What does it mean that know when to stop optimizing?

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

Example: A software engineer spends three weeks refining a data processing pipeline. The first day of work cuts execution time from twelve minutes to four minutes — a 67% improvement that unblocks a team of analysts waiting on fresh data every morning. The second day shaves it to two minutes. By the end of the first week, it runs in forty-five seconds. Good enough for any business purpose. But the engineer keeps going. Week two brings it to thirty seconds. Week three, twenty-two seconds. Each day of optimization now yields fractions of a second. Meanwhile, three feature requests sit untouched, two bugs affect paying customers, and a junior developer has been waiting four days for a code review. The pipeline did not need to run in twenty-two seconds. It needed to run in under five minutes. The engineer crossed that threshold on day one. Everything after was optimization theater — visible effort producing invisible value. The cost was not just the engineer's time. It was the opportunity cost of everything that time could have addressed instead.

Try this: 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.