Model collapse + skill atrophy + competitive pressure = one big feedback loop. Thoughts?

Came across this piece from a consulting firm and it stuck with me more than the usual AI hype/doom stuff. The core argument is that three things people usually discuss separately are actually one feedback loop. Models degrade when they train on AI-generated content (model collapse). Humans lose the ability to spot the resulting errors because we've stopped exercising judgment (they call it cognitive debt, citing that MIT study where ChatGPT users showed less brain activity and 80% couldn't quote a single sentence from "their" essay). And game theory locks everyone in anyway, because if your competitor automates, you have to as well, even if both of you would be better off not doing it. They compare it to the 2010 flash crash where algorithms reacting to algorithms wiped out a trillion dollars in minutes. The line that got me: "the firms that gain the most from AI will be those that are least dependent on it." Basically, when everyone runs the same models on the same data, AI is just infrastructure, like electricity. The differentiator becomes whoever still has humans capable of questioning the output. Yes, it's from a consultancy so there's a "hire us" angle at the end. But the argument itself seems solid to me. What I keep wondering: is there actually a way out of the prisoner's dilemma part? Individually staying skeptical sounds nice, but if your competitor ships decisions 10x faster on autopilot, "we kept our humans sharp" doesn't pay the bills until something breaks. Has anyone seen an org deliberately hold the line on human review and have it pay off, or does the race to the bottom just win? Source