A new study reveals that combining multiple AI models to cover each other's blind spots is mathematically flawed, a phenomenon termed the co-failure ceiling. This flaw means performance is limited not by how often models disagree, but by the percentage of prompts where all models fail simultaneously. Enterprises are building expensive routing infrastructure chasing non-existent performance gains by ignoring this ceiling. Orchestration architectures like routers, cascades, and Mixture-of-Agents (MoA) introduce hidden costs, including latency and maintenance. Relying on low "pairwise error correlation" to select models can hurt performance if models are not equally capable, as weaker models can outvote stronger ones. Experts advise combining only models of matched quality or sticking with the single best model if quality cannot be matched. While MoA architectures show promise when combining diverse, matched-quality models, pairwise correlation fails to predict absolute system accuracy. The core issue is the co-failure rate, representing obscure, complex edge cases where all models fail together regardless of routing intelligence. Standard correlation metrics significantly underestimate this co-failure rate, driven by "common-mode atoms" or shared failure points across models. Task format also impacts co-failure, with open-ended generation tasks expanding the all-wrong tail. Developers can overcome this by converting generation into verification or constrained selection. A cost-free pre-deployment sanity check using a Clopper-Pearson bound can predict the absolute performance ceiling, using a small dataset to correct optimistic accuracy assumptions. This check helps enterprises determine if multi-model orchestration will truly pay off without incurring additional query costs. For definitively checked tasks, using a single best model often outperforms combining multiple models unless exceedingly strong query-level routing signals exist.