Machinists
Machinists — AI exposure, safer roles, and a pivot plan.
Also known as: Carbide Operator · Aircraft Machinist · Conventional Machinist · Auto Machinist (Automotive Machinist) · CNC Machinist (Computer Numerical Control Machinist) · CNC Machinist (Computer Numeric Controlled Machinist)
This score estimates how exposed the tasks in a role are to current and near-term AI capabilities. It does not predict whether a specific person will lose a job.
Most exposed tasks
Highest structured exposure values in this role’s task mix — the work AI systems can already do most of.
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Check work pieces to ensure that they are properly lubricated or cooled.61
Augmentable tasks
Work where AI assists rather than replaces — the productivity frontier of this role.
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Calculate dimensions or tolerances, using instruments, such as micrometers or vernier calipers.59
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Study sample parts, blueprints, drawings, or engineering information to determine methods or sequences of operations needed to fabricate products.59
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Monitor the feed and speed of machines during the machining process.58
Most durable tasks
Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.
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Design fixtures, tooling, or experimental parts to meet special engineering needs.34
Task exposure values and classifications come from the versioned data release — they are structured data, not model output. Bars show exposure contribution relative to this role’s task mix.
What this means
A score of 50 puts Machinists in the third quartile of analyzed occupations. In practice, exposure this high is about the mix: 1 of 20 analyzed tasks lean automatable, 18 augmentable, and 1 durable. The useful question isn’t “will AI take this job” — it’s which tasks go first, which get faster, and where to reposition time. That’s what the personalized report maps against your actual week.
One next move: audit how much of your week sits in the exposed tasks above — then shift time toward the durable set or investigate the adjacent roles below.
Lower-exposure adjacent roles
Shown only when the target is at least 10 points lower under the same score version and skill overlap is at least 50%. These are adjacent roles with lower task exposure — not guaranteed “safe careers”.
Labor-market context
- $58,750median wage
- 287,050employed
- 29,500annual openings
- +0.0%projected growth
Context only — labor statistics are not inputs to the exposure score. See methodology.
Your week probably doesn’t match the average
This page scores the occupation. The $9 Personalized Risk & Action Report scores your task mix — paste what you actually do and get your own score, confidence level, task matrix, human moat, and a 7/30/90-day plan.
Personalize my result — $9Related roles
Adjacent by skills or family — no exposure claim implied.
FAQ — Machinists
- What does a score of 50 mean for a Machinists?
- It means that, weighted across the 20 tasks we analyzed for this role, the task mix sits at 50 on a 0–100 exposure scale — in the third quartile of analyzed occupations. It measures task exposure to current and near-term AI capabilities, not the probability of losing a job.
- Which tasks in this role are most exposed to AI?
- The highest-exposure tasks are: Check work pieces to ensure that they are properly lubricated or cooled. Exposure is scored per task from structured data, not generated by a language model.
- Which parts of this job are most durable?
- The most durable responsibilities are: Design fixtures, tooling, or experimental parts to meet special engineering needs. Durable tasks typically depend on judgment, relationships, physical presence, or accountability.
- Is this score personalized to me?
- No — this page shows the occupation-level baseline. Two people with the same title often do different work. The $9 personalized report recalculates the score from the tasks you actually do and builds a concrete 7/30/90-day plan around them.
Score version jr-v1 · data release 2026.07.11-r1 · updated 2026-07-11 · baseline mapping: 20 of 20 tasks carry source-level provenance · methodology