Innovating in Bets

Every organization, whether small or big, early or late stage — and every individual, whether for themselves or others — makes countless decisions every day, under conditions of uncertainty. The question is, are we allowing that uncertainty to bubble to the surface, and if so, how much and when? Where does consensus, transparency, forecasting, backcasting, pre-mortems, and heck, even regret, usefully come in?

Going beyond the typical discussion of focusing on process vs. outcomes and probabilistic thinking, this episode of the a16z Podcast features Thinking in Bets author Annie Duke — one of the top poker players in the world (and World Series of Poker champ), former psychology PhD, and founder of national decision education movement How I Decide — in conversation with Marc Andreessen and Sonal Chokshi. The episode covers everything from the role of narrative — hagiography or takedown? — to fighting (or embracing) evolution. How do we go from the bottom of the summit to the top of the summit to the entire landscape… and up, down, and opposite?

The first step to understanding what really slows innovation down is understanding good decision-making — because we have conflicting interests, and are sometimes even competing against future versions of ourselves (or of our organizations). And there’s a set of possible futures that result from not making a decision as well. So why feel both pessimistic AND optimistic about all this??

Show Notes

Using a football thought experiment to distinguish skill and luck [0:58]

Balancing outcomes and process [9:49]

Asking the right questions, especially with a negative outcome [11:17]

Discussion of timing in forecasting [15:23], and other practical implications [16:59]

Why not making a decision is also a decision [23:40], and how to evaluate the options you didn’t take [30:15]

Discussion of how widely this type of decision-making will be adopted by the public [34:10]

How to communicate probabilistically [37:24] and how to build uncertainty into an organization [40:21]

Transcript

Sonal: Hi, everyone. Welcome to the “a16z Podcast.” I’m Sonal, and today Mark and I are doing another one of our book author episodes. We’re interviewing Annie Duke, who’s a professional poker player and World Series champ, and is the author of “Thinking in Bets,” which is just out in paperback today. The subtitle of the book is, “Making Smarter Decisions When You Don’t Have All the Facts,” which actually applies to startups and companies of all sizes and ages, quite frankly. I mean, basically, any business or new product line operating under conditions of great uncertainty — which I would argue is my definition of a startup and innovation. So that will be the frame for this episode.

Annie’s also working on her next book right now, and founded howidecide.org, which brings together various stakeholders to create a national education movement around decision education, empowering students to also be better decision makers. So, anyway, Mark and I interview her about all sorts of things in and beyond her book, going from investing, to business, to life. But Annie begins with a thought experiment, even though neither of us really know that much about football.

Skill vs. luck

Annie: So what I’d love to do is, kind of, throw a thought experiment at you guys so that we can have a discussion about this. So I know you guys don’t know a lot about football, but this one’s pretty easy. You’re gonna be able to feel this one. I want you to do this thought experiment. Pete Carroll calls for Marshawn Lynch to actually run the ball.

Sonal: So we’re betting on someone who we know is really good?

Annie: Well, they’re all really good, but we’re betting on the play that everybody’s expecting.

Mark: Yeah, the default.

Annie: This is the default.

Mark: The assumed rational thing to do, right?

Annie: This is the assumed rational thing to do, right. So he has Russell Wilson hand it off to Marshawn Lynch. Marshawn Lynch goes to barrel through the line. He fails. Now they call the timeout — so now they stop the clock. They get another play now, and they hand the ball off to Marshawn Lynch — what everybody expects. Marshawn Lynch, again, attempts to get through that line and he fails. End of game, Patriots win.

My question to you is, are the headlines the next day, “The Worst Call in Super Bowl History”? Is Cris Collinsworth saying, “I can’t believe the call, I can’t believe the call.” Or is he saying something more like, “That’s why the Patriots are so good. Their line is so great. That’s the Patriots’ line that we’ve come to see this whole season. This will seal Belichick’s place in history.” It would’ve all been about the Patriots.

So let’s, sort of, divide things into, like — we can either say the outcomes are due to skill or luck — and luck in this particular case is gonna be anything that has nothing to do with Pete Carroll. And we can agree that the Patriots’ line doesn’t have anything to do with Pete Carroll — Belichick doesn’t have anything to do with Pete Carroll — Tom Brady doesn’t have anything to do with Pete Carroll — as they’re sealing their fifth Super Bowl victory.

So what we can see is there’s two different routes to failure here. One route to failure, you get resulting. And basically what resulting is, is that retrospectively, once you have the outcome of a decision — once there’s a result — it’s really, really hard to work backwards from that single outcome to try to figure out what the decision quality is. This is just very hard for us to do. They say, “Oh my gosh, the outcome was so bad. This is clearly — I’m gonna put that right into the skill bucket. This is because of Pete Carroll’s own doing.” But in the other case, they’re like, “Oh, you know, there’s uncertainty. What could you do?” Weird, right?

Sonal: Yeah.

Annie: Okay, so you can kind of take that and you can say, “Aha, now we can, sort of, understand some things.” Like, for example, people have complained for a very long time that in the NFL they have been very, very slow to adopt what the analytics say that you should be adopting, right? And even though now we’ve got some movement on fourth-down calls, and when are you going for two-point conversions, and things like that, they’re still nowhere close to where they’re supposed to be, and why is that?

Mark: So they don’t make the plays corresponding to the statistical probabilities?

Annie: No. In fact, the analytics show that if you’re on your own one-yard line, and it’s fourth down, you should go for it no matter what. The reason for that is if you kick it, you’re only gonna be able to kick to midfield. So the other team is basically almost guaranteed three points anyway, so you’re supposed to just try to get the yards. Like, when have you ever seen a team on their own one-yard line on fourth down be like, “Yeah, let’s go for it.” That does not happen.

Okay, so we know that they’ve been super slow to do what the analytics say is correct, and so you sit here and you go, “Well, why is that?” And that thought experiment really tells you why, because we’re all human beings. We all understand that there are certain times when we don’t allow uncertainty to bubble up to the surface — is the explanation — and there are certain times when we do. And it seems to be that we do, when we have this, kind of, consensus around the decision, there’s other ways we get there. And so, okay, if I’m a human decision-maker, I’m gonna choose the path where I don’t get yelled at.

Sonal: Yeah, exactly.

Annie: So, basically, we can, kind of, walk back, and we can say, “Are we allowing the uncertainty to bubble to the surface?” and this is gonna be the first step to, kind of, understanding what really slows innovation down — what really slows adoption of what we might know is good decision making, because we have conflicting interests, right? Making the best decision for the long run, or making the best decision to keep us out of a room where we’re getting judged.

Mark: Yelled at, or possibly fired. So let me propose the framework that I use to think about this and see if you agree with it. So it’d be a two-by-two grid, and it’s consensus versus non-consensus, and it’s right versus wrong. And the way we think about it, at least in our business, is basically — consensus right is fine. Non-consensus right is fine. In fact, generally, you get called a genius. Consensus wrong is fine, because, you know, it’s just the same mistake everybody else made.

Sonal: You all agreed, right, it was wrong.

Mark: Non-consensus wrong is really bad.

Annie: Horrible.

Mark: It’s radioactively bad. And then as a consequence of that, and maybe this gets to the innovation stuff that you’ll be talking about — but as a consequence of that, there are only two scripts for talking about people operating in the non-consensus directions. One script is, they’re a genius because it went right — and the other is they’re a complete moron because it went wrong. Does that map?

Annie: That’s exactly it. That’s exactly right. And I think that the problem here is that, what does right and wrong mean? In your two-by-two, wrong and right is really just, did it turn out well or not?

Mark: Yeah, outcomes.

Sonal: Not the process.

Annie: And this is where we really get into this problem, because now what people are doing is they’re trying to swat the outcomes away. And they understand, just as you said, that on that consensus wrong, you will have a cloak of invisibility over you — like, you don’t have to deal with it. <Right.> So let’s think about other things besides consensus. So, consensus is one way to do that, especially when you have complicated cost-benefit analyses going into it. I don’t think that people, when they’re getting in a car, are actually doing any, kind of, calculation about what the cost-benefit analysis is to their own productivity, versus the danger of something very bad happening to them. Like, what is this society? Someone’s done this calculation, we’ve all, kind of, done this together — and so, therefore, getting in a car is totally fine. I’m gonna do that.

Mark: And nobody second-guesses anybody. If somebody dies in a car crash you don’t say, “Wow, what a moron for getting in a car.”

Annie: No. Another way that we can get there is through transparency. So if the decision is pretty transparent, another way to get there is status quo. So a good status quo example that I like to give, because everybody can understand it is — you have to get to a plane, and you’re with your significant other in the car, and you go the usual route.

Sonal: This is a common fight for every couple.

Annie: Yeah, so you go your usual route. Literally, this is the route that you’ve always gone and there is some sort of accident, there’s bad traffic, you miss the plane — and you’re mostly probably comforting each other in the car. It’s like, “What could we do?” You know, eh. But then you get in the car and you announce to your significant other, “I’ve got a great shortcut, so let’s take this shortcut to the airport.” And there’s the same accident, whatever — horrible traffic, you miss the flight. That’s like that status quo versus non-status quo decision.

Sonal: Right, you’re going against what’s familiar and comfortable.

Annie: Exactly. If we go back to the car example, when you look at what the reaction is to a pedestrian dying because of an autonomous vehicle, versus because of a human, we’re very, very harsh with the algorithms. For example, if you get in a car accident and you happen to hit a pedestrian, I can say something like, “Well, you know, Mark didn’t intend to do that.” Because I think that I understand — your mind is not such a black box to me. So I feel like I have some insight into what your decision might be, and so more allowing some of the uncertainty to bubble up there. But if this black box algorithm makes the decision, now all of a sudden I’m like, “Get these cars off the road.”

Sonal: Never mind that the human mind is a black box itself ultimately, right?

Annie: Of course, but we have some sort of illusion that I understand, sort of, what’s going on in there, just like I have an illusion that I understand what’s going on in my own brain. And you can actually see this in some of the language around crashes on Wall Street, too. When you have a crash that comes from human beings selling, people say things like, “The market went down today.” When it’s algorithms, they say, “It’s a flash crash.” So now they’re, sort of, pointing out, like — this is clearly in the skilled category. It’s the algorithm’s fault. We should really have a discussion about algorithmic trading and whether this should be allowed, when obviously the mechanism for the market going down is the same either way.

So now if we understand that, so exactly your matrix. Now we can say, “Well, okay, human beings understand what’s gonna get them in the room.” And pretty much anybody who’s, you know, living and breathing in the top levels of business at this point is gonna tell you, “Process, process, process. I don’t care about your outcomes — process, process, process.” But then the only time they ever have, like, an all-hands-on-deck meeting is when something goes wrong. Let’s say that you’re in a real estate investing group, and so you invest in a particular property based on your model, and the appraisal comes in 10% lower than what you expected. Like, everybody’s in a room, right? You’re all having a discussion. You’re all examining the model, you’re trying to figure out. But what happens when the appraisal comes in 10% higher than expected? Is everyone in the room going, “What happened here?”

Outcomes vs. process

Mark: Now there is the obvious reality, which is, like, we don’t get paid in process, we get paid in outcomes. Poker players, you don’t get paid in process, you get paid in outcome, and so there is a…

Sonal: Incentive alignment.

Mark: It’s not completely emotional. It’s also an actual — there’s a real component to it.

Annie: Yeah, so two things. One is, you have to make it very clear to the people who work for you that you understand that outcomes will come from good process. That’s number one. And then number two, what you have to do is try to align the fact that, as human beings, we tend to be outcome driven — to what you want, in terms of getting an individual’s risk to align with the enterprise risk. Because otherwise you’re gonna get this CYA behavior. And the other thing is that we wanna understand if we have the right assessment of risk. So one of the big problems with the appraisal coming in 10% too high, there, could be that your model is correct. It could be that you could have just a tail result, but it certainly is a trigger for you to go look and say, “Was there risk in this decision that we didn’t know was there?” And it’s really important for deploying resources.

Sonal: I have a question about translating this to, say, non-investing context. So in the example of Mark’s matrix, even if it’s a non-consensus wrong — you are staking money that you are responsible for. In most companies, people do not have that kind of skin in the game. <Right.> So how do you drive accountability in a process-driven environment — that the results actually do matter? You want people to be accountable, yet not overly focused on the outcome. Like, how do you calibrate that?

Annie: So let’s think about, how can we create balance across three dimensions that makes it so that the outcome you care about is the quality of the forecast? So first of all, obviously this demands that you have people making forecasts. You have to state in advance, “Here’s what I think. This is my model of the world. Here are where all the places are gonna fall. So this is what I think.” So now you stated that, and whether the outcome is “good or bad” is — how close are you to whatever that forecast is?

So, now it’s not just like, oh, you won to it, or you lost to it. It’s — was your forecast good? So that’s piece number one, is make sure that you’re trying to be as equal across quality as you can, and focus more on forecast quality as opposed to traditionally what we would think of as outcome quality. So now the second piece is directional. So, when we have a bad outcome and everybody gets in the room, when was the last time that someone suggested, “Well, you know, we really should’ve lost more here?” Like, literally nobody’s saying that, but sometimes that’s true. Sometimes if you examine it, you’ll find out that you didn’t have a big enough position. It turned out, okay, well, maybe we should’ve actually lost more. So you wanna ask both up, down, and orthogonal. So, could we have lost less? Should we have lost more? And then the question of, should we have been in this position at all?

Mark: So in venture capital, after a company works and exits — let’s say it sells for a lot of money, you do often say, “God, I wish we had invested more money.” You never, ever, ever, ever — I have never heard anybody say on a loss, “We should’ve invested more money.”

Annie: See, wouldn’t it be great if someone said that? Wouldn’t you love for someone to come up and say that to you? That would make you so happy.

Sonal: I actually still don’t get…

Mark: And what would be the logic of why they should say that?

Sonal: I still don’t get the point. Exactly. Why does that matter? I don’t really understand that.

Annie: Can I just, like — simple, in a poker example?

Sonal: Yeah.

Annie: So let’s say that I get involved in a hand with you, and I have some idea about how you play. And I have decided that you are somebody that, if I bet X, you will continue to play with me. Let’s say this is a spot where I know that I have the best hand, but if I bet X plus C that you will fold. So if I go above X, I’m not gonna be able to keep you coming along with me, but if I bet X or below, then you will — so I bet X. You call, but you call really fast, in a way that makes me realize, “Oh, I could’ve actually bet X plus C.” You hit a very lucky card on the end, and I happen to lose the pot. I should’ve maximized at the point that I was the mathematical favorite.

Mark: Because your model of me was wrong, which is a learning independent of the win or the loss.

Annie: Exactly. So you need to be exploring those questions in a real honest way.

Mark: Right, because it has to do with how you size future bets.

Sonal: This is exactly like a company betting on a product line.

Annie: Correct.

Sonal: And then picking what the next product line is gonna be, and then not having had the information that would then drive a better decision-making process around that.

Annie: Right. So thinking about the learning loss that’s happening because we’re not exploring that — the negative direction — and now you should do this on wins as well. So if you do ever discuss a win, you always think, like, “How could I press? How could I have won more? How could I have made this even better? How could I do this again in the future? Should we have won less?”

Mark: We oversized the bet and then got bailed out by a fluke.

Annie: We should have actually had less in it, and sometimes not at all, because sometimes the reasons that we invested turned out to be orthogonal to the reasons that it actually ended up playing out in the way that it was. And so, had we had that information, we actually wouldn’t have bet on this at all because it was completely orthogonal. We totally had this wrong. It just turned out that we ended up winning. And that can happen. I mean, obviously that happens in poker all the time, but what does that communicate to the people on your team?

Good, bad, I don’t care. I care about our model. I wanna know that we’re modeling the world well, and that we’re thinking about, “How do we incorporate the things that we learn?” Because we can generally think about stuff in two — stuff we know, and stuff we don’t know. There’s stuff we don’t know we know, obviously — so we don’t worry about that, because we don’t know we don’t know it. But then there’s stuff we could know, and stuff we can’t know. It’s things like the size of the universe, or the thoughts of others, <Exactly.> or what the outcome will actually be. We don’t know that.

Sonal: I have a question about this, though. What is the timeframe for that forecast? So let’s say you have a model of the world — a model of a technology, how it’s gonna adopt, how it’s gonna play out. In some cases, there are companies that can take, like, years to get traction. You wanna get your customers very early to figure that out, right? So you can get that data. But how much time do you give? How do you size that timeframe for the forecast, so you’re not constantly updating with every customer data point, and so you’re also giving it enough time for your model, your plan, your forecast to play out?

Annie: You have to think about —very clearly in advance, “What’s my time horizon? How long do I need for this to play out?” But also, don’t just do this for the big decisions — because there’s things that you can forecast for tomorrow as well, so that you end up bringing it into just the way that people think. And then once you’ve decided, “Okay, this is the time horizon on my forecast,” then you would wanna be thinking about, “What are forecasts we make for a year, two years, five years for this specific decision to play out?” And then just make sure that you talk in advance — at what point you’ll revisit the forecast. So you wanna think in advance, “What are the things that would have to be true for me to be willing to come in and actually revisit this forecast?” Because otherwise, you can start, as you just said, like — it can turn into — super bad.

Sonal: You’re like a leaf in the wind. Right, exactly, because then you’re, like, one bad customer and you suddenly over-rotate on that — when in fact, it could’ve been not even a thing.

Annie: Right, so if you include that in your forecast — here are the circumstances under which we would come in and check on our model — then you’ve already gotten that in advance. So that’s actually creating constraints around the re-activity, which is helpful.

Barriers to logical decision-making

Mark: Two questions on practical implementation of the theory. So what I’m finding is, more and more people understand the logic of what you’re describing, because people are getting exposed to these ideas and, kind of — expanding in importance. And so more and more people intellectually understand this stuff, but there’s two, kind of — I don’t know, so call it emotion-driven warps, or something — that people just really have a hard time with. So one is that you understand this could be true investors, CEO, product-line manager in a company — you know, kind of, anybody in one of these domains — which is you can’t get the non-consensus results unless you’re willing to take the damage, right, the risk on the non-consensus wrong results.

But people cannot cope with the non-consensus wrong outcome. They just emotionally cannot handle it — and they would like to think that they can, and they intellectually understand that they should be able to. But as you say, when they’re in the room it’s such a traumatizing experience that it’s the “touching the hot stove.” They will do anything in the future to avoid that. And so one interpretation would be, that’s just simply flat out human nature — and so, to some extent, the intellectual understanding here — it doesn’t actually matter that much, because there is an emotional override. And so that would be a pessimistic view on our ability as a species to learn these lessons, or do you have a more optimistic view of that?

Annie: I’m gonna be both pessimistic and optimistic at the same time, so let me explain why.

Sonal: Ooh, love it.

Annie: Because I think that if you move this a little bit it’s a huge difference. You, sort of, have two tacks that you wanna take. One is, how much can you move the individual to, sort of, train this kind of thinking for them? And that means, naturally, they’re thinking in forecasts a little bit more — that when they do have those kinds of reactions, which naturally everybody will, they right the ship more quickly, so that they can learn the lessons more quickly, right? I mean, I actually just had this happen. I turned in a draft of my next book — the first part of my next book to my editor — and I just got the worst comments I’ve ever gotten back.

Sonal: Good editor.

Annie: And I had a really bad 24 hours, but after 24 hours, I was like, “You know what? She’s right.” Now, I still had a really bad 24 hours — and I’m the, like, “give me negative feedback” queen. Because I’m a human being. But I got to it fast. I, sort of, got through it pretty quickly after this. I mean, I — you know, on the phone with my agent saying, “I’m standing my ground, this is ridiculous.” And then he got a text the next day being, like, “No, she’s right.” And then I rewrote it, and you know what? It’s so much better for having been re-written, and now I can get to a place of gratitude for having the negative feedback. But I still had the really bad day, so it’s okay.

Sonal: So, it doesn’t go away, right?

Annie: Yeah, and it’s okay. We’re all human, we’re not robots. So number one is, like, how much are you getting the individuals to say, “Okay, I improved 2%, that’s so amazing for my decision making and my learning going forward?” And then the second through-line is, what are you doing to not make it worse? Because obviously for a long time people liked to talk about, “I’m results oriented.” That’s, like, the worst sentence that could come out of somebody’s mouth.

Sonal: Why is that the worst? I’ve heard that a lot, what’s so bad about it?

Annie: Because you’re letting people know that all you care about is, like, “Did you win or lose?” That’s fantastic — be results oriented all you want. You should pay by the piece. You will get much faster work. But the minute that you’re asking people to do intellectual work, results oriented is, like, the worst thing that you could say to somebody. So I think that we need to take responsibility, and the people in our orbit — we can make sure at minimum that we aren’t making it worse. And I think that that — so that’s pessimistic and optimistic. I don’t think anyone is making a full reversal here.

Mark: So the second question then goes to the societal aspect of this. And so we’ll talk about the role of the storytellers — or as they’re sometimes known, the journalists.

Annie: Yeah, and the editors.

Sonal: I love it.

Mark: And the editors, and the publishers. And so the very first reporter I ever met when I was a kid — Jared Sandberg at the Wall Street Journal — you know, the internet was first emerging. There were no stories in the press about the internet, and I used to say, “There’s all this internet stuff happening. Why am I not reading about any of it in any of these newspapers?” And he’s like, “Well, because the story of ‘something is happening’ is not an interesting story.” He said, “There are only two stories that sell newspapers.” He said, “One is, ‘Oh, the glory of it,’ and the other is, ‘Oh, the shame of it.’” And basically he said it’s conflict. So it’s either something wonderful has happened, or something horrible has happened, <Yeah.> those are the two stories. And then you think about business journalism as, kind of, our domain — and you kinda think about it, and it’s like, those are the only two profiles of a CEO or founder you’ll ever read.

It’s just, like, what a super genius for doing something presumably non-consensus and right, or what a moron. Like, what a hopeless idiot for doing something non-consensus and wrong. And so, since I’ve become more aware of this, it’s gotten very hard for me to actually read any of the coverage of the people I know, because — it’s like the people who got non-consensus right, they’re being lavished with too much praise. <Ah.> And the people who got non-consensus wrong, they’re being damned for all kinds of reasons. The traits are actually the same in a lot of cases. And so, I guess, as a consequence — if you read the coverage, it really reinforces this bias of being results-oriented. And it’s like, it’s not our fault that people don’t wanna read a story that says, “Well, you tried something and it didn’t work this time,” right?

Annie: Yes, exactly. But it was mathematically pretty good. If we go back to Pete Carroll, this is a pretty great case. And if we think about options theory, just quickly — the paths preserve the option for two run plays. So if you wanna get three tries at the end zone instead of two, strictly for clock management reasons, you pass first.

Mark: Right, and that’s not gonna kick off ESPN “SportsCenter” that night. And so optimistic or pessimistic that the narrative — the public narrative on these topics will ever move?

Annie: I’m super, super pessimistic on the societal level, but I’m optimistic on — if we’re educating people better, that we can equip them better for this. So I’m really focused on, how do we make sure that we’re equipping people to be able to parse those narratives in a way that’s more rational? And particularly, you know — now there’s so much information, and it’s all about the framing, and the storytelling — and it’s particularly driven by, what’s the interaction of your own point of view? We could think about it as [a] partisan point of view, for example, versus the point of view of the communicator of the information, and how is that interacting with each other. You know, in terms of, how critically are you viewing the information, for example? I think this is another really big piece of the pie, and somewhat actually related to the question about journalism, which is that third dimension of the space.

So we talked about two-dimension, which is, sort of, outcome quality, and how are you allowing that you’re exploring both downside and upside outcomes in a way that’s really looking at forecast? How are you thinking directionally, so that you’re more directionally neutral? But then the other piece of the puzzle is, how are you treating omissions versus commissions?

So one of the things that we know with this issue of resulting is, here’s a really great way to make sure that nobody ever results on you — don’t do anything, okay? So if I just don’t ever make a decision, I’m never gonna be in that room with everybody yelling at me for the stupid decision I made, because I had a bad outcome. But we know that not making a decision is making a decision, we just don’t think about it that way. And it doesn’t have to just be bad investing. You can have a shadow book of your own personal decisions.

Sonal: Personal life, I agree.

Not making a decision is a decision

Annie: So, you know, it’s really interesting — I remember I was giving somebody advice, who — I think he was, like, 23. And so, obviously, newly out of college, had been in this position for a year, and was really, really unhappy in the position. And he was asking me, like, “I don’t know what to do. I don’t know if I should change jobs.” And I said, “Well…” So I did all the tricks, you know, time traveling — and so I was like, “Okay, imagine it’s a year from now. Do you think you’re gonna be happy in this job?” “No.” “Okay, well, maybe you should choose this other — go and try to find another position.” And this is what he said to me — and this, I think, shows you how much people don’t realize that the thing that you’re already doing, the status quo thing — choosing to stay in that really is a decision.

So he said to me, “But if I go and find another position, and then I have to spend another year, which I just spent, trying to learn the ins and outs of the company, and it turns out that I’m not happy there, I’ll have wasted my time.” And I said to him, “Okay, well, let’s think about this, though. The job you’re in, which is a choice to stay in, you’ve now told me it’s 100% in a year that you will be sad. Then if you go to the new job, yes, of course it’s more volatile — but at least you’ve opened the range of outcomes up.” But he didn’t wanna do it because it doesn’t feel — like, staying where he was didn’t feel like somehow he was choosing it, so that he felt like if he went to the other place <Yes.> and ended up sad that somehow that would be his fault and a bad decision.

Sonal: That’s so, so profound. In my case — this might be getting a little too personal, but in my case it was a decision I didn’t know I had made, to not have kids. And it’s still an option, but it’s probably not gonna happen. And my therapist, kind of, told me that my not deciding was a choice — and I was so blown away by that that it, then, allowed me to then examine what was going on there in that framework, in order to not do that for other arenas in my life where I might actually want something. Or maybe I don’t, but at least it’s a choice, that there’s intentionality behind it.

Annie: Well, I appreciate you sharing. I mean, I really wanna thank you for that, because I think that people, first of all, should be sharing this kind of stuff so that people feel like they can talk about these kinds of things, number one.

Sonal: I agree.

Annie: And number two, in my book, I’ve got all these examples in there of, like — how are you making choices about raising your kids when it feels so consequential?

Sonal: When you’re doing decisions for other people?

Annie: Right, and you’re trying to decide, like, “Should I have kids, or shouldn’t I have kids?”

Sonal: Or this school, or that school?

Annie: Or, “Who am I supposed to marry, or where am I supposed to live?” And the thing that I try to get across is, you know — we can talk about investing, like, I’m putting money into some kind of financial instrument, but we all have resources that we’re investing. It’s our time.

Sonal: That’s right. Your time, your energy, your heart. It could be whatever, your friendships, your relationships.

Annie: Right, so you’re deploying resources.

Sonal: Yes, I love that.

Annie: And for the kind of decision that you’re talking about, it’s like — if you choose to have children you’re choosing to deploy certain resources with some expected return. Some of it good, some of it bad. And if you’re choosing not to have children, that’s a different deployment of your resources toward other things.

Sonal: And you need to know that there are limits. Everything isn’t a zero-sum game, <No.> but approaching the world, and the fact that evolution has approached the world as a zero-sum game — and our toolkit makes it a zero-sum game — means that we need to still view everything as a zero-sum game when it comes to those tradeoffs and resources. Because you are losing something every time, even in a non-zero game.

Annie: Right. So I don’t feel like the world is a zero-sum game in terms of, like, <Collaborate, coordinate.> most of the activities that you and I would engage around, we can both win, too. But it’s a zero-sum game, to go back to your therapist. It’s a zero-sum game between you and the other versions of yourself that you don’t choose.

Sonal: Exactly. Or an organization, and the other versions of itself it doesn’t choose.

Annie: Exactly. So there’s a set of possible futures that result from not making a decision as well. So on an individual decision, let’s put things into three categories: clear misses, near misses, and hits. There’s some that would just be a clear miss — throw them out — and there’s some that I’m gonna, sort of, really agonize over and I’m gonna, you know, think about it, and I’m gonna do a lot of analysis on it. So the ones which become a yes go into the hit category, and the other one is a near miss. I came close. What happens with those near misses is they just go away.

So what I realized is that on any given decision — let’s take an investment decision. If I went to you, or you came to me, and said, “Well, tell me what’s happening with the companies that you have under consideration.” On a single decision, when I explain to you why I didn’t invest in a company, it’s gonna sound incredibly reasonable to you.

So you’ll only be able to see in the aggregate, if you look across many of those decisions, that I tend to be having this bias toward missing — towards saying, “You know what? We’re not gonna do it,” so that I don’t wanna stick my neck out. Now this, for you, is incredibly hard to spot because you do have to see it in the aggregate. Because I’m gonna be able to tell you a very good story on any individual decision. So the way to combat that — and again, get people to think about, “What we really care [about] around here is forecast, not really outcomes” — is actually to keep a shadow book. The anti-portfolio should contain basically all of your near misses, but then you have to take a sample of the clear misses as well — which nobody ever looks at. Because the near misses tend to be a little in your periphery if they happen to be big hits.

Mark: So the good news, bad news. So the good news is we have actually done this, and so we call it the shadow portfolio. <Awesome.> And the way that we do it is, we make the investment. We take an equivalent. We take the other equivalent deal of that vintage, of that size — that we almost did but didn’t do — and we put that in the shadow portfolio. And we’re trying to do, kind of, apples-to-apples comparison. In finance theory terms, the shadow portfolio may well outperform the real portfolio, and in finance terms that’s because the shadow portfolio may be higher variance. Higher volatility, higher risk, and therefore, higher return.

Annie: Correct.

Mark: Because, right, the fear is the ones that are hitting are the ones that are less spiky, they’re less volatile, they’re less risky.

Annie: Right. So what’s wonderful about that, when you decide not to invest in a company, you actually model out why. That’s in there.

Mark: It’s often, by the way, a single flaw that we’ve identified. It’s just like, oh, we would do it except for X, <Right.> where X looks like something that’s potentially existentially bad.

Annie: Right, and then that’s just written in there, and so you know that. And then, just make sure those ones that people are just rejecting out of hand, a sample, just a sample.

Mark: Okay, so that’s my question. So we never do that. Let me ask you how to do that, though. So that’s what we don’t do, and as you’re describing, I’m like, “Of course we should be doing that.” I’m trying to think of how we would do that, because the problem is, we reject 99 for every 1 we do.

Annie: Yeah, so you just — literally it’s a sample. You just take a random sample of them.

Mark: A random sample? Okay.

Annie: I mean, as long as it’s just, sort of, being kept in view a little bit. Because what that does is it basically just asks as — pushing against your model. You’re just, sort of, getting people to have the right kind of discussion. So all of that communicates to the people around you, like, “I care about your model.”

Evaluating options you didn’t take

Mark: So let me ask you a different question because you talk about these, sort of, groups of decisions, or portfolios of decisions. So the other question is — so early on in the firm, I happened to have this discussion with a friend of mine, and he basically looked at me and was like, “You’re thinking about this all wrong. You are thinking about this as a decision. You’re thinking about, ‘Invest or not?’” He said, “That’s totally the wrong way to think about this. [The way] you should be thinking about this is, is — is this 1 of the 20 investments of this kind, or of this class size, that you’re gonna put in your portfolio?” When you’re evaluating an opportunity, you are, kind of, definitionally talking about that opportunity. But it’s very hard to abstract that question from the broader concept of a portfolio or a basket.

Annie: Yeah, what I would suggest there is actually just doing some time traveling. That as people are really down in the weeds, to say, “Let’s imagine it’s a year from now, and what does the portfolio look like of these investments of this kind?” So I’m a big proponent of time traveling — of just making sure that you’re always asking that question, “What does this look like in a year? What does this look like in five years? Are we happy? Are we sad? If we imagine that we have this, what percentage of this do we think will have failed? We understand that any one of these individual ones could have failed, so let’s remember that.” And I think that that really allows you to, sort of, get out of what feels like the biggest decision on earth, because that’s the decision you have to be making, and be able to see it in the context of, kind of, all of what’s going on.

Sonal: That’s fantastic. One of the most powerful things my therapist gave me — and it was such a simple construct. It was, sort of, like, doing certain things today is like stealing from my future self. It blew my mind.

Annie: It’s so beautiful.

Sonal: It’s so beautiful. And it seems so, like, you know. Hokey — like, personal, self-helpy — but actually I had never thought of [it]. Because we’re on a continuum. By making discrete individuals — like, Sonal in the past, Sonal today, Sonal, this woman in the future I haven’t met yet. Wow. Like, the idea of stealing from her was, like…

Annie: That’s really a lovely way to put it.

Sonal: Isn’t that so — she’s a fucking awesome therapist, for the record.

Annie: Yeah, she is. I have an amazing therapist.

Sonal: I like talking publicly about therapy because I like lifting the stigma on it.

Annie: No, I’m very, very open about it.

Sonal: Me too.

Annie: Like, let’s not hide it. It’s totally fine.

Sonal: No. There’s no fucking reason to hide it, I totally agree.

Annie: Yeah. Some of the ways that we deal with this is actually prospectively, employing really good decision hygiene — which involves a couple of things. One is some of this good time traveling that we talked about, where you’re really imagining, “What is this gonna look like in the future,” so that that’s metabolized into the decision. Two is making sure that you have pushback once there’s consensus reached. Great, now let’s go disagree with each other. Then the next thing is, in terms of the consensus problem, is to make sure that you’re listening [to] as much input, not in a room with other people. So when somebody has a deal they wanna bring to everybody, that goes to the people individually. They have to, sort of, write their thoughts about it individually, and then it comes into the room after that.

Mark: As opposed to the pile-on effect that tends to happen?

Annie: As opposed to the pile-on effect, and that reduces the sort of effects of consensus anyway. So now this is how you then come up with basically what your forecast of the future is, that then is absolutely memorialized. Because that memorializing of it acts as the prophylactic. First of all, it gives you your forecast, which is what you’re trying to push against anyway. You’re trying to change the attitude to be that the forecast is the outcome that we care about. And it acts as a prophylactic for those emotional issues, right?

Which is now it’s like, okay, well, we all talked about this, and we had our red team over here, and we had a good steel man going on, and we, kind of, really thought about why we were wrong. We questioned — if someone has the outside view, what would this really look like to them? By eliciting the information individually, we were less likely to be in the inside view anyway. We’ve done all of that good hygiene — and then that acts as a way to protect yourself against these kinds of issues in the first place. Again, you’re gonna have a bad 24 hours, I’m just saying. Like, for sure. But you can get out of it more quickly, more often, and get to a place where you can say, “Okay, moving onto the next decision. How do I improve this going forward?”

Sonal: You make better and better decisions.

Mark: Yeah, so building on that, but returning real quick to my optimism, pessimism question. If society is not going to move on these issues, but we can move as individuals — so one form of optimism would be, more of us move as individuals. The other form of optimism could be, there will just always be room in these probabilistic domains for the rare individual who’s actually able to think about this stuff correctly. There will always be an edge. There will always be certain people who are, like, much better at poker than everybody else.

Annie: Oh, I think that’s for sure.

Mark: Okay. Because most people just simply can’t or won’t get there. Like, a few people in every domain might be able to take the time and have the discipline and will power to, kind of, get all the way there, but most people can’t or won’t?

Annie: I think that, in some ways, maybe that’s okay. Like, I mean, I sort of think about it from an evolutionary standpoint. That kind of thinking was selected for for a reason, right? It’s better for survival, likely better for happiness.

Mark: You mean the conventional wisdom of “don’t touch the burning stove twice.”

Annie: Yeah, or run away when you hear rustling in the leaves. Don’t sit around and say, “Well, it’s a probabilistic world. I have to figure out, how often is that a lion that’s gonna come eat me?”

Mark: Most people shouldn’t be playing in the World Series of Poker.

Annie: I have people come up to me all the time and be like, “Oh, you know, I play poker but it’s just a home game,” you know? And I’m like, “Why do you say ‘just a home game?’ There are different purposes to poker. You probably have a great time doing that and it brings you a tremendous amount of enjoyment, and you don’t have an interest in becoming a professional poker player. Just be proud of that, I think that that’s amazing.” Like, I play tennis. I’m not saying, “Oh, but, you know, I’m just playing in USTA 3.5.” I’m really happy with my tennis, I think it’s great.

So I think we need to remember that people have different things that they love. And this kind of thinking, I think that — I would love it if we could spread it more — but of course there are gonna be some people who are going to be ending up in this category more than others, and that’s okay. Not everybody has to think like this. I think it’s all right. So one of the things I get asked all the time is, like, “Well, we can’t really do this because people expect us to be confident in our choices.” <Yes.> Don’t confuse confidence and certainty. So, I can express a lot of uncertainty and still convey confidence. Ready? I’m weighing these three options: A, B, and C. I’ve really done the analysis. Here’s the analysis, and this is what I think. I think that option A is gonna work out 60% of the time. Option B is gonna work out 25% of the time, and option C is gonna work out 15% of the time. So option A is the clear winner. Now I just expressed so much uncertainty in that sentence.

Sonal: But also a lot of confidence.

Annie: But also a lot of confidence. I’ve done my analysis, this is my forecast. And all that I ever ask people to do when they do that is make sure that they ask a question before they bank the decision, which is — is there some piece of information that I could find out that would reverse my decision, that would actually cause — not that would make it go from 60 to 57. I don’t care modulating so much, I care that you’re gonna actually change.

Sonal: Right. And your point is that organizations can then bake that into their process.

Annie: Correct.

Sonal: And not just in the forecasting, but in arriving to that decision. So that then the next time they get to it, right or wrong, they make a better decision.

Annie: Right. And if the answer is yes, go find it. Or sometimes the answer is yes, but the cost is too high. It could be time, it could be actual…

Sonal: Opportunity costs, etc., right.

Annie: Whatever, exactly. So then you just don’t, and then you would say, “Well, then you all recognize as a group, we knew that if we found this out it would change our decision. But we’ve agreed that the cost was too high and so we didn’t.” So then if it reveals itself afterwards, you’re not sad.

Communicating probability to others

Sonal: Yeah, right. Well, you’ve talked a lot about how people should use confidence intervals in communicating — which I love, because we’re both ex-Ph.D psychology people.

Annie: Yes, exactly.

Sonal: Neither finished. So I love that idea. One thing that I struggle with, though, is — again, in the organizational context. If you’re trying to translate this to a big group of people, not just one on one or small group decisions. How do you communicate a confidence interval, and all the variables in it, in an efficient, kind of, compressed way? Like, honestly, part of communication in organizations is emails, and quick decisions — and yes, you can have all the process behind the outcome, but how do you then convey that, even though the people were not part of that room, of that discussion?

Annie: I think that there’s a simpler way to express uncertainty, which is using percentages. Now, obviously, sometimes you can only come up with a range. But for example, if I’m talking to my editor — and this is very quick in an email, I’ll say, “You’ll have the draft by Friday 83% of the time — by Monday, you’ll have it 97% of the time.” Those are inclusive, right?

Sonal: It’s another way of doing a confidence interval, but without making it so wonky.

Annie: Without making it so wonky. So I’m just letting her know — most of the time you’re gonna get it on Friday but I’m building, like, if my kid gets sick, or I have trouble with a particular section of the draft — or whatever it is — and I set the expectations for it that way.

Sonal: That’s fantastic. I mean, we’ve been trying to do forecasting — even for, like, timelines for podcast editing in episodes. And I feel frustrated, because I have a set of frameworks — like, if there’s accents, if there’s more than two voices. If there’s a complex thing, room tone, interaction, feedback, sound effects. I know all the factors that can go into my model, but I don’t know how to put a confidence interval in our pipeline spreadsheet for all the content that’s coming out and predicting it.

Annie: Yeah, so one way to do it is think about — what’s the range? What’s the earliest that I could get it? And you put a percentage on that. And then you think about the latest day they’re gonna get it, and you put a percentage on that.

Sonal: I love that idea.

Annie: And so now, what’s wonderful about that is that — it’s a few things. One is, I’ve set the expectations properly now, so that I’m not getting, you know, yelled at on Friday, like, “Where the hell is the draft?”

Sonal: Exactly, which is half the battle, I’ve learned that.

Annie: And a lot of what happens is that because we think that we have to give a certain answer, it ends up “boy who cried wolf,” right? So that if I’m telling her I’m gonna get it on Friday, and, you know, 25% of the time…

Sonal: Honestly, against your own best judgment sometimes even.

Annie: Right, 25% of the time I’m late, she just starts to not put much stock in what I’ve said anyway. So that’s number one. Number two is — what happens is that you really, kind of, infect other people with this in a good way, where you get them — it just moves them off of that black and white thinking.

Sonal: I love that.

Annie: So, like, one of the things that I love thinking about — and this is the difference between a deadline or, kind of, giving this range — is that I think that we ask ourselves, “Am I sure?” and other people, “Are you sure?” way too often. It’s a terrible question to ask somebody because the only answer is yes or no.

Sonal: So what should we be asking?

Annie: How sure are you?

Uncertainty in an organization

Sonal: How sure are you? I have a quick question for you on this, because earlier you mentioned uncertainty. How do you as an organization build that uncertainty in by default?

Annie: So first of all, we obviously talked a little bit about time traveling and the usefulness of time traveling. So one thing that I like to think about is not [to] overvalue the decision that’s right at hand — the things that are right sitting in front of us, right? So you can kind of think about it, like, how are you gonna figure out the best path? What is it, as you think about what your goals are? And, obviously, the goal that you wanna reach is gonna, sort of, define for you what the best path is.

If you’re standing at the bottom of a mountain that you wanna summit — let’s call the summit your goal — all you can really see is the base of the mountain. So as you’re doing your planning, you’re really worried about, “How do I get the next little bit,” right? “How do I start?” But if you’re at the top of the mountain, having attained your goal, now you can look at the whole landscape. You get this beautiful view of the whole landscape, and now you can really see what the best path looks like. And so we wanna do this not just physically — like, standing up on a mountain, but we wanna figure out a cognitive way to get there, and that’s to do this really good time traveling.

And you do this through backcasting and premortem. And now let’s look backwards, instead of forwards, to try to figure out — this is now the headline. Let me think about why that happened. So you could think about this as a simple weight-loss goal. I wanna lose a certain amount of weight within the next six months. It’s the end of the six months, I’ve lost that weight. What happened? I went to the gym, I avoided bread, I didn’t eat any sweets. I made sure that, you know, whatever. So you now have this list. Then in pairing with that, you wanna do a premortem, which is — I didn’t get to the top of the mountain. I failed to lose the weight. I failed to do whatever it is.

Sonal: And then all the things you can do to counter-program against that?

Annie: Exactly, because that’s gonna reveal really different things. It’s gonna reveal some things that are just, sort of, luck, right? Let me think — can I do something to reduce the influence of luck there? Then there’s gonna be some things that have to do with your decisions. Like, I went into the break room every day and there were doughnuts there, so I couldn’t resist them. So now you can think about, how do I counter that, right? How can I bring other people into the process, and that kind of thing?

And then there’s stuff that’s just — you can figure out it’s just out of your control. It turned out I had a slow metabolism. And now what happens is that you’re just much less reactive, and you’re much more nimble, because you’ve gotten a whole view of the landscape. And you’ve gotten a view of the good part of the landscape and the bad part of the landscape. But I’m sure, as he’s told you, people are very loath to do these premortems, because they think that the imagining of failure feels so much like failure that people are like, “No, and you should, you know — positive visualization, and we should…”

Sonal: I mean, the fact that in brainstorming meetings everyone’s like, “Don’t dump on an idea.” But the exact point is you don’t have to dump on an idea and kill the winnowing of options.

Annie: No.

Sonal: As part of the process you should be, then, premorteming it.

Annie: Exactly. There’s wonderful research by Gabriele Oettingen that I really recommend that people see. The references are in my book. And across domains, what she’s found is that when people do this, sort of, positive fantasizing, the chances that they actually complete the goal are just lower <Interesting!> than if people do this negative fantasizing. And then there’s research that shows that when people do this time travel and this backwards thinking — that increases identifying reasons for success or failure by about 30%. You’re just more likely to see what’s in your way.

So, for example, she did — one of the simple studies was she asked people who were in college, “Who do you have a crush on that you haven’t talked to yet?” She had one group who, you know, it was all positive fantasy. So, “I’m gonna meet them, and I’m gonna ask them out on a date, and it’s gonna be great. And then we’re gonna live happily ever after,” and whatever. And then she had another group that engaged in negative fantasizing. “What if I ask them out and they said no? Like, they said no and I was really embarrassed,” and so on, so forth. And then she revisited them, like, four months later to see which group had actually gone out on a date with the person that they had a crush on. And the ones that did the negative fantasizing were much more likely to have gone out on the date.

Sonal: That’s fantastic.

Annie: Yeah. So one of the things that I say is, like, look — when we’re in teams, to your point, we tend to, sort of, view people as naysayers, right? But we don’t want to think of them as downers. So, I suggest — divide those up into two processes. Have the group individually do a backcast. Have the group individually write a narrative about a premortem. And what that does is, when you’re now doing a premortem, it changes the rules of the game, where being a good team player is now actually identifying the ways that you fail.

Sonal: I love what you said because it’s like having two modes as a way of getting into these two mindsets.

Annie: Right, where you’re not stopping people from feeling like they’re a team player. And I think that that’s the issue, as you said. It’s like, don’t sit there and crap on my goal. Because what are they really saying? You’re not being a team player, so change the rules of the game.

Sonal: You have this line in your book about how regret isn’t unproductive. The issue is that it comes after the fact, not before.

Annie: So the one thing that I don’t want people to do is think about how they feel right after the outcome, because I think that then you’re gonna overweight regret. So you wanna think about regret before you make the decision. You have to get it within the right timeframe. What we wanna do instead is, right in the moment of the outcome, when you’re feeling really sad, you can stop and say, “Am I gonna care about this in a year?”

Think about yourself as a happiness stock. And so if we can, sort of, get that more 10,000-foot-view on our own happiness, and we think about ourselves as — we’re investing in our own happiness stock — we can get to that regret question a lot better. You don’t need to improve that much to get really big dividends. You make thousands of decisions a day. If you can get a little better at this stuff — if you can just, you know, de-bias a little bit, think more probabilistically — really, sort of, wrap your arms around uncertainty, to free yourself up from, sort of, the emotional impact of outcomes — a little bit is gonna have such a huge effect on your future decision making.

Sonal: Well, that’s amazing, Annie. Thank you so much for joining the “a16z Podcast.”

Mark: Thank you very much.

Annie: Yes, thank you.