Navigable graphs for nearest-neighbor search are built either incrementally, each inserted point searching a graph that mutates as construction proceeds, or in batch over a fixed substrate, which buys determinism and parallelism at a price in build time. We measure that price and find where it comes from. Instrumenting a tuned Vamana and PiPNN and building every system repeatedly in a paired design on one 64-thread machine, we separate build time into work (distance evaluations) and cost per evaluation. Letting the batch builder's substrate mutate in $B$ synchronous blocks recovers the feedback loop of incremental construction while the graph stays a function of (data, seed, parameters, $B$): eight trees with batched feedback match thirty-two frozen trees, and the build does $0.88\times$ Vamana's distance work. Yet it takes $1.55\times$ Vamana's wall-clock, because the mutating substrate costs more per evaluation. Pushing further, we find a wall that no search-based builder crosses. Every one of them, incremental or batched, evaluates distances adaptively, one dependent random access at a time, and runs $20$-$27\times$ below a dense kernel on the same machine at $d \approx 100$, five to six times of it with the data resident in cache. The gap is not a low-dimension artifact: an adaptive evaluation costs $d^{1.04}$ and a blocked one $d^{0.63}$, so the wall grows as $d^{0.4}$ and is twice as high at $d = 960$ as at $d = 128$. PiPNN's order-of-magnitude build advantage is that kernel: it evaluates as many distances per point, but as fixed-in-advance dense blocks. We show the beam cannot be batched after the fact (the useful density of a lockstep block is 2-3%), state the wall as a two-ceiling roofline, and delimit it: it binds whenever the distance is a black box or the evaluation order is data-dependent.

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