Will AI replace Cabinetmakers and Bench Carpenters?

Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Cabinetmakers and Bench Carpenters (United States, SOC 51-7011), 3% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 9 out of 100 (range 8–14, 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 “Cabinetmakers and Bench Carpenters” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Estimate the amounts, types, or costs of needed materials” (75/100, high); “Design furniture, using computer-aided drawing programs” (58/100, partial); “Program computers to operate machinery” (56/100, 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 “Cabinetmakers and Bench Carpenters” stay human?

About 87% 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: “Dip, brush, or spray assembled articles with protective or decorative finishes, such as stain, varnish, paint, or lacquer” (0/100, minimal); “Apply Masonite, formica, or vinyl surfacing materials” (0/100, minimal); “Repair or alter wooden furniture, cabinetry, fixtures, paneling, or other pieces” (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 “Cabinetmakers and Bench Carpenters” do about AI?

Start from the ledger rather than the headline: 3% of this job's weighted core work is exposed, and roughly 87% 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 Cabinetmakers and Bench Carpenters 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 20 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

Cabinetmakers and Bench Carpenters

verifying dimensions or checking the quality or fit of pieces to ensure adherence, estimating the amounts, types or costs of needed materials and reinforcing joints with nails or other fasteners to prepare articles. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: verifying dimensions or checking the quality or fit of pieces to ensure adherence to specifications 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: 3%
changing shape: 9%
staying human: 87%

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

Whole-job exposure score 9 out of 100 (8–14 allowing for uncertainty): minimal exposure, across 20 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 cabinetmakers and bench carpenters 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 estimating the amounts, types or costs of needed materials. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Cut, shape, and assemble wooden articles or set up and operate a variety of woodworking machines, such as power saws, jointers, and mortisers to surface, cut, or shape lumber or to fabricate parts for wood products. The job title says “cabinetmakers” or “bench carpenters”: officially one job, two names. The real job is the part underneath: verifying dimensions or checking the quality or fit of pieces to ensure adherence to specifications. 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 cabinetmakers and bench carpenters is not one task. It is 20 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is verifying dimensions or checking the quality or fit of pieces to ensure adherence to specifications, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 3%
changing shape: 9%
staying human: 87%

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.

3 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.

Estimating the amounts, types or costs of needed materials
This is reading one thing and writing another: the amounts, types or costs of needed materials in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Estimate the amounts, types, or costs of needed materials.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high 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: Working out quantities and costs of materials is calculation software already does well from a cut list.
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 0/4 · how much data exists 3/4.

Changing shape

3 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.

Establishing the specifications of articles
The software now makes the first pass at the specifications of articles, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Establish the specifications of articles to be constructed or repaired, or plan the methods or operations for shaping or assembling parts, based on blueprints, drawings, diagrams, or oral or written instructions.” (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: Turning drawings and instructions into specifications and a method is planning work software drafts reasonably.
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 furniture, using computer-aided drawing programs
The software now makes the first pass at furniture, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Design furniture, using computer-aided drawing programs.” (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: Design software and generated concepts help, but production-ready furniture drawings still need real design work.
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.
Programing computers to operate machinery
The software now makes the first pass at computers, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Program computers to operate machinery.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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; mistakes that are cheap to catch.
The rating behind it: Writing machine programs is coding work software does well, though it is proved out on the actual machine.
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 3/4.

Staying human

16 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.

Verifying dimensions or checking the quality or fit of pieces to ensure adherence to specifications
This work happens in the physical world: dimensions, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Verify dimensions or check the quality or fit of pieces to ensure adherence 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: Checking dimensions and how pieces actually fit means handling them with gauges in the workshop.
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 machines
This work happens in the physical world: or operating machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Set up or operate machines, including power saws, jointers, mortisers, tenoners, molders, or shapers, to cut, mold, or shape woodstock or wood substitutes.” (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 saws, jointers and shapers is hands-on machine 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.
Producing or assembling components of articles
This work happens in the physical world: components of articles, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Produce or assemble components of articles, such as store fixtures, office equipment, cabinets, or high-grade furniture.” (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: Making and assembling cabinets and furniture is physical craft work at the bench.
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 parts or subassemblies together to form completed units
This work happens in the physical world: parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Attach parts or subassemblies together to form completed units, using glue, dowels, nails, screws, or clamps.” (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: Fixing parts together with glue, nails and screws is manual assembly 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.
Measuring and marking dimensions of parts on paper or lumber stock prior
This work happens in the physical world: dimensions of parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Measure and mark dimensions of parts on paper or lumber stock prior to cutting, following blueprints, to ensure a tight fit and quality product.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 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: Cut lists can be worked out by software, but marking dimensions onto timber is done by hand.
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 3/4.
Reinforcing joints with nails or other fasteners to prepare articles for finishing
This work happens in the physical world: joints, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Reinforce joints with nails or other fasteners to prepare articles for finishing.” (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: Driving nails and fasteners into joints is physical 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.

Show the other 10 tasks

Drawing up detailed specifications and discussing projects with customers
staying human
The value here is that a specific person handles detailed specifications and stands behind it. That is earned, not computed.
importance 4 · Supplemental
Source: “Draw up detailed specifications and discuss projects with customers.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low 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: Specifications are easy to draft, but customers commit to a commission after talking it through with the maker.
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 2/4 · how much data exists 3/4.
Cutting timber to the right size
staying human
This work happens in the physical world: timber, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Cut timber to the right size, and shape and trim parts of joints to ensure a snug fit, using hand tools, such as planes, chisels, or wood files.” (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: Cutting and paring joints for a snug fit is manual skill with planes and chisels.
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.
Matching materials for color, grain or texture, giving attention to knots or other features of the wood
staying human
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Match materials for color, grain, or texture, giving attention to knots or other features of the wood.” (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: Matching boards for color and grain is done by eye and hand, and the judgment is largely unwritten.
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 0/4.
Trimming, sanding or scrape surfaces or joints to prepare articles for finishing
staying human
This work happens in the physical world: scrape surfaces, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Trim, sand, or scrape surfaces or joints to prepare articles for finishing.” (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: Trimming, sanding and scraping a surface is done by hand at the bench.
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.
Boring holes for insertion of screws or dowels
staying human
This work happens in the physical world: holes, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Bore holes for insertion of screws or dowels, by hand or using boring 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: Boring holes for screws and dowels is hands-on work at the bench.
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.
Performing final touch-ups with sandpaper or steel wool
staying human
This work happens in the physical world: final touch-ups, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform final touch-ups with sandpaper or steel wool.” (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: Final touch-ups with sandpaper or steel wool are done by hand on the piece.
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 hardware, such as hinges, handles, catches or drawer pulls, using hand tools
staying human
This work happens in the physical world: hardware, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Install hardware, such as hinges, handles, catches, or drawer pulls, 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: Fitting hinges, handles and drawer pulls is manual work with hand 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.
Repairing or altering wooden furniture
staying human
This work happens in the physical world: wooden furniture, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Repair or alter wooden furniture, cabinetry, fixtures, paneling, or other pieces.” (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: Repairing or altering existing furniture and paneling is physical craft on the actual piece.
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.
Applying Masonite, formica or vinyl surfacing materials
staying human
This work happens in the physical world: masonite, formica or vinyl surfacing materials, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Apply Masonite, formica, or vinyl surfacing materials.” (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: Applying laminate or vinyl surfacing to a panel 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.
Dipping brush or spraying assembled articles with protective or decorative finishes
staying human
This work happens in the physical world: brush, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Dip, brush, or spray assembled articles with protective or decorative finishes, such as stain, varnish, paint, or lacquer.” (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: Applying stain, varnish or lacquer to an assembled piece is physical finishing 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.

What this job pays, and how many people do it

Median pay: $46,680a 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: 77,170in 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: the amounts, types or costs of needed materials in, a record out. The rows above are exactly that shape: estimating the amounts, types or costs of needed materials and establishing the specifications of articles. What it cannot do is be there in the room, and that is still where dimensions 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: verifying dimensions or checking the quality or fit of pieces to ensure adherence to specifications is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 3% of this job's task weight sits in rows the software is already learning, 9% in rows that change shape rather than disappear, and 87% in rows it is nowhere near. That is the position, measured across 20 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. Estimating the amounts, types or costs of needed materials 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 amounts, types or costs of needed materials, 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 or operating machines 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 establishing the specifications of articles. 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 cabinetmakers and bench carpenters (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was model makers, wood: only about 10% of its durable work is work you already do and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 9/100 here, with only 3% of the task list in the top band, and “verify dimensions or check the quality or fit of pieces to ensure…” 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.

Model Makers, Wood
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already attach parts or subassemblies together to form completed units, using glue, dowels, nails…, and their equivalent is to fit, fasten, and assemble wood parts together to form patterns, models, or sections…. 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. And it is a narrow door: about 280 of those jobs against 77,170 of yours (OEWS May 2025), 0% as many seats.
Look at that job’s page anyway →
Furniture Finishers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already perform final touch-ups with sandpaper or steel wool, and their equivalent is to smooth, shape, and touch up surfaces to prepare them for finishing, using sandpaper…. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 14,480 of those jobs against 77,170 of yours (OEWS May 2025), 19% as many seats.
Look at that job’s page anyway →
Patternmakers, Wood
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already attach parts or subassemblies together to form completed units, using glue, dowels, nails…, and their equivalent is to fit, fasten, and assemble wood parts together to form patterns, models, or sections…. 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. And it is a narrow door: about 220 of those jobs against 77,170 of yours (OEWS May 2025), 0% as many seats.
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: 3% of its task weight, across 20 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 verifying dimensions or checking the quality or fit of pieces to ensure adherence to specifications 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 Furniture makers and other craft woodworkers 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 Furniture makers and other craft woodworkers. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

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Why there is no community here

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Questions people ask about this job

Will AI replace Cabinetmakers and Bench Carpenters?: Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Cabinetmakers and Bench Carpenters (United States, SOC 51-7011), 3% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 9 out of 100 (range 8–14, 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 “Cabinetmakers and Bench Carpenters” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Estimate the amounts, types, or costs of needed materials” (75/100, high); “Design furniture, using computer-aided drawing programs” (58/100, partial); “Program computers to operate machinery” (56/100, 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 “Cabinetmakers and Bench Carpenters” stay human?: About 87% 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: “Dip, brush, or spray assembled articles with protective or decorative finishes, such as stain, varnish, paint, or lacquer” (0/100, minimal); “Apply Masonite, formica, or vinyl surfacing materials” (0/100, minimal); “Repair or alter wooden furniture, cabinetry, fixtures, paneling, or other pieces” (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 “Cabinetmakers and Bench Carpenters” do about AI?: Start from the ledger rather than the headline: 3% of this job's weighted core work is exposed, and roughly 87% 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 Cabinetmakers and Bench Carpenters 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 20 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.
3 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.