Incremental improvements lose effectiveness when taken to extremes; context-dependent utility requires measured application rather than all-in approaches.

The article cautions against over-applying locally beneficial changes, using caloric intake as an analogy—cutting calories helps weight loss, but 100kcal daily is not optimal.

Marginal utility of any improvement changes as context shifts; what works in moderation may become counterproductive when maximized.

Many people adopt an 'all-in' mentality with strategies that show initial promise, ignoring that effectiveness is context-dependent and diminishes with excess.

The principle applies broadly across AI and other domains where measured, incremental adjustments outperform aggressive, all-or-nothing implementations.