Well it depends on your employer/domain, but mostly it's the idea that most of what was learned falls under the category of "premature optimization", and makes

Well it depends on your employer/domain, but mostly it's the idea that most of what was learned falls under the category of "premature optimization", and makes the software more complex while making a negligible difference in efficiency. Of course, if you're dealing with truly big data, like a FAANG company or something, this may not be true at all. But the reality is, 90% of the software needed is satisfied by a standard CRUD setup, and you will not need to use any of the algorithms learned in school. This can be frustrating for a new graduate, who feels an urge to do "real" development and get past the simple stuff. It is mostly simple stuff, and should mostly stay that way. So basically, anything that wasn't CRUD (sort algorithms, processing that avoids just loading it all into memory, etc.) will usually be overkill. But, where you work it could always be different.