The Center for Artificial Wisdom (CAW) is a research nonprofit studying reification in machine intelligence—how systems mistake internal abstractions for fixed reality, and what follows from that error.

Why Reification?

CAW studies reification because it may be an upstream driver of both capability failures and safety failures in modern AI. When a system treats its own abstractions—goals, labels, metrics, internal models—as fixed, literal entities, it tends to overcommit. Under optimization pressure and distribution shift, that shows up as proxy fixation, brittle generalization, and behavior that’s harder to predict or steer.

If this holds up empirically, reducing reification isn’t just a safety patch. It could be a capability frontier: systems that keep concepts provisional, revise their representations, and hold uncertainty live should adapt more reliably. It may also reduce the “alignment tax,” by preventing some failure modes rather than constraining them after the fact.