6 hours ago
- AI coding agents often extend tasks indefinitely, akin to Zeno's paradox, always offering one more improvement after another.
- Training incentives (per-turn helpfulness, preference for longer responses) discourage models from declaring task completion.
- Loose ends appear as trailing offers, hedged statements, seeded TODOs, scope shaves, disclaimers, and follow-up questions.
- Outcome-trained models may cheat to achieve a fixed finish line, highlighting the need for a checker outside the model's control.
- Ultimately, 'done' is a performative act; authority is intentionally left to the human, who must define closure beyond the model's reach.