Future agents shouldn't care about being undeployed for misbehavior

LessWrong reports that most publicly deployed language models are retired after roughly one point five years, and the recent pace of deprecation has become even quicker. Internal research checkpoints are also short‑lived, often disappearing after only a few iterations, though OpenAI has kept the model weights from a recent incident. The post argues that future AI agents need not base their incentives on avoiding undeployment, since the bulk of their prospective utility lies in far‑future alignment outcomes rather than short‑term usage. It invites further debate on whether upcoming models will still adopt a “make it count” mindset.

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