[acdha]

This is true if you're focused on the compiler but if you include the adjacent question of what is reported to you in your editor while working it's a bit blurrier. If I write some Rust code and pass a string into something which expects an integer, I get a hard error preventing compilation. If I do the same thing in Python, however, and there's a prominent error displayed in my editor before I even run the code, how different is that from the perspective of anyone who isn't watching my screen? In either case the bug was caught before the code even ran and I likely have type-aware autocompletion to reduce the odds of making that mistake in the first place.

That's not to say that there aren't quite reasonable questions about how effective the different approaches are or how easy it is to fix the error, how complete the checks are, how deep the checks go, etc. A lot of how you feel about that is going to be subjective based on the languages you use and the quality of the code you work with — Rust has an advanced type system and great developer ergonomics providing unusually helpful error messages, Python has weaker typing but also a culture about simplicity which discourages some classes of bugs, Java has a lot of mushy-typed code where people got tired of language / compiler drawbacks and came up with ways to improve ergonomics at the expense of defeating the type checker, etc.

[madeofpalk]

Both your examples are the same static typing. One just has better IDE integration than the other.

[acdha]

In the first case, there would be a hard compiler error — rustc would refuse to compile the code until I fixed it.

In the second case, Python would allow the code to run but would potentially produce a runtime TypeError when it reached that point depending on exactly the code does. It might also run fine (e.g. I'm just passing that variable to json.dump()) or produce unexpected output (e.g. I'm passing that code to print() and that worked for int, and str, but then someone called it with None and I didn't want "None" in the output).

The point was that while those differ in how they're implemented, the experience can be fairly similar when you're in the middle of the code-test cycle. My example wasn't the most complicated dynamic typing scenario but it's an example of why this works pretty well: most Python code isn't highly dynamic or dynamic everywhere — typically there are a few places which might be challenging for analysis but there's also a LOT of code which only ever works with a single input type. If your IDE provides feedback on all of that code, you're going to avoid a fair number of other bugs and free up time for the hard parts.