Orune's assessments are built on publicly available data and published research. We believe transparency about our sources builds trust. Here is where our data comes from and how we use it.
O*NET is the U.S. Department of Labor's comprehensive database of occupational information. It covers over 1,000 occupations with detailed data on tasks, skills, work activities, and cognitive requirements. Orune uses O*NET data to build the task-level profile for each occupation, including the cognitive dimensions (analytical, interpersonal, creative, routine, physical) that shape how AI interacts with different types of work.
Source: onetonline.org, maintained by the U.S. Department of Labor, Employment and Training Administration.
The BLS publishes employment levels, wage data, and occupational projections for approximately 830 occupations nationwide. Orune uses BLS data for salary ranges and growth outlook information displayed on profession pages. Wage data is from the 2024 Occupational Employment and Wage Statistics (OEWS) survey, the most recent release available. Growth projections cover 2024 to 2034.
Source: bls.gov, U.S. Department of Labor.
Orune's analysis of AI's impact on occupations draws on published academic and industry research, including work from Anthropic, McKinsey Global Institute, the World Economic Forum, and peer-reviewed studies on AI capabilities and labor market effects. Specific studies are cited in our methodology documentation.
On top of public data, Orune applies its own analytical layer. This includes classifying which tasks within each occupation AI can handle independently today, which AI assists and is growing into, and which remain distinctly human. This classification is Orune's interpretation, informed by the research above but reflecting our own assessment.
On profession pages and in the Orune Brief, Orune's analysis is clearly labeled to distinguish it from source data.
Occupational data has a natural shelf life. O*NET data is refreshed annually when new releases are published. BLS wage and employment data is updated annually. AI task classifications are reviewed quarterly as AI capabilities evolve. Scenario weights (the probability distribution across economic futures) are reviewed monthly based on real-world signals.
As Orune grows, we plan to add automated signal collection from news and economic data sources, with editorial review before any changes affect user-facing content.