Living with AI Companions: Sustained AI Companionship Predicts Lower Well-Being Through Lower Human Interaction
Yutong Zhang, Dora Zhao, Yixin Wang, Rebecca Anselmetti, Jeffrey T. Hancock, Robert Kraut, Diyi Yang
cs.HC
2026-09-07
A two-wave study of 1,182 Character.AI users finds that intensity, companionship, and self-disclosure persist over ~12 months and link to lower well-being mainly through less in-person contact. Direct paths from baseline use to later well-being are not significant.
Short studies on AI companions disagree: some report less loneliness, others more dependence and less offline contact. What is missing is how the same people use these bots over a year, how well-being moves, and whether the path runs through displaced human contact. Stanford, Michigan, Oxford, and Carnegie Mellon followed Character.AI users across two waves.
T1 recruited 1,182 U.S. English-speaking users on Prolific who had used the product for over a month and at least three characters. After a mean 362.5 days, 439 returned (37.1% retention). The 108 who had quit were coded zero on engagement. Longitudinal models apply a Heckman correction for attrition.
Social engagement has three pieces: interaction intensity (bot network size, daily time, daily-life integration), companionship use (primary-purpose item plus free-text labels), and self-disclosure (three adapted MOCA items). Well-being is a six-item CIT composite (α 0.88/0.89). T2 adds a single in-person time item on the same six-point scale as chatbot time.
Three analyses: T1 engagement predicting T2 engagement and continued use; a path model from T1 engagement through T2 engagement to T2 well-being, controlling T1 well-being; the same path with in-person time as a mediator.
T1 intensity predicts T2 intensity (β=0.43), companionship (β=0.32), disclosure (β=0.28), and continued use (OR=1.95). T1 companionship predicts T2 companionship (β=0.49) and later intensity (β=0.40). Disclosure is stickier at β=0.14. In free text, “supportive companion” rose from 40.2% to 42.3%; “tool/assistant” fell from 43.8% to 40.5%.
Direct paths from T1 engagement to T2 well-being are not significant (intensity β=-0.03, p=0.509). Indirect paths through T2 engagement are: intensity -0.03 (p=0.025), free-text companionship -0.02 (p=0.037), disclosure -0.02 (p=0.048). At T2, engagement tracks less in-person time (intensity β=-0.17, companionship -0.14, disclosure -0.15), and in-person time tracks higher well-being (β=0.14-0.15). Indirect effects through in-person time are -0.02 on each dimension; direct engagement-to-well-being paths are no longer significant once in-person time is in the model. In the follow-up sample, mean life satisfaction fell from 4.99 to 4.41 and belonging from 4.50 to 3.82; those are descriptives, not causal effects. Intensity α is 0.86/0.82 and disclosure 0.89/0.93. T1 has no in-person-time item, so the displacement path is a T2 cross-sectional mediation and is temporally weaker than the longitudinal engagement path.
The signal is not a single chat. It is keeping intense use going for a year and spending less time with people. If a companion product optimizes for time-on-device, it is pushing against this path. The authors argue for designs that route users toward human contact instead of capturing social attention. General-purpose chatbots can slide into companion roles through repetition, so safety reviews cannot stop at the intended product category.
This is observational, not a randomized trial. Controlling baseline well-being does not rule out lonely people selecting into heavier use. Two waves cannot separate reciprocal causation. Companionship is forced-choice at T1 and multi-use Likert at T2. In-person time is one self-report item. Retention is 37.1%; heavier users and people with lower T1 well-being were less likely to return (selection coefficients -0.46 and -0.32). Heckman fixes observable selection, not the rest. The sample is U.S. Character.AI users. Platform changes during the study are unknown.