Will AI replace Machinists?

Not as a job, but it is already doing parts of the work. Across the 29 official task statements scored for Machinists (United States, SOC 51-4041), 4% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 15 out of 100 (range 12–20, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Machinists” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Program computers or electronic instruments, such as numerically controlled machine tools” (62/100, high); “Prepare working sketches for the illustration of product appearance” (58/100, partial); “Advise clients about the materials being used for finished products” (53/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Machinists” stay human?

About 80% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Maintain machine tools in proper operational condition” (0/100, minimal); “Set up, adjust, or operate basic or specialized machine tools used to perform precision machining operations” (0/100, minimal); “Separate scrap waste and related materials for reuse, recycling, or disposal” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Machinists” do about AI?

Start from the ledger rather than the headline: 4% of this job's weighted core work is exposed, and roughly 80% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Machinists calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 29 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Machinists

calculating dimensions or tolerances, fitting and assembling parts to make or repair machine tools and conferring with numerical control programmers to check and ensure that new programs or machinery will function properly and that output will meet specifications. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: machining parts to specifications, using machine tools, such as lathes, milling machines, shapers or grinders is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 4%
changing shape: 16%
staying human: 80%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 15 out of 100 (12–20 allowing for uncertainty): minimal exposure, across 29 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for machinists is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

What shifts is programing computers or electronic instruments. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Set up and operate a variety of machine tools to produce precision parts and instruments out of metal. Includes precision instrument makers who fabricate, modify, or repair mechanical instruments. May also fabricate and modify parts to make or repair machine tools or maintain industrial machines, applying knowledge of mechanics, mathematics, metal properties, layout, and machining procedures. The job title says “machinists”. The real job is the part underneath: machining parts to specifications, using machine tools, such as lathes, milling machines, shapers or grinders. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But machinists is not one task. It is 29 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is machining parts to specifications, using machine tools, such as lathes, milling machines, shapers or grinders, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 4%
changing shape: 16%
staying human: 80%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

1 task

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Programing computers or electronic instruments
This is reading one thing and writing another: computers in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Program computers or electronic instruments, such as numerically controlled machine tools.” (O*NET task statement)
How this row was scored
Exposure score: 62 out of 100 (55–69 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing the instructions that tell a cutting machine what to do is documented computer work AI drafts well.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.

Changing shape

6 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Evaluating machining procedures and recommending changes or modifications for improved efficiency or adaptability
The software now makes the first pass at procedures, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Evaluate machining procedures and recommend changes or modifications for improved efficiency or adaptability.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Suggesting a faster or cheaper way to make a part is analysis software does well, though it needs a machinist's check.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Studying sample parts, blueprints, drawings or engineering information to determine methods or sequences of operations needed to fabricate products
The software now makes the first pass at sample parts, blueprints, drawings or engineering information, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Study sample parts, blueprints, drawings, or engineering information to determine methods or sequences of operations needed to fabricate products.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Planning the order of cuts from a drawing is something software can rough out, though shop know-how decides the final sequence.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Designing fixtures, tooling or experimental parts to meet special engineering needs
The software now makes the first pass at fixtures, tooling or experimental parts, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Design fixtures, tooling, or experimental parts to meet special engineering needs.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: AI can draft a workable fixture design, but making it hold a real part properly needs an experienced eye and testing.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Establishing work procedures for fabricating new structural products
The software now makes the first pass at work procedures, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Establish work procedures for fabricating new structural products, using a variety of metalworking machines.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing a step-by-step method for making a part is documentation software drafts well, though the method comes from experience.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing working sketches for the illustration of product appearance
The software now makes the first pass at sketches, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Prepare working sketches for the illustration of product appearance.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Producing a neat sketch of how a product should look is drawing work AI handles, though dimensions need a person to confirm.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Staying human

22 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Calculating dimensions or tolerances, using instruments, such as micrometers or vernier calipers
This work happens in the physical world: dimensions, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Calculate dimensions or tolerances, using instruments, such as micrometers or vernier calipers.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (9–23 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The tolerance maths is routine for software, but reading a micrometer means holding the part and the gauge.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Machining parts to specifications, using machine tools, such as lathes, milling machines, shapers or grinders
This work happens in the physical world: parts, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Machine parts to specifications, using machine tools, such as lathes, milling machines, shapers, or grinders.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Machining parts to a drawing means operating the lathe or mill yourself.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Measuring, examining
This work happens in the physical world: completed units, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Measure, examine, or test completed units to check for defects and ensure conformance to specifications, using precision instruments, such as micrometers.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Measuring a finished part for defects means picking it up and putting instruments on it.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Maintaining machine tools in proper operational condition
This work happens in the physical world: machine tools, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Maintain machine tools in proper operational condition.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Keeping machine tools in good order means cleaning, adjusting and servicing them.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Show the other 19 tasks

Advising clients about the materials being used for finished products
changing shape
The software now makes the first pass at clients, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Advise clients about the materials being used for finished products.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Material properties are thoroughly documented, so software can explain the trade-offs, though the client conversation involves a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Conferring with numerical control programmers to check and ensure that new programs or machinery will function properly and that output will meet specifications
staying human
The ratings behind this row put numerical control programmers well outside what today's tools can do on their own.
importance 4 · Core
Source: “Confer with numerical control programmers to check and ensure that new programs or machinery will function properly and that output will meet specifications.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking a program against the part it should make is desk work software helps with, though the conversation matters.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Conferring with engineering, supervisory or manufacturing personnel to exchange technical information
staying human
The value here is that a specific person handles engineering, supervisory or manufacturing personnel and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Confer with engineering, supervisory, or manufacturing personnel to exchange technical information.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Written summaries and drawings can be prepared automatically, but the back-and-forth with engineering is a human conversation.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Testing experimental models under simulated operating conditions
staying human
This work happens in the physical world: experimental models under simulated operating conditions, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Test experimental models under simulated operating conditions, for purposes such as development, standardization, or feasibility of design.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Some of this testing now happens in simulation software, but proving a physical model still means running it on a rig.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring the feed and speed of machines during the machining process
staying human
This work happens in the physical world: the feed and speed of machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Monitor the feed and speed of machines during the machining process.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Machines report their own speeds and feeds, but noticing a cut going wrong still means being at the machine.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Supporting metalworking projects from planning and fabrication through assembly
staying human
This work happens in the physical world: projects, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Support metalworking projects from planning and fabrication through assembly, inspection, and testing, using knowledge of machine functions, metal properties, and mathematics.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: The planning end can be drafted by software, but fabricating, assembling and testing the parts is hands-on work.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Diagnosing machine tool malfunctions to determine need for adjustments or repairs
staying human
This work happens in the physical world: machine tool malfunctions, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Diagnose machine tool malfunctions to determine need for adjustments or repairs.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Fault guides and machine alarms help narrow things down, but working out what is wrong means being at the machine.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Aligning and securing holding fixtures
staying human
This work happens in the physical world: fixtures, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Align and secure holding fixtures, cutting tools, attachments, accessories, or materials onto machines.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Clamping fixtures, tools and material onto a machine is physical setup work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Laying out, measure and marking metal stock to display placement of cuts
staying human
This work happens in the physical world: out, measure and marking metal stock, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Lay out, measure, and mark metal stock to display placement of cuts.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Measuring and marking metal stock is bench work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Checking work pieces to ensure that they are properly
staying human
This work happens in the physical world: work pieces, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Check work pieces to ensure that they are properly lubricated or cooled.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Checking that a workpiece is lubricated and cool means looking at it at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Operating equipment to verify operational efficiency
staying human
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Operate equipment to verify operational efficiency.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Running equipment to see how well it works means operating it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Installing repaired parts into equipment or installing new equipment
staying human
This work happens in the physical world: repaired parts, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Install repaired parts into equipment or install new equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Fitting repaired or new equipment into place is physical installation work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Dismantling machines or equipment
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Dismantle machines or equipment, using hand tools or power tools to examine parts for defects and replace defective parts where needed.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Taking a machine apart to find and replace faulty parts is done with tools.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Fitting and assembling parts to make or repair machine tools
staying human
This work happens in the physical world: parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Fit and assemble parts to make or repair machine tools.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Fitting and assembling parts of a machine tool is done with hands and tools.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Installing experimental parts or assemblies
staying human
This work happens in the physical world: experimental parts, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Install experimental parts or assemblies, such as hydraulic systems, electrical wiring, lubricants, or batteries into machines or mechanisms.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Fitting experimental parts, wiring and hydraulics into a machine is hands-on assembly.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Setting up or operating metalworking
staying human
This work happens in the physical world: this work, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Set up or operate metalworking, brazing, heat-treating, welding, or cutting equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Welding, brazing and heat-treating are skilled physical operations.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Disposing of scrap or waste material in accordance with company policies and environmental regulations
staying human
This work happens in the physical world: scrap, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Dispose of scrap or waste material in accordance with company policies and environmental regulations.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Getting scrap and waste out of the shop and into the right place is physical handling.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Separating scrap waste and related materials
staying human
This work happens in the physical world: scrap waste, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Separate scrap waste and related materials for reuse, recycling, or disposal.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Sorting scrap for reuse or recycling means physically separating materials.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Setting up, adjust or operating basic or specialized machine tools used to perform precision machining operations
staying human
This work happens in the physical world: up, adjust or operating basic or specialized machine, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Set up, adjust, or operate basic or specialized machine tools used to perform precision machining operations.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Setting up and running a machine tool is done standing at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

What this job pays, and how many people do it

Median pay: $58,750a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 287,050in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: computers in, a record out. The rows above are exactly that shape: programing computers or electronic instruments and evaluating machining procedures and recommending changes or modifications for improved efficiency or adaptability. What it cannot do is be there in the room, and that is still where parts get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: machining parts to specifications, using machine tools, such as lathes, milling machines, shapers or grinders is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 4% of this job's task weight sits in rows the software is already learning, 16% in rows that change shape rather than disappear, and 80% in rows it is nowhere near. That is the position, measured across 29 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Programing computers or electronic instruments is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to computers, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether parts are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near evaluating machining procedures and recommending changes or modifications for improved efficiency or adaptability. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to machinists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was computer numerically controlled tool operators: only about 12% of its durable work is work you already do and it pays 13.7% less. And on the numbers you do not need one. This job scores 15/100 here, with only 4% of the task list in the top band, and “calculate dimensions or tolerances, using instruments” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Computer Numerically Controlled Tool Operators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already check work pieces to ensure that they are properly lubricated or cooled, and their equivalent is to check to ensure that workpieces are properly lubricated and cooled during machine operation. Across both published task lists that is about 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $50,690 against your $58,750, 13.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already measure, examine, or test completed units to check for defects and ensure conformance…, and their equivalent is to inspect or measure finished workpieces to determine conformance to specifications, using measuring instruments. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $46,550 against your $58,750, 20.8% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 67,000 of those jobs against 287,050 of yours (OEWS May 2025), 23% as many seats.
Look at that job’s page anyway →
Maintenance and Repair Workers, General
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already diagnose machine tool malfunctions to determine need for adjustments or repairs, and their equivalent is to inspect, operate, or test machinery or equipment to diagnose machine malfunctions. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $49,590 against your $58,750, 15.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 4% of its task weight, across 29 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like machining parts to specifications, using machine tools, such as lathes, milling machines, shapers or grinders arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Metal machining setters and setter-operators is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

Metal working machine operatives

In UK official statistics this job is counted as Metal machining setters and setter-operators and Metal working machine operatives. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Noted, and thank you. We’ll email you if a Space for machinists launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for machinists yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for machinists existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for machinists launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Machinists?: Not as a job, but it is already doing parts of the work. Across the 29 official task statements scored for Machinists (United States, SOC 51-4041), 4% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 15 out of 100 (range 12–20, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Machinists” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Program computers or electronic instruments, such as numerically controlled machine tools” (62/100, high); “Prepare working sketches for the illustration of product appearance” (58/100, partial); “Advise clients about the materials being used for finished products” (53/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Machinists” stay human?: About 80% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Maintain machine tools in proper operational condition” (0/100, minimal); “Set up, adjust, or operate basic or specialized machine tools used to perform precision machining operations” (0/100, minimal); “Separate scrap waste and related materials for reuse, recycling, or disposal” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Machinists” do about AI?: Start from the ledger rather than the headline: 4% of this job's weighted core work is exposed, and roughly 80% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Machinists calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 29 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

How we score a job Download this release Look up another job

Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.