AI Safety

New Study Reveals How US Users Choose AI Assistants: ChatGPT Dominates, Claude Wins Trust

Claude captures 1/3 of coding tasks and is deemed most trustworthy despite small market share.

Deep Dive

A new academic study from Jennifer Zou (arXiv, July 2026) provides the first large-scale, weighted analysis of how US adults choose and use AI assistants. Based on a representative sample of 1,999 users, the research reveals a market that is concentrated but highly differentiated. ChatGPT dominates as the primary assistant for 58% of users, followed by Gemini at 25%, yet smaller platforms hold defensible task niches. Claude, despite only a 7% overall share, captures a third of all coding tasks—a clear sign that task allocation is organized by platform strengths rather than user demographics. Technical use also falls steeply with age, suggesting a generational divide in advanced AI adoption.

Trust and privacy emerge as critical differentiators. The study finds that trust is earned through direct use, not reputation: Claude users rate it as the most trustworthy in every head-to-head comparison, and the gap between its users' and non-users' ratings is the largest of any platform. On privacy, concern is near-universal but action is gated by knowledge, not worry. In a choice experiment, users valued preventing human access to their conversations at $11.20/month—far more than blocking model access—and this valuation rises with task sensitivity. These findings have immediate implications for platform strategy, trust-building, and privacy feature design.

Key Points
  • ChatGPT is the primary assistant for 58% of US users; Gemini for 25%; Claude captures 33% of coding tasks with just 7% overall share.
  • Claude is rated most trustworthy by its users in head-to-head comparisons, showing the largest trust gap between users and non-users.
  • Users are willing to pay $11.20/month to keep humans (not models) out of their conversations, with valuations increasing for sensitive tasks.

Why It Matters

Platforms must differentiate by task-specific strengths and invest in privacy features to win user trust.

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