The Exchanges

Q What's a parallel property direction from 1885 onwards style timeframe that you think will play out in the next era?

A Well, obviously in the world of infrastructure, I think we need something like the grid. For, in the compute infrastructure. So that's what I've spent most of my days on, which is a coordinating mechanism for, uh, that, that allowed this, the, the, the, the, not the commoditization necessarily, but the transition of, uh, coal and electricity from being these resources that were being hoarded to being stable, reliable, uh, commodities that, that the best engineering teams, the best factories had access to, right? That's, so that's, that's what I think about a lot. I think if you're, since you're so talented at media, and you're so talented at storytelling, um, I think I would, and your mission is to push the European continent, I think one of the things, if I was you, is I would be talking, trying to figure out how do we educate The leading capital allocators and infrastructure allocators in Europe about the coming era, whether that's through media, whether that's through educational programs and get them to understand their role in unblocking the bottlenecks for the best scientists and engineers in Europe.

AI assessment note: “I think we need something like the grid. For, in the compute infrastructure.”

Q What was, what was Anthropic and Dario like in the early days?

A Well, so I've known Tom forever. Uh, Tom, you know, was one of the lead authors on GPT-III. Um, we've been friends for many, we'd been friends for many years. Tom gave me a call and said, Anj, you know, we, for various reasons, we want to leave and start this new lab called Enthropic. We're going to need a lot of capital. We're going to need compute. I, I had already sold Ubiquity Six at that point, so I'd kind of gone through the founder journey. Um, and so Dario, Tom, and I started doing these weekly sessions in early 2021 to try to figure out how to turn what was really a research hypothesis, right, which is scale, the scaling Recipe into a business hypothesis. Um, and look, I would say it, it took like really 12 to 24 months. Um, and they did a lot of the hard work on figuring out how, how, how do we really sort of operationalize this, the idea of this AI pair programmer, right? Where you take the context feedback loop of the local repository, the files, the directories of programming and kind of Sort of, you know, in a very methodical way, make predictable progress on the capabilities of, um, of, of software engineering. And I, I thought it was a very, you know, if, if anything, my biggest flaw is as an investor, as a founder, is being too early to things. That, that was my lesson with Ubiquity Six. I was early to the whole computer vision, which is now, you know, obviousl…

AI assessment note: “Dario, Tom, and I started doing these weekly sessions in early 2021”

Q So starting on the world of venture, we said about the unique perspective of having been on both sides of the table, both as VC and as founder. So when we chatted before, you said to me, 90% of venture capital firms are dysfunctional, something that I might agree with having spoken to 2000 times. What a cliffhanger. So what makes you say this, and why are they dysfunctional?

A Okay, so, I mean, I think 90% might, might be a bit too harsh, but yes, in general, I do think there's Some stuff that's structurally wrong with venture capital now than ever before, and I think the benefit of being removed from it now as a founder makes you more introspective about it. You know, the biggest caveat I would have is that, as you know, the bulk of people who ignore history tend to have to, are condemned to reinvent it. I forget whose quote that, I think it might be a Churchill quote, but I find the bulk of VCs very conveniently ignore history all the time because, you know, their job depends many times on framing excitement about things that are new, but often actually things have happened before. Four in the same way, whether that's the seventies, the eighties or the nineties. And when you analyze venture capital as a business, I think it's not controversial to say at the end of the day, it is the business of financing creative hits. No matter how you skin it, I think whether you're talking about consumer software or enterprise SaaS, building anything new from scratch, that is the vision of, you know, a small group of people who want to bring something new into this world in a new form factor, whatever that might be. Their hope is that that will end up being a hit. And when I say hit, whether that's in the sense of traction or revenue, it will end up resonating w…

AI assessment note: “I find the bulk of VCs very conveniently ignore history all the time”

Q How much more cash do we need in Europe for Frontier AI to be what we think it can be? Is it like two X? Is it 10 X?

A That's a good question. I would try to go about it from a top downs approach and bottoms up sizing approach. Um, you know, for us at AMP, when I look at the grid, we are building out, which is sort of a reasoning by analogy, uh, we have started securing about 1.3 gigawatts of computer infrastructure. That's roughly forty billion dollars of cloud spend over the next four years. And that is financed roughly, you know, between with about 20% of equity. The remaining is debt. So 20%, that's about ten billion dollars of equity capital. The remaining is all debt capital. We have a bunch of partners. That help us put together these equity and debt packages to secure computer infrastructure for our companies. I would say in Europe, I would talk to Arthur and figure out how much he thinks is required for the independent ecosystem over there. But in multiples of gigawatt, like if you, if you're doing sort of your atomic unit of math in gigawatts, I would, from a, from a top down perspective, you know, I think Google Is roughly at 12 to 15 gigawatts of that I'm aware of, of, of infrastructure for internal and external deployed needs. Now they have a huge land power shell pipeline coming, but you know, if Europe does not have access to Google level infrastructure, then what are you guys even doing? Right? Like that's roughly what the continent needs for full sovereignty, right? To have as,…

AI assessment note: “that's roughly what the continent needs for full sovereignty”

Q don't blame you for leaving this interview thinking, God, he's got worse over the eight years, not better. But I was watching this interview, speaking of inference, with someone, I think, from base 10, and they were saying that the demand for inference has grown not linearly, but combinatorially, and that is how we would see it progress over the next three to five years. Do you agree with that?

A If we keep scaling capabilities, that will definitely happen. The problem is there are a couple of bottlenecks on scaling capabilities that are quite existential. One of them we've talked about is the four core bottlenecks on capabilities progress we've talked about, right? It's context, compute, capital, and culture. And I think capital allocation, huge problem. We've got to educate people on why this is, why these capabilities are extraordinary. Like, this is, this is like the biggest financial bonanza of all time, if you know where to allocate. I mean, there's a reason why I invested in Anthropic in the seed round. And now, as you've pointed out, like the returns of all the The body of work I've done over the last four years are attracting LPs at the highest levels, but we're just getting started. And so that, that, I think some of these projections you see are correct if we unblock the bottlenecks along the way. In computer infrastructure, secure compute infrastructure that's fungible, that's standardized, that's the biggest bottleneck. I think if there's any reason why OpenAI, Anthropic, Gemini, and so on don't hit their revenue targets over the next few years, it's because they won't have access to enough compute. I will say there's, there's like a related bottleneck. When I was at Stanford many years ago as a kid, I took this class that Peter taught called, uh, I think i…

AI assessment note: “If we keep scaling capabilities, that will definitely happen.”

Q determine what is not going to get Claudified in that vertical model company build out? Because you could look at a cursor and say, Well, they've built their own vertical model end to end, and it's been codified, if we're being blunt. Periodic won't be because of the physical data that's being produced in the labs. How do I know what will be codified versus won't in that model there?

A Yeah, this is a good question. Okay, if we want to sort of unlock frontier progress generally across a bunch of domains, then where are the bottlenecks and where will the Value accrue. Context is Is not necessarily the moat. I would not say yet. I think, I think venture capitalists are very quick to analyze moats, but I would say context feedback loops where you are, you have unique and differentiated access is where progress will be most legible to you. And if there are other teams who don't have access to that context, it'll also be where you have a superior business model. And so here's an example I give in the class, right? Sovereign data. Are you familiar with the cloud act?

AI assessment note: “context feedback loops where you are, you have unique and differentiated access”

Q Can I get back to something you said before, which is like, we're at the industrial revolution stage. And I was like, okay, help me understand that. If we're at the industrial revolution stage, what does that mean for where we're going and how I should be acting as an investor today?

A You have to hold two things. In conflict. That can seem paradoxical. Um, and this is, this is the most important thing I learned from Mark and Ben, which is when the future, the future is not, uh, is, is not determined. And so anyone who tells you that they can predict the future with certainty should be taken with a healthy dose of, Uh, suspicion. And, and instead I try to approach things like a scientist and go, what are the biggest bottlenecks? Let's come up with a hypothesis on how these bottlenecks will be solved and let, let's run multiple experiments in parallel. And then whichever one emerges, you just have to be very truth seeking and, and be willing to claim, like, say you were wrong. Right. And, and, and I would say as an investor, your job is to come up with a hypothesis for where the future is going and be willing to To, to make multiple different experiments that are aligned with your mission in parallel and be willing to be wrong and be honest with your LPs that some of them may be wrong.

AI assessment note: “as an investor, your job is to come up with a hypothesis”

Q What's a parallel property direction from 1885 onwards style timeframe that you think will play out in the next era?

A Well, obviously in the world of infrastructure, I think we need something like the grid. For, in the compute infrastructure. So that's what I've spent most of my days on, which is a coordinating mechanism for, uh, that, that allowed this, the, the, the, the, not the commoditization necessarily, but the transition of, uh, coal and electricity from being these resources that were being hoarded to being stable, reliable, uh, commodities that, that the best engineering teams, the best factories had access to, right? That's, so that's, that's what I think about a lot. I think if you're, since you're so talented at media, and you're so talented at storytelling, um, I think I would, and your mission is to push the European continent, I think one of the things, if I was you, is I would be talking, trying to figure out how do we educate The leading capital allocators and infrastructure allocators in Europe about the coming era, whether that's through media, whether that's through educational programs and get them to understand their role in unblocking the bottlenecks for the best scientists and engineers in Europe.

AI assessment note: “in the world of infrastructure, I think we need something like the grid”

Q determine what is not going to get Claudified in that vertical model company build out? Because you could look at a cursor and say, Well, they've built their own vertical model end to end, and it's been codified, if we're being blunt. Periodic won't be because of the physical data that's being produced in the labs. How do I know what will be codified versus won't in that model there?

A Yeah, this is a good question. Okay, if we want to sort of unlock frontier progress generally across a bunch of domains, then where are the bottlenecks and where will the Value accrue. Context is Is not necessarily the moat. I would not say yet. I think, I think venture capitalists are very quick to analyze moats, but I would say context feedback loops where you are, you have unique and differentiated access is where progress will be most legible to you. And if there are other teams who don't have access to that context, it'll also be where you have a superior business model. And so here's an example I give in the class, right? Sovereign data. Are you familiar with the cloud act?

AI assessment note: “context feedback loops where you are, you have unique and differentiated access”

Q Do Anthropic and OpenAI just accept that and roll over? I, I don't understand, because government is a mega portion of their efforts and workload today. And like, both of them, when I speak to them, are like, oh, we're absolutely coming for Europe. So, so how do they get around that?

A Well, I can't speak for OpenAI too much, uh, because I'm not involved there directly, but Anthropic, I will say, you know, the mission and vision has always been very, um, I think it's always been very American aligned, right? They've always said, hey, America is, The crown jewel of the world in terms of innovation. This is where we're located. Anthropic is located in Silicon Valley. Um, and I think the company really, really wants to do what's best for the American government and the American way of life, which is democracy and freedom. It turns out the world's largest enterprise customers are governments and fortune, 500 companies. And many of those that are overseas need these workloads to be running locally.

AI assessment note: “many of those that are overseas need these workloads to be running locally.”

Q If we just go through them, when we look at that context feedback on the data side, will we see then a generation of vertically integrated foundation model companies, light periodic for a ton of different things? Yeah.

A Yeah. You know, when I went to grad school, uh, for machine learning, I, I went to Stanford for bioinformatics, which was the machine learning applied to healthcare. We were, the space was not as good as marketing as it is today. So super intelligence. Love it. You know, at the end of the day, what are we talking about? We're talking about very powerful models within some domain. And, and we are seeing sort of within distribution, very, very powerful capabilities that are, you can definitely call them superhuman because there's no way, for example, I, As an individual scientist could analyze the reams and reams of data coming out of the lab here without AI models. There's just no chance. And so the fact that you can take all of the data from, you know, training from, from a physical lab and just throw it at a bunch of AI models and ask it to analyze things is a superhuman capability. We didn't have that before. Okay, fine. So let's call that super intelligence. Within coding, within material science, within each of these domain distributions, we are seeing Capabilities that are superhuman. We didn't have them before. And in fact, I would say we're even starting to see automation of those tasks, especially where there's, there's coding involved to starting to be somewhat recursive, right? Where if you have a good coding model, then you can say, okay, let me automate like data an…

AI assessment note: “We're talking about very powerful models within some domain.”

Q What's easier, the equity raise or the debt raise?

A I would say the biggest challenge has been figuring out the right aligned financial structure Across both in a way that's legible to capital allocators at scale. Took me about a year to really cut all the pieces right, but they're very large equity pools. The length of this is a lot of balance sheets, long-term mission line balance sheets in the world who don't have, who have, um, Who are mission aligned at wanting to help frontier scientists, reach researchers, university labs get access to the compute they want, but they don't have operate. They don't have OPEX. They don't have cash to spend on the compute. So if you can find a way to align equity, um, debt balance sheets in a way that's risk sort of, um, De-risked. The fundraising is not a problem. It's, it's actually a systems design problem, which took me again, a year, probably took me four years to get right. But now that we figured it out, it's, it's not been a problem.

AI assessment note: “The fundraising is not a problem. It's, it's actually a systems design problem”

Q don't blame you for leaving this interview thinking, God, he's got worse over the eight years, not better. But I was watching this interview, speaking of inference, with someone, I think, from base 10, and they were saying that the demand for inference has grown not linearly, but combinatorially, and that is how we would see it progress over the next three to five years. Do you agree with that?

A If we keep scaling capabilities, that will definitely happen. The problem is there are a couple of bottlenecks on scaling capabilities that are quite existential. One of them we've talked about is the four core bottlenecks on capabilities progress we've talked about, right? It's context, compute, capital, and culture. And I think capital allocation, huge problem. We've got to educate people on why this is, why these capabilities are extraordinary. Like, this is, this is like the biggest financial bonanza of all time, if you know where to allocate. I mean, there's a reason why I invested in Anthropic in the seed round. And now, as you've pointed out, like the returns of all the The body of work I've done over the last four years are attracting LPs at the highest levels, but we're just getting started. And so that, that, I think some of these projections you see are correct if we unblock the bottlenecks along the way. In computer infrastructure, secure compute infrastructure that's fungible, that's standardized, that's the biggest bottleneck. I think if there's any reason why OpenAI, Anthropic, Gemini, and so on don't hit their revenue targets over the next few years, it's because they won't have access to enough compute. I will say there's, there's like a related bottleneck. When I was at Stanford many years ago as a kid, I took this class that Peter taught called, uh, I think i…

AI assessment note: “If we keep scaling capabilities, that will definitely happen.”

Q If we just go through them, when we look at that context feedback on the data side, will we see then a generation of vertically integrated foundation model companies, light periodic for a ton of different things? Yeah.

A Yeah. You know, when I went to grad school, uh, for machine learning, I, I went to Stanford for bioinformatics, which was the machine learning applied to healthcare. We were, the space was not as good as marketing as it is today. So super intelligence. Love it. You know, at the end of the day, what are we talking about? We're talking about very powerful models within some domain. And, and we are seeing sort of within distribution, very, very powerful capabilities that are, you can definitely call them superhuman because there's no way, for example, I, As an individual scientist could analyze the reams and reams of data coming out of the lab here without AI models. There's just no chance. And so the fact that you can take all of the data from, you know, training from, from a physical lab and just throw it at a bunch of AI models and ask it to analyze things is a superhuman capability. We didn't have that before. Okay, fine. So let's call that super intelligence. Within coding, within material science, within each of these domain distributions, we are seeing Capabilities that are superhuman. We didn't have them before. And in fact, I would say we're even starting to see automation of those tasks, especially where there's, there's coding involved to starting to be somewhat recursive, right? Where if you have a good coding model, then you can say, okay, let me automate like data an…

AI assessment note: “what are we talking about? We're talking about very powerful models within some domain.”

Q What's easier, the equity raise or the debt raise?

A I would say the biggest challenge has been figuring out the right aligned financial structure Across both in a way that's legible to capital allocators at scale. Took me about a year to really cut all the pieces right, but they're very large equity pools. The length of this is a lot of balance sheets, long-term mission line balance sheets in the world who don't have, who have, um, Who are mission aligned at wanting to help frontier scientists, reach researchers, university labs get access to the compute they want, but they don't have operate. They don't have OPEX. They don't have cash to spend on the compute. So if you can find a way to align equity, um, debt balance sheets in a way that's risk sort of, um, De-risked. The fundraising is not a problem. It's, it's actually a systems design problem, which took me again, a year, probably took me four years to get right. But now that we figured it out, it's, it's not been a problem.

AI assessment note: “biggest challenge has been figuring out the right aligned financial structure Across both”

Q How do you escape the money treadmill? I didn't have money when I grew up and I was like, I'll be happy when I get like, you know, X amount of money. Any advice on escaping that money treadmill?

A I was very lucky that, you know, I went to Singapore on a government scholarship and, um, Lee Kuan Yew, who is the, you know, was the founding father of Singapore. I'm a big Lee Kuan Yewist. Realized that, you know, the, the best, like they're, they didn't have many resources. They had, they didn't have, they didn't have money as a founding nation. They didn't have. They didn't have nothing basically other than themselves and their location, their strategic location. And he realized we need to build a talent program. We need to run this country like a company. And I, we would recruit, um, the best talent from across Asia. And because I, I think I was the top 10 or something in some, Public exam. When in the 10th grade in India, I was tapped to be a scholar in Singapore and I took, I was a government scholar. Now I didn't have to actually, I was lucky enough that my parents could have paid for it. We had a family business in telecom, but it was very important to me to be independent from my parents because in Indian culture and a lot of cultures where like, if you don't have financial independence, you are always kind of beholden to somebody else. And then, In the case of community cultures like India, like there's a lot of pressure to adhere to their values and so on. Um, And I, I think I did cause subconsciously I'm very lucky. I have a sister actually who lives in London and …

AI assessment note: “I was very lucky that, you know, I went to Singapore on a government scholarship”

Q How do you escape the money treadmill? I didn't have money when I grew up and I was like, I'll be happy when I get like, you know, X amount of money. Any advice on escaping that money treadmill?

A I was very lucky that, you know, I went to Singapore on a government scholarship and, um, Lee Kuan Yew, who is the, you know, was the founding father of Singapore. I'm a big Lee Kuan Yewist. Realized that, you know, the, the best, like they're, they didn't have many resources. They had, they didn't have, they didn't have money as a founding nation. They didn't have. They didn't have nothing basically other than themselves and their location, their strategic location. And he realized we need to build a talent program. We need to run this country like a company. And I, we would recruit, um, the best talent from across Asia. And because I, I think I was the top 10 or something in some, Public exam. When in the 10th grade in India, I was tapped to be a scholar in Singapore and I took, I was a government scholar. Now I didn't have to actually, I was lucky enough that my parents could have paid for it. We had a family business in telecom, but it was very important to me to be independent from my parents because in Indian culture and a lot of cultures where like, if you don't have financial independence, you are always kind of beholden to somebody else. And then, In the case of community cultures like India, like there's a lot of pressure to adhere to their values and so on. Um, And I, I think I did cause subconsciously I'm very lucky. I have a sister actually who lives in London and …

AI assessment note: “I was very lucky that, you know, I went to Singapore on a government scholarship”