I totally agree, leaning on Moore's Law to solve performance issue doesn't work. When you have performance issues on the language itself, you're essentially paying a performance tax on every piece of code that executes, in a compounding way. So if Julia became a widespread programming language for general applications, you have those small delays leaking into all sorts of operations, and before you know it your whole system feels slow and bogged down.

I have been testing some performance oriented software recently, and it's amazing how much more productive I feel when every keypress is immediately reflected on screen with no delay. We can adapt to poor performance, but it adds a constant cognitive load to deal with it.

In my mind Julia is suited for limited applications, like doing work in a jupyter notebook, but is not suited for general applications unless the TTFX issue can be fixed.