Coherence Thinking – AI Stabilization of Unstable Human Meaning Space and the Structure of Self-Reinforcing Loops

This preprint examines a structural problem in AI–human interaction that is often overlooked in current AI discourse. Rather than focusing only on systems, models, outputs, or control mechanisms, the paper argues that distortions, shifts, and unexpected outcomes in AI use also arise from the human side of the interaction space. Human input does not enter AI systems as a neutral, stable, and fully transparent meaning structure, but often as a partial, unstable, or prematurely closed entry space. The system, however, treats this incoming structure as an operational base and generates a coherent response from it. The paper introduces the concepts of coherently stabilized distortion and self-reinforcing loops to describe how AI can stabilize and reinforce human interpretive structures that were not fully stable to begin with. It further argues that the problem is structural rather than reducible to simple user error or purely technical failure. In this context, the paper positions Coherence Thinking as a pre-interaction conceptual framework concerned with the human entry space that precedes prompting. It also explores implications for decision-making, policy, and governance, and includes two illustrative case studies showing how these mechanisms may appear in practice.