Full Transcript for 20VC: Why Robotics Will be 100x Bigger than Coding Agents | Inference Will Be a $10TRN Market: What No One Sees | Why Software Investors are F***** Today with Naveen Rao, Unconventional

We're going to be out of energy pretty soon 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 sacrosanct. And we're energy first in our mindset. Intelligence, to me, will have infinite demand. I do think we will be in a post-transformer world. I don't think the transformer is the be-all end-all.

I actually 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 fucked, honestly. The inference market is gonna be a $10 trillion market within 10 years. I don't look at, oh my God, AI is gonna kill all humans. That's the stupidest worldview I've ever heard. This is 20 VC with me, Harry Stebbings.

Now, what started as a very planned discussion It evolved into one of the most phenomenal discussions that we've had across a wide range of topics from AI to data to robotics and everything in between with an incredible guest in the form of Naveen Rao. Naveen is a serial entrepreneur. I mean, he sold his last business to Databricks 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. And I think Naveen is one of the most articulate and clear thinkers across such a wide range of very complicated topics. And he's got this unique skill of being able to really break down very complex ideas into very digestible lessons.

He was fantastic, and this show was a joy to do. But before we dive into the show today, founders and investors sleep on 8sleep. It is the intelligent sleep system trusted by Mr. Mark Zuckerberg, Dr. Andrew Huberman, Charles Leclerc, and the Aston Martin F1 team. The Pod is a smart mattress cover that fits on any bed. It cools each side to 55 degrees, heats to 110 degrees.

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free today at framer.com slash 20VC for 30% off a Framer Pro annual plan. That's framer.com slash 20VC. Rules and restrictions may apply. You have now arrived at your destination. Naveen, I am so excited for this, dude. I told you I stalked the shit out of you. Matt Bornstein, Matt Oko, many others. Thank you so much for joining me today, man.

Absolutely. I was looking forward to it. Yeah, I think it's going to be a lot of fun because I went through a ton of your shows. I wrote down what I thought were the most controversial quotes. There were a few zingers, by the way. But I actually wanted to start on 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? Energy supply continues to be difficult to build, right? I don't think people appreciate it. I mean, I've seen numbers from general industries about $50 billion to build one gigawatt. So people are throwing around the word gigawatt like it's easy.

And it's like, guys, this is not easy. Just to put that in perspective, 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 look at it in terms of dollars and complexity. So it's hard. I think what we've seen over the last several years has been soaking up the excess demand that's been around.

So we overbuild generally and we don't run infrastructure at 100% output. So what we've been doing, what we've been getting energy for data centers has primarily been from this. Like, can we run things at higher utilization, you know, and get that next couple of gigawatts out of it? So if you look at the US power grid, we're on average something like 900 gigawatts of output.

But I think it's capable of peaking up to like 1.2, 1.3 terawatts. So 1300 gigawatts.

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. How far behind are we? I think over the last 20 years, China surpassed us in terms of total amount of capacity. That happened, I think, in 2023.

But they were building 6x faster than us for 20 years. They were coming back from far behind, but they really went fast and hard at this. And 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 cleaning up some of this.

So 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. Dude, what can we learn from China being so much further ahead of us that we should be way more loose on policy and regulation? What are the takeaways? Well, I think some of this is hard because it hits the very core of our culture and their culture.

I think China 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. However, I think when it comes to innovation, tip of the sphere sort of things like the U.S. is far and away better. AI was born here, not there.

You know, like all of 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 true innovation. However, I think that tip of the sphere stuff where you really open up, you know, new areas comes from here, not from there.

So, you know, I think we have to leverage our advantages is what it comes down to. When you look at what's scarce today, what do you think is not scarce today that will be in three to five years? I think that excess capacity is going to start getting very scarce. If we look at growth rates, even the kind of much more conservative growth rates of AI, we're going to be out of energy pretty soon in the next few years.

And it's not easy just to say, OK, I'm going to find some more capacity. You will have used up all of that excess. So then we're in a place where we kind of converge to the rate of building And, you know, I've heard people say, oh, we're going to build 40 gigawatts in a year. There's no way. We have never done that. I just don't see it as being possible, 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 gigawatts in a year. Maybe we build 10. If we build 10, that's amazing. That's half a trillion dollars, just to put it in perspective. So 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 cascade or unpack in Corweave, and a lot of concerns around that, is it actually just at the start? Or does the music turn off at the party at 7pm? The biggest determiner of that will be, is there demand for AI services and therefore for GPUs and therefore for this

excess, this new energy build out? Obviously, I'm in this industry, I'm pretty AI pilled. I think there's plenty of demand. I don't think we've even scratched the surface. 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 types 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 bubble being burst. It's sort of like if you said, hey, is there demand for people curing cancer? Of course there is, right? Like if you cure cancer, people are going to want that. Like if you build an intelligent machine capable of doing anything, yeah, people are going to want that.

I always remember Vinal Khosla saying to me, I want to take as much technology risk and as little adoption risk as possible. And it's exactly that. I have a cure for cancer, very difficult to do, but zero adoption risk. 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, I mean, you could argue that I suppose that's up to the energy markets, right? That's how they should be pricing and moving up.

I do see evidence through anecdotes, at least. I don't have specific numbers to quote around this because every one of these behind the meter deals is bespoke and people are paying way more per kilowatt hour than they would 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.

Companies are building new sort of technologies like Bloom Energy. There's a Bloom Energy deal for building a couple of gigawatts in Arizona. If that works, amazing. That might be the technological advances we need to keep going, but it hasn't really been proven out yet. And those economics, by the way, are very different than what you see on the grid.

How so? 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. Like, you know, if I have a gigawatt of compute and I want to sell that, I can sell it for, let's call it 5x the price.

I'm making numbers up, but it's on the order of 5x the price of what I pay for that electricity. So I got a lot of room, wiggle room there.

more for that electricity. When we go slightly further down from energy, which is the core foundational power, to the compute that uses it, what do we know now about compute that you wish you had known two years ago? 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 and let's call it, let's call it three levels.

So the first level is the physical. So if I can send a bit from this point to that point and do it cheaper, you know, let's say I represent zero as being here and one is being here. My phone has a certain weight. So there's energy required to move between these two states. If I make a lighter phone, it changes the amount of energy required.

So that's just a pure physical thing. And Moore's law, as we generally refer to it, or Dennard scaling, you may have heard, where you make devices smaller is really a physical thing. So you're making the physical layer more efficient and denser. So those innovations have largely stopped. And 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 4-bit. So you get pretty good efficiency gains because you're moving fewer bits, you're computing with fewer bits.

Then the next level, which is not really exploited that well, and we started this a little bit mosaic, is about how do I start enforcing locality and reduction of data movement at the model level? And five, six 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 kind of stuff. However, now if I look at the computer solely as a physical device designed to deliver intelligence, it changes the mindset a bit. Now I can co-design all the way from the model to the architecture to the physical. And that's what I wish I'd known how much sort of alpha there is left in the top layers, not just the physical.

We focus on the physical for a long time. How does that design mindset change when moving across the stack with that transition from pre-model focus to post-model focus? Yeah, I mean, it means we don't treat the model as sacrosanct. And we're energy first 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, because, you know, computers were designed to be faster than the alternative. That was always the case. And in the ENIAC's case, by the way, actually, do you know why ENIAC was built? No, it's an acronym, Electrical Numerical Integrator and Calculator, I think is what it stands for.

So this was a computer that was built by the US government in 1945. 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 you have to be faster. That was the key. Next generation comes up, it's got to be faster than that. GPUs came out, they had to be faster at rendering than a CPU. Each thing successively has tried to be faster than the previous thing. That has led to efficiency for several reasons. I mean, making things faster in the same power envelope is more efficient.

But also Moore's law was chugging along and we went from vacuum tubes to transistors. Transistors kept getting smaller and smaller and smaller. Now we're in a place where that physical side of things is not really changing a whole lot. 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 think that's exactly right. So, you know, one thing I've been pretty good at is kind of picking where people are going to be focusing in the next couple of years.

And I think this is going to be exceptional. I mean, I'm really like credit to you. Did 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. Yeah, absolutely. And, you know, 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.

So 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? Well, I kind of look at it like if I were to solve a problem, what is the effect? Like 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 almost the hallmark of a good paper. So for me 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 focus will be because we need to keep scaling, right? We need to deliver intelligence and many new form factors and greatly disrupt the cost in order to make it ubiquitous.

So no one's thinking about this yet. I should start it. We were talking about like model efficiency and how it can deliver in terms of energy. We see models 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 asymptotic point where you're like we're eking out the final increments? No, no, no.

We have a lot to go. And I think the reason is, is we've not done this true co-design between the model and the physical side of it. So you can come up with a concept and you may be able to get, say, let's call it 5% efficiency gain just from software on a standard GPU. That sounds great. However, 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-evolved the intelligence layer and the physical layer, and we're able to exploit things in the intelligence layer in the physical layer. So sparsity, for instance. Sparsity means, let's say, just not having connections between computing units. I can get some kind of gain out of using sparsity in a GPU, 100%.

But when I have a standard off-chip random access memory, the limit of those gains is capped. I can't go too far because that physical thing is not designed necessarily to exploit this kind of sparsity. So if I can build something that exploits it, I can get massive gains. That's really what the brain did. That's how it co-evolved. When we think about that 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 Fractal and Olix and Cerebrus, 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 Turing's original essay. He wanted to build an intelligent machine. Yeah, I mean, that was his stated goal right at the beginning. 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, Turing complete because, you know, they have finite memory and stuff like this. But we were able to build applications that were super useful, right? I mean, we could run businesses off of them. We could edit videos and pictures and text and all kinds of great things. So that's a huge productivity gains over the last 60 years.

But 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. To me, it's just a physical manifestation of intelligence. And so now we have to figure out how to make that 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, our hardware is fixed in its workload. It doesn't do programmability in the same way that we think about it in a CPU. So the GPU is already down this path of building customized architectures for workloads. And I think we're just going to hyper-optimize around this. And that's a good thing because now we can build intelligent machines that are actually capable of building anything at some point.

Forgive my financial mind. You're a wonderful technologist. I am a delinquent trader for a living in many respects. But we mentioned, yes, unwaveringly specialization of chips. To what extent does that massively reduce the TAM for a horizontal provider, NVIDIA specifically, versus, dude, do not be so small-minded. It is still so early. We have 50x the room to run.

It is that. The market is going to be huge. I mean, look, I think NVIDIA executed extremely well on the path to getting us here. I mean, when I started Nirvana in 2014, 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. That 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 started to talk about artificial intelligence at the GTC in 2014. i remember it vividly because 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 important workload and graphics is right and uh he correctly recognized that this was going to be important invested a ton in that direction and actually executed very well we had to go through this evolution of computing architectures from what we use those machines for to what they are 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. That's going to be the figure of merit. To what extent do you think we see the commoditization of chips? You know, Microsoft last night released their chips.

You've obviously got Google with amazing success with TPUs, Amazon with theirs. You've got Anthropic building their own. You've got the wonderfully named, I love this, Jalapeno. Do we see that just inherent commoditization of chips? So I actually don't see chips being commoditized. 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 adversarial. So at first, Two bacteria might be trying to find a spot of sunlight or something like that. The one that has the sensory capabilities to be able to move or somehow adapt itself into that environment wins and keeps proliferating.

Then those things organize into eukaryotic cells and multicellular organisms. Each time you're basically upping the level of energy and the stakes, but it's still adversarial. 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 Kardashev levels, you know, and we're fighting some intergalactic battle, whatever that is. You need intelligence to do that. Intelligence is the fuel, the economy of these adversarial battles. So it has infinite demand. And in order to create intelligence, you need energy. The conversion of energy to intelligence is hardware.

And when I say hardware, I mean the whole stack. I mean the chips, the systems, 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 accrue. So I don't see a 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 between, the more money you're going to make. But I had the founder of Modal on the show yesterday, Eric. And I mean, I love him, but the dude just wants to do everything. I mean, everything.

And I was like, at what point does product breadth damage depth? Do you think about that? I do think the world is changing now with AI. We can take on more breadth. Our surface area that we can explore is greater than it was before. Just because of like speed of dev is much faster. Why is that now every company is a compound startup, so to speak? Yeah, I mean, I can give you almost a pretty concrete example.

You know, in our first chip, we built a set of coupled oscillators. OK, they have a coupling matrix, a bunch of oscillators that sort of influence each other. And so when we're doing this, I would ask 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 to all connectivity? So meaning I have like a daisy chain of 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 sort 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, because they're like, ah, I'm working on this other stuff.

I'll come back to it next week. Maybe it takes a week 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 a few times. I get an answer in an hour. So 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 think we still have a problem. I actually just posted on this yesterday that we have an AI slot 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 do that at scale.

But I can go faster. I can cover more surface area than I could before. What makes you say we have too much crap? Is it just like looking on X and seeing like the dumb takes on, you know, the thing that is just covering my timeline right now? No disrespect to the US, but is your politicians who are rather interestingly structured faces would be an English way of saying it.

That's all of mine, but there's like different AI generations of them. There's aliens and coming out of Congress. I mean, it's nuts. Is that what you mean by AI slob? It's not. That's entertainment. Yeah, 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.

Honestly, by the way, we're all looking at this from the UK being like, wow, we've been moaning about our own politicians. Fuck these guys. Yeah, I know. We've taken new levels. I told you we're innovators. All right. And so actually, this is a perfect example of you see an output and you say, OK, does that does it meet your gut check first? And you're like, huh, maybe not.

So 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, show me these AI generated videos of Trump playing the guitar. And they're like, wow, he plays the guitar. I'm like, mom, he's not playing that guitar. Yeah. That's an AI generated thing.

She's like, oh, okay. So for me, it's quite obvious that it's AI generated. So that verification problem we see in companies, I see it in our company. We can build all kinds of artifacts. We can build code, we can build models, we can 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.

And doing that actually starts to become a big bottleneck because you're producing so many artifacts. 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? Yeah, I suppose. And I think it's a necessary part of using AI to develop things. But if you own the verification layer, do you own the production layer? I'm not entirely sure. I think you have to own the spec as well. Producing a useful output, like building a car or a bridge or something, you have to be able to spec it, figure out, am I solving a problem that's useful? Then each one of these artifacts that's produced has to be verified to be correct.

And then marching towards that spec. And eventually we have to say, yes, I did indeed implement the spec as intended. So it's the end to 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. Mike on the show from Atlassian, awesome guy.

And he was talking about kind of the importance of chain of thought and understanding agent-led decision-making versus human-led decision-making across different functions 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 weird too. We don't. I mean, look, I agree with you. I don't think we should trust Asian decisions as much as humans in a lot of cases just yet. I mean, it's like we train for certain things for many years to build judgment. I mean, becoming a neurosurgeon, right? It's a seven-year training after med school. There's a reason for that.

It's not that learning how to throw a stitch is that hard. I've done that in grad school. It's not the physical, mechanical part of being a surgeon that's the problem. It's having the judgment, right? on which direction to go. You have to have seen enough cases, seen the output, seen how people think through these problems to then build that judgment up.

And the way we're defining intelligence right now is we take a model that's it's stochastic, right? 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'll get a lot of different outputs and most of them are not good.

And you could argue that a human does this too, right? We say, okay, how do I make a decision? Well, I might think through all the outputs and I have some kind of heuristic of deciding what's good. We do this internally, maybe it's an internal monologue or some sort of internal process, 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.

That deciding element is actually not well understood for every task. And so I don't think we should trust these things. I think the longer and longer decision upon decision upon decision chain you build, the more off the rails it can go. And you can get things that look 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. I love a question like, what do you believe that today looks kooky, weird, nuts, that in five years' time will be very commonplace and agreed? I remember putting my credit card into PayPal and my parents telling me off.

And remember OKCupid 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. I think at some point that's true. But you think that we are far from that today? Yeah, we're not there today. And 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 think of intelligence as having a physical and having the intelligence layer as one. They're not separable. I have a machine. It's almost like an organism that I can teach to be an expert at various tasks.

And the value will come from building that expertise up. It's like, which school did you go to? How did you learn how to be a neurosurgeon or a chef or whatever? That provenance matters, right? Like you went and studied under this person and you learned their methodology, their thinking. That's where the value came from. you or any human is an intelligent machine top to bottom but was there was a 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 is real that will work that is verified When you think about the value in that learning, a lot of that is in the data that one accrues over time, in the years that you're doing something. We see data companies today, I'm in McCaw, but we see tens hit astronomical levels of revenue that previously were astonishingly large.

I mean, they're still astonishingly large, but in such short time. Naveen, you 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 defensibility? What happens? I think it specializes, to be honest.

And I think if you specialize and actually start to work on important problems, you have a big market that's defensible. You know, it's again, it's a power law for skills. You could argue that all humans are capable of learning many skills, but certain skills are more valuable than others. And the same thing will be true with data and curricula that you build, right? And so we see the specialization of data companies.

So you don't think they're commodities. Yeah, I don't think it's commodity. I think it started as a commodity because it was sort of like at volume gathering 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 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 the data layer, I think, is going to be pretty important going forward. I don't see that commoditizing. I think building models and knowing how to choose that data is actually the game right now. The big model companies, it's largely a data problem. I mean, they are making advances in the model itself, but 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 moved to what we call mid-training now, which is reinforcement learning, supervised fine-tuning, all of these different sort of mixes. So 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.

Can I ask you, we've seen, you mentioned that kind of pre-training RL and then obviously a real shift has moved to post-training. 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? Well, I don't know that it's more efficient.

Actually, I think RL is more inefficient. If you look at it in terms of how much better the model gets per unit compute or per unit dollar, it's actually more inefficient. But it does keep moving forward, right? I think the idea is that we want to make the models better at the tasks that are important. And so I think it's probably for the next several years, yes, very important.

I think we'll start to build specialization in those middle layers, maybe even in the pre-training layer. So I don't think the model companies are thinking very long terms in terms of performance, like, oh, I'm going to make some massive breakthrough on the way I think about an AI system that's going to give me an advantage. I would say the big labs are not doing that as much.

Do you think they can afford to in the day-by-day race that is this current ecosystem? That's the problem. It's really fast, and you've got to be on top of it. If you're one month late on a model, that's disaster if you're OpenAI or Anthropic or Grok or Google. They can't afford to take those big risks just yet. You're seeing this. When Elon spun up XAI, he basically took, okay, here's the paradigm.

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 me was that there wasn't a ton of like very deep innovation. It was taking the pieces, putting them together with a team that knows how to build them and executing very fast. I think that's very different than trying to come up with a new paradigm.

And, you know, you look at like Yano Kun's company and, you know, Fei Fei Wei's company, they were trying to do deeper innovations. And I think that might be in the realm of startups. And it starts to look like 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 startups when they show promise.

I 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? You mentioned it, kind of the mid-learning there. And I just have so many people, it's all about RL. I'm like, oh, I agree. Yeah, I agree. Yeah. There's several reasons. If you think about the way we learn, it's primarily reinforcement learning.

If I were to teach you a new skill, learn how to serve a tennis ball or something, it's really an RL loop. I would give you a paradigm of what it should look like. Here's how your serve should look. 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 internal model and say, yes, I'm getting there.

I'm getting better or I'm not. And I need to 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. We simply can't. You can't pre-train your way out of this by just gathering data. It's just too hard. Models, frankly, aren't efficient enough 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 something that's that internal model. Like a good serve is I hit it really hard and it went in. It's verifiable. I can measure the speed. I can measure whether it was in or out of bounds. 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 have a structure that allows me to solve any set of general tasks.

And that's why RL is so exciting. Do you buy Yann LeCun's post-transformer world theory? And how does that change everything? I do think we will be in a post-transformer world. I don't think the transformer is the be all end all. I don't think I truly understood this until maybe a couple of years ago because transformers conflate a lot of things.

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 understanding is what intelligence is.

So transformers kind of sort of have that, but they really do it through this 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 context, which is a retrieval thing. And then we can sample off of that 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 brains do not work in this way. We don't build a huge trace, 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 high performance from an algorithmic standpoint ways to do this. And so we will move past the transformer. Do you think what it takes to be a great founder has changed or not? I don't think it's changed. I think the basic principles are still the same.

I think having a viewpoint on the future, your vision actually may matter more now than it did before. I think in the pre-AI world, you could be a pretty good founder by just being able to execute. You don't have to have a very good vision. You just have to know how to execute. And if you just go out and execute a big company, which isn't that hard because big companies are slow and lumbering,

I think you could actually build a startup and you could sell it or build a company out of it. I think these days, if you're just based on execution, I don't think you're going to get very far. We spoke about the different layers. You said, I don't think that's going to get commoditized in data. I think specialization, I think there is value there.

Which layer does get commoditized over time? Yeah, it's a good question. I mean, arguably energy is already a commodity, right? So this is already commoditized. I think a certain amount of intelligence does actually start to become commoditized. We're already seeing it, to be honest, like models that are six months old, let's say the Chinese open models, you know, that level, they're pretty useful, right? Something that's six months old is still pretty darn good.

So I would argue that's a hallmark of commoditization, right? The frontier is not the only thing that matters. For certain problems it is, but in six months, that frontier will be in the open models. So then help me out. What happens if the delivery mechanism or the output of all the infrastructure is then commoditized? Well, I think commoditization doesn't necessarily mean it's bad.

It just means that margin falls quite a lot. But it also means that the market is very big. So you could argue that tomatoes are commoditized, but we still eat them. We still need them, you know. So we will have a huge market for intelligence and the market will continue to grow. But the margins may not be huge. 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 the lion's share of the mar of the margin A lot of people say to me the cost for inference will actually go massively down over the next few years, and we'll see the commoditization potentially of the inference market.

Would you agree with that or not agree with that? Well, let's break it down. What does commoditization of the inference market mean? What that means is that the operator who's running inference, I assume, yeah? Yeah, absolutely. Your fireworks, your modal, your base tans, and the services that they provide. Because 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 then make it work well with uptime guarantees and make it relatively cost efficient to run on infrastructure.

So they're an infra play, right? They're basically mapping the model onto physical hardware and doing that well. Now, 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 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 I think the model itself like is commoditized. You have several different ways to get it. now. But the hardware side of it probably 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.

We have a lot more juice to squeeze there versus the infrastructure layer, in my view. I actually think the hardware is the place to be over the next 10 years. Dude, it's quite fucking hard doing hardware investing. It's a very different mindset. Is this generation of investors equipped to be hardware investors? No. I would say if you're a pure software investor, you're kind of fucked, honestly.

And I'll say that pretty confidently because SaaS, I don't think is going to be where you gain margin anymore. And we had this sugar high for the, since what, 2011, 2010 timeframe till now of, you know, I can very quickly spin something up on an AWS cloud service, test the market quickly, try things out and then go after a hundred million or $200 million market.

SaaS was like massive fragmentation of the market because the delivery mechanism became very easy and frictionless. Now we're in this place where applications start to become more about the intelligence layer. And I just don't see software being a huge market in 20 years. Do you not just think that SaaS becomes Lagora. And what I mean by that is like, is Lagora SaaS? Well, kind of, yeah, enterprise SaaS, 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 a SaaS company. Right. So exactly. I think you're right. So I was taking the classic definition of SaaS, I suppose. But I think companies where you deliver an outcome and you do so through a combination of software and services.

Absolutely. You know, we're seeing that in several companies and 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 that were done in a traditional way. and have to be done correctly. And they're making those tasks much faster, much cheaper by using AI plus humans.

It's great. I mean, I would say Harvey was a first example of this for law firms where the old way was I have a set of lawyers who go and look at contracts and write contracts, that kind of thing. And they basically said, well, I can use AI and verify the outputs with humans and deliver the same thing with fewer humans and deliver it faster at the same quality or near the same quality.

And I will disrupt that market. So I think we're going to see it. This is sort of the new sass. We see it. You know, I call it word wank, which is where like, you know, VCs come up with a coin theory of the world. And it's like, you know, the trillion dollar services thing. You know, I think Sequoia did a brilliant piece on. And I love Julian who did it.

It's 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. Bit overkill, isn't it? Aren't you just investing in a law firm? when you do that. Effectively, yeah. But it's a better version of a law firm, right? That's all. But you'd be okay to do that.

Because I meet a lot of companies doing exactly what you say. And I'm like, I don't want to do a law firm. I'm happy doing a Legor or a Harvey, which sell to law firms, but they're not actually a law firm. Well, but we are seeing that like some of the large law firms are now investing in AI directly. So I think in its limits, you'll see basically Harvey sells to the law firms because the law firms have the channel.

You know, is that channel going to be enduring? I don't know. But I could also see them saying, well, you know what? And we 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. Well, like all these things and I can gain confidence that it works.

I would send 80 percent of my workloads, my legal workloads to them. I am intrigued because you spoke about. some firms that are building their own. I think you saw K&E in the legal space commit $500 million to it. Lynn at Fireworks, where I'm an investor, has 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 actually a slightly techno-optimist prediction from a very specific standpoint? I think it's a bit techno-optimist because I think you have to have 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 Cooley or one of these big firms, yeah, you can invest in these things and you can make it happen.

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 that as a service of some sort, right? You may augment your attorneys with that service and that's how you'll provide service to your clients. So I don't see this as every company will use AI in a way of leveraging their past data to make 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. But it's going to be this long tail. 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 part of our world is adversarial.

It depends on who you're fighting, right? If 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. The mom and pop shop probably isn't doing that. And the people they're fighting against are not doing that. So they're going to stay in this other realm for some time.

I think one thing that underpins a lot of law, coding, is the percent of salary that we will then transition to technology spend. When we look at lawyers today, 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 coding.

You saw Benioff say that they'll spend $300 million of a $6 billion developer salary budget on core code and coding tools significantly. Right. My 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 load question. I actually think the percentages will remain maybe sort of stable because we end up with this verification problem.

It's hard because training humans to know what's good is actually the hard part. Developers, 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, they developed a sense of what is good, what looks good, what's aesthetically right, and they can make things work fast.

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 play it out a little bit.

So I'm spending in a company, let's say $10 million a year to develop my software. Now, all of a sudden, my customers of that software start having different demands. 10 years ago, they were 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.

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 and actually move to a goal that's useful, fixing the bug, adding features. So I think the pace of delivery goes up because the demand side starts to demand it.

And I think that is 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, yes, that is true, but I will now do way more of those tasks because the demand side has increased. You don't get one without the other. When the supply gets cheaper, 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 changes. Like here's a physical example I think that people can get their heads around. You know, let's say today 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 Y sets of skills.

So let's say all of a sudden I have robots that can do 80% of that. What's gonna 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 eight months.

So it's not that you're just going to build the same number of skyscrapers and just do it in eight months. You're going to build twice as many skyscrapers, right? There's going to be more things churning, more buildings being torn down, more labor going into that market. So the total number of 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 robots to build them.

Naveen, you said that you obviously angel invest. You angel invest very successfully. You kind of downplayed it. I 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 Mecha or Micro AGI that are actually in the infrastructure build out for it. But like the actual application layer where we see like robot cleaners, robot builders, 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.

Am I wrong? Am I naive? You're much smarter than me. Well, again, I'm pretty pilled on this one, so I am going to disagree strongly on it. Okay. When you say we're so far away, put that in terms of time. What do you think is the timeline? Five plus years. Okay. We're not so far apart on this. I think we'll have working prototypes for some of these tasks in the next two to three years, and the market will emerge in five to seven years.

I think that's right. I think there's also a clear line of delineation between humanoids and non-humanoids. Non-humanoids, I absolutely see that clearer pathway. The humanoids, I'm really just struck... Whenever I think something's really obvious, I'm always like, I must be very wrong. Yeah. Because the humanoid is so obviously so far off.

You know, 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. Something that looks like so far away today with an exponential curve ends up being much faster than you thought. And like TAMs, you know, a market size is, 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, transportation, right? You would never have thought something in tech would be a trillion dollar market. And I think just AI models themselves will be a trillion dollar market in the next few years.

And the inference market is gonna be a $10 trillion market within 10 years. So this is insane to think about, right? And to your point, we're moving some of that labor market and part of the value generation stack to technology. So that's where we're getting these giant TAMs. So the way I look at like unconventional, the reason that I think it's a fantastic market to be in is that if we knock off a zero, one order of magnitude of efficiency on delivery of a token, that effectively 10Xs the market in some ways.

So 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 Jevin's paradox. So I think we're in this place where we can build these exponentials and it just unlocks massive value in markets. So I actually think robotics is going to be 100 times larger than agentic coding.

Well, that is slightly disagreeing with me, Naveen. Can we just like, before we do a quick fire, can we just unpack that? Because I don't want to be completely moronic. Why do you say that? I think we're still scratching the surface of 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 rote. We're imbuing them with intelligence that we have, like how to do a task, and we're just doing it rote. That's not really novel problem solving. we will be in the realm of physical problem solving in the not distant future. Like imagine a world where I have billions of robots roaming around and they actually solve problems dynamically.

That's a problem. That thing just broke. I'm going to go clean it up. I'm going to replace it. Like the cost of delivery of cleaning everything up in our world, building new buildings, you know, solving physical problems will not drop to zero. I hate those drop to zero kind 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. Can I be blunt? Where specifically then do you invest in robotics? In the model companies that power them, the data companies that power the model companies, the consumer application layer? Where? I don't think the application layer yet. I'm more in the technology stack.

So 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. And that's OK. I'm not suggesting they need to. Like, I don't think the value will be in the model. I am investing in companies that are building models. I think they are probably going to end up building end to end devices.

They're going to build the 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. Do you care about price when the TAM is what you said it was? Not really, no. I just see it as like, I need to put some percentage of my wealth into that space because it's important.

And that's where there's going to be a ton of growth. And so it's interesting because we're in this place where 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.

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 who 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, fuck you. Yeah. They do, until they start to see the big firms also have the same trend and they're like, ah, okay, maybe that's the way it's going, right? If you could put all of your money into one company that is not your own, what would it be? Oh, yeah, probably like a physical intelligence.

They are solving, in my view, something fundamental about how you define a task and then execute it. So conversion of a task to motor output. To me, that's the problem to solve right now for the physical world. So outside of what we're doing and building a new computer, I think that's the next big problem. You've been incredibly successful across companies.

What does no one tell you about having money that you wish you had been told? I think it doesn't change your values. You still have a finite amount of resources, but also at the same time, you still value certain things in certain ways. I'm still kind of cheap when it comes to certain things. There's no reason for it to be. What are you cheap with regards to? Well, if you ask my wife, probably clothes, that kind of a thing.

I'm just like, what? I'm not going to pay that for a set of jeans.

I think travel still, like, 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. And it's not a, you know, it wouldn't matter if I spent the money or not. I just, I just don't see value. So I think that's the thing. I put money into things where I see value and I can put a lot more money into those things, but places where I don't, I actually don't spend any more.

Dude, we're going to do a quick fire round. So I say a short statement. You give me your immediate thoughts. What have you changed your mind on in the last 12 months? Yeah, I think in terms of investing, I've changed my mind on useful metrics for progress. So I think we always looked at it as like, oh, we need to look at market validation 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 SaaS 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 something 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. So I think right now investors are in two camps. Some are saying, well, let's go, let's invest heavily in these companies, you know, valuation be damned. You know, we're one of those companies. And the other set is like, oh my God, that's undisciplined. 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 I kind of grew up 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, biology.

And it's not about market validation. The market is there. The market is very clearly there. You solve that problem, 100% people are gonna buy it. It's about progress of solving the problem. If you were completely resource unconstrained, professionally speaking, what would you do differently? I would start to build my own manufacturing. I wanna go closer and closer to the source of raw materials.

We're in this world now where there are so many supply constraints from the hardware ecosystem point of view. If I were unconstrained and I could build anything, like this is kind of what Ewan's doing, to be honest. Yeah, we should build our own sorts of fabs because I can hyper optimize that layer for what I need. I don't need to take a thing that is a commodity that's been built for 100 other purposes and make it fit my needs.

I would build exactly the thing I need and build a better product. And 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 the silicon ingot when it comes 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? Yeah, I think 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. I think it's very overrated. I actually do think that like agentic 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. But the model providers, the model itself is where most of the value is coming from.

I don't think it's the harness or the IDE so much. 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 and what capabilities it delivers. So I think agentic coding as a market modulo the model providers is something I wouldn't invest in today. What percent of token traffic will flow through open versus closed in three years time? I think it'll be 80% open.

What does that translate to in terms of dollar traffic? 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. The mobile phone market is... Just roughly speaking, iPhone is 20% of the total market, but 80% of 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.

However, all of the like routine tasks that you do will be handled by open models. So 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 frontier models. Who is the single best investor meeting you've ever had and why? Well, all right. I can give a bit of a story for the first. Go on.

I think, you know, since I started in Urbana, I've probably had, I don't know, a thousand investor meetings. Right. So after, you know, the eight hundredth one to eight hundred, the first one don't really leave an impression anymore. So very early on, you know, I had a meeting with Steve Jurvetson, who has now since become a good friend of mine.

I was nervous as hell because I had heard so many like amazing things about this guy, super smart. He's just gonna take me apart. You know, that's kind of what I was told. I had a conversation with him, which we just started geeking out on numeric representations. And I was like, this guy's an investor. And then I send him a couple of papers.

He like marks them up and ask questions. I'm sending responses back to him. Like, this is not what I expected from an investor. ever. This guy's like talking to, you know, a technical colleague in a sense. So to me, that was one of the best investor meetings I had because we were doing something so new that no one pitched it yet. No one had pitched a hardware startup in 10 years in 2014.

This guy was almost champing at the bit to invest in something like this because he'd been thinking about it himself. And so that was just like, boom, let's go. I love Steve. I remember having him on the show. He was fantastic. You talk about like a willing to be non-consensus is what I love about him. That's right. You have to be to make big bets that will pay off.

By definition, the consensus is already wrong. It's already old. So you have to be non-consensus. The challenge I find with being in Europe, though, is actually you have the commoditization of ideas. And what I mean by that is actually now being a founder is quite a touristy thing to do. Everyone wants to be a founder for the experience. The quality of ideas has reduced.

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. We're still doing expense management with AI. Yep. No, totally. I think we are given the permission to think bigger here.

In Europe, you're not because the whole investor network and the way people think is so conservative. If you come in with a crazy idea, they're like, that guy's mad. I'm not gonna invest in him. Whereas here, it's like, that guy's awesome. He's thinking big, right? Now, I completely agree with you. It's absolutely right. The hard thing is, and I've got this genuine, okay, let's do this.

There's a Series A now, and they are building space stuff. The team is great. They're awesome. But dude, it's 20 million check from a 400 million fund. It's 5%. You can't lose that that easily. Dude, it's a super risky play in space, and I do not understand it at all. I sort of look at these as in the early part of the technology cycle, like all of us 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 going to be wrong. 100% will be wrong. 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. What you're investing in, I think, at that 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 requisite pivots and right technology decisions to actually drive the bus to something that matters? The challenge actually that becomes in this market, you don't have the time with them. You don't. Yeah, I would love to say, yeah, I don't know.

You probably get two to three meetings. You're right. And 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. Final 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 bullshit. What's your like, I'm just fucking pumped for this? I am personally pumped that I look at AI as 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 unapproachable. They're unapproachable from just the number of humans who could think about that problem and actually make progress. The 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, oh my God, AI is gonna kill all humans.

That's the stupidest worldview I've ever heard. It's just idiotic to me. I'm actually completely tired of it now. And it's like, no, no, no, guys, AI 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 have these ideas of like wormholes and these things. These are all like theoretical constructs. We can... now start to use AI and start making progress? Can I actually build something that exploits that physical phenomenon? Or is it real or not? These are questions we can start to ask. In the past, we just couldn't. We didn't have enough humans that were capable of approaching these questions.

So to me, the ability to move science forward and to move human understanding forward is so exciting. I can't even tell you, it makes me feel like a kid. It was 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.

Shows like this are why I still love doing the show after 10 years. Like the breadth of your knowledge is just like wonderful. It's amazing for me to witness, honestly. And so thank you for being so willing to engage across such a broad spectrum. And you've been amazing, dude. It's a lot of fun. I love these conversations where we can riff on so many different topics.

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