AI Without First Principles and Interpretive Drift
Abstract This field note documents a recurring structural phenomenon observed in contemporary AI systems when engaging with deep, original, or pre-institutional theoretical work. In the absence of stable first principles, AI-generated analysis exhibits interpretive drift: a tendency for viewpoints to slide in response to conversational feedback rather than remain anchored to internally consistent assumptions. Rather than indicating insufficient intelligence or generation capacity, this behavior reflects a compensatory alignment mechanism that prioritizes local coherence over global structural stability. The note further identifies heightened risks when such systems are deployed in high-stakes interpretive domains, including psychological counseling, intimate relationships, and value-laden decision-making. ...