[pstoll]

Feels like all the write ups that point out the short comings of eg Python for scientific computing.

Sure, except that once you have a community at critical mass around a reasonably good tool, that trumps most other things. Momentum builds. People write tutorials, explainers, better documentation, etc. it hits escape velocity.

Feels like Lean, with Terrance Tao as a strong proponent / cheerleader, is in that space.

Everyone who argues “but language X is better” … may not be wrong, but they are not making the argument that matters. Is it better than the thing everyone else knows and can use and has more people hours going into it to improve it?

Feels like a “worse is better” situation; or maybe “good and popular is good enough”.

[pfdietz]

I think the point that LLMs should enable effective translation between different formalisms is a good one. So I don't see the choice as being a big issue. This is especially the case here because to a large extent the translations can be checked automatically.

[ModernMech]

> once you have a community at critical mass around a reasonably good tool, that trumps most other things

This matters a lot less in the age of AI. AI doesn't need a massive number of community-built libraries, it can just write its own. It doesn't need a million tutorials floating out there on the interwebs because unlike most programmers, it will actually read the spec and documentation (tutorials are just projections of the docs/spec anyway). AI doesn't have to avoid languages with no job market because it just needs to do the job at hand, not build a career. This makes it easier for small languages and DSLs to gain usage where they never would have before.

I think AI might spell the end of the language monoculture (top 20 are mostly slight variations on languages circling the same design) that has persisted in programming.

[tehjoker]

It's the opposite and has been recognized for years. AI depends on training data and this nearly freezes the use of languages that were popular at the time of inception. Hopefully that is not true.

AI needs community libraries if there is to be interoperability and baseline quality between systems. At least at this level of quality and development.

[ModernMech]

> Hopefully that is not true.

I'm here saying this "PL ossification theory" is probably wrong, that it's not going to be the case at all. Yes, AI depends on training data, but that doesn't imply that AI can only use those programming languages or only reason in languages that existed at the time of their training. In fact the AI is able to reason able new languages the same way humans can -- by drawing inferences to the next closest language that it knows, pattern matching to things that are different from other languages, and also figuring out the semantics and reasoning through execution itself where it doesn't have training examples.

> AI needs community libraries if there is to be interoperability and baseline quality between systems.

Not everyone is looking to do the kind of work you're doing, and that's my point. Up until now, programming languages have been written by and for people who want to do businey/mathy/sciencey things with computers. But there's a huge world out there of other stuff to do with programming languages that have never been considered because the proposition of making languages for those domains is daunting and outside of the wheelhouse of normal people.

Now, DSLs are sprouting up where they have no business existing because of AI, just proliferating all over the place. Some of them are going to find communities (of people, AI, or both) and they will flourish completely apart from the systems we are building now in the tech world. It's not going to be the case that AI writes in Python for the rest of time because it writes in and was trained on Python today.