Alec Litowitz, Founder of Magnetar Capital and QStar Capital

A lot of people think about the left tail, and we talked about that already is, I don't care about small losses, where I get feedback back, I learn, and I go redraw. I care about catastrophic losses. But let's think about the right tail, because anybody who's very familiar with decision-making could say, "Alex, it sounds like you're saying say fail or fail safe." There's words for that. But here's where mine differs a little bit, which is that I also believe that when you're experimenting, and this will be very, very well understood by investors who sit around and might be listening going, but wait a minute. Power law, venture. There are certain times where I don't know the answer, and so an opportunity pops up, and should I take it, should I not? And my answer is really simple. If it's not that expensive, meaning if it doesn't wipe you out, so I'm out of money, I have no more chance to iterate experimenting. And it's irreversible. You should take it. Hello, this is Dean Kernut, and welcome to the Alpha Exchange, where we explore topics and financial markets associated with managing risk, generating return, and the deployment of capital, and the alternative investment industry. My guest today on the Alpha Exchange is Alec Liddowicz. He is the founder of Magnetar Capital, of Q-Star Capital, a philanthropist, and now an author. Alec, it's great to have you on the podcast today. I appreciate you having me here. I'm a fan of the Alpha Exchange, and I was laughing thinking about what you called it, because I think I spent 30 years hoarding as much Alpha as I could, and here we are having an Alpha Exchange. So I hope I make the transition well. Yeah, well, listen, we're going to talk about markets. We're going to talk about your book, the adaptability quotient, really fascinating. And I would say timely read for an environment in which it would be almost impossible to say the pace of change is not remarkable, and probably accelerating from here. We'll get into your motivations for writing the book, but I think that's a big part of it. Let's start with the investing side of things. So you've been in this business for 30 odd years. You founded Magnetar, and I want to say 2005, or is it 2006? It was 2005. Yeah, 2005. And the pre-financial crisis period, so things were about to get absolutely haywire in markets. As you look back on your time in markets, and you think about the characteristics that make for truly differentiated investors, what do you think that looks like with the benefit of so many years of doing this, of seeing so many people, of experiencing so many different cycles of markets and volatility? Is there a thread you would say that ties together the success of investors in this business? Yeah, it's a great question. It makes me look back and realize I'm somewhat old, because I have that 30 years of having seen lots of different regimes and working alongside lots that incredibly talented people. What I would say is probably that I think most people think about this not quite right. I think a lot of people would probably have an intuition that it's an IQ contest, and that people think that the highest IQ would win out of the time. And I think the analogy that I like to think about is from the time I spent between where I was one of the original partners at Citadel, to founding Magnetar No 5. I used to do Iron Man triathlons, and you try as best you can to be really good at swimming, biking, and running. And when I started doing open water swims, the goal was always your stroke. Think of IQ as your technique and how good a swimmer you are, and how big your engine is. And the reality is if the course is straight and the wind isn't blowing and everything's perfect, and it's a very stable environment, then probably the person with the best stroke and the biggest engine works. That's sort of your IQ. But I've been in a lot of Iron hand swims and half Iron Man swims in the open water, and it is rarely calm stable. There's often boys. You have to turn around. There could be multiple boys. The wind and the current could be moving. And so you're always stuck with this question of how do you adapt to that? If the wind and the current is blowing, you have to do something not natural. You have to lift your head up. And when you lift your head up, you interrupt your stroke. And why don't people like doing that? Because you've been trained to keep your head down and be efficient. But you can be incredibly good. You can have an incredibly high IQ and just swim faster into the wrong direction. When the world is changing really quickly, you alluded earlier to the fact that this world is changing faster than maybe we've ever seen. In my time over 30 years in different periods, I've realized IQ alone doesn't cut it. That's actually not all that matters. What matters a lot is understanding that there are different conditions. And I like to think about three types of conditions that you have to swim in and not all of them are perfect for someone who has the highest IQ. The first one is a world that is more stable. I think of that as a world of risk. I think probably a lot of the conversations you have on the off-puxed change rightly so are about the world of risk. And that is a world I think a lot of your listeners are familiar with where I describe it as I know the possibilities and I know the probabilities. And that's a world where you can do expected value, mean variance optimization. Sure, you have to think about fat tails and other things. But we can make bets and play that game. And that's a great environment. And that's where IQ helps a lot. It's calculations and otherwise. Then there's another set of the world, which is the opposite side where I don't know any possibilities and I don't know any probabilities. And that's Black Swan's. Nessim Telev has written some phenomenal books on that. And that is survivability and convexity. I think the most important type of environment is the one that's neither of those. And it ironically is the one that gets the least written about. And it's the one that we live in almost all the time. And that is a world of uncertainty. It was well written about by a gentleman named Frank Knight, often it's called "Might end with a K uncertainty." That's a world where we know the possibilities, but we don't know the probabilities. And before someone sits and goes, "Well, I've not heard of that and therefore how much can it apply?" Examples, there are easy ones are, you know, higher someone. I know that they might work out they may not. Could be mediocre. I know the possibilities. I don't know the probabilities. Go meet someone for the first time, date someone for the first time. It could go great. It could go terrible. Maybe it's okay. A lot of life is that way. And a lot of markets are that way. And I think they become the most crucial times for the markets because a lot of the money that gets made by the best type of investors are ones that can navigate all three of those environments. And within uncertainty, the job is to resolve it. And in my career, while sometimes in a world of risk where the market has the distribution, the market has probabilities, I just happen to think there are different ones. Typically the edge is smaller. There's a lot of people looking at it. I think the great investors are the ones that can sit and say, "I know when the world is like a risk world, and I can survive and prosper in that by having better estimations than others. I want to prepare for a black swan. And so I need to survive and have some convexity and understand where my model may be wrong. And I need to be able to survive those kind of outlier situations. But also, and more importantly, the biggest money gets made when there's periods of uncertainty where something's changing. And industry, maybe we'll talk about it later, but energy when hydraulic fracking and horizontal drilling came along rapidly changed. We've seen the production function of lending change in the great financial crisis. Now we're seeing AI. When you get to these periods and you have uncertainty, the big money comes from resolving the uncertainty, going through a process and knowing how to take that uncertainty, ask the right questions, test appropriately, get feedback loops to make better decisions. So my end answer to you is that it's not just strictly IQ. It's the ability to survive and adapt during moments of change that we see as an investor in the market very often. And capitalize on those situations, because often the conversion of uncertainty into risk is where the biggest, biggest money gets made in my opinion. So great if you can make money in risk, great if you can survive black swans, but really great if you can do all three and prosper when everybody else is wondering what's going on and you can actually resolve what's going on. If you go back to let's say the global financial crisis because it's in some ways the biggest test of survivability for banks, for investors, we saw such incredible consequence to potentially miss sizing your exposures to being in the wrong instruments. So those were career ending in some ways or firm ending events. And yet perhaps we look back on these times with more clarity than they really had, but by March 09, the market turns. And so my question is around the folks that might have gotten that risk event right going into it, who saw perhaps the systemic uncertainty, the systemic risk of the banks and then being able to ultimately pivot towards something. And maybe it's not exactly March 09, but something where you finally see the government is putting such weight and capital behind this thing where failure is not an option and that it's ultimately going to pivot. Is that some example of this complex pattern recognition or seeing things change from your perspective? I'm just trying to understand it a little bit better. Sure. I think it goes to what is a Q or adaptability quotient of my eyes. And I often think of as the most important part of a Q, even though it's got sort of multiple pieces to it, three pieces in particular three phases, I call it one of the most important ones is the first one, which is metacognition. Very often people forge their identity with a thesis, especially if you're right. So you go into the crisis, you have a differentiated opinion, you feel like you are investigating and and getting feedback loops from reality and you're just sitting around going, I don't think people see what I see. It's incredible. I have these data points I'm testing it probing and nobody sees what I see and you turn out to be right. It is very easy at that point to make that your identity, that your identity was, you saw this problem and you were right. And what AQ teaches you to do is to have strong opinions weakly held. You can have conviction, but how you hold it has to be weak and it has to be provisional at all times. So it is this razor's edge, as you know, being a great trader is sort of like that confidence and humility, like I'm confident enough to put on a trade. I have enough humility to know constantly probing, where am I wrong, where am I wrong, where am I wrong? I'm not trying to prove that I'm right. I'm trying to prove I'm wrong. So the smartest people were able to sit and go, I have data points, I see what's happening and then constantly in a state of revising based on new feedback loops. I have a phrase in my book that I say, it's better to make decisions right than make the right decision. So at any given moment, I'm not trying to have made the right answer. I don't care what my answer is. I care about my process. My identity is tied to my process of adaptability. I'm in a constant state of wanting to update. If the data says no, then I don't update, but I'm not in a constant state of wanting to be right. Nobody's ever right. I like the Voltaire quote, which is uncertainty is challenging, but certainty is absurd. So at the end of the day, the great investors back to that and what people who played both sides did it well going in survived and come out. Are people that are in a constant state of questioning and even when they get it right, they know that is a provisional. I just got something right that moment in that environment. But if the wind changes or if the current changes, I'm in a constant level of going, okay, is my new model right? Or do I have to go and update that model that just won that last environment, that environment's changing again? And I'm going to be honest, I think that's why this world is really difficult. We're undergoing, maybe we'll talk about later, multiple changes that are epic. Now I talk about that in the book. Later, maybe we'll talk, and if you want about both the fourth industrial revolution and the second cognitive revolution, I talk about it. So there's so much change, it's a very, very good question. How do you constantly get it right? And the answer is you won't, but you can put yourself in a position to be more right than not. Luck is infatuated with the efficient and the efficient people that are great investors are in the constant state of operating in a certain way. And unlike IQ or EQ, I think it's a learnable skill. And the learnable skill is, no when your model no longer is touching reality properly, no how to update your model, and act before the market forces an action on you. That's what I think the key to a great investor is. Well, as I read the book, this strong opinion weekly held, I said this to myself so many times, SOWH, and at least at face value, it feels a little contradictory. And I wanted you to dive into it a little bit more because if I have a strong opinion of convicted, it feels to me like I should have the ability to let that go very quickly. That my prior is strong and that my updating is kind of up against that strong prior. Let's explore that a little bit more. Yeah, I think it is a challenge, right? I understand the language on the son of a Sussan Quanilist, but my mother who, before she became a psychoanalyst, was a linguist, so I'm pretty careful about language. And I could see the concept of aren't these against each other. So let's unpack it in a way I think, hopefully it'll help you understand. So if you go through my process and maybe it'll help for listeners to understand the three of them because that'll tell you what where does a strong opinion come in and where do you start to test it? And so the first phase of building your adaptability quotient is metacognition. And that's important for a variety of reasons. A lot of people's decision processes don't start with that. But the reality is if you're examining a system, if you're examining the facts on the field, reality, et cetera. You are a system. If you think you're seeing reality, nobody sees reality, you bring biases, you have your own process, the functioning of how a brain works, I go a little bit into the book without going too much. And the first important thing is what I call adaptive optics, which is you have to understand your lens and how when you see facts, you're seeing them with a bias. So if you optimize for facts that don't correlate with reality, you can be perfect. You'd have a great swimmer. You're just going in on the wrong direction. So the first part is clear your mind. Don't walk in with a bias. It's a bit of a beginner's mind type of an environment. The second phase is simulation because if the world's changing, you're seeing these facts. And your old model's not, you're sort of, you know, we're all feeling this right now. Wow, things are changing. What does that mean? Is that going in this direction? It's very easy to feel confused. And the first thing you have to do actually is put aside your old model and turn around and simulate a bunch of possibilities. And this is where we get to strong opinion weekly held, which is the goal of the end of the second phase is to, if the first part of it was what's your relationship to yourself, the second part is what's your relationship to possibility? What are the possibilities? What could possibly explain the things that I'm seeing? And you don't get to leave things out. You don't get to come up with a theory that explains two of the four things you're seeing. It's got to explain them all. And there's a word for that, which is abduction, which is a lot of people in venture and other places talk about first principles. You break things down, you start noticing patterns, and you come up with what a strong opinion weekly held is. It's a induction to the best possible answer. And so the end of phase two is not certainty. It is just given what I'm seeing. What is my best hypothesis? Okay? The reason why you hold it strongly is because you're going to go test it. It's just the best among the possible answers that you think you have, but it's provisional. That's the point of phase three. So the strong part is that you are willing to go from there to actually experimenting. The weak part is, I'm not tied to it. It's the best version of what I could see as an explanation, but I'm wedded to this loop, not to the answer that the loop has in this given moment. And so the strong opinion is, I could have called it the strongest opinion. It's the strongest of the ones that I have. Now I'm going to go hold it weekly when I go test it. And phase three is, I'm going to go clash it with reality. I'm going to go experiment. This adventure world is a minimally viable product. Then AB test. Let me figure and must is testing spaceships. I'm going to go see if this theory holds. I can have heat shield and I can have places where there is non places made of metal, places made of a carbon fiber composite. And I'm going to see which one works best. I have a theory, but I'm going to go test. And then you're not done at phase three because you get feedback. And when you get that feedback, you go back in, revise your hypothesis. That one that was held strongly, you revise it instantly. And now it's stronger and you go test it. So it's just this loop. All it means is in phase two, there's a trade-off between exploring and exploiting, which we can go into. I'll leave that if you want to go into it. But ultimately, the goal at phase two is to come up with the strong opinion weekly held. And it means that you have to have it. It is the best one you've got, but you're not wedded to it. What you're wedded to is experimenting and testing it and being open to whatever you feedback is. You don't get to decide the feedback. The world decides the feedback. Yeah. And we'll talk about this in the context of markets and some of the investing that you've done. But one of the tensions that you describe in the book is resisting the temptation to decide too early. This beginner's mind can be an advantage. Sometimes expertise is almost a disadvantage. So going in there, and just with a beginner's sense of exploration, but allowing yourself to take it all in without necessarily deciding too quickly, there's got to be some tension there between that, which is times on your side versus the world's moving fast. Maybe your competitors are moving fast. Maybe just a little bit on that. I know we'll touch on that in the context of some of the specific investing you've done. Yeah. Look, you're highlighting very well the tensions that are purposely present in the world and they're present, of course, in any design of a decision-making apparatus. And so it's good to highlight them. Let me analogize for the listeners, because let's take something abstract and make it really, really concrete. When people are going out to a restaurant wherever they live, or they're going to go on a trip if they're going to travel somewhere, they're in a constant state of this struggle between explore and exploit. It's this question of, well, I heard there's some new restaurants. Should we go to that new one? But we have the ones we love. So I go look at the new ones listed in a magazine. I'm like, maybe that would be a good one, but as good as the one that we love the best. So you're exploring. And then at some point you make a decision. We're going to either go to the one we love or we'll try the new one. That's exploiting. Same thing when you travel. Should we stay at-- should we go to this city or that? Do we stay at this hotel or that? Most days, people are deciding, do I keep researching or do I stop and exploit something? So there is a tension that exists. And for people like me that were born into a family of peel the onion, peel the onion, peel the onion, a little bit more intellectual. And if you're risk averse, there's this desire to just keep peeling the onion back. Why exploit it unless someone's putting a gun to your head, just keep researching, keep researching. But you're absolutely right that to be a great investor and to be in the markets to be a founder, you realize there's pressure. There's capital, there's time, there's competitors. The world's not stopped and moving. And so there's a function in a way that I get past that. And I talk about it in the book. And the way to think about that is-- and this is the release valve of it, which is, listen, what you're trying to do when you come up with a strong opinion weekly held is to avoid when you go experiment things that stop you from learning, effectively that kill you, that are knockouts, or maybe for your audience. You want to avoid a very big left tail. but not all. failures are big left tails. So what I try to do is think about what paths that I could go test when I go test it would eliminate my ability to continue testing it. Those are not viable because I lose my chance to resolve the uncertainty through learning and learning. And I call those typically and the worst type of errors are without going too much detail, type two errors, where I don't think there's a signal but there is. That's Blockbuster saying streaming. Yeah, I don't think it's that big a deal. Those things kill you. Type one errors, where which is sort of the smoke signal goes off but there isn't a fire. That cost me money. It cost me time to do the experiment. But I get to go back and relearn and revise. So the way that the insight, the answer is to where you stop and where you stop exploring and going, exploit and experiment with whatever the best version is at that time is you try with a great partner, play Devils Advocate, have a steel man army, talk about things with people, think through things, come up with the best answer you can that doesn't kill you when you go experiment. At once you've done the best job you can of avoiding the really, really bad left tails, making other errors, those aren't errors, those aren't failures, those are just feedback loops. They make you better, stronger and get you closer to the answer. So I'm completely fine. A lot of people in the market and the investing will know this as like making smaller investments, probing, those are like probes. It makes me focus more. I go talk to the company, et cetera. So think of this as there is this trade off, there is reality that bangs and you have to go execute. The time to go down and actually start experimenting is when you effectively avoided the most disastrous ones, if you can, and allow yourself to make other mistakes because that is the learning. That's actually what happens. That's how I think about it. And so it seems to me that type one, verse type two, at least in markets, maybe it's an other in life in general is about sizing in some ways. The type two error is really going to come down to some version of catastrophic loss that maybe comes from sizing being too big. It sounds to me like you're a big proponent of, again, experimenting with maybe little allocations to different strategies, just trying things out to try to get them up and running so that they can give you feedback. Is that a way to finger? That's an area for assessment. If you're 100% right, at least in my mind, on the right track, think about it this way. Remember are three versions. If you're in a world of uncertainty, you don't know probabilities. Let's do a poker example. Poker in my mind is more of a world of risk other than if someone's bluffing. That's 19 uncertainty. I don't know how you bluff. If you go play someone new, are you going to bet everything on probabilities and play or are you going to play GTO because you have to resolve the uncertainty. You sit play and then begin to get tells you begin to know how the person bluffs. Then you scale up the trading and investing when you get that answer. Remember, I think the world is more uncertain, especially a world like we're in right now. I'm not going to make huge bets, which might caught off my learning when I haven't resolved the probabilities. We can get into details. Some of your listeners might say there's something called subjective bays where you throw in probabilities, but I can just tell you that there's dangers of doing that in many ways. My answer back to people is you go in, you do these probes, you resolve that information and over time, you start to get clarity around the probabilities. That's when you make your bigger bets. When you're getting true big answers, it's back to the equivalent again of like product market fit. I'm starting to see the answer. I'm starting to get feedback loops. Okay. Now I deploy a lot of capital to scale. It's really that same process. Well, there's a statement you make in the book and I took so many notes in this book because I thought there was some just brilliant insights here. So I want you to just reflect on this since you wrote it. If pattern recognition is the seed of human intelligence, pattern editing may be its highest expression. And I think this ties back to understanding when maybe the rules have changed, when the ground underneath us is shifting, I just love to get a little bit deeper on that is I think a big part of why you wrote the book and what a queue is all about. Yeah, I did write it by the way and for better or worse, I wrote it before AI really came along. So that wasn't written with AI at all. Maybe it would have been a lot faster process if I had had AI, but I didn't get to do that. The point that I was making there about editing goes back to whether and again, we could apply it to investing, but I think it applies to life broader, which is that the most successful people, they don't just learn faster. They let go faster. They're willing to edit their thoughts faster back to the quote from another quote from Ed book. It's better to make decisions right than make the right decision. I don't care if I'm wrong. If I have a group of people that I hire and I could build a firm, the people around the table help me edit constantly, give me ideas I didn't have come up with answers that I didn't have that I've done a really good job of hiring people. I don't care if I'm the one with the answer. Why would I care about that? The goal here is to be closer to reality, to be able to make some reasonable level of forecast. I say forecast, not prediction, predictions of the certainty. This will happen. A forecast is there's a range of outcomes. As you and I both know, the best investors don't sit with spot forecast. They think about a range in a distribution. And even if they can't get its spot forecast, they think about the shape of the distribution. Our entire prior conversation was think about the shape of the distribution and don't get caught in the left tail while you're revising uncertainty and getting probabilities. Same thing is true here is it's one thing to go through a process and humans don't like uncertainty. People don't like it. And certainty is the greatest of things. If for those that are scientific bent that are listening, the father of information, Terry Quadchanan, who said that if I tell you something you already know, you've learned nothing. The most important and valuable thing is the thing that's orthogonal and completely different. It's the one with the most potential value to you. You don't have to agree with all the thing that is different, the otherness. But the most potential for you to learn is the complete opposite and his otherness. So when I say that it's one thing to have a pattern, but sticking with it without the willingness to edit it is to sit and say, I know I've got it. And in a world that we're in right now, that is possibly the most dangerous thing you can do. It's just being able to edit yourself, the willingness to do it, the willingness to separate your identity from being right. I keep trying. I think I say it in the book like make your identity that you're an adapter. I have a section of the book where I talk about resistors, adopters, and adapters in this world of extreme change. Resistors sit and go, well, this is a bubble. I think it'll pass. And I'm going to just stick with my old model. Adopters and that sounds good. Adopters sit around and go, this is something big. I'm going to adopt a new technology. But I like to say that AI as an example of the biggest maybe of these changes. AI is a tool at the interface, but it's a regime change at the system level. Adapters not only adopt the tool, they sit step back and go, what does it mean at the system level? What does it mean that the cost of knowledge is going to zero? What does this change? And they begin to see the world differently. They're willing to edit their prior opinions and prior positions because they see it at a bigger level, at a more abstract level. I get it. I'm going to use this tool. It's an incredible tool. But what does it mean for me, for society, for this position, for their investment? And if I said to you in the past, let's take the following things. Let's take real estate. Let's take energy and power and utilities. Let's take semiconductors. Let's take software. These are on conduct. I got a pretty diversified portfolio. They're all correlated now to one thesis, to one hypothesis. The artifact was, we can talk all we want, and I can talk extensively about my history of portfolio construction and risk management in a different world. That world isn't this world. So maybe now I need to diversify by hypothesis because it means something different. That was the artifact of, if you use the current set of tools to do risk management from the prior world, you have the artifact of diversification. You don't have the function. The function's different. You need the function of it, not the artifact. And so if I sit with my prior opinion, I don't edit it, I have the artifact of the prior opinion. I don't have the function of the updating to, well, what do I do now? What is current now? What matters now? What's right now? And in the markets, as we know, that's all that matters. And I think it's true in life. Part of the reason for writing the book is we're on the precipice of this mattering way more than markets in my opinion. As you talk about updating priors and editing based on the flow of information coming your way, I can't help but think of our mutual friend Ross. Ross Stevens, I had a chance to have him at my MacRomine conference. And we talked a lot about his background. The three of us are all University of Chicago folks and his deep background in Bayesian statistics. And this, and you talk about him a little bit in the book of being really patient approaching things with this beginner's mind, but then allowing being, as Ross says, a little less wrong each day. Trying to get yourself a little less wrong is a large part of a Q linked to a craft like Bayesian statistics in your view? Yeah, I consider myself a pretty Bayesian person. Let me say two things and they're not at odds. The first is that I love being a being a Bayesian thinker, but my whole conjecture is it doesn't work in a world of uncertainty because there are no probabilities. And so you can have a Bayesian type approach, but you have to change what you do if you're making decisions in a world of risk and you go to a world that becomes uncertain and you use Bayesian logic. It doesn't work because there are no probabilities. So the key then becomes first, how do you get the probabilities without blowing yourself up? How do I resolve them? Once I get I can be really Bayesian. I can like a Bayesian, but I have to get them first. Black Swan says, "You're never gonna get them. "It's unresolvable. "Some uncertainties are resolvable. "Some isn't." So being Bayesian is, I'm trying to get there. I want to convert the uncertainty into probabilities and then I can go with things, but there are times where that's harder. What I was referencing Ross in close friend of mine and I have incredible admiration respect for what he's built and how his mind works and otherwise I consider him to be very high AQ. And one of the things that he does that I talk a little bit about in the book is not just his patience and trying to get a little better which I know is true. One of the things he does as well as anybody and frankly I had to learn this lesson the hard way a long time ago. And that is that sometimes you're trying to resolve uncertainty and you're doing it appropriately. You're running through the way I explain to people in the book that I think is right, although I'm adaptive. And so someone listening might make me better at my own process. But one of the things that happens is sometimes you bang up and nothing's getting resolved. You're just not getting any feedback loops back. One of the things Ross does really well is he checks his experiment, he checks his thinking. Am I testing it the right way? But there are times where you're just not getting the feedback loop back. And a lot of times at that point, people have this desire like sunk costs. Like, well, I'm already into this. I've spent a lot of time on it. Let me keep banging up against the wall. But sometimes you have to sit and go, I should probably move on to another problem. I'm not going to go build a business around that, a trade around it, 'cause I can't, maybe someone else can't. I can't resolve that uncertainty. And instead of treating it like risk and going and making a bet, I have to be comfortable with the fact that I'm going to leave that one. I can't resolve it. I'm going to move on to another place where I see an uncertainty and go try to resolve it 'cause I'm not getting any feedback loops here. Sometimes when I'm interviewing people, I say is a cleaner version of, is quitting a cop-outer, it is a diskill. Which is it? There is a right way to quit and a reason to stop when you just can't resolve something and you have no business being in that trade or in that business. And then you move on and try to find something that is. And that's a different kind of patience. So that kind of patience is, I'm going to wait and resolve it and improve and get better. But it also is the patience to sit and go and the wisdom to sit and go, this time, I'm not getting anywhere. And I'm going to cut that capital off and go deploying new capital somewhere else rather than trying to run down a dry hole. - Well, let's go back a ways. We're going to go back to your early days at Citadel. You're a freshly minted JB MBA, I think from you of Chicago. You landed Citadel in the very, very early days and you very quickly take responsibility for the risk of our business. And you talk a fair amount about this in the book in terms of the nature of that business. It's obviously got a lot of asymmetry. There's information aspects to it. But it is hard to scale, very difficult to scale, almost by definition. And so you really approached it differently. I'd love for you to take us through that and then of course, how it relates to this adaptive thought process. - I always laughed when I think back to that time because I was pretty freshly minted. I had spent six months at JP Morgan doing investment banking, and which is a great business. Just wasn't right for me. I need very fast feedback loops. And I went to work at what was gonna be called Citadel. It was called Wellington at the time. We had a name the firm contest and Citadel won at that year. But when I went in there, what Ken was really doing to be honest, 'cause I didn't know what risk Arb was, never traded a day in my life. I mean, I was pretty raw. I got there in February of '94. Ken was worried about, he very correctly called that there would be an M&A boom. And he was worried at the time about convertible bond arbitrage and cash takeovers. So at the time, there are some provisions that are different now. But at the time, your short would get hurt and then your conversion would collapse and you would lose a lot of money. And so he said to me in February of '94, right when I started, by April, you have to be in 10 risk hard deals. I don't know that much about this business or I didn't know anything. So long-winded story, I started researching and really thinking, I had no priors. There was not a book around. There was a pamphlet. I could talk to some Wall Street people and there were some very, very helpful, very kind people to me that I acknowledge in the end of my book from Bear Stearns who helped me a lot. But when I was looking at the industry, I saw the following. I went back, did a little research, tried to investigate the facts on the field and I found out that 92% of all risk hard deals go through. Well, that's pretty good. So if I just play all of them, 92% go through. The market treats the deals like 87% go through generally. So if I play all the deals and appropriately size them, then there's like an extra 5% edge in there because the marketplace is paying you for being a liquidity provider. The long only people sell 'cause they don't know how to assess the deals going through it or not and you can play them all. And that's where a lot of people might do that or you decide which trades. I did something different. How do I analyze this different than other? And I looked at the deals and tried to figure out well which deals break, some break, so which ones break. And it turned out deals break for two real reasons. One is financing, the deal just falls apart 'cause they couldn't get their financing. And the other was regulatory, typically antitrust. And I realized well, when markets fall apart for financing, it could be a deusing credit. It could be when the market bumps, right? When the market fails and the capital markets close up and the financing, I'm not too good. I don't think I'm gonna fresh out of this lost JDMBA to predict that. But my favorite class in law school was antitrust. That was my favorite class, great professor. And what I did was I said, well let me think about that problem because here's the thing about antitrust. When a deal gets announced, it goes to the FTC or the DOJ. There are not experts on these industries per se. So what do they do? They call up customers, competitors. They have to do an analysis of the industry and fear out whether the deal is anti-competitive or not. I can call people. I mean, I love antitrust. I kind of know the right questions to ask. So I started thinking, well wait a minute, why don't I become a specialist in the regulatory stuff and the complex deals? I'm not gonna necessarily play everyone because unlike the market where a deal is announced, it trades at a certain level and the market has a probability. So that's a game of risk. I went and said, well where's the uncertainty? The uncertainty is in two places. Pricing when the deals blow up because of financing, pricing when the regulatory gets complicated. I can't resolve one. I think I might be able to resolve the other. So I went into there and said, well I'm gonna do this process. I'm gonna follow these companies when they're going through this complex regulatory process. And then getting to the question that you asked about scaling that, I did something which was pretty novel at the time. And that was that now everybody knows there's like GLG and there's all these expert networks. But I was thinking about the production line. A deal gets announced. It could be across all these industries. I have all these analysts. They're supposed to get up to speed on the deal. I've taught them Briskarb, get on the conference call, read the merger agreement, think of et cetera. And the question was, well, how do I, if a deal gets announced in a particular sector, a classic example would be when Boeing was buying at Donald Douglas, there were only three wide body aircraft manufacturers in the world, those two and Airbus. Usually a three to two to deal breaks. So was it gonna go through, was it not? My analyst does not an expert on that. So what I wound up doing is I wound up building an expert network captive to sit it out. I had hundreds and hundreds of people. In fact, I hired a woman one time to do some research from me on a bank merger in Florida. She was so good. I hired her full time and her job was when a deal got announced to line up the right consultant so that we could get up to speed on the industry and start getting and touch with customers, competitors, et cetera. And so what we wound up doing is we built at scale effectively our own consultant network captive to Citadel. Well wound up happening is that I was, became pretty good at, we had billion dollar positions in the 90s, those are pretty big positions in risk of in the 90s. And I lost money in four of my first 1100 trades. The answer, which is not a gloating thing, it's a process at work. It was just wow, we were pretty good at resolving that uncertainty. And there's some great stories around that of like, well, once you think about, you can make those phone calls, but why is somebody gonna answer the phone? And so thinking and really, really rigorously around, how do we build a network that we can get a hold of people, that we can get in touch with customers and competitors and begin to do the work that the government was doing. That was not typical work that people by themselves one or two people did at a hedge fund was build a production line for the resolution of uncertainty of antitrust risk. We did that and it accrued huge benefits. We debated at one point whether to make a commercial and do it like a GLG. We decided that we were making enough money on the scar internally to keep it captive. But that was an example of going into something. Nobody told me not to do it. Everybody else called a former DOJ lawyer and DC and said, what do you think? Is this deal going through? They'd say, I think it's a 70% chance that goes through. But what am I gonna do with that? If I put it on and it breaks, can I call my investor and go, well, my lawyer said it was 70, 30. That was pretty good. It was trading like it was 50, 50. That wasn't it for me. And so that's where the marketplace offered a risk bet. And I wanted to find out where the uncertainty was, go resolve it. And I find, again, as I alluded to earlier, that if you're the first person to go in and systematize resolving that uncertainty, you get enormous gains from doing that rather than trying to predict long short as a slightly different game. There is a distribution of where the stock's gonna go in earnings. You just have a slightly different one. In antitrust, there was no marketplace for the probability of that deal. There was, I mean, the market traded it somewhere, but it traded based on the fact that it had no model for it. I just tried to build, take that and resolve it into probabilities and that against a deal or back for a deal, which we did both. Mark Mitchell, a famous U of Chicago professor, as paper that he wrote probably in 2000 or so, the risk characteristics of risk arbitrage, essentially illustrating the tail risk, short put. components and we know that these deals they jump to default in some ways, right? They break. That's the term. And that almost conjures up an idea of it just happens. And so that you almost don't get to observe, it seems your process is about observing things and allowing those to inform you. All of these conversations and this deep and wide expert network, does that become the set of information that allows you to really create the mechanism to update your prior, where if you're just in the deal and just waiting for something bad to happen, you don't really get that information. I think that that's right. I'm not pouring cold water in anybody. I read that. I was satiably reading whatever I could around our business and otherwise. I was a practitioner. I mean, I've read 10,000 murders. There are people who have been around a long time. I've been around a long time. I played a lot of risk-arpe. I know a lot about risk-arpe. If there was one thing I might argue that I actually have an expert in the whole world, that might be it and nothing else. And so what I would say is that when I started doing risk-arpe, I didn't know what was important and what wasn't. And so yes, I focused on Amtichrust, but I kept track and built a database before people were at this in their middle '90s of all these aspects around mergers, merger agreements, that I provision, et cetera. And so a lot of people that do those studies, and it was a great study and a great research report. And so I was doing it from data that is available. I had my own proprietary database that came at the time from I had to read the merger agreements and capture data. I had to talk to CEOs. Some of the data was useless. Some was incredibly valuable. And so where I had the advantage was that whereas someone might have X number of data points and they're discerning a pattern from it, I had a lot of data points that I was keeping track of. And there were a lot of times where I'd work with the Quant team at Citadel and then at Magdatar. And I'd ask a lot of questions like, hey, what's the answer to this problem? And even portfolio construction, simple questions maybe like, if a deal is trading at a 95% probability of going through in the market, and I think it's 98, but another deal is trading at a 50% probability. And I think it's 70, which is better. Obviously, I can get into confidence in a virus, et cetera. But there were lots of questions that you could ask when you have enough data points and enough information. That might seem like an easy question, but when a deal is a collar deal, you have to constantly be reassessing what the value of the collar is. If it's a quantum option, not only on the stock, but on the currency, that's harder to calculate. I mean, we can go into an infinite loop on this topic, actually. I've spent a lot of time on this one and always something that one misses. But I would sit and say that going back to your original question, I had a lot of data points that I was sitting on. And it doesn't mean that there isn't jump to defaults. But I find that there isn't true uncertainty that's unresolvable. But I do find that very often, if you're very, very maniacal and looking constantly for where you might be wrong, what I did in risk, was the following. And it's very Bayesian. Operating part of a Bayesian, the equation for Bayes theorem, is not P. It's the not hypothesis. All you're doing is trying to figure out how you're wrong. I would ask anybody, and I have trained a lot of people and they've gone on to build huge businesses. I think that the people who are my mentees, it's way, way over $100 billion under management that they created those businesses, not that they're working for people and that they're running those SM management businesses. And probably they would all tell you, you're not the easiest thing working for me. I was, I'm a pretty intense guy. I hope I'm fair, but I demand a lot. And constantly just saying, listen, where are we wrong? Where are we wrong? Where are we wrong? Where are we wrong? And someone says, well, I just spoke to someone yesterday. I'm like, yeah, but that new piece of data came out, call them back again. And I give her like, what, but I just spoke to the CEO yesterday. I'm like, yeah, but that came out. And now I'm not sure it's right anymore. So call again. So being relentless about that constantly, finding any little piece of data and not necessarily saying, I can ignore it. It's an outlier. We did all our work already. I was in a perpetual state of trying to figure out whether that marginal new piece of information was a new line. And whether it meant that something had changed, the regime changed, something happened. That's what we built really well at Boltz, Ed Allen, Mabditar. Well let's talk about another big investment on MagnaTar's behalf and that's CoreWeave. And I think this really speaks to a different way of seeing an opportunity. I'll let you run with it, but my understanding CoreWeave's a crypto miner in a crypto bear market, but it's got some really interesting assets. And you guys are able to see something there that not a lot of others did. Why don't you walk us through, just would love to get inside that conversation with you and your business partners, how that whole thing materialized. And I should be clear here. So I left three years ago from running day to day MagnaTar. I'm very close there. I still remain an owner and investor and I'm very close to the people running it. So some of this overlap with me, someone was after, but I'm very close to them. But let me step back for one second. I'm not avoiding it. We'll get back to CoreWeave. But I think CoreWeave again is about a process rather than a trade. So let me step back for a second and go fairly quickly. I'll try to talk slowly, but I'll be very quick with it to just give you evidence of a pattern of thinking, which I think is more valuable people than just one trade. And so go back to what we talked about earlier around uncertainty risk and Black Swan. We built MagnaTar. We did something somewhat unusual. When we launched MagnaTar, we said to everybody, source evaluate structure risk manage. And most people on the hedge fund world back then would have said source. What are you sourcing? Risk our bills are announced converts. You get a call from a bank, long short equities. You just see that those are your sector names. And what are you structuring? Most of that stuff just comes to you. We were focused on finding places where uncertainty was and resolving it. And when you find it, sourcing it. So let me give an example and then we'll go right to CoreWeave. In 2005, we launched and right around 2005, 2006, a new technology came out, which was horizontal drilling and fracking. It was a regime change in energy. In regime changes, I consider this will be important for CoreWeave. I consider regime changes to have four components. One is the production function changes. Two is things become abundant that were scarce and things that were scarce become abundant. Three is you get bottlenecks. And four is that the old map doesn't explain what's happening and that changes irreversible. So let's go to energy and then we'll go right to CoreWeave. Energy, production function clearly changed. We weren't vertically drilling, we were horizontally drilling. We went from importing a lot of oil and gas to being as we are today, the number one producer of oil and gas in the world. What became scarce was not carbon. What became scarce was because we had never had it. We had minimal build out of infrastructure, both rigs upstream to do the drilling and also midstream to get the oil from where it was newly found to where it needed to get to the hubs. And so what became scarce was the capital formation. I don't know if anybody was around like I was, you're as old enough, but we had MLPs, maybe five, maybe $10 billion a year of capital formation in energy going to hundreds of billions. So what we did is we stepped in and said, you know, an adopter, Michael, wow, I believe that this change is real. What's going to be the price of oil? We stepped in and said, well, wait a minute, we're adapters. What does this mean for the ecosystem? And where is the bottleneck? Where do we fit in to help resolve that and what can we not resolve? So we went in and said, well, midstream assets, we don't know where the commodity is going to go. Midstream assets with takeer pay contracts. So you get paid regardless of user, not our volumes. You're a midstream and you need to build a pipeline and you need capital and you turn around and you have a take away, a takeer pay contract with BP, very good credit. Then you have an asset that's cash flowing and you're going to build it. What if we lend to you? We can be at various places in the capital structure. If we're too worried, we can be senior. If later we're confident that we could be more in equity or otherwise. But I sit and say you have a core fundamental piece of collateral that cash flows. I want to contain that. I want to get paid back on that. Maybe I'll do a convert or a preferred. But I want upside on your stock. I want to write tail as well as that. And because you're one of the first people to go in there, you source it, you structure that. You try to structure it in a way that's forgiving to the commodity. I run or it's near, you know, simulate a number of scenarios. How do I avoid a left tail of crude goes to 20 or 150? And so take that, leave that aside and go, I'm going to fast forward to core week. And now, yes, you're right at the time. I think a lot of people thinking about the production of Bitcoin. There's Bitcoin miners. It's an asset that generates cash flow. Obviously, it's dependent on the level of Bitcoin and the operations and the cost of electricity, et cetera. But more importantly was that, again, I don't want to speak on behalf of Magdita because I'm not a spokesman for Magdita, but being close to all of them, think about the same thing. If I have GPUs and I need to build a data center, it's a little like amidst, and I'm going to call it a midstream asset, I have a contract with Microsoft, very good credit for the offtake of those GPUs. And I see the production functions changing. For a fourth industrial revolution is going on right now, but what is it producing? It's producing knowledge. And when the cost of knowledge goes to zero, that changes everything. I say, AI is a tool at the interface. It's a regime change at the system level. So now we get to step back and go, great, there's a production function change, scarce abundant. Again, capital formation here is going to be scarce. So we're going to step in just like we did before. We can negotiate and structure something where we can protect ourselves around the left tail and get upside participation. It's the same game, obviously, it's a different disruption, a different regime shift. But what we became and designed our entire firm around from the beginning. It wasn't that later we did it. We designed our whole firm around, how do we go and go into places where others don't like to go because it's not resolved yet? And figure out a way to, so zoom out, think about the system as a whole, figure out where we could be helpful in that system, provide value, and make sure that we, in a fair way, get the appropriate risk reward that we can architect both by having sourced it and structuring it in a way that, great, here are the things we can resolve, here's the things that we can't resolve. If we have our own sourcing and structuring, can we, within the structure, get rid of the things that we can't resolve versus creating investments and having to go as a pool continuously resolve how I hedge all of that when I combine it all. This can be, a lot of it can be done directly through negotiating and structuring. So to me, these trades are emblematic of what we tried to do and what we did again and again and again at magnetar, which is we tended not for the most part to go to where the risk was and say we can do it a little bit better. Long short has a little bit of edge, but sharp ratio is edge times the square root of n. So I diversify a number of it and depends over large n and I can have something if I use leverage. We tended to be not levered or very, very little leverage at magnetar and turn around and go places where we think the money as once that uncertainty is resolved into risk, the marginal person comes in with a much lower cost of capital. The return gets squeezed out and the money we made is having been a participant early on from the uncertainty coming down to the risk level. That's how we think about it. That's what was going through our minds. Obviously, you do a whole episode on just one trade, but that was the gist of the structure and we did it. I gave you two examples. I gave you a third with risk of it. It's how we focused and what our focus is at magnetar. It seems like a lot of your thinking and maybe AQ itself is related in some ways to optionality, being trying to be long optionality. Your efforts to cut off the left tail in risk arb, your core weave financing certainly as a convertible component to it, which embeds call optionality. I'd just love to hear you talk out loud about that. Is there a connection there between adaptive thinking and trying to keep yourself long optionality? 100%. This is a pretty detailed part of my book where people read it and they want to muscle through. I think it's meant to be approachable. You can tell the audience, but there is a slightly more dense part, relatively, when I get into sort of decision theory in chapters 8, 9, 10. I put something in a footnote so that I don't, people want to read it, they can read it, but it is a pretty important point around optionality. Let me unpack that because you're so right and on to something. Let me zoom back for a second. I know given the nature of how you think in your audience, I think we'll be a fruitful little piece. If I'm modeling the world and it's uncertain and I know there are different possibilities, but I don't know the probabilities. I'm going to go imagine things, try to make sure that I avoid the worst case scenarios when I go experiment, but I have a decision tree. I have a line lit through the decision tree, meaning I think this is my strongest guess as to going right left, right left, as you know, an example of the restaurant I built in there. It's like, what is the food, what is the decor, what you have this line through all these different decisions? That's my best answer. But the goal when you go into an experiment is to try to preserve the entire tree. And the reason why is I don't know which the best part of it is. I have what I think is the best line through the tree, but thinking as a Bayesian, like I don't want to prune anything. Now, I have to prune the ones that are going to kill me because if I go in and I land on one of those games over, I can't even go back to the tree. So the first thing you want to do by simulating things, even if you pick a strong opinion weekly out, it might be the wrong line. One of the nodes might be wrong, but I don't want to get it lost. So now you go in and one of the things that happens is, let's say you wind up with a scenario, a lot of people think about the left tail and we talked about that already is I don't care about small losses where I get feedback back. I learn and I go redraw. But let's think about the right tail because anybody who's very familiar with decision making could say, "Alex, it sounds like you're saying say fail or fail safe." There's words for that. But here's where mine differs a little bit, which is that I also believe that when you're experimenting and this will be very, very well understood by investors who sit around and might be listening going, but wait a minute, power law, venture. There are certain times where I don't know the answer and so an opportunity pops up and should I take it, should I not? You should take it. This is a little unusual in decision making and why is it that I say if it's not that expensive option on a big right tail, you should take it. Very easy answer. Here's why. It's because if I don't take it in it's irreversible. I have pruned a tree. If I later find out that was the best node and path, it's not there anymore. I didn't take it. If it's so expensive that I ruin my through the system, I can't take it because I got to keep learning. But if it doesn't cost me much to take it, even though I don't know if it's the right path, if it's irreversible, in Bezos talks about one way to a decision. If it's irreversible, take it precisely because it keeps your optionality open that later when you were fine and you go back and go, "Oh my god, that was on the best line of the decision tree. It's available." So slight level of more granularity to what is slightly unique about my method. It's a little unusual relative to what you'll read about in a literature, whether you get into Bayesian more risk worlds or whether you get into uncertainty and other people's models. The way my mind works is there are times where you do place bets on that, even though you haven't resolved that if you just are going to miss that opportunity. I think about that a little bit. I invested in SpaceX very early on and one of that was, he only raised $9 billion ever for that business. There was a feeling of irreversibility. I mean, it turns out there were a couple other chances, but there weren't many. He did a bunch of secondary trade, but primary trades, he didn't do much. So I didn't know how big the right tail was. Obviously, the left tail is my investment. And I think he's an incredible entrepreneur and we can debate about lots of things about Elon, but in my opinion, he exhibits a lot of high AQ in his decision-making, experimenting and probability and feedback loops. So I wanted to participate alongside that, even though there was no Starlink in the sky. I knew about Starlink and I thought it would be a big opportunity, but I didn't know about AI data centers in the sky and other things like that. So that optionality, I wanted to preserve that optionality and I didn't want to lose it. So I don't let that answer as part of your question. Well, I want to finish with one or two of the investments or areas of focus for UQ Star. Before we do that, if there were one or two just big picture messages that you want folks to come away from the adaptability quotient, how would you frame that out? I think most of what we have talked about here is about investing. I've had a career of that, but the reason why I wrote the book was for a different reason. I spent a 30-year career as we just described, thinking about risk and uncertainty and playing with various points of uncertainty, energy had a regime change, the great financial crisis was a regime change, etc. So when I left magnetar, I did it because I had an opinion that before CHFTP was announced that data compute and material science were converging and that we were going to have biology terms, a punctuated equilibrium, a moment of unbelievable change across a variety of different industries, energy and transition synthetic biology, maybe a new financial rail system, quantum fusion space robotics AI defense. I mean, it's all changing incredibly rapidly. I wanted to be present for that. I wanted to map it and I didn't want any constraints. I wanted to give the way to test whether magnetar was a business was for me to leave and they prospered and been done an incredible job. Great team. But it also, as I started to face this uncertainty, I was looking at it going, man, how do I resolve this? How do I map this? And I realized, oh, I kind of have been napping this kind of uncertainty forever. But when I saw this one, the prior uncertainties I described all centered around an industry, investors, it was a small group of people or a reasonable size group of people. The center of gravity of regime shift has changed to society in this one. And the better thing for me to do would be to hand people the source code. This is how you survive and thrive in a world of uncertainty. I've done it before many times in investing. Everybody's an entrepreneur right now. You either are one, your industry is disrupted in your one or you're a young person whose career path is disrupted. Like, what do you do facing all this uncertainty? So that's what I saw. The second thing is why is it so urgent now? And then what's the takeaway? The urgency is that we've been through industrial revolutions before the first industrial revolution and the second industrial revolution. We manipulated atoms to offload work. We made hammers. We made tools. We even had tools make tools. We did it scale, production, in factories, et cetera. And that was great. We offloaded labor. We went to do other things. The third industrial revolution. We manipulated bits. And bits were storage, retrieval, and computation. But in all of those cases, it changed how we work. And humans were always in the process at some point. But it changed how we work. And when you manipulate a bit, if I have a spreadsheet and I run through some numbers, every time I run it through the numbers come back the same. If I use the same inputs, this is the fourth industrial revolution at the physical layer. We have power, GPUs, we have land, shell, craze we're seeing in the marketplace of what is rising. We can talk We talked about bubbles or not. But what I was going to say is that it's important to think about the fourth industrial revolution as how are we producing knowledge? But what is being produced is tokens and someone on listening will say, but Alec tokens are bits, but they're different. And here's why they're different. A bit doesn't matter when it's next to another bit context. Any all the prior revolutions were fixed and contextless. It didn't matter if the hammer is next to the screwdriver. Didn't matter if it bits next to another bit. Tokens word displaced matters and therefore it has meaning. That means for the first time in history, we have a partner in how we think. We have a partner in meaning making when I use Google and I Google a lot of information. I get all human thought and I have to compress it and make meaning out of it. That's not what happens now. I use AI. It compresses for me. This is something different is the second part. And now what do I want the message to be? The message to be is it's a very dangerous moment that we're in right now and it's an incredibly optimistic moment. I'm going to take social media and I'm going to take AI and combine them as this experiment we're running. And we've already run one for 20 years. And that is you can use both of these tools, which are both regime changes. They both change the environment of how we think, not how we work and offloading prior ones, but how we think. I can use it to augment how I think and connect with people or I could use it to atrophy. How I think and connect to people. And that is a massive, massive import. It's sort of if I was going to leave your audience like there's alpha exchange and then there's the life alpha. This is what is critical for people to understand. We just had an experiment where you gave up attention and return. You were supposed to get connection. And if you used it to connect to your long lost body from high school that you didn't talk to before, that's great. You expanded the set. If you used it to do a reunion with your fellow fraternity brothers, great. But if you used it to replace and make an artifact instead of the function, if I turn around and said I used to date. Now I swipe left and swipe right. Oh, I used to go on vacation with Dean, but now he shows me pictures as a vacation and I thumb up and like it. That is a illusion of connection. And that's why we have an epidemic of loneliness because people aren't using it to expand the function. They're using it as an artifact. It can be used well, but it's not being used well. Now we go to AI. Are we going to run this same experiment on cognition? At the end of the day, I can use it to say play doubles advocate with me. Where am I wrong? Help me think about possible scenarios that I haven't sat through. But if I ask it for an answer, if I just slowly give it away, it atrophies my cognition. And my point is is that my book is really about agency. It's about you are sitting on the greatest machine ever made better than AI. You are a pattern processing superior being and AI and social media are geared in a certain way. I called the double whammy. If you just step back for a moment, what does social media do? Engagement forces it. If you're optimizing for it, that's the reward function. It gives you the extremes. What is AI do? What is an LLM do? It gives you the mean answer that everybody would give. So you're getting the worst of the polarized extremes. And you're getting the middle of the distribution. If you let it think for you and connect for you, you're going to get double whammy. So my point to people is you don't give that away all of a sudden. You know, I tell risk. You give it away bit by bit by how you interact with it. So keep agency. Keep what is uniquely yours. It'll help you become a better investor too, by the way, if you apply it. But this is your life. And if enough people make the choice like we've done with social media, we're just slowly going to fade away to where we have this false illusion of connection, but we don't. False illusion of cognition, but we don't. And we become avatars. I'm very fearful. There's a part of this that is a world of abundance, but it's not all abundance. How you use it is everything. How you think about making decisions is everything. You are the keeper of choice. You are the keeper of agency. Don't give that up lightly. I would say don't ever give it up. And what my book is, basically a repair kit that says, I'll give you the way to compress and be active in thinking, even a world that's confusing. That's uncertain. I'll give you the code on how to do your own compression. Don't offload it. Do it yourself. What's inside the ears of the best investors, the best entrepreneurs. This is what we're doing. It just wasn't written down in one book. There's some great books, but I try down on you to you'd be the judge, but I tried to write it down in one book. This is how to preserve your agency. It doesn't mean don't use these tools, but do not let them take over for your agency. That would be a major, major loss. I think that's a great way of summing up. This is so much insight in this book, but this idea of retaining agency is a very, very strong recommendation or almost warning, as you say, at a time as we started this conversation, it's just incredible change. Just in terms of Q star and you're investing there, are there one or two investments that you're particularly excited about when we talked last, you had this barbell approach of true tech and then much more in person stuff. You mentioned your restaurant, things like that. What's an investment that you're currently either have made or just an area of study for you that you're thinking about right now? You recall correctly. So let me zoom out for a second and describe Q stars, our family office, just our own capital, you know, about 15 people here. I would say the mission of Q star is to compound human flourishing. That's our goal. And we think that there's two aspects to that. One is capability, which is what is the most that humans can do. That tends to be more technology focused capability. That could be health care could be technology. How do we achieve more as humans? What tools and otherwise get built and investing in those could help us live longer, live better, etc. So the second part is what we call belonging, which is, well, great. There's all those advances, but what makes it have any meaning? And that is connectivity and having each other and understanding that despite lots of differences, we really have a lot of the same hopes, dreams, fears, etc. And so the part on the belonging side, you know, so we tend to call the capability side techno centric and the belonging side anthro centric. And we have teams that focus on those two aspects. So thematically, where do we think the world's going? And then as typical you would expect of us bottoms up, what's the most forgiving asymmetric investment we can make across those? So on the anthrop side, we think we have a minority owner and a majorly soccer team. We think a lot about hospitality that can include music or anywhere where people gather. We have a pretty large parcel of real estate that we bought in a symbol in Chicago. And we're going to be building a cultural arts hub that doesn't really exist in Chicago. You can think of what happened in Dumbo. You can think about Miami design district. This is maybe a smaller version, but thinking about what we can bring together people together in a community that, I think because of the Chicago fire, we just don't have a grid system that supported building something like that. But when you accumulate a five city block area, you now control all the blocks. You can do something you might not have before. So that more on the anthrop side. And on the, just as an example, what we're looking at on the more capability and technology side, I mean, one of the things going back to the formulaic piece that we talked about on the production side of the fourth industrial revolution, we think a lot about the electric grid and that thing, a bottleneck and the fact that it's old and we have problems. And so it's great that we have solar and we have wind in addition to carbon-based forms of energy. But they create challenges when they come into the grid for things that need to be five nine. A data center needs to be up 99.99, nine percent of the time. Same thing with some of the reshored heavy industrial device, if you're a manufacturer and you're using special equipment, you can't have to go down in the middle of the run. So there's a concept, not of grid following. So solar and wind work in a certain way that can be good overall for us, but can be destabilizing to the grid for businesses like this. And then there's a new business or a new area called grid forming, which is building products that resolve some of those problems so that we can not have this technology build, disrupt what we need as typical consumers and otherwise. So investing in people solving that problem is interesting to us as a, you know, this is a bottleneck. It's a problem. How do you resolve that problem? How do you repair? We could use a new grid, but in the meanwhile, how do we repair the existing one to support some of these things without, you know, how do we get the good without harming individuals? And I think that's an interesting area that we've spent some time on as an example. Well, Alex, this has been a pleasure to host this conversation. Congrats on the book. Obviously a tremendously heavy lift putting it together. I really, and I mean, this really enjoyed reading it. I think it's at the right time too. And you come back to this idea of retaining agency and this method for trying to think through things in the structured way. So kudos to you for writing it. And thank you so much again for being a guest. Well, I appreciate all of your efforts. I'm a fan. I think it's incredible for you to take your time and democratize a lot of the knowledge that you give to people. I'm hoping that whatever I've said is helpful to people and making it through safely the environment that we're in. It's important to recognize that it's a challenging one and normally quiet, but hopefully speaking out a little bit more than I normally do to give people tools. Hopefully if it helps someone one listener even or people a little bit, then I'll be thankful and hopefully valuable. to the audience, so appreciate you for having me. Yeah, thanks again. You've been listening to the Alpha Exchange. 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