Remote-Capable Knowledge Work Should Default to AI-Enabled Flexibility

This position paper argues that remote-capable knowledge work should default to AI-enabled flexibility because the workflow-integrated foundation-model stack changes the coordination economics that once favored daily co-presence. By foundation-model stack, we mean systems that combine natural-language interaction, multimodal capture, long context, retrieval, transcription, translation, and increasingly bounded tool use inside everyday workflows. Their organizational significance is not generic automation but the accumulation of artifact capital: durable, queryable, reusable traces such as transcripts, summaries, decisions, tickets, code comments, and retrieval layers. The argument rests primarily on capabilities that are already widely deployed—transcription, summarization, retrieval, translation, drafting, and code assistance—with bounded agents treated as an amplifying but not necessary extension. Rather than eliminating the office, this shift supports selective co-presence, reserving in-person time for tasks with high tacitness, high coupling, or high relational stakes, including apprenticeship, conflict repair, trust formation, and early-stage synthesis. Because the same systems can also intensify surveillance, skill atrophy, and compute-related emissions, we outline a machine-learning research agenda centered on team-level evaluation, privacy-preserving memory layers, scaffolded AI for learning, carbon-aware routing, and pro-agency workflow design.