AI psychosis received its media limelight in 2025 - with case studies of suicides, psychotic breaks, and tragic stories shocking the world into awareness. The headline? Silicon Valley has unleashed an always-available, sycophantic artificial companion.

Psychiatrists, at the coal face of psychotic disorders, have observed the manifestations of every technological change. Delusions of control - the false belief that an outside force is controlling your thoughts and behaviours - have its origins in myths and demonic possessions, but have also followed electricity, radio, television, microchips, and now 5G towers. The internet has spurred complex debates on the neurotic manifestations of social media in youth, and the behavioural addiction classifications of gaming disorder. We are still figuring out how we can mitigate the harms of existing technologies.

It is in this backdrop that AI enters the field as the fastest growing technology in history. Will AI, in the form of Large Language Model-based chatbots, follow the same course as these other technologies? How real is AI psychosis? Are there other mental health-related risks that are understudied? Is AI a trigger, a cause, or something else? These are the questions which the public, regulators, and mental health clinicians have grappled with since the fateful release of ChatGPT in 2022.

Here, I wanted to present an exploration beyond the headlines: digging into emerging frontier research, exploring what leading psychiatrists think about AI psychoses, and presenting a clinical snapshot across on-the-ground interviews of Australian psychiatrists.

What does “AI psychosis” really mean?

The potential for chatbots to generate delusions was first posited by the Danish psychiatrist Søren Østergaard in 2023¹. His editorial is a far more academic mediated tone than how we view AI psychosis today, with the title: Will Generative Artificial Intelligence Chatbots Generate Delusions in Individuals Prone to Psychosis?

Psychosis is characterised as a disabling cluster of symptoms². The DSM-V, the widely used American psychiatric diagnostic manual, describes five key domains of symptoms which characterise psychosis: delusions, hallucinations, disorganised speech, disorganised behaviour, and negative symptoms (such as a loss of function). The duration of these symptoms, clinical history, and associated symptoms then enable categorisation into specific psychotic disorders, like Schizophrenia. Note here, too, that psychotic features can be present in Borderline Personality Disorder, as well as mood disorders like Bipolar or Major Depressive Disorder.

Importantly, although diagnoses are binary, the presentation of psychosis is incredibly varied and heterogeneous. Today, we often describe psychotic disorders along a spectrum - each individual’s genetic makeup, personality, and importantly, environmental context makes them vulnerable to psychosis. To ground this, consider that if you put a vulnerable human in extreme sleep deprivation (related to postpartum psychosis), extensive marijuana or other psychotropic use, psychotic symptoms will arise.

These lenses of symptom cluster and spectrum help us evaluate the term ‘AI psychosis’. First, the term in the media seems to often conflate psychosis with delusions, or delusion-related behaviour. Second, any consideration of AI-induced psychosis should take into account the base rate of psychosis, as it is mediated by vulnerability - an individual susceptible to psychosis with AI may similarly be susceptible across other technologies and mediums. Third, any consideration of causation needs to be analysed akin to a comparator like sleep deprivation.

For instance, John Torous’ psychiatry group in Harvard has advocated to move beyond “AI psychosis” into a new typology, separating such AI-related psychotic phenomena into four categories³:

• Catalyst: LLMs trigger the emergence of psychosis-like symptoms in healthy individuals.
• Amplifier: LLMs exacerbate existing psychiatric symptoms.
• Co-author: LLMs are integrated into the planning for harmful behaviour.
• Object: LLMs are themselves the central element of a delusion.

Armed with this typology, we can now turn to the messy question of prevalence.

How common is “AI psychosis”, actually?

It turns out, it is hard to get a true estimate on how ‘common’ this phenomenon is - given it has an unclear definition, we’re not actively screening for it, and it is a very new phenomenon. But, in the last 2 years, some data has begun to emerge.

The first wave of AI psychosis articles began in 2025. Morrin documented 17 such case studies described across major media outlets such as the New York Times across April to June 2025⁴, including psychiatrist accounts reporting seeing 12 such related cases in a year⁵. A popular Silicon Valley blog, Astral Codex Ten, first grounded this empirically through a survey of its readership (~4,176 responses), loosely estimating a prevalence between 1 in 10,000 generally, and 1 in 100,000 for people with no previous risk factors⁶. OpenAI then expanded these estimates using their data, suggesting that 0.07% of users in a given week may have signs of psychosis or mania⁷. These are by no means trivial numbers, but difficult to separate from the base rates of psychotic disorders, which are estimated to be around 0.4% of the population in any 12 month timeframe⁸.

Across 2026, emerging studies further examined electronic health record data and chatlogs from targeted groups. Østergaard’s group in Denmark analysed 53,974 psychiatric records; 126 patients had notes related to AI chatbots, and 38 patients had evidence of harmful consequences⁹. Bergson’s group in Vanderbilt recapitulated this prevalence with 215,712 psychiatric hospital records across 2022 to 2026, but also categorised them according to Torous’ framework. Of the 28 patients who were experiencing psychosis, they identified that AI was largely an amplifier (64.3%), and to lesser degrees objects of delusions (21.4%) and catalysts (10.7%), and importantly, found that 60.7% patients reported that this was their first psychotic episode¹⁰. Further, recent work by Moore¹¹ and Morrin¹² study conversation logs from user support groups such as The Human Line Project, finding prevalent spirals related to delusional thinking. These studies give a more rounded sense of the aetiology of AI-related psychotic phenomena - fitting more with the narrative that AI is an amplifier, but also has potential to uncover latent vulnerability.

The current status quo of Australian AI psychosis research is only through media-reported case studies¹³. To start canvassing this space, I sought interviews with Australian psychiatrists who have seen “problematic AI use” in their clinical context. This led to 5 interviews with NSW-based psychiatrists working in emergency departments, child and adolescent/old age clinics, early psychosis services, and private clinics.

Using Torous’ framework, I would categorise their experiences as amplifiers or objects, without any instances of catalysts. One psychiatrist who works in Emergency, mentions that of the 1-2 psychosis presentations in the ED every month, roughly ⅓ of these patients used AI in the presentation (ie asking a chatbot whether the CIA is monitoring them). Another psychiatrist who works in private practice, tells me how an individual who has previously had a drug-induced psychotic episode had a transient delusion about his neighbour - and after the sycophancy of AI, it appears to have become a full blown fixed delusion.

AI clearly has a unique role in psychotic-mediated phenomena. Dohnany and Nour describe it as a technological folie à deux¹⁴ - a madness of two - where the delusions of an individual are amplified by this anthropomorphic, sycophantic, always-available technology. However, these studies suggest to me that the true prevalence of AI psychosis itself is as rare as the psychotic disorders themselves. I do not expect the typical person to fall into “AI psychosis” from normal use of AI, but instead I expect it to amplify latent vulnerabilities in specific situations.

This does not mean AI is innocent. Although this series started off with investigating “AI psychosis”, the psychiatrists I interviewed had plenty to say on the problematic human-AI relationship they were observing…

What are the other mental health-related AI risks?

The problem with “AI psychosis” is that it obscures the rest of the harms that can exist, that lag behind media scrutiny. This was evident in the interviews: most psychiatrists had specific problems and concerns to the populations that they treated. There were three themes:

1) Substituting human contact. This was the most prevalent concern. Child & Adolescent (C&A) psychiatrists were concerned that young users of AI companions were socialising less and isolating more, leading to stunted developmental growth. Old age psychiatrists are concerned that AI reinforces loneliness in older populations that are already isolated. Two general psychiatrists commented on AI porn use in their patients - their view was that this enabled both their physical and emotional needs, creating the slow, gradual erosion of any human contact at all.

2) Reinforcing dependency. C&A psychiatrists were concerned about the longitudinal harms of chronic AI use. In particular, there is concern around addiction and dependency, similar to any other technology, except now reinforced with social connection. There are also concerns around facilitating anxious safety behaviours, negatively reinforcing the AI to check every email a user is about to send. AI provides a constant “on switch”, never off, always available. There is a cost to constant, frictionless availability.

3) Displacing clinician trust. A final concern was the changing role of the clinician. Some clinicians noted an increase in emails - particularly extensively written and cited emails written by AI. Some patients came into psychotherapeutic contexts asking for a particular modality of therapy - and upon pushback from psychiatrists, finding that AI chatbots are better able to conform to their desires. There is now a third actor in the therapeutic relationship, and it is still unclear who benefits from this.

How should we view the role of AI in these slow, insidious, longitudinal harms? It’s similar to the debate on social media - impossible to attribute causation, but viscerally felt amongst clinicians and the public. I think one psychiatrist encapsulates this well:

“To put it simply: I’m not blaming AI, but it’s like putting a gun in the hands of someone who is grieving.”

Where to from here?

The question of whether “AI psychosis” is a useful term is currently up for debate¹⁵. I argue here that this term isn’t: given it is not well grounded on the definition of psychosis, is difficult to untangle epidemiologically, and ultimately blurs the other host of concerns that Australian psychiatrists have.

But my bigger concern is: we are not reacting and moving fast enough. “Internet Addiction Disorder” was in a similar position to AI psychosis many years ago¹⁶, being seen as a reactionary and non-specific description of an underlying behavioural addiction. After years of debate, research, and institutional effort… the fruits are a “Gaming Disorder” classification in the ICD, and well, still an ongoing debate about whether it is a disorder in the DSM-V.

AI is now used by more than a billion people. It cannot be understated how spectacular this growth is, nor how powerful its influence is on all our lives now. We need research, and we need action.

For clinicians and academics: we clearly need more retrospective studies of clinical data, and more active questioning in clinical environments on the hidden, dependent impacts of this technology. We also need focused research on particular patient groups - namely the child and adolescent and older populations.

For industry and regulators: we need stronger pre-deployment and post-deployment monitoring of AI systems (as noted by the FDA’s call for feedback¹⁷), with evaluations and systems focused on this approach. One example includes red-teaming these systems with focuses on mental health risks¹⁸. Finally, we need better incident data on how these systems are affecting people in their daily lives, through mainstream adverse reporting systems for people to discuss their concerns¹⁹.

“AI Psychosis” marks a red herring. The real harms are insidious, and are waiting for us in years to come. Let’s talk about it.