Artificial Intelligence, Labour Income and Effective Demand: A Theoretical Framework for the Distributional Absorption Threshold of AI-Induced Productivity

Artificial intelligence (AI) may raise productivity by automating tasks, augmenting human work and reducing information-processing costs. Yet productivity gains are not necessarily converted into broadly shared purchasing capacity or fully absorbed output. This conceptual review develops the notion of the Distributional Absorption Threshold of AI-Induced Productivity, defined as the point at which AI-related productivity growth outpaces the growth of broadly distributed real purchasing power and household consumption. The framework links AI-induced productivity to labour income, income distribution, prices, investment, fiscal redistribution, external demand and effective demand. It distinguishes a favourable transmission path, in which productivity gains support wages, disposable income, consumption and output absorption, from a critical path, in which weak distributive transmission may generate absorption tension. The review formulates conceptual propositions and preliminary operational indicators for future empirical research while treating the threshold as an analytical construct rather than a fixed empirical constant. Its contribution is theoretical: it reframes the AI productivity debate beyond both technological optimism and automation anxiety by connecting technological change, distribution and demand-side realisation.