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Platform Choice, Trust, and Privacy in the Consumer AI Assistant Market

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  • Jennifer Zou

Abstract

We study how a representative sample of United States adult AI-assistant users (n=1,999; June 2026) choose among platforms, allocate tasks across them, evaluate provider trustworthiness, and value data-handling features. Estimates are weighted to the AI-user population using external adoption benchmarks. Four patterns emerge. The market is concentrated but internally differentiated: ChatGPT is the primary assistant for 58% of users and Gemini for 25%, yet smaller platforms hold defensible task niches--Claude captures a third of coding tasks despite a 7% overall share. Task allocation is thus organized by platform far more than by user, and technical use falls steeply with age. Trust is earned through use rather than reputation: Claude is ranked most trustworthy in every head-to-head among users of both platforms, and shows by far the largest gap between how its users and non-users rate it. Finally, privacy concern is near-universal but action is gated by knowledge, not concern; in a choice experiment users pay most to keep humans--not models--out of their conversations ($11.20/month), with valuations rising in task sensitivity.

Suggested Citation

  • Jennifer Zou, 2026. "Platform Choice, Trust, and Privacy in the Consumer AI Assistant Market," Papers 2607.15134, arXiv.org.
    • Handle: RePEc:arx:papers:2607.15134
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    References listed on IDEAS

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    1. Susan Athey & Christian Catalini & Catherine Tucker, 2017. "The Digital Privacy Paradox: Small Money, Small Costs, Small Talk," NBER Working Papers 23488, National Bureau of Economic Research, Inc.
    2. Alexander Bick & Adam Blandin & David Deming, 2023. "The Rapid Adoption of Generative AI," On the Economy 98843, Federal Reserve Bank of St. Louis.
    3. Christopher Conlon & Julie Holland Mortimer, 2021. "Empirical properties of diversion ratios," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 693-726, December.
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