The Universal Hierarchy of Life - Prof. Chris Kempes [SFI]

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There are differences in the details of eyes in terms of how exactly they see and how they're constructed and slightly what they're made of and so forth. But in some notion, they're all doing the same sort of physics. MLST is supported by CyberFund. So I'm Phelan. I'm the co-founder and CEO of Prolific. And Prolific is a human data infrastructure company. So we make it easy for people developing frontier AI models and running research to get access to trustworthy, high-quality participants for high-quality online data collection. This podcast is supported by Google. Hey, folks, Stephen Johnson here, co-founder of Notebook LM. As an author, I've always been obsessed with how software could help organize ideas and make connections. So we built Notebook LM as an AI-first tool for anyone trying to make sense of complex information. Upload your documents, and Notebook LM instantly becomes your personal expert, uncovering insights and helping you brainstorm. Try it at notebooklm.google.com. So I'm a professor at the Santa Fe Institute, which is a massively interdisciplinary research institute trying to bring together minds from across the spectrum of academia to really think about some of our hardest problems. My own background is that I was a physicist and then I became a biophysicist. And then that's taken me into ecology and cities and human organizations and thinking about the whole history of life and also origins of life and astrobiology, right? And that's really the SFI thing is to try and take a core expertise and use that as a nucleation to expand into all these different topics and areas. And it's so interesting that you work at the Santa Fe Institute. So I spoke with David Krakow recently, very inspiring gentleman. And what you folks are really kind of doing is, I guess you would call it a multidisciplinary lens on science. You were talking about three cultures in science. So there's the variance culture, where we look at diversity and deviation, the exactitude culture, where we have quite a high resolution mapping of everything, and this coarse-grained abstract culture where we look for principles. And even in machine learning, we have similar terms for this. We have the neats and the scruffies. How can you reconcile these different aspects of science? Part of the inspiration for us in writing that paper was to say, if we look at the history of physics, which has had enormous success, part of that amazing success is that it was such a set of easy questions in relative terms. I mean, biology, the economy, intelligence, those are all much harder questions. But for physics, trying to answer relatively simple questions like gravity and planetary motion, they had what I call the magic loop, right? Which is this observation leading to theory, theory leading back to observation, right? So people took huge sets of observations that we had, tried to find regularities, tried to find simple equations that predicted those observations. And then once they had those theories, they would explore them mathematically. often that gave rise to new sorts of surprising predictions. And then experimentalists would go looking for those predictions, right? And then this loop just continued on and on, as people uncovered most of the laws for most of the fundamental forces. And obviously, there's still open questions in physics, but that trajectory has been really successful. And so I think our proposal in this paper was that we need to do a similar sort of thing for biosciences and for a science of the biosphere. We need more ways to bring the huge amount of observations we have today, together with new types of theory, to sort of get this loop where we're compressing what we know into simple theories, using those theories to make surprising predictions, testing those with data, so forth and so on. And so that's really the variance culture and the coarse grain culture. In our current moment, we have this new thing that we call exactitude culture, which is just the ability to model everything, right? So Galileo didn't have this, he didn't have the ability to write down an arbitrarily many number of equations that he wanted. He couldn't write down an agent-based model for the planets and simulate that in a computer. He was forced to try and find these compressed mathematical representations. In today's world, we can simulate huge numbers of things and make exceptionally complicated simulations. And so that's sort of a third category, right? We have this with certain Earth systems models. We have this with very detailed models of the economy. Artificial intelligence is certainly in that space. And so we think each of these has trade-offs, each of these three cultures, and we really need to find a way to sort of walk amongst the corners of that triangle to get the best knowledge. So there are certain things core screening is best for. There are other things simulation is best for. We always need observation to test both types of knowledge. And that's, I think, where we find ourselves. Chomsky said to us that deep learning is a bit like, I mean, he used the term anything goes. And in his estimation, it didn't kind of, you know, as a theory, it didn't demarcate what something isn't versus what it is. I mean, in your estimation, what makes a good scientific theory? And what does it mean for us to actually understand something? I mean, for me, it's all about compactness and compression, right? So I think the amazing thing about equations is that we can transmit them to each other quickly, easily, in small forms. Now, there's a huge amount of knowledge that one requires to then decode those equations, right? So it's not so simple. Like, you and I might both have to spend 30 years learning all the mathematics it takes to transmit a certain sort of equation to each other. But once we've done that, once we have that training, this compact form is really an easy way to transmit understanding and knowledge. One of my favorite ways to test understanding is to predict the unseen or the unexpected. And I think that's many theorists get very excited when they have a theory that predicts something that hasn't been seen yet. Because it's sort of, I think what Rob Phillips once called a dangerous prediction, right? There's no data for it yet. there's no way you could have tuned your theory to match some old data. You're making a prediction that will either be right or wrong about something that hasn't been seen yet. But that's a real notion of understanding, I think, to say, you know, we've never looked for this, we've never seen this, but if X is true and Y is true, then certainly Z must be out there, let's go find it. And I think that's a real demonstration of understanding. I've just read your paper with David Krakauer, multiple paths to life. And towards the end of the paper, you said, we want to have a universal theory of life. Tell me about that. Yeah, so it's something that's been of interest for, you know, as almost as perhaps as long as human thought, right? What is life? How do we understand living things and so forth? That question has been more and less obvious to people over the history of science, where sometimes people say, of course, we know what life is, it's a frog, You see one there and so forth. But what we're really after is what are the principles that go into a theory of the living, right? How do we have a set of principles that helps us understand how life first came to be, how you get an origin of life, how that life starts to gain more complexity, how eventually it starts to add more and more intelligence, how you get new types of what are called major transitions in evolution as life gets bigger and more complicated. how eventually do you get new types of life, right? And so in that paper, we argue that from certain theoretical perspectives, human culture should be seen as life. It's just life living on a very strange substrate. That substrate is human minds. Language is the same sort of thing. We argue that in silico life, artificial life should also, should be no question that that counts. Again, it's just life living in a very weird substrate, the substrate of computers that are built by humans and so forth. And so our interest was really, what sort of theories, what sort of principles help us understand all of those different cases, right? And in that paper, we're very careful to say, we don't think we know what the theory is yet. We're far from it. But there's a style of thinking that hopefully can push us down that path. And we point to some new emerging theories that are sort of exciting. There are new theories of agency. There are new theories of semantic information. David Krakauer and a bunch of colleagues have a paper on the information theory of individuality, which helps us understand what an individual is. You know, all this work that I and many collaborators do on scaling laws looks like something that we can start to build into theories of life. And in that paper, we say is that these are all ingredients. And at some point in the future, hopefully we'll have some projection of all these ingredients into some new thing that we'll call life. Maybe that's a set of equations. maybe that's a set of concepts maybe it's you know more agency less intelligence we don't know but we think we're in an exciting time where people are building quantitative theories to get at some of the ingredients and again looking at the history of science there are many cases where all the ingredients were there and then people figure out oh this is the right combination this is the right projection to give us the theory that really gives us traction on something so for example he had this paper which was minds brains and programs and what he was basically saying is that the physical instantiation is very important and if you remove the instantiation then it doesn work anymore and you had this wonderful quote in the paper what you know where you were saying that the materials on Earth might be the universal life machine Do we need the material So I think we need a material, right? So I agree with instantiation. I agree with embodiment. Partly it's because embodiment is what connects software or concepts to real constraints. I mean, that's a lot of what I focus on is once you have a cell, once you have an encapsulation for the simplest life, that then has to interact with the physical world in ways where we understand what the dominant physics are. And that allows us to predict quite a lot about life. And so here it's important to separate sort of the universal from the particular or the path dependent or the contingent. There are many reasons why the particular material may not be essential. Right. So I think life is such a hard problem that one way we've gotten traction is just to focus on the things we have, DNA and RNA and ribosomes and the folded proteins that produce a lot of function in cells and the lipid membranes that encapsulate them. All of that is the biochemistry we know. And I think we've gotten very attached to that because we're still trying to understand that, how it came to be, all the things it does, how it gets more complicated. But if we really step back for a second, there's lots of ways to achieve the same sorts of functions, right? And one can make a sort of functionalist argument about what life might be. And then the materials become much less central, right? You could have radically different materials performing the same sort of function, right? And so we sort of talk about this in that paper in computational terms. You can say, you know, there's an abstract algorithm, and then there are often many different hardwares you could implement that algorithm on. And the mapping is what one has to think about. That's where there's some trickiness. You could even say there's a process for which there are many algorithms that will have the same sort of outcome, like sorting. And that then could have multiple softwares and multiple hardwares, and the outcome is the same somehow assorted list, right? And you could talk about relative efficiencies and other sort of constraints. And so I think it's sort of going back and forth between all those layers to say what types of things do we want in life, in understanding life for the same sorts of principles. I'll say this is a hugely emerging topic at the moment to sort of focus on the functional bit of life and to use that to sort of say the materials might be less important. So David and I wrote about it in that paper. Michael Lockman has been working on this. Michael, Sarah Walker, Lee Cronin and I have been working on this in other work. And so I think it's really an exciting time for sort of trying to step away from the particulars of materials and try and understand some principles about life. for more details Whether it's a movie night or just midday, Skinny Pop is a salty snack that keeps on giving. 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Yes, and as an aside, I've spoken to several functionalists this week, like for example, Yoshibok. and he has an information ontology and I suppose you're pointing to a type of functionalism which is compatible with materialism where you could have higher order processes and these processes could ensconce themselves in other types of material and we're not really rejecting materialism but I wanted to talk about your hierarchy so this is absolutely fascinating so there are three levels there's materials so things like chemical bonds and kinetics there's constraints so things like the maximum entropy principle and optimization so things like variational and action methods and we're going to show a graphic on the screen now so you know this this amazing visual hierarchy that you've got in the paper and you kind of show that these levels work together and there's the the space of living at the top where we have these high level principles and that's not completely homogenous but more homogenous and in the middle we have quite a diverse heterogeneous set of constraints. And then we have the set of possible materials at the bottom. Absolutely amazing. Can you explain that? Yeah. So our proposal there is that you could get lots of different origins of life on different planets in radically different materials. And then all of them would undergo one of these phylogenetic trees where organisms compete and diversify and mutate and evolve and so forth. That could be happening on really different substrates. And our proof of concept there is sort of, you know, language, which I said already is evolving on the substrate of human minds. People have written down really nice theories for that, showing that it's an evolutionary dynamic, that you can write down the evolutionary process for language. So there you're getting evolutionary dynamic on top of a new substrate, human minds. Now you could say, well, that substrate is still made of carbon and all the rest, but that's not really the essential piece, right? It's the substrate that behaves in a certain way. It has new properties. And on top of that, you get something like language and then culture. So we think the materials we're completely uncommitted to. The materials can be totally different. They're like different hardware implementations. And we expect that to be quite diverse across the universe for that reason, right? So we don't think you should expect to go to, say, a different solar system and find, if we do find intelligent life, find intelligent life built on exactly the same biochemistry. We think that would be exceptionally unexpected, right? So then you could say, okay, so are we just stuck with that? Are we stuck with this huge diversity of life across the universe? There's nothing more to say about it. Not quite, because all of that still lives in a physical universe. It still lives within the constraints of physics. So if you lift up a layer, then you have the laws of gravity, the laws of diffusion, all sorts of different physical forces that matter to organisms, matter to any organism made of anything at a particular scale. And so there you can then start to say, well, if I write down theories based on, imagine I just have a cell in a fluid that has to interact with a diffusive process. Great. That's a really general model. I haven't actually had to tell you much about the materials. I didn't invoke DNA in telling you that story. I didn't invoke proteins. So now we could say there's something we can say about any sphere of living material that's really small, say bacterial scale, anywhere in the universe, right? And so that's our strong proposal is that once we lift to the layer of constraints like physics, we're collapsing a huge amount of material diversity into a more compressed space of constraints. But there still could be ways materials put you in different constraint spaces, different planets still could have radically different constraints because of things like temperature and pressure and the particular chemistry and how acidic the environment is, all those sorts of things. So we still allow for some diversity there. And then the question is, okay, but what would any living process be doing? And that's what lifts us up to these optimization principles, right? So many people, including David, have written about how evolution is a learning dynamic, right? There's certain ways you can write down the equations. It looks exactly like certain learning dynamics that are used repeatedly. that feels much more like an abstracted principle that should be true of any living system, just that it's following a certain sort of evolutionary dynamic. There's actually this wonderful thing called the air threshold, which is how fast can you mutate given how much information you're trying to propagate to the next generation? The minute principle. Yes, so that is equivalent. Also gets called the air threshold. And if I mutate too quickly, I can't adapt anymore, given how much information I have. Again, there, I haven't said anything about materials. I haven't even said anything about physics. I've just said, you're an evolving process with some error rate, and you're trying to adapt to a new world. There are constraints on how fast you can mutate and still be able to adapt, given the size of the information and how big your population is and some other things that matter for evolutionary theory. But that's really fundamental, right? that should apply to language and culture and cells and genomes and all the rest. Yeah. And even that as a principle of life is fascinating. I mean, that conversation I had with David was just earth shattering for me. But he was talking about these adaptive processes that kind of surpass traditional evolution. So for example, ontogenic hacking, which is where we can store information in our nervous systems and brains and culture and so on. And there was a quote from David's paper, I'm not sure if you're on that paper as well, but the definition of life was the union of two crucial energetic information processes, producing an autonomous system, metabolically encoding and extracting information of survival value and propagating through time. Isn't that fascinating? I love that. Yeah. And I think, again, I think that definition has all of the ingredients that we think will eventually be in a definition or a theory of life. The community at present has different weightings on those different pieces of that, right? So some people are very focused on metabolism and energy. I work a lot on energy, although I'm not, I don't think it's the only thing. Some people are very focused on, you know, autopoiesis, self-generation, replication to the next generation. Other people are very focused on information. Other people are very focused on computation. So I think all the ingredients are there. And there's something exciting happening in the field, because many people's, you know, theories have these overlapping ingredients. And I think it's just about weighting them and quantifying the sort of details of how how we bring each into a sophisticated set of metrics, right? Yeah. What I love about this functionalist perspective is the substrate independence. So there's this beautiful idea of a kind of memeplex where you have certain patterns of functional processing and they can jump into different phylogenies. Can you give me an example of that I mean does that explain potentially how the sophisticated life that we have in our phylogeny could it potentially have transferred from another one Yeah so I think there sort of different ways to think about that One is there is a whole lot of lateral transfer that can happen across the tree of life in lots of different ways, right? So even in bacteria, you have these lateral gene transfers where you can move bits of genome from one genome to another. So you can discover something in one lineage and then bring it over to another lineage. That's great. There's this whole other process of just good old-fashioned convergence, which is an old idea. And it just says that in certain cases, there are targets for a function that if you achieve that function, it's likely to look a certain way. And so you see similar solutions across the tree of life, right? So we get many origins of the eye, right? So you can imagine, look, the eye is so complicated. Maybe it only gets discovered once, and then everything that has an eye has a common ancestor. Not true. Eyes evolve many times. There are differences in the details of eyes in terms of how exactly they see and how they're constructed and slightly what they're made of and so forth. But in some notion, they're all doing the same sort of physics. So that's the convergent bit, right? And the convergent bit is because the physics is a target, right? We understand that, you know, there's as is happening to both of us right now, certain ways to focus light and obtain a picture at a distance and so forth. And so that's really the target for convergence. So I think there are two things happening in lineages. Sometimes it gets discovered in one lineage and transferred, and the other is just something is so predicted by physics that if the function happens, it will look a certain way, even if some of the details are different. I think what's really interesting about human society is that we're really, really good at the transfer bit, right? And so, for example, you can have things discovered in entirely different cultures with different languages. And sometimes those get, things like agriculture get discovered multiple times. That's maybe more of a convergent thing. But then there's other cases where you have some really neat thing discovered in one part of human society, and then it can just immediately propagate to the rest, Because our genomes for intaking and taking on information or rejecting it are really flexible. I mean, genomes are very abstracted in our sort of cultural genomes. And because that process is if you tell me an idea, I can filter it, think about it, decide if I want to integrate it and so forth. So you might try to mutate my mind in some way by giving me a little bit of information, but I get to think about that and process it and decide if I want to integrate it and decide what actions I might want to take on it. That's all really key, and I think that's sort of the extra sauce of society and culture is that we have a higher rate of transfer, but we also have really good procedures for deciding if we want to allow the transfer or not. If I tell you a bad idea, you might remember it for zero seconds, and that's important. And does the convergence happen at multiple levels? So just with this hierarchy that you proposed in mind, I can understand how the principle of least action or something will give rise to the convergence of certain patterns in different parts of the phylogeny. And then perhaps at the optimization level, we might have a higher level of, you know, more abstract form of convergence in different parts of the phylogeny. But I suppose what I'm driving to here is, do you think that there could be some kind of information phylogeny which can eventually transcend the material substrate? You know, does it resemble a basin of attraction where patterns can't really sort of migrate to very different materials? Or do you think there's something higher level going on which really allows patterns to move around? Wouldn't that be cool? Yeah, I think many people have a notion that that might be something like that, if we can find the right projection, a general enough theory. I don't think it's something we currently have a ton of traction on. But maybe it is. I mean, people that are thinking very hard about, okay, so software evolution, how does that look like? Biological evolution, you know, all these different things. and maybe someday we will find a notion where we realize oh right uh life just you know has this process and then when we started to build software systems it did exactly the same thing in terms of a bunch of people working on some open source piece of code that uh then had a very particular evolutionary dynamic i don't think we know what those projections look like other than at these very coarse grained what's the macro scale evolutionary dynamic um and uh so yeah it's there's still some fuzziness there. But I think the hope is that someday one can find that projection. I mean, I think in general, this is a very meta point, but I think in general, theories are often, you know, good theories should either tell you a bunch of things that look different are actually just a projection onto one space when you see it at exactly the right angle, right? All the things lined up, you realize, oh, that was just one thing. Or theories should tell you, no, at the most fundamental level, these things are provably different. There's no way to project them into the same space. They're fundamentally different in some interesting way, or these are the similarities they share, and these are the similarities that we know provably can't be shared by the two things. So that to me is sort of the whole goal of theories to tell us what's the same, but hiding in plain sight, where the sameness is hiding in plain sight, and what's fundamentally different. And similar to how we started the conversation in this paper, you said there are extant theories of life. So comparing existing life, historical theories, so looking at the trajectories and principles, so abstract principles for all possible life. And we are a little bit Earth-centric in how we think about life. And the million-dollar question for me is, could we simulate life in a computer? Oh, it's a very interesting question. So I think if you can get enough constraints and you can get sort of enough complexity in the environment, then there's no reason you couldn't simulate life in a computer. Computer viruses, we say, are lifelike, right? They get passed around and they have functions and they're hard to get rid of and all these other sorts of things. And some of them might even evolve and so forth, right? And so that's an interesting case. there are perpetual debates about whether anything counts as life in any context, right? Whether that's creating synthetic life in the lab or creating artificial life in the computer, there's endless debates about where the threshold would be. I think part of that is that we don't have a strong theory, right? So, you know, if you and I wrote down a two-dimensional simulation for a gas in a computer, and we asked, okay, is this obeying the second law of thermodynamics, you and I would immediately be able to say yes or no. There's just a calculation we could do. And we could say, and not, obviously, your computer is obeying the second law of thermodynamics. It's heating up the room, and there are conditioners fighting against that and all the rest. But even in the environment of the computer, someone could look at my code and run my simulation and say, you know, that's a really interesting simulation you created, but I don't think it obeys, I don't think you've built in at obeying the second law of thermodynamics, right? Now, if we wanted to try and do the same thing for a living system, that becomes a much harder conversation, right? I mean, if we wanted to say like, okay, I really have a simulation of my computer that is life, then it's, then we're into this whole axiom space where the thresholds are, and we just don't have a good compact theory to tell us when we've crossed that threshold. But I think in principle, there's just, there's no reason why with enough compute, with enough constraints, with enough complexity inside some artificial world, you could create life. Now, there's a whole other question here about embodiment. And there are people who have strong commitments to a certain sort of materialism that would rule out certain computer simulations. That's part of a really interesting philosophical debate, right? And for me, I'm mostly interested in principles. And so I don't have strong commitments to those debates yet, because I don't think we understand all the principles. Yeah, I mean, we intuitively know that the causal graph will be different, the energy usage will be different, there are differences. But David said something interesting, which is, you know, he thinks about life in terms of representation, inference and adaptivity. and it just brings up all of these interesting questions like is a virus intelligent is culture intelligent and with the virus one in particular we kind of feel that it doesn't make sense to talk about it as being a life form when it's so obviously parasitic on its instantiation and we are increasingly kind of going to this abstract level now where we're talking about things that supervene on the physical many levels down as being possibly agents or intelligent organisms in their own rights. And just the language that we use, you know, it's the same thing in language models. We use mentalistic terms like beliefs and deception and thinking. And it's hard for us to think about this because the language that we use is so grounded in the way we do it. Yeah. So I think that's a very interesting point. And, you know, we would say, so a lot of the thinking about what is life has been trying to argue about lists of axioms, Right. Life needs to have this and that and that. And then what you put in the list rules in and rules out what is life. And so some lists account for viruses and other lists don't. We really tried to argue in that paper that the dynamic of viruses is clearly living. And then we try to point out that there's a really weird thing about parasitism, right, is how we view it. So if you think about a virus, like language or culture, it's an organism that has a very strange environment. That environment is a host like us or like bacteria. Viruses infect bacteria as well. And so it's a very strange life form with a really complicated environment. But we're a strange life form with a really complicated environment, right? We don't make our own energy directly. You and I didn't sit outside in the sun for an hour and a half before this interview, photosynthesizing and building up enough sugars to have the mental energy to talk about this. We ate a bunch of vegetables and other things that did do all that work. And so we're already, in some sense, all heterotrophs, all predators in some sense are a certain sort of parasite We require other things that are doing the primary work of energy capture and transformation We also have this internal microbiome right So we're a walking ecology. So we're both parasites and in ecology. And so our argument in that paper is if you take a cyanobacterium, which is just a small organism that makes energy directly from the sun, it's simple, it's self-contained, it gets all of its energy right from the sun. Maybe that thing is the most living thing. But as you move away from that towards viruses and us, there's certain perspectives where we'd say we're not necessarily more living than a virus. There are other perspectives where we're clearly more living. You and I are having this conversation. We've put things on the moon. There's a certain sort of agency prediction about the future, technology construction that is clearly unique about many of the things that we do. But in the wrong in the wrong projection, again, thinking about this projection space, the wrong projection, us and viruses look like the same sort of parasites living on different types of really complicated environments. And so I think we can start to think about how that applies to AI and learning in general. And sort of to this other point you made about intelligence, I think a lot of us want to put all of the things that matter to life on a spectrum, right? Which is what physics does a good job of as well, which is to say, I want to say that something has zero intelligence. I don't want to say, or I want to say it has 10 to the minus 20 intelligence in some units, right? I don't want to have this binary, is it intelligent or not? I want to be able to say, to arrange sort of all systems that might be intelligent on a spectrum. And maybe that spectrum goes 10 to the minus some huge number up to some really big number, 10 to the positive huge number, and we're on one end of the spectrum of that. But I think that's what we need for all of these things, for agency, for intelligence, for self-replication. All of those need to be put on a spectrum so that we can fairly compare them. Yeah, and I think it's possible to have categories and spectrums at the same time, so you can have spectrums inside the categories. Because I'm very interested, when we look at the phylogeny, we see these phase changes, and maybe emergence is a term we might use. So, for example, going from abiotic to biotic or also um i speak to a lot of gofi people and and they adopt a form of cognitive chauvinism where they say humans have cognitive properties that other things don't so you know we can have a belief in in in a strong sense our form of intelligence and agency is some something to do with the types of computation we can do so we you know at some point we become touring machines we have this prometheus moment with language or something like that so i suppose the question is what triggers these Promethean moments where we see huge changes? Yeah, so I think that is important, right? That even if you have a metric, like I was just describing, that metric can have sudden jumps. And it's important then to talk about what those jumps are. I mean, there's a reason we call water, water and ice, ice, right? You can write down an order parameter and you get a jump and that's cool, right? That's really nice. So I think we've been doing a lot of work to try and understand why you have those jumps over the history of life. And what we typically see is that you have some set of physical constraints that matter for a class of organisms. That predicts how things will change as the organism gets bigger or smaller. And we have a nice theory predicting that. Those theories also tell you that at the boundary of those scaling relationships, you often get these strong asymptotic behaviors where things go off to infinity or zero. There's some hard limit to a category of organization of organisms. And so that creates a wall, an evolutionary wall. And that is where a phase transition is going to happen. You need to do something to jump over the wall. Now, I'm not sure you always jump over the wall. It's not clear to me you always get these evolutionary transitions, and I'll explain why in a second. But if you're going to get bigger than a certain scale, it says you need to invent a certain sort of architecture that gets you away from that. And that architecture is characteristically different. So going from bacteria to unicellular eukaryotes, you have to put one prokaryote inside another prokaryote. Looks like it was a bacteria inside an archaea. That's a new sort of architecture, right? Now suddenly you have two genomes. You have more internal membranes. There's something really different happening there. When you go to multicellularity, true multicellularity, where you're differentiating cell types and creating organs and tissues and all the rest, not just living in a colony, that's another sort of organization that requires a different kind of regulation, communication, developmental programs that get you there. That's another phase transition. Interestingly, when we look at when those jumps happen, they typically happen around huge shifts in Earth history. So when the environment of the planet radically changes. So we have a paper showing that snowball Earth, which is this period in Earth history, happened a few times where the planet almost completely covers itself in ice, that we think that induces multicellularity, that a bunch of physical conditions become just right then to get bigger and become complex multicellulars. You discover that, and then when the world thaws out again, that is around and spreads and becomes bigger, and you've found that solution. So that's a case where, yes, there are these transitions. We understand what the wall on one side is. And then maybe sometimes you need an environmental inducement to jump the wall. You invoked David Deutsch in your paper. So he had this book, The Beginning of Infinity, and he spoke about the importance of separating matter and logic. What did he mean by that? I could look at a computer and just say, it's a bunch of electrons moving around, right? It's, you know, some complicated network with a current running over it. A river is a complicated network with a current running over it, right? What's unique about a computer is that we've built it in a certain way to perform certain logical operations in a repeatable way so that we can give it inputs and give it outputs. And in the case of generalized computers, we can reprogram it and have sort of an arbitrary notion of what inputs and outputs we want, right? We can write software to it. So I think that's really the key is to say in the crudest sense, the physical description leaves out a whole bunch of information, right? And to understand certain architectures or where the architecture is there, you would need to bring in a logical or a software notion. And I think that's what that separation is about. Yeah. Can we talk about assembly theory? Right. So you've got a paper about this, and it's a way of kind of quantifying complexity, I suppose, by looking at how something can be built step by step. Can you explain what that is? Yeah. So this is a theory initially aimed at trying to search for life in the universe. So a chemist, Lee Cronin, and a theoretical physicist, Sarah Walker, and then a bunch of other of us who've gotten involved, um, were, have been interested in this idea of how do you fairly and without being committed to this past knowledge I was talking about before the biochemistry we have, how do you fairly look for life in the universe? Um, and so assembly theory simply says, uh, one way to do that is to look at the recursive use of parts and what the shortest path to build an object is. Right. And, and so here's the thing you're trying to get away from. I hand you a molecule. And I say, is this molecule complex? And I'm not allowed to tell you anything about the synthesis that we use to make it. I'm not allowed to tell you if a living system made it. I'm not allowed to tell you if that living system shares our biochemistry, has different biochemistry. How do you decide if that molecule is complex or not? And what we want is something that bounds that complexity, right? We want sort of an ultimate bound in that complexity. And one way to do that is just to say, when you have a synthesis pathway, So you have an evolving set of objects that are following some evolutionary lineage. You invent a set of parts. And then the easiest thing to do is take those parts and reuse them in some way, right? And then if you invent a new part, that's sort of a more complicated thing to do. With that sort of notion in mind, you could say, I give you an object, and you're just trying to find the shortest path to build it, where you build up a set of parts and then can recursively use those to make a next set of parts. and you ask how often you have to build a new part or use an old part, each of those counts as a step, and you're just trying to find this shortest path to get something. And that's definitely a bound, right? Certain processes could be much more complicated in building that up, but it sort of lower bounds the complexity. And then by comparing all these lower bounds of complexity, you can sort of fairly compare how complex an object is in the best case sort of shortest path process. And so this gets away from things like if I hand you carbon 60, which has a large molecular weight, you can say, well, that's a very big object, but it's actually not so complex in those terms, right, in this assembly theoretic perspective. And so experimentally, it looks like there's, and this is work that Lee's done in his lab, It looks like there's a threshold where you can go from a biotic to the biotic. Each step in a STEM-ly space is in a very rapidly growing space of combinatorial possibilities. So each step is a really big step. And so you expect sort of a sharp cutoff there somewhere. And it looks like that happens in the experimental data, which is exciting. Chris, this has been absolutely amazing. Thank you so much for joining us. Oh, it's been a blast. Thank you so much. Yeah. you Been out here all morning Not a single bite Guess the fish finally figured it out Just like hackers do when Cisco duo is on guard With Duo's end-to-end fishing resistance, every login, every device, every user stays protected. No hooks, no catches, no bites. Cisco Duo. Fishing season is over.