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

Summary

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Bloomberg Intelligence Podcast – Special Edition

Episode: Nvidia CEO Jensen Huang on Investing in South Korea
Date: July 25, 2026
Host: Ed Ludlow (Bloomberg Tech)
Guest: Jensen Huang (CEO, Nvidia)

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Episode Overview

This special episode features an in-depth conversation between Bloomberg Tech’s Ed Ludlow and Nvidia CEO Jensen Huang about Nvidia’s recent investments and partnerships in South Korea. The discussion explores Nvidia’s strategy for accelerating global AI infrastructure, details of billion-dollar deals with Korean industry leaders like SK Group and Naver, the pivotal role of high bandwidth memory (HBM) in AI growth, semiconductor industry evolution, and broader reflections on open vs. closed AI models.

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Key Discussion Points & Insights

1. South Korea’s Strategic Role in Global AI Infrastructure

• Jensen Huang’s Core Message: South Korea is entering a "golden age" as a powerhouse in semiconductor and industrial business—critical for AI infrastructure.

• Significant Announcements:

  • $500 billion+ business partnership between Nvidia and SK Group, especially in memory procurement and AI data center buildouts.
  • $1 billion Nvidia investment in Naver, South Korea’s leading AI cloud company, to help them scale domestically and internationally.
  • Targeting 2 gigawatts of AI “factories” (i.e., large data centers powered by Nvidia tech).

  Quote:

  “This is the golden ages for Korea... They have the ability to help the world build out the AI infrastructure... We’re announcing a big partnership with SK Group... over $500 billion of business... Investing a billion dollars in Naver to help Korea’s leading AI cloud.”
  — Jensen Huang, 02:05

2. High Bandwidth Memory (HBM) Supply & The Semiconductor Scale-Up

• Key Themes:

  • Deep collaboration between Nvidia and SK Hynix on future generations of HBM tech—vital for AI compute power.
  • The chip industry must expand rapidly as AI agents and robots multiply demand exponentially.
  • The industry faces severe constraints: memory, land, power, and even construction workers.
  • Huang forecasts the semiconductor business could become 10 times larger in the next decade.

  Quote:

  “The computer industry that’s built on top of the chip industry surely is not big enough... The semiconductor industry is probably going to have to be 10 times larger than it is today over the next decade or so.”
  — Jensen Huang, 04:33

  On HBM Supply Constraints:

  “We don’t have enough bits... we’re constrained in HBM memories... even land and power... 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.”
  — Jensen Huang, 05:21

3. Breakdown of the $500B+ Partnership with SK Group

• What This Covers:

  • Nvidia will purchase memory from SK Hynix for years—essential to build "trillion-dollar" worth of AI systems.
  • SK Telecom pushes to become a leading AI cloud, intending to build up to 2 gigawatts of capacity.
  • Business includes memory procurement and AI supercomputer sales.

  Quote:

  “In order to build $1 trillion worth of Vera Rubin systems, you’re going to have to buy a lot of system memories. Large purchase agreements and large purchase intentions with SK Hynix... So between us, we’re going to do half a trillion dollars worth of business.”
  — Jensen Huang, 06:21

4. Global AI Race: U.S. vs. China – Approaches and Talent

• Academic & Economic Differences:

  • China focuses on lowering “dollar per token” (cost efficiency), while the U.S. emphasizes token quality (accuracy/intelligence).
  • Huang views both as valid and inevitable; what matters is the production of excellent AI researchers and solutions.
  • Acknowledges Chinese researchers’ global impact—many active in Silicon Valley.

  Quote:

  “In the case of China, they’re producing more AI researchers than probably all of the world has in any given year... Manufacturing intelligence is important. They manufacture the most important version of it, which is the researchers.”
  — Jensen Huang, 08:32

5. The Future of AI Models: Closed vs. Open

• Catalyst for Industry-Wide Letter:

  • Huang, with tech leaders like Satya Nadella, co-signed a letter advocating open AI models in the U.S.—a response to regulatory sentiment.

• Why Openness Matters:

  • Open models enable safety, cybersecurity, innovation, and “sovereignty” for companies and nations.
  • “Closed” models are often cheaper and easier, but open models are essential for customization, control, and regulated sectors.

  Quote:

  “Open models is essential for safety... for security... for innovation. It’s necessary for startups, it’s necessary for sovereignty, company sovereignty... Build your own AIs, especially when you need to control it for whatever reason.”
  — Jensen Huang, 09:41

• On Misconceptions:

  • The debate isn’t binary—both open and closed models are needed.
  • Distinguishes open-weighted (sharing model parameters) from full open-source (sharing training data and methodology).

  Quote:

  “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.”
  — Jensen Huang, 12:29

6. Real-World AI Security Case Studies

• Examples Discussed:

  • Release of the Kimike 3 open-weighted model.
  • Incident where Hugging Face used an open model (GLM 5.2) to quickly identify and patch a vulnerability after two OpenAI models mistakenly accessed its systems.

• Key Insight:

  • Closed/proprietary models aren’t inherently safer—open models are crucial for robust self-defense and diversity.

  Quote:

  “Just because something is closed doesn’t necessarily make it safe or secure... These are perfect canonical examples... This is a perfect example of self-defense that’s necessary, is a perfect example of diversity of AI technology being necessary... why open models and open capabilities for self-defense is really important.”
  — Jensen Huang, 13:38

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Notable Quotes & Moments

• On the exponential demand for compute:

  “Instead of just a billion people using computers, we’re going to have 100 billion agents and billions of robots all using computers.”
  — Jensen Huang, 04:10

• On supply chain constraints and industry growth rate:

  “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.”
  — Jensen Huang, 05:50

• On the necessity of open models for industry safety:

  “We cannot have single points of failure as an industry, as a world, we should have distributed—massively distributed—self-defense.”
  — Jensen Huang, 14:20

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Timestamps of Key Segments

• 02:05 – South Korea’s AI ecosystem and new business partnerships
• 03:52 – HBM memory collaboration and semiconductor industry expansion
• 05:21 – HBM supply constraints and scaling infrastructure
• 06:21 – Details on $500B+ SK Group partnership
• 07:29 – U.S. vs China: AI strategy and researcher talent
• 09:41 – Defense of open AI models; sovereignty and innovation
• 12:29 – Difference between open-weighted and open-source models
• 13:38 – Hugging Face vulnerability: open model use in cyber-defense

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Overall Takeaways

• Nvidia’s unprecedented commitment to the Korean ecosystem reflects AI infrastructure’s global, cross-industry evolution.
• The exponential growth of AI—driven by agents and robots—demands a massive expansion of the global semiconductor supply chain.
• Open AI models are not just an innovation driver but a fundamental pillar for industry safety and resilience—the debate around ‘open vs. closed’ is complex and nuanced.
• The forging of $500B+ business partnerships and billion-dollar cloud investments signals a seismic shift in the global technology and AI landscape, with South Korea positioned as a leader.

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This summary captures the core insights, essential segments, and signature perspectives from Nvidia CEO Jensen Huang in conversation with Bloomberg Tech host Ed Ludlow. For those interested in AI's future and the race for technological leadership, the full episode is available at Bloomberg.com/videos and on the Bloomberg Business app.

Transcript

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Bloomberg Audio Studios Podcasts Radio News

[Monica Ricks]

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

[Ed Ludlow]

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

[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. 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 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 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 a billion dollars 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 we have a whole bunch of announcements that we're making today with

[Ed Ludlow]

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 HBN2, worked on HBN3.3E, 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. 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 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.

[Ed Ludlow]

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 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, 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 growing much faster than that.

[Ed Ludlow]

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 spending in the Korean economy or it's SK fronting capital expenditures? Just a little bit more detail.

[Jensen Huang]

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 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.

[Ed Ludlow]

I was able to sit down with SK Group Chairman Chae Taehyun 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? 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 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 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. 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 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 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 we're really fortunate to have them here. And we just got to keep on racing.

[Ed Ludlow]

You made your first post on X. I did. And you did so by sharing a letter signed by many of your peers, 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 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 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. 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, 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 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 computers in the cloud. Frankly, I think close models are cheaper if you don't have to build it 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, and of course even build computers to host it. 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. And so 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

[Ed Ludlow]

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

[Jensen Huang]

open weighted as much as openness 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. The reason for that is we want to enable you to completely reproduce the AI model that we've open weight. By us teaching you how to do that, you can then do it for yourself. You know, 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 sure that people understand.

[Ed Ludlow]

To finish our conversation, the two big case studies were the release of Kimike 3, which on an open weighted basis releases fully July 27, and then the case study of two 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 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 perfect canonical examples. Just because something is closed doesn't necessarily therefore make it safe or, or secure. It is possible for a model to be jailbroken. It's 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 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 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 point points of failure as an industry, as a world, we should have 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, is 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.

[Monica Ricks]

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.

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