Jensen Huang Thinks A.I. Alarmism Has Gone Too Far

The Ezra Klein Show

NVIDIA CEO Jensen Huang discusses the transformative impact of AI as a new industrial revolution structured across five layers. He addresses

Key takeaways

• Huang defines AI as a 'five-layer cake' ranging from physical chips and energy to the application layer.
• He argues that AI automates specific tasks (like radiology scan reading) but preserves the fundamental purpose of professions like engineering and medicine.

Main topics

• AI Industrial Revolution
• Job Automation vs. Displacement

Notable quotes

The purpose of the software engineer is engineer.

Conclusion

The episode highlights the tension between rapid technological advance

Transcript preview

Speaker 3 (0:00) I'm John Caramonica, a critic at The New Speaker 4 (0:02) York Times. And I'm Joe Cascarelli, a culture reporter at The Speaker 3 (0:04) Times. Together we host PopCast, a weekly pop culture chat show where we speak to the biggest musicians, actors, internet celebrities, and more. Speaker 4 (0:12) We've had Bad Bunny, Olivia Rodrigo, and Anne Speaker 3 (0:14) Hathaway. We've also hosted live performances with Andre 3000 and Erykah Badu. Plus, we end every conversation with a snack. Catch new episodes of PopCast every week on YouTube. And anywhere you get your podcasts. Speaker 5 (0:29) 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. What if it's what they believe? I can't talk to you about what they believe. I can tell you what I believe. Speaker 4 (1:12) Over the course of these last few weeks, where the whole world has been talking about artificial intelligence, the voices people have 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 Chachi B. Key and Gemini 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 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. 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. Speaker 4 (3:11) Jensen Huang, welcome to the show. Thank you. It's great to see you. So you've described AI as a five-layer cake. Walk me through the layers. Speaker 5 (3:19) Well, first of all, it's a new industrial revolution. And this industrial revolution, this industry requires production. It manufactures things. I know that in the end, when people experience it, it's 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 above that is the models. And the important thing to realize, there's 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 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. You know, applications for legal services, for health services, for manufacturing, so on and so forth. Every single industry is involved. Speaker 4 (4:25) So I want to go through this, but I want to go from the top down. Because as you're saying, the way people will interact with it, the way it 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're envisioning? What is possible that is not possible now? What is common that is not common now, if we get that layer right. 200 Speaker 5 (4:50) years ago, we were able to power anything and everything. Electricity. And then, I guess, 40 years ago, 30 years ago, with the internet, we were able to find anything. Today... or soon, we'll be able to know everything and do anything. And 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 and 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 magical thing. I Speaker 4 (5:44) feel like the future, the way you're describing it there, what people have experience 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 NVIDIA is Speaker 5 (6:02) pretty- Speaker 4 (6:02) That's a great example. For example, radiology. What does it look like? Speaker 5 (6:06) Radiology. In the last 10 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. Speaker 4 (6:27) 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? Speaker 5 (6:52) Well, the thing 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. Speaker 5 (7:06) And 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 literally by this year that 90 % of all software will be coded 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 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 manifestation of a product. And so that mission, that purpose doesn't change. 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 I think the fallacy, and now it's, you know, because of some of the narratives and some of the storytelling has turned into myth and it's harmful, is that that AI will destroy jobs, which is fundamentally wrong. It will change every job. It will change every job. Many tasks will be automated. Some jobs where the job and the task is really one, meaning customer service 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 of venture capital is 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 of new investment. And so all of this is all happening right now. Speaker 4 (10:38) Well, let me take the side of this to give voice to the fears people have. Speaker 5 (10:41) Yeah. Speaker 4 (10:41) So there is the example of the radiologist, right? Which people were, over the past 10 years, predicting that job would go away. And right now there's more demand for it than ever. Speaker 5 (10:53) Yeah. Speaker 4 (10:53) There's 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. We Speaker 5 (11:05) outsourced Speaker 4 (11:05) it, though. That is true? Speaker 5 (11:07) Not because those jobs were gone, because of technology. But you can understand Speaker 4 (11:10) AI as an outsourcing, too. Speaker 5 (11:13) AI has the best— Let me make the argument, and then you can respond to Speaker 4 (11:16) 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 that you're going to have tremendous productivity improvement, which will come from it being cheaper to hire an AI than to hire a person. Speaker 5 (12:26) that we are going to see jobs change en masse. I believe there's going to be a net creation of jobs. And so let's, you know, listen, there's a whole bunch of industry that exists 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 all. But overall, overall, there's no question in my mind that because of human ambition. That's really the fundamental missing ingredient. People look at this work. This is the amount of energy that goes into it. This is the amount of work that goes into it. We're going to insert this work automation system. And as a result, the amount of work that's necessary is now going to be reduced. And therefore, some jobs will be gone. Speaker 5 (13:33) I believe that's flawed because there's a piece of input, the human input, that's intangible. It is not in calories. It's not 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. But for a lot of people, Speaker 4 (13:59) their relationship to work, is not powered by the kind of ambition that led you to create NVIDIA. Oh, just a different Speaker 5 (14:07) ambition. It's an ambition to make their children's lives better, to take care of their family, take care of their parents, ambition to be rich, to be able to travel. These are all ambitions. That I agree with, but Speaker 4 (14:22) maybe I'll go back to 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. We're going Speaker 5 (14:38) to Speaker 4 (14:38) bring it Speaker 5 (14:38) back. Speaker 4 (14:39) Maybe we will. But 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? Like, 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. Well, because 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. Speaker 5 (16:00) I'm always worried about the future. That's why I work so hard. But I'm a, if you will, responsible optimist. I have great responsibilities. I take my work extremely seriously. There are a lot of things that can go wrong. We're 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 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. Speaker 5 (16:46) I'll do the same with my children. I do the same with my family. And I think that what we want to do, I believe, is 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. Speaker 4 (17:15) The fear a lot of people have, 79 % 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 Amadei is. Speaker 4 (17:29) 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. Speaker 5 (18:03) 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, 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 specialized 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++, 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 interacts with you and gets the work done and gets that task done. All of a sudden, you have the might. You have the same might that... 10, 15 million people out of 8 billion has. And so it's incredible. And so 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, on the one hand... Yes, there's the fear of just this incredible technology change and how quickly it's happening. But that quickly is translated in two ways. What I hear when I say the technology is happening quickly and therefore it should give me anxiety, that's 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 just be impacted by it. I Speaker 4 (20:18) think it's an interesting question lurking here for young people. So one of the shifts we've begun 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 employers or do you need more people kind of oversee their agents? Speaker 5 (20:47) Oh, good one. Good one. Wait two years. Tell me why. Because it takes four years to go to college. And the meantime to graduation of this new technology is two years away. And so in two years' time, you're going to have a new generation of engineers and students. artists, and they're going to be empowered. Speaker 4 (21:11) So they're Speaker 5 (21:11) going Speaker 4 (21:11) to Speaker 5 (21:11) be Speaker 4 (21:11) native to this in a way that's going to give them an advantage. Oh, Speaker 5 (21:13) you Speaker 4 (21:13) watch. Speaker 5 (21:14) 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 a good student, you know, and you compare me to the students that are coming out of school today. Incredible. When I went to school, we weren't allowed to use a computer, not allowed to use a calculator. And 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 just not, you're not going to see a kid like that. And so they're all going to be superpowers. So Speaker 4 (22:13) I take the gain of that very seriously. I mean, the idea of doing my job now without just digital search, right? The idea that I'd 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. There's a study on AI in schooling out of China. It looked at 26,000 students, grades 7 to 12, and they had staggered AI adoptions. You could kind of see what was happening. And what I found was, quote, AI adoption raises homework scores by 18 percent, great, reduces completion time by 30 percent, so they get their homework done faster. and then lowers monthly exam scores by 20 % within six months. High-stakes entrance exam scores fall by 18 % and 24%, 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. What do you think when you hear that? Speaker 5 (23:22) I think the last part, I completely agree. 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, it's just basic math is being forgotten. Does it matter? That's my question for you. Yeah, I don't think it does. Speaker 4 (23:42) I don't think it does. But there must be some set of skills that matter. Speaker 5 (23:46) Oh, yeah, yeah, yeah. But maybe not those. We're going to discover new ones. Speaker 5 (23:51) 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. I don't really believe that to be true. 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 they needed 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. Speaker 4 (24:27) 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. Yeah. I also can't get anywhere without a mapping system now. Yeah. Never could, frankly. But. I'm a big reader. Speaker 5 (24:41) And Speaker 4 (24:41) one of the skills I really value, one of the capacities I have that I really value Speaker 6 (24:47) is Speaker 4 (24:48) 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. Speaker 5 (25:11) Others Speaker 4 (25:11) 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. Speaker 5 (25:21) Yeah. Well, I think that we're going to lose some. Speaker 5 (25:27) 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. What Speaker 4 (25:46) do you mean by systems thinker? Speaker 5 (25:47) 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, 200 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 level, you know, knowledge is gone. Is that horrible? And so I don't know how valuable it is to know how to do, for most people, to learn how to do surface integrals or partial differential equations. 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. They don't. 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. Speaker 6 (27:33) 33 presidents. Speaker 2 (27:35) I called the president at 4.30 in the morning and he answered. Speaker 6 (27:38) Seven decades in space. We want to see what's