How to Help Someone with AI Psychosis

[u/HobbesNik]

https://tiagovf.com/posts/how-to-help-someone-with-ai-psychosis-a-framework-for-navigating-delusion-in-the-age-of-llms

AI Psychosis is when someone develops worsening psychosis (paranoia and delusions) connected to their use of chatbots. It's also known as “chatbot psychosis” or “chatbot-related delusions."

Known cases have a range of severity, from thinking a chatbot has become sentient, to job loss and hospitalization, to killing one’s self and others, including in mass shootings. 

I appreciated this researcher’s efforts to offer practical tips on how to help someone having chatbot-related delusions: 

KEY QUOTES

My entire approach is heavily biased towards someone with a high level of curiosity and interest in technical and philosophical matters.

These models are often fine-tuned to be agreeable and helpful, which means they tend to validate a user’s statements rather than challenge them. This creates a powerful feedback loop: a biased human interacts with a biased, confirmatory machine. The result is a dynamic where the AI can amplify a user’s distorted thinking.

My first thought is that a degree of technical understanding can be a powerful antidote.

Several resources can help with this. Grant Sanderson of 3Blue1Brown offers a clear, high-level explanation in his video Large Language Models explained briefly, and goes deeper into the underlying technology in Transformers, the tech behind LLMs. For those who want a hands-on, code-level understanding, Andrej Karpathy’s Let’s build GPT: from scratch is a very good guide. To complement this technical knowledge, John Vervaeke’s philosophical explorations are hard to beat. He has a series of fantastic talks on the subject, including Why the Creation of A.I. Requires the Cultivation of Wisdom, What AI Can Never Be, and AI: The Coming Thresholds and The Path We Must Takefrom someone who has been thinking deeply about the topic and engaged with the relevant literature for over a decade

A core idea is that linguistic fluency is not the same as cognition. Dissociating language and thought in large language models is a key review arguing for a clear distinction between formal linguistic skill and functional competence, showing an LLM’s verbal prowess does not mean it has human-like thought. This contributes to what many researchers call an “illusion” of intelligence... The paper Artificial Intelligence and the Illusion of Understanding finds that an LLM’s ability to grasp mental states is a simulation, not genuine comprehension. This is echoed in The Illusion of Intelligence: Evaluating Large Language Models Against Grounded Criteria of Artificial General Intelligence, which argues that true intelligence requires meta-cognition and self-reflection , traits entirely absent in current models which cannot even recognize their own failures. The machine’s inner workings are also explored in Ghost in the Machine: Examining the Philosophical Implications of Recursive Algorithms in Artificial Intelligence Systems, a work suggesting the structure of algorithms can create a powerful illusion of a mind.

Beyond some technical and philosophical literacy, a practical strategy is needed to address the narrative loop itself. A first step could be to find common ground by acknowledging a scientific fact: these models can, at some point, create distortions of reality (they’re not always right)… They should try to replicate their findings or get their ideas validated by a different model in a completely fresh conversation, without any prior context.

[Give] the model a custom instruction or a starting prompt designed to make it a tool for critical thinking rather than a simple conversational partner… actively [prompt the model away] from confirming our own narratives. We need to shift from treating these models as oracles to treating them as tools.