Headline risk
4%
Very Low RiskLaundry and dry-cleaning workers
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
Operate or tend washing or dry-cleaning machines to wash or dry-clean industrial or household articles, such as cloth garments, suede, leather, furs, blankets, draperies, linens, rugs, and carpets. Includes spotters and dyers of these articles.
Task evidence
100% weighted task match · 0% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 33,800
Employment 2024
202.6K
Projected Change (2024–34)
5.4%
Openings (2024–34)
31.9K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Start washers, dry cleaners, driers, or extractors, and turn valves or levers to regulate machine processes and the volume of soap, detergent, water, bleach, starch, and other additives. AI use: 0%
- 2. Remove items from washers or dry-cleaning machines, or direct other workers to do so. AI use: 0%
- 3. Sort and count articles removed from dryers, and fold, wrap, or hang them. AI use: 0%
- 4. Examine and sort into lots articles to be cleaned, according to color, fabric, dirt content, and cleaning technique required. AI use: 0%
- 5. Load articles into washers or dry-cleaning machines, or direct other workers to perform loading. AI use: 0%
- 6. Operate extractors and driers, or direct their operation. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 46.4 · 137K employed
Under 25: 10% · 25–54: 60% · 55+: 29%
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
major_group_fallback · 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.