WEBVTT - Embracing Imperfectionism, with Charles Conn 0:00:00.320 --> 0:00:02.840 This is Dana Perkins and you're listening to Switched on 0:00:03.120 --> 0:00:08.240 the BNF podcast. Imperfection or the idea of something being imperfect, 0:00:08.360 --> 0:00:12.200 is usually considered a negative in business, in life, in art, 0:00:12.280 --> 0:00:15.440 in sport, perfection or as close as possible is normally 0:00:15.440 --> 0:00:18.119 the objective. But is there something to be said for 0:00:18.280 --> 0:00:22.160 embracing imperfection, figuring out what we can learn from failures 0:00:22.320 --> 0:00:26.200 or just incomplete information, and what mindsets and tools we 0:00:26.280 --> 0:00:29.720 need to utilize imperfect outcomes in the business world. So 0:00:29.800 --> 0:00:33.239 in today's episode, I speak with Charles Kahn. Charles is 0:00:33.240 --> 0:00:37.519 an investor, environmentalist and entrepreneur. He's the former CEO of 0:00:37.560 --> 0:00:40.839 The Roads Trust in Oxford and the current chair of Patagonia. 0:00:41.040 --> 0:00:44.479 Aside from his business in academic experience in positions, he's 0:00:44.520 --> 0:00:48.560 also an author, having previously co written Bulletproof Problem Solving, 0:00:48.760 --> 0:00:51.520 and Charles is here today to talk about his latest book, 0:00:51.760 --> 0:00:57.000 The Imperfectionists Strategic Mindsets for Uncertain Times. Together, we discuss 0:00:57.040 --> 0:00:59.040 a range of topics and of course we give them 0:00:59.080 --> 0:01:03.160 the BNF spin. By looking at decarbonization and the transition 0:01:03.560 --> 0:01:06.480 and how this methodology might be applied. We look at 0:01:06.520 --> 0:01:09.680 the different elements that make up the imperfectionist mindset and 0:01:09.720 --> 0:01:11.600 go through some of the practical ways it can be 0:01:11.640 --> 0:01:14.440 applied to business along with AI learning and how it's 0:01:14.480 --> 0:01:17.520 being harnessed to aid decision making. Now, as always, if 0:01:17.560 --> 0:01:20.120 you like this podcast, make sure to subscribe so that 0:01:20.200 --> 0:01:23.240 you get updates when there are future episodes, and give 0:01:23.319 --> 0:01:25.720 us a review on Apple Podcasts or Spotify to make 0:01:25.760 --> 0:01:28.640 it more discoverable to others. But right now we get 0:01:28.640 --> 0:01:43.800 to have a conversation with Charles about imperfectionism. Charles, thank 0:01:43.800 --> 0:01:45.199 you very much for joining me today. 0:01:45.319 --> 0:01:46.759 Dan, it's such a pleasure to be here. 0:01:46.920 --> 0:01:51.480 So we're here well really because you have recently written 0:01:51.560 --> 0:01:55.200 or co written a book and having read it from 0:01:55.240 --> 0:01:57.760 cover to cover myself, there is a lot of application 0:01:57.840 --> 0:01:59.960 and learnings for the industries that we had been in 0:02:00.160 --> 0:02:03.560 have cover regarding decarbonization in the transition, and I really 0:02:03.640 --> 0:02:06.920 want to start with your motivations. I want to start with, well, 0:02:07.120 --> 0:02:10.120 actually your relationship with your co author. So you previously 0:02:10.160 --> 0:02:13.080 wrote a book called Bulletproof Problem Solving the One skill 0:02:13.160 --> 0:02:16.000 that changes Everything. Why was it time to write a 0:02:16.240 --> 0:02:17.200 second book together? 0:02:17.560 --> 0:02:20.360 Yeah? So the first book was a tool sets book 0:02:20.520 --> 0:02:23.520 about problem solving, how to break complex problems apart and 0:02:23.560 --> 0:02:26.160 solve them creatively, which is I think what both Rob 0:02:26.200 --> 0:02:28.600 and I would say is our life's work. And in 0:02:28.639 --> 0:02:33.280 a world where you have increasing automation and artificial intelligence, 0:02:33.440 --> 0:02:36.079 the one thing that humans can really do is work 0:02:36.120 --> 0:02:39.480 together to solve complex problems creatively. We're still better at 0:02:39.560 --> 0:02:42.440 that than the AI routines. The reason we wrote the 0:02:42.480 --> 0:02:45.960 second book, which is a mindset's book, is in the 0:02:46.000 --> 0:02:49.320 depth of the pandemic. As things were changing so quickly, 0:02:49.560 --> 0:02:52.519 it became clear to us that the first book didn't 0:02:52.560 --> 0:02:56.520 address problem solving under very high uncertainty well enough, and 0:02:56.600 --> 0:02:59.000 so we wanted to write was a book that helped 0:02:59.000 --> 0:03:01.359 people think about what to do when things are changing 0:03:01.480 --> 0:03:04.480 very quickly. When things are changing quickly, people tend to 0:03:04.520 --> 0:03:07.480 do one of two things. They paralyze, and we see 0:03:07.480 --> 0:03:10.600 this a lot in company managements. Now they freeze and 0:03:10.639 --> 0:03:14.480 they want to wait for stasis stasis isn't coming. Or 0:03:15.160 --> 0:03:19.560 they do leap before you look moves where people panic 0:03:19.800 --> 0:03:23.640 and they do something big that's irreversible and difficult. And 0:03:23.680 --> 0:03:26.000 we wanted to show people there's a way to lean 0:03:26.040 --> 0:03:29.560 into risk and to be comfortable solving problems even when 0:03:29.600 --> 0:03:30.480 things are changing. 0:03:30.760 --> 0:03:33.280 And with that you said, it's a mindset's book, So 0:03:33.280 --> 0:03:35.800 there are six different mindsets that you go through in here. 0:03:35.920 --> 0:03:38.120 Let's go through a quick overview so that we can 0:03:38.160 --> 0:03:40.240 then drill down into some of the individual ones. 0:03:40.440 --> 0:03:43.160 Sure, so let's just do a thirty thousand foot view. 0:03:43.200 --> 0:03:45.920 So when things are changing really quickly, the most important 0:03:45.920 --> 0:03:49.800 orientation is curiosity. And it sounds incredibly obvious, but as 0:03:49.880 --> 0:03:52.880 we get older, we forget to be curious. As we 0:03:52.920 --> 0:03:55.480 get good at what we do, we get into ruts. 0:03:55.640 --> 0:03:58.440 And those ruts are even like tying your shoe. You 0:03:58.480 --> 0:04:00.600 don't think about it. And because you don't think about 0:04:00.600 --> 0:04:02.400 it and you're not curious about it, you're not open 0:04:02.440 --> 0:04:05.800 to other ideas. Second, and very much a sister mindset 0:04:05.880 --> 0:04:08.760 we call dragonfly eye, which is an idea we borrowed 0:04:08.760 --> 0:04:12.480 from Philip Tetlock, who's written so beautifully about super forecasters. 0:04:12.760 --> 0:04:14.960 What that means is to make sure to see things 0:04:15.000 --> 0:04:18.040 through multiple perspectives before you make up your mind about 0:04:18.040 --> 0:04:20.400 what strategic path you're going to take to see them. 0:04:20.560 --> 0:04:23.880 We call it sometimes environment vision, which means thinking about 0:04:24.040 --> 0:04:27.680 problems through the perspective of your customers or your suppliers. 0:04:28.080 --> 0:04:31.359 Or a potential competitor, rather than just thinking about things 0:04:31.560 --> 0:04:34.120 through your own industry lens, which is how we tend 0:04:34.120 --> 0:04:36.320 to do things. The third which should be familiar to 0:04:36.360 --> 0:04:38.599 many people but probably not with this name, and we 0:04:38.680 --> 0:04:41.640 call it oh current behavior, which is what actually happens 0:04:41.960 --> 0:04:44.760 rather than what you hope will happen. And for us, 0:04:44.839 --> 0:04:48.680 that is an experimentalist mindset. And I think many people 0:04:48.720 --> 0:04:51.840 think that we can only do experimentation in light industries 0:04:51.920 --> 0:04:54.359 like internet, but we think it's just as important to 0:04:54.400 --> 0:04:58.159 do experimentation in heavy industry of the sorts that you 0:04:58.279 --> 0:05:02.360 often pay attention to. Both mindset we call collective intelligence, 0:05:02.560 --> 0:05:05.279 which is how we can reach outside the boundaries of 0:05:05.320 --> 0:05:10.240 our own organizations and crowdsource in great ideas, and most 0:05:10.400 --> 0:05:12.960 big professional industries are loath to do that because the 0:05:13.000 --> 0:05:15.599 assumption is that the smartest people are already in the room, 0:05:15.800 --> 0:05:19.119 and therefore we miss out that idea of peripheral vision, 0:05:19.200 --> 0:05:22.560 where technologies or ideas from another industry that could be 0:05:22.600 --> 0:05:26.600 applicable we just miss entirely. The fifth mindset we call 0:05:26.880 --> 0:05:29.800 show and tell, which is really that sort of kindergarten 0:05:29.880 --> 0:05:33.360 idea that you would rather than just create a PowerPoint 0:05:33.400 --> 0:05:35.880 slide you actually tell a story if you want to 0:05:36.000 --> 0:05:39.359 rally people around your ideas to do something radically different, 0:05:39.440 --> 0:05:42.520 and of course the whole climate change world requires us 0:05:42.600 --> 0:05:44.680 to do things that are radically different. You need to 0:05:44.720 --> 0:05:47.280 speak to their hearts, not just to their minds, and 0:05:47.560 --> 0:05:50.440 you need to speak to values. And it's very important 0:05:50.480 --> 0:05:53.000 that we all learn how to be better storytellers. And 0:05:53.040 --> 0:05:56.239 then the one mindset that brings all of those together 0:05:56.520 --> 0:06:00.159 we call imperfectionism, which is really about stepping in to 0:06:00.279 --> 0:06:04.600 risk rather than being paralyzed, and using small steps in 0:06:04.680 --> 0:06:07.520 order to build your confidence, to build your understanding, to 0:06:07.560 --> 0:06:10.960 build capability sometimes to build asset positions so that you 0:06:11.120 --> 0:06:14.880 actually move toward your goal without having some grand strategy, 0:06:15.000 --> 0:06:18.080 because when things are changing so quickly, grand strategies the 0:06:18.080 --> 0:06:20.160 way we used to construct them don't work anymore. 0:06:20.320 --> 0:06:22.840 I think you've got us a curious certainly, because one 0:06:22.920 --> 0:06:25.160 of the things I hear over and over again from 0:06:25.200 --> 0:06:27.479 listeners when I run into them, you know, out there 0:06:27.480 --> 0:06:29.440 in the real world and not in the studio, is 0:06:29.440 --> 0:06:32.760 that what they tend to tune into this podcast for 0:06:32.920 --> 0:06:35.960 is to better understand things that are not perfectly in 0:06:36.000 --> 0:06:38.599 their field of vision. So Hopefully this will be a 0:06:38.680 --> 0:06:41.080 journey for them to think about how they approach work 0:06:41.240 --> 0:06:43.599 in a very, very different way. One of the things 0:06:43.640 --> 0:06:46.800 that is very clear from the work that you have 0:06:46.920 --> 0:06:49.000 done in this book is to highlight a number of 0:06:49.040 --> 0:06:52.960 different company examples and just at the fundamental level. How 0:06:52.960 --> 0:06:55.200 many case studies would you say that you read on 0:06:55.240 --> 0:06:58.320 a monthly basis, because it must have been hard to choose. 0:06:58.800 --> 0:07:01.360 Yeah, well, certainly more than one hundred. You know, that's 0:07:01.680 --> 0:07:05.360 the food for our work is case studies, because that's 0:07:05.400 --> 0:07:08.840 what allows us to see through these multiple lenses. Grand 0:07:08.920 --> 0:07:12.000 theory is kind of boring, it's empty until you can 0:07:12.040 --> 0:07:15.360 make it real via case studies. So I think there's 0:07:15.400 --> 0:07:19.280 both that deductive problem solving where you come from a 0:07:19.320 --> 0:07:22.800 big idea to the specific, and then inductive where you 0:07:22.840 --> 0:07:25.160 go from the specific to knit together a bit of 0:07:25.160 --> 0:07:27.200 a bigger idea. I guess we love induction. 0:07:27.640 --> 0:07:29.200 There are a lot of examples that I would say 0:07:29.200 --> 0:07:31.520 are more in the B two C space in this book, 0:07:31.560 --> 0:07:35.200 and a lot of the solutions that I'm looking at 0:07:35.280 --> 0:07:38.480 are really focused on this transition of industry, this transition 0:07:38.560 --> 0:07:41.160 and energy, which is more on the B to B side. 0:07:41.280 --> 0:07:43.960 I guess within the different mindsets, Are there some that 0:07:44.040 --> 0:07:46.560 you think are more applicable to the B to B 0:07:46.640 --> 0:07:49.160 space or is really the entire way of all look 0:07:49.200 --> 0:07:52.720 at looking at all six really relevant And are there 0:07:52.720 --> 0:07:54.880 any examples maybe that you may drop on that you 0:07:54.880 --> 0:07:57.760 think would be really useful to the B to B community. Yeah? 0:07:57.800 --> 0:08:00.080 Absolutely, So the first thing I would say is there 0:08:00.280 --> 0:08:03.120 no B two C bias in this way of thinking. 0:08:03.400 --> 0:08:06.920 And what I would say about heavier industries, which tend 0:08:06.920 --> 0:08:09.880 to be the bigger emitters, is there's no easy answers 0:08:10.200 --> 0:08:14.120 because they do involve enormous capital expenditures. That's the characteristic 0:08:14.200 --> 0:08:18.040 of heavy But I think it's too easy to say 0:08:18.440 --> 0:08:22.080 that those are not amenable to these kind of imperfectionist 0:08:22.160 --> 0:08:25.000 approaches to strategy, and it just doesn't so. And I'll 0:08:25.040 --> 0:08:28.440 give you one example. When we talk about experimentation, folks 0:08:28.520 --> 0:08:32.000 often like to think that that's only relevant when you have, 0:08:32.240 --> 0:08:36.640 for example, media or internet light investment industries. So people 0:08:36.679 --> 0:08:39.640 would say, you can do ab testing with two different 0:08:39.679 --> 0:08:42.600 website designs and you see which one attracts more people. 0:08:42.760 --> 0:08:44.800 That's easy and doesn't cost a lot. But if we 0:08:44.800 --> 0:08:47.000 were to look at, for example, one of the heaviest 0:08:47.000 --> 0:08:49.640 industries of all, which is space. We have a good 0:08:49.679 --> 0:08:53.800 example of an experimental company, right, which is SpaceX. SpaceX 0:08:54.000 --> 0:08:57.360 picked up where NASA left off. And what did they do? 0:08:57.559 --> 0:09:01.040 They massively increase the number of launches per year. NASA 0:09:01.120 --> 0:09:03.520 was doing two or three or four launches a year. 0:09:03.640 --> 0:09:07.160 SpaceX now does twenty or thirty launches a year. NASA 0:09:07.240 --> 0:09:11.480 tried to engineer everything double triple heavy. SpaceX has been 0:09:11.520 --> 0:09:16.040 deliberately experimental, sometimes spectacularly. So you remember the what do 0:09:16.120 --> 0:09:19.600 they call it an unplanned disassembly on the most recent 0:09:19.679 --> 0:09:24.679 large rocket launch. Right crazy to take multimillion dollar launches 0:09:24.800 --> 0:09:28.120 and to view that as an experimental lens, But because 0:09:28.160 --> 0:09:31.559 they've viewed it an experimental lens and they've pioneered, for example, 0:09:31.640 --> 0:09:35.080 three D printing of rocket parts, were usable rocket parts 0:09:35.120 --> 0:09:37.320 like the nose cone they catch in a net which 0:09:37.360 --> 0:09:40.320 saves a huge amount of money, or using new materials 0:09:40.320 --> 0:09:43.120 that come from other industries as heat shields. These are 0:09:43.160 --> 0:09:46.080 all ideas that have been pioneered by SpaceX and the 0:09:46.120 --> 0:09:48.720 frequency of launch. You know, they have what they call 0:09:49.080 --> 0:09:53.040 fly test fix fail or fail fix, I should say, 0:09:53.160 --> 0:09:56.400 as a mentality for their engineers. They've been able to 0:09:56.480 --> 0:09:59.319 drive massively down the cost curve. So it used to 0:09:59.400 --> 0:10:02.000 cost fifty five thousand dollars to put a kilogram into 0:10:02.040 --> 0:10:06.040 space with NASA. Now it costs literally a twentieth of 0:10:06.080 --> 0:10:09.000 that with SpaceX to send that same kilogram into space. 0:10:09.240 --> 0:10:12.800 That's heavy industry, that's an experimentalist mindset. The two can 0:10:12.840 --> 0:10:13.240 go together. 0:10:13.559 --> 0:10:15.920 It's incredible to see these cost of clients as you're 0:10:15.960 --> 0:10:18.320 pointing them out. One of the things that occurs to me, though, 0:10:18.360 --> 0:10:21.440 is that this is a company that essentially grew up 0:10:21.720 --> 0:10:24.360 on its own to tackle this issue. It didn't come 0:10:24.400 --> 0:10:27.680 from within, It didn't come from the inside. And you 0:10:27.920 --> 0:10:30.640 have another example in the book about Ford Motor Company 0:10:30.679 --> 0:10:33.920 and how they ended up making their electric vehicle division 0:10:34.000 --> 0:10:36.680 separate from their internal combustion division in order to give 0:10:36.720 --> 0:10:40.360 them that perspective to think about things with a fresh view. 0:10:40.840 --> 0:10:43.280 Let's go into an example that wasn't in the book, 0:10:43.280 --> 0:10:45.600 but certainly an industry that you have experience with, which 0:10:45.640 --> 0:10:48.480 is oil and gas. So in this umbrella space that 0:10:48.520 --> 0:10:51.440 are the energy companies. So many of the people who 0:10:51.440 --> 0:10:53.760 are making the decisions have been in these companies for 0:10:53.800 --> 0:10:57.280 a long time and very rightfully have deep expertise kind 0:10:57.280 --> 0:10:59.720 of what advice or views do you have on how 0:11:00.160 --> 0:11:03.120 they might be able to think about pivoting their business 0:11:03.120 --> 0:11:05.880 and experimenting in a way that we'll be in line 0:11:05.960 --> 0:11:07.920 with a drive to decarbonize. 0:11:08.240 --> 0:11:11.040 Yeah, and Dana, these are the hardest ones, right anytime 0:11:11.080 --> 0:11:13.520 you have a specialist industry where people have to work 0:11:13.600 --> 0:11:16.480 for many years just to learn the basics before they 0:11:16.480 --> 0:11:18.600 can become useful in the industry, and then they need 0:11:18.600 --> 0:11:21.760 to build industry experience. By definition, you're dealing with a 0:11:21.760 --> 0:11:27.160 deep trench rather than an experimentalist or a multi lens 0:11:27.280 --> 0:11:30.559 viewpoint to begin with. And medicine has the same characteristics 0:11:30.600 --> 0:11:33.160 as oil and gas. I would say it's all the 0:11:33.200 --> 0:11:36.800 more reason why these mindsets are terribly important if you 0:11:36.840 --> 0:11:41.720 want to create innovation inside conventional energy. And let me 0:11:41.720 --> 0:11:45.079 give both examples and then processes. One of the things 0:11:45.160 --> 0:11:48.360 we've learned is if you just follow the existing processes 0:11:48.400 --> 0:11:50.880 in a business, you'll get the same answers. And one 0:11:50.880 --> 0:11:53.120 of the things we like to do is use workshops 0:11:53.160 --> 0:11:56.400 where people do, for example, what's called perspective taking, So 0:11:56.480 --> 0:11:59.280 before you launch into a new strategic plan, you actually 0:11:59.280 --> 0:12:01.760 step back and this is this idea of dragonfly eye, 0:12:01.800 --> 0:12:04.320 where you look at your industry. You know, whether you're 0:12:04.320 --> 0:12:08.559 doing upstream exploration or you're doing refining, for example, or distribution, 0:12:08.880 --> 0:12:12.440 and see that industry or that segment through the lens 0:12:12.520 --> 0:12:16.520 of your customer, your supplier or for example, Gretituneberk. That's 0:12:16.559 --> 0:12:19.360 a very different perspective. How would you see yourself? And 0:12:19.400 --> 0:12:22.760 that puts you outside. We call it anchoring outside. It's 0:12:22.760 --> 0:12:25.480 a term we really like. Anchoring outside gives you a 0:12:25.520 --> 0:12:28.920 better perspective and frees you to think differently than you 0:12:29.000 --> 0:12:31.199 might have done before. So I'm going to give a 0:12:31.240 --> 0:12:33.640 couple of examples that we can make up from oil 0:12:33.679 --> 0:12:37.160 and gas. So flare gas is one of these persistent 0:12:37.200 --> 0:12:39.880 problems in oil and gas. You have a remote location, 0:12:40.240 --> 0:12:43.319 you're producing liquids, gas comes up, you don't have any 0:12:43.320 --> 0:12:46.040 way of handling gas. What do you do while you 0:12:46.080 --> 0:12:48.400 flare it? Right? And we still see that all around 0:12:48.440 --> 0:12:51.040 the world. An idea that I saw recently, and I 0:12:51.040 --> 0:12:52.840 don't you know, I'm not an expert in this, but 0:12:53.080 --> 0:12:55.439 I just thought it was interesting, which is a group 0:12:55.480 --> 0:12:58.560 of folks who had been working in server farms. So 0:12:58.600 --> 0:13:01.520 these are the enormous computer server farms that we use 0:13:01.559 --> 0:13:04.280 for this internet driven economy that we're in today, which 0:13:04.320 --> 0:13:07.079 are usually located near big cities. What if you were 0:13:07.080 --> 0:13:10.360 to locate server farms close to where we're flaring gas, 0:13:10.400 --> 0:13:13.920 and the heating and cooling systems, the electricity generation and 0:13:13.920 --> 0:13:16.920 cooling systems that are required could be powered using that 0:13:17.040 --> 0:13:20.760 gas instead of flaring in so co locating an industry 0:13:20.960 --> 0:13:24.400 that's a heavy user near where you're otherwise having to 0:13:24.440 --> 0:13:27.079 burn gas without any value. It's just it's a different 0:13:27.120 --> 0:13:28.800 way of thinking about it. You do have to think 0:13:28.840 --> 0:13:31.360 about transmission lines. There's a whole bunch, you know, for 0:13:31.400 --> 0:13:33.319 the data that comes out of data rooms. But it's 0:13:33.360 --> 0:13:35.520 kind of a cool idea and that you would get 0:13:35.600 --> 0:13:36.680 from thinking differently. 0:13:36.920 --> 0:13:39.920 And there was another example that you brought up in 0:13:39.920 --> 0:13:42.600 the book that was around water pipelines and water pipe 0:13:42.760 --> 0:13:45.560 water pipe failures, and the first thing that really occurred 0:13:45.559 --> 0:13:47.920 in my mind maybe almost two literals. When I'm thinking 0:13:47.920 --> 0:13:50.840 of pipes, I'm thinking about actually like methane gas leaks, 0:13:50.920 --> 0:13:53.120 and I'm thinking about the fact that you know, increasingly 0:13:53.200 --> 0:13:56.120 satellites are picking up on where this is coming from 0:13:56.400 --> 0:13:59.040 and hopefully then leading us to solutions on then what 0:13:59.040 --> 0:14:01.040 we can do about it. But one of the things 0:14:01.080 --> 0:14:04.600 that you really drilled down on this specific case around 0:14:04.640 --> 0:14:08.320 water pipe failures had to do with AI or probably 0:14:08.480 --> 0:14:11.440 machine learning, depending on how you want to refer to it. Really, 0:14:11.920 --> 0:14:16.360 where do you see the potential for application of machine 0:14:16.440 --> 0:14:19.760 learning and helping us find solutions to these big problems. 0:14:20.040 --> 0:14:22.400 Yeah, And on the way into our conversation, we talked 0:14:22.440 --> 0:14:26.040 about how complex this is. Right, the world of decarbonization 0:14:26.200 --> 0:14:29.440 and of energy transition, there's no silver bullets. You have 0:14:29.520 --> 0:14:32.960 to find innovation in many small things. AI and machine 0:14:33.040 --> 0:14:36.960 learning will become hugely important for this world because pattern 0:14:37.040 --> 0:14:41.040 recognition in incredibly complex systems is something that the machine 0:14:41.040 --> 0:14:43.680 does better than us. I love your example. So in 0:14:43.720 --> 0:14:46.280 the water pipe example, you tend to think in a 0:14:46.360 --> 0:14:50.160 very mechanistic way FIFO right first in, first out, So 0:14:50.200 --> 0:14:53.160 you'd assume that the oldest water pipes or methane pipes 0:14:53.320 --> 0:14:55.280 are the ones that you should be replacing because that's 0:14:55.280 --> 0:14:57.640 where failure is going to occur. Turns out that's not 0:14:57.680 --> 0:15:01.080 a very good model. What the artificial intelligence And this 0:15:01.280 --> 0:15:05.680 was an actual example a mathematics professor in Australia looking 0:15:05.720 --> 0:15:08.120 at the water pipe system in a Citney, like Sydney 0:15:08.160 --> 0:15:12.080 or Melbourne came up with were non parametric artificial intelligence 0:15:12.160 --> 0:15:15.880 models that used other clues and almost by definition non 0:15:15.960 --> 0:15:19.280 parametric models, so you didn't define in advance, for example, 0:15:19.360 --> 0:15:21.800 age of pipe is the key determinant. With those kind 0:15:21.840 --> 0:15:24.200 of models, they ended up being able to predict failure 0:15:24.240 --> 0:15:28.360 points much more accurately because they were often in unexpected places. 0:15:28.400 --> 0:15:30.680 And I think the same would likely be true with 0:15:30.840 --> 0:15:34.200 methane or with even more slippery gases like hydrogen, And 0:15:34.280 --> 0:15:36.320 so I think we're going to find a lot of 0:15:36.320 --> 0:15:40.280 our future solutions using artificial intelligence. And the good news 0:15:40.320 --> 0:15:42.720 there is, of course, these oil and gas engineers will 0:15:42.720 --> 0:15:47.000 find that very comfortable because it's sourcing from something that's 0:15:47.080 --> 0:15:48.880 quite adjacent to what they do already. 0:15:50.480 --> 0:15:53.840 So climate change is a shared problem that is going 0:15:53.920 --> 0:15:57.360 to impact everyone and is not going to be an 0:15:57.360 --> 0:16:02.440 equal measure depending upon where and which companies or countries 0:16:02.480 --> 0:16:04.600 are the emitters. So when we think about the fact 0:16:04.600 --> 0:16:07.840 that this is a shared concern, is there potential then 0:16:07.880 --> 0:16:10.200 for it to also be a way for us to 0:16:10.240 --> 0:16:14.200 actually share the creation of the solutions. And one of 0:16:14.200 --> 0:16:16.160 the things that you point out in this book is 0:16:16.200 --> 0:16:19.680 this well open source technology and then also Joy's law. 0:16:19.720 --> 0:16:21.680 Can you kind of go into that a little bit? 0:16:22.080 --> 0:16:26.680 Sure? So Bill Joy was a founder of Sun Microsystems, 0:16:26.800 --> 0:16:29.920 so one of the first big companies that used Unix, 0:16:30.080 --> 0:16:33.040 and not surprisingly because he'd been at Berkeley before where 0:16:33.040 --> 0:16:36.680 he was part of developing Unix, which originated many years 0:16:36.680 --> 0:16:38.920 before in a joint project with AT and T and 0:16:38.960 --> 0:16:42.960 some other companies. Unix is a wonderful example of where 0:16:43.040 --> 0:16:46.600 Joy's law comes from. Bill Joy said, the smartest people 0:16:46.600 --> 0:16:48.440 and may not be in your room. They may be 0:16:48.560 --> 0:16:51.640 laboring in someone else's garden. This is the core idea 0:16:51.680 --> 0:16:54.920 behind Joy's law, and how do you access them so 0:16:54.960 --> 0:16:58.320 that they can contribute to your project. The example that 0:16:58.400 --> 0:17:02.080 he used was Unix, which was open source, meaning that 0:17:02.200 --> 0:17:06.159 engineers from software engineers from all around different companies and 0:17:06.240 --> 0:17:09.560 academic environments could actually contribute to the building of this 0:17:09.800 --> 0:17:12.879 core infrastructure of software that the kernels of which are 0:17:12.920 --> 0:17:16.800 still literally called kernels are in Microsoft operating system, in 0:17:16.840 --> 0:17:19.920 the Apple operating system, and the operating systems of every 0:17:19.960 --> 0:17:24.040 other major computer language. But that idea of Joy's law 0:17:24.119 --> 0:17:27.120 and open source can be applied much more broadly. One 0:17:27.119 --> 0:17:31.359 of the most famous competitions was the Flying NonStop across 0:17:31.400 --> 0:17:34.399 the Atlantic competition, which was won by Lindberg back in 0:17:34.400 --> 0:17:39.080 the nineteen thirties. That idea of using prize competitions, for example, 0:17:39.280 --> 0:17:44.040 to attract creativity, often from other industries, is another way 0:17:44.080 --> 0:17:48.880 of crowdsourcing intelligence or ideas or technologies from outside. We've 0:17:48.880 --> 0:17:51.080 seen that with the X Prize, which has led to 0:17:51.119 --> 0:17:53.959 innovations in flight and a number of other areas. And 0:17:54.000 --> 0:17:58.040 we've seen it with gamified platforms like Cagele, which have 0:17:58.160 --> 0:18:02.000 allowed the crowdsource seeing of great ideas. So an example 0:18:02.000 --> 0:18:03.879 that we use in the book That I Love is 0:18:03.920 --> 0:18:08.000 the Nature Conservancy was trying to make innovations in how 0:18:08.040 --> 0:18:12.800 to reduce bycatch of endangered fish species. Really complex problem 0:18:12.800 --> 0:18:15.320 because these fish are brought a board, either onlines or 0:18:15.320 --> 0:18:17.800 in nets at sea, bumping up and down in the 0:18:17.800 --> 0:18:20.520 worst weather. And you can put cameras aboard ships, but 0:18:20.600 --> 0:18:22.840 how do you very quickly make an identification of a 0:18:22.880 --> 0:18:24.720 fish that's okay to keep and a fish that you 0:18:24.720 --> 0:18:28.080 should put back gently? And the Nature Conservancy didn't have 0:18:28.240 --> 0:18:32.159 people internally who were experts in computer vision or machine learning, 0:18:32.240 --> 0:18:34.080 and so they put up one hundred and fifty thousand 0:18:34.119 --> 0:18:37.280 dollars prize on the Cagle platform, and they received more 0:18:37.320 --> 0:18:40.400 than three thousand entries from different clever people who had 0:18:40.400 --> 0:18:43.560 built algorithms for recognizing fish according to the shape of 0:18:43.600 --> 0:18:46.680 a gill plate or a fin that worked remarkably well 0:18:46.680 --> 0:18:49.919 to identify fish even in those difficult conditions. It's a 0:18:49.960 --> 0:18:52.840 wonderful example of an organization in this case committed to 0:18:52.880 --> 0:18:57.320 conservation sister to decarbonization, for sure, that crowdsourced in a 0:18:57.320 --> 0:19:00.879 wonderful idea that's now being put to work already, first 0:19:00.880 --> 0:19:03.600 in the Indonesian tuna flee to actually help save the 0:19:03.640 --> 0:19:05.920 biodiversity on the planet at the same time as we're 0:19:05.960 --> 0:19:10.320 trying to slow warming. Just a cool idea. This same idea, 0:19:10.400 --> 0:19:13.400 this family of ideas is also being used in for example, 0:19:13.440 --> 0:19:16.160 AI swarms, which can be used to do much better 0:19:16.200 --> 0:19:19.640 prediction for things like cancer diagnosis. So I think we're 0:19:19.720 --> 0:19:21.920 right at the very beginning of being able to put 0:19:21.960 --> 0:19:26.840 our fingers into revolutionary technologies that come from one industry 0:19:27.000 --> 0:19:31.399 but that may provide solutions, including in the heaviest emitting industries. 0:19:31.800 --> 0:19:34.359 So when you think about a prize, a prize really 0:19:34.400 --> 0:19:39.680 sits outside of a company and really motivates individuals and 0:19:39.760 --> 0:19:42.320 these brilliant minds to tackle problems in a different way. 0:19:42.480 --> 0:19:45.160 Do you think that maybe in some respects net zero 0:19:45.280 --> 0:19:48.720 targets or other I mean, are there other ways essentially 0:19:48.800 --> 0:19:50.800 to motivate companies to want to get involved in this 0:19:50.800 --> 0:19:52.879 because I think about the fact that we can go 0:19:52.920 --> 0:19:54.600 back to oil and gas, or we can call upon 0:19:54.680 --> 0:19:57.360 any of the heavy industries. Really they're still companies, they're 0:19:57.359 --> 0:20:00.239 competitive with one another. There are opportunities to cloud rate 0:20:00.320 --> 0:20:03.080 on solutions, but in reality there's an element to this 0:20:03.200 --> 0:20:05.320 and that not only do you need to have the 0:20:05.320 --> 0:20:07.720 best solution for your customers, but you want to maintain 0:20:07.760 --> 0:20:11.720 market share. Are net zero targets a good proxy for 0:20:11.840 --> 0:20:13.960 a prize when it comes to trying to think about 0:20:14.040 --> 0:20:15.240 motivating large companies. 0:20:15.440 --> 0:20:17.800 I think that's a really cool idea. And my guess 0:20:17.880 --> 0:20:21.640 is that net zero targets are or will become that 0:20:21.680 --> 0:20:25.040 same kind of motivator. Well, we found that Patagonia is 0:20:25.440 --> 0:20:28.439 a net zero target is quite distant, even for a 0:20:28.480 --> 0:20:32.400 company like Patagonia. So when we break that down into 0:20:32.720 --> 0:20:36.639 nearer targets that we can actually achieve, it's more motivating. 0:20:36.720 --> 0:20:39.600 So I'll give you an example that wonderful rain jacket 0:20:39.600 --> 0:20:41.800 that you wear here in London when you're riding your 0:20:41.800 --> 0:20:45.080 bike to work, which sheds water in just a remarkable way, 0:20:45.280 --> 0:20:48.760 does so because it uses really dangerous chemistries. Yet Patagonia 0:20:48.840 --> 0:20:51.320 we've said by twenty twenty five, we are not going 0:20:51.400 --> 0:20:53.879 to use any of those chemistries. And over the course 0:20:53.920 --> 0:20:57.320 of the last four years that's been a huge motivator 0:20:57.359 --> 0:21:00.920 for us because the family that own Patagonia until recently 0:21:01.080 --> 0:21:03.719 gave it a way to fight climate change, said we 0:21:03.760 --> 0:21:07.359