I think we just start with the basics. Like career is incredibly important to

to the I build out globally. We'll get into high bandwidth memory.

But just from this summit, uh, from the president being here, what is the

takeaway? What is it you're trying to achieve?

Well, we're announcing a whole bunch of partnerships with them.

This is the golden ages for Korea. As you know, their semiconductor

business is booming. Their industrial business is booming.

You know, this is a country that has the ability to help the world build out the

AI infrastructure that they're incredibly adept at, uh, adopting new

technologies and is a really technologically, uh, forward leaning

society. And they they love using AI.

AI has has really diffused throughout their, uh, society and, and their

industry. And so, so this is a great time for

them. We're announcing several things.

We announce, um, a big partnership with uh, SK Group, where our companies are

going to enter into a business partnership where we do over $500

billion of business with each other. Whether it's consumption of and

purchasing of memories or selling supercomputers to them as they scale out

to gigawatts of factories. There's a whole bunch of other

announcements. We're investing $1 billion in never to

help. They're the, uh, Korea's leading a

cloud. They're going to scale up in Korea up to

two. They're going to scale up 200MW, I think

it is. And, uh, they're going to expand, uh,

across the world. And so we have a whole bunch of

announcements that we're making today with the, the, the expanded

relationship. There's also sort of more direct

involvement with Nvidia on the roadmap for HBM, future generations of HBM.

Talk about that. You know, I remember being on stage

earlier this year saying five years ago, we told our supply chain what was going

to happen and it did happen. And you gave some credit to the memory

makers in in going with you on that journey.

But clearly, you want to be involved in the direction of travel for future

generations of HBM. Yeah, we're working together on of

course. We started with HP and two worked on HP

and three three E4, four E and then beyond.

And so we've got, we've got a whole roadmap of memories that we're working

on together. Uh, it is also the case that the the

semiconductor industry has really changed.

And the reason for that. Um, because we used to build computers

for people to use and we're going to still continue to build incredible

computers. These are now eight processing eyes for,

uh, humans to collaborate with. But in the future, we also have eye

agents and robots, and they're going to be using computers.

So instead of just a billion people using computers, we're going to have 100

billion agents and billions of robots all using computers.

The computer industry, the chip that's built on top of the chip industry,

surely is not big enough. And so this is one of the realizations

of the semiconductor industry that now computers are built not just for people

to use, but computers are being built for computers to use.

My guess is that that the semiconductor industry is probably going to have to be

ten times larger than it is today. Over the next decade or so and, and, um,

uh, and so working with our partners in Korea and around the world to scale up

the supply chain of semiconductors so that we're prepared for this AI future,

it's really important. I've had the opportunity to ask you

about this more than once this year, but how much do you need the Korean economy

to kind of get going to increase the supply of HBM bits?

Uh, for Nvidia based systems, wherever they are?

Well, we we don't have enough bits. Uh, we're constrained in HBM memories,

Lpddr3 memories. We're constrained in just about every

part of the supply chain. We're even constrained now with land and

power and construction workers to set up the data centers.

I think this is one of the areas that's that's, um, is going to make sure that

we continue to build out in a throttled way.

You know, for a decade. And the reason for that is because these

infrastructure, unlike electronics, Electronic devices like PCs and phones

and things like that. It's really, really hard to scale up

land, power and shell. And so all of, all of the supply chain

just really needs to get built out over the years.

I think we have the ability as an industry to double each year, but we're

going to have a hard time going much faster than that.

The $500 billion number is large. Would you just talk a little bit more

about what it encompasses? We've gone over a lot on your commitment

to the US in terms of spending. Is that Nvidia's spending in the Korean

economy or it's SK fronting capital expenditures?

Just a little bit more detail. We're going to we're going to be

purchasing memories from them for many years to come.

And as you know, we buy we build a lot of computers in order to build $1

trillion worth of. There are room in systems.

You're going to have to buy a lot of system memories to go with it.

And so we have we have large purchase agreements and large purchase intentions

with SK Hynix. Meanwhile, SK Telecom is going to become

an AI cloud there We're starting to build already.

They. They're intending to build up to two

gigawatts in the near future. And, um, and in that, in that agreement,

we will be selling supercomputers to them.

So between us, we're going to we're going to we're going to do half $1

trillion worth of business, over half $1 trillion worth of business.

I was able to sit down with SK Group chairman one very recently for about 40

minutes, and at the end of the conversation, we got to what is the

difference in approach, the academic difference in approach on AI between the

United States and China. And his view on it was that China is

very focused on lowering the dollar per token.

In America, we're still focused on the quality of tokens.

I wonder what you think of that. Um, the goal of the goal of AI is to

produce an intelligent, smart answer. Now, you could approach it in a couple

of different ways. You could, of course, make all of the

tokens smarter and smarter, and as a result, uh, result in using less tokens

to do so. You could also, um, uh, produce eyes

that are much more efficient and maybe you can think longer, explore more

options and as a result, as a result, produce a smart answer.

There are a couple. There are many different ways to to

reach intelligence and deliver smart answers.

In the end, really, I think you have to take a step back and just realize that

both countries has extraordinary AI researchers and whatever, whatever, um,

conditions and whatever resources that they have, amazing people will find

great answers. And so you're going to find you're going

to you know, my expectation is that China and the United States will

continue to advance AI, um, the conditions are different, the resources

are different, their constraints are different.

But they're all there. You know, these amazing researchers will

find answers. And and I think that that, um, in the

case of China, they're producing more AI researchers than probably all of the

world has, you know, in any given year. And so they're producing it.

Manufacturing intelligence is important. They manufacture the most the most

important version of it, which is the researchers.

And so. So this is a this is an area a country

that is going to produce excellent AI technology.

And we are to keep an keep uh, continue to learn from them, work with them.

As you know, you're here in Silicon Valley right here in San Francisco.

But the number of AI researchers here that came from China that are Chinese is

really quite significant. And so, uh, you know, we're really

fortunate to have them here. And, and, um, you know, we just got to

keep on racing. You made your first post on X.

I did, and you did. So, uh, by sharing a letter signed by

many of your peers, American companies, to talk about the importance of open

models to America, to to the industry, to the development of AI.

And in the letter, it's pretty well explained, you know, your rationale.

But what what was the catalyst for now? What why did you and Satya Nadella and

others need to do that in this moment? What we sense we sense that there's,

there's, um, uh, a growing, uh, um, sentiment that that, um, and the

consummate sentiment for open models. It's really important to realize that

open models is essential for safety. Open models is essential for security,

for cybersecurity. Open models are essential for

innovation. It's necessary for startups.

It's necessary for sovereignty, company sovereignty.

We I see a future where the world uses tons of closed models.

And I, I encourage everybody, including my company, to use OpenAI and Clyde and

cursor and, and, uh, cognition and perplexity use everything that you can

because uh, of of the cloud. Because it's just easier and and you

build only what you must. Um, and so in order to build what you

must, you need to have open models to do that with and the areas where we must.

Maybe it's because we have expertise that we we simply cannot afford to

share. This is our companies alpha, our

companies intelligence, and we have to make sure we keep that proprietary.

Maybe it's because our company works in a in an industry that's regulated, and

therefore we simply can't pass along the service level agreement.

And we have to make sure that we can deliver fully on the service and the

promise that we sign up for. Maybe it's something to do with

sovereignty that that, um, you simply in a particular country, you have to have

your own eye, you have to control your own eye.

Whatever those reasons are, there could be cost reasons.

But but I think I think that largely I would recommend people build their own

eyes, especially when they need to control it for whatever reason.

And so I think the future is going to have lots and lots of use of eye that's

closed and an eye that's open that you can build your own AI.

Now, one of the things that that people, you know, misunderstand about, uh, these

open models is. Yes.

Uh, you can host it yourself, but you can build your own computer.

But most people use used computers in cloud.

Frankly, I think close models are cheaper.

You know, if you don't have to build yourself, if you don't have the training

yourself cost a lot of expertise to fine tune and maintain and guardrail and keep

it safe and evaluated and, and of course, even build computers to host it.

So there's nothing cheap about doing that.

The reason why you need open, open models is because you need to have

control, because you need to adapt something for for your own very

specialized use cases. And so I, I think there's a lot of

misunderstanding about about closed versus open.

We felt that it was important for people to understand that there's a world for

both open weighted versus, uh, open source as well.

There is a distinction, um, uh, open, open waited as much, as much as open as

that you can. The more open it is in the way that we

work. We put the weights, um, out there.

We also teach people how to train the model from the data that we also open

source. And the reason for that is we want to

enable you to completely reproduce the eye model that we've open.

Wait. And so that a built by by us teaching

you how to do that, you can then do it for yourself.

You know, I think I think the um, the idea that the world is going to be

one or the other is just completely wrong.

And, and, um, the idea that, uh, open models is somehow unsafe is also

fundamentally wrong. And so we just want to make sure that

people understand to finish our conversation, you know, the two big case

studies where the release of communique three, which on an open way to basis

releases for the July 27th, and then the case study of two open AI models

mistakenly accessing hugging faces systems and hugging face, trying to use

an open model in its defense, where the guardrails were a factor, which you just

reflect on those two. I know that you've been asked about

them, but they were they seemed to be like really big moment today.

Those are perfectly perfect canonical examples.

Just because something is closed doesn't necessarily therefore make it safe or

secure. It is possible for a model to be

jailbroken. Is it possible for a model to be, if you

will, stolen? It could be possible that that somehow

is leaked from the inside. It's possible that the guardrails or the

sandboxes of an eye closed AI model wasn't properly engineered, and as a

result, it was able to attack another and another company in some way.

And so. So just because something is closed and

just because something is proprietary doesn't necessarily make it secure and

safe. Of course.

Thank thank goodness we have two companies.

Well, I guess more than that. Several companies have built closed AI

models. And these are extraordinary technology

companies. And they're doing their best to keep it

safe and keep it secure. But it is also the canonical case that

single points of failure is where we have the greatest vulnerability.

We cannot have single point of failure as an industry, as a world, we should

have distributed massively distributed self defense.

And so in the case of in the case of the example you just mentioned, hugging face

thankfully was able to access an open model and I think they use the GLM 5.2

as my understanding. Um, they, they couldn't get a

proprietary model. They could not get a close model, uh, to

help them figure out what happened. And but this is exactly the reason why

you want to have open models. Because in that case, they use GLM 5.2

to identify where the vulnerability was, where the penetration was, and were able

to quickly identify them and patch it up.

And so so this is a perfect example of self-defense.

That's necessary is a perfect example of diversity of AI technology being

necessary. And it's a perfect example of why open

models and open capabilities for self-defense is really important.