group, we focus on countries with an observed AIPI score exceeding 0.7—corresponding to the population-weighted average and median AIPI scores for HICs (rounded to one decimal)—and surpassing their predicted AIPI score (i.e., above the linear fit line) given their level of trade and research economic complexity. These 10 overperformers, marked in yellow, include Australia, Japan, the Netherlands, New Zealand, three of the four Asian Tigers (Hong Kong SAR, China; Singapore; and Korea), as well as Northern European countries such as Denmark, Finland, and Norway.<sup>19</sup> Their observed AIPI scores are statistically significantly above their predicted scores (at 0.1 percent threshold). The relative prominence of East Asian economies, particularly the three recently developed Asian Tigers and particularly Singapore—ranked #1 in observed AIPI score for 2023, much above its predicted score—aligns with prior BMA findings, which underscore the significant influence of “Confucian cultural values” on AI preparedness. By contrast, other HICs with strong AIPI scores, such as the United States and Switzerland, do not qualify as overperformers. This is because their observed AIPI scores align with the predictions based on their high levels of economic complexity rather than exceeding them. Additionally, the largest economies, like the United States, may face challenges in scaling top-level AI implementation uniformly across regions (Hunt, Cockburn, and Bessen, 2024).

# _III.bLocal overperformers_

Figure 4, weighted by population size, demonstrates a positive but more moderate (yet still economically significant) correlation between AIPI and multidimensional economic complexity for the subgroup of 69 LMICs and UMICs, with an value of 0.45 and of 0.67 (statically significant at 0.1 percent). This suggests that while economic complexity likely plays a role in shaping AI RR<sup>2</sup> oo preparedness for these countries, policy decisions appear to have a more substantial independent impact on AIPI performance. A likely explanation, explored further in the next section, is that some of these countries, especially among UMICs, have greater macro-fiscal capacity than LICs (Choudhary, Ruch, and Skrok, 2024), enabling them to implement ambitious digital and AI-specific policy agendas. However, these countries often lack a fully developed infrastructure, and specifically digital infrastructure (pillar i of AIPI) and a sufficiently skilled workforce (pillar ii of AIPI), which are still in the process of being built, especially in comparison to HICs (World Bank, 2021; 2024c).

> 19 With an impressive AIPI score of 0.76 in 2023 and an ambitious digital agenda, Estonia has the potential to be considered among the AI preparedness overperformers. However, a lack of data on its multidimensional economic complexity limits such classification. Estonia's third national AI Strategy (2024–26) prioritizes advancing AI skills and competencies through education and training reforms, including the introduction of new Master's and PhD programs, elective AI courses, and vocational education. The strategy also emphasizes society-wide upskilling initiatives, such as a digital academy and public sector training programs that aim to train 10 percent of employees annually. Additionally, the Ministry of Education and Research offers specialized AI training for teachers, students, and public sector managers, while the Ministry of Economic Affairs and Communications targets 80 percent AI literacy across society and widespread AI adoption— 100 percent in the public sector and 75 percent in the private sector—by 2030 (Digital FOSS, 2024).

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