Memory Scarcity, Open Models, and the Restructuring of the AI Industry, 2026-2030 -- A quantitative scenario analysis of inference economics, training-cost divergence, and infrastructure solvency

A new quantitative analysis identifies four primary drivers reshaping the artificial intelligence sector between 2026 and 2030, including rising memory costs and the emergence of high-performance open-weight models. These trends suggest a coming shift in industry economics where rapid gains in inference efficiency and local deployment capabilities challenge the current centralized infrastructure model. By balancing infrastructure solvency with technological progress, these factors are expected to fundamentally alter the competitive landscape for both large-scale developers and local AI providers.