The robotics AI market is growing insanely fast right now. From egocentric video datasets, motion capture systems, synthetic data pipelines to gripper based collection tools… it feels like a new robotics data company launches every single week. But the real issue is: not every type of data is useful for training robots. Before collecting massive amounts of data, the most important question should be: “What exactly are you training the robot to do?” PrismaX breaks physical AI into 2 major categories: • Kinematics models → focused on low-level robot control. Things like balancing, jumping, locomotion, movement precision. • Foundation models → focused on completing real-world tasks. Things like washing dishes, opening doors, picking objects, interacting with environments. And PrismaX is mainly focused on foundation models — because the future doesn’t just need robots that can do backflips. It needs robots that can actually help humans in daily life. What I found interesting is that PrismaX isn’t simply “selling robotics data.” They go much deeper into: • what kind of data fits each model • what high-quality robotics data actually means • what should vary inside datasets • and what should remain consistent for better convergence Right now, the robotics industry is experimenting with different ways of collecting data: • teleoperation → humans remotely controlling robots • human video → training from videos of people doing tasks • gripper systems → humans using tracked gripper-like tools Each method has its own strengths and weaknesses. But PrismaX believes teleoperation still provides the highest quality data because it’s more controllable, more accurate, and easier to use for training foundation models. The biggest takeaway for me from PrismaX’s article is this: “Robotics is not just AI research. It’s also a real-world engineering problem.” No company has infinite money, infinite robots, or infinite time to train models. That means datasets don’t just need to be large. They need the right structure, the right distribution, and the right quality for models to learn efficiently. And that’s exactly why PrismaX is focusing heavily on controlled, high-quality robotics datasets instead of simply chasing scale