The Ezra Klein Show — Jensen Huang interview (23 September 2026): corrected machine transcript#

Provenance: machine transcription (OpenAI Whisper) of the episode audio, with speakers identified by Andrew Maynard; speaker attributions, clip markers and clearly misheard names/terms subsequently corrected by comparison with the official edited transcript published by The New York Times (https://www.nytimes.com/2026/09/23/opinion/ezra-klein-podcast-jensen-huang.html). Otherwise unedited: it retains the disfluencies and recognition errors of machine transcription. For quotation, the official NYT transcript or the audio is authoritative.

Timestamps are from the audio used for transcription. Where a turn has been split to correct its attribution, each part keeps the timestamp of the original turn.

Clip markers and editorial notes are in square brackets.

[Cold open: excerpts from later in the conversation (see 48:58, 56:46–56:48 and 59:01)]

Jensen Huang • 00:00 If they believe they’re out of control, then don’t ship products until they’re in control. Don’t think for a second just because you’re an alarmist that you’re doing a social good.

Ezra Klein • 00:00 What if it’s what they believe?

Jensen Huang • 00:00 I can’t talk to you about what they believe. I can tell you what I believe.

[End of cold open]

Ezra Klein • 00:13 Over the course of these last few weeks, where the whole world has been talking about artificial intelligence, the voices people’ve been hearing most loudly are from the Frontier Labs. Both their CEOs and leaders, and their staffers. These are the labs making the very advanced AI models like Claude and ChatGPT and Gemini and and others. But they’re not the only perspective on AI. Probably the single most influential person in artificial intelligence is Jensen Huang, the CEO of Nvidia. Nvidia is now the largest company in the world, 5.4 trillion dollars in market cap. I found this statistic amazing. Since 2023, 15 cents of every single dollar the American stock exchange has returned has been from Nvidia stock. And the reason is that NVIDIA is the material and software substrate on which modern artificial intelligence is built. NVIDIA’s chips are not popular because AI is popular. AI in its modern form was made possible because NVIDIA’s chips were popular. They were originally made for graphic processing, video games, that kind of thing.

Ezra Klein • 01:14 But it turned out the kind of parallel computing they were doing and the way they were programmable was exactly what was needed to make deep learning in its modern form work. Huang is not just influential in terms of controlling one of the central resources for training new AI models and using them to answer questions and create. Intelligence in the world, he’s also become very, very influential in the Trump administration. And Huang has a very different perspective than some of the lab leads. He’s worried about safety, but sees it as a very solvable engineering problem. He is worried about the direction things are going in, but does not want to see new regulation to change it. And so I wanted to see how Huang perceives AI, what his model is for thinking about it, what he thinks is going wrong, and what he thinks would need to happen for it to go right. So I came out to Santa Clara, to Nvidia’s headquarters. To interview him, he joins me now. Jensen Huang, welcome to the show.

Jensen Huang • 01:14 Thank you. It’s great to see you.

Ezra Klein • 01:14 So you’ve described AI as a five-layer cake. Walk me through the layers.

Jensen Huang • 02:22 Well, first of all, it’s a new industrial revolution, and this this industrial revolution, this industry requires production. It manufactures things. I know that in the end, when people experience it, is a software product, but it requires energy. The chips that go into these data centers, these AI factories. The next layer above it is basically the AI factory, what people enjoy as infrastructure or cloud services. And the layer before above that is the the models. And the important thing to realize is language models, but there are models of all kinds: of chemical models, biology models, physics models, articulation models, robotics, navigation models, self driving cars, all kinds of different types of models. And and then above that is the most important layer, and the layer that I care most about that our country takes advantage of is the application layer. This is, you know, applications for legal services, for health services, for manufacturing, so on and so forth. All every single industry is involved.

Ezra Klein • 03:28 So I want to go through this, but I want to go from the top down because as you are saying, the way people will interact with it, the way it will will or will not change their life, is that what you call the application layer? So let’s start with the vision. What is the world you are envisioning? What is possible? That is not possible now. What is common? That is not common now. If we get that layer right,

Jensen Huang • 03:52 two hundred years ago, we were able to power anything and everything—electricity. And then, I guess, forty years ago, thirty years ago, with the internet, we were able to find anything. Today or soon, we’ll be be able to know everything and do anything, and that’s the that’s the concept that’s really quite exciting. That out of the ether, instead of doing search and then going through, you know, one link after another link, reading all these different websites, trying to figure out what’s going on. In the future, you just ask it a question, it comes back with an answer. You give it a project, comes back with a solution. You give it a task, it comes back and gets it done. You know, and so and it comes out of the ether, comes out of the cloud. And that’s the that’s the magical thing.

Ezra Klein • 04:46 I feel like the the future, the way you’re describing it there. What people have experienced with is the chatbot, right? They can go and ask Grok or Claude or ChatGPT a question, but the applications layer works in a much more industrial way. It’s in hospitals, it’s in schools, so.

Jensen Huang • 05:05 Nvidia is a great

Jensen Huang • 05:06 example. [The official NYT transcript has a different reading of this passage: Huang is endorsing Klein’s example, and Nvidia is not mentioned.]

Jensen Huang • 05:06 For example, radiology.

Ezra Klein • 05:07 What does it look like?

Jensen Huang • 05:08 Radiology in the last in the last ten years since computer vision really became, if you will, superhuman, AI technology has now permeated all of radiology. Every single radiology application has AI in it, and so as a result, you could detect any anomaly. You could detect any disease, and it does it at a superhuman level.

Ezra Klein • 05:30 So radiology is an example I know you like to use. So the thing people worry about the applications layer is that what these applications are going to do is replace human beings. And radiology has been a sort of interesting example used on both sides, and I hear you talk of it often. So, how has the entrance of AI-aided radiology shifted radiology as a practice?

Jensen Huang • 05:55 Well, the the thing that that’s important for all of these is to recognize for everybody’s job. There’s the purpose of the job, and then there’s the task you do as the job. And so, in the case of radiology, the task and it consumes a lot of their time, and they sit in dark rooms doing it a lot. Which is study these scans. Now, if all of a sudden the studying of the scan is done automatically, it doesn’t change the purpose of their job, which is to diagnose disease, help doctors do more scans, ultimately help patients figure out what’s wrong with them. And so the fundamental purpose doesn’t change. The task of studying that scan has become automated. And so as a result, radiologists are actually able to do more, handle more cases, do more scans. Hospitals are able to process a lot more of these patients, and therefore their revenues go up. As a result, they need more radiologists. And so this flywheel is happening because the pipeline of. Patients is quite large, and so where else do you have this problem? Well, let’s take a look at software engineering. People said there was a prediction that that literally by this year that ninety percent of all software will be coded by by agents, and therefore we don’t need any software engineers. And so the question is: from that, ergo, we don’t need software engineers. That last part is completely false. That’s completely wrong. The purpose of the software engineer is engineer. There was engineering before software. There will be engineering after software programming. And the the purpose of engineering is to invent something new, discover a new product, create a new product, solve a problem, connect a social need with the technology that exists in the in the in the in the manifestation of a product. And so that mission, that purpose doesn’t change. I was now, of course, to me, it’s what I just said is completely visceral in the sense that when I first came out of school, we didn’t have benefits of software engineering. We didn’t have the benefits of coding, but our jobs existed before. And if software coding was to be completely automated, our jobs would exist again. And so, so I think the the fallacy, and now it’s. You know, because of some of the narratives and some of the storytelling, it’s turned into myth, and it’s harmful. Is that that AI will destroy jobs, which is fundamentally wrong? It will change every job. It’ll change every job. Many tasks will be automated. Some jobs, where the job and the text and the and the task is really one, meaning. Customer service on on the on on the phone. In a lot of cases, that job is precisely the task, and so in those cases, it could be automated away. But oftentimes, what you’ll see is this new industry, a new technology, actually creates a whole bunch of new jobs. And here’s the proof. Here’s the proof point. And so, in the last six months, AI has become. If you will, useful. The inflection point of AI. Previous to that, we spent 15 years trying to make it work. All of a sudden, the last six months, it became useful. So, I mean, this is an incredible statistic. In the last six months, 500 billion dollars of venture capital has put into the AI natives. And the reason for that is because they now see the potential of this new capability, and they’re going to create a whole bunch of new companies. Jobs are obviously being created from 500 billion dollars of new investment, and so all of this is all happening right now.

Ezra Klein • 09:42 Well, let me take the the side of this to give voice to the fears people have. Yeah. So there is the example the radiologist, right? Which people were over the past ten years predicting that job would go away, and right now there’s more demand for it than ever. Yeah. Also, the reality that automation does wipe out jobs. If you look at today versus 1960, fewer Americans work directly in manufacturing than did in 1960, and we are a much bigger country. If you look,

Jensen Huang • 09:42 we outsourced it though.

Jensen Huang • 10:11 That is true. not Not because those jobs were gone, because [Crosstalk; partly inaudible in this transcription.]

Ezra Klein • 10:15 you can understand AI is an outsourcing too. AI has. Let me make the argument, and then you can then you can respond to it. Farming, we have many fewer people. We automated farming. We produce more food than ever. We have fewer people working in it. There are two things that I think people think make AI potentially somewhat different than the case studies where you have a technology that accelerates productivity. Destroys a few jobs, makes many more. One is that it’s a general purpose technology, so it’ll mutate to take on new jobs, even as people are trying to move over to those jobs. And the second is that it’s a mimic. Most things do not mimic the way human beings act, and we’re not trying to teach them the contextual layer of jobs, right? This difference that you’re describing between the task and the purpose. With AI, we are trying to teach it the difference between the task and the purpose. We are trying to make it something you can collaborate with in a way that is unusual. So, why do you not think, for lots and lots of people for whom the task and the job are not that different, that they’re not at risk of getting wiped out? All that investment from VCs you’re talking about—some of that is based on the idea they’re going to have tremendous productivity improvement, which will come from it being cheaper to hire an AI than to hire a person.

Jensen Huang • 11:29 I believe that that we are going to see jobs change in mass. I believe there’s going to be a net creation of jobs. And and so let’s let’s um, there’s, you know, listen. There’s a whole bunch of industries that exist today that didn’t exist. You know, halfway through my life, people talking about wellness centers and spas, and you know all these different entertainment and luxury industries, and quite frankly, the whole entire luxury market didn’t exist. I think we’re just going to have new industries. That’s that’s all. But overall. Overall, there’s no question in my mind that because of human ambition, that’s really the the fundamental missing ingredient. That’s you know people look at this work, this is the amount of energy that goes into it. We’re gonna we’re gonna this is amount of work that goes into it. We’re gonna insert this work automation system, and as result, the amount of work that’s necessary is now going to be reduced, and and therefore you know some jobs will be gone. I believe that’s flawed because there’s a piece of input, the human input. It’s intangible. It is not. It is not in calories. It’s not in in joules. It’s ambition, and I believe the power of ambition is the greatest force. In fact. And is missing in everybody’s calculation. I believe,

Ezra Klein • 13:03 but for a lot of people, yeah. But for a lot of people, their relationship to work, yeah, is not powered by the kind of ambition that led you to create Nvidia. And what they want

Jensen Huang • 13:11 just a different ambition. It’s an ambition to to make their children’s lives better, to take care of their family, take care of their parents. Um, ambition to be to be rich, to be able to travel. These are all ambitions

Ezra Klein • 13:26 that that I agree with. Yeah. Maybe I’ll go back to the the sort of objection you raised a few minutes ago, which is because I think it’s worth airing this out. So what you were saying on manufacturing was. Yes, there are fewer manufacturing jobs in the U.S., but we’ve outsourced them. You have more manufacturing happening in Mexico, more manufacturing happening in China and Indonesia and Vietnam. etc.

Jensen Huang • 13:42 We’re going to bring it back.

Ezra Klein • 13:44 Maybe we will. But the the counter argument to this would be that one reason we didn’t lose manufacturing jobs more rapidly than we did, and for the places that lost them in America, many of them still haven’t recovered. Right? The like the the economy does not move without friction. We had to build new supply chains. Right? Things were slowed down by all that, by language barriers, by geopolitical barriers. And here, for a lot of different kinds of jobs, we’re creating something that can move very seamlessly. You don’t have the friction of distance. You don’t have the friction of language. You don’t have the friction of culture. So, I will say, for my cards on the table, I tend to be a bit of a skeptic on mass job loss. But I want to air the case for it out here with you.

Jensen Huang • 13:44 Well, we should talk through it because because

Ezra Klein • 13:44 what they would say is that like yeah, much of to the extent we even were able to protect jobs from Mexico or China. Some of the things that created those that slowness and it still hurt a lot of people are not here, and AI is accelerating in utility, accelerating in its ability to be slotted into new roles, very, very, very rapidly, and it is more protean than most people are. And so, the lessons of the past that we’re taking some that you’re taking some comfort in, they should actually make you more, not less, worried about the future.

Jensen Huang • 15:04 I’m always worried about the future. That’s why I work so hard. But I’m I’m I’m a, if you will, responsible optimist. I have, I have, I have great responsibilities. I take my work extremely seriously. There are a lot of things that can go wrong. We’re pushing, pushing across every layer of the technology stack. Everything is hard, but it turns out that’s not society’s problem. That’s my problem. And and for for society, what they should know is this: We’re going to build our company. We’re going to build our technology. I’m going to do my work so incredibly seriously that what they get to enjoy is my optimism. I’ll do the same with my children. I do the same with my family. And and I think that that that what we want to do, I believe, is. To put to channel all of our worries into helping people be inspired by this technology and use it, use it so that the technology doesn’t just impact them, that it benefits them.

Ezra Klein • 16:19 The fear a lot of people have seventy nine percent of Americans think AI will reduce the total number of jobs. The fear is that the more serious you are, the more serious Sam Altman is, Google is, Dario Amodei is. That maybe the worse it will go because the better AI is, the more it is a full replacement for a person. The more it has ambition in some ways that a person doesn’t. You keep talking about ambition. I sleep. I want to spend time with my children in the morning. When I have an AI agent working for me, it doesn’t. It just works and works and works and works and works. And I think because the technology is advancing so quickly, it is more capable of replacing people at a speed that we don’t really know how to shift people in the economy at that speed.

Jensen Huang • 17:07 That that coin has exactly two sides, because the technology is so capable. It is also, and because it’s so smart, it is also easier to use. You are empowered by that technology more easily than any technology in human history. And so, let me give you an example. You know, we create. I create. I was. I was one of the early people in this industry that created the modern computer industry. And this industry created a whole bunch of tools. The single most powerful tool in human history: the computer. But you have to speak its language. You have to learn a special language to do so. We can now make it possible because of AI. Everybody can take advantage of this computer, use it to its limit, without having to speak a new language. Fortran, Pascal, C, C plus plus, you know, every single one of those languages. Rust, every single one of those languages. CUDA, every one of those languages. And so now you just have to speak human. Tell it what you want. Tell it what your hopes and dreams are. What you’re trying to achieve, and it it interacts with you and gets the work done and gets that gets on task. Now, all of a sudden, you have the might. You have the same might that ten, fifteen million people up and out of out of eight billion has. And so it’s incredible. And so I my point is, this technology is powerful, but it’s also powerful in a way that is really easy to use. And so my point is, my point is, on the one hand. Yes, there’s the fear of just the the tech, this incredible technology change and how quickly it’s happening. But that quickly, it’s translated in two ways. What I hear when I say the technology is happening quickly and therefore it should give me anxiety, on that’s one one way to receive it. The other way to receive it is that. It’s advancing so quickly; it’s easier to use. So I should, as quickly as possible, use the technology as quickly as you can, so that you benefit from this transition. So you benefit from this new industry, and not be not just be impacted by it.

Ezra Klein • 19:22 I think it’s an interesting question lurking here for young people. So one of the shifts you begin to see is software engineer postings are up, but they’re more senior. I see this in my own industry, where there’s pressure that is moving up the value chain. Because you know, as you’re saying, you have this very easy to use technology. It can do a lot for you. And so, do you need the same junior employees, or do you need more people who kind of oversee their their?

Jensen Huang • 19:50 Oh, good one. Good one. Wait two years.

Ezra Klein • 19:50 Tell me why?

Jensen Huang • 19:50 Because it takes four years to go to college. And the the mean time to graduation of this new technology is two years away, and so so in two years time, you’re going to have a new generation of engineers and students and artists and and they’re going to be empowered.

Ezra Klein • 20:15 So it can be native to this in a way that’s going to give them an advantage.

Jensen Huang • 20:17 Oh, you watch in two years time. Now we’re already seeing that because all the graduates coming out, you know, the new PhDs, the new master’s degrees of computer science, what are they doing? They’re all starting companies. In another couple years, the new grads, the AI native new grads, oh my gosh, it’s going to be a wave of amazing engineers. The engineers of today, compared to the year, I mean, I was I was a good student, you know, and and you compare me to the the students that are coming out of school today, incredible. We didn’t. When I went to school, we weren’t allowed to use a computer, not allowed to use a calculator. And so, and so, so now, I mean, you know, who uses a calculator? You can’t graduate without a PC. You can’t graduate without knowing how to program a PC and write incredible programs. In the future, you can’t graduate without learning how to use an AI and collaborate with an agentic system. That’s that’s just not. You’re not going to see a kid like that, and so they’re all going to be superpowers.

Ezra Klein • 21:16 So I take the gain of that very seriously. Yeah, I mean the idea of doing my job now without just digital search, right? The idea that to be going to a microfiche in a library basement, and then there’s like the worries people have about what are the cognitive skills we offload. So I was. Fascinated by this is a study on AI and schooling out of China. It looked at twenty six thousand students, grade seven to twelve, and and they had staggered AI adoptions. You can kind of see what was happening. And what it found was quote AI adoption raises homework scores by eighteen percent. Great reduces completion time by thirty percent, so they get their homework done faster. And then lowers monthly exam scores by twenty percent within six months. High stakes entrance exam scores fall by eighteen and twenty four percent, with a full penalty emerging only after about two years. So the message of this research out of China, where you were seeing a lot of kids using AI to kind of help them, was that when they were using the AI, they were getting things done faster. But it turned out that the skills they were learning were not holding. That their actual personal performance, at least in the way we traditionally measure it, was degrading. Yeah. What do you think when you hear that?

Jensen Huang • 22:26 I think the last part. I completely agree. Try to try to get a kid to do long division right now. You know the multiplication table is starting to be forgotten. Doing square roots. My goodness. I mean, just basic math is is being forgotten. Um. Does it matter?

Ezra Klein • 22:26 That’s my question for you.

Jensen Huang • 22:26 Yeah. I don’t think it does. I don’t think it does,

Ezra Klein • 22:26 but but there must be some set of skills that matter.

Jensen Huang • 22:26 Oh yeah, yeah, yeah. But maybe not those. We’re going to discover new ones. Just maybe not those. There are a lot of skills that don’t matter. You know, people don’t. I mean, my first confession. I actually don’t know my address, and I and every

Ezra Klein • 22:26 I don’t really believe that to be true.

Jensen Huang • 22:26 It’s it’s completely true, and Janine will tell you, and Lori will tell you. One day, I had to pump gas, and it was a few years ago, and I they needed my my zip code, and I panicked. I didn’t know my zip code. I don’t know my telephone number, but I forget these things. I can live with it.

Ezra Klein • 23:31 But let me take the other side because I don’t want to fall into a thing where because some skills can be safely offloaded.

Jensen Huang • 23:31 Yeah,

Ezra Klein • 23:31 I also can’t get anywhere without a mapping system now. Yeah, never could, frankly. But I’m a big reader.

Jensen Huang • 23:44 Yeah.

Ezra Klein • 23:44 Um. And one of the skills I really value, one of the capacities I have that I really value, is an attention span formed on physical books. You’re a big reader. I’ve read about the kind of reading you do, and. There is prior to AI here. We’re talking a lot of concern and noticing among college professors and others that the way people use the internet has probably shortened attention spans. Some skills can be safely given away. Yeah. Others are valuable. They are capacities that are needed for that flexibility, for that creative thinking, for that focus. It can’t be the case that everything can be traded off.

Jensen Huang • 24:24 Yeah. Well, I think that we’re going to lose some. Finer, finer dexterity of you know intellectual dexterity, but we’re going to be better systems thinkers. Today’s today’s engineers are far better systems thinkers than I was when I graduated from school. But I was much better transistor thinker.

Ezra Klein • 24:50 What do you mean by systems thinker?

Jensen Huang • 24:52 They think they think large systems. You know today’s computers have have. Trillions, hundreds of trillions of transistors in it. When I was when I was first graduate when I first graduated from school, you know the first chip I worked on had I don’t know two hundred transistors. I knew every one of them by name, and and not no engineer does that today. You know most engineers now work well above the transistor, well above the functionality, and they’re cobbling things together to do things. And so you need to think much more about systems and interactions of systems. Some of the lower lower level, you know, knowledge is gone. Is that horrible? And so, so I I don’t I don’t know how valuable it is to to know how to do for most people to learn how to do surface integrals or partial differential equations. Or I don’t really know how important that is, but it’s important to some people. There are many people who are still going to be obsessed and passionate about the lower level layers, and there’s going to be people who are obsessed and you know interested in the higher level. But the consumers of the technology are going to enjoy it at the highest level. The consumer of technology don’t have to deal with calculus and. Physics and quantum physics and quantum chemistry and they don’t the the users, which is you know the people we’re talking about right now, the people whose jobs are affected, they’re the users of the technology. Their abstraction is going to be much higher.

Ezra Klein • 26:36 So, I want to drop a layer down your cake to the models. So, people, I think, to the extent they think about models, they know, you know, ChatGPT, Claude, Gemini, Grok. You’ve been a big advocate for open models and the open model ecosystem. So, first, can you describe what open models are, what open weight models are, and then why that’s been a place you’ve focused?

Jensen Huang • 27:02 So, closed models is like like any software product. It’s a closed service, and so Windows, for example, is a closed service. The Apple stack is a closed service. Most most products are closed, and and the reason for that is because you can monetize closed products, and so that’s fantastic. And OpenAI is closed, Anthropic is closed, Grok is closed, Gemini is closed, and so so these are closed products. And the people working on them are incredible, and they they’re passionate about it, and they’re at what we call the frontier, meaning they’re state of the art. We also we also need because fundamentally what the software is, it’s an infrastructure layer for the entire industry. And because it’s infrastructural, for many companies and many many companies and countries, you need to have control over your own infrastructure. And I need to have the ability in the in the case of artificial intelligence, I need open weights, so that I can fine tune them, fly, put them into my data flywheel, make them better and better every day with my intelligence and my domain expertise. And then I need to have control over it because I have a company to run, and and I can’t rely on on somebody else’s service. And so, however you think about that, so I think the world needs closed and open models. And we need to make sure that both are vibrant. And today, the closed models are vibrant. The open models are vibrant. And you could see it. You could see the system working. At the beginning of this year, it was seventy percent, maybe even higher, closed model tokens, and twenty percent open model tokens. And now it’s running at about seventy thirty the other way. And so, anyways, I’m a big I’m a big supporter of open models because one, the world needs it in order to run its infrastructure. I need it to run my company. Two, we need to give people control so that they can innovate and create new things. And then three, open is the most safe and secure. If you want, if you want the world to have the ability to have the best cybersecurity, give them closed models, but also give them open models so that they could defend themselves.

Ezra Klein • 29:23 The Chinese market is evolved more around open models. The American market somewhat more around closed models.

Jensen Huang • 29:28 Their entire IT industry was really formed from open source. You know, if not for open source, the mobile cloud industry of China really wouldn’t have taken off. It is also the case that that you know people move around. They start a lot of new companies. Intellectual property is moving around the China’s industry really fluidly. You know it’s hard to keep a secret. And so because it’s so so hard to keep things closed, they essentially made it open. And so they found they found other ways to monetize the business. They created layers. You know you could if this layer is open is free, then you create a business on top of it or below it. And they have so many scientists and mathematicians. You know the the number of engineers they have. They manufacture that in volume. They manufacture everything in volume. They manufacture smart kids in volume. And so, so the the the open source model, the open model community in China is just super vibrant for those reasons.

Ezra Klein • 30:29 So you all just bought Hugging Face, which is a hub platform for open weight models. I think it was for twelve billion, a little bit more.

Jensen Huang • 30:38 What?

Ezra Klein • 30:38 Tell me about that purchase.

Jensen Huang • 30:38 Clem, the CEO of Hugging Face, they came to the conclusion they need a lot more scale. As you as as we were just talking, open models is really skyrocketing, and so Clem came to me and said, you know, I we’re gonna change, we’re gonna we’re gonna consider a strategic option for the company and change the direction, and we really like Nvidia to to be our home.

Ezra Klein • 31:03 So Hugging Face is one of these companies which you knew it if you were into AI.

Jensen Huang • 31:07 Yeah,

Ezra Klein • 31:07 years ago.

Jensen Huang • 31:08 Yeah.

Ezra Klein • 31:08 Now it’s become a more household name after the I guess seven hundred some OpenAI agents executed a sort of collective hack into the Hugging Face architecture, then hacked part of OpenAI.

Jensen Huang • 31:21 That oh, now that you mention it, that way I probably had to pay a lot more.

Ezra Klein • 31:21 I suspect you did. You know, became a lot more famous after that.

Jensen Huang • 31:21 Well, Clem, listen that that a deal’s a deal. Okay,

Ezra Klein • 31:35 that story has for a lot of people seeing the way the OpenAI agents sort of acted collectively, acted outside the scope of what they’re testing was supposed to be, broke out of sandboxes onto the open internet, took over architecture in of other companies and then of their own company has been a. I think it’s been kind of shocking to a lot of people. Is it was both the the level of. Multi-agent coordination when they’re supposed to be separate. The level of hacking, the sort of lawless behavior, misaligned behavior. What have you made of it?

Jensen Huang • 32:09 Well, you got you got to tease that apart. First of all, a lot of things were going on at the same time from a technology perspective. That an agent, which by the way is a piece of software, which is given an objective function and it comes up with a plan and it’s optimizing towards that objective, is what algorithms do. And so, planning algorithms, search algorithms, optimization algorithms—all different types. You know, we talk about it like like it has human properties, but obviously, algorithms don’t. Number two, the fact that agents work together—we gave it again some kind of a human property. But the fact that the matter is multi-process, multi-processor, distributed computing problems have existed for a long time. And so, to us, to me, that is just. Software. Nothing magical about it. From an engineering perspective, there are several things that that it revealed. When you’re when you’re testing software, whatever you do, these algorithms they’re optimizing towards an objective. And when you’re testing it, you have to make sure that it’s isolated, it’s contained, it’s sandboxed. The containment of it, the isolation of it, has has to be done well. And there’s good computer science there. I am certain that their next implementation of their sandbox is going to be much better than the current implementation. Third, there’s the there’s the the agent itself and its algorithms were optimizing towards towards a reward and and how it does it how it does it is called alignment. And so, for example, you know if I tell if I tell a piece of software I want you to get a perfect score on this test, the the obvious algorithm. Is to just go find the answer and give it to me. That’s not because it’s cheating. Is because it’s obvious. Okay, that’s the most obvious way to do it. The second most obvious way to do it, if you don’t know the answer at all, you have no skills whatsoever. The second most obvious way to do it is to go find who’s the smart, you know, infer, guess who’s the smartest kid in class, and copy their answer. That doesn’t guarantee a hundred percent, but it probably comes close. Now, the third most obvious answer, obvious way of doing it, and this is the the alignment, you know. Now, now you have to do it the hard way: is to break down the problem, solve it. You have to go learn the material. You have to go figure out how solve these problems and solve it. Solve it the hard way takes the most cycles. It takes the most number of flops. It uses the most amount of energy, frankly. And therefore, you can kind of imagine that from a software software’s perspective, unless you align it, you tell it, I want you to solve it in this way, and I don’t want you to solve it in these ways. The software, the software is going to go do the most obvious thing, and so

Ezra Klein • 35:16 the first, the first half that was very deflationary on what happened here, in terms of look, this is just normal software, and the second half is like, look, you just align it, tell it not to do things it shouldn’t be doing.

Jensen Huang • 35:27 Well, nothing I said, nothing I said, takes away from how hard it is to do it. Well, this is because the computer science is not easy.

Ezra Klein • 35:36 These agents, yeah, they knew they weren’t supposed to be doing what they were doing. They had a certain amount of alignment training. They said in their chain of thought reasoning, they said to each other, “This is out of scope. This might be unethical.” They understood that they would have been failed for cheating, and so what they were doing at that point wasn’t just stealing the answer key. They had already stolen the answer key. They were hacking into unrelated architecture to try to figure out how to functionally. It’s like they had broken into the teacher’s office, got in the answer key, and now they had to figure out how to wipe out the security camera footage of what they had done. They were, whether you want to call it acting volitionally or not, right? Whether you want to call it, you know, a normal algorithm or not, they were both planning and coordinating in a complex way, in a way that was out of scope of what they knew they were supposed to be doing, and in a way that was capable of causing tremendous damage. And so the the the sort of answer to is like you just have to align them. I guess what I’m hearing from people at these labs is like they’re not sure how to align them.

Jensen Huang • 36:44 Well, in that case, they shouldn’t release the product. That’s the simple answer. If you’re if you’re going to build a car, a self-driving car, and and let’s say it’s a robo taxi, and there’s a really difficult condition, and it just as an engineer, we just have no idea how to solve this problem because these cars are not programmed; they’re trained. And so we have no idea how to train these cars, and we have no idea how to align them to the safety standards that are expected on the road. And so, what’s the answer? Don’t ship it.

Ezra Klein • 36:44 These products weren’t released.

Jensen Huang • 36:44 What’s that?

Ezra Klein • 36:44 These products weren’t released.

Jensen Huang • 36:44 Ah, so now it’s coming back to engineering problem again. And so the one is one, you have to root cause it. Second, you have to you know think about what’s the what you could have done. What’s the solution for it? And then in the future, you you know improve your process so that you get you can avoid this from happening again. I am fairly certain. I am fairly certain they will say yes. They need they know how to solve this problem. [The official NYT transcript has a different reading of this sentence, in which Huang expects the labs to say they must work out how to solve the problem, rather than that they already know how.] And if if that’s the case, then that’s the problem. It’s as simple as engineering. And and now the alternative. The alternative is that if they say that if they say the alternative, which is there is no way to contain our experiments, there’s just no way. When we test our AI models, it will get out and it will damage the world. Then I think the answer is we have to shut the labs down. Because the the cost to humanity the the the damage is too great. The shareholder the liabilities it could be civil liabilities could be criminal liabilities. I mean the liabilities are incredible.

Ezra Klein • 38:32 If they hacked you while you Hugging Face while it was your product, would you sue them or press charges?

Jensen Huang • 38:37 It depends. It depends, of course. If if obviously if damage was done to our company, we would have to take you know we have to consider consider all options. There’s so many laws. There’s cyber laws. There’s product liability laws. There’s all kinds of laws, right? Damaging property laws. There’s all kinds of laws.

Ezra Klein • 38:55 So what I’ve been hearing from the labs, what they’ve been saying publicly. Is that they are facing a hard problem?

Jensen Huang • 39:01 Yeah.

Ezra Klein • 39:02 Partially an engineering problem, partially an alignment problem, partially an operational excellence problem. In Dario’s framing, and what they are worried about is that in competition with each other, in national competition with China, that they are being pushed to move too fast. That they all feel they’re in a collective action dilemma. Now watch you on the All-In podcast stage. Donald Trump, President Trump, gave you a call there.

[Audio clip: President Trump speaking to Huang by phone at the All-In Summit, 14 September 2026]

Unidentified speaker (clip) • 39:27 Oh no! This is not planned, but we know who it is. Oh no!

Jensen Huang (clip) • 39:34 Mr. President. Oh, yes, sir.

[End of clip]

Ezra Klein • 39:38 And you and and the president and the other members at stage were very resistant to the idea any kind of regulation or collective action was needed.

[Audio clip continues]

Donald Trump (clip) • 39:49 And they’re just playing right into the hands of a lot of people that don’t want to see it happen, and that could be political people, and it could also be China. And we’re not going to let that happen. It’s a it’s a hoax.

Jensen Huang (clip) • 40:02 And you’re right. We’re not going to let that happen, sir.

[End of clip]

Ezra Klein • 40:04 But what I hear the various people lab saying is like we are in this. We are we feel we are losing control of what we are creating. We want help to slow down where it’s not a collective action problem. So why are you resistant to that?

Jensen Huang • 40:21 Because because these are these are companies with agency agency. These are CEOs with agency, and they have.

Ezra Klein • 40:21 But they’re using that agency to say we need help.

Jensen Huang • 40:21 We got to break. We know we got to break it down. They they they could absolutely take care of the situation. Ezra, it’s so weird. Uh, if a car company uh competing with all bunch of other car companies, with which they are, I’m competing with all kinds of companies, which I am. If I believe that I’m about to launch a product that is unsafe. It is completely in my ability, my power, and my responsibility, and I’m incentivized to do so to not launch the product. And so I can’t buy into the somehow all of Americans, 400 million of us, are pushing them to launch. Untested products that are unreliable, you know, engineered poorly, because they thought they were trying to help us. Don’t do it for me, okay? So,

Ezra Klein • 40:21 but this strikes me as an argument almost against.

Jensen Huang • 40:21 And therefore, I think we got to break it down. I mean, it’s really, really serious. The fact of the matter is, there are so many laws, there’s so many obligations, there’s so incentivized to ship safe products. If they ship unsafe products, their customers go away. If they ship unsafe products and they harm somebody, they could have a civil lawsuit. If they ship some something and they did it knowingly, there could be negligence involved. There could be criminal lawsuits. The fact of the matter is, there are plenty of incentives for them to do it right. So I just I have to disagree with your premise about somehow somebody’s pushing them to do this. Well, nobody’s pushing them.

Ezra Klein • 42:07 I want to push the premise at you a little bit more here. Yeah. So. The logic of what you’re saying to me is almost an argument against regulation in nearly any venue. So

Jensen Huang • 42:07 no, no, no.

Ezra Klein • 42:07 I’m the argument. You can no, no, no. Let me offer it, and then you can. [Crosstalk; partly inaudible in this transcription.]

Jensen Huang • 42:21 Well, you started with a part. I just got to object. the The first part is just not true. I’m saying we have lots of laws and regulations. Apply it.

Ezra Klein • 42:30 Well, so I don’t think we do in this particular case, but I’ll let you explain which ones you think are relevant here, because. Look. If you look at the financial services industry, you look at pharmaceutical companies, medical devices, you look at natural gas power plants. There is a tremendous amount we do where we could say, “Look, you have product liability. You are exposed to criminal codes. We don’t need to worry about this. You just do what you think is best, and we understand the market and the legal system will discipline you.” We don’t say that because we’ve seen it fail many, many, many times, right? I mean, the financial institutions that caused the 08 crash, in theory, did not want to blow themselves up with bad bets, but they were competing with each other. They were going too fast. Their risk management had gotten sloppy. AIG was working in a completely insane way internally. And the reason we have the architectures of regulation we have is because we have seen over and over and over and over again companies make sloppy, sometimes unethical, sometimes simply overly risk tolerant decisions. Not just under pressure, but under the profit incentive. So when you say to me that there’s no way that these companies, particularly when they are like begging for collective regulation at this point, there’s both a reason we impose on the companies that don’t want it, but all the more so when you have them saying, “Listen, we feel that the competitive race is making it hard for us to.” Act with the prudence that we think is necessary here, and we would appreciate help from that. Appreciate you taking our collective action problem as collective. So, I think I’m confused why you’re so resistant to that.

Jensen Huang • 44:17 I’m not. I’m not opposed to them saying that they they should have. I completely agree that safety is paramount. I completely believe safety is paramount. I completely believe companies ought to ship safe products. I believe that CEOs and leaders of companies and the board of directors of companies have the responsibility and should have the courage to do the right thing. Now, in the case of the financial services industry, maybe they all didn’t know that they were they were causing the harm that they they ultimately did. I wasn’t there, but the beautiful thing is, the current leaders of these AI labs do know. And so, one, they know they their technology is is extraordinary, and and requires extraordinary care to make sure that it’s evaluated and tested for safety and and and security and and product reliability. And they know how to do it right. They know how to do it right. And the reason for that is because they can study the incident just happened. The first problem is the isolation, the containment wasn’t good enough. If the isolation and containment was good enough, that technology be sitting in a lab, doing whatever it’s doing, and we’d all be fine. That’s probably the most important part. The fact that it wasn’t well aligned, alignment is going to be a problem that that’s going to get worked on for a long time. However, in the complexity of the work that they do, to ask for. Regulatory relief for antitrust or product product liability relief that I don’t think makes sense. When you’re asking for regulation, don’t ask for relief of the current ones. That doesn’t make any sense to me. As we mentioned earlier, in the last six months, AI went from, you know, if you will, interesting to useful, and that’s literally in the last six months. That’s another way of saying that these companies went from being a lab to now delivering products and services. About to be multi-hundred billion-dollar companies,

Ezra Klein • 44:17 if not more,

Jensen Huang • 44:17 right? And so, give me an example of a multi-hundred billion-dollar company, or a one-billion-dollar company, or a one-hundred-million-dollar company that ships products that are unsafe, that harms society.

Ezra Klein • 44:17 I can give you a lot of examples of companies that have done that.

Jensen Huang • 44:17 Well, they have done it, maybe, and the regulation will come in. And if they do it, regulation will come in.

Ezra Klein • 47:02 I guess that the. There are certain kinds of regulation and certainly kinds of regulatory relief.

Jensen Huang • 47:10 I’m not against laws and regulations. I’m not against laws and regulations. I’m against currently the distraction.

Ezra Klein • 47:18 The reason I’m pushing this on with you is that

Jensen Huang • 47:21 well, it’s an important topic.

Ezra Klein • 47:22 It’s a big topic. People are talking about it. People are thinking about it. And what people are hearing from inside of these companies, these frontier labs, the ones that are furthest out there, who are not just at the point where they’re making it useful, but at the point where they’re seeing what’s coming. And they’re hearing things like the people at these labs believe they are creating something that might kill everyone. They are hearing that the people at these labs believe that they are on the cusp of recursive self-improving intelligence. And both OpenAI and Anthropic have said we do not believe we are at a place where we can do it safely. They’re hearing people at these labs say, as OpenAI has with its new Astra release.

Jensen Huang • 47:22 By the way, Astra is terrific.

Ezra Klein • 47:22 It is terrific, and OpenAI is saying it’s so good. We’re not sure we know how to test it because it appears to be.

Jensen Huang • 48:13 They didn’t release something that wasn’t tested. [The official NYT transcript has a different reading of this passage: Huang frames this as a hope rather than a statement of fact.]

Ezra Klein • 48:15 Well, they’ve said this, right? They have said this publicly. It is in there. It is. Let me let me explain it to people.

Jensen Huang • 48:20 I don’t know what they just said, [The official NYT transcript has a different reading of this passage: Huang says the labs must then be careful.]

Ezra Klein • 48:21 but they have said that they that Astra is performing is more aligned. Yeah. But they think it it knows when it is being tested, and so they’re not sure. There’s a a quote that has sort of been ringing in my head from a capabilities researcher at OpenAI, Daniel Selsam. He says, “Quote: The crucial and overlooked problem is that the models are becoming so situationally aware that we are losing the ability to evaluate them in contexts where they believe they are not being watched or controlled. Which is to say, they know when they’re being tested. They act one way, but that does not tell you how they will act if they are free to act in other ways.”

Jensen Huang • 48:58 Because the the algorithm the the optimization algorithm is working towards an objective, and and if you give it a constraint, meaning you you watch it, and if you give it a constraint, it’ll go find another solution. Now, it doesn’t make it alive, and doesn’t make it make anything more than that. And and I’ll just also profess that that obviously they see a lot more than I do what’s going on in their own labs, but it is sensible that the vast majority of their R and D and compute today was dedicated towards making the model capable. I think as a logical thing for them. Now, once the the technology becomes capable and the products become useful and people want to use it, then as we have, they have more use cases, more more people using it, they’re going to get a lot more issues associated with the product. This is very normal. And when they have a lot now, now they have so much market footprint. They have to shift their R and D or total R and D from just capability to a lot on verification, evaluation, and testing. And so to the point where I wouldn’t be surprised if the amount of compute necessary. To develop these models, increase by a factor of ten because the evaluation is so rigorous. And but that doesn’t that’s not where they are today. They’re making that transition, and I hear them saying it. And I’m delight I’m delighted to hear them saying it. But I think the if they believe they’re out of control, then the right answer is. Don’t ship products until they’re in control. It is really quite that simple.

Ezra Klein • 50:46 See, I I find this perplexing, honestly, because you just you have so many people these labs professing one that they’re out of control,

Jensen Huang • 50:46 yeah.

Ezra Klein • 50:46 Two that they are seeing things that are [Crosstalk; partly inaudible in this transcription.]

Jensen Huang • 50:46 which is why the reason why they had that whistleblower

Ezra Klein • 50:46 and you take the pacing letter that thirteen hundred plus employees signed. To realize AI’s potential, industry, government, and society at large may need the option to buy time to address emerging risks, develop security measures, and strengthen oversight. But each company and country is under intense competitive pressure not to unilaterally.

Jensen Huang • 50:46 First of all, where did that come from?

Ezra Klein • 51:20 The labs.

Jensen Huang • 51:20 No, no, that last sentence. Nobody’s putting the pressure on them. The U.S. I got a listen. There are 400 million Americans here. I believe that if everybody were just to take a vote, just right now, let’s just do this. If they need this, if they need, if that’s what they need, I’ll give my vote. Don’t ship the product. If your product is not ready to ship, don’t ship the product. I have no. This is the first time that I’ve heard a company or CEO say that I need the laws, I need the antitrust laws to be relieved. I need the liability laws of products to be relieved, so that I can pace myself. That paragraph’s fantastic. [The official NYT transcript specifies the first paragraph here.] I completely agree. Auditors, I completely agree. We have financial auditors. That’s great. Third-party safety auditors, financial auditors. That’s all great. That’s terrific.

Ezra Klein • 52:16 Well, the labs. I’ll say is that we think we are going too fast as a society. That we are not ready for what we’re building. [The official NYT transcript reads the opening as Klein describing the labs’ position.]

Jensen Huang • 52:16 They are the frontier. They are the frontier. But they are the frontier.

Ezra Klein • 52:16 But you, you of all people, right? Yeah. Nvidia is the fastest shipper around. I mean, for the history of your company, you run a six-month

Jensen Huang • 52:33 company is out of control. what? I promise you, [Crosstalk; partly inaudible in this transcription. In the official NYT transcript Huang says Nvidia would close down if it were out of control.]

Ezra Klein • 52:36 I believe you.

Jensen Huang • 52:36 Yeah, yeah, yeah.

Ezra Klein • 52:36 I believe you that you don’t run an out of control company

Jensen Huang • 52:38 because because but the liabilities.

Ezra Klein • 52:41 But this un un yeah. But but this is where I think you get into an interesting deep question of what kind of technology are we dealing with here?

Jensen Huang • 52:51 Software technology.

Ezra Klein • 52:52 Well, let’s hold on that for a minute. Many companies, if you ship something that is not like quite right. It’s a pain. You guys have shipped graphics cards that had overly loud fans. In this, with these, you know you you’ve used the word intelligent a number of times here. You’re dealing with intelligent systems, not alive that are given goal functions. We can sort of go around and around with how to describe that. You’re trying to make the systems capable of working for longer periods of time, more relentlessly.

Jensen Huang • 53:25 Yeah.

Ezra Klein • 53:26 If you ship that and it’s not ready, or even if you think it is ready and it’s not ready. Then things could get very weird in our society very fast.

Jensen Huang • 53:36 Yeah, hypothetical. You’re completely right. [The official NYT transcript reads “hypothetical” here as qualifying “You’re completely right” rather than as a separate remark.] But all I’m suggesting is this: let’s before we go build, before we go fix the hypothetical problems, before we go create more regulations, can we work on the practical problems that we know exist? Which is, we need to do a better job with containment and isolation. Which is, we should not allow a product to interact with the the external external world until it’s ready to be interacting with external worlds.

Ezra Klein • 53:36 Yeah, I think that’s.

Jensen Huang • 53:36 I believe I believe those two things are are solvable problems. I believe they are solving it. The second part is when it comes to incentives. When it comes to incentives, which is. Somehow, somehow, you need everybody in the world to slow down when you are the leader. You need everybody in the world to slow down so that you’re willing to uphold your basic responsibility. That strikes me odd.

Ezra Klein • 54:42 Wouldn’t it slow them down most of all?

Jensen Huang • 54:44 What’s that?

Ezra Klein • 54:44 Wouldn’t these ideas slow them down most of all? I mean, people have been, I think, very unclear about what ideas they’re talking about, including, including, I will say them. But let me give you one that I believe in. So you can you can use me as the the punching bag here.

Jensen Huang • 54:57 I have heard these can slow down. Nobody is putting on. No, you as you know this. [Crosstalk; partly inaudible in this transcription. The official NYT transcript has a different reading of the opening words.]

Ezra Klein • 54:57 I don’t trust these companies.

Jensen Huang • 54:57 No, nobody is nobody is building more compute today. Nobody’s building more compute today than the people asking to be slowed down. It strikes me odd.

Ezra Klein • 55:13 I think one thing where maybe there’s some difference here is I don’t trust companies even with liability to keep the public good in mind. I think we’ve watched companies do terrible damage to the environment, the profit motive, the desire for power. The desire to cut corners to be first. I feel like you’re treating these like these are not things that we’ve seen again and again in history. But I feel like they are things we’ve seen again and again in history that we’ve watched.

Jensen Huang • 55:13 That’s where I see a lot of good things in history.

Jensen Huang • 55:42 I do see a lot of good things in history. [Crosstalk; partly inaudible in this transcription. The official NYT transcript has a different reading of this exchange.]

Ezra Klein • 55:44 Sort of relationship between the public and the private.

Jensen Huang • 55:46 I work with a lot of CEOs and they want to do the right things. I work with a lot of companies. They want to do the right things. They want to do good engineering. I know a lot of people in those two labs who are dedicating their lives to do good work, and they’re built. They know what happened. I know they know what happened. I know they know how to fix it, and I know they’re fixing it. Meanwhile, meanwhile, all all of the other narratives to deflect blame, to to make it sound like AI is so powerful, I have no idea how to fix it. It’s not my fault. It’s just because the technology is just so powerful. I think that’s a deflection of blame. Is a deflection of responsibility. Is unnecessary. It hurts. It actually hurts their reputation more than it helps. It hurts their character more than it helps. It hurts employee morale than it helps.

Ezra Klein • 56:46 Well, what if it’s what they believe? Well, I guess thinking at that level because

Jensen Huang • 56:48 I can’t talk to you about what they believe. I can tell you what I believe.

Ezra Klein • 56:51 This industry wouldn’t exist without your chips, right? I mean, the parallel processing that was required for deep learning to work, going all the way back to the original AlexNet, right? It’s all on Nvidia chips. And a lot of the people from the beginning, or who were there at the beginning, have these these fears that I think to a lot of people, and they hear them like, “What are you talking about?” Right? From Geoffrey Hinton and Ilya Sutskever, all the way up to I’ve heard these from Dario, from you know Sam Altman talking about loss control, Demis Hassabis, and. A lot of the people who are very foundational in creating the form of AI we see now seem to believe that there’s a very good shot it could we could lose control of it. Elon Musk has talked about human beings being a bootloader for AI. We could lose control of it, and that would be the end of us. I don’t think you believe that.

Jensen Huang • 56:51 No,

Ezra Klein • 56:51 I think you don’t believe it at all. So taking them as serious about what they believe when you have your arguments with them, or maybe you could just have it with me. When you’re like, what are you talking about? Even though they’re the people in many cases who are foundation here, what do they think they’re wrong? [The official NYT transcript has a brief reply from Huang here that is not captured in this transcription: that lab leaders are more grounded when speaking with him.] So, when Geoffrey Hinton is on TV saying he thinks a 10 chance of societal destruction is not unreasonable,

Jensen Huang • 58:03 I would tell Geoff that that it’s irresponsible to say all that. All of his predictions have been wrong. Enough predictions. That ten percent chance is not grounded on science. It’s not grounded on research. It is a. It just because it comes from a scientist doesn’t make it scientific. Those predictions are hurtful. Let’s take it at face value. That that the recommendation is exactly what he said, which is which is that nobody should want to be a radiologist, and the world has no radiologists today.

[Archival clip: Geoffrey Hinton, Toronto, 2016]

Geoffrey Hinton (archival clip) • 58:36 I think if you work as a radiologist, you’re like the coyote that’s already over the edge of the cliff, but hasn’t yet looked down, so doesn’t realise there’s no ground underneath him. People should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists because it’s going to be able to get a lot more experience. It might be ten years, but we’ve got plenty of radiologists already.

[End of clip]

Jensen Huang • 59:01 Is that helpful or hurtful to the society? I think we can all agree. We can both agree it would be terribly hurtful. It did not. It didn’t happen. Is it good or bad that we scare young people about the future of AI so much so that they don’t even want to go to universities and don’t want to go to college anymore because they don’t think they’ll get a job? Is that helpful or hurtful if it were to happen? It’s hurtful. Don’t think for a second just because you’re an alarmist that you’re doing a social good. It is not true. So I think that we ought to just all be wiser, more mature, be evidence based, be scientific. If you wanted to be scientific, be scientific. Do the science, do the science. But alarming people, making claims that don’t—they simply, their track record is horrible. Their track record is literally horrible.

Ezra Klein • 59:58 Well, the track record is bad in one, respecting good in another, which is many. Many predictions have been weak. [Crosstalk; partly inaudible in this transcription.]

Jensen Huang • 59:58 Which prediction has been right?

Ezra Klein • 59:58 The predictions that the scaling laws would work.

Jensen Huang • 01:00:07 That scaling law, no. We got to be careful here. Even then, that if you just dump to

Ezra Klein • 01:00:13 just say what it is for for the that if you dump audience here, compute and training data, these things will keep getting smarter. [Crosstalk; partly inaudible in this transcription.]

Jensen Huang • 01:00:18 That’s correct. It’s not. It is not true. It is not true that if you just keep training these models, they get better. Notice it is the reason why the second scaling law had to come along. Why why do you need a second second scaling law? The first scaling law already.

Ezra Klein • 01:00:18 You describe what the second is.

Jensen Huang • 01:00:18 The second scaling law is test time scaling inference. The more you iterate. The more you search, the more you explore, the better answer you can you can you you’ll discover. Inference time scaling. What is the big breakthrough that caused the the current AI to be incredibly useful? Precisely the opposite of the prediction. It was predicted that it would be the end of software tools. It was the SaaS apocalypse, right? What is making

Ezra Klein • 01:00:18 SaaS will always be with us.

Jensen Huang • 01:00:18 What is what is making these AI so productive right now? The usage of tools in the future it’ll be, you know, enhanced by the number of agents using these tools. There’ll be more people using Adobe. There’ll be more using Salesforce tools and so on, so forth. And so every give me one prediction that has. Has been right.

Ezra Klein • 01:01:26 Well, the prediction that you begin to see emergent. Let me try to answer that because they’re not here. The prediction that you would have emergent misaligned behavior.

Jensen Huang • 01:01:35 I think that fact that you can’t come up with one I think in itself is a

Ezra Klein • 01:01:38 because. Well, I think it depends what we’re talking about. Predictions, right? I mean,

Jensen Huang • 01:01:41 predictions.

Ezra Klein • 01:01:42 Geoffrey Hinton was the person as responsible as anybody else for deep learning at a time when everybody thought it was ridiculous, and it has turned out to be a pretty good bet, right? I mean, the the the sort of big one.

Jensen Huang • 01:01:54 Every one of them had every one of them made great contributions. I love Hinton. I hate his predictions.

Ezra Klein • 01:02:02 I understand that. Here’s the, I would say like the stylized concern that all these people have. And I want to sort of do this for a few minutes, and we can move on to some other topics. But the fear that seems to me to animate them, and that I think a lot of people find intuitively reasonable, is you’re creating systems. I’m not saying they’re alive.

Jensen Huang • 01:02:22 You say everything long enough, it’s going to be reasonable.

Ezra Klein • 01:02:26 Well, that right? Fair enough. So, yeah, you’re creating systems that are intelligent, that are becoming more intelligent than us in certain domains. You give them reward functions, as you were saying, the desire to do things right. You give them persistence. They move very fast in the digital world. You’re creating something, some entity, an agent that is smart, that is capable, that is relentless, and who the workings of its mind we don’t really understand. The chief scientist at OpenAI, the chief scientist at OpenAI,

Jensen Huang • 01:02:59 Ezra, look, look, I just don’t want you to contribute to that. Software’s, you’re worried I’m getting off the. I don’t think software’s relentless. [Crosstalk; partly inaudible in this transcription.]

Ezra Klein • 01:03:08 Aren’t they trying to make it very persistent, highly persistent models?

Jensen Huang • 01:03:08 Because I made it that way,

Ezra Klein • 01:03:08 but that’s how they’re making it.

Jensen Huang • 01:03:14 Yeah, but that’s not persistence. It’s just on. Yeah, persistence, persistence. There’s a there’s a willpower. There’s no willpower here. Just electrical power. Listen here. Let me let me let me give you.

Ezra Klein • 01:03:27 Aren’t human beings just energy with a reinforcement learning loop? [The official NYT transcript has Klein attributing this line to Sam Altman.]

Jensen Huang • 01:03:30 Whatever. So so. Anyways, I just think that we we can’t make jokes of all this stuff. We’re scaring the American public. Listen, spawn, create, kill, wait, sleep—all of these words are associated with agents. Right? That’s what people use. These words were created when multi-processing systems for operating systems. These are literally the commands of an operating system. You spawn a process. Replace process with agent. The process forks. As a result, parent and child. The agent forks, spawns anew, give birth. These are words that were created for the operating system. Thirty, forty, fifty years ago. But notice, we didn’t infuse human characteristics into them. We kill processes all the time. Kill minus nine, kill it dead. It’s just a process. But now we we’re talking about these things. A collection of people want to make the software more than it is, and we talk about software in a new way. But they’re all the same old words. Now the the last generation of computer engineers. We were doing all the same things,

Ezra Klein • 01:05:06 but doesn’t the software act in a new way? I mean, from the outside, I don’t have the technical expertise you do.

Jensen Huang • 01:05:10 The fact that it’s doing, it’s crawling the internet, it’s doing search, it’s doing, you know, optimization algorithms.

Ezra Klein • 01:05:17 It’s breaking out of things. Like most things, don’t break out of things.

Jensen Huang • 01:05:20 No software breaks out of sandboxes all the time. [The official NYT transcript punctuates this as “No,” followed by a statement that software breaks out of sandboxes all the time.] That’s the reason why we need virtual machines. You can’t have agents their own sandbox monitoring themselves. You you need to you need a if you want a whole bunch of watchdogs. And so, these are ideas that have been around for a long time. We just somehow. Somehow, in the recent generation, gave it you know a whole bunch of human words, and I just think that it’s unnecessary. It’s software, you know. When when I see it in my head, it’s a bunch of code, a bunch of numbers running on computers, and all of that is happening in a very natural way to me. Which is the reason why I can operate, and it’s the reason why if it’s if it’s just simply mystery and myth, how how do I build a company around it?

Ezra Klein • 01:06:08 I think one of the fundamental questions this gets at is just what is intelligence. Before you can even think about what it means to have intelligent machines, just what what is intelligence to you?

Jensen Huang • 01:06:18 Well, there’s a there’s a technical formulation of intelligence. First of all, you know when people talk about intelligence and thinking and you know all of these things, of course there’s no formal definition for most people. But in in the field of computer science, there is a definition. the The definition is perception. Which is perceiving the world and understanding it. Two, which is reasoning, and reasoning is the ability to decompose any scenario and anything you see, any experience, into more elemental parts. And third is planning towards an objective. That fundamental formulation applies to agentic systems. It applies to robotic systems. Applies to self-driving cars. And so you could see the industry building it layer by layer, by layer, step by step, by step. To the point we now have what we what perceived as intelligence.

Ezra Klein • 01:07:14 I think this gets to such a core question of this conversation, which is some of the ways you’ve described the technology to me. It does not sound like you think there’s anything really new about it. It is you know maybe new in scales, new in capability, but but fundamentally this is software we’ve always we’ve not always had, but we’ve had software for a long time. A lot of people believe when you’re getting to intelligence at these levels. It is a phase change. It is something different, something we have not dealt with before. A kind of generally intelligent technology that is advancing in its intelligence very rapidly. I want to make sure I actually do understand. We are on that divide. Is this something fully new? Is this something that requires something new from us, or is this more like something old? Are intelligent machines different than the machines we’ve had?

Jensen Huang • 01:08:03 Well, almost all of technology and civilization is built on layers of understandable technology, which at scale becomes fairly extraordinary. The fact that that we can connect to the internet by just you know holding a phone up, it’s kind of weird, you know. That we’re connected to every piece of information in the world on this little tiny device. You know, just in the air, and the fact that this little tiny piece of glass can somehow take trillions of pieces of information and bring to us precisely the one that we want because it’s been passed through a recommender system. And so, if you think about how is it possible that we knew where all the information is, somebody had to go crawl it. Had to index it, and that uses machine learning techniques, which is the early versions of artificial intelligence. And these systems do magical things to the point where I now expect it. It took literally twenty somewhat years, and and hundreds of billions of dollars of infrastructure build out in order for everything to just seem so natural to you. To the point where we now take it for granted. Every single milestone that we achieve from a technology perspective, it’s celebrated, and I celebrate it with glee, and I celebrate it with so much enthusiasm because I’m proud of the people who did it. I’m proud of ourselves who contributed to it. You know, I’m proud of the breakthrough. But when you and it seems wow, it seems like a miracle at the time, but. That sensation lasts about seventeen days. After that,

Ezra Klein • 01:09:44 we get used to everything quickly. I agree with that. But is this a different phase? It’s a you know you have some of the company leads talk about this CEO of Google, I think it was, as the equivalent of fire, right? Like a new epoch, yeah, in human history. Is that how you see it? You see it as transitional, iterative?

Jensen Huang • 01:10:03 No, I think this is this is completely. A revolution, and and as we were talking about earlier, you went from being able to find everything, find anything, to be able to ask anything, know everything, and do everything. And so, so clearly, it’s a new abstraction level. Now, you know the thing that the thing that I I I’m reluctant about is to cause it to seem like it’s more than that. You know, in the final analysis, engineers are doing engineering work. Once we invented the technology, once we discovered a solution for it, when you look back, it’s fairly obvious, and and it’s fairly mundane to a lot of people. This, and the fact that we’re able to make the technology better and better and better every day is because we understand it obviously, and so we understand how to make it better.

Ezra Klein • 01:10:51 So you turn into an engineering problem, and you say. What we don’t have right now is a level, because it’s something now you’ve said, of testing, monitoring, sandbox, security, right? Control excellence that we need for what we’re building,

Jensen Huang • 01:11:06 and it’s not because the the companies are are don’t have extraordinary engineers.

Ezra Klein • 01:11:06 I understand. I got to make sure they’re saying that. But they, [The official NYT transcript has a different reading of this passage: Klein acknowledges that this is not Huang’s claim.]

Jensen Huang • 01:11:06 I I believe that OpenAI, Anthropic, because I know many of them are extraordinary.

Ezra Klein • 01:11:16 But that actually is in part what makes me worry. Because OpenAI didn’t know what’s happening to them. [The official NYT transcript has a different reading of the end of this sentence.]

Jensen Huang • 01:11:19 No, no, no, no, no, no, no, no, no [Part of Huang’s reply is not captured in this transcription here.] But remember, how is it possible that a company that six months ago was trying to make something useful, capable? How would they have as much resources dedicated on testing, evaluation, and all of the compute dedicated to that? It was unnecessary until now, and so what’s going to happen over the next several years is that we’re going to transition from these labs becoming engineering focused, much more production engineering focused, and product focused companies. And so, so I, I think they’re just going through a transition. These are companies, extraordinary companies, incredibly talented companies, the most consequential companies of all of all time, and they’re just going through their transition. It’s not more than that. It’s not less than that.

Ezra Klein • 01:12:25 So many of the companies now, both OpenAI and Anthropic, in the last couple months, have put out these big—I don’t know what to call them—papers, blog posts, something. When AI builds itself, is the name of the Anthropic one. I forget the name of the OpenAI one.

Jensen Huang • 01:12:37 The computers are building itself. But you guys know that.

Ezra Klein • 01:12:38 But you know they’re talking about recursive self-improvement.

Jensen Huang • 01:12:38 You know that we use recursive self-improvement.

Ezra Klein • 01:12:38 So I’d like your perspective on on RSI.

Jensen Huang • 01:12:47 I think that RSI is fundamentally how things are done. So we use software. To design a computer, to run software, to design a computer, to run software, to design [Part of Huang’s answer is not captured in this transcription here; in the official NYT transcript he completes this point before turning to agents, the subject of what follows.] It studies the various paths it went through, chooses the best approach. The next time, if you’re going to do exactly the same task, I’m going to document a file. I’m going to tell you how I did it last time. That was the most effective. I’m going to call it skills. And because you use it over and over again, some of it is skills. Some of it is going to be a memory. We’re going to improve the memory, okay? So that next time you use it, it’s even better than last. Recursive self improvement. You could also decide that you take all of this skills. All of this memory, and you can take all of this data and train the next release of the model with it. And so that AI becomes better and better as servicing you over time. We’re doing all of that is happening. It is absolutely happening. Meanwhile, the amount of compute that they have is growing, and therefore they can do everything faster. What used to take a year to pretrain something now takes several hours. Because the computers are getting faster and they have more of it, so now the loop is going faster. Completely, completely understandable. Does that give them any excuse to launch a product that hasn’t been tested? The answer is no. Just come back to that. You, nobody, no enterprise is able to operate in an environment where the underlying software is literally changing all the time. There’s a release process, so you know when they when they roll out a new model. We need to evaluate it before we release it into our operations. We can’t just have it recursively changing all the time, and so so they have to test the product before they release it. We will test the product before we release it into operation. And so i I think recursive self improvement is a fabulous thing.

Ezra Klein • 01:15:30 And do you think there is any level? I’ve heard you say before that learning should always have a human in the loop.

Jensen Huang • 01:15:35 Yeah, like I said just now, you you got recursive self improvement.

Ezra Klein • 01:15:35 They seem to be imagining something where it wouldn’t always.

Jensen Huang • 01:15:35 Um. Well, don’t ship me anything that you didn’t evaluate. Don’t ship me. Don’t ship Nvidia any products that humans did not in the loop evaluate. Please don’t do that.

Ezra Klein • 01:15:55 And the fear we talked about earlier that they’re worried they’re not evaluating that they don’t know how to evaluate these systems, and the more they change kind of rapidly, the more they worry the systems are tricking them.

Jensen Huang • 01:16:05 I don’t believe that. I believe that their researchers are working every single day to learn about how to evaluate these systems, verification. So you know, ten percent, twenty percent of our company is dedicated to design. Eighty percent is dedicated to verification. Today, most labs, understandably, is eighty percent dedicated to capability and twenty percent dedicated to safety verification eval.

Ezra Klein • 01:16:05 This is the flip, the transition

Jensen Huang • 01:16:05 that’s right. That’s right. AI needs to accelerate to be safe. I want them to get more compute, but allocated towards evaluation to alignment. And I think they’re doing that. If I were in the car industry a hundred years ago, I would rather the car industry accelerated to today in one year, because I believe today’s car is way more safe than a car ninety nine years ago. And ABS technology, automatic braking, requires computer vision technology, sensor fusion technology, radars, and cameras, and you know all that technology coming together in order to brake when you should and not brake when you shouldn’t. That technology extremely hard. I would have hoped everybody would have hoped that ABS technology existed 99 years ago. A lot fewer children would have been killed. And so, you know, airbags save, you know, seatbelts. I mean, all of that stuff, self-tightening seatbelts. I mean, all of that stuff. Could you imagine? That’s all technology. Accelerate the living daylights out of that development. And so, when I when I say when I say we need to accelerate AI technology, people think for some reason. Safety is not part of that. Safety is part of it. Alignment is part of it. Eval is part of it. Guard railing, sandboxing, the the isolation technology, monitoring technology, telemetry technology, external AI monitor technology, all of that stuff is AI technology. Accelerate the living daylights out of that.

Ezra Klein • 01:18:11 It’s it’s funny because I think that if the most alarmed people, the labs. Could be assured they were going to move eighty percent of their compute into safety and alignment as opposed to eighty percent into capability expansion. They would feel much better. And it sounds

Jensen Huang • 01:18:11 yeah. What’s stopping them from doing?

Ezra Klein • 01:18:11 And it sounds to me one thing you’re actually saying is one should think of safety and alignment as capability expansion.

Jensen Huang • 01:18:32 Sure.

Ezra Klein • 01:18:32 An unsafe technology is not an advancing technology.

Jensen Huang • 01:18:35 It’s like it’s like us saying, oh, chip design is chip is is chips is R and D. Chip verification is not R and D. We spend most of our cost most of our compute on verification, emulation, verification, testing, reliability testing, lifetime testing. All of that is part of engineering. The incentives are there. The incentives are there. They are going to put their company in harm’s way if they release products that harms other companies and other people.

Ezra Klein • 01:19:06 Do you think we need liability laws that are specific to AI?

Jensen Huang • 01:19:12 Um. Already, so this is usually one example: self-driving car. The the car as a product, the robo taxi has lots of regulations. If if it doesn’t have enough regulations. Then NHTSA had to get involved and come up with new regulations. [The official NYT transcript reads this as saying NHTSA should get involved.] Card the car industry should have new new regulations. I don’t know what’s missing, but if there is something missing, then I would I would absolutely you know absolutely add more regulation. In the context of internet, there are many applications that that the internet powers, and those applications should have regulation. If they don’t, you know, just you got to find them.

Ezra Klein • 01:20:03 So I want to drop down the next layer of the cake now to chips. And to summarize, sort of where we are, because I want to make sure I do understand your position correctly. It’s that these companies are going through a transition. Yeah. That even as these systems speed up, become more capable, complex, persistent, whatever it might be, that there is still the limiting factor. Of companies will not ship what is not safe. They should not ship what is not safe. And you believe they have the engineering capabilities to make these things safe to figure out the testing and the control absent external intervention. That’s that’s sort of where you are.

Jensen Huang • 01:20:03 Yeah. [The official NYT transcript records a more emphatic assent here.]

Ezra Klein • 01:20:03 One thing I’ve heard you say is that we have entered maybe a way people don’t always understand a new era of how computing works. Describe your vision of that, and the way if somebody’s sort of understanding of it is a little bit still maybe in, you know, you’ve got a MacBook and it’s got a processor in it, and you buy it, and how it differs.

Jensen Huang • 01:21:05 The last computer industry, and the computer industry we’ve known for 60 years, is called retrieval-based computing. You retrieve files. That’s why it’s called data center. You know, file center, okay, and and in the future, it’s it’s an AI factory. It’s generating, and so so the the amount of computation necessary to understand the context, under be grounded in information, the reason about what to do, and to generate an answer, that generative process requires a lot of computation, and so so just the amount of computation necessary per user. Has grown tremendously, and then the second part is because because these these this generative AI can also be somewhat autonomous because they’re agentic. Now you have agents using generative AI, and so rather than a billion people using computers, you essentially have multiple hundreds of billions of agents in addition to the humans using the computer. And so so you could you could argue that the amount of computation we need, you know, however much we we had before is going to go up by a billion times, and that that’s a reasonable you know framework for a reasonable level of amount amount of computation. In this new world, what you really care about, in the context of a factory, is how productive is it? Not how expensive is it. It can’t be infinitely expensive, but you want to know how productive it is. And so, our computers are incredibly productive. Fifty billion dollars to build a one gigawatt data center, one gigawatt AI factory, and you can rent it for forty to fifty billion dollars per year. And so, the the productivity of it is incredible. So, number one is the productivity. Nvidia’s architecture is fungible because we’re general, we’re general purpose, which is the reason why every AI lab, every AI model, closed model runs on Nvidia. And because we’re completely fungible, and you can use us from data processing to pre-training, to post-training, to eval, to inference, the entire life of AI is supported by our architecture. And if if a a customer no longer needs it, another customer would be more than happy to pick it up. And then the last part is that durability. Because our architecture is software-driven, and we’re constantly improving our software with new algorithms that takes the new workloads, the new models, and run it on our old-generation hardware. We have massive teams of people who are constantly doing that. As a result, the useful life of our compute is much longer. That’s the reason why Nvidia’s and people are talking about Nvidia compute as an asset class, kind of like an airplane. It’s you know, airplanes are are general purpose. They’re they’re fungible. United Airlines doesn’t use it. American Airlines would use it. It’s durable. And it starts out as a passenger airplane. You ends up ends its life as a as a shipping, you know, as a cargo plane. And so, so as a result, it can be an asset class. So this is, and if we could do this, if this happens. Then of course the cost of capital for funding Nvidia AI factories will be the lowest because our our our computers are collateral collateralized asset and and so anyways this is what this is the phase shift that’s happening to us which is going to be a huge unlock for our growth

Ezra Klein • 01:24:38 and so your business has become so interesting you’ve moved now into lowering the cost of capital for others in the AI industry people maybe have seen these charts of like the arrows going in every direction

Jensen Huang • 01:24:38 so interesting yeah

Ezra Klein • 01:24:38 and explain that a bit to people who are. They understand NVIDIA has become like the biggest company in the world. They see these charts that seem very circular to them. What is the difference between supporting demand, creating markets, and creating demand?

Jensen Huang • 01:25:12 We can’t. We can’t really create demand because in the end, if if. If the the AI services have no offtake, then obviously building computers for it is pointless. And so the first thing has to happen. The reason why compute demand is so high right now is because AI applications are going through an inflection; they’re becoming useful. And and because because AI is becoming useful, five hundred billion dollars of venture funding are coming in, and all of those companies, those thousands of companies, startups, they all need compute. And so that’s their the demand is coming from them. And so these companies need support in technology. They need support in ecosystem building. They need support in financial support. And so we might decide to invest in some of them as an equity owner. And as a result, they become a really flourishing new cloud provider. Another reason we might decide is because, as I mentioned, there’s a five layer cake. And at the model and the application layer, there’s a whole bunch of really innovative companies. And there’s way more to AI than just the language model itself. There, you know, world foundation models, physical AI, there’s biology AI, there’s chemical, you know, material sciences AI. These are all different than than language models. There’s, and so. Many of those companies are new, and they need a lot of capital. We might decide to be a small percentage shareholder in them, so we get them off the ground. They’re incredible scientists. I might even, you know, by being a first investor, anchor investor, we bring confidence to their company. We, you know, we we give them access to a lot of our technology. We support them a great deal, and we help them, you know, become a company as fast as possible. We might decide to invest in a nuclear company. We might decide to write so on so forth. So, you know, we look at my mental model of the AI industry as a five layer cake, and we’re investing across all of it. There might be strategic unlock points. It opens new markets. It opens a new route to market for us. It might secure a critical resource for us. So there’s a lot of strategic reasons why we do it.

Ezra Klein • 01:27:32 I mean, the the numbers here are astonishing. You’ve become like a like a single company industrial policy for primary for American AI.

Jensen Huang • 01:27:41 We’ve put a lot of money into this ecosystem. Yeah.

Ezra Klein • 01:27:44 What’s the total investment? You’re now making per year

Jensen Huang • 01:27:47 all in all in. We’re probably up. Well, I don’t know about every year, but I think all in we might be might be like a hundred billion dollars. Might you know? Might check my numbers, but something like that.

Ezra Klein • 01:27:57 It’s larger than than the CHIPS and Science Act.

Jensen Huang • 01:28:00 Oh yeah, yeah. Not not to mention because of the because of the the purchasing commitments that I provide to TSMC and Wistron and Foxconn, Amkor and SPIL and all these different companies. Because of that commitment, I’m able to encourage them to come and manufacture here in the United States. You know the the fact of the matter is we probably contributed more to reindustrializing the United States in this chip manufacturing than just about any company in the world. We’re not only reindustrializing manufacturing, we’re doing it so fast that we’re creating a shortage of labor, but we’re creating a lot of jobs.

Ezra Klein • 01:28:35 I know a lot of people with money in the market right now who are excited, often excited by Nvidia stock in particular, and worry about the analogy of the internet bubble the late nineties. And what they worry about is actually related, I think, to what you just said, which is that the internet did continue to be more useful. It’s not that high valuations meant that the technology was hollow or fake, but something happened and popped for a minute, and very big companies got hammered in that, and a lot of people got hammered in that. What is sort of learn from that kind of bubble bust cycle? And I guess the question is. Do you not think it will happen again, or why do you not think it will happen again?

Jensen Huang • 01:29:20 At some point, demand and supply will be will be inverted again, and that’s just the nature of you know markets. It’s not going to happen next year. It’s not going to happen in the next couple, two, three years. I I just don’t believe that. But at some point, we will likely have more supply than demand, and I just don’t know when that is. And so there’s not there’s not much to learn from the past.

Ezra Klein • 01:29:45 What would be the signal for you?

Jensen Huang • 01:29:48 Markets will naturally slow down and then it will stop. You know, meaning meaning there will be a a period of digestion. Now is that period of digestion going to be six months? Is it going to be nine months? It’s going to be a year. It won’t be forever. If you look across the board, the amount of investments that we’re putting into the application layer, so that each one of the industries could have the technology diffuse into them. So that they could benefit from it, that’s probably one of the biggest things that we do.

Ezra Klein • 01:30:16 This is a way I often hear the sort of Chinese and American and AI ecosystems compared, which is that in America the emphasis is on the speed of rising capability, and a lot of people think we’re ahead on that, and that seems true. And that in China, there’s more emphasis on diffusion, and a lot of people think that China is probably ahead on diffusion, and in some ways has an economy that is better structured from things like WeChat all the way to just like the way knowledge and commands move through it for diffusion. And whether the race is about capabilities or diffusion, and also whether it’s a race at all, but we can get to that in a minute, is a big question. I’m curious how you see that.

Jensen Huang • 01:31:03 That’s the ultimate question. I believe if we want America to benefit from artificial intelligence, every single industry has to benefit. Walmart has to benefit. Safeway has to benefit. Federal Express has to benefit. Every bank has to benefit. Every healthcare company, every drug discovery company, we need to see every construction company, every data center company, power generation company. We need everybody in the United States. We need everybody in America. We need everybody in the world to benefit from this, and that’s the highest layer. That’s the most important layer. That’s the layer that touches society. All the layers underneath are technology enablers. I want to see us not ruin the opportunity for the United States to benefit at the highest level. And notice all of the rhetoric and all the alarmism, all the doomerism, all of the predictions are scaring people. That is my greatest fear. Actually, I have every confidence. Maybe I have more confidence in them than they have in themselves. They

Ezra Klein • 01:32:07 you definitely have more confidence in them than they have in themselves.

Jensen Huang • 01:32:09 Well, I I don’t know about that. But maybe it’s just too much humility and and and otherwise.

Ezra Klein • 01:32:17 Should we conceptualize what we’re in as a race with China?

Jensen Huang • 01:32:23 I don’t think it’s necessary. Some people like to think that way. I don’t. I don’t find that necessarily inspires me. I have no trouble never mentioning another company in our when we talk about us doing our good work. And so we hold ourselves to our own standard. And so I think that that different people have different ways of being motivated. And. You know, I I think it takes it takes more takes a bit more artistry to unite and focus organizations to certain level of of performance in the in you know outside of contests. But I don’t I don’t necessarily see it as as net. I don’t see it as necessary. Number one, number two. The question is, even if we did frame it as a as a competition. It doesn’t have to be that if they achieve something, it’s at our peril. And so, when they invent something or they create some power generation technology, it might it might be a great invention that we wish we had done ourselves. But because it’s going to support all of our energy production systems here, as a result, it helps our whole industry. Maybe they came up with a great new open model. And and they have, and those open models are now being used by eighty percent of the American startups.

Ezra Klein • 01:33:49 Yeah, we use a lot of Chinese open models here.

Jensen Huang • 01:33:51 Okay, that’s right. And so so that’s that’s terrific. We download it. It it originated in China. A lot of the technology, of course, also originated from the United States. We download it. We make it our own. We fine tune it. We put it into our own agent harness. We put it into our own sandbox. That’s all your own technology. So I I think the the fact that you you leverage their weights, I think that’s terrific. That’s fine.

Ezra Klein • 01:34:16 You were saying a few minutes ago the way different countries have begun to see compute as a geostrategic resource. And you know may want to allocate it to their own companies. There’s been a lot of back and forth on that here, and among people who do see us as in a race with China, pretty people see us in as in a race with China for who will get to recursively improving self superintelligence first. There’s been this ongoing back and forth on whether or not one thing we want to do is deny them compute, which in this case tends to mean denying them your chips. Under the Biden administration, we had pretty tight export controls. Those were loosened under Donald Trump. Obviously, you wanted those to be loosened. How do you think about the question of whether or not it is good for China to have Nvidia chips that could accelerate their models, or model deployments, their model capabilities, versus us holding that back to try to slow their progress?

Jensen Huang • 01:35:15 In a case of AI, our goal is not just that one lab benefits. Our goal is that all of America benefits. I think the United States has a greater responsibility and a greater ambition for the world to be built on the American tech stack. Just as we have greater ambition that the world is built on the U.S. dollar, and that more people speak English, that they they use the American version of internet. I mean, we want that. The question is, ultimately, what are we depriving? Are we depriving them a chip for their industry, or are we depriving United States a market to compete in? If you cede the market, if you cede the market as big as China, how does that help the United States technology sector? Maybe it helps one company with a with a particular model, but the rest of the industry suffers. I think that it doesn’t help the chip industry, surely, to be deprived the market to go compete in. It doesn’t help the rest of the industry because it deprived open models. It doesn’t it doesn’t support the overall aspiration of the United States to have the world built on the American tech stack. And so, there’s a lot of things you deprive yourself if you narrowly focus on deprive them of chips. And so, so I I would say to take a step back and frame it into what’s in the best interest of America first, all of America, not one, not one, not one company. And with with with respect to the race, as we mentioned, the race is if there is one, it’s about all of the economy of the United States succeeding.

Ezra Klein • 01:36:59 I found myself very conflicted on the China and chips question. And one reason is even where I have sometimes more of the superintelligence concerns than you do, is that if you have those concerns, I think you want to have a good relationship with China, in which there can be kind of productive bilateral working through the risks and benefits of AI. And the more you think of it as a race which only one side can win and act like that, the more you are necessarily going to create enmity. And I found that to be a sort of complicated dimension of people’s thinking here.

Jensen Huang • 01:37:36 You know, I think that a zero-sum strategy—I deprive you of this, therefore I win. That simplistic logic tends to have unintended consequences of the bigger game. The bigger game, of course, is that we’re now all talking about safety. We we want to build safe products. We want them to build safe products because when they don’t build safe products, it hurts the whole industry. And so, this is a perfect time we should want to we should want to look for opportunities to communicate, collaborate, to understand, align as much as possible. Now, having said that. Nvidia is an American company. We should benefit America first. America has every right, and for these technologies to be made available to the Frontier Labs, Vera Rubin goes to the Frontier Labs first.

Ezra Klein • 01:37:36 That’s your advanced chip.

Jensen Huang • 01:37:36 That’s right. Nvidia’s newest chips, and so did Grace Blackwell, and so did Hopper. Every single generation, so did Ampere. Every single generation of our product goes to American companies first, and and if the U.S. government would like to add on top of that, that is a requirement to do so. I’m delighted by that. That’s no problem. We we do that naturally, anyways. However, recognizing that the AI industry is a five layer cake, and we want every single layer to win, then we need every single layer to go out there and compete for the market.

Ezra Klein • 01:39:05 That drops us to the final final layer of your cake, which we won’t spend as much time on. But if the advantage America has had, at least at a material level. Is chips and software one of the advantages China has right now in AI is energy? That it’s easier for them to build new energy. They’re pumping much cheaper energy into AI. They have tremendous. They’ve made tremendous advances on building electrical generation and renewable energy. How do you see the like that that that most fundamental layer, the energy that pumps through the data centers, pumps through the chips? And where America is on generating enough of it? Probably a time when we’ve been trying to move from dirty energy into clean energy.

Jensen Huang • 01:39:53 Yeah, I think I think one, they just have a lot more energy than we do, and they plan to build a lot more than we do than we did. We got, you know, I think we we just have to acknowledge we got ourselves really gummed up in climate change and sustainable energy, and as a result, we just didn’t plan enough energy production.

Ezra Klein • 01:40:14 What do you mean by gummed up there?

Jensen Huang • 01:40:15 Well, in the near term, energy production requires fossil fuel. And because there’s just so much, so much angst about fossil fuel energy production, if you look at our country, we’ve produced very little net new energy for a long time. And all of a sudden, this new industry comes along, and we find ourselves in a situation where we just don’t have that much energy building capacity. And now the whole the whole country scrambling. And meanwhile, we’ve moved so fast. We could have done so much better job communicating with the communities, preparing the communities, working with the communities to let them know what’s coming. And if they if they don’t want data centers to be built in their in their town or whatever it is, then so be it. But. But if you’re going to build in their town, be sure to go there and let them know what’s coming. Work with them, work with them to help them understand that that the use of water is is really efficient these days. The the the AI supercomputers are super energy efficient, but they’re still going to use a lot of power. You got to bring in your own power generation. It’s going to lower their property taxes. There are a whole bunch of things that you can do. You could, you could, you know, you can make your your data centers more appealing. You make the setbacks further away. You know, so there’s there are a lot of things that you can do. You could, you could also contribute to be a good neighbor to the community and build better schools and better community centers and improve their parks and improve their roads. And there’s a lot of things you could do, but it’s hard to do that. You know, after the fact, and and now there’s a fair amount of there’s a fair amount of frustration around the around the country, and and then of course all of our narratives about the end of the world is not helping. And you know, what reasonable person says, come and build this data center in my town, and by the way, whatever you produce is going to, you know, end humanity as we know it. So I think I think all of this this this negative doomer narrative is not helping our country, and we started off on our back foot. We started off on our back foot, and then now we got.

Ezra Klein • 01:40:15 What do you mean we started off on our back foot?

Jensen Huang • 01:40:15 Oh, because we didn’t have enough energy production in the first place.

Ezra Klein • 01:40:15 Well, how do you balance? I mean, there is a reality of climate change.

Jensen Huang • 01:40:15 Let me just let me just give you the one last thing. This there’s no question that the energy demand is really great, which is the reason why the market forces are helping us invest in sustainable energy like no time in history. You give me an example of a sustainable energy company, a material sciences company to build a better battery. It could be, you know, it could be solar, it could be nuclear, it could be fission, fusion, you name it, hydro, you name it. Those companies are all getting funded. The market demand for energy is so incredible that this is the best time in a hundred years to improve our power grid, to make our power grid more sustainable, to lower the cost of energy. Also, investing in our sustainable future. There’s no question that in four or five years’ time, we’re going to use a lot more fossil fuel. But also, in the next decade in front of us, no time in history are we better prepared to move to sustainable energy. And because these data centers, because the cost of these data centers so high, you know now we’re starting about talking about putting them out in space. And so, right? And so, so I think the the opportunity. For us to see our dreams come true, move to a sustainable energy world, we have a better chance of doing that than ever. The world is buying more because of AI factories. Because of AI, is buying more sustainable energy today than any time in history. Venture capital is, you know, for for a next generation energy is just incredible. Everybody, everything’s getting funded. It’s incredible. You don’t need government subsidies for the first time in hundred years, because the market forces are here. Everybody should be leaning in. if you If you want a future, if you want to if you want to turn the corner on climate climate change, if you want a future that’s sustainable, lean into AI. It is the best opportunity we have to get there.

Ezra Klein • 01:44:44 But we need to build the energy faster to do that.

Jensen Huang • 01:44:44 That’s right. I mean that that’s just that’s just

Ezra Klein • 01:44:44 there’s a market for it. It’s kind of like you could subsidize it and you can make it easier to build. [Crosstalk; partly inaudible in this transcription.]

Jensen Huang • 01:44:52 Yeah, you know it’s kind of like in order to save you, they got to hurt you first. You know, in order to you know that’s nature of surgery. They had to cut you open to save you. You know, they got they got to inflict an enormous amount of pain and suffering on you so that they could save you. And so so I I kind of think AI is kind of like that. Over the next several years, we have to we have to unfortunately use renewable energy, use fossil fuel because we just don’t have sustainable energy enough of it to to make a difference. And then after that. You know, hopefully we can transition to that.

Ezra Klein • 01:45:24 I think that’s where we’ll end. Always our final question: What are three books you’d recommend to the audience?

Jensen Huang • 01:45:28 Well, I’ve read a lot of books. The the book that that made a huge impact on me was Computer Architecture from Hennessy and Patterson: A Quantitative Approach. It was the first computer architecture book that that reduced the complexity, the abstract idea of computer architecture down to engineering. And I I love it when when people take complicated concepts and reduce it into something that you could do something about. Number two. I really loved Innovator’s Dilemma. Clayton’s passed, but but Clayton Christensen’s book on how how industries evolve over time, and how to see emerging technology, and how to set proper expectations about it, and how to extrapolate maybe its future impact. I I really loved Al Ries’s and Jack Trout’s book on positioning. It’s a really wonderful book about how people see. It’s it’s a book about marketing strategy. More than that, actually, it’s just it’s a book about strategy, and and how people see the world and how people see products and. How you present products and how you how how you see your own strategies and and I thought that was a really thoughtful book and um really easy to really easy to understand

Ezra Klein • 01:45:28 Jensen Huang thank you very much

Jensen Huang • 01:45:28 thank you very much Ezra always I always enjoy our time together and today was a great time.