A Canonical Eight-Layer Taxonomy for Contemporary AI Systems (2026)

Artificial Intelligence (AI) has expanded rapidly across modalities, architectures, and deployment ecosystems, yet the field lacks a unified, end-to-end taxonomy that organizes AI from first principles to emergent capabilities. This paper presents the first comprehensive, mathematically grounded, structured Eight-Layer Canonical AI Taxonomy, integrating domain definitions, learning paradigms, model families, deep architectures, foundation model classes, alignment regimes, orchestration systems, and emergent behaviors. The taxonomy is formally justified using category theory, information theory, and systems theory, establishing minimal sufficiency, orthogonality, and vertical compatibility. A forward-looking Ten-Layer Meta Taxonomy introduces meta-governance and atomic intelligence units. Falsifiability criteria, empirical validation pathways, limitations, and a critical evaluation are provided. This unified framework establishes a foundational reference architecture for AI research, engineering, governance, and regulatory alignment in 2026 and beyond.