Why We're Living in a Biological Singularity With Ben Lamm | MOONSHOTS Live #297

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. So I remember at Abundance 360 last March, just before you came on, Elon was there. And you said, yeah, I want a pet woolly mammoth. How are you doing on that project?

We did get that request, yeah, but there was actually an amazing, I 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 they explained CRISPR and it was amazing. And it was amazing 'cause then at the end they're like, yeah, 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 it was amazing 'cause it explains CRISPR, but I was like, We get the requests, like, the number 2 request we get is teacup mammoths.

Teacup?

Teacup mammoths.

Like, like—

Yeah, everybody wants a teacup, like, they wanna take a mammoth and put it like— You can put it in your purse. Yeah, and put it in your purse, right? And so, I mean, we can engineer a new melanin, make it pink, and then Paris may want one. So, we are not working on that currently. But we're making good progress on the mammoth project, so.

Amazing. So, let me kick this off, so, you've started like, 4 successful companies before Colossal, in gaming, in defense, in AI, in mobile. And so you knew nothing about synthetic biology.

Yeah,

true. Before you started, now a company worth over $10 billion. So why, what made you make de-extinction your moonshot? And having no background in synthetic biology, was that a hindrance or an advantage? Oh,

I think it's a massive advantage. Um, you know, I, I think that it, 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 vantage point is like I get to deal with a lot of the same bullshit, but at the same time I can go in and like sit down with like Beth 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? And 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 that old adage of like putting the top smartest women and men around you and 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, they're like, we never thought of it that way, right? Because—

By the way, this is such an important 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 you can attract the best talent around you.

Yeah, George Church, and this is the only person 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 everybody?

Oh, so if you don't know George, George is the father of— arguably the father of synthetic biology. He's the head of genetics at Harvard. And, um, you know, he's a lot of the next-gen read-write 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 outta the lab. It's probably the, probably most active startup biology lab in the world. And, and he's also, he's also like 6'7 with like narcolepsy and hilarious.

And such a sweetheart guy.

Yeah. Yeah. And I mean, he is the most collaborative person ever, right? And so he's, he's also like hardcore, even though he doesn't come from software, he's hardcore into, uh, 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, you know, like solving all disease, curing all pain is just like, highest on the priority list. 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 my first question on like the business model is the foundation models. Like we just saw that like out of the box, Astra can drive a car.

Yeah.

Is it going to do biology out of the box or do you say, no, no, no, no, we need to build our own?

You still have to. So the foundation models now, and we've been very fortunate to work with both OpenAI and Anthropic on, and getting early access to some of that 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 retrain scientists to work in Jira. Yeah. So that's harder than stem cell reprogramming at times. And so, 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? Yeah. They weren't really great at doing like ancestral state reconstruction or comparative genomics, right? Like that, that's where they just didn't work, which would've 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 Deloits or Accentures of the world to build all these systems and reporting, right? So we did a, 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. There's a bunch of companies out there that are standing up, Lila and others, that are trying to do lab automation around it. We've been pretty thoughtful about how and when we invest in that category. 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. For example, if you go search the literature on everything on mice, if you just want to go build a new therapy company or whatever for humans, and you go look at all of the published papers on mice, many of them, they'll call a gene a different thing, they'll classify it differently, they'll run that experiment slightly differently. And so, you know, 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? Yeah. So where I think AI is going to be really helpful in the next kind of wave of— outside of like small molecule drug discovery, is I think it'll be very, very helpful kind of eliminating that gap and bridging kind of that nomenclature between work. There'll be times where we'll do work and we'll find out later that there was a peer-reviewed published paper on one of the things that we were trying to solve, but the way we were looking for it wasn't findable on PubMed or whatnot. I think in the short term, it's going to bridge the communication layer, but I do think you're going to see, and you saw this, I think Anthropic announced this a couple days ago, right? They've, um, and, and you have Ginkgo and others that are now going to test it. You're still gonna need to have a wet lab experiment to test and validate those. Yeah. 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 gonna see AI in 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 gonna come out the other side.

No, you, yeah, but, but at scale, so, so thank you for the tee up. So that is what, that, that is what I fundamentally believe is the importance of our global BioVault system that we're rolling out, right? So we're trying to roll out this like Noah's Ark 2.0 model. Not that Noah didn't get it really right in the first stage, but like at least ours is slightly different. We're doing like ATAC sequencing and other things that he didn't do. And so we're going out and trying to work with governments around the world to stand up localized biovaults to get all of that so that we can do T2T sequencing. Uh-huh. And so to your point, I do not think that a single genome is going to, like you're not going to feed it into Mythos and it's going to be like, oh, well if you make these 6 changes, it's immortal, right? But I think that if you go look at like, you know, thousands of genomes across all these evolutionary lanes in avian 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 with things like P53, immortal jellyfishes, other things, looking at kind of like known outcomes of the species. Mm-hmm. And then try to backtrack it down the tree of life. So we're doing some of that right now. at one of our companies, which we're pretty excited about, and early indications are positive. But, but to your point, you're not just gonna be able to throw a genome in and it's gonna 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. Mm-hmm. But I also think that, so, so I would make the argument that, you know, the dataset that is more valuable than the model.

Yeah, yeah, yeah.

Because I have the dataset.

We've made that point so many times. Yeah. Alex.

Yeah, I'd love to talk a bit about de-extinction. I'm cognizant that you now have 4+ spinoffs, and— but nonetheless, de-extinction, I think, is still what you and Colossal are perhaps best known for.

And certain things we're not supposed to talk about.

We won't talk about those, though. So de-extinction. There was a Russian philosopher, Nikolai Fyodorov, 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 be essentially to develop the technology to revive every human who's ever lived. Russian cosmism, you can look it up. So de-extinction, in some sense you are—

We are not doing that currently.

Good to know. So, but in some sense, by de-extinguishing, de-extincting 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 technical trajectory, to try to go after the entire historical biosphere. So maybe not even just every human who's ever lived, but every non-human organism that's ever lived.

Right.

In an era of superintelligence, 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 every human, do you foresee that becoming possible?

I do not. I think through DNA synthesis, prediction models, and, you know, some level or some components of synthetic biology, we will be able to get pretty close approximates. I don't think though, with 2 big caveats. Number one, just a second, I have to throw this out there. Colossal does not use any of our technologies at Colossal for humans, so we won't do that, even though I think our technology— You'll need a spinoff for that, is that what you're saying? I think that when you start working with humans, you need different, sometimes different investors, sometimes, and definitely different governance, right? And you go through different processes.

And more patience.

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 gonna be in this monotony forever. Go solve, go, go get to the technologies until they plateau before you then take 'em out. But, but back to your question, I think that fundamentally, um, most organisms, remember these are the most organisms that we know, but very few people re— uh, realize this, but there's like— less than like 100 T-Rexes that have ever been— I just want to go down the path, it'll scare you. There's less than like 100 T-Rexes that have ever been found, right? But with that, there's billions that allegedly were on Earth at different points in time, right? And so, very few things leave a fossil record. So, I think it's more likely that 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 it's probably highly likely based on AI superintelligence at some point where we get to the point where we're saying, oh, we're gonna engineer this thing. And to your question, 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 than trying to bring back things. And even if Colossal or a subset of our technologies, both cloning 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 going to, like, solve where the terrible trajectory of music has gone.

Well, just maybe a follow-up question. So for Mozart specifically, we do know quite a bit about Mozart's life. So our arguendo, if we did want to resurrect Mozart, we'd have no problem at all with reconstructing his childhood environment.

To a point, right? 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 where we are taking humans and growing them. It's like you're gonna make— like, you're gonna go down this like Michael Jordan, LeBron, super basketball thing in a second and scare me. Oh yeah. So the— I do think it's highly likely that if we were able— or it's not highly likely, it's 100% accurate to say that we understand their genetic disposition and aptitude towards these certain traits, and under the right environments, if you wanna go like, you know, if you wanna go all simulation design on them, and put them in the right environments, then they could probably, then your Mozart 2.0 could probably be better than the Mozart 1.0.

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 that's bigger— It's getting better.

It's not 100% there yet, but—

But that's the objective, right? If you want an 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 seems to be a weird like fan favorite, you know. People are more accepting, I think, of Pikachu than these large genetic human camps that you're thinking.

Branded species.

Yeah, yeah, it is. Yeah, I think people are more open to that model of genome engineering than—

But it sounds, just maybe to tie a bow on this, it sounds like it isn't a technical objection per se, it's more worries of social, political, regulatory concerns.

Correct, correct, yes, and ethical.

So this is where I'd like to jump in. You know, in Stewart Brand, made this famous comment, Peter, you named the title of your book. He said, we are as gods, we might as well start acting like it, right? And he said that in 1968. We are at a point where you can de-extinct or bring back any species. How do you think through the ethics of what should we bring back? Which ones? Which ones shouldn't we bring back? How do you think through that?

So I'm a huge Stewart fan. You know, I love him, love Ryan. That quote has been out there kind of like, you know, Minnie from a famous dinosaur movie. But I don't know if I would characterize it like Stuart and then get like on a plane. But from my perspective, I would say that like we spend a lot of time. It is not plausible yet to bring back everything or engineer everything from a synthetic biology perspective. Of course. So we try to be very thoughtful and people ask us, is there a checklist? How exactly do you go about selecting your species? Because there's a species that we've been very public about, and then there's species that we have not yet.

Hold on, I'm not thinking— I'm not talking about what you're doing specifically.

Oh, you're saying philosophically?

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 should we be thinking about it?

Speciesism, right?

Yeah.

Yeah, and so it's a great question, and because we're kind of the you know, guinea pig in this world. 100%. The way that we think about it is, what was their contribution to the environment? What was their contribution to the food web? What was their— why did they go extinct? 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 people side of it, right? So I don't think you're gonna walk in and quote Stuart Brand to them, right? And so I think that it's very important to kind of like factor all those and weight those. And we're probably gonna get it wrong, and I think society's gonna get it wrong, but we're gonna 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 Dire Wolves, right, after working with the indigenous people groups and the Red Wolf Coalition teams and all these different components, is we did weigh in the pop culture nature to it. Mm-hmm. We thought, is there a way that we can bring all these people that focus on sci-fi and Game of Thrones and Magic: The Gathering, Can we bring them to wolf conservation, and can we bring them— can we teach them about genome engineering because we brought something back that they thought was only a mythological creature in their fantasy universe, right? So we try to weight all of these things differently. 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 weight that on an individual basis.

Yes, they will.

So, Ben,

I'm sure that all of us are going to get it wrong at some point.

Well, because there's so many different factors involved, right? Like, I'm some 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 going to 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 great.

You're like a walking singularity.

Yeah, we are. We, we do. Uh, I, I think that society doesn't fully understand. I mean, they all saw 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. Is there an inflection point, a singularity in biology where all of a sudden there is a complete knowledge base of all the ingested DNA, all the ingested phenotypes, and you can literally design an organism with—

We have like the CAD software for biology.

You can design, you could prompt Yep. 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, yeah. So I think that world is less than 10 years out.

Okay. All right, so you can prompt your favorite Pikachu or animal in 10 years.

I don't think 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 clades and be able to synthesize them or multiplex engineer them and then grow them ex utero is within a decade.

Okay, ex utero.

Within a decade.

Talk about your— what's that?

Within a decade.

Within a decade.

Holy shit.

Can you make a—

this is unbelievable. Well, let me give you a 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. No. I think that 99% of biopharma and— I'd say 99% of biopharma and academia would do that today. We are delivering 300+ at 90+% efficiency consistently. That was a year ago. Wow. We're now testing 1,000—

That's an exponential growing curve.

That doesn't mean it's going to continue. We're testing 1,000 curves. 1,000, uh, uh, uh, edit deliveries right now. Doesn't mean it's gonna work, but we're working on it.

Tripling year over year base edits?

We don't know if it's gonna work, but we are having—

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 a, a business driver to synthesize DNA after a certain scale. Um, so we just started doing that internally.

So doing the math, tripling year over year, you're at 1,000 base edits right now. That gives you what, 12 years?

We are consistently north of 300. We're testing 1,000. 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 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 gigabase code you want.

I think that has a 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 an avian 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 baby.

Well, you have to, from a technology perspective, I think it's a very different answer than a societal, And acceptance, and ethics, and regulations.

Okay, okay, so let's talk about an artificial womb for a mammal.

Yeah, I think within 24 months, we will— Colossal will birth animals, mammals, fully ex utero.

From gestation through delivery?

Yeah, that never went into a surrogate.

Wow.

Wow.

That's pretty amazing.

Yeah.

That's wild.

Hopefully sooner, but I think that 24 months is highly likely.

Wow. I need to ask a quick question.

Yeah.

Just a show of hands in the audience, is your mind blown? Like, okay, just checking. Okay, got it.

Yeah.

Make sure I'm not alone here.

So what's the iPhone moment in Colossal here? Is it the woolly mammoth stepping onto the stage, or is there something else that like—

I think that— I 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— You have. I think the next major inflection points are when we show the world the next extinct species, right? Like, I think always that kind of, in that kind of 0 to 1 mindset.

That's next week? When is that? Just kidding.

Coming soon. So, so I think, I think that showing another extinct species back through precision gene editing, number one, Number 2, I think that mammalian artificial development and gestation is number 2. And then, you know, we are working on some things that we haven't shared yet that I think are equally as interesting to, like, dire wolves. So we have some more surprise and delights, 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, you know, protein folding just really snuck up on everybody. Yeah. Yeah. Like solved overnight. Yeah. Been just a total goldmine of change for all of biotech. And my daughter uses it every single day.

Yeah, we use it.

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 Wiesner-Gross. Okay, this sequence. Oh, that produced Celine. Great. This is— oh, that's a dodo bird. Okay.

I love this final jump there.

Just a couple of months.

We love that jump, by the way.

So is there a point where, given the dataset 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 we're doing it on a— from a product perspective, we're doing it on a species basis currently. We're trying to extrapolate to large clades of animals. So like sizing's a big one, right? So if you go— if you're taking like our model species that we're working with for the Tasmanian tiger, is, or thylacine, is a fat-tailed dunnart, and it's a 1,500x fold from a marsupial mouse to a marsupial wolf, right? Yep. So understanding that and extrapolating that and how that, whether, 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, but you could probably scale within certain levels of function. If you look at certain species like dogs and also certain species groups of birds, they have tremendous scale functions, right, which are larger than 1,500. Yeah. And so we're— or 1,500x. So we're looking at it on a per— from a non-trait engineering perspective, but from a purist perspective in de-extinction to look specifically at the genes that drove X, Y, and Z. But separately, we are then trying to extrapolate that on a clade basis so that we can say, okay, how can we affect sizing even within some marginal, you know, 20%, 50% offshoot within other species? So I think that it's likely that like coat, coat color, sizing, you know, placate-like things that form skin, skin color, Scales, feathers, all of that will be highly measurable. Yeah. And be able to be inducible very quickly.

Interesting. So you can— all right.

I don't know about everything, but like, you know, we have a whole AI team that's just working on patterning and stripes. It's actually a really hard problem.

Tusks, stripes, length of snout.

Yeah. Hair.

Hair. Yes, hair.

Yeah.

By the way, the hair—

That's been requested quite a bit.

You know, when I took— when Chris and I took our boys down to Dallas to visit, it was a real surprise and delight to see the woolly mice there. How many gene edits did that take?

The first generation, which is what we showed the public, was 8.

Amazing.

In one delivery.

8 base pairs?

8 edits?

8 edits total. 8 base edits in one delivery.

Amazing.

Let's go to some of the audience questions here. This is from Bruno.

Another version at some point that—

Okay.

More interesting.

I can't wait.

Yeah, that's interesting.

We'll have you back on Moonshots to talk about it. Yeah, great. All right, so Bruno asks, must a moonshot tackle one enormous problem?

We had Shatner there the other day and I was like, fuck, if we should do a triple. A triple, oh yeah. Yeah, like if we made Harrier and tailless, we could have like gotten like, you know, Shatner very excited about a triple.

Yeah, the triples, that's right, we need triples. You can bring triples back.

I think back is the wrong word, but I think we could— Forward?

Bring them forward.

Yeah.

All right, so Bruno asked the following of you. Must a moonshot tackle one enormous problem within a single vertical, or can it be horizontal, addressing a seemingly mundane everyday need that cuts across many verticals? And we'll ask that of Astro as well, the Captain Moonshot, shortly.

I think it can go across verticals, right? But remember, I have ADD, and so, like, I think that the lack of focus gives us a larger amount of wisdom across multiple categories, right? And so we look at de-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 I think that if you ground your fundamentals in what 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. How are you planning the ethical issues in creating mammals ex utero? A mammal has emotional needs, and just creating an animal doesn't relieve you of the emotional burden of a living creature. Yeah.

I think that's the same thing. Like, if it's born ex utero, do you give it A family to live with? Yeah. Or raise it in a—

So we do a lot. I, I think most people 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, no, how many total mammoths do you want to bring back?

Do I want to bring back? Tens of thousands.

Tens of thousands of mammoths?

Yeah.

Wow. In LA?

Um, it'd have to be Columbian mammoths for here, or pygmy.

I'm going to steal your line. Mammoth in the room is where do the mammoths go?

But going back to that— wait, wait, wait, wait, wait. Yeah, I want to answer this because I think it's a really important, thoughtful question, right? And so you've had, you know, California condors, you've had all these different close-to-extinct species that people work on, and the rearing of it. And so we fund 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's trying to do. We fund in Botswana an incredible group called Elephant Havens, which is working with— Mm-hmm. orphaned elephants, right, that, that have already been born, not ex-schizo, but have 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 elephants where they— where elephants— 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 everything from satellite imaging to drones to AI, building programs in different elephant migratory patterns and corridors so that we can understand the social dynamics and hierarchy of moving, right? Nice. And so that benefit— all of that technology and that data impacts elephant conservation work today, right? So you don't have, like, elephant— orphaned elephants, you have— you can rewild entire herds. But all of that data also informs us how we are going to rear these animals in a way where if they are born ex utero, 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 have several, but yes.

We're gonna 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 walk around and knock down all the trees.

And elephants actually do this in Africa too.

Yeah, yeah, forest elephants.

Yeah, yeah, they're incredible at this.

So we're like, well, what the hell is the connection to global warming? He goes, well, without the trees, the grass grows. The grass actually sequesters more carbon than the trees do.

Yeah, it's about 6 times more efficient and a 2 to 3x albedo effect for 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'm from India, actually.

Seriously though, where do we build the rest of the

farming? No, but this is a really good point. So in the early days, and I'm a big data guy, so most of my background's in software and a little space hardware. But for the most part, I just want to go where the data takes us, right? Right. So you have high conviction, really smart 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, permafrost, a lowering of 6 to 8 degrees in the summer months. It only melts so far. So, so you, you can extrapolate that out. We have actually done that exercise. Uh-huh. Uh, 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 have that level of impact, right? The good news is that generally speaking, whether they fall on the, how do you solve climate change with mammoths at 10,000+ mammoths in the Arctic, doesn't really matter because they have a net positive benefit on the environment in terms of helping restore that ecosystem. So what I try to do, and Palmer mentioned this in the last thing, is like, how do you get 2 assholes in a room and get them to agree? Well, it gets extrapolated to like the 10th degree when they're both PhDs. And so, 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, or PhDs, 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 latitude-longitude will have this level of impact. But maybe others are right saying that it will have a positive benefit on the flora and fauna, but it won't cure climate change. Either way, it doesn't really matter if it's having a net positive benefit on elephants today as well as Uh, the ecosystem that, uh, 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 wanna say that George is right, but I'll just say I've looked at the math and I think George is pretty smart.

Okay.

Ben, you just spun out Astromech, a multi-billion dollar company from the start. Uh, what is Astromech doing?

So we're looking at, so, so this was kind of a tee-up from your question. The foundation models and AI models aren't quite looking at the entire tree of life, and they're not like, they're not gonna magically overnight give them a genome and give us an answer. So we are trying to build like what are kind of inflection, we, we call 'em internally inflection models. Right. What are inflection models that can plug into those foundational models that can say, okay, we've studied and we understand everything about this genome sequence across how it's 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 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 Astromech's going to have the like magic, you know, Anthropic mythos, you know, $4 trillion or whatever the latest round is that, that, that solves all things always. But what I do think is I think it'll have enough of the unique datasets in how to classify and understand that dataset that it can plug into those so that when you do have global bio vaults and you have millions of samples that you can feed into a Mythos, this can be acting like kind of the, or a Mythos-like competitor. 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 right

now? 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. 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 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 texts and emails, but— Some people use Signal. But, like, for the most part, though, I really think that we have got to right now invest in infrastructure to back those species up, because if you don't like— when we lose those species, we're gonna have negative impacts on ecosystems. When we lose those species, we're gonna have negative impacts in the food web for the animals. But so, if you like ecosystems, you should back it up. If you don't like ecosystems and you hate the environment, but you like animals, you should back it up. If you hate the environment and animals, but you like fucking 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 with an amazing CEO, Bob Hariri, called Cellularity, we have something called Life Bank USA, that when your baby is born, you store all the placental cells. Yep. And you've got basically the original boot disk. You've got your kid's stem cells, T 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 baby. So, I mean, this kind of envisioning of safety, of backups, is amazing. So this is what you're doing right now in Dubai?

We're doing it in Dubai. We just announced a partnership with US Fish and Wildlife here as part of Secretary of the Interior's directive on backing up the natural resources that that make America great. So we're doing that now here domestically. Uh, we have 2 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 talked 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's secret billionaire boys club that wants to Everyone wants to be b-boys.

Are you in a b-boys group?

So anybody that like wants to say— I'm not in any secret chat groups. I have a quick question.

All right, let's close it out with your quick question.

I want to be Ben Lamb with a 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 incredibly creatively as you have done?

So, uh, well, it's very kind. I think that, uh, I'm very curious, right? So 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 go find the answer. What I've also found, which most people— this is big advice that I'd also give everyone— people will help you. I am an optimist. I believe in technology, but I believe in humanity first, and people will help you. Mm-hmm. I don't think people ask for help enough. I think everyone's walking on subways and shit, looking at their phones, but if you just look out for a second and ask for help, people will help you. It's like, when I don't understand something, sometimes I will call people Some of our top advisors at Colossal are advisors because I just cold emailed them like, hey, I don't understand this. My teams are telling me this. You're the world's expert in this. Can you have a meeting with me? This woman or man has no reason to talk to me, but they'll take the call. I feel like I just have this general curiosity wrapped with—

People like you.

They want to help you. No, I think that they'll like everybody. I really do. I think you can just— if people ask for help, 9 out of 10 times, I believe in humanity, they will help you. A big— 9 people will ask.

A quick— give it up for that, right? A curiosity mindset, a purpose-driven mindset. And a quick question from Cathie Wood backstage who said yesterday Anthropic, you know, set up their— announced their wet labs that were able to create something like CRISPR-like. Does this light a fire in your work? Or does it kill forward momentum?

I think it's massively validating. It's a great question. Kathy's great. I think it's really important. There are so many problems to solve in biology. These technologies are— we haven't even opened the door. The door's barely cracked open. I think that what they announced yesterday, people see that and say, oh my gosh, biotechs are going to be dead because it should be Anthropic 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 know, the AI companies that understand, and yes, Dario has 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 I, it was a great thing for this society.

Really short.

Do you have a P-doom or do you not even think about the question?

I'm an optimist. Thank you. I'm an optimist. I don't agree with— I think we're going to have some scary moments. Of course. I think that's okay, because I do believe in human ingenuity to work through those problems. I think that you've got to have a conversation. I do think that the media is overselling that a little bit right now, to be kind. But I do think that, like, you know, we, we really will get there and we're going to have a couple of 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 Lamm.

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