Labour Market Polarisation: How AI Is Rewriting Who Wins and Who Loses at Work

For decades, technology mostly automated routine, repetitive jobs — factory assembly lines, data entry, basic bookkeeping — while higher-skilled, judgment-heavy work stayed safely human. Economists called this pattern ‘labour market polarisation’: middle-skill jobs hollowed out while both high-skill and low-skill service jobs grew.

Generative AI is complicating that story. Unlike earlier automation, it is proving capable of handling tasks long considered ‘high-skill’ — drafting legal documents, writing code, analysing data, producing marketing copy — while many low-wage, hands-on service jobs (cleaning, caregiving, skilled trades) remain largely untouched for now. Early evidence shows US employment growth slowing sharply in white-collar fields most exposed to generative AI, including marketing, graphic design, and office administration, even as demand surges for AI-literate technical roles paying a substantial wage premium.

The result may be a new kind of polarisation — one that does not automatically reward traditional ‘high-skill’ credentials the way past waves of automation did. Whether this trend widens or narrows inequality depends heavily on policy choices: how retraining is funded, whether AI's productivity gains are shared with displaced workers, and how quickly new job categories emerge to absorb workers pushed out of shrinking occupations.