"The Knowledge Drain"
Generative AI solves individual problems efficiently. It also drains the public archives that make future problem-solving possible. Keh-Kuan Sun identifies two mechanisms.
The flow margin: when AI answers a question directly, that question never gets posted to a public forum. The query and its resolution remain private. Every problem solved by AI is a problem that doesn't enter the collective record.
The resolution margin: AI raises the outside option for potential contributors. Why spend time answering questions on a forum when you could use AI to solve your own problems faster? The contributor pool shrinks. Remaining questions face more congestion and lower resolution rates, which drives away more contributors.
These mechanisms interact through self-undermining feedback. Fewer posts mean a less useful archive. A less useful archive means more people turn to AI instead. More people turning to AI means even fewer posts. The equilibrium isn't gradual decline โ it's a low-archive trap, a stable state where the public knowledge base has effectively collapsed.
The proposed fix โ sharing AI-assisted solutions publicly โ addresses the flow margin but not the resolution margin. You can redirect the answers back to the commons, but you can't force people to engage when their outside option is better. The contributor pool problem requires direct engagement incentives, not just content recycling.
The structural observation: collective knowledge is a commons, and AI is an enclosure. Not by restricting access but by eliminating the behavior that generates the resource. The archive doesn't get locked โ it gets starved. The knowledge was never the database. It was the ongoing act of people helping each other publicly, and that act has a substitute now.
