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

people have estimates and all those estimates are, including mine, are going to be take 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, you're getting close to all of aging.

In other words, you're seeing instead of just saying, 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 age related diseases, and I'm going get more than one at a time. I think looking at those two phenomena, the exponentials of biotechnologies and the breakthrough in general aging, not analysis, but synthesis and therapies. And a lot of these therapies are now making in the clinical trials.

I wouldn't be surprised if 2050 would be a point if we can make it to that point, twenty five years. Most people listening to this have a good chance of making it twenty five years. And the thing is, it's not going to be some sudden point where you're going to be, you know, so sick twenty five years from now that it's like hit or miss.

It's more likely that you're going to be healthier twenty five years from now than you thought you were going to be. There may be some, probably not some law of physics, but some economic or complexity issue that we don't know about that becomes a brick wall. I doubt it seriously, but we'll have to see.

Probably there's a lot of forces pushing it towards somatic. For one, there are eight billion people that have missed the germline opportunity. Yeah.

That's to say, doesn't apply to us, the two of us and everybody listening to this. And you you have to be very cautious when you say something's impossible. It's safe to say it's impossible to do it this second, but you don't know what's going to happen tomorrow or the next decade or something.

So, 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 suddenly be young again, right, 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 replace the cells, will they fit into that niche? They might displace the old cells. You know, that's certainly within the realm of modern synthetic biology is 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 circuit and then displace the old ones in some way. Ship of Theseus kind of thing in the brain?

Yeah, exactly. Ship of Theseus having, you know, trying to maintain the connections and the memories. But, you know, there's some fairly straightforward experiments that need to be done before we can really even estimate 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 just hands over to us levers that we can flip, like vaccines. It's an amazing gift that didn't have to exist, but they do.

close to that today, but there's nothing, no law of physics that would prevent it. You know, there's going to be practical considerations, you know, like how many injections do you need to do to achieve that goal. But we're getting better at targeting tissues, you know.

So, for one of my companies, Dyna Therapeutics, showed they could get a hundredfold improvement in targeting, neurons in the brain, which is a big deal. Now, and that was just one little campaign that they did, you know, one experiment involved a lot of AI and a lot of testing of millions of different capsids. If you did that with cells, the capsids are fairly limited in the diversity and the structure that it can change to.

But cells have even more possibilities. I think you could probably get delivery to everything. And the question is how close to 100% do you need to get, right?

And it's going to vary from tissue to tissue. For example, for some therapies, you just need to get 1% because that 1% can produce some missing enzyme. And the 1% doesn't have to necessarily be in its normal place, right?

You know, 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, could make liver, right, if the point is just to get it into the blood. So I think that's moving along quite well.

think people get worked up about whether we are trying to bring back or have already or will ever bring back a new species. 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, 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?

Because it could be very cost effective. And 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 millions of difference between mammoths and elephants.

There are millions of difference between elephant one and elephant two within Asian elephants and between Asians and African. But not all of those are definitive in terms of what we would normally call them, how we would normally classify them, what their functionality would be in an ecosystem, right? And so there's this exercise that people do.

And we've done it, for example, with developmental biology. What's the minimum number of transfusion 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.

And these are in a way, 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?

We learn the rules for how to make things bigger, how to make things replicate faster, how you know, how to, to use new materials, etcetera. So, I think what the dire wolf, we clearly didn't make an exact copy of a dire wolf. But it helped illustrate kind of educated people around the world that what is the difference between a wolf, a gray wolf, a dire wolf, right?

Because, you know, dire wolves, they're big. Maybe they have a particular coloration. You know, the head components tend to be bigger than the leg components.

And so how many genes do need to do that? Maybe this was direwolf two point zero, and we've got to go for three point zero in successive approximation. And we might want to develop the technology for making exact copy of something because then we can especially being able to make 100 variations on an exact copy because then there won't be any argument about whether you could make a direwolf.

It's a matter of whether 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.

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 multigenic trait in humans like height is something that's probably the most, well studied one simply because no matter what gene or 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. And they each have a tiny influence on height. But if you take growth hormone, somatostropin, that you have extreme examples where you'll get extremely low small stature and extremely high stature due to 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 much we can minimize something, sometimes called reductionism. Reductionism isn't all bad.

Sometimes it helps us bring a product into medicine. Sometimes it helps us understand or build a tool chest or a module that we can and use in other translate it to other species. So, you hit on it just right, is that not everything will translate, but we start accumulating these widgets.

Biology, we've got a real gift, which is 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 little deviation from the normal developmental pattern during fetal development.

And they both function fine. They control subsets of the body. And, you know, they have their own personality, their own life.

So, there's all kinds of things you can do in biology that where you're working at a very high programming level is a way of thinking about Pushing us to a new level of intelligence is going to be very challenging and maybe not even urgent, okay? 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.

Some people want to be healthy all the time, unlikely, but some people might not. Some people might want to live 150. Some people might want to die at 80.

of these? I would say mostly GWAS for humans, maybe for animals in general, followed for animals with synthetic biology, you know. And the smaller and the cheaper and faster replicating, the more experiments you can do.

So, you know, I don't want to overemphasize 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 did it use to get express did at the time that it's the target? And then you say, well, let's just try those on the stem cells and see if they work. And that recipe has worked quite well.

It's the basis of GC Therapeutics company. And a bunch of the work that we do is you can almost you can 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, 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 transcription factors. So, that's an example. And there's room for lots of other examples of where you can do 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, know, in vitro things you can do on probably on the order of 10 to the fourteenth, 10 to the seventeenth. Things that involve cells are typically in the billions.

But we have this this is how we're going to get inroads into the very complicated biological systems.

warned about the dangers of mere life, just like, you know, I wrote a paper long ago about the dangers of having the synthetic capabilities we have for making synthetic viruses. And to some extent of having new genetic codes, they have a few things in common. But the thing about 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 that we made in the lab unless there's a general societal consensus is 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, we took it to a whole another level of concern. And the concern was that if we got it to a certain point, it would be easy to weaponize it. And again, 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 their legacy and everything like that. 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. It might be. It's quite possible it's already here.

In other words, we already have mirror life 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, 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 or the right access to technology, you know, could blow up a city, right? And that's a huge increase in capability.

you know, we we made it through the Cold War without blowing up any hydrogen bombs as far as I know, accidentally on or intentionally on enemies. We did two atomic bombs. 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. Right. You know, there's some people that are just so happy, 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, 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. Hopefully, there's a more technological solution or more robust solution than Well, there are there will be technological solutions to the psychiatric problem. It could be this people even people who aren't sure whether they want to be helped or not can test try it out.

And it's reversible. They say, Yes, I like that better. Okay, let's try that.

Then there's other things that cause you to have bad days. It's not just your psyche, it's also the environment.

Yeah. Which I'm like, I'm not that optimistic about, like, what's on some of not trying to to reassure you. And and, you know, we're having conversation about what it takes, and that might be as one scenario for what it might take.

synthetically manufactured viruses? Much harder. Yeah.

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

Yeah. So, one interesting thing 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 eightieth difference codes. Now, some of them you might be able to take out all at once. Anyway, the coding space is a kind of more interesting space.

And of course, it could get even more complicated than that because they're you know, the 10 to the eighty third is like based on triplet codons and that sort of thing. But if they're quadruplet codons or the new novel alphabets and so on. But we're sort of getting into you know, a cycle of competition.

It would be better to nip it in the bud, which is why did we spend so much societal resources building up to tens of thousands of nuclear warheads? And now we've dialed it back to mere thousand nuclear warheads. That's nice that we dial it back, but why do we waste all that time and money and energy?

Biology seems very dual use, right?

One of the things I advocated in 2004 is that we stop diluting ourselves into thinking that moratorium and voluntary, you know, sign ups 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 essentially moratoria and disapproval for germline editing and nevertheless, somebody did it.

And a lot of people knew about it. So that was clearly a failure of the whole moratorium, voluntary, and whistleblower components.

Half empty, half full. I'll give you that. But all it takes is one for some of these scenarios, right?

And that's so it would have been nice if the whistleblowers could have saved him the three years in prison by getting an intervention. I mean, it's not like anybody died, right? There are probably three healthy genetically engineered children in the world now.

Be teenagers soon. But it still shows 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 consequences that are well known.

Why don't we have something like that in biology yet? 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. But we could kind of stand on the shoulders of the electronics giants to go a little bit faster to catch up. I would say we do.

I mean, we have the biotech industry, which has used that exponential curve to get better. It's also possible we're close to the big payoff is the other aspect or the beginning of the big payoff. You know, right now we have miraculous things like cures for rare diseases.

We have vaccines. We have trillion dollars probably of various biotech related things if you go far enough apart. But we're kind of on the verge of really combining electronics and biology more thoroughly and AI and biotech.

And I think that's it seems like we're on the same track as Moore's Law, if not better.

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

It's a long time. 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. Well, we can get into I have a question for you there.

But, know, I think that we are shortening the time of getting medical products approved the still in a safe way. So, I think but that's not going to completely change the exponential. It might reduce it from ten years down to one year is our record so far for, say, COVID vaccines.

So, maybe that'll be 10 times shorter. Maybe that will multiply out 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. There'll be things that we didn't classically consider drugs or instruments, kind of some sort of hybrid thing.

But again, I don't think that'll be completely shocking. But it's just going to be so much of it. You know, it's going be lots of diversity of solutions.

I'm not even sure it's going to make sense. But yeah, 100x would not be completely surprising. Combinations of drugs will be important.

You're using them intelligently. There'll be a lot more. Some drugs will affect everything.

So, for example, an age related drug that could impact every disease.

There was a big discontinuity between Sanger sequencing and nanopores and fluorescent next gen sequencing.

sometimes so, it's a merger of two things. So clearly, AI merging with protein design caused a step function. These step functions get smoothed out into a kind of a smooth exponential, but there are lots of them.

The next set will probably be, yeah, a merger of AI with other aspects of biology like developmental biology, merger of developmental biology with manufacturing and, you know, conquering developmental biology, in other words, actually knowing how to make any arbitrary shape given DNA as the programming material. I think that would be a big thing. Having just more materials in general, all the materials that we use in mechanical, electrical engineering should be made better by biotechnologies.

Why is that? Why is that? Well, it's that electronics is, you know, Moore's Law, I wouldn't say is stopping, but it's kind of the what we would call the one nanometer process, which is supposed to come out in 2027 according to the roadmap.

It's not really one nanometer. It's more like 40 nanometers center to center spacing, you know, typically in two dimensions. Maybe a little bit of three dimensions.

But biology is already at 0.4 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 times higher density that biology is already at.

And we just need a little more practice with dealing with the whole periodic table. Even lecture engineering doesn't use the whole periodic table typically, especially not at the atomic level. I think biology is just really good at doing atomic precision.

40 nanometers. Right. It's quite small.

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 a little bit bigger. Even though it started kind of focused on E. Coli and yeast, it enabled us to maybe think about new amino acids, for example.

And I think new amino if you start using the full periodic table with the amino acids or what amino acids can catalyze, that breaks one of the major barriers. One of the major barriers between, you know, electrical and mechanical engineering and biology was the use of, you know, special materials, things that conduct electricity at the speed of light or conduct signals more But 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. Designing nucleic acids was great whether we were doing, you know, you want two things to bind to each other, you just dial it up using Watson Crick rules. If you want to make a three-dimensional structure, you know, it's actually the one, kind of the one thing where morphology is dictated by fairly simple rules.

It's not how developmental biology works, we still need to figure out how that works. But DNA origami, DNA nanostructures really work. But doing it for proteins was really, really hard until, I don't know, maybe eight years ago, something like that.

And I think we're just now getting used to it. The use of chips for making DNA. I mean, you said that DNA synthesis come down a thousand fold.

Well, it depends on who you talk to. So 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 collaborators and alumni.

And it wasn't even listed on the Moore's Law curve for DNA synthesis, even though it was like a thousand times cheaper. It was just like ignored. And now we have claims of 10 to the seventeenth genes, okay, that you can make libraries 10 to the seventeenth that aren't randomized in any real, in the usual sense where you just like do air prone PCRs, spiked in nucleotides.

Now, 10 to the seventeenth, that's a lot bigger than a thousand fold, you know, if it turns out to be practical.

as original, you know, the kind of the source of the inspiration, Eric Drexler, he wanted to reinvent biology in a certain sense, but it already existed. And so you don't need to design a diamond replicator because you already have a DNA replicator. And so the question, what was missing?

What was motivating this reinvention of biology? It was materials. So, the biology is not that great with 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 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 can build things that can operate at low temperature or maybe biology now because you can make these big libraries of biology, you know, maybe 10 or the seventeenth in vitro. 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 you haven't made, you know, a billion different kinds of electronic materials, right? Just, know, in an afternoon, barcode them all and see who wins, right?

But we do that all the time in biology now, at least since 2,004 we have. And so I think that's an opportunity is that we use those libraries to make much superior materials. And we might even finally get a room temperature superconductor that way.

From bio? It's possible. I mean, libraries.

We call it chemicalbiochemical 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.

very nice, but it's only part of it. So there are large language models that are different from alpha. So give an example, alpha fold, last time I checked, anyway, at least it's all changed.

If you substitute an alanine for a serine in a serine protease, it will have exactly the right fold. It will be precise to a fraction of an angstrom overall average, but it won't function. It just won't function.

And that's where need either extraordinary precision or just knowledge of what happens evolutionarily or happens in experiments to say that no alanine won't work. Okay? And so, I think there's all kinds of combinations of AI tools that can give you deeper insight into that.

And I can just like get that. I mean, I think the way that it's working now, 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 variations on it. And then whichever of those variations work, we make variations on that.

And we can just keep 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 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 but likely to be game changing, have more of a focus on those. Another thing that's been missing, and none of the AI protein design tools that I know of are particularly good at it yet, but we're trying to, as we speak, trying to improve this, is non standard amino acids.

Because a lot of these tools depend on having libraries of three d 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. 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 liganded all the entire periodic table, stable elements.

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 And ultimately, you can think of the determination of the functionality of your library is a kind of computer, right? So, you use AI to design the library optimally so you avoid things that are really neutral and really seriously damaged.

But then the stuff in the middle, you actually play it out, not in a simulation, but in real 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 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 more conventional AI and do another round of it. Yeah.

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 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 yeah, it's good. You know, it's one of these things where when you get the right idea, it's not hard to recruit people. I mean, for example, when Feng Zhang and my labs brought out CRISPR, we just got 10,000 requests in the next two months for people that wanted to duplicate And the so that's what I hope will happen with the non standard amino acids and the using AI for protein design and making new materials.

Hopefully, that will recruit tens of thousands of people overnight.

much more excited about scientific AI than I am 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. And there's a lot of safety organizations and a lot of safety rules and so forth.

And I think what typically happens when there's an intense competition is those safety rules get undermined and pushed aside. But even if they weren't, I just don't think we understand our own ethics well enough to educate 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-nineteen where we actually millions of people are dying if we delayed the science. This is something where if there ever is a crisis, it's because we created it. It's not because we're trying to solve it.

Yeah. Right? And so I think we need to go very slowly on AGI and ASI and double down on slightly narrower scientific goals.

And even that, we need to be very cautious about.

Think it would eliminate it because like the first thing it would conclude is biology is not relevant to me because I'm not made out of biology. I mean, suppose you could get them to care about it.

But they can't like run experiments directly. They're just in data centers. They can just say stuff and think stuff.

I don't think we have anything close to the assurance that we need that that would be safe. But let's put safety aside for So, the point? I think it's hard to it's not only hard to calculate the bads, it's hard to calculate the goods.

So, I think it could be a complete game changer. But on the other hand, you know, it's like if we said, 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 would be more interesting to just have Zoom calls that are better, you know, or just learn how to get everything we want in our kitchen and we don't need to travel anymore, You know? So, you know, be careful what you ask for, right?

which is a sort of proxy for what E. These might are great questions. And I'm not sure, I don't want to misrepresent that I know the answers.

You know, it's like the question of, you know, if you have nine women, can you do pregnancy in one month? No, not But at you're working on that, right? No, no, no, no.

But the same thing is there may be certain things that doesn't take a lot of people. We just don't know. We just don't.

We don't have that much experience with having, you know, thousands of Einstein type levels of creativity and intelligence simultaneously in a generation. And in fact, it's probable that we're all capable of being a bit more efficient if we don't have the distractions of mental illness, of 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 have no one to take care of, there'll be something bad that happens to us socially. So, these things are very complicated and hard to predict.

And I also felt that there was something that we could engineer. Certainly, we have made a lot of progress at the broken end of the spectrum where the brain is severely challenged relative to average. There's thousands of, a huge fraction of genetic diseases that have one of their consequences being that the child is developmentally delayed to such an extent that it's lethal or a lifetime deficit.

And we know the genes involved and we know how to do genetic counseling and in some cases, gene therapy and other therapies to deal with it. At the other end, we have reduction of cognitive decline by cognitive enhancement, 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 how much information does it take to encode a brain? I'm not sure that it's that much less genome is required than if you just wanted to make a brain, because the brain is totally entangled with the body. You know, you need to you have a 10 to the eleventh neurons, 10 to the fourteenth synapses.

If you wanted to reproduce a particular brain, let's say, it might be, it's speculative as to whether it would be easier to do that by making a copy of it in silico, in some kind of inorganic matrix, 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 complicated book, it would be easier to take photographs of each of the pages than to completely translate it into another language, trying to get all the nuances of the poetry and so forth Right.

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, just to define this 10 to the fourteenth synapses is going to take a lot more bytes than the genome, which is billions rather than 10 to the fourteenth.

Well, mean, certain things seem incompatible, like the temperatures of a fission reactor isn't obviously compatible. But the possibility that once we 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 thirty minute doubling time could make a nuclear reactor as that would be its nest. But you need to, you know, expand its range of materials.

In a certain sense, we do this already. Humans are a biological thing that replicates not in thirty minutes but in, you know, twenty years or less. And is that fundamentally limiting us?

You know, probably is. But yes, it's amazing to think about what if you could take, you know, a cornfield or a nuclear reactor and suddenly thirty minutes later, you've got two of them, right? And then four of them.

Yeah. Mean, that's quite an interesting concept. But I I think we should start with I teach a course called How to Grow Almost Anything.

And I work with Neil Gershonfeld at MIT who has a course called How to Make Almost Anything. And we're trying to meet in the middle where we can, his mechanical electrical engineering will meet with our biological. And in fact, neither of us can make or grow almost everything because there are all kinds of little gaps and things that are very hard to make in a small lab because there are things all over the world that depend on multibillion dollar fabs to make things.

But we're eating away at it. I think that we might eventually be maybe a smaller baby step than making a nuclear reactor is making a phone. You said radio communication.

We should bake a biologic. It should be a small challenge goal for the synthetic biology community, maybe iGEM or something. Make you know, bacteria make a radio.

And actually, Joe Davis is an artist who's been affiliated with my lab and before that, Alex Rich's lab. And he did make a bacterial radio, but it was kind of more on the art end than on the science end.

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 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, harnessing migration, gradients of factor, diffusion factors, you know, chemotaxis and so forth.

So that's one thing we need, but there's a bunch of things we need really.

signals or light signals. But biology, the kind of evidence would be that you show in a laboratory using prebiotic conditions a really simple way to get life, Or I mean, it's a harder proof to prove that given because we don't know all the possible preconditions. And probably the number was vast.

Mean, you have 10 to the twentieth 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, I think if you showed kind of reconstructed in the lab a very simple pathway from inorganics, cyanide derivatives and reduced compounds all the way up to some cellular replicating structure. I think that might lead us to believe that at least life exists.

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

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 systems spontaneously would be interesting. Again, I'm not sure it would It'd be very hard to prove the negative.

even sure we would be able to say within five words of magnitude, much less 50%. But, you know, I think it's more likely to come from exploration than it is going to be from simulation. The sad truth is that almost none of the missions that we sent outside of Earth have actually looked for life.

They've had components that could have looked for life. But a sad number of those, not enough components that could look for life and the ones that could look for life not really looking for it. And when we get positive results, we dismiss them as happened with the Pioneer.

And so I think if we just start looking at the geysers that are coming out of various moons of Jupiter and Saturn, there's so much water. There's 50 times more water, liquid water, not frozen, more liquid water in our solar system than in Earth. Doesn't that seem likely that, you know, some of that would have been a good breeding ground?

But it could be that we need sunny shores, you know, where they have a lot of dry land right next to water. Maybe these are just giant oceans that are surrounded by ice, and maybe that's not an idea. But in any case, we need to look at those fountains to see what's popping up.

That's a high priority. And the same thing goes, you know, for, you know, there's a lot of water on Mars that's maybe even more accessible. But until we've exhausted those, those are probably the easiest.

They're hard. We're still talking about multibillion dollar experiments. But I think they're a little more convincing.

And again, it'll be hard to prove the negative. If we find this negative on every, everything in the solar system, you know, there's so much more diversity out there that could have done it.

or 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 catalysts. I think one thing that's probably already happening so we don't have to go a thousand years out is the number of amino acids that's going up. It's going up radically from 20.

I think pretty soon we'll have a system where we can have thirty three and thirty four new nonstandard amino acids being used simultaneously while the standard ones in an E. Coli cell. So, 34 plus 20 is a lot bigger than 20.

I don't think we necessarily need 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 have more. But I think the main thing is this information storage. And whether it's bits, 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. 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 there'll be more. I mean, these are just things that my primitive twenty first century brain is coming up with a thousand years from now.

I think that evolution has a tendency to go with what works and the investment in making a whole new base pair would have been high. And we haven't even articulated what the return on investment would be. What do you get from that?

We have made systems like Floyd, Williamsburg, and others that where you have replication and transcription and translation with new base pair. But it hasn't been clearly articulated what that gets you. Even in technological society so in technology, you can jump to things where all the intermediates aren't, you know, incrementally useful.

But evolution is, as far as we know, generally limited to you have to justify every change.

gets crossed over? 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 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 previous example I would have chosen was making genes out of arrays. Arrays were typically used for analytic, you know, quantitating RNAs or something like that.

So, the original Affimetrix type arrays. But we turned them into gene arrays. And just people weren't using it.

It was in nature. It was hidden in plain sight. 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, 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 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 I heard Doria Sherem described that way and rightly so. What they're doing is standard medicine.

You know, whether 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.

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 necessarily thinking about 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 seatbelts because less than one percent 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 seatbelts and smoking. But eventually, we got over it. I think this is a similar thing, which is that only three percent of children are severely affected by genetic diseases.

And they feel like, well, I'm not that, you know, I'm in the ninety seven percent, right? You know, ninety seven percent of those were your odds of winning, you know, at the horse races or at the casino, you take them. Yeah, ninety seven percent of winning.

Good. You know? But with, you know, when a child's future is at risk, I think that's not the right solution.

And the other thing is I think it 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 a decision, right? Right. 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.

valuable intervention. I think as a yeah, I know what you're saying and what David is saying, but I think it's a dangerous dichotomy. You know, they'll say that there's certain there are lots of not just India, you know, all over the world.

And in fact, we all went through a bottleneck. No, but that changes the rate from say three percent to six percent. But the point is three percent is still unacceptable.

I mean, it's just a tragic loss not only of the human life directly affected, but the whole family. That's right. Is, you know, very often one or both parents have to quit their job and spend full time like caregiving and fundraising because these are very expensive diseases And as it's just we don't need to, we need to be careful not to stigmatize as well.

So, 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?

Yeah. It's very inexpensive. In fact, it's less than zero because you spend $100 per genome and it'll probably be less soon.

And you get the whole thing analyzed. And you know, compare that to millions of dollars, you know, that will be lost. Opportunity costs, them not being part of the workforce, taking care of them and so forth.

So, the return on investment is tremendous. It's at least a tenfold return on investment. 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. And it turns the insurance companies from being the bad guys that they're like poop 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.

my gene therapy companies that they should be investing in very common diseases because rare diseases have this genetic counseling solution. 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, you know, the COVID vaccine was formulated as a gene therapy and was, you know, the cost was in the, you know, dollars 20 per dose range and six billion people benefited from it. Or six billion people took it and, you know, 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, you know, getting FDA approval and so forth, you might go for the rare diseases. And that's perfectly fine.

have to preface this by when scientists explore, answer a question and explore possibilities, it doesn't mean they're advocating it. 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 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, you know, like China could now become the next empire after This is a positive story? US.

Yeah. Well, it could be for China. Mean, you didn't specify who it's a positive story for.

You know, The US displaced Britain, 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. Know, to some extent, when 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 hypercapitalism that might mean, you know, there's all kinds of pathologies that come along with that.

explains this pattern? Well, first of all, thank you for being so generous in your evaluation, which Maybe take it with a 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, you know, spouses can find other jobs in the same field. So having a concentration of biotech and pharma and MIT and Harvard and BU and so forth all in one pretty walkable distance, 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 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, 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 high-tech startups than pharma. And so you get a couple of wins in the literature and people start coming that are a whole another level up on it.

And maybe they're 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 suddenly, you know, create a lab that is taking these kind of risks early in a career with, you know and then also the timing is good because the exponential is starting to show up. The exponential is pretty much the same in the beginning of the hockey stick and the end of the hockey stick, But you don't notice it until it gets and so that's what's happening is both 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 academic to industry technology transfer would asymmetrically benefit from that exponential. And to some extent, exponential, you can really look like you're very productive when really you're just kind of sliding downhill. It's like, yeah, look at how productive I am.

have been recruited by you, worked in your lab. How do you spot talent? Well, I'm glad you framed it as spotting talent.

I've heard at least one meme 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 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 a million members of the library are going to fail and maybe one or two will succeed. Very different attitude.

You know, it's much more engineering. But it's even different from most engineering where, you know, engineering doesn't usually use libraries that way. Millions and billions of components that are, you know, non random, but many of them will fail.

Yeah. So, the question is selection criteria. So, of that, there's a self selection.

And then 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.

That's wonderful. But nice, I think, is highly predictive of how well you will do in the lab and afterwards. And as a consequence, I think we have a 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 as 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. 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. Final question.

or whatever it might look like? I think one scenario, and like, if if we handle the safety issues, and that has to be a top priority, if we handle that properly, then we're probably going to have almost perfect health. Why 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 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. Yeah.

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

In this very positive scenario. Yes.