Nothing is automated as a whole job. A job is a bundle of tasks, and what happens is that some of the tasks are taken — which changes the job that remains rather than removing it.
One job, four tasks: which go first, what is left, and what new work appears beside it.
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What decides
- Predictability, not difficulty. A task with a clear rule and many examples goes first, however skilled it looks.
- Volume. A task done a thousand times a day is worth automating; the same task done twice a week is not.
- Whether an error is cheap. Tasks where a mistake is caught downstream are automated long before tasks where it is not.
- Whether the data already exists. If the work already produces a record, the examples needed are already there.
Why “skilled jobs are safe” was wrong: Reading a scan is a skilled, predictable task with millions of labelled examples. Clearing a table is unskilled, unpredictable and has none. The second is much harder to automate than the first.
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Real-world examples you can name
Platform work and employment status — UK Supreme Court ruling on Uber drivers February 2021; EU platform work directive adopted 2024
Work is allocated by an app rather than a manager. The UK Supreme Court held that Uber drivers are workers entitled to minimum wage and holiday pay, and the EU has since legislated a presumption of employment where a platform controls the work.
Who it affected: Couriers and drivers, and companies whose model assumed self-employment.
Amazon's scrapped CV-screening tool — reported October 2018
An experimental hiring tool was trained on a decade of CVs submitted to the company, most of them from men. It learned to downgrade CVs containing the word 'women's' and graduates of two women's colleges. The project was abandoned.
Who it affected: Women applying for technical roles, and every organization that assumed historical data was neutral.
Boston Dynamics' Spot — commercial sale from 2020
A four-legged robot that walks over ground wheels cannot manage, used for inspecting construction sites, refineries and nuclear facilities. Police trials prompted objections strong enough that some forces returned it.
Who it affected: Inspection workers kept out of hazardous places, and communities uneasy about policing use.
| Group | What they gain | What they lose |
|---|---|---|
| Workers whose routine tasks go | Less repetitive work | The easy parts of the day, and bargaining power |
| Workers who supervise the system | New, better-paid work | Rarely the same people or the same place |
| People entering the field | Fewer entry-level tasks to learn on | The route by which the job used to be learnt |
The point most answers miss: Entry-level tasks are the most automatable, and they were also how people learned the job. Removing them does not just cut jobs — it cuts the path into the work.
How this is tested — this is one of the most frequently examined topics in the subject and the one with the most weak answers. It comes up two ways:
Paper 1 — structured question
- Part b: explain which tasks are automated first
- Part c: evaluate the impact of automation on a workforce
Paper 2 — source-based question
- Q4: synthesise sources disagreeing about jobs
- HL Paper 3: recommend a response to automation
The trap: “robots will take all the jobs”: Tasks are taken, not jobs. Say which tasks, why those, and whether the people who lose them are the people who gain the new ones.
Explain one reason why a skilled job may be automated before an unskilled one.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.
Discuss the claim that automation will leave most people without work.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.