Will AI replace Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic?
Most of the work in Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic still leans on things AI struggles with — research rates its theoretical AI reach at only ~3%, and real-world use lower still.
O*NET-SOC 51-4032
How your 16 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 (~3%). 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 (~3%), 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 16 tasks, ratedBased on real task-level AI scores — click to collapse
- None — AI cannot fully do any core task alone yet.
- Study machining instructions, job orders, or blueprints to determine dimensional or finish specifications, sequences of operations, setups, or tooling requirements.
- Verify conformance of machined work to specifications, using measuring instruments, such as calipers, micrometers, or fixed or telescoping gauges.
- Move machine controls to lower tools to workpieces and to engage automatic feeds.
- Verify that workpiece reference lines are parallel to the axis of table rotation, using dial indicators mounted in spindles.
- Establish zero reference points on workpieces, such as at the intersections of two edges or over hole locations.
- Change worn cutting tools, using wrenches.
- Select and set cutting speeds, feed rates, depths of cuts, and cutting tools, according to machining instructions or knowledge of metal properties.
- Position and secure workpieces on tables, using bolts, jigs, clamps, shims, or other holding devices.
- Observe drilling or boring machine operations to detect any problems.
- Lift workpieces onto work tables either manually or with hoists or direct crane operators to lift and position workpieces.
- Turn valves and direct flow of coolants or cutting oil over cutting areas.
- Install tools in spindles.
- Perform minor assembly, such as fastening parts with nuts, bolts, or screws, using power tools or hand tools.
- Operate single- or multiple-spindle drill presses to bore holes so that machining operations can be performed on metal or plastic workpieces.
- Lay out reference lines and machining locations on work, using layout tools, and applying knowledge of shop math and layout techniques.
- Sharpen cutting tools, using bench grinders.
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.