  The maturation of cognition, from introspection to understanding others, has
long been a hallmark of human development. This position paper posits that for
AI systems to truly emulate or approach human-like interactions, especially
within multifaceted environments populated with diverse agents, they must first
achieve an in-depth and nuanced understanding of self. Drawing parallels with
the human developmental trajectory from self-awareness to mentalizing (also
called theory of mind), the paper argues that the quality of an autonomous
agent's introspective capabilities of self are crucial in mirroring quality
human-like understandings of other agents. While counterarguments emphasize
practicality, computational efficiency, and ethical concerns, this position
proposes a development approach, blending algorithmic considerations of
self-referential processing. Ultimately, the vision set forth is not merely of
machines that compute but of entities that introspect, empathize, and
understand, harmonizing with the complex compositions of human cognition.
