1 00:00:13,039 --> 00:00:16,480 Speaker 1: Welcome to tech Stuff. This is the story. Each week 2 00:00:16,520 --> 00:00:19,200 Speaker 1: on Wednesdays, we bring you an in depth interview with 3 00:00:19,280 --> 00:00:21,319 Speaker 1: someone who has a front row seat to the most 4 00:00:21,320 --> 00:00:26,320 Speaker 1: fascinating things happening in tech today. We're joined by Ben Lamb, 5 00:00:26,720 --> 00:00:31,840 Speaker 1: an entrepreneur and the founder and CEO of Colossal Biosciences. 6 00:00:32,800 --> 00:00:36,000 Speaker 1: By now, you might have heard of the company's wooly mice, 7 00:00:36,640 --> 00:00:39,800 Speaker 1: fluffy creatures with the same type of fur as the 8 00:00:39,880 --> 00:00:43,680 Speaker 1: extinct wooly mammoth. Or maybe you saw the April issue 9 00:00:43,680 --> 00:00:47,279 Speaker 1: of Time magazine, the one with a white direwolf on 10 00:00:47,280 --> 00:00:50,239 Speaker 1: the cover, a type of wolf that hasn't walked the 11 00:00:50,280 --> 00:00:54,640 Speaker 1: Earth in over ten thousand years. It might sound like sorcery, 12 00:00:55,000 --> 00:01:02,120 Speaker 1: but Colossal Biosciences actually genetically engineered these animals ancient DNA, 13 00:01:02,160 --> 00:01:05,920 Speaker 1: and while resurrecting extinct animals is pretty cool on its own, 14 00:01:06,480 --> 00:01:10,200 Speaker 1: our guest today, Ben Lamb, wanted to start the company 15 00:01:10,400 --> 00:01:13,760 Speaker 1: because of what he believed the extinction could do for 16 00:01:13,880 --> 00:01:17,200 Speaker 1: our current ecosystem and the future of science. 17 00:01:17,640 --> 00:01:20,160 Speaker 2: I learned in this process that in the next twenty 18 00:01:20,200 --> 00:01:23,039 Speaker 2: five years, half of species will either be extinct or 19 00:01:23,040 --> 00:01:25,839 Speaker 2: be at least threatened with extinction. So I thought maybe 20 00:01:25,840 --> 00:01:27,800 Speaker 2: there was an opportunity to build a company that we 21 00:01:27,840 --> 00:01:31,800 Speaker 2: could develop tools to help conservation, hopefully inspire kids and 22 00:01:31,800 --> 00:01:34,440 Speaker 2: give people excited about science through something that was like 23 00:01:34,480 --> 00:01:36,640 Speaker 2: truly a moonshot and like the Apollo Days. 24 00:01:37,440 --> 00:01:41,000 Speaker 1: CEO Ben Lamb has a background in tech entrepreneurship, but 25 00:01:41,040 --> 00:01:45,400 Speaker 1: became interested in the extinction after meeting the renowned geneticist 26 00:01:45,640 --> 00:01:50,680 Speaker 1: George Church and having a pivotal experience in his own life. 27 00:01:51,240 --> 00:01:53,680 Speaker 1: Ben Lamb, thank you so much for joining on tech stuff. 28 00:01:53,840 --> 00:01:55,040 Speaker 3: Hey, thanks so much for having me. 29 00:01:55,200 --> 00:01:57,200 Speaker 1: So we're going to talk about Willy mammoths and Willie 30 00:01:57,240 --> 00:02:00,880 Speaker 1: mice and dire wolves and gene editing and fa but 31 00:02:00,920 --> 00:02:03,440 Speaker 1: I kind of want to start with you and your 32 00:02:03,520 --> 00:02:09,480 Speaker 1: drive to create arguably the most engaging, live scientific experiment 33 00:02:09,840 --> 00:02:13,720 Speaker 1: ongoing in the world. So my question is why this 34 00:02:13,840 --> 00:02:16,240 Speaker 1: science project and why this mission for you? 35 00:02:16,840 --> 00:02:20,480 Speaker 2: Yeah, so my background is in just building technology companies 36 00:02:20,520 --> 00:02:23,480 Speaker 2: with much smarter women and men than me, and I 37 00:02:23,600 --> 00:02:26,359 Speaker 2: met this guy, George Church. He's the head of genetics 38 00:02:26,400 --> 00:02:29,320 Speaker 2: at Harvard and he'd actually invented a lot of these 39 00:02:29,360 --> 00:02:31,520 Speaker 2: core technologies. So he's one of the first people to 40 00:02:31,600 --> 00:02:34,600 Speaker 2: ever use Crisper and some of these advanced technologies in 41 00:02:34,680 --> 00:02:38,040 Speaker 2: genome engineering, and I was really fascinated about the intersection 42 00:02:38,680 --> 00:02:42,880 Speaker 2: of synthetic biology, access to compute and AI and really 43 00:02:42,919 --> 00:02:45,519 Speaker 2: being able to direct life and be able to engineer 44 00:02:45,600 --> 00:02:47,640 Speaker 2: life and be able to evolve life. 45 00:02:47,720 --> 00:02:47,880 Speaker 3: Right. 46 00:02:48,000 --> 00:02:51,200 Speaker 2: And one of my employees, my chief strategy officer, was fantastic. 47 00:02:51,240 --> 00:02:53,959 Speaker 2: His name was Greg. He passed away of a sudden 48 00:02:54,000 --> 00:02:57,240 Speaker 2: cardiac event. And it causes you to really kind of 49 00:02:57,480 --> 00:03:00,880 Speaker 2: you reconsider your priorities, and especially when with someone that 50 00:03:00,919 --> 00:03:04,320 Speaker 2: you've known for fifteen years. You're flying back from NASA 51 00:03:04,600 --> 00:03:07,120 Speaker 2: one night joking about UFOs and the next morning you're 52 00:03:07,160 --> 00:03:10,760 Speaker 2: talking to his wife and widow right, and so you 53 00:03:10,760 --> 00:03:15,680 Speaker 2: know those moments where mortality and reality hits you. I 54 00:03:15,720 --> 00:03:18,560 Speaker 2: think you know our major inflection points. So I thought 55 00:03:18,560 --> 00:03:20,799 Speaker 2: I would jump into the weird world of biology. 56 00:03:21,480 --> 00:03:24,000 Speaker 1: You had that moment of how much time might I 57 00:03:24,040 --> 00:03:25,760 Speaker 1: have left? And the time I do have left is 58 00:03:26,120 --> 00:03:28,680 Speaker 1: more of a blessing than I may have considered. Therefore, 59 00:03:28,680 --> 00:03:31,160 Speaker 1: how do I want to spend it? I remember, I 60 00:03:31,200 --> 00:03:35,080 Speaker 1: think before twenty twenty, reading about George Church and reading 61 00:03:35,120 --> 00:03:38,120 Speaker 1: about the Wooly memmoth project and being fascinated by it, 62 00:03:38,160 --> 00:03:41,320 Speaker 1: but how did you catch this de extinction bug? In 63 00:03:41,320 --> 00:03:43,520 Speaker 1: other words, going not just from the mammoth but to 64 00:03:43,560 --> 00:03:46,080 Speaker 1: this larger project you're working on mount. 65 00:03:46,320 --> 00:03:50,000 Speaker 2: So I had met George and was fascinated by the 66 00:03:50,080 --> 00:03:52,600 Speaker 2: fact that this person that's at the top of their 67 00:03:52,680 --> 00:03:54,400 Speaker 2: field had told. 68 00:03:54,160 --> 00:03:56,320 Speaker 3: Me that we have all the tools to do this. 69 00:03:56,320 --> 00:03:57,120 Speaker 3: This is really a. 70 00:03:57,040 --> 00:04:00,720 Speaker 2: Function of funding, and if we could bring the right 71 00:04:00,760 --> 00:04:04,360 Speaker 2: people together with the right focus and the right funding, 72 00:04:04,480 --> 00:04:07,200 Speaker 2: we had the technologies to make extinction a thing of 73 00:04:07,240 --> 00:04:10,000 Speaker 2: the past. But over time, if we can make them 74 00:04:10,240 --> 00:04:14,520 Speaker 2: more efficient, well then that has massive ripples into conservation, 75 00:04:14,640 --> 00:04:15,720 Speaker 2: into human healthcare. 76 00:04:15,920 --> 00:04:16,680 Speaker 3: So he told me that. 77 00:04:16,680 --> 00:04:18,719 Speaker 2: In twenty nineteen, I just kind of went back to 78 00:04:18,720 --> 00:04:20,520 Speaker 2: my day job and I was interested. 79 00:04:20,600 --> 00:04:22,679 Speaker 3: Maybe I'd fund his lab, maybe I'd work with him. 80 00:04:22,720 --> 00:04:25,720 Speaker 2: But then after I went through this introspection period, I 81 00:04:25,760 --> 00:04:28,840 Speaker 2: was like, well, worst case scenario, this is a massive failure, 82 00:04:28,880 --> 00:04:31,479 Speaker 2: and I'll just go back to making software. And then 83 00:04:31,720 --> 00:04:34,800 Speaker 2: what George was right about was there are no real 84 00:04:34,920 --> 00:04:38,599 Speaker 2: science gits. We have all of the tech in some 85 00:04:38,839 --> 00:04:41,560 Speaker 2: form or fashion, and so we really these are in 86 00:04:41,640 --> 00:04:44,880 Speaker 2: some cases innovation problems. And I think that with AI 87 00:04:44,960 --> 00:04:49,440 Speaker 2: and automation, we're seeing scientific discoveries slowly moving from scientific 88 00:04:49,560 --> 00:04:53,919 Speaker 2: experiment to scientific engineering. So I think that science and 89 00:04:53,960 --> 00:04:56,840 Speaker 2: engineering are going to continue to blur and get closer 90 00:04:56,880 --> 00:04:59,400 Speaker 2: and closer together in the next five to ten years. 91 00:05:00,400 --> 00:05:03,240 Speaker 1: What do you actually mean by the extinction? And was 92 00:05:03,279 --> 00:05:06,360 Speaker 1: there an accepted definition or if you kind of created 93 00:05:06,520 --> 00:05:08,560 Speaker 1: to concept it on with George. 94 00:05:08,600 --> 00:05:10,560 Speaker 2: So, the extinction is a made up word, right, like 95 00:05:10,600 --> 00:05:12,400 Speaker 2: a stinction of the word the extinction. You know, we 96 00:05:12,480 --> 00:05:15,800 Speaker 2: learned from Jurassic Park and whatnot. It's become one of 97 00:05:15,800 --> 00:05:19,599 Speaker 2: these these just part of the nomenclature. And up until 98 00:05:19,680 --> 00:05:24,800 Speaker 2: this year, Wikipedia defined the extinction as engineering a species 99 00:05:24,800 --> 00:05:28,479 Speaker 2: to look like an extinct species, or cloning an extinct species. Well, 100 00:05:28,880 --> 00:05:30,920 Speaker 2: I don't like to say things are impossible, but it 101 00:05:30,960 --> 00:05:34,200 Speaker 2: doesn't seem like it is or will be possible to 102 00:05:34,320 --> 00:05:37,200 Speaker 2: clone an extinct species because you don't have living cells. 103 00:05:37,240 --> 00:05:40,159 Speaker 2: You're not just going to take nucleus from one cell 104 00:05:40,240 --> 00:05:42,279 Speaker 2: and put it into another cell because there are no 105 00:05:42,400 --> 00:05:43,560 Speaker 2: living cells. 106 00:05:43,560 --> 00:05:46,080 Speaker 3: For extinct organism that's been dead for quite some time. 107 00:05:46,160 --> 00:05:48,640 Speaker 2: Right, we thought that just engineering something that look like 108 00:05:48,680 --> 00:05:51,839 Speaker 2: an extinct species is kind of like phoning it in, 109 00:05:52,279 --> 00:05:54,160 Speaker 2: and so we came up with this idea that the 110 00:05:54,160 --> 00:05:56,800 Speaker 2: definition of the extinction was flawed, and so we really 111 00:05:56,839 --> 00:05:59,960 Speaker 2: wanted to focus on how do we bring back loss diversity, 112 00:06:00,240 --> 00:06:03,120 Speaker 2: lost genes, how do we ensure that it has the 113 00:06:03,200 --> 00:06:06,360 Speaker 2: core phenotypes or physical attributes, but are also are there 114 00:06:06,440 --> 00:06:07,800 Speaker 2: opportunities for enhancements? 115 00:06:08,080 --> 00:06:12,200 Speaker 1: Right, So I guess you're thinking beyond using genetic engineering 116 00:06:12,279 --> 00:06:17,240 Speaker 1: to make animals that look like extinct animals, but actually 117 00:06:17,279 --> 00:06:22,200 Speaker 1: trying to bring back extinct genes or re express extinct genes, 118 00:06:23,360 --> 00:06:25,159 Speaker 1: and then in turn trying to see if any of 119 00:06:25,200 --> 00:06:28,040 Speaker 1: them can be used to solve genetic problems that are 120 00:06:28,040 --> 00:06:29,200 Speaker 1: found in species today. 121 00:06:29,440 --> 00:06:32,200 Speaker 2: So I'll give you one example of that is like EEHV, 122 00:06:32,320 --> 00:06:35,760 Speaker 2: which is the number one killer of elephants, specifically Asian elephants, 123 00:06:35,760 --> 00:06:37,960 Speaker 2: but it kills about twenty percent of elephants every year. 124 00:06:37,880 --> 00:06:39,279 Speaker 3: More than poaching, more than anything. 125 00:06:39,320 --> 00:06:42,279 Speaker 2: We're working with Baylor College of Medicine and others to 126 00:06:42,320 --> 00:06:46,280 Speaker 2: actually eradicate this disease, and we've actually have a MR 127 00:06:46,320 --> 00:06:48,919 Speaker 2: and A based vaccine that's being tested right now in 128 00:06:49,000 --> 00:06:53,040 Speaker 2: elephants and is conferring resistance, which is awesome for existing elephants. 129 00:06:53,400 --> 00:06:56,960 Speaker 2: But here's the deal, mammas are actually closely related to 130 00:06:57,000 --> 00:06:59,720 Speaker 2: Asian elephants. They are African elephants, and they were susceptible 131 00:06:59,720 --> 00:06:59,960 Speaker 2: to eat. 132 00:07:00,520 --> 00:07:01,039 Speaker 3: We know that. 133 00:07:01,360 --> 00:07:05,400 Speaker 2: And so if you can engineer in resilience at EEHV, 134 00:07:06,360 --> 00:07:07,120 Speaker 2: why wouldn't you. 135 00:07:07,480 --> 00:07:10,080 Speaker 1: I really want to talk about the wooly mice and 136 00:07:10,120 --> 00:07:12,960 Speaker 1: the dire wolves. I was at south By Southwest this 137 00:07:13,080 --> 00:07:16,400 Speaker 1: year as you were, and I think the release of 138 00:07:16,440 --> 00:07:19,480 Speaker 1: the wooly mouse. I've never seen a conference being taken 139 00:07:19,560 --> 00:07:21,800 Speaker 1: by storm, or not just a conference, I mean the 140 00:07:21,840 --> 00:07:24,680 Speaker 1: whole internet, frankly, as the release of. 141 00:07:24,760 --> 00:07:27,200 Speaker 3: The wooly mouse, I think you saw the direwolves, and. 142 00:07:27,240 --> 00:07:29,680 Speaker 1: Then and then you saw the dire wolves, which, yeah, 143 00:07:29,960 --> 00:07:31,960 Speaker 1: which were an apex predator. When it came to hype 144 00:07:32,680 --> 00:07:34,680 Speaker 1: that said, it's a little uncanny to talk about animals 145 00:07:34,720 --> 00:07:38,480 Speaker 1: as though their product releases. But hold my hand and 146 00:07:38,480 --> 00:07:40,560 Speaker 1: walk me through this. You guys are trying to make 147 00:07:40,680 --> 00:07:43,560 Speaker 1: the wooly mammoth, a species that existed and then went 148 00:07:43,600 --> 00:07:48,320 Speaker 1: extinct thousands of years ago, and in that process you 149 00:07:48,360 --> 00:07:51,040 Speaker 1: make an entirely new creature, which is the wooly mouse. 150 00:07:52,200 --> 00:07:54,440 Speaker 1: Why mice? Why not just work on the mammoth? 151 00:07:55,120 --> 00:07:58,160 Speaker 3: So one we wanted to test our Indian pipeline two. 152 00:07:58,280 --> 00:08:00,320 Speaker 2: We want to ensure that if your I didner find 153 00:08:00,320 --> 00:08:03,600 Speaker 2: phenotypes or physical attributes that you believe will be engineered 154 00:08:03,640 --> 00:08:05,920 Speaker 2: that have been lost to time in the Asian elephant lineage, 155 00:08:05,920 --> 00:08:09,120 Speaker 2: but engineered back into that lineage from the mammoth. And 156 00:08:09,160 --> 00:08:13,040 Speaker 2: you want to confer cold tolerance, hair color, hair texture, 157 00:08:13,240 --> 00:08:16,280 Speaker 2: hair thickness, wave length. Your three options, or you make 158 00:08:16,320 --> 00:08:19,440 Speaker 2: a mammoth, right, But going back to an ethics perspective, 159 00:08:19,560 --> 00:08:21,600 Speaker 2: like if you have better ways to test it, let's 160 00:08:21,640 --> 00:08:22,280 Speaker 2: test it there. 161 00:08:22,520 --> 00:08:25,080 Speaker 3: The second is you grow organoids, which this sounds. 162 00:08:24,800 --> 00:08:28,720 Speaker 2: Frankenstein, It's super cool, though, is we actually create stem cells, 163 00:08:29,080 --> 00:08:32,280 Speaker 2: program them into organoids, and we actually grow mammoth hair 164 00:08:32,320 --> 00:08:36,040 Speaker 2: in little follicles and dishes, so it's alive, so we. 165 00:08:36,000 --> 00:08:36,640 Speaker 3: Know it's working. 166 00:08:36,800 --> 00:08:39,000 Speaker 2: But that's still not a complete animal model, right, And 167 00:08:39,080 --> 00:08:43,199 Speaker 2: so taking that same into end pipeline, applying it from 168 00:08:43,440 --> 00:08:48,400 Speaker 2: the specific variance from mammoth to the mouse specific variance. 169 00:08:48,120 --> 00:08:50,520 Speaker 1: In terms of the gene that expresses as hey, you 170 00:08:50,559 --> 00:08:53,000 Speaker 1: can mention the mammoth headed gene to the mouse head. 171 00:08:52,920 --> 00:08:56,040 Speaker 2: Gene exactly exactly when we did this for the mouse, 172 00:08:56,080 --> 00:08:58,720 Speaker 2: It's like there's two hundred million years of genetic divergence 173 00:08:58,960 --> 00:09:02,520 Speaker 2: between an ELpH in the mouse, and so I believe 174 00:09:02,640 --> 00:09:06,440 Speaker 2: it's irresponsible and unethical to just shove it in, cross 175 00:09:06,480 --> 00:09:09,200 Speaker 2: your fingers and see what happens, right, because why would 176 00:09:09,240 --> 00:09:11,240 Speaker 2: we do that, right, Because we're really trying to test 177 00:09:11,280 --> 00:09:14,640 Speaker 2: if the pathways and the edits express the phenotypes or 178 00:09:14,640 --> 00:09:17,320 Speaker 2: physical attributes. Right, So we did an extra step of 179 00:09:17,400 --> 00:09:20,079 Speaker 2: like mapping all to mouse, and what we found was that, 180 00:09:20,400 --> 00:09:23,960 Speaker 2: you know, in twenty days versus twenty two months, that 181 00:09:24,160 --> 00:09:26,400 Speaker 2: all of the hair phenotypes. 182 00:09:25,800 --> 00:09:28,680 Speaker 1: Twenty days versus twenty two months because the gestation period 183 00:09:28,720 --> 00:09:31,200 Speaker 1: of the mouse versus an elephant correct correct. 184 00:09:31,280 --> 00:09:34,240 Speaker 2: And we found that we get healthy wooly mice with 185 00:09:34,320 --> 00:09:38,480 Speaker 2: the exact predicted phenotype that all of our modeling showed. 186 00:09:38,840 --> 00:09:41,559 Speaker 2: And so it's a much faster, easier, and more ethical 187 00:09:41,559 --> 00:09:45,599 Speaker 2: way to test. And then the only unintended consequence is 188 00:09:45,640 --> 00:09:48,600 Speaker 2: that they were objectively cute and they took the Internet 189 00:09:48,600 --> 00:09:52,280 Speaker 2: by storm. We thought it was interesting and we thought 190 00:09:52,320 --> 00:09:54,680 Speaker 2: it proved our endo end process works really well. 191 00:09:55,000 --> 00:09:56,800 Speaker 3: We did in one month, by the way, which I 192 00:09:56,800 --> 00:09:58,720 Speaker 3: think no one seemed to write or care about. 193 00:09:58,800 --> 00:10:01,840 Speaker 2: But that's amazing and most people at the time, we 194 00:10:01,880 --> 00:10:04,439 Speaker 2: made eight edits and seven genes. And what most people 195 00:10:04,600 --> 00:10:07,840 Speaker 2: think is that sometimes the number like eight doesn't sound 196 00:10:07,840 --> 00:10:10,439 Speaker 2: like a big deal, but when you're dealing with genome engineering, 197 00:10:11,040 --> 00:10:14,360 Speaker 2: and most people were making an edit in one generation 198 00:10:14,440 --> 00:10:16,559 Speaker 2: of a mouse, making an edit in the next generation 199 00:10:16,640 --> 00:10:19,800 Speaker 2: of mouse, and so that they were stacking eight generations 200 00:10:19,840 --> 00:10:23,520 Speaker 2: to get to eight edits, right, And so we did 201 00:10:23,559 --> 00:10:27,440 Speaker 2: it all with one multiplex delivery in one shot with 202 00:10:28,080 --> 00:10:30,800 Speaker 2: nearly one hundred percent efficiency, which is insane, But we 203 00:10:30,840 --> 00:10:33,400 Speaker 2: did it because we wanted to really do three things. 204 00:10:33,480 --> 00:10:36,360 Speaker 2: One is, we built this end the end process right 205 00:10:36,440 --> 00:10:40,760 Speaker 2: of taking competitional analysis from ancient DNA, identifying genes, engineering 206 00:10:40,840 --> 00:10:44,240 Speaker 2: those genes, and doing a combination of either editing those 207 00:10:44,320 --> 00:10:48,199 Speaker 2: in the actual embryos themselves or somatic some onny code transfer. 208 00:10:47,840 --> 00:10:50,120 Speaker 3: Where we edit the cell and then move the nucleus. 209 00:10:50,160 --> 00:10:53,199 Speaker 2: And then we wanted to test our monoclonal screening process 210 00:10:53,200 --> 00:10:56,280 Speaker 2: at the end to ensure that all the sequencing. So 211 00:10:56,360 --> 00:10:59,400 Speaker 2: Colossal does a lot of extra steps. Probably the most 212 00:10:59,440 --> 00:11:00,680 Speaker 2: people would in our. 213 00:11:00,960 --> 00:11:03,920 Speaker 1: Boat this just to make sure you don't make Frankenstein 214 00:11:04,000 --> 00:11:06,720 Speaker 1: woody mice switch it, which have horrible illnesses and which 215 00:11:06,720 --> 00:11:08,920 Speaker 1: are in pain and one of those things exactly. 216 00:11:08,960 --> 00:11:10,480 Speaker 3: So none of these technologies are perfect. 217 00:11:10,559 --> 00:11:10,760 Speaker 1: Right. 218 00:11:10,840 --> 00:11:14,080 Speaker 2: You know, before the Wooly Mouse and before the Dire Wolf, 219 00:11:14,120 --> 00:11:16,960 Speaker 2: people were making like one, maybe two edits at a 220 00:11:17,000 --> 00:11:19,600 Speaker 2: time that weren't what are called linear repeats. 221 00:11:19,600 --> 00:11:21,240 Speaker 3: We neither the same edit over and over again. 222 00:11:21,280 --> 00:11:23,400 Speaker 2: Right, there's thousands of those, but those are just copies 223 00:11:23,440 --> 00:11:25,440 Speaker 2: of the same thing. Right, But making a lot of 224 00:11:25,559 --> 00:11:28,560 Speaker 2: edits all over the genome with ninety plus percent efficiency 225 00:11:28,920 --> 00:11:31,760 Speaker 2: and not creating what's called off target effects or unintended 226 00:11:31,760 --> 00:11:35,080 Speaker 2: consequences is what you're talking about. It's really really hard, 227 00:11:35,080 --> 00:11:37,800 Speaker 2: and that's where I think Colossal is really succeeding. But 228 00:11:37,920 --> 00:11:40,920 Speaker 2: still you want to screen, and so we sequence, and 229 00:11:41,000 --> 00:11:42,400 Speaker 2: so think about like sequencing is. 230 00:11:42,400 --> 00:11:43,200 Speaker 3: Reading the DNA. 231 00:11:43,280 --> 00:11:46,840 Speaker 2: We read the DNA at every step and that's insanely 232 00:11:47,280 --> 00:11:49,720 Speaker 2: computationally heavy and it's exanely costly. 233 00:11:50,040 --> 00:11:50,800 Speaker 3: But here's what we know. 234 00:11:51,760 --> 00:11:54,839 Speaker 2: The embryos that we transfer, we know one hundred percent 235 00:11:54,840 --> 00:11:57,240 Speaker 2: of them are healthy. And so we're certified by American 236 00:11:57,280 --> 00:11:59,800 Speaker 2: Humane Society. And we don't do it because that certification, 237 00:11:59,840 --> 00:12:01,640 Speaker 2: but we do it because we care about conservation. 238 00:12:01,679 --> 00:12:02,920 Speaker 3: We care about animal welfare. 239 00:12:03,240 --> 00:12:06,840 Speaker 2: And so the reason why we have successfully birth, you know, 240 00:12:06,920 --> 00:12:10,719 Speaker 2: animals with no unintended consequences, is because we do it 241 00:12:10,800 --> 00:12:13,760 Speaker 2: before the editing, during the editing, after the after the 242 00:12:13,800 --> 00:12:14,640 Speaker 2: embryo creation. 243 00:12:15,000 --> 00:12:16,480 Speaker 3: And I think that's really really important. 244 00:12:16,559 --> 00:12:20,840 Speaker 1: But practically functionally, how much closer to the wooly mice 245 00:12:21,440 --> 00:12:24,079 Speaker 1: take you to the wooly mammoth? Have they knocked off? 246 00:12:24,120 --> 00:12:25,600 Speaker 1: Three months? Six months a year? 247 00:12:25,840 --> 00:12:28,760 Speaker 3: So they don't speed up anything. They're just a validator. 248 00:12:28,760 --> 00:12:32,199 Speaker 2: It's like, oh, okay, well this is behaving exactly as 249 00:12:32,679 --> 00:12:36,760 Speaker 2: we were predicting, and so this is a validation step. 250 00:12:36,800 --> 00:12:39,320 Speaker 2: So we're all the edits that we made the woy mouse. 251 00:12:39,679 --> 00:12:43,120 Speaker 2: We've already made the mammoth equivalent of them in Asian 252 00:12:43,160 --> 00:12:45,920 Speaker 2: elephant cels. We just haven't taken them to term right, 253 00:12:46,320 --> 00:12:50,240 Speaker 2: So I'd say less likely that it will speed up 254 00:12:50,280 --> 00:12:53,480 Speaker 2: the project, more likely that it means that we aren't 255 00:12:53,480 --> 00:12:54,880 Speaker 2: getting the project wrong. 256 00:13:02,240 --> 00:13:04,880 Speaker 1: When we come back. What Ben says is the first 257 00:13:05,320 --> 00:13:16,360 Speaker 1: the extinct species, the dire wolf, stay with us. So 258 00:13:16,440 --> 00:13:20,680 Speaker 1: the Woolli mice were a validator, but the dire wolves were, 259 00:13:21,040 --> 00:13:23,560 Speaker 1: at least as far as you're concerned, the first the 260 00:13:23,600 --> 00:13:24,480 Speaker 1: extinct species. 261 00:13:24,960 --> 00:13:27,520 Speaker 2: Yes, I think the dire wolves were the first extinct 262 00:13:27,559 --> 00:13:31,880 Speaker 2: species brought back. The will mice never existed, so we 263 00:13:31,960 --> 00:13:34,079 Speaker 2: can't classify them in that category. 264 00:13:34,280 --> 00:13:36,880 Speaker 1: And the dire wolf this species that went extinct about 265 00:13:36,960 --> 00:13:39,160 Speaker 1: ten thousand years ago. I guess it was kind of 266 00:13:39,160 --> 00:13:42,559 Speaker 1: reintroduced into the popular imagination, at least by George R. R. 267 00:13:42,679 --> 00:13:44,960 Speaker 1: Martin in the Game of Thrones series, which I know 268 00:13:45,000 --> 00:13:47,880 Speaker 1: you're a fan of. But can you tell me what 269 00:13:47,920 --> 00:13:52,280 Speaker 1: it was like to see these die wolves coming into existence, 270 00:13:52,520 --> 00:13:54,000 Speaker 1: being born? Holding them? 271 00:13:54,360 --> 00:13:56,319 Speaker 3: Yeah, it was highly emotional. 272 00:13:56,400 --> 00:13:59,439 Speaker 2: So I was on FaceTime while they were being born, 273 00:14:00,040 --> 00:14:02,040 Speaker 2: so I was actually in London. 274 00:14:02,320 --> 00:14:03,920 Speaker 3: I remember exactly where I was. 275 00:14:04,320 --> 00:14:07,079 Speaker 2: Your hearts palpitating, You feel like like you want everything 276 00:14:07,080 --> 00:14:08,280 Speaker 2: because everything healthy is. 277 00:14:08,360 --> 00:14:09,920 Speaker 3: I was like, it's like when you have your first child, 278 00:14:09,960 --> 00:14:11,440 Speaker 3: was like to they have the right feet. 279 00:14:11,640 --> 00:14:15,200 Speaker 2: And when they came out, they were white, which you 280 00:14:15,240 --> 00:14:17,640 Speaker 2: know wolf when they're born they're all black or gray 281 00:14:17,800 --> 00:14:20,440 Speaker 2: dark gray, like super dark gray, so that was an 282 00:14:20,440 --> 00:14:21,240 Speaker 2: early indicator. 283 00:14:21,440 --> 00:14:22,480 Speaker 3: They were much larger. 284 00:14:22,560 --> 00:14:26,680 Speaker 2: They're about forty percent larger than normal wolf puppies. So 285 00:14:27,120 --> 00:14:31,440 Speaker 2: I didn't because I'm not an animal veterinarian, like, I 286 00:14:31,480 --> 00:14:35,720 Speaker 2: had no value add I actually waited until they were 287 00:14:35,760 --> 00:14:37,240 Speaker 2: about five weeks. 288 00:14:37,240 --> 00:14:38,360 Speaker 3: And then I got to see them. 289 00:14:38,440 --> 00:14:40,920 Speaker 2: So and when I saw them the first time, I 290 00:14:40,960 --> 00:14:43,000 Speaker 2: got I mean, I still do, I get chill bumped, 291 00:14:43,080 --> 00:14:43,760 Speaker 2: I teared up. 292 00:14:43,760 --> 00:14:44,840 Speaker 3: It's very very emotional. 293 00:14:45,080 --> 00:14:47,960 Speaker 2: And what's been interesting is even people that aren't as 294 00:14:48,560 --> 00:14:53,560 Speaker 2: attached specifically to the Extinction or specifically to Colossal, they 295 00:14:53,600 --> 00:14:58,240 Speaker 2: have a very similar response. And it awakened something in you. 296 00:14:58,680 --> 00:15:02,360 Speaker 2: The importance of the moment isn't lost on you. I 297 00:15:02,400 --> 00:15:06,000 Speaker 2: remembered one of the first people I showed the how 298 00:15:06,160 --> 00:15:08,280 Speaker 2: video that became very very popular on the internet. 299 00:15:08,520 --> 00:15:11,240 Speaker 1: You're talking, of course, about the YouTube video of the 300 00:15:11,360 --> 00:15:14,520 Speaker 1: die Wolf Pops Howling. Who did you show it to? 301 00:15:14,680 --> 00:15:15,480 Speaker 3: Peter Jackson. 302 00:15:15,720 --> 00:15:17,880 Speaker 2: Peter's a dear friend and he's director for Lord of 303 00:15:17,880 --> 00:15:20,720 Speaker 2: the Rings and he's an investor in Colossal, And so 304 00:15:20,880 --> 00:15:23,800 Speaker 2: I was in his living room with his partner fran 305 00:15:24,440 --> 00:15:27,960 Speaker 2: I hooked up old school like HDMI to my laptop. 306 00:15:28,000 --> 00:15:29,720 Speaker 2: I said, I got to show you something, and I 307 00:15:29,760 --> 00:15:32,600 Speaker 2: showed it to him. He was overwhelmed. It's a very 308 00:15:32,720 --> 00:15:35,000 Speaker 2: very surreal experience. 309 00:15:35,520 --> 00:15:40,200 Speaker 1: I can only imagine. Ben, this is the Tech Stuff podcast. 310 00:15:40,320 --> 00:15:41,760 Speaker 1: So I just want to make sure, I do have 311 00:15:41,800 --> 00:15:45,280 Speaker 1: the tech right. I actually wrote down seven bullet points 312 00:15:45,280 --> 00:15:48,560 Speaker 1: for myself about the the extinction process, which includes the 313 00:15:48,640 --> 00:15:51,600 Speaker 1: use of generating tools like CRISPA. But I wonder if 314 00:15:51,640 --> 00:15:53,360 Speaker 1: you could sort of let me know if I've got 315 00:15:53,360 --> 00:15:56,920 Speaker 1: the steps right. Starting with step one, which is you 316 00:15:56,960 --> 00:15:59,800 Speaker 1: go out and find as much DNA as you can 317 00:16:00,440 --> 00:16:01,640 Speaker 1: of an extinct species. 318 00:16:01,960 --> 00:16:04,320 Speaker 2: Yes, and that can come in the form of some 319 00:16:04,440 --> 00:16:07,320 Speaker 2: researchers already have it on their hard drives, sometimes from 320 00:16:07,400 --> 00:16:08,680 Speaker 2: museum specimens. 321 00:16:08,960 --> 00:16:11,200 Speaker 3: Sometimes it's expeditions. 322 00:16:10,640 --> 00:16:11,800 Speaker 1: Digging through permafrost. 323 00:16:11,920 --> 00:16:12,880 Speaker 3: I mean, yeah, you go. 324 00:16:12,840 --> 00:16:16,040 Speaker 2: Into the permafrosts, you go into caves, but it's really 325 00:16:16,080 --> 00:16:19,520 Speaker 2: a global collaborative effort. It's you're in the sub basement 326 00:16:19,560 --> 00:16:22,960 Speaker 2: of a museum talking to a researcher at some university 327 00:16:22,960 --> 00:16:25,720 Speaker 2: that's sent their whole life sequencing it. 328 00:16:25,600 --> 00:16:28,600 Speaker 3: To You're actually out in a cave or in the 329 00:16:28,680 --> 00:16:30,120 Speaker 3: Arctic and whatnot. It's pretty cool. 330 00:16:30,560 --> 00:16:33,000 Speaker 1: Okay, So now I'm going to go through steps two 331 00:16:33,040 --> 00:16:37,400 Speaker 1: to five. You take the sample from the field back 332 00:16:37,400 --> 00:16:40,920 Speaker 1: to the lab and sequence the DNA. Then you cross 333 00:16:40,960 --> 00:16:44,760 Speaker 1: reference to DNA with other samples from the extinct species. 334 00:16:45,800 --> 00:16:48,880 Speaker 1: Then you build as complete a genome as you can 335 00:16:49,280 --> 00:16:53,120 Speaker 1: of the extinct species using AI, and then you cross 336 00:16:53,160 --> 00:16:56,240 Speaker 1: reference with that genome with all living animals to find 337 00:16:56,280 --> 00:16:57,440 Speaker 1: the closest living relative. 338 00:16:58,080 --> 00:17:00,640 Speaker 2: That is correct, But on that last parts to those 339 00:17:00,920 --> 00:17:03,360 Speaker 2: don't exist either, so you actually have to go build 340 00:17:03,360 --> 00:17:06,399 Speaker 2: the reference genome for the closest living relative. There's not 341 00:17:06,560 --> 00:17:09,720 Speaker 2: like a database of all life on Earth. 342 00:17:09,720 --> 00:17:11,880 Speaker 1: So you have a hypothesis about what the closest living 343 00:17:11,920 --> 00:17:14,000 Speaker 1: relative might be and then go and build that genome 344 00:17:14,000 --> 00:17:17,840 Speaker 1: in parallel. Then yeah, okay, yeah, Then you identify the 345 00:17:17,960 --> 00:17:20,280 Speaker 1: key genes that you need to edit in the living 346 00:17:20,359 --> 00:17:24,320 Speaker 1: species to make it genetically closer to the extinct. 347 00:17:23,920 --> 00:17:25,360 Speaker 3: Species exactly right. 348 00:17:25,760 --> 00:17:29,000 Speaker 1: Then you use CRISP gene editing to edit the embryo 349 00:17:29,320 --> 00:17:30,359 Speaker 1: of the living species. 350 00:17:30,480 --> 00:17:33,040 Speaker 3: So this is where it kind of forks a little bit. 351 00:17:33,119 --> 00:17:36,040 Speaker 2: So we use a combination of I think it's better 352 00:17:36,080 --> 00:17:38,679 Speaker 2: to classify it as genome engineering tools. One of the 353 00:17:38,680 --> 00:17:40,960 Speaker 2: things that colossals I think done a really good job 354 00:17:41,000 --> 00:17:44,160 Speaker 2: of is figuring out, we actually built an AI model 355 00:17:44,160 --> 00:17:45,480 Speaker 2: for this, what is. 356 00:17:45,400 --> 00:17:48,720 Speaker 3: The right tool for the right job. 357 00:17:48,960 --> 00:17:53,320 Speaker 2: And with that our system actually now recommends what combination 358 00:17:53,440 --> 00:17:56,480 Speaker 2: of tools in what order, and how that guide design 359 00:17:56,480 --> 00:17:59,960 Speaker 2: should be packaged to deliver the highest levels of efficient 360 00:18:00,080 --> 00:18:01,359 Speaker 2: see AI for us. 361 00:18:01,280 --> 00:18:03,719 Speaker 3: Has been a game changer in how we then deliver 362 00:18:03,800 --> 00:18:04,440 Speaker 3: the edits. 363 00:18:04,640 --> 00:18:07,920 Speaker 1: So then you've made the edits, you go to an embryo. 364 00:18:08,280 --> 00:18:09,320 Speaker 3: Sorry, you've got to cell. 365 00:18:09,520 --> 00:18:11,400 Speaker 2: Okay, And then so the next step is to do 366 00:18:12,000 --> 00:18:13,600 Speaker 2: the same thing they do with Dolly the sheep, which 367 00:18:13,640 --> 00:18:16,200 Speaker 2: is sematic cell nuclear transfer. So you've got two types 368 00:18:16,240 --> 00:18:19,119 Speaker 2: of cells, germ cells and sematic sels. Germ cells are 369 00:18:19,160 --> 00:18:21,760 Speaker 2: like egging sperm. Somatic cells are everything else, right, So 370 00:18:22,000 --> 00:18:25,199 Speaker 2: we're editing sematic cells in most cases. And so you 371 00:18:25,280 --> 00:18:29,640 Speaker 2: take the nucleus from the sematic cell and you put 372 00:18:29,680 --> 00:18:32,200 Speaker 2: it into that of a germ cell, and that almost 373 00:18:32,240 --> 00:18:35,600 Speaker 2: acts like the fertilization process of like when egg and 374 00:18:35,640 --> 00:18:38,439 Speaker 2: sperm meets, and then you go through a process that 375 00:18:38,640 --> 00:18:42,240 Speaker 2: stimulates those cells to start dividing, and then you have 376 00:18:42,320 --> 00:18:44,560 Speaker 2: the precursor to something that you would implant. 377 00:18:45,160 --> 00:18:46,320 Speaker 1: Two big criticisms. 378 00:18:47,280 --> 00:18:51,600 Speaker 3: One is only two that's incredible, fantastic. 379 00:18:51,920 --> 00:18:54,359 Speaker 1: We only have an em no. But one is that 380 00:18:54,680 --> 00:18:58,119 Speaker 1: it's not really the extinction if you're just changing a 381 00:18:58,200 --> 00:19:01,520 Speaker 1: few phenotypes. In other words, you justanging the appearance of 382 00:19:01,560 --> 00:19:04,120 Speaker 1: an animal to look more like an animal that was extinct. 383 00:19:04,600 --> 00:19:07,520 Speaker 1: Some people accuse you guys of doing that. On the 384 00:19:07,560 --> 00:19:09,920 Speaker 1: other end of the spectrum, some people say, no, you're 385 00:19:09,960 --> 00:19:12,760 Speaker 1: playing god. And Jennifer Dowton at the inventor of Chris, 386 00:19:12,760 --> 00:19:14,640 Speaker 1: but she didn't say this in reference to what you're doing, 387 00:19:14,640 --> 00:19:17,119 Speaker 1: but she said more broadly in her book, if we 388 00:19:17,119 --> 00:19:20,800 Speaker 1: can avoid altering nature more than we already have, shouldn't 389 00:19:20,840 --> 00:19:22,000 Speaker 1: we try to do so? 390 00:19:22,000 --> 00:19:26,120 Speaker 2: So I come from software, right is predominantly my background 391 00:19:26,160 --> 00:19:29,440 Speaker 2: in some space hard mostly software. And if you can 392 00:19:29,480 --> 00:19:32,320 Speaker 2: build something to achieve a function that's got three hundred 393 00:19:32,320 --> 00:19:34,679 Speaker 2: lines of code, and then you've got something that can 394 00:19:34,720 --> 00:19:38,200 Speaker 2: do it in seven lines code, every great programmer will 395 00:19:38,240 --> 00:19:40,920 Speaker 2: take you to the second one because there's less room 396 00:19:40,960 --> 00:19:43,960 Speaker 2: for error, there's less things to troubleshoot, it's cleaner, it 397 00:19:44,040 --> 00:19:45,400 Speaker 2: runs fast, it's more efficient. 398 00:19:45,720 --> 00:19:47,120 Speaker 3: There's a million reasons to do that. 399 00:19:47,520 --> 00:19:50,159 Speaker 2: We spend a lot of time and a lot of 400 00:19:50,200 --> 00:19:56,000 Speaker 2: money on compute AI comparative genomics, because no matter how 401 00:19:56,040 --> 00:19:58,880 Speaker 2: good you get at this, we're still in the world 402 00:19:58,960 --> 00:20:01,280 Speaker 2: of discovery. I do think, as I mentioned, it will 403 00:20:01,320 --> 00:20:05,520 Speaker 2: move to the world of strictly engineering and the entire 404 00:20:05,800 --> 00:20:10,679 Speaker 2: idea of genotype of phenotype expression. What genes cause different 405 00:20:10,720 --> 00:20:13,840 Speaker 2: things to do different things that result in a physical 406 00:20:14,280 --> 00:20:15,920 Speaker 2: attribute of an animal is. 407 00:20:15,880 --> 00:20:17,720 Speaker 1: The great medical question of our age. 408 00:20:17,800 --> 00:20:20,920 Speaker 2: It's a huge medical question, right, And that is the 409 00:20:20,960 --> 00:20:23,359 Speaker 2: core of what we were doing at Colossal, right. And 410 00:20:23,400 --> 00:20:26,119 Speaker 2: so you know, I would argue that we're a genetic 411 00:20:26,200 --> 00:20:29,280 Speaker 2: engineering company and a genotype of phenotype company at our core. 412 00:20:29,720 --> 00:20:33,119 Speaker 2: And so when you can do that, and you know, 413 00:20:33,520 --> 00:20:35,480 Speaker 2: if you look at a species, if you ask me 414 00:20:35,920 --> 00:20:38,800 Speaker 2: if you could just make a couple changes to the elephant, 415 00:20:39,280 --> 00:20:43,520 Speaker 2: you know, and it wasn't informed in any way by mammos, 416 00:20:44,040 --> 00:20:45,840 Speaker 2: but you just made like a hairy elephant. 417 00:20:46,280 --> 00:20:48,640 Speaker 3: I wouldn't consider that the extinction. I wouldn't. 418 00:20:48,840 --> 00:20:53,000 Speaker 2: But if you can take data like true data from 419 00:20:53,200 --> 00:20:57,000 Speaker 2: a mammoth and it can tell you what are the 420 00:20:57,119 --> 00:21:01,760 Speaker 2: genes that were fixed over time that really drove these phenotypes, 421 00:21:02,400 --> 00:21:06,760 Speaker 2: and you can either engineer in those exact variants which 422 00:21:06,800 --> 00:21:10,920 Speaker 2: we did in the direwolf, and potential enhancements that are 423 00:21:11,080 --> 00:21:14,440 Speaker 2: in the world of synthetic biology to produce those phenotypes 424 00:21:14,600 --> 00:21:18,919 Speaker 2: that are informed by ancient DNA, that's the extinction. 425 00:21:19,240 --> 00:21:19,920 Speaker 3: And so what's. 426 00:21:19,760 --> 00:21:21,640 Speaker 2: Interesting to me is all the people that saw Jurassic 427 00:21:21,680 --> 00:21:24,520 Speaker 2: Park and say it's a movie about dinosaurs, but then 428 00:21:24,520 --> 00:21:26,840 Speaker 2: don't want to call my direwolf direwolves. It's like, well, 429 00:21:26,840 --> 00:21:30,360 Speaker 2: you're just a fucking hypocrite because those are either genetically 430 00:21:30,440 --> 00:21:36,199 Speaker 2: modified frogs and birds with dino DNA in it, or 431 00:21:36,200 --> 00:21:37,080 Speaker 2: their dinosaurs. 432 00:21:37,160 --> 00:21:37,360 Speaker 3: Right. 433 00:21:37,480 --> 00:21:40,679 Speaker 2: And so this is a semantic question. It is not 434 00:21:41,119 --> 00:21:44,600 Speaker 2: a scientific question. It is a human construct that we're 435 00:21:44,600 --> 00:21:46,879 Speaker 2: putting on this. And so going to your second. 436 00:21:46,640 --> 00:21:49,520 Speaker 1: Question, the accusation that you're playing gold, you know. 437 00:21:49,760 --> 00:21:52,439 Speaker 2: I think that we play God every day, and I 438 00:21:52,480 --> 00:21:55,600 Speaker 2: think that taking cholesterol medication is a form of playing 439 00:21:55,600 --> 00:21:56,960 Speaker 2: God on a personal level. 440 00:21:56,960 --> 00:21:58,040 Speaker 3: It doesn't mean you shouldn't do it. 441 00:21:58,520 --> 00:22:01,480 Speaker 2: I think the idea of cutting down the rainforest or 442 00:22:01,520 --> 00:22:04,080 Speaker 2: overfishing the ocean is playing God. So if you think 443 00:22:04,119 --> 00:22:07,280 Speaker 2: of if you define playing God is interfering with the 444 00:22:07,400 --> 00:22:08,240 Speaker 2: natural order. 445 00:22:08,560 --> 00:22:11,560 Speaker 3: To Jennifer's point, we do that all day long. 446 00:22:12,000 --> 00:22:15,080 Speaker 2: So why not do it in a way that helps 447 00:22:15,119 --> 00:22:18,879 Speaker 2: us develop technologies for human health care, inspires kids, and 448 00:22:18,920 --> 00:22:22,159 Speaker 2: can help with conservation. Because every conservationist will tell you 449 00:22:22,560 --> 00:22:27,080 Speaker 2: that while conserving land and protecting species is the primary 450 00:22:27,080 --> 00:22:29,439 Speaker 2: focus of conservation, which by the way we agree with. 451 00:22:29,520 --> 00:22:31,439 Speaker 2: We think that's where everyone should just spend ninety nine 452 00:22:31,480 --> 00:22:35,240 Speaker 2: percent of their time. Everyone in that field will still 453 00:22:35,280 --> 00:22:37,920 Speaker 2: tell you, even the most hardcore conservations in the world, 454 00:22:38,160 --> 00:22:42,120 Speaker 2: that is a losing battle in the end human progress 455 00:22:42,200 --> 00:22:45,440 Speaker 2: or whatever you want to define it as encroachment, overfishing, 456 00:22:45,560 --> 00:22:50,800 Speaker 2: overfeeding livestock versus natural order of animals that will end 457 00:22:51,680 --> 00:22:56,399 Speaker 2: with massive losses of biodiversity and potentially various ecosystem collapses. 458 00:22:56,760 --> 00:22:59,719 Speaker 2: And so the best thing in the world is for 459 00:23:00,080 --> 00:23:03,879 Speaker 2: us to continue to conserve land and save species because 460 00:23:03,880 --> 00:23:06,200 Speaker 2: of the hell of a lot, cheaper and more efficient 461 00:23:06,200 --> 00:23:08,120 Speaker 2: to save a species then bring back a species. 462 00:23:08,640 --> 00:23:10,920 Speaker 3: But there may be a day in humanity's future. 463 00:23:11,240 --> 00:23:13,840 Speaker 2: And I'm an eternal optimist, so I hope I'm wrong 464 00:23:13,880 --> 00:23:15,480 Speaker 2: on this, even though im running. 465 00:23:15,240 --> 00:23:16,119 Speaker 3: At the Extinction Company. 466 00:23:16,400 --> 00:23:19,200 Speaker 2: There could be a day in human history where there 467 00:23:19,280 --> 00:23:21,879 Speaker 2: is a species that we lose in the near future 468 00:23:22,000 --> 00:23:24,600 Speaker 2: that we have to bring back. You know, not to 469 00:23:24,640 --> 00:23:27,040 Speaker 2: get too Star Trek four on you, but who knows. 470 00:23:27,400 --> 00:23:30,119 Speaker 2: We may have to bring back blue whales, not to 471 00:23:30,359 --> 00:23:34,640 Speaker 2: appease some drone from an alien planet like in Star Trek, 472 00:23:34,880 --> 00:23:39,000 Speaker 2: but to potentially help with ocean currents and the phytal 473 00:23:39,080 --> 00:23:42,480 Speaker 2: plankton turnover in the nutrient cycling in the oceans. Right, 474 00:23:42,840 --> 00:23:45,760 Speaker 2: we don't know, and I think that having these technologies 475 00:23:46,200 --> 00:23:49,760 Speaker 2: are inevitable. In applying them in a way that helps humans, 476 00:23:49,960 --> 00:23:53,520 Speaker 2: helps animals and hopefully inspires kids is probably not a 477 00:23:53,520 --> 00:23:53,920 Speaker 2: bad thing. 478 00:23:54,359 --> 00:23:56,480 Speaker 1: Just to play Demod's evocat though. I mean, the dire 479 00:23:56,560 --> 00:24:01,600 Speaker 1: wolves are living on the enclosure, going to breed. What 480 00:24:01,680 --> 00:24:05,240 Speaker 1: will be different in future about animals that you bring back? 481 00:24:05,320 --> 00:24:07,280 Speaker 1: How will they be integrated in a way that these 482 00:24:07,359 --> 00:24:10,399 Speaker 1: tibles on being to the real world. 483 00:24:10,040 --> 00:24:13,800 Speaker 2: So they could breed. They are physiological capable of breeding. 484 00:24:13,840 --> 00:24:15,879 Speaker 2: We use different cellions as with male and females, right, 485 00:24:16,119 --> 00:24:18,600 Speaker 2: and then you know they are to your point, secure 486 00:24:18,680 --> 00:24:20,000 Speaker 2: expansive ecological preserve. 487 00:24:20,080 --> 00:24:23,520 Speaker 3: But what isn't these days? Like what is truly the wild? 488 00:24:23,560 --> 00:24:26,880 Speaker 2: This Kruger National Park is six million acres in Africa 489 00:24:26,920 --> 00:24:28,719 Speaker 2: that's fully fenced with lots of biodiversity. 490 00:24:29,840 --> 00:24:32,680 Speaker 3: Some would consider that the wild. It's still a park. 491 00:24:32,880 --> 00:24:36,000 Speaker 2: Right, So these animals, I assure you you know, they've 492 00:24:36,000 --> 00:24:37,719 Speaker 2: got ten full time people that take care of them. 493 00:24:37,720 --> 00:24:41,639 Speaker 2: They're on two thousand acres of a yellowstone like environment. 494 00:24:41,960 --> 00:24:45,120 Speaker 2: They live quite quite well, but our long term goal 495 00:24:45,160 --> 00:24:47,120 Speaker 2: with all of our species is to put them back 496 00:24:47,480 --> 00:24:51,280 Speaker 2: into the wild in collaborations with indigenous people groups, private landowners, 497 00:24:51,480 --> 00:24:54,639 Speaker 2: and governments. Right, So those are very long term processes. 498 00:24:54,960 --> 00:24:58,000 Speaker 2: I won't say that we have a clear date that 499 00:24:58,080 --> 00:25:00,199 Speaker 2: we could put dire wolves back in the wild, but 500 00:25:00,200 --> 00:25:02,160 Speaker 2: I also say we won't. Right, that's really not even 501 00:25:02,240 --> 00:25:05,280 Speaker 2: up to colossal, that's up to We've had indigenous people 502 00:25:05,280 --> 00:25:07,800 Speaker 2: groups express interest in wanting that, but we have to 503 00:25:07,800 --> 00:25:09,719 Speaker 2: work with you know, the government, the EPA. 504 00:25:09,880 --> 00:25:11,680 Speaker 3: There's a lot that goes into this, right. 505 00:25:11,960 --> 00:25:14,000 Speaker 2: So, like one of the projects we just announced was 506 00:25:14,040 --> 00:25:18,920 Speaker 2: in partnership with Yellowstone is using AI for bioacoustics for wolves, 507 00:25:19,040 --> 00:25:22,960 Speaker 2: so we understand migratory patterns, so we understand different call patterns, 508 00:25:23,080 --> 00:25:25,680 Speaker 2: so we understand things, right, Because you know, the best 509 00:25:25,720 --> 00:25:27,680 Speaker 2: thing that humans can do is figure out how we 510 00:25:27,720 --> 00:25:30,879 Speaker 2: live with nature, not just bring back nature. And I 511 00:25:30,880 --> 00:25:34,800 Speaker 2: think that those technologies are critical to be developed in 512 00:25:34,880 --> 00:25:38,160 Speaker 2: concert with the extinction projects so that we can make 513 00:25:38,240 --> 00:25:42,360 Speaker 2: rewilding work for the animals themselves and humanity that has 514 00:25:42,440 --> 00:25:43,399 Speaker 2: encroached on their land. 515 00:25:43,880 --> 00:25:45,520 Speaker 1: No, and I like what you said earlier about some 516 00:25:45,600 --> 00:25:48,320 Speaker 1: of these secondary benefits like the vaccine for the elephants. 517 00:25:48,920 --> 00:25:51,080 Speaker 1: I know there are tons and tons of others, including 518 00:25:51,600 --> 00:25:56,200 Speaker 1: you know, potentially bacteria to eat, ocean plastic. We've talked 519 00:25:56,280 --> 00:25:59,199 Speaker 1: at the beginning about mortality, and then we talked a 520 00:25:59,200 --> 00:26:02,560 Speaker 1: lot about de extinction. So I just want to close 521 00:26:02,640 --> 00:26:06,000 Speaker 1: with this question. If you had a Chillncee to read 522 00:26:06,040 --> 00:26:09,480 Speaker 1: your obituary and you're happy with it, how do you 523 00:26:09,520 --> 00:26:12,840 Speaker 1: complete the sentence? He did this because. 524 00:26:14,040 --> 00:26:17,959 Speaker 2: He did this because others were too afraid to do it. 525 00:26:18,720 --> 00:26:19,439 Speaker 1: What do you mean by that? 526 00:26:20,119 --> 00:26:23,640 Speaker 2: I think that making change is hard, and I think 527 00:26:23,680 --> 00:26:29,400 Speaker 2: it requires determination, it requires thick skin. I think that 528 00:26:29,680 --> 00:26:32,959 Speaker 2: some of the biggest and boldest things have had a 529 00:26:33,040 --> 00:26:37,200 Speaker 2: perspective of abundance, and it's also a not zero sum game. 530 00:26:37,720 --> 00:26:40,240 Speaker 2: And I think that's important because I think right now, 531 00:26:40,880 --> 00:26:44,000 Speaker 2: sometimes when you work on new things, it's scary and 532 00:26:44,040 --> 00:26:47,080 Speaker 2: it's hard, and I've been criticized a lot. You know, 533 00:26:47,920 --> 00:26:51,119 Speaker 2: I've been a very long term supporter of developing technologies 534 00:26:51,119 --> 00:26:54,199 Speaker 2: for climate change. Yet I have a lot of people 535 00:26:54,640 --> 00:26:56,720 Speaker 2: that have been very very kind to me for quite 536 00:26:56,720 --> 00:26:58,760 Speaker 2: some times that aren't as kind of me today as 537 00:26:58,760 --> 00:26:59,320 Speaker 2: they used to be. 538 00:26:59,640 --> 00:27:01,440 Speaker 3: But it's okay, because at. 539 00:27:01,400 --> 00:27:04,000 Speaker 2: The end of the day, I am convicted in what 540 00:27:04,040 --> 00:27:06,800 Speaker 2: I'm doing and I'm not afraid to do it because 541 00:27:06,920 --> 00:27:11,280 Speaker 2: I truly believe that we need these technologies for conservation, 542 00:27:12,040 --> 00:27:14,000 Speaker 2: and I think we need to also do big, bold 543 00:27:14,040 --> 00:27:16,879 Speaker 2: things that will inspire the next generation so that we 544 00:27:16,920 --> 00:27:20,560 Speaker 2: have more scientists and astronauts and fewer influencers. 545 00:27:22,119 --> 00:27:24,399 Speaker 1: Thank you, very very well said, Thanks so much for 546 00:27:24,440 --> 00:27:55,240 Speaker 1: having me for tech Stuff. I'm most velocian. This episode 547 00:27:55,280 --> 00:27:58,600 Speaker 1: was produced by Eliza Dennis and Tori Dominguez. It was 548 00:27:58,640 --> 00:28:01,960 Speaker 1: executive produced by me Kara Price and Kate Osborne for 549 00:28:02,040 --> 00:28:07,280 Speaker 1: Kaleidoscope and Katrina Norvel for iHeart Podcasts. Jack Insley mixed 550 00:28:07,280 --> 00:28:11,040 Speaker 1: this episode and Kyle Murdoch wrote our theme song. Join 551 00:28:11,119 --> 00:28:13,560 Speaker 1: us on Friday for the Week in Tech, when Karen 552 00:28:13,600 --> 00:28:15,639 Speaker 1: and I will run through the tech headlines that you 553 00:28:15,680 --> 00:28:19,040 Speaker 1: may have missed, and please do rate, review, and reach 554 00:28:19,080 --> 00:28:22,560 Speaker 1: out to us at tech Stuff podcast at gmail dot com. 555 00:28:22,600 --> 00:28:23,520 Speaker 1: We love hearing from you.