Foundry Mold and Coremakers
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
Make or form wax or sand cores or molds used in the production of metal castings in foundries.
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 |
|---|---|---|
| Assemble products or work aids. | 20.2% | Not observed 0.00 |
| Load products, materials, or equipment for transportation or further processing. | 17.7% | Not observed 0.00 |
| Remove workpieces from production equipment. | 12.2% | Not observed 0.00 |
| Clean tools, equipment, facilities, or work areas. | 9.4% | Not observed 0.00 |
| Smooth surfaces of objects or equipment. | 9.4% | Not observed 0.00 |
| Position tools or equipment. | 7.0% | Not observed 0.00 |
| Operate lifting or moving equipment. | 7.0% | Not observed 0.00 |
| Signal others to coordinate work activities. | 7.0% | Not observed 0.00 |
| Cut materials. | 5.5% | Not observed 0.00 |
| Operate industrial processing or production equipment. | 4.6% | 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.
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: Assemble products or work aids is the one place the data shows any overlap.
Next 90 days
- Deepen the human core. Activities like Assemble products or work aids 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.