ARTIFICIAL INTELLIGENCE

“Is the labour market ready [for AI] ? The honest answer is no. Our study shows that already in advanced economies, one in ten jobs require new skills.” – Kristalina Georgieva – Managing Director, IMF

Kristalina Georgieva, Managing Director of the International Monetary Fund (IMF), delivered this stark assessment during a World Economic Forum town hall in Davos in January 2026, amid discussions on growth dilemmas in an AI-driven era^1,3,4. Her words underscore the IMF’s latest research revealing that artificial intelligence is already reshaping labour markets, with immediate implications for employment and skills development worldwide⁵.

Who is Kristalina Georgieva?

Born in 1953 in Bulgaria, Kristalina Georgieva rose through the ranks of international finance with a career marked by economic expertise and crisis leadership. Holding a PhD in economic modelling from Sofia University, she began at the World Bank in 1993, eventually becoming Chief Executive Officer of its Science and Technology division. She served as European Commission Vice-President for Budget and Human Resources from 2014 to 2016, and as CEO of the World Bank Group from 2017. Appointed IMF Managing Director in 2019, she navigated the institution through the COVID-19 pandemic, the global inflation surge, and geopolitical shocks, advocating for fiscal resilience and inclusive growth^3,5. Georgieva’s tenure has emphasised data-driven policy, particularly on technology’s societal impacts, making her a pivotal voice on AI’s economic ramifications¹.

The Context of the Quote

Spoken at the WEF 2026 Town Hall on ‘Dilemmas around Growth’, the quote reflects IMF analysis showing AI affecting 40% of global jobs-enhanced, eliminated, or transformed-with 60% in advanced economies^3,4. Georgieva highlighted that in advanced economies, one in ten jobs already requires new skills, often IT-related, creating supply shortages⁵. She likened AI’s impact on entry-level roles to a ‘tsunami’, warning of heightened risks for young workers and graduates as routine tasks vanish^1,2. Despite productivity gains-potentially boosting global growth by 0.1% to 0.8%-uneven distribution exacerbates inequality, with low-income countries facing only 20-26% exposure yet lacking adaptation infrastructure⁴.

Leading Theorists on AI and Labour Markets

The IMF’s task-based framework draws from foundational work by economists like David Autor, who pioneered the ‘task approach’ in labour economics. Autor’s research, with co-authors like Frank Levy, posits that jobs consist of discrete tasks, some automatable (routine cognitive or manual) and others not (non-routine creative or interpersonal). AI, unlike prior automation targeting physical routines, encroaches on cognitive tasks, polarising labour markets by hollowing out middle-skill roles³.

Erik Brynjolfsson and Andrew McAfee, MIT scholars and authors of Race Against the Machine (2011) and The Second Machine Age (2014), argue AI heralds a ‘qualitative shift’, automating high-skill analytical work previously safe from machines. Their studies predict widened inequality without intervention, as gains accrue to capital owners and superstars while displacing median workers. Recent IMF-aligned research echoes this, noting AI’s dual potential for productivity surges and job reshaping^3,5.

Other influencers include Carl Benedikt Frey and Michael Osborne, whose 2013 Oxford study estimated 47% of US jobs at high automation risk, catalysing global discourse. Their work influenced IMF models, emphasising reskilling urgency³. Georgieva advocates policies inspired by these theorists: massive investment in adaptable skills-‘learning how to learn’-as seen in Nordic models like Finland and Sweden, where flexibility buffers disruption⁵. Data shows a 1% rise in new skills correlates with 1.3% overall employment growth, countering fears of net job loss⁵.

Broader Implications

Georgieva’s warning arrives amid economic fragmentation-trade tensions, US-China rivalry, and sluggish productivity (global growth at 3.3% versus pre-pandemic 3.8%)⁵. AI could reverse this if harnessed equitably, but demands proactive measures: reskilling for vulnerable youth, social protections, and regulatory frameworks to distribute gains. Advanced economies must lead, while supporting emerging markets to avoid an ‘accordion of opportunities’-expanding in the rich world, contracting elsewhere⁴. Her call to action is clear: policymakers and businesses must use IMF insights to prepare, not react.