How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Dress electrodes, using tip dressers, files, emery cloths, or dressing wheels.71
- Select torch tips, alloys, flux, coil, tubing, or wire, according to metal types or thicknesses, data charts, or records.70
- Set dials and timing controls to regulate electrical current, gas flow pressure, heating or cooling cycles, or shut-off.70
- Fill hoppers and position spouts to direct flow of flux or manually brush flux onto seams of workpieces.70
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Assemble, align, and clamp workpieces into holding fixtures to bond, heat-treat, or solder fabricated metal components.01
- Mark weld points and positions of components on workpieces, using rules, squares, templates, or scribes.02
- Compute and record settings for new work, applying knowledge of metal properties, principles of welding, and shop mathematics.03
- Set up, operate, or tend welding machines that join or bond components to fabricate metal products or assemblies.04
- Turn and press knobs and buttons or enter operating instructions into computers to adjust and start welding machines.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic
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Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
