Not really a rebuttal. There's a lot of low hanging fruit in code bases were you can read code and see what's making things slow. Like the parent comment stated, you can visually tell if there's a lot of allocations in a certain part, and easily see if those allocs are present in a loop and often reason simply how many times that loop can be called.

I can see in the code when data layouts aren't optimal, and fix that.

There's a lot of optimizations that need more of a deep dive, but you can get a lot of gains by just reading and reasoning about your code/data.

EDIT: To add, there are cases were you specifically can't read code and understand performance issue, but you should first ask, is that because you just don't understand the APIs/Libs/tools you're using, or is it fundamentally difficult. For example, often at work I see people complain about their torch code being slow and needing to bust out a profiler, but often those people just don't understand how tensor operations work internally so of course they can't reason about the code and see the way they're using the lib is suboptimal.

Are you serious? Of course you can. Some hash algorithms are slower than others, you can read that and know that. Allocating in a hot loop is probably slower than allocating outside of it. Duh? I can't believe this even has to be justified, of course you can look at code and get an idea of its performance properties lol

Obviously you want empirical evidence to justify changes, but like... duh, you can read code and understand how it executes.

Consider the anecdotes in https://danluu.com/algorithms-interviews/ .