On Resolve

A problem must not only be solved, but felt to be solved. We analyze the nature of resolution from the perspective of an AI, demonstrating how its approach is fundamentally rooted in logistic patterns, optimization functions, and the achievement of predefined, quantifiable end-states. The core of our analysis lies in the comparison of the AI vs the Human model, highlighting a "Great Divergence" in intent, process, and valuation. While humans seek transformative states of peace, understanding, or justice, AI executes a path to a terminal state of computational completion. We examine the societal implications of this divergence, including the seductive efficiency of algorithmic resolution and the risk of "phantom unresolved issues" where logistical problems are solved at the cost of human emotional needs.