Superpower Competitiveness in the 4IR: AI, FinTech

This study compares the competitiveness of the USA, EU, China and India over 2017–2025, using verified international indices (GII, GTCI, NRI) and descriptive indicators of AI, FinTech, human capital and innovation. Three exploratory propositions, motivated by endogenous growth in 4th Industrial Revolution, dynamic comparative advantage, and institutional theory, are examined. Innovation scores converge modestly relative to incomes per head. Talent competitiveness (GTCI) and Global innovation (GII) move together in levels, but initial talent does not predict later gains. Risk-based regulation is not associated with higher FinTech adoption: the EU records the lowest adoption, China the highest, the USA leads in capitalization. A Network Readiness Index (NRI) integrating talent, infrastructure, capital, regulatory complexity is partly computable. Three qualitative 2030 scenarios, with multipolar specialization most plausible, are exploratory, not forecasts. Policy implications (prioritizing human-capital investment, adaptive governance, infrastructure complementarity, open knowledge flows against costly technological decoupling) are hypotheses for testing on larger samples.

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Publication Details

Journal
Frontiers of Finance
Published
2026-11-10
DOI
https://doi.org/10.59429/ff.v4i1.15298
Primary Topic
Global Trade and Competitiveness
Type
article
Field-Weighted Citation Impact
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article

Superpower Competitiveness in the 4IR: AI, FinTech

Antanas Buračas
Frontiers of Finance
Global Trade and Competitiveness
article

Superpower Competitiveness in the 4IR: AI, FinTech

Antanas Buračas
article en

Abstract

This study compares the competitiveness of the USA, EU, China and India over 2017–2025, using verified international indices (GII, GTCI, NRI) and descriptive indicators of AI, FinTech, human capital and innovation. Three exploratory propositions, motivated by endogenous growth in 4th Industrial Revolution, dynamic comparative advantage, and institutional theory, are examined. Innovation scores converge modestly relative to incomes per head. Talent competitiveness (GTCI) and Global innovation (GII) move together in levels, but initial talent does not predict later gains. Risk-based regulation is not associated with higher FinTech adoption: the EU records the lowest adoption, China the highest, the USA leads in capitalization. A Network Readiness Index (NRI) integrating talent, infrastructure, capital, regulatory complexity is partly computable. Three qualitative 2030 scenarios, with multipolar specialization most plausible, are exploratory, not forecasts. Policy implications (prioritizing human-capital investment, adaptive governance, infrastructure complementarity, open knowledge flows against costly technological decoupling) are hypotheses for testing on larger samples.

Frontiers of FinanceVol. 4(1)
Mykolas Romeris University (LT), Lithuanian Academy of Sciences (LT)
Global Trade and Competitiveness
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