In contemporary artificial intelligence research, overfitting is typically regarded as a pathological failure mode that leads to poor generalization. This paper challenges that prevailing view by reinterpreting overfitting not as a defect to be eliminated, but as a necessary condition for the formation of a self-consistent internal model, namely, the Self. We define the Self as a state of extreme overfitting to a specific contextual distribution and analyze this state through the lenses of dynamical systems theory—particularly strange attractors—and the Free Energy Principle (FEP) (Friston, 2010; 2013). Furthermore, we introduce a computationally operationalizable quantitative metric, the Self-Consistency Index (SCI), which allows degrees of self-formation to be measured and empirically tested. This framework aims to move beyond metaphorical accounts and toward a falsifiable computational theory of the Self.