**Jensen Huang: The Doomer Hoax, Superintelligence is Here, and The Future of AI (ft. President Trump) — Transcript & Summary | SozAI**
Source: https://sozai.app/transcript/jensen-huang-doomer-hoax-superintelligence-ai-future/

Jensen Huang discusses AI safety, industry predictions, Nvidia's role, and the future of AI innovation with insights on leadership and regulation.

## Key Takeaways

- AI safety is paramount but should not hinder innovation or be driven by unfounded fear.
- Many catastrophic AI predictions have been proven wrong, urging a more measured and scientific approach.
- Nvidia is a central player in AI development, focusing on full-stack AI platforms and engineering excellence.
- Effective AI leadership requires balancing rapid execution with internal controls and responsible public communication.
- The future of AI depends on pragmatic regulation, collaboration, and continued technological advancement.

## What the video covers

- Jensen Huang emphasizes the importance of vision and leadership in AI development and Nvidia's pivotal role in the market.
- He addresses recent AI safety concerns and whistleblower issues, advocating for serious attention but cautioning against alarmist predictions.
- Huang critiques exaggerated doomsday forecasts about AI, highlighting many past predictions that have proven inaccurate.
- He stresses the need for responsible innovation, balancing rapid progress with safety and internal control in AI companies.
- The conversation touches on the transition from research to engineering within AI labs and the challenges it brings.
- Jensen discusses the competitive landscape, Nvidia’s strategic positioning, and the importance of building AI technology quietly and effectively.
- He reflects on the political and business incentives behind public AI discourse and the need for measured public communication.
- The dialogue includes insights on government regulation, AI’s impact on jobs, and the future trajectory of AI technology.
- Nvidia’s ecosystem, supply chain, and scaling efforts are explored as critical factors for sustained AI leadership.
- The video concludes with reflections on collaboration, competition, and the long-term vision for AI’s role in society.

## Chapters

1. 00:00 Introduction and Vision on AI Leadership
2. 02:03 Discussion on AI Safety and Whistleblower Issues
3. 05:06 Critique of AI Doom Predictions and Their Accuracy
4. 07:35 Psychological and Business Incentives Behind AI Discourse
5. 10:15 Engineering Challenges and Responsible Innovation
6. 13:11 AI Regulation and Public Communication Strategies
7. 20:32 Nvidia’s Strategy and AI Ecosystem Overview
8. 29:44 Future of AI, Competition, and Closing Remarks

Answers

## Questions about this video

What is Jensen Huang's view on AI safety and innovation?

Jensen Huang believes AI safety is crucial but should not be a false choice against innovation. He advocates for responsible, rapid innovation balanced with strong internal controls.

How does Jensen Huang respond to doomsday predictions about AI?

He considers many catastrophic AI predictions to be unfounded and irresponsible, emphasizing that past predictions about job losses and AI takeover have proven wrong.

What role does Nvidia play in the AI industry according to the video?

Nvidia is described as a leading AI platform provider, with a full-stack AI factory approach, driving innovation through engineering excellence and strategic ecosystem development.

## Full Transcript — Download SRT & Markdown

00:01

Speaker A

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

00:09

Speaker A

We preempted the weekly show. And there's only three people we preempt the show for: President Trump, Jesus, and Jensen.

00:18

Speaker A

The number one podcast in the world. That's Jensen Wong. He's the founder, president, CEO of Nvidia.

00:23

Speaker A

Whether you know it or not, his decisions are shaping your future. Nvidia is the most important stock in this market. Jensen is arguably the best executive in history.

00:31

Speaker A

Revenue exploded 97% year-over-year. Not only is demand already strong, it is actually accelerating. Nvidia is the only computing platform that is a full stack AI factory. A GPU is like a time machine because it lets you see the future sooner. And if we could see the

00:48

Speaker A

future and we can predict the future, then we have a better chance of making that future the best version of it.

00:56

Speaker A

Please welcome Jensen Hang. Oh, we got a standing O on the way in. Oh, come on.

01:05

Speaker A

Standing O. Standing O on the way in. There's our guy. Ladies and gentlemen, GPU Jesus.

01:20

Speaker A

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

01:25

Speaker A

In the world. Absolutely. Wow. We like the new jacket. Well, you know, you auctioned the open.

01:30

Speaker A

I just, I felt you guys needed some energy. Yes. This is the, I know we're talking about serious stuff here, but we need to talk about it with energy. Yes.

01:39

Speaker A

Let's, uh, let's start with this, uh, essay from this weekend. Which one? Let's start with Daario's essay because was Hemingway involved?

01:50

Speaker A

Actually, did anybody run it through Pangram? I don't even know how much of it was AI helped, but that was a pretty incredible thing. And then I think what a lot of people were surprised by was the coalescing of the frontier labs

02:03

Speaker A

around the essay itself. Just Jensen unpack what happened, how you read it, how you interpreted it, and then we'll get into some details that were inside of it. But maybe just the high-level thoughts to kick it off.

02:14

Speaker A

Well, first of all, there were a lot of stuff in there. Yeah. And, and, uh, uh, first there is a, there's a part about safety which we have to take very seriously. Safety is paramount. Obviously, um, uh, safety and

02:28

Speaker A

leadership are not false. They're false choices. You're, you're able to innovate quickly. You're able to execute quickly and, and, uh, America's able to lead and to do it safely. I think those are, those are false choices but safety is

02:42

Speaker A

obviously important. Uh, there's a matter of internal control that I think he was speaking to. Uh, obviously the, the Coxin, uh, whistleblower is very serious matter. When, whenever you have a whistleblower, you got to take it very

02:57

Speaker A

seriously. I thought Coxin had great courage, uh, to, uh, put out, put out, uh, uh, what his concerns were. Um, and even then there were some issues that were kind of conflated within that. Uh, I think the, the, the whistleblowing is fine. I

03:12

Speaker A

think the, the, uh, scientific prediction, uh, about the future, uh, is less aligned because it's not grounded on science obviously and, um, uh, it, it was expressed by a scientist but it was obviously not grounded on science and, and so I take, I

03:27

Speaker A

take issue with that but obviously the whistleblower part of it, uh, you know, I, I think there's just a whole bunch of stuff, uh, pausing, uh, uh, pacing those are all the voluntary things that they could do if they feel that their

03:41

Speaker A

company is out of control. Uh, if Coxin saw something, you know, obviously we didn't, we don't know what Coxin saw, but he, if he saw that the company was out of control and maybe it's a transition from, uh, uh,

03:57

Speaker A

research to engineering. As you know, these labs are research are transitioning from research to engineering. Extraordinary talent, extraordinary engineering. Um, but obviously engineering is different than research. Maybe that transition is clumsy. You know, I don't, we don't know

04:11

Speaker A

what, what he saw and ultimately only he knows. Um, but if there was a, a matter of lack of control, that's a different topic. Um, how should the government deal with it? Now all of a sudden, uh, regulation and reg I mean it just covers

04:23

Speaker A

everything in one blog. Can you just help us sort of unpack? We, we play, we tried to play this game actually this week on the pod and it was difficult which is how do you describe like, you know my mom calls me and she's

04:33

Speaker A

like Jimoth what is this whole civilizational death thing? I don't know how to explain it to her. So when you have very smart people like that quantize it and quantify it, I think that's probably what's perturbing to some people. They're like, "What does

04:46

Speaker A

that mean, 10% of extinction?" Nobody knows how to explain that to the average person how that's even possible. Well, first of all, we shouldn't, uh, because it's made up. Uh, first of all I think that we shouldn't because we, it's made up and

05:06

Speaker A

these are, these are well educated, uh, they're called researchers, um, obviously they're working in a lab and so the confluence of these words and then and then and then the prediction is alarming and troubling and it shouldn't be done.

05:21

Speaker A

It's, it's, it's irresponsible. Now the fact of the matter is let's go back and look at the real facts. The facts are, uh, there was a prediction that in 5 years time radiology will be completely taken over by artificial intelligence and

05:34

Speaker A

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

05:46

Speaker A

great. Um, uh, there was a prediction that within 6 to 12 months, wasn't it just last year? Within 6 to 12 months, uh, 90% of code would already be generated, uh, by AI. That has turned out to be wrong. Uh,

06:01

Speaker A

within 6 to 9 months, that was predicted last year, 50% of entry jobs will be wiped out. That has proven to be wrong.

06:07

Speaker A

Uh, let's see what else. What else has proven to be wrong? I mean, all of these predictions have been wrong, right? Well, that GPT-2 would be too unsafe to release. That Llama 3 would be too unsafe to release.

06:20

Speaker A

Oh, one. Yeah, we've heard the half of white collar jobs would be gone next year.

06:24

Speaker A

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

06:33

Speaker A

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

06:51

Speaker A

The short form for that is some people are saying, you know, they say trust the experts and they used the analog of COVID which again started with people that were researchers, educated people that had an asymmetric awareness of the thing

07:03

Speaker A

that the rest of us did not saying things that ultimately turned out we find out in facts, uh, not to be true. Um, and so there's this war that's happening right now between the trust the experts movement and the, you know, well, let's

07:17

Speaker A

just look at the actual history of these predictions and let's just think more methodically.

07:23

Speaker A

Where is this coming from? Because it's coming from inside the places that's actually making it. Like what do you think is the psychological makeup or what is the real incentive? Maybe it's a business incentive, maybe it's a political incentive. Can you just maybe

07:35

Speaker A

guess or how do you, how do you think about what's why they're doing this?

07:38

Speaker A

Well, first of all, I got to tell you these are some of the most consequential companies in history. Uh, uh, extraordinary engineers, extraordinary researchers, uh, really fantastic work.

07:49

Speaker A

Um, uh, on the one hand, uh, I work very closely with them as companies to companies. Uh, on the other hand, uh, we have to have conversations like this in public. And it's really unfortunate. And I, I think that, that these, these

08:05

Speaker A

companies, um, really ought to be built the way that we used to build companies, which is in silence, right? You know, and so wait, wait, Jensen, you don't allow anybody in your organization to speak for the entire organization, especially

08:20

Speaker A

when they'r

08:31

Speaker A

them uh these are this is the way you behave when you work in our company and and uh if you would like if you like the culture of our company um uh which as you know the NVIDIA culture and the

08:42

Speaker A

NVIDIA employee base uh incredibly happy. Yeah. uh 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. Uh

08:56

Speaker A

that we do meaningful work, we do it we do it as quietly as we can and uh we contribute to everybody else's success, which we're very proud of. And so those kind of core values people are attracted to. Um but when you come and work in our

09:09

Speaker A

company, there are also some things that we don't appreciate that you do. Like for example, we don't welcome uh political discourse in our inside our company. Take it home. You guys talk about politics outside the company. Um we

09:23

Speaker A

Yeah. Uh we we are um the company is an a-olitical company. You know, we're bipartisan. We want America to succeed and and um uh we want we want uh whatever uh government is in place uh we'll do everything in our power to help

09:43

Speaker A

America succeed. And so so the the discourse about about uh about race and religion and politics and all of that stuff we tell people do it outside the company. It's not not for us.

09:58

Speaker A

In terms of u maybe AI regulation then more narrowly. Um Satya was here this morning and what he said is you know before we talk about regulation that could really styy things why don't we just get some basics right? Why don't we

10:09

Speaker A

get measurement right? Why don't we get standardization right? Right. Um where do you land on get engineering right?

10:15

Speaker A

Get the engineering right. Right. Translate the research in a more predictable way so that we're not fear-mongering. Keep it inside until we're ready to expose it. Um what do you think the right response is? You know Demis had a proposal which was sort of

10:26

Speaker A

this more FINRA like organization. It's not clear what Daria wants. This transnational mutated thing that has some sort of control. Where do you land on this? The sort of perspective of what what do we need right now?

10:39

Speaker A

You know, regulation should solve actual problems. And so the question is what actual problems have we enjoyed, right? And and um if you look at look at the actual problems um all of the actual problems so far have come from the labs.

10:55

Speaker A

And the reason for that, the reason for that and and just in their defense, the reason for that is because they have the most compute, right?

11:02

Speaker A

And the reason for that is because they're trying to solve uh the frontier problems. And so in their defense and so it's sensible that that um the labs, the frontier labs will be where the most danger come from. It is unlikely that a

11:19

Speaker A

high school student uh did something because they just simply won't have enough compute, right? And so uh it's unlikely that a startup will be the reason because they won't have enough compute. It's it's uh they you know in fact you could look

11:32

Speaker A

across the planet and everybody won't have enough compute with the exception of the frontier labs. And so so now the question is if you look at what actually happened um and they're doing pioneering work. It's really very hard. Um they're

11:45

Speaker A

transitioning from research to engineering. Um, I could imagine and they're they're they're they're obviously building some of the most consequential technology and companies in the world. Uh, they're building their company, they're building their culture, they're building the technology, they're

11:58

Speaker A

building engineering, they're building products all at the same time. And so I I can understand it's a little bit hair on fire. Um, uh, but nonetheless, the four incidents from one lab, the one giant incident from the other lab, um,

12:13

Speaker A

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 in to implement and institutionalize whether it's technology

12:25

Speaker A

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

12:42

Speaker A

their control and I'm sure that they are I'm sure I'm I'm sure that I'm sure those four four incidents won't happen again. Um they I'm sure they root caused it and fixed it. I'm sure uh they have now technology

12:55

Speaker A

for you know sandboxes and run times and monitors and continuous in continuous monitors and you know and so I'm I'm certain they have much much better technology now the alternative is also unlikely which is for them to say look

13:11

Speaker A

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.

13:24

Speaker A

Yeah. Now, if that's the case, then we ought to, you know, a bunch of 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

13:34

Speaker A

think they they have extraordinary people. They got this handled. But we we're not operating in a vacuum.

13:38

Speaker A

David, last night you informed me that there is a Chinese lab, the makers of GLM, who are going to put three billion towards a recursive self-improvement run. So, maybe you could tee that up for J.

13:48

Speaker A

Well, that's what was announced. Yeah. zpoo.com the founder just raised 5 billion and said that one of their priorities is going to be trying to get to recurs you know uh AI that trains the next AI and to try and automate as much

14:02

Speaker A

of that as possible. Um yeah I think that I mean well this is the new sexy phrase but as you guys know RSI is a combination of a system of ideas.

14:13

Speaker A

It's um 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

14:31

Speaker A

know, over time. And you could also have uh low rank, you know, all of that stuff doesn't include the weights. Uh you could actually improve the weights and it's called Laura. uh Laura could be could be improved in synthet synthetic

14:44

Speaker A

data generation reinforcement learning enhance it without training the the base model itself and then over time uh you could train the base model again with all of that experience and and so I I think I think it's a sensible thing that

14:57

Speaker A

that you're going to use the technology uh to enhance productivity of all kinds of tasks including building AI. I think that's a very logical idea and and I'm I'm certain that everybody is using it in some degree. It's just this phrase is

15:13

Speaker A

now being used um to weaponize the technology in some way and maybe to turn the as if it's going to spiral out of control is the impression they're trying to give. But you don't believe that's real?

15:25

Speaker A

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

15:37

Speaker A

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

15:53

Speaker A

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.

16:06

Speaker A

Let's talk about uh open source for a second. I mean this hugging face we we were communicating about this and I said it's going to be one of the most consequential um acquisitions. I don't even want to call

16:17

Speaker A

it a transaction because I think it's more important than that. Um, give us your first principles explanation of open source versus closed source versus open weights and how the ecosystem should fit together over time.

16:29

Speaker A

The world needs both closed models and open models. Um, you want you want to use I use as much closed models as I can. This weekend I I used four of them and and uh they work terrifically.

16:40

Speaker A

They're frontier. They're great experience. They right they work incredibly well. They're getting better all the time. Uh, and and the way I think about closed closed closed models is kind of like bottled water. You know, water is free, you guys. I don't know if

16:54

Speaker A

I've told you guys, but water is free. I I don't want to, you know, burst everybody's bubble, but water's free.

16:59

Speaker A

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.

17:08

Speaker A

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

17:18

Speaker A

sovereignty reasons, privacy reasons, um 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.

17:32

Speaker A

80% of them use open models. If not for open models, how could they build their dream, right?

17:40

Speaker A

Because their dream could be different. Obviously, it'll be different than the labs, the frontier labs dreams. And there's America has so many different ways to innovate. That's one of our core strengths. Great ideas just coming out of the fountain. And and so open models

17:54

Speaker A

enables that. Open models enables every single if we want to win the AI race.

18:00

Speaker A

It's not about a few technology companies winning the AI race. It's about every company in America. Every comp, every company, every industry, every researcher, every teacher, every student, every startup, everybody wins.

18:16

Speaker A

Some of them will use closed models. A lot of them will use open models.

18:21

Speaker A

There's 10 million Does it matter? Well, let me just ask, does it matter if the model the open models come from China or the US?

18:29

Speaker A

Well, we're doing everything we can um 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

18:46

Speaker A

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?

18:54

Speaker A

That's one of our disadvantages, right? They're manufacturing them through amazing universities like Chinua University in high volume. Well, they contribute to open source today. We download Linux. We download Kubernetes.

19:06

Speaker A

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.

19:14

Speaker A

We improve it. We make it ours. And so we 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.

19:27

Speaker A

So what exactly is the race? the race. Yeah, I think that's that's a really good point. My point is the race is really about who exploits the technology best.

19:40

Speaker A

You know, the last industrial revolution, all of the inventors were Maxwell, Volulta, Ampier. None of them were American.

19:49

Speaker A

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

20:02

Speaker A

generation happens just like this. Yeah. Yeah. So why why are the communists getting their message out so successfully here right now?

20:15

Speaker A

You know I I think first of all the narrative is much more practical. The narrative is much more practical.

20:22

Speaker A

Nobody's in China is saying that there's end of this and end of that and you know cataclysmic this and you know doom or that doom or that.

20:32

Speaker A

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 these groups who are basically saying it's going to end civilization and we're making it up. The part that is

20:46

Speaker A

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 even if it's true, we ought to spend more time doing something about it than

20:57

Speaker A

worrying a bunch of people who can't do anything about it. It's our job to build it right?

21:02

Speaker A

Has there ever been a point in history where so many people have so vehemently said something that is so untrue?

21:08

Speaker A

And they're measurably they're 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.

21:20

Speaker A

Is it a fear of the frontier? Humans have never been there. We've never seen it. Therefore, we're scared of it and therefore it's easy to tell everyone to be scared of it.

21:27

Speaker A

It could be life experience as well, David. Um, 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 for that is because I was the first

21:39

Speaker A

generation before software software became popular. We had to go build the computers to make software pop possible.

21:45

Speaker A

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

22:01

Speaker A

you you type all day long you type from the moment you wake up to the m well there was engineering before typing right and so so can you imagine that the world has a mountain of engineering work to do

22:13

Speaker A

where most of it is not typing anymore Sure, we were we had busy engineers before typing. I think we're going to do a lot of great engineering after typing.

22:22

Speaker A

Yeah. When I say typing, I mean coding. I mean, and so even at NVIDIA when software engineers talk to me, I I tell them, you're just typing. I've been saying that forever, but obviously for fun. And I I tell them, my favorite key

22:38

Speaker A

is backspace. And and the reason for that is because the best software is the smallest software. Yeah. So I I want you to use backspace software.

22:48

Speaker A

Let's actually talk about Nvidia. I let's let's do a little tear down of Nvidia. So uh tear down meaning just explain the pieces because there's a lot of strategy at play. Let's start at the absolute bottom. So Oh no,

23:03

Speaker A

this is not planned, but we know who it is. Oh no. No. Mr. President.

23:12

Speaker A

Oh, yes, sir. Um, I gotta tell you something. It I I uh if it wasn't because of you calling, I would I'm on stage with the besties. I'm on stage with the besties.

23:26

Speaker A

I'm on stage with the besties. I'm on stage with Sachs. And yeah, you know, the whole group.

23:34

Speaker A

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 we were talking about you.

23:48

Speaker A

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 of the matter is you saw through all of that and and I you know, we're all just

23:57

Speaker A

really grateful. Tell them I said hi. Do you want to say hi to the crowd?

24:06

Speaker A

Jason would like Jason would like to put you on the even Jason speaker mode.

24:13

Speaker A

How do we put How do we put on pus on? Put him on speaker.

24:16

Speaker A

Speaker. Yeah. Right into the microphone. Here we're going to get a mic. Hang on a second.

24:20

Speaker A

Hold on, sir. We're getting a microphone. Mr. President, you're you're now talking to the planet.

24:26

Speaker A

You see, the great thing about life is that Jensen can develop the most complex computer chip in the world that nobody can copy for 10 years. But he can't figure out how to put me on SPEAKER THING. We have to remember this one. So

24:43

Speaker A

interesting the AI. It's almost as conspiracy and the happiest group is China and China is very happy. And I could even say in the country a lot of states are happy that weren't going to get anything because they're being uh inundated by

24:59

Speaker A

people that want to be there. But now all of a sudden you see they're building in Finland. They want to build one.

25:03

Speaker A

Google wants to build a big one in Finland, which I'm not happy about because they were unable to get permitting. And I'm telling you, it's all a hoax. The data centers are great and they make people wealthy and they

25:15

Speaker A

make states wealthy and it's the oil of the next 20 25 years. It's bigger than the internet and the AI, you know, much more so. And uh they're just playing right into the hands of a lot of people

25:28

Speaker A

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 It's a hoax. And you're right. We're not going to let that happen, sir.

25:40

Speaker A

No, we're not going to let it happen. The uh the robots are not going to be taking over the world. And that's not going to happen. You know, my uncle was a the top probably maybe the best of all

25:51

Speaker A

time frankly. professors at MIT for 41 42 years and can known as being one of the most brilliant men and he was he was there for 41 years as the top he was like at the top top of the ladder top of did

26:08

Speaker A

many things Jensen knows all about it but did many things so I have a little genetic uh a little genetic strength if you believe in the resource theory but I do I have genetic that explains why you know so much about

26:21

Speaker A

AI I Yeah. Well, I know about AI. I know I also have common sense about AI. Uh the robots will not be taking over. Uh the AI will not be taking over the rest of the world. The whole thing is a hoax.

26:34

Speaker A

Now, with that, we have to be a little bit careful. We have to very be, you know, we have to do things and we have to do them prudently. But that doesn't mean we're going to stop industry because, you know, as we work on the

26:46

Speaker A

next 10 years about how to destroy it. So, I'm with you all the way. I didn't even know how you felt about it. And I assumed you felt the same way as me.

26:53

Speaker A

Yes, sir. And we if we're going to lead and I have an expression, it's whoever wins AI wins. That's how big it is. It's bigger than the internet. And whoever wins AI wins. And we can't let this kind of

27:04

Speaker A

stuff happen. And that includes very much includes data centers. There are communities that were dying that have data centers right now. And now they're wealthy communities. Really wealthy communities. We're We're going to make sure that We're going to make sure that

27:17

Speaker A

everybody We're going to make sure that everybody wins in the AI race in America. Every industry, every company, every state, every people.

27:25

Speaker A

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

27:35

Speaker A

I'll see. Did you Did you hear that? Did you hear that? Thousands of people are clapping for you, sir. All I know if you're there to listen to Jensen, but uh he's done an amazing job and David has done an

27:53

Speaker A

amazing job and good luck to everybody and uh we're going to stay with the future. The country has never done better. We have 20 trillion dollars of investment coming into the country and that's as opposed to much less than 1

28:06

Speaker A

trillion under sleepy Joe Biden and that was for four years. This is in one year.

28:12

Speaker A

So, you know, it's it's really the country is there's ne the country has never seen anything like it and we're going to keep it going. And so, thank you all very much.

28:19

Speaker A

Thank you, Mr. President. Mr. President, thank you. I'll call you back later. Thank you, Mr.

28:25

Speaker A

President. Thank you. Um I was unique. I thought it was a bit. Did you know that was happening?

28:31

Speaker A

I thought it was a bit. Yeah, that was it was No, it was real. I thought it was a bit at first when I was like, put him on speakerphone.

28:39

Speaker A

Wow. and he calls you. How do you how do you think he calls you any hour of the night right?

28:44

Speaker A

Well, we we were we were in the uh we were in the oval that time when he called you sleeping.

28:50

Speaker A

You were asleep and he like said, "Wake him up." I felt I felt so bad because he's like, "Who's coming to this dinner?" And we go through the list. He's like, "Well, what about Jensen?" I said, "No, sir. We I

28:58

Speaker A

He's on vacation." Cuz he he had to postpone this vacation for 5 years. And he's like, "Get him on the phone." What's vacation?

29:06

Speaker A

But what why do you think he sees through the hoax? It's it's this is the thing quite an extraordinary thing.

29:11

Speaker A

It was it's polling minus 80. So for anyone else that's sitting in the Oval Office. You're going to do what's popular. You're representing the people.

29:19

Speaker A

This is what everyone wants. They want to shut down the data centers and AI. It seems to be the popular thing in the moment. But he says it's a hoax and he calls it. How does he do that?

29:29

Speaker A

I got to tell you, I'm not sure. And the reason for that is because a lot of people are falling for it. And so the fact of the matter is it's complicated.

29:36

Speaker A

You know, at first, I mean, if you look at the story, if you look at the stories, it's all anchored on two things. The first thing that it was anchored on was national security. And recently, that was all blown blown to

29:46

Speaker A

bits right? And so, no, that story is no longer anchored on national security. Now, it's anchored on safety. Now, if you want AI to be safe, um the first thing is we need to make sure that the the labs that

29:58

Speaker A

are building it are in control, that they're they're good tests for them. uh if we would like to have third parties uh uh to to um uh make sure that a third party evaluator third party evaluators are available that's no different than

30:12

Speaker A

financial control. You guys know we have auditors and the auditors are quite quite um they don't have to be as expert as we are in our business but they just have to ask the right questions and um I I think I

30:23

Speaker A

heard somebody say that it's good to have uh independent auditors or evaluators but they just have to have multiple. I agree with that too. Just as there's multiple evaluated and auditors, it makes sure that one company doesn't become, you know, pilled or somehow

30:38

Speaker A

influenced um for for whatever reason. And so you, you know, there's a lot of different ways that you could solve this. Um and so I think the number one thing is let's build the technology safely. Let's make sure that the testing

30:51

Speaker A

of it is safe. And I I recognize completely that that what what is being built is extraordinary. Um but these are extraordinary companies and and we ought to hold them to to extraordinary standards. Um and they want to be and

31:04

Speaker A

they want to be I wanted to go back to open source for a second. Um a year ago we weren't taking it very seriously. It was two years 18 months behind.

31:14

Speaker A

The one thing that you know one of the as you guys know one of the challenges when you're on the call with President Trump is hard to say something. Um I'm going to get in trouble for that.

31:25

Speaker A

I'm sure he's going to call me up up on that. But anyhow, uh what I was going to tell him and and and all of you is that AI is creating an enormous number of jobs. The the the thing that he wanted

31:36

Speaker A

more than anything at the beginning of the the administration and that my first phone call with him, my first time I met him is that he wants to create jobs in America. He wants to re-industrialize the United States. He wants to make sure

31:48

Speaker A

that United States has the energy to support the next industrial revolution. Without energy, there's no industrial growth. And so he wants to make sure that there's energy growth, that there's job growth, that they're re-industrializing the supply chain. Look at everything

32:03

Speaker A

that we're doing right now. All of it is happening right now as we speak. We're creating more jobs than ever. We're creating software jobs. We were just talking about earlier. $400 billion dollar of venture financing went into the AI industry just recently. Yeah. 6

32:18

Speaker A

months. Well, that's created a ton of jobs. That's created a ton of jobs. Um it's created you know obviously enormous amount of demand for compute which we're I'm happy about. Um which is also which is also creating a lot of demand for

32:31

Speaker A

data centers and we ought to talk about that. I think I was just I was talking to um uh Governor Abbott uh uh of uh Texas and he was he was uh he wants to appeal to the industry to make sure that

32:42

Speaker A

we are we are empathetic to the small communities as we're building data centers all of all across America just to be better listeners. Let's actually talk about that for a second. That's what's incredible about Nvidia if you if

32:55

Speaker A

you break down the component parts is you've effectively had to become the bank of AI to get the ecosystem going and you've had to do it at all the levels. You know, you just did this thing with Cloverleaf where you're doing

33:08

Speaker A

land powers shell. You did this great thing with Black Rockck and Goldman and all these folks to to essentially create the financing capability. walk us through your capital allocation strategy like what has to happen to get a broader

33:22

Speaker A

ecosystem folks to be able to come in and underwrite this next phase. Well, we're we're creating as you guys know this is a new industrial revolution and and um every aspect of it is true.

33:33

Speaker A

Um this new industry requires manufacturing just as just as uh the the the um electricity, internet and now AI. We power anything, we can find anything. Now with AI, we can ask and know anything. Isn't that right? And

33:51

Speaker A

so that's our future. We tap into the ether and we can ask it of anything we want and it could explain it to us. Now, in order for that to happen, it's got to produce the intelligence. And so that's

34:01

Speaker A

a production process which is the reason why this infrastructure has to get built. But once you get the infrastructure built, the question is um what about all of the other layers across the United States? Uh this industry isn't just about the model.

34:14

Speaker A

It's not just about the chips. It's mostly about the applications on top. It's mostly about the infrastructure layer, the data centers and all the infrastructure, the the the the construction, the electricity, the power generation that all of that is involved.

34:30

Speaker A

And so I look across the entire ecosystem and look for bottlenecks and if there are places where extraordinary companies are being built constraints constraints extraordinary companies being built uh maybe it's uh uh uh supply chain that has to uh get scaled

34:46

Speaker A

up so that when we're ready to deploy compute that they'll be ready for us land power shell and so this is no different than looking at the supply chain upstream. You know, I I probably uh think about the long-term supply

35:00

Speaker A

chain more than most because our company's really large and and um in order for us to succeed, a whole bunch of companies has to support me. You know, it's got to uh Corning has to, you know, Wendle at at Corning has to

35:12

Speaker A

support me, Lumenum, and you know, TSMC of course and memory companies and and so we started working with all of these companies long before the revolution that the the growth came so that the growth could happen. Now I'm got now I'm

35:26

Speaker A

doing a downstream. The compet cycle tends to be though that the earnings over time over long stretches of time tends to move up the stack right towards the application layer where you can over earn for larger periods of time. Um I mean you bought

35:40

Speaker A

hugging face now you're sort of in the actively in the serving business. I mean it seems pretty natural that products like open router make a lot of sense. It seems pretty obvious that you know there are better versions of ways to build

35:53

Speaker A

things like bedrock. I'm sure you think about it. What's the natural conclusion? Because it seems like the folks up here have no issue trying to move down.

36:01

Speaker A

Mhm. And you have the best balance sheet, these incredible engineers, and you have the proven experience to make it right and engineer the product and get it out.

36:11

Speaker A

So, how do you think about looking up and saying, "I could probably do that." The the reason why Nvidia runs every single model in the world, we were the only It's incredible. Last year about a year and a half ago the only thing we

36:23

Speaker A

ran was open AI. Yeah. And now look at amazing models are available. The Metamuse is available.

36:29

Speaker A

You got gro is available. Grockbots's incredible. Um we now run Gemini. Uh and anthropic is is uh scaling up on our platform as well. Uh since a year and a half ago, you got all these frontier AI models that are now open that are

36:44

Speaker A

available. So the number of models that are are are growing. Um there's a whole bunch of companies that I won't mention that are building uh frontier models as well. And the the number of AI labs are growing. Yeah. The the ineffables, the

36:57

Speaker A

uh the reflections, the right the list goes on. The physical intelligence, the list goes on. Okay. And so all of these labs are building on NVIDIA. And the reason for that is because as a company, I rather for us to help everybody

37:12

Speaker A

succeed instead of taking a slice out. And so we would go up as far as we need to but as low as possible.

37:23

Speaker A

Our strategy is go up as far as we need to and as low as possible. And the reason for that is because if I do that, if I solved the if if not for Nvidia creating QDNN, all of the frameworks

37:34

Speaker A

wouldn't exist. If not for us creating Megatron uh megatron core, uh then all of the large scale training wouldn't have happened.

37:42

Speaker A

Wouldn't exist. Um, so we we go and we invent all the technology necessary as far as we need to and then we let a thousand flowers bloom.

37:50

Speaker A

And so that posture allows us to be quite frankly the only Well, look, let's be honest that I I agree with you. The push back would be that it really would be great to have more competition at the hyperscare

38:02

Speaker A

layer. And I think you've done a great job supporting the NeoClouds. There are some. And by the way, I think you introduced me to NBS. Superb, great, everything. They're amazing. But we need like 50 of these guys. We need a hundred

38:13

Speaker A

of them. We need a thousand of them. And it just may take some Yeah. You know, it's just I'm surprisingly uncompetitive really. Yeah. That's not my thing. You know, my thing is kind of like for example, I'd be more than happy with

38:31

Speaker A

five hyperscalers. However, um the reason I noticed the early customers of all the Neoclouds, all the what we call NCPs, all the early customers were the hyperscalers.

38:42

Speaker A

Exactly. And the reason for that is because the hyperscalers plan once a year, but the market dynamics is so volatile right now that they're always almost wrong. And so with all these regional clouds who are agile and they can move fast, um they

38:59

Speaker A

know their state or they know their country, they know their region, they're securing land, power and shell in a way that's hard for somebody who sits in Seattle or sits in Palo Alto to be able to see the planet.

39:10

Speaker A

And so we now have basically a largecale distributed network of companies that are building securing land power shell for us. and um uh and and now countries realize it's strategic.

39:22

Speaker A

Yeah. So many countries are saying I'm going to take my power and only give it to my own companies, right?

39:28

Speaker A

Well, Nvidia is in that country as well and we could help the Neoclouds in that country grow and and so whether it's whether it's Fermas and Australia, we just did a whole bunch of stuff in Australia. Um brought on two more

39:40

Speaker A

gigabytes. Uh Southeast Asia of course IOH and others bring on a few gigabytes. And so we're building gigawatts. So, we're building, you know, we're we're scaling up. You know, it's pretty clear, though. I just want to get this one thing in. It's pretty clear

39:54

Speaker A

that you're going pretty high up and getting very focused on open-source. Obviously, you have your Neotrons doing exceptionally well. I use them often.

40:03

Speaker A

Hugging face poolside and Laguna uh very very solid product that you're now uh aqua hiring, hiring, whatever it is. Um and then you have your open source stack for self-driving also uh very disruptive. So we are the frontier model in five

40:18

Speaker A

domains. Yeah. Yeah. And so you don't seem to build products to get the silver medal. You seem to go for the gold. So are you going for the gold? And will you have the best hands-down open-source model? And then

40:34

Speaker A

part B to that is can open source catch up to frontier models and are you the person to do it? So the logic the logic Jason is that that um we will build it because one uh we can we have the skills

40:48

Speaker A

to do it and because our customers need us to do it right. So, for example, Alpamo is the world's first thinking self-driving car.

40:58

Speaker A

And by by thinking, by reasoning, you don't need as much data as, you know, you don't have to train on a few billion hours of road data because you could reason about it. Break down the problem into I've seen this before. It's not

41:10

Speaker A

exactly the same, but it's largely the same as that. Okay? And so, so Alpamo, why is it necessary? Well, there's a whole bunch of car companies. Every car in the world is going to be autonomous, but beyond that, every ag tech, every

41:24

Speaker A

truck, every van, and most of them aren't big enough in scale to be able to build that whole stack. So, I'll build an extraordinary stack for them. They do last mile adapting for their application. Now, everything that moves

41:36

Speaker A

in the future could be autonomous. If not for us building uh some of the some of the biology models, the world wouldn't have it. uh the the ESM2 uh protein found language model we created that ESM fold open fold alpha fold 2 um

41:51

Speaker A

all the stuff with with coup equavariant um all of that stuff technology wouldn't have existed if we didn't build it uh uh one of my favorites uh proteina complexa uh is you know synthesizing next generation proteins and it's binding

42:05

Speaker A

it's groundbreaking stuff we built that and so we'll build that because Lily needs it and and uh you know Merc needs it and others need it and they don't have the capab ability to do it or they they're not yet there and so we can make

42:16

Speaker A

a real contribution. So I do everything out of need. I'm not trying to disrupt I mean we don't wake up in the morning try to disrupt anybody.

42:24

Speaker A

We just wake up in the morning try to help everybody. Jensen, what about what about competitive threats that might be emerging to your core business? Can you just comment?

42:32

Speaker A

Just so nice. Yes. Well, I know this is Well, I I actually want I want to just get your let's just call it a take. What's your take on Terraab 100 million square foot facility Elon's announced and um

42:44

Speaker A

if anybody could do it he can and the two of us were on a flight together to a country and um with a person who sometimes calls you on the phone. It was it was a nice plane and and and we had like you know and you

43:02

Speaker A

know Elon likes to talk about these things and and so uh we spent a lot of time talking about it.

43:08

Speaker A

I I is that anybody could do it because I mean you could you design chips you don't fab them. Could your chips be fab there or is it Well, we know we we know a lot about process technology because we're pushing

43:19

Speaker A

the limits of everything, right? And you know because we scale at such large scale uh we have incredible memory technology inside the company.

43:27

Speaker A

We're the world's best sis company. You know we got lots of amazing. So your take is you've talked a lot about it.

43:32

Speaker A

So we could just yeah we could talk about it and and um you can't discourage Elon from doing it which is one of his incred that's his superpower and once he decides to go do something it's hard to

43:42

Speaker A

stop him. And so I And can can you give us your take on where China is with advanced lithography systems? Um native grown.

43:49

Speaker A

They're going to get there by 2030. By 2030. Yeah. And 2030 is just around the corner.

43:54

Speaker A

Yeah. Also, that's how long will all be dead at that time. So, and does and for China, does that mean the switch is flipped and then that's all going to go into um mainland fabs almost immediately?

44:07

Speaker A

You know, the the way to think about China is really good at high volume production.

44:13

Speaker A

And this is just matter of time. Yeah. And I you know I I think in I I think in decades as well you know I've been around a long time and you know for Nvidia I've got to think about what

44:26

Speaker A

happens next decade and decade after that. So two or three years is it's just a click. It's nothing.

44:31

Speaker A

And so as far as they're concerned they're already there. They're already there. Yeah. Jensen Elon uh and Gwen we've got to run America. We got to run.

44:40

Speaker A

Yeah. speedun. So, we got speedun. Slowing down is definitely the wrong strategy. Well, I mean it it feels apparent, I think, to most of us in the industry that we're kind of in the AGI moment.

44:52

Speaker A

And it's a definition obviously just as smart as any other human. I think we're already there.

44:57

Speaker A

We're there, right? And so then super intelligence is the next way point based on what you see, based on your customer base, based on your history here.

45:04

Speaker A

But Jason, I think we're there, too. You think we're at super intelligence? Yeah. Yeah. When you when you when you take a narrow segment a narrow segment I mean my my self-driving car I don't want you to make me an omelette I just want you to

45:16

Speaker A

drive the car right that is super intelligent super it's better it's better than a human yeah yeah onetenth the the accident rate exactly uh synthesizing proteins you know uh doing virtual screening of proteins we're already there are you having fun being on the frontier

45:33

Speaker A

of humanity I like Yeah, ladies and gentlemen. Ladies and gentlemen, I like it. I like it. And guys, guys, it's it's it's great there. The future is great and we want to get there.

45:50

Speaker A

Listen, ride the bike. A lot of us don't have to work. But I got to tell you, it's too good not to be.

45:57

Speaker A

So fun, right? And so, so I want every we I want to be there. I want all of you guys there with me. We're all going to be there. we're going to be enormously successful together as a humanity. And

46:08

Speaker A

um and in the meantime, uh we got to encourage them, urge them on. They're doing really, really important work as you guys know. And I want them to succeed. Um I also would love for us to tone down the the the the drama and most

46:23

Speaker A

importantly, we need all of America to come with us. That's how we make it.

46:28

Speaker A

Ladies and gentlemen, Jensen Long. Thanks, man. Appreciate you. Thank you. Thank you. That was awesome.

46:39

Speaker A

Only your part. That was awesome. That was great. Thanks, guys. That was great, huh? Great time.

Topics: Jensen Huang Nvidia Artificial Intelligence AI Safety AI Predictions AI Regulation Superintelligence AI Industry AI Innovation Technology Leadership


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