Headline risk
13%
Low RiskMixing and blending machine setters, operators, and tenders
United States AI Work Index tracks this occupation on the shared structural baseline and then layers on local demand resilience, wages, and confidence.
Why This Score
Share of job tasks that overlap with current AI capabilities
Median annual wage
Projected employment change over 10 years
Typical preparation needed for this occupation
Occupation profile
Set up, operate, or tend machines to mix or blend materials, such as chemicals, tobacco, liquids, color pigments, or explosive ingredients.
Task evidence
100% weighted task match · 5% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 47,680
Employment 2024
101.1K
Projected Change (2024–34)
-6.8%
Openings (2024–34)
8.8K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Weigh or measure materials, ingredients, or products to ensure conformance to requirements. AI use: 0%
- 2. Observe production or monitor equipment to ensure safe and efficient operation. AI use: 0%
- 3. Read work orders to determine production specifications or information. AI use: 0%
- 4. Clean work areas. AI use: 0%
- 5. Mix or blend ingredients by starting machines and mixing for specified times. AI use: 0%
- 6. Stop mixing or blending machines when specified product qualities are obtained and open valves and start pumps to transfer mixtures. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 40.6 · 55K employed
Under 25: 4% · 25–54: 80% · 55+: 16%
Related
No direct US role match is available yet for this occupation.
Source coverage
11/11 source families · O*NET 30.2 / OEWS 2024 / ORS 2025 / OOH 2025-08-28 / Projections 2024-34 / CPS 2025 / Anthropic task penetration
Mapping quality
title_match · employment series present
Narrative & sources
Published limitations
This page shows the local country layer, not realised individual job outcomes. The global structural baseline is shared across countries; only the local demand and wage layer changes here.
Built from O*NET occupation descriptions, task statements, technology skills, work context, Job Zones, Anthropic task penetration, BLS OEWS wages, BLS projection tables, BLS ORS requirements, BLS OOH narrative content, BLS skills data, and BLS CPS occupation age tables.