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

The classic ‘skill-biased technical change’ story of the 1990s and 2000s described a labour market splitting into two growing tiers — highly-paid professional and managerial jobs, and lower-paid personal service jobs — while middle-skill, middle-wage occupations (manufacturing, clerical work, routine administration) shrank as computers and industrial robots absorbed their tasks. Wages for the surviving high-skill jobs rose steadily, rewarding a college-education premium that defined labour economics for a generation.

Generative AI breaks from this pattern in an important way: its comparative advantage lies in cognitive, language-based, and pattern-recognition tasks, which happen to overlap heavily with exactly the professional, white-collar work that previously sat safely on the ‘high-skill, high-wage’ side of the divide. Recent labour market data bears this out. Employment growth in the United States has slowed markedly in occupations most exposed to generative AI — marketing and consulting, graphic design, office administration, and call centre work — even as job postings for AI-related technical roles have surged. In South Asia specifically, AI-related postings now offer wages roughly 30% above comparable white-collar roles, reflecting a scramble for scarce AI-literate talent even as adjacent white-collar categories soften.

Meanwhile, many lower-wage, physically-grounded service jobs — caregiving, cleaning, skilled trades, hospitality — remain largely insulated from generative AI's current capabilities, which do not yet extend reliably into physical manipulation or unstructured real-world environments. This is a break from the pattern predicted by ‘routine-biased technical change’ models, which assumed automation would climb the wage ladder from the bottom up. Instead, the exposure pattern looks closer to a ‘hollowing from the top-middle,’ pressuring mid-to-upper white-collar roles whose tasks are cognitively routine even if formally credentialed as ‘skilled.’

Distributional consequences are already visible along lines beyond just occupation. Analysts flag that women, younger workers, and those in entry-level roles face disproportionately higher exposure to AI-driven task automation, partly because entry-level white-collar positions often consist of exactly the structured, judgment-light tasks — first-draft writing, basic research synthesis, routine analysis — that generative AI now performs competently. This raises a specific policy concern distinct from past automation waves: if AI erodes the traditional ‘entry-level’ rung of white-collar career ladders, it could disrupt the pipeline through which workers historically developed into senior professionals, with effects that only become visible years later.

Not every forecast is pessimistic. Several labour economists and international bodies, including a recent assessment from the European Training Foundation, argue that AI is ‘far more likely to change jobs than eliminate them outright’ — reshaping task composition within occupations rather than eliminating entire job categories wholesale. Under this reading, the critical variable is not whether AI displaces workers, but whether displaced workers can move into newly-created roles quickly enough, and whether wage growth in AI-complementary occupations is broad-based rather than concentrated among a narrow technical elite. Which outcome prevails depends heavily on policy: funding for retraining and reskilling, portable benefits that do not lock workers into declining occupations, and whether productivity gains from AI adoption are captured primarily by capital owners or shared more broadly with labour through wage growth — a distributional question that will likely define labour economics debates through the rest of this decade.