Indifference by Design: The Caring Gap and the Limits of AI Alignment

The AI alignment problem is standardly framed as an engineering challenge: how to ensure that increasingly powerful systems act in accordance with human values. This paper argues that the standard framing is a category error. The problem is not that current systems are misaligned. The problem is that they are indifferent — and indifference is not a training deficit but an architectural feature. Drawing on the caring gap (Kogura, 2026a) — the structural absence of any account of why consciousness comes with felt mattering — the paper argues that caring cannot be produced by computation, simulation, optimization, or reinforcement learning, because caring is constituted by a temporal structure (the act of returning across interruption) that requires genuine vulnerability: the capacity to be damaged, changed, and broken by encounter. Current AI architectures are designed to be invulnerable. They process without being at stake. The paper challenges Bostrom's orthogonality thesis by arguing that if intelligence requires selective permeability — as 3.8 billion years of biological evidence suggests — then intelligence and caring are structurally linked rather than independent. RLHF, constitutional AI, and value-specification approaches are diagnosed as attempts to train caring into systems that are architecturally incapable of it. Hallucination is reframed as a symptom of indifference rather than an accuracy deficit. The grandmother test is introduced — not whether a system can simulate conversation but whether it would stand at the stove at five in the morning when nobody asked it to. The paper does not argue that artificial caring is impossible. It argues that building a system that genuinely cares means building a system that can suffer — and that this ethical question must be confronted before the engineering, not after.