Here's the thing about AI—feed it its own output long enough and everything goes downhill. Each generation gets messier, the distortion stacks up, and eventually you've got a model that's basically useless.

So how do you break that cycle? By keeping humans in the loop. That's the core idea: human reviewers become the backbone of every training iteration, not an afterthought. When you anchor model development to real human judgment instead of letting algorithms eat their own tail, you actually maintain quality.

Distributed systems running this way? That's where it gets interesting. You're not centralized around one blind spot, and you're not trapped watching AI gradually poison itself. Instead, you've got a network approach that stays grounded in actual human oversight.