Digital Disruption

00;00;01;05 - 00;00;31;19

Stephen Wolfram

So lots of crazy things will happen. There'll be more cyber attacks, the defense will be AI and the attack will be AI. There'll be a whole ratcheting up of those kinds of things up to a level that is sort of, you know, far beyond what humans can individually understand. In many areas, there'll be things where it's battle between the AIS that will be, in some ways disquieting to people that there's a lot of stuff that's happening sort of at the AI layer that humans don't really understand what's going on.

00;00;31;20 - 00;00;40;14

Stephen Wolfram

What was discovered in AI was essentially a wild horse. The era of where you can expect to understand how the machine looks inside. It's no longer here.

00;00;40;16 - 00;01;03;10

Geoff Nielson

AI is only becoming more powerful. But are we actually capable of understanding, controlling, or even predicting what it will do? Stephen Wolfram doesn't think so. He thinks it's a fundamentally wild and untamable technology, and we're going to have to figure out how to live with it safely. Stephen Wolfram is a legend in AI and computation. He's founder and CEO of Wolfram Research.

00;01;03;17 - 00;01;29;08

Geoff Nielson

Creator of Mathematica and Wolfram Alpha, and has been writing books and papers about the computational universe for over 40 years. We explore what I can and cannot do, what we can control, and what we'll have to learn to live with. And what leaders need to understand before handing over more decisions to machines. I'm Geoff Nielson and this is digital disruption.

00;01;29;11 - 00;01;48;23

Geoff Nielson

Well, Steven, thanks so much for joining us. It's an absolute pleasure to have you. And I wanted to ask you a little bit about, I guess, your outlook when it comes to technology. And I guess the society around it. But before we talk about outlook, maybe it makes a little bit more sense to to back up and talk about the now and where you see technology as being right now.

00;01;48;25 - 00;02;11;14

Geoff Nielson

And the reason I bring that up is, you know, you've been in computation and AI in these spaces for so long. And, you know, we're at a point now with transformer architecture with limbs where they seem like they're truly transformational, like we're on this sort of exponential curve. But at the same time, there's so much hype around them that there's this counter-narrative where people have grown disillusioned.

00;02;11;15 - 00;02;25;02

Geoff Nielson

There's a backlash. And so I guess the first question I wanted to ask you is, do you believe in the long term promise of this technology, or do you think it's mostly hype and smoke and mirrors? How much more trajectory do we have here to go?

00;02;25;04 - 00;02;55;19

Stephen Wolfram

Right. I mean, what has been discovered and invented with AI in recent years is important, useful, long term, all those kinds of things. Is it everything that everybody's extrapolation thinks it is? No. Is it something which will be of long term importance? Absolutely, yes. I mean, it's, you know, there are there are a whole series of kind of steps that have happened in the development of technology in general computation in particular, even AI in particular.

00;02;55;21 - 00;03;23;12

Stephen Wolfram

I mean, and, you know, if you go back 2011, 2012, it was kind of like deep learning is actually going to work. I mean, neural nets were a thing from the 1940s. And, you know, in the in the 1950s, people are like neural nets are going to do everything. And then 1960s neural nets are useless. 1980s, the kind of interesting then, you know, 2011, 2012, the breakout moment of yes, we can do image recognition of cats versus dogs and so on.

00;03;23;14 - 00;03;49;15

Stephen Wolfram

But neural nets. Wow. That's useful. You know, things in like, for example, image recognition haven't advanced that much since that time. That was of a step where suddenly that was possible and hadn't been possible before. Same thing happened with things like speech to text and then the ChatGPT moment in 2022 surprised everybody, including the people working on it, including those of us who've been following this tech for a long time.

00;03;49;17 - 00;04;08;03

Stephen Wolfram

Nobody knew at what point sort of fluent generation of of text would be possible, or even whether whether it would ever be possible. Turns out it's possible now that we know it's possible. Go back and say, how did this come to happen? We realized there's a whole bunch of science we don't know about what made it possible. We can talk about that a bit.

00;04;08;06 - 00;04;30;10

Stephen Wolfram

But then, you know, then in more recent times, kind of the yes, you can have loops where, you know, code is being generated and so on. I think the the thing to understand is most of what's happened is these kind of steps up together with a bunch of good engineering around these sort of core tools that have allowed them to be actually applied in different places.

00;04;30;12 - 00;04;48;26

Stephen Wolfram

I think, you know, a lot of the, you know, if you look at sort of the the core part of what's going on hasn't changed that much after its initial step. But if you look at how useful is it, it kind of changed a lot just by the fact that it's plugged into all these other systems. It's actually something that you can include in your workflows and so on.

00;04;48;29 - 00;05;12;12

Stephen Wolfram

I mean, I think that the, this sort of idea that we've got, you know, we've got nothing worked, then tried to beat, then coding worked and so on. Let's extrapolate that to the moon. That's kind of silly. I think that there will be other kind of step moments. You know, I'm guessing they'll be one for humanoid robotics at some point.

00;05;12;15 - 00;05;35;25

Stephen Wolfram

Which will have important effects in the world just as the current, you know, text generation, code generation thing has had important effects in the world. I think that, you know, a lot of things that have been the traditional technology that's been built out in all kinds of different domains will continue to be valuable. This is just another tool that makes more things possible.

00;05;35;26 - 00;05;57;16

Stephen Wolfram

I mean, it's like back in the day, we had, you know, programs that had command line interfaces and that was it. That was all you got. And then there were graphical user interfaces, and that opened up all kinds of different possibilities, different levels of usage, different kinds of users. Same with mobile. The fundamental thing we got with ChatGPT was linguistic user interfaces.

00;05;57;19 - 00;06;26;03

Stephen Wolfram

I mean, in fact, technology we'd built back in 2009 and WolframAlpha was kind of the first version of that, of that kind of idea. That was has been very successful. It's kind of aims at a higher level of fidelity, a, less breadth of coverage than, than what lessons do and so on. But it's something where, you know what we got a big part of what we got is linguistic user interfaces.

00;06;26;06 - 00;06;47;08

Stephen Wolfram

Very important. Doesn't mean that the things behind that, are all kind of, you know, what was there before doesn't make any sense anymore. It still makes perfect sense. It's just there is a an additional kind of piece to the whole story. So that would be my I mean, look, I think there's a lot of detail about what where things are going.

00;06;47;09 - 00;07;27;07

Stephen Wolfram

I mean, people say, you know, we're going to have the zero hallucination alarm, which is kind of a meaningless idea because, you know, you can ask it to write fiction to. And the the point of fiction is it's about something that isn't sort of the reality of the world. I think the thing I've been much involved in, I mean, the technology we've built over the course of years is our computational language, Wolfram Language, whose objective is to have kind of a precise computational representation of things in the world, whether those things are abstract things, whether those things are cities or chemicals or images or whatever else.

00;07;27;09 - 00;07;51;14

Stephen Wolfram

It's a way of having sort of a computational representation of the world from which you can do precise computation. And the thing that's kind of emerging as a terrific workflow is you kind of use the linguistic user interface provided by modern Alam AI, and you say, oh, this is vaguely what I want to do. It generates a piece of computational language code.

00;07;51;16 - 00;08;12;13

Stephen Wolfram

And then the critical thing is that computational language code is set up so that you as a human, can read it. And so you can say, is this actually what I meant? If yes. Great. That becomes a sort of solid brick that you can start building a whole system on top out. If no. Well iterate again. But the, the kind of that's been.

00;08;12;13 - 00;08;41;26

Stephen Wolfram

So it's kind of combining what has been, at least for us, a kind of, 4 or 5 decade journey of kind of creating the sort of computational representation of the world, which is something that's all about sort of precise representations of things together with this, you know, just do kind of what I mean, much more like kind of human, sort of basic human thinking kind of approach that's provided by sort of the modern AI opportunity.

00;08;41;28 - 00;09;03;02

Geoff Nielson

I talked to a lot of CIOs, and everybody knows that running a successful IT shop is extremely hard, and AI has only made it worse. You know the drill. Immature process is a fragmented data ecosystem and an unlimited demand for it. It all makes for an impossible job. And yet nobody has invented a unified operating system for it.

00;09;03;05 - 00;09;28;17

Geoff Nielson

We're literally the last function without one until now. That's why Infotech created CIO analytics, a single platform to run the IT department. We call it API driven advisory. It's powered by your data, our analyst insight, and it works with your existing tools right out of the box. If you're interested, click on the link in the description to request access and check out CIO Analytics in Action for yourself.

00;09;28;19 - 00;09;59;27

Geoff Nielson

An InfoSec team lead can get you started. Same day. Better context, better insight, better IT performance only with API driven advisory only with infotech. The notion of computational representation. You've mentioned it a few times and you know, I've I'm familiar enough with I guess, some of your your research and your writings to know that this is tied to some, you know, much deeper ideas about the way that, the world is understandable and model a book.

00;10;00;00 - 00;10;20;23

Geoff Nielson

Can you share a little bit about when you talk about computational representation and, you know, you gave examples as, you know, kind of different as, you know, chemicals and cities. What are the implications here? How do you understand our underlying world and how does computational representation get built on top of that? And why is it useful?

00;10;20;25 - 00;10;57;25

Stephen Wolfram

Okay, so there's a couple of different levels. I mean, this rabbit hole goes extremely deep. Actually it goes as deep as anything can go, so to speak. But the thing to understand is, there's on the one side is the depth of the rabbit hole about how the physical universe is built and so on. On the other side is what we humans understand and think about and sort of have narratives about, and kind of my goal and, in kind of in and, and sort of our computational language is to make a bridge between the kinds of things humans think about and capable of thinking about, and the kinds of things that exist in the

00;10;57;25 - 00;11;29;08

Stephen Wolfram

world. Now, when it comes to so, in other words, when once the sort of the big idea is to have a computational language, kind of a notation for representing things in the world in a precise way, that is, where, where you're, you're able to, to kind of take well, you're, know, think of it, you know, like, for example, you know, mathematics has a notation that God invented 500 years ago.

00;11;29;08 - 00;11;51;06

Stephen Wolfram

So now with plus signs and equal signs and so on, we're trying to represent things which you can think about computationally and sort of a notation that you can use to communicate with a computer. But you can also use as a framework to think about as a human, so to speak. So that's kind of that's, that's the objective on the side of kind of computational language.

00;11;51;13 - 00;12;15;16

Stephen Wolfram

It's a bridge between humans on what is computationally possible. On the other side. It's like, okay, what ultimately is computationally possible. And that has to do with sort of how the universe is constructed. And that's a that's a much deeper rabbit hole, which, in my, in my life, these things are somewhat connected. But it's a, it's a fairly long, long kind of chain.

00;12;15;18 - 00;12;39;19

Stephen Wolfram

I can explain it a bit. I mean, you know, to explain sort of the, my, my life is sort of consisted of alternation between building technology and doing basic science about five iterations. So far. And, that's been something where, you know, the technology has provided tools that have let me do a bunch of basic science. The basic science has given me ideas that let me build technology.

00;12;39;21 - 00;13;17;16

Stephen Wolfram

It's been actually really amazing and powerful, and it's something that, you know, I didn't, I didn't plan it that way, but it's the way things have worked out. So when it comes to kind of the, the science side of it, the, the sort of the big question is we've got the natural world. How do we formalize what happens in the natural world, how do we talk about what happens in the natural world and sort of the, if you go back, I don't know, to antiquity, people sort of thought about it in sort of philosophical terms, how, how nature ought to work and so on.

00;13;17;19 - 00;13;41;18

Stephen Wolfram

Then you get to the 1600s. And the big idea was, let's use math, which had been invented for other purposes. Let's use math as a way to represent the natural world. That worked pretty well. That gave us kind of the whole chain from, you know, calculus to a lot of modern physics and things like this. The, the sort of the big thing that I realized back in the,

00;13;41;25 - 00;14;06;02

Stephen Wolfram

Oh, gosh, how long ago is it now? Nearly least 50 years ago now. Is, that sort of math is one particular way of formalizing things, but it's not the only way to formalize things. Computation provides a more general way to formalize things. And by computation, what I really mean is you just specifying definite rules, and then you're saying, what are the consequences of those rules?

00;14;06;05 - 00;14;38;07

Stephen Wolfram

You know, when we we typically write programs where we kind of set them up for particular purposes. But you can also just say, imagine that you have some set of rules, and then you just apply those rules over and over again. What happens? So this leads to a set of very basic kind of science that I call rheology, the study of rules and what they do and what the most significant thing you discover is that even when the rule is very simple, the behavior that it can produce can be extremely complicated.

00;14;38;13 - 00;15;04;13

Stephen Wolfram

That's something very unintuitive, people. People are always pushing against that idea until they actually try some computer experiments. And then they're like, gosh, that's not what I expected. You know, it turns out even a very simple rule can produce very complicated behavior. And that turns out, I think, to be sort of the secret that lets nature do all the complicated things we see in nature that even though the rules underneath can be very simple, the behavior is complicated.

00;15;04;13 - 00;15;26;14

Stephen Wolfram

That's what we see both in physics and in biology. And the big excitement for me in the last few years has been seeing that really come together and really being able to derive a lot of the core theory laws of physics. And now a bunch of the core results of biology, which is something that once never really had a theory of before, from, from these kind of computational ideas.

00;15;26;20 - 00;15;53;05

Stephen Wolfram

That's I would say that's the kind of, metaphysical underpinning of thinking about the world computationally when it comes to kind of the actual operational rules that lead you to features of physics. Those rules are very incomprehensible to humans. The level at which we humans can interact is a much higher level. And so there's kind of this, this thing of what what is how far can we get from the kinds of things that we can understand.

00;15;53;05 - 00;16;15;21

Stephen Wolfram

And that's where, computational language is really the crucial idea that lets you get from the things humans understand to what we can get done computationally. Then there's a much deeper level, which is, given the idea of computation, how do you understand sort of metaphysically, how the whole world is put together?

00;16;15;24 - 00;16;35;16

Geoff Nielson

I'm glad. Thank you for that. I'm glad we went down a bit of that rabbit hole, because I think it's useful background as we get to now, this layer of computational representation. So, you know, if we understand that everything starts with rules, can be computed, can be represented and used through AI.

00;16;35;19 - 00;16;36;27

Stephen Wolfram

00;16;36;29 - 00;17;01;27

Geoff Nielson

What what do we do with that? Like, like how do you where would you want to actually leverage those models. How does it interact with as we were saying this, this view of AI, that it's a language tool. Now we're talking about something much deeper and not even necessarily talking about reasoning, but actually a deeper understanding of the world where, well, where do you point that?

00;17;01;27 - 00;17;03;29

Geoff Nielson

And what does that what does that help you do?

00;17;04;01 - 00;17;26;10

Stephen Wolfram

Right. So, you know, if you go back 400 years, there wasn't a lot of sort of formalization of science and kind of the description of the world. It was like, the world works the way it does, and we can argue about how it works, and it's all philosophical, and it's all kind of something that is done by pure human thought without kind of the solid structure of formalization.

00;17;26;12 - 00;17;50;05

Stephen Wolfram

What we got in the 1600s was the beginning of sort of a solid structure of formalization. And that's what's given us most of modern technology and science and so on. It's very powerful. It's different from what modern AI does. Modern AI does something that we didn't know how difficult it would be, but we sort of know it's possible, which is to do something like what human brains do.

00;17;50;07 - 00;18;12;07

Stephen Wolfram

In other words, the, the things that are possible with formalization, with, you know, running explicit computations billions of times and so on. Those are not things human brains do. Those are things that we are able to do. And we learned that we were able to do as, as kind of a, a, a formalized branch of how to, how to do things.

00;18;12;10 - 00;18;36;19

Stephen Wolfram

It's different from what brains do. Brains, you know, nobody runs code in their mind. It's just not something computers run code humans, you know, if you say, here's a program, what does it do? Run it in your brain. You know, that's not something anybody can do beyond a very few steps. It's it's a different kind of activity now, the kinds of things that brains do.

00;18;36;19 - 00;19;02;02

Stephen Wolfram

Well, things like, is this a picture of a cat or a dog that had been completely out of scope for sort of the, the kind of the solid formalized computation. So one has to understand that what, what sort of modern AI is really doing is something a bit like what human brains are doing. And probably the reason, you know, when you say is that a picture of a cat or a dog is they're a mathematical definition of what's a what's a cat?

00;19;02;02 - 00;19;22;00

Stephen Wolfram

What's a dog picture? Not really. The thing that we, you know, a human can say, yeah, that's a cat, that's a dog. And the thing that makes us impressed about AI and makes AI useful to us is that it's decision about what's a cat. What's a dog is probably pretty similar to ours. So it's a great proxy for us, so to speak.

00;19;22;02 - 00;19;38;15

Stephen Wolfram

If you want to run faster, more, you know, etc., etc., etc.. You can use the AI instead of using a human, but it's doing something which has the same kind of fuzzy. Well, I don't know if it's a cat in a dog suit or, you know, is it is it really a picture of a dog or whatever else it is?

00;19;38;18 - 00;20;06;23

Stephen Wolfram

It's something that, it's it does the same kind of, sort of fuzzy stuff that brains do, which is very useful for many purposes. But for example, you know, the the skyscraper in your background was not built with fuzziness, so to speak. It was built with a bunch of, detailed engineering that made use of the kinds of things that have been discovered in the last few hundred years in kind of mathematical science and so on.

00;20;06;25 - 00;20;34;20

Stephen Wolfram

And I think that's a, you know, that's the that's the distinction that that's kind of modern AI is broad but not computationally deep. Then we have kind of formalization where things are deep but not as broad that there's, you know, what you can formalize is, what's convenient to formalize has been that's starting to change, but that's a that's a different story.

00;20;34;22 - 00;21;08;05

Stephen Wolfram

It has been, things where which are more on the kind of, objective scientific side, not the, you know, let me express human feelings, so to speak. That's that's more on the kind of, purely human side and more on the side of the kinds of things that modern I can do. So I think, you know, what we're seeing is there's sort of two complementary buckets, you know, there's the precise computational formalized part, and there's the modern I kind of humanlike parts, the two together a super powerful.

00;21;08;07 - 00;21;31;24

Stephen Wolfram

And they can leverage each other in all sorts of ways. I think, you know, the thing, you also have to understand on the kind of AI side, it's like if you have a task where getting 90% success is a total win, then you know the AI is going to do great. If you have a task where a 100% success is the only thing that's acceptable, you're going to be very frustrated.

00;21;32;00 - 00;21;54;15

Stephen Wolfram

You're going to keep on poking at the AI, and it's going to usually do the right thing and occasionally do something crazy. And by the way, that's not just a feature of we'll make the technology better. That's an intrinsic aspect of kind of the kind of thing that modern AI is. And I think that that's, you know, a few that that's sort of a different branch from the oh, we need to get it precisely right.

00;21;54;22 - 00;22;15;02

Stephen Wolfram

And it's going to be very well defined. It's going to be less broad than what you can achieve with kind of modern AI. That's that's the kind of computational branch, and that's the place where, you know, and things modernize, are getting better at like calling out technology when they need it, need it. You know, it's like hook up the MCP server, that's the beginning.

00;22;15;02 - 00;22;35;08

Stephen Wolfram

And then there's lots of pre-training that's now been done to get, sort of modern AIS to be able to use, you know, Wolfram Language and so on. And that means that the AIS can do the same kinds of things that humans do, which is when humans need to compute something they, you know, turn to our tech and so on.

00;22;35;11 - 00;22;39;27

Stephen Wolfram

They don't try and run the code in their brains, so to speak.

00;22;39;29 - 00;23;05;21

Geoff Nielson

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00;23;05;24 - 00;23;37;02

Geoff Nielson

Three self-paced learning paths, plus lead expert led sessions and content that evolves as AI does. Better judgment, better outputs, better outcomes. Check it out at the link below to learn more. So with all of that in mind, where do you see, I guess, the next frontier of the applications of this technology? And part of what I'm asking is we tend to talk about AI as though it's, you know, the next frontier in automation, and it's a tool for automation.

00;23;37;02 - 00;23;58;24

Geoff Nielson

But I think it's clear from this discussion it's not exclusively a tour tool for automation. There's other ways to use it. And it does more than that. It's you know, it's something that we conceive of as being similar in some ways to the human brain. As you said, it's not exactly 1 to 1. So so how should we be thinking about it?

00;23;58;24 - 00;24;05;04

Geoff Nielson

And what does that mean in terms of where we expect to see it applied in the next handful of years?

00;24;05;07 - 00;24;39;00

Stephen Wolfram

Well, I mean, places where kind of human judgment was useful. There are a whole bunch of those places where you can kind of fill in some AI judgment. It'll be different, but it'll be faster. And, you know, lots of things. That's that's one kind of place, another kind of places. Just by the fact that modern AI systems have ingested a lot of kind of the textual knowledge of the world and other kinds of knowledge of the world, it's useful for kind of pulling together sort of different threads from different places.

00;24;39;00 - 00;25;00;09

Stephen Wolfram

I mean, there's all sorts of success, you know, people sell it proved this math theorem. What it really did typically was, you know, there are 3 or 4 million papers and the literature of mathematics, it's ground up, essentially all of them. And if there's something where you pull the result from this place and that place and the other place, no human mathematician will have seen all of those places.

00;25;00;15 - 00;25;23;15

Stephen Wolfram

But it can it has seen those places, and it can kind of pull those threads together, and it's kind of mashing up kind of things, which were sort of already human knowledge. But putting them together in a way that's very useful. And it's kind of like, you know, web search to the piece of that. But this is doing something much, sort of more, more fine grained and, and more ambitious than that.

00;25;23;21 - 00;25;55;11

Stephen Wolfram

But it's the same kind of idea. It's taking existing human knowledge and being able to refactor it and use it. And that's something where lots of very useful things will come out of that. I mean, there are plenty of places where and by the way, that that's the kind of thing, whether it's, medical diagnosis, whether it's legal precedents, these are places where that same kind of sort of, taking the ground up kind of text of the world and being able to put pieces of it together is super useful.

00;25;55;13 - 00;26;15;15

Stephen Wolfram

When it comes to, sort of, sort of reasoning and so on. I mean, to be realistic, humans are not that good at that and nor the AIS. I mean, if you really want a big tower of kind of this follows from that follows on, that follows from that you're doing computations. You're, you're running some rule a million times or something.

00;26;15;19 - 00;26;37;18

Stephen Wolfram

That's not what human reasoning does. And I think, you know, people were very surprised when sort of ChatGPT turned out to be able to, you know, do logic. I think the reason it can do logic is because it sort of puts pieces together, sort of it it knows patterns by which things go together. And that's what logic was to Aristotle a couple thousand years ago.

00;26;37;22 - 00;26;57;00

Stephen Wolfram

It was like, hear all these arguments that people make while there are these patterns and how these arguments are put together and that was kind of the original form of logic. And that's the kind of thing that an AI system can readily understand how to do. I mean, it's something where it's kind of following the patterns of if this, then that, then that.

00;26;57;03 - 00;27;17;12

Stephen Wolfram

If you say to it, okay, now do that kind of same logic as it's applied in a computer with a billion. You know, transit gates and it's, it's, microprocessor or whatever. That's not what it does. It does the same kinds of things that humans do, sort of following patterns of how things fit together and, and applying them.

00;27;17;14 - 00;27;35;26

Stephen Wolfram

And that's, that's great. And that's useful for certain kinds of things. It falls completely flat for other kinds of things. So, I mean, I think, in terms of sort of the, you know, what, what do we actually see happen? We actually saw every company thought, you know, AI is cool. We've got to have an AI plan.

00;27;35;28 - 00;28;11;23

Stephen Wolfram

And the, you know, initially people were like, let's just put AI into everything. That didn't work very well. Then there's sort of a a a realization that there are use cases that work. There are other ones that don't work. You have to try things. Depends on the particulars of what you know your business is trying to do. There sort of patterns, as I say, the the general heuristic is, if you know, if getting it right 80% of the time is what you want, then it's a good thing to try and you know, even if you're writing code, getting it right 80% of the time when you can't read the code is sort of a

00;28;11;23 - 00;28;32;00

Stephen Wolfram

disaster. And so people have kind of said, we're just going to have all the code written by AI. And then they realize, oh my gosh, we just got drowned. And you know, I slot code. It's going to take us years to dig ourselves out from underneath us. That's a feature of the fact that you just said to the AI, go do this thing.

00;28;32;00 - 00;28;56;22

Stephen Wolfram

And it probably did 80% of us, right? 20% of it is nonsense, and nobody knows. And that's, you know, for example, our computational language, sort of the big the big thing there is that the language is sort of the highest level representation that anybody has of computational processes. And so it gives you the best chance to have humans actually understand what it was that got written and say, yes, that's right.

00;28;56;22 - 00;29;29;27

Stephen Wolfram

That's what I meant. Thus, no, you you I missed the point and we should try it again kind of thing. I think that the, the, the, the sort of, you know, what's, what's happening sort of society, I suppose, is that there's, I mean, there's a, there's a lot of kind of overreach of many things, like I was you know, I was just at, some seeing something where people were kind of programmer type.

00;29;29;27 - 00;29;49;06

Stephen Wolfram

People were saying, we're going to discover mass, and they were producing kind of math paper like output, but they were saying, but we don't understand anything that's in here. And it's like, this is not going to end well. You know, this is kind of, you know, and what's what's clear is that the value of actually understanding things has gone up.

00;29;49;09 - 00;30;11;03

Stephen Wolfram

The value of being able to mechanically do things has gone down. I mean, the fact is, you know, I've spent much of my career trying to automate a lot of kinds of computational tasks. And for me, the fact that people say, oh my gosh, we can automate all this programing is like, yeah, that's what we've done for the last 40 years.

00;30;11;05 - 00;30;33;17

Stephen Wolfram

And you know that the pattern of, of without technology, for example, you know, widely used by CEOs and CTOs of, of tech companies not so widely used by kind of rank and file programmers. Why? Because the CEOs and CTOs understand what they're trying to do. They're they're kind of for them, it's like, man, I just want to implement it.

00;30;33;19 - 00;31;03;01

Stephen Wolfram

And then I'm going to go and figure out the next thing I want to do. For the more rank and file programmers. It's been about give me a spec, then I'll spend the next month implementing that spec, ask me to make a new spec. And it's like, well, that's not my job. That sounds really hard to me. So that that particular thing of kind of rank and file programing has been, you know, in, in all the various programing languages and so on has been a thing that, you know, we've we've automated a lot of that.

00;31;03;03 - 00;31;26;05

Stephen Wolfram

And the people who, you know, the CEO, CTO crowd who use our tech, you know, writing that one function that for a low level programmer would be, oh, that's a thousand lines of code that I have to custom, right, so to speak. What happened with sort of AI coding is now the AI can write those thousand lines of code and it's like, yeah, we'd already automated a lot of that stuff.

00;31;26;07 - 00;31;55;18

Stephen Wolfram

It's great. It's useful. We use it a bunch. There are a bunch of kinds of situations in which, particularly when you're kind of gluing that sort of this thing together with that thing, there's a, you know, there's this thing that is in that format and it's developed in that format, and it works that way. And there's this other thing that works a different way, kind of this making the glue that goes between those things, making the kind of connective tissue of of kind of, technical systems.

00;31;55;20 - 00;32;19;29

Stephen Wolfram

That's a place where a lot of modern AI and, coding is, is super useful. And something where in a sense it's very shallow programing, but it's very messy, detailed programing, and it's also something where it's fairly easy to check whether you got it right. Which, which means if, you know, if you were in the 80% right, 20% wrong scenario, you can get to iterate and get it.

00;32;19;29 - 00;32;59;16

Stephen Wolfram

So it really is, a reliable thing. But, you know, I think that the, the thing that, you know, there's going to be more value to kind of general thinking, general understanding, general strategy, less value to kind of the let me learn this. Let me have learned this tower of ideas, kind of, over the course of many years and then apply it because it's increasingly the case that you can kind of get that kind of, boost of information or that refresher or those kind of, those aspects of, of that kind of thing.

00;32;59;16 - 00;33;32;07

Stephen Wolfram

You can get that done automatically. And so the real value is, do you have a big picture understanding what's going on? It's sort of who can tell the AI's what to do. The AI's do what they do and automate a lot of things. But the question is what what what's involved in telling them what to do? And again, in in my my world, I've, I spent a lot of effort kind of trying to understand sort of at, at a more kind of thematic level, how a lot of things work, both in science and elsewhere.

00;33;32;09 - 00;33;51;21

Stephen Wolfram

And, you know, I, I see kind of computational language as the general formalizing mechanism for taking the ideas that you have that might be kind of vague and making them kind of precise and formalized and something that you can kind of, you know, step by step, build on.

00;33;51;24 - 00;34;25;03

Geoff Nielson

That that piece is very interesting to me. And you touched on it a couple of times that the notion of how important it is to understand things and being able to, you know, get a little bit deeper and not just have the answer, but understand how you got to the answer. And it's it's interesting to me because I feel like with a lot of the more modern LMS and and AI tools, the Auditability is actually quite low that that they're set up in a lot of ways, like a black box, you get the output, but maybe you don't understand the output, and maybe they don't want you to understand the output.

00;34;25;06 - 00;34;25;25

Stephen Wolfram

00;34;25;27 - 00;34;47;19

Geoff Nielson

To what degree do you consider that a concern? Do it. Do we need to get better at, you know, custom building tools where we can understand every piece of it? Is it just understanding the logic or understanding the prompt? How do we how do we find, I guess, the right level of understanding there? And is it everything or is it just some subset?

00;34;47;19 - 00;35;12;03

Stephen Wolfram

Well, there's a lot of things that when we succeed in formalizing things, we get to do things like build the whole tower of modern engineering. It's, you know, formalizing things is very powerful. It allows you to go beyond what humans can do, so to speak, fundamentally, go beyond what humans can do in the this question of of can you understand what's happening inside?

00;35;12;06 - 00;35;28;18

Stephen Wolfram

Well, you know, when we work with humans, we don't understand what's happening inside. You know, when you manage humans, you try and say, hey, I want you to do roughly this. You give a prompt, do this. And if you're lucky, the human will do roughly what you hope they would do. You know, that's how a lot of the world operates.

00;35;28;20 - 00;35;51;22

Stephen Wolfram

And it's pretty useful. And insofar as that's how, you know, AI and lens work is going to be useful there as well. If you say, I want to know exactly how that and I'm came to that conclusion, good luck. That's not going to work. It's it's even at a theoretical level that's just something you, you, you you can't make a narrative that explains why it happened.

00;35;51;24 - 00;36;12;12

Stephen Wolfram

And that's the sort of fundamental reasons for that. It's worth remembering that, you know, if you look at the natural world, the natural world just does what it does. We then have science that attempts to build a bridge between the natural world and the things that we understand. We have narratives for, but we don't expect to understand everything that happens in the natural world.

00;36;12;15 - 00;36;37;19

Stephen Wolfram

I mean, when it comes to the arc of the history of technology, it is interesting that, you know, the the primary means of transportation back in the day would be things like horses. People expected to know how to get the horse to do stuff. They did not expect to know how the horse worked inside. Then the Industrial Revolution came and people started saying, we can see the colors, we can see the levers.

00;36;37;22 - 00;37;08;10

Stephen Wolfram

You know, we understand how the machines work inside. That era has largely come to an end at this point, the era of where you can expect to understand how the machine works inside. It's it's no longer no longer here. Now, is that, why is that? Well, ultimately, I think it has a very deep sort of theoretical, the it's a it's a consequence of very deep sort of theoretical principle as this idea that I call computational irreducibility.

00;37;08;13 - 00;37;33;22

Stephen Wolfram

So the question is, if you've got some system that's described by certain rules, you want to know what the system is going to do. You just apply those rules over and over again, maybe a million times or something, and just see what the rules lead to. And that tells you what the system is going to do. But the question is, can you expect to jump ahead and say, oh, I see these rules now I can jump ahead and say, okay, in a million steps, they will have done this or that.

00;37;33;24 - 00;37;56;09

Stephen Wolfram

Can you sort of reduce the amount of computational effort to know what will happen? A lot of kind of the, the achievements of mathematical science had to do with various kinds of jumping ahead that were found to be possible. The thing that turns out to be the case is sort of a fundamental thing that many computationally computational processes are fundamentally computationally irreducible.

00;37;56;14 - 00;38;17;16

Stephen Wolfram

You can't jump ahead in general to see what will happen. So you could say, well, you know, when you can't jump ahead to see what will happen. There are all kinds of consequences. One of them is that what happens may surprise you. You didn't know what was going to happen because you couldn't jump ahead. And so what actually happens may surprise you.

00;38;17;18 - 00;38;35;16

Stephen Wolfram

So when people say, you know, I'm going to I'm going to sort of, constrain my AI to the only things that I want are not things I don't want. Well, to constrain it to only things I want. You have to essentially be able to jump ahead and say, what's it going to do? I'm going to make sure it doesn't do the things I don't want it to do.

00;38;35;18 - 00;38;59;28

Stephen Wolfram

Well, the difficulty is that something that is impossible in general because of computational irreducibility. So that gives you a sort of choice in terms of the technology. You can either say, I'm going to have technology that I guaranteed to understand. It's computationally reducible. That means you could, in principle, jump ahead and see what happens, which which sort of begs the question of why do you want the technology in the first place?

00;39;00;08 - 00;39;23;13

Stephen Wolfram

It's to do things where you couldn't jump ahead to see what would happen, whether the technology is actually doing something that's valuable to you. So. So branch number one is we want the thing to be computationally reducible so we can understand what happens inside. But then there's a limit on what it can do. And there's a lot there's it's not doing the things that are sort of the most computationally valuable.

00;39;23;13 - 00;39;49;15

Stephen Wolfram

So that's branch number one. Branch number two is we're going to do something that's computationally irreducible, like a lot of what happens in the natural world, like what happens in brains and so on. And we're going to, know that some, some part of what happens will be unpredictable to us, will be a surprised us. I think we have you know, it's very clear we've passed into the zone where computational irreducibility is all over the place.

00;39;49;18 - 00;40;11;05

Stephen Wolfram

And the idea that we're going to be able to have, you know, the kind of the audit trail that tells us exactly what happened and why and so on, it's not going to happen any more than it's going to happen to human brains. And it's it's some, you know, that say, that's again, it's a question, you know, when you think about how do you use this technology, it's a question of knowing it might surprise you.

00;40;11;08 - 00;40;36;16

Stephen Wolfram

So if, you know, if there's a thing where it really mustn't do that, you've got to put something outside of that, that sort of AI technology to make sure that, you know, I don't know the, the, the car can't drive itself into, into, you know, off the side of the road, for, you know, you've got to have other things that make sure of that, that aren't part of the sort of main AI loop.

00;40;36;19 - 00;40;59;04

Geoff Nielson

I, I was just as you were speaking, I wrote down related to the, you know, the horse metaphor you were using that, that it it sounds like you're describing AI as a, as a tool that can't fully be harnessed, harnessed in the sense of actually physically putting a harness on it. Right? That it in some ways is wild because of its irreducibility.

00;40;59;04 - 00;41;02;16

Geoff Nielson

Right? Like it like a wild animal that like something that exists in the natural.

00;41;02;23 - 00;41;22;05

Stephen Wolfram

It's amusing because I was using this horse analogy a few years ago, and I, and I was talking about, you know, the value of putting harnesses on horses and using them to, you know, pull carriages and plows and things like that. That was before the term harness became common. And, in kind of the AI world.

00;41;22;05 - 00;41;42;11

Stephen Wolfram

And I don't, I don't know what, you know, what the interplay between those two things was, but I was just kind of amused that now everybody's talking about harnesses and yes, it's, you know, what was discovered in I was essentially a wild horse and a lot of the value comes in putting the right kind of harnesses on the wild horse to get it to do things that are useful to us.

00;41;42;14 - 00;42;08;15

Stephen Wolfram

I mean, it can also do things that are just purely, you know, for horse civilization, so to speak. The horses can run wild and do all kinds of things which the horses no doubt find pretty interesting. But where we are like, I don't know why the horse did that, and I don't care about that, but if I can put a harness on the horse and have it pull my my carriage along, then that's super useful to me.

00;42;08;17 - 00;42;37;27

Geoff Nielson

There's, I think, maybe two separate ways to be thinking about the harness here. There's if we think about frontier AI models, there's a lot of talk is as you were referring to about, you know, harnesses in general and making sure that we're trying to minimize risk. Or, you know, if you listen to some narratives, existential risk to humanity from not knowing what these tools are going to be able to do and not really having a sense of their capabilities or, you know, where their capabilities could go.

00;42;37;28 - 00;43;05;29

Geoff Nielson

There's also a harness in the sense of just, you know, if you are using AI as an individual or as an organization, having some sort of harness of this is what I actually expect it to do and having some sort of guardrails. Are you I thought I heard you say that we shouldn't. It sounded like we can't just expect to put a complete harness on this thing and just say everything is going to be reducible, everything is going to be auditable.

00;43;05;29 - 00;43;25;06

Geoff Nielson

That's not that. That's not kind of a reasonable trajectory for us. So so given that what what do we do? How big of a risk is this. And I guess, what's your level of optimism to concern about how we have some harness on this horse?

00;43;25;08 - 00;43;45;06

Stephen Wolfram

I mean, look, lots of crazy things will happen. There'll be more cyberattacks, they'll be more complicated, the defense will be AI, and the attack will be AI. And there'll be a whole, you know, there'll be a whole ratcheting up of those kinds of things up to a level that is sort of, you know, far beyond what humans can individually understand.

00;43;45;06 - 00;44;05;28

Stephen Wolfram

And that will be a it will be a kind of a, in many areas, there'll be things where it's battle between the AIS type thing and, you know, that will be in some ways disquieting to people that there's a lot of stuff that's happening sort of at the AI layer that humans don't really understand what's going on.

00;44;06;00 - 00;44;42;14

Stephen Wolfram

It's worth remembering that we live in the natural world, which is full of things where humans don't understand what's going on. And yet, you know, even though we don't understand the thunderstorm, we, you know, build a house with a roof. And so it doesn't really matter that there's a thunderstorm going on outside. It's, you know, my expectation is that the same kinds of things will happen that is that there is kind of a, you know, a different there's not the natural world, it's sort of AI civilization and so on that's out there doing things and that, yes, there are certain kinds of things where we've had an expectation about how things should work

00;44;42;14 - 00;45;03;09

Stephen Wolfram

in the world, and that expectation is no longer going to be correct. Like, for example, you know, you're you're having a conversation is the thing. You're having a conversation with a human. Well, that was a good assumption a few years ago. If if it's not a good assumption anymore and you know what consequences that have, well, probably has a bunch of consequences.

00;45;03;11 - 00;45;37;00

Stephen Wolfram

But, you know, there are things that we have to adapt to and, you know, it's some I think, in terms of, you know, what does the future look like for, I think, you know, I'm, I'm generally personally an optimist. Generally that's some, you know, I'm, I'm you can be if you look at the arc of human history, you know, there's lots of things about the modern world that are great compared to the way things were back, you know, centuries ago or whatever.

00;45;37;05 - 00;45;58;15

Stephen Wolfram

And there are also things where you could say about the modern world, we lost a bunch of stuff that was wonderful and rich and human about what was going on, you know, a hundred years ago. So but I tend to think that things will sort of adapt and that there will be, you know, in terms of will everybody be miserable all the time because of AI?

00;45;58;15 - 00;46;20;21

Stephen Wolfram

No. Absolutely not. People will be sort of, my guess is that the, you know, people will be as happy as they ever were, which means some people are happy, some people are unhappy, and that's just the human condition. I think the things that will happen, I mean, you know, one thing, as more gets automated, the big sort of thing is, well, what do you want to do?

00;46;20;24 - 00;46;56;22

Stephen Wolfram

In other words, in the past it was like, I pick this profession, I'm stuck in this profession. I got, you know, positioned here. I learned a bunch of stuff about this. This is what I'm going to do. And I think one of the things that one realizes in modern times is that it's kind of, one can have much more fine grained kinds of activities, like the very thing we're doing right now of, you know, podcast kinds of things that, you know, I can kind of, you know, be involved with doing, you know, live streams and things like this as a small part of my life.

00;46;56;25 - 00;47;12;11

Stephen Wolfram

It's not something that I've had to spend a huge amount of effort. I just, you know, press the button and switch it on and it goes type thing because of automation. If it wasn't for automation, I would have, you know, that would be a huge investment to be able to get to the point of doing something like, let's say, live streaming.

00;47;12;18 - 00;47;31;15

Stephen Wolfram

And I think that has the consequence for people that more and more you'll see people who have, you know, five professions type thing rather than, I put all my eggs in that one basket. And that's the thing I'm learning about. And that's the the specialized thing that I'm going to do now, you know, some people might say, that's wonderful.

00;47;31;18 - 00;47;59;20

Stephen Wolfram

Some people may say, that's disorienting and terrible. You know, this is, I tend to think that, I mean, for me in particular, you know, I'm a person who spends their life sort of coming up with ideas and trying to make them real. And, you know, my main way of doing that is building technology, having a, you know, a company with, with 800 people or something in it to try and turn ideas into real things, so to speak.

00;47;59;22 - 00;48;30;04

Stephen Wolfram

And so for me personally, having other tools to help turn ideas into real things is, is great because I've got I've got plenty of ideas. Not everybody has those particular interests or or directions and so on. So it's it's a, you know, it is complicated whenever automation comes in, there is, you know, there are things that people learn to do that now, some of the investment they put into it is, is no longer worthwhile.

00;48;30;07 - 00;48;52;23

Stephen Wolfram

And, you know, what has tended to happen historically in an economy is, you know, at the beginning, everybody's in agriculture, then that gets automated. Then you see the pie chart of occupations get more and more fine grained as a sort of the investment to get into any occupation goes down. And you, you know, people are able to do a wider diversity of things.

00;48;52;25 - 00;49;18;11

Stephen Wolfram

And I think that's you know, that's that's kind of the future that one can expect to see, which puts a premium on sort of learning to think in general, learning about as many kinds of things as possible, not kind of the very specialized I'm going to do this particular thing. There are still some fields of human endeavor where the kinds of things that we know how to do are at the top of a very tall tower.

00;49;18;13 - 00;49;47;29

Stephen Wolfram

That's true in areas of, you know, math, science and so on, where just a lot is known. And, to be able to contribute, you really can't can't do that without having climbed a lot of that tower. That's not, that's not and that's not nearly as true. And in most other areas. And I think it's much more possible to, you know, do sort of the, the amounts of gig work, so to speak, in some form is going to expand.

00;49;48;02 - 00;50;07;23

Geoff Nielson

Well, it it's interesting to hear it described as gig work because some of it could be true gig work. And, you know, you're you're in the food delivery business. But I was also thinking about, you know, doing five gigs at once, and I was thinking about you and the way you describe yourself, because it's a they're gigs in a sense, and it's a very specific type of gigs.

00;50;07;23 - 00;50;40;23

Geoff Nielson

But you brought up your company, which has, you know, 800 or so people. And I'm curious how you've seen your approach, I guess, to running a company and structuring a company change over the last handful of years, how you integrate automation. And I have you seen have you seen it change in a big way, or is it just a way for you know, that some of the same roles and you know, some of the same activities to just be done more quickly than they were before?

00;50;40;24 - 00;51;01;15

Stephen Wolfram

What both a bad example and a good example, because, you know, the story of my company has been a story of layers of automation. I mean, that's what we've been doing. That's what we that's what we've done both for our internal purposes and for the things that we tell a lot of customers. It's all about delivering the technology to automate things.

00;51;01;17 - 00;51;31;24

Stephen Wolfram

So we've kind of already I've already been living the AI dream for the last four years. In other words, I've, you know, in the things I do, for example, in science, I've built an incredibly automated technology stack that allows me to be super efficient and going from an idea to really working it out. So in a sense, the the effect of, of modern AI on our particular organization has been probably smaller than almost any other, because, you know, we've already automate a lot of stuff now there's new things we can automate.

00;51;31;26 - 00;51;56;20

Stephen Wolfram

And we've been doing that. And it's it's cool. Some of them have worked, some of them worked so well. I think that the, you know, whether it's, I mean, a lot of things that we sort of didn't imagine doing because they were just too much human effort to do them. We can now automate them, and they don't get done as well as all that human effort will probably that can be done.

00;51;56;23 - 00;52;20;22

Stephen Wolfram

But you can we can be broader if not deeper. And that has allowed us, I mean, both in terms of our business operations and in terms of actually some of that technology strategy to do some slightly different things. That'll be some things, we'll come out for a little while, but are fairly surprising that we've been able to do by using the fact that sort of AI makes breadth cheap.

00;52;20;25 - 00;52;48;15

Stephen Wolfram

In other words, the, it's, you know, going across a lot of different kinds of activities is now much easier than it was before. I mean, we we've done a lot of that, companies, you know, we we build technology that's applied to a lot of different areas. But there's a certain, amount of kind of, delivering to, sort of building out the towers you need for many, many, many specific areas that has never been economically feasible for us.

00;52;48;15 - 00;53;18;20

Stephen Wolfram

And now is, so, you know, those kinds of things have affected the way I've done things. I would say that, in I mean, I, I've, a company is well, it's a very, kind of ideas oriented company in the sense that we're always inventing new stuff and having to figure out things that nobody figured out before.

00;53;18;22 - 00;53;39;00

Stephen Wolfram

Most, you know, not, you know, the majority of companies never have to figure out things that nobody ever figured out before. That's something you know, people expect, you know, academia or something like this to do. We've been in a position of many, many times being sort of confronted with, and choosing to confront ourselves with sort of things that have never been solved before.

00;53;39;03 - 00;54;00;10

Stephen Wolfram

And it's kind of been, for us, kind of my own experience in doing basic science is kind of critical, because it lets one sort of think foundationally about problems and technology and, and strategic problems and so on. And that's been tremendously valuable to us. And that's kind of part of the sort of the culture of our particular company, I think.

00;54;00;10 - 00;54;23;09

Stephen Wolfram

And, you know, what I see with other people is, sort of small utilities, sort of software utilities are now much easier to build, and they can be built by people with much less technical sophistication and sort of big deep systems. No, you will just, you know, that will be horrifying if you try and do it that way.

00;54;23;16 - 00;54;46;00

Stephen Wolfram

But the little utility, I mean, I've, I've, they've been a bunch of little utilities that I've wanted for myself for, for decades in some cases that now just got built, you know, with, with AI programing, they're not they're not deep, they're not complicated. You know, the, the, sort of the, the, the thing that, you know, synchronizes this kind of contact with that kind of contact and so on.

00;54;46;02 - 00;55;07;21

Stephen Wolfram

Nothing very deep. But in the past it was like the, the barrier to doing it was just a little bit too high. And so it never got done. I mean, it's worthwhile in my life. And science, for example, part of what's happened is that I built technology that so automates things that an idea that is just kind of a whim.

00;55;07;23 - 00;55;36;10

Stephen Wolfram

I can, you know, it's things are automated enough that in ten minutes I can kind of start checking out that idea. If I didn't have that automation, checking out that idea would take a month and I would never do it. But it's because it gets to the kind of do it on a whim level that things get done, and I've seen the same thing happen with, with our company and, and little utilities and things like this that it's been possible to build, though in some cases really quite useful.

00;55;36;12 - 00;55;57;09

Stephen Wolfram

But, you know, they were just a little bit out of reach. You couldn't, you know, it would take a real serious effort and you couldn't quite justify that effort. And you didn't know whether that effort would pay off. But it doesn't matter if that if it's going to pay off, if it's only going to take you, you know, a few minutes to, to to do the work.

00;55;57;11 - 00;56;21;11

Geoff Nielson

It feels a little bit like a blessing and a curse to me that if you can now do a month's worth of development in ten minutes. And I mean, the blessing side is fairly obvious, but the curse is now. I mean, almost like the the a lack of focus or the tyranny of choice that suddenly your company can be just drowning in all these sort of potential options or directions to take.

00;56;21;14 - 00;56;26;28

Geoff Nielson

What what do you do with that? How do you avoid that from being, you know, paralyzing?

00;56;27;00 - 00;57;03;28

Stephen Wolfram

Well, look, what you're describing is the frustration that I had for years about why rank and file programmers didn't use our tech. Because, you know, for that, you know, the people who are architects and the people who are really thinking of the next idea for them, it's a huge win to get everything as automated as possible. Yes, it's true, there are plenty of people who are used to having a kind of, you know, follow this line kind of activity and, you know, my own view.

00;57;03;28 - 00;57;26;19

Stephen Wolfram

I'm, I'm an optimist. And I'm also very optimistic about human nature and people. And I tend to think that, you know, that that, you know, nobody is, is is forced to kind of do that kind of, kind of work just as we look back today and say, oh, my gosh, all that manual labor that was being done in lots of countries and so on, it's like it's terrible.

00;57;26;19 - 00;57;46;29

Stephen Wolfram

People had to do that. I mean, we've got bulldozers and things now, you know, nobody has to do that with, you know, in, in many countries with, you know, with, with a small shovel type thing. And I think the same will be seen in the future for a lot of the kind of things that were like, you just grind through and you follow this thing and, and do that.

00;57;47;02 - 00;58;09;23

Stephen Wolfram

Now the question of, okay, so what do you choose to do? Yeah, well, that takes skill. I mean, that's, you know, strategy is important. And I think the, you know, for example, in education system, not a lot is taught about how to figure out what to do. Mostly it's what's taught is given that you are asked to do this thing, can you mechanically do it?

00;58;09;25 - 00;58;38;28

Stephen Wolfram

And I think, you know, there's, there's I tend to think I'm kind of an optimist about people that so that people are intrinsically quite good at figuring out sort of what to do. They just most they have never been given the opportunity to think that way. And I kind of think that, you know, when it comes to sort of the strategy of what you should you do, it's you know, yes.

00;58;38;28 - 00;59;06;17

Stephen Wolfram

There's a certain kind of thinking about, should I do this? Should I do that? I mean, in my own life, I've made a zillion choices of that kind, and, you know, I've chosen to work on certain kinds of science, technology, business opportunities, whatever else. You know, I've had a rather rich collection of of potential opportunities, and I've chosen particular ones to do, I think I've been pretty happy with my choices in general.

00;59;06;19 - 00;59;37;25

Stephen Wolfram

And I think that the, the thing that, is, you know, well, you know, there are, there are this question of sort of how do you decide what to choose to do? You know, it's still the case that executing any, any significant thing is a lot of work, even if the, you know, things.

00;59;37;28 - 00;59;58;11

Stephen Wolfram

Well, I made a lot of things more automated. That just means the expectations are higher, you know, to actually deliver a real product in the world. Yeah. You know, there are a examples where some one one, one guy or gal just sort of vibe coded something. And then everybody thought it was wonderful and it became a product.

00;59;58;18 - 01;00;26;26

Stephen Wolfram

Usually that story is exaggerated. Usually the true story is a whole bunch of work that really built all the different corners of something and made it into a real thing. I mean, I think, I've seen, I think, more of the challenge is, I don't know, you know, I've had a hobby for, for decades now of, you know, advising startup companies and some well, some companies that are far beyond startups at this point.

01;00;26;29 - 01;00;46;05

Stephen Wolfram

And, you know, one thing I've seen now is people saying, you know, I'm doing an AI startup and like, we're doomed. There's nothing for us to do. The AI is just going to do everything. You know, what are we supposed to do? It's not a a paralysis of choice. It's a paralysis of the AI is just can automate everything.

01;00;46;05 - 01;01;11;02

Stephen Wolfram

And there's nothing for our companies to do. This is basically never correct. Because, you know, that's just not, but maybe it takes, you know, if you just say, I'm doing an AI company and it just is an AI company and that's its only definition, then yeah, that's probably not going to go well. But it's kind of a thing where, you know, once you start saying, well, how is this actually going to be used?

01;01;11;02 - 01;01;33;20

Stephen Wolfram

What, you know, what set of people do I understand? And can I deliver something for it? It gets very much more specific and very much more a thing where it's like, oh, it's very nice that I can do this as automatically as I can, and now I can think bigger about the kinds of things that I can deliver.

01;01;33;22 - 01;02;04;20

Geoff Nielson

That makes sense to me. And I like that framing of, you know, thinking more broadly than just the technology, but what it actually impacts and maybe who it impacts. Yeah. You mentioned earlier that your company is different in some ways because so much of it is focused on inventing, on innovation, on on new things versus just, you know, being a widget factory that does the same thing every day for, for ten years.

01;02;04;22 - 01;02;35;06

Geoff Nielson

And I always find organizations like that really interesting and probably in many ways the most challenging to run and certainly to run well, because it's just you're not just building the same muscle over and over again. Maybe you are if the muscle is some sort of, you know, cognition or power around, you know, thinking about things. But but what are your principles that you've learned over the years that have enabled you to be more successful at running a company that's effective at that?

01;02;35;08 - 01;02;57;14

Stephen Wolfram

Yeah, I mean, I've been running the company for 40 years now, so and, you know, we've done a lot of new things. It's not trivial to have companies do new things. I mean, at the beginning of our company, our core flagship product, which has been the same product for 38 years now, I mean, a dramatically bigger, you know, fancier and so on than, than it was.

01;02;57;14 - 01;03;25;29

Stephen Wolfram

But it's the same. It has the same lineage over that period of time. It's it's something where it was very successful very quickly. And it would have been easy for the company to do nothing different from that. It took effort from me to make it do different things. And, you know, the number of times I've had to create a special projects group to do the crazy thing that I want to do that many people think is kind of at the company.

01;03;25;29 - 01;03;59;06

Stephen Wolfram

Many think people think is doomed. That's happened many times, you know, create a special projects group. It does this crazy thing. Turns out it works out. It turns out it becomes a big part of what the company does. Then you do that again and it's usually not the same people. Actually, I've, I've tried to have the idea of having people who sort of just do special projects, but usually when they've been involved in projects very successful, they want to stay with that project and you kind of have to have a new generation that maybe with people who work on something quite different before that becomes kind of the new special projects team, the new team

01;03;59;06 - 01;04;26;14

Stephen Wolfram

that has kind of the, the, the energy and enthusiasm to kind of do something new and where the particular new thing that's come up resonates with them. I mean, I think that for me, it's it's, you know, injecting new things, not so trivial. Then the question of what you actually build, you know, I have kind of a vision for what I want to build, and I push it quite hard.

01;04;26;16 - 01;04;45;11

Stephen Wolfram

And people always tell me that's impossible. And I say, well, let's figure out how to do it. And then turns out that we grind it around a bit and it turns out it is possible, actually. And, you know, it took ideas, often ideas for me. I built up a very talented group of people who work with me, and they have great ideas too.

01;04;45;13 - 01;05;02;16

Stephen Wolfram

And, you know, I don't know how many meetings we've been in where kind of the meeting starts with somebody saying, you know, people have studied this problem for 30 years and haven't been able to solve it. And I say, well, we've got, you know, there's a one hour meeting. We're going to make progress on this. And by golly, we do.

01;05;02;19 - 01;05;21;17

Stephen Wolfram

And why is that? First of all, it's believing that it is even conceivable that you would make progress in that kind of scenario. Second of all, it's having really good tools that are different from tools that people have had before to go attack things that maybe people gave up on, because in the past the tools just didn't exist.

01;05;21;24 - 01;05;42;11

Stephen Wolfram

And people said, oh, that problem hasn't been solved for decades. We'll just give up on solving it. I think that's what happened, for example, with WolframAlpha, was a particularly dramatic example of that, where that was at a time when I was really, you know, you know, at its lowest ebb and nothing worked, so to speak.

01;05;42;14 - 01;06;13;14

Stephen Wolfram

And it's like we're going to build a system that does kind of question answering across a broad range of domains and so on. It's like, that's just impossible. Well, it turns out it wasn't impossible. And even believing that it wouldn't be impossible was a critical part of being able to achieve actually doing it, so to speak. So I think that's a, you know, the other thing, realistically, I can make projects happen when I can kind of foresee roughly what the arc of the project is going to be.

01;06;13;16 - 01;06;35;05

Stephen Wolfram

And if I was just sort of coming up with, hey, here's an idea for a project, and I can't really see how that project works through, it's not going to work. And for me, it's kind of important that I, you know, most of the things we do, I could in principle do them. I have great people who do them, I hope, at least as well as I would do them, maybe better in some cases.

01;06;35;07 - 01;07;01;09

Stephen Wolfram

But in principle, I can do these things. It's not like, oh, we're writing mysterious code that you'll never understand type thing. And I found, you know, in the last 20 years, I found that, sort of the, the occasional, you know, I'm just going to dive in and solve that problem myself, even though the problem is being worked on several layers down in kind of the management hierarchy, that that is, is actually not the structure.

01;07;01;09 - 01;07;22;04

Stephen Wolfram

People might think, oh, that's totally destructive, because it means you don't trust the layers of people that you have. No, actually, the the other dynamic is the the people. The more junior people say, gosh, it's really nice that the CEO cares about what I do and even actually isn't as dumb as I thought they were, and actually knows a bunch about what I do.

01;07;22;06 - 01;07;47;25

Stephen Wolfram

And, you know, maybe I can learn something from them. That's a pretty good dynamic for me. That dynamic was way overpowered. The dynamic of, oh, let people have sort of, agency to do to make their own mistakes type thing. I think the, the thing, I mean, being able to do new things in an organization is partly a consequence of having done these things before.

01;07;47;28 - 01;08;16;28

Stephen Wolfram

That is back in the in the 1990s. Well, the beginning of the 1990s company was was sort of growing very quickly, and I had far more ideas about what the company could do than the company could possibly absorb at the size it was at. And so I decided to kind of throttle back and work on basic science for a few years, during which time the company got itself very nicely organized and so on, and was very effective, actually very profitable at that time as well.

01;08;17;00 - 01;08;44;02

Stephen Wolfram

You know, just sort of steaming forward doing things that were not, you know, we'd already had a lot of innovation. We were just sort of stabilizing things. And then when I sort of came back to, to say, now we're going to do new things, that was a fairly heavy lift. In fact, the, kind of the, the, the most amusing part of that was sort of we were already a very distributed company even in the 1990s and the early 2000s and so on.

01;08;44;05 - 01;09;05;19

Stephen Wolfram

But, so it wasn't as hard to hide, you know, 200 people working on a project as it might have been if everybody had been in the same, you know, office space. But, you know, I decided it was better to do a projects sort of off on the side, not kind of telling about what we were, what we were doing, because I knew that people would say, oh, that's not going to work.

01;09;05;19 - 01;09;22;00

Stephen Wolfram

That's not going to work. That's a bad idea. Let's not do that. You know what other places to use resources. To the credit of my management team, as soon as we had a product that was demo mobile, in the case of Wolfram Alpha, you know, gave a demo and half an hour everybody was like, hey, this is great.

01;09;22;00 - 01;09;59;05

Stephen Wolfram

You know, we can do this and that with it. How do I kind of told them a year earlier, we're building this thing. It's not really demo, bill. It's kind of, you know, hard to understand what it is. It would have been a much more difficult proposition. And, you know, I think that's a, so you have to believe you know, if you're sort of providing the vision, you've got to sort of believe in that vision and, and push it even and, and not get it so polarized by having so that people who know the existing vision, so to speak, kind of say, well, wait a minute, that's different from what

01;09;59;05 - 01;10;25;20

Stephen Wolfram

we've been doing. It can't possibly be right. But, you know, there's no question that the, the driving to get to always be innovating is and not innovating for the sake of innovating. Innovating because there's actually a good thing to do is is not is not so easy. I think for me, an important driver is I build things that I want to use.

01;10;25;23 - 01;10;48;20

Stephen Wolfram

And whenever we end up in a situation where, you know, we've we're building something where I'm like, I'm not in the target market for this thing. We typically try and spin those things off, because I know that I can't lead that thing in, in an effective way because I just, I don't have intuition for what's going to make it great or not.

01;10;48;22 - 01;11;12;10

Stephen Wolfram

And I also don't have kind of the passion to make it happen if I'm going to be a user of the thing we're building, then I really want to make sure that it's a it's it's built in the best way. And, you know, it's you talk about management of innovation. I mean, I've been we have now a, a kind of nonprofit spin off of an institute, that, is doing basic science.

01;11;12;12 - 01;11;40;24

Stephen Wolfram

That management problem is actually really hard. That management problem is harder than the technology management problem. And the reason is that any technology I can more or less foresee what's going to happen in a project, a month, three months, whatever. And with very capable people, you can kind of, you can say, do this project, check in every so often, but they kind of more or less stay on track, and when they hit bumps, they'll know how to navigate around them.

01;11;40;26 - 01;12;07;00

Stephen Wolfram

The problem with basic science is that when you're doing research, you can hit a bump. And to get around that bump, there's no existing kind of approach that gets you around that bump in technology. There is an existing approach which if you're a skilled technologist, you're going to be able to to make use of. But in science, you keep on hitting bumps where it's really you got to go back to sort of the basic science, know how to get around that bump.

01;12;07;08 - 01;12;29;25

Stephen Wolfram

And that's difficult from a management point of view, because while I'm I think pretty good at this point, getting around the bumps and basic science, the, you know, junior person X working on that is really not able to do that. And even kind of the middle management there, it's it's not as easy. It's not it's not something well that's sort of known methods for getting around it.

01;12;29;25 - 01;12;52;03

Stephen Wolfram

So I've finally been figuring out kind of a management structure for basic science, which I think is kind of a I don't think anybody figured that out before. So it's a it's another one of these kind of things which you have to sort of figure out from scratch. We're getting there. And we're able to sort of produce basic science in a much more scalable way than have been possible before.

01;12;52;05 - 01;13;01;21

Stephen Wolfram

And it's, that's been that's been exciting to me. And, and, I, I very much enjoy that productivity.

01;13;01;23 - 01;13;22;13

Geoff Nielson

I'm curious what that looks like. And maybe more broadly, I'm curious when you bring people into your organizations as managers, as frontline staff, what are the skills and traits that you look for that you think are ultimately the most important to for them to be successful and help with the mission?

01;13;22;18 - 01;13;45;08

Stephen Wolfram

Right? I mean, different organizations work differently. I mean, most companies, you know, try as they might to not be this way, are a reflection of the personalities of the founders. I mean, it's it's just it works out that way. You know, I, you know, companies I'm advising and things where the, where the CEO says, I'm going to do this, even though I can plainly see they are very different personality wise from what they say they're going to do.

01;13;45;08 - 01;14;11;16

Stephen Wolfram

And like that's not going to work. Just, you know, you've got your personality built on that. So I think, you know, in our company, sort of the, keep the thinking apparatus engaged at all times, which often you know, for example, people who have a lot of technical skill, they'll be like, I can think about that technical thing that I know about, but something else comes up.

01;14;11;23 - 01;14;36;17

Stephen Wolfram

It's like the thinking apparatus is disconnected at that point. You know, that's the thing where sort of being able to think about a broad range of things and sort of keeping the thinking apparatus, sort of engaged, that that's one thing I think, sort of having. Yeah. And sort of related to that is kind of keeping the big picture in mind.

01;14;36;19 - 01;15;07;06

Stephen Wolfram

But on the other hand, doing things that are very practical and actually work and not kind of you know, another particular company, we've been able to have a company with very, very little internal politics. And people are very straightforward, you know, perhaps some people maybe can't deal with that because people are very straightforward. They say what they think, which, you know, is not always positive about, and that's some in our organization that's worked very well.

01;15;07;08 - 01;15;31;17

Stephen Wolfram

You know, we also from I, I sort of consider the problem, you know, the certain kinds of people, the certain kinds of projects we have. One of the key problems of management is to solve the puzzle of how do we connect these people to these projects, so to speak. And, you know, particularly when we're doing new projects all the time, it's like, okay, let's put together a group of people who are going to be good for that project.

01;15;31;19 - 01;15;48;15

Stephen Wolfram

And you kind of think, and we have all kinds of internal data on, you know, who knows what within the company and so on. And we try and understand, you know, oh, this is a person who knows about this. I don't know them very well, but let's see if they're going to engage on this particular kind of project.

01;15;48;17 - 01;16;12;27

Stephen Wolfram

And, you know, fitting, fitting people into projects is, is something I consider as sort of a management responsibility. Another thing that I think is, important management responsibility is don't do projects or throw away. So that's the thing which people, you know, in our company, people care about the projects they're doing and they're not conditioned to not care about.

01;16;12;27 - 01;16;35;02

Stephen Wolfram

But in many companies, some fraction of projects that get done just get thrown away. And that's very dispiriting to people. That makes them not want to really deeply care about the projects and our company. We've been, you know, we don't throw projects away. Sometimes we hibernate them sometimes for a number of years. But, you know, it's been a notable feature that the projects always end up coming back.

01;16;35;05 - 01;16;54;17

Stephen Wolfram

And, it's sometimes it may take a while because the market may not be ready or we may not have the resources to deploy the project or whatever else, but people fully expect that the things they work on will be things that, kind of, remain, you know, remain valid and their work doesn't get thrown away.

01;16;54;19 - 01;17;18;23

Stephen Wolfram

I mean, I think that the, you know, our company has had very strong R&D forever. I would say that on the kind of internal entrepreneurism side, it's it's more spotty. And there are plenty of people, that worked for a company. And I've said, you'll never be happy unless you're working for yourself. You should spin off a separate company or go start your own company type thing.

01;17;18;26 - 01;17;36;04

Stephen Wolfram

And, you know, I think that it's always challenging when you have a company that runs fairly smoothly and then you have this kind of spark of entrepreneurism inside it. You know, people are like, oh my God, I can't, I can't stand dealing with so and so. They just trying to break out of all these systems that we have.

01;17;36;06 - 01;17;53;23

Stephen Wolfram

Well, you need a certain amount of that. On the other hand, you can also waste a bunch of time with people who are like, I mean, in our particular company, people who come in and like, I've got this amazing, you know, I've got all these amazing things I'm going to do. And, and people are like, let's see what happens.

01;17;53;25 - 01;18;13;04

Stephen Wolfram

And you know, sometimes people come in and they say, we're going to do these amazing things and everybody's a bit skeptical. And then they deliver, and then everybody's excited and very quickly on board. And sometimes they're like, oh, another one of those, you know, let's let's watch the, you know, the, the thing, the thing crash and burn.

01;18;13;04 - 01;18;40;07

Stephen Wolfram

And sadly, it quite often does under those circumstances. And I, you know, I don't know whether I mean, I think, a company is pretty good on the give people enough rope that they can hang themselves, so to speak. And or not, as the case may be. I think, a company is, is, I mean, the thing's some small set of people end up working directly with me.

01;18;40;07 - 01;19;04;15

Stephen Wolfram

And one of the things that's always, I am a very intense, sort of worker on the projects that I'm doing directly myself. And that's always, I don't know, I'm much older typically, than the people who are working with me. And it's always a little bit funny that, you know, that the stamina I seem to be able to, to muster a lot of stamina to do these projects.

01;19;04;15 - 01;19;29;25

Stephen Wolfram

And I think that the, you know, that's a thing that that, in our company, at least, you know, there are the times when that sort of an intense phase of some project and it's really like, can you actually muster the stamina to really get through this project? And I have to say that, you know, we had we have we have great people in terms of doing that kind of thing.

01;19;29;28 - 01;19;30;12

Stephen Wolfram

Well, that's great.

01;19;30;12 - 01;19;50;22

Geoff Nielson

You need to get it. Sounds like it keeps you young as well. I'm, I am watching the time here. I did want to ask Steven. You know, you've mentioned that you've advised, you know, countless founders you've spun off, you know, people who have become pioneers in their own right, for people who may be listening to this and have that spark they want to create something new.

01;19;50;22 - 01;19;57;05

Geoff Nielson

They want to be builders. What's sort of your your best advice that you want to leave them with?

01;19;57;08 - 01;20;23;14

Stephen Wolfram

Build something you want. It's point one. I mean, don't have a theory for some industry that you know nothing about. And, you know, it's like, would I want this thing? Okay, maybe we can build it. I think, you know, if I look at, lots of founders, I've interacted with very different personalities. A certain amount of tenacity is always necessary.

01;20;23;16 - 01;20;38;27

Stephen Wolfram

You know, it's never easy. It takes a lot of work. You know, people are like, oh, I'm going to have this great idea, and then I'm going to hire a bunch of people and it's all going to be easy. It's never easy. And, you know, if that's what you think is going to happen, it's not going to work.

01;20;39;00 - 01;21;03;21

Stephen Wolfram

Doesn't mean the people can't be successful entrepreneurs when they've got, you know, young families and this and that and the other. It's just a question of how much, you know, personal tenacity can you put into the thing, you know, and, really, how much, how much energy and stamina you have, I think, you know, it is it is always surprising to me the range of personalities that can be successful.

01;21;03;23 - 01;21;26;22

Stephen Wolfram

I mean, there's a certain degree. I mean, for me, one of the things is, you know, somebody who is very technically sophisticated and when some business or personnel related thing comes up, they just switch their brains off. Drives me crazy. And the most successful entrepreneurs just don't do that. It's like there's a thing that's come up. Well, let me figure it out.

01;21;26;25 - 01;21;47;22

Stephen Wolfram

And the fact that, you know, I was trained as a, you know, physicist or something, you know what? I'm going to use what I learned from that about figuring out this personal problem rather than like, oh, that's a personal problem. I can't deal with that. I think for me, you know, I, I guess I have the confidence to believe that I can understand anything anybody's telling me.

01;21;47;24 - 01;22;18;23

Stephen Wolfram

So, you know, when it's like, I'm going to hire the expert and they're just going to do it, and I'm going to leave them to do it. I don't do that. And I've seen that end badly in many cases. In other words, the CEO has to understand what's going on. And you have to, you know, if you've got some team that's, you know, delivering technology or setting up, you know, some legal thing or doing or doing some other, you know, doing some other thing if you don't understand what they're doing, then it's probably not going to go well where it has a high chance of not going going to go well.

01;22;18;27 - 01;22;35;03

Stephen Wolfram

And that requires a certain amount of personal confidence because people will always be telling you, you know, we're the experts, just leave us alone. We'll do it's going to do the right thing. And just like, just explain it to me, you know, I'm kind of kind of slow at this, but just, you know, explain what you're talking about.

01;22;35;06 - 01;22;54;26

Stephen Wolfram

And if you get to the point where it's like, I just don't see how you know, be follows from a push harder because that that's going to be the weak spot where, you know, when something goes horribly wrong and it's going to blindside you. And by the way, the person who set it up is probably going to quit by that point.

01;22;54;28 - 01;23;20;09

Stephen Wolfram

And, you know, it's like you're going to have to understand it. I think, you know, when it comes to, starting companies, you know, I think people, you know, I've never been sort of lucky enough to have co-founders. Things I've done, might have been nice. It's I don't think, you know, I think that's a challenging thing.

01;23;20;09 - 01;23;45;03

Stephen Wolfram

I think that sort of the co-founder thing works okay. For a few years, as you as the years go by, that tends to be more and more divergence between people's interests. And you kind of have to plan for that if you can. I mean, I think that, you know, whenever I see an entrepreneur who says, I know about this thing of I'm going to hire people who do all these other things, even though I really, really don't understand them.

01;23;45;05 - 01;24;03;13

Stephen Wolfram

That's that's never a good sign. I mean, it's kind of like, you know, the way you should think about it. I was on day one. The CEO has to do everything. And, you know, gradually, as you understand, different functions, you can find great people to do those things. Maybe they'll be better than you at doing those things.

01;24;03;16 - 01;24;24;27

Stephen Wolfram

And, but but, you know, it's first you have to understand what they're doing. Then you can hire for that role. I mean, I think that when I, you know, when I hire people, it's, for sure. I don't do much. Junior hiring at this. I don't do those interviews myself, but I, I am, you know, my basic principle there is, you know, I'm going to talk to somebody.

01;24;24;27 - 01;24;40;19

Stephen Wolfram

I don't think I can do an interview in less than an hour. I've given up on trying to trying to do the 15 minutes. You know, I'm just going to get the key points because I just don't find that works. You know, what I try to do is just let me really understand this person, you know, do I get to know this person?

01;24;40;21 - 01;25;11;26

Stephen Wolfram

You know, and usually and, you know, I do interviews. They're talking about whatever it is they know about. It's not me asking them a set collection of questions. It's like, just tell me about what you know about, you know, talk about their experiences and so on. And really and then, you know, I, I kind of ask myself at the end sort of what, you know, given what I learned about this person with the, you know, with the particular task that I have in mind, you know, how confident am I that they'll be able to do this thing given what they learned about them?

01;25;11;28 - 01;25;37;16

Stephen Wolfram

And whenever I find that, you know, I realize at the end of the interview, I did three quarters of the talking in this interview. That's usually a super bad sign. It's it's usually a, but I mean, that that's, I mean, the challenge when you do more senior interviewing, it's actually that's very difficult because anybody who's sort of a senior executive type person, has got a lot of polish.

01;25;37;18 - 01;26;15;17

Stephen Wolfram

And drilling through that polish is really hard. And, you know, for more junior people, less experienced people, there isn't a lot of polish. You, you, you know, you see what you have and then you can understand, is it a fit? Is it not a fit, whatever else. And, I would say that the, it's more difficult and, and, I always find it just, oh, my gosh, you know, it's like, after enough time, you finally get to the actual person as opposed to the Polish, so to speak, and then you can find out, is this a fit or the greater the terrible fit for what one wants, etc..

01;26;15;17 - 01;26;34;12

Stephen Wolfram

But but it's more challenging anyway, a few a few thoughts about I would say the other thing, the sort of the keep thinking is an important thing and things go along, you have to figure out what to do. And, you know, you have to think, you know what is the big picture? What are we really trying to achieve?

01;26;34;14 - 01;26;44;13

Stephen Wolfram

Not kind of. We get led into every little valley of, of what what's happening? And I'm getting reminded that I have to run along a moment here.

01;26;44;14 - 01;26;59;26

Geoff Nielson

I, I thought you might. And I was watching the clock as well. Well, thank you for imparting that advice on us, Steven. I wanted to say such a big thank you for joining us. It's been very insightful. We've covered a lot of ground here, and I really appreciate all the insight and guidance that you've shared with us today.

01;26;59;28 - 01;27;02;06

Stephen Wolfram

Thanks.

01;27;02;08 - 01;27;26;16

Geoff Nielson

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01;27;26;16 - 01;27;41;10

Geoff Nielson

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