Clearly, companies view the context fed to these tools as valuable. And it certainly has value in the abstract, as information about how they're being used or could be improved.

But is it really useful as training data? Sure, some new codebases might be fed in... but after that, the way context works and the way people are "vibe coding", 95% of the novelty being input is just the output of previous LLMs.

While the utility of synthetic data proves that context collapse is not inevitable, it does seem to be a real concern... and I can say definitively based on my own experience that the _median_ quality of LLM-generated code is much worse than the _median_ quality of human-generated code. Especially since this would include all the code that was rejected during the development process.

Without substantial post-processing to filter out the bad input code, I question how valuable the context from coding agents is for training data. Again, it's probably quite useful for other things.