but even terribly performing applications can be "worked around" and thus scaled higher. [...] How can we achieve this? By monkeying with the dataset, queueing

but even terribly performing applications can be "worked around" and thus scaled higher. [...] How can we achieve this? By monkeying with the dataset, queueing and batching operations, invalidating operations that take too long, artificially limiting the number of requests per second, or just passing the request to a completely different app depending on use case. And that's a good thing? Part of my point is that this can be avoided, or at least have a higher probability of being unnecessary, if the proper basics were learned. Wouldn't you wish the author would have just known how to properly sort a list[1] in the first place? "Scaling" the development process can significantly reduce the amount of time needed to solve problems, or complete new features. It can be more beneficial to be able to ship code faster and more reliably, even if it isn't the most efficient code Agreed to that, but as said, computer science is not just about runtime complexity. Knowing computer science might help you avoid those situations, or resolving them quicker. I know that shitty stuff can work, even work "well enough", but going back to my original comment: Maybe it could work much better, simpler, more profitable if that's your favorite metric, with just the application of some basics, if they are known. [1] Noting that "sorting a list" is a stand-in for all sorts of tasks that benefit from CS-knowledge.