Full Transcript for E198 Chris Winter: Scaling Intelligence - The Technology Behind the Future of Nature

--> 00:02:22.479 Um and a Tesla. 00:02:23.439 --> 00:02:26.240 Well, we we always talked about the Tesla moment, right? 00:02:26.400 --> 00:02:26.560 Yeah. 00:02:26.719 --> 00:02:36.960 This thing came up yesterday when Elon came out and said people are gonna pay$90,000 for an electric car when a Prius was the only thing out there, everyone laughed and we said it it would come and it was gonna be that. 00:02:37.039 --> 00:02:38.479 And that's exactly what's arrived. 00:02:38.719 --> 00:02:40.400 We talked about Tesla at the beginning. 00:02:40.800 --> 00:02:53.039 When we launched early days in Oxen Conservation, the article about the Guardian saying carbon credits are worthless, and we were talking about launching the best quality credits in the world, and it was it was almost a laughable idea at that stage. 00:02:53.199 --> 00:02:58.960 But five years on, we have got the best quality credits in the world, they've been independently rated as the best. 00:02:59.199 --> 00:03:04.080 They are credits that you can touch and see, they are landscape restoration schemes that have been delivered. 00:03:04.240 --> 00:03:10.000 Um, I think what's happened in that last five years has been not just what we've done, but you've seen the whole sector completely transform. 00:03:10.159 --> 00:03:19.360 And a key part of that is I mean, what's happened over the last five years in terms of AI and tech and where that's got to, we live in a different world to five years ago. 00:03:19.520 --> 00:03:24.080 What we can do now, you know, at the click of a button was pretty much impossible five years ago. 00:03:24.159 --> 00:03:27.840 And that's really been the kind of revolution in a lot of the natural capital market. 00:03:28.080 --> 00:03:35.360 Uh when you talk about best in the world, so you you mentioned it there, we have got the highest independent rating a nature-based product has ever got. 00:03:35.759 --> 00:03:38.319 And how many world records have you broken for pricing? 00:03:38.560 --> 00:03:39.199 Uh, just two. 00:03:39.439 --> 00:03:39.759 Just two. 00:03:40.000 --> 00:03:42.000 We get a round of applause, Chris, please. 00:03:43.680 --> 00:03:54.560 How when you pause for a sec, which I appreciate is rare for both of us, and you think about what you've achieved over the last five years, that's astonishing, right? 00:03:55.919 --> 00:04:06.080 Yeah, but as as you've seen when we got the double A rating for the carbon credits, my immediate reaction was, Well, how do we get a triple A rating? 00:04:06.400 --> 00:04:08.080 You did get quite focused on that. 00:04:08.400 --> 00:04:10.319 Yeah, I I mean it's never enough. 00:04:10.479 --> 00:04:12.000 It's always got to be better. 00:04:12.319 --> 00:04:17.360 Uh that's but when forced to reflect, it is incredible. 00:04:17.519 --> 00:04:28.800 And if you reflect, like even further back, first shoot room session podcast we had, I talked about how I was disappointed to get rejected from Greg's when I was a younger boy in Liverpool. 00:04:29.040 --> 00:04:38.399 The idea that I would be sat here now as managing director of Oxygen Intelligence, delivering nature restoration at this sort of scale, it's it's unimaginable. 00:04:38.560 --> 00:04:39.519 It's it really is. 00:04:40.000 --> 00:04:44.560 The bit I love is that you've built it like over the last few years. 00:04:44.800 --> 00:04:51.360 Every element, every decision, every data point, every tool technique, all of that that's come together, the people that are in the business. 00:04:51.519 --> 00:05:03.680 You know, when you were in the first five or six people that came in, something like that, and there's 50 of us now, and it's just it's what feels the same about the business and what feels distinctly different. 00:05:05.439 --> 00:05:14.560 There's something about how, like, right today, when everyone's together in Mornocott and everyone's sort of hanging out and walking around this state and chatting, that feels the same. 00:05:14.639 --> 00:05:23.439 It feels like the same people, the same team, the same like shared feeling of like, we're gonna do this crazy thing, we're gonna save the world, we're gonna do something really exciting together. 00:05:23.600 --> 00:05:24.879 That feels the same. 00:05:25.040 --> 00:05:29.920 Um, but then other aspects, you're looking at the scale of what we're doing. 00:05:30.160 --> 00:05:35.920 It's it's so much bigger and faster and more professional uh than when we first started off. 00:05:36.000 --> 00:05:55.040 We've now got teams of people that are delivering everything from like, you know, really complex, comprehensive legal agreements through to on the ground, you know, operational delivery schedules and gant charts and getting machines and stuff delivered on the ground to then a whole entire marketing empire that takes what we do and creates videos and a podcast and a book. 00:05:55.199 --> 00:05:57.920 And it's it's yeah, the scale. 00:05:58.160 --> 00:06:02.000 It feels sometimes it feels the same, and sometimes you step back and look at it. 00:06:02.079 --> 00:06:03.680 It's it's you know, it's completely different. 00:06:04.000 --> 00:06:08.720 Throughout that that growth, there's been moments where you and I have have kind of worried about that, right? 00:06:08.800 --> 00:06:12.720 We we both enjoy the creative chaos, the energy. 00:06:12.879 --> 00:06:20.560 One of our brilliant interns said to me today, OC feels like we're flying in this amazing fighter jet that we're building while it's in the air. 00:06:20.800 --> 00:06:21.680 I was like, I love that. 00:06:22.560 --> 00:06:23.199 Maria. 00:06:24.079 --> 00:06:24.639 Yeah. 00:06:24.879 --> 00:06:26.160 But it's what we want, right? 00:06:26.240 --> 00:06:28.399 We don't want it to become boring and stale. 00:06:28.560 --> 00:06:31.120 Why why is that so important to you that we keep the chaos? 00:06:31.600 --> 00:06:44.079 I think, I mean there is something about like the act of like

problem solving and being creative and finding a solution to something that's really difficult. 00:06:44.319 --> 00:06:48.800 That is inherently really motivating and really exciting. 00:06:48.959 --> 00:06:53.920 Uh, and when you apply that to something that you really care about, it's it's doubly so, you know. 00:06:54.079 --> 00:07:06.959 To be able to look at the big problems we have around us, around climate collapse and biodiversity loss, and to be able to get up day to day and figure out how to solve it, do something different about it, it's it's uh yeah. 00:07:07.519 --> 00:07:08.639 Something very special about that. 00:07:09.680 --> 00:07:10.079 Oh, right. 00:07:10.240 --> 00:07:10.399 Yeah. 00:07:10.720 --> 00:07:17.680 I think that only comes from working with like-minded and equally able and exciting and and very different people. 00:07:17.839 --> 00:07:18.319 Yeah. 00:07:18.800 --> 00:07:24.000 Um your relationship with leadership has changed a lot over the last few years. 00:07:24.399 --> 00:07:30.560 Uh, you famously in our first year of intern rounds when I'm not interested, there's nothing an intern can do to impress me. 00:07:30.720 --> 00:07:31.920 I d I don't remember that. 00:07:32.240 --> 00:07:33.120 L, do you remember that? 00:07:33.759 --> 00:07:35.199 L's off camera remembering that. 00:07:35.360 --> 00:07:37.279 Those of us that were there do remember that. 00:07:37.680 --> 00:07:44.319 And then and then we got James and Esme, and now you have the most incredibly talented team. 00:07:44.720 --> 00:07:47.920 How has your feeling about leading people changed? 00:07:52.160 --> 00:07:59.600 If you're working with people who are brilliant and leading them and managing them, it's like the easiest best thing in the world. 00:08:00.000 --> 00:08:03.600 You know, you're just there to like support them and watch them do great things. 00:08:04.319 --> 00:08:10.319 If you're if it's set up in a really good culture, uh it can be incredibly rewarding. 00:08:10.480 --> 00:08:16.720 I think previous experience I've had with leadership and management have been less less positive, less supportive. 00:08:16.959 --> 00:08:24.480 I think you and George have had the exact same journey that I I realized it when I was uh talking to to both George and Fee earlier. 00:08:24.639 --> 00:08:31.120 Um I should say, depending on the order Penny release these, this is part of a series of announcement videos because we're going to be announcing. 00:08:31.279 --> 00:08:39.600 Obviously, uh we've announced Fee becoming managing director of the conservation part of the business in earlier this year, and also uh you, the second announcement we've made. 00:08:39.679 --> 00:08:44.799 And then there'll also be a podcast coming out announcing George as um the MD of the capital side of the business. 00:08:44.960 --> 00:08:53.919 I think with both you and George, you are such high achievers, so fast-paced, so driven, you weren't given people that could keep up

and help making you better before. 00:08:54.000 --> 00:08:55.919 So actually, it was just frustrating. 00:08:56.080 --> 00:08:57.919 Whereas I was like, hmm, here's a challenge. 00:08:58.000 --> 00:09:00.639 Let's give Chris and George people that they want to work with. 00:09:00.799 --> 00:09:05.279 As soon as that gone in, both of you have just absolutely flown from a leadership perspective. 00:09:05.919 --> 00:09:15.360 Yeah, and I mean it's a testament to the like the role that's been done on the people team, particularly around holding the bar and recruiting people who are brilliant. 00:09:15.600 --> 00:09:17.279 It just makes it lifts everybody. 00:09:17.360 --> 00:09:24.240 When if you just keep breaking bringing in people who are brilliant and surrounding them by people who are brilliant, it just it gets better and better. 00:09:24.720 --> 00:09:30.320 Your first interns are now leads and managers who themselves have interns. 00:09:30.559 --> 00:09:31.919 How does that make you feel? 00:09:32.720 --> 00:09:34.320 It's quite amazing to watch. 00:09:34.399 --> 00:09:49.039 And there's something really there's something really wonderful about seeing them come in and I mean what yesterday Esme was kind of leading a team of people who'd come to see one of our estates with um our intern. 00:09:49.440 --> 00:09:50.639 And it's incredible, right? 00:09:50.720 --> 00:10:02.559 Like a couple of years ago, they were straight into OC, asking loads of questions, trying to figure out what they're doing, and now they are walking people around, people looking up to them as like leading figures in the industry, like they're the ones who've gone out and done it over the last five years. 00:10:02.639 --> 00:10:12.960 They've built this, they've you know built habitat banks, created new nature credits, like planted woodlands, developed carbon credits, and everybody's looking to them to see how that how this could be done. 00:10:13.120 --> 00:10:14.000 How did you do that? 00:10:14.159 --> 00:10:22.480 And that's something like these guys have been their professional careers in a couple of years, they've done more than most people have done in 20, 30 years. 00:10:22.559 --> 00:10:26.080 And those people who've been working for 20, 30 years now looking at them and saying, How did you do it? 00:10:26.159 --> 00:10:27.519 What like it's incredible. 00:10:27.679 --> 00:10:29.600 Like what a yeah. 00:10:30.799 --> 00:10:40.399 I um I feel a bit like the manager of Barcelona when they knew they had like Lionel Messi coming through and Innie Esther and Chabby. 00:10:40.559 --> 00:10:49.039 And I feel like we know we've got this the the the sort of managers here, the James, Esme, Abby, um, Ellie, this year's Ellie Millington. 00:10:49.200 --> 00:10:54.639 Yeah, that that whole that whole band of super penny, that whole band of super talent at that part of the business. 00:10:54.799 --> 00:10:58.559 That the rest of the world think OC's me and you and El and George and Fee. 00:10:58.799 --> 00:11:06.000 And of course it is, but oh my word, they do not know the incredible talent we've got coming for the next five and ten years. 00:11:06.159 --> 00:11:11.600 And because they're now getting interns, we've got the next version of that as well, which is just astonishing. 00:11:11.759 --> 00:11:20.799 And, you know, we all sat there and talked about, again, borrowed from spot, building a culture that could win multiple trophies in multiple years or multiple world records in multiple years. 00:11:21.120 --> 00:11:24.240 And doesn't it just feel like we've got it now in terms of that depth of talent? 00:11:24.480 --> 00:11:29.679 There's some probably hackneyed like comparison to how we're managing landscapes in the same way. 00:11:29.840 --> 00:11:33.840 You see like a beautiful all go throughest, it looks incredible from the top of it. 00:11:33.919 --> 00:11:40.559 But if there's no like regrowth and natural regeneration underneath, it's just slowly dying, and it's not going to be a rainforest there in 50 years. 00:11:40.720 --> 00:11:47.360 Um, a lot of what we're doing in the land is like bringing young trees and establishing them so they kind of grow up through the next generation. 00:11:48.159 --> 00:11:48.399 Sorry. 00:11:49.279 --> 00:11:55.759 Um from the very beginning, data's been really important to you and me personally. 00:11:55.840 --> 00:12:01.120 And from the the birth of Claudia, our sort of first AA platform in terms of acquisition. 00:12:01.360 --> 00:12:04.799 Um can you talk about how important data is to us as a business? 00:12:05.200 --> 00:12:33.120 I mean, I think that was the first the way we first connected was through the project I was doing previously, Nature Capital Laboratory, which was like it basically combined the two things I love most: nature, nature restoration, and like how that could be financed and delivered, and then also technology and data and how you could use those systems and platforms that are coming out in the tech world to make nature restoration more possible, more scalable, uh, more deliverable. 00:12:33.279 --> 00:12:42.320 And so we were doing this like small-scale project across 100 acres, looking at uh it was a rewelding project in Scotland, the Highlands, uh, an incredible landscape. 00:12:42.399 --> 00:12:50.879 And we filled it with drones and tech and sensors, and like the whole ethos of that was like, how do we use this data to make these schemes deliverable in practice and scalable? 00:12:50.960 --> 00:12:54.639 Like, how do we take this from 100 acres to 100,000 acres? 00:12:54.799 --> 00:12:58.320 And then you came along and were like, let's do it over 100,000 acres. 00:12:58.480 --> 00:13:26.159 So, a very from the first day, like a core part of what I was doing here was bringing that right, we need to create a scalable financial model for doing nature restoration, but also a key part of that has got to be using data and tech to make it possible

because you think about how environmental schemes have been delivered for 20, last 20, 30 years, there's a lot of paperwork, a lot of clipboards, there's a lot of stuff that is extremely painstaking and just it's not scalable at 100,000 acres. 00:13:26.240 --> 00:13:31.360 It might be possible on small, you know, pilot scale projects, but it doesn't work at the landscape level. 00:13:31.519 --> 00:13:38.080 And so if we're going to do what we say we're gonna do, which is a score of 250,000 acres of land, there has to be a tech element to it. 00:13:38.240 --> 00:13:40.960 You know, the ha we have to create the solutions that make it work. 00:13:41.279 --> 00:13:47.440 And um, someone else who's played a massive role in this, can you talk a little bit about the impact Lara's had on the business? 00:13:47.840 --> 00:13:52.000 I mean, she was there before me, and she was just Yeah. 00:13:52.240 --> 00:13:56.559 Lara, she's understated, she's absolutely brilliant. 00:13:56.879 --> 00:14:04.399 Since like the first day, she's been collecting all the data that OC's ever had into this increasingly growing and sophisticated platform. 00:14:04.559 --> 00:14:15.360 And you've seen it and her develop from collecting data, creating maps, and spatially representing what we've got to what, and that was essentially what Laura's role is. 00:14:15.519 --> 00:14:29.360 Now she is at the forefront of developing and managing these incredible pieces of AI-led data software platforms, which are pulling together this data that's built up over five years to provide these tools which cut across each aspect of the business. 00:14:29.679 --> 00:14:37.120 It's just incredible to see the work she's done and built over those few years and what we can do now compared to what we started from. 00:14:37.440 --> 00:14:47.679 The ability for us to put drones in the air and turn that into meaningful data from intelligent acquisitions to intelligent estates all the way through to the MRV and intelligent carbon side of what we're doing. 00:14:47.919 --> 00:14:52.320 And prior to this year, we were really we were almost in stealth mode about the tech side, right? 00:14:52.480 --> 00:14:59.679 We talked about the impact, the inventure, the environment side of it, very vocal about that, but we're obviously very protective and discreet about the tech side of it. 00:14:59.840 --> 00:15:04.720 But this year, you've had the opportunity, you and Lara, to showcase it to some of the leading companies in the world. 00:15:04.879 --> 00:15:06.639 What's that feedback been like? 00:15:07.120 --> 00:15:11.440 I mean, it's been overwhelmingly positive and excited. 00:15:11.679 --> 00:15:12.879 You almost swore then, didn't you? 00:15:13.039 --> 00:15:13.360 Yeah. 00:15:15.919 --> 00:15:23.440 It's surprising when you see, you know, because you kind of just when you're in the middle of a bubble, you're kind of working on stuff and you think it's freaking exciting. 00:15:23.759 --> 00:15:33.519 When you

then speak to people externally and they look at and say, wow, this is quite, you know, everybody is surprised and shocked at how much we've built and what we've done in the last few years. 00:15:33.679 --> 00:15:37.360 I think one of the key differences that that we've been doing. 00:15:37.519 --> 00:15:43.759 So Laura's her work has always been very focused on what does oxygen conservation need to scale and build? 00:15:43.840 --> 00:15:47.440 Like what are the tools and tech and platforms that are needed for the business? 00:15:48.159 --> 00:15:49.919 My favorite example being the gate code. 00:15:50.080 --> 00:15:50.320 Yeah. 00:15:50.480 --> 00:15:53.279 So you we have the hundreds of gates across the estate. 00:15:53.440 --> 00:15:55.840 Finding the right code at the right time, absolute nightmare. 00:15:56.000 --> 00:15:57.679 Lara goes, not a problem, leave that to me. 00:15:57.840 --> 00:16:02.399 Build an app, we can onboard a phone, you walk up to a gate, it recognizes you and gives you a gate code. 00:16:02.559 --> 00:16:06.159 What seems like a simple problem, nightmares that hit estates across the country. 00:16:06.240 --> 00:16:06.879 I'll solve that. 00:16:06.960 --> 00:16:07.279 Don't worry. 00:16:07.360 --> 00:16:07.840 Thank you, Lara. 00:16:08.000 --> 00:16:08.639 We appreciate you. 00:16:08.799 --> 00:16:10.159 And it's amazing, right? 00:16:10.240 --> 00:16:14.240 Because you would never start up a tech company that focuses on gate codes. 00:16:14.320 --> 00:16:16.240 Like it doesn't make any sense. 00:16:16.480 --> 00:16:18.240 But then there are a lot of kind of email. 00:16:18.559 --> 00:16:22.240 Can someone check if there's a company that an AI could be the type of company? 00:16:22.480 --> 00:16:23.120 Apologies. 00:16:23.919 --> 00:16:31.519 I think one of the big differences is that what Laura's done the last few years is solve problems day to day for the rest of the team. 00:16:31.759 --> 00:16:42.799 Uh, and what we've got now is this really rich and detailed kind of set of tools and platforms and processes that are built on real-life data linked to real-world problems. 00:16:42.879 --> 00:16:46.480 Uh, and they were never intended to be a scalable tech platform. 00:16:46.639 --> 00:16:49.679 They were tend to intended to solve real-world problems. 00:16:49.840 --> 00:17:01.600 But over five years, when you look at what's been built, we have got the most sophisticated tech platform for designing, delivering, executing natural capital projects because it's we've needed to build out over the last five years. 00:17:01.759 --> 00:17:08.000 And it's only now that we're starting to talk about it as an actual sort of product or service rather than just something we use in-house. 00:17:08.400 --> 00:17:14.400 We um you were doing a demonstration to one of the leading strategy consultants in the world. 00:17:14.640 --> 00:17:18.640 I was across with the team looking at potential new acquisitions in Spain and Portugal. 00:17:18.799 --> 00:17:21.599 And he literally sent me a message saying, I'm with your team. 00:17:21.759 --> 00:17:23.440 This is an entire other business. 00:17:23.519 --> 00:17:25.599 You could raise venture capital on this tomorrow. 00:17:25.759 --> 00:17:26.640 It's incredible. 00:17:26.880 --> 00:17:29.119 Brackets, this isn't hyperbole. 00:17:29.279 --> 00:17:32.160 I mean, that's an astonishing response to the demo. 00:17:32.400 --> 00:17:34.160 Yeah, it's been it's been amazing. 00:17:34.319 --> 00:17:44.799 I think one of the things that's really sort of landed well with people uh at Climate Action Week this year, and again, we weren't launching a tech product or tech software platform. 00:17:44.880 --> 00:17:51.359 We were doing a presentation about quality and trust and how do you build carbon credits that are actually measurable and verifiable. 00:17:51.759 --> 00:17:53.440 Integrity and like how is it real? 00:17:53.599 --> 00:17:54.559 How do you achieve integrity? 00:17:54.880 --> 00:17:56.640 Yeah, and that's that's what the presentation was out. 00:17:56.720 --> 00:17:57.759 That's what we're talking about. 00:17:58.000 --> 00:18:33.759 We're talking about how we develop this really super detailed LIDAR system and then a series of machine learning um models and neural networks that can take high-quality LIDAR information and use it to build individual 3D trees of every single tree we own, and then measure that almost in real time and look at how trees are growing and quantify super accurately carbon and bring that together into like a visual, tangible thing, because a big disconnect previously is what would happen in carbon markets is people would buy a credit on a registry and not really know whether that was linked to a real scheme, even or a real tree, or what was actually happening. 00:18:33.920 --> 00:18:40.960 So being able to actually link you've got a credit, here is the tree that's providing it, and you can see that credit being developed. 00:18:41.119 --> 00:18:44.160 I think was a you know that was a key thing that we were trying to build. 00:18:44.319 --> 00:19:09.119 And sort of almost like an offshoot of that is people are now looking at it and this software platform that we've built, you know, this could be used as a verification tool across not just our schemes, but any anyone who's doing carbon credit schemes, we can now provide this as a piece of software that allows you to actually measure carbon much more accurately at a cheaper price and bring forward the cost the time scales for verification. 00:19:09.279 --> 00:19:15.119 So what previously may have been done every 10 years, we can now do every six months, every six days, even if we needed to. 00:19:15.839 --> 00:19:16.640 It's astonishing. 00:19:17.039 --> 00:19:20.000 What's your ambition for the tech side of the? 00:19:20.640 --> 00:19:29.680 I think the big the big step, and we've just been talking about how everything we've done so far has been to

develop the tools and processes needed for oxygen conservation. 00:19:30.000 --> 00:19:34.400 And oxygen conservation is scaling and growing, you know, really quite really fast. 00:19:34.480 --> 00:19:35.519 It's really exciting. 00:19:35.759 --> 00:19:41.519 But ultimately, 250,000 acres of land, that's a tiny slither of what's happening globally. 00:19:41.759 --> 00:19:46.720 What the tech platform offers is the potential to do this at unimaginably bigger scale. 00:19:46.799 --> 00:19:50.640 So taking what we've done over 250,000 acres and scaling it globally. 00:19:50.880 --> 00:20:09.920 So if we can help other organizations to find land, execute and deliver natural capital projects and deliver super high quality premium credits, then that's gonna facilitate not just the billions of um pounds that we're putting into nature restoration, but billions of pounds for other people to do it and then billions of pounds for others to do it. 00:20:10.000 --> 00:20:11.359 And it's just it compounds. 00:20:11.440 --> 00:20:17.839 And that's how we're gonna build and grow a sector and expand from what we're doing, you know, with our portfolio to portfolios across the world. 00:20:18.079 --> 00:20:22.640 Um, so we're focused on the tech side, the natural capital part of your role. 00:20:22.799 --> 00:20:31.519 Um, you are amongst a small handful of people that literally conceived the potential of a natural capital market from your work in Australia. 00:20:31.839 --> 00:20:36.319 Can you remind people a little bit about that, about how your um work on natural capital began? 00:20:36.799 --> 00:20:47.920 Uh so I did a master's degree in environmental economics and then spent time working in Australia with the government and university there on designing markets for nature. 00:20:48.079 --> 00:21:18.240 So they were looking at how do you how do you create a water market which allows you to um allocate water rights and um restore environmental flows, how do you allocate money better for conservation finance, how do you deliver more with money that's available, and looking at I think for me that the big kind of moment I had in my sort of early career was sort of figuring because I was terrible at economics, hated it, um, found it really boring, but was pretty good at maths and like none wasn't that side of it. 00:21:18.400 --> 00:21:30.559 Um I had this moment of like uh it was an early professor who was talking about phishing and how you if every time it's it's in my contract. 00:21:30.640 --> 00:21:33.359 I have to bring up fishing at every possible opportunity. 00:21:33.759 --> 00:21:47.759 Um but with like basic simple economic modeling, if you apply that to like environmental resource allocation problems like phishing rights and phishing access, you can actually save whole species and stop them from being you know collapsed and um fished to death. 00:21:47.920 --> 00:21:58.720 I had just this like aha moment of actually economics and market systems, they're the most powerful and influential tool that humans have created for like how we allocate resources and make decisions. 00:21:58.880 --> 00:21:59.759 If we're not using them. 00:22:00.160 --> 00:22:03.119 For positive and like applying them to environmental challenges. 00:22:03.839 --> 00:22:06.880 We're never going to solve anything, we're just always going to be skirting around the solution. 00:22:07.039 --> 00:22:08.799 And then, I mean, that was the moment. 00:22:08.880 --> 00:22:13.599 Then that was kind of like the whole you know map out of my career was sort of set at that point. 00:22:13.680 --> 00:22:19.039 It was like, right, let's create you know markets that can allocate finance to restore nature. 00:22:19.759 --> 00:22:30.400 So you've taken that concept and brought it to life through what is one of, if not the only, fully vertically integrated natural capital model in the world. 00:22:30.640 --> 00:22:38.480 How important is that ownership of the land and the real asset to your vision of creating the best natural capital products possible? 00:22:38.960 --> 00:22:49.599 I mean, and again, it a lot of it comes back to one of those foundational principles of when we were talking about how would you do a good carbon credit and looking at what was happening in the market in 2002, 2003. 00:22:49.920 --> 00:22:57.680 If you don't own the land, if you can't go there, see it in person, see it for yourself and for people who are buying the credits, they can come and see it too. 00:22:58.079 --> 00:23:02.000 It's very difficult to actually know what's happening and to be really clear about that. 00:23:02.240 --> 00:23:11.359 And the fact that we can purchase the land, visit the estates year on year, collect the data, pull that data together, measure it, report it, bring people in there. 00:23:11.599 --> 00:23:16.319 It's completely transformative in terms of the ability to actually manage what's happening. 00:23:16.480 --> 00:23:25.200 If there's a problem on the estate, say there's a wildfire, we can be there, you know, we can detect the issue in minutes and like send people out there to resolve it. 00:23:25.440 --> 00:23:32.720 Everything happens much faster and much more care and precision because we're able to actually own and control what's happening. 00:23:32.880 --> 00:23:43.279 And it's just completely different to being in a position where you're relying on third parties, sometimes other end of a country or other end of the world, to deliver a scheme that you kind of you don't actually see day-to-day. 00:23:43.920 --> 00:23:53.200 So despite the, you know, you mentioned maths and data and the sort of base principles you're building from, the last couple of years, all that's been disrupted by AI. 00:23:53.519 --> 00:23:56.960 How much is AI integrated into your day-to-day work? 00:23:57.839 --> 00:24:05.039 I've got a long message thread back and forth with Phil, our wonderful um data science advisor, uh at Oxy Intelligence.

00:24:05.279 --> 00:24:15.119 He's just going, because he's a PhD physicist, brilliant mathematician, he's just bemoaning the idea that maths is kind of you know, it's being solved now by AI. 00:24:15.200 --> 00:24:21.039 And what was previously seen as like impossible theorems are now being solved in a couple of hours by the latest models. 00:24:21.279 --> 00:24:24.319 Like how far and how fast things have changed. 00:24:24.559 --> 00:24:31.119 It's it I mean, it's some it's overwhelming, you know, in the last six months how much has happened. 00:24:31.279 --> 00:24:34.559 And when you extrapolate that forward, it's gonna change everything. 00:24:34.640 --> 00:24:38.720 The way we work, the way everything is done is gonna change forever now. 00:24:38.880 --> 00:24:42.160 Um, and I mean it's it's both terrifying and wonderfully exciting. 00:24:42.880 --> 00:24:43.759 Are you scared yet? 00:24:43.920 --> 00:24:55.440 Yeah, I mean, I think I've got a two-year-old son, and he goes to a nursery where one of the things they do as two-year-olds is use a large language model to program robotic minds so that they can interact with them. 00:24:55.599 --> 00:24:59.039 It's it that wouldn't have happened in my nursery. 00:24:59.599 --> 00:25:01.920 No, like eating crayons. 00:25:02.160 --> 00:25:10.720 Yeah, the the world he the world he grows up in is gonna be fundamentally different to the world we know now. 00:25:11.119 --> 00:25:14.799 We're on the cusp of this technology that's gonna change everything. 00:25:14.960 --> 00:25:18.799 Um so few people realise. 00:25:19.279 --> 00:25:20.400 I think that's the reality. 00:25:20.480 --> 00:25:29.440 Like, you can get the right corner of X or LinkedIn and you and YouTube, and you're like, okay, everyone sees this, and it's like I got I've had a couple of scared moments. 00:25:29.519 --> 00:25:30.559 We were in London one morning. 00:25:30.640 --> 00:25:40.720 I remember messaging you going, the first time I used an agent prompt on GTP and it took over, and I saw it moving around my screen, and I was like, okay, this has changed. 00:25:41.039 --> 00:25:51.359 Another one was yesterday when there was so we were recording this the day after some senior researchers at Anthropic and Open Air Um resigned with scathing letters going, This is fucking scary, people. 00:25:51.519 --> 00:25:53.839 This can do whatever it wants and it's taking over. 00:25:53.920 --> 00:26:00.240 And it's like, Yeah, I mean the hogging face thing, the more you read about that, the more it just I I haven't dove into it. 00:26:00.400 --> 00:26:03.359 It's it it's it's like fascinating and terrifying. 00:26:03.759 --> 00:26:08.960 But didn't they set it up and say no parameters, just keep going until you fuck some stuff up? 00:26:09.279 --> 00:26:17.279 No, that's they they did what everyone in AI is doing at the moment, which is creating goal-seeking agents where you give them a goal and say, do this, and it does it. 00:26:17.359 --> 00:26:26.319 But if you accidentally give something an impossible goal, it will literally do anything to do that goal, including like lie, cheat, create new forms of communication. 00:26:26.480 --> 00:26:28.960 Like Chris, are you describing an AI or you? 00:26:31.359 --> 00:26:32.000 Yeah. 00:26:36.480 --> 00:26:42.960 In in a world whereby AI is influencing things, there's also the amount of autonomation and robotics and things. 00:26:43.039 --> 00:26:48.079 It just I can't help but think about the incredible work that Doris and Chloe do on the drone side of things. 00:26:48.240 --> 00:26:51.039 Can you talk about the importance of data collection of that type? 00:26:51.440 --> 00:26:53.200 That's another huge change in the sector. 00:26:53.440 --> 00:26:58.400 You think about we own some large landscape, say 15,000 acres. 00:26:58.559 --> 00:27:06.559 You think about someone going there and walking across the entire landscape, measuring every single tree, marking it on a map, on a paper map. 00:27:07.200 --> 00:27:08.640 It takes literally years. 00:27:08.960 --> 00:27:21.039 What Doris and Chloe can do is do that in a matter of days and then take that information, process it, make it visually accessible to anybody who wants to use it and run the algorithms to accurately quantify the control tree. 00:27:26.000 --> 00:27:28.559 I mean, Doris has revolutionized the way we do that. 00:27:28.799 --> 00:27:34.640 And what Doris, what was incredible about Doris's approach was she came in and said, everything you're doing is rubbish. 00:27:34.720 --> 00:27:36.880 You need to do it this, like up your game, do it. 00:27:36.960 --> 00:27:40.319 This is the best possible data collection you could do if you want to. 00:27:40.400 --> 00:27:42.880 If you want to do the best carbon credits in the world, this is how you should do it. 00:27:43.039 --> 00:27:45.440 And we looked at it and we're like, this will take months. 00:27:45.519 --> 00:27:47.119 It's gonna be extremely expensive. 00:27:47.200 --> 00:27:49.279 It'll take, it's not doable at scale. 00:27:49.519 --> 00:28:02.160 And then, you know, month after month, with like little tiny tweaks and adjustments and iterations, she's developing systems and processes and the technology gets better and the time and the accuracy, the time decreases and the accuracy increases. 00:28:02.319 --> 00:28:06.079 And what she's able to do now is like months faster than what she previously did. 00:28:06.319 --> 00:28:12.319 And you can see we're on this curve why where each iteration gets faster and better and faster and better. 00:28:12.480 --> 00:28:13.920 And yeah, it's it's incredible. 00:28:14.079 --> 00:28:23.759 We're almost getting to this point where we can run, you know, near instantaneous data collection over large areas, translate that to the cloud and get the results in yeah, record time. 00:28:24.079 --> 00:28:40.880 And the the amount of stacked, vertically integrated, stacked in innovations you need on

that go from the the rules and the regulations about flying the drones, where you can do it, how you can do it, accessing the site in the best way, the power and the energy solutions that need, having to optimize everything to make that possible. 00:28:41.039 --> 00:28:46.559 And I just think that was one of the big steps Doris made, as you say, she came in and said, no, no, no, we can do this in another way. 00:28:46.720 --> 00:28:49.279 And I love that about the intelligence part of the business. 00:28:49.359 --> 00:28:56.000 We're always bringing in a new skill set that doesn't just, yes, we want marginal gains, but we're finding step changes still, which is super exciting. 00:28:56.240 --> 00:28:56.559 Yeah. 00:28:56.799 --> 00:29:01.039 And like what's really exciting is the team is really small. 00:29:01.279 --> 00:29:05.519 What we've done is just the tiny slither of the first bit of what we're gonna do. 00:29:05.680 --> 00:29:09.920 Like, this is just the beginning, and you can kind of see these exponential curves ahead of us everywhere. 00:29:10.160 --> 00:29:17.440 Intelligence is our the tech side of what you do, especially is like a startup within us at the minute, and it's just so ready and poised to go. 00:29:17.759 --> 00:29:22.000 Understanding, measuring, and monitoring these landscapes is obviously incredibly challenging. 00:29:22.319 --> 00:29:29.200 But how difficult is securing the permission, the approvals, and actually delivering the change we need to see on the ground? 00:29:29.519 --> 00:29:35.200 Yeah, I mean, that's like that's basically the most difficult thing we we do as a business, especially when we first get an estate. 00:29:35.680 --> 00:29:41.440 And the best thing we can do to make that easier is to do the work ahead of acquiring estate. 00:29:41.599 --> 00:29:58.240 And so a big part of what the intelligence team has done is to build this model for analyzing data and looking at landscapes and bringing in AI and data and giving very detailed um overviews and understanding of estates before we even get there. 00:29:58.480 --> 00:30:04.240 And that feeds into the work that James does in our team, who leads on all the site acquisition work. 00:30:04.720 --> 00:30:25.039 What used to be a long process of pulling, again, going out to estates and pulling together data and like reading PDFs and pulling all together, he can now automate and process and give us instantaneous views of each estate as they come to market, and we can get a really comprehensive and detailed understanding of what the opportunities could be and whether a site actually does work for natural capital. 00:30:25.200 --> 00:30:28.000 There's some places what we want to do are not going to be appropriate. 00:30:28.160 --> 00:30:31.359 Maybe it's productive farming and we don't want to take it out of food production. 00:30:31.599 --> 00:30:38.720 Maybe there are specific constraints around the peat depth or the birds or whatever archaeology that make

natural capital not appropriate. 00:30:38.880 --> 00:30:42.640 So we can find those estates and screen them in or out early on. 00:30:42.799 --> 00:30:52.240 And by the time we get there, we've got a really good understanding, a really good baseline, and we know what sort of scheme is going to work and how to be able to deliver that to the um requirements that we need. 00:30:52.559 --> 00:30:57.279 You you mentioned James on the way past, um, your first employee here. 00:30:57.680 --> 00:31:02.240 James is one of those people who he's got like the best attitude I've met of anyone. 00:31:02.319 --> 00:31:03.279 It's like a superpower. 00:31:03.440 --> 00:31:14.799 He's just every day he picks something up, he learns, he develops, and he's just more than anyone, I think, over the last five years, he's he's just grown with the business completely. 00:31:14.960 --> 00:31:18.160 His whole career has been here and he's been a part of everything we've done. 00:31:18.319 --> 00:31:23.279 And and we were talking about the site acquisition thing, which I think is a really nice example of what he's been doing. 00:31:23.359 --> 00:31:27.519 He started off um feeding into what other people were doing, and now he runs the whole process. 00:31:28.000 --> 00:31:32.000 He started off finding me and Dan coffee shop where we would go at badly. 00:31:32.160 --> 00:31:34.240 Badly, he's way better at finding a state. 00:31:34.480 --> 00:31:40.720 Um he is simply one of the best human beings in this business, and um we adore you, James. 00:31:41.440 --> 00:31:44.319 Um where are we going next, Chris? 00:31:45.680 --> 00:31:57.599 I think the the single thing that we need to do next as a business is to prove or to take all the stuff that we've done and take it elsewhere and make it scalable and usable by other people. 00:31:57.839 --> 00:32:06.720 So, what we're planning to do next is to start actually testing some of the monitoring, reporting, verification models we've developed on a real life project. 00:32:06.880 --> 00:32:28.319 So partnering with Isometric, what we want to be able to do is use this advanced LIDAR um detection system to provide the most accurate estimate of carbon stocks at our new woodland creation project in Cornwall, and then use that to try and secure finally a triple-A rated credit and show what the best quality carbon credit could actually be. 00:32:28.480 --> 00:32:43.279 Uh, and once we've done that, looking at how we can then become a verified provider of that service so that anybody who's doing carbon projects across the UK or even globally is able to use those tools and techniques and be able to really improve the quality and deliverability of carbon projects. 00:32:43.519 --> 00:32:48.960 I'd be a miss if I didn't ask you to reflect a little upon your fellow managing directors. 00:32:49.279 --> 00:32:53.680 What do you value the most about working with Fee and George? 00:32:54.079 --> 00:32:58.559 I mean, they compensate for basically all the flaws I have as a human. 00:33:00.720 --> 00:33:01.839 Yeah, that's fair. 00:33:02.480 --> 00:33:04.160 George is gonna keep me out of prison. 00:33:04.720 --> 00:33:05.759 Thank you, George, appreciate that. 00:33:05.920 --> 00:33:12.000 And Fee's gonna actually make sure that all the stuff I just sit and talk and hypothesize about actually gets delivered in practice. 00:33:12.319 --> 00:33:21.599 Um both of them are so so intelligent, so dedicated, and so professional and just generally nice and wonderful people. 00:33:22.000 --> 00:33:24.400 I like the business wouldn't work without them. 00:33:24.559 --> 00:33:34.000 They are the people who like do things, make sure it gets done day in, day out, and they they they they deliver and run the business, essentially. 00:33:34.160 --> 00:33:43.359 And all I do is sort of sit in a chair with you and talk about crazy things like drone technology and AI and robotic walls, and yeah, unfortunately, sometimes cricket. 00:33:43.920 --> 00:33:53.519 I don't get imposter syndrome except where it comes to getting to work with you and George and Fee, um, which just means the world to me. 00:33:53.759 --> 00:33:54.640 Thank you very much. 00:33:54.960 --> 00:33:59.920 Bill, like it was almost worth getting rejected at Greg's for this moment. 00:34:00.559 --> 00:34:02.000 Round of applause for Greg.