動画エージェントモデルが次なる潮流へ — xAI のイーサン・ヒー氏、Grok Imagine を語る

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Today’s guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:

He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)

Put it this way: In the near term, the next Sora won’t be a better video model, but a video agent.

Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.

At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.

Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task.

In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA’s Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.

We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines.

Flipbook: The future of Videomaxxing

Video agents are almost a sure bet to be the trend in the coming year. We end with a glance at what’s beyond video agents:

Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.

We discuss:

Why fast iteration mattered more than meetings

Why small training bugs can drive huge model quality gains

Why coding models may make compute the bottleneck again

How image and video models are trained with synthetic captions

The role of VAEs and latent space in frontier video models

Why image models are the foundation for video models

The tradeoff between temporal compression and real-time interactivity

Flipbook, Neural OS, and the future of generative UI

Why future interfaces may go from user intent to pixels

The hidden cost of training video models: storage, egress, and GPU hours

How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster

Grok Imagine 0.9 and large-scale audio-video generation

Why audio-video alignment is harder than text-video alignment

Ethan’s definition of world models

Reference-to-video, video extension, and long-context video generation

Why xAI’s research communication undersells Grok Imagine

How xAI culture shaped the speed of development

AI watermarking, SynthID, and detecting generated media

Why prompt rewriting matters for video models

Grok Imagine Agent and the rise of video agents

Why language models may unlock better video generation

Robotics, physical AI, and embodied world models

Why Ethan left xAI and shifted focus toward LLMs

Self-managed context, memory, and the next frontier for language models

Ethan He

LinkedIn: https://www.linkedin.com/in/ethanhe42

X: https://x.com/EthanHe_42

Timestamps

00:00:00 Introduction

00:01:25 From NVIDIA Cosmos to xAI

00:03:24 Building Grok Imagine from Zero to One

00:10:07 How Image and Video Models Are Trained

00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs

00:22:10 Generative UI, Flipbook, and Neural OS

00:32:10 The Cost of Training Large Video Models

00:37:04 Distillation, GANs, and Fast Video Inference

00:41:21 Audio-Video Generation and Grok Imagine 0.9

00:48:34 What Makes a World Model?

00:55:51 Reference Videos, Long Context, and Video Memory

01:00:11 xAI Culture, Research, and First-Principles Building

01:09:45 AI Safety, Watermarking, and Prompt Rewriting

01:13:10 Video Agents and AI-Assisted Creation

01:27:32 Why Language Models Unlock Better Video

01:31:15 Robotics, Physical AI, and Embodied World Models

01:32:38 Why Ethan Left xAI

01:34:16 Self-Managed Context and the Future of LLMs

01:38:43 Ethan’s Career Path and Closing Thoughts

Transcript

Introduction: Ethan He, Latent Space, and the Path to xAI

Swyx [00:00:00]: We’re here in the studio with Ethan He, most recently of xAI. Welcome.

Ethan [00:00:10]: Thank you. Glad being here.

Swyx [00:00:11]: We’re also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.

Ethan [00:00:23]: I’ve actually, I also presented the MoEs twice at latent space.

Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?

Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It’s very nice.

Ethan [00:00:49]: I learned a lot.

Swyx [00:00:49]: I think three years stop. We haven’t stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.

Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”

Vibhu [00:01:04]: But I might have reached out to you after.

Swyx [00:01:05]: you-- because it’s an amateur club, right?

Swyx [00:01:08]: so it’s very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.

Vibhu [00:01:18]: Came out yesterday.

Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it’s a good one. We’ll, we’ll recommend people to read it.

Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don’t even know when you joined. just like tell the, tell the story about the sort of transition.

From NVIDIA Cosmos to xAI: Scaling Video and World Models

Ethan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it’s a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that’s, that’s why I realized I need to move to somewhere with much more compute resources. That’s how I

Swyx [00:02:13]: Than NVIDIA?

Vibhu [00:02:14]: The GPU rich came themselves.

Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.

Ethan [00:02:25]: It was end of twenty-four.

Vibhu [00:02:28]: End of twenty-four.

Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.

Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.

Building Grok Imagine From Scratch in Three Months

Swyx [00:03:24]: Can you give like a rough roadmap of okay, you’re on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you’re setting up a new team?

Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeah

Swyx [00:03:51]: three months is like

Vibhu [00:03:52]: From everything

Swyx [00:03:52]: actually like very surprisingly fast.

Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It’s, it’s like every day there’s not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it’s, it’s just all building. It was pretty fun at that time.

Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don’t so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.

Iteration Speed, Compute, and Debugging Model Pipelines

Swyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are you

Ethan [00:05:50]: Let’s say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale or

Swyx [00:06:01]: So cycle time for like any hyperparam that you’re searching.

Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?

Ethan [00:06:11]: So

Swyx [00:06:11]: So it’s like before you, someone had already set this up that you can iterate very quickly.

Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.

Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.

Vibhu [00:06:46]: It’s interesting, right? So you say it’s like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it’s interesting to see the other side, right?

Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don’t know.

Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it’s the coding model wasn’t quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I’ve been, I’ve been using it at that time. It’s, it’s helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn’t maintain, and the LLM itself couldn’t figure out what’s, what’s wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don’t-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.

Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.

Swyx [00:08:36]: yeah, I actually, honestly, I think it’s like kind of a stressful job because you’re “Well, I should be trying everything, and if I’m not, then I’m not doing my job well.”

Vibhu [00:08:48]: there’s also the stress of you’re eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.

Swyx [00:08:56]: You got the daddy Elon to

Vibhu [00:08:57]: You got daddy Elon.

Ethan [00:08:59]: It was

Vibhu [00:09:00]: But there’s still finite amount of compute, like you want to use it, you want to use it well, you want more of it.

Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it’s a, it’s a marathon, so you got to maintain good health and, a regular schedule.

Vibhu [00:09:28]: It’s, it’s hard to hear that when you shift from zero to nothing in two months.

Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.

Vibhu [00:09:54]: I think there’s, there’s three things we’re talking about, right? So there’s Video Gen, there’s also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?

Swyx [00:10:06]: Oh, yeah, maybe I got distracted.

How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEs

Vibhu [00:10:07]: Sorry. and then, from there’s Video Gen, there’s Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What’s, what’s the few months like? How do you go to state-art Image Gen model? How do you just start?

Ethan [00:10:28]: I cannot comment specifically how xAI did, but it’s, it’s a quite standard process. I can draw some, examples from Cosmos. So mainly it’s building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don’t naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the comments

Swyx [00:11:11]: Title

Ethan [00:11:11]: of a video, but usually they’re not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I’m so happy today.

Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here’s a question, like how do you, how do you gather VLM to begin with? So if there’s no

Swyx [00:11:55]: You, so you fuse the model, right? Like

Ethan [00:11:57]: Say if there’s no like VLM exists, like how do you generate the text to the beginning, right? It’s, it’s impossible.

Swyx [00:12:04]: I see.

Ethan [00:12:05]: In the beginning, it’s like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that’s in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.

Swyx [00:12:43]: Video or image? You’re talking about images.

Ethan [00:12:44]: Video or image, either one of them.

Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?

Vibhu [00:12:51]: It’s all training on really detailed captioning of images. So same is applied to video, but instead

Ethan [00:12:57]: same applied

Vibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can also

Swyx [00:13:04]: I think there’s this traditional perspective of supervised, or, very highly human curated thing. I feel like there’s a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.

Ethan [00:13:36]: It’s interesting to see that you kind of need both data.

Ethan [00:13:41]: For example, for the

Swyx [00:13:41]: You need it to bootstrap it up. Yeah

Ethan [00:13:43]: for the generative model training, there’s also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it’s, it’s a lot of tokens. So like one image, it’s, a thousand by a thousand, it’s like one million tokens, one million pixels. It’s impossible to train transformer on that. So it’s, you need to train a tokenizer, which can go from image to latent space and latent space back to image.

Swyx [00:14:45]: That’s why we named the podcast.

Swyx [00:14:48]: But, basically, you’re talking about vocabulary science.

Ethan [00:14:50]: so vocab.

Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?

Ethan [00:14:54]: In generative models, the vocab is continuous. It’s a continuous space. We can think about like you map an image to a vector. It’s a, it’s a fixed length vector. It’s sixteen or forty-eight, something like that. And then you map that vector back to the im