Speaker 1: This is Dana Perkins and you're listening to Switched on
Speaker 1: the BNF podcast. Imperfection or the idea of something being imperfect,
Speaker 1: is usually considered a negative in business, in life, in art,
Speaker 1: in sport, perfection or as close as possible is normally
Speaker 1: the objective. But is there something to be said for
Speaker 1: embracing imperfection, figuring out what we can learn from failures
Speaker 1: or just incomplete information, and what mindsets and tools we
Speaker 1: need to utilize imperfect outcomes in the business world. So
Speaker 1: in today's episode, I speak with Charles Kahn. Charles is
Speaker 1: an investor, environmentalist and entrepreneur. He's the former CEO of
Speaker 1: The Roads Trust in Oxford and the current chair of Patagonia.
Speaker 1: Aside from his business in academic experience in positions, he's
Speaker 1: also an author, having previously co written Bulletproof Problem Solving,
Speaker 1: and Charles is here today to talk about his latest book,
Speaker 1: The Imperfectionists Strategic Mindsets for Uncertain Times. Together, we discuss
Speaker 1: a range of topics and of course we give them
Speaker 1: the BNF spin. By looking at decarbonization and the transition
Speaker 1: and how this methodology might be applied. We look at
Speaker 1: the different elements that make up the imperfectionist mindset and
Speaker 1: go through some of the practical ways it can be
Speaker 1: applied to business along with AI learning and how it's
Speaker 1: being harnessed to aid decision making. Now, as always, if
Speaker 1: you like this podcast, make sure to subscribe so that
Speaker 1: you get updates when there are future episodes, and give
Speaker 1: us a review on Apple Podcasts or Spotify to make
Speaker 1: it more discoverable to others. But right now we get
Speaker 1: to have a conversation with Charles about imperfectionism. Charles, thank
Speaker 1: you very much for joining me today.
Speaker 2: Dan, it's such a pleasure to be here.
Speaker 1: So we're here well really because you have recently written
Speaker 1: or co written a book and having read it from
Speaker 1: cover to cover myself, there is a lot of application
Speaker 1: and learnings for the industries that we had been in
Speaker 1: have cover regarding decarbonization in the transition, and I really
Speaker 1: want to start with your motivations. I want to start with, well,
Speaker 1: actually your relationship with your co author. So you previously
Speaker 1: wrote a book called Bulletproof Problem Solving the One skill
Speaker 1: that changes Everything. Why was it time to write a
Speaker 1: second book together?
Speaker 2: Yeah? So the first book was a tool sets book
Speaker 2: about problem solving, how to break complex problems apart and
Speaker 2: solve them creatively, which is I think what both Rob
Speaker 2: and I would say is our life's work. And in
Speaker 2: a world where you have increasing automation and artificial intelligence,
Speaker 2: the one thing that humans can really do is work
Speaker 2: together to solve complex problems creatively. We're still better at
Speaker 2: that than the AI routines. The reason we wrote the
Speaker 2: second book, which is a mindset's book, is in the
Speaker 2: depth of the pandemic. As things were changing so quickly,
Speaker 2: it became clear to us that the first book didn't
Speaker 2: address problem solving under very high uncertainty well enough, and
Speaker 2: so we wanted to write was a book that helped
Speaker 2: people think about what to do when things are changing
Speaker 2: very quickly. When things are changing quickly, people tend to
Speaker 2: do one of two things. They paralyze, and we see
Speaker 2: this a lot in company managements. Now they freeze and
Speaker 2: they want to wait for stasis stasis isn't coming. Or
Speaker 2: they do leap before you look moves where people panic
Speaker 2: and they do something big that's irreversible and difficult. And
Speaker 2: we wanted to show people there's a way to lean
Speaker 2: into risk and to be comfortable solving problems even when
Speaker 2: things are changing.
Speaker 1: And with that you said, it's a mindset's book, So
Speaker 1: there are six different mindsets that you go through in here.
Speaker 1: Let's go through a quick overview so that we can
Speaker 1: then drill down into some of the individual ones.
Speaker 2: Sure, so let's just do a thirty thousand foot view.
Speaker 2: So when things are changing really quickly, the most important
Speaker 2: orientation is curiosity. And it sounds incredibly obvious, but as
Speaker 2: we get older, we forget to be curious. As we
Speaker 2: get good at what we do, we get into ruts.
Speaker 2: And those ruts are even like tying your shoe. You
Speaker 2: don't think about it. And because you don't think about
Speaker 2: it and you're not curious about it, you're not open
Speaker 2: to other ideas. Second, and very much a sister mindset
Speaker 2: we call dragonfly eye, which is an idea we borrowed
Speaker 2: from Philip Tetlock, who's written so beautifully about super forecasters.
Speaker 2: What that means is to make sure to see things
Speaker 2: through multiple perspectives before you make up your mind about
Speaker 2: what strategic path you're going to take to see them.
Speaker 2: We call it sometimes environment vision, which means thinking about
Speaker 2: problems through the perspective of your customers or your suppliers.
Speaker 2: Or a potential competitor, rather than just thinking about things
Speaker 2: through your own industry lens, which is how we tend
Speaker 2: to do things. The third which should be familiar to
Speaker 2: many people but probably not with this name, and we
Speaker 2: call it oh current behavior, which is what actually happens
Speaker 2: rather than what you hope will happen. And for us,
Speaker 2: that is an experimentalist mindset. And I think many people
Speaker 2: think that we can only do experimentation in light industries
Speaker 2: like internet, but we think it's just as important to
Speaker 2: do experimentation in heavy industry of the sorts that you
Speaker 2: often pay attention to. Both mindset we call collective intelligence,
Speaker 2: which is how we can reach outside the boundaries of
Speaker 2: our own organizations and crowdsource in great ideas, and most
Speaker 2: big professional industries are loath to do that because the
Speaker 2: assumption is that the smartest people are already in the room,
Speaker 2: and therefore we miss out that idea of peripheral vision,
Speaker 2: where technologies or ideas from another industry that could be
Speaker 2: applicable we just miss entirely. The fifth mindset we call
Speaker 2: show and tell, which is really that sort of kindergarten
Speaker 2: idea that you would rather than just create a PowerPoint
Speaker 2: slide you actually tell a story if you want to
Speaker 2: rally people around your ideas to do something radically different,
Speaker 2: and of course the whole climate change world requires us
Speaker 2: to do things that are radically different. You need to
Speaker 2: speak to their hearts, not just to their minds, and
Speaker 2: you need to speak to values. And it's very important
Speaker 2: that we all learn how to be better storytellers. And
Speaker 2: then the one mindset that brings all of those together
Speaker 2: we call imperfectionism, which is really about stepping in to
Speaker 2: risk rather than being paralyzed, and using small steps in
Speaker 2: order to build your confidence, to build your understanding, to
Speaker 2: build capability sometimes to build asset positions so that you
Speaker 2: actually move toward your goal without having some grand strategy,
Speaker 2: because when things are changing so quickly, grand strategies the
Speaker 2: way we used to construct them don't work anymore.
Speaker 1: I think you've got us a curious certainly, because one
Speaker 1: of the things I hear over and over again from
Speaker 1: listeners when I run into them, you know, out there
Speaker 1: in the real world and not in the studio, is
Speaker 1: that what they tend to tune into this podcast for
Speaker 1: is to better understand things that are not perfectly in
Speaker 1: their field of vision. So Hopefully this will be a
Speaker 1: journey for them to think about how they approach work
Speaker 1: in a very, very different way. One of the things
Speaker 1: that is very clear from the work that you have
Speaker 1: done in this book is to highlight a number of
Speaker 1: different company examples and just at the fundamental level. How
Speaker 1: many case studies would you say that you read on
Speaker 1: a monthly basis, because it must have been hard to choose.
Speaker 2: Yeah, well, certainly more than one hundred. You know, that's
Speaker 2: the food for our work is case studies, because that's
Speaker 2: what allows us to see through these multiple lenses. Grand
Speaker 2: theory is kind of boring, it's empty until you can
Speaker 2: make it real via case studies. So I think there's
Speaker 2: both that deductive problem solving where you come from a
Speaker 2: big idea to the specific, and then inductive where you
Speaker 2: go from the specific to knit together a bit of
Speaker 2: a bigger idea. I guess we love induction.
Speaker 1: There are a lot of examples that I would say
Speaker 1: are more in the B two C space in this book,
Speaker 1: and a lot of the solutions that I'm looking at
Speaker 1: are really focused on this transition of industry, this transition
Speaker 1: and energy, which is more on the B to B side.
Speaker 1: I guess within the different mindsets, Are there some that
Speaker 1: you think are more applicable to the B to B
Speaker 1: space or is really the entire way of all look
Speaker 1: at looking at all six really relevant And are there
Speaker 1: any examples maybe that you may drop on that you
Speaker 1: think would be really useful to the B to B community. Yeah?
Speaker 2: Absolutely, So the first thing I would say is there
Speaker 2: no B two C bias in this way of thinking.
Speaker 2: And what I would say about heavier industries, which tend
Speaker 2: to be the bigger emitters, is there's no easy answers
Speaker 2: because they do involve enormous capital expenditures. That's the characteristic
Speaker 2: of heavy But I think it's too easy to say
Speaker 2: that those are not amenable to these kind of imperfectionist
Speaker 2: approaches to strategy, and it just doesn't so. And I'll
Speaker 2: give you one example. When we talk about experimentation, folks
Speaker 2: often like to think that that's only relevant when you have,
Speaker 2: for example, media or internet light investment industries. So people
Speaker 2: would say, you can do ab testing with two different
Speaker 2: website designs and you see which one attracts more people.
Speaker 2: That's easy and doesn't cost a lot. But if we
Speaker 2: were to look at, for example, one of the heaviest
Speaker 2: industries of all, which is space. We have a good
Speaker 2: example of an experimental company, right, which is SpaceX. SpaceX
Speaker 2: picked up where NASA left off. And what did they do?
Speaker 2: They massively increase the number of launches per year. NASA
Speaker 2: was doing two or three or four launches a year.
Speaker 2: SpaceX now does twenty or thirty launches a year. NASA
Speaker 2: tried to engineer everything double triple heavy. SpaceX has been
Speaker 2: deliberately experimental, sometimes spectacularly. So you remember the what do
Speaker 2: they call it an unplanned disassembly on the most recent
Speaker 2: large rocket launch. Right crazy to take multimillion dollar launches
Speaker 2: and to view that as an experimental lens, But because
Speaker 2: they've viewed it an experimental lens and they've pioneered, for example,
Speaker 2: three D printing of rocket parts, were usable rocket parts
Speaker 2: like the nose cone they catch in a net which
Speaker 2: saves a huge amount of money, or using new materials
Speaker 2: that come from other industries as heat shields. These are
Speaker 2: all ideas that have been pioneered by SpaceX and the
Speaker 2: frequency of launch. You know, they have what they call
Speaker 2: fly test fix fail or fail fix, I should say,
Speaker 2: as a mentality for their engineers. They've been able to
Speaker 2: drive massively down the cost curve. So it used to
Speaker 2: cost fifty five thousand dollars to put a kilogram into
Speaker 2: space with NASA. Now it costs literally a twentieth of
Speaker 2: that with SpaceX to send that same kilogram into space.
Speaker 2: That's heavy industry, that's an experimentalist mindset. The two can
Speaker 2: go together.
Speaker 1: It's incredible to see these cost of clients as you're
Speaker 1: pointing them out. One of the things that occurs to me, though,
Speaker 1: is that this is a company that essentially grew up
Speaker 1: on its own to tackle this issue. It didn't come
Speaker 1: from within, It didn't come from the inside. And you
Speaker 1: have another example in the book about Ford Motor Company
Speaker 1: and how they ended up making their electric vehicle division
Speaker 1: separate from their internal combustion division in order to give
Speaker 1: them that perspective to think about things with a fresh view.
Speaker 1: Let's go into an example that wasn't in the book,
Speaker 1: but certainly an industry that you have experience with, which
Speaker 1: is oil and gas. So in this umbrella space that
Speaker 1: are the energy companies. So many of the people who
Speaker 1: are making the decisions have been in these companies for
Speaker 1: a long time and very rightfully have deep expertise kind
Speaker 1: of what advice or views do you have on how
Speaker 1: they might be able to think about pivoting their business
Speaker 1: and experimenting in a way that we'll be in line
Speaker 1: with a drive to decarbonize.
Speaker 2: Yeah, and Dana, these are the hardest ones, right anytime
Speaker 2: you have a specialist industry where people have to work
Speaker 2: for many years just to learn the basics before they
Speaker 2: can become useful in the industry, and then they need
Speaker 2: to build industry experience. By definition, you're dealing with a
Speaker 2: deep trench rather than an experimentalist or a multi lens
Speaker 2: viewpoint to begin with. And medicine has the same characteristics
Speaker 2: as oil and gas. I would say it's all the
Speaker 2: more reason why these mindsets are terribly important if you
Speaker 2: want to create innovation inside conventional energy. And let me
Speaker 2: give both examples and then processes. One of the things
Speaker 2: we've learned is if you just follow the existing processes
Speaker 2: in a business, you'll get the same answers. And one
Speaker 2: of the things we like to do is use workshops
Speaker 2: where people do, for example, what's called perspective taking, So
Speaker 2: before you launch into a new strategic plan, you actually
Speaker 2: step back and this is this idea of dragonfly eye,
Speaker 2: where you look at your industry. You know, whether you're
Speaker 2: doing upstream exploration or you're doing refining, for example, or distribution,
Speaker 2: and see that industry or that segment through the lens
Speaker 2: of your customer, your supplier or for example, Gretituneberk. That's
Speaker 2: a very different perspective. How would you see yourself? And
Speaker 2: that puts you outside. We call it anchoring outside. It's
Speaker 2: a term we really like. Anchoring outside gives you a
Speaker 2: better perspective and frees you to think differently than you
Speaker 2: might have done before. So I'm going to give a
Speaker 2: couple of examples that we can make up from oil
Speaker 2: and gas. So flare gas is one of these persistent
Speaker 2: problems in oil and gas. You have a remote location,
Speaker 2: you're producing liquids, gas comes up, you don't have any
Speaker 2: way of handling gas. What do you do while you
Speaker 2: flare it? Right? And we still see that all around
Speaker 2: the world. An idea that I saw recently, and I
Speaker 2: don't you know, I'm not an expert in this, but
Speaker 2: I just thought it was interesting, which is a group
Speaker 2: of folks who had been working in server farms. So
Speaker 2: these are the enormous computer server farms that we use
Speaker 2: for this internet driven economy that we're in today, which
Speaker 2: are usually located near big cities. What if you were
Speaker 2: to locate server farms close to where we're flaring gas,
Speaker 2: and the heating and cooling systems, the electricity generation and
Speaker 2: cooling systems that are required could be powered using that
Speaker 2: gas instead of flaring in so co locating an industry
Speaker 2: that's a heavy user near where you're otherwise having to
Speaker 2: burn gas without any value. It's just it's a different
Speaker 2: way of thinking about it. You do have to think
Speaker 2: about transmission lines. There's a whole bunch, you know, for
Speaker 2: the data that comes out of data rooms. But it's
Speaker 2: kind of a cool idea and that you would get
Speaker 2: from thinking differently.
Speaker 1: And there was another example that you brought up in
Speaker 1: the book that was around water pipelines and water pipe
Speaker 1: water pipe failures, and the first thing that really occurred
Speaker 1: in my mind maybe almost two literals. When I'm thinking
Speaker 1: of pipes, I'm thinking about actually like methane gas leaks,
Speaker 1: and I'm thinking about the fact that you know, increasingly
Speaker 1: satellites are picking up on where this is coming from
Speaker 1: and hopefully then leading us to solutions on then what
Speaker 1: we can do about it. But one of the things
Speaker 1: that you really drilled down on this specific case around
Speaker 1: water pipe failures had to do with AI or probably
Speaker 1: machine learning, depending on how you want to refer to it. Really,
Speaker 1: where do you see the potential for application of machine
Speaker 1: learning and helping us find solutions to these big problems.
Speaker 2: Yeah, And on the way into our conversation, we talked
Speaker 2: about how complex this is. Right, the world of decarbonization
Speaker 2: and of energy transition, there's no silver bullets. You have
Speaker 2: to find innovation in many small things. AI and machine
Speaker 2: learning will become hugely important for this world because pattern
Speaker 2: recognition in incredibly complex systems is something that the machine
Speaker 2: does better than us. I love your example. So in
Speaker 2: the water pipe example, you tend to think in a
Speaker 2: very mechanistic way FIFO right first in, first out, So
Speaker 2: you'd assume that the oldest water pipes or methane pipes
Speaker 2: are the ones that you should be replacing because that's
Speaker 2: where failure is going to occur. Turns out that's not
Speaker 2: a very good model. What the artificial intelligence And this
Speaker 2: was an actual example a mathematics professor in Australia looking
Speaker 2: at the water pipe system in a Citney, like Sydney
Speaker 2: or Melbourne came up with were non parametric artificial intelligence
Speaker 2: models that used other clues and almost by definition non
Speaker 2: parametric models, so you didn't define in advance, for example,
Speaker 2: age of pipe is the key determinant. With those kind
Speaker 2: of models, they ended up being able to predict failure
Speaker 2: points much more accurately because they were often in unexpected places.
Speaker 2: And I think the same would likely be true with
Speaker 2: methane or with even more slippery gases like hydrogen, And
Speaker 2: so I think we're going to find a lot of
Speaker 2: our future solutions using artificial intelligence. And the good news
Speaker 2: there is, of course, these oil and gas engineers will
Speaker 2: find that very comfortable because it's sourcing from something that's
Speaker 2: quite adjacent to what they do already.
Speaker 1: So climate change is a shared problem that is going
Speaker 1: to impact everyone and is not going to be an
Speaker 1: equal measure depending upon where and which companies or countries
Speaker 1: are the emitters. So when we think about the fact
Speaker 1: that this is a shared concern, is there potential then
Speaker 1: for it to also be a way for us to
Speaker 1: actually share the creation of the solutions. And one of
Speaker 1: the things that you point out in this book is
Speaker 1: this well open source technology and then also Joy's law.
Speaker 1: Can you kind of go into that a little bit?
Speaker 2: Sure? So Bill Joy was a founder of Sun Microsystems,
Speaker 2: so one of the first big companies that used Unix,
Speaker 2: and not surprisingly because he'd been at Berkeley before where
Speaker 2: he was part of developing Unix, which originated many years
Speaker 2: before in a joint project with AT and T and
Speaker 2: some other companies. Unix is a wonderful example of where
Speaker 2: Joy's law comes from. Bill Joy said, the smartest people
Speaker 2: and may not be in your room. They may be
Speaker 2: laboring in someone else's garden. This is the core idea
Speaker 2: behind Joy's law, and how do you access them so
Speaker 2: that they can contribute to your project. The example that
Speaker 2: he used was Unix, which was open source, meaning that
Speaker 2: engineers from software engineers from all around different companies and
Speaker 2: academic environments could actually contribute to the building of this
Speaker 2: core infrastructure of software that the kernels of which are
Speaker 2: still literally called kernels are in Microsoft operating system, in
Speaker 2: the Apple operating system, and the operating systems of every
Speaker 2: other major computer language. But that idea of Joy's law
Speaker 2: and open source can be applied much more broadly. One
Speaker 2: of the most famous competitions was the Flying NonStop across
Speaker 2: the Atlantic competition, which was won by Lindberg back in
Speaker 2: the nineteen thirties. That idea of using prize competitions, for example,
Speaker 2: to attract creativity, often from other industries, is another way
Speaker 2: of crowdsourcing intelligence or ideas or technologies from outside. We've
Speaker 2: seen that with the X Prize, which has led to
Speaker 2: innovations in flight and a number of other areas. And
Speaker 2: we've seen it with gamified platforms like Cagele, which have
Speaker 2: allowed the crowdsource seeing of great ideas. So an example
Speaker 2: that we use in the book That I Love is
Speaker 2: the Nature Conservancy was trying to make innovations in how
Speaker 2: to reduce bycatch of endangered fish species. Really complex problem
Speaker 2: because these fish are brought a board, either onlines or
Speaker 2: in nets at sea, bumping up and down in the
Speaker 2: worst weather. And you can put cameras aboard ships, but
Speaker 2: how do you very quickly make an identification of a
Speaker 2: fish that's okay to keep and a fish that you
Speaker 2: should put back gently? And the Nature Conservancy didn't have
Speaker 2: people internally who were experts in computer vision or machine learning,
Speaker 2: and so they put up one hundred and fifty thousand
Speaker 2: dollars prize on the Cagle platform, and they received more
Speaker 2: than three thousand entries from different clever people who had
Speaker 2: built algorithms for recognizing fish according to the shape of
Speaker 2: a gill plate or a fin that worked remarkably well
Speaker 2: to identify fish even in those difficult conditions. It's a
Speaker 2: wonderful example of an organization in this case committed to
Speaker 2: conservation sister to decarbonization, for sure, that crowdsourced in a
Speaker 2: wonderful idea that's now being put to work already, first
Speaker 2: in the Indonesian tuna flee to actually help save the
Speaker 2: biodiversity on the planet at the same time as we're
Speaker 2: trying to slow warming. Just a cool idea. This same idea,
Speaker 2: this family of ideas is also being used in for example,
Speaker 2: AI swarms, which can be used to do much better
Speaker 2: prediction for things like cancer diagnosis. So I think we're
Speaker 2: right at the very beginning of being able to put
Speaker 2: our fingers into revolutionary technologies that come from one industry
Speaker 2: but that may provide solutions, including in the heaviest emitting industries.
Speaker 1: So when you think about a prize, a prize really
Speaker 1: sits outside of a company and really motivates individuals and
Speaker 1: these brilliant minds to tackle problems in a different way.
Speaker 1: Do you think that maybe in some respects net zero
Speaker 1: targets or other I mean, are there other ways essentially
Speaker 1: to motivate companies to want to get involved in this
Speaker 1: because I think about the fact that we can go
Speaker 1: back to oil and gas, or we can call upon
Speaker 1: any of the heavy industries. Really they're still companies, they're
Speaker 1: competitive with one another. There are opportunities to cloud rate
Speaker 1: on solutions, but in reality there's an element to this
Speaker 1: and that not only do you need to have the
Speaker 1: best solution for your customers, but you want to maintain
Speaker 1: market share. Are net zero targets a good proxy for
Speaker 1: a prize when it comes to trying to think about
Speaker 1: motivating large companies.
Speaker 2: I think that's a really cool idea. And my guess
Speaker 2: is that net zero targets are or will become that
Speaker 2: same kind of motivator. Well, we found that Patagonia is
Speaker 2: a net zero target is quite distant, even for a
Speaker 2: company like Patagonia. So when we break that down into
Speaker 2: nearer targets that we can actually achieve, it's more motivating.
Speaker 2: So I'll give you an example that wonderful rain jacket
Speaker 2: that you wear here in London when you're riding your
Speaker 2: bike to work, which sheds water in just a remarkable way,
Speaker 2: does so because it uses really dangerous chemistries. Yet Patagonia
Speaker 2: we've said by twenty twenty five, we are not going
Speaker 2: to use any of those chemistries. And over the course
Speaker 2: of the last four years that's been a huge motivator
Speaker 2: for us because the family that own Patagonia until recently
Speaker 2: gave it a way to fight climate change, said we
Speaker 2: want if we can't find non dangerous chemistries for rain jackets,
Speaker 2: we're out. We're going to stop selling them. And so
Speaker 2: the internal team worked with external organizations like the Gore Organization,
Speaker 2: which is a wonderful fabric chemistry company, and they've cracked it.
Speaker 2: They found a way to use less dangerous chemistries to
Speaker 2: create equally water shedding fabrics. That's incredibly motivating, But it
Speaker 2: was motivating because it was a target that was almost
Speaker 2: out of our grass, but not out of our grass,
Speaker 2: whereas a net zero target for a oil and gas
Speaker 2: company might feel too distant. Well, we'll chip away at
Speaker 2: it by investing a bit and wind.
Speaker 1: How do you, I guess, deal with ethics questions in this,
Speaker 1: And the reason I bring up ethics questions is that
Speaker 1: at the center of this is really experimentation, trying things,
Speaker 1: maybe going out and doing it before you're quite ready.
Speaker 1: And a good parallel for that is, you know, any
Speaker 1: sort of autonomous driving right now, if those vehicles are
Speaker 1: learning how to avoid traffic and deal with not just
Speaker 1: traffic but accidents, and in the long run, the view
Speaker 1: is that it's going to save many, many more lives.
Speaker 1: Right now it already is saving maybe some lives depending
Speaker 1: on how you're looking at the data. But the real
Speaker 1: question is, in that circumstance, how do we then deal
Speaker 1: with the fact that there's some randomness to the way
Speaker 1: human beings work? But once we start relying on experimentation
Speaker 1: is a way of getting us to that ultimate end.
Speaker 1: Can you really experiment when you're dealing with human beings?
Speaker 2: Well, I mean that's such a huge question. I think
Speaker 2: it's a wonderful question too. I don't have a silver
Speaker 2: bullet answer, but let me give you two thoughts. One is,
Speaker 2: when you can do experiment without putting living creatures in
Speaker 2: harms way, you should do that. And so, for example,
Speaker 2: I do a lot of investing in the biotech space,
Speaker 2: Sometimes you have to put a mouse in harm's way
Speaker 2: in order to find out what's going on. But increasingly
Speaker 2: you can do organ on a chip, which are approaches
Speaker 2: that don't involve sacrificing animals or putting people in harms way.
Speaker 2: So whenever you can use an alternative technology, you should
Speaker 2: do it. But there are cases where we introduce technologies
Speaker 2: that do have some risk to humans, and the question
Speaker 2: is how can we reduce the risk to humans. I'll
Speaker 2: use an old example. We could massively reduce highway deaths
Speaker 2: if we went back to fifty five miles an hour,
Speaker 2: and we don't, and that's a trade off, and everybody
Speaker 2: knows it's a trade off. Because people want to get
Speaker 2: where they're going a little bit faster, we accept slightly
Speaker 2: higher highway debts. So that is an example, like your
Speaker 2: autonomous driving example. I guess between those two examples saving
Speaker 2: the mouse and highway deaths, we have to ask is
Speaker 2: autonomous driving at the state yet where we can take
Speaker 2: that incremental risk as we do with speed limits, or
Speaker 2: is it still at the state where we're better off
Speaker 2: having a non living creature in the seat. When I
Speaker 2: was working in Oxford, a lot of the autonomous driving
Speaker 2: work was being done, a lot of the stuff that
Speaker 2: in fact informs the industry today. Very little of that
Speaker 2: put people in harms.
Speaker 1: Way, which then deals with you know, how do you
Speaker 1: do things when there's so much uncertainty and you have
Speaker 1: to move your way up these five different levels of
Speaker 1: uncertainty which you bring up and so I guess taking
Speaker 1: a bit of a turn, so to speak, in terms
Speaker 1: of the subject matter. But when we get back into
Speaker 1: let's say physical risk and we think about going forward
Speaker 1: with climate change, change is really at the center of it, right,
Speaker 1: we are increasingly not able to actually look at weather data.
Speaker 1: How does one really grapple with these different Well, I
Speaker 1: guess first outline the five different levels of uncertainty and
Speaker 1: really where you think the most experimentation really can live.
Speaker 2: Yeah, there's various ways to categorize uncertainty. In the book,
Speaker 2: we talk about one framework that was developed a number
Speaker 2: of years ago, where you have no knowns, which is
Speaker 2: the easy level known unknowns, that is, you know what
Speaker 2: you don't know. Ultimately up to unknown unknowns, which you
Speaker 2: know in the nineteen sixties were called unk unks. Well,
Speaker 2: you literally don't know enough to even characterize the nature
Speaker 2: of uncertainty. Obviously, those are the hardest places to operate.
Speaker 2: I think in the middle uncertainty level two and three,
Speaker 2: you really can use experimentation a lot to learn about,
Speaker 2: especially if you can do that in a way that,
Speaker 2: again that's safe. One of the things we talk about
Speaker 2: is the core idea in the book is an imperfectionist
Speaker 2: approach loves to take steps forward into risk if you
Speaker 2: can do it in ways that are reversible. That is,
Speaker 2: if you don't like where you got to, you can
Speaker 2: go back through the door or where the consequences are
Speaker 2: relatively low rather than existential. And so you can use
Speaker 2: this fundamentally experimentalist or imperfectionist approach to explore level two,
Speaker 2: level three, and even level four risk as long as
Speaker 2: you're doing so in ways that aren't existential risk.
Speaker 1: And we break down big problems into increasingly small problems
Speaker 1: so that we're.
Speaker 2: Able to precisely and as you fail, so experimentation means
Speaker 2: not just winning, but losing, you make sure to consolidate
Speaker 2: those lessons, and that's what ultimately builds organizational capability. Organizational
Speaker 2: capability doesn't come from just in insourcing. A lot of
Speaker 2: it comes from learning. Learning comes from making mistakes. One
Speaker 2: of the most important messages of the book, especially for
Speaker 2: the heavy industries that are the biggest admitters, is not
Speaker 2: to be so afraid of failure and not to punish
Speaker 2: failure in our frontline teams. Engineering cultures hate failure, and
Speaker 2: we often punish people when their projects don't work, and
Speaker 2: I think we need to change that mentality and industry.
Speaker 2: In science we accept that all the time, and we
Speaker 2: write up our results, perhaps not as much as we should,
Speaker 2: and there is a survivor bias and papers too, but especially
Speaker 2: once we get to heavy industry where the investments are significant,
Speaker 2: we often criticize or punish or change the compensation of
Speaker 2: teams that have good ideas that don't work. We need
Speaker 2: to make sure good ideas that don't work are celebrated.
Speaker 1: To and in that there's also articulating what's happening to
Speaker 1: the rest of the world. So one of the mindsets
Speaker 1: is show and tell and explaining this to the world.
Speaker 1: And actually first question within that, would you consider yourself
Speaker 1: a storyteller?
Speaker 2: I sure hope. So I think the most compelling people
Speaker 2: in the world are the storytellers. You know, when you
Speaker 2: think about David Attenborough. You listen to him because he
Speaker 2: tells the stories in a way that sort of brings
Speaker 2: it home to you, rather than in the sort of
Speaker 2: thirty thousand foot science. I think all of us would
Speaker 2: be better storytellers if we didn't just think about the
Speaker 2: logic what's between our ears, but we also thought about
Speaker 2: what's in our hearts and our values. The best storytellers
Speaker 2: are ones that link our reason and our values.
Speaker 1: And you reference this one study in the book that
Speaker 1: called upon the US specifically in saying that when individuals
Speaker 1: were asked where climate ranked in their concerns, it was third,
Speaker 1: but when they thought about their peers, they thought their
Speaker 1: peers ranked it at thirty third. So the question is,
Speaker 1: I guess that are we doing a good enough job
Speaker 1: in the world of those that are actually looking at
Speaker 1: climate solutions of telling the story.
Speaker 2: I don't think we are, and I think unfortunately we've
Speaker 2: done the thing that's hardest for people to act on,
Speaker 2: which is to speak about a future state that's uncertain,
Speaker 2: that is catastrophic, very difficult to know what to do
Speaker 2: with that. We move forward in the world by imperfectionist steps,
Speaker 2: by experimentation, and when we don't give people something that
Speaker 2: they can do tomorrow that's different. All we create is
Speaker 2: fear and no forward movement. And so if we and
Speaker 2: perhaps this kind of program that we're discussing right now
Speaker 2: is exactly the right way to start, each of us
Speaker 2: can do behavior changes that help move us down this
Speaker 2: path and which give us more information more data to
Speaker 2: move us further down the path.
Speaker 1: And you reference data certainly the people I work with
Speaker 1: and I like data a lot, but data and data
Speaker 1: is certainly really inherent to being able to make good
Speaker 1: business decisions and create those bets, if you will, about
Speaker 1: how to move forward. But increasingly there's a distrust of
Speaker 1: science and data, and maybe storytelling is the solution to that.
Speaker 1: But for someone who's not in the business community and
Speaker 1: is maybe looking at this problem and paralyzed by fear,
Speaker 1: as you outline, where does data come in for them?
Speaker 1: And is it being too heavily relied on as the story.
Speaker 2: Yeah, the people who control the biggest levers around decarbonization
Speaker 2: and energy transition don't tend to be the people who
Speaker 2: speak from their hearts. They tend to be the people
Speaker 2: who speak from what's between their ears. And I do think,
Speaker 2: especially in these industries. We need to get better at
Speaker 2: speaking to where people are now and starting there. And
Speaker 2: you can build bridges with every kind of person if
Speaker 2: you start with where they are, if you understand what
Speaker 2: they value. In the book we talk about this, but
Speaker 2: almost everybody cares about their kids. Probably everyone cares about
Speaker 2: their kids. If you start with the future for our children,
Speaker 2: you actually have a bridge that can work with almost everybody.
Speaker 1: I certainly feel that personally, so very good example. Okay,
Speaker 1: so we're going to go through a couple of things
Speaker 1: that I would put in this category on whether or
Speaker 1: not there's something that you're watching closely or perhaps ignoring
Speaker 1: for the moment. So in watch or ignore and pulling
Speaker 1: upon also your experience from Patagonia, where would you put
Speaker 1: circular economy.
Speaker 2: I think it's a watch and lean in, right. I
Speaker 2: really do think that this is a critical which is
Speaker 2: when we purchase something. You know, you have a bicycle,
Speaker 2: you have clothing, that we think about where it goes
Speaker 2: after we're done using it. It's as simple as that,
Speaker 2: and of course the people in manufacture it should also
Speaker 2: be thinking about it. At Patagonia, now, when we make
Speaker 2: a jacket, we make it so that it can be disassembled.
Speaker 2: There was a time when we had all these cool
Speaker 2: welding technologies where we glue everything together with using heat
Speaker 2: and chemistry, and then we realize, oh, we can't recycle
Speaker 2: that because you can't take the zipper out. And so
Speaker 2: if both manufacturers and users consumers can think about where
Speaker 2: it goes after you're done with it, I think that's
Speaker 2: absolutely critical.
Speaker 1: Okay, watch or ignore biodiversity targets.
Speaker 2: But for corporations, you're going to hate this answer, but
Speaker 2: I think the answer is ignore. I just think the
Speaker 2: very few corporations are in a position to pay attention
Speaker 2: to the biodiversity of species. Much more important for them
Speaker 2: is to work toward net zero goals because it's climate
Speaker 2: change that is putting most species at risk. Climate change,
Speaker 2: and then, of course I think the escape of danger chemistries.
Speaker 2: When you look at what's happening with frogs and other amphibians,
Speaker 2: this is something that you can only affect by focusing
Speaker 2: on the big levers, not so much on frog habitat
Speaker 2: because that's being destroyed by climate change and chemistry escape.
Speaker 1: Okay, so final watch and ignore ESG financial ratings and rankings.
Speaker 2: Can I create a third category, which is fix. So
Speaker 2: I don't think you ignore it, and I think you
Speaker 2: need to do more than watch it, because ES and
Speaker 2: G are all good ideas, But trying to create an
Speaker 2: index which captures even one of those things for big
Speaker 2: companies and complex operations is literally absurd. So having a
Speaker 2: single rating for E for a company like McDonald's or
Speaker 2: Coca Cola or any of the big energy companies is
Speaker 2: just silly. And most of the ESG ratings that go
Speaker 2: along with stocks, for example, are meaningless, they're not consistent.
Speaker 2: The rating agencies even don't agree themselves. That doesn't mean
Speaker 2: the idea should be thrown out entirely. What it means
Speaker 2: is that to be useful, we need to be much
Speaker 2: more granular. At Patagonia, we have of measures for environmental
Speaker 2: sustainability for each one of the products that we produce.
Speaker 2: We know the carbon that it emits to produce that,
Speaker 2: we know the water that's required, and we know the
Speaker 2: dangerous chemistries are not that are used in manufacture. So
Speaker 2: ES and G need to become much more granular to
Speaker 2: actually allow us to make action. And today you don't
Speaker 2: see a lot of correlation between higher ESG scores and
Speaker 2: higher returns or lower returns. We don't see a lot
Speaker 2: of correlation at all, and the reason is those are
Speaker 2: not meaningful in their current index form.
Speaker 1: Charles, thank you very much for joining today, and I
Speaker 1: look forward to reading whatever the third collaboration is for
Speaker 1: you and Robert in the future someday.
Speaker 2: Dana, I had so much fun being here. Thank you.
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