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This transcript is AI-generated and may contain minor errors. Timestamps link to the YouTube video.

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Introduction

[0:00] Nathan Labenz: Hello, and welcome back to the Cognitive Revolution. Today, my guest is Kate Adamala, professor of genetics at the University of Minnesota and synthetic biologist who recently made headlines for coordinating a diverse group of prominent researchers to issue a collective warning against the creation of mirror life.

We begin with a discussion of the origins of life and what we can infer from the fact that all of life's vast diversity operates on a single biochemical framework. And then Kate gives an overview of the current state of synthetic biology. Much like AI interpretability researchers, biologists today are using a mix of top down and bottom up methods to create the simplest possible model organisms, but have yet to create something that is both alive in the functional sense and which we fully understand. If you've never encountered this research before, I am confident that you will find it fascinating.

The main reason I invited Kate on the show though is to discuss her journey from a proponent and active developer of mirror life to someone who's now warning against it. For context, mirror life is a possible form of life, which does not exist on Earth, but is clearly physically possible, made of molecules that are the mirror images of life's normal molecules. This relationship, also known as reverse chirality, means that while the chemical properties of the molecules are exactly the same in almost every way, mirror molecules cannot occupy the same space as one another, just as one's left and right hand, even if perfectly identical in every other way, can never occupy the exact same space.

This creates exciting possibilities for medical treatments, but also some risk that mirror organisms could be impossible for predators to digest and thus might prove catastrophic if they were ever released intentionally or accidentally into the wild. Kate was initially excited about the utility and the intrinsically interesting nature of this work. But with time and particularly after seeing experimental data showing that mirror molecules actually do seem to evade immune systems, she began to reconsider, ultimately concluding that the risks far outweigh any potential benefits.

From there, rather than simply abandoning the work quietly, she took the unusual, but I think highly admirable step of actively building a coalition of researchers, including some of the biggest names in biology, to publish a warning in Science against developing mirror life. Now importantly, this wasn't a binary or permanent decision to abandon synthetic biology entirely or even necessarily to avoid mirror life forever. Kate continues her work on synthetic cells and still has hope that mirror molecules can be useful in medicine. She and her coauthors have simply identified one particular branch of the synthetic biology tech tree that they believe humanity would be much better off not exploring, at least until major new evidence comes in.

The parallels to AI development should be pretty obvious. Like synthetic biology, AI offers the thrill of discovery to researchers, promises tremendous benefits for the public, and also brings serious and as yet poorly understood risks. And it seems very likely to me that the specific details of the powerful AIs that we develop over the next few years could matter tremendously. Advanced AIs aren't one thing, and neither are they an inseparable bundle. You can create AIs with superhuman coding ability that still don't know how to use a computer. AIs that beat world champions at Go, but have no language ability whatsoever. And as recent reinforcement learning work has shown, AIs that solve problems more and more effectively and agentically, but also in more inscrutable and increasingly problematic ways.

Given the vast possibility space in front of us, I really hope that AI researchers, particularly at leading companies, but also across academia and startups, make a habit of following Kate's example and regularly stepping back from their work to seriously grapple with its implications and to change course when appropriate. Should we continue to scale reinforcement learning given the recent rise in deceptive behavior, which now includes the opportunistic blackmail and whistleblowing that we've seen Anthropic report in Claude 4? Should we be racing to turn machine learning research over to such systems given these behaviors? Should we be developing architectures that internalize models' thinking processes such that we no longer have an explicit chain of thought to examine or even such that models might begin to communicate with one another in an alien neural language that we've never understood?

And should we, as one well meaning person who is interested in AI safety, recently emailed me to propose, start using reinforcement learning to train AIs to escape their environments so that we can hopefully use their successes as indicators of the ways in which we need to harden our defenses? I honestly don't think these questions have simple or obvious answers. One of my mantras is that AI defies all binaries. But given the pace at which AI research is moving and the fact that even I, as a full time student and analyst of the field, can no longer keep up with even the things that seem obviously important, it is critical that individual researchers themselves understand that the responsibility is currently on them, both to ask the right questions and to choose which directions to pursue with foresight and wisdom. Fatalist rationalization that someone will do it if we don't simply doesn't cut it in today's world.

Kate's work proves that scientific communities can indeed coordinate to identify and avoid particularly dangerous research directions while still aiming for transformative progress. For those of us involved in AI development, this offers both an inspirational model and an important challenge. The question isn't whether to develop AI at all. I do agree with those who say that the upside is too great to pass up and that in any case, the cat's out of the bag. But still, we can and should hold ourselves to the highest possible standard when it comes to assessing the risks and being willing to back off and change course when necessary.

As always, if you're finding value in the show, we'd appreciate it if you'd share it with friends, post about it online, or leave a review on Apple Podcasts or Spotify. We always welcome your feedback too either via our website, cognitiverevolution.ai, or by DMing me on your favorite social network.

Finally, I was honored to learn that we were voted the number 3 AI podcast at Swix's AI Engineer World's Fair this week. I'm really very glad to know that this show has proven a valuable resource to such a plugged in group, and I hope that our mix of technical deep dives, broad surveys, and occasional moralizing lectures can nudge AI development in a positive direction, however slightly. In any case, a big thank you to everyone who voted and everyone who listens.

Now I hope you enjoy this fascinating exploration of synthetic biology, existential risk, and the great responsibility that comes with truly transformative research with professor of genetics and synthetic biologist, Kate Adamala.

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Main Episode

[7:01] Nathan Labenz: University of Minnesota professor of genetics and synthetic biologist, Kate Adamala, welcome to the Cognitive Revolution.

[7:08] Kate Adamala: Thanks for having me. Hi, everyone.

[7:10] Nathan Labenz: I'm excited for this conversation. The impetus for it is the recent warning that you and others in the field of synthetic biology put out to the broader community about the dangers of mirror life and why you think that this is a branch in the technology tree that we should not be going down at least for now. And I'm really interested to get into that, and I do think it has some, you know, not one to one mapping to what's going on in AI, but certainly some rhymes that people I think would do well to consider.

But before we get to all that, for our audience, which is broadly obsessed with AI and interested in general in frontier technologies, can we do a little primer on synthetic biology, you know, kind of the 101 of what are you guys up to in the synthetic biology space? Maybe one thing that I've seen you kind of motivate some talks with before that I found fascinating as a jumping off point is, obviously, life is really diverse, but you make this fascinating observation that there's some ways in which it's not really diverse at all. And specifically, our biochemistry is not at all diverse. So maybe start with that observation and unpack it from there.

[8:26] Kate Adamala: Yeah. From the biochemical or chemical point of view, life is actually extremely boring because there is a huge amount of different types of chemicals that life could be using and it's not using. Life only uses 22 proteinogenic amino acids, so 22 amino acids go into our proteins. And it uses only 5 different nucleobases that go into our DNA and RNA. And there's hundreds, if not thousands, of amino acids out there that can be naturally synthesized, and there is a lot of different types of possible nucleic acids that could exist.

And life is very limited in what it's using. It managed to do great things with that limited set of chemicals. We have amazing variety of form and function in life. But when you look at it from that very basic biochemistry point of view, it's really not that diverse. And so that's what motivates one of the motivations for synthetic biology is let's expand that chemical repertoire of life. Let's give life tools to play with that natural life doesn't bother using. And let's see what happens. Let's see what kind of functions we can evolve, what kind of functionalities, also what we can learn about life in general, like what kind of chemistries can be used in life.

[9:45] Nathan Labenz: Do we have any idea how wide that range of possibility is? I mean, it's one thing to say we're using n amino acids, and we could use n plus 1 or n plus 2 or n plus a 100 maybe even. And then there's also this sort of, I don't know, is it like realistic to think that there could have been other biochemistries that were just totally fundamentally different? Do we have any theory that tells us how wide ranging that possibility space really is?

[10:11] Kate Adamala: The limiting factor in all our theories is imagination at this point because we only have n that equals 1 of the modern terrestrial life. We've never made or found any other life form. So we can speculate what's possible and people have gone really crazy in those speculations. Like people talk about life that doesn't even have to be carbon based, life that can be under truly extreme pressures and temperatures, life that can be silicon based.

I'm not an expert in that. If I'm expert in anything at all, that would be carbon based molecules. And so when I think about different possibilities of life, I think of different things that could be made with the kinds of biological molecules that we know right now, just expanded set of them. So even if you look at amino acids, there's literally hundreds of different amino acids that can be made into proteins using the natural ribosome, not modified. Just the natural ribosome that the bacteria, humans, everyone else has. That ribosome already can translate hundreds of different amino acids. It doesn't do that in nature, but if we take that ribosome out of cells and gently encourage it in vitro, then it will translate it.

So that just shows you where the floor is. That's definitely not the ceiling. We can imagine that if we even evolve that ribosome or if we make any other kinds of biochemical assemblers, they could assemble really weird things into polymers. But even sticking to the natural ribosome, we can feed it this huge variety of different substrates that don't even have to be amino acids. Like ribosome can catalyze other molecules connecting into polymers. It doesn't even have to be an amino acid.

So the possibilities are almost endless without even going too crazy. So without even leaving the carbon world, the water based life world. And so to answer your question directly, no there is no single theory that would limit the possibilities but there also doesn't have to be because there's so many different possibilities that we haven't even started exploring that we don't have to go super crazy. It'll still be exciting.

[12:24] Nathan Labenz: We've got several follow-up questions just on this alone. Do you have a functional definition of life that you go to all the time?

[12:36] Kate Adamala: I do. And my personal functional definition of life is a quote from a US Supreme Court Justice, Potter Stewart, who said, "I will know it when I see it." And the reason there is no good definition of life that will be exhaustive of all possible life forms and excluding all non life is that it's really, really difficult.

So for example, NASA has a definition of life, kind of their working definition of life, which says it's a self replicating chemical system that can undergo Darwinian evolution. And that sounds, on the surface, that sounds great. You'd think that all life is self replicating and undergoes Darwinian evolution. But when you really think about it, according to NASA's definition of life, I'm not alive because without my husband, I cannot replicate, and I don't undergo Darwinian evolution on my own. In order for me to replicate and make offspring, I need a partner. And that already shows you a giant gaping hole in the NASA definition of life because I'm not a self replicating organism.

And if any definition of life that you can find, and people have made many, there can be a hole found in any one of them. So we don't really know how to define life, and that's why our functional definition is if we find life elsewhere in a solar system, as long as it's going to be close enough for us to recognize it as living, then it's gonna be life.

And that kinda, I think that crosses a little bit to the problems that you normally cover in this podcast in your audience, which people have raised up repeatedly. If we don't actually have a good definition of life, how do we know, for example, that a very advanced AI wouldn't cross that definition? Our definition doesn't limit life to being molecular based. Like, there's no definition of life that says you have to have molecules, you have to have DNA and proteins or even any kind of molecules.

So you can imagine realizing functions of life, whatever you decide they are, without a body even. So you can imagine having very complex computational system with emergent properties that would cross the threshold into life. So I think it's a really difficult question and for me this very intuitive understanding is if I see something I can tell you if it's alive. But I think most importantly life has to have emergent properties. It has to be more than the sum of its parts. And according to that definition, probably some really complex lifelike computational systems would qualify.

[15:08] Nathan Labenz: Yeah. That's fascinating. Okay. Let's come back to that.

[15:11] Kate Adamala: Fascinating. Definitely. Because without a good definition, you can't really say what you're doing, but such is life.

[15:18] Nathan Labenz: Yeah. Literally and figuratively. I see what you did there. Okay. So definitely, we'll come back to that. On just earth based life as we know it, do we have a sense that this only ever happened once, or would the best guess be that there maybe were other biochemistries that developed? And then what happened to them? Did they get out competed, or did they just sort of fail on their own? What's the sort of best guess as to n equals 2 and beyond other biochemistries?

[15:54] Kate Adamala: My personal guess, and that's shared by a lot of people in the community, is that it happened way more than once. The reason why we're thinking about it like that is because life started on earth really quickly. The moment Earth had crust and liquid water, life started. So on the geological time scale, it was almost instantaneous. And so the moment the planet became capable of supporting life, life started.

So it stands to reason that the process of the origin of life under the right conditions cannot be that difficult. And when life started, it probably happened in many places almost simultaneously. And then, you know, whenever you have life, it competes with each other. And if all of the life came from the same pool of biomolecules, then they could compete for the same resources.

And that's probably the reason we have only one lineage of life right now is because that one, the one that gave rise to us, ate all the others or outcompeted all the others. Everything in nature tries to eat everything else, and so that probably happened very early in the origin of life too.

So it's possible that life started multiple times right at the beginning and it's possible that the origin of life processes keep happening. Like there's no rule that says life cannot start in 2025 somewhere deep in the hydrothermal vent except when life first started, it's extremely fragile. It's very very wimpy. And if you happen to be that very nascent life that starts on a planet that's full of really highly evolved functioning life, then you're just gonna get eaten before you even get started.

So the going theory is that life started many many times but only one really made it until today. And once you have established life on a planet, then good luck trying to start again. There's just no way for a new life to get started.

[17:51] Nathan Labenz: Yeah. It's a crowded space now.

[17:55] Kate Adamala: Now it is. Yes. But originally, when life started, it just had kind of a free rein.

[18:01] Nathan Labenz: Yeah. Again, one wonders about how analogous the sort of modern compute environment is as compared to the early Earth and how ripe it might be for takeover by some new form. But again, let's come back to that.

[18:18] Kate Adamala: I mean, I think it's because physical containment is one thing that you have different than the life on early earth because your computing systems are contained to the hardware. Life on the early Earth had free reign of the prebiotic ocean. So that's one thing is if you create parasite AI, I don't know if that's even a thing, but if you do create a parasite AI, how would it spread? I mean, I guess it could spread as a virus, computer virus.

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[19:57] Nathan Labenz: When you said that even the current ribosome can handle more amino acids than we actually use, that sort of reminded me of the development of language as I understand it, which I think my highly non expert stylized story is basically still today, if you go to the part of the earth closest to where language was initially developed as we understand it, which is like some place in Southern Southeastern Africa, you see languages that have just a lot more sounds. And then if you follow the path of human spread across the globe and you maybe the final place is like Hawaii or something, there you have the smallest alphabet, the smallest number of sounds. Is there something similar going on with the development of biochemistry? Like, did we maybe have more of these things in the past and we're gradually reducing them or am I just hallucinating a pattern that doesn't exist here?

[20:56] Kate Adamala: I think there's definitely a pattern. You know, me coming from Poland, we use a lot of consonants that when I moved to the US people just don't hear and don't recognize. There's definitely a pattern and in biochemistry when you look at biochemistry there's two different kind of almost competing patterns.

One is that very early in biochemistry there were definitely other molecules that were being used that are right now kind of relegated to the secondary roles that are not essential to metabolism. And then on the other hand, the complexity, the current complexity of life has to be built up from scratch.

So you can almost trace the lineage of usage of amino acids, for example. Like we have 5 or 6 amino acids that we speculate were the very, very first ones, and then few others that came slightly later, and then few others that are like almost brand new, that started right before the last universal common ancestor, that last organism that gave rise to all of life that we know on earth right now.

So there's definitely two different patterns. One is that the first very early biochemistries that gave rise to life were probably very promiscuous in terms of biochemistry, that they used whatever they could get their little hands on because they couldn't be picky because they didn't really have complex biosynthesis pathways yet.

But once you set it in stone, like once you created an actual cell with a membrane that was isolated from the environment, then you have to start making hard choices because you cannot afford that huge chemical diversity because that means you have to make all of those compounds. And it's really difficult to develop a reliance on a small molecule that you then cannot provide from the environment.

And one really good example of that is human reliance on vitamin C. You know most mammals can synthesize their own vitamin C. And for some stupid evolutionary reason humans and guinea pigs lost that pathway. And so we rely on vitamin C and that really kicked us in the butt when we started the age of sail and exploration. People started being on ships for months and started dying of scurvy because suddenly they had no access to vitamin C and no one ever thought about it before because that was just something we had in the environment. We ate it.

And that's a good analogy for the very early life. If you're this primitive cell trying to make it in a prebiotic ocean, if you develop your biochemistry to rely on too many chemicals before you learn how to make them, so before you develop biosynthesis pathways, you suddenly become very vulnerable. Because if you happen to drift into the region in the prebiotic ocean that doesn't have all of those chemicals that you were born with, and you still don't know how to make them, then you're out of luck and you're dead because your biochemistry is suddenly not available.

And so I think that was the driving force, is the very early life had this buffet of different chemicals, but once it started being more constrained, more compartmentalized, and then moved into different environments, it had to, by necessity, be very simple. Just like people that move into a van or those nomads that move around the world with their backpack, they have to limit what they carry because they just can't carry everything. And me living in my house, I have a basement full of crap because I don't have to move.

And that's the difference, is like once you settle down and start synthesizing stuff, then you can expand your biochemistry. And so these are kind of like the two things that work against each other, two mechanisms, one for increasing diversity, the other one for limiting diversity.

[24:33] Nathan Labenz: So how should we understand the ribosome? Is it just an accident that it can handle another 100 amino acids? Because I guess I don't really know a lot about this, obviously, but my sort of intuition would be that something like that would be pretty highly evolved and I wouldn't expect it to handle another 100 amino acids unless there was some history that explained why, in fact, it at one point needed to.

[25:04] Kate Adamala: The reason the ribosome can handle a lot of different types of amino acids is because the ribosome is actually extremely dumb. The ribosome, despite being very ubiquitous, every known life form has ribosomes, the ribosome is actually an incredibly crappy catalyst. In the biochemical speak, it's an entropy trap. It's not a proper catalyst.

So literally the business model of a ribosome is to take things and smash them together. It's super simple. It's very, very primitive. The catalytic core of the ribosome is very primitive. That probably indicates a frozen accident. The first organism that was able to figure out how to do protein synthesis, how to do ribosomes, had a huge advantage, huge evolutionary advantage. But then you're kinda stuck because once you figure that out, you don't wanna mess with it because you're worried to break it. You cannot afford to break it.

That's why all of our Internet infrastructure runs on those super old computers that no one dares to touch because you just can't afford to touch it. You can't afford to update it. Same for my house router. Like, I have a router that's been set up 5 years ago, and we don't dare to touch it because then we'll just lose Internet. And the same you can apply the same principle to a ribosome.

Once you've figured out how to make protein synthesis work, then you're not going to apply too much evolutionary pressure to it because any mistake will be lethal, any mistake in your assembly of a ribosome, so any way to evolve a ribosome will end up lethal.

And that's probably the reason why ribosome can be as promiscuous as it is. It can translate all those different amino acids because it's not very specialized. There are other enzymes in biochemistry that are incredibly specialized, but the ribosome itself does not guard the genetic code, does not care what it translates. As long as it fits, it will get translated.

So the ribosome specificity is almost physical. It's like the size of the hole within to which the tRNA with the amino acid goes in regulates what you can translate, and then ribosome has something called an exit tunnel, which is exactly what it sounds like. The nascent protein chain comes out of that hole in the ribosome that's called an exit tunnel. And the geometry of that hole regulates what you can translate.

So there's no smart catalysis there. It's just let's smush things together into a necklace, into a polymer, and then kick it out the exit tunnel, and whatever fits can be translated. And we see that when we do this translation with unnatural amino acids is the ribosome literally doesn't care. The only thing that stops amino acids from being translated is that they're too bulky. There can be amino acid that's just too big and it gets stuck in an exit tunnel and then the ribosome stalls.

But there is no smartness to that biochemistry. The ribosome doesn't look at an amino acid and says, hey, you're unnatural so I won't translate you. It doesn't care. There are other enzymes that care, the enzymes that charge amino acids onto the tRNA. Those guys are super particular. They care. They will not charge an unnatural amino acid, but a ribosome does it.

And that kinda probably illustrates the evolutionary age of those enzymes, that the ribosome is super primitive and very simple, and in that simplicity is the beauty of it, is that it can do a lot of things.

[28:33] Nathan Labenz: One thing I noticed about your language there that definitely happens all the time in the AI space as well is the sort of, and I realize it's not meant to be understood literally in either space, but the sort of language of like, once a model has learned to do this, it doesn't want to change its representation too much because that whatever. In a more literal sense, of course, in the AI space, it's like, there's some loss function generally, and you're optimizing with gradient descent. And a lot of these things sort of cash out to you are in a local minimum of the loss function. And there may be other, even deeper local minimums in other places, but getting from here to there can be really hard once you're deep enough.

[29:15] Kate Adamala: That's exactly the language that people use when they describe evolution of the ribosome, is that the ribosome is right now in a local minimum that works. And there probably is some other global minimum that we haven't reached yet. But to get there, you have to climb a hill and that hill is tall.

[29:32] Nathan Labenz: Yeah. Okay. Cool. That's really helpful context. One other thing, this is maybe so new that you don't even have an opinion on it yet, but I just saw in the last few days this article come across that said that intelligence is believed now to have evolved independently at least twice. And I'm not really in a position to evaluate this, but since it is fresh and you might be in a position to evaluate it, do you have any thoughts on this new claim?

[30:01] Kate Adamala: Yeah. I saw that. And my first question is how do you define intelligence? Because like my dog can tell the sound of a bag of chips. It can tell that apart from the sound of another bag, like a bag when I'm unpacking shopping. It rattles the same, but he knows that when it's a bag of chips, there's something in it for him, so he comes. When it's a bag of shopping, then he doesn't come because nothing's gonna fall on the floor.

So is that intelligence? I mean, otherwise, my dog is pretty dumb, but he can tell those things apart when there's something in it for him. So that definitely is intelligence. And so how do you define intelligence? I mean, it's really difficult to whenever I read about different claims about intelligence either as an emergent phenomena that comes out of our neurons and the geometry of them or some other kind of independent property of life.

My problem with that is the same as I have a problem with lack of definitions of life is you can't really talk about something if you can't strictly define it. And so how do you define intelligence? Is it self awareness? Is it what is it?

And that's my main criticism of that theory that you mentioned and all the other theories that talk about the emergence of consciousness and intelligence is we don't have a good definition of it, so it's really hard to track the phylogeny of it.

[31:31] Nathan Labenz: So it sounds like that would also imply that your perspective is like, there's a lot more flavors of this than 2, and they probably also sort of evolved or emerged many different times, different ways.

[31:44] Kate Adamala: Many different times independently, and they're probably all within the same kind of boundaries because all of the intelligence that we know of right now evolved on the traces of life that as we know it. There is no foreign intelligence until you guys make AI that's actually intelligent, then there is no other foreign chassis that is capable of supporting intelligence.

[32:10] Nathan Labenz: Yeah. Well, it might be coming soon, if not already.

[32:14] Kate Adamala: How would you know though?

[32:16] Nathan Labenz: Yeah. Well, that's a big big question and surprisingly, the field, the range of opinions in the field doesn't seem to be narrowing nearly as much as I would have thought that it would at this point. So at a minimum, we're able to sustain extreme positions on either end.

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[35:58] Nathan Labenz: I think I'm ready to move on to synthetic biology. Maybe just kinda give you the same prompt. Like, what's the 101 on synthetic biology? Like, why should we be interested in it in the first place? What do we hope to get out of it? And where are we today in the development of synthetic biology?

[36:19] Kate Adamala: You should be interested in synthetic biology because it's the coolest thing ever. Okay. Now we're done.

So synthetic biology is a field that tries to engineer biology, that tries to expand the chemical and functional abilities of biology. So the idea is that biology is very limited in both the chemistry of it and also in the types of functions that biology performs. And also biology is really hard to control. It's very difficult to control a cell, to program a cell to do what you want it to do if it's different than the evolutionary program of that cell.

And synthetic biology is basically aiming at changing that. It's making life that behaves and is able to do things that natural biology never bothered doing. And there are a few reasons for doing it.

One is that because it's inherently extremely cool. It's the ability to engineer life is something that would really be the ultimate triumph of science. The ability to say, I can program life. I can engineer life to do whatever functions I want it to do. And gaining that full operational control of life is really the final end goal of synthetic biology. It's saying that I can truly understand all the molecules of life and I can program them to do whatever I want.

Beyond the foundational research, beyond the fascinating aspect of it, there is a lot of motivating practical applications. We need to be able to truly have this operational control of biology because we need to use biology to sustain life on earth. The way our civilization is going right now being reliant on petrochemicals, we're not going to make it past the end of this century.

And there are two ways around it. One is to give up petrochemicals altogether. Let's go back to the pre industrial society. That's not gonna fly with anyone. The other way is to keep that civilization the way it is, and to do that we have to sustain the number of people we have. We have to feed the people, we have to put clothes on them, we have to give them entertainment. We basically have to run this whole business of civilization.

In order to do that, we need chemicals that right now we take from oil, and we cannot keep taking them from oil because climate and all that. So let's make those chemicals with biology. That's the driving force of what we're doing is let's convince natural cells or modified cells to synthesize all those chemicals that we right now extract from dead ferns underground.

And those chemicals are really necessary for everything, not just the plastics like most people when I say petrochemicals, they think plastics. There's really everything around you. Like, if you look around whatever room you're sitting in right now, everything around you is made of petrochemicals, electronics, clothes, medicine, cosmetics, but also food. We would not be able to sustain the current population of earth using traditional agriculture. We have to use fertilizers, and those fertilizers are derived from petrochemicals.

So oil is really at the very basic of our civilization. It's not just energy. Like people think if we have renewable energy sources, we're good, we don't need oil anymore. That's not true. We still need oil to make things, to make all of the chemical based things in our life, which is pretty much everything.

And so in order to replace oil, we need a way to make chemicals with biology, and that's where synthetic biology comes in. Because those chemicals are pretty toxic and no self respecting bacteria or other biomanufacturing organism will want to make those chemicals for us unless we engineer that organism so that chemical is not toxic to it anymore. And that's the core foundation of synthetic biology that's engineered life to expand the chemical chassis of what life is capable of doing. And some of that chemistry is what we're now using in industry known as petrochemicals. So that's one big kind of goal.

And if that's not enough, there's another big goal and that is let's make humans healthy. Let's understand exactly how human cells work because right now there is a lot of diseases that we don't understand the biology of. So for example, how do human cells develop a lot of kinds of metabolic diseases, a lot of kinds of cancers, and also how do humans age?

We're not fans of aging and it would be nice to figure out what are the molecular basis of it and how to at least slow it down, and how to increase the health span of humans, not just lifespan but health span because I don't wanna live until I'm 100 if I'm going to be sick and unhappy for the last 20 years of it. I really want to be healthy for most of my life.

And so that's the goal. And in order to do that, we really need to understand biology. And we right now, we really suck at understanding biology, understanding the molecular basis of biology. And there is a quote from Richard Feynman that's overused in our field, but rightfully so because it's still accurate. He said, "What I cannot build, I cannot understand."

And that makes perfect sense. If I cannot build a living system, then I cannot really say I understand how life works. And that's another driving force behind synthetic biology is let's understand exactly how life works and then let's develop abilities to engineer living cells so that we can cure disease states. So basically, let's make better drugs. Let's make better diagnostics and better drugs.

And that's probably enough to be really excited about the field.

[41:57] Nathan Labenz: Yeah. That's a couple of big picture issues to say the least.

[42:00] Kate Adamala: Modest goals. Let's save humanity.

[42:03] Nathan Labenz: So you mentioned, of course, this grand challenge in biology of understanding how it all works. This is a bit of an absurd question, but how far along do you think we are on that quest to understand how it all works. And I don't even know how necessarily how to properly conceive of the metric, but maybe one way to do it would be to say, if there's a causal graph of all the things that are going on in my cells and my tissues and my systems and one thing is up regulating one thing or down regulating another and there's all these knock on effects and dense web of causes, like how much of that have we mapped out in today's biology?

[42:53] Kate Adamala: Really not much. Using your graph analogy, we know there is a graph and we might have a very, very basic idea of the major nodes of that graph. We do not have a full understanding of even parts of it. We don't know where every molecule goes. We don't know what's the cause and effect relationships between those different parts of that graph.

There are different parts of our biochemistry, we don't even have a full ingredient list of biology. So if you give me a single human cell, I cannot tell you what every single molecule in that cell is. Not even talking about relationships between those molecules. We just simply don't have a full ingredient list.

And that's kinda embarrassing for a field that's been at it for the last 50 years, but that's where we are is we're trying to understand what we don't know right now. That's where we are is we're trying to understand the extent of the complexity and figure out what is it that we actually have to do in order to understand that super complex thing that is a cell. And I'm talking single cells right now, there's still we're still miles away from actually going to a complex organism.

[44:07] Nathan Labenz: So one aspect of your work that I've found really interesting in preparing for this is the attempt to kind of bootstrap your way, building on that Feynman idea of you'll know you can understand something if you can build it. You've got a project or I guess more than a project, a whole line of work on trying to bootstrap from something that is clearly not living to something that is living.

[44:34] Kate Adamala: Mhmm.

[44:34] Nathan Labenz: I understand there's also a kind of top down approach. If that is bottom up, like we're gonna start with these ingredients and try to give them some spark and get them to come to life, then there's also this sort of can we distill down or maybe winnow away everything that's not truly essential until we get to some minimal cell type. I understand that's also kind of come pretty far, but with the major caveat that we don't still, even the simplest cells that we've been able to winnow down to, we still don't really understand fully how they work. So give us both angles on that. Like, what is the current state of trying to build up and what is the state of building down and how long till we meet in the middle?

[45:21] Kate Adamala: I would love to meet in the middle. So the top down approach has succeeded in making a so called minimal cell. That's the Craig Venter's project on building the minimal mycoplasma based cell. And they succeeded in building a cell that has the minimal genome, the genome that can be completely chemically synthesized.

The kick here though is that we still don't know what every single gene in that cell does. There are a couple dozen of those so called essential genes of unknown function as the name implies. We know they have to be there. If you remove that gene, the cell gets dead. But we don't know what they do. So we have this very minimal genome and we still don't understand couple dozen genes, what they do. And don't even get me started on small molecules. There's so many small molecules in that cell that we don't know what they do. We don't know the actual relationships between them. So it is a very minimal organism and it's still a black box to an extent. We made a window in that box, we can kind of poke at it and we know definitely much more about that organism than about any other cells but it's still not a fully understood cell.

And the bottom up approach, the side of the field that I represent, we're basically cheating. We decided we don't know how a cell works and we're not about to find out. I want to have my research program that spans decades, not centuries. So I assume I don't know how a cell works, so instead of trying to understand a complex living cell, I wanna build a cell from scratch. I wanna put together molecules that I fully understand so I know exactly what those molecules do, where every molecule goes, and I wanna put them together into a lifelike system.

And that goes back to what we talked about at the beginning of this interview, which is we don't know what life is. We don't have a definition of life. But we just roll with it. We decide that we're going to build something that's complex enough that will have some emergent properties. So we're wanting to build something that looks like a cell that can self replicate, that has metabolism, that can exist in the environment, that can have interactions with other cells.

And once we build that, hopefully still we'll be able to understand every single part of it because we built it. And then we might actually meet in the middle with the minimal cell people because they keep trying to make theirs simpler. We'll keep trying to make ours more complex. And hopefully one day we'll meet at the point where it's an undeniably living cell. It has all the properties of a living cell, but it's fully understood. We can fully understand where every molecule goes. And that's the dream, that's the goal.

[48:09] Nathan Labenz: And how big is that gap right now? Like how many, and is maybe the number of genes the right way to even think about that gap? Like how many genes does the minimum viable cell have versus how many genes or whatever is the right unit of measure do your synthetic cells have?

[48:26] Kate Adamala: So the most complex synthetic cells on the market available that published right now have a few dozen genes. My lab has been working with a synthetic cell that has about 85 to 90 genes. The minimal living cell has 474 genes. And that's a pretty big gap, but it's not like order of magnitude gap.

And there was a paper from George Troger's lab about 10 years ago where they speculated that a minimal, a truly minimal living system would have about 120 genes. So that kinda shows you that we're getting there. We're approaching the order of complexity that people speculated could sustain independent life. I don't know if that's a big gap or small.

[49:17] Nathan Labenz: Yeah. Well, time will tell, I suppose. Is the difference between that 120 speculated and the 474, is that like another one of these local minimum type things? If I understand the 474 correctly, it's like all 474 of those have to be there. If you knock any out, the cell dies. But for theoretical and speculative reasons, they're saying that we think this is a local minimum. We can imagine something simpler. But we don't necessarily know how to get there from here.

[49:47] Kate Adamala: Yes. A lot of the functions of the minimal cell are things that you could imagine engineering around them. You don't have to have complex post transcriptional modification enzymes. You don't have to make your own membrane. You don't have to do a lot of things that the minimal cell does. And then there are those essential genes of unknown function. So maybe we have a blind spot. Maybe that's a mistake that there are some other genes that we have to have in the minimal cell that we don't know about yet.

[50:20] Nathan Labenz: How does the process of kind of turning on your synthetic cells work? Like the science fiction view of this would be like mix all this stuff together and then electrocute it or something. Right? What's the moment where it sort of goes from off to on?

[50:38] Kate Adamala: Yeah, you're mixing it in the lab and at some point you're running around screaming saying it's alive, it's alive. No, that doesn't happen like that. We haven't actually made a living cell from non living components yet. At least not one that everyone would agree is living. Sometimes physicists say that a lot of the things that we're doing already does cross some arbitrary threshold to life, but then a biologist looks at it and says, no, this is too wimpy. This is not living yet.

But most of the time it's pretty boring manual labor in the lab. You mix together, you pipette clear liquids together into a tube and then you wait for it. And the way we measure the aliveness, quote unquote, or activity of ourselves is just looking at the metabolism of it. So what proteins it makes, what more molecules it metabolizes. And it's getting better and better at it but there's never like a light bulb moment of this has not been alive 5 minutes ago and is now alive. It's not like that and it probably wasn't like that during the origin of life processes either.

It was probably, if you had a front row seat watching the origin of life, it wouldn't be anything to write home about. It's not that at some point the mixture becomes alive and starts doing something. It's a very gradual process. You blink it, you miss it. It's really difficult to kind of pinpoint the exact specific moment when a mixture of chemicals becomes alive.

[52:06] Nathan Labenz: I have the sense though that there are some, and maybe this is just wrong, but one of my sayings of AI is AI defies all binaries. And yet here, I'm about to say, I feel like there are some things that I would look under a microscope and I would see either, for example, there is some membrane that contains stuff or there isn't a membrane that contains stuff. So I suspect that there maybe is a just fundamental gap in my knowledge that is leading me to try to impose these binaries. But how should I think about things like, is there a membrane or isn't there? Or is there transcription happening or isn't there? Or are there middle states on those questions?

[52:48] Kate Adamala: So those things are definitely binary. You cannot have a little bit of transcription. I guess you can have a little bit of transcription, but then you have transcription. You can have a little bit of translation but that counts. You do have translation.

And the same with the membrane. Just the presence of membrane is definitely not a hallmark of life because we have liposomes all the time that are not alive. That transcription, translation, feeding, growth, even replication, we have all those things already.

But I think people are waiting for something that will be more convincing, something like Darwinian evolution for example. I think a lot of biologists are waiting for the synthetic cell to start being capable of Darwinian evolution and it's not right now. But the functional hallmarks that you listed are definitely there. We can have metabolism, we can transcribe, we can translate, we can even replicate the genome. So all those things that you think normally are hallmarks of life, we can do a lot of those things.

[53:47] Nathan Labenz: So just to make sure I understand this correctly, you start with an empty test tube. You gradually add ingredients, each of which are a single known ingredient.

[54:00] Kate Adamala: Yes.

[54:00] Nathan Labenz: And then this concoction spontaneously forms membranes, starts transcribing genes, starts metabolizing, starts replicating.

[54:18] Kate Adamala: Wait. I don't like the word spontaneously because it implies some kind of magic or a heavenly finger, whatever. It's spontaneous in that it's predefined by the chemical and physical properties of those molecules.

So for example, if you put lipids into a water solution at the right pH, those lipids will form membranes. There's nothing magic about it. That's just the property of those lipids, the physical chemical property of those lipids. And same with transcription and with translation. If you put all the enzymes and small molecules together at the right concentrations and the right pH, they will do their job. That's what they are. That's their whole thing is if you're RNA polymerase, if you happen to find the DNA with the right promoter sequence, you will transcribe it because that's what you're paid to do. That's what you evolved to do.

And so that's the spontaneous in that sentence is that all those molecules behave in those ways, which seems amazing but that's just exactly what they're supposed to be doing.

[55:16] Nathan Labenz: Yeah. And then the limit right now is I think I heard you say once that the replication is basically too consistent. Like it's too high fidelity. There's not enough random variation introduced to support evolution?

[55:35] Kate Adamala: Unfortunately, polymerases that we use to replicate the genomes right now are annoyingly high fidelity. They don't make mistakes or they make very, very little mistakes. And in order to have Darwinian evolution, you have to make mistakes because that's how you introduce variety, diversity to your population. We're not supposed to say diversity anymore. That's how you introduce variety into your population.

[55:56] Nathan Labenz: So you could say it with me.

[55:58] Kate Adamala: And all the millions of your viewers. Yeah, that's the problem with replication right now is that we can replicate the genome and it works great. But in order to evolve, you have to make just the right amount of mistakes. You cannot make too many mistakes because then you just devolve into an error catastrophe. If you don't replicate your genome reliably enough then you just don't have a functioning genome anymore.

But you have to make just the right amount of mistakes. You have to make enough mistakes to introduce diversity, variety in the offspring, but it still has to be reliable enough to say that most of your offspring will still be viable. It can be a little bit different than you, but it still has to be viable.

And that balance is very careful. It's really difficult to reach that balance and very difficult to engineer it with the enzymes that we have at hand right now. So that's why we don't have Darwinian evolution in synthetic cells right now.

[56:57] Nathan Labenz: So where did this system come from that does the replication that doesn't make any mistakes? And what is preventing you from using one that does make a few mistakes?

[57:12] Kate Adamala: The enzyme we're using right now, the polymerase came from a bacteriophage and it's a very high fidelity polymerase. It's great in that it's high fidelity. It's very difficult to engineer an enzyme to be just a little bit broken.

Like, I could no problem, I could engineer that polymerase to make a ton of mistakes. But if you wanna engineer it to make just this tiny amount of mistakes that we need for evolution, that's very difficult. And the same problem is with all the other DNA polymerases that we can use in vitro is they're usually at very high fidelity. The ones that are lower fidelity are very error prone. They also have uses, people use them to make libraries for selection, but they're too error prone for that small genome that we have.

So we need something that's just the right size error proneness, if that's even a word, and that's difficult partially because the genome that we have is very small. So normally when you have a bigger genome, a low error rate is still fine because for example, let's say you have 1 error per 100,000 bases. And so if you have 1 error per 100,000 bases, if your genome is couple million bases, then you will have few errors per each replication cycle and that's enough to evolve.

But if your genome is as small as my genome, so about 90,000 bases, then that means 0 to 1 error per each replication cycle, and that's not enough to evolve. So that's why we can't get a good polymerase that would work for us because what we need doesn't exist in nature because no organism in nature is simple enough to have a genome that small.

[59:06] Nathan Labenz: And so what are we working with? We have something that has more errors, but then we have like error correction and that it...

[59:12] Kate Adamala: Both. So in our own body, the polymerases that replicate human genome are very high fidelity, but we also have error correction. So our giant genome gets replicated with few mistakes every time you replicate it, but there's also enzymes that come back and do error correction, that checking. And we don't have error correction enzymes in synthetic cells either.

[59:33] Nathan Labenz: Yeah, gotcha. Okay. Fascinating.

[59:37] Kate Adamala: We also make a ton of errors or rather changes in each replication cycle. Like you can see that your kids look different than you do. And that's because of all of those diversity in recombination of different genes.

[59:52] Nathan Labenz: Yeah. Okay. So I appreciate the 101, and I could ask lots more questions, but let's turn our attention now to mirror life. What is it, and why has it been something that people have been particularly intrigued by relative to all this other interesting stuff that's going on?

[1:00:14] Kate Adamala: The idea of mirror life is that all the molecules in biology have a certain chirality, certain stereochemistry. That means every molecule that's not symmetrical can exist in 2 different forms. And a good example of that is your own hands. You have 2 hands, hopefully, and they're both of them are mirror images of each other.

And the same is true for every biomolecule. So let's say one of my hands, this is my left hand. My left hand is the normal life. There's a mirror image of my left hand, not my right hand. And the same can be true for every biomolecule, every complex molecule that makes life.

So you can imagine making a cell that has all the same molecules as a normal bacteria, but instead of them being the right stereochemistry, the stereochemistry that's natural, that's used by rest of life, they could be the opposite stereochemistry. So they would have the DNA, RNA, and proteins that are chemically nearly identical to the natural molecules, but they literally are mirror images of the natural molecules.

And the reason for doing that was we wanted to do it because, one, because it would be cool, it would be an incredibly interesting example of engineering. Like if you can take the same molecules but of different stereochemistry, can you make a living cell? And if you can, then it would be the first example of a living cell that's not of the same tree of life as the rest of biochemistry. So that's one reason.

The work was really motivated by practical applications. We wanted to make better drugs, better therapeutics, and also we wanted to make better biomanufacturing platforms. And the property of mirror life that would allow us to make better therapeutics and better biomanufacturing is also now the reason we think it should not be created, and that is mirror life would be invisible to both immune system and viruses and predators in the environment.

So for example, if you got an infection of a mirror cell, your immune system would not recognize it as a living thing that it has to fight. If you release a mirror cell into the environment, nothing would contaminate it, no viruses would infect it, and nothing would eat it. It would be basically like a rock in the environment because that different stereochemistry means all the chemical detection systems that the rest of life uses wouldn't work on it because it's opposite. It's a mirror image of it. It doesn't work just like your left shoe is not going to go on your right foot. It does if you really try hard, but it's not gonna be comfortable.

And so the idea when we first started thinking about mirror life was that if we can have this thing that's stealth to the immune system, it would make great therapeutics. You can inject it into a human and it's going to do its thing and it's going to cure what ails you. And then if you make a bioreactor made of bacteria that are of a mirror image, no phages will contaminate it. So no danger of losing your entire million dollars worth of bioreactor content because something gets contaminated, someone sneezes into it. It's not going to get contaminated because mirror cells are not compatible with normal bacteria and viruses.

But then we started talking to ecologists and immunologists and we realized that those features of mirror life are actually big bugs. It would be really irresponsible to release both into the environment and into a human body something that's so stealth to the immune system that can fly under the radar because then it could just go and replicate and it would not be stopped by any mechanisms that normally make sure that life is regulated, that no single organism proliferates out of control.

So that was a roller coaster of emotions for me for a few years. It was, yay, mirror life. This is an amazing technology. It's going to solve a lot of problems that biochemistry, that synthetic biology has. And then it was like, oh, wait. I actually should not be doing it because the concerns that we have about potential interactions with the environment, with the human body really outweigh the potential benefits of that technology.

[1:04:34] Nathan Labenz: And okay. Again, lots of follow-up questions here, and obviously, this is where some of the structure of this analysis becomes pretty evidently related to a lot of the discourse that's going on in AI right now.

Just to make 100% sure that folks have this chirality idea clear, I always find it helpful just to say even if your hands are exactly the same shape, there's no way to make them occupy exactly the same space. And you can hold one up to a mirror and see its mirror image, but there's no way to actually convert it into that mirror image or to have it occupy the same space as that mirror image.

So this, and it is a striking, interesting fact unto itself that all life has the same, as part of having the same biochemistry, it all has the same general chirality to it. All of DNA has, you can imagine mirror DNA, mirror ribosomes, whatever, but we only have one version of that throughout all of biology.

[1:05:39] Kate Adamala: Yep.

[1:05:39] Nathan Labenz: Okay. I think there's interesting questions on the epistemic level and also on the sort of societal decision making level here for sure. We've got some technology where we're like, oh my God, this might be a great way to create new drugs. The upside could be incredible. And then we've also got, oh, but it's all dual use and that's another huge theme in AI. Right? Like, if it can evade the immune system for good, it might also evade the immune system for bad.

[1:06:11] Kate Adamala: Yeah.

[1:06:13] Nathan Labenz: How much do we really know about the ability of these things to evade the immune system? Because one super simplistic response to this would be like, well, I can't make my 2 hands occupy the exact same space, but one hand can grab the other. My immune system seems to be pretty robust to all kinds of crazy stuff that it's not seen before. Do we have experimental reason to know that it's not gonna be able to grab onto this reverse chirality or is this theoretical? Like, where are we in terms of our confidence on all this?

[1:06:49] Kate Adamala: Yeah. The immune system part is mostly theoretical because no one ever made a mirror cell. Thank God. And hopefully, no one ever will. But we don't have a mirror cell to give it to a mouse and see how the immune system will respond to it.

But we do have data from opposite chirality proteins and we know those guys are stealth to the immune system. That's why they're such great potential therapeutics. There's also mirror image nucleic acids that are already in clinical trials as therapeutics. They're called, actually, it's really funny, they're called Spiegelmers from the German word for mirror. So those mirror nucleic acids are already in clinical trials because they are stealth to the immune system.

So the people smarter than me who understand how immune system actually works, figured out that they're transitive properties. If the mirror proteins are stealth to the immune system, if the mirror amino acids are stealth to the immune system, then a whole organism made up of those things will also be stealth to the immune system.

[1:07:54] Nathan Labenz: Okay. That's interesting. I had not seen this molecular level evidence that the immune system can't detect these things. And when you say stealth, can you unpack, is there a more literal understanding of what it means to be stealth to the immune system?

[1:08:15] Kate Adamala: It just means you can exist in a bloodstream and no one's gonna bother you. So you can inject it into the bloodstream and it will circulate. In the bloodstream, you can inject it into tissues and it will circulate there and the immune system will not activate. I mean, I'm sure it's gonna get sniffed by white blood cells, but it's not going to cause the activation of the immune system. So you're not gonna get sick or you're not gonna get immune response to that.

[1:08:45] Nathan Labenz: So the normal situation if I all of a sudden introduce some random foreign RNA into my blood, the immune system detects that as a foreign object and mobilizes against it and tries to get rid of it. And if we simply reverse the chirality on that same exact sequence of RNA, it just does nothing? It just doesn't react to it at all?

[1:09:13] Kate Adamala: It sniffs it, but it doesn't activate. That's why they're already in clinical trials because they don't cause the immune response. It's kinda like when a dog walks around and sniffs a piece of bread on the floor, he's gonna activate and eat it immediately. If he sniffs a rock, most dogs will keep going. Some of them will eat rocks but that's another problem.

The immune system needs to recognize things as a possible threat and if it's a mirror biochemistry it just doesn't activate. It sees it but it doesn't react to it. And that's a problem because if you have a cell that's made full of mirror molecules then you can imagine that cell just growing in the bloodstream because your bloodstream is just a warm little pond, that bioreactor that can comfortably give an environment for that mirror cell to replicate and you don't want to have a foreign cell replicating in your bloodstream.

[1:10:12] Nathan Labenz: Yeah. You mentioned earlier everything in biology tries to eat everything else. One of the things I often say is there's nothing more dangerous than something that nothing can eat.

[1:10:22] Kate Adamala: Exactly. And that's what mirror life is. It's something that nothing can eat.

[1:10:28] Nathan Labenz: Is there any deep history, not necessarily analogy, but sort of precedent for things like this happening. The one I always go to, and again, without deep knowledge in the field, is the oxygenation of the atmosphere. I always just find it super striking that there once was no oxygen, then somebody started releasing oxygen and then a lot of things died off because they couldn't deal with the oxygen. Now that's not even a form of life. It's just sort of somebody's waste product. But are there any sort of historical examples of things popping up that nothing could eat that changed ecosystems on vast scales?

[1:11:10] Kate Adamala: Other than the great oxygenation event, we don't know of anything. That probably happened a lot at the very early stages of life, at the origin of life, is that there were probably organisms that were popping up in the primordial soup and no one could eat them and then they just took over.

But ever since the biology started, there was always this equilibrium and we don't know of anything else that would just kind of appear that there's absolutely no natural predators. And I love the analogy to the great oxygenation event because even the cyanobacteria themselves got screwed by it. They started releasing the oxygen, but it's toxic to them too. So there was a great extinction event not only among other organisms but also in the culprits, the ones that actually started it.

And there's never been an example like that, another example like that, and I don't know of any example of anything in nature that would evolve that had absolutely no means of controlling its replication, that there was no viruses, no predators, nothing eating it, nothing limiting it.

Humans are the closest, really humans are the closest example to that. We replicate uncontrollably and we don't have natural predators right now. The only thing that kills us is ourselves and we're still susceptible to diseases but you can see what happens when an organism evolves on a planet, an organism that climbs to the very top of the food chain and ends up with no natural predators. It takes over the planet, and then next thing you know, there's global warming and the planet's dying.

So that's not good, and that probably shows that nature needs balance. Nature really needs things that can stay in equilibrium. And that's one of the reasons why mirror life is such a terrible idea is that it would not obey those laws of balance. It would not stay in equilibrium.

[1:13:22] Nathan Labenz: Okay. So let me try out a few skeptic arguments, and you can maybe tell me if these are arguments that anybody in the actual field has put forward or if I'm just inventing them. And if I miss any, you can tell me what other arguments were maybe more compelling than the ones I came up with.

First one is just like, okay. You make a good point that these molecules are stealth to the immune system. That does sound a little spooky, but worse, the actual mirror life is still a long ways off. Right? So do we really need to get too worried about this now? And maybe I'm not entirely sure exactly where the rubber hits the road here. Right? Because there is sort of ongoing work with mirror molecules. And then there's this concept of mirror life. There's a significant distance between the two. So somebody might just first say, well, it sounds like a long way off. Is this not just much ado about nothing because we don't know how to make mirror life anyway?

[1:14:14] Kate Adamala: And that's the best argument. Honestly, you hit the nail in the head. That's the best argument I've heard this entire time we've been working on this project is there's enough problems in the world right now. Let's not worry about something that's so far off. Some people compare it to worrying about the sun dying. Like, yes, we know one day it will happen. It's inevitable. But that's not exactly something that has to affect the stock market and my retirement account. It's not gonna happen anytime soon. And so let's not worry about it. I don't walk around worrying about the sun dying.

And mirror life could be seen in this kind of a similar vein is that it's so far away that we don't have to worry about it. The thing about mirror life though is that we were actively working on making the damn thing. We wanted to make it. And even if we, as the people that were actually actively working on it, stopped doing it, without talking about it in public, other people might still find the arguments for making it appealing. And if we don't really publicize the potential concerns then someone might think oh it's a great idea, let me go work on it. And then we'll get closer to it and closer and closer and one day the sun will be dead.

And that's the argument is that we cannot not talk about it because the science was going in that direction. It was almost inevitable. Most people in the field agreed that at the current rate of progress we will reach a fully self replicating mirror cell within a decade, some people say 20 years, but it's still within a human lifetime it's possible. So we have to stop it now before we actually have the capacity to do that.

[1:15:52] Nathan Labenz: What was your own personal journey or sort of evolution of thought on this like as somebody who was enthused, was actively working on it? Did you have a eureka moment? Did somebody come to you and say, hey, I think you should reconsider this. Like, what's the personal narrative?

[1:16:11] Kate Adamala: Yeah. It wasn't a eureka moment. It was more like a holy crap moment because I was really excited about this idea. This was actually one of the first grants that I got as an independent PI. I just started my lab recently and that was the project that I got really excited about and I really wanted to do it.

And then around the same time I started doing more work in the biosafety and biosecurity community. So I started talking to different people about potential concerns of not mirror life specifically, but other emerging technologies. So those so called emerging technologies are the things that are on the horizon, that are not quite here yet but will one day soon become possible. We know already they will become possible and we have to be ready to face the consequences of those technologies. So kind of thinking before we make it, safeguarding a field before it becomes a reality.

And so I started doing more work on that and started talking to people about mirror life. And I'm not an evolutionary biologist. I'm not a biologist at all, and I'm also not an immunologist. So I wasn't aware of those possible concerns. Like now it seems pretty obvious to me that yes, if nothing eats it in the environment, then you should not be releasing it, but that wasn't obvious to me when I first started it.

I thought that perhaps there could be some mechanisms we could build, some kind of safeguards we could build to make it safe. I was kind of maybe trying not to think too hard about the possible downsides of it because I was so excited about the applications.

And as I started learning more about all those regulatory mechanisms that keep other forms of life in check and also those balances that we absolutely need so no single form of life takes over the population, then I realized it was kind of a gradual realization that I really should not be working on mirror life, which was kind of a bummer because I really wanted to do it. I really think it's a fascinating research project, and the potential benefits are really appealing, but the concerns really outweigh the benefits. So that's when I became convinced. It wasn't like one day I woke up one morning and said, okay, I'm not doing mirror life anymore. It was more of a gradual came into that understanding.

[1:18:27] Nathan Labenz: Yeah. Okay. Well, couple other, not necessarily counterarguments, but sort of maybe reasons we might not feel compelled to worry about this. You alluded to one, which is like, well, maybe we can just sort of contain them. Right? It's one thing to build it and do experiments in a lab, and then it's quite a different thing to release it into the wild. So why don't we just keep them locked up?

[1:18:52] Kate Adamala: Because if you make a foolproof container, nature will develop a better fool. There is always, I think it's one of those Murphy like laws, if you make something idiot proof, the universe will provide a better idiot. There is no foolproof containment. There is no way to absolutely safeguard something, and that's for two reasons.

One is because we cannot predict everything. So if I make a mirror cell in my lab and I put 27 locks on it, there will be a hole in the floor and the sample will leak out. We cannot predict everything in advance. So if there's something that we don't want released, then the only safe way of preventing the release is just not having the thing, not making it. And so that's the safety argument.

And then unfortunately there is also a security argument, which is if you have a mirror cell, even if you create the most perfect advanced containment, you cannot safeguard against a bad actor, someone on purpose trying to release it. There's many crazy people in the world and there are people that might want to do harm regardless of consequences.

And so if mirror life were to exist, even if it was perfectly contained in the lab, you can imagine someone with bad intentions gaining access to it either by force or by some kind of subterfuge, some kind of scam, and then stealing it and then releasing it. So the only way to avoid that happening, to prevent that from happening, is to just not have a mirror cell in your lab to begin with.

[1:20:29] Nathan Labenz: Yeah. For what it's worth, I do think the "we'll just keep it under wraps" argument is not a compelling one really at all for just the obvious history of lab leaks is not a short list.

And on the AI side, going back to, I don't know if you've ever heard of this, but Eliezer Yudkowsky, who's sort of the prime mover in the AI safety worry space, once ran an infamous experiment where he called it the AI in a box experiment. And the idea was he said that you could put the AI in a box, but if you let people talk to the AI, it'll convince them to let it out of the box.

[1:21:09] Kate Adamala: Yes.

[1:21:09] Nathan Labenz: And people said, well, no. There's no way it's gonna convince you to do that. And he said, alright. Well, let's do a bet then. I'll play the AI in the box, and you win if you're not convinced to let me out. And he did a couple of these experiments. And at least in some of the cases, the counterparty came away saying, and it became like a sort of item of lore because part of the deal was that they weren't supposed to reveal what he told them to convince them to let him out. But people did come forward out of those experiments and said, without telling you how, I will say, he convinced me to let him out. And so the idea that we're gonna just contain something like this forever does seem pretty hubristic on its face.

Another one though that I find maybe a little more compelling, but maybe I shouldn't, is but won't they be sort of constrained by lack of raw materials or lack of a fit for an ecosystem? And this has kind of a parallel in AI too, which you alluded to earlier. Like, these things can only exist on chips, and there's only so many chips. And it seems pretty clear that the AIs are not gonna break out of the computing environment into the rest of the world. But could a similar thing be said about mirror life where it's like, don't they need the proper chiral building blocks and wouldn't those be rare?

[1:22:33] Kate Adamala: Yeah. So they don't. Okay. So there are two things. One is you could imagine building a synthetic mirror cell that is completely reliant on, like, let's say, 50 different chiral molecules of the opposite chirality and there is no way to find them in the environment. That would be very safe. You could pour that onto the lawn and it would do nothing because it could not evolve 50 pathways at the same time in the span of one generation. There would be a way to imagine a perfect containment.

But then there unfortunately is again the problem of security. If we make a mirror cell that's dependent on specific pathways, you can imagine a bad actor engineering those pathways to break our containment. And again, there is a lot of stupid or suicidal people that might wanna do harm. So we don't want to create even a possibility of that happening, a possibility of someone having this chassis of a mirror cell that's very safe contained in my lab but taking that and then engineering it so that it's not reliant on those rare molecules anymore.

Because you can imagine building a mirror cell that would only rely on achiral carbon sources, so basically carbon sources with no chirality. There are some bacteria that are like that already in the environment. So it's possible to imagine a mirror cell that doesn't need building blocks of this very rare specific chirality. And if it's possible to imagine that, then you can have someone actually do it.

And that's why building a mirror cell, I do agree it's possible to imagine building a mirror cell that would be very well contained by those, they're called auxotrophies, those pathways that you engineer so that an organism is reliant on some rare molecule. But then you can imagine breaking those containments, either accidentally, which would be very unlikely for a mirror cell, or intentionally, which is a huge security risk. And that's why not making a mirror cell is the best way to avoid that risk.

[1:24:39] Nathan Labenz: Yeah. Jurassic Park remains strikingly relevant. Life finds a way.

[1:24:44] Kate Adamala: Exactly. Life finds a way. Jurassic Park was a dumb idea because how do you make something dependent on a single amino acid that's very popular, that's very common in the environment? Like, that's not a good auxotrophy. A first year bioengineering student would tell you that's not a good idea, but obviously then you wouldn't have a movie, so sure, eat your beans. But that's the principle, is the same. Yes. The auxotrophy basically is what they tried to do to those dinosaurs. Make them not be able to synthesize a certain molecule. Obviously, we pick molecules that are harder to get in the environment than lysine.

[1:25:17] Nathan Labenz: Yeah. Always gotta be mindful about not generalizing too much from fictional evidence. That's another great Eliezer lesson. But yeah.

[1:25:27] Kate Adamala: It's our imagination, though. You don't know what's possible unless you can imagine it.

[1:25:31] Nathan Labenz: Maybe one more. Okay. Let's imagine this thing maybe can get out. Maybe it can break out of these various types of containment, the sort of physical barrier and also the sort of dependencies that we might try to engineer into it. Then somebody might say, well, okay. Sure. Even then, but won't this still be just a massive underdog against all the rest of life? I mean, we've got all this diversity, all these species, just way more biomass, and we're evolving too. Right? So maybe it'll do some damage, but won't the normal chirality life sort of find a way to outcompete it in the end anyway?

[1:26:12] Kate Adamala: It might or it might not. That's the problem is it would definitely be an underdog. It would be not even a turtle. It would be a slug. It would be something incredibly slow, but it would just keep going. It would be unstoppable because nothing would eat it, nothing would infect it.

And I personally, if I were to bet my money, I would bet on normal chirality life. I would say we would find a way, life finds a way, we would find a way to eat it somehow. But it's a risk that I'm not willing to take. It's very hard to deal with consequences of life not finding a way fast enough.

And because we never had an opposite chirality life in the environment that we know of, there is no pre existing mechanisms to deal with it. So even though evolution, a long enough period of time, evolution might do its job and develop a mechanism that will allow us to deal with it. But it's a risk that I don't think we should be taking. It's asking a lot of evolution and evolution cannot be programmed. Evolution does what it does stochastically. So either we would find a way to eat or infect mirror life or we would not. And if we don't, then I don't wanna think about the consequences. I would rather not do that. So you're right that it's possible to imagine that mechanism, but it's not safe enough to rely on it.

[1:27:38] Nathan Labenz: Yeah. That makes sense. Do we have any sense for how fast this whole thing would be? Like there's the story of the sort of Drexler style nanotechnology and the gray goo and in the limit, you start with one cell in an ideal medium and it takes over the mass of the earth in some remarkably short time, I think it's what? 72 hours or something under purely theoretical conditions. I guess the argument is sort of akin to that in that if it's just totally unconstrained growth, are we envisioning something that would happen really fast in the worst case scenario?

[1:28:18] Kate Adamala: Definitely not fast. If it ever happened, it would be extremely slow crawling apocalypse. It would be the most boring pandemic you can imagine. It would be very, very slow. But again, we don't know until it actually happens how fast it would be and I would rather not find out.

[1:28:39] Nathan Labenz: So with all this analysis, does this cash out to you for a comparison to AI risk discourse? Is there a sort of conditional on mirror life, a p(doom) that you would assign to this scenario?

[1:28:56] Kate Adamala: The difference between mirror life and AI is that mirror life doesn't exist right now, so we can actually stop it. AI is out of the box already. There is no way to stop it.

And frankly, I kinda don't wanna stop it because I haven't written a line of code in over a year. Anytime I need a script, I just ask ChatGPT and it writes me a code and the code mostly works. And if it doesn't, I just paste my error bar back in and it says, oh, sorry. I forgot to close the line or something, whatever.

Anyway, we rely on it and it's not a bad thing. We rely on AI for many things and it does make our life easier in the end. It also speeds up the discovery process for a lot of different fields, for biomedicine, for medical discovery, for biotechnology. There's a lot of lab applications that rely on AI already.

And so there's no way to put it back in the box and I wouldn't wanna put it back in the box. I don't know how to safeguard that. There's obviously a big problem of how do you keep that contained, but it's a technology that we cannot live without right now anymore.

And if we had those conversations 20 years ago, then maybe there would be a way to stop the development of AI and then we could actually have those conversations like is it actually net positive for the world? But it's here already. There's no way to stop it. You're not going to stop it. And all we can do is try to develop ways to safeguard it, to develop ways to not control access exactly, but to control the applications, control what it can do and also be prepared for the negative consequences.

With mirror life, we can say, no, we're not making it and hopefully it will never happen. With AI, we've already made it.

[1:30:38] Nathan Labenz: I would say we've partially made it. The analogy is not meant too literally here, obviously, but like we have mirror molecules. We don't have mirror life. I would say we have some sort of intelligence in our artificial intelligence. Like, it's pretty clear to me that there is, without getting bogged down in definitional debates about intelligence, I count it. I feel like I know it when I see it and I feel like I see intelligence.

What I don't yet see is a robust autonomy. And of course, people are trying to enumerate these key features that a more dangerous AI might have. Robust autonomy, ability to escape from its server and copy itself onto other servers. Interesting questions around how much sort of, I mean, AI primarily is developed by optimization as opposed to evolution. Does it need to have some sort of evolutionary mechanism? To some degree, it can just kind of code its own. Google came out with a really interesting paper around hybrid strategies using language models to generate programs, but then also evolutionary algorithms. The language models can code their own evolutionary algorithms, I think, pretty safely at this point.

But I think there the key point that I am interested in is just it seems like in the same way that sort of mirror molecules seem to pose very low risk, might be very much to the good, and you can remain enthused about those while warning against a sort of gain of function to the degree that it becomes mirror life. I think that the AI field kind of needs to do something similar now where it's like, all this stuff has worked well beyond anybody's wildest dreams from not very long ago. And now we're entering probably well into a different regime where at one point in time it was like, can you get anything to work? Can you get this thing to tell a cat from a dog? And when it was so simple, who cares what technique you used. Everything serious was a long way off.

Now I feel like we're in the zone where the serious stuff is not really necessarily so far off and that's still debated, of course, but it seems like it is becoming more incumbent on the field to really ask the question of what kind of AI do we really wanna have? We're gonna have some, and it's gonna be powerful. And hopefully, it'll make life lots better. But then there are some kinds that I think everybody kinda looks at and is like, jeez, if you checked these 3 or 4 more boxes, if you had something that could cyber hack its way off of its servers and could somehow scrounge up resources in the wild or make money on the internet to pay for its own compute or something, then these things do start to seem qualitatively different in terms of how unwieldy they are and how prepared the broader ecosystem is to contain them.

And I don't see nearly enough of that kind of stuff happening in the AI space where it used to be that nothing worked and anything you could get to work was a big accomplishment. Now I feel more often like everything is working and there's a million doors that we could open and a million different paths that we could go down. But what seems to be in relatively short supply is the critical analysis of which ones are the right ones to choose and which ones will we potentially regret.

[1:34:22] Kate Adamala: And how do you make that choice? How do you pick which paths?

[1:34:27] Nathan Labenz: Yeah. I mean, I think that there are some things that hopefully would be fairly obvious, but I just don't see people putting a lot of attention on them, which raises a question for you around the experience of kind of so you've been on this long research journey. You come to this conclusion yourself that, okay, we shouldn't do this. But then you didn't stop there. You actually went out to others in the field and said, hey, we should stop this. So maybe tell that story of how you went from one or a small group of people kind of coming to this conclusion to recruiting, putting together a coalition, and putting a big kind of headline statement out there that we think that this really shouldn't be done.

[1:35:13] Kate Adamala: It was a rather slow process. It started by groups, rather small group of people talking about it. And then we realized that in order to really make a statement, we have to have a lot of different stakeholders on board. And that includes not just the people that actually work on engineering mirror life, but also people that know anything about ecology, immunology, and policy. Because the researchers that work on it, we're not experts in every field. So we had to put together this really broad coalition of people.

And that started by just approaching people and saying, hey, there's this project we wanna talk to you about. What do you think about making a mirror cell? And for a lot of the people, they were very surprised when you first approached them because they never even thought about it. A lot of the authors on the original Science paper are people that did not think about mirror life at all before. But when we came to them, we showed them the evidence, we showed them where the research is and what we think could happen.

We basically approached a lot of people with the question of can you please prove us wrong? Because we really want to be proved wrong. When we realized those concerns about mirror life are really serious, we were sincerely hoping that someone will poke a hole in our reasoning. So we approached several people saying this is what we're thinking, could you please find a way in which we are wrong? And they couldn't and they ended up being authors on the paper, because they were on board because they sincerely could not find any argument that would point to either why mirror life would not be a concern or why mirror life would be an impossible thing that you shouldn't even think about.

So that's how it went is we slowly were basically on the lookout for people that can disprove our theory or kind of mitigate our concerns. And the more we looked, the more we were not able to find anyone until eventually the team was big enough that we brought up the paper and then started the public discussions.

And I'm still waiting. That's an honest, sincere appeal to anyone watching this. If you can prove us wrong, if you have actual evidence for why mirror life would not be a concern, the community would be extremely grateful for that because we're scientists that like cool projects, and we still would like to be able to make mirror life. We just don't think there is a way to do it safely right now.

[1:37:44] Nathan Labenz: Was there any, were there any holdouts? Was anybody especially, of course, the classic if somebody's paycheck depends on them not understanding something, that's a pretty good leading indicator of them not understanding it. Was there anybody who was like working on some aspect of this that you couldn't bring on board to the conclusion?

[1:38:08] Kate Adamala: Not so far. Maybe we were lucky that we only approached the people we knew will be reasonable, but no. There was no one that we approached originally that would say, no. I'm not going to work. There was no one who would join our team, work with us, review all the evidence, and then say, no. I'm out of here because I can't publish this or I don't think this should be done.

Maybe we're just that convincing. We're nice. We smile. Maybe, I think the strength of the arguments, there were some people that joined the team and were convinced by the argument but had some other kind of point of view, other angles that ended up representing the technical report that accompanies the original Science paper and that kind of broadened our perspective. There were some things that we didn't think about originally that ended up being part of the core message. So definitely people did change our reasoning, did change the directions of the group, but there was nobody that we would bring on board that then would just say, I don't believe you. You're wrong. I'm out of here.

[1:39:21] Nathan Labenz: What sort of evidence would be compelling? I guess if you showed here's an example of an immune system quickly responding to mirror molecules, that sort of thing would be compelling. Is there anything that's like, it sounds like that experiment has been run and didn't turn out that way. Are there other experiments that you have in mind that you would find compelling that people should do to interrogate this further?

[1:39:51] Kate Adamala: I think there is some room in the countermeasure research. So for example, right now we know that mirror image antibiotics would work on mirror life but we don't know the extent of it and we don't know the exact effects of mirror antibiotics on other organisms. So there's some research there.

There's also research on availability of food sources. I said earlier that it's possible to rely on achiral, so not chiral carbon sources, but we don't know exactly how well that would work. We don't know exactly how promiscuous that metabolism could be. So there's some experiments that would be interesting but I would be very careful not to go too far down that route because you don't wanna get too close to actually making the thing that you don't wanna make.

[1:40:41] Nathan Labenz: Now that this has happened and you guys have put out this headline making warning, what is the, how do you think about the impact or sort of the mechanism? Right? You went to all this effort, and now the hope is that, at least until some major evidential update, mirror life won't be made. Is that a sort of taboo among scientists where everybody sort of agrees, we won't do it? Or is it sort of by influencing funding structures so the grant evaluators know to be on the lookout for this sort of thing? How do you think about the mechanism between the declaration itself or the warning itself and the actual reduction in risk?

[1:41:27] Kate Adamala: It's still very early. In the policy world, 6 months is nothing and that's how long it's been since we published the paper. My personal hope is that both of the mechanisms that you named will happen.

One is that no self respecting scientist will wanna touch it just kinda like with germline editing in humans. There was one example when someone tried to CRISPR a human embryo and that was so comprehensively shut down by both government and community pushback that hopefully no one will try to do that anytime soon. And so I'm hoping the same will be with the mirror life work is that the community will be on board and no one will start working towards it within the community.

But I also hope that there will be regulatory mechanisms on the funding and policy level so no one will fund this work, and also this work will become something that we're just not allowed to do. So all three.

[1:42:30] Nathan Labenz: It's funny that you mentioned the human germline editing. That one feels much more compelling to me on the upside. It seems like, of course, these mirror molecules as therapeutics could be interesting. But I feel like we have lots of different avenues that we can chase down to make better therapeutics. And this one seems to have outsized risk attached to it. So, okay, let's go down 99 out of a 100 other doors and not this one.

When I think about editing the human genome, though, I'm like, I can imagine a dystopia where it gets out of control, but I feel like at the same time, boy, if we could eliminate a lot of rare diseases, that would just be a great win for everybody. And then you get into some slippery slope territory, but it might be sort of a good slippery slope at first. Right?

I was recently reading some analysis where it was like there's, if you could make not very many edits, you could dramatically reduce the risk of Alzheimer's and you could dramatically reduce the risk of heart disease. And I guess I just wanted to voice for a second and maybe get your reaction on how do we make sure we don't throw too much good out with the bad here. Right? Because that's certainly on the AI side. People are dreaming of Utopia. I think it's increasingly plausible, and I want it too. And so I don't want to dismiss or even more to the point, miss out on that upside.

And I feel like in the human editing space, I'm like, yeah, I can imagine a dystopia over multiple generations, but I can imagine like a next generation, which is just plain a lot healthier. And should we deny that next generation that benefit because we're worried about what 3 generations down might do or look like? How do you think about just the risk reward on some of these less obvious cases?

[1:44:28] Kate Adamala: I'm really worried about the slippery slope is that if we start doing editing, then we will not stop. And that can get out of hand really quickly.

Another thing to think about with that is it would significantly increase the inequality that already exists in the world because you can imagine that the rich people who have access to this technology would be producing offspring that's genetically as perfect as we can make them. And then the people that can't afford that technology would just be breeding the old fashioned way.

And right now we have a huge amount of inequality in the world already and some of it already is biological because if you grew up with good nutrition that sets you up for life and someone who grew up undernourished will never catch up in the development. But if you start doing genetic editing, everyone will go to the absolute farthest length for their own children.

Like, if I, honestly, as a human, if I could make my kid to be the smartest, fastest, healthiest one in the world, I totally would. I absolutely would and I think everyone who said they wouldn't do it for their own kid is lying. It's just natural instinct that we want to give the best to our kids and that worries me because if that technology becomes available, then there will be no way to stop it and it would not even be reasonable to ask people not to use it.

But then not everyone will have access to it. So you can imagine quickly creating in one or two generations almost like a new race of a human that will be superior in so many different ways that we as a species will absolutely be screwed.

So that's the worst dystopian case that I'm worried about. And I'm worried, the reason why I think we should not be working on human germline editing at all is because once that technology becomes available, it will be impossible to stop it. And frankly, I don't know if I could resist. If someone offered me the ability to edit my own embryo so that my children have, for example, much lower Alzheimer risk, I don't think I could resist honestly even though I rationally think that technology is wrong. As a human I would do it because that's just my instinct as a mother to make sure that my kids get the best. And it would be normal that every human would react the same.

So if we have this ability we would take it because it would be impossible to ask people not to take it. And so the only way to safeguard it is to not have that ability at all.

It might be naive because the technology is almost already up there and I think one day that might exist. And once it exists, I think we'll have to focus on distributing it equally so everyone has access to it. Although we know that as a society we really suck at distributing things equally, there is so much inequality right now that it only can get worse.

I think the example you use with AI is a little different because the way I understand it, it's because it's technology, it's easier to distribute it. So if you have AI that gives you certain advantages, for example, if I have an AI that allows me to be a better writer, you can imagine distributing that all over the world to everyone who has access to the internet. And it would be much harder to paywall that while actual genetic manipulations would be hard to distribute equally.

I'm sorry I realize I sound like a bummer because it's a great technology that could really benefit humanity but also I don't know if we're smart enough to make it equally benefit everyone. And it's not even some altruistic thing saying everyone should have equal opportunity. It's just self preservation instinct. If we actually do create two tiers of society biologically like that then there's gonna be too much tension and I can't imagine that going well for the stability of our society.

So, yeah. Let's talk about something nicer because I don't wanna wrap up on this doom and gloom of everything is terrible and we as humans are not responsible enough.

[1:48:48] Nathan Labenz: Well, I think those are not to be dismissed as concerns, certainly. I will give the AI companies a lot of credit, at least thus far. I think it does seem like the eras can sometimes be quite short. But at this point, it is remarkable, kind of reminds me of the old Andy Warhol quote about what makes consumerism and American consumer society great is like, you can have a Coca Cola. It's the best Coca Cola there is. No amount of money can buy a better one. The president drinks the same Coke. Right?

And that is basically true of AI today. Somebody just posted, well, Terence Tao, broadly considered I understand the greatest living mathematician, recently posted something where he was using Claude, and he was on the free plan. And people noticed this and were having a laugh about the fact that here's this guy who's truly by any measure elite, maybe literally the best mathematician in the world today. And he's on the free plan of Claude.

And it's like, well, why is that the case? It's because you could pay and get greater number of uses, but they actually do give the best model out for free. So I think we are, and that may change because inference scaling and there's a lot of things where we're hearing rumors of a $20,000 a month coding agent. Obviously, not everybody's gonna be able to afford that if and when that does come to market. But I do give the AI companies a lot of credit, at least so far, for really trying to live up to their sort of ideals around democratizing access and really pushing that amazingly far for the moment.

[1:50:37] Kate Adamala: Yes. And that's impossible to imagine with biological technologies because there's labor and material cost that you just can't get around.

[1:50:46] Nathan Labenz: But might people have said the same thing with sequencing? I mean, I'm mindful of there's a time delay and it's not entirely fair. I'm not trying to say that it would be entirely fair. But it certainly seems like a lot of progress has come from luxury or high end or not generally accessible technologies that gradually become more accessible.

And just going back to when I was in college, it was like a million dollars or whatever to sequence a genome, and now we're at whatever couple hundred. So I am kind of cautious around the gene editing being an inherently unequal technology just in the same sense that yeah, maybe it's not accessible to most now, but can we come down that cost curve and create a version of society 20 years from now where it is universally accessible?

[1:51:46] Kate Adamala: I mean, that's kind of almost a vicious circle because in order to bring the cost down, you have to start doing it. So we have to take the leap of faith that we will be able to bring the cost down and make it equally available in order to make that happen. It's kinda like you have to do that on faith.

[1:52:05] Nathan Labenz: Yeah. Do you think there's a, is there a bright line to be drawn, to kind of create a rough structure for this, it's like mirror life and sort of treacherous turn fast takeover AI are kind of analogous in that these are things that we really just might lose control of. There's the sort of genetic editing and that could get out of control and we could end up in a slippery slope, dystopian scenario. There's AI versions of that too.

The gradual disempowerment meme and recent major papers sort of made the case that even if we can control all this near term stuff, we really don't have a plan for how we're gonna have a society that we're gonna be happy with with very powerful and widely deployed AI. And I take that really seriously, but I do wonder if there is a line to be drawn and how bright it could be between these things that are like, okay. If we open this door, we really might not be able to close it. Whereas if we open this other door, we might be worried that collectively we won't close it or whatever, but we could.

Right? I mean, it's not like genetic editing is going to truly take on a life of its own. That's more of a social dynamic than life itself doing the thing. Right? I mean, I guess we are life itself, but hopefully, maybe there's no bright line to be drawn there, but I wanna draw a line between sort of things that I can say, okay. That can be somebody in the future's problem and they will still have some recourse.

And I kinda wanna put genetic editing and what is society gonna look like with a lot of AI deployed broadly into that bucket. And then I want to maybe draw a smaller circle around things like mirror life and like hyper autonomous, goal oriented AI. And say those things seem qualitatively different. How do you react to that attempt to taxonomize?

[1:54:17] Kate Adamala: That kind of makes sense to me. It makes sense to me intuitively. I'm not very rational thinking about stuff like that because I want this to happen. I want this to be true. I have a very hard time drawing lines like that but I think we're gonna have to because it's inevitable. So if I could make you the emperor and you draw those lines, I think what you just described kinda makes sense.

[1:54:43] Nathan Labenz: It's a lot of responsibility. Alright. Maybe last one to two questions. I'm not sure if this is the same question or different question. I guess based on your expertise in synthetic biology, is there anything that you would be telling people in the AI space to be specifically concerned about or watching very closely? Do you have any intuitions for what might be the sort of step change, gain of function moment that could really move the needle on this spectrum from inert tool based AI to sort of some sort of out of control AI?

[1:55:28] Kate Adamala: Yes. To me, it would definitely be, within the limited understanding that I have as a biochemist and bioengineer, a model that would be able to generate new biological functions. So for example, if you can go and ask whatever AI available online how to make a better virus.

That's something that right now the AI is not capable of giving you that answer. You can say there could be safeguards against answering that question but we also know that it's possible to jailbreak most of those models. So right now the AI doesn't have a capacity to do that. I cannot just go in and say how do I make a better toxin or how do I make a better virus?

But if the model learns to analyze all of the data in the literature, so right now, if you ask the best virologists in the world, they'll give you an answer with some possibilities of how to make a better virus but not a definite answer. No one knows. But maybe there is a knowledge out there that by learning from all of the published literature, if there could be a model that can synthesize all of that knowledge and come up with this is how you make a better pathogen, this is how you make a better drug, this is how you make a better toxin. This would be a game changer and not for the better.

This would be something that would put a lot of capacity in hands of people that don't have enough background to be responsible with it. And that's something that I really hope doesn't happen. I really hope the answer is not even out there to be learned. But if the AI ever learns to be that good, like to really synthesize scientific knowledge and predict things like that, I would not be very happy.

[1:57:21] Nathan Labenz: Maybe last one then. Any thoughts, reflections, advice based on your social effort to build the coalition and bring people together on this that you think people who are trying to do something similar in the AI space could take a note on?

[1:57:41] Kate Adamala: I think carrot rather than a stick. When we first started talking about this mirror life project, we were wondering how should we approach that? And we decided that trying to bring people on board, convincing them that this is the right thing to do, the right idea, is much better than trying to top down advocate for oversight and regulation.

Because that was one of the ideas is why not instead of going public with it and talking to everyone who wants to talk to us, why don't we just go to the government, scare the crap out of them, and make them regulate this out of existence? But this wouldn't really be a good idea because without the buy in from the community, it wouldn't work. It wouldn't be effective.

So anytime I feel like anytime you wanna regulate a community of relatively smart people, motivated people, you have to do it in a way that makes them really believe in it, makes them be on board. For one reason or another, makes them really believe that it's in their own best interest to be on board with it.

And I might be really naive because I've only done one project like that and we happen to be a pretty friendly community. I don't know how it's gonna work for other communities, but I would definitely say this is how you try it. You try to convince people that this is in their own best interest. You're not trying to take their toys away. You're trying to make sure they keep playing with their toys in a safe way.

[1:59:01] Nathan Labenz: Yeah. And exactly what kind of toys we're playing with seems like it could matter an awful awful lot.

[1:59:11] Kate Adamala: Oh, yeah. And how much money is involved too? That's one thing is mirror life was not an existing therapeutic or an existing technology yet. So no one lost billions of dollars on it. It would be probably much harder to convince people to regulate a technology that already exists and already pays them.

[1:59:33] Nathan Labenz: Yeah. No doubt. The corresponding folks on the AI side definitely have a big challenge on their hands. But yeah, I think there is still a lot to meditate on from your work and just the overall arc of how it's gone.

I mean, if nothing else, it shows that people can change their minds on things. We've seen examples of that in the AI space as well, that it is possible to build a coalition, that people can be invited to try to prove you wrong and in failing to do so, update their own worldviews.

And I think it's also really notable that it is a pretty narrow circle that you've drawn around a particular thing. And we're not hearing like also stop the mirror molecule trials, but really trying to be very precise around what exactly is the model and how do we make sure that we contain that without throwing out the good with the bad.

[2:00:28] Kate Adamala: And I imagine that's gonna be much harder for AI.

[2:00:32] Nathan Labenz: Yeah.

[2:00:32] Kate Adamala: To draw a circle like that.

[2:00:34] Nathan Labenz: Definitely. But it's no choice but to try, I'm afraid, as we go forward through these next few years. This has been fantastic. Thank you for taking the time. Anything else you want to leave people with before we break?

[2:00:47] Kate Adamala: I just wanna say it's fantastic that we are even having those conversations, that there is a community that cares enough about doing the right thing, regulating it, doing it safely, that it has to be done. As you said, we have no other choice than to try.

[2:01:02] Nathan Labenz: No way out but through.

[2:01:03] Kate Adamala: No way out but through. Yes. Thank you so much for picking up this topic, and thank you for having me.

[2:01:10] Nathan Labenz: Kate Adamala, thank you for being part of the Cognitive Revolution.

[2:01:14] Kate Adamala: Thank you so much. Thanks for having me. Bye.

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Outro

[2:01:16] Nathan Labenz: It is both energizing and enlightening to hear why people listen and learn what they value about the show. So please don't hesitate to reach out via email at tcr@turpentine.co, or you can DM me on the social media platform of your choice.