AI Video’s Wild Year – Runway CEO on What’s Next

28:35And those two things are different. And then you have gaming world and software and it's very specialized and specific. But then if you look at something like Alaf or Runway, we don't have very specific software to address all of those needs. It's the same UI, the same product experience, yet somehow we have customers in all those verticals. And I think the underlying, I would say, pattern there is it used to be the case that you pick verticals. I think now you pick principles. And the principles allow you to scale much better than any other previous generation of software. Our principles are the ones I was kind of telling you before, which is generalizable models.

29:13Scale really matters. Data, like quality, really matters. If you pick those principles well enough, you will scale in ways that you couldn't scale before with traditional software. And so, yeah, we don't customize or change the underlying product in any way. We do a lot of the work in the model, and that will continue to be the case. Fascinating. That was at the model level. At the product level, is that true as well? Or do you need to do industry-specific integrations in that architecture or software? No, you don't. And that's the interesting thing. You ask the user to do it on inference time.

29:47Similar to, like, I would say language models these days, where if you want to, if you, like, if you think about it, I don't know if you use, I'm sure you use any, like, a chatbot these days or language models to work. You're using the underlying same model interface that someone in, like, Chile is using to, like, do their high school homework and someone in, like, MIT is using for, like, Biola research. And it's the same underlying interface. I think for video and image models, it's basically the same. You don't have to have specialized UIs. You have to have the user just give you the right instructions.

30:20And if the right instructions are set correctly, then if there's a need for a UI or a slider or something else, I also believe that the models and the products should be able to just generate that as well, which is a completely different paradigm from how we built software before. And I think a much more interesting one for me. Yeah, and speaking of instructions, One of the things that blew my mind as I was, again, playing around with the models is the input modalities. Yes. So for chatbots, we use two prompts. But with Aleph in particular, you can do all sorts of different things in terms of like how you query the model.

30:54Maybe talk about some of those. You can now generate video or content or media without having to like write a single prompt, which for many, I think, is just totally new. and it goes back to kind of what we were saying before, people might need to rethink their mental models around media and videos and images. Aleph works in a way that allows you to input a video and then either select or choose or make a modification with it. And so you get the same video. So you can generate entire new sequence, but in this case, it could be the same video, just with something added or modified in the way that you want.

31:31And in some cases, just a word. In some cases, you can just annotate on top, which happens to be the way most professionals work. You take a video or a frame and you annotate on top what you want and you use that to prompt. So you have a video, your annotations that happens to be images. There's no like language prompts. There's only words perhaps in the reference or in the annotation and you feed that to the model. The model looks at both things at the same time, understands that this is an annotation of this thing and then like makes the changes. Yeah, it could be literally an arrow, right? It could be like a drawing was an arrow.

32:05Literally, like it doesn't have to be words, just like, which again, if you look at how professionals are working these days, it's pretty much like that. You take something like a video and then you either slice it or use something like Frame.io and you start annotating on top and then the annotations you can send to someone, they review it, they interpret them and they make the changes. That process can now be automated having a model that does it for you. That for me is a completely new way of just using the models that just wasn't possible like a couple of even weeks ago. How do you think about quality and testing quality?

32:41Because, you know, a video is ultimately a bit of a subjective kind of product. So yes or no, it follows instructions. But is this a good-looking video? Is it a bad-looking video? So how do you think about building rigor around evaluation and the testing and feedback? It depends what you're solving for. I think we have a high bar for like aesthetics and quality and cinematic outputs and professional outputs. I want to make sure like we can keep raising the bar towards. Part of it is you have to have the right feedback loops for training. And so we have a studio team, a creative team in-house working right next to a research team.

33:26That's how you improve the models on a qualitative kind of like aspect. I think if you aim very high for quality, it would unlock a lot of other use cases that are just downstream of that. I don't think there's one single answer. It's more of just have to be, you have to prioritize for that. How do you manage expectations in this world? And I'm seeing this as a general comment, not about you guys specifically, but it feels like AI video is perhaps the most obvious case of like amazing Twitter demos or like social media demos where it looks fantastic. And then, you know, you get on the tool, a tool, not necessarily you guys.

34:11And then, you know, it's work, right? It's a struggle. Like all creative processes involve struggle. How do you manage this so that people are not disappointed and jump to the conclusion that, oh, AI just doesn't work. It's all Twitter videos or Twitter X demos. I actually wrote a long post about this very recently. But there are a couple of things. The first one is setting the right expectations for people. I think, as you were saying before, most of people's experience with AI these days, if you look at a macro level, has been with chatbots. And the way you interact with a chatbot is you give the system one prompt, one answer and you expect one answer back and it needs to be true and it needs to be good and it needs to be like one single thing that i do right um i think if you're completely new to like creative ai or using ai to make images or videos you might come with a very similar expectation being like i have this incredibly creative thing in my head which is a complex thing that i only can visualize internally i'm going to go into this software i'm going to type the words that i think describe what I'm seeing.

35:16And then once it's out, I'm going to be extremely frustrated because it doesn't match what I had in my head. And the conclusion is therefore that it doesn't work. And so for me, it's like you're watching a Christopher Nolan movie and you realize he used a camera for that. You go and buy the same camera and you press the button to record and you watch the output and you're like, those two things don't compare. This camera does not work. Right. It's not me. It's the camera. The camera doesn't work. I make that kind of comparison because I think ultimately this is a creative medium that requires you to experiment, spend time understanding how it works.

35:50If the assumption is you're going to come and make a film by pressing a button of a camera, you're not going to have, you're not going to understand how cameras work. If your expectation is to come to any AI creative software and type one word and get exactly what you want, you're not fully understanding the extent of how they work. And so part of it is just helping people manage their expectations and understand how things work in this new world, that some of the things that you're expecting might not actually happen the way you will expect them. It just works differently. And it's just a learning curve.

36:21For the first couple of times, it will take you time to adjust until you understand how it works and you start realizing the potential of it and you start understanding how you can bring it in. And I think it's, yeah, probably managing expectations happens to be the most important thing for people who are totally new to the field. What would you say is the current state of the art in video AI runway, but like across the industry, precisely in terms of managing expectation? What is currently possible? What is not yet possible? What's truly working and what's not yet working? So I think there's a lot of things that have worked, but I think this field is still very nascent.

37:03and there's so many things you can solve for. I think image gen is not fully solved, but it's made a lot of progress. I think most of the tasks that people thought would not take many years to solve are instruction-based prompting or instruction-based generations. All of the things that are very specific and detailed, I think models are getting really good at. In the video side, I would say long consistency of scenes or being able to cut and have consistent characters within the same generations, though things are also making a lot of progress. I think real-time is getting closer and closer. And I think it will be perhaps the sole focus of many companies over the next couple of months.

37:43How do you make sure inference happens on like real-time basis? Like you can do with language models these days. You can have a conversation with an assistant. You're going to get to that for video very soon as well. I think there are many things that haven't yet been solved. And there are things that are getting better, like consistency role is getting extremely good. But I think my belief has always been that we solve, as a field, we solve rendering first. We're able to show and create incredibly consistent videos and images. We haven't yet solved control, which is how you make sure the models create the thing that you want to create.

38:19And control has only started to happen over the last couple of years, months even. So there's a lot more focus in control. For a lot of us people following video AI over the last couple of years, and then the broad public, not that long ago, we were in the world of the Will Smith spaghetti and then the six fingers and all the things that were sort of easy to poke fun at. But what's happened in the last year and a half that, you know, all of a sudden we seem to have like this completely mind-blowing results? Is there any kind of like fundamental breakthrough that happened? Any work that you guys did that unlocked this level of quality?

39:06I think it was just time. I think people know if you believe something to be true and you're convinced of it, it's just like a matter of time until like it worked. I think for a long time all of the sculptural moments of like the six fingers and like Will Smith eating spaghetti I think for me those are like focusing on a specific moment in time and trying to extrapolate from that towards the future considering that nothing else will change and I think we have we had just a different perspective being like yeah those things are imperfect but like you're not extrapolating well based on what happened before that and before that and before that, I don't think there was one particular thing.

39:42It was more of, I think many in the field have believed some things are going to be true, some things will scale really well. And building the infrastructure to get there is to have the thing that takes you the longest. Like training a model is not trivial. It takes time. It takes time to do the right captioning, the right annotation, the right infrastructure around it, the right testing. But those things are coming. I mean, they will be solved over time. Yeah. Yeah. Let's talk about that last point in detail, if you will. So let's take Gen 4 and Aleph. From starting with an architecture and algorithm standpoint, are they the same thing directionally as Gen 3?

40:23Are they more of the same thing? Are they different? There's a lot of things that we learn. At every model that you build, you learn something around what works and what doesn't. And I think a model is not just like one single idea. It's a combination of different ideas from how you caption, how you do training, how you task, how you benchmark, how you do different parts of a model. Like if you swap specific architectures on the encoder or the decoder, there's many parts of a model building that for me is more like an art than a science that you're going to learn just by shipping one model, then shipping another model, shipping another model.

40:57Gen 4 has a lot of the things that we've learned over the last three generations of models. And the next generation of models are going to be releasing. They're also going to have a lot of the learnings and the things that work in Aleph and in Gen 4. And I think that should continue to be the case where it's less about one single algorithmic innovation that's going to change the entire field and more about how do you make sure that those pieces are set correctly. Because I think ultimately it's a complex puzzle that has many different parts and you just need to know which ones are working and which ones are not.

41:29How long does it take to train a new model in the hardware world? Yes, pre-training. A couple of months, yeah. I mean, it depends on the standards that you have and how big the model is. And yeah, there's a lot to it. The current ones, LF and Gen 4. It takes a couple months. I mean, so the first model that we ever released that I think was Gen 1 video-wise, it was the first, I think, first model that was ever out publicly and commercially was around 2023. I know that because it was in the front page of the New York Times. It was a big deal at the time. So 2023 was Gen 1. And now we're in Gen 4, Gen 5 almost.

42:07that's been like, what, two and a half years or so, a bit less than two years or two and a half years. And so at the beginning, it was like every 12 months. Then I think every eight months. Then by now, I think things are getting to a point where you can release new pre-trained baseline models every couple of months. I think partially, again, it has to do with the infrastructure. With the amount of work that you, it's hard to see, you know, because like when people judge a model, they just judge the output. and I think it's a fair assumption that like you're judging what you see but it's very hard to understand what went into the model itself and how good that infrastructure knowledge of the organization can be used to ship another model and another model and another model, you know?

42:50And I think that for me is the most valuable part of Runway. It's not a model that we put out because models will like completely like change every now and then. It's the organizational knowledge and the infrastructure that it takes to ship a model like that. And if you're good at that, you're going to start shipping them much faster than before, which happens to be the case for us. And to the extent that you can talk about your infrastructure, any kind of detail about how that works, how do you handle all the compute that is needed to train those models? Are you an AWS shop? What's the stack?

43:27Anything that you can give us a glimpse about on the infra? We started building pretty much, I would say, almost everything from scratch. And so we've spent a lot of time building really good just in research workflows and toolings for researchers. And those are the things that are hard to measure and see because there is no immediate output or no immediate value yet if you're building and spending time on those. But I think if you make the right bets on the way you manage your data, the way you manage your cluster, the way you do deployments, the way you do research and training jobs and all of that infrastructure and knowledge we build internally.

44:06In some cases, there's like, I'm pretty sure if we take some of those internal tools and we make them products, there'll be successful products on their own, you know? But now we have like, a lot of it has to do with knowing exactly why are you building those kind of things. And in some cases now we've managed to like buy some stuff, like we don't have to build everything from scratch. There's enough knowledge in in building those from scratch and knowing why those things work and why others don't work. I think what we can share the most is a lot of what we build is very much custom built. And I think that's an edge if you can afford to do it.

44:42And I think we've managed to afford to do it because we just started. What about the data side? So in AI video, there's this well-publicized debate around in particular using YouTube videos and there's class action lawsuits and all the things. where do you all stand on that and what data do you use to train the current version of the models yeah so we don't disclose what data we use but we've done some like announcements on partnerships around data we have one with Lionsgate we announced another one with GetImages and so we use we have our own internal teams that are collecting data I think quantity matters a lot but also quality so making sure you can like curate the right data garbage in garbage out So if you just put a lot of garbage into the model, you're going to get a lot of bad stuff into it.

45:30But I think quality then goes back to the question you asked me before. It's like, what's good? Like in art or in video or in filmmaking or in just any artistic endeavor, there's no such thing as a right or wrong answer. There is in a chatbot or in a search engine. And so Aloha, we just train the right eye to select and curate the data itself. And so data for us, more than quantity, is a lot of the quality component to it. What about synthetic data? Is that a thing in AI video? It is. I think it's becoming more of a thing, I would say. It still has its challenges, mostly to generate diversity of data, but definitely something we're exploring.

46:15And so on the technology front, you are a closed source. this, you know, obviously this whole back and forth and the theme of open source has been one of the key themes of 2025. Do you, can you imagine that at some point you'll open source some stuff or where do you stand on this question? I do feel that depending on what your goal is, you might just choose whatever is the best outcome for your like mission. And I think for us, it's like we know there's a lot to be built. There's a lot of product momentum that research and product have to work really closely. I don't think open source necessarily gives you that level of control.

46:55It has other benefits. It has other things that I think are extremely valuable. And I think there's a lot yet to be built. And we've open searched a lot of stuff before. But I think for where we're at right now, we'll probably continue to build models just internally. We talked about how one direction the technology was evolving into was one model that could do them all. If you suspend disbelief or be very optimistic for the next few years, from a pure technology standpoint, what do you think happens? In particular, there's this question of 3D worlds where you can explore entire universes. So, like you're in a completely video-generated, AI video-generated environment that keeps being built as you progress.

47:42Is that near term? It's here. Yeah, I think, I mean, we have it. It's more about like deploying it and like the economics around it. I think you're going to start seeing, yeah, real-time becoming more of an interesting use case than things that you can see. And that's what you meant by real-time, just to double-click on what you said earlier. So real-time means it's not just like a customer service chatbot kind of thing. It could be like a whole universe. Correct. you start with let's say a reference image or reference video or a prompt and you're free to basically navigate this world openly as you wish and I think what I mean this is a new medium is the moment you're able to do that and if you've ever tried it and there are a few people that will have tried it it doesn't feel like anything you've experimented before because like if you think about linear media films and videos and ads it's the same video everyone watches and it's the same experience It's the same sequence of actions happening over and over again.

48:47So that's why it's linear media. There's no linear media like games. And in the nonlinear world, you still have instructions being built. There's worlds and parameters around how things are supposed to happen. And so the rules of the system already baked in, and you're kind of exploring something that someone already created. Now, this is different because you're starting from something, let's say an image or a video or an initial starting point. And then if you extrapolate where things are going to go, you might be able to just navigate and move around that world freely. And there's no rules to the world, but the rules that you want to have in the world.

49:27And that in itself, for me, it's not a film, first of all. It might have some of the qualities of a film because you might choose the story and you want to follow the story. But it's also not a game entirely because it doesn't follow the rules and instructions that we know of games. So what is it? I don't know I know it's just different it just feels and tastes and smells different and I think real time is probably one of the things that will unlock many other new use cases that were just never manageable for people before Are you a believer in the same vein in this idea of hyper personalization of content as in the movie format so maybe not as crazy as what you just described but you know I like that tweeted a while ago this idea of that you could have a Netflix series that would be just completely based on I want this actor doing this thing in this scenario and I got a lot of flack for it like people were not happy for whatever reason.

50:29Is that something that you think is possible? Is that something that you hear people in the creative industry talk about? Yeah. Or is it all made up? No, no, I think a lot of people have come to some sort of the same conclusion of like, oh, you can personalize your films or shows. I don't think that's necessarily wrong, although I do think that that's a way of looking at this new medium with the lens of what we know. So it might not be the case that that's something people want to experience. And that doesn't mean that films are going to go away and you're not going to see shows anymore. It just might happen in the case that it's a different experience altogether.

51:07and there might still be Netflix shows in the ways that we know them, but then there's an experience that you're having on the side that could be inspired loosely by the story that you watch on a linear way. You can have both. Like, it doesn't have to necessarily replace. And I think when people get angry or perhaps mad is that they might be interpreting this new way as replacing the entire new thing, you know? And I'm like, no, it's not going to replace it. You're still going to have, like, Guillermo del Toro building, like, the film for you, but you can have another thing on the side. And another thing might be just if you're an experience.

51:40It might not be for everyone. That's fine. Not everyone in the world plays games and it's totally fine. Like, but you can still have both and you're going to get to the point where you can customize experiences in the way that you're like describing. I'm just not sure that they're going to follow the same rules and patterns of films where you have actors and scenes and sequences, you know? I think that, yeah, I don't think that's probably going to happen. All right. So we talked about the product. We talked about the core technology and models. Let's talk about the business side a little bit. Who's your prime customers?

52:11We touch upon a little bit, like this Hollywood, this advertising, this music videos in terms of use case. But are you at this stage a bottoms up kind of company? Like you have this product, which is open to everyone. Or are you targeting the big enterprise deals with Lionsgate and Disney's as you already have? So from a business side, I think our goal is to help people tell stories. Like, ultimately, I think storytelling can take different forms and shapes. Today happens to be the case, again, the most obvious one is, like, the people who make storytelling for a living. All the studios, all the agencies, all the media companies, the brands.

52:50Most of, if not all, of our adoption is just very organic. Like, we've just people coming to a platform. Again, it started with a very small set of subcultures and sub-users in very small niche companies, and they're starting to grow from there. The brand is now, I would say, the product is known by many, mostly because you've seen it somewhere else. Because someone showed it to you or you saw a video of it and like you experienced it and you share your, you start using it. We have hundreds of people and thousands of people just making like videos for their own, for their own like enjoyment. It's an audience of one.

53:20You're just making it for yourself. There's value there. I think there's a lot of like value making experiences that are like just for you. But then you probably work personally somewhere else and you bring runway to your company. I think having your users and your customers be your like sales force in a way, it's great and it's hard, but it's great because it helps you just go into many different places that otherwise would be very hard to get into. That's how we've got into all the studios. It's not us like trying to like deeply sell to them. It's like they reach out being like, hey, we have this use case or like we want to use it because our team was using it.

54:01and we started to see that in other industries like architects same thing like there's some overlap between BFX and architect software and for some it became interesting to experiment with runway and it became a thing and then you started kind of growing like that. So you're mostly inbound or exclusively? 99 % interesting. Will you go outbound at some point or is it just not the thing about the company? I think we will. I think we realize the business is in a position where we need to make sure that we can show this to everyone, even if you haven't seen it before. Like I was traveling, I came through customs a couple of days ago and the officer knew about runway and like, you know, in the visa it said runway.

54:42It's like, oh, runway. Like, that's great. That's amazing. But I have like all that stories where like people now have heard about it. And I think we need to just either, but make sure also that like there's many other people in the world, like 90, I would say most of the world out there just hasn't heard about this. and you're going to get there by just doing everything you need to do. From a pricing standpoint, so you have different tiers where like for 20 bucks you get a certain number of credits and then you have like an enterprise tier. Like how do you charge people? The best option is just unlimited.

55:15Get unlimited. Unlimited is a plan that I would say to generate as much as you want. It's like what,$79? And you can generate everything you want. You can pay for credits if you want faster generations. And for enterprises, it depends. Like we have people who are using the API to generate thousands of videos. And in that case, we charge you per request. And so it's a very flexible, since we do everything, we do model training with deployment, inference, distillation, optimization, product, then we can manage to change everything from that stack that we want to change. I think that one of the things that I would say in AI these days is tough if you're just on a particular like hopper layer is the margins because you don't control the rest.

55:56You're just basically giving the value to whoever built the model. I think there are very few companies out there that can do this full stack approach. You train the models, you deploy them, you do the inference. And so for us, in some cases, we might charge you differently depending on which part of a stack you want. If you just want the API, we can charge you this. If you want the product, we can charge you this. But I think overall, my general sense is that prices will continue to go down, mostly because compute will continue to go down. Yeah, fascinating. Anticipated my question. So you're not in the reported cursor and formerly Windsurf world where because of their reliance on underlying models, they're reported to be operating at negative gross margins.

56:39But here you control the stack, so you're reasonably insulated. Although there's still a cost of inference. Of course, there's cost. But I would say, yeah, I would say most of the AI lab research margins these days for companies to build are between like what's 40 to 70 percent yeah i think eventually you'll get to like best in sas margins like as 80 90 percent over time um and i think most of those companies were in those ranges are the ones who build the models themselves like if you if there's a dollar that comes in and you can get that dollar out for the entire stack then like you're benefiting from it if you're switching that dollar to start giving it to someone else then in some cases you you might not be owners of your destiny in a way.

57:23And I think we're pretty much still owners of our destiny. Speaking of big labs, one other thing that seems to have accelerated in the last year or so is the level of competitive pressure on this part of the market. So in particular, Google and VO3 made quite a splash. And then there are reports or rumors that there's going to be a Sora 2 coming out soon. Innovatively, that will happen. So what's your take on this? Is that validating? Is that scary? How do you think about it? It's great. Again, when you create a market and industry, like if it's interesting enough, you're going to have the best companies try to follow you.

58:06And if you're scared because of that, then I don't think you have the guts to continue leading it. When we started, no one cared. We showed a way, we showed a path. now others have followed and I think that's a great validation it's a great sign I still believe the companies are going to win here the companies are obsessed around the problem at hand are obsessed around not only catching up but like leading the way and I think that just requires a completely cultural like set of approaches on how you build both product and research speed is the uttermost importance these days it needs to be very fast you need to learn a lot and I think for us it will continue to be the case We've seen competition come and go for the last six years.

58:50Every new year, there's a new company people ask me about, and it's like, what do you think about this? But Chris, they have so many PhDs in their team, and they're so well-funded. Great. It's going to move the field forward if they succeed. If not, we'll continue building what we built. And I think that happens to be the case year over year. I'm confident that we'll continue to lead the way. All right, so maybe to close, zooming out, I'm curious about the future for Runway for you, but also for the industry and for users. What should one do today if you're interested in that world of creative film and storytelling?

59:35Do you go all in on AI and what does that mean? or do you still go to film school or are there professions that you should not pick because eventually that's going to be disrupted by, completely disrupted by AI? What's your recommendation when people ask you those questions? Be very open-minded. I think if you have a very consistent and particular way of thinking about how the world has worked, I think that's probably not going to adjust well to change. And I think the fusion of technology has changed. Technology has changed the world, of course, all the time. Like I argue that art is the history of technology and that will continue to be the case over time.

1:00:16But it used to be the case that we used to adjust. We had more time to adjust. We have sometimes like years and decades to adjust. And so the media world had years and decades to adjust to streaming and digital content. I don't think people have now realized that we don't have time to adjust. like if you're there are many companies that I've spoken with over the last couple of years that thought that all that what's happening today was going to supposed to happen in the next 10 years and if your whole strategy has been waiting and seeing I think you're not going to make it and that happens also like at a personal level if you're just waiting and seeing from the sidelines you're going to miss out and I think there's nothing preventing you from like experimenting, trying new things even if it's not perfect I think models have so much value these days in all sorts of domains.

1:01:06So be very open-minded and willing to question the essence of all of the things that you know are true because I think most of them will change. How have you adapted to that whole evolution at Runway? And with that, I'm going towards any kind of surprises in the history of Runway and advice for founders as you build and scale in this super fast-changing environment. It's funny. Someone asked me that same question a couple weeks ago. And I think the way I thought about it is we train models at Runway. So a model is basically an algorithm that learns about data, learns the patterns in the data, and then creates something based on the data.

1:01:49And then if you're good at training models, you can add more data, change the outputs, and keep doing that all the time. I think of Runway as an organization as a model itself, where there's data in the world, and data might be markets, markets, there are technology like competition, like talent. There's data happening. You fit that data for your organization. That happens to be the people and the knowledge between the people. And then an output comes out of it. And then what you need to do is they take that output, put it back as data that you understand and mix with everything else. And you keep doing that all the time.

1:02:22And I think adjusting the weights of that model is the most important thing like I can do right now, which is how do you make sure that as new data comes in and outputs keep changing, this model keeps growing and learning and becoming better. And I think part of it is sometimes in the AI land you start with random weights. You start with no knowledge, no understanding of what the model does until you start training it. And then suddenly something comes out and you think what works. I like to think of an organization that operates in a very similar way. You're constantly learning all the time. You're a system that keeps on learning.

1:02:55The moment you stop learning is the moment you stop pretty much growing. so perhaps it's a self-recurring like answer but yeah I think companies should operate like as an AI models as well Five years from now what does success look like for Runway View if you have it your way and everything goes according to plan Five years that's like five decades in AI Yeah okay three years, five years, ten years I think we started the company because I was just having too much fun with my co-founders like at school really really i'm 100 % honest here like we we finished school and we just love working together and so we we thought that starting a company might be the easiest way to like keep having fun and learning about this and uh i think i still what i enjoy the most about runways i come to the office now for 100 people and sometimes i sit with the brightest minds in a particular field and i'm learning a lot it's just so fun you know um i think success for me is like, well, we'll keep doing that for many more years.

1:03:56And in the process of doing that, you're going to change the world somehow. You're going to either make the world more creative. You're going to help someone make a story that they couldn't do before. You're going to change how people learn, how people see the world. But most importantly, the organization itself is having fun and enjoying and doing it because they care. I think care, like if you fundamentally don't care about what you're working on, like you're not going to get very far. I think we care maybe too much. And so in the next five to 10 years, I want to keep on working stuff that I care deeply about.

1:04:25And then the consequence of that is that you make great stuff and great products. The moment you stop caring is the moment like nothing really works. Well, that's a wonderful place to leave it. Thank you so much, Chris. This was terrific. We appreciate it. Thank you. Hi, it's Matt Turk again. Thanks for listening to this episode of the Matt Podcast. If you enjoyed it, we'd be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from. This really helps us build a podcast and get great guests.

1:04:56Thanks and see you on the next episode.