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Plant and machine operators, and assemblers · code 8141

Rubber products machine operators

A camera can check the tyre. Repairing one is still hands-on.

Checking output for defects is mostly out of AI's reach. It barely touches five of the six tasks, including building and vulcanising tyres and shaping rubber.

What they actually do (and what else they get called)

Rubber products machine operators monitor and operate machines which knead and blend rubber and rubber compounds and produce various components and products from natural and synthetic rubber, such as moulded footwear, domestic articles, insulating materials, industrial accessories or tyres. Tasks include - (a) operating and monitoring machines which knead, mix and blend rubber and rubber compounds for further processing; (b) operating and monitoring machines which produce sheets of rubber or rubberized fabric by a rolling process; (c) operating and monitoring machines which extrude compounded rubber or shape vulcanized rubber by moulding; (d) operating and monitoring machines which build up tyres on a form, vulcanize tyres and mould or rebuild used tyres; (e) examining outputs for defects and conformity to specifications; (f) locating defects and repairing worn and faulty tyres by vulcanizing or other processes. Examples of the occupations classified here: - Latex worker - Rubber extruding machine operator - Rubber milling machine operator - Rubber moulder - Rubber products machine operator - Tyre maker - Tyre repairer - Vulcanizer

  • rubber products machine operator

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

82 / 100

Strongly holds up against AI

Most of this job still needs an actual human. AI would mostly be making the tea.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Not Exposed

120 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 6 tasks for how much of it AI could do. They average 18.2 out of 100, so it holds up at 100 − 18.2 = 81.8.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Pinch of salt: this number is probably too rosy

It only asks what AI software could do, and this job is 70 out of 100 hands-on. A chatbot can't do that part. A robot might, one day, and nobody measures how close that is.

Man v Bot · compare

Who holds up better?

Rubber products machine operators 82

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Automated defect inspection.
  • Machine settings and recipes.
  • Production records.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Running and adjusting machines.
  • Repairing worn tyres.
  • Handling hot, heavy materials safely.

Both

What's arriving

New work that only exists because the AI does.

  • Supervising automated lines.
  • Tyre recycling and retreading.
  • New rubber compounds.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

26 could do without you

It could take over very little of the work on its own. It could help with 58 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

70 how hands-on

Most of this job needs hands, a body or turning up. That's robot territory, and nobody measures how far robots have got well enough to give you a number. We won't make one up.

So could a robot do the physical part?

More fine handwork, and more time in cramped or awkward spots, than a typical hands-on job. That is exactly what machines find hardest: they do best in tidy, predictable places.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 42 out of 100. The higher it is, the longer the robot waits.

70 How
hands-on
42 How fiddly
and cramped

Kind of hands-on work Hands-on, fiddly work in tight spaces

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 8 US jobs , so treat it as rough.

finger skill 45.3 · hand skill 46.1 · cramped spaces 35.2 · averaged · rule: hands-on 50+ · fiddly 39.4+

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 27, against an average of 18. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 27

    Examining outputs for defects and conformity to specifications

    Low
  2. 18

    Operating and monitoring machines which build up tires on a form, vulcanise tires and mould or rebuild used tyres

    Very Low
  3. 17

    Operating and monitoring machines which knead, mix and blend rubber and rubber compounds for further processing

    Very Low
  4. 17

    Operating and monitoring machines which produce sheets of rubber or rubberized fabric by a rolling process

    Very Low
  5. 16

    Operating and monitoring machines which extrude compounded rubber or shape vulcanized rubber by moulding

    Very Low
  6. 15

    Locating defects and repairing worn and faulty tyres by vulcanizing or other processes.

    Very Low

6 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you
58
AI does it for you
26

Sidekick wins, by 32 points.

How good a match is this?

One Australian job matches this one closely, so this is about as reliable as it gets here.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 7115

So, should rubber products machine operators be worried?

Short answer: not really. Put the kettle on.

Not about AI software. The most exposed task is examining products for defects, rated Low; mixing, moulding and vulcanising rate Very Low. Camera inspection and automation are spreading through factories, so operators supervise lines and fix problems.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 39 jobs in the “Plant and machine operators, and assemblers” family, averaging 80. This one is above the family average.

Pit this against any other job →