Embracing Risk-Taking in Leadership: Knowing When to Say Yes with Annie Duke and Paul Misener


This week on World Reimagined, we examine the importance of knowing when to take risks and innovate, and when to walk away.

Innovation always requires some level of risk. For leaders, this means taking the right risks and knowing when to walk away. How do leaders make decisions when the outcome is uncertain?  How do they create a culture that promotes innovation and enables risk taking?

In this episode, host Gautam Mukunda speaks with Annie Duke, Cognitive Scientist, Decision Strategist, and Author of Quit: The Power of Knowing When to Walk Away, and Paul Misener, the VP of Global Innovation Policy and Communication at Amazon about the process of decision-making and innovation in uncertain conditions.

If we are not failing ever, we are not trying hard enough; we are not being innovative enough.
Paul Meisener, VP of Global Innovation Policy and Communications at Amazon
There's a time-accuracy trade-off when we're making decisions. The more time we take, usually, the more accuracy we're going to accrue. The less time we take, the less accuracy we're going to accrue.
Annie Duke, Author


Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts, by Annie Duke

Quit: The Power of Knowing When to Walk Away, by Annie Duke

Guest Information for Embracing Risk-Taking in Leadership

Annie Duke is an author, corporate speaker, and consultant in the decision-making space, as well as Special Partner focused on Decision Science at First Round Capital Partners, a seed stage venture fund. Annie’s latest book, Quit: The Power of Knowing When to Walk Away, will be released October 4, 2022 from Portfolio, a Penguin Random House imprint. Her previous book, Thinking in Bets, is a national bestseller. As a former professional poker player, she has won more than $4 million in tournament poker. During her career, Annie won a World Series of Poker bracelet and is the only woman to have won the World Series of Poker Tournament of Champions and the NBC National Poker Heads-Up Championship. She retired from the game in 2012. Prior to becoming a professional poker player, Annie was awarded a National Science Foundation Fellowship to study Cognitive Psychology at the University of Pennsylvania. 

Annie is the co-founder of The Alliance for Decision Education, a non-profit whose mission is to improve lives by empowering students through decision skills education. She is also a member of the National Board of After-School All-Stars and the Board of Directors of the Franklin Institute. She is serves on the board of the Renew Democracy Initiative. 

Paul Misener is Amazon.com’s Vice President for Global Innovation Policy and Communications. An Amazon VP for over 22 years, Paul has long participated in Amazon’s history, culture, and methods of sustained, customer-obsessed innovation guided by the company’s Leadership Principles. He frequently represents Amazon to external organizations and internal colleagues worldwide.

The founder of Amazon’s global public policy organization, Paul built and led the team and served as the company’s Vice President for Global Public Policy from February 2000 to May 2016. Paul has testified before the United States Congress over 30 times and dozens of times before other policymaking bodies around the world. He has met hundreds of times with political leaders, heads of state, cabinet ministers, US Representatives and Senators, US governors and mayors, and has negotiated policy agreements with many of these governors.

Paul is both an engineer, scientist, and attorney and has brought his expertise to advise numerous policy and technology organizations during his career. He served as Senior Legal Advisor to a commissioner of the US Federal Communications Commission; co-founded and led the computer industry’s Internet Access Coalition, which successfully blocked the imposition of telecom access charges on Internet access; and chaired the technical subcommittee of the US Federal Aviation Administration’s advisory committee that recommended allowing commercial airline passengers to use portable electronics during taxi, takeoff, and landing.

Paul serves on the board of a number of organizations in the US, advising on economic development, technology, education, and public affairs. He lives with his family outside of Washington, D.C. in the United States.

Episode Transcript:

Gautam (00:00):

Sometimes the difference between failure and success is a leader who knows when to walk away.

Annie (00:09):
If you stop something sooner than somebody else would have that isn't working, that's not a failure, that's a success.

Paul (00:16):
A few uncapped successes pay for a whole lot of failures.

Annie (00:21):
One of the worst things about being successful early is that you start trusting your gut.

Speaker 4 (00:30):
World Reimagined, with Gautam Mukunda, a leadership podcast for a changing world, an original podcast from Nasdaq.

Paul (00:40):
But it's up to consumers to decide what is disruptive.

Gautam (00:57):

The rules of blackjack are fairly simple. You're dealt two cards, and you compete with a dealer to see who can get closest to a value of 21 without going over. Get a higher qualifying score than the house, and you win. Get a score that's too low or go over, and you lose.


At face value, it's a fairly evenly matched contest of skill and luck, except built into the rules of the game are small particulars that give the house an edge. For example, the dealer always goes second, meaning they can base their strategy off of your hand, giving them a little bit more data to work with and thus a slight advantage. The overall value of that edge isn't all that big. Most estimates put it between 0.15 and 2%, but at the end of the day, it's enough to ensure that dealers win more often than they lose. Bottom line, individual hands may break your way, but if you play the house an infinite number of times, eventually you'll lose everything.


Now, not everyone who plays blackjack goes broke. If they did, well, nobody would ever play it. People have and regularly do win lots of money by playing this game through a combination of careful study and random chance. And just like decisions that leaders have to make in high leverage situations, the true skill is often knowing when to stay in and when to take your money and go, which of course leads to the obvious question. How do you know when it's time to pick up your chips and walk away? And perhaps more importantly, even when you do know, why is it so hard?

Annie (02:47):
This might be the most valuable option we have as decision makers, but the science shows that we don't exercise it enough.

Gautam (02:54):

Annie Duke is the author of Thinking in Bets and Quit: The Power of Knowing When to Walk Away. She's a cognitive scientist and decision strategist whose area of expertise is decision-making under uncertain conditions. And she's also a former professional poker player whose tournament winnings include $4.3 million and a championship bracelet at the 2004 World Series of Poker. She'll be the first to tell you that she never would have made it that far if she hadn't developed a highly tuned sense of when to stay in the game and when to call it quits.

Annie (03:30):

Part of the reason for that is that that decision is itself made under uncertainty. So if you were to objectively know the time that you were to stop something, nothing particularly dire would be happening in that moment. You would be forecasting into the future and say, "I can see that the probability of this not going the way that I want it to, at this moment, is too high compared to what I could sink those resources into, the other options I could sink those resources into."


And we know that quite a lot of this problem has to do with this issue that Richard Thaler talks about, which is the way that our mental accounting works. So once we set a goal, we're trying to create a project, we're trying to complete a new strategic initiative, we have a product that we're trying to develop, whatever, we have now set a finish line or a goal, right? There's a reason we start it, because we're trying to get to an end point or a finish line. And if we're short of that finish line, cognitively we feel like we're in the losses.


So we do not like to close mental accounts in the losses. We just don't like to do it. It's that thing of going from failing to having failed. So this is really, if you think about across many of the biases that make it very hard for us to stop stuff, this thing about not wanting to lose now is one of the things, is probably the main thing, that causes us not to stop.

Gautam (04:49):

What Annie is saying often feels like a heartfelt biological truth. We as human beings hate to fail. For all that we can learn from it, for all that we can know that we are better off for certain endeavors petering out, there is something none of us like about having to course correct once we've admitted we went down the wrong path. So how do you make decisions in the face of this basic, innate distaste for failure, especially when you make decisions at one of the largest companies in the world?

Paul (05:21):
At Amazon, when we're faced with any kind of a decision of any substance, the first thing we do is decide what kind of a decision it is.

Gautam (05:29):

Paul Misener is the Vice President for Global Innovation Policy and Communications at Amazon. He first joined the company over 20 years ago, back when its future as one of the largest organizations on earth was anything but secure. Back then, Paul had people tell him to his face that Amazon was going to go bankrupt and fail, meaning he has a great deal of experience sticking with ideas that pan out, a skill which according to him is born from nixing ideas that don't.

Paul (06:01):

The bifurcation is, it a one-way door, which as its name suggests, if you take the decision as hard, if not impossible, to untake the decision, or is it a two-way door? And with a one-way door, yeah, you want to gather a lot of information. You want your data, you want to do all the analysis that you can, and maybe it's like 90 or 95% of the available information. But the vast majority of decisions at a company like Amazon are not one-way doors, they're two-way doors. If you decide it and it doesn't work out, you can undecide it. And those sorts of decisions, you want to make with a lot less information to keep the speed up.


The problem that a lot of very successful organizations make is to treat every single decision as if it's a one-way door, and they're not. And part of is a reluctance to admit failure by undoing something that you've started to do. And that's something that at Amazon, we welcome. We're okay with failure, entirely okay with it. In fact, there's an expectation of failure, because if we're not failing ever, we're not trying hard enough, we're not being innovative enough. But one aspect of that failing is trying something, taking a decision, and then recognizing, "Yeah, that wasn't a good decision," and then undoing it. And that's an admission that you made a bad decision.

Annie (07:10):

I love that. So I want to just hone in on people don't like to admit failure. I think that's true, because when you stop something is when you go from that feeling of failing to having failed. That's a transition that we don't like. It's one that we tend to get to too late.


I would argue however, and I'll suggest this for Amazon, that calling it a failure is actually not correct, because if you stop something sooner than somebody else would have that isn't working, that's not a failure, that's a success. So when we're talking about two-way door decisions, what we're saying is, "Look, we know that when we make this decision, there's going to be a bunch of information discovery that occurs afterwards. And we're building that into the decision process." So when we discover that information, the new information after starting, when we react appropriately to it, success. I wouldn't even call that a failure. I actually think failure is the opposite, when you stick to something way too long, even after you've gotten the information that you ought to do the switch.


And I think that we're helping by telling people we're expecting failure, we're celebrating failure. I think in some ways we're making it worse, because we're emphasizing, we're reinforcing the idea that stopping is failure. And stopping is not failure, particularly for two-way door decisions, stopping is actually built into it and mostly you're going to stop those. It's just a question of whether you do those well.

Gautam (08:38):

There's a fairly easy way to identify a great chess player. They win. Chess is one of those rare games in which luck plays no role. The outcome is entirely determined by the skill of the players. So you can say without a doubt, who has skill simply by looking at their performance. Roulette on the other hand, requires absolutely no skill whatsoever. The outcome is entirely based on random chance. So there's no such thing as a great roulette player, just a lucky one.


But what about situations where chance and skill are mixed, as they often are in finance, business, and even politics? How can you tell who's good and who's just lucky? When you're evaluating success, whether it's yours or someone else's, knowing when to weight process and when to weight outcomes is vital. In chess, players win if they make good moves and lose if they make bad ones. But in our work lives, where luck nearly always plays a role, that distinction isn't nearly as clear. That means that when they're analyzing decisions and considering new ones, leaders need to look at their process regardless of, and sometimes in spite of, their results.

Annie (10:06):

Separating process from outcome would not be particularly important in a world where there wasn't uncertainty that was exerting an influence over your decision. So uncertainty comes in two forms; one is hidden information. So if you think like poker, I can't see my opponent's cards. But also, when we choose to do things, we know very little for most things in comparison to all there is to be known. And what that means is that we're generally going to have that feeling after the fact of, "Oh, I wish I knew then what I know now." So that's the first form of uncertainty that's really wreaking a little bit of havoc on our decisions. And the second is just plain luck. So I can make a decision that's going to work out 80% of the time. That means that 20% of the time it's not going to work out and I have no control over when I observed that 20%.


And in fact, that's one of the reasons why you want to separate process from outcomes, because the thing that you don't want to feed back into the process is an outcome, not certainly in the short run and not when you're trying to be innovative. That's going to be actually really bad to do. Instead, you need to have really good process, where you have a record of why you made the decision that you can then connect to whether your thesis turned out to be true. What were the things that you learned after the fact? Could you have known them beforehand? That could be a yes or a no; generally, it's going to be no. Sometimes it's going to be yes, but even then, you have to ask another question, which is, "Could we have afforded to have gotten it?"


And the reason why you ask that is that, as Paul pointed out, gathering information takes time. And sometimes you don't have the time or you're thoughtfully not spending the time because you know that it's very easily reversible. And so yes, even though you could have known it beforehand, you already kind of knew that and you weren't going to go and get that anyway because it was going to cost you too much time. So as long as you're going through that process, then you're going to be pretty good to go in terms of actually creating really good learning loops or feedback loops there.

Paul (12:05):

Annie, I understand what you mean by thinking of it as a success, knowing that you should stop and walk back through that two-way door. And so, I get that. That is true. That is a success to be able to stop doing something that is not working as you would want it to work.


But we embrace the term failure, in part I think, because we think along the lines that scientists think. So scientific theories can never ever be proven right. They only can be proven wrong. And so, we actively work to disconfirm our beliefs, that is to say, kick the tires, try to make the idea fail. And if you fail to make it fail after trying, then you can have some degree of confidence that it will be successful at some level. But you can never prove the success, certainly not up front.

And so, when we talk about failure, it's like to do something truly new and innovative that's never been done before, you have to experiment. You have to do something that is totally different, because if you're just doing what we called in high school, what we called experiments then, all you're really doing is just demonstrating something that's already been done before. You're not doing something truly novel. We like things to fail internally, not because we're embarrassed about it, I'm able to tell you about it, but because we don't want it to get out to customers. They shouldn't be the ones who experience a failed product or service. And so, we had a mobile phone product, as you may recall, for about eight months.

Gautam (13:26):
It's been cited multiple times on this podcast, for I'm pretty sure the reason you're about to say.

Paul (13:30):

Yeah. Well, it was the case that it got out to customers. That was where the real failing was. And so, much better for it to have failed internally and not get out to them. That's why it hurt. It wasn't the $170 million loss. It wasn't that that hurt; it was that it got out to customers.

Annie (13:46):

I think that we're really just sort of quibbling about semantics here, except in as much as generally, when you look at the science around stopping things, we stop almost everything too late. And we also actually tend to be too slow to start things. So let me think about both sides of the equation. So as you pointed out, there's a time accuracy trade-off when we're making decisions. The more time we take, usually the more accuracy we're going to accrue. The less time we take, the less accuracy we're going to accrue.


And so, the way we kind of think about how do we handle that trade-off is to think about the impact that getting it wrong is going to have, right? In other words, making a decision that, in retrospect, you kind of wish you hadn't made. And the reason for that is that if you take less time, you're increasing the probability that you're going to have an error in the decision. That's basically the way you can think about it.


So some decisions just don't have a lot of impact. So if you make a mistake in the intern that you hire, it's not going to have a huge impact on the organization. So you really shouldn't take a lot of time with those types of hiring decisions in comparison to say hiring a CFO, who, if you get that wrong, it will have a very deep impact on the organization.


And then, one way to mitigate impact is reversibility, right? So that's that one-way door, two-way door decision. The more reversible a decision is, the more you can mitigate the impact of an error because you can just stop. So that's on the front end, right? So on that front end, people do still take too long with those decisions. So when you do discover that you have a one-way door decision, people will generally start to gather information beyond the point at which the next piece of information has a very low probability of changing their mind. They'll start to case build. And that's also even true with two-way door decisions. Even when you tell people they can go fast, they will go faster, but they generally still

will not even go fast enough. (15:38):

On the one-way door decisions, you always want to include in your decision process, "Is the next piece of information that I gather going to change my mind?" So this is a kind of a thought experiment. It helps you to stop when you realize, "Very unlikely to," and then you can stop even though you don't build the case.


So part of the reason why I have a quibble with the word failure is because when we then decide that a decision is reversible, we're also way too slow to that decision. So there's a tremendous amount of science from Stephen Levitt, Daniel Kahneman, Richard Thaler, Colin Camerer, it's a big, heavy duty list including some Nobel laureates in there, that shows that as you've pointed out, this option to stop things, this option to be able to quit something that you're doing, is incredibly valuable, particularly if you're trying to be innovative, right? But for anybody, it helps us to deal with this problem of decision- making under uncertainty, right? Is that we're going to learn new stuff, we get to react to that new stuff, and we get to stop.

Gautam (16:38):

Even in the most hyper-rational environments, it can be hard to know when to quit. Economists Vijay Singal and Lily Xu studied 2,300 US mutual funds and found that approximately 30% of them tended to sell their winners too early and their losers too late, resulting in an underperformance of 4-6% a year; a problem severe enough that such funds are less likely to survive than rivals who are quicker to cut their losses. But if this drive to deny our own failures is so powerful, even in the results-driven world of mutual funds, how do we combat it?

Annie (17:18):

I am not saying that experience doesn't matter. I am not saying that pattern recognition doesn't matter. A lot of what options traders are doing are applying experience and pattern recognition because they actually have to make the trades in the moment. They have the quantitative work, but they're making the trades. That was certainly true in the eighties, where it was much more like a combination of the two things.


So let's take somebody who I work with, like Josh Kopelman. Let's all agree that he has an amazing nose. Bill Trenchard, amazing nose for value, right? Very successful. That being said, what we've done is said, "Okay, you have a great gut. What are the things that you're seeing? And let's try to create some sort of rubric around that." So we're not taking subjective judgment out of it. What we're saying is there are certain facts that are inputted into the decision and then there are certain subjective judgments that you're making.


And when you're talking about a nose for these things, you're talking about subjective judgements about, say, the quality of the market, the quality of the founder, the quality of the team, the quality of the product, things like do you think there's going to be good product market fit? So on so forth, right? So let's take those things that are implicit, let's make them explicit, and let's actually have you judge subjectively on a scale of one to seven, what is your opinion about these things that are the inputs into your decision, which stops some of the really malaligned influences on decision-making. (18:38):

Here's a few of them. One, it stops you from subconsciously highlighting the good stuff and downplaying the bad stuff if you like the investment, or vice versa if you don't. That's number one.


Number two, it allows somebody else to examine. And you now see, for example, Josh Kopelman and Bill Trenchard, both great investors. It's helpful within that partnership to see that they see an investment differently. So this allows us to see the gap between their points of view, which then really improves decision-making because we get to explore that gap. It allows much faster transfer of knowledge from the managing partners to the junior partners, because you're saying, "Look, if Josh has a great gut, don't you want the new person who's just come in to also be able to develop that type of pattern recognition?" And it allows us to now do analysis to tie how does the investment actually perform to what was the judgment when it came into the door?


There was just a study that came out that said only 11% of early stage venture actually does that second part of the process. What a waste of a great gut. The whole point is that you want to be able to make explicit what that great gut decision-making is doing. So you can start to close these feedback loops, be better about it, put it up for examination, make it explicit, record it, and transfer that knowledge to the other people on the team, which I imagine you do a lot of it at Amazon.

Paul (20:02):

But it's sort of not a gut transfer, if I can mix that metaphor, but it's more like the establishment of our culture and mechanisms inside the company. And so, I had been at Amazon, I guess about four, four and a half years when we kind of recognized that we were doing things differently than the other places we had worked. So I had worked at Intel Corporation for example, and everybody else came from somewhere else, and we recognized that we did things differently.


And so, we decided to try to articulate, to actually write down what our culture was. And that was the birth of what we call our leadership principles. They're available on our website if you want to check them out. But at that point, it was a description of who we were, but it then immediately became prescriptive to newer Amazonians that, "This is how we do things. We're not claiming a religion here. We're not saying this is the only way. We're not even claiming it's necessarily the best way to do things. It just happens to be way we do things."


And so, articulating that culture in that fashion memorialized maybe the gut or the emotion that had that occurred before. And it also has allowed us to maintain this continuity throughout the company. So we have one and a half million employees at this point, and every one of them is expected to abide by this same set of principles.


We also have some very specific mechanisms that sound a lot like boring process, but they're a way to get at better decision-making. For example, we do not use bullet point presentations to make decisions. They're just simply banned from decision-making, because they're too vague. We want people to articulate in prose, sentences and paragraphs and sections, why they believe in the decision that they're advocating. And so, to us that kind of transfer of gut from Jeff and the leadership team to the rest of the company has come down as kind of a cultural expectation that just says, "This is the way we do things at Amazon."

Annie (21:48):

I completely agree. And I think that what I would say is that the implicit is a Petri dish for a cognitive bias. If you want cognitive bias to grow and thrive, leave things implicit. That is where it's going to live. If you make it explicit, as you just articulated so well Paul, you create a way to contain all the ways that things can start to break apart. You transfer it to the next generation. Things are very clear, expectations become very clear. Bias does not thrive as well in that type of environment. And this is what we're trying to do in terms of our own decision-making.

Gautam (22:27):

If you've ever watched poker on TV, you might have noticed that the broadcast will occasionally cut to commercial only to return several hands later without a major change in the leaderboard. Time will have passed, cards will have been dealt, but most players in that interim felt their hands weren't worth playing and took the most prudent path available. They folded, making a short term sacrifice in the hope of long term gains.


This is a strategy very familiar to Paul at Amazon, where they've been willing to lose money on a number of innovations, sometimes for years, when they're confident about their success in the long run. And this is where great leaders often show themselves. Not only do they know when to stay in the game and when it's time to go home, they know how important it is to cultivate that mindset in their teams.

Paul (23:19):

This must come from the top. It cannot be the case that some mid-level manager says to his boss, "Hey, boss. I'm really sorry. I experimented with something. We just lost $17 million on it. Really sorry, but I'm trying. I'm trying to innovate on behalf of our customers." And the boss, she's going to say, "What? You lost $17 million? Let's have your performance review." Right? So it can't be generated from the middle or bottom-up. This is a cultural feature that has to come from the top.


So there is no doubt in anybody's mind where Jeff and Andy are on this question of failure in the context of experimentation and innovation. They have made crystal clear that this is not only okay, that it's permissible, but it's an expectation; that we're trying so hard that we are failing all the time. Jeff personally takes credit for billions of dollars of failure, and I'm sure he's right. It's that kind of a function at Amazon that we try things, we think big. But the reason it works, this whole failing thing works, is because we're always trying it.


I think a big mistake that particularly very successful enterprises make is to be riding a successful product or service. Maybe it's patent protected, maybe it's just a great product, everybody's happy, customers are happy, employees are happy, investors are happy. And then something changes, and then you say, "Oh my gosh." Suddenly, we need to innovate. We need to do something new to replace what's been going out for decades. And the chances of that working are about zero. It can't happen if you don't have the muscle memory for it. And the fact that we're always trying not only gives us the muscle memory, but a few uncapped successes pay for a whole lot of failures, because there's no upper cap on success, as Jeff likes to point out.


If you go to a football match or a baseball game or whatever your sport, the very best team can be dominant over a bad team in that match. But what does the best team get out of it? They get one victory. But in business, there's no upper cap on success. One product can be 10 victories or 100 or 10,000 victories. There's just no upper cap. And so, if you are cutting off the things that aren't working before they get to be massive losses, then just a few big victories will pay for a lot of failures along the way.

Annie (25:20):

I completely agree. This is a leadership issue. It has to do with the culture of your team. And I think there's kind of two different ways to approach it, right? One is to say, "We expect you to try a bunch of stuff that isn't going to work and we're totally fine with that. What we care about is that you're going to find out as quickly as possible that the stuff isn't working." Because to your point, Paul, if you spend too much time on something that isn't working, your wins are not going to make up for your losses.


So this has to do with a mindset, and this is true whether it's projects. It's true whether it's investments. Even for the best investor in the world, he's doing a little bit of this, which is that what we want to do is we're going to make some bets, and then as we get information, we're going to concentrate our capital on the bets that are working and we're going to let go of the rest. And we're going to go in knowing that that's our strategy. Okay? And that's true even if you have just long hold type of funds, because even they don't hold everything forever, right? So they free some capital up to go do some other things.


So I work at First Round Capital. We're a seed stage venture fund, so obviously, we're talking about investing in highly innovative projects or other companies at a time when you have the least amount of information basically in the cycle. And that's the whole goal, is that we go into it saying, "Every company that we invest in at seed is an option." That's what it is. It's an option that we've bought, and our job is to concentrate capital on the options that are working. And obviously, you're not selling, you're still supporting the companies that aren't, but you're not concentrating capital there. And so, that's built into the way that we think about the world.


And that's true. You have to understand that venture capital is no different than what a company ought to be doing. So a company ought to be thinking about every single thing that they start is an option. You should be trying to start those when they're uncertain. To Paul's point, if you have a lot of certainty that it's going to work, then you're not innovating, you're making incremental improvements. And trust me, the people that First Round invest in are going to eat you, eventually. They will at some point eat you.


So you don't want that to happen, right? So you have to continually be saying, "I want to create these options for myself." And then, our job is to now sort those and try to figure out what are the things that are worthwhile, which is going to be a very small percentage of them, and what are the things that aren't worthwhile? And the better you are at identifying the things that aren't worthwhile and shutting those things down fast, the more that those outsized returns from the things that aren't worthwhile are going to get you to where you want to go.


So we can think about it again in relation to venture capital. If you have a company that's underperforming, and because you are an owner of the company, because maybe as the point partner your identity is wrapped up in some way with that company, and you decide that you are going to continue to invest at later rounds because you know that you can turn it around, that is going to eat into your profit, because you've got the information that you need to go. And the reason why you're continuing down that path has to do with issues of your own identity and ownership and confirmation bias, sunk cost, so on and so forth, where you will no longer be successful if you do that.


And so, you have to create that kind of mindset. You have to actually create that kind of venture capitalist mindset, which is, "We're going to have lots and lots and lots and lots and lots of false positives." The thing we don't want to have is a false negative, but we also want to identify the things that are false positives as quickly as possible. And that's really the way that you have to approach those types of decisions.

Gautam (29:01):

Knowing when it is and isn't time to take a risk and communicating that to their teams is one of the most remarkable things leaders can do on a regular basis. Given that, and given Annie and Paul's expertise on the subject, we wanted to know who had made an impression like that on them? Who had most impress them and why?

Annie (29:22):

The most impressive person that I have gotten to know was my first advisor in graduate school, a woman named Lila Gleitman, who was surely the smartest person I've ever met, also the kindest, also I would say the funniest. And she was a giant in the field of cognitive science at a time when there were very few women. In fact, her first job had to be as a research assistant for her husband, because a wife was not allowed to work at the same university as their husband and the husband like one. And also, women didn't really get tenure track positions at that time.


And so, she came up during a time when it was incredibly hard for her to even get into graduate school. Certainly, it was really hard for someone so incredibly brilliant to actually get a tenure track position. She eventually ended up at the University of Pennsylvania, which is where I studied with her for five years. She just passed at 91 last summer, not this past summer, but the summer before, and was as sharp at 91 as she was when I was working with her when she was 60 or so. And I miss her terribly. She was really a bright light in the world and I really miss her.

Paul (30:38):

I guess it's politically correct and also a good career move to say your boss. But Jeff and I went to the same school. We had the same major. He's a year younger than I, and I hadn't met him until I interviewed with him. So I was going to go fly out to Seattle in December of '99, and his team called me and said, "Can you meet him in New York over the weekend?" And I said, "Sure." That was a lot easier for me. So it was a Sunday, and we sat in an empty restaurant in his hotel and talked for about two and a half hours. He wanted to see if I was adequate for the job, and apparently I was at least adequate.


But I wanted to understand whether he really meant it when he said customer obsession, because obsession is a very strong word for a commercial context. And so, he was talking about obsessing over customers. And he does mean it. He did mean it at the time. He does mean it, and that's the way the company now thinks. And I just thought that that forward look and that real way of analyzing every business decision was phenomenal, and if not unique, certainly unusual.


It also turns out as I was going to leave him that evening to fly back to DC, he says, "When you get home, turn on CNN." And I said, "New product launch? New line of business?" He said, "No, just turn on CNN." Well, it was Jeff getting Times Person of the Year award. And here is a global award, that's why he was in New York for the final taping of it earlier on, and he spent two and a half hours with me without mentioning this. And so, it is another insight into his personality. The man is extremely talented, hard- working, demanding, but also very humble.

Gautam (32:07):

Who dares, wins. That's the motto of Britain's elite Special Air Service. It's inspiring, noble, and practical; because to bet on sure thing is to think safe and small and eventually to lose to people who are willing to take risks. But to innovate is to dare.


As Captain Picard, always a key source of leadership wisdom, once said, "It is possible to commit no mistakes and still lose. That is not a weakness, that is life." Leadership and innovation require taking risks, but being good at them means taking the right risks and knowing when to walk away when those risks don't pan out.


Luck has a role in all of our successes and all of our failures. But if we avoid putting too much of our egos into our decisions, if we embrace process over results, and if we nurture those philosophies in our teams, then we can pick ourselves up, shake off our losses, and dare another day, confident that this time we are even better prepared to win.


Ultimately, none of us can say for certain what the cards have in store. But being comfortable, thoughtful, and transparent about that creates the space that true innovations need to thrive. Adopting that mindset is what leads to truly impactful outcomes, and it also marks the difference between gambling and leading.

Speaker 4 (33:48):
World Reimagined, with Gautam Mukunda, a leadership podcast for a changing world, an original podcast from Nasdaq.

Speaker 5 (34:01):
Gautam Mukunda does not speak on behalf of Rose Park Advisors, LLC or any of its affiliates, and is not soliciting investments or providing investment advice.

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