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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. I'm Monica Ricks in the Bloomberg newsroom in New York with a special conversation. Bloomberg Tech host ed Ludlow sat down within Video CEO Jensen Huang to discuss the tech giant's latest investments in South Korea, which includes teaming up with sk Group to build new data centers and investing a billion dollars in internet and cloud service provider Neighbor. Let's listen into a portion of their conversation, coming not long after the Nvidia CEO appeared at a Korean AI summit.

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Speaker 2: Jens and I think we just start with the basics, like career is incredibly important to the AI built out globally, we'll get intohigh bandwidth memory. But just from this summit, from the president being here, what is the takeaway? What is it you're trying to achieve?

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Speaker 3: Well, we're announcing a whole bunch of partnerships with them. This is the golden ages for Korea. As you know, there's semiconductor businesses booming, their industrial businesses booming. You know, this is a country that has the ability to help the world build out the AI infrastructure that they're incredibly adapt at adopting new technologies, and it's a really technologically forward leaning society and they love using AI. AI has has really diffused throughout their society and and their industry, and so so this is a great time for them. We're announcing several things. We announce a big partnership with SK Group where our companies are going to enter into a business partnership where we do over five hundred billion dollars of business with each other, whether it's consump consumption and purchasing of memories or selling AI supercomputers to them as they scale out to gigawatts of AI factories. There's a whole bunch of other announcements. We're investing a billion dollars in neighbor to help the they're the Korea's leading AI cloud. They're going to scale up in Korea up to two they're going to scale up to in mega watts I think it is, and they're going to expand across the world. And so we have a whole bunch of announcements that we're making today.

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Speaker 2: With the expanded SK relationship. There's also sort of more direct involvement with video on the roadmap for HBM future generations of HBM talk about that. You know, I remember you 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 going with you on that journey. But clearly you want to be involved in the direction of travel for future generations of HBM.

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Speaker 3: Yeah, we're working together on of course, we started with HBM two, worked on HBM three, three E, four, four E, and then beyond, and so 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 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 ais for humans to collaborate with. But in the future we also have AI 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 one hundred 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 so working with our partners in Korea and around the world to scale up the supply chain and the semiconductors so that we're prepared for this AI future. It's really important.

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Speaker 2: I've had the obvious need 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 for Nvidia based systems wherever they are.

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Speaker 3: Well, we don't have enough bits. We're constrained in HBO memories, LPDD our memories. We're constrained, and 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 is going to make sure that we continued to build out in a throttled way, you know, for a decade. And the reason for that is because of 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 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 growing much faster.

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Speaker 2: In that the five hundred billions a number is large. Would you just took 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 in Vidia spending in the in economy or it's it's s K fronting capital expenditures just a little bit more detail.

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Speaker 3: We're gonna we're gonna 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 a trillion dollars worth of a roommate systems, you're gonna 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 s K Heinex. Meanwhile, s K Telecom is going to become an AI cloud. They're going, we're starting to build already, They're going they're intending to build up to two gigawatts in the near future. And uh and in that in that agreement, we will be selling AI supercomputers to them. So between us, we're gonna, we're gonna, we're gonna do half a trillion dollars worth of business. Over half a trillion dollars worth a business.

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Speaker 2: I was able to sit down with SK Group chair and JK One very recently for about forty minutes, and at the end of the conversation we got to what is the difference in approach, the academic difference and approach on AI between the United States in China. And his view on it was that China is very focused on lowering the dollar PA token. In America were still focused on the quality of tokens. I wonder what you think of that.

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Speaker 3: 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 token smart and smarter, and as a result result in using less tokens to do so, you could also produce AIS that are much more efficient, and maybe you can think longer, explore more options and as a result result as a result, produces a smart answer. There are a couple, there are many different ways 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 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. The conditions are different, the resources are different, their constraints are different, but they're all they're you know, these amazing researchers will find answers and and I think that that 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. If manufacturing intelligence is important, they manufacture the most most important version of it, which is the researchers. And so so this is a this is an area a country that's going to produce excellent AI technology. We how to keep and keep continue to learn from them, work with them. As you know, you're here in Silicon Valley, right here in San Francisco. 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 uh, you know, we just got to keep on racing.

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Speaker 2: You made your first post on x I did and you did so 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? Why did you and saty Inodella and others need to do that in this moment?

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Speaker 3: What we sense we sense that there's there's a growing sentiment that that and the wrong 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 and as a server for startups. It's necessary for sovereignty company sovereignty. I see a future where the world uses tons of closed models, and I encourage everybody, including my company, to use open AI and cloud and cursor and cognition and perplexity, use everything that you can because it's out of the cloud, because it's just easier, and you build only what you must, and so in order to build what you must, you need to have open models to do that with. And the area is where we must. Maybe it's 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 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 you simply in a particular country you have to have your own AI, you have to control your own AI. Whatever those reasons are, it could be cost reasons, but I think I think that largely I would recommend people build their own AIS, 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 AI that's closed and AI that's open that you can build your own AI. Now, one of the things that people, you know, misunderstand about these open models is yes, you can host it yourself, but you can build your own computer, but most people use computers in a cloud. Frankly, I think close models are cheaper. You know, if you don't have to build yourself, if you don't have to train it yourself. It costs a lot of expertise to fine tune and maintain and guard rail and keep it safe and evaluate it 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 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.

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Speaker 2: Both weighted versus open source as well. There is a distinction.

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Speaker 3: I open open weighted 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 out there, we also teach people how to train the model from the data that we also open source. And the reason that is we want to enable you to completely reproduce the AI model that we've open weighted, and so that ability by us teaching you how to do that, you can then do it for yourself. You know, I think, I think the the idea that the world is going to be one or the other is just completely wrong, and and the idea that open models is somehow unsafe is also fundamentally wrong, and so we just want to make sure that people understand.

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Speaker 2: To finish our conversation, you know, the two big case studies where the release of can communicate three which on an open way to basis releases fully July twenty seventh, 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 guard rails were a factor. Would you just reflect on those two I know that you've been asked about them. They seem to be like really big moments in AI.

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Speaker 3: Over 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, as possible for a model to be if you will, stolen. It could be possible that that somehow has leaked from the inside. It's possible that the guardrails or the sandboxes of an AI closed AI model wasn't properly engineered, and as a result, it was able to attack another company in some way. And so just because something is closed, and just because something is proprietary, doesn't necessary really make it secure and safe. Of course, thank goodness, we have two companies, well, I guess more than that several companies that build 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 cainetical case that single points of failure is where we have the greatest vulnerability. We cannot have single points 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 used GLM five point two. As my understanding, 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, because in that case, they use GLM five point two to identify where the vulnerability was, where the penetration was, and we're able to quickly identify them and patch it up. And so this is a perfect example of self defense that's necessary, as a perfect example of diversity of AI technology being necessary, and so a perfect example of why open models and open capabilities for self defense is really important.

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Speaker 1: That's in Nvidia CEO Jensen Huang in a special conversation with Bloomberg Tech host ed Ludlow. You can watch the full interview now at Bloomberg dot com, slash Videos and on the Bloomberg Business app. You can also listen by subscribing to the Bloomberg Tech podcast feed. I'm Monica Ricks, thanks for listening. This is Bloomberg