Jensen Huang's Top 10 Rules for Success

Long before Nvidia became one of the world's biggest companies, Jensen Huang was betting on a future almost nobody could see.

We have to believe in what we're doing. We have to go back to our core values. As you know, in almost everything great that's done, you have to go back to your core foundations.

He knew exactly what he wanted.

When you're designing a company, you should first think about what is it that you want the company to produce.

He built his company one step at a time instead of chasing shortcuts.

The company has defined that Really narrow path, step by step by step.

He outworked the competition when the stakes were highest.

If you don't work hard every single day, you will perish.

And he embraced new technology long before the rest of the world saw what was coming.

AI is obviously a, a word that people are just learning now. It's made your computer so much more accessible.

So today we're breaking down Jensen Huang's top 10 rules of success, featuring powerful lessons on leadership, innovation, resilience, and building the future. So if you wanna think bigger, solve harder problems, and create extraordinary success, this video's for you. Need motivation?

What's up, 10 Great Believenation?

If you've been here before, drop a hashtag believe in the comments below so I can feature you here in a future video. And if you're new, welcome to Believe Nation, the only channel that helps you believe in yourself daily, one video at a time. Okay, let's kick it off with rule number 1, believe in what you're doing.

First of all, we have to believe in what we're doing. We have to go back to our core values. As you know, in almost everything great that's done, you have to go back to your core foundations. You have to demonstrate that, that you are determined to go pursue that, that you see that future in your mind's eye, even though nobody else can. You see that future in your mind's eye. You have to tell the story so that everybody else could see it in their mind's eye. And you have to believe it yourself.

Rule number 2, have a clear goal.

When you're designing a computer, you have to have operating system of computers. When you're designing a company, you should first think about what is it that you want the company to produce? You know, I see a lot of companies' organization charts and they all look the same. Hamburger organization charts, software organization charts, and car company organization charts, they all look the same. And it doesn't make any sense to me. You know, the goal of a company is to be the machinery, the mechanism, The system that produces the output, and that output is the product that we'd like to create. It is also designed, the architecture of the company should reflect the environment by which it exists. It almost directly says what you should do with the organization.

Rule number 3, have core values.

I would always retreat back to first principles. I would always reason from first principles when I'm uncertain about the future, when things are moving too fast, you always go back to first principles. What are my core values? What makes me great today?

Right.

What do I aspire for? And to go back to that, I would say to young people that it's possible to have multiple feelings at the same time. It's possible to be grateful for everything that you have, to be unsatisfied with where we are at, and to have aspirations for greatness. You're allowed to have all of those feelings at exactly the same time. Doing technology change, doing world change is the only opportunity for greatness. Status quo, you know, it's really hard to make a difference. And so I would deeply dive into the capabilities of artificial intelligence and use it in every possible way. And it's not just about master of science, mastery of science, but it's about mastery of using artificial intelligence in my field of science. Your purpose, if you decide to go into medicine, is to care for people. It's different than studying a radiology scan. Your purpose as an engineer is to either solve a known problem or discover problems that have never been solved, that are worthy to be solved. There's a fundamental difference between the tasks that we do in our job versus the purpose of our job. Everybody's purpose, every job's purpose, surprisingly consists of tasks, but not defined by the tasks.

Rule number 4, build step by step.

We started out as an accelerator company, but the problem with accelerators is that the application domain's too narrow. It has the benefit of being incredibly optimized for the job. Any specialist has that benefit. The problem with intense specialization is that, of course, your market reach is narrower, but that's even fine. The problem is the market size also dictates your R&D capacity, and your R&D capacity ultimately dictates the influence and impact that you can possibly have in computing. And so when we first started out as an accelerator, very specific accelerator, we always knew that that was gonna be our first step. We had to find a way to become accelerated computing. But the problem is when you become a computing company, it's too general purpose and it takes away from your specialization. I connected 2 words they actually have fundamental tension. The better computing company we become, the worse we became as a specialist. The more of a specialist, the less capacity we have to do overall computing. And so that— and I connected those 2 words together on purpose, that the company has to find that really narrow path, step by step by step, to expand our aperture of computing, but not give up on the most important specialization that we had.

Okay.

So the first step that we took beyond acceleration was we invented the programmable pixel shader. So that was the first step towards programmability. It was our first journey towards moving into the world of computing. The second thing that we did was we created— we put FP32 into our shaders. That FP32 step, IEEE-compatible FP32, was a huge step in the direction of computing. It was the reason why all of the people who were working on stream processors and other types of data flow processors discovered us. And they said, hey, all of a sudden, we might be able to use this GPU that's incredibly computationally intensive, and it's now compliant with IEEE.

Mm-hmm.

I can take my software that I was writing previously on CPUs, and I can see about using the GPU for that, which led us to create— put C on top of FP32, what we call CG. That CG path took us to eventually CUDA, CUDA step by step by step. Well, putting CUDA on GeForce, that was a strategic decision that was very, very hard to do because it cost the company enormous amounts of our profits and we couldn't afford it at the time, but we did it anyways because we wanted to be a computing A computing company has a computing architecture. A computing architecture has to be compatible across all of the chips that we build.

Also, if you wanna take real action after this video, I made a free worksheet just for you. It covers the top lessons from today, gives you space to write your biggest takeaways, and helps you build a simple action plan. It's 100% free. Just click the link in the description below to go grab it, and I'll see you there. Rule number 5, work hard.

When everybody's playing by the rules, Because the rules and laws are for good reason, then at least you could find where there are segments of the market that are underserved.

Mm-hmm.

And that you can rely on that. You can rely on the fact that you can create something great and take it to market in a way that serves a market that has a demand. And that it wouldn't be foreclosed on you randomly, arbitrarily, unknowingly, you know, unpredictably. Those things that entrepreneurs rely on, you know, are alive and well here.

Yeah.

You know, as an immigrant, you come here by choice. You witness a miracle because compared to where you came from, the circumstances you came from, it is a miracle. The resources are incredible, are abundant. You want to work hard because you're desperate to succeed. An entrepreneur, you're desperate to succeed. If you don't work hard every single day, you will perish.

Rule number 6, take risks.

The thing that— the decision that we made that was good was we said, hey, look, ultimately it's about installed base and what is the best way we could get a new computing architecture into the world? By that timeframe, GeForce had become successful. We were already selling Millions and millions of GeForce GPUs a year. And we said, we ought to put CUDA on GeForce and put it into every single PC, whether customers use it or not, and use it as a starting point of cultivating our installed base. Meanwhile, we'll go and attract developers. And we went to universities and wrote books and taught classes and—

CUDA everywhere.

And eventually people will discover— and at the time, the PC was the primary computing vehicle. There was no cloud. And we could put a supercomputer in the hands of every researcher in school, every scientist, every engineer in school, every student in school, and eventually something amazing will happen. Well, the problem was CUDA increased our cost of that GPU, which is a consumer product, so tremendously it It completely consumed all of the company's gross profit dollars. And so at the time, the company was probably, you know, worth, I don't know, at the time, $8— was it like $8 billion or something like that? $6, $7 billion or something like that. After we launched CUDA, I recognized that it was going to add so much cost, but it was something we believed in. You know, our market cap went down to like $1.5 billion. And so we were down there for a while and we clawed our way back slowly, but we carried CUDA on GeForce. I always say that NVIDIA is the house that GeForce built because it was GeForce that took CUDA out to everybody, researchers, scientists. They discovered CUDA on GeForce because they were all, many of 'em were gamers, many of them built their own PCs anyways. In a university lab, many of them clusters themselves using PC components. And so that's kind of how we got going.

Rule number 7, take action.

Strategy is not words. Strategy is action. And so if the company has a set of strategies, but the people's actions, their top 5 things are not that, then they're obviously not executing the strategy. And so the strategy, it turns out, isn't what I say. Is what they do.

Rule number 8, think outside the box.

I heard from someone that there were pro—

that you could—

there was never a problem you couldn't fix.

Do you think that's true, or was there a problem that you couldn't fix? There's some problems you can't fix. I don't recall one. And the reason for that is because either you find a solution for it or you change the problem so that you can find a solution for it. I think, in a lot of ways, some people call that thinking out of the box. If you can't solve something, either sometimes it's not meant to be solved, or you can change the problem in a way that is solvable. Now, there are some problems that if you try to solve it in the most literal sense, it would take a long time to solve. It might take a lot of money to solve. It might take an enormous amount of cost in time or resource or whatever it is, or materials. But if you change the problem and you truly understand what is the person really trying to ask, what is at the core of that problem, maybe you can express that problem in a slightly different way. And as a result, it becomes solvable. problem and a solution can be found. Maybe you can simplify the problem. Maybe you can break the problem down into 10 pieces and that you would solve it over time. There's a lot of different ways to look at the problem. Smart thinking about a problem is really at the beginning of engineering. It's asking the question, what is this problem really about? What is this problem really about? Oftentimes, by asking that question, what is this problem really about? What are they really asking? What are we really looking for? How can I express this problem in different ways? When you think about it that way, all of a sudden, the number of solutions really, really expand.

Number 9, leverage technology.

AI is obviously a word that people are just learning now. But it's just, you know, what would— it's made your computer so much more accessible. It is easier to prompt ChatGPT to ask it anything you like than to go do the research yourself. And so we've lowered the barrier of understanding. We've lowered the barrier of knowledge. We've lowered the barrier of intelligence. And everybody really ought to just go try it. You know, the thing that's really, really crazy is if I put a computer in front of somebody and they've never used a computer, there is no chance they're going to learn that computer in a day. There's just no chance. Somebody really has to show it to you. And yet with ChatGPT, if you don't know how to use it, all you have to do is type in, I don't know how to use ChatGPT, tell me. And it would come back and give you some examples. And so that's the amazing thing. The amazing thing about intelligence is it'll help you along the way and make you superhuman, you know, along the way.

And rule number 10, the last one before a very special bonus clip, is think different.

You can succeed in doing something, inventing a future, even if you were not informed about it at all. And it's kind of my attitude about everything now. You know, when somebody tells me about something and I've never heard of it before, or if I've heard of it and never— 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. And so I spend a lot of time reading archive papers. And it's true. It's true. You can— 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 could 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 1950s and 1900s? How would I build a computer today? How would I write software today? Does it make sense? And so I go back to first principles all the time, even in the company today, and just reset ourselves, you know, because the world has changed. And the way we wrote software in the past was monolithic, and it's designed for supercomputers, but now it's disaggregated, it's, you know, And so on and so forth. And how we think about software today, how we think about computers today, how we think— just always cause your company, always cause yourself to go back to first principles, and it creates lots and lots of opportunities. So the thing is, when you want to build something great, it's not easy to do. And when you're doing something that's not easy to do, you're not always enjoying it. I don't love every day of my job. I don't think every day brings me joy, nor does joy have to be the definition of a good day. And every day I'm not happy, every year I'm not happy about the company, but I love the company every single second. And so I think that what people misunderstand is somehow the best jobs are the ones that brings you happiness all the time. I don't think that that's right. You have to suffer, you have to struggle, you have to endeavor, you have to do those hard things and work through it in order to really appreciate what you've done. And there are no such things that are great that was easy to do. And so by definition, I would say therefore, I wish upon you Greatness, which by my way of saying it, I wish upon you plenty of pain and suffering.

Congratulations, you're one video closer to who you're meant to be. Believe. For some more incredible Jensen Huang advice in 25 minutes, check the video right there next to me. I think you'll love it. Continue to believe, and I'll see you there.

The question is determination and commitment versus stubbornness. And that line is fuzzy. Look, I gut-checked.