The Future of Expertise in an AI-Driven World.

A Davos 2026 chart and recent labour data suggest AI is thinning the middle rungs of professional hierarchies by cutting entry-level work: a Stanford update found 22–25-year-olds in highly AI-exposed US occupations down 13% since late 2022, with sharper drops in software engineering and customer service, while senior roles held steady and starting wages in AI-exposed firms fell 4.5%.

Profiles in the Guardian show workers pre-emptively leaving AI-vulnerable careers for manual trades. The argument is framed as “apprenticeship severance”: automating junior “pretext” tasks disrupts the transfer of tacit and collective knowledge described by Michael Polanyi and Harry Collins.

Evidence from robotic surgery shows residents get 10–20x less hands-on practice, pushing “shadow learning.” While some firms expand hiring and retrain juniors, a review-first workflow risks asking for judgement without the formative experience that builds it, creating long-term, hard-to-measure expertise decay unless mentorship and deliberate developmental design are prioritised.