Going public is an exchange of professional investors for a different class of investors at a lower cost of capital, but for most companies it remains the path to legitimacy and high valuation — only a handful of AI companies can raise public-market-sized rounds privately.

(00:00:00) Netflix used to deliver DVDs and envelopes. (00:00:02) And when the internet got fast, they became a movie studio. (00:00:05) It opened up an entirely new business, something fundamentally different. (00:00:10) That's what happens with speed. (00:00:12) And I think that's what fast AI does. (00:00:15) Right now, we're replacing things that everybody can see, like coding, design, the SaaS tools. (00:00:20) 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. (00:00:28) And I'm eager for that. (00:00:30) That's so cool. (00:00:37) Today on no priors we have Andrew Feldman, the co-founder and CEO of Cerebras. (00:00:42) 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 into very fast inference for the foundation model world that we live in today. (00:00:54) Cerebras recently went public and is currently worth about $63 billion in the stock market. (00:01:00) So, Andrew, thank you for joining us in our priors. (00:01:02) Oh, what a pleasure. (00:01:02) It's good to see you guys again. (00:01:03) Yeah, so first of all, congratulations. (00:01:05) So your company, Cerebras, just went public. (00:01:08) As of today, it's a $60 billion market cap, which is pretty amazing. (00:01:12) Pretty amazing. (00:01:13) 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. (00:01:20) Could 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? (00:01:30) AI computers, right? (00:01:31) Computers designed and optimized to accelerate AI workloads. (00:01:37) And right now we're the fastest at inference, not by a little, but by a lot, 15, 18, 20x faster than GPUs. (00:01:44) And so what happened was, starting in about 20, 25, AI models got smart enough to be useful. (00:01:52) People began using them. (00:01:53) And you know, we make AI with training and we (00:01:56) We use it with inference. (00:01:57) So as people began to use it, began to sort of be integrated into their day-to-day work. (00:02:04) Speed became fundamentally important and we were just crushed with demand. (00:02:09) Is this faster across the board or is this specific use cases? (00:02:12) Faster across the board. (00:02:13) Big models, small models, US models, Chinese models. (00:02:18) trillion parameter models, 1 billion parameter models across the board. (00:02:21) 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. (00:02:31) And then in March, we signed an agreement with AWS where we will be deployed in their data centers going forward. (00:02:38) And so it was just a whirlwind year and a half of chasing the (00:02:45) chasing supply and trying to sort of meet the demand. (00:02:49) And what shifted in the last year and a half? (00:02:51) Was it the ramp in manufacturing? (00:02:53) Was it a new chip design? (00:02:54) Was it something else? (00:02:55) Could you help educate folks on? (00:02:56) What happened was we built a really, really fast machine, and for a long time, nobody cared. (00:03:04) Actually, forgive me for saying so, but a lot of people objected and said, this is just a weird architecture. (00:03:12) They called it wrong. (00:03:13) Like Cerebrus called it wrong. (00:03:14) Yeah. (00:03:15) they did. (00:03:16) I think to be radically better, right? (00:03:21) You can't build something that is a similar architecture, right? (00:03:26) You're not going to get 15 or 20 times better than the GPU. (00:03:29) with a minor modification to their architecture. (00:03:32) 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. (00:03:40) And from the beginning, 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. (00:03:51) They told us we were out of our mind, it would never work. (00:03:54) They listed reasons why it was impossible. (00:03:58) But in 2019, we proved it was possible. (00:04:00) We began delivering it and we improved on it and we improved on it. (00:04:06) But we were fast when AI was a novelty. (00:04:09) And when it's a novelty, nobody cares that you're fast because it's not being used. (00:04:14) 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. (00:04:23) And once you use something every day in your work, it can't be slow. (00:04:27) I mean, how long will you guys wait for a website to resolve? (00:04:30) I'll have no attentions. (00:04:31) Right, that's exactly right. (00:04:33) That's exactly the way it is. (00:04:34) I mean, how big is the market for slow search? (00:04:37) It's 0. (00:04:38) How big is the market for dial-up internet? (00:04:40) It's 0. (00:04:41) That's how big the market for slow inference will be. (00:04:44) But we had to wait until it was smart enough to be useful. (00:04:47) And that happened in 2025. (00:04:49) And that's why you got this sort of (00:04:51) explosion of demand and companies like Cognition and Cursor and Lovable and just all these others that began ramping extraordinary. (00:05:01) Many of the ones you guys have invested in are ramping like crazy, OpenAI and others. (00:05:08) And we were right there with the right product. (00:05:12) I think I first met you back in 2016 or something like that. (00:05:14) And at the time, people weren't even, like saying AI sounded weird, right? (00:05:18) You were talking about machine learning. (00:05:20) And the models of the time were convolutional neural networks and RNNs, and just the emergence of GANs and things like that. (00:05:27) We were trying to tell the difference between a chair and a cat. (00:05:30) That was Quockly's great. (00:05:32) So his PhD is like a cat or a chair. (00:05:35) It's like, whoa, look how far we've come. (00:05:37) I mean, it's unbelievable. (00:05:38) Yeah, What do you think gave you the foresight to build against the market? (00:05:43) 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. (00:05:50) 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. (00:05:58) What gave you the conviction back then to do this? (00:06:01) Combination of vision, the right co-founders, and a little bit of arrogance, a little bit of luck. (00:06:08) You know, we saw AI on the horizon as a new workload. (00:06:12) And as computer architects, new workloads are opportunity. (00:06:15) It's very, very hard to enter (00:06:19) in the x86 world, right? (00:06:21) Where there's not, nothing new is happening there and nothing has happened for generations. (00:06:26) But when graphics emerged, you got the discrete GPU and you got Nvidia. (00:06:33) And when the mobile compute hit, you got ARM. (00:06:39) 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. (00:06:47) And so we knew that (00:06:49) This new workload would eat a lot of compute. (00:06:54) It would require a new architecture, a dedicated architecture, and it ought to be very different. (00:06:59) The architecture could not be a derivative of what's existing. (00:07:04) Those were our big bets, and they were 100% contrarian. (00:07:07) And they turned out to be dead right. (00:07:10) Were there moments where you just doubted whether this would work, given that? (00:07:13) Oh, for sure. (00:07:14) Yeah. (00:07:14) We had a period, you know, we're solving a problem that had never been solved before. (00:07:20) I mean, there'd been efforts across the entire 70-year history of the compute industry to build a wafer-scale product. (00:07:26) In fact, Gene Amdahl, sort of one of the fathers of our field, one of the guys on Mount Rushmore of compute, (00:07:33) failed miserably to do it. (00:07:35) We had a period between about 2017, middle of 2017 and middle of 2019, where we couldn't build it. (00:07:43) We were spending about 8 million a month. (00:07:45) You're having board meetings every six weeks saying, I can't build it. (00:07:50) No, it's still not working. (00:07:52) And right, Oof is right. (00:07:54) I mean, that's a huge amount of money and a huge amount of conviction your investors have. (00:07:59) And each time we did a failure analysis, we got a little bit better at it. (00:08:03) We got a little bit better at it. (00:08:05) And then in the summer of 19, we yielded it and it began to work. (00:08:11) And 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. (00:08:19) Yeah, 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? (00:08:27) It's like, nobody's been able to do this, and it's working, and we did this. (00:08:32) And it was all amazing, because that's the technical side of it. (00:08:34) And then there's a market side, right? (00:08:35) And also on the market side, to your point, it took time to get to the point where these workloads were really important. (00:08:40) So were there moments where you doubted whether the market existed? (00:08:43) we solved it and we solved this sort of the hardest problem in the computer industry and nobody cared. (00:08:50) Nobody. (00:08:51) It was like, the first Gen. (00:08:53) we might have sold a dozen. (00:08:55) The second Gen. (00:08:56) we probably sold 300 and now we're still going to sell 10s of thousands in the third Gen. (00:09:01) We had a two or three-year period where we were ahead of the market. (00:09:05) And absolutely nobody cared that we were blisteringly fast. (00:09:09) And you found some pioneering customers that were like atypical in terms of starting point, right? (00:09:14) There was some sovereigns who really bought ahead. (00:09:17) Like how did you think about being resilient to this period of being ahead of demand? (00:09:21) I think there's a path that has been laid down by new computer architectures. (00:09:27) And often you begin in the supercomputer world because (00:09:31) Those guys love speed and they don't care if your software is immature. (00:09:35) And so we sort of ran the table there. (00:09:37) We won at Argonne National Labs and at Lawrence Livermore and at Sandia and in Europe, at European Parallel Computing Center at LRZ. (00:09:44) So we ran the table there. (00:09:46) And 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. (00:09:55) But then historically, there's this giant chasm. (00:09:58) because none of them provide the volume to get to mainstream. (00:10:02) And we won a sovereign, a G42, and they became a strategic partner and close friends, and they placed a billion dollar order on us. (00:10:14) And with that, we were able to sort of transform the company. (00:10:18) We're able to change our supply chain. (00:10:19) We're able to deploy equipment in big enough clusters that we could battle test at scale. (00:10:26) 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? (00:10:34) I mean, you can't put $100 million in your QA lab worth of your own gear. (00:10:38) And they worked with us and we began training models for them. (00:10:42) We began doing inference for them. (00:10:45) They've been an extraordinary partner. (00:10:46) This is Pang, who's CEO of G42 and his chairman, Shikh Tuck Noon. (00:10:51) We couldn't ask for better partners. (00:10:53) And so we were able to, when OpenAI came along, when AWS came along, we had the capacity. (00:10:59) We were ready, right? (00:11:01) We'd battle tested. (00:11:02) We'd sort of gotten over the chasm. (00:11:05) We'd had a bridge. (00:11:06) And so we could meet the demand. (00:11:09) Yeah, I think that kind of path dependence is sometimes undervalued in this field, because the ability for you to go from a (00:11:18) 10s, $100 million order to 20 billion of backlog. (00:11:21) Like there's got to be, there's got to be something in the middle of somebody. (00:11:23) It's years of work. (00:11:24) Yeah. (00:11:24) It's years of work. (00:11:25) 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? (00:11:36) But when you're building things, right, you have to, you want to double, you got to call your manufacturing partner, your CM. (00:11:43) They have to find power. (00:11:45) They have to run a building. (00:11:46) They have to add more lines. (00:11:47) You have to make test fixtures, right? (00:11:50) Each step takes real time and effort to grow. (00:11:54) We're going to try to increase manufacturing 10x this year, right? (00:11:58) That's about as fast as anybody in the history. (00:12:00) It's also a maturity of the software stack for you guys that's more scale, right? (00:12:05) You know, when we started the company, Sarah, one of my co-founders, I do remember. (00:12:10) I know. (00:12:11) We presented to you, one of my co-founders said, Andrew, it's going to take about 10 years to build a compiler. (00:12:18) I said, no, that's crazy. (00:12:19) That's big company talk. (00:12:20) We can do it in five. (00:12:22) It takes about 10 years. (00:12:24) It takes a long time to build a compiler. (00:12:27) It is an extraordinarily difficult piece of software. (00:12:31) And now we've got a good software stack. (00:12:35) 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. (00:12:42) How much is all of this AI-generated coding relevant for Cerebras internally? (00:12:48) Hugely. (00:12:49) I would say that eight months ago, we weren't spending $1,000 in engineering. (00:12:54) on tokens and we're probably at 25 or 30,000 right now and it's ripping. (00:12:59) I think it's not useful for everybody. (00:13:01) I think that's truth. (00:13:02) I think there are some people who have sort of the perfect mindset for it. (00:13:08) right? (00:13:08) And they are running 8 or 10 agents, 7 by 24. (00:13:13) They've moved their coding style to being one in which they govern agents, whether they think about how to QA. (00:13:20) So they've got a QA agent running. (00:13:22) They think about how to sort of remedy some of the weaknesses in the coding models, right? (00:13:26) They're often verbose. (00:13:27) They often cut out comments. (00:13:29) So they've really thought about, and it's a type of puzzle (00:13:32) They're the perfect fit for their mind. (00:13:34) And they've gone from being sort of 10x guys to being 100x guys. (00:13:38) I think the rest of us, myself included, we're sort of limping along. (00:13:42) 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. (00:13:53) But for a small number, it is such a tool. (00:13:57) And then the rest, we try and show them what others are doing, what best practices are. (00:14:03) You're about 800 people now. (00:14:04) 800, 850, yeah. (00:14:06) It's a lot of market cap per person. (00:14:08) I like that. (00:14:08) Yeah, that's great. (00:14:09) Yeah, it's a good, metric overall. (00:14:11) When you think about where to go from here, (00:14:15) making business bigger, strategic directions, like what do you predict and where can you go from here? (00:14:20) I think we... (00:14:21) Besides delivery. (00:14:23) Well, when you've got a backlog that's north of 20 billion, delivery is pretty important every day. (00:14:29) I think we have to continue to sort of be fearless. (00:14:34) I think one of the malaise of companies as they get to 1000 to 2000, 3000 people is they stop taking the type of risks. (00:14:44) that they were taking before, right? (00:14:45) You move from being a fearless engineering culture to sort of being, what can we get in the timeframe of the next rev? (00:14:53) And I think that's extraordinarily damaging. (00:14:56) And we take such pride in doing fearless work. (00:15:00) We want to hire people who do fearless work. (00:15:02) We 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. (00:15:10) That is a horrible thing to do. (00:15:12) And so those are some of the things that worry me. (00:15:15) I think recruiting, right? (00:15:16) You have so many openings and it's so easy to settle. (00:15:21) And it's so easy to just try and put a butt in the seat. (00:15:23) Yeah, pretty good. (00:15:24) Let's get that butt in the seat. (00:15:25) I mean, that is death. (00:15:27) And so we think really hard and I spend a meaningful part of every day in talking to candidates. (00:15:33) Those are things that sort of I worry about, I think about every day. (00:15:37) We 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. (00:15:49) They think about how to hire from 800 to several thousand. (00:15:54) We talked about the managing of your own psychology when you're like, am I right for this decade? (00:15:59) How did you like keep and motivate employees when there wasn't external feedback for this long period of time? (00:16:07) Well, first, I have empathy for them. (00:16:10) I mean, being CEO is an extraordinarily lonely thing. (00:16:14) And you're building a business, you're building a business. (00:16:18) You guys know this, that being a leader is lonely and it's not easy. (00:16:23) 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. (00:16:33) You sort of, you gain fire from that chip on your shoulder, right? (00:16:37) When they say it can't be solved, you say in your head, you can't solve it. (00:16:42) Right. (00:16:42) I thought that was just my. (00:16:45) That's right. (00:16:45) That's exactly right. (00:16:48) You know, you were a top venture firm. (00:16:50) You want to do it your way. (00:16:51) right? (00:16:52) And so you stepped out and doing it your way. (00:16:54) And you say to yourself, I can do this. (00:16:56) And it's not easy. (00:16:58) And that's one thing. (00:17:01) The other thing is you have to love the journey, right? (00:17:05) This things we do are too hard if you don't like the building, right? (00:17:09) That you do this for the money is a horrible thing. (00:17:13) There are way easier ways to make money than trying to create something extraordinary and compete with somebody as strong as Nvidia. (00:17:21) That is not the easiest path. (00:17:23) You got to love being a David, right? (00:17:25) I'm a professional David. (00:17:26) This is my fifth startup. (00:17:27) I compete against Goliath. (00:17:30) That is what I do for a living. (00:17:32) And I think to myself that every dollar, every $1,000,000, every billion we sell, if it wasn't for our brains, their muscle would have taken it in a heartbeat. (00:17:43) And you got to love that. (00:17:45) And if you don't love that, it's a very long road. (00:17:48) When do you think, because there's sort of two views of the world in terms of when to give up on something. (00:17:55) And one argument is just going no matter what, and hopefully things work out, or eventually, well, the other view of the world is, you should be constantly reassessing whether the journey you're on is the right one. (00:18:06) And there's some moments where actually giving up is the smartest possible thing you can do. (00:18:10) What's your view on that? (00:18:11) Or how do you think about when's the right time to give up on something? (00:18:14) I 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. (00:18:25) And they all come back negative. (00:18:27) but I see people kind of do this sequentially, right? (00:18:29) They say, I just need to test one more thing and they test and it doesn't work. (00:18:32) I say, I need to test one more. (00:18:33) And so the slippery slope is a beast. (00:18:35) 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? (00:18:49) And I think sometimes having other former CEOs or other really seasoned entrepreneurs (00:18:57) who are on your side and who can share with you. (00:19:00) Remember a year ago, you said, if you got to this point, you didn't have this and to remind you, so they pull you back off that slippery slope, right? (00:19:10) They said, the old frog in the warm water thing is like, you said if it got this hot, you were going to get out. (00:19:16) And it slowly kept getting warmer. (00:19:19) But basically, can other people keep you effectively accountable towards both directions? (00:19:23) That's right, accountable to your own thinking. (00:19:24) Yeah. (00:19:26) If you understand why it's not working, right, if there are some things that you can articulate that have to change in order for it to work, and you can put some sort of time frame on it. (00:19:42) But that is an extraordinarily hard question. (00:19:46) And I think it's probably the case that (00:19:51) lots of efforts ought to be truncated. (00:19:55) And those people sort of redeploy their efforts to new and different ideas that they have. (00:20:00) Yeah, it's kind of like I view it as opportunity cost on life. (00:20:02) And for some people, it's the best moment of their lives in terms of productivity or things they could do. (00:20:06) And so, you know, the cost of time is extremely high. (00:20:10) You know, in your guys' case, obviously it worked out. (00:20:12) What made you all decide to go public? (00:20:13) Similarly, there's differing opinions on when to go public, why to go public, what's the benefits, what's the drawbacks. (00:20:18) What was that in your mind and what made you decide to go out now? (00:20:21) First, sort of going public is exchanging some professional investors, venture capitalists who specialize in technology investing for a different class of investors. (00:20:34) And in so doing, reducing your cost of capital a little bit, right? (00:20:37) This is really what's happening. (00:20:40) Suddenly we go from pros like you to my dad, right? (00:20:44) That's sort of the trade-off. (00:20:46) And in return for that, (00:20:49) you have to agree to be governed by a set of extraordinarily stringent rules. (00:20:53) 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. (00:21:04) That this was never a thing before OpenAI and Anthropic and maybe DanaBricks. (00:21:11) Do you know where the (00:21:13) Yeah, it used to be how long it would take you to get public. (00:21:18) Exactly. (00:21:19) Right. (00:21:19) It used to be four years. (00:21:21) Right. (00:21:21) It used to be four years. (00:21:23) And that was the way you got a valuation in the hundreds of millions. (00:21:27) Right. (00:21:28) But I think. (00:21:29) Now people have a tender cycle. (00:21:30) That's right. (00:21:31) At a certain scale. (00:21:32) It took us 10. (00:21:35) And I think that changes A lot. (00:21:37) right? (00:21:38) What we did is we opened up the secondary market and let people sell, right? (00:21:41) If you're going to bet big chunks of your career with us, we thought it would be perfectly reasonable for you to find modest liquidity as you went along. (00:21:51) I think you have to think very differently if it's going to take you a decade. (00:21:54) 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. (00:22:07) 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. (00:22:21) 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. (00:22:32) And I think all of those are reasonable reasons. (00:22:35) I also think we could offer the public market something unique, right? (00:22:39) We would be the first and only for a period of time, AI pure play. (00:22:43) We are the only company that you can 100% of the revenue comes to this exact market. (00:22:52) There's no gaming, there's no graphics, there's no PC, this is it. (00:22:57) And that was an opportunity, a differentiator that we thought was interesting. (00:23:01) I think there are ways around all the other things. (00:23:04) You can deliver returns to your investors. (00:23:06) 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. (00:23:18) But I think more than anything, for us, it was an opportunity to graduate from corporate adolescence to corporate adulthood. (00:23:28) Can you talk a little bit about, I'm so curious, like how did the open AI deal happen? (00:23:34) what do you think was the point at which you knew that you were a good fit for them? (00:23:42) 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. (00:23:57) We now see the importance.