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

Today I have the pleasure of interviewing George Church. I don't know how to introduce you. This is not even an exaggeration—it would honestly be easier to list 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, and de-extinction. So you weren't exactly an easy prep.

Okay, let's start here. By what year would it be the case that, if you make it to that year, technology in biology will keep progressing to such an extent that your lifespan will increase by a year every year, or more? Escape velocity is sometimes what it's called for aging.

Different people have estimates, and all those estimates, including mine, are going to be taken with a big grain of salt. Mainly looking at the exponentials in biotechnology and the progress that's been made in understanding—not just understanding the causes of aging, but seeing real examples where you can reverse subsets of the aging phenotype—you're getting close to all of aging.

In other words, 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 to get more than one at a time.”

Looking at those 2 phenomena—the exponentials in biotechnology and the breakthroughs in gerontology, not just analysis but synthesis and therapies, with a lot of these therapies now making it into clinical trials—I wouldn't be surprised if 2050 would be a point. If we can make it to that point, 25 years… Most people listening to this have a good chance of making it 25 years.

The thing is, it's not going to be some sudden point where you're going to be so sick 25 years from now that it's 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 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.

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

Bowhead whales, 200 years.

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

Probably there are a lot of forces pushing it toward somatic. For one, there are 8 billion people who have missed the germline opportunity. That’s to say, it doesn't apply to us—the 2 of us and everybody listening to this.

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, in the next decade, or something.

I think there's a lot that could be done. In particular, since aging is a fairly cellular phenomenon, with proteins and other factors going through the blood, signaling, and so forth, you could imagine that if you replaced every nucleus in the body, it would suddenly be young again without going all the way back to the embryo and forward again.

There are 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. That's certainly within the realm of modern synthetic biology, for cells to take over niches.

I think the hardest part is the brain. Even there, 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, learn the circuit, and then displace the old ones in some way. A Ship of Theseus kind of thing in the brain.

A Ship of Theseus kind of thing in the brain.

Yeah, exactly—a Ship of Theseus, trying to maintain the connections and the memories. There are some fairly straightforward experiments that need to be done before we can really even estimate how hard that problem is.

Very often, there's low-hanging fruit that people just think is improbable. But it's there because biology has all these gifts where it just hands over to us levers that we can flip. Vaccines are this amazing gift that didn't have to exist, but they do.

Is there an existing gene-delivery mechanism that could deliver gene therapy to every single cell in the body?

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

One of my companies, Dyno Therapeutics, showed that they could get a 100-fold improvement in targeting neurons in the brain, which is a big deal. That was just 1 little campaign that they did. That 1 experiment involved a lot of AI and a lot of testing of millions of different capsids.

Capsids are fairly limited in the diversity of structures that they can change to. But cells have even more possibilities. I think you could probably get delivery to everything. The question is how close to 100% you need to get. 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 that 1% doesn't necessarily have to be in its normal place. You can turn a muscle into part of the immune system temporarily for a vaccine. An enzyme that's normally made in, let's say, the brain could be made in the liver if the point is just to get it into the blood. So I think that's moving along quite well.

1. Finding the master switch for any trait

You're one of the co-founders of Colossal, which recently announced that they de-extincted a dire wolf. Now you're working on the woolly mammoth. Do you really think we're going to bring back a woolly mammoth? The difference between an elephant and a woolly mammoth might be like 1 million base pairs. How do we think about the kind of thing we're actually bringing back?

People get worked up about whether we are trying to bring back, have already brought back, or will ever bring back a new species. If you think of it not as a natural thing that we're trying to do, but as synthetic biology with goals that have potential societal benefits, people also get worked up as to whether this could possibly benefit society in any way.

Can we really fix an environment to suit humans or fix the global climate to suit humans? The answer is we don't know. But it's worth a try, isn't it? Because it could be very cost-effective.

The other aspect of it is that there's a whole discipline within synthetic biology of asking, “What's the minimum?” People often phrase it as, “What's the maximum? What can we do?” I'm interested in both.

Yes, there are millions of differences between mammoths and elephants. There are millions of differences between elephant 1 and elephant 2, within Asian elephants and between Asian and African elephants. But not all of those are definitive in terms of what we would normally call them, how we would normally classify them, and what their functionality would be in an ecosystem.

There's this exercise that people do. 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? What's the minimum number of base pairs it takes to make something that will replicate? That was something that was done in Mycoplasma originally.

In a way, these are more interesting than, “Can we make a perfect copy of something?” What's the minimum number of things we have to do to make it completely functional, or even functional in a particular category? How do we make it bigger?

We learn the rules for how to make things bigger, how to make things replicate faster, how to use new materials, and so on. With the dire wolf, we clearly didn't make an exact copy of a dire wolf. But it helped illustrate and educate people around the world on, “What is the difference between a gray wolf and a dire wolf?”

Because dire wolves are big. Maybe they have a particular coloration. The head components tend to be bigger than the leg components. How many genes do you need to do that?

Maybe this was Dire Wolf 2.0, and we're going to go for 3.0 and successive approximations. We might want to develop the technology for making exact copies of something, 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 dire wolf.

It'll be a matter of what you should make and what would be most beneficial for the species that you're making, for the environment it lives in, and for humans.

Does this teach us something interesting about phenotypes that you think are downstream from many genes, and 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? You might think there must be thousands of genes that are relevant, but there are, like, 20 edits you need to make to be in a totally different ballgame.

You're hitting on a very interesting question. It's related to “What's the minimum?” For example, you almost said it. Take a very multigenic trait in humans. Height is probably the most well-studied one, simply because no matter what gene or medical condition you're studying, you collect information on height and weight and things like that. They tracked it down to something on the order of 10,000 genes, of which we have 20,000 protein-coding genes. Some of them are RNA-coding genes. They each have a tiny influence on height.

But if you take growth hormone, somatotropin, you have extreme examples where you'll get extremely low stature and extremely high stature due to that one alone. In fact, it's used clinically as well for 7 different medical treatments. 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 could use in other cases and translate it to other species.

You hit on it just right. Not everything will translate, but we start accumulating these widgets. It's kind of like all the electronic widgets that we accumulate over time. If you just want to slap it into the next circuit, you might be able to.

What implications does this have for gene therapy in general? What is preventing us from finding the latent knob for every single phenotype we might care about, in terms of helping with disabilities or enhancement? Is it the case that for any phenotype we care about, there will be one thing that is like HGH for height? How do we find it?

With biology, we've got a real gift, which is that it's both much more complicated than almost anything we've designed from scratch, but it also is a lot more forgiving in a certain sense. You can have an animal or even a human that has 2 heads, which evolutionarily there was no selection specifically to have. 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 they have their own personality, their own life.

There's all kinds of things you can do in biology where you're working at a very high programming level. That's a way of thinking about it.

But pushing us to a new level of intelligence is going to be very challenging and maybe not even urgent. To some extent, actualizing the people that we currently have would be quite impactful, just getting them all up to whatever speed they want to be up to within the range that's been demonstrated. Some people are going to want to be like Einstein; some people won't. Some people will want to be healthy all the time. Unlikely, but some people might not.

Some people might want to live to be 150; some people might want to die at 80. But if you give them that range, that capability, what if we had 8 billion super-healthy people who didn't need to worry about food and drugs, with super-healthy, Einstein-level intelligence and the best education level we could come up with? That would be a completely different world.

Just getting everybody to the healthy level, how much gene therapy would that take? It sounds like it wouldn't take that much if you think that there are a couple of knobs which control very high-level functions. So do you find them through GWAS, genome-wide association studies? Is it through simulations?

I would say mostly GWAS for humans, maybe for animals in general. For animals with synthetic biology, the smaller, cheaper, and faster-replicating they are, the more experiments you can do. 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.

For example, I mentioned earlier, what's the minimum number of transcription factors it takes to turn a stem cell into a neuron? There's a bunch of recipes where you can do it with 1. Maybe you want a specific neuron; you might need a few more. But then you can quickly go to the answer by looking at each target cell type that exists.

You can see what transcription factors it expresses at the time that it's the target. Then you say, “Let's just try those on the stem cell and see if they work.” That recipe has worked quite well. It's the basis of GC Therapeutics and a bunch of the work that we do. You can get a recipe for almost every cell type in the body.

Now, that's not new cell types, but at least 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 1, 2, 3, maybe 7 changed transcription factors. That's an example.

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

2. Weaponized mirror life

Can I ask you some questions about biodefense? Because some of the stuff you guys work on, or quite responsibly choose not to work on, can keep one up at night.

Mirror life: given the fact that it's physically possible, why doesn't it just happen at some point? Some day it'll get cheap enough. Somebody will care about it enough that somebody just does it. What's the equilibrium here?

I was a co-author on a paper that warned about the dangers of mirror life. Just like 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.

The advance that we were recognizing in our Science paper warning about mirror life was not only that we had to calculate the possibility of error-prone 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 that it's a good thing. So far, there aren't any examples of that.

Mirror life, if it can be weaponized, would take it to a whole other level of concern. The concern was that if we got it to a certain point, then it would be easy to weaponize it. Again, there are practical considerations that maybe most people who consider weaponizing mirror life would probably be satisfied with weaponizing viruses that already exist, that are already pathogens. They wouldn't want to destroy themselves and their family and their legacy and everything like that.

But all it takes is 1 group, probably, or 1 person. 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.

What we were saying in the Science paper is that this seems like the sort of thing that could wipe out all competing life if it were properly weaponized. But there are probably a few things like that. 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 and some of which will be from 1 disgruntled person who has essentially too much power.

Over the history of humanity, the amount of things that a single person can do has grown very significantly. It used to be, when you had your bare hands, there was kind of a limit to what 1 person could do. A large number of people could team up and get a mammoth or something like that. Today, 1 person with the right connections or the right access to technology could blow up a city. That's a huge increase in capability. I think we want to start dialing that back a little bit somehow.

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

Offense often does have an advantage, but so far we haven't… We made it through the Cold War without blowing up any hydrogen bombs, as far as I know, accidentally or intentionally on enemies. We did do 2 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 that are harder and harder to detect, and more and more subtle compared with the stochastic variation between people.

There are some people who are just so happy they would never want to do anything close to that. Or they're so responsible or ethical or whatever. Then there are other people who, whenever they have a bad day, want to take a lot of people with them. 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 that.

Well, there will be technological solutions to the psychiatric problem. It could be that even people who aren't sure whether they want to be helped or not can try it out, and it's reversible. They say, “Yes, I like that better.” Okay, let's try that then.

Then there are other things that cause you to have bad days. It's not just your psyche; it's also the environment. So if your people are being starved, there's infectious disease, or you're being shot at or something like that, those are things that are subject to sociological and technological solutions. If we could really solve a lot of that stuff, we could reduce the probability that one person…

This is maybe pessimistic because you're basically saying we have to solve all of society's problems before we don't have to worry about synthetic biology, which I'm not that optimistic about.

We'll solve some of them.

Right? You shouldn't be.

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

You had an interesting scheme for remapping the codons in a genome so that it's impervious to naturally evolved viruses. Is there a way in which this scheme would also work against synthetically manufactured viruses?

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

Which will limit the transmissibility?

Yeah. So one interesting thing is that there's only 2 chiralities. There's the current chirality and the mirror chirality. But there are maybe 10^80 different codes. Some of them you might be able to take out all at once.

Anyway, coding space is a more interesting space. Of course, it could get even more complicated than that because the 10^83 is based on triplet codons and that sort of thing. But if there are quadruplet codons or novel alphabets and so on…

We're sort of getting into a cycle of competition. It'd be better to nip it in the bud. Why did we spend so much societal resources building up to tens of thousands of nuclear warheads? Now we've dialed it back to a mere 10,000 nuclear warheads. It's nice that we dialed it back, but why did we waste all that time and money?

Biology seems very dual-use, right? The mere fact that you—literally you—are making sequencing cheaper will just have this dual-use effect in a way that's not necessarily true for nuclear weapons. And we want that, right? We want biotechnology to advance. It's hard to pound nuclear weapons into ploughshares, as they say.

I guess I am curious if there is some long-run vision. 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, or are we just stuck in a world where offense will be privileged, so we'll just have to limit access to these tools and have better monitoring, but there's not a more robust solution?

One of the things I advocated in 2004 is that we stop deluding ourselves into thinking that a moratorium and voluntary sign-ups to be good citizens are going to be sufficient. We need to also have surveillance and consequences, and mechanisms for whistleblowers to make it easy for people to report things that they think are out of line.

We had essentially moratoria and disapproval for germline editing. Nevertheless, somebody did it, and a lot of people knew about it. That was clearly a failure of the whole moratorium, voluntary, and whistleblower components. It worked for 5 years with only 1 defector. That's quite impressive.

Okay, half empty, half full. I'll give you that. But all it takes is 1 for some of these scenarios.

It would have been nice if the whistleblowers could have saved him the 3 years in prison by getting an intervention. It's not like anybody died.

Right.

There are probably 3 healthy genetically engineered children in the world now. They'll be teenagers soon. But it was a good test run that 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.

3. Why hasn’t sequencing/synthesis led to biotech revolution?

Over the last couple of decades, we've had a 1,000,000-fold decrease in the cost of sequencing DNA and a 1,000-fold decrease in synthesis costs. We have gene-editing tools like CRISPR and massively parallel experiments through multiplex techniques that have come about.

Of course, much of this work has been led by your lab. Despite all of this, why is it the case that we don't have a huge Industrial Revolution—a huge burst of new drugs, or cures for Alzheimer's and cancer that have already come about?

When you look at other trends in other fields, we have Moore's law, and here's my iPhone. Why don't we have something like that in biology yet?

We have something that's about the same speed, a little bit faster than Moore's law, in biology. It's more recent; that's one aspect of it. We could stand on the shoulders of the electronics giants to go a little bit faster to catch up. I would say we do.

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, or the beginning of the big payoff.

Right now, we have miraculous things like cures for rare diseases. We have vaccines. We have a trillion dollars, probably, of various biotech-related things if you go far enough apart. We're on the verge of really combining electronics and biology more thoroughly, and AI and biotech. It seems like we're on the same track as Moore's law, if not better.

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

Well, with 2040, we're talking about only 15 years. That's maybe 2 cycles of FDA approval. We are shortening the time it takes to get medical products approved while still doing it safely, but that's not going to completely change the exponential.

It might reduce it from 10 years down to… One year is our record so far for, say, COVID vaccines. Maybe that'll be 10 times shorter. Maybe that will multiply out a little bit.

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, some sort of hybrid thing. But again, that won't be completely shocking. It's just going to be so much of it. There's going to be lots of diversity of solutions.

2040 is post-AGI. 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. There are lots of cool things we can do with just super AI, but we need to be very cautious with AGI. Anyway, we can get into that. I have questions for you there.

How much more are we talking about? Are we going to have 10x the amount of drugs? 100x?

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

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

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?

The cost curves are affected by new tools. It's not just some automatic thing. There was a big discontinuity between Sanger sequencing, nanopores, and fluorescent next-generation sequencing. Sometimes it's a merger of 2 things. Clearly, AI merging with protein design caused a step function. These step functions get smoothed out into a smooth exponential, but there are lots of them.

The next set will probably be a merger of AI with other aspects of biology, like developmental biology. After that, there will be the merger of developmental biology with manufacturing: conquering developmental biology. In other words, it would be actually knowing how to make any arbitrary shape given DNA as the programming material. That would be a big thing.

Just having more materials in general. All the materials that we use in mechanical and electrical engineering should be made better by biotechnologies. Why is that? Well, in electronics, I wouldn’t say Moore’s law is stopping, but think about what we would call the 1-nanometer process, which is supposed to come out in 2027 according to the roadmap. It’s not really 1 nanometer; it’s more like 40 nanometers, center-to-center spacing, typically in 2 dimensions, maybe a little bit of 3 dimensions.

Biology is already at 0.4-nanometer resolution, and it is in 3 dimensions. Depending on how you count that third dimension, it could be a billion times higher density than electronics is already at. We just need a little more practice with dealing with the whole periodic table. Even electrical and mechanical engineering don’t typically use the whole periodic table, especially not at the atomic level. Biology is just really good at doing atomic precision.

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

40 nanometers.

Right. It’s quite small. It’s a thousand times bigger than biology, linearly. But the progress we have made hasn’t been related to biology so far. It seems like we’ve made Moore’s law happen. People in the 1990s were saying ultimately we’d have these biomachines that are doing the computing, but it seems like we’ve just been using conventional manufacturing processes. What exactly is it that changes that allows us to use bio to make these things?

A few things. One is the arrival of synthetic biology. We were already kind of doing synthetic biology before; we were doing recombinant DNA, a kind of genetic engineering. It 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. If you start using the full periodic table with the amino acids, or what amino acids can catalyze, that breaks one of the major barriers. One of the major barriers between electrical and mechanical engineering and biology was the use of special materials, things that conduct electricity at the speed of light or conduct signals more generally.

But there are definitely polymers that biology can make that will conduct at the speed of light. We could make a mixed neuronal system that has conventional neurons and processes that conduct at the speed of light. That would be interesting. So I think that our ability to design proteins was particularly difficult.

Designing nucleic acids was great. You want 2 things to bind to each other? You just dial it up using Watson-Crick rules. If you want to make a 3-dimensional structure, it’s actually the one kind of 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 and DNA nanostructures really work.

But doing it for proteins was really, really hard until maybe 8 years ago, something like that. I think we’re just now getting used to it.

The use of chips for making DNA—you said that DNA synthesis has come down a thousandfold; it depends on who you talk to.

When we came out with the first chip-based genes in a 2004 Nature paper, basically people dismissed it for about a decade. The only people that used it were collaborators and alumni. It wasn’t even listed on the Moore’s law curve for DNA synthesis, even though it was a thousand times cheaper. It was just ignored.

Now we have claims of 10^17 genes that you can make libraries of, that aren’t randomized in the usual sense, where you just do error-prone PCR or spiked nucleotides. 10^17—that’s a lot bigger than a thousandfold if it turns out to be practical.

Speaking of protein design, another thing you could have thought of in the 1990s—people were writing about nanotechnology, Eric Drexler and so forth. Now 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? Why didn’t AlphaFold cause that?

Part of it is that nanotechnology, as originally conceived by its source of inspiration, Eric Drexler, was meant to reinvent biology in a certain sense, but it already existed. So you don’t need to design a diamond replicator because you already have a DNA replicator.

The question was: what was missing? What was motivating this reinvention of biology? It was materials. Biology is not that great with materials that are, say, superconductors, conductors, semiconductors, and light speed. But it’s getting there. Rather than going the route of everything having to be based on first-principles nanostructures, you can meet in the middle, where biology can build things.

Of course, when you go down to liquid nitrogen and colder temperatures, biology as we currently know it stops functioning. 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 if biology can build things that can operate at low temperatures.

Or, because you can make these big libraries of biology—maybe 10^17 in vitro—you can flip through them quickly, barcode them, and see which ones work. 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 a billion different kinds of electronic materials just in an afternoon, barcoded them all, and seen who wins. But we do it all the time in biology now; at least since 2004, we have.

So I think that’s an opportunity. 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, from libraries. We call it chemical, biochemical, exotic material libraries. The point is that they’re libraries. They’re essentially based, in some sense, on polymers, even though pieces of them don’t necessarily have to be polymers.

Do you have a prediction by when we’ll see this materials science revolution? What is standing between now and that? Because we’ve got AlphaFold right now. So what is the thing that we need? Do we need more data?

AlphaFold is very nice, but it’s only part of it. There are large language models that are different from AlphaFold. To give an example, with AlphaFold—last time I checked, anyway—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.

That’s where you need either extraordinary precision or just knowledge of what happens evolutionarily or in experiments, to say, “No, alanine won’t work. Okay?” So I think there are all kinds of combinations of AI tools that can give you deeper insight into that.

If AlphaFold predicting the structure doesn’t tell you whether the thing will actually function, then what is needed before I can say, “I want a nanomachine that does X thing, or I want a material that does that Y thing, and I can just get that”?

The way that it’s working now—which will get us a long way, but won’t get us the whole way—is that we have something that kind of works, and we make libraries inspired by that. We make variations on it, and then whichever of those variations work, we make variations on that. We can just keep going. It’s kind of like the way evolution worked, except now we can do it at incredibly high speeds.

In principle, evolution might incorporate a few base-pair changes in a million years. Now we can make billions of changes in an afternoon. 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 and have more of a focus on those.

Another thing that’s been missing is nonstandard amino acids. None of the AI protein design tools that I know of are particularly good at that yet, but as we speak, we’re trying to improve them. That’s because a lot of these tools depend on libraries of 3D structures that use 20 amino acids, and on large language models where you line up all the sequences of 20 amino acids. We have very little experience with extra ones.

But there’s a revolution going on in generating nonstandard amino acids, where the amino acids can either have all the stable elements in the entire periodic table as a covalent part of them or have them easily liganded. Each of those we’ll 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.

Ultimately, the determination of the functionality of your library is a kind of computer. You use AI to design the library optimally. You avoid things that are really neutral and really seriously damaged. But the stuff in the middle, you actually play it out—not in a simulation, but in real life.

But it’s so inexpensive, and it’s so fast, and it’s so exact. It’s 100% precision, because you’re not simulating. You’re not making assumptions. 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. So you’re doing a kind of natural computing.

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.

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 a couple more drugs by 2040?

Well, I didn’t mean to stop there. I knew the conversation would continue. I’m not pinning down a particular year either, but this is poised to go pretty quickly. There are very few practitioners, which is the thing that will stop it for a while.

Materials actually should go faster, though, because they don’t require quite as much regulatory approval. It’s one of these things where, when you get the right idea, it’s not hard to recruit people. For example, when Feng Zhang and my labs brought out CRISPR, we each got 10,000 requests in the next 2 months from people who wanted to duplicate the system.

That’s what I hope will happen with the nonstandard amino acids, using AI for protein design, and making new materials. Hopefully, that will recruit tens of thousands of people overnight.

4. Impact of AGI on biology research progress

Are you more excited about AI which thinks in protein space or capsid space, just predicting some biological or DNA sequences? Or are you more optimistic about LLMs just trained on language, which can write in English and tell you, “Here’s the experiment you should run,” in English? Which of those 2 approaches—or is it some combination—when you think about AI and bio, is more promising?

I’m much more excited about scientific AI than I am about language AI. With languages, we’re in pretty good shape already. What worries me is that getting to the next level of language requires AGI or ASI. That’s very dangerous. I don’t think we have quite figured out how to handle that.

There are a lot of safety organizations and a lot of safety rules and so forth. What typically happens when there’s intense competition is that those safety rules get undermined and pushed aside. Even if they weren’t, I don’t think we understand our own ethics well enough to educate a completely foreign type of intelligence. We barely know how to pass it on to the next generation of humans.

So we need time to sort that out. There’s no rush. This is a completely artificial emergency. This is not like COVID-19, where millions of people were 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.

So I think we need to go very slowly on AGI and ASI, and double down on slightly narrower scientific goals. With even that, we need to be very cautious. We need to have kind of an international consensus on what constitutes safe AI.

Suppose we did build safe superintelligence. How much would that speed up bio progress? There’s a million George Churches in data centers just thinking all the time. Is it a 10x speed-up?

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

Suppose you could get them to care about it. There’s a million copies of you in a data center. How much faster is bio progress? They can’t 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 we need that that would be safe.

But let’s put safety aside for a moment. It’s not only hard to calculate the bads; it’s hard to calculate the goods. It could be a complete game changer. But on the other hand, it’s like if we said we could get instantaneous transport all over the Earth. Well, we could say, “Yes, that could be a game changer.” But do we really need it? Is that really important?

Maybe it’d be more interesting to just have Zoom calls that are better, or we can just learn how to get everything we want in our kitchen and we don’t need to travel anymore. So be careful what you ask for. You could tip our priorities towards something that we really don’t care about, that we shouldn’t care about, or we might wish we didn’t care about.

But I’m curious: you’ve still got to run the experiments, and you still need these other things. So does that bottleneck the impact of the millionth copy of you, or do you still get some speed-up? Basically, how much faster can biology go if there are just more smart people thinking, which is a sort of proxy for what AI might do?

These are great questions, and I don’t want to misrepresent that I know the answers. But it’s like the question of, “If you have 9 women, can you do a pregnancy in 1 month?”

No, not at present. But you’re working on that, right?

No, but the same thing is that there may be certain things that don’t take a lot of people. We just don’t know. We don’t have that much experience with having thousands of Einstein-levels of creativity and intelligence simultaneously in a generation.

In fact, it’s probable that we’re all capable of being a bit more efficient if we don’t have distractions of mental illness or taking care of other people. Now, taking care of other people may be a very good thing. Maybe if we have no one to take care of, there’ll be something bad that happens to us socially.

So these things are very complicated and hard to predict. I think right now, the baby step—or actually, the pretty big baby step—is to eliminate diseases, or at least make it possible for people to eliminate their own diseases as they see fit.

You’ve worked on brain organoids and brain connectome and so forth. How has that work shifted your view on how fundamentally complex intelligence is? Are you more bullish on AI because you realized the organoids are not that complicated, or rather, very little information is required to describe how to grow them? Or are you like, “No, this is actually much more gnarly than I realized”?

I always felt it was very gnarly. I also felt that it 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.

A huge fraction of genetic diseases have, as one of their consequences, the child being developmentally delayed to such an extent that it’s lethal or causes a lifetime deficit. 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, we have 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 having a late-stage component.

But what about how much information it takes to encode a brain?

I’m not sure that much less genome is required than if you wanted to make a brain, because the brain is totally entangled with the body. You have 10^11 neurons and 10^14 synapses.

If you wanted to reproduce a particular brain, 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—if your goal is just to replicate it. The same thing might be true of the brain. But replicating a brain probably involves a lot more information than synthesizing it.

Just to define this, 10^14 synapses are going to take a lot more bytes than the genome, which is billions rather than 10^14. But there might be reasons that you want to replicate a particular brain configuration rather than just make another animal that starts from scratch as an infant.

5. Biobots that use the best of biological and human engineering

Going back to the engineering stuff, often people will argue, “Look, you have this existence proof that E. coli can duplicate every 30 minutes. Insects can duplicate really fast as well. But with our ability to manufacture stuff through human engineering, we can do things that nothing in biology can do, like radio communication, fission power, or jet engines.” How plausible to you is the idea that we could have biobots that can duplicate at the speed of insects—there could be trillions of them running around—but they could also have access to jet engines, radio communication, and so forth? Are those two things compatible?

Certain things seem incompatible, like the temperature of a fission reactor. But it is a possibility that a biological system can make other things. For example, it can make a nest. A bird can make a nest. You consider the whole nest as part of the replication cycle of the bird.

So you can say the biological thing that replicates at a 30-minute doubling time could make a nuclear reactor. That would be its nest, but you need to expand its range of materials. In a certain sense, we do this already. Humans are a biological thing that replicates not in 30 minutes, but in 20 years or less.

Is that fundamentally limiting us?

Yeah, probably it is. But it’s amazing to think about.

What if you could take a cornfield or a nuclear reactor, and suddenly 30 minutes later you’ve got 2 of them, then 4 of them, and 8 of them? That’s quite an interesting concept.

I teach a course called How to Grow (Almost) Anything. I work with Neil Gershenfeld at MIT, who has a course called How to Make (Almost) Anything. We’re trying to meet in the middle, where his mechanical and electrical engineering will meet with our biology.

In fact, neither of us can make or grow almost anything because there are all kinds of little gaps and things that are very hard to make in a small lab. There are things all over the world that depend on multibillion-dollar fabs to make things. But we’re eating away at it.

Maybe a smaller baby step than making a nuclear reactor is making a phone. You said radio communication. It should be a small challenge goal for the synthetic biology community, maybe iGEM: make bacteria make a radio. Actually, Joe Davis is an artist—he’s been affiliated with my lab and, before that, Alex Rich’s lab—and he did make a bacterial radio, but it was more on the art end than on the science end. I think that would be a good goal.

What would it take to do whole-genome engineering to such a level that, for even a phenotype that doesn’t exist in the existing pool of human variation, you could manifest it because your understanding is so high? For example, if I wanted wings, is the bottleneck our understanding? Is the bottleneck our ability to make that many changes to my genome?

Part of this has to do with just learning the rules of developmental biology, like I said. We can determine morphology at the molecular level now: proteins and nucleic acids. At the cellular and multicellular level, there are a lot more things you can do, and a lot faster. But we don’t know the language yet.

I think we’re on the cusp of getting the tools to do that, like the transcription factor I was talking about earlier, harnessing migration, gradients of diffusible factors, chemotaxis, and so forth. That’s one thing we need, but there’s a bunch of things we need, really.

6. Odds of life in universe

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

Oh, I see what you’re getting at. In astronomy, it might be that we would detect radio signals or light signals. With biology, the kind of evidence would be that you show, in a laboratory using prebiotic conditions, a really simple way to get life.

It’s harder to prove the negative because we don’t know all the possible prebiotic conditions. Probably the number was vast. You have 10^20 liters of water at various different salinities, drying up on the ocean, with the sun and the lightning and all this stuff.

I think if you reconstructed in the lab a very simple pathway from inorganics, cyanide derivatives, and reduced compounds all the way up to some cellular replicating structure, that might lead us to believe that at least life exists. Now there are other parts of the Drake equation that might kick in. Maybe it’s hard to get intelligent life 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 us. That’s hard to do experiments on.

But to your question, an experiment that showed multiple different ways of getting to a living system from nonliving systems spontaneously would be interesting. Again, I’m not sure. It would be very hard to prove the negative.

Between intelligent life and some sort of primordial RNA thing, what is the step at which, if there is any, you say there’s a less-than-50% chance something at this level exists elsewhere in the Milky Way?

These are very challenging problems. I’m not even sure we would be able to say within 5 orders of magnitude, much less 50. 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’ve sent outside of Earth have actually looked for life. They’ve had components that could have looked for life. A sad number of those had not enough components that could look for life. The ones that could look for life were not really looking for it. When we get positive results, we dismiss them, as happened with Viking.

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 liquid water—not frozen but liquid water—in our solar system than on Earth. Doesn’t that seem likely that some of that would have been a good breeding ground?

It could be that we need sunny shores, where you have a lot of dry land right next to water. Maybe these are just giant oceans that are surrounded by ice, and maybe that’s not ideal. In any case, we need to look at those fountains to see what’s popping up. That’s a high priority.

The same thing goes for 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.

Again, it’ll be hard to prove the negative. If we find this negative on everything in the solar system, there’s so much more diversity out there that could have done it.

7. Is DNA the ultimate data storage?

If in 1,000 years we’re still using DNA, RNA, and proteins for top-end manufacturing—for the frontiers of engineering—how surprised would you be? Would you think, “Oh, that makes sense. Evolution designed these systems for billions of years”? Or would you think, “Oh, it’s surprising that these ended up being the systems. Whatever evolution found just happened to be the best way to manufacture or to store information”?

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 catalysts, or maybe in some cases as a scaffold as well as catalysts.

One thing that’s probably already happening—we don’t have to go 1,000 years out—is that 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 3 or 4 new nonstandard amino acids being used simultaneously with the standard ones in an E. coli cell. 3 or 4 plus 20 is a lot bigger than 20.

I don’t think we necessarily need more than 4 nucleic acid components. 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—whether it’s bits, digital binary—it’s just 0s and 1s. That works pretty well for 99% of what we do electronically. Having 4 is better than 2, maybe, but do we really need 6? I don’t know.

I wouldn't be surprised. Another possibility is if we changed the backbone of DNA. Maybe we keep the ACGT, but make it out of peptides now, a little bit smaller, a little bit more compatible. I don't know. It could be part of the new amino acid collection. There'll be more.

These are just things that my primitive 21st-century brain is coming up with. A thousand years from now, it'll be a whole new millennium. It makes sense why evolution wouldn't have discovered radio technology. But things like more than 20 amino acids, or these different bases so that you can store more than 2 bits per base pair, or, for example, the codon remapping scheme—this redundancy, which it seems like, based on your work, was this extra information you could have used for other things. Is there some explanation for why 4 billion years of evolution didn't already give living organisms these capabilities?

I think that evolution has a tendency to go with what works. The investment in making a whole new base pair would have been high. We haven't even articulated what the return on investment would be. What do you get from that? We have made systems, like Floyd Romesberg and others, where you have replication and transcription and translation with a new base pair.

But it hasn't been clearly articulated what that gets you, even in technological society. In technology, you can jump to things where all the intermediates aren't incrementally useful. But evolution, as far as we know, is generally limited to… You have to justify every change, like some bureaucracy: “If you're going to put this sidewalk in, you have to justify that before you build a city.”

8. Curing rare diseases with genetic counseling

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

It's hard to say because as soon as you say it, it becomes hyped. If I've ever been asked this question before, it's too late. One thing I think is very ripe and very well understood in a certain sense, but is nevertheless ignored… The previous example I would have chosen was making genes out of arrays.

Arrays were typically used for analytics, quantitating RNA—the original Affymetrix-type arrays. But we turned them into gene arrays, and people just weren't using it. It was in Nature. It was hidden in plain sight. It was somehow underhyped.

What I would say is that genetic counseling is underhyped. It is clearly competitive with gene therapy in a certain sense, clearly not for people that are already born, but for people in the future—not even the distant future, but in the next couple of years. We've got a chance of diagnosing them or diagnosing the potential parents and dodging these diseases.

This has been in practice since 1985 in Dor Yeshorim, a perfectly reasonable community response to it. It eliminated or greatly reduced all sorts of very serious inherited diseases. Sometimes, depending on how it's presented, it's dismissed as eugenics. Rarely have I heard Dor Yeshorim described that way, and rightly so. What they're doing is standard medicine, whether you cure these kids as soon as they are newborns or whether you counsel the parents so the same disease is missing.

The problem with eugenics was that it was forced. The government forced it on people. It wasn't that it enabled people to make a choice. 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 because when people are 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.

There was great resistance to seat belts because less than 1% of people died in automobile accidents or even got hurt. There was great resistance to stopping smoking. It's hard even for us to imagine how great the resistance was to seat belts and smoking. But eventually we got over it. I think this is a similar thing.

Only 3% of children are severely affected by genetic diseases, and they feel like, “I'm not that unlucky. I'm in the 97%.” If those were your odds of winning at the horse races or at the casino, you'd take them. A 97% chance of winning? Good. But when a child's future is at risk, I think that's not the right solution.

The other thing is that I think it has to do with the trolley problem. If you don't influence it, it's not your fault. But actually everything is your fault. Not doing something is a decision. So I think it's like, “If I just don't do anything and they come out damaged, it's not my fault,” but it is.

David Reich was talking about how in India—especially because of the long-running history of caste and endogamous coupling—there have been these small subpopulations that have high amounts of recessive diseases. So there, it's an especially valuable intervention.

I know what you're saying, and what David is saying, but I think it's a dangerous dichotomy. There are lots of them, not just in India but all over the world. In fact, we all went through a bottleneck. But that changes the rate from, say, 3% to 6%. But the point is, 3% is still unacceptable.

It's just a tragic loss, not only of the human life directly affected, but of the whole family. Very often, one or both parents have to quit their job and spend full-time caregiving and fundraising, because these are very expensive diseases as well.

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. But I think as word spreads and you see the positive outcomes, I think it will be seen as one of the simplest bits of medicine ever.

It's very inexpensive. In fact, it's less than zero because you spend $100 per genome. It'll probably be less soon. You get the whole thing analyzed. Compare that to millions of dollars that will be lost in opportunity costs and 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 10-fold return on investment.

It's a no-brainer from a public health standpoint. We should be able to pay for this through the National Health Service in England, through insurance companies in the United States. It turns the insurance companies from being the bad guys snooping in on your personal life and then raising your rates to giving you this free information, and you can do with it as you wish. If you take the advice, then you save them millions of dollars.

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

Absolutely. I've actually counseled 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 genetic counseling. But the rare recessives can be handled at matchmaking and at every level.

Anyway, I counseled my gene therapy companies that they should invest in common diseases like age-related diseases and infectious diseases. In fact, the COVID vaccine was formulated as a gene therapy, and the cost was in the $20-per-dose range. 6 billion people took it, and it was proven over the whole population.

So I think that's the more appropriate usage of gene therapy. For practical reasons, getting FDA approval and so forth, you might go for the rare diseases, and that's perfectly fine. The sweet spot for gene therapy is for age-related diseases, and the sweet spot for rare diseases is genetic counseling.

9. NIH & NSF budget cuts

All right, some final questions to close us off. 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 DOGE’d 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 to maybe come up with a different funding structure? Basically, what is the best-case scenario if this postwar system of basic research is upended?

I have to preface this.

When scientists answer a question and explore possibilities, it doesn't mean they're advocating for them. 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. I'm not enthusiastic about NIH and NSF budgets being cut.

You could say that it forces us to think more seriously about philanthropy and industry-sponsored research. That could be a positive thing. It could be that it makes us listen more carefully to what society actually needs rather than just doing basic research. I'm a big proponent of basic research, but maybe I'm more than average at connecting 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.

That could be a positive. It could be that it creates another nation-state that now is the dominant force. China could now become the next empire after the US. Is this a positive story? It could be for China. You didn't specify who it's a positive story for. The US displaced Britain, which displaced Spain and Portugal. It keeps moving. Fresh blood is sometimes a good thing.

Again, I preface this by saying I'm not advocating this. Let's see, what else could go well? There are just certain things that society is fairly good at doing collectively that we're not good at doing individually. Building roads, schools, and science are examples of that. It doesn't mean we couldn't learn how to do that. 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 that there's all kinds of pathologies that come along with that.

10. How one lab spawned 100 biotech companies

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 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. Is it something about the nature of your academic lab? Is it something about the nature of biology research? What explains this pattern?

First of all, thank you for being so generous in your evaluation. Maybe take it with a grain of salt. But I think that what it is is being in the right place at the right time.

Boston is a unique culture. It attracts some of the best and brightest students and postdocs automatically. It is dense enough. Sometimes people want to spread the wealth out evenly all over the universe or the planet. There are advantages to having it clustered. Spouses can find other jobs in the same field. Having a concentration of biotech and pharma, and MIT and Harvard and BU and so forth, all within 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. It starts building up a positive feedback loop, just like the building of Boston was a positive feedback loop.

The more Harvards and MITs and high-tech startups, then pharma, you get a couple of wins in the literature and people start coming that are a whole other level up. Maybe they're already aiming for entrepreneurship, whereas before they weren't. Anyway, it evolves in a way that you can't just jump-start from scratch. You couldn't just suddenly create Harvard and MIT in the middle of the desert and suddenly create a lab that is taking these kinds of risks early in a career.

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 at the end of the hockey stick, but you don't notice it until it gets going. That's what's happening in computing, AI, and biotech. They're all peaking at this point.

So whichever lab happened to already have that positive feedback loop going with the academic-to-industry technology transfer would asymmetrically benefit from that exponential. To some extent, with the 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. I just jumped out of a plane and am accelerating steadily."

Yesterday, I had dinner with a bunch of biotech founders. I mentioned that I was going to interview you tomorrow. 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?" I think 70% or 80% of the people raised their hands. 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 been recruited by you, have worked in your lab. So 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. 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 like you make a library where you have 1 million members of the library that are going to fail and maybe 1 or 2 will succeed. It's a very different attitude. It's much more engineering, but it's even different from most engineering. Engineering doesn't usually use libraries that way—millions and billions of components that are nonrandom, but many of them will fail.

So the question was selection criteria. There's self-selection. The next thing is, in the interview, I typically tell them that I'm looking for people who 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.

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 nice is one criterion.

Multidisciplinarity. It's hard to build a multidisciplinary team from disciplinarians. If you have 2 people who each know 2 languages or 2 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 the skill that connects them. That's the third thing. Those are the 3 main things I would say.

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—given that's the case, what is the vision? We're going to very plausibly have a world with genuine AGI within the next 20 years. What is the vision for biology given that fact?

If AI were 100 years away, we could say we've got this research we're doing with the brain or with gene therapies and so forth, which might help us cope or might help us stay on the same page. Given how fast AI is happening, what is the vision for this bio-AI co-evolution, or whatever it might look like?

If we handle the safety issues—and that has to be a top priority—then we're probably going to have almost perfect health. Why wouldn't we? It's going to go so fast. It's going to go pretty fast with just regular AI without AGI. But if you add to it AGI...

It'll also be a positive feedback loop, because the more people that get fixed or get access to good healthcare, the more people will be helping prompt the AI, if that's necessary. I think it probably will be. The more hybrid systems we'll have of people and machines working together in harmony in this very positive scenario.

Well, that's a good vision to end on. George, thank you so much for coming on.

Thank you.