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Research Question 6: Use Case Evolution Over Time
Primary Question
How are AI use cases evolving across platforms, and what does this reveal about user learning, platform maturation, and market trajectories?
Key Hypothesis
AI use cases are broadening from technical early-adopter tasks toward mainstream personal and professional applications, with different platforms following different evolutionary paths.
Significance
Understanding use case evolution is critical for:
- Predicting future AI application areas
- Platform positioning and feature development
- Identifying emerging market opportunities
- Understanding user sophistication progression
- Labor market impact forecasting
Key Findings Summary
- Decline in technical help: ChatGPT technical help requests 12% → 5% (58% decline, July 2024 → July 2025) (OpenAI)
- Work → Personal shift: ChatGPT non-work usage 53% → 72% (mid-2024 → mid-2025) (OpenAI)
- Education growth: Claude educational tasks 9% → 12% (early 2025 → August 2025) (Anthropic)
- Scientific growth: Claude scientific tasks 6% → 7% (Anthropic)
- Broadening beyond code: Copilot fewer programming conversations, more culture/history (Jan → Sep 2025) (Microsoft)
- Device-context shift: Copilot desktop = work, mobile = health/wellness (different use cases by device) (Microsoft)
- Automation rising: Claude automation 27% → 39% (late 2024 → Aug 2025) (Anthropic)
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