The Exchanges

Q And you can create video using Runway with eight different kind of inputs?

A Yeah, that's critical for us. We always think about Um, we're thinking about, like, who's using this, and for what? And if you're creative, if you want to tell a story, if there's something you want to say, You want to have full control. If you don't have control over the way you're using something, then it won't probably like matter, uh, because it will be just like a system creating things for you without actually you being the one with, with the agency. Um, and so we have like nine different ways of controlling those models and not all of them will last. Some will change. We'll come up with new ones. And so we're experimenting with these new medium, but there is, for example, um, motion brush. So motion brush is this idea that We train a model and a way of manipulating video just by, this is inspiration that comes a lot from how filmmakers and art directors actually give reference on, like, films. When you're, when you have a photogram, you sometimes just take a pen and, like, draw on top of it and, like, define how you want movement to happen. And so we took that inspiration, like, you have a video, an image, you can basically draw on top, define how things you want to move, and then, like, the model will actually move them. And that, and so you have, that's, that's motion brush. Um, and you have different, like, like eight different pens you can have for that. Another one …

AI assessment note: “we have like nine different ways of controlling those models”

Q Hollywood and filmmaking, that certainly something that I've heard you and you and I have over the years spoken a bunch about, but sort of feels looking in 2025 that this is just one use case and that the spectrum and the range of use cases that Runway Powers has expanded pretty dramatically over the last 12 months. Is that, is that fair?

A Yeah, I think that's, that's a fair representation. And I think it also has to do with, like, our philosophy of really what we're trying to achieve and our vision. Um, we, we always spoke about runway, um, as a new kind of camera. Like, we always speak about it as a, as a new medium. It's, in, in a, a new medium in the way that cameras were a new medium in the late 1800. It allowed people and artists and a bunch of, uh, um, a bunch of people to see the world in completely different ways. And the camera gave birth to photography. It gave birth to filmmaking and, and, and so on. Um, I think for me, AI is somehow a new kind of camera. And that camera has, of course, obvious applications in the fields where the camera, the real camera is still like use useful, uh, which is like cinema and filmmaking and video making and ads, which has been the first stepping stone for video models and world models to function. Um, but for us is that's the stepping stone. It's the first function. Cameras in the same way were first used mostly for the arts. They were mostly used for, like, theater recording and stage recording and then, of course, film. But then cameras have a bunch of other applications beyond that. Cameras are now in, like, self-driving cars. They're in, like, space. They're satellites, right? Uh, they're monitoring many things of, uh, of our, like, organs and bodies and using all …

AI assessment note: “Yeah, I think that's, that's a fair representation.”

Q Fascinating. That was at the model level, at the product level. Is that true as well, or do you need to Do, uh, industry-specific integrations in that architecture, software, or?

A No, you, you don't, and that's, uh, that's the interesting thing. You ask the user to do it on inference time. Similar to, like, I would take language models these days, where if you wanna, um, if you, like, if you think about it, I don't know if you use, I'm sure you use any, uh, 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, biology 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, and 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.

AI assessment note: “No, you, you don't, and that's, uh, that's the interesting thing.”

Q you get on the, on the, on the tool, a tool, not necessarily you guys. Uh, and then, you know, it's, it's, It's, it's work, right? It's a, it's a struggle, like all creative processes involve struggle. Uh, how do you manage this so that, uh, people are not disappointed and jump to the conclusion that, oh, yeah, it just doesn't work. It's all Twitter videos or Twitter X demos.

A I actually wrote a long post about this very recently, but, uh, my, 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 the 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 Uh, internally, I'm gonna go into this software, I'm gonna type the words that I think describe what I'm seeing, and then once it's out, I'm gonna be extremely frustrated, because it doesn't match what I had in my head. And the conclusion is, therefore, that it doesn't work. Um, 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.

AI assessment note: “The first one is setting the right expectations for people.”

Q Password to today. I want to ask you about something really interesting you wrote recently. What do people get wrong about generative AI media?

A Um, I think there is a common misconception that the first way to think about images and videos and content and audio and all sorts of like multimedia things that you can generate with these models is that you're going to make the previous thing that we make, but AI. So you hear a lot about AI films or AI novels or AI images, right? But that for me is like only the first step towards something much more interesting. Um, and I keep comparing this to, um, perhaps a very similar moment that we had, like as a society, specifically when it comes to storytelling, when the invention of the camera happened like a 120, 10 years ago. The camera is not a better paintbrush. It doesn't work in the same way that you use a paintbrush. You can get inspiration from how painting works. And that's actually how filmmakers used to think about the camera and how photographers used to think about the camera. But then eventually the camera, um, evolved as its own medium. It evolved in its own ways with its own mechanisms and metaphors and primitives and artists. And eventually it actually created an entire new art form that we call cinema. Cinema is a new art form that was created entirely by a Technological breakthrough. And so for me, we're in that early journey, journey of, of understanding AI as some sort of a new camera. And the first thing we think is like, oh, this is going to be like painting.…

AI assessment note: “a common misconception that the first way to think about images... is that you're going to make the previous thing”

Q And you can create video using Runway with eight different kind of inputs?

A Yeah, that's critical for us. We always think about Um, we're thinking about, like, who's using this, and for what? And if you're creative, if you want to tell a story, if there's something you want to say, You want to have full control. If you don't have control over the way you're using something, then it won't probably like matter, uh, because it will be just like a system creating things for you without actually you being the one with, with the agency. Um, and so we have like nine different ways of controlling those models and not all of them will last. Some will change. We'll come up with new ones. And so we're experimenting with these new medium, but there is, for example, um, motion brush. So motion brush is this idea that We train a model and a way of manipulating video just by, this is inspiration that comes a lot from how filmmakers and art directors actually give reference on, like, films. When you're, when you have a photogram, you sometimes just take a pen and, like, draw on top of it and, like, define how you want movement to happen. And so we took that inspiration, like, you have a video, an image, you can basically draw on top, define how things you want to move, and then, like, the model will actually move them. And that, and so you have, that's, that's motion brush. Um, and you have different, like, like eight different pens you can have for that. Another one …

AI assessment note: “we have like nine different ways of controlling those models”

Q From a pricing standpoint, um, so you have, uh, different tiers where, like, for 20 bucks you get, like, a certain number of credits, and then you have, like, an enterprise tier. Like, how do you, how do you charge people?

A Uh, the best, the best option is just Unlimited. Get Unlimited. Unlimited is a plan that allows you to generate as much as you want. It's like, what, 79 dollars? Um, and you can generate everything you, you want. Um, 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, like, request. And so it's a very flexible, since we do, we do everything. We do model training with deployment inference, distillation, optimization, Product. Then we can manage to change everything from that stack. They want to change. I think that, um, 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. You're just basically given the value to whoever built the model. Um, 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've, 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, like, the product, we can charge you this. Um, but I think overall, my general sense is that prices will continue to go down, mostly because compute will continue to go do…

AI assessment note: “Unlimited is a plan that allows you to generate as much as you want.”

Q you get on the, on the, on the tool, a tool, not necessarily you guys. Uh, and then, you know, it's, it's, It's, it's work, right? It's a, it's a struggle, like all creative processes involve struggle. Uh, how do you manage this so that, uh, people are not disappointed and jump to the conclusion that, oh, yeah, it just doesn't work. It's all Twitter videos or Twitter X demos.

A I actually wrote a long post about this very recently, but, uh, my, 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 the 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 Uh, internally, I'm gonna go into this software, I'm gonna type the words that I think describe what I'm seeing, and then once it's out, I'm gonna be extremely frustrated, because it doesn't match what I had in my head. And the conclusion is, therefore, that it doesn't work. Um, 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.

AI assessment note: “The first one is setting the right expectations for people.”

Q From a pricing standpoint, um, so you have, uh, different tiers where, like, for 20 bucks you get, like, a certain number of credits, and then you have, like, an enterprise tier. Like, how do you, how do you charge people?

A Uh, the best, the best option is just Unlimited. Get Unlimited. Unlimited is a plan that allows you to generate as much as you want. It's like, what, 79 dollars? Um, and you can generate everything you, you want. Um, 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, like, request. And so it's a very flexible, since we do, we do everything. We do model training with deployment inference, distillation, optimization, Product. Then we can manage to change everything from that stack. They want to change. I think that, um, 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. You're just basically given the value to whoever built the model. Um, 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've, 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, like, the product, we can charge you this. Um, but I think overall, my general sense is that prices will continue to go down, mostly because compute will continue to go do…

AI assessment note: “Unlimited is a plan that allows you to generate as much as you want.”

Q In other words, it's not just enterprise, it's for everyone?

A No, so, yes. So we have, I mean, video is now everywhere. We have TikTok creators and YouTubers who are using Runway to create just like viral videos, and that happens a lot. Um, and we have teams, small teams, large teams, and just, uh, production studios building, um, like, AAA movies. Um, there was a movie that was the other day in the movie, in the movies, um, there was, um, there uses Runway to edit a few scenes there. Um, a lot of ads, a lot of just YouTube ads and, um, Instagram ads as well, the Runway. It's a very, it's a self-serve system. You can just sign up, use the product online. You don't have to create an account. And then if you're gonna go really deep, you can go as deep as you want. Um, yep.

AI assessment note: “TikTok creators and YouTubers who are using Runway to create just like viral videos”

Q What did you start with? Like in 2018 or 19, what was the first product?

A So the first product was, so the, the vision was pretty much the same today, which is, um, I was actually reviewing a deck that we had from 2018. We, we, we used to, I think, drive the guys, the way people describe AI these days, uh, we used to call it synthetic media back then. And, and where our thesis was like, look, We're now able to generate these very small patches of images. Um, and so the first product was a bunch of models that would allow you to generate or use AI in creative ways. Sometimes, uh, creating images, but in these very blurry, inconsistent ways. Um, and our thesis was like, um, this is going to scale, and the moment it scales, you're going to be able to generate anything you want. Um, and so we started with what was possible at the time, which very small. It was using GANs at the time and LSTMs for writing tags. A set of experiments of products that you can plug into existing software, and so we had a Photoshop plugin that allows you to generate images inside Photoshop. We had, um, um, there was a, before Figma, there was a software called Sketch, um, that, uh, so we had a bunch of plugins in Sketch. Um, then we have a plugin in Unity that allow you to, like, create renders. A bunch of, like, different explorations, I would say, how to use AI within, within creative workflows.

AI assessment note: “we had a Photoshop plugin that allows you to generate images inside Photoshop”

Q In other words, it's not just enterprise, it's for everyone?

A No, so, yes. So we have, I mean, video is now everywhere. We have TikTok creators and YouTubers who are using Runway to create just like viral videos, and that happens a lot. Um, and we have teams, small teams, large teams, and just, uh, production studios building, um, like, AAA movies. Um, there was a movie that was the other day in the movie, in the movies, um, there was, um, there uses Runway to edit a few scenes there. Um, a lot of ads, a lot of just YouTube ads and, um, Instagram ads as well, the Runway. It's a very, it's a self-serve system. You can just sign up, use the product online. You don't have to create an account. And then if you're gonna go really deep, you can go as deep as you want. Um, yep.

AI assessment note: “It's a very, it's a self-serve system. You can just sign up”

Q And how do you make researchers and product people work together? Because researchers, uh, presumably are going to be drawn to the frontier and the theoretical and the product people are going to be drawn towards, uh, this is what our customers want to see tomorrow. How do you balance it?

A That's sometimes the case, but I would say for the best people and maybe something we do when we hire is try to find people who Can understand a little bit of both worlds. I think there's definitely a simplification of thinking of researchers just as like, like academics who want to like publish. I think many of them are seeing their impact in the real world and want to build products. And there's a lot of great engineers who understand research and want to make sure they can bring their research to their like product development. Um, I think it's just speaking the right people. There's definitely a lot of both people who are just obsessed with one or the other. But I would say these days, intersection of folks who can speak both languages is, is a bit more common. So you just start to find, find those people.

AI assessment note: “try to find people who Can understand a little bit of both worlds”

Q Smith's spaghetti, and then the six fingers, and all the things that were sort of Easy to poke fun at. What, what's happened in the last year and a half that, uh, 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, any work that you guys did that unlocked this level of quality?

A I think it was just time. I think people know if you believe something to be true and you're convinced that it, it's just like a matter of time until like it worked. I think for a long time, all of these cultural 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. It was more of, um, 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, it's 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.

AI assessment note: “I don't think there was one particular thing. It was more of”

Q And, uh, so do you use Stable Diffusion still at the core? Is, is Gen-one a product or is that a model?

A No, it's, uh, it's, it's both. Well, the, the model is, um, we published a paper for Gen-one. We haven't yet published a paper for Gen-two. Um, but Stable Diffusion is, like, it's now like a backbone, I would say, of, like, a lot of, like, image generation tools. Um, maybe not the entirety of it, but some ideas, and that's actually how research Tends to, like, evolve. It's not that you take the model and just, like, implement it. Actually, you can take the ideas of the backbone, or the ideas of how the data is fine-tuned, or some ideas on how you start the training and the noise schedules. And so, stable diffusion, I think, and latent diffusion, which was the first model was, was a good set of ideas on how to get to good resolution of images in fast and reliable ways, yeah.

AI assessment note: “No, it's, uh, it's, it's both.”

Q And when the Transformer paper came out and Generative AI started becoming a thing, was that, uh, super obvious to you guys, and you started, uh, switching away from GANs into, uh, into Transformer-type models? Where was the, the path to that?

A So Transformers, for the most part, early on, were just used for language. I think we're using other approaches for Pixel and Video, and Diffusions were kind of, I would say, a big, uh, transition for what people were using at the time, where we're mostly GAN, and now people are using Combinations of transformers and diffusion systems. Uh, I think, um, I think it was a, it was a validation of sorts. It's like we're into something, um, and it took us a while to prove that we're right in a way. Um, I think it also brought a lot more attention to the field. A lot of companies started to appear. There was way more competition than before, but I think for us it was just another reminder, like, try to keep, remain focused and remain, uh, obsessed. What you know is, is true. There was a lot of noise early on in 20, 22. There was too many things going on. And I think at some point it was easy to get, like, busy, you know, with stuff going on and companies popping up and everyone offering everything. And I think we're like, yeah, just like, keep doing the thing we know we're good at and everything else will, like, follow. Um, and so, yeah, I think it was, and, and still today it's, feels very, very competitive environment, which is in a way great.

AI assessment note: “now people are using Combinations of transformers and diffusion systems. Uh, I think”

Q Where are we from your perspective in the adoption cycle for a technology like generative images, generative video? Who are your early users? You mentioned Madonna, like I read the ASAP Rocky and Slash and like celebrities like those, but are they hobbyists? Are they celebrities? Or are you already in the enterprise?

A I measure adoption cycles in reactions of executives at Hollywood companies. So I pitched, um, or spoke with, like, some folks in, like, a large production team, like, five years ago, and this idea that you can generate images and videos was so far-fetched that it felt like, it's like, come on, it's like the metaverse, I don't know, it's some obscure thing. Um, I talked with the same guy, like, a year and a half ago, and he was like, oh yeah, keep me posted, you know, it's coming into, I saw one, yeah, it's kind of, like, cool. And then I had a, I knocked in a chat with him last week, and he was like, what should I do? Like, I'm freaking out. Like, I'm not late. Like, I was thinking about, I just, he's, he actually left his current production team he's working on for 20 years, and he started a new company. He's like, should I not start that studio? Should I, like, invest in, like, what should I do? And I think that, like, um, I'm seeing more of that. I'm seeing a lot of, um, People and studios and companies like realizing that, um, it's just like the tipping point of a major transformation for them. Some of them are like freaking out a little bit because their business is like, Like on risk if you don't like catch up. So no one wants to be blockbuster. Like you're seeing the streaming for the first time and you're like better catch up. Um, and so adoption for us is like tied to…

AI assessment note: “it's just like the tipping point of a major transformation for them.”

Q you're interested in that world of like, you know, creative, uh, film and, uh, uh, storytelling? Do you go like all in on AI and what does that mean? Or do you still go to film school or should, are there professions that you should not Pick because, uh, eventually that's gonna be, uh, disrupted by, completely disrupted by AI. Like, what's your recommendation when people ask you those questions?

A Be very open-minded. I think if you've, um, if you have a very consistent and particular way of thinking about how the world has worked, I think that's probably not gonna adjust well to change. And I think diffusion 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, but 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 a personal level. If you're just waiting and seeing from the sidelines, uh, you're going to miss out. And I think it's, 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, so be very open-minded and willing to, like, question the essence of all of the things that …

AI assessment note: “Be very open-minded... if you're just waiting and seeing from the sidelines, you're going to miss out.”

Q What about the data side? So in, uh, AI video, there's, uh, this well-publicized, uh, you know, debate around, uh, in particular using YouTube videos, and there's, uh, class action lawsuits, and all the things. Where do you all stand on that, and, uh, what data do you use to train the current version of the models?

A Yeah, so we don't, uh, we don't disclose what data we use, but we've, we've done some, like, announcements on partnerships around data. We have one with Lionsgate, We announce another one, we get images, 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, like, garbage into the model, you're gonna get a lot of, like, bad stuff into it. But I think quality then goes back to the question you asked me before. It's like, how do you, what's good? Like, video, like, in art, or in video, or in filmmaking, or in just, like, any artistic mean endeavor, There's no such thing as a right or wrong answer. There is in, like, there is in, in, in a chatbot or in a search engine. And so a lot has to do with just training the right eye to, like, select and curate the data itself. And so data for us, more than quantity, is a lot of the quality component to it.

AI assessment note: “we don't disclose what data we use, but we've done some”

Q So every year you guys at Runway do this very fun thing, uh, called the AI Film Festival, and, uh, as we record this, just yesterday you had the showing of the results at, uh, various IMAX theaters around the city. Any standouts for you from, from this year?

A Yeah, so, uh, the Film Festival is, uh, a festival we've been putting together since, uh, I think, Open call for filmmakers to submit, like, films that are somehow using AI. We started, um, and it was a small collection of, like, artists. We had, like, I don't know, 300 submissions. I think that was, uh, the first call was a very small set of submissions from people. Um, this year, it's the third time we've, we've done it, uh, we got, like, around 6000 submissions from people over the world. And these are, like, mostly short films. And, uh, we sold out the Lincoln Center, which, where we did our, kind of, uh, premiere. Amazing. Beautiful event, venue here in New York, and then we did a second show in LA. Um, and then after that, we've, um, we, we managed to partner with IMAX to do screenings, uh, pretty much all over the US to show the, the winning finalists. Uh, and so, um, those are happening, I think, this week and next, and then after that, we're gonna open, uh, the videos so anyone can watch them online. A couple reflections, I think, well, um, it's wild to see the growth, I would say, of, Starting with this very small venue in like Chinatown, trying to like get people to come to like films and AI films and watch what it meant three years ago was felt, uh, felt like niche and small now. And now you're like selling out the Lincoln Center, which, which, uh, which is insane. …

AI assessment note: “it's wild to see the growth, I would say, of”

Q And when the Transformer paper came out and Generative AI started becoming a thing, was that, uh, super obvious to you guys, and you started, uh, switching away from GANs into, uh, into Transformer-type models? Where was the, the path to that?

A So Transformers, for the most part, early on, were just used for language. I think we're using other approaches for Pixel and Video, and Diffusions were kind of, I would say, a big, uh, transition for what people were using at the time, where we're mostly GAN, and now people are using Combinations of transformers and diffusion systems. Uh, I think, um, I think it was a, it was a validation of sorts. It's like we're into something, um, and it took us a while to prove that we're right in a way. Um, I think it also brought a lot more attention to the field. A lot of companies started to appear. There was way more competition than before, but I think for us it was just another reminder, like, try to keep, remain focused and remain, uh, obsessed. What you know is, is true. There was a lot of noise early on in 20, 22. There was too many things going on. And I think at some point it was easy to get, like, busy, you know, with stuff going on and companies popping up and everyone offering everything. And I think we're like, yeah, just like, keep doing the thing we know we're good at and everything else will, like, follow. Um, and so, yeah, I think it was, and, and still today it's, feels very, very competitive environment, which is in a way great.

AI assessment note: “Transformers, for the most part, early on, were just used for language.”

Q So every year you guys at Runway do this very fun thing, uh, called the AI Film Festival, and, uh, as we record this, just yesterday you had the showing of the results at, uh, various IMAX theaters around the city. Any standouts for you from, from this year?

A Yeah, so, uh, the Film Festival is, uh, a festival we've been putting together since, uh, I think, Open call for filmmakers to submit, like, films that are somehow using AI. We started, um, and it was a small collection of, like, artists. We had, like, I don't know, 300 submissions. I think that was, uh, the first call was a very small set of submissions from people. Um, this year, it's the third time we've, we've done it, uh, we got, like, around 6000 submissions from people over the world. And these are, like, mostly short films. And, uh, we sold out the Lincoln Center, which, where we did our, kind of, uh, premiere. Amazing. Beautiful event, venue here in New York, and then we did a second show in LA. Um, and then after that, we've, um, we, we managed to partner with IMAX to do screenings, uh, pretty much all over the US to show the, the winning finalists. Uh, and so, um, those are happening, I think, this week and next, and then after that, we're gonna open, uh, the videos so anyone can watch them online. A couple reflections, I think, well, um, it's wild to see the growth, I would say, of, Starting with this very small venue in like Chinatown, trying to like get people to come to like films and AI films and watch what it meant three years ago was felt, uh, felt like niche and small now. And now you're like selling out the Lincoln Center, which, which, uh, which is insane. …

AI assessment note: “A couple reflections, I think, well, um, it's wild to see the growth”