The Rise of Generative Media: fal's Bet on Video, Infrastructure, and Speed

fal is building the infrastructure layer for the generative media boom. In this episode, founders Gorkem Yurtseven, Burkay Gur, and Head of Engineering Batuhan Taskaya explain why video models present a completely different optimization problem than LLMs, one that is compute-bound, architecturally volatile, and changing every 30 days. They discuss how fal's tracing compiler, custom kernels, and globally distributed GPU fleet enable them to run more than 600 image and video models simultaneously, often faster than the labs that trained them. The team also shares what they’re seeing from the demand side: AI-native studios, personalized education, programmatic advertising, and early engagement from Hollywood. They argue that generative video is following a trajectory similar to early CGI—initial skepticism giving way to a new medium with its own workflows, aesthetics, and economic models. Hosted by Sonya Huang, Sequoia Capital

• 0:00Speaker 00

  We recently had our first generative media conference. And Jeffrey Katzenberg, former CEO of DreamWorks, was there. And he made a comparison. He said, this is exactly playing out how animation, when it first came out, people revolted against it. It was all hand drawn before that and computer graphics. It was new and there was a lot of rebellion against computer driven animation. And something very similar is happening with AI right now. But there's no way of stopping technology. It's just gonna happen. You're either gonna be part of it or not.

• 0:53Speaker 01

  In this episode, we sit down with a team from Fall, the developer platform and infrastructure powering generative video at scale. Fall is a place that developers can go to access more than 600 generative media models simultaneously, from OpenAI Sora and Google VIO to open weight models like Kling. We'll discuss why video models present fundamentally different optimization challenges than LLMs, why the open source ecosystem for video has a thriving long tail in ways that text models never did, and why the top video models have a half-life of just 30 days. The team also shares insights from the demand side of the video model equation. We discuss what's happening in the app layer, from AI native studios to personalized education, what's happening in Hollywood and more. Enjoy the show.

• 1:37Speaker 01

  Barkai, Gorkam, Batuan, thank you so much for joining us today. I want to start with the problem space that you decided to tackle. So Fall is a developer API and platform for generative video and image models. Video is massive, obviously. It is more than 80% of the internet's bandwidth. And it follows that generative video is going to be similarly massive. But there's not that many companies that are focused on this problem. Why do you think that is?

• 2:01Speaker 00

  Yeah, in a way, generative image and then video was a overlooked market in this current phase of AI. In my opinion, for two reasons. Number one, there wasn't a very clear industry use case that people were going after. There wasn't vibe coding that automates software engineering, or there wasn't search, which seems LLM market is going after, or customer support, anything like that. Also, number two, the investment on the research side wasn't as big three years ago. And then that ramped up a little bit slower than LLMs, but still considerably since then.

• 2:46Speaker 00

  And now the models are much more capable, much more useful, and real industry use cases. Compared to what it was three years ago, it felt like a toy use case. This was just gonna be for fun on the side, and it's gonna be a small market at the end. And now we can see that it's gonna be a massive market with very unique use cases and customers compared to the LLM market.

• 3:11Speaker 02

  Like, if you actually go back to like, as we were experiencing it, I think that was an interesting time. We were, we were working on some like Python compute infrastructure. And then these, these models like DALL-E 2 had just come out. And then soon after that, ChatGPT had come out, and then Llama, Llama had come out. And we were just like, we were initially, we didn't know that, that, you know, image and video market was gonna get that big. We were actually just curious about like running image models much faster. That was, that was like our initial entry point. And then we saw like the initial growth. We had a few customers and they were growing really fast. We were like, what the heck is going on?

• 3:56Speaker 02

  And, and then, you know, a few customers later, we actually thought, hey, we should double down here. And around that time, also, the other thing that was happening was, people were over-indexed on language models. This like story of AGI was being told and, and you know, that attracted all the dollars, that attracted all the talent. So everyone was just like working on that, where we thought like, we had something niche, like growing fast, you know, don't tell anyone. And then, and then we just like. Started focusing on that and and soon after, like as we got more familiar with the models, we thought the uh I remember, I think we changed our website copy to say generative media,

• 4:38Speaker 02

  like generative media platform. And then it was only like two or three months after that, uh Sora was announced. Uh so so we were definitely like ahead, but but we we really saw the whole like future kind of coming with like better image models, video models, etc. So so yeah, we we made this early bet.

• 4:55Speaker 01

  I mean, you guys have a front row seat to the sorts of new experiences people are building. I think the market's only going to expand from the media market that we know today.

• 5:03Speaker 02

  Yeah, absolutely. I think like Al Alcoa Carpati tweet, you know, no, no good podcast without it. Uh he he did. He did one like recently where he was talking about like why he's excited about the, you know, media models. And one of the things he said was that, like, he he also mentioned that, right? You know, people are visual and we're gonna, we have more, so much more video than than text, like wall of text. And he was saying like, um, he was making a point around like education and a lot of the like content you consume just to like, like learn things. I think right now, the model quality is just like, like relatively, it's just so much like, it's so much worse than what it can be, where like you could actually like have, you know, I do a lot of learning on

• 5:46Speaker 02

  chat G P T, but it's like through text. But if if if it actually rendered a video where it could compress like a concept, right, instead of, you know, ten thousand characters, if it could do it in in like fifteen seconds, uh it'd be so much better. I thi- I think like there's there's sort of like, uh, the quality bar where if like it's it's gonna go up and and if you have, once we have that, we're gonna have even more more penetration. So it's a it's really a function of the quality right now. And we're just like in the very early beginnings.

• 6:17Speaker 01

  Totally.

• 6:17Speaker 00

  Education market almost untouched right now with with video generation. And there's so much potential there. And it's just waiting the quality, the predictability to get there. And I think it's gonna have a lot of potential.

• 6:30Speaker 01

  Totally. I mean, you guys sent me that generative video Bible app. I think it's a much better way to learn some of the lessons from the Bible. And it's, you know, if you're capturing consumers' attention right where they are. Exactly. I think it's, I agree with you, we're just at the beginning. So Fall is an infrastructure company, and so we're going to structure today's interview. I love infrastructure companies in terms of the technical layer cake. So we're going to start from the core inference engine compilers, kernels that you've built. We're going to go up to the model layer and then the workflows and then end with some observations on the markets and what people are building. Let's do it. Sounds exciting. Okay, let's do it. The inference engine. Batswen, how old are you?

• 7:13Speaker 03

  Twenty-two.

• 7:14Speaker 01

  You're twenty-two years old, okay. Say it in your background, I think it's super badass and makes complete sense why this company is so hardcore at inference.

• 7:21Speaker 03

  I started working on compilers when I was fourteen, so in a way I have like a lot of experience on that front. You know, it's not just that. But like I started working on open source projects. So my first contributions were around tooling around the Python language. And then I started to slowly contribute back to the Python language. Core compiler, core parser, and the core interpreter itself. And became like one of the core maintainers of it. I think at the time I was like the youngest core maintainer of the language. And this kind of gave me like a unique appreciation of compilers and how flexible they are. So when we first started like working on serving these image models at Fall. The main idea was, okay, there is like these like three different image models, three different architectures.

• 8:05Speaker 03

  But this is surely going to explode, there's upscalers, there's like, you know, there's going to be video models, we were predicting that. And we didn't want to go optimize a single model, put our eggs into a single basket and then go, became invalidated when the next model comes. So we started building this inference engine, which is a tracing compiler that traces the execution. And essentially tries to find common patterns that are fitting within the templated kernels that we do. So our bread and butter is like spending, we have a ten percent performance team that's spending all their efforts into writing kernels that are like ninety-five percent there, but like generalized with templates. So we traced the execution of a model and find common patterns that could like replace these templated semi-generated kernels to like specialized kernels at runtime.

• 8:49Speaker 03

  And optimize the performance of these models. And we found this technique to yield like pretty much superior results from anything that's out there in the market. And this led us to claim like number one spot on performance on all the benchmarks. Another big thing about this is we specialize in doing like this sort of kernel level mathematically correct sound abstractions that let us like, you know, maintain the same quality of these models, which is a very high bar when you're in this media industry and when you really care about the output that you're getting.

• 9:18Speaker 01

  What's different between optimizing a diffusion model versus another aggressive LLM?

• 9:23Speaker 03

  In other aggressive LLMs, like your bottleneck is how fast you can move all those like giant weights from memory to SRAM, because you have like a 600 billion dollar parameter model and you're trying to predict the next token. You're doing the attention for like all the tokens, like a couple tokens before that. In diffusion models, you're trying to denoise like thousands, tens of thousands of tokens for a video at the same time, doing attention of it. So you're essentially saturating all the compute bandwidth of these of these GPUs. You're not necessarily bound on memory bandwidth, but like the computational operations that you do are like fully saturated. So you're trying to find better ways to execute around the GPU. This could be like writing more efficient kernels, or this could be overlapping of softmax with GAMS that you do. Like, it's essentially like

• 10:05Speaker 03

  you're you're trying to use all of the power of the GPU, leverage it in a way that like, you know, that gets you all the capabilities.

• 10:12Speaker 01

  So it's a different binding constraint. It's on the compute versus the memory.

• 10:15Speaker 03

  Exactly.

• 10:15Speaker 01

  And what's the intuition for why LLMs are relatively memory constrained and why video models are by comparison relatively compute constrained, but not as large in terms of just sheer number of parameters?

• 10:26Speaker 03

  I I think it's scaling issue, right? Like in terms of like, if you scale these video models of 600 billion parameters with the same dense architecture, you're gonna have to do attention with all those full like hundred, like let's say a single video has 100,000 tokens. And you do this attention step, or like you do this like denoising step 50 times. And every 50 times you do like attention over all these like 100,000 tokens. It's insanely, insanely expensive. So I think the constraint there is like just how fast you can do the inference. And the same applies to LLMs at like larger batch sizes, but like at like the traffic patterns that people do, like, you know, the batch sizes are not that much. And you're mainly constrained by memory bandwidth. So people do optimizations like speculative decoding and other factors to like reduce that a lot.

• 11:05Speaker 01

  Yeah. What exactly goes into being at the top of the leaderboard in terms of performance? Because I would imagine there's other teams that are also very smart people. And you know, this is, this is my Olympics. And so what exactly goes into? I imagine people have like very similar ideas on the techniques and different optimizations they can do.

• 11:22Speaker 03

  I don't think anyone cares about it as much as us. We are literally obsessed with Geronimo media. We are literally obsessed with these models. We have a team that's like just focusing on this. Like so far, like it seems like and from Nvidia to other inference players, everyone is like super obsessed with language models. Everyone is trying to get like one more tokens per second on like deep seek benchmarks, whatever. And like we are like on a, on a different lane. We have like competitors, but like no one close to us because I think we assembled one of the best teams. We found out like the best way to optimize these general models. And we just focus on this. This is like a purely focused thing, right? Like at the end of the day, you're constrained by the hardware. There's nothing unique about it, but like we're just like three months ahead, six months ahead. Like when we benchmark Torch, like the latest version of Torch against like, you know, our inference engine from a year ago.

• 12:07Speaker 03

  We're clearly, we are clearly underperforming because like Torch caught up. The same thing is going to happen with other players. You're gonna always like the lead that you can maintain is three months, six months ahead at most. The thing that matters is just focus. If you focus on it, if you purely put all your energy into it, I think that's like there's, it's very hard to get outcompeted by others.

• 12:25Speaker 00

  Because models are slightly changing each month, each release. So it's still the same general architecture, but there are slight differences where we can go in and optimize where that's different. And no one else is paying that much attention to it. Also, hardware is changing as well. So we were able to adapt to B two hundreds earlier than anyone else. And we were able to run video models much faster, basically throughout the year because of that obsession with running video models with the latest hardware.

• 12:54Speaker 01

  Yeah, got it. What are the hardest technical problems that you think you're solving?

• 12:59Speaker 00

  So one thing people don't appreciate it as much is we are running six hundred different models at the same time. We have to be running them. We have to be so good at running them that we should be running a single one of them better than as if someone else is running a single model. Because when a foundational lab is running models, maybe they have a single version of the model, maybe they have like a couple other different versions, and that's what all they care about. We have to be better than them at running those models. And we have to be doing all six hundred at the same time. So on top of the inference optimizations that happens in the GPU, a lot of optimizations on the infrastructure level needs to happen.

• 13:39Speaker 00

  We need to manage the GPU cluster in a way that's efficient to load, unload these models at the right times. We need to route traffic to the right GPUs who have the warm cache of these models. We need to be smart about choosing the right kind of machines, which which kind of chips are running what kind of models. And the the customer traffic is changing all the time, and we need to adapt towards that. So on top of the inference engine, the the overall infrastructure is also really, really hard beast to manage.

• 14:09Speaker 03

  Yeah.

• 14:10Speaker 00

  And so far, we've done an incredible job at that. Would you add anything to?

• 14:14Speaker 03

  I I I think that's a pretty fair uh fair explanation of what we do. Like, I call this distributed supercomputing. I don't know why people don't like that name, but like, you know. I like it. Uh, but like, the idea is we are at like twenty eight, thirty, like, this was like a month ago, now we are probably at thirty five different data centers. And you have these like heterogeneous groups of compute that's split across with their own like, you know, different specs, different networking, whatever. And you're trying to schedule workloads as if it's like a homogeneous cluster that you got from a hyperscale. It doesn't work like that. So we built like, we spent the last three years building that traction over it from our own orchestrator to building our own CDN. Like, we go back to like, you know, fundamentals of web and we built our own CDN service,

• 14:57Speaker 03

  deploying Racks to Colos, like, you know, just like routing traffic. So we built all these technologies to essentially make sure that we can tap into capacity wherever it is and schedule our workloads, which is like very different than like a traditional enterprise L M usage pattern, you know, like the the the use case that we have are like so much more spread across, so much more, you know, like, uh. More consumer facing and like when you consider that like there is so much investment going into making sure we can tap into this scarce capacity of GPUs.

• 15:25Speaker 01

  Yeah, you mentioned hyperscalers and you know I hear distributed compute and I hear managing giant clusters and I naturally think that's somewhere where hyperscalers should have the incumbent advantage. Why do you think that you've been able to so far execute them on the core engine?

• 15:40Speaker 03

  There's two things about the core engine, right? There's the inference part where none of the hyperscalers have any expertise. This is a net new field. I think this has been only happening for the past three years, inference optimization. So it's like a brand new lane that we have been outcompeting anyone in our field. I think that that's like a pretty much answer of its own. And the second one is infrastructure. I think right now hyperscalers are very busy with their traditional pattern of, oh, we have this data center capacity, we'll just deploy GPUs and we don't care about the rest. This has been changing recently, you know, even like Microsoft is going buying from NeoClouds. This is like there's like an interesting pattern happening because the GPUs and the demand and the growth of GPUs doesn't fit to the patterns of these hyperscalers, the growth patterns they expect.

• 16:23Speaker 03

  So I think at this age, not even hyperscalers have that big of an advantage of scale because like they're going buying GPUs from NeoClouds, like the tables have turned a bit.

• 16:33Speaker 02

  Yeah, it almost helps to also be like slightly earlier, like in the company journey, right? Like if you're a public company, you also have to kind of abide by like what the what the market's expecting of you. So like the other thing is that there's a huge price discrepancy with hyperscalers and NeoClouds, right? So like it's maybe sometimes two X, three X more expensive to use things, you know, through hyperscalers.

• 17:02Speaker 01

  What's driving that?

• 17:04Speaker 02

  Well, I think I think one is like market pressure, right? And also like there's added kind of operational expenses that hyperscalers have for like having, you know, better they just have a better service, right? Better uptime and better SLAs and all of these things add up. And then on top of that, there's kind of an established like cloud margin, right? And you know, the market expects the cloud margin to be a certain level. Whereas like if you have a three-year-old NeoCloud, you know, you're a private company. Maybe you don't have as much pressure.

• 17:38Speaker 01

  Yeah.

• 17:39Speaker 02

  And there's like, assuming infinite demand and, and limited capacity, you can actually, you know, hyperscalers can keep their prices high and, and, you know, they will fill out the capacity and also like get, get slightly better economics. Whereas like NeoClouds compete over the whole, whole like infinite demand and, and that, that pushes the prices down.

• 18:00Speaker 01

  Perfect price competition. What does it take to run image versus video models well? Like you guys started the company around a stable, stable diffusion moment. It was, the field was mostly image at the time. How does running video models compare to image?

• 18:12Speaker 00

  Let's actually do text, image, video. Okay. Even let's compare all three of them. And so for, for, let's say a SOTA L L M, like we don't know, let's say DeepSeek or, or something like that where we know the numbers. Running a single prompt, like 200 tokens. And let's say it takes 1X of, of, of teraflops. Uh, I think it's tens of teraflops, but let's call that unit one. Uh, one image is around 100X of, of that. And if you are doing a five second video, 24 FPS, that is around 120 frames. So 100X from one image.

• 18:52Speaker 00

  So you, you are already a 100X of, uh, of 100X. So you are at, you're at 1,000, 10,000X for, for a stand, law, law of standard definition video. And if you wanna do 4K, that's another 10X. So 10, 10,000X compared to a, a single 200 token L L M input. So it is a lot more compute intensive in terms of, uh, amount of flops you are doing.

• 19:22Speaker 03

  Yeah. Uh, in, in, in, in general, like when we started with image, the infrastructure was relatively easier to do because it's like, it takes three sec, or it took 15 seconds back in days. It takes 15 seconds to generate an image. You don't need necessarily shave like that 50 MS, 100 MS you have overall in the system. And then when we went to video, it's like even like easier because it takes like 20 seconds, 30 seconds to generate the video. The way that has been happening in the past like couple of months is real time video, where you need to stream twenty four FPS videos over the, or like a network link to the, from these GPUs. That's where we actually spend like now some of our time. We started this progress with like speech to speech models a year ago. We started optimizing them where we were able to like reduce the latency of our system with like globally distributed GPU fleet.

• 20:07Speaker 03

  When you send a request, we route to the closest GPU, minimize our own overhead and then do stuff like, you know, we pick the best runner, whatever, stuff like that. So we are now applying those same optimizations we did to real time video. And we actually see like really good, interesting demand there where people want to experience these stuff like as they type, as they prompt. And that's where like some of the infrastructure technical challenges differ from traditionally running image and video models. Because image and video are similar-ish, you know, like just more compute expensive. But like you actually need to care about infrastructure stuff when you go from like less than a second generation time for some of these models.

• 20:43Speaker 02

  Yeah, another, another interesting thing is like image, image models especially, you were able to run them on a single GPU. Like the parameter counts is actually much smaller. So that actually makes it like a little bit easier for us as opposed to L L M's. And then with video, parameter count is going up. Right now, I think we're around like for the open source ones, I don't know, thirty billion parameter. Whereas, you know, we, we, we hear rumors about, you know, G P T four being like in the trillions, G P T five, maybe more. So, so that's, that's another, that's like, you know, on the, on the flip side, it's a little bit easier. But it doesn't mean video models are not gonna grow, right?

• 21:26Speaker 02

  There's, you know, rumors around numbers for V O, numbers around Sora. So like there's also a like increase in parameter count. So, so that, that you're gonna have to kind of, you know, use more distributed computing. But, but if you're just, you know, eight nodes, that's a, or one node or eight nodes, you, you kind of have a slight advantage.

• 21:45Speaker 01

  Yeah, totally. Okay, let's pop one layer up the stack to the models.

• 21:49Speaker 02

  Let's do it.

• 21:50Speaker 01

  So one thing I think people don't fully appreciate at the media space, and you mentioned this, you alluded to this before, is that there are, there's a very, very long tail of models that are actually used in practice. And so I was hoping you'd give people a sense of on your platform, how many models are people actively using? How's it distributed and like, why do you think there's such a long tail of models being used compared to the L M space?

• 22:11Speaker 00

  This is actually one of the things I would say three years ago, people got it wrong. I mean, jury is still out, but people, right after the chat G P T, people start talking about Omni models. There's gonna be these giant models that they're gonna be able to generate video, audio, image, and code, text, every type of token. This, this might still happen, I think, but it's more clear that you are better off if you optimize for a certain type of output. Even this is true for code generation, definitely true for image or video output. So that's one thing, when we were pitching three years ago, everyone, like, that's one feedback we got.

• 22:53Speaker 00

  Oh, there's gonna be Omni models and there's gonna be a single way of running these. It's gonna be hard to create an edge on, on the modality. But turns out, it's, it's, it's not true. And it actually makes sense to, to have a technical edge on the modality. And this is one of the reasons why there's also a variety of models. Because still the best upscaling model is just doing upscaling. And the best image editing model, even the best text to image model is different from the image editing model. So all these special tasks require their, their own model. It might be the, the, the similar model, model family or similar architecture.

• 23:35Speaker 00

  But at the end of the day, it has its own weights that needs to be deployed independently. And that creates the variety in the ecosystem.

• 23:44Speaker 03

  I, I think there's also the, like, this also applies to language models where even in the same modality, there is different families of models with different tastes, different characteristics, there's different personas. And this happens with like language models still, like the code that Claude writes is very different than the code G P T five does, right? Like, and like, we see this happening, but the, the good thing about here is like, there's like these three, four different personas on top of like different like categories, upscaling, editing, video, text, video, whatever, stuff like that. So it gets, it gets you like, you know, close to fifty models that are active at any point in time. And then you have a very long tail of models that people still choose because they might like the person of that better.

• 24:23Speaker 01

  Yeah, totally. Speaking of model personalities, what are some of the most popular models on your platform? What do you think are the personalities of them?

• 24:32Speaker 00

  So one thing that's been true since the beginning, the popular models change all the time. So there is always new releases from different labs that take over the other. And it's always a moving target. But that being said, there is two types of models usually preferred by our customers. It's usually there is a one big expensive model that has the best quality on video generation. This could be V O, this could be cling, this could be Sora. And then there's usually a workhorse model, which is cheaper, smaller, but good enough. And people usually use that at higher volumes. I would say this has been true for the past almost two years that there's an expensive, high-quality model.

• 25:18Speaker 00

  That keeps changing. There's a cheaper, good enough model that keeps changing. But overall, this has been constant.

• 25:26Speaker 01

  And is the workhorse model for prototyping? And then you run it through the big expensive model for the final product? Or what do people use the workhorse?

• 25:33Speaker 00

  It's for higher volume use cases. And depending on the application you are building, you might encourage different like lots of variations of the same output maybe. But it's very application specific, I would say.

• 25:48Speaker 02

  Yeah, there's also another dimension, I think, that's like kind of happening in real time right now, which is based on like the different use case you want to use the model for. So like one, when OpenAI released GPT Image Editing, that model had just like like superior text editing, text generation and editing capabilities. And for things that require like a lot of text, people started going, you know, and choosing that model versus the other models. So it also tends to correlate with like different capabilities models are bringing. And also like what they're like, what they're good at, right?

• 26:27Speaker 02

  So like cling, for example, people really like it for visual effects type types of workflows. Because, you know, they had they had that kind of data in their data set. As opposed to, you know, some other models. For example, C dance is like very good at like detailed textures and artistic diversity, things like that. So it's really, it's really a matter of also like this, this sort of use case dimension that that models excel at.

• 26:59Speaker 03

  An interesting metric that we saw on Q2 and Q3 was half-life of a top-five model was thirty days.

• 27:06Speaker 01

  Wow.

• 27:07Speaker 03

  It's very, very interesting to me, where like, you know, these models are continuously shifting, like, the top five of the models are continuously shifting.

• 27:15Speaker 01

  Tough depreciation schedule for the for the model providers.

• 27:18Speaker 00

  Hopefully, they are building on top of the work that they already done, so it's, it's, you know, additive to the end, but yeah.

• 27:26Speaker 01

  Yeah, I'm teasing, and the model probably is in a more turbulent state right now than what the end state will probably be.

• 27:34Speaker 01

  What do you guys think is the most underrated model? Like, what's your personal favorite?

• 27:38Speaker 00

  I usually like cling models for video.

• 27:43Speaker 00

  But this kind of has been changing because they don't have sound. For sound, we have VO3 and Sora, they are the only ones. A lot of people are working on it, so would love to have more variety there as well.

• 27:58Speaker 03

  Image models, I like Rev's model, and like Flux still holds like a very nostalgic, even though it's been a year, value for me, you know, like, I still go back to Flux. There's like variations of Flux models now that I like.

• 28:11Speaker 02

  I'll go with Midjourney, which is not on file, it's not available on API. I just like the, how they navigated the space, I think, I think is very interesting. Like, they kind of brought this like photorealism, which was, you know, that was like a very big deal at the time, you know, no model could do it. And then now they're more like this artsy model, right? Like, it's no, photorealism is kind of cracked and like, no one cares about it. So now they have this like niche, very artistic like visuals, which is very cool.

• 28:43Speaker 01

  Yeah, I'd love to chat about the marketplace dynamics a little bit. So I understand your business as a little bit of a marketplace, where you aggregate developers on one side of the market, that's the demand side. And you aggregate model vendors on the other side of the market, that's the supply side. And the model vendors are both, you know, proprietary APIs, model labs, that view you as a distribution partner. And then also open models that you, that you host and run yourselves. And so maybe talk a little bit about, for the closed model providers, you have partnerships with OpenAI Sora, with DeepMind on VO. What's in it for them? Why did they choose to partner with you?

• 29:23Speaker 00

  We were one of the first platforms that accumulated.

• 29:28Speaker 00

  The developer love and, you know, following from that, these developers work at big companies. So they started working with us and we really built the platform for simplicity and being able to

• 29:43Speaker 00

  get going really fast. And because the thing Batuwan mentioned, the half-life of these models is is really short. People usually work with many different models at the same time. So we were able to claim that we have this big developer base that that love the platform and not tied into any single model and here for the platform.

• 30:06Speaker 01

  Yeah.

• 30:06Speaker 00

  And model research labs see this and they they they use the platform for as a distribution channel and tap into the developer ecosystem that we built.

• 30:17Speaker 01

  Yeah.

• 30:17Speaker 00

  On the other side, this helps us with the next model provider because they see all the all the developers, they wanna they wanna be on the platform as well, which attracts more developers on the platform and creates a very nice positive flywheel for us.

• 30:32Speaker 01

  Yeah, it very much is a marketplace business and for developers, it's a single choke point to be able to access multiple model vendors. And to your point on like the model space is changing so quickly, I think they really do value that choice.

• 30:44Speaker 00

  Yeah, we call it marketplace plus plus because we get to provide infrastructure to the research labs as well, also to the developers. So there's additional benefits with ties into the flywheel effect that we are creating. So it's marketplace plus other services next to it.

• 31:02Speaker 01

  How do you position yourselves to get, you know, in some cases, day zero launch access, sometimes exclusive launch access to models like Kling and Minimax? Have you done that?

• 31:12Speaker 00

  Yeah, throughout the last two years, you were able to build a very robust marketing machine as well. And this this this is our connection point with the developers who are on the platform. Every time we release something, this creates another opportunity for us to Introduce a new capability, introduce a new model. And model developers also see that. And we usually do co-marketing together. And part of that co-marketing, we get exclusive release access for a certain period of time. Sometimes, sometimes forever. We are like, we have a couple competitors that are on the smaller side. So model developers wanna work with the biggest platform out there. And increasingly, that platform is ours.

• 31:56Speaker 00

  And we get to have these exclusive benefits with the model providers.

• 32:01Speaker 01

  That's awesome. Why do you think it is that the open source model ecosystem has been so vibrant for video models? You know, it almost feels like the text models are just consistently a generation behind. Whereas in video, you know, there's, there's so much that's happening in, in the open source realm.

• 32:17Speaker 00

  Video and also image editing, image editing as well.

• 32:20Speaker 01

  Why do you think that is?

• 32:21Speaker 00

  It started with Stability. They first open sourced Stable Diffusion and got insane adoption. And almost the same team then started Black Forest Labs. And they knew the power of open source, how it, it helps them create the ecosystem. And with image and video models, the ecosystem actually matters. When developers are training LoRAs, they are building adapters, they are building on top of your model. It really brings free marketing, but also creates stickiness. So the, the developer, there are still people who are using Stable Diffusion models because they, they like that ecosystem, because it was so open. So the Flex team saw this from their experience at Stability.

• 33:02Speaker 00

  And they had a very smart strategy of having at least some models that are open source, some that are closed source. And a lot, a lot of video model providers that came after is, is following the same playbook. Because you can have a very robust ecosystem. It gives you a lot of advantages in terms of marketing, in terms of developer love. And I think it's gonna keep, keep going like this.

• 33:27Speaker 02

  I wanna add on to that, like, the domain is also very interesting. Like, I think in the visual domain, like, ecosystem actually matters more. Like, I think when, when, like, Llama 2 first came out, there was like... Many fine tunes out there, but like if you actually downloaded it and start using one, like you can't, I mean.

• 33:45Speaker 00

  Can't tell it's a fine tune.

• 33:47Speaker 02

  You can't tell like the difference, like you can't really, you know, if you're using a, uh, I don't know, like a control net. Like that concept doesn't even exist, like it doesn't, you know, language models are a lot more general, like generalized. So you can't really understand like the difference if you, if you were to actually fine tune it, right? So, so it kind of just ends up being very monolithic a- as opposed to like in the visual realm. It's just like, um, any small adjustment you make to the model, it can actually, um, you know, uh, it can actually have huge implications, right? And so, and so it's, it's just, it's just, uh, you know, very fertile ground for like a lot of, a lot of customization.

• 34:31Speaker 01

  Yeah. I mean, speaking of Midjourney, David Holt's, one of his, one of his quotes that I like is, you know, he's curating the aesthetic space uh, with Midjourney and I, I very much think you just have this combinatorial explosion of, of styles.

• 34:44Speaker 02

  Yeah.

• 34:44Speaker 01

  Aesthetically and I, I think that's the reason why some of the, I think some of the models on your platform are, are fine tunes of other.

• 34:51Speaker 02

  Hundred percent.

• 34:52Speaker 01

  Models, right?

• 34:52Speaker 02

  Yes. And, and like the, the thing is like, even if you add a lot of diversity of aesthetics, like if you, if you actually train on everything, like if you have trained on too many, you may not be able to like actually get the exact. Like, like there's so many times you want the exact aesthetics.

• 35:09Speaker 01

  Yeah.

• 35:10Speaker 02

  And then you still, you, you may still have to like fine tune the model to get exactly the output you want. Whereas like with L L Ms, that's not really like how you operate. You don't exactly want a particular outcome. It's, it's like a different, it's a different problem. Uh, so, so this is a lot more, you know, it's, it's very, uh, subjective. So like you, you kind of have to do, do these like post-training things on top of the models. Sora is another like good example, like Sora two is very fine tuned on like social looking stuff, right? And, and, and so, you know, you could, you could probably, you know, you, you can have tens of different styles and you, you still wanna probably, uh, push the model towards that direction with post-training.

• 35:51Speaker 01

  Yeah, absolutely.

• 35:52Speaker 00

  It all depends on the use case too. Like a customer support chatbot does not need personality. Like you want it to be as vanilla as possible. But we are talking about filmmakers, marketing teams. They all want to add the personality of their style or their brand. So they want to have greater control over the outputs. Whereas maybe in L L M's, that's not necessarily true all the time. If you have an agent, if you are doing code generation, there is no equivalent of style and personality.

• 36:24Speaker 01

  Yeah, okay. That's a good segue for us to go one more layer up the stack. Let's go to workflows. What's the, what is the average developer workflow inside Fall look like today?

• 36:35Speaker 00

  They are using many different models, first of all. So if you looked this up recently, our top hundred customers, they are using fourteen different models at the same time. These are sometimes chained to each other. So one text image model, one upscaler, one image to video model, all part of a same workflow or like a many, a more complicated combination of this part of a same workflow or different models used in different use cases. I think that's the most interesting part, the variety of the models people use on the platform. We do have a no-code workflow builder as well. We built this in collaboration with Shopify.

• 37:18Speaker 00

  And this is usually very good for their P M's, their marketing teams, the non-technical members of the team who are playing with these models. It's really good for trying different things, really good for comparing different models. But eventually this makes it into the product as well. You can reach to this workflow through an A P I. It's been very popular recently and more and more people in a typical software engineering organization is now interested in image and video models. So the users of this platform has been increasing.

• 37:54Speaker 01

  Okay, so the average workflow is not just text to prompt. It's not create a five-minute commercial that does it. If I wanted to create a five-minute commercial, what would the workflow be?

• 38:03Speaker 00

  Yeah, so for this reason, people actually prefer open, like that's one of the reasons why people prefer open source models. Because they get to have more control over, over the model and they, they can add things here and there to steer the model towards the output they want.

• 38:20Speaker 01

  Yeah.

• 38:21Speaker 00

  And when we, when we go talk to studios or more professional marketing teams, they all love working with the open source models because of the pieces they can replace and control they can add into it. And then these workflows are usually like the ones, if you've seen any like big comfy UI workflows with many different nodes, it resembles those where each different piece of the model can be replaced to create more control for the creator.

• 38:50Speaker 01

  Hmm, got it.

• 38:51Speaker 02

  Yeah, and I think like what we have, like our workflow tool, it's not the final form of, like there's almost like a, another layer of abstraction maybe on top in terms of workflow and like as we talk to like these studios, we actually figure out like there's so many ways of, just like there's so many ways of using Photoshop, like there's no single workflow. In fact, like there's probably like, based on your role, right, like you're a marketing person or you're a animator or whatever, like you have different workflows, right? And so I think, I think that is also emerging, like as, as more and more like professionals are actually starting to use these tools, like you see the emergence of like, uh, very particular workflows, right?

• 39:35Speaker 02

  One of our favorite creators is PJ Ace. Uh, he actually like shares his workflows, uh, online. And every time like he posts things, you know, every month, he actually has like a different kind of workflow. It's, it's really driven by like the new models, like based on, based on new model, he may have a completely new, new workflow next time. I think, I think once like we sort of reach some sort of, uh, I guess like some sort of productivity and, and you know, some professionals actually adopting these tools, there will probably be more sort of standardized like best practices around, around using these, these abstractions. But like, you know, it's not, I don't think anyone knows like the final, final form yet.

• 40:20Speaker 01

  Yeah.

• 40:20Speaker 02

  And it's, it's like every day we see new things and we try to like update our product to make sure like it, it caters to those people.

• 40:28Speaker 01

  Totally. One of the workflows I'm seeing somewhat commonly is You know, you have an idea for a high level, what you want, and you type that in and then um and the aesthetics that you want, and you iterate on the aesthetics from an image model.

• 40:39Speaker 02

  Uh huh.

• 40:39Speaker 01

  And then use that image model with the aesthetics you want to then generate a series of images, which then form the storyboard.

• 40:45Speaker 02

  Yes.

• 40:46Speaker 01

  So to speak.

• 40:46Speaker 02

  And then it's cascades down.

• 40:49Speaker 01

  Exactly. And then the video models kind of interpolate in between them. And it's funny, because that's actually how, you know, that's how, you know, Pixar and all these companies work, right?

• 40:57Speaker 02

  Exactly.

• 40:57Speaker 01

  In terms of storyboards and so.

• 40:59Speaker 02

  I think it was a cost thing in the beginning.

• 41:01Speaker 01

  Yeah.

• 41:01Speaker 02

  Like, that's why they had to do it like that. But like, it actually also makes sense, right? Like, it makes sense in so many ways to do it, to do it like that. And yeah, they they call the the that stuff pre-production and then, you know, post or production. Right? So pre-production is all the all the tooling around storyboarding, et cetera. Like, that's what everyone does, like even today, uh even though it was like a very cost costing. Now it's more of a speed thing.

• 41:27Speaker 00

  And AI makes the workflow, you know, ve- very interesting where you have everything laid out and let's say a new model, ne- new text to image model comes out, they they built it in such a way that, okay, you can press a button and now all different combinations are going to be generated with this other model. And then you can like generate all the videos again. We we've seen those insane workflows. You you want to update one thing and the whole thing is going to cost like a thousand dollars to rerun it again. But these individuals, like, they spend a ton of money on on on creator platforms. I've seen bills like half a million dollars just spent by a single individual.

• 42:06Speaker 00

  Uh maybe even even more when it's it's a small production studio, stuff like that. So it's it's it's pretty incredible.

• 42:14Speaker 01

  Totally. Wonderful. Okay, speaking of studios who are building building on your platform, let's let's go. Our final layer of the stack, let's talk about customers and and markets and then what the future might hold. Um maybe what are the coolest things that people are building on your platform today? And are with are they what we would think of as traditional media businesses? Or are they net new businesses?

• 42:36Speaker 02

  It's it's all over the place. Like, what's most what's so exciting about this space is that it just goes across like all of the. You know, markets you can possibly imagine. Like, I'll give you some more, um, I guess, long-tail stuff first. Cause, cause it's, it's super fun and interesting. There's a security company that's building on top of all. And they basically have these like trainings and the trainings are generated on the fly. And, and the content is all dynamic. Obviously, they have some scripts, I'm, I'm guessing to, to kind of fit like the curriculum. But like the, the content you get, you know, per person, um, is, is, is all dynamic.

• 43:15Speaker 01

  Is Brian Long's company?

• 43:17Speaker 02

  Yeah, this is adaptive security.

• 43:18Speaker 01

  Yep.

• 43:19Speaker 02

  Uh, yeah, they, they do, they do some really cool stuff. Uh, I think that's one of the like most unique, uh, use cases. You can see how that translates into like rest of education. I think that market is like kind of picking up. Uh, another one, I think like, um, you know, this, this is more common use case, I guess, is, is, uh, like AI native studios. You, you mentioned like the Bible app.

• 43:42Speaker 01

  Yeah.

• 43:42Speaker 02

  That was, that was one of my favorites. Uh, it's called Faith. It's one of the like highest ranked apps on the, on the app store. And yeah, they have like stories for each of the stories from the Bible and, and they're like really well-produced. Uh, and you know, this, this sort of category of AI native studios, uh, either in the form of, you know, applications or, or like they're doing like, you know, feature, um, uh, feature films and, and you know, series and things like that. That's a huge category. So I would call this like maybe new media or like AI native media and entertainment.

• 44:20Speaker 02

  There is also a lot of like design and productivity, like out of our, uh, public, uh, customers, like Canva is one of those, Adobe is one of those. So they're integrating kind of like in this, um, you know, in this older tooling, they're integrating new, new models. Um, ads is a big one. Uh, so, and ads kind of come in many flavors.

• 44:45Speaker 02

  Basically, there's like the UGC style ads, like the stuff you see, like there's a person, you know, demoing a product. That's like a very big category. Uh, so AI-generated versions of those. There's also kind of like older. Styles of ads, right? More professional looking, higher, higher production. Maybe you saw the Coca-Cola ad.

• 45:04Speaker 01

  Yeah.

• 45:04Speaker 02

  Uh, that came up recently.

• 45:05Speaker 00

  Controversy.

• 45:06Speaker 02

  Yeah, yeah. Uh, so that's, that's like a kind of a higher production. You know, style of ads, but, but you know, what we're excited about is also like programmatic ads. Right? So where, where you can do personalized, um, you know, to the degree of like, like literally individuals. You know, yourself being the ad or in the movies, whatever. So like, that's, that's also a, a big like growing use case.

• 45:31Speaker 01

  I'm most excited for the education use case. I think that, you know, ads is, ads is, you know, the backbone of the, of commerce and the internet. And so like, like super compelling business case, but education is a market that's like so important. It has never really had that many compelling business cases behind it.

• 45:47Speaker 02

  Yes.

• 45:48Speaker 01

  And part of the challenge with education, I mean, the challenge has been the bottleneck to creating high quality content at scale. That's actually ideal for the, for the learner. And so I'm personally most excited about education.

• 45:59Speaker 02

  Same, like I, I really love the education use cases and I actually think that like. Chat G P T or like just, you know, L L M's in general, I think they are already solving it in a way. But it's not the right form factor. Like if you, if you actually wanna fully realize like the, the, the, the. Sort of power that these models are bringing, you actually need to go into the visual space. Cause then, you know, it's so much more compact, it's more approachable. And, and yeah, I think once we actually crack like visual learning, like through these video models. That's when it's gonna, you know, really just like impact people.

• 46:37Speaker 01

  Do you think that the advent of generative media is going to increase the value of existing I P? So like Mario Brothers, Nintendo, Disney, Pikachu, all these things?

• 46:48Speaker 02

  Yeah.

• 46:48Speaker 01

  Or do you think it's gonna lead to the de- democratization of the creation of I P?

• 46:52Speaker 00

  I love this question because it felt like, I would say six months ago, it felt like. This was all happening too fast for Hollywood, the, the I P holders to adapt and be part of it. And from our viewpoint, we thought, alright, these AI native studios, they're just gonna take over and Hollywood is just gonna be too slow and this is just go past them and they're gonna be left behind. But this summer, something changed and we've been talking to a lot of usual suspects from the Hollywood. We recently had our first generative media conference and Jeffrey Katzenberg, former CEO of DreamWorks, was there.

• 47:34Speaker 00

  And he made a comparison, he said, this is exactly playing out how animation, when it first came out, people revolted against it, it was all hand drawn before that and computer graphics, it was new. And there was a lot of rebellion against computer-driven animation. And something very similar is happening with AI right now, but there's no way of stopping technology. It's just gonna happen, you're either gonna be part of it or not. So we are seeing a lot of existing IP holders are now taking this very seriously. And at least for the medium term, I think they are pretty well positioned because

• 48:15Speaker 00

  they have the technical people who are actually really interested behind the scenes in this technology. They also have the IP, but they also have storytelling and filmmaking know-how. You still need quite large budgets, maybe things are gonna get cheaper, but in the medium term, filmmaking is still gonna be expensive. Yes, AI is gonna make it maybe a little bit cheaper, but we need these deeply technical people who know filmmaking, who has the IP, who knows storytelling to actually, in the beginning, be part of this. And I think they're gonna play a big role in the next coming years in the AI ecosystem.

• 48:52Speaker 01

  Yeah, when there's infinite content generation, it almost puts a value on the things that are finite. And I think, you know, for those of us who grew up with Power Rangers or Naopets or whatever, there is just this nostalgia element and this finite supply of IP that really resonates with us.

• 49:09Speaker 00

  The opposite is true too also, there's a lot of new, like, we had little toys of these Italian Renegade characters. These are characters with no IP, no one owns them, they are completely AI-generated from like the internet community. And once you have cheap generation of content, very different permutations of it, things that people like.

• 49:32Speaker 02

  Catches on and it becomes part of the zeitgeist.

• 49:36Speaker 01

  Yeah, totally.

• 49:37Speaker 02

  The opposite, there's signs of opposite being true as well.

• 49:39Speaker 01

  Yeah, both are true. How do you, a related question, how do we prevent like the infinite slop machine state of the world? You know, there's this, you know, version where we're just connected to this machine that knows how to personalize stuff for us. And we're just, you know, uh, we're just hooked up to the infinite slop, slop machine. And there's a version where there's, you know, human creativity and artistry and uh, things like that involves. Like, how do you think the world plays out?

• 50:04Speaker 02

  I think um, humans eventually like converge on, on the things that are more meaningful uh, in general. Like, I don't know, like, no matter how much slop we fill the world with, I think, I think, you know, taste prevails. Uh, and, and people are drawn to like, you know, experiences that are personal and, and human and, you know. I, I just think that uh, that's gonna happen. One interesting um, example of this was like when Meta announced Vibes uh, and then OpenAI announced Sora 2. Like, the reception was very different.

• 50:43Speaker 02

  And one of the like reasons in my mind was like Vibes was like positioned as this um, you know, slop machine ki- like kind of thing where, you know, they, they didn't, they didn't have the product out at the time. But, but it was just like these AI-generated, like, just you have no relation to the characters, etc., right? Like, it was kind of this like detached thing. Whereas like Sora really made it about friends, right? Like cameo and, and you know, they were very vocal.

• 51:11Speaker 01

  And now you can cameo your pets.

• 51:13Speaker 02

  There you go. It's huge, right? Um, so yeah, I think, I think like this, this connection to like friends and, and pets and, and things like that that actually made, and Sora was also like, they were being very personal about it. They were, they were very uh, adamant about like, hey, we wanna make this about friends. We wanna make this about, you know, um, you know, the, these connections as opposed to, you know, infinite slop machine. So, so I, I think that's, you know, that, that, that perception was also, I think, a good, good signal that like, there's ways to uh, work, make this technology work, uh, you know, in a, in a good way.

• 51:48Speaker 01

  Absolutely. Okay, I'm gonna get your perspective on timelines and what's feasible today and what's feasible to come. I guess, do you think that we'll see Hollywood-grade feature film length?

• 52:01Speaker 01

  Films entirely generated by AI and and if so, on what timeline?

• 52:06Speaker 03

  What does entirely generated by AI means? Is it like no human involvement or like?

• 52:09Speaker 01

  No human filming, so hu- human involvement.

• 52:12Speaker 00

  Editing.

• 52:13Speaker 01

  Yes, absolutely, human editing, but no, no human filming.

• 52:16Speaker 03

  I think less than a year, we'll have like, you know, advanced video models with combined with the storyboarding that people have been doing. You'll have feature grade short films, uh, like less than twenty minutes. I think that's that's a fair estimation, like even today, I think you can like do really great films. It's just like not enough investment of time is go- going into these.

• 52:36Speaker 01

  Yeah.

• 52:36Speaker 03

  But like with enough investment of time and the model quality, I think we'll be there.

• 52:39Speaker 01

  I think we're already there, okay. And you you think it's photorealistic, you think it's anime, you think it what like what categories do you think are more likely to happen sooner?

• 52:48Speaker 03

  I think photorealistic is like what everyone is like targeting, but like anime would be a cool one, right? Like it's like you don't see that many anime specialized models, why not? I think it's there needs to be a market for that.

• 53:00Speaker 01

  Hmm.

• 53:01Speaker 03

  Clearly.

• 53:02Speaker 00

  I I I think it's gonna be animation or or anime or cartoon like one like not photorealistic, like as far away from photorealistic as possible, maybe even like as fantasy as possible, because filming photorealism is cheap and doable already, like it that's not what costs money when people are making movies. It's the non-photorealistic stuff that's actually expensive. And um, you know, even if you look at the animated movies, some of the my favorite movies are animated. To- the Toy Story series, How to Train Your Dragon, Shrek, Ratatouille. Uh, and pe- people like these things, not because it it it reminds them photorealism.

• 53:45Speaker 00

  It it's the storytelling that matters and this this created a new medium. I think AI is gonna be similar to animation and how that brought a whole different angle to filmmaking.

• 53:56Speaker 03

  Yeah, I think feature films are hard, like because yeah, with photorealism, like you you you typically, I mean, people usually like the movies that their like favorite actors are in, whatever actors actresses. And that's, you know, so so it's like it's like one step removed from.

• 54:13Speaker 00

  That's the thing that costs money to get the actors.

• 54:15Speaker 03

  Yeah, yeah, exactly. So so so that's the, you know, we we first need to build a connection to this AI, you know, AI generated character.

• 54:22Speaker 01

  Yeah.

• 54:22Speaker 03

  Before we can turn turn into a film.

• 54:24Speaker 01

  Yeah.

• 54:24Speaker 03

  Um, but but I think like, yeah, I I think I think it's.

• 54:29Speaker 02

  Among like different kinds of content, like shorts, you know, uh, I think Italian Brainrot is an amazing example. Right? It was first like these characters and then it became a Roblox game.

• 54:40Speaker 01

  Uh huh.

• 54:40Speaker 02

  Uh, and making, I don't even know, like, you know, a lot of revenue. So, so yeah, I think, I think like AI native stuff is uh, and, and shorter uh, form content is, is probably gonna be very, very big.

• 54:53Speaker 03

  We saw this with VFX where like the VFX FX, like one of the most expensive parts of like um, producing these videos or films is, is like, got like AI-fied very, very quickly because it's very easy for AI to do like explosions, right? Like, or a building collapse. It's like almost perfect now. And I think it's just gonna continue along on that dimension.

• 55:13Speaker 01

  Hmm.

• 55:13Speaker 02

  And maybe facial expressions are gonna be hard.

• 55:17Speaker 03

  Yes.

• 55:17Speaker 02

  And.

• 55:17Speaker 03

  They're very hard.

• 55:18Speaker 02

  You don't have to do face, facial expressions. That's gonna be okay.

• 55:21Speaker 01

  But now they can do gymnastics.

• 55:23Speaker 02

  Yeah, gymnastics are important. Good thing we have a lot of footage of Olympics, so.

• 55:29Speaker 01

  Um, what about, you mentioned Roblox. At what point do you, do you think we'll have interactive video games that are generated in real time?

• 55:37Speaker 02

  Yes, I think so. I, I'm very excited about it, actually. Like, I think, I think like in, in one world, I think the, the sort of next reasonable step for text to video, like if you, if you think text to video is the continuation of text to image, I would say like a text to game is the continuation of text to video. Um, because, you know, with a, with a game, you would, you know, you're essentially making the video interactive, right? That's, that's kind of what that means. And I, I actually think that there is a world where this like hyper I know, hyper casual games exist, but this is like another level of hyper casual where it's actually discardable.

• 56:18Speaker 02

  Uh, I think, I think we're not too far away from that. I, I actually feel like pretty. Um, pretty bullish on having like these, you know, one time playable games, like very short games.

• 56:31Speaker 01

  Yeah.

• 56:32Speaker 02

  I thi- I think that's probably gonna happen. I think that's a good use case for world models, other than many other great use cases. Uh, but, but I think, I think it's gonna happen.

• 56:41Speaker 01

  What about AAA quality games? Will we, will these models at least, you know, assist and change the development pipeline of those games, or?

• 56:48Speaker 02

  Yeah, I think they're already impacting, like at least L L Ms are impacting like conversations, there's like, you know, dynamic conversations, things like that. Um, I think, um, pre-production stuff is impacted already. Um, I think like, kind of side quests, like IP stuff is impacted, right? Like where you have the assets and you can make a mini game. I think people are using it actually, not very public, but like that is already happening. I think like, using for AAA production, or like generating that with a model that's like, I don't know, at least like three, four years ahead. Uh, for me, and, and yeah, it's, it's, I mean, that would be insane if, if we can actually do that.

• 57:30Speaker 02

  But, but you know, along the way there, just like the, just like the, uh, you know, video space. I think along the way to the AAA, there's like many other things, and I think those are gonna be very big.

• 57:42Speaker 01

  Yeah. The video model space has just exploded in terms of options, quality, et cetera. As you look ahead towards what's needed to get us to the promised land for everything that generative media can be. Do you think that there's, you know, future R and D breakthroughs that are needed on the horizon? Like fundamental R and D breakthroughs, or do you think we're very much in the engineering scale up, uh, leg of the race?

• 58:07Speaker 02

  I think the architecture needs to like slightly change, at least like if you think about like scaling these models by ten X, hundred X, I think the architecture is a big.

• 58:15Speaker 03

  bottleneck right now in terms of the inference efficiency, right? Like the more compression of the video space, then that's definitely needed. Like we we saw this with image. Image models used to be like much less compressed. And then later, like or like you were operating at the pixel space. And then we introduced latent space. And then like even inside that latent space, you took like sixty four pixels and made them a single pixel. Uh, and now like with video, we are compressing on a time dimension where we are seeing like four X ratios. Why not like twenty four X or whatever like you? You need to like increase that like compression. And like I think that's gonna be a big driver of improving both inference efficiency as well as training efficiency. But like I think like any model, like I think at this age that we are afraid like any model you take.

• 58:56Speaker 03

  On the generating media side, we are far from being like scaled up engineering wise. Like I think there's not enough investment being put into. Or like it just started happening in the within the past six months. Like Google showed this with like their models and how quickly they were able to catch up. They didn't need to innovate that much. It's just like they have the resources, they can put more more effort into it. But at the same time, smaller labs uh are able to demonstrate this because like there's so much like unique and noble stuff that you can do at the data level to train these models. So I think that's also like helping contributing. And there's the factor of, you know, like outside, like, you know, mid-tier labs that raise like hundred to a billion dollars. That's also trying to come up with models, releasing them open source or like contributing to the ecosystem.

• 59:38Speaker 01

  Yeah.

• 59:38Speaker 00

  That's what's so exciting about this space. There's so much more work to do. Like so far, the research community did the simplest thing possible. They captioned images and trained the model on on text to text prompt. And now like we are doing video image editing that requires a lot more uh data engineering to to create the data sets. But luckily, seemingly we have a lot of abundant free video data. We are not, we are gonna run out of compute before we run out of video data. So that means there is a lot more work to do and a lot more room for improvement.

• 1:00:12Speaker 02

  I mean, earlier on, like Gerkem's math also indicates that like if you want to get to 4K video, real time, that is like, I mean, that that means like, I don't know, 100x, maybe more in um in like compute or architecture, something has to, something has to give to to to get us there, right?

• 1:00:33Speaker 02

  And yeah, like right now, a lot of models are like not that usable, like for for uh for professionals especially, right? Or or even for like consumer, right? Like if you're if you're sitting there, like for the best models, you still have to wait like 40 seconds. I don't know. Sometimes you have to wait two minutes, three minutes. Like, that's not really acceptable in a world where like we want everything like on demand. So so yeah, I think I think something needs to change.

• 1:01:01Speaker 01

  Yeah.

• 1:01:01Speaker 02

  Uh, and probably pace of like hardware getting faster is not enough. I think if if that's the case, you know, it'll take much longer, will have longer timelines. So I think architecture needs to get better.

• 1:01:15Speaker 01

  Awesome. Thank you, guys. You made a very high conviction bet on generative media as a theme, I think, way before it was obvious. I think we are just at the start of, I think, what's going to be an explosion of generative media. And it's been really cool to hear about everything you've built from the kernel optimizations and the compiler, all the way up to uh the workflows and and what you're seeing from customers with with new and and old media alike. And so um, thank you for joining us on the show today.

• 1:01:45Speaker 00

  Thank you. Thank you so much. This was a lot of fun.