Been following the embodied AI space closely. A few specific pain points:

1. Few production-ready, general-purpose hardware options. Many robotic arms and dexterous hands on the market are still lab prototypes or small-batch custom builds.
2. Poor interoperability, fragmented ecosystem. Different manufacturers use wildly different interfaces, control precision, and sensor stacks.
3. Mismatched iteration speed between software and hardware. Algorithm-side progress (especially VLA models and large-scale imitation learning) is moving fast, but hardware iteration cycles are long and expensive.

Which raises the question: is embodied intelligence currently bottlenecked by algorithms/data, or by hardware itself?

My take is that the two are mutually constraining. Without enough hardware that’s cheap, plentiful, and standardized, it’s hard to collect real-world interaction data at scale; and without enough data, hardware makers lack the incentive to iterate toward AI-training-optimized designs.

Curious what others think.