Jen-Hsun Huang's latest 10,000-word interview: AGI is coming, AI will revolutionize productivity

GPUs play an increasingly important role in AI computing

Brad Gerstner:

That was, that was something that we were very excited about on November 22nd because we had people like Mustafa from Inflection and we didn't have people from Character coming to our office talking about investing in their company. They said, well, if you can't invest in our company, then buy Nvidia, because everyone in the world is trying to get Nvidia chips to build these apps that are going to change the world. Of course, the Cambrian moment happened on ChatGPT. Nonetheless, these 25 analysts remain so focused on cryptocurrency winners that they can't imagine what's happening in the world. So it ended up being much bigger. In very plain English, the demand for Blackwell is insane, and it's going to stay that way for as long as you can, for as long as you can foresee. Of course, the future is unknown and unknowable. But why are the critics so wrong in thinking this won't be overbuilt like Cisco was in 2000.

Huang Jen-hsun (1944-), Chinese-American physicist::

The best way to think about the future is from first principles, right? Okay, so, to the question, what are the first principles of what we're doing? Number one, what are we doing? The first thing we're doing is reinventing computing, isn't it? We just said that the future of computing will be highly machine-learning. Yes, highly machine-learning. Okay, almost everything we do, almost every application, Word, Excel, Powerpoint, Photoshop, Premier, AutoCAD, your favorite application is designed by hand. I promise you, in the future it will be highly machine-learning. Right? So all of these tools will be so, and most importantly, you'll have machines, agents to help you use them. Right. So now we know that's true. Right? We've reinvented computing. We're not going back. The entire computing technology stack is being reinvented. Okay. Now that we've done that, we said software would be different. What software can write will be different. The way we use software will be different. So now let's acknowledge that. So those are my basic facts right now. Yes.

The question now is what happens? Let's look back at home computing of the past. There was $1 trillion invested in computing in the past. Let's look at it, just open the door, look at the data center, look at it. Are these computers the future you want? The answer is no. You have all these CPUs in there. we know what it can and cannot do. All we know is that we have $1 trillion that we need to modernize our data centers. So right now, as we speak, if we're going to modernize these old things over the next four or five years. That's not unreasonable.

So we have a trend where you're talking to people who have to modernize it. Yes, they are modernizing it on the GPU. That's it.

Let's do another test. You have $50 billion in capital expenditures. You like to spend Option A, Option B, to build capex for the future, right?

Or build capital expenditures as you have in the past, and now you have the capital expenditures of the past, right? Yeah, right. It's right there. It's not much better anyway. Morse's Law is basically over. So why rebuild it?

We only took out $50 billion and put it into generative AI, right? So now your company is better. Right? Now how much of that $50 billion would you put in? Well, I would put $50 billion into 100% because I've had four years of infrastructure that's past that.

So now you're just, I'm just reasoning from the perspective of somebody thinking from first principles, and that's what they're doing. Smart people doing smart things. Now, the second part is this. Then we have a trillion dollars worth of capacity. Go for it, Bill.

Trillions of dollars worth of infrastructure. It's about $150 billion. OK. So we have a trillion dollars of infrastructure that needs to be built over the next four or five years. Well, the second thing we observed is that software is written differently, but software is used differently.

In the future, we'll have agents. Our companies will have digital employees. In your inbox, you'll see these little dots on low profile faces. In the future, things mean low profile icons for AIS. Right? I'll send those to them.

I'm not programming computers in C++ anymore, I'm going to program AI with hints. right? Now, that's no different than me talking to me this morning.

I wrote a lot of emails before I came here. I was certainly prompting my team. I would describe the background, I would describe the basic limitations that I knew, I would describe their tasks. I'd leave enough space, I'd give enough direction so that they understood what I needed. I wanted to be as clear as possible about what the outcome should be, but I left enough room for ambiguity, a little room for creativity, so they could surprise me.

Right? It's no different than the way I prompted AI today. Yes, that's exactly how I'm suggesting AI. So there's going to be a new infrastructure on top of the infrastructure that we're going to modernize. This new infrastructure is going to be operating thesedigital personof AI factories, they will run 24/7.

We will have them for all companies around the world. We will have them in our factories and we will have them in our autonomous systems. Right? So there's a whole layer of computing architecture. This whole layer I call the AI factory, which the world has to make, but simply doesn't exist today.

So the question is, how big is this. It's not known yet. It could be in the trillions of dollars.I know what's going on right now, but as we sit here and build it, the wonderful thing is that the modern architecture of this new data center is the same as the architecture of the AI factory. That's a good thing.

Brad Gerstner:

Can you make it clear that you have a trillion dollars of old stuff. You have to modernize. You have at least a trillion new AI workloads coming. Yes, you will have $125 billion in revenue this year. You were once told that the market capitalization of this company would never exceed $1 billion. As you sit here today, is there any reason for that? Yes, if you only have $125 billion out of trillions of Tam, then your future revenue will not be 2x or 3x what it is now. Is there any reason why your revenue is not growing? There isn't.

Jen-Hsun Huang:

As you know, not everything is like that, companies are only limited by the size of their fishponds, and goldfish ponds can only be so big. So the question is, what is our fish pond? What is our pond? It takes a lot of imagination, which is why market makers think about the future without creating new fishponds. It's hard to figure that out looking back and trying to capture market share. Right. Share takers can only be so big. Certainly. Market makers can be very big. Sure.

So I think the good fortune that our company has is that from the very beginning of our company we had to create the market in order to swim in it. People didn't realize it at the time, but now people do, but we were at the beginning of creating the 3D gaming PC market. We basically invented that market and all the ecosystems and graphics card ecosystems that we invented. So the need to invent a new market to serve it later is a very comfortable thing for us.

Jen-Hsun Huang: I'm happy for OpenAI's success

Brad Gerstner:

As you know, OpenAI raised $6.5 billion this week at a $150 billion valuation. We're all in.

Jen-Hsun Huang:

Yeah, really happy for them, really glad they came together. Yeah, they did a great job and the team did a great job.

Brad Gerstner:

They are reportedly going to have revenues or operating income of about $5 billion this year and possibly $10 billion next year. If you look at the business today, it's about twice as much revenue as it was at the time of Google's initial public offering. They have 250 million, yes, 250 million average weekly users, which we estimate is twice what Google had at the time of its initial public offering. If you look at the P/E of this company, if you believe it's going to be $10 billion next year, it's about 15 times the expected revenue, which is what Google and Meta had at the time of their IPO. Imagine a company that 22 months ago had zero revenue and zero average weekly users.

Talk to us about how important OpenAI is to you as a partner and the power of OpenAI as a driver of public awareness and use of AI.

Jen-Hsun Huang:

Okay.This is one of the most important companies of our time, a pure AI company pursuing an AGI vision. Whatever the definition of it is. I hardly think it matters at all what the definition is, and I don't think timing matters either. One thing I do know is that AI will have a capability roadmap over time. And that capability roadmap is going to be spectacular and peculiar. And in the process, long before it reaches anyone's definition of AGI, we'll make the most of it.

你所要做的就是，现在，在我们说话的时候，去和数字生物学家、气候技术研究人员、材料研究人员、物理科学家、天体物理学家、量子化学家交谈。你可以去问视频game design师、制造工程师、机器人专家。选你最喜欢的。无论你想选择哪个行业，你都要深入研究，和重要的人交谈，问他们，AI是否彻底改变了你的工作方式。你收集这些数据点，然后回头问问自己，你想有多怀疑。因为他们不是在谈论AI的概念优势。他们现在谈论的是将来使用AI。现在，农业技术、材料技术、气候技术，你选择你的技术，你选择你的科学领域。它们正在进步。AI正在帮助他们推进他们的工作。

Now, as we said, every industry, every company, every height, every university. Unbelievable. Right? Absolutely. I'm gonna change business somehow. We know that. I mean, we know it's so tangible.

Today. It's happening. It's happening. So, I think the awakening of ChatGPT triggered it, which is totally incredible.I love their speed and their unique goal of moving the field forward, which is really important.

Brad Gerstner:

They build the economic engine that can fund the next modeling frontier. I think there was a consensus emerging in Silicon Valley that the whole modeling layer, the commoditization of Llama, enabled a lot of people to build models very inexpensively. So early on we had a lot of modeling companies. These, features, tone and cohesion were on the list.

A lot of people question whether these companies will be able to build escape velocity on the economic engine that will continue to fund the next generation. My own feeling is that's why you see consolidation. openAI clearly reached velocity. They can fund their future. I'm not sure many other companies can. Is that a fair assessment of the current state of the modeling layer? We're going to do what we've done in many other markets, and we're going to bring this integration to the market leaders who can afford it, who have the economic engines and the applications that will allow them to continue to invest.

Just having a powerful GPU doesn't guarantee a company's success in AI

Jen-Hsun Huang:

First, there is a fundamental difference between models and AI. It is. Models are essential elements. Right. It is necessary but not sufficient for AI. Right. So AI is a capability, but for what, right? And what is it used for? Right? AI for software-driven cars is related to, but not the same as, AI for human robots, which is related to, but not the same as, AI for chatbots.

So you have to understand the taxonomy. Yes, the taxonomy of the stack. At every level of the stack, there are opportunities, but not every level of the stack offers unlimited opportunities for everyone.

Now, I just made a comment that what you're doing is replacing the word model with GPU. In fact, that was a great observation that we made as a company 32 years ago, that there is a fundamental difference between GPUs, graphics chips, or GPUs, and accelerated computing. Accelerated computing is not the same as what we're doing with AI infrastructure. They are related, but not identical. They are superimposed on each other. They are not identical. And each of these abstraction layers requires a completely different skill set.

People who are really good at building GPUs don't know how to become an accelerated computing company.I can give you an example, there's a lot of people that make GPUs. i don't know which one came later, we invented GPUs, but you know we're not, we're not the only company that makes GPUs today, right? There are GPUs everywhere, but they're not accelerated computing companies. There are a lot of them that do. They have gas pedals that do application acceleration, but that's not the same as an accelerated computing company. For example, a very specialized AI application, right, that could be a very successful thing, right?

Brad Gerstner:

This is MTIA (Mata's self-developed next-generation AI acceleration chip).

Jen-Hsun Huang:

Right.But it may not be the kind of company that brings influence and capability.So you have to decide what you want to be. There may be opportunities in all these different areas. But just like building a company, you have to be mindful of how the ecosystem changes and what gets commoditized over time, recognizing what's a feature, what's a product, and yes, what's a company. OK. I just talked about, well, there's a lot of different ways you can think about this.

xAI and the Memphis supercomputer cluster have reached "the age of 200,000 to 300,000 GPU clusters."

Brad Gerstner:

Of course, there is one new entrant with money, smarts and ambition. That would be xAI. Yes, right. And there are reports that you had dinner with Larry Ellison and Musk. They convinced you to give up 100,000 H100 chips. They went to Memphis and built a large coherent supercluster in a matter of months.

Jen-Hsun Huang:

Three o'clock. Don't equate, okay? Yes, I had dinner with them.

Brad Gerstner:

Do you think they have the capacity to build this supercluster? There are rumors that they want another 100,000 H200s, right, to scale up this super cluster. First of all, talk to us about X and their ambitions and what they've accomplished, but at the same time, have we reached the age of 200,000 to 300,000 GPU clusters?

Jen-Hsun Huang:

The answer is yes.And then first, recognizing the achievement. From the moment of conception to the moment the data center was ready for NVIDIA to install our equipment there to the moment we fired it up, hooked it up, and did our first training session, it was all worth it.

Jen-Hsun Huang:

Okay.So the first part of it is building a huge plant in such a short period of time, water-cooling it, electrifying it, getting permits, I mean, it's like Superman.Yes, as far as I know, there is only one person in the world who can do that. I mean, Musk's understanding of engineering and building large systems and marshaling resources is unique. Yes, it's incredible. And of course, his engineering team is great. I mean, the software team is great, the network team is great, the infrastructure team is great. Musk understands that.

From the moment we decided to start planning with the engineering team, the network team or the infrastructure computing team, the software team, all the preparations were ahead of schedule. And then all of the infrastructure, all of the logistics, the amount of technology and equipment that was shipped that day, the video infrastructure and the computing infrastructure, and all of the technology that was needed for the training, 19 days were up in the air, do you want anything? Did.

Take a step back and think about it. Do you know how many days are 19, how many weeks are 19? Right? If you look at it in person, the amount of technology is incredible. All the wiring and the networking, the networking of the NVIDIA devices is very different than the networking of a hyperscale data center. Okay, how many wires does a node need. The backs of computers are full of wires, and to integrate this whole bunch of technology and all the software together is incredible.

So I think what Musk and the X-team have done, and I'm very grateful to him for recognizing the engineering work that we've done with him and the planning work and so forth. But what they've accomplished is unique and has never been done before. Just from that perspective. A hundred thousand GPUs, as a cluster, that's easily the fastest supercomputer on the planet. The supercomputers you build usually take three years of planning. Then they deliver the equipment and it takes a year to get them all up and running. Yes, we're talking 19 days.

Clark Tang:

What is NVIDIA's credit?

Jen-Hsun Huang:

Everything is already working. Yes, of course, there's a whole bunch of X algorithms, X frameworks, X stacks, and so on. We said we had tons of reverse integration to do, but the planning was excellent. Just pre-planning.