The Chip That Could Unlock AGI.

Naveen Rao is cofounder and CEO of Unconventional AI, an AI chip startup building analog computing systems designed specifically for intelligence. Previously, Naveen led AI at Databricks and founded two successful companies: Mosaic (cloud computing) and Nervana (AI accelerators, acquired by Intel). In this episode, a16z’s Matt Bornstein sits down with Naveen at NeurIPS to discuss why 80 years of digital computing may be the wrong substrate for AI, how the brain runs on 20 watts while data centers consume 4% of the US energy grid, the physics of causality and what it might mean for AGI, and why now is the moment to take this unconventional bet. Timecodes: 00:00 - Trailer 00:56 - Exploring hardware for running AI workloads 02:02 - Why Naveen built lots of software in a "hardware company" 03:22 - Why start a new chip company? 05:13 - How computing systems went digital 09:26 - Why intelligence is a good fit for analog computer systems 12:30 - What tradeoffs Naveen faced in pursuing his own path 15:23 - The Data modalities Unconventional chips will be best for 16:54 - Does this get us closer to AGI? 21:00 - Where Naveen gets his excitement and motivation 22:37 - What makes Naveen confident that Unconventional will work 24:43 - Unconventional's hiring priorities 26:27 - Career advice for young people 28:19 - What Naveen has done best in his companies Resources: Follow Naveen on X: https://twitter.com/NaveenGRao Follow Matt on X: https://twitter.com/BornsteinMatt Stay Updated: Follow a16z on X: https://twitter.com/a16z Follow a16z on LinkedIn: https://www.linkedin.com/company/a16z Follow the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Follow the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details, please see http://a16z.com/disclosures.

• 0:00Speaker 0

  I think AI is the next evolution of humanity. I think it takes us to a new level, allows us to collaborate and understand the world in much deeper ways.

• 0:11Speaker 2

  Probably one of the smartest guys in this domain. He sees things well before anybody else sees them.

• 0:15Speaker 1

  You had a lot of success doing Nirvana Mosaic and Databricks. Why start a new

• 0:19Speaker 0

  chip company now? 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 physical systems.

• 0:27Speaker 1

  Nvidia, TSMC, Google are these potential allies for unconventional or these competitors.

• 0:32Speaker 0

  So I think TSMC is absolutely going to be a partner. Google kind of has everything internally. And NVIDIA, of course, they built the platform that everyone programs on today. So are we going to 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. Has

• 0:46Speaker 1

  anyone called you crazy yet for doing this? Oh yeah, plenty of people.

• 0:56Speaker 1

  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 Nirvana uh doing AI chip accelerators uh before it was cool. Um we're here reporting from NERUPS. Um and uh yeah, great to great to have you on the on the podcast, and being welcome. Thanks. Thanks for having me. So you were kind of at the vanguard thinking about what the proper hardware is for running AI workloads.

• 1:29Speaker 0

  Aaron Powell 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 this kind of thing. So I my career was uh early part of my career was all about that. And uh, you know, then I went back to academia, did a PhD in neuroscience. And so you still kind of look at it like, hey, can I make something that's better that's more

• 2:10Speaker 1

  efficient? Aaron Powell And so you sold Nirvana to Intel. Yeah. Um 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?

• 2:30Speaker 0

  Well, I think I was I don't know, I guess you would call it an OG kind of full stack. Now, full stack engineer means something different that it did meant back then. I think back then, and then 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 uh a full stack engineer. And I was I I actually had touched all those topics. So to me, it was very natural to kind of think across these boundaries. You know, to me, like uh software and hardware isn't is not really natural boundary. 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 going to consume something? Where is the problem and then you know, right size and figure out the the solution to go and hit it.

• 3:18Speaker 0

  Aaron Powell Now full stack means I know JavaScript and

• 3:20Speaker 1

  Python. That's right. 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?

• 3:27Speaker 0

  Aaron Powell It is kind of crazy. It's it's one of these things like uh I actually was first off say it's it's not a chip company per se. Most of what we're doing is at the beginning is theory and uh really kind of looking at first principles of how learning works in a in a in a physical system. Um the reason to go back and 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, uh, when I learned about, you know, the the termics 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.

• 4:14Speaker 0

  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 right sizes 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 want to build this cool thing, therefore I go build it. Maybe in academia you can do that. But in the and in sort of the real world, I can't. And uh now it's exciting because those concepts are super relevant.

• 5:02Speaker 0

  We're at a point in time where uh computing is is bound by energy at the global level, which just was never true in all of humanity. And so

• 5:10Speaker 1

  for for those of us who aren't experts, 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 digital focused over over decades, as you said. Aaron

• 5:25Speaker 0

  Powell Yeah. I mean very simply digital computers uh implement numerics and numerics with some sort of estimation, right? I mean, in uh in a digital computer, a number is represented by a fixed number of bits. And uh that has some precision error and things like this. It's it's just a way we implement the system. If you make it enough bits like 64 bits, you can largely say that maybe the error is small, you don't have to think about it. Um 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.

• 6:12Speaker 0

  And so this became obviously computer science, uh the 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 it 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. Um analog computers are actually some of the first computers, and uh they worked really well, they're 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 higher low very reliably. I can't characterize the in-between very well, but I can I can say it's higher low. And so that was kind of where we went to digital abstraction and then we could scale up.

• 7:02Speaker 0

  ENIAC, which was built in 1945 had 18,000 vacuum tubes. Wow. So 18,000 is 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 effectively using the physics of the uh underlying medium to do the computation. Aaron Powell And so in digital computers, we

• 7:39Speaker 1

  have transistors. Um just to make it sort of concrete. What kind of substrates are you talking about for for analog computers?

• 7:46Speaker 0

  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 and a track or an airplane, and I want to 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 to know what the real system is 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.

• 8:34Speaker 0

  So what we're doing today is is more specified than what we're 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. The neural network is actually a stochastic machine. And so why are we using the 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.

• 9:22Speaker 1

  That's a pretty wild idea, isn't it? Um maybe unpack it one level deeper. Because 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. That's right. And I don't want to like burn up on re-entry. So like a computer can help us with this incredible degree of uh precision. Um we're now kind of going the opposite direction, right? We're actually trying to encode more sort of um fuzziness into computer systems. Um yeah, so so go maybe just a little bit deeper on this idea of an analog and um Why intelligence is a good fit for anal for analog systems. Aaron Powell

• 10:04Speaker 0

  Well, I mean, the best examples we have of uh intelligent systems in nature are brains. 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 a million brains generally, actually extremely efficient, like a squirrel or a cat, it's like a tenth of a lot. 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, neural network dynamics are implemented 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 there's some visual stimulus, for instance, that directly activates

• 10:49Speaker 1

  uh an uh actual neural network and and produces some somatic response, that sort of thing. Aaron Powell

• 10:54Speaker 0

  Exactly. And those things are mediated by chemical diffusion and you know, uh uh just 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 uh a hundred percent true. Uh can we do it and build build uh a product out of it is really the question we're asking your unconventional.

• 11:20Speaker 1

  And is part of the idea that now is the right time because AI is a both a huge and a unique workload. Aaron Powell

• 11:26Speaker 0

  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's not going to 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 it takes time. You can't you can only bring online so many kilowatts or gigawatts per year. So something on the order of four per year.

• 12:12Speaker 0

  Uh by some estimates, we need 400 gigawatts additional capacity over the next 10 years to power the demand for AI.

• 12:19Speaker 1

  Well.

• 12:19Speaker 0

  So we have a huge shortfall. And so we really just need to rethink this. Aaron Powell

• 12:23Speaker 1

  The you know, 15-year-old sci-fi nerd in me says, like, wow, we're we're mobilizing, you know, species scale resources to like invent the future. We are and 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 going to melt under the under the load. So yeah, so so there's very serious sort of infrastructure hurdles to this, I think. Aaron Powell

• 12:47Speaker 0

  It's hard to get

• 12:48Speaker 1

  a lot of humans to act together,

• 12:49Speaker 0

  right?

• 12:49Speaker 1

  It's just the reality.

• 12:51Speaker 0

  That's what has to happen to solve these problems.

• 12:54Speaker 1

  What trade-offs do you think this entails, you know, sort of the path you're pursuing versus the the mainstream digital path, no? Aaron Powell

• 13:00Speaker 0

  Yeah, I actually don't see it as you know, it's digital or analog. It doesn't work like that. I think there are certain types of workloads that are amenable to these analog approaches, especially the ones that are that can be expressed as dynamical system, the dynamics meaning time. They have time associated with them. In the real world, every physical process has time. And in the computing world, like the numeric computing world, we actually don't have that concept. You simulate time 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 we like you said, it's a bit fuzzier. I'm trying to retrieve and summarize across multiple inputs.

• 13:46Speaker 0

  That's actually what brains do really well, right? They can they can uh take in tons of data and sort of formulate a uh a model of how those things interact. And sometimes those models can be actually extremely accurate. Like look at an athlete, you know, um you know, Alex Honnell who climbed uh uh El Capitan, right? Like this thing about the precision that's required. It still scares me every time

• 14:07Speaker 1

  I'm saying, right? Yeah. And if he slips,

• 14:09Speaker 0

  like just he's off by a millimeter in some places. He dies, right? And that's true for like every top-level athlete. And they're someone who's you know at the Olympics.

• 14:20Speaker 1

  Steph Curry, you know, the story is he set up a special tracking system so he can make sure the ball was sitting in the middle of the rim, not just going through. Aaron Powell So the level of precision these guys hit

• 14:30Speaker 0

  with a neural network that's noisy is actually quite high. So uh neural systems can actually do a lot of precision under certain circumstances. But it what's interesting about these situations is Steph Curry, when he shoots a ball is never going to shoot it under ideal circumstances in a game. Always it's a unique input. And there's a lot of different input variables 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 kind of put them all together and integrate them and still have very accurate behavior. So uh 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.

• 15:15Speaker 0

  Uh but it doesn't mean we don't still use computational uh substrates to do actual computation. This is kind of intelligent substrate. Aaron Powell

• 15:23Speaker 1

  And so what types of AI models or or data modalities do you expect uh your your um partware will be well suited for? Aaron Powell

• 15:31Speaker 0

  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 energy-based models are actually pretty interesting because they inherently have dynamics as part of them. They're literally written as an ordinary ordinary differential equation. So 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 principled way of uh 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 let people play around.

• 16:18Speaker 0

  But um, transformers are really uh they're a big innovation because they they made the constructs of the 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. Transformers parameter is a function of the nonlinearities and the way that whole thing is set up with attention. There's gonna be some kind of uh mapping between transformer parameter spaces and these other parameter spaces. And it's uh transformers are I think have uh kind of use lots of tran uh lots of parameters to accomplish what they do. Aaron Powell I

• 16:52Speaker 1

  have to ask, just since you mentioned energy-based models and Jan Lakun has been um, you know, uh writing quite a lot about this. Um 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.

• 17:10Speaker 0

  Honestly, I do. Uh the reason I feel that way, and again, this is this is hand wavy. I'm

• 17:14Speaker 1

  gonna be really honest. I don't know. That's why I'm putting quotes around AGI. I

• 17:17Speaker 0

  think that the discussion is necessarily hand-wavy. Trevor Burrus, it's gotta be, because we just don't know. Uh but my intuition says that uh 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. So we've largely tried to remove that. And you know, you know, a lot of times you can write math down, 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 have do have time evolution. I think that's the right basis to build something that understands causation. 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.

• 18:05Speaker 0

  They're very useful tools, but they're they're they're not what it's not like working with a person, right? I think it's a way at that. Aaron Powell

• 18:12Speaker 1

  That's actually really interesting. So 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. Yeah. And that training in more dynamic sort of regime may impart this kind of like a parent understanding causality better than what we have now. Aaron Powell Yeah. And again, hand-wavy, but yes.

• 18:39Speaker 0

  Uh I mean, look, you 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 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 something 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. Aaron Powell

• 19:09Speaker 1

  Put unconventional in the context of the broader industry for me, like NVIDIA, TSMC, Google, are these um, you know, potential allies for unconventional or these competitors, how do you think about it? Aaron Powell Yeah, I mean, uh a couple

• 19:24Speaker 0

  of things that we set out to do when we built we're 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 global energy problem. So we need to have somebody say, okay, go build 10 million of these things, right? So I think TSMC is absolutely gonna be a partner forward, you know, met with them recently. And you know, we wanna we want to work closely with them to To make sure we get what we need, get fast turnaround times to prototype and all of that. Google, NVIDIA, Microsoft, all these guys are, you know, at the forefront of where the application space is.

• 20:13Speaker 0

  Obviously, Google kind of has everything internally. And I think they're working on sort of lower risk, but you know, continual improvements for their hardware with TPUs, you mean with CPUs. 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 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, we're open to all of these things.

• 21:00Speaker 1

  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 own startup. Um what you know, what's

• 21:09Speaker 0

  exciting about this to you. I don't know. I just it's it's a weird thing. Like if you haven't worked in hardware, it's hard. Um I've had the been fortunate to work in hardware and software. And you know, I I love writing a bunch of software and then hitting a pile and seeing it work. That's that's that's a good dopamine hit. But man, when you work on a piece of hardware 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-fi thing. 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 will come back and be like, I want to see. Let's see what happens. So these turn about sometimes you turn it on, it's like you see the little puff of spoke.

• 21:56Speaker 0

  That's not good. But you want to be there, be part of the moment. But uh I I think that's part of it. I think for me personally, 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. Totally agree. So every technology has negatives, but uh how 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.

• 22:36Speaker 1

  I think 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. Yeah. Yeah. 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. Aaron Powell That's a short-term thing.

• 22:52Speaker 0

  I mean, there will be there will be bumps along the way, right? Technology does that.

• 22:55Speaker 1

  That's that's what happens when you've seen too many sci-fi movies. That's right. Um look at Star Trek. Yeah.

• 23:01Speaker 0

  Yeah, exactly.

• 23:02Speaker 1

  Totally. It's great. Um this is a really big swing, right? Like this is a very ambitious company. Um what gives you confidence that it's that it's gonna work or you know, has reasonable

• 23:13Speaker 0

  shot of working. 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 it's yeah, uh engineers are kind of like

• 24:05Speaker 1

  uh

• 24:06Speaker 0

  the opposite of theorists. It's like, well, all right, that thing doesn't quite fit, stand it down and make it fit, right? So it's like we gotta do a little bit of that right now, and then we can build something

• 24:13Speaker 1

  and put it all together. That's awesome. Uh has anyone called you crazy yet for doing this? Oh, yeah, plenty of people.

• 24:19Speaker 0

  Is it

• 24:19Speaker 1

  lays

• 24:19Speaker 0

  it like everybody? Well, it's I I'm used to this at this point, you know. My family I would call crazy. I was called crazy going back to grad school um years ago when I had a very 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 about humanity out of Africa, all that. Well, the crazy people went out. We would be lost without with how crazy. You need some crazy in there. So it's okay. I'm fine with that. And

• 24:43Speaker 1

  so what kind of people uh are you looking to bring on to the team of a very ambitious goal? Um who should be interested in joining

• 24:52Speaker 0

  you. Yeah, I mean, I think some of the traditional, traditional ish, when I say traditional, I mean over the last five years, this feel 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 and 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 kind of 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.

• 25:42Speaker 0

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

• 26:04Speaker 1

  are. So you can get in on that cerebrus Jensen game where they're each pulling the biggest possible wafer out of out of an oven. You can't do it. Something like that. Yeah, yeah, yeah. Exactly, right? Put a few vacuum tubes on top for for effect. Yeah, we can I I I need blinking lights. Yeah,

• 26:17Speaker 0

  exactly. We're not gonna have cool heat sinks. It's gonna be super cool. It's gonna be cold. Like you don't need biggest things, you know. So I hope they make something that looks

• 26:25Speaker 1

  looks interesting here. This is a funny time for for um top AI people, right? Where you have sort of the option, if you want to 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.

• 26:43Speaker 0

  Yeah.

• 26:44Speaker 1

  Um or you know, people can join a startup like unconventional that, you know, has a lot of the nice aspects people look for and in sort of AI careers and are taking like super sort of big swings. Um 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? I

• 27:04Speaker 0

  think you get such a breadth of working in a startup that at the beginning of your career that will pay dividends later on. Because like I said, like the reason I can think across the stack is because 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 aimless 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. You're 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 want to 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.

• 27:43Speaker 1

  Yeah, that's interesting.

• 27:44Speaker 0

  Is

• 27:44Speaker 1

  it fair to say unconventional, sort of a practical research lab? Is that kind of the culture

• 27:48Speaker 0

  you're going for? Absolutely. Yeah. I mean, the first few years, it really is open-ended. I I don't want to close doors. Like I'm really specific about this. Like I always try to bring the conversation back because those people are like, oh, that's gonna be hard to manufacturer. Uh-uh, stop. Don't think about that. Will it work? First come up with existence proofs. Then we go back and try to engineer it. And you know, all the trade-offs there in. But if you make those trade-offs up up front, 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.

• 28:18Speaker 1

  And and to your point, it takes not only people with diverse skill sets, but um people with kind of high agency to try new things and learn new things and kind of integrate across the stack. Aaron Powell

• 28:28Speaker 0

  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 kind of lends itself to smart people wanting to come in and try to solve them. They they see a challenge like here's the Mount Everest of climate. 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 style decision, maybe a global, globally better for the company in the short term. 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 very passionate about trying something. It's like, okay, what you really want to do this. That makes sense, go for it, you know. And then you own it. You own both the good and the bad, right?

• 29:14Speaker 0

  And that's agency to me. It's like you gotta you're gonna say, like, okay, I fucked up. No, this wasn't that's okay too. But give people the room to do that, you know. Anything else you want to wanna

• 29:25Speaker 1

  say before we wrap

• 29:26Speaker 0

  up? 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.