Motley Fool Money - Pixar Co-Founder on AI and Storytelling

Episode Date: January 21, 2024

Ed Catmull is a computer scientist – and a force of creativity. He helped bring to life beloved, generation-defining movies like Toy Story, Finding Nemo, Ratatouille, and more.  Ricky Mulvey caught up with Catmull to discuss:  Being in the “business of exponential change”  AI’s potential upheaval of the animation industry How technology and story advance each other Tickers discussed: DIS Host: Ricky Mulvey Guest: Ed Catmull Producer: Mary Long Engineer: Rick Engdahl Learn more about your ad choices. Visit megaphone.fm/adchoices

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We wanted to do research, I don't mean research in a technical sense, research where you go out into the world and find out something you don't know. And the reason is you actually need stakes to get away from the stereotypes. So if your expertise is something you've learned from movies, then you tend to copy what's in movies, which makes everything derivative. So for us, it was to say, well, we need to go out in the world and discover that which is not obvious. I'm Mary Long, and that's Ed Catmull, co-founder of Pixar and a pioneer in both computer animation and in storytelling. Ricky Moldy caught up with Catmull to discuss AI's impact on the animation industry, stakes in storytelling,

and why imagining oneself, riding bareback on a herd of wild horses, can help in dealing with unpredictable talents. Ed, I kind of want to start at a jumping off place, which is Pixar characters are often admired for their curiosity. I know you have to be a curious person as you have developed software and stories. So right now, what are some things you're curious about? Well, there are several things I'm curious about. There are actually too many things now. but one of them is just looking back at the implications of the rate of change and over my entire history it's been fairly clear that the underlying change in the

cost of computing was at an exponential rate about I think is a practical metal I 30% per year of improvement. I think the actual numbers, it isn't. Forget transistor count. It's really the effectiveness of the chips and the cost. It's probably more like 20, 25% annual increase over a 50 year period. Now, having said that, the companies along the way, like the workstation companies and the other companies,

were in the business of building on this exponential change, and yet they didn't see the implications of it. This, for me, it was mind-blowing. What in the world was going on? This is separate from the cultural questions, how you solve the technical problems. It's just looking at the industry as a whole that I was in the midst of thinking,

why is it that people weren't seeing this? And now I've got more time is to talk with some of the people involved and to find that actually a lot of the engineers did know this, but the leaders and managers, the company, in thinking about management and growth and the pressures on them, we're not listening to people, after their own people and their engineers, one of the results is none of the workstation companies

have survived until today. Now, for me, this is curious. So now I'm going back looking at it and working with a friend at MIT Sloan is to think, what is it that's going on? Why are people missing something which is so clear and instead reacting when something actually becomes so big it hits them in the face where they could have prepared ahead of time? What are the implications? So in the cost of computer power declining, in the rate of change

in the amount of computing power that people get going up, what are the implications that you and the engineers were worried about that the leaders were not? Well, if you could actually clearly see that something is going to change by more than an order of magnitude or some major component of your business is going to change, it's going to have an impact. And that impact means that you have to think out five, ten, ten, fifteen years, and you really have to put some attention on it. and most industries didn't even have anybody inside who could see that to hit and if they did they typically ignored them so it was this this uh an ongoing problem

uh i think in the case of what we're seeing now with an even faster exponential growth rate with uh machine learning, the growth rate, the annual growth rate of processing power and cost for the GPUs is even greater than it was for CPUs and has been for several years. So we're now seeing the consequences of that and those consequences regarding machine learning are, they're not very obvious, but we don't typically have people in companies that can even

think about the implications of the change. I'm sure you also have thoughts on the effects of AI on the animation business, and I hope you will forgive me for reading a take from Jeffrey Katzenberg about this. This has made some news. He said, quote, at the Bloomberg New Economy Summit, it. In the good old days when I made an animated movie, it took 500 artists five years to make a world-class animated movie. I don't think it will take 10% of that. Literally, I don't think it will take 10% of that three years from now, end quote. I think in terms of, let's say, making an animated film, the number of people who knew how to use the tools for doing 3D animation, It was very small to begin with.

So as Pixar started, we had to train people, and we wanted people who were trained as artists at observational skills. We didn't actually care whether or not they knew how to use the tools because we trained them to use the tools. They weren't the skills or the skills that we needed. Now, as we grew, we were, we tried to be very careful. just turning back at Lucasfilm is to understand that this was not a about technology was about telling stories. So if we had a technological tour of force, then we were to fail.

But if we got the story right, we would succeed. So there is that issue of what is the optimal group for creating something which is thoughtful and able to make a good story, but used the technology in the process of doing it. Now that the number of people that we would take to make films is, I'm going to say roughly on the order of 300 people dedicated to it near the tail end. There's a small group. to begin with and then it's only when you figure out what it is that you add a lot of skilled people to do it. But the real question for us was, from a creative point of view, what's an optimal number of people? So as the technology has gotten a lot less expensive and the,

in all different fronts, tools, also the number of people trained to use them is significantly more than it was before, and they're very good. So that's in a different kind of world that we're now in today. So that the barrier to entry, if you, if you will, to make anything, is actually very low. So if you want to produce something which is very, B or C level, it doesn't take very much to do it. It's cheaper to do typically a 3D film for young children on Saturday morning than it is by hand. So the economics have changed and a continuing to change. A generative AI is clearly going to continue to change that.

So that's the course that we're on, and you can't ignore that that's the process that's going on any more than the workstation company should have ignored what was happening to the CPUs underneath them at their peril. So, but that doesn't ask the question that if you want a rich full film, what's the optimal number of people? And I think that's the better question that you want. to ask for a really good film. But the answer is, what would it take to produce a C-level film is the answer is it's going to be pretty cheap, like very cheap.

So a brilliant person or two using generative AI could do something which is great, which is what happens with arts. And I was just a single person will do something. I think if you've got a very rich film, then I've got contributions to a fair number of people. I'd always estimate it, and it's just a gut feeling, that because you've got looks and appearances,

but you're also trying to draw in ideas from a number of people, that an optimal optimal number is probably around 100 that when you get larger than that it's very difficult to change course because you've got it's very complicated and if it gets too small

in general, not always then you don't have as much richness and depth that you'd like. Yeah, I think and please correct me, we may be maybe a few years away from someone who can maybe draw a little bit and use natural language commands to make their own animated movie. Well, I think, I don't think that's even far away in order to be able to do something. I mean, the answer is probably a few years. but you can easily imagine that.

But in order to do that, it's you're describing something, and with generative AI, it's creating the dialogue and it's creating the imagery. But you can draw a comparison. If you look back at six years ago at the Scooby-Doo,

cartoons which I watched professional reasons not because I enjoyed them but I was more fascinated by the fact that there were only three plots or four plots there was the one that took place in the haunted mansion

there was the one that took place in the mountains with the Yeti there was one in the cave I forget where the other one was. So the settings were very repended with each other, and the format was fairly easy. So the cost of riding them was actually very simple and it was very low cost.

But it was also only watchable by children. So, and I sometimes, listened, because my wife will listen to a series on her iPad. I'm in the other room. I'm just hearing the voice. I'm listening to, how are these people speaking? And I think, well, actually, all of it sounds not very good, not very real estate.

I think when you actually watch it with the visuals, it's more compelling because you have something with the actors, but it all sounds like something which you really could generate because there's nothing particularly interesting that I find just in listening to them. So another question is, will that be generated? And it can easily be generated and the answer is yes. And then everyone saw you'll see something which was really refreshing a new and real, even though it's a fantasy in many senses, which is what art does and what art is,

but it would not have been generated. So we'll be able to fill the air with stuff which is pre-dributive, How far away is that? I don't think it's terribly far away, and it will happen. I want to go to Creativity, Inc. for a little bit because one part of it that I found useful for me is especially the section on mental models and how we use them whether or not we're cognizant of them and how especially creative people use mental models to sort of stay centered. One example is a producer from Pixar, John Walker, who imagined balancing an upside-down pyramid on his fingertip. You wrote that when you were at Lucasfilm, your model was riding bareback on a herd of wild horses.

Why was that a useful model during your time at Lucasfilm? Well, for me, it was accepting that in order for us to move forward, we had to have these sort of unpredictable talents around. And there was something about just saying, okay, I'm not really totally in charge of this group. and nominally I'm the leader of it, but if I thought I was trying to control what the people were doing

or where they were going even, I would actually slow things down. So it was to think of it in terms of like, this is a wild ride. And it was. So for me a lot of times, it's an acceptance of the nature of the problem. And just accepting the unpredictability of it, I've always felt to be a useful tool to have, as well as I found it was useful to know that I was frequently wrong.

Pixar was famous for essentially where story advanced the technology and technology would advance the story. Well, there's a million examples. One would be figuring out subdivision in a bug's life to make essentially triangles very, very small. So you have smooth surfaces appeared on the insects. And while we know the times that it works, were there ever times where maybe you're in a story trust meeting where you have to stop an idea because you're like, this is wonderful, but this is not technically possible at this moment. I think of, as you referenced, James Cameron, he had to delay Avatar for more than a decade because he was like the text just not there yet. Well, in the, for the first show, obviously for Toy Story, essentially these are characters that are made out of plastic.

The humans didn't look realistic. We had, the complexity was, were things that we'd worked out, like the leaves and the trees with massive amounts of stuff. But essentially, we're dealing with flat surfaces. The next film was Bug's Life, where we dealt with more characters, but even at that time, the sheer number of ants had the people working on

and saying, we don't think we can do that many. So it was like a question whether or not we could even do it. But the technology represented limits, and so we were designing around what we knew was possible. it also had uneven terrain. Now, it didn't take long before we reached the point where that really wasn't a major limitation, and as the rate of improvement in the technology

became then for the next few years of which are the things that are going to be the new problems that we should transform. And so we'd focus on that. And it ultimately reached the point where the technology became accessible enough that it almost, for me, in some of it felt it became a problem because it was in excess. So one of the difficulties with making a film is that working out the story is really hard. Going in the screening room and looking all the fun visuals is just that. It's fun.

So it then becomes a distraction from the story. And you've seen movies, as I've seen movies, where they look great and the story suck. So why aren't they spending more time on the story and less time on the visuals? It's kind of getting seduced in the candy store, because you don't want to do your homework. So that's the rage you went over from it. That's our limitation to actually we can do anything we want, and it gets in the way because we're spending time

of doing anything we want rather than what should be done. I think one thing Pixar also really understands is setting stakes in storytelling. And I think that's what maybe a lot of the visual feasts that you've described missed. I struggle with it, with a lot of, I see executives wanting optionality in a story, and I worry that comes to pass with multiversal storytelling. How did you, I guess what was maybe not the magic,

but how did you think about setting stakes in a story at Pixar? Well, the reason why I think that there's a better number, It's not like there is the actual number, but you want several people involved with it. But it isn't just you've got several people involved. For us, we wanted to do research, I don't mean research in a technical sense, research where you go out into the world

and find out something you don't know. And the reason is you actually need stakes to get away from the stereotypes. So if your expertise is something you've learned from movies, then you tend to copy what's in movies, which makes everything derivative. So for us, it was to say, well, we need to go out in the world and discover that which is not obvious. So as an example, although there are several of them, but one of them is Ratatouille, which is several years ago. The premise was a hard one, and it's a

important for us to take on hard premises. That is, these are ideas that would not pass the elevator test because you can't describe quickly why this is a good idea. So it's a challenge. Then you have to go out and not only solve this challenge, you have to find out things that the audience wouldn't know. So in the case of Ratatouille, we basically have typically cooking in our home so we know what it means to cook. So we just cook. We see cooking in restaurants. We can watch

cooking channel. But basically almost none of us have been inside of a professional high-end kitchen. What is that really like? So in this case, the research, and some research

isn't as nice, but you go into to high-end restaurants. You get to know them. And in the case of the French laundry, for instance, they went in and they worked in the kitchen. So Thomas Keller had the people in there in the kitchen. And then they went to France to the high-end restaurants. And they got to know, who are these people? What are they thinking? How are they trained? And then how do you quickly describe that in a film. And the thing about this is the audience doesn't know whether or not it's true,

but they sense that it is. And that's what you're trying to capture is something that they don't know, but you sense is true. So that's one element. The other one is you really want, ideas and problems that people can relate to. It isn't just that you can tell a story or even a fun story. And there are good movies that are just fun or they're comedic or they're adventurers.

So, you know, I enjoy them also. But every once in a while you get a movie which touches you or addresses the kind of thing that you're worried about. So that's what Pixar was trying to do, is to say, of all the kinds of films that we do, what are the ones that can touch us? And animation is, you know, it's abstract, it's an exaggeration, and it's metaphorical by its nature. So in the case of up, well, the characters in your head are real, look like real people. they have eyes and mouths and they're sort of like people but they're not but they're representative

but you're trying to capture something and explain something which is hard to explain which is emotions inside a person and the fact that the emotions are sometimes in contradiction with each other and in an abstract level we know that's true but it's not obvious to some people that if a child is distressed, that they've got this conflicting emotions. So in order to do that, the research is to talk with child psychiatrist as well as to think about your own children

and what happens there, and then to convey that in a story. So that becomes the underlying theme for anything to capture something which is, real and do it in a way that draws people in and gives them something new. It's not talking down to them. It's appealing to what they know

and what they don't know and their desire to know things that are new to them. I appreciate that. I want to go back to mental models real quick. You're now an advisor at an independent game company, that game company. a well-known game called Sky. Are there any mental models that you find applicable to you right now in this new project?

Well, I've only met with them a few times. I mean, the truth is often when I'm, my name is used to say that I'm a advisor, but I don't meet with them that much, to be honest. So I don't actually have an answer for that one. I honestly more of my advising has to do with other technical companies. How do they think about the technology and the rate of change and what it's being used for and how they might think about long-term implications when we don't really know. The nature of a mental model typically, if you think,

about the ones that people of God is how they're dealing with the unknown. And for me, that's the more profound question is, is how do you both extrapolate from where you are, how do you make an estimate about what will take place in the future, and accept the fact that you really don't know a lot, so you're taking steps towards something that is unknown, which means you're going to completely or frequently alter what you think is where you're going. Now, the problem is this is a hard concept

because a lot of leaders want to have more clarity about where they're going. In fact, a lot of people in the companies want the clarity. The problem is often the clarity is just wrong, in which case the company goes over the cliff. So dealing with that reality of unknown change in a world in which you were judged by immediate returns, is a pretty difficult position, and most people have difficulty navigating. Yeah, well, Ed, I want to be mindful of your time. It's been an absolute honor to be able to have a chance to talk to you, as I'm sure.

Many people who speak with you, Pixar, was a very important part of my childhood and my understanding of how to tell stories. Thank you for your wisdom and your time spent with us, listeners on Motley Full Money. That's my pleasure to talk with you. As always, people on the program may have interests in the stocks they talk about. And the Motley Fool may have formal recommendations for or against, so don't buy ourselves stocks based solely on what you have. here. I'm Mary Long. Thanks for listening. We'll see you tomorrow.