Co-Evolving with Magical Technologies feat. Sam Arbesman

Too often when we were confronted with very complex systems, we zoom off into two extremes. Either we have this like, insane fear of the unknown, or we almost create this like sense of like reverence or awe, or almost like worship of these systems we don't fully understand. Like the truth is, neither one is productive. And this idea of humility, of there are going to be limits to human understanding, not only is that, I think, more reasonable approach, but it's actually much more productive because those two extremes of responses, they're so emotional, they actually cut off any further understanding.

They say, oh, we can't fully understand this stuff, let's just stop entirely, and either worship this thing, or be scared out of our wits. But the true answer is no, we might never fully understand anything, but it doesn't necessarily cut off any sort of inquiry, or any sort of questioning. And so I think that is what responsibility we have in relationships with these technologies, is being okay with recognizing we're not going to fully understand things, but still trying, because I think the alternative, which is still living in this sort of stylized enlightenment version, which is, oh, we can understand every single thing that we set our minds to, if you have that mindset, and then you then are confronted with a system you don't understand, you're going to be blindsided. And so rather than being blindsided, recognize that, no, this is the world we already live in, and that's okay.

I don't know what the right balance is, but for me, it's more just a matter of we just need to be incredibly aware and deliberate about the kinds of changes and choices we're making and how we relate to our technologies. Because I think if we don't, then we just begin adopting new technologies and new systems and new pieces of software, whatever it is, like, willy-nilly, and just like, oh, this is a new thing, I'll do this. And as long as though we have kind of a sense of like, okay, what do we, I don't know, to go really philosophical and vague, if I have a sense of what the good life is, then I will be much more deliberate about the kinds of technologies I choose to incorporate into my life, because I want to make sure they allow me to kind of live that good life better. I don't necessarily think we're ever going to have a situation where, as a society, we are going to have, let's talk about like, what is the good life?

But I do think we can have a little bit more of that kind of thinking, and I think that's not a bad thing, because that will give us a better sense of how we might think about adopting the kind of things we want to build, or just the way in which we think about these systems on a daily basis. Welcome to the 26th episode of Humans on the Loop. I'm your host, Michael Garfield, and this week we talked to scientist, author, idea, import, export, professional, and my friend, Sam Arbusman, weaving together and plucking at the idea strings in his latest book, The Magic of Code. It's almost trite these days to mention Arthur C.

Clark's third law that any sufficiently advanced technology is indistinguishable from magic. But if it is trite, it's because we feel the truth of it in our everyday lives. As Sam observes at length and in depth in his work, yes, we are living in a magical age. Even if the pre-modern maps don't translate perfectly onto the present day, the history of software environments that form an increasingly significant, if often invisible and frequently misunderstood layer of 21st century life checks all the boxes.

Arcane, powerful, anchored in the application of language to the transformation of consciousness, practiced by warring esoteric white hats and black hats, summoning both wonders and monsters, theoretically accessible to anyone, but harboring bottomless complexity, all of the above and more. A core theme of the show is that magic is code poetry, and code is magical working. As natural language and brain machine interfaces become more common, what we say and even what we think will shape our built environments, the gap between what we imagine and our physical reality is shrinking. A software eats the world and biology becomes the domain of culture, virtual environments spill out into shared spaces, and it's up to us to reimagine initiation for an age of cyborg wizards.

So who better to riff on this with me than Sam, scientist and residents at Lux Capital, adjunct professor and ex-lab senior fellow at Case Western Reserve University's Weatherhead School of Management, senior fellow at the Silicon Flatiron Center for Law, Technology and Entrepreneurship at CU Boulder, and research fellow at the Long Now Foundation. He's also the host of the orthogonal bet, compiles ideas and trends at the over-edge catalog and the humanistic computation project, and designs typefaces in his spare time. In other words, a delightful geek and well-rounded character. Sam and I don't see perfectly eye to eye about just where the analogy of code as magic works and where it falls apart, but that tiny bit of friction makes for a fascinating joint exploration into the liminal zones where our categories fray and their distinctions are, just like lines of code in the human operating system constantly rewritten.

At some point, it is worth it to accept that our explanations are only explanations, that there is always more to discover and deeper knowledge awaiting us, and yet there are undefined limits to our understanding. In this episode, we discuss code as algebra and fire, open source development and open-ended innovation, rethinking the nature of failure in the so-called technocene, navigating simplicity and complexity, acceptable and unacceptable sacrifices to the incomprehensibility of our technologies, the squishy overlap between tech and biology, the codomestication of software bugs and people, and the emerging age of ephemerality. It was, as it always is with Sam, a joy. I hope you get as much out of it as we did, and that you visit the show notes for Links to Sam's book, his other work, and all of the illuminating resources we mention.

But before we begin, a heads-up to fellow explorers that I just released an interactive portal to the course I taught this summer at Weirdosphere, How to Live in the Future, as a public notebook on Notebook LM. This was a notoriously challenging five weeks of material covering everything from physics and evolutionary biology to media theory and the philosophy of language. Now, you can explore it in a structured mind map and chat with all of the lecture and discussion transcripts. I'm deeply impressed at how much easier this tool makes my unwieldy web of ideas to understand, and I invite you to visit the show notes for a link to check it out.

Founding members on Substack and Patreon also get free access to all of the recordings and readings, and discounts for upcoming courses about which I will have more to say soon. Also, our next member's hangout is this Saturday, October 4th, at 10am Pacific Time. It's a weird time to be alive, which makes it a perfect time to link up with other listeners for open-ended conversation and see what grows there. We've got a great group, and I hope you'll join us.

Thank you for listening and for your support. Without further ado, Sam Arbusman. We've got a bag of macadamia nuts here. I was just...

Do you know Clive Thompson? He writes for Wired and Near Times. I don't know him personally, but yeah. He's awesome.

And so we were talking about coding and just the future of that and everything like that. Yeah, you should definitely come on for a final one. Yeah, I was doing that one. Yeah, I'm talking to someone next week who wrote a novel about where everyone kind of...

It's like seven years after it's been revealed that everyone's living in a simulation, and now people are just like taking a bus tour of all the weird glitches in America. It's amazing. Dude, that's great. That's like straight out of your book.

Yeah, so this is the kind of stuff I love. Yeah, so yeah, Lux is very supportive of me just talking to all these weird, interesting people about this stuff. So that's great. Yeah, I mean, I probably can't help but get into this during our conversation because it's so fresh in my mind.

But Lawrence and I talked about thinking through evolutionary biology about how the future of work is actually an economy of play. So I've been gathering notes for an essay on this about jobs are done, human labor isn't done, human labor is just going to be whatever you want. Yeah, like that. Oh, I think about...

As you can probably tell from the book, I am deep into the world of Star Trek. And so I think a lot about like, okay, in this post-scarcity world, what does this mean? How do we think about work and meaning and purpose and all the... And Lawrence is...

He's thinking about this too. And he got really deep into this by like deep research basically, slapping him across the face and saying like, the AI world is here. So I find this fascinating. Well, right on.

Sam, this is the first time you and I are on public record. I always go mad, I can't share the secret conversations I've got with you. But yeah, man, look at this. Oh, wow.

This is what happens when I don't use a highlighter. Okay. But this is good. I'd rather have that than kind of the pristine, like, that's untouched book.

Yeah, well, I mean, otherwise I end up highlighting half the book. That's my deal. It's not... Then what's the point?

It's like all I'm doing is... And then I think about Kevin Kelly and you know, he's such a kindle enthusiast. If I'd have been reading this on Kindle, then highlighting half the book might have been useful to other people. But there's no way to share that when it's just sitting.

So anyway, yeah, the magic of code. Very cool. Lots to talk about, but I like to address people before we start. So you and I have played this game before.

That's a good question. How deep do you want me to go back in terms of the origins of Sam? Yeah. I mean, when you talk about, at least as it pertains to thinking about code, you talk about it quite a bit in this book.

So something that's like off the rails, it's not on the page here would be nice. Okay. Yeah. Yeah.

And certainly one major touchstone in my life, I would say, was my grandfather. So he lived to the age of 99. He was a dentist and a retired dentist. But he was also a lifelong fan of science fiction.

As well as science fiction. I think he read Scientific American. I'm like, no, sorry. He read popular science.

I think he was 70 years straight or something crazy enough that he was actually featured in the magazine as like, the longest reader or something. Or one of the longest readers or whatever was, but he was this lifelong fan of science fiction basically since the modern dawn of the genre. And so he transferred that love of science fiction and that kind of thinking about science and technology in the future to me. And I think he read doing when it was like serialized in a magazine.

He was like very early on. And you could really not surprise him by any sort of science fiction plot. But actually related to kind of the code kind of stuff and how to think about it. I remember actually when, and I don't think I included this in the book, around the time when the iPhone came out, we, I went with my grandfather, my father went to the Apple store to kind of check it out.

So I see this new thing and we're playing with it and my grandfather's looks and he goes, this is it. This is the object I've been reading about for all these years. And now it's a reality. And so yeah, so in terms like my own, yeah, what I do and my own deal.

And so I grew up in Buffalo surrounded by science fiction and computing and kind of these thinking about science and all these different ideas went to college and grad school, initially planned on doing kind of a traditional academic more academic career. And then I kind of swerved because I realized my interests were too interdisciplinary to fit into anything kind of traditionally academic. And I also liked writing and engaging with the public as opposed to just other academics. And so I kind of switched into the foundation world.

And now I work for Lex Capital, the largest venture capital firm as their scientists and residents, which is kind of a fun title. And obviously my job is to really survey the frontier of science and technology and find individuals and communities and weird topics that could be relevant, maybe sometimes only several years online, maybe sometimes they're not actually even good investment. But they're kind of just kind of things that are worthwhile thinking about. And so as a result, I guess I kind of live the, a lot of the earlier things that I've been thinking about, like actually engaging with science fiction and thinking about, okay, are some of the things that we've thought about for decades, a reality are the things that we want, are the paths that we hope we would move towards the future actually becoming reality or becoming more distant than ever before.

And so yeah, I get to have this experience like smashing together lots of different topics together and seeing what happens is a part of accelerator, but for ideas and smashing together and doing import export of ideas and people. And yeah, and that's, I don't know, that is a long way of saying, I'm not really sure what my deal is, but I'm having fun playing with ideas and writing and engaging with stuff. Yeah, there's the idea import export starter pack that includes the black V neck. So it's funny, like I was saying, I was just talking to Lawrence Lundy Brian and I was like, Oh yeah, he's a research analyst.

You know, I'm always like trying to find the language for the illegible. And I was like, Oh, research catalyst. That's the thing. So in the spirit of research and catalysis, I just want to start actually trying to stitch some of these bits in your book together, talking about the magic of code, an area of shared adoration.

You say computer code is not concrete. It's not steel, but it's also not just text software consists of spells of crystallized thought. And then a few pages later, you talk about enchantment and disenchantment that the worlds of enchantment are really wonder and disenchantment have existed alongside each other in the realm of computing. The utilitarian road of state and corporate coding has always run beside the path walk by those delighted by the wonders of these machines, those who have played with computers at a more human scale.

The chapter is about getting into the alchemical analogy, algebra and fire. Our Argentine writer Jorge Luis Borhas was prone to highlighting the intersection of algebra and fire where the logical enriches meets the fiery and living. In my view, computation itself, this animated logic is algebra mixed with fire. So this is the left brain, right brain terrain.

I want to traverse with you and take this wherever you will. But you know, the information is in the difference between like efficiency and curiosity or the way that we use technology for leverage and then the way that we use it to screw around. And I just want to hear you riff on this. Yeah.

So when I think about how people think about technology and computing now, I feel like there's kind of this broken conversation or broken relationship where like we're either just using it for work or sometimes we're adversarial towards it, we're worried about it or ignorant of it and we're like, oh, like, it's all under the world and maybe that's true. But there's kind of, there's, when I think back to my own experience with computers, when I was younger, it wasn't that adversarial or wasn't that kind of broken. It wasn't like an either or these things are just terrible or they're just or whatever. It was like, there was, it was also stitched together through with this sense of like wonder and delight, which is kind of like what I'm trying to allude to that, which is that there was that a computation, like it didn't feel like it was just this, you know, branch of engineering or kind of just a tool for development or whatever it was, it really, at least in my mind, like it was this humanistic liberal art that like when you think about it properly, it should connect to language and philosophy and biology and art and how we think and all the other different kinds of things.

And so for me, and I think part of the goal of the book is to kind of like reenchant and to be clear, I'm not using like enchantment in kind of the idea that like we're surrounded by spirits and things like that. Or maybe I should actually use this more at least when it comes to computing, but it's more about just the sense that there is this sense of wonder. And I mentioned in the book that the truth is like this enchantment and disenchantment at least when it comes to computing, having a sense of wonder versus kind of a more utilitarian perspective, like they have always co-existed. But I think too often the wandering stance has either been forgotten by most people or has been too separated from the utilitarian sense, even though recognizing properly like these things should be tied together.

The internet was a source of wonder, but it was also a place where people got worked on and shared information like early days in the internet or whatever it was. These things weren't an either or. And so for me, yeah, when I think back to my childhood, my family was not like a computer engineer, my father, he's a retired dermatologist, but he was just like an early adopter of computers. He was like, this is the future.

I want to get involved with these things. And our first computer was like, come over 20, then we kind of moved to early Macintosh's. But surrounded by early programming, these early computers, the world of fractals and simsies, like there was just this sense of wonder. And code itself also is this embodiment of these desires that we've had probably for millennia of using words and text to coerce the world around us.

That is at sorcery to a certain degree in the magic. And it's not just the realm of ancient or medieval desires or like stories that we tell ourselves in fantasy or whatever it is. We now actually can do this kind of thing for real. And so therefore, there's something to be said for taking it seriously, taking it as this kind of enchanting medium that is this weird thing that is a combination of text and idea and thought stuff, but also very real.

And I talk about this later in the book, like computing is deeply physical. And we forget this often until weird things happen with bugs and glitches and you realize, oh, wait a second. I don't know. There's some story I read where I think I included in the book where someone's wifi only worked when it was raining or like someone took up this one department in some university can only send emails about 500 miles away and like these things sound crazy.

But it turns out it's because the world of the internet and code is still based on something very physical. We need to recognize that the physicality and the magic and the ways in which code and computation touches upon all these different things is weird and wonderful and deeply messy. And to simply say, oh, we can engineer some of this away or we should have you it as this very state discipline. It means we lose a lot of that messiness and weirdness and wonder.

And so anyway, I'll pause my my riff right there. But yeah, those are a lot of the ideas that we're kind of bouncing around my head as I was thinking about these things. Yeah. So in service of my perverse fascination with dissolving our categories, like I think over the course of this book, if I'm to treat it synchronically, like thinking back on the whole text as a single gesture, you start off making these distinctions, well, you're like, oh, you know, magic is like code.

There are these really important distinctions between computers and biological systems, but then it's like watching the show with the reveal again. And I go back and I'm looking at how you are describing computing earlier on in the book starts to sound more like the way that you're describing the unique features of biology by the end of the book. Like for instance, talking about this embodiment of the physicality of computing and all of these weird little variables that are outside of the code itself, like about the epigenetic interaction, right, between what we are actually thinking about as executable instructions and this whole complex world. And you mentioned in Lev Grossman's The Magicians, Lev describes magic as even the simplest spell had to be modified and tweaked and inflected to agree with the time of day, the phase of moon, the intention and purpose and precise circumstances of its casting and a hundred other factors.

And then it's okay, maybe setting aside the issue of is magic real. They were definitely onto something about the relationship between the intent and the complexity. Like I listened to a great episode of Weird Studies where they were talking, I was spacing on his name, I'll put it in the show notes. They were talking with a chaos magician and they were saying that chaos is the underlying reality in which we are tunneling with our ideas and our theories.

And it sounds a lot like the chaotic dynamics of like later on in the book when you're talking about strange attractors in biological systems and you're like, well, we can't really predict precisely what goes on in real complex systems with world models, but they give us plausible constraints. And so yeah, there's this relationship between the granularity of our understanding and then the way that you make this point that it's not just about writing code, it's like the debugging and maintenance, it's like the lion's share of this, the effort of working code. So yeah. Yeah, I guess one of the underlying things I think of what you're talking about there is almost just this sense of humility.

There are limits to our knowledge or to how we can build systems or whatever it is like we are going to bump up against these things where it's debugging is a lot more challenging than we realize the systems are a lot more deeply physical or kind of weird. And I think that is kind of the at least one window into kind of viewing computing is almost like human, more humanistic endeavor. But we're like, it's not just trying to build a system that does X or trying to understand some biologicals and whatever it is. When properly construed, like this computing should be in computation, like it has this like universal solvent kind of thing that's like turn all j's in the book of it dissolves all the different things that we think about.

And then hopefully will in turn, please in my mind, give us a better sense of hopefully ourselves and like our own limits. Like when you think about these things, then you learn, not only do you learn about a lot of different topics, but hopefully you learn about how humans kind of relate to the world and kind of where our place is and what we can fully understand what we cannot understand. And I use the example of the field of philology. And so it was kind of this like all encompassing humanistic domain of, oh, I was like, and it was like studying like the history of languages, but I didn't incorporate history and anthropology and archaeology and linguistics and all these different topics.

And then it kind of fractured and then we kind of went to, okay, each of these are their own just different disciplines. And I think there's something good about having expertise and specialization, but you also lose something when there is that fracturing. And I think computing has a little bit of that same thing as well, where like early days of computing, and you look at this like when people were like early on talking about AI and like people were coming from all different fields and talking about different things and it's kind of messy. And then it kind of became this thing in the world of engineering or the sciences or related to mathematics and it is all those things.

But it's also so much more and going back to kind of the humanistic stuff and the humility, I think being willing to smash all these topics together gets you to take some of these things seriously, realize where these things break down, where the underlying chaos really is that we can't fully understand. And yeah, just kind of appreciate it. It's not one of these things where you have to then have a single answer. Okay, what is computing?

How do we think about computers? How do we think about technology in humans in relation to it? I don't have the answer to it, but we should just think about it a lot more flexibly and openly in just with a sense of wonder and to like to go back to that. I think we need to just be a little bit playful in how we think about these systems because we don't fully understand that.

We don't fully understand the full range of applications these tools can have. And I think that's a great thing. It just means there's so much more to be able to do. So yeah, that links for me into your chapter on open source and open ended innovation.

And I was like, you're looking at the list of pre-publication readers at the end. And I'm like, okay, that must have been the one he gave Nadia Asparahova is like having written that whole book on open source, like trying to peg reader to chapter. Yeah, but so it's interesting because you talk about, we talk about this all the time on the show that the open ended innovation of computing, you say, if creating open endedness is the goal, giving up a certain amount of control is key. Some degree of mess is part and parcel of open source.

So there's the mess and the information that you used to characterize biology. And I went back before the call and I really wanted to get into some of these real specific cases with you because you talk about coding history and these fun, weird little vignettes and like little bits of coding culture. And you're talking about the way that variables operate and how, as you say, let's say variables can often have deep and complicated structures within them. And you say, talk about like the true names in like Ursula K.

Le Guand, like if you know this thing, you've got the power over it. You have like power over it. You have the actual level access of somebody. You're talking about how global variables for which the scope is editability of an entire program.

You say a global variable is a true name with far too much power. This is a source of a magnitude that no programmer should be granted. Hence the general rule is to avoid global variables as much as possible. So it's funny because like as the code gets more and more complex, the top down kind of God level access becomes something that's actually ill advised in working with these systems.

Like you actually know you don't want to give it full spectrum antibiotics because you don't know what will happen with this kind of thing. So I have to imagine that people here are fairly savvy, but I did learn a lot about the inner workings in this book. So like a bit on, yeah, the way that software development rhymes with negotiating living systems, the way it rhymes with myth and culture, the similarities that you draw between these things. Yeah, software I think certainly with AI right now, like we know that our software systems are massively complex.

And of course, this is true well before these AI systems. We can't fully interrogate it. And like you look at Microsoft Office or various operating systems are available to software that runs your cars and because there's actually a lot of code inside cars, these systems are really complicated and big. And and especially when it comes to like open source systems, they and there is certainly a degree of design, but there's also degree of evolution and change over time.

And so I think that the way in which we can build these systems needs to include and incorporate this idea that these systems are very complex. We don't fully understand necessarily or how these things will respond when we make changes. And so therefore the most the most logical way to actually handle such complex systems is through this kind of iterative evolutionary tinkering approach. Like you change a little bit here, you change a little bit there.

And then over time, okay, when I change this little thing, did it bite back? Did something go horribly awry? And which is why I'm going back to global variables. The reason you don't want to use them too much is because they can change a lot in ways that you have no way of anticipating.

And so you don't want to do that. You want to change a little bit at a time. And so in terms of how we think about certainly open source, but kind of just software more generally is that when it is this large complex system that is a collaborative endeavor with lots of people engaging with it, you want to think about it as almost this tradition. And I kind of use this example of like ancient mythology or other textual traditions where there is a core, but then over time, people modify and change it.

And so in the same way that like ancient message like an ancient Greek, Greek story, there is no real like one canonical version. There's kind of canonical versions over time or people, there's a story and then people retell the story, people add to it, they modify it. And if people like those modifications, then it gets taken up by the next generation and then people kind of do that. And the truth is whether it's variants of Unix or other parts of software, whatever, like that kind of tradition is the same way in which any large complex system happens and it evolves over time.

And in order to do that though, you need to have this amount of open end in this where people or it's not a single monolithic thing that can never be modified or there is this possibility for combining with other things, allowing, you know, for interfaces that allow one part of the system to interact with another system somewhere else or where people can recombine bits and pieces together and see how they change over time. It's very high level, but I do think that, and you mentioned your quote in this idea, like innovation and open end in this, these ideas around tinkering and modifying complex systems and humility and open end in this innovation and how we tell stories over time and traditions and how they're modified and maintained and this balance between maintenance and innovation, these things, they're all delicate balances and they're all ultimately interconnected, whether or not you're talking about, I don't know, a large scale infrastructure system, piece of software, a story that has been told for millennia. The ways in which these things evolve are have deep similarities. And of course, they're different in many different ways, but the beauty of thinking about complex systems more broadly is that if you abstract away the details, you can then find certain fundamental similarities, you find differences too, but the similarities, I think, are really powerful and allow us to then say, okay, where does the analogy work?

Where does it break down? How can we use this to then potentially take some idea from one complex system and apply it to how we think about research, like, e-tremethology has worked really well for millennia or whatever it is, can we use some of those ideas for how we think about software or whatever it is, and so I think there's deep power to finding the similarities between these systems that are all just, they're large and complex isn't that of all over time. And so I think there's really something interesting there. So being somebody who loves this, you're talking about how debugging as a kind of, you don't use this term, but it seems to me like kind of a spiritual practice and your chapter on confronting the edges of software and how even Ada Lovelace's first computer program ever had buggers in it that people found out years later, but she couldn't find them because she couldn't run the code.

Yeah, like who is very understandable. Yeah, so there's this, you talk about how to augment the debugging process, some software engineers actually inject failure into the system in order to learn that it's, yeah, once it crosses from complicated into complex, then you have to bring noise back in and you talk about in the history of computing the original book of random variables that there's like a paper book of randomness and it just strikes me that, yeah, as we go up this curve that taking shots in the dark or like tinkering, as you said, becomes more important. I love this joke, you tell the software engineer walks into a bar, he orders a beer, orders zero beers, orders 9,999,999 beers, orders a lizard, orders minus one beers, orders a new convenience for a computer. First, real customer walks in and asks where the bathroom is, the bar bursts into flames killing everyone.

Okay, so failure injection. The last piece here in talking about tools for thought, you say, helping people wrap their heads around AI, AI can provide a kind of engineered serendipity or even augmented imagination. And one of the themes, the big motifs of this show is that maybe we're thinking about advanced computing in the wrong way by trying to use it primarily as a tool for prediction and primarily as an enhancement of our leverage, because what it seems to be doing is, it's like the the fire and the algebra grow together. And that once the quote unquote algebra of these systems exceeds our ability to understand them, the fire exceeds our ability to predict them.

And so yeah, I'm curious how you're thinking about the culture around computing and this balance over time of the left-brained instrumental approach and the right-brained intrinsic approach. And maybe it's just because I am undisciplined. But it seems to me like at the human scale, games, and I know you talked about the Hyam in gold on the show, and so did I. That play is edging out over games in the sense of clearly specified wind conditions, the success of SimCity and the success of complex system science shows a tilting of the scales toward fuck around and find out in the way that we relate to technology.

Yeah, let me think about this for a moment. Certainly as these systems are getting more complex, there is this need to give up a little bit of sense of control, whether or not we had control in the first place. That's one thing. But separate from that, we have to give up the need for control or relinquish a little bit of it because we need to be more comfortable playing with the systems, mentioning with the SimCity or whatever, it is one of the ways in which we think about these systems and understand them is sometimes through a sense of play.

And actually, there was this book probably now it's 20 years ago. So Stephen Johnson wrote the book, Everything Bad Is Good For You. That's about, I think, the fact that it's like, why today's popular culture makes it smarter. But in one of the chapters, for one of the sections, he talks about how games kind of teach you this sort of almost like scientific method approach to where you're in playing a computer game.

You are interrogating the edges of the system and trying to understand where it works, where it breaks down. From there, you can hopefully beat the game. And I think, and going back to what you're talking about in terms of injecting randomness and injecting failures, that is a certain element of that kind of process where we are trying to understand the system through sometimes seeing where it fails. And I think there's this complex interplay there between, I'm going to keep on harping on this idea of humility, but it's like the idea of like humility, limited understanding, playfully experimenting with the edge cases and the boundaries of a system in order to actually learn more or build something better.

And so I'm not really sure it's kind of an either or there in terms of like, right brain versus left brain, oh, we're going to play with it. Certainly, I mean, there are many aspects of computing that are very playful. There's a whole realm of creative coding and things like that. But it's also deeply intertwined with the rigor of computation and kind of like iterating things many times in the same way that sometimes injecting error and randomness into a system is part of the process of making a piece of software better.

And so I don't necessarily think it's an either or, but I do think that when people think about software and computation, they have to recognize that it involves error, edge cases, unexpectedly, messiness and humility, because in the end, like we're never going to, we understand certain systems pretty well. But once things become a certain level of complexity, we're not necessarily going to understand these things fully. And at that point, it becomes incumbent upon us to kind of take this almost like more like philosophical approach to these systems and say, okay, we're going to do the best we can. We're going to try to make these things rigorous or understandable as best we can or at least reliable.

But going back to the joke that I mentioned in the book, these systems are into kind of borrow a phrase from certain aspects like the physics literature, they're robust yet fragile. They're robust to all the things that we've anticipated, but they can be entirely fragile to the few exceptions that we could never have possibly imagined. But of course, those are going to be a few things that actually the system doesn't counter. And so you kind of have to have this balance of saying, okay, we're going to try to make these systems as rigorous and solid and robust as possible.

And in order to do that, we're going to have a certain amount of playfulness and edge cases and failures and randomness, but we're also going to recognize that this is a constant process and we're kind of like asymptotically approaching a set of reliability and understanding rather than it kind of being this binary condition where we can fully get it right. And now we got it right. And now we're done. So the sweet spot, right, between these things, I want to link that to the piece in your chapter on numerical modeling and the reference William Alonzo's 1968 article predicting best with imperfect data and other authors that talk about this curve where the more variables you try to include in your simulation, the more likely that a measurement error in one of those variables will throw the entire output of that simulation.

And then in science, there's that relationship also between high compute time and complexity required for effective prediction versus the efficiency of like a parsimonious theory. And then also this shows up in the aesthetics of code, like how different programmers, like you talk about Lisp being one of these incredibly elegant languages that specifies so much with so little. And so yeah, there is still like that tension toward making things simple because simple is beautiful. And there's also this like veracity to fly as high as we can to see how many variables it takes before it starts spitting out garbage.

I don't know. Just to set the expectation moving forward, like I don't know, like I put in questions together so much as giving you little bits of your book to chew on in response to your last thing. And these are good provocations. Yeah, I think we definitely have a bias, which is in our minds towards simplicity and beauty.

And the question becomes whether or not that accords with the actual reality of the universe. And I and certainly the simple, like simple explanations for the world, like in science and physics and things like that are going to be simpler, just because it's like a low hanging fruit. But the world is deeply complex and weird and like full of all these educations that we're talking about earlier. And so when it comes to like simulation, actually, there's some research that shows that when it comes to like large AI systems, like where that kind of that curve, it kind of goes up and then actually if you add a huge amounts of data, huge amounts of data or variables and other different parameters, then the error actually goes down again.

But at the same time, though, you're sacrificing something. And one of the things you're sacrificing is understanding because I mentioned this in terms of like when we think about what these models are for, sometimes they're for prediction and creation is really powerful and something that we want and we want to understand and whether really well or certain things around mobility, these things are really important. But another really important aspect of a model is to actually gain some sort of insight into how the system works. And our brains cannot handle a lot of variables and a lot of parameters.

We don't do well with really complex models. And so we need a certain amount of simplification. And so a model, even if it's not that precise, can actually be very powerful as long as you're not thinking of it solely in terms of prediction. And I think this kind of goes back to one of the ideas I'm going back with the game, like SimCity, for example, a drastic simplification of a city.

And in some cases, a lot of the models and ideas behind it, many people would argue are wrong or deeply flawed or things that they would not actually choose if they were kind of building their own SimCity. But for people who just want to kind of have some sort of intuition about how a complex system operates or what are certain of the features you should be thinking about when you think about urban design or whatever it is, SimCity can be enormously valuable. You can just gain a certain amount of intuition around a system that blokes back. And then in itself, whether or not, it actually fits the way a real city operates.

So independent of that, because it might not fit into our city at all, that can be really powerful and something really valuable. And so I think when we think about simplicity and complexity, we kind of have to think about what we're using these systems for. And going back even like programming languages, and you mentioned LISP, LISP is very elegant and very powerful. And I've used kind of like simpler versions of it, like Scheme, for example, which is related to LISP in some way, but kind of its own thing, but it has a similar sort of structure.

The downside of the elegance is that when you're writing code, I remember doing this, I think that's my freshman year of college when because we were learning things in Scheme, is that your programs look like just like a huge amount of parentheses. And there's just like parentheses everywhere. And you look at it and you're trying to hold it on your head. It's really hard, but the language is very elegant, but it doesn't necessarily lend itself to certain types of expression.

And so that's the thing. So this tradeoff between simplicity and beauty, expressiveness, these are all what the goal is for a system or piece of software. These are all things that have to be held in balance. And so, and I actually, I don't mention this in the book, but like one of the languages, Pearl, that's a very, one time it was a very popular programming language that was lost, I guess, and currently in the past, like there.

So, but the, I'm pretty sure that the creator Larry Wall, one of the things he talks about is like that Pearl does one thing well, which is it does, it takes ideas from a ton of other languages. And so, it's just trying to be pristine. It's just trying to allow you to do certain things. Now, I was never a big user.

Pearl was kind of messy. I had issues with it. And so, you always, whether or not, where that balance between beauty and usefulness lies is I think always a kind of personal choice. But you have to really think about all these tradeoffs and kind of how you use computing, how you try to understand the world.

And yeah, and just, and anything you're doing, there's always these tradeoffs. And I think certain aspects of computing just kind of make it that much clearer in terms of understanding these things. I actually really like that section on recursion in LISP because I was thinking about, okay, where is, like you're saying, you're having to hold it all together in your mind. And one of the places that matters in real concrete terms, again, like you go through the curve and then more data, more and more.

And then finally, you get back up to something that's predicting better again, but it's over the rainbow, right? The black box comes up a lot on the show, as I'm sure you can imagine. I like in your chapter in Digital Alchemy, you say, do you want a powerful system that can generate text or code or images? Then you must place a large corpus of humanity's creations upon its altar.

Because I know you're not saying that these are actual demons. But of course, you make the point later that ideas like the simulation hypothesis are filling this need in human beings for a myth. So let's just leave that to the side for now. This idea of the sacrifice of what we lose.

You talk in your chapter on the spreadsheet about accessibility of programming. It's so basic that most people don't even realize that they're programming. And throughout the book, you make a real case for software literacy. And then the last piece of this is you say, even if code becomes generated by software, we still need to take responsibility for it.

If you don't know how a piece of code works or accept suggestions without interrogating them, something really can be lost. So I think of human language. I know through the book you're trying to keep these in their distinct buckets. But in a much more general category of language that includes both human language and software code, human language is more like Lisp and that requires more of the head space and that the more we're interacting with computers, the more things get redistributed.

And I'm curious about this issue of responsibility because the thing that made me realize that technology is where magical objects was that you cannot understand them mechanistically. You know that they work, right? That practical concern takes over. Because you don't know how they work, it has agency over you, which is of course the thing that when people are saying it takes teams of thousands of people to build smartphones and that no one person has the like God's-eye view of the phone.

And so therein lies the opportunity for all kinds of dark patterns to control our behavior. And the question of how we legislate the effects of these technologies is a wicked problem. And then software literacy seems on alloyed good. But even that is only going to take us so far.

Like it'll only take us to the bounds of our ability to understand things as people. So like what do you see as worth sacrificing here? Where do we place responsibility in these systems that are like when William Gibson says that surprise AI is actually a coral reef made out of people, right? But except through AI, there isn't a like a single latency coordinating space where decisions are being made.

And so like what is our relationship to that? Can you say a little bit about the last thing you mentioned? Yeah, well, like the behavior of something like Chad GPT is something like the behavior of a corporation or a market where you can argue that people do have inputs of different sizes to that, but that no one variable in the code of society that we can't reduce it to us. And so the more we become entangled in the structures that emerge out of our interactions, like years ago, I remember seeing David Crack I ever gave that talk about how cumulative culture means that our collective understanding grows, but that our individual understanding becomes more and more partial relative to that.

And so it's really not just a question about code, but like using the lens of code on this massive collaborative piece of software we call civilization, like how can we start thinking better about our responsibilities to understand what we're participating in and how to conceive of and allocate responsibility through that. Texas says that I won't believe corporations are people until Texas executes one. This is really interesting. I think I actually discussed this a little bit more in my last but over complicated where I was talking about this like technological complexity and increasing incomprehensibility.

And the way I think about it is understanding and responsibility. These are not the kind of understanding we need in order to be responsible actors in this space. It's not a binary of either complete understanding or complete ignorance. It's like understanding what's on a spectrum and probably it's some sort of high dimensional surface of like different types of understanding whether it's certain aspects of software or kind of a lot of systems or entire society and civilization.

There are lots of different ways of understanding these systems. It could be like I understand one really small part really well. I have an incomplete understanding of something kind of at a higher level or whatever it is. But I think the way to think about this and you mentioned kind of like this idea of everything being entangled.

So the computer scientist Danny Hill is the idea that we kind of move from the enlightenment. We had a complete understanding of the world to the entanglement where everything is so hopelessly interconnected. We no longer fully understand the system that we are part of. And I think there's a great deal of wisdom there because in some ways this because we're maybe no longer in the enlightenment mode.

We can actually then look to some of the ideas prior to the enlightenment to see how people thought about these things. So if you go back, it's like medieval thinkers. I'll use one example that I'm familiar with. I assume there are others.

I don't know, but this is a medieval scholar. So it's the philosopher and rabbi and physician Moses, my monodies and in his master word. And so he lived, I think in the year like in the 1100s. Maybe he died in the early 1200s and he in his master piece of philosophy called the God of the perplexed, he talks about things that humans will never fully understand.

Like he actually gives a list. He's not like a huge list. There are things like that only like the mind of God will understand it. But he seemed fully okay with that.

And I wonder if because of the idea of the enlightenment that we've lost a comfort with recognizing our limits to understanding. Now, at the same time, I'm very much a creature of the enlightenment and I love trying to understand things as much as possible. I want us to keep on trying, but we should be okay when we bump up against those limits. And so for me, it's like when I think about the, like in terms of what is our responsibility in engaging with these technologies, too often when we're confronted with very complex systems, we zoom off into two extremes.

Either we have this like insane fear of the unknown or we almost create this like sense of like reverence or all or almost like worship of these systems. We don't fully understand. Neither one is productive. And going back to humility, this idea of humility of there are going to be limits to human understanding.

Not only is that I think a more more reasonable approach, but it's actually much more productive because those two extremes of responses, they're so emotional, they actually cut off any further understanding. They say, oh, we can't fully understand this stuff. This kind of thing. Let's just stop entirely and either worship this thing or be scared out of our wits.

But the true answer is no, we might never fully understand anything, but it doesn't necessarily cut off any sort of inquiry or any sort of questioning. And so I think that is what responsibility we have in relationships with these technologies is being okay with recognizing we're not going to fully understand things, but still trying. Because I think the alternative, which is still living in this sort of like stylized enlightenment version, which is, oh, we understand every single thing that we saw our minds do. If you have that mindset and then you then are confronted with a system you don't understand, you're going to be blindsided.

And so rather than being blindsided, recognize that, no, this is the world we already live in. And that's okay. And I think about this in the context of the, I think it was when like the Apple Watch came out. There was some article, I think it was in the Wall Street Journal where it was people, it was discussing whether or not people are still going to buy fancy mechanical watches.

And the answer is people still do this kind of thing. But they interviewed some guy who is, I think a fan of mechanical watches and he said, yeah, when I think about the complexity involved in mechanical watch as opposed to smartwatch, which is just a chip. And I'm thinking like, just a chip, like these things are orders of magnitude more complex than a mechanical watch, but you've been shielded from it. And so like when you are fully confronted by it, it's going to blow your mind.

And I think that is the world we live in. Like we are already surrounded by all these systems that you're talking about, but too often we're confronted by it. And so we need to have windows into these systems, even just a little bit in order to kind of ease us into the fact that we are surrounded by these very complex systems that, to understanding and to recognize it's not a binary, we can find some ways of these things. That kind of tinkering approach is probably the best way to appreciate kind of approach these systems, but we have to at least recognize this world with open eyes.

Yeah. So you mentioned in here, you don't quote them side by side, there's several pages apart, but there was a kind of an AB rhyme in here where you mentioned Michael Levin talking about biological systems as the possible future of machines. And then in the next chapter, you mentioned Chris Langton working on A-Life and saying that software organisms might be the possible future of biology. And so I've been thinking about when you say we're surrounded by these things, but you also make points such as computer errors illustrate both how we think about software and how computation is built from and impinges on reality.

So back to this kind of murky intertidal zone between what is biology, what is code, I've been thinking about the bit that you wrote about code injection and you taught me a new word I love, homo-iconicity. Before I even try to go here, I would like, if you can just give people a little primer on this particular thing as a place to stand on. Yeah, sure. And so the idea of homo-iconicity or software or language that's homo-iconic, is it has this equation between code and data.

And I'm trying not to do it justice, but the idea is that languages that exhibit this property, they can treat code itself like the text as data. And then the data can then in turn be actually executed. So it's kind of like this first class object that can then be manipulated itself. And so you could have a computer program that inspects itself or inspects other code.

I mean, a lot of languages have this kind of varying degrees and how people define this as a little murky and a little messy. And I imagine you're going to go into it in the direction of biology, but biology also kind of has a certain degree of this kind of thing where it's the fact that you can treat DNA or DNA can operate on itself. And DNA, your genetic information can be manipulated. And so there is this kind of very code-like property and data-like property to biology.

And that was the context within which I was discussing this idea of homo-iconicity. Yeah, so you make the point the code that describes living organisms DNA can be converted into proteins but can also be operated on by proteins with enzymes themselves derived from DNA, modifying DNA itself. That's the homo-iconicity piece. And then you talk about later breaking software through code injection.

For example, buffer overflow where a whole bunch of data is inputted to a computer program and because it is more than the program expects that data rewrites other parts of the program as well since the data is now viewed by the machine as instructions instead of input and you give an example of hackers reprogramming Super Mario World with a specific pattern of jumping into the computer game Flappy Bird, right? And so I'm thinking about that in terms of when we again like when we talk about the built environment when we take a like post thinking of nature and technology as separate kind of view in which the flooding of oxygen into the atmosphere by early photosynthetic bacteria is deeply like modern industrial pollution. They built an atmosphere. This is like that love lock in Margulis Gaia hypothesis.

It's like, no, we're not the first industrial pollution disaster. Human beings live in this world that is the output of the industrial activity of billions of years of stuff before it. But now that we've built this thing that like our primary environment is this extra layer of code that is operating on us and this is where it gets back to the magic piece of it, right? That again, the wizards that I'm willing to listen to people like Alan Moore talk about writing as magic.

He says because magic is simply the operation on your consciousness of language. And so I feel like the buffer overflow thing is strongly analogous to like reading a book that blows your mind or the way that our own psychology seems to be changing as we move into new media environments. There's that question I talked about with Adder Paris. Is there a relationship between the fact that we spend so much time online and that so many people now are self-diagnosing as ADHD, which seems to be this attention pattern that is adaptive to really highly variable and information rich environments?

You say we've come full circle. We started by speculating about computer code as magic and now by taking seriously at least a bit the idea that we are living in a computer simulation. This is on the simulation hypothesis as thought experiment that we don't need to come to a conclusion about chapter. We might have to think about magic as high level hacking.

Yeah, like I just I wonder about again the effect of computer code on human language, human consciousness, human biology. And you're somebody who scans the horizon of the frothy edge of technical implementations of this stuff. Even if right now you have an enlightenment or attitude about these things being distant categories, how might you see them remixing and troubling these categories as we move forward? Yeah, I mean, that's a fun question.

And certainly, and going back to you mentioned Mike Leven's work, I think that some of his work is kind of one of these like leading indicators in the chapter right, discussed biology and computation in which they're similar and different and the ways that kind of analogy of biology is code kind of breaks down. One of the interesting things though is going back to what Mike Leven says is if you rather than viewing computing as a certain thing and then trying to shoehorn other aspects of our world into that thing, oh, like we now understand computers and so like the brain is a computer or the cell is a computer or whatever, which is not really right because the brain is its own thing. The cell is his own thing. If instead you say no, no, no, they're all just information processors.

So let's instead of trying to shoehorn the brain into a computer instead say no, let's think about how what the brain does or whatever it is can give us insight into how broad computing and information processing can be. And so rather than trying to fit these things by analogy, expand our sense of what computers can be. And I think those are the really provocative approaches where we're saying, okay, let's actually try to look around and see like rather than like where the boundaries are, like where these boundaries kind of break down. And so whether it's looking at the entire earth and how that processes information to a certain degree or these kind of like you mentioned, code injection, but for overflow kind of things.

When I think about a lot of these systems and software and code, I'm always drawn to the edge cases. And so and for me, like sometimes the edge is because you learn something new, like when it comes to debugging or whatever it is or like the deep leaf is called major computers, but it also can sometimes give you a hint of a new definition for the system. So like when you say, okay, you're the following 10 ways in which a cell is similar to a computer, that's great. But now give me the 10 ways in which a cell is not a computer.

And then let's use those ideas to help us expand how we should think about computers or how we should build new types of machinery. And then in turn, like when we think about the brain is computer, whatever it is, or the like, because with AI, for example, right now, like these large language models, they are good at certain things. They're also very bad at many things. I don't actually know the current state of the art, but there was a certain period where LMs are really bad at doing mathematics because they're based on language.

And rather than trying to make them better at math, although I think that's a lot of all, instead say, okay, what are the features of, let's like lean into that like that badness. And so people talk, I think a friend of mine is a fussy processor or something like that. Let's actually just take these things as they are and use that to expand the sense of the ideas of processing and chips and computers or whatever it is. And I think that's always going to be much more fruitful in some really exciting ways because it forces you to expand your definitions and change how you think about these things or think about, okay, what are the truly fundamental things and processes that these systems are doing, whether it's like information processing, whether it's a laptop or a cell or an ant colony, like an ant tail, they're all doing similar kinds of things in some ways and other ways they're doing deeply different things.

But let's see where the similarities are, where the differences are, where the edge cases are, and then use that to expand our notion of what these systems are. So in the spirit of that, you mentioned your friend, Matt Bitker, who refers to some of the process of debugging as actually not getting rid of, but domesticating bugs. You say, Max will take unexpected and anomalous behaviors he discovers in one setting and repurpose them, turning these weird new actions into features elsewhere. This gives new meaning to the old defensive commoner programmers.

It's not a bug, it's a feature. So this is, I think this is the boundary I keep kind of swishing around, which is the boundary of intentionality or like interoperability, right, where it's the ability to turn a bug into something intentional points to the same kind of co-evolutionary nexus that I'm curious about, about when you make the specifications about how human language differs from programming language, and you acknowledge that we're in a time when English is at a very abstract level of programming. Getting to be programming, but what's also happening is that living in this built environment always has, Jessica Flack talks about when we come together in collective computation, the top down pressure, the social contract, and we have a contract that's embodied in the design of our spaces that I talked about with Robert Point and how like having what you intend to be the same conversation, but you've set the people up around a different table or you're holding it in a different room or whatever, that constrains it. And as part of, in the broadest sense, the language or the information processing or the code or whatever of the structures that are not merely software or hardware, the last pieces, I had Adam Aronovich on back when this was still future fossils and he keeps this blog of the weird things that people do on social media.

And like one of them was the TikTok phenomenon of people pretending to be robots, which I felt really spoke to the vibe right now of like, as we try to make machines more lifelike, the pressure that these environments put on us is to become more and more regulated in our behavior. Like when Doug Rushkoff talks about the influence of a central clock tower on medieval cities or like the wristwatch, like suddenly we are keeping time by the second. And so I'm thinking about the code domestication of software bugs and of people who are like bugs in this enormous technological evolving thing. You know, you spend a lot of time in this book about retaining our humanity.

We're like using technology to serve our humanity, but also that we need to not make the God of the gaps mistake of just saying whatever people are is what machines can't do. So yeah, I hate collapsing all of this down. It's good. It's only starting with the God of the gaps.

I think it's very interesting. Right. Because what our systems are able to do, I think is going to continue changing and moving forward. And like anytime you say, oh, it's great.

Like, I can do this, but I can't do X. And so therefore that's the thing that makes human special and we'll always be able to do that. It seems to be a constantly moving target. And so I think it's much more important to say, let's think about what we care about doing.

I talk about this as like what is our quintessential humanity and then focus on building systems that allow us to do those kinds of things better as opposed to kind of aligning those kinds of things away. But more broadly when you're talking about like domestic things bugs, domestic and humans, how do you think about this? I think this goes back to what we're talking about earlier, kind of that balance that dance between like enchantment and disenchantment within computing, which is on the one hand, there's all this weirdness. On the other hand, we want these systems to just get jobs done and we have to balance that.

And I feel like there's like a wave that we have to ride. Where on the one hand, we need to use the weirdness of computers and harness it for productive goods. On the other hand, though, the productivity of the machine and the power of computers themselves can also be a great source of weirdness and delight. And we can't forget that.

And we have to always balance those two things together in order to make sure that the systems are working in service of our humanity. Now how to do that? Not always easy. But when I think about like, like, domesticating people around computers, I think I mentioned this in the beginning of one of the chapters around like the QWERTY keyboard, like we learned how to express ourselves in text by learning how to use a specific really weird layout of a keyboard.

It's not particularly natural. And programming languages are also not always natural. And so there's always this dance. Like, how do we balance these things?

Now, of course, and I say it's also like on the one hand, some things might not be natural. But at the same time, we are also tool users. And like, we're always just learning how to use new tools. And that sounds like a much better way of describing this kind of thing than bending ourselves towards a machine in some sort of way that's unnatural.

And I don't know what the right balance is, but for me, it's more just a matter of we just need to be incredibly aware and deliberate about the kinds of changes and choices we're making in how we relate to our technologies. Because I think if we don't, then we just begin adopting new technologies and new systems and new pieces of software, whatever it is, like, willy-nilly. Oh, there's a new thing. I'll do this.

And as long as we have kind of a sense of like, okay, what do we, I don't know, I need to go really philosophical and vague. If I have a sense of what the good life is, then I will be much more deliberate about the kinds of technologies I choose to incorporate into my life. Because I want to make sure they allow me to kind of live that good life better. And I think I don't necessarily think we're ever going to have a situation where as a society, we are going to have, let's talk about like, what is the good life?

But I do think we can have a little bit more of that kind of thinking. And I think that's not a bad thing because that will give us a better sense of how we might think about adopting these things or the kind of things we want to build, or just the way in which we think about these systems on a daily basis. So I think if I'm aiming for something specific here, not just with you, but like with this series, it's a frame shift, right? So like I just watched Monica Anderson, who I hope to get into dialogues and who's been working in AI since the 80s, hugely interesting person, shared this video this morning with Jeremy Utley, who's a design and innovation professor at Stanford.

And this was really, you know, I think like 90% of people using AI are using it wrong, because you can ask the tool quote unquote questions. And so you can interact with it in a way that refines your own questions, that helps you learn to speak its language. You know, he says like, instead of treating like a tool, treat it like a collaborator, if it doesn't do what you want it to do, then you can converse with it and say, what kind of question should I ask you in order to improve the output? And so toward the end of the book, when you're talking about the wisdom of computation, you say about these step changes between technological ecosystems, like the horse and car, technological change is not a simple extrapolation of what we have now, only a bit faster or sleeker.

It will be qualitatively different. I keep going back to this qualitative difference about the thing that concerns me the most right now is the thing that Utley is saying is like, most people are still thinking of this as an it instead of a vow. And so like when you say, we do what is easiest for us to do because the companies that have designed the hardware and software have incentivized these kinds of behaviors, or if we really want to be glued, it's utilitarianism. But in terms of the utility that we as people are providing to the world of computing, we humans are the ones being used by these technologies and creators.

And I think that is the logic that we can trace back to the beginning of this, like Facebook as central cybernetic control chamber of social memetics. But the qualitative difference that you glance into in this book, and that we've been kind of dancing around this whole conversation, is one in which it's not about using or being used because it's really hard to draw the line now. Yeah, there are important distinctions that we can still make, like you're saying, between what information processing looks like in silicon or in carbon. But we can't actually, like Joshua de Colleo says, epistemic specificity without onological separability.

Like this, we're part of this and this is part of us. And so where does that kind of thinking take you when you imagine like the best possible outcome here? For me, it seems like the best possible outcome is it's Kennedy. Ask not what your country can do for you, but what you can do for your country is still seeing you and your country as separable entities.

And so it's like, we've got to get out of this master slave dynamic. We've got to get past the genie or the golem or the stuff that is as you write in this book, deep in the mythic history of AI. So where does that put us? Yeah, I don't know if I have a great answer of that vision.

I'm drawn to, I don't know if I mentioned it in the book, but there's this poem by Richard Broadigan, the machines of Love and Grace. I mean, he talks about us living in some like cybernetic meadow altogether, whatever it is. And I want, I mean, it sounds very like hippie very much like of that moment, like when that poem was made. But the truth is I feel like we could do a lot worse than that vision of this kind of this like mutualistic relationship between these things.

Because I think exactly what you're saying on the one hand, it's harder and harder to distance ourselves from these technologies. That ship is settled. We are like, these things are very much part of the world that we live in. And like, even when I think about when I was younger and was not surrounded by like the ever present web and the internet in the same way that I am now, like it's hard for me to actually imagine that world because it's almost been so completely overwritten by the world that we have now, which is so different in some ways.

And people talk about things in terms of like race, stagnation, whatever, it's like certain major technologies around cars and airplanes and dishwashers and things like that. They've not necessarily appreciably changed in 50, 60 years. But when it comes to information processing, that world is completely different. People play that down.

And so everything looks kind of the same versus you watch like some sitcom from the 60s or 70s other than the internet and computers. I'm like, no, that's a huge portion of the way in which we spend our days and believe in our minds. And I think there is this really deep cybernetic thing going on. And I don't necessarily think we can undo all that.

And I'm not necessarily sure we even want to, or we should. But in terms of what is the ideal vision, it's I think being able to live in this cybernetic meta or whatever brought it's also being able to imagine a little bit of the world without that. And people who observe some sort of digital status or whatever, where they say, okay, we're going to take one 24, 25 hour period each week and just live without some of these technologies. That is something really, really powerful because it allows you to see that this is not always the default.

There are other ways of living. Now, the truth is, it's a lot easier to do that when you're doing it as part of the community. If you kind of do it by yourself, it can feel very isolating. And so do this kind of thing like deliberately and carefully.

But I think there's something there of like being able to create this balance and like live in this world, but also periodically take some steps backwards in order to realize that it needn't be this way. And therefore, the things that I kind of choose to add or remove should be done consciously. And so maybe the doodles have it as one of the ways of kind of providing that means to it. But yeah, anyway, that's a little bit of how I think about this.

Yeah, the question of what to remove. I think the last bonus round point I want to touch on with you is that you end this book talking about, again, because research fellow at long now, Stuart Branson, the bibliography. So I'm guessing that you're thinking on this and mine are both deeply influenced by his book, The Clock of the Long Now and his comments on the ephemeral nature of code. And you speak to this toward the end of the book about how the world that we take for granted in a particular paradigm of technology is transient and that we have a hard time imagining past it.

And there's something about if I'm going to take a temperature on the zeitgeist right now, it's about the way that moment and what Federico Campania talks about worlding, like the way we make sense of the chaos, like the future of ancient Rome is not us. And because their future was a different future and they're not really like in another weird way, like they're not really our past, like they are the like the simulated reconstruction from where we're standing. And so there's this, when you talk about, you quote Alan Pirlis, computer scientist, he says, is it possible that software is not like anything else, that it is meant to be discarded? The whole point is to see it as a soap bubble.

And I think about vibe coding. And I think about how the more lifelike technology becomes, the less it looks like we invented that. And now we have it there forever. It's these ephemeral utilities that we call into being saying like, like Star Trek, I need you to do this.

And then it writes a piece of code for that function. And then in the next scene, that memory and that compute has been reallocated to something else. I think a lot of people are scared at that kind of a thought because we imagine that we are building incrementally on progress and that every new thing expands it. But I'm curious to hear you riff just enclosing on like this show nine years ago, started with this idea that one day I'll be able to dig all these up and make a computer simulation of myself.

Not me, somebody will do this. And then Rolf Potts, the renowned travel writer said something that kind of broke my thinking on this, which I needed, which was like, when I asked him at the end, like, what would you say to the future? And he said, I'm not really sure that anything I could say would matter to them. And I kind of hope it doesn't.

Like I kind of hope that they have their own problems. So like that's the interference pattern I want to leave you with and hear you think about. Certainly one thing I've thought about is that potentially one of the reasons why Big Tech has been so warping of our culture is because it's trying to change the world while building upon something that is inherently transient. Like it's using software, which is like this very evanescent kind of thing, but it's also trying to change the world very quickly.

So as a result, it has to change the world as quickly as possible before all this stuff kind of pops like so bubbles. Now I had an oversimplification, but I do think we need to be aware of like when there are these mismatches of time scales. And I agree. I think it's okay to do things that are going to be ephemeral.

I read something somewhere. I don't know. I get this as a study or as a study or as a study or as a paper, but it was in some book. I can remember exactly.

But it was like the vast majority of humans are going to be forgotten. I think 70 years after they die, which is basically because people they knew after they die, like you're just completely forgotten. And that I think for some people can be enormously scary, but for other people, it can be almost liberating. What you are doing will hopefully make an impact, but maybe it's even more important to make an impact in the here and now.

And so there will be a change, but it's going to be progress in this much more diffusive sort of way, which I've heard other people talk about as well. And I think that whether you're thinking about software in this kind of evidence and so public quality or just kind of making impacts and changes more broadly, we need to recognize, right, that nothing is truly permanent. And that is okay. And if you kind of truly imbibe this and internalize this idea that nothing is permanent, maybe gives you the freedom or gives you the flexibility or gives you just the ability to make an impact on the here and now and whether it's building software that can get things done right now.

And then you can just kind of float away or you can do things not because you're hoping to be inscribed in the history books or creating monuments or things like, you know, you're just trying to help the people around you and the people you care about in love that can be enough. And you can still make an impact and it can still be very powerful, but it's a very different way of thinking and going back to this little hand, really, like I think it was as a C jobs talk about creating like a dent in the universe or whatever with his work. It's kind of crazy. I'm glad the Macintosh exists and Apple computers is around, but I think that's wildly at odds with the way in which the world works.

And so when we think about making an impact or making change, we have to just take into account like the world seriously. And that is the fact that everything is transient and that's okay. And I'm not going to be ending on this very like a clisiest moment. Like it's all transient or vanity or whatever, you want to use.

But I actually think that sort of wisdom literature can actually teach us a lot about the way in which we should approach how we think about technologies. And I also think that when we think about technology more broadly, just as this thing that is evanescent or is personal or something that we build for ourselves or for our loved ones, as opposed to it has to be something that has to be useful for every single person on the planet for decades or generations. We're going to have a lot healthier kind of relationship with technology. Yeah, I really appreciate how much of the Torah you bring into the book.

Actually, I was like, this is precisely the like Jewish scientist style gloss I want on these ideas. Well, thank you. Yeah, like you were saying earlier, like we have this opportunity to go back now and make sense of things in a new way with what happened before the great success story of the modern era. Now is the time to sit with that rhyme and see how we make sense of it.

I don't know. Yeah. Yeah, we're in an era of great change and a lot of interesting new things. And if not now, this is like a lot of other time, it would be more appropriate to actually look at previous generations and wisdom and other literature that we found that we have, because these traditions, they speak to something permanent or perennial within human nature.

And it's worthwhile examining and you can kind of hold it lightly and hold it tightly. But at least it's worth looking at. On that note, I hope that publishing this conversation confers some sort of instrumental reward to you and the success of this book, but it's always intrinsically satisfying to talk. Likewise.

Thank you very much. Listen to his shows, folks. Plural. Come on.

Thank you. Thank you. And remember, imagination and attention are our greatest natural resources.