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

1. 0:00 – 0:41

   Cold Open

   1. AFAndrew Feldman0:00

      Netflix used to deliver DVDs in envelopes, and when the internet got fast, they became a movie studio, right? 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.

   2. EGElad Gil0:33

      [upbeat music] Today on No Priors, we have Andrew Feldman, the co-founder and CEO of

2. 0:41 – 0:48

   Andrew Feldman Introduction

   1. EGElad Gil0:41

      Cerebras. Cerebras was founded in the mid-2010s to focus on new workloads for AI, particularly the machine

3. 0:48 – 2:17

   Cerebras’ Evolution

   1. EGElad Gil0:48

      learning world, and then has made the transition into 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 on No Priors.

   2. AFAndrew Feldman1:01

      Oh, what a pleasure. It's good to see you guys again.

   3. EGElad Gil1:03

      Yeah. So first of all, congratulations. So, um, your company, Cerebras, just went public. Um, as of today, it's a sixty billion dollar market cap, which is pretty amazing.

   4. AFAndrew Feldman1:11

      Pretty amazing.

   5. EGElad Gil1:12

      Yeah. And you, 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. 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-

   6. AFAndrew Feldman1:27

      Yeah. Sure

   7. EGElad Gil1:27

      ... how you're moving forward.

   8. AFAndrew Feldman1:28

      We, we build AI computers, right? Computers, computers designed to and optimized to accelerate AI workloads. And right now we're the, the fastest at inference, not by a little, but by a lot, 15, 18, 20x faster than GPUs. And so what happened was, um, starting in about twenty twenty-five, 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. So as people began to, to use it, it began to, to sort of be integrated into their day-to-day work. Um, speed became fundamentally important, and we were just crushed with demand.

   9. EGElad Gil2:08

      Is it, is this faster across the board, or is it specific use cases?

   10. AFAndrew Feldman2:12

       Faster across the board. Big models, small models, US models, Chinese models, um,

4. 2:17 – 6:38

   Wafer-Scale Bet Pays Off

   1. AFAndrew Feldman2:17

      trillion-parameter models or one billion-parameter models, across the board.

   2. EGElad Gil2:21

      Mm-hmm.

   3. AFAndrew Feldman2:22

      And then what happened was, uh, at the end of the year, we signed a, a deal with, 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. And so it was just a whirlwind year and a half-

   4. EGElad Gil2:41

      Mm-hmm

   5. AFAndrew Feldman2:41

      ... of ch-chasing the, chasing supply and trying to, trying to sort of m-meet the demand.

   6. EGElad Gil2:49

      And what, what shifted in the year, in, 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-

   7. AFAndrew Feldman2:55

      You know, y- what, what, what happened was we'd built a really, really fast machine, and for a long time nobody cared. [laughs] And they, right? That's-

   8. EGElad Gil3:05

      Mm-hmm

   9. AFAndrew Feldman3:05

      ... because AI-

   10. SGSarah Guo3:06

       Actually, forgive me for saying so, but a lot of people objected and said, "This is just a weird architecture." They, they called it wrong. Like, Cerebras called it wrong. Yeah?

   11. AFAndrew Feldman3:14

       Yeah, they, they did. I, I think, um, to be radically better, right? You, you, you can't build something that, that is a similar architecture, right? You're not gonna get fifteen or twenty times better than the GPU w-with a minor modification to their architecture. And that's probably true across the board, that if you're going to aspire to a, a radical improvement, your design has to be different. And from the beginning, you know, we chose wafer scale, which means we build a forty-six thousand 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, 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. Um, but 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.

   12. EGElad Gil4:22

       Mm-hmm.

   13. AFAndrew Feldman4:23

       And once you use something every day in your work, it can't be slow. I mean, how, how long will you guys wait for a website to resolve?

   14. SGSarah Guo4:30

       I'll have no attention span.

   15. AFAndrew Feldman4:31

       Right. That's exactly right. That, 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. But 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, of demand and companies like Cognition and Cursor and Lovable and, and just all these others that began ramping extraordinarily. Many of the o-ones you guys have invested in are, are ramping like crazy, OpenAI and, and, and, and others. And, and we were right there with the right product.

   16. EGElad Gil5:11

       Mm-hmm. I think I first met you back in 2016 or something like that, and at the time, uh, people weren't ev-weren't ev-- Like, saying AI sounded weird, right? You were talking about machine learning, and the models at the time were, uh, convolutional neural networks and RNNs and, you know, just the emergence of GANs and things like that.

   17. AFAndrew Feldman5:27

       We were trying to tell the difference between a chair and a cat, right?

   18. EGElad Gil5:30

       Yeah.

   19. AFAndrew Feldman5:30

       That was clockwise great. [laughs]

   20. EGElad Gil5:31

       That's true. It was the cupcake versus the dogs and-

   21. AFAndrew Feldman5:32

       So sort of his PhD is like a cat and, or a chair. It's like, whoa-

   22. EGElad Gil5:35

       Yeah

   23. AFAndrew Feldman5:35

       ... look how far we've come. I mean, it's unbelievable.

   24. EGElad Gil5:38

       Yeah, yeah. What do you think, um, gave you the foresight to build against the market? Because to your point, I think a, a lot of us believed then 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, uh, to your point now, it's, it's this massive use case. People really care about speed of inference and other things. Um, what gave you the conviction back then to do this?

   25. AFAndrew Feldman6:01

       Combination of, of vision, um, the right co-founders, and a little bit of arrogance-

   26. EGElad Gil6:06

       Mm-hmm

   27. AFAndrew Feldman6:06

       ... a little bit of luck. You know, we, we saw AI on the horizon as a new workload, and as computer architects, new workloads are opportunity, right? It's very, very hard to, to, to enter i-in the x86 world, [chuckles] right? Where there's not-

   28. EGElad Gil6:21

       Mm-hmm.

   29. AFAndrew Feldman6:21

       Nothing new is happening there, and nothing has happened for generations. But, you know, when graphics emerged, you got the discrete GPU, and you, you, you got, uh, Nvidia, and, and when, uh, when the mobile, uh, compute hit, y- you got Arm.

5. 6:38 – 8:37

   Challenges and Breakthroughs

   1. AFAndrew Feldman6:38

      And it was interesting that, that not Intel, not AMD, not all sorts of people who you would've thought have been really well-positioned to win in that business, they all got no share. And so we knew that, that, that this new workload would eat a lot of compute. It would require, uh, a new architecture, dedicated architecture, and it 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.

   2. EGElad Gil7:06

      Mm-hmm.

   3. AFAndrew Feldman7:07

      And they turned out to be dead right.

   4. EGElad Gil7:09

      Were there moments where you just doubted whether this would work, given that it took time for-

   5. AFAndrew Feldman7:12

      Oh, for sure.

   6. EGElad Gil7:13

      Yeah.

   7. AFAndrew Feldman7:14

      We had a period, uh... You know, w- we're solving a problem that, that had never been solved before.

   8. EGElad Gil7:19

      Mm-hmm.

   9. AFAndrew Feldman7:19

      I mean, there'd been efforts across the entire 70-year history of the computer industry to build a wafer-scale product. In fact, Gene Amdahl, sort of one of the fathers of our field, one of the, the, the guys on Mount Rushmore of compute, failed miserably to do it.

   10. EGElad Gil7:34

       Mm-hmm.

   11. AFAndrew Feldman7:35

       We had a period between about 2017, middle of 2017, and middle of 2019, where we couldn't build it. We were spending about eight million a month.

   12. EGElad Gil7:45

       Mm-hmm.

   13. AFAndrew Feldman7:45

       You're having board meetings every six weeks saying, "I, I can't build it. [chuckles] No, 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, at it, we got a little bit better at it. Um, and then in the summer of, of '19 we, we yielded it, and it began to work. And the first time, we were sitting in a, 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-

   14. EGElad Gil8:23

       Mm-hmm

   15. AFAndrew Feldman8:23

       ... and it's working, and we just... We couldn't speak for half an hour, right? [chuckles] It's like, "Nobody had been able to do this, and it's working, and we did this." And it was all-

   16. EGElad Gil8:32

       Yeah. It's amazing, 'cause that, that's the technical side of it, and then there's the market side, right? And also on the market side, to your point, it took time to

6. 8:37 – 10:38

   Crossing the Market Chasm

   1. EGElad Gil8:37

      get to the point where these workloads were really important. Um, so were there moments where you doubted whether the market existed?

   2. AFAndrew Feldman8:43

      Oh. You know, we, we, we, we solved it, and we solved this sort of the hardest problem in the computer industry, and nobody cared. Nobody. [chuckles] 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 selling, gonna 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.

   3. EGElad Gil9:08

      Hmm.

   4. SGSarah Guo9:09

      And you found some pioneering customers that were, like, atypical-

   5. AFAndrew Feldman9:12

      We did

   6. SGSarah Guo9:12

      ... in terms of s- of starting point, right? There was a-

   7. AFAndrew Feldman9:14

      We did

   8. SGSarah Guo9:14

      ... 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. AFAndrew Feldman9:21

      Well, I, I think there's a, there's a, a, a path that has been laid down by new computer architectures, and often you begin in the, in the super compute world because those guys love speed, and they don't care if your software is immature.

   10. EGElad Gil9:33

       Mm-hmm.

   11. SGSarah Guo9:34

       Hmm.

   12. AFAndrew Feldman9:34

       And so we, we sort of ran the table there. We, 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, and then we, we won some guys in, 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 [chuckles] because none of them provide the volume to get to mainstream. And we won a, a sovereign, uh, G42, um, and they became, uh, a strategic partner and, and close friends. Um, 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, that we could battle test at scale. You know, one of the challenges in hardware is y- your QA lab c- can't be as big as some of the customers you wanna deploy to.

   13. SGSarah Guo10:32

       Mm-hmm.

   14. AFAndrew Feldman10:33

       Right? But you can't put $100 million in your QA lab worth of your own gear. And

7. 10:38 – 12:03

   Scaling Software and Hardware

   1. AFAndrew Feldman10:38

      they worked with us, and we, 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 of G42, and his chairman, Shi Tuknung. We couldn't ask for better partners. And so we, we were able to, when OpenAI came along, when AWS came along, we, we had the capacity. We were ready, right? We'd battle tested. We'd sort of gotten over the, the chasm. We, we'd had a bridge, and so we could, could meet the, the demand.

   2. SGSarah Guo11:09

      Yeah. I think that kind of path dependence is sometimes undervalued in, in this field because the ability for you to go from a, you know, like, tens, hundred million dollar order to 20 billion of backlog, like, there's gotta be, there's gotta be something in the middle. Is somebody-

   3. AFAndrew Feldman11:23

      It's years of work.

   4. SGSarah Guo11:24

      Yeah.

   5. AFAndrew Feldman11:24

      It's years of work. And, and you know, it's... I, I think often, and I'm sure many of your listeners, um, are in the software world and, and you guys can scale so fast.

   6. EGElad Gil11:34

      Mm-hmm.

   7. AFAndrew Feldman11:35

      Right? But, but when you're building things, right, you, you have to... You wanna double, you gotta call your manufacturing partner, your CM. [chuckles] You gotta... They have to find power. They have to rent a building. They have to add more lines. They have to make test fixtures, right? The... E- each step takes real time and effort to, to grow. We're gonna try to increase manufacturing 10X this year.

   8. SGSarah Guo11:57

      Mm-hmm.

   9. AFAndrew Feldman11:57

      Right? That's about as fast as anybody in the history of hardware.

   10. SGSarah Guo12:01

       And it's also maturity of the software stack for

8. 12:03 – 13:31

   Relevance of AI-Generated Coding

   1. SGSarah Guo12:03

      you guys that's more scale, right?

   2. AFAndrew Feldman12:04

      You know, when, when we started the company, Sarah, ourOne of my co-founders, Gary

   3. SGSarah Guo12:08

      I do remember. [laughs]

   4. AFAndrew Feldman12:09

      I know. Uh, we, we presented to you, uh, one of my co-founders said, "Andrew, it's gonna take about 10 years to, to build a compiler." I said, "No, that's crazy. That's big company talk. We can do it in five." Takes about 10 years. [laughs]

   5. SGSarah Guo12:23

      [laughs] Turns out.

   6. AFAndrew Feldman12:24

      It takes a long time to build a compiler, is an extraordinarily difficult piece of software. And, um, now we've got good, a good software stack.

   7. SGSarah Guo12:34

      Can I ask you as an aside, actually, just because you, you have, for more than a decade, believed that this revolution's gonna happen, uh, how much is all of this, um, AI-generated coding relevant for Cerebras internally?

   8. AFAndrew Feldman12:48

      Uh, hugely. I would say that, that, you know, eight months ago, we weren't spending $1,000 in engineer on tokens, and w- we're probably at 25 or 30,000 right now, and it's ripping. I, I think it's not useful for everybody. I, I think that's the truth. I, I think there are some, some people who have sort of the perfect mindset for it, right? And y- you- they are running eight or 10 agents, seven by 24. They've moved their coding st- style to being one in which they govern agents.

   9. SGSarah Guo13:17

      Mm-hmm.

   10. AFAndrew Feldman13:17

       Whether they think about h- 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. Th- they often cut out comments. So they've really thought about, and

9. 13:31 – 17:16

   Leadership and Hiring Culture

   1. AFAndrew Feldman13:31

      it's a type of puzzle that's a perfect fit for their mind, and they've gone from being sorta 10X guys to being 100X guys. I think the rest of us, myself included, we're sort of limping along. We, we're, we're trying to figure out h- how we can make it work for, for our different jobs, for being the CEO, for being the CFO, for being accountants, for being in marketing. Um, but for a, a small number, it is such a tool. And then the rest, we try and, try and show them what, what, what others are doing, what best practices are.

   2. SGSarah Guo14:02

      You're about 800 people now?

   3. AFAndrew Feldman14:04

      800, 850, yeah.

   4. SGSarah Guo14:06

      Um, it's a lot of market cap per person.

   5. AFAndrew Feldman14:07

      I like that, yeah.

   6. SGSarah Guo14:08

      Yeah.

   7. AFAndrew Feldman14:08

      That's great.

   8. SGSarah Guo14:09

      It's a good, good metric overall. Um, when you think about where to go from here, you know, making business bigger, strategic directions, like, what do you, what do you predict, and where, where can you go from here?

   9. AFAndrew Feldman14:20

      I, I, I think we-

   10. SGSarah Guo14:21

       Besides delivery. [laughs]

   11. AFAndrew Feldman14:22

       Well, wh- when you've got a backlog that's north of 20 billion, delivery's pretty important every day. Um, I, I think we have to, uh, continue to, to, to sort of be fearless. I, 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, to, to sort of being, "What, what can we get in in the timeframe of the next rev?" And I, I, I think that's extraordinarily damaging, and we take such pride in doing fearless work. Um, we wanna hire people who do fearless work. We wanna, wanna kinda sort of guard that culture that, that says we would much rather fail in pursuit of the extraordinary than succeed in the ordinary. That, that is a horrible thing to do. And so those are some of the things that worry me. I think recruiting, right? You, you have so many openings, and it, it's so easy to settle, and it's so easy to just try and put a butt in a seat. Yeah, pretty good. Let's get that butt in the seat. I mean, that, that is death. And so, um, we think really hard, and I spend a meaningful part of every day in talking to candidates. So those are things that, that sort of I, I worry about and I think about every day.

   12. SGSarah Guo15:37

       W- we have a, um, a lot of founders and leaders who, you know, 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. Um, there-- we've talked about the managing of your own psychology when you're like, "Am I right for this decade?" How, uh, how do you, like, keep and motivate employees when there wasn't external feedback for this long period of time?

   13. AFAndrew Feldman16:06

       W- well, first, I, I, I have empathy for them. I mean, being CEO is an extraordinarily lonely thing. And, uh, you, you're building a business. You're building a business. You, you guys know this, that, that being a leader i- 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, you sort of, you, you, you gain fire from that chip on your s- on your shoulder, right? When, when they say it can't be solved, you say in your head, "You can't solve it." [laughs]

   14. SGSarah Guo16:41

       [laughs]

   15. AFAndrew Feldman16:42

       Right? Right. That's-

   16. SGSarah Guo16:43

       I hope that was just my head. [laughs]

   17. AFAndrew Feldman16:44

       That, no, that's right. That's exactly right. You know. Um, you know, you, 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 say to yourself, "I can do this," and it's not easy. And, um, that, that's o- one thing. The, the, the other thing is you have to love the journey, right? Th- this, things we do are too hard if you don't like the building, right? That you do this for the money is, is a horrible thing. There are way easier ways to make money than, than trying to create

10. 17:16 – 19:40

    When to Quit vs. Persist

    1. AFAndrew Feldman17:16

       something extraordinary and compete with somebody as strong as, as Nvidia. That is not the easiest path. You, you gotta love being a David, right? I'm a professional David. This is my fifth startup. I compete against Goliath. Um, that is what I do for a living. And I, I, 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've taken it-

    2. SGSarah Guo17:40

       Mm-hmm

    3. AFAndrew Feldman17:41

       ... in a heartbeat. And you gotta love that. And, and if you don't love that, it's a, it's a very long road.

    4. EGElad Gil17:48

       When do you think, um... 'Cause there's sort of two views of the world in terms of, um, when to give up on something. And, you know, one argument is just keep 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.Um, what's your view on that? Or, or how do you think about when's the right time to give up on something?

    5. AFAndrew Feldman18:13

       I think it is clearly the s- the, the right time to give up when, uh, you, you've laid out a set of hypotheses about what it's gonna take-

    6. EGElad Gil18:22

       Mm-hmm

    7. AFAndrew Feldman18:23

       ... to, to win, and they all come back negative.

    8. EGElad Gil18:27

       Yeah.

    9. AFAndrew Feldman18:27

       Right?

    10. EGElad Gil18:27

        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-

    11. AFAndrew Feldman18:33

        The, the-

    12. EGElad Gil18:33

        I see a lot of founders just-

    13. AFAndrew Feldman18:33

        ... slippery slope is a beast.

    14. EGElad Gil18:35

        Yeah.

    15. AFAndrew Feldman18:35

        The slippery slope in, in, in all things.

    16. EGElad Gil18:37

        Yeah.

    17. AFAndrew Feldman18:38

        In ethical situations, in your life. I mean, the slippery slope is really, um, something you have to guard against, right? And I, I think sometimes having other former CEOs or other really seasoned entrepreneurs who are on your side a-and who can share with you, "R-remember a year ago you said if you got to this point and you didn't have this," and to remind you, so, so they pull you back off that slippery slope, right? They, they said, you know, the old frog in the, the warm water thing is like, "You said if it got this hot, you were gonna get out." [chuckles]

    18. EGElad Gil19:16

        Yeah, yeah.

    19. AFAndrew Feldman19:16

        And it slowly kept getting warmer.

    20. EGElad Gil19:18

        So it's basically can other people keep you effectively accountable-

    21. AFAndrew Feldman19:21

        That's right

    22. EGElad Gil19:22

        ... towards both directions. Yeah.

    23. AFAndrew Feldman19:23

        A-accountable to your own thinking.

    24. EGElad Gil19:24

        Yeah.

    25. AFAndrew Feldman19:25

        Um, i-if you understand why it's not working, right? I-if there are some things that, that you can articulate that have to change-

    26. EGElad Gil19:35

        Yeah

    27. AFAndrew Feldman19:36

        ... um, in order for it to work, and, and you can put some sort of timeframe on it.

11. 19:40 – 22:57

    Why Cerebras Went Public

    1. AFAndrew Feldman19:40

       Um, but th-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.

    2. EGElad Gil19:54

       Mm-hmm. Yeah.

    3. AFAndrew Feldman19:55

       And those people sort of redeploy their efforts to new and different ideas that they have.

    4. EGElad Gil20:00

       Yeah, it's kinda like I, I view it as opportunity cost on life, and for some people-

    5. AFAndrew Feldman20:02

       It is

    6. EGElad Gil20:03

       ... 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. Um, 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. What, what was that in your mind, and what made you decide to go out now?

    7. AFAndrew Feldman20:21

       First, uh, sort of go-going public is exchanging some professional investors, venture capitalists who specialize in technology investing, for a different class of investors and, and in so doing, reducing your cost of cap a little bit, right? Th-this is really what's happening.

    8. EGElad Gil20:38

       Mm-hmm.

    9. AFAndrew Feldman20:39

       Um, suddenly we go from pros like you to my dad, r-right? [chuckles] That, that, that's sort of the trade-off. Um, and in return for that, uh, you have to agree to, 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. That this was never a thing before OpenAI and Anthropic and, uh, maybe, uh, Databricks.

    10. EGElad Gil21:11

        Do you know where, do, do you know where the, um, option package timeline from Silicon for Silicon Valley comes from? It's like a four-year timeline, isn't it?

    11. AFAndrew Feldman21:16

        Yeah, it used to be how long it would take you to get public.

    12. EGElad Gil21:18

        Exactly.

    13. SGSarah Guo21:18

        Mm-hmm.

    14. EGElad Gil21:18

        Yeah.

    15. AFAndrew Feldman21:19

        Right? It used to be-

    16. EGElad Gil21:20

        So it used to be four years, huh?

    17. AFAndrew Feldman21:20

        Right. It used to be four years, and that was the way you got evaluation in the hundreds of millions.

    18. EGElad Gil21:26

        Yeah.

    19. AFAndrew Feldman21:27

        Right? But I, I think-

    20. SGSarah Guo21:28

        Now people have a tender cycle.

    21. AFAndrew Feldman21:30

        That's right. [chuckles]

    22. SGSarah Guo21:31

        At a certain scale.

    23. AFAndrew Feldman21:32

        It took us ten. Um, and I, I, 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 gonna bet big chunks of your career with us, we thought it would be perfectly reasonable for you to, to, to find, uh, modest liquidity as you went along. I think you have to think very differently if it's gonna take you a decade.

    24. EGElad Gil21:53

        Mm-hmm.

    25. AFAndrew Feldman21:54

        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 vi-valuations in the private market. Um, I think for the rest of the world, if you want super high, uh, valuations, if you want, uh, the legitimacy that comes with it, um, historically, large companies like doing business with other public companies in the US. And you, you get a credibility and a legitimacy-

    26. EGElad Gil22:24

        Mm-hmm

    27. AFAndrew Feldman22:24

        ... from having your books audited, from them being able to see who you are, that, that is different than when you're private.

    28. EGElad Gil22:31

        Mm-hmm.

    29. AFAndrew Feldman22:31

        And I, I think all of those are, 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-

    30. EGElad Gil22:41

        Mm-hmm
12. 22:57 – 25:54

    The OpenAI Deal

    1. AFAndrew Feldman22:57

       an opportunity, a differentiator, um, that, that we thought was interesting. I, I think there are ways around all the other things. You, you can deliver returns to your investors. I, I think both, uh, Elon and Ali have been really creative about allowing employees to sell and, and allowing investors who, who have ten-year funds to, to, to find some liquidity in the process. But I, I, I think m-more than anything, um, for us, it was an opportunity to graduate from corporate adolescence to corporate adulthood.

    2. SGSarah Guo23:28

       Can you talk a little bit about, I'm so curious, like, how did the OpenAI deal happen? You know, what were, um, what do you think was the point at which you, you knew that you were a good fit for them?

    3. AFAndrew Feldman23:41

       I, I think I spoke to Sam in, in sort of middle of summer in, in '25, and he said for the first time, he, he said, "W-we're, we've been trying so hard just to keep up with demand. We, we now see the importance of fast inference." That produced, uh, a set of trials and some testing that w- that was done, um, and we were so much faster than the, than the competition. It felt really good. And whenWell, we love talking to super smart customers, right? I mean, I, I can't-- I know you do consumer too. I, I can't do consumer. I have a rule that if my, my mother buys it or uses it, I don't wanna make it or sell it. Um, [chuckles] 'cause I, I, I, I really want super smart customers who are doing really interesting things with our stuff. And so we got in with, um, some of their guys, and they were like, "Whoa, this is co- we understand now." And at Thanksgiving, the night before Thanksgiving, we signed a term sheet. And, you know, four weeks later, on the twenty-fourth of December, we signed, uh, a big master agreement. And so, um-

    4. SGSarah Guo24:48

       That's incredibly fast. Yeah

    5. AFAndrew Feldman24:50

       ... you know what? Th-they can fly. And, uh, you know, we were working seven days a week. I mean, they had several law firm. I mean, it, it was a hu- for a $20-plus billion deal, to, to do it in four and a half weeks was, was exceptional.

    6. EGElad Gil25:04

       Mm-hmm.

    7. SGSarah Guo25:04

       I actually think that's like a crazy characteristic of, uh, this market that I've not personally experienced before, which is everybody's trying to keep up with demand.

    8. AFAndrew Feldman25:12

       I, I-

    9. SGSarah Guo25:12

       Yeah

    10. AFAndrew Feldman25:12

        ... and, and I think, you know, I, I talked to the guys at, uh, at Cognition, right? They bought Windsurf over a weekend, right? I, I think many of the things that we thought were speed of light weren't.

    11. EGElad Gil25:24

        Mm-hmm.

    12. AFAndrew Feldman25:25

        Right? Could be done much faster. And I, I think, you know, the, the rate at which Elon has been able to build data centers, right? Everybody's, "Oh, you can't do it that way," except if you're him, in which case you can, or you can't buy a three-hundred-million-dollar company in three... Actually, you can. You can do a deal like this in, in 24 days. But if you work on it every day-

    13. EGElad Gil25:44

        Mm-hmm

    14. AFAndrew Feldman25:44

        ... for 8 or 10 hours a day, you can. And I, I think the, the art of the possible has been expanded by, by this push in a, in a way

13. 25:54 – 27:37

    Open Source and Post-Trained Workloads

    1. AFAndrew Feldman25:54

       I'd never have expected.

    2. EGElad Gil25:55

       Mm-hmm.

    3. SGSarah Guo25:56

       And I think it's a huge advantage to have the ambition for speed if you believe it is possible.

    4. AFAndrew Feldman26:00

       That, that's right. I, I, I think we have seen some extraordinary operators, uh, in this market build amazing things, right? I mean, uh, the guys at Cursor and Cogni- you, you see sort of growth we've never seen before. You can't grow that fast. Well, actually, you can. Um, you can't build data centers. You can't do deals. It just... Th-those were sort of truncated aspirations, which is interesting.

    5. SGSarah Guo26:25

       Speaking about these companies like Cog and Cursor and such, uh, uh, the growth of the open source ecosystem has enabled a generation of companies to do really impressive things like-

    6. AFAndrew Feldman26:37

       Super, super impressive

    7. SGSarah Guo26:39

       ... uh, you know, Devin on Cerebras is a really magical experience.

    8. AFAndrew Feldman26:42

       It, it's cool.

    9. SGSarah Guo26:42

       Coding on Cerebras is like, uh, like high performance at massive speed is really special. Um, how do you, how do you think about, you know, open source and post-trained workloads and, and your perspective on that going forward?

    10. AFAndrew Feldman26:56

        Th-they have fed this market, right? When, uh, closed source was, was too expensive, the open source community ha-has sort of kept the interest alive a-and kept the flame going, and I, I think that, that the, uh, and pushed the, uh, the closed source guys. I, I think the, the sort of techniques that we saw by, uh, some of the Chinese makers, like, whoa, we, we gotta stay ahead of that, right? We, we can't rest on our laurels. We, we can't depend on the fact that we have, uh, bigger training clusters and more data.

14. 27:37 – 30:07

    How Speed Opens Up New Business

    1. AFAndrew Feldman27:37

       Um, and I think that's made for an extraordinarily vibrant ecosystem, right? I, I think it's, uh, made for creativity, uh, or, and allowed creativity to, to, to, to take root and, and really produce interesting results, and that's fun to be in the mix of, right? It-it's fun to see other people's ideas do interesting things on your hardware, and that's-- if you don't love that, your infrastructure is not right for you. You gotta love other people's ideas to take flight on, on what you built.

    2. SGSarah Guo28:08

       Uh, when you think about, um, experiences you imagine will be possible only on Cerebras, is there anything you're excited about in a couple years from now that we should all look out for?

    3. AFAndrew Feldman28:19

       You know what? When I think about w-what speed does, um, it, it, it doesn't make the existing business models a little better, right? Um, you know, Netflix used to deliver DVDs in envelopes, and they thought their competition was Blockbuster, and when the internet got fast, they became a movie studio, right? That's what happens with speed. I mean, it wasn't a, a... They didn't get better incrementally at, at more efficient at delivering DVDs, right? It opened up an entirely new business, s-something fundamentally different. Um, and th-then they sort of became a movie, movie studio. They bought existing movie studios, and, and I think that's what, what, what fast AI does i-is it will present entirely new, um, sort of business models that, that are available. I, I think the easy and the obvious is to replace existing, and we, we know that, that, that when the, the PC came in, it replaced, right, typewriters and general ledger accounting, but the big jump in productivity was when it reorganized how we did work, and you got the cloud, and then with the cloud, you were able to get SaaS, and with SaaS, you were able to get tools that you previously couldn't afford 'cause they were so expensive to the individual company and to the small number of seats, right? Then you got this massive jump in productivity, and I, I think AI is in the same way, that right now we're, we're, we're replacing things that, that everybody can see, like coding, design, right, some of the, the SaaS tools. But once we start sort of fundamentally reorganizing around this, you're gonna see this sort of new business models and fundamental jumps in productivity, and I'm eager for that. That's so cool.

15. 30:07 – 30:32

    Conclusion

    1. EGElad Gil30:07

       Very exciting. Thank you so much for joining us today.

    2. AFAndrew Feldman30:09

       Guys, thank you so much for having me-

    3. EGElad Gil30:10

       It's great to have you

    4. AFAndrew Feldman30:10

       ... on your show. Really appreciate it.

    5. SGSarah Guo30:12

       Congratulations.

    6. EGElad Gil30:12

       Yeah.

    7. AFAndrew Feldman30:12

       Thank you so much.

    8. SGSarah Guo30:14

       [upbeat music] Find us on Twitter at no priers pod. Subscribe to our YouTube channel if you wanna see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.

Episode duration: 30:33