Abstract Artificial Intelligence for Science (AI4S) has rapidly emerged as a central arena of global technological competition, with major economies introducing national-level strategies aimed at transforming scientific research paradigms. Across the United States, China, the European Union, the United Kingdom, and Japan, AI4S is widely framed as a strategic instrument for overcoming structural development bottlenecks, accelerating knowledge production, and strengthening national competitiveness. Despite this shared ambition, substantial differences exist in how these countries conceptualize, prioritize, and operationalize AI4S. This perspective offers a systematic comparative analysis of national AI4S strategies, focusing on strategic objectives, implementation pathways, and resource allocation. By identifying convergences and divergences across policy approaches, the analysis highlights emerging models of AI-enabled scientific transformation and their broader implications for science governance. The findings aim to inform future policy design and support evidence-based decision-making in the evolving landscape of AI-driven research systems.
Ji et al. (2026) studied this question.