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

**Andrew Feldman** (0:00)
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 fundamentally reorganizing around this, you're going to see this new business models and fundamental jumps in productivity. I'm eager for that. That's so cool.

**Elad Gil** (0:37)
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 at No Priors.

**Andrew Feldman** (1:01)
Oh, what a pleasure. It's good to see you guys again.

**Elad Gil** (1:03)
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** (1:11)
Pretty amazing.

**Elad Gil** (1:12)
Yeah.
I think you were with us a year or two ago on the show in one of the earlier episodes, and it was a pleasure to talk to you then, and obviously we're very excited to have you on today. Can you tell us a bit how the business evolved since that time and just a reminder for our audience, what you do, what you're focused on, how you're moving forward?

**Andrew Feldman** (1:28)
We build AI computers, computers designed and optimized to accelerate AI workloads. Right now, we're the fastest at inference, not by a little bit, but by a lot, 15, 18, 20x 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 we 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.

**Elad Gil** (2:09)
Is this faster across the board or is this specific use cases?

**Andrew Feldman** (2:11)
Faster across the board. Big models, small models, US models, Chinese models, trillion parameter models, one billion parameter models across the board. Then what happened was at the end of the year, we signed a deal with OpenAI, one of the biggest deals ever in Silicon Valley north of $20 billion. Then in March, we signed an agreement with AWS, where we will 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.

**Elad Gil** (2:49)
What shifted in the last year and a half? Was it the ramp in manufacturing? Was it a new chip design? Was it something else? Could you help educate folks on?

**Andrew Feldman** (2:56)
What happened was we built a really fast machine, and for a long time, nobody cared.

**Sarah Guo** (3:06)
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** (3:14)
Yeah, they did. I think to be radically better, 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. 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 work. And once you use something every day in your work, you can't be slow. I mean, how long will you guys wait for a website to resolve?

**Sarah Guo** (4:30)
I'll have no attention.