What if one AI brain could run every robot on the planet—a humanoid, a warehouse arm, and a dog-like inspection bot—all at once? That's not a thought experiment. That's what Skild AI is building right now. Deepak Pathak (CEO and Co-Founder) and Abhinav Gupta (President and Co-Founder) of Skild AI break down Skild Brain—a universal, general-purpose AI model designed to power robots of any form factor, tackling any task, from a single shared intelligence. 🔬 Topics covered: Why robotics is fundamentally a data problem—and why there's no "internet of robot data" Skild's three-layer data strategy: real robot teleoperation, video pretraining, and simulation How the pretraining/post-training recipe that made LLMs work is now being applied to physical AI The data flywheel—how every robot deployment makes the shared brain smarter for the next task The deployment roadmap: factories and warehouses → hospitals and hotels → home consumer robots Skild's NVIDIA partnership: co-developing Newton physics solvers and using Cosmos for data augmentation, Isaac Sim for simulation, and edge compute for on-device robot inference How Skild tests Skild Brain before deployment—from task KPIs to generalization stress tests to safety guardrails When will robots actually be ready for your home? The co-founders give their honest (and surprisingly humble) take 🔗 Resources mentioned: Skild AI: https://www.skild.ai Skild AI Blog: https://www.skild.ai/blogs Chapters: 00:00 Introduction: What is Skild AI? 03:19 Why robotics needs a horizontal platform 03:50 The 90% wall: how corner cases killed traditional robotics 08:34 The three sources of robot training data 10:57 Pretraining vs. post-training in physical AI 16:32 The data flywheel and cross-vertical deployment strategy 17:34 How Skild AI works with NVIDIA (Isaac Sim, Cosmos, Newton, edge compute) 18:59 Testing Skild Brain: task KPIs, generalization, safety guardrails 23:06 The future of robotics: factories now, homes later? 27:54 What's next for Skild AI

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