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Formulating inference economics in dollars per petabyte of bandwidth delivered (\\$/PB) -- model-agnostic for bandwidth-bound decode -- we show the entrant-incumbent cost gap never closes: a depreciation conveyor delivers newly amortized fleets to incumbents faster than hardware prices normalize (3.2x in 2026, 1.9x in 2027, re-widening to 3-4x by 2029-30). Training bifurcates into a luxury tier (\\$18-38B per frontier run by 2030) and a mass tier (previous-frontier parity via RL/distillation falling toward \\$5M). Solvency of the announced buildout is confined to a corridor requiring roughly 2x annual","title":"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","url":"https://arxiv.org/abs/2607.07207","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.07207v1 Announce Type: cross \nAbstract: We analyze how four forces restructure the AI industry over 2026-2030: the DRAM/HBM price surge, frontier-capable open-weight models (GLM-5.2), rapid inference-efficiency gains (near-Shannon-limit KV-cache compression, lightweight local runtimes), and the entry of Meta and xAI into compute resale on fleets bought before the memory repricing. 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