NVIDIA's Plan To Build AI That Understands The Real World — With Rev Lebaredian

Rev Lebaredian is the Vice President, Omniverse & Simulation Technology at NVIDIA. He joins Big Technology Podcast for a conversation about NVIDIA's push to develop AI that understands the dynamics of the real world, including physics. In this conversation, we cover how NVIDIA is building this technology, what it might be useful for (things like robotics and building common sense into AI models), how it will change labor, and even potentially warfare. We also cover how AI videos today possess a solid understanding of the real world. Tune in for the first few minutes where we discuss Lebaredian's perspective on DeepSeek and Jevon's Paradox. Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Alex Kantrowitz?

  Let's talk about Nvidia's push to generate AI that understands the real world with technology that can influence the future of robotics, labor, cars, Hollywood, and more. We're joined by the company's VP of Omniverse and Simulation Technologies, right after this. This episode is brought to you by Genesis. What does it actually take to put a Gentic AI to work across an entire enterprise? I recently spoke with Genesis Chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience, and what companies need to get right in the AI era. Then, I sat down with Adam Mitchell, Head of Enterprise Business Solutions at Voya Financial, to go inside a large-scale

• 0:42Alex Kantrowitz?

  Agentic CX transformation, including the business decisions, trade-offs, and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get Frontier Compute. How do you know where to look, who to trust, or how much to pay? Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation.

• 1:20Alex Kantrowitz?

  Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. Today, we're joined by Rev Lebedev. He's the Vice President of Omniverse and Simulation Technology at Nvidia for a fascinating conversation about what may well be the next stage of AI progress, the pursuit of world models that provide common sense to AIs. Rev, I'm so happy to see you here. We actually spent some time at your headquarters a couple of months back, and I'm really glad that you're here today and to introduce you to the Big Technology audience. Welcome to the show. Thank you for having me. All right, before we jump into world models, obviously, we're having this conversation in the wake of the DeepSeek revolution, I don't know what you want to call it. And

• 2:03Alex Kantrowitz?

  everyone is talking about Nvidia now. You're in quiet period, so we're not going to go into financials, but I can and do want to ask you about the technology side of this, specifically about Jevon's Paradox. I keep hearing Nvidia, Jevon's Paradox, Jevon's Paradox, Nvidia. What is Jevon's Paradox, and what do you think about it?

• 2:25Speaker 01

  My understanding of what Jevon's paradox is, is essentially an economic kind of principle that as you reduce the cost of something, of running it, you create more demand for it because it unlocks essentially more uses of that technology when it becomes more economically feasible to use that. I think that really does apply in this case in the same way that it applies to almost every other important computing innovation over the last 40, 50 years, or at least as long as I've been alive.

• 3:02Speaker 01

  You know, at the inception of Nvidia in 1993, Nvidia selected, very carefully selected, the very first computing problem to address in order to create the conditions by which we could continue innovating and keep growing that market. And this was the problem of computer graphics and particularly rendering within computer graphics, generating these images. The reason we selected it is because it's an endless problem. No matter how much compute you throw at it, no matter how much innovation we throw at

• 3:46Speaker 01

  it, you always want more. And throughout the time I've been at Nvidia, which is now 23 years, you know, many times I've heard, "Well, graphics are good enough, rendering is good enough, and so soon Nvidia's big GPUs and more computing power is not going to be necessary. We'll just get consumed by SoCs or integrated into another chip as integrated graphics and it'll disappear." But that never happened because the fundamental problem of simulating the physics of light and matter was endless. We see this in almost every important computing domain.

• 4:32Speaker 01

  AI is one of these things. I mean, can we really say that that we have now reached the point where our computers are intelligent enough, or the intelligence we create is good enough, and so it's just going to shrink? We're not going to have any more use for more compute power there? I don't think so. I think intelligence is something that is probably the most endless of all all computing problems. If we can throw more compute at the problem, we can make more intelligence and do it better and better. So making making AI more efficient will just increase its economic value in in many, many of the applications

• 5:16Speaker 01

  we want to apply it to and increase demand.

• 5:19Alex Kantrowitz?

  And can we talk about the progression of AI models becoming more efficient? I know it's like a hot topic right now, but it does seem to me that over the past couple of years, we've definitely seen models become more and more efficient. So what can you tell us about this? We'll just talk about large language models on this front, the efficiency gains that we've seen over time with them.

• 5:40Speaker 01

  I mean, this is this isn't new. This has been happening for the past 10, 12 years or so, essentially since we first discovered deep learning on our GPUs with AlexNet. If you look at the the computational curve, what our GPUs can do in terms of tensor operations, the AI kind of math that we need to do, over the last 10 years, we've had essentially a million X performance increase. And that increase isn't isn't just from the raw hardware.

• 6:22Speaker 01

  It's also through through many layers of the software algorithms. So we're getting these these benefits, these speed ups continuously at a very rapid rate exponentially by compounding many.

• 6:38Speaker 01

  Many layers, all the different layers at which this computing happens, from the fundamental hardware, the chips themselves, at systems level, networking, system software, algorithms, frameworks, and so on. So what we've seen here with DeepSeek is a great advancement that's on the same curve that we've been on for a decade now.

• 7:02Alex Kantrowitz?

  Okay, and 23 years at Nvidia, I'm going to save a question to ask you about that as we get later on, or towards the end of the interview, because I'm very curious what your experience has been being at Nvidia for so long, especially given that the company's technology, at least from the outside world, was viewed as in favor, and then people questioned it, then back in favor, if people questioned, obviously, we see what's going on now. I mean, we're leaving through a mini-cycle at this point. So very curious about your experience, but I want to talk about the technology first. And let me just bring you into a conversation that we had here on the show with Yan LaKun, who's met as a chief AI scientist, really right after ChatGPT came out. And one of the things that Yan did was, he said, "Go ask ChatGPT what happens if you

• 7:47Alex Kantrowitz?

  let go of a piece of paper with your left hand." And I typed it in, it gave a very convincing answer, it was completely wrong, because with text, you don't have the common sense about physics. And try as you might to teach a model physics with text, you can't. There's just not enough literature that describes what happens when you drop a paper with a hand, and therefore, the models are limited. And Yan's point here was basically like, if you want to get to truly intelligent machines, you need to build something into the AI that teaches common sense, that teaches physics, and you need to look beyond words to do that.

• 8:27Alex Kantrowitz?

  And so now I turn it over to you, Rev, because I do think that right now, within Nvidia, a big initiative is to build a picture of the world to teach AI models that common sense that Yan had mentioned was lacking. And I have some follow-ups about it, but I want to hear first a little bit about what you're doing and whether your efforts are geared towards solving the problem that Yan brought up.

• 8:52Speaker 01

  Well, what Jan said is absolutely true. And it it makes intuitive sense, right? If a an AI has only been trained on words, on on text that that we've digitized, how can it possibly know about concepts from our physical world? Like what the color red really is? Or what what it means to hear sound? What it what it means to to feel felt? You know, it can't it can't know those things because it never experienced them. When when we train a model, essentially what we're doing is we're providing life experience

• 9:36Speaker 01

  to that model and it's and it's pulling apart patterns or it's discerning patterns from all of the experience that we give it. And what's what was really, really amazing about GPT, the advancements with L L M's, you know, starting with the transformer, is that we could take

• 9:57Speaker 01

  this really, really complex

• 10:01Speaker 01

  set of rules that humans had no way of actually

• 10:07Speaker 01

  defining directly in a in a clear and robust manner the rules of language. And we were able to pull that out of a corpus of data. We we took all of this text, all the all these books and and whatever information you could scrape from the Internet about that. And somehow this model figured out what all the patterns of language it are in many different languages. And could then, because it understands the fundamental rules of language, do some amazing things. It could generate new texts. It could style some texts that you give it in a different way. It can translate text from one form to another, from one language to another.

• 10:51Speaker 01

  It can do do all of this awesome stuff. But it lacks any information about our world other than what's been described in those words. And so the next step is, the next step in AI is for us to take the same fundamental technology we have, this machine we have, where we can feed it life experience and it figures out what the patterns and the rules are, and feed it with actual data about our physical world and about how our world works, so that it could apply that same learning to the rules of physics. Instead of the rules of grammar, the rules of language, it's going to understand how

• 11:36Speaker 01

  the physical world around us works. And our thesis is that from all the AIs we're going to create into the future, the most valuable ones are going to be the ones that can interact with our physical world, the world that we experience around us, the world created out of atoms. Today, the AIs that we're creating are largely about our world of knowledge, our world of information, ones and zeros, things that you could easily represent inside a computer in the, in the digital world. But, but if we can apply the same AI technology to the physical world around us, then essentially we unlock robotics.

• 12:18Speaker 01

  We can have these agents with, with these, with this intelligence and even superintelligence in specific tasks, do amazing things in the world around us, which is, if you look at global markets, if you look at all of the commerce happening in the world and GDP, the world of knowledge, information technology is somewhere between two to five trillion dollars a year, but everything else, transportation, manufacturing, supply chain, warehouse and

• 12:59Speaker 01

  logistics, creating drugs, all this stuff in the physical world, that's about a hundred trillion dollars. So the application of, of, of this kind of AI to the physical world is going to bring more value to us.

• 13:14Alex Kantrowitz?

  So it's interesting, it's not just basically inputting that real world knowledge into LLMs, right, so they can get the question about dropping the paper with a hand correct. It is also something that you're working on is building the foundation for robots to go out into our world and operate within it.

• 13:39Speaker 01

  So, yes, it's not, it's not inputting it in the same way that we do for these text models. We're not just going to describe with words how, what happens when you drop a piece of paper. We're gonna give these models other senses during the learning process. So they'll, they'll watch, um, watch videos of, of paper dropping. We can also give it uh, more, more accurate, specific information in the 3D realm. Uh, because we can simulate these physical worlds inside a computer today, we have physics simulations of worlds. We

• 14:22Speaker 01

  can pull ground truth data about, about the position and orientation and, and state of things inside that 3D world and use that as another mode of input into these models. And so what we'll end up with is a, a world foundation model that was trained on many different modes of data, essentially different sense, senses. It can see, it can hear, it can touch and feel and do, do many of the things we can do or many things other animals or, or even things no, no creature can do because we can provide it with sensors that don't exist uh, inside, inside the natural world. And uh, it can, from that, kind of decipher what are the actual

• 15:08Speaker 01

  Combined rules of the world. And this encoding of the knowledge of how the physical world works can then be the basis for us to build agents inside the real world, to build the brains of these agents, otherwise known as physical robots.

• 15:28Alex Kantrowitz?

  Right. And so this is your recently announced Cosmos project. So talk a little bit about like what Cosmos is. I mean, obviously, it's a world foundational model. But where, how long you been building it? And what type of companies are and developers might use it? And what they might use it for?

• 15:50Speaker 01

  We've been, we've been working towards Cosmos for probably about 10 years. We envisioned that eventually this new technology that had formed with deep learning, that that was going to be the the critical technology necessary for us to create robot brains. And that, that, that was, is ultimately what's going to unlock this incredible amount of value for us. So we started working towards this a long time ago. We realized early on that the big problem we were going to have is in order to train such a model,

• 16:35Speaker 01

  to train a robot brain to understand the physical world and to work within it. We're going to have to give it experience. We're going to have to give it the data that represents the physical world. And capturing this data from the real world is, is not really an easy thing to do. It's very expensive and in some cases, very dangerous. For example, for self-driving cars, which is a type of robot, it's a robot that can autonomously, you know, on its own, figure out how to get from point A to point B by controlling this physical being, a car, by braking and accelerating and steering.

• 17:17Speaker 01

  How are we going to ensure that a self-driving car really understands when a child runs into the street as it's barreling down the street, that it should stop? And how can we be sure that it's actually going to do that without actually? Doing that in the real world. We don't want to go capture data of a child running across the street. Well, we can do that by simulating it inside the, the, inside a computer. And so we realized this early on, so we set about applying all of the technologies we'd been working on up until that point with computer graphics and for video games and

• 17:57Speaker 01

  video game engines and physics inside these worlds to create a system to do world simulation that was physically accurate so that we could then train these AIs. And so we call that operating system, if you will, Omniverse. It's a system to create these physics simulations, which we then use to train AIs that we could test in that same simulation before we put them out in the real world. So we use it for self-driving cars and other, other robots out there. So building Cosmos actually starts first with simulating the world. And so we've

• 18:41Speaker 01

  been building that stack and those computers for quite a while. Once, uh, once the transformer model was introduced and we started seeing the, the amazing things large language models can do and the ChatGPT moment came, um, we understood that this had essentially unlocked the one thing that we needed to really push forward in robotics, which is the ability to, to have this kind of general intelligence about a really complex set of things, complex set of rules. And so, so we set about, um, building what is Cosmos today, essentially

• 19:26Speaker 01

  a few years ago, using, using all of the technology we had built before with simulation and AI training. And what Cosmos is, is, uh, it's actually a few things. It's a collection of, uh, some open weight models, uh, that, that were, that we've made freely available.

• 19:48Speaker 01

  Along with it, we also provide essentially all of the tooling and pipelines you need to create a new world foundation model. So we give you the world foundation models that we've started training, which are world class, especially for the purposes of building physical AI. And we also have a what's called a tokenizer, which are AIs themselves that are world class. It's a critical element of of building world foundation models. And then we have curation pipelines. The data that you you select and curate to feed into the training of your world foundation

• 20:33Speaker 01

  model is critical. And just selecting the right data requires a lot of AI in it of itself. So we released all of this stuff and we put it out there in in the open so that the whole community can join us in in building physical AI.

• 20:50Alex Kantrowitz?

  And so who's going to use it? Is it going to be robotics developers? Is it going to be somebody that's building, let's say, L M based application, but just wants them to be a little smarter? Both?

• 21:00Speaker 01

  It will be all of them. Yes, it's a we we feel that we're as a as as the industry, the world is right at the beginnings of this physical AI revolution. And no one company, no one organization is going to be able to build everything that's that that we need. So so we're building it out there in the open to encourage others to come build on top of what we've built and come build it with us. And this is going to be essentially anybody that has an application that involves the physical world.

• 21:44Speaker 01

  And so that's definitely robotics companies are part of this and robotics in the very general sense. That includes self-driving car companies, robot taxi companies, and as well as companies building robots that are in our factories and warehouses. Anybody that wants to make intelligent robots that have perception and operate autonomously inside the real world, they want this. But it's not it's not only about robots in the way we think about them as as these agents that move around. We have sensors that we're placing

• 22:21Speaker 01

  in our spaces, in our cities, in urban environments, inside buildings. These sensors need to understand what's happening in that world, maybe for security reasons, for for coordinating other robots, changing the climate and and energy efficiency of of our buildings and data centers. So there's there's many applications of physical AI that are broader than what we generally think of as as these what you imagine when you say a robotic application. There's going

• 23:00Speaker 01

  to be thousands and thousands of companies that that build these physical AIs. And this is just the beginning.

• 23:09Alex Kantrowitz?

  Now, you mentioned that the transformer model was an important development on this path. And that obviously was the thing that underpinned a lot of the real innovation we've seen with large language models. Can the real world AI learn from the knowledge base that has been sort of turned into these AI models with text? Like if you're if you have a model that's trying to understand the world with common sense, do they take text as an input?

• 23:36Speaker 01

  They take all of it as input.

• 23:38Alex Kantrowitz?

  How does it work then with with text? I mean, it's very interesting because it seems like that's like when we talk about the progression towards general intelligence, that is a very, you know, kind of amazing application of being able to read something and then sort of intuit what it means in a physical space, don't you think?

• 23:56Speaker 01

  Yeah, I think the the way I think about it, and I think this is right, is these AIs learn the same way we do.

• 24:07Speaker 01

  When when you're brought into this world, you don't know who is mommy, who is daddy, you don't even know how to see it, you don't have depth perception, you can't see color or understand what it is, you don't know language, you don't know these things. But you learn by being bombarded with all of this information simultaneously through the many different senses. So when when your mommy looks at you and says, I'm mommy, pointing, you're getting multiple modes of information coming coming at you, including essentially that text that's coming through an audio form there.

• 24:48Speaker 01

  And then eventually, when you learn how to read, you you learn how to read because a teacher points at letters and then words and sounds them out. So you have this association that you build between the information that you understand, like like mommy, and the letters that mean that thing. AIs learn in the same way. When we train them, if you give them all of these modes of information associated with each other at the same time, it'll it'll associate them together. That's how image generators work today. When you go generate an image using a text prompt, the reason why it can generate, you

• 25:33Speaker 01

  know, a an image of a red ball in a in a grass field in an overcast day is because when it was trained, there was an association of some text along with the images that were fed into it.

• 25:54Speaker 01

  It knew that during the training process that these words were related to that image. And so we can gather that understanding from that association. What we're trying to do with world foundation models is take it to the next level by giving it more modes of information and richer information. But part of that will still include text. We'll feed in the text along with the video and other ground truth information from the physical state of the world.

• 26:30Alex Kantrowitz?

  Yeah, so this is going to be a multi-part question and I apologize, but I don't really know another way to ask it. So what are the other modes of information that you're feeding in there? And do you really need to go through this simulation process? And I'll tell you, it all sounds like a worthwhile endeavor to me, and I'm sure it is. But I also see video models today. And that is something that's really surprised me when we've seen the video generation models is that they really have an understanding of physics. Like, they know, just as an image, like, an image generation is not moving, right? So you know that, let's say, the guy sits on the chair. But video, you could see people walking through a field and you watch the grass move.

• 27:14Alex Kantrowitz?

  And that means that those models inherently have a concept of how physics works, I think. And I'm going to run it by you because you're the expert here. But like, again, and Jan's going to come on the show in a couple of weeks. So maybe this is just in my mind because I'm gearing up and thinking about our last conversation, but I'm going to put this to you also. Maybe I'll ask what your answers, I'll ask him to weigh in on your answers on this. But the thing that he always talked about is a human mind is able to sort of see infinite possibilities and accept that. It doesn't break us. So if you have a pencil and you hold it up, you know it's going to fall, but you know it could fall in infinite ways, but it's still going to fall. For an AI that's been trained on different scenarios, it's very difficult for them to

• 27:56Alex Kantrowitz?

  understand that that pencil might fall in infinite ways when asked to generate it. However, they've been doing a very good job with the video generators of like showing that they understand that. So just to sort of reiterate, what different modes of information are you using? And why do we need this broader simulation environment or this Cosmos tool if we are getting such good results from video generation already?

• 28:23Speaker 01

  All very, very good questions. So first off, we use many, many modes. The primary one, though, for training Cosmos is video. Just like the video generation models. But along with that, there's text. We also feed it extra information and labels that we can gather from

• 28:45Speaker 01

  data, particularly when we do, when we train, when we generate the data synthetically. If you use a simulator to generate the videos, you have perfect information about everything that's going on in every pixel in that video. We know how far each object is in each pixel. We know the depth. We know

• 29:13Speaker 01

  what the object is in each pixel. You can segment out all of that stuff. Traditionally, what we've done for perception training for autonomous vehicles. So we've used humans to go through and label all that information from hours and hours of video that's been collected. And it's inaccurate and not, not complete. So, so from simulation, we can get perfect information about the actual videos themselves. Now, that being said, your, your question about, you know, these video models seem to really know physics and do, and know it, know it well.

• 29:56Speaker 01

  I, I think it is pretty amazing, you know, how much physics they do know.

• 30:02Speaker 01

  And, and it's kind of surprising we're here at this point. Like, had you asked me five years ago, would we be able to generate videos with this, this much physics plausibility at this stage? I wasn't sure, actually, because I continually had been wrong for years prior to that. I didn't expect to see image classification in my lifetime until we saw it with AlexNet. But, but I, I would have bet against it. And so, so we're pretty far along. That being said, there's a lot of flaws in the physics we see. So you see this in the video. One of the, one of the basic things is object permanence. If

• 30:46Speaker 01

  you direct the video to move the camera, point away and come back. Objects that were there at the beginning of the video are no longer there or they're different, right? And so that is such a fundamental violation of the laws of physics. It's kind of hard to say, well, these models currently understand physics well. And there's a whole bunch of other things in there. You know, my, my life's work has been primarily computer graphics and specifically rendering, which is a, 3D rendering is essentially a physics simulation. It's the simulation of how light interacts with matter and eventually reaches a sensor of some sort.

• 31:31Speaker 01

  We simulate what a camera would do in a 3D world and, and, and what image it would, it would gather from the world. When I look at a lot of these videos that are generated, I see tons and tons of flaws because when we do those simulations and rendering, we're attuned to seeing when shadows are wrong and reflections are wrong and, and, and these sorts of things. To the untrained eye, it looks plausible. It looks, it looks correct, but I think people can still kind of feel something is wrong, you know, when, when it's AI generated, when it's not in the same way that for, for decades

• 32:15Speaker 01

  now, since we introduced computer graphics to visual effects in the movies, you know, when some, you don't, you don't know what it is, but, but if the, the rendering's not great in there, it just feels CG, it feels wrong. We still have that kind of uncanny valley thing going on. That all being said, I think we're going to rapidly get better and better. So, so the, the models today have, have an amazing amount of knowledge about the physical world, but they're maybe at like 5, 10% of what they should understand. We need to get them to 90, 95%.

• 32:50Alex Kantrowitz?

  Right, yeah, I just saw a video of a tidal wave hitting some island. And I looked at it, it was like, super realistic. It was, of course, it was on Instagram, because that's all Instagram is right now, is 3D generated, I mean, AI-generated video. And it took me a second, and it's more frequently taking me a minute to be like, oh, that's AI-generated. And sometimes I have to look in the comments and just sort of trust the wisdom of the crowds on that front. But I, look, we could talk about-

• 33:14Speaker 01

  But you might not be the best judge of it as well. Humans, I mean, we're not particularly good at knowing whether physics will really be accurate or not. This is why movies, directors can take such license with, with the physics when they do explosions and, and all kinds of other fun stuff like tidal waves in there.

• 33:34Alex Kantrowitz?

  Yeah, well, it's, it's interesting, like, some comedian made this joke. They're like, Neil deGrasse Tyson likes to come out after these movies like Gravity and talk about how they're like scientifically incorrect. And some comedian's like, yeah, well, how about the fact that George Clooney and Sandra Bullock are the astronauts? That didn't bother you at all? But it is interesting that we can watch these videos, watch these movies, and fully believe, at least in the moment, that they're real. Like, we can allow ourselves to like, sort of lose ourselves in the moment. Exactly, and just be like, yep, I'm, I'm in this story. I feel emotion right now, watching, you know, George Clooney in a spaceship, even though I know he's no astronaut.

• 34:15Speaker 01

  And I think for that purpose, I mean, I worked on movies before I was at Nvidia. That's, that's what I did, computer graphics for visual effects.

• 34:24Speaker 01

  That is a perfectly legitimate use of that technology. It's just that, that level of simulation is, is not sufficient for building physical AI that are, that are going to be the underpinnings or the fundamental components of a robot brain. I don't want my, my self-driving car or my robot operating heavy machinery in a, in, in a factory to be trained on physics that's not, that doesn't match the real world. Even if it looks right to us, if, if, if it's not right, then it's not going to behave correctly, and that's dangerous. So, so it's a, it's a different purpose.

• 35:09Speaker 01

  That's why what we're doing with Cosmos, it's-

• 35:15Speaker 01

  It is really a different class of AI than video generators. You can use it to generate videos, but the purpose is different. It's not about generating beautiful imagery or interesting imagery as for art. This is about simulating the physical world, using AI to to create the the simulation.

• 35:43Alex Kantrowitz?

  Rev, I want to ask you one more follow up question about not the flaws, but the video generator's ability to get things right. And then we're going to move on from this topic. But it is just surprising and interesting for me to hear you and Demis Hassabis, the CEO of Google DeepMind, who was just on, who commented on this, talk about how these video generators have been surprisingly good at understanding physics. And Jan also, basically in our conversations previously, effectively saying that it's very difficult for AI to solve these problems. I won't say they've solved it, but everybody's surprised they've gotten to this point. So what is your best understanding of how they've been, though flawed, but this good?

• 36:28Speaker 01

  You know, this is the trillion dollar question, I guess. You know, we've been we've been betting now for years that if we just throw more compute and more data at at the problem, that the scaling laws are going to give us a level of intelligence that's really, really meaningful, that that will be like step function changes in in capabilities. There's no way for us to know for sure. It's very hard to predict that. It feels like we're on an ex we are on an exponential curve with this.

• 37:08Speaker 01

  But which part of the exponential curve we're on, we can't tell. So we don't know how fast that's going to happen. Honestly, I'm I've been surprised at how how well these transformer models have been able to extract the laws of physics at this to this level by this point in time. I have. At this point, I believe in a few years, we're going to get to a level of physics understanding with our AIs that's going to unlock the majority of the applications

• 37:51Speaker 01

  we need to apply them in, in robotics.

• 37:54Alex Kantrowitz?

  Let me ask you one more question about this, then we're going to take a break and talk about some of the societal implications of putting robotics, let's say, in the workforce and in, I don't know, in all different areas of our lives. There's definitely a sizable portion of the population that is going to be surprised, maybe not our listeners, but a sizable portion of the population that would be surprised to hear that Nvidia itself is building these world foundational models, releasing weights to help others build on top of them. The perception, I think, from some on the outside is, hey, isn't Nvidia just a company that makes those chips? So what do you say to that, Rev?

• 38:34Speaker 01

  Well, yeah, that's been the perception. It's been the perception since I started Nvidia 23 years ago, and it's never been true that we just build chips. Chips are a very, very important part of what we do. They're the foundation that we build on. But when I joined the company, there were about 1,000 people, 1,000 employees at the time. The grand majority of them were engineers, just like today, the majority of our employees are engineers, and the majority of those engineers are software engineers. I myself am a software engineer. I wouldn't know the first thing about making a chip.

• 39:14Speaker 01

  And so our form of computing, accelerated computing, the form of computing we invented, is a full-stack problem. It's not just a chip that we throw over the fence and leave it to others to figure out how to make use of it. It doesn't work unless we have these layers of software, and these layers of software have to have algorithms that are harmonized with the architecture of our chips and our systems. So we have to go in these new markets that we enter, what Jensen calls zero-billion-dollar industries.

• 39:57Speaker 01

  We have to actually go invent these new things kind of top to bottom because they don't exist yet and nobody else is going to likely to do it. So we build a lot of software and we build a lot of AI these days because that's what's necessary in order to build the computers to power all of this stuff. We did this with L L M's early on. Many, many years ago, we trained the, at the time, what was the largest model in terms of number of parameters for an L L M. It was called Megatron. And because we did that, we build our computers, our chips and, and computers and the system software and the,

• 40:42Speaker 01

  the frameworks and, and pipelines and everything. We, we were able to tune them to do these large scale things. And we put all of that, all of that software out there, which was then used to create all the L L M's we enjoy today. Had we had not done that, I don't think we would have had Chat G P T. And so, so this is essentially the same thing. We're, we're creating a new market, a new capability that doesn't exist. We see this as being an endeavor that is greater than Nvidia. We need many, many others to participate in this. But there are some things that we're uniquely positioned to contribute, given

• 41:26Speaker 01

  our scale and our particular expertise. And so we're going to go do that. And then we're going to make that freely available to others so they can build on it.

• 41:34Alex Kantrowitz?

  Yep. For those wondering why Nvidia has such a hold in the market right now, I think you, you just heard the response. So I do want to take a break and then I want to talk about the implications for society when we have, let's say, humanoid robots doing labor in that part of the economy that we simply, you know, haven't really put AI into yet. And what it means when it's many more trillions of dollars than the knowledge work. So we're going to do that when we're back right after this. This episode is brought to you by AVPoint. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data.

• 42:18Alex Kantrowitz?

  And most teams have no single way to see it, secure it, and prove it's under control. That's exactly what AVPoint does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended across your entire AI estate. Your data, your cloud, and the agents acting on your behalf. It's how more than 28,000 organizations deploy AI with confidence. So innovation scales without scaling risk. AVPoint, the unifying trust layer for AI. Head to AVPoint.com to see how enterprises deploy AI with confidence. Learn more at AVPT.co/bigtechnologypodcast.

• 43:00Alex Kantrowitz?

  That's AVPT.co/bigtechnologypodcast. Violent threats against executives are growing at an alarming rate. When a company experiences backlash, executives are the first people blamed. When their address and other personal details are sitting online, this backlash can lead right to their front door. The team at Ironwell knows this better than anyone. They've protected some of the most targeted executives and individuals on the planet for almost two decades. As a wide-glove enterprise security service, they protect your people with continuous personal data removal, proactive prevention tools, and human-led emergency support. So when someone goes looking for your executives, Ironwell ensures they hit a dead end. So here's what to do. Go to ironwell.com/bigtechnology,

• 43:44Alex Kantrowitz?

  fill in the quick form, and request your free risk assessment. Their team will show you just how exposed your executives are and how to lock it down before a threat reaches their front door. That's ironwell.com/bigtechnology. Learn how to stop online threats before they become real-world attacks. The link's in the show notes.

• 44:05Alex Kantrowitz?

  And we're back here on Big Technology Podcast with Rev Leberedian. He's the Vice President of Omniverse and Simulation Technology at Nvidia. Rev, I want to just ask you the question that obviously has been bouncing around my mind since we started talking about the fact that you're going to enable robotics to be able to sort of take over, I don't know, is take over the right word? Take over a lot of what we do currently in the workforce. I mean, what do you think the labor implications are here? Because, yeah, if you've spent your entire life, you know, working at a certain manual task and next thing you know, someone uses the, you know, the Cosmos platform or you're

• 44:49Alex Kantrowitz?

  new, I think it's like a Groot, it's called, what is it called? Groot?

• 44:52Speaker 01

  Groot, that's our project for humanoid robots for building and training humanoid robot brains.

• 44:59Alex Kantrowitz?

  So, all right, so Groot, you know, that some company uses Groot to start to put a humanoid work for, humanoid labor in, let's say, a factory or even as a care robot. And I'm a nurse and all of a sudden some Groot-built robot is now helping take care of the elderly. What are the labor implications of that?

• 45:21Speaker 01

  Well, first and foremost, I think we need to understand that this is a really hard problem. It's not like overnight, we're going to have robots replace everything humans do everywhere. It's a very, very difficult problem. We're just now at an inflection point where we can finally, we see a line of sight to building the technology we needed to unlock the possibility of these kind of general purpose robots. And that's, we can now build a general purpose robot brain.

• 46:02Speaker 01

  Twenty years ago, that was not true. We could have built the physical robot, the actual body of a robot, but it would have been useless because we couldn't give it a brain that would let it operate in the world in a general-purpose manner. We couldn't interact with it or program it in a useful way to do anything. So that's what's been unlocked here. I talked to a lot of CEOs and executives for companies in the industrial sector, in manufacturing, in warehousing,

• 46:44Speaker 01

  to retail companies. In all of these companies I talked to, in every geography, there's a recurring theme. There's a demographic problem the whole world is facing. We don't have as many young people who want to do the jobs that the older people who are retiring now have been doing. If you go to an automotive factory in Detroit or in Germany, go look around. Most of the factory workers are aging and they're quickly retiring.

• 47:27Speaker 01

  And these CEOs that I'm talking to, their biggest concern is all of that knowledge they have on how to operate those factories and work in them. It's going to be lost. The young people don't want to come and do these jobs. And so we have to solve that problem. If we're going to maintain, not just grow our economy, but just maintain where the economy is at and produce the same amount of things, we need to find some solution to this problem. We don't have enough workers. We've been seeing it in transportation. There's not enough truck drivers in the world to go deliver all the stuff that's moving around in our supply chains.

• 48:11Speaker 01

  We can't hire enough of them and there's less and less young people that want to do that job every year. So we need to have self-driving trucks. We need to have self-driving cars to solve that problem. So I think before we talk about replacing jobs that humans want to do, we should first be talking about using these robots to fill in the gap that's being left by humans because they don't want to do it anymore.

• 48:41Alex Kantrowitz?

  Right. And there could be specialization. Like, take nursing, for example. The nurse that injects me with a vaccine, or the nurse that like puts medication in my IV, maybe we keep that human for a while, even though, you know, they make mistakes too. But I'd feel a lot more comfortable if that was human. The nurse that takes me for a walk down the hall after I've gotten a knee replacement, that could be a robot. It'd be better.

• 49:05Speaker 01

  Yeah, we'll see how this plays out. We're, we believe that the first place we're going to see general-purpose robots, like the humanoid robots, really take off is in the industrial sector because of two things. One, the demand is great there because we have the shortage of workers. And also because it makes, it makes more sense to have them adopted in these spaces where a company just decides to put them in there and mostly warehouses and factories are kind of unseen. I think the last place we're going to start seeing humanoid show up is in

• 49:47Speaker 01

  our homes, in your kitchen.

• 49:51Alex Kantrowitz?

  Don't tell Jeff Bezos that.

• 49:53Speaker 01

  Well, they will show up there and I think it's going to be uneven. It'll depend uneven geographically. They'll probably show up in a kitchen in somebody's home in Japan before they show up in a kitchen in somebody's home in Munich, in Germany. And I think that's a cultural thing. You know, I, I personally don't even want another human in my kitchen. I like...

• 50:20Speaker 01

  Being in my kitchen and preparing stuff myself. My wife and I are always in each other's space there, so we get kind of annoyed. So having a humanoid robot would be kind of weird. I don't even want to hire somebody else to do that. We kind of do that ourselves. So that's a kind of personal decision. I think things like, jobs like caring for our elderly and healthcare, those are very human professions. You know, there's a lot of what the care is. It's not really about the physical thing that they're doing. It's about the emotional connection with another human.

• 51:03Speaker 01

  And for that, I don't think robots are going to take that away from us anytime soon.

• 51:11Alex Kantrowitz?

  Well, the question is, do we have enough care professionals to take those jobs? That's the one that really seems in danger.

• 51:16Speaker 01

  And so what's likely to happen is it'll be a combination. The care professionals we do have will do the things that require EQ, that require empathy, that requires, you know, really understanding the other human you're taking care of. And then they can instruct the robots around them to assist them, to do all of the more mundane things like cleaning and maybe giving the shots and IVs. I don't know.

• 51:46Alex Kantrowitz?

  How long away is that future, Rev? How long do you think?

• 51:53Speaker 01

  You know, I wouldn't venture to guess on that kind of interaction in a hospital or a care situation quite yet. I believe it's going to happen in the industrial sector first. And I believe that it's within a few years, we're going to see humanoid robots widely used in the most advanced manufacturing and warehousing.

• 52:19Alex Kantrowitz?

  Wild. Okay, I want to ask you about Hollywood before we go. I guess I have this question rattling in my mind, which is... Are we just gonna see like movies not that movies that look real but are computer generated like we have computer generated movies now with the CGI but they all look pretty CGI but I imagine

• 52:43Speaker 01

  Well, they don't all look CGI. Some of them look

• 52:45Alex Kantrowitz?

  Right.

• 52:46Speaker 01

  Pretty amazing.

• 52:47Alex Kantrowitz?

  Somewhat real but I'm curious like do you think that like is Hollywood gonna move to a area where it's super real and just simulated? Go ahead.

• 52:56Speaker 01

  Absolutely. I mean, well, was it a year or two ago when the last Planet of the Apes came out? I went to go see it with my wife. Now, my wife and I have been together since I worked at Disney in the mid 90s, working on visual effects and rendering. We I had a startup company doing rendering and she was a part of that. So she she has a good eye and she she's been around computer graphics and rendering for decades now. When we went to go see Planet of the Apes, even though obviously those apes were not real, at one point she turned around and said, that's all CG, right? She couldn't quite believe

• 53:41Speaker 01

  it. I think what Weta did there is is amazing. It's indistinguishable from real life, except for the fact that the apes were talking. Like, other than that, it's indistinguishable. The the problem with with that, though, is to do that level of CG in the traditional way that we've done it requires an incredible amount of artistry and and skills that only only a few studios in the world can do with the teams that they have and the pipelines they build. And it's incredibly expensive to produce that.

• 54:25Speaker 01

  What we're building with AI, with generative AI, and particularly with world foundation models, that, once we get to the point where they really understand the depth of the physics that they need to, to produce something like Planet of the Apes, once we have that, of course, of course, they're going to use those technologies to produce the same images because it's gonna, it's gonna be a lot faster and it's gonna be a lot, a lot less expensive to do the same things. It's already starting to happen.

• 54:59Alex Kantrowitz?

  Rev, I know we're getting close to time. Do I have time for two more questions or?

• 55:03Speaker 01

  Absolutely.

• 55:04Alex Kantrowitz?

  Okay, so the more I think about robotics, the more I think about sort of what the application in war might be. I know that like, you can't think of every permutation when you're developing the foundational technology, but we are living in a world where war is becoming much more roboticized and it's sort of like remarkable that we have some wars going on or people are still fighting in trenches. So I'm just curious if you've given any thought to like how robotics might be applied in warfare and whether there's a way to prevent some of like the, the bad uses that might come about because of it.

• 55:44Speaker 01

  You know, I'm, I'm not really an expert in, in warfare, so I, I don't feel that I'm the best person to, to talk about how my bees are not, but I can say this. This isn't the first time where a new technology has been introduced that is so powerful that not only can we imagine great uses of it that are beneficial to people, but also really, really scary devastating consequences of it being used, particularly in warfare. And somehow we've managed to, to not, not have that kind of devastation.

• 56:28Speaker 01

  And in general, the world has gotten better and better, more peaceful and safer despite what it might feel like today. By almost any measure, we have less lives lost through wars and, and.

• 56:42Speaker 01

  These sorts of tragedies than ever before in mankind's history. The big one, of course, everybody always talks about is nuclear technology. I mean, I grew up, I was a little kid in the eighties. This is kind of the height of the Cold War, the end of it. But every day I remember thinking, thinking, you know, it's might happen. We might, we might have some ICBMs arrive in Los Angeles at any point. And it hasn't happened because somehow the general understanding by everyone collectively,

• 57:25Speaker 01

  such that this would be so bad for everyone, that we put together systems, even though we had intense rivalry and even enemies

• 57:39Speaker 01

  between the Soviet Union and the US.

• 57:43Speaker 01

  We somehow figured out that we should create a system that prevents that sort of thing. We've done the same with biological weapons and chemical weapons. Largely, they haven't been used, even though the technologies existed there. And so I think that's a, that's a good indicator of, of, of what's, how, how, how we should deal with this new technology, this new powerful technology of AI. And a reason for us to be optimistic that it's possible to, to actually have this technology and not have it be so devastating. We can set up rules and conventions that say, even though it's possible to use AI in this way,

• 58:26Speaker 01

  that we shouldn't and we should all agree on that. And anybody that skirts the line on that, you know, there should be ramifications to it to disincentivize them from using it that way.

• 58:40Alex Kantrowitz?

  Yep, I, I hope you're right on that. It seems like it's something that we're going to, as a society, deal with more and more as this stuff becomes more advanced. All right, so last one for you. You've been at Nvidia, we've talked about a couple of times, 23 years, I already teased this. So I want to, I just want to ask you, you know, the technology's been in favor, it's not been in favor. You're at the top of the world right now, even though, you know, there was some hiccup last week, but whatever. Doesn't seem like it's going to be a long-term issue. Just what's, what is like one insight you can tell us, you know, that you can draw from your time at Nvidia about the way that the technology world works?

• 59:20Speaker 01

  About, well, first I can tell you about how Nvidia works. And the reason I'm here, I've, I've been here for 23 years and this will be the last job I ever have. I'm positive of it. When I joined Nvidia, that wasn't the plan. I thought I'd be here one year, two years max. And now it's been 23 years. When I hit my 20-year mark,

• 59:45Speaker 01

  Jensen at our next company meeting had rattled off a bunch of stats on how long various groups have been here. How many, how many people had been there for a year, two years and so on. When you got to 20, there were more than 650 people that were at 20-year. Now, earlier I had said, when I joined the company, there were about 1,000 people. So this means that most of the people that were there when I started, when I started Nvidia were still there after 20 years. I wasn't as special as I thought I was when I hit my 20-year mark. And so this is actually a very strange thing about Nvidia. We have people that, that have been here a long time and haven't left.

• 1:00:29Speaker 01

  It's strange in general for most companies, but particularly for Silicon Valley tech companies. The people move around a lot. And I believe the reason why we've stayed here through, through all of our trials and tribulations and whatnot is because

• 1:00:48Speaker 01

  Fundamentally, what Jensen has built here is a company where people come to do their lives work, and we really mean it. Like, you feel it when you're here. This is more than just about making some money or having a job. You come here to do great work and to do your life's work. And so the idea of leaving, it feels painful to me, and I think it is to many others. That's what's actually, I think, behind why, despite the fact that

• 1:01:26Speaker 01

  Nvidia's had its ups and downs. And you can go back to look at our stock chart going back to like, the mid 2000s. We introduced CUDA in 2006, and that was a really important thing, and we stuck to it. The analysts and nobody wanted us to keep sticking to it, but we kept investing in it. And our stock price took a huge hit, and it was flat there for a long time, flat or dropping. And then, it finally happened. AI was born on our GPU, that's what we were waiting for. And we went, we went all in on that, and we've had ups and downs since then. We'll, we'll continue to have ups and downs, but I think the trend

• 1:02:11Speaker 01

  is going to still be up and to the right. Because, this is an amazing place where, where people who want to do their life's work, the best people in the world at what we do, who wanted their life's work, they come here and they stay here.

• 1:02:28Alex Kantrowitz?

  Yep. Well, Rev, look, it's always such a pleasure to speak with you. I really enjoyed our time together at Nvidia headquarters. It was a really fun day. We did some cool demos, and I appreciate that. And I'm just thrilled to get a chance to speak with you about this technology today. It is fascinating technology. It is cutting edge. Obviously, it brings up a lot of questions, some of which we got to today. I'm sure we could have talked for three hours. And, and I hope to keep the conversation up. So thanks for coming on the show.

• 1:02:53Speaker 01

  Thank you for inviting me and hope we do talk for three hours one day.

• 1:02:57Alex Kantrowitz?

  That'll be great. All right, everybody. Thank you for listening. Ranjan and I will be back to break down the news on Friday. Already a lot of news this week with OpenAI's deep research coming out. I just paid $200 for ChatGPT, which is a lot more than I ever thought I would for a month, but that's where we are today. So we're going to talk about that and more on Friday. Thanks for listening and we'll see you next time on Big Technology Podcast.

• 1:03:19Speaker 02

  Now, more than ever, we need someone on our side, but Rob Whitman? Whitman's votes drove up costs, cut Medicaid and risked 350,000 Virginians healthcare to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, the House Majority PAC dot com, not authorized by any candidate or candidate's committee.

• 1:03:50Speaker 03

  Every mile matters. Whether you're driving your dream car or your daily commuter, the Mobile One team knows that. It's why we created Mobile One Advanced Clean Motor Oil. It cleans your engine in one oil change and helps extend engine life, delivering performance, protection and cleanliness for vehicles of all ages, all to help keep your engine running like new, whatever you're driving. Visit mobileone.com/advancedclean to learn more.