This paper conducts a comparative legal analysis of the European Union’s and the United States’ regulatory architectures for artificial intelligence (AI) through a computational methodology grounded in natural language processing (NLP). By applying techniques such as TF-IDF vectorization, word embedding, principal component analysis, and sentiment analysis, the study examines the semantic structure, lexical density, and normative rationalities embedded in the EU Artificial Intelligence Act (Regulation (EU) 2024/1689) and the recent U.S. Executive Orders on AI. The empirical findings reveal a profound structural and conceptual divergence between the two regulatory systems. The European corpus exhibits a high concentration of juridical and procedural terminology—high-risk, compliance, provider, surveillance, conformity—reflecting an ex ante, risk-preventive, and rights-protective legal rationality. Conversely, the U.S. corpus displays a more heterogeneous lexical distribution dominated by terms such as policy, national, promote, leadership, and agency, indicative of an executive-driven model of governance grounded in discretion, inter-agency coordination, and innovation enablement. Quantitative and semantic analyses confirm the existence of two distinct regulatory epistemologies: a European model characterized by legal formalism, normative determinacy, and precautionary proportionality; and a U.S. model distinguished by procedural agility, policy pragmatism, and strategic competitiveness. An additional interpretive layer employing large language models (ChatGPT, Copilot, Gemini, and Mistral) substantiates these results, demonstrating that LLMs are capable—under controlled conditions—of identifying latent regulatory tensions, coherence asymmetries, and potential avenues for transatlantic harmonization. At a theoretical level, the paper deconstructs what it terms the AI regulatory paradox: both jurisdictions pursue the same goals of safety, trust, and accountability, yet they do so through structurally incommensurable legal grammars. Building on this finding, the study advances a proposal for an adaptive, risk-based governance paradigm that integrates the European model’s normative density and rights-based safeguards with the American system’s procedural flexibility and innovation-oriented design. Beyond providing empirical evidence of transatlantic divergence, the paper demonstrates how NLP-based computational tools can serve as meta-regulatory instruments for the design, calibration, and critical assessment of future legal architectures in the age of algorithmic governance.