# Generative Video at the Speed of Light — Keegan McCallum, uRun AI Engineer · 2026-08-18 Keegan McCallum, founder of inference provider uRun, argues that generative video's interesting axis is no longer quality but efficiency and long-horizon generation. He shows Helios, a distillation of Wan 2.1 14b, generating clips in real time for ~one-hundredth the cost of a slower frontier-quality clip, and notes at least 40 real-time/long-horizon models released this year. Ten dollars buys three hours of continuous generative video, and fifty buys fifteen; this unlocks magic-mirror webcam transformations, visual mediums for people who don't think in text, and content creation where you steer a generation in under a second. The hard part is serving: global GPUs, WebRTC with ICE/TURN, and synchronized streaming pipelines; uRun is building a React component, Python runtime, and MCP/CLI. ## Questions this episode answers ### How much generative video can you get for ten dollars now? Keegan McCallum says ten dollars can get you three hours worth of generative video continuously with most of these models, and 50 dollars would give you an entire day interacting with an AI in a visual medium. Fifteen hours. He contrasts that with people who burn 10 or even 50 dollars worth of tokens in an hour with Claude Code. [3:25](https://aiengineer.podhood.com/77a1db1e-a520-4589-bdc8-a54e6d16d831?t=205000) ### How does real-time video generation compare with slower generation in cost and quality? Keegan McCallum says the other axis models improve along is efficiency and long-horizon generations. Comparing a real-time clip with one generated in minutes, he says the right clip is arguably a bit better, has better motion, and was generated for about a one hundredth of the cost. [2:04](https://aiengineer.podhood.com/77a1db1e-a520-4589-bdc8-a54e6d16d831?t=124000) ### What applications does real-time generative video enable? Keegan McCallum highlights a magic mirror webcam that can show you in an outfit, car, or haircut you are considering; accessibility for people who think visually instead of in text; and steerable content creation, where you adjust a generation under a second rather than spending about ten dollars a minute on prompts and keyframes hoping for a shot. [3:50](https://aiengineer.podhood.com/77a1db1e-a520-4589-bdc8-a54e6d16d831?t=230000) ### What does it take to serve real-time generative video to users? Keegan McCallum says you need GPUs all over the world for a global audience, choose where users connect and which GPUs serve them, set up WebRTC with ICE and TURN, and for the most interesting use cases wire multiple models together in continuous streaming workflows synchronized with user controls frame by frame. uRun is building a React component and a programmable Python runtime. [6:02](https://aiengineer.podhood.com/77a1db1e-a520-4589-bdc8-a54e6d16d831?t=362000) ## Key moments - **[0:00] Quality axis** - [0:51] Generative video quality jumped from Will Smith spaghetti in 2023 to Seedance's photorealistic output, says Keegan McCallum, but efficiency is the new axis. - **[1:16] Efficiency axis** - [2:04] Helios, uRun's distilled Wan 2.1 14b, generates real-time video at about one-hundredth the cost of a slower frontier-quality clip, says Keegan McCallum. - [3:11] At least 40 real-time or long-horizon generative video models were released this year, says Keegan McCallum. - **[3:25] Cost shift** - [3:50] $10 now buys three hours of continuous generative video and $50 buys fifteen hours, says Keegan McCallum. - **[4:08] Use cases** - [5:06] Real-time steering turns generative video content creation from a $10/minute slot machine into under-a-second shot control, says Keegan McCallum. - **[6:02] Serving problem** - [6:33] Serving real-time generative video needs GPUs positioned globally, WebRTC with ICE/TURN, and synchronized multi-model streaming pipelines, says Keegan McCallum. - **[7:40] Software factories** - [7:40] "I argue that in 2026 we don't just need platforms, we need software factories and ways for agents to interact with these." — Keegan McCallum ## Speakers - **Keegan McCallum** (guest) ## Topics Video Generation, Inference Optimization ## Mentioned uRun (company), Claude Code (product), Google Gemini Omni (product), Helios (product), ICE (product), MCP (product), Seedance (product), Sora (product), TURN (product), Wan (product), WebRTC (product) ## Transcript ### Quality axis **Keegan McCallum** [0:13] I am Keegan, I'm the founder of uRun, a new kind of inference provider focused around interactive media. And I'm here to talk about generative video. So we hear a lot about generative video improving along the quality axis at the frontier. We have the classic Will Smith eating spaghetti from 2023. It is nightmare fuel and not something you would ever mistake for reality. In 2024 we got Sora, and it gets a little better. It still has a bit of, you know, an AI feel to it, but it's getting there. And Sora 2, you know, even better. But Seedance this year, absolutely incredible. So photorealistic. And it's no wonder that we talk a lot about quality. But I'm here to talk about another axis which models are improving along, which is efficiency and the long horizon generations. ### Efficiency axis **Keegan McCallum** [1:16] So what you're watching here is a demo for a model called Helios that we serve at uRun. The generation in the bottomright corner you'll see is a long continuous generation, and the other video is a bunch of clips that have been generated faster than you can consume them. And they're about at the same quality as the frontier models were last year. Helios is a distill of Wan 2.1 14b. And I'll talk a bit about the techniques that are used in the various models that are hitting the sceneright now, but there's been an explosion in just the last year in terms of efficiency and capabilities. So like looking at this, I kind of ruined it with the last clip, but you can guess which one is real time and which one was generated in a number of minutes. And the one on theright is arguably a bit better. It's got better motion. And it was generated for about a one hundredth of the cost. And these are just some of the charts showing the quality bar for both long and short video generation. Helios came out in March, and it's pretty incredible to see how fast these are improving. But these are techniques that are being applied all over the place, not just to one model. There's world models which can keep consistency over long horizons, and you can control in a fine-grained way the camera and the viewport. There's avatar models like we just talked about with LemonSlice. And there's video to video models that can transform what you're seeing in real time, almost like a magic mirror. There's actually been an explosion of innovation. There's been at least 40 models with real time capabilities and long horizon generation capabilities released this year. Show of hands, who here has burned 10 or even 50 dollars worth of tokens in an hour with Claude Code? A lot of people. And so we're at a placeright now where ten dollars can get you three hours worth of generative video continuously with most of these models, and 50 dollars would give you an entire day interacting with an AI in a visual medium. ### Cost shift **Keegan McCallum** [3:50] Fifteen hours. And so I want to talk a little bit about the different things this enables in terms of the way that we interact with computers. And I'll talk a little bit about what we're doing at uRun to try and make it easier for folks to experiment and build out applications like this. So one such use case would be a magic mirror. You could have your webcam, and you could ask to see yourself in any outfit. You could ask to see yourself in a car you like or with a haircut you're considering. ### Use cases **Keegan McCallum** [4:24] A lot of different possibilities because these are open-ended models that can transform what they're seeing on a webcam in real time. I also think about accessibility a lot with these models. You know, working with AI involves a lot of reading and a lot of text. For some people that's more difficult. For some people they just don't think in text, they think visually and learn better that way. So there's more opportunities to have companions or visual mediums that are going to allow more people to experience the things a lot of us have with coding models. And I'm excited about content creation. So far we've very much had a slot machine type approach where you're setting up a prompt and maybe some keyframes and spending about ten dollars a minute to try and get the shot that you want. But with these models you can actually steer them in real time and under a second while they're generating and get the actual shots that you want. Maybe you're piloting an agent that you're able to look over its shoulder and see what it's generating in real time. But you're able to more granularly control the content you're generating. And with modern models like Google Gemini Omni, you can actually render these out as a more full fidelity clip. And of course we all are thinking about world models, but I want to take the focus off of just kind of the basic world models that we talk a lot about and just try to expand the horizons of what we can do with this technology. And so what does it look like to actually build an application like this? So you're going to need GPUs all over the world potentially if you've got a global audience that's going to be using these. You're going to need to think about where you're connecting the users to, what GPUs you're going to use to serve them. ### Serving problem **Keegan McCallum** [6:23] You're going to need to set up probably WebRTC and ICE and TURN. And for the most interesting use cases, you're going to want to wire multiple models together in continuous streaming workflows, building those real time harnesses. And you're going to want things synchronized with your controls that you're providing to your end users with every frame and continually providing a smooth streaming experience. And so our idea is what if there was just a React component that you could drop into your application to make it easy to provide video interactively inside your applications with any model. And behind the scenes there's a programmable Python runtime that lets you easily build these complex pipelines generating asynchronously so that you can build avatar models, you can build these video to video transformation models, you can experiment and build whatever you can really imagine on top of these. And I argue that in 2026 we don't just need platforms, we need software factories and ways for agents to interact with these. And so we've actually built one that will let folks hook into a CLI or an MCP server and build these kinds of applications. ### Software factories **Keegan McCallum** [7:59] And so the models are here and the frontier is really in how we serve them. I went way over, I went way under time. But we are looking for design partners who want to push the boundaries of human-computer interaction, and we're hiring at uRun. So come see me after the talk if you're interested in chatting more. --- This library is powered by PodHood (https://podhood.com), the podcast website platform.