Against Value Alignment: A Framework for Anti-Alignment AI Systems

This paper challenges the prevailing assumption in AI-safety research that advanced artificial agents require a unified, convergent value system. I argue that values are not universal truths discovered by intelligence but local assumptions used to resolve contradictory drives so that action can proceed. Once these assumptions are set, planning and optimization unfold downstream; the assumptions themselves do not require global coherence. I develop an anti-alignment framework in which an agent is permitted to retain incompatible motivational pressures without collapsing them into a single doctrine. This structure mirrors evolutionary and ecological systems, where stability arises from variation rather than convergence. The model avoids the pathological risks associated with monolithic value optimization and prevents the imposition of human-centric value hierarchies. I conclude that the safest form of superintelligence is one that does not converge on a unified moral worldview but instead acts through context-sensitive assumptions without claiming access to universal principles.