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Demand for Conversational AI

Author

Listed:
  • Elliott Ash
  • Francesco Capozza
  • Sergio Galletta

Abstract

We estimate demand for five conversational AI use cases — administrative assistance, studying, wellness, friendship, and romance — using a preregistered, within-respondent conjoint experiment in a U.S. online sample (N=1,989). Purpose dominates interest: instrumental uses rank highest, romance lowest. Free pricing raises interest sharply but is attenuated for relational purposes, revealing non-monetary signaling costs that price reductions cannot eliminate. Privacy and personalization shift demand; interaction modality does not. Second-order beliefs exceed own interest in proportion to the purpose penalty, consistent with a single stigma parameter suppressing stated below latent demand. Corrected consumer surplus for a free romantic companion turns positive once suppression costs are recovered — the welfare mirror image of social media’s collective trap.

Suggested Citation

  • Elliott Ash & Francesco Capozza & Sergio Galletta, 2026. "Demand for Conversational AI," CESifo Working Paper Series 12673, CESifo.
    • Handle: RePEc:ces:ceswps:_12673
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    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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