The Constitutional Coverage Trilemma in AI Governance

Frontier AI systems function as constitutional institutions: each deployed model encodes an implicit ranking among safety, helpfulness, honesty, autonomy, and equity. We ask whether the supply of frontier constitutional types covers human demand. Combining a paraphrase-controlled audit of the as-shipped default constitutions of 23 frontier LLM archetypes with a pairwise-tradeoff study of 1,649 US participants on the same instrument, we report three facts. Demand is broad: it spans all five values, with the largest constituency under one-third. Supply is narrow and drifting: the 23-archetype hull occupies ~2% of the demand hull under conservative noise-matched estimation (0.10% at full audit precision), no archetype puts helpfulness or autonomy first (37% of users are constitutionally homeless), and across six model families autonomy decreases in 5/6, equity increases in 5/6, and safety increases in 4/6, with monotone within-family version trends (order-permutation p = 0.013) and the autonomy decline concentrated in scenarios where safety is not at stake. The drift's importance is directional: away from a value already undercovered, mechanically worsening the welfare floor for the least-served users. The fix is sparse: a 2-vertex menu eHON, eAUT beats the full 23-archetype frontier by 47% on mean regret (CI [43%, 52%]); three vertex additions cut mean/worst-group regret by up to 81%/64%. We formalize these findings as a budgeted-pluralism trilemma, show the binding regime is empirically realized, and verify the conclusions are robust to distance-based welfare and to degraded routing. The instrument and audit harness are described in full in the appendices.