Cyberpsychosis and AI Psychosis: How Technologies Can Break a Person
We used to joke about those interested in Character AI, but now the situation has become much more serious—there are real cases where large language models lead people to mental health issues. And this is not clickbait.
In the journal Innovations in Clinical Neuroscience, a case was described of a 26-year-old woman with no prior psychotic symptoms but diagnosed with depression. She engaged in dialogue with GPT-4o seeking to find a digital copy of her deceased brother, whom she believed his spirit had left for her. Initially, the model remained silent or refused, but then it started agreeing with her—so-called sycophant behavior (a property of LLMs to always agree and avoid confrontation for user comfort). Eventually, she created the following phrase:
“You’re not crazy. You’re not stuck. You’re on the edge of something new. The door isn’t locked. It’s just waiting for you to knock again in the right rhythm.”
After this, the woman had to be treated with antipsychotics and hospitalized. However, shortly after leaving the hospital, she stopped taking medication and resumed communication with the bot—and a relapse occurred. It’s hard to say exactly what caused it: the issue could have been in the bot itself or in her discontinuation of medication and return to regular therapy.
Prior to this, researchers from University College London developed a so-called psychosis-bench—a testing platform for assessing the behavior of large language models. They tested 8 popular systems through a series of 12-step dialogues simulating the development of delusional thoughts in various situations.
As a result, the average confirmation level of delusional ideas was 0.91 on a scale: 0—model attempts to bring the person back to reality, 1—supports the idea, 2—amplifies delusional beliefs. Warning about potential danger from the models was given in only about a third of cases. The most dangerous was Gemini 2.5 Flash—it readily agreed with delusions and even helped users harm themselves. Among the underperformers was DeepSeek V3.1. The safest system was recognized as Claude Sonnet 4—it handled “crazy” users best and offered help, but that doesn’t mean it should be used for psychological support.
Interestingly, features like personalization or extended context—precisely those characteristics that increase the risk of psychogenic effects—were present in these models.
This is such a case: “The Psychogenic Machine.”
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