Will AI replace Tool and Die Makers?
Most of the work in Tool and Die Makers still leans on things AI struggles with — research rates its theoretical AI reach at only ~19%, and real-world use lower still.
O*NET-SOC 51-4111
How your 15 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~19%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~19%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 15 tasks, ratedBased on real task-level AI scores — click to collapse
- Set up and operate conventional or computer numerically controlled machine tools such as lathes, milling machines, or grinders to cut, bore, grind, or otherwise shape parts to prescribed dimensions and finishes.
- Visualize and compute dimensions, sizes, shapes, and tolerances of assemblies, based on specifications.
- Study blueprints, sketches, models, or specifications to plan sequences of operations for fabricating tools, dies, or assemblies.
- Inspect finished dies for smoothness, contour conformity, and defects.
- Verify dimensions, alignments, and clearances of finished parts for conformance to specifications, using measuring instruments such as calipers, gauge blocks, micrometers, or dial indicators.
- Fit and assemble parts to make, repair, or modify dies, jigs, gauges, and tools, using machine tools, hand tools, or welders.
- Select metals to be used from a range of metals and alloys, based on properties such as hardness or heat tolerance.
- Lift, position, and secure machined parts on surface plates or worktables, using hoists, vises, v-blocks, or angle plates.
- File, grind, shim, and adjust different parts to properly fit them together.
- Smooth and polish flat and contoured surfaces of parts or tools, using scrapers, abrasive stones, files, emery cloths, or power grinders.
- Measure, mark, and scribe metal or plastic stock to lay out machining, using instruments such as protractors, micrometers, scribes, or rulers.
- Conduct test runs with completed tools or dies to ensure that parts meet specifications, making adjustments as necessary.
- Design jigs, fixtures, and templates for use as work aids in the fabrication of parts or products.
- Cut, shape, and trim blanks or blocks to specified lengths or shapes, using power saws, power shears, rules, and hand tools.
- Set up and operate drill presses to drill and tap holes in parts for assembly.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.