Why does when to stop optimizing fail?

The most common reason when to stop optimizing fails: The most common failure is not refusing to stop — it is never defining when to stop in the first place. Without an explicit stopping criterion, optimization becomes open-ended by default. You keep refining because there is always something to refine, and each micro-improvement feels productive in the moment. The deeper trap is identity-driven: if you see yourself as someone who does excellent work, stopping at 'good enough' feels like a betrayal of that identity. You reframe continued optimization as standards rather than compulsion. The antidote is to separate the quality of the output from the quality of the decision about where to allocate attention. Stopping at good enough is not low standards. It is the highest standard of resource allocation.

The fix: Identify one thing in your life you are currently optimizing — a workflow, a habit, a project, a skill, a system. Write down the specific threshold at which it would be 'good enough' for its actual purpose. Then honestly assess: are you above or below that threshold? If you are above it, write a one-sentence stop rule — a concrete criterion that, once met, triggers you to move your attention to the next thing. If you are below it, calculate how much more effort is needed to reach the threshold, not to reach perfection. Post the stop rule somewhere you will see it during your next optimization session.

The underlying principle is straightforward: The optimal amount of optimization is not infinite — there is a point where you should stop and move on.