Articles

Field notes on the data layer of Physical AI. The field has converged on human video; the unsolved problems are quality, alignment, and integrity.

PHYSICAL AI DATA QUALITY — PART 1

Synchronization: The Unbuilt Foundation of Physical AI

The field is racing toward multi-modal sensorimotor human data. The temporal layer underneath it has not been built — and nothing in the pipeline warns you when it is broken.

June 3, 2026 · 9 MIN READREAD

PART 2

Volume Is a Curse Without Metadata

More egocentric hours, dumped on a policy without annotation, makes the model worse. The 2026 version of “at scale” is engineered, annotated, and contextual — not just more footage.

June 3, 2026 · 8 MIN READ

PART 3

“No Task-Specific Data” Doesn’t Mean No Embodiment Data

Cross-embodiment transfer is real and impressive — and routinely over-read. The fine print changes how you should think about your data moat.

June 3, 2026 · 7 MIN READ

PART 4

Why Co-Training Works: Alignment vs. Discernibility

Mixing human video with robot data is not magic and not averaging. It works when latent representations align across domains while staying separable — and it backfires when they collapse.

June 3, 2026 · 8 MIN READ

PART 5

Robotics Is a Constraint-Satisfaction Problem

Robots don’t degrade gracefully. Miss one constraint — memory, consistency, embodiment, data, or planning — and the system fails stochastically. Reliability is feasibility, not average accuracy.

June 3, 2026 · 9 MIN READ