No Priors: Artificial Intelligence | Technology | Startups
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?
00:05:38 · Elad Gil, Andrew Feldman
[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? [00:06:00] S0: Combination of of vision, the right cofounders, and a little bit of arrogance, a little bit of luck. You know, we we saw AI on the horizon as a new workload. And as computer architects, new workloads are opportunity. Right? It's very, very hard to to to enter in the x 86 world, right, where there's not nothing new is happening there, and nothing has happened for generations. But, you know, when graphics emerged, you got the discrete GPU, and you you you got NVIDIA, and and when when the mobile compute hit, you you got ARM. And it was interesting that 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 that that this new workload would eat a lot of compute. It would require a new architecture, dedicated architecture, and that ought to be very different. The architecture could not be a derivative of what's existing. Those were our big bets, and they were a 100% contrarian. [00:07:06] S1: Mhmm.
00:07:06 · Elad Gil, Andrew Feldman
[00:07:06] S1: Mhmm. [00:07:07] S0: And they turned out to be dead right. [00:07:09] S1: Were there moments where you just doubted whether this would work given the [00:07:12] S0: different for sure. Yeah. We had a period you know, we're solving a problem that had never been solved before. I mean, there'd been efforts across the entire seventy year history of the compute industry to build a wafer scale product. In fact, Gene Amdahl, sort of one of fathers of our field, one of the guys on Mount Rushmore of compute, failed miserably to do it. We had a period between about 2017, middle of twenty seventeen and middle of twenty nineteen, where we couldn't build it. We were spending about 8,000,000 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 of nineteen, we we yielded it, and it began to work. And the first time, we were sitting in a a little makeshift shift 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 Mhmm. 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.
00:08:31 · Elad Gil, Andrew Feldman, Sarah Guo
[00:08:31] S1: That's amazing. Because that that's the technical side of it, 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? [00:08:42] S0: You know, 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 selling in a sense of tens of thousands in 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. [00:09:08] S2: And you found some pioneering customers that were like atypical in terms of a starting point, right? Were some sovereigns who really bought ahead. Like, how did you think about being resilient to this period of being ahead of demand?