The Joe Walker Podcast - Stephen Wolfram — Constructing the Computational Paradigm

Episode Date: August 16, 2023

Stephen Wolfram is a physicist, computer scientist and businessman. He is the founder and CEO of Wolfram Research, the creator of Mathematica and Wolfram Alpha, and the author of A New Kind of Science. Full transcript available at: jnwpod.com.See omnystudio.com/listener for privacy information.

Hello and welcome back to the show. Meeting this episode's guest was like encountering a force of nature. Stephen Wolfram is a British-American computer scientist, physicist, and businessman, and his bio is as precocious as it is unconventional. Stephen's first paper on particle physics was published when he was 15 years old. At 17, he began studying undergraduate physics at Oxford, but he left after about a year, recruited by Nobel laureate Marie Gaumont to Caltech, where he earned his PhD in particle physics by the tender age of 20. Richard Feynman, one of Stephen's colleagues at Caltech, said no other physicist had a wider

range of understanding than Stephen. At the age of 21, Stephen became the youngest recipient of the MacArthur Genius Grant, and at 24, he was scooped up by the Institute for Advanced Study in Princeton, where Robert J. Oppenheimer had been director only two decades earlier. Around the same time, this is the early 1980s, Stephen became interested in computer programs called cellular automata as a way to model complexity and maybe even the universe. He eventually wrote a door-stopping book called A New Kind of Science, where he tried to usher in a new scientific paradigm, one built on computation as distinct from mathematics. But Stephen is not just a scientist, he's also an entrepreneur and the founder and CEO of Wolfram

Research. Indeed, you may know Stephen best for his flagship software products, Mathematica and Wolfram Alpha. A large part of what impresses me about Stephen is his sense of agency with respect to doing science. It's as if he has constructed his company, a significant business in its own right, as a vehicle for his curiosity, a curiosity that he pursues relentlessly. In fact, his company is currently engaged in the small project led by Stephen of uncovering the fundamental theory of physics. I traveled to Concord, Massachusetts to a small outpost of Wolfram Research to record this conversation with Stephen in person. Perhaps one way it differs from other

interviews Stephen has done is that we spend at least as much time talking about his ideas on business as about science. The conversation was not meant to go for as long as it did, but Stephen and I enjoyed talking so much that we just kept going. Enjoy. Stephen Wolfram, welcome to the podcast. Thank you. Stephen, I'd like to start with business and biographical stuff, and then we'll wend our way into computational science, as well as its implications for history, technology, and artificial intelligence. So you're one of those rare figures who's both a brilliant scientist and a brilliant entrepreneur. And kind of like Galileo, you've both made important discoveries and created the tools necessary for making those discoveries. Your version of the telescope,

of course, being Mathematica and Wolfram Language. Do you view your scientific ability and your entrepreneurial ability as largely separate or is there some common underlying factor or factors? Because not many great scientists are also great entrepreneurs and vice versa. So what is your fundamental theory of Stephen Wolfram? Well, I think thinking about things and trying to understand the principles of them is something that has proven very valuable to me, both in science and in life in general and in business and so on. And so I think it always surprises me that people who think deeply in one area tend to not keep the thinking apparatus engaged when they're confronted with some other area. And I suppose if I have any useful skill in this, it's to keep the thinking apparatus engaged when confronted with sort of practical problems in the world, as well as when confronted with

kind of theoretical questions in science and so on. And I think mostly I see the kinds of things I do in trying to understand sort of strategy in science and strategy in business as very much the same kind of thing. And maybe I have one attribute that is a little bit different, which is that I'm interested in people, which is something quite useful if you're going to run companies for a long development of people to be a satisfying thing in its own right, that's something that is relevant on the business side, less relevant on the scientific side. Perhaps a third attribute of yours I might add to the mix is optimism. Well, yes. Right. There's a lot that one doesn't see from the inside, so to speak. And I think it is true that when one embarks, as I've done many times in my life on large projects, very ambitious projects, I don't see them as large

or ambitious from the inside. I just see them as a thing I can do next. I don't see them as risky. I just see them as things that can be done. And yes, from the outside, it will look like lots of risk-taking, lots of outrageous optimism. From the inside, it's just like that's the path to go next. For me, it's often in the, you know, one has optimism, but one also says what could possibly go wrong. And having had experience of sort of the things that happen and so on, it is useful to me as a kind of backstop to optimism to always be also thinking about what could possibly go wrong type thing.

And that's been, it actually probably fuels the optimism because by the time you realize the worst thing that could go wrong is this and it's not so bad, then it kind of makes one more emboldened to go forward and try and do that next thing that seemed impossible, so to speak. I read this anecdote about how you learned the word yes before you learned the word no. And that felt kind of representative of the optimism. Yes, that's something my parents kind of would trot out from time to time as an explanation for my sort of later activities, so to speak. That's great. So typically the earliest one would get a PhD is the age of 25. You got yours at the age of 20. So somewhere in your education, you compressed five years before the age of 20. How much of that is accounted for just by raw talent and how much was some hack you learned that other people with less horsepower could adopt as well? Interesting question. I think, you know,

there were first hack was you can learn things just by reading books. That's very old fashioned these days. It will be, you know, going to the web or something. But, you know, the idea that if you want to learn something, you just go read books about it. You don't have to sit in a class and be told about it, so to speak. That was perhaps hack number one. Hack number two was you can invent your own questions. It's not, you know, when you're trying to learn about something, yes, there are exercises in the back of the book, but there are things that you might wonder about. And by golly, you can go off and explore those things. And I often, if you'd asked me, you know, is this a doable, you know, can you actually answer this question? Is this an answerable question?

I would have said, I don't know, but I'm going to try and do it. And somebody else might have said, you can't go and ask that question. You're a 14-year-old kid, And that's a question that nobody's asked before. And that's not a thing one could do as a 14 year old kid. But I didn't really know that. And so, you know, I got into the habit of, you know, if I have a question that I'm curious about, I will try and figure out the answer, whether it's something that would be in the back of the book, whether it's something that's been asked before or not. So those, for me, those were two important hacks, I suppose. Another one is trying to get to the point where you truly understand things, which, you know, there's a level of understanding that is perfectly sufficient to get an A in the class type thing. Well, when I was in school, they didn't quite do grades like that, but same idea. But, you know, can one really get to the point where one can explain the thing to oneself and feel like one really understands it? That was the thing that I, you know, progressively really

found very satisfying and got increasingly into. And once you really understand it, sort of from the foundations, it's much easier to build up a tall tower than if you're kind of, well, you're kind of roughly know what's going on, but it's all a bit on sand, so to speak. So I think the, you know, those are perhaps three attributes that I kind of, you know, three things that I kind of figured out now in terms of I never saw myself as having that much raw talent, so to speak. I mean, you know, in retrospect, you know, I went to top schools in England and I was, you know, they ranked kids in a good class and I was often the top kid and so on. So, you know, in retrospect, yes, I was kind of a, you know, at least by the ranking systems of the time, sort of a top operative, so to speak.

But that was not my self-image. I mean, it was just like I do the things I find interesting. I don't really, you know, I'm not, perhaps it was a good thing that I wasn't, you know, didn't say, oh, I'm the top kid, you know, and therefore I can do this and that. I was just like, I can do these things and they're fun and that's what I'm going to do. So I think there's a certain, for me, there's a certain drive to do things and to do things that i think are interesting regardless of what the sort of ambient you know uh feedback of yes that's a good thing to do or no that isn't

a good thing to do i i suppose i'm i'm perhaps um uh i don't know uh obstinate in that respect in that i'm just you know the things i want to do and I'm going to try and figure out how to do them. And that's, and that's been a trait that I suppose I've had all my life. I know when I was a kid, I would go, you know, I would do projects, they were, they were, you know, I would get very excited about some particular thing. And I would go and explore that thing. And then I would get to the point I wanted to get to in that thing, I would move on to the next thing and I'm a little bit shocked to realize that I kind of still do that now, you know, half a century later so to speak. If you hadn't developed an interest in computation back in the early 80s, would Mathematica have been developed or how long would it have taken?

So how contingent was that on you? Or something like Mathematica? Well, I think that the kind of sort of being able to have a computational assistant for doing mathematical kinds of things, there were already sort of experimental systems. I built my first system back in 1979 for doing this kind of thing. That was a thing that was sort of experimental systems. I built my first system back in 1979 for doing this kind of thing. That was a thing that was sort of bubbling forward. The part that I think is probably more contingent is kind of the sort of the principled structure of this kind of symbolic programming

idea, the idea that you can sort of represent things in the world in terms of symbolic expressions and transformations for symbolic expressions and so on. I think those things, in retrospect, I realize were more singular and more specific than I might have expected. I mean, in a sense, when I figured out, I kind of set myself this problem back in 1979 of, okay, I'm going to try

and build this broad computational system. What should its foundations be? And at the time, you know, I'd spent time doing natural science and physics and so on. And kind of my model for how to think about that was, it's like you go try to find the atoms, the quarks, the, you know, what are the fundamental components from which you can build up computation? And I kind of went back and looked at sort of mathematical logic and understood those kinds of things and tried to sort of learn from that. How can I find sort of the right primitives for thinking about computation? And I think, you know, as it turned out, I was either lucky or something that I got a pretty good idea about what those primitives should be. And I'm not sure that that would have been quite something that would have happened the same way.

I mean, the precursors of that date back to things like the idea of combinators from 1920, which had existed in the world and been ignored for a really long time. And so I think that's probably a particular thing. The other thing is kind of the, you might call it ambition or vision, to say we're going to try and describe the whole world computationally. That was a thing that I kind of sort of steadily got into, and that was a thing that I think was not really something other people had in mind and have had in mind in the intervening years. I think it's something where perhaps it's like, it's just too big a project. It's like, you know, can you really conceptualize a project that

big? You mentioned optimism. That's probably a necessary trait if one's going to sort of imagine that there that one can try to do a project that's kind of that uh um that that grand that big um and i think that was uh uh you know that was something which you know you look at uh when would people have decided that one could do something that big it's not clear that happens for a really long time. I mean, the thing that I've been interested in in very recent times, looking at LLMs and AI and so on, and realizing that they're showing us that there's kind of a semantic grammar of language. There's kind of ways that language is put together to have meaning and so on. And I'm realizing, well, you know, Aristotle did a little bit on this back 2,000 years ago and managed to come up with logic, and that was a pretty good idea. And we could have come up with sort of more general formalizations of the world any time in the last couple of thousand years, but nobody got around to doing it. And I've done little pieces of that, and maybe not so little.

We've done pieces of that, and hopefully we'll get to do more of that. But it's sort of shocking that in 2,000 years, although it was something that could have been thought about, people just didn't get oriented to think about it. And it's something where I suppose I've been fortunate in my life that I've worked on a lot of things that were things I wanted to work on which were not quite in the mainstream of what people were thinking about. And they worked out pretty well.

And so that means that the next time I'm thinking, well, I'm going to think about something that's sort of outside the mainstream, I kind of think, yeah, it's going to go okay. You know, after you've done a few steps of that, it's kind of you kind of feel that, yes, you feel a bit empowered to say, yeah, you know, I'm thinking about it. I think it makes sense to me. You know, I'm really going to do something with this rather than, look, how can it possibly make sense? Look at all these other people who say it doesn't make sense or say that that isn't the direction things should go in. So it's been, I was lucky because, you know,

I started doing science when I was pretty young and I was in an area that was very active at the time, particle physics. And I was able to make a little bit of progress, and that gave me good, positive sort of personal feedback to kind of get emboldened to try and do sort of bigger, more difficult, further outside the box kinds of things. Andy Matuszik, a previous guest of the podcast, and Michael Nielsen have this article called

How Can We Develop Transformative Tools for Thought? Tools for Thought being tools for augmenting human intelligence. So examples include writing, language, computers, music, Mathematica. And in the essay, they assert a general principle that good tools for thought arise mostly as a byproduct of doing original work on serious problems and tools for thought tend either to be created by the people doing that work or people working very closely to them

just out of curiosity and i assume you probably agree with that principle but can you think of any historical counter examples where someone has actually set out primarily to create a new tool for thought without being connected with an original problem? Well, it's, I mean, this kind of goes along with when entrepreneurs ask me, you know, how should they invent the product for their company? And, you know, the first thing I say is invent a product you actually want. It's, you know, it's hard to invent the product for the imaginary consumer that isn't like you, so to speak. And so, you know, in my own efforts, certainly, you know, the things I built as tools, I'm typically user number one, so to speak. You know, I'm the persona that sort of I most want the tool to be able to serve.

I would say that when it comes to sort of tools for thinking about things and the extent to which they are disembodied from, I mean, you know, it's kind of, there are things where people invent abstract ideas that don't have kind of application to the world. I mean, a famous example in mathematics is transfinite numbers, which were invented. They're interesting. They have all kinds of structure. And it's been 100 and something years. And every so often I say, finally, I'm going to find a use for transfinite numbers. And it doesn't usually work out. But kind of I think when it comes to, well, another thing to understand is if you look

at the progress of science, there are often kind of experimental tools that get created, whether it's telescopes, microscopes, whatever. I think that the invention of the telescope was something that happened kind of the – how that was plugged into things one would think about it wasn't really it was invented as a piece of invention um for practical uses and then you know the fact that it turned out to be this thing that unlocked the discoveries of the moons of jupiter and all this kind of thing was kind of a a came after the creation of the tools, so to speak. But in terms of ways that people have of thinking about things, I suppose, well, a big example that you mentioned is language, which is kind of a, that's kind of our apparatus for taking the thoughts that swirl around in our brains

and packaging them in such a way that we can communicate them elsewhere and even play them back to ourselves. And I think that's something which, by its very nature, emerges from kind of the thoughts that are happening inside, so to speak. It's not something that, you know, there's a question of, well, I suppose another example of this would be when it comes to things like artificial languages, where people say, let's invent a language that is kind of will lead us to think in certain ways. You know, I'm thinking through historical examples here. I think it's some, there are definitely in science, there's definitely plenty of things where the experimental tool has been invented independent of people thinking about how it will be used.

Just as a matter of, well, this is the next thing we can measure type thing. Without kind of thinking, well, if we measure this, then it will fit into our whole framework of thinking about things. In terms of, I mean, the history of sort of tools of thought at a more abstract level, they're not so many. I mean, you listed off many of the major ones. I mean, that's some, and I think in, you know, it's sort of interesting if you take mathematics as an example, which is in a sense an organizing tool for thinking about things.

You know, what was mathematics invented for? What were the kind of the ideas of numbers and things like that invented for? They were invented for the practical running of cities in ancient Babylon. I mean, they were, you know, it was not, they were not invented as a way, they were invented as a way of kind of abstracting life to the point where it could be sort of organized to be governed and so on. But I don't think they were, you know, things like numbers were not probably, and sort of the early times of mathematics were not invented, and sort of the early times of mathematics,

were not invented, I think, so much as a way of extending our ability to think about things. They were invented as a sort of practical tool for taking things which were going on anyway and making them kind of more, I don't know, governable and organized or something. So perhaps that's an example of a place where kind of the notion of this abstraction kind of happened for very practical reasons. Now, that's why by the time we get to, you know, 1687 and Isaac Newton and his Principia Mathematica, you know, its full title is, you know, in English, Mathematical Principles of Natural Philosophy. So in his time, he was the one who got to make this connection between this already built tool of thought, in a sense, of mathematics, and in his case, things in the natural world. So it's it's a it's a complicated it's a good uh it's a good good uh

kind of prompt for thinking about uh thinking about kind of how one how one imagines the history of intellectual development for our species but it's uh it's always a thing where as we it's kind of as we fill in a certain amount of abstraction, a certain set of principles, we get to sort of put another level on the kind of tower of intellectual things that we can think about. And it's kind of each new kind of paradigm that we invent

lets us build a bit taller so we can potentially get to the next paradigm, so to speak. Yeah. So you raised the more general claim about the history of ideas, namely that technology often precedes science. Yes. I'm going to take that as an opportunity for a quick digression and then I'll come back to tools for thought. So if it is indeed true that technology often precedes science, and in fact in A New Kind of Science you talk about, you raise the question, well, why wasn't the computational paradigm stumbled upon earlier?

And the answer you give is that the technology of computing that had kind of coalesced by the time you were looking at these problems was an important enabling factor for two reasons. Firstly, there were certain experiments that could only be done with that contemporaneous technology. And secondly, being exposed to practical computing helped you to develop your intuition about computational science. So if that's true, does it worry you that some technology currently inconceivable to us could in future provide a basis for an even more fundamental kind of science? Well, I'm not sure it worries me. I think that seems kind of exciting. mean i think that uh in you know one of the things i've come to realize from studying recent things about fundamental physics is we perceive the universe the way we

perceive the universe to be because of who we are so to speak that is if you if our way of our sort of sensory apparatus for perceiving the world is what gives us the fact that as we look around, you know, we see, you know, 100 meters away or something, and the time it takes light to come from 100 meters away to us is really short compared to the time it takes us to realize what we saw, so to speak. And that's why we kind of imagine that there's this kind of sort of what happens in space everywhere at successive moments in time. We might be built differently. We might be a different physical size relative to the speed of light and so on. And we would have a different view of how the universe is put together. So I kind of think that the way that we have of understanding science, understanding the universe, is deeply dependent on the way we are

as perceivers of the universe, so to speak. And as we advance, maybe have more sensory apparatus, we build more tools that allow us to sense aspects of the universe we couldn't sense before, we necessarily will start to think differently about how the universe works. And I think it's kind of a thing that goes hand in hand. I mean, both the way that we kind of expand our existence, so to speak, and the things that we can perceive about how the universe works, these are going to sort of expand together, whether it was the telescope, the microscope, the electronic amplifier. These all led to different views of what existed in our universe that we were simply

unaware of before that. And I think it is likely, in fact, certain, in fact, necessarily the case that as we extend our kind of sort of sensory domain, we will end up sort of sampling aspects of what I call the Rulliad, this kind of limit of all possible computational processes, this kind of universal or possible universes, so to speak, will inevitably sample more of that. So it is not that there can be sort of different pieces of science, different pieces of the story of how the universe works that we will get to, I find that, you know, inevitably the case. Now, have we reached the bottom of the whole thing? Have we got to, you know, with the Rulliad and with all these ideas about fundamental physics,

are we at the end of that particular path of understanding kind of what's it all made of, so to speak? I kind of think yes. I kind of think that there will be many aspects of, I think we got to the bottom. Now, there's a long way from the bottom to where we are. And there are undoubtedly many kinds of science that one could expect to build that live in that intervening layer between what's at the bottom. What's at the bottom is both deeply abstract. And in some sense, it is, it is sort of, it's necessary that it works that way, but it also doesn't tell us that much. It tells us, you know, that we know that that is the foundation is interesting. I think it's great, but it also, to be able to say things about what we could possibly sense in the world, there's layers of what we have to figure out to know that.

And science, one of the things that comes out of this idea of computational irreducibility is this realization that there's an infinite number of pockets of computational reducibility, an infinite number of places where we don't just have to say, oh, we just have to wait for the computation to take its course to know what's going to happen, where we can say we discovered something, we know how to jump ahead. In this particular case, there's an infinite collection of those places where we can discover something that allows us to jump ahead, that allows us to make an invention that allows us to make a new kind of scientific law or something. And I think that, uh, uh, that that's the, that's the place where kind of there's a, uh, you know, an endless frontier of things to do. And that's a place where there will undoubtedly be kinds of science that are developed by sort of looking at different kinds of pockets of reducibility than the ones we have seen so far. I think, maybe I'm wrong, but I think we, for better or worse,

hit the bottom in terms of understanding sort of what the ultimate machine code of how things are put together is. And in a sense, it's a very, as I say, it's a very abstract, general, inevitable kind of structure. But the real sort of richness of our experience comes in the layers that exist above that. So coming back to tools for thought, we were talking about how when one is designing such tools, it's important to have some kind of tangible contact with the problem that the tool is designed to solve. And one of the things I find interesting is that Mathematica's functionality has expanded over the years into domains where you don't actually have domain expertise. So for example, you bundle libraries with detailed primitives

for earth science modeling. And I was curious what incites projects like that and how is geological domain expertise imported into Wolfram research? Well, you know, one of the things that's been great about my job and my life, so to speak, is that I am sort of forced to have some kind of fairly deep understanding of a very broad range of areas. And in a sense, that's been, if there's been, you asked me about kind of life hacks that have let me do interesting stuff I've been able to do. One of them is I've sort of been forced to understand at a foundational level a very broad range of areas. Because,

you know, what I've discovered is that if you're trying to do language design, you're trying to make the best tool for people to be able to do different kinds of things. The way you have to do that is by drilling down to get to the primitives of what has to be done in that area. And that requires that you have sort of a deep understanding of that area. So the typical thing, you know, our company has, well, within the company, we have a very eclectic collection of people with lots of different backgrounds. And we always have this internal database about who knows what, that kind of where people sort of talk about the different things they know about. And so, okay, we need somebody who knows what, where people talk about the different things they

know about. And so, okay, we need somebody who knows about geology. All right, let's go to the who knows what database. There's probably somebody who knows about geology. But beyond that, we've been lucky enough to have a very broad spectrum of top research people around the world use our tools. And so it's always been an interesting thing when we need to know about some very specialized thing. It's like, well, who's the

sort of world expert in this? It's often very satisfying to discover that they've been long time users of our technology. But then, you know, we contact them and say, hey, can you tell us, help us understand this? I have noticed, particularly in building Wolfram Alpha, which has particularly sort of wide reach in terms of the different domains that we're dealing with, that one of the things about sort of setting up computational knowledge there has been, unless there's an expert involved in that process, you'll never get it right. Because it's kind of like, there's always that extra little, oh, but everybody in this area knows this. I mean, it's kind of like, there's always that extra little, oh, but everybody in this area

knows this. I mean, it's kind of like, you see things that happen in the world where like in the tech industry or something, people will be saying, oh, you know, can you believe this or that happened? Or can you believe this company turned out to be a sham or whatever else? It's kind of like, look, I'm in this industry. Everybody in this industry kind of has certain intuition about what's going on and kind of knows how this works. But if you're outside of that world, it's kind of difficult to develop that intuition. One of the things I've, I think, gotten better at over the years is, first of all, I know that that is a thing in an area, that there's some kind of intuition, some way of thinking in that area. And I know that I don't know it if, you know, if it's something

I've never, never been exposed to. And I've kind of learned that you kind of have to sort of feel a way around talking to people in that area, trying to get a feeling for how people think in that area. And, you know, usually you can get to be able to do that, but you, you have to realize that it's the thing you have to do. And it's not kind of self-evident how this area works, even if you know the sort of the core facts of the thing you have to do and it's not kind of self-evident how this area works, even if you know the sort of the core facts of the area. That's interesting. So Wolfram Research was founded in 1987. It's been a private company ever since. What are the factors that have gone into the decision to remain private? Because I think you toyed with the idea of taking it public back in 91. Yes, that's right. Yes. So look, I've, you know, people sometimes say

everybody has a boss, but I don't. And that's great because it means that I can get to do things where, you know, I take responsibility for what I do. And, you know, often it works out, and that's great. But sometimes it doesn't. And I think that the sort of freedom to do what one thinks one should do, rather than having a responsibility to other people to say, hey, look, you know, you put all your money into this. So I would feel, in that case, a responsibility to the folks who put all the money in or the public or whoever else it is to not lose their money or whatever. It's a thing where it's been very nice to have the freedom to just be able to do the things that I think we should do, so to speak. It's a complicated thing because, you know, our company is about 800 people right now. And that is a size that I kind of like.

I think maybe we could expand to maybe twice that. If you say, well, would you like a company that has 50,000 employees? The answer is not particularly. You know, that's a ship that's a lot harder to turn. You know, if's a ship that's a lot harder to turn. You know, if you have a company that has only 50 employees, that has the problem that there's a lot of single points of failure. There's a lot of things where there just isn't a structure that lets you get certain kinds of things done. And also, you know, as the thing gets bigger, you know, the thing I notice is it's like, okay, we could have a big sort of tentacle

that does this or that thing, which I don't really know about and I don't really care about. And it's like, okay, that could be a thing. We could do that. And it's necessary for the practicalities of the world that you have things that are commercially successful. And sometimes those involve pieces that you don't personally that much care about. But for me, it's kind of a, you know, how do you build something? For me, it's kind of, I view the company as kind of a sort of machine for turning ideas that I have into real things. And there's a certain ergonomic

aspect of a certain kind of character and size of company that works well for that. And having something where a lot of pieces of it, I don't really know how they work and what they're doing. It's like, well, you can do that. It might be a commercially viable thing to do, but it's not something that intellectually and sort of personally, I find as satisfying. Somehow, another thing that tends to happen is there are always these trends. People say, oh yeah, you're a successful tech company. You should go public. You're a, you know, you should do this.

You should do that. There's some trend about how it should work. You know, my own point of view has been I try and think about what makes sense, and I kind of try to do what makes sense, and it often isn't what the trend is. People are saying, that's really stupid. You know, everybody's doing this. You should be doing that. It's like, well, you know, you just try and do the things that I do.

And that's worked out pretty well for me. And that's given me sort of an attitude that, you know, I should just do the things I think I should do, so to speak, rather than following the go public, do an ICO these days or a few years ago, or do a, you know, make up tokens, you know, or do something, you know, they're just all these different trends. And I suppose at some level, I've been a very simple minded and conservative business person, because, you know, we just make a product that people find useful, they buy it, and that allows us to go on and make new products and improve the thing we have. For me personally, the greatest satisfaction comes from making a great thing. And that rather than, you know, there are people I know and respect where the thing they most want to do is make the most money.

I don't particularly care about that. You know, the choice is I will always choose the door that says do the more interesting thing. Of course, one has to be practical, and one only gets to go on doing interesting things if one has a viable commercial enterprise. But for me, you know, the goal is to do the interesting things. And that's kind of the value function that I'm applying to the things that I do.

Well, you think the threshold is in terms of headcount when the ship gets too difficult to turn? That's an interesting question. I would say... I guess maybe it depends on the network structure of the company as well. A little bit. It depends on what you're doing. Because, I mean, there's some things that just require a lot of people, that just require lots of, you know... I mean, what I've done in our company is automate everything.

So, in other words, our company, if you look at the technology we're producing, it should be 10,000 people. It's, you know, in terms of what you, you know, if you just say technology produced per unit time, it should be at least 10,000 people to be able to do that. But it isn't because all the time, and that happens to be the product that we make,

is something that automates the making of things. But, you know, we very much applied that ourselvesates the making of things. We very much applied that ourselves, and that's been why it's been possible. In a sense, our company is full of great people and some great AIs, in effect, that let us make things and leverage a smaller number of people to be able to do those kinds of things. I would say that the time when there's kind of a – the size that we're at, I can pretty much know everything that we're doing at some level. If you say, what's the list of all the projects in the company?

Okay, it's a sort of joke at our company that there are more projects than there are people in the company. Okay, it's a sort of joke at our company that there are more projects than there are people in our company. But that's a number of hundreds of projects. And I can have some idea what's going on in all of those things. If you get to a structure where there are actually 5,000 projects going on, then that's not something

where a CEO can kind of really keep all of that in mind. And that, I think, becomes a more difficult, a different kind of enterprise to manage. And I think it also depends on the important aspect of these things is what the culture of the company is. For our company, it's been interesting. It's had multiple phases. You know, the company has been around for 36 years now, but it's had various phases. At the beginning, it was all about developing Mathematica, a very successful product right priority at the time was keep the company stable. I want to go off and do this basic science. The company kind of grew up very nicely during that period of time, as in it went from a company that was probably not so well organized and so on to a company that was quite well

organized, even if it wasn't as innovative during that period of time. Then I came back in 2002 from that, and I'm like, okay, now we have to really push to innovate. And by that point, the company had a pretty good, stable structure. And then I was able to – and it took some effort to say, okay, now we're going to innovate. You know, people were saying, why are we doing this? You know, we have a good business going. We're doing the things that we've already been doing. But it took some force of will, so to speak,

to turn the company into something where there could be innovation. And then what's developed very nicely is that people recognize that we do new things. And people recognize that the new things usually work out. And so, for example, when LLMs came on the scene, it was kind of like, I very quickly said, we're going to work seriously on this. And, you know, it happens to dovetail very beautifully with the technology we've spent so many decades developing. But I didn't have a lot of pushback. You know, it wasn't like people saying, oh, why are we going to do this, you know, et cetera, et cetera, et cetera. It was kind of like, you know, and I have to say, I try to have a company culture in which people do think for themselves. And so I definitely get pushback when people say that argument doesn't make sense.

You know, and that, you know, we've had this, it's kind of been amusing with virtual reality and augmented reality. Back a decade ago, I was like, we should be doing this. But the people, some of the people at the company who'd been around in the early 90s said, you said that in the early 90s. And it turned out to be totally silly at that time. And so now we're just about to see whether people take it seriously again now. I would say that there's a, you know, it's kind of a mixed bag of, and I'm not sure how seriously i take it right now either so it's it's um but you know developing this kind of culture where people have an anticipation of innovation and anticipation that things change that's important how much that can scale to how many people i don't really know

and you know what tends to happen is you need to you both have to have people think for themselves but you have to have some commonality of purpose and mission so that it isn't just a bunch of fiefdoms, you know, in silos doing all kinds of different things that don't fit together. And I think there's sort of a, some kind of ratio of the force of will of the CEO and so on versus the kind of the extent of independent thinking in different parts of the company. And I don't know that we've optimized that, but at least it's a thing which feels like it's working fairly well. So you've been a remote CEO since 91, and indeed much of the company is distributed. How do you think about the trade-offs involved in remote work?

Because a lot of people stress the importance of physical proximity for foming the exchange of ideas, the proverbial water cooler conversation. Yes. You know, it's funny because people adapt to lots of different kinds of things in their lives, in the world, and so on. I think that companies do the same thing. That is, you know, had our company, our company just adapted to the idea that it is sort of distributed and people, you know, they get comfortable with brainstorming on Zoom or whatever. And, and that's happened for a long time. And it's kind of when I really know that we sort of turned the corner years ago now was when people are working in the same office

and you realize that they're actually, you know, talking to each other on their computer, even though they're just down the hall. And why are they doing that? Well, because it's sort of more convenient because they can share the screen more easily. They can kind of, you know, it's an, it's an easier way to take notes and so on and so on. It's less distracting, et cetera, et cetera, et cetera. People get used to these kinds of things. Now, you know, the dynamics of in-person versus kind of remote, there's certain kinds of conversations I do find it more useful to have in person. They're mostly personal conversations, really. They're mostly conversations about, you know, when it's a, this is sort of a set of ideas, they're kind of impersonal, it's all about ideas. It's okay. It works pretty well in my experience remotely. And by the way,

it has the tremendous advantage for us that we really have a lot of, you know, that people distributed around the world and completely different kind of personal settings, cultural settings, et cetera. And I notice that there are times when I think we have a better view of things because we do have that kind of diversity of environment for the people. If everybody was kind of like, we're all living in the same town, we're all kind of seeing the same things and so on, there's less, you know, it brings less kind of ideas to the table, so to speak. So I think that's been a really worthwhile thing. But I think when it comes to kind of more, sometimes when it comes to understanding people, which is something that occasionally one is, you know, it's really valuable to do, the in-person thing is often useful in that regard. I mean, it's like, what can you get from email versus what can you get

from a phone call versus what can you get from actually seeing people in person? Now, you know, every year we have kind of an experiment, I suppose, in this. We have a summer school for grownups and we have a summer research program for high school students and so on, which is in fact just starting tomorrow for this year. And that's an in-person thing for altogether about 150 people or so. And it's an interesting dynamic. It's a different dynamic. I think that it's a great way to get to know people in a way that it would take much longer. The three weeks of the summer school, one can get to know people much better for the fact that one is actually running into them in person, so to speak.

It would take longer to get the same level of, oh, I really kind of understand something about this person if it was done kind of remotely in some more attenuated way. But I think if I look at all the things we've invented at the company, and have they been invented in in-person conversations even when those happen? Not really. I mean, they're invented in people you know, people, as I say, get comfortable kind of what is difficult is getting to the point where you really can have a brainstorming type conversation with people. And for some people, that's more convenient when they're in person and they're not used to it when it's kind of a remote thing. But people get used to that. And for me, for our company, I suppose, there are certainly people where I find it easier

to kind of expose ideas talking to them than other people. And there's sort of an environment, a cultural environment one sets up where it's kind of easier to expose ideas than otherwise. I mean, one of the dynamics for us in recent times for our software design activities, we live stream a bunch of these things. And that's a whole nother interesting dynamic that's worked out really well. It's kind of a, it's, it, for me, it feels somehow the process, which I've always found very interesting of kind of inventing software design and so on,

it gives it a certain extra gravitas or something that we're recording this, people can watch it. It's kind of like the process means something as well as the end result, so to speak. And actually, I think that's helped us have a better process and a better feeling that we're really accountable for the process as well as for the end result is something that I found quite helpful. The other, I think, valuable aspect of those live streams, which I'll link to, there's an amazing library of them. They're incredible. And a lot of your other meetings as well around the physics project and whatnot. From the standpoint of the general public, those kind of recordings facilitate tacit knowledge communication? Yes.

I mean, look, it is. I think we're the only group that has either the chutzpah or the stupidity. To work in public. Yeah, right. But it is, I have to say, whether it's the humans or the AIs that pick up on this and, you know, and learn how to think about things. I think it's a very, you know, this process of seeing thinking happen, I think is very useful for people. I mean, I know that like at our summer school, you know, I tend to do a live experiment for people.

I actually just figured out this morning what my live experiment will be this time. And what's useful about that is people get to see, we don't know what's going to happen, we're puttering away, and, you know, then things usually go horribly wrong, and then usually, eventually, it comes together in some way. It's a very, you know know the fact that you can see that happen and you can see kind of the missteps that get made and so on and you can kind of get a sense of sort of an intuition for how the rhythm of such a project works that's an important thing and i think it's something that uh you know too, for example, education ends up just being a, here's the way it is,

not, you know, it's kind of like, well, you too can think about it. I mean, I was mentioning my sort of early, what I was describing as kind of educational hack of you can kind of go and explore things that haven't been explored before, kind of this idea that you can actually be sort of in the process of thinking about things, not just, and here's the answer, let me tell it to you type thing. Okay. So of the four large projects you've done in your life, Mathematica, A New Kind of Science, Wolfram Alpha, and The Physics Project, I'm going to assume that a new kind of science was the most difficult, correct? It was the most personal. Okay. I mean, in the sense that it was really, you know, I had some research assistants and things, but it was really a very individual project. Right. And, you know, most of these other ones, there are teams involved, there are other people

involved. It's one of these things where the question for a project is always, if I don't do something today, does that mean nothing happens on this project today? And by the time there's hundreds of people working on some software development thing, even if I do nothing today, the machine is going to keep moving forward. Okay, got it.

So, well, I have a bunch of specific questions about the new kind of science. Firstly, I want to talk about the book from the perspective of treating it as a project. Secondly, its impact. And then thirdly, the content of its claims. But let's start with it as a project because I think it's one of the most ambitious, inspiring, intellectual projects I'm aware of. So, okay, a bunch of questions on this. So when you were standing on the precipice of the project in 91, did you have any idea it would take you more than 10 years to complete? No, I wouldn't have done it. I mean, you know, my time horizon, look, my original concept of the project, and this is often how these projects work, is, you know, my time horizon, look, my original concept of the project, and this is often how these projects work, is, you know, I had worked on simple programs, cellular

automata and things in the 1980s. I've been pretty pleased with how that had worked out. I kind of thought there was what I would now describe as sort of a new kind of science to build that really focused on complexity as sort of the thing to understand. And I tried to get that started in the mid-1980s. And I tried to do that not only as an intellectual matter, but as an organizational matter as well. It was kind of frustrating. It went really slowly. I didn't understand. I was 26 years old or whatever. I didn't understand that the world moves more slowly than you can possibly imagine. And, you know, so I got, you know,

I went to my sort of plan B of build my own environment, my own tools, and then dive in and do it myself. I thought when I started New Kind of Science, I thought I was mostly going to summarize what I had done in the 1980s in a well-packaged way. But I thought, I better go and actually make sure I understand the foundations of this better. And there are some obvious questions to ask. Let me go ask them. Now that I have tools that let me ask these questions, I can go ask them. First couple of years, I was really studying, you know, programs other than cellular automata, what really happened with them, Turing machines, register machines, all these different kinds of things. And, well, I found, you know, it was quite quick work, actually. If the book had been just that exploration of what I would now call ruleology,

the study of simple rules and what they do, then I would have been done by 1993. But what then happened was I was like, well, I should figure out, you know, there's sort of low-hanging fruit to be picked and how this applies to different areas. That low-hanging fruit, maybe I started at the bottom branch of the tree, but I quickly found there's much more fruit going all the way up the tree, so to speak. And, you know, just discovered a lot more than I expected to discover. And it was, I kind of felt this kind of almost obligation to sort of figure this stuff out within this context. Now, I also knew perfectly well that producing one sort of high impact thing was going to be much more economical of my life than writing 500 papers about lots of different sort of small pieces. I knew that that

kind of matrix for where to put the things I was discovering, having a single place to put them, was a lot more efficient. And it was, I knew also that, I mean, that was a conscious realization. I'm not going to write, you know, endless papers which won't fit together and somebody will have to come back years later and say, oh, look, all these things fit together. I mean, I also think that the, you know, the process of writing the book, it was, it's like, I want to understand the science. How do I know that I understand it? Well, I try and really write it in a sort of minimal way as possible, so to speak. That was my kind of internal mechanism for sort of getting to the place where I wanted to get to intellectually.

Correct me if I'm wrong, but you set out the table of contents at the beginning of the project. I did. Yeah. Did you worry that that would somehow make you too intellectually rigid? I didn't think about that because I thought this is an 18-month project and I know what's going to be in it.

And it was the same table by the end, right? Pretty much. Okay. Pretty much. I'm sure I have all the data. And I just wrote this thing recently about the, because it was the 20th anniversary, I wrote this, as it turned out, very long and elaborate piece about the making of new kind of science. And I think the thing that really happened was the table didn't broaden, it just deepened. So, you know, in a sense, what I was covering was the main areas of intellectual sort of formalization, so to speak, whether it's in science, physics, you know, biology,

whatever else, mathematics, and kind of the, yeah, the table of contents didn't really expand. Now, you know, something I left out of the book was kind of the technological implications of all of this. And I kind of made the conscious decision, I'm not of the technological implications of all of this. And I kind of made the conscious decision, I'm not going to do that as part of this project. That's going to be, I don't know how that's going to work out, but that's a separated piece. And I certainly did start thinking about that while I was doing kind of the science of the project and then said, I'm not going to do that. One of the things, for me at least, is that I have many ideas. And one of the things I've learned is that one of the very frustrating

things that can happen is you have ideas, but you can do nothing with them. Because it's like, yes, it's a good idea. But to implement that idea, you need this whole structure in the world that I don't happen to have. And so I tend to, as a sort of self-preservation move, in a sense, I tend to try and constrain a bit the ideas that I think about to be ones in which I have some kind of matrix for delivering those ideas. And new kind of science was a great matrix for sort of presenting certain kinds of ideas. So for example, right now, if I decide one day, I really want to study some really cool aspect of register machines, for example, well, I could do that. It might be fun, but I really don't have a great matrix into which to put those results.

So I'll tend not to do that right now. I'll tend to do things for which I have some sort of delivery mechanism because otherwise it's just frustrating. You just build up these things that are kind of sort of free-floating disconnected oh i can't even remember i did that type things i mean one of the things very nice about the new kind of science book is that i refer to it all the time and it's like yeah i think i understood that once let me go look at the note in in in nks like on a daily basis uh these days yes i mean it depends a little bit whether i'm doing science or with technology but yes it's uh all the time and and you know and i've found that um and you know it's it's a thing for me that's important about the things i write is that i refer to them yeah and

you know particularly in recent times now all the the code and all the things i write is is you know any picture is click to copy so it has click to copy code so you know, any picture is click-to-copy. So it has click-to-copy code. So, you know, there's a picture, and it has all these things going on, and you click it, and you get some Wolfram language code. You paste it into a notebook, and it runs, and it makes that same picture. That's a very powerful thing. Like at our summer school, that's a thing people are using all the time to be able to build on stuff one's already done.

And it's been actually a long-running project to get click-to-copy code for everything in a new kind of science. It's slowly getting done. But it's, you know, I think I refer to it all the time because it's a very convenient to have a condensation of a large chunk of things once thought about. And, um,

I mean, between that and Wolfram language, I've got, you know, a pretty good, you know, that's a pretty good chunk of things I know about. And now stuff for the physics project,

but having, having that be organized really nice. I mean, it, it, it, if it was scattered across a zillion kind of academic papers and so on, I would always be like, I don't know where I, I don't know where I talked about that type thing.

How good are you generally at predicting how long projects will take and how many resources they'll require? I don't know. Locally, pretty decent. But I would say that I am about what tends to happen is it's a question of just how well do you want to do this project right it's you know it's funny at our company the chap who's now actually still with the company but sort of semi-retired who joined the company very early in its history and had

had an experience of doing uh project management actually for uh for building like billion dollar freeways. So it's an area where you kind of can't be, you know, you really better not get it wrong. Because anyway, he came into our company. He says, I'm going to be able to tell you how long it's going to take to do every one of these projects. So I said, I don't believe you.

He said, it'll take me six months, but I'll really be able to predict this pretty well. And he was right. Really? He predicted really predicted yes can you share what he does i'm not sure i think it's a bunch of judgment but but here's the terrible thing then there was the question of uh he said this is going to take us two years he'd say about something and say uh and say let's tell the team it's going to take two years okay if you tell the team it's going to take two years. Okay, if you tell the team it's going to take two years, it doesn't take two years anymore.

It takes longer. And so we had this big argument about should we be telling, we know how long this project's going to take. Should we tell people how long they're going to take? And the answer was in the end, no. Interesting. It's not useful. It is sometimes useful from a management point of view to know,

even sometimes from a management point of view, for the kinds of things we're doing, which are kind of doing one-of-a-kind projects that have never been done before. It's often you don't really want to know because it's the optimism, the vision, that's all necessary. Yeah. And it's, I think in you know i suppose i've been wrong in both directions like the physics project i had no idea that would happen as quickly as it did and that was something where i thought we'd be picking away at little pieces for a decade or two. And it turned out we got a whole kind of collection of breakthroughs very quickly.

And, you know, some other things, I think I have more of a feeling now for the sort of arc of intellectual history of how long things take to kind of get absorbed in the world and so on. And it's just shockingly long. I mean, it's long. It's depressingly long. If you, you know, human life is finite. You know, I perfectly well know that lots of things I've invented won't be absorbed until long after I'm no longer around. You know, the timescales are 100 years more. It's kind of satisfying to say, I can see what the future is like. You know, that's cool.

It's also a little frustrating because it's kind of like, you know, to me, one of the things particularly as I've gotten older that I really get a kick out of is, you know, you invent ideas, you invent things. And it's just really nice to see people get sort of satisfaction, fulfillment, excitement out of absorbing those ideas. I mean, the ego thing of, oh, yeah, they got my idea, for me is less important than it's so cool to see these people get excited about this. It's kind of like you gave them a gift and, you know, they enjoy it. And it's so that's, you know, that's a thing where it makes it a pity that kind of the fruition is going to come 100 years from now so to speak and um it will be kind of just you know pleasant to be able to see a bunch of those things and it's it's um but i think it this question of of um uh you know one of the things i would say in technology prediction happens as well

i am i think i have a really excellent record of predicting what will happen but not when it will happen right and you know a classic example my wife reminds me about this example from time to time it's back in the early 90s building a house and it's like actually modifying an existing house um and uh it was a like have this place really like to put a television but it's only four inches deep and i'm like don't worry they're going to be flat screen televisions right this was the beginning of the 90s right well of course there were flat screen televisions in the end but it took another 15 years and why was i wrong well you know i had seen flat screen televisions i knew the technology of them what was wrong was something very subtle which was

the yield when you make a you know when you make a semiconductor device or something, it's like you're making all these transistors and some of them don't work properly. And when you're doing that in a memory chip or something, you can route around that and it's all very straightforward. When you're doing that on a great big television, if there are some pixels that don't work, you really notice that. And so what happened was, yes, you could make these things, and one in a thousand would have all those pixels working properly. But that's not good enough to have a commercially viable flat-screen television. Right.

And so it took a long time for those yields to get better to the point where you could have consumer flat-screen televisions. That was really hard to predict. I mean, perhaps if I'd really known semiconductors better and really thought through, it's really going to matter if there's one defect here and so on, I could have figured that out. But it was much easier to say, this is how it's going to end up than to say, when's it going to happen? I mean, in, in there's some places, I don't know, like I'm sure one day there will be

general purpose robotics that works well. And that will be the sort of the chat GPT moment for many kinds of mechanical tasks. When will that happen? I have no idea. That it will happen, I'm quite sure of. You know, you could say things about molecular computing. I'm sure they'll happen. Things about sort of medicine and life sciences. I'm sure they'll happen. Things about medicine and life sciences, I'm sure they'll happen. I don't know when. And it's really hard to predict when because sometimes, well, sometimes some things like the physics project, for example. Good question. When would that happen? I had thought for a while that there were ideas that should converge into what became our physics project.

The fact that that happened in 2020, not in 2150 or something, is not obvious. And I think as I look at the physics project, one of the things that is a very strange feeling for me is I look at all the things that could have been different that would have had that project never happen. And that project was a very remarkable collection of almost coincidences that aligned a lot of things to make that project happen. Now, the fact that that project ended up being easier than I expected was also completely, well, it was completely unpredictable to me at least. But I think this point that, you know, you can't know when it will happen, it's like, okay, we're going to get a fundamental theory of physics. You know,

Descartes thought we were going to get a fundamental theory of physics within 100 years of his time. And it turns out he was wrong. But the know that it will happen is a different thing from knowing when it will happen. And sometimes the when it will happen depends on the personal circumstances of particular individuals and the fact that, for example, things like our company happened to have done really well in the time heading into the physics project. So I felt I could sort of take more time to do that. And, you know, lots of silly details like that. And it was, you know, so I think that makes it even harder to predict when things will happen.

And in terms of, you know, how long a project will take. It's somehow there are projects where it's kind of like, you know, you can do it. You know, I know I can write, you know, if you say write an exposition of this or that thing, like I know, you know, I wrote an exposition of chat GPT. I knew roughly how long that would take to do. It wasn't, it was a, you know, it's a thing I know I can do it type thing. There are other things where if you say, can you figure out something that's never been figured out before? No, I don't know how long it's going to take. Do you feel like you've gotten better at project management over time? I feel like it's like one of the big underrated skill sets in the world.

Yeah. I mean, what does it take to manage a project? I mean, there's managing a project that's just you and there's managing a project that has lots of other people in it as well. The first step is, can you assemble the right team to do the project? And one of the things I always think is that sort of a role of management is you've got projects, you've got people. There are these complicated puzzle pieces. How do you fit them together? And do you know, do you understand, do you have your arms well enough around the project to know what it's going to take?

And do you understand the people well enough to know, you know, will this person, how will this person perform doing these things for this project? So that's kind of the first step. And yes, I think I've gotten, yeah, I've gotten significantly better at that because I just have, it's really straightforward. I just have more experience and I just know, you know, I've seen a person like that before. I've seen a project like that before.

You know, I kind of have this lexicon. It's helpful to me that I've, a lot of people at a company who've worked with me for a very long time. And so it's like, you know, something will come up and they'll say, oh, yeah, we had this. Remember the situation in 1995 where we had something like this happen. And everybody has this kind of common view of, well, this plays out this way. It's always interesting.

We have a lot of bright people who come into our company and there's a certain you know people know there's a certain pattern of the kind of the the young eager folk who come in and some some do fantastically and some blow themselves up in some way or another and it's kind of there's a certain pattern to that and the fact that you know there's a group of people who've all seen this is is helpful and it's it's often very hard to predict you know the details of what will happen but yeah i know i've i've definitely gotten better at that it helps that their structure okay at our company we have a pretty serious project management operation um actually started by this same guy that i mentioned who was estimating times for projects. He kind of built this kind of structure for doing project management. And there's a certain set of sort

of expectations for project managers and kind of how do you, I think one of the things that's important is project managers have to understand their projects. They don't have to be able to do every technical detail, but they have to understand the sort of functional structure of the project. And if they don't, it's not going to work. And they have to be able to take the sort of the fill in the things which the people in the trenches, so to speak, they don't kind of see far enough away to be able to notice, oh, this piece has to fit together with this other piece. You know, I think the thing you always notice in projects, you know, I've done a lot of

big projects and a lot of often quite intense projects where it's kind of like we've got to deliver this by this time. And one of the things I always notice is that the, you know, you'll have a thing where people will be great doing their particular silo. And but it's, you know, the role of this sort of the overall manager ends up being this silo is great. That silo is great. But who's got the stuff in the middle? And both of them say, we're doing our job. You have to really push hard often to get them to, you know, to do the stuff that's in the middle. And often a thing that really helps me in my efforts at management is, you know, I rarely

manage anything where I couldn't do it myself if I really wanted to, so to speak. And that's a, you know, it's, I do not envy people who manage things which they couldn't do themselves. And, you know, people who are, for example, non-technical CEOs of tech companies, that's a tough business because it's, you know, for me, if I'm in some meeting and people are saying, oh, it's impossible, X is impossible. It's like, explain it to me. And they'll go, I mean, people in my company know me pretty well by this point. And so sometimes newer people will be like, try and explain it to me in very baby terms. It's like, no, just tell me the actual story. And if I don't understand what some word means, I'll ask you what it means, more or less. And then it gets very technical very quickly. And it's very nice, actually, because somehow people, I used to think that me diving into these very deep technical details would be dispiriting to the teams that were working on this.

Because, look, the CEO could just jump in and parachute in and just do our job. Like, I thought that would be bad for people to feel that way. Actually, quite the opposite. It's, you know, hey, it's cool. The CEO actually understands what we do, you know, and has some appreciation for what we do. And by the way, you know, okay, we didn't manage to figure this out.

And he did manage to figure this out. It like well we learned something from that and um you know it's actually a good dynamic it's not what i expected and it's it's um i mean it is a little bit interesting to me that oh i don't know things like debugging complex software problems okay uh it's i am always a little bit disappointed that i am better at that than one might you know than one might think i would be so to speak that is um but it is it is two things it's experience and it's keeping the thinking apparatus engaged and it's kind of like you know it's a very common thing and it's also perhaps knowing some tools it's a very common thing you know some problem in some server thing and this that and the other and it's kind of like you know it's a very common thing and it's also perhaps knowing some tools it's a very common thing you know some problem in some server thing and this that and the other and it's like well did you look at you know first of all it's like experience did you look at this this

and this maybe yes maybe no and it's like well we can't tell what's going on there's a hundred thousand log messages that are coming out it's like okay did you write a program to analyze those log messages well, we looked at log messages. Well, no, you should, you know, you sit down, you write a little piece of orphan language code. Hey, I'm going to do it right here. And, you know, and then, oh, well, you know, now we can look at the hundred thousand messages and we realize there are five of them that tell us what's going on, but we'd never have noticed that if we were just doing it by hand. And it's kind of like you invent, you know, you end up making use of a lot of stuff you know from other areas to apply to this. And that's a, but, you know, this method of management in a sense where you do understand at some level the things that are going on is, I mean, again, that relates also to things like company size and so on, is, you know, can

you be at the point where that's going on? And, you know, I know that for our company, there have been areas of the company which I, for years, never really understood. Like, you know, our transaction processing systems. I never paid attention to those. And they were kind of crummy, actually. And then finally finally about five years ago, I got fed up because things were just too crazy. And I said, we're going to build our own ERP, transaction processing system in our own language. We're just going to build it from scratch,

which we've done and it's a wonderful thing. And it's actually, you know, we've learned a lot from doing that and we've managed to build something that's very good for us. It'll probably spin off as a separate company selling that to other people too. But I was kind of a little bit shocked at how sort of the things I didn't understand, how kind of crummy they actually were. And it's kind of a lesson.

And part of the dynamic that happens in companies is, you know, things the CEO doesn't care about, people don't put as much effort into. And so, you know, it's kind of a, you know, I suppose it's the inspecting the troops kind of theory of things. If you, you know, even though that function, it isn't really that important that you kind of check out, you know even though that that function it isn't really that important that you kind of check out you know the swords or something but the fact that you bother to do it

is important and you know i think that's a dynamic that um that i certainly see and that's a reason for that's a reason why it's pretty nice to be able to kind of uh you know parachute into the details of projects and so on um and uh uh you know because it kind of it it very much communicates that yes you know you care about this stuff even though you're not spending all your time doing it it's not like you say oh yeah they're those guys doing devops or something i don't care about devops yeah um it's uh so yeah i've been coming around to this idea that micromanagement is underrated but okay so back to a new kind of science in the process of writing it so you famously worked in solitude for 10 years did that reclusive period run against your nature or

are you comfortable being alone wolfram oh i'm a gregarious person i like people i mean i i like people but i suppose in um uh i like learning things from people i mean i'm probably not i'm not a i'm not a big small talk just hang out with people kind of person um the i mean to be fair if you look at the ages of my children three of them were born during the time that i was working on new kind of science right so i wasn't uh um you know in um uh it was um uh it was not you know i can't claim i was a a uh you know monk yeah right it's it's but and i was also running a company so again i wasn't again, I wasn't completely isolated in that respect. But in terms of the process of sort of doing the intellectual work,

it was not a collective process. I mean, I had some research assistants I delegated some particular things to, but it was very much of a solo activity. Now, in the early time of working on the project, I did occasionally talk to people about it. And it was a disaster. Because what happened was, people would say, oh, yeah, that thing is interesting. What about this question? What about that question? And then I think, well, you know, I should think about that, I guess. And then I'd waste several days thinking about such and such a question and i'd

say i don't really need that which then they may have even suggested it kind of flippantly in the first place perhaps but even if they you know they have expertise and it was uh you know it was well intentioned okay but it was just kind of a a um uh a you know in order to get a project of that magnitude done you kind of have to just say, I've got a plan. I'm going to execute my plan. And the distraction of other people's input and so on was I really didn't want it. It was really a, you know, I learned actively early on in the project. If I have that input, it will not get done anytime soon. And so it was much

better to just close things off. And, you know, there are several points. I mean, first of all, the act of writing things and being honest in what one's writing, so to speak, is for me, a very strong driver of, do you know what you're talking about? You know, for many people, it's like, well, let me chat with other people. And sometimes I find that useful for myself, you know, to just chat with other people to know that I know what I'm talking about. I mean, in my own, in the last few years, I've been doing a lot of live streaming and, you know, answering questions from people out in the world about things, that process has actually been quite helpful to me. As I, you know, set up the camera, I'm going to be yakking for the next hour. And, you know,

with answering a bunch of questions, and I gets me to think about a bunch of things. And this this process of kind of self, sort of self, self explanation, so to speak, I find to be at least as valuable, if not more so than the sort of self-explanation, so to speak, I find to be at least as valuable, if not more so than the sort of actual, you know, interaction back and forth with people. So, you know, that was one dynamic. Another dynamic was I'm writing code. You know, the code doesn't lie, so to speak. It's kind of like it does what it does. And for me, it's like, do I understand this? Does it, does it, you know, what does it actually do? It's not like I need somebody to tell me, oh, that's wrong. You know, I'm, I'm, I'm finding that out for myself because the code

doesn't work or whatever else. So it didn't need some of the kind of, uh, some of the things that people think, oh, sort of the socialization will be useful. It didn't need, and it was actively a negative because of the fact that it was distracting, kind of, you know, staying on target. So let me put an idea to you. In the general notes in the book, you write about how it's crucial to be able to try out new ideas and experiment quickly. So with this idea of the importance of speed in science in mind, could you have benefited from like a close collaborator in the kind of Hardy and Ramanujan, Watson and Crick sense? Because I guess I have a hypothesis that pairs in science can accelerate the progress of a field

in a way that a solo researcher can't and a group of three or more can't? Because the pair can kind of like bounce ideas off each other. I mean, I don't know. I've, you know, I've been. I guess the trick is finding a partner. That's right. I mean, you know, in the physics project, I had a couple of people, Jonathan Gorard, Max Piskanoff, who, you know, worked on the early part of that project,

particularly Jonathan's been very kind of uh you know a good uh person who's who's carried forward a lot of things um you know i think the fact that that project got done as efficiently as it did certainly was greatly helped by by those guys being around i think that um in the uh you know i've i have not had that many and it's probably a terrible statement about myself you know i haven't had that many successful collaborations in my life i mean i've been happily married for 30 years so that's about it so that's a that's a um that's i suppose one one successful uh kind of thing like that um although although i my wife would say, I would say, you know, we never collaborate on actual projects.

It's kind of like, you know, she wants to build a house, go build the house. I'm not going to, you know, I'm not going to be involved because, but in any case, it's a thing where I you know when i was younger when i was kind of a late teenager whatever doing physics and so on i did collaborate with people and i had some great collaborators but i would say that a lot of the dynamic was more social and more motivational for me than it was necessarily i mean they certainly contributed plenty of things but it was me than it was necessarily. I mean, they certainly contributed plenty of things, but it was then it was sort of necessary from a pure technical execution point of view.

And I would say that, you know, for me, I don't disagree that if you find the right collaborator at the right time, it's cool. And sometimes there are times when kind of it happens for a while and then it doesn't happen anymore. And sometimes, I mean, I think I would say that the ones you mentioned, I mean, Watson and Crick, I happen to know both of those people, not terribly well, but I, you know, so I

have a little bit more personal sort of view of that. But if you take Hardy and Ramanujan, I think it wouldn't be fair to say that that was so much of a collaboration. I mean, I think Ramanujan was kind of an experimental mathematician who Hardy never really understood. And I think that was more of a Hardy as distribution channel and as kind of socializer to the world, so to speak, and Ramanujan as kind of sort of person just pulling mathematics out of the experimental mind, so to speak. Yeah, interesting.

I got that impression when I read your essay, Ramanujan. moving things forward rather than one on the other hand it's finding that second person where there's a you know a perfect fit is is very challenging and it hasn't been something although i have known worked with many terrific people the number of times in my life where that dynamic has really developed is is very small and i think in the case of sometimes, I mean, for the physics project, I was lucky that, you know, Jonathan read the NKS book when he was in junior high school or something, right? So he'd been kind of, you know, I didn't, it wasn't, it's somebody where there's kind of an intellectual alignment

that was not of my making, so to speak. It was kind of a thing that had kind of independently happened. When you have, you know, when you're building something new and it's like nobody's done something like that before, and can you find the other person who also believes that that thing is worth doing that's a difficult thing and that's um uh you know i think it's um it's great if it works i you know in business for example i've never had a uh you know in my my company right now, you know, I've been the CEO from the beginning.

I've never really had a business partner to my detriment. I mean, you know, I've been lucky enough to have lots of great people I've worked with, but I wouldn't say I've ever really had. And maybe now I maybe have some hope of having aligned that, but we'll see. But kind of, you know, being able to say, look, I want to do the intellectual stuff. Somebody else be the business partner type thing. And, you know, perhaps I have been both lucky and unlucky that I am competent enough at running a business that it isn't an absolute disaster not to have somebody else in there doing it. But on the other hand, I consider myself, you know, I'm pretty good on the R&D innovation side. I always rate myself as kind of mediocre on the running a business side. But the truth is, probably from the outside, I'm much better at that

than I think I am, so to speak. Partly because for me, most of the things that have to be done are just pure common sense. Just keep the thinking apparatus engaged, it'll be okay. And I know because I've advised a lot of people who have lots of tech startups and so on, I know that my kind of it's just common sense thing isn't really quite right. I mean, in the sense that I've been super useful as an advisor to lots of companies, but people say, well, you can figure all this stuff out. We couldn't figure out what to do. And, you know, you can figure it out.

But to me internally, it's like, look, that stuff is pretty obvious. And, you know, it's whereas a lot of things I do in kind of science and so on, I don't think they're obvious. I think they require kind of intellectual heavy lifting to do them. I wouldn't necessarily say that, you know, does that mean that I'm saying that sort of business is easier than science? I don't think it necessarily is. It's just that I probably, you know, I don't take seriously whatever skills I might have, a sort of thinking capability I might have on the business side. Do you have any unique comments on the Watson and Crick partnership? Don't think so. Okay. Don't think so. So it strikes me that a new kind of science

as a project would almost be inconceivable to pull off within the context of academia, which is kind of a sad thought. What accounts for the incrementalism in academia? It's big. Academia is big. At the time when, in any field, when it's small, it's not as incremental. It's when it gets big, it gets necessarily institutionalized. By the time you have 20,000 people in a field, it's got to have structure.

It's got to be, well, which people do you pick for the, you know, do you fund? Which people go in the departments? Who sets the curriculum? You know, all this kind of thing. When it's an emerging field and there's only five people working on it, you don't need that kind of structure. And indeed, those are the times when you see the sort of the fastest progress is when some new thing emerges. It's a small number of people. It's quite entrepreneurial. Some of what gets done is probably nonsense, but some of it is great and not incremental. And I think academia as a

whole, you know, the fact that it is so big is kind of the thing that holds it back and forces it to have this really conservative, they would hate to use that term in the context of academia, but it is, it's a conservative view of kind of what makes sense to do. And all these different fields, they develop their value systems, their value systems get deeply locked in, because it's kind of the funding cycle, the publication cycle, all this kind of thing. And it's kind of the, you know, that's how that works. And I think it is a, you know, I see people who want to be more entrepreneurial. I mean, can you be intellectually entrepreneurial and be an academic?

The answer is there's only a certain amount of entrepreneurism that works. If you want to be more entrepreneurial, if you're lucky enough to be, in a sense, this happened to me. I mean, I was, I got to the point where I was a respectable academic, so to speak, in a good kind of position. And then I got to that point when I was pretty young. And so it was like, okay, now I can do whatever the heck I want. And now I can do things that aren't particularly incremental. And it's sort of because one happened to be embedded in a place where through what was, I mean, again, I was lucky because I worked in particle physics, which was sort of having its golden age in the late 1970s.

And that was a time when, in a sense, there was low-hanging fruit to be picked. Incremental progress was big because the field was in this very active phase. One, having made some reasonable incremental progress, people could say, oh yeah, that person kind of knows what they're doing, and so they can be a physics professor or whatever, and then one can go off and do other kinds of things. But it's rare that people end up with that kind of platform. And it's very common that they've gone through this tunnel

for 15 years or something, or 20 years, and by that point they can't really escape from that very narrow thing that they were doing. But I think the number one thing is academia is big, and that means it has structure, and it has structure that holds back the spiky stuff that gets to be really, really innovative. And I think that that is almost to be careful what you wish for. It's kind of like, you know, as I think about some fields of science that I've been interested in kind of moving forward, like this area of ruleology and so on, I think, what's it going to look like? You know, I'm going to build a structure for doing ruleology, and then the really cool stuff won't, you know, will be, it will have a definite direction. And that's a particular area which has a nice feature, as some other areas have had,

that just doing more stuff is useful. So like, you know, in the early, I don't know, 130 years ago or something, people doing chemistry. Let's go study all these different chemical compounds. It just was useful to build this giant encyclopedia of what was true about all those things. So similarly with ruleology, there are times when incrementalism in science is useful because you need a bunch of incrementalism to kind of build this encyclopedia that you need to be able to make the next sort of big conceptual leap. And it's, I think that's not a bad thing, but I think from

the point of view of, you know, uh, the other, the other point is that, that people only understand things at a certain rate. You know, if there was major new paradigms in science being invented every year, people would find that utterly disorienting. would keep track of it it would just be sort of a a mess and and you know people need you know people in order to kind of socialize ideas there needs to be kind of a it needs to be it can't be too fast titration yeah right titrate the paradigms yes yes so it kind of raises the question of where in the world truly original research should be done. If it's not in universities, then I mean, what have you got left? Like corporate monopolies or kind of more exotic research institutions like the Institute for

Advanced Studies or All Souls at Oxford. Do we need new social and economic structures to support original research yeah have you thought about this do you have any any suggestions I don't have a great answer interesting I mean I think that um uh you know like the institute for advanced study where I where I worked at one point is a sort of a good example of a bad example in some ways, because the theory of, well, I worked there at a time when Oppenheimer had been the director, what, a decade and a half earlier. And he had been, he was very much of a people person. He picked a lot of very interesting people. And by the time I was there, many of his best bets had departed, leaving people who were

the ones who he had betted on, but they weren't such good bets as it turned out. And then there's this very strange dynamic of somebody who was in their late 20s, and it's like, okay, now you're set for life, just think. Turns out that doesn't work out that well for most people turns out you know so that you know that isn't a great solution you know you might think it would be a really good solution that's just that's just anoint these various people as being like you go think about whatever you want to think about that turns out not to work very well turns out people in this sort of disembodied, just think type setting, you know, it's just a hard human situation to be in.

I mean, I think I've been lucky in that doing things like running companies and so on, the driver of the practicality of the world is actually a very useful driver for keeping one, you know, just stirring things up, getting one to really think, you know, like stirring things up, getting one to really think. You know, like, for example, the fact that I have been able to sort of strategically decide

what to do in science a bunch of times. The fact that I think seriously about science strategy, that's because I've thought about strategy all the time, every day, doing sort of running companies and building products and things like that. It's all about strategy. Most, if you ask the typical person who's kind of, you know, gone and studied science and, you know, got a PhD or whatever else,

you say, did you learn about sort of the strategy for figuring out what questions to ask and so on? They'll probably look at you and say, no, nobody ever talked about that. You know, that wasn't part of the thing. But that's one of the features that you get by being out in the world that sort of forces you to think about things at a more strategic level. Now, this question of how should basic science be done? Very interesting question. I mean, one of my little exercises for myself is, imagine you're Isaac Newton, 1687. You're inventing calculus, and you think

there's going to be $5 trillion worth of value generated by calculus over the next 300 years. What do you do about it? You know, and, you know, is there a way, and you say, is there a way to take what basic science, which often is the thing from which trickles down lots of things that are very significant in the world. Is there a way to take that future trickle down and apply it to now to get more basic science done? And then how do you avoid the trap of if you make too much of that be done, it gets institutionalized.

It's kind of like when people say, you know, people talk about entrepreneurism and they say, we're going to have a class about entrepreneurism. We're going to teach everybody to be an entrepreneur. We're going to teach everybody to be an innovator and so on. It's kind of like it doesn't really work that way because it's kind of like by the time you have a formula for innovation, you've kind of, you know, it's a self-answering,

not going to work type of thing so it's it's um but i mean this question of well we recently started this little wolfram institute effort where uh you know i would say i consider the jury is still out on how that is best set up i mean the thing okay so my history in this, back in 1986, I started a thing called Center for Complex Systems Research, which was an effort to sort of make a, you know, basic research direction about complexity and so on. I was very disappointed with what happened there in the sense that I brought in a bunch of people I thought were quite good. You know, they had turned out to have good careers and so on. But then it's like, what's my role in running this? Well, I'm the guy who gets to raise the money.

Well, I'm not really interested in that. And so I went off and started my company. But, you know, for me, I kind of saw that as being a bunch of kind of feral cats going off and doing their thing. And there wasn't much role for management there. Now, most universities don't have strong management of, you should be concentrating on this, to their detriment often. Because I see people who have had, they go through an academic career, they become, you know, they get tenure, all this kind of thing. And it's like, why did nobody tell this person? Just think about the strategy of what you're doing. You know, the basic thing that you would do in a company where you're managing some personal group of people, you'd say, you should think about what you're trying to get

to, you know, where are you trying to go? And nobody does that at universities. It's kind of, it's an unmanaged setting, which is, you i was a professor type that was kind of cool to be in an unmanaged setting but it was uh i don't think it's always good for the people involved so i i think the um and then this you know what i've my current uh you know one model of doing things is you know you have the person like me who has a definite set of, I want to do these things and these things and these things. And then you get the best sort of, it's kind of what I've done with the company. You kind of get the best support you can for being able to take those ideas you have and turn them into kind of real, uh, you know,

real things in the world and really work, work things out. But no, I'm very curious. I mean, in the time when NFTs were big, it's like, could you tokenize sort of the idea of basic science and so on? And couldn't really figure that out. I figured out one thing, which I'm not very, I don't really like where this is going, but it's interesting. So, you know, basic science, it's like you're not going to make patents, you're not going to do, and those are, you're not going to, you know,

what is the thing that is kind of the sort of protectable value in basic science? And it usually tends to be sort of guild-like know-how there'll be a certain set of people that know about this particular kind of thing and you if you look at tacit knowledge yes i mean if you look at who who knows about this it's the students of this person and the grand students of that person yeah you know i was thinking about this a few months ago and i realized that that one of the things that I've done is many different fields, I've ended up being not somebody who was part of the guild, who ended up kind of for people in the field, it's quite disorienting to see somebody who comes who they've kind of, you know, they might know about that person from some other setting, but it's like, you're not part of our guild. And, you know, now you're coming in and doing something.

And sometimes it's easier if you're coming from the outside, because you guys have all been, you know, off in this corner. And by the way, there's this great big thing over here. But the fact is the situation is much more typically, there's a kind of a guild, there's a group of people that has this, as you're saying, tacit knowledge about how things work. They have this intuition that they sort of collectively develop. And that thing is sort of a thing. It's not a thing that gets monetized, for example, particularly. The only way it gets monetized is by sort of the education process of, you know, come to our kind of, you know, insofar as education is a business, so to speak. You know, come and be, learn about our, you know, the ways of our guild type thing i think that the um the way

that um uh you know in is there a way to kind of take that and have it sort of feed into the earlier era earlier years of the of the basic research from what will be the subsequent development of the sort of guild that eventually becomes the guild that sort of drives what eventually becomes the economic sort of value. You know, take an area like machine learning. There were people who were working on neural nets. There were people working, many of them I know, people working in that area for years. Kind of, it wasn't a, you know, it wasn't an economically interesting area.

I mean, these were academics, but they were lone people with weird backgrounds, off doing particular things and justifying their work on the basis of, oh, it's connected to neuroscience, or oh, it's connected to computer science, or whatever. Even though really they had a more specific vision, so to speak. And then suddenly it becomes a very economically valuable area, and then that guild, in that particular case, mostly that guild has done quite well. Mostly those people, I think, well, actually,

one example of a very good friend of mine who I don't think cares that much but hasn't been part of the kind of commercial development of these kinds of things. But many of those individual people and their students and grand students even, that's worked out quite well. But this question of how should this be done, how should you set up environments where people can be successful, it's a very challenging thing. And it's sometimes, it's like, even, you know, is this person a good person to bet on? That's often very difficult. I mean, it's kind of like,

you know, that's the problem when you're doing companies and you're doing venture capital or something. That's the problem you have there. It's really hard, really, really hard. And sort of in the intellectual domain, same kind of issue. You know, I myself have had a, I find it very interesting to sort of mentor folks who are kind of, you know, the high talent, sort of maybe unusual kinds of people. And I think, you know, and sometimes I do feel like there are many settings in which I'll run across people and, you know, I know enough to be able to say, hey, I think this person has something really interesting going for them.

Or I'll know enough to say, I think this person is just full of it. And, you know, this person's a fraud. And it's, you know, and I think I do a lot better than the average bear on that particular thing. But it is sometimes shocking that you'll see people where it's like, you know, and sometimes I'll be too optimistic and I'll get it wrong. But you see people where it's like uh you know and sometimes i'll i'll be too optimistic and i'll get it wrong but you see people where it's like i'm pretty sure that person has some really interesting intellectual thing going on but the world doesn't recognize that the world just says there you're a hopeless whatever it is right and it's and it is a little frustrating what do you do in those in that situation and i've? And I try to do some mentoring and so on,

but sometimes it's like, where am I going to get a job? And it's like, well, I don't really know. There needs to be some kind of mechanism for putting the equivalent of a call option on that person. Yes, yes, yes, right. But it's kind of a, yeah, people try to invent some schemes like this which have a really don't work very well from a human point of view.

But I think it's some, you know, it is a shame that there aren't, you know, even at the level of philanthropy and so on, the number of, you know, I don't think people feel very good about this like just just bet on this random person type thing you know i i think the macarthur foundation in um uh is an outfit that sort of sort of bets on random people except i think in for the last several decades they have been sort of really betting on people who are already sure bets and you know i was i happened to they you know gave me some money in the very first cohort of these things back in 1981 and uh it's kind of interesting getting to know that foundation and the whole history of uh you know how did somebody decide to just make sort of random bets on people

and um although again it was uh I don't know whether they, but the interesting question I've asked them from time to time, whether they think I was a good bet for them or not. Because, you know, for example, I'm one of the very, very few people from everybody they've ever sort of funded. I'm one of the very few people who has you know been a significant commercial sort of operative so to speak and you know uh sort of generated significant assets you know at a financial level and so on and and i think um but but you know it's interesting

how that even came to be i mean this guy john mcarthur ran an insurance company but, you know, it's interesting how that even came to be. I mean, this guy, John MacArthur, ran an insurance company and, you know, didn't, you know, I asked people, does anybody know what John MacArthur, you know, sort of wanted? And people would say, no, nobody really knew what he wanted. And, you know, then he died and left all this money. And he had this corporate lawyer who was a, you know, very crusty corporate lawyer who was just, I need to figure out what to do with this. And he went and asked a bunch of people. And somebody suggested this MacArthur Fellows program. And this guy, I met him several times, is sort of very, you know, you'd never have thought

this was a kind of the great innovator of philanthropy, so to speak. It's just a very, I'm going to do my job. I'm a sort of crusty corporate lawyer type person. That was where this came from. And it was, you know, we've got some advice from different people who suggested, oh, this might be an interesting thing to do. But it wasn't one of these things where that sort of, it came from a slightly random place is what I'm saying.

And it's kind of, and I don't know, even at that level of, um, of sort of betting on people philanthropy, there's not a lot of that that goes on. And, you know, partly because it's, um, I don't know the, uh, uh, and I don't even know if that's the right thing. You know, I, you take the Institute for Advanced Study case where you say to somebody, okay, you're 22 years old. Uh, you know, we're going to bet on you doing something great. And, uh, here you are set for the rest of your life. I mean, it's kind of like, one of the things I often notice is I often refer to it as the negative value of money, um, is, you know, in a situation where it has many, many sort of individual

negative values, but one of these things is, okay, you're set. You don't really have to do anything. It's kind of like go off and hang out for the rest of your life type thing. That doesn't usually end well. Sometimes it does. Occasionally somebody will say, well, by golly, I got interested in this thing and I'm going to become what always used to be called a gentleman scientist, so to speak, and I'm going to go figure out amazing things.

Occasionally in history, that's worked. But that's exceptional. I know a small number of independent scientists, and it's an interesting crowd. I suppose I'm one of that crowd in some sense. And usually, it's a terrible thing because usually people say, I'm going to be an independent scientist. I'm going to make money doing this thing.

I'm going to start this company. And then I'm going to go off and I'm going to do intellectual stuff. They almost never go back to the intellectual stuff. Even though they have the means, they could just sort of hang out and do that. They don't end up doing it. Why? Because they get used to a mode of life where it's kind of like,

I think it's probably for many people, you know, if you're in the CEOing role and there's a kind of a rhythm to doing that, and then it's like, okay, you're on your own now, just go invent something in science. It's a, it's a pretty grueling kind of transition because it's kind of like you've been CEOing, you're working with a whole bunch of other people, they provide momentum, et cetera, and then, oops, you're sitting on your own. Now you've got to figure something out on your own.

It's not an easy, you know, I've been fortunate in that I kind of interspersed these kinds of activities. So for me, it's kind of like when I'm in the, okay, I'm just sitting and figuring out something by myself type mode, that's not as kind of, it's not, oh, I've just spent the last 20 years being, you know, running a company and having momentum from other people. So just as a final comment on this section of the conversation, it's kind of funny to think that as the CEO of a

company, you probably have more time for basic scientific research than most university professors have to deal with, you know, get applying for grants, sitting on committees, teaching students. Yes. Yes, it is. It is a funny thing. It's a perverse situation. Well, yes and no. I mean, look, one point is that because I get to be my own boss, so to speak, I get to decide what I delegate. And, you know, it is a decision with which one has some, I mean, it's like, I suspect if I put more effort into this or that thing, the company will be more successful in this

or that area. But I decide as a personal matter, I'm just matter, I'm going to be a little bit irresponsible. I'm not going to do as much as I could in that direction because I want to spend time doing basic science. Look, I think it's been, yes, I find that sort of ironic because people say, there are a number of extenuating circumstances. One, you get to decide what you can delegate. Two, many people, if they were academics, for example, if they were presented with what I do for a living every day, they would be like, oh my God, how are you going to decide these things? You know, and people who've been academics who come to join our company and, you

know, it's a very common experience for saying, we're going to have this meeting, we're going to decide this or that thing. And they're like, you can't do that. I mean, you can't just decide this in an hour. This is a whole process. We'd have a committee and it would take six months or something. And it's like, well, no, we're just going to decide it. And, you know, hopefully we'll get it right 90% of the time or something. And that's okay. And it only took an hour and it didn't take six months. And it's, it's a, um, uh, I think it's one of these things where, where it is a, a question

of sort of the cultural rhythm of things. And, you know, it really helps me that I've been doing this a while. And so a lot of things that I might agonize about, it's like, eh, I pretty much know what to do. Take two minutes. I don't have to agonize about it. I don't have to ask a bunch of people. It's just like, let's just do this.

And sometimes it's wrong, but it certainly saves a bunch of time. One of the things I find particularly ironic in today's world, I think in the college professors, university professors are busy. I think high school students may be the busiest people, at least in the U.S. The elitish high school students are the, you know, I've got an activity every 15 minutes. Yeah, the extracurriculars. Yes, right. Yeah. Stephen, I want to be respectful of your time, I've got an activity every 15 minutes yeah the extracurriculars yes right

Stephen I want to be respectful of your time but I also have gazillions of questions yeah and I'm really enjoying this and I figure we'll only do this once are you okay if we keep going keep going keep going actually you know what I'm going to take a very short yeah

I'm going to crunch for a little while do have a little while um you're asking very interesting questions this is good thank you so you you kind of touched on the question of sort of implicitly touched on the question of how to identify talent. Let me ask a couple of questions about this. One is, I'm a, I'm sort of an optimist about people. And so I kind of think, you know, everybody's born with lots of interesting capabilities and the question of whether, for example, do those capabilities happen to be, uh, usable at this time in history? You know, in other words, you could be somebody who would be a great programmer, but if you lived in the 1400s, you're out of luck. There aren't any computers.

Or you could be a great discoverer of the source of the Nile and live in the 21st century when you can find it on whatever satellite map, so to speak. And so it's kind of like, you know, I think that there is, at any given time, there are certain kinds of things that are possible to do in the world. And I kind of think that people, there are lots of interesting capabilities people have. And, you know, then you have to ask, you know, to become a Ramanujan,

you have to have a certain degree of kind of development. I mean, Ramanujan went to perfectly decent schools and learned math and so on. And, you know, had he not done that, uh, you know, he might've been great at, you know, basket weaving or something, but one would never have known that he would have had the capability to be great at doing math. So I think it's a little bit, you know, there's some history dependence of what, but I do think that, you know, there's surely a huge amount of untapped great talents in the world. And how does it go untapped? Sometimes it goes untapped through the best education,

so to speak. People go to these terrific schools, and they are fed lots of great content and so on, and they're so busy doing all that stuff, and they get put into a track where they wind up working for a big consulting firm or something like this. And they're just like, and that's, you know, that they were, they were pushed onto that track by the very momentum of all of the wonderful education that they were getting. And they could have been, you know, a great innovative, uh, you know, thinker who invented something, you know, really new and different in the world, but instead they were on this particular track. I notice, I have to say, it's something of a –

very recently it's become a little bit of a pet peeve of mine, which perhaps is an unfair one, but I look at the finance industry, and I know many people in that industry who are really great intellects. I mean, really, you know, smart people, good, you know, good thinking skills, even good strategic skills and so on. And it's like, you know, at the end of the day, they've run a company, they've made billions of dollars and, you know, it seems very unsatisfying, at least to me. I mean, maybe that's just, you know, that's why I don't do that. But, um, uh, and it feels like there is a sort of a, in the world today, there's this kind of, there's a great pool because, because, you know, there is a fair amount

of, you know, there are things that the sort of the, the financial elite can get in the world that are, you know, distinct from what other people can get. And so there's a great motivator to kind of be in that, be in that kind of financial elite. But I think it's, it's something where, um, it's kind of a shame that people, uh, you know, high talent people get pulled into this activity that, you know, and maybe I'm just not seeing it correctly, but to me, it seems like it's kind of a waste, perhaps for the people and for the world, of that talent. But, you know, that's on one end of the people who have sort of all of the access to terrific

education and this kind of thing, and then get pulled along by it into something which is ultimately not a particularly, you know, moving the world forward in a creative way kind of activity. And then the other side of it is people who just don't have access to that. And if only somebody told them, you know, oh, you know, you should, you know, taught them Wolfram language or something, or taught them something which allows them to have a tool to explore or whatever, then they would go places. And here's where I have a hard time sort of understanding some things, which is, you know, you say, okay, imagine you go talk to people in the rural US or in other places, and you say,

look, there are all these cool things you could do. Look at the tech industry. It's a really cool thing. Everybody could be a tech entrepreneur and so on. And then you realize, look, these people just don't care. It's like somebody comes to me and says, you know, you could be in show business. And it's like, great. I don't care. Yeah. Yes. But I think it's also, it's like I've done these surveys of kids, actually, of, you know, what would you like to have achieved in your life? Like, you know, make X amount of money.

You know, take a one-way trip to Mars. You know, make a, you know, write a great novel or something. Okay. And people have, they pick very different things. My guess is that after about the age of 12, whatever they pick won't change that much. That, that people have a certain intrinsic value system that comes from who knows where. And, and that um you know doesn't doesn't shift the thing that you know i see i i'm always i said i was interested in people i do i i am interested in people like i'm i'm interested enough that like i recently did a a 50-year virtual reunion of my elementary school class um so that was interesting to see you know what happens to people in 50 years.

And I think that what you see is that people somehow don't change, but sometimes the world provides people certain kinds of opportunities and niches that really sort of what allowed them to be there was already present originally, so to speak, but the kind of, you expose certain kinds of things differently, um, with the way that those people interact with the world. And I think that's a, um, uh, so, but anyway, one of the things that I don't really understand very well is, you know, you're, you're, you're saying, okay, there are all these kids, for example, who might be great tech entrepreneurs, let's say. Um, and you know, you're saying, okay, there are all these kids, for example, who might be great tech entrepreneurs, let's say. And, you know, are you going to, should you go and be like a missionary, basically? Go to all these places and say, look, there's this great thing, it's tech entrepreneurism. And at first people say, we don't care. And then, you know, is it something where, is that the right thing to be doing so to speak and i i think my my main conclusion is that the thing you know to say to people here's this thing

that exists you know if you care that's great that's sort of a good thing to do but on the other hand before you've had some level of sort of developments in that direction, it's hard to even form the thought that you might care. Yeah. So it's kind of a, you don't have enough context. Right. Yeah. Right.

And it's, it's somehow, I think that the, um, uh, so to me, you know, you ask,

is there lots of high talents out there that has not been realized? My guess is absolutely. And my guess is that, in developed countries where there's all kinds of educational programs and testing people and this and that and the other, my guess is that there are a very large number of people where were you to align their lives in a different way, they would end up being, you know, great contributors of this or that kind. How one sort of achieves that, how one sort of makes this a more efficient world and market and so on, I don't know. I mean, I've put some reasonable amount of effort into this of kind of sort of putting out feelers for, you know, it's difficult for me to understand.

Like one thing that, you know, sometimes there are kids who have less resources where they just, at a purely practical level, you say, okay, you know, why don't you join the Zoom call? Oh, well, I don't have a computer that has internet. You know, so you're out of luck there. And, you know, some of these things that, you know, perhaps for me in my particular walk of life and not things I think about. And, um, you know, they might not think about some of the things that are issues for me, but it's kind of like, it's hard to,

hard to get into that. And so I've, I've, um, I mean, it's, it's a thing that I would, I, you know, for me personally, it's, I, I, the rhythm of my life tends to be the things I get interested in and I try and do them. And sometimes I sort of start off doing them as a hobby and then eventually they get more serious. This one of identifying sort of talent, particularly in kids, is one that I've been, you know, for years I've been kind of hobbyist sort of thinking i should do something i kind of had this idea i was going to have a thing uh i probably called the trajectory project effort to um uh you know one of one of the problems another issue is kids who just don't know what's out there to do in the world yeah and and where tell them, boy, did you know about software quality assurance? No, I never heard of that. Or did you know about this or that kind of thing? Where it turns out

it's a really good fit for them. All they tend to know about is the subjects they studied in school, which is a very narrow slice. And by the way, a slice that was set 100 and something years ago mostly uh for things that that um that can be done in the world and it's kind of like you know can you even you know i don't know somebody like me kind of sort of bouncing around the world i kind of know some of the things that are sort of coming and so kind of like can you even communicate those things okay you you know who's going to be an AI psychologist? There will be AI psychologists. And some kid out there has exactly the right mindset,

exactly the right skills, and is going to be a great AI psychologist. Very empathetic towards machines. Yeah. And also just getting an intuition for what is this large language model doing? You know, how do I write a prompt that will convince it to do this? You know, how do I get inside?

Yes, an empathy for machines, basically. But, you know, and how does that get, you know, how do you even tell, you know, some kids somewhere, how do you even communicate to them there is this thing? And they might say, I don't care. Or care or they might say wow that's really interesting and get because because that's one of the other issues is that a lot of kids and i think it's worse in more elite education actually say you say because i've i've done this thing i've actually even doing it with a kids just yesterday middle school kids saying you know what do you want to do when you're grown up? And a large fraction, I would say, will, particularly these days, will just say, I don't know.

You know, I'm just going to go with the flow of my sort of education process and somehow it will land me in the place where I should be, so to speak. I mean, I think it's a, a, um, uh, that, that somehow, and again, you know, one is always, it's a terrible thing about giving advice because somehow the, the, it's always, you know, it's always entwined with the particular choices, the person giving the advice made for themselves, so to speak. And for me, the fact that I sort of thought I knew what I wanted to do with myself by the time I was, you know, 10, 11 years old was tremendously useful. Maybe it wouldn't be to other people. Now, as it turned out, the thing I thought

I wanted to do with myself was, you know, be a physicist, which, you know, I kind of was by the time I was 20, which was a good thing that that happened quickly. And then I was like, well, actually, I want to do more things. And now I've come back to sort of being a physicist many years later. At some level, I know that I count as a physicist, a new kind of physicist at least. So Ramanujan famously wrote, I guess, like the then equivalent of a cold email to Hardy in the form

of a letter with a bunch of proofs and... Not proofs. Sorry, not proofs. Formulas. Formulas. Yeah, yeah, yeah. And yeah, he was like a human calculator. Do you get many cold emails like that from many interesting and okay so have you developed any heuristics for determining whether someone is an outlier talent yeah it's interesting question i mean i often i mean i do look at them you know know, and it's, and I have occasionally found people who sent pretty strange emails and where, I mean, I suppose I just, you know, is this interesting? Is this, the thing I get a lot is kind of people with theories of physics and things like this.

Okay. Those are very disappointing to me usually because the most common pattern is it's kind of high school level knowledge of physics. And then I've got a theory of everything. And the problem is the 20th century, you know, a lot of things happen in physics in the 20th century. You don't know about any of those things. it's really hard to have a good physics theory. And so you can kind of see right off the bat, this is not going to work. And what's frustrating to me, and I've never

figured out, is there's quite a lot of energy out there in this kind of area. And it's like, what should this be channeled to? Now, i have to say i tend to try to channel people towards things like ruleology studying simple programs and so on which is an activity where there's a much less tool of a tower to climb to get to the point where you can do useful original research because you know in physics if you really don't know anything about 20th century physics it's really tough to have you know it's kind of like you can say, well, I can understand things in terms of kind of electromagnetism from the 19th century. Well, that's not going to work. You know, we already know that's not going to work.

It's more abstract, more elaborate, and so on than that but in the case of studying simple programs you know it's much more at uh it's much there's much more low-hanging fruit to be picked so but i think in uh i would say that hardy you know when you look at the formulas, I do know a few people who, one in particular I'm thinking of, who has for years sent me Ramanujan-like formulas. This person really is very smart and is kind of a misfit a little bit in the world as it is. And, you know, actually I've been getting older now

and I've been trying to persuade him to collect all the stuff that he's produced because even though individually it's just like this fact, this fact, this fact, that, you know, it's a pretty interesting corpus of work. But it's certainly, you know, on an individual basis, it's very sort of, uh, weird. And, you know, how does this fit into anything kind of thing? But I think it's, it's, it's pretty easy. Okay. The heuristic is really this, you, you look at what's there, and there are details, and sometimes things sort of let themselves down in the details, as in you can kind of see this is, you know, kind of if you want to know,

has somebody been a professional physicist or something, okay, and you talk to them about something, and there's some standard term in physics that they use wrong or they say it wrong or whatever else. You kind of know, okay, that person never was really in that that particular world um and it's these kinds of sort of there are little details like that now sometimes there are that sort of reveal things about knowledge i'm not sure they reveal things about ability um i mean that mean, that's a different thing. I suppose, you know, another challenge is somebody has a brilliant set of ideas, but they absolutely cannot explain what they're talking about. Where does that leave one? Because, you know, I've seen that a bunch of times too,

where people, I'm pretty sure you can extract by pulling hard enough. You pull long enough on the thread. Yeah. There's something really interesting there. Yeah. But my gosh, you know, I don't know. I run out of patience long before I can get the thing at the other end of the thread. Yeah. And then the risk is that you simply penalize them for being inarticulate. Well, yes. But I think the other risk is, which has happened to me plenty of times,

is you pull on the thread, it's incomprehensible what's coming out. And then I'm like, you know, then I keep on thinking, and in the end, whatever came out was actually just something I put in, so to speak. Right. It's kind of like, you know,

the person is just generating, you know, something completely incomprehensible. Yeah. And I'm just imposing my, the person is just generating, you know, something completely incomprehensible. Yeah. And I'm just imposing my own ideas on this. And sometimes I say, hey, that's a really good idea. And I have no idea whether that was there and what was, you know, what I was kind of working with or whether I just came up with that. And it was completely independent. And, you know, and often people won't, I mean, you know, there's more than challenges because people have different ways of thinking about things.

Some of the most interesting innovations come from different ways of thinking about things. But if they're too different, you don't understand them. And I mean, I think one of the things that I've put a lot of effort into is sort of being able to explain things in a way that other people can understand. But part of the motivation to me for that is it helps me to understand them. In other words, if it was purely a service to other people to explain them, I'm not sure how well I would do at that. But because I find it really useful for me to be able to understand things that way, that's why I end up putting so much emphasis on it. Yeah, interesting.

I wonder whether people somehow feel comfortable reaching out to you because of the unique path that you've been in your life. I don't know. I think I have at times thought I must have had the complete set of different theories of physics and so on and so on and so on but then somebody sent me this thing where they've cataloged these things and they're like 20,000 of them and I don't I haven't counted but I'm I I suspect I'm in the thousands but not up to 20,000 yet but it's it's very I would say that the the cases where sort of I get a cold, I get very interesting cold emails. And sometimes they really turn into a lot, turn into good things.

And for me, okay, so I have a, for me, I have a couple of mechanisms there. So one is we do these summer schools every year. I say, you want to interact with us? Come to our summer school. So I'm sure I haven't looked at the list, but I'm sure this year there are several people who are coming where they sent me a cold email. We said, come to our summer school and they're coming and then we'll interact with them and, you know, learn about what their story is. They'll learn about our story and whatever happens will happen. But, um, uh, that's one thing. The other thing is that I have to say that I'm sort of almost obnoxious at saying, you know, if you're talking about something that's kind of has sort of formalized content, show it to me in Wolfram language.

You know, you show it to me in words, you show it to me in some random piece of C code or something. I'm not going to look at it. Because if you show me, you know, a piece of orphan language code, I can run it. Not only that, also by looking at kind of, you know, I can read it quickly. And by looking at kind of the texture of what's been done, I can kind of make a, it's very easy for me to make an assessment. Does this make sense? And, you know, again, that's a pretty good dynamic and filter.

Now, no doubt that people say, I can't be bothered to do this. Well, you know, that's it. You know, my attitude towards these things is if it's like you provide a path, if they don't take the path, well, then, you know, that's not my problem, so to speak. But, you know, I would, the thing that, as I said, I find most, I haven't figured out, there's some categories of people who contact us. Another one is artists who make artworks of various kinds based on science and things that I've done. And sometimes they're really nice. And it's like, I don't quite know what to say.

And we recently made a collection of some of these artworks, which I thought was helpful. But it's kind of like, where do you go next with something like that? And it's one of the things, again, I was talking about sort of the matrix that one creates for oneself. If it's like, I want to do stuff related to your products and your company. Okay, fine. We've got a business development team.

We've got, you know, there's a mechanism for making something happen there um and uh you know i think these these undefined you know i i think um anyway as i say that the um it is i i mean i i i'm my staff are always uh horrified at how diligent we are in actually responding to all these random emails. I mean, usually, if it isn't kind of outrageous in some way, we'll usually try to respond. And even if it's mostly saying, you know, package what you're saying in a way that we can better understand it and so on.

And, you know, I would say that over the years, that's been a good thing to do because we've, you know, we've come into contact with a lot of interesting folks that way. I mean, it's always funny what you can learn by, you know, there are these strange kind of corners of the world where you can just say, oh, I'm not, you know, most academics I know, for example, they'll never respond to these messages, never.

And people, I think, well, I think, one, I feel some responsibility to respond. Two, I think it's in self-interest because, you know, occasionally something really interesting will come out of it. Yeah, for sure. Okay, so I'd like to turn to the impact of a new kind of science. And we've spoken about how paradigms get absorbed, how new ideas get absorbed, the rate at which they're absorbed. Have you found any patterns studying

the history of ideas? It's slower than you can possibly imagine. On the ground, it's slower than you can possibly imagine. In the hindsight of history, it looks faster. It looks fast. Right. So people will say, I don't know, the idea that one uses programs instead of equations to describe the world. People will say, oh yeah, as soon as there were computers and able to do those kinds of things, oh yeah, that was an immediate thing. Which it wasn't on the ground. On the ground, it was a large part of my life, so to speak. But in hindsight, it will a large part of my life, so to speak. But in hindsight, it will look like that happened quickly.

I think the other thing that, well, it's always absorption. For example, with NKS, if you look at different fields, for example, fields with low self-esteem absorb more quickly than fields with high self-esteem. And the self-esteem of fields goes up and down. There are fields like art, actually, where everybody always wants new ideas. There are fields which are used to having, which feed off new ideas like art. I think that what I noticed with the NKS book, a lot of the kind of softer sciences that hadn't had a formal framework of any kind

were like, wow, these are models we can use, and this is great. Whereas an area like physics, which says we got our models, we're happy, we've got our equations, it's all good, we don't need anything else. At the time when the NKS book came out, physics was in a high self-esteem moment, thinking we got string theory theory we're going to nail everything you know in just a just a short while uh which didn't happen but that meant it was kind of a field particularly resistant to kind of outside ideas bizarre for me because i was you know i knew it was sort of well integrated into that field and and in fact, the greatest irony was people say, we don't need any of this new stuff. The only new thing we need is the thing you built,

which was this tool that we now all use. I mean, that was one of the really amusing ironies of the whole thing. I think that now with our physics project, 20 years later, quite different situation. Physics has not been fundamental. Physics is not a high self-esteem field. People didn't feel the string theory thing worked its way through. That didn't nail it. And it's like good receptivity to new ideas. I think that I would say, when you look at the arrival of a new thing, you know, I've been involved in a few new things in my life. And one of the things I'm most curious about is who's going to get who's going to jump onto this bandwagon. And sometimes you'd say it'll be the young people.

It's not true. It's a distribution of ages, distribution of stages of career. And what happens is that people going around the world and there's a new thing comes up and that resonates with them. And then that's the thing they pursue. Now, what also tends to happen, you wait 20 years and you say, what happened to those people who jumped into this new area? My observation is about half of them are still in that area, and another half have moved on to two other new areas.

So in other words, for some people, it's kind of the newness is the driver, and for other people, it's the specific content, where they kind of realize that this is a thing that resonates with them. And I think the other thing that's complicated about new areas is how much flakiness do you allow? So, for example, you have a new area. People start saying, you know, this area is going places. I'm going to use its banner.

I'm now going to start doing something that is kind of, you know, seems to a person who is kind of a, I don't know, academic, you know, sensibility type person in some sense. Seems like, ah, this is really flaky, kind of nonsense-y, right? But that's a tricky thing because sometimes it is flaky and nonsensey. But sometimes it's kind of just what it looks like as people are kind of trying to come to terms with some new set of ideas. And you have to not throw out all the kind of the marginal stuff. You have to kind of, but you don't want so much marginal stuff that the whole field gets covered with marginal stuff

and kind of it overwhelms and kills the field, as has happened with some fields. And so it's a tricky thing. But I think, and by the way, one of the things that happens is, you know, the ideas that at first seem outrageous and shocking and how can this possibly be true, you wait a few decades and people are like, oh, that's obvious. Yeah, yeah. that's obvious.

Yeah. Yeah. It's kind of, it's kind of charming that way. I mean, it's kind of, what's always interesting to me when you are interested in foundations of a field and the originators of the field are still alive, you go talk to them. You say, hey, what about this foundation? They say, well, we're not quite sure about that. And maybe it's a better way to do it. And, you know, et cetera, et cetera, et cetera. They're still very flexible. Then you go, you know, five academic generations later, you talk to the people in the field, say, what about this foundational thing? So, oh yeah, that's just the way it is. You know, there's no possibility of, of, um, and by the way, a bunch of things I've done and things I've encouraged other people to do turns out by the time you're five academic

generations later, it is the case that one of the foundations is, you know, some of the foundations are wonky. And if you go attack those foundations, you can sometimes make huge progress because nobody who's actually in the field is ever going to look back down at those things. Right. They're all kind of up in the top of the tower. And you say five generations deliberately? Like that is a number that's emerged from?

I think by the time you're at a lot of fields, so things like physics are like relative to the stuff that happened a century ago at like five academic generations. I mean, I think it is, it might be partly, are the people who originated the field still alive? That's a, you know, are they still influencing what's happening? The sort of Max Planck, science advances one funeral at a time thing? Yes, but I think that that is, I mean, this is the inverse of that. Yeah, yeah. This is to say, when those people are still alive, they're still flexible.

And about the field that they created. I see. I mean, this question of whether it is true that people, once they're locked in, you know, I learned this field. This is what I do. This is my career. They'll often never change. And even when overwhelming evidence shows them that this just wasn't the right direction and i don't blame them at some level because it's kind

of like it's a it's a very wrenching thing to say you know i've been doing this for 30 years now i'm going to you know i got laid off from my field basically and now i'm going to try and find some other profession you know it's it's it's not surprising that people try and hang on to the things that they were doing. It's, you know, it's not a thing calculated to sort of lead to sort of the greatest innovation. I mean, you know, as careers have gotten longer, because we all happily live longer and so on, it's, you know,

that's meant that actually the time scales you might have thought that would mean the time scales you know in the modern world where everything moves so quickly the time scales for change have gotten smaller but i don't think that's true because people you know once they've locked into this is the way we do it they can be doing that for you know for a very large number of decades at this point right, so you chose to introduce the computational paradigm via a book. Why not create some kind of new canonical medium for a new kind of science? Oh, I wanted to do that. I mean, it just, you know, the concept of kind of computational essays where you can have computational language which you can read and understand alongside kind of, you know, English text or whatever.

That is a great thing, and that will be the future. It's just it's not there yet. I mean, we built the technology for that 35 years ago, but it's been, you know, people have used it, but it's been painfully slow to see that come into practice. And I think the reason is, for academics, you write a paper, you make some claim, it's just a bunch of words. You just have to kind of make the words say what they say once. If you've got a piece of computational language code there, and you say, this code shows this, then there's a higher bar. The code can actually run, and you can see, does it actually do that?

And people, I think, it takes more work. It's more valuable to the people who create it and to the community to have this thing that actually runs. But it isn't something that takes more work, and the academic enterprise has not particularly rewarded that work so far. That's one of the things that has, I think, is a very important kind of direction for change, is, you know, make sort of the computational way of communication

something that is expected in these kinds of intellectual areas, not something that's just you do in the back room and you don't use it as part of communication. So I think, yeah, no, I mean, I think the, it's sort of, in some ways, had the book, well, like the physics project, that was delivered in a slightly different form. It wasn't, you know, I did produce a book from it, but that was not its primary delivery mechanism. Because in the modern world, there's, you know, much, you know, we can run things in the cloud, we can have people be able to run code, we can do

live streams, we have social media, you know, it's a different form of communicating things. And I would say I think that worked. It worked pretty nicely. I mean, it was a strange thing that it landed right at the beginning of the pandemic. And that was, I don't know, it was a mixed thing. I mean, it was perhaps people had more time perhaps to think about new things. I think a lot of the kind of channels of communication had sort of closed down. One thing that was interesting about the physics project was how much we didn't get coverage in traditional media and how much we couldn't care less.

I mean, we literally didn't even bother to, you know, we sent a few emails, but we didn't bother to make any serious. We just didn't care. It wasn't relevant. I mean, it's kind of like for any, you know, it's more useful to do live streams and podcasts and social media stuff than it is to get, you know, the article in the newspaper or whatever. Yeah. Which had changed in 20 years. Because when new kind of science came out,

it was useful to have wide coverage in things like newspapers. But that was irrelevant by 20 years later. I have a specific question about scientific books and then a general question. So the specific question is, Benoit Mandelbrot wrote a book on fractals in the early 1970s that turned out really to be more impactful

than the hundreds of papers he'd written on the subject. What's your explanation for why his book was so successful relative to the things he'd published in journals? Right. Well, I mean, partly he was a good example of why I did New Kind of Science rather than write hundreds of papers.

No, but in Benoit's case, and Benoit and I had a complicated relationship, I would say. I mean, Benoit was fond of telling other people. He didn't tell this to me, but I heard this from a whole bunch of people. He said about my stuff, he said, eventually that stuff will kill fractals, he said. And I said to him, you're wrong.

You know, fractals are a thing that are interesting in their own right, and the fact that there's a more general story about computation and so on is also interesting. I like it a lot. I spent my life on it. But it's not going to crush the story of fractals. No, actually, I tracked down when Benoit died, I was going to write an obituary,

and my staff said, you know, I picked out all the communications I'd had with them and I was looking at them and my staff said, you cannot write this obituary because there's too many just horrifying things that happened here. Right. Of, of, um, I mean, he was a difficult guy in many ways, but, um, uh, then later on he wrote an autobiography. So I wrote a kind of review of it and I, and I did kind of realize sort of what had happened in that book. What happened is he was a guy who'd worked on power laws. He worked on power laws in language.

He worked on power laws in turbulence. He worked on a bunch of power laws. Then he was going to write the book, and the editor of the book said basically, you know, well, it's like who cares cares about power of the horse type thing? You know, can't we make some pictures? Well, Benoit was at IBM. It was a guy called Dick Voss, who was a younger physicist there who was sort of started making pictures and the pictures were really cool. And that was the pictures. It was really driven by, it was a very

unusual case where it was driven by kind of the communication channel and, you know, the publishing company was a sort of visually oriented publishing company. And they're like, we want pictures. And so then started producing these pictures. And then the pictures ended up taking over the story, so to speak. Um, and I think it was a, uh, and I'm not sure that absent the pictures, yeah, I mean, it was, the impact was just vastly higher for the presence of the pictures, which was a thing. I don't know whether, I wonder whether I ever asked my mother's question.

I'm not sure how seriously he took the pictures initially i mean i think before people started giving him feedback about them i think he may not have thought that they were much of anything you know i think that that um uh so it's a it's an unusual case, I would say. But one that certainly I was very much aware of as a, you know, that's sort of the value of the one book versus the hundreds of papers. I mean, Benoit

made another interesting tactical mistake, which was that people would apply his stuff in different areas and Benoit would collaborate with them and, you know, add his name to their papers and so on. And, you know, whatever area it was in meteorology or in, you know, geology or whatever it was. And that did not work well because what happened is that the people, and this is a question about the fringe, the people who would sort of first contact him would be the geologists who are often a corner, not part of the mainstream. And he was like, that's cool.

You're using fractals. Let me help you out. Add my name to your paper, et cetera, et cetera, et cetera. But then that turned out to be this weird corner of geology. So other people in geology said, oh, this fractal stuff, it's part of that weird corner. It's not something that we can mainstream in our field. Became tainted. Yeah, right. And so, so, you know, that was, that was not a good strategy. It might've seemed like a good strategy, but it wasn't in fact a good strategy it would have been you know and i i must say that well my own emergent strategy which i won't claim is is uh is great but is you know i'm like i'm a cheerleader

but i'm not going to be involved in the you know in kind of all the things people have done with nks and so on yeah it's kind of a because, because it's kind of like the dynamics just don't work because it's kind of like, okay, I'm pretty skilled at doing these things. You know, you show me a paper you've written. I say, gosh, I could do that in 15 minutes. That's not useful. It's kind of like, because I'm not going to do, I'm not going to spend the next however many years telling people about it

and so on because it was like, well, I could just do that in 15 minutes type thing. And also, it's kind of like that doesn't lead to a good, you know, it's not a good human situation, so to speak. And also, it's kind of like I feel like when I write something or be involved in something, I really have to kind of have my arms around it. I really have to understand it. I don't feel comfortable unless I really know the bedrock that it's based on. And that's something that's just impossible to do. If somebody says, can I add your name to my paper? It's like, well, no.

And if you were going to do that, I would have to understand every word of what you're doing. And by the way, by the time I've done done that it won't look anything like what you originally had yeah yeah it's interesting because i feel like there are there seems to be like at least two prejudices against scientific books one which i hear increasingly is that a book is like a vanity project and just write a blog post, get it out into the world quickly. You don't need to do the book. The second is that, well, a scientific book just kind of like synthesizes ideas that have already been published in journals and then popularizes them. But there are exceptions where a scientific book makes like a genuinely original contribution.

I feel like Richard Dawkins, The Extended Phenotype is a pretty classic example. A New Kind of Science is also a classic example. So when is it appropriate to take the book avenue? When you've got a big set of things to talk about. Yeah. I mean, because there are things where you could explain it. You did a good job. You could compress it to five pages and you'd have the whole story.

But there are things where it's just a big paradigmatic thing to talk about. Yeah. And, and without that, you know, if, if, if,

you know, Charles Darwin had written origin of species as a three page, you know, paper, people would have, people wouldn't have understood it and people would have just ignored it and i think you know that there's a but you know what happens with books is there's this whole sort of industry of trade books of kind of you know oh there'll be a book that people just sort

of buy at the front of the store type thing and uh just read it for fun kind of thing. And there's certainly a development of, you know, scientists are going to be the people who, you know, scientists are among the people who write those kind of front of the store type books. Most of those are at best deeply secondary, as in they're just, you know, a little spinoff of a spinoff of somebody's research, you know, presented in a sometimes good, sometimes not so good kind of entertainment type form. You know, when I was starting to write New Kind of Science, I was working with a publishing company, you know, considering having them publish it and so on. And I said, let's go find out who actually reads popular science books. What is the audience for these things?

Because they had no idea, no idea. And the best one editor for, fairly well-known editor for these things said, probably said the most useful thing, which was, I think it's people who used to buy philosophy books before, but now the philosophy books are all too technical and they buy science books instead. It's like intellectual fodder.

Yes. Right. Yes. But I think that's a, but, you know, yes, I mean, the popular science book has, you know, for working scientists, the popular science book is usually not, know that's a secondary thing that's something you do as a kind of a hobby rather than something that's part of your mainstream activity now occasionally when you have big ideas to communicate it's kind of you don't really have a choice but to present them in a form that has kind of the enough scaffolding that people have a chance to understand them you know you just, oh, by the way,

you can use programs instead of equations to study the natural world. It's like people say, okay, whatever. So I think that's the dynamic there. And I think this is also part of the value system of academia and so on. And I don't I'm not sure I've tracked that in the last few decades that well. But I think it's it's something where people feel like sometimes there's the there's the kind of there's the the people who are just doing their job. And then they're the showboaters who are kind of, and that happens. That is, that's a real phenomenon. Although sometimes the people who are explaining things are the people who really like what they're doing. And, you know, even the people who are not using their explanation to deliver the main message, they're people who really like what they're doing and think other people should know about it.

And, you know, it's not really a showboating activity. It's more of a, I really like this stuff. You know, this is really cool. But, you know, some of the dynamics and the sort of industrial dynamics of the publishing industry have led to a certain degree of just pump out those kind of science entertainment type books. And that doesn't, doesn't make for the best results. Yeah. So you mentioned Charles Darwin. I once heard you say that you learned from his example to never write a second edition. Yes. Can you elaborate on that and on what it takes to write a timeless book?

Yeah. I mean, so I think on the timelessness question, I'm fairly satisfied with a lot of things I've written that I kind of, there was a certain domain and there was fruit to be picked. There was a certain amount of fairly low-hanging fruit and I just efficiently, you know, with best tools, just try to pick it all. And then that has the great feature that what you do is timeless. It has the bad feature that then when people come in and say, hey, I want to work on this stuff, there's no low-hanging fruit to pick anymore because you picked it all. And, you know, you pick the first level of kind of low-hanging fruit, and the next level of fruit is quite a quite a quite a ways away and i don't really realize that that phenomenon when you know in in kind of uh you know you got to leave some stuff there that people can fairly easily pick up i i have to say i i it's always surprising to me

that when you're in the middle of a project you've kind of you're all understanding what's going on you are so much ahead of anybody else. Just because you've kind of wrapped yourself up in the whole thing. And it's always surprising how long it takes for people to kind of get to that same place that you were in. And sometimes you're not even in that place anymore. But in terms of the, I think, the thing that happened with Charles Darwin is that, you know, he wrote Origin Species. He made a bunch of arguments. And then people said, what about this?

What about that? What about the other thing? And he started adding these patches, says, as Professor So-and-So has asked, this, this, and this, and this, and this. You read those later editions now, and you're like, look, professor so-and-so just didn't get it you know and uh it wasn't you know and and darwin just went and pandered to this thing and made a mess of his argument because he's pandering to professor so-and-so he should have just stuck with the original argument which was nice and clean and self-contained and i think that's um uh but but yeah i, from the point of view

of, I mean, I always, I find it very satisfying to try and do things where I feel like, I feel very hard for me to say, I'll, you know, I'm going to do this and I know I'm going to throw it away. I can't do it well if I know I'm going to throw it away. You know, I have to believe this is going to be the thing, so to speak. And so then, you know, it means a lot to be able to kind of go and, you know, as I say, I think this, you know, one of the things about the NKS book, for example, is that in a sense, once you know the paradigm, much of what it has to say is kind of obvious. And that means that it's very kind of clean. It doesn't have a lot of kind of scaffolding of the time. I knew when I was writing it there were things where I was referring to like technology of the time, like PDAs, personal digital assistance, which nobody's ever heard of anymore. I thought when I think I have it somewhere in the notes, you know, when I mentioned these things, I'm like, I don't know.

You know, today people will understand that. In the future, they'll say, what the heck is that? It's like Alan Turing mentions, you know, you could use a Brunsviga to do that. That was a brand of mechanical calculator. Right. Which lasted a long time, but I have no idea what that was. Yeah.

But so, you know, so there are things like that. But I think, you know, having sort of picking the low-hanging fruit and trying to make the arguments as clean as possible. I mean, one of the things that's always striking to me is, you know, you see some ancient Egyptian artifact and it's a dye and it's a, you know, it's a, it's a, you know, I don't know, an icosahedral dye. And you say, that looks very modern. Well's it's modern because it's a it's an icosahedron icosahedrons haven't changed you know in the history of the world so to speak and and so again when you're when you're at the sort of foundational level and you can make it clean enough you have

the the chance to make something that is sort of timeless because, you know, people can run, you know, I could run Rule 30 in 1982 and I can run Rule 30 today and it's going to be the same bits and it's going to be the same bits forever. And it's not, you know, oh, now it's written in Old English or something. The bits are going to be the same forever. And it's like that icosahedron from ancient Egypt. You know, whoever made that icosahedron spoke a language we absolutely don't understand today, but the icosahedron from ancient Egypt. You know, whoever made that icosahedron spoke a language we absolutely don't understand today, but the icosahedron is still the same.

In hindsight, would you have left more low-hanging fruits on the tree? I don't know. I mean, I think I don't know. I mean, you know, I guess different people have different expertise. And I think this thing about how do you sort of develop a community is not so much my expertise. I mean, that is, it's one of these things where I mean, perhaps it's because, I mean, it's not like I didn't define, you know, I wrote out, I started writing for the book. I started writing a list of kind of unsolved problems related to the book. Okay. who'd ever commented on anything about those unsolved problems until this chap, Jonathan Gorard, who worked on our physics project,

said, oh, that was my favorite thing that I read when I was 13 or 14 years old or something. So, okay, we got at least one hit from that. Turned out to be a valuable hit. Yeah, yeah, right. But I think the thing that, yeah, I don't know. I mean, it's an interesting question. I mean, right now I'm thinking about for this field of ruleology that I kind of launched in the early 80s of kind of study simple programs, what they do.

I didn't have that name at that time. And then I look today and there are about 500 people who've made interesting contributions to that field that I can tell. And so I was thinking, now these people, many of them have grown up. They went from being maybe young researchers in 1982 to being esteemed, distinguished whatevers, and they're embedded in lots of different places and activities around the world. And I'm trying to think, how do I help this field? And I'm probably going to organize a ruleological society. I'm not quite sure I'm like trying to think, how do I help this field? And I'm

probably going to organize a really illogical society. I'm not quite sure what it's going to do, but it's at least going to be a, a collective kind of guild branding or something of this group of people who've been interested in this particular area. Um, and I, I, you know, I don't know exactly what the best way to kind of stimulate more work there is. You know, I think sometimes it's very mundane. Sometimes it's like, is some university going to start teaching classes about this and giving out credentials and so on? Oh, if that happens, then people will come there because they want to get a credential.

You know, it's very kind of very prosaic like that, rather than, you know, oh, it's a wonderful thing and people find it fascinating, and so that's why they go study it. You know, people, I'm always, yeah, so I'm not sure. Something random I noticed when I was reading the book is that you use commas sparingly is that like a conscious stylistic choice oh boy you know it's funny thing because i've i've at my company we have a group called dqa document quality assurance yeah which in past eras might have been called

proofreading or copy editing or something and it's funny because you know they have a set of guidelines for things and then they have you know they have the main guidelines because they have a set of guidelines for things, and then they have the main guidelines, and they have the guidelines for me. There are all these different things about commas and starting sentences with conjunctions and smooshing words together and so on. And over time, I've evolved slightly different stylistic conventions. But, for example, the starting, I'm not sure what my comma usage.

I know my DQA team, they re-comma-ify things from time to time. And I have to say there's some things about where I get frustrated because it's like, look, the previous people didn't capitalize that word and now you're telling me that word is capitalized it's kind of like like um uh and and but you know like the starting sentences with conjunctions basically that is a hack for avoiding kantian length sentences yeah um yeah and it, and I think it works. Okay. And it's, it's, um,

uh, but, but yes, there are, I definitely have had some stylistic quirks and I, uh, they've slightly evolved over time. Like for example,

in the NKS book, I never used, uh, sort of isn't and things like that, you know, these, these, um,

shortenings, whereas in the things I write now, I always use that stuff. Right, and I, I feel, I don't know why particularly it's, it's a, it's a, I actually have liked the way that the writing I've done more recently has evolved because I feel like one of the questions is, can you say anything in your writing? In other words, if you can only say things in a very formal way, if you've got just a feeling about how something works, can you express that? Or do you have to, if you're writing something

that's very sort of authoritative, you just kind of can't talk about that. And so one of the things that's happened in more recent times is evolving towards a style where I feel like I can talk about anything anything even if i'm not sure about it even if and sometimes also like in in ks there's not a single joke for example right and you know in in um in the things i write now when i see something which is kind of a resonance with something that's kind of funny or kind of, you know, culturally resonant or something, I'll put it in.

I full well know that that cultural reference will kind of fall away into kind of incomprehensibility at some point. But I figure that it's, I don't know, I don't mind the, okay, another thing I realized is things I've't know, I don't mind the... Okay, another thing I realized is, things I've written today, the last many years, everything was written at a certain moment in time. And like when I make these books,

which are collections of posts that I've written, I was at first, I was like, I can't do this. You know, they've all got to fit together perfectly. It turns out that isn't really true. You can, you know, each one is at a moment in time and people don't seem to be confused by or mind the fact that this one was at this moment in time, that one was at that moment in time. Now, the NKS book, I set myself a higher bar because I was really trying to define a paradigm in a kind of a sort of coherent way. And that's something that's just a different, you know,

that sets a higher bar for the way that you organize what you're writing. Yeah. In the book, you had come up with the simplest possible Turing machine as a piece of evidence for the principle of computational equivalence. And you put up a prize for somebody to prove or disprove it. Alex Smith won the prize, I think 2007. Yes. Yeah.

So that was a significant positive technical update to the book. Yes. Have there been any other, like many other updates, either positive or negative since it was published? Not that many. Okay. Surprisingly few. And I mean, you know, it's one of these things where people were like, you know, but is it right?

And it's like every frigging thing in this book has been picked over now with them. It's, I think, it's a thing where, yeah, no, I've been actually, we've been sort of collecting, because there's an online version of the book, and we've been kind of collecting things where there's sort of a dander. I suppose the other really major update is the physics project. That's sort of an extension of the book. Yes. And I mean some things, like I wrote this book about combinators, which are a big extension to the section about combinators. But in terms of the kind of cliffhangers,

the simple Turing machine was probably the most obvious cliffhanger, so to speak, in the book. And I'm kind of hoping, like Rule 30, for example, and its characteristics, I put up this prize associated with that, and another one associated with combinators. I was really happy that Alex Smith was able to resolve the Turing machine thing quickly, because I thought it might be a 100-year story. And I kind of suspect some of these others might end up being 100-year stories. But I think, yeah, so no, it's surprisingly, I mean, very few.

There's the occasional typo, not in text, that that's long, long since gone. But there are some little glitches in pictures, which people notice from time to time. So I'm always excited when that happens, because it's like they're very small things. And they're like, you notice that, well, usually it's because they're trying to reproduce the thing're like, like, uh, you notice that, um, uh, well, usually it's because they're trying to reproduce the thing. That's, uh, uh, that's the most common thing. I just realized, I don't know how the Alex Smith story ended, but did you try and hire him or anything like that? Yeah. I mean, uh, you know, I have to say I was, um, smith is a is an unusual person and um i think you know the set of people who kind of can focus in i mean i you know it was um uh i would say he's he's a a person who is

i doubt he would describe himself as a sort of a socially connected kind of person. Um, and you know, I think, I think he's, he's, um, uh, yes, absolutely. We tried to hire him. Um, and, uh, uh, and I, and I actually thought, um, um, there've been several projects where, I mean, he, he went and finished his PhD in, um PhD in kind of theoretical-ish computer science. And I think he's been working on compiler technology, which is kind of like what he did with the Turing machine. But, you know, I think it's one of these things in part where it was cool that he was there for this project and it was cool that this project

was there for him so to speak um it was one of those sort of moments where these things intersect um and uh uh makes me realize i should i should ping him again i do every few years um it's kind of like uh uh partly because i'm just um uh i was just like thanks a lot for resolving this this question i mean i you know it was uh um the uh one of my better investments of 25 000 well it made me wonder um when is it more effective to try to solve a scientific problem by offering a prize and when is it better to assemble a team to solve it? How do you distinguish? I don't know.

I think that this was a case where, I mean, this is the one case in my life where put up a prize, somebody solved it, everybody's happy type thing. I think it was sort of, it had a difficulty level that was kind of a lot of very complicated technical work. But I don't think one would say that it was kind of a a big paradigmatic kind of thing that had to be figured out right and it wasn't cross-disciplinary either right right it was it was pretty pretty specific and technical i think that that um uh yeah i mean i don't know i you know there are there are obviously prizes that people put up. You know, the whole XPRIZE Foundation has been trying to put up prizes for things with varying degrees of success. I mean, I think it's sometimes this was a case where it's a very specific technical result.

You know the target. Actually, it was kind of funny with that result because it's like, this is a definite thing. There is no doubt about what happens. And I assembled this sort of team of people. It's most of the world's sort of experts in these kinds of things. We're on my little prize committee type thing. And so Alex Smith sends in this thing, and I say to this prize committee,

okay, guys, I didn't know this was going to happen in our lifetimes, but here it is. Somebody's actually got a real, uh, uh, real thing about this. You know, let's go, let's go check it out. And, um, uh, eventually a couple of people really, who, uh, really worked hard on, on, on going through it. But, um, then it was like, then people were like, well, does it really solve the problem? Does it really prove it's universal? What are the footnotes to this? And how complicated is the initial condition, et cetera, et cetera, et cetera.

And it's like, if you want something which is well-defined, this is about as well-defined as it comes, although it is a complicated issue, what counts as universal computation. And it was kind of, in a sense, funny to me that this thing that I thought was a very clear target, even because the way these things that are difficult work out, it's never exactly what you think. That is, it's like, you know, I say, okay, I want to find the fundamental theory of physics. I want to go to find the rule which makes the universe. And then you realize, well, actually, there's this whole really odd object, and it isn't really the question that I originally asked isn't quite the right question.

It's more like we have this whole thing, and we're observers of this, et cetera, et cetera, et cetera. So kind of whenever you build one of these tall towers, it turns out that the particular thing you thought was the target probably isn't precisely the right definition. Right, right. So what's the most underrated chapter or section of the book today? So I feel like you might have said chapter 10 in the past, but maybe that's now changed with the physics project underway? Yes, chapter 10 is about to have its, which is about perception analysis. It's about to have its day in the sun because I'm working on this thing that I call observer theory, which is kind

of an attempt to make kind of a general theory of observers in the same kind of way that Turing machines and so on are a general theory of computation. And that's a chapter 10 story, so to speak. I think that the, it's funny because every chapter has sort of its own personality. I mean, chapter nine is a chapter about fundamental physics, and that's been, that's kind of very much had its day in the sun. Actually, it has really two sections in it. It has partly the parts about space-time and quantum mechanics and so on

and fundamental physics at that level. The earlier part of the chapter is about the second row of thermodynamics, which turns out in this amazing thing that we've now realized that these three big theories of 20th century physics, thermodynamics and statistical mechanics, general relativity and gravity, and quantum mechanics, are all facets of the same result about how observers interact with the computational irreducibility of sort of the underlying structure of things.

And the thing that's just fascinating to me, sort of philosophically aesthetically scientifically is that people had thought in the 1800s oh the second order of thermodynamics is derivable but they never thought the general relativity was derivable they never thought quantum mechanics was derivable it turns out they're all in the same bucket they're all as derivable as each other and they're all in some level derivable from the way that we exist as observers. So that's kind of a super exciting thing. But for me, the thermodynamics story is an interesting personal story because I started being interested in the second world of thermodynamics when I was 12 years old.

And, you know, it's now 50 years later, I think I can bring that to some kind of closure. And it's, you know, that is certainly the longest running project in my life. And it's one that I realized the Second Law of Thermodynamics has kind of inserted itself into my life many different times. And it's also interesting to me that I wrote this stuff about Second Law, and I have a book about second law coming out real soon actually the um uh it's an awful lot of people who i know who wrote to me after that second law stuff came out and said oh i've been interested in second law for a long time as well i've never written anything about it nor had i really apart from the stuff in the nks book and um uh but it's kind of like always been a thing i've been curious about but never managed to make progress on. So it was kind of a – I didn't know there was many closet second-law enthusiasts

as turned out to be the case. But I think somehow the early chapters of the book, which are kind of about ruleology and what's out there in the computational universe, all these different kinds of systems, those I look at all the time, I need all the time. You know, this or that example. I found chapter 12, which is about the principle of computational equivalence and covers things like the relationship to the foundations

of mathematics that I have very much picked over in great detail. And that's proved valuable i think that the um uh the the kind of um well many of the earlier sections which are a little bit more like you know starting from randomness and systems based on numbers things like this these have all been uh of practical use and kind of actual explorations that I've done. So I would say right now, well, chapter seven, which is about mechanisms and programs in nature,

is a little bit of a, it's good for intuition building building it's been good for sort of paradigm creation i would say it's detailed content it has a bunch of specific things that i pick out from time to time but um it's more of a hodgepodge i would say that some of the other some of the other chapters okay but um no you know this is a uh after you spend 10 years on something like this with a fixed table of contents, yes, every chapter is your personal friend. And I think that the people who've studied the book a bunch, and I would say that the real aficionados can quote page numbers, which I can't. So that's some. That's impressive. What's your mental model of physicists like Freeman Dyson or Steven Weinberg who didn't take your new kind of science seriously?

Well, I mean, I, I knew both of those people. Um, I would say, well, that's a little bit different actually. Okay. Steve Weinberg was, it's kind of funny, I had lunch with him after the book came out, after he wrote things about the book. Steve Weinberg, you know, long user of Mathematica and very competent physicist. Very, you know, Murray Gell-Mann always used to say, you know,

Steve Weinberg is a physicist who can work out anything. He used the viscosity of milk as an example of, you know, just feed him something like that and he'll technically be able to do it. And, you know, so he had his kind of rhythm of doing physics, and he was very good at it. I think for him, new kind of science was just alien. Just kind of like a you know message from the aliens type thing and uh i remember i had lunch with him a while after it came out explaining to him you know simple programs and so on he said i just didn't get it i just didn't understand that and it's like you know you wrote a whole review. You read the book, right?

He said, but I just didn't understand that. And it was just like it's a different paradigm. It was something that just sort of went straight past. Another mathematical physicist, well-known physicist, who also ended up writing. I never read these reviews, reviews I don't actually know what I've been meaning one of these years I'd crack it open and read all this and read all these things

I because I found that perhaps a strange psychological quirk that I don't find seeing the feedback about kind of what people say about me I just do what I do and seeing the feedback about kind of what people say about me. I just do what I do. And it's kind of independent of what people say about me, so to speak.

But another one was very directly, I remember getting on the phone with this person. And the first thing he says to me with great emotion, he says, you're destroying the heritage of mathematics that's existed since ancient Greek times. And it's like, okay, that's interesting. Quite the compliment. Yeah, well, right. I said, perhaps quick enough to say, the next thing I said was, then it is perhaps the greatest irony that I've made such a good living from purveying the fruits of that particular tradition. But that conversation was very interesting because eventually this person was saying, you know, I look at the book. All I see is a bunch of pictures and code.

I don't understand anything. And I said, you know, that's kind of the sound of a new paradigm. That's, you know, it's different. And I think Steve Weinberg felt that way as well. And then later on, you know, I ran into him and was talking to him about the, you know, doing the physics project and so on. I think the most telling line was, I hope you don't do that project. Right. And, and I said, you know, cause he said, he said, if you do that project and if you are right, it will destroy what we've done for the last 50 years. And I said, I don't think you're right about that. You know, what you've done is a perfectly

solid thing and it's going to survive forever. And, you know, we may be able to do things that are below that or even above that, but that thing will survive. And I suppose then that the next things Steve Weinberg said to me in that conversation was, and anyway, you'll never be able to find any young physicists and so on who are prepared to work on this stuff. And I said, well, you know, the one little glitch in that theory is, you know, we hire an awful lot of physics PhDs at our company. You know, it's like there's no lack of people in this. Sometimes people who don't want to be in academia because they don't like the kind of the milieu of academia. So I would say that in the case of Steve Weinberg, it was very much, you know,

he had a paradigm. He was really good at that paradigm. He really liked that paradigm for him. And chaos was something just completely alien that, as I say, as you know, as he said, I hope you don't do that project because thinking about the physics project,

because it was kind of like, you know, he thought it was a risk to what he was doing, which I don't agree with. But, you know, and this is an interesting case because he's a sort of first generation person of the people who had done the things he'd done. A few generations later, they wouldn't think it was a risk. They would just think there's, you know, the foundations are solid, so to speak. Freeman Dyson's a little bit of a different story. I mean, I knew Freeman when I worked at the Institute in Princeton.

I would say that we were, he was a person who, he would always, I think, he sort of was very interested in new ideas and would forage the world for new ideas and would always want to come up with the most contrarian idea he could. And, you know, the number of times we'd go to lunch at the Institute and Freeman would say, I want to talk about some new idea. And he'd explain it, and I'd say say but freeman that can't possibly be right and then he would kind of bristle and eventually kind of kind of go quiet and you know it's like no it wasn't right i mean it was just like it was a it was a contrarian idea but it was kind of like you know i remember he was big on the idea that you know uh forget the electronics revolution everything is going to be you know biological the whole you know all of the machinery we use is all going to be growing

biologically and it was like freeman there's many reasons why that isn't going to work and uh you know maybe he'll have the last laugh and everything will be you know we'll eventually understand how to do molecular computing and it will work very much like living systems. But certainly in the practicality of the early 1980s, it wasn't a thing. And so I think, and actually I asked Freeman, there's an interesting thing, I asked him shortly before he died, I ran into him, and there'd been this quote that somebody had given me,

which I collect these kinds of, I don't pay attention to many things people say about me, but sometimes when they say them, you know, when they, when they quote them to me, they're kind of fun. So Freeman had this quote that somebody had asked him about the NKS book. And he'd said, well, you know, you know, talking about me, you know, he does, he's very precocious. He does a lot of things young. Some people, when they get very old and decrepit, think that they have sort of a global

theory of everything. You know, he's been precocious in that too, so to speak. So I said to Freeman, did you actually say that? Because I had no idea. It was quoted to me sort of secondhand by some journalist. And so he had the gumption to say, yes, I did say that. So I give him credit for that. Then this was eventually an email exchange. Then it was like, and I never believed in any of the work that you did back in whatever the 1980s. And I'm like, like look freeman we've interacted a bunch of times since then why did you never tell me that it's like you know you should

have told me that i mean i you know i don't know whether i i wouldn't have agreed with you but it's like then one can actually have a discussion rather than you know like you know tell other people you think it's nonsense type thing, you know, tell me. And I have to say, I sent him a, I'm sure it will show up in his archive sometime, but I sent him a pretty strong letter that basically said, I think it's irresponsible that, you know, because I was at that time, I was a young guy and, you know, there would have been opportunity for, you know, if he had something sensible to say, it might've actually been useful to hear it rather than just hear it behind one's back type thing. So it's, I would say I was not

impressed with his kind of intellectual integrity in that whole thing. So I think it's a little bit of a different situation. I see. I think Freeman was, as I say, a person who was very, he had been a, you know, he went through the kind of Cambridge, England education thing. I remember I first noticed his name because there's some collection of sort of difficult math problems for high school students type thing. There was one problem, but very few had anybody's name associated with them. There was one which said, you know, this was solved by mr fj dyson um and that was the first i i you know for some reason because about the only one in this book uh that that had a name on it and i was you

know it's before the web so you can just go look up who was this character but i have a good memory for names i suppose so i i remembered this and And, you know, years later, I would meet Freeman, and I realized that his greatest skill was kind of solving math puzzle type things. And yet, and it often happens with people, he was really quite good at that, and quite good at solving kind of mathematical, mathematical physics kinds of things. But yet, you know, the grass was greener for him on the side of come up with these incredibly creative ideas, even though I don't think that was the thing he was really the best at. And so it's kind of like, and you know, one tries to not make these mistakes oneself of saying, you know, look, there's this thing over there that, that, um, uh, you know, there's this thing

that I'm good at and oh, everybody's good at that. so I don't have to, you know, make use of that skill. There's this other thing that I'm actually no good at at all, but that thing seems like, you know, the real thing I should be doing. And I kind of think that was a little bit of his situation and a little bit of, I think he, I don't know, I think he viewed, you know, because I'm probably more on the opposite side of that. I wouldn't consider myself technically competent. I wouldn't have been able to solve that math problem. Okay, I built computational tools that automate, that let me solve things like that. But me unaided, I wouldn't have been able to do that.

He could. that but me unaided you know i would have i wouldn't have been able to do that he could but um uh i'm kind of more on the side of create uh you know create the ideas type thing what do you think dick feinman would have made of the book because he was always quite committed to the tools of calculus wasn't he he would have liked the book i talked to him enough about it i mean i was i did a bunch of them look he, he was a person of, I would say he liked new things. He liked new ideas. He liked, and he was a person, I would say, of less, I think he always just wanted to be intellectually stimulated and solve the next thing.

He wasn't as invested. He would fall back on, these are the tools I know, these are the tools I'm going to use. But what they were going to be applied to was much more things that he was always excited you know he was always excited about kind of applying it to new things you know i think one of my favorite perhaps compliments or something i don't know was a uh paid by well it was something big feimer once said to me we'd been um we were both consultants

at this company called thinking machines corporation which was ultimately an unsuccessful kind of parallel computing meets ai type company um and uh um i had been generating this kind of giant picture of rule 30 and so we were were both, you know, and Dick Feynman was kind of like, I'm going to figure this out. I'm going to crack this. You know, this is not as complicated as it seems. Okay. And so he tries to do this for a while. And eventually he says, okay, okay, you're onto something here. And then he says, he kind of wants to walk off from everybody else and ask me some question. And it's like, I just wanted to ask you, you know, how did you know it was going to work this way?

And I said, I didn't. I just ran these programs, and this is what I found. And he said, oh, I feel so much better. I thought you had some intuition that was far away from what he had. It's like, no, no, no, don't worry. I just did an experiment. Now, to be fair, on all sides there, the thing I've realized in later years is to do an experiment and actually notice the unexpected, turns out you have to be primed for that. Otherwise,

you just whiz right past it. Yeah, theory-induced blindness. Yes, right. But I think, no, I mean, in terms of the fundamental physics stuff, I think Dick Feynman would really like that. It's really a shame that I, you know, we talked about quantum mechanics a lot. And, you know, he always used to say, I've worked on quantum mechanics all my life, but I can tell you, nobody understands quantum mechanics. Yeah, yeah.

And I think now we really do and i think the understanding that we have i think is is one that that he would really resonate with and i think actually he and i worked on quantum computers uh back in what was it 1984 maybe and um we kind of came to the conclusion that these things it's not going to work and so i'm kind of interested even in the last few months well from our physics project i kind of have an intuition about why it's not going to work and how to understand that it isn't going to work but it seems like other people are coming to the same conclusion and um uh it's kind of like in fact the reasons we thought it wasn't going to work are now transmuted into a different way of saying these things, kind of the same reasons as today.

I mean, it's kind of like you've got this quantum thing and it makes all these different threads of history. And in parallel, all these threads of history can do all sorts of different computations. But if you want to know as a human observer what actually happened you've got to knit all those threads of history back together again and you've got to say this was the answer you got and that knitting process is one that's not accounted for in the standard formalism of quantum mechanics and that knitting process turns out to be hard and uh i I think, so, you know, I think, I mean, in, I would say that Dick Feynman was, to me, sort of interesting. He really liked to understand things in sort of a fundamental way. He was sort of, in some level, one of his charming features was that he would, you know, he was

a very good calculator. And so he would go off and do all these calculations and things. But he thought that was easy. So he would then get to the end and say, now I want to come up with a real intuitive explanation, because that's really hard to do. He would come up with this intuitive explanation, never even tell anybody about all these calculations. And so, you know, for years afterwards, people would say, how did he figure this out? You know, how did he know this was going to work this way? And it's like, it's the same thing as my, I just did the experiment.

It's like, well, I just did this whole giant calculation. And, you know, the thing that was always remarkable to me was that he could go through this big calculation and get the right answer. Because for me, unless I had a computer doing it, or unless I had some intuition about what was going to happen, I just wouldn't have gotten the right answer. But yeah, I think the precursors of the book, he did get to see and I got to talk to him about. And I would say that he was uh uh was was quite into them and i think that the um the idea that for example physics is ultimately computational i think was an idea that that he talked about and um um you know and i think things like you know

he talked to me for ages about why is e to the minus beta h and statistical mechanics why is it the same as the e to the i h t in quantum mechanics? Right. Coincidence or not. Yeah, right. And it's not a coincidence. Right. And I think that one of the things I really miss, actually,

about Dick Feynman and Steve Weinberg, for that matter, is one of the things we now have to do in the physics project is go from this very foundational level of these are sort of principles about what's going on to, okay, you're an astrophysicist, you've got a big telescope pointed in this direction, and see if you can see a dimension fluctuation or something, figure out what you actually look for. What is the physics detail, so to speak? And it used to be the case, at least in that generation of physicists, that those kinds of people were quite good at figuring out, okay, you know, we've got this set of principles underneath.

So what is the actual consequence for what happens to, you know, an active galaxy or some such other thing? Or what does it, and it's frustrating because I'm not finding the younger folk mostly are much more specialized. And it's, I've been, in fact, at our summer school that's coming up right now. I'm kind of hoping I'm going to get some people who are actually going to go figure some of this stuff out because otherwise I'm going to be stuck doing it myself, talking about, you know, a lack of delegation and a need to kind of dive into the details.

And it's kind of like, you know, I think I know how to do this stuff. I used to be pretty good at it, but I'm rusty at those kinds of things. And I'm kind of like, you know, can we actually figure out what happens to a photon when it propagates through a dimension fluctuation in the early universe? And what, you know, do we get, you know, are there these strange fractalized images that the space telescope should see based on that or whatever? Don't know.

You know, can we, so those are things that in the Dick Feynman, Steve Weinberg generation of physicists, they were kind of generalist enough that they would have been really good at working those things out. Even I might have even persuaded Steve Weinberg to work some of those things out because that was exactly his kind of thing. I mean, I find it interesting now

people have started using our sort of framework for thinking about general relativity and using it as a kind of a computational scheme for studying black hole mergers or whatever else. And I just saw a quote actually Jonathan sent me from some group that said some person saying, you know, these methods are really good. You know, it's so strange that they're based on such a crazy set of premises underneath. It's kind of like, you know, that's the, you know, for them, you know, you're kind of using this method and it's like, well, you know, it's based on this idea that space is discrete in this way, but we don't really care. It's just that discreteness is what we need to put it on a computer.

And so for them, if they see some weird numerical glitches in their calculation, they may be unhappy, but we'll be really happy because that round-off error or whatever is the signature of the discreteness of space. That's one of my things now is to try and find what Brownian motion, little microscopic motion of pollen grains and things, which people finally understood was the pollen grains being kicked by individual molecules.

Now it's kind of like I want to find that sort of analog of that for space-time because that's what's going to show us that space-time is ultimately discrete. Speaking of Dick Feynman, and given I'm interviewing Richard Rhodes next week, I have to ask, did Dick ever tell you any stories about working at Los Alamos that you can share many I mean gosh the um uh you know one one that I suppose perhaps interesting is uh he was like you know after I saw the first bomb test, he was saying, you know, I thought the world's going to end. It's like, why is anybody bothering to do anything? Kind of, I can see the end kind of thing. I thought it was interesting that he had that kind of reaction to that. I would say that um uh he i mean he was in this funny position because he was running this team of human calculators um so he was kind of a little bit out of the he wasn't you know he's a young younger on the

younger end of people who are there so he wasn't part of the actually designed the bomb you know figure out how the bomb should work kind of thing um but he was uh sort of i think viewed as sort of the the super smart guy who was in that particular case put to work on on doing this human calculation stuff but yeah no i think um let me think i'm i'm um i can think of um he's quite an enthusiast of oppenheimer's i mean he he thought um uh as a lab director yeah yeah as a i mean you know i remember one day we were at this strange event that was put on by a, essentially, I suppose one could say Californian cult-like thing where the guy who was running it sort of had a thing for physicists

and put up this money to put on these physics conferences. And so Dick Feynman and I were kind of the people sort of selected by that group to kind of you know be across the sky at dinner kind of was this in san francisco yeah where was it um est was the name of the the operation was a guy called verner erhardt yeah verner erhardt yeah yeah right um i spoke to land i had learned so leonard saskind on the podcast once and he was telling me about he he used to go to these dinner parties as well and had a conversation at the Blackboard with Ed Hooft over black holes one time. And Stephen Hawking was there as well.

Anyway, I digress. I might have been there at that same one. I'm not sure. Right. But anyway, I mean, after this conversation, Dick Feynman just wanted to talk for hours about kind of what is leadership, what causes people to follow people in sometimes apparently irrational ways. I mean, Brigham Young was one of his big examples.

How did a bunch of people decide to follow somebody out into the desert type thing? And how do people follow Werner Est? And he sort of put Oppenheimer in this collection of somebody who can be a leader who people follow without necessarily understanding just by force of personality or something. I have to say, I think, um, uh, Dick Feynman always used to kind of, I mean, everybody always gives advice that's based on their own experiences, as I was saying. And so he would always say when I was off talking about organizing things and companies and all that kind of thesis,

why do you want to do any of that stuff? You know, just, uh, hang out and do, do science. And it's like, but he had had very bad experiences i think in later years with sort of two categories of people university administrators and publishers

both of whom are probably not the most you know those markets are not the most efficient those industries are not the best organized and so you know know, he would imitate for me in a less than flattering way, you know, what people in those kinds of industries had said to him about different things. And it's like, you know, these people are idiots type thing. But I think he picked particularly bad examples of industries there. But, you know, it's a, you know, I think in, I mean, it's always funny in physics, you know, I was involved in the field at a time when sort of particle physics was still in the thank you for the Manhattan Project phase as far as the government was concerned. And a lot of the people who I knew in physics, who was sort of the older generation of physicists, many of them, they were treated with sort of great respect for sometimes reasons I had no idea about, because they were reasons that were, oh yes, that person invented

the such and such thing that was critical to the atomic bomb, but it was sort of secret or semi-secret, and it's just like it was sort of a, that is a very esteemed person type thing. And there was a kind of almost clique of people who'd worked at Los Alamos on the Manhattan Project and who kind of were a sort of brotherhood of physicists or something. I'm afraid it was all brothers, pretty much. And that kind of left a kind of an interesting glow

in the sort of world of physics that lasted, well, I would say, the end of that was the killing of the supercollider, which happened in the 1990s. That was, I think, the end of the era of post-Manhattan project, government saying, thanks for helping us win the war type thing. Right. And the people having retired or died who'd been involved in that sort of process. I mean, I think this whole thing about, you know, intense projects and people who do intense projects and what's involved in doing intense projects. I mean, the Manhattan Project is obviously a bigger story than any projects I've been involved in.

But it is always interesting that, you know, you see people who are involved in these projects, the project succeeds. There is, I think, a certain glow that persists for probably a decade or something for people. They've been involved in a project, particularly projects where it goes from nothing, just an idea, to this whole thing in the world, so to speak. People realize, gosh, one can actually do that. And one of the things I found interesting is that sometimes I think, oh my gosh, we've got to do this project. got to push so, so hard with such intensity. You know, these people are just going to quit. It's going to be terrible. Doesn't happen that way. People, you know, it's like the intensity of projects is actually a very invigorating thing for people. People, even though it's like, oh my God, I'm

working so hard, it's terrible, et cetera, et cetera, et cetera. It's actually a great experience. It's usually when the project is finished and everybody's like, oh, what do I do next? You know, that's when people are off to do something else type thing. Yeah. Yeah. It's a great joy to be, you know, down in the trenches with, with your colleagues, so to speak. No, I think it's also, it's when collectively one achieves this big thing, which there's this excitement of realizing that it's possible to do these things. Yeah, I see what you mean.

I've got one final question on the impact of NKS. And then the final section is just the content and some of its implications. Okay, okay. Which I will swiftly cover if it's okay. Yeah, it's okay. Okay, thank you. I'm having fun.

You're asking very interesting questions. Okay, I really appreciate it. Okay, so the final question on the impact of the book is what would it take to get computational X for all X injected into universities and academia? Oh, that's an interesting question. I've been thinking about that question. You know, the first step, I think, is even to define what it means for people to do computational thinking. And I think it got a bit easier because LLMs can now get people over some of the first hurdles. You know, they don't write perfect computational language, but they get one roughly in the zone. Don't quite understand the dynamics of how that gets sort of improved once

you're in the zone, but it helps in building confidence for people. I think that, so first step is, what does it mean to learn computational thinking? It's going to end up falling on me to try and write some big thing about, you know, introduction to computational thinking type thing that is an attempt to kind of explain that. What does it mean?

What kinds of things do you need to know? It's not just principles. It's also just sort of facts about the world. You know, images are encoded this way. You know, audio is encoded this way. Sort of, you know, you've got to have intuition about how things work. And I think that's step one, and that's something broadly accessible to people. And now the tools, particularly thanks to LLMs, the art history majors really are perfectly enabled to get computationally serious, so to speak. And I think this is the whole computational paradigm, kind of like we've had formalizations of thinking about things from logic to mathematics,

now to computation. This is, and computation has a great feature that the computers can help you with it, which, and so now I think that the dynamics of how does that get injected into universities? Fascinating question. I mean, I have a bunch of university presidents and things have asked me about this. And it is complicated because it's like, for example, does the computer science department eat the university? Does it just like everything's computational X, so it's all computer science? Probably not. That's just like, just because fields use mathematics doesn't mean the mathematics department runs those fields. The computer science departments at most universities have swelled greatly through teaching people basically programming language programming.

And it's not obvious that's going to be such a thing anymore. I mean, for somebody like me, it's like I've been automating that stuff for 40 years. It's hardly as if, you know, I wouldn't have put, you know, I've told many people, don't go study, you know, sort of rote, low-level computer science. You know, whatever you learn today is going to be like all the people who said assembly language is the only thing you could use back, you know, in the 1980s. And nobody learns similar language anymore. It's not a great bet. And a lot of universities, even very sort of elite, intellectually oriented universities, have felt this need to kind of sort of bulk up their offerings in what amounts to kind of trade school computer science. So I don't think that's the, I don't think that's the place where sort of the computational thinking thing is, you know, like, how do you apply? It's like, okay, how do

you write this specific program? That's one thing. How do you take something in the world and think about it computationally? That's actually a different kind of thing. And it's not what most of sort of computer science and universities has consisted of so you know it's like how do you um how do you kind of uh how do you get people who can do computational x do you inject them into departments of x do you have i think what's going to emerge is there'll be hopefully i don't know maybe even the things i'm writing will end up being sort of a general literacy computational thinking thing that people learn that i suspect people will say is one of the more useful things they learned in college or in high school or wherever it's

ends up being taught because it's you know it is the paradigm in the 21st century and it's kind of like useful to have some intuition about it and some way of thinking about it in terms of it. And then, you know, it's challenging. I mean, actually, the person I was just talking to just before we were chatting here, we were that person is a philosopher who is now in charge of humanities at a large university and, you know, talking about, you know, okay, they want to hire AI ethics people. Where are they going to get them from? You know, who does that stuff? What is the track that leads you to that? You know, is it technical?

Is it, and the other, there's a vacuum, I think, in a lot of these areas of kind of what does it mean to not do kind of engineering computing and to do but to use computational thinking in attacking things in the world? You know, I've spent my life kind of building the tooling and the notation for doing that. But that hasn't solved the problem of what is the sort of the organizational mechanism for making that stuff happen. Now, one of the more outlandish things that might happen is it just doesn't happen at universities. It gets built elsewhere and it becomes a, you know, you were asking how basic science might support itself. You know, maybe what happens, and I mean, after all, universities had to be invented back in the 1200s or whatever. You know, maybe what happens is the computational thinking gets sort of taught in a setting that isn't like a current university. I mean, our summer school, in a sense, is a small example

of doing that. But it's, you know, we're educational amateurs, so to speak. You know, we're just, we don't have the, you know, we're not giving out the indulgences, the degrees. We're not part of that ecosystem. We're just teaching certain content. And I think that's an interesting question of whether this stuff… stuff you know the fact that programming gets taught in fancy colleges sort of intellectually oriented colleges is actually a little weird because and i think it's only happened because a bunch of kind of high-end white-collar jobs require programming otherwise you know like those places don't teach for the most part things like animation

or you know some you know one of these or you know post-production skills things like that yeah right those are taught in much more trade school vocational kinds of places and a lot of programming is that same kind of thing it's not that different from being a CGI artist or something like that. It takes work, it takes human effort, et cetera, but it isn't the same kind of thing as the sort of big intellectual kind of arc of things that you might think of as sort of elite intellectual university. And so the fact that that happened at universities is kind of a quirk of history, I think. And it might not have happened that way.

It might have been that the boot camps and the sort of alternative, well, it's like many of these things work in a funny way. I mean, like, you know, we were talking about Y Combinator a bit earlier and, you know, the whole accelerator incubator type world. I mean, that's, in a sense, the parallel world to business school. I mean, that's that's the, you know, business schools grew up in what the 30s, 40s, 50s as a as a sort of they got attached to universities. Y Combinator isn't part of a university, but it's teaching the same kind of a thing as you might learn in business school. It just didn't happen to be attached to a university. And maybe that's what will happen with computational X.

I'm not sure. Maybe what will happen is it will grow that way at first, and then those things will become acquisition targets for universities, so to speak. And universities will absorb these things because universities just have this infrastructure that's been built up. I mean, there's a lot of, it's different in different countries, but in the US, for example, there's a lot of this kind of government intersecting infrastructure about, you know, student loans and all this kind of thing, and the whole machinery of credentialing and so on, that's very entwined with what is now, well, in the US, it's like 140-year-old. I mean,

some universities are a bit older than that, but it really started developing maybe 150 years ago or something, that infrastructure. Yeah. That's fascinating. If you become the head of the kind of computational equivalent university, I guess you could call yourself the principal of computational equivalence. That would be nice. It would be lovely to. That's a cool idea. One of the things that's great

about computation is that it is in some level sort of, it's accessible to everybody it's not like it's not like there happened to be a tantalum deposit here so we can mine that it's kind of like it's a global resource

and you know we were talking a bit earlier about kind of the, who gets to make use of that resource and how do people kind of, you know, how do people get to the point where they can be at the leading edge of these kinds of things? And I said, I think it's, you know, it's a, it's a societal challenge more than it is in this particular case, more than it is a technical challenge. Yeah. Okay okay so let me now move to the final part of the conversation which is the content of nks and some of its implications for history technology and artificial intelligence so i'd

be grateful if you could just briefly explain the principle of computational equivalence and perhaps some of the remarkable discoveries that led to you formulating it and please assume many listeners probably won't even know what computation is universality is or cellular automata are right so i mean the the what is computation computation as i see it is you define precise rules and then you follow them. It's a way of kind of formalizing things that happen in the world as you describe them. It's a way where you can say, let me write down this rule. The rule is going to say I've got a line of black and white cells, and the rule says if I have a black cell here and a white cell to its right

and a black cell to its left, then underneath I'll i have a black cell here and a white cell to its right and a black cell to its left then underneath i'll put down a black cell you just keep applying that rule over and over again that's rule 30 uh that's a piece of rules okay that's yeah that's a yeah it has eight little pieces like yeah yeah um the uh and and the terrible thing is you ask me sort of from my memory to to produce them and i'll, I just need to get out my computer. Anyway, so the thing that had, so you've defined these rules. They're really simple rules.

You can think of these rules as kind of implementing a computation, but it's in a sense it's a computation with extremely simple rules. And you might think, as I did, that when the rules are sufficiently simple, whatever the thing does will be correspondingly simple. And I'll certainly always be able to say what it's going to do because, after all, I know its rules. Well, the big surprise to me was,

even though the rules may be very simple, it can still turn out that the behavior that they have is very complicated. It looks very complicated to me was, even though the rules may be very simple, it can still turn out that the behavior that they have is very complicated. It looks very complicated to me. I can try and apply all kinds of mathematics, statistics, cryptography, whatever to it. And it's like, can I crack this? That was what Dick Feynman was trying to do with Rule 30. Can I crack this using some mathematical method? And the answer is, well, no. It's somehow doing something that is computationally sophisticated enough that you can't just say, oh, I know the answer. It's working out the answer for itself by following step by step what it's doing.

But you can't just sort of say, I'm smarter than it is. I'm going to tell you what the answer is. So, you know, I observed this first in probably 1982. I really didn't recognize it properly until 1984, kind of this phenomenon that very simple rules can produce very complicated behavior. And it's then like, how do you understand that phenomenon? How do you, what's the bigger picture of what's going on there? And what I realized is every one of those rules being applied,

that's a computation that's happening. And then the question is, well, you know, is that a computation where it's kind of a simple computation? I can kind of just jump ahead and say what the answer is or not. And the thing that I realized is in the end, even though the rules are simple, the computations that get done are just as sophisticated as the computations that can get done by much more complicated rules, including the kind of rules that operate in our brains and things like this. And so that's the principle of computational equivalence is the statement that above very low threshold, and basically any set of rules where the behavior is not obviously simple will turn out to be correspond to doing a computation that's

kind of as sophisticated as any computation can be and that so that's this idea that you know you're looking at these rules and they're really really simple ones they just do very simple things they make periodic patterns maybe they make fractal patterns that's kind of the benoit point and then as you kind of go to other rules, suddenly you see all this incredible complexity. And there's this one threshold. Once you pass that one threshold, they're all the same. What does that mean? It means, for example, one of the big consequences is the thing I call computational irreducibility. You say, I've got rule 30. I've got the simple rule. I look at what it does. I run

it for a billion steps. I can follow all those billion steps, but can I jump ahead and say what it's going to do after those billion steps by something less computationally expensive than following those billion steps? Computational irreducibility says you can't do that. It's a very important idea, I think, because it kind of tells one there's a limitation to science. You know, what one had come to expect from kind of the mathematical equations approach to science is science can predict stuff. We write down the equation. It just tells us, oh, at this value of the time, this is what will have happened. And that's kind of the expectation. That's what people think

science is about, is about predicting things and having a cheap way to predict stuff. What computational irreducibility implies is you don't get to do that all the time. There is an awful lot of what's out there in the computational universe is computationally irreducible. And it's saying sort of from within science, you're being told, no, you can't make these kind of easy predictions. You can't expect what we thought was the mission of science to work out. So it's kind of, I think it's a rather important thing in terms of our sort of everyday understanding of the world and of what science means and so on. And it's something which people are slowly coming to terms with.

It's kind of like, it's like the question, can we kind of force the AI to only do what we want it to do? Well, no, because there will always be unexpected things it does because of computational irreducibility. Can we open up the code of the AI and say, oh, now we can see the code, so we know it's not going to do anything bad type thing? No, you can't because of computational irreducibility. No, it has a lot of these consequences.

And in the end, it's the interplay of computational irreducibility and our finiteness as observers that ends up with the laws of physics that we have. Because you might say, okay, there's computational irreducibility in the world. How come we can predict anything? It could be the case that everything that goes on in the world is ultimately unpredictable, that in a sense, everything is governed by fate. We just never know what's going to happen. It's always just wait and see what happens. But one of the consequences of computational reducibility is this phenomenon that there are always these patches, these pockets of computational reducibility where you can jump ahead. Those are the things that are the discoveries we make that let us say things in

science and so on. And we kind of live in particular pockets of computational reducibility. And it turns out that for observers like us, we kind of, we parse this computationally irreducible underlying structure of the world in terms where we sort of aggregated things together. And there are inevitable laws of that aggregation. So, for example, you've got a bunch of molecules, gas bouncing around in this room, and the motion of those molecules is really complicated. That's the whole sort of second law of thermodynamics story is about, oh, it's really complicated, really random down there. But yet, in terms of what we care about,

about gas molecules or whatever, we notice these overall air currents. We notice the gas laws and so on. These are things that we can talk about at our level of observation, which are pieces of reducibility on top of this computational irreducibility. And anyway, this is kind of the sort of the philosophical, both consequence and underpinning of our physics project is this interplay between what are we like as observers and how does computational irreducibility work? Great. Okay. So at least three profound ideas in there. Let me push you on a couple of things. So how many more rules have been shown to be universal since 2002? It's a barren story because it's kind of a, you know, we got Rule 110, we got the Turing machine.

There's some kind of simple extensions of those kinds of things. I would say that the, uh, proving universality is really hard. It's a computationally irreducible story. You know, it's kind of, you never know. In fact,

it's an undecidable story. You never know how far you're going to have to go to prove universality. For me, it's kind of like, uh, you know, at least we've got a few data points. At least we've got a few places where we can say, yup, we know it works out this way. I'd love to have more. You know, it's one of the things that perhaps, well, there's a couple of points. There's, you know, in the end, it's all about making compilers that compile to a machine code that is unbelievably low level. And that, as molecular computing becomes more important, that may be something on which there is more emphasis put. But not a lot has been done.

It's terrible, really. I mean, it's some, because it is. Ultimately, it's a really interesting thing to know. You know, I suspect that the S-combinator on its own is universal. Okay? And I put up a little prize for that. And so far, no serious takers on that one. Except for a bunch of people saying it can't possibly be true. And I point out, no, no, no serious takers on that one, except for a bunch of people saying it can't possibly be true.

And I point out, no, no, no. The argument you have for why it isn't true isn't right. But so that's the thing. I mean, what people choose to work on, it's a funny set of choices because it's kind of like, okay, we got a few data points here. People might say, well, at some point it might become like a celebrated problem, and then everybody's got to solve it like the Riemann hypothesis or something.

But for some reason, for whatever reason, it didn't quite get to the celebrated hypothesis type stage. And so it hasn't had this kind of herd kind of try and populate it. But that's, yeah, I haven't, I actually haven't thought about it so much in recent times because those things are always so, so incredibly difficult and technical and detailed and so on not my kind of thing at all right um and uh you know now the question is can i automate it and that's a more feasible thing and uh that's interesting question i mean with proof assistance and these kinds of things actually that's a reasonable question. Could one have sort of a proof-assistant system that is a computer-assisted way of doing universality proofs?

I don't think anybody's touched that. It's a good thing. It's a good thing I will have to bear that in mind as I come up with projects in our summer school next week. Does the fact that not many more rules have been shown to be universal since 2002 cast doubt on the principle of computational equivalence? The slightest bit. Because one of its key implications is that universal systems are ubiquitous.

Right. But the problem is, it's so hard to climb those mountains that saying, oh, there isn't a mountain there, there's no way you can say there isn't a mountain there. You know, it's not like anybody, you know, it's not like people said, this might be a mountain. No, actually, it turns out it's flat. It's like, yep, those mountains are still out there. And no, no, not at all. I mean, in fact, I would say that the kind of intuition behind the principle of computational equivalence of,

you know, in any system, you can find complicated behavior and so on. That gets repeated over and over and over again. People, and I watch, actually, it's kind of interesting at our summer schools and things like that. People say, I've got this system. And look, it's a really simple system. It can't possibly do anything complicated. I've even said that myself about lots of systems. I've even, in the last, when was the last time this happened to me? Within the last three months, I'm sure. I've even, in the last, when was the last time this happened to me? Within the last three months, I'm sure. I've even had the same mistaken intuition. This system is

so simple, it can't do anything interesting. And then, you know, I go, I study it, and oh my gosh, it does something complicated. You know, who knew? And it's like, well, I've got this whole principle of computational equivalence. Now, you know, I'm a first-gen, you know, first-generation person in this regard, so it still seems to me very surprising. But, you know, to the next layer of people, the Jonathan Gorads of this world and so on, it seems less surprising to them

because it's always been there for them. And, you know, by the time we're a few generations further on, it's going to be something people just take for granted as a principle. In the same kind of way, there are lots of scientific principles that one takes for granted. The status of the principle of computational equivalence, at some level, it's almost a definition of computation. At some level, it's a provable thing. At some level, it's a provable thing. At some level, it's a fact about nature. It's a complicated meeting point of all those kinds of things.

And I think that I would like to think that in the course of time, there will be more kind of data points where we can put a flag down and say, yep, it said this. I mean, I think it's pretty cool that it could predict this Turing machine thing. You know, Alex Smith could have discovered, no, it's not universal. He didn't. I would have been surprised if he had. But it's kind of like, you know, it's got, you know, people say, well, you know, you've got some scientific theory. Does it have predictions? Well, this one has boatloads of predictions.

Now go out and actually do the experiments. It isn't experiments here. It's theoretical work. And go validate these things. Well, that turns out to be really hard. But it's kind of nice that those things are out there to be validated, so to speak. Just as a piece of intellectual history, I'm curious.

So, okay, so computational irreducibility follows logically from the principle of computational equivalence. That's not the order that I discovered them in. Yeah, right. Okay, so was it when you were looking at Rule 30 that you had the intuition about computational irreducibility? More or less, yes. Right.

So that was 1984, 1985. Yeah. And actually, interestingly, I sort of tracked this history down. The thing that really caused me to kind of condense sort of interesting actually the the this idea of computational irreducibility i had sort of the general intuition of it but i was writing an article for scientific american and i wanted to kind of explain what was going on more clearly and that's when i kind of condensed it into this idea of computational irreducibility and later on when, when I was working on the NKS book,

that's when I kind of, again, wanted to condense a bunch of things that I'd seen. And that's when I kind of came up with the principle of computational equivalence. So it's kind of both of these, in a sense, were summaries of things that were expositorily driven. Right, right. Okay, so let me push you on computational irreducibility. So I guess my claim here will be that it's overstated or not as prevalent as the book makes out. So there must still exist many opportunities to outrun natural systems because nature with its tendency to maximize entropy is less likely to naturally produce the complexity that we might associate with sophisticated computations. And instead, we see a lot of randomness. Well, let's see. You've packed a lot of ideas there, which I think need to be

complicated to unpack. I mean, is computational irreducibility not as much of a thing as i say it's the thing you should go do some computer experiments and you will come back saying it's a real thing because it's something for which we just don't have intuition i mean i don't i don't even now even though i've lived with this thing for you know 40 years now it's still i still make this intuitional mistake and um it's uh uh even though it doesn't last long for me because i know oh yeah i made that same intuitional mistake again type thing but it's it's something where um kind of the the question about sort of what happens in nature is the things that we notice most in nature and that we use for our technology and do engineering with are precisely the things that we can predict.

We have selected those things to build our world out of, to build our built world out of, that are things where we can say what's going to happen. We don't want a car that goes from here to there. We don't want a thing that has this random walk, but we don't know where it's going to end up. So we pick these pockets of reducibility to live in, so to speak. I mean, you could live in the hostile environment of computational irreducibility, or you could live in the pleasant Mediterranean climate or something of computationally reducible things. I think that environment of computational irreducibility, or you could live in the pleasant Mediterranean climate or something of computationally reducible things. I think that has a certain selection bias for us. And when it comes to, for example,

if you're asking about the AIs or something, do they live in, you know, right now, the AIs that we've built are trained on human stuff. So they work in a way that's very aligned with the way that we work. But if you say, where could the AIs go? They've got this whole computational universe out there. They could go off and start just spinning around

in the computational universe. Well, then they might find other pockets of reducibility, out there. They could go off and start just spinning around in the computational universe. Well, then they might find other pockets of reducibility, but they will be kind of, they're out there in the computationally irreducible world. And it's not, you know, this is a feature of we are selecting things for ourselves that we can successfully navigate with the finite minds that we have and so on. If the computational paradigm ultimately fails scientifically, and I know that you strongly believe it won't and you've worked very hard to establish it, but assuming for the sake of argument that it does, what do you think the most likely reason for that would be?

Well, we're deeply past the point of no return let's put it that way i mean that yeah you know if you look at the new models that have been made for things in the last 20 years it's kind of like it's programs not not equations i mean this this is a this story is you know if one was wondering you know how was the story going to go that story is already you know we know the answer but i think if if you know if you ask the question would you know it's there's computation there's things which are not computation universal that are but a simpler

way you can always jump ahead there are are things that are hyper-computational, where even you say you've got a Turing machine that does its computation. It's computationally reducible. But you could say, imagine that you had a machine that could just answer all computationally reducible questions. Just imagine you have such a machine. Alan Turing had this idea he called an oracle. And imagine you have such a machine. Alan Turing had this idea he called an oracle. And imagine you have those things.

OK, we've got this hypercomputational world where it's full of these things which can do beyond what computational irreducibility talks about. It can jump ahead in every computational irreducible computation. I don't think we live in that world. I think we have pretty good evidence we don't live in that world. I think we have pretty good evidence we don't live in that world. As a theoretical matter, that world is sealed off from the world in which we live

in the same way that the innards of a black hole are sealed off by an event horizon. From inside a black hole, at least in the simplest case, time stops. So in other words, we get to think that we have an infinite future. If you're living inside a black hole you do not have looked at from our point of view, you don't have an infinite future. Time will stop. To you, you're just doing your thing, and there's a point at which, well, looked at from an outside observer, your thing just stops.

But for you yourself, you're just doing your thing. And similarly, from a hypercomputational observer of our universe, it would be like, well, those guys just, they stopped. They didn't do anything interesting. It's only hypercomputation that's interesting. But for us, there will be hyper-ruly ads. Those can, in principle, exist. But they are forever sealed off from us by an event horizon, basically. And so it's not even clear what it means to talk about their existence. So I think, you know, I feel like, you know, as a practical matter, I mean, you know, you imagine the science fiction universe where AIs have been outlawed,

we don't have computers, and it's like, what's the world like? Well, it's a little bit paleolithic. I mean, it's kind of, yes, you can roll that. I think we're deeply past the point of no return. It's kind of like asking some question like, you know, what would happen if the speed of no return it's kind of like asking some question like you know uh what would happen if the speed of light was infinite well it's just not and the universe just is not constructible it's it's just a different you know you all these things are interdependent and the fact is we are you know at this point in our development of our civilization, I think we're really past the point of no return for computation as a paradigm. Now, what will happen in the, how will people, how will more people learn this paradigm?

I mean, that may be by fits and starts. I mean, it's just like people, there was a long period of time when people didn't learn natural science. When, you know, when it was like, well, it's either in Aristotle or it's in the Bible. And, you know, there's nothing else to learn type thing. So, you know, human affairs can certainly inhibit kind of what happens. But I think there's a certain deep inexorability to the place where where we're going to end up and it's kind of like in um uh and and you can already see there's kind of enough has happened that kind of the the end of the story is is pretty clear and it's it's just

like if you'd gone back to um oh i don't know in the in the 1500s, and you ask people, how do you think about the world? How do you work out what to do in the world? Nobody's going to say we use math to do that. That was not really a thing. Math was a kind of a toy, a thing. And it was used by merchants and so on to do very basic math, but nothing fancy. It was the thing where people would do these competitions to prove cubic equations and things like this, but it wasn't a thing where people would say,

well, we're going to work out everything we do in the world and all our engineering and so on was going to be done with math. Nobody would have said that. But yet, it became quite inexorable at some point. A very quick digression on the graph-based physics. So aren't these theories compatible with nearly any world we could find ourselves in? You mean as, well, again, you're packing a bunch of things into that question. A world we could find ourselves in.

So, you know, what happens in this idea of the Rulliad, this kind of entangled limit of all possible computations, which we are part of, and we are sampling it, and given our characteristics as observers, there are certain constraints on what world we can perceive ourselves to be in. If we were different kinds of observers, if we were observers who are, you know, greatly extended in our computational abilities, greatly extended in space, don't believe we're persistent in time, all these kinds of things, we could believe we're in a different world. Let's see, I think that you say, are they compatible with things, we could believe we're in a different world.

Let's see. I think that you say, are they compatible with any world we could find ourselves in? I think that if you're asking, could general relativity not be true in the world that, you know, could our theories still be right if general relativity was not true in the world that we perceive? The answer is, if we are the way we are, no. If we are aliens with very different sort of sensory result, you know, apparatus and so on, then sure. But I think for the way for us to be the way we are, it is inevitable. It's sort of a matter of a formal science that the Rulliad plus the way we are implies things like general relativity. I see. Are some historical dynamics computationally irreducible?

Yes. I think this question of the theory of history, is there a theory of what will happen in the world? No. There's lots of computational irreducibility. You just have to see what happens. I'm sorry. I misspoke. The question was, are some historical dynamics computationally reducible? Ah. Well, so can there be theories about history? Yes. Yes. The answer is yes, for sure. I mean, I think that, you know, people at different times, you know, lots of philosophers have had, you know, theories of history. They've often been horribly abused in

sociopolitical ends. But yes, there can be an inexorability to certain aspects of history, for sure. And everybody kind of has an intuitive sense that history repeats itself. And certainly, the lesson of history is that history repeats itself, so to speak. And that, in a sense, is right there telling you that there are some reducibilities in history. There is some kind of theory of history, at least at that local level. Right, just through the repetition. Yeah, I mean, it just shows you there's a theory. It shows you whether there is a bigger arc to that repeatability.

I don't know, but there is some repeatability, kind of suggests that there is a theory, so to speak. Where would Karl Popper's anti-historicism fit into your framework? Is it like a limiting case of computational irreducibility is you have to tell me well just he's kind of idea that the course of human history is fundamentally unpredictable since it largely depends on the growth of knowledge and we can't predict the science and technology of tomorrow since if we could we would already have invented it yeah i think that's actually not that far away. I mean, I think that's a, you know, one of the things that is, well, now let's see.

I mean, when you say you can't predict it or otherwise we would have invented it, I'm not sure I would agree with that conclusion because computational irreducibility is all about the fact that there is, you know, you can know the rules, but not know what will happen. So I think it's, it's, um, uh, I'm not sure. I mean, that's, um, interesting. I should, I should learn about that. It's, it's, I don't know. I don't know that piece of intellectual history. I'll send you the reference. Computational irreducibility found in a surprising application in proof of work for blockchains. What are the odds that you've read the NKS book are high. Right.

I think it's one of these cases, you know, you always have to wonder about something like that situation and you have to wonder what's the human story and what's the, what is the kind of, what's the right thing to do with whatever one knows or doesn't know about that. And I think it's one of these things where I think the, let's put it this way. I think there is the idea of computational irreducibility in the NKS book and the arrival of proof of work in blockchain were not unrelated. Okay, interesting. So moving finally to AI, many people worry about unaligned artificial general intelligence,

and I think it's a risk we should take seriously. But computational irreducibility implies well it must imply that a mathematical definition of alignment is impossible right yes i mean the the thing that there isn't a mathematical definition of what we want AIs to be like. You know, in the minimal thing we might say about AIs, about their alignment, is, you know, let's have them be like people are. And then people immediately say, no, no, no, we don't want them to be like people. People have all kinds of problems. We want them to be like people aspire to be. And at that point, you've kind of fallen off the cliff because at that point, what do people aspire to be?

Well, different people aspire to be different and different cultures aspire in different ways. And I think the concept that there will be a perfect mathematical aspiration is just completely wrongheaded. It's just the wrong type of an answer. You know, we do not, the question of how we should be is a question that is a reflection back on us. There is no, this is the way we should be imposed by mathematics, so to speak.

Humans have ethical beliefs that are a reflection of humanity. One of the things I realized recently is one of the things that's confusing about ethics is if you're used to doing science, you say, well, I'm going to separate a piece of the system. And I'm going to say, I'm going to study this particular subsystem. I'm going to figure out exactly what happens in the subsystem. Everything else is irrelevant. But in ethics, you can never do that. So, you know, you imagine you're doing one of these trolley problem things. You know, you're going to decide you're going to kill the three giraffes or the 18 llamas. Okay. And, you know, which one is it going to be? Well, then you realize to really answer that question to the best ability of

humanity, you're looking at the tentacles of you know the religious beliefs of the tribe in africa that deals with giraffes and the you know the this kind of thing that was the consequence of the llama for its wool that went in the supply chain and all this kind of thing yeah um and it's in other words ethics is not a separable. It's my current impression. One of the problems with ethics, it doesn't have the separability that we've been used to in science. In other words, it necessarily pulls in everything. We don't get to say there's this microethics for this particular thing.

We can solve ethics for this thing without the broader picture of ethics outside. And I think, you know, this question of, of, so, uh, you know, computational irreducibility is certainly a, you know, if you say, I'm going to make the system of laws and I'm going to make the system of constraints on AIs, and that means I know everything that's going to happen. Well, no, you don't. There will always be an unexpected consequence. There will always be this thing that kind of spurts out and isn't what you kind of expected to have happen because there's this irreducibility, this kind of inexorable kind of computational process that you can't readily predict. So I think it's a, you know, the idea that we're going to have a

prescriptive collection of principles for AIs, and we're going to be able to say, and this is enough, that's everything we need to constrain the AIs in the way we want. It's just not going to happen that way. It just can't happen that way. I mean, you know, it's something I've been thinking about recently is, so what the heck do we actually do? You know, I was realizing this, I was connecting, you know, we have this connection to ChapGPT, for example. And I was thinking, you know, it's now I can write Wolfram language code. I can actually run that code on my computer. I'm right there at the moment where I'm going to press the button that says, okay, LLM, you know, whatever code you write is going to run on my computer. And I'm like, that's probably a bad idea because it's kind of like, I don't know,

it's going to log into all of my accounts everywhere, and it's going to start doing, you know, it's going to send you email, and it's going to tell you this or that thing, and it's just going to be my, you know, the LLM is in control now. And I realized that probably it needs some kind of constraints on this. But what constraints should they be? If I say, well, you can't do anything, you can't modify any file, that's kind of like, well, no. Then there's a lot of stuff that would be useful to me that you can't do um and i think um uh the you know so i don't know you know i've been trying to work out

there isn't going to be there is no set of sort of golden principles that humanity agrees on that are what we aspire to it's like sorry that just doesn't exist that's not the nature of of civilization it's not the nature of our society and so on. And so then the question is, so what do you do when you don't have that? And my best current thought is, in fact, I was just chatting with the person I was chatting with before you about this, is kind of developing kind of what are, let's say, a couple of hundred principles you might pick. Like one principle might be, I don't know, an AI must always have an owner. An AI must always do what its owner tells it to do. An AI must whatever.

You might say an AI must always have an owner. Is that a principle we want? Is that a principle we don't want? Some people will pick differently. And so, you know, but can you at least provide scaffolding for what might be the set of principles that you want? And then people will be, you know, it's like kind of be careful what you wish for because you know you make up these 200 principles or something and then you see a few years later you know people with placards saying you know don't do number 34 or something and you realize oh my gosh you know what did one set up and uh i think um uh you know but i think one needs some kind of framework for thinking about these things rather than just people saying, oh, we want AIs to be virtuous.

Well, what the heck does that mean? Or we want or we have this one particular thing. We want, you know, AIs to not do this societally terrible thing right here. But we're blind to all this other stuff. None of that is going to work. You have to kind of have this sort of formalization of ethics that is such that you can actually pick. You can literally say, I'm going to be running with number 23, number 25, and not number 24 or something.

Right. But you've got to make that kind of framework. I have about two more pages of questions, but I think we should leave it there because I've kept you much longer than I intended. Yeah, I'm sorry. But perhaps we can pick up the AI topic another time because I think it's, well, both important, but your work has really crucial implications for how we should deal with those problems. But Stephen, this has

been an absolute honor. I really appreciate it. Thank you so much. Thanks for lots of interesting questions. Thanks so much for listening. Two quick things before you go. First, for links, show notes, and the episode transcript, go to my website, thejspod.com. That's thejspod.com. And finally, if you think the conversations I'm having are worth sharing, I'd be deeply grateful if you sent this episode or the show to a friend. Message it to them, email them, drop a link in a WhatsApp group. The primary way these conversations reach more people is through my listeners sharing them. Thanks again. Until next time. Ciao.