Special Edition: Nvidia CEO Jensen Huang on Investing in South Korea

Summary

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Bloomberg Surveillance - Special Edition: Nvidia CEO Jensen Huang on Investing in South Korea

Date: July 25, 2026
Host: Ed Ludlow (Bloomberg Tech)
Guest: Jensen Huang (Nvidia CEO)

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Episode Overview

This special edition of Bloomberg Surveillance features an in-depth conversation between Bloomberg Tech host Ed Ludlow and Nvidia CEO Jensen Huang. The primary focus is Nvidia’s substantial investments in South Korea, its partnership with major Korean tech and industrial groups (notably SK Group and Naver), and strategic insights on the global AI and semiconductor industries. The discussion further delves into the future of AI architectures (open vs. closed models), international perspectives on AI research, and the necessity of scaling infrastructure.

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Key Discussion Points & Insights

1. Nvidia’s Strategic Push into South Korea

(Starts at 01:51)

• Golden Era for Korea: Huang describes this as a "golden age" for Korea due to the boom in its semiconductor and industrial sectors, positioning Korea as integral to global AI infrastructure.
  • Quote: “This is the golden ages for Korea...this is a country that has the ability to help the world build out the infrastructure...technologically forward leaning society and they love using AI.” (02:04, Jensen Huang)
• Major Announcements:
  • $500B+ business deal between Nvidia and SK Group, spanning memory purchasing and AI supercomputer sales (02:04, 03:21).
  • $1B investment into Naver, Korea’s leading AI cloud, to help it expand domestically and internationally.
  • SK Telecom to build out 2 gigawatts of AI factories/data centers, leveraging Nvidia hardware.
• Infrastructure Limitations: Demand is so high that growth is bottlenecked by shortages in memory, land, power, and workers.
  • Quote: “We're constrained in HBM memories, LPDDR 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...” (05:21, Jensen Huang)

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2. The Semiconductor Industry’s Future

(03:51 — 05:09)

• Industry Transforming: Transition from computing primarily for people to creating computers for “agents and robots”—predicts exponential growth in demand.
  • Quote: “Instead of just a billion people using computers, we're going to have 100 billion agents and billions of robots all using computers...the semiconductor industry is probably going to have to be 10 times larger than it is today.” (04:29, Jensen Huang)
• Collaboration on Memory (HBM): Nvidia’s roadmap with SK Hynix—from HBM2 through future generations (HBM3, HBM3E, HBM4, etc.)—is critical for scaling new forms of AI computing.

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3. Korea’s Role in the AI & Memory Supply Chain

(05:09 — 06:04)

• Korean Industry’s Importance: Nvidia relies on Korea to ramp up HBM (high bandwidth memory) production.
  • Power, land, and construction capacity are now as crucial as compute itself to data center expansion.
  • Quote: “As an industry [we can] double each year, but we're going to have a hard time growing much faster than that.” (05:54, Jensen Huang)

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4. $500B+ Deal: Structure & Impact

(06:04 — 07:03)

• Deal Details:
  • Major purchases of memory from SK Hynix for Nvidia’s massive system builds.
  • SK Telecom also building its own AI cloud and data centers with Nvidia’s AI servers.
  • “Over half a trillion dollars worth of business.” (06:21, Jensen Huang)

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5. US vs. China: Differing AI Roadmaps

(07:03 — 09:14)

• Contrast in Approaches: China seeks to lower “dollar per token”, US focuses on “quality of tokens”.
  • Huang’s perspective: Both tactics are valid, and both nations have world-class AI researchers.
  • China is a powerhouse in “manufacturing intelligence” by training vast numbers of AI researchers each year.
  • Quote: “They manufacture the most important version of it, which is the researchers...this is an area, a country that's going to produce excellent AI technology. We ought to continue to learn from them, work with them.” (08:22, Jensen Huang)

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6. Open vs. Closed AI Models: Safety, Sovereignty, and Innovation

(09:14 — 13:38)

• Catalyst for Open Model Advocacy:
  • Growing sentiment against open models prompted a joint industry letter (with peers like Satya Nadella).
  • Huang’s core argument: Open models are “essential for safety...security...cybersecurity...innovation...startups...sovereignty.” (09:41)
  • Use closed models when needed, but open models are necessary for control, customization, and, crucially, robust system defense.
  • Quote: “I think there's a lot of misunderstanding about closed versus open. We felt that it was important for people to understand there's a world for both.” (11:54, Jensen Huang)
• Open Weighted vs. Open Source:
  • Nvidia supports open weighted models—providing model weights and, where possible, training data and instructions for full reproducibility. (12:29)
  • “The idea that open models is somehow unsafe is also fundamentally wrong.” (13:00, Jensen Huang)

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7. Case Studies: Kimik 3 & Hugging Face Incidents

(13:09 — 15:37)

• Lessons from Recent Incidents:
  • Kimik 3: Major open weighted release, exemplifies the openness movement.
  • Hugging Face: When closed/proprietary models couldn’t help, the open model GLM 5.2 enabled rapid vulnerability detection and response.
    • Quote: “Just because something is closed and just because something is proprietary doesn't necessarily make it secure and safe...Hugging Face...used GLM 5.2 to identify where the vulnerability was...This is a perfect example of self defense that's necessary....” (13:38–15:08, Jensen Huang)
• Danger of Single Points of Failure: Diversity in AI architectures (open and closed) is critical for global cyber-resilience.

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Notable Quotes & Memorable Moments

• “This is the golden ages for Korea...they love using AI. AI has really diffused throughout their society and their industry.” (02:04, Jensen Huang)
• “...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.” (04:29, Jensen Huang)
• "We're constrained in HBM memories...constrained in just about every part of the supply chain. Even constrained now with land and power and construction workers..." (05:21, Jensen Huang)
• “They manufacture the most important version of it, which is the researchers....We ought to continue to learn from them.” (08:22, Jensen Huang)
• “Open models is essential for safety. Open models is essential for security, for cybersecurity. Open models are essential for innovation.” (09:41, Jensen Huang)
• "Just because something is closed and just because something is proprietary doesn't necessarily make it secure and safe....the canonical case that single points of failure is where we have the greatest vulnerability.” (13:38–15:08, Jensen Huang)

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Timestamps for Key Segments

• 01:51 — Overview of Nvidia’s Korea strategy; importance of Korea in AI infrastructure
• 03:21 — Roadmap for next-gen AI memory with SK; shift in the chip industry
• 05:21 — Supply chain bottlenecks; global demand outpacing capacity
• 06:21 — Structure and implications of the Nvidia-SK half-trillion-dollar deal
• 07:29 — US vs. China AI approaches; the importance of researcher generation
• 09:41 — Debate over open vs. closed AI models; rationale for advocating openness
• 12:29 — Open weighted vs. open source models; reproducibility & safety
• 13:38 — Kimik 3 and Hugging Face incidents as case studies for security
• 15:37 — Close of interview

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Conclusion

Jensen Huang’s special Bloomberg interview provides rare clarity on Nvidia’s massive Korean investments, the transformation of the semiconductor industry for the AI era, and the urgent need for both open and closed AI models for innovation and security. The theme throughout: success in AI will depend on collaborative scale, diverse research strategies, and a globally inclusive, robust technological architecture.

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Transcript

[Jensen Huang]

So like 100% of investors think that protection is important. Only about 70% of advisors are like talking to their clients about that.

[Ed Ludlow]

Where do you think the disconnect is happening?

[Jensen Huang]

There's this huge differences that exist in terms of what advisors think they're talking about their clients, what clients are actually hearing.

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[Ed Ludlow]

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 with Nvidia 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 $1 billion in Internet and cloud service provider Naver. Let's listen into a portion of their conversation. Coming not long after the Nvidia CEO appeared at a Korean AI summit, Jensen

[Interviewer]

I think we just start with the basics like career is incredibly important to the AI. Build out globally will 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?

[Jensen Huang]

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 infrastructure that they're incredibly adept at adopting new technologies and is a really technologically forward leaning society and they love using AI. AI has really diffused throughout their society and their industry. 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 consumption of and purchasing of memories or selling AI supercomputers to them as they scale out 2 gigawatts of AI factories. There's a whole bunch of other announcements. We're investing $1 billion in Naver to help. They're the Korea's leading AI cloud. They're going to scale up in Korea up to 2. They're going to scale up 200 megawatts, I think it is, and they're going to expand across the world. And so. But we have a whole bunch of

[Interviewer]

announcements that we're making today with the expanded SK relationship. There's also sort of more direct involvement with Nvidia on the roadmap for hbm. Future generations of HBM talk about that. 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.

[Jensen Huang]

Yeah, we're working together on, of course, we started with HBM2, worked on HBM3, 3E, 4.4E 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 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 100 billion agents and billions of robots all using computers. The computer industry that's built on top of the chip industry surely is not big enough. 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 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 and the semiconductors so that we're prepared for this future is really important.

[Interviewer]

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 for Nvidia based systems wherever they are.

[Jensen Huang]

Well, we, we don't have enough bits. We're constrained in HBM memories, LPDDR 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 is going to make sure that we continue to build out in a throttled way for a decade. And the reason for that is because these infrastructure, unlike 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 than that.

[Interviewer]

The $500 billion number is large. Could 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 spending in the Korean economy or it's SK fronting capital expenditures? Just a little bit more detail.

[Jensen Huang]

We're going to be purchasing memories from them for many years to come. And as you know, we build a lot of computers. In order to build $1 trillion worth of Vera Ruben systems, you're going to have to buy a lot of system memories to go with it. And so we have large purchase agreements and large purchase intentions with SK Hynix. Meanwhile, SK Telecom is going to become an AI cloud. We're starting to build already. They're intending to build up to 2 gigawatts in the near future. And in that agreement we will be selling AI supercomputers to them. So between us, we're going to do half a trillion dollars worth of business. Over half a trillion dollars worth of business.

[Interviewer]

I was able to sit down with SK Group Chairman Chetaewon 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 turn token. In America we're still focused on the quality of tokens. I wondered what you think of that.

[Jensen Huang]

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, result in using less tokens to do so. You could also produce AIs that are much more efficient and maybe you can throw. 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, really, I think you have to take a step back and just realize that both countries has extraordinary AI researchers. And 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 United States will continue to advance AI. The conditions are different, the resources are different, their constraints are different, but they're all, you know, these amazing researchers will find answers. And I think 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. Manufacturing intelligence is important. They manufacture the most important version of it, which is the researchers. And so this is an area, a country that's going to produce excellent AI technology. We ought to 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, you know, we're really fortunate to have them here. And, you know, we just got to keep on racing.

[Interviewer]

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 the industry, to the development of AI. And in the letter, it's pretty well explained, you know, your rationale. But what was the catalyst for now? Why did you and Satya, Nadella and others need to do that in this moment?

[Jensen Huang]

Well, we sense that there's a growing sentiment 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 necessary 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 OpenAI and Claude and Cursor and, and cognition and perplexity. Use everything that you can

[Ed Ludlow]

out of

[Jensen Huang]

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 areas 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, but 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 misunderstand about these open models is yes, you can host it yourself, but you can build your own computer. But most people use use computers in the 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. 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

[Interviewer]

for both open weighted versus open source as well. There is a distinction

[Jensen Huang]

open weighted as much as open as 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 for that is we want to enable you to completely reproduce the AI model that we've open weighed. And so that ability 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. And the idea that open models is somehow unsafe is also fundamentally wrong. And so we just want to make

[Interviewer]

sure that people understand to finish our conversation. The two big case studies were the release of Kimik 3, which on an open weighted basis releases fully July 27. And then the case study of two OpenAI models mistakenly accessing Hugging Faces 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 two? I know that you've been asked about them, but they seem to be like really big moments in AI overall.

[Jensen Huang]

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. It's possible for a model to be, if you will see, stolen. It could be possible that, that somehow it's 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, 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. Now, 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 canonical 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, distributed massively distributed self defense. And so 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. Was 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 5.2 to identify where the vulnerability was, where the penetration was, and were able to quickly identify them and patch it up. This is a perfect example of self defense that's necessary. As a perfect example of diversity of AI technology being necessary. It's a perfect example of why open models and open capabilities for self defense is really important.

[Ed Ludlow]

That's Nvidia CEO Jensen Huang in a special conversation with Bloomberg Tech host Ed Ludlow. You can watch the full Interview [e-mail] 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.

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[Jensen Huang]

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[Jensen Huang]

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[Jensen Huang]

Maybe we're saved.

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