In embodied AI, it is a widely held belief that cross-embodiment data—a diverse mixture encompassing both heterogeneous robot data and egocentric human videos—enhances robot models. Yet, this consensus remains an opaque black box: we know it works, but we rarely understand exactly what this broad spectrum of data is teaching the robot. To demystify this, we introduce Fe0, an embodied foundation model. Rather than scaling data to chase benchmarks, we designed a rigorous controlled experiment. We deliberately constrained our in-domain teleoperation data to the minimum: just 3 hours of trajectories featuring only a single, atomic pick-and-place primitive, where 95% of the data involves moving a single object to a single receptacle from a highly restricted vocabulary. By establishing this extreme baseline, we guarantee that any zero-shot generalization Fe0 exhibits beyond in-domain grasping is unequivocally powered by cross-embodiment learning. But how do we measure this transfer scientifically? Instead of ad-hoc testing, we constructed a systematic L1–L5 capability framework—spanning from basic visual robustness and semantic grounding, up to complex relational reasoning, compositional planning, and novel action execution. We evaluate the model through both rigorous open-loop trajectory scoring and extensive closed-loop real-world trials on the IRON-R01-1.11 robot to unpack the true anatomy of cross-embodiment data. We reveal a clear capability boundary: while heterogeneous data effectively teaches the model visual semantics and basic task logic, it falters at mid-level relational and sequential reasoning, and hits a hard ceiling when transferring precise physical execution. By systematically evaluating exactly where cross-embodiment data succeeds and where it fails, we illuminate what this heterogeneous experience actually teaches an embodied foundation model, and what it takes to push beyond current limits.