"Learning springs forth from the gap in what is expected and what is real." — The Prolific Creator
Learning exists in the space between what is expected and what is real. We possess a distillation of our experiences in the form of knowledge (untested) and wisdom (tested). We proceed in life by applying both to every act.
In the application of what is learned, we find there is more to learn. Life is the teacher, and this world is the classroom. Our very existence is a constant process of course correction along the asymptote of enlightenment.
The smaller and tighter we make this cycle, the better. A life of smaller experiments allows for more frequent course correction. We microdose clarity.
Must we always be proven wrong?
The short answer is YES.
The long answer is YES... but to varying degrees.
Even when the space between what is expected and what is real appears to be in perfect alignment, there is always a minor adjustment to be made. This is because no single act occurs under the exact same conditions. And if you don't see it, then might I suggest you look closer and magnify the result?
There is a reason why small observations are the spark that lights the match of innovation. Most of us miss what is in plain view, because our tolerance for what is correct is set to high. When we lessen the tolerance in an engineering sense, the difference between what we expect and what happens becomes apparent... sometimes shockingly so.
Now that we know the nature of learning, we can optimize for it by...
Just as there will never be a universal theory of everything, so too will the learning never end. Just as the universe is infinite, so too is the gap between what we know and what we have yet to discover.
Are you willing to acknowledge there is always more to learn by zooming in on the space between what you expect and what you experience?
Are you microdosing clarity or spending much of your time lost at sea?
The key to defeating your fear of failure is to recognize the true nature of learning. Conduct small, frequent experiments. Look at the results as neither good nor bad. Learn from them. Now apply what you have learned to the next experiment. Test new insights and questions that arise... and they always do.
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