Moonshots / Peter Diamandis
Anthropic’s Wet Lab, Artificial Wombs in 24 Months, and Scalable Gene Editing | MOONSHOTS Live #297
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▶ Mammoth requests and the founder’s background
The conversation opens with an anecdote about Palmer worrying that a screen said he should not say something, followed by a question about a previously mentioned wish for a pet woolly mammoth. The guest says teacup mammoths are the company’s second most common request, jokes about making one pink, and says Colossal is not working on that currently but is making progress on its mammoth project. Asked why he entered synthetic biology despite having no background in it, he calls that a major advantage: he can ask questions, learn from specialists, and hire people smarter than himself. He says he knew how to ask the right questions throughout his career and recounts long conversations with George Church, whom he describes as a leading synthetic biology figure, Harvard genetics leader, collaborator, and advocate for open source and making genome sequencing cheaper.
▶ AI, biology, and the value of data
The guest says Colossal initially used language models as middleware to connect lab notebooks and Jira, and for infrastructure and reporting; he says retraining scientists to work in Jira was sometimes harder than stem-cell reprogramming. He says earlier models were good at writing papers but not ancestral-state reconstruction or comparative genomics. He expects AI to help connect inconsistent scientific nomenclature and literature, noting his claim that repeating published experiments can have a 40–60% failure rate. He expects simulation and experiment design to be useful over the next 3–5 years, but says wet-lab experiments will still be needed for validation. In response to a question about feeding a gene sequence into a model, he argues that a single genome will not yield a simple prescription, but large-scale comparative genomic data across species and evolutionary lineages may reveal patterns. He says Colossal is pursuing localized biosampling with governments and references exploratory work on p-53 and immortal jellyfish, with early indications he describes as positive. The interviewer argues that datasets may be more valuable than models.
▶ Limits and ethics of bringing species back
The interviewer frames de-extinction as potentially part of a much broader effort to revive past organisms. The guest says Colossal is not pursuing human revival and does not think every past organism can be brought back. He expects DNA synthesis, prediction models, and synthetic biology to produce close approximations, but says fossil records are incomplete and it may be more feasible to engineer useful life than to recreate everything that once existed. He notes that cloning would not reproduce a person’s original experiences or environment, though he suggests genetic dispositions and carefully designed environments could shape traits. Asked whether engineered animals such as Pikachu are technically possible, he says people seem more open to engineered branded species; he agrees that his reservations about human applications are social, political, regulatory, and ethical rather than purely technical. He says work involving humans would require different investors, governance, and patience, and recounts advice to develop technologies before taking them into human applications because of a lengthy FDA process.
▶ Choosing species and the power of the technology
Asked how to decide which species to bring back, the guest says there is no perfect internal checklist. He says the company considers a species’ ecological and food-web contributions, why it went extinct, Indigenous perspectives and spiritual connections, and possible educational benefits. He describes the direwolf project as an opportunity to interest people drawn to fantasy and pop culture in wolf conservation and genome engineering, after consultation with Indigenous groups and conservation partners. He expects governments deploying the technologies to weigh these issues case by case and says they may get decisions wrong. He warns that society does not fully understand the power of technologies at the intersection of synthetic biology, computing, and AI, with quantum computing potentially added later; he claims they could be far more powerful than any weapon system created so far.
▶ Projected capabilities and gene editing
Asked whether biology may reach a point where organisms can be designed from prompts, the guest says DNA synthesis is not there yet but predicts the ability to engineer key phenotypes in basic organisms, synthesize or multiplex-engineer them, and grow them outside the body within a decade. He says Colossal had previously considered 20 edits a major achievement and now consistently delivers more than 300 edits at over 90% efficiency, while testing 1,000 edits; he cautions that the larger tests may not work and says early indicators have low efficiency. He suggests DNA synthesis may replace multiplex editing sooner and says it has a higher likelihood of success in the nearer term. On human artificial wombs, he distinguishes technical feasibility from societal acceptance, ethics, and regulation. For nonhuman mammals, he says Colossal is likely to gestate and deliver animals fully ex utero within 24 months, possibly sooner. Asked about Colossal’s next major public milestones, he names showing another extinct species through precision gene editing and mammalian artificial development and gestation, with other unannounced work also underway.
▶ Genotype, phenotype, and measurable traits
The guest says Colossal currently works on a species basis and is trying to extrapolate to larger animal clades. He gives the example of a 1,500-fold size difference between a marsupial mouse and a marsupial wolf and says understanding developmental regulation, not just genes, matters for extrapolating traits. He says some traits, such as coat color, size, and structures forming skin, scales, or feathers, may become measurable and inducible, while noting that patterning and stripes are a difficult problem for the company’s AI team. Asked about the first-generation direwolves shown publicly, he says they involved eight edits delivered at once. He hints that another version may be more interesting but gives no further details.
▶ Moonshots, animal welfare, and conservation work
The guest says a moonshot can span multiple verticals if its underlying systems and fundamentals apply broadly, citing potential applications of Colossal’s system modeling to conservation and human healthcare. In response to a question about the needs of animals gestated ex utero, he says Colossal has a foundation that open-sources its conservation technologies and has 75 global partners. He says mammoths and elephants are social, so the goal would be to bring back herds rather than a single mammoth; he says 16 lines are being worked on and, when asked how many mammoths he wants, answers tens of thousands. He describes funding work in Botswana with Elephant Havens on orphaned elephants and how to form social herds, as well as research using satellite imaging, drones, and AI to understand elephant migration, social dynamics, and hierarchy. He says that work can inform how ex-utero-born animals might be raised within a social structure.
▶ Mammoths, tundra, and climate claims
The interviewer recounts George Church’s argument that woolly mammoths could help address global warming by knocking down trees in the tundra, allowing grass to grow and sequester more carbon; the interviewer attributes to the argument a claim that grass is about six times more efficient and has a 2–3x albedo effect. The guest says he follows the data and reports that some scientists project mammoth density could lower summer permafrost temperatures by six to eight degrees, while others, including people at Colossal, doubt that degree of impact. He says he has examined the calculations and sees a possible net positive effect on the tundra ecosystem even if mammoths do not solve climate change. He emphasizes that the tundra is degraded and says both sides can agree on improving it, while not definitively endorsing the stronger climate claim.
▶ A specialized model and biodiversity backups
The guest describes a newly spun-out company, whose name is rendered inconsistently in the transcript, as developing specialized models to plug into foundational AI models. He says they aim to characterize how genome sequences evolved, when and why changes occurred or did not occur in related clades, and how factors such as climate may have influenced them. He expects these models and datasets to help direct analysis of millions of samples rather than magically solve biology alone. Asked what major problem needs more attention, he predicts half of biodiversity will be lost in the next 25 years and calls for billions of dollars in federal funding across governments to back up life. He argues that preserving species data can support ecosystems, food webs, and humanity. The interviewer describes a company storing placental cells at birth. The guest says Colossal has announced a partnership with the U.S. Fish and Wildlife Service under a Department of the Interior directive, has two other government partners not yet announced, and makes its conservation framework open to nonprofits, academic institutions, foundations, and private individuals.
▶ Curiosity, AI competition, and optimism
Asked how to develop his creative approach, the guest credits curiosity, admitting what he does not know, learning with a childlike sense of wonder, and asking experts for help. He says people will help when asked and estimates that nine out of ten times they will. Asked whether Anthropic’s announced wet labs threaten Colossal’s work, he calls the news validating and says biology has many unsolved problems; he argues that AI companies entering biology will not make biotech obsolete and sees healthier families and longer lives as widely accepted goals. Asked about a possible downturn, he calls himself an optimist, expects some frightening moments, and says human ingenuity can work through problems. He also says media coverage is overselling the danger somewhat and compares setbacks to turbulence on a plane. The segment closes with the host introducing him as the “Oscars of Optimism” and applause/music.
Full transcript
[0:05] I was a little terrified backstage when Palmer's like, "There's something on the screen that says I'm not supposed to say this." I was like, "For the love of God, whatever it is, please not be please don't make it about colossal. >> Please don't say it." Uh, so I remember at uh at Abundance 360 last March um just before you came on, Elon was there. Uh and you you said uh yeah, I want a pet woolly mammoth. How you doing on that project? >> Uh we get that request. Yeah. Yeah. But but there was actually an amazing uh I
[0:37] think it was either American Dad or Family Guy episode where they explained Did you see this? I don't know if you guys saw this, but there's like flame crisper and it was amazing. And it was amazing cuz then at the end they're like, "Yeah, and the woolly mammoths came out and they're about the size of a dog. That's actually how big they were. The fossils were just wrong, right?" So, [laughter] uh, it was like amazing cuz it explains Christopher, but I was like, you know, uh, we get the request like the number two request we get is teacup mammoths. >> Teacup. >> Teacup mammoth. Like, >> yeah, everybody wants a teacup. Like
[1:07] they want to they want to take a mammoth and put it like Yeah. Put it and put it in your purse, right? And so when I mean we can engineer a new melanin and make it pink and then Paris may want [laughter] it. So we're uh uh we are not working on that currently. Um but we're making good progress on the Mammoth project. So >> amazing. Uh so let me kick this off. So you've started like four successful companies before Colossal and in gaming in defense in AI in mobile and so you knew nothing about synthetic biology. I
[1:38] yeah before before you started now a company worth over10 billion dollars. >> So why what made you make the extinction your moonshot >> and having no background in synthetic biology? Was that a hindrance or an advantage? >> Oh, I think it's a massive uh advantage. Um you know I I think that it affords me the opportunity to go into rooms of you know Palmer talked a little bit about this too of like hiring people to replace you and hiring people that are much smarter than you, right? And so I get, what's great about this from my
[2:09] vantage point is like I get to deal with a lot of the same [ __ ] but at the same time I can go in and like sit down with like best Shapiro and learn about ancient DNA extraction and like how not to do things, but then at the same but then like the next meeting I can go learn about like where we're pushing the boundaries of multiplex editing and how many edits that we can make at once, right? So, I would say that um you know I I knew how to and I guess in all of my career I've known how to ask the right questions because I'm really curious. But um I really do kind of subscribe to
[2:39] that old adage of like putting the top smartest women and men around you just asking them the questions, right? And so I get to go to meetings to just ask questions. And you know 90% of the time I think people are like okay if you knew more about biology this meeting would go faster. But then 10% of the time but then 10% of the time they're like we never thought of it that way. Right? is >> by the way this is such an important uh lesson for all of us as entrepreneurs right just because you're not an expert in an area doesn't make it an area that you shouldn't go into if you love if
[3:10] you're attract the best talent around you >> yeah George Church and like this is the only brag that I will say which I'm very proud of George Church says that I am the best student he's never had because I will literally just pepper him and be like hey my favorite times of the year are during holiday seasons when like no one's working because I will just get on calls for hours and hours and hours with George Church and just talk about the possibilities with synthetic biology. >> Who's George Church for a bit? >> So, so if you don't know George, George is the father of arguably the father of synthetic biology. He's the head of
[3:41] genetics at Harvard and um you know he's a lot of the nextgen readr technologies that were invented came out of the church lab and his lab's prolific. It's there's been numerous multi-billion dollar companies that have spun out of the lab. is probably the probably most active startup biology lab in the world and and he's also he's also like 67 with like narcolepsy and hilarious >> and such a sweetheart guy. >> Yeah. And I mean he is the most collaborative person ever, right? And so he's he's also like hardcore even though it doesn't come from software he's
[4:11] hardcore into open source and just like trying the democratization of technologies like genome sequencing. he wanted that to go from billions to $100, which he was very active in that category. And so, um, he is literally just the most collaborative co-founder I've ever, you know, had the pleasure of working with. >> Dave, yeah, I'm really curious about the business model of biology. And this is like I think we all know that of all the use cases of AI that are imminent, like solving all disease, curing all pain is just like highest on the priority list.
[4:42] Uh, the business model in biology is just always >> Yeah. So I mean and it's great to get your perspective as a serial entrepreneur who came into biology from the outside. So so my first question on like the business model is uh the foundation models like we just saw that like out of the box Astra can drive a car. >> Yeah. Yeah. Is it going to do biology out of the box or do you say no no no no no no no we need to build our own have to so the foundation models now and we've been very fortunate to work with both open AAI and um anthropic on and getting early access to some of that
[5:13] stuff which has been great for a long time we were leveraging it for kind of like thoughtful middleware right coming from a software perspective where we were connecting lab notebooks and Jira I mean one of the hardest things that we did at the company was uh retrain scientists to work in Jira So that's harder than stem cell re programming at times. And so uh and so we we actually for a long time, you know, these the LLMs before the frontier models, uh were really good at writing term papers, right? But they weren't
[5:43] really great at doing like ancestral state reconstruction or comparative genomics, right? Like that that's where they just didn't work, which would have been amazing. So for a long time, we were leveraging them for kind of like middleware layers of infrastructure. So you don't have to go hire the deoits or accenturers of the world to build all these systems and reporting. Right? So we did a I think we were very successful at deploying that for a long long time. Now they're getting pretty far where you can run simulation uh experiments. Uh there's a bunch of companies out there that are standing up Laya and others that are like trying to do lab automation around it. Right? We've been
[6:14] pretty thoughtful about how and when we invest in that category. But I think that what you're going to find is you're going to find interesting insights between connections that are really still language problems, right? So like, so for example, if you go search the literature on, you know, everything on mice, you know, if you just want to go build a a new therapy company or or whatever for humans and you go look at all of the published papers on mice, many of them uh they'll call a gene a different thing. They'll classify it differently, they'll run that experiment
[6:45] slightly differently. And so so [clears throat] you know if you if you go try to run the exact same experiment you're going to get like somewhere between 40 and 60% failure rate on published work which is terrible right so where I think AI is going to be really helpful in the next kind of wave of uh outside of like small molecule drug discovery I think it'll be very very helpful uh kind of eliminating that gap and bridging kind of that nomenclature between work like there'll be times where we'll do work and we'll find out later that there was a peer-reviewed published paper on on one
[7:15] of the things that we trying to solve, but the way we were looking for it wasn't findable um on PubMot. So, so I think in the short term it's going to bridge the communication layer, but I do think you're going to, and you saw this, I think Anthropic announced this a couple days ago, right, where they've um and you have Genko and others that are now going to test it. You're still going to need to have a wet lab experiment to test and validate those. But running simulation uh design across a myriad of different experiments and helping creatively come up with the next experiment. I think that's where we're
[7:45] going to see AI and biology for the next, you know, 3 to 5 years. But what about this like like if if I think about text data and research results and going through, you know, thousands and thousands of prior tests, that's all LLM city. >> But what if my input vector is just a gene sequence? Like if I dump that right now into Anthropic, pretty sure nothing good is going to come out the other. >> Yeah. But but at scale. So, so thank you for the tea up. Um so that is what that that is what I fundamentally believe is
[8:16] the importance of our global bio system that we're rolling out. Right. So we're trying to roll out this like Noah's Arc 2.0 model. Uh not that Noah didn't get it really right in the first stage, but um like at least our ours is slightly different. We're doing like a tax sequencing and other things that he didn't do. Um, and so we're going out and and trying to work with governments around the world to stand up localized bios to get all of that so that we can do TTOT sequencing. And so to your point, I do not think that a single genome is going to uh like you're not
[8:47] going to feed it into mythos and it's going to be like, oh, if you make these six changes, it's immortal, right? But I think that if you go look at like you know uh uh you know thousands of genomes across uh all these evolutionary uh lanes in in aven species and see that they are not susceptible to many of these diseases. You'll look at that right we're doing that on a small scale um with uh things like p-53 immortal jellyfishes other things looking at at kind of like known outcomes of the species and then try to backtrack it
[9:18] down the tree of life. We're we're doing some of that right now at one of our companies which we're pretty excited about and early indications are positive. Um but but to your point, you're not just going to be able throw a genome in and it's going to be like oh here's how you fix it and makes it perfect. But I do think that that amount of data from a comparative genomics perspective at scale is will get you there. >> But I also think that um so so I would make the argument that you know the data set the is more valuable than the model. >> Yeah. Yeah. >> Because I have the data set. >> We've made that point so [laughter] many times. Alex. Huh.
[9:49] >> Yeah. I'd love to talk a bit about deextinction. I'm cognizant that you now have four plus spin-offs and but nonetheless deextinction I think is still what you and Colossal are perhaps best >> and certain things we're not supposed to talk about. >> We won't talk about those though. So the extinction there was a a Russian philosopher Nikolai Fodorov late 19th century parent of a strain of philosophy called Russian cosmism that argued that the ultimate trajectory of humanity in developing science and technology would
[10:20] be essentially to develop the technology to revive every human who's ever lived. Russian cosmism you can look it up. So deextinction in in some sense you are >> we are not doing that currently. >> Uh [laughter] good to know. >> So in but in some sense by uh deexttinguishing deextincting various model species including the woolly mammoth you're the first company to my knowledge on earth that at least has a plausible business model or at least
[10:51] technical trajectory to try to go after the entire historical biosphere. So maybe not even just every human who's ever lived, but every nonhuman organism that's ever lived. >> Right? >> In an era of super intelligence where grand challenges are falling left and right, do you think that humanity's common task as the 19th century Russian cosmists thought that we'll ultimately have the technology to revive every organism that's ever lived or at least
[11:21] every human? Do you foresee that becoming possible? >> I do not. I think through syn through DNA synthesis prediction models and uh you know some level some components of synthetic biology we will be able to get pretty close approximates. I don't think though with two big caveats. Number one just like I have those other colossal does not use any of our technologies at Colossal for humans. So we won't do that even though I think our >> You don't need a spin-off for that. Is
[11:51] that what you're saying? I think that that when you start working with humans, you need different uh sometimes different investors sometime and definitely different governance, right? And you go through a different process >> and more patience >> and and more patience, right? That was actually really good advice I got from Bob Nelson. Bob Nelson in the early days was like, hey, don't apply any of this to humans for a while because you effectively go into like code freeze with the FDA and it's just a like you're just going to be in this monotony forever. go sol go get to the technologies until they plateau before you then take them out. But but back to
[12:22] your question, I think that fundamentally um most organisms remember these are the most organisms that we know. Very few people re uh realize this, but there's like less than like a hundred T-Rexes that have ever been I just I just want to go down the path that will scare you. There's there's uh less than like a hundred T-Rexes that have ever been been found, right? But with that, you know, there's billions that allegedly were on Earth at at at different points in time, right? And so very few things uh leave a fossil record. So I think it's more likely that
[12:52] we will be able to engineer life from a programmable life perspective in the way that we want it than just bring back everything that ever existed because I don't even think we know, right? I think is probably highly likely based on uh AI super intelligence at some point where we get to the point where we're saying oh we're going to engineer this thing and to to your question uh maybe it did exist but we just never know because it left no fossil records right so I think it's more likely that we will be engineering life to our advantage and trying to bring back things and even if
[13:22] colossal or a subset of our technologies both cloning and and genome engineering can bring back things as you know environmental factors epigenetics all these other things like if we could clone Mozart, that doesn't mean that he's going to come out and be like, "Oh, I'm I'm I'm going to like solve where where the terrible trajectory of music has gone." >> Well, just maybe a follow-up question. So, for for Mozart specifically, we do know quite a bit about Mozart's life. So, our argu if we did want to resurrect Mozart, we'd have no problem at all with reconstructing his childhood environment.
[13:52] >> I to to to a point, right? Like like to a point, right? And so I'm not in and by the way, just to be very clear, I am not encouraging in any way this line of questioning, but we are taking humans and growing them. I was like, you're going to make like that you're going to go down this like Michael Jordan LeBron super basketball team in a second and scare me. So um the uh I do think it's highly likely that that if we were able or it's not highly it's 100% accurate to say that we understand their genetic disposition and aptitude towards these
[14:24] certain uh traits and under the right uh environments. If you want to go like you know um if if if you want to go all simulation design on them and put them in the right environments then they could probably then then your Mozart 2.0 could probably be better than the mo most. >> But but Ben, let's bring it back to the animal kingdom because right now the whole thesis is that the AI systems can design the genome sequence that reflect a phenotype. So if you want an animal
[14:55] that's not 100% there yet, but >> but it's that's that's the objective, right? If you want animal that's got a longer snout or an animal that has wings. So, like I asked you on stage at FII, you know, could you make a Pikachu? >> Yeah, that that seems to be a weird like fan favorite. You know, people are more accepting, I think, of Pikachu than um these large genetic human camps that that you're thinking >> branded species. >> Yeah. Yeah. Yes. Yeah. I think people are more uh open to that model of of of genome engineering than
[15:25] >> But it's it sounds just maybe to tie a bow on this, it sounds like it isn't a technical objection per se. It's it's more worries of social, political, regulatory concern. >> That's correct. Yes. And ethical. >> So this is where this is where I'd like to jump in. You know, in Stuart Brand made this famous comment, Peter named the title of your book. He said, "We are as gods. We might as well start acting like them, right?" And he said that in 1968. Um we are at a point where you can deextinct or bring back any species. How
[15:55] do you g think through the ethics of what should we bring back? Which ones? Which ones shouldn't we bring back? >> I'm a huge Stewart fan. You know, I love him. Love Ryan. That quote has been out there kind of like, you know, uh, Minnie from a a famous dinosaur movie. Um, but, uh, I don't know if I would characterize it like Steuart would then get like on a plane. But, um, but from my perspective, um, I would say that like we spend a lot of time. It is not plausible yet to
[16:25] bring back everything or engineer everything from a synthetic biology perspective. So we try to be very thoughtful and people ask us is there a checklist? How exactly do you go about selecting your species? Uh because there's a species that we've been very public about and then there species that we have not hold on. I'm not think I'm not talking about what you're doing specifically. >> Oh, you're saying philosophy? >> Generally philosophically, right? If we could bring back any species, who gets to decide? What evidence do you use to say oh bring this back or that? just from a societal ethics perspective, how
[16:55] should we be >> speciesism, right? >> Yeah. >> Yeah. And so it it's a great question and because we're kind of the um you know guinea pig in this world. Uh the way that we think about it is what was their contribution to the environment? What was their contribution uh to the food web? What was their why did they go extinct? Uh what how do indigenous people feel about it? Right? Like some of the species that we work on, >> they have a deep spiritual connection to the indigenous uh people side of it. Right? Right? So, I don't think you're going to walk in and quote Steuart Brand to him, right? And so, I think that it's
[17:27] very important to kind of like factor all those and weight those and we're probably going to get it wrong and I think societyy's going to get it wrong, but we're going to continue to try. But then we also look at like is there an educational benefit to it, right? Like one of the things that we did when we did the direwolves, right, after working with the indigenous people groups and uh the redwolf coalition teams and all these different components is we did we did weigh in the pop culture nature to it. We thought, is there a way that we can bring all these people that focus on um sci-fi in Game of Thrones and Magic the Gathering? Can we bring them to Wolf
[17:58] Conservation and can we bring them can we teach them about genome engineering because we put we we brought something back that they thought was only a mythological creature in their fantasy universe. Right? So, so we we try to weight all of these things differently. um there's not a perfect kind of internal algorithm for how we look at it. But I do think that as the technologies proliferate and more governments start deploying our technologies, they will have to wait that on an individual basis. >> They will. >> So Ben, >> and I'm sure that all of us are going to get it wrong at some point. >> Well, because it's there's so many
[18:28] different factors involved, right? Like I'm some uh indigenous tribe and I worship the Tasmanian devil. That doesn't mean you should or shouldn't bring it back. There's all sorts of other factors. We're gonna have to think through that at a very because the power that you're bringing to the table here is something that we've never seen in the history of humanity. >> Yeah. Yeah. I mean it is it is >> you're like you're like a walking singularity. >> Yeah. We are um >> we we do uh I I think that society
[18:58] doesn't fully understand. I mean they also address Jurassic Park and I think that they've all heard like Ian Malcolm like talk about it but I do think he was really right. Like I do think that the technologies that are being developed with the kind of intersection of synthetic biology, compute, AI, and then eventually quantum will be more powerful than any weapon system that's ever been created by far. >> Ben, we talk about solving everything. We talk about, you know, math is cooked, physics is next, chemistry and biology.
[19:29] Is there an inflection point, a singularity in biology where all of a sudden there's a complete knowledge base of all the ingested DNA, all the all the ingested phenotypes, and you can literally design an or software for biology. >> You can design you could you could prompt, >> give me an animal that does this. >> Yeah, I think that DNA synthesis isn't quite there yet. >> But project for me here. >> Yeah. Yeah. So So I think that world is less than 10 years out. >> Okay. All right. So you can prompt your
[19:59] favorite Pikachu or animal within 10 years. >> Whoever owns those technologies should do it for you. But I do think that the technologies of being able to engineer key phenotypes on base level organisms in different clays and be able to uh synthesize them or multiplex engineer them and then grow them exudo is within a decade. >> Okay. Exuter within. >> Talk about talk about your your what's that? >> Within a decade. >> Within a decade. >> Holy [ __ ] This is unbelievable. >> Well, well, let me let me give you a
[20:30] current curve and this doesn't obviously mean that it's going to continue on this curve, but you know, we were taking victory laps at 20 edits deliver and I think that 99% of biioarma and uh I'd say 99% of biioarma and academia would do that today. Um we are delivering 300 plus at 90 plus% efficiency consistently. Right. That was that was a year ago. >> Wow. >> Right. We're now testing thousand uh >> so that's an exponential growing curve
[21:00] right >> that doesn't mean it's going to continue right we're testing thousand curves we're working we're a thousand uh edit deliveries right now doesn't mean it's going to work but we're working on it >> tripling year-over-year base edits >> we don't know if it's going to work but we are >> but you think it might >> it could early indicators have low efficiency but it's working but there is a point where large cargo swaps with DNA synthesis is just better unfortunately the people in that category don't really have an a business driver to synthesize DNA after a certain scale. Um, so we've
[21:31] just started doing that internally. >> So doing the math, tripling year-over-year, you're at a thousand base edits right now. That gives you like four years. >> We are consistently north of 300. We're testing a,000. I don't want I think it's highly likely we will get that working. >> What's the point at which it kind of really goes crazy and you can do whatever you want. Well, I I think the synthesis is going to I think synthesis will replace multiplex editing faster, >> right? So, basically a machine that generates the you know the the gigabase code you want. >> I think that's high I think that's has a
[22:02] higher likelihood of success faster. >> Okay. >> So, Ben, you've come up with the artificial egg. >> Not the kind you eat, but the kind that gives birth to a aven species. How far are we from a lady here in the audience, a young lady or older lady in the audience having a baby in an artificial womb? a human. >> Well, you have from a technology perspective, I think it's a very different answer than a societal and acceptance and ethics and regul. >> Okay. Okay. So, let's talk about an
[22:32] artificial womb for mammal. >> Yeah. Uh I I think within 24 months we will colossal will birth animal mammals um fully exudo >> from gestation through delivery. >> Yeah. That never went into a surrogate. >> Wow. >> Wow. >> That's pretty amazing. >> Yeah. That's wild. [applause] >> Hopefully hopefully sooner, but I I think that 24 months is highly likely. >> Wow. I I need I need to ask a quick question. >> Yeah. >> Just a show of hands in the audience. Is your mind blown?
[23:03] >> Like, okay, just checking. Okay, got it. >> Yeah. >> Make sure I'm not alone here. >> So, what's what's the iPhone moment in in Colossal here? Or is it is it the Willie Mammoth stepping onto the stage or is there something else that I >> I think that I [clears throat] think it's I think >> a WTF moment. >> I think well I mean >> I think we've had a little bit of those already, but I think that I think the next major inflection points are when we show the world the next extinct species, right? Like I think always at kind of in
[23:33] that kind of zero to one week. When is that? [laughter] >> Just kidding. >> Coming [clears throat] soon. Uh so so I think I think that showing another extinct species back um through precision gene editing number one. Number two I think that mamillian artificial development and gestation is number two. And then you know we are working some on some things that we haven't shared yet that I think are equally as interesting to like direwolves. So we have some more
[24:03] surprise and delights. If if that surprised you and delighted you, if that surprised and scared, well then we have that for you, too. So, >> can I ask you about the data set actually? So, so you know, protein folding just really snuck up on everybody. It's like solved overnight. Been just a total gold mine of change for all of biotech and >> my daughter uses it every single day. >> Yeah, we use I mean, yeah, >> massive. So, the equivalent the genotype to phenotype mapping problem where you say, okay, this sequence produced that, oh, that's Alex Wisner Gross. Okay, this
[24:33] sequence. Oh, that produced the lane. Great. This is Oh, that's a dodo bird. [laughter] Okay, >> I love this final jump there. >> Just a couple. >> We love that jump, by the [laughter] way. >> Um, so is there a point where given the data set you're accumulating, you can interpolate and you can say, "Okay, now I don't have to create it. I know exactly what would come out." >> So, so we're doing it on a from a product perspective, we're doing it on a species basis currently, but we're trying to extrapolate to large clades of animals. So like sizing is a big one,
[25:03] right? So if you go if you're taking like our model species that we're working with for the Tasmanian tiger uh is orthotine is a fat tail done dunart and it's a 1500x fold from a marsupial mouse to a marupial wolf. Right? So understanding that and and extrapolating that and how that whether what what regulates that not just the genes but how and when it regulates in development how does that transfer to like you know can you make a killer whale the size of you know your pet goldfish probably not
[25:34] but you could probably scale within certain levels of function. If you look at certain uh species like dogs and also certain certain species groups of birds, they have tremendous scale functions, right? Which are larger than 1500 um and so so we're or 1500x. So, so we're looking at it on a per from a um non-trait engineering perspective but from a purist perspective in uh deextinction to look specifically at the genes that drove X Y and Z but separately we are then trying to
[26:04] extrapolate that on a clay basis so that we can say okay how can we affect sizing even within some marginal you know 20 50% uh offshoot within other species so I think that it's it's likely that like coat code coat color sizing, um, you know, placket like things that form skin, scales, feathers, all of that will be highly measurable. >> Um, and and be able to be inducible very quickly. >> Interesting. So, all right.
[26:34] >> So, I don't know about everything, but like, you know, we have a whole AI team that's just working on uh patterning and stripes. It's actually a really hard problem. >> Tusks, stripes, length of snout, >> hair. >> Hair. Yes. Hair. Yeah. >> By the way, quite a bit. >> You know, I when I took uh when Chris and I took our boys down to Dallas to visit, it was a real surprise and delight to see the William ice there. >> Yeah. >> How many gene edits did that take? >> Uh the first generation, which is what
[27:04] we show the public uh was eight. >> Yeah. Amazing. >> In one delivery. >> Eight base pairs. >> Uh >> eight edits. >> Eight edits total in one. Eight base edits in one delivery. >> Got it. >> Amazing. Let's go to some of the uh audience questions here. from Bruno. >> We may we may have a another version at some point that more interesting. >> I can't wait. >> Yeah, that's interesting. >> We'll have you back on Moonshots to talk about it. >> Great. >> All right. So, Bruno asked, "Must a moonshot tackle problem?" >> We had we had Shatner there the other day and I was like, "Fuck, if we
[27:34] like and tailless, we could have like gotten like, you know, Shatner very excited about Trible." So, >> the tripbles. That's right. We need tribles. You could bring triples back >> just without >> I think back is the wrong word, but I think we could forward. >> Bring them forward. >> Bring it forward. [laughter] >> All right. So, Bruno asked the following of you. Uh, must a moonshot tackle one enormous problem within a single vertical or can it be horizontal addressing a seemingly mundane everyday uh need that cuts across many verticals?
[28:06] And we'll ask that of Astro as well, Captain Moonshot shortly. >> Um, I think it can go ac like cross verticals, right? But but remember I have ADD and so like I think that the that the the lack of focus gives us a larger amount of wisdom across multiple categories, right? And so we look at the extinction as a systems problem, but that same system modeling that can be used to preserve species, bring back species can also be used to do all kinds of work specifically in human healthcare. So, so I think that if if you ground your fundamentals in what
[28:37] you're trying to build, I think you can apply it to many use cases and it doesn't have to be so narrow that like if you miss that window, it doesn't have other broader applicability. Here's a great question from Teresa. Uh, how are you planning the ethical issues in creating mammals exut? A mammal has emotional needs and just creating an animal doesn't relieve you of the emotional burden of a living creature. I think that's the same thing like if if it's born ex utero do you give it a family to live with it in a >> so we do a lot I I think most people
[29:08] don't know this because like you know the media doesn't uh always cover all of the stuff that we do we have a foundation we open source all of our technologies for uh conservation so anybody can use any of our technologies for conservation for free we have 75 global partners we're very grateful for them but we've also funded projects specifically around this right so like ma uh mammoths and elephants are highly social animals, right? So, we're not going to bring back a mammoth. We're bringing back herds of them. We have 16 different lines being worked on at the same time. >> How many? >> We have 16 different lines working. >> No. How how many total mammoths do you
[29:39] want to bring back? >> Do I want to bring back tens of thousands? >> Tens of thousands of mammoths. >> Yeah. >> Wow. In LA. >> Um, they have to be Colombian mammoths for here or Py. >> I'm going to steal I'm gonna steal your line. The mammoth in the room is where do the mammoths go? >> But going back going back to that Wait, wait, wait, wait. Yeah, I want to answer because it's I think it's a really important, thoughtful question, right? And so you've had you've had uh you know California condors, you've had all these different um uh close to extinct species that that people work on and in and rear in the rearing of it. And so we fund uh
[30:11] in in while there's a halo effect of the positivity of this for today for elephants. It also has a broader implication for what Colossal is trying to do. We fund uh in Batswana an incredible group called elephant havens which is working with orphaned elephants right that that have already been born not actually but been born and been abandoned for whatever reason and they are working to use they use AI they use a lot of different tools to figure out how do we how do they create synthetic herds from a very matriarchal society of of elephants where they where elephants
[30:41] where they don't currently get that how do they rear those elephants and train them to be elephants and also work together in a herd right because because that's how elephants behave. Separately, we're funding and doing research in uh everything from satellite imaging to drones to AI, building programs in uh uh different elephant migratory patterns in corridors so that we can understand the social dynamics and hierarchy of of moving, right? And so that benefit all of that technology in in that that data uh impacts elephant conservation work
[31:12] today, right? Right. So you don't you don't have like elephant orphaned elephants you have you can rew wild entire herds but all that that data also informs us how we are going to rear these animals in a in a way where if they are born exudo how do they grow up in a social dynamic with the right hierarchy >> Dave you were going to say >> so true story your guy George Church was at a presentation that we had at MIT and the topic was global warming and he said well I have the you know the study I have the cure for global warming we're
[31:42] going to bring several but Yes, we're going to bring back the woolly mammoth. The woolly mammoth's native habitat is Siberia and northern Canada. >> The tundras. >> The tundras. And back when they were around, there were no trees because the woolly mammoths >> Yes. >> walk around and knock down all the trees. >> And elephants actually do this in Africa, too. >> Yeah. Yeah. Force elephants. Yeah. Yeah. They're incredible at this. >> So, so we're like, well, what the hell is the connection to global warming? Because, well, >> without the trees, the grass grows. The grass actually sequesters more carbon than the trees do. It's about six times
[32:14] more efficient in a 2 to 3x albido effect with light reflection to space. >> Yeah. So I think I don't think you guys checked in with the Canadians to see if it's okay, but you turn them loose. Turn them loose in Canada. >> I don't live in the tundra. >> I I'm from India actually. >> Seriously though, where do we build? >> So no, but this is a this is a a really good point. So in the early days um and I'm a big data guy so I most my background is in software and a little space hardware but for the most part I just want to go where the data takes us right >> so you have high conviction really smart
[32:46] scientists like George that will say if you have this mammoth density at these places uh in in the in the tundra they'll have this impact on the uh perafrost a lowering of six to eight degrees in the summer months it only melts so far so so you you can extrapolate that out we have actually done that exercise and it's quite interesting then but it goes back down to a top down versus a bottom up approach of how they affect the environment. There's other people in the scientific community including at Colossal that think that they will not
[33:16] have that level of impact. Right? And so um but the good news is is that generally speaking whether they fall on the what does how do you solve climate change uh with mammoth uh at 10,000 plus mammoth in the in the Arctic doesn't really matter because they have a net positive benefit on the environment in terms of like helping restore that ecosystem. So what I try to do and and Palmer mentioned this in the last thing is like how do you get two [ __ ] in a room and get them to agree well it gets extrapolated to like the 10th degree when they're both PhDs and so [laughter]
[33:46] uh which is like in my experience I don't have a PhD and people think that I have a war on academia at times but like the pretty hard to deal with people out there um or PhDs uh in my experience they actually have a model where I have found though is when you sit them down and tell them that they're both right and you help them walk through that it actually works. And so what I've said is maybe George is right that this level of density of mammoths at this at this lat long at latitude longitude will have this level of impact. But maybe others
[34:16] are right saying that it will have a positive benefit on the flora and fauna, but it won't cure carbon uh climate trees. Either way, it doesn't really matter if it's having a net positive benefit on uh elephants today as well as uh the ecosystem that of the tundra which is highly degraded. So, everyone can agree that the the ecosystem sucks and we need to make it better, right? And so, so I I've done that and you know, I don't know. I I don't want to say that George is right, but I'll just say I've looked at the math >> and I think George is pretty smart. >> Okay,
[34:46] >> Ben, you just spun out Astromec, multi-billion dollar company from the start. What is Astromemech doing? >> So, we're looking at So, so this was kind of a tea up from your question. um the uh foundation models and ad models aren't quite looking at the entire tree of life and they're not like uh they're not going to magically overnight give them a genome and give us an answer. So we are trying to build like what are kind of infle we we call them internally inflection models. What are deflection models that can plug into those
[35:16] foundational models that can say okay we've studied and we understand everything about this genome sequence across how it's uh evolved and more importantly when it evolved and why it didn't evolve in related clades and then we're looking at everything from like climate like like what what spurred that because we want to build essentially a prediction model to say okay where did that go and why did it go because I don't think that astromeme's going to have the like magic you know anthropic mythos those, you know, $4 trillion or whatever the latest round is that that
[35:47] that solves all things all ways. But what I do think is I think it'll have enough of the unique data sets in how to classify and understand that data data set that it can plug into those so that when you do have global bio volts and you have millions of samples that you can feed into a mythos, this can be acting like kind of the um or a mythos like competitor that this can be acting as like your traffic control cop of where to go and where to focus. >> Yeah. Yeah. >> Yeah. Let me synthesize a couple of questions here. What's the biggest problem you wish people were working on? The biggest moonshot that people are not
[36:18] right now. >> I think I think that the we are going to lose half of biodiversity in the next 25 years. And everyone who loves us and hates us agrees with that. So we need to do something about it. Governments like this is not going to be solved by a zoo or a nonprofit. We have to have billions of dollars of federal funding. across multiple governments working together to at least back up life. We back up everything else. We back up our, you
[36:48] know, we back up our photos, we back up our texts, our emails, we back up everything. But ultimately, I think most people back up their text and emails, but so some people use Signal. But like for the most part [laughter] though, um I I I really think that we we have got to right now uh invest in infrastructure to back those species up because if you don't like when we lose those species, we're going to have negative impacts on ecosystems. When we lose those species, we're going to have negative impacts in the food web for the animals. But so if you like ecosystems, you should back it
[37:20] up. If you don't like ecosystems and you hate the environment, but you like animals, you should back it back up. You hate the environment and animals, but you like [ __ ] humans. You should back it up. You know, there's data in there that will help humanity. >> Just as a quick aside, you know, one of my companies uh with an amazing CEO, Bob Hurer, called Cellularity, we have something called LifeBank USA that when your baby is born, you store all the placental cells. Yep. >> And you've got basically the original boot desk. You've got your kids stem
[37:50] cells, te- cells, natural killer cells, everything. And it's like if your baby came with an extra set of organs, would you throw them away? Probably not. But why throw away, you know, the placenta is the 3D printer that creates the creates the baby. So, I mean, this kind of envisioning >> of of of safety of backups is amazing. So, this is what this is what you're doing right now in Dubai. >> We're doing in Dubai. We're we just announced a partnership with uh US Fish and Wildlife here as part of Secretary
[38:20] Interior's directive on backing up the natural resources that that that make America great. So, we're doing that now here domestically. Uh we have two other governments we haven't announced yet uh that we'll announce when they want to announce that are part of our framework. Um and then it's also kind of like what George and I also talk about with open source. It's completely open. So we any nonprofit, any academic institution, any big foundation, any private individuals to anybody in Palmer secret billionaire boys club that wants to throw money
[38:51] wants boys. Are you in the B boys group? >> So So the anybody [laughter] that like I'm not I'm not in any secret Jack groups. >> I have a quick question. >> All right, let's close it out with your quick question. I want to be Ben Lamb with the mind as creative and crazy as yours to envision these things. >> How do I go about doing that? >> Oh, how you do what? >> How do I go about >> how did you become >> how do I become take on the mindsets that you have to apply technology as
[39:22] incredibly creatively as you have done? >> So I uh well it's very kind. I think that uh I I'm very curious, right? So, I I like to just learn new things. And I think in a world, especially with AI, where everyone's got every answer to their um at their tool at their fingertips, I'm pretty good at telling people what I don't know. So, I think I kind of take a childlike wonder to things and just say, "Hey, I don't know this, but I'm sure I could uh go find the answer." And what I've also found, which most people, this is big advice that I'd also give everyone, people will
[39:52] help you. Like, I am an optimist. I believe in technology, but I believe in humanity first. And people will help you. So like I don't think people ask enough on subways and [ __ ] looking at their phones, but if you just look out for a second and ask for help, people will help you. And so it's like when I don't understand something, sometimes I will call people in this in like some of our top adviserss at Colossal are adviserss cuz I just cold email them like, "Hey, I don't understand this. My teams are telling me this. You're the world's expert of this. Can you have a meeting
[40:22] with me?" They have no this this woman or man has no reason to talk to me, right? But they'll take the call. And so I feel like I just have this general curiosity wrapped with >> people like you. They want to help. >> No, I think that they'll like everybody like I really do. I think you can just if people ask for help, nine out of 10 times I believe in humanity, they will help you. Ask >> a quick give it up for that. Right. >> [applause] >> uh a curiosity mindset, a purpose- driven mindset, and a quick question from Kathy Wood backstage who said, "Yesterday, Anthropic um uh you know set
[40:55] up their announced their wet labs to uh that were able to create something like crisper-like. Does this light a fire in your work or does it you know, kill forward momentum?" >> So, I think it's massively validating, right? Like um it's a great question. Kathy's great. I think it's really important, right? Like there are so many problems to solve in biology. The these technologies are we are just like we haven't even opened the door like the doors barely cracked barely barely cracked open. I think that what they announced yesterday like people see that and say oh my gosh biotech is going to
[41:26] be dead because it should be anthropic in their in their lives. That's not true right that that's just not true. And so I think that that was a huge watershed moment for the industry to show that like you have the AI companies that understand and yes background in biology but but you have the AI companies that that understand that that will be one of the most accepted use cases and deployments of their technology and that people want to have healthier families healthier longer lives right so it was a great thing for the society >> really sure do you have a pd doom or do you need to even think about the
[41:57] question >> you I'm an optimist Like I I am an optimist. Like I don't agree with, you know, I I think we're going to have some scary moments and I think that's okay, right? Because I do believe in human ingenuity to work through those problems, right? But I think that you've got to have a conversation. I do think that the media is overselling that a little bit right now. um to to to be kind. But I do think that like you know we we really will get
[42:27] there and you we're going to have a couple scary moments, but it's like you have turbulence on planes and everyone still lands, right? Like that's okay. >> All right, welcome to the Oscars of Optimism. Give it up for Ben Lamb. [music]
[0:37] think it was either American Dad or Family Guy episode where they explained Did you see this? I don't know if you guys saw this, but there's like flame crisper and it was amazing. And it was amazing cuz then at the end they're like, "Yeah, and the woolly mammoths came out and they're about the size of a dog. That's actually how big they were. The fossils were just wrong, right?" So, [laughter] uh, it was like amazing cuz it explains Christopher, but I was like, you know, uh, we get the request like the number two request we get is teacup mammoths. >> Teacup. >> Teacup mammoth. Like, >> yeah, everybody wants a teacup. Like
[1:07] they want to they want to take a mammoth and put it like Yeah. Put it and put it in your purse, right? And so when I mean we can engineer a new melanin and make it pink and then Paris may want [laughter] it. So we're uh uh we are not working on that currently. Um but we're making good progress on the Mammoth project. So >> amazing. Uh so let me kick this off. So you've started like four successful companies before Colossal and in gaming in defense in AI in mobile and so you knew nothing about synthetic biology. I
[1:38] yeah before before you started now a company worth over10 billion dollars. >> So why what made you make the extinction your moonshot >> and having no background in synthetic biology? Was that a hindrance or an advantage? >> Oh, I think it's a massive uh advantage. Um you know I I think that it affords me the opportunity to go into rooms of you know Palmer talked a little bit about this too of like hiring people to replace you and hiring people that are much smarter than you, right? And so I get, what's great about this from my
[2:09] vantage point is like I get to deal with a lot of the same [ __ ] but at the same time I can go in and like sit down with like best Shapiro and learn about ancient DNA extraction and like how not to do things, but then at the same but then like the next meeting I can go learn about like where we're pushing the boundaries of multiplex editing and how many edits that we can make at once, right? So, I would say that um you know I I knew how to and I guess in all of my career I've known how to ask the right questions because I'm really curious. But um I really do kind of subscribe to
[2:39] that old adage of like putting the top smartest women and men around you just asking them the questions, right? And so I get to go to meetings to just ask questions. And you know 90% of the time I think people are like okay if you knew more about biology this meeting would go faster. But then 10% of the time but then 10% of the time they're like we never thought of it that way. Right? is >> by the way this is such an important uh lesson for all of us as entrepreneurs right just because you're not an expert in an area doesn't make it an area that you shouldn't go into if you love if
[3:10] you're attract the best talent around you >> yeah George Church and like this is the only brag that I will say which I'm very proud of George Church says that I am the best student he's never had because I will literally just pepper him and be like hey my favorite times of the year are during holiday seasons when like no one's working because I will just get on calls for hours and hours and hours with George Church and just talk about the possibilities with synthetic biology. >> Who's George Church for a bit? >> So, so if you don't know George, George is the father of arguably the father of synthetic biology. He's the head of
[3:41] genetics at Harvard and um you know he's a lot of the nextgen readr technologies that were invented came out of the church lab and his lab's prolific. It's there's been numerous multi-billion dollar companies that have spun out of the lab. is probably the probably most active startup biology lab in the world and and he's also he's also like 67 with like narcolepsy and hilarious >> and such a sweetheart guy. >> Yeah. And I mean he is the most collaborative person ever, right? And so he's he's also like hardcore even though it doesn't come from software he's
[4:11] hardcore into open source and just like trying the democratization of technologies like genome sequencing. he wanted that to go from billions to $100, which he was very active in that category. And so, um, he is literally just the most collaborative co-founder I've ever, you know, had the pleasure of working with. >> Dave, yeah, I'm really curious about the business model of biology. And this is like I think we all know that of all the use cases of AI that are imminent, like solving all disease, curing all pain is just like highest on the priority list.
[4:42] Uh, the business model in biology is just always >> Yeah. So I mean and it's great to get your perspective as a serial entrepreneur who came into biology from the outside. So so my first question on like the business model is uh the foundation models like we just saw that like out of the box Astra can drive a car. >> Yeah. Yeah. Is it going to do biology out of the box or do you say no no no no no no no we need to build our own have to so the foundation models now and we've been very fortunate to work with both open AAI and um anthropic on and getting early access to some of that
[5:13] stuff which has been great for a long time we were leveraging it for kind of like thoughtful middleware right coming from a software perspective where we were connecting lab notebooks and Jira I mean one of the hardest things that we did at the company was uh retrain scientists to work in Jira So that's harder than stem cell re programming at times. And so uh and so we we actually for a long time, you know, these the LLMs before the frontier models, uh were really good at writing term papers, right? But they weren't
[5:43] really great at doing like ancestral state reconstruction or comparative genomics, right? Like that that's where they just didn't work, which would have been amazing. So for a long time, we were leveraging them for kind of like middleware layers of infrastructure. So you don't have to go hire the deoits or accenturers of the world to build all these systems and reporting. Right? So we did a I think we were very successful at deploying that for a long long time. Now they're getting pretty far where you can run simulation uh experiments. Uh there's a bunch of companies out there that are standing up Laya and others that are like trying to do lab automation around it. Right? We've been
[6:14] pretty thoughtful about how and when we invest in that category. But I think that what you're going to find is you're going to find interesting insights between connections that are really still language problems, right? So like, so for example, if you go search the literature on, you know, everything on mice, you know, if you just want to go build a a new therapy company or or whatever for humans and you go look at all of the published papers on mice, many of them uh they'll call a gene a different thing. They'll classify it differently, they'll run that experiment
[6:45] slightly differently. And so so [clears throat] you know if you if you go try to run the exact same experiment you're going to get like somewhere between 40 and 60% failure rate on published work which is terrible right so where I think AI is going to be really helpful in the next kind of wave of uh outside of like small molecule drug discovery I think it'll be very very helpful uh kind of eliminating that gap and bridging kind of that nomenclature between work like there'll be times where we'll do work and we'll find out later that there was a peer-reviewed published paper on on one
[7:15] of the things that we trying to solve, but the way we were looking for it wasn't findable um on PubMot. So, so I think in the short term it's going to bridge the communication layer, but I do think you're going to, and you saw this, I think Anthropic announced this a couple days ago, right, where they've um and you have Genko and others that are now going to test it. You're still going to need to have a wet lab experiment to test and validate those. But running simulation uh design across a myriad of different experiments and helping creatively come up with the next experiment. I think that's where we're
[7:45] going to see AI and biology for the next, you know, 3 to 5 years. But what about this like like if if I think about text data and research results and going through, you know, thousands and thousands of prior tests, that's all LLM city. >> But what if my input vector is just a gene sequence? Like if I dump that right now into Anthropic, pretty sure nothing good is going to come out the other. >> Yeah. But but at scale. So, so thank you for the tea up. Um so that is what that that is what I fundamentally believe is
[8:16] the importance of our global bio system that we're rolling out. Right. So we're trying to roll out this like Noah's Arc 2.0 model. Uh not that Noah didn't get it really right in the first stage, but um like at least our ours is slightly different. We're doing like a tax sequencing and other things that he didn't do. Um, and so we're going out and and trying to work with governments around the world to stand up localized bios to get all of that so that we can do TTOT sequencing. And so to your point, I do not think that a single genome is going to uh like you're not
[8:47] going to feed it into mythos and it's going to be like, oh, if you make these six changes, it's immortal, right? But I think that if you go look at like you know uh uh you know thousands of genomes across uh all these evolutionary uh lanes in in aven species and see that they are not susceptible to many of these diseases. You'll look at that right we're doing that on a small scale um with uh things like p-53 immortal jellyfishes other things looking at at kind of like known outcomes of the species and then try to backtrack it
[9:18] down the tree of life. We're we're doing some of that right now at one of our companies which we're pretty excited about and early indications are positive. Um but but to your point, you're not just going to be able throw a genome in and it's going to be like oh here's how you fix it and makes it perfect. But I do think that that amount of data from a comparative genomics perspective at scale is will get you there. >> But I also think that um so so I would make the argument that you know the data set the is more valuable than the model. >> Yeah. Yeah. >> Because I have the data set. >> We've made that point so [laughter] many times. Alex. Huh.
[9:49] >> Yeah. I'd love to talk a bit about deextinction. I'm cognizant that you now have four plus spin-offs and but nonetheless deextinction I think is still what you and Colossal are perhaps best >> and certain things we're not supposed to talk about. >> We won't talk about those though. So the extinction there was a a Russian philosopher Nikolai Fodorov late 19th century parent of a strain of philosophy called Russian cosmism that argued that the ultimate trajectory of humanity in developing science and technology would
[10:20] be essentially to develop the technology to revive every human who's ever lived. Russian cosmism you can look it up. So deextinction in in some sense you are >> we are not doing that currently. >> Uh [laughter] good to know. >> So in but in some sense by uh deexttinguishing deextincting various model species including the woolly mammoth you're the first company to my knowledge on earth that at least has a plausible business model or at least
[10:51] technical trajectory to try to go after the entire historical biosphere. So maybe not even just every human who's ever lived, but every nonhuman organism that's ever lived. >> Right? >> In an era of super intelligence where grand challenges are falling left and right, do you think that humanity's common task as the 19th century Russian cosmists thought that we'll ultimately have the technology to revive every organism that's ever lived or at least
[11:21] every human? Do you foresee that becoming possible? >> I do not. I think through syn through DNA synthesis prediction models and uh you know some level some components of synthetic biology we will be able to get pretty close approximates. I don't think though with two big caveats. Number one just like I have those other colossal does not use any of our technologies at Colossal for humans. So we won't do that even though I think our >> You don't need a spin-off for that. Is
[11:51] that what you're saying? I think that that when you start working with humans, you need different uh sometimes different investors sometime and definitely different governance, right? And you go through a different process >> and more patience >> and and more patience, right? That was actually really good advice I got from Bob Nelson. Bob Nelson in the early days was like, hey, don't apply any of this to humans for a while because you effectively go into like code freeze with the FDA and it's just a like you're just going to be in this monotony forever. go sol go get to the technologies until they plateau before you then take them out. But but back to
[12:22] your question, I think that fundamentally um most organisms remember these are the most organisms that we know. Very few people re uh realize this, but there's like less than like a hundred T-Rexes that have ever been I just I just want to go down the path that will scare you. There's there's uh less than like a hundred T-Rexes that have ever been been found, right? But with that, you know, there's billions that allegedly were on Earth at at at different points in time, right? And so very few things uh leave a fossil record. So I think it's more likely that
[12:52] we will be able to engineer life from a programmable life perspective in the way that we want it than just bring back everything that ever existed because I don't even think we know, right? I think is probably highly likely based on uh AI super intelligence at some point where we get to the point where we're saying oh we're going to engineer this thing and to to your question uh maybe it did exist but we just never know because it left no fossil records right so I think it's more likely that we will be engineering life to our advantage and trying to bring back things and even if
[13:22] colossal or a subset of our technologies both cloning and and genome engineering can bring back things as you know environmental factors epigenetics all these other things like if we could clone Mozart, that doesn't mean that he's going to come out and be like, "Oh, I'm I'm I'm going to like solve where where the terrible trajectory of music has gone." >> Well, just maybe a follow-up question. So, for for Mozart specifically, we do know quite a bit about Mozart's life. So, our argu if we did want to resurrect Mozart, we'd have no problem at all with reconstructing his childhood environment.
[13:52] >> I to to to a point, right? Like like to a point, right? And so I'm not in and by the way, just to be very clear, I am not encouraging in any way this line of questioning, but we are taking humans and growing them. I was like, you're going to make like that you're going to go down this like Michael Jordan LeBron super basketball team in a second and scare me. So um the uh I do think it's highly likely that that if we were able or it's not highly it's 100% accurate to say that we understand their genetic disposition and aptitude towards these
[14:24] certain uh traits and under the right uh environments. If you want to go like you know um if if if you want to go all simulation design on them and put them in the right environments then they could probably then then your Mozart 2.0 could probably be better than the mo most. >> But but Ben, let's bring it back to the animal kingdom because right now the whole thesis is that the AI systems can design the genome sequence that reflect a phenotype. So if you want an animal
[14:55] that's not 100% there yet, but >> but it's that's that's the objective, right? If you want animal that's got a longer snout or an animal that has wings. So, like I asked you on stage at FII, you know, could you make a Pikachu? >> Yeah, that that seems to be a weird like fan favorite. You know, people are more accepting, I think, of Pikachu than um these large genetic human camps that that you're thinking >> branded species. >> Yeah. Yeah. Yes. Yeah. I think people are more uh open to that model of of of genome engineering than
[15:25] >> But it's it sounds just maybe to tie a bow on this, it sounds like it isn't a technical objection per se. It's it's more worries of social, political, regulatory concern. >> That's correct. Yes. And ethical. >> So this is where this is where I'd like to jump in. You know, in Stuart Brand made this famous comment, Peter named the title of your book. He said, "We are as gods. We might as well start acting like them, right?" And he said that in 1968. Um we are at a point where you can deextinct or bring back any species. How
[15:55] do you g think through the ethics of what should we bring back? Which ones? Which ones shouldn't we bring back? >> I'm a huge Stewart fan. You know, I love him. Love Ryan. That quote has been out there kind of like, you know, uh, Minnie from a a famous dinosaur movie. Um, but, uh, I don't know if I would characterize it like Steuart would then get like on a plane. But, um, but from my perspective, um, I would say that like we spend a lot of time. It is not plausible yet to
[16:25] bring back everything or engineer everything from a synthetic biology perspective. So we try to be very thoughtful and people ask us is there a checklist? How exactly do you go about selecting your species? Uh because there's a species that we've been very public about and then there species that we have not hold on. I'm not think I'm not talking about what you're doing specifically. >> Oh, you're saying philosophy? >> Generally philosophically, right? If we could bring back any species, who gets to decide? What evidence do you use to say oh bring this back or that? just from a societal ethics perspective, how
[16:55] should we be >> speciesism, right? >> Yeah. >> Yeah. And so it it's a great question and because we're kind of the um you know guinea pig in this world. Uh the way that we think about it is what was their contribution to the environment? What was their contribution uh to the food web? What was their why did they go extinct? Uh what how do indigenous people feel about it? Right? Like some of the species that we work on, >> they have a deep spiritual connection to the indigenous uh people side of it. Right? Right? So, I don't think you're going to walk in and quote Steuart Brand to him, right? And so, I think that it's
[17:27] very important to kind of like factor all those and weight those and we're probably going to get it wrong and I think societyy's going to get it wrong, but we're going to continue to try. But then we also look at like is there an educational benefit to it, right? Like one of the things that we did when we did the direwolves, right, after working with the indigenous people groups and uh the redwolf coalition teams and all these different components is we did we did weigh in the pop culture nature to it. We thought, is there a way that we can bring all these people that focus on um sci-fi in Game of Thrones and Magic the Gathering? Can we bring them to Wolf
[17:58] Conservation and can we bring them can we teach them about genome engineering because we put we we brought something back that they thought was only a mythological creature in their fantasy universe. Right? So, so we we try to weight all of these things differently. um there's not a perfect kind of internal algorithm for how we look at it. But I do think that as the technologies proliferate and more governments start deploying our technologies, they will have to wait that on an individual basis. >> They will. >> So Ben, >> and I'm sure that all of us are going to get it wrong at some point. >> Well, because it's there's so many
[18:28] different factors involved, right? Like I'm some uh indigenous tribe and I worship the Tasmanian devil. That doesn't mean you should or shouldn't bring it back. There's all sorts of other factors. We're gonna have to think through that at a very because the power that you're bringing to the table here is something that we've never seen in the history of humanity. >> Yeah. Yeah. I mean it is it is >> you're like you're like a walking singularity. >> Yeah. We are um >> we we do uh I I think that society
[18:58] doesn't fully understand. I mean they also address Jurassic Park and I think that they've all heard like Ian Malcolm like talk about it but I do think he was really right. Like I do think that the technologies that are being developed with the kind of intersection of synthetic biology, compute, AI, and then eventually quantum will be more powerful than any weapon system that's ever been created by far. >> Ben, we talk about solving everything. We talk about, you know, math is cooked, physics is next, chemistry and biology.
[19:29] Is there an inflection point, a singularity in biology where all of a sudden there's a complete knowledge base of all the ingested DNA, all the all the ingested phenotypes, and you can literally design an or software for biology. >> You can design you could you could prompt, >> give me an animal that does this. >> Yeah, I think that DNA synthesis isn't quite there yet. >> But project for me here. >> Yeah. Yeah. So So I think that world is less than 10 years out. >> Okay. All right. So you can prompt your
[19:59] favorite Pikachu or animal within 10 years. >> Whoever owns those technologies should do it for you. But I do think that the technologies of being able to engineer key phenotypes on base level organisms in different clays and be able to uh synthesize them or multiplex engineer them and then grow them exudo is within a decade. >> Okay. Exuter within. >> Talk about talk about your your what's that? >> Within a decade. >> Within a decade. >> Holy [ __ ] This is unbelievable. >> Well, well, let me let me give you a
[20:30] current curve and this doesn't obviously mean that it's going to continue on this curve, but you know, we were taking victory laps at 20 edits deliver and I think that 99% of biioarma and uh I'd say 99% of biioarma and academia would do that today. Um we are delivering 300 plus at 90 plus% efficiency consistently. Right. That was that was a year ago. >> Wow. >> Right. We're now testing thousand uh >> so that's an exponential growing curve
[21:00] right >> that doesn't mean it's going to continue right we're testing thousand curves we're working we're a thousand uh edit deliveries right now doesn't mean it's going to work but we're working on it >> tripling year-over-year base edits >> we don't know if it's going to work but we are >> but you think it might >> it could early indicators have low efficiency but it's working but there is a point where large cargo swaps with DNA synthesis is just better unfortunately the people in that category don't really have an a business driver to synthesize DNA after a certain scale. Um, so we've
[21:31] just started doing that internally. >> So doing the math, tripling year-over-year, you're at a thousand base edits right now. That gives you like four years. >> We are consistently north of 300. We're testing a,000. I don't want I think it's highly likely we will get that working. >> What's the point at which it kind of really goes crazy and you can do whatever you want. Well, I I think the synthesis is going to I think synthesis will replace multiplex editing faster, >> right? So, basically a machine that generates the you know the the gigabase code you want. >> I think that's high I think that's has a
[22:02] higher likelihood of success faster. >> Okay. >> So, Ben, you've come up with the artificial egg. >> Not the kind you eat, but the kind that gives birth to a aven species. How far are we from a lady here in the audience, a young lady or older lady in the audience having a baby in an artificial womb? a human. >> Well, you have from a technology perspective, I think it's a very different answer than a societal and acceptance and ethics and regul. >> Okay. Okay. So, let's talk about an
[22:32] artificial womb for mammal. >> Yeah. Uh I I think within 24 months we will colossal will birth animal mammals um fully exudo >> from gestation through delivery. >> Yeah. That never went into a surrogate. >> Wow. >> Wow. >> That's pretty amazing. >> Yeah. That's wild. [applause] >> Hopefully hopefully sooner, but I I think that 24 months is highly likely. >> Wow. I I need I need to ask a quick question. >> Yeah. >> Just a show of hands in the audience. Is your mind blown?
[23:03] >> Like, okay, just checking. Okay, got it. >> Yeah. >> Make sure I'm not alone here. >> So, what's what's the iPhone moment in in Colossal here? Or is it is it the Willie Mammoth stepping onto the stage or is there something else that I >> I think that I [clears throat] think it's I think >> a WTF moment. >> I think well I mean >> I think we've had a little bit of those already, but I think that I think the next major inflection points are when we show the world the next extinct species, right? Like I think always at kind of in
[23:33] that kind of zero to one week. When is that? [laughter] >> Just kidding. >> Coming [clears throat] soon. Uh so so I think I think that showing another extinct species back um through precision gene editing number one. Number two I think that mamillian artificial development and gestation is number two. And then you know we are working some on some things that we haven't shared yet that I think are equally as interesting to like direwolves. So we have some more
[24:03] surprise and delights. If if that surprised you and delighted you, if that surprised and scared, well then we have that for you, too. So, >> can I ask you about the data set actually? So, so you know, protein folding just really snuck up on everybody. It's like solved overnight. Been just a total gold mine of change for all of biotech and >> my daughter uses it every single day. >> Yeah, we use I mean, yeah, >> massive. So, the equivalent the genotype to phenotype mapping problem where you say, okay, this sequence produced that, oh, that's Alex Wisner Gross. Okay, this
[24:33] sequence. Oh, that produced the lane. Great. This is Oh, that's a dodo bird. [laughter] Okay, >> I love this final jump there. >> Just a couple. >> We love that jump, by the [laughter] way. >> Um, so is there a point where given the data set you're accumulating, you can interpolate and you can say, "Okay, now I don't have to create it. I know exactly what would come out." >> So, so we're doing it on a from a product perspective, we're doing it on a species basis currently, but we're trying to extrapolate to large clades of animals. So like sizing is a big one,
[25:03] right? So if you go if you're taking like our model species that we're working with for the Tasmanian tiger uh is orthotine is a fat tail done dunart and it's a 1500x fold from a marsupial mouse to a marupial wolf. Right? So understanding that and and extrapolating that and how that whether what what regulates that not just the genes but how and when it regulates in development how does that transfer to like you know can you make a killer whale the size of you know your pet goldfish probably not
[25:34] but you could probably scale within certain levels of function. If you look at certain uh species like dogs and also certain certain species groups of birds, they have tremendous scale functions, right? Which are larger than 1500 um and so so we're or 1500x. So, so we're looking at it on a per from a um non-trait engineering perspective but from a purist perspective in uh deextinction to look specifically at the genes that drove X Y and Z but separately we are then trying to
[26:04] extrapolate that on a clay basis so that we can say okay how can we affect sizing even within some marginal you know 20 50% uh offshoot within other species so I think that it's it's likely that like coat code coat color sizing, um, you know, placket like things that form skin, scales, feathers, all of that will be highly measurable. >> Um, and and be able to be inducible very quickly. >> Interesting. So, all right.
[26:34] >> So, I don't know about everything, but like, you know, we have a whole AI team that's just working on uh patterning and stripes. It's actually a really hard problem. >> Tusks, stripes, length of snout, >> hair. >> Hair. Yes. Hair. Yeah. >> By the way, quite a bit. >> You know, I when I took uh when Chris and I took our boys down to Dallas to visit, it was a real surprise and delight to see the William ice there. >> Yeah. >> How many gene edits did that take? >> Uh the first generation, which is what
[27:04] we show the public uh was eight. >> Yeah. Amazing. >> In one delivery. >> Eight base pairs. >> Uh >> eight edits. >> Eight edits total in one. Eight base edits in one delivery. >> Got it. >> Amazing. Let's go to some of the uh audience questions here. from Bruno. >> We may we may have a another version at some point that more interesting. >> I can't wait. >> Yeah, that's interesting. >> We'll have you back on Moonshots to talk about it. >> Great. >> All right. So, Bruno asked, "Must a moonshot tackle problem?" >> We had we had Shatner there the other day and I was like, "Fuck, if we
[27:34] like and tailless, we could have like gotten like, you know, Shatner very excited about Trible." So, >> the tripbles. That's right. We need tribles. You could bring triples back >> just without >> I think back is the wrong word, but I think we could forward. >> Bring them forward. >> Bring it forward. [laughter] >> All right. So, Bruno asked the following of you. Uh, must a moonshot tackle one enormous problem within a single vertical or can it be horizontal addressing a seemingly mundane everyday uh need that cuts across many verticals?
[28:06] And we'll ask that of Astro as well, Captain Moonshot shortly. >> Um, I think it can go ac like cross verticals, right? But but remember I have ADD and so like I think that the that the the lack of focus gives us a larger amount of wisdom across multiple categories, right? And so we look at the extinction as a systems problem, but that same system modeling that can be used to preserve species, bring back species can also be used to do all kinds of work specifically in human healthcare. So, so I think that if if you ground your fundamentals in what
[28:37] you're trying to build, I think you can apply it to many use cases and it doesn't have to be so narrow that like if you miss that window, it doesn't have other broader applicability. Here's a great question from Teresa. Uh, how are you planning the ethical issues in creating mammals exut? A mammal has emotional needs and just creating an animal doesn't relieve you of the emotional burden of a living creature. I think that's the same thing like if if it's born ex utero do you give it a family to live with it in a >> so we do a lot I I think most people
[29:08] don't know this because like you know the media doesn't uh always cover all of the stuff that we do we have a foundation we open source all of our technologies for uh conservation so anybody can use any of our technologies for conservation for free we have 75 global partners we're very grateful for them but we've also funded projects specifically around this right so like ma uh mammoths and elephants are highly social animals, right? So, we're not going to bring back a mammoth. We're bringing back herds of them. We have 16 different lines being worked on at the same time. >> How many? >> We have 16 different lines working. >> No. How how many total mammoths do you
[29:39] want to bring back? >> Do I want to bring back tens of thousands? >> Tens of thousands of mammoths. >> Yeah. >> Wow. In LA. >> Um, they have to be Colombian mammoths for here or Py. >> I'm going to steal I'm gonna steal your line. The mammoth in the room is where do the mammoths go? >> But going back going back to that Wait, wait, wait, wait. Yeah, I want to answer because it's I think it's a really important, thoughtful question, right? And so you've had you've had uh you know California condors, you've had all these different um uh close to extinct species that that people work on and in and rear in the rearing of it. And so we fund uh
[30:11] in in while there's a halo effect of the positivity of this for today for elephants. It also has a broader implication for what Colossal is trying to do. We fund uh in Batswana an incredible group called elephant havens which is working with orphaned elephants right that that have already been born not actually but been born and been abandoned for whatever reason and they are working to use they use AI they use a lot of different tools to figure out how do we how do they create synthetic herds from a very matriarchal society of of elephants where they where elephants
[30:41] where they don't currently get that how do they rear those elephants and train them to be elephants and also work together in a herd right because because that's how elephants behave. Separately, we're funding and doing research in uh everything from satellite imaging to drones to AI, building programs in uh uh different elephant migratory patterns in corridors so that we can understand the social dynamics and hierarchy of of moving, right? And so that benefit all of that technology in in that that data uh impacts elephant conservation work
[31:12] today, right? Right. So you don't you don't have like elephant orphaned elephants you have you can rew wild entire herds but all that that data also informs us how we are going to rear these animals in a in a way where if they are born exudo how do they grow up in a social dynamic with the right hierarchy >> Dave you were going to say >> so true story your guy George Church was at a presentation that we had at MIT and the topic was global warming and he said well I have the you know the study I have the cure for global warming we're
[31:42] going to bring several but Yes, we're going to bring back the woolly mammoth. The woolly mammoth's native habitat is Siberia and northern Canada. >> The tundras. >> The tundras. And back when they were around, there were no trees because the woolly mammoths >> Yes. >> walk around and knock down all the trees. >> And elephants actually do this in Africa, too. >> Yeah. Yeah. Force elephants. Yeah. Yeah. They're incredible at this. >> So, so we're like, well, what the hell is the connection to global warming? Because, well, >> without the trees, the grass grows. The grass actually sequesters more carbon than the trees do. It's about six times
[32:14] more efficient in a 2 to 3x albido effect with light reflection to space. >> Yeah. So I think I don't think you guys checked in with the Canadians to see if it's okay, but you turn them loose. Turn them loose in Canada. >> I don't live in the tundra. >> I I'm from India actually. >> Seriously though, where do we build? >> So no, but this is a this is a a really good point. So in the early days um and I'm a big data guy so I most my background is in software and a little space hardware but for the most part I just want to go where the data takes us right >> so you have high conviction really smart
[32:46] scientists like George that will say if you have this mammoth density at these places uh in in the in the tundra they'll have this impact on the uh perafrost a lowering of six to eight degrees in the summer months it only melts so far so so you you can extrapolate that out we have actually done that exercise and it's quite interesting then but it goes back down to a top down versus a bottom up approach of how they affect the environment. There's other people in the scientific community including at Colossal that think that they will not
[33:16] have that level of impact. Right? And so um but the good news is is that generally speaking whether they fall on the what does how do you solve climate change uh with mammoth uh at 10,000 plus mammoth in the in the Arctic doesn't really matter because they have a net positive benefit on the environment in terms of like helping restore that ecosystem. So what I try to do and and Palmer mentioned this in the last thing is like how do you get two [ __ ] in a room and get them to agree well it gets extrapolated to like the 10th degree when they're both PhDs and so [laughter]
[33:46] uh which is like in my experience I don't have a PhD and people think that I have a war on academia at times but like the pretty hard to deal with people out there um or PhDs uh in my experience they actually have a model where I have found though is when you sit them down and tell them that they're both right and you help them walk through that it actually works. And so what I've said is maybe George is right that this level of density of mammoths at this at this lat long at latitude longitude will have this level of impact. But maybe others
[34:16] are right saying that it will have a positive benefit on the flora and fauna, but it won't cure carbon uh climate trees. Either way, it doesn't really matter if it's having a net positive benefit on uh elephants today as well as uh the ecosystem that of the tundra which is highly degraded. So, everyone can agree that the the ecosystem sucks and we need to make it better, right? And so, so I I've done that and you know, I don't know. I I don't want to say that George is right, but I'll just say I've looked at the math >> and I think George is pretty smart. >> Okay,
[34:46] >> Ben, you just spun out Astromec, multi-billion dollar company from the start. What is Astromemech doing? >> So, we're looking at So, so this was kind of a tea up from your question. um the uh foundation models and ad models aren't quite looking at the entire tree of life and they're not like uh they're not going to magically overnight give them a genome and give us an answer. So we are trying to build like what are kind of infle we we call them internally inflection models. What are deflection models that can plug into those
[35:16] foundational models that can say okay we've studied and we understand everything about this genome sequence across how it's uh evolved and more importantly when it evolved and why it didn't evolve in related clades and then we're looking at everything from like climate like like what what spurred that because we want to build essentially a prediction model to say okay where did that go and why did it go because I don't think that astromeme's going to have the like magic you know anthropic mythos those, you know, $4 trillion or whatever the latest round is that that
[35:47] that solves all things all ways. But what I do think is I think it'll have enough of the unique data sets in how to classify and understand that data data set that it can plug into those so that when you do have global bio volts and you have millions of samples that you can feed into a mythos, this can be acting like kind of the um or a mythos like competitor that this can be acting as like your traffic control cop of where to go and where to focus. >> Yeah. Yeah. >> Yeah. Let me synthesize a couple of questions here. What's the biggest problem you wish people were working on? The biggest moonshot that people are not
[36:18] right now. >> I think I think that the we are going to lose half of biodiversity in the next 25 years. And everyone who loves us and hates us agrees with that. So we need to do something about it. Governments like this is not going to be solved by a zoo or a nonprofit. We have to have billions of dollars of federal funding. across multiple governments working together to at least back up life. We back up everything else. We back up our, you
[36:48] know, we back up our photos, we back up our texts, our emails, we back up everything. But ultimately, I think most people back up their text and emails, but so some people use Signal. But like for the most part [laughter] though, um I I I really think that we we have got to right now uh invest in infrastructure to back those species up because if you don't like when we lose those species, we're going to have negative impacts on ecosystems. When we lose those species, we're going to have negative impacts in the food web for the animals. But so if you like ecosystems, you should back it
[37:20] up. If you don't like ecosystems and you hate the environment, but you like animals, you should back it back up. You hate the environment and animals, but you like [ __ ] humans. You should back it up. You know, there's data in there that will help humanity. >> Just as a quick aside, you know, one of my companies uh with an amazing CEO, Bob Hurer, called Cellularity, we have something called LifeBank USA that when your baby is born, you store all the placental cells. Yep. >> And you've got basically the original boot desk. You've got your kids stem
[37:50] cells, te- cells, natural killer cells, everything. And it's like if your baby came with an extra set of organs, would you throw them away? Probably not. But why throw away, you know, the placenta is the 3D printer that creates the creates the baby. So, I mean, this kind of envisioning >> of of of safety of backups is amazing. So, this is what this is what you're doing right now in Dubai. >> We're doing in Dubai. We're we just announced a partnership with uh US Fish and Wildlife here as part of Secretary
[38:20] Interior's directive on backing up the natural resources that that that make America great. So, we're doing that now here domestically. Uh we have two other governments we haven't announced yet uh that we'll announce when they want to announce that are part of our framework. Um and then it's also kind of like what George and I also talk about with open source. It's completely open. So we any nonprofit, any academic institution, any big foundation, any private individuals to anybody in Palmer secret billionaire boys club that wants to throw money
[38:51] wants boys. Are you in the B boys group? >> So So the anybody [laughter] that like I'm not I'm not in any secret Jack groups. >> I have a quick question. >> All right, let's close it out with your quick question. I want to be Ben Lamb with the mind as creative and crazy as yours to envision these things. >> How do I go about doing that? >> Oh, how you do what? >> How do I go about >> how did you become >> how do I become take on the mindsets that you have to apply technology as
[39:22] incredibly creatively as you have done? >> So I uh well it's very kind. I think that uh I I'm very curious, right? So, I I like to just learn new things. And I think in a world, especially with AI, where everyone's got every answer to their um at their tool at their fingertips, I'm pretty good at telling people what I don't know. So, I think I kind of take a childlike wonder to things and just say, "Hey, I don't know this, but I'm sure I could uh go find the answer." And what I've also found, which most people, this is big advice that I'd also give everyone, people will
[39:52] help you. Like, I am an optimist. I believe in technology, but I believe in humanity first. And people will help you. So like I don't think people ask enough on subways and [ __ ] looking at their phones, but if you just look out for a second and ask for help, people will help you. And so it's like when I don't understand something, sometimes I will call people in this in like some of our top adviserss at Colossal are adviserss cuz I just cold email them like, "Hey, I don't understand this. My teams are telling me this. You're the world's expert of this. Can you have a meeting
[40:22] with me?" They have no this this woman or man has no reason to talk to me, right? But they'll take the call. And so I feel like I just have this general curiosity wrapped with >> people like you. They want to help. >> No, I think that they'll like everybody like I really do. I think you can just if people ask for help, nine out of 10 times I believe in humanity, they will help you. Ask >> a quick give it up for that. Right. >> [applause] >> uh a curiosity mindset, a purpose- driven mindset, and a quick question from Kathy Wood backstage who said, "Yesterday, Anthropic um uh you know set
[40:55] up their announced their wet labs to uh that were able to create something like crisper-like. Does this light a fire in your work or does it you know, kill forward momentum?" >> So, I think it's massively validating, right? Like um it's a great question. Kathy's great. I think it's really important, right? Like there are so many problems to solve in biology. The these technologies are we are just like we haven't even opened the door like the doors barely cracked barely barely cracked open. I think that what they announced yesterday like people see that and say oh my gosh biotech is going to
[41:26] be dead because it should be anthropic in their in their lives. That's not true right that that's just not true. And so I think that that was a huge watershed moment for the industry to show that like you have the AI companies that understand and yes background in biology but but you have the AI companies that that understand that that will be one of the most accepted use cases and deployments of their technology and that people want to have healthier families healthier longer lives right so it was a great thing for the society >> really sure do you have a pd doom or do you need to even think about the
[41:57] question >> you I'm an optimist Like I I am an optimist. Like I don't agree with, you know, I I think we're going to have some scary moments and I think that's okay, right? Because I do believe in human ingenuity to work through those problems, right? But I think that you've got to have a conversation. I do think that the media is overselling that a little bit right now. um to to to be kind. But I do think that like you know we we really will get
[42:27] there and you we're going to have a couple scary moments, but it's like you have turbulence on planes and everyone still lands, right? Like that's okay. >> All right, welcome to the Oscars of Optimism. Give it up for Ben Lamb. [music]