64
/100

SOC 51-9041

Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders

High RiskFrey/Osborne: 93.0%

Risk Score

โš ๏ธ

64/100

High Risk

US Employment

๐Ÿ‘ฅ

57,310

Total workers

Median Wage

๐Ÿ’ฐ

$45K

$35K โ€“ $65K

Projected Growth

๐Ÿ“ˆ

+2%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

48/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders face a risk score of 64/100 โ€” 20 points above the national average of 44. With only 48/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $45K. 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 64/100, Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders faces moderate automation pressure. While tasks like industrial robotics replacing manual assembly tasks are increasingly handled by AI, the role retains significant human elements. The 57,310 workers in this occupation should focus on strengthening skills in troubleshooting complex equipment malfunctions and quality judgment requiring tactile and visual inspection to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders?

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

โš ๏ธ Top Risk Factors

1

Industrial robotics replacing manual assembly tasks

2

AI quality inspection via computer vision systems

3

Automated CNC programming and machine operation

4

Predictive maintenance reducing manual inspection roles

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Troubleshooting complex equipment malfunctions

โœ“

Quality judgment requiring tactile and visual inspection

โœ“

Setup and calibration of custom production runs

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

Adjust machine components to regulate speeds, pressures, and temperatures, and amounts, dimensions, and flow of materials or ingredients.

โ€ข

Press control buttons to activate machinery and equipment.

โ€ข

Examine, measure, and weigh materials or products to verify conformance to standards, using measuring devices such as templates, micrometers, or scales.

โ€ข

Monitor machine operations and observe lights and gauges to detect malfunctions.

โ€ข

Clear jams, and remove defective or substandard materials or products.

๐Ÿ’ป Technology Skills

โ€ข

Enterprise resource planning ERP software

โ€ข

Spreadsheet software

โ€ข

Office suite software

โ€ข

Electronic mail software

โ€ข

Word processing software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

Mechanical

โ€ข

Computers and Electronics

โ€ข

Mathematics

โ€ข

Engineering and Technology

๐Ÿ“Š vs National Average

Median Wage$45K
$-1K

National avg: $46K

Risk Score64/100
+20

National avg: 44/100

GenAI Exposure48/100
+10

National avg: 38/100

Projected Growth2.0%
-1.7%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K53%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K63%
Fabric and Apparel Patternmakers33$68K77%