Attachment Point, Not Router: A Ceiling on Conditional Computation in Spiking Speech Models

Conditional computation is widely proposed as a route to energy-efficient spiking speech transformers: a gate decides, per frame, whether the attention operator runs, and savings of 30 to 49% are attributed to the gate. We show that the mechanism cannot produce them. The operations a query-side router can remove are bounded by a product of three factors, $\Delta C/C\le u_{\mathrm{id}}{s}_{\mathrm{attn}}\rho$, and the third has been overlooked. Because a frame's key and value are read by its neighbours, and because the projections bracketing the operator run regardless of the routing decision, only $\rho$ = 22 to 28% of the attention operator belongs to the router in streaming speech models. Across eight audio and nine vision configurations from published architectures the ceiling is below 2.9% for every streaming-capable speech model and below 7% for every windowed architecture; it survives removing the width bottleneck, tightens under a better window implementation, and within this family admits no feasible configuration above 5.3% at the temporal resolution these benchmarks use. Energy is not operations, and we keep the two apart: forcing a trained gate across the whole skip axis removes 7.1% of operations and 10.9% of measured energy, while accuracy falls 9.8 points. An ablation ladder that adds one change at a time locates the 49.5% we ourselves recorded for this mechanism in a static width reduction, with a further 8 to 17.5 points of it an artefact of the energy formula in common use. Finally we reach the higher ceiling the analysis predicts. Holding the routing rule, rate and training protocol fixed and varying only where the mask is applied, one random rule removes 2.7% of energy at the attention operator, 11.0% at the channel MLP and 25.9% at the whole block, each within a few points of the ceiling derived for it, at accuracy flat to within a point. The obstacle is not the router but where it is attached.