But that is probably the most important thing. I don't have time to re-write 40+ years of code in a new language just because it's trendy or safer or whatever. I have new science to do!
In some ways science suffers because of this, but it is also nice to have a relatively common ecosystem and institutional knowledge that doesn't change every 3 years.
In the short term it is of course convenient to just use the most popular thing always (in which case we'd all still be using PHP and Flash), but eventually things do switch on a large scale, and it benefits all of us to push this along when we can.
The more we double-down on an inherently sub-optimal ecosystem, the more we are trapped by it.
Imagine if all the effort making Python usable over the last decade had instead been spent on giving a compiled language better dev UX and GPU support...
I'm not even a fan of Julia, but I probably would be if it had received the same attention Python has for the last ten years.
Conversely, I'm _still_ not a fan of Python, even after all this time and effort has been expended, because the foundation being built upon is just simply a bad one for high performance and high security domains. Anything they manage to get working is akin to a hack, and is working in spite of the language in which it is built.
It's one thing to compare languages in the abstract, for e.g., to compare and contrast iteration or exceptions or whatever. It's a different thing to compare languages for doing something specific -- in this case, scientific computing. If someone want to get something done in a realistic ( or affordable) time frame, then things like like libraries, community, and documentation become especially important.
1.) The rewrite never seems to have all the features of the original (for many reasons), so you end up keeping the original around because science can be very niche. Now you have two or more packages to deal with.
2.) There is always a better language. Science (or at least parts of science) have switched before - from Fortran to C to C++ to Python. Some of the gains have materialized (some safety, performance, borrowing stuff from outside science). But it has come at a cost (language fragmentation, and also packaging is an absolute shit show right now, partly because #1).
But I'm sure the next batch of languages will finally solve all our problems once and for all, and we will never have to switch again.
(In general, I am talking about non-AI type science. I am a computational chemist, and our code really does date back > 40 years at times. That is not always a bad thing).
Sure and that's reasonable, but in the cases where performance/throughput/training time/RAM usage is the main limiting factor of a field, suddenly this stuff matters a lot. There's plenty of useful code written in COBOL and Basic from back in the day, but you don't see people using those things to train LLMs
Imagine if instead of letting Basic effectively die, we had improved it with a myriad of extensions to the point where you can run LLMs and GPU code and things in a performant-ish way on it. Now replace the word Basic with Python