Measuring the convergence of artificial intelligence and deep technologies: A PCA-based composite index and logistic forecastinG (2013–2050)

Vollständiger Abstract

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The article addresses the problem of measuring and forecasting the technological convergence between artificial intelligence (AI) and adjacent science-intensive domains from the perspective of innovation economics. The relevance of the task stems from a systematic gap between hype-driven narratives and the observed technological potential: public projections are predominantly linear and fail to account for saturation effects and mutual constraints related to energy supply, computing capacity and regulatory lags. The purpose of the study is to construct a reproducible quantitative framework for assessing and forecasting the technological strength of seven technology areas and to quantify the risk of an infrastructure bottleneck over the period 2027–2030. The methodology comprises: min-max normalization of three primary indicators (investment, scientific publications and patent applications); dimensionality reduction via principal component analysis (PCA) yielding a composite technological index I_tech; parameterization of diffusion through the Verhulst — Fisher — Pry logistic curve (parameters L, k, t_0); and a cross-correlation analysis of time lags between science, patenting and investment. The empirical base covers aggregated time series for 2013–2024 with a forecasting horizon up to 2050. Three key results are obtained: (1) the diffusion-rate gap between the cognitive cluster (AI, k≈0.44) and the infrastructure cluster (energy, k≈0.08) is quantified; (2) the science-to-patent innovation lag in AI is shown to have compressed to 2–3 years compared with 5–7 years in traditional industries; (3) a two-dimensional 'speed × power' typological map is proposed for strategic planning. The results are applicable to the design of technology and investment policy as well as to corporate analytics functions engaged in long-term capital planning.