71
/100

SOC 51-4034

Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic

High RiskFrey/Osborne: 84.0%

Risk Score

โš ๏ธ

71/100

High Risk

US Employment

๐Ÿ‘ฅ

18,970

Total workers

Median Wage

๐Ÿ’ฐ

$49K

$36K โ€“ $65K

Projected Growth

๐Ÿ“ˆ

-13.6%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

34/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic face a risk score of 71/100 โ€” 27 points above the national average of 44. With only 34/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $49K ($2K 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 71/100, Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic faces significant automation pressure. Key threats include cobots handling repetitive material handling tasks and smart factory scheduling and production optimization. The 18,970 Americans in this role should actively develop skills in coordinating workflow across diverse production teams and setup and calibration of custom production runs to remain competitive. Workers who proactively adapt will find new opportunities even as traditional tasks are automated.

Will AI Replace Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic?

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

โš ๏ธ Top Risk Factors

1

Cobots handling repetitive material handling tasks

2

Smart factory scheduling and production optimization

3

Predictive maintenance reducing manual inspection roles

4

Automated CNC programming and machine operation

5

AI quality inspection via computer vision systems

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Coordinating workflow across diverse production teams

โœ“

Setup and calibration of custom production runs

โœ“

Handling non-standard materials and configurations

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 controls and change tool settings to keep dimensions within specified tolerances.

โ€ข

Move controls to set cutting speeds and depths and feed rates, and to position tools in relation to workpieces.

โ€ข

Study blueprints, layouts or charts, and job orders for information on specifications and tooling instructions, and to determine material requirements and operational sequences.

โ€ข

Inspect sample workpieces to verify conformance with specifications, using instruments such as gauges, micrometers, and dial indicators.

โ€ข

Replace worn tools, and sharpen dull cutting tools and dies, using bench grinders or cutter-grinding machines.

๐Ÿ’ป Technology Skills

โ€ข

Industrial control software

โ€ข

Object or component oriented development software

โ€ข

Inventory management software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

Mechanical

โ€ข

Engineering and Technology

โ€ข

Education and Training

โ€ข

Administration and Management

๐Ÿ“Š vs National Average

Median Wage$49K
+$2K

National avg: $46K

Risk Score71/100
+27

National avg: 44/100

GenAI Exposure34/100
-4

National avg: 38/100

Projected Growth-13.6%
-17.3%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K67%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K57%
Fabric and Apparel Patternmakers33$68K76%