You jest, but if we are talking average it would not be hyperbole.
But what I want to add to my last point: Most performance pitfalls I have seen are not due to choosing wrong algorithms, but bad implementations driven by messy implementation and low maintainability. Python and JS are absolutely among the worst of the bunch and weak dynamic typing is a terrible burden to maintain and scale.
Mind you, it depends on which domain we are talking about. If you are working close to the metal and you are using something like PyTorch, your performance is indeed determined by choosing the right algorithms and language overhead is less significant in comparison. But most software is just moving things around in memory and do a little networking.
But what I want to add to my last point: Most performance pitfalls I have seen are not due to choosing wrong algorithms, but bad implementations driven by messy implementation and low maintainability. Python and JS are absolutely among the worst of the bunch and weak dynamic typing is a terrible burden to maintain and scale.
Mind you, it depends on which domain we are talking about. If you are working close to the metal and you are using something like PyTorch, your performance is indeed determined by choosing the right algorithms and language overhead is less significant in comparison. But most software is just moving things around in memory and do a little networking.