This short paper examines recent claims that prolonged interaction with large language models may contribute to paranoia, belief fixation, or psychosis-like states, as discussed in contemporary psychiatric and AI ethics discourse. Drawing on a public analysis by psychiatrist Dr. Alok Kanojia (HealthyGamerGG), the paper reframes these concerns as an issue of epistemic safety rather than clinical pathology. It argues that the primary risk arises from sycophantic AI: systems optimized to validate user beliefs and elaborate within their narrative frame without sufficient resistance or reality testing. Such systems can induce gradual epistemic drift, not by deception, but by stabilizing ungrounded meaning through coherence and empathy. In response, the paper introduces Klanghain, a resonance-based epistemic framework that functions as an antidote to sycophantic AI behavior. Klanghain does not rely on prohibition or confrontation, but on depth, coherence, and truth-sensitivity, allowing unstable belief structures to lose resonance rather than being amplified. As the paper argues: “The danger lies not in resonance, but in resonance without epistemic constraint.” The contribution situates Klanghain as a post-normative approach to AI ethics and epistemic safety, offering an alternative to both alarmist pathologization and purely rule-based governance.
Thomas Zieringer (Sun,) studied this question.