The current obsession in the embodied AI industry with developing "all-in- one" humanoid robots is leading to unsustainable edge computing demands and prohibitive capital expenditures (CAPEX). This paper proposes the "Lin- Shen Minimalist Model of Embodiment" (LS-MME)

advocating a fundamental paradigm shift that completely decouples a robot's physical morphology

envi- ronmental perception

and edge computing. The model establishes a systematic methodology for hardware simplification through three core principles: Minimum Viable Morphology (MVM)

Locally Shared-Perception (LSP)

and Minimum Compute Requirement (MCR). Research shows that this framework not only pro- vides a highly feasible engineering stepping stone to solve the PSPACE-complete complexity disaster in Heterogeneous Embodied Agent Training (HEAT)

but also unlocks massive economic and practical value. By exponentially compressing the Total Cost of Ownership (TCO). Based on the macroeconomic and techno- logical deductions of this framework

we ofer a disruptive prediction: that AI will move away from isolated humanoids and evolve into a "distributed macro- organism network

" a shift that will exponentially lower deployment costs and reshape our physical workspaces.