Ep112 "How is computer code like magic?" (with Sam Arbesman)

Published Jul 14, 2025, 10:00 AM

What is code, and can it be thought of like a magic spell? Are we building a world so complex that we will lose the ability to understand its operations -- and has that already happened? What does any of this have to do with SimCity, or knowledge that already exists but no one has put together, or how coding will evolve in the near future? Join Eagleman with scientist Sam Arbesman, who has just written a book asking the question: what is code, really?

What is software code and can it be thought of like a magic spell? Are we in the process of building a world so complex that we will lose the ability to understand it? Or has that already happened a long time ago? And what does any of this have to do with SimCity or knowledge that already exists but no one is thought to put together, or inventions that evolve beyond the grasp of their creators. And what coding looks like in the near future. Welcome to Inner Cosmos with Me and David Eagleman. I'm a neuroscientist and author at Stanford and in these episodes we look at the world inside us and around us to understand why and how our lives look the way they do. Today's episode is about computer code. We live in a world increasingly built out of symbols. We've got strings of code and lines of logic and invisible layers of computation stacked very deeply in our lives. This goes from traffic lights to financial markets, to weather predictions, to streaming recommendations, to all of our apps and our AI and on and on. We are surrounded by systems that hum quietly in the background, and that orchestrate everything about our modern lives. But it's very uncommonly that we stop to ask, what is code? Really? It's a tool, but it's also a massive force that has taken over the world. So where does it come from? Where is it taking us? And what does it mean to live inside systems that we ourselves have written but we can't possibly fully understand. So today's guest invites us to see code as having an aspect of magic. We're going to talk with Samuel Arbisman. He's a complexity scientist and a writer who thinks about the evolving relationship between humans and the tools that we build, and how our creations can outpace our comprehension. He's just written a new book called The Magic of Code, and here he takes a dive into the joy and the deeper nature of programming, in other words, beyond just an engineering discipline, but instead as a hopeful and enchanted practice. I think I would describe this book as a blurring of the boundaries between science and art, between logic and myth, between our intentions with writing code and what can actually emerge. Because the paradox is that code is built from rigid languages with strict syntax and rules, but it lets us create whole worlds from scratch. We can simulate galaxies and evolve virtual creatures, and test new economies and reimagine cities, and it gives rise to things that are complex and unpredictable and often quite beautiful. So here's my interview with Sam Arbusman. So, Sam, you're a scientist with very broad interests. What drew you to writing about your latest book on the subject of code?

Yeah, So, one of the things I think about when I think about how people are talking about technology and computing and code is that right now, it feels like there's almost this kind of like a broken conversation in society where when we talk about code or computing the world of tech, there is this average real stance towards it, or or just worried about it. Sometimes people are just ignorant about it and unwilling to kind of learn more. And certainly some of the adversarial stuff is reasonable. But for me, like when I think about my own experience with computers growing up, it wasn't adversarial. It was kind of full of this kind of like wonder and delight. It also didn't feel that like computing was really just this branch of engineering. It also really connected to lots of different things. It was almost like humanistic liberal arts that drew in language and philosophy and biology and art and how we think in all these different areas. And so for me, I wanted to try to explain how to think about code and computing as this almost like liberal art that attracts all these different topics.

And in the book, you compare this to philology, Right, what's this you tell us about philology?

Yeah, so, so philology it's this branch of humanistic study that within the humanities, it was devoted to understanding the origin of words and kind of the nature of the history of language. But philology, in the process of doing philology, it required knowing about archaeology and anthropology and history and all these different topics. And then eventually philology sort of fractured, and that's kind of where we got a lot of the different domains within the humanities. And I kind of think that in some ways computing has at least some aspect of that kind of philology as unifier of lots of different topics. And so for me, my goal was to kind of try to show how computing and code can actually have that connective tissue between all these different domains.

And so you described code as being magical.

Why and so when I say magical, I'm not saying that it's like magic in the sense of like, oh, like this piece of software just works, it works like magic, although there is some of that. For me, it's actually this idea that when we think about the nature of code or magic, and we've had as a society this desire for millennia to kind of coerce the world around us through our language and our texts and our speech and make the world kind of do our bidding. And only in the past, I don't know, seventy five odd years since the advent of the modern digital computer, has this been a reality where we can actually write text, we can write code, and it can actually do things in the world. And so for me, there is this deep similarity between how we've thought about magic in the ancient or medieval days, or even in the stories that we tell ourselves and the reality of code. And so, of course this analogy and metaphor can only be taken so far before it kind of breaks down. I certainly take it to the bending point, if not the breaking point. But there are a lot of deep similarities between how to think about this. So, for example, magic often requires in our stories a certain amount of training and knowledge. And it's a craft. It's not just like a thing like that just works. It actually requires you to learn certain things. And so we have Hogwarts School of Witchcraft and Wizardry. You got to go there for seven years or whatever it is. And so too with code, like it doesn't necessarily just work. You actually have to understand how like the nature of syntax and the details of code and so and so that example, as well as other ones, I kind of us to show the ways in which this this idea of like the kind of the analogy of magic actually can be a productive and useful one to help us better understand how code works.

So it's not just a set of instructions. It's like a spell in the sense that you puts a strange set of symbols and it does stuff in the world that moves electrons or launches rockets, or models pandemics or creates simulations. And that's the sense in which it's got this magic to it. But also you point to the fact that there's often unpredictability and emergence of things we didn't expect from code. So can you give us an example of this dual nature of code?

Yeah, I mean so certainly. In the world of magic, we have a lot of these stories, and like there's like the story of like if the source is Apprentice, which I think there's like the old version and then the Disney makeme Out version, where some sort of magic has unanticipated consequences ands only you have you have brooms kind of walking around and flooding and flooding a basement, And the same kind of thing is true with code. Where in code, I think people who might not be familiar with with programming think of it as, oh, like I have this idea in my mind and I'm going to instantiate it into a computer program. And there is that, but there's also a huge amount of debugging and frustration because oftentimes when you write a program, there's a gap between how you think it will actually work and how it actually does work. And oftentimes the reality of the program and better understanding of it is only revealed through these bugs, through these glitches and edge cases and things like that, And so there are many situations where we only see these bizarre errors, and then based on those errors and these kind of unanticipated consequences, do we realize, oh, how this thing actually works, and so it can be So there's a there's a well known story of someone who I think was like a systems administrator for some university department. He was told by the chair of the department that their email was only able to be sent about five hundred miles away, and the systems like this is insane, like email, that's not how email works. And it turned out by delving into it, and he was able to find that I think that there was like an older piece of software that hadn't been upgraded, but the newer system didn't realize this and like would time out, but only after like some very small amount of time. And it turns out, based on like the speed of sound and that small amount of time, it ended up working out to about five hundred miles and it was this weird unanticipated consequence. But of course, and there's also other things that just the fact that when you stitch systems together and pieces of software together, they all interact in unexpected ways. And that's also the kind of unanticipated consequences we see. Whether it's like some weird little system fails to get upgraded and then suddenly all like the the airline systems go down for a first certain amount of time or whatever it is. And so there is that kind of unanticipated consequence in lots of different ways. And we're seeing this, of course even more so with AI.

Okay, So this is the thing that you and I both love is the emergence of comp complexity in the world. And with code it's a very specific, detailed set of instructions, and yet it can break, it can decay, it can be opaque. All kinds of things can happen. And sometimes when we try to model complexity, we actually unleash complexity. So tell us your take on how code can do things that we didn't expect it to do.

Yeah, and so when we think about and just engineered systems more broadly, and certainly when it comes to code, you think, oh, it's designed by people. It sounds very logical, it's kind of derived from mathematics. It should be very simple and straightforward, and very small bits of code are that. But the truth is it adds up and then through this combination of the sizes of programs, growing them beingcoming connected to various other bits of code that are out there, as well as also just engaging with kind of the messiness of the world around us, you end up getting sort of a certain amount of unexpectedness as well as kind of just a reduced understanding. And part of this is because there's and you mentioned this kind of like it's like breaking and things kind of growing over time. There is this whole phenomenon of legacy code where code has been around for a very very long time, and we have systems that are still being you like, that are still being used that were developed decades ago, but they might be involved in kind of the irs, but they were developed first developed or in the Kennedy administration. Like there's all these kind of crazy examples where things that have been developed the people who first made them they might be and they might be long retired, they might be dead. And we also just don't fully understand these systems. And so it's this weird situation where we have to recognize that even the systems of our own construction are actually like when they become big enough, they actually have this kind of qualitative difference where they almost they almost become almost biological or organic in their complexity, and as a result, we have a reduced understanding and we have to kind of take almost biological modes of studying these systems, whether it's kind of like the days of old with like the natural is kind of going out and collecting bugs in this case could be bugs and errors. It could be bugs like insects, as well as just kind of like trying to tinker at the edges and better understand a system, because the thing overall you fully don't understand. So there's this weird situation where these systems are engineered, but they also involve kind of a certain amount of humility in trying to understand these systems. And part of that is also because one of the other features of computing and software is this idea of abstraction that you can kind of build things on top of other pieces, and those pieces are then sophisticated kind of units, and you can kind of use them as standalone bits and then don't have to worry about the things underneath it. And so that modularity is very, very powerful, but as a result, there is a decreased amount of understanding, and so sometimes not understanding what's going on under the hood or kind of underneath these pieces, even when yourself are programming them or programming kind of the things that interact with them, means that you can kind of also have a certain amount of anticipated consequences.

And so what are the key limitations that you see when we try to model very complex systems?

So one of the things I think about when I think about modeling complex systems is is what is the goal of modeling the complex system? So there's some situations where we really want to have perfect fidelity to a real world system. And so for predictions, like with weather, as a weather prediction, like you want to and you want to not just understand kind of how kind of air moves around, You want to really understand whether or not it's going to rain tomorrow or in an hour or two hours or whatever it is. And and based on that you have to have a great deal of data and a great deal of complexity. And then oftentimes the resulting models might be very powerful, very sophisticated, but there might be a reduced amount of understanding and actually how these things are doing what they're doing. On the other hand, if you want to just understand the features of a system, you can sometimes get away with a much simpler model, which might not necessarily be exactly the way that the model works, but could at least capture some of the complexity and kind of the and the emergence of what you were talking about of that system. And so, for example, and this is a kind of trivial example, but the computer game SimCity. It is not modeling an actual city, but to give you an intuitive sense of how feedback operates, or unanticipated consequences work, or just the fact that, like complex systems can bite back and do weird things that you might not expect. SimCity is great for that kind of thing. And so, and it can also kind of give you a sense of, oh, when I do this kind of thing, according to this model of how Will Wright or whoever was programming it thought cities would work, this maybe would be the way it works. Whether or not that is actually how the city operates, that's entirely different, different thing. But so for me, I often think about, like, yeah, what is the ultimate goal with the model? Is the goal to kind of understand things? And we have to recognizing our human minds are really limited when it comes to understanding complex and nonlinear systems, and so we need these simplified models. If it is actually just prediction, then sometimes a really complex model can work, but at the cost of reduced understanding.

So as AI generated code becomes more common, what does that do to our sense of authorship and even understanding? And could we end up in a situation where we're surrounded by systems that are running our world that we don't understand at all.

I mean, to be honest, I think we're probably there already. It's just the situation where I think many people are not aware of that fact. This is also one of these situations where as we build more and more complex systems and everyday users are kind of more distant from them, we just don't realize the sheer complexity. When the Apple Watch first came out, this is years ago, there was an article in the Wall Street Journal, I think it was like the Style section about like, are people going to still use like biomechanical watch? As the answer is they still are that. They interviewed this one guy about it, like whether or not you want to buy mechanical watch or just smart watches, and this guy said something to the effect of, of course I want a mechanical watch. When I think about a mechanical watch. It's so complex as opposed to a smart watch, which is just a chip, and the thing is like a chip is orders of magnitude more complex than a mechanical watch. But we've been shielded from it, and I think as we have AI generated code, we're going to kind of have another level of shielding. I do think we need better mechanisms for so interrogating the system. So I actually so I think one of these situations we're On the one hand, it is very good that we can now generate code via AI and build simple tools and actually democratize the software development. I think there's lots of interesting things there. But I still also think understanding code to a certain degree and allowing you to kind of like dive into the code that is being generated and tweak it, not only does it give you a better understanding of what you're doing, but it's still actually really good to help make sure that it is doing at least partly what you hope for. That being said, our systems have always been imperfect, and I think right now, this is this moment is kind of just heightening that fact, and maybe we'll give people a better awareness that these systems have always been enormously complex, enormously imperfect, made by humans at least at some level, but maybe give us a greater appreciation for building systems on top of these, maybe also AI generated as well, that can allow us to make sure that the the unanticipated consequences are as minimal as possible.

You know, I was just thinking about right after I asked my question about could we end up living inside systems that are too complex for us to understand. Obviously, we live inside our biology, and that is for sure that we don't understand. But a fraction of what's going on inside is biologically. But we try to eat the right foods and get exercise and just just right on top of this system. So we're actually quite used to living inside systems that are beyond our understanding.

Yeah, And I think and that goes back to kind of like the biological nature of these massive, complex computational systems. Like, the more we recognize that these systems are really complex and almost have like this organic quality, we will have to realize, yeah, that we need different ways of approaching them and the way we approach our bodies. Right, It's not, oh like I have total ignorance about how the system works or have complete understanding. There's there's a lot in between, and using rules of thumbs and things are are very powerful and I but at the same time, though, we don't want to necessarily kind of like succumb to like the like the biohacking trend, which I feel like five ten years ago was a really big thing where it's like, oh, if I can just find this one chemical to ingest or this one cool trick to do, then I'll never need to sleep again or I'll be held and we realize that, I mean, yeah, our system, our bodies have evolved over millions of years and are optimizing a huge number of different things, and they're going to be imperfect and weird, and so it's going to be maybe we can find those things, but the odds that we are going to is very low. And I think the same kind of thing that that same kind of approach needs to be used when we think about these complex technologies that were building around us as well.

So let me ask you this, if we fast forward one hundred years, are we still coding by using symbols and syntax or is it a completely different sort of thing where we are setting initial conditions and letting complexity evolve.

I would say it's very different. I don't think we need to necessarily go even one hundred years into the future. It could be like five ten years until we're kind of managing these AI generated systems. I mean. But the truth is, when I think about what coding is, it's always been this moving target, like it's always been changing. So the way I learned how to program in some of the languages I learned, they're not totally extinct, but they're not things I would ever consider using nowadays. But also even when I think about how people before me learned how to program, that too was something very very different. It was like plugging in cables or flipping switches or writing things in binary or assembly code. Like I never did those kinds of things. Nor do I really have a strong desire too. And that's okay, And I think it's going to continue changing. And so one of the ways I think about this is actually I tell this story in the book, a story from the Tumud actually where it's this conversation that's describing a conversation between God and Moses and they're discussing like, oh, like who's going to be the greatest scholar in the future, and God says, oh, it's going to be some rabbi, like a thousand and two thousand years in the future, and Moses says, can you show him to me. So there's this weird time travel moment where he's transported to the hall of Study, like a thousand years in the future whatever it is, and Moses is sitting in the back listening to this illustrious scholar talking about things, and he realizes he doesn't understand anything this guy's talking about, and he's kind of overwhelmed. He's like, Oh, I'm the one who received the law from Heaven and I don't get it until at the very last moment, the rabbi says, oh, in the way we understand all this is because of the law received from Moses at Sinai, And at that point he's calmed and because he realizes that even if he doesn't understand it, there's this clear, continuous line and kind of continuous tradition. And I feel like when it comes to code, the same kind of thing is true. Coding has changed, it will continue to change. It'll be much more like managing AI systems or some other thing. But Ultimately, it's all about taking some idea in our heads and finding some way of instantiing it into a machine and actually getting the machine to do something, to kind of do our bidding. And what that looks like is always going to be changing and so but as long as we recognize it's all part of this long tradition, I think, then I kind of do it. It's all coding, whether or not it's syntax or Python or Pearl or whatever it is, it will definitely not be that. But that's okay.

First of all, I love that story. I had no idea that there was time travel and the toallment. That's amazing. I want to come back to this point that we're talking about that we're already living inside a system that is so complex. We don't understand this because you know, we program simulations, we program other things. But increasingly what it means is we're really living inside of this opaque simulation. And this will be even more true for our descendants, where they'll be living inside this world of creation that they can't understand explicitly.

Danny Danny hillis the computer scientist. He has this great term where he talks about how we've kind of moved from the enlightenment to this kind of age where we could take our take our mind and kind of apply it to the world around us and really understand it. To the entanglement. We've kind of moved to this area where everything is so hopelessly interconnected, we're never going to fully understand it. And I think we've been in the entanglement for quite some time, and it's it's really just a matter of becoming a little bit more aware of it. I think, I mean going back to kind of the analogy of biology and things like that with technology, and biology is a form of technology. Someone once told me that the way he kind of thought about it is like the most complicated engineered system that humans have ever made are domesticating dogs, because like these are we made them. I we we evolve them basically through like through our artificial selection, but they're an enormously complicated system. And I think that's those kinds of approaches, whether it's kind of like tinkering with systems, kind of evolving them, wrecking these things as enormously complicated, those might be the kind of approaches that that we need. That being said, I would say one of the other things though that at least gives me a certain amount of hope. Though that it is kind of weird when you think about it, is that the extent to which humans are also really good at adapting to the world around us. And so we think about all the technological changes that have come over the past couple hundred years, and these things were enormously destabilizing, but in many ways we kind of now take them for granted, and even like more modern and more modern ones. I'm not just talking about I like air travel or certain things around the internet, or the industrial Revolution. So my grandfather, he was he lived at the age of ninety nine. He was a retired dentist. But he also read science fiction since like the modern dawn of the genre, Like he read his entire life, and I remember he read I think he read Dune when it was sialized in a magazine, so like no story could surprise him. And I remember when when the iPhone first came out. I went with my grandfather as well as my father to the Apple store to kind of check out the iPhone and we're playing with them looking at it, and he looks at and one point he goes, this is it, Like this is the object I've been reading about for all these years, and we've moved though from like, oh, the iPhone is this object of wonder and science fiction in the future, to like like complaining about like camera resolution or battery life and things like that. Like we've so quickly adapted, which, on the one hand, is good and kind of gives me hope that we are going to figure out ways of adapting to new types of complexity. On the other hand, though, it means that we sometimes don't necessarily retain that capacity for wonder or when it comes to complexity and the complex systems around us, maybe a more critical stance and actually saying okay, like how like what are the kind of systems we want to be embedded within and as opposed to kind of just allowing them to kind of wash over us. But I do think that kind of adaptive capacity does give me a little bit of hope.

Yes, you probably know this routine from the comedian Louis c k where the first time he's ever on an airplane and they announce we have Wi Fi in the airplane and he's amazed. Everyone on the plane is amazing I ever heard of this, And then ten minutes into the flight, the Wi Fi breaks, it stops working in the guy next to him starts complaining, and Luisy cases you know, ten minutes ago you didn't even know this existed, and now you're complaining about it. So yes, it is true that we adapt so quickly to that. Okay, so let me ask you something really random. Given the evolution of the complexity all around us, what is your opinion on whether we are already living in a simulation?

For me, I like to think about it much more as if you kind of not necessarily take the simulation hypothesis seriously as this like question of great importance, but as like, oh, a question that kind of leads me to think about more things around physics and computing. Then I think it can actually be very productive. So the question becomes like, in the same way that people talk about the simulation hypothesis, there are interesting aspects around, like breaking out of a computer program when you were inside it, or like the like the high resolution fidelity of computer games, or even just the ways in which physic in reality and computing intersect. Oftentimes, when we think about computation, we think of it as this kind of like ephemeral information stuff, and I think that's a really powerful way of thinking about it. But the truth is, like computing and computers, like they are deeply physical. So the like the Internet, like the Internet is not just information kind of whizzing around. It is kind of to use the term from like the that senator a number of years ago who was like widely mocked, like it is a series of tubes. And actually there's a book called Tubes based on that of like the physical infrastructure of the Internet, Like there is a lot of this physicality, and and I think thinking about that the physical nature of our computing can be really powerful. And sometimes the simulation hypothesis, like thinking about it can can help heighten that or can just make you realize, oh, there's some interesting bugs that are worth thinking about. So for example, there was a story I read where I think it was like in some hospital people noticed that iPhones stopped working when they were near one MRI machine. But it wasn't Android phones, it wasn't on there. It was like just Apple products. And it turned out it happened to be that some sort of switch or some other component within these Apple devices it had some small enough gap that it happened to be this MRI machine had had a helium leak, and the helium atoms were just the right size to kind of get into this machine but didn't affect Android devices or other things. And so it was this wild thing that just brought home like the deep, deeply physical nature of computing. And so for me, when I think about the simulation hypothesis, I don't think about it as like, oh no, like I'm being controlled by aliens or humans in the future or whatever it is. It's much more about, Okay, how do I think about breaking open computer games, or like the deeply physical nature of bugs and like all this, like that's the kind of stuff that I find most interesting about the simulation hypothesis. I also think about it as for me, it's almost this like cry for myth in like the in the tech world where it's like, oh, like we're a deeply like like the Silton Valley world is like deeply rational, deeply logical, but we still kind of need some sort of myth or store organizing story in our world. And the simulation hypothesis and ideas around the singularity, certain ideas around longevity or AI or things like that, they often many many times they're also based on technology, but when they kind of get big enough, those ideas kind of veer into kind of myth and storyland. And so for me, it's I view that as kind of when people take those ideas a little bit more seriously, it's much more around okay, fitting kind of a certain amount of myth into kind of that myth shape hole for those for those type people.

So let's return to your grandfather and the iPhone. So how do you recommend in your book and in your life preserving our sense of magic around the technology that we have.

When I think about like magic and wonder and kind of delight in computing, it's never really been an either or of like, oh, like there's either kind of like corporate SaaS software or kind of like the fun weird things. And I feel like you can kind of tell some people might tell a story of like oh, there used to be more of that kind of wondrous stuff and now we've kind of it's all like we're we're just kind of locked into large social media sites or just we're just using these like a large, kind of bland, beige pieces of software. I think there is an element of that, but the truth is these two aspects of computing, they've always co existed, and so like alongside like the really big mainframe or refrigerator sized computers, there were people trying to build like early computer games, and then once we had personal computers, there was a lot of fun, weird things, people experimenting with fractals, but also people using spreadsheets in businesses, and so it's not an either or any and the truth is even on the web now alongside kind of the large websites, there are also people talking about there's a term called the poetic web, where it's like the kind of the more human scale, fun, funkier and weirder sort of websites. And for me, it's really just a matter of trying to actually like discover these kinds of things and realize that it's that it's always been out there and it's really just a matter of being able to find it. And so for me, I kind of view some of the ideas in the book almost as like a proof of existence of like, oh, these things do exist out that out there. You don't necessarily have to be as excited as I am by some of the examples I give, but let that be a guide to ohkay, there are other things out there that are just worth enjoying and experiencing and delighting it. And I think part of that often is just kind of at the smaller scale. And I do actually think that one of the exciting things about AI generated code is it really allows for this kind of democratization of building soft where and so people have talked about this kind of thing for a very long time of like it shouldn't just be the domain of like big companies or kind of serious software developers of building things that are going to be used by like millions or hundreds of millions of people. There should be a way for each individual user to kind of build the bespoke thing they want. And so the novelist Robin Sloan has this phrase. I think it's like an app can be a home cooked meal, this idea that like, you don't necessarily need to build something for everyone. You can build like a little program for yourself or for your loved ones, and that's fine, and that's great. In fact, and the true this spreadsheets were actually a simple version of this kind of thing, because you can actually program in very simple ways. But then there was and there was HyperCard with kind of some of the early macintoshes where it was like this authoring program to kind of build like weird little sort of like pseudo website programs on your own computer. But now with AI generated code, I really see this kind of democratization potential really blossoming. And and so for me, like that is the kind of thing that really can hopefully induce a sense of wonder and people where like they can now build all the programs that they want, and so to have to be that if you kind of explored the world and kind of went about your day and looked at the world and noticed interesting problems that could maybe be solved by software, if you weren't a software developer, you would kind of have to shut down that portion of your mind because you couldn't do anything about it. But now you can turn it back on because now anyone can build those kinds of things. And so I think that is actually a really interesting source for wonder.

And you know, to my mind, there's the flip side of that coin, not just for the individual, but for society. What's going to come out of this? So tell us about Dawn Swanson's paper from what was that I think the eighties or something about undiscovered public knowledge. Tell us about that.

Yeah, so Don Swanson, Yeah, he's this information scientist. In the nineteen eighties, he wrote this paper called Undisovered Public Knowledge, where the idea behind it was and he begins kind of like with a thought experiment. He says, Okay, imagine, somewhere in the scientific literature there's a paper that says A implies B, and then somewhere else in the literature could be in the same field, it could be an entirely different field. There's another paper that says BE implies C. And so if you would connect to them together, you would say, oh, maybe in fact A IMPLYI C. But because the scientific literature is so vast, no one has actually been able to read these two papers, and so that that knowledge, the connection was undiscovered, but it was public because it was out there. And so it's one of these things where if we actually had ways of stitching together all the scientific knowledge that was out there, we would actually be able to make new discoveries that were kind of just lying out there, ready for the taking. The interesting thing with Swanson is that he was not content with leaving this as a thought experiment. He actually tried to test it in the real world, and he used in the then cutting edge technology, which was I think like using keyword searches on like the medline database. But he actually found this relationship between I think I think it was consuming fish oil and then helping treat some sort of circulatory disorder, and then he was I think he was able to publish it in a medical journal even though he himself had no medical training, which was kind of wild and so and I think with all like we had with a lot of these AI tools, we are now going to be able to kind of stitch together lots of different ideas and kind of navigate them like the latent space of knowledge or however you want to describe it, in a way that that has really never before been possible.

This is actually my highest hope with large language models is tackling the biomedical data, in putting facts together that anyone could know, but nobody is going to because they're published in totally different journals. I wrote a paper a couple of years ago now on a meaningful test for intelligence in AI, and I think that what I just described that's going to be enormously helpful for science. But the next level of intelligence which I don't think llms are at yet is actually questioning whether something is true and coming up with alternative models and then simulating those and evaluating them. For example, you know, saying, hey, what if I were writing on a photon of light, what would that look like? And then getting to the theory of relativity and realizing the trajectory of merch can be explained by that, and so on. So that's the sort of thing that lms don't do now. But yes, I think they're going to be enormously helpful in this discovery process within the public knowledge. It's already sitting out there.

People are already talking about, like AI scientists and things like that, like whether or not it's going to be helping with or it's stitching together the knowledge hypothesis generation. Maybe eventually even yeah, this kind of like thought experiment and then examining like what are the implications of the thought experiments. But I do think, yeah, even if they're not necessarily able to kind of do everything on their own, the potential for this kind of like science like human scientist machine partnership will well hopefully unlock a lot of information and knowledge that is already out there, but we just don't even realize it.

What's one thing that you wish more people understood about the coded systems that surround them.

One aspect about code is that the extent to which there's like a craft and a style and almost an art to it. And when people think about like programming languages or kind of which language they want to program, and the truth is there's there's a lot of personal choice and a lot of a lot of opinion, very strong opinions about what kind of languages work, but also even kind of the way in which you program. And so for example, there's actually this book called If Hemingway Wrote JavaScript, where it actually takes I think, like the same coding task and then programs in different ways accordinated kind of different like authorial styles, and to show that it is a deeply human kind of thing. Now, of course, and many people compare it to writing or fiction or poetry and things like that, and there are aspects of that, and I certainly, but I don't want to push it too far because the code still has to do something, it still has to operate. But there really are many almost like artistic aspects to code, and I think that kind of interesting combination of extreme logic and practicality and efficacy combined with style and art and problem solving, I think is something that maybe people who are kind of outside of the world of code just don't realize.

So here's a random question, the relationship between software code and let's say, biological code like DNA. Is this just a metaphorical thing or is there something deeper there?

We are wet, squishy, messy things, and then down at the sollular or substolular level, it's incredibly incredibly stochastic and random, and there's things just all vibrating around, and it's wildly different from the way in which we think about coding. But the one exciting aspect about this is that, like Mike Levin and some of his and his collaborators, they've talked about this idea that really traditional computation is really just a subset of kind of information processing as a whole, and biology is just another mode of doing that kind of thing. So I think by looking at what biology is doing and comparing it to how code operates and how computers operate, where they are similar and where they're different, just shows you the sheer number of different ways that computing can be done, and when it comes to kind of more engineered traditional computing, we are still only beginning to scratch the surface. So I think in that way, comparing contrasting the way biology and computation are similar and different can be enormously valuable.

So let's end with telling us what your message is at the heart of the magic of Code, your new book. What do you hope that people will take away from it in seeing the world around them?

Like Steve Jobs has this idea that computers are the bicycle for the mind. And the idea behind this was that he was reading I think some old scientific American article where it was like a chart of like the energy efficiency of different organisms and humans were kind of mediocre, and like maybe some birds were much better, but then everything changed when a human got on a bicycle, because suddenly they were much more efficient. And his idea was that computers they should be this bicycle for the mind, for helping accelerate how we think, how we interact engage with the world. And that's really ultimately what it's all about. And so for me, whether I'm thinking about like trends in like super powerful AI or certain other things around the Internet or whatever it is we all have to be thinking about, not just saying, oh, these are interesting trends. I wonder what things are going to look like in the future, but more no, like these tools are for me, what is the future that I want to live in? And how can I kind of make that human centered future with technology that much more possible? And so the book is kind of a guide to kind of think about all these different sort of human centered ideas, to hopefully provide a guide for that sense of wonder and delight and humane aspects of computing.

That was my interview with complexity scientist and lover of code, Sam Arbusman. We are right now at the beginning of a centuries long experiment in computation. For the first time in history, we can build these dynamic worlds that evolve and adapt. We can simulate climate futures, or economic collapses, or entire societies that rise and fall in silic But Sam talks about code not just as a set of instructions, but more generally like a kind of spell, a system of symbols that does something real in the outside world. And part of what I find the most amazing about our current moment in time is the way that code can and has evolved past the understanding of its creators. And that's the paradox we're sitting with, and in some sense we have been sitting with for some centuries, now that we are building systems more powerful than our ability to fully understand them. There's one more thing I just want to touch on from Sam's book, the idea that code, like language or like myth, offers us a kind of mirror. Code can reflect our values and our metaphors, and our hopes for control, and our particular curiosities about how the world works. I suspect that someday there are going to be code anthropologists who look back on the kind of programs written by different civilizations at different time points, and it will tell them as much about those civilizations as their books and plays and religious practices, because our code reflects the assumptions that we build into them and the blind spots that we forget to consider. Every simulation has some of us in there. So with the passing of decades, we're going to go beyond how do we code the world to questions about who's doing the coding, and what do we make sure is in there, and what do we choose to leave out? And what are the limits of what we can simulate? And when does it matter that those limits shape our conclusions? In any case, as I think about what Sam and I talked about, I come to something like this conclusion. As we look into the deep future, we may find ourselves less in control than we thought, but also more creative than we ever thought possible. Our minds will be riding bicycles and eventually motorcycles and jets. And that is the sense in which even the most logical systems we've ever built have a healthy dose of magic. Go to eagleman dot com slash podcast for more information and find further reading. Join the weekly discussions on my substack, and check out and subscribe to Inner Cosmos on YouTube for videos of each episode and to leave comments until next time. I'm David Eagleman, and this is Inner Cosmos.