I think that this is a naive take. Current AI capabilities like ChatGPT can be compared to Pascal and C. We will get the Python and Java equivalent when we abstract things with hyperparameters and scale up the number of params at some point.
Not saying that there won't be good use-cases for Large Language Models, just saying that the analogy doesn't work: these things will be useful if used by competent people in very specific use-cases. The way ChatGPT is used now is just to generate a lot of hype and middlebrow content: with that in mind, I think its low barrier of entry is actually a negative (it causes things like this: https://www.npr.org/2023/02/24/1159286436/ai-chatbot-chatgpt...)
I would say quite explicitly that Python is a lot better than C, at least as long as you value pure concentration on the business logic of whatever you are implementing. Its libraries are helpful, huge and well documented.
I am overjoyed by the fact that I don't have to a) either reinvent the wheel myself (e.g. writing a JSON parser from scratch) or b) rely on some OSS library with a bus factor of 1, whenever I just need to store or read data etc.
It's unlikely that Python type annotations will ever be able to achieve something comparable to what many of these languages had during the 90s. They're useful, but if you need strong static safety guarantees, Python is the wrong tool for the task.