Will AI replace Machine Feeders and Offbearers?

Not as a job, but it is already doing parts of the work. Across the 13 official task statements scored for Machine Feeders and Offbearers (United States, SOC 53-7063), 10% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 7 out of 100 (range 7–12, 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 “Machine Feeders and Offbearers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Record production and operational data, such as amount of materials processed” (69/100, high); “Inspect materials and products for defects, and to ensure conformance to specifications” (0/100, minimal); “Remove materials and products from machines and equipment, and place them in boxes, trucks or conveyors, using hand tools and moving devices” (0/100, 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 “Machine Feeders and Offbearers” stay human?

About 90% 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: “Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport” (0/100, minimal); “Open and close gates of belt and pneumatic conveyors on machines that are fed directly from preceding machines” (0/100, minimal); “Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks” (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 “Machine Feeders and Offbearers” do about AI?

Start from the ledger rather than the headline: 10% of this job's weighted core work is exposed, and roughly 90% 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 Machine Feeders and Offbearers 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 13 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

Machine Feeders and Offbearers

inspecting materials and products, identifying and marking materials, products and samples, following instructions and shoveling or scooping materials into containers. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: inspecting materials and products 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: 10%
changing shape: 0%
staying human: 90%

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

Whole-job exposure score 7 out of 100 (7–12 allowing for uncertainty): minimal exposure, across 13 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 machine feeders and offbearers 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 recording production and operational data: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Feed materials into or remove materials from machines or equipment that is automatic or tended by other workers. The job title says “machine feeders” or “offbearers”: officially one job, two names. The real job is the part underneath: inspecting materials and products. 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 machine feeders and offbearers is not one task. It is 13 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is inspecting materials and products, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 10%
changing shape: 0%
staying human: 90%

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.

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.

Recording production and operational data
This is reading one thing and writing another: production in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record production and operational data, such as amount of materials processed.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 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: Logging output and material used is simple data entry, often captured by the machine already.
The five ratings: output a model can produce 4/4 · 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.

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

12 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.

Inspecting materials and products
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect materials and products for defects, and to ensure conformance to specifications.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 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: Checking materials for flaws happens with the items in your hands at the machine.
The five ratings: output a model can produce 2/4 · 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.
Cleaning and maintaining machinery, equipment and work areas to ensure proper functioning and safe working conditions
This work happens in the physical world: machinery, equipment and work areas, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean and maintain machinery, equipment, and work areas to ensure proper functioning and safe working conditions.” (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 maintaining machines and work areas 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.
Pushing dual control buttons and moving controls
This work happens in the physical world: dual control buttons, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Push dual control buttons and move controls to start, stop, or adjust machinery and 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: Starting and adjusting machinery means pressing the controls 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 2/4.
Weighing or measuring materials or products to ensure conformance to specifications
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Weigh or measure materials or products to ensure conformance to specifications.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 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: Weighing and measuring material means physically handling it at the line.
The five ratings: output a model can produce 2/4 · 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.
Identifying and marking materials, products and samples, following instructions
This work happens in the physical world: materials, products and samples, following instructions, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Identify and mark materials, products, and samples, following instructions.” (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: Marking and labeling products is done by hand on the item.
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 2/4.
Transferring materials and products to and from machinery and equipment
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks.” (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: Moving material around with hand or industrial trucks 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 1/4.
Loading materials and products into machines and equipment
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Load materials and products into machines and equipment, or onto conveyors, using hand tools and moving devices.” (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: Loading materials into machines and onto conveyors is manual 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.
Fastening, packaging or stacking materials and products, using hand tools and fastening equipment
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Fasten, package, or stack materials and products, using hand tools and fastening 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: Fastening, packing and stacking products is 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 1/4.
Removing materials and products from machines and equipment
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Remove materials and products from machines and equipment, and place them in boxes, trucks or conveyors, using hand tools and moving devices.” (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 products off machines and boxing them is manual 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.

Show the other 3 tasks

Adding chemicals, solutions or ingredients to machines or equipment as required by the manufacturing process
staying human
This work happens in the physical world: chemicals, solutions or ingredients, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Add chemicals, solutions, or ingredients to machines or equipment as required by the manufacturing process.” (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: Adding chemicals or ingredients to a machine means being there to put them in.
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.
Opening and closing gates of belt and pneumatic conveyors on machines that are fed directly from preceding machines
staying human
This work happens in the physical world: gates of belt, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Open and close gates of belt and pneumatic conveyors on machines that are fed directly from preceding 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: Opening and closing conveyor gates is done 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 1/4.
Shoveling or scooping materials into containers
staying human
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport.” (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: Shovelling material into containers or machines is entirely physical.
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: $41,220a 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: 42,330in 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: production in, a record out. The rows above are exactly that shape: recording production and operational data. What it cannot do is be there in the room, and that is still where materials 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: inspecting materials and products is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 10% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 90% in rows it is nowhere near. That is the position, measured across 13 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. Recording production and operational data 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 production, 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 machinery, equipment and work areas 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 inspecting materials and products. 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 machine feeders and offbearers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was mixing and blending machine setters, operators, and tenders: only about 18% of its durable work is work you already do. And on the numbers you do not need one. This job scores 7/100 here, with only 10% of the task list in the top band, and “inspect materials and products for defects, and to ensure conformance to specifications” 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.

Mixing and Blending Machine Setters, Operators, and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already weigh or measure materials or products to ensure conformance to specifications, and their equivalent is to weigh or measure materials, ingredients, or products to ensure conformance to requirements. Across both published task lists that is about 18% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 18% 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 →
Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already weigh or measure materials or products to ensure conformance to specifications, and their equivalent is to weigh or measure materials, ingredients, or products at specified intervals to ensure conformance…. Across both published task lists that is about 18% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 18% 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 →
Packers and Packagers, Hand
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already weigh or measure materials or products to ensure conformance to specifications, and their equivalent is to measure, weigh, and count products and materials. Across both published task lists that is about 15% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 15% 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: $36,280 against your $41,220, 12.0% 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: 10% of its task weight, across 13 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 inspecting materials and products 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 Elementary process plant occupations n.e.c. 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:

Industrial cleaning process occupations

In UK official statistics this job is counted as Elementary process plant occupations n.e.c. and Industrial cleaning process occupations. 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

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for machine feeders / offbearers, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 10% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for machine feeders / offbearers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for machine feeders / offbearers launches. Nothing else.

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No Space for machine feeders / offbearers 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 machine feeders / offbearers 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 machine feeders / offbearers 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 Machine Feeders and Offbearers?: Not as a job, but it is already doing parts of the work. Across the 13 official task statements scored for Machine Feeders and Offbearers (United States, SOC 53-7063), 10% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 7 out of 100 (range 7–12, 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 “Machine Feeders and Offbearers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record production and operational data, such as amount of materials processed” (69/100, high); “Inspect materials and products for defects, and to ensure conformance to specifications” (0/100, minimal); “Remove materials and products from machines and equipment, and place them in boxes, trucks or conveyors, using hand tools and moving devices” (0/100, 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 “Machine Feeders and Offbearers” stay human?: About 90% 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: “Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport” (0/100, minimal); “Open and close gates of belt and pneumatic conveyors on machines that are fed directly from preceding machines” (0/100, minimal); “Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks” (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 “Machine Feeders and Offbearers” do about AI?: Start from the ledger rather than the headline: 10% of this job's weighted core work is exposed, and roughly 90% 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 Machine Feeders and Offbearers 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 13 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 2026-08-05.
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.