At a glance
This measures AI task-usage patterns, not wages or employment. The API and Claude.ai channels serve different populations (developers vs consumers) — treat them separately unless you’re explicitly comparing.
Tables
Both tables have the same schema. The Claude.ai table is larger because it includes country and state-level geographic breakdowns (the API table is global-only).
How the data is structured
Each row is one metric observation. The key columns:Types of metrics
The data includes counts (volume), percentages (shares), averages, index values (ratio vs global average, where 1.0 = average), confidence intervals, and distribution histograms.What’s measured
Try asking
Use-case mix
- “What share of Claude.ai usage is work vs personal vs coursework?”
- “Top use-case clusters by volume on the API?”
- “How does the use-case mix differ between API and Claude.ai?”
Task outcomes
- “Overall task success rate on each platform?”
- “Which O*NET tasks does AI complete most successfully?”
- “How much time does AI save vs human-only completion?”
Complexity and autonomy
- “What education level are the tasks AI is handling?”
- “How does AI autonomy differ between API and Claude.ai?”
- “Which tasks require the most education years?”
Collaboration
- “What share of interactions are directive vs feedback loop vs validation?”
- “How many tasks are flagged as requiring uniquely human ability?”
Geography
- “Which countries use Claude.ai the most?”
- “Top US states by usage volume?”
- “How many countries are represented?”