I've +1'd because while I disagree, yours is a reasonable opinion that's provoking a useful discussion, and I think the downvotes you're getting are not appropriate.
The problem with aiming for success from the start is that it promotes a highly conservative, over-engineered solution. If it does work, it also acts as a brake on iterative improvement because your system already works, why risk failure by trying to make it more optimal? Also if you have a proven system, people start making bigger and bigger bets on it's success (more expensive payloads, for example), so the cost of any failure goes up.
If you aim for optimal design form the start, you're almost bound to experience failures. That's because say there are 100 parameters you need to tone and you aim for the estimated optimum for all of them. Statistically, half of those estimates are goign to turn out to be sub-optimal. As you iterate on the design, you fix those untill you end up with a truly optimal design. At each iteration, you have a positive incentive to make the necessary changes.
Depending on the cost of failure, this can be more expensive than aiming for a conservative, over-engineered design to start with, but not necessarily. Over-engineered systems, for a high chance of initial success, are by definition more costly than optimised designs and there's still a non-zero chance of initial failures anyway.