Stanford: AI hits entry-level jobs hardest — what employers should do

A Stanford University study has documented what the market sensed intuitively: artificial intelligence cuts demand for junior positions hardest. The logic is brutal: novices' work contains the most routine — drafts, cross-checks, boilerplate writing — and that is exactly what automation takes first.

For an individual company the temptation is obvious: skip the junior hire, buy a subscription. But on a multi-year horizon that saws off the branch: mid-levels and seniors do not appear out of thin air — they grow out of juniors. Organizations that fully stop hiring novices will hit a talent pit in five years that no budget can fill.

The sensible strategy for business in Kazakhstan is to rebuild entry roles rather than abolish them: hand the routine to algorithms and train newcomers from day one in what machines don't do — client communication, verifying AI output, owning a stretch of work. A telling example from accounting: routine questions from colleagues — "where is the act", "what is the balance on this contract" — are already answered calmly by an agent from 1C data, as it works in 1Cifer, so a young specialist can spend their first month analyzing rather than hunting for numbers. A junior with AI tools is not a discounted headcount; it is an accelerated path to mid-level.