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.