# Andrew Feldman on Building Cerebras and the Future of Chips | Ep. 57 Markdown copy of https://realalpha.app/v/HFOhG1jH2tk/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy. Andrew Feldman says chip startups need radically different architectures, not incremental gains, to challenge incumbents. - Show: Uncapped - Hosts: [Jack Altman](https://realalpha.app/people/jack-altman/) & Eric Vishria - Guest: [Andrew Feldman](https://realalpha.app/builders/andrew-feldman/) is cofounder and CEO of Cerebras Systems. - Published: Sep 15, 2026 - Length: 1 h 3 m ## The best minutes - [4:09](https://realalpha.app/v/HFOhG1jH2tk/?t=249) [Attacking Goliath requires a radically better product](https://realalpha.app/s/attacking-goliath-requires-a-radically-better-product/) (6 min) - [9:37](https://realalpha.app/v/HFOhG1jH2tk/?t=577) [Failure analysis kept Cerebras going through 18 months of setbacks](https://realalpha.app/s/failure-analysis-kept-cerebras-going-through-18-months-of/) (3 min) - [16:13](https://realalpha.app/v/HFOhG1jH2tk/?t=973) [Chip fabs are a rare, tightly coupled supply chain](https://realalpha.app/s/chip-fabs-are-a-rare-tightly-coupled-supply-chain/) (7 min) ## Also worth watching - [43:57](https://realalpha.app/v/HFOhG1jH2tk/?t=2637) [Data center delivery depends on power gear and site readiness](https://realalpha.app/s/data-center-delivery-depends-on-power-gear-and-site/) (6 min) - [51:57](https://realalpha.app/v/HFOhG1jH2tk/?t=3117) [Faster inference can open new markets and increase throughput](https://realalpha.app/s/faster-inference-can-open-new-markets-and-increase/) (3 min) - [55:12](https://realalpha.app/v/HFOhG1jH2tk/?t=3312) [Nvidia's long fight as a public company shaped its grit](https://realalpha.app/s/nvidias-long-fight-as-a-public-company-shaped-its-grit/) (2 min) - [29:00](https://realalpha.app/v/HFOhG1jH2tk/?t=1740) [Cerebras made architectural choices to adapt as AI changed](https://realalpha.app/s/cerebras-made-architectural-choices-to-adapt-as-ai-changed/) (4 min) - [12:55](https://realalpha.app/v/HFOhG1jH2tk/?t=775) [Cerebras is developing memory and optical links alongside compute](https://realalpha.app/s/cerebras-is-developing-memory-and-optical-links-alongside/) (3 min) Skip the opening investor backstory, the board and hiring discussions, supplier relations, and the closing childhood and education stories. ## Summary Andrew Feldman is the cofounder and CEO of Cerebras Systems, which he founded in 2016. Cerebras makes the Wafer Scale Engine, described as the world’s largest chip. Andrew Feldman says Cerebras was built around a different architecture because incremental gains cannot dislodge incumbents that keep improving. He describes the technical and financial risk of wafer-scale computing, and says the board helped by financing the work without trying to solve engineering problems. Feldman explains how Cerebras moved from training toward inference as AI’s use became clearer, while chips, fabs, power and data centers remain constraints. He argues that speed can create new markets and credits NVIDIA’s grit, rather than CUDA or chip architecture, for its success. ### Minute by minute - [0:44](https://realalpha.app/v/HFOhG1jH2tk/?t=44) **A chip startup needs a radical edge.** Feldman says he saw AI as a computationally intensive workload that might justify a dedicated chip, unlike the power-intensive rise of ARM processors for cell phones. Eric Vishria says his experience showed him how hard company-building is, and that investors need some naivete to attempt difficult ventures again. - [4:09](https://realalpha.app/v/HFOhG1jH2tk/?t=249) **Incumbents demand a radical advantage.** Feldman says a startup challenging a giant like NVIDIA cannot win by being slightly better or cheaper, since the incumbent can cut prices or bundle products. He argues for a 10-, 100- or 500-times-faster product and says Cerebras took control of the full stack, from chip and system to software and API, to pursue that advantage. - [9:37](https://realalpha.app/v/HFOhG1jH2tk/?t=577) **Wafer-scale computing took 18 months to prove.** Feldman says Cerebras spent about 18 months unable to make its wafer-scale design work, while spending about $8 million a month. The team analyzed each failure and made new mistakes rather than repeating old ones. In July 2019, after progress from wafers shattering in seconds to running for a day, he says they solved a problem no one had solved in 75 years of computing. - [13:21](https://realalpha.app/v/HFOhG1jH2tk/?t=801) **Cerebras is pursuing three compute bottlenecks.** Feldman breaks computing into calculation, memory and moving data to where it is useful. He says Cerebras is working on faster cores, memory capacity and speed, and I/O. Its programs include stacking HBM onto an SRAM-based wafer and putting optical switching close to compute. - [15:52](https://realalpha.app/v/HFOhG1jH2tk/?t=952) **Fab capacity cannot catch fast demand.** Feldman explains that advanced fabs cost $40 billion or $50 billion and take years to build, while ASML currently makes the lithography machines they use. He says demand has outpaced the capacity to build fabs, and that specialized manufacturing skills are scarce. He blames decades of U.S. policy for fabs and related suppliers leaving, and proposes a 20-year period of waiving local ordinances to encourage domestic fab construction. - [23:49](https://realalpha.app/v/HFOhG1jH2tk/?t=1429) **Hardware boards must respect long cycles.** Vishria says his expertise did not include chip design, so he focused on what he could contribute. Feldman says good board members help in their areas of expertise, ask useful questions and support financing, while leaving technical choices to engineers. He says hardware companies can spend years building a chip before getting meaningful customer feedback. - [29:00](https://realalpha.app/v/HFOhG1jH2tk/?t=1740) **An inference pivot emerged step by step.** Feldman says Cerebras made decisions one step at a time, revising its view as the market changed. As AI became more useful, he concluded inference would put heavy pressure on compute infrastructure. He says the first architecture accelerated the algebra underlying AI rather than specializing in convolutional networks, which let it handle transformers that had not yet been invented. - [33:04](https://realalpha.app/v/HFOhG1jH2tk/?t=1984) **Hardware teams need selective experience.** Feldman says Cerebras promoted young product leaders from within when insight and ability mattered more than experience. For chip design and mechanical systems, he says years of experience building hardware are a better predictor of success. He contrasts this with software, where teams can often correct a decision quickly; chip mistakes can take years to fix. - [39:22](https://realalpha.app/v/HFOhG1jH2tk/?t=2362) **Hardware depends on durable supplier trust.** Feldman says Cerebras’s long-standing relationships with TSMC and contract manufacturers mattered during a period of supply contention. He advises keeping commitments, sharing information early and treating new partners well. Vishria notes that hardware depends on dozens of specialized suppliers, unlike software companies that may rely on only a few major vendors. - [42:13](https://realalpha.app/v/HFOhG1jH2tk/?t=2533) **Data centers expose power and community gaps.** Feldman says data centers are a constraint whether customers rent cloud capacity or their own facilities. He points to an aging U.S. grid and describes new interest in generators, batteries, fuel cells and nuclear power. He says the industry also failed to communicate with local communities about costs, water use, jobs and tax revenue. - [47:05](https://realalpha.app/v/HFOhG1jH2tk/?t=2825) **Data center construction has long poles.** Feldman says a good builder can bring 50 megawatts online in 18 months from raw land and permits, while some operators are retrofitting old factories with existing power access. Generators, electrical transmission switches, cooling and electrical work can take time to secure. He expects the industry will still be chasing data centers and chips five years out. - [52:47](https://realalpha.app/v/HFOhG1jH2tk/?t=3167) **Speed can unlock new markets.** Feldman argues that speed creates markets, using Netflix’s shift from mailing DVDs to making films as an example of what faster internet enabled. He says Cerebras is working with AMD and AWS on disaggregating inference workloads. He reports five times as much throughput with AMD at the same speed, with similar numbers from AWS. - [55:20](https://realalpha.app/v/HFOhG1jH2tk/?t=3320) **Feldman credits NVIDIA's grit.** Feldman argues that NVIDIA’s success came less from CUDA or its chip architecture than from the grit and intensity it developed during a difficult decade as a public company. He describes Cerebras as a challenger energized by proving doubters wrong, from making wafer-scale chips to producing and packaging them in volume and winning government, sovereign cloud, OpenAI and hyperscaler customers. - [59:49](https://realalpha.app/v/HFOhG1jH2tk/?t=3589) **Feldman recalls an academic childhood.** Feldman says he grew up on the Stanford campus, where his neighbors included William Shockley and Amos Tversky, and that intellectual ability was the neighborhood’s main currency. He expected to become an academic and was working toward a PhD, but says he got bored, went to Stanford’s business school and was drawn into business. ## Transcript Uncapped, hosted by Jack Altman and Eric Vishria, with Andrew Feldman. Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there. [0:00](https://realalpha.app/v/HFOhG1jH2tk/?t=0) You got 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. And Eric's like, should I get in? Can I do anything to help? That's right. They're like, can we help? I was like, no. But we always believed that if we could make it, there would be huge demand for it. 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 Cerebrus, which is obviously one of the most important chips companies. [0:33](https://realalpha.app/v/HFOhG1jH2tk/?t=33) 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 Cerebrus 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 building when you started the company, and what was the thinking? Well, Jack, thanks for having me. It's always fun to hang out with Eric, and so I appreciate you having me. [1:13](https://realalpha.app/v/HFOhG1jH2tk/?t=73) 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. Unlike, for example, the rise of ARM processors for cell phones, 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 the insight and then it was a a long hard slog 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 and probably never will be again probably never will be again [2:47](https://realalpha.app/v/HFOhG1jH2tk/?t=167) like would you do that every time again was there something that made it clear to you or was it what happened for you when you made the investment? 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 will you do it again? And I ask myself that all the time. You actually need some naivete. um 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 um 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 the company was going to come to the brain. [3:50](https://realalpha.app/v/HFOhG1jH2tk/?t=230) 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. Don't tell them. I'm doing great. It's a really hard problem. All's good, man. 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? No, never. So how's it go? So what happened? 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. [4:30](https://realalpha.app/v/HFOhG1jH2tk/?t=270) And 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. They can do a hundred other things. What that means is you have to go out with something way better. Ten, a hundred, five hundred times faster. [5:05](https://realalpha.app/v/HFOhG1jH2tk/?t=305) **Andrew Feldman:** You have to come up with a product that has a value proposition that even if the other guys give it away, they can't compete. [5:13](https://realalpha.app/v/HFOhG1jH2tk/?t=313) 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, 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. So what has to be in your control? You have to have all aspects of the design, the implementation. [5:59](https://realalpha.app/v/HFOhG1jH2tk/?t=359) 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 ways to fail there. When you do radical innovation, there are no vendors waiting for you. When you build a chip the size of a dinner plate, you can't go to a catalog and find a heat sink because nobody had ever built one bigger than a postage stamp. 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, the surrounding components. But the result of that is you develop this extraordinary expertise. We didn't start world leaders in packaging. We're right now the best in the world of 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 how you can innovate across the the the various elements of of the full solution and push as hard as you can i i think like andrew taught me this in in 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 [7:33](https://realalpha.app/v/HFOhG1jH2tk/?t=453) 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 they 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... the fifth. So that puts you at 32X. And then you need at least a multiple advantage of that. So you 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, these big companies with a lot of resources are actually doing, they're doing great work. [8:37](https://realalpha.app/v/HFOhG1jH2tk/?t=517) NVIDIA is advancing the ball materially every year, like very significantly. NVIDIA and Google and Tranium, they're moving the ball. And so you got to aim at 100, 500,000 times better if you're going to arrive, if you're going to intersect the market ahead of them. And so 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 wafer scale, SRAM, whatever it is. Like those things matter. You can't get there with a collection of modest improvements, right? [9:23](https://realalpha.app/v/HFOhG1jH2tk/?t=563) Your biggest competitor buys silicon for less than you. They buy manufacturing capacity for less than you, right? They probably pay less for their EDA tools. And so you can't go at them. You can't run it at Goliath straight on. I mean, you've got to think about how you can deliver something profoundly different. 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 I had some near-death experience when you can't build the thing you're supposed to build yeah 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 a 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? I think in our space, we were always confident if we could make it, we'd sell it. Right? There are sort of two axes in our life. There's can you make it? And the other axis, can you sell it? Right? And we always believed that if we could make it, [10:39](https://realalpha.app/v/HFOhG1jH2tk/?t=639) there would be huge demand for it. I think nobody had ever done wafer 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 18 months, and we couldn't make it. 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 going to talk about i mean still can't make it again and again and again and i i give a huge right right they're like should i get in can i do anything to help that's right they're like can we help like no you can't oh there's nothing nothing that can be done nothing to be done But we believed because we had ideas still. [11:36](https://realalpha.app/v/HFOhG1jH2tk/?t=696) **Andrew Feldman:** We hadn't run out of ideas. And each time we built it and it failed, we'd go through sort of good engineering practice. [11:46](https://realalpha.app/v/HFOhG1jH2tk/?t=706) We'd do a full failure analysis. We'd understand it. We would, and we wouldn't fail that way again. All new mistakes. All new mistakes. So we sort of had a, that was sort of our mantra, only new mistakes. Right? Only new failures. And over time, 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 an hour. And then we shattered some in minutes again. And then we built back up to the point we had a day in sort of July of 2019 where we were running and temperature 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 and stared at a server, which is about as exciting as looking at paint dry. [12:42](https://realalpha.app/v/HFOhG1jH2tk/?t=762) I said, holy crap, we've solved this problem that nobody in 75 years of compute had ever solved. And that was one of the great minutes of my life. That's cool. When you look forward, like if you, so, you know, there's the wafer scale, and then everything around that, the packaging and cooling and powering it, all these things. but you know when you look at the r&d envelope that you have going forward over the next five years 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 i i think I think if you simplify what we build down to its most fundamental elements, a computer is built of three things. [13:41](https://realalpha.app/v/HFOhG1jH2tk/?t=821) We do calculations, we store the results, and then we move the results to where they're useful. So 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's how we ship the results to somewhere where it's useful. I think if you're in the computer business like we are for AI, you better be working on all three. You're going to be thinking about how to make your course faster, how to make them tuned for AI, but general enough to to withstand the innovation happening in AI 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 got to be thinking about how you get the results off your chip and somewhere where they're useful and that's your IO and so we're working on all three of those and we have programs with the U.S. government already, big programs where we're thinking about how to stack memory, [14:49](https://realalpha.app/v/HFOhG1jH2tk/?t=889) **Andrew Feldman:** right, how to put HBM onto an SRAM-based wafer. And what this does is it enables HBM to behave like SRAM, which is exactly what everybody wants. You get the capacity of HBM and the speed of SRAM, [15:05](https://realalpha.app/v/HFOhG1jH2tk/?t=905) right we're working on on uh optical wafer stacking so you put an optical switch onto a wafer this would change the world um you know jack dangara said and 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's that's the io part and so thinking about how to how to solve that problem by bringing optical switching smack up against compute is something that we also have large government contracts for and we're enormously excited about so you know you got to get faster you got to find ways to store more and get to memory faster and you got to find ways to move that move those results at ten hundred a thousand x faster to where they're useful I want to go to a bit broader in the supply chain. [16:03](https://realalpha.app/v/HFOhG1jH2tk/?t=963) And I think it's kind of well understood right now that AI is very supply constrained. But I, like probably many other people, I wouldn't say I have a perfect understanding of the supply chain. And one of my favorite things to do on this podcast is you get a successful person like you, and because there's cameras on, I get to ask you a really simple question, and I get to 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 gpt answer dude it's just tsmc there's a black box we call that tsmc fed by another black box called asml and out the other end comes comes great we're supply constrained but i don't know what this can you teach me what [16:49](https://realalpha.app/v/HFOhG1jH2tk/?t=1009) **Andrew Feldman:** the supply how it works yeah i i think the first thing to to to think about is that that a fab and especially a fab that builds at cutting edge geometries is a modern pyramid. It's one of the greatest things humans make. What is it? It is a collection of machines that take a chunk of [17:11](https://realalpha.app/v/HFOhG1jH2tk/?t=1031) 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 is a collection of, I mean, this is a factory. It's just a reasonable way, a real factory. It costs 40 or 50 billion to make. It has a four or five year lifetime. How big is it? Football fields. When Samsung was building a factory in Texas, they began building a power plant. The power plant was used to make concrete they ran concrete trucks hundreds of concrete trucks 7x24 for years to pour enough concrete to build the foundation on which to put the fab right these are unbelievably complicated things so asml makes the machines asml makes makes uh the the machine that does the photolithography um what's that machine like it's the size of uh each machine is what is it 50 or 60 feet long and 20 feet high costs what half a billion yeah they're they're expensive um 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 tsmc able to to to achieve things that others can't and can anyone else make these machines? Is it really just ASML that can make these machines? Right now it's only [18:42](https://realalpha.app/v/HFOhG1jH2tk/?t=1122) ASML. In the world? In the world. That's weird, isn't it? Yeah. Yeah. I mean, there are very few things that this is a true monopoly, not born of what De Beers did, which was try and control supply. They have technology that others haven't been able to replicate. Are we supply constrained on lithography machines um probably not i i think the the challenge in our business is that that demand moves extremely quickly and if you've looked at sort of a great company like NVIDIA's demand it's it's exponential but you you can't build fabs that fast if the fab takes five years to build right and you see demand increasing there's no way anybody would have expected demand to shoot up the way it has for chips. They're always behind. And they're investing huge capital blocks in the construction of these facilities. And demand is racing past the ability to make these huge five-year bets. And we have the same problem right now with data centers, right? AI is moving at the speed of software, and data centers are moving at the speed of real estate. And that's why we're behind. That's just it. [19:58](https://realalpha.app/v/HFOhG1jH2tk/?t=1198) Good articulation is good. OK. So. TSMC buys the lithography machines. Right. So TSMC buys lithography machines. They have decades of experience. They wrap it together. They take an ingot, which is a tube of silicon. It's sliced into disks 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 and you send the results to TSMC and they run it through a factory that cost $40 or $50 billion to make and took years to make. [20:58](https://realalpha.app/v/HFOhG1jH2tk/?t=1258) And out the other end comes a $22 chip. It blows your mind. What do you think about it? Each one of these took sort of the smartest people in their field decades. It's the most impressive thing humans have ever done. Right. It's among the most impressive things And a burrito is $23. That's right. Right, exactly, right. And you go down the street here in the Mission District and you pay $17 for a burrito. That's right. Right, and so... But once you have the chip, you're done, right? Then you're good. Once you have the chip, you've solved about a fourth of your problems. Okay, but TSMC, they receive a design, they operate the machine, they give you the chip. [21:39](https://realalpha.app/v/HFOhG1jH2tk/?t=1299) Yes. But that itself seems like not replicable. Like we don't have fabs like this in the US. We have them run by TSMC and by Samsung. And Global Foundries have got one, too. How much of the bottleneck is at the TSMC level? A huge amount. Okay. Why? Huge amount. Is that because of the speed of building fabs? Yeah, it's because the demand has outpaced the ability to build fabs. And it's not like 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 even there, they can't do 12 at once, right? [22:22](https://realalpha.app/v/HFOhG1jH2tk/?t=1342) The skills to make these things are so rare and in so few hands that you can't just knock them out. It's not a cookie cutter. And that's why we don't have enough of them right now. And the fact in the US, sort of three decades of bad policy that pushed the fabs away. When the fabs left, the tool vendors, the collection of vendors who provided services to them, they all left. The next step in the process called packaging, and those are companies like Amcor and ASC, they left. And we just punted a strategic industry. And we got to do better. That's not smart. Yeah. So what do you think should happen there? [23:08](https://realalpha.app/v/HFOhG1jH2tk/?t=1388) 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 think this is about building U.S. domestic fab capacity. yeah um i think i mean look when when 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 yeah right i mean 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 country for the economy 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 issues. [24:00](https://realalpha.app/v/HFOhG1jH2tk/?t=1440) lying to me i think you've managed to be very helpful like what eric was extremely helpful so like how did you approach this 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 um 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 i think I think there are a couple things that made Eric a good board member, and I think that are foundational in being a good member. [24:55](https://realalpha.app/v/HFOhG1jH2tk/?t=1495) You don't know about everything, and share and make us better in those domains where you're a real expert, and don't talk about those other domains, right? Sometimes there's a lot of words, and we were lucky. We had a really good board, and everybody knew what they were good at, and they helped us in those domains in which they had real expertise, right? I mean, one of the advantages of the venture world is you can see across an industry, right? When we're deep in it, we're going deep. They can see wide, right? But there weren't efforts by the board to try and solve technical problems. They didn't have, that wasn't their expertise. [25:35](https://realalpha.app/v/HFOhG1jH2tk/?t=1535) Talking about how we might finance the company, thinking about all sorts of other things, they were enormously helpful, and they were patient. And I think they asked thoughtful questions, and our board meetings made us better. It's like, what should a board do in a hardware company in general, when you expect that there's going to be much less to contribute on the product? Is it financing and recruiting? I think the question when you've got a long, hard project, and this is the opposite of SaaS, right 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 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 sort of put their foot on the throat of somebody else's idea, right? They say, yeah, yeah, it's great. It takes you three years to two and a half years to get to the point where you can bring it to a customer. I think what you can do as investors is understand that trajectory, understand that your first chip is very rarely a good one, [26:53](https://realalpha.app/v/HFOhG1jH2tk/?t=1613) 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 fourth or 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 um 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 uh specialization and flexibility right these are questions that can really help sharpen our thinking but you know whether to use this one technique or this other technique in design, whether you use one tool vendor or the other, you got to let that, you got to let the team pick that. [27:48](https://realalpha.app/v/HFOhG1jH2tk/?t=1668) Did it feel really different for you versus like an infrastructure role? Yeah, totally. It was just very different. I mean, your time to revenue is so much longer. The revenue comes in like giant chunks. um the initial customers you know we had u.s government customers then we had you know huge sovereign cloud and g42 you know then you have now today obviously open ai and so you have these like things you're so your customer concentrations more it's just like every dynamic's different the investment in go-to-market so much less so i think 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. [28:36](https://realalpha.app/v/HFOhG1jH2tk/?t=1716) That part's actually the same. And then the market is very dynamic. I mean, think about it. Pre-Transformer, you started the company pre-Transformer. We did. At that time, TensorFlow was dominant. TensorFlow was dominant. ResNet was still a thing. Right. These were very small, very simple networks compared to where we are today. Yeah, the actual ML, the AI, looked nothing like it does today. Well, actually, I think that's really, you know, for entrepreneurs 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 back prop. [29:21](https://realalpha.app/v/HFOhG1jH2tk/?t=1761) top 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 2022, 2023, maybe 24, we pivoted the company, or I don't know if pivot's the right word, but like to inference, how and why and where did we get lucky and where did you have real foresight on that? I think there's sort of, 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. [30:16](https://realalpha.app/v/HFOhG1jH2tk/?t=1816) And that's a circuit, we think. 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. [30:39](https://realalpha.app/v/HFOhG1jH2tk/?t=1839) **Andrew Feldman:** 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? [30:53](https://realalpha.app/v/HFOhG1jH2tk/?t=1853) Yes. Can we make it work in routing? Yes. Is routing now where everybody's focusing? That's where they're focusing. Can we be a fast router? Yes. But now we're seeing the lay of the land, we're seeing the unfolding of AI, the rapid growth of intelligence. Well, who's going to use it? We use AI through inference. So we're in a position, we're 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 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 how to get there and so decision hop you get to a point of view Think carefully. Earn the next viewpoint. Earn the next viewpoint. [32:06](https://realalpha.app/v/HFOhG1jH2tk/?t=1926) Well, it's interesting because with software, you obviously can both think nimbly and change everything the next day. Right. It's hard where you can't do it like that. 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 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 the algebra that underpins all ai we knew about that was a really good decision because when transformers came out we were the fastest at those two even though we've never seen them and never heard of them and they hadn't been invented when we set the architecture and so it helps to make a few good decisions yeah it's interesting like on the you know on the backs of you know companies like spacex andrel cerebrus you know palantir maybe others but obviously like hardware is now hotter than hot and everyone's to fund hardware but it's it does just talking this through it is crazy hard it's really hard yeah it's hard and uh it requires enormous internal fortitude and it requires uh success has [33:38](https://realalpha.app/v/HFOhG1jH2tk/?t=2018) historically been predicted by some experience in the field yeah different than like you know AI where you see the advantage to? 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? That's true. That's different from- 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 do something and then you have to live with that for a year. [34:27](https://realalpha.app/v/HFOhG1jH2tk/?t=2067) Years. Years. You can't just be wrong and fix it tomorrow. 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. One of the things that I think is interesting about how you built the company, which is you and you mentioned a lot of the early team have done multiple companies together. A lot of you guys have worked together for a long time. You're older and more experienced. I think you're not that old. Somebody called me a boomer. I'm just like, my dad's a boomer. A boomer? [35:14](https://realalpha.app/v/HFOhG1jH2tk/?t=2114) You know, at some point you're looking up and it's all the same. When you're that low, you're looking up and it's just like when you're eight. He's really old, like 27. But you know what's interesting actually is if I look at the product leaders in your organization, if I look at some of the go-to-market leadership, they're actually very young. They are. and um 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 i think we tried to think really hard about where experience mattered we're blistering intelligence matters um you know if you look at at our product organization um unbelievably smart i mean some of the best product people i've ever seen um all young all promoted from within um you know we we don't have big company rules you 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 um you know my co-founder sean uh you know one of the [36:36](https://realalpha.app/v/HFOhG1jH2tk/?t=2196) five of us i mean in my last company we we hired him as an individual contributor and when we were acquired four years later the guy was 25 or 26 when we 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 And now he's a founder and he's CTO of a public company. And we do that sort of ruthlessly. 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. [37:22](https://realalpha.app/v/HFOhG1jH2tk/?t=2242) 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 from 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. 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. [38:04](https://realalpha.app/v/HFOhG1jH2tk/?t=2284) And so it takes some time. Are the relationships something that are critical or can those be earned quickly by young people? 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. Your specific question is, does it take a lot of time to build trust? [38:43](https://realalpha.app/v/HFOhG1jH2tk/?t=2323) I think within the six or eight weeks of working with someone, you can tell if they're extraordinary. I mean, extraordinary people, in the first email they send you, you go, whoa, that's exactly what I needed, 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, and then they just chew through work. [39:18](https://realalpha.app/v/HFOhG1jH2tk/?t=2358) I always thought with recruiting, it's like if I came away 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. The best. The best. It's so good. The best. What about the external relationships? You've got to work with a lot of people outside your company. You've got to be in Taiwan. You've got to work with – It helps to bring those with you a little bit, some of them. We've 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 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. [40:07](https://realalpha.app/v/HFOhG1jH2tk/?t=2407) That's really, really important. I think you earn relationships with new partners in exactly the same way. You're good to your word. You get them information early. You write. I mean, it's, you know, 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. This, to me, was one of the big learnings, 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 matters is AWS. And maybe now their vendor that matters is AWS and a foundational model company or something like that. [40:55](https://realalpha.app/v/HFOhG1jH2tk/?t=2455) There's like two vendors that matter. This is like, I don't know what the vendor list is, but it it's it's insane it's dozens right it's 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 – Software guy coming to grips with a supply chain. [41:33](https://realalpha.app/v/HFOhG1jH2tk/?t=2493) Whoa. Yeah, exactly. They call it a chain for a reason. And it really is. It 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 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. [42:15](https://realalpha.app/v/HFOhG1jH2tk/?t=2535) And so I, that, that was a big learning for me in just terms of like how much complexity there is in that 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 and it is hard to wrap your head around the size of the ai build-out that's happening yeah and the capex going in you know i actually saw a chart this morning that was like one percent of gdp was spent per year on highway and telecom right and then it was two percent for railroads yeah and then this ai build-out is like three and a half percent of gdp it's like only one person got it right which was your brother no that's true 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. [43:18](https://realalpha.app/v/HFOhG1jH2tk/?t=2598) Now it's going to slow down. He was like, no. I remember the Stargate, like $7 trillion or whatever. Right. I mean, now the initial Stargate is sadly small. Yeah. Right? And it was mind-boggling. Yeah, people were laughing. 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. 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 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, via a cloud, or you deliver it what we call on-prem, it impacts both case. [44:31](https://realalpha.app/v/HFOhG1jH2tk/?t=2671) In one case, you rent the capacity and you put your equipment in it. In the other case, your customer rents the data center. And so it is a limitation in the market right now. It exposes a whole bunch of weaknesses in the U.S. Our grid is pathetic and sort of built on 1940s or 50s technology. 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. That's right. It showed that 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 generator and diesel gensets for decades. Now suddenly there's innovation. [45:36](https://realalpha.app/v/HFOhG1jH2tk/?t=2736) The guys at Boom want to use what they designed for jets to power data centers. We're seeing all sorts of interesting things 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 think the industry did itself no favors by doing some dumb stuff at the beginning, tried to pawn off some costs on local communities. It tried to take advantage of local municipalities. There is no reason a data center shouldn't 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 U.S. use less than California almond growers. Not by 1x or 2x or 4x, but between four and seven times the almond growers use more. So, I mean... [46:37](https://realalpha.app/v/HFOhG1jH2tk/?t=2797) The water thing's just not a thing. The water thing's not just a thing. 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, ten thousands 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. I mean, it kind of goes with the whole theme of tech doing a terrible job, communicating about AI in general. Horrible job. We're doing a horrible job. I think the data center thing, the other part of the data center thing that's interesting to me is, like, if I ask you, I don't know, 2018 2020 whatever what's the probability that data centers were going to be a critical factor get another thing i'd have gotten wrong right yeah we'd all got it wrong we got it wrong 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 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 [47:37](https://realalpha.app/v/HFOhG1jH2tk/?t=2857) on a data center shovel ready is an expression you hear in the data center world it means we haven't done shit we want to sell you a pile of dirt ready for your shovels to start doing something it does sound better it does sound better i've got raw land or i've got dirt in oklahoma right Permits and other things that are important. Sometimes, sometimes not. We would never have expected to be where we are today. If you have a good builder, it takes probably from raw land and permits to stand up 50 megawatts, which is a reasonable block. [48:22](https://realalpha.app/v/HFOhG1jH2tk/?t=2902) **Andrew Feldman:** And often even the big sites unfold in 50 megawatt blocks. 50 megawatt blocks is 18 months. [48:29](https://realalpha.app/v/HFOhG1jH2tk/?t=2909) 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 uh because they had a lot of power right that they had the permits for power already and retrofitting them so how much of their kind of supply chain bottleneck is energy versus chips versus the construction versus permitting versus whatever else each chunk of the supply chain has its own supply chain so data centers need uh there's plenty of concrete uh 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 um generators and uh 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 um but then you have to fit it out and turn it into a high-powered hotel for for compute Yeah. [49:48](https://realalpha.app/v/HFOhG1jH2tk/?t=2988) Right? And that takes electricians, takes cooling, chillers, all these other things. Those all are long lead time right now. I was really, I happened to fly over, leaving Memphis last night, I happened to fly over macro hard and macro harder. It was astonishing. Like the size of- Nobody can build like Elon. It's unbelievable. The size of buildings and construction nearby and everything, I was like, whoa, that is tremendous. 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, and for 10 more years, it's always going to not be enough. [50:30](https://realalpha.app/v/HFOhG1jH2tk/?t=3030) Or because of the dynamics of the lead time and what's going on right now, you could imagine like an overbuilt. And betting on which of those worlds you think is going to come out, there's probably some alpha there for whoever calls it. We won't have enough data centers. I'm just, yeah. We won't certainly, I mean, I, seeing out 10 years in our space right now 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. [51:08](https://realalpha.app/v/HFOhG1jH2tk/?t=3068) And for those who use HBM, they'll still be chasing HBM. Yeah. It'll be interesting also how politics plays in all this, because now you've got probably more government interest and involvement in tech and AI specifically than we've ever had, obviously. 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 important decisions. You know, I think this is an area where they're really thoughtful discussions to be had about what's good for the US and good for different municipalities, different rural areas. [51:50](https://realalpha.app/v/HFOhG1jH2tk/?t=3110) And those aren't being had by the politicians, right? They're knee-jerk, they're sort of... Let me ask you a question on this. So don't move away from Paul. 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 and the demand is 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 task per watt maybe and then so like if that's the case then you have cerebrus has a speed advantage a huge speed advantage but like how do you think about that dynamic if we fast forward in terms of what the constraint actually is? [52:51](https://realalpha.app/v/HFOhG1jH2tk/?t=3171) I think there are a couple 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's no market for slow search, right? There's no market for dial-up. Your kids 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 right this is a real punishment let him use it for a week at dial-up speed slowly right that why is we laugh and then we say it's okay for for ai to be slow i think think about it i mean when when the internet was slow netflix delivered dvds and envelopes yeah and when the internet got fast they became a movie studio all right that's not They didn't get better at their other thing. [53:46](https://realalpha.app/v/HFOhG1jH2tk/?t=3226) They became something entirely new. And I think what we're seeing with the launch of GPT-5-6 Sol, right, it's put out in limited availability last week, that people are thinking of whole new applications. You've got frontier intelligence instantly. And 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 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 result is higher throughput? [54:37](https://realalpha.app/v/HFOhG1jH2tk/?t=3277) **Andrew Feldman:** And, you know, with AMD, we're seeing 5x additional throughput, I mean, five times as much throughput, while keeping the speed the same. [54:47](https://realalpha.app/v/HFOhG1jH2tk/?t=3287) And we're seeing similar numbers with AWS. We have an opportunity, because of our architecture, to do that with the entire GPU landscape. We could do it across the board. And so, you know, there are four major chip makers right now in our category. Obviously, NVIDIA, we'd love to partner with them. There's AMD, there's the Google TPU, and there's AWS with their training parts, and we're already working with two. So that's something we've thought a great deal about, and as a vector, we are extremely interested in chasing down. What has it been about NVIDIA that has made them have the just crazy run that they've had? [55:27](https://realalpha.app/v/HFOhG1jH2tk/?t=3327) I think most people are wrong about what makes NVIDIA great. First, NVIDIA is, you know, in the first quarter of this century, they're the great company without any question. And I think people look to a bunch of things. [55:45](https://realalpha.app/v/HFOhG1jH2tk/?t=3345) **Andrew Feldman:** They look to CUDA. I don't think it's CUDA. They look to their chip architecture. I don't think it's their chip architecture. [55:55](https://realalpha.app/v/HFOhG1jH2tk/?t=3355) It's this unbelievable grit and intensity that was born of a A decade of not having success as a public company. I think if you look at their stock chart between about 2003 and 2013, 2004, 2014, for a decade, 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 it's it's not this these other things these are just things people say but that that sort of a decade of being a public company and fighting and fighting and fighting that gets in your dna and that's awesome yeah i mean you like you see jensen like still at events fighting tooth and nail yeah right i mean where could you have imagined five trillion dollar companies could you imagine old tech leaders before jensen doing that no right right that kind of fight yeah i mean that in my view is what's awesome yeah um that level of fight I think the other stuff, cool, good, but when I look at what I can do better, when I look [57:24](https://realalpha.app/v/HFOhG1jH2tk/?t=3444) at sort of what I ought to be thinking about as being a CEO, those are the things I look to. Do you think, did your near-death experiences give your company some of that DNA? You know, we are, and I think this is what's interesting about Jensen too, is he sees himself still as the underdog. 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 um 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 uh we we have sort of bigger competitors right we have bigger challenges we have more people throwing stones at us. And so I hope we take that. I personally wake up every day with that passion and that drive. I mean, every day I think to myself, when we started, they said, it would never work. You can't do wafer scale. We made wafer scale. And then they said, all right, you did wafer scale, but you can't yield it in volume. And then we yield it in volume. They said, okay, you can yield it in volume, but you can't package it in volume production. [58:35](https://realalpha.app/v/HFOhG1jH2tk/?t=3515) 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 frontier lab. And then we won OpenAI. And then they said, okay, you've got government, you've got sovereign clouds, you've got a frontier lab, you don't have a hyperscaler. Then we won a hyperscaler. right each time they said then they said you've got all these cool customers but you couldn't you couldn't do big models now we're serving GPT what are they saying what's that now like what are they saying you can't do now the CEO's a boomer but each time I became a millennial and I shut down you know right and so I think when you're sort of a professional David 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 other people can't do that's why we get up every morning 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 [59:55](https://realalpha.app/v/HFOhG1jH2tk/?t=3595) people can't build them Eric told me that a pretty cool and unique childhood I don't know if that fed in. to this at all but like how'd you grow up I grew up on the Stanford campus my parents were faculty and there's a little neighborhood where all your all your neighbors are other professors my neighbor was William Shockley that's crazy transistor so all we knew about him a you know we're 10 or 8 is that his wife gave out full-size candy bars at Halloween the inventory was at Bell Labs he invent he brought Silicon Valley right 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 think there were a couple things that were glorious first the only currency was intellectual horsepower right that That was, nobody cared if you were rich. [1:00:55](https://realalpha.app/v/HFOhG1jH2tk/?t=3655) Nobody cared if you'd started a company. This was the 70s. What they cared about was, well, that dude's really smart. He does good work. My other neighbor was Amos Tversky. And for his work with David Kahneman, they got a Nobel Prize in economics. My dad's tennis match. There were six or eight guys in rotation. They played doubles on Saturday and Sunday. Somewhere in my mid-20s, I realized three had Nobel Prizes and one had a Fields medal. Right? He sucks at doubles. That's right. No, no. Let me tell you, it was some old man tennis. I mean, their serves were grim. Their physics was good. It's a crazy way to grow up. [1:01:36](https://realalpha.app/v/HFOhG1jH2tk/?t=3696) It's a crazy way to grow up. And, you know, the neighborhood was safe. We'd get on our bikes and we'd just go all summer and come back when it was dark. And so did you think you'd be an academic? I did. Actually, I was working on a PhD. I got a little bored. I went to the business school at Stanford while I completed my qualifying exams for my Ph.D. And I sort of got sucked into this by mistake. And, you know, my dad still asks me, he's like, you're going to finish your Ph.D., Andrew. I'm like, Dad, all my professors are dead. Nobody left. Yeah, amazing. All right, well, Andrew, this was a blast. [1:02:13](https://realalpha.app/v/HFOhG1jH2tk/?t=3733) 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. It's a pleasure to be here and chat with you guys and you know Benchmark was an extraordinary partner. I mean I think if you do hardware you're going to be in bed with your backers for a decade and pick good ones and I'm proud we did. Thank you.