1 00:00:00,520 --> 00:00:02,200 Speaker 1: Your child is going to go to school and be 2 00:00:02,240 --> 00:00:04,720 Speaker 1: like Daddy's bringing back the wooly mammoth, and. 3 00:00:04,760 --> 00:00:07,560 Speaker 2: Like the teachers are going to be like what's happening 4 00:00:09,039 --> 00:00:09,360 Speaker 2: of them. 5 00:00:09,480 --> 00:00:13,760 Speaker 1: It's amazing when we talk about rapid advancements of technology, 6 00:00:14,040 --> 00:00:16,640 Speaker 1: like where we are today and what we're capable of 7 00:00:16,720 --> 00:00:19,759 Speaker 1: and where we could go? How big do we want 8 00:00:19,760 --> 00:00:24,560 Speaker 1: to think? For Ben Lamb and his company Colossal, the 9 00:00:24,560 --> 00:00:28,760 Speaker 1: weight of innovation is in the tons. That's because Colossal 10 00:00:28,880 --> 00:00:31,000 Speaker 1: is working right now to bring. 11 00:00:30,840 --> 00:00:31,920 Speaker 2: Back the wooly mammoth. 12 00:00:32,040 --> 00:00:35,440 Speaker 1: Yes, that's right, the six ton, prehistoric relative of the 13 00:00:35,479 --> 00:00:40,520 Speaker 1: elephant that went extinct ten thousand years ago. It's all 14 00:00:40,560 --> 00:00:43,360 Speaker 1: part of his de extinction project that dares to bring 15 00:00:43,400 --> 00:00:47,519 Speaker 1: back species like the dire wolf, the dodo, the wooly mammoth, 16 00:00:47,680 --> 00:00:51,240 Speaker 1: and the blue buck. The blue buck is Colossal's newest target. 17 00:00:51,479 --> 00:00:54,960 Speaker 1: It's an extinct species of antelope with a beautiful blue coat. 18 00:00:55,280 --> 00:00:58,280 Speaker 1: They lived in South Africa until around the eighteen hundreds. 19 00:00:58,520 --> 00:01:02,720 Speaker 1: But all this isn't just for scientific spectacle. Lamb believes 20 00:01:02,800 --> 00:01:06,400 Speaker 1: these species could be essential to solving our planet's biggest 21 00:01:06,400 --> 00:01:07,679 Speaker 1: conservation challenges. 22 00:01:08,680 --> 00:01:11,760 Speaker 2: So how exactly is he going to accomplish this life. 23 00:01:13,240 --> 00:01:15,039 Speaker 3: Finds a winning In. 24 00:01:14,959 --> 00:01:17,680 Speaker 1: This conversation, we're going to dig into the technology and 25 00:01:17,760 --> 00:01:21,039 Speaker 1: what Lamb sees as the benefits to our ecosystem. We're 26 00:01:21,080 --> 00:01:23,119 Speaker 1: also going to talk about how such a project could 27 00:01:23,160 --> 00:01:27,720 Speaker 1: reinvigorate national moonshots scientific goals, and we're going to answer 28 00:01:27,720 --> 00:01:31,600 Speaker 1: the question I'm sure you're all thinking, so is this 29 00:01:31,720 --> 00:01:35,640 Speaker 1: Jurassic Park in real life? I'm Larie Siegel, and you're 30 00:01:35,680 --> 00:01:41,320 Speaker 1: listening to Mostly Human, a tech podcast through a human lens. Well, 31 00:01:41,360 --> 00:01:43,600 Speaker 1: thank you for being here, and I'm so excited for 32 00:01:43,640 --> 00:01:46,720 Speaker 1: this conversation because what in extraordinary time to be talking 33 00:01:46,760 --> 00:01:47,280 Speaker 1: about this. 34 00:01:48,040 --> 00:01:49,680 Speaker 2: And I was looking at your background, which is interesting. 35 00:01:49,720 --> 00:01:54,080 Speaker 1: You're a serial entrepreneur gaming artificial intelligence, and now you 36 00:01:54,160 --> 00:01:57,480 Speaker 1: are in the de extinction genetic conservation business. 37 00:01:57,480 --> 00:01:59,520 Speaker 2: So that's the next billion dollar opportunity. 38 00:01:59,080 --> 00:02:01,680 Speaker 4: Ye think? Yeah? So I've I've always been very very 39 00:02:01,720 --> 00:02:03,640 Speaker 4: lucky to have great teams and people helped me build 40 00:02:03,680 --> 00:02:06,000 Speaker 4: some pretty cool companies over the years. But I only 41 00:02:06,040 --> 00:02:08,360 Speaker 4: I've ever been as in love as I am with 42 00:02:08,480 --> 00:02:12,519 Speaker 4: this company, right because it's such a The ramifications of 43 00:02:12,600 --> 00:02:16,160 Speaker 4: our success are you know, truly, you know, I think 44 00:02:16,360 --> 00:02:18,639 Speaker 4: you know, I think not to quote unquote change the world, 45 00:02:18,639 --> 00:02:21,280 Speaker 4: but I think it also will really inspire like the 46 00:02:21,320 --> 00:02:24,040 Speaker 4: next generation for science. And I think we have no 47 00:02:24,160 --> 00:02:27,680 Speaker 4: idea what that halo effect could really be, and so 48 00:02:28,160 --> 00:02:30,800 Speaker 4: it's something that's pretty amazing to think about as we 49 00:02:30,919 --> 00:02:32,440 Speaker 4: kind of like started this journey. 50 00:02:32,680 --> 00:02:35,080 Speaker 1: So take me to the moment that you decided. So 51 00:02:35,240 --> 00:02:39,079 Speaker 1: you spent your career. You are from Austin, Texas, you 52 00:02:39,120 --> 00:02:41,520 Speaker 1: didn't grow up in the Bay Area. You've done AI, 53 00:02:41,680 --> 00:02:45,239 Speaker 1: you've done gaming, Like, it's just not overnight that someone 54 00:02:45,360 --> 00:02:48,040 Speaker 1: is like, well, I'm going to try to do de 55 00:02:48,240 --> 00:02:51,080 Speaker 1: extinction and bring back animals that are far gone and 56 00:02:51,120 --> 00:02:53,000 Speaker 1: actually have a different conversation about science. 57 00:02:53,040 --> 00:02:55,359 Speaker 2: So like, go back to it. 58 00:02:55,480 --> 00:02:56,919 Speaker 3: Well, it's really George Church's fault. 59 00:02:56,960 --> 00:03:00,400 Speaker 4: Like, so if people love it, it's his, they should 60 00:03:00,400 --> 00:03:02,320 Speaker 4: give him the credit. They hate it, they should say 61 00:03:02,320 --> 00:03:04,639 Speaker 4: it's kind of his fault. I met George Church and 62 00:03:04,680 --> 00:03:07,639 Speaker 4: I was just massively inspired. Right, George is awesome. He's funny, 63 00:03:07,639 --> 00:03:10,239 Speaker 4: He's six foot seven, he looks like Santa Claus, a 64 00:03:10,320 --> 00:03:11,240 Speaker 4: much skinnier version. 65 00:03:11,320 --> 00:03:13,120 Speaker 1: And tell us, who for those who don't know who 66 00:03:13,120 --> 00:03:15,800 Speaker 1: George Church is, George tell us about that first meeting. 67 00:03:15,600 --> 00:03:19,639 Speaker 4: So George is arguably the father of synthetic biology. He's 68 00:03:19,680 --> 00:03:22,800 Speaker 4: the head of genetics at Harvard. He was some of 69 00:03:22,800 --> 00:03:27,760 Speaker 4: the initial read write technologies for genome engineering. Was all George, 70 00:03:28,160 --> 00:03:30,680 Speaker 4: and he's amazing and he really thinks outside of the box. 71 00:03:30,720 --> 00:03:33,760 Speaker 4: He also doesn't believe in using the word impossible. And 72 00:03:33,840 --> 00:03:36,960 Speaker 4: so I got introduced to George because I was running 73 00:03:37,000 --> 00:03:41,120 Speaker 4: a satellite software and defense business leveraging AI and I 74 00:03:41,120 --> 00:03:43,840 Speaker 4: got introduced to George and they're like, Oh, synthetic biology 75 00:03:43,920 --> 00:03:46,360 Speaker 4: is the future. You should talk to this guy who's 76 00:03:46,440 --> 00:03:48,440 Speaker 4: like the leader in it. So I reached out to George. 77 00:03:48,680 --> 00:03:50,600 Speaker 4: He answered all my questions in like three minutes because 78 00:03:50,600 --> 00:03:52,600 Speaker 4: he's a genius. And then after that, I was like, 79 00:03:53,240 --> 00:03:55,760 Speaker 4: what else you're working on? What are you excited about? 80 00:03:55,800 --> 00:03:57,800 Speaker 4: He started listing because he's got one hundred people in 81 00:03:57,840 --> 00:03:58,680 Speaker 4: his lab at Harvard. 82 00:03:58,840 --> 00:04:00,960 Speaker 3: He was just walking through all the these crazy projects. 83 00:04:01,720 --> 00:04:03,720 Speaker 4: So they're doing like, like one of the projects at 84 00:04:03,760 --> 00:04:06,480 Speaker 4: the time he was working on was neural regeneration. So 85 00:04:06,800 --> 00:04:09,600 Speaker 4: how do you look at taking it and swapping out 86 00:04:09,600 --> 00:04:11,880 Speaker 4: the nucleus of a neuron? 87 00:04:12,120 --> 00:04:14,280 Speaker 3: So because your neural net would already be built. 88 00:04:14,080 --> 00:04:15,920 Speaker 4: And if you could do that at scale, you already 89 00:04:15,960 --> 00:04:18,400 Speaker 4: had the infrastructure already built, and then you could kind 90 00:04:18,400 --> 00:04:19,480 Speaker 4: of regenerate neurons. 91 00:04:19,960 --> 00:04:21,840 Speaker 3: So anyway, he's working on a lot these cool projects. 92 00:04:21,920 --> 00:04:23,280 Speaker 4: And then towards the end of the call, I was 93 00:04:23,320 --> 00:04:25,520 Speaker 4: smart enough to say, well, if you had one of 94 00:04:25,560 --> 00:04:27,600 Speaker 4: these projects to work on for the rest of your 95 00:04:27,600 --> 00:04:29,760 Speaker 4: life and unlimited capital, what would you do? 96 00:04:30,080 --> 00:04:31,839 Speaker 3: And it was like a mic drop moment. 97 00:04:31,880 --> 00:04:33,920 Speaker 4: He's like, I'd work to bring back the wooly manmouth, 98 00:04:33,920 --> 00:04:38,160 Speaker 4: apply those technologies to conservation, apply those technologies to human healthcare, 99 00:04:38,360 --> 00:04:40,840 Speaker 4: and had to rewild the animals to help the ecosystems. 100 00:04:41,120 --> 00:04:42,600 Speaker 4: And then and then the call was over and he's like, 101 00:04:42,600 --> 00:04:43,960 Speaker 4: all right, I have to go to my next call. 102 00:04:44,040 --> 00:04:45,960 Speaker 4: I was like wait what And I was like, I 103 00:04:46,000 --> 00:04:47,800 Speaker 4: was like, wait, you're joking. This is like you we 104 00:04:47,839 --> 00:04:49,919 Speaker 4: are you kidding? And he was just incredible and you 105 00:04:49,920 --> 00:04:53,440 Speaker 4: could hear this like like childlike wonder and inflection in 106 00:04:53,480 --> 00:04:57,720 Speaker 4: his voice, and it became abundantly clear, like after like 107 00:04:57,800 --> 00:05:00,200 Speaker 4: listening to like ten podcasts and a few interviews, that 108 00:05:00,320 --> 00:05:02,440 Speaker 4: this is like actually what he wants to do with 109 00:05:02,520 --> 00:05:04,680 Speaker 4: his life. So I called him back the next day 110 00:05:04,760 --> 00:05:06,960 Speaker 4: and I was like can I come see you? Next week, 111 00:05:07,120 --> 00:05:09,560 Speaker 4: I was on a plane to his labbit at Harvard. 112 00:05:09,880 --> 00:05:13,200 Speaker 4: We sat down for three hours and we decided that 113 00:05:13,240 --> 00:05:16,279 Speaker 4: we were a pretty good culture fit with each other. 114 00:05:16,360 --> 00:05:18,640 Speaker 3: And I was like, all right, let's go do this. 115 00:05:19,000 --> 00:05:22,240 Speaker 4: And a week later, you know, we started building out 116 00:05:22,240 --> 00:05:23,480 Speaker 4: the plan of how we go to it. 117 00:05:23,760 --> 00:05:26,360 Speaker 1: And when we say go do it, do you really 118 00:05:26,400 --> 00:05:28,080 Speaker 1: think you can bring back the wooly Mammo? 119 00:05:28,279 --> 00:05:30,800 Speaker 3: Yeah? I mean after you know, I'm a trust but 120 00:05:30,920 --> 00:05:31,479 Speaker 3: verified guys. 121 00:05:31,480 --> 00:05:33,640 Speaker 4: So I spent that six months talking to other scientists 122 00:05:33,680 --> 00:05:36,400 Speaker 4: and they and all roads led back to George. Everyone's like, yeah, 123 00:05:36,440 --> 00:05:38,279 Speaker 4: we think you do this, but you know you should 124 00:05:38,279 --> 00:05:40,040 Speaker 4: talk to about this George Church. And it was like 125 00:05:40,080 --> 00:05:42,480 Speaker 4: George Church, George, you know, like those commercialders like George Shurch, 126 00:05:42,480 --> 00:05:44,799 Speaker 4: George Church. And so I ended up saying, okay, great. 127 00:05:45,680 --> 00:05:47,400 Speaker 4: I reached out to him and I said, and we 128 00:05:47,440 --> 00:05:49,960 Speaker 4: walked through it. We built the plan, and it really 129 00:05:50,080 --> 00:05:53,200 Speaker 4: was a function of focus and funding. It's like that 130 00:05:53,279 --> 00:05:56,640 Speaker 4: all of the technologies existed in some fashion, so it 131 00:05:56,720 --> 00:05:58,440 Speaker 4: wasn't like we had to have It's like, oh, we 132 00:05:58,480 --> 00:06:00,599 Speaker 4: got it all, but we have to like all faster 133 00:06:00,680 --> 00:06:02,640 Speaker 4: than light travel and then we'll have a mammoth. Right, 134 00:06:03,120 --> 00:06:05,800 Speaker 4: All the technologies existed on some level. We had to 135 00:06:06,000 --> 00:06:07,960 Speaker 4: innovate on them, we had to improve them, and we 136 00:06:08,000 --> 00:06:10,720 Speaker 4: had to make them better. But with enough focus in funding, 137 00:06:10,760 --> 00:06:13,400 Speaker 4: we would get there. And at that point I was like, Okay, 138 00:06:13,600 --> 00:06:13,960 Speaker 4: we're in. 139 00:06:14,240 --> 00:06:15,719 Speaker 2: I mean, it's just it's so fascinating. 140 00:06:15,720 --> 00:06:17,000 Speaker 1: First of all, it sounds like it could also be 141 00:06:17,080 --> 00:06:20,120 Speaker 1: the beginning of another Jurassic partner to be so I've 142 00:06:20,160 --> 00:06:22,240 Speaker 1: heard that. No, it literally it's like they started with 143 00:06:22,360 --> 00:06:24,719 Speaker 1: algae and then they sat down and decided to bring 144 00:06:24,720 --> 00:06:26,880 Speaker 1: the wily mammoth back to life for science. Then they're 145 00:06:27,400 --> 00:06:30,440 Speaker 1: what But it is really fascinating and I would love 146 00:06:30,480 --> 00:06:32,520 Speaker 1: to talk a little bit about the technology behind it, 147 00:06:32,560 --> 00:06:35,000 Speaker 1: sure because Class will and correct me if I'm wrong. 148 00:06:35,000 --> 00:06:38,359 Speaker 1: Class was the first company to use crisper technology for 149 00:06:38,400 --> 00:06:40,640 Speaker 1: the purposes of de extinction. 150 00:06:40,880 --> 00:06:42,960 Speaker 4: I think we're the first for using for the extinction 151 00:06:43,040 --> 00:06:44,840 Speaker 4: and for conservation and for conservation. 152 00:06:45,120 --> 00:06:48,280 Speaker 2: So can you walk me through what that what that 153 00:06:48,320 --> 00:06:48,760 Speaker 2: looks like? 154 00:06:48,960 --> 00:06:53,520 Speaker 4: Yeah, so what's been great is like the extinction and 155 00:06:54,200 --> 00:06:57,240 Speaker 4: knew this this kind of like end in technology stact 156 00:06:57,279 --> 00:07:01,920 Speaker 4: that we're building is pretty complicated, but it requires a 157 00:07:02,000 --> 00:07:04,360 Speaker 4: lot of innovation across a lot of different categories. So 158 00:07:04,400 --> 00:07:07,040 Speaker 4: we do a lot with computational analysis, a lot with 159 00:07:07,320 --> 00:07:10,800 Speaker 4: genome reading compared with genomics trying to understand the differences 160 00:07:10,800 --> 00:07:13,320 Speaker 4: between a wooly mammoth and an Asian elephant or the 161 00:07:13,360 --> 00:07:17,200 Speaker 4: other species that we're working on the genome engineering side, 162 00:07:17,360 --> 00:07:20,840 Speaker 4: Crisper has become this like catch all for like genome engineering. Right, 163 00:07:20,880 --> 00:07:23,120 Speaker 4: it's oh, it's just crisper, right, which is great because 164 00:07:23,120 --> 00:07:25,600 Speaker 4: the people are just saying that that's good enough. But 165 00:07:26,400 --> 00:07:28,800 Speaker 4: we use a combination of tools like Crisper that you know, 166 00:07:28,840 --> 00:07:30,400 Speaker 4: where you knock things out of the genome, where you 167 00:07:30,400 --> 00:07:32,240 Speaker 4: actually identify things as they we want to knock things 168 00:07:32,280 --> 00:07:34,040 Speaker 4: out or knock things in. We're going to put things 169 00:07:34,080 --> 00:07:37,160 Speaker 4: in there. We're also doing a bunch of individual editing 170 00:07:37,400 --> 00:07:41,640 Speaker 4: with if you think of that twisted ladder that is DNA, 171 00:07:41,720 --> 00:07:44,240 Speaker 4: each one of those rungs, right, and half of it's 172 00:07:44,240 --> 00:07:47,040 Speaker 4: the nucleotide. We are actually editing each half of that 173 00:07:47,160 --> 00:07:50,280 Speaker 4: rung individually, which is another technology that we're using, which 174 00:07:50,320 --> 00:07:53,720 Speaker 4: is pretty cool. And then we're also synthesizing DNA. So 175 00:07:53,760 --> 00:07:55,960 Speaker 4: if there's a bunch of changes in this block of DNA, 176 00:07:56,280 --> 00:07:59,240 Speaker 4: we'll actually synthesize, we'll actually write that DNA from scratch 177 00:07:59,480 --> 00:08:01,840 Speaker 4: and then and then swap it in. And I think 178 00:08:01,840 --> 00:08:04,480 Speaker 4: the thing that Colossal and while Colossal has been innovating 179 00:08:04,480 --> 00:08:06,680 Speaker 4: across all three of those, right, we didn't discover cross 180 00:08:06,720 --> 00:08:09,600 Speaker 4: or didn' discover some of these tools. But what we've done, 181 00:08:09,720 --> 00:08:12,320 Speaker 4: I think really really well is created new tools on 182 00:08:12,360 --> 00:08:14,800 Speaker 4: top of it, increase the efficiency. And then we've built 183 00:08:14,800 --> 00:08:18,240 Speaker 4: this clustered wrapping system so that we can deliver lots 184 00:08:18,280 --> 00:08:20,840 Speaker 4: of different edits all at the same time. And that, 185 00:08:21,680 --> 00:08:24,600 Speaker 4: to me is part of the true magic of what 186 00:08:24,640 --> 00:08:27,360 Speaker 4: we built because it allows us to say, instead of 187 00:08:27,360 --> 00:08:29,560 Speaker 4: making like a single edit at a single point in 188 00:08:29,560 --> 00:08:31,760 Speaker 4: the genome, like a lot of labs are making like 189 00:08:32,000 --> 00:08:34,760 Speaker 4: two edits, we're doing over ninety five edits at a 190 00:08:34,840 --> 00:08:38,360 Speaker 4: time right in one delivery, which is really really powerful. 191 00:08:38,400 --> 00:08:40,560 Speaker 4: And if you want to have the number of changes 192 00:08:40,600 --> 00:08:43,280 Speaker 4: that we want in our species, that's what we have 193 00:08:43,360 --> 00:08:43,800 Speaker 4: to achieve. 194 00:08:44,040 --> 00:08:46,720 Speaker 1: So can you apply that what you just talked about 195 00:08:46,760 --> 00:08:50,480 Speaker 1: to extinct animals that you're working on, whether it's a 196 00:08:50,520 --> 00:08:54,559 Speaker 1: wooly mammoth. I know that in twenty twenty four you'll 197 00:08:54,600 --> 00:08:57,320 Speaker 1: announce that you de extincted the dire wolves. 198 00:08:57,360 --> 00:08:59,880 Speaker 4: So can you get and are objectively cute wilya My 199 00:08:59,880 --> 00:09:02,760 Speaker 4: ide that we didn't sync, but we may be engineered. 200 00:09:02,800 --> 00:09:05,920 Speaker 1: So you engineered wooly mice. I mean, Matt, I know 201 00:09:06,000 --> 00:09:07,960 Speaker 1: you're not the mad scientist. I know that I know 202 00:09:08,040 --> 00:09:09,840 Speaker 1: that you brought in the mad scientists, but could you 203 00:09:09,920 --> 00:09:11,920 Speaker 1: just like geek out mad science with us for a 204 00:09:11,960 --> 00:09:13,480 Speaker 1: little bit, like how did you do it for each 205 00:09:13,520 --> 00:09:14,959 Speaker 1: of each of these animals? 206 00:09:15,040 --> 00:09:17,079 Speaker 2: What's next? Like let's go into the lake. 207 00:09:17,160 --> 00:09:20,120 Speaker 4: Yeah, So in the dire wolf and the boy mouse 208 00:09:20,160 --> 00:09:22,160 Speaker 4: were a little bit of a surprising delight because we 209 00:09:22,200 --> 00:09:24,760 Speaker 4: had announced the wooly mammoth, the Tasmanian tiger, and the 210 00:09:24,800 --> 00:09:27,360 Speaker 4: Dodo is kind of like three flagship projects and so 211 00:09:27,480 --> 00:09:30,000 Speaker 4: we've announced those, but you know, we wanted to show 212 00:09:30,120 --> 00:09:32,760 Speaker 4: proofcase on the woi mice. Right. What people don't realize 213 00:09:32,800 --> 00:09:35,760 Speaker 4: is that the wooly mice, while they're objectively cute and amazing, 214 00:09:36,520 --> 00:09:38,720 Speaker 4: they are actually a marvel of science. Right because we 215 00:09:38,800 --> 00:09:42,240 Speaker 4: delivered eight edits all at once, and you know, there 216 00:09:42,280 --> 00:09:45,040 Speaker 4: was I think some confusion when we announced because people said, well, 217 00:09:45,080 --> 00:09:47,840 Speaker 4: people have done eight edits in mice, like yeah, but 218 00:09:47,840 --> 00:09:50,480 Speaker 4: they did it over multiple generations, right, we did eight 219 00:09:50,840 --> 00:09:52,959 Speaker 4: at once. All of the mice that were born had 220 00:09:53,000 --> 00:09:56,160 Speaker 4: exactly the phenotypes or physical attributes that we were looking for, 221 00:09:56,200 --> 00:09:59,480 Speaker 4: like one hundred percent, like what so, so we targeted 222 00:09:59,480 --> 00:10:01,679 Speaker 4: a couple of core things. We targeted to make them 223 00:10:01,679 --> 00:10:05,400 Speaker 4: a little bit fatter. We targeted a goal the color 224 00:10:05,400 --> 00:10:07,840 Speaker 4: of the hair, the thickness of the hair, the length 225 00:10:07,880 --> 00:10:09,640 Speaker 4: of the hair, the density of the hair, and the 226 00:10:09,679 --> 00:10:10,559 Speaker 4: direction that even some. 227 00:10:10,520 --> 00:10:11,200 Speaker 3: Of the hairs grow. 228 00:10:11,320 --> 00:10:14,360 Speaker 4: Right. And so what we did from a computational analysis 229 00:10:14,360 --> 00:10:17,240 Speaker 4: perspective and an AI perspective. 230 00:10:17,000 --> 00:10:18,680 Speaker 3: Is we looked at the mammoth genomes. 231 00:10:18,800 --> 00:10:21,280 Speaker 4: We have about one hundred and four mammoth genomes ranging 232 00:10:21,280 --> 00:10:24,040 Speaker 4: about one point two million years. It's a long period 233 00:10:24,040 --> 00:10:26,280 Speaker 4: of time, so that but that's great because that allows 234 00:10:26,320 --> 00:10:29,000 Speaker 4: us to understand what genes really made a mammoth mamoth 235 00:10:29,040 --> 00:10:31,520 Speaker 4: because there's a lot of genetic drift and a lot 236 00:10:31,559 --> 00:10:34,320 Speaker 4: of genetic diversity over a million years. So then we 237 00:10:34,320 --> 00:10:36,880 Speaker 4: could say, okay, if with these core physical attributes or 238 00:10:36,880 --> 00:10:40,280 Speaker 4: phenotypes around like cold tolerance, like that shaggy coat or 239 00:10:40,360 --> 00:10:43,480 Speaker 4: that fat layer, what made a mammoth? And mammoth, like 240 00:10:43,520 --> 00:10:45,959 Speaker 4: what truly made a mammoth math? And then we use 241 00:10:46,040 --> 00:10:48,720 Speaker 4: AI to say, okay, what are the mouse equivalents of that? 242 00:10:48,760 --> 00:10:51,360 Speaker 4: Because there's about two hundred million years between you know, 243 00:10:51,559 --> 00:10:53,760 Speaker 4: elephants and mice. So we weren't just going to jam. 244 00:10:53,880 --> 00:10:56,400 Speaker 4: We weren't trying to do anything too weird. We wanted 245 00:10:56,400 --> 00:10:58,800 Speaker 4: to actually do something to be healthy for the animals. 246 00:10:58,880 --> 00:11:02,079 Speaker 4: And so we then engineer the mouse equivalent of our 247 00:11:02,160 --> 00:11:06,120 Speaker 4: mammoth edits into mice. And what was amazing is we 248 00:11:06,160 --> 00:11:08,520 Speaker 4: did that. Like, while that's all sounds interesting and cool, 249 00:11:08,720 --> 00:11:11,600 Speaker 4: what the most amazing part was everything was healthy. Everything 250 00:11:11,679 --> 00:11:14,240 Speaker 4: was exactly what we predicted, which is great, and it 251 00:11:14,280 --> 00:11:16,439 Speaker 4: all happened within thirty days. Wow. 252 00:11:16,600 --> 00:11:30,320 Speaker 1: Yeah, So tell me where are you with the wily mammoth? 253 00:11:30,360 --> 00:11:31,880 Speaker 2: We hear we have the willly mouse? 254 00:11:31,960 --> 00:11:32,720 Speaker 3: Yes, we have the mouse. 255 00:11:32,760 --> 00:11:34,480 Speaker 4: And well, the mouse is a great test case, right 256 00:11:34,559 --> 00:11:38,240 Speaker 4: because it's twenty days of gestation versus twenty two months 257 00:11:38,240 --> 00:11:42,000 Speaker 4: of gestation and elephants, and so it allows us to say, 258 00:11:42,040 --> 00:11:44,480 Speaker 4: are the edits that we're making in our Asian elephant cells, 259 00:11:44,480 --> 00:11:46,880 Speaker 4: which is the closest living relative to the mammoth, are 260 00:11:46,880 --> 00:11:49,160 Speaker 4: they the right edits. Are we doing the right edits 261 00:11:49,200 --> 00:11:51,240 Speaker 4: to them? Are we making the right are we getting 262 00:11:51,480 --> 00:11:54,199 Speaker 4: what we expected right? And so that's a great way. 263 00:11:54,240 --> 00:11:56,640 Speaker 4: It's a great model organism to test with. So we're 264 00:11:56,679 --> 00:11:58,960 Speaker 4: in the editing phase. We've done all the competation analysis, 265 00:11:58,960 --> 00:12:02,840 Speaker 4: we've done the ancient DNAs simply, we're in the editing phase. 266 00:12:03,040 --> 00:12:06,760 Speaker 4: We've actually created effectively stem cells for elephants. We can 267 00:12:06,880 --> 00:12:10,559 Speaker 4: differentiate into different tissues and make sure it's working as predicted. 268 00:12:10,880 --> 00:12:11,920 Speaker 3: And we're in the editing phase. 269 00:12:11,960 --> 00:12:14,679 Speaker 4: And you know, we're over halfway through the editing and 270 00:12:14,760 --> 00:12:17,840 Speaker 4: from there it'll then go into the cloning phase and 271 00:12:17,880 --> 00:12:20,200 Speaker 4: then it will go into the gestation phase. 272 00:12:20,320 --> 00:12:22,520 Speaker 1: And keeping it on the wily mammoth, because it seems 273 00:12:22,520 --> 00:12:24,280 Speaker 1: as though everybody, as you say, is obsessed with this 274 00:12:24,360 --> 00:12:26,040 Speaker 1: idea of the will of mammoth, which I think is 275 00:12:26,080 --> 00:12:28,840 Speaker 1: interesting and I'd love to ask you about. But is 276 00:12:28,880 --> 00:12:31,040 Speaker 1: there because I know there's kind of a larger goal 277 00:12:31,160 --> 00:12:33,120 Speaker 1: with bringing these animals back, which we'll get to, but 278 00:12:33,200 --> 00:12:35,040 Speaker 1: is there any data about how the wily mammoth would 279 00:12:35,040 --> 00:12:36,840 Speaker 1: actually affect ecosystems in the long run? 280 00:12:36,960 --> 00:12:39,960 Speaker 4: Yeah? Yeah, And so this is this is a great 281 00:12:40,040 --> 00:12:42,719 Speaker 4: question and so, and hopefully we'll get to talk a 282 00:12:42,760 --> 00:12:45,200 Speaker 4: little bit about how the technologies on that path can 283 00:12:45,240 --> 00:12:48,600 Speaker 4: be applicable today to modern elephants, which I'd love to 284 00:12:48,640 --> 00:12:50,840 Speaker 4: talk a little bit about its possible. So our goal 285 00:12:50,880 --> 00:12:53,640 Speaker 4: is we think that the extinction and species preservation goes 286 00:12:53,679 --> 00:12:56,320 Speaker 4: hand in hand, right, And so it's forecasted and this 287 00:12:56,360 --> 00:13:00,200 Speaker 4: is terrifying, but I'm a big optimist, but it's it's 288 00:13:00,280 --> 00:13:02,440 Speaker 4: terrifying that we are forecasting that we could lose up 289 00:13:02,440 --> 00:13:05,280 Speaker 4: to fifty percent of all biodiversity in the next twenty 290 00:13:05,320 --> 00:13:07,520 Speaker 4: five plus years, which is which is terrifying, right, like 291 00:13:07,559 --> 00:13:09,880 Speaker 4: we are in the six mass extinctions. 292 00:13:09,880 --> 00:13:11,800 Speaker 3: So we need new tools and technologies, but. 293 00:13:11,760 --> 00:13:16,559 Speaker 4: We also need to revitalize ecosystems because ecosystem revitalization actually 294 00:13:16,720 --> 00:13:20,600 Speaker 4: increases natural birth rates of flora and fauna, right. And 295 00:13:20,640 --> 00:13:23,400 Speaker 4: so we've seen that there's this concept called rewilding where 296 00:13:23,640 --> 00:13:27,280 Speaker 4: where humans reintroduce species back into their natural habitat that 297 00:13:27,320 --> 00:13:30,360 Speaker 4: they were removed for whatever reason. So probably the most 298 00:13:30,360 --> 00:13:34,040 Speaker 4: famous case of that is Yellowstone in nineteen twenty five. 299 00:13:34,320 --> 00:13:36,640 Speaker 4: They called the wolves because they're like predators are bad, 300 00:13:36,679 --> 00:13:40,120 Speaker 4: they're awful. That's actually not true. Wolves are and predators 301 00:13:40,120 --> 00:13:43,400 Speaker 4: are pretty important to ecosystems, right. They probably on the young, 302 00:13:43,480 --> 00:13:45,560 Speaker 4: the sick, and the olds. It's very survival of the fittest. 303 00:13:45,640 --> 00:13:47,679 Speaker 4: But they also get rid of a lot of the sick, 304 00:13:47,760 --> 00:13:50,640 Speaker 4: which is key. Right. And without wolves, they saw that 305 00:13:50,679 --> 00:13:54,520 Speaker 4: the elk and the other populations became sedentary, they overpopulated, 306 00:13:54,720 --> 00:13:58,360 Speaker 4: they stopped migrating, they stopped they started eating all of 307 00:13:59,200 --> 00:14:01,760 Speaker 4: the materials that vivers would use to make dams. And 308 00:14:01,840 --> 00:14:05,000 Speaker 4: so what we know about things like mammos and large 309 00:14:05,000 --> 00:14:10,480 Speaker 4: herbi wars is they're also very big environmental modifiers and 310 00:14:10,480 --> 00:14:13,319 Speaker 4: and and and this is where people in the scientific 311 00:14:13,360 --> 00:14:16,120 Speaker 4: community kind of split, right even within our own population. 312 00:14:16,200 --> 00:14:18,240 Speaker 4: Like George will show you all the math of how 313 00:14:18,280 --> 00:14:20,800 Speaker 4: if we introduce this number of thousands of elephants it 314 00:14:20,880 --> 00:14:24,600 Speaker 4: will help completely change the ecosystem and and actually help 315 00:14:24,640 --> 00:14:25,520 Speaker 4: with climate change. 316 00:14:25,560 --> 00:14:27,760 Speaker 3: We have other scientists like our chief Sigence sofs for Best. 317 00:14:27,640 --> 00:14:30,960 Speaker 4: Shapiro, who use amazing and a genius that will show you, well, 318 00:14:31,160 --> 00:14:33,120 Speaker 4: it'll wait, won't have at that level of impact, it 319 00:14:33,160 --> 00:14:35,520 Speaker 4: will have this level impact, but the net benefit of 320 00:14:35,600 --> 00:14:39,080 Speaker 4: just reintroduction of cold tolerant megafauna back into the Arctic 321 00:14:39,320 --> 00:14:42,840 Speaker 4: will help revitalize that that that ecosystem. I think the 322 00:14:42,880 --> 00:14:46,080 Speaker 4: big thing to that that I wouldn't you know, say 323 00:14:46,240 --> 00:14:48,240 Speaker 4: in exact number, because I don't think we know until 324 00:14:48,240 --> 00:14:51,160 Speaker 4: we truly measure it. But generally speaking, everyone agrees that 325 00:14:51,160 --> 00:14:54,000 Speaker 4: the reintroduction of these species will have a net positive 326 00:14:54,040 --> 00:14:56,680 Speaker 4: benefit on the ecosystem, which then has that that kind 327 00:14:56,680 --> 00:14:59,320 Speaker 4: of cast getting effect like we've seen with the wolves. 328 00:14:58,960 --> 00:15:01,520 Speaker 1: And specific to the will mammoth, You believe that this 329 00:15:01,720 --> 00:15:03,320 Speaker 1: would have a net positive benefit. 330 00:15:03,520 --> 00:15:06,160 Speaker 4: Yes, yes, And so I mean because elephants if you 331 00:15:06,200 --> 00:15:08,640 Speaker 4: look at like the foreself ands in Gabon and you 332 00:15:08,640 --> 00:15:11,880 Speaker 4: look at what Savannah elephants who they're great at spreading 333 00:15:11,880 --> 00:15:16,200 Speaker 4: seeds through defecation, They're great at removing dead and dying 334 00:15:16,280 --> 00:15:19,640 Speaker 4: trees that are not great carbon stores that are actually decomposing. 335 00:15:19,920 --> 00:15:20,880 Speaker 3: It is pretty interesting. 336 00:15:20,960 --> 00:15:23,640 Speaker 4: And yeah, you know, I think from a geoengineering perspective, 337 00:15:23,640 --> 00:15:26,120 Speaker 4: we have to like do it and test it. But 338 00:15:26,280 --> 00:15:29,640 Speaker 4: I think generally speaking, everyone agrees that a more biodiverse 339 00:15:29,680 --> 00:15:32,200 Speaker 4: ecosystem is a better and healthy ecosystem. 340 00:15:32,280 --> 00:15:34,400 Speaker 1: And I mean there are very different ways that people 341 00:15:34,440 --> 00:15:36,720 Speaker 1: actually which I didn't know this before I started doing 342 00:15:36,720 --> 00:15:38,680 Speaker 1: research on this, but there are different ways that people 343 00:15:38,920 --> 00:15:41,080 Speaker 1: really define what the extension would look like. 344 00:15:41,200 --> 00:15:43,480 Speaker 2: What does colossal mean when when you define it? 345 00:15:43,560 --> 00:15:45,520 Speaker 4: Yeah, well, I think we define it the only way 346 00:15:45,520 --> 00:15:46,240 Speaker 4: it's possible. 347 00:15:46,320 --> 00:15:47,600 Speaker 3: But I think that's that's my view. 348 00:15:47,840 --> 00:15:51,720 Speaker 4: Sometimes people think that the extinction is cloning, like you 349 00:15:51,800 --> 00:15:54,520 Speaker 4: have to clone. Now, cloning is a step in the process. Yeah, 350 00:15:54,520 --> 00:15:57,280 Speaker 4: but you can't clone from a cell that doesn't exist, right. 351 00:15:57,200 --> 00:15:59,360 Speaker 1: And this is I guess the big question is like 352 00:15:59,400 --> 00:16:02,880 Speaker 1: if the willy mammoth is so far gone sadly, you know, 353 00:16:03,040 --> 00:16:04,200 Speaker 1: like you can't clone it. 354 00:16:04,600 --> 00:16:05,520 Speaker 3: You can't clone it, right. 355 00:16:05,560 --> 00:16:07,720 Speaker 4: And so if we go back to that one hundred 356 00:16:07,720 --> 00:16:11,240 Speaker 4: and four mammoth example, which one is a mammoth, is 357 00:16:11,280 --> 00:16:13,880 Speaker 4: it the mammoth that is it the og mammoth from 358 00:16:13,880 --> 00:16:15,560 Speaker 4: one point two million years ago that was on the 359 00:16:15,560 --> 00:16:17,840 Speaker 4: mammoth step or is it one that died on Wrangell 360 00:16:17,880 --> 00:16:20,840 Speaker 4: Island four thousand years ago? Right? And so the way 361 00:16:20,880 --> 00:16:23,800 Speaker 4: that we think about the extinction is we try to 362 00:16:23,960 --> 00:16:25,840 Speaker 4: make sure people don't miss the force for the trees 363 00:16:25,880 --> 00:16:28,000 Speaker 4: and say we try to educate people and say, look, 364 00:16:28,040 --> 00:16:30,680 Speaker 4: you can't clone an extinct species, right because it's gone. 365 00:16:31,080 --> 00:16:33,520 Speaker 4: And even if you did. It depends on what geographic 366 00:16:33,600 --> 00:16:37,080 Speaker 4: location you cloned it from whether what it would actually be. 367 00:16:37,400 --> 00:16:39,520 Speaker 4: And so for us, we try to say, okay, what 368 00:16:39,560 --> 00:16:42,960 Speaker 4: are the genes that have been lost from a biodiversity perspective, 369 00:16:43,000 --> 00:16:46,720 Speaker 4: so what are the genes that gave them the adaptability 370 00:16:46,720 --> 00:16:49,040 Speaker 4: to the environment so that they can solve that same 371 00:16:49,160 --> 00:16:50,240 Speaker 4: ecological function? 372 00:16:50,560 --> 00:16:52,840 Speaker 3: And sometimes we call that functional de extinction. 373 00:16:53,160 --> 00:16:56,160 Speaker 4: But in the reality, that's the only way to kind of, 374 00:16:56,160 --> 00:16:57,560 Speaker 4: like I think, to do it, because I don't think 375 00:16:57,560 --> 00:17:01,640 Speaker 4: we'll ever find living mammo cell, living dodo cells, right, 376 00:17:01,720 --> 00:17:03,200 Speaker 4: I think this is the only way to do it. 377 00:17:03,360 --> 00:17:06,800 Speaker 4: And so it's almost about like rebuilding extinct species for today. 378 00:17:07,240 --> 00:17:09,639 Speaker 1: And I guess this brings me to my next question, 379 00:17:09,680 --> 00:17:12,880 Speaker 1: which is you'll have something like over one hundred scientists 380 00:17:13,160 --> 00:17:14,000 Speaker 1: now working for you. 381 00:17:14,160 --> 00:17:16,000 Speaker 3: Yes, we have about two hundred and sixty scientists. 382 00:17:16,000 --> 00:17:18,720 Speaker 1: Wow, okay, so two hundred and sixty scientists working to 383 00:17:18,720 --> 00:17:21,560 Speaker 1: bring back extinct species from the dead. How do you 384 00:17:21,600 --> 00:17:25,000 Speaker 1: decide which animals? I mean, I just for some reason, 385 00:17:25,040 --> 00:17:26,520 Speaker 1: I don't know why. Maybe it's like an episode of 386 00:17:26,520 --> 00:17:28,679 Speaker 1: Silicon Valley. I just like imagine a bunch of people 387 00:17:28,720 --> 00:17:31,560 Speaker 1: around and were like just roll it ice, Yeah, how 388 00:17:31,560 --> 00:17:32,720 Speaker 1: about this one? How about this one? 389 00:17:32,720 --> 00:17:32,840 Speaker 4: Oh? 390 00:17:32,840 --> 00:17:34,320 Speaker 2: That one seems kind of cool, But how do you 391 00:17:34,320 --> 00:17:34,880 Speaker 2: actually do it? 392 00:17:35,080 --> 00:17:36,760 Speaker 3: We don't have like a set criteria. 393 00:17:36,840 --> 00:17:38,840 Speaker 4: We just kind of have a framework and it is 394 00:17:39,680 --> 00:17:42,760 Speaker 4: very democratic with the things like our chief science officer 395 00:17:42,800 --> 00:17:45,159 Speaker 4: doesn't choose, I don't choose, our board doesn't choose. We 396 00:17:45,240 --> 00:17:47,520 Speaker 4: really kind of bring everyone together talk about it. So 397 00:17:48,080 --> 00:17:50,080 Speaker 4: the things that kind of go in that mix are 398 00:17:50,400 --> 00:17:51,480 Speaker 4: is it possible? Right? 399 00:17:51,480 --> 00:17:54,000 Speaker 3: Like, we're not trying to do trade engineering. We're not 400 00:17:54,000 --> 00:17:54,520 Speaker 3: trying to make. 401 00:17:54,440 --> 00:17:56,879 Speaker 4: Something just look like a dire wolf or look like 402 00:17:56,880 --> 00:18:00,000 Speaker 4: commandmuth Right, we want to understand what were those genes. 403 00:18:00,000 --> 00:18:01,399 Speaker 4: We actually want to do the hard work in the 404 00:18:01,400 --> 00:18:03,080 Speaker 4: research of that, right because we want to bring in 405 00:18:03,119 --> 00:18:05,919 Speaker 4: that lost by a diversity. So is their DNA? Is 406 00:18:05,920 --> 00:18:09,879 Speaker 4: there possible? So just to jump ahead, uh, there is 407 00:18:09,920 --> 00:18:12,199 Speaker 4: no dino DNA. I don't like to say impossible, but 408 00:18:12,240 --> 00:18:14,320 Speaker 4: I don't see a path where there's dyno DNA that 409 00:18:14,400 --> 00:18:15,359 Speaker 4: sixty five million years ago. 410 00:18:15,400 --> 00:18:16,840 Speaker 3: So I don't see a path for you to to 411 00:18:16,960 --> 00:18:17,960 Speaker 3: dcing to dinosaurs. 412 00:18:17,960 --> 00:18:20,359 Speaker 2: He's like, do not ask me if I'm bringing back down. 413 00:18:20,720 --> 00:18:23,200 Speaker 3: I get that, We get that question. Four hundred times 414 00:18:23,200 --> 00:18:23,520 Speaker 3: a day. 415 00:18:23,720 --> 00:18:25,440 Speaker 4: Sometimes it makes people cry when we tell them no, 416 00:18:25,840 --> 00:18:29,080 Speaker 4: but the very few people say oh thank god. Most 417 00:18:29,119 --> 00:18:31,160 Speaker 4: people are like really sad about it, which is interesting. 418 00:18:31,320 --> 00:18:32,840 Speaker 4: By the way, if you love dinosaurs, that's great. We 419 00:18:32,880 --> 00:18:33,080 Speaker 4: love that. 420 00:18:33,240 --> 00:18:36,080 Speaker 1: You're like, I'm not the headline ars, I'm not anti dinosaurs. 421 00:18:36,119 --> 00:18:37,280 Speaker 4: I'm also not. 422 00:18:37,200 --> 00:18:38,080 Speaker 2: Building Jurassic Park. 423 00:18:38,160 --> 00:18:39,760 Speaker 3: Yeah, but we're not building Dressed But there. 424 00:18:39,720 --> 00:18:41,160 Speaker 2: Are some kind of parallels. 425 00:18:41,240 --> 00:18:42,840 Speaker 4: Yeah, well, I mean dress Work did a lot of 426 00:18:42,880 --> 00:18:45,920 Speaker 4: great stuff, like it taught like people that aren't scientists. 427 00:18:46,480 --> 00:18:48,760 Speaker 4: There's this thing called DNA, and we now have this 428 00:18:48,840 --> 00:18:51,119 Speaker 4: ability to edit it, and we as humans have that, 429 00:18:51,160 --> 00:18:52,919 Speaker 4: you know. And obviously there's a lot of humorsalon and 430 00:18:52,920 --> 00:18:54,920 Speaker 4: a lot of bad things go wrong that entertained us all. 431 00:18:55,000 --> 00:18:57,760 Speaker 4: But it is really interesting, like mister DNA taught the 432 00:18:57,920 --> 00:19:00,960 Speaker 4: world at the right time that there's there's this amazing 433 00:19:00,800 --> 00:19:02,879 Speaker 4: thing called DNA and we now have the ability to 434 00:19:03,040 --> 00:19:05,800 Speaker 4: edit it. And I think that's I think it's so 435 00:19:05,840 --> 00:19:08,879 Speaker 4: anyway to back to your question, what is it like, 436 00:19:08,960 --> 00:19:11,520 Speaker 4: is it possible? Number one? Number two is like does 437 00:19:11,520 --> 00:19:14,880 Speaker 4: the ecosystem exist? Can they exist? Can they thrive? Number three? 438 00:19:15,080 --> 00:19:18,639 Speaker 4: Is there an ability to bring them back in a 439 00:19:18,680 --> 00:19:21,920 Speaker 4: way that benefits other species like it does the tech 440 00:19:22,520 --> 00:19:24,560 Speaker 4: benefit of other species, Like in the case of the 441 00:19:24,560 --> 00:19:28,040 Speaker 4: dire wolves, we invented a way to clone from isolated 442 00:19:28,040 --> 00:19:31,560 Speaker 4: cells within the blood, so it's massively not invasive blood cloning, 443 00:19:31,560 --> 00:19:34,280 Speaker 4: which is awesome. And we use the red wolf as 444 00:19:34,320 --> 00:19:36,600 Speaker 4: our example, which is the most endangered wolf in the world, 445 00:19:36,640 --> 00:19:39,320 Speaker 4: which is the American red wolf, which is a travesty, right, 446 00:19:39,520 --> 00:19:41,960 Speaker 4: and so we invented a new way to do cryo 447 00:19:42,040 --> 00:19:44,840 Speaker 4: preservation of those cells, in a new way to clone 448 00:19:44,920 --> 00:19:48,040 Speaker 4: that doesn't have to take different skin biopsies from the animal. 449 00:19:48,080 --> 00:19:49,920 Speaker 4: You can simply just take a blood draw, which is 450 00:19:49,960 --> 00:19:51,760 Speaker 4: awesome from an animal welfare perspective. 451 00:19:51,880 --> 00:19:52,840 Speaker 3: And so there is. 452 00:19:52,760 --> 00:19:55,440 Speaker 4: There a benefit that comes from this, right And then 453 00:19:55,720 --> 00:19:57,160 Speaker 4: is there educational benefits? 454 00:19:57,200 --> 00:19:58,360 Speaker 3: Are there cultural benefits? 455 00:19:58,359 --> 00:20:00,280 Speaker 4: Some of the species we work on their there's a 456 00:20:00,440 --> 00:20:03,240 Speaker 4: connection to the animal, right, Like we would never work 457 00:20:03,280 --> 00:20:06,920 Speaker 4: on the moa in South Island, New Zealand without getting 458 00:20:06,960 --> 00:20:11,080 Speaker 4: permission from the Maori people because it's part of their tonga, 459 00:20:11,119 --> 00:20:13,760 Speaker 4: their sacred species, right, and so there's a lot that 460 00:20:13,840 --> 00:20:16,040 Speaker 4: kind of goes into it. And then there is with 461 00:20:16,080 --> 00:20:18,720 Speaker 4: the dire wolves. So specifically, we thought, and I don't 462 00:20:18,760 --> 00:20:19,919 Speaker 4: know if this will be the case with all these 463 00:20:19,960 --> 00:20:22,760 Speaker 4: other species, but with the diarolves, we were excited because 464 00:20:22,920 --> 00:20:25,199 Speaker 4: we thought it was also a really fun way to 465 00:20:25,400 --> 00:20:28,119 Speaker 4: bridge people in from fantasy. Like people that are like 466 00:20:28,160 --> 00:20:31,120 Speaker 4: Game of Thrones fans or match Gathering fans or World 467 00:20:31,119 --> 00:20:32,879 Speaker 4: of Warkcraft fans were dire wolves, Like, how do you 468 00:20:32,920 --> 00:20:35,240 Speaker 4: bring them? Is they like these are actually real animals. 469 00:20:35,400 --> 00:20:38,280 Speaker 4: I won't say who, but numerous people on the Game 470 00:20:38,320 --> 00:20:43,040 Speaker 4: of Thrones set in the cast believe that they were 471 00:20:43,240 --> 00:20:46,160 Speaker 4: mythical creatures, right, And so the fact that we could 472 00:20:46,200 --> 00:20:50,680 Speaker 4: like bring this like you know, cult following into science 473 00:20:50,760 --> 00:20:54,680 Speaker 4: and get people excited about science, that was that kind 474 00:20:54,680 --> 00:20:55,880 Speaker 4: of weighed in that decision too. 475 00:20:56,000 --> 00:20:58,440 Speaker 1: It's interesting because when I was when I was researching, 476 00:20:58,560 --> 00:21:00,919 Speaker 1: I'm my god, they really have a large, like a 477 00:21:00,960 --> 00:21:04,840 Speaker 1: public footprint right there. You aren't science behind closed doors, 478 00:21:04,920 --> 00:21:07,679 Speaker 1: I mean a big presence on social You've built a 479 00:21:07,720 --> 00:21:09,960 Speaker 1: brand around it. You have folks like Paris Hilton and 480 00:21:10,000 --> 00:21:11,520 Speaker 1: Tom Brady on the cap table, but. 481 00:21:11,480 --> 00:21:13,600 Speaker 4: Those people just be clear, we didn't seek those people 482 00:21:13,600 --> 00:21:17,200 Speaker 4: out right thirty percent. I said this earlier, like free people, 483 00:21:17,240 --> 00:21:20,359 Speaker 4: like what the thirty percent of our investors came from 484 00:21:20,400 --> 00:21:20,879 Speaker 4: their kids. 485 00:21:21,280 --> 00:21:23,679 Speaker 3: Wow. So like Tom Brady, Tom reached. 486 00:21:23,400 --> 00:21:25,840 Speaker 4: Out because his kids read about it and they were 487 00:21:25,880 --> 00:21:27,760 Speaker 4: excited about it and they were just pining in. And 488 00:21:27,760 --> 00:21:29,520 Speaker 4: then he reached out to a friend of mine and 489 00:21:29,560 --> 00:21:31,960 Speaker 4: then we talked and now we talked pretty regularly about 490 00:21:32,000 --> 00:21:34,439 Speaker 4: it because he loves he wants to give the updates 491 00:21:34,440 --> 00:21:47,640 Speaker 4: to his kids. 492 00:21:48,800 --> 00:21:50,359 Speaker 2: What do we do wrong about science? 493 00:21:50,400 --> 00:21:50,560 Speaker 3: Why? 494 00:21:50,800 --> 00:21:54,199 Speaker 1: Why do you think science hasn't been built out in 495 00:21:54,240 --> 00:21:57,199 Speaker 1: a way that feels even though it is probably one 496 00:21:57,200 --> 00:21:59,840 Speaker 1: of the most exciting things we could possibly talk about, 497 00:22:00,080 --> 00:22:00,680 Speaker 1: it doesn't. 498 00:22:00,480 --> 00:22:02,520 Speaker 2: Feel that exciting to talk about. Why is it? 499 00:22:03,080 --> 00:22:05,240 Speaker 4: I think that it did for a long time, right. 500 00:22:05,320 --> 00:22:07,600 Speaker 4: I think that for a long time, you had like 501 00:22:07,720 --> 00:22:09,800 Speaker 4: the Apollo program, and you had these kind of like 502 00:22:10,000 --> 00:22:13,880 Speaker 4: nationalistic like moonshots number one. And then I think we're 503 00:22:13,920 --> 00:22:17,040 Speaker 4: all promised things like you know, flying cars and all 504 00:22:17,119 --> 00:22:19,159 Speaker 4: kinds of cool stuff, and we got like, you know, 505 00:22:19,640 --> 00:22:21,960 Speaker 4: a new messaging platform, like that's what we got, Like 506 00:22:22,160 --> 00:22:24,360 Speaker 4: that was the that was where our flying cars went. 507 00:22:24,560 --> 00:22:27,360 Speaker 4: But then I feel like post COVID, I think more 508 00:22:27,359 --> 00:22:29,960 Speaker 4: and more people are doing bigger moonshots. I think we 509 00:22:30,000 --> 00:22:32,439 Speaker 4: all sat at home during COVID and we're like, we 510 00:22:32,440 --> 00:22:34,600 Speaker 4: don't want to just watch Netflix. We got there's got 511 00:22:34,640 --> 00:22:36,760 Speaker 4: to be more to life and do cooler stuff. And 512 00:22:36,760 --> 00:22:40,080 Speaker 4: so so I'm hopeful that we have this like you know, uh, 513 00:22:40,640 --> 00:22:44,320 Speaker 4: you know, reinvention and reignition of a big moonshot thinking. 514 00:22:44,560 --> 00:22:46,919 Speaker 4: And I think that if we are successful in our journey, 515 00:22:47,320 --> 00:22:50,840 Speaker 4: we will bring people into biology or into being a 516 00:22:50,880 --> 00:22:54,160 Speaker 4: conservationist because they just because they thought what we did 517 00:22:54,240 --> 00:22:54,600 Speaker 4: was cool. 518 00:22:54,840 --> 00:22:57,719 Speaker 1: And the larger mission of course, it's let's bring back 519 00:22:57,760 --> 00:23:00,880 Speaker 1: the wooly mammoth. Let's you know, bring back these these 520 00:23:00,880 --> 00:23:03,080 Speaker 1: animals that we never thought possible. 521 00:23:03,240 --> 00:23:05,240 Speaker 2: Yeah, but it's about a. 522 00:23:05,160 --> 00:23:07,520 Speaker 1: Lot more than that, and that seems to be kind 523 00:23:07,520 --> 00:23:10,320 Speaker 1: of what we're dancing around. So what is the real 524 00:23:10,359 --> 00:23:11,399 Speaker 1: moonshop for you as what? 525 00:23:11,880 --> 00:23:13,399 Speaker 3: So we care? 526 00:23:13,240 --> 00:23:16,360 Speaker 4: We care, We think about the business and what we're 527 00:23:16,359 --> 00:23:19,400 Speaker 4: building around value creation, impact, inspiration. We talked a little 528 00:23:19,400 --> 00:23:21,680 Speaker 4: about a lot about the inspiration, right, Like we try 529 00:23:21,720 --> 00:23:24,720 Speaker 4: to make the science accessible for kids and for parents 530 00:23:24,720 --> 00:23:27,359 Speaker 4: and teachers. Like every single week we get like parents 531 00:23:27,440 --> 00:23:29,800 Speaker 4: writing into us. We have like kids drawing pictures of 532 00:23:29,840 --> 00:23:32,400 Speaker 4: Mamison dodos. Right, So with that inspiration side, we've talked 533 00:23:32,400 --> 00:23:35,280 Speaker 4: about right value creation. There's lots of impacts through these 534 00:23:35,320 --> 00:23:39,240 Speaker 4: technologies to industrial use cases like you know, using genome 535 00:23:39,320 --> 00:23:42,840 Speaker 4: engineering competition analysis to like engineer drought resistant you know, 536 00:23:43,040 --> 00:23:47,320 Speaker 4: animals or plants, or engineering microbes to break down plastics. 537 00:23:47,320 --> 00:23:50,440 Speaker 4: That's another company we built right and so species preservation 538 00:23:50,680 --> 00:23:52,440 Speaker 4: is key. We always say that we're a de extinction 539 00:23:52,640 --> 00:23:55,760 Speaker 4: and species preservation company. Most people don't say the second part, 540 00:23:55,840 --> 00:23:58,960 Speaker 4: they just say the first part. And what that means 541 00:23:59,000 --> 00:24:02,280 Speaker 4: is that all of these technologies that we develop that 542 00:24:02,320 --> 00:24:05,360 Speaker 4: have an application of conservation, we open source for the community. 543 00:24:05,400 --> 00:24:08,040 Speaker 4: We even started a foundation which is called the Colossal 544 00:24:08,040 --> 00:24:11,080 Speaker 4: Foundation that we raised one hundred million dollars for where 545 00:24:11,080 --> 00:24:14,520 Speaker 4: we are have seventy five global partners leveraging our technologies 546 00:24:14,760 --> 00:24:18,480 Speaker 4: to innovate conservation because we are kind of fighting this 547 00:24:18,600 --> 00:24:23,199 Speaker 4: losing war globally against against losing species and extinction. And 548 00:24:23,240 --> 00:24:25,800 Speaker 4: so for us, I think if you fast forward the clock, 549 00:24:25,960 --> 00:24:28,760 Speaker 4: like you know, ten years, I think perfect success in 550 00:24:28,800 --> 00:24:31,320 Speaker 4: our perfect mission be is that we've successfully delivered on 551 00:24:31,359 --> 00:24:33,800 Speaker 4: our promises to the world in terms of our the 552 00:24:33,880 --> 00:24:37,600 Speaker 4: extinction projects and successfully rewilded them back into their habitats 553 00:24:37,600 --> 00:24:41,080 Speaker 4: where they're thriving with indigenous people and governments and everyone 554 00:24:41,080 --> 00:24:43,320 Speaker 4: excited about it. But I think we've also created, like 555 00:24:43,720 --> 00:24:46,240 Speaker 4: you know, a Noah's Ark two dot oh if you will, 556 00:24:46,320 --> 00:24:50,720 Speaker 4: of species that exists today while applying those technologies to 557 00:24:50,760 --> 00:24:52,480 Speaker 4: help save existing species today. 558 00:24:52,680 --> 00:24:54,639 Speaker 3: And I think that that would be kind of our 559 00:24:54,840 --> 00:24:55,560 Speaker 3: ultimate dream. 560 00:24:55,800 --> 00:24:58,240 Speaker 1: Okay, so we're in the dream paint the world. I'm 561 00:24:58,240 --> 00:25:01,159 Speaker 1: walking around what an am I seeing what does the 562 00:25:01,200 --> 00:25:03,520 Speaker 1: world kind of look like if you're able to accomplish 563 00:25:03,840 --> 00:25:05,760 Speaker 1: all of these things that you're hoping to accomplish. 564 00:25:05,840 --> 00:25:08,200 Speaker 4: Yeah, So I think that if we're able to accomplish 565 00:25:08,200 --> 00:25:10,399 Speaker 4: everything that we want to do, I think you're going 566 00:25:10,480 --> 00:25:14,520 Speaker 4: into areas like Alaska and Canada and others in the 567 00:25:14,600 --> 00:25:17,920 Speaker 4: Artic and you're seeing healthy populations of mamos thriving. I 568 00:25:17,920 --> 00:25:20,680 Speaker 4: think you're going into Mauritius and seeing dodos back in 569 00:25:20,720 --> 00:25:24,160 Speaker 4: Marsia's mos back in the South Island of New Zealand. 570 00:25:24,280 --> 00:25:26,400 Speaker 3: So I think you see that, right, and I think you. 571 00:25:26,359 --> 00:25:29,359 Speaker 4: Hopefully see the benefits of more animals and more plants 572 00:25:29,400 --> 00:25:31,800 Speaker 4: and stuff of that in there. I also think that 573 00:25:31,880 --> 00:25:34,480 Speaker 4: you I think hopefully we've done it in a way 574 00:25:34,720 --> 00:25:38,480 Speaker 4: that from an education perspective, people can go and see 575 00:25:38,600 --> 00:25:41,520 Speaker 4: kind of like these seed vaults of animals and what 576 00:25:41,640 --> 00:25:43,960 Speaker 4: could have gone extinct and how it didn't go extinct 577 00:25:44,119 --> 00:25:46,480 Speaker 4: because of innovation and technology. And I think that we 578 00:25:46,560 --> 00:25:49,040 Speaker 4: have to think as a as a species, how do 579 00:25:49,119 --> 00:25:52,240 Speaker 4: we out innovate the take that we have from nature, right, 580 00:25:52,280 --> 00:25:54,919 Speaker 4: because we still have to live our lives, right, and 581 00:25:54,960 --> 00:25:57,800 Speaker 4: we're still going to develop technologies, We're going to make plastics, 582 00:25:57,800 --> 00:25:59,720 Speaker 4: We're still going to consume things. So we have to 583 00:25:59,760 --> 00:26:01,840 Speaker 4: figure fgure out how we do that and live a 584 00:26:01,880 --> 00:26:05,160 Speaker 4: life of abundance while we're also giving back an abundance 585 00:26:05,160 --> 00:26:08,160 Speaker 4: to the world. And so I think in a world 586 00:26:08,200 --> 00:26:10,960 Speaker 4: where kids can also learn not just they see a 587 00:26:11,000 --> 00:26:13,040 Speaker 4: mammoth and they see it in the ecosism, but how 588 00:26:13,119 --> 00:26:15,240 Speaker 4: it was made and then I hope by that time 589 00:26:15,280 --> 00:26:17,760 Speaker 4: that technology has been applied to save elephants and save 590 00:26:18,040 --> 00:26:21,679 Speaker 4: ten other species, right, And then drawing that bridge for people, 591 00:26:21,760 --> 00:26:24,280 Speaker 4: I think is equally important to actually just delivering on 592 00:26:24,320 --> 00:26:24,880 Speaker 4: that message. 593 00:26:24,960 --> 00:26:27,160 Speaker 1: It's almost like you guys are looking at your content. 594 00:26:27,160 --> 00:26:29,080 Speaker 1: It almost feel like a bit of a media company. 595 00:26:29,200 --> 00:26:30,960 Speaker 4: I feel like we're an education of it. Like people 596 00:26:31,000 --> 00:26:32,760 Speaker 4: ask me like well, you know, you guys are the 597 00:26:32,800 --> 00:26:35,080 Speaker 4: most cutting edge Jim eagineering company world is. I think 598 00:26:35,080 --> 00:26:36,879 Speaker 4: we're an education company. I think it's our job to 599 00:26:37,000 --> 00:26:39,280 Speaker 4: educate people on these technologies. 600 00:26:39,359 --> 00:26:41,760 Speaker 1: Well, the most interesting companies now build out a brand, 601 00:26:41,800 --> 00:26:43,920 Speaker 1: and your brand is a different type of science that 602 00:26:44,560 --> 00:26:46,720 Speaker 1: a lot of younger people are talking about right now. 603 00:26:46,920 --> 00:26:49,480 Speaker 1: Are there any animals you decided not to de extinct 604 00:26:49,520 --> 00:26:53,600 Speaker 1: because they were too dangerous or for the reasons anything 605 00:26:53,600 --> 00:26:55,359 Speaker 1: that we do what's on the cutting room floor. 606 00:26:55,480 --> 00:27:00,480 Speaker 4: So there's so we tried it. We tried to in 607 00:27:00,560 --> 00:27:02,879 Speaker 4: kind of that relevant ranking. We tried to focus on 608 00:27:02,920 --> 00:27:07,119 Speaker 4: species that we thought or had an ecological impact, had 609 00:27:07,119 --> 00:27:10,480 Speaker 4: a cultural impact that would inspire people. Right, Like, there's 610 00:27:10,520 --> 00:27:12,879 Speaker 4: a lot of like extinct rats and stuff like that, 611 00:27:12,920 --> 00:27:14,600 Speaker 4: but the world doesn't need more of that. Raps are 612 00:27:14,600 --> 00:27:16,880 Speaker 4: innovasive species on a lot of islands and other things. 613 00:27:17,000 --> 00:27:19,240 Speaker 4: So we didn't do anything like that. You know, we're 614 00:27:19,520 --> 00:27:23,679 Speaker 4: we're not working on anything that's crazy, you know, to 615 00:27:23,760 --> 00:27:27,280 Speaker 4: be transparent though, you know, elephants are you know, people 616 00:27:27,280 --> 00:27:28,960 Speaker 4: were worried about from in about wolves. It was like 617 00:27:29,080 --> 00:27:31,399 Speaker 4: elephants come more people than wolves do, right, And so 618 00:27:32,200 --> 00:27:34,760 Speaker 4: by like five hundred to one. I mean, deers kill 619 00:27:34,840 --> 00:27:37,720 Speaker 4: more people than wolves do, which just insane. So we 620 00:27:37,800 --> 00:27:41,439 Speaker 4: haven't like, I think we never really had species in 621 00:27:41,480 --> 00:27:45,359 Speaker 4: that selection criteria that ever kind of met enough that 622 00:27:45,400 --> 00:27:47,080 Speaker 4: we're like, okay, we should do that, but it's like 623 00:27:47,400 --> 00:27:49,840 Speaker 4: super dangerous. I'd say one that we probably would never 624 00:27:49,920 --> 00:27:53,320 Speaker 4: do is as the short face bear, which is like 625 00:27:53,320 --> 00:27:56,280 Speaker 4: an eighteen foot massive bear. Oh my gosh, yes, we 626 00:27:56,320 --> 00:27:56,840 Speaker 4: would do that. 627 00:27:57,040 --> 00:27:57,920 Speaker 2: The eighteen foot bear. 628 00:27:58,040 --> 00:28:01,120 Speaker 4: Yeah, I know, I don't know, yeah, because you would 629 00:28:01,160 --> 00:28:04,080 Speaker 4: have an instant like super apex predator. And then you 630 00:28:04,119 --> 00:28:05,760 Speaker 4: know what's crazy. We get a lot of requests for this, 631 00:28:05,760 --> 00:28:07,360 Speaker 4: which I don't understand this at all. 632 00:28:07,520 --> 00:28:10,240 Speaker 2: Okay, tell me, maybe I can try Megalanon. 633 00:28:10,480 --> 00:28:14,320 Speaker 4: Way Meglanon. It's like a giant giant sharks, a prehistoric shark. 634 00:28:15,400 --> 00:28:18,520 Speaker 4: Actually made movies about it called the meg which are 635 00:28:18,640 --> 00:28:23,000 Speaker 4: ridiculous best. But what's interesting is, no, I'm not weird. 636 00:28:23,240 --> 00:28:25,040 Speaker 4: At first of all, there's not DNA, so that should 637 00:28:25,119 --> 00:28:26,560 Speaker 4: everyone should just take a big we. 638 00:28:26,560 --> 00:28:27,359 Speaker 2: Can all go swimming. 639 00:28:27,640 --> 00:28:29,880 Speaker 4: But it's like, you know, the ocean is amazing, Yeah, 640 00:28:30,240 --> 00:28:33,119 Speaker 4: absolutely fucking terrifying at times. So it's like I would 641 00:28:33,119 --> 00:28:35,760 Speaker 4: never want to be like knowing that it's out there, 642 00:28:35,800 --> 00:28:38,440 Speaker 4: I would still be scary enough. But we that's another 643 00:28:38,480 --> 00:28:40,640 Speaker 4: weird thing that a lot of people please make the 644 00:28:40,960 --> 00:28:42,760 Speaker 4: megalodon us, Like what do. 645 00:28:42,800 --> 00:28:44,880 Speaker 2: You just have people sliding into your dms being like 646 00:28:44,960 --> 00:28:45,800 Speaker 2: make this may. 647 00:28:45,760 --> 00:28:47,920 Speaker 4: Get a lot of Yeah, I get a lot of stuff. 648 00:28:47,960 --> 00:28:49,320 Speaker 4: Our teams gets a lot of people. 649 00:28:50,120 --> 00:28:52,000 Speaker 1: Well, you brought and I know we have to ensume, 650 00:28:52,040 --> 00:28:53,520 Speaker 1: but I do want to talk because you talked a 651 00:28:53,560 --> 00:28:55,479 Speaker 1: little bit about Because y'all are so out there, your 652 00:28:55,600 --> 00:28:58,920 Speaker 1: big company, big voice, a big idea, you also have critics, right, 653 00:28:58,960 --> 00:29:00,160 Speaker 1: you have the people who are in your camp. If 654 00:29:00,200 --> 00:29:03,320 Speaker 1: you have the people who are like absolutely not scientists. 655 00:29:02,840 --> 00:29:05,120 Speaker 4: Some of the critics, you know, have been the best 656 00:29:05,160 --> 00:29:07,400 Speaker 4: thing for our business, right, because it's like you know, 657 00:29:07,480 --> 00:29:09,200 Speaker 4: I think the I think probably one of the things 658 00:29:09,200 --> 00:29:12,320 Speaker 4: we did right early on was we ran towards critics, right, 659 00:29:12,360 --> 00:29:14,200 Speaker 4: and so like you know, I will say. 660 00:29:14,040 --> 00:29:16,120 Speaker 1: That it's very modern, just like you know, just to 661 00:29:16,200 --> 00:29:17,440 Speaker 1: kind of confront them right on. 662 00:29:17,640 --> 00:29:19,360 Speaker 4: Yeah, there's well there's two no, not even con front 663 00:29:19,360 --> 00:29:22,120 Speaker 4: of that. There's there's two categories of critics. And there's 664 00:29:22,200 --> 00:29:24,080 Speaker 4: ones I care about and ones I don't. The ones 665 00:29:24,120 --> 00:29:25,440 Speaker 4: I care about are the ones that are what I 666 00:29:25,440 --> 00:29:29,160 Speaker 4: call inform critics best. Shapira Archie, science officer, huge critic. 667 00:29:29,320 --> 00:29:32,400 Speaker 4: She wrote a book before I started this company called 668 00:29:32,520 --> 00:29:34,479 Speaker 4: how to Clone a Mammoth and it ends with you 669 00:29:34,520 --> 00:29:36,880 Speaker 4: can't I mean that? And I was like yeah, But 670 00:29:37,480 --> 00:29:38,560 Speaker 4: so I went and said down with Beth and I 671 00:29:38,640 --> 00:29:40,440 Speaker 4: was like, but she's the number one ancient DNA expert 672 00:29:40,480 --> 00:29:43,000 Speaker 4: in the world, so I would downstep down best, like, 673 00:29:43,200 --> 00:29:44,920 Speaker 4: you can't clone a mamith. I totally right there, but 674 00:29:44,960 --> 00:29:47,600 Speaker 4: you can engineer one. And she was like, I think 675 00:29:47,640 --> 00:29:49,800 Speaker 4: you could engineer one, right and so and so I 676 00:29:49,800 --> 00:29:50,080 Speaker 4: look that. 677 00:29:50,080 --> 00:29:51,720 Speaker 1: You just recruited the person who wrote the book that 678 00:29:51,760 --> 00:29:53,800 Speaker 1: said you can't write that you can't bring back the 679 00:29:53,800 --> 00:29:55,360 Speaker 1: wille mammis actually. 680 00:29:55,040 --> 00:29:57,080 Speaker 4: Like she had thought through the process. So anyway, we 681 00:29:57,080 --> 00:29:59,120 Speaker 4: have lots of really great critics or sorry, not great. 682 00:29:59,200 --> 00:30:01,040 Speaker 4: We have lots of critics. It give us lots of 683 00:30:01,040 --> 00:30:02,400 Speaker 4: feedback that's pretty valuable. 684 00:30:02,520 --> 00:30:02,720 Speaker 1: Yeah. 685 00:30:02,840 --> 00:30:05,960 Speaker 4: I got called out when we were launching before launched 686 00:30:05,960 --> 00:30:08,200 Speaker 4: the thioscene and we launched the Mammoth, and you know, 687 00:30:08,480 --> 00:30:10,480 Speaker 4: a very big critic. It was like, has he ever 688 00:30:10,520 --> 00:30:12,520 Speaker 4: even been to Alaska? Has he been to Canah has 689 00:30:12,520 --> 00:30:14,760 Speaker 4: he talked to indigenous people about the mammoth. And I 690 00:30:14,800 --> 00:30:17,400 Speaker 4: was like, that's a really good point. So I got 691 00:30:17,480 --> 00:30:19,520 Speaker 4: my ass on a plane and flew up there. And 692 00:30:19,560 --> 00:30:23,200 Speaker 4: then before we launched the Thiolascene, I flew to Tasmania 693 00:30:23,240 --> 00:30:24,720 Speaker 4: and I flued it and I went to Central I 694 00:30:24,720 --> 00:30:26,560 Speaker 4: went and met with everyone I could because I wanted 695 00:30:26,680 --> 00:30:28,400 Speaker 4: to actually hear from them. But then you do have 696 00:30:28,400 --> 00:30:30,080 Speaker 4: the other critics that are like, I don't like it 697 00:30:30,120 --> 00:30:32,800 Speaker 4: because it's Tuesday, and you know, we can't do much 698 00:30:32,840 --> 00:30:33,200 Speaker 4: about that. 699 00:30:33,520 --> 00:30:36,840 Speaker 1: Well, I saw someone they said this is Someone said 700 00:30:37,320 --> 00:30:41,360 Speaker 1: Colossal's attempts are genetically engineered, poor copies at best, passed 701 00:30:41,360 --> 00:30:44,280 Speaker 1: off as the real deal in Colossal spreading misinformation. So 702 00:30:44,360 --> 00:30:47,360 Speaker 1: it's like pretty and I see this too. You get 703 00:30:47,360 --> 00:30:49,480 Speaker 1: this a lot. So what do you say to someone 704 00:30:49,480 --> 00:30:50,880 Speaker 1: who works in a lab who says that. 705 00:30:51,040 --> 00:30:53,920 Speaker 4: So we're big freedom of speech people, right, we believe 706 00:30:53,960 --> 00:30:56,200 Speaker 4: in everyone's right, Like, if you want to call our 707 00:30:56,280 --> 00:30:59,400 Speaker 4: mammoth a wooly mammoth, you should do that. If you 708 00:30:59,440 --> 00:31:02,920 Speaker 4: want to call our mammoth a cold tolerant, genetically modified 709 00:31:03,000 --> 00:31:06,440 Speaker 4: Asian elephant with mammoth alliles across one hundred and four 710 00:31:06,760 --> 00:31:11,960 Speaker 4: different mammoth genomes with targeted select edits that have been 711 00:31:12,080 --> 00:31:15,080 Speaker 4: conserved over a one point two million years. You can 712 00:31:15,080 --> 00:31:17,400 Speaker 4: say that too, like it's you're right, But the reality 713 00:31:17,480 --> 00:31:20,479 Speaker 4: is is that that argument is that speciation argument. This 714 00:31:20,520 --> 00:31:24,280 Speaker 4: is a semantic argument that really is in the eye 715 00:31:24,320 --> 00:31:26,520 Speaker 4: of the beholder. Because humans like to put things in boxes. 716 00:31:26,560 --> 00:31:29,960 Speaker 4: And so if someone like whoever said that feels a way, 717 00:31:30,560 --> 00:31:32,560 Speaker 4: that's that's okay for them to feel that way, right. 718 00:31:32,640 --> 00:31:35,240 Speaker 4: It's like, I don't think in this world if you do, 719 00:31:35,360 --> 00:31:39,240 Speaker 4: if you're doing things right and you're innovating, and you're 720 00:31:39,240 --> 00:31:41,680 Speaker 4: not going to have one hundred percent of people behind you, right, 721 00:31:41,720 --> 00:31:43,800 Speaker 4: And but I think that that person has every right 722 00:31:43,840 --> 00:31:44,480 Speaker 4: to that opinion. 723 00:31:44,680 --> 00:31:46,960 Speaker 1: And I want to go back and end kind of 724 00:31:47,000 --> 00:31:49,800 Speaker 1: where we started. There is something I don't know what 725 00:31:49,840 --> 00:31:52,440 Speaker 1: it is about the wooly mammoth that people are so 726 00:31:52,560 --> 00:31:55,400 Speaker 1: drawn to or feel I mean nostalgic for even though it. 727 00:31:56,680 --> 00:31:58,880 Speaker 3: Ray you know, like you did the Ice Age movies, 728 00:31:58,920 --> 00:31:59,320 Speaker 3: and yeah, I. 729 00:32:00,120 --> 00:32:00,640 Speaker 2: Think it is. 730 00:32:00,640 --> 00:32:02,960 Speaker 4: Is it just touching on our culture that I think 731 00:32:03,000 --> 00:32:06,080 Speaker 4: it's something that's just out of memory, right, Like, so 732 00:32:06,120 --> 00:32:08,720 Speaker 4: the thilasine went extinct you know, a little ol eighty 733 00:32:08,920 --> 00:32:12,040 Speaker 4: years ago and so like it is in generational memory, right, 734 00:32:12,040 --> 00:32:14,920 Speaker 4: But the wooly mammoth is like people put it in 735 00:32:14,960 --> 00:32:17,560 Speaker 4: the camp of dinosaurs, right, yeah, even though there were 736 00:32:17,560 --> 00:32:21,480 Speaker 4: sixty five million years of genetic divergence of time between that. 737 00:32:21,800 --> 00:32:25,000 Speaker 4: But it's just out of our reach, right in a way, 738 00:32:25,040 --> 00:32:27,240 Speaker 4: Like people don't realize this, but there were mammoths on 739 00:32:27,320 --> 00:32:30,560 Speaker 4: the Earth while we were building pyramids. Like that's crazy, right, 740 00:32:30,600 --> 00:32:33,200 Speaker 4: and so like it wasn't just out of our reach, 741 00:32:33,240 --> 00:32:35,040 Speaker 4: but it was just out of humanity's reach, right, And 742 00:32:35,080 --> 00:32:38,080 Speaker 4: so it feels like we were there because we did 743 00:32:38,120 --> 00:32:40,840 Speaker 4: coexist for some point. And so then they're also a 744 00:32:40,840 --> 00:32:45,000 Speaker 4: big cute you know, you know megafauna. That's charismatic. So 745 00:32:45,200 --> 00:32:47,280 Speaker 4: I think that it checks a lot of boxes. 746 00:32:47,440 --> 00:32:50,720 Speaker 1: Yeah, I think I was saying before we started the interview, 747 00:32:51,000 --> 00:32:52,720 Speaker 1: a new mom, I have an eleven month old, you're 748 00:32:52,720 --> 00:32:56,720 Speaker 1: a new dad, and I cannot and this sounds so cliche, 749 00:32:56,800 --> 00:32:58,120 Speaker 1: so I don't want to end on a cliche, but 750 00:32:58,160 --> 00:33:00,560 Speaker 1: I cannot stop thinking about the world that my son 751 00:33:00,680 --> 00:33:04,280 Speaker 1: is going to grow up in. And I imagine having 752 00:33:04,680 --> 00:33:09,200 Speaker 1: little ones has changed you in every way possible and 753 00:33:09,720 --> 00:33:11,880 Speaker 1: viewing the work you do. So I guess my last 754 00:33:11,920 --> 00:33:17,480 Speaker 1: question is how has actually having a child shaped your viewpoint? 755 00:33:17,840 --> 00:33:20,719 Speaker 4: I think it, you know, I think it just i' 756 00:33:20,760 --> 00:33:23,080 Speaker 4: say in two ways, right, I think I think we 757 00:33:23,120 --> 00:33:25,120 Speaker 4: inherited a lot of problems. I think we created a 758 00:33:25,160 --> 00:33:26,640 Speaker 4: lot of problems, and I think our kids are going 759 00:33:26,680 --> 00:33:29,120 Speaker 4: to inherit those some of those problems, but they're also 760 00:33:29,160 --> 00:33:31,680 Speaker 4: going to inherit our solutions. So a lot of times 761 00:33:31,720 --> 00:33:33,479 Speaker 4: I think people are quick to jump and say, we 762 00:33:33,520 --> 00:33:35,320 Speaker 4: just need to go make this because we'll saw that X. 763 00:33:35,560 --> 00:33:37,840 Speaker 4: But our kids are also going to are and get 764 00:33:37,880 --> 00:33:40,880 Speaker 4: inherit our solutions. Are we doing the solutions ethically? Are 765 00:33:40,880 --> 00:33:43,280 Speaker 4: we doing that? So I try to think about that 766 00:33:43,400 --> 00:33:46,560 Speaker 4: as we build Colossal, because ultimately, you know, you know, 767 00:33:46,600 --> 00:33:48,800 Speaker 4: I don't want my son and daughter to both you know, 768 00:33:48,920 --> 00:33:51,840 Speaker 4: inherit the problems of ours but also hate the way 769 00:33:51,880 --> 00:33:53,920 Speaker 4: that we are trying to solve them, right, And so 770 00:33:53,920 --> 00:33:55,720 Speaker 4: so I think that our kids are going to also 771 00:33:55,760 --> 00:33:58,440 Speaker 4: inherit those solutions, and I think it's just really important 772 00:33:58,480 --> 00:33:59,640 Speaker 4: to kind of think through that. 773 00:34:03,320 --> 00:34:05,960 Speaker 1: Mostly Human is a production of iHeart Podcasts and mostly 774 00:34:06,040 --> 00:34:09,320 Speaker 1: Human Media. It's produced and edited by Laurie Siegel, Lauren Hanson, 775 00:34:09,440 --> 00:34:13,040 Speaker 1: and Nicole Bouchet. Sound design and mixing by Derek Clements, 776 00:34:13,360 --> 00:34:16,360 Speaker 1: additional production help from Abooz of Bar special thanks to 777 00:34:16,400 --> 00:34:20,000 Speaker 1: Mark Weinhaus. Find us on all socials at mostly human Media. 778 00:34:20,239 --> 00:34:22,600 Speaker 1: You can also watch mostly Human on our YouTube page. 779 00:34:22,680 --> 00:34:24,480 Speaker 1: If you want to get in touch, email us at 780 00:34:24,480 --> 00:34:27,520 Speaker 1: hello at mostlyhuman dot com. And if you like what 781 00:34:27,560 --> 00:34:29,839 Speaker 1: you're here, please rate and review the show and share 782 00:34:29,880 --> 00:34:30,560 Speaker 1: it with your friends. 783 00:34:30,600 --> 00:34:31,359 Speaker 2: See you next week.