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.
- Manipulate valves, switches, and buttons, or key commands into control panels to start semiconductor processing cycles.80
- Stamp, etch, or scribe identifying information on finished component according to specifications.80
- Study work orders, instructions, formulas, and processing charts to determine specifications and sequence of operations.78
- Place semiconductor wafers in processing containers or equipment holders, using vacuum wand or tweezers.77
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.
- Load and unload equipment chambers and transport finished product to storage or to area for further processing.01
- Calculate etching time based on thickness of material to be removed from wafers or crystals.02
- Clean semiconductor wafers using cleaning equipment, such as chemical baths, automatic wafer cleaners, or blow-off wands.03
- Clean and maintain equipment, including replacing etching and rinsing solutions and cleaning bath containers and work area.04
- Measure and weigh amounts of crystal growing materials, mix and grind materials, load materials into container, and monitor processing procedures to help identify crystal growing problems.05
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Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
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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.
