In practice 'good iteration velocity' is often dependent on using a language/stack that you and/or your team is comfortable with and proficient in.

When one can choose between multiple equally-mastered stacks, then choosing the most appropriate/performant one is perhaps a good choice. But even then there are other things to consider.

For example, I worked for a startup where my ideal choice would've been a performant stack that I'm pretty good with (Elixir/Phoenix), but we ended up going for Python/Django because the founders felt that 1) it would be easier to find more programmers locally, and 2) apparently some investors do care about this. Both seemed like valid reasons to me.

> In practice 'good iteration velocity' is often dependent on using a language/stack that you and/or your team is comfortable with and proficient in.

This is true, but some stacks impose such a high cost that the costs exceed gains from familiarity. Basically you need to evaluate the savings of switching to a better stack and how long it's going to take before that savings equals the cost of switching to the new, less-familiar stack. For greenfield projects, the cost of switching is just the opportunity cost of slower initial development velocity while the team learns the new stack; for existing projects, there may also be the cost of rewriting in the new stack, which quickly becomes prohibitive.

Every maintained project is a continuous Ship of Theseus rewrite, but yes, if there's a language your team is more productive at writing correct code in and that provides better performance, there's an easy choice to make. That's very often not the case, though, and as well as team it's often project scale dependent, and projects often start at a scale where the calculus is very different from where it might be years down the road.