Jensen Huang: The Doomer Hoax, Superintelligence is Here, and The Future of AI (ft. President Trump)

Some people call it vision. Vision is an awfully big word to me because I believe first of all vision matters.

Oh, we got a standing O on the way in.

Thank you. I love you back. Number one podcast in the world.

Absolutely.

Well, you know, you auctioned the open.

I just I felt you guys needed some energy.

Which one?

Well, first of all, there were a lot of stuff in there. And first there is a part about safety which we have to take very seriously. Safety is paramount. Obviously, safety and leadership are not false. They're false choices. You're able to innovate quickly. You're able to execute quickly and America's able to lead and to do it safely. I think those are false choices but safety is obviously important. There's a matter of internal control that I think he was speaking to. Obviously the Coxin whistleblower is very serious matter. Whenever you have a whistleblower, you got to take it very seriously. I thought Coxin had great courage to put out what his concerns were. And even then there were some issues that were kind of conflated within that. I think the whistleblowing is fine. I think the scientific prediction about the future is less aligned because it's not grounded on science obviously and it was expressed by a scientist but it was obviously not grounded on science and so I take issue with that but obviously the whistleblower part of it, you know, I think there's just a whole bunch of stuff, pausing, pacing, those are all the voluntary things that they could do if they feel that their company is out of control. If Coxin saw something, you know, obviously we don't know what Coxin saw, but if he saw that the company was out of control and maybe it's a transition from research to engineering. As you know, these labs are transitioning from research to engineering. Extraordinary talent, extraordinary engineering. But obviously engineering is different than research. Maybe that transition is clumsy. You know, we don't know what he saw and ultimately only he knows. But if there was a matter of lack of control, that's a different topic. How should the government deal with it? Now all of a sudden, regulation and reg... I mean it just covers everything in one blog.

Well, first of all, we shouldn't because it's made up. First of all, I think that we shouldn't because it's made up and these are well educated, they're called researchers, obviously they're working in a lab and so the confluence of these words and then the prediction is alarming and troubling and it shouldn't be done. It's irresponsible. Now the fact of the matter is let's go back and look at the real facts. The facts are there was a prediction that in 5 years time radiology will be completely taken over by artificial intelligence and there'll be no radiologists in the world. That has proven to be exactly the opposite. We need more radiologists than ever in the world. However, AI has taken over radiology completely which is great, is automated scan reading which is great. There was a prediction that within 6 to 12 months, wasn't it just last year? Within 6 to 12 months, 90% of code would already be generated by AI. That has turned out to be wrong. Within 6 to 9 months, that was predicted last year, 50% of entry jobs will be wiped out. That has proven to be wrong. Let's see what else. What else has proven to be wrong? I mean, all of these predictions have been wrong, right?

We have to take accountability. We have to take account for all of the stupid predictions that were made, right?

And so we ought to just keep track of all that. And of course people do and remind us that those predictions are inconsistent with ultimately America winning the AI race.

Well, first of all, I got to tell you these are some of the most consequential companies in history. Extraordinary engineers, extraordinary researchers, really fantastic work. On the one hand, I work very closely with them as companies to companies. On the other hand, we have to have conversations like this in public. And it's really unfortunate. And I think that these companies really ought to be built the way that we used to build companies, which is in silence, right? You know, and so...

No, because well that's what they decided when they came to work for us and we told them these are, this is the way you behave when you work in our company and if you like the culture of our company, which as you know the NVIDIA culture and the NVIDIA employee base, incredibly happy. They like the fact that the company is consistent, that we're stable, that our core values are consistent with taking care of the families and creating the conditions by which they can do their life's work. That we do meaningful work, we do it as quietly as we can and we contribute to everybody else's success, which we're very proud of. And so those kind of core values people are attracted to. But when you come and work in our company, there are also some things that we don't appreciate that you do. Like for example, we don't welcome political discourse inside our company. Take it home. You guys talk about politics outside the company. We...

We are, the company is an apolitical company. You know, we're bipartisan. We want America to succeed and we want whatever government is in place, we'll do everything in our power to help America succeed. And so the discourse about race and religion and politics and all of that stuff we tell people do it outside the company. It's not for us.

Get engineering right?

You know, regulation should solve actual problems. And so the question is what actual problems have we enjoyed, right? And if you look at the actual problems, all of the actual problems so far have come from the labs. And the reason for that, and just in their defense, the reason for that is because they have the most compute, right? And the reason for that is because they're trying to solve the frontier problems. And so in their defense, it's sensible that the labs, the frontier labs will be where the most danger come from. It is unlikely that a high school student did something because they just simply won't have enough compute, right? And so it's unlikely that a startup will be the reason because they won't have enough compute. In fact you could look across the planet and everybody won't have enough compute with the exception of the frontier labs. And so now the question is if you look at what actually happened and they're doing pioneering work. It's really very hard. They're transitioning from research to engineering. I could imagine and they're obviously building some of the most consequential technology and companies in the world. They're building their company, they're building their culture, they're building the technology, they're building engineering, they're building products all at the same time. And so I can understand it's a little bit hair on fire. But nonetheless, the four incidents from one lab, the one giant incident from the other lab, the first thing that you have to do is just root cause the problem from an engineering perspective. What happened, what could we have done differently and what are we going to implement and institutionalize whether it's technology or methods or processes and make sure that we don't let it happen again. Now, I would bet you money that in every single one of those cases is within their control in the future to prevent it because the alternative if it's not in their control and I'm sure that they are, I'm sure those four incidents won't happen again. They, I'm sure they root caused it and fixed it. I'm sure they have now technology for, you know, sandboxes and run times and monitors and continuous monitors and so I'm certain they have much much better technology. Now the alternative is also unlikely which is for them to say look we had these incidents after we're done analyzing it we came to the conclusion we don't know anything that happened and we have no idea how to control it and we're asking society for help.

Now, if that's the case, then we ought to, you know, a bunch of companies with engineers ought to send engineers in. I mean, and we should advise them if we can, but I doubt it. I think they have extraordinary people. They got this handled.

Well, this is the new sexy phrase but as you guys know RSI is a combination of a system of ideas. It starts everything with in-context stuff. It starts with skills. It starts with reflection. It starts with, you know, reinforcement learning and synthetic data generation. And these are all very sensible ideas that causes AI to get better at solving a problem, you know, over time. And you could also have low rank, you know, all of that stuff doesn't include the weights. You could actually improve the weights and it's called LoRA. LoRA could be improved in synthetic data generation, reinforcement learning, enhance it without training the base model itself and then over time you could train the base model again with all of that experience. And so I think it's a sensible thing that you're going to use the technology to enhance productivity of all kinds of tasks including building AI. I think that's a very logical idea and I'm certain that everybody is using it in some degree. It's just this phrase is now being used to weaponize the technology in some way and maybe to turn the...

No. No, of course not. And the reason for that is because you could RSI all day long inside your company, but when you release a product, you've got to evaluate it, don't you? You have to test it again, don't you? You have to make sure that there's no regression, right? And so the basic process of control. These labs are going to as they move from labs to engineering, they will have much much better control, right? And when they have much better control that and control comes from methods and knowledge and practice and tools and technology all of those things that leads to better control, verification and evals. It's going to enable RSI to be done inside the company and for good products to be released outside.

The world needs both closed models and open models. You want to use, I use as much closed models as I can. This weekend I used four of them and they work terrifically. They're frontier. They're great experience. They work incredibly well. They're getting better all the time. And the way I think about closed models is kind of like bottled water. You know, water is free, you guys. I don't know if I've told you guys, but water is free. I don't want to, you know, burst everybody's bubble, but water's free. And this morning, I used a lot of free water taking a shower. And so, you use the right water in the right places. And this is no different than electricity. This is, you know, this is no different than all kinds of commodities that we use in the world. You need both. Now in the case of open, the reason why that you need it is because it could be for sovereignty reasons, privacy reasons, proprietary technology reasons. Look at the facts. The facts are in the last 6 months $400 billion of venture funding went into AI native companies. 80% of them use open models. If not for open models, how could they build their dream, right? Because their dream could be different. Obviously, it'll be different than the labs, the frontier labs dreams. And America has so many different ways to innovate. That's one of our core strengths. Great ideas just coming out of the fountain. And so open models enables that. Open models enables every single, if we want to win the AI race. It's not about a few technology companies winning the AI race. It's about every company in America. Every company, every industry, every researcher, every teacher, every student, every startup, everybody wins. Some of them will use closed models. A lot of them will use open models. There's 10 million...

Well, we're doing everything we can to make a contribution in open models. However, the moment you download, like for example, probably the vast majority of the world's contribution to open source today is coming from China. They just have a lot more engineers. They produce everything in large scale because it's a larger country. And so they produce science and math students in volume, right?

They're manufacturing them through amazing universities like Tsinghua University in high volume. Well, they contribute to open source today. We download Linux. We download Kubernetes. We download all the software. A lot of it has been touched by Chinese. And once you download it, it's yours. We fork it. We improve it. We make it ours. And so when you download one of these Chinese models, it just happens to be made by some really great researchers in China, but it's now yours. Whatever you want to do with it.

Yeah, I think that's a really good point. My point is the race is really about who exploits the technology best. You know, the last industrial revolution, all of the inventors were Maxwell, Volta, Ampere. None of them were American. They were right. The last industrial revolution came from Europe. But we exploited it. We took advantage of it socially better than anybody else in the world. Look how it turned out for us. I want to make sure that this next generation happens just like this.

You know, I think first of all the narrative is much more practical. The narrative is much more practical. Nobody in China is saying that there's end of this and end of that and cataclysmic this and doom or that. They're much more pragmatic about it. They see AI as a technology that's going to advance their economy, advance their society and they don't have these groups who are basically saying it's going to end civilization and we're making it up. The part that is frustrating is if it was true, if it was true then we ought to talk about it and go do something about it, right? Even if it's true, we ought to spend more time doing something about it than worrying a bunch of people who can't do anything about it. It's our job to build it, right?

And they're measurably, they're actually demonstrably untrue and it actually makes sense as untrue. It's not based on science. It's not based on research. Everything that's based on science and research proves otherwise.

It could be life experience as well, David. So let me give you an example. When I first graduated from school, I was an engineer and I didn't do that much typing. And the reason for that is because I was the first generation before software became popular. We had to go build the computers to make software possible. Could you imagine in this generation every single engineer who came into the world of engineering you spend all your time typing. Literally that's what you do when you get a job, they give you a laptop, they give you a chair and you start typing. You type all day long, you type from the moment you wake up to the... well there was engineering before typing, right? And so can you imagine that the world has a mountain of engineering work to do where most of it is not typing anymore? Sure, we had busy engineers before typing. I think we're going to do a lot of great engineering after typing.

When I say typing, I mean coding. I mean, and so even at NVIDIA when software engineers talk to me, I tell them, you're just typing. I've been saying that forever, but obviously for fun. And I tell them, my favorite key is backspace. And the reason for that is because the best software is the smallest software. So I want you to use backspace software.

Oh no.

Oh no. No. Mr. President. Oh, yes, sir. I gotta tell you something. If it wasn't because of you calling, I would... I'm on stage with the besties. I'm on stage with the besties. I'm on stage with the besties. I'm on stage with Sachs. And yeah, you know, the whole group. Yeah. Jason's here. Chamat's here. David and David is here. Yeah. I'm sitting in front of a few thousand people and we're talking as it turned out we were talking about you. Good job, sir. Good job. The fact that you saw through all of that, I mean, there's a lot of complexity and the fact...

You're right. We're not going to let that happen, sir.

Yes, sir.

Good. Well, I feel strongly about it and I have the position that can do something about it. We're not going to let that stuff happen. So, I have no idea who's at the meeting. I have no idea who the hell I'm talking to, but I'll see.