[jmsdnns]

There is another angle to this too.

Prior to LLMs, it was amusing to consider how ML folks and software folks would talk passed each other. It was amusing because both sides were great at what they do, neither side understood the other side, and they had to work together anyway.

After LLMs, we now have lots of ML folks talking about the future of software, so ething previously established to be so outside their expertise that communication with software engineers was an amusing challenge.

So I must ask, are ML folks actually qualified to know the future of software engineering? Shouldnt we be listening to software engineers instead?

[tomrod]

> So I must ask, are ML folks actually qualified to know the future of software engineering?

Probably not CRUD apps typical to back office or website software, but don't forget that ML folks come from the stock of people that built Apollo, Mars Landers, etc. Scientific computing shares some significant overlap with SWE, and ML is a subset of that.

IMHO, the average SWE and ML person are different types when it comes to how they cargocult develop, but the top 10% show significant understanding and re speed across domains.

[abeppu]

This seems to be overstating the separation. For people doing applied ML, there's often been a dual responsibility that included a significant amount of software engineering. I wouldn't necessarily listen to such declarations from an ML researcher whose primary output is papers, but from ML engineers who have built and shipped products/services/libraries I think it's much more reasonable.