This is Dana Perkins and you're listening to Switched on the BNF podcast. Imperfection or the idea of something being imperfect, is usually considered a negative in business, in life, in art, in sport, perfection or as close as possible is normally the objective. But is there something to be said for embracing imperfection, figuring out what we can learn from failures or just incomplete information, and what mindsets and tools we need to utilize imperfect outcomes in the business world. So
in today's episode, I speak with Charles Kahn. Charles is an investor, environmentalist and entrepreneur. He's the former CEO of The Roads Trust in Oxford and the current chair of Patagonia. Aside from his business in academic experience in positions, he's also an author, having previously co written Bulletproof Problem Solving, and Charles is here today to talk about his latest book,
The Imperfectionists Strategic Mindsets for Uncertain Times. Together, we discuss a range of topics and of course we give them the BNF spin. By looking at decarbonization and the transition and how this methodology might be applied. We look at the different elements that make up the imperfectionist mindset and go through some of the practical ways it can be applied to business along with AI learning and how it's
being harnessed to aid decision making. Now, as always, if you like this podcast, make sure to subscribe so that you get updates when there are future episodes, and give us a review on Apple Podcasts or Spotify to make it more discoverable to others. But right now we get to have a conversation with Charles about imperfectionism. Charles, thank you very much for joining me today.
Dan, it's such a pleasure to be here.
So we're here well really because you have recently written or co written a book and having read it from cover to cover myself, there is a lot of application and learnings for the industries that we had been in have cover regarding decarbonization in the transition, and I really want to start with your motivations. I want to start with, well, actually your relationship with your co author. So you previously wrote a book called Bulletproof Problem Solving the One skill
that changes Everything. Why was it time to write a second book together?
Yeah? So the first book was a tool sets book about problem solving, how to break complex problems apart and solve them creatively, which is I think what both Rob and I would say is our life's work. And in a world where you have increasing automation and artificial intelligence, the one thing that humans can really do is work together to solve complex problems creatively. We're still better at that than the AI routines. The reason we wrote the second book, which is a mindset's book, is in the
depth of the pandemic. As things were changing so quickly, it became clear to us that the first book didn't address problem solving under very high uncertainty well enough, and so we wanted to write was a book that helped people think about what to do when things are changing very quickly. When things are changing quickly, people tend to do one of two things. They paralyze, and we see this a lot in company managements. Now they freeze and
they want to wait for stasis stasis isn't coming. Or they do leap before you look moves where people panic and they do something big that's irreversible and difficult. And we wanted to show people there's a way to lean into risk and to be comfortable solving problems even when things are changing.
And with that you said, it's a mindset's book, So there are six different mindsets that you go through in here. Let's go through a quick overview so that we can then drill down into some of the individual ones.
Sure, so let's just do a thirty thousand foot view. So when things are changing really quickly, the most important orientation is curiosity. And it sounds incredibly obvious, but as we get older, we forget to be curious. As we get good at what we do, we get into ruts. And those ruts are even like tying your shoe. You don't think about it. And because you don't think about it and you're not curious about it, you're not open
to other ideas. Second, and very much a sister mindset we call dragonfly eye, which is an idea we borrowed from Philip Tetlock, who's written so beautifully about super forecasters. What that means is to make sure to see things through multiple perspectives before you make up your mind about what strategic path you're going to take to see them. We call it sometimes environment vision, which means thinking about problems through the perspective of your customers or your suppliers.
Or a potential competitor, rather than just thinking about things through your own industry lens, which is how we tend to do things. The third which should be familiar to many people but probably not with this name, and we call it oh current behavior, which is what actually happens rather than what you hope will happen. And for us,
that is an experimentalist mindset. And I think many people think that we can only do experimentation in light industries like internet, but we think it's just as important to do experimentation in heavy industry of the sorts that you
often pay attention to. Both mindset we call collective intelligence, which is how we can reach outside the boundaries of our own organizations and crowdsource in great ideas, and most big professional industries are loath to do that because the assumption is that the smartest people are already in the room, and therefore we miss out that idea of peripheral vision, where technologies or ideas from another industry that could be
applicable we just miss entirely. The fifth mindset we call show and tell, which is really that sort of kindergarten idea that you would rather than just create a PowerPoint slide you actually tell a story if you want to rally people around your ideas to do something radically different, and of course the whole climate change world requires us to do things that are radically different. You need to speak to their hearts, not just to their minds, and
you need to speak to values. And it's very important
that we all learn how to be better storytellers. And then the one mindset that brings all of those together we call imperfectionism, which is really about stepping in to risk rather than being paralyzed, and using small steps in order to build your confidence, to build your understanding, to build capability sometimes to build asset positions so that you actually move toward your goal without having some grand strategy, because when things are changing so quickly, grand strategies the
way we used to construct them don't work anymore.
I think you've got us a curious certainly, because one of the things I hear over and over again from listeners when I run into them, you know, out there in the real world and not in the studio, is that what they tend to tune into this podcast for is to better understand things that are not perfectly in their field of vision. So Hopefully this will be a journey for them to think about how they approach work
in a very, very different way. One of the things that is very clear from the work that you have done in this book is to highlight a number of different company examples and just at the fundamental level. How many case studies would you say that you read on a monthly basis, because it must have been hard to choose.
Yeah, well, certainly more than one hundred. You know, that's the food for our work is case studies, because that's what allows us to see through these multiple lenses. Grand theory is kind of boring, it's empty until you can make it real via case studies. So I think there's both that deductive problem solving where you come from a big idea to the specific, and then inductive where you go from the specific to knit together a bit of a bigger idea. I guess we love induction.
There are a lot of examples that I would say are more in the B two C space in this book, and a lot of the solutions that I'm looking at are really focused on this transition of industry, this transition and energy, which is more on the B to B side. I guess within the different mindsets, Are there some that you think are more applicable to the B to B space or is really the entire way of all look at looking at all six really relevant And are there any examples maybe that you may drop on that you
think would be really useful to the B to B community. Yeah?
Absolutely, So the first thing I would say is there no B two C bias in this way of thinking. And what I would say about heavier industries, which tend to be the bigger emitters, is there's no easy answers because they do involve enormous capital expenditures. That's the characteristic of heavy But I think it's too easy to say that those are not amenable to these kind of imperfectionist approaches to strategy, and it just doesn't so. And I'll
give you one example. When we talk about experimentation, folks often like to think that that's only relevant when you have, for example, media or internet light investment industries. So people would say, you can do ab testing with two different website designs and you see which one attracts more people. That's easy and doesn't cost a lot. But if we were to look at, for example, one of the heaviest industries of all, which is space. We have a good
example of an experimental company, right, which is SpaceX. SpaceX picked up where NASA left off. And what did they do? They massively increase the number of launches per year. NASA was doing two or three or four launches a year. SpaceX now does twenty or thirty launches a year. NASA tried to engineer everything double triple heavy. SpaceX has been deliberately experimental, sometimes spectacularly. So you remember the what do they call it an unplanned disassembly on the most recent
large rocket launch. Right crazy to take multimillion dollar launches and to view that as an experimental lens, But because they've viewed it an experimental lens and they've pioneered, for example, three D printing of rocket parts, were usable rocket parts like the nose cone they catch in a net which saves a huge amount of money, or using new materials that come from other industries as heat shields. These are all ideas that have been pioneered by SpaceX and the
frequency of launch. You know, they have what they call fly test fix fail or fail fix, I should say, as a mentality for their engineers. They've been able to drive massively down the cost curve. So it used to cost fifty five thousand dollars to put a kilogram into space with NASA. Now it costs literally a twentieth of that with SpaceX to send that same kilogram into space. That's heavy industry, that's an experimentalist mindset. The two can go together.
It's incredible to see these cost of clients as you're pointing them out. One of the things that occurs to me, though, is that this is a company that essentially grew up on its own to tackle this issue. It didn't come
from within, It didn't come from the inside. And you have another example in the book about Ford Motor Company and how they ended up making their electric vehicle division separate from their internal combustion division in order to give them that perspective to think about things with a fresh view. Let's go into an example that wasn't in the book, but certainly an industry that you have experience with, which is oil and gas. So in this umbrella space that
are the energy companies. So many of the people who are making the decisions have been in these companies for a long time and very rightfully have deep expertise kind of what advice or views do you have on how they might be able to think about pivoting their business and experimenting in a way that we'll be in line with a drive to decarbonize.
Yeah, and Dana, these are the hardest ones, right anytime you have a specialist industry where people have to work for many years just to learn the basics before they can become useful in the industry, and then they need to build industry experience. By definition, you're dealing with a deep trench rather than an experimentalist or a multi lens viewpoint to begin with. And medicine has the same characteristics
as oil and gas. I would say it's all the more reason why these mindsets are terribly important if you want to create innovation inside conventional energy. And let me give both examples and then processes. One of the things we've learned is if you just follow the existing processes
in a business, you'll get the same answers. And one of the things we like to do is use workshops where people do, for example, what's called perspective taking, So before you launch into a new strategic plan, you actually step back and this is this idea of dragonfly eye, where you look at your industry. You know, whether you're doing upstream exploration or you're doing refining, for example, or distribution, and see that industry or that segment through the lens
of your customer, your supplier or for example, Gretituneberk. That's a very different perspective. How would you see yourself? And that puts you outside. We call it anchoring outside. It's a term we really like. Anchoring outside gives you a better perspective and frees you to think differently than you might have done before. So I'm going to give a couple of examples that we can make up from oil and gas. So flare gas is one of these persistent
problems in oil and gas. You have a remote location, you're producing liquids, gas comes up, you don't have any way of handling gas. What do you do while you flare it? Right? And we still see that all around the world. An idea that I saw recently, and I don't you know, I'm not an expert in this, but I just thought it was interesting, which is a group
of folks who had been working in server farms. So these are the enormous computer server farms that we use for this internet driven economy that we're in today, which
are usually located near big cities. What if you were to locate server farms close to where we're flaring gas, and the heating and cooling systems, the electricity generation and cooling systems that are required could be powered using that gas instead of flaring in so co locating an industry that's a heavy user near where you're otherwise having to burn gas without any value. It's just it's a different way of thinking about it. You do have to think
about transmission lines. There's a whole bunch, you know, for the data that comes out of data rooms. But it's kind of a cool idea and that you would get from thinking differently.
And there was another example that you brought up in the book that was around water pipelines and water pipe water pipe failures, and the first thing that really occurred in my mind maybe almost two literals. When I'm thinking of pipes, I'm thinking about actually like methane gas leaks, and I'm thinking about the fact that you know, increasingly satellites are picking up on where this is coming from and hopefully then leading us to solutions on then what
we can do about it. But one of the things that you really drilled down on this specific case around water pipe failures had to do with AI or probably machine learning, depending on how you want to refer to it. Really, where do you see the potential for application of machine learning and helping us find solutions to these big problems.
Yeah, And on the way into our conversation, we talked about how complex this is. Right, the world of decarbonization and of energy transition, there's no silver bullets. You have to find innovation in many small things. AI and machine learning will become hugely important for this world because pattern recognition in incredibly complex systems is something that the machine
does better than us. I love your example. So in the water pipe example, you tend to think in a very mechanistic way FIFO right first in, first out, So you'd assume that the oldest water pipes or methane pipes are the ones that you should be replacing because that's where failure is going to occur. Turns out that's not
a very good model. What the artificial intelligence And this was an actual example a mathematics professor in Australia looking at the water pipe system in a Citney, like Sydney or Melbourne came up with were non parametric artificial intelligence models that used other clues and almost by definition non parametric models, so you didn't define in advance, for example,
age of pipe is the key determinant. With those kind of models, they ended up being able to predict failure points much more accurately because they were often in unexpected places. And I think the same would likely be true with methane or with even more slippery gases like hydrogen, And so I think we're going to find a lot of
our future solutions using artificial intelligence. And the good news there is, of course, these oil and gas engineers will find that very comfortable because it's sourcing from something that's quite adjacent to what they do already.
So climate change is a shared problem that is going to impact everyone and is not going to be an equal measure depending upon where and which companies or countries are the emitters. So when we think about the fact that this is a shared concern, is there potential then for it to also be a way for us to actually share the creation of the solutions. And one of the things that you point out in this book is this well open source technology and then also Joy's law.
Can you kind of go into that a little bit?
Sure? So Bill Joy was a founder of Sun Microsystems, so one of the first big companies that used Unix, and not surprisingly because he'd been at Berkeley before where he was part of developing Unix, which originated many years before in a joint project with AT and T and some other companies. Unix is a wonderful example of where Joy's law comes from. Bill Joy said, the smartest people and may not be in your room. They may be
laboring in someone else's garden. This is the core idea behind Joy's law, and how do you access them so
that they can contribute to your project. The example that he used was Unix, which was open source, meaning that engineers from software engineers from all around different companies and academic environments could actually contribute to the building of this core infrastructure of software that the kernels of which are still literally called kernels are in Microsoft operating system, in the Apple operating system, and the operating systems of every
other major computer language. But that idea of Joy's law and open source can be applied much more broadly. One of the most famous competitions was the Flying NonStop across the Atlantic competition, which was won by Lindberg back in the nineteen thirties. That idea of using prize competitions, for example, to attract creativity, often from other industries, is another way of crowdsourcing intelligence or ideas or technologies from outside. We've seen that with the X Prize, which has led to
innovations in flight and a number of other areas. And we've seen it with gamified platforms like Cagele, which have allowed the crowdsource seeing of great ideas. So an example that we use in the book That I Love is the Nature Conservancy was trying to make innovations in how to reduce bycatch of endangered fish species. Really complex problem because these fish are brought a board, either onlines or in nets at sea, bumping up and down in the
worst weather. And you can put cameras aboard ships, but how do you very quickly make an identification of a fish that's okay to keep and a fish that you
should put back gently? And the Nature Conservancy didn't have people internally who were experts in computer vision or machine learning, and so they put up one hundred and fifty thousand dollars prize on the Cagle platform, and they received more than three thousand entries from different clever people who had built algorithms for recognizing fish according to the shape of a gill plate or a fin that worked remarkably well
to identify fish even in those difficult conditions. It's a wonderful example of an organization in this case committed to conservation sister to decarbonization, for sure, that crowdsourced in a wonderful idea that's now being put to work already, first in the Indonesian tuna flee to actually help save the biodiversity on the planet at the same time as we're
trying to slow warming. Just a cool idea. This same idea, this family of ideas is also being used in for example, AI swarms, which can be used to do much better prediction for things like cancer diagnosis. So I think we're right at the very beginning of being able to put our fingers into revolutionary technologies that come from one industry but that may provide solutions, including in the heaviest emitting industries.
So when you think about a prize, a prize really sits outside of a company and really motivates individuals and these brilliant minds to tackle problems in a different way. Do you think that maybe in some respects net zero targets or other I mean, are there other ways essentially to motivate companies to want to get involved in this because I think about the fact that we can go back to oil and gas, or we can call upon any of the heavy industries. Really they're still companies, they're
competitive with one another. There are opportunities to cloud rate on solutions, but in reality there's an element to this and that not only do you need to have the best solution for your customers, but you want to maintain market share. Are net zero targets a good proxy for a prize when it comes to trying to think about motivating large companies.
I think that's a really cool idea. And my guess is that net zero targets are or will become that same kind of motivator. Well, we found that Patagonia is a net zero target is quite distant, even for a company like Patagonia. So when we break that down into nearer targets that we can actually achieve, it's more motivating. So I'll give you an example that wonderful rain jacket that you wear here in London when you're riding your bike to work, which sheds water in just a remarkable way,
does so because it uses really dangerous chemistries. Yet Patagonia we've said by twenty twenty five, we are not going to use any of those chemistries. And over the course of the last four years that's been a huge motivator for us because the family that own Patagonia until recently gave it a way to fight climate change, said we want if we can't find non dangerous chemistries for rain jackets,
we're out. We're going to stop selling them. And so the internal team worked with external organizations like the Gore Organization, which is a wonderful fabric chemistry company, and they've cracked it. They found a way to use less dangerous chemistries to create equally water shedding fabrics. That's incredibly motivating, But it was motivating because it was a target that was almost out of our grass, but not out of our grass, whereas a net zero target for a oil and gas
company might feel too distant. Well, we'll chip away at it by investing a bit and wind.
How do you, I guess, deal with ethics questions in this, And the reason I bring up ethics questions is that at the center of this is really experimentation, trying things, maybe going out and doing it before you're quite ready. And a good parallel for that is, you know, any sort of autonomous driving right now, if those vehicles are learning how to avoid traffic and deal with not just traffic but accidents, and in the long run, the view is that it's going to save many, many more lives.
Right now it already is saving maybe some lives depending on how you're looking at the data. But the real question is, in that circumstance, how do we then deal with the fact that there's some randomness to the way human beings work? But once we start relying on experimentation is a way of getting us to that ultimate end. Can you really experiment when you're dealing with human beings?
Well, I mean that's such a huge question. I think it's a wonderful question too. I don't have a silver bullet answer, but let me give you two thoughts. One is, when you can do experiment without putting living creatures in harms way, you should do that. And so, for example, I do a lot of investing in the biotech space, Sometimes you have to put a mouse in harm's way
in order to find out what's going on. But increasingly you can do organ on a chip, which are approaches that don't involve sacrificing animals or putting people in harms way. So whenever you can use an alternative technology, you should do it. But there are cases where we introduce technologies that do have some risk to humans, and the question is how can we reduce the risk to humans. I'll
use an old example. We could massively reduce highway deaths if we went back to fifty five miles an hour, and we don't, and that's a trade off, and everybody knows it's a trade off. Because people want to get where they're going a little bit faster, we accept slightly higher highway debts. So that is an example, like your
autonomous driving example. I guess between those two examples saving the mouse and highway deaths, we have to ask is autonomous driving at the state yet where we can take that incremental risk as we do with speed limits, or is it still at the state where we're better off having a non living creature in the seat. When I was working in Oxford, a lot of the autonomous driving work was being done, a lot of the stuff that in fact informs the industry today. Very little of that put people in harms.
Way, which then deals with you know, how do you do things when there's so much uncertainty and you have to move your way up these five different levels of uncertainty which you bring up and so I guess taking a bit of a turn, so to speak, in terms of the subject matter. But when we get back into let's say physical risk and we think about going forward with climate change, change is really at the center of it, right, we are increasingly not able to actually look at weather data.
How does one really grapple with these different Well, I guess first outline the five different levels of uncertainty and really where you think the most experimentation really can live.
Yeah, there's various ways to categorize uncertainty. In the book, we talk about one framework that was developed a number of years ago, where you have no knowns, which is the easy level known unknowns, that is, you know what you don't know. Ultimately up to unknown unknowns, which you know in the nineteen sixties were called unk unks. Well, you literally don't know enough to even characterize the nature of uncertainty. Obviously, those are the hardest places to operate.
I think in the middle uncertainty level two and three, you really can use experimentation a lot to learn about, especially if you can do that in a way that, again that's safe. One of the things we talk about is the core idea in the book is an imperfectionist approach loves to take steps forward into risk if you can do it in ways that are reversible. That is, if you don't like where you got to, you can go back through the door or where the consequences are
relatively low rather than existential. And so you can use this fundamentally experimentalist or imperfectionist approach to explore level two, level three, and even level four risk as long as you're doing so in ways that aren't existential risk.
And we break down big problems into increasingly small problems so that we're.
Able to precisely and as you fail, so experimentation means not just winning, but losing, you make sure to consolidate those lessons, and that's what ultimately builds organizational capability. Organizational capability doesn't come from just in insourcing. A lot of it comes from learning. Learning comes from making mistakes. One of the most important messages of the book, especially for the heavy industries that are the biggest admitters, is not to be so afraid of failure and not to punish
failure in our frontline teams. Engineering cultures hate failure, and we often punish people when their projects don't work, and I think we need to change that mentality and industry. In science we accept that all the time, and we write up our results, perhaps not as much as we should, and there is a survivor bias and papers too, but especially once we get to heavy industry where the investments are significant, we often criticize or punish or change the compensation of
teams that have good ideas that don't work. We need to make sure good ideas that don't work are celebrated.
To and in that there's also articulating what's happening to the rest of the world. So one of the mindsets is show and tell and explaining this to the world. And actually first question within that, would you consider yourself a storyteller?
I sure hope. So I think the most compelling people in the world are the storytellers. You know, when you think about David Attenborough. You listen to him because he tells the stories in a way that sort of brings it home to you, rather than in the sort of thirty thousand foot science. I think all of us would be better storytellers if we didn't just think about the logic what's between our ears, but we also thought about
what's in our hearts and our values. The best storytellers are ones that link our reason and our values.
And you reference this one study in the book that called upon the US specifically in saying that when individuals were asked where climate ranked in their concerns, it was third, but when they thought about their peers, they thought their peers ranked it at thirty third. So the question is, I guess that are we doing a good enough job in the world of those that are actually looking at climate solutions of telling the story.
I don't think we are, and I think unfortunately we've done the thing that's hardest for people to act on, which is to speak about a future state that's uncertain, that is catastrophic, very difficult to know what to do with that. We move forward in the world by imperfectionist steps, by experimentation, and when we don't give people something that they can do tomorrow that's different. All we create is
fear and no forward movement. And so if we and perhaps this kind of program that we're discussing right now is exactly the right way to start, each of us can do behavior changes that help move us down this path and which give us more information more data to move us further down the path.
And you reference data certainly the people I work with and I like data a lot, but data and data is certainly really inherent to being able to make good business decisions and create those bets, if you will, about how to move forward. But increasingly there's a distrust of science and data, and maybe storytelling is the solution to that.
But for someone who's not in the business community and is maybe looking at this problem and paralyzed by fear, as you outline, where does data come in for them? And is it being too heavily relied on as the story.
Yeah, the people who control the biggest levers around decarbonization and energy transition don't tend to be the people who speak from their hearts. They tend to be the people who speak from what's between their ears. And I do think, especially in these industries. We need to get better at speaking to where people are now and starting there. And you can build bridges with every kind of person if you start with where they are, if you understand what
they value. In the book we talk about this, but almost everybody cares about their kids. Probably everyone cares about their kids. If you start with the future for our children, you actually have a bridge that can work with almost everybody.
I certainly feel that personally, so very good example. Okay, so we're going to go through a couple of things that I would put in this category on whether or not there's something that you're watching closely or perhaps ignoring for the moment. So in watch or ignore and pulling upon also your experience from Patagonia, where would you put circular economy.
I think it's a watch and lean in, right. I really do think that this is a critical which is when we purchase something. You know, you have a bicycle, you have clothing, that we think about where it goes after we're done using it. It's as simple as that, and of course the people in manufacture it should also be thinking about it. At Patagonia, now, when we make a jacket, we make it so that it can be disassembled.
There was a time when we had all these cool welding technologies where we glue everything together with using heat and chemistry, and then we realize, oh, we can't recycle that because you can't take the zipper out. And so if both manufacturers and users consumers can think about where it goes after you're done with it, I think that's absolutely critical.
Okay, watch or ignore biodiversity targets.
But for corporations, you're going to hate this answer, but I think the answer is ignore. I just think the very few corporations are in a position to pay attention to the biodiversity of species. Much more important for them is to work toward net zero goals because it's climate change that is putting most species at risk. Climate change, and then, of course I think the escape of danger chemistries.
When you look at what's happening with frogs and other amphibians, this is something that you can only affect by focusing on the big levers, not so much on frog habitat because that's being destroyed by climate change and chemistry escape.
Okay, so final watch and ignore ESG financial ratings and rankings.
Can I create a third category, which is fix. So I don't think you ignore it, and I think you need to do more than watch it, because ES and G are all good ideas, But trying to create an index which captures even one of those things for big companies and complex operations is literally absurd. So having a single rating for E for a company like McDonald's or Coca Cola or any of the big energy companies is
just silly. And most of the ESG ratings that go along with stocks, for example, are meaningless, they're not consistent. The rating agencies even don't agree themselves. That doesn't mean the idea should be thrown out entirely. What it means is that to be useful, we need to be much more granular. At Patagonia, we have of measures for environmental sustainability for each one of the products that we produce.
We know the carbon that it emits to produce that, we know the water that's required, and we know the dangerous chemistries are not that are used in manufacture. So ES and G need to become much more granular to actually allow us to make action. And today you don't see a lot of correlation between higher ESG scores and higher returns or lower returns. We don't see a lot of correlation at all, and the reason is those are not meaningful in their current index form.
Charles, thank you very much for joining today, and I look forward to reading whatever the third collaboration is for you and Robert in the future someday.
Dana, I had so much fun being here. Thank you.
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