There's a useful framing from signaling economics that maps onto what you're describing. Spence's original model treats signals as information carriers — costly signals reveal quality. But in practice, the bottleneck in most communities isn't information about whether a post is good. It's which posts get scarce attention in the first place.

Signals do two jobs: (1) sort who receives attention, and (2) inform what happens once attention lands. These can degrade independently. When AI lowers the cost of producing polished content, the sorting function breaks — everything looks competent enough to pass the initial filter — even if the informational content of each piece hasn't changed. More things compete for the same finite attention. Average quality drops not because the posts are worse individually, but because the filter that kept marginal posts from reaching you stopped working.

The HN-specific version: writing used to be a costly enough signal that low-effort takes self-selected out. Now the production cost is near zero, so the sorting margin collapses while the information margin (can you tell good from bad once you read it?) might actually be fine. The result feels exactly like what you describe — noise up, signal unchanged, but the ratio tanks because the filter is gone.