Good morning everyone. It's really nice to be back here in Congress Hall. Hopefully everybody had a good day yesterday and are enjoying it today. It is my real pleasure to introduce Jensen Huang, who is somebody I admire, somebody I've watched, and somebody who has been a teacher to me on the journey of learning about technology and AI. It is amazing watching how he led Nvidia. And I don't measure myself on comparisons, but I like this one comparison. So since Nvidia has been public, which was in 1999, same year as BlackRock. Oh boy. Okay. No. Nvidia's total return for its shareholders, it's been a compounded 37%. Just think about that. What would that mean to every pension fund if they invested in Nvidia as an IPO? The amount of successes we have with everybody's retirement. At the same time, BlackRock's annualized total return has been 21%. Not so bad for a financial services company but it certainly pales. And so that is just a really great indication of Jensen's leadership, the positioning of Nvidia, and also it is a great statement about what the world believes in the future of Nvidia. So Jensen, congratulations on that journey and I know we have many more years of that journey ahead of us.
Thank you. I appreciate that. My only regret was at the IPO, after the IPO, I wanted to buy my parents something nice and so I sold Nvidia stock at a valuation of $300 million. The company was at a valuation $300 million and I bought them a Mercedes S-Class. It is the most expensive car in the world.
Yeah, they still have it. Yeah.
Good. Let me go into the subject matter now, but I just want to say, you know, the debate on AI is about how it's going to change the world and the global economy. Today I want to talk about how AI can add to the world economy and how AI can increasingly become a foundational technology that everyone in this room can be utilizing, enhancing our lives, enhancing the lives of everyone in the world. And we need to talk about how it's going to reshape productivity, labor, infrastructure across virtually every other sector, but importantly, how it's going to reshape the world and how can more segments of the world benefit from AI and how can we ensure that we have a broadening of the global economy, not a narrowing of the global economy. And I can't think of another person who has a clearer view on not just what AI is, but the infrastructure around it. The infrastructure that is necessary to build around it. And because so many of the major hyperscalers are utilizers of what Nvidia creates and the whole engagement around the infrastructure around AI, the potential of AI, I think we have a great voice to listen to this morning. So Jensen once again thank you. This is his first time here at the World Economic Forum in Davos and I know you have a really busy schedule so thank you for taking that time.
So let me go right into it. Why do you believe that AI has the potential to be that significant engine of growth and what makes this moment, this technology different than past technology cycles?
Yeah, this is, first of all when you think about AI and you're interacting with AI in all these different ways, ChatGPT of course, using Gemini of course, using Anthropic Claude of course, and the magical things that it could do. It's helpful to reason back to the first principles of fundamentally what is happening to the computing stack. This is a platform shift. A platform is something where applications are built on top of. And this is a platform shift like the platform shift to PCs. New applications were developed to run on a new type of computer, a platform shift to the internet, a new type of computing platform hosted all kinds of new applications, a platform shift to mobile cloud. In each and every one of these platform shifts, the computing stack was reinvented and new applications were created. This is a new platform shift in the sense that today you're using ChatGPT. It's important to understand that itself is an application but very importantly new applications will be built on top of ChatGPT. New applications will be built on top of Anthropic Claude for example. And so it's a platform shift in that way. AI is really easy to understand if you realize what it can do that you could never do before. Software in the past was effectively pre-recorded. Humans would type and describe the algorithm or the recipe for the computer to execute. It was able to process structured information, meaning you've got to put the name, the address, their account number, their age, where they live. You create these structured tables that software would then go and retrieve information from. We call it SQL queries. SQL is the single most important database engine the world's ever known. Almost everything ran on SQL before. Now we have a computer that can understand unstructured information, meaning it could look at an image and understand it. It could look at text and understand it. It's completely unstructured. It could listen to sound and understand it, understand the meaning of it, understand the structure of it, and reason about what to do about it. And so for the first time we now have a computer that is not pre-recorded but it's processed in real time meaning that it's able to take the context of the circumstance, whatever the environmental information, the contextual information and whatever information you give it. It could reason about what is the meaning of that information and reason about your intent which could be described in a really unstructured way. You describe it however you want to describe it. We call it prompts. But you describe it however you like to describe it and to the extent that it can understand your intention, it could perform a task for you. Now the important thing about this is that because we're reinventing that entire computing stack the question is what is AI? When you think about AI you think about the AI models but it's really important to understand industrially AI is actually essentially a five layer cake. At the bottom is energy. AI because it's processed in real time and it generates intelligence in real time. It needs energy to do so. Energy is the first layer. The second layer is the layer that I live in. It's chips. Chips and computing infrastructure. The next layer above it is the cloud infrastructure, the cloud, the cloud services. The layer above that is the AI models. This is where most people think AI is. But don't forget that in order for those models to happen, you have to have all of the layers underneath it. But the most important layer and this is the layer that's happening right now. The reason why last year was an incredible year frankly for AI is that the AI models made so much progress that the layer above it which is ultimately the layer that we all need to succeed, the application layer above that. And so this application layer could be in financial services, it could be in healthcare, it could be in manufacturing. This layer on top ultimately is where economic benefit will happen. But the important thing though because this computing platform requires all of the layers underneath it. It has started and you guys are everybody's seeing it right now has started the largest infrastructure buildout in human history. We're now a few hundred billion dollars into it.
We're a few hundred billion dollars into it. Larry and I, we get the opportunity to work on many projects together. There are trillions of dollars of infrastructure that needs to be built out. And it's sensible. It's sensible because all of these contexts have to be processed so that the AI, so that the models can generate the intelligence necessary to power the applications that ultimately sit on top. And so when you go back and when you reason about it layer by layer by layer and you realize that the energy sector is now seeing extraordinary growth. The chip sector, TSMC just announced they're going to build 20 new chip plants. Foxconn working with us and Wistron and Quanta building 30 new computer plants which then go into these AI factories. So we have chip factories, computer factories and AI factories all being built around the world.
And memory. Right. Exactly. Those chip fabs, Micron has started investing $200 billion in the United States. SK Hynix is doing incredibly. Samsung is doing incredibly. You could see that entire chip layer growing incredibly today. And now of course we pay a lot of attention to the model layer but it's really exciting that the layer above them is really doing fantastically. And now one indicator is where the VC funding going into. Last year 2025 was one of the largest years in VC funding ever. And last year most of the funding went to what is called AI native companies. These are companies in healthcare, the company in robotics, incoming manufacturing, financial services, all of the large industries in the world. You're seeing huge investments going into those AI natives because for the first time, the models are good enough to build on top of.
So, let's just dive a little further. Obviously, everybody, I'm sure, uses their own chatbot and getting information. But you're talking about the dispersion of AI is going to be the key. Let's talk about it like go into a little more upside ideas related to the dispersion of it in the physical world. You mentioned obviously healthcare is a great example of that. But where do you see the transformational opportunities in areas like transportation or science?
Well, last year I would say three major things happened in AI in the AI technology layer, the model layer. The first one is that the models themselves started out being curious and interesting but they hallucinated a great deal and last year you could, we can all reasonably accept that these models are better grounded. They could do research. They can reason about circumstances that maybe they weren't trained on. Break it down into step-by-step reasoning steps and come up with a plan to answer your question. Do your research or perform the task. So last year we saw the evolution of language models becoming AI systems that we call agentic systems. Agentic AI. The second major breakthrough is the breakthrough of open models. Several years ago, was it a year ago, DeepSeek came out and DeepSeek was, a lot of people were quite concerned about it. Frankly, DeepSeek was a huge event for most of the industries, most of the companies around the world because it's the world's first open reasoning model. Since then a whole bunch of open reasoning models have emerged and open models has enabled companies and industries, researchers, educators, universities, startups to be able to use these open models to start something and create something that's domain specific or specialized for their needs. The third area that had enormous progress last year was the concept of physical intelligence, of physical AI. AI that understands not just language but AI understands, you know, if you will, nature. And it could be AI that understands the physical world here. AIs that understand proteins, chemicals, natural physics, for example, fluid dynamics, particle physics, quantum physics, AIs that are now learning all these different structures and different languages, if you will. Proteins is essentially a language. And so all of these AIs are now making such enormous progress that these industrial companies, whether it's manufacturing or drug discovery are really making great progress and one of the great indicators is a partnership that we had with Eli Lilly that they realize now that AI has made such extraordinary progress in understanding the structure of proteins and the structure of chemicals. Essentially be able to interact and talk to the proteins like we talk to ChatGPT, we're going to see some really big breakthroughs.
So all these breakthroughs raises concerns about the human element. You and I have had many conversations on this but we need to tell the whole audience there is a huge concern that AI is going to displace jobs. And you've been arguing the opposite. Obviously the buildout of AI as you talked about the biggest infrastructure buildout in history is going to occur which...
Energy is creating jobs, industries creating jobs, the infrastructure layers creating jobs, land, power, and shell jobs. I mean right, it's incredible.
So let's get into that a little more detail so you actually believe we're going to face labor shortages and so how do you see that AI and robotics changing the nature of work rather than eliminating it?
There's several different ways that we could think through it. First of all, this is the largest infrastructure buildout in human history. That's going to create a lot of jobs. And it's wonderful that the jobs are related to tradecraft. And we're going to have plumbers and electricians and construction and steel workers and network technicians and people who install and fit out the equipment and all of these jobs we're going to, in the United States we're seeing quite a significant boom in this area. Salaries have gone up nearly doubled.
