The International Monetary Fund Managing Director Kristalina Georgieva highlighted on 12 October 2026 that the flood of capital into artificial intelligence infrastructure is a key factor keeping the world economy expanding at roughly a 3% pace. Speaking at the opening session of the IMF-World Bank meetings in Bangkok, she warned that the same wave of spending could also reshape labour markets, education systems and public-finance structures.
In the face of an ongoing energy-supply shock that threatens to dampen activity, the investment shock generated by AI projects has acted as a counterbalance. The Fund’s latest outlook, released in July, already projected 3% global growth for 2026. A fresh assessment to be published later this week is expected to reaffirm that figure, attributing much of the resilience to the rapid expansion of AI investment across both advanced and emerging economies.
AI spending lifts global growth despite energy shock
Georgieva explained that the surge in funding for data centres, specialised chips and cloud platforms is creating a new source of demand that offsets the drag from higher energy costs. The IMF estimates that this “investment shock” could add between 0.5 and 1 percentage point to the growth rates of advanced Asian economies, and 0.2 to 0.8 points for emerging Asian markets. In practical terms, countries that were already on a moderate growth trajectory may see their GDP trajectories nudged upward, simply because firms are rushing to secure the compute power required for the next generation of AI applications.
Asian economies reap hardware and infrastructure benefits
A defining characteristic of the current cycle is the geographic concentration of the supply chain for AI hardware. Nations such as Thailand, Vietnam, Malaysia and the Philippines host factories that assemble semiconductors, produce printed-circuit boards and build the servers that power large-scale models. The IMF points to Thailand as a concrete example of a country that could see a measurable boost to its output as multinational firms expand their production capacities in the region.
The United States, China and India remain the largest developers of AI algorithms, but the physical backbone – the chips, the cooling systems, the networking equipment – is increasingly sourced from Southeast Asian manufacturers. This dynamic is creating a virtuous loop: higher global AI spend spurs orders for Asian-made components, which in turn stimulates domestic investment, employment and export earnings.
Supply-chain concentration and policy implications
Policymakers in the region are therefore faced with a choice. By fostering a favourable business climate – through tax incentives, skilled-labour programmes and infrastructure upgrades – they can capture a larger slice of the value chain. Conversely, without strategic planning, they risk becoming mere assemblers while higher-margin activities such as AI research and software development stay abroad.
Employment reshaped: opportunities and challenges
While the macroeconomic picture looks upbeat, Georgieva warned that the labour market could experience a “slow-moving tsunami” of change. The IMF’s internal modelling suggests that up to 40% of existing jobs could be augmented, downgraded or fully automated over the coming decades. Early evidence from the United States already shows that roughly 10% of occupations now require some degree of AI-related skill.
Routine, repetitive tasks are the most vulnerable, leading to a contraction in entry-level positions that traditionally serve as the gateway to the workforce. At the same time, new demand is emerging in sectors such as data-centre operations, low-cost construction for server farms, and specialised maintenance roles – often lower-paid but growing in absolute terms. World Bank President Ajay Banga highlighted the potential of smaller, purpose-built AI tools to boost productivity in agriculture, health and education, especially in developing markets where large-scale models are less accessible.
Singapore’s President Tharman Shanmugaratnam stressed that governments must intervene before the gap between graduate-level and entry-level opportunities widens irreparably. He argued that AI should be harnessed to create “good jobs” that allow workers to acquire experience and progress, rather than simply displacing them.
From a fiscal perspective, the shift toward capital-intensive AI systems could erode traditional labour-tax bases, pressuring governments to rethink revenue collection. As firms adopt more automation, the proportion of income derived from wages may drop, prompting a reevaluation of corporate-tax structures or the introduction of new digital-services levies.
Policy pathways under discussion
During the Bangkok sessions, several finance ministers – including the U.S. Treasury Secretary and Spain’s finance minister – signalled their intention to monitor the AI-driven transition closely, even as some chose to focus on domestic agendas. The consensus was clear: capturing the productivity gains of AI while safeguarding inclusive employment will require coordinated investment in education, reskilling programmes and adaptive tax frameworks.
Yet the same engine brings a seismic shift to the job market demanding swift policy action to ensure that the benefits are widely shared.
