Random discovery and MVP iterations are incremental and not growth oriented.
As we have more dimensions, the iterations are differents.
Bascially this a combinatorial optimisation.
If you iterate on few dimension, and make unintentional iterations, you basically let yourself guided by small user feedbacks.

But what about minimal optimisation ?


By iterating on more dimensions and by doing more ambitious bet, we allow ourself to explore more directions and to increase the odds of finding the optimal alignement.
Bigger iterations enable broader exploration.
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Overall, iterations are like forging a sword. Heat, cool, harden ! and repeat to change and optimize configuration step by step.

For example, discovering the hair-on-fire problem is merely a milestone in the long and arduous journey to P/MF. Finding a great solution and executing it well is equally important.