I understand static type annotations would be an extension to current Python semantics, it would require a flag such as "my type annotations are correct and ple

I understand static type annotations would be an extension to current Python semantics, it would require a flag such as "my type annotations are correct and please use them for optimizations" (this idea really only makes sense if PyPy can assume the annotations are not wrong, otherwise the arguments against this idea are just trivial). TypeScript is doing something similar for Javascript; it extends the language but is a strict superset. Type annotations can work together with JIT optimizations, because the annotations could simply provide a better starting point for JIT, requiring it to infer less, and perhaps providing information which would otherwise not be inferred from running the code. PyPy would still need to specialize further, e.g., from Int to int64 as you say, but already knowing that it's not float or str or list already seems useful. Gradual typing is relevant in this regard, which makes it possible to combine static and dynamic types and do type inference. See e.g. https://github.com/tomprimozic/type-systems/tree/master/gradual_typing https://github.com/tomprimozic/type-systems/tree/master/grad... Now of course PyPy may hold the position that it's going to eschew static types on principle, but then it's a matter of ideology, not a technical reason, which causes these type annotations to "never have any value for PyPy".