Video Generation

Inside xAI: Building Grok Imagine in 3 Months, Videogen vs World Models, and Video Agents— Ethan He

Jun 1, 2026 · 1:44:43

Ethan He, former xAI and NVIDIA Cosmos researcher, explains how xAI built its first image and video models (Grok Imagine 0.9) from zero to one in three months, attributing rapid iteration to small teams with minimal meetings and strong infra that enabled fixing tiny data and training bugs for biggest quality gains. He argues that most improvements in video generation now come from language models and agents rather than diffusion technology, predicting that by end of 2025 video agents will produce production-grade content for ads. He defines world models as real-time, interactive, long-horizon videos, and details challenges like temporal compression, context management, and the high cost of storing and moving video data (e.g., tens of petabytes for a billion videos). Ethan also shares why he left xAI to focus on language model research, believing the next frontier is models that manage their own context length, similar to solutions already being explored in video generation.

A Technical History of Generative Media

Sep 8, 2025 · 1:04:44

Fal.ai founders Gorkem and Batuhan detail their pivot from dbt pipelines to generative media inference, which now serves 2M developers and 350 models, crossed $100M ARR, and raised a $125M Series C. Key inflection points included Stable Diffusion 1.5 (company pivot), SDXL (first $1M revenue), Flux (jump from $2M to $10M monthly revenue), and Veo 3 (text-to-video with perfect lip-sync). They built a proprietary inference engine with 100+ custom kernels and a serverless GPU stack managing 10,000+ H100 equivalents across 6 cloud providers, typically delivering 1.5-10x speedups over stock PyTorch. Video models now drive 50% of revenue, up from 18% in February, fueled by open-source models like Hunyuan and partnerships with closed labs such as Play.ht and Google DeepMind. They argue advertising is the killer application for generative media and that image/video RL, specialized data pipelines, and cheaper conversational video models are underexplored startup opportunities.

AI Video Is Eating The World — Olivia and Justine Moore, a16z

Jul 9, 2025 · 49:28

Alessio and Shawn (hosts of Latent Space) talk with a16z partners Justine and Olivia Moore about the explosion of AI-generated video on TikTok, Instagram, and YouTube. The Moores trace how trends moved from Reddit to consumer platforms, with examples like Italian Brainrot, Kim the Gorilla, and fruit-slicing ASMR. They detail their own hands-on creation: Olivia used Veo 3 and MiniMax to make viral clips (gold bar squishing, Tide Pod consumption), spending up to 8 generations per usable video and hitting Veo 3’s $125/month plan limits. They explain monetization paths—creator fund payouts (~$20 per million views), merch (Breadclump sweatshirts), consulting, and licensing to Netflix—and recommend tools like Overlap (clips 30–140 seconds) and ComfyUI for control. The episode closes with prompt theory: AI characters questioning their existence, and a bet that Gen Alpha’s brainrot consumption will reshape long-form content.

2024 Year in Review: The Big Scaling Debate, the Four Wars of AI, Top Themes and the Rise of Agents

Jan 1, 2025 · 1:51:07

In their 100th episode, hosts Alessio and Swyx recap 2024 in AI, arguing that pre-training scaling has hit a wall—backed by Ilya Sutskever and others at NeurIPS—and that inference-time compute (o1, o3) is the new frontier. They dissect the "four wars": data quality (lawsuits vs. synthetic data), GPU haves vs. have-nots (with the middle class dying), multimodality (Sora, Veo 2, Gemini 2.0's native image output), and the LLM OS/agents stack (LangChain, E2B, memory). Market share shifted from OpenAI's 95% to 50-75% as Anthropic and Gemini gained ground; prices dropped ~3 orders of magnitude for same ELO. The episode predicts 2025 as the year agents finally enter production, driven by models like o1 and tools like Devin, and warns that AI will set the skill floor for roles.

Best of 2024 in Vision [LS Live @ NeurIPS]

Dec 22, 2024 · 55:46

Isaac Robinson and Peter Robicheaux from Roboflow and Vik Korrapati from Moondream recap the biggest shifts in computer vision in 2024, naming Sora as the year's most significant paper despite being a blog post, and highlighting its replication efforts (OpenSora) that use MAGViT V2 and diffusion transformers. They detail how SAM 2 extends the SAM approach to video with a memory bank and real-time performance, and how DETR-based detectors (RT-DETR, LW-DETR, DeFine) now surpass YOLOs on COCO by 4.6 AP at the same latency. Peter explains the CLIP-blind phenomenon: MMVP shows leading VLMs (ChatGPT, Gemini, LLaVA) fail at fine-grained tasks like reading clock hands, but models like PaliGemma 2 and AimV2 improve by incorporating pixel-level understanding and autoregressive image reconstruction, with AimV2 approaching 60.2 mAP on COCO. Vik introduces Moondream's 0.5B parameter pruned VLM and demonstrates a grounded chain-of-thought approach that dramatically improves sample efficiency for analog gauge reading, reducing the need for millions of synthetic examples.