Dell Technologies World 2026 Keynote | May 18–21 | Las Vegas

Please join me in welcoming a great partner and friend, a true leader and visionary of the AI age, Nvidia's Jensen Wong. Nice to see you, >> Jensen. Go. >> Thank you, Jensen. >> Jensen, it's great to have you back here at Dell Tech World. You know, we've been talking about agents and >> I'm here every year selling Dell. >> We we we appreciate it, man. We appreciate it. >> So, give us your perspective on where we are in this agentic recursive self-improvement world. It looks like every time, you know, we wake up there's been some leap in the model and the capabilities and sort of it doesn't seem to be plateauing. give us your perspective you know from from the front lines. >> Well uh two years ago when I was here we had just started with the agentic oh excuse me generative AI journey right generative AI can of course generate content but remember it can also generate content to think with generate thoughts which led us to reasoning which led us to planning which led us to agentic systems. So now we have we now have for the very first time useful AI which is the reason why your demand my demand is going parabolic utterly parabolic >> because right it's going parabolic because uh agentic systems the AI has to understand has to reason has to think it has to plan use tools look at the results of the tools think about it some more come up with maybe an improved plan and so it iterates until it can get the job done using a whole bunch of different tools. Well, the amount of computation necessary because it's running autonomously for so long instead of just responding to a query, the amount of computation has grown a 100x, a thousandx, and depending on the work that you're doing, sometimes we'll kick off a software programming job, it doesn't finish for a week. Of course, it did a week what would have taken a whole team a month to do. And so big deal in productivity but gigantic leap in computation requirement. So that let's say just let's just say that computing went up by a hundred or a thousand times. Meanwhile, because it's so useful, the number of people using agents is now all over the place. Every enterprise, all our company, your company, we're everybody's using agents all over the place to do software development, devops, s sur, all of our CI/CD work, QA testing. The amount of software work that we do in the company now supported by agents is incredible. One engineer, a really good engineer today is working with an agent, but a really great engineer in the future is going to be orchestrating a whole bunch of agents who are going to be orchestrating a whole bunch of sub aents to do work. Well, between the amount of computation and the amount of demand use going up, the co the product of that, that's our demand. And so we've now arrived at the era of useful AI which is really just really exciting for all of us because until now it's been novel, interesting, incredibly exciting but really at the enterprise level many of you have said before last couple years the impact of AI is the the potential is incredible but the actual use was minimal. Now it's taken off >> and we're starting to see now, you know, we didn't a couple years ago when we started this, we didn't have 5,000 enterprise customers. Now we have, you know, the biggest companies in the world like you saw with, you know, Lily and Samsung and Honeywell, you know, they're piling into this in a big way, but really that's just starting, right? and and you know to reimagine their workflows to be able to understand you know the trajectory of how this is improving and how it can affect ultimately what they their companies can become >> that really is just an idea I mean it it we we haven't seen that in any scale I mean there are some companies that are doing it but it's it's a very small number today >> no doubt and well you're experiencing and I'm experiencing our company has always gone fast, but it's gone. Yeah, it's it it's really going fast now. And just the amount of content that's being created internally, uh the progress that's being made, you know, people said that AI is going to make us more productive. There's no question about that. What took months now takes weeks. What took weeks now takes days. And what takes days now takes hours. And things that would take an hour, you know, you and I pretty much expect it instantly now. And so what has really changed is that our ambition has changed. There's no question my ambition has changed. You know, I wanted to be somebody to do something, make a contribution, but that that was the old Jensen, you know, the new Jensen. I got big ambitions now. >> You got to you got to you know, we all have to ask ourselves the question, how high is up? Well, it's pretty high, right? >> Yeah. Yeah. So, so we've got all these great products, you know, we're embedding AI in everything and enabling, you know, this distributed inference and intelligence. This is the best room I've ever been in. >> Which is which which is your favorite? >> Well, I you know, it's hard to I got I love all my kids. >> How about How about the one on the end? You got to love that one most, though. There's Yeah. Well, this is incredible. And so we we uh Michael and I have been working on a whole new line of computers uh to run agents. The way to think about agents is this. There's a large language model. It's gigantic. It's the most computationally intensive piece of software the world's ever known. And that would run in that system towards the end that Envy 72 it the world's largest scale up single domain computer. Okay, it's just one giant system operating as one computer. And that system has a large language model in it. It could, you know, one terabyte, 10 terabyte of parameters, no problem. And so that's one giant system. That's the brain. However, an agent starts with the harness. That harness has to sit in a secure and governed container. We call it a sandbox. And so the NVIDIA open shell open open- sourced sandbox uh is the security system that just about everybody in the in the industry is using. Inside we have a reference harness we call Nemo cloud. >> Nemoclaw runs on a CPU and that CPU could be a CPU in there, a CPU in here, a CPU in there. Y >> it can also run Neotron or any of the models uh open source models that you would create your own specialized agents for your own companies trained for your own special domain of data or skills and that would run locally if you like and then the large language models could run in the cloud and so you have this hybrid AI. The thing that's really cool is that Nvidia's architecture is the only architecture in the world that runs every frontier AI model. And lately uh recently in the last year or so, Anthropic has been really leaning into the NVIDIA architecture. So now we support every single frontier model. We support every open- source model and they we support them in the cloud or locally. Uh as you can see our computer is the first one in the world that runs in every cloud but it also runs locally. And if you have your models that you're quite sensitive about, our systems are built with confidential computing. This way you don't have to trust the operator that's operating the data center with your secure uh data. And so all of these architectures now run at every cloud, runs every model, runs hybrid AI, and runs agentic systems. And so you have your harness, it runs on a CPU. The CPU also uses tools and the this the CPU we created I think you mentioned it earlier called Vera. >> Yes. >> The CPUs of the past the CPUs of the past were built for hypers scale clouds and so you're renting the CPU cores and so you're optimizing for as many CPU cores as possible. Well agents in this new world you're generating tokens. You're not renting CPU cores anymore. you're generating tokens and marketing that that's the e the economics of this AI era. And so the AI wants to generate run its run its work and generate as many tokens as possible as quickly as possible because that's the output of the intelligence. Vera CPU has the highest single threaded performance of any CPU in the world. It has three times the memory bandwidth of the fastest CPU in the world. And as a result, uh, Starburst, uh, Duck DB, all these databases run incredibly fast because the agents are pounding on the databases. So the CPUs better be super fast. The agents want to get through its work. So the CPU has got to be super fast. Otherwise, that big machine down there is waiting for the agent to get its work done. And so now you have the harness running on the systems here, local AI models running on the systems here, and giant models running in the cloud or in your own data center on that big machine down there. >> Well, let's go check it out. But as we do that, you know, um what's exciting is to think about uh you know, in the past as humans, we would do work, right, and we would pass it on to the next human. But now we have, you know, all these agents that we're managing, right? and uh you know an an individual can manage I don't know you know a thousand 100 agents and so the possibilities that that unlocks in terms of human creativity and what humans are going to be able to do and I love this idea of the unmetered intelligence right where you've got the power in your own PC in your own data center and you can use it with your own data uh you know that's that's sort of Super cool. That's I think >> you don't have to struggle with token anxiety. >> Yeah. Yeah. >> You know, you tell you tell your >> employees run out of tokens or you know, get get that that get that dreaded bill. >> But Jensen, since you're here, >> would you do us the honor >> autographing this uh this this this latest uh >> All right. Well, we got we got to climb up here. I'm not as tall as you are. >> All right. All right. This is uh what's the date today? 18th. >> May 18th. >> We're not going to sell this one. This This one is >> You got to sign it, too. >> I already signed it. >> You did? Yeah. >> Well, you got it right here. Dell del Here we go. Here we go. Michael's already signed it right here. And so this is Michael. This is this is a hundred times. Yeah. Larger than this one, the station, >> right? Exactly the same architecture. >> The GB300. >> GB300 in here. That's a 100 times larger than this one. This is the only computer deskside computer in the world that can run a terabyte a one trillion parameter AI model. Okay, so this is incredible. >> Would have been unimaginable even a year or two ago. >> I know it one trillion. I mean this this would basically be a cloud just two days ago, you know. And so so this is this is a station and this this is 100 times bigger. So, >> yep. >> This one. And that's five, six times larger than this one. >> And this is one of my favorites. This is a the smallest GP10. >> Same architecture as that one is that one. >> It's incredible, right? One architecture. >> Incredible. >> Uh we really appreciate you being here. We treasure the incredible partnership with Nvidia and all we've been able to do for customers around the world and uh look forward to to >> and we grew up together practically. >> Yes, we did. 31 years we've been doing this. >> Yeah. >> Thank you guys. Keep up the great work. Thank you everybody. >> Thank you Jensen. Bye Dell.