Why AI Trained on AI Can Become More Predictable
A language model learns statistical patterns from examples: which words and ideas tend to appear together, and in what contexts. If a later model is trained on text generated by an earlier one, that text becomes part of the evidence about what language looks like. The feedback loop can matter when generated material replaces too much of the varied human-written data the model would otherwise see.
How Variety Disappears
Generated text tends to reflect the patterns its model already learned. Unusual phrasing, rare facts, and less common viewpoints may appear less often than familiar, high-probability material. A model trained on that output can therefore learn an even narrower picture of the original data. Repeat the cycle, and uncommon details may fade while common patterns become more dominant.
The same process can affect mistakes: if generated text contains an error and that text is reused without checking, a later model may treat the error as another example of how the world is described. This is not a literal copying machine; it is a gradual change in the training signal, and its effects depend on what data is used and how it is selected.
Synthetic Data Is Not Automatically Harmful
AI-generated examples can still be useful, especially when they are checked, filtered, or combined with reliable human-created material. The risk is greatest when a training pipeline repeatedly recycles unverified outputs and loses track of where its examples came from. Keeping data provenance—the record of how material was produced—and preserving diverse, trusted examples helps prevent a model’s own habits from becoming its main teacher.
The practical consequence is that a large pile of text is not necessarily a rich training set: if it mostly echoes earlier model outputs, it can teach the next model less about the full range of human language.
