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Research brief
The Aggregation Paradox of AI: Why do micro-economic productivity gains from AI disappear at scale
This research brief examines why strong productivity gains from artificial intelligence (AI) observed at the task and individual worker level have not yet translated into measurable productivity growth at the firm, sectoral or macroeconomic level. It reviews emerging evidence on AI adoption, diffusion and workplace transformation, while exploring the economic and institutional conditions that shape how productivity gains scale across the economy.
Artificial Intelligence (AI) delivers large productivity gains at the task level (typically 10-70 per cent), with the strongest effects for less experienced workers and well-defined, text-intensive tasks.
At the firm level, evidence is more mixed and AI adoption remains uneven; productivity gains are concentrated in larger, digitally advanced enterprises, while many firms report little measurable impact beyond pilots.
At sectoral and macroeconomic levels, no clear AI-driven productivity growth has yet appeared in official statistics, consistent with historical patterns of slow diffusion and delayed productivity gains (the “productivity J-curve”) as well as persistent measurement gaps.
Translating micro-level gains into aggregate productivity growth depends on broad diffusion, complementary investment in workplace re-organisation and skills, supportive macroeconomic conditions and effective competition policy.
Collective bargaining and social dialogue can shape AI adoption, working conditions and the distribution of productivity gains, including through provisions on transparency, training rights, work organization and data protection.
Historical experience with electrification and information and communications technology (ICT) shows that technological revolutions only raised aggregate productivity after substantial organizational change and institutional adaptation. AI is likely to follow a similar path, though with broader reach into cognitive and service-sector tasks.
Without targeted policy interventions to improve on skills, digital infrastructure, social protection, competition and collective bargaining frameworks, AI risks widening productivity and income gaps across firms, workers and countries rather than closing them.
