Ep17. Welcome Jensen Huang | BG2 w/ Bill Gurley & Brad Gerstner

29:30My roadmap goes way deeper to our friends at Azure and AWS and others. We have no trouble doing any of that, even as they're building their own A6. I think, you know, when people observe the business, you said recently that the demand for Blackwell is insane. You said one of the hardest parts of your job is the emotional toll of saying no to people in a world that has a shortage of the compute that you can produce and have on offer. But critics say this is just a moment time, right? They say this is just like Cisco in 2000, we're over building fiber, it's gonna be boom and bust. You know, I think about the start of 23 when we were having dinner.

30:17The forecast for Nvidia at that dinner in January of 23 was that you would do 26 billion of revenue for the year 2023. You did 60 billion, right? The 25 people. It was just, let's let the truth be known. That is the single greatest failure of forecasting the world has ever seen. Right, right, right. Can we all at least admit that? What, what, what, to me? That was my takeaway. I just go. And that was, and that was, we got so excited in November 22 because we had folks like Mustafa from inflection and no one from character coming in our office talking about investing in their companies and they said, well, if you can't, pencil out investing in our companies, then buy Nvidia because everybody in the world is trying to get Nvidia chips to build these applications that are gonna change the world.

31:07And of course, the Cambrian moment occurred with chat GPT and notwithstanding that fact, these 25 analysts were so focused on the crypto winner that they couldn't get their head around an imagination of what was happening in the world. Okay, so it ended up being way bigger. You say in very plain English, the demand is insane for black well that it's going to be that way for as far as you, you know, for as far as you can see. Of course, the future is unknown and unknowable, but why are the critics so wrong that this isn't going to be the Cisco -like situation of overbuilding in 2000? Yeah. Yeah.

31:48The best way to think about the future is reason about it from first principles. Correct. Okay, so the question is, what are the first principles of what we're doing? Number one, what are we doing? What are we doing? The first thing that we are doing is we are reinventing computing. Do we not? We just said that. The way that computing will be done the future will be highly machine learned. Yes. Highly machine learned. Okay, almost everything that we do, almost every single application, word, Excel, PowerPoint, Photoshop, Premiere, you know, AutoCAD, you give me your favorite application that was all hand engineered.

32:29I promise you it will be highly machine learned in the future, isn't it, right? And so all these tools will be, and on top of that, you're going to have machines, agents that you help you use them. Right. Okay, and so we know this for a fact at this point, right? Isn't that right? We've reinvented computing. We're not going back. The entire computing technology stack has been reinvented. Okay, so now that we've done that, we said that software is going to be different. What software can write is going to be different. How we use software will be different. So let's now acknowledge that. Though those are my ground truth now.

33:01Yes. Now the question therefore is what happens? And so let's go back and let's just take a look at how is computing done in the past. So we have a trillion dollars with the computers in the past. We look at, just open the door, look at the data center and you look at it, are those the computers you want doing that? Doing that in the future? And the answer's no. Right, you got all these CPUs back there. We know that what it can do and what it can't do. And we just know that we have a trillion dollars with the data centers that we have to modernize. And so right now as we speak, if we were to have a trajectory over the next four, five years to modernize that old stuff, that's not unreasonable, sensible.

33:37So we have a trillion. And you're having those conversations with the people who have to modernize it. Yeah. And they're modernizing it on GPUs. That's right. I mean, well, let's make another test. You have 50 billion dollars of CapEx, you like to spend, option A, option B, build CapEx for the future, or build CapEx like the past. You already have the CapEx of the past. It's sitting right there. It's not getting much better anyways, and more so largely ended. And so why rebuild that? Let's just take 50 billion dollars, put it into generative AI, isn't that right? And so now your company just got better.

34:13Now how much of that 50 billion would you put in? Well, I would put in 100 % of the 50 billion, because I already got four years of infrastructure behind me, that's the of the past. And so now you just, I just reasoned about it from the perspective of somebody thinking about it from first principles. And that's what they're doing. Smart people are doing smart things. Now the second part is that, so now we have a trillion dollars with a capacity to go build, right? Trillion dollars with a infrastructure. We're about, you know, call it 150 billion dollars into it. Right. Okay? So we have a trillion dollars in infrastructure, infrastructure to go build over the next four or five years.

34:46Well, the second thing that we observe is that the way that software is written is different, but how software is gonna be used is different. In the future, we're gonna have agents, isn't that right? We're gonna have digital employees in our company. In your inbox, you have all these little dots and these little faces. It's in the future, it's in the little icons of AIs, isn't that right? I'm gonna be sending them, I'm gonna be no longer gonna program computers with C++. I'm gonna program AIs with prompting, isn't that right? Now there's no different than me talking to my, you know, this morning I wrote a bunch of emails before I came here.

35:24I was prompting my team, right? And I would describe the context. I would describe the fundamental constraints that I know of. And I would describe the mission for them. I would leave it sufficiently, I would be sufficiently directional so that they understand what I need. I wanna be clear about what the outcome should be as clear as I can be, but I leave enough ambiguous space on a creativity space so they can surprise me, isn't that right? There's no different than how I prompt an AI today. It's exactly how I prompt an AI. And so what's gonna happen is, is on top of this infrastructure of IT that we're gonna modernize, there's gonna be a new infrastructure.

36:03This new infrastructure are going to be AI factories that operate these digital humans. And they're gonna be running all the time, 24 -7. We're gonna have them for all of our companies all over the world. We're gonna have them in factories. We're gonna have them in autonomous systems, isn't that right? So there's a whole layer of computing fabric, a whole layer of what I call AI factories that the world has to make that doesn't exist today at all. So the question is how big is that? Unknowable at the moment, probably a few trillion dollars. Unknowable at the moment, but as we're sitting here building into the beautiful thing is, the architecture for this modernizing this new data center, and the architecture for the AI factory is the same.

36:49That's the nice thing. And you made this clear. You've got a trillion of old stuff, you've got to modernize, you at least have a trillion of new AI workloads coming on. You give or take, you'll do 125 billion in revenue this year. You know, there was one point somebody told you the company would never be worth more than a billion. As you sit here today, is there any reason, right? If you're only 125 billion out of a multi trillion, that you're not going to have 2X the revenue, 3X the revenue in the future that you have today. Is there any reason your revenue doesn't? No, yeah. As you know, it's not about, it's not about, everything is, you know, companies, companies are only limited by the size of the fish pond.

37:36You know, a gold fishing can only be so big. And so the question is, what is our fish pond? What is our pond? And that requires a little imagination. And this is the reason why market makers think about that future, without creating that new fish pond, it's hard to figure this out looking backwards and try to take share. You know, share takers can only be so big. Market makers can be quite large. For sure. Yeah. And so, you know, I think the good fortune that our company has is that since the very beginning of our company, we had to invent the market for us to go swim in. That mark, and people don't realize this back then, but anymore, but, you know, we were at the, at the ground zero of creating the 3D gaming PC market.

38:22Right. Right. We largely invented this market and all the ecosystem and all the graphics card ecosystem, we invented all that. And so the need to invent a new market to go serve it later is something that's very comfortable for us. Exactly. And speaking to somebody who's invented a new market, you know, let's shift gears a little bit to models and open AI, open AI raised as you know, six and a half billion dollars this week. At like $150 billion valuation, we both participated. Yeah, really happy for them, really happy that came together. Right. Yeah, they did a great stand and the team did a great job.

39:03Reports are that they'll do five billionish of revenue or run rate revenue this year, maybe going to 10 billion next year. If you look at the business today, it's about twice the revenue as Google was at the time of its IPO. They have 250 million. Yeah, 250 million weekly average users, which we estimate is twice the amount Google had at the time of its IPO. And if you look at the multiple of the business, if you believe 10 billion next year, it's about 15 times the forward revenue, which is about the multiple of Google and Met at the time of their IPO. When you think about a company that had zero revenue, zero weekly average users 22 months ago.

39:44Brad has an incredible command of history. When you think about that, talk to us about the importance of OpenAI as a partner to you and OpenAI as a force and driving forward kind of public awareness and usage around AI. Well, this is one of the most consequential companies of our time. The a pure play AI company pursuing the the vision of AGI and whatever its definition. I almost don't think it matters fully what the definition is. Nor do I, really believe that the timing matters. The one thing that I know is that that AI is going to have a road map of capabilities over time. And that road map of capabilities over time is going to be quite spectacular.

40:53And along the way, long before even gets to anybody's definition of AGI, we're going to put it to great use. All you have to do is right now as we speak, go talk to digital biologists, climatech, researchers, material researchers, physical sciences, astrophysicists, quantum chemists. You go ask video game designers, manufacturing engineers, roboticists, pick your favorite, or whatever industry you want to go pick. And you go deep in there and you talk to the people that matter and you ask them, has AI revolutionized the way you work. And you take those data points and you come back and you then get to ask yourself how skeptical do you want to be?

41:50Because they're not talking about AI as a conceptual benefit someday. They're talking about using AI right now. Right now, ag tech, material tech, climate tech, you pick your tech, you pick your field of science. They are advancing, AI is helping them advancing their work right now as we speak. Every single industry, every single company, every university, unbelievable, isn't that right? Right. It is absolutely going to somehow transform business. We know that. Right. I mean, we know it's so tangible you could. It's happening today. It's happening today. It's happening today. And so I think the awakening of AI, Chatchy PT, triggered, is completely incredible.

42:48And I love their velocity and their singular purpose of advancing this field. And so really, really consequential. And they build an economic engine that can finance the next frontier of models. Right. And I think there's a growing consensus in Silicon Valley that the whole model layers commoditizing. Lama is making it very cheap for lots of people to build models. And so early on here, we had a lot of model companies, you know, character and inflection and cohere and Mistral and go through the list. And a lot of people question whether or not those companies can build the escape velocity on the economic engine that can continue funding those next generation.

43:35My own sense is that there's going to be, that's why you're seeing the consolidation, right? It's open AI clearly has hit that escape velocity. They can fund their own future. It's not clear to me that many of these other companies can. Is that a fair kind of review of the state of things in the model layer that we're going to have this consolidation? Like we have in lots of other markets to market leaders who can afford, who have an economic engine and application that allows them to continue to invest?

44:07There's a first of all, there's a different fundamental difference between a model and artificial intelligence. Right. Yeah. A model is an essential ingredient for artificial intelligence. It's necessary but not sufficient. Correct. And so, and artificial intelligence is a capability but for what? Then what's the application? The artificial intelligence for software and cars is related to the artificial intelligence for human or robots but it's not the same. Which is related to the artificial intelligence for a chatbot but not the same. Correct. And so you have to understand the taxonomy of the stack.

44:46Yeah, of the stack. And at every layer of the stack, there will be opportunities but not infinite opportunities for everybody at every single layer of the stack. Now, I just said something. All you have to do is replace the word model with GPU. In fact, this was the great observation of our company 32 years ago that there's a fundamental difference between GPU, graphics chip or GPU versus accelerated computing. And accelerated computing is a different thing than the work that we do with AI infrastructure. It's related but it's not exactly the same. It's built on top of each other. It's not exactly the same.

45:26And each one of these layers of abstraction requires fundamental different skills. Somebody who's really really good at building GPUs have no clue how to be an accelerated computing company. I can, I can, there are a lot of people who build GPUs. And I don't know which one came, we invented the GPU but you know that we're not, we're not the only company that makes GPUs today. And so, they're GPUs everywhere. And but they're not accelerated computing companies. And there are a lot of people who, you know, they're accelerators, accelerators that does application acceleration. But that's different than an accelerated computing company.

46:06And so, for example, a very specialized AI application. Right. Could that, could be a very successful thing? Correct. Meta's MTIA. That's right. But it might not be the type of company that, that had broad reach and broad capabilities. And so, so you've got to, you've got to decide where you want to be. There's opportunities probably in all these different areas. But like building companies, you have to be mindful of the, the, the shifting of the ecosystem and what gets commoditized over time. Recognizing what's a feature versus a product versus a company. For sure. Okay. I just, I just went through, okay.

46:41And there's a lot of different ways you can think about this. Of course, there's one new entrant that has the money, the smarts, the ambition. That's an X .AI. Yeah. Right. And, um, well, there are reports out there that, that you and Larry and Elon had dinner. They talked you out of 100 ,000 H100s. They went to Memphis and built a large coherent supercluster in a matter of months. You know, so first, three, three points don't make a line. Yes, I had dinner with them.

47:16Cossality is it. What do you think about their ability to stand up that supercluster? And there's talk out there that they won another 100 ,000 H200s, right to expand the size of that supercluster. You know, first talk to us a little bit about X and their ambitions and what they've achieved. But also, are we are ready at the age of clusters of 200 and 300 ,000 GPUs? The answer is yes. And then the first, first of all, acknowledgement of achievement, where it's deserved. From the moment of concept to a data center that's ready for NVIDIA to have our gear, gear there to the moment that we powered it on, had it all hooked up, and it did its first training.

48:09Okay. So that first part, just building a massive factory, liquid cooled, energized, permitted, in the short time that was done. I mean, that is like superhuman. Yeah, there's, and as far as I know, there's only one person in the world who could do that. You know, I mean, Elon is singular in this understanding of engineering and construction and large systems and marshalling resources. Incredible. Yeah, just it's unbelievable. And then, and of course, then his engineering team is extraordinary. I mean, the software team is great, the network team is great, the infrastructure team is great. You know, Elon understands this deeply.

48:58And from the moment that we decided to get to go, the planning of with our engineering team, our networking team, our infrastructure computing team, the software team, all of the preparation and advance, then all of the infrastructure, all of the logistics and the amount of technology and equipment that came in on that day, and video and video infrastructure and computing infrastructure and all that technology, to training 19 days. Hang on, you just, you know what, do you don't want to do? Did anybody sleep 24 or 7? No question, then nobody slept. But, but first of all, yeah, some 19 days is incredible.

49:41But it's also kind of nice to just take a step back and just, do you know how many days 19 days this, this is a couple of weeks? Yeah, right. And the amount of technology, if you're ever to see it, is unbelievable. All of the wiring and the networking and, you know, networking and video gear is very different than networking, hyper scale data centers. Okay. The number of wires that go in one node, the back of a computer is all wires. I'm just getting this mountain of technology integrated and all the software, incredible. Yeah, so, so I think, I think what Elon and next team did, and I'm really appreciative that he acknowledges the engineering work that we did with him, and the planning work and all that stuff.

50:24But what they achieved is, is singular, never been done before. Just a put in perspective, 100 ,000 GPUs, that's, you know, easily the fastest supercomputer in the planet, that's one cluster. A supercomputer that you would build would take normally three years to plan. Right. And then they deliver the equipment and it takes one year to get it all working. Yes. We're talking about 19 days. Wow. What's the credit of the Nvidia platform, right? That it's the whole processes are hardened. That's right. Yeah. Everything's already working. Yeah. And of course, there's a whole bunch of, you know, ex algorithms and ex framework and ex stack and things like that.

51:08And we said we got a ton of integration we have to do. But the planning of it was extraordinary. Just pre -planning of it to, you know, end of one is right. Elon isn't end of one. But you answered that question by starting off saying, yes, 200 to 300 ,000 GPU clusters are here. Right. Does that scale to 500 ,000? Does it scale to a million? And does the demand for your products depend on it scaling to millions? That part, the last part is no. My sense is that distributed training will have to work. And my sense is that distributed computing will be invented. Right. And in some form of federated learning and distributed, you know, asynchronous distributed computing is going to be discovered.

52:06And I'm very enthusiastic and very optimistic about that. The, of course, of course, the thing to realize is that the scaling law used to be about pre -training. Now we've gone to multi -modality. We've gone to synthetic data generation. Post -training has now scaled up incredibly synthetic data generation, reward systems, reinforcement learning based. And then now inference scaling has gone through the roof. The idea that a model before it answers your answer had already done internal inference. Incredible. 10 ,000 times is probably not unreasonable. And it's probably done treaser, just probably done reinforcement learning on that.

52:52It's probably done some simulations, surely done a lot of reflection. It probably looked up some data. It looks some information, isn't it? So this context is probably fairly large. I mean, this type of intelligence is, well, that's what we do. That's what we do, isn't that right? And so the ability, this scaling, if you did that math, and you compounded it with, you compounded that with 4x per year, on model size and computing size. And then on the other hand, demand continues to grow in usage. Do we think that we need millions of GPUs? No doubt. Yeah, that is a for certainty now. And so the question is, how do we architect it from a data center perspective?

53:40And that has a lot to do with, are there data centers that are gigawatts at a time, or are there 250 megawatts at a time? And my sense is that you're going to get both. I think analysts always focus on the current architectural bet. But I think one of the biggest takeaways from this conversation is that you're thinking about the entire ecosystem, and many years out. So the idea that because Nvidia is just scaling up or scaling out, it's to meet the future. It's not such that you're only dependent on a world where there's a 500 ,000 or a million GPU cluster. It's by the time there's distributed training, you'll have written the software to enable that.

54:27That's right. Remember without Megatron, that we developed with some seven years ago now, the scaling of these large training jobs would have happened. And so we invented Megatron, we invented Nickel, GPU Direct, all of the work that we did with our DMA. That made it possible for easily to do pipeline parallelism. All the model parallelism that's being done, all the breaking of the distributed training, and all the batching, and all that. All of that stuff is because we did the early work. And now we're doing the early work for the future generation. So let's talk about strawberry and O1. I want to be respectful of your time.

55:12So we'll all the time in the world. Actually, well, you're very generous. Yeah, we've got all the time in the world. But first, I think it's cool that they named O1 after the O1 visa, which is about recruiting the world's best and brightest, and bringing them to the United States. It's something I know we're both deeply passionate about. So I love the idea that building a model that thinks and that takes us to the next level of scaling intelligence. Is an homage to the fact that it's these people who come to the United States by way of immigration that have made us what we are. Bring their collective intelligence to you.

55:51Surely an alien intelligence. Certainly. It was spearheaded by our friend, Noam Brown. Of course, he worked at Pluribus in Cicero when he was at Metta. How big of a deal is inference time reasoning? As a totally new vector of scaling intelligence, separate and distinct from, just building larger models. It's a huge deal. It's a huge deal. A lot of intelligence can't be done a priori. Right. You know. And a lot of computing, even a lot of computing can't be reordered. I mean, just, you know, out of order execution can't be done a priori. And so a lot of things can only be done in runtime. And so whether you think about it from a computer science perspective, or you think about it from intelligence perspective, too much of it requires context.

56:48The circumstance. Right. The quality, the type of answer you're looking for. Sometimes just a quick answer is good enough. It depends on the consequential impact of the answer. You know, it depends on the nature of the usage of that answer. So some answers, please take a night. Some answers take a week. Yes. Is that right? So I could totally imagine me sending off a prompt to my AI. And telling it, you know, think about it for night. Right. Think about it overnight. Don't tell me right away. Right. I want you to think about it all night. And then come back and tell me tomorrow what your best answer and reason about it for me.

57:30And so I think the, the, the, the quality, the segmentation of intelligence from now from a product perspective, there's going to be one shot versions of it. Right. Sure. Yeah. And then there'll be some that take five minutes, you know. And the intelligence layer that roots those questions to the right model. Yeah. For the right use case. I mean, we were using advanced voicemode and O1 preview last night. I was, I was coaching my son for his AP history test. And it was like having the world's best AP history teacher. Yeah, right. Sitting right next to you thinking about these questions, it was truly extraordinary.

58:09Again, my tutor is an AI today. Right. I'm sure of course they're here today, which comes back to this. You know, over 40 % of your revenue today is inference. But inference is about ready because of chain of reasoning. Yeah. Right. It's about to go up by a billion times. Right. By by by by a million X by a billion X. That's right. That's the part that most people have, you know, haven't completely internalized. This is that industry we were talking about, but this is the industrial revolution. That's the production of intelligence. That's right. Right. And yeah, it's going to go up a billion times.

58:47Right. And so, you know, everybody's so hyper focused on Nvidia as kind of like doing training on bigger models. Yeah. Right. Isn't it the case that your revenue if it's 50, 50 today, you're going to do way more inference in the future. Yeah. Right. Then I mean, training will always be important. But just the growth of inference is going to be way larger than we hope than training. We hope. It's almost impossible to conceive otherwise. Yeah, we hope. That's right. That's right. Right. Yeah, I mean, it's good to go to school. Yes. But the goal is so that you can be productive in society later. And so it's good that we train these models.

59:22But the goal is to inference them, you know. Are you already using chain of reason and, you know, tools like O1 in your own business to improve your own business? Yeah. Our cybersecurity system today can't run without without our own agents. We have agents help with design chips. Hopper wouldn't be possible. Blackwold may possible. Ruben, don't even think about it. We have digital. We have AI chip designers, AI software engineers, AI verification engineers. And we build them all inside because, you know, we have the ability and we rather we use the opportunity to explore the technology ourselves.

1:00:01You know, when I walked into the building today, somebody came up to me and said, you know, Ash Jensen about the culture. It's all about the culture. I look at the business, you know, we talk a lot about fitness and efficiency, flat organizations that can execute quickly, smaller teams. You know, Nvidia is in a league of its own, really, you know, about four million of revenue per employee, about two million of profits or free cash flow per employee. You've built a culture of efficiency that really has unleashed creativity and innovation and ownership and responsibility. You've broken the mold on kind of functional management.

1:00:39Everybody likes to talk about all of your direct reports. Is the leveraging of AI, the thing that's going to continue to allow you to be hyper creative while at the same time be inefficient? No question. I'm hoping that someday, Nvidia has 32 ,000 employees today. And we have four, we have 4 ,000 families in Israel. I hope they're well. Thinking of you guys. And I'm hoping that Nvidia someday will be a 50 ,000 employee company with 100 million, you know, AI assistants. And they're in every single group. We'll have a whole directory of AI's that are just generally good at doing things. We'll also have our inbox is going to fall of directories of AI's that we work.

1:01:35We know are really good specialized at our skill. So, AI's will recruit other AI's to solve problems. AI's will be in slack channels with each other. And with humans. And with humans. So, we'll just be one large employee base, if you will. Some of them are digital and AI, some of our biological. And I'm hoping some of them even make electronics. I think from a business perspective, it's something that's greatly misunderstood. You just described a company that's producing the output of a company with 150 ,000 people. But you're doing it with 50 ,000 people. That's right. Now, you didn't say I was going to get rid of all my employees.

1:02:17You're still growing the number of employees in the organization. But the output of that organization is going to be dramatically more. This is often misunderstood. AI will change every job. AI will have a seismic impact on how people think about work. Let's acknowledge that. AI has the potential to do incredible good. It has the potential to do harm. We have to build safe AI. Let's just make that foundational. The part that is overlooked is when companies become more productive using artificial intelligence, it is likely that it manifests itself into either better earnings or better growth or both.

1:03:08And when that happens, the next email from the CEO is likely not a layoff announcement. Of course. Because you're growing. Yeah. And the reason for that is because we have more ideas than we can explore. And we need people to help us think through it before we automate it. And so the automation part of it, AI can help us do. Obviously, it's going to help us think through it as well. But it's still going to require us to go figure out what problems do I want to solve. There are trillion things we can go solve. What problems does this company have to go solve? And select those ideas and figure out a way to automate and scale.

1:03:47And so as a result, we're going to hire more people as we become more productive. People forget that. And if you go back in time, obviously, we have more ideas today than 200 years ago. That's the reason why it's a piece of larger and more people are employed. And even though we're automating like crazy underneath. It's such an important point of this period that we're entering. One, almost all human productivity, almost all human prosperity is the byproduct of the automation and the technology of the last 200 years. You can look at from Adam Smith and Shumpeter's creative destruction, you can look at chart of GDP growth per person over the course of last 200 years.

1:04:31And it's just accelerated. Which leads me to this question. If you look at the 90s, our productivity growth in the United States was about 2 .5 to 3 % a year. And then in the 2000s, it slowed down to about 8%. And then the last 10 years has been the slowest productivity growth. So that's the amount of labor and capital or the amount of output we have for a fixed amount of labor and capital. The slowest we've had on record actually. And a lot of people have debated the reasoning for this. But if the world is as you just described and we're going to leverage and manufacture intelligence, then isn't it the case that we're on the verge of a dramatic expansion in terms of human productivity?

1:05:12That's our hope. That's our hope. And of course, we live in this world, so we have direct evidence of it. Right. We have direct evidence of it, either as isolated a case as an individual researcher for sure, who is able to with AI now explore science at such an extraordinary scale that is unimaginable. That's productivity. Right. 100 % Measure productivity. Or that we're designing chips that are so incredible at such a high pace. And the chip complexities and the computer complexities we're building are going up exponentially while the company's employee base is not measure of productivity. Correct.

1:05:58The software that we're developing better and better and better because we're using AI and supercomputers to help us, the number of employees is growing barely linearly. Okay. Okay. Another demonstration of productivity. I can go into, I can spot check it in a whole bunch of different industries. I could gut check it myself. Yes. You're right. That's right. And so I can, you know, and of course, you can't, we could be over -fed. But the artistry of course is to generalize what is it that we're observing and whether this could manifest in other industries. And there's no question that intelligence is the single most valuable commodity the worlds are known.

1:06:43Right. And now we're going to manufacture it at scale. And we, we, all of us, have to get good at, you know, what would happen if you're surrounded by these AIs. And they're doing things so incredibly well and so much better than you. Right. And when I reflect on that, that's my life. I have 60 direct reports. Right. The reason why they're on, the reason why they're on E -staff is because they're world -class of what they do and they do it better than I do. Much better than I do. Right. I have no trouble interacting with them. And I have no trouble prompt engineering them. Right. You guys got to start.

1:07:22I have no trouble programming them. Right. Right. And so, so I think that that's, that's the thing that that people are going to learn is that they're all going to be CEOs. Right. They're all going to be CEOs of AI agents. Right. And their, their ability to have the creativity, the will, the, the, the, the, the, the, the, and some knowledge and how to reason break problems down so that, so that you can program these AIs to help you achieve something like I do. Right. That's called running companies. Right. Now, it's, you mentioned something, this alignment, the Safe AI. Right. You mentioned the tragedy going on in the Middle East.

1:08:09You know, we have a lot of autonomy and a lot of AI that's being used in different parts of the world. So let's talk for a second about bad actors, about Safe AI, about coordination with Washington. How do you feel today? Are we on the right path? Do we have a sufficient level of coordination? You know, I think Mark Zuckerberg has said the way we beat the bad AIs is we make the good AIs better. Is how would you characterize your view of how we make sure that this is a positive net bit, benefit for humanity as opposed to, you know, leaving us in this dystopian world without purpose? The conversation about safety is really important and good.

1:08:53Yes. The abstracted view, this conceptual view of AI being a large, giant, neuro network, not so good. Right. Right. Okay. And the reason for that is because, because as we know, artificial intelligence and large language models are related, not the same. There are many things that are being done that I think are excellent. One, open sourcing models so that the entire community of researchers and every single industry and every single company can engage AI and go learn how to harness this capability for their application. Excellent. Number two, it is under celebrated the amount of technology that is dedicated to inventing AI to keep AI safe.

1:09:41Yes. AI is to carry data to carry information to train an AI. AI created to align AI synthetic data generation AI to expand the knowledge of AI, the cause of to hallucinate less. All of the AI's that are being created to, for vectorization or graphing or whatever it is, to inform an AI, guard railing AI, AI's to monitor other AI's, that the system of AI's to create safe AI is under celebrated. Right. Then we've already built. That we're building everybody all over the industry. The methodology is to red teaming the process, the model cards, the evaluation systems, the benchmarking systems, all of the harnesses that are being built at the velocity that's been built is incredible.

1:10:36I wonder if they are. Under celebrated, do you guys understand? Yes. You know, the world's the thing. And there's no, there's no government regulation saying you have to do this. Yeah. This is the actors in the space today who are building these AI's are taking seriously and coordinating around best practices with respect to these critical matters. That's right. Exactly. And so that's under celebrated, under understood. Yes. Somebody needs to to, well, everybody needs to start talking about AI as a system of AI's and system of of engineered systems, engineered systems that are that are well engineered, built from first principles, well tested, so on and so forth.

1:11:16Regulation. Remember, remember AI is a capability that can be applied.

1:11:27It's necessary to have regulation for important technologies. But it's also don't overreach to the point where some of the regulation ought to be done, most of the regulation ought to be done at the applications. The FAA, NITSA, FDA, you name it, right? All of the different, all of the different ecosystems that already regulate applications of technology. Now have to regulate the application of technology that is now infused with AI. And so I think, I think there's, don't misunderstand, don't overlook the overwhelming amount of regulation in the world that are going to have to be activated for AI.

1:12:14And don't rely on just one universal galactic, you know, AI council that's going to possibly be able to do this because there's a reason why all of these different agencies were created. There was, there was a reason why all these different regulatory bodies were created and go back to first principles again. I'd get in trouble by my partner Bill Gurley if I didn't go back to the open source point. You guys launched a very important, very large, very capable open source model. Yeah. You were trying recently. Obviously, meta is making significant contributions to open source. I find when I read Twitter, you know, you have this kind of open versus closed, a lot of, a lot of chatter about it.

1:12:59How do you feel about open source, your own open source models, ability to keep up with frontier? That would be the first question. The second question would be, is that, you know, having that open source model and also having closed source models, you know, that are powering commercial operations? Is that what you see into the future and do those two things? Does that create the healthy tension for safety? Open source versus closed source is related to safety, but not only about safety. You know, so for example, there's absolutely nothing wrong with having closed source models that are the engines of an economic model necessary to sustain innovation.

1:13:45Okay, I celebrate that wholeheartedly. Right. It is, I believe, wrong -minded to be closed versus open. It should be closed and - Last open. Yeah, right. Because open is necessary for many industries to be activated. Right now, we didn't have open source. How would all these different fields of science be able to activate be activated on AI? Right. Because they have to develop their own domain -specific AI's and and they have to develop their own using open source models create domain -specific AI's. They're related, again, not the same. Right. Just because you have an open source model, there's a mean of an AI.

1:14:25And so you have to have that open source model to enable the creation of AI's. So financial services, health, care, transportation, delusive industries, fields of science that has now been enabled as a result of open source, unbelievable. Are you seeing a lot of demand for your open source models? Our open source models, so first of all, Lama downloads, right? Obviously, yeah, Mark and the work that they've done, incredible. Off to charts. And it completely activated and engaged every single industry, every single field of science. Right. It's true. The reason why we didn't nimotron was for synthetic data generation.

1:15:05Intuitively, the idea that one AI would somehow sit there and loop and generate data to learn itself, it sounds brittle. And how many times you can go around that infinite loop, that loop, you know, questionable. However, it's kind of my mental image is kind of like, like a, you get super smart person, put him into a padded room, close the door for about a month. You know, what comes out is probably not a smarter person. So, but the idea that you could have two or three people sit around and we have different AIs, we have different distributions of knowledge, and we can go QA back and forth. All three of us can come out smarter.

1:15:51And so the idea that you can have AI models, exchanging, interacting, going back and forth, debating, reinforcement learning, synthetic data generation, for example, kind of intuitively suggests and makes sense. And so our model, nimotron 350B is, 340B is the best model in the world for reward systems. And so it is the best critique. Okay. Interesting. Yeah. And so, fantastic model for enhancing everybody else's model. Irrespective of how great somebody else's model is, I'd have recommend using nimotron 340B to enhance and make it better. And we've already seen Mates, Nama better, made all the other models better.

1:16:38Well, we're coming to the end. Thank goodness. As somebody who delivered DGX1 in 2016, it's really been an incredible journey. Your journey is unlikely and incredible at the same time. Thank you. You survived. Just surviving the early days was pretty extraordinary. You delivered the first DGX1 in 2016. We had this Cambrian moment in 2022. So I'm going to ask you the question, I often get answer, get asked, which is how long can you sustain what you're doing today? With 60 direct reports, you're everywhere. You're driving this revolution. Are you having fun? And is there something else that you would rather be doing?

1:17:39This is a question about the last hour and a half. The answer is, I great time. I great time. I couldn't imagine anything else I'd rather be doing. Let's see. I don't think it's right to leave the impression that our job is fun all the time. My job is fun all the time. Do I expect it to be fun all the time? Was that ever an expectation that it was fun all the time? I think it's important all the time. I don't take myself too seriously. I take the work very seriously. I take our responsibility very seriously. I take our contribution and our moment in time very seriously. Is that always fun? No.

1:18:28But do I always love it? Yes. Like all things. Whether as family friends, children, is it always fun? No. Do we always love it? Absolutely, deeply.

1:18:45How long can I do this? The real question is, how long can I be relevant? That piece of information, that question can only be answered with how am I going to continue to learn? I am a lot more optimistic today. I'm not saying this simply because of our topic today. I'm a lot more optimistic about my ability to say relevant and continue to learn because of AI. I use it, I don't know, but I'm sure you guys do. I use it literally every day. There's not one piece of research that I don't involve AI with. There's not one question that I even if I know the answer, I double check on it with AI. Surprisingly, the next two or three questions I ask it reveals something I didn't know.

1:19:40You pick your topic. You pick your topic. I think that AI is a tutor, AI is an assistant, AI as a partner to brainstorm with. Double check my work. You know, boy, you guys, this is completely revolutionary. I'm an information worker. My output is information. I think the contributions that all have on society is pretty extraordinary. If that's the case, if I could stay relevant like this and I can continue to make a contribution, I know that the work is important enough for me to want to continue to pursue it. My quality of life is incredible. I can't imagine you and I been at this for a few decades.

1:20:35I can't imagine missing this moment. The most consequential moment of earth's years. We're deeply grateful for the partnership. Don't miss the next 10 years. For the thought partnership, you make it smarter. I think you're really important as part of the leadership that's going to optimistically and safely lead this forward. Thank you. Really enjoyed it. Really. Thanks, Brad. Thanks, Clark. Good job.

1:21:09As a reminder to everybody, just our opinions, not investment advice.