Will AI replace Timing Device Assemblers and Adjusters?
AI reaches little of this job’s work.
0/100
Low exposure
More exposed to AI than 2% of US workers.
- AI already used in
- 5% of the work
- As AI tools mature: up to
- 0% of the work
What the job involves
Perform precision assembling or adjusting, within narrow tolerances, of timing devices such as digital clocks or timing devices with electrical or electronic components.
Where AI reaches the work
The job’s main tasks, heaviest first, by how far AI reaches them.
Already happening(1)
- Review blueprints, sketches, or work orders to gather information about tasks to be completed.
Out of reach(16)
- Observe operation of timepiece parts and subassemblies to determine accuracy of movement, and to diagnose causes of defects.
- Test operation and fit of timepiece parts and subassemblies, using electronic testing equipment, tweezers, watchmakers' tools, and loupes.
- Replace specified parts to repair malfunctioning timepieces, using watchmakers' tools, loupes, and holding fixtures.
- Disassemble timepieces such as watches, clocks, and chronometers so that repairs can be made.
- Examine components of timepieces such as watches, clocks, or chronometers for defects, using loupes or microscopes.
and 11 more
Also called
Calibrator · Banking Pin Adjuster · Hairspring Vibrator · Timing Adjuster · Watch Train Assembler · Time Stamp Assembler · Balance and Hairspring Assembler · Chronometer Adjuster
Related jobs
What these numbers mean
The score is the share of this job’s work, weighted by how important and frequent each task is, that AI could do in half the time: tasks an AI model could do alone count in full, tasks that need software built on AI count half.
“AI already used” is the share of the work in tasks where people are recorded using AI today. “As AI tools mature” counts every task AI could halve, with or without extra software.
The percentile compares the score with every job’s, weighted by how many people in the US do each one. Exposure means AI could change how the work is done, not that the job goes away. These are estimates for the typical job, not predictions.
Sources
- O*NET 31.0 Database, U.S. Department of Labor, Employment and Training Administration
- GPTs are GPTs: Labor market impact potential of LLMs (task labels), Eloundou, Manning, Mishkin & Rock (2023), OpenAI and University of Pennsylvania
- Anthropic Economic Index: labor market impacts (task penetration), Anthropic
- Occupational Employment and Wage Statistics, May 2025 (national and metropolitan areas), U.S. Bureau of Labor Statistics