Nvidia Founder and CEO Jensen Huang on the AI revolution

I think it's fun to be inventing the next computer era. It's fun to see all these amazing applications being created. The part of it that is just really intense is just, you know, the world on our shoulders. I hope everybody's been enjoying the conference. It's a fantastic event. Lots of great companies, a couple thousand people here, and so really, really terrific. And obviously, a real highlight and a real privilege to have Jensen, the president and CEO of NVIDIA here. Since you found NVIDIA in 1993, you've pioneered accelerating computing. The company's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefining computers and igniting the era of modern AI. Jensen holds a B.S.E. degree from Oregon State University and M.S.E. degree from Stanford. And so I want to start by welcoming you, Jensen. Everybody, please welcome Jensen to the stage. Thank you. Thank you. Thank you. Thirty-one years ago, founded the company, you've transformed yourself from a gaming-centric GPU company to one that offers a broad range of hardware, software to the data center industry. And I'd just like you to start by talking a little bit about the journey. You know, when you started, what were you thinking? How has it evolved? Because it's been a pretty extraordinary journey. Yeah, David, it's great to be here. The thing that we got right, I would say, is that our vision that there would be another form of computing that could augment general-purpose computing to solve problems that a general-purpose instrument won't ever be good at. And that processor would start out doing something that was insanely hard for CPUs to do, and it was computer graphics. But that we would expand that over time to do other things. The first thing that we chose, of course, was image processing, which was just complementary to computer graphics. We extended it to physics simulation because in the domain, the application domain that we selected, video games, you want it to be beautiful, but you also want it to be dynamic to create virtual worlds. We took step by step by step, and we took it into scientific computing. Beyond that, one of the first applications was molecular dynamic simulation. Another was seismic processing, which is basically inverse physics. Seismic processing is very similar to CT reconstruction, another form of inverse physics. And so we just took it step by step by step, reasoned about complementary types of algorithms, adjacent industries, kind of solved our way here, if you will. But the common vision at the time was that accelerated computing would be able to solve problems that are interesting. And that if we were able to keep the architecture consistent, meaning have an architecture where software that you develop today could run on a large install base that you've left behind, and the software that you created in the past would be accelerated even further by new technology. This way of thinking about architecture compatibility, creating large install base, taking the software investment of the ecosystem along with us, that psychology started in 1993, and we carried it to this day, which is the reason why NVIDIA's CUDA has such a massive install base, because we always protected it. Protecting the investment of software developers has been the number one priority of our company since the very beginning. And going forward, some of the things that we solved along the way, of course, learning how to be a founder, learning how to be a CEO, learning how to conduct a business, learning how to build a company. Not easy stuff. These are all new skills. And we're just kind of like learning how to invent the modern computer gaming industry. NVIDIA, people don't know this, but NVIDIA's the largest install base of video game architecture in the world. GeForce is some 300 million gamers in the world, still growing incredibly well, super vibrant. And so, I think every single time we had to go and enter into a new market, we had to learn new algorithms, new market dynamics, create new ecosystems. And the reason why we have to do that is because unlike a general purpose computer, if you built that processor, then everything eventually just kind of works. But we're an accelerated computer, which means the question you have to ask yourself is, what do you accelerate? There's no such thing as a universal accelerator because, yeah. Dig down on this a little bit deeper. Just talk about the differences between general purpose and accelerating computer. If you look at software, out of your body of software that you wrote, there's a part of the software inside which has some of the magic kernels, the magic algorithms. And these algorithms are different depending on whether it's computer graphics or image processing or whatever it happens to be. It could be fluids, it could be particles, it could be inverse physics as I mentioned, it could be image domain type stuff. And so, all these different algorithms are different. And if you created a processor that is somehow really, really good at those algorithms, and you complement the CPU where the CPU does whatever it's good at, then theoretically, you could take an application and speed it up tremendously. And the reason for that is because usually, some 5-10% of the code represents 99.999% of the runtime. And so, if you take that 5% of the code and you offloaded it on our accelerator, then technically, you should be able to speed up the application 100 times. And it's not abnormal that we do that. It's not unusual. And so, we'll speed up image processing by 500 times. And now we do data processing. Data processing is one of my favorite applications because almost everything related to machine learning, which is a data-driven way of doing software, data processing is involved. And we accelerate the living daylights out of that. But in order to do that, you have to create that library. And so, we just did it one domain after another domain after another domain. We have a rich library for self-driving cars. We have a fantastic library for robotics. An incredible library for virtual screening, whether it's physics-based virtual screening or neural network-based virtual screening. An incredible library for climate tech. And so, one domain after another domain. And so, we have to go meet friends and create the market. And so, what NVIDIA is really good at, as it turns out, is creating new markets. And we've done it for now so long that it seems like NVIDIA's accelerated computing is everywhere. Yeah. But we really had to do it one at a time. One industry at a time. So, I know that many investors in the audience are super focused on the data center market. And it would be interesting to kind of get your perspective, the company's perspective on the medium and long-term opportunity set. Obviously, your industry is enabling your term, the next industrial revolution. What are the challenges the industry faces? Talk a little bit about how you view the data center market as we sit here today. These giant data centers are super inefficient because it's filled with air, and air is a lousy conductor of electricity. And so, what we want to do is take that few, call it 50-, 100-, 200-megawatt data center, which is sprawling, and you densify it into a really, really small data center. And so, if you look at one of our server racks, NVIDIA server racks look expensive, and it could be a couple of million dollars per rack, but it replaces thousands of nodes. The amazing thing is just the cables of connecting old general-purpose computing systems cost more than replacing all of those and densifying into one rack. The benefit of densifying also is now that you've densified it, you can liquid cool it because it's hard to liquid cool a data center that's very large, but you can liquid cool the data center that's very small. And so, the first thing that we're doing is modernizing data centers, accelerating it, densifying it, making it more energy efficient. You save money, you save power, you save much more efficient. If we just focused on that, that's the next 10 years. We'll just accelerate that. Now, of course, there's a second dynamic because NVIDIA's accelerated computing brought such enormous cost reductions to computing. It's like, in the last 10 years, instead of Moore's law being 100x, we scaled computing by a million x in the last 10 years. And so, the question is, what would you do different if your plane traveled a million times faster? What would you do different if… And so, all of a sudden, people said, hey, listen, why don't we just use computers to write software? Instead of us trying to figure out what the features are, instead of us trying to figure out what the algorithms are, we'll just give the data, all the data, all the predictive data to the computer and let it figure out what the algorithm is. Machine learning, generative AI. And so, we did it in such large scale on so many different data domains that now computers understand not just how to process the data, but the meaning of the data. And because it understands multiple modalities at the same time, it can translate data. And so, it can go from English to images, images to English, English to proteins, proteins to chemicals. And so, because it understood all of the data at one time, it can now do all this translation we call generative AI. Large amount of text into small amount of text, small amount of text into large amount of text, you know, and so on and so forth. We're now in this computer revolution. Now, what's amazing is, so the first trillion dollars of data centers is going to get accelerated and invented this new type of software called generative AI. This generative AI is not just a tool, it is a skill. And so, this is the interesting thing. This is why a new industry has been created. And the reason for that is, if you look at the whole IT industry up until now, we've been making instruments and tools that people use. For the very first time, we're going to create skills that augment people. And so, that's why people think that AI is going to expand beyond the trillion dollars of data centers and IT and into the world of skills. So, what's a skill? A digital chauffeur is a skill, autonomous. You know, a digital assembly line worker, robot. You know, a digital customer service, chatbot. A digital employee for planning NVIDIA supply chain. We use a lot of service now in our companies and we have digital, you know, employee service. And so, now we have all these digital humans, essentially. And that's the wave of AI that we're in now. So, step back, shift a little. Based on everything you just said, there's definitely an ongoing debate in financial markets as to whether or not as we continue to build this AI infrastructure, there is an adequate return on investment. How would you assess customer ROI at this point in the cycle? And if you look back and you kind of think about, you know, PCs, cloud computing, when they were at similar points in their adoption cycles, how did the ROIs, you know, look then compared to where we are now as we continue to scale? Yeah. So, let's take a look. Before cloud, the major trend was virtualization, if you guys remember that. And virtualization basically said, let's take all of the hardware we have in the data center, let's virtualize it into, essentially, virtual data center, and then we could move workload across the data center instead of associating it directly to a particular computer. As a result, the tendency and the utilization of that data center improved. And we saw, essentially, a two to one, you know, two and a half to one, if you will, cost reduction in data centers overnight. Virtualization. The second thing that we then said was, after we virtualized it, we put those virtual computers right into the cloud. As a result, multiple companies, not just one company's many applications, multiple companies can share the same resource. Another cost reduction. The utilization, again, went up. By the way, this last 10 years of all this stuff, 15 years of all this stuff happening, masked the fundamental dynamic which was happening underneath, which is Moore's Law ending. We found a 2x, another 2x in cost reduction, and it hid the end of the transistor scaling. It hid the transistor, the CPU scaling. Then, all of a sudden, we already got the utilization cost reductions out of both of these things. We're now out. And that's the reason why we see data center and computing inflation happening right now. And so, the first thing that's happening is accelerated computing. And so, it's not uncommon for you to take your data processing work, and there's this thing called Spark. Any one of you, Spark is probably the most used data processing engine in the world today. If you use Spark and you accelerate it with NVIDIA in the cloud, it's not unusual to see a 20 to 1 speedup. And so, you're going to save 10, and you pay, of course, you got the NVIDIA GPU, augments the CPU, so the computing cost goes up a little bit. It goes, maybe it doubles, but you reduce the computing time by about 20 times. And so, you get a 10x savings. And it's not unusual to see this kind of ROI for accelerated computing. So, I would encourage all of you, everything that you can accelerate, to accelerate. And then once you accelerate it, run it with GPUs. And so, that's the instant ROI that you get by acceleration. Now, beyond that, the generative AI conversation is in the first wave of Gen AI, which is where the infrastructure players like ourselves and all the cloud service providers put the infrastructure in the cloud so that developers could use these machines to train the models or fine tune the models, guardrail the models, so on and so forth. And the return on that is fantastic because the demand is so great that for every dollar that they spend with us translates to $5 worth of rentals. And that's happening all over the world and everything is all sold out. And so, the demand for this is just incredible. Some of the applications that we already know about, of course, the famous ones, OpenAI's ChatGPT or GitHub Copilot or co-generators that we use in our company, the productivity gains are just incredible. There's not one software engineer in our company today who don't use co-generators, either the ones that we build ourselves for CUDA or USD, which is another language that we use in the company, or Verilog or C and C++ and co-generation. And so, I think the days of every line of code being written by software engineers, those are completely over. And the idea that every one of our software engineers would essentially have companion digital engineers working with them 24-7, that's the future. And so, the way I look at NVIDIA, we have 32,000 employees, but those 32,000 employees are surrounded by, hopefully, 100X more digital engineers. Sure. Lots of industries embracing this. What cases, use cases, industries are you most excited about? Well, in our company, we use it for computer graphics. We can't do computer graphics anymore without artificial intelligence. We compute one pixel. We infer the other 32. I mean, it's incredible. And so, we hallucinate, if you will, the other 32. And it looks temporally stable. It looks photorealistic. And the image quality is incredible. The performance is incredible. The amount of energy we save, computing one pixel takes a lot of energy. That's computation. Inferencing the other 32 takes very little energy. And you can do it incredibly fast. So, one of the takeaways there is, AI isn't just about training the model. Of course, that's just the first step. It's about using the model. And so, when you use the model, you save enormous amounts of energy. You save enormous amount of time, processing time. So, we use it for computer graphics. If not for AI, we wouldn't be able to serve the autonomous vehicle industry. If not for AI, the work that we're doing in robotics, digital biology, just about every tech bio company that I meet these days are built on top of NVIDIA. And so, they're using it for data processing or generating proteins or for new things. That seems like a super exciting space. Oh, it's incredible. Yeah. Small molecule generation, virtual screening. I mean, just that whole space is going to get reinvented for the very first time with computer-aided drug discovery because of artificial intelligence. So, incredible work being done there. Yeah. Talk about competition. Talk about your competitive moat. There's certainly a group of public and private companies looking to disrupt your leadership position. How do you think about your competitive moat? Well, first of all, I think there are several things that are very different about us. The first thing is to remember that AI is not about a chip. AI is about an infrastructure. Today's computing is not build a chip and people come buy your chips, put it into a computer. That's really kind of 1990s. The way that computers are built today, if you look at our new Blackwell system, we designed seven different types of chips to create the system. Blackwell is one of them. So, the amazing thing is when you want to build this AI computer, people say words like supercluster, infrastructure, supercomputer, for good reasons. Because it's not a chip, it's not a computer per se. And so, we're building entire data centers. By building the entire data center, if you just ever look at one of these superclusters, imagine the software that has to go into it to run it. All the software that's inside that computer is completely bespoke. Somebody has to go write that. So, the person who designs the chip and the company that designs that supercomputer, that supercluster, and all the software that goes into it, it makes sense that it's the same company because it'll be more optimized, it'll be more performant, more energy efficient, more cost effective. And so, that's the first thing. The second thing is AI is about algorithms. And we're really, really good at understanding what is the algorithm, what's the implication to the computing stack underneath, and how do I distribute this computation across millions of processors, run it for days on end with the computer being as resilient as possible, achieving great energy efficiency, getting the job done as fast as possible, so on and so forth. And so, we're really, really good at that. And then lastly, in the end, AI is computing. AI is software running on computers. And we know that the most important thing for computers is install base, having the same architecture across every cloud, across on-prem to cloud, and having the same architecture available, whether you're building it in the cloud in your own supercomputer or trying to run it in your car or some robot or some PC, that having that same identical architecture that runs all the same software is a big deal. It's called install base. And so, the discipline that we've had for the last 30 years has really led to today. And it's the reason why the most obvious architecture to use if you were to start a company is to use NVIDIA's architecture, because we're in every cloud. We're anywhere you like to buy it. And whatever computer you pick up, so long as it says NVIDIA inside, you know you can take the software and run it. Yeah. You're innovating at incredibly fast pace. I want you to talk a little bit more about Blackwell, four times faster on training, 30 times faster inference than its predecessor, Hopper. It just seems like you're innovating at such a quick pace. Can you keep up this rapid pace of innovation? And when you think about your partners, how do your partners keep up with the pace of innovation you're delivering? The pace of innovation, our basic methodology is to take, because remember, we're building an infrastructure, there's seven different chips. Each chip's rhythm is probably, at best, two years. At best, two years. We could give it a midlife kicker every year, but architecturally, if you're coming up with a new architecture every two years, you're running at the speed of light. Okay, you're running insanely fast. Now, we have seven different chips, and they all contribute to the performance. And so, we could innovate and bring a new AI cluster, a super cluster to the market every single year that's better than the last generation, because we have so many different pieces to work around. And so, when Blackwell is three times the performance for somebody who has a given amount of power, say one gigawatt, that's three times more revenues. That performance translates to throughput. That throughput translates to revenues. And so, for somebody who has a gigawatt of power to use, you get three times the revenues. There's no way you can give somebody a cost reduction or discount on chips to make up for three times the revenues. And so, the ability for us to deliver that much more performance through the integration of all these different parts and optimizing across the whole stack and optimizing across the whole cluster, we can now deliver better and better value at much higher rates. The opposite of that is equally true. For any amount of money you want to spend, so for ISO power, you get three times the revenues. For ISO spend, you get three times the performance, which is another way of saying cost reduction. And so, we have the best perf per watt, which is your revenues. We have the best perf per TCO, which means your gross margins. And so, if we keep pushing this out to the marketplace, customers get to benefit from that, not once every two years, and it's architecturally compatible. And so, the software you developed yesterday will run tomorrow. The software you developed today will run across your entire install base. So, we can run incredibly fast. If every single architecture was different, then you can't do this. It takes a year just to cobble together a system. Because we built everything together, the day we ship it to you, and it's pretty famous, somebody tweeted out that in 19 days after we shipped the systems to them, they had a super cluster up and running. Nineteen days. You can't do that if you were cobbling together all these different chips and writing the software. You'll be lucky if you could do it in a year. So, I think our ability to transfer our innovation pace to customers, getting more revenues, getting better gross margins, that's a fantastic thing. The majority of your supply chain partners operate out of Asia, particularly Taiwan. Given what's going on geopolitically, how are you thinking about that as you look forward? Yeah, the Asia supply chain, as you know, is really, really sprawling and interconnected. People think that when we say GPUs, because a long time ago, when I announced a new chip, a new generation of chips, I would hold up the chip. And so, that was a new GPU. NVIDIA's new GPUs are 35,000 parts, weighs 80 pounds, consumes 10,000 amps. When you rack it up, it weighs 3,000 pounds. And so, these GPUs are so complex, it's built like an electric car, components like an electric car. And so, the ecosystem is really diverse and really interconnected in Asia. We try to design diversity and redundancy into every aspect wherever we can. And then, the last part of it is to have enough intellectual property in our company. In the event that we have to shift from one fab to another, we have the ability to do it. Maybe the process technology is not as great. Maybe we won't be able to get the same level of performance or cost, but we will be able to provide the supply. And so, I think in the event anything were to happen, we should be able to pick up and fab it somewhere else. We're fabbing at a TSMC because it's the world's best. And it's the world's best not by a small margin. It's the world's best by an incredible margin. And so, not only just the long history of working with them, the great chemistry, their agility, the fact that they could scale. Remember, NVIDIA last year's revenue had a major hockey stick. That major hockey stick wouldn't have been possible if not for the supply chain responding. And so, the agility of that supply chain, including TSMC, is incredible. And in just less than a year, we've scaled up COAS capacity tremendously. And we're going to have to scale it up even more next year and scale up even more the year after that. But nonetheless, the agility and their capability to respond to our needs is just incredible. And so, we use them because they're great. But if necessary, of course, we can always bring up others. Yeah. Company's incredibly well-positioned. A lot of great stuff we've talked about. What do you worry about? Our company works with every AI company in the world today. We're working with every single data center in the world today. I don't know one data center, one cloud service provider, one computer maker we're not working with. And so, what comes with that is an enormous responsibility. And we have a lot of people on our shoulders and everybody's counting on us. And demand is so great that delivery of our components and our technology and our infrastructure and software is really emotional for people because it directly affects their revenues, it directly affects their competitiveness. And so, we probably have more emotional customers today and deservedly so. And if we could fulfill everybody's needs, then the emotion would go away. But it's very emotional. It's really tense. We've got a lot of responsibility on our shoulder and we're trying to do the best we can. And here we are ramping Blackwell. And it's in full production. We'll ship in Q4 and start scaling in Q4 and into next year. And the demand on it is so great. And everybody wants to be first. And everybody wants to be most. And everybody wants to be... And so, the intensity is really quite extraordinary. And so, I think it's fun to be inventing the next computer era. It's fun to see all these amazing applications being created. It's incredible to see robots walking around. It's incredible to have these digital agents coming together as a team solving problems in your computer. It's amazing to see the AIs that we're using to design the chips that will run our AIs. All of that stuff is incredible to see. The part of it that is just really intense is just the world on our shoulders. Sure. So, less sleep is fine. Three solid hours, that's all we need. Well, good for you. I need more than that. I could spend another half hour. Unfortunately, we've got to stop. Jensen, thank you very much. Thank you for being here and sharing with us. Thank you. Appreciate it. Thank you.