Most publicly available code is educational, experimental, or simply below production standards. When large language models ingest it indiscriminately, that distribution becomes the model’s prior. The consequences ripple through pretraining, fine-tuning, and the behavior of autonomous code generation tools that plan, write, and refactor code without constant human supervision. This article explains how low-signal code skews model internals, what failure modes appear in autonomous coding, and the concrete data and system fixes that produce production-grade outcomes.