The Exchanges

Q Even with the, like, irreversible decisions, be it leaving companies, leaving, you know, things that you can't go back on bluntly, like, do you still take that mindset? Like, how do you think about that?

A Yeah, so that's interesting. So the first thing that I would say is a lot of decisions that we think are irreversible are actually reversible. I think that we feel like we can't go to options, back to options that we've rejected in the past. Much more than is actually true of the world. So yeah, it's true that sometimes when you leave a company, you sort of think about that as irreversible, but, you know, mostly, particularly if you're leaving your own volition in a situation where you have done a good job there, it's actually not true that you probably can't go back to that. So I would say, number one, that you should always really ask yourself pretty deeply, does it feel like I can't go back, or can I go back, right? I think that's number one. Number two is something can be reversed. I think about something as Reversible, even if, just if you can get off the option that you're on. So you might not be able to go back to the, to an option that you rejected, that you had already rejected, but you certainly should be able to go, there should be other options that are available to you, right? So don't think about reversibility just as, can I get back to the thing that I did before? But just, can I undo this decision and get to a different option, right? So then again, I think that that kind of helps you there. And then lastly, I would say, absolutely, there are lots and lots of th…

AI assessment note: “a lot of decisions that we think are irreversible are actually reversible”

Q kind of developing skills and mindsets that you didn't have before, when you think about, like, critical thinking, a lot of emerging grads, a lot of investors listen to the show, and very keen to kind of engage their critical thinking mindset. Are there things that you'd recommend in terms of encouraging critical thinking, encouraging expansive mindsets, being open and plastic to new opportunities? What are some things that work?

A Look, I think that the big thing is that, number one, this idea of eliciting opinions prior to actually talking to a group naturally ends up expanding your mindset, because we all have, there's actually a bias that we think that other people hold the same reality that we do and have the same opinions we do. So as much as you can be putting into your process, and you can do this in your own life, not in a partnership, because I can just say to you, what did you think of the American election? And not tell you what I thought. Right, so just that little trick is really huge, right, because now I'm going to get more of that dispersion out of you. So the more that you kind of set yourself up, that you can actually see that other people actually have a wide range of opinions that aren't as overlapping as your own, as you thought, and these are people that are smart, that you respect, that immediately causes you to get into this place where you start to expand your horizons more, because you're sort of exploring all these different perspectives on the world. I think that's really important in terms of number one. Number two is you really have to get into a Forecasting mindset. I mean, people should, everybody should be reading super forecasting from Phil Tetlock, because what forecasting does is it says, I care not so much about my identity in terms of the idea of, like, my, all the b…

AI assessment note: “eliciting opinions prior to actually talking to a group naturally ends up expanding your mindset”

Q Even with the, like, irreversible decisions, be it leaving companies, leaving, you know, things that you can't go back on bluntly, like, do you still take that mindset? Like, how do you think about that?

A Yeah, so that's interesting. So the first thing that I would say is a lot of decisions that we think are irreversible are actually reversible. I think that we feel like we can't go to options, back to options that we've rejected in the past. Much more than is actually true of the world. So yeah, it's true that sometimes when you leave a company, you sort of think about that as irreversible, but, you know, mostly, particularly if you're leaving your own volition in a situation where you have done a good job there, it's actually not true that you probably can't go back to that. So I would say, number one, that you should always really ask yourself pretty deeply, does it feel like I can't go back, or can I go back, right? I think that's number one. Number two is something can be reversed. I think about something as Reversible, even if, just if you can get off the option that you're on. So you might not be able to go back to the, to an option that you rejected, that you had already rejected, but you certainly should be able to go, there should be other options that are available to you, right? So don't think about reversibility just as, can I get back to the thing that I did before? But just, can I undo this decision and get to a different option, right? So then again, I think that that kind of helps you there. And then lastly, I would say, absolutely, there are lots and lots of th…

AI assessment note: “a lot of decisions that we think are irreversible are actually reversible.”

Q again, parallels kind of poker and venture, but also sticks to this kind of sunk cost and kind of continuous monitoring, which is like, say you have a couple of tournament series investments that are actually just continuously bad, or you have a couple that are great. How do you prevent prior success slash failure from impacting future performance and mindset? Because that's a big one, I think, in both.

A Yeah, so that's an interesting question. I think that's how people can get stuck on particular verticals, for example, or, you know, very particular models. You know, what I would say is kind of number one, that's a little bit why you have a partnership, because I think there's no doubt that individuals are going to get affected by the things that have worked for them in the past, but if you have a group of individuals, then you should be balancing each other out in order to naturally get a balanced portfolio, so I think that that's number one. I think number two is That you need to do a lot of tracking, you know, and you need to say, what are the opportunities that I'm pulling down into my funnel? What am I rejecting? And then look at what the market thinks about the ones that you're rejecting from the funnel. There are ways to go find that out because you can find out, like, what are people funding, right? And then when you look at that set of things that people are funding that you chose not to, you need to sort of scrape the data and say, what's the commonality there, right? What's the commonality in the things that I'm funding, right? Versus what are the commonalities in the things that other people are funding that I've chosen not to. Not that I didn't win, because that's different, because when you don't win a deal, you've decided to fund it, and you should treat it as s…

AI assessment note: “number one, that's a little bit why you have a partnership”

Q a lot of people have asked you about risk, especially when it comes to investing and, you know, poker playing, in terms of, like, personal attitudes to risk, going from, like, you know, professor and academic to poker player, as you said, when it was more thought of as a vice, so to speak, it's a bit risky move. Like, how do you personally think about risk applied to yourself?

A Yeah, it's interesting, because I didn't think about that as a risky move. First of all, I, I just really loved the game, and I did pretty well from the start, so that was a little bit helpful. The people I was playing against weren't that good, so that was helpful, as it always is. You know, I mean, the thing about poker that's kind of interesting that I think that people should remember is there wasn't as much information at the time about, you know, how to play and how to play well. It wasn't on television. You couldn't run, you know, Monte Carlo simulations on a computer at the time, like, You would have needed something more powerful than what would be available to you at home. So we were kind of like working out a lot of the math by hand. So the gap between the people who like understood the game and knew the game and everybody else was so wide that as a professional, you didn't need to be nearly as good then as you would need to be now in order to win. Like the way I think about it is like, you know, what happens to any kind of game where you're trading on players Sort of informational advantage when the information market becomes more liquid. That's kind of how I think about it. So, like, I've talked to people who were trading options in, like, 1982, and this was when, first, like, the people trading options didn't even know the Black-Scholes model. Like, literally, the…

AI assessment note: “I didn't think about that as a risky move.”

Q So what do we do? Because we're just chucking cash and skipping the steps.

A Yeah, that's a really big problem. So there's a couple of solutions that you can do for that. One is another way to To mitigate the cost of being less accurate is it's essentially just a portfolio theory, right? Like if you can do a lot of bets in parallel, then the penalty for being less accurate on any single one of them is lower. So assuming that you think that generally you're winning to the market, right? So imagine the situation, you have two investments that you can consider that you could throw some money at, and both of them long run are positive expected value. If you had lots of time to build a really great model and to really dig into that company, You may find that company A has a higher expected value than company B, and you should prefer to be on company A, right, to invest in company A over company B if you have a choice between the two. Remember, but both are positive expectancy. So there's a couple of reasons why we may not be able to distinguish once we sort of thresholded the company, because that's really what we're talking about is a thresholding problem. Once we've gotten past the threshold of this seems like it's probably positive expectancy, there's two reasons why we might not be able to distinguish between the two. One is time. So this is what you're saying. So it may just be that we have to move too fast, and so what we really care about is the sorti…

AI assessment note: “there's a couple of solutions that you can do for that. One is”

Q kind of developing skills and mindsets that you didn't have before, when you think about, like, critical thinking, a lot of emerging grads, a lot of investors listen to the show, and very keen to kind of engage their critical thinking mindset. Are there things that you'd recommend in terms of encouraging critical thinking, encouraging expansive mindsets, being open and plastic to new opportunities? What are some things that work?

A Look, I think that the big thing is that, number one, this idea of eliciting opinions prior to actually talking to a group naturally ends up expanding your mindset, because we all have, there's actually a bias that we think that other people hold the same reality that we do and have the same opinions we do. So as much as you can be putting into your process, and you can do this in your own life, not in a partnership, because I can just say to you, what did you think of the American election? And not tell you what I thought. Right, so just that little trick is really huge, right, because now I'm going to get more of that dispersion out of you. So the more that you kind of set yourself up, that you can actually see that other people actually have a wide range of opinions that aren't as overlapping as your own, as you thought, and these are people that are smart, that you respect, that immediately causes you to get into this place where you start to expand your horizons more, because you're sort of exploring all these different perspectives on the world. I think that's really important in terms of number one. Number two is you really have to get into a Forecasting mindset. I mean, people should, everybody should be reading super forecasting from Phil Tetlock, because what forecasting does is it says, I care not so much about my identity in terms of the idea of, like, my, all the b…

AI assessment note: “number one, this idea of eliciting opinions prior to actually talking to a group”

Q again, parallels kind of poker and venture, but also sticks to this kind of sunk cost and kind of continuous monitoring, which is like, say you have a couple of tournament series investments that are actually just continuously bad, or you have a couple that are great. How do you prevent prior success slash failure from impacting future performance and mindset? Because that's a big one, I think, in both.

A Yeah, so that's an interesting question. I think that's how people can get stuck on particular verticals, for example, or, you know, very particular models. You know, what I would say is kind of number one, that's a little bit why you have a partnership, because I think there's no doubt that individuals are going to get affected by the things that have worked for them in the past, but if you have a group of individuals, then you should be balancing each other out in order to naturally get a balanced portfolio, so I think that that's number one. I think number two is That you need to do a lot of tracking, you know, and you need to say, what are the opportunities that I'm pulling down into my funnel? What am I rejecting? And then look at what the market thinks about the ones that you're rejecting from the funnel. There are ways to go find that out because you can find out, like, what are people funding, right? And then when you look at that set of things that people are funding that you chose not to, you need to sort of scrape the data and say, what's the commonality there, right? What's the commonality in the things that I'm funding, right? Versus what are the commonalities in the things that other people are funding that I've chosen not to. Not that I didn't win, because that's different, because when you don't win a deal, you've decided to fund it, and you should treat it as s…

AI assessment note: “that's a little bit why you have a partnership”

Q Perfect. Listen, I'm adding it to my list. Tell me, what's the biggest misconception of risk?

A Oh, that's a really interesting question. You know what? I think the biggest misconception about risk is that, this is going to be kind of weird, is people spend a lot of time trying to calculate their risk, but they don't think enough about whether they actually... Have the expected value, right? Like, are they winning or losing? So I think that we can get caught up in discussions about, like, what should our bet sizing be, and, you know, how broad should our portfolio be, and whatnot, and we don't circle back to say, are we actually winning in the first place enough? So I would say that, that investors think that risk is kind of, in a lot of ways, the most important question, when there's a, just a much more basic question that's more important. I don't know if that really answers your question, but that's kind of what I think.

AI assessment note: “the biggest misconception about risk is that people spend a lot of time trying to calculate”

Q So what do we do? Because we're just chucking cash and skipping the steps.

A Yeah, that's a really big problem. So there's a couple of solutions that you can do for that. One is another way to To mitigate the cost of being less accurate is it's essentially just a portfolio theory, right? Like if you can do a lot of bets in parallel, then the penalty for being less accurate on any single one of them is lower. So assuming that you think that generally you're winning to the market, right? So imagine the situation, you have two investments that you can consider that you could throw some money at, and both of them long run are positive expected value. If you had lots of time to build a really great model and to really dig into that company, You may find that company A has a higher expected value than company B, and you should prefer to be on company A, right, to invest in company A over company B if you have a choice between the two. Remember, but both are positive expectancy. So there's a couple of reasons why we may not be able to distinguish once we sort of thresholded the company, because that's really what we're talking about is a thresholding problem. Once we've gotten past the threshold of this seems like it's probably positive expectancy, there's two reasons why we might not be able to distinguish between the two. One is time. So this is what you're saying. So it may just be that we have to move too fast, and so what we really care about is the sorti…

AI assessment note: “if you can do a lot of bets in parallel, then the penalty for being less accurate”

Q there will be a positive ROI on those investments at a base case. And, you know, poker is a really interesting parallel because it's like, Solo decision making. Partnerships in venture are different in terms of collective decision making. How do you think about effective decision making in a partnership and collective decision making model? What can one do to optimize that process, but also have conversations of truth? Yes.

A Sure. So there's a few big things, and we can sort of tackle them sort of one on one. One is that when we're making really large judgment, we know that a lot of bias can enter into the And actually a lot of noise as well. Like if I catch you on a Monday and I show you an opportunity, you might have a different opinion about it than if I caught you on a Thursday. So, you know, because your opinions aren't necessarily consistent in terms of the way that you judge opportunities that come toward you. But also people have biases, right? Like you could be overly optimistic or, uh, you could particularly like a particular type of vertical or a particular type of founder where it's not really rooted in reality that that founder would be better than another one. It just, it's just, you have a biased Toward that type of personality or whatever, or a bias toward a particular vertical or a bias toward whatever, right? Like, okay, so we know that there, there can be bias and there can be noise. So one of the things we want to do is break sort of broad judgments down into their component part. So for example, instead of saying, I really like this founder, you should want to break it down into the component parts. And I can ask you like, what specifically are the things that you look in for in a founder? So I'll just like ask you that question because Because we, no, but obviously you're thin…

AI assessment note: “break sort of broad judgments down into their component part”

Q a lot of people have asked you about risk, especially when it comes to investing and, you know, poker playing, in terms of, like, personal attitudes to risk, going from, like, you know, professor and academic to poker player, as you said, when it was more thought of as a vice, so to speak, it's a bit risky move. Like, how do you personally think about risk applied to yourself?

A Yeah, it's interesting, because I didn't think about that as a risky move. First of all, I, I just really loved the game, and I did pretty well from the start, so that was a little bit helpful. The people I was playing against weren't that good, so that was helpful, as it always is. You know, I mean, the thing about poker that's kind of interesting that I think that people should remember is there wasn't as much information at the time about, you know, how to play and how to play well. It wasn't on television. You couldn't run, you know, Monte Carlo simulations on a computer at the time, like, You would have needed something more powerful than what would be available to you at home. So we were kind of like working out a lot of the math by hand. So the gap between the people who like understood the game and knew the game and everybody else was so wide that as a professional, you didn't need to be nearly as good then as you would need to be now in order to win. Like the way I think about it is like, you know, what happens to any kind of game where you're trading on players Sort of informational advantage when the information market becomes more liquid. That's kind of how I think about it. So, like, I've talked to people who were trading options in, like, 1982, and this was when, first, like, the people trading options didn't even know the Black-Scholes model. Like, literally, the…

AI assessment note: “I didn't think about that as a risky move.”

Q there will be a positive ROI on those investments at a base case. And, you know, poker is a really interesting parallel because it's like, Solo decision making. Partnerships in venture are different in terms of collective decision making. How do you think about effective decision making in a partnership and collective decision making model? What can one do to optimize that process, but also have conversations of truth? Yes.

A Sure. So there's a few big things, and we can sort of tackle them sort of one on one. One is that when we're making really large judgment, we know that a lot of bias can enter into the And actually a lot of noise as well. Like if I catch you on a Monday and I show you an opportunity, you might have a different opinion about it than if I caught you on a Thursday. So, you know, because your opinions aren't necessarily consistent in terms of the way that you judge opportunities that come toward you. But also people have biases, right? Like you could be overly optimistic or, uh, you could particularly like a particular type of vertical or a particular type of founder where it's not really rooted in reality that that founder would be better than another one. It just, it's just, you have a biased Toward that type of personality or whatever, or a bias toward a particular vertical or a bias toward whatever, right? Like, okay, so we know that there, there can be bias and there can be noise. So one of the things we want to do is break sort of broad judgments down into their component part. So for example, instead of saying, I really like this founder, you should want to break it down into the component parts. And I can ask you like, what specifically are the things that you look in for in a founder? So I'll just like ask you that question because Because we, no, but obviously you're thin…

AI assessment note: “one of the things we want to do is break sort of broad judgments down”

Q Well, that is very, very kind of you, but I would love to contest that a little bit. So talk to me. How did you make your way into the world of poker in particular and become to be the kind of very renowned author that you are today?

A Oh my gosh. You know, I have such a long and winding journey that, you know, I think it's like, it's so heavily influenced by luck. I mean, that's kind of how I think. Think about it, but, you know, I started off my adult life in cognitive science. I was actually doing PhD work at the University of Pennsylvania. I was going to become a professor, and really, like, had no intention of doing anything else. Like, I was going to be a professor, and, you know, do research, and have a lab, and on my way to a tenure-track job, and right at the end of it, I got sick, and so I needed to take a year off, and, but this was also, just, just for context for people, because I think that people don't really realize, like, there was a time before poker was on television, so poker started getting really big on TV, and, like, 2002, 2000 three-ish, like right around there. And prior to that, it wasn't on television. I was doing this in the nineties when people didn't really understand that poker was something you could make money at. It was really kind of firmly in the category of vices, you know, it's like gambling. So the only reason why I knew about it was because my brother was really interested in chess. Actually, I've been watching Queen's Gambit, which is so wonderful.

AI assessment note: “I started off my adult life in cognitive science. I was actually doing PhD work”

Q as you thought it could be, but your chips are down, and you've already put, I don't know, whatever, name your number, a skin in the game, and it's like, do I go all in and fuck it, or do I pull back and retreat, and then lose what I have? So like, I'm too intrigued. How do you think about sunk cost, and the right way to approach that?

A Yeah, so that's actually a really interesting problem. It's a lot of what I'm going to be writing about next, actually, because this is an issue of like, when do you actually decide enough is enough, and abandon an opportunity? So I There's obviously a lot, you know, grit is such a great topic, and, you know, sticking to things that are hard and not getting kind of stuck in local minima is a really, really important feature of people who are successful. But the question is, like, you know, there's a dark side to grit, right, which is sunk cost is really kind of the dark side to grit, right? We don't want to stick to things long after we're supposed to. So the question is, like, how do you actually solve for that, right? How do you actually get the grit just right? So you can think about it this way, and I think that this is something that should be intuitive to people, is that we have the intuition as humans that when the world gives us signals that we should abandon ship, that we will do it. But we know all the science tells you is that it's actually quite the opposite. There's really amazing work, some from Katie Milkman and Maurice Schweitzer, which shows this escalation of commitment, that when things are going poorly, we actually escalate our commitment. So that's kind of interesting, right? So we have this intuition, well, you know, if we're getting kind of bad signals fr…

AI assessment note: “sunk cost is really kind of the dark side to grit”

Q It totally does. What's your biggest strength and your biggest weakness?

A My biggest weakness is, like, I'm really bad at saying no, and I also, like, talk way too fast, and, but no, I'm really bad at saying no, and it's actually, like, a huge weakness for me, and I set up a whole bunch of things, like, barriers, so that people can't ask me to do things, because, like, it's a terrible thing that I do. My biggest strength, I'm also, by the way, on weaknesses, I have many weaknesses. I'm also very, very anxious. I get in people's heads too much, which may be why I was good at poker. I don't know, but I'm, like, always worried about, like, what other people think. I don't know. My weaknesses are too great to list. I have so many of them. Also, by the way, I think I'm, I'm a little bit lazy, which is really bad. I hate losing so much, which is also really awful. I don't know. I overcommit. Whatever. Okay. On my strengths. You know, I think my biggest strength is that, like, I think I'm curious. I would say that that's my big strength, is that, like, the reasons why I've done a lot of things in my life is because I just sort of get curious about something, and then I love to go deep.

AI assessment note: “my biggest strength is that, like, I think I'm curious.”

Q It totally does. What's your biggest strength and your biggest weakness?

A My biggest weakness is, like, I'm really bad at saying no, and I also, like, talk way too fast, and, but no, I'm really bad at saying no, and it's actually, like, a huge weakness for me, and I set up a whole bunch of things, like, barriers, so that people can't ask me to do things, because, like, it's a terrible thing that I do. My biggest strength, I'm also, by the way, on weaknesses, I have many weaknesses. I'm also very, very anxious. I get in people's heads too much, which may be why I was good at poker. I don't know, but I'm, like, always worried about, like, what other people think. I don't know. My weaknesses are too great to list. I have so many of them. Also, by the way, I think I'm, I'm a little bit lazy, which is really bad. I hate losing so much, which is also really awful. I don't know. I overcommit. Whatever. Okay. On my strengths. You know, I think my biggest strength is that, like, I think I'm curious. I would say that that's my big strength, is that, like, the reasons why I've done a lot of things in my life is because I just sort of get curious about something, and then I love to go deep.

AI assessment note: “My biggest weakness is, like, I'm really bad at saying no”

Q Penultimate one. Have you angel invested since, and, uh, what have been some learnings?

A So I have done some angel investing, but I'll tell you how it's been. So actually, this is a strength of mine. I don't bet my own opinion when I feel like there's lots of people who are way better at the thing that I would be betting my opinion on. So I'll bet poker, right? Although I don't know if I would do it today because I haven't played in a long time. But like, I've never, like, my stock portfolio is indexed because there's people who spend every single day trading in that market, and I hardly think that I'm smarter than them. And I think that's true of venture as well. So I have done angel investing, but it's always a long So I'm really betting somebody else's opinion who I think is, like, much smarter than I am.

AI assessment note: “I'm really betting somebody else's opinion who I think is, like, much smarter”

Q as you thought it could be, but your chips are down, and you've already put, I don't know, whatever, name your number, a skin in the game, and it's like, do I go all in and fuck it, or do I pull back and retreat, and then lose what I have? So like, I'm too intrigued. How do you think about sunk cost, and the right way to approach that?

A Yeah, so that's actually a really interesting problem. It's a lot of what I'm going to be writing about next, actually, because this is an issue of like, when do you actually decide enough is enough, and abandon an opportunity? So I There's obviously a lot, you know, grit is such a great topic, and, you know, sticking to things that are hard and not getting kind of stuck in local minima is a really, really important feature of people who are successful. But the question is, like, you know, there's a dark side to grit, right, which is sunk cost is really kind of the dark side to grit, right? We don't want to stick to things long after we're supposed to. So the question is, like, how do you actually solve for that, right? How do you actually get the grit just right? So you can think about it this way, and I think that this is something that should be intuitive to people, is that we have the intuition as humans that when the world gives us signals that we should abandon ship, that we will do it. But we know all the science tells you is that it's actually quite the opposite. There's really amazing work, some from Katie Milkman and Maurice Schweitzer, which shows this escalation of commitment, that when things are going poorly, we actually escalate our commitment. So that's kind of interesting, right? So we have this intuition, well, you know, if we're getting kind of bad signals fr…

AI assessment note: “when things are going poorly, we actually escalate our commitment.”

Q Well, that is very, very kind of you, but I would love to contest that a little bit. So talk to me. How did you make your way into the world of poker in particular and become to be the kind of very renowned author that you are today?

A Oh my gosh. You know, I have such a long and winding journey that, you know, I think it's like, it's so heavily influenced by luck. I mean, that's kind of how I think. Think about it, but, you know, I started off my adult life in cognitive science. I was actually doing PhD work at the University of Pennsylvania. I was going to become a professor, and really, like, had no intention of doing anything else. Like, I was going to be a professor, and, you know, do research, and have a lab, and on my way to a tenure-track job, and right at the end of it, I got sick, and so I needed to take a year off, and, but this was also, just, just for context for people, because I think that people don't really realize, like, there was a time before poker was on television, so poker started getting really big on TV, and, like, 2002, 2000 three-ish, like right around there. And prior to that, it wasn't on television. I was doing this in the nineties when people didn't really understand that poker was something you could make money at. It was really kind of firmly in the category of vices, you know, it's like gambling. So the only reason why I knew about it was because my brother was really interested in chess. Actually, I've been watching Queen's Gambit, which is so wonderful.

AI assessment note: “I started off my adult life in cognitive science. I was actually doing PhD work”