AI is creating jobs as well as erasing them – but how rewarding are they? - Yahoo Finance UK

Don't believe the hype about AI taking over all forms of work. Most of the evidence to date suggests large-scale job losses are limited. But what is changing is the way work is organised.

One area of increased employment is in people "cleaning up" after large language models (LLMs), or quality controlling them.

The sales pitch is that these AI systems can program code, write reports, analyse data and automate routine decisions faster and better than humans, all at negligible marginal cost. In theory, this should free people to focus on higher-level thinking, and leisure.

In practice, AI agents produce vast amounts of "almost right" output – but there are still errors, hallucinations and odd gaps in judgment that a real person has to catch.

The resulting jobs, often known as AI remediation, vary in their skill and remuneration levels. Some AI companies are now hiring experienced professionals in many different fields to ensure their agents are producing work of requisite quality – and to help them change that if not.

But there is also a more subtle shift in job responsibilities within many organisations. Some white-collar workers in industries such as finance, tech and retail are suggesting that AI can add to, rather than reduce, their workload, because of the need to monitor so-called "workslop". This can also undermine trust in colleagues who are using AI agents.

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A 2025 Harvard Business Review report quoted a retail director who lamented spending "more time following up on the information [provided by AI] and checking it with my own research. I then had to waste even more time setting up meetings with other supervisors to address the issue."

Public sector slop

The public sector risks being drawn into this "AI repair" logic. The UK government (among many others) is experimenting with AI for tasks such as summarising consultation responses, drafting correspondence and analysing sentiment in citizen feedback. Official guidance for civil servants encourages cautious use of generative AI, while warning that its outputs can be misleading and must be independently verified.

This means, if public services start producing large volumes of AI-generated analysis and letters, they will also have to create new positions whose main responsibility is to monitor, check and correct that output before it reaches citizens or feeds into policy.

How much of the apparent AI efficiency gain will be consumed by people silently tidying up after systems that are sold as automatic?