We study the feasibility limitations of generic QAOA for permutation-constrained optimization, where valid configurations occupy an exponentially small fraction of the Boolean hypercube. Using random edge-cost TSP/ATSP ensembles, we establish high-probability bounds showing that optimized generic QAOA retains exponentially small feasible probability over specified parameter domains. At depth one, the bound holds uniformly over all mixer angles and a cost-angle grid that may be exponentially large in n^2. The analysis combines Walsh-Fourier/Krawtchouk estimates, which suppress low- and high-degree contributions, with transition-feature collision bounds controlling objective-dependent interference. Complementary cost-angle averaging, fourth-moment estimates, and light-cone analysis characterize energy-fibre coherence, typical-angle concentration, and constraint-correlation geometry. We compare generic QAOA with a constraint-enhanced kernel (CE-QAOA) initialized in a product one-hot subspace and evolved with a block-local XY mixer. For N = n^2 binary variables, CE-QAOA attains feasible probability at least n!/n^n at every depth, yielding an exp[Omega(n^2)] advantage over optimized generic QAOA at depth one with high probability. For linked cost-angle schedules with independently optimized mixer angles, the generic feasibility obstruction and exponential CE-QAOA separation extend throughout linear-depth windows p <= alpha*n, for fixed alpha > 0. These parameter-uniform bounds also imply exponential raw-sample requirements for adaptive feasible sampling within the prescribed domains.

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