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.
- Position and clamp workpieces, plates, or rollers in holding fixtures.80
- Prepare etching chemicals according to formulas, diluting acid with water to obtain solutions of specified concentration.80
- Brush or wipe acid over engraving to darken or highlight inscriptions.80
- Expose workpieces to acid to develop etch patterns such as designs, lettering, or figures.79
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.
- Set reduction scales to attain specified sizes of reproduction on workpieces, and set pantograph controls for required heights, depths, and widths of cuts.01
- Prepare workpieces for etching or engraving by cutting, sanding, cleaning, polishing, or treating them with wax, acid resist, lime, etching powder, or light-sensitive enamel.02
- Remove wax or tape from etched glassware by using a stylus or knife, or by immersing ware in hot water.03
- Adjust depths and sizes of cuts by adjusting heights of worktables, or by adjusting machine-arm gauges.04
- Examine engraving for quality of cut, burrs, rough spots, and irregular or incomplete engraving.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Patternmakers, 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.
