FULL DISCUSSION: Nvidia CEO Huang on AI’s Future: Transforming Jobs, Economy & Global Growth — AC1E

[00:00:00] Speaker 1: Good morning, everyone. It's really nice to be back here at Congress Hall. Hopefully everybody had a good day yesterday and are enjoying it today. It is my real pleasure to introduce Jensen Wong, 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. Now, 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 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, but 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. [00:02:02] Jensen Wong: And so, I sold NVIDIA stock at a valuation of $300 million. The company was at a valuation of $300 million. And I bought them a Mercedes S-Class. It is the most expensive car in the world. Now, they regret it. They regret it. Do they still have it? Oh, sure. Yeah, they still have it, yeah. Good. Let me go into the subject matter now. But I just want to say, you know, the [00:02:29] Speaker 1: debate on A.I. is about how it's going to change the world and the global economy. Today, I want to talk about how A.I. can add to the world economy and how A.I. can add to the world economy. And how A.I. 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 A.I. 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 A.I. 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 the video creates and the whole engagement around the infrastructure around A.I., the potential of A.I., I think we have a great voice to listen to this afternoon or 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. I appreciate that. So let me go right into it. Why do you believe that A.I. has the potential to be that significant engine of growth? And what makes this moment, this technology different than past technology cycles? [00:04:38] Jensen Wong: Yeah, this is a first of all, when you when you think about A.I. and you're interacting with A.I. in all these different ways. ChatGPT, of course, using Gemini, of course, using Anthropic Cloud, 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 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 Cloud, for example. 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 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, you know, 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. 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? 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 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, it has started the largest infrastructure buildout in human history. We're now a few hundred billion dollars into it. So 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, 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 Wishtron 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. And memory, right, exactly. Those chip labs. 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 are 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, the company in manufacturing, financial services, all of 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. [00:11:28] Speaker 1: So let's just dive a little further. Obviously, everybody, I'm sure, uses their own chat bot 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, that healthcare is a great example of that. But where do you see the transformational opportunities in areas like transportation or science? [00:12:00] Jensen Wong: 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, 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, DeepSeq came out? That's true. And DeepSeq was, a lot of people were quite concerned about it. Frankly, DeepSeq 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. 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, 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 in different languages, if you will. Proteins is essentially a language. And so, all of 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 Lilly that they realize now that AI has made such extraordinary progress in understanding the structure of proteins and the structure of chemicals, essentially being able to interact, essentially being able to interact and talk to the proteins, like we talked to ChadGBT, we're going to see some really big breakthroughs. [00:14:56] Speaker 1: 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 build-out of AI, as you've talked about, the biggest infrastructure build-out in history is going to occur, which is... [00:15:20] Jensen Wong: Energy is creating jobs. Chips, industries, creating jobs. The infrastructure layer is creating jobs. Land, power, and shell. Jobs, jobs, jobs. I mean, right? It's incredible. [00:15:31] Speaker 1: So let's get into that in 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? [00:15:43] Jensen Wong: There's several different ways that we could think through it. First of all, 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. 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, 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. And 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. You know, everybody should be able to make a great living. You don't need to have a Ph.D. in computer science to do so. And so I'm delighted to see that. The second thing to realize, and so we theorize about the automation of tasks and things like that, and what is the implication to jobs. You know, I'll just offer some anecdotes. These are real-world anecdotes of what has actually happened. Remember, 10 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 computer vision. And one of the largest applications of computer vision 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%, and the impact is completely real. However, not surprisingly, I say not surprisingly, if you reason from first principles, not surprisingly, the number of radiologists have gone up. [00:18:04] Speaker 1: Is that because of the lack of trust of, or is that because the human interaction with the results of AI is a better outcome? [00:18:15] Jensen Wong: 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, interacting with the patients, interacting with other clinicians. Well, surprisingly, also not surprisingly, actually, as a result of that, the number of patients that the hospital can see has gone up because, you know, 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, they hire more radiologists. This is the same thing that's happening to nurses. We're 5 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. Documenting. And now they could use AI technology in one particular company, a bridge, a partner of ours doing incredible work. As a result of that, the nurses could spend more time visiting patients. Give them a touch. That's right. And because you could now see more patients and we're no longer less bottlenecked by the number of nurses, more patients could get into the hospital sooner. As a result, hospitals do better. They hire more nurses. And so surprisingly, AI is increasing their product. Not surprisingly. AI is increasing their productivity. As a result, the hospitals are doing better. They want to hire more people. You have too many people waiting too long to get into hospitals. And so these are two perfect examples. Now, the easiest way to think about whether, what is the impact of AI on a particular job is to understand whether the job, 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 automate so much word prediction and 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, is to care for people? And 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, I think it's a helpful framework. [00:21:00] Speaker 1: Let's move this beyond the developed economies, helping understand how AI is it abroad in the world and help 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 they're even seeing the educated component of each society being heavily more utilized. Obviously, they're using it against their own model plot, so it may have its own biases. So, how do we ensure that AI is a transformational technology, maybe like what a Wi-Fi and 5G was for the emerging world? And when you intersect that, what does it mean for the emerging world and jump? How do we broaden the global economy? And two, you know, 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, you know, 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? [00:22:30] Jensen Wong: Well, first of all, AI is infrastructure, and there's not one country in the world I can't imagine that you need to have AI as part of your infrastructure. Because every country has its electricity, you have your roads, you should have AI as part of 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, you should be able to create models that are helpful to your own country. And 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. So, I think 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 a couple of two, three years, it's coming up to almost a billion people. I think, first of all, Claude is incredible. Anthropic has made a huge progress, huge leap in developing Claude. We use it all over our company. The coding capability of Claude, its reasoning capability, its ability is just really incredible. And anybody who has a software company really ought to get involved 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, I think everybody should get involved. 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, 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, you know, carbon-based AIs, in the future, we're also going to have digital versions of AIs, silicon versions of AIs. And we'll have to manage them. They're just part of our digital workforce, if you will. And so 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 actually fairly optimistic about the potential of AI to lift the countries that are emerging. And for many people who haven't had computer science degree, all of you can be programmers now, you know. And so 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, you know. And if you don't know how to use an AI, just go up to the AI and say, I don't know how to use an AI. How do I use an AI? And it would explain it to you. And, you know, you say, I like to write a program to create my own website. How do I do that? And it says, it would ask you a whole bunch of questions about what kind of website you would like to build and then write you the code. And so, it is that easy to use. And that's, of course, the incredible, you know, power of AI, which is exciting. Two quick questions, then we're going to run out of time. [00:26:18] Speaker 1: We're sitting here in Europe. When we were talking about a lot of companies, we mentioned a lot of U.S. 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? [00:26:38] Jensen Wong: Well, NVIDIA has the benefit of working with every AI company in the world. And because we're low in the infrastructure layer and we power AI across the board. And we power AI that are languages, you know, their biology, their physics, their world models and related to manufacturing and robotics. And the thing that's really, really quite exciting for Europe is, remember, 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. And 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 whether it's, you know, well, all of the countries that I visit here, 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. [00:28:05] Speaker ?: Right. [00:28:06] Jensen Wong: And the deep sciences now have the benefit of applying artificial intelligence to accelerate your discovery. And so I think that it's fairly certain that you have to 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. [00:28:31] Speaker 1: 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 are so many people talking about a bubble. But the question is, what I'm hearing from you is, you know, are we investing enough to do what we need to do to broaden the global economy? [00:28:51] Jensen Wong: And so one good test on the AI bubble is to recognize that NVIDIA has now millions of NVIDIA GPUs in the cloud. We're in every cloud, you know, 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, the number of companies shifting their R&D budget. Lillia is a great example. Three years ago, most of their R&D budget, all of their R&D budget, it was probably wet labs. Notice the big AI supercomputer that they've invested in, the big AI lab. Increasingly, that R&D budget is going to shift towards AI. And so 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. Everybody ought to get engaged. We need more energy. I think that we all recognize that. We need more 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, you know, 20, 30 years. But it's still incredibly strong here in Europe. It's an extraordinary opportunity you ought to take advantage of. And so I would, you know, I know that where Larry and I work, we see the investment opportunities and the investment scale is going up. The number of startups, as I mentioned earlier, that last 2025, the largest investment year in VC history, over $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, you know, and go build this future. [00:31:12] Speaker 1: And I actually believe it's going to be a great investment for pension funds around the world to be a part of that, to grow with this AI world. And this is one of my messages to so many political leaders. We need to make sure that the average pensioneer, 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. [00:31:33] Jensen Wong: And we want to invest in infrastructure. Right. In fact, infrastructure is a great investment opportunity. This is the single largest infrastructure build-out in human history. [00:31:43] Speaker 1: Get involved. We're out of time. Hopefully, everybody in the audience and everybody on the web streaming seeing the power of Jensen Wong 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, having that leadership from the heart and the soul. So, thank you, everyone. [00:32:07] Jensen Wong: Thank you, everyone. [00:32:36] Speaker ?: Thank you, everyone.