You don't need to do it artificially. If you look long enough in most code bases, you will eventually find a spectacular problem. Case in point, I was working

You don't need to do it artificially. If you look long enough in most code bases, you will eventually find a spectacular problem. Case in point, I was working for several months for a client, and I had a slow afternoon, so I decided to convert some calculation to numpy, see if we gained any free perf. We got a x100 local speed up, which was very fishy. Gaining speed is common with vectorization, but two orders of magnitude is a lot. So I looked at the original algo. There was a glaring mistake in it, that I fixed by my numpy code without noticing, and just changing that in the pure Python algo, without using numpy at all, made it X50 faster. I could then call my client, and celebrate the good news. Not "there was a mistake", no. But "we found margin for progress".