Usually languages are not the issue. It is the code that we write. As long as languages help us to find/debug a problem caused by crappy code - we should be good. Coding is kinda creative work. There is no standard to measure creativity or pitfalls of using wrong patterns. The incidents & RCAs usually find these. But most of the times it is already too late to fix core problem.

This is definitely true... as a mod/admin on EchoJS, can't tell you the number of times I've seen unnecessary IoC/DI libraries created in JS/TS to match the style of Java or C#.

The reality is that as a scripted environment, there are provisions to override dependencies for testability.... so unless you literally need multiple implementations of a given adapter, you don't need a DI/IoC framework and adding one only detracts from your overall solution. I'm a strong believer in that abstractions should mostly serve to hide relative complexity to make the rest of the application easier to reason with.

I'm also a big fan of the first version of anything being done in a scripted language with an emphasis on correct behavior. JS/TS and Python are more adaptable earlier on without committing to Java/C# or even Rust or Go. I understand a desire for homogeny, but that often can hold you back from creating something functional and easy to replace first.

I think that's generally true of most knowledge domains for anything resembling complex work. Govt, banking, pharma, medical, SaaS all will have a lot of specific requirements that come from deep historical, regulatory or business needs.

I would suggest that the rise of Python is very similar, as it's common in education circles. It's not even that Java as a language is particularly bad, it's how it is used in practice. Though I really do favor C# over Java, there's a similar stigma that comes from the community itself, not the language or it's baseline abilities. And this is a valid criticism.

Getting up to speed on the business logic was arguably the easiest part of my last job where the product was a liquid biopsy. Regardless of industry, you have to learn how to communicate with stakeholders and collect requirements. The existing software being a mess was a much more significant challenge. They also had similar issues on the data science side, where I would argue they did not lean into modern ML nearly enough and instead opted to do the familiar thing.