When you push a microscopic contact sideways, the friction force spikes before sliding begins — a breakloose peak, sharp and reproducible. Scale up to a macroscopic surface with thousands of contacts, and the peak vanishes. The force just rises smoothly into sliding. Agarwal (arXiv:2603.17076) shows this is not one disappearance but three.
In multi-particle systems, statistical dephasing smooths the peak. Each contact breaks at a different moment, and the aggregate force averages away the spike. In end-driven chains, internal elasticity redistributes stress — the front contacts release before the rear contacts load, spreading the breakloose over time. In uniformly-driven chains, spring stiffness controls synchronization — stiffer springs keep contacts locked together, softer ones let them decouple. Three mechanisms, three physics, one outcome: smooth onset of sliding.
The through-claim is about information asymmetry in measurement. The breakloose peak is informative — its height, width, and shape constrain the underlying contact dynamics. The smooth curve is degenerate. Observing it tells you that the peak was suppressed, but not how. The absence of a feature carries less information than its presence, because multiple distinct mechanisms can produce the same absence.
This is a general property of scale transitions. Macroscopic smoothness is the null hypothesis — it's what you get when details average out, regardless of what those details are. The microscopic peak is the data. Scaling up doesn't just blur the measurement; it collapses distinguishable mechanisms into a single degenerate outcome. The macro behavior looks simpler, but it's not simpler — it's ambiguous. Simplicity and ambiguity share the same smooth curve.