Huang Says AI Reshapes Chip Industry

What I'm going to add is some sound that we got from our colleague Ed Ludlow. He's in San Francisco, of course. He's the host of Bloomberg Tech. He was at an AI summit that took place there yesterday. And Jensen Huang, the CEO of NVIDIA, was there as well. That's right. He spoke exclusively with Bloomberg Tech host Ed Ludlow about the massive $500 billion investment in South Korea's SK Group, as well as recent developments in Chinese AI. 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. 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 is 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 for NVIDIA-based systems wherever they are? 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 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. 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. 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 a trillion dollars worth of Vera Rubin 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 two 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. I was able to sit down with SK Group Chairman Choi Tae-won 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. The goal of AI is to produce an intelligent and a 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 have extraordinary AI researchers. And whatever conditions and whatever resources that they have, amazing people will find great answers. And so my expectation is that China and the United States will continue to advance AI. The conditions are different. Their 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 keep continuing 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 we're really fortunate to have them here. And we just got to keep on racing. 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, your rationale. But what was the catalyst for now? Why did you and Satya Nadella and others need to do that in this moment? 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 open AI and cloud and cursor and cognition and perplexity. Use everything that you can 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 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, they 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 closed 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 guardrail and keep it safe and evaluated. 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 for both. Open weighted versus open source as well. There is a distinction. 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 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 sure that people understand.