Headline risk
12%
Low RiskGraders and sorters, agricultural products
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
Grade, sort, or classify unprocessed food and other agricultural products by size, weight, color, or condition.
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 35,430
Employment 2024
38.9K
Projected Change (2024–34)
-5.4%
Openings (2024–34)
5.1K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Discard inferior or defective products or foreign matter, and place acceptable products in containers for further processing. AI use: 0%
- 2. Weigh products or estimate their weight, visually or by feel. AI use: 0%
- 3. Grade and sort products according to factors such as color, species, length, width, appearance, feel, smell, and quality to ensure correct processing and usage. AI use: 0%
- 4. Place products in containers according to grade and mark grades on containers. AI use: 0%
- 5. Record grade or identification numbers on tags or on shipping, receiving, or sales sheets. AI use: 0%
Technologies
Work context
Worker profile
Median age 45.4 · 58K employed
Under 25: 9% · 25–54: 57% · 55+: 34%
Related
Source coverage
10/11 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.