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Skilled agricultural, forestry and fishery workers · code 6224

Hunters and trappers

AI can identify an animal track from a photo. It can't set the trap.

AI barely touches any of the five tasks, from setting traps and hunting to maintaining gear and selling live animals.

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

Hunters and trappers catch and kill mammals, birds or reptiles mainly for meat, skin, feathers and other products for sale or delivery on a regular basis to wholesale buyers, marketing organizations or at markets. Tasks include - (a) setting traps to catch mammals, birds or reptiles; (b) killing trapped or free mammals, birds or reptiles with firearms or other weapons; (c) skinning and otherwise treating killed mammals, birds or reptiles to obtain desired products for sale or delivery; (d) delivering or selling trapped live mammals, birds or reptiles; (e) repairing and maintaining equipment. Examples of the occupations classified here: - Fur trapper - Seal hunter

  • hunter

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

91 / 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

4 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 5 tasks for how much of it AI could do. They average 9 out of 100, so it holds up at 100 − 9 = 91.

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 69 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?

Hunters and trappers 91

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.

  • Keeping records and permits up to date.
  • Identifying species and signs from photos.
  • Selling and arranging deliveries online.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Reading land, weather and animal behaviour in the field.
  • Handling animals safely and humanely.
  • Skinning and preparing products by hand.

Both

What's arriving

New work that only exists because the AI does.

  • Pest and invasive species control contracts.
  • Wildlife monitoring with trail cameras and sensors.
  • Stricter welfare and traceability records.

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.

– could do without you

We don't have a measure of how much of this job AI could take over.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

69 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?

Less fine handwork and fewer cramped spaces than a typical hands-on job. That clears one hurdle for machines. It doesn't mean machines are doing this work yet.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 39 out of 100. The higher it is, the longer the robot waits.

69 How
hands-on
39 How fiddly
and cramped

Kind of hands-on work Hands-on, less fiddly work

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 2 US jobs .

finger skill 40.2 · hand skill 40.2 · cramped spaces 35.9 · averaged · rule: hands-on 50+ · fiddly under 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 14, against an average of 9. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 14

    Repairing and maintaining equipment.

    Very Low
  2. 10

    Delivering or selling trapped live mammals, birds or reptiles

    Very Low
  3. 9

    Skinning and otherwise treating killed mammals, birds or reptiles to obtain desired products for sale or delivery

    Very Low
  4. 6

    Setting traps to catch mammals, birds or reptiles

    Very Low
  5. 6

    Killing trapped or free mammals, birds or reptiles with firearms or other weapons

    Very Low

5 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

So, should hunters and trappers be worried?

Short answer: not really. Put the kettle on.

No. Setting traps, handling animals and treating skins all rate Very Low, and even the most exposed task, repairing equipment, stays Very Low. The bigger pressures on this work are regulation and conservation, not software.

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 18 jobs in the “Skilled agricultural, forestry and fishery workers” family, averaging 83. This one is above the family average.

Pit this against any other job →