Transcript: Howard Lutnick Interviews Nvidia CEO Jensen Huang at G20 Innovation Summit

Artificial intelligence can learn and understand and predict almost any information that has structure. So, biology has structure, chemicals have structure, the laws of physics have structure, and so each one of the regions around the world specializes in different areas in AI, and AI cuts across all of it, different languages, different technologies, different information theories. And so AI cuts across all of that, and there’s a whole bunch of AI models.

But the most important part of AI is the 5th layer, the layer above that, which is data and applications. The way to benefit— the United States is more deeply invested in inventing, building, developing the entire 5-layer cake. However, every single region, every single country should decide which one of the layers they want to invest in. You don’t have to win in every single one of them. You don’t have to develop all of them. The most important part is that every single country should advocate for AI diffusion or the adoption of AI into your industries. And the industries that we’re talking about span everything from education to healthcare, to manufacturing, to sciences, every single industry in between.

And so the way to think about AI is just to think about it from the 5-layer stack. And the United States is going to lead in the development of AI across these 5-layer stacks, but we’d love to partner with everybody so that everybody could take advantage of this. NVIDIA has built it in such a way that every single model in every single field of science and every single modality runs. And so it runs American models, it runs international models, it runs models in biology and chemicals and physics and robotics.

AI at the Edge: From Software to Physical Infrastructure

HOWARD LUTNICK (00:07:58 – 00:08:25): So when— if I go to a dinner party and I say AI, everybody thinks it’s software. But what we’re seeing now is really AI at the edge. It’s hardware, it’s robotics, it’s advanced manufacturing. So how do you see AI and the difference between the physical infrastructure of factories and sort of the general perception that AI is software out there?

Agents, Robots, and the Infrastructure Imperative

JENSEN HUANG (00:08:26 – 00:11:43): Well, AI is intelligence and starts with software. And in fact, if you look at AI in the last year, we started by talking about large language models, this LLM— this concept that the world would know what LLM stands for. It’s kind of an amazing thing. And so, large language models. What made AIs useful is putting an exoskeleton around the LLM. That exoskeleton is what is called an agent harness. It essentially gives the brain the parts necessary to retrieve knowledge, have working memory, use tools, be able to collaborate, so on and so forth, and so solve problems. And so the first thing is that the digital version of AI is what is called an agent.

When you take that digital version, you could put it— instead of using a tool like spreadsheet or PowerPoint or Excel or web browsers, you could make it use a tool, essentially a machinery. And so this agent would be embodied inside a physical body, and all of a sudden it becomes a robot. You could take this agent and put it inside a machine that has 4 wheels, self-driving cars. You could put this agent inside a manipulator, and then it becomes a pick-and-place or a manufacturing robotics arm. You could put this agent inside something that essentially is a grocery delivery or logistics solution, delivery vehicle, or a surgical robot, or an autonomous drug discovery laboratory. All of these different versions are basically the same idea: large language models with an agentic system around it. You could use this agent— agentic system could operate whether it’s digital tools or physical tools.

Now, in the end, what every single country will have to do is you need to recognize that this is infrastructure, just as it’s water, roads, electricity, the internet. Every single country needs to build infrastructure so that you could support your own local economy. It becomes the capacity, the intellectual capacity, the digital intellectual capacity for you to support your researchers and students and society and industries and startups. And every single country that we’ve worked with— and there are so many here that we’ve partnered with already— the moment we create an infrastructure locally, all of a sudden the startups, the researchers just really get activated, and so it’s really a fantastic investment to make.

We’ve had the benefit in the United States to have built one of the largest infrastructures, and because of the pro-energy growth and the speed and the regulatory environments in the United States, we’ve been able to build out really quite substantially. And you look at what’s going on in the United States. AI is now generating jobs across every single sector, from chip fabs to computer plants to AI factories. Just the number of jobs that we’re creating is just hundreds of thousands. And as you know, this year alone, we’re going to invest close to $1 trillion into the United States infrastructure. And that’s generating enormous economic growth and, of course, a lot of jobs.

Embracing Change: AI and the Industrial Revolution

HOWARD LUTNICK (00:11:44 – 00:11:48): So the change makes people nervous.

JENSEN HUANG (00:11:48 – 00:11:49): Yeah.

HOWARD LUTNICK (00:11:49 – 00:12:11): You know, it gets all sorts of people to say all sorts of things. You know, this AI change, how do you rank it as compared to the other change all the way back to Industrial Revolution? Where do you put it? Is it small? Is it big from your perspective? And what’s the best way for our friends here to embrace it, from your perspective?

AI Safety, Regulation, and the Responsibility of Progress

JENSEN HUANG (00:12:11 – 00:17:01): Well, as with all new technology, at first it seems magical. You could just imagine in the beginning of the production of electricity that somehow you could be far, far away and all of a sudden, a light could turn on. It seems like magic. And in the beginning of all technology innovations, it seems like magic.

But the fact of the matter is, all of us who are building it, this is engineering. There’s computers, there’s software, there’s mathematics involved, there’s probabilities involved, and there’s a lot— just as in every technology industry, when we’re building something, whether it’s a car or a plane, it is the responsibility of the people building it to do it safely and responsibly, and to continue to advance the technology so that as it advances, it becomes safer.

I prefer to drive today’s car with so much more technology in it than to drive a car from 100 years ago. I prefer to fly in a plane today, with all of the technology advances, than a plane 100 years ago. And so in a lot of ways, we want to accelerate the advancement of the technology so it could be more functionally able to deliver on its promise so that it’s safer to use. And so the same thing is going on with AI.

We have to remind the industry that— and I think the dialogue around safety and security is all fantastic. However, if you think about each one of your countries, what is the worst outcome that could happen in this technology industrial revolution? The worst outcome is that you don’t take advantage of it, that you are left behind. That is the single worst outcome.

And that outcome comes from— that outcome could happen if we talk about the technology with fear, with uncertainty, if the leadership talks about it with so much concern, so much fear, and all the conversations around safety and security. It has to be balanced with a lot of conversation about prosperity and a lot of conversation about what it can do. And so I don’t think it’s about one or the other. We have to make sure that we’re balanced in talking about it.

You probably know that in the history of all of the technology advancements, one of the stories I was just reading about recently has to do with the airline industry. There was a time when all of them spent a lot of their advertising dollars and marketing campaigns on, “my plane is safer than everybody else’s plane.” Turns out, nobody wanted to hear about that. They wanted the companies to take care of that.

What they wanted to hear was that the airlines could take them to new places, expand their horizon, make the world smaller so that we could learn about each other’s culture, create new economies. That’s what they wanted to hear about. They wanted it to be our responsibility to make it safe, not to make it their burden to think about safety all day long.

I think this is the same thing that’s happened. It happened in the auto industry, it happens to the power generation industry, it happened to the airline industry. I think it’s a responsibility for all of the leaders, all of us, to make sure that we take safety and security and building the technology properly, our responsibility.

Working with regulators, and of course, every single— there’s so many regulations and so many laws already. Intelligence is a digital version of what we do, and so you would think digital version of some tasks that humans do. In fact, humans have regulations across so many different domains already, whether it’s the FDA or NHTSA or whatever it is, there’s so many different agencies that already have regulations. I’m sure there’s a way to formulate AI into that.

And so, I think the balance of precise regulation, don’t regulate hypothetical, theoretical harm, regulate actual and pragmatic harm, and give this technology, which is at the early stages of this, S-curve innovation an opportunity to advance quickly so that it could be safe.

And so some of the people— if you just go back and think about some of the— and remind yourself of some of the ideas of slowing down technology advancement 3 years ago when it was just being formed, in fact, the advancement is what made it safer. The advancement made it not hallucinate, grounded on real truth, iterate until the answer is scientifically sound. All of those technologies were made possible through technology advancements. And so we’re making it safer but advancing it more.

NVIDIA’s GPU Architecture and Why It Matters

HOWARD LUTNICK (00:17:02 – 00:17:26): So let’s turn a little bit to your business for a moment. Your chips, GPUs, they’re different from what other people are hoping to build, right? And so maybe can you talk to the audience about your products and why are GPUs different than any other letter before the PU, if you will?

JENSEN HUANG (00:17:27 – 00:19:47): Well, today we started out, we invented a technology called a GPU, and basically a GPU does parallel processing tasks and it solves things like fluid dynamics and particle physics and quantum chemistry and computer graphics, image processing, image reconstruction, and of course one of the things that it does incredibly well is artificial intelligence.

Our architecture, our technology is general purpose, meaning that any— we support every single closed model, we support every single open model, we support languages, we support large models, small models. We support physical models, biological models. And so our architecture is fungible in the sense that it runs every AI model, runs all data processing and parallel processing tasks.

As a result, we’re everywhere. We’re in every cloud, we’re on-prem, we could be at the edge, we’re in robotic systems and self-driving cars. And the company’s been around, we’ve been working on this for so long, we’re in our 14th generation of our architecture. And so NVIDIA’s basically everywhere.

One of the advantages of doing that is that these infrastructures are so expensive. 1 gigawatt is about $50, $60 billion. Back in the old days, people thought building a fab, $25 billion, was a giant investment. And now, literally, 1 gigawatt is twice that. And we’re building, between now and the end of the decade, 100 gigawatts, because we want to produce intelligence for the world.

And so when you’re building, investing in infrastructure that’s that costly, you want to make sure that it is fungible and it’s durable, because otherwise, if you build something that’s too specialized and the world’s architecture and algorithm changes— and these AI models and technologies are changing so fast— if it changes, then all of a sudden you could obsolete the investment that you’ve made.

And so the reason why NVIDIA’s growth is accelerating is because people are starting to realize that we’re the safest architecture to invest in. It’s fungible, it is durable, and it continues to improve over time through software, and it runs everything. And so we run every American model, every international model, and every modality of models that you have.

The Big Picture: Where AI Is Taking Us

HOWARD LUTNICK (00:19:49 – 00:20:17): So people are always interested on where the world is going. If you were going to give us sort of your big picture view, where are we going? What should we be thinking about that we don’t always talk about? What you see, everything, really, all the companies in the world sort of come and visit with you and sort of tell you where they’re going. How would you sort of give us a big picture of a set of ideas that we don’t usually hear about?

JENSEN HUANG (00:20:18 – 00:27:03): Well, there’s some things that we hear, and then I’ll juxtapose that against what I expect. And so, for example, in the next couple of years, we are going to achieve essentially what people call AGI. And that’s not an illogical thing to me. In fact, I would argue that we’re practically there today. And so the question is, what does that mean? It either means a lot or it doesn’t mean anything.

So let me give you an example. If we said that in the next couple of years we’re going to achieve artificial general intelligence, and there’s no question everybody’s tasks, the things that we do, whether it’s just learning or working or every single industry, will be impacted. It will benefit everybody.

However, the part that is not thought through is, in fact, the thought experiment— let me just give you a thought experiment. Suppose, and you know that several hundred years ago, humans, in order to have a more civil society, manufactured one of the most important things in the world, which is called education, intelligence. Through universities and schools, we manufacture intelligence and we manufacture stability at scale. Without it, how would we have civilization?

And so now we’re creating the digital version of that. We’re manufacturing now intelligence digitally at scale so that even countries and people who don’t have access to the highest levels of education now have the benefits of the highest levels of education. And so that’s the simplest way of thinking about what is happening right now.

So the question is, what’s the benefit to society as a result of that? Well, let’s do a thought experiment. Suppose every single young person that we hire out of school is incredibly well-educated. Well, that’s pretty much consistent with everybody I hire. They come out of Stanford or Harvard or MIT or ETH Zurich, and these are incredibly smart kids.

However, when these kids come to our company, and every company, and every industry, we now still have to invest quite a bit in surrounding them with context. So, as it turns out, even when we get to AGI, it’s not as if every company’s problems are solved. It’s not as if all of a sudden, in 2 years’ time, I say, “okay, let me connect it to this AI service, and I just sit back, and all of a sudden my company becomes more and more productive overnight.” That’s just not going to happen at all.

You’re going to spend all of your energy, just like you currently do, onboarding a new college grad with a PhD from MIT, which is surrounding them with context, purpose, relevance, access to all of these other things, which is essentially, if you will, the harness around these AIs, the environment around these AIs so that they could do productive work. All of that harnessing, all of that environment creation is what companies do. It’s what leaders do.

And as it turns out, our jobs won’t change. Our jobs is defined by purpose, context, meaning. That’s what a job is. In that job, there’s a lot of typing and talking and things like that. Those skills are going to be automated by AI. The tasks are going to be automated, but our job’s purpose remains.

And so I think what’s going to happen is that we’re all just going to be supercharged, just like we’re currently supercharged. NVIDIA is— we use AI across the whole company today. We use off-the-shelf AIs like Anthropic’s and OpenAI’s and Cursor, and we build a lot of our own custom AI, and I encourage everybody to apply the same idea.

You should use AI off the shelf wherever you can, but don’t forget you have to invest in building your own intelligence. Every country, every company, you can’t outsource all of your intelligence to somebody else, and so you have to use as much of it as you can, build as much as you need to. And so we do that at the company, and so that’s the first idea, is just that we’re going to be supercharged.

The idea that somehow all the jobs are going to be eliminated, removed, that’s just nonsense. And the reason for that is because of all the things that I’ve just said.

Now, the other thing that is the case is that all of our ambitions are going to be supercharged. Just as we are more ambitious today than any time in human civilization. We think about doing things that people 100 years ago would just think, “that’s insane, that’s got to be science fiction.” And yet we perfectly expect it. And so just imagine how ambitious we’re going to be in 5, 10 years. That’s the exciting time.

I’ve been working a long time and surely I don’t have to work, but I don’t want to miss, I’m definitely not going to miss the next 10, 15 years. The ambition of the industry, the ambition of my colleagues, my own ambition has really been supercharged.

This is another way of saying that all of you, all of the countries, your ambitions have to be supercharged. This is the time when you have to feel enlightened, inspired, supercharged, lifted, because you now have this incredible technology behind you.

And so, what do I expect in the next 5, 10 years? Things that take 10 years are going to take 1. Things that take 1 will take a month. And so, if you think that this is going to cost $1 million to do, well, back a long time ago, just years ago, $1 million seems like a big investment. But these days, just think about the level of investments we’re talking about. Trillions of dollars in investment.

And the reason for that is this: it is now possible for average humans like ourselves to think that we can make an impact on a $100 trillion industry. For the first time, you’re hearing people— isn’t that right? For the first time, you’re hearing innovators, people, companies, countries talking about elevating a $100 trillion industry. That’s ambition. To think that you’re going to add $20, $50 trillion of economic benefit around the world, that’s ambition. No time in history have we been able to say things like that.

Closing Remarks

HOWARD LUTNICK (00:27:05 – 00:27:26): So my goal for today when we’re together is growth. That’s our goal, that we can all grow and benefit our countries and benefit society. And I think what you’ve done, Jensen, which we really appreciate— I mean, you are an American treasure.

JENSEN HUANG (00:27:26 – 00:27:27): Thank you.

Closing Remarks

HOWARD LUTNICK (00:27:27 – 00:28:35): Your company is leading us in a path that none of us when we were young ever dreamed possible. Maybe we dreamed it a little bit. That’s what sort of sci-fi was about. But now we’re there. I think what you’ve laid out for us is a vision of that harnessing AI to using it both from surrounding it, contextualizing it, and directing it is how we’re going to succeed, that each of the countries here needs to invest in its infrastructure in order to capture that for themselves, to make sure that their citizens and their society benefits from it.

They’ve got to make that infrastructure investment. They’ve got to build the data centers. They’ve got to harness the AI for themselves. That’s because what you’ve said is that education has been democratized, but it’s made available for everybody. You now can have a PhD in everything. That’s right, at your fingertips. And you don’t have to find that person because you’ve helped find them and sort of capture that education in a bottle.

JENSEN HUANG (00:28:35 – 00:28:37): You are that person now, right?

HOWARD LUTNICK (00:28:37 – 00:28:59): Everybody is that person now. So I think our goal is to have that idea of growth, that ambition for growth. And what you’ve done is you’ve laid us out that our dreams need to be higher because they can be higher. Because if you have all of this intelligence at your fingertips, how could you not dream higher?

JENSEN HUANG (00:28:59 – 00:28:59): That’s right.

HOWARD LUTNICK (00:29:00 – 00:29:21): And I think really for a company and for a leader of a company to have such a brilliantly positive vision for where we’re going, I think was the exact way to lead off today. So I want to thank you for coming. Everybody, I would like to thank Jensen Huang for coming and joining us. Thank you.

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