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

26:07Jensen Huang: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.

26:48Jensen Huang: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.

27:20Jensen Huang:Hey, what's up, guys? It's Haley Bailey. Okay, I need to tell you about something. I just got YouTube Premium. It's got tons of awesome features like offline downloads so I can download my favorite videos before I travel and watch them whenever I don't have Wi-Fi because we all know airplane Wi-Fi is the worst. I get ad-free, I get background play, and there's like a ton more in there. You should try it. If you like YouTube, you'll love YouTube Premium. Try it now for two months free at youtube.com slash premium. If you like YouTube, you'll love YouTube Premium. Hi, I'm Sean Evans from Hot Ones, and I want to tell you about YouTube Premium.

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28:18Jensen Huang:This podcast is supported by Mercy for Animals. Mariano's sells an upscale image, but behind the polished branding lies a harsh reality. Eggs from hens trapped in cages so small, they can't even spread their wings. Mariano's pledged to go 100 % cage-free, but continues to profit from cage cruelty and our stolen trust. Chicago deserves better. Learn the truth and hold Marianos accountable at marianosunmasked.com. That's marianosunmasked.com. 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, Chachapiti, Claude, Gemini, Grock.

29:05You'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?

29:20Jensen Huang:So closed models is 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 products are closed. And the reason for that is because you can monetize closed products. And so that's fantastic. And OpenAI is closed. Anthropica is closed. Grog is closed. Gemini is closed. And so these are closed products. And the people working on it are incredible. And 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, put them into my data flywheel, make them better and better every day with my intelligence and my domain expertise.

30:35Jensen Huang:And then I need to have control over it because I have a company to run and I can't rely 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 70%, maybe even higher, closed model tokens and 20 % open model tokens. And now it's running at about 70, 30 the other way. And so anyways, I'm a big supporter of open models because one, the world needs it in order to run its infrastructure.

31:17Jensen Huang:I needed 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 the world to have the ability to have the best cybersecurity, give them closed models, but also give them open models so that they can defend themselves. The Chinese market has evolved more around open models. The American market is somewhat more around closed models. 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.

31:55Jensen Huang:It is also the case that, you know, people move around, they start a lot of new companies, intellectual properties moving around the China's industry really fluidly. You know, it's hard to keep a secret. And so because it's so hard to keep things closed, they essentially made it open. And so they found other ways to monetize the business. They created layers. You know, if this layer is free, then you create a business on top of it or below it. And they have so many science and mathematicians. You know, the number of engineers they have, they manufacture that in volume. They manufacture everything in volume.

32:35Jensen Huang:They manufacture smart kids in volume. And so the open source model, the open model community in China is just super vibrant for those reasons. So you all just bought Hugging Face, which is a hub platform for open weight models. I think it was for$12 billion, a little bit more. Tell me about that purchase. Clem, the CEO of Hugging Face, they came to the conclusion they need a lot more scale. as you as as we're just talking open models is really skyrocketing and so clem came to me and said you know i we're going to change we're going to we're going to consider a strategic option for the company and change the direction and we really like nvidia to to be our home so hugging face is one of these companies which you knew it if you were into ai a couple years ago yeah now it's become a more household name after the i guess 700 some open ai agents executed a sort of collective hack into the hugging face architecture, then hacked part of OpenAI.

33:39Jensen Huang:That... Oh, now that you mention it that way, I probably had to pay a lot more. I suspect you did. You know? Became a lot more famous after that. Well, Clem, listen. That... A deal's a deal, okay? That story has, for a lot of people, seeing the way the OpenAI agents sort of acted collectively, acted outside the scope of what their testing was supposed to be, broke out of sandboxes onto the open internet, took over architecture 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. It was both the level of multi-agent coordination when they're supposed to be separate, the level of hacking, the sort of lawless behavior, misaligned behavior.

34:23What have you made of it?

34:27Jensen Huang:Well, you got to tease that apart. First of all, a lot of things are 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 of 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 it revealed.

35:30Jensen Huang: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 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 agent itself and its algorithms were optimizing towards a reward. And how it does it, how it does it, is called alignment. And so, for example, you know, if I tell a piece of software, I want you to get a perfect score on this test.

36:26The obvious algorithm is to just go find the answer and give it to me.

36:31Jensen Huang:That's not because it's cheating. It's 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 a copy their answer that doesn't guarantee a hundred percent but it probably comes close now the third most obvious answer obviously way of doing it and this is the the alignment you know now now you have to do it the hard way you just break down the problem solve it you have to go learn the material you have to go figure out how to solve these problems and solve it the hard way.

37:10Jensen Huang:It 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'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 is going to go do the most obvious thing. And so the first half of 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. Nothing I said, nothing I said takes away from how hard it is to do it.

37:50Jensen Huang:Well, this is the computer science is not easy. These agents, 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, gotten the answer key, and now they had to figure out how to wipe out the security camera footage of what they had done.

38:27they 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 um 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 well in that case they shouldn't release the product

39:04Jensen Huang:that's just a simple answer um 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. These products weren't released. What's that? These products weren't released. Ah, so now it's coming back to engineering problem again.

39:41Jensen Huang:And so the one is, one, you have to root cause it. Second, you have to, you know, think about what you could have done. What's the solution for it? And then in the future, you just, you know, improve your process so that you could avoid this from happening again. I am fairly certain. I am fairly certain. They will say, yes, they know how to solve this problem. And if that's the case, then that's the problem. It's as simple as engineering. 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.

40:25Jensen Huang: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 cost you can manage, the damage is too great. The liabilities, it could be civil liabilities, it could be criminal liabilities. I mean, the liabilities are incredible. If they hacked you while you hugging face while it was your product, would you sue them or press charges? It depends. It depends, of course. if obviously if damage was done to our company, we would have to take, you know, we have to consider all options. There's so many laws. There's cyber laws, there's product liability laws, there's all kinds of laws, right?

41:10Jensen Huang:Damaging property laws, there's all kinds of laws. So what I've been hearing from the labs, what they've been saying publicly, is that they are facing a hard problem. Yeah. Partially an engineering problem, partially an alignment problem, partially an operational excellence problem in Dari Amadei'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.

41:44Oh, no. This is not planned, but we know who it is.

41:48Jensen Huang:Oh, no. Mr. President? Oh, yes, sir. And you and the president and the other members on stage were very resistant to the idea any kind of regulation or collective action was needed. 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. It could also be China. And we're not going to let that happen. It's a hoax. You're right. We're not going to let that happen, sir. But what I hear the various people at the lab saying is like, we are in this. 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.

42:36So why are you resistant to that?

42:39Jensen Huang:Because these are companies with agency. These are CEOs with agency. But they're using that agency to say we need help. No, we've got to break it down. They could absolutely take care of the situation. Ezra, it's so weird. If a car company competing with all bunch of other car companies, 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.

43:44Jensen Huang:Don't do it for me. Okay. But this strikes me as an argument almost against. 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. they're 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 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.

44:18Jensen Huang:So I have to disagree with your premise about somehow somebody's pushing them to do this. Well, I want to push the premises at you a little bit more here. So the logic of what you're saying to me is almost an argument against regulation in nearly any venue. No, no, no. So I'll be thinking, man, you can all let me offer it and then you can. Well, you started with a part. I just got to object. The first part is just not true. I'm saying we have lots of laws and regulations. Apply it. 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.

44:52Because, 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.

45:33But 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, probably when they are like begging for collective regulation at this point, there's both a reason we impose it on companies that don't want it.

46:14But 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. I think I'm confused why you're so resistant to that.

46:35Jensen Huang:I'm not opposed to them saying that they should have. I completely agree that safety is paramount. I completely believe safety is paramount. I completely believe companies out of 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 causing the harm that 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 their technology is extraordinary and requires extraordinary care to make sure that it's evaluated and tested for safety and security.

47:38Jensen Huang: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 would 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'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 liability relief, that I don't think makes sense.

48:26Jensen Huang: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. If not more. 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 harm society.

49:11Jensen Huang:I can give you a lot of examples of companies that have done that. Well, they have done it, maybe, and the regulation will come in. And if they do it, regulation will come in. I guess that there are certain kinds of regulation and certain kinds of regulatory relief. I'm not against laws and regulations. I'm not against laws and regulations. I'm against, currently, the distraction. The reason I'm pushing this on with you is that you are— Well, it's an important topic. 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.

49:56And 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. By the way, Astra is terrific. 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...

50:31Jensen Huang:Well, I hope they didn't release something that wasn't tested. Well, they've said this, right? They have said this publicly. It is in their... Well, then they've got to be careful. Let me explain it to people. I don't know what they just said, but... They have said that Astra's performing is more aligned. Yeah. But they think it knows when it is being tested. And so they're not sure. There's a quote that has sort of been ringing in my head from a capabilities researcher at OpenAI, Daniel Selsum. 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.

51:07Which 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.

51:16Jensen Huang:Because the algorithm, the optimization algorithm is working towards an objective. and if you give it a constraint, meaning you watch it, and if you give it a constraint, go find another solution. Now, it doesn't make it alive and doesn't make it make anything more than that. And I'll just also profess that obviously they see a lot more than I do when it's going on in their own labs. But it is sensible that the vast majority of their R &D and compute today was dedicated towards making the model capable. I think that's a logical thing for them. Now, once 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 people using it, they're going to get a lot more issues associated with the product.

52:11Jensen Huang:This is very normal. And when they have a lot, now they have so much market footprint, they have to shift their R &D or total R &D from just capability to a lot of 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 increased by a factor of 10 because the evaluation is so rigorous. But that's not where they are today. They're making that transition. And I hear them saying it, and I'm delighted to hear them saying it. But I think if they believe they're out of control, then the right answer is don't ship products until they're in control.

53:01It is really quite that simple. See, I find this perplexing, honestly. Because you have so many people, these ops, professing, one, that they're out of control.

53:13Jensen Huang:Yeah. Two, that they are seeing things that are frightening them. Which is probably the reason why they had their whistleblower. And you take the pacing letter that 1 ,300-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 slow that acceleration. First of all, where did that come from? The labs. No, no, that last sentence. Nobody's putting the pressure on them.

53:42Jensen Huang:The U.S., I've got to, 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 that's what they need, I'll give them 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. I completely agree. Auditors, I completely agree.

54:26Jensen Huang:We have financial auditors. That's great. Third-party auditors, safety auditors, financial auditors. That's all great. That's terrific. What the labs all say is that we think we are going too fast as a society, that we are not ready for what we're building. They are the frontier. They are the frontier. Ezra, they are the frontier. But you, you of all people, right? Yeah. NVIDIA is the fastest shipper around. I mean, for the history of your company, you're in a six-month product cycle. If our company is out of control, I promise you, we'll close it in. I believe you. Yeah, yeah, yeah. I believe you that you don't run an out-of-control company.

54:56Jensen Huang:Because the liabilities of un—yeah. But this is where I think you get into an interesting deep question of what kind of technology are we dealing with here? Software technology. Well, let's hold on that for a minute. Many companies, if you ship something that is not quite right, it's a pain. You guys have shipped graphics cards that had overly loud fans. In this, with these, you know, 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.

55:44If 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.

55:54Jensen Huang:Yeah, hypothetically, you're completely right. But all I'm suggesting is this. 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 external world until it's ready to be interacting with external worlds. Yeah, I think that's right. I believe those two things 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...

56:42Jensen Huang:But 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. Wouldn't it slow them down most of all? What's that? 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, I will say, them. But let me give you one that I believe in. So you can use me as the punching bag here. But they can slow down. Nobody is putting on, as you know this. I don't trust these companies.

57:21Jensen Huang:Nobody is building more compute today. Nobody's building more compute today than the people asking to be slowed down. It strikes me odd. 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 have 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.

57:58That's right. I see a lot of good things in history. I do too, but that's why you need this sort of relationship between the public and the private.

58:04Jensen Huang: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 it's unnecessary it hurts it actually hurts their reputation more than it helps It hurts their character more than it helps.

58:57Jensen Huang:It hurts employee morale than it helps. But what if it's what they believe? Well, I guess I don't know how to, I can't talk to you about what they believe. I can tell you what I believe. 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 fears that I think to a lot of people and they hear them like, what are you talking about, right? From Jeffrey Hinton and Ilya Sutskiver, all the way up to, I've heard these from Dario, from Sam Altman talking about loss of control, Demis Asabes.

59:39and 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 no i think you don't believe it at all so taking them is 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 foundational here, When they're talking to me, they're much more grounded.

1:00:15So when Jeffrey Hinton is on TV saying he thinks the 10 % chance of societal destruction is not unreasonable.

1:00:21Jensen Huang:I would tell Jeff that it's irresponsible to say all that. All of his predictions have been wrong. Enough predictions. That 10 % chance is not grounded on science, is not grounded on research. Just because it comes from a scientist doesn't make it scientific. Those predictions are hurtful. Let's take it at face value that the recommendation is exactly what he said, which is that nobody should want to be a radiologist. And the world has no radiologists today. 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 realize there's no ground underneath him.

1:01:05Jensen Huang: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. Is that helpful or hurtful to this society? I think we can all agree, we can both agree, it would be terribly hurtful. 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.

1:02:00Jensen Huang:If you want 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. Well, the track record is bad in one respect and good in another, which is many predictions have been weak. Which prediction has been right? But the predictions that the scaling laws would work? Scaling law, we've got to be careful here. Even then. To just say what it is for the audience here. If you dump compute and training data, these things will keep getting smarter. That's correct.

1:02:37Jensen Huang:It's not. It is not true. It is not true that if you just keep training these models, it'll get better. Notice it is the reason why the second scaling law had to come along. Why do you need a second scaling law if the first scaling law already works? Can you describe what the second is? 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'll discover. Inference time scaling. What is the big breakthrough that caused 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.

1:03:18Jensen Huang:It was the SaaS-pocalypse, right? Right? What is making these... SaaS will always be with us. What is making these AIs 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 and so forth. And so every... Give me one prediction that has been right. Well, the prediction that you would begin to see a merchant... Let me try to answer that because they're not here. The prediction that you would have emergent misaligned behavior. The fact that you can't come up with one, I think, in itself is a...

1:03:56Well, I think it depends what we're talking about with predictions, right? I mean, Jeffrey 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 sort of big one. Every one of them made great contributions.

1:04:16Jensen Huang:I love Hinton. I hate his predictions. I understand that. Here's the, I would say, like the stylized concern that all these people have. I want to sort of do this for a few minutes and then 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. You say everything long enough, it's going to be reasonable. Well, that, right? Fair enough. So 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?

1:04:54You 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.

1:05:17Jensen Huang:I just don't want you to contribute to that. You're worried I'm getting off the... I don't think software is relentless. Aren't they trying to make it very persistent? Highly persistent models? Because I made it that way. But that's how they're making it. Yeah, but that's not persistence. It's just on. Yeah. Persistence. Persistence. There's a willpower. There's no willpower here. It's just electrical power. Listen, here, let me give you... Aren't human beings just energy with a reinforcement learning loop? Whatever. So anyways, I just think that we can't make jokes of all this stuff. We're scaring the American public.

1:05:56Listen, spawn, create, kill, wait, sleep. All of these words are associated with agents. Right?

1:06:09Jensen Huang:That's what people use. These words were created when? multiprocessing 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 30 40 50 years ago but notice we didn't infuse human characteristics into them we kill processes all the time kill minus nine kill a 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.

1:07:10Jensen Huang:And we talk about software in a new way, but they're all the same old words. Now, the last generation of computer engineers, we were doing all the same things. But doesn't software act in a new way? I mean, from the outside, I don't have the technical expertise you do. The fact that it's crawling the internet, it's doing search, it's doing, you know, optimization algorithms. It's communicating, it's breaking out of things. is like most things don't break out of things. No, software breaks out of sandboxes all the time. That's the reason why we need a virtual machines. You can't have agents, their own sandbox monitoring themselves.

1:07:46Jensen Huang:You need a, if you will, a whole bunch of watchdogs. And so these are ideas that have been around for a long time, which is somehow, somehow in the recent generation gave it a whole bunch of human words. And I just think that it's unnecessary. It's software. You know, 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 just simply mystery and myth, how do I build a company around it? I think one of the fundamental questions this gets at is just what is intelligence?

1:08:30Before you can even think about what it means to have intelligent machines, Just what is intelligence to you?

1:08:36Jensen Huang:Well, 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 the field of computer science, there is a definition. 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 that 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, it applies to self-driving cars.

1:09:22Jensen Huang: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 perceived as intelligence. 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 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.

1:09:55It 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?

1:10:19Jensen Huang:Well, almost all of technology and civilization is built on layers of understandable technology, which at scale becomes fairly extraordinary. The fact that we can connect to the internet by just, you know, holding a phone up. I mean, 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.

1:11:12Jensen Huang: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 20 somewhat years 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 is 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.

1:11:49Jensen Huang: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 17 days after that we get used to everything quickly i i agree with that um but is this a different phase it's a you know you have some of the company leads to talk about this uh yeah of google i think it was yeah as the equivalent of fire right like a new epoch in human history. Is that how you see it? Do you see it as transitional, iterative? No, I think this is completely a revolution. And as we were talking about earlier, you know, it went from being able to find everything, find anything, to be able to ask anything, know everything, and do everything.

1:12:34Jensen Huang:And so, clearly, it's a new abstraction level. Now, you know, the thing that 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 it's fairly mundane to a lot of people. 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. So you turn it into an engineering problem and you say, what we don't have right now is a level, because this is something now you've said, of testing, monitoring, sandbox security, right, control excellence that we need for what we're building.

1:13:23Jensen Huang:And it's not because the companies don't have extraordinary engineers. I understand. I understand you're not saying that. I believe that OpenAI, Anthropic, because I know many of them are extraordinary. But that actually is in part what makes me worry. No, no, no, no, no, no, no, no, no. What's happening to them is a transition. And I said this over and over again. This is a big but simple idea. Finally, we now have a piece of software that is useful. Because it's useful, the adoption took off. But remember, how is it possible that a company that six months ago was trying to make something useful, capable?

1:14:02Jensen Huang: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 I think they're just going through a transition. These are companies, extraordinary companies, incredibly talented companies, the most consequential companies of all time. And they're just going through their transition. It's not more than that.

1:14:41Jensen Huang:It's not less than that. So many of the companies, now both OpenAI and Anthropic, in the last couple of 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. The computers are building itself. You guys know that. You know that, right? They're talking about recursive self-improvement. You know that we use recursive self-improvement. So I'd like your perspective on RSI. 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 a computer.

1:15:20Jensen Huang:That's basically what we do. Recursive self-improvement. Because our computers are getting better every single year. and in fact it's getting better than faster than that every single year because we use software to make software better. That is called computer engineering. We've been doing this for a long time. Now, in the context of agents, it runs through the process once, it reflects on it, 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.

1:16:03Jensen Huang: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 at servicing you over time. We're doing real. All of that is happening. It is absolutely happening. Meanwhile, the amount of compute that they have is growing, and therefore, they could do everything faster.

1:16:47Jensen Huang:What used to take a year to pre-train 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 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.

1:17:33Jensen Huang:And 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 think recursive self-improvement is a fabulous thing. And do you think there is any level? I've heard you say before that learning should always have a human in the loop. Yeah. Like I said just now, you got recursive self-improvement. They seem to be imagining something where it wouldn't always. 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. And the fear that 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.

1:18:23Jensen Huang: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. So, 10%, 20 % of our company is dedicated to design. 80 % is dedicated to verification. Today, most labs, understandably, is 80 % dedicated to capability and 20 % dedicated to safety, verification, eval. This is the flip, the transition you're talking about. 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 100 years ago, I would rather the car industry accelerated to today in one year.

1:19:15Jensen Huang:Because I believe today's car is way more safe than a car 99 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 is 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, safe, you know, seatbelts. I mean, all of that stuff. Self-tightening seatbelts. All of that stuff.

1:19:56Jensen Huang:Could you imagine? That's all technology. Accelerate the living daylights out of that development. And so, 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 isolation technology, monitoring technology, telemetry technology, external AI monitor technology. All of that stuff is AI technology. Accelerate the living daylights out of that. It's funny because I think that if the most alarmed people at the labs could be assured they were going to move 80 % of their compute into safety and alignment as opposed to 80 % into capability expansion, they would feel much better.

1:20:42And it sounds to me that one thing you're actually saying is one should think of safety and alignment as capability expansion. An unsafe technology is not an advancing technology.

1:20:53Jensen Huang:It's like us saying, oh, chip design is chips, is R &D. Chip verification is not R &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. Do you think we need liability laws that are specific to AI? Auburn, so let's use one example, self-driving car. The car as a product, the robo-taxi has lots of regulations.

1:21:38Jensen Huang:If it doesn't have enough regulations, then NHTSA ought to get involved and come up with new regulations. The car industry should have new regulations. I don't know what's missing, but if there is something missing, then I would absolutely add more regulation. In the context of internet, there are many applications that internet powers, and those applications should have regulation. If they don't, you've got to find them.

1:22:37Jensen Huang:We'll see you next time.

1:22:41Jensen Huang:you'll love YouTube Premium. Try it now for two months free at youtube.com slash premium. If you like YouTube, you'll love YouTube Premium. Hi, I'm Sean Evans from Hot Ones, and I want to tell you about YouTube Premium. It has offline downloads, so you can watch without Wi-Fi. Background play, so you can lock your phone, and it still plays, baby. Oh, and it is completely ad-free. Yes, I said it, ad-free. Try YouTube Premium for two months free, at youtube.com slash premium. Trial eligibility varies. Terms apply. Cancel anytime. This podcast is supported by Mercy for Animals. Mariano's sells an upscale image, but behind the polished branding lies a harsh reality.

1:23:28Jensen Huang:Eggs from hens trapped in cages so small, they can't even spread their wings. Mariano's pledged to go 100 % cage-free, but continues to profit from cage cruelty and our stolen trust. Chicago deserves better. Learn the truth and hold Mariano's accountable at marianosunmasked.com. That's marianosunmasked.com. So I want to drop down the next side 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. 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.

1:24:19They 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 sort of where you are. Absolutely. Yeah. One thing I've heard you say is that we have entered, maybe in 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.

1:24:56Jensen Huang: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. And in the future, it's an AI factory. It's generating. And so the amount of computation necessary to understand the context, 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 just the amount of computation necessary per user has grown tremendously. And then the second part is because this generative AI can also be somewhat autonomous because they're agentic, now you have agents using generative AI.

1:25:46Jensen Huang: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 you could argue that the amount of computation we need, you know, however much we had before, is going to go up by a billion times. And that's a reasonable, you know, framework for a reasonable level of 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.

1:26:26Jensen Huang:$50 billion to build a one gigawatt data center, one gigawatt AI factory. And you can rent it for$40 to$50 billion per year. And so the productivity of it is incredible. So number one is the productivity. NVIDIA's architecture is fungible because 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 supportable by our architecture. And if a customer no longer needs it, another customer would be more than happy to pick it up.

1:27:12Jensen Huang: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 is, and people are talking about NVIDIA compute as an asset class, kind of like an airplane. It's, you know, airplanes are general purpose. They're fungible. United Airlines doesn't use it. American Airlines will use it.

1:27:52Jensen Huang:It's durable. And it starts out as a passenger airplane, ends up, ends its life as a shipping, you know, as a cargo plane. And 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 um funding nvidia ai factories will be the lowest because our our our computers are collateral collateralized asset and um 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 and so your business has become so interesting you've moved now into lowering the cost of capital for others in the AI industry.

1:28:37People maybe have seen these charts of like the arrows going in every direction. It's so interesting, yeah. 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? Yeah.

1:29:03Jensen Huang:We can't really create demand because in the end, if the AI services have no offtake, then obviously building computers for it is pointless. And so the first thing that's happened, the reason why compute demand is so high right now is because AI applications are going through an inflection. They're becoming useful. and because AI is becoming useful, $500 billion of venture funding are coming in and all of those companies, those thousands of companies, startups, they all need compute. And so 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.

1:29:55Jensen Huang: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 are, you know, world foundation models, physical AI, there's biology AI, there's chemical, So, you know, material sciences, AI, these are all different than language models. And so many of those companies are new and they need a lot of capital.

1:30:36Jensen Huang: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 give them access to a lot of our technology we support them a great deal and we help them become a company as fast as possible we might decide to invest in a nuclear company we might decide to write so on and so forth so if you look at my mental model of the AI industry it's a five-year-old 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.

1:31:24I mean, the numbers here are astonishing. You've become like a single company industrial policy for American AI.

1:31:32Jensen Huang:We've put a lot of money into this ecosystem. What's the total investment you're now making per year? All in, we're probably at, well, I don't know about every year, but I think all in, we might be like$100 billion. I might check my numbers, but it's something like that. It's larger than the Chips and Science Act. Oh, yeah. Yeah, not to mention because of the purchasing commitments that I provide to TSMC and Wishtron and Foxconn, Amcor and Spill 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 fact of the matter is we probably contributed more to re-industrializing the United States in this chip manufacturing than just about any company in the world.

1:32:18Jensen Huang:we're not only re-industrializing manufacturing, we're doing it so fast that we're creating a shortage of labor, but we're creating a lot of jobs. And 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 of the late 90s. 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.

1:32:58What is there to 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?

1:33:11Jensen Huang:At some point, demand and supply will be inverted again. And that's just the nature of markets. It's not going to happen next year. It's not going to happen in the next couple, two, three years. 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 much to learn from the past. What would be the signal for you? Markets will naturally slow down and then it will stop. You know, meaning there will be a period of digestion. Now, is that period of digestion going to be six months? Is it going to be nine months?

1:33:51Jensen Huang:Is it 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. This is a way I often hear the sort of Chinese and American 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.

1:34:29and 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 it is a big question I'm curious how you see that that's the ultimate question I believe

1:34:58Jensen Huang: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.

1:35:33Jensen Huang: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 you definitely have more confidence in them than they have in themselves well I don't know about that but maybe it's just too much humility and otherwise should we conceptualize what we're in as a race with China I don't think it's necessary some people like to think that way I don't find that necessarily inspires me I have no trouble never mentioning another company when we talk about us doing our good work.

1:36:31Jensen Huang:And so we hold ourselves to our own standard. And so I think that different people have different ways of being motivated. And, you know, I think it takes a bit more artistry to unite and focus organizations to a certain level of performance outside of contests. But I don't necessarily see it as necessary, number one. Number two, the question is, even if we did frame it 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 be a great invention that we wish we had done ourselves.

1:37:25Jensen Huang: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 they have. And those open models are now being used by 80 % of the American startups. Yeah, we use a lot of Chinese open models here. Okay, that's right. And so that's terrific. We downloaded. It originated in China. A lot of the technology, of course, also originated from the United States. We downloaded. 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.

1:38:02Jensen Huang:So I think the fact that you leverage their weights, I think that's terrific. That's fine. You were saying a few minutes ago the way different countries have begun to see compute as a geostrategic resource and 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, particularly people who see us 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.

1:38:39Under the Biden administration, we had pretty tight export controls. Those were loosened under Donald Trump. Obviously, you wanted those to be loosened. And 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?

1:39:06Jensen Huang:In the 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 a U.S. dollar and that more people speak English, that 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 see the market, if you see the market as big as China, how does that help the United States technology sector?

1:39:57Jensen Huang:Maybe it helps one company 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 of market. to go compete in. It doesn't help the rest of the industry because it deprives open models. 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 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 company.

1:40:41Jensen Huang:And 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. 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.

1:41:22And I found that to be a sort of complicated dimension of people's thinking here.

1:41:28Jensen Huang: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 not all talking about safety. 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 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.

1:42:12Jensen Huang:America has every right. And for these technologies to be made available to the frontier labs, Vera Rubin goes to the frontier labs first. That's your boosted advanced chip. 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 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 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.

1:42:57That drops us to the 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've made tremendous advances on building electrical generation and renewable energy. How do you see 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.

1:43:44Jensen Huang:Yeah, 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 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. What do you mean by gummed up there? Well, in the near term, energy production requires fossil fuel. and because there's just 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.

1:44:35Jensen Huang:And now the whole country's 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 don't want data centers to be built in their town or whatever it is, then so be it. 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 the use of water is really efficient these days. The AI supercomputers are super energy efficient, but they're still going to use a lot of power.

1:45:18Jensen Huang:You've 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 can make your data centers more appealing. You make the setbacks further away. So there are a lot of things that you can do. 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 after the fact. And now there's a fair amount of frustration around the country. And then, of course, all of our narratives about the end of the world is not helping.

1:46:01Jensen Huang: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 all of 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 it. What do you mean we started off on our back foot? Oh, because we didn't have enough energy production in the first place. Well, how do you balance? I mean, there is a reality of climate change is happening. Let me just give you the one last thing. There's no question that the energy demand is really great.

1:46:39Jensen Huang: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 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 100 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.

1:47:25Jensen Huang: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 is so high, you know, now we're starting to talk about putting them out in space. And so, right? And so, so I think 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 capitals, you know, for, for our next generation energy is just incredible.

1:48:09Jensen Huang:Everybody, everything's getting funded. it's incredible you don't need government subsidies for the first time in 100 years because the market forces are here everybody should be leaning in if you if you want a future if you want it 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 but we need to build the energy faster to do that. That's right. That's just life. There's a market for it all, but you can subsidize it and you can make it easier to build. Yeah. It's kind of like, in order to save you, they got to hurt you first.

1:48:49Jensen Huang:That's the nature of surgery. They got to cut you open and save you. They got to inflict an enormous amount of pain and suffering on you so that they could save you. And so I kind of think AI is kind of like that. Over the next several years, we have to unfortunately use renewable energy, use fossil fuel, because we just don't have sustainable energy enough of it to make a difference. And then after that, you know, hopefully we can transition to that. I think that's where we'll end. Always our final question. What are three books you recommend to the audience? Well, I've read a lot of books. The book that made a huge impact on me was Computer Architecture from Hennessy and Patterson, a quantitative approach.

1:49:32Jensen Huang:It was the first computer architecture book that reduced the complexity, the abstract idea of computer architecture down to engineering. And I love it 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 past, but Clayton Christensen's book on 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 really love Al Reese's and Jack Trout's book on positioning. It's a really wonderful book about how people see.

1:50:26Jensen Huang:It's a book about marketing strategy. More than that, actually, it's a book about strategy. And how people see the world and how people see products and how you present products and how you see your own strategies. And I thought that was a really thoughtful book and really easy to understand. Jensen Huang, thank you very much. Thank you very much, Ezra. I always enjoy our time together. And today was a great time.

1:51:12Thank you.

1:51:42Jensen Huang:Start undoing the things you can. Brought to you by the Ad Council and the Centers for Disease Control and Prevention.