Will AI replace Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic?
Most of the work in Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic still leans on things AI struggles with — research rates its theoretical AI reach at only ~10%, and real-world use lower still.
O*NET-SOC 51-4081
How your 18 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 (~10%). 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 (~10%), 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 18 tasks, ratedBased on real task-level AI scores — click to collapse
- Compute data, such as gear dimensions or machine settings, applying knowledge of shop mathematics.
- Read blueprints or job orders to determine product specifications and tooling instructions and to plan operational sequences.
- Inspect workpieces for defects, and measure workpieces to determine accuracy of machine operation, using rules, templates, or other measuring instruments.
- Position, adjust, and secure stock material or workpieces against stops, on arbors, or in chucks, fixtures, or automatic feeding mechanisms, manually or using hoists.
- Select, install, and adjust alignment of drills, cutters, dies, guides, and holding devices, using templates, measuring instruments, and hand tools.
- Observe machine operation to detect workpiece defects or machine malfunctions, adjusting machines as necessary.
- Set up and operate machines, such as lathes, cutters, shears, borers, millers, grinders, presses, drills, or auxiliary machines, to make metallic and plastic workpieces.
- Change worn machine accessories, such as cutting tools or brushes, using hand tools.
- Set machine stops or guides to specified lengths as indicated by scales, rules, or templates.
- Select the proper coolants and lubricants and start their flow.
- Remove burrs, sharp edges, rust, or scale from workpieces, using files, hand grinders, wire brushes, or power tools.
- Perform minor machine maintenance, such as oiling or cleaning machines, dies, or workpieces, or adding coolant to machine reservoirs.
- Make minor electrical and mechanical repairs and adjustments to machines and notify supervisors when major service is required.
- Start machines and turn handwheels or valves to engage feeding, cooling, and lubricating mechanisms.
- Move controls or mount gears, cams, or templates in machines to set feed rates and cutting speeds, depths, and angles.
- Instruct other workers in machine set-up and operation.
- Record operational data, such as pressure readings, lengths of strokes, feed rates, or speeds.
- Extract or lift jammed pieces from machines, using fingers, wire hooks, or lift bars.
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.