70
/100

SOC 47-5044

Loading and Moving Machine Operators, Underground Mining

High RiskFrey/Osborne: 59.0%

Risk Score

โš ๏ธ

70/100

High Risk

US Employment

๐Ÿ‘ฅ

6,130

Total workers

Median Wage

๐Ÿ’ฐ

$69K

$48K โ€“ $83K

Projected Growth

๐Ÿ“ˆ

-22.3%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

42/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Loading and Moving Machine Operators, Underground Mining face a risk score of 70/100 โ€” 26 points above the national average of 44. With only 42/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $69K ($23K above the national median). 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 70/100, Loading and Moving Machine Operators, Underground Mining faces significant automation pressure. Key threats include autonomous heavy equipment operation and robotic bricklaying and prefabrication automation. The 6,130 Americans in this role should actively develop skills in fine motor craftsmanship in custom installations and physical work in confined or elevated spaces to remain competitive. Workers who proactively adapt will find new opportunities even as traditional tasks are automated.

Will AI Replace Loading and Moving Machine Operators, Underground Mining?

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

โš ๏ธ Top Risk Factors

1

Autonomous heavy equipment operation

2

Robotic bricklaying and prefabrication automation

3

BIM-integrated automated progress tracking

4

3D printing of building components

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Fine motor craftsmanship in custom installations

โœ“

Physical work in confined or elevated spaces

โœ“

Adapting to unique building configurations on-site

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

Handle high voltage sources and hang electrical cables.

โ€ข

Drive loaded shuttle cars to ramps and move controls to discharge loads into mine cars or onto conveyors.

โ€ข

Pry off loose material from roofs and move it into the paths of machines, using crowbars.

โ€ข

Move trailing electrical cables clear of obstructions, using rubber safety gloves.

โ€ข

Control conveyors that run the entire length of shuttle cars to distribute loads as loading progresses.

๐Ÿ’ป Technology Skills

โ€ข

Industrial control software

โ€ข

Inventory management software

โ€ข

Facilities management software

โ€ข

Spreadsheet software

โ€ข

Electronic mail software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Mechanical

โ€ข

Education and Training

โ€ข

Law and Government

โ€ข

English Language

โ€ข

Production and Processing

๐Ÿ“Š vs National Average

Median Wage$69K
+$23K

National avg: $46K

Risk Score70/100
+26

National avg: 44/100

GenAI Exposure42/100
+4

National avg: 38/100

Projected Growth-22.3%
-26.0%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K65%
Supervisors of Construction and Extraction Workers33$79K74%
Extraction Workers29$56K72%