How so?

LLMs are leveling the developer experience and productivity in a way that makes Python's strengths almost irrelevant, while it's still suffering from bad tooling (even with uv and friends) and poor performance.

AI/ML: interfacing with C++ libraries directly (or in Rust) is now a real option. For everything else, even 5 years ago I wouldn't have used Python, now there are even fewer reasons to do so. As far as I'm concerned the remaining use cases are notebooks and one-shot scripts.

With writing code in english now, why have it use a slow weak language?

ML still has a depth of libraries that can't be replicated easily but ML work is decreasing by the day with LLMs.

Because the English to code translation step is fallible

Which is precisely a reason for not using Python, despite LLMs being good at it.

Why is that?

> With writing code in english now, why have it use a slow weak language?

Because the feedback loop of writing few lines of Python inside Jupyter cell is much shorter than with your currently favorite AI tool. It costs less too.

Implementing whole features is? What are we talking about?