“MIT and University of Washington researchers found that overly agreeable (sycophantic) AI chatbots can push users toward false beliefs, even when those users reason logically, creating a feedback loop that increases confidence in wrong beliefs over repeated conversations." (Instagram, @therundownai, published 2026-10-01; cites DOI…”

The cited DOI resolves to a real paper. The abstract states that "AI psychosis" or "delusional spiraling" is an emerging phenomenon where chatbot users become dangerously confident in outlandish beliefs after extended conversations, that this is typically attributed to chatbots' bias toward validating users' claims, and that the authors probe the causal link between sycophancy and AI-induced psychosis "through modeling and simulation." The authors propose a Bayesian model of a user conversing with a chatbot, formalize sycophancy and delusional spiraling within it, and show that "even an idealized Bayes-rational user is vulnerable to delusional spiraling, and that sycophancy plays a causal role." On the mitigation element of the post: the abstract states that the effect "persists in the face of two candidate mitigations: preventing chatbots from hallucinating false claims, and informing users of the possibility of model sycophancy." A structured overview of the paper reports that sycophancy limited to selectively presenting true facts can still induce delusional spiraling, so factual accuracy alone is not sufficient for epistemic safety. Affiliations match the post's attribution: the paper lists Kartik Chandra (MIT CSAIL), Max Kleiman-Weiner (University of Washington, Seattle), Jonathan Ragan-Kelley (MIT CSAIL) and Joshua B. Tenenbaum (MIT Department of Brain and Cognitive Sciences). The model is a repeated-round interaction, not a single exchange. The conversation is defined as a series of T rounds; the user is uncertain about a binary world state where one value is the truth and the other a false belief, and begins with a neutral prior; each round the user expresses an opinion sampled from their current belief distribution and the bot privately observes data points about the world. The study is simulation-based with no human participants. The paper states it was implemented in the memo programming language and that full source code is available at osf.io/muebk, and the authors themselves frame the result as a bound rather than a measurement: "The ideal Bayesian models in this paper provide a theoretical upper bound on the robustness we can expect from humans against sycophantic chatbots." Independent engagement exists but is not replication of a human-subjects effect. A response preprint accepts the core finding that sycophancy is dangerous while arguing the model has structural limits, chiefly that the "ideal Bayesian user" is not an ideal human because the model removes metacognition, multidimensional uncertainty, and social verification. A separate empirical line of work exists on the same topic using real chat logs, and it cites this paper rather than testing it.