The Chip That Could Unlock AGI.

Speaker 0:00 I think AI is the next evolution of humanity. I think it takes us to a new level. It allows us to collaborate and understand the world in much deeper ways. Naveen Rao is here, expert in AI. Naveen Rao, probably one of the smartest guys in this domain. He sees things well before anybody else sees them.

Matt Bornstein 0:15 You had a lot of success doing Nervana, Mosaic, and Databricks. Why start a new chip company now?

Speaker 0:20 First off, it's not a chip company per se. Most of what we're doing is really kind of looking at first principles of how learning works in a physical system.

Matt Bornstein 0:27 Nvidia, TSMC, Google, are these potential allies for Unconventional, or are these competitors?

Speaker 0:32 So I think TSMC is absolutely gonna be a partner. Google kinda has everything internally, and Nvidia, of course, they built the platform that everyone programs on today. So are we gonna be at odds with Nvidia going forward? I don't know. We'll see what the world looks like, but there could be a world where we collaborate.

Matt Bornstein 0:46 Has anyone called you crazy yet for doing this?

Speaker 0:48 Oh, yeah, plenty of people.

Matt Bornstein 0:49 [rock music]

Matt Bornstein 0:56 Our guest today is Naveen Rao, co-founder and CEO of Unconventional AI, which is an AI chip startup. Prior to that, Naveen was at Databricks as head of AI and co-founder of two successful companies, Mosaic in the, uh, cloud computing world and Nervana, uh, doing AI chip accelerators, uh, before it was cool. Um, uh, we're here reporting from NeurIPS. Um, and, uh, yeah, great to, great to have you on the, on the podcast, Naveen. Welcome.

Speaker 1:22 Thanks. Thanks for having me.

Matt Bornstein 1:23 So you were kind of at the vanguard thinking about what the proper hardware is for running AI workloads.

Speaker 1:29 Absolutely. I mean, you know, it's like, uh, when you have a hammer, everything's a nail, I suppose. But the early part of my career was really about how do I take certain algorithms and, uh, capabilities and shrink them, make them faster, put them into form factors that, uh, make them, make those use cases proliferate, like wireless, uh, technology or video compression. You know, you couldn't do video compression real time on a, on a, on a laptop back then. It was just, there wasn't enough, uh, computing power. So you actually needed to build hardware to do those kind of things. So, uh, my career was-- Early part of my career was all about that.

Speaker 2:02 And, uh, you know, then I went back to academia, did a PhD in neuroscience. And so you still kinda look at it like, "Hey, can I make something better that's more efficient?"

Matt Bornstein 2:10 And so you sold Nervana to Intel.

Speaker 2:12 Yeah.

Matt Bornstein 2:13 Um, and, and then, uh, founded Mosaic, which is a cloud company. You know, it's interesting to sort of cross domains like that, I think, to be able to look at hardware and software. You know, I would sort of argue Mosaic was really a software company. You know, how, how'd you make that decision, and, and why, you know, why, you know, why do you think you have these diverse interests?

Speaker 2:30 Well, I think I was, I don't know, I guess you would call the OG kind of full stack. Now, full stack engineer means something different than it did meant back then. I think back then it meant someone who understands potentially devices like silicon, how to do, um, uh, logic design, computer architecture, uh, low-level software, maybe OS-level software, and then application. That was a, a full stack engineer. And I was-- I, I actually had touched all those topics. So to me, it's very natural to kind of think across these boundaries. You know, to me, like, uh, software and hardware isn't, is not really a natural boundary.

Matt Bornstein 3:03 Mm.

Speaker 3:03 It's just where we decide to draw the line and say, "Okay, this is something I make configurable or I don't." And, uh, you know, it's, it's like where, where is the world gonna consume something? Where is the problem?

Matt Bornstein 3:14 Mm.

Speaker 3:14 And then, you know, rightsize and figure out the, the solution to go and hit it.

Matt Bornstein 3:18 Now full stack means I know JavaScript and Python.

Speaker 3:21 That's right.

Speaker 3:22 [chuckles]

Matt Bornstein 3:22 You know, so you've had a lot of success doing both of those things and at Databricks. Um, why start a new chip company now?

Speaker 3:28 It is kinda crazy. It's, it's one of these things like, um, I actually was first off say, it's not a chip company per se. Most of what we're doing is, at the beginning, it's theory and, uh, really kind of looking at first principles of how learning works in a, in a, in a, in a physical system. Um, and the reason I can do all that, can do this is just purely out of passion. I think we can, we can change how a computer is built. We've been building largely the same kind of computer for 80 years. We went digital back in the 1940s. And, um, you know, in undergrad in the 1990s when I learned about, you know, the, the thermodynamics of the brain, like the brain's 20 watts of energy and, you know, the kind of computations that can happen inside of brain and neural systems, I, I was just blown away then, and I'm still blown away by it. And I think, um, we haven't really scratched the surface of how we can get close to that. You know, biology is exquisitely efficient. It's very fast. It rightsizes itself to the application at hand. Like, you know, when you're chilling out, you don't use much energy. Like, you just... But you're still aware of other threats and things like this. And then once, you know, a threat happens, like, everything turns on. It's very dynamic. And we really haven't built systems like this. And, you know, I, I've been in the industry long enough to know that we have to have an incentive to build things. You can't, you can't just say, "Hey, I wanna build this cool thing, and therefore I go build it." Maybe in academia you can do that, but in the, in, in sort of the real world, I can't. And, uh, now it's exciting because those concepts are super relevant. We're at a point in time where, uh, computing is, is, is bound by energy at the global level, which just was never true in all of humanity.

Matt Bornstein 5:09 And, and so for, for those of us who aren't experts,

Matt Bornstein 5:13 um, can you describe the difference between digital and analog computing systems and, and, you know, like, why do you think the, the architecture has evolved the way it has, sort of more, more digital focused over, over decades, as you said?

Speaker 5:26 Yeah. I mean, very simply, digital computers, uh, implement numerics and numerics with some sort of estimation, right? I mean, in a, in a digital computer, a number is represented by a num- fixed number of bits, and, uh, that has some precision error and things like this. It's just, it's just the way we implement the system. If you make it enough bits, like 64 bits, you can largely say that maybe the error is small and you don't have to think about it. Um, and so when we-- The, the digital computer, uh, is capable of simulating anything that you can express as numbers and arithmetic. So it became a very general machine. I can literally, uh, simulate any physical process. All of physics we try to do... computational physics, right? I have an equation. I can then write, um, numeric solvers that sort of deal with those, uh, those imprecisions, uh, in, in the number of bits. And so this became obviously computer science, uh, as an entire field now. And we went that direction actually very early on because, uh, we couldn't scale up, uh, computation. It was-- It's actually kind of an interesting conversation if you look from back then, not that I was there, of course, but if you look at the papers and things, they actually look very similar to today in terms of scaling up GPUs.

Matt Bornstein 6:36 Hmm.

Speaker 6:36 Um, analog computers are actually some of the first computers, and, um, they worked really well. They were very efficient, but they couldn't be scaled up because of manufacturing variability. So someone said, "Oh, okay, you know what? I can actually say that I can make a, um, vacuum tube behave as a high or low very reliably. I can't characterize the in-between very well, but I can, I can say it's high or low." And so that was kind of where we went to digital abstraction, and then we could scale up. ENIAC, which was built in 1945, had 18,000 vacuum tubes.

Matt Bornstein 7:06 Wow.

Speaker 7:07 So 18,000's kind of similar to how many GPUs people use now, right, for large-scale training. So scaling things up is, is always a hard problem, and once you figure out how to do it, it makes a paradigm happen. And I think that's why we went to digital. But analog still is inherently more efficient because you-- It's, it's actually analogous computing is the way to think about it. Like, can I build a physical system that is similar to the quantity I'm trying to express or compute over? Um, you're, you're effectively using the physics of the, uh, underlying medium to do the computation.

Matt Bornstein 7:38 And so in digital computers, we have transistors. Um, ju-just to make it sort of concrete, what kind of substrates are you talking about for, for analog computers?

Speaker 7:46 Yeah, I mean, analog computers can be lots of different things. You know, um, there's... Wind tunnels are a great example of an analog computer in a sense, where, um, I have, you know, a race car on a track or an airplane, and I wanna understand how the, uh, the wind moves around it. And you can, in theory, solve those things computationally. The problem is you're always gonna be off. It's very hard-

Matt Bornstein 8:08 Hmm

Speaker 8:08 ... to know what the real system's gonna look like, and doing things with computational fluid dynamics accurately is pretty hard. So people still build wind tunnels. That's actually modeling that. That's, that's an analog computer. You know, I think, uh, we still have lots of reasons to build these analogous-type computers. Now, in the situation we're talking about, we can actually build circuits in silicon, uh, to recapitulate behaviors of, of, uh, neural networks. So what we're doing today is, is more specified than what we were doing 80 years ago, in a sense, is that then we were trying to automate generic calculation, which was used to calculate artillery trajectories. It was used to calculate finances, maybe some, you know, physics problems like going into space, things like that. Those require determinism and, you know, specificity around these numbers and these computations. Intelligence is a different beast. You can build it out of numbers, but is it naturally built out of numbers? I don't know. A neural network is actually a stochastic machine, and so why are we using this substrate that is highly precise and deterministic for something that's actually stochastic and distributed in nature? So we believe we can find this, the right isomorphism in electrical circuits that can subserve intelligence.

Matt Bornstein 9:23 It's a pretty wild idea, isn't it? Um,

Matt Bornstein 9:26 maybe unpack it one level deeper 'ca- 'cause I totally, I totally agree with you, right? It's like, um, computers for, for decades have been sort of the complement to human intelligence, right? It's like, hey, my brain isn't really great at computing an orbital trajectory.

Speaker 9:41 That's right.

Matt Bornstein 9:41 And I don't wanna, like, burn up on re-entry, so, like, a computer can help us with this incredible degree of pres- of precision. Um, we're now kind of going the opposite direction, right?

Speaker 9:50 That's right.

Matt Bornstein 9:50 We're actually trying to encode more sort of, um, fuzziness into computer systems. Um, so, so yeah. So, so go maybe just a little bit deeper on this idea of an analog and, um, you know, why intelligence is a good fit for, for analo- for analog systems.

Speaker 10:03 Well, I mean, the best examples we have of, uh, intelligent systems in nature are brains.

Matt Bornstein 10:08 Hmm. Mm-hmm.

Speaker 10:09 And, you know, it's often been said, you know, human brains run on 20 watts of energy. That's, that is true, but if you look at mammalian brains generally, they're actually extremely efficient, like a squirrel or a cat. It's like a tenth of a watt. And so, uh, there's something there that we're still missing, and, uh, not to say that we, we understand all of it, but part of what I think we're missing is we have lots of abstractions in a computer that are quite lossy. In a brain, the neural network, ne- neural network dynamics are implemented-

Matt Bornstein 10:37 Hmm

Speaker 10:37 ... physically, so there is no abstraction. Intelligence is the physics. They're one and the same. There's no, you know, OS and, you know, some sort of API and this and that. It's like-

Matt Bornstein 10:46 So there's some visual stimulus, for instance, that directly activates a, an, a actual neural network and, and produces-

Speaker 10:52 Yeah

Matt Bornstein 10:52 ... some, some somatic response, that sort of thing.

Speaker 10:54 Exactly. And those things are mediated by chemical diffusion and, you know, uh, uh, just the, the, the, the physical properties of the neuron, the physics itself. So I think absolutely it's possible to build something that's much more efficient and, uh, by using physics in an analogous way. That is 100% true. Uh, can we do it and build s- build, uh, products out of it is really the question we're asking here at Unconventional.

Matt Bornstein 11:20 A- and is part of the idea that now is the right time because AI is a, both a huge and a unique workload?

Speaker 11:26 Yeah, absolutely. You know, it's, it's interesting. So just maybe some stats here, like the US is about 50% of the world's data center capacity, and today we put about 4% of the energy grid, of the US energy grid into those data centers. And this, this past year, 2025, was the first time we started to see news articles about brownouts in the Southwest during the summer. And, you know, just imagine what happens when this goes to 8%, 10% of the energy grid. It, it's not gonna be a good place that we're in. So can we build more power? Absolutely, we should. Building power generation is very hard, expensive, and it's infrastructure. Like, it takes, takes time. You can't, you can only bring online so many kilowatts or gigawatts per year, and so something on the order of four per year. Uh, by some estimates, we need 400 gigawatts additional capacity over the next 10 years to power the demand for AI.

Matt Bornstein 12:19 Hmm. Wow.

Speaker 12:19 So we have a huge shortfall, and so we really just need to rethink this.

Matt Bornstein 12:23 The, the, you know, 15-year-old sci-fi nerd in me says like, "Wow, we're, we're mobilizing, you know, species

Matt Bornstein 12:30 scale resources to, like, invent the future."

Speaker 12:32 We are.

Matt Bornstein 12:32 Then, then, then there's the practical. It's like even if we add 400 gigawatts of production capacity, our, our, you know, 1970s era transmission grid is probably gonna melt under the, under the load. So yeah, so, so there's very serious sort of infrastructure hurdles to this, I think.

Speaker 12:47 It's hard to get a lot of humans to act together, right? It's just a reality. That's what has to happen to, to solve these problems.

Matt Bornstein 12:53 Hmm. What trade-offs do you think this entails, you know, sort of the path you're pursuing versus the, the mainstream digital path now?

Speaker 13:00 Yeah. I actually don't see it as, you know, it's digital or analog. This doesn't work like that. I think there are certain types of workloads that are amenable to-

Matt Bornstein 13:08 Mm-hmm

Speaker 13:08 ... these analog approaches, especially the ones that are, that can be expressed as, um, dynamical systems, dynamics meaning time. They have time associated with them. In the real world, every physical process has time, and in the computing world, like a numeric computing world, we actually don't have that concept. You simulate time-

Matt Bornstein 13:25 Mm-hmm

Speaker 13:25 ... with numbers. Actually, simulating time is very useful in certain, uh, certain problems. So I think we should still build those things, and we should still have those, uh, capabilities for the problems that we need to solve that way. Uh, but for these problems where, you know, like, like you said, it's a bit fuzzier. I'm trying to retrieve and summarize across multiple inputs. That's actually what brains do really well, right? They can, they can, uh, take in tons of data and sort of formulate a, a, a model of how those things interact. And sometimes those models can be actually extremely accurate. Like, look at an athlete.

Matt Bornstein 13:58 Hmm.

Speaker 13:58 You know? Um, you know, Alex Honnold climbed, uh, uh, El Capitan, right? Like, just think about the precision that's required.

Matt Bornstein 14:06 Oof. It still scares me every time I see it [chuckles] .

Speaker 14:08 It's insane, right?

Matt Bornstein 14:08 Yeah.

Speaker 14:09 And if he slips, like just-

Matt Bornstein 14:10 Ugh

Speaker 14:10 ... if he's off by a millimeter in some places-

Matt Bornstein 14:13 That's wild

Speaker 14:13 ... he dies, right? And that's true for, like, every top level athlete. They're someone who's, you know, at the, the Olympics.

Matt Bornstein 14:20 Yes. Steph Curry, you know, the story is he set up a special tracking system so he can make sure the ball was hitting the middle of the rim, not, not just-

Speaker 14:26 Yeah

Matt Bornstein 14:27 ... going through.

Speaker 14:28 So the level of precision these guys hit with a neural network that's noisy-

Matt Bornstein 14:32 Yeah, yeah, yeah

Speaker 14:32 ... is actually quite high. So, uh, neural systems can actually do a lot of precision under certain-

Matt Bornstein 14:37 Hmm

Speaker 14:37 ... circumstances. But what's interesting about these situations is Steph Curry, when he shoots a ball, is never gonna shoot it under ideal circumstances in a game.

Matt Bornstein 14:45 Mm-hmm.

Speaker 14:46 Always it's a unique input, and there's a lot of different in-input variables-

Matt Bornstein 14:51 Hmm

Speaker 14:51 ... coming at you, like where the other players are, precisely where you're standing. Maybe your shoes are different. Maybe the surface is a little different. Like, maybe the ball is tackier or your hands are sweaty. Like, there's so many inputs, and we, we kinda put them all together and integrate them and still have very accurate behavior. So brains are exceptionally good at this and, you know, that's a set of problems that is actually very useful to solve, and now we're approaching those problems. Uh, but it doesn't mean we don't still use computational, uh, substrates to do actual computation. This is kind of an intelligence substrate.

Matt Bornstein 15:23 And so what types of AI models or, or data modalities do you expect, uh, your, your, um, hardware will be well suited for?

Speaker 15:31 Yeah. So we're, we're obviously starting with, uh, the state of the art today, like transformers, diffusion models. They, they work. They do really good stuff, so we shouldn't throw that out. And, uh, diffusion models and flow models are actually, and energy-based models are actually pretty interesting because they inherently have dynamics as part of them.

Matt Bornstein 15:49 Hmm.

Speaker 15:49 They're literally written as an ordinary, ordinary differential equation. So, um, that makes it such that, hey, can I map those dynamics onto the dynamics of a physical system in some way that's either fixed or, um, has some principle way of, of evolving? And then can I basically use that physical system to implement that thing and do it very efficiently with physics? So that's, that's kind of the nature of what we're doing, and we will be releasing, uh, some open source and things around this to, to let people play around. But, um, you know, transformers are really, uh, they're a big innovation because they, they made the constructs of a GPU work extremely well. And it doesn't mean it's wrong, but I don't think... There's, there's nothing natural, there's no natural law about the parameter of a transformer.

Matt Bornstein 16:34 Hmm.

Speaker 16:35 Transformer's parameter is a function of the nonlinearities and the way that whole thing is set up with the tension. There's gonna be some kind of, uh, mapping between transformer parameter spaces and these other parameter spaces, and it's, transformers are, I, I think, have, um, kinda use lots of tran- lots of parameters to accomplish what they do.

Matt Bornstein 16:52 I have to ask, just since

Matt Bornstein 16:54 you mentioned energy-based models and Yann LeCun has been, um, you know, uh, writing quite a lot about this. Um, do, do you think pursuing these sorts of paths that you're talking about is, is, uh, gets us closer on the path to AGI, whatever, whatever AGI means?

Speaker 17:10 Honestly, I do. Uh, the reason I feel that way, and again, this is, this is hand-wavy. I'm gonna be really honest. I don't-

Matt Bornstein 17:16 That, that's why I'm putting quotes around AGI.

Speaker 17:17 Yeah. Yeah.

Matt Bornstein 17:17 I think that the discussion is necessarily hand-wavy.

Speaker 17:20 It's gotta be 'cause we just don't know. Uh, but my intuition says that, um, anything where the basis is dynamic, which has time and causality as part of it, will be a better basis than something that's not.

Matt Bornstein 17:32 Mm-hmm.

Speaker 17:32 So we've largely tried to remove that and, you know, you know, a lot of times you can write math down and it's reversible in time and things like that, but the physical world tends not to be, uh, at least the way we, we perceive it. And so can we build out of elements of the physical world that are, you know, uh, do, do have time evolution? I think that's the right basis to build something that understands causation.

Matt Bornstein 17:52 Hmm.

Speaker 17:53 So I do think we'll, we'll have something that is better, uh, and will give us something closer to what we really think is intelligence. Because yes, we have intelligence in these machines. I, I don't think they're anywhere close to AGI because, I mean, they still make stupid errors. They're very useful tools, but they're, they're, they're not what... It's not like working with a person, right?

Matt Bornstein 18:10 Yeah, totally.

Speaker 18:10 I think most people would admit that.

Matt Bornstein 18:12 That, that's actually really interesting. So the, the sort of thing that's missing in, in AI behavior, which, which I think a lot of us see that there's something missing but can't quite put a name to it, it sounds like you're arguing part of that is, is sort of a real sense of causality-

Speaker 18:28 Yeah

Matt Bornstein 18:28 ... and that training in more dynamicSort of regime may, may impart this kind of like apparent understanding of causality better than what we have now

Speaker 18:37 Yeah. And again-

Matt Bornstein 18:38 It's really interesting

Speaker 18:38 ... hand-wavy, but yes. Uh, I mean, look, y-you have kids, little kids, and you see them. I mean, children kind of innately understand causality in some ways. Like, you know, this happened, then that happened. And yes, I know you can say like it's r- reinforcement learning or whatever, that's some part of it, but there's something innate that we understand causality. In fact, that's how we move our limbs and all of that. I know if I send a certain command to my arm, it'll do so- do something. So I, I think there's something innate about the way our brains are wired, built out of primitives that are, that do understand causation.

Matt Bornstein 19:09 Put unconventional in the context of the broader industry for me, like NVIDIA, TSMC, Google, are, are these, um, you know, potential allies for Unconventional? Are these competitors? How do you think about it?

Speaker 19:22 Yeah, I mean, a couple of things that we set out to do when we built-- we were starting this company was see if we can find a paradigm that's analogous to intelligence within five years. Uh, and then at the five-year mark, we should be able to build something that's scalable from a manufacturing standpoint. So, you know, you can, you can think about building a computer out of many different things, but if it's not scalable from a manufacturing standpoint, we can't intercept this, this global energy problem. So we need to have something to say, "Okay, go build 10 million of these things," right? So I think TSMC is absolutely gonna be a partner going forward. You know, met with them recently and, you know, we wanna, we wanna work closely with them to make sure we get what we need, get fast turnaround times to prototype and all of that. Um, Google, NVIDIA, Microsoft, all these guys are, you know, at the forefront of where the application space is. Uh, obviously Google kinda has everything, uh, internally, and I think they're working on sort of lower risk, but, you know, continual improvements for their hardware and-

Matt Bornstein 20:22 With TPUs, you mean?

Speaker 20:23 With TPUs, yeah. That, from what I can see, you know, uh, just publicly is it makes total sense, right? They have a business to run, and they're trying to make their margins better and, you know, how can I do that with all the tools I have at, you know, uh, in front of me? Um, NVIDIA, of course, you know, they, they, they've built the, um, the platform that everyone programs on today. So i- is it-- are we gonna be at odds with NVIDIA going forward? I, I don't know. We'll see what the world looks like. But I mean, we are trying to, to build a better substrate than matrix multiply. Um, there could be a world where we collaborate, uh, on such solutions. Um, and you know, we're open to all of these things.

Matt Bornstein 21:00 Where, where do you, where do you personally get the motivation to get up in the, in the morning and build this company? I mean, you've had a lot of success in your career. This is your third startup. Um, what, what, you know, what's exciting about this to you?

Speaker 21:12 I don't know. I j- it's, it's a weird thing. Like if you haven't worked in hardware, it's hard. Um, I've had the f- been fortunate to work in hardware and software and, you know, I love writing a bunch of software and then hitting compile and seeing it work. That's, that's, that's a good dopamine hit. But man, when you work on a piece of hardware-

Matt Bornstein 21:28 [laughs]

Speaker 21:28 ... and you turn that thing on, that's a big dopamine hit. That's like, it's just like celebration, jumping, you know, jumping up in the air, high-fiving. It, it's a different thing and I don't know, you sort of live for these moments, you know? Uh, like when I was at Intel, like I was one of the only execs who would go to the lab when the first chip would come back, and I'm like, "I wanna see it when you turn it on."

Matt Bornstein 21:46 [laughs]

Speaker 21:47 "Let's, let's see what happens." Sometimes you turn it on, sometimes you turn it on, it's like [laughs]

Matt Bornstein 21:50 You see the little puff of smoke come out.

Speaker 21:52 You're like, "Oh, sh-"

Matt Bornstein 21:52 You're like, "Uh-oh." [laughs]

Speaker 21:54 That's not good. But you wanna be there. You wanna be part of the moment. But, uh, uh, I, I think that's part of it. I think for me personally, I feel like we, we have this opportunity now that we can really change the world of computing and make AI ubiquitous. I, I'm the opposite of an AI doomer. I think AI is the next evolution of humanity. I think it takes us to a new level, allows us to collaborate, understand each other, and understand the world in much deeper ways.

Matt Bornstein 22:20 Totally agree. Totally agree.

Speaker 22:21 So e-every technology has negatives, but the, the positives to me so far outweigh it, and, uh, the only way we're gonna get to ubiquity is we have to change the computer. The current paradigm, as good as it is, and as far as it's taken us, is not gonna take us to that level.

Matt Bornstein 22:36 I think

Matt Bornstein 22:37 that's such a great way to say it. AI actually can help us understand each other better, help us understand ourselves better, understand the natural world better.

Speaker 22:44 Yeah.

Matt Bornstein 22:44 I, I don't think it's at all what, what some of the doomers think of, of, you know, replacing sort of human, human experience.

Speaker 22:51 That-that's a short-term thing. I mean, there will be, there will be bumps along the way, right? Technology does that.

Matt Bornstein 22:56 That's, that's what happens when you've seen too many sci-fi movies.

Speaker 22:58 That's right.

Matt Bornstein 22:59 Um-

Speaker 22:59 But look at Star Trek.

Matt Bornstein 23:00 Yeah. Yeah, no, totally. Totally.

Speaker 23:02 It's great.

Matt Bornstein 23:03 Um, uh, this is a really big swing, right?

Speaker 23:07 It is.

Matt Bornstein 23:07 Like, this is a very ambitious company. Um, what gives you confidence that it's, that it's gonna work or, or, you know, has a reasonable shot of working?

Speaker 23:14 Um, there's a, there's a number of data points. Of course, like I said, the brains are existence proof. Um, but there's also 40-plus years of, of academic research, which is showing a lot of promise here. Um, people have built different devices, albeit not in the latest technology with professional engineering teams, but they have built proofs of concept that actually show some of these things work. Um, we've also, from a theory standpoint, both from neuroscience and just pure, uh, dynamical systems and math theory, uh, do start to understand how these, these systems can work. So I think we now have pieces at different parts of the stack that show, hey, if I can combine these things the right way, I, I can build this. And, uh, you know, that's what great engineering is all about, is like, you know, exploiting this thing that someone else built for something else, exploiting that thing and then-

Matt Bornstein 24:01 [laughs]

Speaker 24:02 And it's... E-engineers are kind of like, uh, the opposite of theorists. It's like, well, all right, that thing doesn't quite fit. Sand it down and make it fit, right?

Matt Bornstein 24:11 [laughs]

Speaker 24:11 So it's like we gotta do a little bit of that right now, and then we can build something and put it all together.

Matt Bornstein 24:14 That's funny.

Speaker 24:14 Yeah.

Matt Bornstein 24:14 That's awesome. H-has anyone called you crazy yet for doing this?

Speaker 24:18 Oh, yeah. Plenty of people. That's fine.

Matt Bornstein 24:19 Is it, is it like everybody? [chuckles]

Speaker 24:20 Well, it's... I'm, I'm used to this at this point, you know?

Matt Bornstein 24:22 [laughs]

Speaker 24:23 My family would be called crazy. I was called crazy going back to grad school, um, years ago when I had a very good, good career in tech. Um, so it, it's fine. I think that's, that... You need crazy people to go out and explore. I mean, if you think abouthumanity out of Africa, all that. It was the crazy people who went out

Matt Bornstein 24:39 We would be lost without, without crazy, right?

Speaker 24:40 You need some crazy in there, so it's okay. I'm fine with that.

Matt Bornstein 24:43 And so what kind of people are, are you looking to bring on to the team? A very ambitious goal. Um, who should be interested in joining you?

Speaker 24:52 Yeah, I mean, I think some of the traditional, traditional-ish, when I say traditional, I mean over the last five years, this field of AI systems has evolved. Like people who are really good at taking algorithms and mapping them very effectively to physical substrates. Uh, those folks who understand energy-based models, flow models, gradient, uh, gradient descent in different ways, uh, you know, this, this kind of thing is what we need there. We need theorists who, uh, can think about different ways of building coupled systems, how I can characterize a richness of dynamical systems and relating that to neural networks, so there is a theory aspect of this. Uh, then there's folks who are like kinda at the system architecture level. It's like, all right, here's what the theory says. This is what I can really build. How do I bridge that gap? And then there's the people actually physically building this stuff, like analog circuit people, actually digital circuit people too. We're gonna have a mixed signal here. So, uh, that's, that's the whole stack. The stack is-- It's hard because these are all things that no one's really pushed to that level. Like-

Matt Bornstein 25:52 Mm.

Speaker 25:53 When we build this chip, our first prototype, it's gonna be probably one of the larger, maybe the largest analog chip people have ever built, which is kinda weird. First time you do something, things don't usually work the way you think they do.

Matt Bornstein 26:04 So you can get in on that Cerebras-Jensen game where they were each pulling the biggest possible wafer out, out of an oven. You, [chuckles] you-

Speaker 26:10 Something like that, yeah.

Matt Bornstein 26:11 Yeah, yeah, yeah.

Speaker 26:11 Exactly, right?

Matt Bornstein 26:12 Put a few vacuum tubes on top for, for effect.

Speaker 26:15 Yeah, we c- I, I, I need blinking lights. We need cool-

Matt Bornstein 26:17 [laughs] Yeah, exactly.

Speaker 26:18 We're not gonna have cool heat sinks. It's gonna be super c- it's gonna be cold.

Matt Bornstein 26:21 [laughs]

Speaker 26:21 Like, you don't need big heat sinks, you know? So I hope we make something that looks, looks interesting here.

Matt Bornstein 26:26 This is

Matt Bornstein 26:27 a funny time for, for, um, top AI people, right?

Speaker 26:31 Yeah.

Matt Bornstein 26:31 Where you have sort of the option, if you wanna start a company, there's a lot of venture capitalists who probably would fund you. If you want to get a cushy job at, at a big company, you can get a very cushy job and, and kind of do some interesting things.

Speaker 26:43 Yeah.

Matt Bornstein 26:44 Um, or, you know, people can join a startup like Unconventional that, you know, has a lot of the nice aspects people look for in, in sort of AI careers and are taking, like, super sort of big swings. Um, I, I'm just sort of curious. You've been on all sides of this. Like, do you have any advice for, for, um, you know, younger people starting out in their careers? Or, or how do you think about this?

Speaker 27:03 I think you get such a breadth of working at a startup that a-at the beginning of your career, that will pay dividends later on. 'Cause like I said, like the reason I can think across the stack is 'cause I did all those things very early in my career, you know? I built hardware, I built software, I built applications. And, um, in big companies, it's not, it's not anyone's fault. It's just the way it is. Like, you get hired to do a thing, and you do that thing over and over again, and you get really good at doing that thing. And that's fine. You need people who are really good at doing specific things. But, um, if you wanna be prepared for change in the future, being really good at one thing is probably less valuable than being very good at, but slightly good at a lot of things.

Matt Bornstein 27:43 Oh, that's interesting. Is it fair to say Unconventional is sort of a practical research lab? Is that kinda the culture you're going for?

Speaker 27:48 Absolutely, yeah. I mean, first few years, it really is open-ended. I, I don't wanna close doors. Like, I, I'm really specific about this. Like, I always try to bring the conversation back, because there's people like, "Oh, that's gonna be hard to manufacture." It's like, "Eh, eh, stop. Don't think about that. Will it work?" F-first come up with existence proofs. Then we go back and try to engineer it and, you know, all the trade-offs therein. But if you make those trade-offs up, upfront, you don't go into a good place. So yes, we're really thinking wide open, but with an eye on the future. We are building a product.

Matt Bornstein 28:18 And,

Matt Bornstein 28:19 and to your point, it takes not only people with diverse skillsets, but, um, people with kind of high agency to try new things and learn new things and kind of-

Speaker 28:26 So-

Matt Bornstein 28:26 ... integrate across the stack.

Speaker 28:28 Yeah, I mean, I, I think what I've done really well across the companies I've built has been, uh, going after hard problems, which kinda lends itself to smart people wanting to come in and try to solve them. They, they see a challenge as like, "Here's a Mount Everest. Go climb it." Um, but then giving them agency, and I sort of look at it like, what decisions can I make as a leader to increase agency of the org overall? Like, me making top-down decisions may be a global, globally better for the company in the short term.

Matt Bornstein 28:57 Mm-hmm.

Speaker 28:58 But I think long term, we will, we'll do better if more people have agency and can, and try more things out. So personally, I like to find ways to get out of the way when I see people who are, who are very passionate about trying something. It's like, okay, what, you really wanna do this. That makes sense. Go for it, you know? And then you own it. You own both the good and the bad, right? And that's agency to me, is like, you got, you gotta-

Matt Bornstein 29:18 Yeah.

Speaker 29:18 You just be like, "Okay, I fucked up. No, this wasn't gonna work."

Matt Bornstein 29:20 [laughs]

Speaker 29:21 That's okay too, but give people the room to do that, you know?

Matt Bornstein 29:24 Anything else you wanna, wanna say before we wrap up?

Speaker 29:26 I mean, I think this is, like, uh, an opportunity to do something that is generationally will be felt. You know? To me, that's, that's what gets me up in the morning, is, you know, you can go work on a product and make a tweak, and people will use it. That's great. But, like, in five years, many times people forget those things. But if we are successful here, the world will not forget this for a very long time, right? This will be written in history books. And so I feel like those opportunities are rare. [upbeat music]