economic complexity for a given income level, aiming to uncover the factors driving their overperformance. Economic complexity refers to the knowledge intensity of an economy, captured by the diversity and sophistication of the goods and services it produces. The Economic Complexity Index (ECI), initially developed by Hidalgo and Hausmann (2009), measures this complexity by analyzing a country's export mix, reflecting its capabilities to produce and export diverse, high-value products. More recently, the ECI expanded to technology, based on patents data, and research, based on research papers (Stojkoski, Koch, and Hidalgo, 2023), since trade data can miss key information about innovative activities.

Using this methodology, we categorize distinct groups of AI overperformers based on their Economic Complexity Index (ECI) and their preparedness and readiness levels. Notably, overperformers are not simply the countries with the highest AIPI scores but those that (i) meet a minimum AIPI threshold and (ii) outperform their predicted scores given their economic complexity. Among high-income economies, global overperformers include Australia, Japan, the Netherlands, New Zealand, three of the four Asian Tigers (Hong Kong SAR, China; Singapore; and the Republic of Korea), and Northern European countries such as Denmark, Finland, and Norway—aligning with findings on the long-term determinants of AIPI detailed later. Upper-middle-income local overperformers comprise Albania, China, Costa Rica, Indonesia, Kazakhstan, Malaysia, and Ukraine. Finally, lower-middle- and lowincome local overperformers include Ghana, India, Morocco, Rwanda, Sri Lanka, Tunisia, and Viet Nam.

From this list of AI overperformers, we observe that AI regulation and ethics consistently emerge as top drivers of AI preparedness across income groups, emphasizing the universal importance of institutions in managing AI integration. In low- and lower-middle-income countries (LICs/LMICs), regulation and workforce development dominate, reflecting foundational priorities amid resource constraints, while digital infrastructure and innovation receive less emphasis. For upper-middleincome countries (UMICs), regulation and human capital remain critical, but digital infrastructure becomes increasingly significant as technological capacities expand. High-income countries (HICs) prioritize regulation alongside well-established digital infrastructure and innovation systems, supported by advanced workforce readiness strategies.

This progression illustrates how AI preparedness strategies align with the unique capacities and priorities of each income group, offering tailored policy pathways for countries aiming to enhance their AI readiness. By understanding these patterns, policy makers can adopt fitted strategies to develop their countries’ AI ecosystems based on their specific economic and institutional contexts and might effectively bridge the gap in the global AI divide.

The rest of the paper is organized as follows: Section II outlines the data and methodology, Section III presents the main empirical findings, Section IV examines how different categories of countries overperform in AI preparedness, and Section V concludes and opens avenue for future research on AI preparedness.

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