Conversation with Jensen Huang, President and CEO of NVIDIA

21 Jan 2026 10:30h - 11:00h

Session at a glance

Summary

This discussion between BlackRock CEO Laurence Fink and NVIDIA CEO Jensen Huang at the World Economic Forum focused on AI’s transformative potential for the global economy and its role as a foundational technology. Huang began by explaining that AI represents a fundamental platform shift comparable to the transitions to PCs, internet, and mobile computing, but with the unique capability to process unstructured information and generate real-time intelligence rather than executing pre-recorded software. He described AI as a five-layer infrastructure comprising energy, chips and computing, cloud services, AI models, and applications, emphasizing that this has triggered the largest infrastructure build-out in human history worth trillions of dollars.

Regarding employment concerns, Huang argued that AI will create rather than eliminate jobs, citing examples from radiology and nursing where AI automation of tasks has actually increased demand for professionals by enhancing their productivity and allowing them to focus on human-centered work. He emphasized that the massive infrastructure development is creating numerous jobs in trades like construction, electrical work, and manufacturing, with salaries nearly doubling in some sectors. For developing countries, Huang advocated that AI could close the technology divide due to its accessibility and ease of use, encouraging nations to build their own AI infrastructure and leverage their cultural and linguistic resources.

The discussion concluded with both leaders rejecting the notion of an AI bubble, instead suggesting that current investments may be insufficient given the enormous demand for AI infrastructure and the transformative opportunities ahead for global economic growth.

Keypoints

Major Discussion Points:

– AI as a transformational platform shift: Huang explains AI as a fundamental computing platform change comparable to the PC, internet, and mobile revolutions, describing it as a five-layer infrastructure (energy, chips, cloud services, AI models, and applications) that requires the largest infrastructure build-out in human history.

– AI’s impact on employment and labor markets: Rather than eliminating jobs, both speakers argue AI will create labor shortages and new opportunities, particularly in skilled trades needed for infrastructure development, while augmenting rather than replacing human workers in fields like healthcare.

– Global democratization of AI technology: Discussion of how AI can broaden economic opportunities worldwide, especially for developing countries, through accessible open models and the ease of AI interaction that doesn’t require traditional programming skills.

– Massive investment opportunities and infrastructure needs: Emphasis on the trillions of dollars in infrastructure investment required, from energy and chip manufacturing to AI factories, presenting significant opportunities for pension funds and institutional investors.

– Europe’s strategic positioning in the AI revolution: Specific focus on how Europe can leverage its strong industrial manufacturing base and deep science capabilities to compete in the AI era, particularly in physical AI and robotics applications.

Overall Purpose:

The discussion aims to reframe the AI narrative from one of concern about job displacement and economic disruption to one of unprecedented opportunity for global economic growth, infrastructure investment, and technological advancement. Fink and Huang seek to educate the World Economic Forum audience about AI’s potential to broaden rather than narrow economic participation worldwide.

Overall Tone:

The conversation maintains a consistently optimistic and enthusiastic tone throughout. Both speakers demonstrate genuine excitement about AI’s potential, with Huang serving as an educational voice explaining complex technical concepts in accessible terms, while Fink acts as an engaged interviewer focused on practical economic implications. The tone is collaborative and forward-looking, with no significant shifts in mood or perspective during the discussion.

Speakers

– Laurence Fink: CEO of BlackRock (financial services company), moderating the discussion at the World Economic Forum in Davos

– Huang Jen-Hsun: CEO and leader of NVIDIA (technology/AI company), expertise in artificial intelligence, computing infrastructure, and technology platforms

Additional speakers:

None identified in the transcript.

Full session report

Comprehensive Discussion Report: AI’s Transformative Potential for Global Economic Growth

Executive Summary

This World Economic Forum discussion between BlackRock CEO Laurence Fink and NVIDIA CEO Jensen Huang examined artificial intelligence’s role as a foundational technology driving global economic transformation. The conversation addressed AI’s technical capabilities, infrastructure requirements, employment implications, and investment opportunities. Both leaders demonstrated strong consensus on AI’s potential to broaden economic participation worldwide, with current investments potentially insufficient given the enormous infrastructure demands ahead.

AI as a Fundamental Platform Transformation

Technical Foundation and Capabilities

Huang established AI’s revolutionary nature by distinguishing it from traditional computing paradigms. He explained that while historical software was “effectively prerecorded,” AI represents the first computing system capable of processing unstructured information in real-time. This fundamental capability allows AI 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.”

This technical foundation underpins what Huang characterized as a complete platform shift comparable to transitions from mainframes to PCs, PCs to internet, and internet to mobile cloud computing. However, he emphasized that AI’s unique capability to understand and process unstructured data represents a more fundamental change than previous technological transitions.

Five-Layer Infrastructure Architecture

Huang described AI’s infrastructure requirements as “essentially a five-layer cake” comprising:

1. Energy infrastructure – forming the foundational layer

2. Chips and computing infrastructure – providing the computational backbone

3. Cloud infrastructure – enabling distributed access and processing

4. AI models – delivering the intelligence capabilities

5. Applications – representing the most crucial layer where value is realized

This framework revealed why AI development requires what Huang termed “the largest infrastructure build-out in human history,” with investment requirements reaching trillions of dollars across chip factories, computer factories, and AI factories globally.

Employment Impact and Labour Market Transformation

Job Enhancement and Creation

The discussion revealed optimistic perspectives on AI’s employment implications. Huang presented compelling evidence from healthcare, noting that despite widespread predictions of job losses, “the number of radiologists have gone up” over the past decade following AI integration. He attributed this to AI’s role in automating routine tasks while enhancing job purpose, allowing radiologists to focus on patient diagnosis rather than merely studying scans.

Fink acknowledged that while some jobs may be displaced, new opportunities are simultaneously created. Both speakers agreed that the net effect could be positive, particularly given the massive infrastructure development requirements.

Infrastructure-Driven Job Creation

Both speakers highlighted that AI infrastructure development is generating substantial employment opportunities in skilled trades. Huang noted that sectors such as plumbing, electrical work, and construction are experiencing significant demand, with salaries “nearly doubling” in some areas and reaching six-figure levels. He mentioned that nurses are “five million short in the United States,” indicating substantial job opportunities in essential services.

The infrastructure boom requires extensive human capital across multiple industries. Huang cited specific examples: TSMC building 20 new chip plants, Foxconn, Wistron, and Quanta building 30 new computer plants, and Micron investing $200 billion in the United States.

Framework for Analyzing Job Impact

Huang provided a practical analytical framework distinguishing between job “purpose” and job “tasks.” He noted that while external observation might suggest that he and Fink are “typists” due to their computer usage, their actual purpose involves strategic leadership and decision-making. This framework suggests that AI typically automates routine tasks while enhancing workers’ ability to fulfill their core professional purpose.

Global Democratization and Development Opportunities

AI as National Infrastructure

Both speakers advocated for treating AI as essential national infrastructure comparable to electricity and transportation systems. Huang argued that “every country should develop their own AI capabilities using local language and culture,” emphasizing that AI development should reflect national characteristics and priorities.

This perspective suggests that AI’s accessibility could enable developing countries to leapfrog conventional development stages. Huang noted that AI represents “the most accessible programming tool in history” because natural language interaction eliminates traditional coding barriers.

Addressing Digital Divides

Fink raised concerns about ensuring broad participation in AI’s economic benefits, noting the importance of making sure “average pensioners and savers participate in AI growth.” Huang emphasized that AI’s intuitive interface could actually “close the technology divide” by making advanced computing capabilities accessible to broader populations without requiring traditional technical skills.

European Strategic Positioning

Industrial and Scientific Advantages

The discussion specifically addressed Europe’s competitive positioning in the global AI landscape. Huang identified significant opportunities for European nations to leverage their “strong industrial manufacturing base,” “deep science capabilities,” and “strong trade-skill workforce” in the AI era. He suggested that Europe could excel particularly in physical AI and robotics applications by combining traditional industrial strength with AI capabilities.

This analysis positioned Europe not as a follower in AI development but as a region with unique advantages that could be enhanced through AI integration. Huang emphasized that European scientific research capabilities, when combined with AI tools, could accelerate discovery and innovation across multiple fields.

Investment Landscape and Market Dynamics

Scale of Investment Opportunity

The discussion revealed the enormous scale of AI-related investment opportunities. Huang reported that 2024 saw “over $100 billion in VC funding, mostly directed toward AI-native companies” across healthcare, robotics, manufacturing, and financial services. This investment level reflects the comprehensive infrastructure requirements across multiple economic sectors.

Fink positioned this as a generational investment opportunity for institutional investors, particularly pension funds seeking long-term returns. The infrastructure requirements create investment opportunities across energy, manufacturing, real estate, and technology sectors.

Market Evidence of Genuine Demand

Both speakers rejected characterizations of current AI investment as speculative or bubble-driven. Huang provided concrete market evidence, noting that NVIDIA GPU rental prices are increasing even for “two-generation-old GPUs,” indicating genuine demand rather than speculative investment. The difficulty in securing GPU rental capacity suggests that demand significantly exceeds current supply.

Fink reframed the discussion from bubble concerns to investment adequacy, questioning “whether we’re investing enough in AI infrastructure rather than whether there’s an AI bubble.” This perspective suggests that current investment levels may be insufficient given the infrastructure requirements and economic opportunities ahead.

Key Areas of Consensus

Infrastructure Investment Imperative

The speakers demonstrated strong consensus on AI infrastructure representing unprecedented investment opportunities. They agreed that the five-layer infrastructure requirements justify massive capital deployment across energy, computing, cloud services, model development, and applications. Both viewed current investment levels as potentially inadequate rather than excessive.

Democratization and Accessibility

Both leaders emphasized the importance of ensuring broad participation in AI’s economic benefits. They agreed that preventing economic exclusion is crucial for social stability and continued AI development, with AI’s natural language interface serving as a democratizing force.

National Infrastructure Approach

Both speakers advocated for treating AI as essential national infrastructure requiring coordinated investment and strategic planning. This consensus between technology and financial leadership suggests mature understanding of AI’s systemic importance.

Notable Moments and Personal Insights

Huang shared a humorous anecdote about his early days at NVIDIA, mentioning that he sold NVIDIA stock to buy his parents a Mercedes, adding a personal touch to the discussion. He also noted this was his first time at Davos, explaining his fresh perspective on the global economic implications of AI development.

The conversation highlighted specific partnerships and breakthroughs, including collaborations with companies like Lilly and the recent DeepSeek breakthrough, demonstrating the rapid pace of AI advancement across industries.

Conclusion

This discussion presented AI as a transformative infrastructure opportunity requiring unprecedented coordination and investment. The strong consensus between financial and technology leadership on AI’s potential suggests that current policy and investment frameworks may need to scale up significantly for the transformation ahead.

The conversation’s emphasis on democratization and broad participation indicates that successful AI adoption requires ensuring that AI’s benefits reach beyond educated elites in developed economies. The speakers’ rejection of bubble concerns in favor of investment adequacy questions suggests that the primary risk may be underinvestment rather than speculation.

The discussion positioned AI as a generational opportunity for global economic growth, provided that societies can successfully navigate the employment transitions and infrastructure requirements that accompany such fundamental technological transformation. The path forward requires coordinated action across technology development, infrastructure investment, and international cooperation to realize AI’s potential for broadening global economic participation.

Session transcript

Laurence Fink

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 Jen-Hsun 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. But that is just a really great indication of Jen-Hsun’s leadership, the positioning of NVIDIA, and also it is a great statement about what the world believes in the future of NVIDIA.

So Jen-Hsun, congratulations on that journey, and I know we have many more years of that journey ahead of us.

Huang Jen-Hsun

Thank you. I appreciate that. My only regretwas 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 of $300 million, and I bought them a Mercedes S-Class. It is the most expensive car in the world. They regret it.

Laurence Fink

Do they still have it?

Huang Jen-Hsun

Oh, sure. Yeah, they still have it, yeah.

Laurence Fink

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 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 AI has the potential to be that significant engine of growth? And what makes this moment, this technology, different than past technology cycles?

Huang Jen-Hsun

Yeah, this is, first of all, when you think about AI and you’re interacting with AI in all these different ways, 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, 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 chat GPT, it’s important to understand that itself is an application, but very importantly, new applications will be built on top of chat GPT.

New applications will be built on top of anthropic cloud, 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 prerecorded. 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. 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 prerecorded, 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. 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 build-out in human history.

We’re now a few hundred billion dollars into it. We’re a few hundred billion dollars into it. Laurence Fink 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 and you realize that the energy sector is now seeing extraordinary growth.

The chip sector, TSMC just announced they’re going to manufacture, 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. And memory, right, exactly. Those chip labs.

Micron has started investing 200 billion dollars 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 health care, the company in robotics, incoming 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.

Laurence Fink

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, health care is a great example of that, but where do you see the transformational opportunities in areas like transportation or science?

Huang Jen-Hsun

Well, last year, I would say, 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 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, 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 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 and talk to the proteins.

Like we talked to Chat GPT, we’re gonna see some really great big breakthroughs.

Laurence Fink

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 talked about, the biggest infrastructure build-out in history is going to occur, which is

Huang Jen-Hsun

energy is creating jobs, ships, industries, creating jobs, the infrastructure layer is creating jobs, land power and shell, jobs, jobs, jobs. I mean, it’s incredible.

Laurence Fink

So let’s get into a little more detail. So you actually believe we’re gonna face labor shortages. And so how do you see that AI and robotics changing the nature of work rather than eliminating it?

Huang Jen-Hsun

There’s several different ways that we can think through it. First of all, this is the largest infrastructure build-out in human history. That’s gonna create a lot of jobs.

And it’s wonderful that the jobs are related to tradecraft. And we’re gonna 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 gonna, 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 PhD 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 a hundred percent, 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.

Laurence Fink

Is that because a lack of trust? Or is that because the human interaction with the results of AI is a better outcome?

Huang Jen-Hsun

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 to get their scans done. And so now, because the 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 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 spend half of their time charting. And now they could use AI technology in one particular company, a partner of ours, doing incredible work.

As a result of that, the nurses could spend more time visiting patients.

Laurence Fink

Human touch.

Huang Jen-Hsun

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 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, it’s 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.

Laurence Fink

Let’s move this beyond the developed economies. Helping understand how AI is it a broadened 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 against their own model caught, so 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? 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, 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?

Huang Jen-Hsun

Well, first of all, AI is infrastructure, and there is not one country in the world I can 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.

And so I think that’s number one. 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.

In just a couple of two, three years, it’s coming up to almost a billion people. I think, first of all, Cloud is incredible. Anthropic has made a huge progress, huge leap in developing Cloud.

We use it all over our company. The coding capability of Cloud, 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.

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, 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 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 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 a computer science degree, all of you can be programmers now. 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? 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 say, I’d like to write a program to create my own website. How do I do that?

And 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 power of AI, which is exciting.

Laurence Fink

Two quick questions, then we’re going to run out of time. 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. How do you see NVIDIA play that role here in Europe?

Huang Jen-Hsun

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 that are languages, that are biology, that are physics, that are world models, and related to manufacturing and robotics. And the thing that’s 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.

Robotics is a once-in-a-generation opportunity for the European nations, whether it’s, 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. 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.

Laurence Fink

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, what I’m hearing from you is, are we investing enough to do what we need to do to broaden the global economy?

Huang Jen-Hsun

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, 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, Lilia’s a great example.

Three years ago, most of their R&D budget, all of their R&D budget 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-skill 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.

But it’s still incredibly strong here in Europe. It’s an extraordinary opportunity you ought to take advantage of. And so, I know that where Laurence Fink and I work, we see the investment opportunities and the investment scale is going up.

The number of startups, as I mentioned earlier, 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.

Laurence Fink

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 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.

Huang Jen-Hsun

And we want to invest in infrastructure.

Laurence Fink

Right.

Huang Jen-Hsun

Infrastructure is a great investment opportunity. This is the single largest infrastructure build-out in human history. Get involved.

Laurence Fink

We’re out of time. Hopefully, everybody in the audience and everybody on the web streaming is seeing the power of Jensen Wang 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. Thank you, everybody.

Huang Jen-Hsun

Speech speed

150 words per minute

Speech length

3533 words

Speech time

1404 seconds

AI represents a fundamental platform shift comparable to PCs, internet, and mobile cloud, requiring complete reinvention of the computing stack

Explanation

Huang argues that AI is a new computing platform where applications are built on top, similar to how PCs, internet, and mobile cloud created new application ecosystems. This platform shift involves reinventing the entire computing stack and enables computers to process unstructured information in real-time rather than just executing prerecorded algorithms.

Evidence

Examples include AI’s ability to understand images, text, and sound without structured data formats like SQL databases. New applications are being built on top of ChatGPT and Anthropic Claude, demonstrating the platform nature of AI.

Major discussion point

AI as a Platform Shift and Infrastructure Revolution

Topics

Infrastructure | Economic

AI is a five-layer infrastructure requiring energy, chips, cloud services, AI models, and applications, driving the largest infrastructure build-out in human history

Explanation

Huang describes AI as a five-layer system starting with energy at the bottom, followed by chips and computing infrastructure, cloud services, AI models, and applications at the top. He emphasizes that this comprehensive infrastructure requirement is driving unprecedented global investment in building the necessary foundation.

Evidence

AI’s ease of use makes it the most accessible programming tool in history, potentially closing the technology divide for developing countries

Explanation

Huang argues that AI’s intuitive interface through natural language makes programming accessible to people without computer science backgrounds. He suggests this democratization of programming capability could help developing countries leapfrog traditional technology barriers and participate more fully in the digital economy.

Evidence

Topics

Economic | Digital business models

High demand for NVIDIA GPUs with rising spot prices indicates strong underlying demand rather than a bubble

Explanation

Huang uses GPU rental market dynamics as evidence that AI investment is driven by genuine demand rather than speculative bubble behavior. He points to the difficulty of renting GPUs and rising prices for even older generation hardware as indicators of real, sustained demand from AI companies and researchers.

Evidence

NVIDIA has millions of GPUs deployed across all major clouds, GPU rentals are extremely difficult to obtain, and spot prices are rising for both current and two-generation-old GPUs due to increasing numbers of AI companies and R&D budget shifts toward AI.

Major discussion point

AI Investment Landscape and Market Dynamics

Topics

Economic | Digital business models

Agreed with

– Laurence Fink

Agreed on

The scale of AI investment is justified by genuine demand rather than bubble dynamics

The investment scale is justified by the need to build infrastructure for all layers of AI applications

Explanation

Huang defends the large scale of AI investments by explaining that the comprehensive infrastructure requirements across all five layers of the AI stack necessitate substantial capital deployment. He argues that these investments are rational given the need to support the growing ecosystem of AI applications being built on top of the infrastructure.

Evidence

Companies like Eli Lilly are shifting R&D budgets from traditional wet labs to AI supercomputers and AI labs, demonstrating the real demand for AI infrastructure across industries.

Major discussion point

AI Investment Landscape and Market Dynamics

Topics

Economic | Infrastructure

Agreed with

– Laurence Fink

Agreed on

The scale of AI investment is justified by genuine demand rather than bubble dynamics

Laurence Fink

Speech speed

137 words per minute

Speech length

1170 words

Speech time

511 seconds

This infrastructure build-out creates significant investment opportunities for pension funds and institutional investors

Explanation

Fink argues that the massive AI infrastructure development represents a major investment opportunity that institutional investors, particularly pension funds, should participate in. He emphasizes the importance of ensuring that average savers and pensioners benefit from AI-driven economic growth rather than being left on the sidelines.

Evidence

Fink mentions working with Huang on many projects and references the trillions of dollars in infrastructure investment opportunities.

Major discussion point

AI as a Platform Shift and Infrastructure Revolution

Topics

Economic | Infrastructure

Agreed with

– Huang Jen-Hsun

Agreed on

AI infrastructure represents the largest infrastructure build-out in human history with massive investment opportunities

There are legitimate concerns about AI displacing certain analytical and professional jobs in sectors like finance and law

Explanation

Fink acknowledges that while AI may create jobs in some sectors, it will likely reduce demand for certain professional roles, particularly analytical positions in financial institutions and legal professionals. He suggests that AI’s ability to process and analyze data more efficiently will impact these knowledge-worker roles differently than the healthcare examples Huang provided.

Evidence

Agreed with

– Huang Jen-Hsun

Agreed on

Ensuring broad participation in AI economic benefits is crucial for social stability

The question for Europe and globally is whether we’re investing enough in AI infrastructure rather than whether there’s an AI bubble

Explanation

Fink reframes the common concern about an AI investment bubble by suggesting that the real issue is whether investment levels are adequate to meet the infrastructure needs for broad AI adoption. He argues that rather than worrying about overinvestment, policymakers should focus on ensuring sufficient investment to democratize AI benefits globally.

Evidence

Fink notes that many people are talking about an AI bubble, but based on his conversation with Huang, he concludes the question should be about investment adequacy for broadening the global economy.

Major discussion point

Europe’s AI Opportunity and Industrial Advantage

Topics

Economic | Infrastructure

Agreed with

– Huang Jen-Hsun

Agreed on

The scale of AI investment is justified by genuine demand rather than bubble dynamics

This represents a generational investment opportunity that institutional investors should participate in

Explanation

Fink positions AI infrastructure development as a once-in-a-generation investment opportunity comparable to major historical infrastructure build-outs. He advocates for institutional investors, particularly pension funds, to actively participate in financing this infrastructure to ensure their beneficiaries share in the economic returns.

Evidence

Fink references his work with Huang on investment projects and emphasizes that this is the single largest infrastructure build-out in human history, making it an exceptional investment opportunity.

Major discussion point

AI Investment Landscape and Market Dynamics

Topics

Economic | Infrastructure

Agreements

Development | Economic | Digital access

Similar viewpoints

Both speakers acknowledge that AI will have mixed effects on employment – creating jobs in some sectors while potentially displacing others. Huang emphasizes job creation in tradecraft, while Fink acknowledges displacement in analytical roles.

Speakers

– Huang Jen-Hsun
– Laurence Fink

Arguments

AI infrastructure development is creating substantial job opportunities in tradecraft sectors like plumbing, electrical work, and construction with six-figure salaries

There are legitimate concerns about AI displacing certain analytical and professional jobs in sectors like finance and law

Topics

Economic | Future of work

Both speakers see Europe as having unique advantages in the AI era, with Huang emphasizing industrial and scientific strengths, while Fink views it as a major investment opportunity for European institutional investors.

Speakers

– Huang Jen-Hsun
– Laurence Fink

Arguments

Europe has a strong industrial manufacturing base that can be enhanced with AI to leap into physical AI and robotics applications

European deep science capabilities combined with AI can accelerate discovery and innovation

This represents a generational investment opportunity that institutional investors should participate in

Topics

Economic | Infrastructure

Unexpected consensus

Arguments

Real-world examples show AI augments rather than replaces jobs, as seen with radiologists and nurses who use AI to increase productivity and patient care

There are legitimate concerns about AI displacing certain analytical and professional jobs in sectors like finance and law

Summary

Huang argues that AI primarily augments jobs rather than replacing them, using healthcare examples where employment increased despite AI integration. Fink acknowledges this but points out that certain professional roles, particularly analytical positions in finance and law, will likely face displacement as AI can process data more efficiently than humans.

Topics

Economic | Future of work

Unexpected differences

Scope of job displacement concerns

Speakers

– Huang Jen-Hsun
– Laurence Fink

Arguments

The key distinction is between job purpose (caring for patients) versus job tasks (studying scans), where AI automates tasks but enhances purpose

There are legitimate concerns about AI displacing certain analytical and professional jobs in sectors like finance and law

Explanation

This disagreement is somewhat unexpected given their generally aligned perspective on AI’s benefits. While Huang provides a framework suggesting AI typically enhances rather than replaces jobs by automating tasks while enhancing purpose, Fink specifically challenges this by pointing to knowledge work where the ‘purpose’ itself may be automated, not just the tasks. This suggests a more nuanced view of AI’s impact than Huang’s framework might accommodate.

Topics

Economic | Future of work

Overall assessment

Summary

The speakers show remarkably high alignment on most major issues, with disagreement primarily centered on the extent and nature of job displacement from AI adoption. Their main difference lies in Huang’s more optimistic view of AI’s job impact versus Fink’s more cautious acknowledgment of displacement in certain professional sectors.

Disagreement level

Low to moderate disagreement level. The speakers are largely aligned on AI’s transformative potential, infrastructure investment needs, and global democratization goals. Their disagreements are more about nuance and emphasis rather than fundamental opposition. This suggests a strong foundation for collaborative approaches to AI development and deployment, with the main challenge being how to address legitimate concerns about job displacement while capitalizing on AI’s economic benefits.

Partial agreements

Similar viewpoints

Both speakers acknowledge that AI will have mixed effects on employment – creating jobs in some sectors while potentially displacing others. Huang emphasizes job creation in tradecraft, while Fink acknowledges displacement in analytical roles.

Speakers

– Huang Jen-Hsun
– Laurence Fink

Arguments

AI infrastructure development is creating substantial job opportunities in tradecraft sectors like plumbing, electrical work, and construction with six-figure salaries

There are legitimate concerns about AI displacing certain analytical and professional jobs in sectors like finance and law

Topics

Economic | Future of work

Both speakers see Europe as having unique advantages in the AI era, with Huang emphasizing industrial and scientific strengths, while Fink views it as a major investment opportunity for European institutional investors.

Speakers

– Huang Jen-Hsun
– Laurence Fink

Arguments

Europe has a strong industrial manufacturing base that can be enhanced with AI to leap into physical AI and robotics applications

European deep science capabilities combined with AI can accelerate discovery and innovation

This represents a generational investment opportunity that institutional investors should participate in

Topics

Economic | Infrastructure

Takeaways

Key takeaways

AI represents a fundamental platform shift requiring a five-layer infrastructure (energy, chips, cloud services, AI models, applications) that is driving the largest infrastructure build-out in human history worth trillions of dollars

AI augments rather than replaces jobs by automating tasks while enhancing job purpose, as demonstrated by radiologists and nurses who became more productive and led to increased hiring in their fields

Every country should develop AI as essential national infrastructure using their local language and culture, with AI’s ease of use potentially closing the technology divide for developing nations

Europe has a significant opportunity to leverage its strong industrial manufacturing base and deep science capabilities by integrating AI, particularly in physical AI and robotics applications

The current investment scale is justified by underlying demand rather than representing a bubble, as evidenced by rising GPU rental prices and record VC funding of over $100 billion in 2025 directed toward AI-native companies

Ensuring broad participation in AI growth, including average pensioners and savers through institutional investment, is crucial to prevent economic exclusion

Resolutions and action items

Countries should invest in building AI infrastructure and develop their own AI capabilities using local expertise, language, and culture

Europe needs to increase energy supply and invest in AI infrastructure to build a rich AI ecosystem

Everyone should learn essential AI skills including how to direct, prompt, manage, guardrail, and evaluate AI systems

Institutional investors and pension funds should participate in AI infrastructure investments to ensure broad economic participation

There is a need for more energy infrastructure, land power and shell, and trade-skill workers to support the AI build-out

Unresolved issues

How to ensure equitable AI access when current utilization is heavily skewed toward educated populations in developed economies

Managing the displacement of certain analytical and professional jobs in sectors like finance and law while new jobs are created elsewhere

Specific mechanisms for ensuring average pensioners and savers participate in AI growth benefits

Detailed strategies for developing countries to build AI infrastructure and capabilities with limited resources

How to balance the massive energy requirements of AI infrastructure with sustainability concerns

Suggested compromises

Impact

This practical market evidence effectively countered Fink’s bubble question and reinforced the investment thesis. It provided concrete validation for the infrastructure buildout argument and supported the case for continued investment in AI infrastructure.

Overall assessment

Explanation

Based on Anthropic research showing AI usage concentrated among educated users, this addresses equity and accessibility concerns in AI adoption

Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.