0:00We're going to be out of energy in the next few years. This is a blip. It's not some kind of bubble being burst. We don't treat the model as sacrian. And we're energy first in our mindset.
0:10Naveen Ralph is a serial entrepreneur. I mean, he sold his last business to data bricks for $1.3 billion. Mosaic ML. Now he's back with unconventional AI, which has raised hundreds of millions of dollars. Their last round close to $5 billion in valuation. Navine is one of the most articulate and clear thinkers across such a wide range of very complicated topics. He was fantastic and this show was a joy to do. Intelligence to me will have infinite demand. I
0:39actually think the hardware is the place to be over the next 10 years. I would say if you're a pure software investor, you're kind of [ __ ] The inference market is going to be a 10 trillion market within 10 years. I don't look at, you know, oh my god, AI is going to kill all humans. That's the stupidest worldview I've ever heard. Ready to go.
1:05Naven, I am so excited for this, dude. I told you I sto the [ __ ] out of you. Matt Bournestein, Matt Oo, many others. Thank you so much for joining me today, man.
1:14Absolutely. Yeah, I was looking forward to it.
1:16Yeah, I think it's going to be a lot of fun because I I went through a ton of your shows. I wrote down what I thought were the most controversial quotes.
1:23There were a few zingers, by the way, but I actually wanted to start on like the foundational layer of this infrastructure, which is energy. You've spoken before about how it will be the bottleneck and the importance of it. How do you evaluate the state of energy supply today and what are we not seeing that we should see?
1:44Energy supply continues to be difficult to build, right? I I don't think people appreciate it. I mean, I've seen, you know, numbers from uh general industry is about $50 billion to build one gigawatt. So, people are throwing around the around the word gigawatt like it's like it's easy. And it's like, guys, this is not easy. Just to put that in perspective, um building a fab like a semiconductor fab is on the order of 20 or 30 billion. So, this is one gigawatt is more than building a fab like if you
2:13if you look at it in terms of dollars and complexity. So, it's hard. Um I think what we've seen over the last several years has been uh soaking up the excess demand that's been around. So we overbuild generally and we don't run uh infrastructure at 100% um output. So what we've been doing where we've been getting energy for data centers is primarily been from this like can we run things at higher utilization and uh you know and get that ne next couple of
2:42gigawatts out of it. So yeah, I think if you look at the US power grid, we're on average something like 900 um gawatts of of output, but I think it's capable of peaking up to like 1.2 1.3 terowatts. So 1,300 uh gawatt. So there is some excess capacity and that's primarily what we've been going after. That's what you can get access to quickly. But building infrastructure is hard and we need to build more. We're behind.
3:09How far behind are we? Well, I think over the last 20 years um China surpassed us in terms of total um uh amount of capacity that happened I think in 2023. Uh but they were building 6x faster than us for 20 years. They were coming back from far behind but they uh they really went fast and hard at this.
3:31And we criticized that a lot because we said oh they're using dirty methods to do this coal burning this and that. True, but they're also trying to grow as fast as possible. And I think now they're sort of going back and and cleaning up some of this. So, uh, you know, meanwhile, we didn't really invest in energy infrastructure and that's going to hurt us because it's going to take time to really get there.
3:51Dude, what can we learn from China being so much further ahead of us that we should be like way more loose on policy and regulation?
3:59What are the takeaways?
4:00Well, I I think some of this is hard because it it hits to the very core of our culture and and their culture. I think uh China you know can centrally plan. We don't and there are advantages to both of these things right and in these things where you have these giant infrastructural rollouts yeah there's an advantage no doubt about it uh however I think when it comes to innovation tip of the sphere sort of things like the US is far and away better so uh AI was born
4:28here not there you know like uh all of these these latest innovations have all come from here now they're doing some really great work in China also I don't want to diminish that because it's not just distillation they're really doing some some true innovation. However, I think that that tip of the spear stuff where you really open up, you know, new areas comes from here, not from there.
4:48So, you know, I think we have to leverage our advantages is what it comes down to.
4:51When you look at what's scarce today, what do you think is not scarce today that will be in 3 to 5 years?
4:59I think that excess capacity is going to start getting very scarce. uh you know if we look at growth rates even even the kind of uh much more conservative growth rates of AI we're going to be out of energy pretty soon in the next few years and and it's not easy just to say okay I'm going to find some more capacity you will have used up all of that excess so then we're in a we're in a place where we kind of converge to the rate of building and you know I I've heard people say oh we're going to build 40
5:28gawatts in a year there's no way we have never done that I I I just don't see as being possible. Uh, at least not right now. Maybe in a few years we'll come up with some new inventions and we will build our way out. But as it stands today, we're not going to build 40 gawatts in a year. Maybe we build 10. If we build 10, that's amazing. That's half a trillion dollars, just to put it into perspective.
5:49So when we look at the debt cycle that is already being talked about as being incredibly toppy and we're seeing that now starting to uh cascade or unpack in core and a lot of concerns around that.
6:03Yeah. Is it a is it actually just at the start or does the music turn off at the party at 700 p.m.?
6:11I I I think uh the biggest the biggest turner of that will be is there demand for AI services for therefore for GPUs and therefore for this excess uh this new energy buildout. I mean obviously I I'm in this industry. I'm I'm pretty AI pill. I I think there's plenty of demand. I don't think we've even scratched the surface. um you know there's a lot of problems we have to solve in AI still but as we solve those and we will over the next couple of years it's just going to open up new new
6:40types of demand that we don't even really know about today. So my sense is that this is a blip. It's not some kind of uh bubble being burst. Uh I mean it's sort of like if you said hey is there demand for people curing cancer? Of course there is right? Like if if you cure cancer people are going to want that. Like if you build an intelligent machine capable of doing anything, yeah, people are gonna want that.
7:03I always remember Vin or Kosa saying to me, I want to take as much technology risk and as little adoption risk as possible.
7:09Yeah, it's exactly that. I have a cure for cancer. Very difficult to do. Uh but zero adoption risk. Um I totally get that. But then does that mean that we're mispricing the excess supply today if we're literally staring down the barrel of 18 months running out? Uh, I mean, you could argue that. I suppose that's up to the the energy markets, right? Um, that's that's how they should be pricing and moving up. I I do see evidence through anecdotes at least. I don't have
7:39specific numbers to quote around this because every one of these behind the meter deals is is bespoke and people are paying way more per kilowatt hour than they would off the off the standard grid. So, I think we're already seeing a two-tiered pricing occurring where the value of electricity for data centers is sort of higher than it is for general use, right? So, there's a lot of behind the meter stuff going on right now. U companies are are are building new sort of technologies like Bloom Energy. Uh
8:09there's a Bloom Energy deal for building, you know, a couple gigawatts in uh in Arizona. Um if that works, amazing. That might be the technological advances we need to keep going. But, you know, it hasn't really really been proven out yet. And those economics, by the way, are very different than what you see on the grid.
8:26I mean, I think if you just boil it down to how much people are willing to pay per kilowatt hour for energy for data centers, it's just higher. You know, you can tolerate a higher price because the margin structure is just so much better.
8:39Like basically, you know, if I have uh uh a gigawatt of compute and I want to sell that, I I can sell it for let's call it uh 5x the price. I'm making numbers up, but it's on the on the order of 5x the price of what I pay for that electricity. So, you know, I'm I I got a lot of room wiggle room there. So, I could pay 30% 40% more for that electricity. when we kind of go slightly further down from energy which is kind of the core foundational power uh to the
9:11What do we know now about compute that you wish you had known 2 years ago? I I think one thing I wish I'd known is the fact that we don't really see physical efficiency gains anymore. So you can separate out the way we build a computer in let's call it three levels. So uh the first level is the physical. So if I can send a bit from this point to that point and do it cheaper like you know let's say I I represent zero as being here and
9:39one as being here and my phone has a certain weight. So there's energy required to move between these two states. Uh if I make a lighter phone it changes the amount of energy required right? Uh so that's just a pure physical uh uh thing. And Moor's law as we generally refer to it or Dennard scaling you may have heard uh where you make devices smaller is really a physical thing. So you're making the physical layer more efficient and and denser. Uh so those innovations have largely
10:07stopped and you know if we look at the last few generations of GPUs or other chips you don't see efficiency gains much anymore. You see efficiency gains from the next layer up which is kind of the computing architecture. So I would say most of the efficiency gains we've seen over the last several years or several generations have come from smaller data types going from 32-bit to 16 bit to 8 bit to 4bit. So you get pretty good efficiency gains because you're moving fewer bits, you're computing with fewer bits. Um then the
10:36next level which is not really exploited that well and we started this a little bit mosaic is about um how do I start enforcing uh locality and reduction of data movement at the model level and you know 5 6 years ago we didn't think about it like this because the model was not the primary driver of compute or even yeah maybe six years ago it was still compute was a general purpose thing I run websites and databases and all that
11:04kind of stuff. However, now if I look at the computer solely as a physical device designed to to to deliver intelligence, it changes the mindset a bit. Now I can co-design all the way from the model through the architecture to the physical. And that's what I wish I had known how much sort of alpha there's there is left in that in the top layer, not just the physical. We focus on the physical for a long time. Can I ask how does that design mindset change when moving across the stack with that
11:32transition from premodel focus to postmodel focus?
11:36Yeah. I mean it it means we can we don't treat the model as sacrian right and and we're energy first in our in our mindset meaning that everything we do has to give us an energy advantage. It sounds like a simple thing but it's not how anyone has designed these things in the past. It's always been about speed. And this actually has its roots that go all the way back to ENIAC, you know, in the 1940s. Uh because, you know, computers were designed to be faster than the alternative. That was always the case.
12:04And in the ENIAC's case, uh by the way, actually, do you know why ENIAC was built? It's an acronym. Uh electrical numerical integrator and calculator, I think is what it stands for.
12:17Yeah. So, this was a computer that was uh built by the US government in 1945.
12:23Uh it was one of the first large-scale digital computers built and its purpose like every other machine that's ever been built has a purpose. Its purpose was to do artillery trajectory calculation and do it faster and more accurately than the alternative. The alternative at that time were humans who literally did the maths. So uh you have to be faster. That was the key. Next generation comes out, it's got to be faster than that. Um you know GPUs came out, they had to be faster at rendering than a CPU. uh each thing successively
12:52has tried to be faster than the previous thing that has led to efficiency for several reasons. I mean making things faster and the same power envelope isn't more efficient. Uh but also Moore's law was chugging along and you know we went from vacuum tubes to transistors.
13:06Transistors kept getting smaller and smaller and smaller. Uh and now we're in a place where that physical side of things is not really changing a whole lot. Like transistors haven't gotten much better. However, we have a lot of room left on the model and architectural levels. Does that mean we're seeing a commoditization of models or does that mean we're seeing actually a separation of model quality in those that are able to save energy and have efficiency gains versus those that aren't? I I think that's exactly right. So, you know, one thing I've been pretty good at
13:35is kind of picking where people are going to be focusing in the next uh couple of years. And I think this is going to be a big exceptional Naveen. And really like credit to you. Do you walk through when you're picking a market and go like this is where I think the world's going to go and so I'm going to position myself here?
13:52Yeah, absolutely. And you know it it also comes down just to interest. You know, this is for me one of the most interesting problems there is to build a better computer, you know.
14:02So you sat down here pre- unconventional in the idea maze and you were like problem set that I'm going to focus on is dot dot dot. What was that?
14:12Well, I kind of look at it like if I were to solve a problem, what is the effect? Like what what what does it unlock? In fact, it's kind of like being a scientist. Like being a good scientist is all about asking the right question that if you answer it, it unlocks many more questions. That's that's almost the hallmark of a good uh of a good paper.
14:31So for me in this in this endeavor, it was clearly like, okay, well, we're going to run out of energy. That's clear. The paradigm we have hasn't really been focused on this. I think this is going to be where the the the focus will be because we need to keep scaling, right? Like we need to deliver intelligence in many new form factors and and and and greatly disrupt the cost in order to make it ubiquitous. So no one's thinking about this yet. I should start it. Can I we were talking about like model efficiency and how it can deliver in terms of energy. We see
15:01models getting more and more efficient today. How do we think about where we are on that model efficiency curve? Is there a lot of room to run there or are we kind of at the asmtotic point where you're like we're ekking out the final increments?
15:14No, no, no. We have a lot to go and I think the reason is is we've not done this true codees between the model and the physical side of it. So, uh you can come up with a concept and you may be able to get say let's call it 5% efficiency gain uh just from software on a standard GPU. Uh that sounds great.
15:33However, you can only exploit whatever concept so much because the physical thing is built in a certain way. If you look at our brain, they co-evolved. We co-evolve the intelligence layer and the physical layer and we're able to exploit things in the intelligence layer in the physical layer. So, sparity for instance, sparity means uh let's say just not having connections between computing units. Um, I can I can get some kind of gain out of using sparsity
16:02in a GPU 100%. But when I have a standard offchip random access memory, the limit of those gains is not is is capped. I can't go too far because that physical thing is not designed necessarily to exploit this kind of sparity. So if I can build something that exploits it, I can get massive gains. That's really what the brain did.
16:23That's how it co-eolved. When we think about that like co-evolution or that specialization of architecture to the task almost does that mean that we'll unwaveringly have the specialization of chips we see etched and we see fractile and Olix and a number cerebrous obviously but a number of people going after specific workloads is that the future of compute how do you think about that 100% it is and you know this actually goes back to even Touring's original
16:52essay he wanted to build an intelligent machine Yeah, I mean that was that was his stated goal right at the beginning.
16:57Um, so we went down this path of we took his machine which was a theoretical construct and we built physical versions of it. So anything we build is not truly quote unquote touring complete because you know they have finite memory and stuff like this. Uh, but we were able to build applications that were super useful, right? I mean we could run businesses off of them. We could we could edit videos and pictures and text and all kinds of great things. So that's a huge productivity gains over the last
17:2460 years. Uh but it it deviated from that original goal and now we've kind of come back. So the purpose of the computer I think is to provide intelligence is to me it's it's just a physical manifestation of intelligence.
17:37And so now we have to figure out how to make that uh better. And what's interesting is that if you make a better physical manifestation of intelligence, you made something general. Our brains in some ways are general. However, hardware is fixed in its workload. but it doesn't do uh programmability in the same way that we think about it uh in a CPU say. So the GPU is already down this path of building customized architectures for workloads and I think we're just going to hyper optimize
18:06around this and uh and that's a good thing because now we can build intelligent machines that are actually capable of building anything at some point. Listen, forgive my financial mind. You're a wonderful technologist. I am a delinquent trader for a living uh in in many respects, but we mentioned like yes, unwaveringly specialization of chips.
18:26To what extent does that massively reduce the TAM for a horizontal provider Nvidia specifically versus dude do not be so smallminded. It is still so early. We have 50x the room to run. Yeah, I mean it it is that the the market is going to be huge. Um I mean look I I think Nvidia obviously executed extremely well uh on the path to getting us here. I mean you know when
18:54I started Nirvana in 2014, you know we didn't really understand how big the market was going to be. It was clear to me that we needed a new computing architecture around neural networks.
19:05That was a new way to describe compute and to me it was like the most important thing a computer could do. I think Jensen recognized this relatively early on as well. I mean he he started to talk about artificial intelligence at the uh GTC in 2014. I remember it vividly because uh a lot of the graphics guys were pissed off about this. They were like why did you spend so much time on AI? It's nothing. It's it's it's not a uh important workload. And uh and graphics is right. and and uh he correctly recognized that this was going
19:35to be important, invested a ton in in in in that direction and actually executed very well. Uh so we had to go through this evolution of computing architectures from what we use those machines for to what they were eventually going to become. But I think now it's pretty clear to me that I don't need to care about anything else. I only need to care about running a neural network very very efficiently, very very scalably. Um and and that that's going to be the figure of merit.
20:04To what extent do you think we see the commoditization of chips? You know, Microsoft last night released their chips. You've obviously got um you Google with amazing success with TPUs, Amazon with theirs. You got Anthropic building their own. You've got the wonderfully named I love this Jalapino.
20:20Um I wonder what the next version will be. Yeah. Yeah. Um but do we see that just inherent commoditization of of chips? Well, first off, I think the next one's got to be habanero because it's gonna be it's gonna be hotter. I would think ghost pepper or something eventually, right?
20:35I think you should trademark it otherwise Sam might do it.
20:39I'll ask him. Um, so I actually don't see chips being commoditized. Uh, it it I I I I sort of look at it as it runs in parallel to what we saw in evolution. So I have a very simple worldview of how all this stuff works. So intelligence to me it will have infinite demand. There is no limit of demand of intelligence because the world that we live in the universe is is uh adversarial.
21:09So at first two bacteria might be trying to find a spot of sunlight or something like that. the one that has the the sensory capabilities to be able to move or or somehow adapt itself into that environment wins and keeps proliferating. Um then those things organize into uh ukarotic cells and multi multi-ellular organisms each each time you're basically upping the level of energy and the stakes but it's still
21:38adversarial. And right now we see this between human populations, right? We talk about US versus China or whatever it'll be something versus something and you know at some level we start to harness more of our sun like this is like the Cardesev levels you know and we're fighting some intergalactic battle whatever that is you need intelligence to do that. Intelligence is the uh is the fuel the economy of of these adversarial battles. So it has infinite demand and in order to create intelligence you need energy. The
22:06conversion of energy to intelligence is hardware. And when I say hardware, I mean the whole stack. I mean the chips, the systems, the the cloud, the networks, all of those things. So to me, if I look at it just from an economy, like I'm going to be selling the capability to convert energy to intelligence. And the better I do that, the more value I acrew.
22:28So I don't see it commoditizing anytime soon as far as the market goes. Do you worry that you bite off more than you can chew and then deprecate product quality in that transition from energy to intelligence? The more that you can eat in the in between, the more money you're going to make.
22:45Yeah, but I had the founder of Modal on the show yesterday, Eric.
22:50And I mean, I I love him. But the dude just wants to do everything. I mean everything. And I was like, at what point does product breath damage depth?
23:03Do you think about that?
23:04I we do all the time. Focus is super important. Uh I do think the world is changing now with AI. We can take on more breath. Our surface area that we can explore is greater than it was before just because of like speed of dev is much faster. Like why is that now every company's a compound startup so to speak? You know, in our first chip, we we built a set of coupled oscillators.
23:27Okay, they they have a coupling matrix.
23:29They have a bunch of oscillators that sort of uh uh influence each other. And so when we're doing this, I would ask I you know, in in the old world, say before AI, I might have a question like, okay, well, how does noise propagate if we don't do all toall connectivity? So meaning I have like a a daisy chain of of uh oscillators and I have some noise characteristic at the beginning. I want to see how that degrades information over say four or six hops. In the old world, I'd have to ask my research team, hey, can you go model this? Put some
23:58sort of noise model in. And I just want a zero with order understanding here, right? Just to kind of guide my own thoughts. That might take a week or so cuz they're like, I'm working on this other stuff. I'll I'll come back to it next week. Maybe it takes a week to to write the code and verify it and give me an output. So, I got a couple of weeks to get an answer. I can literally ask this question to a model. It spits out the code. I can introspect the code. I can go back and forth with it with it a few times. I get an answer in an hour.
24:23So something that would have taken me two weeks to think through has now taken me an hour. So I can think through more things per unit time because of AI. Now I I think we still have a problem. I actually just posted on this yesterday that we have an AI slop problem. We have too much crap coming out of these models and it's very hard to verify it. Some of it's good, some of it's not. And we need to figure out how to how to to do that at scale. But I can go faster. I can cover more surface area than I could before.
24:50What makes you say we have too much crap?
24:53Is it just like looking on X and seeing like the the the dumb takes on, you know, the thing that is just covering my timeline right now? No disrespect to the US, but it's your politicians who are rather in interestingly structured faces. Um would be an English way of saying it. Um that's all of mine, but there's like different AI generations of them. There's aliens and coming out of Cong. I mean, it's nuts. Is that what you mean by AI slob?
25:21It's not. Um, it's not.
25:24That's entertainment.
25:25Yeah, that's more entertainment. I will say, by the way, the English have cornered the market on self-deprecating humor. It is so much better than we can ever do in the US. So, I honestly, by the way, we're all looking at this from the UK being like, "Wow, we've been moaning about our own politicians." [ __ ] these guys.
25:42Yeah, I know. No, we we've taken to new levels. No doubt about it. I told you we're innovators. All right.
25:49And so, actually, this is a perfect example of you see an output and you say, "Okay, does that kind of uh uh does it meet your gut check first?" And you're like, "Huh, maybe not." So, I think what's really interesting is older people today see images and they don't have that gut check. They're like, "Wow, that's real." I mean, I've had my parents say like, you know, show me these AI generated videos of Trump playing the guitar and they're like, "Wow, he plays the guitar." I'm like,
26:15"Mom, he's not playing that guitar. That's an AI generated thing." She's like, "Oh, okay." So, for me, it's quite obvious that it's AI generated. So, I think that verification problem we see in companies, I see it in our company.
26:30We can build all kinds of artifacts. We can we can build code. We can build models. We can uh do all kinds of things. But you have to close the loop and make sure each one of those things is correct to actually trust the result.
26:43And doing that actually starts to become a big bottleneck because you're producing so many artifacts. So I I think we've moved the problem from producing to verification and the value of verification is if you own the verification layer then you own the automation layer because you have like verifiability that you can then progress there on. Is that it? Um, yeah, I suppose I think it's a necessary part of of using AI to develop things, but uh, if you own the verification layer,
27:11do you own the production layer? I I'm not entirely sure. I think you have to own the spec as well. So, producing the a useful output like building a car or a bridge or something, you have to you have to be able to spec it, uh, you know, figure out am I solving a problem that's useful? Then each one of these uh artifacts that's produced has to be verified to be correct and then marching towards that spec and eventually have to say yes I did indeed implement the spec
27:40as intended. So it's endto-end process not just verification. So verification is a piece of it. I don't know how one would value that. I think I value it as part of the whole like getting to the end result.
27:53We we had Mike on the show from Atlassian. Awesome guy. um and and he was talking about kind of the importance of kind of chain of thought and understanding, you know, agent-led decision-m versus humanled decision-m across different functions in in companies. And part of me was like, I totally get that and that makes absolute sense. And part of me was like that feels kind of old to think of it like that and like what does that mean? We don't trust agent decisions as much as we trust humans. That feels kind of
28:24How do you think about Yeah.
28:27Uh I mean look I I agree with you. I think I don't think we should trust Asian decisions as much as humans in a lot of cases just yet. We train for certain things for many years to build judgment.
28:38I mean becoming a neurosurgeon, right?
28:40It's a seven-year training after uh after med school, you know, and there's a reason for that. It's not that learning how to throw a stitch is that hard, you know? I I've done that in grad school. It's not the physical mechanical part of of being a surgeon that's the problem. It's having the judgment on which direction to go. You have to have seen enough cases, seen the outputs, seen how people think through these problems to then build that judgment up.
29:06And uh I think the way we're defining intelligence right now is we take a model that's it's stochastic, right? We can u we can prompt it several times and we'll get different sorts of outputs. And what you're doing is you're exploring the distribution of possible solutions. And then we need to pick which of those solutions is good. you know, you you'll get a lot of different outputs and most of them are not good.
29:27And you could argue that a human does this too, right? We say, okay, um what how do I make a decision? Well, I might think through all the outputs and I have some kind of heristic of deciding what's good. We do this internally, maybe it's an internal monologue or some sort of internal process. Uh but a model spits out a chain of thought and then we have many chains of thought and we decide what's good and we take that path. uh that deciding element is actually not well understood for every task and so I don't think we should trust these things. I
29:56think the longer and longer deci decision upon decision upon decision chain you build the more off the rails it can go and you can get things that look very very robust but they're completely wrong. So that's where we are today. That will probably change in the future. I think we need a lot a few more breakthroughs to get there but that's where we are today. So I personally I I love a question like what do you believe that today looks kooky weird nuts that
30:25in 5 years time will be very common place and agreed. I remember putting my credit card into PayPal and my parents telling me off and like remember like okay cupid and being like you find your husband or wife online what a ridiculous idea nuts at the time. I think that we will trust our agents more than we trust ourselves.
30:44I think at some point that's true. Yeah.
30:47But you think that we are far from that today?
30:49Yeah. We're not there today. And uh expertise doesn't really go out of style. It's just going to take on different forms. And what I think a future will look like is that we start building, we'll call it models, but I actually don't think it's a model. I think it's the whole machine. I I think of I think of uh intelligence as having a physical and having the intelligence layer as one. They're they're not separable. So uh I have a machine. It's almost like an organism that I can teach
31:18to be an expert at various tasks and the value will come from building that expertise up. It's like you know which school did you go to? How did you learn how to be a neurosurgeon or or a chef or whatever that providence matters right?
31:34Like you went and studied under this person and you learned their methodology their thinking. That's where the value came from. like you you or any human is an intelligent machine top to bottom, but was it was the way you were taught that actually made you valuable. And I think we're going to start to see something similar in machines. And we can replicate that intelligence and all of that, but but the way you were taught is actually going to be the important thing. And so part of that is actually knowing how to get to an end result that
32:02is real, that will work, that is verified.
32:05I wasn't really taught much. I spent very little time in education, Naveen, but thank you. I I hope there is some value in the human being. Um, no I I get you totally when you think about the value in that learning. A lot of that is in the data that one occurs over time in the years that you're doing something.
32:22We see data companies today uh I mean but we see tens hit astronomical levels of revenue that previously were like astonishingly large. I mean they're still astonishingly large but in such short time. Naveen, dude, you're such a smart dude. It's such a joy talking to you. I just kind of want to ask you lots of questions about the world. What happens to this market? Does it just consolidate over time? Does it unbundle more and more and you build more
32:49defensibility? What happens? I I think it specializes to be honest and I think if you specialize and actually start to work on on important problems, you have a big market that's defensible.
33:00You know, it's it's again it's a power law for skills. You know, you you could argue that all humans are capable of learning many skills, but certain skills are more valuable than others. And the same thing would be true with data and curricula that you you build, right?
33:18Totally get that. And so we see the specialization of data companies. So you don't think they're commodities?
33:25Yeah, I I don't think it's commodity. I I think it started as a commodity because it it was sort of like uh you know at volume gathering of of observations right but as you start to construct higher quality data sets that's what we call it in the AI world like higher quality for coding higher quality for uh uh chain of thought or whatever you actually start to have a lot of value in the data the model is almost just a manifestation of what it learned in a sense so uh the the data
33:53layer I think is going to be pretty important going forward I I don't see that commoditizing. I think building models and knowing how to choose that data is actually the game right now. The the big model companies, it's largely a data problem. I mean, are they they they are making advances on the model itself.
34:10But that's that's not been where most of the capabilities come from. It's been from curating data using different sorts of loss functions. Like we had pre-training before which was a very particular kind of loss. Then we move to what we call mid-training now which is reinforcement learning supervised fine-tuning all these different sort of mixes. So that that curriculum how you went from pre-training to all these different sorts of trainings to modify the behavior of the model is actually the secret sauce.
34:37Can I ask you we've seen you mentioned there kind of pre-training RL and then obviously a real shift has moved to post-training. Yeah. With that shift of emphasis from pre-training to post-training, do you think that is an enduring permanent shift in the efficiency of model improvements and how we get there or will we move back to RL being the primary or pre being the primary? How does that look in terms of importance over time?
35:02Well, I I don't know that it's more efficient actually. I think RL is more inefficient. uh if you look at it in terms of you know how much better the model gets per unit compute or per unit dollar it's actually more inefficient uh but it it does keep moving forward right I think the idea is that we want to make the models better at all at the tasks that are important and so I think it's it's probably for the next several years yes very important I think we'll start to build specialization in
35:29those middle layers maybe even in the pre-training layer so it's hard to say exactly you know what will be the paradigm of the future because these aren't these are these are very contrived scenarios that we just engineer our way to the next problem, right? It's it's like I'm here and I want to get to there so I I engineer some way to get there and then I'm here and I want to get to here so I engineer some way to get there and you end up with this kind of random walk of points and that's what that's how the industry evolves. I don't think the model companies are thinking very long terms in in terms of performance like oh I'm
35:58going to make some massive breakthrough on the way I think about a uh an AI system that's going to give me an advantage. I would say the big labs are not doing that as much.
36:08Do you think they can afford to in the daybyday race that is this current ecosystem?
36:16It's really fast. I mean you've got to be on top of it. I mean like if you're one month late on a model that's that's disaster, right? if you're OpenAI or Anthropic or Grock or Google. So, yeah, they they they can't afford to take those big risks just yet. And, you know, you're seeing this like, you know, when Elon spun up uh XAI, you know, he he basically took, okay, here's the paradigm. Let's go, let's go hire the people. And boom, he got to the frontier pretty fast because he had the resources. So, what that points out to
36:44me was that there wasn't a ton of like very deep innovation. you know it was taking the pieces putting them together with a team that knows how to build them and executing very fast. Um I think that's very different than trying to come up with a new paradigm and you know you look at like Yanukun's company and you know uh uh Few Wei's company they they were trying to do deeper innovations and I think that's what that might be in the realm of startups and it sort starts to look like uh drug development right like all the big risks are taken by startups and all the incremental stuff is done by big companies. The big companies buy the
37:13startups when they show promise.
37:16I sometimes ask simple questions but I don't know the answers and you're very smart. Why does everyone get so excited by RL today? Like you mentioned it kind of the mid learning there and I just have so many people I just it's all about RL. I'm like oh I agree. Yeah, I agree 100%.
37:33I mean there's several reasons. I if you think about the way we learn it's primarily reinforcement learning. If I were to teach you a new skill, you know, learn how to serve a tennis ball or something, it's it's really an RL whoop. you know, you're I would give you a uh a paradigm of what it should look like.
37:50Here is how your serve should look and you know, maybe demonstrate that or show you a video or something and it will allow you to build an internal model of what good is or great is. And then you keep judging yourself against that that internal model and say yes, I'm getting there, I'm getting better or I'm not and I need to like ask for help. So that is RL. And I think the reason it's important is we cannot gather enough data to solve every task in the world.
38:17We simply can't. You can't pre-train your way out of this by just gathering data. It's just too hard. And the models frankly aren't efficient enough to to make use of it. So you need to have a paradigm where you can start to say, okay, these are the 10 most important tasks. And then I have some sort of what we call a verifiable reward. I have some something that's that internal model.
38:36Like a good serve is I hit it really hard and it went in, right? That's very it's verifiable. I can measure the speed. I can measure whether it was in or out of bounds. And uh when I have that verifiable reward, then I can start to say, okay, did the actions I took, did those actions lead to that good outcome or not? So now I I have a structure that allows me to solve any set of general tasks and that that's why RL is so exciting. Do you buy Yan Lun's
39:06post transformer world theory and how does that change everything?
39:10I do think we will be in a post transformer world. I don't think the transformer is the be all end all. Um I don't think I truly understood this until maybe a couple of years ago because transformers conflate a lot of things. Uh what we care about is reasoning. Being able to reason through a problem. I don't know who said it, but it's like intelligence is what you do when you don't have the information, not what you do when you do have the information. So, being able to reason through a problem from some sort of mechanistic or first principles
39:40understanding is what intelligence is.
39:42So, uh transformers kind of sort of have that, but they really do it through this agent, uh agentic kind of flow where you're taking multiple samples and you're deciding what's good. So I take one trace and then compare it to another trace and I say okay this trace is better. So I'm going to call that my reasoning trace. And so the way we kind of implement this reasoning thing is through a retrieval mechanism. So attention basically has this this context which is a retrieval thing and
40:12then we we can we can sample off of that and and eventually get to something that looks like reasoning. I think this is extremely inefficient for how we do reasoning. We don't need to build reasoning this way. Our our brains do not work in this way. We don't build a huge trace, pipe pump it into the input of another model and then produce another trace. That's not how our brains work. So there are way more efficient ways to do this and way more uh uh high performance from an algorithmic st
40:40standpoint ways to do this and so we will move past the transformer.
40:44Do you think what it takes to be a great founder has changed or not?
40:49Uh I don't think it's changed. I think the basic principles are still the same. I think having a viewpoint on the future, the your vision actually may matter more now than it did before. You know, I think uh in the in the preAI world, you could be a a pretty good founder by just being able to execute.
41:06You don't have to have a very good vision. You just have to know how to execute. And you know, you just go out execute a big company, which isn't that hard because big companies are slow and you know, lumbering. I think you could actually build a startup and you could uh you could sell it or or build a company out of it. I think these days if you're just based on execution I I don't think you're going to get very far.
41:26I agree uh incredibly. Can I ask you we we spoke about the different layers and you said I don't think it's going to get commoditized in data. I think specialization I think there is value there. Which layer does get commoditized over time?
41:39Yeah, it's a good question. Um I mean arguably energy is already a commodity, right? So this is already commoditized.
41:45I think a certain amount of intelligence does actually start to become commoditized. We're already seeing it to be honest. Like models that are 6 months old, let's say the Chinese open models, you know, that level, they're pretty useful, right? Something that's 6 months old is still pretty darn good. Uh so I would argue that's that's a hallmark of uh commoditization, right? The frontier is not the only thing that matters. For certain problems, it is, but in 6 months that frontier will be in the open models. So So then help me out. What happens if the
42:15delivery mechanism or the output of all the infrastructure is then commoditized?
42:21Well, I I think commoditization doesn't necessarily mean it's bad. It just means that margin yeah falls quite a lot, but it also means that the market is very big. So you you could argue that uh tomatoes are commoditized, but we still eat them. We still need them, you know.
42:38So we will have a huge market for intelligence and the market will continue to grow, but the margins may not be huge. I I actually don't think the model companies will be the ones who own the margin of the whole stack. It's not even true today. Like Nvidia owns the margin of the stack today, right? I would say they're probably the one who take a lion share of the mar of the margin.
42:57A lot of people say to me the cost of inference will actually go massively down over the next few years and we'll see the commoditization potentially of the inference market.
43:06Would you agree with that or not not agree with that?
43:09Well, let's break it down. What does what does commoditization of the inference market mean? What that means is that uh the operator who's who's running inference I assume. Yeah.
43:19Yeah. Yeah. Absolutely. Your fireworks, your model, your base tans and the services that they provide.
43:26Because if you look at their value prop, what they've done and they've done very well is take a model that exists an open model say and and then make it work well, you know, with uptime guarantees and make it, you know, relatively costefficient to run on infrastructure. So they're an infra play, right? They're basically mapping the model onto physical hardware and doing that well.
43:48Now, uh I I think there will be value there in the future, but the price of inference, like what people are willing to pay per token will drop. And so they're basically they're they have to squeeze more margin out of the hardware's capabilities to the delivery of that token. And that, you know, the amount of juice in that layer, I think, will decrease. So I think it'll result in them having lower margins and and I think the model itself like um is commoditized. You have
44:15several different ways to get it now. But the hardware side of it probably won't won't collapse. I think that part of it is still going to have an advantage because you can still squeeze more juice out of like hey I convert energy to intelligence more efficiently.
44:30The we have more a lot more juice to squeeze there versus the infrastructure layer in my view. So I I I actually think the hardware is the place to be over the next 10 years.
44:39Do I need to change who I am as an investor? I know that sounds strange, Naveen, but like I'm I'm a software investor. I've invested in SAS for the last 10 years. I've done it well.
44:49Yeah, dude. It's quite [ __ ] hard doing hardware investing. I It's a very different mindset. Is this generation of investors equipped to be hardware investors? advice.
45:04I would say if you're a pure software investor, you're kind of [ __ ] honestly. And I'll say that pretty confidently because SAS I don't think is going to be where you gain margin anymore. And we had this this sugar high for the since what 2011 2010 time frame till now of you know I can very quickly spin something up on an AWS cloud service uh test the market quickly uh try things out and then go after a h 100
45:32million or $200 million market. SAS was like massive fragmentation of the market because the delivery mechanism became very easy and frictionless. Um, now we're in this place where applications start to become more about the intelligence layer and uh I just don't see software being a huge market in 20 years.
45:51Do you not do you not just think SAS becomes Lorra? And what I mean by that is like is Lora SAS? Well, I mean kind of yeah enterprise SAS but kind of not. No. So does like the semantics not just change? I don't think anyone really is like starting fresh being like, "Oh, we're we're a SAS company, right?" Yeah. I So, exactly. I think you're right. So, I was taking the classic definition of SAS, I suppose.
46:16Uh but I think companies where you deliver an outcome and you do so through a combination of software and services.
46:22Absolutely. And uh you know we're seeing that in uh in several companies and I I invest pretty actively in startups generate the seed stage and you know I'm seeing a crop of companies come where they're taking engineering tasks that have existed for a long time uh that were done in a traditional way and have to be done correctly and they're making those tasks much more much faster much cheaper by using AI plus humans. It's great. And that's I mean I would say Harvey was a first example of this for
46:52law firms where you know you the old way was I have you know a set of lawyers who go and look at contracts and you know write contracts that kind of thing and they basically said well I can use humans and verifi or sorry use AI and verify the outputs with humans and deliver the same thing with fewer humans and uh deliver it faster at the same quality or near the same quality and I I will disrupt that market. So I think we're going to see this is sort of the new SA. And I we see you know I call it
47:20word wank which is where like you know VCs come up with a coin theory of the world and it's like you know the the trillion dollar services thing you know I think did a brilliant piece on um and I love Julian who did it. Very good. But like are we not just slightly jazzing it up? That's just the future. People using AI to do things efficiently this like whole glamorization of it.
47:47Bit bit overkill, isn't it?
47:48I I I think you're right.
47:50Aren't you just Aren't you just investing in a law firm when you do that?
47:53Effectively, yeah, but you're just it's a better version of a law firm, right?
47:56That's all. And you but but you'd be okay to do that cuz I meet a lot of companies doing exactly what you say and I'm like I don't want to do a law firm.
48:04Like I'm happy doing a Lorra or a Harvey which sell to law firms but they're not actually a law firm. You get me?
48:10Well, but we are seeing that like some of the large law firms are now investing in AI directly. So I think I think in its limits you'll see basically Harvey sells to the law firms because the law firms have the channel.
48:23Uh you know is that channel going to be enduring? I don't know. But I could also see them saying, "Well, you know what?
48:29We could just sell directly to companies." Like, personally, I would buy that if Harvey said like, "Hey, I have a solution that does all your NDAs. It does it reviews your contracts, blah, like all these things, and I I can gain uh uh, you know, confidence that it works. I would send 80% of my workloads, my legal workloads to them."
48:49I am intrigued because you spoke about some firms that are building their own. And I think you saw K& in the legal space commit like $500 million to it.
48:58Um, you know, Lynn at Fireworks Roman investor spoken very openly about owning your own intelligence and the importance of companies leveraging their own data to build their own models. Do you think that is a very viable future or or actually a slightly techno optimist kind of prediction from a very specific standpoint? I think it's a bit techno optimist because uh I I think you have to have a certain number a certain amount of scale in order to build your own intelligence and own it. Keep going with law firms for instance. If you're
49:27Kulie or one of these big firms, yeah, you can invest in these things and you can you can make it happen. Um if you're a mom and pop shop in the middle of Nebraska, you can't do that. So what you're going to use then is a service of some sort, right? you may augment your attorneys with that service and that's how you'll you'll provide service to your clients. So I I I don't see this as every company will use AI in a way of um uh of leveraging their past data to to
49:57make their own models better. They're not going to do that. It's just it's too hard for them. They want an out-of-the-box solution. So I think there is a market for selling those out of the box solutions. Uh but it's it's going to be this long tale. the big the big players will start to customize because they have the scale to justify it. It makes sense. And it goes back to this idea that every you know every part of our world is adversarial. Like it it depends on who you're fighting, right?
50:24If you're fighting another big law firm and that big law firm goes and uses AI to supercharge their margins, well, you kind of have to do the same. Uh the mom and pop shop probably isn't doing that and the the people they're, you know, fighting against are not doing that. uh so they're going to stay in this other other realm for some time.
50:42I think one thing that underpins a lot of law coding is the percent of salary that we will then transition to technology spend. You know when we look at lawyers today you know I think it's about a $300 billion legal market in the US and you're looking at that going well how much of that will be eaten by these providers. I think you see that same question there with um coding. You saw Ben off say that they'll spend $300 million of a $6 billion developer salary
51:09budget on claw code and coding tools significantly.
51:13My question to you is will that stay at 5% for developers? Will that go up to 30%, 50%. Or will it come down to 1%. Because that is the mother lode question. I I actually think the percentages will remain maybe sort of stable because we end up with this verification problem.
51:34So it it actually it's hard because training humans to to know what's good is actually the hard part. So developers, you know, it's actually not about what your direct output is on this day. It's not like lines of code. It's actually like quality of code that matters. So very experienced, very top-notch developers, those 10x developers, whatever. Um, you know, they developed a sense of what is good, what looks good, what's aesthetically right, and they can they can make things work
52:03fast. In this new world, you know, those coders, those people who know what's good can actually verify the output of models and become more efficient. So I think what happens is we just produce more and the market size let's say that we're paying for humans plus AI doesn't change drastically just their output greatly changes. That's the way I look at it because let's let's play it out a little bit. So um I'm spending in a company let's say $10 million a year to
52:31develop my software. Um now all of a sudden my customers of that software start having different demands. you know, 10 years ago they're like happy with one year release cycles. Now they're saying, "Hey, I want my bug fixed in a week." And so the only way to do that is actually to use AI with the humans. So I'm not doing less, I'm actually doing way more. I'm fixing those bugs in a week and eventually fixing them within an hour, but I'm still paying $10 million to keep developing this whole thing. And I need the humans that can verify those outputs
53:01and actually move to a a goal that's that's useful, fixing the bug, adding features. So I think the the the pace of delivery goes up because the demand side starts to demand it. And and I think that is the the key thing here that's being left out is that it's not that everyone's job goes away because the same task could be done for less with AI plus humans. It's that now I yes that is true but I will I will now do way more of those tasks because the demand side is increased. You you don't get one without the other. When the supply gets
53:31cheaper you get it changes the demand side as well. So I think that's what's going to happen is that we just end up doing more. We have more release cycles, more uh changes. Like here's a a physical example I think that people can get their heads around. You know, let's say today uh we have a rate of building a building. If I want to build a skyscraper, it takes two years of construction, say, and that's based upon x number of humans with, you know, y sets of skills. So let's say all of a sudden I have robots that can do 80% of
54:00that. What's going to happen? Well, okay, there might be a disruption in physical labor for a moment, but then you'll say, you know what, all of a sudden, my cost and time to build that same skyscraper, it was two years, but now 80% of those tasks are done by robots. I can bring the whole thing down to 8 months. So, it's not that you're just going to build the same number of skyscrapers and just do it in 8 months.
54:22You're going to build twice as many skyscrapers, right? there's going to be more things churning, more buildings being torn down, more more labor going into that market. So, the total number of uh projects will go up. The amount of dollars going into all of those projects may be the same, but it'll be a combination of humans and and robots to build them.
54:43Naveen, you said that you um obviously angel invest, you angel invest very successfully. You kind of downplayed it.
54:49I think where I invest today a contrarian thought for me is I do not like robotics investments. I think we are so far from where we need to be and I do think that timing is important. Now I'm not saying the infrastructure to power robotics. I think the data sets around them are very interesting. We see a lot of people like Mecca or microagg infrastructure buildout for it. But like the actual like application layer where we see like robot cleaners, robot
55:17builders, we are so far away from where we need to be. I don't want to be there and I don't want to take the timing risk.
55:25Am I wrong? Am I naive? You're much smarter than me.
55:30Well, again, I'm pretty pilledled on this one, so I am going to disagree uh strongly on it. U Okay. When you say uh we're so far away, put that in put that in terms of time. What do you what do you think is the timeline?
55:44Okay. We're not so far apart on this. I think we'll have working prototypes for some of these tasks in the next 2 to 3 years and the market will emerge in 5 to seven years. I think that's right.
55:55I think there's also a clear line of delineation between like humanoids and non-humanoids. Correct. Like non-humanoids I absolutely see that clearer pathway. the humanoids. I'm I'm really just str whenever I think something's really obvious, I'm always like, I must be very wrong because the humanoids is so obviously so far off.
56:16Um, you know, I I think we're going to be in this weird world where we live in exponential development cycles. It's really hard to apply our old models.
56:28It is. So something that looks like so far away today with an exponential curve ends up being much faster than you thought and and like tams like what you know a market size is you know like I think the inference market what do I know now that I wish I'd known 24 months ago that the inference market was just like unbounded it's unbounded it's multi- trillions like if you asked me 10 years ago you know what were trillion dollar markets it's basic needs it's housing food
56:56transportation right you would never have thought something in tech would be a trillion dollar market and and I think uh just AI models themselves will be a trillion dollar market in the next few years and the inference market is going to be a $10 trillion market within 10 years. So this is insane to think about right and and to your point we have we're moving some of that labor market and part of the de of the um of the value generation stack to technology. So that's where we're getting these giant
57:26TAMs. So, you know, the way I look at like unconventional, the reason that I think it's a it's a fantastic market to be in is that if we knock off a zero, one order of magnitude efficiency on delivery of a token, that effectively 10xes the market in some ways, right?
57:42So, something that was a trillion dollar market, now all of a sudden you have way more applicability. The base of potential uses just increases more than 10x. This is Jeban's paradox. So I I think we're in this place where we can build these exponentials and just unlocks massive value and market. So I I actually think robotics is going to be 100 times larger than a coding.
58:02Whoa. Well, that is slightly disagreeing with me. Naveen, can we just like before before we do a quick fire, can we just unpack that because I I don't want to be completely moronic. Why do you say that?
58:13I I I think we we're still scratching the surface of how to uh how to see the output of intelligence. So the physical world is something we've just not been in at all for intelligence. Like I would argue robotics to date like industrial robotics are not really intelligent. They're wrote we're imbuing them with intelligence that we have like how to do a task and we're just doing it wrote.
58:36That's not really novel sol problem solving. Uh we will be in the realm of physical problem solving not in the not distant future. Like imagine a world where I have billions of robots roaming around and they actually solve problems dynamically. like that's a problem. That thing just broke. I'm going to go clean it up. I'm going to I'm going to replace it. Like the cost of delivery of cleaning everything up in our world, building new buildings, um you know, solving physical problems will will not drop to zero. I hate those drop to zero
59:06kind of statements. It will greatly reduce. So, I think that market will be enormous. And that's why I think it's just going to be much bigger than what we see today in the virtual world.
59:15Can I be blunt? Where specifically then do you invest in robotics? Like in the model companies that power them, the data companies that power the model companies, the the the consumer application layer where I don't think the application layer yet. I'm more in the technology stack. So I I actually think these guys who are doing the models haven't really figured out what they're going to look like when they grow up. Uh and that's okay. I'm not suggesting they need to like building I I don't think the value will be in the model. I I am investing in companies
59:44that are building models. Uh, I think they are probably going to end up building end-to-end devices. They're going to build a robot with a model that does something very, very well. That's how they're going to sell it. My personal take is that it's going to go together. You're not going to have a separation.
59:58Do you care about price when the TAM is what you said it was?
1:00:02Not really. No. I I I just see it as like I need to put I need to put uh some percentage of, you know, my wealth into that space because it's important. And that's where that's where there's going to be a ton of growth. And so it's interesting because we're in this place where you know value investors or whatever you want to call it like traditional VCs are like oh you know that's way overpriced. Like guys when we're talking about trillion plus dollar TAMs the price is almost irrelevant. you
1:00:31know, do you know I I completely agree with you. The challenge is very often we have a customer too or a boss too and it's LPs and the LPs are an even lagging indicator if you think that ownership and price are very important and when you rock up to your AGM and you say hey ownership's gone down and price has gone up they go [ __ ] you.
1:00:52They do until they start to see the big firms also have the same trend and they're like ah okay maybe maybe that's where the way it's going right um if you could put all of your money into one company that is not your own what would it be yeah probably like a physical intelligence you know they they are thinking about as a model I think there are several companies doing that as well like I think roa and a few others are doing are thinking about it like this I think they are solving in my view something fundamental about how you define a task and then execute it so
1:01:21conversion of a task to motor output. To me, that's that's the problem to solve right now for the physical world. So, outside of uh what we're doing and building a new computer, I think that is that's the next big problem.
1:01:35You've been incredibly successful across companies, what does no one tell you about having money that you wish you had been told?
1:01:43I think it's it doesn't change your values. You still have a finite amount of resources, but also at the same time, you you still value certain things in certain ways. Like I I'm still kind of cheap when it comes to certain things. It there's no reason for it to be.
1:01:56What What are you cheap with regards to?
1:01:59Uh well, if you ask my wife, you know, probably uh clothes, that kind of a thing, you know, I just like what? I'm not going to pay that for this for a set of jeans.
1:02:08I think travel still like I you know, I'm getting to a point where I will try to optimize my time, but overall I'm like ah that costs too much. I'm just not going to do it. It's not a you know, it wouldn't have mattered if I spent the money or not. I just I just don't see value. So, uh I think that's the thing.
1:02:24I I I I I put money into things where I see value and I can put a lot more money into those things, but the places where I don't, I actually don't spend anymore.
1:02:33Dude, we're going to do a quick fire around. So, I say a short statement, you give me your immediate thoughts. What have you changed your mind on in the last 12 months?
1:02:41Yeah, I think in terms of investing, I've changed my mind on on useful metrics for progress. We always looked at it as like, oh, we need to look at market validation as as the main metric and I think that's an old way of looking at it. In fact, I think it comes a lot from SAS. Uh because market validation makes sense when there's not a deep technology route. Like when you're saying, well, I'm building a product that serves a purpose. It's all about market validation. But when I have
1:03:09something that is on its face almost true by inspection like I'm going to cure cancer. You don't need to validate the market. You just need to validate the progress on the technical problem. Right now investors are in are in two camps. Some are saying well let's go let's let's invest heavily in these companies. You know valuation be damned. You know we're one of those companies.
1:03:32And the other set is like oh my god that's undisiplined. It's like you don't have market validation. I actually think that's fundamentally wrong and a lot of growth investors are still in that bucket. So, and I I I I kind of grew up, you know, reading all the books about how you look at risk and investing. And I've changed my mind quite significantly on this. Not just because my company is doing it, but I think now we're in this place where we're solving really deep technical problems, robotics, computing, you know, biology. And it's not about
1:04:02market validation. The market is there. The market is very clearly there. you you solve that problem, 100% people are going to buy it. It's about progress of solving the problem.
1:04:10Yeah, that's a rare mindset to have in Europe especially. Um, if you were completely resource unconstrained, professionally speaking, what would you do differently?
1:04:20I would start to build my own manufacturing uh because I want to go closer and closer to the source of raw materials. I think we're in this world now where there are so many supply constraints from uh from the hardware ecosystem point of view. If I were unconstrained and I could build anything like this is kind of what Elon's doing to be honest. Um yeah, we should build our own sorts of fabs because I can hyper optimize that layer for what I
1:04:48need. I don't need to take a thing that is a commodity that's been built for a hundred other purposes and make it fit my needs. I would build exactly the thing I need and build a better product.
1:04:58And frankly, I see it as all the value of a computer comes from delivering intelligence. If I can hyper optimize that all the way down to how I process, you know, the silicon ingot when it comes, you know, from the factory and how I slice it, I absolutely want to do that. Great answer. What area of AI do you think is most overrated today where you would not invest? So like for me I think customer support AI is like one of the most overp proliferated non-
1:05:26differentiated spaces that's just like a an entry point to try and kill Salesforce which is quite uninteresting.
1:05:33Yeah I I think those those quick hits where you have a standard thing that's done by humans I'm just going to try to automate it. That's the worst place to invest right now. I think uh I think it's very overrated. I actually do think that uh like aentic coding stuff is quite overrated in terms of a market because I think it moves to the model provider. I don't think you get much value from the others. I know cursor had a great exit and that's wonderful. Uh but the model providers, the model itself is is where most of the value is coming from. I don't think it's the
1:06:02harness or the IDE so much. Uh it's not super sticky. I've seen it in my own company. People move between these things pretty fluidly. So it's the model that matters uh and what what capabilities it delivers. So uh I think aentic coding as a market you know modulo the model providers is is something I wouldn't invest in today.
1:06:21What percent of token traffic will flow through open versus closed in 3 years time?
1:06:28I think it would be 80% open.
1:06:31What does that translate to in terms of dollar traffic?
1:06:34Uh that's a better question actually. I think it's going to probably converge on something that looks very much like the cell phone market. Just roughly speaking, iPhone is 20% of the total market, but 80% of the of the the margin. Something similar to this. We'll probably see that there's a relatively small number of very important problems that Frontier models are needed for, and you'll pay much more for those things.
1:06:57However, all of the like routine tasks that you do will be handled by open models. So, uh I I actually think there'll be a rough 50/50 split on spend, but in terms of tokens, it'll be only 20% to the uh to the frontier models. Who is the single best investor meeting you've ever had and why?
1:07:16I can give a bit of a story for first.
1:07:19Um I think you know since I started Nirvana, I've probably had I don't know a thousand investor meetings, right? So after you know the 800th one to the 800 the first one don't really leave an impression anymore. So um so very early on uh who you know I had a meeting with Steve Jervson who has now since become a good friend of mine and uh I was nervous as hell because I'd heard so many like amazing things about this guy super smart he's going to take me apart you know that's kind of what I was I was told I had a conversation with him which
1:07:49we just started geeking out on numeric representations and I was like this guy's an investor and then I send him a couple papers he like marks him up and asks questions I'm sending responses back to him like this is not what I expected from an investor ever, you know, this guy's like talking to, you know, like a a technical u you know, colleague in a sense. So to me that was one of the best the best uh investor meetings I had because we were doing something so new that no one had pitched it yet. No one had pitched a hardware
1:08:18startup in 10 years in 2014. And this guy was almost champing at the bit to invest in something like this because he had been thinking about it himself. And so that was just like boom, let's go.
1:08:31It's a great answer. I I love Steve. I remember um having him on the show. He was he was fantastic. You talk about like willing to be non- consensus is what I love about him.
1:08:40That's right. You have to be to make to get make big bets that will pay off. By definition, the consensus is already wrong. right? It's already old. So you have to be non-conensus.
1:08:51The challenge I find with being in Europe though is actually you have like the commoditization of ideas. And what I mean by that is actually now is being a founder is quite a touristy thing to do.
1:09:01Everyone wants to be a founder for the experience. The quality of ideas has reduced. And I think I don't think Americans are inherently better, smarter, more talented. But I do think you are more innovative around idea selection. And that's probably because of exposure, what you see, how you think, where to play in the structure. You know, we're still doing expense management with AI.
1:09:21Yep. No, totally. I think I think we are given the permission to think bigger here.
1:09:26In Europe, you're not because the whole investor network and the way people think is so conservative. You know, if you come in with a crazy idea, they're like, "That guy's mad. I'm not going to invest in him." Whereas here, it's like, "That guy's awesome. He's thinking big."
1:09:41Yeah. No, I completely agree with you.
1:09:43It's absolutely right. The hard thing is, and I've got this problem, I've got this genuine, okay, let's do this. There's a series A now, and they are building space stuff. I've got no [ __ ] clue. The team is great. They're awesome, but dude, it's 20 million check from a 400 million fund. That's a It's 5%. You You can't lose that that easily, dude.
1:10:06It's a super risky play in space and I do not understand it at all. I sort of look at these as you know in the early part of the technology cycle like you know all of us in who are technologists are going to give you a story and that's what we need to do is build a narrative that story is is going to be wrong 100% will be wrong I I don't know anything else but I do know it's wrong I don't know exactly where it's wrong I don't know how wrong it'll be but it is wrong and what you're investing in I think at
1:10:34that stage is someone to drive the bus not the bus itself right So the idea is right, the direction is right and the team is what you care about. Can they drive that? Can they make the uh uh requisite pivots and and uh and right technology decisions to actually drive the bus to something that matters?
1:10:56The challenge actually there becomes in this market you don't have the time with them.
1:11:00Yeah, I would love to say yeah I don't know. Um you probably get two to three meetings.
1:11:06You're right. And you you got to do as much work as you can. Talk to people that they know and whatnot, but it's hard. Yeah, I agree.
1:11:13Final one for you, dude. What are you most excited for? I like optimism. I don't like the cynicism that we have around bubbles and [ __ ] What's your like I I'm just [ __ ] pumped for this.
1:11:23I look at AI as a a tool and capability that enables us to ask much bigger questions about the universe. There's so many things we don't understand in science, and it's because they're un approachable. they're unapproachable from um you know just the number of humans who are who could think about that problem and actually make progress.
1:11:43The surface area of problems we can go after and start to solve starts to increase. And to me that's extremely exciting. I don't look at you know oh my god AI is going to kill all humans. That's the stupidest worldview I've ever heard. It's just it's just idiotic to me. Actually completely tired of it now.
1:11:59And it's like no no no guys AI is is the thing that enables humans to actually start to crack these bigger problems like how can we explore more of our universe we can do that when we start to understand deeper parts of physics you know we we have these ideas of like wormholes and these things these are all like theoretical constructs we can now start to use AI to start making progress can I actually build something that exploits that that that physical phenomenon or is it real or not like these are questions we can start to ask
1:12:28in the past we we just couldn't. We didn't have enough humans that were capable of of approaching these questions. So, to me, the ability to move science forward and to to move human understanding forward is so exciting. I I I can't even tell you. It makes me feel like a kid. You know, there's a world of wonder out there that we just couldn't even think about. We got browbeaten into our own capabilities of like this is all we can look at. And now all of a sudden, that whole world is open to us.
1:12:54I mean, shows like this are why I still love doing the show after 10 years. like the breadth of your knowledge is is just like wonderful. Um it's it's amazing for me to like witness honestly. And so thank you for being so willing to engage across such a broad spectrum. And you've been amazing, dude.
1:13:10It's a lot of fun. I love uh I love these conversations where we can riff on so many different topics.
