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

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, and I'm eager for that. That's so cool. Today on our priors, 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 into 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 in our priors. Oh, what a pleasure. It's good to see you guys again. 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 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. 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. 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 little bit, but by a lot. Fifteen, eighteen, 20 x 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, 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. Speed became fundamentally important, and we were just crushed with demand. Is it is this faster across the board, or is it specific use cases? Faster across the board. Big model, small models, US models, Chinese models, trillion parameter models, 1,000,000,000 parameter models across the board. Mhmm. And then what happened was, 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 $20,000,000,000. 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 …