Interdisciplinary Skills: Decision-Making – Annie Duke (2022)

[Music] On Ted's sideys, and this is Capital Allicators. For the second year, our Summer Series will share some of the best episodes of the past, curated from our favorites and yours. You can pull down many of the great ones on our Spotify playlists. This series pushes them to your regular listening feed. Our 2026 Summer Series focuses on improving the investment process. Over the next six weeks, we'll share 10 gems from interdisciplinary skill sets that will help you level up your investing, across decision making, interviewing, storytelling, negotiations, and habit formation. Our Summer Series kickoff is with one of the all-time greats, Annie Duke. The former world-class poker player has become a noted academic and practitioner in the art of decision making. Her bestseller, Thinking in Bets, is a one-of-a-kind set of tools and tips to try, emphasis on try, to get past our embedded behavioral biases. And her follow-up works, how to decide, and quit, pull different threads in the decision-making process. Since writing Thinking in Bets, Annie has consulted several investment organizations to apply her insights to investing, including becoming a special partner focused on decision science, adventure firm, first-round capital. For the series, we've shared two of Annie's most popular appearances on the show, coinciding with the launch of Thinking in Bets in 2018 and quit in 2022. Before we get going, we're hiring at Capital Allicators. Two roles that will shape our next chapter. There are two of the best jobs in the business, at least in my opinion, that will help us bring together our community of allocators and managers to compound knowledge and relationships. And best of all, we get to serve this community without really selling them anything. Full descriptions of the role are available at capitalallicators.com. 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Now every time we talk about one of your books, you and I agree that neither one of us is going to write another book. Well, that's true. So here we are again. What was the path to coming up with the idea for quit? So here's the thing. So after the last book, I really did say, like very much, "Swear up and down that I wasn't going to write another book." So what I realized though was that sometimes you need to write a book. Like you just have to. And I think that that was true with my first general audience book, Thinking in Bat. So it was something that I'd been thinking about for a decade before I actually kind of made the decision that I was going to sit down and write it. And so I think that was something that had been brewing in me for a long time. When I wrote How to Decide, it was meant to give people a practical way to implement Thinking in Bat. But it wouldn't say it was a book like I had to write. It was a book that I felt was important to write for readers in relation to Thinking in Bat. So after that, I was really like, I'm never writing another book again because it's such torture to write a book. It's a lot of work. I swear that up and down to you. It was probably two months later that I asked you to get on a Zoom with me to talk about this topic of quitting. So what happened? Right? That's the question. Like, how did that happen so quickly? So it's a good example of overconfidence. Basically here's what happened. I'm doing podcasts like this one here for How to Decide. And there's lots and lots of materials and how to decide. But as you kind of roll around to chapter seven, which is a chapter about how do you make faster decisions? When is it okay to sacrifice quality for time? That's what that chapter is about. There's maybe a page in there, ish, it's a small section on quitting in the sense of optionality that when you think about what Bezos or Branson says about one way or two way door decisions that having a two way door decision, one that you can reverse or let's call it quit, makes it so that you just have more margin of error on the initial choice. Okay, so that's this little tiny section in How to Decide. And during these podcasts, I kept directing the topic over to this little section. Nobody really wanted to ask me about it. I just made them talk about it. So I kind of just paid attention to that. And I said, but this is a very weird thing that's happening because there's all sorts of other things that I could talk about in this book and I really want to talk about this topic. And then it just became like a brainworm. And going beyond just the issue of optionality, I just started thinking about all of this work in cognitive science that really shows that quitting is really hard. We're not very good at it. And thinking about my life in poker and how incredibly important quitting a K folding is in terms of the skill elements of that game and how much elite poker players really know that you have to be able to cut your losses. This is an incredibly important skill element of the game. In fact, I would argue that most of the skill would go out of poker if you didn't have the option to quit and couldn't get good at exercising that option. And of course, you as an investor know that it's an optionality or liquidity for at least just so highly valued. So I couldn't let that go. And I just started thinking about that. And then I moved on to this issue, which is that I think that grit has captured the popular mind. If you're gritty, you're showing character. It's a virtue. And then quitters are losers. And I said, well, that's absurd. So I then started just calling people to see if they were as excited about the topic as I was called my agent was my first call. You were one of my very first calls. I called Michael Moveson. He's probably the first person I taught you because he's always my number one person to go to. I would recommend for anybody if you have an idea called Michael Moveson. I also got in touch with Danny Coniman to just sort of gauge. Did he think this was something worthwhile to explore? And what I discovered was that all the people that I could connected with on the topic, we're honestly as excited as I was about it. At which point, I literally sort of like my shoulders, I was, everything went out. Oh, I think I need to write this book. And that's kind of where that happened. So Annie, is this book comes out and you're going to go on the normal tour to spread the message about why this is so important? What's the mean conclusion that you drew after being compelled to dive into the research leading to yet another book, which I believe will be another bestseller. I got to write with Morgan Housel's book and I think I'm going to be right with this one too. Well, I mean, to be fair, Morgan Housel's book is incredible. And let's just say right now, all of you have probably bought the psychology of money, but like on the off chance you have it, you should just like stop listening to us right now and go buy it. What's your elevator pitch when you go on the today's show to talk about quit? Yeah. So we think of grit as a virtue and quit as a vice, but that's not true. They're the exact same decision. By definition, if you choose to stick to something, you're choosing not to quit it. And if you choose to quit something, you're choosing not to stick with it. And we need to understand that all the skill is telling the difference between the two, because it's all about context. One is not a virtue and one is not a vice. They're the same decision. And that's the skill that we need to develop is when is it worthwhile to stick to things and when isn't it? And here's the thing that I want people to really, really, deeply understand that usually, if you quit at the moment that it's objectively correct, it will feel like you're quitting way, way, way too early. That's the thing that we need to watch out for, because as human beings, we just generally stick to things too long and life's too short for that. So you mentioned in public that grit is this heroic thing that people aspire to. And I just love some of the stories that you picked out where grit wasn't necessarily the best answer. So why don't you dive in maybe with that just initial story in the book, which is such a good one. So we all say things like quitters never win and winters never quit. But then it's so easy for me to just give you an example, which is so like in your face, that you go, oh, wait a minute, maybe quitting is an okay thing. So let's start our conversation on the top of Everest, because climbing Everest is obviously an amazing example of grit. You're super uncomfortable. You got to climb up and down up and down up and down like takes months, you're freezing cold, you get frostbite. And yet you continue up to the summit. So if I wanted to write a book about grit, I might also start it on the top of Everest. So let's start a book about quitting on the top of Everest as well. And this particular story is about three climbers, Dr. Stewart Hutchinson, John Tasky and Luke Cossisky. And they're in a group of eight climbers. There's like a few climbing sherpas and there's the expedition leader. They become friends and down at base camp, they get given these turnaround times. And turnaround times are just simply put for any day's climb. There's some point in the day where no matter where you are, whether you've reached your final destination or not, you have to turn around and go back to the camp that you came from. So the way that climbing Everest works is you start at base camp and you climb up and down to camp one a few times because you're trying to acclimate. And then you climb from camp two to camp three a few times and so on and so forth. And on summit day, you're going from camp four, which you usually leave around midnight. You go to the summit that day and then you try to get back down. And so these turnaround times basically say at time X, no matter where you are, whether you've gotten to the summit or not, let's say you must turn around. And the turnaround time on summit day is set at 1 p.m. So this is very clear to everybody. And the reason why it's set at 1 p.m. is that in order to go up to the summit or actually back down, you have to navigate the southeast ridge, which is very narrow and very dangerous. You have to go in single file. If you fall, you're going to fall like 8,000 feet into Nepal or 12,000 feet into Tibet. So probably don't want to do that. So one of the reasons that you leave at midnight from camp four is actually so that you get to the southeast ridge in daylight. And one of the reasons that you're supposed to turn around at 1 p.m. no matter where you are is because if you get to the summit past 1 p.m. there's too good of a chance that you're not going to get back to the southeast ridge in daylight. And most of the dangers are on the descent. That's when most people die is coming back down where you're tired and you don't have as much adrenaline and obviously your oxygen is low. And if you're in darkness, all of those things present grave dangers to the climbers. So they're given the turnaround time. So now it's summit day. They have got the turnaround time of 1 p.m. They leave at midnight and it's a particularly slow day on the mountain. As you might know, climbing ever started to get super popular in the 90s, which is when they happen to be climbing the mountain. And so there were more and more people trying to get up to the summit. And so there's only a single rope that you can go up and so you can sort of get a traffic jam. So they're in a little bit of a traffic jam. And it's getting on toward about 11 30 a.m. And their expedition leader actually comes up from behind them. And Hutchinson says to the expedition leader, hey, it seems like we're moving pretty slow, how much farther until the summit. And the expedition leader says, I think it's going to be about three hours. And he then goes past them and continues up the mountain. Hutchinson holds Tascane Kassitsky back. And he says, okay, look, I think we have a problem. It's just about 11 30. Our expedition leader just told us, this is going to be three hours until we get to the summit. I can do some math. That seems like we're going to get there at 230, which is going to be well past the turnaround time. So it seems to me, given that we know now that we're not going to get to the summit by one p.m. We out to turn around now. Kassitsky was a little reluctant. Tascane agreed right away. Kassitsky was actually on his seventh summit. That's climbing the seven tallest mountains in the world. So obviously he wanted to sort of finish, but he got convinced pretty quickly and they turned around and they left. Now it might not surprise you that you don't really know who these guys are. Because that's kind of a boring story, right? Nobody's going to make a movie out of that story. Where's the hero? There's three guys. They followed the rule. They went back to base camp. They lived. Yon. But here's the thing that's amazing about this story is that they climbed that mountain in 1996. A year that was chronicled by John Crackauer in Interthan air. Not only that, they were part of Rob Hall's expedition. Rob Hall famously going to the top of the mountain, arriving at 2 p.m. Waiting for Doug Hanson, one of the other climbers in his expedition until 4 p.m. Doug Hanson got to the top at 4 p.m. No, it's just well past the turnaround time. And basically died immediately by that time Rob Hall, who had been up there so long, didn't have the strength to come back down. And he also perished on top of the mountain. And guess what? He was the expedition leader who told Tascay, Kaczytskiy and Hutchinson that it was going to be three hours to the top of the mountain. So this was a year when a bunch of people died. A lot of people did not follow that turnaround time. But these three people did, they turned around, they lived to climb many more mountains, spend a lot of time with their families, because they actually followed a quitting role. And in the context of understanding what happened and the disaster that occurred, we should understand why now quitting is so important. And yet those quitters are totally invisible to us. Because here's the thing, I assume anybody listening to this is like I hadn't really heard of those guys. But if anybody's read that book, Crack Hour's book, or by the way, seeing the documentary or watch the movie, they're in there. In fact, Crack Hour says they were the best decision-makers on the mountain that day, because they turned around when they were supposed to and they quit at the right time. And I just think that that's such a good example of two things. One is people, we don't even see, like they're invisible to us, we don't see people who quit. That's sort of the best case scenario, or we see them as quitters, which is an insult. And the heroes of the story are people who had the exact same information as these three climbers and yet continued on even so to great disaster. I think that's like the whole shabang right there. Yeah, so there's so much to unpack from that. You've touched on the case for quitting, right? This idea that there's optionality, there's more time. What are those key things as you've done your research that you're laying out? This is why learning how to quit, learning how to change your mind is so important. And I just want to say something really clearly. I think everybody should go by grit. I think everybody should read Angela Duckworth's work. I think it's amazing, because grit is really good for getting you to stick to hard things that are worthwhile. The problem that I'm trying to tackle with quit is that grit also gets you to stick to hard things that are not worthwhile. And the key is to tell the difference between the two. Figure out as quickly as possible when it's not worthwhile. You know, this is my whole jam. Is that pretty much any decision you make is under the influence of some form of uncertainty. I've spent pretty much my whole life dealing with how do you make decisions under uncertainty? And the uncertainty comes in two forms. One is that the world is stochastic. There's just the influence of luck on the way that things turn out. Having completely independent of the decision quality, you can make a decision that's going to work out 95% of the time. And by definition, that means you're going to observe a bad outcome 5% of the time. And sorry for you, you don't have control over when you observe that 5%. Okay, so that's one thing is that there's just luck that influences how anything turns out. And then the second influence of uncertainty is hidden information that for most decisions that we make, we know very little in comparison to all there is to be known. Okay, so those are big problems in decision making, right? So whenever we choose to start something, in other words, we make a choice to go forward in some way or to choose some sort of path, we're making that usually under extreme uncertainty, where we may find out things later. And we say this all the time, right? If I had known then what I know now, I would have made a different choice. That's that feeling of the influence of hidden information, right? And then the other thing is that after the decision is made, you may observe the influence of luck in some way. So simple as sense, like I could be playing a hand a poker and understand that if a four hits the board, it's really bad for me. Now a four is only going to hit the board on the next card 8% of the time, but I'm going to observe that for 8% of the time. And when I observe that, I'm going to know something new. I'm going to understand how luck is influencing the way that things are turning out. So we have this really big problem. We have to make decisions when we don't have all the facts, when there's lots of luck that's going to influence the way it's going to turn out. And that makes decision making really hard, but lucky for us, we have the option after we find that new information out to change our minds and to do something different. And that option to quit is so valuable. That's the thing that when I was talking about the other book, I just wanted to tell people all the time is yes, decision making under uncertainties really hard, but you have optionality. When you learn the new information, when you discover how luck is influencing the outcome of the thing that you chose to do, you can do something different. You can go back to previous options that you might have rejected in the past. You can go to new options that you hadn't even considered yet. And this is what gets us out of that bind. That right there is the reason why quitting is so incredibly valuable. But here's the rub. The way that I try to put it is imagine this. Imagine if the first person you ever dated you had to marry. How hard would it be to make the decision to go on a date? How could you ever go on a date? It's the option to say that date wasn't very good. I don't want to go out on another date with you that allows us to go out on dates, right? And it's not just dates. It's like we can take a job because maybe if we don't like it or it turns out the boss is toxic, we can walk away from it. Some things are harder to quit than others, but you can quit being married, for example. Now that's harder to do so you should probably have more certainty around who you marry than who you date. But in either case you do have an option to walk away. You can change colleges, so on, so forth. So that's all really good. Yeah, so lots of good reasons to understand why it's important to be able to change paths. You also hinted in that ever story that there's a lot of obstacles in being able to quit effectively. And I'd love to hear what you learned from the research you did that went into this book about what are some of those obstacles? I'm going to give the top line first and then we can dig in as you want to some of the details because in a nutshell, it turns out that the way that our cognition works, it's very hard for us to walk away from things. There's a lot of biases, like biases that you might think are independent, but that you can pull a single thread through that have to do with an inability to quit. Let me just talk top line. The decision to start things is made under uncertainty. We can do that because we have the option to quit. But here's the problem. Exercising the option to quit is also a decision made under uncertainty. So here's where it gets really hard is that you're in the middle of something, you're doing something, you're climbing Everest, and you have to make some sort of forecast of the future. Because in general, the right time to quit is before you're already at the summit and it's 4 p.m. You're supposed to quit a lot earlier than that. And what that means is that basically, if you get to a point where you're very close to 100% certain that you're supposed to quit, it's probably already too late. As Richard Daler said to me, the only time that we're willing to quit is usually when it's no longer a decision. Because you already fell into the crevasse, what are you going to do? It's like your startup is already out of money. So at that point, you kind of know, like I know I don't have any other choice, I have to quit, but by then it's like well past the time that you should quit. So if we think about when you should actually quit, it's when your expected value goes negative. So you're thinking about am I winning to the path that I'm on in comparison to other paths that I could take. So that's the thing that you have to figure out, but we know with those types of calculations that generally things don't look so bad in the moment that you figure that thing out. Let's go back to Hutchinson Tasking and Kaczyczki. At the moment they figured out that well, I'm supposed to turn around because my expected value looks pretty bad here, which they knew because of the turnaround time that had been set. They have tons of oxygen. They're not in a dangerous place on the mountain. There's no snowstorm. Other people are continuing ahead of them. So they're like super brilliant. They're amazing. They're going against every single instinct that a human being has and they're turning around. They really are the heroes of the story because they're able to look and peek into the future in a way that other people weren't able to there and make the decision to turn around because at the moment that it's correct, it will be made under uncertainty. It's going to be probabilistic. You're never going to be 100% sure that you're supposed to quit. And here's where it gets really bad. The only way to know how the thing you're doing turns out is to stick with it. Right. So now we can see this problem. It's like we're beings that want to know. We're very uncomfortable with uncertainty. And the only way I can know for sure is to grid it out to persevere. Then I'm going to find out for sure. And that's going to be good, not just for me personally and feeling like, well, I didn't fail because I didn't have a choice. That's Taylor's point. Like what else could I do? I tried everything. But also in the way that we think that other people are going to view us. Because we worry that other people are going to think we gave up too soon. We're worried that other people are going to think we're not gritty that we lack character. They're going to call us quitters. So you mentioned a thread of all these behavioral biases. And some of what you were just saying is, okay, how other people are going to perceive us is certainly one. You mentioned opportunity cost, the difficulty of assessing something that it's okay. It's fine. It's working. There could be something better. There's an element of inertia. There's clearly some cost involved. Part of the way up Everest all the time you spent in that. What are some of those other threads? And how do they weave together in the things that create the obstacles to effective quitting? Sure. Okay. So let me just list them off. And then you can tell me what you want to talk about. So there's sure loss of version. Now I just want to be clear that's different than loss of version. Both of these come from conumin and diversky. Conumin obviously, Nobel laureate. Loss of version is not wanting to start things for fear of the losses you might incur later. So let's think about loss of version as something that stops you from starting. Sure loss of version as conumin says that talks about is not wanting to turn a loss on paper into a realized loss. So loss of version stops us from starting. Sure loss of version stops us from stopping. Okay. Then there's all these things that go under this rubric of kind of escalation of commitment. A escalation of commitment simply put it like this. We have the intuition that when we get bad news that will stop doing what we're doing. And actually we don't stop when we get bad news. We actually double down. We escalate our commitment to the cause. And what goes under that umbrella, which is more of a motivational explanation would be a lot of the cognitive explanations. We're going to put some cost under that rubric and the endowment effect things we own. We value more than things we don't own omission commission bias status quo bias ambiguity aversion mental accounting actually becomes a thread that goes across here, which is that the way that we sort of think about losses and gains as not being fungible across all the things that we're doing. We open accounts for things that we start and we don't like to close those accounts and losses. So that's another piece of it. Then we have all of these things that have to do with identity, internal and external validity, cognitive dissonance, the desire for consistency or to be seen as consistent. Then the opportunity cost piece doesn't stop us from quitting. It's that sunk costs makes us stick to things that then make us incur opportunity costs. And then we have all of the stuff around goals that goals are graded as pass failed. They cause this myopia progress along the way. It doesn't matter. And so that also will stop us from quitting because it gives us a target where if we don't get to the finish line, we failed. It's like the whole of behavioral science. Once you sort of look at it through the lens of quitting it, I think that's why I got so excited about the topic was that you start to think about over optimism. I forgot that one. Overconfidence. I forgot that one. You start to think about like so much of what people know from thinking fast and slow, for example. From this world that we think of this exciting world of behavioral economics and behavioral psychology, you say, oh my gosh, I can literally pull this quit thread all the way through it. That just really excited me. It is literally the entire smorgasbord. It's used laid out of behavioral economics. I think to dive into a couple of those, why don't we start with this recent academic research on money managers that although they tend to make quite good buy decisions, they tend not to make good sell decisions which is effectively quitting. I imagine a lot of these examples factor into it, but would love to hear your thoughts on what you've seen from money managers sell decisions. Okay, so let me just start by talking about what regular people do because I just think it's helpful to frame in terms of what money managers do. Usually, we quit too late. I don't know if you've heard that old management adage. The first time you think about firing someone you should. People don't. That's probably already way past when you should have. That's for most things. Most of the time when we decide to turn around on Everest, it's way later than we should have. That's because we're trying to accrue certainty, much more certainty than we actually need before we make the choice to turn around. That's not always true. Sometimes we quit too early. If we think about what Taylor talks about with mental accounting, we don't like to close mental accounts in the losses. Let's think about that as in a simple way. If I buy a stock and I bought it at 50 and it goes to 40, I don't want to sell it because I have to close that mental account in the losses. This has to do with sure loss aversion. But if it goes to 60, I now really want to sell it because I would like to take the sure gate. Okay, so when we're in the gains, we like to close the account and when we're in the losses, we do not like to close the account. When you look at retail investors, this displays as people cancel their stop loss orders all the time to blow through them. It's like, you've got to stop loss at 40 and you're like, I'm canceling it because now the stock's really cheap. Then you go to 35, then you go to 30. This is a very well-known behavior from retail investors. Then obviously, they also cancel their take-game orders, but that's to not reach the take-game. They buy it at 50, they've got a take-game order at 60. It gets to 57. They're canceling it and selling. Again, because they want to close that account in the gains. You can do about this way. If I leave the position on, I've got luck going for me. The stock could turn around. I want to leave the position on. When I'm at 57, having bought in at 50, in other words, I'm in the gains, I actually don't want to recruit luck into the equation because luck could go against me and cause me to erase those gains. Now, obviously, this is totally irrational. It should just be, is the stock still positive expectancy? That's all you should care about. But we don't. Let me just set that up there. Now, so now we can say, all right, if retail investors are doing that, what are expert investors doing? Because surely, they're not doing that. The answer is surely, they're not. You're correct. So, when you look at expert investors on the buy side, they're doing really well. They're generally doing somewhere around 120 bits or so better than the beta. So, they're alpha generators on the buy, and you can see why they're thinking through the Thesis, they've got quants working on it, so on, so forth. So, they're great at that. So, the question is, let's look at what's happening on the lost side. And it's not as simple as they're selling winners too soon and they're keeping losers on. In fact, you don't see that behavior from them at all. But yeah, when you look at their sell side decisions, they're usually about 70 bits worse than the benchmark. So, if you were to just randomly free the capital up from your portfolio, how would you do? That's the benchmark. When you look at their actual decisions, and this is work that comes from Alex Emas, who's amazing, I would highly recommend that people read his work. But when you actually look compared to the benchmark, they're about 70 bits worse than, oh, I just randomly chose to sell stuff. And so, Emas asked why? Like, why is that? Why would they be so much worse? And it turns out that what they're doing is just looking at the tails. So, when they have to free capital up, they're either looking at the extreme winners or they extreme losers. And they're not actually considering the whole set. Now, I sort of introduced that pretty early in the book. I get into later how you can solve for that. And I've actually coached PMs who are in that exact same situation about how you can get better at those decisions about when you sell. But that's sort of the short story is that's the behavior you're seeing from institutional investors. So, the question is, you know, why? Why is that happening? Well, obviously, those are attention grabbers. But we know that on the sell side, they're so good. And I think it's because my conjecture, and actually Alex's conjecture as well, is that, well, on the buy side, you're getting constant feedback because the position's on your book. So, you can see it tick up and down every single day. You have some thesis about what the fundamentals are going to look like. And every day, you can compare your thesis to how the world is actually unfolding. So, on the buy side, it's just a feedback machine. Just feedback loops, right? But once you sell it, it's off your books. You're not looking at how that would have done. And so, you're not getting any feedback. And in general, what I find is that they don't know that they're losing to those quitting decisions because it's a little like out of sight out of mind. And if you're not getting any feedback on it, you're not getting experience how you're going to learn. We're going to take a quick break in the action to tell you about Ridgeline. Ridgeline makes your day unrecognizable. That's how refreshingly different it is from legacy investment management technology. With Ridgeline's front-to-back AI native platform, your typical tech pains disappear. No integration headaches, no data discrepancies, and no upgrade cycles. Instead, you get real-time data flowing through everything from portfolio accounting to reporting to reconciliation, trading, compliance, and more. In the AI era, asset and wealth management firms moving to Ridgeline gain a decided advantage. That's why customers call it miraculous game changing and an awakening. If that's not how you would describe your investment management tech, request a demo at Ridgeline.ai. And now back to the show. So one of the most interesting things that you mentioned is this problem of identity, which certainly hits all of us in money management. And we tell someone about the positions we own, and therefore we own those positions and make them less likely to sell. What's your favorite anecdote to describe that problem? Okay, I'm going to tell you one that's completely bonkers if that's okay. It's about a retail chain called Sears. Have you ever heard of it? We've heard of it. What's left of it? Right. Well, yeah. So here's the interesting thing about Sears. So it's founded in the late 1800s. It's a catalog company. I think the original one had like 512 pages in it. This was when the mail routes were just opening up. So all of a sudden, people in remote areas could get stuff that people in the cities could get like bicycles. And they sold just about everything. Everything from bicycles to like kitchen cheerers, like I think you could buy cars in the catalog. And they were incredibly successful. This was really the most successful retailer. And so then all of a sudden like right around the 30s cars become really big and people become much more mobile. And what they find is that this is kind of eating into like their catalog business. And so they start to open retail locations. And just so you understand how successful Sears ends up being as a retailer, at one point they represented 1% of the total GNP of the United States. So they're big. But then we also know the sort of decline of Sears. It starts faltering in the 70s as these other bargain retailers start coming along like Walmart and Kmart for example, eventually target in the 90s. That starts to eat into Sears as business, particularly because they're kind of getting squeezed out. You've got the high end retailers like Nordstroms and Neiman Marcus that are kind of pushing them from the top. And then you've got the Kmarts, Walmart's and eventually the targets that are squeezing them from the bottom. And their share of that market starts getting smaller and smaller until in the beginning of the 90s, they actually end up not being the number one retailer anymore for the first time in ever. Eventually, as we know, they declare bankruptcy and they go out of business. Okay, so that's kind of the story of Sears that everybody knows. But there's another story of Sears, which I find much more interesting. And it's not as Sears, the retailer, but Sears, the financial services company. And most people don't know that Sears was a financial services company. So they start a banking division when they just have the catalog because they were offering their consumers credit. But when all of the cars start happening and they open those retail locations, they realize, hold on a second, people drive their cars to our retail locations. So they open up a new business to sell car insurance to all of these new car owners. And that business is called all-state insurance. Now all-state ends up branching out obviously and ends up becoming the largest actually seller of all sorts of liability insurance, home, auto, fire, life, so on and so forth. And that goes on for a long time. So they found that in the 1930s, they continue to own all state for a very long time. Then in the 70s, they decide that this financial services business is really doing really well. And they acquire another company called Dean Whitter. And they also create their own credit card called the Discover card. That's pretty big. And then they also acquire Coldwell Banker. So let's think about the worth of those companies. So Dean Whitter and the Discover card gets actually bought by Morgan Stanley. And at the time, it represented 40% of the worth of Morgan Stanley. So we calculate whatever Morgan Stanley is worth. And that's whatever that is. Okay. And then you have obviously all state, which is worth billions. Coldwell Banker ended up merging into reality. But I think that their market cap is 2.2 billion or something like that. And I think all state is worth about something like 40 billion. It's a lot. So we've got billions and billions and billions of dollars of worth from the financial services industry. So I don't know about you Ted, but I'm like, how did Sears go broke? That seems very strange. Because they owned a lot of stuff. So that doesn't really make any sense. So here's what happens, right? Remember that starting around the 80s, the profits from the retail business start to falter and decline. Eventually, they start to lose money on it. And at some point, the board of directors have to make a decision about what they're going to do about this faltering business. Now, from the outside looking in, it seems pretty obvious what your choice should be. Why don't we get rid of this stupid retail business? Because look at our financial services business. We have Dean Whitter. We have Coldwell Banker. We have the Discover Card. We have all state. Let's just go with that because that's just making money hand over fist. And let's get rid of this retail business, which is losing money. But the board of directors puts out a statement saying that what we've realized is that we have to get back to our retailing roots. They essentially say if we're thinking about it from an identity standpoint, that Sears is identity is retail. It is who we are. We are retailers. And certainly from the standpoint of the public, that is how they knew them because I don't think you knew this. I certainly didn't know this before I did research for the book that they even ever owned all state. Why would I have known that? So what do they do at this point? This is in the 90s. They spin off all state in its own IPO. They sell off Coldwell Banker. They sell off Dean Whitter and Discover and they do get back to the retailing roots, which are losing money. And that's it. And that basically becomes the end of Sears. So that seems so bonkers, right? Like it's so incredibly bonkers. How could you do that when it's so clear that you have this thriving business? And why would you sell these thriving businesses in order to raise capital to try to save something that's doing so poorly in the face of a very competitive market? And it's because of what they said we had to get back to our roots because the hardest thing to quit is who you are. Even for Sears. Yeah. Amazing. All right. Before we get to some of the ways that we can get better at some of these quit decisions, there's one other example I'd love to talk about, which has come from the endowment effect. This idea that you value the things you own more than the things you don't. And you use the example of sports teams and draft picks anytime I can weave in sports and sports analytics. I'm always going to do it. So how did that play out as you saw it in the research? So Barry Stau, who's larger than life, scientist and character, his father is actually a character in the book, who was also in doubt. I had a little bit of a problem with the downwind, but I'll let people read the book to read about Barry Stau's dad, Harold. But it's pretty amazing. He did a lot of the original work on escalation of commitment, starting with a paper called knee deep in the big muddy, which was about escalation of commitment and sort of first identifying this issue where it's like it just goes against your intuition. But when you get bad news, you don't walk away. You don't cut your losses. You actually doubled out. So he did some work with Ross in the 90s on this and then Colin Cameron with his colleagues replicated the work again. And then it was just recently replicated again. And what he found was he was looking at drafts from a few years in the 80s to look at whether draft order had any effect on later decisions that had to do with quitting. And you can think about what those might be. Like, are you getting playtime on the court? So if you're not benching somebody, obviously, you're doubling down your escalating commitment to them. Are you trading them? How long is their career? Those kinds of questions. What he did was he looked at just the metrics. What is the player productivity? So he's got all these measures of player productivity. And what we know is that basketball teams ought to be highly motivated to have their most productive players on the court as much as possible. Not only that, they have the data to be able to do it. So I think that sometimes we think, we're going to make good quitting decisions if we have the right motivation to quit. And the incentives are aligned to make you quit and you have enough information. Maybe we just don't quit because of what I said before is that at the moment that you're supposed to quit, probably nothing particularly bad is happening. So it may be just an informational deficit. But now we're in a world where there is no informational deficit. You have the stats for that player every single day. And this is what he found is that draft order was predictive. Independent of the skill of the player for how much playing time the player got and for how long their contract was so much so that I believe that from one to two was 23 minutes a season. From two to three was 23 minutes a season. It was like that was how big this effect was that they found it was humongous. And I think their contracts ended up being about two years longer. Now again, I want to say this is controlling for skill. This is saying if you look at the number one pick in the first round versus the number one pick in the second round and you now control for their on court productivity, you still get this effective draft order. So what is that telling you? Well, two things. It's not just a sunk cost effect, right? Obviously you have more invested in them in terms of the contract sizes are bigger and that kind of thing. But it also brings in this issue of endowment and identity. Your team's identity is really tied to those really high draft picks. So it's very hard to give up that piece of you. One of the things I say in the book is one of the hardest things to quit is who you are. And your number one draft pick is going to be part of who you are as a team and the people who made the decision to spend that very valuable resource are going to be very much endowed to that decision. You can see this all comes through and that should be really alarming to us because the financial incentives are totally there and the information is totally there. It's totally available to these teams and yet they don't have their most productive players on the court and they're keeping them around a lot longer than they ought to simply having to do with what point they were drafted in the draft. We have all of these things working against us, which does remind me a lot of thinking about. And fortunately, in thinking about you gave some prescriptive things about how to make better decisions. And so again, here in Quit, you have laid out some of the things that we can do to get better at decision making around this all important quit decision. And I just love to hear some of your thoughts and some of those prescriptions. I'm going to be so excited to tell everybody what to do right. But first I want to tell people a story about a quitting disaster. Just not quitting when you should. I think it's going to help us understand if we understand this particular story. I think it helps us understand what we can do to be better. So this is a story about the California bullet train. And sadly, it's not fiction. This is a real life, true adventure. So for those not familiar, the California bullet train is a very audacious project in California. They started thinking about it and I think it was about 2008. And the idea was to build high speed rail like in Japan or other places that would connect San Francisco and Silicon Valley to LA and San Diego to the South. The reason that they wanted to do that was obvious. Those two areas are the economic engines of the state of California. And so they wanted to connect those to the interior. What was lying in between. Number one, just for like economic prosperity, but also number two, to make it so that you could relieve the congestion in terms of the housing market in LA San Diego and San Francisco and Silicon Valley. Because those areas obviously because they're economically prosperous, they attract a lot of people. It was a very congested housing market. So if you could somehow make that commute easier for people, you could essentially spread people out. And that would be really helpful for everybody in the state. This was the idea. OK, so great. They floated a bond in 2010, which was for $9 billion to begin construction. The total budget initially was estimated at $33 billion. The projection was that enough of the line would be functional by 2021 that it would be in the black. And it would essentially be able to generate enough income to complete the rest of the line on its own without taking any more taxpayer money. And that was going to be through both fees for ridership and also public private partnerships. So that was the idea. It's 2022. I haven't heard about this real. Yeah, right. Oh, that's weird. Because it's supposed to be done in 2021. So, hmm, okay, so let's figure out what happened. So they approve track between, I think it was Madera and Fresno. That's where they're going to start. It's pretty far to the south of San Francisco, I'm pretty far to the North Belay, but that's where they're going to start building the track. It takes them five years to break ground. That's a problem. So they approve this bond in 2010. They don't break ground until 2015. That's probably assigned things aren't going well. But then what happens is that somewhere around that time, leading up into 2018, they figure out that there's a problem. In fact, there's two problems. And the two problems are mountain ranges. One, which is the Tachoppy Mountains, which are to the north of LA, separating LA from Bakersfield. But then even worse than the Tachoppy Mountains is the Diablo range. And the Diablo range sits to the south of Silicon Valley, separating that from everything to the south. And again, this is the whole reason why these areas aren't connected in the first place. So somewhere along the way, somewhere between 2015 and 2018, they go, oops, we didn't really think about the Titanic engineering challenges that these two mountain ranges are going to present in terms of actually connecting point A to point B. In other words, San Francisco basically to LA, because we got to figure out how to blast through these mountains. So there's a report they have to the authority, which is the body, it's literally called the authority, is sort of like governing this. And they put this report out that's like, oh, oops, we made a mistake. Because actually the budget's going to be more like $81 billion. We're not even sure it's going to be that low because we don't actually know if we can blast through these mountains and actually run track through them. So this now goes to in 2018 to Gavin Newsom, the governor. Now you might think that once your budget's gone from like 30 billionish to 80 billionish, you might go, oh, well, we've only laid this little bit of track. Why don't we stop now? Except that's not what happens. Instead, Newsom says, no, we're going to keep going. And here's what we're going to do. We're going to build track next between Bakersfield and Merced. So notice this is to the north of the Tetch Hoppy Mountains. This isn't addressing the engineering issue. And then after we're done with that, we're going to build track between San Francisco and Silicon Valley, which by the way, already connected by roads and to the north of the Diablo range. So instead of just shutting the whole darn thing down at that point, they actually escalate their commitment, right? Like this is what we mean by escalation of commitment. And they say, no, I don't know what's going on with those two challenges that we can't even completely look the line if we don't finish it. But we're now going to build two other pieces of track on flat land. Essentially, I mean, if we think about it in terms of the original goal, they're building from nowhere to nowhere, at least not anywhere that anybody was trying to connect in any way. The last report that came out, which was just this last year, now estimates the cost at about 120 billion. It still says, like it's very uncertain, that we're going to actually be able to conquer these engineering challenges. So obviously, you can see there's some cost issues in there. There's endowment issues. There's issue about identity. This problem that we feel of waste, I don't want to waste the taxpayers' money. Except waste should not be a backward looking problem. It should be a forward looking one. The nine billions already spent. Are you supposed to put another billion into this thing? Or by the way, another dollar. Right, so this now can start to get us to a solution. I know that's weird because it's such a disaster of a story, but it can get us to a solution. And it gives us a clue to one of the first ways to think about quitting well. And it's a mental model that I absolutely love that comes from Astro Teller, who's the CEO otherwise known as Captain of Moon Shots, over at Axe, which is the Innovation Hub at Google. And the mental model is called monkeys and pedestals. This is one of my favorite things ever. So imagine Ted that you need some dough. So you decide you're going to come up with a side hustle. And the side hustle is going to be that you're going to train a monkey to juggle flaming torches while standing on a pedestal in the town square. So can we agree? Like, people would probably throw a lot of money in your hat. Sure. That would be pretty cool. Okay. So what Astro says is, what are you supposed to do first? That's the really hard thing about this. Because one thing is a possibly intractable challenge. And the other thing is building the pedestal. So we think about building the pedestal for the monkey to stand on. We already know that we can do that. Because people have been building pedestals for many thousands of years. You could probably buy a pedestal from somewhere or you could like turn a milk crate upside down. The thing that might be a bottleneck to unlocking the whole system is actually, can you train this monkey to juggle the flaming torches? Okay. So if we take monkeys and pedestals, basically it's hashtag monkey first. That when you're approaching a problem, you want to think about what are the bottlenecks? What's the really hard thing that needs to happen in order for me to be able to unlock this problem? And that's the thing that you're supposed to go after first. You're not supposed to build the pedestals. So let's think about why that might be. Why is this such a helpful way to think about approaching problems? Well, first of all, can we agree? This is the opposite way that most people approach problems because I don't know about you, Ted. But have you ever heard someone say, let's tackle the low hanging fruit? The low hanging fruit, yeah. Yeah. So look, I don't have a problem with eventually building a few pedestals, right? Like you should eventually tackle the low hanging fruit, but not before you figure it out, whether you can get to the top of the tree. But we do that because we like the sense of progress. And this is one of the most important points of what Astroteller says is that there's progress and there's false progress or the illusion of progress. And if you build a pedestal, it's the illusion of progress because you already knew you could do it. If I build track between Bakersfield and Merced, it's the illusion of progress because I already knew I could do it. That's not really the problem that I have in building this train or training my monkey. The problem, the real progress, would be in solving something that you don't know yet whether you can solve. Figure out, can I get that monkey to juggle those torches? Or can I actually blast through the Diablo range and get track to run through it and do that thing first? This is so important because every minute that you spend building a pedestal, every dollar that you spend building a pedestal before you've tackled that monkey, those are some costs that you're accumulating. Like the taxpayers, they've now accumulated $9 billion in costs, building pedestals, track that they already knew that they could build. So we don't want to do that because the sunk cost, the problem is that it like snowballs on you, right? So you accumulate the sunk cost that makes it harder for you to stop, which then causes you to accumulate more costs, which then makes it harder for you to stop. So if you start with low hanging fruit, if you start building pedestals first, you're now going to make it harder for you to quit when you actually butt up against a monkey that you can't solve. And from Teller's standpoint, he says, look, we're going for moonshots here, right? If I can figure out after spending $2 million that we can't do it, instead of figuring that out after we've spent $9 million, that's not a waste of $2 million. It's a saving of $7 million. And this is the way that we need to understand that. So now let's go back to the California bullet train. Well, the monkeys are the mountains. The pedestals is any track that you build other than the stuff that's at the mountains. And look at what they're doing. They're just pedestal building. I'm sure you can hear somebody in the room saying, well, we shouldn't cancel it. Let's tackle the low hanging fruit. Has anyone gotten you in front of Gavin Newsom? Well, no, but the book hasn't been published yet. So I don't know. Maybe somebody will. But you can think about this for anything. And in fact, so now I'm going to turn into interviewer for a second. Because when we chatted about monkeys and pedestals, you actually brought up an example from your own life, which I think is really enlightening in terms of the way that you think about approaching projects this way. I want to hear from you what that was. Oh, I could talk about that briefly because there's a lot more that we need to get to. But the example that we talked about was when I was planning to launch a fund earlier this year, the obvious monkey is how easy is it going to be raised the money. And some of the pedestals are getting your legal docs done and all the back office stuff, which is a nuisance and a lot of work and cost. And so I put a few pedestals, but what I said to you is, oh my God, I built all these pedestals before I tackled the monkey and could have done different types of market testing to all that kind of stuff. Another great example. And I see that all the time with fund managers that are going to launch, they assume that the market is going to be there. And then they go and build all the pedestals. So yeah, another good example of that. I think that's such a good example. And let me just say that the fact that X is called X is literally Astroteller living this concept of monkeys and pedestals because what happened was when they founded the innovation hub, they decided they would save the name for later because they just wanted to figure out whether this was going to work, whether it would make sense. And their charter is to take huge ideas from inception to commercial viability in five to 10 years. That's what their charter is. And so they wanted to sort of work on that stuff first and they decided they would figure out the name later. So they just called it X for the time being. And that's such a great example. So I want to encapsulate it. I think it's do the hard thing first and be aware of false progress. What are some of the other key remedies? to help people quit better. - Yeah, so another one that actually goes, I think really well with monkeys and pedestals is what I call kill criteria. So let me just set this up by saying, I think Dan O'Coniman says this really well that the worst time to make a decision is when you're in it. So what does he mean by in it? I like to describe it as you decided that you wanted to eat healthier. Now there was a cupcake sitting in front of you. (laughing) That's what it means to be in it. It's really hard to actually do that. Okay, so can we figure out a way to make these decisions when we're not in it? Because that should improve behavior. So kill criteria comes from this idea. Actually it's from, I got the idea mainly from some work of Barry Staws. One of the things that doesn't help and I think this is very, very important for financial professionals to hear is one of the things that does not help is saying just treat the decision like it's fresh. That's like intuitive, right? Well, if the problem is that I made the initial allocation, then what I should do is think, well, what if I were one of those people who was new to the decision? And I've actually heard people that I work with say this all the time, oh, I tell my traders to say, what would you do if you had to buy it today? So it's sort of trying to do that mind trick of think about it as if you were new to the decision as if you can somehow sweep the cognitive debris out of your brain of having made the initial choice. So he actually tested that and found that it made no difference. So if you said, like, don't worry about what's happened in the past, think about it as a new decision. What would you do if you were fresh to it? Like all that stuff, it does not help. So stop doing that. I just want to say that, like, stop saying, oh, I just say what I buy it today, 'cause you're fooling yourself into thinking that you're making more rational choice, you are not. But one of the things that he found that did actually make a difference is equivalent to a turnaround time. What he said was when people made the initial choice, he said, I want you to list off what the benchmarks are that you would expect this division to hit that would show you that this allocation was successful. Okay, so he's asking them to think about in the future when the participants see the performance going forward, what are the signals that it's failing? What are the signals that it's succeeding? So he has them do that right it down and now he asks them to go and make an allocation. And when they do that, this gets you to a decision that looks more fresh. Now, this should sound a lot like turnaround times. That's what you're doing. At the base of the mountain, you're saying, what's the point at which I ought to turn around and you're making a commitment to that? Here's an example of kill criteria. This is something that I did with a group of sellers at a company that I consult for, it's called the M Particle, they offer a CDP. So I said to the sellers, okay, you get lead through an RFP or RFI, it's six months later and you've lost the deal. Looking back, you realized there were early signals that the deal was not to be won. What were they? So it's like 40 sellers, they all generate this independently and asymptlessly, that's really important. You can go for how to decide to figure out why that's really important. But they come up with a lot of lists and some of the more common ones were the lead in the first meeting all they wanted to talk about was price. They didn't ask anything about our technology or where we're different or whatever, they just were asking for price. Now, obviously a terrible signal, probably it means they're just trying to beat somebody else down or check pricing. Another one they came up with was that they couldn't get a decision maker in the room. So they weren't able to get an executive or someone who could actually make the decision to buy in a room. So we sort of generated this list and then we turned those into kill criteria. So the kill criteria are, if you take a first meeting with someone and all they wanna talk to you about is price, don't spend any more time on them. Why don't you wanna spend any more time on them? And this is very important for people like sellers who are naturally very gritty. The reason why is that you know that it's very low expected value now that probably they're already pretty far down the road with a competitor and they're just trying to beat them down on price and you're a stocking horse. So why are you wasting your time for someone who's not actually gonna buy from you? So that sort of gets into like, don't accumulate more sunk costs, don't spend a whole bunch of time on them. And also you're missing the opportunity of cost if you do that. In the case of not being able to get an executive in the room, they didn't kill right away, but it triggered a new action which was offer up executive alignment at the next meeting. We'll get, bring an executive from our side, you bring an executive from your side. If they said yes to that, then you would continue if they said no, you would kill. Okay, so when we generated a very long list of these kill criteria. So what this allows them to do is when they're not actually facing the decision down, they've now thought in the abstract about what are the negative signals that I might see in the world? What are the snowstorms that might come my way? Well, I'm on the top of this mountain that would tell me that I ought to quit. But we know that I'm probably gonna escalate my commitment. We know I'm probably gonna double-dap, we know I'm probably gonna persevere and stick to it anyway. And I don't wanna do that because there's too much cost involved both in the time that I'm spending on something that's low expected value, but also in not spending that time on something that's higher value and we turn that into a pre-commitment, which then allows you to manage them much better. Because one of the problems from a management perspective is that we manage to outcomes. Did you close the deal or not? That's how I decide whether you're a good seller. Now, if I've got a large enough end over a large enough course of time that is reasonable for me to say in the last year what business has Ted closed. But it's not reasonable on a single lead that you might be pursuing. And yet this is what we do when Ted loses the deal, I'm quizzing Ted about it. Because that's how I'm measuring whether Ted is doing well or not. When we develop this set of kill criteria, not only are you more likely to follow it because you're sort of looking for those signals. And we've made some public declarations about how we're supposed to behave toward those. But as a manager, it allows me to manage to the kill criteria and to the win. So this now gives Ted two ways to win. I can close the business or I can follow the kill criteria and when my manager says, "How's it going?" And I say, "Oh, the first meeting all they did was talk about price." And I say, "So I didn't pursue further." The manager goes, "That's awesome." And I get a win for that as well. And that's one of the most powerful ways to start to develop really good quitting habits. - So a lot of the monkey and pedestals in the kill criteria, you can imagine an individual decision maker working through that. How can you leverage a team that's working together to make better decisions? - Yeah, so again, I said a lot of the problems they were trying to make these decisions when we're in it. And we know we're not very good at that. So I kind of think about two ways to not be in it. One is to think in advance. So that's to not be in it on your own timeline. Okay, like I'm on my own timeline and I'm thinking about it far in advance. And monkeys and pedestals would be doing that. It's think about what's the hard part of the problem first. Maybe I should do an engineering study, feasibility study on the mountains before I start building any track. That's thinking in advance on how do I approach the project to figure out if I can solve for the bottleneck first. Kill criteria is the same thing. What basically saying to yourself in a way that feels counterintuitive, what are the signals that I might see in the future that would tell me that I ought to quit? And that's counterintuitive because we think what we have a thesis is we enter into say an investment. And obviously when the world's turning against our thesis, we're gonna quit. So I don't need to do this advanced step, but you actually do. And so that's one way to do it. But another way to do it is to be on your own timeline. So in that moment in time, even when you might be in it and talk to somebody who's not in it. In other words, just get yourself a quitting coach. And we all know this. We can see really clearly that someone should be quitting something. We can see better than most people when they should be out of a relationship. We can see better than most people when they should quit their job. We can see better than most people when someone should shut a project down, when someone should sell, you know, so and so forth. It basically, it's like, I don't carry with me all the stuff that you carry with you, all the cognitive debris that you carry with you, when you've already made that allocation decision to a particular division. 'Cause I didn't make the original allocation decision. So I don't have the sunk cost associated with it. I'm not in doubt to the original decision. None of my identity is tied up with that decision. I don't carry any career risk that might be associated with that decision. So I'm not in it with you. So I can look from the outside in and see it much more clearly in the same way that we can look from the outside in at the California bullet train and go, this is nuts. But the people who are in it making the decision are saying, "We don't want to waste taxpayer money." So this really came through to me, I think in a really clear way in a conversation with Dan Economan when he told me, he has a quitting coach, his name is Richard Deller. We should all be lucky enough to have quitting coaches who are no ballerillas. Let me go find one of those for myself. But he said, "Basically, you have to find someone who loves you but doesn't care much about hurt feelings in the moment." So what does he mean by that? Well, one of the things that might stop me from giving you a really good perspective, a fresh perspective is that I don't wanna hurt your feelings. I don't wanna make you feel bad. It's hard to tell someone that they're failing, that they need to shut their thing down. But we need to be able to do that if we care about how things turn out for them in the long run. This has probably happened to you before I said, I'm guessing, it's like you break up with someone or you quit a job or something and people say, "Oh, I'm so glad you did that." You should have done that six months ago. And what do you always say, "Well, why didn't you tell me six months ago?" Like, what is that? Why didn't you tell me? And you get kind of mad about it, right? Well, that's because if we leave things implicit, [BLANK_AUDIO] I don't want to hurt your feelings because I love you. That's sort of the implicit understanding. So even if you tell me tell me the truth, I'm going to tell you what I think you want to hear. And nobody wants to hear, you should close that mental account in the losses. Right? Nobody really wants to hear that. So what you have to do is say to your quitting coach, I want you to tell me the truth because I want what's best for myself in the long run. And if you can do that, you can get people to really help you with these types of decisions. So I think one of the best examples, like if we combine all of this together, comes from Ron Conway, who is the founder of SB Angel, one of the most successful angel investors ever. And when you look at his career and you look at the companies he's funded, there's a long list of things that you might think he's super proud of. But what's interesting is the thing he's the most proud of is actually coaching founders to quit, which really goes against what we think about as Silicon Valley like VCs, who like they want everybody just to grid it out. But that's not true. Like what they want is the people who are spending their time on something really worthwhile to stick to it, even through the hard times. And that's the wonderful thing about grit. So this is what Ron Conway is the most proud of. And so he told me like what he does with founders is he'll sit down when he sort of can see that maybe the company is floundering, maybe things aren't going so well. And he'll have a conversation with them about what he sees. And he said, you know, pretty much every single time the founders like, no, I can turn it around. And I'm sure you can relate to this in terms of conversations you've had with employees before by the way, where it's like, no, I can turn it around. And he actually does something interesting at this point, which is he doesn't disagree with him. He actually agrees with them. And he says, oh, I totally believe you can turn it around. Okay, so let's see what does turn around look like? So if we think about what's going to happen in the next two months, let's imagine that we're two months from now. Tell me in detail, what are the things that are different that tell us both that you have turned it around? So that should sound a lot like kill criteria. So he's asking for details. So they work that out together, what does turning it around look like? And then he says, great, let's talk again in two months. And let's agree right now that if you haven't hit those benchmarks, then you ought to return the capital to your investors. And he comes back in two months. And you know what, if they've hit the benchmarks, great, they keep going. But if they haven't, now what he's allowed those founders to do is think about that decision long before they're actually in it, long before they're having to actually shut it down. They've had two months of very clear criteria that they're trying to meet. And if they don't meet it, it becomes an easier decision that allows them to return the capital to the investors, which is as he points out, better for them. His whole thing that he says to everybody is life's too short. If you're a founder, you're probably brilliant. And by the way, incredibly gritty. You probably have that very good quality of being very gritty, super smart, driven, wanting to create a world that's 10x better. So let's agree that when we both figure out that this isn't the thing, that you should be spending your time on, that life's too short for you to do something this hard if it's not worthwhile. And you need to move on to something that is going to be worthwhile. And I think it's such an incredibly important way to think about things. And that's really what he's doing. It's just acting as that quitting coach. He's acting as the outside voice to help those founders see what he can see in a way that they can process so that they can actually get to that sooner. And one of the things he said to me, which I think is really important is he's like, look, when I talk to them, I usually think they should shut it down that day. So do they keep it going for a few months longer? Sure. But they might have kept it going for two years longer. And that brings us right back to Astro Teller. Get there in 2 million instead of nine. He's just so just fascinating stuff in the book. We didn't even cover all the great stories in the book, Muhammad Ali, marathon runners, all kinds of great stuff. So I really encourage everyone to go read the book. Don't forget Barry Stodd's father, Harold. It's one of my favorites. Oh, that's right. Yeah. Well, the combination of those two is just an extraordinary story. I can't let you leave without a couple of the new closing questions that I've had since the last time you're on the show. So here we go. Which two people have had the biggest impact on your professional life? I would remiss if I didn't say Lila Glitman, who is for sure a number one. She was my advisor in graduate school. She just passed last year, actually, I'm devastated about it. And she's just like an intellectual giant and kind of shaped the way that I think about the world. So for sure, she's number one. Then the problem is, well, I've had so many different professional lives that it starts to get really hard. So I could say my brother, because he's the one who started me playing poker, I could say Eric Sydow for a similar reason. I can say Michael Mobison, who would be quite honest. I sort of try to hopefully model myself in some small way after the way that he thinks about the world, his brilliance, just how incredibly nice he is as a human, how intellectually generous he is. And I hope that I can do about 10% of what he thinks in terms of thinking about my professional world. That aside, the person that I would have to name, like today, would be Phil Tetlach and Barb Mellers. Because I started doing a bunch of work with them on forecasting and really starting to think about how do we actually improve our ability to make predictions, which that's all the decision is, our predictions. And so currently, I would say that would be the biggest. And I know that that wasn't too. And then I missed a whole bunch of people, but it's hard for me because, first of all, I'm old. And I've quit a lot of things. And so there's too many places for my mind to go. What are your biggest blind spots, let's just say in decision making? Yeah, the thing is, I think it's so hard for any of us to see ourselves in any kind of rational way. And so I try to be really mindful of the mistakes that I make. I'm very decisive, which I think a lot of people think of as a very good thing. But I decide a lot of things way too fast. I talk to people about thinking about what's the impact of getting it wrong. And I try to get people to think about that. And I actually think that I probably don't think about that enough. A pretty quick decision maker. And so I think that's a really big blind spot for me. I really wish that I were better at that. Last one, and this one is custom made for you. Oh no. Which is, what's the best decision process you've gone through that led to quitting, regardless of how it turned out? Sometime around 2002, I got asked to give a talk about how poker might inform cognitive science to a group of options traders for a fund that was called Parallax Fund founded by Roger Lowe. And I really enjoyed it. I hadn't been in academics at that point for eight years. And I don't know. I think I had kind of forgotten how much I really enjoyed teaching. I hadn't been thinking as explicitly about the connection between the cognitive science that I have been doing when I was in academic and poker and the way that those could inform each other. So I really loved it. So for the first two years, I just started getting referrals. And then I decided to start building that business. And I very purposely kept two things going at once, which was my poker career and my speaking and consulting career. And I did that until I felt like I could spin off and actually be successful in that speaking and consulting. And then that actually ended up leading me back into academics. So I think that doing that in parallel and starting off with this sort of exploratory line, like, oh, this is an interesting kind of shiny object. Let me explore that without having to quit the other thing, which I think is something that people miss, is that you sometimes don't have to quit. You sometimes can just do some exploring so that you can gain more information before you actually make the switch. And I think I did actually a pretty good job of that. And so it took a while after I quit for me to get thinking and bet published. I think it was about six years. So you could call that a good or bad outcome depending on what time you sort of enter the timeline. But that's irrelevant because I think that I did it in a good way. I think it's much more interesting for me to tell you like the worst quitting decision that I ever made, which was quitting academics in the first place. And the reason why I say it was a terrible decision is as I said, I sometimes decide things too fast and with too little thought, I was sick. I had to take a year off from graduate school. I started playing poker. I really liked it. And I never really thought about it after that. I just started playing poker. And I don't know, maybe I would have in the end made a decision to leave academics and keep playing poker if I actually went through and built out a decision tree and thought about it and talk to other people about whether they thought it was a reasonable decision and really explored my options. But I just started playing poker and never stopped to be real. Like that's basically what happened. And I think that's like the worst decision that I ever made in my whole life. I'm embarrassed of that decision. It was so bad. But you know what? It ended up working out okay. But I wish that I could go back and redo that one and think it through in a different way. And again, maybe I would have stayed playing poker maybe I wouldn't have. But I would at least like to look back and be like proud of the way that I thought about it, which I'm not. I'm like completely mortified. - Annie, this is so much fun as always full of insights. - Well, thank you. And I just would like to say, because people don't necessarily know this. But I just want to give Ted here a shout out. And I'm going to say you're not allowed to cut this from the podcast because I'm going to listen and I'm going to be really mad if you cut up from the podcast. Okay, so I'm just saying that publicly. So when I'm writing a book, I usually get a couple of people to read while I'm writing the book. And I try to think pretty clearly about like, what are the categories of person that I would like to read it? I usually want an academic to read it because I'm very nervous about translating the science poorly. And then I also think, about other categories and this particular time for two reasons I landed on UTET. Reason number one was that when you read my last book, you had all sorts of amazing comments which I wish I had known beforehand because I would have actually changed the book based on those comments. So that was number one was like, well, I'm not doing that again. I'm going to get the comments beforehand. But the other is that you read lots of books and you do podcasts for living and you talked to lots of authors and you talked to lots of people who read books. And I just felt like you would have a good commercial eye for what I was reading as well. And I sent you a few chapters at a time. And as expected, you sent me back incredible comments, including one which I thought was really important which was, could you put a summary at the end of each chapter? Which it's something that wouldn't necessarily be my instinct to do. But I think your instincts were absolutely right. And for people who pick up the book, they'll see that there are now summaries at the end of each chapter. Just like so helpful just in terms of me being able to gut check is what I'm writing readable. It's an interesting, does it catch your interest? Do you understand what I'm saying? I'm being repetitive. So on so forth. And I don't think this book would be what it was without you having read it. And so I would just like this take this opportunity to thank you in a public way because otherwise people wouldn't know that you were very instrumental in helping me along. Well, thanks, Annie. It was a lot of fun. It's a fabulous book and people are going to get out a lot of it. So I'm glad I was able to have a very, very small part in its success. Well, I wouldn't call it small, but thank you. Thanks so much. And I'm wishing you all the success with the launch of this book. Thanks for listening to the show. If you like what you heard, hop on our website at capitalislecators.com where you can access past shows, join our mailing list, and sign up for premium content. Have a good one and see you next time.