In my experience the zero to something stage lasts way longer than people think and the cost of using immature and badly suited stacks like Typescript/etc bites way sooner than people think. They just keep pushing through it and telling themselves this is faster and everyone else is doing it so it can't be wrong.
Case and point I built a new service recently in Kotlin on JVM. It's a SQL translation gateway that maps an internal representation of simplified relational query model onto other databases so you can connect your own MySQL, PostgreSQL, Oracle, SQL Server, etc. Because Java has such a deep ecosystem in this area I was able to leverage JDBC and jOOQ to deliver support for all the require database types in only a few days of work with very clean code and full test coverage that tests compatibility with all the target databases.
Imagine trying to do that in Typescript. You don't have a standardised interface to database drivers so you would need to first choose that abstraction and implement it yourself + adapters for each driver you need to support (i.e re-inventing JDBC). Then you need to handle the different SQL dialects as they all have different quoting rules, different LIMIT/OFFSET syntax, different supported LIKE/ILIKE/etc, there is no library for handling this in Typescript, especially not with support for commercial DBs like Oracle, SQL Server and -definitely- not for more esoteric stuff like HANA and Informix, etc. Just writing a dialect agnostic query builder abstraction is weeks of work, actually handling all the dialects and emulating anything that isn't native for your targets is much longer.
So you might say, "but that is just libraries"... however that -is- the entire point. When you buy JVM you are buying the ecosystem of libraries primarily. The very nice runtime/GC/languages is really just icing. Same way that when you buy Python you are really buying numpy/pandas/PyTorch/etc, the language itself isn't the important part.
Or you could say this is just a problem uniquely suited for JVM and you wouldn't be wrong but isn't that also the point? Use the right tool for the job. JVM is to business logic and solving business problems what Python is to data science and machine learning.
The big epiphany I had recently was that when I build on JVM I don't write much code. I make a design, assemble some libraries in the correct way, iterate on it by refactoring until the abstraction is clean and then turning it on. There is very little mechanical coding involved because everything fits together very cleanly with only business logic being surfaced in actual "code".
I feel more like an architect, less like a code monkey and I can ship results way faster because I don't have to work on code that doesn't directly solve the problem. i.e there are much less yaks to shave.