Jensen Huang

We never talk about market share in our company. And the reason for that is because why are you talking about, I have 23 % market share and they have 27 % market share. Why are you fighting people for market share? Because the whole concept of market share says that there are a whole bunch of other people who are doing the same thing. And if they're doing the same thing, why are we doing it? Why am I squandering the lives of these incredibly talented people to go do something that's already been done? And so that's one way of thinking, to go do something that's never been done before.

The other way is to demonstrate that is to walk away from businesses that has been commoditized. Through our own initiative or otherwise, we've walked away from many businesses in the past. And so that demonstrates very clearly to your employees that we're not going to go do commodity work. And so the combination of choosing the right work and walking away from the wrong work, that is the best way to create the conditions.

In our company, the information is disseminated fairly quickly to a lot of different people. What are the trade -offs associated with that versus the traditional structure? The downside is the pressure on the leaders is fairly high. And the reason for that is because in a command and control system, the person who you report to has more power than you. And the reason why they have more power than you is because they're closer to the source of information than you are. For example, just now I was in our robotics meeting, and we're talking about certain things, and we're making some decisions, and there are new college grads in the room.

There's three vice presidents in the room. There's two e -staffs in the room. And at the moment that we decided together, we reasoned through some stuff. We made a decision. Everybody heard it exactly the same time. So nobody has more power than anybody else. The new college grad learned at exactly the same time as the e -staff. The executive staff and the leaders that work for me and myself, you earn the right to have your job based on your ability to reason through problems and helping other people succeed. And it's not because you have some privileged information that I knew the answer was 3.5. And 7, and only I knew.

Everybody knew.

And this is one of the concerns that I have about the doomers describing the end of work and killing of jobs. If we discourage people from being software engineers, we're going to run out of software engineers. And the same prediction 10 years ago, some of the doomers were saying that whatever you do, don't be a radiologist. Radiology is going to be the first career to go. The world's not going to need any more radiologists. Guess what we're short of? Radiologists. If we scare this country into thinking that AI is somehow a nuclear bomb so that everybody hates AI, if we scare everybody out of doing software engineering jobs because it's going to kill every software engineer job, and we don't have any software engineers as a result of that, we're doing a disservice to the United States.

We misunderstand the difference between the job and the task, the job of a radiologist, patient care, task, to read a scan. If we misunderstand that so profoundly, we're not going to have enough radiologists and good enough health care.

maybe you give feedback on something that you see somebody systematically not doing so well, again, conventionally supposed to do in the one -on -one. Do you not do those things, or do you do them in a different way? Really good question. I do it right there. I give you feedback right there in front of everybody. Feedback is learning. For what reason are you the only person who should learn this? We should all learn from that opportunity. So you created the conditions, but we should all learn from it. So for me to explain to you why that doesn't make sense or how I differ from it, not as half the time I'm not right, but for me to reason through it in front of everybody helps everybody learn how to reason through it.

The problem I have with one -on -ones and taking feedback aside is you deprive a whole bunch of people that same learning. Learning from mistakes, other people's mistakes, is the best way to learn.

Having simpler ideas that you can execute perfectly is sometimes better than having a grandiose idea that your company can't execute on. And so when you get large as a company and when you're trying to do complicated things, in fact, it is best, it is most prudent to keep it simple. Many ideas surface, and a lot of people say, Jensen, why didn't you guys do this? Why didn't you do this? Or our engineer is asking, why didn't we put this feature in? We could have done all that. I mean, it wasn't because we didn't have the ability to have the idea. You have to decide as a company, you have to decide as an engineering team or as an innovator to say, you know what, I don't need to change the world overnight.

I'm going to change the world over the next 50 years. I don't need to build a killer product overnight. I just need to build a winning product. And the goal of winning is so that you can play again. It's just like pinball. If you could just play well enough to get another game, you could be there for a long time. Most companies just need to realize that, in fact, this is a long road and that you can't build that perfect product. So once you do that, then you keep the project's scope confined. You keep the project now simple. You have a long -term vision, but your product definition is rather simple.

You execute flawlessly on that because your people now can execute flawlessly on a simple plan. And you come back and do it again.

Stanford has a lot of aspiring entrepreneurs. What advice would you give them to improve their chances of success?

You know, one of my, I think one of my great advantages is that I have very low expectations.

And I mean that. Most of the Stanford graduates have very high expectations. And you deserve to have high expectations because you came from a great school.

You were very successful. You're at the top of your class. Obviously, you were able to pay for tuition.

And then you're graduating from one of the finest institutions on the planet. You're surrounded by other kids that are just incredible. You should have very, you naturally have very high expectations. People with very high expectations have very low resilience.

And unfortunately, resilience matters in success.

I don't know how to teach it to you except for I hope suffering happens to you.

And I was fortunate that I grew up with my parents providing a condition for us to be successful on the one hand. And, but there were plenty of opportunities for setbacks and suffering and, you know. And to this day, I use the word, the phrase pain and suffering inside our company with great glee. And the reason, and I mean that, you know, boy, this is going to cause a lot of pain and suffering. And I mean that in a happy way.

Because, because you want to train, you want to refine the character of your company. You want the, you want greatness out of them. And greatness is not intelligence as you know. Greatness comes from character. And character isn't, isn't formed out of smart people. It's formed out of people who suffered. And, and so, so that's, that's kind of the, and so if I could, if I could wish upon you, I don't know how to do it. But, you know, for all of you Stanford students, I wish upon you, you know, ample doses of pain and suffering.

I wouldn't do it.

I know. The reason why I wouldn't do it, and it goes back to why it's so hard, is building a company and building a video turned out to have been a million times harder than any of us expected it to be. At that time, if we realized the pain and suffering and 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. And I think 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?

And to this day, I trick my brain into thinking, how hard can it be? You have to get yourself to believe that it's not that hard because it's way harder than you think. And 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. Do you have any suggestions on any kind of support system or a way to get through the emotional trauma that comes with building something like this? Family and friends and all the colleagues we have here. I'm surrounded by these people that never one time gave up on me to be able to go home and to have your family be fully committed to everything that you're trying to do.

And they're proud of you and proud of the company. And you need that. You need the unwavering support of people around you. And I'm pretty sure that almost every successful company and entrepreneurs that have gone through some difficult challenges, they had that support system around them. And thenlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlettlett

You once told me that you really didn't like firing people and very seldom did it. Can you elaborate on that?

Well, I'd rather improve you than give up on you. There are a lot of things in life that I believe you can learn and you just have to be given the opportunity to learn it. I had the benefit of watching a lot of smart people do a lot of things. They probably don't realize it, but I'm learning constantly from every single one of them. I don't like giving up on people because I think they could improve. People know that I'd rather torture them into greatness. That was the phrase that I was hoping to uncover. Yeah, I remember you mentioned that. Yeah, so I'd rather torture you into greatness because I believe in you.

And I think coaches that really believe in their team torture them into greatness. And oftentimes, they're so close. Don't give up. They're so close. You know, greatness kind of, it comes all of a sudden one day, it's like, I got it. That feeling that you didn't get it yesterday and all of a sudden one day something clicked, you go, oh, I got it. But could you imagine you gave up just that moment right before you got it?

Be careful with the analogies and the science fiction stories that this is Terminator and words like singularity and ideas that somebody say, 20 % chance this will be the end of humanity as we know it. Those kind of articulations of AI is just nonsense. It is complete nonsense. Oh, we have no idea how it works. This is so mysterious, we don't even know how it works. It might just get up out of its seat and walk out tomorrow morning.

There's no question in my mind it's computer and software. And there's no question in my mind they know how it works. And do you know how I know they know how it works? Because every single year apparently it's getting better. If you don't know how something works, how do you make it better? I have no idea how it works, but I know how to make it better.

That's nonsense. So why are they saying these things? That's an interesting question. However, don't let it scare you. You must engage it. You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI.

When NVIDIA first started, there were 60 3D graphics companies. We are the only one that survived. If you would have taken those 60 graphics companies and asked yourself which one was going to make it, NVIDIA would be the top of that list not to make it. You know, this is long before you, but NVIDIA's graphics architecture was precisely wrong. And it was an impossible thing for developers to support. It was never going to make it. We reasoned about it from good first principles, but we ended up in the wrong solution. And everybody would have counted us out. And here we are. And so I have enough humility to recognize that, you know, don't pick winners.

There are so many great, amazing foundation model companies, and we try to invest in all of them. And it's part of our joy of doing so. It's an imperative to our business. We also go out of our way not to pick winners. And so when I invest in one of them, I invest in all of them. Either let them all take care of themselves or take care of all of them.

The idea that China won't be able to have AI chips is completely nonsense. The amount of energy they have is incredible, isn't that right? AI is a parallel computing problem, isn't it? Why can't they just put four, ten times as much chips together? Because energy is free. They have so much energy. They have data centers that are sitting completely empty, fully powered. You know, they have ghost cities, they have ghost data centers. They have so much capacity of infrastructure. And their capacity of building chips is one of the largest in the world. Now, of course, if you ask me, would the United States be further ahead if the entire world had no compute at all?

But that's just not an outcome. That's not a scenario that's true. They have plenty of compute already. The amount of threshold they need for the concern you worry about, they've already reached that threshold and beyond. When you have abundance of energy, it makes up for chips. If you have abundance of chips, it makes up for energy. For example, the United States is scarce on energy, which is the reason why NVIDIA has to keep advancing our architecture and do this extreme co -design so that with the few chips that we ship, because the amount of energy is so limited, our throughput per watt is off the charts.

But if your amount of watts is completely abundant, it's free. What do you care about performance per watt for? But then there's a question of, okay, well, can they actually manufacture enough chips? But they do. Huawei just had the largest single year in the history of the company. How many chips did they ship? A ton. Millions. Millions is way more than Anthropic has. Right. But as you know, the bottleneck often in trading and doing inference on these models is the amount of bandwidth. So if you HBM2, I don't know the numbers offhand, but like versus the newest thing you have, you can be almost an order of magnitude difference of memory bandwidth, which is huge.

Always a networking company. But that doesn't change the fact that you need an EUV for the most advanced HBM. Not true. Not at all true. You could gang them together just like we gang them together with MB -Link 72. They've already demonstrated silicon photonics connecting all of these compute together into one giant supercomputer. Your premise is just wrong.

Acquiring Mellanox and you were super aggressive in acquiring that company. Why did you see that when no one else saw that? So speaking of the speed of life, InfiniBand, you totally saw that InfiniBand would be way more useful, way sooner than anyone else realized. 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. A desktop computer in a data center uses the same CPUs, uses the same GPUs apparently. 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.

So those characteristics are associated with Mellanox, not NVIDIA. The day that I concluded that really NVIDIA wants to be computers of the future and computers of the future are going to be data centers, embodied in data centers, and we want to be data center oriented company, then we really need to get into networking.