AI coding tools are everywhere but most teams plateau at "prompt and review." This talk presents the AI Codebase Maturity Model, a 6-level framework defining maturity not by what the AI can do but by what your codebase encodes — instructions, tests, metrics, feedback loops. Each level depends on the previous; you cannot skip. We walk from Level 1 (ad-hoc prompting) to Level 6 (autonomous multi-agent orchestration), validated against a CNCF project. The central insight: automating a repo with AI agents demands the same rigor as running a large open-source project — testing, coverage gates, governance. A solo dev with AI agents IS a large team. Takeaways: (1) flaky tests in autonomous workflows are dangerous, not annoying, (2) intelligence lives in the infrastructure, not the model, (3) full autonomy needs more human judgment, not less. We demo Hive, the open-source Level 6 implementation. Attendees leave with a self-assessment and artifacts to reach each level.