Will AI replace Mixing and Blending Machine Setters, Operators, and Tenders?

Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Mixing and Blending Machine Setters, Operators, and Tenders (United States, SOC 51-9023), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 5–10, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

What should someone working in “Mixing and Blending Machine Setters, 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 94% 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.

US dataswitch to UK

Mixing and Blending Machine Setters, Operators, and Tenders

weighing or measuring materials, ingredients or products to ensure conformance, examining materials, collecting samples of materials or products for laboratory testing and unloading mixtures into containers or onto conveyors for further processing. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: weighing or measuring materials, ingredients or products to ensure conformance to requirements 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: 6%
staying human: 94%

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

Whole-job exposure score 5 out of 100 (5–10 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 mixing and blending machine setters, 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 reading work orders to determine production specifications or information: the paper around the work, not the work.

Your week, as this page understands it

Set up, operate, or tend machines to mix or blend materials, such as chemicals, tobacco, liquids, color pigments, or explosive ingredients. The job title says “mixing”, “blending machine setters”, “operators” or “tenders”: officially one job, several names. The real job is the part underneath: weighing or measuring materials, ingredients or products to ensure conformance to requirements. 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 mixing and blending machine setters, operators, and tenders 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 weighing or measuring materials, ingredients or products to ensure conformance to requirements, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 0%
changing shape: 6%
staying human: 94%

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

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.

Reading work orders to determine production specifications or information
The software now makes the first pass at work orders, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Read work orders to determine production specifications or information.” (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: Pulling production details out of a work order is straightforward reading that software handles reliably.
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

19 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.

Weighing or measuring materials, ingredients or products to ensure conformance to requirements
This work happens in the physical world: materials, ingredients or products, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Weigh or measure materials, ingredients, or products to ensure conformance to requirements.” (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 and measuring physical ingredients has to be done by someone at the scale.
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.
Observing production or monitoring equipment to ensure safe and efficient operation
This work happens in the physical world: production, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Observe production or monitor equipment to ensure safe and efficient operation.” (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: work that happens in the physical world.
The rating behind it: Watching the line for safe running means being there with 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.
Cleaning work areas
This work happens in the physical world: work areas, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean work areas.” (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 a work area is physical work in that space.
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.
Mixing or blending ingredients by starting machines and mixing for specified times
This work happens in the physical world: ingredients, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Mix or blend ingredients by starting machines and mixing for specified times.” (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 and running a mixer is physical 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.
Stopping mixing or blending machines when specified product qualities are obtained and open valves and starting pumps to transfer mixtures
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Stop mixing or blending machines when specified product qualities are obtained and open valves and start pumps to transfer mixtures.” (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: Stopping machines and opening valves at the right moment is done by hand at the equipment.
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.
Cleaning and maintaining equipment, using hand tools
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean and maintain equipment, 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: Cleaning and maintaining equipment with hand tools 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.
Recording operational or production data on specified forms
This work happens in the physical world: operational, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Record operational or production data on specified forms.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Writing down production figures is simple record work, but the readings come from the machine itself.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Examining materials, ingredients or products visually or with hands to ensure conformance to established standards
This work happens in the physical world: materials, ingredients or products, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Examine materials, ingredients, or products visually or with hands to ensure conformance to established standards.” (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 materials by eye and by touch requires being at the product.
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.
Unloading mixtures into containers or onto conveyors for further processing
This work happens in the physical world: mixtures, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Unload mixtures into containers or onto conveyors for further processing.” (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: Unloading mixtures into containers or 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.

Show the other 10 tasks

Testing samples of materials or products to ensure compliance with specifications
staying human
This work happens in the physical world: samples of materials, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Test samples of materials or products to ensure compliance with specifications, using test equipment.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Running a physical test on a sample needs someone at the equipment, though the result write-up does not.
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 or tending machines to mix or blend any of a wide variety of materials
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Operate or tend machines to mix or blend any of a wide variety of materials, such as spices, dough batter, tobacco, fruit juices, chemicals, livestock feed, food products, color pigments, or explosive ingredients.” (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: Operating and tending mixing machinery is physical work on the plant floor.
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.
Dumping or pouring specified amounts of materials into machinery or equipment
staying human
This work happens in the physical world: specified amounts of materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Dump or pour specified amounts of materials into machinery or 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: Pouring measured materials into machinery is a physical act.
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.
Collecting samples of materials or products for laboratory testing
staying human
This work happens in the physical world: samples of materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Collect samples of materials or products for laboratory testing.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Taking a physical sample for the lab means handling the product.
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.
Adding or mixing chemicals or ingredients
staying human
This work happens in the physical world: chemicals, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Add or mix chemicals or ingredients for processing, using hand tools or other 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: Adding and mixing chemicals with hand tools 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 2/4.
Transferring materials, supplies or products between work areas, using moving equipment or hand tools
staying human
This work happens in the physical world: materials, supplies or products, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Transfer materials, supplies, or products between work areas, using moving equipment or 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: Moving materials between work areas is physical handling.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Tending accessory equipment, such as pumps or conveyors, to move materials or ingredients through production processes
staying human
This work happens in the physical world: accessory equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Tend accessory equipment, such as pumps or conveyors, to move materials or ingredients through production processes.” (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: Tending pumps and conveyors means being on the floor with them.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Compounding or processing ingredients or dyes
staying human
This work happens in the physical world: ingredients, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Compound or process ingredients or dyes, according to formulas.” (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: Compounding ingredients to a formula means physically handling the materials.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Dislodging and clearing jammed materials or other items from machinery or equipment
staying human
This work happens in the physical world: jammed materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Dislodge and clear jammed materials or other items from machinery or equipment, 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: Clearing a jam from machinery has to be done by hand at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Opening valves to drain slurry from mixers into storage tanks
staying human
This work happens in the physical world: valves, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Open valves to drain slurry from mixers into storage tanks.” (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 valves to drain a mixer is a physical action at the tank.
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: $48,990a 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: 94,920in 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: work orders in, a record out. The rows above are exactly that shape: reading work orders to determine production specifications or information. What it cannot do is be there in the room, and that is still where materials, ingredients or products 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: weighing or measuring materials, ingredients or products to ensure conformance to requirements 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, 6% in rows that change shape rather than disappear, and 94% 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. Reading work orders to determine production specifications or information 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 work orders, 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 materials, ingredients or products 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 reading work orders to determine production specifications or information. 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

You did not come here for a career change and I am not selling you one. But one road out of here is worth knowing about, so here it is with the bill attached.

Mixing and Blending Machine Setters, Operators, and TendersCrushing, Grinding, and Polishing Machine Setters, Operators, and Tenders
a year or morematched on shared tasks
One of the tasks on your list is on theirs in the same words: “clean work areas”. Take both published task lists together and about 55% of the work in that job that the software is not taking is work you are doing today.
Half of what they do, you do already. The argument is about the other half, not about starting again.
The work the two jobs share
- You already do
  “Clean work areas.”
  They do
  “Clean work areas.”
- You already do
  “Tend accessory equipment, such as pumps or conveyors, to move materials or ingredients through production processes.”
  They do
  “Tend accessory equipment, such as pumps and conveyors, to move materials or ingredients through production processes.”
- You already do
  “Clean and maintain equipment, using hand tools.”
  They do
  “Clean, adjust, and maintain equipment, using hand tools.”
What you would not already have: Nothing in your task list touches “notify supervisors of needed repairs”, “move controls to start, stop, or adjust machinery and equipment that crushes…” or “inspect chains, belts, or scrolls for signs of wear”. That is the part you would be learning from scratch, and it is roughly the 45% of their durable work you do not already hold.
The honest bill
A pay cut: $48,540 against your $48,990 (OEWS May 2025 (both)). I am saying the words: you would earn less. Decide that on purpose, not by accident.
- The licence gate: Default-closed. This release carries no licence-register snapshot, so I could not check whether that job is regulated, which means I have to assume it might be. Before you spend a penny, look it up on the US Labor Department’s licensed-occupations finder; the free routes below link straight to it. The route is banded a year or more because of that unknown, not in spite of it.
- The entry ticket: Typical entry-level education is published for only ten occupations in this release, and neither this job nor that one is among them. So I cannot tell you whether a qualification stands in the way. Treat that as an open question to settle before you commit, not as a green light.
- What the pay gap is telling you: $48,540 against your $48,990 (OEWS May 2025 (both)): within a percent of each other, which is to say the same money. Whatever this move is for, it is not for the pay.
- What you live on meanwhile: Nobody is going to pay you to retrain. This is evenings and weekends alongside the job you already have, for a year or more, and if that is not possible right now then this route is not open right now, which is worth knowing before you start. The free American Job Center service listed below will talk training funding through with you before you pay anyone.
- Is the target job itself holding up: Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders scores 7/100 on this site’s own exposure measure (minimal), with 0% of its tasks in the top band. Employment projections are not published for this occupation in this release, so this is the exposure leg of the check only. It passed, which is the only reason it is here.
How long: A year or more, part-time, alongside the job you have. That band is set by the unchecked licence question and by the 45% of their work you would be learning, not by any one course.
See what AI is doing to crushing, grinding, and polishing machine setters, operators, and tenders

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.

Machine Feeders and Offbearers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already weigh or measure materials, ingredients, or products to ensure conformance to requirements, and their equivalent is to weigh or measure materials or products to ensure conformance to specifications. Across both published task lists that is about 22% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 22% 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: $41,220 against your $48,990, 15.9% 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 or measure materials, ingredients, or products to ensure conformance to requirements, and their equivalent is to measure or weigh ingredients, using scales or measuring containers. 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. It is a pay cut, in those words: $41,590 against your $48,990, 15.1% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
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 collect samples of materials or products for laboratory testing, and their equivalent is to examine or test samples of processed substances, or collect samples for laboratory testing…. 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 14,280 of those jobs against 94,920 of yours (OEWS May 2025), 15% 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: 0% 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 weighing or measuring materials, ingredients or products to ensure conformance to requirements 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 Process operatives 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

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

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Nearby moves

The jobs above, as pages you can read the same way as this one. Your job shares its core work with these. That is what the match is, and it is all it is.

Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders →Half of what they do, you do already. The argument is about the other half, not about starting again.

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Questions people ask about this job

Will AI replace Mixing and Blending Machine Setters, Operators, and Tenders?: Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Mixing and Blending Machine Setters, Operators, and Tenders (United States, SOC 51-9023), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 5–10, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Mixing and Blending Machine Setters, Operators, and Tenders” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Read work orders to determine production specifications or information” (56/100, partial); “Record operational or production data on specified forms” (38/100, low); “Test samples of materials or products to ensure compliance with specifications, using test equipment” (8/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Mixing and Blending Machine Setters, Operators, and Tenders” stay human?: About 94% 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: “Clean work areas” (0/100, minimal); “Mix or blend ingredients by starting machines and mixing for specified times” (0/100, minimal); “Open valves to drain slurry from mixers into storage tanks” (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 “Mixing and Blending Machine Setters, 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 94% 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 Mixing and Blending Machine Setters, 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 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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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.