The Exchanges

Q You came out of stealth in December and you disclosed a very large seed round. How much have you raised a date and how much do you think you're going to need to raise to get this company to escape velocity?

A Yeah, I mean, it's a good question. So we raised four 75. Um, there's actually interest to do more, so we'll probably, probably raise some more in a, in a short time frame. I think to get to actual first product, it's probably a billion and a half is my guess. And it sounds like a huge number, but I think the opportunity is enormous. So this, this initial money will get us through much of the exploration and sort of a product, not productization, but like What it takes to build a product. So there's a lot of engineering required to get from the science to this is something I can productize. Then the actual productization will probably be the last billion or so. And it's just because we have to build a bunch of infrastructure. We have to, we're going to do a whole stack. Like we're not just going to sell a chip. You can't do that because we're changing the whole paradigm. So we do need a fair bit of money to do that, but that's a few years down the line.

AI assessment note: “we raised four 75... to get to actual first product, it's probably a billion and a half”

Q How did you get into this sport and how does it make you a better CEO?

A Um, well, I, I got into this before because I just like cars. I mean, I'm an engineer, you know, and cars are a thing you can engineer. They're also beautiful and they make cool sounds and, and all that. Um, there's something very visceral about them. It is, it is sort of a form of moving art in a sense, right? And experience. So, um, learning to do race car driving stuff, I actually just was pretty good at it when I first started. And, um, I raced, you know, like racing go-karts years ago, like in the early 2000. I didn't do anything for a long time because I, Went back to grad school. Had kids and, and all of these things. Um, after I sold Nirvana, I didn't want anything. I stayed in the same house and all that kind of stuff, but I wanted a Ferrari. I bought a Ferrari, took it to the track, and that, that just started again. The addiction. It was like that first hit. And, uh, yeah, so then I started to like, uh, I started racing in, in that series and sort of, you know, kept on upping the dose. And actually I'm right in the middle of a race at the moment. The Daytona 24 hour race is happening on Saturday. I was at the track all weekend because we had our practices this past weekend. I'm literally here and I go back to Daytona tomorrow. So, um, it's something, how does it make me a better CEO? I think there's actually a lot of lessons to learn from, from racing. It's, it's pre…

AI assessment note: “how does it make me a better CEO? I think there's actually a lot”

Q What are your principles as a founder and a leader?

A Um, well, it's interesting because, um, I think as a founder, you change some of these things depending on the company to some degree. Of course, you know, truth seeking, we don't want to have these ideas of, you know, I'm just going to defend my idea because it's mine and, and I don't care if it's, it's true. I mean, you have to have this, you have to have people at the beginning of a company who, who set the stage and are, and are really, truly, um, about solving the problem. Actually, I've seen this impedance mismatch happen when those people go into a big company, because in big companies the incentives are very different many times. It's not about solving the problem, it's about getting promoted or something like that, and so oftentimes when you get these very mission-driven people, uh, in those scenarios it, it causes problems. So I think being mission-driven, uh, going after the problem and caring about it, that's, that's an important thing. I think that's job one. Um, I think, um, trying to find the The quickest way to get information is sort of a cultural, uh, cultural, um, I don't know, attribute you need to have, like, You know, think, try to think efficiently, even if you've raised a bunch of money. It doesn't matter, right? It has to be about, do I need to do all these experiments? What's the cheapest way to get there? That, I still think, even at any scale, will o…

AI assessment note: “being mission-driven, uh, going after the problem and caring about it”

Q So how did you get the team together, and how big is the team?

A Um, I think the team will be like maybe 24 or so by the end of this month. Um, Yeah, that's a hard one. I mean, you know, uh, we got to find people who are deep experts in their field. I mean, everybody in this company is smarter than I am on each of these elements. I don't, I am not a deep expert on all, all these components by, by any means. So at the same time, you've got to have that expertise, which means you probably did go through these kind of conventional routes and then have the right attitude to think outside of it. I think I usually do sort of the attitude check about it. It's like, well, yes, you've been doing it this way, but Are you open to trying other things? Are you, uh, what do you do outside of work? You know, do you do things that are, um, that maybe are risks that require risk tolerance? So I think these are the kind of like, you know, personality attributes you need to build a team. And our hiring bar is very high. It takes a long time. It takes a bunch of interviews for us to hire someone. So it is what it is. We're human limited at the moment, uh, for sure. But we are, we're building, building a team as quickly as we can.

AI assessment note: “I think the team will be like maybe 24 or so by the end”

Q And so you founded Nirvana. You founded Mosaic ML. You got acquired by Databricks, became chief AI officer there. What compelled you to start unconventional AI?

A Yeah, I mean, it's a good question. You know, I think you gotta look at the, the motivations of different folks. I mean, my motivation in this space is, you know, it's been about this obsession of why can we not build a computer that acts like biology? The single thing hit me when I was an undergrad, like, 30 years ago, and that's when I learned about 20 watts of energy in your brain and, you know, just how, how, how biology kind of does computation. It doesn't really do computation, by the way, it's sort of, um, It's, it's like dynamical systems, but it's a, we can get into that later. Um, and my whole career has been about that actually. When I came out of undergrad, I learned how to, I was like, okay, I want to build computers. How do I, how do I build those? So I actually became a computer architect. I, you know, worked on Ultra Spark three and a bunch of processors and did a bunch of ASICs. Then went back to school to do a PhD in neuroscience. So I'm like, okay, now I know how to build a machine. I've done it several times, sort of from scratch. I'm still no closer to the answer of why computers aren't as good as brains, right? At that time, we didn't really understand what brains were doing. I think now we kind of did. But at that time, we almost thought of brains as sort of magical. Like, this is the time of Deep Blue, if anyone remembers that. That was the chess compute…

AI assessment note: “my motivation in this space is, you know, it's been about this obsession”

Q So to start things out, I'd love to maybe start at the macro perspective. We may be in the early innings of these things and the super cycle, but exactly where are we in the super cycle? What is the thick of it? What do you see going on?

A I mean, it's impossible to say for sure, because you have to see the whole super cycle to know where we are, I guess. But, uh, um, I mean, as, as Peter pointed out, right, I mean, even 10 years ago, we were still kind of working our way out of traditional compute workloads, that kind of a thing. It's sort of like, uh, that was the primordial mist of, of the, of the, um, next level of intelligent evolution. And I think we're at this point where, okay, we figured out some stuff, you know, like, uh, we can use Numbers and, you know, algorithms on existing hardware to build something that's actually demonstrating intelligence and learning adaptation, but it's really bad. Like, I mean, just, just think about for a moment, like the errors that are made, like they're, they're, they're errors that are so obvious to any biological system, even like a, you know, a child or a rat or something like can, can reason through those. So we're still, we still have not really cracked the code on exactly how to build that. My personal take is that we need innovation on the hardware substrate to actually build true intelligence. You're not going to get there with this sort of rudimentary thing that we have. So, um, I mean, a lot of the things that Peter was talking about in terms of investment, I mean, some of those things will pan out, some won't, but like, that's what we have to start doing. We h…

AI assessment note: “we're at this point where, okay, we figured out some stuff, but it's really bad”