“We collectively, including the fund, did not appreciate the backlash against globalisation that came from the fact that, yes, the world economy is doing better as a whole, but many communities were hollowed out because their jobs disappeared and there was not enough attention to them. I’ll tell you what I’m very keen not to see repeated is the same with artificial intelligence.” – Kristalina Georgieva – International Monetary Fund (IMF) Managing Director
The central issue is not whether a new technology makes economies more productive, but whether the gains arrive faster and more visibly than the losses. When job destruction is concentrated in particular towns, sectors, and skill groups, aggregate growth can look healthy while the social fabric in affected places weakens, and that imbalance has become a defining political risk around artificial intelligence. Kristalina Georgieva, who has served as Managing Director of the International Monetary Fund since October 1, 2019 and began a second term on October 1, 2024, has made that warning from a position of institutional authority that was shaped by the IMF’s experience of multiple global shocks.1
The remark reflects a lesson that global institutions learned, often slowly, from the era of rapid trade integration. The world economy can be better off on paper even as specific communities lose stable work, local spending power, and a sense of economic purpose. That distinction matters because politics is rarely organised around the global average. It is organised around visible closures, wage stagnation, and the feeling that national and international leaders celebrated efficiency while leaving the costs of adjustment to be absorbed locally. Georgieva’s concern is that artificial intelligence could repeat that pattern on a faster clock, with the benefits accruing to firms, capital owners, and highly adaptable workers while the disruption lands on those whose tasks are easiest to automate.1,12
From globalisation’s backlash to AI’s distributional shock
The comparison with globalisation is not rhetorical flourish; it is an argument about political economy. In her interview, Georgieva said that the IMF and others had not sufficiently appreciated the backlash against globalisation because they focused on the fact that the world economy was doing better as a whole, while many communities were hollowed out when jobs disappeared.1,4,7 That description captures the core failure of technocratic optimism: it can measure aggregate welfare precisely while underweighting the geography of decline. A region that loses a factory, a port function, a back-office cluster, or a processing plant does not experience the economy as a statistical average. It experiences it as closure, migration, and social churn.
Artificial intelligence creates a similar tension because it is best understood as a general-purpose technology whose economic effect is broad, uneven, and delayed. The IMF has estimated that almost 40% of global employment is exposed to AI, rising to about 60% in advanced economies.12 Exposure does not mean every exposed job vanishes, but it does mean that a substantial share of routine cognitive work, administrative handling, analysis, and content production may be altered, compressed, or partially automated. The IMF also noted that in advanced economies roughly half of the exposed jobs may benefit from AI integration, while the other half may see lower labour demand, lower wages, or in some cases disappearance.12
This is why the social question is not merely about total output. If AI raises productivity by making firms leaner and faster, the headline number can be positive even when bargaining power shifts away from labour. Goldman Sachs has argued that generative AI could raise global GDP by 7% and lift productivity growth by 1,5 percentage points over 10 years, while also exposing the equivalent of 300 million full-time jobs to automation.3 Those figures are not incompatible. They describe a world in which technology expands the economic pie while simultaneously changing who gets the slices and who is left waiting outside the bakery.
The IMF’s warning is also a warning about timing
One reason AI is politically delicate is that its benefits may be diffused over time, while its costs are immediate and local. Productivity gains can take years to appear in national accounts because firms need to adapt workflows, train staff, redesign products, and learn how to trust new systems. By contrast, a call centre that reduces headcount, a law office that automates first-draft work, or a media business that cuts junior roles can do so quickly. The result is a familiar asymmetry: the burden of adjustment arrives before the compensation mechanisms are ready.12,14
This timing problem helps explain why economists disagree so sharply on the size of the prize. Optimistic estimates stress economy-wide efficiency gains, new products, and the value of complementary tasks. More restrained work emphasises that only a fraction of tasks can be profitably automated once implementation costs, error rates, oversight, regulation, and customer preferences are included. Daron Acemoglu has argued that the medium-term productivity effect may be far smaller than the largest headline estimates, with a much more modest uplift in output once only economically viable uses are counted.9 The disagreement matters because policy should not be built on the most dramatic forecast, nor should it ignore the possibility that adoption will be slower and less comprehensive than enthusiasts predict.9,14
Georgieva’s intervention sits between those poles. She is not denying that AI can boost growth. Indeed, the IMF itself has argued that AI is on the brink of a technological revolution that could jumpstart productivity, boost global growth, and raise incomes around the world.12 The warning is that the distributional consequences could still be severe enough to deepen inequality and social tension if governments assume that aggregate gains will automatically trickle down. In other words, the productivity story and the social story are not rivals. They are two halves of the same policy problem.12
Why global institutions are especially sensitive to this pattern
The IMF’s interest in this issue is not accidental. A multilateral lender and surveillance institution sees macroeconomic stability through the lens of crises, capital flows, unemployment, and political backlash. If a major technology wave deepens inequality inside countries, it can also change fiscal politics, trade politics, and attitudes towards international cooperation. Francine Lacqua, the interviewer in the podcast series, is a Bloomberg anchor who regularly speaks with central bankers, finance ministers, and senior officials, which makes the conversation part of a broader public debate about how economic power is being reorganised.2,11
Georgieva’s own background reinforces the institutional seriousness of the warning. Since the IMF has already had to manage the economic consequences of the pandemic and other global disruptions, it has become increasingly alert to the fact that resilience cannot be treated as an abstract ideal. It must be built in advance through labour-market policy, social protection, training, competition rules, and investment in digital capacity.1,12,14 That is especially true because AI does not affect all countries equally. The IMF has said exposure is highest in advanced economies, while emerging markets and low-income countries face lower but still significant exposure.12 That means the immediate labour-market shock may be concentrated in wealthier countries, but the longer-term diffusion of AI capabilities could widen the gap between economies that can adopt, regulate, and complement the technology, and those that cannot.
What was missed during globalisation
The phrase about communities being hollowed out points to a specific historical failure. Policymakers often treated trade and integration as a sum-of-parts problem: if the nation as a whole is richer, then the policy is successful. But local economies do not adjust frictionlessly. Workers in declining industries are not instantly reallocated to new sectors. Skills are not perfectly transferable. Housing markets are sticky, family ties matter, and the social meaning of work is not captured by GDP. When those frictions are ignored, resentment accumulates and eventually seeks political expression.
That experience is directly relevant to AI because the technology may hollow out different kinds of places. Globalisation often hit manufacturing towns, logistics hubs, and regions dependent on tradable goods. AI may instead pressure administrative centres, shared-service locations, media organisations, some professional services, and entry-level white-collar pathways. The political consequence may therefore be different in detail but similar in structure: whole ladders of advancement can be shortened before replacements are fully visible. For younger workers, especially, the problem is not just displacement but the erosion of the first rung of a career ladder.12,14
There is also a deeper ideological parallel. During the globalisation era, many advocates implicitly assumed that efficiency was self-justifying. If something was cheaper, faster, and better for consumers, the distributional pain was treated as secondary. AI could repeat that error if firms and governments measure success by adoption rates alone. But broad adoption is not the same as broad benefit. A technology can be commercially successful, strategically important, and still socially destabilising if the gains are narrowly held.12,14
The strategic debate: productivity engine or inequality accelerator?
The strongest argument in favour of AI is that it can raise productivity in economies that have struggled with weak growth, labour shortages, and ageing populations. Goldman Sachs’ estimate of a 7% lift in global GDP captures the scale of ambition that surrounds the technology, while the IMF has stressed that AI could improve incomes and support growth if it is deployed well.3,12 In sectors from healthcare to education to finance, AI systems can reduce routine workload, accelerate analysis, and improve service quality. The promise is not only cost cutting but the creation of new products and business models.6
The strongest argument against complacency is that AI may amplify existing inequalities in capital, data, and skill. Firms with the best models, the most data, and the strongest distribution channels will capture disproportionate value. Workers with high complementary skills may see their productivity rise, while workers in modular, repeatable tasks face stagnation or displacement. Countries with advanced digital infrastructure may use AI to widen their advantage, while countries with weaker institutional capacity struggle to keep up.12,14 Even when the overall effect on employment is positive in the long run, the short run may still bring a wave of churn that outpaces retraining and policy response.
This is why the debate is not really about whether AI is good or bad. It is about whether societies will manage transition costs with enough seriousness. The IMF has argued that policymakers should proactively address inequality to prevent AI from further stoking social tensions.12 That implies practical choices: stronger safety nets, wage insurance, mobility support, lifelong learning, and public investment in digital skills. It also implies a regulatory stance that encourages adoption while checking abuses, such as excessive market concentration or labour substitution without offsetting investment in human capability.12,14
Why the warning matters now
Georgieva’s message matters because it shifts the debate from hype to governance. It is easy to celebrate a technology when its promised benefits are still theoretical. It is harder to govern it when its disruptions are already visible. The IMF chief’s insistence that the world should not repeat the mistakes of globalisation is a reminder that economic success measured at the top can coexist with social fracture at the base.1,12 If AI is allowed to proceed as a private efficiency project with public consequences ignored until later, then the backlash will not be surprising; it will be predictable.4,7
That is the practical consequence buried inside the warning. AI can make economies richer, but it can also make societies less stable if the transition is unmanaged. The policy challenge is to ensure that productivity gains are not treated as an excuse to forget the communities and career paths that bear the cost of change. If that lesson is missed again, the political response may be harsher than the technology itself.
References
1. “Leaders With Francine Lacqua: IMF Chief Kristalina Georgieva” – https://www.moneyweb.co.za/news/economy/imf-chief-warns-world-isnt-ready-for-shocks-that-are-piling-up/
2. Kristalina Georgieva – International Monetary Fund – 2026-04-09 – https://www.imf.org/en/about/senior-officials/bios/kristalina-georgieva
3. Francine Lacqua – Building Bridges – 2025-06-24 – https://www.buildingbridges.org/speaker/francine-lacqua/
4. Generative AI could raise global GDP by 7% – Goldman Sachs – 2023-04-05 – https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent
5. IMF chief warns world isn’t ready for shocks that are piling up – 2026-06-08 – https://theedgemalaysia.com/node/806193
6. Leaders with Francine Lacqua – Apple Podcasts – 2026-05-25 – https://podcasts.apple.com/mt/podcast/leaders-with-francine-lacqua/id1623682235
7. Global Economic Impact of AI: Horizon 2040 | Holistic Data Solutions – 2025-06-04 – https://holisticds.com/en/articulo/ai-economy/
8. IMF chief warns world isn’t ready for shocks that are piling up – 2026-06-08 – https://www.straitstimes.com/business/economy/imf-chief-warns-world-isnt-ready-for-shocks-that-are-piling-up
9. IMF Chief Says Wake Up, Next Global Shock is Coming – YouTube – 2026-06-08 – https://www.youtube.com/watch?v=cu4_RUTqoBM
10. A new look at the economics of AI | MIT Sloan – 2025-01-21 – https://mitsloan.mit.edu/ideas-made-to-matter/a-new-look-economics-ai
11. Full interview: IMF managing director Kristalina Georgieva – YouTube – 2026-04-12 – https://www.youtube.com/watch?v=2HWcL9KYUqA
12. Francine Lacqua – Bloomberg Philanthropies – 2024-09-18 – https://www.bloomberg.org/people/francine-lacqua/
13. AI Will Transform the Global Economy. Let’s Make Sure It Benefits … – 2024-01-14 – https://www.imf.org/en/blogs/articles/2024/01/14/ai-will-transform-the-global-economy-lets-make-sure-it-benefits-humanity
14. Inside Europe’s Economic Crises With Christine Lagarde – YouTube – 2026-01-11 – https://www.youtube.com/watch?v=Q934O_uJCiY
15. Global | The impact of IA on employment and productivity – 2025-01-31 – https://www.bbvaresearch.com/en/publicaciones/global-the-impact-of-ia-on-employment-and-productivity/
16. Exclusive: ECB President Lagarde Speaks to Bloomberg – YouTube – 2026-04-14 – https://www.youtube.com/watch?v=CWx9436zHKQ
