Locksmiths and Safe Repairers
AI is not doing this work today.
This occupation's activities barely register in measured AI usage. Generative AI is not doing this work today. The pressures that matter here are more likely economic or robotic than linguistic.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Repair and open locks, make keys, change locks and safe combinations, and install and repair safes.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Disassemble equipment. | 18.4% | Not observed 0.00 |
| Repair tools or equipment. | 18.1% | Not observed 0.00 |
| Build structures. | 12.1% | Not observed 0.00 |
| Cut materials. | 10.0% | Not observed 0.00 |
| Fabricate devices or components. | 9.7% | Not observed 0.00 |
| Drill holes in earth or materials. | 8.4% | Not observed 0.00 |
| Set up equipment. | 7.8% | High overlap 0.75 |
| Maintain operational records. | 7.6% | Not observed 0.00 |
| Maintain facilities or equipment. | 4.1% | High overlap 0.66 |
| Assemble equipment or components. | 2.7% | Not observed 0.00 |
| Apply protective solutions or coatings. | 1.1% | Not observed 0.00 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is set up equipment; the most important activity AI is not observed doing is disassemble equipment. The plan below is built around exactly that split.
Low exposure is an asset. Spend it deliberately.
Next 30 days
- Don't buy the panic. This occupation's activities barely appear in AI usage data. Your near-term exposure is other people's headlines, not your tasks.
- Skim the gains anyway. Even here, an assistant helps with the thin admin layer: Set up equipment is the one place the data shows any overlap.
Next 90 days
- Deepen the human core. Activities like Disassemble equipment are measured as fully outside current AI. Mastery there is compounding, defensible value.
- Mentor the exposed. Colleagues in adjacent office-heavy roles are the ones facing redesign; understanding their tools makes you a better coordinator and leader.
The longer game
- Watch the actual frontier for this work: usually physical automation, demographics or regulation rather than language models. This dataset intentionally measures only generative AI.
- Revisit after major data updates. If this page's vintage changes and your band moves, the plan moves with it.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.