There is no incentive to learn something when you have no desire to do more than solve an immediate problem.
To the extent that all learning is an investment that pays off over extended use, leveraged by more advanced learning, close-ended problems are hell.
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Economies of scale are economies of learning. To learn, you *must* intend to scale to the minimum size necessary for the payoff to be positive.
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A thing feels like "maintenance" when you're forced to keep learning long after you've abandoned all intentions of growing. Taxes are the prototypical example of this kind of desire-limited learning vector.
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The more arbitrary the "learning" the stronger this effect. Learning feels arbitrary when there is a big distance between the foundational principles and what you need to know to apply them. Consider gap between understanding the Laffer curve and the 1040 form.
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Is this (no incentive) only because the learning can not be reapplied in the future? That is seldom the case, no? Even (short) time bound learning can be tactical learning for long term problems?
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Feels like when in organizations everybody wants to innovate safely. You can't do that, maybe limit the scope in time or resources, but innovating needs unlearning and then learning. A lot can go wrong.
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