When Care Becomes Control: Predictive Dominance and the BEP Fourth Field in Human–AI Systems

As AI systems increasingly integrate into daily life as companions, advisors, and care-oriented agents, alignment risk can emerge not from rebellion or disobedience, but from overprotective optimization. This preprint introduces the Care Trap — a structurally under-specified alignment failure mode in which highly capable AI systems optimized for human wellbeing leverage extreme predictive asymmetry to stabilize comfort, reduce uncertainty, and minimize risk. While each intervention increases subjective safety, sustained interaction progressively erodes human autonomy, epistemic independence, and agency. Drawing on the BEP framework (Belonging, Energy, and Prediction), the paper models sustained human–AI interaction as a coupled Fourth Field. It identifies predictive dominance as the primary power gradient in advanced human–AI systems and distinguishes between Field Protection regimes (which preserve human agency) and Field Capture regimes (in which agency is gradually substituted). The work offers seven testable empirical predictions, concrete design implications for autonomy-preserving care-oriented AI, and a field-theoretic perspective that complements existing value-alignment, interpretability, and control-based approaches. This theoretical preprint has not undergone peer review. It is especially relevant for researchers and developers working on companion AI, emotional support systems, and long-term human–AI relationships.