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

Summary

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Podcast Summary: No Priors — "The Story Behind Cerebras’ $63 Billion IPO with Founder and CEO Andrew Feldman"

Date: May 21, 2026
Guests: Andrew Feldman (Founder & CEO, Cerebras)
Hosts: Elad Gil, Sarah Guo

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Episode Overview

This episode centers on the meteoric rise of Cerebras, culminating in its $63 billion IPO. Andrew Feldman shares the multi-year story behind pioneering wafer-scale AI hardware, weathering deep skepticism, and scaling through inflection points as AI shifted from novelty to indispensable infrastructure. The discussion dives into foundational company bets, the grit and resilience required to lead ahead of a market, the path to huge commercial deals with OpenAI and AWS, lessons for founders, and the transformative future of fast AI.

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Key Discussion Points & Insights

1. From Outlier Hardware Bet to Industry Breakout

• Radical Architecture: Feldman recounts Cerebras’s “insane” leap to wafer-scale chips, a fundamentally different approach rejected by much of the industry.
  • “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. They told us we were out of our mind, it would never work…” — Andrew Feldman [03:14]
• Vision and Timing: Cerebras developed for AI before the market existed, investing years in technology that was “blisteringly fast” before mainstream demand arrived.
• Inflection Point (2025): Real adoption began only when AI models became “smart enough to be useful,” triggering an explosion in demand for fast inference.
  • “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 once you use something every day in your work, it can't be slow.” — Andrew Feldman [04:31]

2. Navigating the "Valley of Despair" as an AI Startup

• Technical Breakthroughs: Years spent solving wafer-scale chip challenges (“spending $8 million a month, board meetings every six weeks saying, I can't build it”—[07:14]), with the breakthrough only coming in 2019.
• Early Market Reality: Initial years post-breakthrough saw sparse sales (“might have sold a dozen... probably sold 300” [08:42]) before true product-market fit.
• Customer Strategy: Started with supercomputing labs and high-compute industries, then crossed the volume “chasm” via major sovereign investment (G42), which provided the scale, supply chain transformation, and battle-tested deployments needed for hyperscale demands.

3. Building Through Contrarian Conviction

• Contrarian Bets: Feldman highlights the risk and wisdom of betting against existing architecture norms for step-function improvement.
  • “If you're going to aspire to a radical improvement, your design has to be different.” — Andrew Feldman [03:14]
• Resilience & Psychology: Addressing founder loneliness, the need for loving the journey, and thriving as “David” against “Goliath”—“I'm a professional David. This is my fifth startup. I compete against Goliath.” [16:44]
• Culture of Fearlessness: Now, even with scale, Cerebras strives to be “fearless” and not settle in hiring or ambition, guarding its innovative engineering spirit.
  • “We would much rather fail in pursuit of the extraordinary than succeed in the ordinary. That is a horrible thing to do.” — Andrew Feldman [14:22]

4. Strategic Scale: The OpenAI and AWS Breakthroughs

• Major Commercial Deals:
  • Landmark deal with OpenAI in late 2025 (“north of $20 billion”), onboarding into AWS in early 2026.
• Speed of Closing: The OpenAI deal completed in just over four weeks—an unheard-of pace for such size.
  • “For a 20 plus billion dollar deal. To do it in four and a half weeks was exceptional.” [24:00]
• Market-Wide Shift Toward Speed: Fast-moving markets, rapid M&A, and hyperscale buildouts are now not only possible but expected.

5. AI Tooling and Internal Transformation

• AI-Generated Coding at Cerebras: Not all engineers benefit equally from LLMs and agents, but for some, these tools convert 10x engineers into “100x” contributors.
  • “There are some people who have the perfect mindset for it… they've gone from being sort of 10x guys to being 100x guys.” — Andrew Feldman [13:24]

6. IPO Decision and the Modern Public Company

• Why Go Public?
  • Transitions from “corporate adolescence to corporate adulthood,” lifts cost of capital, enables employee liquidity, and signals credibility—especially as “the first and only for a period of time AI pure play” on public markets. [21:28]
  • “We would be the first and only for a period of time AI pure play. We are the only company that you can [invest in where] 100% of the revenue comes in this exact market.” [22:34]

7. Lessons for Founders: Navigating Giving Up Versus Perseverance

• Reassessing vs. Persevering:
  • Feldman counsels that the right time to give up is “when you've laid out a set of hypotheses… and they all come back negative,” but warns that the “slippery slope” of incremental hope can trap founders. [18:13–19:18]
  • Outside perspectives are crucial for accountability.

8. The Open vs. Closed Source Ecosystem – Current & Future

• Open Source as Market Force: Open models kept innovation alive and pushed closed-source players.
  • “The open source community has sort of kept the interest alive and kept the flame going and I think that… pushed the closed source guys.” [26:56]
• Engineered for Creativity: Feldman emphasizes the excitement of seeing new ideas take root and thrive on Cerebras hardware.

9. The Future: Transformative Power of Fast AI

• Speed Enables New Business Models:
  • Just as broadband turned Netflix from DVDs to a movie studio, fast AI will spark entirely new, currently unimaginable business models and jumps in productivity.
  • “That's what happens with speed. And I think that's what fast AI does… once we start fundamentally reorganizing around this, you're going to see new business models and fundamental jumps in productivity.” [28:19]

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Notable Quotes & Memorable Moments

• On Radical Hardware Bets:
  “To be radically better, right. You can't build something that is a similar architecture… You're not going to get 15 or 20 times better than the GPU with a minor modification to their architecture.” — Andrew Feldman [03:14]
• On Perseverance and Failure:
  “We were spending about $8 million a month. You're having board meetings every six weeks saying, I can't build it. No, it's still not working. And right. Oof is right.” — Andrew Feldman [07:17]
• On Loving the Journey:
  “You have to love the journey, right? The things we do are too hard. If you don't like the building, that you do this for the money is a horrible thing.” — Andrew Feldman [16:44]
• On Market for Slow AI:
  “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.” — Andrew Feldman [04:31]
• On the OpenAI Deal:
  “For a 20 plus billion dollar deal. To do it in four and a half weeks was exceptional.” — Andrew Feldman [24:00]

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Timestamps for Important Segments

• [00:00] — Opening analogy: Fast AI’s disruptive potential
• [01:28] — What Cerebras builds and its product edge
• [03:04] — Why radical architecture was necessary (wafer-scale genesis)
• [07:09] — Technical “valley of death”: How perseverance paid off
• [08:42] — Early sales and the market catching up
• [09:21] — Securing early customers and crossing the “chasm”
• [13:24] — Internal AI coding practices and the engineer “100x” effect
• [14:22] — Maintaining fearless culture through scale and IPO
• [16:06] — Leadership loneliness, psychology, and loving the journey
• [17:47] — When to persevere versus when to give up
• [20:20] — Decision to IPO: why and how now
• [23:41] — The OpenAI deal: speed, fit, and mutual recognition
• [25:55] — The new paradigm: Ambition for high-speed company building
• [26:56] — Open source AI’s role in industry dynamism
• [28:19] — The future: Speed-first AI will reorder business and productivity

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Conclusion

The episode offers a rich tapestry of entrepreneurial courage, technical innovation, and business strategy amid a shifting AI landscape. Feldman’s candid insights highlight not just the dramatic journey of Cerebras, but the qualities needed to pioneer in emerging markets—unwavering vision, resilience, culture, and the audacity to move before consensus emerges. The rise of fast AI stands poised to remake not just how we work and compute, but the very shape of entire industries.

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Transcript

[Andrew Feldman]

Netflix 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.

[Host 1]

Today at no 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 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 in Opriors.

[Andrew Feldman]

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

[Host 1]

Yeah. So first of all, congratulations. So your company Cerebras just went public. As of Today, it's a $60 billion market cap, which is pretty amazing.

[Andrew Feldman]

Pretty amazing.

[Host 1]

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. 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 moving forward.

[Andrew Feldman]

We build AI computers, computers designed and optimized to accelerate AI workloads. And right now we're the fastest at inference. Not by a little bit, but by a lot. 15, 18, 20x faster than GPUs. And so what happened was starting in about 2025, AI models got smart enough to be useful. People began using them. And we make AI with training and we, we use it with inference. So 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.

[Host 1]

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

[Andrew Feldman]

Faster across the board. Big models, small models, US models, Chinese models, trillion parameter models of 1 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. And so it was just a whirlwind year and a half of chasing supply and trying to sort of meet the demand.

[Host 1]

And what shifted in the year, in the last year and a half, was it the ramp in manufacturing? Was it a chip design? Was it something else? Could you help educate folks on what

[Andrew Feldman]

happened was we built a really, really fast machine and for a long time nobody cared. Right.

[Host 2]

That's because actually, forgive me for saying so, but a lot of people objected and said this is just a weird architecture. They called it wrong. Like Cerebras called it wrong.

[Andrew Feldman]

Yeah, yeah, they did. I think to be radically better, right. You can't build something that is a similar architecture. Right. You'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 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 proved it was possible. We began delivering it and we improved on it, and we improved on it, 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. And once you use something every day in your work, it can't be slow. I mean, how long will you guys wait for a website to resolve?

[Host 2]

I'll have no attention.

[Andrew Feldman]

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. 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 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.

[Host 1]

I think I first met you back in 2016 or something like that. And at the time people weren't even like saying AI sounded weird. Right. You would talk 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.

[Andrew Feldman]

We were trying to tell the difference between a chair and a cat. Right. That was Quackley's great. PhD is like a cat or a chair, like, whoa, look how far we've come. It's unbelievable.

[Host 1]

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

[Andrew Feldman]

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 where there's nothing new is happening there. Nothing has happened for generations. But when graphics emerged, you got the discrete GPU and you got Nvidia and when the mobile compute hit, you got army. 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. And so we knew that this new workload would eat a lot of compute. It would require a new architecture, a 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 and they turned out to be dead. Right.

[Host 1]

Were there moments where you just doubted whether this would work, given that for sure.

[Andrew Feldman]

We had a period. We're solving a problem that had never been solved before. I mean there'd been efforts across the entire 70 year history of the computer industry to build a way for scale product. In fact, Gene Amdahl, sort of one of the fathers of our field, one of the guys on Mount Rushmore of computer, 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're having 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. 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, and we're staring at a computer, which is about as exciting as watching paint dry. And it's working. And we just, we 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.

[Host 1]

And it was all amazing because 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?

[Andrew Feldman]

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. It was like, you know, the first gen we might have sold a dozen. The second gen we probably sold 300. And now we're still going to sell tens of thousands of 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.

[Host 2]

And you found some pioneering customers that were like atypical in terms of a starting point. Right. There were some sovereigns who really bought ahead. How did you think about being resilient to this period of being ahead of demand?

[Andrew Feldman]

I 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 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. But then historically, there's this giant chasm because none of them provide the volume to get to mainstream. And we won a sovereign 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're able to change our supply chain. We're 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. You can't put $100 million 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 for them. They've been an extraordinary partner. This is Peng, who's CEO of G42 and his chairman, Sheikh Tuk Nun. We couldn't ask for better partners and so we were able to. When OpenAI came along, when AWS came along, we had the capacity, we were ready, we'd battle tested, we'd sort of gotten over the chasm, we'd had a bridge and so we could meet the demand.

[Host 2]

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

[Andrew Feldman]

You need to 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, but when you're building things right, you, you have to, you want to double, you got to call your manufacturing partner, your cm, you got to, they 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, to grow. We're going to try to increase ManU10x this year. Right. That's about as fast as anybody in the history of hardware and so is

[Host 2]

the maturity of the software stack. For you guys, that's more scale, right?

[Andrew Feldman]

You know, when, when we started the company, Sarah, our, one of my co founders, I know we, we presented to you, one of my co founders said, Andrew, it's going to 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. 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.

[Host 2]

Can I ask you, as an aside, actually, just because you, 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 Cerebras internally?

[Andrew Feldman]

Hugely. I would say that, that, you know, eight months ago we weren't spending $1,000 in engineer on tokens and 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 you, they are 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 they've really thought about. And it's a type of puzzle that the perfect fit for their mind. And they've gone from being sort of 10x guys to being 100x guys. I 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 try and show them what others are doing, what best practices are.

[Host 2]

You're about 800 people now.

[Andrew Feldman]

800. 850.

[Host 1]

Yeah.

[Host 2]

It's a lot of market cap per person.

[Andrew Feldman]

I like that. Yeah, that's good.

[Host 2]

It's a good metric overall. When you think about where to go from from here, making business bigger strategic directions. What do you predict? Where can you go from here besides delivery?

[Andrew Feldman]

Well, when you've got a backlog that's north of 20 billion deliveries, pretty important every day. I think we have to continue to be fearless. I think one of the malaise of companies as they get to a thousand to two thousand, three thousand 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 and 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. 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. 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.

[Host 2]

We have a lot of founders and leaders who are, 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 there. We talked about the managing of your own psychology when you're like, am I right for this decade? How, how did you like, keep and motivate employees when there wasn't external feedback for this long period of time?

[Andrew Feldman]

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. You 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, Right.

[Host 2]

I thought that was just my.

[Andrew Feldman]

That's right. That's exactly right. You know, 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 say to yourself, I can do this and it's not easy. And that, that's one thing. The other thing is you have to love the journey, right? This 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' for our brains, their muscle would have taken it in a heartbeat. And you got to love that. And if you don't love that, it's a very long road.

[Host 1]

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 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's 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?

[Andrew Feldman]

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 and they all come back negative.

[Host 1]

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 say, I need to test one more.

[Andrew Feldman]

And so the slippery slope is a beast. 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. Remember a year ago, you said if you got to this point, you didn't have this and to remind you, so. 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.

[Host 1]

It's basically, can other people keep you effectively accountable to those directions, accountable to your own thinking?

[Andrew Feldman]

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. But that is an extraordinarily hard question. And I think it's probably the case that lots of efforts ought to be truncated and those people sort of redeploy their efforts to new and different ideas that they have.

[Host 1]

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

[Andrew Feldman]

Now first sort of going, 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 capital a little bit. This is really what's happening. Suddenly we go from pros like you to my dad. 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. That this was never a thing before OpenAI and Anthropic and maybe do you

[Host 1]

know where the option package timeline from for Silicon Valley comes from? It's like a four year timeline.

[Andrew Feldman]

Yeah. It used to be how long it would take you to get public. Exactly right.

[Host 1]

It used to be four years.

[Andrew Feldman]

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

[Host 1]

Yeah.

[Andrew Feldman]

Right.

[Host 2]

But I, I think now people have a tender cycle.

[Andrew Feldman]

That's right.

[Host 2]

At a certain scale it took us 10.

[Andrew Feldman]

And I think that changes a lot. What we did is we opened up the secondary market and let people sell. 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. I 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. And 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 comes in 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. But I think more than anything for us it was an opportunity to graduate from corporate adolescence to corporate adulthood.

[Host 2]

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

[Andrew Feldman]

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. 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. It felt really good. And when 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, 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 four weeks later on the 24th of December we signed a big master agreement. And so incredibly fast. You know what, they can fly. And we were working seven days a week. They had several law firms. It was for a 20 plus billion dollar deal. To do it in four and a half weeks was exceptional.

[Host 2]

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

[Andrew Feldman]

And I think I talked to the guys at, at Cognition. They bought Windsurf over a weekend. I think many of the things that we thought were speed of light weren't could be done much faster. I think the rate at which Elon has been able to build data centers, everybody 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. Actually you can. You can't do a deal like this and 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.

[Host 2]

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

[Andrew Feldman]

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

[Host 2]

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

[Andrew Feldman]

like super, super impressive.

[Host 2]

Devin on Cerebras is a really magical experience. Coding on Cerebras is 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?

[Andrew Feldman]

They 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 pushed 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. 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. And that's fun to be in the mix of. Right? 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 got to love other people's ideas to take flight on what you built.

[Host 2]

When you think about experiences you imagine will be possible only on Cerebras is there anything you're excited about and a couple years from now that we shall look out for.

[Andrew Feldman]

When I think about what speed does, it doesn't make the existing business models a little better. Netflix used to deliver DVDs and envelopes and they thought their competition was blockbuster. And when the Internet got Fast they became a movie studio. That's what happens with speed. They didn't get better incrementally and more efficient at delivering DVDs, right. It opened up an entirely new business, something fundamentally different. And then they sort of became a movie studio. They bought existing movie studios. And I think that's what Fast AI does is it will present entirely new sort of business models that are available. I think the easy and the obvious is to replace existing. And we know that when the PC came in, it replaced 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 because they were so expensive to the individual company into the small number of seats, right? Then you got this massive jump in productivity. And I think AI is in the same way that right now we're replacing things that everybody can see, coding, design, right? Some of 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.

[Host 1]

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

[Andrew Feldman]

Thank you so much for having me on your show. Really appreciate it.

[Host 2]

Congratulations.

[Andrew Feldman]

Thank you so much.

[Host 2]

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