Overview
In HUMAN AGAIN, I argued that modern AI can learn nearly everything about human experience while remaining structurally “external” to it, because nothing can truly go wrong inside the system in a way that forces reprioritization. I proposed a technical analog of pain: a continuity-sensitive disturbance function defined over the AI’s internal state space, with nonlinear escalation near the boundary of coherent operation, and with proportional coupling to measurable human suffering signals.
