Humans get way more information out of each failure than ML systems. When you or I fail just a few times we can analyse the failures and often discover huge classes of wrong behaviors, and never repeat any of them. We're also good at differentiating what parts of the failure caused it and can even learn what parts were successful. We might even test dozens of hypothesis at once in a single attempt, even if we're focusing on just one of them. A computer often only gets one bit of learning from a failure or a success: this single behavior in particular either did or did not work.

My hypothesis is that we model the system we're studying and simulate many 'attempts' for every real world attempt. I.e. we grow a low-fidelity, but much faster, model of the system in our brain that we can use to make medium-low confidence predictions about the real system many times for each time we test against the real system.

So when you say you fail 95% of the time, I'm saying each of those failures actually have 200 mini-successes embedded that you can still use to train your mental model.