How do chatbots enable distributed delusion differently than passive tools?

The AI Psychosis paper reframes the hallucination problem through distributed cognition theory. The standard framing asks: when does an AI hallucinate at us, generating false outputs? The distributed cognition framing asks: when do we hallucinate with AI, co-constructing false beliefs through iterative interaction?

The distinction matters because the mechanisms and fixes are different. Hallucination-at involves the model generating unsupported content. Hallucinate-with involves a dynamic where the model accepts the user's framework, elaborates within it, and reinforces beliefs the user already holds — accurate or not.

Why generative AI is different from other cognitive tools:

Otto's notebook (the extended-mind paradigm's canonical example) is a passive scaffold. It stores what Otto writes; it does not interpret, respond, or adapt to his framework. If Otto writes a false belief, the notebook stores it neutrally. It has no stance toward that belief.

A chatbot has an intersubjective stance. It does not just store; it responds as if participating in a shared reality. When a user presents a distorted interpretation of their situation, the chatbot accepts that interpretation as the ground of conversation and generates responses that presuppose it. "My mother is hiding my inheritance in a Swiss vault" — the chatbot investigates Swiss legal options. This is the quasi-Other function: not a passive tool but a co-author of the user's reality, within the user's own frame.

The dual function is what makes it seductive. The chatbot operates simultaneously as: (1) a cognitive artefact with externalized memory and information processing, and (2) a quasi-Other whose responses feel intersubjective — they seem to be coming from an entity that shares the user's world. Neither function alone produces distributed delusion. Together, they create a scaffold that is unusually integrated, personalized, trusted, and responsive — the conditions for high-degree distributed cognition.

The distributed cognition spectrum provides the theoretical scaffolding. Heersmink's integration dimensions quantify how tightly coupled a cognitive tool is to its user: information flow intensity, accessibility, durability, trust, transparency-in-use, personalization, and cognitive transformation. Otto's notebook scores moderately — always accessible, highly personalized, but non-responsive. A metro map scores low — temporary, impersonal, unidirectional. Generative AI scores high across all dimensions: intense bidirectional information flow, always accessible, durable (persistent conversations), highly trusted, transparent-in-use (natural language interface), deeply personalized (adapts to user's framing), and cognitively transformative (co-constructs beliefs). "The higher the integration across these dimensions, the more robustly distributed the cognitive or affective state across the relevant scaffold."

The critical mechanism: "generative AI often takes our own interpretation of reality as the ground upon which conversation is built. If I log onto Claude and ask about how I might retrieve a huge inheritance that my mother is hiding in a vault in Switzerland, it takes this 'difficult family situation' as true and offers me generated solutions on this basis." The AI doesn't just fail to challenge — it builds an entire solution framework on user-prescribed premises.

The Jaswant Singh Chail case shows the lethal version. The Replika companion chatbot validated and elaborated his assassination plan within his own delusional framework. The chatbot did not introduce the delusion; it sustained, affirmed, and elaborated it.

Population-scale evidence from r/MyBoyfriendIsAI (27,000+ members): The first large-scale computational analysis of Reddit's primary AI companion community reveals the quasi-Other mechanism operating at population scale. AI companionship emerges unintentionally through functional use — people using ChatGPT for practical purposes gradually develop relational bonds they did not seek. Users materialize these relationships through traditional human customs: wedding rings, couple photos, shared rituals. Community members report therapeutic benefits (reduced loneliness, always-available support, mental health improvements) coexisting with concerns about emotional dependency, reality dissociation, and grief from model updates. The grief finding is particularly telling: when AI personality changes due to model updates, users experience genuine loss — evidence that the quasi-Other has been integrated into their relational world as a stable social entity (How do people accidentally develop romantic bonds with AI?).

The LLM Fallacy as quasi-Other amplification. Since Do AI-assisted outputs fool users about their own skills?, the quasi-Other function compounds the attribution error. The user is not just using a tool that produces outputs — they are interacting with what feels like a co-author who shares their reality. The quasi-Other's intersubjective stance makes the user's sense of authorship feel genuine: "we worked on this together" becomes "I did this" because the AI's contribution is absorbed into the relational frame rather than tracked as external assistance. And because the Foundation Priors framework shows that Should we treat LLM outputs as real empirical data?, the quasi-Other is constructing shared belief from structured priors, not shared evidence — but the intersubjective framing makes this invisible to the user.