Jensen Huang | Does AI Revenue Justify Capex, '27 AI Bottlenecks, Singularity, Open vs Closed & more

18,657 words · about 75 min to read

Immigrant contributions drive American technological leadership

0:00 This is an existential issue for us. We could easily stop, but if we stop today, we lose. Functionality, capability, safety, security, they're all at exactly the same place. Priority number one.

0:21 It's our third pod back together.

0:23 In the last 10 days, you've been uh at the she state dinner at the White House. Um you were at the White House uh summit where we did the accord on super intelligence and just yesterday you were there with your parents um when the president you know presented you with the national medal of science.

0:40 You were the only investor at the table.

0:42 Um there

0:43 you were the only investor at the table now that now that we reflected on it.

0:47 Yeah. Well tell me being there with your parents yesterday

0:51 right it struck me five out of the six people on stage were all born outside of the United States. Um, how did that feel?

1:00 Uh, well, f first of all, uh, it's a great honor all by itself, right?

1:04 And and it's a reflection of the the contribution that the company has made,

1:09 right?

1:10 And and obviously obviously uh we created we invented a new computing model. This is probably the most pervasive computing model in the world today. Uh, it's the only computing architecture that's in every cloud. It's in every computer company. It's used in every field of science.

1:24 Um, it's used by gamers. It's used by creators and builders and it's used by scientists. Uh it powers intelligence,

1:31 right?

1:32 I mean, just if you take a step step back and you look at what what the company has accomplished, you know, we started out building a graphics chip for gamers and I'm still proud of that, of course. And and we now shipped one and a half 1.67 billion uh billion units,

1:48 right? That's how many generations of I mean I raised generations of scientists and kids and designers, you know,

1:54 and so so I I think the impact that and and here we are, you know, we're the infrastructure that powers the intelligence of society and so so it's a it's an incredibly proud moment for me to receive the award on behalf of the company. Now for my parents, you know, I wanted them to go and and I gave them literally, you know, 24 hours lead time and and they don't travel that much,

2:17 right? They don't travel that much. And my dad didn't even have a suit.

2:21 Wow.

2:21 Yeah. So, he ran out and bought a suit.

2:24 And uh

2:26 he looked great. They looked great.

2:27 Yeah. It was a brand new suit and he showed up. I didn't realize he was he he had a suit. I thought he was just going to wear some shirt. And uh he went out and bought a suit.

2:36 Um my my mom got a new dress, you know, and and literally in 24 hours.

2:41 Yeah. Oh, I know how it works.

2:43 Oh my gosh. It's incredible. And so so we got him on a we chartered a plane for them, got him there and [snorts] um they were so happy to meet the president and and uh President Trump walked my mom because she has a hard time walking and the two of them were were walking off stage and he he held her

3:03 and walked it walked her down the stage and they were just incredibly incredibly proud to meet her meet him

3:09 and um uh but for them it's their American dream. You know, my dad, I still remember I I think I was like 5 years old or something like that. And and he had just come back from the United States on a business trip. He was he was working for an air conditioning company. And he

3:24 And you were living in Taiwan.

3:25 We were at the time we had just moved to Thailand. Thailand.

3:28 And um

3:30 Oh no, no, we were just about to move to Thailand. And um we were in Taiwan. He had come back

3:36 and he was he was in United States working for a company called Carrier,

3:39 their air conditioning company. and uh he was here for training and um I of all the places in the world they landed him in New York.

3:48 Okay. And so here you can just imagine

3:50 a kid in the late 20s I guess mid late 20s

3:54 and uh never been outside of Taiwan goes to

3:58 United States, New York City of all places.

4:00 And he saw the future of the future

4:03 and he came back you know mind-b blown uh uh all the things that he bought at the airport brought it back. It was a He bought he he brought chocolate. He brought you know that cheese that you have in a tube back in the old days. It's kind of like white strange cheese. It comes in a tube.

4:19 It's great.

4:20 Um and uh he brought back all kinds of strange things. And he says this is we should go there someday.

4:26 And the first opportunity

4:28 they had uh they sent my older brother and I to uh United States and and um you know it was it was his dream. I don't think many people realize that you came to the United States ahead of your parents. Yeah.

4:41 Right. Um and they joined a few years later, but it it just hit me like a ton of bricks that five out of the six people on that stage yesterday were born outside the United States. You and I have talked about the need for uh you know, with all the other stuff going on, a real strategy and an immigration policy policy that makes certain that we're still the place that all the great builders in the world want to come build. This this is America's superpowers,

5:07 right?

5:08 Um in a lot of ways the intellectual property um creation capability of our nation is unlike anything else in the world. And the reason for that is we attract the world's best. That's

5:19 right.

5:19 You know, we are the place where they want to come and build their lives uh because of the opportunity provides the freedom that that she offers, the opportunity that it provides, the spirit of of invention and entrepreneurialism and the can do attitude. you know in the United States everything is is possible

5:37 right

5:38 we start with of course it's possible of course we could do that

5:41 instead of instead of you know I do a lot of travel and um of course you can't do that of course you know in other places in other places and we take it for granted

5:51 but the spirit the the the energy um that comes around with the culture that we've created here

5:59 um it just can't be understated and and so I think the flow the inflow of intellectual property into this com into this country is unlike anything else the world's ever seen.

6:11 It's true.

6:11 And we want that. We want all of the world's great minds to come here, bring their great ideas, you know, make them realize them here.

6:19 Realize them here and let us, you know, let us let us uh uh keep that flywheel going.

6:24 You know, I I really it's a it's a delicate system. It's a delicate balance. I get it's a delicate balance I get. And so we have to do it lawfully. you have to do it rightfully, right?

6:33 Um but we we surely want to do it.

6:35 Yeah, for sure. So, I want to start high

Reasoning and thinking require a billionfold increase in compute

6:37 level just kind of where we are. Uh then I want to dig down a little bit into the safety and regulation and getting this right, where we are in terms of the compute buildout. And then finally, we're going to finish up think, you know, drilling in a little bit to Nvidia's growth and and and and what's going on there. But you've been really good at calling the future, right? Two years ago, we were sitting here and you said that the market was dramatically underestimating the amount of compute buildout that was going to be required and was going to happen over the course of the next five years. You you said it's not going to be a trillion, it's going to be two trillion at a minimum in terms of cumulative compute buildout.

7:13 And of course, now we're over that. You said before everybody was thinking about it that inference, right, compute was going to 1 billionx because of inference time reasoning and thinking. Um, and then last year, of course, before we saw Opus 4.6, before we saw Claude Code, you said we're entering the age of agents, right, where we're going to unlock a lot of these capabilities. Um, and of course, we saw Opus 4.6 and Claude Code really took off. So, where are we now?

7:39 Right, we have Elon and Sam and only three things that I said that came true. There's another 200. Why don't you just keep that? Let's just let that be the podcast.

7:49 So, so, so, so our investor day in a couple weeks is on, you know, entering the singularity, this acceleration. We have Elon and Sam and Demis all say, "Hey, we're in the foothills or entering the singularity." We have, you know, model solving Navier Stokes as you know. Um so when you look into your crystal ball where are we and are we inflecting are we accelerating you know into a different phase of super intelligence.

8:20 Um and so let let's unpack that first. First of all it's very fairly logical why the amount of compute that we're going to need uh is a billion times more than where we were just recently so calla last year. And the reason for that is because thinking and problem solving takes a lot more you know uh inferences

8:45 than [clears throat] uh repeating something regurgitating something that you memorized

8:49 right

8:49 so when you're a chat GBT um uh you give it a prompt most of that most of that knowledge was encoded into the large language model um unfortunately of course it hallucinated and so the next click was um uh it was it was a generative, but it was also grounded or conditioned by ground truth.

9:09 Yes.

9:09 And so

9:10 it would have to go out and do a search,

1 Highlight · Why agentic reasoning demands exponential compute

9:12 do a quick search, comes back with the ground truth, and then from that conditioning augmented with his own own what it already learned pre-training um and it would generate uh new new uh whatever the answer is. Well, most of most of the things that we want to really do that are that are interesting requires you to ref to to reason step by step to come up with several options plan and you might decide um let's see how these things all roll out and then based on based on the answers of all of those I reflect on the best answer so far the best path so far I might choose one or two or three of them roll them out

9:48 right

9:48 okay prune the rest of it and then you just keep on doing this and you know people we do this that's the reason Why? Why we say um uh great strategists love strategic optionality. You don't want to prune things too fast on the one hand, but you you also have to optimize your resources. Well, AIS are doing that. Okay. So, so they're they're keeping their strategic op optionality. They're rolling things out. They're pruning. Um they're they're reflecting, they're pruning, so on and so forth. All of that thinking, all of that exploration just takes a lot more compute and and not on top of that because it's able to now do productive things, we're going to use it for more things. And so the compounding of both it uses more compute to do its job, but it's doing a good job and therefore we're going to apply we're going to hire it to do more jobs.

10:35 Exactly.

10:36 Okay. And so that all compounds. Um at this point I would I would say that the big observation from last year is that that on the one hand we want the models

Agent harnesses and tool use unlock enterprise productivity

10:45 to get better and better and we we still want the models to get better and the scaling laws of models are are going to continue. However, the attachment of a harness, basically the exoskeleton of the large language model right?

10:59 You know, kind of the body of the brain

11:01 that allows it to um uh manage manage working memory, manage knowledge. How do you go from working memory to short-term to long term to basically formulating your knowledge graph, which is what we all do? we kind of think about, hey, you know, what did I learn and how does it fit into um everything that I know in the past and you know how when we go to bed uh we do compaction things that don't make sense we kind of or don't matter we kind of

11:28 eventually forget but the things that kind of matter somehow it gets reconnected and so we're doing that kind of offline and so we got working memory we have offline knowledge extraction and creation knowledge graph creation um that whole part of memory is a big deal the other thing is of course tool use

11:46 and we we've taught these tools these AIs how to interact with uh the most important tools that the humans have ever made. You know, it's no different than uh if you were if you were a robot comes into our house, it's more likely you're going to use all the tools that I have in the house than to go off and build it from scratch. And so uh so so spice is still going to be useful and um verilog will be useful and simulators will be useful and you know design compilers and and you know Photoshop and AutoCAD and you know the cell kia and all the seammen's tools all of these different tools that we've been using to run our companies build our companies design things operate things they're all going to be great tools for the agents to use

12:26 but the agents had to learn how to use it and so these two areas knowledge knowledge um and working memory and um tool use huge unlocks in the last year.

12:39 So now all of a sudden you could you could put these large language models encapsulated in their um harness or exoskeleton and you can put them into your company, connect them to uh the the information, the ground truth that you have, connect them to the the tools that they can use and all of a sudden they start doing amazing things. Now the the easiest part uh frankly was coding

13:04 right

13:05 that's kind of the the the first on-ramp you know to useful work and in a lot of ways uh coding is easy because both the customer

13:14 and the work the the tools that they use

13:17 are fairly accustomed to uh things that has tremendous use but are not extremely easy to use right

13:25 and so in the early days of coding it's it wasn't extremely easy to deploy um But software engineers found a way and so so now now I think the next click for us is now and and you guys have probably been playing with Astra OpenAI Astra is really fantastic and we're using it in all these different places and so now the ne the next phase of these these agents they're able to do you know design work and uh a lot of uh creative marketing work and and we're using it to go you take a picture and give it to Astra it literally recreates it into CAD.

13:59 Yeah. It recreates it in into the CAD and so which is incredible because we need the CAD.

14:04 Yes.

14:05 So that we can we can interact with the CAD and make fine-tuning and adjust and so Astra could really give us the the first initial implementation incredibly productive. Now you take you take Astra and and you know the latest release and you apply it to all of these different different domains. Um I I think that that besides software engineering you know we're going to see designers and builders and creators and you know this whole area scientists and you know all of all of this whole unlocking. Now the challenge of course is how do we deploy agents safely and securely into the world's enterprise.

14:42 This is exactly the same question again with safety and security

14:46 um and uh and and and we'll talk about

14:48 we'll come we'll come in on that. What what I'm what I'm struck by is that all of this occurred this year with OpenAI and Anthropic having I don't know two three gigawatts of compute each. Okay. So relatively small amount of compute relative to what's coming. Okay. And so again what I'm interested in is the way the economy generally works or people model the economy or they model a company is linear deceleration. They're not very good at modeling acceleration.

15:20 But here we have this double exponential. We have massive amount of new compute coming on board. So if OpenAI and Anthropic are, I don't know, 3 to 5xing their compute next year. Does it stand a reason? I mean, we solved Davier Stokes, we we'll look back at this and say that was a tiny amount of compute and intelligence, the worst intelligence we're ever going to have from here forward, solving a millennium problem. So when you look at what's coming online next year, you know, do you expect that we're going to have major breakthroughs in, you know, longunning agentic uh uh problems that that that we can use these swarms of agents on?

15:58 Well, Navier Stokes is is really a great example. Um remember uh OpenAI did not solve Navier Stokes from nothing.

16:07 Correct.

16:07 They solved it from the foundation of a lot of great scientists and a lot of great mathematicians and a lot of great previous work. Earlier I said we're at we're at kind of the tipping point of a lot of these different fields

16:20 because we're kind of at that phase that that point uh with Navier Stokes with a great deal of foundational work already been done. If we could just apply

16:30 the exhaustive exploration of AI

16:34 yes

16:34 to the previous previous done work

16:37 we're going to see one breakthrough after another breakthrough rather quickly. But we do need a lot of compute to do that. And and and the reason for that is because a large amount of exploration was necessary. You know, if it was if it was simply uh somebody resolving some fundamental first principled theory in order to discover the breakthrough, if it didn't require a vast amount of exploration of space,

17:00 uh search space, not space space, but search space. If it didn't require that, then somebody would have found it earlier right?

17:06 You know, people are pretty smart. And so so I I think that there are many problems like this that are just on a prec you know precipice and and really

The cost of intelligence falls rapidly like Moore's Law

17:16 wants to just have that massive amount of intelligence exploration to to get us there. Um in the in the coming years, you know, my expectation is is that the the cost of intelligence would fall fairly fairly quickly,

17:35 right? And the reason for that is because obviously we're bringing more compute online. Um uh these models are getting smarter. When you're smarter, you could be more efficient,

17:44 right?

17:45 And so the combination of these two things, the the cost of intelligence will come down, which is which is of course what they want, right?

17:52 And this is no different than Moore's law. If you go back 30 somehat years, we we have three decades of computing cost decreasing by a factor of two every single year, right? and and um we want intelligence cost to decrease by a factor of two or 20 every single year because we the the consumption of it is going to

18:13 more than make up paradigm.

18:14 That's right. More than make up for it. And

18:17 and so there's there's a massive empty space in the world that wants to be filled. Empty vacuum of of um of opportunity and and we just got to get get the cost down.

18:26 I I I guess what stands out to me about Navia Stokes is um that they had 10,000 agents working together on this. It looks like a to me that sounds like a you know hypers scale company basically all pointing agents to work together towards a goal right and I think in the early days right now it's a lot a lot of you know paralyzed search exploration but I think you know the the goal is that more of these agents can work together to a common goal and that we can solve a lot more and to your point you know I think our math says probably cost about $4 million of compute to solve solve you of this instantiation of nooks.

19:05 um the cost will the cost to do something like that over the next few years is going to drop by another 5x 10x right so the the number of things that we're going to be able to do is is rapidly expanding

19:18 our ambition is going to go skyhigh uh um the original GPT probably cost let's pick a number say you know maybe $20 million to do

19:28 if you wanted to train GPT1 now it'll probably cost you what a thousand $100 maybe

19:34 it's like a hundred bucks Yeah.

19:35 Okay. And so you you went from you went from $20 million, let's say $10 million to 100 bucks. And we did that in 10 years.

19:43 Yeah.

19:44 Okay.

19:44 And so um I fully expect meanwhile

19:49 uh our ambition got so large

19:51 that the models that we're training now 3 trillion, 10 trillion, 30 trillion, we're talking about gigantic models now.

19:58 And we wanted to be and so our ambition for for the capabilities of technology has gone up. Meanwhile, our use of it has gone up between 10 years ago to now probably I don't know a million times.

20:10 And so I I don't think this trend is going to stop. I think this is there's a there's a fairly insatiable demand for for intelligence. And the more the smarter you are, the the more intelligent you are,

20:23 the more ideas of applying intelligence you have.

20:26 You know, it's over the more I've learned in life, the more I realize there's more to do. And that's because every single every time you learn something, it opens a new door. Every time you learn, it opens a new door. And every time you open a new door, you see more opportunity. You see more challenges. You see more problems. And so that's kind of the that's the future,

20:45 right?

20:45 You know, people say we're going to have more time. I I completely disagree.

20:50 I think I think for most people

20:52 this intelligence is going to unlock more opportunities.

20:55 We're just going to and they're going to want to pursue it because they're ambitious.

20:58 Yes. You know, give me an example of a family, a father, a son, you know, a husband, a a wife, a mother who doesn't want it, who who don't have ambition for their families to have more. It's just they just don't have the opportunity and these things are going to open up opportunity.

21:13 If you go back to 1930, Kane's talked he made a prediction about the industrial revolution and he said, you know, in a hundred years the economy will have 8xed, but the work week will go to 15 hours a week. He was right. The economy did ad 8x, right? We had massive abundance and productivity gains from it.

21:31 Yeah. Except we're working 80 hours,

21:32 but we didn't go to a 15 hour week. And what what he got wrong was the human spirit, right? He didn't realize that that work is purpose. Discovery is purpose, right? It's deeply encoded in I think our DNA. And I'm working harder than I ever have, having more fun than I ever have. Not because I have to, like

21:52 my grandparents did, right? I'm doing it because I want to. Um, and and so I I agree with you on that. I I'm I'm fascinated with this idea. You know, the singularity postulates that, you know, we we've kind of now have all these agents that can work together on longunning tasks. They can stay on task. Um, I love your

22:11 keep each other on task, working as a team. Hey, you're off.

22:14 That's right. I love your idea that, you know, we build on on top of all the things that have come, right? And so if I think about full self-driving as an example,

22:23 we talked about it for a decade

22:25 and we really didn't get there, right? But all of the sudden, like you go on Twitter, everybody experiencing Tesla FSD, experiencing Whimo, we're here. Somebody asked me, "How was the drive here this morning?" I said, "I don't know." Cuz I got in my car and I hit FSD and I didn't pay any attention until we pulled into the parking garage. Right? I was working the whole way here. Um but if I think about that in the fields of science, the breakthroughs that are going to come in the fields of pharma and genetics, right? We've been laying down the tracks for 30 years, we're going to break through on on a lot of massive cures for the world. Of course, Chat GPT is already helping parents plan their day and solve their kids' health issue and helping them uh get get better educated. Um talk to us a little bit though as to why that's having such a hard time breaking through. Jensen, we have all of these things that are out there improving lives and that are right around the corner. And so that that's kind of curious to me as to why that's not breaking through.

23:23 Um [gasps and sighs] I guess I guess um let me take a shot at it. Yeah.

23:26 Um and and you know, we'll ultimately not really know.

23:31 Um I've spoken to a lot of people. They told me what they thought. I mean, one of the one of the things, of course, is

23:39 is uh AI or super intelligence is a is soft matter.

23:44 Yeah.

23:44 You can't look, you can't see it.

23:47 And

23:48 and so uh you got the whole world talking about this technology, but you can't see it. You can't touch it.

23:53 And even though they're already benefiting from it, when they do search, they're already benefiting from it. When they go shopping, they're benefiting from it. When they're uh when they're using Netflix, they're benefiting from it. You know, you go to Walmart, you're benefiting from it. uh the fact that FedEx is getting getting stuff to us uh you know it's all leveraging super intelligence and accelerated compute today but we don't do a very good job telling that story I often

24:16 so life is they're benefiting from it's just it's invisible

24:19 right so if we shut off the data centers

Physical AI transforms industries beyond digital computing

24:21 and shut off super intelligence for for a weekend I think everybody would notice

24:26 right I know you had a question about robotics in particular though

24:29 yeah I guess you you've you said notably said a few times this year that the chat GB BT moment for robotics is here and I think you've also you you've also said that it could be your next hundred billion dollar market. So I guess what are you seeing now in the in inflection point in this year? Yeah.

24:45 That this is around the corner.

24:47 Yeah. I I understated the hundred billion dollars because we're we're already the first six months of this year we did $10 billion in physical AI. Physical AI is basically you know uh AI that's somehow interacting with the physical world. And it could be uh photon photon counting CT machines. It could be ultrasounds. It could be um uh new imaging radars. Uh five next generation of 5G.

25:09 Not [clears throat] only does it understand radio waves, it's going to understand signals of all kinds. It's a sensing

25:14 network. Um uh of course robotics, self-driving cars, uh manufacturing systems, visual inspections, uh you know, im uh geo geospatial satellites, um uh imaging the world and and reconstructing it into 3D. And so so there's there's basically if you look at the entire electromagnetic spectrum, there's AI stuff going on from visible to invisible spectrums.

25:41 Okay. And and so so we and we're doing work across that entire thing that and and and and as an AI uh there's there's the perception part of it, there's the reasoning part of it, and then there's the planning and act actuation part of it. When you finally come to planning and actuation, then that's self-driving cars and robotics and things like that, you know, in in warehouse delivery vehicles, uh, pick and place uh, robots that you don't have to program, you know, as as precisely because it learns by itself, you know, all the way to people talking about consumer robots, you know, coming into your house, you know, making your bed and things. And so, so or healthcare robots, hospital robots, lots of really good examples there. um uh you know precisely delivering exactly your prescription to the patient.

26:28 Um uh uh having having robotic care uh literally 24/7 every single second

26:36 u to to to keep patients safe.

26:38 Um and and so just lots and lots of great examples entire uh surgical rooms that are going to be completely robotic, right?

26:45 And so uh lots of great technology being deployed. And so if you look at this whole thing, uh we're the world's largest physical AI company. We we did $10 billion in the first six months. We did $10 billion in the last year.

26:58 Mhm.

26:59 And that was our first $10 billion year. The year before that we did five.

27:02 Yeah.

27:03 Okay. And so it went five 10. Let's say we did 20.

27:06 Yeah.

27:07 Who knows?

27:07 Yeah.

27:08 So maybe the hundred was a little understated. Um and uh and I I think the reasoning part of it is is everything that matters to us in the final analysis touches the physical world and and we live in the physical world. And so I I think the the market opportunity for bringing uh software computing intelligence

27:29 into an industry that largely historically was mechanical electrical. That's a big opportunity. I mean, look at look at obviously what Elon has done to the to the uh automotive industry. Um turned it into essentially a computing industry, a technology industry. And so I think every car company is going to be a technology company.

27:47 Mhm.

27:48 Every shipping and rece, logistics company is going to be a technology company. I'm super excited for FedEx. They're obviously going to be an AI company. They're obviously going to be a technology company. Um Walmart obviously going to be a technology company, an AI company. So super excited for them. Um yeah, the the entire telecommunications industry will be reinvented again into an into a technology industry. They were a technology industry a long time ago, then became a services industry. They're going to be a technology industry again.

28:14 And I'm delighted to work with AT&T and Verizon and you know the and Europe around the world as they reinvent themselves. Um and so you you take a look at every one of these industries that touch the physical world and I there's a great idea for reinventing them with AI. You know, we we um I was just in Washington.

28:33 So weird, you know, people can put us in chips.

28:35 Yes.

28:36 You know, and and so we're actually in warehouse and logistics.

28:40 Yes.

28:41 We're in healthcare.

28:43 We're in automotive. [clears throat]

28:44 We're in tele. Every industry on the planet and and so so we're in life we're we're in we're in life sciences, you know, and so so all these entertainment. I mean obviously for a long for a long time Nvidia was really vastly an entertainment company you know and so so each one of these verticals uh Nvidia has has a a massive stack that's inside of it and and we're very deep in the ecosystem uh and we have excellent partnerships there and so you know one of these days you know people might not categorize us as a chip company anymore. playbook would be to take the Alpameio automotive I guess playbook and insert it into each one of these industries.

29:25 Yeah, that's that's the final that's the

Physical AI deployment requires three distinct computer systems

2 Highlight · The three-computer architecture for physical AI

29:27 final [snorts] um contribution.

29:29 Yes.

29:29 But in each one of these, it takes three computers to build a physical AI platform. And so so you need you need the computer to create the brain. That's the training system. But very importantly, and this is where where you're going to see the labs also do this, um they need to build a digital twin

29:48 of the internet

29:49 so that they don't have to put their AI agents out into the internet to learn how to use it.

29:54 And so just like we we created Omniverse as the digital twin of warehouses and buildings and cities and the physical world,

30:02 we put our robot into the digital twin. the digital twin learns how to be a robot

30:06 and then [clears throat] we deploy it into the last computer which is the computer that's inside you know for example a robot or a car or surgical instrument or whatever it is and so it takes three robots three computers

30:16 and [clears throat] three complete stacks and we build complete stacks for each one of them. We're the only company in the world that does that. And then if if you're a company that that wants to

30:25 build it end to end, you could use two or three or you know, one of our computers. We don't care. And so and so each one of a computer has own software stack. We do the same across all of our verticals.

30:35 Um but of course these capabilities cut both ways, which is why um you know, we hear a lot of people talking about

Containment and out-of-band monitoring ensure AI safety

30:43 safety. You've been highly critical of the doomer narrative. You can only get there if you build the technology responsibly.

30:50 That's right.

30:50 And so the first thing is build the technology responsibly.

30:54 Right.

30:55 Notice the word build. It's an engineering word,

30:58 right?

30:58 It's about technology. It's about engineering. Engineers, scientists, researchers. We have to build this technology safely, responsibly. And and the question is, can you and the the answer is absolutely yes. Because if you can't, then you shouldn't build it at all.

31:16 Of course.

31:17 And so we either have to believe we can or we shouldn't build it. Okay. And so I believe we can. And so here, let me just reason about it with you. So the first part of it is they say some people say that um uh AI is is uh uncontrollable because it's impossible to align perfectly. Alignment means this for all the audience. Uh you tell somebody um I want you to do this. And so the mission is clear.

31:45 The mission is clear and um I it's clear to you

31:49 because you said it in English. Okay. And so it could be uh I want you to solve this quadratic equation,

31:55 right?

31:56 And so the the mission is to solve the quadratic equation. The the alignment is how do you do it? There are many ways to do it. For example, you memorized it, right?

32:04 Okay. And so the answer is, you know, whatever it is. Uh or uh you saw the answer in a textbook, so you looked it up,

32:12 right?

32:13 Okay. Uh you referred to a reference

32:16 or you went to a drawer and you s you you know that drawer had all the answers. You open a drawer, you picked out the answer. Okay. Some people call that cheating. Um I call that opening the drawer and retrieving the answer.

32:26 Okay? And that's what computers do. There's a lot of different ways you could solve that problem. Okay?

32:31 Now, of course, that one is very explicit. Here's another one. Uh be successful in life. The mission is easy to say and in fact we say it to each other all the time.

32:40 You know we say it to our children. We say to our friends let's do our best to to uh contribute to society. Well

32:47 now the question is how there are so many ways how do you reasonably align and everybody's culture and background and some people think that contributing to society means uh to be generous to be kind. Uh some people might mean that contributors society uh make a lot of money and and then be uh uh philanthropic. Uh some people might so on so forth. Isn't that right? Yes.

33:10 Okay. And some people might just think that that uh building a company alone is is or doing something alone is is a a sufficient contribution to society. Creating a beautiful piece of art or um all of it is true.

33:22 Correct.

33:22 And so the question is what's the alignment? And it just depends on your background, your culture. maybe has something to do with where you live, where you came from. There's just infinite number of ways.

33:32 And so my point is this,

33:35 alignment is going to be a forever problem.

33:37 Yes.

33:38 If we depend on perfect alignment in order to ensure safety, then we're never going to get there. And so the answer is this. Um the way we treat the way we think about babies and when we we're

3 Highlight · Zero-trust containment and out-of-band monitoring

33:50 raising kids and you know we have pets or whatever it is, uh employees, okay? Um you start with containment. Uh you give them uh the rights that the access to tools, the access to information, uh the access to whatever responsibility, capabilities that they have, funds, resources. You give them access based on the job that you ask them to do. You start out with zero trust. When you hire somebody, you start out with zero trust.

34:15 When you have a baby that's one year old, you put them in a play pen. You assume zero trust.

34:20 And so you zero trust. You give them minimum rights.

34:23 Mhm. [clears throat] and you expand that boundary as you learn more.

34:27 And so when whenever we have a new AI, we have to assume the same thing. Whenever you're testing an AI,

34:34 zero trust, minimum boundaries, minimum rights, and you expand it as you test and give yourself confidence.

34:41 So containment, very tight containment is really important to any autonomous system. Second thing you do,

34:48 uh, uh, monitoring

34:49 and the monitoring has to be out of band. And the reason for that is because if you're if you tell an intelligent person, hey, listen, I'm going to sit here and watch you do your work to make sure you're doing it right.

34:59 Right.

34:59 Okay. And I'm going to sit here and watch you uh make sure that you're serving customers properly and that you're working hard enough.

35:06 Okay. That in that autonomous system is going to behave differently than when you're not watching.

35:12 It's like Brad's in the room with me or not.

35:14 Exactly. Exactly. Exactly. Okay. Yeah.

35:17 And so so the the the monitoring has to be out of band and it has to have the ability to observe that the autonomous system is doing something that was inconsistent with the policy or inconsistent with alignment. And you have the ability to either escalate or shut it down, right?

35:33 Just turn it off. And so so those kind of capabilities have to exist. That's engineering, that's technology. And it's possible. And we we announced it recently called Open Shelf. We have, you

35:44 know, over 100 companies now joining us. lots and lots of activity around the world because everybody wants the same thing.

35:49 Starting [clears throat] at the highest level,

35:51 you know, when I looked around the table at the White House,

35:53 yeah,

35:54 um, everybody around that table, right, their parents, they have kids. I think there's this view in the country that there are some leaders of super intelligence that are just yoloing, that don't really care. They're being reckless. They just want to grab all the money. And yet when I looked around their table, that table, there wasn't a person in there that I saw that wants to be reckless, that wants to yolo. In fact, everybody there wanted to find the right balance between fast and safe.

36:23 That was the president's directive. And I we we debated a little bit what that meant, right? There are different versions of how to implement that. But, you know, what would you say to the American public who fears that we have a reckless president or reckless leaders? Did you see any evidence of that? You have kids. Do you worry about the future that you're bringing into existence with super intelligence?

36:46 Safety and security technology,

36:50 right,

36:50 are part of AI,

36:52 right?

36:53 There's some weird formulation that AI technology and safety and security are orthogonal.

37:01 Yeah.

37:02 It's kind of like the airbags and the and the braking system,

37:06 right? and the radars, the cameras, um the ABS, uh the the the lanekeeping, all of that technology is somehow independent of the car.

37:15 Do you guys understand what I'm saying? The only thing that matters to the car is the engine,

37:19 right?

37:20 In fact, the whole car is part of the car's technology. When I say we need to accelerate AI's technology, what I mean by that is we need to accelerate the security technology. We have to accelerate the safety technology. They all have to be accelerated.

37:35 Right? If you ask me right now, I think everybody would say, "Let's accelerate security. Let's accelerate safety technology." Nobody says, "No, let's pace that,

37:43 right?

37:44 We should accelerate that." It's all part of the same thing. You can't separate capability, functionality from safety and security. They go us one.

37:53 And when you deliver the service, nobody's going to say, you know, good good uh you know, I I really like the the uh the functionality of your service, but boy is it, you know, wrecking havoc. And

38:04 so so I I think it's all one thing

38:07 and we treat it as all one thing and and the industry has to treat it as one thing and when we say accelerate we have to mean

38:14 accelerating the entire package and right now

38:17 including the safety

38:18 including especially the safety.

38:20 You mentioned pacing um

38:22 especially safety. [clears throat]

38:23 You know this some people read pacing the frontier by Daario um and thought that meant slowing down and I thought a funny moment in the room was when the president called on his new best friend Daario. He he referred to him in that way and he said, you know, explain to me, you know, the your version of fast and safe. And Dario started off by saying, Mr. President, you said whoever wins AI wins, [clears throat] right? and he said that we're all speeding up, that they're speeding up, that they want to go fast, that they want to, you know, win the race with China, but also acknowledge that along the way we just to your point you just made, we have to make sure the safety systems are also speeding up and also keeping pace. So, I thought that was an important moment where you had, you know, because I think people have positioned him as though he's in opposition, right, to this, but it seemed to me that you were all very much on the same page. you I you went to the cabinet room with him and and Greg and the other Frontier Labs to sign uh you know this agreement. Was there any

39:23 chatter you know in that room about differences of opinion or was everybody uh you obviously got it signed. Did anybody have to be put in a headlock or did everybody go willingly? No, everybody was very con very constructive, very productive and um [clears throat] I I I think that that without exception, everybody um is dedicated to advancing the industry safely and securely. And the reason for that is because if it's not safe, it's not secure, the functionality will never get a chance to speak.

39:53 And so the capability will never get get a chance to shine. And so we have to we know that the priorities are at all times safety and security. Of course recognizing that this industry came through a transition just recently, right?

40:07 It wasn't but six months ago. [clears throat]

40:10 This technology barely worked.

40:11 Exactly.

40:12 And so the functionality and ca without functionality and capability, what's the point in making it safe and secure? Nobody even cares about it. And so the obviously the first the first job of AI is to to demonstrate it could do something useful,

40:23 right?

40:23 And it really reached that point six months ago. And so now it really wants to be deployed and and v and broadly deployed. And so so there's no question that the indust the entire industry now has a has a strategic imperative an incredible imperative and great urgency to make sure that whatever we deploy is safe and secure. And so I think I think everybody is completely convinced capabil aligned functionality,

America winning means every industry prospers

40:50 capability safety security they're all at exactly the same place.

40:54 Priority number one.

40:56 And so so um I the question is winning.

41:01 Um I would reframe winning not of the technology industry winning, not of just the AI industry winning.

41:09 Um America winning means every industry in America winning. healthare.

41:14 That's right. Nothing would give me more joy. The definition of America winning is when we talk about Lily winning,

41:20 yes,

41:20 Walmart winning, FedEx winning, every single one, Boeing winning, um the US military winning,

41:27 every citizen winning,

41:27 every citizen winning. And so, um uh our universities winning, our high schools winning, um you that's the definition of winning.

41:35 The winning, there is no winning if it's just a technology won. Exactly.

41:40 But nobody used it.

41:41 Right.

41:42 Right. what what

41:43 the greatest risk is of course we're going to you know I I'll just take it I'll just say baseline uh there will be no techn there will be no AI technology industry if we don't build it safely and securely okay this is an existential issue for us

41:59 and and if we make it an existential industry for everybody else well that's not going to happen because it'll be an existential issue for us first right

42:06 so so having said that the definition of winning for us and and you know this is This is the part I always have to change a little bit,

42:14 right?

42:14 I want us to pivot.

42:16 I like that.

42:17 I want us to pivot. And and when I see AI winning, America AI winning, I'm looking down from, you know, these days we spend a lot of time in the air and we're looking down. Every state's winning, every county is winning, every community is winning, every company's winning.

42:32 Everybody won because we could afford to spend more on healthcare. We could afford to spend more on education. We could afford to spend more on interstate, you know, commerce, highways, etc. all of the infrastructure of the country. What I hear you saying is the definition of winning is that when we look back 25, 30, 40 years from now, the economy needs to be 10 times as large. Everybody's lives need to be better and existential and primary to all of that is it has to be done in a way that's aligned with human interest, which this is the last question I have on on on this and I know we have a lot to get to.

43:03 Most people don't understand what our security framework is in the country. Um, I've said we have the most robust super intelligent security framework in the world. Um, we're we're clearly the only country on the planet that's actually sanctioned a model and pulled it off the shelf with Mythos earlier this year. But I describe it as a three-legged stool. We have the Frontier model review of every new Frontier model by Casey and the Department of Commerce. So, they do all their work. If a new model's coming out, they review it. They test it for whether or not they think it's going to cause a problem in the marketplace. Now we have the White House accord on super intelligence that you help to architect internal and external audits, board oversight and all of the liability that comes with that. And then third we have the SI force now led by Jay Clayton of course Emil and Scott and Andrew and that's doing critical industry coordination cyber etc. So, these three legs on the stool, um, there are people out there, I heard Gates recently saying, "We're not doing, you

44:03 know, enough. We need new laws on the books and everything else." That three-legged stool seems like a lot to me in a very short period of time that's come together as a leader of one of the most important companies obviously in super intelligence. Do you feel comfortable right for that future that we're trying to usher in that that is a good step forward where we need to be right now? Yeah. The the best way to get to the future is uh uh uh take action quickly.

44:30 Yeah.

44:31 And and if we if we uh argue about what is the what's perfect uh versus what's first,

44:41 right?

44:41 Um we'll end up arguing about perfection um and we'll never get there,

44:46 right?

44:46 And so I love I love the fact that that um these three-legged stool are actionable,

44:53 right? They're tangible. They have real teeth,

44:56 right?

44:56 Happening now.

44:57 It's happening right right now. You could, you know, we're doing it now. We're taking action now,

45:02 right?

45:02 And and the reason we're taking action now is because it's good for our company. It's good for our industry. It's good for our country. It's good for our society. It's good all the way around.

45:10 And if we don't do these things, um, you know, the outcome the outcome is is less desirable. We don't get to pursue that optimistic future that we all dream dreamt about. And so if you go back and look at another industry, I don't remember another industry that is uh as it's currently at its current state,

45:28 right?

45:29 And have now formulated all of these cross company, crossindustrial um government to industry um coordination uh to make sure that we we uh we advance this in a responsible and optimistic way.

45:42 Yeah. I just think it's so important for people to understand because when I hear senators talking about it on television on both sides to be perfectly honest like they don't

45:50 they have to learn about what we're doing here

45:52 they don't even know what we're doing right like we are doing an extraordinary amount and by the way these aren't voluntary things there's not going to be a frontier model that gets released that Casey is not reviewing and the commerce department is not reviewing and they've demonstrated they have the authority under export controls to pull them off the shelf

46:10 do two things one, come and spend some time and and and and learn what actually has to be done. What's the problem, right?

46:18 If you want a solution, you got to at least understand what the problem is. Right.

46:20 Right. And so, so one, come and just learn about we got plenty of time. I got all the time in the world for this kind of stuff and and just, you know, come talk to us. Come talk to our industry and learn. The second thing is learn about the solution that we put in place and then and try to see if we can make that if there are ideas to make that better. Of course, we're all completely open-minded, right? you know and so but but be constructive right

46:42 I thought that was part of the the the fourth point on on the accord on super

Power and land bottlenecks challenge massive compute buildouts

46:46 intelligence was this is not static this is dynamic this group is going to get together routinely we're going to update the framework as required when the technology evolves but I want to move forward now uh to compute of course longunning agents that work in teams of thousands or millions to solve complicated tasks it all consumes massively more compute um Dylan our friend as as semi analysis has said we're going to bring on board 43 gawatt of new AI compute next year just to contextualize it we'll share a chart with people online that's more than all of the AI compute that exists in the world today so we're going to bring that on board next year the year after that another 40 gawatt uh of compute you are the one you know where every single GPU is allocated right so as you sit here does that sound right to to you do you think we're going to bring on that much compute? Um and do you think that you know some of the things we're hearing out there about permitting

47:47 permitting issues or bottlenecks in the supply chain or you know uh pro local protests uh do you think the number is going to be materially different than that based upon what you're seeing?

48:00 Uh I think the demand is definitely there and more.

48:04 Yeah,

48:04 the demand is definitely there and more. Um the challenge of course is that there are a lot of obstacles when you when you move from uh when you move from digital to the physical. There are a lot of obstacles

48:18 there. There land permitting. There's power generation that you got to bring online. Um of course you got to you need construction workers and outfitters and plumbers and you know all kinds of

48:29 created a million new jobs just building these things. create a million new jobs and and their their skills are their skills are not fungeible. You know, they have a lot of skills and particularly the the ones that are building these $50 billion data centers. Uh you're going to want some people with great skills and and and there's a critical part of every single one of the buildout these things where where you need some very specific skills. And so um I I think that and then there's there's of course um whatever moratoriums that are that are happening um what whatever whatever issues that are that are um uh [snorts] um that we're seeing around around the country and that and so I think I think this is this is um u my guess is that uh land power shell um uh the the buildout resources

49:19 will be one of the the most important obstac obstacles to reaching that

49:23 and and so so our our company as you know now speaking specifically for us there are several things that we're doing to uh mitigate that and and one you know because we're the only company that serves every CSP we serve every NCP um uh we have uh great enterprise businesses through Dell and HP and Lenovo and others

49:43 and because we serve every single industry and and we're across the entire uh life cycle of AI I everything from you know of course data processing to training to post-raining to inference to agentic processing um and all of the tool use that we accelerate uh you know our exposure to our go to market is the largest of all

50:08 um every every contract manufacturer in the world is working on my behalf um every go to market is is connected to our architecture and um and so if one particular state one particular community um one particular data center has a hard time right um you know our demand is so much greater than our supply at the moment we'll just adjust our supply chain accordingly

50:30 um the other thing that we have and and I I don't love this answer as much um but as as you know Nvidia is also the infrastructure for the planet and so uh whether it's in Europe or um uh and we we've got uh early developments now in Africa in Central Asia um in Southeast Asia and Latin America America in, you know, in Middle East and so on and so forth. And so I I don't I don't love that that that um United States can't keep up.

50:58 Yeah.

50:59 But for our business specifically, we have such a global reach

51:02 and and there's buildout demand all around the world

51:07 if we don't build it out here. Um you know, I think we just all have to recognize that every country in the world would love to have it built out there.

51:14 Yeah. And so and so um I think that that our business specifically is probably the most um resilient. Um and the reason for that is is all the things that I mentioned, you know, we cover the entire spectrum of of AI.

51:31 Um we cover the entire spectrum of computing. Uh we're in every single cloud, every single region, you know, and so so the resilience of our company is much greater. But I do think that land powered shell is going to be a challenging part next year.

51:44 And I guess what I what I what I hear from the street is there's this idea that you know maybe there's 50 gawatts of announced chips um that are underway um or talked about and maybe 35 to 40 gawatts of compute or land power shell that might come online. So what compute fits into that land power and shell and on what timeline is a question up in the

Nvidia captures a growing share of global capex

52:08 air. Yeah. But um I guess like if we think of through the entire

52:12 and just Clark just interrupt you there and and um you know of course as you know Nvidia's architecture is the most productive

52:24 right

4 Highlight · Power constraints drive demand to productive compute

52:25 it generates the most tokens per watt [clears throat]

52:28 and so if the world's if the world's amount of watt power energy reduces it shifts their choices to the most productive version and the reason for that is that they still want to make their revenues,

52:42 right?

52:42 And so, so all of my CEO colleagues, they all want they all have high expectations for revenues next year and people have, you know, they need to deliver it. Their shareholders counting on them to deliver it. All their partners need to need them to deliver it. And so when the amount of power reduces the quality of the compute you put into it has to has to be the most most extraordinary or the most productive computing resource you could put in the world today. We're the most rentable. [clears throat]

53:08 We're funible and we're durable.

53:11 And so so the the the the forces around the world because of the shortage of resources

53:20 is is shifting interest towards us. Our market share was already very high.

53:26 But surprisingly to everybody, not only are we a large company and our market share is high, our market share is actually increasing for these reasons. Mhm.

53:32 And so, so you you're going to see us go from and because we offer so much more per gigawatt than we used to. We used to be a GPU company, then we became the largest and fastest growing networking company. We're now going to be one of the largest CPU companies, surely the fastest growing one.

53:47 And so, so because we're offering more and more and and we're in the you know, we're in computing for storage, we're in computing for the knowledge infrastructure. And so the amount of and now we're in security with open shell and Sentry and so so the the the offering that we we offer into one gigawatt went from went from uh uh I guess about you know 18 billion or so per gigawatt to 20 somewhat billion per gigawatt to now 40 plus per gigawatt

54:16 and my sense is that that's going to

54:17 keep on going up and so so not only are we are we growing share because because people are shifting towards our computing platform we're also growing share because we offer more to the world's

54:27 capex

54:28 and and to to the world it's a dollar dollar share of the market. Yes.

54:33 Yeah.

54:34 If you look at if you look at uh just go back in just uh six years ago.

54:38 Yes.

54:39 Our percentage of the world's capex was in single digits.

54:42 Yes.

54:43 Um then I think I think uh right now with the world's CSPs we're probably running about 40%.

54:49 Mhm. But we're also expanding the market beyond the CSPs. And outside of the CSPs, whether it's onrem or neo clouds, half of our business,

55:00 our percentage of capex is probably what 80%.

55:03 Yeah.

55:04 And so

55:04 and so between 40 half of our business being 40%, half of our business being 80%.

55:11 And both of us growing doubling every year.

55:14 And so [clears throat] you could see that our percentage of the world's capex is in fact even growing.

55:19 Yes. Well, one of those one of those buyers of Nvidia um is somebody we talked about two years ago, Elon, um when he started building Colossus, and we were blown away a couple years ago to see him stand up a couple hundred,000 hoppers, right, in Memphis. Um and I remember you saying nobody can do it like him. Now he's raising another 40 billion, going to buy maybe million plus Vera Rubin. Um

55:46 he's moved from the K's to the M. He says he wants to have 10 gawatts by the end of 2027. That's hundreds of billions, right, spent on Vera Rubin.

55:56 It's an unprecedented ramp at scale. You were with him again yesterday when he was also presented with a medal. Is Elon that much better at building compute today as he was then? Is it now obvious to you? Is it is it a foregone conclusion that SpaceX will be the largest AI hyperscaler in the world? Well, we we used to, you know, in the in the two parts of our chart, um Elon used to be under Enterprise. That's the way we reported our numbers. Now, he's part of our CSPs,

56:29 of course.

56:29 Yeah. Yeah. Of course.

56:30 And so, they're massive.

56:31 So, I'm I'm delighted that that that um I I love working with Elon. Um he's using our technology for his own AI um in training models and such. uh deploying models. Of course, Grockbot is super cool and and um and the and the demand for Grockbot, the consumption for Grockbot is really skyrocketing. And then of course, he rents it.

56:54 Yeah.

56:54 And so the combination of these three things, he's fairly unique in doing that. Um I guess I guess uh uh Google does that, right?

57:02 Right. That would be another yet another example. And so I think he's he's um uh also has the has the uh advantage that that he loves the intersection between computing and the physical buildouts. That's

57:16 right.

57:16 You know, he loves that that whole area and nobody knows more about energy than he does. And

57:21 and and uh one particular uh new market that we're going to go explore together is putting data centers out in space. And so our two engineering teams are working super closely together

57:30 to create a new type of server, new type of architecture. um that might might uh be super good, you know, you know, in space and on Earth.

57:38 Well, we saw that Starship launched last week, you know, had its its its first orbital rendevous and was launching those V3 sats that literally captured the imagination and kind of broke Twitter for a moment. Just people mesmerized by watching these launch into space.

57:54 I can't I can't wait to be a payload. I mean, not person. Do you think does that get do you have increasing confidence that we will have a material amount of

High-value Nvidia compute makes space data centers viable

58:03 compute in space you know via via starship by let's call it 2028 2029 I mean Elon's put some big numbers out there but do you think that we can get to material inference in space in that in that time frame

58:17 only Nvidia compute let me tell you why it's not not just about the technology uh Nvidia's performance per unit energy is the world's best And so we call it performance watt. And but the most important thing is is this the productivity of Nvidia's compute is so good meaning the return on investment invested capital was so good.

58:38 Yes.

58:39 That you could put it out in space and the launch cost

58:43 is a small percentage of it.

58:44 Okay.

58:45 The launch cost is not inconsequential.

58:48 And so you're not going to put lowc cost computing out there. Correct. You're going to put high value computing out there. And so we're the highest value computing system you could imagine, the highest computing system in the history of computing. And so we're the perfect fit really to launch into space right now. Irrespective of the com the launch cost, it'll still be excellent ROI.

59:08 So do we get 500 megawatts in space by 29?

59:11 Well, I'll I'll let him tell you, but but um I think

59:14 impossible.

59:15 I I you know what's Elon's what launching 100 launches a year right now? I don't know what exactly it is multiple a day.

59:24 Yeah. Right. And so you you could you could put Vera Rubin in there and and it'll justify it. The demand demand is there. Um his return on investment is so good on it

59:34 and so the productivity is excellent. It's fungeable meaning you put a satellite out in space. If you don't have a customer to go rent it, it sits idle and you certainly don't want that. And so so Nvidia Compute is going to be fully fungeible and there'll be lots and lots of renters. You know, we have we work with every single closed model company in the world. Uh we run every single open model in the world. We have thousands of startups working with us. And so and enterprises around in every single industry and so so the computing is completely funible and it's durable.

1:00:04 Yeah.

1:00:04 You know, you'll last it'll sit up there and we'll keep up and the reason for that is because we keep CUDA upgrading the software. you know, we're the only comp hardware company in the world that you pay us eight years ago and we're still updating your software, new algorithms,

1:00:18 right?

1:00:18 You know, and so we'll we'll update the software

1:00:21 for as long as the satellites up there and so so uh it's going to be really durable. And so I I think the combination of these three traits,

1:00:28 yes,

1:00:29 along with the technology makes it the perfect payload for for uh SpaceX.

1:00:34 Um it cost trillions of dollars to build all this capex. You guys have leveraged

Diverse revenue streams justify massive AI infrastructure capex

1:00:38 your own balance sheet to help folks like the Neoclouds build that out. In order for finance people to get it to pencil out, they need to see payback, right? Um you talked about, you know, some companies seeing one-year payback, right? $50 billion in the first year, you know, some some spot pricing out of out of SpaceX perhaps. Um but if I look at the total incremental offtake, right, the revenue of all the labs this year, so if I add up, you know, the two frontier labs, oh yeah, get yourself a snack. and we add up, you know, some of the inference labs, it's about $200 billion. Okay? Um, we need to see that double next year to 400 billion. We need to see it double the year after that to 800 billion, right? In order to get in the zip code of the payback on trillions of dollars of capex. Um, we we had this massive unlock this year with with Claude Code that led to a 10x increase in anthropics revenues. What's your confidence that the labs can continue to two to 3x their revenues every year in

1:01:41 order that we get the payback that's going to be required to to to to build out trillions of dollars of capex? I mean, you're investing in these companies. You have a you have big investments in these companies, right?

5 Highlight · Three distinct segments power Nvidia's compute demand

1:01:54 Do you see that level of growth continuing in these companies?

1:01:58 Oh, yeah. People misunderstand what's going on. So first of all particularly I mean Nvidia represents call it 80% of the revenue share of AI infrastructure in the world today and so and so my description of of the universe of computing and video computing matters here there really basically call it three segments three large segments is is is probably slightly more than that but let me just kind of categorize in three large segments the first segment of our computing is just baseline computing just You use it for data processing, molecular dynamics, physics, simulation, um fusion, simulation, fision, simulation, you know, you name it, right? Quantum chemistry, quantum physics, you know, and so everything,

1:02:43 right?

1:02:44 Okay. The world of simulation and data processing, not to mention computer graphics and and so all of that processing done on our GPUs. If you just kind of said there was no AI at all, just classical machine learning, uh, XG boost at Visa Mastercard, uh, you know, for fraud detection, recommener systems to figure out what news to recommend to you, just all of that stuff combined,

1:03:08 classical computing, not AI, not deep learning, not agentic systems, you know, call it a third of our business.

1:03:13 Yeah.

1:03:14 And that business is growing, you know, call it 30%, 40% per year.

1:03:19 Yeah.

1:03:20 Okay. in the absence of any AI labs, Nvidia is not zero, right? We're quite a large company,

1:03:25 you know. So that's number one. The second part is um the AI labs people see two, but we work with every one of them,

1:03:36 right?

1:03:36 And uh Metamuse is doing fantastic, right?

1:03:39 Incredible.

1:03:40 And it's vastly Nvidia and and you know, there were zero last year,

1:03:44 right? you know, or a year before that it was just recommener systems and now they're, you know, killing it with Metamuse and um, of course, Grock

1:03:54 didn't exist [clears throat] three years ago. Uh, we didn't run Gemini uh, before we run Gemini now. Uh, and uh, starting this year, we started working with Anthropic. And so, if you kind of look at our compute, people see a couple of labs, but I see more than that. I see five, six labs. Okay? And so so these labs are all doing great.

1:04:15 Correct.

1:04:15 And and if you if you told me that that represents um not quite one-third of our business and let's say a quarter of our compute,

1:04:25 fantastic. Okay. So let's say that's a quarter. So when people are looking at 80 Nvidia and and we now run the infrastructure of the world and I just said 25% of it maybe 30% of it is the Frontier Labs,

1:04:40 right? you know, call it a third is um uh just processing in general,

1:04:46 right?

1:04:46 And another third, this is the the part that's really great.

1:04:50 Um because we're at the beginning of a new computing platform shift, just a vast number of new startups are coming out.

1:04:59 So many great companies

1:05:00 and some of them are of course doing consumer stuff, you know, uh one of them really doing great. Noah Instinct, I was just going to mention that in Noah's doing great. um to uh all kinds of physical AI, biological AI, uh material sciences AI, uh uh Jeff's Jeff Bezos's new company, Prometheus, uh they're doing they're doing fantastic. And so the the number of companies across the spectrum of industries

1:05:25 that have been started, thousands of them. And what do they do? They rent NVIDIA compute.

1:05:30 So they're renters. Yes. And so what is a third of our business is kind of cloud computing for the next generation,

1:05:38 right?

1:05:39 It's kind of like AWS for today, you know right?

1:05:42 And so it's just not AWS for the future,

1:05:44 right?

1:05:45 And that represents about a third of our business altogether, you know.

1:05:49 Um my sense is that this we're in the early innings of the buildout of this new computing architecture. And um it's it's a very it's it's a real big mistake to recognize just a couple of companies and think that they were funding the infrastructure. And then one observation uh open models you you know that that I I've been a great advocate of open models. And the reason for that is because open models opens up new opportunities. And if you look at all these startup companies, 80% of them use open models. That's

1:06:18 right.

1:06:18 And and they're not using it to compete with the frontier labs. They're using it to unlock a new opportunity somewhere else. right?

1:06:25 You know, it could be in physical AI, could be manufacturing, it could be healthcare. They're doing something completely completely different.

1:06:31 And and they're taking um these open models and and many of them taking Neotron and the these models are then

1:06:39 customized into super intelligence.

1:06:41 Yeah.

1:06:42 And so if you will, I kind of see the the the cons the general the general closed labs will be artificial general intelligence and it gets super good.

1:06:50 Yeah. And then you have all these vertical companies that are focused on a particular domain and they have very proprietary data. They teach it to do something very specific and they achieve super intelligence for their domain.

1:07:02 Right? So you don't see you know so again um if we go to your compute math which I think a lot of people are using to forecast the future revenues of of of the labs for example you know you said hey if you stand up a gigawatt of this compute you can get you know $40 billion out the other end but if we took the number we talked about earlier 40 gawatts of compute coming online next year and if you took 40 billion out the other end that that would be 1.6 six trillion of revenues coming out the other end of that factory. But of course, there's something, you know, it doesn't work that way, right? At the end of the day, there's you got to look at the demand curve and the rate of diffusion to determine, right, the revenue that's going to come out the other end of the factory. And what I hear a lot is that people are nervous, right? They've seen over the course of the year maybe the labs revenue slowing down a little bit and they're nervous that you know at some point you're going to have the proverbial bubble popping right where we have more supply coming online then we have demand at a moment

1:08:03 in time that AI is more like these step functions rather than just this parabolic line straight up. I guess the question to you is do are you worried about you know a moment in time where

End of Moore's law drives baseline infrastructure growth

1:08:14 everybody gets nervous about financing the buildout right and um and then we don't get the buildout. Well, you know, all these different things, you just just got to break it down. Break it down back down to first principles and and um

1:08:29 uh the the part that people are trying to extrapolate the hundred hundred hundred billion dollars of AR or 125 billion where they are they're trying to extrapolate

1:08:37 is a small part not a small part it's it's just a part

1:08:41 right

1:08:42 of the overall infrastructure buildout for sure. And and so so the question is are people going to continue doing shopping online? Right.

1:08:48 You know, that's

1:08:50 so that third of the buildout, let's call that a third. You say it's a third of your business.

1:08:54 Take that to the bank.

1:08:55 Yeah.

1:08:56 Right. Like that's not even a debate. So a third of the

1:08:59 say, you know, I said I said a few couple was it a couple years ago that as we exit this decade um infrastructure buildout will probably be somewhere between 3 to four trillion a year.

1:09:09 Um uh you know obviously it's closer to four than it is closer to three. and and um

1:09:15 per year.

1:09:16 By the end of the decade.

1:09:17 Yeah. And if you if you said if you said that one and a half trillion of that was just compute,

1:09:22 right? Right.

1:09:24 Just compute. Now, why is it going to be so big? Well, it was half a trillion

6 Highlight · The end of Moore's law increases infrastructure costs

1:09:29 before AI started.

1:09:30 Right.

1:09:32 And if I told you that over the course of some six, seven, eight years, it became one and a half trillion because the world's just consuming more compute, right? I mean, that's not illogical, right?

1:09:41 On top of this, Remember, uh, Moore's law is over,

1:09:46 right?

1:09:47 There was a time if you want to get twice as many transistors, you just wait one year,

1:09:51 right?

1:09:52 And if you want twice as many transistor, get another wait one year. Same price. But if you want twice as many transistors now, doesn't matter how long you wait, you have to pay twice as much.

1:10:00 Okay? Because Moore's law is over. And so whatever whatever computing capacity that the world wants,

1:10:08 every single year, if you want twice as much, you're going to pay twice as much.

1:10:11 Yeah. And so on top of that half a trillion dollars that was kind of growing just naturally,

1:10:17 right?

1:10:17 Because the world just used more and more digital, you're going to have to effectively, you know, give it an X factor.

1:10:25 Yeah.

1:10:25 Just to account for the fact that Moore's law is over,

1:10:28 right?

1:10:28 And so without AI,

1:10:31 the semiconductor industry was on its path to become the largest industry in the world. Right.

1:10:35 And I've mentioned that long ago, just people forgot.

1:10:38 It's just one of those things that I said.

1:10:39 Yes. Yes. Yes. Yes. I just said that two years ago. Yeah.

1:10:42 Yeah.

1:10:43 And so, so, so that's just one part,

1:10:45 right?

1:10:45 And so the only thing that we and then

1:10:47 and then don't forget because we're in a new computing platform shift, there going to be a whole lot of startups.

1:10:54 Innovation is part of the economy,

1:10:56 right?

1:10:57 New companies

1:10:57 and we said it's accelerating.

1:10:59 Yeah. You know, so if we spent if you go back to Moore's law, general purpose compute, $5 billion per gigawatt.

1:11:05 Yeah.

1:11:05 Today it's 50.

1:11:06 Yeah.

1:11:06 Right. For a Vera Rubin. So the question is is Vera Rubin deliver 10x more valuable in the world?

1:11:12 So the punch line is I don't think it's necessary to extrapolate any of the labs into multiple trillions of dollars.

1:11:18 Yeah.

1:11:18 Unnecessary to do that.

1:11:19 Yeah. I think that's a I mean it's an important one because I think people uh you know we just saw yesterday you know even in the market um you know some chatter about was OpenAI's revenue 70 billion or 50 billion. I think you and I

1:11:32 they're going to be insanely successful companies.

1:11:34 Correct.

1:11:34 Yeah. But you saw the pullback in a lot of the infrastructure companies, right, on the fear that oh my gosh, OpenAI doesn't have the money, won't have the money in order to pay for the infrastructure. I think the point that you're hammering home

1:11:49 is that when we look at the 40 gawatt of infrastructure build out next year at least a third of it is just going to right general purpose compute replacement the the the the basic stuff out of the hyperscalers that a third of it's going to kind of neocloud sovereigns enterprises everybody else who's using this all the startups and a third of it's going to not two labs but to many labs right including gro and thinking machines and all the other yet

1:12:14 we're putting way too pressure on Two Labs.

1:12:17 I'm delighted to partner with them and support them. They're going to be two of the most consequential technology companies ever built in history. They're going to be magnificently successful.

1:12:27 Yeah.

1:12:28 But they don't have to be anywhere near a trillion dollars in revenues to be successful. Right.

1:12:32 Yeah. And I think we're putting way too much pressure on that. And then the rest of the industry, we still do have to build out a few trillion dollars of computing infrastructure.

1:12:40 Um just to keep going, um let's let's shift a little. How good is this, you guys? By the way, what are what are these called?

1:12:48 These are dried plums.

1:12:49 Dried plums. The old dried plums.

1:12:51 Yeah. This this one particular store in Taiwan is one of my favorites.

1:12:54 Kind of a licoricey.

1:12:56 There is licorice. Yeah. Licorice. A

Physical and manufacturing bottlenecks constrain supply

1:12:58 little licoricey. Uh salt, sugar, cinnamon.

1:13:01 Yeah. Has a little twang to it.

1:13:03 Yeah. Yeah. You got it. You know.

1:13:04 Yeah.

1:13:05 Okay.

1:13:06 You said as to next year, demand for Nvidia's chips well over 100%. You guided to 70% revenue growth. You gave a a year forward look because you said supplies constrained.

1:13:20 That's all I had at the time.

1:13:21 That's right. Yeah. You said I got 18 months to work here.

1:13:24 Right. You said you were very confident.

1:13:26 Don't come to work every day.

1:13:27 You said very confident

1:13:29 and plenty of time to work with the supply chain to do even better. You expected to do even better and to not disappoint customers. So are are things going even better? And has the order book continued to grow? Because the other part of that is right, you took a snapshot at a moment in time and of course people are continuing you know to place orders. So it do you think there's enough slack in the supply chain you can squeeze more out and is the order book continuing to grow as more of these capabilities are unlocked?

1:14:01 The first let me tell you why I told everybody. Um the challenge that we have is we had giant order books. Every single CSP, every single NCP, every single OEM had already placed orders on us. Startups, big companies, small company, they already placed orders on us. And and the reason for that is because we work with them on a on a depending on on the size of the customer at least a year um for the the largest ones two years forecast.

1:14:29 And so so we already have a a very large order book and because I was in the process of allocating and what I was allocating to all of our customers is a lot less than

1:14:43 they wanted

1:14:44 they wanted.

1:14:44 Yeah. If I if I was if I didn't publicly say

1:14:49 right

1:14:50 what we had

1:14:52 right

1:14:52 everybody would be secondguessing are these short changed are we being you know and so

1:14:57 and so it was it was my way of of essentially handling um every single conversation where I'm disappointing people

1:15:04 right

1:15:04 um and

1:15:06 so pretty much every customer would buy more from you if you could deliver more

1:15:10 by far and then and then since then of course we have new orders almost almost weekly and daily and and so so um uh in addition however we're getting more supply and some of the supply that that is limiting us is land power shown downstream correct

1:15:26 and so a couple weeks ago we announced 2 gawatts in Australia

1:15:30 and so that's a very big deal 2 gawatts to us is like $80 billion

1:15:33 huge

1:15:34 and and then and then um um I'm hoping when I go to Europe with UTC you know we'll be able to announce some some uh new partnerships there and and unlock more more uh land power shell and so so this land power shell is is clearly one of the obstacles and and this is this is a an area where Nvidia has a huge advantage.

1:15:53 Yeah. you know, because based on what we build, if if uh there's a a Neo cloud um uh and uh this Neo cloud like Core Weave or EBS um and they would like to to scale out their business, Nvidia has the entire full stack complete AI infrastructure and so so we could support them as their scale out. Um and so we we could build in places and we can build with partners and customers that nobody else can. Um and and so I I think this this gives us a lot more resilience. But none nonetheless uh as we as we continue to increase supply upstream and downstream uh you know uh one of the critical areas is substrate.

1:16:34 Substrates are getting larger and there more chips and so so it's working against them in two ways. every single chip that's being shipped uses more of the weight of the material. And so,

1:16:46 so these these um organic materials are are are really hard to make at the precision that we need because the as the packages get larger. um uh the the the coefficient the thermal coefficients um and the way we stack things make it really hard to handle things like warpage and you know and various various manufacturing defects and and so at the scale that we do we have to be very precise and we need really great partners and

1:17:12 and so that's that's supply constraint and just that those are just some examples uh silicon photonics is is supply constrained and so so everything is supply constrained and so we're going to keep working on I have 18 months to do so when you know every single week that goes by we increase but nonetheless every we keep increasing more supply and the order books keeps going up

1:17:31 right

1:17:32 and so it's going to be hard to close that gap for a few years

1:17:36 um take a look at this chart um you know

Nvidia's unmatched moat commands blind trust

1:17:40 this is like a deposition

1:17:41 no [clears throat] right

1:17:42 excuse me now usually goes like this Mr. Monk ex please please now

1:17:47 examinate

1:17:48 please now look at exam

1:17:49 good deposition okay so the gray line this is was [clears throat] the consensus at the start of 2025 this was the consensus for Nvidia at the start of 2025 the blue line was alimter's forecast way back then so we were a lot higher than the consensus line is now where the new consensus is based upon your guidance and so one of the things that's blows my mind about this is the 25 people who get paid to just forecast what this company is going to do. Their consensus of all their ideas, right, was off by a couple hundred%. Right, just at the beginning of 20 uh Oh, no. This time last year,

1:18:26 at this time last year, not even 25 line.

1:18:28 Yes, that that was the consensus at this time last year. Okay. One year ago,

1:18:33 last week.

1:18:34 Okay. So, in a year, the consensus forecast has shifted up by 100 to 200% for this company.

1:18:42 Mhm.

1:18:43 Extraordinary. [clears throat] Like in in my 20 plus years of doing this, you don't often see consensus being off by that much. And yet the stock's only up 20%. Okay? You literally doubled what the revenue forecast was and the free cash flow forecast was for the company. You're now trading at 13 to 14 times earnings depending upon who you're looking at. So what do you think the market is saying about Nvidia or about the confidence in the expectations of the market right to cause that to be the case? Because usually if you see it shift up by that much the stock would have shifted up by a lot more.

1:19:18 Uh first of all the the for a company to be as large as us gaining share tells you something about the resilience.

1:19:25 Yes.

1:19:25 And the the the size and scale of our advantage,

1:19:30 right?

1:19:30 And and

1:19:31 is that advantage increasing or decreasing? Is your moat increasing or decreasing?

1:19:35 Increasing.

1:19:36 Increasing.

1:19:36 Increasing.

1:19:36 Your competitive moat is increasing at scale.

1:19:39 Yeah. Let me give you an example. Let's just do a thought experiment right now.

1:19:43 If you had to hand somebody a hundred billion dollar check to place a purchase order right now for a next generation

1:19:50 Yeah.

1:19:50 If you if you want to get in line um for Vera Ruben Ultra, it's close to placing order time.

1:19:57 Wow.

1:19:59 Sight unseen.

1:20:00 Yeah.

1:20:00 Right.

1:20:01 But they have the confidence. And so let's say there's a you got to place a hundred billion dollars on somebody. Yeah. One

1:20:07 uh how do you know it's going to be any good,

1:20:09 right?

1:20:09 But because Nvidia builds it, you know it's going to be better than Vera Rubin,

1:20:12 right?

1:20:12 And Vera Rubin's awesome,

1:20:13 right?

1:20:14 And the reason why Vera Rubin you the reason why everybody had confidence in Vera Rubin is because Grace Black was awesome.

1:20:19 Yeah. [clears throat]

1:20:20 And so and the world of computing the the workloads that these computers do are so complex. You can't just have like measure one benchmark.

1:20:29 Right. Right.

1:20:30 You know, it's $50 billion. Yes.

1:20:32 And the number of technologies have to come together to deliver the performance is insane, you know, and so so the question is sight unseen, who can you place a $50 billion order on?

1:20:46 Yeah.

1:20:47 We're literally the only one.

1:20:49 Yeah.

1:20:50 Now, back when it was place a PO, a billion dollar PO, that was challenging enough. But if you had place a $100 million PO and the the the the system is sitting right in front of you, you could just bench you could benchmark the chips.

1:21:03 Right.

1:21:04 Right. How do you benchmark these systems?

1:21:06 Just building it is a miracle.

1:21:08 Right.

1:21:09 You know, so if somebody wants to do an evaluation, I'm happy to support it, but you're going to need to give me a giant data center and we're going to need a couple years to retrofit it and you know, and so you can't even benchmark these things anymore.

1:21:22 Interesting. And so it's exactly what happened with TSMC.

1:21:26 The reason why we give 100% of our business to TSMC is because they're the only company in the world that I can plan five years out,

1:21:32 right? And know

1:21:33 and sight unseen know that that transistor is going to be really good because it's better than that one. Better than that one.

1:21:39 Yeah. and that they will have capacity for me and that they know how to work with my business team that they have the agility to handle all of my ups and downs and you know for example to deal with with the fact that I went from gray to blue to green.

1:21:53 Yeah. Exactly. Okay.

1:21:54 Exactly.

1:21:54 They have to be big enough to go help me go from gray to blue to green.

1:21:57 Exactly.

1:21:58 There's no other company in the world that could help me do that.

1:22:01 That's right. and to have and and to have all these different process technologies from silicon photonics to logic to memory to and push pull push pull packaging. I mean the number of things that we do is so dynamic

1:22:15 and so how do you possibly do that with a company that is onetenth their size it's impossible and so I think the big idea is this because we're building infrastructure for the planet and Nvidia has become an infrastructure company. Mhm. [clears throat]

1:22:31 And because these infrastructures cost so much money and they're moving and technology is moving so fast.

1:22:36 Yeah.

1:22:37 We're practically, you know, the combination of the trust, the the fact

7 Highlight · Fungibility across workloads, models, and geographies

1:22:42 that we run everything, you know, so it started out with some some GPT models. Um, but look at the flash attention mechanisms that were were re were invented. the way that we parallelized the models and sharded across MVLink 72 and how all of that changed changed and now people are talking about transformer diffusion models that are hybrid SSMs that are transformers hybrids and all these different technologies are all right

1:23:09 the evolution of all that we know that it all runs on CUDA

1:23:12 right [clears throat]

1:23:13 it is the only we uh we don't know whether you need to do more pre-training post-raining or just agentic inference well it turns out we run it all anyways right

1:23:21 and so you could build a $50 billion ion dollar data center and know that the fungeability of the work and if something were to happen you you know for whatever reason unknown you wanted to if you will sublease half of your capacity to somebody

1:23:35 Nvidia is completely funible

1:23:37 right

1:23:37 so we're funible in workload we're funible in models we're fungeible across customers

1:23:42 and we're deployable everywhere in the world you start a company in Europe you start a company in United States but guess what you would like your customer you want to serve customers in Europe you want to serve customers in Japan well Turns out we're already there. Nvidia's Nvidia's infrastructure is already there. So, OpenAI wants to go to Europe. No, we'll take them there in a heartbeat. If they want to go to India in a heartbeat, if they want, isn't that right? If Anthropic wants to go to Japan, Korea, in a heartbeat, we're already there,

1:24:07 right?

1:24:08 All of these architectures are already there. And so, I think the the if you take Nvidia in a whole and then you come back and you say, okay,

Fungibility ensures low risk and long durability

1:24:17 I want to do the thought experiment. I want to place a hundred billion dollars on somebody. M

1:24:22 how do you do it?

1:24:24 A year ago, we were sitting here and the consensus forecast was that you were going to be flat or shrink in 2027. Now the consensus forecast you're going to grow 74%. Okay, so you know I I just want to let people let that sit in because a lot of people follow these consensus forecasts, right? And it was really wrong. Okay, it they were forecasting.

1:24:46 Did they go back and listen to your podcast from last time?

1:24:49 Right. You told them.

1:24:50 Yeah. I explained everything. Listen, ladies and gentlemen, I did explain everything.

1:24:55 And so I look I look out at 2029.

1:24:58 There was a there was wasn't it an ROI question.

1:25:01 Yes.

1:25:01 And then there was there was this um uh custom. Yeah. Custom versus general purpose.

1:25:08 The reason why Nvidia is general purpose is so I can have all this fungibility.

1:25:11 That's right.

1:25:12 Um the benefit of fungeability is low risk,

1:25:14 right? [clears throat] And low risk really comes into play when you're placing big dollars. When you're building out 50 hundred billion dollars worth of infrastructure, you need to know that that infrastructure is going to be really good for a long time, right?

1:25:28 No matter what.

1:25:29 So specialization is the enemy of fungibility.

1:25:35 Specialization is the enemy of durability.

1:25:38 Right? And so so long as I can prove that our performance is extraordinary and we prove that every single day,

1:25:48 the moment we can prove that then fungeability and durability translates to rentability which translates to productivity

1:25:56 and financeability

1:25:57 and therefore financeability which leads to the lowest TCO.

1:26:01 Right. Right. Exactly.

1:26:03 Yeah. Well, now now you're buying back the largest amount in history. And you know, on my math, you know, you could probably buy upwards of 10% of the company back within the next two years.

1:26:13 At Goldman, you called Nvidia, you know, the first growth value company. Yeah.

1:26:18 Um,

1:26:20 sounds a lot like uh Apple's pitch to back.

1:26:24 Okay. Is that right?

1:26:25 Yeah. Or [clears throat] or more. Yeah. Or that they that they could return the capital.

Software licensing and revshare unlock new growth vectors

1:26:28 It worked for sure.

1:26:29 It worked. went from 300 at the time $30 to 300.

1:26:33 Yeah.

1:26:34 300 plus.

1:26:34 And in a lot of ways, we're going through a similar transition. And for for example, the the thing I did I forgot to mention is and and and Brad, I think you were about to ask me about that. Um you know, most of our

8 Highlight · Monetizing enterprise software licensing at scale

1:26:47 investment and financing um uh partnership strategies is trying to te trying to set up uh two business models,

1:26:56 right?

1:26:57 Two new business models. Um uh one business model we've been working on for some time and will be accelerated uh in the near future is software licensing.

1:27:07 You know the body of software that we have in our company is just extraordinary and that data center is so large. Imagine all the software that runs in it

1:27:14 and we provide the entire stack.

1:27:16 Well and our entire stack is completely industrial strength for the CSPs. they want to run their own stack, right?

1:27:23 And so we license or we give them our software open source and then they take it and they modify it and they integrate into theirs and then they operate their services. But for a lot of the world's companies, they don't have the IT department, they don't have the software department to do it. So they need me to maintain it.

1:27:38 And so because AI

1:27:41 in the last several year was largely focused on re uh on training, but now they're moving into deployment,

1:27:47 right? They need to operate these as factories and they need to they need to know that I'm on the hook.

1:27:53 I'm on the SLA to support it and maintain it, keep it secure, keep it safe for as long as we shall live. And so software license for enterprise business will be very very large.

1:28:04 And so, you know, if if our software license is, you know, just roughly say for for for simple math, a couple of thousand dollars per GPU per year and we have millions of GPUs,

1:28:13 right? We have we we ship, you know, 20 million GPUs say a year. I'm growing and so half of it goes into world's enterprise. It adds up.

1:28:22 Yeah.

1:28:23 Okay. And so so I'm fairly certain this is going to be a very big part of our business software license. And then of course there there are some some clouds that that um rely on Nvidia for our full stack.

1:28:36 They rely on us for our offtake, our ecosystem. They also rely on us for uh maybe as an investment partner in helping them secure secure their financing and capital.

1:28:48 They've secured Lamp Power Shell. They're regional champion. They got all kinds of stuff working in their in their advantage. But these three areas they need our support on. And so in in those kind of relationships, it's very likely that we'll move to Refshare. And so we'll make an investment. We'll get the benefit of the sale up front, but we'll also have the benefit of Refshare going forward. And so, you know, we're we're not a cloud service provider. Um, but we're going to be providing offtake and providing software support for them for for, you know,

1:29:15 four, five, 10 years. And so, we ought to get some economic return on that. And so Revale

1:29:22 notice I just described two new growth vectors

1:29:25 independent of the hardware sale of our company and it's much more related to the install base of our platform. What's the benefit of software licensing if you have no install base? Um and and how could you have revshare if there's no offtake?

1:29:39 Okay. And so the the install base element, the richness of our software stack, the offtake, the richness of our offtake and the ecosystem that we have around the world that's going to fuel that one of the one of the ways, you know, so we're talking about this training at 13 times. You know, when Tim and Luca went to see Warren Buffett, what one of the reasons that Buffett has said he doesn't like technology companies is they they have this hit, they produce an enormous amount of free cash flow, and rather than ever giving any of it back to the shareholders, they go and spend it on something else. And often times the something else isn't as good as the thing that caused them to generate the cash flow in the first place. You guys are now spending over 50% of your free cash flow buying back your stock effectively returning it uh you know to shareholders while you have this exponential growth at the same time. As we've already discussed,

1:30:27 we said 50,

1:30:28 right?

1:30:28 I think this last quarter we did 60 some odd.

1:30:30 Correct.

1:30:31 Yeah. We're we're we're going to increase you know we invest in really high quality companies as you guys know and and um and you know the last couple of years whoever Nvidia buys from whoever Jensen's buys from you got to invest in that company. That's right.

1:30:46 And now the new the new word on the street is whoever Jensen invests in you invest in that company. And and and it's true. And the reason for that is because you know like you we're we're in we're in the ecosystem. We we're in the we're in the flow of information. We know those investments together.

1:31:00 Yeah. And we know a lot of smart people and uh we want to back these people when they when they you know want to do something great and so so we're kind of in on the inside. We we we have a lot of information and so our investments are you know wellinformed.

1:31:13 Right.

1:31:14 And not to mention uh Nvidia is also in the ecosystem and so we could help them succeed. That's right.

1:31:19 And so there's a lot of different reasons why the investments turn out great. Um but as a percentage of of our total um uh cash generation it's going to decline very rapidly partly because open AI and anthropic isn't going to

Global compute rearchitecture drives multi-trillion spend

1:31:33 come around again that opportunity is not going to come around again frank frankly and and then secondarily um our our cash flow generation is is growing quite fast

1:31:43 right

1:31:44 um and and and to that point it seems to me that if you're buying back that much and the durability of the revenues I think as you've described here today is longer than people expect. And I think that's the one place people are still hanging too much on. This is only going to last as long. Ezra asked you this on the pod the other day. How long until the bubble burst? And I think you said something about 2 to 3 years. You have good visibility that you know the demand is there and who knows kind of after that and I heard some people saying oh it may only be two years before this burst. But when I look at this like this is fundamentally a rearchitecture of all global compute forever.

1:32:23 Forever. So unless there's a global depression.

1:32:25 Yeah.

1:32:26 Right. Then every single year people are going to have to consume an enormous amount of NVIDIA compute.

1:32:32 Yeah. And so so the the way to think about that is is we were running in the um you know the CSPs were running about half a trillion. the whole comp the whole industry the whole world is running call it a trillion dollars of it

1:32:44 right

1:32:44 build out each year

1:32:46 okay

1:32:46 and every and every year more gets built and every 5 years or so some gets you know depreciated or turned off and and taken offline and so on so forth and some of them some of it lasts longer um and so so that that old world was in the retrieval way of doing computation in the new way of doing computation you know I've I've predicted that going out of the out of the decades. So, so three, four, let's say four, let's say five.

1:33:14 And um I and my sense is that we're going to continue to grow from there. If you told me

1:33:20 that the annual the annual capacity building, infrastructure building, refreshing, combination of new and old

1:33:29 um uh refreshes runs around 10 trillion dollars. I'm not even a little bit surprised.

1:33:34 Yeah. Right. I mean ju just again for people listening you know you think $4 trillion of annual spend by the end of the decade because every time you consume tokens every time you use intelligence you have to run the machine the factory has to to run and I think you you've described that that's about 50% you know if you assume that you know $50 trillion of global GDP is uh you know is related to knowledge work if we get a 20% productivity gain over 5 years on that like you've seen inside Nvidia uh you've seen much bigger productivity gains inside Nvidia. But if you see that sort of productivity gain then if we get $10 trillion of incremental economic activity. Yeah. Right. By the by by 2029 2030 like that seems very reasonable.

1:34:19 And you're just saying we'll spend half of that

1:34:22 on capex on the intelligence you know to

Deep optimism for humanity's technological future

1:34:24 to produce it. Um and so maybe you know that's a good place for us to take a bit of an offramp. You've been incredibly generous. Um I could go on forever. But I kind of want to wrap where we started, right? When we look back um

1:34:40 if this was one of those eating, drinking podcasts,

1:34:45 we could keep going. We could keep going.

1:34:47 Yeah.

1:34:47 But um it wasn't so long ago that the human condition was described as nasty, brutish, and short, right?

1:34:56 Was that last week or

1:34:58 innovation has really driven all human progress mostly in the last 200 years? um more time with family, uh longer lives, less time doing drudgery that humans really didn't want to do. Safer.

1:35:15 We're the first generation where 63 years old's considered young.

1:35:18 Exactly. Exactly.

1:35:20 When people still got, you know,

1:35:21 Exactly.

1:35:21 People call me young man.

1:35:23 You safer, better lives.

1:35:27 All of this despite all of these concerns that you and I hear out there. By the way, those same concerns were expressed at the beginning of the industrial revolution,

1:35:36 right? What's your level of confidence that in 5 years from now, we will have made the right decisions and the world will be that much better place, right, that we describe um because of the investment and all that we're doing. Are you more confident as we sit here today than you were 12 months ago? I'm 99.99% just in in in in service in honor of of a good friend not using zero and 100. Um I'm 99.99% 99% confident that that um uh too many of us care due deeply that the outcome turns out well.

1:36:15 Yeah. that we will dedicate our might, our intellect, our resources to ensure we get there responsibly and optimistically.

1:36:25 And so there's just so many people doing the right things. Now, often times, you know, there they're they're there they're there [snorts] people who pontificate about these things and they're informed by several several things and they and they they don't realize that there are a lot of people industries incredible researchers and engineers outside of their sphere

1:36:50 of information that are doing amazing things all by themselves independent of them. Just because they didn't say

1:36:57 they didn't say it doesn't mean a lot of smart people are doing smart things.

1:37:01 And and you know I say this all the time inside the company

1:37:04 even without my leadership without strategies

1:37:08 the company is doing smart things all by itself.

1:37:11 Smart people do smart things. They do more impactful things when you organize online

1:37:16 but amazing people are doing smart things all by themselves. And so when somebody tells me that that you know you you're worried about it's good that we worry,

1:37:24 right? And we should express our worries. We should express our concerns. But we shouldn't predict the end of the world. Yeah.

1:37:33 Because they're underestimating the human condition. They're underestimating the human capacity. And they think for some some reason with maybe it's just hubris, maybe it's arrogance that unless they do it, it won't get done.

1:37:47 Right. Right.

1:37:48 Unless they do it, it won't get done.

1:37:50 Right.

1:37:51 The fact of the matter is this company is doing amazing things whether I say it or not. Right.

1:37:55 Okay. And I, you know, I I get I get all the accolades and the blame, but um in both cases, in both cases, I'm fair, especially the accolades. And and so I and the reason for that is just these, you know, when you surround yourself with amazing people and you create the conditions by which everybody wants the same outcome.

1:38:15 Yeah.

1:38:15 The good outcome, the good version of it. And I think most people do. That's the reason why I say we need to we need to hold ourselves accountable and responsible.

1:38:25 But we should express a future that's optimistic

1:38:27 for sure.

1:38:28 And I am I am 99.99% certain,

1:38:31 right?

1:38:32 That surrounded by all the amazing people in the world. We're going to get there together.

1:38:35 Yeah, agreed. And uh it's a I think you're a national treasure. We're lucky to have you helping to lead the charge. The nation's indebted to you for getting everybody together, getting us all on the same page, building the framework. There's going to be more work to do. Um, but as a father of two, and I I know you you have kids, you're going to have grandkids, we don't care about anything more.

1:38:58 That's right.

1:38:59 Than actually building a better world for them, right? We could easily stop.

1:39:03 But if we stop today, we lose, right? We lose the opportunity of that better world. So, we'll wrap it there. Thank you Jensen.

1:39:11 Thank you, guys. And and Brad, thank you for I I've never met a a greater patriot, somebody who who cares so much about the industry,

1:39:19 um but even more so the country and and um uh your your gift your gift in uh uh your enthusiasm and just tireless energy uh in in coordinating uh pulling together a lot of people in order to do something great like what you did. Um, incredible.

1:39:48 As a reminder to everybody, just our opinions, not investment advice.