The $4.5B Startup Building Brain-Inspired Chips for AI

28:02But I mean, you know, when you have these new roles that come up, like, I don't know, chief product officer is one of those things that came up, what, like 10, 15 years ago. you sort of define it yourself to some degree. And I think there is some latitude in these roles. I mean, you know, I think some of it is obviously defining a strategy around AI. That's important. Defining products, defining who the important customers are. It's kind of like being a CEO, a piece of it in a sense. But then outside of that, you know, what are your responsibilities? I don't know. I mean, I think it's, there's enough latitude and it's nebulous enough that you can make it work.

28:36And I think it just has to make it fit into what makes the company successful. And so, you know, Ali and I had just sort of figured that out. I would end up doing a lot of the, you know, public speaking and press and stuff like that around AI. And that was a very hot topic or is a very hot topic for Databricks. So I think it was good to get that, to get Databricks established in that space, you know? What's your official title at Unconventional? Is it UnCEO? Yeah. I feel like Naveen can't take a normal title. I think it has to be created as a new is part of that and you're a trailblazer. Everything in the company, we got to think unconventionally.

29:11So we have an un-CFO. I saw that. I was asking for a financial update and I heard from her un-CFO. I said, wait a second. I do want these numbers to be correct, not odd. We'll make it correct. But I think the key is that everything about this company is about breaking the old paradigm and coming up with something new. And so it's actually really hard, like for me too, not just, you know, for any human, it's very hard to break out of what you've been taught and what you've, you know, know works. So like, you know, as like we talk about analog engineers all the time, like analog engineering has largely been about making circuits act linearly, linearly, predictably.

29:53what's really interesting is if something is linear and predictable it actually is very easy to simulate if you can simulate in a digital system it's less interesting it turns out like silicon has all these beautiful physics that are associated with it we just try to like minimize we say like where can i make it look as linear as possible that's the least interesting part of it and that's the part that everyone's been trained to go after so now we're saying okay open it open the aperture, look at the places where it's ugly, quote unquote, right? And nonlinear. That's where the interesting things happen.

30:26And incidentally, those things are extremely hard to simulate on a digital machine. When it's hard to simulate a digital machine, it's doing something interesting. Now it's about harnessing that and making it work for us, right? And that's engineering. And that's what we have to do. But that's where the interesting parts come from. If you're just doing something that's easy to simulate in a digital computer, just use a digital computer. There's nothing new here. So anyway, that's why everything is on. You have to be outside that normal way of thinking, break some of the assumptions that you've grown up with, maybe for 30 years of your career, and think differently.

31:00So how did you get the team together and how big is the team? 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.

31:37It's like, well, yes, you've been doing it this way, but are you open to trying other things? What do you do outside of work? Do you do things that require risk tolerance? So I think these are the kind of 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, for sure. But we are building a team as quickly as we can. You came out of stealth in December and you disclosed a very large seed round. How much have you raised to date and how much do you think you're going to need to raise to get this company to escape velocity?

32:16Yeah, I mean, it's a good question. So we raised$475. There's actually interest to do more. So we'll probably raise some more 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 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.

32:57We're going to do a whole stack. 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. With capitally intensive businesses, Constantine, I'm really curious. In the current state of the AI cycle, there's huge rounds, there's smaller rounds, and there's midsize rounds. But I'm curious, from your perspective, Do you think these very massive rounds are reasonable, unreasonable? What becomes the breaking point? It's all about the company. So a round is meaningless without the context of the company.

33:36And when you're set up to go after a massive problem that you're uniquely positioned to go after, that societally and individually and as an investor warrants a significant investment. In the case of unconventional, it is singular, I'd say, in their ability to aggregate this type of talent. If you want to be in analog computers, if this sounds interesting, if this is an area that you want to spend your life in and you think can be transformational, first of all, this is the company for you. And secondly, Novena is the person to be leading that for years and decades to come. And when you can aggregate that kind of talent in one space and form the best talent in the world, and you believe that a space is sufficiently important, I think most of us would agree that massive reduction of power in order to do digital cognitive processes is sufficiently important, you can build something very large and impressive.

34:36To go even further, how do you feel about valuations? um i'd always like them lower molly of course that's an investor um yeah how do you feel about he wants them higher i want them higher actually i i don't i think you want them at the right in the right range because valuations to me as an entrepreneur are expectations right so when you set them higher your expectations are higher that's what a good founder thinks that's very that is truly very good because it is the life i mean you're a repeat founder you've actually seen it. I find first-time founders are 0 % calibrated to that type of thinking.

35:12And a few years in, it is very difficult for them. And then repeat founders, of whom I work with several, always have a very similar logic because they've seen what happens, even if it's not to them, to their friends, once they've set those expectations. And even if they surpass it, it doesn't feel as good for the entire company. Or if they fall short, it feels much worse. So that is a very experienced way of looking at it. Do you feel pressure?

35:40No, not really. Because this is, I don't know, I just feel like this is the path I was meant to be on, right? Because I feel like this is the thing that we need to do as a society. I mean, just kind of put it in context. I'm sure someone in this room knows how much money do we spend on energy generation, electricity generation in the world. Does anyone have an idea? I don't know, actually i know it's a really big number i should know this number a lot navine uh 20 trillion 10 10 trillion 20 trillion something in this order right half a billion dollars is a drop in the fucking bucket like it's it's if you can solve one tenth of a percent of that problem you've made an enormous company so i think that's what it comes down to here is that like the scale of the problem is so big.

36:27No, I don't feel pressure because are we going to hit a thousand X in the first go? Maybe, maybe not. I don't know. There's some percentage chance that we will hit it, but I think we're going to hit 50 X with a very high chance. If we do that and we have a path to getting to a thousand and getting to 10 ,000 and a hundred thousand, now we've created something really big. So to me, it's about like, let's keep pushing this and be as unconventional as we can get to a new paradigm and then keep on pushing that. That's another 20 or 30 years of innovation. So all I can think about is excitement. Honestly, I don't think about pressure at all.

37:03Of course, I have investors and I have to make sure that I take care of them. But at the same time, I think they're on the journey with me. We were talking about this a little bit beforehand, but clearly capital is not a bottleneck. Power is a bottleneck. And you have a hot take on this. Do you want to share what your take was? Yeah. I mean, you know, everybody here, I'm sure, has heard all the hype, right? Everything about, oh, we're going to do all this build out in data centers. We're going to have this giant advantage. I'm not going to name company names, but you probably know who I'm talking about.

37:34None of this is physically realizable, right? That's what's really kind of annoying to me. I'm like, okay, you're talking about, you know, a trillion dollars of GPUs. Where the fuck are you going to get the power for that? I'm sorry, right? Like, you're not going to be able to build these things physically. And the things that have been realized, I mean, like the XAI clusters and stuff like that, valiant efforts, no doubt about it. And it's amazing to push through and get it done. But it broke a lot of rules and is doing a lot of stuff very inefficiently. So it's not really something you can do over and over again.

38:02To me, you really start changing the world when you make something that's repeatable and you can sustain it. When I say sustain, I don't necessarily mean from any ideological standpoint. I mean just economically. Like can you do this thing over and over again? Does it get cheaper? can you keep scaling it? And so that's important. You have to have this. So doing something as a one-off is not super exciting to me. Doing something where I can now go from that one-off to something that's scalable, repeatable, will continue to get cheaper, will continue to scale, and will kind of hit these Jeevan's paradox sort of dynamics, that's interesting to me.

38:37What are your principles as a founder and a leader? Well, it's interesting because I think as a founder, you change some of these things depending on the company to some degree. Of course, truth seeking, we don't want to have these ideas of, I'm just going to defend my idea because it's mine and I don't care if it's true. I mean, you have to have people at the beginning of a company who set the stage and are really, truly 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.

39:14It'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 in those scenarios, it causes problems. So I think being mission-driven, going after the problem and caring about it, that's an important thing. I think that's job one. I think trying to find the quickest way to get information is sort of a cultural attribute you need to have. Try to think efficiently, even if you've raised a bunch of money. It doesn't matter. 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 only help you so i think that's an important attribute um i think going after the original source of information you know like we want to keep our organization as flat as possible um efficiency drops really precipitously as you go over like 80 to 100 people that's what i've seen over and over again um what's that uh number called 150 people uh it's particular no not mapcal no yeah but there's a word for the number it's something number i forget the name now but anyway yeah over 150 people basically dunbars that's it yeah there you go thank you chat jbt i had a phone in a friend there um yeah so dunbars number right like when you break that you start to see these massive inefficiencies because coordination starts to take over so i think keeping things flat going to the source of information is very important and you know always just being i want to be humbled by the people that i hire i want everybody to feel that way and we will just keep getting better at the after the 20th hire i probably would never have been hired at the company you know i mean that's kind of my goal so i think having these sort of attributes in everybody's mind uh just makes the company better and it's it's hard to keep that going.

41:14It's just, you see it over and over again, but we're in a research mode for a while. So I think we can, we can adhere to these principles for a number of years. So I think those are kind of the high level ideas that I like to keep. Constantine, what are your principles? You work with many amazing founders, including Naveen. What have you learned from them? Interesting. I'll say for investing or for company building? Investing. Okay. So for investing, the first principle is honesty and integrity, without question. Working with people who are honest, say things the way they are, have clear communication with me and genuine trust, that's non-negotiable and high integrity on all these things.

41:57It has to be a true relationship. I'd always rather know bad news, honestly, than the opposite. And frankly, that's the kind of relationship that I have with my founders and I'm really grateful for. And partly that's because when there is tough news, which by the way, if you do something important, there's going to be a lot of tough news. Always. If there's no tough news, you're probably doing something unimportant. So when there is tough news, the response is level-headed. Or you're lying. Yeah, or you're lying. Exactly. And when there is tough news, the way that these relationships actually grow are by responding with ability, taking responsibility and responding with ability and saying, okay, this is what we can do.

42:36This is how we can help you. This is how we can put this on track or just listening and solving it together. So the first two are always honesty and integrity. And then the next is I'm looking for a spike in the person, something that they're amazing at. I'm not looking for someone that checks every box. I'm looking for someone who's unbelievable in some particular area. And the other areas we can help, you know, that's the job. I'm not here to say, okay, well, you're really great at this, but how about we teach you these things that you don't know? Instead, it's, you're great at this, be the best in the world, is the saying, run incredibly fast, and we will help complement you in the areas that you don't know.

43:17And the third is, make no small plans. There's a Daniel Burnham quote, he's an architect of the World's Fair, and he said, make no small plans because they have no way of making men's blood boil and probably will never be realized. So if you're going to do it, do something big. What companies do you think are going to fail?

43:45I would rather not answer that. And the reality is we can get into that, but I also don't focus on that. My job is to focus on the things that can be successful. Let me think on that, if there's any category of issues. I worry about companies that chase revenue in the short term, just to be very explicit. You can always buy revenue, and this has been true in every single cycle. It's especially true in financial technology companies and insurance companies and the like, where you can buy revenue for less. But it's even true in software businesses in an AI era where you're acquiring customers and the long-term uniconomics aren't attractive.

44:31And so anytime you see a business growing really, really quickly, I like to see that the founder's thinking about the long-term as opposed to the short-term. So categorically, businesses that chase revenue just because it's attractive to investors and it makes the employees and team feel good, I'd be very worried. And I really like businesses. I feel really excited when I meet a founder who's taken no shortcuts and done the really hard thing up front and knows the revenue can follow. What do both of you think about all the circular deals that are going on at the top? Well, I mean, there are some games definitely being played.

45:10But at the same time, I think the bet is that, yeah, I know we talk about it looks bad or whatever, but the bet is that the demand will come. and I think that's actually a good bet. So I see it as like, all right, fine. It doesn't look great in the short term and I can see why people are irked by it because there are some sort of circular dependencies on these things. But if out of the results of this, you create AIs that start to do very interesting things, which we are doing, and real demand happens, you will see societal, secular shifts. We are seeing it already, right? I think software engineering is probably one of the areas that's being hit first.

45:52And I don't say hit's the wrong word. I think being changed first. And we're seeing people have a willingness to pay for those solutions. And so once that demand is established, all of these games don't matter anymore. It's like, okay, well, yeah, you did that deal and it pumped up your valuation, but then you grew into the valuation because the demand came. So I think that's what's been irking people is that the demand wasn't there and many of these things happened earlier. So I actually don't see it as a terrible thing. I think I saw a very similar thing happen in the early 2000s. There were all kinds of crazy shenanigans that happened.

46:25Some of the biggest businesses in the world now came out of that. Before we open it up to Q &A, I think we're almost there. I want to talk about race car driving. Okay. Happy to do that. How did you get into this sport and how does it make you a better CEO? um well i got into this for because i just like cars i mean i'm an engineer you know and cars are a thing you can engineer that's 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 uh i raced you know like racing go-karts years ago like in the early 2000s i didn't do anything for a long time because i went back to grad school i had kids 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 i 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 like uh I started racing in that series and sort of kept on upping the dose.

47:38And 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 how does it make me a better CEO? I think there's actually a lot of lessons to learn from racing. It's precise execution. So people have this, in both racing and in entrepreneurship, people have this like romanticized or just incorrect notion that like, you know, if you're racing, it's just like, just go out. You just have like, you know, massive cojones and you just go really fast.

48:14It couldn't be farther from the truth. Being a race car driver is about precision. It's like you hit the mark perfectly every time. And it's just tiny, tiny movements, right? Same thing with being an entrepreneur. Like you have to be precise. You the way you hire and the way you talk about things and the way you focus the team. It's not something where it's just like, oh, I talk about some high level bullshit and then everything just falls into place. No, there's a disciplined execution that happens. And so those things are actually kind of similar, just the timescales are compressed. And I think for me personally, it's just like, it's a way to just break my mind out of a local minima.

48:53It's like when you're in a race car, you're in a complete zone. Like there is nothing else outside of that. I mean, I had, I was at Le Mans in the summer last year and, you know, I did a quadruple stint, which is quite a long time. It's like two hours, 45 minutes in the car straight. I can't remember the time. You're literally just in there, just boom. I'm just doing the thing over and over again. Like you come out and you're, you had this Zen feeling. It's really, there are probably much cheaper ways to accomplish that, mind you, but I love it. get a one week meditation retreat yeah just silent meditation safer we need you healthy that's no fun though come on do you have a key man clause or anything okay man don't ask about why do we have to go there um no but race cars are pretty safe so but i don't know it's actually i don't know there's a competitive element to it also like being strategic and you know uh judging risk.

49:48Everything you do in a racetrack is about a risk-reward trade-off. I'm going to pass this guy now. I'm in a 24-hour race. I'm in hour one. Is it worth it? Maybe not. You have to make these micro decisions all the time, which again is very similar to running a company. You're constantly making tiny decisions. The success or failure of that company is a culmination of all those little decisions. Same thing in a race. 24-hour race, the decision you made over a three millisecond period might have changed the outcome. So make good decisions. And I think these are the kind of things you actually learn.

50:21I think there's a lot of parallels. And that's probably true for many sports, but you know. How fast can you go? Depends on the track. Well, that was probably the fastest track. We hit about 200 miles an hour on that track. Okay. That's nice. Constantine, any hobbies on your side? I work all the time. Okay, well, I think we'll open it up to the Q &A if anybody has any questions. We got one open. Or I guess we have mics, but... We'll come back. It's okay. So my question to Naveen is what do you think of combining the physical and virtual systems? So creating a thing that would create a new kind of symbolic system based on topology of the object.

51:17I'm talking specifically about Hopfions, but Hopfions, it's a node of light. And so it's possible to create a kind of new alphabet or more complex just a language and describe it in Hopfion based on the topology of the object. And my other question to Konstantin is what in case of mind, like people, like the evolution of mind will be getting further questions. What is coming after Heidegger? Heidegger is existentialist. And so, yeah, that's it. Well, I'll attempt to take the first one. I think you're saying virtual, and so doing it in an existing numeric machine. The not applied is. OK. But combining with Bueschel is, I mean, the one zero, but.

52:22So, OK. I mean, I would argue that all computers... Thank you for giving that one to Naveen. So all computers are that, right? So any computation or memory does result in a physical change. We cannot represent information without a physical change. In fact, that's fundamental to this Landauer principle that Constantine was talking about earlier. So it's about what kind of change can you use to represent information. And so we're very good at building semiconductors that can use charge to represent information. And we can manufacture those at scale. That is by no means the only way to represent information.

52:59So I think what we're doing on convention, I would say, is can we change the way we think about computing instead of becoming fixed bit with digital numeric arithmetic? Can we think about it as like a dynamical system that evolves with some sort of time varying physics? If I do that, now I can represent information on physical media that have their own dynamics. First one is going to be silicon because we know how to build it. Again, when someone comes up, when I say, I said when, not if, but when someone comes up with a better way of representing information, it's smaller, you know, more energy efficient, denser, whatever, we can use that.

53:42But we have to move away from that abstraction in a sense. So I'm open to any new ideas, but we have to be able to build it and manufacture it. That's the five your problem. So I don't see anything else that fits that bill right now. I'll jump into your answer about the mind and what happens next in this era where we have massive artificial cognitive scale. So there's the physical and then there's the metaphysical. I'll leave the metaphysical because that's a matter of belief and religion. And I think that that is its own very important set of things. But on the physical, I think that we're going to have more and more computation doing more and more cognitive work.

54:27And I don't think that that replaces the most fundamental human interactions or the fundamental needs of the mind. And I'll go back to the physical world again. Theoretically, it makes sense for all of us to be moved by mechanical objects. because the cost of driving mechanical scooters everywhere, everyone in this room being on a mechanical scooter to exert zero effort so we can move faster between destinations and doing everything over Zoom, this is a packed room, but theoretically we could do this without physical exertion at all, would say if that technology was allowed, humans would all be sitting in front of a screen from a distance and minimizing physical exertion of effort.

55:11If that would happen, that would suggest that when cognitive processes occur, automation of cognitive processes, that we'll also minimize human cognitive processes and offload everything to machines. So I don't think we ever do that. I don't think we ever offload everything to machines because there's this old Greek philosopher that on the physics would say the man is the measure of all things. The person is the measure of all things. And I think that we will choose to keep our minds largely as they are, just like we've chosen to keep our bodies largely as they are even after the Industrial Revolution and the automation of the vast majority of physical work.

55:51Guthrie, up in the front, what was your question? If the ultimate output of AI is the completion of a cognitive task, to your point, how do you actually measure the efficiency improvement across many different types of tasks? Yeah, that's a great question. I mean, we have a convenient way of doing that now because of tokenization. So at least in the near term, the way we're measuring it is joules per token. It's pretty straightforward. It's very hard to game, right? So, you know, with ISO quality, holding the quality constants and joules per token. Yeah, beyond that, though, you could argue that, okay, is the token the right atom of that task?

56:32Maybe, maybe not, right? So that starts opening up some gamification. So I think when you start moving away from that thing being the unit of measure, it starts to open this up. And that will happen. Absolutely. So then it will come down to, well, okay, how quickly can you do it for or how much of the cost to do it will eventually come into cost. and I think at some point there will be some tasks still that humans will probably still be pretty good at or at least in guiding the system because it's going to have different attributes than a human brain in terms of what it's good at, what it's fast at, what it's slow at, what it's energetically intense at, what it's not energetically intense at.

57:10So I think there will be this space where we start to trade off between different tasks at some point.

57:19so that these uh previous approaches like spiking or computing memory they've been tried they've been successful as much as we would like compared to artificial neural networks can you comment on that and how that is different from what you're thinking yeah so first off just because something is tried does not mean it's wrong and it is absolutely the wrong reason not to do something. I've heard that so many times throughout my life. It's absolutely not the right reason to not do something. Was it flawed? I don't think fundamentally flawed. I think it was flawed from an execution standpoint. Many neuromorphic approaches actually started very early, and it was just from a pure biomimicry standpoint.

58:01Also, I think because we didn't understand learning systems like 20 years ago, it was very hard to actually put some kind of frame and what's useful out of these. Like the question that was just asked about how do you evaluate it? That wasn't even clear 20 years ago, which now it is. So I don't think neuromorphic stuff was wrong. I think it was just maybe a bit early. Now, what we're doing differently is we're not being so literal about biology. Biology is built out of very different stuff than silicon. We are choosing silicon because we can manufacture it. The nonlinear dynamics of silicon are different than biology.

58:37I'm not saying better. I don't know if it's better or worse, but they are different. So that might necessitate different ways of thinking about it. Like spiking in a neuromorphic system, you don't need to make it spiking. If there's something that, if it solves a problem for us, like transmission information or something like that in a particular energy envelope, sure, we'll do it. But we're pretty open about how to build these systems. So we're trying to build the systems kind of for what their attributes are versus like trying to fit a paradigm on top of it. So I think that's a big difference.

59:07And also, you know, we are, we do have these almost not ground truth, but sort of a golden model in a sense, like I can compare it to a digital version now, which I couldn't do before. I remember the neuromorphic stuff back in the 90s. It was like, oh, I can do some sort of classification or something like that, which was very constrained and not super comparable to a digital system. Now I can directly compare and I can say what I'm better at. So I think just having that framing actually makes it successful. But we're going to make it work in silicon and make it manufacturable, and it doesn't need to look like biology.

59:45And I'm really glad you asked that because it's partly what's so exciting here. The understanding is that perhaps this is a field that has been tried. But the reality is the world is full of fields that have been tried but have not been brought to their completion. I mentioned earlier electrification of vehicles, so the EV revolution. and that happened very recently, even though the electric motor has been along here for a very, very long time. And there are so many benefits because when you come up with the internal combustion engine, you have to deal with all sorts of physics and transmissions and coolants and all these things that you don't have to deal with when you go with just the fundamental basics of the physics of an EV.

1:00:37Similarly, with analog computation, there's this whole space littered with just gems of information over the decades from analog computation that the theoretical limits are even better. You know, if you take it to the extreme of where the physics can go, it's even better. And it also reminds me of neural networks, which have gone through phases. And neural networks, I'm an AI engineer. It's been the cornerstone of my career for the past 15 years. During that time, neural networks have been out of fashion and in fashion, not nearly to the extreme as they were in the 80s and 70s when they were completely in a backwater.

1:01:18But let's not forget that the folks at OpenAI were unbelievably courageous because they did something that the vast majority of academia thought was not going to work. Remember, the vast majority of academia, when OpenAI was doing their research that now has changed the world, would have thought that that would fail. And you can say that with confidence, because otherwise, academia probably would have been working on it. So it's so exciting as an investor to find areas that have just the laws of physics and science and the promise of what can happen on their side, and a great team to go after it.

1:01:57I think we have time for one more question.

1:02:03I have a pretty loud voice. I can go for it. Thank you. Awesome. Again, congratulations. Super excited. I have a question. Where do you think is the hard wall today, 2026? Is it the energy constraint that you have to fundamentally deal with up front? Data movement, memory, materials? I know there's a lot of race with materials, foundational materials, across the world, particularly with China. So what do you envision, like, just going into 2026 that you're going to be... With existing paradigms, you mean? Yeah. Starting with the new paradigm that you're beginning to build, but you still have to deal with some existing remnants.

1:02:46Well, yeah. I mean, if you look at an existing digital system, the vast majority of energy actually goes into data movement. so that doesn't go away like to move data like i said before all information has to be represented physically and when you move information something physical has to change work equals force time distance that's still true if it's charge or if i'm moving something around i have to physically move it so that is still true we cannot get away from this i mean arguably some of the optics people would would say we can um maybe at some point that'd be great or quantum people would say we can.

1:03:24There's still a lot of hard stuff to solve there. So that absolutely is going to be there. I think from a material standpoint, lithography is kind of hitting physical limits. Yeah, we've gone to EUV. Now we have two nanometer, three nanometer, four nanometer. They're all very similar density. They kind of have just different attributes. Some are better at this and worse at that. So we're kind of in this now trading off space. Just to kind of give you an example. Again, I go back to biology because biology is sort of an example of a system that we could have observed in the past and had gone through changes.

1:03:56As a trained neuroscientist, I can look at what we call the cytoarchitecture, basically the cell structure of the cortex of a rat and a human. I can't tell the difference. So the evolutionary biology version of Moore's Law kind of ended about 150 million years ago. The circuit didn't change very much at the micro level. The macro level changed, the architecture of the brain changed and other things, but the circuit didn't change so much. And so I think maybe we're getting to some kind of limit there with the current lithography techniques. Now there are people building new lithography techniques, and that's what I think is interesting.

1:04:35It's going to necessitate what we're doing. If we don't think about how I can use new materials that have different properties and new ways, And if we just put our blinders on and say it must be digital and it must act this way, I think that's going to limit what we can do physically. So that's actually what I'm even more excited about beyond just what we're going to do in five years. It's like, what are we going to do in 20 years? And I'll say, I'll add to this, which is a perfect example of the physical world and processes where we're limiting. What I see as limiting or the limiting factor is not talent, but the matching problem for talent.

1:05:14To this day, the vast majority of problems are still out there. They're ahead of us. And the amount of creativity and courage necessary to solve them is still way under talent. And I actually do think we have immense talent in this world, not in this room, but in this whole world. and finding and developing and accessing and placing that talent in the right places is so under-optimized, so radically under-optimized. There are so many brilliant minds who can solve things in their own unique ways and for a million different reasons are not in their best and highest use. And so that is if I could optimize one variable to maximize the success of humanity, that's the variable that I'd push on.

1:05:58And frankly, it's a variable I feel like I get to work on every day. Wow, what a good place to end. What? There was one extra question. A bonus round. Okay. One last thing. When do the robots take over? No, actually. So you mentioned architecture between the rat and the human. And given your neuroscience background, the one element you didn't talk about neurocomputing that strikes me, you talked about analog, stochastic, right, that's nonlinear. It's also recurrent. And if you look at works of Antonia and Hanna-Damazia, for example, or Francisco Varela, a generation earlier, that is the defining feature, right, that the insula tracks the body state, the change.

1:06:43How do you think about bringing that into what you're doing? And does it play any role at all at this point? Are you just doing the neurons? No, no, no, 100 % it does. So this is what I mean by nonlinear dynamics. So if you look at these recurrent systems, you know we can study them in crude ways which would dunk a bunch of electrodes into a brain and watch what happens you actually see that like there's there's a a ringing state so you can perturb this state and it moves through these like transitions these transitions actually are computation right that is how our brain does computation it's done through this evolving state so you perturb it from some kind of outside stimulus and you get this kind of computation those states are learnable and changeable depending upon experience.

1:07:26So we're building systems like this. Exactly that. They are recurrent. And we can model some of this stuff, and we have modeled it. In fact, if you look at what a transformer is really doing, like a transformer neural network, if anyone knows what a KV cache is, you get a bunch of tokens in, you do all this stuff where you perturb the system, right? You basically, you set up a new set of desired states for the system. It walks through, it puts another token out, it jams that back in, and it does it again. That's recurrence. The whole thing is actually a dynamical system that is being simulated in this horrific algorithmic way that's order n squared in a digital system.

1:08:11But it is a dynamical system that sort of kind of converges. In fact, it doesn't really converge because sometimes it gives bad answers. Convergence would be giving good answers. But that's exactly what it is. It is a recurrent system. So now can we think about how do we physically build something like this that does converge and uses the physics in our favor instead of having to simulate every step of the way? Fantastic. Okay. That was a good bonus round. Well, first off, thank you so much to Playground, Peter, Hadley, and the team here. and second off, thank you so much to our wonderful guests, speakers, leaders of the frontier of AI, Naveen and Konstantin.

1:08:51Thank you. Thanks for the great question. Thank you, Malik. Thank you, Naveen. Thanks for the subject.