Davos 2026: Conversation with Jensen Huang, President and CEO of NVIDIA

Linda Lacina, Meet The Leader Welcome to Meet the Leader, the podcast where top leaders share how they’re tackling the word’s biggest challenges. In today’s special episode, recorded at the World Economic Forum annual meeting in NVIDIA CEO Jensen Huang talks to Larry Fink, the Forum’s own Interim Co-Chair and BlackRock’s President and CEO. It’s a conversation tech and innovation - and how we can deploy it responsibly and at scale. I’ll let them get right into it.

Laurence Fink, WEF: 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. 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). Nvidia's total return for its shareholders has 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 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. That is just a great indication of Jensen's leadership, the positioning of NVIDIA, and 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.

Jensen Huang, NVIDIA: I appreciate that. My only regret was at the IPO, after the IPO I wanted to buy my parents something nice. So, I sold NVIDIA stock at a valuation of $300 million. The company was at an evaluation of $30 million and I bought them a Mercedes S-Class. It is the most expensive car in the world. They regret it.

Laurence Fink, WEF: Do they still have it? Oh, sure. Yeah, they still haven't. Good. I just want to say 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.

We need to talk about how it's going to reshape productivity, labor, infrastructure, across virtually every other sector. But importantly, how it is going to re-shape 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 globally economy.

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.

The whole engagement around the infrastructure, around AI, the potential of AI, I think we have a great voice to listen to today.

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 busy schedule, so thank you for taking the time. I appreciate that.

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?

Jensen Huang, NVIDIA: Firstly, when you think about AI and you're interacting with AI in all these different ways through ChatGPT, Gemini, and Claude. In 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. Platform shifts to the internet: a new type of computing platform, hosted all kinds of new applications, a platform shift to mobile cloud. In each 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 you're using ChatGPT and 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 easy to understand if you realize what it can do that you could ever 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 sequel queries. Sequel is the single most important database engine the world's ever known.

Almost everything ran on sequel queries before now. Now we have a computer that can understand unstructured information, meaning it can 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?

You're asked, when you think about AI, you think about the AI models, but it's important to understand industrially that 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 computer chips and computing infrastructure. The next layer above it is the cloud infrastructure, the cloud services. The layer above that is the AI models. This is where most people think AI is. Don't forget that in order for those models to happen, you have to have all 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 is the application layer above that.

This application layer could be in financial services, it could be health care, it could 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 everybody seeing it right now, has started the largest infrastructure build out in human history. We're now a few hundred billion dollars into it. 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 must be processed so that the AI 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 and you realize that the energy sector is now seeing extraordinary growth. In the chip sector TSMC just announced they're going to build 20 new chip plants. Foxconn, Wishtron and Quanta are building 30 new computer plants, which then go into these AI factories. Micron has started investing $200 billion in the United States. SK Hynix and Samsung is doing incredibly well in investment too. So, we have chip factories, computer factories, and AI factories, all being built around the world. You could see that entire chip layer growing incredibly today.

And now, of course, we pay a lot of attention to the model layer. It’s exciting that the layer above them is really doing fantastically. One indicator of growth is where the VC funding is 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, robotics, manufacturing, financial services – all the large industries in the world. You're seeing huge investments going in to those AI natives because for the first time the models are good enough to build on top of.

Laurence Fink, WEF: So, let's just dive a little further. Obviously, everybody, I'm sure, uses their own chatbot. But you're talking about the dispersion of AI, which is going to be the key. Let's talk about it and the more upside ideas related to the dispersion of it in the physical world. You mentioned, obviously, healthcare. Where do you see the transformational opportunities in areas like transportation or science?

Jensen Huang, NVIDIA: Last year, I would say three major things happened in AI and 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, these models became better grounded. They could do research, reason about circumstances that maybe they weren't trained on, break it down into step-by-step reasoning and come up with a plan to answer questions, and perform the task. So, last year we saw the evolution of language models becoming AI systems that we call agentic systems or agentic AI.

The second breakthrough is in open models. Last year DeepSeek came out and it was for a lot of people something to be quite concerned about. Frankly, DeepSeek was a huge event for most of the industries, most of 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 have enabled companies, industries, researchers, educators, universities, startups, to be able to use these to create something that's domain-specific or specialized for their needs. The third area is the concept of physical intelligence, of physical AI. AI that understands not just language, but AI that understands nature. AI that understands the physical world here, AIs that understand proteins, chemicals, natural physics, fluid dynamics, particle physics, quantum physics. AIs that are now learning all these different structures and different languages. Proteins is essentially a language. All these AIs are now making such enormous progress that these industries, 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 Lily where they saw that AI has made such extraordinary progress in understanding the structure of proteins and the structure of chemicals. Here, AI is essentially interacting and talking to the proteins like we talked to ChatGPT. We’re going to see really big breakthroughs.

Laurence Fink, WEF: So, all these breakthroughs raise 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 build out of AI as you talked about, the biggest infrastructure build out in history is going to occur. So, let's get into that in a little more detail. So, you actually believe we're going to face labor shortages. So, how do you see that AI and robotics changing the nature of work rather than eliminating it?

Jensen Huang, NVIDIA: There are different ways that we can think through it. Firstly, this is the largest infrastructure build-out in human history. That's going to create a lot of jobs. And it's wonderful that the jobs are related to tradecraft. 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 – all these jobs in the United States, we're seeing quite a significant boom in this area.

Salaries have gone up, nearly doubled, and so we're talking about six figure salaries for people who are building chip factories or computer factories or AI factories. We have a great shortage in that, and I'm really delighted to see so many people in so many countries really recognizing this important area. Everybody should be able to make a great living. You don't need to have a PhD in computer science to do so. I'm delighted to that.

The second thing to realize, and so we theorize about the automation of tasks and what is the implication to jobs. I'll just offer some anecdotes. These are real world anecdotes of what has happened. Ten years ago, one of the first professions that everybody thought was going to get wiped out, was radiology. And the reason for that was the first AI that became superhuman in capability was Computervision. And one of the largest applications of Computervision is studying scans by radiologists. Well, 10 years later, it is true that AI has now completely permeated and diffused into every aspect of radiology. And it is true that radiologists use AI to study scans now. The impact is 100%. The impact is completely real. However, not surprisingly, if you reason from first principles, not surprisingly the number of radiologists have gone up.

Laurence Fink, WEF: Is that because of the lack of trust of AI or is that because the human interaction with the results of AI has a better outcome?

Jensen Huang, NVIDIA: Exactly. The reason for that is because a radiologist, their job, the purpose of their job is to diagnose disease, to help patients diagnose disease. That's the purpose of their job. The task of the job includes studying scans. The fact that they are able to study scans, now infinitely fast, allows them to spend more time with patients diagnosing their disease. They are now interacting with the patients, interacting with other clinicians.

As a result of that, the number of patients that the hospital can see has gone up. Because there are a lot of people waiting a long time to get their scans done. And so now, because the number of patients have gone up, the revenues of the hospital has gone up, so they can hire more radiologists.

This is the same thing as happening to nurses. We're five million nurses short in the United States. As a result of using AI to do the charting and the transcription of the patient visits, nurses spent half of their time charting. And now they could use AI and one particular company, Abridged, a partner of ours, is doing incredible work. As a result of that, the nurses could spend more time visiting patients. And because you can now see more patients, and we're less bottlenecked, and more patients could get into the hospital sooner. As a results, hospitals do better. They hire more nurses. And so, AI is increasing their product, AI is increase in their productivity. As a result, the hospitals are doing better. They want to hire more people.

These are two perfect examples. Now, the easiest way to think about what is the impact of AI on a particular job is to understand what is the purpose of the job. And what is the task of the job? If you just put a camera on the two of us and just watched us, you would probably think the two of us are typists because I spend all of my time typing. And so, if AI could help us type, then we would be out of jobs. But obviously, that's not our purpose. And so, the question is, what is the purpose of your job in the case of radiologists and nurses? It is to care for people. That purpose is enhanced and made more productive because the task has been automated. And so, to the extent that you can reason about each one of the people's purpose versus the task.

Laurence Fink, WEF: Let's move this beyond the developed economies. Help me understand how AI is has broadened the world and helped the world. I read an Anthropic piece this past weekend that basically said the utilization of AI most recently is very dominant by the educated society. And there you even see of the educated component of each society being heavily more utilized. Obviously, they're using it against their own model plot. It may have its own biases. So, how do we ensure that AI is a transformational technology, maybe like what Wi-Fi and 5G was for the emerging world? And when you intersect that, what does it mean for the emerging world and how do we broaden the global economy? And two, getting back to the whole job situation with robotics and AI, there is going to be some substitution there. And there's substitution in the United States already going on. We may be creating more plumbers and electricians, but we probably need less analysts at financial institutions. Lawyers need less people because they're able to accumulate the data faster. So, let's just pivot on to the emerging world for a second, and the developing world. How do you see that play out?

Jensen Huang, NVIDIA Well, AI is infrastructure, and there is not one country in the world, I can't imagine, that you don’t need to have AI as part of your infrastructure. Because every country has its electricity, roads, you should have AI as part your infrastructure. Of course, you could always import AI, but AI is not so incredibly hard to train these days. And because there are so many open models, these open models, with your local expertise, should be able to create models that are helpful to your own country. So, I really believe that every country should get involved to build AI infrastructure, build your own AI, take advantage of your fundamental natural resource, which is your language and culture, develop your AI, continue to refine it, and have your national intelligence be part of your ecosystem. That's number one.

And number two, remember, AI is super easy to use. It is the easiest software to use in history, and that's the reason why it's the fastest growing and most rapidly adopted. I mean, in just two or three years, it's coming up to almost a billion people. I think, first of all, Claude is incredible. Anthropic has made huge progress, huge leap in developing Claude. We use it all over our company. The coding capability of Claude, its reasoning capability, its ability, just incredible. And anybody who has a software company really ought to get involved in and use it.

On the other hand, ChatGPT is probably the most successful consumer AI in history, and its ease of use and its approachability means everybody should get involved. And whether it's somebody in a developing country or a student, it is very clear that it is essential to learn how to use AI, how to direct an AI, how to prompt an AI, how to manage an AI, how to guardrail the AI, and evaluate the AI. These skills are no different than leading people, managing people, things that you and I do all the time. So, in the future, instead of biological, carbon-based Ais, in the feature we're also going to have digital and silicon versions of AIs. We must manage them. They're just part of our digital workforce.

I would advocate that for the developing countries, build your infrastructure, get engaged in AI, and recognize that AI is likely to close the technology divide, because it is so easy to use and so abundant and so accessible. And so, you know, I'm optimistic about the potential of AI to lift the countries that are emerging. And for many people, who haven't had a computer science degree, all of you can be programmers now. In the past, we had to learn how to program a computer. Now you program a computer by saying to the computer “how do I program you?” And if you don't know how to use an AI, just go up to the AI and say, “I don't how to use an AI”. And it would explain it to you. And you say, “I'd like to write a program to create my own website. How do I do that?” And it would do it. And so it is that easy to use, and that's, of course, the incredible power of AI, which is exciting.

Laurence Fink, WEF: We're sitting here in Europe. When we were talking about a lot of companies, we mentioned a lot of US companies and Asian companies. Talk to us about how AI and the success of Europe and the future of Europe can intersect. And how do you see NVIDIA play that role here in Europe?

Jensen Huang, NVIDIA: Well, NVIDIA has the benefit of working with every AI company in the world, because we're low in the infrastructure layer, and we power AI across the board. And we power AI languages, their biology, their physics, their world models and their manufacturing and robotics. And the thing that's really quite exciting for Europe is, your industrial base is so strong. The industrial manufacturing base in Europe is incredibly strong. This is your opportunity to now leap past the era of software. The United States really led the era of software.

AI is software that doesn't need to write software. You don't write AI, you teach AI. And so get in early now so that you can now fuse your industrial capability, your manufacturing capability, with artificial intelligence. That brings you into the world of physical AI or robotics.

You know robotics is a once in a generation opportunity for the European nations and because their industrial base is really, really strong. The other thing to realize is that so much of the deep sciences are still very, very strong here in Europe. And the deep sciences now have the benefit of applying artificial intelligence to accelerate your discovery. And so, you must get serious about increasing your energy supply so that you could invest in the infrastructure layer, so that you could have a rich ecosystem of artificial intelligence here in Europe.

Laurence Fink, WEF: So, what I'm hearing is we're far from an AI bubble. The question is, are we investing enough? Let's turn it around, because there's so many people talking about a bubble, but the question is: Are we invested enough to do what we need to do to broaden the global economy?

Jensen Huang, NVIDIA: One good test on the AI bubble is to recognize that NVIDIA has millions of Nvidia GPUs in the cloud. We’re in every cloud, we're used everywhere. And if you try to rent an NVIDIA GPU these days, it's so incredibly hard. And the spot price of GPU rentals is going up. Not just the latest generation, but two-generation-old GPUs. The spot price of rentals are going up. And the reason for that is because the number of AI companies that are being created and the number of companies shifting their R&D budget is changing. Lily’s a great example. Three years ago, most of their R& D budget was probably wet labs. And now there is a big AI supercomputer that they've invested in, the big A.I. Lab, so R&D will shift towards AI.

The AI bubble comes about because the investments are large. And the investments are large because we have to build the infrastructure necessary for all of the layers of AI above it. And so, I think the opportunity is really quite extraordinary. And everybody ought to get involved and engaged. We need more energy. We need land power and shell. We need more trade-scale workers, and in fact, that population of workforce is so strong here in Europe. In a lot of ways, the United States lost that in the last 20, 30 years. It's still incredibly strong here in Europe, it's an extraordinary opportunity you have to take advantage of. We see that investment opportunities and investment scale is going up. 2025 was the largest investment year in VC history, over a $100 billion around the world. Most of it was AI natives. And so, these AI companies are building basically the application layer above and they're going to need infrastructure. They're going to need our investment to go build this future.

Laurence Fink, WEF: And I believe it's going to be a great investment for pension funds around the world to be a part of that growth. And this is one of my messages as so many political leaders we need to make sure that the average pensioner, the average saver is a part of that growth. If they're just watching it from the sidelines, you know, they're going to feel left out.

Jensen Huang, NVIDIA: We want to see investment in infrastructure, infrastructure is a great investment. This is the single largest infrastructure build out in human history.

Laurence Fink, WEF: Get involved. Hopefully everybody in the audience and everybody on the web streaming is seeing the power of Jensen Huang as a leader, not just a leader in technology and AI, but a leader in business, and also a leader in heart and soul, which is really important today.