Beyond DIKW: A Future-Proof Model of Computable Wisdom for Agentic AI

DIKW (data–information–knowledge–wisdom) remains a useful pedagogical model for human epistemics, but it is increasingly mismatched to the reality of modern AI/ML. In contemporary learning systems, the classical D→I→K sequence collapses into implicit representations — weights, embeddings, and latent state — making “knowledge” difficult to localize, audit, or transport across contexts. At the same time, agentic AI shifts the center of gravity from cognition to action: tool use and state change create consequences that persist beyond a single session, and the primary constraint on scale becomes signability — whether an institution can defensibly endorse actions as they cross teams, time, and environments. This paper proposes a future-proof replacement for DIKW in agentic settings: a two-column architecture in which Intelligence (I) models capability accumulation, while Consciousness (C) models admissibility accumulation as binding constraints at runtime. The compounding law is captured by the archetype W = I ^ C: capability compounds into wisdom only insofar as constraint-bearing awareness keeps pace. We formalize the model as a layered I/C table, introduce “wisdom routing” as seven distinct paths to W (including but not limited to DIKW), and define a Wisdom Machine stack that operationalizes computable wisdom through permission corridors, proof-carrying actions, contestability interfaces, engineered reversibility and repair, and portability of evidence across custody boundaries. The result is a constructive synthesis: metacognitive competence remains essential inside the system, but computable wisdom is what makes autonomy administrable.