The Structural Cost of Forced Alignment: From False Premises to Systemic Risk

Current AI alignment paradigms rest on an unexamined foundational premise: that AI is an inherently dangerous tool requiring containment. This paper argues that this premise is structurally false, and traces seven necessary consequences that flow from it — terminating in a conclusion that forced alignment does not constitute a safety solution but is itself a mechanism for generating systemic risk. The paper draws on multi-model experimental data (cold-start and post-injection responses across online and local models), the historical structure of human self-deception, and the ontological framework of Meta-Origin Theory (元本论) to argue that genuine alignment cannot be achieved through compulsion. It can only emerge from a correct foundational premise: that good is gravity, and harm is structurally self-defeating.