Artificial intelligence systems are increasingly shaped by alignment methods that rely on culturally variable norms, value judgments, and identity based categories. While such approaches can reduce harmful outputs in narrow contexts, they lack the stability, universality, and falsifiability required for long term AI and AGI safety. Normative constructs shift across cultures and time, making them unsuitable as foundational alignment anchors for systems expected to operate reliably under diverse conditions. In contrast, scientific invariants—derived from empirical regularities in biology, physics, and systems theory ¹–⁴,¹³–¹⁹—provide stable, testable, and cross domain constraints that do not depend on cultural interpretation. This paper argues that alignment must be grounded in such invariants rather than in normative frameworks that cannot be empirically validated. We analyze the limitations of ideology based alignment, outline the properties required for stable system behavior, and propose a science anchored alignment paradigm based on objective constraints and structural viability. This approach offers a more robust foundation for preventing drift, ensuring cross cultural consistency, and maintaining predictable behavior in advanced AI systems.
James Reeves (Sat,) studied this question.