When the IMF places artificial intelligence on the same level as climate and demographics among the mega-structural factors of global growth for 2026-2035, the question is no longer whether new technologies are transforming the economy, but how to measure the extent of this transformation. Several recent indicators allow us to map the areas of friction and real gains.
AI as a Buffer Against Geopolitical Shocks on Global Growth
The most underestimated angle in current analyses relates to the counter-cyclical role of investment in artificial intelligence. The IMF describes a situation of tension between a negative supply shock, linked to the risks of closure of the Strait of Hormuz, and a positive demand shock fueled by the investment boom in AI.
This mechanism is not trivial. It has helped to maintain the global growth forecast around 3% in 2026, despite the simultaneous deterioration of the energy and trade context. The effects remain asymmetric: countries heavily exposed to hydrocarbon imports suffer more from the supply shock, while those positioned on the AI value chain capture most of the positive demand shock.
An analysis that addresses the impact of technologies according to Claravox puts these intertwined dynamics between innovation and the geo-economic context into perspective.
In other words, AI acts as a partial counter-shock to energy crises, not merely as a linear productivity engine. This distinction changes the interpretation of macroeconomic forecasts for the coming years.
Adoption of AI by Businesses: Comparative Table of Sectoral Gaps
The adoption of artificial intelligence is not progressing uniformly. The gaps between sectors and between economies reveal fault lines that will structure competitiveness in the coming years.
| Factor | Advanced Economies | Developing Economies |
|---|---|---|
| Position in the AI Value Chain | Design, infrastructure, high-value services | Downstream use, dependence on foreign platforms |
| Effect of the AI Boom on Growth | Direct positive demand shock | Indirect effect, often delayed |
| Exposure to Energy Shock | Variable depending on energy mix | High exposure to hydrocarbon imports |
| Employment Risk | Skilled jobs exposed to AI complementarity | Low-skilled jobs threatened by automation |
The IMF emphasizes that the risk of falling behind is deepest in developing economies. The Managing Director of the IMF explicitly pointed out this risk, marking a change in tone from more general analyses on digitization.
Data and Automation in Financial Services
Financial services account for a major share of adoption. The automation of management, compliance, and data analysis processes has reached a level that redefines the necessary workforce. Central banks are beginning to integrate questions of financial stability related to AI into their prudential analyses.
The BIS (Bank for International Settlements) has dedicated recent work to the links between artificial intelligence, growth, and financial stability, indicating that the topic now transcends the corporate framework to enter the realm of macroprudential regulation.
Digital Divide and Development: The Ethical Stakes of Technological Deployment
Digital transformation does not produce the same effects everywhere. Developing countries face a dual challenge: capturing the productivity gains of digitalization while avoiding structural dependence on infrastructures and platforms designed elsewhere.
- Access to big data remains concentrated in a few economies, creating an asymmetry in the ability to train competitive AI models.
- Advanced digital skills (AI engineering, data science) are unevenly distributed, with a talent drain effect towards advanced economies.
- Ethical and regulatory frameworks for AI remain embryonic in many countries, exposing populations to risks of algorithmic bias without effective recourse.
The World Bank and the EIB are financing connectivity and digital training programs in developing countries, but the digital divide is widening faster than catch-up policies. The OECD now ranks digitalization among the priority levers of the global economy, alongside trade or taxation.
Labor Market and Artificial Intelligence: Complementarity or Substitution
The IMF has published an analysis on the impacts of AI on the global labor market. The central finding: AI now affects skilled jobs, not just repetitive tasks. This exposure concerns a significant share of jobs in advanced economies.
In contrast, in developing economies, it is low-skilled jobs that remain the most threatened by traditional automation (robotics, digitization of processes). Generative AI adds an additional layer of uncertainty, as it affects cognitive functions that were thought to be protected: writing, legal analysis, assisted medical diagnosis.
What Early Field Data Shows
Research covering the period 2022-2026 on the labor market in the face of AI is beginning to provide empirical data. Initial results suggest a complementary effect in companies that invest simultaneously in training and tools, and a net substitution effect where adoption occurs without support.
The productivity gap between adopting and non-adopting companies is widening rapidly, posing a medium-term economic concentration problem. The ethical challenges related to algorithmic surveillance of employees and the transparency of automated decisions remain largely open.
The global growth forecast maintained at 3% for 2026, despite a deteriorated geopolitical context, is partly due to this investment boom in AI. However, this figure masks considerable disparities between countries, sectors, and categories of workers. The key data point is not the overall growth rate, but the speed at which the gap is widening between those who integrate AI and those who are subjected to it.
