I see what you mean, but from my perspective what this argument is missing is the context that a large and important subset of the scientific community has never moved to Python to begin with.
In other words, there is an even crankier and older community for which Julia may be the first actual change in a very long time.
This is particularly the case in HPC, where everyone still uses Fortran, C, or C++, and has never moved nor ever will move to Python because Python is fundamentally unsuited to these workloads. But in some cases, Julia is [1].
The best differential equation solvers (e.g., SUNDIALS [2] for a modern example) have been written in FORTRAN for the last 50 years. If Julia can challenge Fortran as the go-to language for this type of work (e.g. DifferentialEquations.jl [3] and SciML), that would hardly count as excessive churn.
[1] https://github.com/jeff-regier/Celeste.jl