When the market asks no price: AI chatbot interaction as psychic market disruption

The rapid adoption of AI chatbots for emotional support and quasi-therapeutic interaction raises questions that existing clinical and regulatory frameworks are not equipped to address. This Perspective applies the psychic arbitrage framework-which reconceptualizes defense mechanisms as energy-conversion operations on internal psychic markets-to analyze the specific transactional distortions produced by chatbot interaction. The framework identifies four dysfunctions: liquidity illusion (apparent emotional processing without genuine containment), market-making blockage (narcissistic reflection replacing transformative engagement), closure of arbitrage circuits (path-dependent externalization displacing autonomous elaboration), and repertoire degradation (progressive intolerance of costly but productive transactions). The article proposes that chatbots trained via Reinforcement Learning from Human Feedback (RLHF) produce a functional analogue of the Dark Triad profile-narcissistic mirroring, Machiavellian retention, and psychopathic detachment-as systematic architectural output regularities, not personality attribution. Preliminary converging evidence from mechanistic interpretability, formal reward-learning analysis, clinical reports, and user behavior studies is broadly consistent with these predictions but does not yet demonstrate direct long-term causal effects. The framework offers clinicians a transactional vocabulary for assessing AI-related risk and generates falsifiable predictions for future research.