George Church — A Billion Years of Evolution in a Single Afternoon

30:39It's more recent is one aspect of it. But we could kind of stand on the shoulders of the electronics giants to go a little bit faster to catch up. I would say we do. I mean, we have the biotech industry, which has used that exponential curve to get better. It's also possible we're close to the big payoff as the other aspects or the beginning of the big payoff. Right now we have miraculous things like cures for rare diseases. We have vaccines. We have, you know, trillion dollars probably of various biotech related things if you go far enough apart. But we're kind of on the verge of really combining electronics and biology more thoroughly and AI and biotech.

31:41And I think that's the, it seems like we're on the same track as Moore's Law if not better. What exactly are we on the verge of? What does 2040 look like? Well, 2040, we're telling them only 15 years, which is like one and maybe two cycles of FDA approval. 2040s post -AGI. It's a long time. I hope it's not post -AGI. I think we're rushing a little bit to get the AGI. And there's lots of cool things we can do with just super AI. But we need to be very cautious, I think, that AGI. Anyway, we can get into that question. But, you know, I think that we are shortening the time of getting medical products approved in still in a safe way.

32:35So I think, but that's not going to completely change the exponential. It might reduce it from 10 years down to one year as our record so far for say COVID vaccines. So maybe they'll be 10 times shorter. Maybe that will multiply out a little bit. But I think the big thing is that our designs will become better. So there'll be fewer failures. The cost per drug will drop. There'll be things that we didn't classically consider drugs or instruments be kind of sort of a hybrid thing. But again, I don't think that'll be completely shocking. But it's just going to be so much of it. You know, this is going to be lots of diversity of solutions.

33:24How much more are we talking? Is that are we going to have 10 X? They might have drugs 100 X? I'm not even sure it's going to make sense. But yeah, 100 X would not be completely surprising. Combinations of drugs will be important. When you're using them intelligently, there'll be a lot more. Some drugs will affect everything. So for example, age -related drug that could impact every disease. It could be, I'm not sure the number is going to matter so much as the quality and the impact and intersection and software that helps physicians and other regular citizens make decisions. What's specifically changing that's enabling this is just existing cost curves continuing or is it some new technique or tool that we'll come about?

34:11Well, the cost curves are affected by new tools. I mean, it's not just some automatic thing. There was a big discontent of the between sanger sequencing and nanopores and fluorescent next -gen sequencing. That was. And so, you know, I think, sometimes it's a merger too thing. So clearly AI merging with protein design causes a step function. These step functions get smoothed out into a kind of a smooth exponential, but there are lots of them. Next, the next set will probably be a merger of AI with other aspects of biology, like developmental biology, merger of developmental biology with manufacturing and conquering developmental biology.

35:02There was actually knowing how to make any arbitrary shape given, you know, DNA is the programming material. I think that would be a big thing. Having just more materials in general, all the materials we use in mechanical, electrical engineering should be made better by biotechnologies. Why is that? Why is that? Well, that electronics is, you know, more so I wouldn't say is stopping, but it's kind of the, that what we would call the one nanometer process, which is supposed to come out in 2027 according to the roadmap. It's not really one nanometer. It's more like 40 nanometers, centers, centers, spacing, you know, in typically in two dimensions.

35:57Maybe a little bit of three dimensions, but biology is already at point four nanometer resolution and it is in three dimensions. And so, depending on how you count that third dimension, that could be a billion times higher density that biology is already at. And, you know, we just need a little more practice with dealing with the whole periodic table, even lecture, lecture engineering, and we can't, doesn't use the whole periodic table typically, but we, but especially not at the atomic level. So, I think biology is just really good at doing atomic precision. So then what's the reason that over the last many decades, and we have, we do have not atomic, but close to atomic level manufacturing with semiconductors.

36:4440 nanometers. Right. It's quite small. It's a thousand times bigger than biology, linearly. But the progress that you have made hasn't been related to biology, so far. It seems like the, we've made more stuff happen. I don't know, people in the 90s were saying, you know, ultimately we'll have these bio machines that are doing the computing, but it seems like we've just been using conventional manufacturing processes. What exactly is the changes that allows us to use bio to make these things? The few things that one is the arrival of synthetic biology, where you sort of, we were already kind of doing synthetic biology before, you know, we were doing recombinant DNA.

37:24It was kind of, you know, genetic engineering was called, this kind of in that direction, but synthetic biology really liberated us to think a little bit bigger, even though it started kind of focused on E. coli and yeast. It, it, it, it, it, it, it, it, it, it, it, it, it, it, it, it, it, it, it, it, it, it's a, maybe think, about new amino acids, for example. And I think new amino, if you start using the full periodic table with the amino acids or what amino acids can catalyze that breaks one of the major barriers. One of the major barriers between electrical and mechanical engineering and biology was the use of special materials, things that conduct electricity at a speed of light or conduct signals more generally.

38:15But there's definitely polymers, the biology can make that will conduct at the speed of light. And we could make a mixed neuronal system that has conventional neurons and processes that conduct at the speed of light that would be interesting. I think that our ability to design proteins was particularly difficult. Designing nucleic acids was great, whether we were doing, you know, you want two things to bind to each other. You just dial it up using Watson Crick rules. If you want to make a three -dimensional structure, you know, it's actually the one kind of the one thing where morphology is dictated by fairly simple rules.

38:59It's not how developmental biology works and we still need to figure out how that works. But DNA or GAMI, DNA nanostructures really worked. But doing it for proteins was really, really hard until, I don't know, maybe eight years ago, so we have that. And I think we're just now getting used to it. The use of chips for making DNA. I mean, you said that DNA synthesis come down a thousandfold with the pens and who you talk to. So that when we came out with the first chip -based genes in 2004 nature paper, you know, basically people dismissed it for about a decade. The only people they used that were, you know, collaborators in an alumni.

39:40And it wasn't even listed on the Moore's Locker for DNA synthesis, even that was like a thousand times cheaper, who was just like ignored. And now we have claims of 10 to the 17th genes, okay, that you can make libraries 10 to the 17th that aren't randomized in any real, in the usual sense where you just like do's air prone PCR or spiked in nucleotides, 10 to the 17th. That's a lot bigger than a thousandfold data. If it turns out to be practical, yeah. Publicly available data is running out. So major AI labs partner with scale to push the boundaries of what's possible. Through scales data foundry, major labs get access to high quality data to fuel post training, including advanced reasoning capabilities.

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41:05If you're an AI researcher or engineer and you want to learn more about how scales data foundry and research lab can help you go beyond the current frontier of capabilities go to scale .com slash the war cache. Okay, so speaking of protein design, another thing you could have thought in the 90s is when people were writing about nanotechnology, air, directional, or so forth. And now we have, we can go from a function that we want this tiny molecular machine to do back to the sequence that can give it that function. Why isn't this resulting in some nanotech revolution or will it eventually, like, why didn't alpha -phola cause that?

41:45I think part of it is that the nanotechnology as original, you know, the kind of the source of the inspiration Eric Drexler, he wanted to reinvent biology in a certain sense, but it already existed. And so, you don't need to design a diamond replicator because you already have a DNA replicator. And so the question of what was missing, what was motivating this reinvention of biology, it was materials, so the biologies, not that great, with, you know, materials that are, say, superconductors or conductors, periods, and conductors and light speed. But it's getting there. I mean, you know, rather than going the root of having everything has to be based on first principle nanostructures, you can meet in the middle where biology can build things.

42:45Now, of course, when you go down to you know, liquid nitrogen and colder temperatures, biology, as we currently know it, stops functioning. Now, it's not to say that you can't have things moving in liquid nitrogen, you can, but that hasn't been explored and doesn't really need to be because if biology can build things that can operate at low temperature, or maybe biology, now because you can make these big libraries of biology, maybe 10 to the 17th in vitro, and you can flip through them quickly and you can barcode them and you can, this is something you've just never been done in electronics.

43:29I'm not a billion different kinds of electronic materials, right? Just in an afternoon, barcode them all and see who wins, right? But we do the all the time in biology now, at least since 2004 we have. And so I think that's an opportunity, is that we use those libraries to make much superior materials, and we might even finally get room temperature superconductor that way. From bio? This is possible. I mean from libraries. We call it chemical slash biochemical slash exotic material libraries, but the point is they're libraries. They're essentially based in some sense on polymers, even though pieces of them don't necessarily have to be polymers.

44:14Do you have a prediction by when we'll see this material science revolution? What is basically standing between, because we've got, we've got off of photos right now, right? So what is the thing that we need? Do we need more data? Well, alpha -folds are a nice, but it's only part of it. So there are large language models that are different from alpha -folds. So give an example. Alpha -fold last time I checked, anyway, at least it's all changed. If you substitute an alamine for serine in a serine protease, it will have exactly the right fold. It will be precise to, you know, fraction of an angstrom overall average, but it won't function.

44:59It just won't function. And that's where you need either extraordinary precision or just knowledge of what happens evolutionarily or happens in experiments to say that no, an alamine won't work. Okay. And so I think there's all kinds of combinations of AI tools that can give you deeper insight into that. If alpha -fold predicting the structure doesn't tell you whether the thing will actually function. The mod is needed before I can say, I want a nano machine that does X thing or I want a material that does Y thing. And I can just like get that. I mean, I think the way it is working now, which will get us a long way, I won't get us the whole way, is we have something that kind of works and we make libraries inspired by that, make variations on it.

45:47And then whichever of those variations work, we make variations on that, and we could just keep going. It's kind of like a way evolution worked, except now we can do it incredibly high speeds. And in principle, you know, what might, you know, evolution might incorporate a few base -beard changes in a million years, now we can make, you know, billions of changes in an afternoon. And so, and it's all guided in such a way that you get rid of the wastefulness of having a bunch of neutral mutations and a bunch of lethal mutations. You can have things that are quasi -neutral and, but likely to be game -changing, have more of a focus on those.

46:32Another thing that's been missing and none of the AI protein design tools that I know of are particularly good at it yet, but we're trying to, we're, as we speak, trying to improve this, is non -standard amino acids. Because a lot of these tools depend on having libraries of 3D structures, which use 20 amino acids, and large language models that are you line up all the sequences of 20 amino acids. And we have very little experience with extra ones, but I think there's a revolution going on in generating non -standard amino acids, where the amino acids can either have as part, covalent part of them, or as easily liganded all the entire periodic table, stable elements.

47:20And that will, you know, each of those will have to blend in and train our models on, but as soon as that comes in, then we're going to have a whole series of new materials very quickly. And ultimately, you can think of the determination of the functionality of your library is a kind of computer, right? So you use AI to make, to design the library optimally. So you avoid things that are really neutral and really seriously damaged. But then the stuff in the middle, you actually play it out, not in a simulation, but in real life. But it's so inexpensive and it's so fast and it's so exact. I mean, it's 100 % precision because you're not simulating, right?

48:11You're not making assumptions, you know, you're not going from quantum electrodynamics, this is an assumption, quantum mechanics, this is an assumption, through, you know, molecular mechanics, which are full of assumptions. You're really doing the real thing. And so you're doing a kind of natural computing. And then you can take that data and harvest it in various ways very efficiently, pump it back into the, you know, the more conventional AI, and do another round of it. Yeah. It seems like if I listened to these words, it seems like I should be expecting the world to physically look a lot different.

48:42But then why are you only getting like a couple more drugs by 2040? Well, I didn't mean to stop there. I mean, I knew the conversation would continue. Right. I'm not pinning down a particular year either. But I think this is poised to go pretty quickly. There are very few practitioners as a thing that will stop it for a while. Since materials will actually go, should go faster though, because they don't require quite as much regulatory approval. So it's good. You know, it's one of these things where when you get the right idea, it's not hard to recruit people. I mean, for example, when Feng Zhang and my lab's product, Christopher, we just got 10 ,000 requests in the next two months for people that wanted to duplicate the system.

49:30And so that's that's what I hope will happen with the non -standard amino acids. And they're using AI for protein design and making new materials. Hopefully that will recruit tens of thousands of people overnight. Are you more excited about AI? I wish things in protein space or like capsid space or like just, you know, it's like predicting some biological or DNA sequences. Or are you more optimistic about just L and strain on language, which can like write in English and tell you here's the experiment you should run in English? Which of those two approaches or is this some combination that when you think about AI and bio is more promising?

50:08I'm much more excited about scientific AI than about language AI. I think languages were in pretty good shape already. And what worries me is that to get to the next level of language requires a GI or ASI, you know, artificial superintelligence. And that's very dangerous. I don't think we have quite figured out how to, there's a lot of safety organizations and a lot of safety rules and so forth. And I think what typically happens when there's an intense competition is those safety rules get undermined and pushed aside. But even if they weren't, I just don't think we, I don't think we understand our own ethics well enough to educate a completely foreign type of intelligence.

51:00I mean, barely know how to pass it on to the next generation of humans. So I think we need time to sort that out. And there's no rush. This is a completely artificial emergency. This is not like COVID -19 where we actually millions of people are dying if we delayed the science. This is something where if there ever is a crisis, it's because we created this not because we're trying to solve it. Yeah. Right. And so I think we need to go very slowly on AGI and ASI and double down on slightly narrower scientific goals. And even now we need to be very cautious about we need to have kind of an international consensus on what constitutes safe AI.

51:46I suppose we did, we'll save superintelligence. How much would that speed up bioprogress? There's a million George churches in data centers just like thinking all the time. Is it a 10x speedout? I think it was slow it down. I think it would eliminate it because the first thing would conclude is biology is not relevant to me because I'm not made out of biology. I mean, I suppose you could get them to care about it. There's a copy of you in a data center. How do you comment run experiments directly? They're just in data centers. They can just say stuff and think stuff. I don't think we have anything close to the assurance that we need that that would be safe.

52:26But let's put safety aside. It's not only hard to calculate the the bads, it's hard to calculate the goods. So I think it could be a complete game changer. But on the other hand, it's like if we said we could get instantaneous transport all over the earth, right? Well, we could say yes, that could be a game changer, but do we really need it? Is that really important? Maybe it would be more interesting to just have zoom calls and they're better or just learn how to get everything we want in our kitchen and we don't need to travel anymore. Be careful what you ask for, because you could tip our priorities towards something that we really don't care about.

53:20That we shouldn't care about or might wish we didn't care about. But I'm curious what you've still got to run the experiments. You still need these other things. So does that bottleneck the impact of the millionth copy of you? Do you still get some speed up? How much faster can biology basically go if they're just more smart people thinking, which is this sort of proxy for what AI do? These are great questions and I'm not sure I don't want to miss or present that I know the answers, but it's like the question of if you have nine women, can you do pregnancy in one month? No, not not not present.

53:57But you're working on that, right? No, no, no. But the same thing is there may be certain things that doesn't take a lot of people. We just don't know. We don't have that much experience with having thousands of Einstein -type levels of creativity and intelligence simultaneously in a generation. And in fact, it's probable that we're all capable of being a bit more efficient if we don't have distractions of mental illness, of taking care of other people. Now, taking care of other people may be a very good thing. Maybe if we have no one to take care of, there'll be something bad that happens to us socially.

54:51So these things are very complicated, hard to predict. I think right now, I think the baby step or actually the pretty big baby step is to eliminate diseases or at least make it possible for people to eliminate their own diseases as they see fit. You worked on brain brain connect home and so forth. That work, how has it shifted your view on fundamentally how complex intelligence is? In the sense of like, how are you like more bullish on AI because I realize that organoids are not that complicated or it's like very little information is required to describe how to grow it. Or are you like, no, this is actually much more gnarly than I realized?

55:35I think I always felt it was very gnarly. I also felt that there was something that we could engineer. Certainly, we have made a lot of progress at the broken end of the spectrum where the brain is severely challenged relative to average. There's thousands of a huge fraction of genetic diseases that have one of their consequences being that the child is developmentally delayed to such an extent that it's lethal or lifetime deficit. We know how to, we know the genes evolve and we know how to do genetic counseling in some cases gene therapy and other therapies to deal with it. At the other end, we have reduction of cognitive decline by cognitive enhancement, which is showing some promise.

56:51But again, that's kind of like this early stage severe impediment to cognition has a late stage component. But how much information does it take to encode a brain? I'm not sure that that much less genome is required than if you just wanted to make a brain because the brain is totally entangled with the body. You know, you need to, you have a 10 to the 11th neurons, 10 to the 14th synapses. If you wanted to reproduce a particular brain, let's say, it might be it's speculative as the weather would be easier to do that by making a copy of it in in solaco in some kind of an organic matrix or making a copy of it.

57:41Both of those are going to be hard. I would say that if you wanted to make a copy of a complicated book, it would be easier to take photographs of each of the pages and to completely translate it into another language, trying to get all the nuances and the poetry and so forth. If your goal is just to replicate it, and I think the same thing might be true of a brain. But replicating a brain probably involves a lot more information than synthesizing it. So I mean, you would have just defined the 10 to the 14 synapses is going to take a lot more bites than the genome, which is billions rather than 10 to 14th.

58:20But there might be reasons that you want to replicate a particular brain configuration rather than just make another animal that starts from scratch as an infant. Given how little I knew about biology, I might prefer this episode, basically look like one minute of trying to read some paper and then chatting with an LLM like Gemini 30 minutes afterwards and asking it to explain a concept to me using secratic tutoring. And the fact that this model has enough theory of mind to understand what conceptual holes a student is likely to have and ask the exact right questions in the exact right order to clear up these misunderstandings is honestly been one of the most feel the AGI moments that I've ever experienced.

59:11This is probably the single biggest change in my research process. Honestly, since I started the podcast for this episode, I think I probably spend on the order of 70 % of my prep time talking with LLMs rather than reading source material directly because it was just more useful to do it that way. And given how much time I spend with Gemini in prep for these episodes, improvements in style and structure go a really long way towards making the experience more useful for me. That's why I'm really excited about the newly updated Gemini 2 .5 Pro, which you can access in AI Studio at AI .dev. All right, back to George.

59:51Going back to the engineering stuff, often people will argue that look, you have this existence to prove that you co -elect and multiply it every or duplicate every 30 minutes and sexton duplicate really fast as well. But then with our ability to manufacture stuff with human engineering, we can do things that nothing in biology can do like radio communication or vision power or jet engines. So like how plausible to you is the idea that we could have biobots, which are like can duplicate at the speed of insects and you could be trillions of them running around. But they also have access to jet engines and radio communication and so forth.

1:00:30Are those two things compatible? Well, I mean, a certain thing seem incompatible, like the temperatures of a vision reactor isn't obviously compatible. But the possibility that once we that a biological system can make other things, for example, it can make a nest, a bird can make a nest. And you consider the whole nest as part of the replication cycle of the bird. So you can say biological thing that replicates at 30 minutes, doubling time, could make a clear reactor as that would be its nest. But you need to expand this range of materials. And as certain as we do this already humans are a biological thing that replicates not in 30 minutes, but in 20 years or less.

1:01:30And is that fundamentally limiting us, probably is. But yes, it's amazing to think about what if you could take a cornfield or a nuclear rapture and suddenly 30 minutes later you got two of them, right? And then four of them. And yeah, I mean, that's quite an interesting concept. But I think we should start with, I teach a course called how to grow almost anything. And I work with Neil Gershianfeld who had MIT who has a course called how to make almost anything. And we're trying to meet in the middle where we can, in his mechanical, electrical engineering, we'll meet with our biological. And in fact, neither of us can make or grow almost everything because there are all kinds of little gaps in things that are very hard to make in a small lab.

1:02:25Because there are things all over the world that depend on multi -billion dollar fabs to make things. But we're eating away at it. I think that we might eventually be, maybe a smaller, maybe a step than making a nuclear reactor is making a phone. You said radio communication. We should make a biolot. It should be a small challenge goal for the synthetic biology community, maybe Igem or something. Make bacteria make a radio. And I actually, Joe Davis is a artist who's been affiliated with my lab before the Alex Rich's lab. And he did bake a bacterial radio, but it was kind of more on the art and on the science.

1:03:10But I think that would be a good good goal. What would it take to do whole genome engineering to such a level that for even a phenotype which doesn't exist in the existing pool of human variation, you could manifest it because your understanding is so high that you can, for example, if I wanted wings, it's a bottleneck or understanding, it's a bottleneck or ability to make that many changes to my genome. So part of this has to do with just learning the rules of development biology, like I said. We can determine morphology at the sort of the molecular level now, proteins, nucleic acids, determining at the cellular multicellular level, there's a lot more things you can do and a lot faster, but we don't know the language yet.

1:03:58So we got to that, I think we're on the cusp of getting the tools to do that, like the transcription factor that I was talking about earlier, harnessing migration, gradients of diffusion factors, chemotaxis and so forth. That's one thing we need, but there's a bunch of things we need, really. What discovery in biology, not an astronomy or some other field, in biology, would make you convinced that life on Earth is the only life in the galaxy. Conversely, what might convince you that no, it must have arisen independently thousands of times in the galaxy? Oh, I figured you're getting at it. Astronomy might be, we would detect radio signals or light signals, but biology, what the kind of evidence would be that you show in a laboratory using prebiotic conditions, a really simple way to get life.

1:05:10Or it's a harder proof to prove that given, because we don't know what all the possible pre -coded bioconditions, and probably the number was vast. You have 10 to 20 liters of water, and it varies different solenities and drying up on the ocean, and the sun, and the lightning, and all this stuff. But yes, I think if you show reconstructed in the lab a very simple pathway from inorganics, cyanide derivatives, and reduced compounds all the way up to some cellular replicating structure, I think that might be the least life exists. Now, there are other parts of the Drake equation that might kick in, which is maybe it's hard to get intelligent life, because intelligent isn't necessarily in your best interest.

1:06:10And if you get intelligent life, it's hard to maintain that without societal collapse or without robotics taking over and then killing themselves. And that's hard to do experiments. But I think to your question, I think, an experiment that showed, you know, maybe multiple different ways of getting to a living system from non -loving systems spontaneously would be interesting. Again, I'm not sure it would be very hard to prove the negative. So I'm curious between intelligent life and some sort of primordial RNA thing. Yeah. What is the step at which, if there is any, where you say there's a less than 50 % chance, something like at this level exists elsewhere in the Milky Way.

1:06:57Yeah, I think these are very challenging problems. I'm not even sure we would be able to say within five words of magnitude, much less 50%. But I think it's more likely to come from exploration than it is going to be from simulation.

1:07:23The sad truth is that almost none of the missions that we sent outside of Earth have actually looked for life. They've had components that could have looked for life, but a sad number of those in a not enough components that could look for life and the ones that could look for life, not really looking for it. And when we get positive results, we dismiss them as tapped with the pioneer. And so I think if we just start looking at the, you know, the geysers that are coming out of various moons of super and Saturn, there's so much water, there's 50 times more water, liquid water, not frozen, more liquid water in our solar system than in Earth.

1:08:12Doesn't that seem likely that, you know, some of that would have been a good breeding ground. But it could be that we need sunny shores, you know, where you have a lot of dry land, right next to water. Maybe these are just giant oceans that are surrounded by ice, and maybe that's not an idea. But in any case, we need to look at those founts to see what's popping up. That's a high priority. And the same thing goes, you know, for, you know, there's a lot of water on Mars that's maybe even more accessible. But until we've exhausted those, I think those are probably the easiest, they're hard. You're still talking about multi -billion dollar experiments.

1:08:58But I think they're a little more convincing. And again, it'll be hard to prove the negative. If we find this negative on every, everything in the solar system, you know, there's so much more diversity out there that could have done it. Even a thousand years, we're still using DNA and RNA proteins for top end manufacturing, the frontiers of engineering. How surprised would you be? Would you think like, oh, that makes sense? Evolution designed these systems for billions of years. Would you think like, oh, it's surprising that these ended up being the systems that whatever evolution found just happened to be the best way to manufacture or to store information or.

1:09:37Yeah, I don't think I'd be surprised either way. I make an imagine it going either way. I can imagine making truly amazing materials using proteins as the catalysts, or maybe in some cases as a scaffold, as well as catalysts. I think one thing that's probably already happening, so we don't have to go a thousand years out is the number of amino acids is going up. It's going up radically from 20. I think pretty soon we'll have a system where we can have 33, 34 new non -staylorine amino acids being used simultaneously with all the standard ones in an E. coli cell. 34 plus 20 is a lot bigger than 20.

1:10:18I don't think we necessarily need more than four nucleic acid components. I mean, you certainly have plenty of modified ones. There's a bunch of alternative base pairs, some of which don't even involve hydrogen bonds, so we could have more. But I think the main thing is this information storage, and whether it's bits, digital binary is just zeroes of ones. That works pretty well for 99 % of what we do electronically. So having four is better than two maybe, but do we really need six? I don't know. So yeah, I wouldn't be surprised if we had another possibility is that we changed the backbone of DNA.

1:11:05So maybe keep the ACGT, but make it peptides now. A little bit smaller, a little bit more compatible. I don't know. Or maybe that'll just be just a slight, you know, it could be part of the new amino acid collection. And there'll be more. I mean, these are just things that my primitive 21st century brain is coming up with 1000 years from now, it'll be a whole new millennium. So it makes sense when evolution wouldn't have discovered like radio technology, right? But things like more than 20 million acids or these different bases so that you can have store more than two bits per base pair. Or for example, the code code on remapping scheme, this redundancy, which it seems like based on your work, you can, there was this extra information you could have used for other things.

1:12:02So is there some explanation for why four billion years of evolution didn't already give living organisms these capabilities? I think that the evolution has tendency to go with what works and the investment in making a whole new base pair would have been high. And we haven't even articulated what that, what the return on investment would be. What do you get from that? We have made systems like Floyd, Roinsburg and others that where you have replication and transcription and translation with this, with a second with a new base pair. But it hasn't clearly articulated what that gets you. Even in technological society, so in technology, you can jump to things where all the intermediates aren't incrementally useful with evolution as far as we know, generally limited to you have to justify every change.

1:13:04It's like some bureaucracies. You put this side walk in, you have to justify that before you've been built a city. What is one, so we've talked about many different technologies you worked on or are working on right now from gene editing to the extinction to age reversal. What is an underhyped technology in research portfolio, which you think more people should be talking about, but gets glossed over? It's hard to say because as soon as you say it, it becomes hyped. If I've ever been asked this question before, it's too late. I would say one thing I think is very ripe and is very well understood in a certain sense, but it's nevertheless ignored.

1:13:52It's kind of like the previous example I would have chosen was making genes out of arrays. Arrays were typically used for analytic, quantitating RNAs or something like that, the original aphometrics type of arrays, but we turn them into gene arrays. People weren't using it. It was in nature, it was hidden in plain sight. It was somehow underhyped. What I would say is genetic counseling is underhyped. It is clearly competitive with gene therapy in a certain sense. Clearly not for people that are already born, but for people in the future. Not even distant future in the next couple of years. We've got a chance of diagnosing them or diagnosing the potential parents and dodging.

1:14:48This has been in practice since 1985 in Doria Shureem. Perfectly reasonable community response to it, eliminated or greatly reduced all sorts of very, very serious inherited diseases. It's sometimes, depending on how it's presented, it's dismissed as eugenics. I think it's rarely that I heard Doria Shureem describe that way. And rightly so, what they're doing is standard medicine. Whether you cure these kids as soon as or newborns or whether you counsel the parents, so the same disease is missing. The problem with eugenics was that it was forced, the government forced it on people. It wasn't that it enabled people to make a choice.

1:15:40Is it it removed the choice from the people? That was what was wrong. And that's the confusion. But I don't think that's explanation for why this is underhyped. I think it's people, when they're dating, they're not thinking about reproduction necessarily. And when they're thinking about reproduction, they're not necessarily thinking about serious genetic diseases, because they're rare. I think it's difficult with dealing with rare things. It's like there was great resistance to seatbelts, because less than 1 % of people died in all of the old accidents, or even got hurt. Great resistance to stopping smoking.

1:16:21Really, it's hard even for some magic how great the resistance was for seatbelts and smoking. But eventually we got over it. I think this is a similar thing, which is that only 3 % of children are severely affected. My genetic disease doesn't actually feel like, well, I'm not that unlucky. I'm in the 97%. 97 % of those were your odds of winning at the horror races or the casino. You take them. 97 % of winning good. But with when a child's future is at risk, I think that's not the right solution. And the other thing is I think has to do with a trolley problem. It's like if you don't influence it, it's not your fault.

1:17:08But actually everything is your fault. Not doing something is a decision. And so I think it's like, if I just don't do anything and they come out damaged, well, it's not my fault. But it is. Yeah. Dude, right. We're talking about how in India, especially because of the long running history of caste and endogamous coupling, that they're having these small soft populations that have high amounts of excessive diseases. And so like there, it's especially valuable intervention. I think I said, yeah, I know what you're saying and what David is saying. But I think it's a dangerous dichotomy. You know, they'll say there's certain, there are lots of not just India, you know, all over the world.

1:17:50And in fact, and with fact, we all went through a bottleneck. No, but that changes the rate from say 3 % to 6%. But the point is 3 % is still acceptable. I mean, it's just it's just a tragic loss, not only of the human life directly affected, but the whole family. Is very often one or both parents have to quit their job and spend full time like caregiving and fundraising because it's very, these are very expensive diseases as well. And it's just we don't need to, we need to be careful not to stigmatize as well. So when if a bunch of families get fixed, we shouldn't point a finger at the ones that are unwilling to get fixed because that's their choice, you know.

1:18:43But I think as as word spreads and you see the positive outcomes, I think there will be it will be seed as as one of the simplest bits of medicine ever. I mean, it's in fact, it's something. It's vaccination. Yeah, it's like fine. It's very inexpensive. In fact, in fact, it's it's less than zero because you spend $100 per genome and it'll probably be less soon. And you get the whole thing analyzed. And you know, compare that to millions of dollars that would be lost opportunity cost and not being part of the workforce that taking care of them and so forth. So the return on investment is tremendous.

1:19:29It's at least a tenfold return on investments. It's not it's a no brainer from public health standpoint. We should be able to pay for this through, you know, national health services in England through insurance companies, United States. And it turns the insurance companies from being the bad guys that they're that they're like prup you're snooping in on your personal life and then raising your rates to, oh, they're giving you this free information and you can do what it is you wish. And you could if you take the advice, then you save them millions of dollars. Right. Do you think genetic counseling is a more important intervention or even in even in the future, we'll continue to be have a bigger impact than even gene therapy for these.

1:20:09I've actually counseled my gene therapy companies that that they should be investing in very common diseases because rare diseases have the genetic counseling solution with the exception of spontaneous mutations and dominance, which probably are IVF clinic type solutions rather than but but the rare recessives can be handled matchmaking at every level. I mean my counsel, my genetic therapy companies that they should invest in common diseases like age related diseases and infectious diseases. And in fact, you know, the COVID vaccine was formulated as a gene therapy and was, you know, the cost was in the, you know, $20 per dose range and 6 billion people benefited from it or 6 billion people took it and, you know, and it was, you know, proven over the whole population.

1:21:11So I think that's the more appropriate usage in therapy but I think for practical reasons, you know, getting FDA approval and so forth, you might go for the rare diseases and that's it's perfectly fine. But I think the cost effectiveness of the sweet spot for gene therapy is for age related diseases and the sweet spot for rare diseases is genetic counseling. All right. So I have some final questions to close this off. If 20 years from now, if there's some scenario in which we all look back and say, you know what, I think on net, it was a good thing that the NSF and the NIH and all these budgets were blown off and got doged and so forth.

1:21:59I'm not saying you think this is likely, but suppose there ends up being a positive story told in retrospect, what might it be? Would it have to maybe become up with a different funding structure? Basically, like, yeah, what is the best case scenario if this post -war system of basic research is upended? I have to preface this by you know, when scientists explore, answer a question, explore possibilities. It doesn't mean they're advocating it. In the past, people have asked me off the wall questions about Neanderthals, for example, and then it was described as if I was enthusiastic about it. So not enthusiastic about NIH and NSF budgets being cut.

1:22:44You could say, well, it forces us to think more seriously about philanthropy and industrial sponsored research. That could be a positive thing. It could be that that makes us listen more carefully to what society actually needs, rather than doing basic research. I'm a big proponent of basic research, but also maybe I'm more than average connecting the basic research to societal needs from the get -go. It does, I don't think it actually interferes with basic research to think and act on societal needs at the same time. So that could be a positive. It could be that it creates another nation state that now is the dominant force, like China could now become the next empire after.

1:23:34This is a positive story. What could be for China? He didn't specify who it's a positive story for. The US displaced Britain, which was displaced, Spain and Portugal, it keeps moving. Fresh blood is sometimes a good thing. Again, I prefaced a saying, I'm advocating this. What else could go well? There's just certain things that the society is fairly good at doing collectively that we're not good at doing individually. Building roads, schools, and science, or examples of that, doesn't mean we couldn't learn how to do that. Something said, when you build a gated community, a lot of that is done with private funding.

1:24:23It's possible we could figure out how to build roads and schools. Just about everything, it means we're going to run into some kind of hyper capitalism that might mean there's all kinds of pathologies that come along with that. What is it about the nature of your work, maybe by all the way, generally, that makes it possible for one lab to be behind so many advancements? I don't think there's an analogous thing in computer science, which is I feel that I'm more familiar with where you could go to one lab and one academic lab. Yeah, sorry, one academic lab. Then a hundred different companies have been formed out of it, including the ones that are most exciting and doing a bunch of groundbreaking work.

1:25:11Is it something about the nature of your academic lab? Is it something about the nature of biology, research? What explains this pattern? First of all, thank you for being so generous in your evaluation, which may be taking with great assault. But I think that what it is being in the right place at the right time, so boss it is unique culture. It attracts some of the best and brightest students and postdocs automatically. It is dense enough. Sometimes people want to spread the wealth out evenly all over the universe or the planet. There's advantages to having it clustered. If you have spouses can find other jobs in the same field.

1:26:01So having a concentration of biotech and pharma and MIT and Harvard and BU and so for all in one, pretty walkable distance, not spread out all along the east or west coast, but actually walkable city is one thing. That's the starting point. Then a lab that chooses from an early stage to keep this dynamic between basic science and societal needs going at all costs, causing great trauma when the lab starts. But then getting a couple of wins and it starts building up a positive feedback loop where just like the building of Boston was a positive feedback loop, the more Harvard's and MIT's and high tech startups than pharma.

1:27:04So you get a couple of wins in the literature and people start coming that are a whole other level up on it and maybe they're already aiming for entrepreneurship while before they weren't. It evolves in a way that you can't just jump start from. You couldn't suddenly create Harvard and MIT in the middle of the desert and suddenly create a lab that is taking these risks early in a career.

1:27:39Also the timing is good because the exponential is starting to show up. The exponential is pretty much the same in the beginning of the hockey stick and the end of this happening is both the computing AI biotech. They're all peaking at this point. So whichever lab happened to already have that positive feedback loop going with the academic industry technology transfer would asymmetrically benefit from that exponential. To some extent, exponential, you can really look like you're very productive when really you're just kind of sliding down hill. It's like, yeah, looking out productive, I am, I just jumped out of a plane and I accelerated steadily.

1:28:36So yesterday I had a dinner with a bunch of biotech founders and I mentioned that I was going to interview you tomorrow. So somebody asked, wait, how many of the people here have worked in Georgia's lab at some point or worked with them at some point? And I think 70 % or 80 % of the people raised their hand. And one of the people suggested, oh, you should ask him, how does he spot talent? Because it is the case that many of the people who are building these leading companies or doing groundbreaking research have done, have been recruited by you, have worked in your lab. So how do you spot talent?

1:29:09I'm glad you framed it as spotting talent. I've heard at least one meme that, oh, you have to do a show up and you'll get into my lab, which is definitely not true. First of all, there's a lot of self selection. Frankly, we're an acquired taste. You know, technology development is not at all the same skill set as regular biology, where you, you know, you pick a gene, you pick a disease, you pick a phenomenon and you hammer away at it for your whole life. This is more you make a library where you have, you know, a million members of the library are going to fail and maybe one or two will succeed.

1:29:51Very different attitude. You know, it's much more engineering, but it's even different from most engineering, where, you know, engineering doesn't usually use libraries that way. Millions and billions of components that are, you know, non -random, but many of them will fail. Yeah. So the question is selection criteria. So of that, there's a self selection, and the next thing is in the interview, I typically tell them, I'm looking at people that are nice. I'm not in the solo looking for geniuses. We end up with a lot of geniuses. It's wonderful, but nice, I think, is highly predictive of how well you will do in the lab and afterwards.

1:30:38And as a cusp, once I think we have a, you know, kind of international set of alumni, they're quite nice to each other, even though they're supposedly in cutthroat fields. And I think they're nice to other people as well. So that's nice is one criteria. Multi -disciplinary is hard to build in a multi -disciplinary team from disciplinarians. So if you have two people that each know two languages or two skills, even if they don't have anything in common, they have shown that they can learn a new skill, and then they'll each add a new, the skill that connects them as a third thing. So those are the three main things I would say.

1:31:24Final question. Given the fast pace of AI progress, your point taken that we should be cautious of the technology, but by default, I expect it to go quite fast, and they're not being some sort of global moratorium on AI progress. Given that's the case, what is the vision for we're going to have a world with, we're going to very plausibly have a world with like genuine AGI within the next 20 years. What is the vision for biology given that fact? Because if AI was 100 years away, we could say, well, we've got this research for doing with the brain or with gene therapies and so forth, which might help us cope or might help us, you know, stay on the same page.

1:32:06Given how fast AI is happening, what is the vision for this biotei co -evolution or whatever it might look like? I think one scenario, unlike if we handle the safety issues and that has to be a top priority, if we handle that properly, then we're probably going to have almost perfect health. Why, why, why wouldn't we? You know, it's going to go so fast. And I mean, it's going to go pretty fast with just regular AI without AGI, but if you add to it AGI, and it'll be a positive feedback loop because the more people they get fixed, you know, or get access to good health care, the more people will be helping prompt the AI if that's necessary.

1:32:51And I think it probably will be and the more hybrid systems will have of people and and machines working together in harmony. Hopefully. In this very positive scenario. Yes. Well, that's a good vision to end on. Okay. George, thank you so much for coming on. Yeah, thank you. I hope you enjoyed this episode. If you did, the most helpful thing you can do is just share it with other people who you think might enjoy it. Send it to your friends, your group chats, Twitter, wherever else. Just let the word go forth. Other than that, super helpful if you can subscribe on YouTube and leave a five star review on Apple podcasts and Spotify.

1:33:29Check out the sponsors in the description below. If you want to sponsor a future episode, go to doarkesh .com slash advertise. Thank you for tuning in. I'll see you on the next one.