Inside Nvidia’s “Thinking Machine”
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Jensen's hardware platform, Jensen is not going to build the next version of GPT. He'll build one internally just to test it, but he's not attempting to compete directly with OpenAI because he knows if he does, then his customers will be incentivized to come back and compete against him.

I'm Mary Long, and that's Stephen Witt. He's a journalist and author of a number of books, including, most recently, The Thinking Machine, Jensen Huang, NVIDIA, and the world's most coveted microchip. The Motley Fool's chief investment officer, Andy Cross, and Fool contributor, Jose Naharro, caught up with Witt for a conversation about what Jensen Huang is afraid of, whether anything can stop the current CapEx cycle, and where NVIDIA's next $3 trillion in market cap could come from.

Heads up that this conversation was recorded last Friday, April 11th. Keep that in mind, especially when you get to the tariff part of the conversation. It's all still relevant, but as we know, that situation seems to change just about every day. The full version of this conversation is available on our live stream, Fool 24. We'll drop a link in the show notes in case you want to listen to more. ♪

Hello, Fools. Welcome to another Motley Fool conversation. I'm Andy Cross, the Chief Investment Officer at The Motley Fool. I'm here with Jose Najarro, one of our Motley Fool contributors who specializes really in tech, many things technology, Jose, and NVIDIA. And we're really happy

And excited to talk to Steven Witt, who's the author of The Thinking Machine: Jensen Huang, NVIDIA, and the World's Most Coveted Microchip. Among other things, Steven, you've been pretty prolific in your career, but NVIDIA is such a hot topic right now. We wanted to have you on board with The Motley Fool. Thank you for being here with us. Yeah, definitely the biggest story I've ever covered. Certainly the biggest business story, I would say, in terms of its long-term impact.

on society and the human species, like the biggest story. I mean, and is that specifically is that specifically about NVIDIA or is that more NVIDIA and AI? We'll just get right into the conversation. Well, it's both, but AI wouldn't exist.

Without NVIDIA. Not in its current form. I mean, I was talking to one AI scientist and he was like, without Jensen and his innovations, we'd be 10 years behind on this technology. And you can't really say that for any other player in AI. OpenAI, I mean, what they do is brilliant, but Google could do it. MITRE could do it. It doesn't seem like anyone's been successful in recreating what NVIDIA is capable of doing. And so they really, if you think of AI as a stage...

All the players on the stage are open AI. We know who they are. They're Anthropic. They're XAI. Elon Musk. The theater is owned by Jensen. Jensen owns the theater. And that's what's happening in AI right now. And Stephen and then Jose, we'll bounce back and forth our questions here. My follow-up question is,

If you go back 10 years, NVIDIA has gone through all of the cycles around different technologies it has supported with its GPUs. Gaming, crypto mining, the list is almost endless. And then AI, of course, over the last really two or three years. But when you go back and look over the history of NVIDIA, does that surprise you? And what is in the DNA that got Gensyn and NVIDIA together?

where it is on the AI vanguard. Here's the link, and it's a little subtle. In the 90s, Jensen and his team realized that there was going to be infinite demand for 3D graphics rendering for video games. No matter how well you rendered the sprites, no matter how well you rendered the characters, the customer is always going to want it better. So you couldn't throw enough computing in it. There was no limit

Jensen saw that, and for 10 years, he looked for another application that had that profile. Then they did a bunch of stuff. They did science stuff. They did high academic computing. As you said, they did crypto. But all of those, the demand was ultimately satisfied very quickly. There wasn't a huge market for it until AI came along, intelligence came along.

And Jensen and his team said, here is the other application with infinite demand. No matter how much computing power we throw at AI, we think the customer will always come back to us and ask for twice as much or even 10 times more.

And that insight turned out to be correct. That's exactly what happened. And so Jensen was always hunting for that. He didn't know it was AI specifically. And I think that came as something as a surprise to him. But the moment AI arrived, he pivoted his whole company toward it because he could see just like 3D graphics. I'm never going to satisfy the customer here. No matter how fast these chips go, no matter how much hardware I build, the customer is going to come back to me and ask for 10 times more. Mm hmm.

So that's the link. Yeah. Thank you. Still jumping to another question here. I'm pretty jealous, right? Cause you got to, to spend a lot of time at scene with Jensen and his team and something as, um, Nvidia investor, I can only hear him during a earnings call or kind of like, um, uh, just random interviews he does here and there, but kind of jumping almost to the end of the book, you talk about kind of this last chat, uh, the last interview you had in the book. And it was kind of, um,

Not as pleasant as the others. It can be kind of hard to be around.

And it definitely seems a little bit more on the negative side of the experience, like you mentioned. He's tough to be around with. Did that have any big impact on the way you saw NVIDIA in forms of management or the company after that experience you had with him? I knew that Jensen had this aspect to him from interviewing so many people around him. I just never thought I would see it myself because he's kind of guarded around the media.

But basically, Jensen is different, in my opinion, than most Silicon Valley executives who are driven either by greed or maybe ego or just a vision for the future. Jensen is totally driven by anxiety.

He's completely afraid that NVIDIA will fail and that he will be disgraced. It's totally how he motivates himself, totally from negative emotions. So even when NVIDIA was literally the single most valuable company on the planet, he's sitting there thinking, oh, my stock's going to go down and my firm's going to go bankrupt.

And there were times in NVIDIA's history where, in fact, this did almost happen. So it's not a completely academic thing, but it's just how he motivates himself. He's constantly beating himself up, saying, I'm not good enough. My company is not good enough. And what we're producing isn't good enough. And this is true even when they dominate the entire space. So it's kind of a very unusual thing. It's really a different way to motivate yourself. I think it's hard. I think it's hard on Jensen. And it's hard on the people around him because he has this kind of fiery temper.

He will explode in anger if he thinks that his executives or even the people around him just aren't prepared. And he'll even do that at people outside his company. He's done it to me. He did it to, you know, I think he's done it to other executives in the field. He's got a bad temper. Now, for Jensen's point of view,

He told me my mind is racing, and I'm thinking faster than I can put words into. I can't put my thoughts into words fast enough, and that comes out as anger. And maybe that's true, but I must say it also seemed a little bit self-indulgent.

There's this interesting topic that in the most recent GTC, Jensen talked about a lot, Stephen, and it's what he calls AI factories. So when he mentions AI factories, this is completely different from your traditional kind of cloud infrastructure where your typical cloud infrastructure is meant to host data, to host information, to run applications. This form of AI factories only have one job, is to just generate your tokens. That's all. Running 24-7, generating that token.

thinking energy of AI. And this is something he mentions is completely new, the market, they're building it themselves. It's always hard to kind of think of something new being developed, especially kind of in this, I feel even though AI has been here for a while now, almost two, three years from the Chad GPT moment, it still seems like sci-fi to some.

Yeah. Your experience, what do you think about these AI factories? Is this something that's really, really going to be happening? Oh, it's happening already. No, it's real. So what happens when you put a request into ChatGPT? What happens? So what happens is it takes your request on a data pipe. It shoots it off to some industrial warehouse on the edge of the city somewhere, which is filled with racks of NVIDIA hardware, right? It's filled with 10,000 GPUs or more, all acting in concert.

It takes your request. Let's say we want to make ourselves look like a Princess Mononoke, Studio Ghibli avatar. That's actually an expensive request. It requires a certainly large amount of computing power, enough to run probably a microwave oven for an hour.

It processes that request inside the industrial warehouse, inside the kind of NVIDIA computing stack. And then, yeah, as you say, it returns a bunch of tokens, which are used to generate this image. So data goes in, requests come in, and tokens come out. It really is a factory. And they already exist.

I've been inside one of these things. There's no humans on the floor. It's completely sterile. They're liquid and air-cooled. The supercomputing equipment itself is inside kind of sealed pods. It's very loud because there's hundreds of thousands, actually, of fans spinning all the time. You can barely hear yourself think. These are the thinking machines.

As I say in my book, I mean, these are the things that think for you and they take your request and they give it back. They are AI factories. No, Jensen might be compared to Thomas Edison. I think it's not an inaccurate comparison. That's who I think of when I think of Jensen and building these things.

Which is just very quickly, GTC stands for GPU Technology Conference, I think. It's their big, it's become the big AI conference that NVIDIA hosts every spring, I believe. Is that right? Yeah, that's right. Initially, it was not an AI conference. They were actually looking for any kind of use for these GPUs. They didn't know, imagine that you built a propeller with an engine attached to it and

And you were like, what do I use this thing for? For like 10 years before the airplane was invented. What is this for? Who could use this? And you're just going out to everyone with your airplane propeller and an engine attached to it. Does anyone want to build an airplane around this? That's kind of like what NVIDIA did.

Stephen, tariffs are just such in the news. So I do want to spend a little bit of time about this. When you think about this as an analyst investor yourself, but you look at NVIDIA, what you learn from the book or just thinking about the environment with NVIDIA, where do you see NVIDIA into the tariff mix these days right now? Yeah, so I was just in Taiwan, and they are terrified of these tariffs. It's a sense of dread.

It would really have an incredibly negative impact for them. Now, there was a carve-out actually for semiconductors in the original tariff plan. But even so, if you look at Jensen's presentations at Computex, the big Taiwanese computing conference, he'll list like 40 or 50 Taiwanese suppliers. NVIDIA doesn't make anything. They don't manufacture anything. It's all outsourced. They're really just a very cutting-edge R&D laboratory. Basically, most of their components come from Taiwan.

And that's not easy to replicate in the United States. Personally, I think these tariffs are extremely misguided and bad for everyone involved. But who's going to argue with Trump about it? So it's not a good thing for this company if the tariffs are imposed. NVIDIA earns a 90% profit margin on its most advanced equipment. So they can afford to pay. And they will pay if they have to. I mean, they'll pay the tariff. I don't think...

I think the cost of production and trying to onshore to the United States is just more than 20, more than whatever the tariff is, 30% or whatever. So they'll just pay it, but it will hurt their bottom line for sure. Do you see it from the investing side? Do you see that as the biggest risk that's facing NVIDIA? Let's just call it this year. Or do you see it more around the AI data centers spend? So I will tell you what I think the biggest risk was.

The biggest risk was that Jensen could not sell his equipment to China. And this created an opportunity for Chinese vendors to create a second NVIDIA, a low-cost NVIDIA, right, that doesn't earn a 90% margin, that earns a 10% margin and destroys NVIDIA's economics. And Jensen was terrified of that, I can tell you. Now, most recently, perhaps you saw this, Jensen paid a million dollars or NVIDIA paid a million dollars to be at some kind of fundraising dinner with Trump. And what's the first thing that's announced after that?

oh yeah well we're going to delay the export ban on these chips to china that sentence biggest concern he is terrified of someone in china building the stack again because they can do it he's always had a fear of asian manufacturers knocking off his equipment this goes way back and in fact it was kind of the whole impetus for cuda in the first place he was making the state-of-the-art 3d graphics hardware but he said you know if we're not

What is this company? We don't make anything. We're just a bunch of guys in a laboratory. So if we're not constantly innovating new products, manufacturers in Asia are going to knock off what we're doing and compete our margins down to zero. Yeah.

and so they have to stay on the front lines of kind of that and they can't it's bad for jensen if he cannot sell his chips uh to the world's largest market yeah i think also the the deep seek experience has shown there's no point to this ban anyway like what are you going to ban it's not going to stop chinese innovation in ai it had no real impact on that so it's counterproductive and it could cost nvidia its leadership position i think that was his biggest concern

Having said that, the tariffs are bad for NVIDIA. They are a huge business risk. If Trump got his way and put a 35% tariff on Taiwan, even if semiconductors were exempted from that, NVIDIA's cost of production would go up a ton. It would eat into their margins. Now, if any company in the world can afford a little margin reduction, it's NVIDIA. But still, the stock price would go down 100%.

Discussing kind of current fears, one being the tariffs, the other I keep hearing is kind of just the fear. And I feel like we've heard this since this AI market boom spending started. It's just the fear of overcapacity being built. Is that something that maybe through your experience writing this book or just like you mentioned, you were just in Taiwan. Is this something that you would consider a yellow flag or a red flag at the moment? I've heard people talk about the CapEx cycle.

I don't think so. I don't think anything is going to stop. Even a recession, I don't think, I mean, yeah, it'll impact it somewhat. But if you look at a 10-year projection, just about how much capacity computing power AI is going to need, how many power plants we're going to build. I mean, Facebook is trying to co-locate with a nuclear power plant to build its AI data center.

I know, yeah, maybe timing issues related to CapEx over a period of a few months might affect the stock price, sure. But I think over the long term, we're at the beginning of a gigantic CapEx cycle. And trying to manage around order flow every few months for that CapEx cycle is maybe to miss the forest for the trees. I think the longer term trend is...

practically vertical for this stuff. And I don't see any reason why that wouldn't be the case. It certainly could be the case now that a cheaper competitor could appear or that NVIDIA could even miss a production cycle and flub something. But the scenario where

The AI ultimate inference demand for consumer or businesses doesn't materialize for some reason seems relatively remote to me. I think these products are delivering what people have said they will deliver. I think they are a pretty special product. Every academic computer scientist I talk to views this as a civilizational advancement.

If we were talking about kind of the dawn of the era of electricity or the dawn of the era of the internet, sure, probably in a month-to-month basis, there's some CapEx stuff to talk about. But if we're talking about the 20-year trend, it's a meaningless blip in the longer term of what's going on. AI is just moving in so many directions. And AI, it's just this huge umbrella that you can kind of trickle into all different types of markets. Three that NVIDIA really kind of talks about are one kind of

robotic space, AI and robotics being your autonomous humanoid robots, industrial robots, or even kind of autonomous vehicles. The second I want to say is the healthcare industry. The healthcare industry is one that Jensen and I forget, Kimber, I forget the VP of health always says a lot of how that could be such a massive market opportunity for NVIDIA. And then the third is just kind of like your software AI agentic solutions. Yeah.

First of all, you nailed it, Jose. This is exactly correct. The big question is, all right, where does the next $3 trillion in market capitalization come from? What could possibly be left to build?

But I think Jensen's perspective is, yeah, there's areas that are just waiting to be revolutionized. One, as you mentioned, in healthcare. And there they have like 30 initiatives, right? There's diagnostic initiatives, medical imaging, drug discovery, this kind of thing. I think the most interesting thing I heard about is basically using neural networks to essentially build almost a biological compiler.

If we can think of the elementary nucleotides of RNA, I think it's A, C, G, and U. I didn't go to medical school, so if I got that wrong, let me know in the comments. But those are the zeros and ones of programmable biology.

And so many people want to build essentially a biological compiler that runs on top of these nucleotide bases and then builds bespoke custom drugs and maybe even like replacement tissue for damage. The possibilities are just incredible.

So intermediating that, making that happen is going to be some kind of neural network somewhere. And it's going to require an incredible amount of computing power from NVIDIA. So NVIDIA wants to be, just as they grew the AI platform around it, they want that vendor lock. They want to be right in there. When you're building a system like this, you go through the NVIDIA toll booth. They're trying to be there.

The other one, as you mentioned, huge, and this is, I think, Jensen's biggest focus, is robotics. So let's say, Fei-Fei Li, a Stanford academic who actually ran the neural net competition that really broke through NVIDIA and neural nets. Her new thing is robots. And so what she did is she created a survey. The survey had one question. How much would you benefit if a robot did this for you? And they took it to a bunch of people.

And the number one task that people would benefit the most from if a robot did, I think, was washing dishes. And the number two was like washing the toilet or cleaning the toilet. Number three is cleaning up after a crazy party. So these are the tasks that people least want to do and would be the most profitable for a robot to do if you could sell a robot to do this to consumers. So how do you train a robot to wash the dishes?

Well, if you try and do it in the real world using a neural network strategy with reinforcement learning, you're going to break 10 million dishes along the way. The sink is going to be a mess. It's going to be so expensive to do that.

What Jensen wants to do is build a digital gymnasium, a kind of high fidelity physics simulation that the robot can learn to wash dishes in. This is called Omniverse. Perhaps you've heard about it if you followed NVIDIA. This is what Omniverse is. It's basically a robotics training platform, a high fidelity reality simulator. In that simulator, the robot can break one billion dishes. Right? Who cares? It's all digital.

Then when the brain is trained, the neural network is trained, we download that and stick it into a real-world body and send that off to work at the sink. And it's not going to break anything because it learned in this digital gymnasium. Now, Jensen will charge his robotics customers a very large amount to participate, you know, gym membership, basically, which, as you know, has fantastic kind of subscription economics. Right.

And he'll say, well, I'm saving you money. Look at all the money I saved you if you didn't do it this way. You'd have a laboratory with 10 million shattered dishes in it. You don't have to do that. Now, that's hard.

Getting the physics right is hard. It's not just a game simulator. We've got to get very, very precise tactile physics. We've got to get fluid dynamics right because the dishes are going to be wet. We've got to get a whole lot of things right, surfaces. It's not easy. But I think Jensen feels like, all right, so we do that and then we're going to be at the center of the robotics revolution. That's another trillion dollars in market capitalization for us because that's a trillion dollar industry and we're going to earn a multiple on it.

So those would be the two things I would, I know Jensen is paying a ton of attention to these things. I asked him, what looks like CUDA? What's the bet you're making now that looks like CUDA used to look like 10, 15 years ago? And his first answer, write up one word, omniverse. This is his next CUDA. I would pay attention to that, see if they're actually getting customers. I mean, right now it's, you know, it's an idea. I mean, it exists. They don't have a huge customer base right now. So it's kind of conceptual, but they're working very hard on it.

What about companies that are building their own silicon, especially clients of NVIDIA? Where do you see that on the risk spectrum? Yeah, it's a great question. I, at first, was like, well, why don't I just knock this off? Why don't I go with AMD and just make the same things? And Jay Prabhu, who designed circuits at NVIDIA, was like, yeah.

They can make silicon just as good as we can. There's nothing that we can build that they can't just pry open the lid and look at with a metallurgical microscope and rebuild it. There's no trade secret in the metal. The trade secret is these software development kits that we build. The trade secret essentially is that they're not as good as being on the front lines with the scientists and building them tools as Jensen is. And they don't have that same...

sense of, oh my God, if I don't build this scientist a tool, I'm going to die. My company's going to fail and I'll be disgraced. Jensen thinks about things. But do you think there's an advantage just if they were going to build it just for their specific uses? One of my thoughts was maybe there's an advantage, and Jose, jump in here, maybe there's an advantage if they are building it just for what they really needed to do.

Where NVIDIA is building it tied to CUDA and tied to lots of other clients. Yeah, sure. But remember, if they're doing it just for what they need to do, that's not a large market. Maybe they carve out 1% to 2% of the market. Google had its TPUs for a while. They haven't made a great amount of traction. AMD was trying its approach. They mostly are not successful.

So a whole wave of this cycle has kind of already happened once, and NVIDIA was really just almost completely unaffected. Yeah. I was just thinking like Microsoft or Facebook, Meta, Meta especially maybe. You know, the integrate – outside of Apple –

These companies don't have the DNA of that kind of hardware manufacturing. In a certain sense, one of the longer-term lessons of computing, Apple accepted, but one of the longer-term lessons is it makes sense to separate software and hardware. It makes sense to have your hardware stack built by someone else and to focus on your competitive advantage. I don't think Facebook's competitive advantage is building microchips. And I don't think that's going to change

I don't think Microsoft's competitive advantage is building hardware. I don't think that's going to change. Now, I can be wrong. And Apple, in fact, has built silicon. It works great. And it is integrated tightly into their product. They don't use NVIDIA stuff. So maybe someone else can do that. But you got to remember, most of what NVIDIA sells is basically it's very easy to swap it in and out of the data center. They're all on racks. I can just pull out the rack and stick in another one.

Right. So there's not really any kind of stickiness happening at the physical level like there maybe would be with it. It's all modular hardware that's highly commoditized and can be, well, not commoditized, but it's modular hardware that can be very easily replaced. Right. Yeah.

NVIDIA, though, operating that environment has done just fine. So I think the fact that you can pull the rack out of the data center and replace it with a different chip, very few people are doing that right now. So I don't see that in the immediate horizon. I think also NVIDIA benefits for the same. Let me say this. Jensen learned a ton from TSMC and its founder, Morris Chang. And one of the things he learned is do not compete with your customers. Right.

So the reason TSMC succeeded with its foundry business and the reason Samsung did not do as well is people were paranoid that if they manufactured their chips at the Samsung fab, Samsung was going to steal their idea. Yeah. But TSMC wasn't selling any chips. They were just fabricating them. Yeah. Jensen's hardware platform, Jensen is not going to build the next version of GPT. He'll build one internally just to test it, but he's not attempting to compete directly with OpenAI. Mm-hmm.

Because he knows if he does, then his customers will be incentivized to come back and compete against him. If Microsoft builds a training chip and at the same time they're backing open AI, it's not an open platform. And for other competitors, they're not going to want to use Microsoft's product, right? They're going to say, well, I'm going to train my thing on Microsoft's product and then Microsoft's going to steal my idea and wrap it in a GPT. No way. Yeah, right. And so I think this is part of NVIDIA's competitive advantage as well. What was your...

biggest shift in your thinking about NVIDIA as you were starting thinking about the book and as you ended the book and finished the final word? My biggest shift in thinking was personal. So,

I was originally like, I'm toast. I'm cooked. Like, I can't be a writer anymore. ChatGPT is going to write in two, three years. It's going to write better than I am. It's going to just produce books that are better than mine on demand in five minutes. It's going to take whatever I do. I'm going to feed my interview notes into the knowledge engine, and it's going to produce a bunch of tokens. And those tokens will be a fantastic bestselling book. That still can happen. And maybe even that will happen. But I've learned to think differently about that.

So now I think about it this way, and this was from exposure to Jensen and watching how he thinks about things. I did something like 200, 300 hours worth of reporting. It's just interviews for this book, right? And maybe 1% of that knowledge actually makes it into the finished product, and the rest is just sitting in some database somewhere. Maybe what should happen...

And I have to do this. I'm constantly having to decide, what am I going to put in the book? What does the general reader want? Who is the general reader? I'm trying to guess what the reader wants to read about. But what if the reader came to me and the reader said, well, listen, I'm an electrical engineer with 10 years experience in designing microchips. Or I manage a portfolio and I need to know more about NVIDIA's stock price. Or I'm a teenager and I'm interested in this field. I don't know anything about it. And then the AI wrote the book on the fly.

to meet the demands of this particular customer. And so the book stops being this kind of static paper document and evolves onto something more like a knowledge base that you can query. But maybe my voice is in there in some way, too. And maybe there's still a compelling narrative that brings the reader through the book, but also customizes or tailors certain sections to meet the reads of the needer kind of in real time. So this is how I think about this now.

The other thing I have observed is that chess has long, long surpassed humans. And yet very few humans are interested in watching two computers play chess against each other, which is completely incomprehensible. And in fact, weirdly, this has actually turbocharged the personality-driven aspects of chess.

It's become this kind of like actually more popular than it was before the computers beat it. And now rather than kind of thinking like this and being boring, you have these like chess personalities who are like Twitch streamers. They're fun. You know, Magnus Carlsen has leaned into this and become almost like a celebrity. Yeah. Scandals, drama, drama. Yeah. I mean, they kind of always existed to some extent in the chess world, but now it's like, or also like, you know, hot people playing chess. Like, come on, that did not exist before. I don't think too much. So maybe even if the computer can't,

writer better book than me. Yeah. Maybe no one would read it. Maybe they still want a person, an author behind the book. And I'm growing a little more comfortable with this to some extent.

As always, people on the program may have interests in the stocks they talk about, and The Motley Fool may have formal recommendations for or against, so don't buy or sell stocks based solely on what you hear. All personal finance content follows Motley Fool editorial standards and is not approved by advertisers. The Motley Fool only picks products that it would personally recommend to friends like you. For The Motley Fool Money Team, I'm Mary Long. Thanks for listening, and we'll see you on Monday.