Inside Nvidia's "Thinking Machine"

"AI wouldn't exist without Nvidia. Not in its current form." So says Stephen Witt, a journalist and author of the book The Thinking Machine: Jensen Huang, Nvidia, and the World' Most Coveted Microchip.

In this podcast, Andy Cross, The Motley Fool's chief investment officer, and Motley Fool contributor Jose Najarro caught up with Witt for a conversation about:

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• What Jensen Huang is afraid of.
• Whether anything can stop the current capital expenditure (capex) cycle.
• Where Nvidia's next $3 trillion in market cap could come from.

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A full transcript is below.

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This video was recorded on April 19, 2025

Stephen Witt: 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.

Mary Long: 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. Motley Fool's Chief Investment Officer, Andy Cross and Fool contributor Jose Najarro 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 three trillion dollars 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 livestream Fool 24. We'll drop a link at the show notes in case you want to listen to more.

Andy Cross: 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 Stephen Witt who is the author of The Thinking Machine: Jensen Huang, Nvidia and the World's Most Coveted Microchip. Among other things, Stephen, you've been pretty prolific in your career, but Nvidia is such a hot topic right now. We wanted to have you on board at the Motley Fool and thank you for being here with us.

Stephen Witt: 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.

Andy Cross: Is that specifically about Nvidia, or is that more Nvidia and AI? We'll just get right into the conversation.

Stephen Witt: Well, it's both, but AI wouldn't exist without Nvidia. Not in its current form. I remember 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. You can't really say that for any other player in AI. OpenAI what they do is brilliant, but Google could do it. Meta could do it. It doesn't seem like anyone's been successful in recreating what Nvidia is capable of doing. If you think of AI as a stage, all the players on the stage are OpenAI. We know who they are. They're Anthropic, they're xAI, Elon Musk. Jensen owns a Theater, and that's what's happening in AI right now.

Andy Cross: 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, cryptomining. 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 Jensen and Nvidia where it is on the AI vanguard?

Stephen Witt: 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 was always going to want it better. 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. 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. That insight turned out to be correct.

That's exactly what happened. Jensen was always hunting for that. He didn't know it was AI specifically, and I think that came 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. That's the link.

Andy Cross: Thank you.

Jose Najarro: Stephen, jumping to another question here, I'm pretty jealous because you got to spend a lot of time it seem with Jensen and his team and something. As Nvidia investor, I can only hear him during earnings call or just random interviews he does here and there. But jumping almost to the end of the book, you talk about the last interview you had in the book. It was not as pleasant as the others.

Stephen Witt: Jensen can be hard to be around. [laughs]

Jose Najarro: 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?

Stephen Witt: 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 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. Even when Nvidia was literally the single most valuable company on the planet, he's sitting there thinking, my stock is going to go down and my firm is going to go bankrupt. There were times in Nvidia's history where in fact this did almost happen. 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.

This is true even when they dominate the entire space. It's a very unusual thing. It's really a different way to motivate yourself. I think it's hard on Jensen, and it's hard on the people around him because he has this fiery temper. He will explode in anger if he thinks that his executives or even the people around him just aren't prepared. He'll even do that at people outside his company. He's done it to me. 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 like, I can't put my thoughts into words fast enough and that comes out as anger. Maybe that's true, but I must say it also seemed a little bit self indulgent.[laughs]

Jose Najarro: There's this interesting topic that in the most recent GTC, Jensen talked about a lot, Stephen. Is what he calls AI factories. Well, he mentions AI factories, this is completely different from your traditional 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 thinking energy of AI. This is something he mentions is completely new. The market, they're building it themselves. It's always hard to think of something new being developed, especially in this, I feel even though AI has been here for a while now, almost two, three years from the ChatGPT moment, it still seems like sci-fi to some. Your experience, what do you think about these AI factories? Is this something that's really going to be happening or [OVERLAPPING] already?

Stephen Witt: It's happening. No, it's real. What happens when you put a request into ChatGPT? 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. 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 certain 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 Nvidia computing stack and then as you say, it returns a bunch of tokens which are used to generate this image. 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 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, these are the things that think for you and they take your request and they give it back. They are AI factories. Now, 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.

Andy Cross: Just very quickly, GTC stands for GPU technology conference, I think. It's become the big AI conference that Nvidia hosts every spring, I believe, is that right?

Stephen Witt: That's right. Initially, it was not an AI conference. They were actually looking for any use for these GPUs. Imagine that you built a propeller with an engine attached to it and you're like, what do I use this thing for, for 10 years before the airplane was invented. What is this for? Who could use this? You're just going out to everyone with your airplane propeller and engine attached to it. Does anyone want to build an airplane around this? That's what Nvidia did.

Andy Cross: Stephen, tariffs are just such in the news. 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 learned from the book or just thinking about the environment with Nvidia. Where do you see Nvidia into the tariff mix these days right now?

Stephen Witt: 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 Computech, the big Taiwanese Imputing conference, he'll list 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. 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. 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. They can afford to pay and they will pay if they have to. They'll pay the tariff. I think the cost of production and trying to onshore in the United States is just more than whatever the tariff is, 30% or whatever. They'll just pay it, but it will hurt their bottom line, for sure.

Andy Cross: 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 center spend?

Stephen Witt: I will tell you what I think the biggest risk was. The biggest risk was that Jensen could not sell his equipment to China. This created an opportunity for Chinese vendors to create a second Nvidia, a low cost Nvidia that doesn't earn a 90% margin, that earns a 10% margin and destroys Nvidia's economics. Jensen was terrified of that, I can tell you. Now, most recently, perhaps you saw this, Nvidia paid a million dollars to be at some fundraising dinner with Trump. What's the first thing that's announced after that? Well, we're going to delay the export ban on these chips to China. That's Jensen's 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. In fact, it was the whole impetus for CUDA in the first place. He was making the state-of-the art 3D graphics hardware, but he said, if we're not, what is this company? We don't make anything. We're just a bunch of guys in a laboratory.

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. They have to stay on the front lines of that and it's bad for Jensen if he cannot sell his chips to the world's largest market. I think also the DeepSeek experience has shown, there's no point to this ban anyway. What are you going to ban? It's not going to stop Chinese innovation in AI. It had no real impact on that. 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 and 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%.

Jose Najarro: Discussing current fears, one being the tariffs. The other I keep hearing is 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 like you mentioned, you were just in Taiwan. Is this something that you would consider a yellow flag or a florid flag at the moment?

Stephen Witt: I've heard people talk about the CapEx cycle. I don't think so. I don't think anything is going to stop. Even recession, I don't think 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. Facebook is trying to co-locate with a nuclear power plant to build its AI data center. 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. 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. 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 flood 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 pretty special product.

Every academic computer scientist I talk to views this as a civilizational advancement. If we were talking about 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 you're talking about the 20 year trend, it's a meaningless blip in the longer term of what's going on.

Jose Najarro: AI is just moving in so many directions. AI, it's just this huge umbrella that you can trickle into all different types of markets. Three, that Nvidia really talks about one robotic space, AI and robotics being your autonomous humanoid robots, industrial robots, or even autonomous vehicles. The second, I want to say, is the healthcare industry. The healthcare industry is one that Jensen and I forget the VP of Health always says a lot about how that could be such a massive market opportunity for Nvidia. Then the third is just like your software AI agonic solutions.

Stephen Witt: First of all, you nailed it, Jose. This is exactly correct. The big question is, where does the next three trillion dollars in market capitalization come? What could possibly be left to build? But I think Jensen's perspective is, there's areas that are just waiting to be revolutionized. One is you mentioned in healthcare, and there they have 30 initiatives. 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. That runs over 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. [laughs] But those are the zeros and ones of programmable biology. 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 replacement tissue for damage.

The possibilities are just incredible. Intermediating that, making that happen is going to be some neural network somewhere. It's going to require an incredible amount of computing power from Nvidia. 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 Jensen's biggest focus is robotics. 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. 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? They took it to a bunch of people. The number 1 task that people would benefit the most from if a robot did it, I think was washing dishes. Then number 2 was washing the toilet or cleaning the toilet and number 3 is cleaning up after a crazy party. 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. 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 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 follow Nvidia. This is what Omniverse is. It's basically robotics training platform, a high-fidelity reality simulator. In that simulator, the robot can break one billion dishes. 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 it 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, gym membership, basically, which as you know, has fantastic subscription economics. 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 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, 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. Those would be the two things. 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. 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. Right now, it's an idea. It exists. They don't have a huge customer base right now, so it's conceptual, but they're working very hard on it.

Andy Cross: What about companies that are building their own silicon? Especially clients of Nvidia. Where do you see that on the risk spectrum?

Stephen Witt: It's a great question. I at first was like, well, why don't I just knock this off. I don't go with A&D and just make the same thing. J. 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. [laughs] Jensen thinks about things.

Andy Cross: But do you think there's an advantage if they're going to build it just for their specific uses? One of my thoughts was, 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 tied to CUDA and tied to lots of other clients.

Stephen Witt: Sure. But remember, if they're doing it just what they need to do, that's not a large market. Maybe you can carve out 1-2% of the market. Google had its TPUs for a while. They haven't made a great amount of traction. AMD was trying its approach. The mostly are not successful. A whole wave of this cycle has kind of already happened once, and Nvidia was really just almost completely unaffected.

Andy Cross: I just saying like Microsoft or Facebook, Meta specially, maybe.

Stephen Witt: Outside of Apple, these companies don't have the DNA of that hardware manufacturing. In a certain sense, one of the longer term lessons of computing, Apple accepted. But 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 to focus on your competitive advantage. I don't think Facebook's competitive advantage is building microchips. 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 could be wrong. Apple, in fact, has built Silicon. It works great and it is integrated tightly into their product, they don't use Nvidia stuff. Maybe someone else can do that, but you got to remember, most of what Nvidia sells 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 microchip. There's not really any stickiness happening at the physical level like there maybe would be. It's all modular hardware that can be very easily replaced. Nvidia, though, operating that environment has done just fine. 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.

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. One of the things he learned is do not compete with your customers. 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. But TSMC wasn't selling any chips. They were just fabricating them. 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. If Microsoft builds a training chip, and at the same time, they're backing OpenAI, it's not an open platform. For other competitors, they're not going to want to use Microsoft's product. He said, well, I'm going to train my thing on Microsoft's product, and then Microsoft can steal my idea and wrap it into GPT. No way. I think this is part of Nvidia's competitive advantage, as well.

Andy Cross: 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?

Stephen Witt: My biggest shift in thinking was personal. I was originally like, I'm toast. I'm cooked. I can't be a writer anymore. ChatGPT 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 best selling book. That still can happen, and maybe even that will happen, but I've learned to think differently about that. Now I think about it this way, and this is from exposure to Jensen and watching how he thinks about things. I did something like 300 hours worth of reporting just interviews for this book. 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? 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, 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 stock price. Or I'm a teenager, and I'm interested in this field. I don't know anything about. Then the AI wrote the book on the fly to meet the demands of this particular customer. The book stops being this static paper document and evolves them to something more a knowledge base that you can query. But maybe my voice is in there in some way too. 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 reader in real time. This is how I think about this now. The other thing I have observed is that chess has long surpassed humans yet very few humans are interested in watching two computers play chess against each other, which is completely incomprehensible. In fact, weirdly, this has actually turbo charged the personality-driven aspects of chess. It's become this actually more popular than it was before the computers beat it and now, rather than thinking like this and being boring, you have these chess personalities like Twitch dreamers. They're fun. Magnus Carlson has leaned into this and become almost like a celebrity.

Andy Cross: Scandals, drama, all kinds of stuff.

Stephen Witt: Scandals and drama they always existed to some extent, the chess board but now or also like hot people playing chess? Come on. That did not exist before. I don't think too much. Maybe even if the computer can write a better book than me, maybe no one would read it. Maybe they still want a person, an author behind the book. I'm growing a little more comfortable with this to some extent.

Mary Long: As always, people on the program may have interest 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 a on Monday.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Randi Zuckerberg, a former director of market development and spokeswoman for Facebook and sister to Meta Platforms CEO Mark Zuckerberg, is a member of The Motley Fool's board of directors. Andy Cross has positions in Alphabet, Apple, Meta Platforms, Microsoft, and Nvidia. Jose Najarro has positions in Advanced Micro Devices, Alphabet, Meta Platforms, Microsoft, and Nvidia. Mary Long has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Apple, Meta Platforms, Microsoft, Nvidia, and Taiwan Semiconductor Manufacturing. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.