Flux

Why AI Labs With Unlimited GPUs Still Fail — Anjney Midha, AMP

Jun 18, 2026 · 1:00:37

Anjney Midha, CEO of AMP, argues that AI labs with unlimited GPUs still fail due to misaligned culture and infrastructure waste, proposing a compute grid modeled on independent system operators to pool demand and supply. At Google, 95% node utilization was considered an outage, yet most clusters today don't reach that, with waste compounding at scale. AMP’s grid, starting at scheduling, aims to make FLOPs flow like megawatts, having secured 1.3 gigawatts of demand. Midha explains Anthropic cracked coding because 'luck favors the prepared mind'—their four years of paranoia and scarcity created a culture that OpenAI’s abundance couldn't replicate. He also shares a 14-year mission in end-of-life prediction, arguing AI can reduce the 30% of Medicare/Medicaid spend on end-of-life care. He warns that too much capital too early makes labs fragile because without hardship they fail to define their P0.

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 Engineering for Art - with comfyanonymous

Jan 4, 2025 · 52:09

Comfy Anonymous, the anonymous creator of ComfyUI, details in his first-ever podcast interview how his node-based image generation tool overtook Automatic1111 through superior memory management and early support for SDXL, becoming the de facto interface for advanced diffusion workflows. He started coding on January 1, 2023, and released the first version on January 16, driven by a desire to chain models and experiment with custom samplers. His work at Stability AI from June 2023 ensured ComfyUI efficiently handled SDXL, which leaked via early access and forced users from less powerful GPUs to adopt his tool. He discusses model preferences: Flux for consistency, SD 3.5 for creativity, and SD1.5 remaining popular. For video, he highlights Mochi as a 'true' video model with 3D latents, already implemented in Comfy. Now with a core team, ComfyUI is targeting a v1 release with an easy installer on Windows and Mac, while planning monetization through cloud and enterprise features—but keeping the open-source core free.

[Paper Club] Intro to Diffusion Models and OpenAI sCM: Simple, Stable, Scalable Consistency Models

Nov 2, 2024 · 52:22

RJ Honicky presents OpenAI's sCM paper, which introduces techniques to simplify, stabilize, and scale continuous-time consistency models for faster image generation. The paper addresses instability issues by modifying skip connections, using cosine/sine schedules, adjusting the noise embedding scale to 0.02, applying adaptive double normalization, and implementing tangent warm-up over the first 10K iterations. These innovations enable one-step generation with FID scores rivaling GANs and multi-step diffusion models, while scaling experiments demonstrate performance up to 1.5B parameters. Distillation from scratch outperforms distillation from a teacher, and the episode contrasts these continuous-time methods with discrete-time approaches, explaining the intuition behind the mathematical choices.