The Recoverability Boundary: Societal-Level Misalignment in Frontier AI Provider Framing
This paper reconstructs how major frontier AI providers framed the socioeconomic implications of advanced AI deployment during April 2025 to April 2026. Through an interpretive comparative analysis of official documents from OpenAI, Anthropic, Google, Microsoft, and Meta, it identifies four recurrent frame families: diffusionist adoption, diagnostic governance, managed disruption, and material absorption. Read across the corpus, these frames converge into a broader grammar of managed absorption: a recoverability-preserving language that translates disruption into problems of evidence, training, transition, infrastructure, and institutional redesign. This grammar remains bounded by institutional recoverability, the assumption that existing institutions can absorb frontier AI if they adapt quickly enough. The omitted paradigm is structural incompatibility, the possibility that some deployment paths may erode the institutions through which societies absorb technological change. The paper develops societal-level misalignment as a safety-relevant category for this gap between model-level success and institutional absorptive capacity.