Will AI replace Metal-Refining Furnace Operators and Tenders?

Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Metal-Refining Furnace Operators and Tenders (United States, SOC 51-4051), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 10 out of 100 (range 7–14, 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 “Metal-Refining Furnace Operators and Tenders” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Record production data, and maintain production logs” (56/100, partial); “Draw smelted metal samples from furnaces or kettles for analysis, and calculate types and amounts of materials needed to ensure that materials meet specifica…” (25/100, low); “Observe operations inside furnaces, using television screens, to ensure that problems do not occur” (24/100, low). 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 “Metal-Refining Furnace Operators and Tenders” stay human?

About 92% 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: “Scrape accumulations of metal oxides from floors, molds, and crucibles, and sift and store them for reclamation” (0/100, minimal); “Prepare material to load into furnaces, including cleaning, crushing, or applying chemicals, by using crushing machines, shovels, rakes, or sprayers” (0/100, minimal); “Sprinkle chemicals over molten metal to bring impurities to the surface” (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 “Metal-Refining Furnace Operators and Tenders” do about AI?

Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 92% 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 Metal-Refining Furnace Operators and Tenders 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 15 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

Metal-Refining Furnace Operators and Tenders

regulating supplies of fuel and air, observing air and temperature gauges or metal color and fluidity and operating controls to move or discharge metal workpieces from furnaces. 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 furnaces and equipment to locate defects and wear 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: 8%
staying human: 92%

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

Whole-job exposure score 10 out of 100 (7–14 allowing for uncertainty): minimal exposure, across 15 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 metal-refining furnace operators and tenders 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 data and maintaining production logs: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Operate or tend furnaces, such as gas, oil, coal, electric-arc or electric induction, open-hearth, or oxygen furnaces, to melt and refine metal before casting or to produce specified types of steel. The job title says “metal-refining furnace operators” or “tenders”: officially one job, two names. The real job is the part underneath: inspecting furnaces and equipment to locate defects and wear. 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 metal-refining furnace operators and tenders is not one task. It is 15 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is inspecting furnaces and equipment to locate defects and wear, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 0%
changing shape: 8%
staying human: 92%

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.

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

1 task

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.

Recording production data and maintaining production logs
The software now makes the first pass at production data, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Record production data, and maintain production logs.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (52–60 allowing for uncertainty): partial 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: Production logs are structured records that software fills in from instrument data.
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

14 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 furnaces and equipment to locate defects and wear
This work happens in the physical world: furnaces, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect furnaces and equipment to locate defects and wear.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Finding wear and damage on a furnace means walking up to it and looking.
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.
Observing air and temperature gauges or metal color and fluidity
This work happens in the physical world: air, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Observe air and temperature gauges or metal color and fluidity, and turn fuel valves or adjust controls to maintain required temperatures.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 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 metal color and turning fuel valves means standing at the furnace.
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.
Regulating supplies of fuel and air
This work happens in the physical world: supplies of fuel, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Regulate supplies of fuel and air, or control flow of electric current and water coolant to heat furnaces and adjust temperatures.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 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 fuel, air and coolant on a live furnace means being at the controls on the plant floor.
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.
Operating controls to move or discharge metal workpieces from furnaces
This work happens in the physical world: controls, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Operate controls to move or discharge metal workpieces from furnaces.” (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 hot metal out of a furnace 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.
Drawing smelted metal samples from furnaces or kettles
This work happens in the physical world: smelted metal samples, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Draw smelted metal samples from furnaces or kettles for analysis, and calculate types and amounts of materials needed to ensure that materials meet specifications.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The charge calculations are straightforward math for software, but drawing a sample from molten metal is hands on.
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 2/4.
Draining, transferring
This work happens in the physical world: molten metal, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Drain, transfer, or remove molten metal from furnaces, and place it into molds, using hoists, pumps, or ladles.” (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: Handling molten metal with ladles and hoists 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.
Weighing materials to be charged into furnaces
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Weigh materials to be charged into furnaces, using scales.” (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: Weighing material into a furnace 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 2/4.
Kindling fires
This work happens in the physical world: fires, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Kindle fires, and shovel fuel and other materials into furnaces or onto conveyors by hand, with hoists, or by directing crane operators.” (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: Shoveling fuel and materials into a furnace is manual labor.
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.
Preparing material to load into furnaces
This work happens in the physical world: material, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Prepare material to load into furnaces, including cleaning, crushing, or applying chemicals, by using crushing machines, shovels, rakes, or sprayers.” (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: Crushing, cleaning and loading furnace material is manual work with tools and machines.
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 5 tasks

Observing operations inside furnaces, using television screens, to ensure that problems do not occur
staying human
The ratings behind this row put operations inside furnaces well outside what today's tools can do on their own.
importance 4 · Supplemental
Source: “Observe operations inside furnaces, using television screens, to ensure that problems do not occur.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the five ratings behind the score, with no single dominant reason.
The rating behind it: This is watching a screen, but software that reliably watches a furnace is not something an employer can simply buy.
The five ratings: output a model can produce 1/4 · 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.
Directing work crews in the cleaning and repair of furnace walls and flooring
staying human
This work happens in the physical world: work crews, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Direct work crews in the cleaning and repair of furnace walls and flooring.” (O*NET task statement)
How this row was scored
Exposure score: 4 out of 100 (0–8 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Directing a repair crew inside a furnace means being on site with them.
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 2/4 · how much data exists 1/4.
Removing impurities from the surface of molten metal
staying human
This work happens in the physical world: impurities, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Remove impurities from the surface of molten metal, using strainers.” (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: Skimming impurities off molten metal is done by hand with a strainer.
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.
Sprinkling chemicals over molten metal to bring impurities to the surface
staying human
This work happens in the physical world: chemicals over molten metal, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Sprinkle chemicals over molten metal to bring impurities to the surface.” (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: Sprinkling chemicals onto molten metal is hands on work at the furnace.
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.
Scraping accumulations of metal oxides from floors
staying human
This work happens in the physical world: accumulations of metal oxides, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Scrape accumulations of metal oxides from floors, molds, and crucibles, and sift and store them for reclamation.” (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: Scraping and sifting metal oxide off floors and molds is manual 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.

What this job pays, and how many people do it

Median pay: $54,430a 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: 16,780in 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 data in, a record out. The rows above are exactly that shape: recording production data and maintaining production logs. What it cannot do is be there in the room, and that is still where furnaces 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 furnaces and equipment to locate defects and wear 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, 8% in rows that change shape rather than disappear, and 92% in rows it is nowhere near. That is the position, measured across 15 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 data and maintaining production logs 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 data, 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 furnaces 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 recording production data and maintaining production logs. 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 metal-refining furnace operators and tenders (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was furnace, kiln, oven, drier, and kettle operators and tenders: only about 8% of its durable work is work you already do and it pays 11.7% less. And on the numbers you do not need one. This job scores 10/100 here, with only 0% of the task list in the top band, and “inspect furnaces and equipment to locate defects and wear” 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.

Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already weigh materials to be charged into furnaces, using scales, and their equivalent is to weigh or measure specified amounts of ingredients or materials for processing, using devices. 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. It is a pay cut, in those words: $48,040 against your $54,430, 11.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Food Cooking Machine Operators and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already weigh materials to be charged into furnaces, using scales, and their equivalent is to measure or weigh ingredients, using scales or measuring containers. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $41,590 against your $54,430, 23.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Extruding, Forming, Pressing, and Compacting 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 materials to be charged into furnaces, using scales, and their equivalent is to examine, measure, and weigh materials or products to verify conformance to standards, using…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $45,760 against your $54,430, 15.9% 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: 0% of its task weight, across 15 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 furnaces and equipment to locate defects and wear 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 making and treating process 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 making and treating process 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 metal-refining furnace operators / tenders launches. Nothing else.

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

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

No Space for metal-refining furnace operators / tenders 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 metal-refining furnace operators / tenders 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 metal-refining furnace operators / tenders 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 Metal-Refining Furnace Operators and Tenders?: Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Metal-Refining Furnace Operators and Tenders (United States, SOC 51-4051), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 10 out of 100 (range 7–14, 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 “Metal-Refining Furnace Operators and Tenders” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record production data, and maintain production logs” (56/100, partial); “Draw smelted metal samples from furnaces or kettles for analysis, and calculate types and amounts of materials needed to ensure that materials meet specifica…” (25/100, low); “Observe operations inside furnaces, using television screens, to ensure that problems do not occur” (24/100, low). 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 “Metal-Refining Furnace Operators and Tenders” stay human?: About 92% 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: “Scrape accumulations of metal oxides from floors, molds, and crucibles, and sift and store them for reclamation” (0/100, minimal); “Prepare material to load into furnaces, including cleaning, crushing, or applying chemicals, by using crushing machines, shovels, rakes, or sprayers” (0/100, minimal); “Sprinkle chemicals over molten metal to bring impurities to the surface” (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 “Metal-Refining Furnace Operators and Tenders” do about AI?: Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 92% 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 Metal-Refining Furnace Operators and Tenders 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 15 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.
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.

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

Using these figures?

Cite this

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

Plain text

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

BibTeX

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

Permalink

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

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