Cerebras 630亿美元IPO背后的故事：创始人兼CEO Andrew Feldman

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: coding, design, the SaaS tools. But once we start fundamentally reorganizing around this, you're going to see these new business models and fundamental jumps in productivity, and I'm eager for that.

That's so cool. Today in 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 in the machine learning world, and then 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 on the stock market. So, Andrew, thank you for joining us on No Priors.

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

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

Pretty amazing.

Yeah, and I think you were with us a year or two ago on the show, in one of the earlier episodes. 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 about how the business has evolved since that time? As a reminder for our audience, what do you do, what are you focused on, and how are you moving forward?

We build AI computers—computers designed and optimized to accelerate AI workloads. Right now, we're the fastest at inference, not by a little but by a lot: 15, 18, 20× faster than GPUs.

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 use it with inference. As people began to use it, it began to be integrated into their day-to-day work. Speed became fundamentally important, and we were just crushed with demand.

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

Faster across the board. Big models, small models, U.S. models, Chinese models, trillion-parameter models, or 1-billion-parameter models—across the board.

Mhm.

Then, at the end of the year, we signed a deal with OpenAI. It's one of the biggest deals ever in Silicon Valley, north of $20 billion. In March, we signed an agreement with AWS, where we'll be deployed in their data centers going forward.

It was just a whirlwind year and a half of chasing supply and trying to meet the demand.

What you have done in the last year and a half—was it the ramp in manufacturing, a new chip design, or something else? Could you help educate folks on what happened?

We built a really, really fast machine, and for a long time nobody cared.

Actually, forgive me for saying so, but a lot of people objected and said this was just a weird architecture. They called it wrong—like, “Cerebras called it wrong.”

Yeah, they did. I think to be radically better, you can't build something with a similar architecture. You're not going to get 15 or 20 times better than the GPU with a minor modification to its architecture. That's probably true across the board: if you're going to aspire to a radical improvement, your design has to be different.

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 minds and that 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 improved on it.

But we were fast when it was a novelty. When it's a novelty, nobody cares if you're fast because it's not being used. From about 2023 to the beginning of 2025, people pointed at AI, but nobody used it every day in their work.

Mhm.

Once you use something every day in your work, it can't be slow. How long will you guys wait for a website to resolve?

I have no patience.

Right. That's exactly right. 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. That's why you got this explosion of demand, with companies like Cognition, Cursor, Lovable, and all these others ramping extraordinarily fast. Many of the ones you guys have invested in are ramping like crazy—OpenAI and others. We were right there with the right product.

Mhm.

I think I first met you back in 2016 or something like that. At the time, people weren't saying AI; it sounded weird, right? You were talking about machine learning. The models of the time were convolutional neural networks and RNNs, and there was just the emergence of GANs and things like that.

We were trying to tell the difference between a chair and a cat. That was great. So, his PhD is like a cat or a chair. It's like, “Whoa, look how far we've come.” I mean, it's unbelievable.

Yeah. What do you think gave you the foresight to build against the market? To your point, I think a lot of us believed that this market would be really important, and you more than others, 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, it's now this massive use case. People really care about speed of inference and other things. What gave you the conviction back then to do this?

A combination of vision, the right co-founders, a little bit of arrogance, and a little bit of luck. We saw AI on the horizon as a new workload. As computer architects, new workloads are an opportunity.

It's very, very hard to enter the x86 world, where there's nothing new happening there and nothing has happened for generations. But when graphics emerged, you got the discrete GPU, and you got NVIDIA. When mobile compute hit, you got ARM. It was interesting that not Intel, not AMD, and not all sorts of people who you would have thought were really well positioned to win in that business got any share.

We knew that this new workload would eat a lot of compute. It would require a new, dedicated architecture, and that ought to be very different. The architecture could not be a derivative of what existed. Those were our big bets, and they were 100% contrarian.

Mhm.

They turned out to be dead right.

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

Yeah. We had a period where we were solving a problem that had never been solved before. There had been efforts across the entire 70-year history of the computer industry to build a wafer-scale product. In fact, Gene Amdahl, one of the fathers of our field—one of the guys on the Mount Rushmore of compute—failed miserably to do it.

Mhm.

We had a period between about the middle of 2017 and the middle of 2019 where we couldn't build it. We were spending about $8 million a month. You have a board meeting every 6 weeks saying, “I can't build it. No, still not working.”

Mhm.

“Oof” is right. That's a huge amount of money and a huge amount of conviction from your investors. Each time we did a failure analysis, we got a little bit better at it. Then, in the summer of 2019, we yielded it, and it began to work.

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. We were staring at a computer, which is about as exciting as watching paint dry, and it was working. We just couldn't speak for half an hour. It was like, “Nobody's been able to do this, and it's working. And we did this.”

That's amazing, because that's the technical side of it. Then there's a market side, right? On the market side, to your point, it took time to get to the point where these workloads were really important. Were there moments where you doubted whether the market existed?

You know, we solved it, and we solved the hardest problem in the computer industry, and nobody cared. Nobody. The first generation, we might have sold a dozen. The second generation, we probably sold 300. Now we're going to sell tens of thousands in the third generation.

We had a 2- or 3-year period where we were ahead of the market, and absolutely nobody cared that we were blisteringly fast.

You found some pioneering customers that were atypical in terms of their 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?

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 wanted National Labs, at Lawrence Livermore and at Sandia, and in Europe, at the European Parallel Computing Centre at LRZ. So, we ran the table there, and then we won some guys in the oil and gas space and 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. They became a strategic partner and close friends, and they placed a $1 billion order with us. And with that, we were able to sort of transform the company. We were able to change our supply chain. We were able to deploy equipment in big enough clusters that we could battle-test at scale.

One of the challenges in hardware is that your QA lab can't be as big as some of the customers you want to deploy to.

Mhm.

Right? I mean, you can't put $100 million worth of your own gear in your QA lab. 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 Sheikh Tahnoun, its chairman. We couldn't ask for better partners.

And so, 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.

Yeah, I think that kind of path dependence is sometimes undervalued in this field because the ability for you to go from a $10 million or $100 million order to $20 billion of backlog—there's got to be something in the middle. It's years of work.

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

Mhm.

But when you're building things, if you want to double, you've got to call your manufacturing partner, your CM. They have to find power. They have to rent a building. They have to add more lines. They have to make test fixtures. Each step takes real time and effort to grow. We're going to try and increase manufacturing 10x this year. That's about as fast as anybody in the history of hardware.

And so, the maturity of the software stack for you guys—that's more scale, right?

When we started the company, Sarah, one of my co-founders, Gary, and I presented to you. One of my co-founders said, “Andrew, it's going to take about 10 years to build a compiler.” I said, “No, that's crazy. That's big-company talk. We can do it in 5.” It takes about 10 years.

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

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

Hugely. I would say that 8 months ago we weren't spending $1,000 per engineer on tokens, and we're probably at $25,000 or $30,000 right now, and it's ripping. I think that's the truth.

I think there are some people who have the perfect mindset for it. They are running 8 or 10 agents 24/7. They've moved their coding style to being one in which they govern agents. They think about how to QA, so they've got a QA agent running. They think about how to remedy some of the weaknesses in the coding models. They're often verbose. They often cut out comments.

So, they've really thought about it, and it's a type of puzzle that's the perfect fit for their mind. They've gone from being 10x guys to being 100x guys. I think the rest of us, myself included, are 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, for the rest, we try to show them what others are doing and what best practices are.

You're about 800 people now?

800, 850, yeah.

It's a lot of market cap per person.

I like that, yeah. That's good.

A good metric overall. When you think about where to go from here—making the business bigger, strategic directions—what do you predict? Where can you go from here? Besides delivery.

I think we have to continue to be fearless. I think one of the malaises of companies as they get to 1,000, 2,000, 3,000 people is that they stop taking the type of risks that they were taking before. You move from being a fearless engineering culture to asking, “What can we get in the timeframe in the next rev?” And I think that's extraordinarily damaging.

We take such pride in doing fearless work. We want to hire people who do fearless work. We're going to 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.

Those are some of the things that worry me. I think recruiting is one of them. You have so many openings, and it is so easy to settle. It's so easy to just try and put a butt in a seat. “Yeah, pretty good. Let's get that butt in a seat.” I mean, that is death. And so, we think really hard, and I spend a meaningful part of every day talking to candidates. Those are things that I worry about and think about every day.

We have a lot of founders and leaders who listen to the podcast, who are thinking about—they may have a successful business, and they're managing through the period of waiting for the market or trying to figure out if they're still right. They think about how to hire from 800 to several thousand. We talked about managing your own psychology when you're asking, “Am I right for this decade?” How do you keep and motivate employees when there wasn't external feedback for this long period of time?

Well, first, I have empathy for them. Being CEO is an extraordinarily lonely thing. You're building a business. You guys know this: being a leader is lonely. And it's not easy. People don't like to say that.

It's especially true for those of us who like to solve problems—specifically, the problems everyone else says can't be solved. 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.

No, that's right. That's exactly right. You were at a top venture firm. You wanted to do it your way, right? And so, you stepped out to do it your way. You said to yourself, “I can do this.” And it's not easy.

The other thing is, you have to love the journey. The things we do are too hard if you don't like the building. To 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've got to love being a David. I'm a professional David. This is my fifth startup. I compete against Goliath. That is what I do for a living. And I think to myself that every dollar, every million dollars, every billion dollars we sell, if it wasn't for our brains, their muscle would have taken it in a heartbeat. And you've got to love that. If you don't love that, it's a very long road.

When do you think—because there are sort of 2 views of the world in terms of when to give up on something? One argument is just keep going no matter what, and hopefully things work out eventually. The other view of the world is that you should be constantly reassessing whether the journey you're on is the right one, and there are some moments where actually giving up is the smartest possible thing you can do. What's your view on that? How do you think about when the right time is to give up on something?

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.

Yeah, but I see people do this sequentially, right? They say, “Oh, I just need to test one more thing.” They test it, and it doesn't work, and they say, “I need to test one more.” And so—

The slippery slope is a beast. The slippery slope in all things—in ethical situations, in your life—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 and you didn't have this...” and remind you. So, they pull you back off that slippery slope, right? They say, 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.

Can other people keep you effectively accountable to your own thinking?

Yeah. If you understand why it's not working, right? If there are some things that you can articulate that have to change—

Yeah.

—in order for it to work, and you can put some sort of time frame on it, but that is an extraordinarily hard question. I think it's probably the case that lots of efforts ought to be truncated.

Mhm. Yeah.

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

Yeah, there's opportunity cost in life, and for some people it's the best moment of their lives in terms of productivity or things they could do, so the cost of time is extremely high. In your guys' case, obviously, it worked out. What made you all decide to go public? Similarly, there are differing opinions on when to go public, why to go public, what the benefits are, and what the drawbacks are. What was in your mind, and what made you decide to go out now?

First, going public is exchanging some professional investors—venture capitalists who specialize in technology investing—for a different class of investors, and in so doing reducing your cost of capital a little bit. This is really what's happening.

Mhm.

Suddenly, we go from pros like you to my dad. That's sort of the trade-off. 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, 4 or 5 companies that can raise huge amounts of money without going public. That was never a thing before OpenAI and Anthropic and maybe Databricks.

The option-package timeline for Silicon Valley—it's like a 4-year timeline.

Yeah, it used to be how long it would take you to get public. Right, it used to be 4 years, and that was the way you got a valuation in the hundreds of millions, right? But I think—

—and have a tender cycle.

That's right.

And at a certain scale—

It took us 10. 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 of 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 3 in particular, they've been able to raise 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. You get a credibility and a legitimacy from having your books audited, from them being able to see who you are, that is different from when you're private. I think all of those are reasonable reasons.

I also think we could offer the public market something unique. We would be the first and only, for a period of time, AI pure play. We are the only company that has 100% of its revenue in this exact market. There's no gaming, there's no graphics, there's no PC; this is it. 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.

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

I think I spoke to Sam in the middle of summer in 2025. He said, for the first time, “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. We were so much faster than the competition. It felt really good.

What we love is talking to super-smart customers, right? I know you do consumer, too. I can't do consumer. I have a rule that if my mother buys it or uses it, I don't want to make it or sell it, because I—

[Laughter.]

—I really want super-smart customers who are doing really interesting things with our stuff. So we got in with some of their guys, and they were like, “Whoa. This is—we understand now.”

At Thanksgiving, the night before Thanksgiving, we signed a term sheet. Four weeks later, on December 24, we signed a big master agreement.

Incredibly fast.

You know what? They can fly. We were working 7 days a week. They had several law firms. I mean, it was huge. For a $20-plus-billion deal to do it in 4½ weeks was exceptional.

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

And I think I talked to the guys at Cognition, right? They bought Windsurf over a weekend. I think many of the things that we thought were speed of light weren't. They could be done much faster.

I think the rate at which Elon has been able to build data centers—everyone says, “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 3 days.” Actually, you can. “You can't do a deal like this in 24 days.” But if you work on it every day, 8 or 10 hours a day, you can.

I think the art of the possible has been expanded by this push in a way I never would have expected.

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

That's right. I think we have seen some extraordinary operators in this market build amazing things. I mean, the guys at Cursor or Cognition—you see 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. Those were sort of truncated aspirations, which is interesting.

Speaking about Cognition and Cursor and such, the growth of the open-source ecosystem has enabled a generation of companies to do really impressive things. Like—

Super, super impressive.

You know, Devin on Cerebras is a really magical experience. Coding on Cerebras, with high performance at massive speed, is really special. How do you think about open source and post-training workloads, and your perspective on that going forward?

They have fed this market. When closed source was too expensive, the open-source community sort of kept the interest alive and kept the flame going. I think it pushed the closed-source guys.

I think the sort of techniques that we saw from some of the Chinese makers were like, “Whoa. We’ve got to stay ahead of that, right? We can’t rest on our laurels. We can’t depend on the fact that we have bigger training clusters and more data.” 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. That's fun to be in the mix of. It's fun to see other people's ideas do interesting things on your hardware. If you don't love that, your infrastructure's not right for you. You have to love other people's ideas taking flight on what you built.

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

You know what? When I think about what speed does, it doesn't make the existing business models a little better. Netflix used to deliver DVDs in envelopes, and they thought their competition was Blockbuster. When the internet got fast, they became a movie studio. That's what happens with speed.

It wasn't that they got incrementally better and more efficient at delivering DVDs. It opened up an entirely new business, something fundamentally different. Then they sort of became a movie studio. They bought existing movie studios.

I think that's what fast AI does: it will present entirely new business models that are available. I think the easy and the obvious is to replace existing ones. 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. Then with the cloud you were able to get SaaS, and with SaaS we were able to get tools that you previously couldn't afford because they were so expensive for the individual company and the small number of seats. Then you got this massive jump in productivity.

I think AI is the same way. Right now, we're replacing things that everybody can see, like coding, design, and some of the SaaS tools.

But once we start fundamentally reorganizing around this, you're going to see new business models and fundamental jumps in productivity, and I'm eager for that.

That's so cool. Very exciting. Thank you so much for joining us today.

Guys, thank you so much for having me on your show. Really appreciate it.

Congratulations.

Thank you so much.

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