01:34:43 · Rodrigo Liang
[01:34:43] S4: Well, look, if you look at the AI ecosystems up to date, we've been really focused on hyperscale clouds, putting out the neo clouds. The frontier models, we're really focused on building those out, you know, gigawatts here, megawatts there. I mean, you know, just this large scale deployment. And then what you're still thinking about is, well, how do I get as many tokens out per, you know, megawatt per kilo you know, kilowatt, megawatt, and gigawatt that I have. Right? And so that's kind of we're very focused on that. Some of them will play into that are really, really efficient models. We run the big models really fast. That's really what we're known for. Mhmm. Huge models. Right? You know, the battleground for power and data center and all that is about huge models. It's not about the little models, it's about the trillion, multi trillion per model models. That's what we focus on and we run that faster than anybody else in the world. But then you flip it over to the enterprise, and for them, it's not as much about how many thousands of racks are going to fit into an environment. It's about data privacy. It's about security. It's about having the capacity when I want it. It's about low latency. It's about these things that, again, you go back to traditional computing. It's what they've always wanted. They always wanted low latency or high performance, all security They and always wanted that. What we can do is go on over is that because our our rack is a 10 kilowatt air cooled rack compared to what traditional GPs are 140 kilowatts per rack. With 10 kilowatt air cooled rack producing state of the art AI.
01:36:06 · John Coogan, Rodrigo Liang
[01:36:06] S1: Sure. [01:36:07] S4: Now you can go into your existing data centers. Don't need to go buy new data. You already have data centers. Roll out the old gear, bring in the new gear, off we go, right? And you're running the latest and greatest models at great performance. So that's kind of what's exciting about it. You know, there are first and foremost, our customer, you know, they're buying racks and deploying them, but then broadly, they're really also a co developer when it comes to enterprise and you secure AI for enterprise, right? Because they are the most knowledgeable when it comes to deploying these types of applications. They're just so smart about this stuff. And so we can partner with them and figure out how to bring the enterprise on board, which again, if we all think about it, there's three classes of AI clients. You've got the model makers. You've the clouds, and people forget, enterprise is just waking up. Historically, there have been a massive amount of spend. Mass, Right? And so they they're just looking at when you see at Jake Morgan, some of the banks, and some of the the other retails, some to come aboard. And I think that's gonna be a part of the ecosystem. I think it's gonna be something that's gonna be around.
01:37:08 · John Coogan
[01:37:08] S1: Yeah. Last question for me. Jordy, you can take whatever after. But walk me through the pre chat GPT era for your company. I imagine that since 2022, 2023, it's been on fire, and and and demand has been through the roof. But how did the company survive from 2017 to 2022? What were you doing? Who were the customers? Did you have revenue? Like, what was was it all r and d and venture funding? Like, what was the what was the early history of the company? Because that felt that feels like a very potentially rough period, but how did you get through that that that time?
01:37:43 · Rodrigo Liang, John Coogan
[01:37:43] S4: Well, look. No. I mean, I mean, I've been in chip and design for a long time. Well, I always know that the long term investment. And so, I mean, back in 2017, we're still trying to recognize cats and dogs on the Web. It's just a cat or a dog. That's what we're doing. We're trying to figure out what do we do with this thing. We're building recommender models. We've also submitted a thing for people, but the reality is what OpenAI did, it just fleshed out all of that noise pre OpenAI and focused all the value around, let's start here. It doesn't mean that images aren't important. Doesn't mean voice mail. All these other things are But what it did is focused us as a global market to say, let's start here. Let's just start with language. And it's a very good place to start, and that allowed us to actually go all of us can come come together to say hardware, software, models, everybody start focusing in on language as the use case. And now you've got so much production traction, and now you can focus on other things. Like, we're doing great in voice, and we're doing great in video. These are things that are also coming, but the ability to sequence these things in production so that you can actually see value has been incredibly important. [01:38:51] S1: Love it.
01:38:51 · John Coogan, Jordi Hays
[01:38:51] S1: Love it. [01:38:51] S0: On on that note, going forward, how are you thinking about, you know, reacting to customer needs? Like this partnership with with JPM and some of your other, you know, new customers feels like reacting to this, real time need of, like, okay, we understand the potential. We're willing to invest a lot here, but we have certain kind of criteria around how we wanna roll these products out versus skating to where the puck is going and trying to make predictions around r and d that you do today that will pay dividends three, four, five years out.
01:39:22 · Rodrigo Liang, John Coogan
[01:39:22] S4: Yeah. Well, when your chip design, as you guys know, it takes you two years to design and you've to go through TSMC, you've got to build these wafers, and you've got to build the systems all mean, there's a three or four year cycle. Right? So you're always it's a it's a blend between engineering and, fortune telling. Right? And so you're kind of mixing those. But but the reality the reality is you're always doing that. But here's the difference when when it comes to enterprise. And we we you know, not only we have JPMorgan, but last month, we announced a huge partnership with Vista Equity, has 92 portfolio companies building all these applications. And so what is the biggest thing that we run out with AI is we don't have offtakes. We don't have offtakes. Now what you're seeing is the JP Morgans of the world, the Empress of the world, the Vistas of the world, all these applications, all turning SaaS companies into AI first companies, and you're seeing the demand pull through. What we're excited about this is not only the web, the chips. You got connected with the data center that's there. Oh, the next thing is connected with the end user, the end off taker that applies AI into something that's useful to new businesses. And so so we're super excited about the JPMorgan, the Vista, all of these type of That's our that's our mentality is what are people using it for? Solve that problem. Everything underneath follows. [01:40:34] S1: Mhmm. Thank you so much for coming on the show. Great. Thank Congratulations on the massive round.