Nvidia CEO Jensen Huang Talks AI Golden Age in South Korea, New Naver Investment

Bloomberg

I think we just start with the basics. [likely Korea] is incredibly important to the AI buildout globally. We'll get into high-bandwidth memory, but just from this summit—from the president being here—what is the takeaway? What is it you're trying to achieve?

1. Nvidia Expands Korea Partnerships

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, and their industrial business is booming. This is a country that has the ability to help the world build out the AI infrastructure that they're incredibly adept at adopting new technologies and is a really technologically forward-leaning society.

They love using AI. AI has really diffused throughout their society and industry, and so this is a great time for them. We're announcing several things. We announced a big partnership with 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 the consumption and purchasing of memory, 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 likely Naver to help them. They're Korea's leading AI cloud. They're going to scale up in Korea to 200 megawatts, I think it is, and they're going to expand across the world. We have a whole bunch of announcements that we're making today with the expanded relationship.

2. Nvidia Shapes the HBM Roadmap

Bloomberg

There's also more direct involvement with NVIDIA on the roadmap for HBM—future generations of HBM. Talk about that. You know, I remember being onstage 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 for 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, of course. We started with HBM2 and worked on HBM3, HBM3E, HBM4, HBM4E, and beyond. We've got a whole roadmap of memories that we're working on together.

It is also the case that the semiconductor industry has really changed, and the reason for that is because we used to build computers for people to use, and we're still going to continue to build incredible computers. These are now AI-processing computers for humans to collaborate with. But in the future, we also have AI agents and robots, and they're going to be using computers.

Instead of just 1 billion people using computers, we're going to have 100 billion agents and billions of robots all using computers. The computer industry—the industry that's built on top of the chip industry—is surely not big enough. This is one of the realizations of the semiconductor industry: Computers are built not just for people to use, but computers are being built for computers to use.

My guess is that the semiconductor industry is probably going to have to be 10 times larger than it is today over the next decade or 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 is really important.

Bloomberg

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 get going to increase the supply of HBM bits for NVIDIA-based systems, wherever they are?

3. AI Supply Chains Constrain Buildout

Well, we don't have enough bits. We're constrained in HBM memories and LPDDR3 memories. We're constrained in just about every part of the supply chain. We're even constrained now with land, power, and construction workers to set up the data centers.

I think this is one of the areas that's going to make sure that we continue to build out in a throttled way for a decade. The reason for that is because this 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.

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.

Bloomberg

The $500 billion number is large. Would you just talk a little bit more about what it encompasses? We've gone over a lot about your commitment to the U.S. in terms of spending. Is that NVIDIA's spending in the Korean economy, or is it SK fronting capital expenditures? Just a little bit more detail.

4. SK Deal Exceeds $500 Billion

We're going to be purchasing memories from them for many years to come. As you know, we buy—we build a lot of computers. In order to build $1 trillion worth of AI systems, you're going to have to buy a lot of system memory to go with it. We have large purchase agreements and large purchase intentions with SK Hynix.

Meanwhile, SK Telecom is going to become an AI cloud. They're starting to build already. They're intending to build up to 2 gigawatts in the near future. In that agreement, we will be selling supercomputers to them.

So between us, we're going to do over half a trillion dollars' worth of business.

Bloomberg

I was able to sit down with the SK Group chairman just recently for about 40 minutes, and at the end of the conversation, we got to the question of what is the difference in approach—the academic difference in approach on AI—between the United States and China. His view 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.

5. China And America Race Ahead

The goal of AI is to produce an intelligent, smart answer. 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, use fewer tokens to do so. You could also produce AIs that are much more efficient, and maybe they can think longer, explore more options, and, as a result, produce a smart answer.

There are many different ways to reach intelligence and deliver smart answers. In the end, I think you have to take a step back and realize that both countries have extraordinary AI researchers. Whatever conditions and whatever resources they have, amazing people will find great answers.

My expectation is that China and the United States will continue to advance AI. The conditions are different, the resources are different, and the constraints are different. But they're all there. These amazing researchers will find answers.

I think that, in the case of China, they're producing more AI researchers than probably all of the world has in any given year. They're producing it. Manufacturing intelligence is important. They manufacture the most important version of it, which is the researchers.

This is a country that's going to produce excellent AI technology, and we have to continue to learn from them and work with them.

Bloomberg

As you know, you're here in Silicon Valley, right here in San Francisco. But the number of AI researchers here who came from China, who are Chinese, is really quite significant. We're really fortunate to have them here, and we just have to keep on racing. You made your first post on X.

I did.

Bloomberg

You made your first post on X by sharing a letter signed by many of your peers—American companies—to talk about the importance of open models to America, to the industry, and to the development of AI. In the letter, your rationale is pretty well explained, but what was the catalyst for now? Why did you and Satya Nadella and others need to do that in this moment?

6. Open Models Protect Innovation

What we sense is that there's a growing sentiment against open models, and a concomitant sentiment for closed models. It's really important to realize that open models are essential for safety. Open models are essential for security and cybersecurity. Open models are essential for innovation. They're necessary for startups. They're necessary for sovereignty—company sovereignty.

I see a future where the world uses tons of closed models. I encourage everybody, including my company, to use OpenAI, Claude, Cursor, Cognition, and Perplexity. Use everything that you can because of the cloud. It's just easier, and you build only what you must.

In order to build what you must, you need to have open models to do that with. The areas where we must build our own AI may be because we have expertise that we simply cannot afford to share. This is our company's alpha, our company's intelligence, and we have to make sure we keep that proprietary.

Maybe it's because our company works in an industry that's regulated, and therefore we simply can't pass along the service-level agreement. 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 in a particular country, you simply have to have your own AI; you have to control your own AI. Whatever those reasons are, there could be cost reasons. But I think, largely, I would recommend people build their own AIs, especially when they need to control them for whatever reason.

I think the future is going to have lots and lots of use of AI that's closed and AI that's open, where you can build your own AI. Now, one of the things that people misunderstand about these open models is: Yes, you can host it yourself, but you can build your own computer.

But most people use these computers in the cloud. Frankly, I think closed models are cheaper if you don't have to build them yourself. If you don't have to train them yourself, it costs a lot of expertise to fine-tune, maintain, and guardrail them, keep them safe and evaluated, and, of course, even build computers to host them. So there's nothing cheap about doing that.

The reason why you need open models is because you need to have control, because you need to adapt something for your own very specialized use cases. I think there's a lot of misunderstanding about closed versus open. We felt that it was important for people to understand that there's a world for both open-weight and open-source models as well.

There is a distinction. Open-weight is as open as you can get. The more open it is, in the way that we work, the more we put out there. We put the weights out there. We also teach people how to train the model from the data that we also open-source. The reason for that is we want to enable you to completely reproduce the AI model that we've open-weighted. By us teaching you how to do that, you can then do it for yourself.

I think the idea that the world is going to be one or the other is just completely wrong. The idea that open models are somehow unsafe is also fundamentally wrong. We just want to make sure that people understand that.

Bloomberg

To finish our conversation, the 2 big case studies were the release of [likely Kimi] K3, which was released on an open-weight basis July 27th, and then the case study of 2 OpenAI models mistakenly accessing Hugging Face's systems, and Hugging Face trying to use an open model in its defense, where the guardrails were a factor. Would you just reflect on those 2? I know that you've been asked about them, but they seemed to be really big moments today.

7. Open Models Defend AI Systems

Those are perfect canonical examples. Just because something is closed doesn't necessarily make it safe or secure. It is possible for a model to be jailbroken. It is possible for a model to be, if you will, stolen. It could be possible that somehow it's leaked from the inside. It's possible that the guardrails or the sandboxes of a closed AI model weren't properly engineered, and as a result, it was able to attack another company in some way.

So just because something is closed and just because something is proprietary doesn't necessarily make it secure and safe. Of course, thank goodness we have 2 companies—well, I guess more than that. Several companies have built closed AI models. These are extraordinary technology companies, and they're doing their best to keep them safe and secure.

But it is also the canonical case that single points of failure are where we have the greatest vulnerability. We cannot have a single point of failure as an industry, as a world. We should have distributed, massively distributed self-defense.

In the case of the example you just mentioned, Hugging Face thankfully was able to access an open model, and I think they used GLM 5.2, as I understand it. They couldn't get a proprietary model. They could not get a closed model to help them figure out what happened. But this is exactly the reason why you want to have open models.

In that case, they used GLM-4.5 to identify where the vulnerability was, where the penetration was, and were able to quickly identify it and patch it up. This is a perfect example of the self-defense that's necessary, a perfect example of the diversity of AI technology being necessary, and a perfect example of why open models and open capabilities for self-defense are really important.