In AI, it is critically important to write clear and understandable code, because we often have to set up several experiments, and then try multiple models, multiple data, and a ton of hyperparameters. Even more obvious is this difficulty when you have to review code written by other people.
Surely you too have had the experience of writing code that seemed to work fine and that was pretty clear, but then you reread it a day or a week later and it looked incomprehensible. In the field of data science and programming in general, it is very important to be able to write code that is easy to read and maintain. Five Tips for Writing Clean, Efficient and readable Code in PyTorch Introduction
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