AI-Generated Code Risks Diluting Training Data Quality for Future Models
DuaneJRich · x · 2026-08-11
A developer raised a deep concern regarding the training data quality of Large Language Models (LLMs): classic open-source libraries like NumPy embody years of human thinking, experimentation, and tuning, serving as a highly dense knowledge source that significantly levels up model capabilities during training.
However, if the vast majority of new code is generated by AI in the future and human contribution is reduced to simple approvals, we will produce massive amounts of code with far less underlying information about how software should be written. This lack of high-quality, human-derived logic could starve future AI models of the dense data needed for substantial capability leaps.
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