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<p>While Artificial Intelligence (AI) is heralded as a transformative General-Purpose Technology (GPT), its short-to-medium-run associations with sectoral growth remain underexplored. This study investigates the heterogeneous and dynamic conditional correlations between AI development and growth in both the agricultural and industrial sectors across 11 top-ranking AI economies (2012–2023). Utilizing a novel multidimensional AI Development Index and a multi-staged panel estimation strategy, we classify nations into three institutional clusters: AI Superpowers, Advanced AI Economies, and Strategic Implementers. AI functions as a "Maturation Technology" positively correlated with industrial growth at the superpower frontier, while serving as a "Transformative Equalizer" associated with short-run agricultural gains indicative of leapfrogging dynamics in implementer nations. To address the "Productivity Paradox," a pooled interaction model with time-lagged specifications identifies a "Three-Speed" adjustment dynamic. Strategic Implementers exhibit robust positive associations across both sectors, whereas Superpowers show a positive industrial association consistent with frontier innovation and initial adjustment patterns. Crucially, Advanced AI Economies face an "Institutional Trap," where growth associations remain dormant reflecting structural rigidities. The validity of these dynamics is rigorously tested against spurious co-trending using First-Difference estimators, Year Fixed Effects, and dual placebo falsifications. Finally, utilizing an Institutional Quality Index derived via Principal Component Analysis (PCA), mechanism analysis indicates that the sectoral growth associations of AI—particularly for Advanced Economies—are conditional upon high institutional quality, with governance serving as a critical factor for absorptive capacity. These findings provide suggestive pathways to inform SDG 8 policy frameworks, highlighting that AI-associated reallocation is a sector-contingent process filtered through the quality of national institutions.</p>

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The dual frontier: heterogeneous associations of Artificial Intelligence with sectoral growth and the moderating role of institutional quality

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Nadeem Aljonaid,
  • Edmund Li Sheng,
  • Deyaaldeen Almajeedy,
  • Ahmed Alwadeai

摘要

While Artificial Intelligence (AI) is heralded as a transformative General-Purpose Technology (GPT), its short-to-medium-run associations with sectoral growth remain underexplored. This study investigates the heterogeneous and dynamic conditional correlations between AI development and growth in both the agricultural and industrial sectors across 11 top-ranking AI economies (2012–2023). Utilizing a novel multidimensional AI Development Index and a multi-staged panel estimation strategy, we classify nations into three institutional clusters: AI Superpowers, Advanced AI Economies, and Strategic Implementers. AI functions as a "Maturation Technology" positively correlated with industrial growth at the superpower frontier, while serving as a "Transformative Equalizer" associated with short-run agricultural gains indicative of leapfrogging dynamics in implementer nations. To address the "Productivity Paradox," a pooled interaction model with time-lagged specifications identifies a "Three-Speed" adjustment dynamic. Strategic Implementers exhibit robust positive associations across both sectors, whereas Superpowers show a positive industrial association consistent with frontier innovation and initial adjustment patterns. Crucially, Advanced AI Economies face an "Institutional Trap," where growth associations remain dormant reflecting structural rigidities. The validity of these dynamics is rigorously tested against spurious co-trending using First-Difference estimators, Year Fixed Effects, and dual placebo falsifications. Finally, utilizing an Institutional Quality Index derived via Principal Component Analysis (PCA), mechanism analysis indicates that the sectoral growth associations of AI—particularly for Advanced Economies—are conditional upon high institutional quality, with governance serving as a critical factor for absorptive capacity. These findings provide suggestive pathways to inform SDG 8 policy frameworks, highlighting that AI-associated reallocation is a sector-contingent process filtered through the quality of national institutions.