A billion years of evolution in a single afternoon — George Church

George Church is the godfather of modern synthetic biology and has been involved with basically every major biotech breakthrough in the last few decades. Professor Church thinks that these improvements (e.g., orders of magnitude decrease in sequencing & synthesis costs, precise gene editing tools like CRISPR, AlphaFold-type AIs, & the ability to conduct massively parallel multiplex experiments) have put us on the verge of some massive payoffs: de-aging, de-extinction, biobots that combine the best of human and natural engineering, and (unfortunately) weaponized mirror life. Watch on YouTube https://youtu.be/olmHHxFQwxo; listen on Apple Podcasts https://podcasts.apple.com/us/podcast/dwarkesh-podcast/id1516093381?i=1000714690480 or Spotify https://open.spotify.com/episode/7i0apPkNdLsZPfUulrAzHu?si=d3e1c0312c0c4a9b. Sponsors * WorkOS Radar https://workos.com/radar ensures your product is ready for AI agents. Radar is an anti-fraud solution that categorizes different types of automated traffic, blocking harmful bots while allowing helpful agents. Future-proof your roadmap today at workos.com/radar https://workos.com/radar. * Scale https://scale.com/dwarkesh is building the infrastructure for smarter, safer AI. In addition to their Data Foundry, they recently released Scale Evaluation, a tool that diagnoses model limitations. Learn how Scale can help you push the frontier at scale.com/dwarkesh https://scale.com/dwarkesh. * Gemini 2.5 Pro https://aistudio.google.com/https://aistudio.google.com/ was invaluable during our prep for this episode: it perfectly explained complex biology and helped us understand the most important papers. Gemini’s recently improved structure and style also made using it surprisingly enjoyable. Start building with it today at https://aistudio.google.com To sponsor a future episode, visit dwarkesh.com/advertise https://www.dwarkesh.com/advertise. Timestamps (0:00:00) – Aging solved by 2050 (0:07:37) – Finding the master switch for any trait (0:19:50) – Weaponized mirror life (0:30:40) – Why hasn’t sequencing/synthesis led to biotech revolution? (0:50:26) – Impact of AGI on biology research progress (1:00:35) – Biobots that use the best of biological and human engineering (1:05:09) – Odds of life in universe (1:09:57) – Is DNA the ultimate data storage? (1:13:55) – Curing rare diseases with genetic counseling (1:22:23) – NIH & NSF budget cuts (1:25:26) – How one lab spawned 100 biotech companies This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com https://www.dwarkesh.com?utm_medium=podcast&utm_campaign=CTA_1

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Dwarkesh Patel?

  Today, I have the pleasure of interviewing George Church. I don't know how to introduce you. It would honestly, this is not even a exaggeration. It would honestly be easier to list out the major breakthroughs in biology over the last few decades that you haven't been involved in. From the Human Genome Project to CRISPR, age reversal to de-extinction. So you weren't exactly an easy prep.

• 0:21Speaker 01

  Sorry.

• 0:21Dwarkesh Patel?

  Okay, so let's start here. By what year would it be the case that if you make it to that year, technology will keep, in bio, will keep progressing to such an extent that your lifespan will increase by a year, every year, or more?

• 0:34Speaker 01

  Escape velocity is sometimes what is called for aging. Different people have estimates, and all those estimates are, including mine, are going to be taken with a big grain of salt. I think that looking at how, mainly looking at the exponentials in biotechnology and the progress that's been made in understanding, not just understanding causes of aging, but seeing real examples where you can reverse subsets of the aging phenotype. You know, so you're getting close to all of aging. In other words, you're seeing, instead of just saying,

• 1:12Speaker 01

  "Oh, I'm going to fix the damage in this collagen, in this tendon, in this limb", you're saying, "Oh, I'm going to change a lot of things that are common to aged-related diseases, and I'm going to get more than one at a time." I think looking at those two phenomena, the exponentials in biotechnologies and the breakthrough in general aging, not just observa, not just analysis, but synthesis and therapies. And a lot of these therapies are now making in the clinical trials. I would not, wouldn't be surprised if 2050 would be a point, if we can make it to that point, 25 years.

• 1:56Speaker 01

  Most people listening to this have a good chance of making it 25 years. And the thing is, it's not going to be some sudden point where you're going to be, you know, so sick 25 years from now that it's like hit or miss. It's more likely that you're going to be healthier 25 years from now than you thought you were going to be. There may be some, probably not some law of physics, but some...

• 2:20Speaker 01

  Economic or complexity issue that we don't know about that that becomes a brick wall. I doubt it seriously, but we'll have to see.

• 2:30Dwarkesh Patel?

  Given the number of things you would have to solve to get, give us a lifespan of humpback whales.

• 2:36Speaker 01

  Bowhead, bowhead whales, yeah.

• 2:38Dwarkesh Patel?

  Sorry, yeah.

• 2:38Speaker 01

  Two hundred years, yeah.

• 2:39Dwarkesh Patel?

  Is there any hope for doing that from somatic gene therapy alone, or would that have to be germline gene therapy?

• 2:45Speaker 01

  Probably there's a lot of forces pushing it towards somatic. For one, there's eight billion.

• 2:52Dwarkesh Patel?

  Right.

• 2:52Speaker 01

  People that have missed the germ line opportunity.

• 2:55Dwarkesh Patel?

  Yeah.

• 2:56Speaker 01

  As I say, doesn't apply to us, the two of us and everybody listening to this. And, you know, you have to be very cautious when you say something's impossible. It's it's safe to say it's impossible to do it this second, but you don't know what's going to happen tomorrow and the next decade or something. So I, I, I think there's a lot that could be done, in particular, since aging is a fairly cellular phenomenon with proteins going through the blood and other factors going through the blood that signaling and so forth. You could imagine if you replaced, let's say, every cell in the body, every nucleus in the body, you know, it would, it would suddenly be young again, right?

• 3:40Speaker 01

  Without going all the way back to the embryo and forward again. And there's various other things that are just short of that. If you, if you replace the cells, will they, you know, they'll fit into that niche? They might displace the old cells, you know, that's within, certainly within the realm of modern synthetic biology is to, is to, for cells to take over niches. I think the hardest part is brain, but even there, you know, there's some evidence that if you bring, even though the brain doesn't really use stem cells that much, you could artificially bring in stem cells and they could artificially fit into a circuit and learn the, the circuit and then displace the old ones in some way.

• 4:25Dwarkesh Patel?

  Ship of Theseus kind of thing in the brain?

• 4:26Speaker 01

  Yeah, exactly. Ship of Theseus, having.

• 4:30Speaker 01

  You know, trying to maintain the connections and the memories. Uh, but, but you know, there's some fairly straightforward experiments that need to be done before we can really even estimate how, how hard that problem is. Or, you know, very often there's low hanging fruit that people just think is improbable, but it's there because biology has all these gifts that, you know, where the, uh, just hands over to us, uh, levers that we can flip, like, like vaccines is amazing gift. It didn't have to exist, but they, they do.

• 5:03Dwarkesh Patel?

  Yeah. Is there an existing gene delivery mechanism which could deliver gene therapy to every single cell in the body?

• 5:10Speaker 01

  There is nothing close to that today, but there's nothing, no law of physics that would prevent it. You know, there's, there's going to be practical considerations, you know, like, you know, how many injections do you need to do, um, to get, to achieve that goal? But we're getting better at targeting tissues, you know? So for one of my companies, Dyno Therapeutics, um, showed they could get a hundredfold improvement in targeting, uh, neurons in the brain, which is a big deal. Now, if, and that was just one little campaign that they did, you know, uh, one experiment

• 5:50Speaker 01

  involves a lot of AI and a lot of, uh, testing of millions of different, uh, capsules. If you did that with cells or capsules are, are fairly limited in the diversity and the, and the structure that it can, can change to, but cells that have even more, uh, possibilities, I think you could probably get delivery to everything. You know, and the question is how close to a hundred percent do you need to get? Right? And it's going to vary from tissue to tissue. Um, you know, some, yeah, for example, for some therapies, you just need to get one percent because that one percent can produce some missing enzyme. And the one percent doesn't have to necessarily be in its normal place, right?

• 6:30Speaker 01

  You know, you can, you can turn a muscle into part of the immune system temporarily for a vaccine. You can, you know, an enzyme that's normally made in, let's say, the brain, you could make in the liver, right? If the point is just to get it into the blood. So I think we're, that's moving along quite well.

• 6:52Dwarkesh Patel?

  Hmm. You're one of the co-founders of Colossus, which recently announced that they de-extincted a dire wolf. And now you're working on the woolly mammoth.

• 7:00Speaker 01

  Yeah.

• 7:01Dwarkesh Patel?

  Do you really think we're going to bring back like a woolly mammoth? Or how, because like the difference between an elephant and a woolly mammoth might be like a million base pairs. So how do you think about what is the, how do you think about the kind of thing we're actually bringing back?

• 7:13Speaker 01

  Well, so, so I think, I think people get worked up about, you know, whether we, whether we are trying to bring back or have already, or will ever bring back a new species. And, and I think of it, if you think of it rather than as a natural thing that we're trying to do, but as a synthetic biology with goals that have potential societal. And people also get worked up as to whether this could possibly benefit society in any way. You know, can we really, you know, fix an environment to suit humans? Or fix the global carbon to suit humans? And the answer is we don't know, but it's worth a try, isn't it?

• 7:56Speaker 01

  Because it's, could be very cost effective. And the other thing, the other aspect of it is there's a whole discipline within synthetic biology of asking, what's the minimum? Right? And so people often phrase it into what's the maximum, you know, like what can we do? And I'm interested in both, but you know, it's like, oh yes, there's a millions of difference between mammoths and elephants. There are millions of difference between elephant one and elephant two within, within Asian elephants and between Asians and Africans. But not all of those are definitive in terms of what we would normally call them, you know, you know, how we would normally classify. By them, what their functionality would be in an ecosystem, right?

• 8:39Speaker 01

  And so, so there's this exercise that people do, and we've done it, for example, with developmental biology. What's the minimum number of transcription factors it takes to make a neuron from a pluripotent stem cell, right? What's the minimum number of base pairs it takes to make something that will replicate to something that, you know, was done in mycoplasma originally? Yeah. And these are, in a way, these are more interesting than can we make a perfect copy of something, right? It's can we make, what's the minimum things we have to do to make it completely functionally? Or even functionally in a particular category, right? How do we make it bigger?

• 9:19Speaker 01

  We learn the rules for how to make things bigger, how to make things replicate faster, how to, you know, how to use new materials, et cetera. So I think what the dire wolf, we clearly didn't make an exact copy of a dire wolf. But the, but it helped illustrate kind of educated people around the world that, what is the difference between a wolf, a gray wolf and a dire wolf, right? Because, you know, dire wolves, they're, they're big, maybe they have a particular coloration. You know, you know, the heads, head components tend to be bigger than the, than the leg components.

• 9:57Speaker 01

  And so how many, how many genes you need to do that? Maybe this was dire wolf, you know, 2.0, and we're gonna go for, go for 3.0 and in successive approximation. And we might want to develop the technology for making an exact copy of something. Cause then we can, especially being able to make 100 variations on an exact copy. Cause then you, then there won't be any argument about whether you could make a dire wolf. It's a matter of whether, what, what should you make? And what would be most beneficial for the species that you're making, for the environment it lives in, and for humans?

• 10:33Dwarkesh Patel?

  Does this teach us something interesting about phenotypes which you think require are downstream from many genes are in fact modifiable by very few changes? Basically, could we do this to other species or to other things you might care about like intelligence where you might think like, oh, there must be thousands of genes that are relevant, but there's like 20 edits you need to make really to be in a totally different ballgame?

• 10:54Speaker 01

  Yeah, I think you're hitting on a very interesting question, and it's related to, you know, what's the minimum? So, for example, you almost said it, which was, you know, for, take a very multigenetic trait in humans, like height, is something that's probably the most well-studied one, simply because no matter what gene you're, no matter what medical condition you're studying, you collect information on height and weight and things like that. Anyway, they tracked it down to, on the order of 10,000 genes, of which we have 20,000 protein coding genes, and some of them are RNA coding genes.

• 11:35Speaker 01

  And they each have a tiny influence on height. But if you take growth hormone, serumatus tropin, that you have extreme examples where you'll get extremely low, small stature and extremely high stature do that one alone. And in fact, it's used clinically as well, for seven different medical treatments. So, that's a perfect example of how, how much we can minimize something, sometimes called reductionism. Reductionism isn't all bad. Sometimes it helps us bring a product into medicine.

• 12:19Speaker 01

  Sometimes it helps us understand or build a tool chest or a module that we can use in other cases and translate it to other species. So, you hit on it just right, is, is that not everything will translate, but we start accumulating these widgets. It's kind of like all the electronic widgets, so we accumulate it over time, that if you just want to slap it into the next circuit, you might be able to.

• 12:46Dwarkesh Patel?

  What implications does this have for gene therapy in general? Like, what is preventing us from finding the latent knob for like every single phenotype we might care about in terms of helping with disabilities or enhancement?

• 13:00Dwarkesh Patel?

  Is it the case that for any phenotype we care about, there will be one thing that is like HGH for height? And how would you, how do you find it?

• 13:08Speaker 01

  Biology, we, we've got a real gift, which is that it's both very, much more complicated than almost anything we've designed from scratch. But it also is a lot more forgiving in a certain sense is that you can have an animal or even a human that has two heads, which is not something that they evolutionarily, there was not evolutionary selection specifically to have two heads. But just a, just a little deviation from the normal developmental pattern during, you know, fetal development and, and they both function fine. They control subsets of the body.

• 13:48Speaker 01

  And, um, you know, they have their own personality, their own life. So this, you can, there's all kinds of things you can do in biology that where you're working at a very high programming level is a, is a way of thinking about it.

• 14:05Speaker 01

  Pushing us to a new level of intelligence is going to be very challenging. And, and maybe not even urgent. Okay.

• 14:17Speaker 01

  To some extent, actualizing the people that we currently have would be quite, you know, just getting them all up to whatever speed they want to be up to within the range that's been demonstrated. So like, some people are going to want to be like Einstein, some people won't.

• 14:34Speaker 01

  Some people want to be healthy all the time. Unlikely, but some people might not. Some people might want to live to 150. Some people might want to die at 80. But if you give them that, that range, that capability, you know, what if we had eight billion super healthy, don't need to worry about, you know, food and drugs. Super healthy Einstein level of intelligence.

• 15:03Speaker 01

  Education level, best we can come up with. That would be a completely different world. Right?

• 15:09Dwarkesh Patel?

  But just getting everybody to the healthy level, like how many? How much gene therapy would that take? It it it sounds like it wouldn't take that much if you think that

• 15:18Speaker 01

  Yeah.

• 15:18Dwarkesh Patel?

  there are these couple of knobs which control very high level functions.

• 15:22Speaker 01

  Yeah.

• 15:22Dwarkesh Patel?

  So do you find them through the GWAS genome wide association studies? Is it through like simulations of these?

• 15:29Speaker 01

  I would say mostly GWAS.

• 15:31Dwarkesh Patel?

  Okay.

• 15:31Speaker 01

  Uh, for humans, uh, maybe for animals in general. Followed for animals with synthetic biology, you know? Um, and the the smaller and the cheaper and faster replicating, the the more experiments you can do. So, you know, um,

• 15:52Speaker 01

  I don't want to over emphasize how single genes can do these amazing things. But there's also the possibility that multiple genes can be hypothesized and tested quickly. So, for example, I mentioned earlier, you know, what's the minimum number of transcription factors it takes to turn a stem cell into a neuron? Well, there's a bunch of recipes where you can do it with one, right? Maybe you want a specific neuron, you might need a few more. But then you can get, you can kind of quickly go to the answer by looking at each target cell type that exists. And you can see, well, what transcription factors

• 16:34Speaker 01

  did it use to get, that it had, this is expressed at the time that it's the target? And then you say, well, let's just try those on the stem cells if they work. And that recipe is is worked quite well. It's the it's the basis of GC Therapeutics, the company and and a bunch of the work that we do is you can almost, you could get a recipe for almost every cell type in the body. Now, that's not new cell types, but at least you can, you've learned of to to your point about reducing the number of genes we need to manipulate in order to get to a particular goal. Here's a whole series of goals and we can get them with one, two, three, you know, maybe seven change

• 17:14Speaker 01

  transcription factors. So, um, that's a example, uh, in.

• 17:21Speaker 01

  There's room for lots of other examples of where you can do a reduction and do not just reductionistic virality, but then constructionistic, where you take it back up and make a whole complex system and see what happens. And then you can do lots of those combinations and you debug them and so forth. Some of these things you can do, you know, in vitro things you can do on probably on the order of 10 to the 14th, 10 to the 17th. Things that involve cells typically in the billions. But we have this, this is how we're going to get inroads into the very complicated biological systems.

• 18:02Dwarkesh Patel?

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• 18:47Dwarkesh Patel?

  roadmap. If you start using Radar for traditional fraud protection, when you do ship your first feature intended for agents, you can just update Radar's behavior with no engineering required. Learn more about AI-native fraud prevention at workos.com/radar. All right, back to George. Can I ask you some questions about biodefense?

• 19:09Speaker 01

  Yeah.

• 19:10Dwarkesh Patel?

  Because some of the stuff you guys work on, or, you know, quite responsibly choose not to work on, can keep one up at night. Mirror life.

• 19:16Speaker 01

  Yes.

• 19:17Dwarkesh Patel?

  Given the fact that it's like physically possible, why doesn't it just happen at some point? Like someday it'll get cheap enough, or somebody will care about it enough, then somebody just does it. What's the equilibrium here?

• 19:27Speaker 01

  Right. You know, I was a co-author on a paper that warned about the dangers of mirror life, just like. You know, I wrote a paper long ago about the dangers of of having the synthetic capabilities we have for making synthetic viruses. And and to some extent of having new genetic codes, they have in a few things in common. But the thing about the the advance that we were recognizing in our science paper that was warning about mirror life is that we not only had to calculate what the possibility of error prone. You know, escape or something like that. We don't want anything to escape.

• 20:11Speaker 01

  That we made in the lab unless there's a general societal consensus, it's a good thing. And so far, there aren't too many examples of that. But aren't any examples of that. But mirror life, if it can be weaponized, then it we took it to a whole nother level of concern. And the and the concern was that if we got it to a certain point, then it would be easy to weaponize it. And again, this there's practical considerations that may be that most people who would consider weaponizing mirror life would probably be satisfied with weaponizing viruses that already exist, that are already pathogens. And they wouldn't want to destroy themselves and their family and and their legacy and everything like that.

• 20:55Speaker 01

  But all it takes is one, you know, one group probably or one person. But your question is, is it inevitable? I don't know, could might be, it's quite possible it's already here. In other words, we already have mirror life in in our in our solar system, or maybe even on our planet. It just hasn't been weaponized, right? And so it's just like what we were saying in the science paper is this seems like the sort of thing that could wipe out. All competing life, if we're properly weaponized. But there are probably a few things like that. And what we really need to do is reduce the motivation to do that.

• 21:39Speaker 01

  Maybe increase our preparedness for a variety of existential threats, some of which will be natural, some of which will be, you know, one disgruntled person who has essentially too much power because, you know, over history of humanity, the amount of things that a single person can do has grown very significantly. I mean, it used to be when you had your bare hands, there's kind of a limit to what one person could do. A large number of people could team up and get a, let's say, a mammoth or something like that. But yeah, but today, one person with the right connections

• 22:19Speaker 01

  or right access to technology, you know, could blow up a city, right? And that's a huge increase in capability. And I think we need to want to start dialing that back a little bit somehow.

• 22:34Dwarkesh Patel?

  And what does that look like in terms of not just mirror life, but synthetic biology in general? You know, maybe we're at an elevated period of the ratio to offense and defense. But how do we get to an end state where even if there's lots of people running around with bad motivations, that somehow there's defenses built up that we would still survive, that we're robust against that kind of thing? Or is such an equilibrium possible? Or will offense always be privileged in this game?

• 23:06Speaker 01

  Often, offense often does have an advantage, but so far we haven't, you know, we made it through the Cold War without blowing up any hydrogen bombs, as far as I know, accidentally or intentionally on enemies.

• 23:27Speaker 01

  We did two atomic bombs.

• 23:31Speaker 01

  But a lot of that is based on the difficulty of building hydrogen or atomic bombs. The thing that's alarming to people like me is that biotechnology enables smaller and smaller efforts, harder and harder to detect, harder and more and more subtle to the stochastic variation between people. You know, there's some people that are just so happy, they would, you know, they would never want to do anything close to that. Or they're so responsible or ethical or whatever. And then there are other people who like, whenever they have a bad day, they want to take a lot of people with them, right?

• 24:12Speaker 01

  And, you know, maybe some progress in psychiatric medicine would help. Again, you don't want to force that on people. You want to make sure that if they don't want to get cured, you can't force them, but you can make it available to them. That might help.

• 24:30Dwarkesh Patel?

  Hopefully, there's more technological solution or more robust solution than.

• 24:34Speaker 01

  Well, there are, there will be technological solutions to the psychiatric problem. It could be this, even people who aren't sure whether they want to be helped or not can test, try it out. And it's reversible and they say, yes, I like that better. Okay, let's try that. Then, then there's other things that cause you to have bad days. It's not just your psyche, it's also the environment. So if you, if you're surrounded by your people, you know, being starved or infectious disease or being shot at or something like that, those are things that are subject to sociological and technological solutions.

• 25:16Speaker 01

  And if we could really solve a lot of that stuff, we could reduce the probability that one person.

• 25:21Dwarkesh Patel?

  This is making me pessimistic because you're basically saying we got to solve all of society's problems before we get on our way about synthetic biology.

• 25:27Speaker 01

  Yeah.

• 25:28Dwarkesh Patel?

  Which I'm like, I'm not that optimistic about, like, we'll solve some of them.

• 25:31Speaker 01

  I'm not trying to reassure you. And, you know, we're having a conversation about what it takes and that might be. That's one scenario for what it might take.

• 25:42Dwarkesh Patel?

  You had a an interesting scheme for remapping the codons in in a genome so that it's impervious to naturally evolved viruses.

• 25:52Speaker 01

  Right.

• 25:53Dwarkesh Patel?

  Is there a way in which this scheme would also work against synthetically manufactured viruses?

• 25:58Speaker 01

  Much harder.

• 25:59Dwarkesh Patel?

  Yeah.

• 25:59Speaker 01

  Again, it's the the the offense has the advantage. We can make a lot of different codes.

• 26:07Dwarkesh Patel?

  Which will limit the transmissibility.

• 26:09Speaker 01

  Yeah, so so one interesting thing is is that there's only two chiralities. You know, there's the current chirality and the mirror chirality. But there's maybe 10 to the 80th difference codes. Now, some of them you might be able to take out all at once. Anyway, the codes coding space is a is a kind of more interesting space. And of course, it could get even more complicated than that because there, you know, the 10 to the 83rd is like based on triplet codons and and that sort of thing. But if they're quadruplet codons or the new novel alphabets and so on.

• 26:49Speaker 01

  But we're sort of getting into a, you know, a the cycle of of competition. It'd be better than nip it in the bud, which is, you know, why why are we? Why did we spend so much societal resources building up to tens of thousands of nuclear warheads? And now we've

• 27:12Dwarkesh Patel?

  Right.

• 27:12Speaker 01

  dialed it back to to mere thousand nuclear warheads. That's nice that we dial it back, but why do we waste all that time and money and?

• 27:21Dwarkesh Patel?

  Energy seems very dual use, right? So

• 27:23Speaker 01

  Oh, yes.

• 27:24Dwarkesh Patel?

  the mere fact that you're you like literally you are making sequencing cheaper.

• 27:27Speaker 01

  Yeah.

• 27:27Dwarkesh Patel?

  Will will just have this dual use effect in a way that's not necessarily true for nuclear weapons.

• 27:33Speaker 01

  Right, yeah.

• 27:34Dwarkesh Patel?

  And we want that, right? We want biotechnology.

• 27:36Speaker 01

  It's hard to to pound nuclear weapons into plowshares, as they say.

• 27:40Dwarkesh Patel?

  I guess I I am curious if there is some.

• 27:45Dwarkesh Patel?

  Long run vision where, to give another example, in cybersecurity, as time has gone on, I think our systems are more secure today than they were in the past because we found vulnerabilities and we've come up with new encryption schemes and so forth. Is there such a plausible vision in biology? Are we just like stuck in a world where offense will be privileged and so we would just have to limit access to these tools and have better monitoring? But there's no, there's not a more robust solution?

• 28:13Speaker 01

  You know, one of the things I advocated in 2004 is that we stop deluding ourselves into thinking that moratorium and voluntary, you know, signups to be good citizens is going to be sufficient. We need to also have surveillance and consequences and mechanisms for whistleblowers, you know, to make it easy for people to report things that they think are out of line. And we had, we had essentially moratoria and disapproval for germline editing, nevertheless, somebody did it. And a lot of people knew about it.

• 28:57Speaker 01

  So that was clearly a failure of the whole moratorium, voluntary and whistleblower components.

• 29:04Dwarkesh Patel?

  I worked for five years with only one defector. That's quite impressive.

• 29:08Speaker 01

  Okay. Half empty, half full. I'll give you that, but, but all it takes is one for some of these scenarios, right? And that's, and that's, so it would have been nice if the whistleblowers could have, you know, saved him the three years in prison by, you know, getting an intervention.

• 29:29Dwarkesh Patel?

  Yeah, yeah.

• 29:30Speaker 01

  I mean, it's not like anybody died, right? There are probably three healthy genetically engineered.

• 29:38Speaker 01

  Children in the world now.

• 29:39Dwarkesh Patel?

  Yeah.

• 29:40Speaker 01

  Be teenagers soon.

• 29:43Speaker 01

  But it's still, it shows, it was a good test run, shows a failure of the system. We need to have better surveillance of all the things we don't want and, and consequences that are well known.

• 29:56Dwarkesh Patel?

  Over the last couple of decades, we've had a million fold decrease in the cost of sequencing DNA, a thousand fold in synthesis. We have gene editing tools at CRISPR, massive, uh, parallel experiments through multiplex techniques that have come about. And of, of course, much of this work has been, um, led by your lab. Despite all of this, why is it the case that we don't have some huge industrial revolution, some huge burst of new drugs or some, uh, cures for Alzheimer's and cancer that have already come about? When you look at other trends in other fields, right? Like we have Moore's law and here's my iPhone. Why don't we have something like that in biology yet?

• 30:33Speaker 01

  Yes, so we have something that's about the same speed, a little bit faster than Moore's law in biology. It's more recent is one aspect of it. So we had, but we had, we could kind of stand on the shoulders of the electronics giants to, to go a little bit faster to catch up. I would say we do. I mean, we have the biotech industry, which, uh, has

• 30:59Speaker 01

  used that, uh, exponential curve to, to get better. It's also possible we're close to the big payoff, uh, is other aspects, or the beginning of the big payoff.

• 31:12Speaker 01

  You know, right now we have miraculous things like, um, cures for rare diseases. We have vaccines. We have, you know, trillion dollars probably of, of various biotech related things if you go far enough apart.

• 31:29Speaker 01

  But we're kind of on the verge of really, um, combining.

• 31:35Speaker 01

  Electronics and biology more thoroughly and AI and biotech. And I think that's, it seems like we're on the same track as Moore's Law, if not better.

• 31:48Dwarkesh Patel?

  What exactly are we on the verge of? What does 2040 look like?

• 31:51Speaker 01

  Well, 2040, we're talking about only 15 years, which is, you know, like one and a half, you know, maybe two cycles of FDA approval.

• 32:04Dwarkesh Patel?

  240 is post AGI. It's a long time.

• 32:07Speaker 01

  Well, I hope it's not post AGI. I think we're rushing a little bit to get to 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 could get into that.

• 32:23Dwarkesh Patel?

  I have questions for you there.

• 32:25Speaker 01

  You know, I think that we are shortening the time of getting medical products approved in still in a safe way. So I think, but that's not going to completely change the exponential. It will, you know, might reduce from 10 years down to one year is our record so far for, say, COVID vaccines. So maybe that'll be 10 times shorter. Maybe that that will multiply a little bit. But I think the big thing is that all our designs will become better. So there'll be fewer failures. The cost per drug will drop.

• 33:06Speaker 01

  There'll be things that we didn't classically consider drugs or instruments. Be kind of some sort of hybrid thing. But again, I don't think that'll be completely shocking. But it's just, it's just going to be so much of it, you know? It's just, it's going to be lots of diversity of solutions.

• 33:24Dwarkesh Patel?

  How much more are we talking? Is it, are we going to have 10x the amount of drugs, 100x?

• 33:28Speaker 01

  I'm not even sure it's going to make sense, but that, yeah, 100x would not be completely surprising. Combinations of drugs will be important. You're using them intelligently. There'll be a lot, lot more. Some drugs will affect everything. So for example, an age-related drug that could, that could impact every disease and it could.

• 33:50Speaker 01

  I'm not sure the number is going to matter so much as the the quality and the impact and intersection and and and software that helps physicians and other and regular citizens make decisions.

• 34:03Dwarkesh Patel?

  And what specifically is changing that's enabling this? Is it just existing cost curves continuing or is it some new technique or tool that will come about?

• 34:11Speaker 01

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

• 34:51Speaker 01

  like developmental biology, merger of developmental biology with with, you know, manufacturing and, you know, conquering developmental biology, in other words, actually knowing how to make any arbitrary shape, given, you know, DNA is the 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 and electrical engineering should be made better by biotechnologies.

• 35:26Dwarkesh Patel?

  Why is that?

• 35:28Speaker 01

  Why is that? Well, the electronics is, you know, more so, I wouldn't say is stopping, but it's it's kind of the the what we would call the one nanometer process, which is supposed to come out in 2027, according to the roadmap, is not really one nanometer, is more like 40 nanometers, center to center spacing, you know, in the in typically in two dimensions, maybe a little bit of three dimensions. But but biology is already at point four nanometer resolution, and it is in three dimensions. And so. You know, depending on how you count that third dimension, that could be a billion

• 36:10Speaker 01

  times higher density than 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 can't, it doesn't use the whole periodic table typically. But we, but especially not at the atomic level. So that's, I think biology is just really good at doing atomic precision.

• 36:34Dwarkesh Patel?

  So then what's the reason that over the last many decades, and we have, we do have, um, not atomic, but close to atomic level manufacturing with semiconductors?

• 36:44Speaker 01

  Forty nanometers.

• 36:46Dwarkesh Patel?

  Right. It's quite small.

• 36:47Speaker 01

  It's a thousand times bigger than biology, linearly.

• 36:51Dwarkesh Patel?

  But the, the progress we have made hasn't been related to biology so far. It seems like the, the, we've, we've made Moore's law happen. Um, I don't know, people in the nineties were saying, you know, ultimately we'll have these biomachines that are, uh, doing the computing. But it seems like we've just been using conventional, uh, manufacturing processes. What exactly is it that changes that allows us to use bio to make these things?

• 37:14Speaker 01

  A few things. One is the, the arrival of synthetic biology. Where you're, where you sort of, we were already kind of doing synthetic biology before, you know, we were doing recombinant DNA was kind of, you know, genetic engineering was called, was kind of in that direction. But synthetic biology really liberated us to think, um, a little bit bigger, um, even though it started kind of focused on E. coli and yeast, it, it, it enabled us to maybe think, uh, 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, um, that, that breaks one of the major barriers, one

• 37:59Speaker 01

  of the major barriers between. You know, electrical, mechanical engineering and biology was was the use of, you know, special materials, things that conduct electricity at the speed of light, or conducts signals more more generally. But there's there's absolutely, there's definitely polymers that biology can make that will conduct at the speed of light. And, you know, 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.

• 38:42Speaker 01

  Designing nucleic acids was great, whether we were doing, you know, you 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. It's not how developmental biology works, and we still need to figure out how that works, but DNA origami, DNA nanostructures really worked. But doing it for proteins was really, really hard until, I don't know, maybe eight years ago, something like that. And 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 thousand fold, what depends on who you talk to.

• 39:26Speaker 01

  So that when we came out with the first chip based genes in 2004 Nature paper, basically, people dismissed it for about a decade. The only people that used it were, you know, collaborators and alumni. And it wasn't even listed on the Moore's law curve for for DNA synthesis, even though it was like a thousand times cheaper. It was just like ignored. And and now we have claims of 10 to the 17th genes, okay, that you can make, you make libraries 10 to 17th that aren't randomized in any in any real, in the usual sense where you just like do air prone PCR spiked in nucleotides. Now, 10 to 17th, that's a lot bigger than a thousand fold, you know, if if, you know,

• 40:11Speaker 01

  if it turns out to be practical, yeah.

• 40:13Dwarkesh Patel?

  Publicly available data is running out, so major AI labs partner with Scale to push the boundaries of what's possible. Through Scale's data foundry, major labs get access to high-quality data to fuel post-training, including advanced reasoning capabilities. Scale's research team, SEAL, is creating the foundations for integrating advanced AI into society through practical AI safety frameworks and public leaderboards around safety and alignment. Their latest leaderboards include Humanities Last Exam, Enigma Eval, MultiChallenge, and Vista, which test a range of capabilities from expert-level reasoning to multimodal puzzle solving to performance on multi-turn conversations. Scale also just released Scale Evaluation, which helps diagnose model limitations.

• 40:58Dwarkesh Patel?

  Leading frontier model developers rely on Scale Evaluation to improve the reasoning capabilities of their best models. If you're an AI researcher or engineer and you want to learn more about how Scale's data foundry and research lab can help you go beyond the current frontier of capabilities, go to scale.com/thewarcash. Okay, so speaking of protein design, another thing you could have thought in the 90s is when people were writing about nanotechnology, Eric Drexler and 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?

• 41:42Dwarkesh Patel?

  Like, why didn't Alpha Foal cause that?

• 41:45Speaker 01

  I think part of it is that the nanotechnology as originally, 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, what was missing? What was motivating this reinvention of biology? It was material. So the biology's not that great with...

• 42:19Speaker 01

  You know, materials that are, say, superconductors or conductors, periods, semiconductors and light speed. But it's getting there. I mean, you know, rather than going the route of having everything has to be based on first principle nanostructures. You can meet in the middle where biology can build things. Now, of course, when you go down to, you know, liquid nitrogen in 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

• 43:04Speaker 01

  can build things that can operate at low temperature.

• 43:09Speaker 01

  Or maybe biology now because you can make these big libraries of biology, maybe 10 of the 17th in vitro.

• 43:17Speaker 01

  And you can flip through them quickly and you can barcode them and you can. This is something that's never been done in electronics. I'm not saying you can't do it in electronics, but you haven't made, you know, a billion different kinds of electronic materials, right? Just, you know, in an afternoon, barcode them all and see who wins, right? But we did it all the time in biology now, at least since 2004 we have. And so, so I think that's an opportunity is that we use those libraries to make much superior materials and we might even finally get a room temperature superconductor that way.

• 43:57Dwarkesh Patel?

  From bio?

• 43:58Speaker 01

  It's possible. I mean, from libraries. We call it chemical slash biochemical slash, you know, 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.

• 44:14Dwarkesh Patel?

  Do 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 alpha folds right now, right? So what is the thing that we need? Do we need more data?

• 44:26Speaker 01

  Well, I alpha fold is very nice, but but it's it's only part of it. So so there there are large language models that that are different from alpha. So give an example. Alpha fold. Last time I checked, anyway, these all change. If you substitute an alanine for a serine in a serine protease, it will have exactly the right fold. It will be precise to, you know, you know, a fraction of an angstrom overall average. But it won't function. It just won't function. And that's where you you need either extraordinary precision or just knowledge of what happens evolutionarily or happens in experiments to say that no, an alanine won't work.

• 45:11Speaker 01

  Okay. And so I think there's all kinds of combinations of AI tools that can give you deeper insight into that.

• 45:19Dwarkesh Patel?

  If alpha fold predicting the structure doesn't tell you whether the thing will actually function, then what is needed before I can say, I want a nano machine that does X thing, or I want a material that does that Y thing?

• 45:31Speaker 01

  Yeah.

• 45:31Dwarkesh Patel?

  And I can just like get that?

• 45:33Speaker 01

  I mean, I think the way it is working now, which which will get us a long way, won't get us the whole way, is we make is we have something that kind of works and we make libraries inspired by that. Make make variations on it. And then whichever of those variations work,

• 45:51Speaker 01

  we make variations on that and we can just keep going. It's kind of like the way evolution worked. Except now we can do it at incredibly high speeds. And in principle, you know, what might, you know, evolution might incorporate a few base pair 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. Another thing that's been missing,

• 46:35Speaker 01

  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 nonstandard 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 where you line up all the sequences of 20 amino acids. And we have very little experience with extra ones. But but I think there's a revolution going on in generating nonstandard amino acids that can where the amino acids can either have as part covalent part of them or as easily ligand did all the entire periodic table, stable elements.

• 47:20Speaker 01

  And and 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 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,

• 48:01Speaker 01

  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? You're not making assumptions, you know, you're not going from quantum electrodynamics, which is an assumption, to quantum mechanics, which is an assumption, to, 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 convinced, you know, the more conventional AI and do another round of it.

• 48:35Dwarkesh Patel?

  Yeah, it seems like. If I listen to these words, it seems like I should be expecting the world to physically look a lot different, but then why are we only getting like a couple more drugs by 2040?

• 48:45Speaker 01

  Well, I didn't mean to stop there. I mean, I knew the conversation would continue.

• 48:49Dwarkesh Patel?

  Right.

• 48:50Speaker 01

  I'm not pinning down a particular year either, but I think this is poised to go pretty quickly. There are very few practitioners is the 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, 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 labs brought out CRISPR, we just got 10,000 requests in the next two months for people that wanted to duplicate the system. And so that's, that's what I

• 49:31Speaker 01

  hope will happen with the non-sinor amino acids and the, and they're using AI for protein design and making new materials. Hopefully that will recruit tens of thousands of people overnight. Yeah.

• 49:42Dwarkesh Patel?

  Are you more excited about AI, which thinks in protein space or a cap set space or like just, you know, it's like predicting some biological or DNA sequences? Or are you more optimistic about just L uns trained 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 there some combination that when you think about AI in bio is more promising?

• 50:08Speaker 01

  I'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 AGI 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.

• 50:48Speaker 01

  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

• 50:56Dwarkesh Patel?

  Yeah.

• 50:56Speaker 01

  a completely foreign type of intelligence. I mean, we 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, if there ever is a crisis, it's because we created it. It's not because we're trying to solve it.

• 51:23Dwarkesh Patel?

  Yeah.

• 51:24Speaker 01

  Right? And so I think we need to go very slowly on AGI and ASI and, 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.

• 51:46Dwarkesh Patel?

  I suppose we did build safe superintelligence. How much would that speed up bio progress? It's just like, there's a, there's a million George churches on data, in data centers, just like thinking all the time. Is it, is it a 10x speed up?

• 52:00Speaker 01

  I think it would slow it down. I think, I think it would eliminate it because like, the first thing it would conclude is biology is not, not relevant to me because I'm not made out of biology.

• 52:09Dwarkesh Patel?

  I mean, suppose you could get them to care about it. There's just like copies of you, there's a million copies of you in a data center. How much faster is bio progress? But they can't like run experiments directly. They're just in data centers. They can, they can just say stuff and think stuff.

• 52:21Speaker 01

  I don't think we have anything close to the assurance that we need that that would be safe. But let's put safety aside for a moment. I think it's hard to, I think it's hard to, it's not only hard to calculate the, the bads, it's hard to calculate the goods. So I think it could completely be a complete game changer. But on the other hand,

• 52:40Speaker 01

  Um, you know, it's like if we said, um, you know, 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? Right? Is that really important? Maybe it'd be more interesting to just have Zoom calls and that are better, you know, or, or just learn how to get everything we want in our kitchen and we don't need to travel anymore.

• 53:07Dwarkesh Patel?

  Yeah.

• 53:08Speaker 01

  Right? You know?

• 53:10Speaker 01

  So, you know, be careful what you ask for, right? You know, because you could tip our priorities towards something that we really don't care about, uh, that where we shouldn't care about or, or might wish we didn't care about, right?

• 53:25Dwarkesh Patel?

  But, but I'm curious what, um, you've still got to run the experiments, you still need, um, these other things. So does that bottleneck the impact of the millionth copy of you? Or do you still get some speed up? How, how, how much faster can biology basically go if they're just like more smart people thinking, which is a sort of proxy for what AI might do?

• 53:44Speaker 01

  These are great questions and I'm not sure, I don't want to misrepresent that I know the answers, but, but you know, it's like, it's like the question of, you know, if you have nine women, can you do pregnancy in one month? No, not, not, not at present.

• 53:57Dwarkesh Patel?

  But you're working on that, right?

• 53:58Speaker 01

  No, no, no, no. I mean, just, but the same thing is, there may be certain things that, um, doesn't take a lot of people. We just don't know. We just don't, we don't have that much experience with having, uh, you know, thousands of Einstein type levels of creativity and intelligence simultaneously in a generation.

• 54:24Speaker 01

  And in fact, it's probable that we're all capable of being a bit more efficient if we don't have, you know, distractions of mental illness, uh, of, you know, taking care of other people. Now, taking care of other people may be a very good thing, you know? It may be that if we, we have no one to take care of, there'll be something bad that happens to us, uh, socially.

• 54:51Speaker 01

  So 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.

• 55:10Dwarkesh Patel?

  You worked on brain organoids and brain connectome and so forth. That work, how has it shifted your view on fundamentally how complex intelligence is? In the sense of like, how, you know, are you like more bullish on AI because I realize the 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?

• 55:35Speaker 01

  I think I always felt it was very gnarly. And I also felt that there was something that we could engineer. Certainly, we have made a lot of progress in, at the broken end of the spectrum where the brain is severely challenged relative to average. There, you know, there's thousands of, a huge fraction of genetic diseases that are, that have one of their consequences being that the child is developmentally delayed

• 56:20Speaker 01

  to such an extent that it's lethal or, you know, or a lifetime deficit. And we know how to, we know the genes involved, and we know how to do genetic counseling, in some cases, gene therapy and other therapies to deal with it. At the other end, you know, we have a reduction of cognitive decline by cognitive enhancement, which is showing some promise. But again, that's kind of like this early-stage severe impediment to cognition has a late-stage component. But what about?

• 57:04Speaker 01

  Uh, 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, uh, cause 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, um, it's speculative as to whether it would be easier to do that by making a copy of it in, in, in silicone, uh, in some kind of inorganic matrix, uh, or making a copy of it. Both of those are going to be hard. I would say that if you wanted to make a copy of a, you know, complicated book, it

• 57:48Speaker 01

  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. Uh, if, 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, we're just to define this 10 to the 14 synapses is going to take a lot more bytes than the, than the genome, which is billions rather than, um, 10 to the 14th. But you might, there might be reasons that you want to replicate a particular brain

• 58:24Speaker 01

  configuration rather than just, uh, make another, um, animal, uh, that is, um, you know, starts from scratch as a, as a, as an infant.

• 58:38Dwarkesh Patel?

  Given how little I knew about biology, my prep for this episode basically looked 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 Socratic 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. This is probably the single biggest change in my research. Process, honestly, since I started the podcast. For this episode, I think I probably spent on the order of 70% of my prep time

• 59:23Dwarkesh Patel?

  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. Going back to the engineering stuff, often people will argue that, look, you have this existence group that E. coli can multiply every or duplicate every 30 minutes. Insects can duplicate really fast as well. But then with our ability to manufacture stuff with human engineering,

• 1:00:09Dwarkesh Patel?

  you know, we can do things that no, nothing in biology can do, like radio communication, or fission power, or jet engines, right? So like, how plausible to you is the idea that we could have biobots, which are, you know, like, can duplicate at the speed of insects? And there could be trillions of them running around. But they also can have access to jet engines and radio communication and so forth. Are those two things compatible?

• 1:00:32Speaker 01

  Well, I mean, a certain things seem incompatible, like the temperatures of a fission reactor isn't obviously compatible. But, but, but the, the possibility that once we, that, that a biological system can make other things, you know, for example, it can, you know, it can make a nest. A bird can make a nest, okay? 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 nuclear reactor, as that would be its nest. But you need to, you know, expand this range of, of materials.

• 1:01:18Speaker 01

  In a certain sense, we do this already. Humans are a biological thing that replicates, not in 30 minutes, but in, you know, 20 years or, or less.

• 1:01:30Speaker 01

  And is that fundamentally limiting us? Yeah, probably is. But, but yes, it's amazing to think about. What if you could take, you know, a cornfield or a nuclear reactor? And suddenly 30 minutes later, you've got two of them, right? And then four of them. And yeah, I mean, that's, that's quite an interesting concept. But I mean, I think we should start with, I teach a course called How to Grow Almost Anything. That's that, and I work with Neil Gershenfeld, who at MIT, who had, has a course called How to Make Almost Anything. And we're trying to meet in the middle where we can, in, you know, his, you know,

• 1:02:12Speaker 01

  mechanical electrical engineering will meet with our biological. And in fact, neither of us can make or, or grow almost everything. Cause there's all kinds of little gaps and things that are very hard to make in a, in a small lab. Cause there's things all over the world that depend on, you know, multi-billion dollar fabs to, to make things. But, you know, we're eating away at it. I think that we might eventually be, you know, maybe a smaller baby step than making a nuclear reactor is making a, a phone. You know, he said radio communication. We should bake a biolot, it should be a small challenge goal for the synthetic biology community. Maybe iGEM or something. Make, you know, bacteria make a radio.

• 1:02:55Speaker 01

  Now, actually, Joe Davis is a, is a, a artist that's been affiliated with my lab and before that Alex Rich's lab. And, and he did bake a bacterial radio, but it was kind of more on the art end than on the science end. But I think that would be a good, good goal.

• 1:03:15Dwarkesh Patel?

  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.

• 1:03:32Speaker 01

  Yeah, right.

• 1:03:33Dwarkesh Patel?

  Is the bottleneck our understanding? Is the bottleneck our ability to make that many changes to my, to my genome?

• 1:03:40Speaker 01

  So part of this has to do with just learning the rules of developmental biology. Like I said, we can determine morphology at 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. So 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, you know, harnessing, migration, you know, gradients of factor, you know, diffusion factors, you know, chemotaxis and so forth. So I, that's one thing we need, but there's a bunch of things we need, really.

• 1:04:26Dwarkesh Patel?

  What discovery in biology, so not in astronomy or some other field, in biology, would make you convinced that life on Earth is the only life in the galaxy? And conversely, what might convince you that, no, it must have arisen independently thousands of times in this galaxy?

• 1:04:44Speaker 01

  Oh, I see what you're getting at, right? So astronomy might be, we would detect.

• 1:04:49Dwarkesh Patel?

  That's right.

• 1:04:51Speaker 01

  You know, radio signals or light signals.

• 1:04:54Dwarkesh Patel?

  Right.

• 1:04:55Speaker 01

  But biology, what you, the kind of evidence would be that you show in a laboratory using prebiotic conditions, a really simple way to get life.

• 1:05:09Dwarkesh Patel?

  Yeah.

• 1:05:10Speaker 01

  Right? Or, I mean, it's a harder proof to prove that, given, because we don't know what, all the possible prebiotic conditions.

• 1:05:21Speaker 01

  And probably the number was vast. I mean, you have 10 to the 20th liters of water and, you know, at various different salinities and drying up on the ocean and the sun and the lightning and all this stuff. But yes, if you, I think if you showed, kind of reconstructed in lab, a very simple pathway from inorganics, cyanide derivatives and reduced compounds, all the way up to, you know, some cellular replicating structure, I think that might.

• 1:05:56Speaker 01

  It might lead us to believe that at least life exists.

• 1:06:00Dwarkesh Patel?

  Yeah.

• 1:06:00Speaker 01

  Now, there are other parts of Drake equation that might kick in, which is maybe it's hard to get intelligent life.

• 1:06:07Dwarkesh Patel?

  Right.

• 1:06:07Speaker 01

  Because intelligence isn't necessarily in your best interest. And if you get intelligent life, it's hard to maintain that without societal collapse or without robotics taking over and then killing themselves. Right? 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-living system spontaneously would be interesting. Again, I'm not sure it would. It'd be very hard to prove the negative.

• 1:06:39Dwarkesh Patel?

  Mm hmm. So I'm curious, between intelligent life and some sort of primordial RNA thing.

• 1:06:45Speaker 01

  Yeah.

• 1:06:46Dwarkesh Patel?

  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?

• 1:06:56Speaker 01

  Yeah, 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%.

• 1:07:07Speaker 01

  But, you know,

• 1:07:13Speaker 01

  I think it's more likely to come from exploration than it is going to be from simulation.

• 1:07:21Speaker 01

  If, you know, the sad truth is that almost none of the missions that we sent out outside of Earth have actually looked for life. They've had components that could have looked for life.

• 1:07:39Speaker 01

  But a sad number of those, not enough components that could look for life and the ones that could live for life, not really looking for it. And when we get positive results, we dismiss them. As happened with the. Pioneer. And so I think if we just start looking at the, you know, the the geysers that are coming out of various moons of Jupiter and Saturn.

• 1:08:03Speaker 01

  There's so much water. There's 50 times more water. Liquid water, not not frozen, more liquid water in our solar system than in Earth. Doesn't that seem likely that, you know, some of that would have been a good breeding ground?

• 1:08:20Speaker 01

  But it could be that we need sunny shores, you know, where they have a lot of dry land and right next to water. Maybe these are just giant oceans that are surrounded by ice and maybe that's not added. But in any case, we we need to look at those fountains to see what's popping up. That's a that's a high priority. And the same thing goes, you know, for, you know, there's a lot of

• 1:08:45Speaker 01

  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, but I think they're a little more convincing. And again, it'll be hard to prove the negative if we if we find this negative on every, every, everything in the solar system. You know, there's so much more diversity out there that could have done it.

• 1:09:14Dwarkesh Patel?

  If in a thousand years, we're still using DNA and RNA and proteins for top end manufacturing, the 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? Or 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?

• 1:09:37Speaker 01

  Yeah, I don't think I'd be surprised either way. I can imagine it going either way. I can imagine making truly amazing materials using proteins as the catalyst, or maybe in some cases as a scaffold as well as catalyst. 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-standard amino acids being used simultaneously with all the standard ones in a E. coli cell. So 34.

• 1:10:16Speaker 01

  Plus 20 is a lot bigger than 20. I don't think we necessarily need more, more than four nucleic acid components. I mean, certainly there are plenty of modified ones. There's a bunch of alternative base pairs, some of which don't even involve hydrogen bonds. So we could, we could have more. But I think the main thing is this information storage. And whether it's bits, you know, this, you know, digital binary is just zeros and 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? You know, I don't know.

• 1:10:59Speaker 01

  So, so, yeah, I wouldn't be surprised if we had. Another possibility is that we change the backbone of DNA. So maybe keep the ACGT, but make it out of 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, and there'll be more. I mean, these are just things that, that my primitive 21st century brain is coming up with a thousand years from now. It'll, it'll be a whole new millennium.

• 1:11:37Dwarkesh Patel?

  So it, it makes sense why evolution wouldn't have discovered like radio technology, right? But things like more than 20 amino acids or these different bases so that you can have, store more than two bits per base pair. Or for example, the codon 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. So is there some explanation for why four billions of years of evolution didn't already give living organisms these capabilities?

• 1:12:09Speaker 01

  I think that, yeah, evolution has a tendency to go with what works. And the investment in making a whole new base pair would have been high. And, and we haven't even articulated what. What that, what the return on investment would be? Um, what do you get from that? We, we have made systems, uh, like Floyd Williamsburg and others that where you have replication and transcription and translation with this, with a sec, with a new base pair. But it hasn't been clearly articulated what that gets you. Um, uh, even in technological society, so in technology, you can, you can jump to

• 1:12:51Speaker 01

  things that where all the intermediates aren't, you know, incrementally useful. With evolution is, as far as we know, generally limited to, you have to justify every change. Uh, uh, it's like some bureaucracies says, well, if you're gonna, you know, you put this sidewalk in, you know, you have to justify that before you even build a city.

• 1:13:12Dwarkesh Patel?

  What is one, um, so we, we've talked about many, many different technologies you've worked on or are working on right now. Um, uh, from gene editing to de-extinction to age reversal. What is, um, what is an underhyped, uh, technology in our research portfolio, which you think more people should be talking about, but gets glossed over?

• 1:13:33Speaker 01

  It's, it's hard to say because as soon as you say it, it becomes hyped. So if I've ever been asked this question before, it's too late. But, um, you know, I would say, one thing I think is very ripe and is very well understood in a certain sense, but is nevertheless ignored. It's kind of like the, the, the previous example I would have chosen was been making genes out of arrays. Arrays were typically used for analytic, you know, um, quantitating RNAs or something like that. So the original affometrics type arrays. But we turned them into gene arrays and, and just, people weren't using it. Uh, it was in nature, it was, it was hidden in plain sight.

• 1:14:17Speaker 01

  But anyway, it was somehow underhyped. What I would say is genetic counseling is underhyped. It is clearly competitive with gene therapy in a certain sense. I mean, clearly not for people that are already born, but for people in the future, not even distant future, the next couple of years. We've got a chance of diagnosing them or diagnosing the potential parents and dodging. And this has been in practice since 1985 in Doria Shireem. Perfectly reasonable community response to it, eliminated or greatly reduced all sorts

• 1:15:01Speaker 01

  of very, very serious inherited diseases. It's sometimes, you know, depending on how it's presented, it's dismissed as eugenics. I think it's rarely that I heard Doria Shireem described that way, and rightly so. What they're doing is standard medicine, you know, whether you, you know, cure these kids as soon as they are newborns, or whether you counsel the parents so that 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, it's that it removed the choice from the people. That was what was wrong. And that's the confusion sometimes.

• 1:15:44Speaker 01

  But I don't think that's the 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, you know, they're not necessarily thinking about the serious genetic diseases because they're rare. I think it's our difficulty with dealing with rare things. It's like there was great resistance to seat belts because less than 1% of people died in automobile accidents or even got hurt. Great resistance to stopping smoking. Really, it's hard even for us to imagine how great the resistance was for seat belts and smoking. But eventually, we got over it.

• 1:16:29Speaker 01

  I think this is a similar thing, which is that only 3% of children are severely affected by genetic diseases. And I feel like, well... I'm not that unlucky, you know, I'm, I'm, I'm in the 97%, right? You know, 97% of those were your odds of winning, you know, at the horse races or at the casino, you take them, 97% of winning, good. You know, but with, with, you know, when a children's, when a child's future is at risk, I think that's, uh, it's not the right

• 1:17:01Dwarkesh Patel?

  Yeah.

• 1:17:01Speaker 01

  solution. And the other thing is, I think, is has to do with the trolley problem. It's like, if you don't influence it, it's not your fault. But actually, everything is your fault, you know? Not doing something is the decision, right?

• 1:17:14Dwarkesh Patel?

  Right.

• 1:17:14Speaker 01

  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.

• 1:17:21Dwarkesh Patel?

  Uh, David Reich was talking about how in India, especially because of the long running history of caste and, um, endogamous coupling, that they're having these small, soft populations that have high amounts of recessive diseases. And so like there, it's especially valuable intervention.

• 1:17:37Speaker 01

  I think I said, yeah, I, I know what you're saying and what David is saying, but I think it's a dangerous dichotomy. Uh, yeah, they'll say that there's certain, there are lots of, not just India, you know, uh, all over the world. And in fact, um, and in fact, we all went through a bottleneck. No, but that changes the, the, the rate from say 3% to 6%.

• 1:18:00Dwarkesh Patel?

  Mm hmm.

• 1:18:01Speaker 01

  But the point is 3% is still unacceptable. I mean, it's just, it's just a tragic loss, not only of the human life directly affected, but the whole family

• 1:18:11Dwarkesh Patel?

  That's right.

• 1:18:11Speaker 01

  Uh, is, you know, very often one or both parents have to quit their job and spend full time like caregiving and fundraising. Cause it's very, these are very expensive diseases as well. And it's just, we don't need to, uh,

• 1:18:29Speaker 01

  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. But I think as as word spreads and you see the positive outcomes, I think there will be it will be seen as as as one of the simplest bits of medicine ever. I mean, it's in fact, it's something.

• 1:18:56Dwarkesh Patel?

  Infection.

• 1:18:57Speaker 01

  Yeah, it's just like it's very inexpensive. In fact, in fact, it's it's it's less than zero because you spend hundred dollars per genome and it'll probably be less soon and you you get the whole thing analyzed and you know, compare that to millions of dollars that it you know, that will be lost. Opportunity costs and not being part of the workforce, the taking care of them and so forth. So the return on investment is tremendous. It's at least a tenfold return on investment. So it's a no, it's not, it's a no brainer from a public health standpoint. We should be able to pay for this through, you know, national health services in England, through insurance companies in the United States,

• 1:19:42Speaker 01

  and it turns the insurance companies from being the bad guys that they're that they're like snooping in on your personal life and then raising your rates to, oh, they're giving you this free information and you can do with it as you wish. And you could, if you take the advice, then you save them millions of dollars.

• 1:19:59Dwarkesh Patel?

  Right. Do you think genetic counseling is a more important intervention or even in even in the future will continue to be have a bigger impact than even gene therapy for these monogenic?

• 1:20:10Speaker 01

  I've actually counseled my my gene therapy companies that that they should be investing in very common diseases because rare diseases have this genetic counseling solution.

• 1:20:23Speaker 01

  With the exception of spontaneous mutations and dominance, which probably are IVF clinic type solutions rather than, but the rare recessives can be handled at matchmaking at every level. Anyway, I counsel my genetic therapy companies that they should invest in common diseases like age-related diseases and infectious diseases. And in fact, the COVID vaccine was formulated as a gene therapy and was, the cost was in the $20 per dose range and 6 billion people benefited from it.

• 1:21:04Speaker 01

  Or 6 billion people took it and it was proven over the whole population. So I think that's the more appropriate use of gene therapy. But I think for practical reasons, getting FDA approval and so forth, you might go for the rare diseases and that'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.

• 1:21:39Dwarkesh Patel?

  All right, 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 up and got dogged and so forth. I'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 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?

• 1:22:20Speaker 01

  I have to preface this by, 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. You 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

• 1:23:04Speaker 01

  big proponent of basic research, but also, maybe I'm more than average, connecting the basic research to societal needs from the get-go. 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...

• 1:23:34Dwarkesh Patel?

  This is a positive story?

• 1:23:35Speaker 01

  Yeah, well, it could be for China. I mean, you didn't specify who it's a positive story for. You know, the US displaced Britain, which displaced, you know, Spain and Portugal, you know. It keeps moving. Fresh blood is sometimes a good thing. Again, I preface this by saying I'm not advocating this. What else could go well? You know, there's just certain things that we, the society is fairly good at doing collectively, that we're not good at doing individually. You know, building roads, schools, and science are examples of that. Doesn't mean we couldn't learn how to do that, you know. To some extent, when

• 1:24:19Speaker 01

  you build a gated community, a lot of that is done with private funding. It's possible we could figure out how to build roads and schools and just about everything. It means we're going to run into some kind of hyper-capitalism that...

• 1:24:36Speaker 01

  Might mean, you know, there's all kinds of pathologies that come along with that.

• 1:24:41Dwarkesh Patel?

  What is it about the nature of your work, maybe biology more 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 a field I'm more familiar with, where you could go to one lab and

• 1:25:00Speaker 01

  One academic lab, yeah.

• 1:25:01Dwarkesh Patel?

  Yeah, sorry, one academic lab. And then 100 different companies have been formed out of it, including the ones that are most exciting and doing a bunch of groundbreaking work. So is it something about the nature of your academic lab? Is it something about the nature of biology research? What explains this pattern?

• 1:25:19Speaker 01

  First of all, thank you for being so generous in your evaluation, which maybe take with grain of salt. But I think that what it is, being in the right place at the right time. So Boston is a unique culture. It attracts some of the best and brightest students and postdocs automatically. It is dense enough. You know, sometimes people want to spread the wealth out evenly all over the universe, or the planet. And there's advantages to having it clustered, you know? So like if you have spouses can find other jobs in the same field.

• 1:26:01Speaker 01

  So having a concentration of biotech and pharma, and MIT and Harvard and BU and so forth, all in one, pretty walkable distance. You know, not spread out all along the east or west coast, but actually in a walkable city is one thing. That's the starting point. And then a lab that chooses from an early stage to

• 1:26:31Speaker 01

  You know, 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, you know, a, it's a positive feedback loop where, just like the building of Boston was a positive feedback loop, the more Harvard's and MIT's and, and high-tech startups than pharma. And so you get a couple of wins in the literature and people start coming that are, you know, a whole nother level up on it. And maybe they're

• 1:27:13Speaker 01

  already aiming for entrepreneurship well before they weren't. Anyway, it evolves in a way that you can't just jumpstart from, you couldn't just suddenly create Harvard and MIT in the middle of the desert and, and suddenly, you know, create a lab that is taking these kind of risks early in one, in a, in a career with, you know, and then, and then the, also the timing is good because the exponential is starting to show up. The exponential is, is pretty much the same in, in the beginning of the hockey stick and the end of the hockey stick, but you don't notice it until it gets, and so what, that's

• 1:27:54Speaker 01

  what's happening is both the, the computing, AI, biotech, they're all peaking at this point. And so whichever lab happened to already have that positive feedback loop going with the academic to industry technology transfer would asymmetrically benefit from that, that exponential. And to some extent, exponential, you can really look like you're very productive when really you're just kind of, you're just kind of sliding downhill. It's like, yeah, look at how productive I am. I just jumped out of a plane and I'm accelerating steadily.

• 1:28:36Dwarkesh Patel?

  So yesterday I had a dinner with a bunch of biotech founders and I mentioned that I was going to interview you tomorrow. And so somebody asked, wait, how many of the people here have worked in George's lab at some point or worked with him 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?

• 1:29:09Speaker 01

  Well, I'm glad you framed it as spotting talent. I've heard at least one meme that that all you have to do is 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. Very different

• 1:29:52Speaker 01

  attitude. 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 for people that are nice. I'm not necessarily looking for geniuses. We end up with a lot of geniuses. It's wonderful. But nice, I think,

• 1:30:32Speaker 01

  is highly predictive of how well you will do in the lab and afterwards. And as a consequence, I think we have a, you know, kind of international set of alumni that are 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. Multidisciplinarity. It's hard to build a multidisciplinary 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.

• 1:31:13Speaker 01

  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.

• 1:31:24Dwarkesh Patel?

  Final question. Given the fast pace of AI progress, your point taken that we should be cautious of this technology. But by default, I expect it to go quite fast and there not being some sort of global moratorium on AI progress.

• 1:31:37Speaker 01

  Yeah.

• 1:31:38Dwarkesh Patel?

  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.

• 1:31:46Speaker 01

  Yeah.

• 1:31:47Dwarkesh Patel?

  Within the next 20 years.

• 1:31:48Speaker 01

  Yeah.

• 1:31:49Dwarkesh Patel?

  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 we're doing with the brain or with the gene therapies and so forth, which might help us cope or might help us, you know, stay on the same page. Given this, given how fast AI is happening, what is the vision for this bio AI co-evolution? Or whatever it might look like?

• 1:32:14Speaker 01

  I think one scenario, or like if, if we handle the safety issues and that has to be a top priority. If we handle that properly, then, 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 healthcare. The more people will be helping prompt the AI if that's necessary.

• 1:32:51Dwarkesh Patel?

  Yeah.

• 1:32:51Speaker 01

  And I think it probably will be. And the more hybrid systems we'll have of people and, and machines working together in harmony.

• 1:33:00Dwarkesh Patel?

  Hopefully.

• 1:33:01Speaker 01

  In this very positive scenario. Yes.

• 1:33:05Dwarkesh Patel?

  Well, that's a good vision to end on.

• 1:33:07Speaker 01

  Okay.

• 1:33:08Dwarkesh Patel?

  George, thank you so much for coming on.

• 1:33:10Speaker 01

  Yeah, thank you.

• 1:33:11Dwarkesh Patel?

  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. Check out the sponsors in the description below. If you want to sponsor a future episode, go to dwarkesh.com/advertise. Thank you for tuning in. I'll see you on the next one.