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

The theoretical starting point for understanding labour market polarisation is the task-based framework developed by economists such as Daron Acemoglu and David Autor, which models occupations not as fixed skill bundles but as collections of discrete tasks, each with different degrees of automatability. Under this framework, technology does not automate ‘jobs’ wholesale but substitutes for specific tasks within jobs, reallocating remaining tasks toward workers whose comparative advantage lies in judgment, creativity, or physical dexterity that machines cannot yet replicate. The routine-biased technical change (RBTC) literature that emerged from this framework in the 1990s-2010s explained polarisation as a U-shaped employment response: routine middle-skill task bundles were disproportionately automated by industrial robotics and enterprise software, while non-routine cognitive tasks and non-routine manual tasks both grew as a share of employment.

Generative AI complicates the RBTC framework because large language models exhibit comparative advantage in a task category the framework did not clearly anticipate: non-routine cognitive tasks that are nonetheless language-based, pattern-recognition-heavy, and reproducible from training data — first-draft writing, code generation, data synthesis, image creation, customer correspondence. These tasks were previously assumed safe from automation precisely because they require ‘judgment’ in some informal sense, yet large language models can now perform serviceable, if imperfect, versions of them at a fraction of the marginal cost of human labour. Recent theoretical work formalises this using an ‘AI automation exposure index,’ applying frameworks derived from Moravec's Paradox — the long-standing observation in robotics and AI research that tasks easy for humans (physical manipulation, sensorimotor coordination) are often hard for machines, while tasks hard for humans (formal logic, pattern recognition across large datasets, drafting from templates) are often comparatively easy for machines. Generative AI is, in effect, automating precisely the tasks Moravec's Paradox identifies as ‘AI-easy’ — which happen to be concentrated in professional, credentialed, historically well-paid white-collar work.

Empirically, this produces occupational employment data that looks different from the classic hollowing-out-the-middle pattern. Employment growth has slowed in marketing consulting, graphic design, office administration, and call centre roles — occupations that sit toward the upper-middle of the wage distribution and require post-secondary education, not the blue-collar middle-skill occupations RBTC models predicted would be most exposed. This has prompted labour economists to describe an emerging ‘reverse polarisation’ or at minimum a modification of the traditional U-shape, where the most AI-exposed occupations cluster in a band that RBTC-era models treated as relatively automation-resistant.

The wage and inequality implications operate through at least three separate channels. First, a direct displacement channel: workers in highly AI-exposed occupations face reduced labour demand, pushing down wages or employment in those specific roles, particularly for entry-level positions where task content is most standardised. Second, a complementarity channel: workers who can direct, verify, and integrate AI outputs into higher-value work see rising demand and wages, creating a new, narrower category of ‘AI-complementary’ skilled labour distinct from the broader category of credentialed professional labour. Evidence of this channel is visible in the roughly 30% wage premium reported for AI-related technical postings in South Asian labour markets relative to comparable white-collar roles. Third, a capital-labour distribution channel operates at the macro level: to the extent AI substitutes for labour and raises capital's share of value added, aggregate wage growth could lag productivity growth economy-wide, a dynamic economists studying the distribution of income between capital and labour have flagged as a first-order concern.

Demographic exposure compounds these channels unevenly. Analyses consistently find women, younger workers, and entry-level employees disproportionately exposed to AI-driven task automation — partly a function of occupational segregation and partly a function of career-stage task content, since entry-level roles across many professions consist disproportionately of the structured, judgment-light tasks generative AI performs competently. If this pattern holds, it raises a structural concern for career-ladder economics: professions have historically trained senior practitioners by having them perform junior, task-routine work under supervision before advancing to judgment-intensive responsibilities. If AI absorbs the junior tier of tasks across law, consulting, journalism, and software engineering simultaneously, the traditional apprenticeship-style pipeline into senior roles could narrow, with consequences for skill formation that would only become measurable in aggregate data five to ten years hence.

Policy responses under discussion internationally include portable, sector-agnostic retraining funds, stronger social insurance for displaced professional workers who previously had limited exposure to unemployment risk, and — more speculatively — mechanisms to ensure AI-driven productivity gains are shared with labour through wage growth or reduced working hours rather than accruing predominantly to capital owners and a narrow technical elite. Which combination of policies gains traction will likely differ substantially by country, shaped by existing labour market institutions, union density, and the political salience of AI-driven displacement relative to other economic concerns competing for policy attention in 2026.