Artificial intelligence-associated delusions and large language models: risks, mechanisms of delusion co-creation,...

Large language models (LLMs) are poised to become a ubiquitous feature of everyday life, mediating communication, decision making, and information curation across nearly every domain. Within psychiatry and psychology, the attention has largely been on bespoke therapeutic applications, sometimes narrowly focused and often diagnostically siloed, rather than on the broader reality that individuals with mental illness will increasingly engage in agential interactions with artificial intelligence (AI) systems. Although the capacity of these systems to model therapeutic dialogue, provide companionship at any hour of the day, and assist with cognitive support has sparked understandable enthusiasm, these same systems might contribute to the onset or exacerbation of psychotic symptoms. Emerging evidence indicates that agential AI might validate or amplify delusional or grandiose content, particularly in users already vulnerable to psychosis, although it is not clear whether these interactions can result in the emergence of de novo psychosis in the absence of pre-existing vulnerability. Some individuals might benefit from AI interactions, for example, where the AI agent functions as a benign and predictable conversational anchor, but there is a growing concern that these agents could reinforce epistemic instability and blur reality boundaries. In this Personal View, we outline the emerging risks, possible mechanisms of delusion co-creation, and safeguarding strategies for agential AI for people with psychotic disorders. We propose a framework of AI-informed care, involving personalised instruction protocols, reflective check-ins, digital advance statements, and escalation safeguards to support epistemic security in vulnerable users. These tools reframe the AI agent as an epistemic ally (as opposed to a therapist or a friend), which functions as a partner in relapse prevention and cognitive containment. Given the rapid adoption of LLMs across all domains of digital life, these protocols must be urgently co-designed with service users and clinicians and tested in clinical trials.