FULL REMARKS: Nvidia CEO Jensen Huang Outlines AI Future As Global Economic Game Changer | AI14

structure. The laws of physics has structure. And so each one of the

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

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 fifth layer, the layer above that, which is data and

applications. The way to benefit, you

applications. The way to benefit, you know, United States is more deeply invested in

uh inventing, building uh developing the entire five 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

industries. And the industries that we're talking about span everything from education to healthcare to manufacturing to sciences, you know, every single industry in between. And so the way to

think about AI is just to think about it from the five layer stack. And United

States is going to lead in the development of AI across these five layer stacks. But we'd love to partner

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 uh runs and so it runs American American uh models, it runs international models, it runs models in biology and chemicals and you know physics and robotics.

So when if I go to a dinner party Yeah.

and I say AI, everybody thinks it's software.

Yeah. But what we're seeing now is really AI at the edge. It's 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?

Well, AI is intelligence and and starts with software. Um and in fact if you

with software. Um and in fact if you look at AI in the last year we started by talking about large language models.

This LLM you know 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 AI useful is putting an exoskeleton around the LLM. That exoskeleton is what

is called an agent harness. essential

essentially gives the brain the the parts necessary to uh retrieve knowledge, have working memory, uh use tools, be able to collaborate, so on so

forth, and so solve problems. And so the first thing is that the digital version of AI is is what is called an agent.

When you take that doodle 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 use uh would be embodied inside a physical physical body and all of a sudden it becomes a robot.

You could take this agent and put it inside uh a machine that has four wheels, self-driving cars. You could uh put this agent inside a manipulator and

it becomes a pick and place or a manufacturing robotics arm. Uh you could put this uh agent inside something that that essentially is a grocery delivery

or or logistics delivery vehicle or a surgical robot or a autonomous uh drug discovery laboratory. You know, all of

discovery laboratory. You know, all of these different versions are basically the same idea. Large language models with an agentic system around it. you

could use this this agent agentic system could operate um whether it's digital tools or physical tools. Now in the end what 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 every single country that we've worked with and there's so many here that we've partnered with

already when 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 uh to have built one of the largest infrastructures and and um uh because because of the pro-energy growth and the the speed um and the regulatory

environments in the United States, we've been able to build out really quite substantially. And you you look at

substantially. And you 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. Um boy just the number of jobs that we're creating is just hundreds of thousands and as you know and and uh this year alone we're going to invest close to a trillion

dollars into the world's into the United States infrastructure and that's generating enormous economic growth and of course a lot of jobs. So the change

makes people nervous. Yeah.

You know it gets all sorts of people to say all sorts of uh of things. you know

this AI change how how do you rank it as compared to the other change all the way back to industrial revolution you know where do you put it is it small is it big from your perspective and and what's

the best way for uh for our our friends here to embrace it from your perspective well as with all new technology uh at first it see it seems uh uh magical you

could just imagine in the beginning of the production of electricity that somehow uh You could be far far away and all of a sudden you know a light could

turn on. It seems like magic. And in the

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. Um there's probabilities

involved. Um there's probabilities involved and and uh uh there's a lot there 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. um it's with all of the technology advances than a plane a

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 um uh able to deliver on its promise so that it's safer to to uh

to use. And so the same thing is is

to use. And so the same thing is is going on with AI. We have to rem remind the industry um that and I think the dialogue around safety and security is

all fantastic. Uh however,

all fantastic. Uh 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

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 two that outcome could happen. If we talk about the technology

happen. If we talk about the technology with fear, with uncertainty, the leadership um talks about it uh with so much concern, so much fear and uh 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 so I don't think it's about

do. And so 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.

Um you you probably know that that uh in the history of of all of the technology advancements, one of the one of the stories I was just reading about recently uh has to do with the the

airline industry. There was a time when

airline industry. There was a time when all of them spent, you know, a lot of their advertising dollars and and marketing campaigns on my plane is safer than everybody else's plane. Turns out

nobody wanted to hear about that. They

wanted the c they wanted the companies to take care of that. What they wanted to hear was that 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 that to think about safety all day all day long. And so I think this is the same thing that's hap it happened in

the auto industry. It happens to to the power generation industry. It happened

to the airline industry. And so I think it's a responsibility for all of our all of the leaders all of us to make sure that we take safety and security and building the technology properly our

responsibility and uh working with regulators and of course every single there are so many regulations and so many laws already. Intelligence is a digital digital version of what we do

and so you would think digital version of some task that humans do. So in fact humans are have regulations across so many different domains already whether

it's the FDA FDA or Nitsa 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 uh I think the the balance of of precise regulation uh don't regulate

hypothetical theoretical harm regulate actual and pragmatic harm. um and give this technology which is at the at the early stages of the S-curve innovation

an opportunity to advance quickly so that it could be safe. And so some of the people uh to if you just go back and think about some of the and remind yourself of some of the ideas of slowing

down technology advancement three years ago when it was just being formed. In

fact, the advancement is what made it safer. The advancement made it not

safer. The advancement made it not hallucinate grounded on real truth. um

iterate until that the the answer uh is uh uh uh scientifically sound. Um all of those technologies made were made possible through technology advancements. And so we're we're making

advancements. And so we're we're making it safer but advancing it more.

So let let's turn a little bit to your business for a moment. Um your your chips uh GPUs, they're they're different from what other people are hoping to

build, right? And so maybe can you talk

build, right? And so maybe can you talk to the audience about about your products and and why are GPUs different than so any other letter before the PU

if you will? 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 one of the computer graphics, image processing, um image reconstruction.

Uh and of course one of the things that it does incredibly well is artificial intelligence. Um our architecture, our

intelligence. Um our architecture, our technology is general purpose meaning that uh any we support every single

closed models, we support every single open model, we support languages, we support large models, small models, we support um physical models, biological

models and so our architecture is funible in the sense that it runs every AI model, runs all data processing and and parallel processing tasks. As a

result, we're everywhere. We're in every cloud. We're on prem. We could be at the

cloud. We're on prem. We could be at the edge. We're in robotic systems and

edge. We're in robotic systems and self-driving cars. Um, and the com the

self-driving cars. Um, and the com the company's been around. We've been

working on this for so long. We're in

our 14th generation of our architecture.

And, and so, so Nvidia's basically everywhere. One of the advantages of

everywhere. One of the advantages of doing that is that these infrastructures are so expensive. One gigawatt is about 50 60 billion dollars, right? You know,

back in the old days, people thought building a fab $25 billion was a giant investment. And now, literally one

investment. And now, literally one 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 fungeable 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 it could obsolete the investment that

you've made. And so the reason why

you've made. And so the reason why Nvidia's uh growth is accelerating is because people are starting to realize that we're the safest architecture to

invest in. It's fungeible. It is durable

invest in. It's fungeible. It is durable and it continues to improve over time through software and uh it runs everything. And so we run every American

everything. And so we run every American model, every international model and every modality of models that you have.

So people are always interested on where the world is going. If if you were going to give us sort of your big picture view, where where are we going? Um what

should we be thinking about that we don't always talk about? You know, what you you see everything really all the companies of the world sort of come and visit with you and sort of tell you

where they're going. How how would you sort of give us a big picture of a set of ideas that we don't usually hear about?

Well, there's some things that that we hear and and then I'll I'll I'll juxtapose that. um against what I

juxtapose that. um against what I expect. And so, for example, in the next

expect. And so, for example, in the next couple of years, we are going to achieve essentially what people call AGI.

And and um uh and that's not an illogical thing to me. In fact, I would argue that we're practically there today. Okay? And and so the question is

today. Okay? And and so the question is what does that mean? Um it either means a lot or it doesn't mean anything. And

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 um and every and there's no question everybody's everybody's tasks the things that we do

whether it's in um just learning or working or uh every single industry will be impacted it will benefit everybody.

However the part that is that is not not thought through is in fact uh the thought experiment let me just give you a thought experiment. Suppose and you know that several se several hundred

years ago uh humans in order to have a more civil society uh manufactured one of the most important things in the world which is called education intelligence we manu 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 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. Okay? 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

pretend let's do a thought experiment.

Suppose every single uh every single you know young person that we hire out of school uh is incredibly well educated.

Well, that's pretty much consistent with everybody I hire. They come out of Stanford or Harvard or MIT or you know ETH or and these are incredibly smart

kids. However, when these kids come to

kids. However, when these kids come to come to our company and every 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 two years time I say, "Okay, let me connect it to this a a this AI service and I just sit sit back and all of a sudden my company becomes more and more productive

overnight." That's just not going to

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 pro surrounding them with context, purpose, relevance, access to, you know, 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, is what leaders do. And as it turns out, our

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

meaning. That's what a job is. In that

job, there's a lot of typing and talking and, you know, things like that. Those

skills are going to be automated by AI.

the tasks are going to be automated, but our our jobs purpose re remain. 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

company today. We use off-the-shelf AIs like like anthropics and open AIs and cursor and um and we build a lot of our own custom AI and I encourage everybody

to apply the same the same idea. You

should use AI off the shelf wherever you can. But don't forget, you have to

can. But don't forget, you have to invest in building your own intelligence. Every country, every

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,

uh, we do that at the company. And and,

uh, and so that's the the first idea is just that we're going to be supercharged. um the idea that somehow

supercharged. um the idea that somehow all the jobs are going to be eliminated or removed that's just nonsense. Okay?

And the reason for that is because of all the things that I've just said. Now

the other thing that that is the case is that all of our ambitions are going to be supercharged.

Just as you know we are more ambitious today than any time in human civilization. We

think about doing things that people a hundred years ago would just that's insane. That's got to be science

insane. That's got to be science fiction.

True.

And yet we perfectly expla perfectly expected. And so just imagine how

expected. And so just imagine how ambitious we're going to be in 5 10 years. That's the exciting time. You

years. That's the exciting time. You

know, I've been working a long time and surely I don't have to work but I don't want to miss I I'm definitely not going to miss the next 1015 years. The

ambition of the industry, the ambition of my colleagues, my own ambition has really been supercharged. And this is another way of saying that all of all of you, all of the countries, your

ambitions have to be supercharged. This

is the time when you have to feel enlightened, inspired, you know, supercharged, lifted because you now have this incredible technology behind you. And so, you know, what do I expect

you. And so, you know, what do I expect in the next five, 10 years? Uh, things

that takes 10 years going to take one.

things that take one will take a month.

And so, um, you know, if you if you think that that this is going to cost a million dollars to do, uh, well, back a long time ago, just, you know, years

ago, a million dollars 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

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 hundred trillion dollar 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 hundred trillion dollar industry. That's ambition. To think that

industry. That's ambition. To think that you're going to add 20, 50 trillion dollars of economic benefit around the world, that's ambition.

No time in history have we been able to say things like that.

So my goal for today when we're together is growth, right? That's our goal. That

we can all grow and benefit our countries and benefit society. And I

think what you've done uh Jensen, which we really appreciate, I mean, you are an American treasure.

Thank you.

Right. Your company, uh, 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,

you know, 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 uh surrounding it, contextualizing it and directing it is how we're going to

succeed. That each of the countries here

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 right.

Because what you've said is that uh 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. You are that

person now, right? Everybody is that person now. So I think you know our goal

person now. So I think you know our goal is to uh 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?

That's right. And I think really for a company and for a leader of a company to have such a uh 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

today. So I want to thank you for coming everybody. I would like to thank Jensen

everybody. I would like to thank Jensen W for coming and joining us. [applause]

Thank you.

[applause] Thank you everybody.