My job is pretty awesome. But I had to get a PhD first, something that in my experience is risky for students' mental health. I don't recommend it to people unless they show a high tolerance for repeated failure (aka "research").
As for your question: NP-hardness refers to the worst-case performance, not the average case. Many individual instances of NP-hard problems are solvable. Stochastic algorithms do quite well, and protein folding in nature is a kind of stochastic algorithm (classically we would call it simulated annealing, although really it is quantum annealing). So as far as we know, there's nothing nature is doing to solve NP-hard problems faster than known algorithms. But it does do quantum annealing very fast, something we would really like to exploit using quantum computers.
For more like this - the intersection between computability and nature - I HIGHLY recommend Scott Aaronson's "Quantum Computing Since Democritus".
As for your question: NP-hardness refers to the worst-case performance, not the average case. Many individual instances of NP-hard problems are solvable. Stochastic algorithms do quite well, and protein folding in nature is a kind of stochastic algorithm (classically we would call it simulated annealing, although really it is quantum annealing). So as far as we know, there's nothing nature is doing to solve NP-hard problems faster than known algorithms. But it does do quantum annealing very fast, something we would really like to exploit using quantum computers.
For more like this - the intersection between computability and nature - I HIGHLY recommend Scott Aaronson's "Quantum Computing Since Democritus".