Not so many years ago, leaving college felt electric. I remember it as a moment charged with promise, rich with possibility, and alive with the feeling that a dozen doors had swung open at once. Graduates’ ambitions or enthusiasm haven’t changed today. But the job market they’re entering is unrecognizable. 

Tasks that once defined a junior’s role—analyzing data, drafting reports, and taking meeting notes—are being automated, changing the shape and availability of early-career work.

The Pew Research Center reports that 73% of people under 30 now believe artificial intelligence will lead to fewer jobs, up from 61% two years ago. Their anxiety isn’t misplaced; entry-level vacancies are shrinking. 

For those who do make it through the door, the nature of the work is shifting, too. The “seniorization” of junior roles means that in AI-exposed fields, junior hires are now seven times as likely to require midcareer capabilities as hires into roles less connected with AI. And rather than “learning by doing,” many are “learning by reviewing,” spending time tinkering with AI-generated outputs, rather than developing hard-won skills like judgment, pattern recognition, and critical thinking. 

Against this backdrop, a problem emerges. Working effectively with AI demands a degree of experience many juniors might lack, new to the workforce as they are. Experience is what helps us distinguish helpful AI outputs from bad. While the risks aren’t solely associated with those in entry-level jobs—senior colleagues can also be guilty of overrelying on AI to crunch through an ever-expanding list of tasks—it’s reasonable to assume that junior colleagues are the most exposed to AI-enhanced errors and authoritative slop.

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