No Priors: Artificial Intelligence | Technology | Startups
00:00:00 · Andrew Feldman, Elad Gil
[00:00:00] S0: Netflix used to deliver DVDs and 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 gonna see this sort of new business models and fundamental jumps in productivity. I'm eager for that. That's so cool. [00:00:37] S1: Today at No Prize, we have Andrew Feldman, the cofounder and CEO of Cerebras. Cerebras was founded in the mid two thousand tens to focus on new workloads for AI, particularly the machine learning world, and then has made the transition in a very fast inference for the foundation model world that we live in today. Cerebras recently went public and is currently worth about $63,000,000,000 in the stock market. So Andrew, thank you for joining us on Opriors. [00:01:01] S0: Oh, what a pleasure. It's good to see you guys again.
00:01:01 · Andrew Feldman, Elad Gil
[00:01:01] S0: Oh, what a pleasure. It's good to see you guys again. [00:01:03] S1: Yeah. So first of all, congratulations. So your company, Cerebras, just went public. As of today, it's a 60,000,000,000 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. Obviously, we're very excited to have you on today. Can 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? [00:01:28] S0: We build AI computers. Right? Computers designed to and optimized to accelerate AI workloads. And right now, we're the the fastest at inference, not by a little bit, but by a lot. Fifteen, eighteen, 20x faster than GPUs. And so what happened was starting at about 2025, AI models got smart enough to be useful. People began using them, and you know, we make AI with training, and we use it with inference. So as people began to use it, it began to to sort of be integrated into their day to day work. Speed became fundamentally important, and we were just crushed with demand. [00:02:08] S1: Is it is this faster across the board, is this specific use cases? Faster across the board.
00:02:08 · Elad Gil, Andrew Feldman, Sarah Guo
[00:02:08] S1: Is it is this faster across the board, is this specific use cases? Faster across the board. [00:02:13] S0: Big models, small models, US models, Chinese models, trillion parameter models, 1,000,000,000 parameter models across the board. And then what happened was at the end of the year, we signed a deal with OpenAI, of one of the biggest deals ever in Silicon Valley, sort of north of $20,000,000,000 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 the chasing supply and trying to trying to sort of meet [00:02:48] S1: the demand. And what what shifted in the year and 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 What folks on [00:02:56] S0: what what happened was we built a really, really fast machine, and for a long time nobody cared. Right? That's because Actually, forgive [00:03:07] S2: me for saying so, but a lot of people objected and said this is just a weird architecture. They called it wrong. Like Cerubis called it wrong.
00:03:07 · Sarah Guo, Andrew Feldman
[00:03:07] S2: me for saying so, but a lot of people objected and said this is just a weird architecture. They called it wrong. Like Cerubis called it wrong. [00:03:14] S0: Yeah. Yeah. They did. I think to be radically better, 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, you know, 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 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. Mhmm. 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? [00:04:30] S2: You'll have no attention.
00:04:30 · Sarah Guo, Andrew Feldman, Elad Gil
[00:04:30] S2: You'll have no attention. [00:04:31] S0: 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 demand in 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. [00:05:11] S1: I think I first met you back in 2016 or something like that. And at the time, people weren't like, saying AI sounded weird, right? You were talking about machine learning, and the models at the time were convolutional neural networks and RNNs and just the emergence of GANs and things [00:05:26] S0: like that. We were trying to tell the difference between a chair and a cat. Right? That was Quackley's great swarm of his face. Like a cat and or a chair. Like, woah. Look how far we've come. I mean, it's unbelievable.
00:05:26 · Andrew Feldman, Elad Gil
[00:05:26] S0: like that. We were trying to tell the difference between a chair and a cat. Right? That was Quackley's great swarm of his face. Like a cat and or a chair. Like, woah. Look how far we've come. I mean, it's unbelievable. [00:05:38] S1: Yeah. Yeah. What do you think gave you the foresight to build against the market? Because to your point, think a lot of us believed in 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 to do this?