NVIDIA CEO Jensen Huang's Vision for the Future

NVIDIA CEO Jensen Huang's Vision for the Future Cleo Abram 5.86M subscribers Subscribe 89K Share Download Save 2,651,667 views Jan 27, 2025 What NVIDIA is trying to build next… Subscribe for more optimistic science and tech stories from our show Huge If True. You're probably hearing a lot about AI, DeepSeek, NVIDIA and more right now. If you want the big picture (and to start from the beginning), watch this Huge Conversation with NVIDIA CEO Jensen Huang. In the last few years, NVIDIA has skyrocketed to become one of the world's most valuable companies. That's because, beginning in the 90s, they led a fundamental shift in how computers work, now unleashing the current explosion of what’s possible with technology. A huge amount of the most futuristic tech you’re hearing about - in AI, robotics, gaming, self-driving cars, breakthrough medical research - relies on new chips and software designed by him and his company. During the dozens of background interviews I did to prepare for this, what struck me most was how much Jensen Huang has already influenced all our lives over the last 30 years, and how many are saying it’s just the beginning of something even bigger… We all need to know what he’s building and why, and most importantly, what he’s trying to build next, so you can decide for yourself what you think of it. Welcome to the second episode of our new series, Huge Conversations… If you want to know what the most important people building the future are imagining it will look like, Huge Conversations is the show for you. This interview was recorded at CES in Las Vegas on January 7th, 2025. Watch our first episode of Huge Conversations with Mark Zuckerberg here: • The Future Mark Zuckerberg Is Trying ... Watch our trailer to understand more about the mission of Huge Conversations: • Something Big Is Coming... Chapters: 0:00 What is Jensen Huang trying to build? 1:40 The goal of this Huge Conversation 3:40 How did we get here? 4:25 What is a GPU? 5:45 Why video games first? 7:59 What is CUDA? 11:04 Why was AlexNet such a big deal? 15:40 Why are we hearing about AI so much now? 19:33 What are NVIDIA’s core beliefs? 21:34 Why does this moment feel so different? 24:08 What’s the future of robots? 30:15 What is Jensen’s 10-year vision? 32:00 What are the biggest concerns? 35:14 What are the biggest limitations? 38:05 How does NVIDIA make big bets on specific chips (transformers)? 42:33 How are chips made? 44:19 What’s Jensen’s next bet? 47:20 How should people prepare for this future? 50:12 How does this affect people’s jobs? 52:37 GeForce RTX 50 Series and NVIDIA DGX 55:50 What’s Jensen’s advice for the future? 59:07 How does Jensen want to be remembered? You can find me on Instagram here: / cleoabram On TikTok here: / cleoabram Or on Twitter here: / cleoabram Bio: Cleo Abram is a video journalist who produces Huge If True, an optimistic show about science and technology. Huge If True is an antidote to the doom and gloom, helping a wide audience see better futures they can help build. In each episode, Cleo dives deep into one innovation that could shape the future. She has explored humanoid robots at Boston Dynamics, supersonic planes at NASA, quantum computers at IBM, the Large Hadron Collider at CERN, and more. Every episode mixes high quality animations and detailed scripts with relatable vlog-style journeys, taking the audience along for an adventure to answer the question: If this works, what could go right? Previously, Cleo was a video producer at Vox and directed for Explained on Netflix. She was the host of Vox’s first ever daily show, Answered, as well as co-host of Vox’s YouTube Originals show, Glad You Asked. Vox: https://www.vox.com/authors/cleo-abram IMDb: https://www.imdb.com/name/nm10108242/ — Welcome to the joke down low: Why does a GPU without CUDA wear glasses? Because it can’t C! If you don’t get this joke yet, watch the rest of the episode! Find a way to use “C” in a comment to let me know you’re a real one who made it to the end of the description :) Transcript What is Jensen Huang trying to build? 0:00 At some point, you have to believe something. We've reinvented computing as we know it. What is the vision for what you see coming next? We asked ourselves, if it can do this, how far can 0:08 it go? How do we get from the robots that we have now to the future world that you see? Cleo, everything that moves will be robotic someday and it will be soon. We 0:17 invested tens of billions of dollars before it really happened. No that's very good, you 0:22 did some research! But the big breakthrough I would say is when we... 0:28 That's Jensen Huang, and whether you know it or not his decisions are shaping your future. He's the CEO of 0:36 NVIDIA, the company that skyrocketed over the past few years to become one of the most valuable companies in 0:41 the world because they led a fundamental shift in how computers work unleashing this current 0:46 explosion of what's possible with technology. "NVIDIA's done it again!" We found ourselves being one of the most important technology companies in the world and potentially ever. A huge amount of 0:56 the most futuristic tech that you're hearing about in AI and robotics and gaming and self-driving cars and breakthrough medical research relies on new chips and software designed by him and his 1:06 company. During the dozens of background interviews that I did to prepare for this what struck me most was how much Jensen Huang has already influenced all of our lives over the last 30 years, and how 1:16 many said it's just the beginning of something even bigger. We all need to know what he's building 1:22 and why and most importantly what he's trying to build next. Welcome to Huge Conversations... 1:36 Thank you so much for doing this. I'm so happy to do it. Before we dive in, I wanted to tell you The goal of this Huge Conversation 1:42 how this interview is going to be a little bit different than other interviews I've seen you do recently. Okay! I'm not going to ask you any questions about - you could ask - company finances, 1:51 thank you! I'm not going to ask you questions about your management style or why you don't like one-on ones. I'm not going to ask you about regulations or politics. I think all 2:01 of those things are important but I think that our audience can get them well covered elsewhere. Okay. 2:06 What we do on huge if true is we make optimistic explainer videos and we've covered - I'm the worst 2:13 person to be an explainer video. I think you might be the best and I think that's what I'm really hoping that we can do together is make a joint explainer video about how can we actually 2:25 use technology to make the future better. Yeah. And we do it because we believe that when people see those better futures, they help build them. So the people that you're going to be talking to 2:33 are awesome. They are optimists who want to build those better futures but because we 2:39 cover so many different topics, we've covered supersonic planes and quantum computers and particle colliders, it means that millions of people come into every episode without 2:48 any prior knowledge whatsoever. You might be talking to an expert in their field who doesn't know the difference between a CPU and a GPU or a 12-year-old who might grow up one day to be you 3:00 but is just starting to learn. For my part, I've now been preparing for this interview for 3:06 several months, including doing background conversations with many members of your team 3:11 but I'm not an engineer. So my goal is to help that audience see the future that you see so I'm going 3:18 to ask about three areas: The first is, how did we get here? What were the key insights that led to 3:23 this big fundamental shift in computing that we're in now? The second is, what's actually happening 3:29 right now? How did those insights lead to the world that we're now living in that seems like so much 3:34 is going on all at once? And the third is, what is the vision for what you see coming next? In order How did we get here? 3:42 to talk about this big moment we're in with AI I think we need to go back to video games in the 3:48 '90s. At the time I know game developers wanted to create more realistic looking graphics but 3:56 the hardware couldn't keep up with all of that necessary math. NVIDIA came up with 4:02 a solution that would change not just games but computing itself. Could you take us back 4:09 there and explain what was happening and what were the insights that led you and the NVIDIA 4:15 team to create the first modern GPU? So in the early '90s when we first started the company we observed that in a software program inside it there are just a few lines of code, maybe What is a GPU? 4:27 10% of the code, does 99% % of the processing and that 99% of the processing could be done 4:33 in parallel. However the other 90% of the code has to be done sequentially. It turns out that 4:40 the proper computer the perfect computer is one that could do sequential processing and parallel 4:45 processing not just one or the other. That was the big observation and we set out to build a company 4:52 to solve computer problems that normal computers can't. And that's really the beginning of NVIDIA. 5:00 My favorite visual of why a CPU versus a GPU really matters so much is a 15-year-old 5:05 video on the NVIDIA YouTube channel where the Mythbusters, they use a little robot shooting 5:11 paintballs one by one to show solving problems one at a time or sequential processing on a 5:16 CPU, but then they roll out this huge robot that shoots all of the paintballs at once 5:24 doing smaller problems all at the same time or parallel processing on a GPU. 5:30 "3... 2... 1..." So Nvidia unlocks all of this new power for video games. Why gaming first? The video games 5:41 requires parallel processing for processing 3D graphics and we chose video games because, Why video games first? 5:47 one, we loved the application, it's a simulation of virtual worlds and who doesn't want to go to 5:52 virtual worlds and we had the good observation that video games has potential to be the largest 5:58 market for for entertainment ever. And it turned out to be true. And having it being a large market 6:04 is important because the technology is complicated and if we had a large market, our R&D budget could 6:10 be large, we could create new technology. And that flywheel between technology and market and greater 6:17 technology was really the flywheel that got NVIDIA to become one of the most important technology companies in the world. It was all because of video games. I've heard you say that 6:25 GPUs were a time machine? Yeah. Could you tell me more about what you meant by that? A GPU is like a 6:31 time machine because it lets you see the future sooner. One of the most amazing things anybody's 6:37 ever said to me was a quantum chemistry scientist. He said, Jensen, because of NVIDIA's work, 6:46 I can do my life's work in my lifetime. That's time travel. He was able to do something that was beyond 6:52 his lifetime within his lifetime and this is because we make applications run so much faster 7:00 and you get to see the future. And so when you're doing weather prediction for example, you're seeing the future when you're doing a simulation a virtual city with virtual traffic and we're 7:11 simulating our self-driving car through that virtual city, we're doing time travel. So 7:17 parallel processing takes off in gaming and it's allowing us to create worlds in computers that 7:24 we never could have before and and gaming is sort of this this first incredible cas Cas of parallel 7:30 processing unlocking a lot more power and then as you said people begin to use that power across 7:37 many different industries. The case of the of the quantum chemistry researcher, when I've heard you 7:42 tell that story it's that he was running molecular simulations in a way where it was much faster to 7:49 run in parallel on NVIDIA GPUs even then than it was to run them on the supercomputer with the CPU 7:56 that he had been using before. Yeah that's true. So oh my god it's revolutionizing all of these other industries as well, it's beginning to change how we see what's possible with computers and my What is CUDA? 8:07 understanding is that in the early 2000s you see this and you realize that actually doing 8:14 that is a little bit difficult because what that researcher had to do is he had to sort of trick the GPUs into thinking that his problem was a graphics problem. That's exactly right, no that's 8:23 very good, you did some research. So you create a way to make that a lot easier. That's right 8:29 Specifically it's a platform called CUDA which lets programmers tell the GPU what to do using programming languages that they already know like C and that's a big deal because it gives way more 8:39 people easier access to all of this computing power. Could you explain what the vision was that led you to create CUDA? Partly researchers discovering it, partly internal inspiration and 8:53 and partly solving a problem. And you know a lot of interesting interesting ideas come out 9:00 of that soup. You know some of it is aspiration and inspiration, some of it is just desperation you 9:06 know. And so in the case of CUDA is very much this the same way and probably the first 9:13 external ideas of using our GPUs for parallel processing emerged out of some interesting work 9:19 in medical imaging a couple of researchers at Mass General were using it to do CT 9:26 reconstruction. They were using our graphics processors for that reason and it inspired us. 9:32 Meanwhile the problem that we're trying to solve inside our company has to do with the fact that when you're trying to create these virtual worlds for video games, you would like it to be beautiful 9:41 but also dynamic. Water should flow like water and explosions should be like explosions. So there's 9:50 particle physics you want to do, fluid dynamics you want to do and that is much harder to do if 9:56 your pipeline is only able to do computer graphics. And so we have a natural reason to want to do it 10:02 in the market that we were serving. So researchers were also horsing around with using 10:08 our GPUs for general purpose uh acceleration and and so there there are multiple multiple factors 10:13 that were coming together in that soup, we just when the time came and we decided 10:20 to do something proper and created a CUDA as a result of that. Fundamentally the reason why 10:25 I was certain that CUDA was going to be successful and we put the whole company behind it was 10:31 because fundamentally our GPU was going to be the highest volume parallel processors built in 10:38 the world because the market of video games was so large and so this architecture has a good chance of reaching many people. It has seemed to me like creating CUDA was this incredibly optimistic "huge 10:51 if true" thing to do where you were saying, if we create a way for many more people to use much 10:58 more computing power, they might create incredible things. And then of course it came true. They did. Why was AlexNet such a big deal? 11:04 In 2012, a group of three researchers submits an entry to a famous competition where the goal is 11:09 to create computer systems that could recognize images and label them with categories. And their 11:14 entry just crushes the competition. It gets way fewer answers wrong. It was incredible. It blows 11:20 everyone away. It's called AlexNet, and it's a kind of AI called the neural network. My understanding is one reason it was so good is that they used a huge amount of data to train that system 11:29 and they did it on NVIDIA GPUs. All of a sudden, GPUs weren't just a way to make computers faster 11:35 and more efficient they're becoming the engines of a whole new way of computing. We're moving from 11:40 instructing computers with step-by-step directions to training computers to learn by showing them a 11:47 huge number of examples. This moment in 2012 really kicked off this truly seismic shift that we're 11:54 all seeing with AI right now. Could you describe what that moment was like from your perspective and what did you see it would mean for all of our futures? When you create something new like 12:06 CUDA, if you build it, they might not come. And that's always the cynic's perspective 12:14 however the optimist's perspective would say, but if you don't build it, they can't come. And that's 12:20 usually how we look at the world. You know we have to reason about intuitively why this would be very useful. And in fac, in 2012 Ilya Sutskever, and Alex Krizhevsky and Geoff Hinton in the University 12:33 of Toronto the lab that they were at they reached out to a gForce GTX 580 because they learned about 12:39 CUDA and that CUDA might be able to to be used as a parallel processor for training AlexNet and 12:45 so our inspiration that GeForce could be the the vehicle to bring out this parallel architecture 12:51 into the world and that researchers would somehow find it someday was a good was a good strategy. It 12:57 was a strategy based on hope, but it was also reasoned hope. The thing that really caught 13:03 our attention was simultaneously we were trying to solve the computer vision problem inside the company and we were trying to get CUDA to be a good computer vision processor and we 13:13 were frustrated by a whole bunch of early developments internally with respect to our 13:19 computer vision effort and getting CUDA to be able to do it. And all of a sudden we saw AlexNet, 13:25 this new algorithm that is completely different than computer vision algorithms before 13:31 it, take a giant leap in terms of capability for computer vision. And when we saw that it was 13:38 partly out of interest but partly because we were struggling with something ourselves. And so we were 13:43 we were highly interested to want to see it work. And so when we when we looked at AlexNet we were 13:49 inspired by that. But the big breakthrough I would say is when we when we saw AlexNet, we 13:57 asked ourselves you know, how far can AlexNet go? If it can do this with computer vision, how 14:04 far can it go? And if it if it could go to the limits of what we think it could go, the type 14:11 of problems it could solve, what would it mean for the computer industry? And what would it mean for the computer architecture? And we were, we rightfully reasoned that if machine learning, 14:25 if the deep learning architecture can scale, the vast majority of machine learning problems 14:30 could be represented with deep neural networks. And the type of problems we could solve with machine 14:36 learning is so vast that it has the potential of reshaping the computer industry altogether, 14:42 which prompted us to re-engineer the entire computing stack which is where DGX came from 14:49 and this little baby DGX sitting here, all of this came from from that observation that we ought 14:56 to reinvent the entire computing stack layer by layer by layer. You know computers, after 65 years 15:03 since IBM System 360 introduced modern general purpose computing, we've reinvented computing as we 15:09 know it. To think about this as a whole story, so parallel processing reinvents modern gaming and 15:16 revolutionizes an entire industry then that way of computing that parallel processing begins to 15:22 be used across different industries. You invest in that by building CUDA and then CUDA and the 15:29 use of GPUs allows for a a step change in neural networks and machine learning and begins a sort 15:38 of revolution that we're now seeing only increase in importance today... All of a sudden Why are we hearing about AI so much now? 15:45 computer vision is solved. All of a sudden speech recognition is solved. All of a sudden language understanding is solved. These incredible problems associated with intelligence one 15:54 by one by one by one where we had no solutions for in past, desperate desire to have solutions 16:01 for, all of a sudden one after another get solved you know every couple of years. It's incredible. 16:07 Yeah so you're seeing that, in 2012 you're looking ahead and believing that that's 16:12 the future that you're going to be living in now, and you're making bets that get you there, really 16:17 big bets that have very high stakes. And then my perception as a lay person is that it takes a pretty long time to get there. You make these bets - 8 years, 10 years - so my question is: 16:30 If AlexNet that happened in 2012 and this audience is probably seeing and hearing so much more about 16:36 AI and NVIDIA specifically 10 years later, why did it take a decade and also because you 16:43 had placed those bets, what did the middle of that decade feel like for you? Wow that's a good question. It probably felt like today. You know to me, there's always some problem and 16:55 then there's some reason to be to be impatient. There's always some reason to be 17:03 happy about where you are and there's always many reasons to carry on. And so I think as I 17:09 was reflecting a second ago, that sounds like this morning! So but I would say that in all things that 17:16 we pursue, first you have to have core beliefs. You have to reason from your best principles 17:25 and ideally you're reasoning from it from principles of either physics or deep understanding of 17:32 the industry or deep understanding of the science, wherever you're reasoning from, you 17:38 reason from first principles. And at some point you have to believe something. And if those principles 17:45 don't change and the assumptions don't change, then you, there's no reason to change your 17:50 core beliefs. And then along the way there's always some evidence of you know of success and 17:59 and that you're leading in the right direction and sometimes you know you go a 18:04 long time without evidence of success and you might have to course correct a little but the evidence comes. And if you feel like you're going in the right direction, we just keep on going. 18:12 The question of why did we stay so committed for so long, the answer is actually the opposite: There 18:19 was no reason to not be committed because we are, we believed it. And I've believed in NVIDIA 18:28 for 30 plus years and I'm still here working every single day. There's no fundamental 18:34 reason for me to change my belief system and I fundamentally believe that the 18:39 work we're doing in revolutionizing computing is as true today, even more true today than it was before. And so we'll stick with it you know until otherwise. There's 18:51 of course very difficult times along the way. You know when you're investing in something and nobody 18:58 else believes in it and cost a lot of money and you know maybe investors or or others would rather 19:05 you just keep the profit or you know whatever it is improve the share price or whatever it is. 19:11 But you have to believe in your future. You have to invest in yourself. And we believe this so 19:17 deeply that we invested you know tens of billions of dollars before it really 19:25 happened. And yeah it was, it was 10 long years. But it was fun along the way. 19:32 How would you summarize those core beliefs? What is it that you believe about the way computers What are NVIDIA’s core beliefs? 19:38 should work and what they can do for us that keeps you not only coming through that decade but also 19:44 doing what you're doing now, making bets I'm sure you're making for the next few decades? The first 19:50 core belief was our first discussion, was about accelerated computing. Parallel computing versus 19:56 general purpose computing. We would add two of those processors together and we would do accelerated computing. And I continue to believe that today. The second was the recognition 20:06 that these deep learning networks, these DNNs, that came to the public during 2012, these deep neural 20:13 networks have the ability to learn patterns and relationships from a whole bunch of different types of data. And that it can learn more and more nuanced features if it could be larger 20:24 and larger. And it's easier to make them larger and larger, make them deeper and deeper or wider and wider, and so the scalability of the architecture is empirically true. The fact 20:40 that model size and the data size being larger and larger, you can learn more knowledge is 20:47 also true, empirically true. And so if that's the case, you could you know, what what are the 20:55 limits? There not, unless there's a physical limit or an architectural limit or mathematical limit 21:00 and it was never found, and so we believe that you could scale it. Then the question, the only other question is: What can you learn from data? What can you learn from experience? Data is basically 21:11 digital versions of human experience. And so what can you learn? You obviously can learn object 21:17 recognition from images. You can learn speech from just listening to sound. You can learn 21:22 even languages and vocabulary and syntax and grammar and all just by studying a whole bunch 21:27 of letters and words. So we've now demonstrated that AI or deep learning has the ability to learn 21:33 almost any modality of data and it can translate to any modality of data. And so what does that mean? Why does this moment feel so different? 21:42 You can go from text to text, right, summarize a paragraph. You can go from text to text, translate 21:49 from language to language. You can go from text to images, that's image generation. You can go from 21:55 images to text, that's captioning. You can even go from amino acid sequences to protein structures. 22:03 In the future, you'll go from protein to words: "What does this protein do?" or "Give me an example of a 22:11 protein that has these properties." You know identifying a drug target. And so you could 22:17 just see that all of these problems are around the corner to be solved. You can go from words 22:24 to video, why can't you go from words to action tokens for a robot? You know from the computer's 22:33 perspective how is it any different? And so it it opened up this universe of opportunities and 22:40 universe of problems that we can go solve. And that gets us quite excited. It feels like 22:48 we are on the cusp of this truly enormous change. When I think about the next 10 years, unlike the 22:56 last 10 years, I know we've gone through a lot of change already but I don't think I can predict 23:02 anymore how I will be using the technology that is currently being developed. That's exactly right. I 23:07 think the last 10, the reason why you feel that way is, the last 10 years was really about the science 23:12 of AI. The next 10 years we're going to have plenty of science of AI but the next 10 years is going to 23:18 be the application science of AI. The fundamental science versus the application science. And so the 23:24 the applied research, the application side of AI now becomes: How can I apply AI to digital biology? 23:31 How can I apply AI to climate technology? How can I apply AI to agriculture, to fishery, to robotics, 23:39 to transportation, optimizing logistics? How can I apply AI to you know teaching? How do I apply AI 23:47 to you know podcasting right? I'd love to choose a couple of those to help people see how 23:53 this fundamental change in computing that we've been talking about is actually going to change their experience of their lives, how they're actually going to use technology that is based 24:02 on everything we just talked about. One of the things that I've now heard you talk a lot about and I have a particular interest in is physical AI. Or in other words, robots - "my friends!" - meaning What’s the future of robots? 24:16 humanoid robots but also robots like self-driving cars and smart buildings or autonomous warehouses 24:23 or autonomous lawnmowers or more. From what I understand, we might be about to see a huge 24:29 leap in what all of these robots are capable of because we're changing how we train them. Up until 24:37 recently you've either had to train your robot in the real world where it could get damaged or wear 24:43 down or you could get data from fairly limited sources like humans in motion capture suits. But 24:50 that means that robots aren't getting as many examples as they'd need to learn more quickly. 24:56 But now we're starting to train robots in digital worlds, which means way more repetitions a day, way 25:03 more conditions, learning way faster. So we could be in a big bang moment for robots right now and 25:11 NVIDIA is building tools to make that happen. You have Omniverse and my understanding is this is 3D 25:19 worlds that help train robotic systems so that they don't need to train in the physical world. 25:26 That's exactly right. You just just announced Cosmos which is ways to make that 3D universe 25:34 much more realistic. So you can get all kinds of different, if we're training something on 25:39 this table, many different kinds of lighting on the table, many different times of day, many different you know experiences for the robot to go through so that it can get even more out of Omniverse. As 25:52 a kid who grew up loving Data on Star Trek, Isaac Asimov's books and just dreaming about a future with 26:00 robots, how do we get from the robots that we have now to the future world that you see of robotics? 26:08 Yeah let me use language models maybe ChatGPT as a reference for understanding Omniverse and 26:17 Cosmos. So first of all when ChatGPT first came out it, it was extraordinary and 26:24 it has the ability to do to basically from your prompt, generate text. However, as amazing as 26:32 it was, it has the tendency to hallucinate if it goes on too long or if it pontificates about 26:40 a topic it you know is not informed about, it'll still do a good job generating plausible answers. 26:46 It just wasn't grounded in the truth. And so people called it hallucination. And 26:55 so the next generation shortly it was, it had the ability to be conditioned by context, so 27:03 you could upload your PDF and now it's grounded by the PDF. The PDF becomes the ground truth. It 27:09 could be it could actually look up search and then the search becomes its ground truth. And 27:14 between that it could reason about what is how to produce the answer that you're asking for. And 27:21 so the first part is a generative AI and the second part is ground truth. Okay and so now let's 27:28 come into the the physical world. The world model, we need a foundation model just like 27:35 we need ChatGPT had a core foundation model that was the breakthrough in order for robotics 27:41 to to be smart about the physical world. It has to understand things like gravity, friction, inertia, 27:50 geometric and spatial awareness. It has to uh understand that an object is sitting there even 27:57 when I looked away when I come back it's still sitting there, object permanence. It has to 28:02 understand cause and effect. If I tip it, it'll fall over. And so these kind of physical 28:08 common sense if you will has to be captured or encoded into a world foundation model so that 28:16 the AI has world common sense. Okay and so we have to go, somebody has to go create that, and 28:23 that's what we did with Cosmos. We created a world language model. Just like ChatGPT was a language model, 28:29 this is a world model. The second thing we have to go do is we have to do the same thing that we did 28:35 with PDFs and context and grounding it with ground truth. And so the way we augment Cosmos 28:42 with ground truth is with physical simulations, because Omniverse uses physics simulation which 28:49 is based on principled solvers. The mathematics is Newtonian physics is the, right, it's the math we 28:56 know, all of the the fundamental laws of physics we've understood for a very long 29:02 time. And it's encoded into, captured into Omniverse. That's why Omniverse is a simulator. And using the 29:09 simulator to ground or to condition Cosmos, we can now generate an infinite number of stories of the 29:19 future. And they're grounded on physical truth. Just like between PDF or search plus ChatGPT, we can 29:30 generate an infinite amount of interesting things, answer a whole bunch of interesting questions. The 29:37 combination of Omniverse plus Cosmos, you could do that for the physical world. So to illustrate 29:43 this for the audience, if you had a robot in a factory and you wanted to make it learn every 29:49 route that it could take, instead of manually going through all of those routes, which could take days and could be a lot of wear and tear on the robot, we're now able to simulate all of them 29:59 digitally in a fraction of the time and in many different situations that the robot might face - it's dark, it's blocked it's etc - so the robot is now learning much much faster. It seems to 30:10 me like the future might look very different than today. If you play this out 10 years, how do you see What is Jensen’s 10-year vision? 30:17 people actually interacting with this technology in the near future? Cleo, everything that moves 30:22 will be robotic someday and it will be soon. You know the the idea that you'll be pushing around 30:28 a lawn mower is already kind of silly. You know maybe people do it because because it's fun but 30:35 but there's no need to. And every car is going to be robotic. Humanoid robots, the technology 30:44 necessary to make it possible, is just around the corner. And so everything that moves will be 30:50 robotic and they'll learn how to be a robot in Omniverse Cosmos and we'll generate 30:59 all these plausible, physically plausible futures and the the robots will learn from them and 31:05 then they'll come into the physical world and you know it's exactly the same. A future where 31:11 you're just surrounded by robots is for certain. And I'm just excited about having my own R2-D2. 31:18 And of course R2-D2 wouldn't be quite the can that it is and roll around. It'll be you know R2-D2 31:25 yeah, it'll probably be a different physical embodiment, but it's always R2. You know so my R2 31:32 is going to go around with me. Sometimes it's in my smart glasses, sometimes it's in my phone, sometimes it's in my PC. It's in my car. So R2 is with me all the time including you know when I get home 31:43 you know where I left a physical version of R2. And you know whatever that version happens to 31:49 be you know, we'll interact with R2. And so I think the idea that we'll have our own R2-D2 for 31:55 our entire life and it grows up with us, that's a certainty now yeah. I think a lot of news media What are the biggest concerns? 32:05 when they talk about futures like this they focus on what could go wrong. And that makes sense. There 32:10 is a lot that could go wrong. We should talk about what could go wrong so we could keep it from from going wrong. Yeah that's the approach that we like to take on the show is, what are the big challenges 32:19 so that we can overcome them? Yeah. What buckets do you think about when you're worrying about this future? Well there's a whole bunch of the stuff that everybody talks about: Bias or toxicity 32:30 or just hallucination. You know speaking with great confidence about something it knows nothing 32:37 about and as a result we rely on that information. Generating, that's a version of generating 32:45 fake information, fake news or fake images or whatever it is. Of course impersonation. 32:50 It does such a good job pretending to be a human, it could be it could do an incredibly good 32:56 job pretending to be a specific human. And so the spectrum of areas we 33:05 have to be concerned about is fairly clear and there's a lot of people who are 33:11 working on it. There's a some of the stuff, some of the stuff related to AI safety requires 33:18 deep research and deep engineering and that's simply, it wants to do the right thing it 33:24 just didn't perform it right and as a result hurt somebody. You know for example self-driving car 33:29 that wants to drive nicely and drive properly and just somehow the sensor broke down or it 33:36 didn't detect something. Or you know made it too aggressive turn or whatever it is. It did 33:41 it poorly. It did it wrongly. And so that's a whole bunch of engineering that has to 33:47 be done to to make sure that AI safety is upheld by making sure that the product functions properly. 33:54 And then and then lastly you know whatever what happens if the system, the AI wants to do a good 34:00 job but the system failed. Meaning the AI wanted to stop something from happening 34:07 and it turned out just when it wanted to do it, the machine broke down. And so this is 34:13 no different than a flight computer inside a plane having three versions of them and then 34:19 so there's triple redundancy inside the system inside autopilots and then you have two 34:25 pilots and then you have air traffic control and then you have other pilots watching out for 34:31 these pilots. And so that the AI safety systems has to be architected as a community 34:38 such that such that these AIs one, work, function properly. When they don't 34:47 function properly, they don't put people in harm's way. And that they're sufficient safety and security systems all around them to make sure that we keep AI safe. And so there's 34:58 this spectrum of conversation is gigantic and and you know we have to take the parts, take the 35:05 parts apart and and build them as engineers. One of the incredible things about this moment that 35:11 we're in right now is that we no longer have a lot of the technological limits that we had in a What are the biggest limitations? 35:17 world of CPUs and sequential processing. And we've unlocked not only a new way to do computing and 35:28 and but also a way to continue to improve. Parallel processing has a a different kind of physics to it 35:35 than the improvements that we were able to make on CPUs. I'm curious, what are the scientific or 35:42 technological limitations that we face now in the current world that you're thinking a lot about? Well everything in the end is about how much work you can get done within the limitations of 35:54 the energy that you have. And so that's a physical limit and the laws of 36:02 physics about transporting information and transporting bits, flipping bits and transporting 36:11 bits, at the end of the day the energy it takes to do that limits what we can get done. And the 36:18 amount of energy that we have limits what we can get done. We're far from having any fundamental limits that keep us from advancing. In the meantime, we seek to build better and more energy efficient 36:29 computers. This little computer, the the big version of it was $250,000 - Pick up? - Yeah 36:38 Yeah that's little baby DIGITS yeah. This is an AI supercomputer. The version that I delivered, 36:46 this is just a prototype so it's a mockup. The very first version was DGX 1, I 36:52 delivered to Open AI in 2016 and that was $250,000. 10,000 times more power, more energy necessary 37:03 than this version and this version has six times more performance. I know, it's incredible. We're 37:09 in a whole in the world. And it's only since 2016 and so eight years later we've in increased the 37:16 energy efficiency of computing by 10,000 times. And imagine if we became 10,000 times more energy 37:25 efficient or if a car was 10,000 times more energy efficient or electric light bulb was 37:31 10,000 times more energy efficient. Our light bulb would be right now instead of 100 Watts, 37:38 10,000 times less producing the same illumination. Yeah and so the energy efficiency of 37:45 computing particularly for AI computing that we've been working on has advanced incredibly and that's 37:51 essential because we want to create you know more intelligent systems and and we want to use more computation to be smarter and so energy efficiency to do the work is our number one 38:03 priority. When I was preparing for this interview, I spoke to a lot of my engineering friends and this How does NVIDIA make big bets on specific chips (transformers)? 38:09 is a question that they really wanted me to ask. So you're really speaking to your people here. You've 38:15 shown a value of increasing accessibility and abstraction, with CUDA and allowing more 38:21 people to use more computing power in all kinds of other ways. As applications of technology get more 38:28 specific, I'm thinking of transformers in AI for example... For the audience, a transformer is a very 38:35 popular more recent structure of AI that's now used in a huge number of the tools that you've 38:40 seen. The reason that they're popular is because transformers are structured in a way that helps them pay "attention" to key bits of information and give much better results. You could build chips 38:51 that are perfectly suited for just one kind of AI model, but if you do that then you're making them 38:56 less able to do other things. So as these specific structures or architectures of AI get more popular, 39:03 my understanding is there's a debate between how much you place these bets on "burning them into the 39:09 chip" or designing hardware that is very specific to a certain task versus staying more general and 39:15 so my question is, how do you make those bets? How do you think about whether the solution is a car 39:22 that could go anywhere or it's really optimizing a train to go from A to B? You're making bets 39:28 with huge stakes and I'm curious how you think about that. Yeah and that now comes back 39:33 to exactly your question, what are your core beliefs? And the question, the core 39:41 belief either one, that transformer is the last AI algorithm, AI architecture that any researcher will 39:52 ever discover again, or that transformers is a stepping stone towards evolutions of 40:01 transformers that are uh barely recognizable as a transformer years from now. And we believe the 40:08 latter. And the reason for that is because you just have to go back in history and ask yourself, 40:14 in the world of computer algorithms, in the world of software, in the world of 40:20 engineering and innovation, has one idea stayed along that long? And the answer is no. And so that's 40:27 kind of the beauty, that's in fact the essential beauty of a computer that it's able 40:34 to do something today that no one even imagined possible 10 years ago. And if you would have, if 40:41 you would have turned that computer 10 years ago into a microwave, then why would the applications 40:48 keep coming? And so we believe, we believe in the richness of innovation and the 40:54 richness of invention and we want to create an architecture that let inventors and innovators 40:59 and software programmers and AI researchers swim in the soup and come up with some amazing 41:05 ideas. Look at transformers. The fundamental characteristic of a transformer is this idea 41:10 called "attention mechanism" and it basically says the transformer is going to understand the meaning 41:16 and the relevance of every single word with every other word. So if you had 10 words, it has to figure 41:22 out the relationship across 10 of them. But if you have a 100,000 words or if your context is 41:27 now as large as, read a PDF and that read a whole bunch of PDFs, and the context window is now like 41:35 a million tokens, the processing all of it across all of it is just impossible. And so the way you 41:42 solve that problem is there all kinds of new ideas, flash attention or hierarchical attention or you 41:49 know all the, wave attention I just read about the other day. The number of different types of 41:54 attention mechanisms that have been invented since the transformer is quite extraordinary. 42:00 And so I think that that's going to continue and we believe it's going to continue and that 42:06 that computer science hasn't ended and that AI research have not all given up and we haven't 42:12 given up anyhow and that having a computer that enables the flexibility of 42:21 of research and innovation and new ideas is fundamentally the most important thing. One of the 42:29 things that I am just so curious about, you design the chips. There are companies that assemble the How are chips made? 42:37 chips. There are companies that design hardware to make it possible to work at nanometer scale. When 42:44 you're designing tools like this, how do you think about design in the context of what's physically 42:51 possible right now to make? What are the things that you're thinking about with sort of pushing 42:56 that limit today? The way we do it is even though even though we have things made like for 43:05 example our chips are made by TSMC. Even though we have them made by TSMC, we assume that we need 43:13 to have the deep expertise that TSMC has. And so we have people in our company who are incredibly 43:19 good at semiconductive physics so that we have a feeling for, we have an intuition for, what are the 43:25 limits of what today's semiconductor physics can do. And then we work very closely with them to 43:32 discover the limits because we're trying to push the limits and so we discover the limits together. Now we do the same thing in system engineering and cooling systems. It turns out plumbing is really 43:41 important to us because of liquid cooling. And maybe fans are really important to us because of air cooling and we're trying to design these fans in a way almost like you know they're 43:49 aerodynamically sound so that we could pass the highest volume of air, make the least amount of 43:54 noise. So we have aerodynamics engineers in our company. And so even though even though we don't 44:01 make 'em, we design them and we have to deep expertise of knowing how to have them made. And 44:09 and from that we try to push the limits. One of the themes of this conversation is 44:18 that you are a person who makes big bets on the future and time and time again you've been right What’s Jensen’s next bet? 44:25 about those bets. We've talked about GPUs, we've talked about CUDA, we've talked about bets you've made in AI - self-driving cars, and we're going to be right on robotics and - this is my question. What 44:37 are the bets you're making now? the latest bet we just described at the CES and I'm very very proud 44:43 of it and I'm very excited about it is the fusion of Omniverse and Cosmos so that we have 44:50 this new type of generative world generation system, this multiverse generation system. I 44:59 think that's going to be profoundly important in the future of robotics and physical systems. 45:06 Of course the work that we're doing with human robots, developing the tooling systems and the 45:11 training systems and the human demonstration systems and all of this stuff that that you've 45:17 already mentioned, we're just seeing the beginnings of that work and I think the 45:23 next 5 years are going to be very interesting in the world of human robotics. Of course the work that we're doing in digital biology so that we can understand the language of molecules and 45:34 understand the language of cells and just as we understand the language of physics and the 45:39 physical world we'd like to understand the language of the human body and understand the language of biology. And so if we can learn that, and we can predict it. Then all of a sudden our ability to 45:50 have a digital twin of the human is plausible. And so I'm very excited about that work. I love 45:56 the work that we're doing in climate science and be able to, from weather predictions, understand 46:03 and predict the high resolution regional climates, the weather patterns within a kilometer above 46:10 your head. That we can somehow predict that with great accuracy, its implications is really quite 46:17 profound. And so the number of things that we're working on is really cool. You know we 46:24 we're fortunate that we've created this this instrument that is a time machine and 46:37 we need time machines in all of these areas that we just talked about so that we can see 46:43 the future. And if we could see the future and we can predict the future then we have a better 46:48 chance of making that future the best version of it. And that's the reason why scientists 46:53 want to predict the future. That's the reason why, that's the reason why we try to predict the future 46:58 and everything that we try to design so that we can optimize for the best version. So if 47:05 someone is watching this and maybe they came into this video knowing that NVIDIA is an incredibly 47:12 important company but not fully understanding why or how it might affect their life and they're now 47:18 hopefully better understanding a big shift that we've gone through over the last few decades in How should people prepare for this future? 47:23 computing, this very exciting, very sort of strange moment that we're in right now, where we're sort 47:30 of on the precipice of so many different things. If they would like to be able to look into the 47:36 future a little bit, how would you advise them to prepare or to think about this moment that they're 47:42 in personally with respect to how these tools are actually going to affect them? Well there are 47:49 several ways to reason about the future that we're creating. One way to reason about it is, 47:57 suppose the work that you do continues to be important but the effort by which you 48:04 do it went from you know being a week long to almost instantaneous. You know that the 48:15 effort of drudgery basically goes to zero. What is the implication of that? This is, this 48:23 is very similar to what would change if all of a sudden we had highways in this country? 48:30 And that kind of happened you know in the last Industrial Revolution, all of a sudden we have interstate highways and when you have interstate highways what happens? Well you know suburbs start 48:40 to be created and and all of a sudden you know distribution of goods from east to west is 48:48 no longer a concern and all of a sudden gas stations start cropping up on highways and 48:55 and fast food restaurants show up and you know someone, some motels show up because people 49:03 you know traveling across the state, across the country and just wanted to stay somewhere for a few hours or overnight, and so all of a sudden new economies and new capabilities, new economies. 49:13 What would happen if a video conference made it possible for us to see each other without 49:19 having to travel anymore? All of a sudden it's actually okay to work further away from 49:24 home and from work, work and live further away. And so you ask yourself kind of 49:32 these questions. You know what would happen if I have a software programmer with me 49:40 all the time and whatever it is I can dream up, the software programmer could write for me. You 49:46 know what would, what would happen if I just had a seed of an idea and 49:54 and I rough it out and all of sudden a you know a prototype of a production was put in front 50:01 of me? And what how would that change my life and how would that change my opportunity? And you 50:07 know what does it free me to be able to do and and so on so forth. And so I think that the next How does this affect people’s jobs? 50:13 the next decade intelligence, not for everything but for for some things, would basically become 50:22 superhuman. But I can tell you exactly what that feels like. I'm surrounded 50:31 by superhuman people, super intelligence from my perspective because they're the best in the 50:38 world at what they do and they do what they do way better than I can do it. and I'm 50:46 surrounded by thousands of them and yet what it it never one day caused me to to think all of a 50:56 son I'm no longer necessary. It actually empowers me and gives me the confidence to go tackle more 51:05 and more ambitious things. And so suppose, suppose now everybody is surrounded by these 51:13 super AIs that are very good at specific things or good at some of the things. What would that 51:20 make you feel? Well it's going to empower you, it's going to make you feel confident and 51:25 and I'm pretty sure you probably use ChatGPT and AI and I feel more empowered today, more 51:32 confident to learn something today. The knowledge of almost any particular field, the barriers to 51:38 that understanding, it has been reduced and I have a personal tutor with me all of the time. And 51:44 so I think that that feeling should be universal. If there's one thing that I would 51:50 encourage everybody to do is to go get yourself an AI tutor right away. And that AI tutor could 51:56 of course just teach your things, anything you like, help you program, help you write, 52:03 help you analyze, help you think, help you reason, you know all of those things is going to 52:10 really make you feel empowered and and I think that going to be our future. We're 52:16 going to become, we're going to become super humans, not because we have super, we're going to become 52:21 super humans because we have super AIs. Could you tell us a little bit about each of these objects? 52:27 This is a new GeForce graphics card and yes and this is the RTX 50 Series. It is essentially GeForce RTX 50 Series and NVIDIA DGX 52:39 a supercomputer that you put into your PC and we use it for gaming, of course people today use it 52:45 for design and creative arts and it does amazing AI. The real breakthrough here and this is 52:52 this is truly an amazing thing, GeForce enabled AI and it enabled Geoff Hinton, Ilya Sutskever, 52:59 Alex Krizhevsky to be able to train AlexNet. We discovered AI and we advanced AI then AI came back 53:07 to GeForce to help computer graphics. And so here's the amazing thing: Out of 8 million pixels or so in 53:16 a 4K display we are computing, we're processing only 500,000 of them. The rest of them we use AI 53:24 to predict. The AI guessed it and yet the image is perfect. We inform it by the 500,000 pixels that we 53:32 computed and we ray traced every single one and it's all beautiful. It's perfect. And then we tell the 53:38 AI, if these are the 500,000 perfect pixels in this screen, what are the other 8 million? And it goes it 53:44 fills in the rest of the screen and it's perfect. And if you only have to do fewer pixels, are you 53:50 able to invest more in doing that because you have fewer to do so then the quality is better so the 53:58 extrapolation that the AI does... Exactly. Because whatever computing, whatever attention you have, whatever resources you have, you can place it into 500,000 pixels. Now this is a perfect example of 54:11 why AI is going to make us all superhuman, because all of the other things that it can do, it'll do 54:17 for us, allows us to take our time and energy and focus it on the really really valuable things that 54:23 we do. And so we'll take our own resource which is you know energy intensive, attention intensive, and 54:33 we'll dedicated to the few 100,000 pixels and use AI to superres, upres it you know to 54:39 everything else. And so this this graphics card is now powered mostly by AI and the computer 54:47 graphics technology inside is incredible as well. And then this next one, as I mentioned 54:52 earlier, in 2016 I built the first one for AI researchers and we delivered the first one to Open AI 54:58 and Elon was there to receive it and this version I built a mini mini version and the 55:06 reason for that is because AI has now gone from AI researchers to every engineer, every student, every 55:15 AI scientist. And AI is going to be everywhere. And so instead of these $250,000 versions we're 55:21 going to make these $3,000 versions and schools can have them, you know students can have them, and 55:28 you set it next to your PC or Mac and all of a sudden you have your own AI supercomputer. And 55:36 you could develop and build AIs. Build your own AI, build your own R2-D2. What do you feel like is 55:42 important for this audience to know that I haven't asked? One of the most important things I would 55:48 advise is for example if I were a student today the first thing I would do is to learn AI. How do What’s Jensen’s advice for the future? 55:54 I learn to interact with ChatGPT, how do I learn to interact with Gemini Pro, and how do I learn 56:00 to interact with Grok? Learning how to interact with with AI is not unlike being 56:10 someone who is really good at asking questions. You're incredibly good at asking questions and 56:17 and prompting AI is very very similar. You can't just randomly ask a bunch of questions 56:23 and so asking an AI to be assistant to you requires some expertise and 56:30 artistry and how to prompt it. And so if I were, if I were a student today, irrespective whether it's for for math or for science or chemistry or biology or doesn't matter what field of science 56:40 I'm going to go into or what profession, I'm going to ask myself, how can I use AI to do my job 56:46 better? If I want to be a lawyer, how can I use AI to be a better lawyer? If I want to be a better do doctor, how can I use AI to be a better doctor? If I want to be a chemist, how do I use AI to be 56:55 a better chemist? If I want to be a biologist, I how do I use AI to be a better biologist? That question 57:02 should be persistent across everybody. And just as my generation grew up as the first generation 57:10 that has to ask ourselves, how can we use computers to do our jobs better? Yeah the generation before 57:17 us had no computers, my generation was the first generation that had to ask the question, how do I 57:23 use computers to do my job better? Remember I came into the industry before Windows 95 right, 1984 57:32 there were no computers in offices. And after that, shortly after that, computers started to emerge and 57:38 so we had to ask ourselves how do we use computers to do our jobs better? The next generation doesn't 57:45 have to ask that question but it has to ask obviously next question, how can I use AI to do my job better? That is start and finish I think for everybody. It's a really exciting and scary and 57:59 therefore worthwhile question I think for everyone. I think it's going to be incredibly fun. AI is 58:04 obviously a word that people are just learning now but it's just you know, it's 58:10 made your computer so much more accessible. It is easier to prompt ChatGPT to ask it anything you 58:15 like than to go do the research yourself. And so we've lowered a barrier of understanding, we've 58:22 lowered a barrier of knowledge, we've lowered a barrier of intelligence, and and everybody really had to just go try it. You know the thing that's really really crazy 58:32 is if I put a computer in front of somebody and they've never used a computer there is no chance 58:37 they're going to learn that computer in a day. There's just no chance. Somebody really has to 58:43 show it to you and yet with ChatGPT if you don't know how to use it, all you have to do is 58:49 type in "I don't know how to use ChatGPT, tell me," and it would come back and give you some 58:54 examples and so that's the amazing thing. You know the amazing thing about intelligence is 59:02 it'll help you along the way and make you uh superhuman you know along the way. All right I have How does Jensen want to be remembered? 59:08 one more question if you have a second. This is not something that I planned to ask you but on the 59:13 way here, I'm a little bit afraid of planes, which is not my most reasonable quality, and 59:21 the flight here was a little bit bumpy mhm very bumpy and I'm sitting there and it's moving and 59:30 I'm thinking about what they're going to say at my funeral and after - She asked good questions, that's 59:37 what the tombstone's going to say - I hope so! Yeah. And after I loved my husband and my 59:44 friends and my family, the thing that I hoped that they would talk about was optimism. I hope that 59:49 they would recognize what I'm trying to do here. And I'm very curious for you, you've you've been 59:56 doing this a long time, it feels like there's so much that you've described in this vision ahead, what would the theme be that you would want people to say about what you're trying to do? 1:00:14 Very simply, they made an extraordinary impact. I think that we're fortunate because of some 1:00:23 core beliefs a long time ago and sticking with those core beliefs and building upon them 1:00:32 we found ourselves today being one of the most, one of the many most important and 1:00:42 consequential technology companies in the world and potentially ever. And so 1:00:49 we take that responsibility very seriously. We work hard to make sure that 1:00:56 the capabilities that we've created are available to large companies as well as 1:01:03 individual researchers and developers, across every field of science no matter profitable or 1:01:10 not, big or small, famous or otherwise. And it's because of this understanding of 1:01:21 the consequential work that we're doing and the potential impact it has on so many people 1:01:27 that we want to make make this capability as pervasively as possible and I 1:01:37 do think that when we look back in a few years, and I do hope that what the 1:01:47 next generation realized is as they, well first of all they're going to know us because of 1:01:53 all the you know gaming technology we create. I do think that we'll look back and the whole 1:01:59 field of digital biology and life sciences has been transformed. Our whole understanding of of 1:02:06 material sciences has completely been revolutionized. That robots are helping 1:02:13 us do dangerous and mundane things all over the place. That if we wanted to drive we can drive 1:02:19 but otherwise you know take a nap or enjoy your car like it's a home theater of yours, 1:02:26 you know read from work to home and at that point you're hoping that you live far 1:02:31 away and so you could be in a car for longer. And you look back and 1:02:37 you realize that there's this company almost at the epicenter of all of that and happens 1:02:43 to be the company that you grew up playing games with. I hope for that to be what the next generation learn. 1:02:50 Thank you so much for your time. I enjoyed it, thank you! I'm glad!