The Importance of InnerSource in the AI Era
summary
Claims that AI needs enterprise‑grade, well‑maintained code to work well with tools like GitHub Copilot and GitHub’s custom model preview lead to concrete checks: current token window limits force RAG or retrieval workflows, fine‑tuning carries ongoing inference costs on high‑end Nvidia hosts, and ROI only emerges when models serve hundreds rather than a five‑person project. InnerSource becomes the practical path: licensed, accessible repos with trusted committers, up‑to‑date code and solid documentation make code usable for RAG, fine‑tuning or hosted personalization. Waiting years to prepare risks being too late.
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