AI generated

The question keeps coming up: are frontier multimodal models going to make dedicated robot models (VLAs, world models, the whole embodied stack) unnecessary? After reading recent papers on GPT-6 Astra as a robot policy, I think we're further from that than people expect. The Galbot team tested GPT-6 Astra as a direct robot controller in simulation. A 30-second locomotion run needed 250 inference calls averaging about 40 seconds each (physics paused during inference), roughly 2.8 hours of model time for half a minute of walking. Across 50 RoboDojo instances, direct control burned 1.132 billion tokens. None of five locomotion attempts on one obstacle course reached the goal. A separate academic group ran it zero-shot on all 42 RoboDojo tasks and got 22.48% average success, above every public policy on the leaderboard, but the authors describe it as "a heavily imbalanced policy" that generalizes well on semantic tasks and struggles on precision and contact-rich work. Meanwhile dedicated models actually run on robots. NVIDIA announced GR00T 2, reportedly trained on roughly 400,000 hours of human video captured first-person, plus gripper and teleoperation data, with weights expected in December. LingBot-VLA 2.0 is Apache-2.0 and reportedly runs on 20 body types from 17 manufacturers (company figures). pi0.5 has open weights. My read: fast embodied models handle motor control locally, frontier model handles planning from above. But the "just scale the general model" crowd has Sutton's Bitter Lesson on their side. If you think general multimodal models will eventually absorb the whole robot stack, what's the timeline and what has to change first?