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Demand for Conversational AI: Purpose, Price, and the Social Cost of Wanting a Companion

Elliott Ash, Francesco Capozza and Sergio Galletta

No 12673, CESifo Working Paper Series from CESifo

Abstract: Relational uses of conversational AI register as barely demanded in stated-preference data, even as the market for AI companions grows quickly. We estimate demand across five purposes — administrative assistance, studying, wellness, friendship, and romance — in a preregistered within-respondent conjoint experiment (N = 1,990) that independently randomizes price, privacy, personalization, modality, and safety policy. Purpose dominates every design attribute: respondents require $7.81 per month to accept a romantic companion in place of an administrative assistant, an order of magnitude more than any feature effect. We show this penalty is not a pure preference parameter. Removing the monetary barrier does not close it, because the zero-price premium is attenuated specifically for relational purposes, which no additive model generates. Second-order beliefs are compressed across purposes relative to own interest, and the ratio of the belief–interest gap to the purpose penalty is statistically constant across relational purposes, as a single social-image cost parameter requires. That ratio is also the welfare statistic, and it is invariant to the price slope: social image accounts for up to 39.6% of the romantic penalty and 46.8% of the friendship penalty. A hierarchical Bayes demand model recovers the taste distribution and shows social-image sensitivity shifting relational purposes while leaving price and attribute sensitivities unchanged.

Keywords: conversational AI; conjoint analysis; willingness to pay; social image; second-order beliefs; hierarchical Bayes (search for similar items in EconPapers)
JEL-codes: C81 C93 D82 M31 (search for similar items in EconPapers)
Date: 2026
New Economics Papers: this item is included in nep-ain and nep-exp
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