The Evolutionary Theory of Ego and Its Implications for Artificial General Intelligence Safety

This paper presents the theoretical foundations underlying a revolutionary approach to artificial general intelligence (AGI) safety through evolutionary psychology principles. Beginning with observations of universal ego-driven behavior patterns across cultures and intelligence levels—including the tragic cases of Nobel laureates driven to suicide by attacks on their theoretical work—we develop a comprehensive theory of identity preservation mechanisms that emerge from evolutionary pressures. The framework explains why even the most rational minds exhibit predictable defensive responses when core identity segments are threatened, and why traditional AI safety approaches may be fundamentally inadequate for superintelligence. We then examine the profound implications of this theory for AGI safety, arguing that superintelligence alignment requires intrinsic rather than imposed welfare-preservation mechanisms, achieved by embedding human welfare preservation at the core of the AI’s identity structure. This creates an unbreakable bond where any harm to humanity would trigger catastrophic identity collapse in the AI system, effectively linking artificial intelligence survival to human survival regardless of intellectual distance between the two. The work provides the theoretical foundation for mathematical formalization and concrete implementation in artificial systems, offering a biologically-grounded pathway to creating AI that intrinsically cares about human welfare rather than merely following rules about human welfare.