GLOBAL AI ECOSYSTEM DEVELOPMENT LEVEL: AN INTEGRATED INDEX METHODOLOGY AND THE TURING SCALE
Abstract
The rapid expansion of artificial intelligence has been accompanied by numerous international indices that assess national AI activity, government readiness, investment potential, infrastructure, human capital and regulation. These instruments differ in their objects of measurement, units of analysis, time frames, geographical coverage and aggregation rules; consequently, they do not yield an integrated, time-comparable picture of the global AI ecosystem. This article conceptualises the Global AI Ecosystem Development Level (GAIEDL) and proposes a reproducible methodology for its measurement. The construct is defined as a formative multidimensional composite comprising seven interdependent dimensions: technological capabilities; research and innovation; compute, energy and data infrastructure; human capital and education; investment and commercialisation; adoption and socio-economic impact; and governance, safety and inclusion. Six international measurement systems are used as an information base rather than as six equal votes: their headline scores are not averaged. Instead, the methodology requires disaggregation, an indicator-to-dimension mapping matrix, lineage-based deduplication of overlapping variables, fixed-goalpost normalisation and adjustments for data quality and recency. The results provide a structured approach to integrating heterogeneous indicators into a comparable assessment of AI ecosystem development. The framework combines the seven dimensions through weighted geometric aggregation and introduces the turing (Tr) as a common unit for interpreting ecosystem-level development. Procedures for validity assessment, sensitivity analysis and historical backcasting are specified. Procedures for content, convergent and discriminant validity, sensitivity analysis and historical backcasting are specified. The framework supports strategic management, innovation-maturity assessment, global-gap analysis, resource allocation and scenario planning, providing a transparent basis for annual monitoring and long-term interpretation of AI development.
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