Quoting benkuhn.net:

The real world is the polar opposite. You’ll have some ultra-vague end goal, like “help people in sub-Saharan Africa solve their money problems,” based on which you’ll need to prioritize many different sub-problems. A solution’s performance has many different dimensions (speed, reliability, usability, repeatability, cost, …)—you probably don’t even know what all the dimensions are, let alone which are the most important. The range of plausible outcomes covers orders of magnitude and the ceiling is saving billions of lives. The habits you learn by working on problem sets won’t help you here.
Because of these differences, most graduates of elite schools—including me—start out being completely unable to identify which work is actually important. (And if some important work does happen to hit us over the head, it won’t come in the form of a puzzle with a grading rubric, so we won’t know how to execute it well.) Instead, we’ll keep trying to run our college playbook, and look for hard problems.
Frequently, we’ll find them by making easy problems hard, with hilarious/depressing results. The upper ranks of Big Tech are filled with people who made their careers writing bizarre custom databases, or building Big Data infrastructure that could be replaced with a laptop.

In school, if you pick an easy problem instead of a hard one, you lose leverage because your extra problem-solving ability goes to waste. But in real life, you can redirect it to prioritizing which problems to solve, or working more quickly, or building a machine that solves the problems for you.