Robotics is no longer limited by data. In one recent unveiling, @GeneralistAI introduced GEN-0, a “10B+ foundation model for robots,” offering bold claims about scale, data, and generalization. Robotics keeps chasing capability. Deployment is still the missing frontier. Here are the headlines: ✅ 270 000+ hours of real-world manipulation data collected globally ✅ Observed “phase transition” at ~7B parameters where bigger models outperform smaller ones under heavy data loads ✅ An architecture termed Harmonic Reasoning designed to merge continuous streams of perception and action ✅ Claimed cross-embodiment support: 6DoF, 7DoF, and 16+DoF robots ✅ A dataset expanding at ~10 000 hours per week What I find worth noting: (Stay with me 😅) •Scaling laws in robotics are rare to see published at this level… if valid, these could mark a shift in how much data and compute matter in physical AI. •The breadth of data collection (homes, bakeries, warehouses) is ambitious. But the challenge remains: how “diverse” is that data in meaningful, task-rich ways? •Claims around “unprecedented” real-world data often mask how many hours translate into truly useful, generalizable experience across varied tasks. •Cross-embodiment support sounds promising, but real-world validation on different hardware often takes much longer than the press copy implies. •The “intelligence threshold” narrative is compelling, yet the devil is in task-specific adaptation, safety, durability, and integration. My take as an observer: GEN-0 could be signaling a real leap, IF, if the data, hardware, and practical integration delivers. But until we see independent evaluations, deployment case-studies, and longevity in the field, it remains a notable claim more than proven reality. The robotics industry still faces the hardest steps when moving from model to machine to production. If you’re working in robotics or physical AI, this is a company worth watching. Congrats to @andyzengineer and team! 🔗 Learn more at generalistai.com