I do use and like both fastapi and pydantic but this situiation is something they brought to themselves by adopting experimenral features into their mainline code and sell it as production ready. Python designers are not responsible for time-to-market decisions of other projects. It comes with the "no liability" part of opensource.
Thought it has been there for some time, it was added to core Python on a "provisional" basis (it says so in the docs), meaning that they were not bound to keep backwards compatibility with it. In other words, it's an experimental feature.
It's definitely experimental. No other language has an established system like this, although Ruby seems to have acquired one in 3.0. We still aren't close to uncovering all the unsolved problems yet, let alone solving said problems.
You and I seem to have different definitions of "experimental feature" then. By your definition, I don't think they did anything wrong. dataclasses in the standard library also rely on runtime type annotations. (how they don't have the same issue with 3.10 I don't know). I would hope people would start relying on new language features years after their release.
Data classes don't do runtime type checking, but they do provide dynamic type info through dataclasses.fields(). This function returns a tuple of Field objects, and according to the documentation[1], Field includes the attribute "type":
type: The type of the field.
In Python 3.10, this now returns a string. In my opinion that's a bug (perhaps in the docs).
I verified this using a short test program:
import dataclasses
@dataclasses.dataclass
class Apa:
bepa: int
first_field = dataclasses.fields(Apa)[0]
print(type(first_field.type))