Global Advisors | Quantified Strategy Consulting

“We assess that 40% of jobs globally are going to be impacted by AI over the next couple of years – either enhanced, eliminated, or transformed. In advanced economies, it’s 60%.” – Kristalina Georgieva – Managing Director, IMF

Kristalina Georgieva’s assessment of AI’s labour market impact represents one of the most consequential economic forecasts of our time. Speaking at the World Economic Forum in Davos in January 2026, the Managing Director of the International Monetary Fund articulated a sobering reality: artificial intelligence is not a distant threat but an immediate force already reshaping employment globally. Her invocation of a “tsunami”-a natural disaster of overwhelming force and scale-captures the simultaneity and inevitability of this transformation.

The Scale of Disruption

Georgieva’s figures warrant careful examination. The IMF calculates that 40 per cent of jobs globally will be touched by AI, with each affected role falling into one of three categories: enhancement (where AI augments human capability), elimination (where automation replaces human labour), or transformation (where roles are fundamentally altered without necessarily improving compensation). This is not speculative projection but empirical assessment grounded in IMF research across member economies.

The geographical disparity is striking and consequential. In advanced economies-the United States, Western Europe, Japan, and similar developed nations-the figure reaches 60 per cent. By contrast, in low-income countries, the impact ranges from 20 to 26 per cent. This divergence is not accidental; it reflects the concentration of AI infrastructure, capital investment, and digital integration in wealthy nations. The IMF’s concern, as Georgieva articulated, is what she termed an “accordion of opportunities”-a compression and expansion of economic possibility that varies dramatically by geography and development status.

Understanding the Context: AI as Economic Transformation

Georgieva’s warning must be situated within the broader economic moment of early 2026. The global economy faces simultaneous pressures: geopolitical fragmentation, demographic shifts, climate transition, and technological disruption occurring in parallel. AI is not the sole driver of economic uncertainty, but it is perhaps the most visible and immediate.

The IMF’s analysis distinguishes between AI’s productivity benefits and its labour market risks. Georgieva acknowledged that AI is generating genuine economic gains across sectors-agriculture, healthcare, education, and transport have all experienced productivity enhancements. Translation and interpretation services have been enhanced rather than eliminated; research analysts have found their work augmented by AI tools. Yet these gains are unevenly distributed, and the labour market adjustment required is unprecedented in speed and scale.

The productivity question is central to Georgieva’s economic outlook. Global growth has been underwhelming in recent years, with productivity growth stagnant except in the United States. AI represents the most potent force for reversing this trend, with potential to boost global growth between 0.1 and 0.8 per cent annually. A 0.8 per cent productivity gain would restore growth to pre-pandemic levels. Yet this upside scenario depends entirely on successful labour market adjustment and equitable distribution of AI’s benefits.

The Theoretical Foundations: Labour Economics and Technological Disruption

Georgieva’s analysis draws on decades of labour economics scholarship examining technological displacement. The intellectual lineage traces to economists such as David Autor, who has extensively studied how technological change reshapes labour markets. Autor’s research demonstrates that whilst technology eliminates routine tasks, it simultaneously creates demand for new skills and complementary labour. However, this adjustment is neither automatic nor painless; workers displaced from routine cognitive tasks often face years of unemployment or underemployment before transitioning to new roles.

The “task-based” framework of labour economics-developed by scholars including Autor and Frank Levy-provides the theoretical scaffolding for understanding AI’s impact. Rather than viewing jobs as monolithic units, this approach recognises that occupations comprise multiple tasks. AI may automate certain tasks within a role whilst leaving others intact, fundamentally altering job content and skill requirements. A radiologist’s role, for instance, may be transformed by AI’s superior pattern recognition in image analysis, but the radiologist’s diagnostic judgment, patient communication, and clinical decision-making remain valuable.

Erik Brynjolfsson and Andrew McAfee, prominent technology economists, have argued that AI represents a qualitative shift from previous technological waves. Unlike earlier automation, which primarily affected routine manual labour, AI threatens cognitive work across income levels. Their research suggests that without deliberate policy intervention, AI could exacerbate inequality rather than reduce it, concentrating gains among capital owners and highly skilled workers whilst displacing middle-skill employment.

Daron Acemoglu, the MIT economist, has been particularly critical of “so-so automation”-technology that increases productivity marginally whilst displacing workers without creating sufficient new opportunities. His work emphasises that technological outcomes are not predetermined; they depend on institutional choices, investment priorities, and policy frameworks. This perspective is crucial for understanding Georgieva’s policy recommendations.

The Policy Imperative

Georgieva’s framing of the challenge as a policy problem rather than an inevitable outcome reflects this economic thinking. She has consistently advocated for three policy pillars: investment in skills development, meaningful regulation and ethical frameworks, and ensuring AI’s benefits penetrate across sectors and geographies rather than concentrating in advanced economies.

The IMF’s own research indicates that one in ten jobs in advanced economies already require substantially new skills-a figure that will accelerate. Yet educational and training systems globally remain poorly aligned with AI-era skill demands. Georgieva has urged governments to invest in reskilling programmes, particularly targeting workers in roles most vulnerable to displacement.

Her emphasis on regulation and ethics reflects growing recognition that AI’s trajectory is not technologically determined. The choice between AI as a tool for broad-based productivity enhancement versus a mechanism for labour displacement and inequality concentration remains open. This aligns with the work of scholars such as Shoshana Zuboff, who argues that technological systems embody political choices about power distribution and social organisation.

The Global Inequality Dimension

Perhaps most significant is Georgieva’s concern about the “accordion of opportunities.” The 60 per cent figure for advanced economies versus 20-26 per cent for low-income countries reflects not merely different levels of AI adoption but fundamentally different economic trajectories. Advanced economies possess the infrastructure, capital, and institutional capacity to invest in AI whilst simultaneously managing labour market transition. Low-income countries risk being left behind-neither benefiting from AI’s productivity gains nor receiving the investment in skills and social protection that might cushion displacement.

This concern echoes the work of development economists such as Dani Rodrik, who has documented how technological change can bypass developing economies entirely, leaving them trapped in low-productivity sectors. If AI concentrates in advanced economies and wealthy sectors, developing nations may face a new form of technological colonialism-dependent on imported AI solutions without developing indigenous capacity or capturing value creation.

The Measurement Challenge

Georgieva’s 40 per cent figure, whilst grounded in IMF research, represents a probabilistic assessment rather than a precise prediction. The IMF acknowledges a “fairly big range” of potential impacts on global growth (0.1 to 0.8 per cent), reflecting genuine uncertainty about AI’s trajectory. This uncertainty itself is significant; it suggests that outcomes remain contingent on policy choices, investment decisions, and institutional responses.

The distinction between jobs “touched” by AI and jobs eliminated is crucial. Enhancement and transformation may be preferable to elimination, but they still require worker adjustment, skill development, and potentially geographic mobility. A job that is transformed but offers no wage improvement-as Georgieva noted-may be economically worse for the worker even if technically retained.

The Broader Economic Context

Georgieva’s warning arrives amid broader economic fragmentation. Trade tensions, geopolitical competition, and the shift from a rules-based global economic order toward competing blocs create additional uncertainty. AI development is increasingly intertwined with strategic competition between major powers, particularly between the United States and China. This geopolitical dimension means that AI’s labour market impact cannot be separated from questions of technological sovereignty, supply chain resilience, and economic security.

The IMF chief has also emphasised that AI’s benefits are not automatic. She personally undertook training in AI productivity tools, including Microsoft Copilot, and urged IMF staff to embrace AI-based enhancements. Yet this individual adoption, multiplied across millions of workers and organisations, requires deliberate choice, investment in training, and organisational restructuring. The productivity gains Georgieva projects depend on this active embrace rather than passive exposure to AI technology.

Implications for Policy and Strategy

Georgieva’s analysis suggests several imperatives for policymakers. First, labour market adjustment cannot be left to market forces alone; deliberate investment in education, training, and social protection is essential. Second, the distribution of AI’s benefits matters as much as aggregate productivity gains; without attention to equity, AI could deepen inequality within and between nations. Third, regulation and ethical frameworks must be established proactively rather than reactively, shaping AI development toward socially beneficial outcomes.

Her invocation of a “tsunami” is not mere rhetoric but a precise characterisation of the challenge’s scale and urgency. Tsunamis cannot be prevented, but their impact can be mitigated through preparation, early warning systems, and coordinated response. Similarly, AI’s labour market impact is largely inevitable, but its consequences-whether broadly shared prosperity or concentrated disruption-remain subject to human choice and institutional design.

“Productivity growth has been slow over the last two decades. AI holds a promise to significantly lift it. We calculated that the impact on global growth could be between 0,1% and 0,8%. That is very significant. However, it is happening incredibly quickly.” – Kristalina Georgieva – Managing Director, IMF

Kristalina Georgieva, Managing Director of the International Monetary Fund, has emerged as one of the most influential voices in the global conversation about artificial intelligence’s economic impact. Her observation about productivity growth-and AI’s potential to reverse it-reflects a fundamental shift in how policymakers understand the relationship between technological innovation and economic resilience.

The Productivity Crisis That Defined Two Decades

To understand Georgieva’s urgency about AI, one must first grasp the economic malaise that has characterised the past twenty years. Since the 2008 financial crisis, advanced economies have experienced persistently weak productivity growth-the measure of how much output an economy generates per unit of input. This sluggish productivity has become the primary culprit behind anaemic economic growth across developed nations. Georgieva has repeatedly emphasised that approximately half of the slow growth experienced globally stems directly from this productivity deficit, a structural problem that conventional policy tools have struggled to address.

This two-decade productivity drought represents more than a statistical curiosity. It reflects an economy that, despite technological advancement, has failed to translate innovation into widespread efficiency gains. Workers produce less per hour worked. Businesses struggle to achieve meaningful cost reductions. Investment returns diminish. The result is an economy trapped in a low-growth equilibrium, unable to generate the dynamism required to address mounting fiscal challenges, rising inequality, and demographic pressures.

AI as Economic Catalyst: The Quantified Promise

Georgieva’s confidence in AI stems from rigorous analysis rather than technological evangelism. The IMF has calculated that artificial intelligence could boost global growth by between 0.1 and 0.8 percentage points-a range that, whilst appearing modest in isolation, becomes transformative when contextualised against current growth trajectories. For an advanced economy growing at 1-2 percent annually, an additional 0.8 percentage points represents a 40-80 percent acceleration. For developing economies, the multiplier effect could be even more pronounced.

This quantification matters because it grounds AI’s potential in measurable economic impact rather than speculative hype. The IMF’s methodology reflects analysis of AI’s capacity to enhance productivity across multiple sectors-from agriculture and healthcare to education and transportation. Unlike previous technological revolutions that took decades to diffuse through economies, AI applications are already penetrating operational workflows at unprecedented speed.

The Velocity Problem: Why Speed Reshapes the Equation

Georgieva’s most critical insight concerns not the magnitude of AI’s impact but its velocity. Technological transformations typically unfold gradually, allowing labour markets, educational systems, and social safety nets time to adapt. The Industrial Revolution took generations. The digital revolution unfolded over decades. AI, by contrast, is compressing transformation into years.

This acceleration creates what Georgieva describes as a “tsunami” effect on labour markets. The IMF’s assessment indicates that 40 percent of global jobs will be impacted by AI within the coming years-either enhanced through augmentation, fundamentally transformed, or eliminated entirely. In advanced economies, the figure rises to 60 percent. Simultaneously, preliminary data suggests that one in ten jobs in advanced economies already require new skills, a proportion that will accelerate dramatically.

The velocity problem generates a dual challenge: whilst AI promises to solve the productivity crisis that has constrained growth for two decades, it simultaneously threatens to outpace society’s capacity to manage labour market disruption. This is why Georgieva emphasises that the economic benefits of AI cannot be assumed to distribute evenly or automatically. The speed of technological change can easily outstrip the speed of policy adaptation, education reform, and social support systems.

Theoretical Foundations: Understanding Productivity and Growth

Georgieva’s analysis builds upon decades of economic theory regarding the relationship between productivity and growth. The Solow growth model, developed by Nobel laureate Robert Solow in the 1950s, established that long-term economic growth depends primarily on technological progress and productivity improvements rather than capital accumulation alone. This framework explains why economies with similar capital stocks can diverge dramatically based on their capacity to innovate and improve efficiency.

The productivity slowdown that has characterised recent decades puzzled economists, leading to what some termed the “productivity paradox”-the observation that despite massive investment in information technology, measured productivity growth remained disappointingly weak. Erik Brynjolfsson and Andrew McAfee, leading scholars of technology’s economic impact, have argued that this paradox reflects a measurement problem: much of technology’s benefit accrues as consumer surplus rather than measured output, and the transition period between technological eras involves disruption that temporarily suppresses measured productivity.

AI potentially resolves this paradox by offering productivity gains that are both measurable and broad-based. Unlike previous waves of automation that concentrated benefits in specific sectors, AI’s general-purpose nature means it can enhance productivity across virtually every economic activity. This aligns with the theoretical work of economists like Daron Acemoglu, who emphasises that sustained growth requires technologies that complement rather than simply replace human labour, creating new opportunities for value creation.

The IMF’s Institutional Perspective

As Managing Director of the IMF, Georgieva speaks from an institution uniquely positioned to assess global economic trends. The Fund monitors economic performance across 190 member countries, providing unparalleled visibility into comparative growth patterns, labour market dynamics, and policy effectiveness. Her warnings about AI’s labour market impact carry weight precisely because they emerge from this comprehensive global perspective rather than from any single national vantage point.

The IMF’s own experience with AI implementation reinforces Georgieva’s optimism about productivity gains. As a data-intensive institution, the Fund has deployed AI-powered tools to enhance analytical capacity, accelerate research, and improve forecasting accuracy. Georgieva has personally engaged with productivity-enhancing AI tools, including Microsoft Copilot and fund-specific AI assistants, and reports measurable gains in institutional output. This first-hand experience lends credibility to her broader claims about AI’s transformative potential.

The Policy Imperative: Managing Transformation

Georgieva’s framing of AI’s impact as both opportunity and risk reflects a sophisticated understanding of technological change. The productivity gains she describes will not materialise automatically; they require deliberate policy choices. For advanced economies, she counsels concentration on three areas: ensuring AI penetration across all economic sectors rather than concentrating benefits in technology-intensive industries; establishing meaningful regulatory frameworks that reduce risks of misuse and unintended consequences; and building ethical foundations that maintain public trust in AI systems.

Critically, Georgieva emphasises that the labour market challenge demands proactive intervention. The speed of AI adoption means that waiting for market forces to naturally realign skills and employment will result in unnecessary disruption and inequality. Instead, she advocates for policies that support reskilling, particularly targeting workers in roles most vulnerable to displacement. The IMF’s research suggests that higher-skilled workers benefit disproportionately from AI augmentation, creating a risk of widening inequality unless deliberate efforts ensure that lower-skilled workers also gain access to AI-enhanced productivity tools.

Global Context: Divergence and Opportunity

Georgieva’s analysis of AI’s growth potential must be understood within the broader context of global economic divergence. The United States, which has emerged as the global leader in large-language model development and AI commercialisation, stands to capture disproportionate benefits from AI-driven productivity gains. This concentration of AI capability in a single economy risks exacerbating existing inequalities between advanced and developing nations.

However, Georgieva’s emphasis on AI’s application layer-rather than merely its development-suggests opportunities for broader participation. Countries with strong capabilities in enterprise software, business process outsourcing, and operational integration, such as India, can leverage AI to enhance service delivery and create new value propositions. This perspective challenges the notion that AI benefits will concentrate exclusively in technology-leading nations, though it requires deliberate policy choices to realise this potential.

The Uncertainty Framework

Georgieva frequently describes the contemporary global environment as one where “uncertainty is the new normal.” This framing contextualises her AI analysis within a broader landscape of simultaneous transformations-geopolitical fragmentation, demographic shifts, climate change, and trade tensions all accelerating simultaneously. AI does not exist in isolation; it emerges as one force among many reshaping the global economy.

This multiplicity of transformations creates what Georgieva terms “more fog within which we operate.” Policymakers cannot assume that historical relationships between variables will hold. The interaction between AI-driven productivity gains, trade tensions, demographic decline in advanced economies, and climate-related resource constraints creates a genuinely novel economic environment. This is why Georgieva emphasises the need for international coordination, adaptive policy frameworks, and institutional flexibility.

Conclusion: The Productivity Imperative

Georgieva’s statement about AI and productivity growth reflects a conviction grounded in both rigorous analysis and institutional responsibility. The two-decade productivity drought has constrained growth, limited policy options, and contributed to the political instability and inequality that characterise contemporary democracies. AI offers a genuine opportunity to reverse this trajectory, but only if its benefits are deliberately distributed and its disruptions actively managed. The speed of AI’s development means that the window for shaping this outcome is narrow. Policymakers who treat AI as merely a technological phenomenon rather than as an economic and social challenge risk squandering the productivity gains Georgieva describes, converting opportunity into disruption.