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Overoptimized systems often lose resilience and become fragile. Slop provides a buffer for give and take. This is analogous to materials that have a wide plastic region vs materials that have very high rigidity but a small plastic region.

I've often seen this with coding -- another reason (if you needed another) to avoid premature optimisation is, in my experience, it often specialises code to a special purpose, and can make it harder to make changes later.

Often a simple implementation of an algorithm can be within 10% of the performance of a much fancier one, and is much easier to adapt later to a new use. Even if the less optimal implementation is ten times slower, that still doesn't matter if it isn't on the critical path.

Recalls a fundamental lesson about nature/reality preferring redundancy to the detriment of pure efficiency by Nassim Taleb: https://youtu.be/AcTvkt8k0sE?t=563

Pure optimization is a very ruin-prone path to take over time.