Welcome to the stage, Nvidia founder and CEO, Jensen Huang.
Hello Las Vegas. Happy New Year. Welcome to CES. Well, we have about 15 keynotes worth of material to pack in here. I'm so happy to see all of you. You got 3,000 people in this auditorium. There's 2,000 people in a courtyard watching us. There's another thousand people apparently in the fourth floor where there were supposed to be Nvidia show floors all watching this keynote. And of course, millions around the world are going to be watching this to kick off this new year.
Well, every 10 to 15 years, the computer industry resets. A new platform shift happens from mainframe to PC, PC to internet, internet to cloud, cloud to mobile. Each time the world of applications target a new platform, that's why it's called a platform shift. You write new applications for a new computer.
Except this time there are two simultaneous platform shifts in fact happening at the same time. While we now move to AI, applications are now going to be built on top of AI. At first people thought AIs are applications and in fact AIs are applications but you're going to build applications on top of AIs.
But in addition to that, how you run the software, how you develop the software fundamentally changed. The entire vocabulary stack of the computer industry is being reinvented. You no longer program the software, you train the software. You don't run it on CPUs, you run it on GPUs.
And whereas applications were pre-recorded, pre-compiled and run on your device, now applications understand the context and generate every single pixel, every single token completely from scratch every single time. Computing has been fundamentally reshaped as a result of accelerated computing, as a result of artificial intelligence. Every single layer of that five-layer cake is now being reinvented.
Well, what that means is some 10 trillion dollars or so of the last decade of computing is now being modernized to this new way of doing computing. What that means is hundreds of billions of dollars, a couple hundred billion dollars in VC funding each year is going into modernizing and inventing this new world. And what it means is a hundred trillion dollars of industry, several percent of which is R&D budget is shifting over to artificial intelligence. People ask where is the money coming from? That's where the money is coming from. The modernization of AI to AI, the shifting of R&D budgets from classical methods to now artificial intelligence methods.
Enormous amounts of investments coming into this industry, which explains why we're so busy. And this last year was no difference. This last year was incredible.
This last year, there's a slide coming. This is what happens when you don't practice. This is the first keynote of the year. I hope it's your first keynote of the year. Otherwise, you can you have been pretty pretty busy. This is our first keynote of the year. We're going to get the spider webs out. And so 2025 was an incredible year.
It's just see it seemed like everything was happening all at the same time and it in fact it probably was. The first thing of course is scaling laws.
In 2015 the first language model that I thought was really going to make a difference made a huge difference. It was called BERT. 2017 Transformers came. It wasn't until 5 years later 2022 that ChatGPT moment happened and it awakened the world to the possibilities of artificial intelligence.
Something very important happened a year after that. The first o1 model from ChatGPT, the first reasoning model, completely revolutionary, invented this idea called test time scaling which is very common sensical thing. Not only do we pre-train a model to learn, we post-train it with our reinforcement learning so that it could learn skills. And now we also have test time scaling which is another way of saying thinking. You think in real time. Each one of these phases of artificial intelligence requires enormous amount of compute and the computing law continued to scale. Large language models continue to get better.
Meanwhile, another breakthrough happened and this breakthrough happened in 2024. Agentic systems started to emerge in 2025. It started to proliferate just about everywhere. Agentic models that have the ability to reason, look up information, do research, use tools, plan futures, simulate outcomes.
All of a sudden started to solve very, very important problems. One of my favorite agentic models is called Cursor which revolutionized the way we do software programming at NVIDIA. Agentic systems are going to really take off from here. Of course, there were other types of AI. We know that large language models isn't the only type of information. Wherever the universe has information, wherever the universe has structure, we could teach a large language model, a form of language model to go understand that information, to understand its representation and to turn that into an AI.
One of the biggest most important one is physical AI. AIs that understand the laws of nature. And then of course physical AI is about AI interacting with the world. But the world itself has information encoded information and that's called AI physics. AI that in the case of physical AI you have AI that interacts with the physical world and you have AI physics AI that understands the laws of physics.
And then lastly one of the most important things that happened last year the advancement of open models. We can now know that AI is going to proliferate everywhere when open source, when open innovation, when innovation across every single company and every industry around the world is activated. At the same time, open models really took off last year. In fact, last year we saw the advance of DeepSeek R1, the first open model that's a reasoning system. It caught the world by surprise and it activated literally this entire movement. Really, really exciting work.
We're so happy with it. Now we have open model systems all over the world of all different kinds and we now know that open models have also reached the frontier. Still solidly a six months behind the frontier models but every single six months a new model is emerging and these models are getting smarter and smarter.
Because of that you could see the number of downloads has exploded. The number of downloads is growing so fast because startups want to participate in the AI revolution, large companies want to, researchers want to, students want to, just about every single country wants to. How is it possible that intelligence, the digital form of intelligence will leave anyone behind? And so open models has really revolutionized artificial intelligence last year. This entire industry is going to be reshaped as a result of that.
Now we had this inkling some time ago you might have heard that several years ago we started to build and operate our own AI supercomputers we call them DGX clouds. A lot of people asked, are you going into the cloud business? The answer is no. We're building these DGX supercomputers for our own use. Well, it turns out we have billions of dollars of supercomputers in operation so that we could develop our open models. I am so pleased with the work that we're doing.
It is starting to attract attention all over the world and all over the industries because we are doing frontier AI model work in so many different domains. The work that we did in proteins in digital biology lab, Proteina, to be able to synthesize and generate proteins. OpenFold3 to understand the structure of proteins.
EVO2, how to understand and generate multiple proteins, otherwise the beginnings of cellular representation. Earth2 AI that understands laws of physics, the work that we did with ForecastNet, the work that we did with CorrDiff really revolutionized the way that people are doing weather prediction. Neotron, we've now doing groundbreaking work there. The first hybrid transformer SSM model that's incredibly fast and therefore can think for a very long time or can think very quickly with that for not a very long time and produce very very smart intelligent answers.
Nemotron3 is groundbreaking work and you can expect us to deliver other versions of Nemotron3 in the near future. Cosmos, a frontier open world foundation model, one that understands how the world works. Groot, a humanoid robotic system, articulation, mobility, locomotion, these models, these technologies are now being integrated and in each one of these cases open to the world. Frontier humanoid robotics models open to the world.
And then today we're going to talk a little bit about Alpamo, the work that we've been doing in self-driving cars. Not only do we open source the models, we also open source the data that we use to train those models because that in that way only in that way can you truly trust how the models came to be. We open source all the models. We help you make derivatives from them. We have a whole suite of libraries. We call them the NeMo libraries, Physics NeMo libraries and the Clarion libraries, BioNeMo libraries. Each one of these libraries are life cycle management systems of AIs so that you could process the data, you could generate data, you could train the model, you could create the model, evaluate the model, guardrail the model all the way to deploying the model. Each one of these libraries are incredibly complex and all of it is open sourced.
And so now on top of this platform, Nvidia is a frontier AI model builder and we build it in a very special way. We build it completely in the open so that we can enable every company, every industry, every country to be part of this AI revolution. I'm incredibly proud of the work that we're doing there. In fact, if you notice the charts, the chart shows that our contribution to this industry is bar none. And you're going to see us in fact continue to do that if not accelerate. These models are also world class.
All systems are down. This never happens in Santa Clara. Is it because of Las Vegas? Somebody must have won a jackpot outside. All systems are down.
Okay. I think my system's still down, but that's okay. I make it up as I go. And so not only are these models frontier capable, not only are they open, they're also top the leaderboards. This is an area where we're very proud. They top leaderboards and intelligence. We have important models that understand multimodality documents otherwise known as PDFs. The most valuable content in the world are captured in PDFs. But it takes artificial intelligence to find out what's inside, interpret what's inside, and help you read it. And so our PDF retrievers, our PDF parsers are world class.
Our speech recognition models absolutely world class. Our retrieval models, basically search, semantic search, AI search, the database engine of the modern AI era, world class. So we're on top of leaderboards constantly. This is an area we're very proud of and all of that is in service of your ability to build AI agents. This is really a groundbreaking area of development.
You know, at first when ChatGPT came out, people said, you know, gosh, it produced really interesting results, but it hallucinated greatly. And the reason why it hallucinated, of course, it could memorize everything in the past, but it can't memorize everything in the future, in the current. And so it needs to be grounded in research, has to do fundamental research before it answers a question. The ability to reason about do I have to do research? Do I have to use tools? How do I break up a problem into steps? Each one of these steps something that the AI model knows how to do. And together it is able to compose it into a sequence of steps to perform something it's never done before, never been trained to do.
This is the wonderful capability of reasoning. We could encounter a circumstance we've never seen before and break it down into circumstances and knowledge or rules that we know how to do because we've experienced it in the past. And so the ability for AI models now to be able to reason is incredibly powerful.
The reasoning capability of agents open the doors to all of these different applications. We no longer have to train an AI model to know everything on day one. Just as we don't have to know everything on day one that we should be able to in every circumstance reason about how to solve that problem. Large language models has now made this fundamental leap. The ability to use reinforcement learning and chain of thought and you know search and planning and all these different techniques and reinforcement learning has made it possible for us to have this basic capability and it's also now completely open sourced.
But the thing that's really terrific is another breakthrough that happened and the first time I saw it was with Arvin's Perplexity. Perplexity the search company, the AI search company, really innovative company and the first time I realized they were using multiple models at the same time. I thought it was completely genius. Of course, we would do that. Of course, an AI would also call upon all of the world's great AIs to solve the problem it wants to solve at any part of the reasoning chain.
And this is the reason why AIs are really multi-modal, meaning they understand speech and images and text and videos and 3D graphics and proteins. It's multimodal. It's also multi-model meaning that it should be able to use any model that best fits the task. It is multicloud by definition. Therefore, because these AI models are sitting in all these different places and it also is hybrid cloud because if you're an enterprise company or you built a robot or whatever that device is, sometimes it's at the edge, sometimes a radio cell tower, maybe sometimes it's in an enterprise or maybe it's a place where a hospital where you need to have the data in real time right next to you.
Whatever those applications are, we know now this is what an AI application looks like in the future. Or another way to think about that because future applications are built on AIs. This is the basic framework of future applications.
This basic framework, this basic structure of agentic AIs that could do the things that I'm talking about that is multi-model has now turbocharged AI startups of all kinds. And now you can also because of all of the open models and all the tools that we provided you, you could also customize your AIs to teach your AI skills that nobody else is teaching. Nobody else is causing their AI to become intelligent or smart in that way. You could do it for yourself. And that's the work that we do with Nemotron, NeMo, and all of the things that we do with open models is intended to do.
You put a smart router in front of it. And that router is essentially a manager that decides which one of the task based on the intention of the prompts that you give it, which one of the models is best fit for that application for that solving that problem. Okay. So now when you think about this architecture, what do you have?
When you think about this architecture, all of a sudden you have an AI that's on the one hand completely customizable by you. Something that you could teach to do your own very skills for your company. Something that's domain secret, something where you have deep domain expertise. Maybe you've got all of the data that you need to train that AI model. On the other hand, your AI is always at the frontier by definition. You're always at the frontier on the one hand. You're always customized. On the other hand, it should just run.
And so we thought we would make the simplest of examples to make it available to you. This entire framework we call a blueprint and we have blueprints that are integrated into enterprise SaaS platforms all over the world and we're really pleased with the progress. But what we do is show you a short example of something that anybody can do.
Let's build a personal assistant. I wanted to help me with my calendar, emails, to-do lists, and even keep an eye on my home. I use Brev to turn my DGX Spark into a personal cloud. So, I can use the same interface whether I'm using a cloud GPU or a DGX Spark. I use a Frontier model API to easily get started.
I want them to help me with my emails. So, I create an email tool for my agent to call. I want my emails to stay private. So, I'll add an open model that's running locally on the Spark.
Now, for any job, I want the agent to use the right model for the right task. So, I'll use an intent-based model router. This way, prompts that need email will stay on my Spark, and everything else can call the Frontier model. I want my assistant to interact with my world, so I'll hook it up to Hugging Face's Reachy mini robot.
My agent controls the head, ears, and camera of the Reachy with tool calls. I want to give Reachy a voice, and I really like Eleven Labs, so I'll hook up their API.
Hi, I'm Reachy running on DGX Spark.
Hey Reachy, what's on my to-do list today?
Your to-do list today. Grab groceries, eggs, milk, butter, and send Jensen the new script.
Okay, let's send Jensen an update. Tell him we'll have it for him by the end of the day.
We'll do.
Reachy, there's a sketch, too. Can you turn it into an architectural rendering?
Sure.
Nice. Now make a video and show me around the room.
Here you go. That's great.
With Brev, I can share access to my Spark and Reachy. So, I'm going to share it with Anna.
Hey Reachy, what's Potato up to?
He's on the couch. I remember you don't like this. I'll tell him to get off. Potato, off the couch.
With all the progress in open source, it's incredible to see what you can build. I'd love to see what you create.
Isn't that incredible? Now, the amazing thing is that is utterly trivial now. That is utterly trivial now. And yet, just a couple years ago, all of that would have been impossible. Absolutely unimaginable.
Well, this basic framework, this basic way of building applications using language models, using language models that are pre-trained and they're proprietary, they're frontier. Combine it with customized language models into an agentic framework, a reasoning framework that allows you to access tools and files and maybe even connect to other agents.
This is basically the architecture of AI applications or applications in the modern age and the ability for us to create these applications are incredibly fast. And notice if you give it this application information that it's never seen before or in a structure that is not represented exactly as you thought, it can still reason through it and make it best effort to reason through the data, the information to try to understand how to solve the problem. Artificial intelligence.
Okay. Okay, so this basic framework is now being integrated and everything that I just described, we had the benefit of working with some of the world's leading enterprise platform companies. Palantir for example, their entire AI and data processing platform is being integrated, accelerated by Nvidia today. ServiceNow, the world's leading customer service and employee service platform. Snowflake, the world's top data platform in the cloud. Incredible work that is being done there. Code Rabbit, we're using Code Rabbit all over Nvidia. CrowdStrike creating AIs to detect, to find AI threats. NetApp, their AI, their data platform now has NVIDIA semantic AI on top of it and agentic systems on top of it for them to do customer service.
But the important thing is this. Not only is this the way that you develop applications now, this is going to be the user interface of your platform. So whether it's Palantir or ServiceNow or Snowflake and many other companies that we're working with, the agentic system is the interface. It's no longer Excel with a bunch of, you know, squares that you enter information into. Maybe it's no longer just command line. All of that multimodality information is now possible and the way you interact with your platform is much more, well if you will, simple like you're interacting with people. And so that's enterprise AI being revolutionized by agentic systems.
The next thing is physical AI. This is an area that you've seen me talk about for several years. In fact, we've been working on this for eight years. The question is, how do you take something that is intelligent inside a computer and interacts with you with screens and speakers to something that can interact with the world, meaning it can understand the common sense of how the world works.
Object permanence. If I look away and I look back, that object is still there. Causality. If I push it, it tips over. It understands friction and gravity. It understands inertia. That a heavy truck rolling down the road is going to need a little bit more time to stop. That a ball is going to keep on rolling.
These ideas are common sense to even a little child, but for AI, it's completely unknown. And so we have to create a system that allows AIs to learn the common sense of the physical world, learn its laws, but also to be able to of course learn from data and the data is quite scarce and to be able to evaluate whether that AI is working, meaning it has to simulate in an environment. How does an AI know that the actions that it's performing is consistent with what it should do if it doesn't have the ability to simulate the response of the physical world back on its actions? The response of its actions is really important to simulate. Otherwise, there's no way to evaluate it. It's different every time.
And so this basic system requires three computers. One computer of course the one that we know that Nvidia builds for training the AI models. Another computer that we know is to inference the models. Inferencing the model is essentially a robotics computer that runs in a car or runs in a robot or runs in a factory runs anywhere at the edge. But there has to be another computer that's designed for simulation. And simulation is at the heart of almost everything Nvidia does. This is where we are most comfortable and simulation was really the foundations of almost everything that we've done with physical AI.
So we have three computers and multiple stacks that run on these computers, these libraries to make them useful. Omniverse is our digital twin physically based simulation world. Cosmos as I mentioned earlier is our foundation model not a foundation model for language but a foundation model of the world. And is also aligned with language. You could say something like, you know, what's happening to the ball and they'll tell you the ball's rolling down the street. And so a world foundation model and then of course the robotics models. We have two of them. One of them is called Groot. The other one's called Alpamo that I'm going to tell you about.
Now the one of the most important things that we have to do with physical AI is to create the data to train the AI in the first place. Where does that data come from? Rather than instead of having languages because we created a bunch of texts that are what we consider ground truth that the AI can learn from. How do we teach an AI the ground truth of physics? There lots and lots of videos, lots and lots of videos, but hardly enough to capture the diversity and the type of interactions that we need. And so this is where great minds came together and transformed what used to be compute into data.
Now using synthetic data generation that is grounded and conditioned by the laws of physics, grounded and conditioned by ground truth, we can now selectively cleverly generate data that we can then use to train the AI. So for example, what comes into this AI, this Cosmos AI world model on the left over here is the output of a traffic simulator.
Now this traffic simulator is hardly enough for an AI to learn from. We can take this, put it into a Cosmos foundation model and generate surround video that is physically based and physically plausible that the AI can now learn from. And there are so many examples of this. Let me show you what Cosmos can do.
The ChatGPT moment for physical AI is nearly here, but the challenge is clear. The physical world is diverse and unpredictable. Collecting real world training data is slow and costly and it's never enough. The answer is synthetic data. It starts with NVIDIA Cosmos, an open frontier world foundation model for physical AI pre-trained on internet scale video, real driving and robotics data and 3D simulation.
Cosmos learned a unified representation of the world able to align language, images, 3D and action. It performs physical AI skills like generation, reasoning, and trajectory prediction from a single image. Cosmos generates realistic video from 3D scene descriptions, physically coherent motion, from driving telemetry and sensor logs, surround video, from planning simulators, multi-camera environments, or from scenario prompts. It brings edge cases to life.
Developers can run interactive closed loop simulations in Cosmos. When actions are made, the world responds. Cosmos reasons. It analyzes edge scenarios, breaks them down into familiar physical interactions, and reasons about what could happen next. Cosmos turns compute into data, training AVs for the long tail and robots how to adapt for every scenario.
I know it's incredible. Cosmos is the world's leading foundation model, world foundation model. It's been downloaded millions of times, used all over the world getting the world ready for this new era of physical AI. We use it ourselves as well. We use it ourselves to create our self-driving car.
Using it for scenario generation and using it for evaluation. We could have something that allows us to effectively travel billions, trillions of miles, but doing it inside a computer. And we've made enormous progress. Today we're announcing Alpamo, the world's first thinking reasoning autonomous vehicle AI.
Alpamo is trained end to end, literally from camera in to actuation out. The camera in lots and lots of miles that are driven by itself where we human drive it using human demonstration and we have lots and lots of miles that are generated by Cosmos. In addition to that, hundreds of thousands of examples are labeled very very carefully so that we could teach the car how to drive.
Alpamo does something that's really special. Not only does it take sensor input and activates steering wheel, brakes and acceleration, it also reasons about what action it is about to take. It tells you what action it's going to take, the reasons by which it came about that action, and then of course the trajectory. All of these are coupled directly and trained very specifically by a large combination of human trained and as well as Cosmos generated data.
The result of it is just really incredible. Not only does your car drive as you would expect it to drive and it drives so naturally because it learned directly from human demonstrators but in every single scenario when it comes up to the scenario it reasons about it, tells you what it's going to do and it reasons about what's about to do. Now the reason why this is so important is because of the long tail of driving there. It's impossible for us to simply collect every single possible scenario for everything that could ever happen in every single country in every single circumstance that's possibly ever going to happen for all of population.
However, it is very likely that every scenario if decomposed into a whole bunch of other smaller scenarios are quite normal for you to understand. And so these long tails will be decomposed into quite normal circumstances that the car knows how to deal with. It just needs to reason about it. And so let's take a look. Everything you're about to see is one shot. It's a no hands.
Routing to your destination. Buckle up.
We started working on self-driving cars eight years ago. And the reason for that is because we reasoned early on that deep learning and artificial intelligence was going to reinvent the entire computing stack. And if we were ever going to understand how to navigate ourselves and how to guide the industry towards this new future, we have to get good at building the entire stack.
Well, as I mentioned earlier, AI is a five layer cake. The lowest layer is land, power and shell. In the case of robotics, the lowest layer is the car. The next layer above it is chips, GPUs, networking chips, CPUs, all that kind of stuff. The next layer above that is the infrastructure. That infrastructure in this particular case as I mentioned with physical AI is Omniverse and Cosmos. And then above that are the models. And in the case of the models above that I just shown you. The model here is called Alpamo. And Alpamo today is open sourced.
We this incredible body of work. It took several thousand people. Our AV team is several thousand people. Just to put in perspective, our partner Ola, I think Ola's here in the audience somewhere. Mercedes agreed to partner with us five years ago to go make all of this possible. We imagine that someday a billion cars on a road will all be autonomous. You could either have it be a robo taxi that you're orchestrating and renting from somebody or you could own it and it's driving by itself or you could decide to drive for yourself and so but every single car will have autonomous vehicle capability. Every single car will be AI powered.
And so the model layer in this case is Alpamo and the application above that is the Mercedes-Benz. Okay. And so this entire stack is our first Nvidia first entire stack endeavor and we've been working on it for this entire time. And I'm just so happy that the first AV car from Nvidia is going to be on the road in Q1 and then it goes Europe in Q2 here in the United States in Q1, then Europe in Q2, and I think it's Asia in Q3 and Q4. And the powerful thing is that we're going to keep on updating it with next versions of Alpamo and versions after that.
There's no question in my mind now that this is going to be one of the largest robotics industries. And I'm so happy that we worked on it. And it taught us enormous amount about how to help the rest of the world build robotic systems. That deep understanding and knowing how to build it ourselves, building the entire infrastructure ourselves and knowing what kind of chips a robotic system would need. In this particular case, dual Orins, the next generation dual Thors. These processors are designed for robotic systems and was designed for the highest level of safety capability.
This car just got rated. It just went to production. The Mercedes-Benz CLA was just rated by NCAP, the world's safest car.
It is the only system that I know that has every single line of code, the chip, the system, every line of code safety certified. The entire model system is based on this. Sensors are diverse and redundant and so is the self-driving car stack. The Alpamo stack is trained end to end and has incredible skills. However, nobody knows until you drive it forever that it's going to be perfectly safe.
And so the way we guardrail that is with another software stack, an entire AV stack underneath. That entire AV stack is built to be fully traceable and it's taken us some five years to build that. Some six, seven years actually to build that second stack. These two software stacks are mirroring each other and then we have a policy and safety evaluator decide is this something that I'm very confident and can reason about driving very safely. If so, I'm going to have Alpamo do it. If it's a circumstance that I'm not very confident in and the safety policy evaluator decide that we're going to go back to a very a simpler safer guardrail system, then it goes back to the classical AV stack where the only car in the world with both of these AV stacks running and all safety systems should have diversity and redundancy.
Well, our vision is that someday every single car, every single truck will be autonomous and we've been working towards that future. This entire stack is vertically integrated. Of course, in the case of Mercedes-Benz, we built the entire stack together. We're going to deploy the car. We're going to operate the stack. We're going to maintain the stack for as long as we shall live. However, like everything else we do as a company, we build the entire stack, but the entire stack is open for the ecosystem. And these the ecosystem working with us to build L4 and robo taxis is expanding and it's going everywhere.
I fully expect this to be well this is already a giant business for us. It's a giant business for us because they use it for training our training data processing data and training their models. They use it for synthetic data generation in some cases in some car and some companies they pretty much just build the computers the chips that are inside the car and some companies work with us full stack some companies work with us some partial part of that. Okay. So it doesn't matter how much you decide to use. You know my only request is use a little bit of video wherever you can and you know but the entire thing is open. Now this is going to be the first large-scale
Mainstream AI physical AI market, and this is now, I think we can all agree fully here, and this inflection point of going from not autonomous vehicles to autonomous vehicles is probably happening right about this time. In the next 10 years, I'm fairly certain a very, very large percentage of the world's cars will be autonomous or highly autonomous. But this basic technique that I just described, in using the three computers, using synthetic data generation and simulation, applies to every form of robotic systems. It could be a robot that is just an articulator, a manipulator, maybe it's a mobile robot, maybe it's a fully humanoid robot. And so the next journey, the next era for robotic systems is going to be, you know, robots. And these robots are going to come in all kinds of different sizes. And I invited some friends. Did they come?
Hey guys, hurry up. I got a lot of stuff to cover. Come on, hurry. Did you tell R2-D2 you were going to be here? Did you? And C3PO. Okay. All right. Come here. Before now, one of the things that's really... You have Jetsons. They have little Jetson computers inside them. They're trained inside Omniverse. And how about this? Let's show everybody the simulator that you guys learned how to be robots in. You guys want to look at that? Okay, let's look at that. Run it, please.
Are you kidding me? See... baby. Isn't that amazing? That's how you learn to be a robot. You did it all inside Omniverse. And the robot simulator is called Isaac. Isaac Sim and Isaac Lab. And anybody who wants to build a robot, you know, nobody's going to be as cute as you. But now we have all these friends that we have building robots. We're building big ones. No, like I said, nobody's as cute as you guys are. But we have new robot and we have Aubot. Aubot over there, you know. We have LG over here. They just announced a new robot. Caterpillar. They've got the largest robots ever. That one delivers food to your house. That's connected to Uber Eats. And that's Surf Robot. I love those guys. Agility, Boston Dynamics, incredible. You got surgical robots, you got manipulator robots from Franka, you got universal robotics robot, incredible number of different robots. And so this is the next chapter. We're going to talk a lot more about robotics in the future, but it's not just about the robots in the end. I know everything's about you guys. It's about getting there. And one of the most important industries in the world that will be revolutionized by physical AI and AI physics is the industry that started all of us at Nvidia. It wouldn't be possible if not for the companies that I'm about to talk to. And I'm so happy that all of them, starting with Cadence, is going to accelerate everything. Cadence CUDA X integrated into all of their simulations and solvers. They've got Nvidia physical AIs that they're going to use for different physical plants and plant simulations. You got AI physics being integrated into these systems. So whether it's an EDA or STA, and in the future robotic systems, we're going to have basically the same technology that made you guys possible now completely revolutionized these design stacks. Synopsis, without Synopsis, you know, Synopsis and Cadence are completely indispensable in the world of chip design. Synopsis leads in logic design and IP, in the case of Cadence, they lead physical design, the place and route, and emulation and verification. Cadence is incredible at emulation and verification. Both of them are moving into the world of system design and system simulation. And so in the future, we're going to design your chips inside Cadence and inside Synopsis. We're going to design your systems and emulate the whole thing and simulate everything inside these tools. That's your future. We're going to give... Yeah, you're going to be born inside these platforms. Pretty amazing, right? And so we're so happy that we're working with these industries just as we've integrated Nvidia into Palantir and ServiceNow, we're integrating Nvidia into the most computationally intensive simulation industries, Synopsis and Cadence. And today we're announcing that Siemens is also doing the same thing. We're going to integrate CUDA X, physical AI, agentic AI, Neotron deeply integrated into the world of Siemens. And the reason for that is this. First, we designed the chips and all of it in the future will be accelerated by Nvidia. You're going to be very happy about that. We're going to have agentic chip designers and system designers working with us, helping us do design just as we have agentic software engineers helping our software engineers code today. And so, we'll have agentic chip designers and system designers. We're going to create you inside this. But then we have to build you. We have to build the plants, the factories that manufacture you. We have to design the manufacturing lines that assemble all of you. And these manufacturing plants are going to be essentially gigantic robots. Incredible, isn't that right? I know. I know. And so you're going to be designed in a computer. You're going to be made in a computer. You're going to be tested and evaluated in a computer long before you have to spend any time dealing with gravity. I know. Do you know how to deal with gravity? Can you jump? Can you jump?
Okay. All right. Don't show off. Okay. So, so this... so now the industry that made Nvidia possible, I'm just so happy that now the technology that we're creating is at a level of sophistication and capability that we can now help them revolutionize their industry. And so what started with them, we now have the opportunity to go back and help them revolutionize theirs. Let's take a look at the stuff that we're going to do with Siemens. Come on.
