Welcome to the Knowledge Project on your host, Shane Parrish, curator behind Furnham Street and online intellectual hub of interestingness, covering topics like human misjudgment, decision making strategy and philosophy. Today, we're going to be talking about technology.
The knowledge project allows me to interview amazing people from around the world to deconstruct why they're good at what they do. More conversation than prescription on this episode I have. Sam Eversmann is a complexity scientist whose work focuses on the nature of scientific and technological change. He's currently the scientist in residence at Lux Capital, which is a venture capital firm focused on big, daring ideas in science and technology. Sam's also written two books that I love The Half Life of Facts and Overcomplicated.
On this episode, we're going to get to know him better and explore overcomplicated and a relationship with technology in the process. We're going to learn about the difference between physics thinking and biological thinking, which I think you'll particularly enjoy.
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Sam, welcome to the Knowledge Project. I'm so happy to have you on. We're going to talk about your new book, but it's been a long time coming. I loved your old book. The Half Life of Facts Overcomplicate.
It's coming out soon. Thanks for being here. Thank you. It's great to be able to talk to you.
So I want to I've taken a bit of a different format with the podcast recently.
Somebody gave me these table topic cards and I've been going through all these questions and I've just been randomly asking people. But I think we'll start with what was your first job?
My first job. And this is including like even like a little part time jobs and stuff like that. Yeah. The first job that I can really remember I spent and I don't even know, I sure I was paid for this, but it felt job like I spent a summer being essentially like a lab tech in a like electrochemistry company that was owned by family, friends of ours. And I spent essentially kind of like a like a bottle washer slash data collector.
But it gave me my first exposure to the world of science and research. Cool.
That sounds like a I mean, it's impact on you, right? Oh, yeah. So I did that. And then, of course, I think like the next summer, several summers after that, I was a camp counselor at a day camp. So it really it wasn't like, oh, from there I was like doing nothing but science. But but yeah, I guess it had an impact.
How did you end up writing your first book, The Half Life of That was your first book, right? Yes, that was my first book.
So you mean like the process or how I like what made you want to write that book?
I've always been interested in writing and kind of writing for popular audiences, and I had been playing in the space of thinking about the science of science and kind of the nature of how what we know changes and kind of like looking actually at the regularities of this and trying to understand how to quantify this kind of area. And and and I had even begun I'm kind of in the early stages of doing some research related to that. But then I actually wrote this little piece about what I called measle facts.
So these are facts were sort of facts that bits of information that change neither too easily nor too rapidly, they're sort of at the middle or Misael scale. The idea is that it's of knowledge, the change very, very rarely like how many constants there are on the planet. You learn those once you're good, then there's bits of information that change fairly quickly. Like what the stock market closed out yesterday or or what the weather would be like tomorrow.
We're pretty good at updating those kinds of information. But in between, there's a whole slew of things that we often learn. The same way we learned the unchanging facts, like things like how many billions of people there are on the planet or how many elements there are on the periodic table. These things change, but they might change over the course of decades or over the course of human lifetime. And these kind of measle facts are they're sort of in this weird category where we learn them once, but we really should be updating them mentally.
So I wrote this little essay about it and it got a lot of attention. A number of agents and publishers contacted me and said, Do you think there's a book in there? And since I've been thinking about this, I said yes. And I'm excited to kind of tell the story of the larger picture of how knowledge grows and changes in kind of a science of science and science of information, growth and change has that.
Oh, I have so many questions right now. Has that changed how you learn things like do you view do you prioritize learning differently now because of that?
Um, I think ideally I think practically maybe I don't always as much as I should, but I know. And when people talk about I maybe it's more like how I kind of think about information. So, for example, when people talk about how the Internet and Google is ruining our brains or our memories, I am actually much more positively inclined towards those technologies simply. Because if we don't necessarily have as good memories anymore, it means that we now actually have to look things up more often and make sure we actually have the information correct, which means we are more likely to have the most up to date bit of knowledge.
So I think those kinds of things are really good. And I think for me, after working on that book, I think it's also I just I've gotten better at delighting in being wrong and having the facts that I thought were true, being overturned, because I know this is the way of the world. And I think this is kind of for me, it's more just having a scientific mindset towards everything around it and science and sciences. It's a body of knowledge.
But even more important than that, science is really a means of carrying the world. And I think recognizing that everything we know is constantly in draft form is really that's the way we should be thinking about the world. And so whether or not you're doing science or simply just living your life and reading magazines and having bits of knowledge and information about nutrition or how to take care of babies being overturned, I think these things are all really, really good and they're really exciting.
And I think we just need to make that more explicit in our everyday lives.
What was your process for writing? Like, what did you do every day? How did you go about?
So for for this most recent book, which I have maybe a better memory of, the way I did it was I set myself a goal of kind of just a certain amount of text to create per day, and at least is the initial stuff. I said something like a thousand words a day. And so I just wrote a thousand words and didn't have to be on a single topic. I didn't have to be good. It could be kind of on various things that I wanted to write about and kind of it could be multiple things that I want to write about related to the book over the course of The Thousand Words.
And then when I got something that was book length, then I kind of took it all, printed it out, kind of began rearranging it, realized that there were parts in it that I wanted to remove their parts that need to be fleshed out significantly. There were things that need to be connected. And then I kind of went through this kind of iterative smoothing process and eventually I felt like I had something. Then I took the first chapter, kind of the introductory chapter.
So it's my wife. She read it and said, this is garbage. And and I said, well, what do you mean by that? And so we had this long discussion about the discrepancy between what I thought I was writing about and what I actually had written. And then I explained to her what my goal, my goals were. And she's like, oh, write that explicitly. So I went back, worked on that chapter, clarify it.
She said, OK, this actually makes sense now and then went kind of retooled the rest of the book then went through and I'm sure it's my editor and kind of went from there, kind of this constant iterative, repetitive process of kind of going through and making sure that there was little or at least smaller and smaller differences between what I thought I wanted to write and what I actually was writing. At the same time, though, there were also like whole sections and whole topics that I eventually realized were just beyond the mandate.
And the theme of the book, while they were kind of interesting, they really ended up not adding much and probably confusing things. And so I slowly but surely kind of tightened and tightened the actual theme and narrative of of the actual story I wanted to tell. And so I think initially for this most recent book, I wanted to include huge amounts about philosophy of science and the nature of the complexity of our scientific models. And I think there might still be some some of that in there, but the vast majority of that has been found on the cutting room floor.
I want to talk about your new book. We'll get into that in a little bit. Do you write more in the morning or at night?
I write in the morning. So my goal is to always get the majority of the writing done as soon as possible so that it no longer hangs over my head the rest of the day. And so I can feel it kind of like whatever I do for the rest of my work, I have accomplished I have accomplished the goal that I need to do writing wise, which is which is nice.
To what extent would you say science or art is more essential to humanity as somebody you're a practitioner of both.
Right? Oh, that that is an interesting question. I think. I mean, so here's the thing. So first of all, if you ask a lot of scientists, I think they would discuss how there's a lot of art within the science and how science is conducted and the way in which you ask questions. And of course, you still have to answer those questions and very in a very rigorous and kind of scientific and perhaps non artistic way. But the way you can ask questions and sometimes even the way you answer them, if you can think of like a very clever, clever experiment, there's often a certain amount of art to that as well.
So so I think a number of scientists would would kind of push back at that question. At the same time, though, I think I think they're both a very necessary I think they're both kind of different. And I'm not even sure how different ways they are about thinking about the world. They're kind of both approaches and they're both approaches to curing the world. But there are also ways of producing this beautiful things. And so in science is almost like beautiful output constrained by reality.
And art is also beautiful, but constrained by reality of the media that you're using in some very different sort of ways. I'm not entirely sure if that was a nonanswer, but I think they're both very good. And at the same time, though, I mean, there are certainly certain types of forms that speak to me more than others, like certain types of. Contemporary art, I have more trouble with than others, other ones I like a lot, and I also think there's a lot of very interesting points of interaction.
So there's like one whole I like the whole realm of like computational and generative art.
What's that? It's where you almost create you create algorithms that are responsible for generating the the images that we're looking at or some sort of artistic output. So like, for example, you could create a small computer program that generates a tree or an entire forest of trees. And these are all computationally generated. So no one really was the person who kind of painted the tree or kind of drew the tree. But they're all beautiful and they're all they're all really, really wonderful to look at.
And you can also you can also do this where people are now using machine learning techniques to generate text in response to images that are being shown to the computer program. And that also generates some really interesting things. Now, of course, the question is, what is the actual artistic output? Is it is it the computer program, the generator? That is the result? I think in some ways, maybe sometimes those questions are kind of beside the point, because in the end, the output is very interesting, whatever level of output you're looking at.
But, yeah, there's some very interesting points of connection with this kind of computational art and generative art of generative design, where people are using computer programs to design objects that almost have this like biological appearance to them that are so functional. And they also are really beautiful and different sorts of ways than we might think of kind of traditional design objects.
Do you think that we will have the same emotional connection as humans to art that is derived from an algorithm as we do art that's derived from a person?
Uh, maybe. I imagine if you don't tell someone where it comes from, you probably can get a similar kind of response, especially if it's almost indistinguishable. So, like there are actually there are there computer techniques where you can give an image to a computer program and it will actually generate it in the style of famous artists? And some cases that actually looks pretty good. And I wonder if it would generate a kind of yield, the same sort of emotional reaction.
At the same time, though, I think and maybe some emotional reactions for computational general, it would be maybe flat and people would say, oh, it's not doesn't feel as authentic. Or maybe it would be frightening to some people when they realize that computers can kind of be creative in certain ways. For me, it's really exciting just to kind of see the extent to which computationally generate art can be as beautiful as it is. I think that's actually really interesting to look at.
And so I'm I'm maybe less bothered by some of the epistemological issues and just kind of saying, oh, these outputs are really cool. And plus it's all so if they're they're beautiful and they're also they're always different. And so you can see there's even kind of going back like years ago when people first started doing like fractal art, either kind of just like zooming into like the Mandelbrot set or generating random mountain shapes that actually look like mountains but are entirely computer generated.
These are really beautiful. They eventually kind of and there's only so many of them you can look at before you kind of seen everything, that everything kind of looks the same.
But at the same time, they're really, really interesting to look at and kind of see how the variety, the sheer variety of things that can be generated computation. And there are a lot of people who are really at the cutting edge of this, both artists as well as computer scientists and practitioners. Yeah, and I'm just really impressed by the state of the space and what is really happening. I sort of at the frontier of computers and science blended with art.
Yeah, I think I mean, like the next decade or so is going to be fascinating to watch how that plays in.
Yes. Who's the best teacher you've ever had and what made them the best teacher. The best teacher I've ever had.
Who probably my parents. And they they were very good in not only encouraging me to kind of always look things up and kind of showing me and guiding me on how to learn new things. But also really they were really instrumental in just making sure I always kind of like thought about the world in playful ways. And actually so the the thing that I was always told by my father before I left school, before I left for school, when I was little was the phrase think, have fun and be a mensch and and be a mensch, a sort of mensches like Yiddish for kind of like being a good person.
And essentially he wanted to kind of instill in me these like three ideas that you have to think about the world. You can't you can't just kind of have this kind of unquestioning attitude towards it. If you have to have fun, you have to have kind of this, like, playful attitude towards the world. It's not just like and especially when you're learning new things. It shouldn't be wrote, it should be boring. It should be playful and should be exciting.
You should have this kind of always the sense of curiosity. And of course, you should also be a good person while doing all these these three these different things. And I think that kind of attitude and a perspective, I've really tried to live up to that. And and it's really stuck with me. And I think those kind of kind of goals are. Those are the kind of things that I'm going to be instilling into my own children. Those are things that get passed down from family to family because it really resonated with you.
Are you going to be telling your kids to look things up in physical books or you're going to be telling them to look it up on the Internet or wherever you can find things?
I happen to have a lot of physical books, but the Internet's also a great source of information. And I would say physical books are a great hedge against technological change. And I know, like my physical book, if I like 20 years from now, it will still be kind of backwards compatible with the human eye. But as an e-book, not entirely clear. But at the same time, if you want the most up to date knowledge in some ways and the Internet and like Wikipedia in particular, it's kind of the closest thing we have to complete world knowledge.
And so that kind of flippantly. But I think there's some truth to that. And I think having access to those to that kind of up to date information is really, really powerful and really important. And I think a balance between the old and the perennially wise encapsulated kind of maybe in my older books versus kind of the up to date and the recent encapsulated things you can look up online is a really good balance to kind of think about the world and look at and understand it.
While you were saying that, I had an interesting question in my mind, which is as an author, you deliver e-books, Kindle books. As a book lover, you have a very physical connection with books that are unchanging.
I mean, and the connections on changing with the texts and changing. How do you feel about the ability to update books like Update Kindle books to reflect this latest information? Maybe you go in, you add an extra chapter, you change some things around, you fix some errors. That's not possible in physical books.
It is possible in the electronic kind of medium. How do you feel about that as an author, a scientist, given your research?
So I think the possibility is interesting. At the same time, though, maybe as an author at a certain point and you know, the book is never done, there's always new things to add or other bits of information to update as we kind of learn new things about the world. But at the same time, maybe as an author, we kind of want to just be done and say, OK, this is a finished product and you have to be willing to release it out into the world.
And so I think there are different types of products that are maybe like output's that are more well suited to constantly being updated and other ones that are kind of just like this is a finished product and we can be done with it. And I've heard stories of like even like with novels, like people editing them, like right before they're going like on stage two to to do a reading of their novels, even though, of course, the novel is done because they just realize, oh, they want to say things a little bit differently.
And of course they can do that novel. And it's not even like, oh, the novel is being updated with new information. It just being up. There's always tinkering to be done. I think there's always tinkering to be done. But I think you also have to kind of recognize that you spent a lot of time with it. And it's like right now you have to be willing to kind of move on to the next thing. So I think there's I think with with science, though, especially being able to not necessarily constantly update a single paper, but maybe link papers to newer papers that have information or kind of caveats or or a response to the original research.
I think that kind of thing is very, very powerful. And I think in the scientific world, the ability to have all of our research open and interconnected and constantly updated is really the key to actually making sure that as the scientific community, people can really query the most up to date state of the field and really test and see if it is the way things are as opposed to right now. And in science, sometimes you have things that are out of date, but also because knowledge might be eager, walk behind journal paywalls or or these articles are not as interconnected as they could be.
People sometimes end up reinventing certain ideas, sometimes not just simply due to jargon boundaries, where one field might reinvent a model that had been known for several decades and some other field because they just didn't even know that this thing was done with using slightly different terms on this kind of thing, that they will probably never stop happening. But I think by having open, interconnected science, we will able we'll be able to kind of make sure that the resource that we don't waste resources and we are kind of doing research in the best possible way.
Switching gears a little bit, would you choose to be the worst player on a winning team or the best player on a losing team?
He said so worst player on a winning team or best player on a losing team? Yeah, interesting.
Um, I think if you're well, certainly being the worst player, kind of cynically the worst player on a winning team is exciting because you still get to win. But I think the lesson of reason for that would be if you're the worst player on a winning team, there's people around you who are all going to kind of force you to get better. So that might be kind of a less cynical reason to be on it. The best player on a losing team might be a little bit discouraging.
I'm not sure. I think I think it'd be more fun to be on a winning team, even if I'm not doing that great, because I always have good or some good role models to look up to and I'll be winning alongside.
That dovetails into my next question, which is how do you how do you go about defining success for yourself?
So that's a great question. So I think success and certainly the external metrics and I there's a there's a great book called The Guide to the Good Life by William Irvine. I believe it's a it's about essentially stoic wisdom. And I think you might have even mention it and discussed it on your blog. But essentially the idea of like and one of the things he talks about is when you think about success, there's there's only so much you can control, especially externally in terms of one's success and the best way to try to be successful and at the same time be happy with one's attempts is to internalize the success metrics.
So like, for example, let's say you're trying to write a novel if you and there's the external metrics like that selling novel, well regarded, critically acclaimed, all these different kinds of things. But then there's the the internal metrics, which are did you make the best version of of the book possible? And I think and those are the kind of things you actually do have control over. And I have tried and if I'm more and more and not always successfully, this is kind of a continuing thing.
But to try to kind of make the make metrics of success much more internal where you try to do you try to be the best version of yourself possible and kind of be true to yourself and true to your abilities. And and if there are external metrics that come along with it, that's wonderful. But there's only so much you control. And so therefore, you have to be you have to recognize that. And I think if you're only if you're only measures of success are truly external measures, you're really never going to be happy.
So in that case, while being on a winning team is an external metric of success being the worst player? Well, I guess the question is, are you the worst player or are you a bad player? And if you're if you're the best you can be, but still the worst on on a good team, then I think you've done your best.
So it sounds like you have inherently an inner scorecard, primarily to I mean, there's no it's a false duality between the two.
Everybody is somewhere in the middle. But it sounds like you lean towards more internal metrics, things you can control the process I strong I would say I strive.
And sometimes that's always aspirational because I like I'm a person. I'm like, there's always things I want to do that are kind of like that are kind of independent of me. I don't have control over. And and sometimes you can be disappointed if they don't work out. But I think for long term happiness and feelings of success, you have to be more kind of those. You need to have those internal scorecards more so. Otherwise, there's always things you can either always people you can compare yourself to and come up short.
And there's always things you could be doing or succeeding at externally and you're not. And so therefore, if you only use those external scorecards, you'll you'll never be happy. And I'd much rather be happy than simply just kind of checkboxes. I agree with that.
Are there any other books that kind of dramatically impacted your life?
So I would say so. Another book that I really I would say was probably one of the best books I've read in the past few years in the nonfiction realm is the book Immortality by the philosopher Stephen Cave, where he looks at the different ways in which humanity has tried to live forever, both kind of since ancient times as well, kind of the modern versions, like things like immortality of the soul or just simply immortality through not dying. And he looks at the ancient the ancient version of it, as well as the modern version of it.
And eventually he actually goes through all of these and and finds them all wanting, which is kind of interesting. It's an unbelievable book, finds them all wanting. And then he says, OK, what is the what? How should we respond and think he ends up falling back on wisdom, literature, sort of things like Ecclesiastes or the the kind of writings of the Stoics and says these are the kind of things like recognizing that like even in the transience of life, you can still make life meaningful.
And so the book is unbelievable. It kind of deals with these. Very, very deep themes of humanity like mortality and mortality, meaning life and but it also weaves in amazing stories about Alexander the Great Life or the first emperor of China and his goals, as well as a lot of philosophy and other ideas. It's fantastic. I would say that was really that was that was a really influential book. And then another one, I would say, in a very different way, is the Foundation trilogy by Isaac Asimov.
So I don't know if you're familiar with it. It's a sort of science fiction novels written in the 50s, but a long time ago. And it takes place in the far future. And the idea behind it is that there is this the scientist, Harry Seldin, who realizes that the Galactic Empire is going to is going to fall. There's going to be this there's going to be a period of the dark ages. And so he creates this foundation to kind of shorten the amount of time there's going to be a dark age.
But the idea behind is that this man, Harry Seldin, he his branch of science is called psychohistory, which is essentially this quantitative science of human societies. And the idea is that even though each individual human is not predictable, if you get a whole bunch of people together in large enough groups, then suddenly there are actually that there are regularities, there are rules to understanding how human societies operate. And and so for me, that was actually one of the things that got me interested in thinking about computational social science and kind of quantitative science of human organizations and societies and cities.
And it turns out if you actually if you if you ask a lot of social scientists or scientists were kind of involved in computational social science or quantitative or networked science and things like that, a lot of them read foundation at an early age and were actually very interested, very influenced by it.
I've never read that. I'm going to have to pick that up, but that's their fun. And anything else come to mind.
I remember when I was younger liking the novel Childhood's End by Arthur C. Clarke a lot, although I have to say I had actually forgotten most of it. So there was a mini series version of it on the sci fi channel recently. And I watch it and I and and while I was watching it, I was kind of like rediscovering a lot of the plot points because I had forgotten so much of it. So that was kind of interesting thing. So I'm trying to I would say those are those are some pretty influential books of mine.
But the Foundation trilogy got the good life immortality. And I guess also I would say, I guess in the fiction realm, another one that I really enjoyed, Neal Stephenson's Cryptonomicon, just this the idea that there can be a book that weaves together an amazing plot as well as some really, really profound ideas on philosophy and computer science and technology together. I felt like that was, I think, one of the first times I'd seen a book that had really done this work.
There were these unbelievably informational pieces as well as unbelievably fun read like it was. It's also an unbelievably fun read, and I think I'm a big fan of most of Stephen's work. I love his stuff. But I would say Cryptonomicon was one in particular that really kind of demonstrated that you could do this kind of thing together. I really enjoyed it.
We know he's a big reader.
Yes, I can. I can remember when I was younger, there was a period where I think I set myself the goal of like reading like five five Arthur C. Clarke novels over the course of four days. I was trying to do like more than one book per day and seeing if that was possible. And I think I succeeded. I thought I was a big reader. But I also I would say I read heavily science fiction when I was younger as well as actually I read a lot of like the collected columns of Martin Gardner, who wrote like Recreational Mathematics, the recreational mathematics column in Scientific American.
So that was like one of the first places where people read about Conway's game of life, the sort of cellular automaton world, as well as a whole bunch of other things. I would kind of read like recreational mathematics, science fiction, and pretty much anything that was like super nerdy that combined philosophy and science and mathematics and technology.
That was my catnip.
Did you have a lot of physical books in your house? Yes. Yes. Yeah, we actually did. We had a lot of books.
And how do they encourage you to read or was it just something you picked up or you saw them doing?
Or I imagine it was probably leading by example, because I think I can remember I know stories of like even even at a young age, I was like trying to model my parents and I kind of walking around with, like a little books, like even if it was like some like a little grey address book that really I wasn't reading because obviously it was a dress book. I don't even know if I could read at that point, but I wanted to kind of be a reader.
And so I think it was a company of modeling, having the books around and also being encouraged because I didn't know something. I going to look it up and I can remember, like I like at dinners with my family and I. When my grandparents were over, we'd have these debates and then we would like one of us would go rush over and grab the volume from the encyclopedia and try to actually look things up and see if one was correct.
And even I. I still do I still do do those things now at meals just because I feel like it's it's great to have that.
Yeah. I mean, I do that all the time on my phone. So overcomplicated.
Can you give me or give us I guess the audience an overview. I've read the book but most people haven't. Can you give us an overview of the book and how it started?
Sure, yeah. So the idea behind the book is that we all know that technologies are becoming more and more complex and kind of more complicated over time. But increasingly, technologies are becoming so complicated that not only just does the average everyday person not fully understand them, but but increasingly even the experts who work on them with a daily basis or even the people who built them don't necessarily fully understand them or their implications any longer. And so the book looks at kind of what what are the forces that lead us towards this ever greater incomprehensibility and what do we do about it?
Do we simply say we're in trouble? Like this is kind of the new the new state of being, or are there ways of meeting these technologies halfway? And so I happen to be kind of a fairly optimistic person by disposition. So I think there are ways of meeting these technologies halfway. And so I kind of layout different perspectives and ways of approaching our technologies. And again, I'm using technology fairly broadly. I'm using it to mean everything from like the software on your computer to the entire Internet, to our urban infrastructure, to even our legal systems, our legal codes.
What surprised you the most as you were developing the book? And the idea of fleshing out the idea is to do you didn't have that preordained before you went in? I suspect so.
I think with with this it was more initially. I knew the way I knew there were many examples of this kind of thing. But as I was writing the book and as I was talking to people, it just became abundantly clear that in area after area, this kind of things and this incomprehensibility wasn't at the limits and edges of our experience like this. Incomprehensibility is found in aspects of every part of our lives. And so it's like the software on our computers, on our desktops.
It can be found in like medical devices, kitchen appliances, in our cars with like millions of lines of code. This, like reduced understanding and vast complexity of technology is really everywhere we look. And I would say it's probably been accelerated significantly due to the fact that we now have computation embedded within everything else. There's only so many levels of kind of levels of hierarchy of complexity you can have if you don't have the ability to have code within something.
And once you do, though, then suddenly it can baffle your mind. And I think that that was really interesting to see. And every time I discuss it with someone from no matter what the domain, people would just give me more and more examples. And it was it was fantastic. And and a little little surprising to see that. What was your favorite chapter in the book?
So I would say my favorite chapter might be the final chapter where I look at one of the perspectives of technology that I think I would say people sometimes when they're confronted with technology they don't understand or maybe they can't even understand, is they often respond with one of two extremes of either fear in the face of the unknown or this reverential, almost religious sense of awe towards technology. They don't understand. And I think both of these, while they're fairly common, they're not good, mainly because they end up cutting off questioning, because if you're really afraid of something, you're so afraid you can't even question it.
And if you have this reverential towards something, you you don't realize that maybe it's actually a lot messier than it really is. And I think somewhere in between, almost like a humble but constantly questioning approach toward technology is really the way we need to think about it. And and I use it and I use the analogy. I kind of look at the way certain philosophers in the Middle Ages approach the world around them. So I use the example of Moses Minorities' philosopher from the 12th century, I believe.
And he recognized that there were things that we would never understand, like there are natural limits to what the human mind can understand. And I think people kind of recognize us. But at the same time, over the past maybe a century or two, there's been this like somewhat triumphalist sense when it comes to science that if there's a question, no matter what, if we put our minds to it, we can understand everything. And and I think that's that's not always true.
There are limits and we're going to bump up against our limits understanding. And that's true even for the technologies that we ourselves have made. And we think, oh, that we're these rational, logical creatures and therefore the constructions we make should also be logical and rational. And that's not true. These things evolve over time. They're kludgy, they're messy. There's a lot in there that really. No one fully understands anymore, and that's OK, as long as we still have a way of kind of like slowly iteratively and with a sense of humility approaching our technology, I think then we'll never be overwhelmed by what we ourselves have built.
I like that. I think for me, the chapter that resonated or actually the two ways of thinking that I took away from the book that I thought were incredibly insightful were the biological versus physical thinking, specifically Chapter five. Can you walk us through the differences between those two and how they manifest themselves when viewing technology?
Sure. So so I use this kind of dichotomy of two modes of thinking, which I call physics thinking or biological thinking. And it's oversimplification. And of course, not all biologists use biological thinking. Not all physicists use physics style thinking. So with that caveat in mind, the two modes are that the the physics mode of thinking takes up thing and and tries to kind of create a simple a simple means of understanding the vast majority. So like, for example, like a single equation that explains the vast majority of the motion of objects and or or you can write down a single formula and it would explain 50 percent of what's going on in cities and how they and how they work on something, something like that.
On the other extreme, you have the the biological approach, which says that you actually need to focus on the details and that the details and kind of cataloging the diverse instances of things that don't make sense. Not only do the details matter, but sometimes that's like those are wonderful and they're really, really exciting. And and you can see kind of those those trends in certain biol that's like kind of the naturalist's of old who would go around and collect butterflies, as well as certain types of biologists who would just kind of a focus on certain molecular pathway or the relationship of of the or the interactions of two different species within a larger ecosystem.
Recognizing that you can still even you can even create models of understanding these things. They're not they're still amenable kind of mathematical modeling, if that's what you want. But focusing on the details is very, very important for a larger understanding. While the physics approach might be let's kind of sweep away the details and just focus on kind of the abstractions that we can learn from. And and I think when we think about technologies, oftentimes we might feel that we need to have the the physics style approach because we've built these systems.
And so therefore, they should be amenable to kind of a simplified understanding. And oftentimes that's not true when we have a large technol technological system that interacts with the real world. The real world is complicated. The technological system needs to be complicated, needs to deal with, like all the many edge cases and weird exceptions, like when you build a self-driving car, for example, it can't just deal with the like one case of driving on a highway in perfect weather.
It might need to deal with rain or sleet or people jumping out into the middle of the road or or animals doing doing their thing or glare. And and when you build a technological system that mirrors the world in all of its complexity, you have to be kind of aware of all the details. You end up making something that's more biological in structure and ends up therefore being more amenable to kind of this biological approach. And so oftentimes when you're confronted with a really complex technological system, you might have this desire to really think about it in a simple way.
But in fact, it might be more appropriate because and if if if a technology has a massive organic feel to it has evolved over time and kind of created bits and pieces, if it looks biological, maybe we can actually learn from how biologists look at biological systems and so we can look at the details and kind of catalog bugs in order to kind of gain further understanding or in the case like maybe a really complicated machine learning system. We might we might have a really powerful output, but it might require, but it might be really difficult, kind of understand what's going on under the hood and how it arrived at that output.
And so therefore, we have to kind of use this iterative, slowly, like, slow approach to understanding how the system did what it did. This almost like more biological approach where you kind of get bits and pieces from based on that, slowly get a larger picture of what's going on. I think you need both. You need kind of both the physics mode of thinking as well as the biological approach. But but we certainly we certainly should not give short shrift to the biological mode of thinking when we're dealing with our own technologies.
After we get into almost like a false duality in these frames of thinking. It's like this is the one way to see the problem. Even if you have these multiple models like biological or physical thinking in your head, you end up kind of narrow and pigeon.
But if you look at it through both lenses, you end up removing some of your blind spots.
Oh, yeah, no, I think yeah, the more models you have, the better you're going to be able to approach these systems in another. Approach that I advocate with these technologies, like having simulations like the same way that in playing with like playing SIM City, you might gain a better sense of the complexity of a city rather than not necessarily the details of the city, because SIM City is an oversimplification of a real city, but you still kind of understand the nonlinearities in a city's performance or the bounds of how it operates.
I think the same kind of thing. If you have a simulation for a technology, you'll gain a better appreciation for how it works and maybe even how it bites back and does weird things you want to expect. And I think that kind of thing is really important.
So if I'm a manager in an organization, how how should this change how I go about managing the complexity not only in the software, but increasingly in the vectors of the organizations?
So I would say I certainly approach it with a sense of humility. I don't think that, like, a single mode of thinking can explain everything and have multiple different types of models, but also don't try to change too much too quickly. I think, especially in some kind of comes into an organization, there's a tendency to really make a mark and change a lot of different things. And that desire should be should be tempered with this recognition that when a system might be highly nonlinear, highly interconnected and and sensitive to change in ways you can expect, therefore you have to you have to respect that.
You have to kind of respect the complexity of the system. And only in doing that can you kind of recognize what is in your capability and ability to actually change.
Do you think that becomes increasingly hard as things like machine learning maybe encroach more and more into the day to day the workplace, where even if we understood the technology that created the algorithms, the algorithms are now creating their own algorithms. So at some point, through so many iterations, we lose track of what's actually happening.
We just knew we get the output and that creates a dependence.
So I think, yeah, there can be no concerns. But I think as long as you recognize that these technologies and their tools or instruments for doing things like this, and we've always used tools to kind of be better able to do our jobs and complete our tasks. These feel in some ways qualitatively different because of our reduced understanding. But I think as long as we kind of recognize what they're what the end goal is and and still strive to always try to understand them as best we can, I think I think it can be OK.
Is there any other advice you would have for people to integrate their thinking about technology and how to approach it?
So I would say I mean, I would say one one way that a lot of people think about technology like every day when you're looking at your your iPhone and stuff and it does something weird. We we often think with technology it's OK to kind of outsource our understanding because there's always some experts, like an Apple genius who can fix it or can understand it. And I think we recognize that in many cases that might not actually be true. There might be no expert who can fully understand the system.
Then it'll be more than then we'll be a little bit more deliberate and actually trying to understand these technologies. And I think one thing that from an engineering perspective, we need to build into these technologies as well, something I think people just need to be mindful of looking for is trying to find ways of kind of glimpsing what's happening underneath the hood of a technology. And because I think for too long, we think a system is like perfect or really nice until something goes wrong and something or the actual complexity and complication is revealed to us.
And it's a lot better to have ways of kind of glimpsing the underlying complexity underneath something that feels kind of very simplified and very pristine. And so whether or not that's as simple as like playing with like the command line up on your computer if you have access to it or or following more carefully what what's happening with a progress bar or something is being installed, even if sometimes those progress bars are fairly divorced from the reality of what's happening underneath.
I think paying attention to those little details can actually provide you a little bit better sense of what's happening. Certainly not enough always. But but it can least give give us a hope of getting a glimpse of what what's going on kind of beneath the hood. I like that.
I mean, it's moving us closer to technology because I don't think technology is going anywhere. So it's kind of integrating us a little bit closer into it. Although the complexity of it will even the simplification of the complexity will be fascinating to watch how that kind of comes out in the future. Oh, certainly. Yeah. I have no idea where that's going to go.
But at some level, I know, like, the map is not the territory, right? When you simplify to such a degree of like red versus green, for instance, on some corporate dashboards, you're missing the inherent complexity. And over time, you forget about what variables drive the equation that showing you that suit, you lose touch, certainly.
So I think you're having ways of not trying not to lose touch, even if just not not losing. Just quickly, I think it's really important. Awesome, listen, I'm cognizant of the time. One more question before we go. What's on your nightstand right now?
So two books. I'm kind of in the very early stages of reading, but I'm excited by our Kevin Kelly is the inevitable about technology trends as well as algorithms to live by by Brian Christian and Tom Griffiths. I'm really excited about those.
Somebody recommended that to me today, I think, actually.
Oh, wow, that's fantastic. So I would say those are good. Let me ask you also the another one, which I have not yet started is Robin Hanson, the age of. I'm really excited to kind of see the subtitle for that one is Work, Love and Life When Robots Rule the Earth.
And I've heard unbelievable things about it. So I'm very excited and looking forward to that one as well. Awesome.
Sam, thank you so much. This has been a real pleasure. I really appreciate you taking the time.
Hey, guys, this is Shane again, just a few more things before we wrap up. You can find show notes at Farnam Street blog, dotcom slash podcast. That's fair. And S-T, our blog, dotcom slash podcast. You can also find information there on how to get a transcript.
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