which is why it's not a good analogy: you make a powerful tool like "programming language" easier to use, you get more people with the power to write programs. You make an unreliable and brittle tool like ChatGPT easier to use, you get more crap content on the Internet.
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 started writing in Python this January, after spending years in Java, C, C++ ad PHP. (In academia, Pascal.)
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.
The Python honeymoon period lasts a long time. For me it lasted until the monolith I was working on had grown to ~500k lines of code. At that scale the smallest changes would cause (seemingly) unrelated breakage, despite many thousands of tests saying all was good. Python’s “anything is anything” philosophy made me miss the predictability of C and Java. Granted, that was before type annotations had taken off so maybe things are better nowadays.
In that case, blessed is me, because my Python projects are small-ish mutually independent modules ("probes") that only communicate using a specific JSON-based protocol over HTTPS.
I should elaborate that when I write "better than nothing", I'm someone with a background in strongly-typed programming languages (OCaml, Haskell, Rust) and even more strongly-typed programming languages (Idris, Coq, Twelf).
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.
