If I were in the position of making or advising someone making the decision of whether or not to use Julia, an unsubstantiated, untestable assertion like that makes virtually no difference in my decision or advice. I hope it's clear this is not an insult, it's just that if I have no way to evaluate your assertion or its applicability, there's no way for me to incorporate it into my decision or advice.
By contrast, if you could point to even one concrete example, I could file it away in my head as "someone on HN pointed out that Julia has X, Y, and Z problem". If I were in a position of making or advising a decision on Julia, I would be able to evaluate whether it applies the use case in question, whether it points to some deeper design problems with Julia, or whether I even think it's a problem at all.
Until then - I concede, please take it with a grain of salt and use your own judgement.
Rather than rewriting code in Python, we took an approach of using PyCall. It turns out that the overhead of calling Python is very small... at least not to an extent that I had to worry about. For my production system, we used this strategy to access Oracle and Apache Kafka. The code still looks very clean as the calls to the Python packages are just like normal Julia function calls.