Written by Rahim Hirji, author of SuperSkills (Kogan Page, 2026), founder of The SuperSkills Intelligence Company. The research is published openly with every source graded. Found an error? Tell me and it is corrected on the page.
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For talent, graduate recruitment and professional servicesEvery profession built its senior people the same way: by giving juniors the work nobody senior wanted to do. That work is the first thing AI takes. The question nobody in the room has answered is what replaces it.
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A feel for the room before you put me in front of yours.
AI rarely takes a whole job. It takes the first draft, the initial analysis, the routine review. Those were also the tasks through which people built the judgement that made them senior. The work still ships, the output often improves, and nothing triggers an alarm for about three years.
The consequences have names in this research. The missed reps are the repetitions that built judgement, now handed to the machine. The missing rungs are the junior tasks removed before anyone noticed they were load-bearing. Synthetic seniority is the result in an individual: output that looks like judgement without the judgement underneath.
France's national statistics office found employment of 15 to 29 year olds, excluding apprentices, down 7.4 per cent year on year in IT services in the fourth quarter of 2025, against minus 0.7 per cent across the market sector. INSEE explicitly cautions against attributing that to AI alone, and so does this keynote. Korea found the effects of adoption concentrated on younger, tertiary-educated workers. Two countries, two methods, one silhouette.
What is not established is the size of the effect or how fast it moves. The room gets that stated plainly rather than smoothed over. The full position, including what the data cannot yet support, is at will AI replace entry-level jobs.
A way to tell whether a junior cohort is developing or being carried, which is not visible in output quality and breaks most existing performance systems. The method is at assessing capability rather than output.
And the supervision problem underneath it: what happens when the person reviewing AI-assisted work has never produced that kind of work unaided. That is at who supervises work they cannot do.
Output that looks like the work of an experienced professional, produced by somebody who has not built the judgement that work normally requires. It matters because organisations assess people on output, so a pipeline can look healthy for years while producing nobody capable of the senior role.
There are signals rather than proof, and the distinction is kept on the page. INSEE found French employment of 15 to 29 year olds excluding apprentices down 7.4 per cent year on year in IT services in Q4 2025, against minus 0.7 per cent across the market sector, while cautioning against attributing that to AI alone. Korea found realised effects concentrated on younger, tertiary-educated workers. The pattern is consistent across independent methods, which makes it harder to dismiss without making it proven.
Professional services firms, graduate recruitment and early-careers teams, talent and L&D leaders, and professional bodies concerned with training routes. It also suits partner or leadership audiences in law, consulting and accountancy, where the apprenticeship model is most exposed.
Written by Rahim Hirji, author of SuperSkills (Kogan Page, 2026), founder of The SuperSkills Intelligence Company. The research is published openly with every source graded. Found an error? Tell me and it is corrected on the page.
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