[aqsalose]

>The academia is characterized by world's top experts in a narrow niche investigating speculative problems few people have any idea of. More often than not, that research turns out to be a dead end, "wasting" years of work.

I believe this is very much dependent on where and with whom you are working with. By definition, not everyone is a top expert. Even rarer to publish a top paper. Sometimes entering a narrow niche field makes it possible to work in an insulated silo where niche's favorite problem statements and research programs can escape critique from experts of other disciplines.

As a practical, though not quite disastrous example, I did an applied maths MSc with focus on ML and data science, and then spent some time in bioinformatics oriented data science grad program. It was only after I entered the pharma industry that I found a field where it was expected to have serious interest in doing ones best with causal inference while acknowledging its limitations ... with methods which apparently have been bread and butter of econometrics and maybe some biostatistics for decades. On the other side of fence, typically only people exposed to particular statistics textbook or ML fields are interested in running LOO/cross-validation model validation checks for their model fits. I see some more communication between the disciplines could absolutely improve the work of everyone involved. And these are big fields. Small niche fields with niche problems where everyone publishes in a niche journal can become worse.

[jltsiren]

The world is small, but there are many things to study. If you want to make a career in academic research, you have to build a profile other people in the field know you for. That profile quite likely makes you a top expert in something.