Jensen Huang: Founder and CEO of NVIDIA (YouTube Video Transcript)

(00:00:00) Your YouTube transcript will appear here (00:00:09) So, (00:00:09) >> nice to be here. (00:00:10) >> Thanks. Welcome to the inaugural podcast (00:00:12) for a bit personal. (00:00:14) >> The inaugural? (00:00:14) >> Yeah, the inaugural. You're the first (00:00:16) one. So, (00:00:16) >> I'm the first. (00:00:17) >> You were the first that I asked and you (00:00:19) said yes at one second. (00:00:21) >> Oh dear. (00:00:21) >> So, you regret you said yes. (00:00:23) >> Well, I didn't realize it was going to (00:00:25) be a bit personal. (00:00:26) >> Yeah. (00:00:27) >> Yeah. We're going to go deep. (00:00:29) >> Okay. Yeah. Well, just keep it nice and (00:00:30) shallow. (00:00:32) >> You like that better anyway. So, okay. (00:00:34) So, what do you So, the concept of the (00:00:36) podcast Yeah. (00:00:37) >> is that the general public is really (00:00:40) very interested in people like you that (00:00:43) are determining the future of (00:00:44) technology, which is the future of their (00:00:46) world, right? So, it's let's find out (00:00:49) what your values are, your personal (00:00:51) story behind your public success. (00:00:53) >> You like that concept? (00:00:55) >> No, not really. (00:00:56) >> Yeah. (00:00:56) >> Not really. But (00:00:58) >> you're a celebrity. (00:00:59) >> I (00:00:59) >> People want to know about celebrities. (00:01:01) >> I don't see myself as a celebrity and (00:01:04) and um I'm not a celebrity. (00:01:07) >> I I just happen to run a very important (00:01:09) company and and I'm the the CEO of of uh (00:01:13) one of the most successful technology (00:01:15) companies in history. Um we uh we made (00:01:19) some good decisions a long time ago. uh (00:01:21) you know 1993 uh we wanted to reinvent (00:01:25) computing and we had a perspective about (00:01:26) how computers ought to be built and and (00:01:29) it was it was a not very popular view (00:01:32) for a very long time and it was rather (00:01:34) controversial in fact and everybody (00:01:36) thought that uh microprocessors and CPUs (00:01:40) and this is the time you and I met I (00:01:41) mean you know most people don't realize (00:01:43) I mean the audience probably don't (00:01:44) realize (00:01:45) >> that you and you and I met 1994 probably (00:01:48) 1993 late you know that kind of Right. (00:01:51) >> And so, uh, Nvidia was was, uh, uh, (00:01:54) trying to do then what we're trying to (00:01:56) do now, reinvent computing. And, and, (00:01:59) um, at the time in Silicon Valley, as (00:02:02) you know, uh, it was during the era of (00:02:05) CPUs and Moore's law and the PC (00:02:07) revolution. (00:02:08) >> Mh. (00:02:08) >> And in fact, all of your early (00:02:09) customers, right, were all PC chipset (00:02:13) startups. (00:02:14) >> They they were the formation of the (00:02:16) fabulous semiconductor industry. Cirrus (00:02:18) Logic, S3, Western Digital, Trident. (00:02:22) Remember all those companies? (00:02:23) >> Yes. (00:02:23) >> Those those were the the the forefathers (00:02:26) of of Nvidia. And uh um and and here we (00:02:32) here we are. Uh we're trying to we're (00:02:34) trying to create a new computing (00:02:36) approach. And it took it took 33 years (00:02:38) for this to happen. Um but I just I just (00:02:41) happened to be the CEO of that company. (00:02:43) That's all. And it it happened really um (00:02:46) for maybe not for you but for the world (00:02:48) it happened very suddenly. It was (00:02:51) basically November of 20 2023 the whole (00:02:54) world changed. (00:02:56) >> So how was that transition? (00:02:59) >> Well (00:03:01) you know in in order to create the (00:03:04) future you have to live in the future (00:03:06) long before it happens. (00:03:09) And it's to be honest when we first when (00:03:12) we first started CUDA we invented the (00:03:14) technology. The thing that I'm really (00:03:16) proud about Nvidia is we're great at (00:03:19) inventing technology (00:03:21) but then inventing products to carry the (00:03:25) technology to market. You know there are (00:03:28) countless companies who and researchers (00:03:30) inventors who have created technology (00:03:31) and they they're the people that said uh (00:03:34) that say things like oh um I did that (00:03:36) before. uh and or I thought of that or I (00:03:40) invented that or you know um and so (00:03:43) anyways it always it always kills me um (00:03:46) that all these great inventors didn't (00:03:49) also have the benefit of having great (00:03:51) product inventors. These are the (00:03:54) innovators that take these inventions (00:03:57) that then invent products to take it to (00:04:00) market. But then you also have to invent (00:04:02) strategies to take it to market. (00:04:03) >> Right? (00:04:04) >> And then you have to invent in fact even (00:04:06) the market. You have to shape the market (00:04:09) to receive your products that you (00:04:11) invented and the strategies that you (00:04:13) developed and and Nvidia was was a is (00:04:17) really built to be a company that can (00:04:19) invent technology, invent products, (00:04:21) invent strategies and invent ecosystems (00:04:23) and markets and we've done that (00:04:24) repeatedly. And so I I think the in a (00:04:27) lot of ways I've been living in this (00:04:29) future for a long time. (00:04:31) >> There was a there was a strategy a long (00:04:32) time ago. (00:04:34) We don't do it as much anymore, but it (00:04:35) was called CUDA everywhere. and and (00:04:38) people tell tell stories of me shleing (00:04:41) cuda, you know, to universities and (00:04:43) startup companies and established (00:04:45) companies and um I I shle CUDA (00:04:48) everywhere and and um sometimes there'll (00:04:50) be a there'll be an audience of (00:04:52) literally three people and I would pull (00:04:55) out my and back then, you know, I pull (00:04:56) out my laptop and and you know, and (00:04:59) present CUDA and tell them about why (00:05:01) this is going to be why this is going to (00:05:03) change the world. And I I visited (00:05:05) researchers and laboratories and went to (00:05:07) conferences. Uh you know, I put put on (00:05:10) more more coupile than any human in the (00:05:13) world. And and so so I for a for a long (00:05:16) time I've been living in this future. (00:05:18) You tell the story long enough, you kind (00:05:20) of feel like it's happened. (00:05:21) >> Mhm. (00:05:22) >> Um and and so I I think I think all of (00:05:27) this is still a great great delight. And (00:05:30) um uh (00:05:33) you know in in my mind it's not (00:05:35) surprising because the first principles (00:05:38) that we built the company on are (00:05:40) fundamental principles. They're not (00:05:42) based on a hunch or it's not based on (00:05:45) you know a taste or it it's on (00:05:48) fundamental principles of computer (00:05:50) science. And and so what is happening (00:05:53) now uh isn't in a lot of ways (00:05:56) inevitable. The thing that that I would (00:05:58) say though is that um by making (00:06:02) something go incredibly fast, if you (00:06:04) would make something go a thousand times (00:06:06) faster or a thousand times larger or a (00:06:08) thousand times smaller, what whatever (00:06:10) that is, some phase change happens (00:06:14) and that phase change and the and the (00:06:18) the result of that the state that it (00:06:22) results in is surprising. (00:06:23) >> Okay. And so so I think in a lot of ways (00:06:27) um we knew that deep learning could be a (00:06:29) lot larger. That's which is the reason (00:06:31) why we pivoted the whole company behind (00:06:32) it. We knew that Alexet couldn't have (00:06:34) been the end of it and that the (00:06:36) architecture is something that's quite (00:06:38) scalable and the amount of data in the (00:06:41) world is abundant. And so that was a (00:06:43) natural resource we felt that was was (00:06:46) achievable. The one technology that that (00:06:48) I knew was going to be an obstacle to us (00:06:50) was unsupervised learning or (00:06:52) self-supervised learning that the that (00:06:55) the computer could learn by itself (00:06:57) without human labeling because humans (00:06:59) would be the bottleneck and and when (00:07:01) that happened uh I knew we were off to (00:07:04) the races and and you know people still (00:07:07) I I was just on an investor road show (00:07:09) and and people tell me that that I told (00:07:11) them right around that time that there (00:07:14) was a phase change and if you go back (00:07:16) and listen to my earnings call when I (00:07:17) jump into jump into a topic that that's (00:07:20) really important for the world. Um I (00:07:22) emphasized it really really clearly and (00:07:24) I talked about on every single uh (00:07:25) investor road show and everywhere I go I (00:07:27) would talk about those things and that (00:07:29) unsupervised learning is or self-super (00:07:32) supervised learning has really made a (00:07:33) great achievement and then now we're (00:07:35) then the scaling laws were unleashed and (00:07:37) then we were boom you know off to the (00:07:39) races. Now the the the type of problems (00:07:43) we're able to solve as a result of that (00:07:45) is still surprising to me, you know, (00:07:46) because you knew the face shift would (00:07:48) come and you knew the the platform shift (00:07:50) would come. (00:07:51) But all as a result of of all that, (00:07:54) we're now learning the language of (00:07:56) proteins and we're learning the language (00:07:58) of cells and we're learning the language (00:07:59) of you know quantum and we're learning (00:08:02) the language of all these (00:08:03) representations of all these different (00:08:04) things and and the language that we used (00:08:08) to represent information in the past is (00:08:12) now being reinvented. you know, (00:08:15) everything from geometries and textures (00:08:17) to now 3D gouge and splats and and all (00:08:19) of these different representation, you (00:08:21) know, it's kind of like like all of a (00:08:23) sudden we became so smart that the (00:08:26) English language actually got changed, (00:08:28) >> okay? (00:08:28) >> That we no longer use the words and (00:08:30) vocabulary and structure and grammar (00:08:31) that that we use because all of a sudden (00:08:34) we became so much smarter that we we can (00:08:38) communicate in another dimension. And so (00:08:41) we're maybe beeping and blpping at each (00:08:43) other. You know, it's, you know, it's (00:08:45) kind of like that that movie Arrival, (00:08:46) you know, all of a sudden we're just (00:08:48) looking at shapes. And the amount of (00:08:50) information just looking at shapes, (00:08:53) um, you know, causes to be able allows (00:08:55) us to communicate at much much deeper (00:08:57) way and much faster way. And so, so it (00:09:00) it's incredible that we're we're not (00:09:02) only solving problems that are (00:09:04) completely unimaginable before and and (00:09:08) we're doing it at a speed now. what used (00:09:10) to be Moor's law time, you know, (00:09:12) Nvidia's time, Nvidia law time is (00:09:14) completely (00:09:15) >> right. (00:09:15) >> I mean, it's a thousand times faster. (00:09:18) And so the next 10 years is going to be (00:09:20) extraordinary. That that I think is that (00:09:22) that's exciting. (00:09:23) >> So the the kind of confidence that it (00:09:25) takes to do what you did to be able to (00:09:28) see into the future and be absolutely (00:09:30) confident that it's going to happen. (00:09:32) >> So we, as you said earlier, we met in (00:09:34) 1994 (00:09:35) >> So (00:09:36) >> I've been the same since. (00:09:37) >> I know. Yeah. I was in my 20s. You maybe (00:09:39) are a little bit older than me (00:09:40) hopefully. (00:09:41) >> I was 29 when 30 when Yeah. (00:09:44) >> So I remember our very first meeting at (00:09:47) the um headquarters in Sunnyville (00:09:50) way or something. (00:09:51) >> Exactly. It's a it's a massage parlor or (00:09:53) something. Yeah. Acupuncture parlor. (00:09:56) Yeah. (00:09:57) >> That makes sense. (00:09:59) Anyway, I I was interviewing you for a (00:10:01) magazine and you and I said, "So, (00:10:03) Jensen, are you worried about sort of (00:10:05) the rotating door in Silicon Valley, (00:10:07) people coming and going?" Because a lot (00:10:08) of CEOs are complaining about this. And (00:10:11) again, you're 29 or 30 years old. And (00:10:13) you were like, "Hm, Jody, Nvidia is (00:10:16) neither a church nor a prison. You don't (00:10:19) have to come and you don't have to (00:10:20) stay." (00:10:21) >> And I remember being so impressed by I (00:10:23) was like, "Who is this guy?" Such (00:10:25) confidence and such wisdom at a young (00:10:27) age. And I remember Morris Chang has a (00:10:29) similar story when he first met you that (00:10:31) you immediately said, "I'm going to be (00:10:32) your biggest customer or one of your (00:10:34) biggest customers." And he's like, (00:10:36) >> "Wow, that is that's a lot of gumption." (00:10:38) >> Mhm. (00:10:39) >> So, where did that confidence come from (00:10:42) at such an early age? (00:10:43) >> Well, you know, (00:10:46) it it's it's rough to know everything, (00:10:48) you know. I'm just kidding. No, I by the (00:10:52) way, Morris will be happy to know Nvidia (00:10:54) is TSMC's largest customer now. Yeah. (00:10:57) Yeah. I'm sure he's very proud of you. (00:10:59) >> Yeah. And and I'm proud of him. (00:11:02) The the uh we were his largest customer (00:11:06) during the PC revolution. (00:11:07) >> Okay. (00:11:08) >> And now we're his largest customer (00:11:10) again, and I'm very happy about that. Um (00:11:16) you know, I I say that you you you have (00:11:19) to believe what you believe. And so so (00:11:23) your belief shouldn't be based on (00:11:25) anecdotal (00:11:27) you know somebody said something (00:11:28) therefore you believe it. You have to (00:11:30) reason through for what reason do you (00:11:32) believe this and break down um your (00:11:36) reasoning into into sound first (00:11:38) principles and and and then you have to (00:11:41) you have to check it on a regular basis (00:11:43) that that these these principles that (00:11:46) you're you're building everything that (00:11:48) you you believe on believe in and are (00:11:50) doing are sound that the foundation is (00:11:53) sound. And if it's if it's not sound, if (00:11:55) if it changed for some reason, it wasn't (00:11:57) a first principles, maybe it wasn't (00:11:59) wasn't defined by anchored in physics or (00:12:02) anchored in ground truth or something (00:12:03) like that. If that changes, then you (00:12:05) reevaluate and you move on. And so so (00:12:08) I've always lived in that way. (00:12:09) >> Okay. (00:12:10) >> Um and so if you believe in something, (00:12:12) you owe it owe it to yourself to do (00:12:14) something about it. And uh I believed in (00:12:17) in what we're doing. I believed it in (00:12:19) 1993. I believe it today. and and and (00:12:22) therefore if you believe this (00:12:26) then so what (00:12:28) and just keep reasoning through it and (00:12:31) then you know I I I'm doing that (00:12:33) reasoning exercise in my head (00:12:35) continuously (00:12:37) >> I'm constantly you know re-evaluating (00:12:40) constantly extrapolating I'm constantly (00:12:43) re-evaluating the past and that's you (00:12:45) know if you're having meetings with me (00:12:47) and yesterday we're we're we're having (00:12:49) meetings uh uh in so many different (00:12:52) meetings, I would reason through um uh (00:12:55) the past again. And this is how we got (00:12:58) here and and notice um all of those (00:13:02) assumptions were right, but some of (00:13:03) those assumptions were in fact wrong. (00:13:05) And as it led to this led to this (00:13:07) moment, we we were agile and we (00:13:09) readjusted. But it's always good to go (00:13:12) back in time and re re-evaluate and (00:13:15) re-reason through that. It it teaches (00:13:17) you how to reason forward. And so, so (00:13:19) because I've always done that, um, (00:13:23) I you know, I I just live in that truth. (00:13:26) And, uh, to this day, I still believe (00:13:29) that I still feel like I'm an employee (00:13:32) of this company. And, uh, I care about (00:13:36) this company a lot, but there are a lot (00:13:39) of employees that care about this (00:13:40) company a lot. (00:13:43) The CEO was always designed to be uh in (00:13:48) a well-governing well-governed cu uh (00:13:51) company. (00:13:52) uh the CEO was was always designed to (00:13:55) report to the board of directors and the (00:13:57) board of directors report to (00:13:58) shareholders and and um uh if the CEO uh (00:14:02) doesn't do his job you know according to (00:14:05) 12 13 15 board members who are however (00:14:08) size the the board is the CEO is let go (00:14:12) and so so therefore it's an employment (00:14:16) in in an institution (00:14:18) um and and it's not like a church (00:14:21) because not everybody gets to come and (00:14:24) it's not like a prison because not (00:14:26) everybody has to stay, (00:14:27) >> right? (00:14:28) >> And so (00:14:30) that state of mind keeps you grounded. (00:14:32) It keeps you humble. It keeps you um (00:14:37) keeps you fresh. You're always earning (00:14:39) your job. And you know, sometimes people (00:14:44) ask me, you know, Jensen, do do you love (00:14:46) your job? Um, I don't love my job every (00:14:51) day, but I do it to my mightiest every (00:14:53) day, (00:14:54) >> right? (00:14:55) >> And and um (00:14:58) I and I think that that that comes from (00:15:00) that whole package of of recognizing (00:15:04) that one, I'm the best person for the (00:15:06) job. I believe that. And two, I have to (00:15:10) earn being the best person for that job (00:15:12) every day. (00:15:14) So I mean you have been you are Nvidia (00:15:17) and Nvidia is you. I mean that's you've (00:15:19) become that (00:15:20) >> I'm the most frequently taken picture of (00:15:23) the people of Nvidia. Yeah. But whoever (00:15:26) is the next CEO of this company is going (00:15:27) to be the (00:15:28) >> is can there really be a next CEO of (00:15:30) this company? (00:15:31) >> Well there will never be one like me (00:15:34) and and um and the reason for that is (00:15:37) because (00:15:38) because I was raised by the company. You (00:15:41) know, when I first started started in (00:15:42) video, I didn't know anything about (00:15:44) being CEO or strategist or or product (00:15:47) maker or, you know, industry creator or, (00:15:50) you know, I I I didn't know how to do (00:15:51) any of that. Um, I knew how to raise (00:15:54) money. I didn't know how to talk to (00:15:55) shareholders. Um, understand the (00:15:58) sensibility of shareholders and policy (00:16:00) makers and, you know, country leaders (00:16:02) and, um, company leaders. I didn't know (00:16:05) any of that. I didn't know the (00:16:06) sensibility of employees and how to (00:16:08) create a culture. and what does it even (00:16:09) mean to to say culture? Um I I couldn't (00:16:14) formulate a company strategy if I tried. (00:16:16) And so that was day one. And in that 30 (00:16:19) in this 33 years, uh I I've become (00:16:22) better at all of that. And and um I you (00:16:27) know, if there's if there's a if there's (00:16:29) ever a Yoda of company strategies, you (00:16:32) know, and and and industry creators, you (00:16:34) know, probably looks, you know, a short (00:16:36) little guy like me. And so, and so I I (00:16:39) think the um I I've dedicated my my (00:16:42) career learning these things and I'm a (00:16:44) good student. Um and I also bring bring (00:16:48) to the job a level of intensity and and (00:16:51) deep care that it's harder to hire into. (00:16:55) you know, there in a lot of ways, you (00:16:58) know, Nvidia is is one of my children (00:17:01) and and um I care about it uh as if it's (00:17:05) one of my children and and um and my (00:17:09) children even help me, you know, raise (00:17:12) those children and and and so so in a (00:17:16) lot of ways (00:17:17) in a lot of ways I feel about it um that (00:17:21) that is hard to replace that that is (00:17:23) true, but that's because I've been doing (00:17:25) it for 33 years and I I've seen every (00:17:27) aspect of it. It's its successes and (00:17:29) failures and setbacks and um things that (00:17:32) it did, you know, smart and dumb and (00:17:34) stupid and, you know, I I I I've seen (00:17:36) all that. And so you I have a feeling (00:17:39) about this company that you can't you (00:17:41) can't easily replace by hiring somebody (00:17:43) who's who's just good at doing (00:17:45) something. And so so I get that. Um uh (00:17:49) on the other hand uh the way that the (00:17:52) Nvidia management team is set up uh I've (00:17:54) got almost 60 direct reports (00:17:56) >> right (00:17:57) >> and and I have six there are 60 people (00:18:00) who could be world-class CEOs for many (00:18:02) other companies and I reason in front of (00:18:05) them constantly I mean literally all the (00:18:08) all the time and every single decision I (00:18:10) made I've made in front of them. I've (00:18:12) I've reasoned through it in front of (00:18:14) them. Um uh I've spoken about successes (00:18:19) and setbacks and challenges and (00:18:21) adversity all in front of them. And so (00:18:23) in a lot of ways the Nvidia has 61 CEOs (00:18:28) and and so um and they care deeply about (00:18:32) this company. You know, many of them (00:18:34) have been here for a long time, 33 years (00:18:36) in some cases. And so so I I um I think (00:18:40) Nvidia has just been built uh like no (00:18:43) other company ever has been built and it (00:18:47) also (00:18:48) speaks to our resilience like no other (00:18:51) company will have. (00:18:52) >> So obviously that kind of um structure (00:18:55) that you have is very legendary in the (00:18:56) industry now. Um everybody talks about (00:18:58) it these nearly 60 reports. So in order (00:19:01) for that to work those people have to be (00:19:03) exceptional. (00:19:04) >> Yeah. (00:19:05) >> Okay. and not just brilliant because (00:19:06) there's a lot of brilliant people in (00:19:07) Silicon Valley. They have to be (00:19:08) exceptional specifically for Nvidia. (00:19:10) >> Yeah. (00:19:11) >> So tell me a little bit about how you (00:19:13) curate those people. And then secondly, (00:19:16) there's been many times that you didn't (00:19:18) hire in a position until you found the (00:19:21) right person. And I'm thinking (00:19:22) specifically of Colette. You interviewed (00:19:25) 22 CFOs before you hired her. And now (00:19:27) she's, you know, she's a legend in her (00:19:29) her own right uh on Wall Street. So (00:19:32) maybe how did you choose her and how do (00:19:34) you how do you curate those types of (00:19:36) people? What do you look for? (00:19:39) >> Uh (00:19:42) an empty empty chair is better than a (00:19:45) chair filled with the wrong person (00:19:48) and so I'm never in a hurry. (00:19:51) um (00:19:53) the company will keep moving on and and (00:19:59) whether it's a missing CEO or missing (00:20:02) you know VP of anything the company will (00:20:04) keep moving on (00:20:05) >> and and um and so you just have to have (00:20:09) the the confidence of what I just said. (00:20:12) If you if you can convince yourself of (00:20:14) what I just said (00:20:16) that these two ideas, the empty chair (00:20:19) and the company is going to keep moving (00:20:22) on, then it buys you enormous amounts of (00:20:25) time until you find somebody that is a (00:20:28) combination of a lot of things, (00:20:29) including you just like them. (00:20:31) >> Mhm. you know, uh, Colette on his on on (00:20:37) her first week, I think she asked me, (00:20:40) um, you know, Jensen, how long do you (00:20:41) want me to be your CFO? And I said, for (00:20:43) as long as we shall live (00:20:45) >> and (00:20:45) >> death do his part. (00:20:46) >> Yeah. Yeah. Because the alternative (00:20:48) doesn't make sense. Any other answer is (00:20:51) the wrong answer. (00:20:52) >> For what reason is there an end date? (00:20:55) And the end date is when when um I you (00:21:00) know she decides Nvidia is no longer (00:21:02) right for her. That applies to Colette. (00:21:04) That applies to all 60 of the Nvidia to (00:21:07) work reports and and um I I keep chairs (00:21:10) open for a long time. Mhm. (00:21:13) >> And the company just keeps on carrying (00:21:15) on and and people people swarm the the (00:21:19) mission, you know, whatever whatever the (00:21:22) mission is, whatever the job that needs (00:21:23) to be done, people will swarm it (00:21:24) anyways. And worst case, I'll do my best (00:21:27) and just carry on, (00:21:28) >> right? (00:21:29) >> You know, and so so I that that's just a (00:21:32) philosophy. Don't don't ever fill a (00:21:34) chair with the wrong person. Wait until (00:21:36) the right person comes along. and that (00:21:37) right person, you know, I'm asked all (00:21:40) the time, what what makes a great what (00:21:42) makes a great employee, what makes a (00:21:44) great leader. Surprisingly, I don't have (00:21:46) the answer. (00:21:47) >> Okay? (00:21:47) >> And the reason for that is this. Um, (00:21:50) they're all smart. (00:21:53) They're all competent. (00:21:55) You find me a you find me a a CFO (00:21:58) somewhere and I promise you they're (00:22:00) competent. (00:22:02) and they're competent enough (00:22:05) and so so that you hire you find me a (00:22:09) you you find me a whole bunch of (00:22:10) functions you find me a CEO (00:22:13) and I'm I work with CEOs all over the (00:22:15) world and they are all competent let's (00:22:18) just be clear about that (00:22:19) >> right (00:22:20) >> and and many of them when I'm working (00:22:22) with them I was like gosh you know (00:22:25) you're super competent and super smart (00:22:28) it's all completely true and yet in the (00:22:32) end what makes the magic of Nvidia is a (00:22:36) combination of the the the chemistry of (00:22:39) the people that are together. Um um but (00:22:43) mostly I would tell you (00:22:46) that it's it's just corporate character (00:22:49) and that character comes from somewhere. (00:22:52) That's what defines great companies. (00:22:54) Somehow uh there are a lot of companies (00:22:57) building chips. We invented the GPU, but (00:23:02) we're from a volume perspective, we're (00:23:05) the smallest GPU company in the world. I (00:23:07) know it sounds weird, but we are. (00:23:09) Everybody makes more GPUs than I do. (00:23:11) It's like, you know, some random person (00:23:13) makes more GPUs than we do. And so, so (00:23:16) clearly, (00:23:18) clearly it's not that. Um (00:23:23) and so so I think the (00:23:28) the somehow somehow there's a magic in (00:23:31) in in the corporate culture, the (00:23:34) corporate character. Um (00:23:38) how how teams come together during (00:23:41) adversity. (00:23:42) Um, I mean, people see us just kind of (00:23:46) strolling through life, but (00:23:49) getting Grace Blackwell into production, (00:23:52) uh, almost broke our company's back, but (00:23:54) it but we wouldn't let it. Um, it is (00:23:57) just extraordinarily complicated and (00:24:00) incredibly large scale and the (00:24:03) expectations were incredible and for us (00:24:06) to live up to it and exceed it um with (00:24:09) with it almost breaking our back (00:24:12) that that's 100% character, (00:24:15) >> right? (00:24:15) >> That's not intelligence. That's not hard (00:24:17) work. Um, there are a lot of people that (00:24:19) work hard. There are a lot of people (00:24:21) that are super smart. That is 100% (00:24:23) character. where that comes from. Um, (00:24:28) you you just you you can't interview (00:24:31) that into existence, you know. And the (00:24:34) thing that that I believe is this. (00:24:37) I actually kind of believe that you can (00:24:40) bring almost anybody into Nvidia and we (00:24:43) will (00:24:45) instill character into you. (00:24:48) and and um (00:24:52) that I think is the magic of our company (00:24:54) that that somehow we could we could (00:24:57) suffer pain (00:24:59) and we can we can endure incredible (00:25:02) challenges and come out of the other (00:25:04) side, (00:25:05) >> right? (00:25:05) >> And we could do it over and over and (00:25:07) over again. And very few companies can (00:25:10) do that as a team. Usually somebody gets (00:25:12) left behind. You know, usually what (00:25:14) happens is you you go through one of (00:25:16) these incredible challenges and then (00:25:19) somebody leaves because of a bad feeling (00:25:23) or because they were blamed and they (00:25:26) were fired or they were um uh they they (00:25:31) somehow felt uh it's always, by the way, (00:25:34) it's always somebody's fault. I I don't (00:25:35) want to, you know, let's let's be clear (00:25:37) about about building companies and and (00:25:39) teams. (00:25:41) at the end of the game we lost as a team (00:25:44) but there's no question who dropped the (00:25:46) pass and so we have to be clear about (00:25:48) that and and and we are clear about that (00:25:51) and because because we have such a safe (00:25:53) environment (00:25:55) um all the people who drop passes in the (00:25:58) in the past including myself and I've (00:26:00) dropped plenty passes and and the the (00:26:02) passes I dropped you know everybody's (00:26:04) watching the (00:26:07) nobody's been fired for dropping passes (00:26:10) And so, so this company has has (00:26:13) developed a culture, a personality, um, (00:26:16) a lot of it reflecting our own that (00:26:19) tolerance and forgiveness and and and (00:26:23) um, learning from mistakes and so long (00:26:25) as so long as maybe a couple things (00:26:28) that's really super important to me. So (00:26:31) long as (00:26:33) the team play the teammates gave (00:26:35) everything of themselves, (00:26:38) um, that's good enough for me. (00:26:40) So yeah, is that I mean you have a (00:26:43) reputation for really not liking to fire (00:26:44) people and hopefully no one likes to (00:26:46) fire people, but so that's your theory (00:26:48) that it's that you have to make these (00:26:50) people better or your team has to make (00:26:52) them better. (00:26:52) >> Yeah. Like the company made me better. (00:26:54) >> Mhm. (00:26:56) >> You know, I I wasn't that I I wasn't (00:26:59) then what I am today, you know, and and (00:27:02) what I know today. Um you know, no (00:27:05) volume encyclopedia could hold it. and (00:27:08) and um you know if somebody were to ask (00:27:11) me what did I learn over at Nvidia and (00:27:14) write a book I I wouldn't even know (00:27:16) where to start you know and just and so (00:27:19) so this company gave me the chance to (00:27:23) become what I am and this company also (00:27:27) gave the entire management team the (00:27:29) opportunity to become what they are (00:27:32) and I gotta tell you 100% of those 60 (00:27:36) people are different today than they (00:27:39) were when they started. (00:27:40) >> I can tell you they're great today. We (00:27:43) were fine in the beginning. We're good (00:27:45) in the beginning like anybody else. And (00:27:47) so, so the the company tortured (00:27:50) greatness out of us. And the the company (00:27:53) forged incredible character into us. (00:27:57) That's the magic of this company (00:28:00) >> that you could do that not lose the (00:28:02) person (00:28:04) and the company not giving up on you, (00:28:06) >> right? simultaneously. That's our (00:28:08) greatness, I think. And and can you can (00:28:11) you can you hire people into that? Yes, (00:28:15) I believe. So, and I've proven (00:28:18) repeatedly that we've done that. And and (00:28:21) and the people that come in, they're (00:28:23) good. And I see them and and I'm good. (00:28:25) And they're good. So long as I enjoy (00:28:28) working with them, you know, they have (00:28:29) to they they can't be a jerk. And so (00:28:32) long as they can't be self- serving, (00:28:34) they can't be, you know, I can't I can't (00:28:37) work with people that that can't answer (00:28:39) simple questions, they that's my (00:28:41) trigger. Um I to the extent that they (00:28:44) really want to be part of the team. Um (00:28:46) they can be transparent. They can be (00:28:47) vulnerable and they can learn. (00:28:51) They don't have to know it all, you (00:28:53) know, they just have to learn it all, (00:28:54) you know, and and um to the extent that (00:28:58) that all of that is true, we'll we'll um (00:29:01) we'll forge greatness into them. (00:29:04) >> This episode is brought to you by GSME. (00:29:07) GSME is a leading global provider of (00:29:10) tailored silicon solutions dedicated to (00:29:13) empowering semiconductor and system (00:29:15) companies with cutting edge technology (00:29:17) and unparalleled expertise. Founded in (00:29:20) 2022 by Farhot Jawenir, its (00:29:23) comprehensive range of services include (00:29:25) end-to-end chip design, full turnkey (00:29:27) manufacturing capabilities, rigorous (00:29:30) quality assurance, and strategic (00:29:31) incubation to help its partners bring (00:29:33) innovative products to market. At GSME, (00:29:36) they are committed to transforming (00:29:38) semiconductor manufacturing landscape by (00:29:41) optimizing processes, accelerating (00:29:43) product development cycles, and ensuring (00:29:45) faster time to market for next (00:29:47) generation applications. GSME gives full (00:29:50) visibility of the supply chain to its (00:29:52) customers. Now, back to the episode. (00:29:55) >> So, you talk a lot about pain and (00:29:56) suffering as kind of building blocks of (00:29:58) Nvidia and you've (00:29:59) >> That's our secret sauce. Yeah. (00:30:01) >> And you've said before that (00:30:03) >> Yeah. Come work with me. That's my gift. (00:30:06) >> It's very attractive. (00:30:07) >> Yeah. Exactly. Yeah. So what do you (00:30:10) think about I mean if you're if you're (00:30:13) person (00:30:14) it's you know when people when people (00:30:17) ask you know why why come work at Nvidia (00:30:21) >> pain and suffering is a a big part of it (00:30:23) >> right was there ever any sort of (00:30:26) sacrifice that you made that was too big (00:30:28) for what you accomplished at Nvidia (00:30:32) >> no (00:30:34) >> everything was worth it (00:30:37) >> um you you have to do it Right. I think (00:30:39) I think um (00:30:42) I I was I was fortunate in my case it (00:30:44) was for I was fortunate because Lori (00:30:47) Lori Madison Spencer (00:30:50) were were kind of you know grew up with (00:30:54) the company (00:30:56) and and um (00:30:59) I was fortunate that Lori always had a (00:31:01) great interest in a company and and she (00:31:04) didn't met all but she knew everything (00:31:07) about the company. she just, you know, (00:31:09) dedicated herself to read everything, (00:31:12) learn everything, and always be there. (00:31:14) And and um she's never missed an event. (00:31:18) Um she's never even missed one of our (00:31:21) campy little shareholder meetings back (00:31:24) in the old days when we did it live. and (00:31:26) and and so so um uh and her interest and (00:31:30) dedication to supporting the company and (00:31:32) me um rubbed off on the kids and and the (00:31:36) kids read everything, watched (00:31:38) everything, came to everything. Um you (00:31:42) know, they they probably they probably (00:31:43) listened to more bad speeches of mine (00:31:45) than any human ever. And (00:31:47) >> and um (00:31:48) >> the pain and suffering (00:31:49) >> the pain and suffering of that. And so I (00:31:51) was I was fortunate that they they had (00:31:54) the interest in the company and loved (00:31:55) the company the way I loved the company. (00:31:57) And and so so my sacrifices (00:32:02) for the company um probably for that (00:32:05) reason (00:32:07) didn't translate directly into a (00:32:09) sacrifice for them. Mhm. (00:32:10) >> I I missed most of the most of the (00:32:13) karate tournaments and I missed most of (00:32:15) their practices and um well nearly most (00:32:19) is is too generous for me but I would (00:32:21) say nearly all. Um and so (00:32:25) and back in the old days we didn't have (00:32:27) smartphones and so the the definition of (00:32:30) going to work means going to work and it (00:32:33) meant missing every dinner. It miss it (00:32:36) meant missing every weekend. Um, and so, (00:32:41) you know, I that's what we did. And so I (00:32:44) was fortunate that that um, you know, (00:32:48) our family chemistry made it made it (00:32:49) possible for for them to not feel (00:32:53) alienated, (00:32:54) >> you know, they they felt part of it the (00:32:56) whole time. (00:32:56) >> Right. Right. (00:32:57) >> You know, and (00:32:57) >> Yeah. No, I think that integration of (00:32:59) family life and work life, I mean, it it (00:33:01) works for some people. It worked for me, (00:33:02) too. I mean, my children, the um my (00:33:06) youngest went to meet Morris Chain when (00:33:07) he was four months old. (00:33:08) >> Yeah. (00:33:09) >> You know, so they um they've known this (00:33:11) industry and been around it for the (00:33:12) whole time, too. So, (00:33:13) >> and which one did I meet? (00:33:14) >> You met Elijah. (00:33:15) >> Elijah, right? (00:33:16) >> Then my youngest is Hudson. (00:33:18) >> Okay. Okay. (00:33:19) >> Yeah. (00:33:20) >> Yeah. Yeah. He interviewed you when (00:33:21) >> That's right. Yeah. He was terrific. (00:33:23) Yeah. What's he doing now? (00:33:24) >> He works at Ferrari. (00:33:26) >> Wow. No way. (00:33:27) >> Very cool. (00:33:28) >> No way. Okay. Well, I know who to call (00:33:29) to get the first EV. (00:33:31) >> Yeah. Exactly. Yeah. Yeah. Yeah. (00:33:33) >> And then the youngest is um is going to (00:33:36) be a filmmaker. So he's at NYU film (00:33:38) school. (00:33:39) >> Is that right? (00:33:40) >> Wow. Okay. Well, one of these days when (00:33:42) they make a documentary of you, I guess. (00:33:44) >> Yeah. Exactly. (00:33:45) >> Yeah. (00:33:45) >> Exactly. one of um one of the um I think (00:33:49) one of your best characteristics or (00:33:51) skill because you could say it's either (00:33:54) is that although you're for sure the (00:33:57) hardest working billionaire in the world (00:33:59) maybe Elon would argue with that but (00:34:02) but you are when you're with someone (00:34:04) you're allin (00:34:06) >> they have your undivided attention it's (00:34:08) as if you have nothing better to do than (00:34:10) sit down and talk to me (00:34:12) >> and you make people feel special (00:34:14) >> that's a rare gift. So, tell me a little (00:34:16) bit about that intentionality and what (00:34:19) others can learn from it because I think (00:34:20) it's one of the most important gifts (00:34:22) >> or again skills. I'm not sure which it (00:34:25) is. (00:34:25) >> Mhm. I appreciate that. Um (00:34:29) I I think it's it's a humility and (00:34:32) respect, you know. I I think the the um (00:34:37) I love watching people cook. I don't (00:34:40) know about you, but I love watching (00:34:41) people cook. I love watching people do (00:34:43) gardening. I love watching people do (00:34:47) things they they love and that they're (00:34:50) good at. Um, when we go to restaurants, (00:34:53) I I always prefer to sit at the bar. So, (00:34:55) I mean, closer to the kitchen. Um, I (00:34:58) love watching people do their work. Um, (00:35:02) because, you know, I I respect the the (00:35:05) their artistry. I respect their craft. (00:35:07) that respect. I I I'm, you know, I'm (00:35:11) inspired by by them dedicate dedicating (00:35:15) themselves to the work that they do. And (00:35:18) um and you're always learning something. (00:35:20) M (00:35:20) >> you know you you come out of that that (00:35:22) moment and you you're enriched you're (00:35:25) slightly enriched you know by by uh (00:35:29) something you learned or greatly (00:35:31) enriched by a new thought or you know (00:35:34) and and so so I I think from that (00:35:36) perspective (00:35:38) another perspective is is I always want (00:35:40) to help (00:35:43) I want your show to be great not for me (00:35:47) but for you. (00:35:48) >> Thank you. And I want your your work to (00:35:51) be great. And when somebody comes to me (00:35:54) to ask me for help, I want them to (00:35:57) succeed. (00:35:58) >> If a CEO calls me and and these, you (00:36:01) know, these days I have a lot of CEOs (00:36:02) who call me to ask for partnership or (00:36:05) what, I want them to succeed, not for my (00:36:07) benefit, for his benefit, for their (00:36:08) benefit. And and um I en always enjoyed (00:36:13) meeting Julie Sweet. I want her to (00:36:15) succeed. (00:36:16) >> You know, she's a fantastic CEO. I want (00:36:17) her to succeed. (00:36:18) >> Sure. Right. (00:36:19) >> Yeah. And and so the list goes on. (00:36:22) >> You know, I I want um I love watching (00:36:25) people other people succeed (00:36:28) >> and and I love that I was able to help a (00:36:30) little bit. (00:36:32) And um (00:36:35) I'm not sure where that comes from, but (00:36:38) it's a combination of these two things. (00:36:40) You know, I'm I'm here because I want to (00:36:42) I want you to be able to (00:36:46) get the most out of your opportunity. I (00:36:50) want you to be able to launch this (00:36:53) inaugural, (00:36:54) you know, a little bit too personal. Is (00:36:58) that what you call? (00:36:58) >> Yeah. A bit person. A bit personal. (00:37:00) >> Okay. A little bit too personal. (00:37:04) >> Yeah. That's That's the new That's my (00:37:06) new title for you. A little bit too (00:37:07) personal. We'll (00:37:08) >> change it. You know, I bet a little bit (00:37:10) too personal is probably catchier. (00:37:13) >> Yeah. (00:37:13) >> Yeah. (00:37:14) >> We'll go rebrand it. (00:37:17) >> It came from Jensen. (00:37:18) >> Anyhow, that's I think that's the reason (00:37:20) why. (00:37:21) >> So, you know, this philosophy of um of (00:37:23) pain and suffering talk about it a (00:37:25) little bit more. So, I recently heard (00:37:26) Andy Karp on a podcast (00:37:28) >> and I don't wear a watch. (00:37:30) >> Yeah. (00:37:30) >> And as you know, uh everybody everybody (00:37:33) at Nvidia is told when I'm doing (00:37:36) something, you know, don't bother me. (00:37:38) Okay. (00:37:38) >> Everything else can wait. (00:37:39) >> We'll be here till lunch (00:37:41) >> if that's what it takes. If that's what (00:37:43) it takes. (00:37:44) >> So Andy Carb said that um you can either (00:37:47) enjoy your 20s or you can be successful. (00:37:51) >> Do you believe that philosophy? I mean (00:37:53) is it I mean is it really that a person (00:37:55) has to I mean you know not everybody is (00:37:57) going to be the CEO of Palunteer, the (00:37:59) CEO of Nvidia, but what does it take for (00:38:02) a young person? What is the message to (00:38:04) young people about their career and (00:38:06) their success? M yeah. Um gosh, Alex is (00:38:12) so smart and he's got all kinds of very (00:38:15) deep philosophies and um I the the uh (00:38:21) I guess I'm kind of low-key about all (00:38:23) that stuff. (00:38:25) Um (00:38:27) you know, I I personally think it's (00:38:29) pretty incredible that Morris worked (00:38:32) until his 80s. he's still (00:38:36) sharp as a knife and (00:38:38) >> and that, you know, if there's a (00:38:40) definition of a late bloomer, you know, (00:38:43) look up late bloomer in Wikipedia, it's (00:38:45) probably going to be, you know, picture (00:38:47) of Morris. And so, (00:38:50) how is that a bad thing that that you (00:38:52) get to you get to enjoy the the most (00:38:55) productive (00:38:57) times of your life and you get to do it (00:38:59) for 50 years, (00:39:02) you know? (00:39:02) >> Mhm. And and and if that's the case, if (00:39:06) and and I'm kind of of that same cut, (00:39:08) same cut. I mean, I I would really love (00:39:12) that I'm doing something productive (00:39:14) rather than, you know, the clicheing (00:39:17) things, you know, I'm going to go travel (00:39:19) the world for the rest 20 years of my (00:39:21) life and or which is fine, you know, (00:39:24) which is fine. I mean, but I'm traveling (00:39:26) the world now. And and um (00:39:30) I I I also think that that during our (00:39:33) 20s (00:39:36) I I will have to agree that in my 20s I (00:39:39) feel smarter. I can concentrate with (00:39:42) greater intensity. Um, I think faster (00:39:47) and but but the thing that that I would (00:39:50) say is completely missed is all of the (00:39:54) ability to be wiser, to be more nuanced, (00:39:57) um, to be more strategic, (00:39:59) uh, to think to think longer term. Um, (00:40:05) I I think all of that is missed in the (00:40:08) 20s. And I don't I don't I don't know (00:40:11) how you learn those things by not living (00:40:15) those things. You could always repeat (00:40:16) those things by reading it. (00:40:18) >> Mh. And you could always, you know, (00:40:20) these days you could always watch (00:40:21) YouTube and and and and be and if you're (00:40:24) s sufficiently empathetic, you could (00:40:27) kind of, you know, feel what other (00:40:32) people are are going through. So you (00:40:34) could maybe live their life, you know, (00:40:36) through live their life through them, (00:40:38) live your life through them and and (00:40:40) somehow uh gain that wisdom by watching. (00:40:44) Uh so so imitation learning is a real (00:40:47) thing. (00:40:47) And and so I think that that's terrific. (00:40:49) But there's but there's a there's a (00:40:52) there's a (00:40:53) the the grit um that comes along with (00:40:57) enduring (00:40:58) um the the the knowledge of how to deal (00:41:01) with pain and suffering, the feelings of (00:41:02) it. Not not the physical feelings, but (00:41:05) the emotional toil and going through the (00:41:09) the (00:41:10) agony part of it, the fear part of it. (00:41:13) Um and there are real fears. I mean, you (00:41:16) know, fear is a real thing in running (00:41:19) companies. (00:41:21) We're we have to we have the lives of (00:41:23) tens of thousands of people and, you (00:41:25) know, in the decisions we make. Uh when (00:41:27) things are not going well, uh to not (00:41:30) feel fear, (00:41:32) um to not feel anxiety, to not feel (00:41:36) vulnerability, (00:41:38) uh you know, makes you in fact a bad (00:41:41) leader. (00:41:42) to be so crass that that you don't even (00:41:44) care that how things are going to turn (00:41:46) out. And so I don't know how you how you (00:41:49) learn those things without actually (00:41:52) going through it. (00:41:53) >> And and so I I I I guess I see it both (00:41:57) ways. If you if you could succeed early, (00:41:59) your energy is abundant. You know, you (00:42:02) could stay up later, pull allnighters, (00:42:04) and you could work 10 times harder. Um (00:42:08) uh but there's something that's just (00:42:09) really (00:42:11) that I that I feel I have today that I I (00:42:15) really didn't have in my 30s. And and as (00:42:18) a result, even though I'm not thinking (00:42:20) as fast as I used to, I come to the (00:42:23) right answers faster (00:42:26) >> because I have the benefit of wisdom and (00:42:28) patterns and, you know, better strategy (00:42:30) thinking and (00:42:31) >> and so, you know, I'll go toe-to-toe (00:42:34) with a 20-year-old all day long. (00:42:37) I believe that. (00:42:38) >> Yeah. Yeah. They got they got nothing on (00:42:40) me. (00:42:40) >> So, let's get a little bit too personal. (00:42:42) >> Okay. (00:42:42) >> Okay. (00:42:44) So, tell me about um a little bit about (00:42:47) kind of the highlights and low lightss (00:42:49) of your childhood that you think (00:42:51) impacted that you can specifically trace (00:42:53) to impact some characteristic that you (00:42:55) have now. maybe just kind of walk us (00:42:57) through (00:42:59) the um coming from Taiwan to to the US (00:43:02) and (00:43:04) that ordeal or that experience, the (00:43:06) journey (00:43:08) >> when when I was (00:43:11) I I I don't think I'm extraordinarily or (00:43:14) spectacularly intelligent. I'm I'm not (00:43:16) any I don't think I'm an outlier. um (00:43:18) when I was a kid (00:43:21) and during that time even entering (00:43:25) schools you you have to take tests and (00:43:28) and apparently I did very very well in (00:43:30) the test and and this is you know back (00:43:33) then you have to do national tests and (00:43:34) things like that and I did very well on (00:43:36) the test and and I I kind of remember my (00:43:39) mom always just telling everybody (00:43:41) telling me that that you're incredibly (00:43:43) smart (00:43:45) and and um whether that whether that was (00:43:49) actually true or not, the fact that my (00:43:52) mom kept saying it over and over again (00:43:55) probably was was helpful. And it it kind (00:43:59) of put a it put a burden on me to need (00:44:01) to be smart. And so maybe maybe um maybe (00:44:06) that's that's one of those things about (00:44:09) about parenting and leadership that when (00:44:11) you when you set expectations that are (00:44:13) beyond (00:44:15) um beyond reason (00:44:17) uh on some people and on on your company (00:44:20) in a lot of ways they rise to it. (00:44:23) And um (00:44:26) you could also imagine people cowering (00:44:28) from it. Um but in my case it just (00:44:31) didn't do that. it it it helped me rise (00:44:33) to it. Um that that's that was an (00:44:36) important (00:44:39) you know it comes to mind I guess. Uh (00:44:41) another one another one I I would say is (00:44:44) just witnessing (00:44:46) witnessing um someone do something. You (00:44:49) know we were learning how to speak (00:44:51) English and my my mom didn't even know (00:44:53) how to speak English but it didn't stop (00:44:55) her from every single day teaching us (00:44:59) English. I mean, how how is it possible (00:45:01) that somebody who has no clue of English (00:45:03) be teaching us English? And my mom (00:45:06) didn't even graduated from high school, (00:45:08) I don't think. (00:45:08) >> Okay. (00:45:09) >> And and um and so she just bought a (00:45:12) Webster's dictionary and and uh you (00:45:15) know, wrote wrote the English word. She (00:45:18) learned how just by looking at the (00:45:20) patterns, wrote the word and wrote the (00:45:22) Chinese translation, folded a piece of (00:45:25) paper in half and and then made made us (00:45:28) uh you know memorize all these words (00:45:33) and um I don't know if we were (00:45:35) pronouncing it right and but anyhow that (00:45:39) that that taught me something about (00:45:41) about someone with with incredible will (00:45:45) that even if you don't know how to do (00:45:46) something (00:45:48) it shouldn't stop you, you know, how (00:45:50) hard can it be, (00:45:51) >> right? (00:45:52) >> And and so um I remember that when I was (00:45:55) a kid. Um I remembered I remembered um (00:46:01) uh going to Kentucky and my job. I was (00:46:04) the youngest kid in school (00:46:06) and and um Onita Baptist Institute is on (00:46:11) top of a hill and every day I had to (00:46:14) walk down this this hill and cross a (00:46:17) river and then cross you know a really (00:46:21) large field and then there's a little (00:46:23) there's a little school there and that's (00:46:25) where I go and um and along the way uh (00:46:30) you know kids would would you know (00:46:32) because I the first Chinese kid ever (00:46:34) show up in Kentucky. It was 1973. (00:46:38) And so (00:46:40) so the the the town kids were were kind (00:46:43) of rough and they were rough rough on me (00:46:46) when I crossed that bridge. And the the (00:46:48) hanging bridge had wooden planks (00:46:51) and the bridge and the water's way down (00:46:53) there and and um I got across this and (00:46:56) some of the planks were missing (00:46:59) um and and they'd be on the other side (00:47:02) waiting for me. (00:47:05) you know, (00:47:06) and um I'm 9 years old. (00:47:09) >> Wow. (00:47:11) >> And I did that every day. (00:47:14) >> Pain and suffering. (00:47:16) >> You know, (00:47:17) you're 9 years old. Here's a river. (00:47:20) Here's a hanging bridge. Wow. (00:47:21) >> Wooden planks. Some of them are missing (00:47:23) on the other side. (00:47:25) >> That's the worst news. (00:47:28) >> You make you live to make it across the (00:47:30) bridge. That's when you then you're in (00:47:32) trouble, (00:47:33) >> right? (00:47:33) >> Yeah. But I I did that every day, every (00:47:36) morning. (00:47:36) >> And um and then in the afternoons, I (00:47:39) come home and my job was to clean the (00:47:41) bathrooms, (00:47:43) you know, every every kid had every kid (00:47:45) had a job. My older brother, he's he was (00:47:48) 11. His job was to work in the tobacco (00:47:50) farm. And um that was my job, clean the (00:47:53) bathrooms, and I did that every day. And (00:47:56) >> you think any of those people know where (00:47:57) you are now? (00:47:58) Uh uh the president of Anita Baptist (00:48:01) Institute just sent me an email. They're (00:48:03) quite they send me Christmas present (00:48:05) every year. (00:48:05) >> Okay. (00:48:06) >> And they sent me uh they they know I (00:48:08) love sausage and gravy and biscuits. (00:48:10) >> And (00:48:11) >> you learned that in Kentucky. (00:48:12) >> Oh yeah. Yeah. Yeah. Oh. Oh my gosh. I (00:48:14) And when I went back uh I think it was (00:48:18) my 45th birthday or something like that. (00:48:19) My my family took me back there and and (00:48:23) the and the the cafeteria ladies that (00:48:27) cooked when I was there, they're still (00:48:29) alive and they came back to cook a meal (00:48:31) for me. (00:48:31) >> Wow. (00:48:32) >> Yeah, (00:48:32) >> that's incredible. (00:48:33) >> It was incredible. Yeah, they made they (00:48:35) made um Kentucky uh sausage and gravy (00:48:38) and biscuits. It was It was delicious. (00:48:42) >> So, did your parents get to see your (00:48:44) success? (00:48:45) >> Yeah. Yeah, they're still around. (00:48:46) They're great. (00:48:47) >> Good. Good. (00:48:47) >> Yeah. Yeah. I just saw (00:48:48) >> they're so proud of you. (00:48:49) >> They are. Yeah. Yeah, they are. (00:48:51) >> Okay. (00:48:52) >> Yeah. They they know every detail. My (00:48:54) dad reads everything. (00:48:55) >> Okay. (00:48:56) >> He he he uh he he he reads everything (00:48:58) and and everybody who says, you know, (00:49:00) things that that are that are some (00:49:02) somewhat derogatory or adversarial (00:49:05) towards me, you know, he he gets mad. (00:49:09) And so I tell him, don't read (00:49:10) everything. You're going to be mad all (00:49:12) the time. (00:49:12) >> Don't read the bad press. (00:49:13) >> Yeah. You're gonna be mad all the time. (00:49:15) >> That's cute. (00:49:16) >> Yeah. So, what do you what do you miss (00:49:19) about sort of life before all this (00:49:21) insanity? So, you know, you were maybe (00:49:24) the mundane things that you don't you're (00:49:25) a car guy. You don't even get to drive (00:49:26) anymore. No, (00:49:27) >> you were the the first and only person (00:49:29) that I've ever known that owned a um (00:49:31) what is it? Keg. (00:49:32) >> A Koenigseg. (00:49:33) >> Kenseg. (00:49:34) >> Yeah. Christian's an amazing architect. (00:49:36) He's an amazing designer. That's a great (00:49:38) car. (00:49:39) >> Yeah. (00:49:40) >> When you turn it when you turn it on, it (00:49:42) sounds exactly like a Batmobile. (00:49:43) >> Wow. (00:49:44) >> Yeah. And it's a like a sevenstep (00:49:46) process to turn it on (00:49:48) >> because it's that it's that powerful. (00:49:49) You can't just let anybody turn on. Wow. (00:49:51) Yeah. (00:49:52) >> I don't have it. I don't have it (00:49:53) anymore. (00:49:53) >> Okay. (00:49:54) >> I don't I don't drive anymore. (00:49:55) >> Right. You miss that? (00:49:57) >> Yeah. A little bit. Yeah. Yeah. It's (00:49:59) kind of cool to I mean, I still look at (00:50:00) the cars, you know, the new Ferraris (00:50:02) and, you know, I I I still I still enjoy (00:50:06) looking at them. I think they're pretty (00:50:08) terrific. (00:50:08) >> Yeah. (00:50:09) >> Yeah. Great feats of engineering. (00:50:11) >> They really are. It's amazing. I've been (00:50:13) to the Ferrari factory and it's amazing (00:50:14) to watch (00:50:15) >> and to to learn that that what started (00:50:18) out as an industrial instrument (00:50:21) equipment (00:50:22) >> then evolved into uh (00:50:27) you know our our largest consumer (00:50:29) consumption and and and (00:50:32) uh and then now it now many of these are (00:50:35) pieces of art. You know (00:50:37) >> they are (00:50:37) >> Yeah, they're incredible. Yeah. So, it's (00:50:39) great to see that (00:50:41) >> this episode is brought to you by (00:50:42) Maverick Silicon. (00:50:45) So, if uh if we're sitting here five (00:50:47) years from now, podcast is hugely (00:50:49) successful because of this inaugural (00:50:51) >> podcast. (00:50:52) What's the world look like in five years (00:50:54) from now and what what's going to (00:50:57) surprise us the most about what the (00:51:00) world looks like? (00:51:03) Um so if you if we go back to first (00:51:06) principles (00:51:08) and and then we gauge it with with (00:51:11) pragmatism and practicality (00:51:15) um and then all the sensibilities about (00:51:18) about adoption of technology and (00:51:22) um the impact of technology there's (00:51:25) several things that I would say I I (00:51:26) think first of all the work that we're (00:51:28) involved in and what Nvidia's work in (00:51:31) and artificial intelligence and and the (00:51:33) rest of industry's work in this area. (00:51:36) Um there's no question that that the (00:51:39) computer um has will completely (00:51:42) transform from something that we uh that (00:51:46) we we uh program uh to something that (00:51:50) programs itself with a great deal of (00:51:53) guidance from us. And so we we still had (00:51:55) to tell it, you know, what what what do (00:51:57) we want you to go learn? And so in the (00:52:00) in the past we would we would uh teach (00:52:03) uh a computer Japanese. Uh but in the (00:52:06) future we would tell the computer to go (00:52:09) learn Japanese. And so so now the the (00:52:12) way the way that we use computers will (00:52:15) be transformed of course. Uh the (00:52:17) computer will be able to deal with with (00:52:19) problem sizes that are a billion times (00:52:22) larger than anything we're working on (00:52:24) today. And so in in a way we can't even (00:52:28) comprehend what that means because (00:52:30) because coming up with a solution is one (00:52:32) thing even formulating a problem in our (00:52:35) head to go solve is a completely (00:52:37) different thing. And many problems (00:52:40) uh many solvable problems are limited by (00:52:42) own imagination about how to formulate (00:52:44) the problem and how to think about the (00:52:46) problem. And so the size of the problem (00:52:48) that we can we can engage whether it's (00:52:50) the complexities of digital biology um (00:52:53) or the complexities of of physical (00:52:55) sciences or quantum physics or you know (00:52:58) all of the things that are material (00:53:00) sciences that's going to be easy. Um all (00:53:02) of the type of things you know even (00:53:05) mundane things like traffic jams a lot (00:53:07) of these things are going to be largely (00:53:09) easy. Um smart grids there's so much (00:53:11) waste in the grid. um you know (00:53:14) artificial intelligence will go figure (00:53:15) out how to uh how to how to deploy (00:53:19) energy just enough energy (00:53:21) >> okay (00:53:22) >> and instead of overprovisioning energy (00:53:24) we've got a lot of energy that's wasted (00:53:26) and so so the the idea of of um uh of AI (00:53:31) being able to solve those what is (00:53:34) largely mundane problems is is going to (00:53:36) be quite incredible and so every field (00:53:38) of science will be infected will be (00:53:40) affected every every hard problem today (00:53:42) will be turbocharged and um when the (00:53:45) tool when the instrument when the tool (00:53:47) is way way faster then the problem looks (00:53:50) way smaller and so let me give you an (00:53:52) example if if if an airplane were to (00:53:55) travel (00:53:57) you know Mach 10 um then obviously the (00:54:00) world becomes a smaller place (00:54:02) >> and because of because of jet planes we (00:54:04) made the world smaller world used to be (00:54:06) a lot bigger (00:54:07) >> right (00:54:08) >> and so so it's the same thing with with (00:54:10) the computers Nvidia makes because of (00:54:12) what we make is so much faster. We made (00:54:15) every problem smaller to the to the (00:54:18) point where one day researchers at (00:54:20) OpenAI said, "Hey, why don't we just (00:54:22) take all of the internet data and just (00:54:24) give it to this computer?" Because all (00:54:26) of a sudden, all of the world's internet (00:54:27) data looks so small. These days, when we (00:54:30) look at all of the world's internet (00:54:32) data, it looks tiny to us because the (00:54:34) computers have become so fast. And that (00:54:37) that attitude will pervade almost every (00:54:40) field of science. (00:54:42) >> You know, back in the old days, (00:54:43) everybody goes, "Wow, this is a really (00:54:44) hard problem." Now it's going to look (00:54:45) really simple. (00:54:47) >> And and so in five years time, that is (00:54:50) going to be the state of mind of every (00:54:52) scientist, engineer, entrepreneur, (00:54:56) innovator, all of those hard problems (00:54:59) now just look really simple. And so as a (00:55:02) result, we're going to solve more (00:55:03) problems. (00:55:05) So, so that's that's one outcome. (00:55:07) Another outcome of course is that (00:55:09) companies will be incredibly productive. (00:55:12) What is hard problems today, what are (00:55:14) hard problems today are going to be (00:55:16) simple problems tomorrow. And so, um, (00:55:18) managing our supply chain will be way (00:55:20) easier so we have hardly any waste. Um, (00:55:23) designing our computers are going to be (00:55:25) way easier and so we can try more (00:55:28) examples. Not that we'll not that we'll (00:55:30) we'll release more computers than we are (00:55:33) today. We do it once a year, but we'll (00:55:35) we'll try more iterations of these (00:55:37) examples of computers so that the one (00:55:39) that we release every year is so much (00:55:40) better. (00:55:41) >> Um I (00:55:45) uh let's see that's that's one. And so (00:55:48) our company will be more productive. (00:55:49) We'll make more profit. Every company (00:55:51) will be more profitable. (00:55:52) >> All be richer. (00:55:53) >> Yeah. We we'll be more profitable. Um, (00:55:55) one of one of the manifestations of it (00:55:57) though (00:55:59) as as I connect these two ideas is if (00:56:03) every problem that that that we dream up (00:56:06) looks more more tenable, then (00:56:11) we're going to come up with more (00:56:12) problems to solve. And so instead of (00:56:16) instead of having no lesser job fewer (00:56:19) jobs, I actually feel what's likely to (00:56:21) happen is that we're going to be busier (00:56:23) than ever. (00:56:25) And the reason for that is because we're (00:56:26) going to think of more and more ideas of (00:56:28) things that we can solve now that we (00:56:29) didn't used to be able to solve. All (00:56:31) those things that were off the table are (00:56:33) now on the table. (00:56:35) >> And all of the experiments we it was too (00:56:38) expensive to try, they're all every (00:56:41) experiment should be tried and or the AI (00:56:43) is going to help us go try those (00:56:45) experiments. and and so to the extent (00:56:48) that we have imagination and we have a (00:56:50) lot of problems that we we were (00:56:52) deferring or we couldn't solve before I (00:56:55) think they're all going to be on the (00:56:56) table. Um, one thought experiment is (00:57:00) imagining. So today when I when I'm (00:57:02) working, I'm surrounded by 60 geniuses (00:57:06) and they're surrounded by a few thousand (00:57:08) geniuses. And (00:57:11) in my case, I'm surrounded by 60 people (00:57:14) who are all better at what they do than (00:57:16) I am. In a lot of ways, they're (00:57:18) basically artificial super intelligent (00:57:20) relative to me in in their field. And (00:57:23) yet, I've got no trouble working with (00:57:25) all of them. And so I I think that in (00:57:28) the future and and the AIs that I use (00:57:30) now with OpenAI and Gemini and Grock and (00:57:33) you know I use Perplexity and and um (00:57:36) Anthropic and you know all of these all (00:57:38) of these AIs in in their way they're (00:57:40) already smarter than I am (00:57:43) and and yet I've got no trouble working (00:57:45) with them every day. And so that's (00:57:47) number one. But what's really (00:57:48) interesting though is that when I (00:57:51) formulate problems for for my team to go (00:57:54) do, I often have the benefit of waiting (00:57:57) the two or three or four days for them (00:57:58) to go find the answer or formulate the (00:58:01) answer back to me. That allows me to (00:58:04) then (00:58:06) go think about the next step and then (00:58:08) thinking about the next step because in (00:58:09) order for me to think through my steps, (00:58:11) I need intermediate answers to come (00:58:13) back. What if those answers come back (00:58:15) basically in a second? (00:58:17) That's the thought experiment. (00:58:19) >> My day, my days would be insanely busy (00:58:22) now because I'm now the critical path of (00:58:25) everything. (00:58:26) And so I've got to go, okay, now I've (00:58:28) got the answer of that. Therefore, I got (00:58:29) to think about this. I got to kick off (00:58:31) another experiment. Now I've got the (00:58:33) answer to that. I, you know, and so I I (00:58:36) feel that we're busier today because (00:58:39) information technology is faster today. (00:58:41) Wouldn't you say? (00:58:42) >> Yes. (00:58:43) We're getting information and knowledge (00:58:45) and, you know, (00:58:47) answers so fast now. It puts us in a (00:58:51) critical path. Therefore, we're busier (00:58:52) than ever. I have a feeling that a lot (00:58:54) of people are going to feel that way. (00:58:55) And then and then lastly (00:58:58) um for the people that that um weren't (00:59:02) didn't benefit from uh the technology (00:59:05) industry that you and I had the benefit (00:59:07) of being part of all of a sudden (00:59:09) artificial intelligence closed that (00:59:11) technology divide. You know, one of the (00:59:13) one of my favorite things is just vibe (00:59:15) coding. Anybody could be a software (00:59:17) programmer now. And and vibe coding is (00:59:19) creating software that you know is (00:59:22) better than a lot of software (00:59:23) programmers. And so I love the work that (00:59:25) Curser does. I love, you know, I met the (00:59:28) CEO of Loveable the other day and he's (00:59:29) terrific guy and and a startup in Sweden (00:59:32) and and I'm really happy to see that. (00:59:35) And so so I think that that AI is going (00:59:37) to close the technology divide and (00:59:39) everybody who who are really gifted at (00:59:42) their craft, but maybe they don't know (00:59:45) how to scale themselves with technology, (00:59:47) they now have AI to help scale them. And (00:59:50) so one of the stories that that the (00:59:52) Loveable CEO was telling me is all these (00:59:55) companies, all these people are creating (00:59:56) basically small businesses and from the (00:59:59) software that was written by Lovable, (01:00:02) they're making $23 million a year now. (01:00:05) That's incredible. (01:00:06) >> It is. (01:00:07) >> And so they're welcomed into, you know, (01:00:09) the world's economy um not burdened by (01:00:13) technology anymore because AI made that (01:00:15) possible. And so, so I I have a feeling (01:00:17) that that the five years from now it is (01:00:20) likely that we're all going to be more (01:00:23) gainfully employed that the economy is (01:00:26) going to be more productive uh hopefully (01:00:29) the GDP actually grows (01:00:32) uh because of the short you know (01:00:34) overcoming the shortage the labor (01:00:35) shortage that we have um and uh (01:00:39) inflation will go down um you know a lot (01:00:41) more fields of sciences are are being (01:00:43) tackled. Now, of course, there's the (01:00:46) there's the doomer view, the ext the (01:00:48) other view, which is, you know, half of (01:00:50) the world's jobs will be lost and things (01:00:52) like that. I I think that that it's more (01:00:54) likely that 100% of the world's jobs (01:00:58) will change (01:01:01) than 50% of the world's jobs will be (01:01:03) lost, (01:01:04) >> right? (01:01:04) >> And and it's very likely that 100% of (01:01:08) people who don't have jobs today because (01:01:11) of AI can make a living. (01:01:14) And (01:01:16) um and of course you know our technology (01:01:18) would change a lot but that that's the (01:01:20) part that that's less interesting. I (01:01:22) think I think you know in 5 years time (01:01:26) to us (01:01:28) the computer is still a computer. The (01:01:31) applications are just smarter but they (01:01:33) look like applications (01:01:35) and they're still software. (01:01:38) We're doing e-commerce. (01:01:40) um maybe we don't go to websites (01:01:42) anymore, but our agents do the shopping (01:01:44) for us, but they're still buying it from (01:01:46) Amazon and others. Right. (01:01:48) >> Right. (01:01:48) >> And so I think a lot of things will (01:01:50) probably stay the same. And then maybe (01:01:52) maybe just one wish list is you know I (01:01:55) wish that that um and I hope that not (01:01:58) wish but I hope that the work that we're (01:02:00) doing with with robotics and and human (01:02:03) robotics you know turn into something (01:02:05) >> and and uh you know we we all have our (01:02:08) own version of R2-D2 and C3PO's running (01:02:11) around and (01:02:12) >> you know they're they're cute and (01:02:13) adorable. you know, like during during (01:02:15) GTC, my my uh at the end I always have (01:02:17) the the Disney robots on stage and how (01:02:20) adorable are they? And you know, why (01:02:22) shouldn't everybody have them? And you (01:02:24) know, and and I and I hope I hope that (01:02:26) that um I hope that uh that Disney (01:02:31) decides to to merchandise them because, (01:02:33) you know, they're so adorable. They're (01:02:35) so incredible. My pets my pets need (01:02:38) pets, right? And so (01:02:39) >> Momo and Kuma needs their their own (01:02:41) version of pets. And and so I I I hope (01:02:43) that that that happens because there are (01:02:45) a lot of lonely people (01:02:49) and and I actually have been approached (01:02:50) by several that that that hopes to have (01:02:54) um robots that they can interact with at (01:02:56) home because they're living by (01:02:58) themselves and they're getting older and (01:03:00) >> uh you know and so there there are a lot (01:03:03) of different reasons why these robots (01:03:04) could be quite quite helpful and not not (01:03:06) to mention they're just adorable. And so (01:03:08) that's that's an extra bonus of all the (01:03:10) things that we're doing. (01:03:11) someone to cook and clean, will you (01:03:13) watch them cook as they're will you (01:03:14) enjoy it as much when the robots are (01:03:16) cooking? (01:03:17) >> You know, well, the answer is yes. And (01:03:19) the reason for that is because I have (01:03:21) all the resources today to not cook. And (01:03:22) I yet I do. (01:03:23) >> Yeah. (01:03:24) >> Right. Right. (01:03:24) >> And so I don't have to. I choose to. And (01:03:27) we could be surrounded by all kinds of (01:03:29) staff, but we're not. You know, Lori and (01:03:30) I are just by ourselves. And and uh she (01:03:33) made chili last night. It was really (01:03:35) terrific. And you know, she made it by (01:03:36) herself. And we'll probably we'll (01:03:39) probably continue to do all that. And (01:03:40) and our favorite moments are our (01:03:43) favorite moments my my single favorite (01:03:45) moment is is when when the kids you know (01:03:49) come over and and um you know and (01:03:53) and we're all cooking and (01:03:56) you know enjoying cocktail and that that (01:03:59) that's the perfect perfect day. (01:04:00) >> Great bonding in the kitchen that goes (01:04:02) on. (01:04:03) >> Yeah. That's that is that's as good as (01:04:05) it gets. You know that's what we do all (01:04:06) this for for that moment. (01:04:08) >> That's exactly right. (01:04:09) >> Yeah. So, how do you at the end of the (01:04:10) day, how do you want to be remembered (01:04:13) when it's all said and done? (01:04:16) Um, (01:04:24) well, first of all, it's nice to be (01:04:25) remembered and and um I I (01:04:30) I'm fortunate (01:04:32) that (01:04:34) because of of what Nvidia has done (01:04:38) um and what we've built and the impact (01:04:41) that we have in the single most (01:04:44) important technology industry in the (01:04:46) world. Um the most important instrument (01:04:50) of humanity, computers, (01:04:52) uh that that Nvidia will likely (01:04:57) um long beyond long long beyond me uh be (01:05:02) be important to the world. And (01:05:06) uh I was I was fortunate to have been a (01:05:08) a founder with Chris and Curtis. Uh I (01:05:11) was fortunate to have um stayed on top (01:05:14) of the pile um you know as and and (01:05:18) continue to learn and and and not be not (01:05:21) be the reason the company went out of (01:05:24) business but but often times the reason (01:05:26) the company stayed in business (01:05:31) that I've built something um that is (01:05:34) quite consequential to the world that it (01:05:37) wasn't just consequential to an industry (01:05:39) or to a community but but a company (01:05:42) that's that's really genuinely (01:05:44) consequential to the world like not not (01:05:47) many people in the world gets to say (01:05:49) that (01:05:49) >> right (01:05:50) >> you know that that I was the founder and (01:05:54) I'm here um enjoying this and and doing (01:05:57) this that the company turned out to have (01:05:59) been what it was what it is and um to (01:06:03) have the impact on on so many other (01:06:05) industries literally every single (01:06:07) industry in the world (01:06:09) uh to have employees that have been here (01:06:12) for 33 years and and their lives (01:06:14) enriched. Uh second generation and even (01:06:18) third generation employees are now (01:06:19) starting to work here. (01:06:21) um uh that we're able to to uh grow an (01:06:25) employee base, you know, around the (01:06:27) world that they're they're in is Israel (01:06:29) and and um uh and and be able to to to (01:06:35) share with them um you know, their (01:06:38) desperation and then now their joy and (01:06:40) their hope and and and to share with (01:06:43) them their sorrows. And um (01:06:46) not many people get to say they they (01:06:49) they're not not many get to experience (01:06:52) that and to be part of that and and then (01:06:55) and then build an employee base all over (01:06:57) the world. And you know in China, I'm (01:06:58) proud of them. Um our employees in (01:07:01) Taiwan, I'm deeply proud of them. And (01:07:03) and all of our employees in India, I'm (01:07:05) so proud of them. and and you know and (01:07:07) um our European employees, you know, and (01:07:10) and so (01:07:12) many of my Canadian employees, you know, (01:07:14) we have we're growing a large Canadian (01:07:16) base and (01:07:17) >> and uh and and one of these days, I'm (01:07:19) hoping that that Nvidia gets to gets to (01:07:22) extend ourselves into the global south, (01:07:24) you know, the the rest of the world that (01:07:26) that wants to be part of what we what we (01:07:28) all enjoy today. Um uh I was just (01:07:31) talking to somebody yesterday about the (01:07:33) work that we're doing in Africa and um (01:07:36) uh the work that we should be doing more (01:07:38) in in Latin America and there's so in (01:07:41) Southeast Asia um I'm so proud of of of (01:07:45) um the impact that our company has and (01:07:47) so how do people remember me? um they'll (01:07:51) probably remember me as um as you know a (01:07:57) founder and and builder of Nvidia. (01:08:00) That's probably (01:08:02) that's probably (01:08:03) >> and a good guy. (01:08:05) >> Well, that that goes without saying. you (01:08:08) know, he has a great he's a he has a (01:08:10) great sense of humor and (01:08:12) >> and he doesn't take himself too (01:08:13) seriously and and um I (01:08:18) you know he in a lot of ways I'm still a (01:08:20) reluctant CEO. (01:08:23) You know, I I I like being inside the (01:08:25) company more than I like being outside (01:08:26) the company. Um, I like not giving (01:08:29) speeches than giving speeches and and um (01:08:34) I like not giving keynotes at all and (01:08:38) and yet, you know, I have to do it and (01:08:40) and so I'm I'm a highly reluctant CEO. (01:08:44) Um, but I'm a very enthusiastic (01:08:47) Nvidia builder and and any aspect of my (01:08:50) job that that is necess necessary to do (01:08:53) to do that, I'll do. And and so that's a (01:08:57) long-winded answer for I have no idea (01:08:59) what people remember me as. (01:09:03) >> Well, I think it's always fun when the (01:09:05) when the good guys win. So, I love I (01:09:08) love watching your success. I've loved (01:09:10) it all of these years. It's been fun to (01:09:12) watch all the ups and downs. (01:09:15) >> You've seen everyone (01:09:16) >> to the right. Yeah, it's great. (01:09:17) >> You've seen everyone. You've seen (01:09:19) absolutely everyone. and and and just as (01:09:21) a reminder to to all the CEOs, you know, (01:09:24) nobody does it alone. (01:09:25) >> Mhm. you know it we are the CEO but (01:09:29) somebody had to be and and um if not for (01:09:34) the generosity really you know your (01:09:37) generosity early days um talking about (01:09:40) Nvidia and and uh um (01:09:44) you know all of those all of those uh (01:09:47) Morris Chang awards didn't didn't hurt (01:09:49) you know that was probably the Morris (01:09:53) Chang award was probably the first award (01:09:55) I ever received that was that meant (01:09:57) something. (01:09:57) >> Wow, that's very cool. I love that. (01:10:00) >> You know, that that was in somebody (01:10:01) else's name (01:10:03) and and that he he um uh actually played (01:10:07) a role in in selecting (01:10:10) >> and um that meant a lot to me. It means (01:10:13) a lot to me today. (01:10:16) And (01:10:17) um (01:10:19) and and and the generosity of all the (01:10:21) companies that we work with, (01:10:23) you know, I (01:10:26) I'm, you know, CEOs CEOs need help and I (01:10:29) I have no idea how many times I've I've (01:10:31) started conversations with that. I need (01:10:33) your help, (01:10:33) >> right? (01:10:34) >> You know, and and often times I really (01:10:37) need your help and you're really the (01:10:39) only person who can help me and and um (01:10:43) and people have been generous and (01:10:45) gracious to to uh help me along the way, (01:10:48) you know, uh share with me their (01:10:50) knowledge and uh teach me how to do (01:10:52) things and help me get things done. And (01:10:54) you know, and so I I that's maybe the (01:10:57) real life lesson of CEOs. It's (01:11:00) surprisingly a vulnerable. (01:11:02) >> Yeah. A lonely position too, right? (01:11:05) >> Yeah. It can be. It can be. Yeah, it can (01:11:08) be. And but Jody, I would say it's (01:11:11) probably lonely in our heads. (01:11:12) >> Mhm. (01:11:14) >> It's in our head. It's lonely. in our (01:11:16) headsp space is lonely because you're (01:11:18) trying to solve a problem that sometimes (01:11:20) are kind of tricky and and you're (01:11:24) talking to yourself, you know, and for a (01:11:26) long period of time, (01:11:27) >> right? (01:11:28) >> You know, so many many many challenges (01:11:31) almost every transition in our company, (01:11:33) every phase shift, every time I reinvent (01:11:35) the company, I've spoken to myself (01:11:37) probably I don't know how many thousands (01:11:38) of hours, (01:11:39) >> right? and and in that time you're quite (01:11:42) lonely, you know, and but but it but we (01:11:47) we also have to recognize that everybody (01:11:49) wants to help us. (01:11:50) >> They want us to succeed and like you (01:11:52) know, you said earlier, you you like (01:11:54) watching me succeed. I know you want me (01:11:56) to succeed and I want you to succeed. (01:11:58) And so in a lot of ways, we're not alone (01:12:00) that way, (01:12:00) >> right? (01:12:01) >> And so so we're, you know, CEOs are a (01:12:04) surprisingly vulnerable (01:12:07) skill. it's a or profession maybe that (01:12:10) you know it you're you can't do anything (01:12:12) alone (01:12:14) >> and so you're you're at the mercy of (01:12:16) almost everybody. you're the charity of (01:12:18) everybody (01:12:19) and and you know maybe maybe the world (01:12:22) make makes it look like we're formidable (01:12:25) leaders but we're the most vulnerable of (01:12:28) all the people in the company and and um (01:12:32) and and I often say you know I'm the (01:12:34) only person in the company who can't get (01:12:35) anything done without other people's (01:12:37) help. (01:12:38) >> Yeah. and (01:12:41) and that I think that's true for all for (01:12:43) most CEOs. And and so that that's the (01:12:46) that's the learning maybe, (01:12:48) you know, that that CEOs are are um more (01:12:53) vulnerable than than they they allow (01:12:56) themselves to feel. And but I got no (01:12:58) trouble with that. (01:13:01) I'm not I've got no trouble feeling (01:13:03) vulnerable. (01:13:04) >> This episode is brought to you by Morgan (01:13:06) Stanley. Morgan Stanley celebrated its (01:13:09) 90th anniversary last year and its tech (01:13:11) investment banking team has prided (01:13:13) itself in working with the most (01:13:15) innovative entrepreneurs and leadership (01:13:17) companies for decades. During that time, (01:13:20) they've led IPOs for hundreds of tech (01:13:22) companies including Apple, ARM, Astera (01:13:26) Labs, Broadcom, Cisco, Facebook, Google, (01:13:30) Nvidia, Salesforce, and Uber. In (01:13:33) addition, they worked as a trusted (01:13:35) adviser and executed thousands of M&A (01:13:37) transactions for some of the most (01:13:39) industry-defining deals in technology. (01:13:42) Throughout the years, Morgan Stanley's (01:13:44) tech investment banking team success has (01:13:47) been achieved by dedication, (01:13:49) consistency, and a northstar purpose of (01:13:52) helping its clients to maximize their (01:13:54) opportunities and overcome their (01:13:56) challenges. Now, let's get back to the (01:13:58) episode. (01:14:00) Okay. So, um I'm about to wear out my (01:14:03) welcome. I think I've been here a long (01:14:04) time. (01:14:05) >> Why are people You know, they've been (01:14:07) they've been taught to not do that. (01:14:08) >> No, no, no. No one has. No one has. I'm (01:14:10) just kidding. So, (01:14:11) >> um (01:14:12) >> we're going to end on um some rapid fire (01:14:14) questions that I call the last tape out. (01:14:16) >> Wow. Okay. (01:14:18) >> Okay. (01:14:19) >> Don't tell me um don't Yeah. Yeah. Don't (01:14:22) Don't tell me when my last tape out is. (01:14:24) >> See, now I'm I'm holding myself because (01:14:26) this is the part I don't like. Okay. Go. (01:14:28) Who's the smartest person you you've (01:14:30) ever met? (01:14:32) >> Who's the smartest person I've ever met? (01:14:35) Um, I can't answer that question. And I (01:14:38) know I know what people are thinking. (01:14:41) The definition of smart is somebody (01:14:43) who's intelligent, solve problems, (01:14:45) technical, (01:14:47) and and um (01:14:51) but I find that that's a commodity. And (01:14:54) we're not we're about to prove that (01:14:55) artificial intelligence is able to (01:14:57) handle that part easiest, (01:14:59) >> right? (01:15:00) >> Yeah. And so so as it turns out um let (01:15:04) me give you another example. Uh (01:15:06) everybody thought software programming (01:15:07) is the ultimate smart profession. Look (01:15:09) what is the first thing that AI is (01:15:11) solving? Software programming. And so it (01:15:14) turns out that the definition of smart (01:15:16) is very different than most people (01:15:19) think. (01:15:20) And um (01:15:24) I think long-term the definition of (01:15:26) smart and my my personal de definition (01:15:30) of smart uh is is um someone who sits at (01:15:35) that intersection (01:15:37) of of being (01:15:40) um technically astute (01:15:43) but but human (01:15:47) empathy (01:15:49) and um and (01:15:52) having the ability to infer the (01:15:54) unspoken, (01:15:56) the around the corners, the unknowables. (01:16:00) Um (01:16:01) you know, people who are able to see (01:16:03) around corners are are truly truly smart (01:16:06) and and um and that their value is (01:16:10) incredible. uh to be able to preempt (01:16:14) um uh preempt problems before they show (01:16:17) up just because you feel the vibe. And (01:16:21) and the vibe came from a combination of (01:16:25) uh data, analysis, first principle, life (01:16:29) experience, wisdom, um sensing other (01:16:33) people. Mhm. (01:16:35) >> Uh that vibe that I think that's smart (01:16:40) that I think is going to be the future (01:16:42) definition of smart and that person (01:16:45) might actually score horribly on the (01:16:47) SAT. (01:16:48) >> Right. (01:16:49) >> And so anyhow, (01:16:51) >> okay. Okay. (01:16:53) What's a misconception people have about (01:16:54) you? (01:17:00) >> These are hard questions. First of all, (01:17:02) I don't know what they're what's their (01:17:03) give me an example of a conception they (01:17:05) have of me (01:17:05) >> that you um love to be in the public (01:17:08) eye, that you're a great you know, (01:17:10) you're a great speaker, so you love to (01:17:11) give speeches. And you already said that (01:17:13) that's not true. (01:17:14) >> Yeah, that is not true. Right. That is (01:17:15) in fact exactly the opposite of what I (01:17:17) love. Um public speaking scares the (01:17:22) living daylights out of me. It scares me (01:17:25) not at the moment. It's right now I am I (01:17:29) am in deep anxiety over GTC Washington (01:17:33) DC (01:17:33) >> coming up. (01:17:34) >> It's two weeks away. Okay. (01:17:35) >> Less than two weeks away. (01:17:37) >> And I am deeply anxious. I've been (01:17:39) deeply anxious for a week, excuse me, a (01:17:41) month. (01:17:43) >> And so so these things these things wear (01:17:45) on me. Um they're always in my mind and (01:17:49) and uh they stress me out. Um, (01:17:55) so I I I don't know how to explain it. (01:17:57) >> Okay. (01:17:58) >> Yep. (01:17:58) >> If you work (01:17:59) >> companies meetings scare me to death. (01:18:01) >> Yes. (01:18:03) >> Scare me to death because I'm I'm, you (01:18:06) know, I'm on stage and they're the most (01:18:08) important people in the world to me. It (01:18:11) is the single most important speech that (01:18:12) I give in a lot of ways. But it's (01:18:15) impossible to prepare for it. and and um (01:18:19) and everything I can tell them on a (01:18:22) presentation I've already said on some (01:18:24) video somewhere. They ought to just go (01:18:26) watch that. And so I hate I hate to (01:18:28) regurgitate, (01:18:30) you know, talks to them. Uh because they (01:18:34) you would never do that to your family. (01:18:36) You wouldn't you would never come home (01:18:37) and and give your GTC talk, you know, to (01:18:40) your family. And and I hate doing that. (01:18:43) And so it has to be genuine. It has to (01:18:45) be unique. It has to be useful, has to (01:18:48) be meaningful to them, has to make a (01:18:50) difference. I'm still leading the (01:18:51) company. There's still an out outcome (01:18:53) that I'm looking for. And so, you got to (01:18:55) do all of that and you've got to do it (01:18:57) fresh. That is and and so I have no idea (01:19:01) how it's going to turn out until it (01:19:02) turns out. And so, the entire time, you (01:19:06) know, during earnings week, people think (01:19:07) that the earnings is stressful to me. (01:19:09) Not even a little bit. (01:19:10) >> Okay. (01:19:11) >> The company meeting stresses me out. So, (01:19:15) so the conception is exactly wrong. (01:19:17) >> Right. Right. (01:19:17) >> Yeah. (01:19:18) >> That's good. (01:19:20) >> Okay. What is your favorite vacation (01:19:21) spot? (01:19:24) >> Uh, wherever my family is and wherever (01:19:26) we're cooking, having cocktail. (01:19:28) >> Okay. (01:19:29) >> And and uh but if I had to name two (01:19:31) places and one place or two, it's hard (01:19:33) for me. Um but I can tell it's my (01:19:37) favorite vacation spot because I know (01:19:39) I'm happy landing. (01:19:42) I'm always happy landing in in Hawaii. (01:19:44) >> Okay. (01:19:45) >> And because we're all all we're we're (01:19:47) typically together. Um I'm I'm really (01:19:50) happy when I when we land in Taiwan and (01:19:54) and the reason for that is because the (01:19:56) people are great. Um uh I've got really (01:20:00) important partners there and long-term (01:20:02) friendships there and my family is (01:20:04) oftentimes with me. Um um I'm I'm (01:20:08) genuinely happy when I land in Japan. (01:20:12) >> Okay. (01:20:13) >> And and the reason for that is because (01:20:16) um I have very very long memories of (01:20:18) Japan and how important it was to saving (01:20:20) our company and and uh the early (01:20:23) business trips that I've I've been (01:20:24) there. Uh even though business-wise (01:20:27) largely unproductive over the years, I (01:20:30) still I still um en somehow have have (01:20:33) great joy landing there. And so maybe (01:20:36) those are kind of three places and and (01:20:37) and my family's my family's oftentimes (01:20:39) with me when I'm landing. (01:20:40) >> Okay, that's good. (01:20:41) >> Yeah. (01:20:43) >> Pet peeve. (01:20:46) >> People who don't listen to my question, (01:20:48) understand my question, answer my (01:20:50) question during important times. (01:20:52) >> Okay. (01:20:53) >> When we're dealing with very hard (01:20:54) situation, very difficult times, and we (01:20:56) need we need facts. We need facts. and I (01:20:59) ask a question like that, if somebody (01:21:01) doesn't answer it, it triggers me almost (01:21:03) instantaneously. (01:21:05) >> Good. (01:21:05) >> And and the reason for that is because I (01:21:08) don't understand why the person (01:21:12) underappreciates the context of this (01:21:14) meeting that we're doing something (01:21:16) really important (01:21:18) and that we're trying to get to the (01:21:20) truth and we're trying to get there. We (01:21:22) need to get there fast. I just don't (01:21:24) understand. I never understand and I (01:21:27) don't understand to this day. (01:21:29) >> Okay. (01:21:29) >> And somehow it always triggers me. And (01:21:33) if somebody wants to trigger me, that's (01:21:35) the way to do it. (01:21:35) >> That's the way to do it. (01:21:36) >> We know the trick to making Jensen mad. (01:21:39) >> Okay. Um, one last question that someone (01:21:41) asked me recently, and I love this (01:21:43) question. It's if you if you had to be (01:21:45) 20 years old all over again, would you (01:21:47) do it today or would you relive in our (01:21:50) day? (01:21:52) >> I would relive it in our day. And the (01:21:54) reason for for that is because I thought (01:21:56) that our 20s (01:21:59) um was happier than these 20s. (01:22:04) And and um (01:22:08) I think every I think everyone deserves (01:22:11) some time uh to be oblivious (01:22:17) and not to have not to wear all of the (01:22:20) world's problems on their shoulder on (01:22:23) day one. (01:22:25) I I just don't think it's necessary. (01:22:28) Nobody can convince me of it otherwise. (01:22:31) that that that there's some there's some (01:22:33) joy in ignorance and there's there's a (01:22:36) superpower in ignorance. (01:22:39) Nvidia it would not be power would not (01:22:41) be would not be power it would not be (01:22:43) possible today if not for the fact that (01:22:45) I was ignorant to the fact that it's (01:22:47) impossible to build Nvidia. (01:22:49) >> Right. Right. (01:22:50) >> In fact it's impossible to build Nvidia. (01:22:53) You can't build Nvidia. (01:22:55) You just can't. (01:22:57) But nobody can convince me otherwise (01:22:59) because I didn't know any better. And (01:23:04) I think that (01:23:07) optimistic people, you can't convince (01:23:10) them (01:23:12) that they can't make it better. (01:23:15) They're so ignorant. (01:23:18) They're so oblivious to the truth that (01:23:22) they are optimistic. (01:23:24) How is that a bad thing? (01:23:26) And I feel that we're we're we're (01:23:28) raising generation of of very cynical, (01:23:32) too informed. They're cynical not (01:23:34) because they're they're they're (01:23:37) inherently cynical. They're cynical (01:23:39) because they just see so much stuff, (01:23:41) >> right? (01:23:42) >> It's too much stuff. There's time for (01:23:44) that. You know, we got to we have to (01:23:47) build up some internal reserve of (01:23:49) optimism. We have to build up some (01:23:51) internal reserve of goodness that you (01:23:56) see only the good. You have to find a (01:23:58) way to to to build up that that muscle. (01:24:02) >> Yeah. We we had the opportunity to do (01:24:04) that way more than than people do today. (01:24:06) >> Yeah. And we did that in our 20s. (01:24:07) >> Yeah. (01:24:08) >> When we're optimistic, we're super human (01:24:10) and and everything was possible, you (01:24:13) know. And so that I would choose my (01:24:16) early 20s. (01:24:17) >> Okay. A good note to end on. Ignorance (01:24:20) is bliss. (01:24:21) >> Ignorance is bliss. And ignorance is a (01:24:23) superpower. And anybody who who um uh (01:24:27) who tackles a a new adventure, (01:24:31) um (01:24:33) if if not for ignorance, they they would (01:24:36) they would think it's too hard to even (01:24:38) engage it. And uh I'm I'm I'm (01:24:43) really really quite fortunate to have (01:24:45) been so infor I've been I was informed (01:24:48) and and hardworking um you know I I had (01:24:52) some capabilities (01:24:54) but but the ignorance was was um helpful (01:24:57) to me and I approached everything with (01:25:00) this attitude how hard can it be? (01:25:03) >> Now it turns out it's really hard. (01:25:06) >> You have no idea. (01:25:07) >> Yeah. You have no idea, right? Look at (01:25:09) what you built. And if you would have (01:25:11) known everything then that you know now (01:25:15) and all the feelings and all the (01:25:16) setbacks and all the disappointments and (01:25:19) you bottle all of that up and you put it (01:25:21) all in one place, you would never do it, (01:25:24) you know, and and I would never do it. (01:25:26) And and so I think the the ignorance was (01:25:29) a superpower. And and then the other the (01:25:31) other thing that's a superpower is (01:25:32) having, you know, having no endgame. (01:25:38) Nvidia has no endgame. People ask me, (01:25:40) Justin, what's your plan? We don't have (01:25:42) one. (01:25:44) Staying in business is our plan. You (01:25:47) know, we we we have we have future (01:25:49) dreams of the world and we imagine how (01:25:52) technology would be different, but (01:25:54) staying in business is 100% the plan. (01:25:56) And and somebody asked me once, and I (01:25:59) get asked all the time, I guess, Jensen, (01:26:01) what are your what are your what are (01:26:03) your goals, life goals? I don't have (01:26:06) any, you know, just working, staying (01:26:10) employed, being able to do good work, (01:26:12) surrounded by amazing people. That's (01:26:14) that's the goal. And and so in a lot of (01:26:16) ways, you know, having no endgame is is (01:26:20) uh really has been really helpful to (01:26:21) Nvidia. (01:26:22) >> Okay. (01:26:22) >> Yeah. So, anyways, that's my endgame (01:26:26) point for the end of the show. (01:26:27) >> I love it. (01:26:29) >> Thanks for being my first guest. This (01:26:30) was super fun. (01:26:31) >> Thank you. This was terrific. Great to (01:26:33) see you. Great to hang out with you. (01:26:34) Yeah, you too.