Every previous wave of automation eventually made more work than it destroyed. The live question in 2026 is whether the AI wave behaves the same way, or whether it hits cognitive and white-collar work, the very jobs that used to be safe, in a way the old reassurance no longer covers.
An AI-and-work question rewards a student who can hold the economic history and the 2026 evidence in the same hand. The reassuring story, that automation displaces but also creates, is true of past waves. Argue whether it still holds when the technology automates judgement, not just muscle, and when the first jobs to thin are the ones graduates start in.
Technology and work is one of the most reliably recurring strands in the bank. It surfaced as a single career for life in 2018, as AI replacing human beings in 2019, and as learning facts when information sits online in 2025. The vehicle shifts; the worry about machines and human usefulness does not.
The news has made it sharper than at any point since the question last appeared. Through 2026 the talk has moved from chatbots that draft text to agentic systems that plan and execute multi-step tasks, and the early pressure shows up exactly where graduates enter: junior analysis, paralegal work, basic coding. An examiner reading that cannot miss it.
Framed honestly: SEAB sets the paper, and nobody outside it knows the wording. No one knows what the examiners will set. What we can say is that AI and work is a near-permanent fixture, and the 2026 version of it, agentic AI against the entry-level job, is its freshest and most arguable form.
set at A-Level most recent appearance. A frequent visitor that changes costume. The automation-and-jobs worry recurs every few years; agentic AI is its 2026 form.
These questions test whether you can separate task from job. AI is automating tasks at speed, but a job is a bundle of tasks, and the arguable claim is whether the bundles dissolve faster than new ones form. The weak script says 'robots will take our jobs'; the strong one asks which tasks, in which jobs, and what is left over.
They also test economic literacy without becoming an economics essay. The lump-of-labour fallacy, the idea that there is a fixed amount of work to go round, is the trap the question is often built to catch. A strong answer knows the historical pattern and then asks, with evidence, whether this wave is the exception.
Two distinct, defensible routes through the question. A strong script commits to one and uses the other as the concession it answers, rather than sitting on the fence.
Every major wave of automation has destroyed particular jobs while creating more work in total, and the reasonable bet is that AI follows the same path, raising what one worker can do rather than emptying the workforce.
Past automation took muscle and routine; this wave takes judgement, drafting and analysis, so it strikes the educated, white-collar jobs that used to be the safe ground, and it hollows out the entry rungs young workers climb.
The IMF's headline that 40 percent of jobs are exposed to AI is a measure of exposure, not of jobs lost; exposure can mean a role is helped as easily as harmed, which is why the 'task versus job' distinction matters so much in an essay.Source: IMF, as of 2026-06
Singapore's policy answer to automation is SkillsFuture, a national lifelong-learning scheme giving every citizen aged 25 and above subsidised reskilling, a bet on adapting workers rather than protecting specific jobs.Source: SkillsFuture Singapore, as of 2026-06
The shift in 2026 talk from chatbots to agentic AI, systems that plan and carry out multi-step tasks rather than just answering, is what moved the worry from drafting to whole workflows.Source: general technology reporting, 2026, as of 2026-06
AI may not kill your job before it kills the path to your first one. The danger is not the worker replaced, but the junior never hired.The entry-level case, stated sharply
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