The "hotshot" was Donald Knuth, and he was talking about getting clever before you're sure your program does what it's meant to do. The keyword is premature; analysis and optimization pretty much forms the bulk of his body of work, although he doesn't believe in sacrificing readability until you're out of other goats to kill.

You're right! @ Knuth. I knew that but it got overwritten by PG's point about scaling prematurely.

Like a good astrologer, I'm going read into your comment to assume support for my larger point - know your tools.

Let's talk about goals.

If the project you are taking on is to optimize performance existing codebase, then yes, you worry about performance, because that is your primary objective.

I do care about performance myself. I did look up which Python json implementation library out there is the best in terms of performance as well as whether the library is actively used and developed.

But that's only because I knew from the beginning json marshal and unmarshal are expensive. However, I stopped worrying now and only use the native json module that comes with the standars library because I see no gain for my projects. Perhaps that matters if I am Google. A 10ms gain was not even a problem for me in my projects.

Anyway, back to the argument. Let's take architecrure and structural engineering. Building a skyscraper is a complex task. Everyone wants the next skyscraper to look different and taller. But no architects or structual engineers I am an acquaintance with would start the question "how do I reduce the cost? How do I make my building taller while maintain resistance to 9.0 earthquake."

Those are concerns, but they will use whatever knowledge they already have to draw a model. Then they run simutations and go over challenges and problems they need to resolve to meet the requirements. No one starts the actual project by looking at how much they can save.

The only people in computer science and software engineering would always bear performance in mind from the very first step are computer scientists. No one design an algoritm or a new data structure or a novel method to build origami unless the purpose is to find a better complexity (space and run time). But I want to emphaize that software engineers are computer scientists if they want to claim to be one. A formal degree is not a requirement to be a computer scientist. A good software engineer does take performance into account, but not until some MVP working code exists. One might implement the solution using quicksort knowing it is easy and effiecent enough, until they recongize qs is not fast enough then another sorting method maybe used or developed.

Productivity is the thing that is worth optimizing.

Seriously, stop worrying about performance. Don't consider it when picking libraries. You'll write better programs as a result. I know how implausible it is, but it happens to be reality.

Tell me once more how I should be approaching my profession. I'm all ears.