78
/100

SOC 53-7063

Machine Feeders and Offbearers

High RiskFrey/Osborne: 93.0%

Risk Score

โš ๏ธ

78/100

High Risk

US Employment

๐Ÿ‘ฅ

46,690

Total workers

Median Wage

๐Ÿ’ฐ

$40K

$31K โ€“ $57K

Projected Growth

๐Ÿ“ˆ

-13%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

53/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Machine Feeders and Offbearers face a risk score of 78/100 โ€” 34 points above the national average of 44. With 53/100 GenAI exposure, this occupation faces significant pressure from AI tools despite weak projected growth. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $40K. The 3 recommended career transitions all maintain competitive wages while reducing automation exposure. Explore transition paths โ†’

๐Ÿ” AI Impact Analysis

With a risk score of 78/100, Machine Feeders and Offbearers faces significant automation pressure. Key threats include autonomous vehicle and self-driving truck technology and drone delivery displacing last-mile logistics workers. The 46,690 Americans in this role should actively develop skills in manual loading of irregular and fragile cargo and navigating unpredictable road and weather conditions to remain competitive. Workers who proactively adapt will find new opportunities even as traditional tasks are automated.

Will AI Replace Machine Feeders and Offbearers?

Read our full analysis with verdict, risk factors, safe tasks, and career transition paths โ†’

โš ๏ธ Top Risk Factors

1

Autonomous vehicle and self-driving truck technology

2

Drone delivery displacing last-mile logistics workers

3

AI route optimization reducing dispatcher roles

4

AI traffic management and fleet coordination

5

Automated warehouse sorting and loading systems

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Manual loading of irregular and fragile cargo

โœ“

Navigating unpredictable road and weather conditions

โœ“

Customer interaction and conflict resolution during delivery

๐Ÿ“Š Task Automation Breakdown

Based on O*NET task analysis and GenAI exposure scoring. Shows the estimated proportion of this occupation's core tasks that are automatable by current AI, augmented by AI tools, or require essential human skills.

๐Ÿ“‹ O*NET Task Profile

โ€ข

Inspect materials and products for defects, and to ensure conformance to specifications.

โ€ข

Record production and operational data, such as amount of materials processed.

โ€ข

Push dual control buttons and move controls to start, stop, or adjust machinery and equipment.

โ€ข

Weigh or measure materials or products to ensure conformance to specifications.

โ€ข

Identify and mark materials, products, and samples, following instructions.

๐Ÿ’ป Technology Skills

โ€ข

Industrial control software

โ€ข

Spreadsheet software

โ€ข

Office suite software

โ€ข

Electronic mail software

โ€ข

Time accounting software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

Mechanical

โ€ข

Mathematics

โ€ข

English Language

โ€ข

Public Safety and Security

๐Ÿ“Š vs National Average

Median Wage$40K
$-7K

National avg: $46K

Risk Score78/100
+34

National avg: 44/100

GenAI Exposure53/100
+15

National avg: 38/100

Projected Growth-13.0%
-16.7%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K76%
Aircraft Cargo Handling Supervisors29$64K83%
Air Transportation Workers31$107K79%