The Story Behind Cerebras’ $63 Billion IPO with Founder and CEO Andrew Feldman

0:00Netflix used to deliver DVDs and envelopes. And when the internet got fast, they became a movie studio. It opened up an entirely new business, something fundamentally different. That's what happens with speed. And I think that's what fast AI does. Right now, we're replacing things that everybody can see, like coding, design, the SaaS tools. But once we start sort of fundamentally reorganizing around this, you're going to see this sort of new business models and fundamental jumps in productivity. And I'm eager for that. That's so cool.

0:36cool. Today I know priors we have Andrew Feldman, the co-founder and CEO of Cerebras. Cerebras was founded in the mid-2010s to focus on new workloads for AI, particularly the machine learning world, and then has made the transition in a very fast inference for the foundation model world that we live in today. Cerebras recently went public and is currently worth about$63 billion in the stock market. So Andrew, thank you for joining us in O 'Priors. Oh, what a pleasure. It's good to see you guys again. Yeah. So first of all, congratulations. So your company's three gross just went public. As of today, it's a$60 billion market cap, which is pretty amazing.

1:11Pretty amazing. Yeah. And I think you were with us a year or two ago on the show in one of the earlier episodes. And it was a pleasure to talk to you then. And obviously we're very excited to have you on today. Can you tell us a bit how the business evolved since that time and what you folks, just a reminder for our audience, what you do, what you're focused on, how you're going forward we we build ai computers right computers computers designed to and optimize to accelerate ai workloads and right now we're the the fastest at inference not by a little bit but by a lot 15 18 20x faster than gpus and so what happened was um starting in about 2025 ai models got smart enough to be useful people began using them and you know we make ai with training and we We use it with inference.

1:57So as people began to use it, it began to sort of be integrated into their day-to-day work. Speed became fundamentally important, and we were just crushed with demand. Is this faster across the board, or is it specific use cases? Faster across the board. Big models, small models, U.S. models, Chinese models, trillion-parameter models, one billion-parameter models across the board. And then what happened was at the end of the year, we signed a deal with OpenAI, sort of one of the biggest deals ever in Silicon Valley, sort of north of$20 billion. And then in March, we signed an agreement with AWS, where we will be deployed in their data centers going forward.

2:37And so it was just a whirlwind year and a half of chasing supply and trying to sort of meet the demand. And what shifted in the last year and a half? Was it the ramp in manufacturing? Was it a new chip design? Was it something else? Could you help educate folks on? What happened was we built a really, really fast machine and for a long time, nobody cared. Actually, forgive me for saying so, but a lot of people objected and said, this is just a weird architecture. They called it wrong. Cerebris called it wrong. Yeah, they did. I think to be radically better, right, you can't build something that is a similar architecture, right?

3:25You're not going to get 15 or 20 times better than the GPU with a minor modification to their architecture. And that's probably true across the board. That if you're going to aspire to a radical improvement, your design has to be different. and from the beginning you know we chose wafer scale which means we build a 46 ,000 square millimeter chip a chip the size of a dinner plate whereas everybody else is building chips the size of postage stamps they told us we were out of our mind it would never work they listed reasons why it was impossible but in 2019 we we proved it was possible we began delivering it and we improved on it and we improved on it.

4:05But we were fast when AI was a novelty. And when it's a novelty, nobody cares that you're fast because it's not being used. And so from about 2023 to the beginning of 25, sort of people pointed at AI, but nobody used it every day in their work. And once you use something every day in your work, you can't be slow. I mean, how long will you guys wait for website to resolve. I'll have no attention. Right. That's exactly right. That's exactly the way it is. I mean, how big is the market for slow search? It's zero. How big is the market for dial-up internet? It's zero. That's how big the market for slow inference will be.

4:43But we had to wait until it was smart enough to be useful. And that happened in 2025. And that's why you got this sort of explosion of demand and companies like Cognition and Cursor and Lovable and just all these others that began ramping, extraordinary. Many of the ones you guys have invested in are ramping like crazy, OpenAI and others. And we were right there with the right product. I think I first met you back in 2016 or something like that. And at the time, people were, like saying AI sounded weird, right? You were talking about machine learning and the models of the time were convolutional neural networks and RNNs and just the emergence of GANs and things like that.

5:27We were trying to tell the difference between a chair and a cat, right? That was Kwok Lee's great. So his PhD is like a cat or a chair. It's like, whoa, look how far we've come. I mean, it's unbelievable. Yeah. What do you think gave you the foresight to build against the market? Because to your point, I think a lot of us believed in that this market would be really important and you more than others, right? Since you actually started a company in it. But then it took some time for the market to really expand to the point where, to your point now, it's this massive use case, People really care about speed of inference and other things.

5:58What gave you the conviction back then to do this? Combination of vision, the right co-founders, and a little bit of arrogance, a little bit of luck. You know, we saw AI on the horizon as a new workload. And as computer architects, new workloads are opportunity. It's very, very hard to enter in the x86 world, right? Where there's not nothing new is happening there and nothing has happened for generations. But, you know, when graphics emerged, you got the discrete GPU and you got NVIDIA. And when the mobile compute hit, you got ARM. And it was interesting that not Intel, not AMD, not all sorts of people who you would have thought have been really well positioned to win in that business, they all got no share.

6:46And so we knew that this new workload would eat a lot of compute. it would require a new architecture, dedicated architecture, and that ought to be very different. The architecture could not be a derivative of what's existing. Those were our big bets, and they were 100 % contrarian, and they turned out to be dead right. Were there moments where you just doubted whether this would work, given that it took time for - Oh, for sure. We had a period, we're solving a problem that had never been solved before. I mean, they've been efforts across the entire 70-year history of the computer industry to build a wafer-scale product.

7:26In fact, Gene Amdahl, sort of one of the fathers of our field, one of the guys on Mount Rushmore of compute, failed miserably to do it. We had a period between about 2017, middle of 2017, and middle of 2019, where we couldn't build it. We were spending about$8 million a month. You have in board meetings every six weeks saying, I can't build it. No, it's still not working. And right, oof is right. I mean, that's a huge amount of money and a huge amount of conviction your investors have. And each time we did a failure analysis, we got a little bit better at it. We got a little bit better at it. And then in the summer of 19, we yielded it and it began to work.

8:10and the first time we were sitting in a little makeshift office in downtown Los Altos in a building that was not designed for hardware guys. And we're staring at a computer, which is about as exciting as watching paint dry and it's working. And we just couldn't speak for half an hour. Right. It's like, nobody had been able to do this and it's working and we did this. And it was all amazing. Cause that's the technical side of it. And then there's a market side, right. And also on the market side, to your point, it took time to get to the point where these workloads were really important. So were there moments where you doubted whether the market existed?

8:42Oh, you know, we solved it and we solved this sort of, the hardest problem in the computer industry and nobody cared. Nobody. It was like, you know, the first gen we might've sold a dozen. The second gen we probably sold 300 and now we're still going to sell tens of thousands in the third gen. We had a two or three year period where we were ahead of the market and absolutely nobody cared that we were blisteringly fast. And you found some pioneering customers that were like atypical in terms of starting point, right? There were some sovereigns who really bought ahead. Like, how did you think about being resilient to this period of being ahead of demand?

9:21I think there's a path that has been laid down by new computer architectures. And often you begin in the supercomputer world because those guys love speed and they don't care if your software is immature. And so we sort of ran the table there. We won at Argonne National Labs and at Lawrence Livermore and at Sandia and in Europe at European Parallel Computing Center at LRZ. So we ran the table there. Then we won some guys in the oil and gas space and we won some guys in pharma, all of whom have long histories of using extraordinary amounts of compute. But then historically, there's this giant chasm because none of them provide the volume to get to mainstream.

10:01And we won a sovereign, a G42. And they became a strategic partner and close friends. And they placed a billion-dollar order on us. And with that, we were able to sort of transform the company. We were able to change our supply chain. We were able to deploy equipment in big enough clusters that we could battle test at scale. One of the challenges in hardware is your QA lab can't be as big as some of the customers you want to deploy to. right i mean you can't put a hundred million dollars in your qa lab worth of your own gear and they worked with us and we began training models for them we began doing inference with uh for them they've been an extraordinary partner this is peng who's ceo g42 and his chairman we couldn't ask for better partners and so we we were able to when open ai came along when aws came along, we had the capacity.

10:59We were ready, right? We'd battle tested. We'd sort of gotten over the chasm. We'd had a bridge. And so we could meet the demand. Yeah. I think that kind of path dependence is sometimes undervalued in this field because the ability for you to go from a, you know, like tens, a hundred million dollar order to 20 billion of backlog, like there's gotta be, there's gotta be something in the middle. It's years of work. Yeah. It's years of work. And, you know, it's, I think often, and I'm sure many of your listeners are in the software world and you guys can scale so fast, right? But when you're building things, right, you have to, you want to double, you got to call your manufacturing partner, your CM.

11:42They have to find power. They have to rent a building. They have to add more lines. They have to make test fixtures, right? Each step takes real time and effort to grow. We're going to try to increase manufacturing 10x this year, right? That's about as fast as anybody in the history of hardware. It's also maturity of the software stack for you guys. That's more scale, right? When we started the company, Sarah, one of my co-founders, Jerry. I do remember. I know. So we presented to you, one of my co-founders said, Andrew, it's going to take about 10 years to build a compiler. I said, no, that's crazy.

12:19That's big company talk. We can do it in five. It takes about 10 years. It takes a long time to build a compiler. It is an extraordinarily difficult piece of software. And now we've got a good software stack. Can I ask you as an aside, actually, just because you have for more than a decade believed that this revolution is going to happen. How much is all of this AI-generated coding relevant for Cerebris internally? Hugely. I would say that eight months ago, we weren't spending$1 ,000 in engineer on tokens, and we're probably at$25 ,000 or$30 ,000 right now, and it's ripping. I think it's not useful for everybody.

13:00I think that's the truth. I think there are some people who have sort of the perfect mindset for it, right? and they're running eight or 10 agents, seven by 24. They've moved their coding style to being one in which they govern agents, whether they think about how to QA. So they've got a QA agent running. They think about how to sort of remedy some of the weaknesses in the coding models, right? They're often verbose. They often cut out comments. So they've really thought about, and it's a type of puzzle that's the perfect fit for their mind. And they've gone from being sort of 10X guys to being 100X guys.

13:38I think the rest of us, myself included, we're sort of limping along. We're trying to figure out how we can make it work for our different jobs, for being the CEO, for being the CFO, for being accountants, for being in marketing. But for a small number, it is such a tool. And then the rest, we try and show them what others are doing, what best practices are. You're about 800 people now? 800, 850, yeah. That's a lot of market cap per person. I like that. Yeah. Yeah. It's a good, good metric overall. When you think about where to go from here, you know, making business bigger, strategic directions, like what do you, what do you predict?

14:18Where can you go from here? I think we - Besides delivery. Well, when you've got a backlog that's north of 20 billion, delivery is pretty important every day. I think we have to continue to sort of be fearless. I think one of the malaise of companies as they get to 1 ,000 to 2 ,000, 3 ,000 people is they stop taking the type of risks that they were taking before, right? You move from being a fearless engineering culture to sort of being, what can we get in in the time frame of the next rev? And I think that's extraordinarily damaging. And we take such pride in doing fearless work. We want to hire people who do fearless work.

15:01We want to kind of sort of guard that culture that says we would much rather fail in pursuit of the extraordinary than succeed in the ordinary. That is a horrible thing to do. And so those are some of the things that worry me. I think recruiting, right? You have so many openings and it's so easy to settle and it's so easy to just try and put a butt in the seat. Yeah, pretty good. Let's get that butt in the seat. I mean, that is death. And so we think really hard and I spend a meaningful part of every day and talking to candidates. Those are things that sort of I worry about, I think about every day.

15:37We have a lot of founders and leaders who listen to the podcast, who are thinking about maybe they have a successful business and they're managing through the period of waiting for the market or trying to figure out if they're still right. They think about how to hire from 800 to several thousand. We talked about the managing of your own psychology when you're like, am I right for this decade? How did you keep and motivate employees when there wasn't external feedback for this long period of time? Well, first, I have empathy for them. I mean, being CEO is an extraordinarily lonely thing. And you're building a business, you're building a business.

16:17You guys know this, that being a leader is lonely and it's not easy. And people don't like to say that, especially for those of us who like to solve problems, specifically the problems everyone else says can't be solved. You sort of, you gain fire from that chip on your shoulder, right? When they say it can't be solved, you say in your head, you can't solve it. Right? I don't know if it's just my head. That's right. That's exactly right. You know, you were a top venture firm. You want to do it your way. Right? And so you stepped out and doing it your way. And you said to yourself, I can do this.

16:56And it's not easy. And that's one thing. The other thing is you have to love the journey. Right? Things we do are too hard if you don't like the building. That you do this for the money is a horrible thing. There are way easier ways to make money than trying to create something extraordinary and compete with somebody as strong as NVIDIA. That is not the easiest path. You got to love being a David. I'm a professional David. This is my fifth startup. I compete against Goliath. That is what I do for a living. And I think to myself that every dollar, every million dollars, every billion dollars we sell, if it wasn't for our brains, their muscle would have taken it in a heartbeat.

17:42And you got to love that. And if you don't love that, it's a very long road. When do you think, because there's sort of two views of the world in terms of when to give up on something. And, you know, one argument is just going no matter what. And, you know, hopefully things work out or eventually they will. The other view of the world is, you know, you should be constantly reassessing whether the journey you're on is the right one. And there are some moments where actually giving up is the smartest possible thing you can do. What's your view on that? Or how do you think about when's the right time to give up on something?

18:13I think it is clearly the right time to give up when you've laid out a set of hypotheses about what it's going to take to win. And they all come back negative. Yeah, but I see people kind of do this sequentially, right? They say, oh, I just need to test one more thing. And they test it and it doesn't work. And they say, I need to test one more. And so - The slippery slope is a beast. Yeah. The slippery slope in all things, in ethical situations, in your life. I mean, the slippery slope is really something you have to guard against, right? And I think sometimes having other former CEOs or other really seasoned entrepreneurs who are on your side and who can share with you.

19:00Remember a year ago, you said, if you got to this point and you didn't have this and to remind you, so they pull you back off that slippery slope, right? They said, you know, the old frog in the warm water thing is like, you said, if it got this hot, you were going to get out. And it slowly kept getting warmer. So basically, can other people keep you effectively accountable towards both directions? Accountable to your own thinking. Yeah. If you understand why it's not working. Right. If there are some things that you can articulate that have to change. Yeah. In order for it to work and you can put some sort of time frame on it.

19:41um but that is an extraordinarily hard question and i i think uh it's probably the case that that lots of of of efforts ought to be truncated and those people sort of redeploy their efforts to new and different ideas that they have yeah it's kind of like i view it as opportunity costs on life and for some people it's the best moment of their lives that in terms of productivity or things they could do. And so, you know, the cost of time is extremely high. You know, in your guys' case, obviously it worked out. What made you all decide to go public? Similarly, there's differing opinions on when to go public, why to go public, what's the benefits, what's the drawbacks?

20:18What was that in your mind and what made you decide to go out now? First, sort of going public is exchanging some professional investors, venture capitalists who specialize in technology investing for a different class of investors. And in so doing, reducing your cost of capital a little bit, right? This is really what's happening. Suddenly we go from pros like you to my dad, right? That's sort of the trade-off. And in return for that, you have to agree to be governed by a set of extraordinarily stringent rules. I think your question is complicated by the fact that there have been, for the first time in history, four or five companies that can raise huge amounts of money without going public, This was never a thing before OpenAI and Anthropic and maybe Databricks.

21:11Do you know where the option package timeline for Silicon Valley comes from? It's like a four-year timeline. Yeah, it used to be how long it would take you to get public. Exactly. Right? It used to be four years. Right? It used to be four years. And that was the way you got a valuation in the hundreds of millions. Yeah. Right? But I think - Now people have a tender cycle. That's right. At a certain scale. It took us 10. and I think that changes a lot, right? What we did is we opened up the secondary market and let people sell, right? If you're going to bet big chunks your career with us, we thought it would be perfectly reasonable for you to find modest liquidity as you went along.

21:50I think you have to think very differently if it's going to take you a decade. But I think for a very small number of companies, those three in particular, they've been able to raise sort of public market money at public market valuations in the private market. I think for the rest of the world, if you want super high valuations, if you want the legitimacy that comes with it, historically, large companies like doing business with other public companies in the US. And you get a credibility and a legitimacy from having your books audited, from them being able to see who you are that is different than when you're private.

22:31And I think all of those are reasonable reasons. I also think we could offer the public market something unique, right? We would be the first and only for a period of time, AI pure play. We are the only company that you can 100 % of the revenue, this exact market. There's no gaming, there's no graphics, there's no PC, this is it. And that was an opportunity, a differentiator that we thought was interesting. I think there are ways around all the other things. You can deliver returns to your investors. I think both Elon and Ali have been really creative about allowing employees to sell and allowing investors who have 10-year funds to find some liquidity in the process.

23:17But I think more than anything, for us, it was an opportunity to graduate from corporate adolescence to corporate adulthood. Can you talk a little bit about, I'm so curious, like how did the opening idea all happen? You know, what were, what do you think was the point at which you knew that you were a good fit for them? I think I spoke to Sam in sort of middle of the summer in 25. And he said for the first time, he said, we've been trying so hard just to keep up with demand. And we now see the importance of fast inference. That produced a set of trials and some testing that was done. And we were so much faster than the competition.

24:07It felt really good. And we love talking to super smart customers, right? I mean, I can't, I know you do consumer too. I can't do consumer. I have a rule that if my mother buys it or uses it, I don't want to make it or sell it. Because I really want super smart customers who are doing really interesting things with our stuff. And so we got in with some of their guys and they were like, whoa, this is, we understand now. And at Thanksgiving, the night before Thanksgiving, we signed a term sheet. And, you know, four weeks later on the 24th of December, we signed a big master agreement. And so - That's incredibly fast.

24:50You know what? They can fly. And, you know, we were working seven days a week. I mean, they had several law firms. I mean, it was a heap for a 20 plus billion dollar deal to do it in four and a half weeks was exceptional. I actually think that's like a crazy characteristic of this market that I have not personally experienced before, which is everybody's trying to keep up with demand. And I think, you know, I talked to the guys at Cognition, right? They bought WindSurf over a weekend, right? I think many of the things that we thought were speed of light weren't, right? Could be done much faster.

25:27And I think, you know, the rate at which Elon has been able to build data centers, right? We say, oh, you can't do it that way, except if you're him, in which case you can, or you can't buy a$300 million company in three days. Actually, you can. You can't do a deal like this in 24 days. But if you work on it every day for eight or 10 hours a day, you can. And I think the art of the possible has been expanded by this push in a way I'd never have expected. And I think it's a huge advantage to have the ambition for speed if you believe it is possible. That's right. I think we have seen some extraordinary operators in this market build amazing things, right?

26:08I mean, the guys at Cursor and Cognito, you've seen sort of growth we've never seen before. You can't grow that fast. Well, actually, you can't. You can't build data centers. You can't do deals. Those were sort of truncated aspirations, which is interesting. Speaking about these companies like COG and Cursor and such, the growth of the open source ecosystem has enabled a generation of companies to do really impressive things. Super, super impressive. You know, Devin on Cerebrus is a really magical experience. Coding on Cerebrus is like high performance at massive speed is really special. How do you think about open source and post-trained workloads and your perspective on that going forward?

26:56They have fed this market, right? When closed source was too expensive, the open source community has sort of kept the interest alive and kept the flame going. And I think that the, and push the closed source guys, I think the sort of techniques that we saw by some of the Chinese makers, like, whoa, we got to stay ahead of that, right? We can't rest on our laurels. We can't depend on the fact that we have bigger training clusters and more data. And I think that's made for an extraordinarily vibrant ecosystem. I think it's made for creativity and allowed creativity to take root and really produce interesting results.