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Ben: No one has ever taken ancient DNA, put it in a living species and also done it through multiplex editing and got a hundred percent efficiency. I view it as like forced evolution. I love that idea. You're forcing the gray wolf to become a dire wolf through like little tweaks in the DNA. We're now making over 50 edits at once right now, which is insane.
How do you think through the PR side versus the practicality side? Mammoths are closely related to Asian elephants than Asian elephants are to African elephant. Those blows people's mind.
Logan: Ben, thanks for doing this.
Ben: Yeah, thanks for having me.
Logan: So, uh, for people that don't know, can you use just a quick primer on what Colossal is,
Ben: Yeah. So, uh, colossal is the world's first de-extinction and species preservation company. We use a lot of genetic engineering, computational biology, solve some pretty hard challenges in genotype to phenotype relationships. And we open source all the technology for conservation.
Logan: you've been at it for how long
Ben: Uh, since 2021. So nearly four
Logan: and raised.
Ben: For, uh, we raised four $35 [00:01:00] million for the company. And then we have a foundation where we raised $50 million
Logan: And so a, as you thought about this, we'll get into the background and how all this stuff came to be, but
Ben: complete accident too.
Logan: Yeah. Uh, well, so, so maybe, maybe give that short version of like how you, how you sort of fell into
Ben: So I, I'm a software guy and I've only known how to build, like, teams of software. I try to hire a bunch smarter women and men than me. And I've always just thought about like the system, right? You know, you're, you've been in software, right? You invest in software. So it's all, it's all just system design, right?
So I think that really good software engineers and really good system designers can really just figure out how to ask the right questions. Like I don't have to be an expert in, in biology. I just know how to kinda ask the right questions and hire people that can answer them at least better than I can.
Right? Um, and so I, I reached, I thought that, you know, looking at kind of access to compute AI and synthetic biology, this idea that we can engineer life, that we can do directed evolution, that we can do accelerated evolution was gonna be really interesting. And then if you layer on quantum, which I hear is.
Only two years away. Every two years. Um, that's gonna be really crazy. And I, and I, I think we're gonna [00:02:00] enter this whole new world.
Ben: So I reached out to George Church, who's the father of synthetic biology. He's head of genetics at Harvard. Uh, some of the core read write technologies that exist in the world were all invented by him and his lab, as well as David Lou up at Harvard.
So I thought I'd reach out to him, uh, halfway into the conversation I was like, what else are you working on? 'cause I'm weird and curious. And he started going down this path of like all these different projects he was working on. And it was, it was a little overwhelming. And, and then I asked, you know, the, the, probably the most interesting question of my life, which is, if you had unlimited capital, what would you work on and why?
And it was the mammoth. He's like, I'd, I'd work to bring back distinct species, make technologies for human healthcare, make technologies for, uh, eventually for, uh, conservation.
Logan: And was the mammoth just like, uh, forgive the, uh, guess colloquialism but a, a pet project of his, or was there some like derivative consideration of,
Ben: No. Well he, he thought that we had like the amalgamation of all the tech, right.
Ben: It's also majorly not efficient right now, so you need to innovate on all this stuff. But there wasn't any major like science gaps, right? We could get ancient DNA, we could do comparative genomics, [00:03:00] you could build sequencing, you could make edits, even if it only make one edit at a time, and then you could figure out kinda the embryology.
So there's some things to figure out on the gestational side, but for the most part, like we had all the cortex. And so his vision was at the time he raised a hundred thousand dollars for his laugh from Peter Thiel, um, who just gave him a donation and he was stretching that out over the course of, uh, 10 years.
So he wasn't very far.
Logan: And, and so it turns out
Ben: Turns out it's a lot more expensive to,
Logan: I can imagine. Well, I guess to that end, like as you, as you set out to go do this, did you have some business that it could ultimately be? Because when you, when you talk about this and you start to unpack it, it sounds almost like a, a research project.
Yeah. In a lot of ways.
Ben: But like a lot of academic research projects, they result in papers and not products. Um, not, there's anything wrong with papers. I get a lot of feedback on my views of papers. Right. But it's, at the end of the day, you can't ship a, a paper to, to anyone, um, except the scientific community.
Um, and so, so I felt like there was, there's gotta be a way. Our first pitch deck was pretty shitty. It [00:04:00] was, you know, George thinks he can bring back a mammoth. We are pretty confident we know how to build teams. We're pretty sure there's value there, right? Like that, that literally was the first pitch deck.
So I guess the short answer in the beginning was no. Um, over time we realized that, you know, to really make this work at the speed we wanted to work, the impact we wanted to work, uh, and the scale we wanted to work, we had to innovate on a lot of technologies. There's a little bit we had invent, but for the most, we had to innovate.
And all those created, you know, massive opportunities, both human healthcare and to some indu industrial use cases. So we thought that like kind of that Apollo esque model to start would be interesting. Over time there's been other business models that have arisen that we think are even equally interesting.
Logan: Uh, I wanna dive into the process and I'll, I'll, uh. All of that stuff as well.
Logan: But, um, so recently you announced, uh, the de-extinction of dire wolves.
Ben: Yeah. Dire wolves.
Logan: And you have three.
Ben: So we have three. Yeah. So we've got, uh, two males and one female.
Logan: Yeah. Uh, maybe we can dive into Yeah. Like the actual process of how to actually
Zach: Also the shirt is awesome.
Logan: It's [00:05:00] sick.
Ben: Yeah, we do. We, we don't, we make all of our own stuff and we just, like, we wanted, we work with a lot of top designers and we don't sell it. We just give it to our team. And so when you come to the lab, you guys should come see, check out the lab, 'cause lab's way cooler than talking to me. And it's literally, it's like a hodgepodge of all these people wearing cool.
Colossal shirt. So we'll send you one.
Zach: That's awesome. Yeah. I was gonna mug you afterwards for the shirt, so I'm glad you
Ben: yeah. I don't have an undershirt, so please don't travesty to the world.
Zach: I thought just, you know, as a, as a non-scientist in science as well. Yeah,
Ben: same. Yeah. You, you've done well in as a non-scientist in
Zach: yeah. So I pretend I understand, but I just kind of memorize the, the phrases, but, um, I
Ben: like I don't do that for every species. Like I had to learn a lot about elephants and it's like the ultimate like college cram, right? Where it's like, okay, I've learned everything about marsupial gestation. I have to learn everything about this.
So it's cool 'cause you actually get to learn a lot.
Zach: yeah, I find the same. It's like, okay, I'm gonna go learn about like, biologics today and try and remember the words and then do you know
Ben: It's its own normal. It's so, it's so weird. So may
Zach: maybe to try and keep it like somewhat high level, but in the [00:06:00] science, when I think about this step by step and curious to learn like how, how you do this, we will start with a di wolf.
So you've got old di wolf, DNA, right? Let's say, so we kind of know the underlying DNA of
Ben: Which is hard in itself
Zach: to find
Ben: Yeah,
Zach: Yeah.
Ben: no, to to find it. So like in, in like LaBrea Tarpits, they have thousands of di wolf skulls, but they've all been, uh, uh, stressed due to heat and acidification. So there's no DNA or at least, at least no recoverable DNA.
No problem. So, so there's kind of this, like, before it all starts, there's like this search, like you've gotta have something to start. And we found that you need about six x coverage to be able to do this. So that means that you have to have, uh, you probably know this, but for everyone else's, like you need to be able to read the full genome about six times.
You need to get enough destructive sampling that you can get about six passes of the full genome to get enough of the, of the core regions. To assemble it.
Zach: Yeah. So let's say, and we can simplify, you know, this is what we're aiming for. Yeah. If you will. So you've got kind of like that DNA, and then on the other side of it, you've got [00:07:00] an existing live species. Yes. That is close enough. Close enough, but not the same. Not the same. So walk us through, how do you determine where in the DNA, the differences that matter lie, right?
Because there's lots of differences,
Ben: Lots of differences, yeah. Yeah. So yeah, that 0.5, like people sometimes get confused and they're like, well, an Asian elephant's 99.6% a, a mammoth.
But they're like, but that point's that, that 0.4 is quite, quite large as as, as you know. Um, what, what's interesting though about certain species like mammoths are closer to rel mammoths are closer related to Asian elephants than Asian elephants are to African elephants always blows people's mind.
Right. And so, so we, we look for the closest living relatives. 'cause I think if you think about it, both from a reproductive track perspective and from an kind of an evolutionary biology perspective, they will have the highest likelihood of less number of. Impactful edits. And so what we try to do is very much like software.
So I think it's nice to talk to someone about this. In software, we think of this as like functional extinction. We're not trying, we've always said this, we are not trying to clone an extinct [00:08:00] species. There's no living cells for direwolves or, or mammoths or anything, regardless of what, like we are TikTok, someone occasionally sees about like there's living d yeah, there's dinosaur cells.
I see that. And then like I get a, a flurry of like people sending me dms. They're like, they found di dinosaur cells, like they did not find dinosaur cells or the dinosaur embryo. You got pricing seen on Instagram. It's like, that's not, that's fossilized. It's not real. It's real. It's not, it's not, it's not viable.
And so what, what you do is you, you look for the closest living relative and then we're really looking for the genes that are fixed and conserved. Across it. Now, if you can get multiple samples. So on the mammoth we have about 65 mammoth genomes, right? Ranging from about 1.2 million years, uh, old to about 4,000 years old, which is great because then we can really identify what truly made a mammoth and mammoth.
'cause as you know, a lot of that's like genetic diversity, genetic drift and whatnot. But what are the core things that drive core phenotypes? And so we think of ourselves as more of an engineering company. The more computational biology we can do, the smarter we can be about the edits, the lettuce [00:09:00] edits we can make with the highest impacts.
You know, if you can write something. He write software, 3000 lines of code, and he writes software that that's six lines of code. You want the second one every day of the week for efficiency, for bug detection, for all of these, these things, we think of it the same way. And so we spend a lot of time trying to identify what genes are fixed and what are in the regions that we already know from literature that drive like size, hair, phenotypes, coloration, and whatnot.
And you know, like on the mammoth it's about 85 genes, so there's about 85. It's still a lot, but it's not like you, you don't have to make, you know, 2 million changes to get the core phenotypes. That really made a mammoth. A mammoth.
Logan: All right. And before we go into like the specifics on making the two things work together, but so, so did you start top down like, Hey, we wanna make a wooly mammoth and a dire wolf and then figure out the ways to do it? Or did you have like 15 different things and what's viable that we could actually
Ben: So we started with the system, right? We're like, how, how would you, we started with the mammoth and then we said, how do you make a mammoth? Right? So we, so we've kind of like built out like that core system. It's, it has [00:10:00] ancient DNA extraction. It has DNA assembly, it has comparative genomics, it's got tissue culture, it's got cellular engineering, genetic engineering, embryology, somatic, uh, cloning.
Effectively it's got all embryology like IVF and elephants. So we had to go figure out the whole system.
Logan: And where'd you actually find the DNA of the mammoths?
Ben: we actually worked with, so two, uh, uh, twofold. One is I'm on the board of trustees of the Explorers Club. And so they actually have people that got into the Arctic and have expeditions.
George Church. It's actually been to Siberia and Church Key and all these, and brought back samples. Um, most of our samples actually come from, uh, uh, university of Stockholm. So we work with, uh, Lou Vid Dahlin, who's the number one mammoth researcher in the world. And he actually provides us with, uh, uh, DNA in samples,
Logan: And is it preserved in a way that's
Ben: So it, it, it, it, it depends, right? So it's all, and it comes in all shapes and sizes. So, um, you know, what's great about, you know, mammoths is a lot of them died in cold places, so it was preserved on some level, but most of it comes not from that fleshy tissue. People think that that's it, or mummification.
[00:11:00] Most of it comes from like teeth and, uh, the petros bone, which is like an inner ear bone that's really, really like, uh, condensed and it doesn't change a lot after you're born. So it's a great DNA storage vessel. It's Amber is not the best DNA storage film, if you've seen a movie about it.
Zach: Yeah. And I think this nuance, especially like when I've talked about this, this idea with people, 'cause I had, it's, you know, it's cool that, that maybe people miss in the beginning is you're not actually trying to take the mammoth DNA or the di world DNA and just like stick it into Yeah. And grow it. And, and, and that's actually I think what everybody thinks at first,
Ben: I know we had the same Yeah,
Zach: because it's Jurassic Park, you're like, I'll just grow the dinosaur.
And that's, that's not how it works. But what you're actually trying to do is take the closest living mm-hmm. Relative to a dire wolf in this case. What, what's the name of the
Ben: species? Oh, it's for Diw Wolf. It's a gray wolf. Gray wolf. Gray wolf.
Zach: Gray Wolff Graywolf. That's what I was missing. And you're saying, all right, if I make a few changes in the gray Wolf, DNA, I'm gonna get things that look like diols, maybe not an exact copy, but inching closer to
Ben: in, in.
They are the, and like, so if you [00:12:00] were to take the, uh, if you were to take, like, uh, for example, L Coral, it's a gene that drives size. If you take, if you take L Coral from the Dar wolf and l Coral from, uh, a gray wolf, and you were to take them out right to this dress park example, and um, and then stick them back in, what we've done is we take out the, the, um, well in this case, we actually synthesize, we synthesize L Coral with the same code.
'cause the, the, what people don't realize is like, or what some people don't realize is that the DNA. Those cells are dead. So we're, we're, we're basically getting the code right? We're getting what are the right letters in the right order. It's
Zach: but not actually viable.
Ben: It's not, it's not actual viable DNA.
It's not like we're taking blood out and injecting blood. We're reading the code and then we're either changing what's in there or synthesizing a piece and swapping it in. So if, if, you know, if, if, if you had blinders on and you then cut that exact gene out, it is the dire wolf gene. It's just been synthesized.
Right? 'cause you can't move it over there. Sometimes people think we've got like the smallest, like scissors in the smallest, like, [00:13:00] like little tweezers and we move shit. And that's just not, that's just not the case.
Logan: And this is maybe a stupid like definitional thing, but when you announced this, there were so many people that were parsing like the, the language kind of to what Zach is saying of like, so.
Logan: Like you didn't actually, you didn't bring them back from extinction you, and so I guess, how did you think about what the appropriate end state
Ben: no, it's a great, it's a, it's a great question. So we've said from day one, in, in, in, in fairness to. Semantical semantic arguments is that like we've been very transparent and clear with what we're doing from day one. Like we say, we made it this many edits, these are the edits we made. These are what they do.
They're like, we, we didn't say, Hey, we made a mammoth. We promise it's a secret. And, and we don't, like we, we said all of that. Like, like when we made our wooly mouse, we're like, here's every edit we made in every single region, and here's the map. Like we've been very clear about it, right? So there, there's, make no mistake on that.
But what's been interesting is we've also talked about functional de-extinction. We've said we cannot clone mammoths. We not clone these species, these cells don't exist. We've been very, very clear about it. Right? [00:14:00] Uh, but you know, there's a, a group called the IUCN, which is like the. You know, it's in the Species Survival Commission, which is like the UN of species.
And they came out five years before we started the business said, here are the things that you have to do in order to make an extinct species right, to be classified as extinct species. And what other people don't know is there's actually like 31 different ways. I didn't know this 'cause I'm not a biologist or a paleontologist.
There's 31 ways, uh, kinda like Baskin Robbins of like how you define a species, right? So we were probably taught, or I was taught, uh, in, in, in my schools that the biological species concept where it's like if things can't inter breeded than their unique species. But if you see a polar bear in a brown bear, guess what?
They're unique species. They, they have a different species, same genus, but separate species names. Most wolves and canids fall in that same category as where, as well as most bears, but by like the biological definition of species, which is one of the 31. They're not the same. They should be classified as the same species.
If you ask most paleontologists, they will agree that they should be not separate species. You [00:15:00] know, morphologically one's white, one's archly adapted and one's aquatic. But they have, they have babies that are super healthy all the time. There's tons of interbreeding that happens there. And, and I think that, that what, what people don't always realize is that speciation is a human construct that we're trying to put on these things.
Right? And so for us, you know, we've been very clear that we're doing functional de-extinction. We've been very clear you can't clone an extinct species. We've always said that we're trying to identify the core genes that make a species of species and bring those back, right. To make proxies for that. So we've been super clear on that.
Um, and what, what, what, what's been the, the two things that, one sad, one's funny. What's been sad about some of the reaction of the dire wolf is that yes, while it, while it broke the internet, the people that spent hundreds of hours, like Time Magazine and the New Yorker and Rolling Stone, they spent hundreds of hours with our teams, went to our labs.
Talked to external validators talked to us. Their conclusion was Yeah, this is about as, this is as as dire wolf as wherever we're gonna get. Right? Because to your 0.99, five point 0.5% was the same. So they came to that [00:16:00] conclusion, right? Like we didn't write those headlines. Um, you know, there will always be people that debate semantics, but the sad thing is they kind of miss the bigger picture here.
Right? Like that, you know, we're inspiring kids, we're bringing technology to conservation, and we've done genetic feats that no one else has done. No one has ever taken ancient DNA put it in a living species, uh, and also done it through multiplex editing and got a hundred percent efficiency. I mean, you know, from your background, like, that's insane.
And so they've kind of like, ya like the Seinfeld, they're like yada yada ya over the science. And they just wanna argue the name. And it's like, if the worst thing that happens in our dire wolf is that people wanna argue the semantics of how to classify it, then I think we've done Okay.
Zach: I think I, I view it as like. Forced evolution is the method
Ben: I love that idea.
Zach: right? Because you're not, you're not kind of like, I mean, you're working backwards in the logic, but the actual way the science happens is you're, you're forcing the gray wolf Yeah. To become a dire wolf through like little tweaks in the DNA.
Yeah. And that's, that's how you get there. By the way. That's probably how the dire wolf evolved in the
Ben: so, so, so, so three, so three things on that one, you're, [00:17:00] I love force evolution. That's a great way. Um, I always say accelerate. That's, that is force evolution. But what's interesting is that people don't realize this, but we have a 13,000 year old tooth.
And, uh, we have a 60, uh, 3000 year old, or, or or 62,000 or 72,000 year old skull. So we have 60,000 years of genetic divergence. There's more genetic divergence between our two dire wolf samples than the living wolf cells and our other, and our tooth people. Forget that part, right? So from an evolutionary distance perspective, there's actually more evolutionary distance between our two di wolf samples than our dire wolf and gray wolf sample, right?
And so people kind of forget that. And then the other thing is, I, I'll love to ask people like, did you see dra? Because we get the, believe it or not, we get the Jurassic part question
Logan: I can imagine.
Ben: And, and, um,
Logan: that's actually, uh, page two of
Ben: Okay? It's, it's all page
Zach: photos
Ben: so, and, and, and so, uh, and so, um, we always get the question, and I always ask people like, well, did you watch dress work?
And they're like, yeah. I was like, what's the movie about? Some people get cute and they're like, about hu, human hubris. But, but what is the movie really about? It's about Diana fucking dinosaurs, right? And so, uh, in that I'm [00:18:00] like, well, are they. Because they, they're genetically modified birds with ancient DNA and with frog DNA and then the latest one, they've got everything under the sun in it.
Right? And so they are synthetic biology, genetically modified organisms. So are they dinosaurs or are they that, so it's like, if you classify what you see in dress park as dinosaurs going by Jurassic Park Tech, right? Which is, which is similar to what we do. Uh, then, you know, our Daryls are dls. If you wanna argue that our mammoths are gonna be cold adapted, uh, elephants with mammoth alleles, then say that.
Logan: actually the perfect example of like the, well, actually of the internet. Yeah. It's uh, it's like, it's just people want to debate. It's, it's this super cool thing that has never been possible happens and people are, well actually it's not quite as cool as you think. It's not
Ben: but it's, it's, it's an amazing achievement from a sci I feel bad for the scientists. Right. 'cause people have, like, I've been asked, I was like, does this upset you? And I was like, I, I just did this thing in the LA Times. And they were writing, they were writing an editorial that was quite negative and I was like, they're like, are you frustrated with it?
I was like, no. I was like, what? [00:19:00] Story besides tariffs in the world three weeks later, you're still talking about, I was like, what? What the people that hate colossal the most, uh, about that are these like armchair scientists, right? That are not at the top of their field. Remember, we have, people love to say, well, scientists disagree.
I have 95 of the top scientific advisors in noble laureates in the world, right? I've got all the top ancient DNA experts are working with Colossal, and then I have 170 people with 140 of those being scientists that we pulled from the top university. So do I. They
Zach: with? The little
Ben: they want the classification.
Classification,
Zach: Ah, the branding. They,
Ben: they want, you can't call 'em Wolf. So I'm like, well then call 'em whatever you want. And people hate when I say that, but I was like, I if you wanna
Zach: call it Dire Wolf 2.0
Ben: yeah. Or call or call it. How about this colossal di wolf? And they're like, ah. And I was like, no, no, but that's fine.
If you wanna keep arguing the semantics on this, what you're doing is you're just driving more attention to my company. So I couldn't be happier with people arguing the debate on what to call them.
Zach: I'm sorry, I'm gonna go back to [00:20:00] the, the, the methodology,
Ben: yeah. No, sorry, sorry. Yeah.
Zach: fascinating. So you take the gray wolf, DNA, if you will, and we'll talk about like, what, what are you actually taking? Like is it a cell line? Do you synthesize it? But uh,
Ben: take cell lines.
Zach: you take the cell
Ben: Yeah. And, and, and, and what we found is that there's, because these are all non-model species, meaning they're not a mouse or a pig, these are species that really aren't that well studied, right?
And so we find that different cell types and different species work better with specifically, uh, uh, the wolves. A lot of the cells were sing and they just could, we weren't, we, we didn't go through the process of making pluripotent stem cells making basically reprogramable cells back to their most na uh, nascent state.
We actually, uh, uh, discovered a new type, and it's not, we didn't discover the cell. We discovered a new way to use a cell, like the inner lining of like our, our veins. They, they kinda slough off this, uh, these things called ept EPCs, endothelial progenitor cells, which are not fully differentiated because you can't clone from blood because there's no nucleus in red blood cells.
But you can capture from blood these cells, which is awesome for cloning 'cause they're highly efficient to [00:21:00] clone. They're super easy to biobank. And from an animal welfare perspective, you just take a blood draw. And so you don't have to take like skin biopsies or liver tissue. You don't have to anesthetize the animal.
And so for our diols, we actually use EPCs. EPCs.
Zach: EBCs. Yeah. So just to translate for, for people or at least the way I, I think about it is there are, you know, in a gray wolf there's how many cell types?
Ben: Uh, a lot.
Zach: lot. And you know, they don't love to be messed with No. Right. Like you start messing with the cells, they, they, they, they're not happy. They die.
They die. Yeah, exactly. And so there's only a certain, it depends on the animal I assume, but like certain type of cell that you can even like tweak
Ben: And some are in, so a lot of times it's, uh, so getting pluripotent stem cells is typically the best. Embryonic stem cells are great, but in the pluripotent stem cells are great because we can differentiate different tissue types. So we can test the edits before you even have to make the
Zach: Yeah. That's where I was gonna go with. So, you know, you're, you're in, in the beginning I assume you're, you're kinda like guessing at the edits. Yeah. Like you think based on whatever various studies and [00:22:00] wolves and you kind of know are roughly, this looks like it's the hair and this looks like at the set.
So before you ever kind of commit to the new, the new embryo, if you will, you do edits in the gray wolf. So, so whichever cell line you're using Yeah. And you can, you can generate tissue from that cell
Ben: You, you can, you can generate tissue from the selling. So I'll give you the best example that I think people understand is, wait, did you see our Willie Mouse
Zach: Yeah, yeah, yeah, yeah, yeah, yeah, yeah.
Ben: fan favorite, kid favorite, uh, people. By the way, we got negative feedback from the scientific community, uh, when we launched the Dire Wolf. And they're like, we thought they were only working in mice. 'cause they were, we got some of the negative feedback when we launched. Willie Mouse is like, they'll never get to a mammoth because these idiots are only working in mice and they're, they're not very far.
They're never gonna, and then, and they were frustrated at us because while we were taking this negative feedback. We had these dire wolves that were already been born. And so, which are obviously not much harder project, but, but with, but to that example, there's three ways that we can really test, uh, whether the edits that we make in a mammoth and an elephant will be a mammoth, [00:23:00] right?
You can grow one which is risk. 22 months of 22 months of gestation. Quite a long time. That's option one. Option two is you can do some, you can do some modeling and some like molecular test and is it exuding the right protein? Like you can do that test. I guess there's four. The third is you can grow organoids.
This is a tissue. So we actually have grown, uh, we've created elephant pl puttin stem cells and we've differentiated them into organoids for hair. So we have like kind of creepy and super satisfied. So we actually have like hair growing in, in
Zach: like a Petri dish.
Ben: Yeah. Which, which, which is a lie, but it's not full animal.
But then you can use mouse models, right? So what we've done is we took. We didn't just take the mammoth genes and ram them into a, a mouse, 'cause there's about 200 million years of genetic divergence. We, we then looked at, okay, what are the specific, uh, uh, variants in mice that are in the same genes? So we're looking at those same gene families in the, in the sub pathways, and then let's make those edits.
And then we did eight edits in seven genes. Once again, all using multiplex with a hundred percent [00:24:00] efficiency. We screened for off targets. So this is, this was pretty amazing science, like just that in itself, that, that delivery mechanism was incredible. Uh, so, so we did that and then 21 days later we got our woolly mice that had the color, the coat type, the, the hair length, the, the way the hair is growing, different ways, the thickness.
And, and so we were able to visibly see those phenotypes. So in longer gestational species, we can do that. Now, the good things about wolves and dire wolves is they're dogs. So we, we've done a lot of genetics on dogs, so we went to. Bridgette von Holt, who's the number one, uh, uh, wolf, uh, expert, uh, specifically around red wolves, which is one of the closest living relatives to, to the Dar wolves, which is critically endangered.
The most endangered wolf on the planet is the red wolf here in America. And then we went to the Broad Institute in Eleanor Carlson, and we, and she's the number one candidate, expert geneticist in, in canon. So then we could look at doing all of that comparative genomics so that we weren't making choice, uh, gene target effects that could have negative impact
Zach: To translate, I think for, for, for people. [00:25:00] You think, you know what? In the DNA you need to edit based on a variety of different, like ways you can research it. You're not a hundred percent sure you're right. Yeah. Uh, and so you have to kind of make the edit and see what happens. There's only so much, see what happens You can do before you actually grow one.
Yes. So you're looking for proxies like skin and I assume hair color,
Ben: color. So, so on the hair color, I'll give you an, I'll give you an interesting one. And, and once again, this goes back to this whole stupid, I think, stupid purist perspective, right? So we found out, so before we brought back the diols, here's what the world knew about Diols. We knew roughly where they lived.
We knew roughly when they went extinct. Uh, we knew they were roughly 20 to 25% bigger. And we knew that based on muscle, and they had a larger, a slightly larger skull. Um, and then we knew based on, uh, the bone density, we think they were
Zach: What? What's the defining characteristic you think that gets normal people to believe? It's a dire wolf.
Ben: I think it's a, no, I think it's size. I think it's size. And that that did know darel. It's it's size, muscle [00:26:00] structure and cranial facial, the bigger
Zach: the snout?
Ben: Yeah. Those are the, those are things that we, so that's what we knew. Yeah. Right before this. Now what we found out, um, and this is the good and annoying thing about science.
Five years ago there was a paper about Diols. Right. And a misconception. So the result of that paper was, are they, what are they, where do they fall in the canid lineage? The results of that paper, not our paper, but that paper, which be Shapiro, our Chief Science Officer, and many of our scientists were on that paper, uh, came out to, we don't know, we don't have enough data.
They had 0.15 x the genome. That means that they had less than a full read of the full genome. We have 13 X reads. We got 500 times more data. We've got a lot. We've have, and then we have, uh, sorry, we have two specimens out. So what's interesting about that, that was, you know, once again, the press cycles because of Game of Thrones, you know, people in the paper it mentioned jackals 'cause they were somewhere in, in the candidate lineage.
So the paper, uh, there were stories that came out five years ago that said. Dire. Wolves weren't wolves, they were jackals. And then there's paleo artists that drew them to look like jackals, so they were red. So when we [00:27:00] launched. Uh, our thing, people were like, no, no, they're not wolves. They're closely related to jekyll's.
By the way, the original paper doesn't even say that. And, but the paleo artists that we all see in our minds from that original paper, they were red. So what we also found out when we redid the resampling was one, they are closer to wolves. So we put out a paper about that. We also found out they were white.
'cause there's no dire wolf, DNA, there's no D of hair laying around. So we didn't know that. So we found that. And, and going back to your gene question, here's the, what we found, the specific edit and the, or sorry, the specific variant, uh, that causes white, uh, hair, uh, white hair in Canaan. So in do all dogs, the this specific truncation in the variant, the, the, the, the, the specific one, uh, has been known that di that, that at least our two diols had with 60 million years of genetic divergence.
Um, so we know that they're white, but it, it, it does sometimes cause, uh, blindness and deafness in some wolf and dog breeds. So this goes back to choice. So if we're rebuilding Diol 2.0 or, or rebuilding [00:28:00] extinct species for today, like we think about a purist perspective is just go stick that gene in and see what happens.
Let's roll the dice right From an animal welfare perspective, 'cause we're certified by American Humane Society, the oldest human organization in the world, uh, that's a terrible idea.
Ben: Not just 'cause our certification, but because of like, just true animal welfare. So what we didn't, what we then did is we worked with the broad and other people and said, what is the same gene that causes the same coloration that we can put in that we know has no negative
Zach: So when you're, when you think, and I, I have a note here. It's like roughly 20 edits across 14 genes that
Ben: rough it was, it was 20 ads or 14
Zach: Are tho like, talk to me about the edits themselves. Are these like individual base pair edits? Are you inserting
Ben: both a combination? So these were all, these were all snips. So these were all individual base pair? Uh, base base, uh, in, in individual base pair edits.
Logan: And, and, sorry, are you doing this in gray wolves
Ben: We're doing this in, in on, on the gray wolf. We call it the genetic donor, the genetic.
Logan: but then the validation process. You use the woolly mouse as an example of getting to the woolly mammoth. Uh, [00:29:00] what are you doing
Ben: So we didn't, we didn't do a woolly ma uh, mouse type project for this, because we know so much about dog genetics and wolf genetics. Right.
Logan: because we've
Ben: like a pug is a wolf. Right. So it's just like,
Zach: You make, you make the edits. Let's, there's some validation that, okay, we think these are gonna, these are gonna work. How do you handle like the actual editing itself? The tech, do you do it in-house, outta house? And then there's this, there's this concept of like off target,
Ben: Yeah. Yeah. Great. These are all
Zach: talk about, which is like you edit here, but also something happens over here and you
Ben: unintended consequences are off tech effects.
So these are awesome questions.
Ben: So a lot of times, a lot of these editing tools, uh, uh, essentially have like 15 to like 20% efficiency, and I think that's a good day in some of these cases, right? We're seeing 90 plus percent efficiency before we do monoclonal screening, meaning we screen all the cells, which we'll get to in a second.
This answers your off targett effect. Um, so what we do is we edit a lot of cells. One of the things that Colossals done, I think that it is novel, is we've really focused in two categories. DNA synthesis of synthesizing big [00:30:00] blocks that we can swap in safely, number one. And number two, multiplex editing.
Ben: And I, I would argue, and I think that our investors would all argue that we're probably the number one company in the world in terms of multiplex editing. Being able, and, and what that means in kind of layman's terms is instead of making one change, we're making a bunch of change. Now, where we are getting pretty interesting is we're clustering all of that.
So we're building a raise to deliver mul, not just multiplex editing of single nucleotides or, uh, knockouts, knockins or even, uh, and, and then these large cargo inserts we're, we're wrapping all that. And we've got a novel way of delivering a lot of edits at once. So we're now making over 50 edits at once right now, which is, which is insane.
Zach: Yeah. The, the thing I think is sometimes hard to understand is like the more stuff you're trying to change in a single cell, the more chances it goes
Ben: Yeah. The more chances it goes wrong, causes, breaks, causes unintended consequences, like off-target effects. And it's also the higher probability that the cell just, I mean, you're, you're, you're hitting the cell, right? Think about you're punching the cell.
Zach: it's just like [00:31:00] a crisper driven sweat
Ben: it's, it's a, it's a combination.
So CRISPR's one from a knockout perspective, uh, we're doing single, we're doing some, using some of the work. Uh, we've got licenses to the basin, prime editing from, uh, the broad and from and from, uh, uh, David L. David L's lab. And so we're using a combination of, of all these tools and then we're packaging all of it in, in different ways.
And I think one of the things that's interesting about Colossal is our packaging, our delivery mechanisms are creating high, very high degrees of efficiency with how we're delivering. So
Zach: you get, yeah. What maybe we can make is the numbers up, but you got your cell line. Yeah. You make a bunch of edits. You hope, obviously, that the
Ben: And then you screen, but then
Zach: then you screen after the
Ben: You screen all the edits.
Ben: And then one of the things that we do is we then do, we do full, uh, genome, we do full, uh, gene sequencing right. On, all on the edited cells. And then once we go through the, uh, somatic cell nuco transfer or cloning process, once we tra, once we transfer the nucleus, we then resequence 'em all, which, which is a lot of se
Zach: the [00:32:00] fertilized, roughly, if you call it
Ben: Yeah, yeah, yeah,
Zach: Yeah. So you're so step by step, basically like you've got your cells, the, the, the gray wolf cells. You've got a bunch of cool technology to go and edit them. That is actually very hard to do successfully at any scale because you just get like errors in issues along the way.
Making 20 edits is, is not an easy thing to do
Ben: Yeah. I mean, it's never been, uh, there's only one other group that's ever done 20 edits, uh, that we've seen. I mean, maybe there's someone that we haven't seen that's made 20 edits and they were just, they were random knockouts, so they weren't, they weren't really like what we call precision editing. Right.
They weren't like, let's change this letter to this
Zach: Yeah. Or like take this whole thing out and swap a new piece of DNA in, which is like a big change. You then have the
Ben: we've actually done over a hundred kb swaps, which is crazy.
Zach: It's a very long swap. Very long swap. So it's gotta be real precise of like, where do you start and where do you stop? And there's a bunch of like new technologies, let's call it, that allow you to do this. I mean, 10 years ago, impossible.
Ben: 10 years ago. Yeah, probably,
Zach: Probably [00:33:00] impossible. Yeah. It's the timing as well. And
Logan: wait, what's the big change in the, like what do you think has allowed
Ben: So I, I'd say, I'd say there's the, the, the three things. One, AI and compute. The ai and access to compute is definitely, like, this would be impossible without that for us,
Zach: which part of it
Ben: of it? So all the, all of the DNA synthesis work, uh, would be impossible. All of the comparative genomics work, some of the ancient DNA assembly, we've built some proprietary tools around
Zach: like know what to change and the analysis
Ben: Yeah, yeah. We actually, we actually have a tool that's pretty cool. So we, we use this, we do a lot of interesting things.
When you come from a software background, you try to, oh, we can solve everything with software. Right? And, and no, I, I, no, but I, but I, I naively thought that, so everything, you know, it's like you have software hammers, everything looks like a software nail. So, um, but we have done some pretty cool stuff. Like we have one thing that is not interesting to probably anyone else, but we now, we feted enough data over enough editing with different types of editing modalities that we can now, uh, effectively say.
It, we're gonna go make this change in this part of the genome, which is the best tool. And it tell, it actually relevance [00:34:00] ranks the tools for us of what we should use, which will, which, which have the highest likelihood of less off-target
Zach: Yeah, those, those, I assume the answer would be the actual editing technology to like go into the cell, into the nucleus and swap DNA. And the spot that you want is reasonably new
Ben: yeah. It's reasonably new, like last five years. Six. Yeah.
Zach: So to do, maybe you could do like a single base pair swap
Ben: but people are still doing that. I mean, people are climbing victory, which is great. I'm, I'm all about this for a small knockout, for just a single change. Like people are doing that all the time.
Like, you know, like when, when the William, one of the negative feedbacks we got when we launched the William Mouse project, uh, which I would say is pretty objectively cute project, uh, they, uh, uh, one of the feedbacks was like, well, people have made eight changes in a miles. And I was like, not at once.
Zach: Yeah. Individually.
Ben: Yeah. They, they've done it over eight generations of vice. Right. And so, so we like the, these are the things that I, and, and I think that, I believe that, um, I, I, I don't know what the current upper limit [00:35:00] is. I don't, I of changes. Yeah. Yeah. I mean, we're, we're
Zach: and it just goes
Ben: We're just doing, we're doing, we're hollering it so well that we're doing 50 plus edits right now.
And so this, like, this goes back to your point of, of risk, right? Like one of the feedbacks that we got on the classification of firewalls is like, why didn't they make a thousand changes? It's not gonna make on a percentage basis of three and a half billion, uh, base pairs. Guess what? It's not gonna, it's
Zach: may not
Ben: It's not gonna make it more dire wolf and it's not gonna change the phenotypes anymore. So the physical attributes, it's not gonna make it look more like a dire wolf, and it's not gonna make it more dire wolf from a percentage basis. Right? And, and all you're doing is, is doing more, you're just creating more risk for what.
So that you can show the strength of genome engineering. Well, I can show that in a cell. I don't have to take the risk with a live animal.
Zach: So in the new cell, you take some of them, you sequence them, you make sure you didn't have off target issues, meaning like, Hey, we thought we were gonna edit this thing. And
Ben: edited, but oh, we
Zach: we also edited over here. 'cause DNA A is complicated and it's not as simple as just like, you know, a straight, it doesn't, doesn't work that way.[00:36:00]
Uh, and so the notion of like off target can be really bad.
Ben: Yeah, exactly.
Zach: Uh, either because like it just makes the cells not viable
Ben: And so that's where we spend a lot, we, we spend a lot of money and time on sequencing.
We, we kind of like belts and Yeah. But it, but it is belts. It is belts and suspenders. Right. Like I think from a, when you're living doing not just cell work, but your goal is to have living healthy animals, like, I think you have a responsibility to do that. And so people are like, oh, you raised so much money.
It's like, well, when do we have to build the whole system? Two, we have to have the best people in the world. This technology is really expensive. Oh, and by the way, like we, we are adding in layers of safety protocols throughout the whole thing. 'cause the last thing we want is some like, you know, stillborn animal or something because we made the wrong change and we, and we didn't screen
Zach: Yeah, you get a wolf with mental issues or behavioral issues or physical issues.
Ben: I mean, that happens all the time. I, I won't say which one, but there is a, uh, a, a zoo in the, in the United States that recently had a, a stillborn giraffe that had multiple heads and multiple [00:37:00] legs and they weren't gene editing.
That's just biology. Biology goes wrong. Right. It's not perfect. And so, so we have a, I think we have a responsibility when we're taking in that forced evolution, right? That, that we need to actually, uh, overprotect to ensure that the things we're doing won't have
Zach: By the way, and this is like probably a longer conversation for another day, but the whole like biology goes wrong thing I think is like for normal people, uh, who aren't in science day to day, which was me, you know, as of like three years ago. Now I get it a little bit better, but you forget that like the, the ability for biology to go wrong is also what enables evolution in the first place.
Yeah. And so you need a little bit of like, Nope, that's not good. That's not good to catch the one that that is
Ben: is, that, that, that, that advances the ball
Zach: alright, so you get the, you get, let's call it like the clean new, yeah. DNA, the
Ben: the the cells you're super stoked about.
Zach: Now you gotta. Get 'em into, essentially, you could think, I think of it as kind of like into like an egg, right?
Yeah. If you will.
Ben: yeah. It's a great way to look at it. So, um, this is the same thing that we did.
Uh, we as humanity, not colossal. Did, uh, with Dolly [00:38:00] the sheep, right? Only they were using like archaic tools, like jamming stuff together. We use a combination of robotics. We actually have a laser drill. We have a, a whole system that use computer vision to drill into a hole into the, the outer shell so that we're not just hit hitting.
And mam, like literally physically just jamming the
Zach: You, you take the whole nucleus out and pop it in. Yes.
Ben: So we take the whole and, and we cut it. It's pretty interesting what, what
Zach: Can, can you explain that one in like, kind of simple? I mean Yeah. Dumb it down, but
Ben: so, so instead of like, like before we, like, we, like they, we, they would have needles, uh, and kind of, kind of blunt instruments that are just like jamming the nucleus to extract the DNA.
We aren't doing that. So we actually cut a hole, uh, in the outer shell, the Z of palooza. We go in, uh, we actually fabricate our own, uh, we, we actually fabricate our own removal devices as well, uh, because there was nothing that could do exactly what we wanted to. And different species have different things.
Like, for example, we needed to build a, uh, one made out of quartz. Uh, it was the only way that we could do, uh, get into the, uh, uh, get [00:39:00] past the zoma palosa in, uh, that we vibrated at, at insanely high frequency, uh, to get into the zoma palosa for marsupial cells. Because, and, and it sounds weird 'cause you're like, oh, big giant elephant, little tiny marsupial, but their outer shell is insanely hard.
That, that we hit it as hard as we could with a laser and wouldn't even burst a hole. Yeah. So it's crazy. So we do that, we, and we take out that middle kind of brain, and then we put it into that of an egg cell where we kinda do the exact same process in reverse, suck it out and kind of make space for it.
And then we drop it in. And then essentially you have a, an embryo, right? You have the precursor to to, to an embryo, which is pretty, which is
Zach: And you take that and implant it back into a gray Wolf
Ben: So you grow it, so you grow it in, uh, in culture for a little bit, right? So once you get to a little bit further, you put it, we use domestic dogs, so we put 'em in, in, in domestic dogs.
Zach: So like it's ac the actual growth, what, what percentage of the, from like the actual embryo to growth is in the dog versus in the, in the lab.
Ben: 99% in the dog. Yeah.
Zach: And in, in
Ben: we do wol, we do weekly ultrasounds. We do, yeah. [00:40:00] It's pretty involved. And
Logan: why do a dog versus a wolf.
Ben: Well, wolves are endangered. Yeah.
And so, oh, really? Yeah. And so you, and so one of the things that, that colossal, we do have a 17 person team to get, we can talk about this, get weirder in a minute. We, um, we have a 17 person team working on artificial wombs. So long term we love to grow everything exhaustion, like through kinda, um, uh, execute our development systems.
But, um, short term, we're still using surrogates, right? And so we have to get good at, uh, for many reasons at, uh, inner species, sematic, sonic co transfer. So,
Zach: Well, yeah, if you're gro, I didn't realize they were extinct or not extinct. Endangered, yeah. And so you've gotta go to like a slightly adjacent species to grow in, in that whole process. Anything in there where. You felt like this is just insanely complicated. We couldn't do it, and you figured it out. I assume there's tooling and
Ben: Oh yeah. It's all like, I mean, delivering the number of edits is really hard. Getting culture conditions is really hard. And different projects have different issues. So, so for example, um, you, you do a different [00:41:00] process with birds and do mammals. 'cause there's not a clear way to do, uh, somatics on like, do the cloning s seven and birds.
So you have to use generational birds. So you actually, instead of editing like, um, uh, uh, stem cells, you, or, or editing like EPCs with wolves, we're editing what's called primordial germ serial cells, the precursor to egg and sperm. But just to get the culture, like the, this is the media, it's like getting just the media conditions for those, what they're called PGCs to grow outside of a, of a bird body.
Is insanely hard. It's only being done publicly in chickens. Um, we're very close to having it done in pigeons 'cause dodos were pigeons. Um, and so it, it's very, very hard just to get the media conditions correct on, on that stuff. So we have to innovate across the entire spectrum. But once again, it's like, it's not like we know like, well, like when you do reprogramming, we know the of cellular reprogramming.
We know that there's a cocktail of transcription factors. We just have to get the right cocktail.
Logan: On the surrogate side, like how do you ensure cross species [00:42:00] compatibility of the surrogate? So,
Ben: The big, so, so we typically use the egg cell from that species, right? And so the, the, the further genetic distance you get, you could run into mitochondrial rejection issues of the, not of the parent, but of the host, uh, of the host egg right. Of, of, and so we use the
Zach: Of the dog in this
Ben: the dog in, in this case. And so, so one of the things that, that we are working on in addition to artificial wombs is we are working on, and, and we're not there yet on artificial wombs, and we're not there on this other concept, but we are working on a universal egg concept of like, how do you map, how do you have like, which I think has weird, interesting applications to mitochondrial health applications outside of colossal, but how do you map, uh, and, and change mit ba basically have a universal egg that you could accept any type of nucleus in it and you have a low probability of mitochondrial
Logan: like the simplest form of an egg that you possibly can that will be transferrable. Hmm. The primitives of an egg.
Zach: So the wolf shows up [00:43:00] and now I assume there's like a whole bunch of optimization work to make sure that, you know, the, the throughput and the yield, if you will, this whole process just like inches up and up. 'cause you're, you're probably wasting money in a lot of areas. Yeah.
Ben: I mean, the biggest thing, the, the biggest thing now for us is, I'd say the, the two biggest areas for us, uh, from an innovation perspective that we are, is, is delivering DNA, you know, how do we expand DNA synthesis and delivery? How do we, uh, also deliver multiplex edit more and more edits at a high degree of efficiency?
'cause we can screen in the sequencing that, that, that allows us to know what we did wrong.
Zach: Why? Why do you think you need more edits? I mean, the wolf looked pretty, pretty wolf like,
Ben: I mean, it looks just like a Dar wolf. Yeah. So, uh, at least at this stage, morphological, I mean, they're 85 pounds right now and they're six months old. Right. As a typical wolf is like 75 to a hundred pounds. Right? So we're on the trajectory. The wolfs grow for about 18 months. We're on the trajectory for these to be 140, 150 pounds, which is roughly what Dar Wolfs were.
So, um, and then, and also the everything, they're stockier. If you go look at a five month old, [00:44:00] if you go to the internet and look at a five month old wolf pop, and you look at ours, you're like, yeah, those don't look the same. You can, like ours look like lineback, the linebackers of the, of the, uh, wolf world.
Um, well, it is just different species, right? So, like, um, on the Thine or Tasmanian tiger, there's 70 million years of genetic divergence between the thine and, and the, uh, fat tailed donnar, which is, it's a marsupial carnivorous mouse. So we're turning a marsupial mouse into a carnivorous wolf. Well, it's not a wolf, but it's a, it's a, it's a wolf shaped
Zach: So different species are gonna require more edits essentially,
Ben: Yeah. And so, um, now the better we do at Comp Bio, we think that it's about 500 edits for cranial facial morphology. So we get that hyper carni, uh, skull. Like the, the thine is a super hyper, it was a super hyper carnivore. So it's about, we think it's about 500 edits, um, uh, which we've made in a, in one cell line, which is great.
Um, but some of the, the work on sizing and some of the, the patterning could be, could be more. And so I think the thine project, it's hard to say, but it'll be thousands of edits.
Logan: Hmm.
Zach: maybe [00:45:00] a controversial question. So. Business model for a second. Right. And, and, and obviously there'll be some pieces of this, which is like, we made a technology, it's cool. And we spun it out. We spin it
Ben: so we spun out, so we spun out three companies so far, a computational biology platform for human healthcare, a company called Breaking, which is a plastic degradation company. So we've accelerated, uh, uh, there's, there's an Enzo, uh, microbe that makes its enzyme that literally breaks the chemical bonds of plastics.
Uh, and so that's what we call it breaking. Uh, and that's now we've raised capital for both those businesses. We spun out another company we haven't announced yet that's valued it way over a hundred million dollars, which is great. Um, so, so, so spin out technologies
Zach: and, and each of those carry their own unique esoteric risks of
Ben: Yeah, yeah, for
Zach: startups. You've gotta, the Jack has to work, you've gotta scale it. Someone has to pay for it,
Ben: Yeah. Someone's a right at We're not, I don't run it.
Zach: yeah, yeah. Find a CEO. So, but on, on the
Ben: I try to stay on the board as little as time as possible.
Zach: it's also a lot of work on, on, on the core business itself.
Zach: Do you view this as like, people will actually buy the wolf as pets? I mean, they're not gonna buy a Willie Mammoth as a pet, I don't [00:46:00] think I.
Ben: So we got, so we got a tremendous amount of ask for everyone ranging from the, the request on the woolly mouse. Were ranging for, um, kids pets, school pets, uh, uh, large household named, uh, I don't wanna out them, but, uh, museums want them. We've even had some museums reach out and say, well, mice don't live forever, can we taxidermy?
I'm gonna put 'em in a display. And so like, so we've gotten all kinds of those types of requests, right? We get the apparel requests, we get all these types of requests. Um, you know, I, I do think that there's lots of people that would love to buy our animals. That is not our business model, our business model, uh, uh, and it's, we're learning.
If, if you ask us when we started, it's like. Non-existent. If you asked us two years ago, it was technology spin outs because we thought, hey, we can create enough option value that, you know, if you innovate across all these things, and you, if we can deliver thousands of edits with zero off target effects, that company loans worth a hundred billion dollars.
Just that spin out.
Zach: Oh, be your cash runs out. Which is the challenge in that model, right? Because you got core cash
Ben: and you spin 'em out. Yeah. And we
Zach: you spin 'em out, and then they need their own money and
Ben: they, they, they [00:47:00] need their own money. We don't, we don't take, we don't fund them. Right. They, they go out.
Zach: Oh, I just mean without like a core
Ben: No, without core credit. Soon. Yeah. Yeah. So, so we are kind of raising money. So that, that's, that, that was kind of model one. Um, there's a huge economic driver, and you've probably heard about carbon credit seeing the carbon credit model, right? It it,
Zach: fraud. But we
Ben: but Well, no. Yeah, just like crypto. Like crypto, like,
Zach: Well, no, I mean, well,
Ben: I would argue crypto is more of a religion than a currency, but that's another.
Argument.
Zach: meant more like a lot of the carbon credit businesses
Ben: Them are
Zach: them are fraudulent.
Ben: And so, so just like the early days of, of crypto, um, but what you've seen in that market to a couple things that you've seen in that market. One is you've seen this move to biodiversity credits 'cause it's highly trackable versus like, so that's like saying they, there's now been studies that have been peer reviewed.
This says an elephant in Gabon has this annual, uh, ecosystem restoration and carbon impact, right? So you have people that aren't like, Hey, like the, the fraud that you're talking about is like, people are like, oh, like I'm gonna go buy a bunch of forest and I'm gonna keep all the animals alive. And they're like, wink at you.
And [00:48:00] then, and then it's like, and that's where the trillion dollars of carbon
Zach: or like, here, here's this forest. We didn't cut down and either the forest never existed in the first place, or they did cut it down and they just didn't tell you like,
Ben: It's, it's been wrought with that. But what you found is that if you look at like, you know, the Paris agreement, 66% of people's agreements say that they have to move to nature-based solutions like rewilding, like some of these things.
So, um, if you look at things like, uh, rewilding elephants, if you look at kind of like the, the, the total economic value, and I'm not talking about anyone that's currently monetizing it, but from peer reviewed papers shows that there is over $500 billion of, of value that's being added to the African. Um, and they take into ecotourism, take all these things into it to protect, uh, to, to, to essentially protect the force elephant, which is critically endangered, right?
And so, uh, what we're seeing, if you look at now PWC entering the game, you see TPG, uh, you know, raising $9 billion fund around this. You see, um, uh, who else did, oh, uh, Lloyd's of London's now certifying it. You're now [00:49:00] seeing people saying, yeah. This market was nascent. Lots of people running around. But here's the other thing.
We know that ExxonMobil, and we know that, you know, a, in this extractive economy that we, uh, live in, we know that like Sumitomo Corporation, Mitsubishi in strip mining and, and where do think lithium comes from? Does it like magically grow on trees? Right? And so synthetic biology dot solve the, the house plant that makes lithium problem, right?
And so, um, and
Zach: nor is it likely
Ben: no. Nor nor nor is it very, very likely. And so I I, I think that, um, those companies, whether it's social pressures, ESG or government regulations, like in, in the eu, they, they have to buy these offset credits, right? And so. Regardless of where the market was, there's now this maturation of the market.
So for us, it's a perfect market timing. 'cause you know, a lot of these pioneers got arrows in the back, which is great. Now you've got people that are actually coming in and having to certify and really understand it. So if we can go in and say, Hey, we're gonna return the thine back into Tasmania, we think the tropic, uh, cascading effects could be this.
Let's go measure it. Let's [00:50:00] bring in a college and conservationist. Let's then bring in Lloyd's, let's certify it, and then let's also get government subsidies for,
Zach: is the most progressive last business model I've ever heard. I love it.
Ben: Yeah. It's, it's interesting. So we have both sides of the aisle.
We're very, yeah, we're very, very, uh,
Zach: I, so I, it's not a domestic safari that's like really expensive to visit. 'cause that I think I, I, I would go for that. Everyone
Ben: would go for that. Right. And so.
Logan: Is that like an ethical consideration? I mean, like a, a fancy zoo. Sounds awesome. Yeah.
Ben: I mean, look, I, I am not
Zach: hotel. The di will present the Yeah, yeah, yeah.
Logan: Singita brings you
Ben: No, no, but no, but, but, but two things. Like if you look at like, um, on the ecotourism front, like we talked to, we talked to the, uh, ministry of tourism from, uh, Mauritius where the Dodo was.
And he's like, you're gonna five XR tourism, right? And I said, well, look, we would love for you to give us land that goes on our balance sheet. We would love to get the carbon and biodiversity credits that we can go sell, that we can get certain that we can, that isn't just like, Hey, we promise you there's dodos, there's wink, wink.
But like, let, let's actually measure it. Let's have let third party verifications, [00:51:00] let's go sell it. And the other thing about the carbon and biodiversity market is based on the sexy factor. Like the, the car, the credits try to at a higher value, right? Because ExxonMobil wants to show that they're doing stuff with like.
You know, alternative things like ethanol or algae, right? So they do trade at a higher premium. So, so if we can do all that, and then if we can, if we can say, oh, well let's have a four seasons or a one and only earn a mine, be there and they do an eco lodge and we get a, a cut of that, then that's great, right?
But also the local people get a cut of it and whatnot. And if, and if it causes people in Mauritius to clean up the environment and remove the invasive species that led to the dodos demise, then that's a win also for conservation. And so, so I think that you can find a way to put a value on nature. Don't you remember forever people were like, well if we, we charge a hundred thousand dollars to kill a lion, then we're putting a value.
That was like the argument for like hunting lions. But what they, what, regardless of how you feel about hunting, what they were right about is. If an animal has some value and isn't just seen as some random animal that doesn't have value, someone will at least think about that. Right? And so, [00:52:00] um, and so if we can do it in the opposite way, where you're not killing them, you're encouraging them to have an ecosystem, uh, function, I think that's, that's pretty interesting.
Um, and, and to your zoo question, we're not against zoos. Like we, we think like people like the a ZA and people that have great zoos like San Diego Zoo, they do a lot of stuff for conservation. They do really good stuff. We, we work and collaborate with some of them on different projects. You know, I, I think that bringing back extinct species, just to put them in zoos seems like a weird optics,
Zach: Yeah, it could be one, one business line of, you know, many. Yeah. I, I was gonna go to, to, to maybe
Ben: that.
But, but, but we think that, that, we really do believe the restoration work, the biodiversity credits, where we think it's gonna go is certified Nature credits plus ecotourism. We think, we think that's billions of dollars in ARR for us.
Zach: I mean the tourism appeal is, is likely massive. Right? Just given
Ben: and government subsidies.
Yeah. So
Zach: Human embryo editing.
Ben: We are not doing
Zach: I assume we, someone, someone's brought it up.
Ben: Yeah, we, we do have that question. Oh yeah.
Zach: So what, what's the, uh, so the, the reason I ask is, you know, theoretically, and, [00:53:00] and tell me where I'm wrong, a lot of the underlying technology is to do like safe editing here.
There's no reason why those technologies don't also apply to editing
Ben: it goes into germline editing, right? And there's a general
Zach: on the germline side. Yes, yes,
Ben: Yes, I'd say so. I would say that, um, so colossal does not work on anything that is human, uh, neanderthal, like precursor to human or, um. Not human primates, just that there, there's just like, we already get enough cons.
You know, INQ tell is an investor, we, we get a lot of conspiracy stuff already, right? We don't need more of that. Right? So we are like, there's enough for us to do, we're not gonna do some of that stuff. And then if there's applications, some of our technologies, whether it's artificial wounds or multiplex editing or computational biology, we'll spin those out.
Other people can go through the FDA, they can do all that process. That's not our problem, right? We can benefit from it, but that's not our problem. Um, all of the technologies that we're developing, you know, the work that we, uh, we're also looking at different delivery mechanisms. So outside of doing somatic cell nuclear transfer for, [00:54:00] uh, which we're doing for the cloning, we did micro injection in the embryo for, uh, for, uh, the woolly mice.
So we actually are editing, so we're delivering, uh, a way that also doesn't create kymera. So you're actually getting all the delivery in. So it's really cool tech. And so all of that could be applied to, uh, embryo editing. We aren't doing that.
Logan: Wait, at what point, like the prioritization in all of this is probably an interesting thing of, there's, there's probably, I don't know how many novelty animals you can work on. I don't know if that's the right phrase. What? What, yeah. Just like continuing to do. Stuff that's sick and like would get me excited about it, but at some point the dodos like a little bit more practical or has more practical considerations.
How do you think through the, the PR side versus the practicality side?
Ben: the three, our three like flagship projects, right? Mammoth, thine, and dodo all have app. Th tiger, uh, have applications to rewilding and to, uh, and to ecosystem restoration, right? So, so that, so that, those are our three flagship projects of the highest staff projects are [00:55:00] the ones we're working on the most, number one.
Number two is we pair each one of those with the critically endangered species, right? So with elephants. It's, it's the mammoth, it's the pink pigeon with the dodo, and it's the northern quoll with the, with the um, uh, uh, thialysine. And what's interesting is like, you know, while we made three direwolves, we also, we use that same EPC technology to clone four red wolves.
There's only 15 red wolves left in the wild. It's the most endangered wolf in the world, and it's the only wolf that's only endemic to America.
Zach: And you think you can grow it in a dog?
Ben: I already, I already have. You have? I have four.
Zach: four? There's four
Ben: I have four red wolves. Yeah.
Zach: Is that public or? Yeah,
Ben: Yeah, it's public.
Yeah. Just no one cared.
Zach: That's amazing.
Ben: Yeah. So,
Logan: And, and, uh, I guess as you
Ben: think, I shouldn't say that no one cared in relevant in, in a relative number. Last, uh, we, we did bring. We did, uh, increase by a hundred thousand fold Google searches. 'cause we, I'm a data guy, so I look at all the data and, uh, there were 7 billion media hits alone on red wolves.
And there was, uh, I think 398 or 498 [00:56:00] stories on just red wolves. So people cared. But you know, there's 180 billion impressions on, on dire wolves. So, so relatively speaking, they didn't care as much. But then a week later, after we were on the Today Show, a week later, the Today Show just ran a special on Red Wolves.
I don't think they would've done that with, without.
Ben: So, so, so we are building technologies to help conservation, right? Because another business line that we're finding while we open source, I mean, the open source software is a great example, right? So you build a community, you build some tech, you build a community, you do it, people use it and whatnot.
That's all great. But then you have the enterprise use cases, right? Where you've got someone like Microsoft or someone saying, Hey, cool, this is cute. Our developers have used this technology. We love your technology. We don't want our developers to implement this for the Department of Defense. We need, you guys invented the technology.
You guys need to do it right. So a lot of these open source communities will then have their private in instincts. So while we open source all of our technologies for conservation, so like, you know, we're cloning red wolves, we're open sourcing all of that technology to make it more efficient, better for cloning.
You know, I think we could [00:57:00] productionize endangered species, which I think is an interesting concept. So how do we get species off the endangered species list? Because we're actually recovering them. You know, we're not just saving their habitat, which is saving their habitat is critical. Uh, and it has to be done.
So this isn't a replacement for this. This is an and, but then how do we also make more of them?
Ben: But now governments are coming to us on a new business line where they're saying, Hey, we're using all, we, we love some of the papers you put out. We love some of the concepts that our teams are working on, but this one government for uh, for example, has a specific type of, of, of cat.
And they were like, we want, uh, we need to get to 50 breathable females. Well, it's gonna take them at their best rate based on the male to female ratio that, that they, uh, their breeding cycle. The male or female that are born, it's gonna take them 23 years to, to get there. It's gonna cost 'em about $300 million to, to get to their 50 genetically diverse females.
We can do that 'cause we can select for females, we can engineer in genetic diversity. We can do that in three years. And we, and if they paid us a hundred million dollars, we'd still be making a fortune on that [00:58:00] a hundred million. So we could save them hundreds of millions of dollars, but more importantly, we could save them, we could deliver a better product.
We can engineer in more genetic diversity and we can do it in two years.
Logan: Isn't preservation versus de-extinction, like pretty, um, separate in terms of like potential implications, bringing back more red wolves. We know how they do in the wild. We know generally, uh, why they've, you know, decreased in number versus bringing back a wooly mammoth. I guess we don't, do we, do we know why they went extinct and do we know like the implications of bringing them back into the
Ben: So it, it's a great, it's a great question. We know a lot about Rewilding 'cause we know, and we know Rewilding works 'cause we've done it. It's a very systematic process that has indigenous people, groups, private landowners, governments, uh, you know, local, uh, private landowners.
So it's a very stage gated process. Like nobody just like. You know, like in the Yellowstone wolffer rewilding, right? No one just made a bunch of wolves and opened the gates and like crossed their fingers to roll the dye, right? Like no one did that. It, it's a very thoughtful stage gated process where you're in a managed care facility, you're in less managed care, you're in less managed care, then eventually the wild, [00:59:00] right?
So that's a very, it's a very measured process that, that I thinks, uh, well documented and, and, and it's, it's very inclusive of the community, uh, to do that. So we do know, we've also, there's also been studies that show what the reintroduction of cold tolerant megafauna back into the Arctic looks like and how it revitalizes the ecosystem and kind of makes this mosaic landscape, which is, which is actually really good for, uh, uh, adding biodiversity flora and fauna as well as carbon sequestration.
And so, so we know all that. 'cause there's been, people have done this and tested it, obviously not with mammos, but other species. Um, so, so I will say that, you know, uh, from a conservation perspective, you know, we think that, that this is not a replacement for these technologies. This is just a new set of tools like biobanking, genetic engineering, engineering, and genetic diversity.
Cloning. This could all just be new tools in the tool belt because it's forecasted that we're gonna lose about 50% of all biodiversity between now and 2050. So we need new tools. Like we're fail. We, we, we as the world are, are failing. Loss of biodiversity.
Zach: Why no non-human [01:00:00] primates? Monkeys, essentially.
Ben: Yeah. I mean, it, it just, it's just one step closer, right?
Like what, what We're gonna make giant pentheus. Like, that's everyone. It seems to be a fan favorite. It's like a small King Kong. It's like, I I, we're not gonna do that.
Zach: The use case, actually, I'm curious about, and we can talk offline about it actually, is, is in, uh, animal testing for new therapeutics? For new drugs. Yeah. Um, and
Ben: I, do you think that AI and do you think AI will get to the point from a simulation design perspective, that it can simulate all of that?
Zach: not even close. I would, I would put that at 50 plus years
Ben: I, I think it's one of the hardest things.
Zach: because the data to know
Ben: On a, on an,
Zach: what, what happens in a live like human system, animal system is, is insane.
Ben: people and pe and people don't, I I I I'm a hundred percent with you, like fusion and so many other technologies are so much closer than that. Yes. Yeah. I, I think that's a, I think that is a very, very hard problem.
So when I read these things about, oh, well AI, and you're eventually gonna be able to do simulation design, it's like, I think you're gonna be able to deal local simulation design, but I don't think you're gonna be able to do it on [01:01:00] a system model, a full system model.
Zach: I mean the, one of my co-founders at Curie, he, because I was asking these same questions of like, you know, why can't we do it with computers as a computer? Yeah. And, and at one point he said to me, you know, uh, a Petri dish is not a human. Yeah. And I think like, just
Ben: and a mouse model's, not
Zach: a mouse model, a mouse model is not a human.
You know, a monkey is not a human. You
Ben: all that in, in, in these therapeutics. So
Zach: yeah. And, and all the training data for a lot of these biology systems are on a Petri dish, which is a piece of plastic. Yeah. And so it's as close as we can get to a human, but it's not a live system. It's like, you know, a cell and a piece of plastic.
Ben: Yeah. Yeah. Totally agree. I totally agree.
Zach: So when you look at animal testing, if you think about, we would call non, uh, non-human primate testing. Right. So essentially monkey testing is an absolutely required step for many
Ben: Yeah, I bet you could build a large language model for, uh, a large like, animal model for non-human primates,
Zach: Well, I think the non-human primate thing that could be really interesting with the, with the editing side of this is you want. Non-human primates that have human-like disease. Yeah. [01:02:00] Because that's gonna give you a better data set. Yeah. And you, you know, the way you can give a non-human primate disease post birth is limited.
Yep. Versus if you could edit Yeah. Uh, and grow, you know, in an, in an HP that is more likely to develop X, Y, and Z disease. And while that sounds, you know, rough from like a, an animal standpoint, it saves human lives. Yeah. Uh, because now you can actually test and get more predictive data on, on a
Ben: When you, you, you also don't have to have, uh. Infinite cycles of that. That's, it's a somewhat contained loop where it's like you do enough, you get enough training data that you feel confident, at least in this species of monkey that
Logan: Maybe give a literal example, like some cancer or,
Zach: I mean, the one that I would now we don't know what causes, from what we can tell, you cannot give a, a monkey, uh, an NHP Alzheimer's or dementia at this point. At least no one's figured out how to do it. I mean, in theory, you could kind of like try and have them live forever and see if they naturally develop it, but you can't, uh, as of today,
Logan: you can't give it to it [01:03:00] artificially can, can you, can you monitor to the point of showing
Zach: they die too fast. Uh, and like that's an oversimplification, but basically they don't get it. They don't get it. And so, and you can't give a mouse dementia. Uh, and so when you're developing. A new dementia drug or a new Alzheimer's drug. The only point in which you get any real data on whether this thing works is when you put it into a person.
Yeah. And that could be a hundred million dollars just to get to that point. And the chances of it working are really low. 'cause there's no feedback loop, right? Like there's no ability to,
Ben: a long feedback loop, isn't it?
Zach: it? Well, you have to wait for the human, uh, so it's even longer than you think. Um, and so you, you know, you test it in, in, in cell lines and you test it in mice, but like, it's not really predictive.
Logan: We need the woolly mouse of, uh.
Zach: so, oh, this is gonna sound insane, but like if you had a non-human primate edited so that it develops something like dementia or Alzheimer's, well, not great for the primate, great for the people because now you [01:04:00] can get feedback on your new potential drug faster and cheaper. And it's not like 5% faster and cheaper.
It's like 90% faster and cheaper. So
Ben: or, or zero to never cheaper. Or, or, or faster.
Zach: mean, the amount of things that we simply don't do or don't test because getting a drug into a human for Alzheimer's and dementia, you know, essentially clinical trials is so expensive and time consuming that you just,
Ben: what, what do you think about what do, just a absolute tangent, but what do you think about the, uh, the whole notion that like, uh, like Alzheimer is, um, you know, type three diabetes
Zach: I mean, there's a lot of cool theories. I think the reality of biology, it's probably all of the above would be my guess, but like, what do I know? I mean, this is the single most complicated organ in the history of the world. Yeah, right. The human brain. We barely understand how it works. So like what drives this kind of stuff?
It could be like nine things all at once. Yeah. Uh, wouldn't it be nice to be able to study it? In a monkey. [01:05:00] Yeah. Uh, and that would accelerate, you know, research faster. So that's why I was asking about it. And, and it's not just self service and dementia, but there, there are probably edits you could make.
And now I'm speaking a little bit outta my ass, but like, I bet you there are, are edits you could make in a monkey that are predictive of disease.
Ben: that are pre Yeah, yeah. I mean you, you, you're seeing that with like PGT testing. Like I just had a kid and we used Orchid Health, uh, to look at, uh, where they actually do full genome sequencing. And so outside of the specific, uh, you know, major things people, screens for and specific things we were screening for.
Um, there was also some, there's also a, um, uh, there, there they also kind of give you this, and it's somewhat, I don't know if it's controversial, I think not everyone subscribes to it, but they give you a distributed kind of like bell curve of like, of, of what you could have predis, what your child could have the, or that embryo could have a predisposition to including, you know, um, early on, early onset Alzheimer's and others and, and things like diabetes and whatnot.
So you're at least understanding because to your point, we don't know all of the things, [01:06:00] but at least we know that there are specific genes and specific, uh, mutations that ha that create a higher likelihood, um, within environmental
Zach: Yeah, it's great. It's just, you know, to, to drug it
Ben: Yeah.
Zach: is, is an
Ben: I'm, I'm glad I don't have to make that decision because we made this decision not to be in therapeutics because I think those are hard.
I think those are really hard decisions. Right. I think, I think from, from where where I do, I think that people should explore all ways to make human life better. If you've ever had anyone that has gone through dementia or Alzheimer's, you don't wish it on your worst enemies in the world. So if you don't, you don't.
And so, so if, if there are ways to achieve it, I think that's something that we should, we at as humanity should, you know, if we achieve longevity, escape velocity, but we don't solve that well then
Logan: What's the point? Yeah.
Zach: Lo
Ben: So, I, I, I,
Zach: just wants to base edit so his kids are Knicks
Logan: Yeah, exactly.
Zach: Is there a K Nix gene in there somewhere?
Ben: Street? He already
Logan: two, two games in five days of life. So, uh, he is moving along pretty well on Nick's
Ben: Yeah.
Zach: Play off basketball. Best
Logan: basketball.
Logan: I'm curious, like you bump into, uh, religious zeal, you [01:07:00] bump into regulatory considerations, like you're sort of in the Petri dish, I guess to, you know, use a cute term of like society's third rails, it feels like in so many different ways.
Not to mention the PR storm that you guys get caught up in or benefit from. Depending on the, the day
Ben: we actually, before Di Wlf we were at 98% positive neutral
Logan: Is that right? Woolly mouse. People love it.
Ben: Well, even before that,
Zach: Mamo.
Ben: yeah. And if, if you extract out, 'cause we, we, once again, data, if you, if you extract out classification of direwolves, 98% positive, also
Logan: And what was it that was the dire wolf that just sort of set off such a storm?
Ben: can't call a di wolf.
Logan: It was really just that, it was that
Ben: argument. Yeah. Silly argument
Zach: I think the look too, it's ave like just the look of it gets national attention because, uh, it's folklore, right? Yeah. Like they're in Game of Thrones ish and, and others, which I think it just causes people to be interested in it. Maybe, I guess more so than the, what was it, the Red Wolf?
Logan: Was it a step function in [01:08:00] PR too as well? Like
Ben: I mean, we were, I mean we, we, we kinda got fucked because we, we spent a lot of time with what we do is hard and it's complicated, right?
Ben: We spent a lot of time educating, like sometimes we get these, these articles where they're like, you know, the, the most annoying was, was we, we launched our foundation, right?
So we make all these technologies available for, for, uh, for conservation. We then, uh, we then open source all of that technology. We then also pick a species that we help. So, so for example, 20, the number one killer of elephants in the world is, is a, is a herpes ourves, EEHV, 20% of elephants more than poaching human elephant conflict combined.
It's terrible. We have actually worked with Baylor College of Medicine, some of these a ZA accredited zoos to actually get. Uh, in, they're in trials in elephants. That's conferring resistance using an MRNA, uh, approach that, that we helped
Zach: You're vaccinating the
Ben: Yeah. Yeah. And so, but, but check this out.
That's great. If Colossal does nothing else and we just saved, we'll, we'll save more if that works, which is against the number one strain of EHV. There's, I think there's five strains. Uh, but [01:09:00] this is the one that takes like 75% of ba it kills baby elephants. It's like fucking awful. That alone will save more elephants than all elephant conservation combined.
Right. And so, so we have to deal with, so, so even, even with all of that, right? Uh, you know, most people, like, there's still articles out there that, like we launched, we didn't launch the foundation where we raised $50 million separately for the foundation just to fund other people to use R Tech. 'cause maybe you don't have the money that you wanna use our tech, but you don't have labs or whatnot.
So we'll fund you to do it if it's helping a species or if you're developing new tech as long as it's open source. So we're doing all that, right? And then, uh, and then like we launched that in London and CN was one of our launch partners. Like, then we launched the Woolly Mouse in, in one of the lines, in the same article by the same journalist is like.
They should do stuff for conservation. I was like, but you, you know, we do stuff for conservation because we launched our foundation with you. Um, and so, so there's always an education thing. And, and the, the, the problem with, with, uh, colossal times is it's highly nuanced, right? Like, everyone's busy. Every, our, every PR company or every, like media company's looking for click bait.
[01:10:00] And so it's like, you know, dire. Like there was an article that was like George r Mar. George RR Martin hasn't finished the last, uh, book of, of his, uh, a song and ice series because of Colossal. That's literally the title. There was another title in Bloomberg, right? So that the launch title with Bloomberg was, uh, colossal, brought back Diwal because we spent, you know, 20 hours with Bloomberg.
They got deep in the science, they really understood it, and their result was, oh yeah, we're not gonna argue classification. This is a dwell, it's closest, approximate thing. We're gonna call it dwell. Same thing with time, same thing with New Yorker. Same thing with, with, um. Uh, with many others. But then what happens is then you get wave two, which is all the clickbait stuff, which is like, oh, we wanna argue.
There was, there was actually an article that was one of my favorite titles by someone else at Bloomberg, uh, later that's like the tar, uh, colossals, uh, direwolves and tariffs, uh, have one thing in common. It's like, this is weirdest. So you're starting to get stuff like you,
Logan: was that, by the way?
Ben: I didn't read the, I don't read the articles.
Yeah. Like there, there's billions of
Logan: What's a bigger risk for the business? Uh, funding and they're not mutually exclusive, I guess, but funding or [01:11:00] PR
Ben: I think neither. I really don't, I I don't, I think there's, there's a huge app. We've been very fortunate. We have a huge appetite for funding. Um, you know, we, uh, uh, we're not. Announcing new funding at this time. But you know, we, at this publicly to this day, we've raised four $35 million. I think that's highly likely to change in the
Logan: Yeah. Um, so you have more than enough interest on the funding side and then
Ben: There's unlimited interest.
Logan: and do, do you, don't worry then
Ben: the p and the PR side isn't, the PR thing is really just about education, not persuasion. Right. 'cause we, we take this attitude like some of our biggest critics in the world, like my chief science officer, best sha number one, ancient DNA expert in the world.
She joined the, she was the most, one of the most negative person when we left, when we started the company. And we just reached out to her, you know, why she's the number one, she's the number one in her field. So regardless of whether she likes this or not, we would be fools not to reach out to her. So we've had that attitude, right.
We, we don't have an attitude. Are we ever gonna fix the click bitty stuff? No.
Logan: but I, I, I guess the question I was more getting at is, uh, the clickbaity stuff, um, can often lead [01:12:00] to derivative consideration on the policy side that might
Ben: We work really closely. That's a great question. We work with both sides of the aisle. Like, you know, you, you said that we had, the less most
Zach: Well, I, I was joking about the
Ben: crime. Yeah, yeah, yeah, yeah. But, but I'm saying is like, like we, we work with both sides. Like one of the things that we found on the hill is that that loss, regardless of how people feel about climate change and some of these other things, loss of biodiversity is a, is a bipartisan issue.
Everyone agrees losing animals is bad. They may have different motivations behind it, but nobody, neither side seems to really wanna lose animals. Right. So like we have a great relationship with the left. We have a great relationship with the right. We, we, uh, you know, the Department of Interior and Secretary Bergham, uh, of this administration acknowledged our work and, um, you know, thought this was one of the most innovative things that's ever happened, uh, which is a really kind statement.
And also said that the technologies that that de-extinction technologies now need to be a tool in conservation. Now that was weaponized in the press and said, oh, well now, now we don't have to save species. He is like, that's not what he said
Logan: Trump's pick for, you know,
Zach: I'll give, I'll give you another business model. Uh, maybe this works, maybe it [01:13:00] doesn't, but. You know, while I agree a lot of groups are interested in like keeping animals around, uh, there's obviously a bunch of like environmental review for rare species that we find on some like giant development, and we spend three years arguing about like whether we care about the species and then the development doesn't happen, which harms people.
Yeah, right. It makes houses more expensive. Could you work with developers to say, look, you find some rare species that you need to protect, we'll just make more of them and
Ben: So, so that's, that's the argument that, that I think it was weaponized by, um. Uh, so, so part of, we, we got, we got, uh, Washington Post wrote a, a a by other people's standards, not mine. A, a moderately unfair thing because something like that, that exact example was brought up in a cabinet meeting. Right. And that was just a discussion, right?
It, it's, it's, I view it as it's okay to have these conversations. And the response to that was that means we don't need the Endangered Species Act. Nobody's saying that We don't have any, like colossal believe you need the Endangered Species Act. Right. It's like, but at the same [01:14:00] time, like there are certain times where you take, like, there there's been numerous cases where there's been a population of frogs.
They put a road in, they're now two populations of frogs. And this population's smaller. The, they're now classified as two different species because there's a geographic species definition. There's, well, one of your Baskin Robbins 31. And so there's a difference. So, so what I, I think a, what I think a better model is to that is we should, regardless of why we're losing species, we do need to protect habitats.
And we know that current conservation works just doesn't work at the speed of which we're eradicating species. So I think we have to continue to, to protect land, right? Even, even at some, uh, uh, uh, cost to humans. Um, so I think we have to do that and I think we just have to balance that, right? Uh, and then at the same time we also have to look at what are opportunities for us to, um, uh, also biobank species.
'cause no matter how good we are at conserving land, there's gonna be some trade off to humans where land development's gonna move forward. Land development's not gonna move forward. So we have to biobank everything [01:15:00] because we know overfishing and all these other things are gonna continue to eradicate.
Uh, some of our species. So, so I think that, that having, like we have insurance policies for everything yet, um, you know, like the, the government and military is like, one of their number one things is like readiness. How ready are they for a conflict? It's like one of the, the, the core metrics that the military judges itself on, why don't we have that for biodiversity?
And so sometimes I'm a very big advocate for having like the seed vault like in, in Northern Europe for species. Like we need biova, a nationalized biova vault program. But then people say, well then you're just trying to build an insurance program so that we can do with everything species. And that's not what we're saying.
I, I think this is an and conversation. Not an or
Zach: know. I mean, you could make the argument that if this technology works at, at any scale, it's expensive. Totally.
Ben: it's time consuming.
Zach: So is endangered species. Yeah. Uh, you know, I think that argument of like,
Ben: but if you can productionize endangered species and you can put, and you, and you can conserve land and do, if you can take modern conservation and layer on the ability to, to productionize, then, then that's, that could be a win for everybody.
[01:16:00] Right. And, and I think what sometimes people, what people recently have said was, well, if you could productionize a nature species, then you don't need the land. It's like, that's not what we're saying. That's not, you know, other, other people say that that's not, I can't control the world of what they say or what they choose to interpret the technologies.
It's kinda like for a while where it, everyone hated computer vision because people were in, in China we're using computer vision to segregate populations based on specific profile. Like, yeah, that's bad, but that doesn't mean computer vision's bad or facial recognition's bad. That means it was bad. It was a bad application of that technology.
And so, so I do think though, if we can, like, if we can get animals off the endangered species list because we have one conserve land done existing, maybe it's the same land, maybe it's different lands, right? But if we can conserve that land and do modern conservation and then productionize and make a thousand or 2000 or however many you need, uh, red wolves that have engineered in genetic diversity so that they have.
A population that won't go through a bottleneck, that should be a win for everybody.
Zach: Yeah, then you don't need the actual endang. Endered species acts in the long run, or you just need it [01:17:00] in certain geographical areas.
Ben: need to, you still need to protect species. 'cause you're, you are gonna have, you're going to have areas, uh, both foreign and domestic where people don't care.
Right. I think, like we all care. Right. But it's like we all wanna figure out the, the hard thing is always figuring out human progress and, and our
Zach: to be clear, I don't care. I don't, I get why people care. I, I, I don't believe in the endangered species acts. I think nature takes species extinct and has been doing it for 4 billion years and
Ben: does.
But we have been accelerating it. Right? And so I will say,
Zach: but so were, you know, like lions accelerated the, you know, some animal they
Logan: are a, on a different pace
Zach: that's just how it works.
Earth has survived. You know, we go up and we go down, we
Logan: think it's forced extinction, uh, a
Zach: Well, I, I, I get why people care. I, I, I don't, but
Ben: But I, but I'm saying, what I'm saying is, is regardless of what you're, I, I think there's, I think that if every, I think if both sides of the argument open the aperture and look at technological solutions, look at exist, like what currently works.
We know conserving land works. We do know that works a
Zach: absolutely. Absolutely. Yeah. We[01:18:00]
Ben: we also know that sometimes conservative land can come at a cost. So where are the balances? And I, and I think those are important questions, but what, what I have found, the only decisiveness I've found on the biodiversity topic.
Is this, is like, is that we have different, there's different opposing views on, on how to save 'em, but generally speaking, people are mostly in favor of figuring out how to save them.
Ben: And what we're saying is we are not the solution. We're just one new tool. So throw our, throw our tool in this whole conversation and see how it
Zach: yeah, you guys with the right land could pull it off.
Logan: There's
Ben: but it's, but it is still, I will tell you, and, and once again, I run the de-extinction company, right. I will tell you it is way more expensive, at least today, to bring back a species, uh, than to just say, Hey, let's keep it alive here. It is. It, it is, it is more expensive.
Zach: Yeah.
Logan: From, from a regulatory standpoint, are there things that you've bumped up against that are just feel nonsensical? And if you could wave your magic [01:19:00] wand or you know, do something, it's like, gosh, this is just rooted in 19 hundreds
Ben: th there, there, I think there's different, different things. Like we, we, we work very closely with, um, uh, all the organizations, right? Like we we're very transparent about it. The, the Intelligence Committee, uh, our community is an investor in the business. Uh, North Dakota is an investor in the business.
We have other states that. Uh, uh, have
Logan: North Dakota like pension.
Ben: Yeah. They, they're, uh, economic and commerce development fund. Um, and then, um, there's other, uh, vehicles within the United States government that's interested in, in supporting the efforts. Right. We work closely with Fish wildlife, so we work with all these different things.
Right. I, I think that, you know, I, I do think that sometimes there are things that could be changed and, and accelerated. Um, you know, one thing in in particular is there was this moratorium for a long time on GMOs. Now we aren't creating genetically modified organisms for consumption, even though we do get asked if people could eat our mammos, which is weird.
Um, but I do think that that, that we got that question quite often when we launched. It
Zach: It's kind of a great que, I mean, whether you wanna [01:20:00] do it or not, it's an
Logan: Oh yeah.
Ben: But, but, but, so I understand the need from A-U-S-D-A in an FDA perspective to look at. The classifications of, uh, of, of, of genetically modified organisms for consumption.
But for example, so, so we aren't doing this, but let me give, so that goes straight to regulation. Um, the, the FDA has historically taken the stance that if anything has a edit and it, it should be treated as a drug, right? Well, okay, well man, aren't gonna go through clinical trials. And the other thing is like, there's a process.
Uh, we're not in the cattle industry, but there are people that are in the cattle industry and a lot of times they have a pretty. Unhumane model of dehorning cows because when they put them, they're not like running around like Yellowstone National Park, right? When they put them in these large things, they, they have horns, they spear each other.
That leads to infection, that leads to disease. That's, that can create all kinds of bad stuff for human downstream, for human healthcare that could kill off the cattle. It's really, really bad. So they, so they dehorn them, right? Which is a pretty inhumane process if you really, really look at it. But in the whole, it's good for the herd.
[01:21:00] It's not good for the individual animal. But there's actually, uh, um, some researchers that have identified making, uh, I think it's either one or a couple edits that make it where they just don't grow horns. Right? Way more humane for the cows or whatever. But I don't, I, from my, I'm not in it, but from my conversations with people that are in that field.
That hasn't gotten through because it's classified as a drug. Well, that, that's silly. Like genetic, like we have been doing genetic modifications to animals and plants for years. We've just done it inefficiently and we've been doing it the old fashioned way with, with breeding. Yeah. Yeah. Yeah. And so I would say we're just doing to your forced evolution perspective, we're just doing it way more
Zach: Can you get the cows that don't release methane?
Ben: You know, there was a study, we, we were, I don't know where this went. There was a study that was, that happened in Australia where they were, try, I, I don't know where this went, so I'm only giving you a half truth on this, but marsupials give different levels of.
Of milk because so much of the gestation happens outside. So they give you like high fat, then they give you like 2% and then they give you like skim. So the, the marsupial bought it is [01:22:00] incredible. So they, they, they, the moms produce these different tiers of milk, which is really cool. And so there was a study, I don't know if it went anywhere, uh, we, we, we didn't work on, but some, one of 'em, our Australian partners at the University of Melbourne and told me about this, where they actually, uh, were trying to figure out how do you engineer that into cows so that you could have like 2% milk cows.
Zach: Oh, the one I always think about
Ben: is No, I, I know you're talking about the meth
Zach: Cow farts. Yeah. Which everyone laughs at, but it's like a greater contributor to emissions than, you know,
Logan: Climate change.
Ben: Yeah.
Zach: 'cause you need the cows and they, you know, they, they fart methane
Ben: Yeah. And, and there's a lot of cows.
Zach: Yeah. Yeah,
Logan: What about what, I mean there's uh, a ton of the super ones probably like that, but what about the religious element of like playing God and altering,
Ben: I mean, I think, I think we have a pretty, once again, I, we, we try to engage with everyone, right? You know, so we go on the most conservative shows, we go on the most liberal shows. Um, we try to be pretty bipartisan and, and, and, and pretty inclusive, right? We've got some of the strongest atheists behind us.
We, uh, we also have some of the strongest, you know, Christians and, and people from various religions and Jews. And [01:23:00] so it, it, it, so, so for us, I, I think that, you know, we have attack that, and, and this goes to you probably where you fall. It's like, we've been playing God for quite some time. Like we we're, we're eradicating forest, where we're fishing the ocean, we're polluting shit.
We're like, like I take drugs. Is that a form of playing God? Like, uh, I, I like, like I lure my cholesterol with a shot twice a year or twice a month. So does that, does that mean
Zach: I mean, we screen human embryos for disease already.
Ben: Yeah. I, I did that with my son. So it's like, so is that a form of playing God?
I, I built a distributed, uh, uh. Risk matrix for, uh, embryo selection for our kid, and we chose him because he came out on top from a weighted average that I made. So, um, BA based on a polygenic risk score, right? So it it's a, um, so I think we do that all the time, right? And it's like, it know, I view it as, you know, there's not enough money going into conservation.
We open source all the time. We look, I look at this as like a free research and development arm for conservation, right? Like, we're doing all this stuff that is industrial and human application and [01:24:00] other things that we'll make a lot of money on. We do think we'll make money from an a RR perspective on rewilding and carbon credits and biodiversity credits, ecotourism, but separately.
We're just giving all this stuff away from conservation. So anyone that's been in research and development knows it's a huge fucking r and a very small D right. Very little goes actually into development. So we're, we're burdening that cost and risk for the entire community.
Ben: And so, so, so I, I, I think that, you know, we're definitely not gonna do everything right, but I think that we're trying to do everything and work with critics that are at least informed to the point that we can get where, you know, people are, uh, excited about it, but the, the playing godwin doesn't, um, come up as much as I think one would think.
We get asked the question, do you get that question a lot? But, but for the most part, I think people realize that, you know, the intentionality behind what we're doing. Like if we weren't open sourcing stuff for conservation and we're just like, yeah, we're just making whatever we want f off, then I could see how that could,
Logan: I, I, I found that logic can be, uh, totally orthogonal to sentiment oftentimes. Yes.
Ben: Yeah, that's a great point. I.
Zach: It's really cool. [01:25:00] I mean, just leaving the business model stuff aside for a second, just the, the underlying technologies to do this without like, you know, insane failure rates up, up and down the stack are gonna be used in a bunch of other settings as you guys, you know, bring the tech to market. So it's really cool.
Ben: and it's much harder to do with ancient DNA than existing, so we're making the tools like super hardened. Yeah. So
Logan: Thanks for doing this.
Ben: Yeah, thanks for having me. This is awesome.