Jensen Huang, Founder and CEO of NVIDIA

1. 0:00 – 0:04

   Intro

   1. SJShantam Jain0:00

      [upbeat music]

2. 0:04 – 3:22

   From LSI Logic to founding NVIDIA: the Denny’s origin story and mission

   1. SJShantam Jain0:04

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

   2. JHJensen Huang0:07

      I'm delighted to be here. Thank you.

   3. SJShantam Jain0:09

      In honor of your return to Stanford, I decided we'd start talking about the time when you first left.

   4. JHJensen Huang0:15

      Yeah.

   5. SJShantam Jain0:15

      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 decide to leave to become a founder. What motivated you?

   6. JHJensen Huang0:30

      Uh, uh, Chris and Curtis. Chris and Curtis, uh, uh, I was an engineer at LSI Logic, and Chris and Curtis were at Sun. And I was working with, with, uh, uh, some of the brightest minds in computer science at the time, uh, uh, of all time, uh, including Andy Bechtolsheim and others, uh, building, building workstations and graphics workstations and so on and so forth. And, uh, Chris and Curtis, uh, uh, said one day that they'd like to leave Sun, and they'd like, uh, me to go figure out where they're gonna go leave for. And, and, um, uh, I had a great job. Uh, but they, they insisted that I, uh, f-figure out, you know, with them, uh, how to, how to build a company, and so, so we hung out at Denny's whenev-whenever they drop by, and, and, uh, uh, which was, which is, by the way, my alma mater, my-

   7. SJShantam Jain1:25

      [laughs]

   8. JHJensen Huang1:25

      ... my first company. Uh, you know, my first job before, for b- before CEO was a, was a dishwasher, and so, and, and I did that very well. [laughs]

   9. SJShantam Jain1:35

      [laughs]

   10. SPSpeaker1:36

       [laughs]

   11. JHJensen Huang1:36

       And, and so anyways, uh, we got together and, and we, we deci- and it was during the, the microprocessor revolution. This is 1993, and, and 1992 when we were getting together. Uh, the PC revolution was just getting going. You, you know, that Windows 95, obviously, which is the revolutionary version of Windows, uh, didn't even come to the market yet, and Pentium wasn't even announced yet. And so, and this is, this is all before the, right before the PC revolution and, and it was, it was pretty clear that, that, uh, the microprocessor was going to be very important. And we, we thought, you know, why don't we build a company, uh, to go solve problems that a normal computer that is, uh, powered by general purpose computing can't? And, and so that, that became the company's mission, uh, to go, to go build a computer, uh, the type of computers and solve problems that normal computers can't. And to this day, uh, we're focused on that. And if you look at all the, the problems that, that, um, in the markets that we opened up as a result, uh, it's, you know, things like, uh, computational drug design, um, uh, weather simulation, materials design. These are all things that we're really, really proud of. Uh, robotics, uh, self-driving cars, uh, autonomous, autonomous, uh, software we call artificial intelligence. Uh, and then all, you know, of course, uh, we, uh, we drove the, the, uh, uh, the technology so hard that, that eventually the computational cost, uh, uh, went to approximately zero, and it enabled, enabled a whole new way of developing software, where the computer wrote the software itself, artificial intelligence as we know it today. And so, so I, that was, that was it. That was the journey.

   12. SJShantam Jain3:17

       Yeah.

   13. JHJensen Huang3:18

       Thank you all for coming.

   14. SJShantam Jain3:19

       [laughs]

   15. SPSpeaker3:20

       [laughs]

3. 3:22 – 6:58

   Getting funded without a business plan: reputation, referrals, and “have a good past”

   1. SJShantam Jain3:22

      Well, these applications are on all of our minds today, but back then, the CEO of LSI Logic convinced his biggest investor, Don Valentine, to meet with you. He is obviously the founder of Sequoia.

   2. JHJensen Huang3:36

      Yeah.

   3. SJShantam Jain3:36

      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?

   4. JHJensen Huang3:51

      I, I didn't know how to write a business plan. And, and, uh, uh, so I went to a, went to a bookstore, and back then there were bookstores and, and, and, um-

   5. SPSpeaker4:04

      [laughs]

   6. JHJensen Huang4:04

      ... in the business book section, there was this book, and it was written by somebody I knew, Gordon Bell. And this book, uh, I should go find it again, but it's, it's a very large book, and the book says, "How to Write a Business Plan."

   7. SPSpeaker4:16

      [laughs]

   8. JHJensen Huang4:18

      And, and that was, you know, a highly specific title for a very niche market.

   9. SJShantam Jain4:23

      [laughs]

   10. JHJensen Huang4:24

       And it seems like he wrote it for, like, you know, 14 people-

   11. SJShantam Jain4:26

       [laughs]

   12. JHJensen Huang4:26

       ... and I was one of them.

   13. SPSpeaker4:27

       [laughs]

   14. JHJensen Huang4:28

       And, and so I, I bought the book. I, I should've known right away that that was a bad idea because it had a, you know, Gordon is super sm- super smart, and super smart people have a lot to say.

   15. SPSpeaker4:40

       [laughs]

   16. JHJensen Huang4:41

       And, and they wanna, you know, and I, I'm pretty sure Gordon wants to teach me how to write a business plan, uh, completely. And so I, I picked up this book. It's like 450 pages long. Well, I never got through it.

   17. SJShantam Jain4:53

       [laughs]

   18. JHJensen Huang4:54

       Not even close. I, I flipped through it a f- few pages, and I go, "You know what? By the time I'm done reading this thing, I'll be out of business."

   19. SJShantam Jain5:01

       [laughs]

   20. SPSpeaker5:01

       [laughs]

   21. JHJensen Huang5:01

       "I'll be out of money." And, and, uh, Lori and I only had about six months, uh, in the bank. Uh, we had already Spencer and Madison and, and, uh, and a dog. And so the five of us had to live off of, you know, uh, whatever money we had in the bank. And, and so I didn't have much time. Uh, and so instead of writing the business plan, uh, I just went to talk to, to Wilf Corgan. He turned-- He called me one day and said, "Hey, you know, 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 I explained it to Wilf. And Wilf, Wilf, at the end of it, he, he said, "I have no idea what you said."

   22. SPSpeaker5:37

       [laughs]

   23. JHJensen Huang5:39

       And, and, um, uh, "That's one of the worst elevator pitches I've ever heard."

   24. SJShantam Jain5:44

       [laughs]

   25. SPSpeaker5:45

       [laughs]

   26. JHJensen Huang5:46

       Um, and then he picked up the phone, and he called Don Valentine. And he, he called Don, and he says, "Don, I want you to give... I'm gonna send a kid over. I want you to give him money. He's one of the best employees LSI Logic ever ha- ever had." And, um, uh, and, and so the thing I learned is, is, uh- Uh, you, you can make up a great interview. You could even have a bad interview. But you can't run away from your past. And so have a good past.

   27. SJShantam Jain6:18

       [laughs]

   28. JHJensen Huang6:19

       You know, try to have a good past. And, and, and in a lot of ways, I was serious when I said I was a good dishwasher. I was probably Denny's best dishwasher.

   29. SJShantam Jain6:27

       [laughs]

   30. JHJensen Huang6:28

       Um, I, I planned my work. I was organized. You know, I was mise en place, and then I washed the living daylights out of the dish- dishes. Uh, and then, and then, you know, they promoted me to busboy. I was certain I'm the best busboy Denny's ever had.
4. 6:58 – 10:17

   Choosing the first killer app: making 3D graphics for PC gaming when the market didn’t exist

   1. SJShantam Jain6:58

      You talk about being the best. You needed to be the best among 89 other companies that were funded after you to build the same thing.

   2. JHJensen Huang7:06

      Mm.

   3. SJShantam Jain7:06

      And then with six to nine months of runway left, you realized that the initial vision was just not gonna work.

   4. JHJensen Huang7:14

      Mm-hmm.

   5. SJShantam Jain7:14

      How did you decide what to do next to save the company when the cards were so stacked against you?

   6. JHJensen Huang7:21

      Well, we started, uh, this company called for accelerated computing, and the question is: What's it for? What's the killer app? And, and, uh, that was, that, that came our first great decision. Um, and this is what Sequoia funded. Uh, the first great decision was the first killer app was gonna be 3D graphics, and the, the, the t- the technology was gonna be 3D graphics, and the application was gonna be video games. At the time, 3D graphics was impossible to make cheap. It was million-dollar image generators from Silicon Graphics. And the video g- and so it was a million dollars and, and s- it's hard to make cheap. Um, and the video game market was zero billion dollars. So you have this incredible technology that's hard to, uh, commoditize and commercialize, and then you have this market that doesn't exist. That was, that intersection was the founding of our company. And, and I still remember, uh, when, when Don, at the end of my presentation, uh, you know, Don was still kind of... He, he said, you know, one of the things he said to me, whi- which made a lot of sense back then, it s- it makes a lot of sense today. He says, "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. And, and then he, on the way out, he's, he reminded me that Electronic Arts' CTO is 14 years old-

   7. SJShantam Jain8:57

      [laughs]

   8. JHJensen Huang8:58

      ... and had to be driven to work by his mom.

   9. SJShantam Jain9:00

      [laughs]

   10. JHJensen Huang9:02

       And he just wanted to remind me that that's who I'm relying on.

   11. SJShantam Jain9:04

       [laughs]

   12. JHJensen Huang9:05

       That, that... [laughs] And then, [laughs] and, uh, and then after that, he said, "If you lose my money, I'll kill you." And that-

   13. SJShantam Jain9:12

       [laughs]

   14. JHJensen Huang9:13

       ... that was, that was kind of my memories of that first meeting.

   15. SJShantam Jain9:15

       [laughs]

   16. JHJensen Huang9:17

       Uh, but nonetheless, uh, we created, uh, we created something. Uh, we went on, uh, the next several years to go create the market, to create the gaming market for PCs, and it took a long time to do so. We're still doing it today. Uh, we realized that not only do you have to create the technology and, uh, invent a new way of doing computer graphics so that what was a million dollars is now, you know, three, $400, $500, um, that fits in a computer, and you have to go create this new market. So we had to create technology, create markets. The idea that a company would create technology, create markets defines NVIDIA today. Almost everything we do, we create technology, we create markets. That's the reason why people say we have a, you know, uh, people call it a stack, an ecosystem, words like that. Um, but that's basically it. At the core, for 30 years, what NVIDIA realized we had to do is in order to, uh, create the conditions by which somebody could buy our products, we had to go invent this new market. And,

5. 10:17 – 14:54

   Direct3D disruption and the company reset: learning fast from manuals and rebuilding around a new pipeline

   1. JHJensen Huang10:17

      uh, it's the reason why we're early in autonomous driving. It was the reason why we're early in deep learning. It was the reason why we're early in just about all these things, including, uh, computational drug discu- drug design and, and discovery. Um, all these different areas, we're trying to create the market while we're creating the technology. And so that, that's, um... Uh, uh, okay, and then we got c- we got going and, and then, and then, um, Microsoft introduced, uh, a standard called Direct3D, and that spawned off hundreds of companies. And we found ourselves, a couple years later, competing with just about everybody. And, and the thing that, that we invented, the company, the technology we invented, uh, 3D graphics with, to consumerize 3D with, turns out to be incompatible with Direct3D. So we started this company. We had this 3D graphics thing. We, you know, 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. A- and, um, uh, so we had to reset the company, uh, or go out of business. Uh, but we didn't know how to, we didn't know how to build it the way that Microsoft had defined it. And, um, and, uh, I remember, I remember a meeting at, at, you know, on a weekend, and the conversation was, you know, we now have 89 competitors. Uh, I understand that the way we do it is not, not right, but we don't know how to do it the right way. And, and, um, uh, thankfully, uh, there was another bookstore and, um-

   2. SJShantam Jain11:49

      [laughs]

   3. JHJensen Huang11:50

      ... uh, and the bookstore is called Fry's, Fry's Electronics. I, I don't think it, I, I don't know if it's still here. Um, and so I had, I had, I had, um, I, I, I think I drove Madison, my daughter, uh, on a weekend to Fry's, and, and it was sitting right there. The OpenGL manual, uh, which would define, uh, how Silicon Graphics did computer graphics. And so it was, it was right there. It was like $68 a book. And so 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." And I handed out... I had the three versions of it. I handed out. Had a big, nice cen- centerfold. You know, the centerfold is the OpenGL pipeline, which is the computer graphics pipeline. And, um, I, and I handed it to, uh, the same geniuses that I founded the company with, and we implemented the OpenGL pipeline like nobody had ever o- implemented the OpenGL pipeline, and we built something the world never seen. And so, uh, a lot of lessons are right there. That moment in time for our company, uh, 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 it's kind of the, my attitude about everything now. You know, when somebody tells me about something and I've never heard of it before, uh, or if I've heard of it, never, don't understand how it works at all, my first thought is always, you know, how hard can it be? [laughs] And it's probably just a textbook away. You know, is you're probably one archive paper away from figuring this out. And so I spend a lot of time reading archive papers and, um, uh, and it, it's true. It's true. You can, you can, um... 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, um, given the conditions today, given my motivation, given the instruments, the tools, um, given, you know, how things have changed, how would I redo this? How would I reinvent this whole thing? How would I design a... 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, uh, even in the company today, and just reset ourselves, you know? Because the world has changed, and, um, 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, so on and so forth. And, uh, 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, first principles, and it creates lots and lots of opportunities.

   4. SJShantam Jain14:50

      Yeah. The way you applied this technology turns to be revolutionary.

   5. JHJensen Huang14:54

      Yeah.

6. 14:54 – 17:52

   From graphics to programmable computing: shaders, CG, and early evidence of broader demand

   1. SJShantam Jain14:54

      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 pivot 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?

   2. JHJensen Huang15:22

      Uh, remember, at the core, the company was, uh, pioneering a new way of doing computing. Computer graphics was the first application. Uh, but we already always knew that there would be other applications, and so image processing came, particle physics came, fluids came, so on and so forth. All, all kinds of interesting things that we wanted to do. Uh, we made the processor more programmable so that we could express more algorithms, if you will. And then one day we invented, um, uh, programmable shaders, which made all forms of imaging and computer graphics programmable. That was a great breakthrough, so we invented that. Uh, on top of that, we invented, uh, we, we tried to ex- uh, uh, look for ways to express, um, uh, more com- more sophisticated algorithms, uh, that could be computation- that could be computed on our processor, which is very different than a CPU. And so we, we created this thing called CG. And so I think it was 2003 or so. C for GPUs. It predated CUDA by about three years. Um, the same person who wrote the textbook that saved the company, Mark Kilgard, wrote that textbook. And, um, I... And so CG was, was super cool. We wrote textbooks about it. We started teaching people to, how to use it. We developed tools and such. Um, and then several, several researchers discovered it. Uh, many of the researchers here, students here at Stanford, was using it. Um, many of the, the engineers that, that then became, uh, engineers at NVIDIA were, were, uh, playing with it. Uh, um, a, a doctor, a, a, a couple of doctors at, at, uh, Mass General picked it up and used it for, uh, CT reconstruction. So I flew out and saw them and said, "You know, what are you guys doing with this thing?" And, uh, they told me about that. And then, and then, uh, a, um, uh, a computational, a, a quantum chemist, uh, used it to, um, uh, express his, his algorithms. And so I, I realized that, that there's, there's some evidence that people might wanna use this. Uh, and, and it gave, it gave us, gave us, you know, incrementally more, more confidence that, that we ought to go do this. That, that this field, this form of computing could solve problems that normal computers really can't and, and, um, uh, reinforced our belief and, and kept us going.

   3. SJShantam Jain17:47

      Uh, every time you heard something new, you really savored that surprise.

   4. JHJensen Huang17:52

      Mm.

7. 17:52 – 21:37

   Finding conviction years before markets exist: core beliefs and “early indicators of future success”

   1. SJShantam Jain17:52

      And that seems to be a theme throughout your leadership at NVIDIA. Uh, 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. How do you find the conviction?

   2. JHJensen Huang18:12

      Always seems like a diving catch.

   3. SJShantam Jain18:13

      [laughs]

   4. JHJensen Huang18:14

      Always does seem like a diving catch. You do things based on core beliefs. You know, we, we, uh, we, we deeply believe that, that we, uh, we could, you know, create a computer that solves problems normal processing can't do. Uh, that 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, uh, that we can go solve. The question, the question is always are those inc- interesting problems only, or are they, 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, was computer graphics. Uh, for 10, 15 years, the markets that fuels NVIDIA today just didn't exist. And so, so how do you continue, um, uh, with all of the people around you, you know, our company and, you know, NVIDIA's management team and all of the amazing engineers that are there creating this future with me, um, all of your shareholders, your board of directors, all your partners. You're, you're taking everybody with you, and there's no evidence, uh, of a market. That is really, really challenging, you know? The fact that the technology can solve problems and the fact that you have research papers that, that are used, that, 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. You know? We, we have this phrase in the company is, is it... You know, there, 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? You know, a lot of people, you know, when, when we look for KPIs, you go gross margins. That's not a KPI. That's a result. You know, you're looking for something that's an early indicators of future positive results, okay? And as early as possible. And the reason for that is because you want early indic- you, that early sign that you're going in the right direction. And so we have this phrase is called E-O-I-F-S-S, uh, F-S. You know, early indicators, E-O-I-F-S. Early indicators of future success. And, and, um, and it helps people, uh, uh, 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 they were important problems, and that's what the company's about, to solve these problems. Uh, we wanna be sustainable. Uh, a- and therefore the markets have to exist at some point. Um, but, uh, you, you want, you wanna decouple the result from, um, uh, from, uh, evidence that you're doing the right thing, okay? And so, so, so that's how you, that's how you kinda solve this problem of investing into something that's very, very far away, um, and having the, the conviction, uh, to stay on the road, is to find as early as possible the indicators that you're doing the right things. And so, uh, start with a core belief. Unless something, you know, changes your mind, you continue to believe in it. And, um, uh, look for early indicators of future success.

8. 21:37 – 25:11

   Example indicator: deep learning work, cuDNN, and choosing “important work” over immediate ROI

   1. SJShantam Jain21:37

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

   2. JHJensen Huang21:43

      Uh, all kinds. Um, uh, I saw, I saw, um, I saw a, uh, a paper. Uh, long before I saw the paper, I met some people that needed my help on, on, um, uh, on this thing called deep learning. At the time, I didn't even know what deep learning was and, um, and they needed us to create a domain-specific language so that, um, all of their algorithms could be expressed easily on our, on our processors. And we created this thing called cuDNN, and it's essentially the SQL, um, uh, SQL is in, in storage computing. This is, um, neural network computing. And, uh, we created a l- a language, if you will, domain-specific language for that. You know, kinda like the OpenGL of, of, uh, deep learning. And so we, we, uh, they needed us to do that so that they, they could express their mathematics and, uh, they didn't understand CUDA, but they understood their deep learning. And so we created this thing in the middle for them. Uh, and the reason why we did it was because, uh, even though there were zero... I mean, this is, you know, these researchers had no money. Uh, and, and, uh, this is kinda one of the, the great skills of our company, that, that you're willing to do something even though the financial returns are completely nonexistent or maybe very, very far out, even if you believed in it. Uh, we, we ask ourselves, you know, "Is this worthy work to do? Um, does this advance a field of science somewhere that matters?" Notice, this is something that I, I've been talking about. You know, since the very beginning of time, uh, we ex- we, we find inspiration, uh, not from the size of a market, from, but from the importance of the work. Uh, because the importance of the work is the early indicators of a future market. And nobody has to write a, nobody has to do a, a, um, a business case on it. Nobody has to show me a, a, a P&L. Uh, nobody has to show me a financial forecast. The only question is, is this important work? And if we didn't do it, uh, would it happen without us? Now, if we didn't do something and something could happen without us, it gives me tremendous joy, actually. And 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, you know, of, of, uh, ultimate laziness. And, and, and in a lot of ways, in a lot of ways, you want that habit, and the reason for that is this: Uh, 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 wor- the world would fall apart. You have to convince yourself of that. That if I don't do this, it won't get done. That is inc- a- and, and 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, uh, deep learning was just one of them. And the first indicator of, of the success of that was this, you know, fuzzy cat that, that Andrew Ng came up with. And, um, uh, then, uh, Alex K- Krizhevsky, uh, uh, detected cats. Um, you know, not all the time, but [laughs] you know, successfully enough that it was, "You know, this might take us somewhere." And, and we reasoned about the structure of deep learning, and, you know, we're computer scientists, and we understand how things work. And, and so we, we, uh, we convinced ourselves this could change everything. And, and, um, and anyhow, that... But that's an e- that's an example.

9. 25:11 – 30:02

   Leading through collapses: staying steady when stock drops and focusing the company on fundamentals

   1. SJShantam Jain25:11

      So these selections that you've made, they've paid huge dividends, both literally and figuratively. Um, but you've had to steer the company through some very challenging times, like when it lost 80% of its market cap amid the financial crisis, 'cause what, Wall Street didn't believe in your bet on ML?

   2. JHJensen Huang25:29

      Oh, yeah.

   3. SJShantam Jain25:29

      Um, in times like these, how do you steer the company and keep the employees motivated at the task at hand?

   4. JHJensen Huang25:37

      Uh, it's... This is the... My reaction during that time is the same reaction I had about this week. Uh, y- 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. Um, I- and so the opposite of that, you know, when you drop 80%, um, it... Don't get me wrong. When, when your share price drops 80%, it's a little embarrassing, okay? And, and, um, y- you just wanna s- you just wanna wear a T-shirt that says, "Wasn't my fault."

   5. SJShantam Jain26:14

      [laughs]

   6. SPSpeaker26:14

      [laughs]

   7. JHJensen Huang26:16

      Um, uh, but even more than that, you just, you just don't wanna... You won- you don't wanna get out of your bed, you don't wanna leave the house. Um, all of that is true. All of that is true. Um, but then you go back to, go back to just doing your job. Uh, I woke up at the same time, uh, prioritized my day in the same way. I, I go back to what do I believe. Uh, you gotta gut check, always gut check back to the core, you know? What do you believe? Uh, what are the most important things? Uh, and, uh, just check 'em off. You know, sometimes, sometimes it's helpful to, you know, [laughs] family loves me? Okay, check. Um, you know, double-check, you know? Right? And so you just gotta check it off, and, and you go back to your core, um, and then go back to work.

   8. SJShantam Jain27:00

      Yeah.

   9. JHJensen Huang27:01

      And, and then every conversations go back to the core. Uh, 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, did all of the things that, that, that we assumed, uh, that we believed, that led to our decision, did any of those things change? Because if those ch- things change, you gotta change everything. But if none of those things change, you change nothing. Keep on going.

   10. SJShantam Jain27:28

       Yeah.

   11. SPSpeaker27:28

       Yeah.

   12. JHJensen Huang27:28

       That's how you do it.

   13. SJShantam Jain27:29

       [laughs] In, uh, speaking with your employees, they say that you-

   14. JHJensen Huang27:33

       And try to avoid the public.

   15. SJShantam Jain27:34

       [laughs]

   16. SPSpeaker27:35

       [laughs]

   17. JHJensen Huang27:36

       In speaking with your employees, they've said that your leadership style- Including the employees.

   18. SPSpeaker27:40

       [laughs]

   19. SJShantam Jain27:41

       [laughs]

   20. JHJensen Huang27:41

       I'm just kidding. [laughs] No, lead- lead- leaders have to be seen, unfortunately. That's the hard, that's the hard part. You know, I, I, I was, I was, I was a, I, I was, I was an electrical engineering student, and I was quite young when I went to school. Um, uh, when I went to, went to college, I was, I was still 16 years old, and so I was, I was young when I did everything. And, and so I was a bit of an introvert. I'm kinda sh- you know, I'm shy. I don't enjoy public speaking. I'm delighted to be here. I'm not suggesting-

   21. SJShantam Jain28:10

       [laughs]

   22. JHJensen Huang28:11

       Um, but, but it's, it's not something that I do naturally. And, and, um, I... And so, so when, when things are challenging, um, uh, it's not easy to be in front of, uh, precisely the people that you care most about, you know? And the reason for that is because could you imagine a company meeting, we just, our stock price just dropped by 80%? And the most important thing I have to do as the CEO is this: to come and face you, explain it. And partly you're not sure why, partly you're not sure how long, uh, how bad. Uh, you, you just don't know these things. And, and, but you still gotta explain it. Face, face all these people, and y- you know what they're thinking. You know, you, you know, some of them are probably thinking we're doomed. Uh, some people are probably thinking you're an idiot. And some people are probably thinking, you know, something else. And so, uh, um, there are a lot of things that people are thinking, and you know that they're thinking those things. Um, but you still have to get in front of them and, and, and deal, you know, do the hard work.

   23. SJShantam Jain29:18

       They may be thinking of those things, but yet not a single person of your leadership team left during times like this. And in fact-

   24. JHJensen Huang29:25

       They're unemployable.

   25. SJShantam Jain29:26

       [laughs]

   26. SPSpeaker29:27

       [laughs]

   27. JHJensen Huang29:28

       That's what I keep reminding them.

   28. SPSpeaker29:29

       [laughs]

   29. SJShantam Jain29:30

       [laughs]

   30. JHJensen Huang29:30

       I'm just kidding. I'm surrounded by geniuses. I'm surrounded by geniuses, yeah. Other, other, other geniuses. Un- un- un- unbelievable. Uh, 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, uh, just incredible. Engineering teams, like, research teams. Unbelievable. Yeah.

10. 30:02 – 36:39

    Flat organization by design: transparency, many direct reports, and empowering decision-making at scale

    1. SJShantam Jain30:02

       Yeah. 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.

    2. JHJensen Huang30:19

       Yeah.

    3. SJShantam Jain30:19

       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?

    4. JHJensen Huang30:29

       Uh, no task is, is-- to me, no task is beneath me because remember I used to be a dishwasher, and I, and I mean that. You know, I used to clean toilets. I mean, you know, I cleaned a lot of toilets. I've cleaned more toilets than all of you combined. [laughing]

    5. SPSpeaker30:41

       [laughing]

    6. JHJensen Huang30:42

       And, and, uh, some of them you just can't unsee.

    7. SPSpeaker30:45

       [laughing]

    8. JHJensen Huang30:47

       [laughing] I don't know like wh- I don't know what to tell you, you know? That's life. And, and so, so, uh, uh, you can't show me an-- you can't show me a task that is, that's beneath me. Um, now I'm not doing it, I'm not doing it, uh, only because, because of, uh, you know, whether it's beneath me or not beneath me. Uh, if you send me something and you want my input on it, uh, and I can be of, of service to you, and in my, in my review of it share with you how I reason through it, uh, I've made a contribution to you. I've made, I've made it possible for you to see how I reason through something. And, 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, you know, seems to be very scary. This is how you seem... Do you understand? And, and so I show people how to reason through things all the time. Strategy things, you know, how to forecast something, how to break a problem down. Uh, and, and you're just-- you're empowering people all over the place. And so that's how I see it. If you send me something, you want me to help review it, uh, I'll do my best and I'll show you how I would do it. Um, I, uh, in the process of doing that, of course, I learned a lot from you. Is that right? You gave me the seed of a lot of information. I learned a lot. And so I, I feel rewarded by the process. Um, it does take a lot of energy sometimes because, you know, you gotta, 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 head space, and that's really hard. That's really hard. Um, and that takes just an enormous amount of emotional and intellectual energy. And so I feel exhausted after, after I, I work on things like that. Um, I'm surrounded by, by a lot of great people. A CEO should have the most direct reports, um, uh, 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 f- the knowledge, the information of a CEO is supposedly so s- so valuable, so secretive, you can only share it with two other people or three. And their information is so invaluable, so incredibly secretive, that they can only share it with a couple more. Well, um, I don't believe in, in, in a culture or an environment where the information that you possess is the reason why you have power. I would like us all to, to, 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, um, achieve greatness, um, inspire, empower other people, um, support other people. Those are the reasons why the, the management team exists, in service of all of the other people that work in the company. To create the conditions by which all of the, all of these amazing people volunteer to come work for you instead of all the other amazing s- high-tech companies around the world. They elected, they volunteered to work for you, and so you should create the conditions by which they could do their life's work, which is my mission. Um, you know, you, you probably heard it. I've, I've said that, you know, pretty clearly and, and, and I, and I believe that. What my job is is very simply to create the conditions by which you could do your life's work. And so how do I do that? What does that condition look like? Well, that condition should, um, result in great deal of empowerment. You should-- you can only be empowered if you understand the circumstance. Isn't that right? You have to understand the con- 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, uh, it's very often that I'm reasoning through things like in an au- audience like this. I say, "First of all, this is the beginning facts. These are the data that we have. Um, this is how I would reason through it. These are some of the assumptions. These are some of the unknowns. These are some of the knowns." And so you reason through it, and now you've created an organization that's highly empowered. NVIDIA's 30,000 people, and 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, you know, they understand that they understand my condition. They understand my condition. I'm very transparent with people. Um, uh, and, uh, and I believe it, that I can trust you with the information. Uh, oftentimes the information is hard to hear and, uh, the, the situations are complicated, uh, but I trust that you can handle it. You're, you're, you know, a lot of people hear me say, "You know, these-- you're, you're adults here. You can handle this." Sometimes they're not really adults and they just graduated. [laughing] I'm just kidding.

    9. SPSpeaker36:24

       [laughing]

    10. JHJensen Huang36:25

        You know, I know that when I first graduated I was barely an adult, and, um, I, but I, I was, I was fortunate that I was trusted with, with, uh, with, uh, uh, uh, important information. And so I want to do that. I want to create the conditions for people to do that.

11. 36:39 – 41:30

    Reasoning about generative AI from first principles: meaning, translation, and a shift from retrieval to generation

    1. SJShantam Jain36:39

       Okay. I do want to now address the topic that is on everybody's mind, AI.

    2. JHJensen Huang36:44

       Mm.

    3. SJShantam Jain36:45

       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?

    4. JHJensen Huang36:58

       Well, you have to go back to first principles and ask yourself, what is generative AI? What happened? Um, what happened was we have a, we now have the ability to have software that can understand something. They, they can understand, uh, why, you know, what is... First of all, y- you, we digitized everything. That was, you know, like for example, gene sequencing, you digitize genes. But what does it mean? That sequence of genes, what does it mean? Uh, we've digitized amino acids, um, but what does it mean? Uh, and so we now have the ability, we digi- digitize words, we digitize sounds, uh, we digitize, uh, images and videos. We digitize a lot of things, but what does it mean? We now have the ability through, um, a lot of studying, a lot of da- data and from the patterns and relationships, we, we now understand what they mean. Not only do we understand what they mean, we n- 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, we learned about speech and, and words and, and paragraphs and vocabulary in the same context. And so we found correlations between them, and they're all, you know, registered, if you will. Registered to each other. And so now we, uh, not only do we understand, uh, the modality, the meaning of each modality, we can understand how to translate between them. And so, uh, for obvious things, you could caption video to text, that's captioning. Uh, text to, uh, images, Midjourney. Uh, text to text, ChatGPT, amazing things. And so, so we now, we now know that, uh, we understand meaning and we can translate. Uh, the translation of something is generation of information. And, and, um, uh, and all of a sudden you, you have to take your, you take a step back and ask yourself, um, uh, what is the implication in every single layer of everything that we do? And so I'm exercising in front of you, I'm reasoning in front of you, uh, the same thing I did a quarter, uh, 15 years ago when I first saw, um, uh, Alex Net, uh, some 13, 14 years ago, I guess, um, uh, how I reasoned through it. Uh, what did I see? How interesting. What can it do? Very cool. But then most importantly, what does it mean? What does it mean? What does it mean to every single layer of computing? Because, you know, we're in the world of computing. And so what it means is that, that the way that we, um, process information fundamentally will be different in the future. That's what NVIDIA builds, you know, 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, write in the future will be different, new applications. And then als- also the processing of those applications will be different. What was historically a retrieval-based model where, uh, inf- uh, information was pre- prerecorded, if you will, almost. You know, we wrote the text, prerecord it, and we retrieved it based on, uh, some recommender system algorithm. In the future, uh, some seed of information will be, will be, uh, the starting point. We call them prompts, you, as you guys know. And then we generate the rest of it. And so the future of computing will be highly generated. Well, let me give you an example of what's happening. For example, uh, we're having a conversation right now. Very little of the information I'm trans- I'm conveying to you is retrieved. Most of it is generated. It's called intelligence. And so in the future, we're gonna have a lot more generative. Our computers will, will perform in that way. It's gonna be highly generative instead of highly retrieval-based. Then you go back and you're gonna ask yourself, you know, n- now for, for, you know, entrepreneurs, you gotta ask yourself, uh, what industries will be disrupted therefore? Will we think about networking the same way? Will we think about storage the same way? Will we think about... Would 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, every question. So we don't have to be a- as abusive of, of transformation, information transporting as we used to. Um, uh, what's gonna be more? What's gonna be less? Uh, what kind of applications? You know, et ce- et cetera, et cetera. So you can go through the entire industrial spread and ask yourself what's gonna get disrupted, what's gonna get, be different, what's gonna get newed, you know, so on and so forth. And, and that reasoning starts from what is happening. What is generative AI? Found- foundationally, what is happening? Go back to first principles with all things. There

12. 41:30 – 44:06

    Designing organizations from first principles: information flow, purpose, and culture as an operating system

    1. JHJensen Huang41:30

       was something I was gonna tell you about organization. You asked the question and I forgot to answer it. The way you create an organization, by the way, someday, um, don't worry about how other companies' org charts look. You start from first principles. Remember what an organization is designed to do. The organizations of the past where there's a king, you know, CEO, and then, then you have all these, you know, the royal subjects, you know, the royal court, and then e-staff, and then you keep working your way down. Eventually, they're employees. Well, the reason why it was designed that way is because they, 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. I don't h- I only have 30,000 employees. I would like them, none of them to die.

    2. SJShantam Jain42:25

       [laughs]

    3. JHJensen Huang42:27

       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. Second, the question is what is NVIDIA, what does NVIDIA build? An organization is designed so that we could build what it, whatever it is we build better. And so if we all build different things, why, why are we organized the same way? Why would, why would this organizational machinery be exactly the same irrespective of what you build? It doesn't make any sense. You build computers, you organize this way. You build healthcare services, you're built exactly the same way. It makes no sense whatsoever. And so you had to go back to first principles. Just ask yourself what kind of machinery, what, what is the input, what is the output? What are the properties of this environment? You know, what are the, what is the, what is the, what is the forest that this animal has to live in? What are its characteristics? Is, is it stable most of the time and you're trying to squeeze out the last drop of water? Or is it changing all the time, being attacked by everybody? And so you gotta understand s- you know, for... 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. And I was fortunate that, that when I was 29 years old, you know, I had the benefit of, of, of taking a step back and asking myself, you know, how would I build this company for the future and what would it look like? And, you know, what's the operating system, which is called culture. What do we, what kind of behavior do we en- en- encourage, enhance, and what, what do we discourage and not enhance? You know, so on and so forth. And anyways.

13. 44:06 – 46:49

    Redefining tomorrow: purpose, working backwards from impact, and NVIDIA’s long arc

    1. SJShantam Jain44:06

       I wanna save time for audience questions, but, uh, this year's theme for View From The Top is Redefining Tomorrow. And one question we've asked all of our guests is, Jensen, as the co-founder and CEO of NVIDIA, if you were to close your eyes and magically change one thing about tomorrow, what would it be?

    2. JHJensen Huang44:29

       D- w- were we supposed to think about this in advance?

    3. SJShantam Jain44:31

       [laughs]

    4. SPSpeaker44:34

       [laughs]

    5. JHJensen Huang44:36

       I, I, I'm gonna give you a horrible answer. Um, l- I, I don't know that it's one thing. Look, there are a lot of things we don't control. You know, there are a lot of things we don't control. Um, 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, um, everybody says, you know, the world was better because you were here. And so I think that the, that to me, um, I live, I live my life kinda like this. I go forward in time, and I look backwards. So you asked me a question that's exactly from a, 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 kinda read my history. We did this, and we did that way, and we broke that problem down. Does that make sense? And so it's a little bit like, um, how you guys solve problems. You figure out what is the end result that you're looking for, and you work backwards to achieve it. And so, uh, I imagine NVIDIA, uh, making a unique contribution to advancing the f- the future of, of, uh, of computing, which is the single most important instrument of all humanity. Now, it's not about our self, 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. And, uh, I, when I look backwards, I believe that we made, uh... I believe that, that we're gonna be remembered as a company that kinda 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.

14. 46:49 – 49:59

    Audience Q&A: technical and market challenges ahead, biology and humanoid robotics, and broader societal concerns

    1. SPSpeaker46:49

       I'm part of the GSP 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?

    2. JHJensen Huang47:03

       First of all, can I just tell you what was going on through my head? As you say, what challenges, the list that flew by my head-

    3. SPSpeaker47:12

       [laughs]

    4. JHJensen Huang47:13

       ... was so, so large, uh, that, that I was trying to figure out what to select. Um, now, the, the honest truth is, is that when you ask 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 were to, you know, chosen yesterday, um, it might've been market creation challenges. There are some markets that I, gosh, I just desperately would love to create. I just... Can't we just do it already? You know? 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. And, and so some of the things that I would love, I would love for the world of biology to, to be at a point where it's kinda like the world of chip design 40 years ago. Computer-aided and design, um, uh, EDA, that entire industry really made possible for us today. And I believe we're gonna 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, a combination of a whole bunch of genes. Um, what does a cell mean? It's kinda like, what does that paragraph mean? Well, if we could understand a, uh, a cell like we can understand a paragraph, imagine what we could do. And so, uh, so, so I'm, I'm anxious for that to happen. You know? I'm kind of excited about that. Uh, there's some that I'm just excited about, uh, that I know we're around the corner on. For example, uh, humanoid robotics.

    5. SJShantam Jain49:06

       Yeah.

    6. JHJensen Huang49:06

       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, uh, manipulation? And so, so these kind of computer science techniques, you- once you figure something out, you ask yourself, "Well, if I can do that, why can't I do that?" And so I'm excited about those kind of things. Um, and, and so that challenge is kind of a happy challenge. Uh, some of the, uh, some of the other challenges, some of the other challenges, of course, are industrial, and geopolitical, and they're social, and, and... But you've heard all that stuff before. These are all true. You know? The social issues in, in, in the world, uh, the geopolitical issues in the world, uh, why can't we just get along, uh, at things in the world? Why do I have to say those kind of things in the world? Um, why do we have to say those things and then amplify them in the world? Uh, why do we have to judge people so much in the world? Uh, you know, all those things, you guys all know that. I don't have to say those things over again.

15. 49:59 – 54:14

    AI pace and regulation: safety technologies, domain-specific regulation, and open social questions

    1. SJShantam Jain49:59

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

    2. JHJensen Huang50:14

       Uh, yeah, that's, uh, the answer is yes and no. Um, we need, uh, you, you know the, the, the, the greatest breakthrough in, uh, modern AI, of course, deep learning, and it, it e- enabled great progress. But another incredible breakthrough is something that, that humans know and we practice all the time, uh, and we just invented it for, uh, for language models, called, uh, grounding, reinforcement learning, human feedback. Um, I provide reinforcement learning human feedback every day. That's my job. Um, and for their, for the parents in the room, uh, you're providing reinforcement learning human feedback all the time. Okay? Now, we just figured out how to do that, um, at a system- systematic level for artificial intelligence. There are a whole bunch of other technology n- necessary to, uh, guardrail, uh, fine-tune, ground. For example, how do I generate, um, how do I generate, uh, uh, uh, tokens that obey the laws of physics? You know, right now things are floating in space and doing things, and they don't l- they don't obey the laws of physics. Um, uh, how do- That requires technology. Guardrailing requires technology. Fine-tuning requires technology. Alignment requires technology. Safety requires technology. The reason why planes are so safe is because, you know, all of the autopilot systems are, are surrounded by diversity and redundancy, and all kinds of different functional safety and active safety systems that were invented. I need all of that to be invented much, much faster. Uh, you also know that, that the border between security and artificial intelligence, 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, in order to protect us from artificial intelligence. And so, so in a lot of ways, we need technology to go faster. A lot faster. Okay? Uh, regulation, there's two types of regulation. Uh, there's social regulation. I don't know what to do about that. But there's product and services regulation. Know exactly what to do about that. Okay, so, um, uh, the FA- the FAA, the, uh, FDA, the, uh, NHTSA, you name it, all the, all the Fs and all theNs and all the, you know, FCCs, the, uh, the, they all have regulations for products and services that are, have particular use cases. Uh, um, uh, bar exams, and doctors, and, you know, so on and so forth, um, you all have, uh, qual- qualification exams. You all have standards that you have to reach. You all have to, uh, continuously be certified, uh, 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 of it. The regulator who is regulating accounting should not be the regulator that regulates a doctor. You know? I love accountants, um, but, uh, uh, I just w- w- you know, i- if I ever need a open heart surgery, the fact that they can close books is interesting, but not sufficient.

    3. SJShantam Jain53:15

       [laughs]

    4. JHJensen Huang53:15

       And so-

    5. SJShantam Jain53:16

       [laughs]

    6. JHJensen Huang53:16

       And so I, I would like, I would like, um, all of those, all of those fields that already have products and services, um, to also enhance their regulations in context of, in the context of AI. Okay? But I left out this one very big one, which is the s- the social implication of AI, and how do you, how do you deal with that? I don't have great answers for that. Um, but, you know, enough people are talking about it. But it's important to subdivide all of this into chunks. Does that make sense? So that we don't, 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, and, you know, that doesn't make any sense. We should make sure that we, we do the right things there. Okay? Very practical things. May I take one more question?

    7. SJShantam Jain54:01

       Jensen- Well, we have-

    8. JHJensen Huang54:02

       Since-

    9. SJShantam Jain54:03

       ... some rapid-fire questions for you, as View From The Top tradition.

    10. JHJensen Huang54:06

        Ugh. Okay.

    11. SJShantam Jain54:08

        Which we can-

    12. JHJensen Huang54:08

        I was trying to avoid that.

    13. SJShantam Jain54:09

        [laughs] Okay. All right.

    14. JHJensen Huang54:13

        All right.

    15. SJShantam Jain54:13

        Fire away. Fire away.

    16. JHJensen Huang54:14

        Okay.

16. 54:14 – 56:06

    Rapid-fire close: Denny’s memories, jackets, refusing the textbook, and a final core-belief message

    1. SJShantam Jain54:14

       Well, your first job was at Denny's. They now have a booth dedicated to you. What was your fondest memory of working-

    2. JHJensen Huang54:20

       My second job was AMD, by the way.

    3. SJShantam Jain54:21

       [laughs]

    4. JHJensen Huang54:24

       Is there a booth dedicated to me there?

    5. SJShantam Jain54:26

       [laughs]

    6. JHJensen Huang54:27

       I'm just kidding. [laughs]

    7. SJShantam Jain54:32

       Um, I'm gonna-

    8. JHJensen Huang54:32

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

    9. SJShantam Jain54:36

       Yeah. Um, if there were a worldwide shortage of black leather jackets, what would we be see you wearing?

    10. JHJensen Huang54:44

        Oh, no, I've g- I've got a large reservoir of black jackets.

    11. SJShantam Jain54:47

        [laughs]

    12. JHJensen Huang54:48

        I'm the, uh, I'll be the only person who is, who is not concerned.

    13. SJShantam Jain54:52

        [laughs]

    14. JHJensen Huang54:52

        [laughs]

    15. SJShantam Jain54:54

        Um, you spoke a lot about textbooks. If you had to write one, what would it be called?

    16. JHJensen Huang55:02

        I, I wouldn't write one.

    17. SJShantam Jain55:02

        [laughs]

    18. JHJensen Huang55:04

        You're asking me a hypothetical question that has no possibility of me-

    19. SJShantam Jain55:07

        [laughs]

    20. JHJensen Huang55:08

        ... of, of, uh...

    21. SJShantam Jain55:09

        That's fair. And finally, if you could share one parting piece of advice to broadcast across Stanford, what would it be?

    22. JHJensen Huang55:17

        Uh, it, it's not a word, but, but, um, uh, you know, have a core belief. Um, gut check it every day. Uh, pursue it with all your might. Pursue it for a very long time. Surround yourself with people you love, and take them on that ride. So that's the story of NVIDIA.

    23. SJShantam Jain55:48

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

    24. JHJensen Huang55:50

        Thank you very much.

    25. SJShantam Jain55:51

        [clapping] Thank you. Thank you. [outro music]

Episode duration: 56:26