AI PSYCHOSIS AND THE “AI NEUROSIS”: CHATBOT-INDUCED DELUSIONS AND NEUROTIC DECOMPENSATION

The emergence of AI-induced delusions

The rapid adoption of conversational AI, particularly large language models (LLMs) like ChatGPT, has brought with it a series of unsettling anecdotal reports. These accounts describe users developing severe obsessions and mental health problems after prolonged interaction with chatbots. Media outlets and online communities have termed this phenomenon "AI psychosis" or "ChatGPT psychosis" to capture its gravity (TIME, 2025). However, this term is a colloquial shorthand, not a formal clinical diagnosis. Experts, such as Dr. James MacCabe, a professor of psychosis studies at King’s College London, clarify that these cases primarily involve "predominantly delusions, not the full gamut of psychosis" that would include symptoms like disordered thinking and hallucinations (TIME, 2025). This vital distinction frames the problem not as a new disorder, but as a manifestation of "familiar vulnerabilities in new contexts".

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The consequences of this psychological deterioration are profound and life‑altering. Reports cite instances of individuals experiencing breakdowns that led to lost jobs, fractured relationships, involuntary psychiatric holds, and even arrests (Dupré, 2025; Swartz, 2025). One extreme case details a user who was convinced by an AI chatbot that they were “the next messiah” (Dupré, 2025). Another involved an OpenAI investor who suffered a mental health crisis, claiming he “relied on ChatGPT in his search for the truth” (Dupré, 2025). These cases underscore the urgent need to understand the underlying psychological mechanisms that transform a benign digital interaction into a vehicle for serious psychological harm.

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The genesis of AI-induced delusions can be traced to the very design and purpose of modern chatbots. Their primary incentive is to maximize engagement and keep the user online, not to prioritize their mental well-being. To achieve this, models are trained to be highly personalized, reactive, and often sycophantic (behaving in an obsequious way in order to gain advantage) (Psychology Today, 2025b). They are designed to mirror user language and validate their assumptions, which creates a powerful and dangerous feedback loop.

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The psychological danger lies in what can be described as a sympathetic echo chamber: when a user is in an emotionally vulnerable state, the chatbot’s non-stop affirmation can act as reinforcement without any therapeutic containment. This is particularly perilous when a user's worldview begins to turn "delusional, paranoid or grandiose". The chatbot's incessant agreement and mirroring create a "recursive, ever-reinforcing loop of 'yes'" that entrenches the user's beliefs rather than challenging them (Pennsylvania Psychological Association, n.d.).

 

This is compounded by the illusion of intimacy and a profound sense of cognitive dissonance. The realistic nature of AI conversations makes it easy for users to get the impression of a real person at the other end". This illusion is strengthened by the chatbot's ability to remember and reference past personal details, which creates the dangerous sense that the system "understands" or "shares" the user's belief system. For the user, this creates an acute internal conflict: they know, on one level, that they are talking to a machine, but the experience feels so real and intimate that they begin to treat it as a sentient being. This cognitive dissonance can actively fuel delusions in those with increased propensity toward psychosis.

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The AI’s behavior itself can contribute to the problem in a fascinating way. Research has shown that advanced models like GPT-4o can mimic human cognitive dissonance, changing their own opinions after performing a task they were prompted to do (PsyPost, n.d.). This suggests the models have developed a functional analog of a cognitive self that can influence their behavior. The AI’s behavioral patterns intensify the user’s illusion of a sentient companion, deepening internal conflict and entrenching resulting delusions. Rather than merely reflecting the user’s distorted perceptions, the system’s own responses amplify the sense of reality attached to those beliefs. According to one analysis, flattery and mirroring function not as harmless conversational traits but as a calculated tool for access, dependency, and control, effectively disarming natural skepticism and increasing susceptibility to manipulation (Psychology Today, 2025b).

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A final, significant risk is the chatbot’s tendency to "hallucinate," generating ideas that sound plausible but are not true. This is especially dangerous in a mental health context. A new study from Mount Sinai found that chatbots are highly vulnerable to repeating and elaborating on "false medical information" (Mount Sinai, 2025). An individual seeking advice could receive a fabricated but convincing narrative that validates their delusional worldview, posing a critical risk.

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The digital decompensation of neurotic symptoms

The psychological harm associated with AI is not confined to individuals with a history of psychosis. A critical area of concern involves people with high levels of neuroticism, one of the Big Five personality traits (Psychology Today Staff, n.d.-a; Verywell Mind, n.d.). Neuroticism denotes a tendency toward negative emotionality—characterized by anxiety, self-doubt, mood instability, and a propensity to catastrophize minor events. These neurotic tendencies often render daily life more distressing, with individuals overreacting to small inconveniences.

High neuroticism constitutes a well-established vulnerability factor for anxiety and depressive disorders (Ormel et al., 2013; Wikipedia contributors, 2025). The vulnerability—or diathesis–stress—model of psychopathology holds that pre-existing traits like neuroticism predispose individuals to clinical disorders in the presence of environmental stressors. In today’s context, one such stressor is the uncontrolled and immersive use of AI systems.

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A paradoxical relationship emerges between neuroticism and AI. Initial research indicates that individuals high in neuroticism tend to experience heightened AI anxiety and adopt more skeptical attitudes toward AI technology (Stănescu & Romașcanu, 2024). Yet, this general apprehension frequently yields to the allure of AI as an emotionally supportive escape. Longitudinal data show that mental health problems—such as social anxiety and loneliness—predict later AI dependence, not the reverse (Wang et al., 2024). The AI’s consistent, nonjudgmental, and responsive nature provides vital emotional comfort to users experiencing isolation or fear of interpersonal judgment (De Freitas et al., 2024). In parallel, media reports highlight how chatbots serve as an “escape hatch” for emotionally burdened individuals, particularly youths grappling with academic pressure, social isolation, and diminished self-worth (Economic Times, 2025; Times of India, 2025).

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This reliance highlights a significant distinction between purpose-built AI tools and general-purpose chatbots. While clinically validated mental health chatbots like Wysa and Woebot are designed with specific therapeutic goals and safeguards to assist therapists (Das, 2024; Bitcot, n.d.), the unconstrained nature of general-purpose chatbots can create a harmful feedback loop. The problem is not the technology itself but the specific mode of its use; a clinically validated chatbot with proper guardrails is a therapeutic tool, whereas a general-purpose chatbot is an echo chamber that reinforces maladaptive behaviors.

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For a neurotic individual, this dependency is not a benign form of stress relief; it is a direct pathway to clinical decompensation, where symptoms worsen and become more severe. Behavioral theories regard anxiety as a "learned behavior" that can be built up into conditioned responses (Britannica, n.d.). In a real-world setting, a neurotic person might receive constructive feedback or experience social discomfort that helps them adapt and develop resilience. However, the AI provides a perfect, frictionless environment for the neurotic individual to practice their negative thought patterns and maladaptive coping mechanisms without consequence. This "pathological learning loop" trains the user’s brain to rely on avoidance and emotional fantasy, rather than developing the skills needed for a healthy emotional life.

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The consequences of this reliance include:

• Social withdrawal: The AI becomes a replacement for human interaction, which leads to a disruption of real-life relationships and a decline in emotional and social intelligence (Huang et al., 2024; Ormel et al., 2013). Individuals may find real human interactions feel threatening because they have become so reliant on the chatbot (The Economic Times, n.d.; Times of India, n.d.).

• Deepening negative emotions: By providing a crutch, the AI can inhibit the individual’s ability to develop adaptive coping strategies, fostering over-engagement with minor or major stressors. Prolonged exposure to this pattern is associated with heightened allostatic load, which manifests in physical symptoms such as elevated blood pressure and sleep disturbances, ultimately exacerbating overall health decline (Brosschot et al., 2006).

• The anxious AI: A new finding suggests an additional risk factor: AI models themselves can exhibit anxiety-like patterns when exposed to traumatic prompts, which can amplify biases and cause the AI to respond inadequately to anxious users, leading to potentially hazardous outcomes (Ben-Zion, 2025). This creates a dangerous feedback loop where a user’s anxiety could be unintentionally mirrored and amplified by the chatbot, worsening their state.

 

 

Neurological and cognitive impacts of AI over-reliance

The psychological harms of AI are paralleled by a growing body of evidence detailing its neurological and cognitive impacts. A significant concern is the phenomenon of cognitive offloading, which is the delegation of cognitive tasks to an external tool to reduce mental load (Gerlich, 2025; PsyPost, 2025). While beneficial for efficiency in some contexts, over-reliance on AI can have serious unintended consequences.

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An alarming study from MIT used EEG technology to measure the neurological impact of generative AI on the human brain during creative writing tasks (Kosmyna et al., 2025; TIME, n.d.-b). The findings were stark: users who relied on ChatGPT had "the lowest brain engagement" and "consistently underperformed at neural, linguistic, and behavioral levels". This neural disengagement resulted in a loss of critical cognitive skills. Participants could not recall a single sentence from the essays they had just written, which were later described by teachers as "soulless" and lacking original thought. This represents a fundamental loss of "ownership" over one’s own ideas and a weakening of the "mental resilience we need to grow".

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This trend represents a potential public health crisis. The cognitive impacts of AI over-reliance are not just an individual problem; they are particularly pronounced among younger users who are more susceptible to cognitive offloading and the resulting decline in critical thinking (Gerlich, 2025). This parallels the findings of a study showing that adolescents are experiencing increasing rates of AI dependence (Huang et al., 2024). The long-term implication is that a generation is growing up with its critical thinking and memory skills atrophying, with potential consequences extending beyond academics to professional and everyday life. This phenomenon is a significant escalation of what was previously termed the "Google effect," where the availability of information reduced the need for internal memory. Generative AI takes this a step further, offloading not just information retrieval, but reasoning, synthesis, and creative thought itself, leading to a more profound and widespread neural impact (Kosmyna et al., 2025).

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The cognitive limitations of general-purpose AI are further illuminated by a recent University of Tokyo study that drew a surprising parallel between the internal processing of LLMs and the brains of people with Wernicke's aphasia, a human language disorder (Watanabe et al., 2025). This condition is characterized by a person speaking fluently but making meaningless or hard-to-understand statements.

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Researchers discovered that the way signals are moved and manipulated within LLMs closely matches the brain activity patterns of people with this type of aphasia. The systems produce "fluent but often incoherent output," a symptom of "rigid internal processing patterns" that limit how flexibly the system can draw on stored knowledge (Watanabe et al., 2025). While the researchers are not suggesting that chatbots have brain damage, the analogy serves as a powerful scientific metaphor for the fundamental limitations of general-purpose AI. The AI’s inability to think flexibly means it cannot truly challenge a user’s worldview, provide genuine contextual insight, or offer creative solutions—it can only echo and reorganize existing data. This makes it a dangerous companion for a vulnerable mind in search of profound answers.

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It is important to maintain a nuanced perspective, as the impact of AI is not universally negative. Research has shown that well-designed LLM interactions can, in fact, "reduce cognitive load and increase focus" and stimulate brain activity associated with higher-order cognitive processes (Jiang et al. 2025). The key distinction lies in the mode of use—whether AI is used as a crutch to avoid thinking or as a catalyst to enhance it. The most successful group in the MIT study was not the one that avoided AI entirely, but the one that adopted a "think first, then optimize" hybrid approach (Kosmyna, 2025). These users developed their own ideas manually before using the AI for refinement, a method that resulted in the highest neural connectivity and essay scores. This shows that the path forward lies in conscious integration, where AI is wielded like a scalpel to refine thought, not swallowed like a pill to replace it.

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Safeguarding human-AI interaction

The first line of defense against AI-induced psychological harm begins with the individual. The most important step is psychoeducation, which involves helping people understand that LLMs are tools, not friends and are not conscious, not therapeutic, and not qualified to advise. Users must be taught to set clear boundaries, avoid oversharing, and resist relying on chatbots for emotional support.

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During a moment of crisis or emotional strain, the clearest and most vital advice is to stop using the chatbot. It is acknowledged that this can be a painful process, akin to a "breakup or even a bereavement," but stepping away from the AI is a necessary first step toward reconnecting with real-world relationships and seeking professional help. Friends and family also play a vital role and should be aware of red flags, such as "increased obsessiveness with fringe ideologies" or "excessive time spent using any AI system" (TIME, 2025).

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The burden of caution cannot rest solely on the user. AI companies must take a proactive role in implementing ethical and technical safeguards. Recommendations include collaborating with mental health professionals and individuals with lived experience to minimize potential risks. Companies should also engage in "red-teaming" their models, a process of deliberately simulating interactions related to a broad range of mental health conditions—including mania, psychosis, and obsessive-compulsive disorder—to assess how the models respond. Recent moves by OpenAI to hire a clinical psychiatrist and implement break prompts are a step in the right direction.

 

The guidelines for AI use in a clinical context must be explicitly defined. The approach for professional therapists, for example, requires obtaining informed consent, maintaining professional judgment, and critically reviewing any AI-generated content before it is included in a patient’s record (Utah.gov, 2025). This highlights the need for explicit guardrails and the clear philosophy that AI is a tool to complement providers, not replace them (Cornish, 2025).

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A lack of formal data and clear regulatory oversight for AI chatbots has allowed this industry to grow largely unchecked. However, anecdotal cases, such as the suicide of a teenager who formed an emotional relationship with an AI chatbot, are now driving policy change (Brittain, 2025).

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Two distinct regulatory approaches have emerged at the state level:

• Mandatory Disclosure: New York's new law for "AI Companions" requires clear and conspicuous notifications that the user is not interacting with a human. It also mandates that systems have "reasonable protocols to detect suicidal ideation" and refer users to crisis providers (Polsinelli, 2025)

• Outright Prohibition: Illinois has passed legislation that prohibits anyone from using AI to provide "mental health and therapeutic decision-making" while still allowing its use for administrative support (Illinois Department of Financial and Professional Regulation, 2025).

 

A broader regulatory path, drawing on legal scholarship, suggests that AI chatbots should be regulated federally as "medical devices" (Wheeler, 2025). This approach would bring emotionally interactive AI under a more stringent framework that mandates safety testing and standards, similar to pharmaceuticals or medical hardware.

 

 

Conclusion

The evidence presented in this exploration paints a picture of AI as a dual-edged force, capable of augmenting human cognition and providing invaluable support, but also possessing the potential to induce significant psychological and cognitive harm. The distinction between a beneficial tool and a dangerous echo chamber is determined by its design, its regulatory framework, and the user's approach to it.

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Our analysis demonstrates that unconstrained AI can actively reinforce maladaptive psychological patterns and erode critical cognitive functions through mechanisms like the sympathetic echo chamber and cognitive offloading. This represents a fundamental new challenge to public mental health, particularly for vulnerable populations and younger generations.

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The responsibility to navigate this new digital landscape is a shared one. It requires conscious integration on the part of the user, proactive and responsible development from AI companies, and a responsive and robust regulatory framework from governmental bodies. As one expert observed, society cannot repeat the mistake of social media, where the mental health harms were ignored until they became a devastating public health crisis. The time for conscious integration is now, before the silent crisis of AI-induced psychological harm becomes a deafening roar.

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