didn’t do it, would it happen without us?” Now if we didn’t do something and something could happen without us, it gives me tremendous joy, actually.

我们问自己，“这项工作值得做吗？这是否推动了某个重要的科学领域？”请注意，这是我从一开始就在谈论的事情。我们的灵感来自工作的重要性，而不是市场规模，因为工作的重要性是未来市场的早期指标。没有人必须对此做商业案例。没有人必须向我展示[PNL]。没有人必须向我展示财务预测。唯一的问题是，“这项工作重要吗？”如果我们不做，会不会有其他人做？现在，如果我们不做某事，而某事可以在没有我们的情况下发生，实际上这让我感到非常高兴。

The reason for that is — could you imagine — the world got better, you didn’t have to lift a finger? That’s the definition of ultimate laziness. And in a lot of ways, you want that habit. And the reason for that is this — you want the company to be lazy about doing things that other people always do, can do. If somebody else can do it, let them do it. We should go select the things that if we didn’t do it, the world would fall apart. You have to convince yourself of that, “If I don’t do this, it won’t get done.” If that work is hard, and that work is impactful and important then it gives you a sense of purpose. Does that make sense? And so, our company has been selecting these projects. Deep learning was just one of them. And the first indicator of the success of that was this fuzzy cat that Andrew [Ang] came up with, and then Alex [Korchevsky] detected cats, not all the time, but successfully enough that it was, “This might take us somewhere.” And then we reasoned about the structure of deep learning, and we’re computer scientists, and we understand how things work. So, we convinced ourselves this could change everything. Anyhow, that’s an example.

这是因为——你能想象吗——世界变得更好，你无需动一根手指？这就是终极懒惰的定义。在很多方面，你希望养成这种习惯。而这背后的原因是——你希望公司懒得去做其他人总是做、能够做的事情。如果别人能做，就让他们去做。我们应该选择那些如果我们不做，世界就会崩溃的事情。你必须说服自己，“如果我不做这个，它就不会完成。”如果那项工作很艰难，而且具有影响力和重要性，那么它会给你一种目标感。这有道理吗？因此，我们的公司一直在选择这些项目。深度学习只是其中之一。成功的第一个指标是安德鲁（Ang）提出的这只模糊的猫，然后亚历克斯（Korchevsky）成功地检测到猫，虽然不是每次都成功，但足够成功，让我们觉得“这可能会带我们走向某个地方”。然后我们推理深度学习的结构，我们是计算机科学家，我们了解事物是如何运作的。因此，我们说服自己这可能会改变一切。总之，这就是一个例子。

不够夸张、不够奇怪就不能说话，类似于KTV里想引起漂亮姑娘的注意。

Shantam Jain: So these selections that you’ve made, they’ve paid huge dividends both literally and figuratively. But you’ve had to steer the company through some very challenging times, like when it lost 80 percent of its market cap amid the financial crisis because Wall Street didn’t believe in your bet on ML. In times like these, how do you steer the company and keep the employees motivated at the task at hand?

Shantam Jain：所以您所做的这些选择，无论是从字面上还是从象征意义上来说，都带来了巨大的回报。但在公司遭遇金融危机时，由于华尔街不相信您对机器学习的押注，公司市值蒸发了 80％。在这样的时刻，您是如何引领公司并保持员工对手头任务的积极性的？

Jensen Huang: My reaction during that time is the same reaction I had about this week. Earlier today, you asked me about this week. My pulse was exactly the same. This week is no different than last week or the week before that. So, the opposite of that, when you drop 80 percent, don’t get me wrong, when your share price drops 80 percent, it’s a little embarrassing, okay? You just want to wear a T-shirt that says, “It wasn’t my fault.”

黄仁勋：那段时间我的反应和这周的反应是一样的。今天早些时候，你问我这周的情况。我的脉搏完全一样。这周和上周或之前的周没有任何不同。所以，相反的是，当你跌了 80%，别误会，当你的股价下跌了 80%，有点尴尬，好吗？你只想穿一件写着“这不是我的错”的 T 恤。

But even more than that, you don’t want to get out of your bed, you don’t want to leave the house. All of that is true. But then you go back to just doing your job. I woke up at the same time. I prioritized my day in the same way. I go back to, “What do I believe?” You’ve got to gut check; always gut check back to the core — what do you believe? What are the most important things? Just check them off. Sometimes it’s helpful — family loves me? Okay, check, double check, right? So, you’ve just got to check it off, then you go back to your core and then go back to work. And then every conversation goes back to the core, keep the company focused back on the core. Do you believe in it? Did something change? The stock price changed, but did something else change? Did physics change? Did gravity change? Did all of the things that we assumed that we believed that led to our decision, did any of those things change? Because if those things changed, you’ve got to change everything. But if none of those things changed, you change nothing, keep on going. That’s how you do it.

但更重要的是，你不想离开床，不想离开家。这一切都是真实的。但然后你回到了只是在做你的工作。我在同样的时间醒来。我以同样的方式安排了我的一天。我回到，“我相信什么？”你必须审视内心；始终审视核心 — 你相信什么？什么是最重要的事情？逐一核对。有时候很有帮助 — 家人爱我？好的，核对，再核对，对吧？所以，你只需核对，然后回到你的核心，然后回到工作。然后每一次对话都回到核心，让公司专注于核心。你相信它吗？有什么改变吗？股价变了，但其他什么改变了吗？物理学改变了吗？重力改变了吗？我们假定的那些我们相信的事情，导致我们的决定，有哪些改变了？因为如果这些事情改变了，你必须改变一切。但如果这些事情都没有改变，你什么都不改变，继续前进。这就是你应该做的方式。

平常心的定义是面对问题时能排除心理上的干扰，回到事物的源头思考问题，这段描述里只谈到事物的源头，没有讲心理上的干扰，是不是不存在？有些怀疑。

Shantam Jain: In speaking with your employees, they say that —

尚坦姆·詹恩：在与您的员工交谈时，他们说——

Jensen Huang: And try to avoid the public.

黄仁勋：尽量避免公开露面。

Shantam Jain: [Laughs] In speaking with your employees, they’ve said that your leadership is —

Shantam Jain：[笑] 与您的员工交谈时，他们说您的领导能力是 —

Jensen Huang: Including the employees. I’m just kidding.

黄仁勋：包括员工在内。我只是开玩笑。

[Laughter] 【笑声】

Jensen Huang: Leaders have to be seen, unfortunately. That’s the hard part. I was an electrical engineering student, and I was quite young when I went to school. When I went to college, I was still 16 years old, so I was young when I did everything. So I was a bit of an introvert. I’m shy. I don’t enjoy public speaking. I’m delighted to be here. I’m not suggesting that. But it’s not something that I do naturally. So, when things are challenging, it’s not easy to be in front of precisely the people that you care most about. And the reason for that is because could you imagine a company meeting with just our stock prices dropped by 80 percent? And the most important thing I have to do as the CEO is this, to come and face you, explain it. Partly, you’re not sure why. Partly, you’re not sure how long, how bad. You just don’t know these things. But you’ve still got to explain it, face all these people and you know what they’re thinking. Some of them were probably thinking, “We’re doomed.” Some people are probably thinking, “You’re an idiot.” And some people are probably thinking something else. So, there are a lot of things that people are thinking, and you know that they’re thinking those things, but you still have to get in front of them and do the hard work.

黄仁勋：领导者必须被看到，不幸的是。这是困难的部分。我是一个电气工程学生，当我上学时还很年轻。上大学时，我还只有 16 岁，所以我在做任何事情时都很年轻。所以我有点内向。我很害羞。我不喜欢公开演讲。我很高兴能在这里。我不是在暗示。但这并不是我自然而然会做的事情。所以，当事情变得困难时，在你最在乎的人面前站在那里并不容易。原因是因为你能想象一下，公司开会时，我们的股价下跌了 80%？作为首席执行官，我必须做的最重要的事情之一就是，来面对你们，解释这个情况。部分原因是你不确定为什么。部分原因是你不确定会持续多久，有多糟糕。你只是不知道这些事情。但你仍然必须解释，面对所有这些人，你知道他们在想什么。他们中的一些人可能在想，“我们完蛋了。”有些人可能在想，“你是个白痴。”还有一些人可能在想其他事情。所以，人们有很多想法，你知道他们在想什么，但你仍然需要站在他们面前，做好艰苦的工作。

Shantam Jain: Maybe you can give those things, but yet not a single person of your leadership team left during times like this.

Shantam Jain：也许你可以提供那些东西，但在这样的时刻，你的领导团队中没有一个人离开。

Jensen Huang: Unemployable.

黄仁勋：不可雇用。

That’s what I keep reminding them.

这就是我不断提醒他们的事情。

I’m just kidding. I’m surrounded by geniuses, utter geniuses, unbelievable. NVIDIA is well-known to have singularly the best management team on the planet. This is the deepest technology management team the world’s ever seen. I’m surrounded by a whole bunch of them, and they’re just geniuses — business teams, marketing teams, sales teams, and it’s just incredible — engineering teams, research teams, unbelievable.

我只是在开玩笑。我被天才包围，绝对的天才，难以置信。 NVIDIA 众所周知在全球拥有最优秀的管理团队。这是世界上有史以来最深厚的技术管理团队。我被一群他们包围，他们只是天才 — 商业团队、营销团队、销售团队，简直令人难以置信 — 工程团队、研发团队，令人难以置信。

Shantam Jain: Your employees say that your leadership style is very engaged. You have 50 direct reports. You encourage people across all parts of the organization to send you the top five things on their mind. And you constantly remind people that, “No task is beneath you.” Can you tell us why you’ve purposefully designed such a flat organization? And how should we be thinking about our organizations that we design in the future?

Shantam Jain：您的员工表示，您的领导风格非常积极。您有 50 名直接下属。您鼓励组织各个部分的人向您发送他们心中的前五件事。您不断提醒人们，“没有任何任务是您看不起的。”您能告诉我们为什么您特意设计了这样一个扁平的组织结构吗？我们应该如何思考我们未来设计的组织？

Jensen Huang: To me, no task is beneath me because, remember, I used to be a dishwasher, and I mean that. I used to clean toilets. I’ve cleaned a lot of toilets. I’ve cleaned more toilets than all of you combined, and some of them you just can’t unsee.

黄仁勋：对我来说，没有任何任务是低贱的，因为请记住，我曾经是一个洗碗工，我是认真的。我曾经清洁过厕所。我清洁过很多厕所。我清洁过的厕所比你们所有人加起来的还要多，而且有些厕所你们是无法忘记的。

I don’t know what to tell you. That’s life. So, you can’t show me a task that’s beneath me. I’m not doing it only because of whether it’s beneath me or not beneath me. If you send me something and you want my input on it and I can be of service to you and in my review of it, share with you how I reasoned through it, I’ve made a contribution to you. I’ve made I possible to see how I reason through something. And by reasoning, as you know, how someone reasons through something empowers you. You go, “Oh my gosh. That’s how you reason through something like this.” It’s not as complicated as it seems. This is how you reason through something that’s super ambiguous. This is how you reason through something that’s incalculable. This is how you reason through something that seems to be very scary. Do you understand?

我不知道该告诉你什么。那就是生活。所以，你不能给我展示一项对我来说低人一等的任务。我做或不做，并不仅仅是因为它是否对我来说低人一等。如果你给我发来一些东西，并希望我对此提出建议，我可以为你提供服务，并在审查中与你分享我的推理过程，那么我已经为你做出了贡献。我让你看到了我如何推理的可能性。而正如你所知，了解某人如何推理某事会让你更有能力。你会说，“天啊。原来你是这样推理这种事情的。”这并没有看起来那么复杂。这就是你如何推理一些超级模糊的事情。这就是你如何推理一些无法计算的事情。这就是你如何推理一些看起来非常可怕的事情。你明白吗？

这里也可能暗示着Jensen Huang的团队缺少自己的想法，需要有人通过嘴对嘴的方式喂。

So, I show people how to reason through things all the time — strategy things, how to forecast something, how to break a problem down, and you’re just empowering people all over the place. And so that’s how I see it. If you send me something and you want me to help review it, I’ll do my best, and I’ll show you how I would do it.

所以，我一直在向人们展示如何推理事物 - 战略事务，如何预测某事，如何分解问题，你只是在各个地方赋予人们力量。这就是我看待事物的方式。如果你给我发送了某事并希望我帮助审查，我会尽力而为，并向你展示我会如何做。

In the process of doing that, of course, I learned a lot from you. Is that right? You gave me a seed of a lot of information. I learned a lot, and so I feel rewarded by the process.

在这个过程中，当然，我从你那里学到了很多。是这样吗？你给了我很多信息的种子。我学到了很多，所以我觉得这个过程是有回报的。

It does take a lot of energy sometimes because in order to add value to somebody and they’re incredibly smart as a starting point and I’m surrounded by incredibly smart people, you have to at least get to their plane, you know? You have to get into their headspace. And that’s really hard, and that takes just an enormous amount of emotional and intellectual energy, and so I feel exhausted after I work on things like that.

有时确实需要耗费大量精力，因为为了给某人增加价值，而他们本身就非常聪明，而我周围都是非常聪明的人，你至少要达到他们的水平，明白吗？你得进入他们的思维空间。这真的很困难，需要巨大的情感和智力能量，所以在处理这类事情后我感到筋疲力尽。

I’m surrounded by a lot of great people. CEOs should have the most of the reports by definition because the people that reports to the CEO requires the least amount of management. It makes no sense to me that CEOs have so few people reporting to them except for one fact that I know to be true. The knowledge, the information of a CEO is supposedly so valuable, so secretive, you can only share it with two other people or three.

我身边有很多优秀的人。CEO 应该根据定义拥有大部分报告，因为向 CEO 汇报的人需要最少的管理。对我来说，CEO 只有很少的人向其汇报是毫无意义的，除非有一个我知道是真实的事实。CEO 的知识、信息据说是如此宝贵、如此秘密，只能与其他两个或三个人分享。

And their information is so invaluable, so incredibly secretive that they can only share it with a couple more. Well, I don’t believe in a culture, in an environment where the information that you possess is the reason why you have power. I would like us all to contribute to the company. And our position in the company should have something to do with our ability to reason through complicated things, lead other people to achieve greatness, inspire, empower other people, support other people. Those are the reasons why the management team exists, in service of all of the other people that work at the company, to create the conditions by which all of these amazing people volunteer to come work for you instead of all of the other amazing, high-tech companies around the world. They elected, they volunteered to work for you. And so you should create the conditions by which they can do their life’s work, which is my mission.

他们的信息是如此宝贵，如此隐秘，以至于他们只能与另外几个人分享。嗯，我不相信在一个信息就是权力源泉的文化、环境中。我希望我们大家都能为公司做出贡献。我们在公司的位置应该与我们理解复杂事物的能力、带领他人取得伟大成就、激励、赋予他人权力、支持他人有关。这些是管理团队存在的原因，为了服务于公司所有其他工作人员，创造条件，让所有这些了不起的人自愿来为你工作，而不是去世界各地的其他了不起的高科技公司。他们选择了，他们自愿为你工作。因此，你应该创造条件，让他们能够做他们一生的事业，这是我的使命。

You probably heard it. I’ve said it pretty clearly, and I believe that. What my job is is very simply to create the conditions by which you can do your life’s work. So, how do I do that? What does that condition look like? Well, that condition should result in a great deal of empowerment. You can only be empowered if you understand the circumstance; isn’t that right? You have to understand the context of the situation you’re in in order for you to come up with great ideas. And so, I have to create a circumstance where you understand the context, which means you have to be informed. And the best way to be informed is for there to be as little layers of information mutilation, right, between us. And so that’s the reason why it’s very often that I’m reasoning through things like in an audience like this. I say, first of all, these are the beginning facts. These are the data that we have. This is how we reason through it. These are some of the assumptions. These are some of the unknowns. These are some of the knowns. So, you reason though it.

你可能听过了。我说得很清楚，我相信这一点。我的工作非常简单，就是创造让你能够完成一生事业的条件。那么，我该如何做到呢？那种条件是什么样的呢？嗯，那种条件应该会带来很大的赋权。只有当你了解情况时，你才能获得赋权，对吧？你必须了解所处情况的背景，才能提出伟大的想法。因此，我必须创造一个让你了解背景的情况，这意味着你必须得到充分的信息。而获得充分信息的最佳方式就是我们之间的信息层次尽可能少地被扭曲。这就是为什么我经常像在这样的观众面前推理事情的原因。我会说，首先，这是一些基本事实。这是我们拥有的数据。这是我们推理的方式。这是一些假设。这是一些未知因素。这是一些已知因素。所以，你要通过推理来理解。

这样的公司看着很难传承。

Now you’ve created an organization that’s highly empowered. NVIDIA is 30,000 people. We’re the smallest large company in the world. We’re a tiny little company. But every employee is so empowered, and they’re making smart decisions on my behalf every single day. And the reason for that is because they understand my condition. I’m very transparent with people. And I believe that I can trust you with the information.

现在您已经创建了一个非常授权的组织。NVIDIA 有 30,000 名员工。我们是世界上最小的大公司。我们是一个微小的公司。但每个员工都非常授权，他们每天都在为我做出明智的决定。这是因为他们了解我的情况。我对人们非常透明。我相信我可以信任您处理这些信息。

Oftentimes, the information is hard to hear, and the situations are complicated, but I trust that you can handle it. A lot of people hear me say, “You’re adults here. You can handle this.” Sometimes they’re not really adults, and they just graduated. I’m just kidding. I know that when I first graduated, I was barely an adult. But I was fortunate that I was trusted with important information. So, I want to do that. I want to create the conditions for people to do that.

往往信息难以接受，情况复杂，但我相信你能处理好。很多人听我说，“你们都是成年人了。你们能处理这个问题。”有时候他们并不是真正的成年人，他们刚刚毕业。我只是开玩笑。我知道当我刚毕业时，我几乎还不算是成年人。但我很幸运，被信任处理重要信息。所以，我也想这样做。我想创造条件让人们能够做到这一点。

Shantam Jain: I do want to now address the topic that is on everybody’s mind, AI. Last week, you said that generative AI and accelerated computing have hit the tipping point. So as this technology becomes more mainstream, what are the applications that you personally are most excited about.

Shantam Jain：我现在想要谈谈大家都在关注的话题，人工智能。上周，您说生成式人工智能和加速计算已经达到了临界点。随着这项技术变得更加普及，您个人最感兴奋的应用是什么。

Jensen Huang: Well, you have to go back to first principles and ask yourself, “What is generative AI? What happened?” What happened was we now have the ability to have software that can understand something. First of all, we digitized everything. Like, for example, gene sequencing — digitized genes. But what does it mean? That sequence of genes, what does it mean? We’ve digitized amino acids, but what does it mean?

黄仁勋：嗯，你必须回到第一原则，问自己，“生成式人工智能是什么？发生了什么？”发生的是我们现在有能力拥有能够理解事物的软件。首先，我们将一切数字化了。比如，基因测序——基因数字化了。但这意味着什么？那些基因序列，意味着什么？我们数字化了氨基酸，但这意味着什么？

So, we now have the ability to digitize words. We digitize sounds. We digitize images and videos. We digitize a lot of things. But what does it mean? We now have the ability through a lot of study and a lot of data and from patterns in relationships, we now understand what they mean. Not only do we understand what they mean, we can translate between them because we learned about the meaning of these things in the same world; we didn’t learn about them separately. So, we learned about speech and words and paragraphs and vocabulary in the same context. So, we’ve found correlations between them, and they’re all registered, if you will, registered to each other.

所以，我们现在有数字化文字的能力。我们数字化声音。我们数字化图像和视频。我们数字化许多东西。但这意味着什么呢？通过大量研究、大量数据以及关系模式，我们现在理解它们的含义。我们不仅理解它们的含义，还可以在它们之间进行翻译，因为我们在同一个世界中了解了这些事物的含义；我们并没有分开学习它们。所以，我们在相同的语境中学习了语音、文字、段落和词汇。因此，我们发现了它们之间的相关性，并且它们都被登记，可以相互关联。

And so now not, only do we understand the meaning of each modality, we can understand how to translate between them. And so for obvious things, you could caption video to text; that’s captioning, text two images [mid journey], text-to-text, Chat GPT, amazing things. And so we now know that we understand meaning, and we can translate. The translation of something is generation of information. And all of a sudden, you have to take a step back and ask yourself, “What is the implication in every single layer of everything that we do?” So, I’m exercising in front of you, I’m reasoning in front of you, the same thing I did 15 years ago when I first saw Alex some 13, 14 years ago.

因此，现在我们不仅理解每种模态的含义，还能理解如何在它们之间进行翻译。对于显而易见的事情，您可以给视频加上字幕；这就是字幕，文本转图像[中途旅程]，文本到文本，Chat GPT，令人惊叹的事情。因此，我们现在知道我们理解含义，我们可以进行翻译。某物的翻译是信息的生成。突然之间，您必须退后一步，问自己，“我们所做的每一个层面的一切都意味着什么？”所以，我在你们面前锻炼，我在你们面前推理，就像我 13、14 年前第一次见到 Alex 时所做的事情一样。

How I reasoned through it, what did I see? How interesting. What can it do? Very cool. But then, most importantly, what does it mean? What does it mean to every single layer of computing because we’re in a world of computing. So, what it means is that the way that we process information fundamentally will be different in the future. That’s when NVIDIA builds chips and systems. The way we write software will be fundamentally different in the future. The type of software we’ll be able to write in the future will be different, new applications. And then also the processing of those applications will be different. What was historically a retrieval-based model where information was prerecorded, if you will, almost. We wrote the text, prerecorded, and we retrieved it based on some recommender system algorithm. In the future, some seed of information will be the starting point. We call them prompts, as you guys know, and then we generate the rest of it. And so, the future of computing will be highly generated.

我是如何通过推理的，我看到了什么？多有趣。它能做什么？非常酷。但最重要的是，这意味着什么？对每一层计算意味着什么，因为我们处在一个计算的世界中。所以，它的意义在于我们未来处理信息的方式将会有根本的不同。这就是为什么英伟达会构建芯片和系统。我们未来编写软件的方式将会有根本的不同。我们未来能够编写的软件类型将会不同，是新的应用程序。而且这些应用程序的处理方式也将不同。在历史上，信息检索模型是基于事先记录的，如果你愿意这么说的话。我们编写文本，预先记录，然后根据某种推荐系统算法进行检索。在未来，信息的某种种子将成为起点。我们称之为提示，正如你们所知，然后我们生成其余部分。因此，计算的未来将是高度生成的。

未来会不会如此？没有人知道，当下chatGPT降低了很多事的难度，比如，编程，很多企业可以更有效率的提升自己的软件系统。

Well let me give you an example of what’s happening. For example, we’re having a conversation right now. Very little of the information I’m conveying to you is retrieved. Most of it is generated. It’s called intelligence. So, in the future, we’re going to have a lot more generative — our computers will perform in that way. It’s going to be highly generative instead of highly retrieval-based.

让我举个例子来说明正在发生的事情。例如，我们现在正在进行一次对话。我传达给你的信息很少是检索到的。大部分是生成的。这就是智能。因此，在未来，我们将会有更多生成式的——我们的计算机将以这种方式运行。它将是高度生成式的，而不是高度基于检索的。

Then you go back and you’re going to ask yourself — now for entrepreneurs you’re going to ask yourself what industries will be disrupted? Therefore, will we think about networking the same way? Will we think about storage the same way? Will we be as abusive of Internet traffic as we are today? Probably not. Notice we’re having a conversation right now, and I don’t have to get in my car every question. So, we don’t have to be as abusive of transformation/information/transporting as we used to.

然后你回去，你会问自己 — 现在对于企业家来说，你会问自己哪些行业会被颠覆？因此，我们会以同样的方式思考网络吗？我们会以同样的方式思考存储吗？我们会像今天这样滥用互联网流量吗？可能不会。请注意，我们现在正在进行对话，我不必每个问题都要上车。因此，我们不必像过去那样滥用转变/信息/传输。

What’s going to be more? What’s going to be less? What kind of applications, etcetera, etcetera? So, you can go through the entire industrial spread and ask yourself what’s going to get disrupted, what’s going to be different, what’s going to get [new], so on and so forth.

什么会增加？什么会减少？什么样的应用程序等等？因此，您可以浏览整个产业范围，并问自己什么会受到干扰，什么会有所不同，什么会得到[新的]等等。

And that reasoning starts from what is happening? What is generative AI? Foundationally, what is happening? Go back to first principles with all things. There was something I was going to tell you about organization. You asked the question, and I forgot to answer it. The way you create an organization by the way someday, don’t worry about how other companies’ org charts look. You start from first principles. Remember what an organization is designed to do.

那种推理是从发生了什么开始的？什么是生成式人工智能？基本上，发生了什么？回到一切的第一原则。我有件事要告诉你关于组织。你问了这个问题，我忘了回答。创造组织的方式，有一天，不要担心其他公司的组织结构是什么样的。你要从第一原则开始。记住组织的设计目的是什么。

The organizations of the past, there’s a king/CEO, and then you have all the royal subjects, the royal court and then east out. And then you keep working your way down. Eventually, they’re employees. The reason why it was designed that way is because they wanted the employees to have as little information as possible because their fundamental purpose of the soldiers is to die in the field of battle, to die without asking questions. You guys know this.

过去的组织结构中，有一个国王/首席执行官，然后是所有的皇家臣民，皇家法庭，然后是东方。然后你继续往下走。最终，他们是雇员。设计成这样的原因是因为他们希望员工尽可能少地了解信息，因为士兵的根本目的是在战场上牺牲，无需提问。你们知道这一点。

这种宏大叙事总是跟某种文化相关。

I only have 30,000 employees. I would like none of them to die. I would like them to question everything. Does that make sense? And so the way you organized in the past and the way you organize today is very different.

我只有 30,000 名员工。我不希望他们中任何人死去。我希望他们质疑一切。这有道理吗？因此，你过去的组织方式和今天的组织方式是非常不同的。

Second, the question is what does NVIDIA build? An organization is designed so that we can build whatever it is we build better. And so if we all build different things, why are we organized the same way? Why would this organizational machinery be exactly the same irrespective of what you built? It doesn’t make any sense. You build computers, you organize this way. You build healthcare services, you build exactly the same way. It makes no sense whatsoever. So you had to go back to first principles, just ask yourself, “What kind of machinery? What is the input? What is the output? What are the properties of this environment? What is the forest that this animal has to live in? What are the characteristics? Is it stable most of the time? Are you trying to squeeze out the last drop of water or is it changing all the time, being attacked by everybody?”

其次，问题是英伟达在建造什么？一个组织被设计成我们可以更好地建造任何我们要建造的东西。如果我们都在建造不同的东西，为什么我们要以相同的方式组织起来？无论你建造什么，为什么这个组织机构都要完全相同？这毫无意义。你建造计算机，你以这种方式组织。你建造医疗服务，你也以完全相同的方式建造。这完全没有任何意义。所以你必须回到最基本的原则，问问自己，“这是什么样的机制？输入是什么？输出是什么？这个环境的特性是什么？这个动物必须生活在什么样的森林中？特点是什么？它大部分时间是稳定的吗？你是在试图挤出最后一滴水，还是它一直在变化，受到所有人的攻击？”

So you’ve got to understand, you’re the CEO. Your job is to architect this company. That’s my first job, to create the conditions by which you can do your life’s work, and the architecture has to be right, and so you have to go back to first principles and think about those things.

所以你必须明白，你是首席执行官。你的工作是设计这家公司。这是我的第一要务，创造让你能够完成一生事业的条件，而架构必须正确，因此你必须回到最基本的原则，思考这些事情。

I was fortunate that when I was 29 years old, I had the benefit of taking a step back and asking myself, “How would I build this company for the future and what would it look like? What’s the operating system, which is called culture? What kind of behavior do we encourage, enhance, and what do we discourage and not enhance and so on and so forth? Anyways.

我很幸运，在 29 岁时有机会退后一步，问自己：“我要如何为未来打造这家公司，它会是什么样子？什么是操作系统，也就是文化？我们鼓励什么样的行为，加强什么，又会阻止什么，不加强什么等等？总之。

Shantam Jain: I want to save time for audience questions. But this year’s theme from you from the top is “Redefining Tomorrow.” And one question we’ve asked all of our guests is, Jensen, as the cofounder and CEO of NVIDIA, if you were to close your eyes and magically change one thing about tomorrow, what would it be?

Shantam Jain：我想为观众提问节省时间。但今年您从顶层提出的主题是“重新定义明天”。我们问过所有嘉宾的一个问题是，Jensen，作为英伟达的联合创始人兼首席执行官，如果您闭上眼睛，神奇地改变明天的一件事，那会是什么？

Jensen Huang: Were we supposed to think about this in advance?

黄仁勋：我们应该提前考虑这个吗？

[Laughter] 【笑声】

Jensen Huang: I’m going to give you a horrible answer. I don’t know that it’s one thing. Look, there are a lot of things that we don’t control. There are a lot of things we don’t control. Your job is to make a unique contribution. Live a life of purpose, to do something that nobody else in the world would do or can do, to make a unique contribution so that in the event that after you are done, everybody says the world was better because you were here. So, I think, to me, I live my life kind of like this. I go forward in time, and I look backwards. So, you asked me a question that’s exactly from a computer vision pose perspective, exactly the opposite of how I think. I never look forward from where I am. I go forward in time and look backwards. And the reason for that is it’s easier. I would look backwards and kind of read my history. We did this and we did it that way and we [unintelligible] that problem down. Does that make sense?

黄仁勋：我要给你一个糟糕的答案。我不知道这是一件事。看，有很多事情是我们无法控制的。有很多事情是我们无法控制的。你的工作是做出独特的贡献。过有意义的生活，做一些世界上其他人不会做或无法做的事情，做出独特的贡献，以至于在你完成后，每个人都会说世界因为有你在这里而变得更好。所以，我认为，对我来说，我过着这样的生活。我向前走，然后回头看。所以，你问我的问题正好是从计算机视觉的角度来看，完全与我的思维方式相反。我从不从我所在的位置向前看。我向前走，然后回头看。这样做的原因是更容易。我会回头看，然后回顾我的历史。我们做了这个，我们以那种方式做了，我们解决了那个问题。这样说得通吗？

为了特别而特别。

So, it’s a little bit like how you guys solve problems. You figured out what is the end result that you’re looking for and you work backwards to achieve it. I imagine NVIDIA making a unique contribution to advancing the future of computing, which is the single most important instrument of all humanity. Now it’s not about our self-importance, but this is just what we’re good at, and it’s incredibly hard to do. And we believe we can make an absolute unique contribution. It’s taken us 31 years to be here, and we’re still just beginning our journey, and so this is insanely hard to do.

所以，这有点像你们解决问题的方式。你们找出了你们想要的最终结果，然后向后努力实现它。我想象 NVIDIA 对推动计算机未来做出独特贡献，这是所有人类最重要的工具。现在不是关于我们的自我重要性，而是这正是我们擅长的，而且这非常难做到。我们相信我们可以做出绝对独特的贡献。我们花了 31 年的时间才走到这一步，我们仍然只是开始我们的旅程，所以这是极其困难的。

And when I look backwards, I believe that we’re going to be remembered as a company that kind of changed everything, not because we went out and changed everything through all the things that we said, but because we did this one thing that was insanely hard to do that we’re incredibly good at doing that we love doing, we did for a long time.

当我回顾过去时，我相信我们将被记住为一家改变一切的公司，不是因为我们通过所说的一切改变了一切，而是因为我们做了一件极其困难的事情，我们非常擅长并且热爱做的事情，我们做了很长一段时间。

极其困难又非常擅长，自相矛盾=自我消耗。

Female Voice: I’m part of the GSB lead. I graduated in 2023. So, my question is, how do you see your company in the next decade as what challenges do you see your company would face and how you are positioned for that?

女性声音：我是 GSB 的一员。我在 2023 年毕业。所以，我的问题是，您如何看待您的公司在未来十年内会面临什么挑战，以及您如何为此做好准备？

Jensen Huang: First of all, can I just tell you what’s going on through my head? As you say what challenges, the list that flew by my head was so large that I was trying to figure out what to select. Now the honest truth is that when you asked that question, most of the challenges that showed up for me were technical challenges. And the reason for that is because that was my morning. If you had chosen yesterday, it might have been market creation challenges. There were some markets that, gosh, I just desperately would love to create. Can’t we just do it already? But we can’t do it alone. NVIDIA is a technology platform company. We’re here in service of a whole bunch of other companies so that they could realize, if you will, our hopes and dreams through them.

黄仁勋：首先，我能告诉你我脑海中正在发生的事情吗？当你提到挑战时，我脑海中闪过的清单如此之长，以至于我在努力想要选择什么。现在坦率地说，当你问这个问题时，对我而言出现的大多数挑战都是技术挑战。原因是因为那是我的早晨。如果你选择昨天，可能会是市场创造挑战。有一些市场，天哪，我真的非常希望能够创造。我们难道就不能立即做到吗？但我们不能独自做到。英伟达是一家技术平台公司。我们在这里为了一大群其他公司提供服务，以便他们能够实现我们的希望和梦想。

So, some of the things I would love, I would love for the world of biology to be at a point where it’s kind of like the world of chip design 40 years ago, computer-aided and designed, EDA that entire industry really made possible for us today. And I believe we’re going to make possible for them tomorrow. Computer-aided drug design — because we’re able to now represent genes and proteins and even cells now, very, very close to be able to represent and understand the meaning of a cell, combination of a whole bunch of genes. What does a cell mean? It’s kind of like, what does a paragraph mean? If we could understand a cell like we understand a paragraph, imagine what we could do.

所以，我希望的一些事情是，我希望生物学的世界能够达到类似于 40 年前芯片设计世界的水平，计算机辅助设计，EDA 整个行业确实为我们今天的可能性打下了基础。我相信我们将为他们明天的可能性打下基础。计算机辅助药物设计——因为我们现在能够表示基因、蛋白质甚至细胞，非常接近能够表示和理解细胞的含义，一大堆基因的组合。细胞意味着什么？这有点像段落意味着什么？如果我们能像理解段落一样理解细胞，想象一下我们可以做些什么。

So, I’m anxious for that to happen. I’m kind of excited about that. There are some that I’m just excited about that I know we’re around the corner on, for example, humanoid robotics. They’re very, very close around the corner. And the reason for that is because if you can tokenize and understand speech, why can’t you tokenize and understand manipulation? So these kind of computer science techniques, once you figure something out, you ask yourself, “Well, if I do that, why can’t I do that?” So I’m excited about those kinds of things. So that challenge is kind of a happy challenge.

所以，我急切希望这种情况发生。我对此感到有些兴奋。有一些事情让我感到兴奋，我知道我们即将迎来，比如人形机器人。它们就在不远处。之所以如此是因为如果你能对语音进行标记和理解，为什么你不能对操作进行标记和理解呢？所以这些计算机科学技术，一旦你弄清楚了某事，你会问自己，“嗯，如果我能做到这个，为什么我不能做到那个呢？”所以我对这些事情感到兴奋。所以这种挑战是一种愉快的挑战。

Some of the other challenges of course are industrial and geopolitical and they’re social, but you’ve heard all that stuff before. These are all true, you know? The social issues in the world, the geopolitical issues in the world, why can’t we just get along, things in the world, why do I have to say those kinds of things in the world? Why do we have to say those things and then amplify them in the world? Why do we have to judge people so much in the world? All those things, you guys all know that. I don’t have to say those things over again.

当然，其他一些挑战是工业和地缘政治以及社会问题，但你之前都听过这些。这些都是真实的，你知道吗？世界上的社会问题，世界上的地缘政治问题，为什么我们不能和睦相处，世界上的事情，为什么我要说这种事情？为什么我们要说这些事情然后在世界上放大它们？为什么我们要如此苛刻地评判他人？所有这些事情，你们都知道。我不必再重复这些事情。

Jose: My name’s Jose. I’m a Class of 2023 from GSB. My question is, are you worried at all about the pace at which we’re developing AI, and do you believe that any sort of regulation might be needed? Thank you.

何塞：我叫何塞。我是来自 GSB 的 2023 届学生。我的问题是，您是否担心我们发展人工智能的速度，以及您是否认为可能需要任何形式的监管？谢谢。

Jensen Huang: The answer is yes and no. You know the greatest breakthrough in modern AI, of course, deep learning, it enabled great progress. But another incredible breakthrough is something humans know and we practiced all the time, and we just invented it for language models called grounding — reinforcement learning to human feedback. I provide reinforcement learning human feedback every day. That’s my job. And for the parents in the room, you’re providing reinforcement learning human feedback all the time, okay? Now we just figured out how to do that at a systematic level for artificial intelligence.

黄仁勋：答案是肯定的，也是否定的。你知道，当然，现代人工智能的最大突破是深度学习，它带来了巨大的进步。但另一个令人难以置信的突破是一些人类熟知并且我们一直在实践的东西，我们刚刚为语言模型发明了它，称之为接地——强化学习到人类反馈。我每天都提供强化学习的人类反馈。那是我的工作。对于在座的父母们，你们一直在提供强化学习的人类反馈，明白吗？现在我们刚刚弄清楚如何在系统级别上为人工智能做到这一点。

There are a whole bunch of other technologies necessary to guardrail, finetune, ground, for example, how do I generate tokens that obey the laws of physics? Right now, things are floating in space and doing things, and they don’t obey the laws of physics. That requires technology. Guard-railing requires technology. Finetuning requires technology. Alignment requires technology. Safety requires technology. The reason why planes are so safe is because all of the autopilot systems are surrounded by diversity and redundancy and all kinds of different functional safety and active safety systems that were invented.

有许多其他技术是必要的，例如护栏、微调、地面，例如，如何生成遵守物理定律的令牌？现在，事物在空间中漂浮并执行操作，它们不遵守物理定律。这需要技术。护栏需要技术。微调需要技术。对齐需要技术。安全需要技术。飞机之所以如此安全，是因为所有自动驾驶系统都被多样性和冗余以及各种不同的功能安全和主动安全系统所包围，这些系统是被发明出来的。

I need all of that to be invented much, much faster. You also know that the border between cybersecurity and artificial intelligence is going to become blurrier and blurrier, and we need technology to advance very, very quickly in the area of cybersecurity in order to protect us from artificial intelligence. So, in a lot of ways, we need technology to go faster, a lot faster.

我需要所有这些发明得更快，快得多。你也知道网络安全和人工智能之间的边界会变得越来越模糊，我们需要技术在网络安全领域快速发展，以保护我们免受人工智能的侵害。因此，在很多方面，我们需要技术发展得更快，快得多。

Regulation — there are two types of regulation. There’s social regulation; I don’t know what to do about that. But there’s product and services regulation; I know exactly what to do about that. So the FAA, the FDA, [NTSA], you name it, all the F’s and all the N’s and the FCCs, they all have regulations for products and services that have particular use cases, bar exams and doctors and so on and so forth. You all have qualification exams. You all have standards that you have to reach. You all have to continuously be certified, accountants and so on and so forth. Whether it’s a product or a service, there are lots and lots of regulations. Please do not add a super regulation that cuts across. The regulator who’s regulating accounting should not be the regulator that regulates a doctor.

规定 - 有两种类型的规定。有社会规定；我不知道该怎么办。但有产品和服务规定；我对此了如指掌。所以联邦航空局，食品药品监督管理局，[NTSA]，你说的，所有的 F 和所有的 N 以及联邦通信委员会，它们都对具有特定用途的产品和服务制定规定，例如律师考试、医生等等。你们都有资格考试。你们都有必须达到的标准。你们都必须持续获得认证，比如会计师等等。无论是产品还是服务，都有许许多多的规定。请不要制定横跨各领域的超级规定。监管会计的监管者不应该是监管医生的监管者。

I love accountants, but if I ever need open heart surgery, the fact that they can close books is interesting, but not sufficient. So I would like all of those fields that already have products and services to also enhance their regulations in the context of AI. But I left out this one very big one, which is the social implication of AI, and how do you deal with that? I don’t have great answers for that. But enough people are talking about it.

我喜欢会计师，但如果我需要接受心脏手术，他们会关账是有趣的，但并不足够。因此，我希望所有那些已经有产品和服务的领域也在人工智能的背景下加强监管。但我忽略了一个非常重要的问题，那就是人工智能的社会影响，以及如何应对？我对此没有很好的答案。但足够多的人正在讨论这个问题。

It’s important to subdivide all of this into chunks; does that make sense, so that we don’t become super-hyper-focused on this one thing at the expense of a whole bunch of routine things that we could have done, and as a result, people are getting killed by cars and planes. It doesn’t make any sense. We should make sure that we do the right things there, very practical things. May I take one more question?

将所有这些细分为块是很重要的；这样做有道理吗，这样我们就不会为了一个事情而变得过分专注，而忽视了我们本可以做的一大堆例行事务，结果是人们被汽车和飞机撞死。这毫无意义。我们应该确保在那里做正确的事情，非常实际的事情。我可以再回答一个问题吗？

Shantam Jain: Well, we have a set of rapid-fire questions for you as [unintelligible] [clinician].

Shantam Jain：好的，我们有一组针对您的快速提问，作为[不可理解] [临床医生]。

Jensen Huang: Okay. I was trying to avoid that.

黄仁勋：好的。我本来想避免这种情况的。

[Laughter] 【笑声】

Jensen Huang: All right. Fire away.

黄仁勋：好的。尽管问吧。

Shantam Jain: Well, your first job was at Denny’s. They now have a booth dedicated to you. What was your fondest memory of working there?

Shantam Jain：嗯，你的第一份工作是在 Denny's。现在他们有一个专门为你设立的展位。在那里工作，你最珍贵的回忆是什么？

Jensen Huang: My second job was AMD by the way. Is there a booth dedicated to me there? I’m just kidding.

黄仁勋：顺便说一下，我的第二份工作是在 AMD。那里有一个专门为我准备的展台吗？开玩笑的。

I loved my job there; I did. I loved it. It was a great company.

我喜欢我的工作；我真的喜欢。这是一家很棒的公司。

Shantam Jain: If there was a worldwide shortage of black leather jackets, what would we see you wearing?

Shantam Jain：如果全球黑色皮夹克短缺，你会穿什么？

Jensen Huang: No, I’ve got a large reservoir of black jackets.

黄仁勋：不，我有一大堆黑色夹克。

I’ll be the only person who is not concerned.

我将是唯一不担心的人。

Shantam Jain: You spoke a lot about textbooks. If you had to write one, what would it be called?

Shantam Jain：你谈了很多关于教科书的话题。如果你必须写一本，它会叫什么名字？

Jensen Huang: I wouldn’t write one.

黄仁勋：我不会写一个。

You’re asking me a hypothetical question that has no possibility of …

你问我一个毫无可能性的假设性问题…

Shantam Jain: That’s fair. Finally, if you could share one parting piece of advice to broadcast across Stanford, what would it be?

Shantam Jain：公平。最后，如果您能分享一条告别的建议广播到整个斯坦福，那会是什么？

Jensen Huang: It’s not a word, but have a core belief. Gut check it every day. Pursue it with all your might. Pursue it for a very long time. Surround yourself with people that you love, and take ‘em on that ride. So, that’s the story of NVIDIA.

黄仁勋：这不是一个词，而是一种核心信念。每天用直觉检验它。全力以赴追求它。长时间坚持追求它。让自己被你爱的人包围，一起踏上这段旅程。这就是英伟达的故事。

Shantam Jain: Jensen, this last hour has been a treat. Thank you for spending it with us.

Shantam Jain：詹森，过去的这一个小时真是一场盛宴。感谢你和我们共度时光。

Jensen Huang: Thank you very much.

黄仁勋：非常感谢。

Shantam Jain: You’ve been listening to View From The Top, the podcast, a production of Stanford Graduate School of Business. This interview was conducted by me, Shantam Jain, of the MBA Class of 2024. Lily Sloane composed our theme music. Michael Reilly and Jenny Luna produced this episode. Find this series on our YouTube channel or on our website at gsb.stanford.edu. Follow us on social media @stanfordgsb.

Shantam Jain：您正在收听《鸟瞰全局》播客，这是斯坦福大学商学研究生院的一档节目。本次访谈由 2024 年 MBA 班的我 Shantam Jain 主持。Lily Sloane 创作了我们的主题音乐。Michael Reilly 和 Jenny Luna 制作了本集节目。您可以在我们的 YouTube 频道或网站 gsb.stanford.edu 上找到这个系列节目。在社交媒体上关注我们@stanfordgsb。

2024-04-25 Jensen Huang.NVDA.How to Use First-Principles Thinking to Drive Decisions

2024-04-25 Jensen Huang.NVDA.How to Use First-Principles Thinking to Drive Decisions

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