Machinists and tool and die makers: AI exposure and career outlook
Machinists and tool and die makers (SOC 51-4041) sit at the 32nd percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 16% of tasks are already automated and 36% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 28% follows an automation pattern and 72% an augmentation pattern, which places it in the Orchestrator opportunity group: AI reaches deep into this work and amplifies it. The leverage goes to whoever directs it, and that can be you. In Canada the role maps to NOC 72100 (Machinists and machining and tooling inspectors), and ESDC's COPS 2024–2033 outlook for that unit group is moderate risk of shortage.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 51-4041
- Canada NOC 2021
- 72100
- TEER level
- 2 — College / Apprenticeship 2+ yrs
- COPS outlook
- Moderate risk of Shortage
What the measurements actually say
Three independent studies measure AI exposure for this occupation directly. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 16% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. |
| Observed AI usage | 0% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). |
| Academic AI exposure | 25th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. |
| Automation-pattern usage | 28% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). |
| Augmentation-pattern usage | 72% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 16% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 36% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
AI is transforming how this work is done. Professionals who adapt their workflows will thrive; those who don't face increasing competition.
In Canada: NOC 72100
Machinists and tool and die makers map to NOC 72100 — Machinists and machining and tooling inspectors at TEER 2 (College / Apprenticeship 2+ yrs). ESDC's COPS 2024–2033 projection for this unit group is Moderate risk of Shortage.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables. TEER is the second digit of the official NOC code, so it cannot disagree with it.
What this job actually involves
These are the O*NET task statements for Machinists and tool and die makers— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 29, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 16% of this occupation's work activities are covered by observed AI usage, but not which ones.
Estimating the Quantifiable Characteristics of Products, Events, or Information · 2 tasks
Calculate dimensions or tolerances, using instruments, such as micrometers or vernier calipers.
Core task · Importance 4.8/5 (O*NET)
Measure, examine, or test completed units to check for defects and ensure conformance to specifications, using precision instruments, such as micrometers.
Core task · Importance 4.6/5 (O*NET)
Handling and Moving Objects · 2 tasks
Fit and assemble parts to make or repair machine tools.
Core task · Importance 4.4/5 (O*NET)
Align and secure holding fixtures, cutting tools, attachments, accessories, or materials onto machines.
Core task · Importance 4.3/5 (O*NET)
Controlling Machines and Processes · 2 tasks
Machine parts to specifications, using machine tools, such as lathes, milling machines, shapers, or grinders.
Core task · Importance 4.8/5 (O*NET)
Set up, adjust, or operate basic or specialized machine tools used to perform precision machining operations.
Core task · Importance 4.6/5 (O*NET)
Getting Information · 1 task
Study sample parts, blueprints, drawings, or engineering information to determine methods or sequences of operations needed to fabricate products.
Core task · Importance 4.5/5 (O*NET)
Monitoring Processes, Materials, or Surroundings · 1 task
Monitor the feed and speed of machines during the machining process.
Core task · Importance 4.5/5 (O*NET)
Inspecting Equipment, Structures, or Materials · 1 task
Operate equipment to verify operational efficiency.
Core task · Importance 4.3/5 (O*NET)
Working with Computers · 1 task
Program computers or electronic instruments, such as numerically controlled machine tools.
Core task · Importance 4.5/5 (O*NET)
Repairing and Maintaining Electronic Equipment · 1 task
Maintain machine tools in proper operational condition.
Core task · Importance 4.5/5 (O*NET)
Communicating with Supervisors, Peers, or Subordinates · 1 task
Confer with numerical control programmers to check and ensure that new programs or machinery will function properly and that output will meet specifications.
Core task · Importance 4.3/5 (O*NET)
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Machinists and tool and die makers as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
Upskill in AI-driven automation (e.g., Make, Zapier) and generative tools in your domain. The opportunity is to pivot from execution to directing AI-augmented workflows.
AI is transforming tasks rather than replacing entire jobs. Routine scheduling and communication automate, but human decision-making remains critical.
Master workflow automation platforms (e.g., Make, Zapier, CRM integrations) to double your output and position for fractional leadership roles.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the reshaping band, not an occupation-specific research finding about machinists and tool and die makers.
Sources for this occupation
Only the sources that hold data for machinists and tool and die makers are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Anthropic Economic Index: Measured observed AI usage per occupation, from Anthropic's open per-SOC dataset (CC-BY 4.0).
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
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