Objectives:
The objective of this article is to explain the sources of the EU’s AI competitiveness gap. The article tests whether structural and investment constraints, rather than regulation per se, limit scaling and commercialisation, and identifies the enabling conditions under which the EU’s normative-regulatory capacity becomes competitiveness enhancing

Material and methods:
The material and methods used in the article comprise a qualitative analytical approach grounded in institutional and political economics and endogenous growth theory. The analysis synthesises EU legal and policy documents with secondary indicators (AI investment, R&D spending, adoption, publications, patents) and peer-reviewed research and analytical reports, using the United States and China as comparative reference points

Results:
The results show that the EU’s AI competitiveness gap is driven mainly by structural and investment constraints: fragmented capital and data markets, insufficient compute infrastructure, weak scale-up finance and commercialisation pathways, and uneven firm adoption. These factors raise scaling costs and uncertainty, depressing private investment and slowing diffusion. As a result, the EU converts scientific strength into market outcomes more slowly than its global competitors

Conclusions:
The conclusions indicate that regulation is not the binding constraint per se: the EU’s normative-regulatory model can be an asset (trust, legal certainty, standards), but its payoff depends on complementary scaling conditions. Strengthening EU AI competitiveness requires coordinated investment mobilisation, stronger coordination, and EU wide scaling mechanisms (capital market integration, interoperable data/compute infrastructures), enabling the EU to leverage the Brussels effect without sacrificing rights-based governance