Will AI replace Tool Grinders, Filers, and Sharpeners?

Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Tool Grinders, Filers, and Sharpeners (United States, SOC 51-4194), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 4–10, 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 “Tool Grinders, Filers, and Sharpeners” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Study blueprints or layouts of metal workpieces to determine grinding procedures, and to plan machine setups and operational sequences” (38/100, low); “Compute numbers, widths, and angles of cutting tools, micrometers, scales, and gauges, and adjust tools to produce specified cuts” (29/100, low); “Monitor machine operations to determine whether adjustments are necessary, stopping machines when problems occur” (8/100, minimal). 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 “Tool Grinders, Filers, and Sharpeners” stay human?

About 100% 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: “Fit parts together in pre-assembly to ensure that dimensions are accurate” (0/100, minimal); “Place workpieces in electroplating solutions or apply pigments to surfaces of workpieces to highlight ridges and grooves” (0/100, minimal); “Attach workpieces to grinding machines and form specified sections and repair cracks, using welding or brazing equipment” (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 “Tool Grinders, Filers, and Sharpeners” do about AI?

Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 100% 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 Tool Grinders, Filers, and Sharpeners 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 18 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

Tool Grinders, Filers, and Sharpeners

monitoring machine operations to determine whether adjustments, setting up and operating grinding or polishing machines to grind metal workpieces and dressing grinding wheels, according to 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: selecting and mounting grinding wheels on machines 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: 0%
changing shape: 0%
staying human: 100%

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

Whole-job exposure score 5 out of 100 (4–10 allowing for uncertainty): minimal exposure, across 18 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 tool grinders, filers, and sharpeners 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 2026-08-05. Read the full method.

What shifts is the routine end of the work: the paper around the work, not the work.

Your week, as this page understands it

Perform precision smoothing, sharpening, polishing, or grinding of metal objects. The job title says “tool grinders”, “filers” or “sharpeners”: officially one job, several names. The real job is the part underneath: selecting and mounting grinding wheels on machines. 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 tool grinders, filers, and sharpeners is not one task. It is 18 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is selecting and mounting grinding wheels on machines, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 0%
changing shape: 0%
staying human: 100%

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 2026-08-05. 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.

1 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

0 tasks

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.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Changing shape

0 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.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Staying human

18 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.

Monitoring machine operations to determine whether adjustments
This work happens in the physical world: machine operations, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Monitor machine operations to determine whether adjustments are necessary, stopping machines when problems occur.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Spotting trouble comes from the sound and feel of the machine while standing at it.
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 2/4.
Selecting and mounting grinding wheels on machines
This work happens in the physical world: wheels, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Select and mount grinding wheels on machines, according to specifications, using hand tools and applying knowledge of abrasives and grinding procedures.” (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: Choosing and fitting a grinding wheel means physically mounting it on 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 2/4.
Inspecting feel and measuring workpieces to ensure that surfaces and dimensions meet specifications
This work happens in the physical world: feel, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Inspect, feel, and measure workpieces to ensure that surfaces and dimensions meet specifications.” (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: Judging a finished surface by feel and by measuring it needs the part in your hands.
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 1/4.
Computing numbers, widths and angles of cutting tools, micrometers, scales and gauges and adjusting tools to produce specified cuts
This work happens in the physical world: numbers, widths and angles of cutting tools, micrometers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Compute numbers, widths, and angles of cutting tools, micrometers, scales, and gauges, and adjust tools to produce specified cuts.” (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: The maths behind the cut is easy to automate, but the tool still has to be adjusted by hand.
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.
Performing basic maintenance, such as cleaning and lubricating machine parts
This work happens in the physical world: basic maintenance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform basic maintenance, such as cleaning and lubricating machine parts.” (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: Cleaning and oiling machine parts is hands-on maintenance.
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.
Removing and replacing worn or broken machine parts
This work happens in the physical world: worn, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Remove and replace worn or broken machine parts, using hand 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: Swapping a worn machine part means using tools on the machine itself.
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.
Studying blueprints or layouts of metal workpieces to determine grinding procedures
The ratings behind this row put blueprints well outside what today's tools can do on their own.
importance 4 · Core
Source: “Study blueprints or layouts of metal workpieces to determine grinding procedures, and to plan machine setups and operational sequences.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Reading a drawing and planning the grinding sequence is thinking work software can draft, though shop know-how still decides.
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 2/4.
Dressing grinding wheels, according to specifications
This work happens in the physical world: wheels, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Dress grinding wheels, according to specifications.” (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: Dressing a wheel is done by hand 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 2/4.
Setting up and operating grinding or polishing machines to grind metal workpieces
This work happens in the physical world: and operating grinding or polishing machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Set up and operate grinding or polishing machines to grind metal workpieces, such as dies, parts, and 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: Setting up and running a grinder is physical work 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 2/4.
Turning valves to direct flow of coolant against cutting wheels and workpieces during grinding
This work happens in the physical world: valves, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Turn valves to direct flow of coolant against cutting wheels and workpieces during grinding.” (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: Turning a coolant valve is a hands-on action 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 2/4.

Show the other 8 tasks

Removing finished workpieces from machines and placing them in boxes or on racks
staying human
This work happens in the physical world: finished workpieces, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Remove finished workpieces from machines and place them in boxes or on racks, setting aside pieces that are defective.” (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: Lifting finished parts off the machine and sorting them 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 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Filing or finishing surfaces of workpieces
staying human
This work happens in the physical world: surfaces of workpieces, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “File or finish surfaces of workpieces, using prescribed hand 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: Filing and finishing a surface by hand cannot be done from a screen.
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.
Inspecting dies to detect defects
staying human
This work happens in the physical world: dies, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inspect dies to detect defects, assess wear, and verify specifications, using micrometers, steel gauge pins, and loupes.” (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: Checking a die with a micrometer and eyeglass needs the die in front of you.
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.
Duplicating workpiece contours, using tracer attachments
staying human
This work happens in the physical world: workpiece contours, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Duplicate workpiece contours, using tracer attachments.” (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: Copying a contour with a tracer attachment is machine work done by hand.
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.
Straightening workpieces and removing dents
staying human
This work happens in the physical world: workpieces, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Straighten workpieces and remove dents, using straightening presses and hammers.” (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: Straightening a bent part with a press or hammer is physical 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.
Attaching workpieces to grinding machines and form specified sections and repairing cracks
staying human
This work happens in the physical world: workpieces, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Attach workpieces to grinding machines and form specified sections and repair cracks, using welding or brazing 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: Clamping work in place and welding cracks is physical 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.
Placing workpieces in electroplating solutions or applying pigments to surfaces of workpieces to highlight ridges and grooves
staying human
This work happens in the physical world: workpieces, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Place workpieces in electroplating solutions or apply pigments to surfaces of workpieces to highlight ridges and grooves.” (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: Dipping parts in plating solution or applying pigment is hands-on 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.
Fitting parts together in pre-assembly to ensure that dimensions are accurate
staying human
This work happens in the physical world: parts together, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Fit parts together in pre-assembly to ensure that dimensions are accurate.” (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: Trial-fitting parts to check dimensions is done with the parts in your hands.
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 1/4.

What this job pays, and how many people do it

Median pay: $50,060a 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: 5,600in 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. Almost none of this job is reading one thing and writing another (the shape today's tools are built for), because the work turns on wheels, which happens with people and things rather than on a screen. The rows above are the evidence rather than the reassurance: monitoring machine operations to determine whether adjustments and selecting and mounting grinding wheels on machines. The parts that are changing are the paperwork and the tools around the job, not the middle of it, 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: selecting and mounting grinding wheels on machines is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 0% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 100% in rows it is nowhere near. That is the position, measured across 18 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. The routine end of the work 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 the routine work, 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 machine operations 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 monitoring machine operations to determine whether adjustments. 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 tool grinders, filers, and sharpeners (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was grinding, lapping, polishing, and buffing machine tool setters, operators, and tenders, metal and plastic: only about 14% of its durable work is work you already do. And on the numbers you do not need one. This job scores 5/100 here, with only 0% of the task list in the top band, and “monitor machine operations to determine whether adjustments are necessary, stopping machines when…” 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.

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 set up and operate grinding or polishing machines to grind metal workpieces, and their equivalent is to set up, operate, or tend grinding and related tools that remove excess material…. Across both published task lists that is about 14% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 14% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Grinding and Polishing Workers, Hand
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already set up and operate grinding or polishing machines to grind metal workpieces, and their equivalent is to move controls to adjust, start, or stop equipment during grinding and polishing processes. Across both published task lists that is about 14% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 14% 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: $42,660 against your $50,060, 14.8% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
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 remove and replace worn or broken machine parts, using hand tools, and their equivalent is to maintain machines and remove and replace broken or worn machine tools, using hand…. Across both published task lists that is about 10% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 10% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
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: 0% of its task weight, across 18 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 selecting and mounting grinding wheels on machines 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 working machine operatives 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

In UK official statistics this job is counted as Metal working machine operatives. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

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 tool grinders / filers / sharpeners launches. Nothing else.

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No Space for tool grinders / filers / sharpeners yet. Should there be one?

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Questions people ask about this job

Will AI replace Tool Grinders, Filers, and Sharpeners?: Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Tool Grinders, Filers, and Sharpeners (United States, SOC 51-4194), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 4–10, 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 “Tool Grinders, Filers, and Sharpeners” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Study blueprints or layouts of metal workpieces to determine grinding procedures, and to plan machine setups and operational sequences” (38/100, low); “Compute numbers, widths, and angles of cutting tools, micrometers, scales, and gauges, and adjust tools to produce specified cuts” (29/100, low); “Monitor machine operations to determine whether adjustments are necessary, stopping machines when problems occur” (8/100, minimal). 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 “Tool Grinders, Filers, and Sharpeners” stay human?: About 100% 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: “Fit parts together in pre-assembly to ensure that dimensions are accurate” (0/100, minimal); “Place workpieces in electroplating solutions or apply pigments to surfaces of workpieces to highlight ridges and grooves” (0/100, minimal); “Attach workpieces to grinding machines and form specified sections and repair cracks, using welding or brazing equipment” (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 “Tool Grinders, Filers, and Sharpeners” do about AI?: Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 100% 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 Tool Grinders, Filers, and Sharpeners 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 18 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.
1 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 2026-08-05.
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.

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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.