I've written an essay exploring a second-order risk of AI model collapse. The technical safeguards proposed to prevent it—filtering out "AI-like" text—create a perverse cultural incentive: they risk systematically discarding clear, polished human thought while rewarding noisy, imperfect text as "authentic."

This isn't just a data problem. It becomes a gatekeeping problem, affecting academia, journalism, and publishing, and could ultimately feed degraded language back into future AI training. I trace the feedback loop from technical mechanics to cultural distortion.

https://borisljevar.substack.com/p/too-perfect-to-learn-from...

I'm interested in the HN community's thoughts on this paradox and potential solutions beyond filtering.

I really like the DNA analogy, hadn't encountered it before, it is very poignant. > The system becomes like a gene pool without fresh DNA: predictable, fragile, and prone to degeneration.

This is beautifully written article.