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

Summary

Capital Allocators – Interdisciplinary Skills: Decision-Making with Annie Duke

Host: Ted Seides
Guest: Annie Duke – Decision-Making Expert, Former Professional Poker Player, Author
Air Date: August 3, 2026

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Episode Overview

This episode features Annie Duke, acclaimed decision-making strategist, best-selling author, and former professional poker player. Ted and Annie take a deep dive into the nuanced art and science of quitting—an often-neglected but critical skill for investors, leaders, and anyone navigating uncertainty. Drawing on Annie’s latest book Quit and years of research, the conversation explores why, when, and how to walk away from investments, projects, and commitments, and how behavioral biases, identity, and culture conspire to make quitting so hard.

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Key Discussion Points & Insights

The Rationale for Writing "Quit"

• Reluctance and Overconfidence: Annie swore she wouldn't write another book after How to Decide, but found herself compelled to address the poorly-understood topic of quitting. She identifies overconfidence (“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.” — [06:44])
• Origins of the Idea: The concept began as a minor section in her prior book but became “a brain worm.” Systemic challenges and the life-and-death importance of quitting in poker and investing were major influences.
• Grit Versus Quit: Annie notes that while grit has become glorified, “quitters are losers” is over-simplified and often wrong. “It’s absurd. They are the exact same decision.” — [11:00]

The Core Message: Grit and Quit Are Two Sides of the Same Decision

• Elevator Pitch ([11:00]):

  “We think of grit as a virtue and quit as a vice, but that's not true. They're the exact same decision. ... All the skill is telling the difference between the two, because it's all about context.”
  — Annie Duke [11:00]

• Hidden Heroism of Quitting: The Everest story ([12:22–19:09]) illustrates that quitting at the right time saves lives, but these “heroes” become invisible, overshadowed by stories of (sometimes disastrous) perseverance.

The Value and Mechanics of Quitting

• Optionality: Quitting unlocks valuable flexibility, particularly under uncertainty. The ability to change course after learning more is what saves us from poor prior commitments.
• Uncertainty and Decision Making: “Any decision you make is under the influence of some form of uncertainty...” — Annie [19:30].
• Analogy: If you had to marry the first person you dated, “How could you ever go on a date?” The option to quit makes initial decisions feasible.

Obstacles to Effective Quitting

1. Behavioral Biases

• Sure Loss Aversion vs. Loss Aversion ([27:51]):
  • Loss Aversion: Keeps us from starting for fear of future loss.
  • Sure Loss Aversion: Prevents us from accepting losses by quitting (“not wanting to turn a loss on paper into a realized loss”).
• Escalation of Commitment: When faced with setbacks, people double down instead of withdrawing—a phenomenon observed in both retail investors (with stocks) and institutional allocators ([31:03–35:56]).
• Endowment Effect and Identity: We overvalue things we own or identify with—whether assets, sports draft picks ([43:34]), or failing business lines ([37:14–43:09]).

2. Cultural Narratives:

• Societal norms celebrate sticking it out but stigmatize quitting, making early, correct quitting feel “way too early” even when it’s objectively correct ([11:00]).

3. Feedback Deficits:

• In asset management, robust feedback exists on buy decisions (ongoing price movements, thesis checks), but little feedback happens on positions exited—causing systematic errors in selling/quitting ([31:03–35:56]).

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Stories and Examples

Everest Climb (Quitting as Virtue) — [12:22–19:09]

• Three climbers turn around at the set turnaround time, survive, but are forgotten.
• Meanwhile, others (including the expedition leader) push on, break the rule, and die.
• “Those quitters are totally invisible to us... but they turned around when they were supposed to and quit at the right time.” — Annie Duke [18:22]
• Illustrates why we don’t notice optimal quitters and why being a hero often means only surviving quietly.

Money Managers and Sell Decisions — [31:03–35:56]

• Money managers excel at choosing what to buy but underperform benchmarks on sales/exits.
• Behavioral explanations: focus on the highest winners/losers, not the entire opportunity set; less feedback on sells; and biases like mental accounting and endowment.

Sears: The Power of Identity and Endowment — [37:14–43:09]

• Sears clung to its dying retail identity, selling off its hugely successful Allstate, Dean Witter, and Discover assets in pursuit of “getting back to retailing roots”—ultimately leading to bankruptcy.
• “The hardest thing to quit is who you are, even for Sears.” — Annie Duke [43:07]

Sports Analytics: Draft Picks and the Endowment Effect — [43:34–47:20]

• Teams give more playtime and longer contracts to high draft picks, regardless of productivity, exemplifying escalation of commitment, sunk cost, and identity traps.

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Prescriptions: How to Quit Better

1. Monkeys and Pedestals: Do the Hard Thing First ([49:52–59:57])

• Derived from Astro Teller at Google X: Identify the “monkey” (the key uncertainty/bottleneck) and address it first, rather than wasting time on “pedestals” (easy, illusory progress).
• Avoid building sunk costs in non-essential areas before learning if the real challenge is tractable.

  “Do the hard thing first and beware of false progress.” — Annie Duke [59:57]

2. Kill Criteria: Pre-Commitment to Quitting Benchmarks ([60:02–66:32])

• Inspired by research (Barry Staw), instead of treating every decision “as fresh” (which doesn’t work), establish exit criteria in advance.
• Examples: Set turnaround times (Everest), sales targets for business leads, or financial benchmarks.

  “The worst time to make a decision is when you’re in it.” — Daniel Kahneman (as referenced by Annie Duke [60:02])

• Enables process-oriented wins (not just outcomes): following the process for exiting at the right signals is rewarded, not punished.

3. Leverage an Outside Perspective: The Quitting Coach ([66:44–73:58])

• Enlist trusted outsiders not attached to the original decision—quitting coaches or honest advisors (Kahneman’s quitting coach is Nobel laureate Richard Thaler!).
• Group “quitting” exercises, create explicit criteria together, and agree to stick to them.

“You have to find someone who loves you but doesn’t care much about hurt feelings in the moment.” — Annie Duke [66:44]

• Ron Conway’s approach to coaching founders: agree on concrete turnaround goals; revisit in two months to determine whether to quit ([70:32–73:58]).

  “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.” — Annie Duke (citing Ron Conway) [73:41]

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Notable Quotes & Memorable Moments

• On grit and quit:

  “They're the exact same decision, and we need to understand that all the skill is telling the difference between the two, because it's all about context.” — Annie Duke [11:00]

• On quitting too late:

  “If you quit at the moment that it’s objectively correct, it will feel like you’re quitting way, way, way too early.” — Annie Duke [11:20]

• On identity and quitting:

  “The hardest thing to quit is who you are, even for Sears.” — Annie Duke [43:07]

• On the importance of quitting coaches:

  “You have to find someone who loves you but doesn’t care much about hurt feelings in the moment.” — Annie Duke [66:53]

• On decision blind spots:

  “I'm very decisive... but I think I decide a lot of things way too fast.” — Annie Duke [75:55]

• On her personal best and worst quit decisions:

  “The worst quitting decision that I ever made was quitting academics in the first place... I just started playing poker and never stopped to be real...” — Annie Duke [77:36]

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Timestamps for Key Segments

| Timestamp | Segment | |-----------|----------------------------------------------------------------| | 06:09–10:16 | Annie discusses the path to writing "Quit" | | 11:00–12:00 | Elevator pitch: Grit vs. Quit, key insight | | 12:22–19:09 | Everest story: quitting as heroic | | 19:30–23:25 | Why quitting is vital under uncertainty | | 23:49–30:33 | Behavioral obstacles to quitting | | 31:03–35:56 | Money managers and quitting/selling failures | | 37:14–43:09 | Sears: identity, endowment, and disastrous non-quitting | | 43:34–47:20 | Sports analytics: draft picks and escalation | | 49:52–59:57 | California bullet train, solutions: “monkeys & pedestals” | | 60:02–66:32 | Kill criteria: prescriptive frameworks for quitting | | 66:44–73:58 | Role of teams, quitting coaches, and VC founder stories | | 74:28–79:21 | Annie’s influences, blind spots, and personal quit stories |

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Further Nuggets & Reflections

• Culture Rewards Grit, Hides Good Quitting: Annie repeatedly emphasizes that the best quitters don’t stand out—they’re invisible success stories.
• Sunk Cost is a Self-Reinforcing Trap: The more you invest in something, especially in the name of progress, the harder it becomes to walk away.
• Process Over Outcome in Quitting: Shift management and personal evaluations from outcome-only to following a rational, criterion-based process.

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Closing Thoughts

The episode masterfully blends compelling stories, rigorous behavioral science, and practical tools for becoming not just a better decision-maker, but a better “quitter.” For institutional investors, allocators, and anyone facing complex choices under uncertainty, Annie Duke’s frameworks around quitting are essential additions to the decision-making toolbox.

Recommendation:
If you found value in these insights, Annie’s book Quit and earlier works (Thinking in Bets, How to Decide) are highly recommended for deeper exploration.

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For more episodes and resources, visit capitalallocators.com.

Transcript

[A]

Foreign.

[B]

I'm Ted Seides and this is Capital Allocators. 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 Impro 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 Bets is a one of a kind set of 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 hold different threads in the decision making process. Since writing Thinking and Bets, Annie has consulted several investment organizations to apply her insights to investing, including becoming a special partner focused on decision science at venture firm First Round Capital. For the series, we've shared two of Andy's most popular appearances on the coinciding with the launch of Thinking and bets in 2018 and quit in 2022. Before we get going, we're hiring at Capital Allocators, two roles that will shape our next chapter. They're two of the best jobs in the business, at least in my opinion. That'll 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 [e-mail] and thanks for spreading the word about our two new roles at Capital Allocators. Capital Allocators is brought to you by AlphaSense. Here's something for you. Most AI tools today are very good at sounding right, but can you actually trace it back to a filing, transcript, or specific passage that drove the answer? Or are you just trusting the confidence of the output? For Allocators, that's not a minor concern. A missed filing, incorrect source, or context that gets lost somewhere in a retrieval chain aren't edge cases, they're how decisions go wrong. AlphaSense is the AI platform built specifically for this. They own the content, over 500 million curated documents, from broker research and expert transcripts to filings and earnings calls, and they own the retrieval layer on top of it. That means every answer can link back to an exact verifiable source because the answer is only as good as what's underneath it. And with AlphaSense, you know exactly what that is. See it for yourself. Try a free [e-mail] capital. That's AlphaSense.com with a hyphen in the middle slash. Capital Capital Allocators is also brought to you by intap Deal Cloud. You already know intap Deal Cloud. It's where your firm keeps its pipeline, its relationships, and years of deal history. Now meet Celeste, Intap's new agentic AI and an expert coworker that works right inside DealCloud. Together, DealCloud with Celeste changes what your firm can do. Celeste runs on your firm's own data and your own playbook. How you source, diligence and decide. It works inside your compliance rules from day one. So it screens your inbound deals against your thesis, far more than your team could reach on its own. It tells you which LPs are most likely to re up in your next race. Hand it a sim and it builds your first LBO model in minutes. Every firm can only chase as many deals as its people have hours to work. Celeste lifts that ceiling. Now the deals you walk away from are the ones you meant to dealcloud with. Celeste is generally available now from Intap. Learn more and book your demo at intap.com capitalallocators Capital Allocators is also brought to you by Admired Leadership. Back in April, on episode 497, I sat down with Randall Stuttman, the executive coach behind Admired Leadership, who's advised more than 500 CEOs, including some of the most respected names in asset management. Randall introduced me to Alex, an AI leadership coach. His team built on 40 years of proprietary research into what the best leaders actually do for investment professionals. That means your entire team gets on demand coaching, grounded in the behaviors that drive results and build the kind of followership that retains your top talent. We use Alex, and our team at Capital Allocators swears by it. Try Alex for yourself at the link in our show notes tryalex admiredleadership.com Please enjoy these great conversations with Annie Duke. Annie, great to see you.

[A]

Good to see you too.

[B]

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.

[A]

Well, that's true.

[B]

So here we are again. What was the path to coming up with the idea for Quit?

[A]

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 Bats. 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 bats. But I 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 bets. 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 swore 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? That's the question. 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 on 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 brain worm. 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, AKA 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, like, it's 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 Maubouson. He's probably the first person I talked to because he's always my number one person to go to. Which, by the way, I would recommend for anybody. If you have an idea, call Michael Maubouson. I also got in touch with Danny Kahneman 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 connected with on the topic were honestly as excited as I was about it. At which point I literally sort of like, my shoulders, I was like, everything went down. Oh, I think I need to write this book. And that's kind of where that happened.

[B]

So, Annie, as 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 main 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 it right with Morgan Housel's book, and I think I'm going to be right with this one, too.

[A]

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 haven't, you should just, like, stop listening to us right now and go buy it.

[B]

What's your elevator pitch when you go on the Today Show? To talk about quit.

[A]

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. And 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.

[B]

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.

[A]

So we all say things like, quitters never win, and winners 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. It, 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. Stuart Hutchinson, John Taske, and Lou Kasischki. 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 1 a few times because you're trying to acclimate. And then you climb from Camp 2 to Camp 3 a few times, and so on and so forth. And on summit day, you're going from Camp 4, 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 1pm so this is very clear to everybody. And the reason why it's set at 1pm 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 8,000ft into Nepal or 12,000ft into Tibet. So probably don't want to do that. So one of the reasons that you leave at midnight from Camp 4 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 1pm no matter where you are, is because if you get to the summit past 1pm 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 1pm 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 in a traffic jam. So they're in a little bit of a traffic jam, and it's getting on toward about 11:30am 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 Taske and Kasitzki back, and he says, okay, look, I think we have a problem. It's just about 11:30. Our expedition leader just told us there's 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 2:30, 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 1pm we ought to turn around now. Kasichki was a little reluctant. Taske agreed right away. Kasischki 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 lived. 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? Like, nobody's going to make a movie out of that story. Where is the hero? There's three guys. They followed the rule. They went back to base camp. They lived. Yawn. 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 Jon Krakauer in Into Thin Air. Not only that, they were part of Rob Hall's expedition. Rob hall famously going to the top of the mountain, arriving at 2pm waiting for Doug Hanson, one of the other climbers in his expedition, until 4pm Doug Hansen got to the top at 4pm no, this is well past the turnaround time, collapsed 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 Taskey, Kasitzki 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 rule. 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, Krakauer's book, or by the way, seen the documentary or watched the movie, they're in there. In fact, Krakauer 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 the whole shebang right there.

[B]

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.

[A]

And I just want to say something really clearly. I think everybody should go buy 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 could 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%. That's one thing, is that there's just luck that influences how anything turns out. 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. Those are big problems in decision making 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 simplest sense, like I could be playing a hand of 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 4, 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 uncertainty is 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 know, you can change colleges, so on and so forth. So that's all really good.

[B]

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?

[A]

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 4pm 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 Thaler said to me, the only time that we're willing to quit is usually when it's no longer a decision because, like, 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, Taskey and Kasichki. 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 are 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 grit 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 Thaler'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.

[B]

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, okay, it's fine, it's working. There could be something better. There's an element of inertia. There's clearly sunk 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?

[A]

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 aversion. Now I just want to be clear. That's different than loss aversion. Both of these come from Kahneman and Tversky, Kahneman, obviously Nobel Laureate. Loss aversion is not wanting to start things for fear of the losses you might incur later. So let's think about loss aversion as something that stops you from starting. Sure. Loss aversion, as Kahneman says talks about, is not wanting to turn a loss on paper into a realized loss. So loss aversion stops us from starting. Sure. Loss aversion stops us from stopping. So there's sure loss aversion. Then there's all these things that go under this rubric of escalation of commitment. Escalation of commitment, simply put, is like 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 are going to put sunk 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 cost makes us stick to things that then make us incur opportunity costs. And then we have all of this stuff around goals that goals are graded as pass fail, they cause dysmyopia, progress along the way 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, and 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 and you say, oh my gosh, I can literally pull this quit thread all the way through it. And that just really excited me.

[B]

Yeah, it is literally the entire smorgasbord issues 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 we'd love to hear your thoughts on what you've seen from money managers sell decisions.

[A]

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. So usually we quit too late. I don't know if you've heard that old management adage, like the first time you think about firing someone, you should. People don't like that's is probably already way past when you should have. And that's for most things most of the time. Like when we decide to turn around on Everest, it's way later than we should have. And 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 Thaler talks about with mental accounting, we don't like to close mental accounts and 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. Right. This has to do with short loss aversion. But if it goes to 60, I now really want to sell it because I would like to take the share gain 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, right? It's like you've got a stop loss at 40 and you're like canceling it because now the stock's really cheap and then you go to 35, then you go to 30. So this is very well known behavior from retail investors. And then obviously they also cancel their take gain orders, but that's to not reach the take gate, right? So they buy it at 50, they've got a take gain order at 60, it gets to 57, they're canceling it and selling again because they want to close that account in the gains. So you can think about it this way. If I leave the position on, I've got luck going for me, right? Like the stock could turn around and so 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. And 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 like 100, 120bps or so better than the beta. All right, so they're alpha generators on the buy. And you can see why they're thinking through the theses, they've got quants working on it, so on and so forth. So they're great at that. So the question is, let's look at what's happening on the loss 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 yet when you look at their sell side decisions, they're usually about 70bps 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 bips worse than, oh, I just randomly chose to sell stuff. And so Emos 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 the 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 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 are you going to learn?

[B]

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[A]

Okay, I'm going to tell you one that's completely bonkers, if that's okay. Sure. It's about a retail chain called Sears. Have you ever heard of it?

[B]

Yeah, we've heard of it.

[A]

Okay.

[B]

What's left of it, Right?

[A]

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 shears to 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 that 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 business, particularly because they're kind of getting squeezed out. You've got the high end retailers like Nordstrom's and Neiman Marcus that are kind of pushing them from the top. And then you've got the Kmarts, Walmarts 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 the 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 Allstate Insurance. Now, Allstate 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, so forth. And that goes on for a long time. So they found that in the 1930s, they continued to own Allstate 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 Witter, 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 Witter 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 can calculate whatever Morgan Stanley is worth, and that's whatever that is. Okay? And then you have obviously Allstate, which is worth billions. Coldwell Banker ended up merging into Rheology, but I think that their market cap is 2.2 billion or something like that. And I think Allstate 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. Here's what happens. 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 Witter, we have Coldwell Banker, we have the Discover Card, we have Allstate, 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 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 Allstate. Why would I have known that? So what do they do at this point? This is in the 90s. They spin off Allstate in its own IPO. They sell off Coldwell Banker, they sell off Dean Witter 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.

[B]

Yeah. Amazing.

[A]

All right.

[B]

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 just comes 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?

[A]

So Barry Sta, who's a larger than life scientist and character, his father is actually a character in the book who was also endowed, had a little bit of a problem with endowment. But I'll let people read the book to read about Barry Star'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 double down. 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, you're 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, oh, 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 1 to 2 was 23 minutes a season, from 2 to 3 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 come 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 at what point they were drafted in the draft.

[B]

We have all of these things working against us, which does remind me a lot of thinking in bets. Yes, and fortunately in thinking of bets 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'd just love to hear some of your thoughts and some of those prescriptions.

[A]

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. Sadly, it's not fiction. This is a real life true adventure. 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 want to do that was obvious. Those two areas are the economic engines of the state of California. And so they want 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, is 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. Okay, 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.

[B]

It's 2022. I haven't heard about this rail.

[A]

Yeah, right. Oh, that's weird because it's supposed to be done in 2021. So, 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 and pretty far to the north of la, 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 20. 2015. That's probably a sign 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 Tehapi Mountains, which are to the north of la, separating LA from Bakersfield. But then, even worse than the Tehapi 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 gotta 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 gonna be more like $81 billion. We're not even sure it's gonna 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 billion ish to 80 billion ish, 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. Now notice this is to the north of the Techapi 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 is 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 complete 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 and still says it's very uncertain that we're going to actually be able to conquer these engineering challenges. Obviously you can see there's sunk 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 taxpayer's money. Except waste should not be a backward looking problem, it should be a forward looking one. The 9 billion is already spent. Are you supposed to put another billion into this thing or by the way, another dollar? Okay, 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. 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 moonshots over at Axe, which is the innovation hub at Google. And the mental model is called Monkeys and Pedestals. So 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? What'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 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 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 it's 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?

[B]

The low hanging fruit, yeah.

[A]

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 figured 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 Astro Teller 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 sunk costs that you're accumulating. Like the taxpayers, They've now accumulated $9 billion in cost 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 snowballs on you. 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 this 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.

[B]

Has anyone gotten you in front of Gavin Newsom?

[A]

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.

[B]

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. But another great example, and I see that all the time with fund managers that are going to launch or 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.

[A]

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. They wanted to 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. That's such a great example. I want to encapsulate it. I think it's do the hard thing first and beware of false progress.

[B]

What are some of the other key remedies to help people quit better?

[A]

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 Daniel Kahneman 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's a cupcake sitting in front of you. 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 Stoss. 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, would I buy it today? Because 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. 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, write 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 that's called mparticle. They offer a cdp. So I said to the sellers, okay, you get a lead through an RFP or rfi. It's six months later and you've lost the deal. Looking back, you realize 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 asynchronously, that's really important. You can go read 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 why 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 turn those into kill criteria. So the kill criteria are if you take a first meeting with someone and all they want to talk to you about is price, don't spend any more time on them. Why don't you want to 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 stalking horse. So why are you wasting your time for someone who's not actually going to 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 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 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. We generated a very long list of these kill criteria. 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 while I'm on the top of this mountain, that would tell me that I ought to quit. But we know that I'm probably going to escalate my commitment. We know I'm probably going to double down. We know I'm probably going to persevere. And stick to it anyway. And I don't want to 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 n 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.

[B]

So a lot of the monkey and pedestals and 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?

[A]

Yeah. So again, I said a lot of the problem is that we're 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, a feasibility study on the mountains before I start building any Track, that's thinking 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, well, we have a thesis as we enter into, say, an investment, and obviously, when the world's turning against our thesis, we're going to 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 on and so forth. 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. Because I didn't make the original allocation decision, so I don't have the sunk cost associated with it. I'm not endowed 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 Danny Kahneman, when he told me he has a quitting coach, his name is Richard Thaler. We should all be lucky enough to have quitting coaches who are Nobel laureates. 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 want to hurt your feelings. I don't want to 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, Ted, I'm guessing. You know, 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? Like, 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, it's, 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 and 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, kind of 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 whole 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 VCs, who they want everybody just to grit it out. But that's not true. 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 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 pretty much every single time, the founder's 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. He said, oh, I totally believe you can turn it around. Okay, so let's see, what does turnaround 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, is 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 did 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 9.

[B]

And this is so just fascinating stuff. And 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.

[A]

Don't forget Barry Sta's father, Harold. It's one of my favorites.

[B]

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?

[A]

I would remiss if I didn't say Lila Gleitman, who is for sure 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, right? So I could say my brother because he's the one who started me playing poker. I could say Eric Seidel for a similar reason. I can say Michael Mauboussin, who, to 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 Tetlock and Bart Mellor's, because I've started doing a bunch of work with them on forecasting and really starting to think about how do we actually. How do we actually improve our ability to make predictions, which that's all a 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.

[B]

What are your biggest blind spots? Let's just say in decision making.

[A]

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 think 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. I'm 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

[B]

at that last one. And this one is custom made for you.

[A]

Oh, no.

[B]

Which is what's the best decision process you've gone through that led to quitting, regardless of how it turned out?

[A]

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 Low. 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 had been doing when I was an 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 sort of 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 bets 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 talked to other people about what, 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.

[B]

Annie, this is so much fun. As always, full of insights.

[A]

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 it 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 utep. 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, I 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 a living and you talk to lots of authors and you talk 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 is 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. But just like, so helpful, just in terms of me being able to gut check, is what I'm writing readable? Is it interesting? Does it catch your interest? Do you understand what I'm saying? Am I being repetitive? So on and 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.

[B]

Well, thanks, Annie. It was a lot of fun. It's a fabulous book and people are going to get a lot out of it. So I'm glad I was able to have a very, very small part and its success.

[A]

Well, I wouldn't call it small, but thank you.

[B]

Thanks so much and 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 [e-mail] 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.

[A]

All opinions expressed by TED and podcast guests are solely their own opinions and do not reflect the opinion of Capital Allocators or their firms. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Capital Allocators or podcast guests may maintain positions in securities discussed on this podcast.