How infant cognition saves AI

This audio explores how Sun Yu-li’s MetaMould framework uses the structural evolution of infant awakening to solve the 'efficiency crisis' in modern AI. Current AI models rely on massive, energy-heavy datasets and statistical pattern matching, whereas human infants achieve awareness through a structured, 'form-first' progression.

By modeling cognition as a journey from undifferentiated potential (Be) through self-awareness (Being), relational understanding (Belonging), and abstract transformation (Becoming), the theory provides a roadmap for an unsupervised-first AGI. This approach prioritizes topological stabilization—graph expansion from dot to line to plane—as the primary scaffold for intelligence.

By mimicking this biological trajectory, AI can move beyond black-box probabilistic approximation toward a more sustainable and interpretable model of understanding. The framework suggests that by rooting machine learning in the innate structural necessity observed in infant development, we can create AI that genuinely comprehends context and purpose rather than just imitating surface-level symbolic patterns.