As of August 2026, Computer Numerically Controlled Tool Operators has an AI-exposure score of 58/100 (Elevated exposure) on the AI-Safe Careers index. This is an estimate of task exposure, not a prediction of job loss.
Score inputs for this role: This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Anthropic Economic Index (June 26, 2026). BLS labor-market figures are separate context, not score inputs.

AI Exposure Score for

Computer Numerically Controlled Tool Operators

58/100
Elevated exposure
LowModerateElevatedHighVery High

More exposed than 57% of the roles we track. Median pay ~US$50,690. About 13,500 projected openings a year (BLS 2024–34 - growth plus replacement).

Pay & demand figures are US medians (BLS, in USD) - your local figures will differ. Your exposure score applies broadly.

Will AI replace Computer Numerically Controlled Tool Operators?

No exposure score can predict whether AI will replace this role. The 58/100 score means our current model estimates elevated task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.

Early-career context (study ages 22-25)

This role's task mix is automation-heavy, but it sits in the Elevated exposure band rather than our highest-exposure bands. That is only a partial match to the study's cohort, so the headline figure is context rather than a direct forecast for this role.

The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.

Use the task map to identify durable work and the skills worth building early.

Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision

This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.

Data sources, not endorsements: O*NET and curated role data; empirical AI signals where matched; BLS employment, pay, and demand as separate context.Methodology Data sources
Role snapshot

What this role usually involves

O*NET 29.1 exact occupation

Operate computer-controlled tools, machines, or robots to machine or process parts, tools, or other work pieces made of metal, plastic, wood, stone, or other materials. May also set up and maintain equipment.

Common titles
CNC Machine Operator (Computer Numerical Control Machine Operator)CNC Machinist (Computer Numerical Control Machinist)CNC Operator (Computer Numerical Control Operator)Machine OperatorCNC Gear Operator (Computer Numerical Control Gear Operator)CNC Lathe Operator (Computer Numerical Control Lathe Operator)
O*NET job-zone preparation
Job Zone 2 · Job Zone 1-2: Very Little to Some Preparation Needed Usually requires a high school diploma or GED, though some occupations may not. Some occupations may need little or no previous experience; others require several months to a year of experience.

Broad guidance for this preparation level; exact requirements vary by role and employer.

Skills and knowledge
Operation and ControlCritical ThinkingMonitoringComplex Problem SolvingJudgment and Decision MakingSpeaking
Work context
Decision latitudeFrequent contact with othersRepeating tasksIndoor controlled settingConsequence of error

Source: O*NET 29.1 exact occupation - Computer Numerically Controlled Tool Operators, SOC 51-9161.00. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.

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Computer Numerically Controlled Tool Operators tasks, by AI exposure

Automatable
  • Implement changes to machine programs, and enter new specifications, using computers.
  • Calculate machine speed and feed ratios and the size and position of cuts.
  • Check to ensure that workpieces are properly lubricated and cooled during machine operation.
17 more automatable tasks locked in the report.
Augmentable

No augmentable tasks identified for this role - all 20 of its individually-assessed tasks read as automatable.

Durable

No durable tasks identified for this role - all 20 of its individually-assessed tasks read as automatable.

We analyzed all 20 Computer Numerically Controlled Tool Operators tasks - 20 automatable. The full task map - every task with exactly what to do about each - is in your Career Report.

Your report unlocks three concrete artifacts

Full task map

Every task scored with what to automate, augment, or protect.

Adjacent path comparison

Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.

30/60/90 plan + PDF

A keepable roadmap plus resume and LinkedIn repositioning.

Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.

Adjacent career paths

Relatedness shows how reachable a move may be. A path is labeled lower exposure only when its score is at least 6 points lower; every row shows the measured difference.

Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic
80% skills overlap; 6 points lower - lower exposure; Elevated band; ~US$52,800
View path
52
7 more adjacent paths with exposure deltas, salary, demand, and reachability in your Career Report.
Agent Reality Check (on-site workflow preview)

Implement changes to machine programs, and enter new specifications, using computers.

Check what deployed AI agents can attempt on tasks like this using dated capability evidence. You review the real task before any analysis runs.
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Your AI-Safe Career Report

Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.

Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026

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Important: This is an estimate of AI exposure, not a prediction that your job will disappear. It is designed to help you understand how your role may change and improve your career resilience.

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Computer Numerically Controlled Tool Operators - median pay by US state (BLS OEWS, USD)

California: US$56,510New York: US$53,340Texas: US$48,480Florida: US$46,170

Median annual wage, in USD. US national: US$50,690. More states are being added.

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