π‘ Model Makers, Wood have a composite risk score of 68/100 (Frey-Osborne probability: 96%, GenAI exposure: 59/100). With 360 workers in the US, this is one of the most AI-vulnerable occupations. Full occupation profile β
π― The Verdict
Likely for many tasks. The role will look very different in 5β10 years.
With 360 workers and a median wage of $52K,model makers, wood represent a significant portion of the US workforce. Their GenAI exposure index is 59%, meaning a majority of their core tasks overlap with current generative AI capabilities.
Risk Score
68/100
Employment
360
Median Wage
$52K
GenAI Exposure
59%
β οΈ Top Risk Factors
Predictive maintenance reducing manual inspection roles
Automated CNC programming and machine operation
Cobots handling repetitive material handling tasks
Smart factory scheduling and production optimization
π‘οΈ Tasks AI Can't Easily Replace
Setup and calibration of custom production runs
Quality judgment requiring tactile and visual inspection
Troubleshooting complex equipment malfunctions
π Career Transition Paths
Related occupations with lower AI risk and high skills overlap:
Engineers
61% skills overlap Β· $106K median wage
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors
51% skills overlap Β· $62K median wage
Fabric and Apparel Patternmakers
70% skills overlap Β· $68K median wage
β Frequently Asked Questions
Will AI completely replace model makers, wood?
Likely for many tasks. The role will look very different in 5β10 years.
What is the AI risk score for model makers, wood?
Model Makers, Wood have a composite AI automation risk score of 68 out of 100, classified as "High Risk".
How many model makers, wood are there in the US?
There are approximately 360 model makers, wood employed in the United States.
What do model makers, wood earn?
The median annual wage for model makers, wood is $52K.
What skills should model makers, wood develop?
Focus on tasks AI can't easily replicate: setup and calibration of custom production runs, quality judgment requiring tactile and visual inspection, troubleshooting complex equipment malfunctions. These human-centric skills will become more valuable as routine tasks are automated.