This week's chart shows employment for workers aged 22 to 25 since 2021, grouped into five bands by how exposed their occupation is to AI. The dark lines are the most exposed jobs, such as software development and customer service. The pale lines are the least exposed, such as nursing, maintenance and management. Stanford's revised Canaries in the Coal Mine paper puts the gap between them at 19%, up from 15% last July. The lab has also launched a Canaries Dashboard with ADP Research, refreshing these figures every month rather than once a year.
That figure is relative, not a count of jobs lost. Employment in the most exposed bands fell by about 11% while the least exposed grew by about 10%. The adjustment is happening through slower hiring rather than redundancies, and pay has barely moved. The authors describe their results as patterns, not proof of cause. The gap narrows when education is taken into account, and their payroll sample shows larger effects than national surveys.
The mechanism is more useful than the number. Jobs built on codified knowledge are losing young hires. Jobs built on knowledge learned by doing are gaining experienced ones.
Retraining may not always be the answer. Anthropic reviewed 56 randomised trials this week and found average gains of 1.7 percentage points of employment and about $800 a year, at a cost of $13,000 per person. Only programmes designed with employers returned real value.
Takeaways: Employers are not replacing young workers outright; they are declining to hire juniors whose skills are codified while paying more for senior experience that only junior work produces. Because that stock of experience cannot be replenished by generic retraining, cutting early-career rungs shrinks the future talent pipeline.