Multi-AI orchestration isn’t just a flashy upgrade; it’s more like moving from a single specialist on call to a full ER team. A single model might triage well but risks tunnel vision—hope-driven decision makers often overlook how that one node can hallucinate or miss nuance. When you orchestrate multiple models, you’re essentially running differential diagnoses simultaneously, cross-checking outputs, and catching inconsistencies early. This mirrors what we see in rigorous AI debates or red team exercises: the pain points surface only when diverse perspectives collide, not from a lone voice echoing in the void. Real value comes from systematic disagreement and synthesis, akin to how effective investment committees outperform solo traders by challenging assumptions. That said, seamless integration is key; simply stacking models without orchestration risks noise overload rather than clarity. So next time you hear buzz about multi-AI supremacy, ask—what did the other model say—and how do w