00:16:23.640And I think there's a whole bunch of that in history and in a lot of government projects.
00:16:27.320Like when I was reading about some of the history of government works, there was one phase under Defense Secretary Robert McNamara, who was the Defense Secretary during Vietnam, where people would talk about paper wars.
00:16:38.300There was like they had so much stuff, like paper moving all the time on projects, that it's like nobody ever had a point to stop and figure out what it was they had decided to do because they were always changing all the specifications.
00:16:51.640Yeah. And I imagine this is only going to get worse with artificial intelligence or LLMs because
00:16:56.760you can just generate new stuff. You're doing the vibe coding, right? You're like, oh,
00:17:01.040it'd be cool if you had this feature. Here's prompt. I got the feature now.
00:17:05.640The promise is incredible, but it has never been easier to do too much. And I've been seeing this
00:17:12.000with... So for the last year, just to kind of educate myself, I spent a bunch of time with
00:17:16.320one particular AI company that helps other companies implement AI. And one of the things
00:17:22.100I saw is that a lot of companies said, we need AI, right? It's really alluring. Our competitors
00:17:26.900have it. And so they implement and it sprawls and it turns into what researchers are now calling
00:17:31.580work slop, where you just generate this insane volume of stuff that never gets finished or just
00:17:36.660piles up at some bottleneck. Whereas the organizations that I think are having a better
00:17:41.620run of it, start by mapping the jobs to be done or defining a problem and then saying,
00:17:47.160how does the tool fit this problem? So they lead with the problem instead of leading with the
00:17:51.720technology and having these sprawling implementations. Because it's just like
00:17:55.080so much easier for people to start an infinite number of things that they will never finish now.
00:17:59.520And I think that's a real challenge and why we're not seeing in many cases the expected
00:18:04.160productivity benefits, even while adoption has been really rapid. Yeah. And you highlight research,
00:18:08.680why we have this tendency to keep adding and adding and adding when we don't have constraints.
00:18:13.720We actually have like a natural bias towards that. Like when we're given a choice to make
00:18:18.620something better, humans typically like to add things. And so, yeah, like addition can be good
00:18:24.600sometimes, but what is it about addition that tends to muck things up? I mean, I think we
00:18:29.640talked about one of them. You just get the slop sort of stuff, but what else is going on there?
00:18:33.060Yeah. I mean, you're right about that. This appears to be a hardwired bias.
00:18:37.800So there's these series of studies that show that people will overlook solutions that involve
00:18:42.980subtraction, even if they're obviously better, cheaper, easier, et cetera.
00:18:46.820And it's actually called subtraction neglect bias.
00:18:49.700So like in one of the fun studies, this researcher at the University of Virginia named Leidy Klotz
00:18:54.120and his colleagues had people, he gave people this Lego structure and they were supposed
00:18:57.740to bolster it so it would balance a masonry brick over the head of a stormtrooper action
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00:27:50.580and now back to the show all right so a counter example you gave to general magic
00:27:57.040of an organization that put constraints on themselves proactively so they could get stuff
00:28:04.040done is pixar how did pixar work within constraints to eventually make toy story which
00:28:09.840would go on to revolutionize animated filmmaking yeah i liked using pixar because their vision was
00:28:16.620created at the same time as general magic and unfolded over about the same period basically
00:28:23.140but unlike general magic so ed catmull who led pixar for many many years and was the co-founder
00:28:28.200he also in the mid 70s decided he wanted to make the first fully computer animated feature film
00:28:32.980initially he wanted to be a disney animator but it wasn't that great of a drawer so a little bit
00:28:36.080of a problem and en route to making the first computer animated feature film which was toy story
00:28:41.180Instead of jumping straight from big idea to big execution, like General Magic did, he was relentless. He and his colleagues were relentless about defining what is the next tiny step. Okay, here's the big vision, but what is the next tiny, tiny step? They were always making estimates of how many pixels will we need? You know, how many polygons will we need? Where's the technology now? How far does that mean it needs to go to get there? Okay, we're not there yet. So we're going to work on all these proximate problems.
00:29:08.540And so it almost seems like when I was spending time with Ed, you'd almost seem like a killjoy in a sense, like, yes, we have this great vision, but we can't rush toward it. And so he was always keeping things as small as possible for as long as possible.
00:29:22.800And that continued even once Pixar started making movies where they let directors stay in with a tiny team in story development for years, simplifying the core of a story because it's easy to make changes then.
00:29:37.660And the costs only explode once you move into production.
00:29:40.460And so they really prioritize staying as small as possible as long as humanly possible.
00:29:45.780Yeah, it's amazing the difference between General Magic and Pixar.
00:29:48.920Like Pixar worked within the technology they had.
00:29:51.320yeah general magic got a you know a little head on their skis a little too forward on their skis
00:29:56.380yeah and so i mean it's funny you can look at toy story like the first animated video or film that
00:30:02.120pixar did was this thing called tin toy it was about this little toy figure and it was like the
00:30:07.020precursor to toy story it was made in 1988 and it was only like five minutes long and it actually
00:30:13.880looks pretty good i'll put a clip of it in the show notes um but they had to work with the processing
00:30:19.040power that they had at the time, all that stuff. And so it had to be really short. And then they
00:30:23.500built on that to the point where they can make a full length featured film. Those shorts, by the
00:30:28.740way, I don't know if people remember, but if you went to Pixar movies in theaters, there were often
00:30:33.380shorts, you know, five, 10 minutes that would run before the main film. And they used those
00:30:39.600as their little labs, test new animation techniques, test new story techniques. So they
00:30:45.000were constantly doing things in this really, really small way. Even though they had big ideas,
00:30:51.300they were always looking for ways like, what is the smallest possible way we can try this thing
00:30:55.100out? So it seems like they had a process established in order to avoid that bloat
00:30:59.920and to maintain those creative constraints. Yeah. And they had a bunch of important rules
00:31:05.120that were always changing because the staff were changing, the environment was changing.
00:31:08.640But to go back to that calendar story that I told about General Magic, where it just got bigger and
00:31:14.080bigger and bigger, even though it was not important. Ed told me about something at Pixar
00:31:19.660they called the beautifully shaded penny problem, where directors or animators are super conscientious
00:31:25.420and they want to get all the details right. And so they would be obsessing over the shading on
00:31:29.740a penny that would be in the background of some scene that viewer would never even notice. And
00:31:33.360they'd be working away on that for weeks and ignoring main characters that still need to be
00:31:38.080animated. And so they came up with a system. Here's a high-tech system. Popsicle sticks velcroed
00:31:43.760to a board where each Popsicle stick represented the amount of work that one animator could do in
00:31:49.380one week. And if the director wanted those animators to keep working on that penny, then he
00:31:54.000had to start taking Popsicle sticks away from some other character that needed to be animated.
00:31:58.240And so again, it was a way to visualize the priorities and force them to be ruthless.
00:32:02.580And General Magic had nothing like that. So they had all these minor priorities competing with
00:32:07.060major priorities, nobody differentiating them. And so I think Ed and his team were great about
00:32:12.160that, about forcing people into situations where they had to really clarify their priorities.
00:32:17.000I imagine everyone's experienced that shading of the penny problem in an organization. Like
00:32:21.660there's always a group of people or an individual that gets like, okay, this little tiny thing is
00:32:26.240the most important thing. And it takes up like 80% of the time. You get tunnel vision, you know,
00:32:31.060tunnel vision, like on those things, especially when it's your thing. Right. And you don't see
00:32:35.000how it connects to the bigger strategy. You got to kill your darling sometimes. You also highlight
00:32:39.540another organization that used constraints very effectively to put out a great product. And you
00:32:44.480actually had an experience with this organization, This American Life, the famous NPR radio show with
00:32:49.900Ira Glass, famous for their driveway moments where they have these shows you're listening to in your
00:32:54.740car and then you get to your driveway and you want to keep listening. What did This American Life do
00:32:59.400with constraints to create those driveway moments shows.
00:33:04.160Yeah. Again, like Pixar, I think they had this system that I came to think of as
00:33:07.680putting like bumpers in a bowling alley where you're not telling someone exactly what to do,
00:33:12.160but you're keeping them trundling in the right direction. And I had a story pitch accepted by
00:33:16.820them and I had to write a 35 minute radio script. The story was about a woman with two rare diseases
00:33:23.460of fat and muscle wasting. And she identified one of them in an Olympic medalist sprinter
00:33:30.000who had fat wasting and explosive muscle growth and felt like they shared some physiological
00:33:35.380mechanism. She turned out to be right. So I'm supposed to write a 35 minute script on this,
00:33:38.860and I've never written one second for radio before. And so I go in, I try to write a script
00:33:43.740and, you know, we do some interviewing and the way it works is you do a read through where people
00:33:48.060get together in a room and Ira Glass is holding a stopwatch, timing it, and you have your producer
00:33:52.420are there, I'm reading the narration and the producer's hitting play on the audio anytime,
00:33:58.160you know, it comes to an interview that we want to cut in. And so it's sort of like listening to
00:34:01.780a rough draft. And at the end, people get to say what they're confused about. And in my case,
00:34:06.320people were confused about a lot because I was used to writing a lot of scientific detail.
00:34:10.460And in a magazine story, people can stop and go back over that. But when it's flying by in audio,
00:34:16.760that's much more difficult. So people were confused and I was seven minutes over length.
00:34:21.260So they identified all these points of confusion and you're obligated in their process to fix them
00:34:27.180But they they don't tell you how they're not going to tell you how you have to do it
00:35:43.200I think, you know, I want them to just love it.
00:35:44.960And they come and say like, I didn't, I was lost on this or that.
00:35:47.760but also you feel like you have a ton of agency like they're telling you things to do but it's
00:35:53.080really up to you to then spread your wings as a problem solver within that and so it's actually
00:35:57.900kind of a gift to have someone well define a problem for you and then unleash you on solving
00:36:02.660it how long did that whole process take you to refine your piece for this american gosh did
00:36:07.980that take it i mean it dragged over months because oh dang i was thinking maybe like a week or
00:36:13.340something. No, no, no. So there are a few reasons for that though, because Ira's attention was at
00:36:17.980a premium. So there were a lot of different stories going through this process at once
00:36:22.580and mine didn't have like a time peg in the news or anything. Okay. So there was no need to
00:36:28.420rush the read-throughs, but also since I was a brand new in radio, there were some cases where
00:36:33.240somebody highlighted some confusion and I realized that the way to fix it was I had to go do some
00:36:37.880more interviewing. And that often meant, you know, lining up schedules and recording and all these
00:36:42.580things. So, so it went on. And again, it was a 35 minute piece and I was seven minutes over
00:36:47.380on the first draft. So that necessitated a serious reorganization. But a major reason why it took a
00:36:54.100long time was just, there's a whole bunch of these in process at any one time. And so you can't just
00:36:58.500say like, tomorrow I'm doing a read through, you know, you sort of have to get Ira's attention
00:37:02.200scheduled. Did that process going through that, did that change your writing at all?
00:37:06.800It did change my writing. I think for one, it made me a lot more likely to look for a naive
00:37:11.760reader and say, what's confusing you here? Not one of my own editors, you know, who has a lot
00:37:17.400of similar knowledge in some ways to what I have. It also led me to simplify. So I think I had a
00:37:25.480tendency if I find some scientific, you know, I, before I was a writer, I was training to be a
00:37:30.180scientist and I switched careers. And I have a tendency, if I think some aspect of science is
00:37:34.320really interesting to want to get it in no matter what, just get it in. Cause I think it's really
00:37:38.060interesting. And I had to cut so much scientific stuff that I thought was interesting from that
00:37:43.600This American Life piece. And yet the piece turned out amazing. It had probably the best response of
00:37:47.300anything I've ever worked on, maybe like along with my previous book. And so I think it showed
00:37:55.040me that the reader or listener doesn't know what's not there. And so what you have to make sure is
00:38:00.360that the stuff that is there is interesting and really clear. And so I think it made me much more
00:38:05.400aggressive in cutting back in the interest of clarity. Yeah. That this American life bit that
00:38:11.940made me like, okay, I need to be better about my editing. Cause I, my, my wife edits all of our
00:38:16.780writing and sometimes she'll be like, I'm confused here. I'm like, well, are you confused? Like,
00:38:19.940cause it makes perfect sense in my head. What are you talking about? And she'll be like, well,
00:38:23.720it could be confusing to a reader and it would be better if you rework these sentences like this.
00:38:28.280And I'm like, okay, yeah, that is better. There you go. Yeah. Well, let's talk about the idea
00:38:33.180of bottlenecks because you highlight a book that I read a long time ago and I forgot about,
00:38:38.420but I still think about it. I still think about the ideas. This book is called The Goal. It's
00:38:42.920all about thinking about our problems and looking for bottlenecks. So the big idea is if you want
00:38:48.460to be more productive in anything, whether it's work, I mean, this focuses on manufacturing and
00:38:53.600work, but I think it's applicable to your personal life as well. You got to look for bottlenecks.
00:38:58.420Yeah. Tell us more about this idea in this book, The Goal.
00:39:00.960Yeah, this book is bizarre, by the way, but fascinating. And it was written by this physicist
00:39:10.940named Ellie Goldratt, who was like studying the behavior of atoms and crystals when a friend of
00:39:15.940his with a small chicken coop building business asked him to help increase production. And the
00:39:20.740friend had been hiring new help, but it wasn't increasing the number of coops they were producing.
00:39:26.260And so Goldratt studied the process and found that that's because no matter how fast some steps in the assembly process were working, they just piled up at the single slowest step, what he called the bottleneck.
00:39:37.280And so he ended up moving one worker from a fast step to the slowest step, and it increased overall production by threefold.
00:39:44.520And this became the core of his idea, what he called the theory of constraints, that every system is limited by its single slowest step or bottleneck.
00:39:52.240And so he writes this book, The Goal, to try to explain the idea.
00:39:55.060And it's a business novel where this plant manager is facing shutdown and his Jedi-like
00:40:00.840surprise, surprise physics professor shows up and gives him these Socratic lessons.
00:40:05.500And like, he starts to see the whole world in bottlenecks where he takes his son's Boy
00:40:09.080Scout troop on a hiking trip and realizes some of the kids are really fast, but this
00:40:13.220kid Herbie is really slow and the whole group can only move at the speed of Herbie.
00:40:16.980So he decides to redistribute the weight from packs.
00:40:19.380So the fast kids have more and Herbie has less.
00:40:21.280And suddenly the whole group is moving faster.
00:40:22.900and it's a strange book and yet it sold 10 million copies and jeff bezos forced all his
00:40:30.620executives to read it and hosted a full-day book club on it and it just became a phenomenon
00:40:34.180but the core idea is really simple it's that the constraint the system constraint shows you where
00:40:40.600to focus because if you apply energy somewhere else it doesn't change the outcome of the overall
00:40:46.180system because that's all limited by this single least effective step. And it turned out to become
00:40:53.060one of the most impactful ideas in management and even spread into personal improvement as well.
00:41:00.540Yeah. When we don't have the output that we want, we typically think, well, we just need to input
00:41:04.600more, right? We got to do more and more and then we'll get more output. But if there's a bottleneck
00:41:09.480somewhere, you can keep putting in more and more input, but the output is going to stay the same
00:41:13.540it's all getting held up at that bottleneck. And so instead of adding more, just remove that
00:41:17.840bottleneck or somehow widen the bottleneck. Yeah. So the bottleneck shows you where to focus.
00:41:22.260It's like, it's the highest leverage place to apply energy. And some, in some cases it's the
00:41:26.700only place with any leverage that will make a difference if you apply any energy, but yeah.
00:41:31.940And so I think it's a really effective, I don't know if we should get into those stories, but
00:41:35.200really effective for personal improvement as well. I had a, I tell the story of an athlete who
00:41:39.260applied it in the book. Okay. Cause it is also really resonated with my own athletic journey.
00:41:44.440So the story I tell in the book is about this swimmer named Sheila Tarmina. She was at the
00:41:49.400university of Georgia. And in 1992, she goes to the Olympic trials, tries to make the team,
00:41:54.900the 200 meter freestyle, doesn't make it, isn't close, retires. But then for one of her last
00:41:59.740classes at university of Georgia, she takes management five 77 in which she learns about
00:42:04.020the theory of constraints and decides to do a class project on using it to create a plan to
00:42:09.200drop three seconds in the 200 meter freestyle. And so she looks for, she kind of audits her
00:42:14.880training and what's her bottleneck? Well, she determines its strength and power. She's five
00:42:18.280foot two, which is really small for a elite swimmer. She has an incredible aerobic engine,
00:42:23.140world-class, and all her coaches have her working on is aerobic endurance and not her strength and
00:42:28.000power. So she's continuing to feed the thing that is not limiting her, that she already has. So
00:42:33.180with this class plan, she decides to unretire, find a new coach who will work with her on strength
00:42:39.600and power. And four years later, she makes the Olympic team and then is part of the relay team
00:42:45.320that wins an Olympic gold medal. It's crazy. If you Google her, you'll see pictures of her with
00:42:49.080the other three women in the relay and she's about a foot shorter than them. And so she retires after
00:42:54.360this now as an Olympic champion. And then just concerned about her health, she comes out of
00:42:59.120retirement and starts doing triathlons. And now she has this new view on training, right? To look
00:43:03.680for what is her actual limiting factor. She wins the U.S. National Championships triathlon, goes
00:43:07.880the Olympics, finishes sixth, goes the next Olympics in triathlon, retires again, unretires
00:43:12.500again, takes up fencing and horse jumping and goes the Olympics in modern pentathlon. She's the only
00:43:17.740woman ever to have competed in four summer Olympics in three different sports. And she was about to
00:43:23.440retire if she hadn't learned about the theory of constraints in a management class. So I thought
00:43:27.600was an amazing story and it, it was very similar to my, my less illustrious, but I was also a
00:43:33.360college athlete, not at the level of Sheila Tower Mina, but had a very similar story where my
00:43:37.360bottleneck was my ability to recover from workouts. And once I realized that I was an 800 meter runner
00:43:42.280and scheduled class over one workout a week. So I'd have an excuse not to show up. I improved
00:43:46.580like rocket fuel. I became a university record holder, you know, went from walk-on to university
00:43:49.960record holder by targeting the thing that was limiting me. All right. Look for bottlenecks.
00:43:53.520I think that's a big takeaway. Well, let's talk about constraints to make collaboration more
00:43:57.920effective. So I think all of us have worked in a group. We might've done brainstorming sessions.
00:44:02.540We have those meetings where we're all just throwing out ideas and we have probably all
00:44:06.500experienced those meetings are not very productive. Why don't traditional brainstorm sessions work
00:44:12.440usually? Yeah, there are a few reasons. So there was some psychologists recently did an international
00:44:17.340survey of known creativity myths where things that we know are not true from research. And
00:44:22.620the top two mistaken beliefs where people are most creative when they're most free and that
00:44:26.860brainstorming is the best way to come up with lots of creative ideas. And it doesn't work for a few
00:44:33.160reasons. One is because it's too open-ended and people don't tend to come up with creative ideas
00:44:37.360when something is really open-ended. You're much better giving them a specific problem almost no
00:44:42.180matter what it is and they'll come up with more creative ideas. But also people tend to be
00:44:47.900confused by the norms of brainstorming. So there'll be conflicting norms, like say whatever comes to
00:44:53.520mind, but also don't criticize. Those things can be quite mutually exclusive. And there's a lot of
00:44:58.320what's called production blocking, where people who might have something interesting to say won't
00:45:02.480share it because they're embarrassed or they're not that eloquent or because of the person who
00:45:07.360spoke before them. So there's a term called HIPPO, highest paid person's opinion. And once that
00:45:12.160person shares their opinion in a group, other opinions will tend to coalesce around it, not
00:45:16.600because it's a better opinion, just because they're the highest paid. And so there are all
00:45:20.020these factors that sort of make the norms of the situation unclear for people so much so that brain
00:45:26.920writing works much better where if people are allowed to write ideas separately before they
00:45:31.400come together and evaluate them. All right. So that's a constraint you can put in instead of
00:45:34.800having vocal brainstorm sessions, have everyone write a memo of ideas they have, and then they
00:45:41.480submit it to the group? First, yeah, separate it and really trying to keep sort of equal social
00:45:47.940norms. So the best team, this shows up in all sorts of research, like Google did all this
00:45:51.420internal research on it, but in other places that the best teams for problem solving are those that
00:45:56.480have relatively equal conversational turn taking, not in every task they're doing, but like over the
00:46:01.600course of a day. So you need to be really careful to put certain constraints in place to make sure
00:46:05.980that happens. So at Pixar, for example, they banned Steve Jobs from certain meetings because they were
00:46:11.260worried about that hippo effect. They knew his larger than life persona, that his opinion would
00:46:14.960carry too much weight. And then other people wouldn't share some of the important things
00:46:18.780because not every person who has value to add is super eloquent. And so you have to be careful
00:46:22.540about making everyone feel like they're going to have a turn. But again, also because you want to
00:46:28.780give a specific problem. If it's too open-ended, people do not come up with creative ideas.
00:46:32.540Yeah. Well, that's the point I want to talk about you highlight in the book is this idea
00:46:36.440of settling for good enough that it can help you get more great things done what's going on there
00:46:42.760yeah this the last chapter gets kind of more personal philosophical and a major idea in it
00:46:48.840is called satisficing which is a word that's a combination of satisfy and suffice and was coined
00:46:55.760by herbert simon whose work is sprinkled throughout the book and he was a trained as a political
00:46:59.880scientist but he won the highest award in computer science he was a founder of ai he he won the
00:47:05.340highest award in psychology and he won the Nobel prize in economics. And one of his major ideas was
00:47:09.940satisficing where humans do not conform to the rational actor model of classical economics,
00:47:16.120where we evaluate all the options and make the best decision because we have limited cognitive
00:47:20.780bandwidth. We can't evaluate all the options. We can't predict all the repercussions of our
00:47:24.400decisions. And so what Simon argued is that we should proactively satisfy, like we should set
00:47:30.240good enough rules for our decisions. And when they're met, make the decision and never look
00:47:34.880back. Because the opposite, if you're not a satisficer, then the opposite end of the spectrum
00:47:39.000is what's called a maximizer, who really does try to evaluate every possible option and make the
00:47:43.300best decision. Maybe we'd call it an optimizer today. And it turns out that in psychological
00:47:48.120research, it's almost always bad to be a maximizer. They are less satisfied with their decisions,
00:47:53.600less satisfied with their lives, more prone to regret, often prefer reversible decisions,
00:47:59.640even though it leads them to not really commit
00:49:25.800like if I'm trying to buy something, here are the three things I needed to do. That's the job I want
00:49:29.900to hire it for. Once I see those three things, I'm done looking. I'm not reading every single
00:49:34.000review. And that's been really helpful for me. When do you think maximizing is beneficial or
00:49:38.380is it ever beneficial? It's a good question because Simon, you know, I mean, he did all
00:49:43.160these things where, you know, he wore one kind of beret only and one color of socks. And he said,
00:49:46.800you only need three pairs of clothes, one on your body, one in the, in the closet right to wear and
00:49:50.700one in the wash. And he had the same breakfast every day, lived in the same house for 46 years.
00:49:54.480And what he was saying was, look, this freed up my cognitive bandwidth to focus on the things that were really important, like his research. And so I think there are things where, you know, he famously said the best is the enemy of the good.
00:50:08.800and I think it's almost always good to be a satisficer in terms of making progress
00:50:15.840but I think there are also cases where once you have enough experience to know the kind of things
00:50:21.620the ways that you want to spend your time that it can be okay to be more of a maximizer in trying to
00:50:28.120craft your work life so that you're spending more of the time working on something that you think is
00:50:32.320ideal, basically. And again, I think aiming for perfection is way too much, but being okay with
00:50:39.260dithering more on that and saying, you know, how can I really work my way towards spending my time
00:50:44.420working on things that I think are really important? And again, not that you have to
00:50:49.380jump to that immediately, but over the course of a working career, not just necessarily saying,
00:50:53.940this is kind of a good enough thing, but once you get to good enough thinking about, well,
00:50:57.320could I go a little farther? So I think it's okay to do that. But again, I think it should be done
00:51:00.560in steps as opposed to maximization right from the beginning. Well, David, this has been a great
00:51:05.240conversation. Where can people go to learn more about the book and your work? DavidEpstein.com.
00:51:10.060Got links to the book, free newsletter, all that kind of stuff. You're on Substack now, right?
00:51:15.400I'm on Substack. Yep. And there's a link for that on my website for all that stuff. And there's some
00:51:19.040free sort of tip sheets related to Inside the Box on the site also.
00:51:22.180Well, fantastic. David Epstein, thanks for your time. It's been a pleasure.