Episode Ten: Daniel Adamson

In this episode of the News Items Podcast, John Ellis speaks with venture capitalist Daniel Adamson about investing at the cutting edge of global innovation. Adamson explains his “network model,” partnering with elite investors worldwide to access breakthroughs across AI, biotech, robotics, and even fashion. The conversation cuts through AI hype, arguing for a more nuanced view of its strengths, particularly in solving complex, data-heavy, rule-based problems like drug discovery. Adamson highlights transformative opportunities, from automated science labs to humanitarian applications, while warning of disruption across sectors like infrastructure and real estate. Ultimately, the episode explores how rapid technological change is reshaping markets, portfolios, and even our understanding of what it means to be human.

Daniel Adamson operates as a network-driven investor rather than a traditional venture capitalist. Instead of selecting individual startups, he invests in leading venture capital firms and partners with major global institutions such as pensions and sovereign funds. His approach centers on identifying top investors across industries like biotech, fintech, and energy, then aligning with them by taking stakes in their firms and co-investing in their strongest opportunities.

This model provides access to high-quality deals, diversified exposure, and a steady flow of insight from experienced managers. By connecting capital with specialized expertise, it leverages collective knowledge rather than relying on a single viewpoint.

The advantage is early access to innovation and efficient deployment of capital. Rather than trying to outperform specialists directly, his role is to build and manage a system that consistently directs investment toward the most promising opportunities worldwide.

News-Items.com

Hosted by John Ellis

Produced by Dale Eisinger

Transcript

SPEAKER_00 0:00

Hello and welcome back to the News Items Podcast. I'm John Ellis. I'm the founder and editor of two Substack newsletters. One is called News Items, the other is called Political News Items. You can find them both at news-items.com. Our guest today is Daniel Adamson. Dan is a venture capitalist and the founder of Collective Global, a firm that works with a dozen of the world's largest, most sophisticated investors across Scandinavia, Australia, California, and beyond. The firm buys stakes in top venture managers at the general partner management company level, and then co-invests alongside them in their best deals. Dan's firm covers industries from AI and biotech to financial technology, energy, and interestingly, fashion. I wouldn't have guessed that. Dan's educational background is extraordinary. He's a Sumacum Laddie graduate of Yale College, a Marshall Scholar at Oxford University, and a graduate of the Yale Law School. Dan, I'm going to overlook the fact that you weren't the editor of the Yale Law Review and ask you: once you graduated from Yale College, did you go to work at a law firm or did you go straight into the financial world?

SPEAKER_01 1:13

You know, I quickly realized that I was not cut out to be a lawyer. So I actually graduated from the law school, but spent my third year working for a venture capital firm in New York City and just went back to New Haven to take my exams. So you could look at that as an incredibly dumb decision in that I spent all that tuition money and what did I really get for it? On the flip side, I was already 26, been in school for a long time, was itching to get out into the world, knew what I wanted to do, and New York City was just a little over an hour by train.

SPEAKER_00 1:45

Yeah. I would say that the best training for anything is to go to law school because it teaches you how to think, and therefore you're going to be better at anything else you might do. That proven true for you?

SPEAKER_01 1:57

You know, in my case, boy, those those other kids at the law school are awfully smart. And so I think it taught me that I was not going to be the best at anything. I've got three kids, ages uh 11, 12, and 13. My 12-year-old is realizing at the moment that he is not going to play for the New York Yankees. Um and I I kind of had the parallel realization about every professional track uh by meeting the quality of my peers at at Yale Law School. And so I made a pivotal decision then, which has influenced my career ever since, which was if I'm not going to be the best at anything in particular, let me at least try to be the best at partnering and go find the best people in each area. And you know, combine that with enthusiasm as a kind of lifelong learner, enthusiasm for innovation, joy of making investments, and well, you get a company like the one you described.

SPEAKER_00 2:51

So you describe your business employing a network model. Can you tell us exactly? I mean, I think of you know, there are there are hedge funds and then there are fund hedge fund of funds, I guess you would call them. And your model seems to be loosely like that in that you don't have a venture fund, you invest in the best venture funds and then co-invest alongside them. Is that is that more or less correct?

SPEAKER_01 3:16

Yeah. Um the the way I describe it in the simplest terms is just that we are building a better bridge between capital and innovation. It seems to me that if there's one thing, and there may only be one thing that everybody in the world can agree on right now, it is that the pace of innovation is astonishing. I mean, pedal to the metal, sort of back of your head hits the headrest in the car, right? That level of pace. And if you're gonna invest in that way, you are at the cutting edge. And by definition, you're not gonna understand everything that about everything. Best thing you can do is to partner. So rather than model ourselves in a conventional way where we're just trying to have as many investors as we can, do as many deals as we can and hope that they all go well, hundreds of investors, hundreds of deals. We've taken an opposite approach, really, where we we work with deep-pocketed, sophisticated investors, intentionally global, and limit the number of them so that we can really mind meld with them. And then on the manager side, we try to find the best managers in the world, groups like General Catalyst, others that that are household names, and we buy stakes in their top coat. So we are a minority owner of their business, not just an investor in their funds or a co-investor alongside them, those, though we do those things as well. And what you get is a kind of network. I mean, everybody's talking these days. We're obsessed with the idea of taking nodes, connecting them together, and building artificial intelligence. I'm sure we're gonna talk about that today. I hope we have one of the first actually interesting conversations about AI in some time because it feels like it's all anyone wants to talk about. And I hear the same things all the time. So we'll try to say some different things. But one thing that um we do differently is we're building a network, a kind of neural network, if you will. But rather than those nodes being neurons or chips, they are organizations and people who are expert in what they do. And they're from all over the world, and they know areas like energy and you mentioned fashion, right? We've got a consumer business that we back. We we we look at biotech, we look at fintech, and we work together not just in the way that people chat when they're at Davos, you know, everyone's puffing out their their peacock feathers and and trying to be the biggest organization in town. Our strategy is different. We actually are in business together. So we're not just meeting at a cocktail party. We own each other's businesses. That creates a permanent connective tissue in this sort of neural network. And then because the network is built out of people, not chips or neurons, we can actually get together. And that's when it comes alive. So every year we host our company, Collective Global. It's actually hosted by one of our asset owner, pension, or sovereign partners. Last year it was in Stockholm, and we had our major event at the hall where they give out the Nobel Prizes. This year it'll be in Nice, France, next year in Sydney, Australia. And that's when this kind of, I won't call it it's not an artificial intelligence, but this kind of organizational mind comes together. And it helps us all become better thinkers, sharper thinkers about what's happening, especially in innovation.

SPEAKER_00 6:40

So we have to talk about AI. The three topics that I think top of mind for investors, obviously, AI is one of them. Robotics, I saw it was described as the largest addressable market in human history. So there's robotics, and then there's another kind of code, which is genetic code. You're active in all three of these areas, but let's start with artificial intelligence. We have, you know, we the the discussion is doomsday or unlimited promise. You argue that that's not the way to look at it. Tell us why.

SPEAKER_01 7:14

Yeah, I mean, first of all, I I think if you ask 12 experts on AI what they think, you'll get what's the joke, 13 different opinions. Right. And nobody really knows because the pace of change is just so incredibly fast. I will make a promise here that even though I'm not a STEM person per se, I was a philosophy grad student and then a lawyer, I think that actually can help us cut through some jargon and talk about uh AI in a slightly different way. So instead of just throwing around words like deep learning, foundational models, reinforcement learning, genetic token, is it a large language model? Oh, then one day it's supposed to be what about small language models and you know, uh multimodal. And my favorite word is hallucination. I mean, what are we really, really talking about here? So I think that rather than having a simplistic doomsday, you know, versus utopian Rorschach test where you just say AI and you see whether people's blood pressure goes up or down, it is possible to have a nuanced view of what AI is good at. We are spending roughly 1% of gross world product, a trillion plus a year, on the stack of what is AI, from energy through to data centers, through to chips, through to foundational models, through to apps, through to agents, ultimately, in some cases, as you mentioned, through to robots and actual experiments, we're spending a ton of money on that. You would have to go back to more than 100 years ago. I think some historical context is helpful, if you'll forgive me. Like when was the last time we did that as a private sector? I know we spent 3% of GDP on putting a man on the moon, but when was the last time that the world just organically decided through the minds of investors and business people to put this much into one coherent area of transformation? I would say, you know, the call it 30 to 50 years where we invested in railroads, electrification, and agricultural mechanization, which were obviously three linked technologies. The transformation that came from spending 1% of gross world product in those three areas over the call it roughly second half of the 19th century was incredible. Imagine grabbing somebody from the year 1800 in America when 90% of people worked on farms, and telling them that in 2026 we were going to have 2% of people working on farms, and unemployment would be less than 5%. And then asking them, what's everybody gonna do for a living? I don't think, I mean, they certainly wouldn't come up with management consultant or podcast host or electrician, right? Those were not on the menu at the time. So I just say that because the amount that we don't know compared to the amount that we do know is so vast. What we don't know is an ocean, what we do know is a drop of water. We can say some things about that drop of water, and I'm, you know, I think we can take out the microscopes and say some interesting things about it. There's a lot that we still don't know. So jargon's not going to help. It just sells news stories and gets people anxious and maybe drives up valuations on certain investments. But there are coherent things that we can say about AI. Certainly we can expect transformation that is at least equivalent to the transformation that we saw coming out of that tri-pronged era of innovation with railroads, electrification, and agri agricultural mechanization that I talked about, in which the world became the one that we know today. And the difference here is it's going to happen much faster. It's already happening much faster. And it's happening in ways that will have some predictable consequences and some second and third order consequences that are very difficult to predict.

SPEAKER_00 11:15

I saw a brief clip this morning on YouTube. It was one of the leading AI algorithm writers, I guess you would call them. And he was saying that he recently attended an AI conference and was himself astonished by what was projected to happen, not in the next six years, but in the next six months? I wonder, as somebody who invests in funds that invest in AI and that, you know, co-invest on a number of those deals. Is the pace of change AI so extraordinary that it sort of freezes you, if you will, from from investing? Do you have a pretty good idea of what you want to invest in and there's a rationale behind that?

SPEAKER_01 11:58

Aaron Ross Powell It's a little bit of both. I think you need to be humble in recognizing that we don't know a lot, like I just said. But I also said that, and I made a promise that we'd try to have a conversation that was coherent here about AI, which is rare these days. And let's start with a theory of what kind of intelligence we're building. I think everyone's talking about we're building AI, and they they feel like that's enough. Oh, I just said we're building artificial intelligence, as if that's a sufficient description. It's not a helpful description. What type of intelligence is it? What is it good at? What is it not good at? When is it going to be good at the different things that it could help us with in society? So let's try to unpack that. When did we all first hear about AI? Well, I think it's when Deep Mind beat Kasparov at chess. Right. I forget the year, but that you know chess was the first example. Then AI had a win again in a more complex game, a Go, where beat the world champ Lee Sadal. If you put those two data points on a chart, and now I'm going to put a third one up there because I've just I can report an a very interesting breakthrough, a company that I serve on the board of called Lila Sciences, where they have crafted an mRNA molecule that in vivo has an order of magnitude better results in fighting certain cancers in the human body than Big Pharma has come up with. And they did it with AI. What do those three data points have in common? I think they're examples of where AI is flourishing as a type of intelligence. They have in common that they are all based on rules. Chess has rules, Go has rules, DNA, mRNA have has rules. Secondly, you've got immense amounts of data, just brute force, more data than a human being can hold in its mind at any given time. So the the analogy I like to use is if you've got 10 light switches on a wall, they can be in a thousand and twenty-four different arrangements. If you've got a hundred light switches on a wall, there's more potential arrangements than there are atoms in the universe. Now think of a chess board rules. You got huge amounts of potential formations of all the pieces, and you're quickly, you know, at more than the number of arrangements than there are atoms in the universe. Go was supposed to be impossible for AI because it was an order of magnitude or more complex than chess. Well, mRNA, of course, exists, and RNA exists because it's how nature chose to encode information. And the combinatorial landscape, to get fancy about our language here for a second, that is created through four base pairs interacting in almost an endless variety of ways and then coding proteins, which can then express in an almost endless variety of ways, is shocking. It's important, and it's something that humans are just not very good at doing. We have made great progress. I don't mean to suggest that the last 450 years of science post-Bacon and Descartes and others in the early 1600s hasn't been incredible. But the pace at which we can conquer this particular type of problem, one with rules, one with vast amounts of data, and one with huge combinatorial landscapes, is is now newly solvable. So what's in that space and what's not in that space? Well, I would argue that certainly this is going to be the century of biology, right? Biology happens to be really complicated. And so it fits what I just described to a T. So we've done a lot of investing in enabling AI to access biological systems. They often do it through robots, because robots can absorb much more data. And biological systems are inherently incredibly complex. I think there are some non-biological scientific systems, like how do we replace the, what is it, seven rare earths that China's given us trouble over in terms of their use as catalysts in different industrial and other military processes? Well, that's the sort of thing you need to run experiments on. And turns out nature is complicated. You can't just think your way through it. You need to actually go run the experiments, onboard the data, analyze the data, synthesize the data, and then run the next best experiment after that. So those are some areas where AI can be helpful. I'll give you one more just because I don't want both of my examples to be focused on science. You know, AI gets a wrap these days. You talk about the dystopian angle, it's going to replace all of our jobs, et cetera. There are also some huge humanitarian crises that don't get discussed enough in the world, in my view, where AI could, if allowed, be incredibly helpful. The UN is a UN statistic. There are 154 million orphans. That is a shocking number. It's 2% of all humans alive that are under age 18 and don't have parents. Um it's something I know personally because my wife and I have been very active and adopted a kid. And uh the reason there are so many is well, it's very complicated and it's it's it's geopolitical and it's it's we just don't live in a fair world. But one other reason is that there's countless languages, tons of paperwork, and 154 million is a big number. Well, what's AI good at? Reading and understanding any language, following any legal or bureaucratic process, and capturing and processing enormous amounts of data. Think about the amount of money we've spent on dating apps to match, you know, Joe and Sue, who live down the street from another, one another in, you know, in Urbana or in Columbus or in Atlanta. How about we spent a fraction of that matching kids with families that want kids? I'm not saying that it's such an easy problem, but it is a problem that AI could help with because it is a large combinatorial space that is rule-based with a ton of data.

SPEAKER_00 18:07

I wanted to talk a little bit about Lila. I'm just fascinated. How did it, presumably you'd never heard of it like three years ago, now you're invested in it? How did how did you come to know about it? What convinced you to invest in it, et cetera?

SPEAKER_01 18:22

Yeah, I certainly hadn't heard of it. It was in stealth mode. But one of our manager partners, General Catalyst, who I mentioned earlier, and we've got now six manager partners. Collective Global, my firm, is is is relatively new. I've been doing some variant of this kind of investing for 25 years, but we decided to focus on innovation in 2023. I think our timing was good. And we backed General Catalyst alongside McKinsey, JP Morgan, and Amazon. We were one of their backers at the top co level. And they've been wonderful partners. And turned out that, if I understand it right, the biggest safe, which is a particular type of early stage investment that GC General Catalyst had ever done in its history was within a company called Lila Sciences. So immediately my my ears perk up and they say, You want to see the future of science? Go to Boston, check out this lab. And they send me a picture of Bill Gates, you know, practically fogging up his glasses. He's so excited about what he's looking at as he's in a lab coat in the Lila Sciences facility. The most interesting thing about the photo is that nobody is touching anything. So if you think about the way science has been done for the last several hundred years, right, since the age of Louis Pasteur, right? We had grad students with pipettes or the equivalent, moving something from Petri dish A to Petri dish B. Then they spill their coffee on it. Then, you know, the their their thesis advisors argue with them about it. Then they tenured professor publishes something on it. Then they argue with a different tenured professor about whether they were right or this other tenured professor was right, and did they falsify the data? And it's it's a mess. Human science is a mess. It's also the most productive thing we've ever figured out how to do. So don't I don't I'm I'm not demeaning science, right? I'm just telling you, it's a messy business. It takes a while. Now picture instead the inside of one of these semiconductor slash chip manufacturers, you know, with the clean hazmat suits and the 30-foot ceilings in places like Taiwan. It's just incredible. Imagine we were applying that precision to the biological sciences, to the material sciences in other domains. Well, that's what Lila does. And so I could appreciate that the entire wheel of science itself needed an update, and that putting AI with the human in the loop at the top of that chain, and then using robots that could ultimately synthesize their own equipment through 3D printing to improve recursively the quality of the experiments that they're running, run them 24-7, they don't spill coffee, they don't make a mistake about which petri dish they just put the sample in, they suck up orders of magnitude, more data, they process it faster, and because they can hold in their mind at one time a much larger combinatorial space, like the space of all mRNA to fight a particular type of cancer or other disease, they can progress much faster. And so this is one of those areas where whether it takes six months, as you said, John, or or or or six years or even twenty years, if you're a big asset owner, that's what I call the sovereign's pensions, insurance companies, and others that that I work for really, that I help connect to innovation, it almost doesn't matter if it's six months from now or six years from now. You've got liabilities to your pensioners that last 10, 15, 20 years, sometimes longer. If these disruptions through the sciences or in other areas, we all witnessed the SAS pocalypse recently. Which we could talk more about later, where private credit got thrown under the bus. If these changes are going to happen in the life cycle of your liabilities as a pension or sovereign, you need to be worried about them. And it is evident to me with, and I don't have a crystal ball, but I do have call it 12 to 24 months advance warning on what's happening because of the investments that we're making that are in small privately held companies. This stuff is coming not just for new discoveries. It's going to change the other 98% of your portfolio in ways that aren't always positive. So you need to be eyes up, chin up, thinking hard about innovation today in a way that five years ago you could maybe justify not having a venture growth portfolio. Today, I don't think any CIO in the world can hold their head high unless they really understand and make an effort to understand the pace of change.

SPEAKER_00 22:55

Aaron Powell Just as an example, the impact of the change that is coming on other parts of one's portfolio. Are there specific sectors that you think are most at risk or specific companies that you think are most challenged, I guess is the polite way to put it?

SPEAKER_01 23:12

Aaron Powell Well, yes. I actually think, for what it's worth, that this recent SaaS pocalypse, as I called it, software as a service getting hammered. Uh and in particular, I think it was 40% type drops in the listed private credit asset managers like Aries and Blackstone and Blue Owl was an overreaction, personally. I think a lot of these SaaS businesses are good businesses. They got hit hard because, as you know, John Claude Code came out, and all of a sudden people are worried, well, maybe I'm not going to need to buy Salesforce or some other SaaS business. And if you're an Aries and you're lending to them and you've got a duration on your private loan to that company that that goes into the 2030s, and all of a sudden you're thinking that the equity might be wiped out, and maybe even they won't be able to cover all their debt in 2032. Well, that starts to be a problem, and the market, I think, overreacted, priced that in quickly. My particular view, which might be wrong, might be right as to why that was an overreaction, is that, you know, let's take ourselves back in time to when YouTube came out. When YouTube came out, it floundered briefly because everyone thought, why do we need more video? There's tons of high-quality video. You can stream it this way, you can rent it that way. Turns out we had a lot less video than we really needed, as my tw you know, fifth, sixth, and seventh graders' behavior patterns every day will testify to. So maybe we just have a lot less code than we really need. And so this reaction to say, okay, now companies that provide code, software as a service, are going to die, I think has been overstated. So that happens to be one I'm not worried about, but you are not as worried about. You asked me about some areas that I am worried about. So here's a big one. Like what happens when self-driving cars actually turn on? I mean, we we all believe that that's gonna happen, right, John? I mean, it could be some of it's already happening, but i i i i is it gonna be five years, ten years, fifteen years when this horrible corridor that you and I deal with between Fairfield County and New York City starts to flow like like water instead of like molasses because it's self-driving cars. Well, maybe your public infrastructure bets, which you thought were the safest part of your institutional portfolio, your light rail investments in cities like LA or Toronto, which were built around the car that you thought were safe, maybe they're not so safe. I'll give you another example. Your commercial real estate plays in the life sciences, where you thought, oh, well, if I if I have if I have wet lab space near research universities, boy, that's probably going to be a good 10 to 20 year investment. Well, I've just given you a picture of what science might look like, will look like in my view, in five, 10, 15 years. And turns out robots don't like nine-foot ceilings or 10-foot ceilings. They need 20-foot ceilings, depending on exactly what's happening in the space. So all of a sudden, what seemed like a safe bet isn't. And to me, that's the it's both the returns you can get from innovation, but also the insights that you need to have, the kind of early warning system, if you will, that's gonna predict the seismic activity in other parts of your portfolio. I don't see people talking about that. Just like I don't see people talking about the type of intelligence that AI is actually smart at. I mean, there's tons of things AI is not good at, by the way. Like I wouldn't pick it to be my sports agent if I were a good athlete, right? It's not gonna negotiate a deal and understand all the moving parts in a complex human situation. But it's very good at these kinds of combinatorial space thinking that I talked about. Similarly, there are industries that won't be impacted. I think live entertainment is fairly AI proof. My kid will still be able to root for the Yankees in 20 years, even if he won't be able to play for them. But there are lots of areas that will be impacted.

SPEAKER_00 27:08

Aaron Ross Powell There's a group out in California called Rethink X, and they did a paper on transportation as a service. And so in this model that they built, the cars are electric, they're self-driving. You might own one, but you only use it an hour a day, so you could rent it out three or four hours a day. The electric car has 128 moving parts. Uh combustion engine has, you know, a thousand moving parts. So maintenance of that car is, let's put it this way, much less than it would be for a combustion engine car. Because it's self-driving and presumably safer than current situation, insurance costs will go low. That kind of disruption if you're looking at transportation, is that something that that you're thinking, okay, this is really going to be totally different, and how do we invest in this space? Or I guess you know you'd just say transportation's too c you know too too outside of our space, so we'll leave it be.

SPEAKER_01 28:10

Well, we we may not be, uh part from our investments in self-driving cars, which we have, we may not be focused on big public infrastructure type investments like the light rail or or or commuter rail that I I talked about a moment ago. But our pensions and sovereigns are, and those are their biggest bets sometimes. Multi, multi-billion dollar infrastructure plays that they thought of, oh, well, this will give me a five, six, seven percent yield consistently for the next 25 years. Losing steam is a much bigger deal than missing out on the next trillion dollar startup for them.

unknown 28:42

Right.

SPEAKER_01 28:43

Right? Because they're gonna own a small percentage of that startup, but they already own a big percentage of those infrastructure plays. And I think what we need to get comfortable with, and we started the conversation by talking about the sheer pace of change. Talk about electric cars, right? You you press the pedal to the metal, so to speak, in a you know, an electric car and the torque throws your head against the headrest. That's the pace of technological change today. And outside of wartime in human history, we're just not used to thinking about the future being so dramatically different than the present in a fairly short period of time, five to ten years, maybe less. So we've got to be on the balls of our feet, the tips of our toes as investors. And even that will just give us an edge, not uh not a right to win. So, but but an edge is is better than than sticking your head in the sand in this moment. So I'm hoping that more CIOs will pay attention to the innovation economy and think about these second and third order consequences for what they thought of as the quote unquote safe part of their book. You know, the the deeper questions here that that you know maybe maybe we end with are, you know, how is this gonna even change the role of a CIO, of a decision maker as an investor? You know, it's in in every era we humans have thought of ourselves as the protagonists. We're the ones driving history. And we're now entering an era in which that is, I would say at best uncertain. And to me, again, you know, you can look at that and and just get depressed, or you can look at that and get really excited. And probably you can look at it and feel both ways at the same time. But I'll tell you what makes me excited about it is that you know, we don't really have a a tradition that helps us think about what humans can offer if it's not being the protagonist, if it's not being the smartest, the toolmaker, the species above the others. And and if we are no longer the smartest, if we are no longer the toolmakers, if we are no longer the species above the others, because we we gave birth to it in the form of AI, it's going to force us to ask all these really interesting questions about what does, if anything, make us special? And I think I get excited about, you know, we started this conversation talking about my my family, and you know, we've grown it through adoption. We I've I've also I think the the greatest single act of goodwill I've ever done is that I'm originally from Atlanta and I've allowed my 12-year-old son to be a Yankees fan. Just think about that for a minute. Right. What that's real personal sacrifice that involves. But so so like we've got to reimagine what it means to be a human in the context of not being the smartest, of not necessarily being the most efficient, most productive protagonist uh on earth. And I think that's gonna lead to, I hope, a revival of of certain types of arts and culture that I know we both care deeply about. And uh I'm excited. I'm excited both for the economic side of things and for this non-economic side.

SPEAKER_00 31:50

Aaron Powell The question people always ask people like you is you're you're obviously plugged into a network of extraordinarily intelligent people, but you also read a lot. What do you read?

SPEAKER_01 32:02

As you know, John, we trade books. So I'm working my way down the book list you sent me. But ever since I discovered audiobooks, and I can read them, read them as I'm falling asleep, I read them while I wake up in the morning, I read them when I'm on the train, I read them when I'm between meetings. I now get a chance to read more than I have since college. I felt really guilty about that for a while. I thought it was somehow cheating. And then I remembered that, well, for most of the call it 30,000 years that human beings have been telling stories, give or take, it's been oral, not written. So I thought, okay, I could I could Homer was mostly, you know, an oral tradition. Ultimately somebody wrote it down, kept it in a in a medieval monastery so that it could be passed down to us. But so once I got over that shame of being an audiobook guy, I read pretty much everything, John, and more nonfiction. I I feel like I've I've come to the end of what I can read in physics and uh life sciences because I'm not smart enough to read the scientific papers, and all of the popular literature has just become repetitive for me at this point. So I read a lot of history and I just try to find stuff I've never heard about. So I just finished a wonderful book called Sea Peoples about the history of Polynesia, and then I went there with my family. It was so great. So I'm just looking for areas I don't know about, and then I'll click on that.

SPEAKER_00 33:23

Aaron Powell We've reached, uh I think uh we've taken enough of your time here, but I wanted to ask you one last question, which is you're invested in AI, you're invested in companies that do code across artificial intelligence, robotics, genomics, but you're also invested in fashion. So the question is why?

SPEAKER_01 33:43

Aaron Powell You know, it's funny. The investment in my portfolio that my kids are the most excited about is not Lila Sciences, all respect to them. It's not vast data, it's not any of these big, you know, unicorns in the AI space. It's the fact that we own indirectly a bit of Justin Bieber's fashion company. And I asked my 12-year-old, as part of my diligence for the investment, John, I said, What do you think? Justin Bieber, fashion. He said, Dad, Biebes is back. Beeps is back. Beebs is back. And so I thought, okay, I'll take that into consideration. And then he asked me a question, which I thought was, if I can brag on my own son for a minute, I feel like that's allowed. He asked me a question that I thought, okay, this kid could have a future in venture capital. So he said to me, Dad, does he own the company? Or is it like one of those Jordan Nike things where he just owns the brand? Well, great question. He owns the company. And so then my son said, You better, you better invest. Piebes is back. He owns the company. Go for it. So we did. So anyway, I mean, it just to come to your question, innovation, it's an it's an easy time to be reductivist about innovation and just think all innovation is AI. It's not. Like we're also putting nuclear into space and we're investing in Justin Pieper's fashion company, right? Like it's it's the one thing AI is actually not doing is originating truly new content at the frontier of any field yet. And that includes fashion. And so if we can be there with the best partners in the world who actually understand these things that I certainly don't, because I started this by saying I'm not the best in the world at anything, but I try to be the best partner. If we can be there with them, sourcing and then executing on, and then hopefully adding value to the best investments in innovation, it'll span the whole waterfront from AI to fashion.

SPEAKER_00 35:35

I think we've found the uh headline for this podcast The Beebs is back. Uh Dan, thank you very much for your time today. Uh we look forward to speaking to you again in the future. Pleasure, John. Thanks so much.