“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.
References
1. https://time.com/collections/davos-2026/7339218/ai-trade-global-economy-kristalina-georgieva-imf/
2. https://www.youtube.com/watch?v=4ANV7yuaTuA
