Uncapped #57 | Andrew Feldman from Cerebras

Andrew Feldman is the co-founder and CEO of Cerebras Systems, the AI chip company he founded in 2016 around a single radical insight: that winning in compute requires not incremental improvement but a fundamentally different architecture. Cerebras is the creator of the world's largest chip, the Wafer Scale Engine, and counts the US government, sovereign cloud providers, and OpenAI among its customers. Alongside Eric Vishria from Benchmark, we discussed why Andrew believes that if you are going to attack Goliath, being 10% or even twice as good is not an available strategy and you have to aim for 100x or 500x better. Andrew walked through Cerebras's near-death experience: 18 months of board meetings where the only thing to report was "still can't make it," spending $8 million a month, and what kept the team going. He explained how we go from sand to a ChatGPT answer and why the US semiconductor supply chain is in a precarious position. Andrew shared what most people get wrong about what makes Nvidia great (it’s not CUDA), and why he thinks of himself as a professional David in an ongoing battle with Goliath. Timestamps: (0:00) Intro (1:07) Why Andrew started Cerebras in 2016 (2:54) Eric on why he invested despite having no chip experience (4:10) Attacking Goliath (9:44) Near-death experiences and the Valley of Death (10:44) 18 months of "still can't make it" (12:16) Solving a 75-year-old compute problem (13:26) What comes after Wafer Scale (16:19) The chip supply chain explained (22:30) Why the US punted a strategic industry (25:51) How to be a good hardware board member (27:50) Hardware vs. software investing (29:05) The pivot from training to inference (27:00) Specialization vs. flexibility (35:22) Young product leaders and seasoned hardware engineers (39:14) External relationships and TSMC (42:20) The AI infrastructure buildout (44:12) The data center supply chain (53:14) Speed creates markets (54:10) Disaggregation with AMD and AWS (55:24) What actually makes Nvidia great (57:30) Near-death experiences and the DNA of a Goliath fighter (59:58) Andrew's childhood next to William Shockley Links: https://x.com/andrewdfeldman https://x.com/cerebras https://x.com/ericvishria https://x.com/jaltma https://uncappedpod.com/ [email protected]

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  You have board meetings every six weeks. All you've got to say is, still can't make it. Right? That's the board meeting. I mean, what else are we going to talk about? Still can't make it again and again.

• 0:10Jack Altman?

  And they're just like, should I get in? Can I do anything to help?

• 0:13Speaker 00

  That's right. They're like, can we help? Like, no. But we always believed that if we could make it, there would be huge demand for it.

• 0:24Jack Altman?

  All right. Super excited to be doing this today. Andrew, thanks a bunch for being here. So Andrew, you're the CEO and founder of Cerebras, which is obviously one of the most important chips companies. And also really happy to have my partner Eric here, who's going to be kind of half host, half guest, who has been working with Andrew since the beginning. But thank you both for making time. What I want to start with is it's 2026 now. And obviously Cerebras is very important in the sort of AI landscape. But you started the company in 2016 and AI was not what it is now back then. So I guess what was the kind of headspace then? What was the idea, the insight? Like, what were you, what were you building when you started the company? And what was the thinking?

• 1:07Speaker 00

  Well, Jack, thanks for having me. It's always fun to hang out with Eric. So appreciate you having me.

• 1:14Speaker 00

  I think as a computer architect, when you see a new workload on the horizon, you get excited. Right? It's very, very difficult to win share in a mature market in compute. And so when something new emerges, you get excited and you lean forward and you say, well, can I make it faster? Can I build a chip that is better at this work? And then you ask the other question is, is there enough of this work to justify building a chip that's better? So you have two questions. Can I and should I? And what we saw with AI was an extremely computationally intensive workload.

• 1:56Speaker 00

  Unlike, for example, the rise of ARM processors for cell phones, that they weren't computationally intensive. They were power intensive. Right? We saw a problem that was going to be hard on compute. And we saw that what was being used was an architecture that was being remodeled for it. And that we could build something better. And that was sort of the insight. And then it was a long, hard slog.

• 2:30Jack Altman?

  Before we go to the long, hard slog, Eric, obviously for you, like as an investor, you know, you were not, you know, also in this AI era, but you saw something that made you invest. And you were, I think, you know, previously not a chips investor and you've done more like-

• 2:44Speaker 02

  And probably never will be again.

• 2:45Jack Altman?

  Probably never will be again. Like, would you do that every time again? Was there something that made it clear to you? Or was it like, what happened for you when you made the investment?

• 2:55Speaker 02

  I think one of the scariest things about venture, and I think about this all the time, is the more you work on companies and you see the challenges and you see how hard it is, you build up scar tissue. And then the thing is like, well, you should do it again, but like, will you do it again? And I ask myself that all the time. Like, you actually need some naivete. Like, you need to have this like, oh, can do attitude and this like naivete around it to attempt it. Like, otherwise you just don't do it again. And I've definitely built up a bunch of scar tissue.

• 3:33Speaker 02

  And I don't know, had I actually had any idea how hard it was going to be for them, like how much science and technology had to be built? How many innovations? How many times like the company was going to come to the-

• 3:50Speaker 00

  Don't tell your VCs how hard your shit is. The moral of the story is, tell them it's no problem. Keep it inside, push it down, push it down. Don't tell them.

• 4:00Jack Altman?

  I'm doing great.

• 4:00Speaker 00

  It's a hard problem, all's good, man.

• 4:02Jack Altman?

  Yeah. So what happened? So you basically, you know, you start the company, you get to the tape out and then you're done, right? It's like that?

• 4:08Speaker 00

  No, never.

• 4:09Jack Altman?

  So how's it go? So what happened?

• 4:10Speaker 00

  I think we had a, you know, this isn't our first chip company. This is my fifth. And the team had been together, the founding team had been together at the last one. And we had some really clear sort of philosophies on what you should do. And I, our view is that if you're going to attack Goliath, if there's a giant standing in the market, like Nvidia was even at that time, that being a little bit better or a little bit cheaper is not an available strategy. Right? They had high margins. So if you come in and even if you're two thirds the price, they can just cut costs. They can just charge less. They can bundle it.

• 4:55Speaker 00

  They can do 100 other things. What that means is you have to go out with something way better. 10, 100, 500 times faster. You have to come up with a product that has a value proposition that even if the other guys give it away, all right, they can't compete. And so that's sort of one direction of our thinking. And the other building off that is we want to do hard things that produce that advantage. Right? That we want all that hard stuff within the building because that's under our control. All the other stuff's not under our control. But if we can build something that is so fast, nobody else can do it. You can't give away your parts to achieve what we can achieve.

• 5:42Speaker 00

  Then you're on to something. And the only way to do that, in our opinion, is radical innovation. You can't incremental your way to vastly better.

• 5:51Jack Altman?

  So what has to be in your control?

• 5:53Speaker 00

  You have to have all aspects of the design, the implementation. You have to understand the manufacturing. You have to understand every part. And for us, that meant chip, board, system, software, all the way up to the API. And that was not easy. That makes it more expensive. It makes it take longer. It means there are more, more ways to fail there. When you do radical innovation, there are no vendors waiting for you. Right? When you build a chip the size of a dinner plate, you can't go to a catalog and find a heat sink. Right? Because nobody'd ever built one bigger than a postage stamp.

• 6:37Speaker 00

  So nobody has stuff ready for you. And so you end up investing an enormous amount of time in building things that support your thing. Right? The surrounding components. But the result of that is, is you develop this extraordinary expertise. We didn't start world leaders in packaging. We're right now the best in the world at packaging. We earned it, failure after failure, year after year, until we got it. And so what you want to do is you want to think about sort of

• 7:10Speaker 00

  how you can innovate across the various elements of a full solution and push as hard as you can.

• 7:20Speaker 02

  I think like Andrew taught me this in semis and everything else, just to like make this like what you need to be shooting for, concrete. So if you think of whoever the incumbent is in whatever market, you know, they're getting, say, twice as good every year. In a market like this, it's very dynamic. For a new company to get to scale, to start to get to scale, it's going to take five years at least. Let's say with everyone moving as fast as it can, AI tools, you nail everything. First tape out works, first bring up works. You know, everything goes right five years. Okay. To like, to get to scale. And so you're basically at two to the fifth. So that puts you at 32X. And then you need at least a multiple advantage of that.

• 8:06Speaker 02

  So you'd say 3X. So you're basically at 100X. So one of the things that happens when you hear a lot about these new ideas is they're like, hey, we're going to be 50% better. We're going to be two times as better. But what they are, they're like, once we have this design, it's going to be even 10 times better than what exists today. But what exists today isn't the target. Because if you're looking at the companies that are out there right now,

• 8:33Speaker 02

  these big companies with a lot of resources are actually doing, they're doing great work. Everyone's, Nvidia is advancing the ball materially every year, like very significantly.

• 8:42Speaker 00

  Nvidia and Google and Tranium, they're moving AMD. I mean, they're moving the ball. And so you got to aim at 100, 500,000 times better. If you're going to arrive.

• 8:54Jack Altman?

  Yeah.

• 8:54Speaker 00

  If you're going to intersect the market ahead of them.

• 8:57Speaker 02

  And so that, that, just like when you realize that, you're like, oh, wait, it can't be small changes in something. There has to be a very big underlying, like architectural change, a big thought. Like, there has to be something materially different, whether it's way for scale, SRAM, whatever it is. Like, those things matter.

• 9:19Speaker 00

  You can't get there with a collection of modest improvements. Right? Your biggest competitor buys silicon for less than you. They buy manufacturing capacity for less than you. Right? They have, they probably pay less for their EDA tools. And so you can't go at them, you know, you can't run at Goliath straight on. I mean, you got to think about how you can deliver something profoundly different.

• 9:44Jack Altman?

  What were the, like, along the way after some of these like early moments? I know that the company had like, you know, some near death experiences or whatever. Like, what were the, what were sort of the hard hurdles to get through?

• 9:55Speaker 00

  I had some near death experiences.

• 9:59Speaker 00

  Wait, you can't build the thing you're supposed to build? Yeah.

• 10:02Jack Altman?

  Like, what are those, like, how would you frame what those periods are where you're like, we just need, I don't know. Was it a certain amount of time and a certain amount of capital? And it's just going to be valley of death? Is it that you just don't know whether you're going to make a technical breakthrough? Like, what are the things that lead to those?

• 10:17Speaker 00

  I think in our space, we were always confident if we could make it with selling. Right? There are sort of two axes in our life. There is, can you make it? And the other axis is, can you sell it? Right? And we always believed that if we could make it, there would be huge demand for it. I think nobody had ever done way for scale. There was a whole sort of collection of people who were saying it could never work. And we were unsure too. I mean, we believed we could do it, but there was no evidence. And there was a period of time, about eighteen months, and we couldn't make it.

• 11:03Speaker 00

  And we're spending about eight million a month. You have board meetings every six weeks. All you've got to say is, still can't make it. Right? That's the board meeting. I mean, what else are we gonna talk about? Still can't make it again and again and again. And I give a huge, right, right.

• 11:21Jack Altman?

  And they're like, should I get in? Can I do anything to help?

• 11:24Speaker 00

  That's right. They're like, can we help? I was like, no. There's nothing, nothing that can be done.

• 11:29Jack Altman?

  Nothing to be done.

• 11:30Speaker 00

  Nothing to be done. But we believed because we, we had ideas still. We hadn't run out of ideas. And each time we, we built it and it failed, we'd go through sort of good engineering practice. We'd do a full failure analysis. We'd understand it. We would. And we wouldn't fail that way again.

• 11:53Jack Altman?

  All new mistakes.

• 11:54Speaker 00

  All new mistakes. So we, we sort of had a, that, that was sort of our mantra. Only new mistakes, right? Only new failures. And over time, we, we could see progress. I mean, in the beginning, we were shattering wafers, right, in seconds. And then it took minutes. And then we had one run for, for an hour. And then we shattered seven minutes again. We, we, we built back up to the point, we had a day in sort of July of, of two thousand nineteen, where we were running and temperature was, was flat. And we just stood there in a tiny little office that had been converted into a lab. And we drilled a hole in the wall to suck the air out.

• 12:38Speaker 00

  Stared at a server, which is about as exciting as looking at, at paint dry. Said, holy crap. We've solved this problem that nobody in seventy-five years of compute had ever solved. And that was one of the great minutes of my life.

• 12:54Jack Altman?

  That's cool. When you look forward, like if you, so, you know, there's the wafer scale. And then there's everything around that, the packaging and, and, and cooling and powering it, all these things. But, you know, when you look at the R and D envelope that you have going forward over the next five years. I, I know there's like things you're excited about, but like, where do you take, like, where do you take this like giant thing? So what could be the next wafer scale innovation?

• 13:27Speaker 00

  I, I think if you simplify

• 13:33Speaker 00

  what we build down to its most fundamental elements.

• 13:38Speaker 00

  A computer is built of, of, of three things. We do calculations. We store the results. And then we move the results to where they're useful. So we, we build a core that does the calculation. We use memory and we move data to and from memory where we store the results. And then we have I O. And that, that, that's how we ship the results to somewhere where it's useful. I, I think if, if you're in the computer business like we are for A I, you better be working on all three. You gotta be thinking about how to make your cores faster, how to make them tuned for A I. But general enough to, to withstand the innovation happening in A I.

• 14:23Speaker 00

  You better be thinking about memory and both capacity, how much you can store and how fast you can get data on and off it. And then you gotta be thinking about how you get the results off your chip and somewhere where they're useful. And that's your I O. And so we're working on all three of those. And we have programs with the, with the U S government already, big programs where we're thinking about how to stack memory. Right? How, how to put H B M on, on, on to an S RAM based wafer. And what this does is it, it enables H B M to behave like S RAM, which is exactly what everybody wants. You get the, the capacity of H B M and the speed of S RAM, right?

• 15:07Speaker 00

  We're, we're working on, on optical wafer stacking. So you put an optical switch onto a wafer. This would change the world.

• 15:19Speaker 00

  You know, Jack Dungara said, and he, he's sort of one of the, the pioneers in, in big computing. He said, we've, we've been better at making flops and moving flops. Right? And that, that, that's, that's the I O part. And so thinking about how to, how to solve that problem by bringing optical switching smack up against compute i- is something that, that we also have large government contracts for and we're enormously excited about. So, you know, you gotta get faster. You gotta find ways to store more and get to memory faster. And you gotta find ways to move that, move those results a- at ten, hundred, a thousand X faster to where they're useful.

• 15:59Jack Altman?

  I wanna go to a bit broader in the supply chain. And, you know, I, I think it's kind of like well understood right now that A I is very supply constrained. But I, like probably many other people, I wouldn't say I have like a perfect understanding of the supply chain. And one of my favorite things to do on this podcast is you get a, you know, successful person like you. And because there's cameras on, I get to ask you a really simple question. And I get to, you know, be humored with it. So could you explain sort of in a somewhat simple way? How do we go from like sand to a chat G P T answer? Dude, it's just T S M C.

• 16:38Speaker 00

  There's a black box. Call that T S M C. By another black box called A S M L. And out the other end comes, comes great.

• 16:45Jack Altman?

  We're supply constrained, but I don't know what the supply. Can you teach me what the supply, how it works?

• 16:50Speaker 00

  Yeah, I, I think the first thing to, to, to think about is that, that, that a fab, and especially a, a fab that, that builds at cutting edge geometries is a, is a modern pyramid. It's one of the greatest things humans make.

• 17:05Jack Altman?

  What is it?

• 17:06Speaker 00

  It is a collection of machines that, that take a chunk of silicon and use a photolithographic process in which they etch transistors into that piece of silicon. Such that when you deliver power to it, they do calculations. And it, it is a collection of, I mean, this is a factory. It's just a reasonable way, a real factory.

• 17:32Speaker 00

  It costs forty or fifty billion to make. It has a four or five year lifetime.

• 17:36Jack Altman?

  How big is it?

• 17:38Speaker 00

  Football fields.

• 17:39Jack Altman?

  Okay.

• 17:39Speaker 00

  When they, when they, when Samsung was building a factory in Texas, you, they began building a power plant. The power plant was used to make concrete. They ran concrete trucks, hundreds of concrete trucks, seven by twenty-four for years to pour enough concrete to build the foundation on which to put the, the fab. Right? That, that, these are unbelievably complicated things.

• 18:03Jack Altman?

  So A S M L makes the machines?

• 18:04Speaker 00

  A S M L makes, makes a, the, the machine that does the photolithography.

• 18:11Jack Altman?

  What's that machine like?

• 18:13Speaker 00

  It's the size of, each machine is, what is it? Fifty or sixty feet long and twenty feet high.

• 18:19Jack Altman?

  Costs what? Half a billion?

• 18:21Speaker 00

  Yeah, they're, they're expensive.

• 18:25Speaker 00

  What's interesting is that they sell the same machines to different fabs. And fabs use them in different ways and are able to do different things with T S M C able to, to, to, to achieve things that others can't.

• 18:38Jack Altman?

  And can anyone else make these machine? Is it really just A S M L that can make these machines?

• 18:41Speaker 00

  Right now, it's only A S M L.

• 18:43Jack Altman?

  In the world?

• 18:44Speaker 00

  In the world. That's weird, isn't it?

• 18:47Jack Altman?

  Yeah.

• 18:47Speaker 00

  Yeah. I mean, there, there are very few things that, that, this is a true monopoly, not born of, you know, what De Beers did, which was try and control supply. They have technology that others haven't been able to, to replicate.

• 19:03Jack Altman?

  Are we supply constrained on lithography machines?

• 19:07Speaker 00

  Probably not. I, I think the, the, the challenge in our business is that, that demand moves extremely quickly. And if you have looked at sort of a great company like Nvidia's demand, it's, it's exponential. But you, you, you can't build fabs that fast. If a fab takes five years to build. Right? And you see demand increasing. There's no way anybody would have expected demand to, to shoot up the way it has for chips. They're always behind.

• 19:33Jack Altman?

  Yeah.

• 19:34Speaker 00

  And they're investing huge capital blocks in, in, in the, the construction of these facilities. And demand is, is racing past the ability to make these huge five year bets. And we have the same problem right now at data centers, right? A I is moving at the speed of software and data centers are moving at the speed of real estate.

• 19:56Speaker 00

  And that, that's why we're behind. That's just it.

• 19:58Jack Altman?

  Good articulation. That's good. Okay, so T S M C buys the lithography machine?

• 20:02Speaker 00

  Right. So T S M C buys lithography machines. They have decades of experience. They, they wrap it together. They take an ingot, which is a, a tube of silicon. It's sliced into discs. We call wafers. The wafers are sent through the process. The process etches through a lithographic process, transistors into the silicon. It moves through the process. They're cut. We call it diced, or in our case, they're not cut. Out the other end comes chips. And what's amazing about this is you take sort of some of the smartest people you've ever met. And you put them on a project for two years.

• 20:42Speaker 00

  And you send the, the, the, the, the results to T S M C. And they run it through a factory that costs forty or fifty billion to make. And took years to make. And out the other end comes a twenty-two dollar chip.

• 21:04Speaker 00

  It blows your mind what you think about it. Each one of these took sort of the smartest people in their field.

• 21:12Jack Altman?

  It's the most impressive thing humans have ever done.

• 21:15Speaker 00

  Right, it's among the most impressive.

• 21:16Jack Altman?

  And a burrito is $23.

• 21:17Speaker 00

  That's right, right, exactly, right. And you go down the street here in the Mission District and you pay $17 for a burrito.

• 21:23Jack Altman?

  That's right.

• 21:23Speaker 00

  Right, and so.

• 21:24Jack Altman?

  And but once you have the chip, you're done, right? Then you're good.

• 21:27Speaker 00

  No, once you have the chip, you've you've solved about a a fourth of your problem.

• 21:32Jack Altman?

  Okay, but TSMC.

• 21:34Speaker 00

  Yes.

• 21:34Jack Altman?

  They operate them, they they receive a design, they operate the machine, they give you the chip.

• 21:39Speaker 00

  Yes.

• 21:40Jack Altman?

  But that itself seems like not replicable, like we don't have fabs like this in the US.

• 21:45Speaker 00

  We have them run by TSMC and by Samsung.

• 21:47Jack Altman?

  So what.

• 21:48Speaker 00

  And Global Foundries have got one too.

• 21:50Jack Altman?

  How much of the bottleneck is at the TSMC level?

• 21:53Speaker 00

  Huge amount.

• 21:54Jack Altman?

  Okay, why?

• 21:55Speaker 00

  Huge.

• 21:55Jack Altman?

  Is that because of the speed of building fabs?

• 21:57Speaker 00

  It's because the demand has outpaced the the ability to build fabs. And it's not like, it's it's not like a

• 22:06Speaker 00

  an apartment building where there are hundreds of builders you could choose to build your apartment building. The actual making of the fab is skills that only TSMC has. And so and even there, they can't do 12 at once, right? The the the skills to make these things are so rare and in so few hands that that you can't just knock them out, it's not a cookie cutter. And and that's why we we don't have enough of them right now. And the fact in the US, sort of three decades of bad policy, pushed the fabs away. When the fabs left, the tool vendors, the the collection of of vendors who who provided services to them,

• 22:51Speaker 00

  they all left. The next step in the process called packaging, and those are companies like Amcor and ASE, they left. And we just punted a strategic industry. And we we gotta do better, that that was, that's not smart.

• 23:06Jack Altman?

  Yeah, so what do you think should happen there, like like?

• 23:08Speaker 00

  Well, I think I think we should sit down with Global Foundries and TSMC and Samsung and have a 20-year period where we waive all local ordinances to allow them to build fabs. I I I think this is about building US domestic fab capacity.

• 23:27Jack Altman?

  Yeah.

• 23:28Speaker 00

  Um, I I think, I mean, when when a a big ship, right, tried to parallel park in the Suez Canal, right? We were delayed in chips and we couldn't buy washing machines.

• 23:41Jack Altman?

  Yeah.

• 23:42Speaker 00

  Right? If we lost our chip capacity, it would be catastrophic for our for our industry. Not just for our industry, but for the country, for the economy.

• 23:53Jack Altman?

  Eric, how did you as a board member, when you were like learning this stuff as you went? You still obviously, you know, I don't think Andrew's been lying to me, I think you've managed to be very helpful, like what?

• 24:03Speaker 00

  Eric was extremely helpful.

• 24:04Jack Altman?

  So like, how did you approach this?

• 24:08Speaker 02

  Um, I have a very small circle of competence. It is not any of the stuff that he talked about. If he if he if you asked me to explain what packaging is right now, I could not do it. I certainly could not do it in an adequate way. And Andrew explains things to me all the time.

• 24:23Speaker 02

  And, you know, so I think it's important just to be like, hey, this is where you what you can do and this is what you can't do. You know, and there are periods of time in any company, particularly if you're doing technological innovation, where there's just like, there's just, you just have to let the engineers engineer and like and the scientists do their thing and and and stay out of the way and keep them financed. Maybe keeping them financed was very important, I guess, for a huge part of it.

• 24:48Speaker 00

  There are a couple of things that that made Eric a good board member and I think that are foundational in being a good member, right? Here, you don't know about everything and and share and make us better in those domains where you're a real expert. And don't talk about those other domains, right? That sometimes there's a lot of words and and we were lucky, we had a really good board and everybody knew what they were good at. And they they helped us in those domains in which they had real expertise, right? I mean, one of the the advantages of of of the venture world is you you can see across an industry, right? When when we're deep in it, we're going deep, they can see wide, right?

• 25:28Speaker 00

  But there weren't efforts by the board to try and solve technical problems, they they didn't have, that that wasn't their their expertise.

• 25:36Speaker 00

  Talking about how we might finance a company, thinking about all sorts of other things, they were enormously helpful and they were patient. And I I think they asked thoughtful questions and

• 25:49Speaker 00

  our board member, our our our board meetings made us better.

• 25:52Jack Altman?

  It's like, what what what what should a board do in a hardware company in general? When, you know, you expect that there's going to be much less to contribute on the product? Is it financing and recruiting and?

• 26:03Speaker 00

  I I I think the the question when you've got a long hard project, right? And this is the opposite of SaaS, right? You you raise money and then you spend two or three years to build one before you have any real idea what the customer is going to say. You go and you talk to customers and they say, yeah, yeah, that sounds great. Because who's not going to say, yeah, yeah, that sounds great, right? And and so the product management is unbelievably difficult. You survey customers and because it's no cost and it's easy to say, yeah, yeah, this is great. Nobody wants to to to sort of put their foot on the throat of of somebody else's idea, right? They say, yeah, yeah, it's great. It takes you three years to to to two and a half years to get to the point where you can bring it to a customer.

• 26:46Speaker 00

  I think what you can do as investors is understand that trajectory, understand that your first chip is very rarely a good one. And it's a second or third one. I mean, even really strong teams like Google's TPU team, the fourth one was good. The first two were, oh, yeah, the the fourth, fifth were really good parts. It takes years. You have to to know that going in, you have to know it's a long game, right? Ask questions related to the long game, right? Are you hiring the right people, right? Are you thinking about this in the, is it, have you made the right decision between specialization and flexibility? Right, th- these are questions that that can really help sharpen our thinking.

• 27:30Speaker 00

  But, you know, wh- whether to use this one technique or this other technique in in in design, whether to use

• 27:42Speaker 00

  one tool vendor or the other, you you gotta let that, you gotta let the team.

• 27:45Jack Altman?

  Yeah.

• 27:46Speaker 00

  Team pick that.

• 27:48Jack Altman?

  Did it feel really different for you versus like an infrastructure role?

• 27:51Speaker 02

  Yeah, totally, it's just very different. I mean, your time to revenue is so much longer, the revenue comes in like giant chunks. It it, um, the initial customers, you know, we had US government customers, then we had, you know, huge.

• 28:06Speaker 00

  Sovereign Cloud.

• 28:07Speaker 02

  Sovereign Cloud and G42, you know, then you have now today, obviously, OpenAI. And so you have these like things, your so your customer concentration is more, it it just like every dynamic is different.

• 28:18Jack Altman?

  Yeah.

• 28:19Speaker 02

  The investment and go to market is so much less. So I think it it it feels really, really different. The things that are the same, by the way, are you need really good people from a whole bunch of domains coming together who stay motivated over a really long period of time. That part's actually the same. And and then the market is very dynamic, like, I mean, think about it. Pre-Transformer, you started the company pre-Transformer.

• 28:45Speaker 00

  We did.

• 28:46Speaker 02

  Right, at that time, TensorFlow was dominant.

• 28:49Speaker 00

  TensorFlow was dominant, ResNet was was still a thing.

• 28:53Jack Altman?

  Right.

• 28:54Speaker 00

  Um, th- these were very small, very simple networks compared to where we are today.

• 29:02Speaker 00

  Yeah, the the actual ML, the AI, looked nothing like it does today.

• 29:07Speaker 02

  Well, actually, I think that's really, you know, for entrepreneurs who who listen to this, like, it was started as a training system, like part of the vision that you pitched and part of what you explained was like, training is a harder problem because of backprop. Inference, we thought, was going to be on devices and computers and really distributed, which may still end up happening. And like, fast forward to to 2022, 2023, maybe 24, we pivoted the company. Or I don't know if pivot is the right word, but like to to inference, where how and why? And where did we get lucky and where where did you have real foresight on that?

• 29:45Speaker 00

  I think there is sort of a.

• 29:50Speaker 00

  There are two ways to make decisions and to think about it. One, I think about like an old telephone circuit, right? They set up a dedicated view all the way to your brother in New York. And that's one way to do vision, right? That's one way, that you've got this idea of the way the future is going to look way out there. And that's a circuit, we think.

• 30:19Jack Altman?

  Right.

• 30:20Speaker 00

  The other way to do it is the way routers work, where the internet works, where you go hop to hop. You go to Cleveland and then there's a decision whether it's best to take the next hop. And that's sort of the way we worked, where we knew there was a pot of gold out there. But the path to it, we knew was unknowable. And so each time you get over a new mountain, you look around and you re-decide. So it's this sort of hop to hop thinking. So can we build it? Yes. Can we make it work in routing? Yes. Is routing now where everybody is focusing? That's where they're focusing. Can we be a fast router? Yes.

• 31:04Speaker 00

  But now we're seeing the lay of the land. We're seeing the sort of unfolding of AI, the rapid growth of intelligence. Well, who's going to use it?

• 31:15Speaker 00

  We use AI through inference. So we were in a position, we were engaged in conversations, we had product being used, where it allowed us to see something new. And that new thing was, holy cow, the trajectory of AI will make it smart enough that everybody will want to use it. If everybody wants to use it, inference is going to crush, right? The compute infrastructure, we better be there. And so each time we achieved something, it sort of moved us up a mountain or a hill, gave us a new view of the landscape. We could make some new decisions. And we were always sort of moving in the same direction, but we didn't know how to get there.

• 31:53Speaker 00

  And so decision, hop, you get to a point of view, think carefully, earn the next viewpoint.

• 32:04Jack Altman?

  Yeah.

• 32:05Speaker 00

  Earn the next.

• 32:06Jack Altman?

  Well, it's interesting because with software, you obviously can both like think nimbly and change everything the next day.

• 32:12Speaker 00

  Right.

• 32:13Jack Altman?

  Hardware, you can't do it like that.

• 32:14Speaker 00

  That's right. Ours, we have bigger discrete decisions, right? And that's why the choice in your chip of specialization versus flexibility is so important. We made a couple of really good decisions in our first architecture, where we decided not to sort of embed technology that would accelerate convolutional networks. Instead, we said, we don't know how long those will last. If we work underneath that and accelerate the algebra that underpins all AI we knew about.

• 32:57Speaker 00

  That was a really good decision because when transformers came out, we were the fastest at those two, even though we'd never seen them and never heard of them. They hadn't been invented when we set the architecture. And so it helps to make a few good decisions.

• 33:09Jack Altman?

  Yeah. It's interesting, like on the backs of companies like SpaceX, Andro, Cerebras, Palantir, maybe others. But obviously, like hardware is now hotter than hot and everyone wants to fund hardware. But it's, just talking this through, it is crazy hard.

• 33:27Speaker 00

  It's really hard.

• 33:28Jack Altman?

  Yeah.

• 33:29Speaker 00

  It's hard and it requires enormous internal fortitude. And it requires, success has historically been predicted by some experience in the field.

• 33:42Jack Altman?

  Yeah. Different than like, you know, AI where you see.

• 33:46Speaker 00

  I think in AI and social networking, a lot of the founders and the leaders were building tools for themselves and their classmates and their friends. And there they had unique insight. I mean, if you look at cognition, if you look at cursor, these are some of the best engineers, software engineers in the world. They're building tools for themselves, right?

• 34:08Jack Altman?

  That's true.

• 34:09Speaker 00

  That's different from.

• 34:11Jack Altman?

  I also wonder if this last topic probably plays a big role in it, which is when you can change your opinion the next day and it's fine that you were wrong yesterday, speed and decision-making speed trumps versus if you just, you know, you do something and then you have to live with that for a year.

• 34:27Speaker 00

  Years.

• 34:27Jack Altman?

  Years.

• 34:28Speaker 00

  Years.

• 34:28Jack Altman?

  You gotta, you can't just be wrong and fix it tomorrow.

• 34:32Speaker 00

  I think in chips, we measure three times and cut once, right? It is really unforgiving to big mistakes. And so you have to be very, very sure that you get the big items right.

• 34:48Jack Altman?

  Yeah.

• 34:49Speaker 02

  One of the things that I think is interesting about how you built the company, which is, you know, you and you mentioned a lot of the early team have done multiple companies together. Like a lot of you guys had worked together for a long time. You're older and more experienced.

• 35:06Speaker 00

  I think you're not that old.

• 35:07Speaker 02

  But.

• 35:08Speaker 00

  Somebody called me a boomer.

• 35:10Speaker 02

  Yeah.

• 35:10Speaker 00

  I'm just like, my dad's a boomer. A boomer?

• 35:14Speaker 02

  You know, at some point you're looking up and it's all the same.

• 35:17Speaker 00

  When you're that low, you're looking up. It's like, like when you were eight, he's really old, like 27.

• 35:24Speaker 02

  But you know what's interesting actually is if I look at the product leaders in your organization, if I look at somebody who go to market leadership.

• 35:32Speaker 00

  Yeah.

• 35:33Speaker 02

  These are, they're actually very young.

• 35:35Speaker 00

  They are.

• 35:36Speaker 02

  And I'm just interested in like how, was there intentionality around that? Not necessarily ageist, obviously, but just like how you combined the perspective of, you know, young, at the cutting edge, product leaders with seasoned hardware engineers.

• 35:55Speaker 00

  I think we tried to think really hard about where experience mattered, where blistering intelligence matters. You know, if you look at our product organization, unbelievably smart. I mean, some of the best product people I've ever seen. All young, all promoted from within. You know, we don't have big company rules. You've got to be in a job for this amount of time before you can get promoted, right? I mean, if you're extraordinary, we're going to give you more and more responsibility and more and more. And if you do a great job with it, there's no end to where we will, we will take you.

• 36:33Speaker 00

  You know, my co-founder Sean, you know, one of the five of us, I mean, in my last company, we hired him as an individual contributor. And when we were acquired four years later, the guy was 25 or 26 when we hired him in 2007, right? When we were acquired by AMD, we made him a corporate fellow, right? There was just no end. I mean, and now, then he's a founder and he's CTO of a public company.

• 37:03Speaker 00

  And we do that sort of ruthlessly.

• 37:07Speaker 00

  And there are some areas where to be exceptional requires a tremendous amount of experience. There are some areas where it doesn't require any experience. We don't have a long history of understanding what customers want in AI. So their methodology, smarts, insight trump experience. In other areas, in the making of chips, it's been my experience that some odd years of previously building chips is the best predictor of whether you're going to deliver exceptional chips. And that's also true on the mechanical side, on the system side. There's just not a lot of chance in college or in graduate school to actually build silicon, to actually build a machine.

• 37:52Speaker 00

  They're so expensive, they're so hard to build. They take so long that even in a doctoral program, you don't get a chance to tape out a chip, to deliver it, to bring it up, to put it on a board, to power it, to write the software for it. And so it takes some time.

• 38:06Jack Altman?

  Are the relationships something that are critical or can those be earned quickly by young people?

• 38:13Speaker 00

  I think the following. First, five is too many founders, without question, except that we'd worked together before. And so everybody knew what the other folks were good at, right? And so there wasn't a lot of head butting at all. I mean, we'd all worked together previously. We all had tremendous respect for what the others could do and some humility about what we couldn't do. And so we were able to do that.

• 38:40Speaker 00

  Your specific question is, does it take a lot of time to build trust? I think within the six or eight weeks of working with someone, you can tell if they're extraordinary. Right? I mean, extraordinary people, in the first email they send you, you go, whoa, that's exactly what I needed.

• 38:55Jack Altman?

  Yeah.

• 38:55Speaker 00

  Right? Every list is in descending order of importance. There's not a lot of fluff, there's high signal, there's, you go, whoa. And then you see that again. And then you watch the way they run a meeting and you go, whoa. And then you watch them deliver something. They motivate a bunch of people around them, they can work across the organization. You say, well, that's a person I need on the next important project.

• 39:14Jack Altman?

  Yeah.

• 39:15Speaker 00

  And then they just, they chew through work.

• 39:18Jack Altman?

  I always thought with recruiting, it's like if I came away like learning real things and I wanted to have another interview or meeting just because I was like, I'm going to learn more stuff. I was like, that's my best.

• 39:28Speaker 02

  Yeah. The best.

• 39:29Jack Altman?

  So good.

• 39:30Speaker 02

  The best.

• 39:30Jack Altman?

  What about the external relationships? Like, you know, you've got to work with a lot of people outside your company. And you know, you got to be in Taiwan, you got to work with, you know.

• 39:39Speaker 00

  You know, it helps to bring those with you a little bit, some of them, right? So, you know, we'd been building chips with TSMC for decades. We'd been working with our contract manufacturers for decades. And so especially in a time of contention, that those relationships had been in place for years, that you'd been good to your word, not once, not twice, not just in good times, but you'd been good to your word over good and bad times. That was really, really important. I think you earn relationships with new partners in exactly the same way.

• 40:16Speaker 00

  You're good to your word, you get them information early, you write, I mean, it's, write a thank you note. No, really, I mean, do what your mother said, right? Be a good person, write a thank you note, do what you say you're going to do.

• 40:33Speaker 02

  I think this, to me, was one of the big learnings, like particularly during the COVID era and everything. If I think of the software companies, even software infrastructure companies we work on, it's really like their vendor that matter.

• 40:48Speaker 02

  It's AWS and like maybe now their vendor that matters is AWS and you know, a foundational model company, something like that. There's like two vendors that matter. This is like, I don't know what the vendor list is, but it's it's insane. It's dozens, right? It's it is the all of these components that go into the system, all of these specialty manufacturers that build the cooling plate and part of the water system and like, and so you have all of these things that have to kind of come together and and we just aren't used to that like that. Those meant that many dependencies in order to deliver our product, or at least I wasn't, that was like a big thing and you have to kind of work with them on.

• 41:29Speaker 00

  Software guy coming to grips with the supply chain.

• 41:31Speaker 02

  Yeah.

• 41:32Speaker 00

  They call it a chain for a reason.

• 41:37Speaker 02

  And it really is. And you're like, wait a minute, holy cow, this, all of this stuff. And when you're growing exponentially, like then everything becomes even more complicated, right? Because it's like, you're putting in, I had never thought about this, you know, it's like AWS, we want more capacity, you know, you go online and you add capacity. Like you want more capacity on in terms of like number of wafers, like that's a 15 month lead time kind of decision and a huge amount of capital and your vendor is also making a huge allocation decision. And so they have to buy into it, too. And so I, that was a big learning for me in just terms of like how much complexity there is in that.

• 42:21Jack Altman?

  One of the things we've been talking about a lot internally is that it feels like no matter how big you think it is, it is hard to wrap your head around the size of the AI build out that's happening and the capex going in, you know, I actually saw a chart this morning that was like 1% of GDP was spent per year on highway and telecom.

• 42:43Speaker 02

  Right.

• 42:44Jack Altman?

  And then it was 2% for railroads.

• 42:46Speaker 02

  Yeah.

• 42:47Jack Altman?

  And then this AI build out is like three and a half percent of GDP. It's like.

• 42:51Speaker 00

  Only one person got it right.

• 42:52Jack Altman?

  Which was?

• 42:53Speaker 00

  Your brother.

• 42:54Jack Altman?

  No, that's true.

• 42:55Speaker 00

  Yeah. I mean, the only person and everybody thought he was out of his mind. I mean, what, what, what Sam's really good at, and I think is so hard, is he saw an exponential and wasn't afraid, right? You take that exponential out two or three or four or five years, you go, holy crap, right? He wasn't afraid. Everybody else was afraid. And now it's going to slow down, it's gonna, and he was like, no.

• 43:21Jack Altman?

  I remember the Stargate, like seven trillion or whatever.

• 43:23Speaker 00

  Right. I mean, now, now the initial Stargate is sadly small.

• 43:27Jack Altman?

  Yeah.

• 43:28Speaker 00

  Right? And it was mind boggling.

• 43:30Jack Altman?

  People were laughing.

• 43:31Speaker 00

  They were laughing. And so all of us got it wrong, except him. I think we got it wrong. I think TSMC got it wrong. I think Nvidia got it wrong. Everybody, the memory guys got it wrong. We all got it wrong, except Sam.

• 43:43Jack Altman?

  Yeah. So, you know, you've got this insane build out going and, you know, the, the demand for, the demand for inference seems like it's going even faster than no matter what we're going to be able to build. You know, and so then there's a lot of questions about, you know, what will that mean for the price of compute and what will it mean in terms of how much will be available and token spend and all of this. But I'm curious, like, day to day, what you're seeing on the data center side and your experience, you know, now, what, what is that like for you?

• 44:13Speaker 00

  The data center is one of the, the links in the supply chain to deliver compute by the cloud. And the truth is, it's whether you deliver it by the cloud to via a cloud or you deliver it what we call on prem, it impacts both case. In one case, you rent the capacity and you put your, your equipment in it. In the other case, your customer rents the data center. And so it's that it is a limitation in the market right now. It exposes a whole bunch of weaknesses in the US. Our grid is pathetic and sort of built on 1940s

• 44:53Speaker 00

  or 50s technology. We, we stopped doing work in very interesting technologies that turn out to be extremely clean, like nuclear. I mean, wouldn't it be ironic if it took AI to bring us back to doing nuclear? Right? I mean, that's right. Pretty, right. It showed that, you know, in these data centers, we use generators as backups and these are either diesel or LNG or one form of gas, right? And these come from GE Vernova or they come from Caterpillar. There'd been no innovation in, in generator diesel gen sets for, for decades. Now suddenly there's innovation. The guys at Boom want to use what they designed

• 45:38Speaker 00

  for jets to, to power data centers. We're seeing all sorts of interesting things in, in battery backup and like from Bloom Energy, you're seeing interesting fuel cell. I mean, there's just this enormous innovation because there's necessity driving it. We don't have enough data centers. They're coming on too slowly. The grid is old. And so we're, it's an area of tremendous innovation. I, I think the industry did itself no, no favors by doing some dumb stuff at the beginning. Tried to pawn off some costs on local communities. I tried to take advantage of local municipalities. There, there is no reason a data center shouldn't

• 46:21Speaker 00

  pay its way. There's no reason why it should use very much water. We use closed loop systems, right? All the data centers in the US use less than the California almond growers, not by one X or two X or four X, but between four and seven times the almond growers use more. So, I mean.

• 46:37Jack Altman?

  The water thing's just not a thing.

• 46:38Speaker 00

  The water thing's not just a thing.

• 46:40Jack Altman?

  Yeah.

• 46:41Speaker 00

  But as a community, we didn't do a good job of communicating with local communities, getting their buy off, showing them that we're going to bring thousands and in some cases, 10,000 of high paying construction jobs. We're going to pay ongoing jobs and their tax base ought to go down over time. And we didn't do a good job of that and now we're paying the price.

• 47:00Jack Altman?

  I mean, it kind of goes with the whole theme of tech doing a terrible job communicating about AI in general.

• 47:06Speaker 00

  Horrible job. We're doing a horrible job.

• 47:07Jack Altman?

  Yeah.

• 47:08Speaker 02

  I think the data center thing, the other part of the data center thing is interesting to me is like, if you, if I asked you, I don't know, 2018, 2020, whatever, what's the probability that data centers were going to be a critical factor?

• 47:21Speaker 00

  Yet another thing I'd have gotten wrong.

• 47:22Speaker 02

  Right?

• 47:23Speaker 00

  Yeah.

• 47:24Speaker 02

  We'd all got it wrong.

• 47:25Speaker 00

  We got it wrong.

• 47:26Speaker 02

  And it just, and that was just like a, it's a new thing. It's like, okay, now you have to build it all the way through and deliver it, but.

• 47:30Jack Altman?

  How long does it take to go from shovel? I know you joked about shovel. I actually want to ask you about that, but how long does it take to go from start to finish on a data center?

• 47:38Speaker 00

  Shovel ready is an expression you hear in the data center world. It means we haven't done shit.

• 47:44Jack Altman?

  But we're ready to.

• 47:47Speaker 00

  We want to sell you a pile of dirt, ready for your shovels to start doing something.

• 47:52Jack Altman?

  Does sound better.

• 47:53Speaker 00

  It does sound better than, I've got raw land or I've got dirt in Oklahoma. Right.

• 47:59Speaker 02

  Doesn't it mean you have permits and other things that are important?

• 48:02Speaker 00

  Sometimes, sometimes not. We, we would never have expected to get where we, to be where we are today.

• 48:11Jack Altman?

  Yeah.

• 48:12Speaker 00

  From, if you have a good builder, it takes probably from raw land and permits to stand up 50 megawatts, which is a reasonable block. And often even the big sites unfold in 50 megawatt blocks. 50 megawatt blocks is 18 months. Now, if there's an existing building there and there's grid power already, we call those brownfield sites. So a lot of what's going on right now is people are going to the Rust Belt and they're buying old factories, paper mills, because they had a lot of power, right? That they had the permits for power already and retrofitting them.

• 48:50Jack Altman?

  So how much of their kind of supply chain bottleneck is energy versus chips versus the construction versus permitting versus whatever else?

• 49:00Speaker 00

  Each chunk of the supply chain has its own supply chain. So data centers need, there's plenty of concrete. In most places, there's labor, although in Wyoming, where there was a battle for all these data center sites, they were bringing in electricians from as far away as Denver. Generators and electrical transmission switches are long lead time items right now. And those are hard to come by. And so those are the long poles usually. Usually you can get up what's called a cold shell. You can build a concrete tilt up building or a metal building fairly quickly. But then you have to fit it out

• 49:43Speaker 00

  and turn it into a, a high powered hotel for, for compute.

• 49:47Jack Altman?

  Yeah.

• 49:48Speaker 00

  Right? And that takes electricians, takes cooling, chillers, all these other things. Those all are long lead time right now.

• 49:57Speaker 02

  I was really, I happened to fly over, leaving Memphis last night, I happened to fly over macro hard and macro harder. I, it, it was astonishing, like the size.

• 50:10Speaker 00

  Nobody can build like Elon. It's unbelievable.

• 50:12Speaker 02

  The, the size of buildings and, you know, the construction nearby and everything. I was like, whoa.

• 50:17Jack Altman?

  Yeah.

• 50:18Speaker 02

  That is tremendous.

• 50:19Jack Altman?

  Which leads to an interesting question now, which is like, I could imagine a world where either it'll just never be enough data centers, you know, and for 10 more years, it's always going to not be enough. Or because of the dynamics of the lead time and what's going on right now, you could imagine like an overbuild and betting on which of those worlds you think is going to come out is, there's probably some alpha there for whoever.

• 50:40Speaker 02

  We won't have enough data centers.

• 50:42Speaker 00

  We won't certainly, I mean, I, seeing out 10 years in our space right now is, is really hard, right? 10 years ago, transformers weren't running, people were running tiny little models and trying to identify faces or cats and chairs or whatever. I mean, it was, 2016 was not a big AI year, right? But five years, we will still be chasing data centers and we'll still be chasing chips. And for those who use HBM, they'll still be chasing HBM.

• 51:12Jack Altman?

  Yeah. It'll be interesting also how politics plays in all this because like now you got probably, you know, more government interest and involvement in tech and AI specifically than we've ever had, obviously.

• 51:23Speaker 00

  No, what we need is more people who don't understand making decisions. That's clearly what we need. I mean, I think more people with absolutely no clue, making, making important decisions. You know, I, I think this is an area where there are really thoughtful discussions to be had about what's good for the US.

• 51:45Jack Altman?

  Yeah.

• 51:46Speaker 00

  And good for different municipalities, different rural areas. And those aren't being had by the politicians.

• 51:53Jack Altman?

  Yeah.

• 51:54Speaker 00

  Right? They're knee jerk, they're sort of.

• 51:57Speaker 02

  Let me ask you a question on this. I'll move away from politics for a second. Let's say we do agree on this like compute thing. The compute is constrained in some form or another, whether it's memory or chips or data center space, whatever. There's some constraint there.

• 52:11Speaker 02

  And the demand is overwhelming. Doesn't that argue then the only thing that matters is like tokens per watt? Like basically, we're gonna have a limited number of denominator. And so however much, whether it's tokens per watt, or tokens per facility, or tokens per chip, or I don't know, something like that. But it feels like we need the most of the numerator for the limited denominator.

• 52:36Jack Altman?

  Task per watt, maybe.

• 52:37Speaker 02

  And then so like, if that's the case, then you have, Cerebras has a speed advantage, a huge speed advantage.

• 52:46Speaker 02

  But like, how do you think about that dynamic if we fast forward in terms of what the constraint actually is?

• 52:51Speaker 00

  I think there are a couple of things. First, speed, which is what we've chosen to focus on, has historically created markets. Right? And if you think about it, right, there is no market for slow search, right? There's no market for dial-up.

• 53:11Speaker 00

  Your kids are, are what, 14 and 12? If you want to punish them, right, don't take away their phone. Ratchet it back to dial-up speed.

• 53:22Speaker 00

  Right? This is a real punishment. Let them use it for a week at dial-up speed, slowly, right? That, why is, we laugh and then we say, oh, it's okay for AI to be slow. Think about it. And when, when the internet was slow, Netflix delivered DVDs in envelopes.

• 53:40Speaker 02

  Yeah.

• 53:41Speaker 00

  And when the internet got fast, they became a movie studio. Right? That's not, they didn't get better at their other thing. They became something entirely new. And I, I think what we're seeing with the launch of GPT-5, 6, Soul, right? It's put out in limited, limited availability last week, that people are thinking of whole new applications. You've got frontier intelligence instantly. And that, that opens up all sorts of new opportunities. So that's one thing you do with speed. The other thing you do is you try and think about how you can drive up throughput, how you can make more tokens. And one of the ways we're doing that is by partnering in something called disaggregation. And we're doing it with AMD, we're doing it with AWS, where you think about, in the

• 54:26Speaker 00

  work of inference, is there a part that can be done by somebody else? And is there a part that can be done by you, such that the, the result is higher throughput? And, you know, with, with AMD, we're seeing 5X additional throughput. I mean, five times as much throughput, while keeping the speed the same. And we're seeing similar numbers with, with AWS. We have an opportunity, because of our architecture, to do that with, with the entire GPU landscape. We, we, we could do it across the board. And so, you know, there, there are four major chip makers right now in, in our category. Obviously Nvidia, we'd love to partner with them.

• 55:09Speaker 00

  There's AMD, there's the Google TPU, and there's AWS with their training in parts. And we're already working with two. So that's something we've thought a great deal about, and is a vector we are extremely interested in, in chasing down.

• 55:23Jack Altman?

  What has it been about Nvidia that has made them have the just crazy run that they've had? You know, like I've just.

• 55:28Speaker 00

  I think most people are, are wrong about in, in what makes Nvidia great. First, Nvidia's, you know, in, in the first quarter of this century, they're, they're the great company, without any question.

• 55:42Speaker 00

  And I, I think people look to a bunch of things. They look to CUDA, they, I, I don't think it's CUDA, they, they look to their chip architecture, I don't think it's their chip architecture.

• 55:55Speaker 00

  It's this unbelievable grit and intensity that was born of a decade of not having success as a public company. I think if you look at their stock chart between about 2000, what, 2003 and 2013, 2004, 2014, for a decade, right? They traded horribly. And you're a public company, and you're fighting tooth and nail, and no one's listening to you, and you can't sell very much. And the relentlessness and the grit that that takes is awesome. And to come out of that as the most valuable company a decade later, right? The most valuable company in the world, that sort of intensity and grit for a company of their size, in my view, makes them one of the great companies in history.

• 56:40Speaker 00

  It's, it's not, there's these other things, these are just things people say. But the, the, the sort of decade of being a public company and fighting, and fighting, and fighting, that gets in your DNA. And that, that's awesome.

• 56:55Jack Altman?

  Yeah, I mean, you're like, you see Jensen, like, still at events.

• 56:59Speaker 00

  Fighting tooth and nail.

• 57:00Jack Altman?

  Yeah.

• 57:01Speaker 00

  Right? I mean, where, could you have imagined?

• 57:03Jack Altman?

  CEO of a five trillion dollar company.

• 57:04Speaker 00

  Could you imagine old tech leaders before Jensen doing that?

• 57:08Jack Altman?

  No.

• 57:08Speaker 00

  Right? Right.

• 57:09Speaker 02

  How that kind of fights.

• 57:11Speaker 00

  I mean, that, that in my view, is what's awesome.

• 57:14Jack Altman?

  Yeah.

• 57:15Speaker 00

  That level of fight. I, I think the other stuff, cool, good, but, but when, when I look at what, what I can do better, when I look at sort of what I ought to be thinking about as being a CEO, those are the things I look to.

• 57:29Jack Altman?

  Do you think, did your near-death experiences give your company some of that DNA?

• 57:33Speaker 00

  You know, we, we, we are, and I, I think this is what's interesting about Jensen, too, is he sees himself still as the underdog. Now, now he's the big dog. But, you know, this is my, my fifth startup. You know, we sold three and took one public previously, and we've now taken this one public. I'm a, I'm a professional David in the battle with Goliath. And I, I wake up every day with that mentality. And that we're now bigger, we, we have sort of bigger competitors, right? We have bigger challenges. We have more people throwing stones at us. And so I, I, I hope we take that. I personally wake up every day with that passion and that drive.

• 58:17Speaker 00

  I mean, every day, I think to myself, when we started, they said, it would never work, you can't do way for scale. And we made way for scale. And then they said, all right, you did way for scale, but you, you can't yield it in volume. And then we yielded in volume. They said, okay, you can yield it in volume, but you can't package it in volume production. And then we packaged it in volume production. They said, okay, now you've packaged it in volume production, you only have a government customer. And we said, okay. Then we won a sovereign cloud. And then they said, you don't have a, a hyper, you don't have a, a, a frontier lab. And then we won OpenAI. And then they said, okay, you've got government, you, you've got sovereign clouds, you've got a hyper, you've got a, a, a frontier lab, you don't have a hyperscale. Then we won a hyperscale.

• 59:03Speaker 00

  Right? Each, each time, they said, then they said, you, you've got all these cool customers, but you couldn't, you couldn't do big models. Now we're serving GPT-5, 6.

• 59:11Jack Altman?

  What are they saying? What, what's that now? Like, what are they saying?

• 59:13Speaker 00

  The CEO's a boomer.

• 59:19Speaker 00

  But each time.

• 59:20Jack Altman?

  And then I became a millennial and I shut down.

• 59:24Speaker 00

  You know, it, it, right? And so I, I think when you're sort of a professional David, when, when you are an entrepreneur at heart, right? Each one of those fires you up, right? We're only interested in solving problems that other people can't solve. That we're only interested in doing things that, that other people can't do. That's why we get up every morning.

• 59:45Speaker 00

  It, it's not the money, it's not, it's not notoriety, it's because we love building cool things. And we really like building cool things that are so hard that other people can't build them.

• 59:56Jack Altman?

  Eric told me you had a pretty cool and unique childhood. I don't know if that fed into this at all, but like, how'd you grow up?

• 1:00:01Speaker 00

  I grew up on the Stanford campus. My parents were faculty, and there's a little neighborhood where all your, all your neighbors are, are other professors.

• 1:00:14Speaker 00

  My neighbor was William Shockley, the inventor and transistor.

• 1:00:19Jack Altman?

  That's so crazy.

• 1:00:21Speaker 00

  So all we knew about him, you know, we're 10 or eight, is that his wife gave out full-size candy bars at Halloween.

• 1:00:29Speaker 00

  The inventor was at Bell Labs, he invent, he brought Silicon Valley, right? He, he, but the movement of Shockley to the West Coast created the foundation for Silicon Valley, and we're thinking he gives Big Three musketeer bars. Right? But I, I think there were a couple of things that were glorious. First, the only currency was intellectual horsepower.

• 1:00:52Speaker 00

  Right? That, that was, nobody cared if you're rich, nobody cared if you'd started a company, that, that this was the seventies, nobody cared. What they cared about was, oh, that dude's really smart, he does good work. My other neighbor was Amos Tversky. And for his work with David Kahneman, they got Nobel Prize in Economics. My dad's tennis match, there were six or eight guys in rotation, they played doubles on Saturday and Sunday.

• 1:01:19Speaker 00

  Somewhere in my mid twenties, I realized three had Nobel Prizes and one had a Fields Medal.

• 1:01:24Speaker 00

  Right?

• 1:01:26Jack Altman?

  Eric, he sucks at doubles, but.

• 1:01:28Speaker 00

  Let me tell you, it was some old man tennis. I mean, their serves were grim. Their physics was good.

• 1:01:34Jack Altman?

  It's a crazy way to grow up.

• 1:01:36Speaker 00

  It's a crazy way to grow up. And, you know, we, the neighborhood was safe, we'd get on our bikes, it would just go all summer and come back at when it was dark.

• 1:01:45Jack Altman?

  And so did you think you'd be an academic?

• 1:01:47Speaker 00

  I did. I actually, I was working on a PhD. I got a little bored, I went to the Business School of Stanford while I completed my qualifying exams for my PhD. And I sort of got, got sucked into this by mistake. And, you know, my dad still asks me, he'd say, you're gonna finish your PhD, Andrew? I'm like, Dad, all my professors are dead. Nobody left.

• 1:02:11Speaker 00

  So.

• 1:02:11Jack Altman?

  Yeah, amazing. All right, well, Andrew, this was a blast. Thanks a ton for doing this with us. And obviously, you know, you've built something extremely special, and it's been cool to just watch and learn vicariously through Eric. So thanks for everything.

• 1:02:22Speaker 00

  It's a pleasure to, to be here and chat with you guys. And, you know, Benchmark was an extraordinary partner. I mean, I, I think if you do hardware, you, you're gonna be in bed with your, your, your backers for a decade. And pick good ones. And I'm proud we did.

• 1:02:41Jack Altman?

  Thank you.