In the ML, DL world or in the physical simulators, people just compose a task and throw it to a CPU/GPU/TPU or a cluster of these and let it run for a long time

In the ML, DL world or in the physical simulators, people just compose a task and throw it to a CPU/GPU/TPU or a cluster of these and let it run for a long time. I don't see how Julia will be different for this kind of tasks. I understand that in Julia you solve the 2-language problem and all the goodies that multiple dispatch brings but the Python ecosystem progressed a lot in the past 2 years, now you have Numba JIT, Jax JIT, PyTorch JIT, XLA JIT and many other proprietary JITs that are not open-sourced. Since JAX (as an example) is mostly numpy and Python, you can leverage your existing knowledge instead of having to learn a fundamentally new paradigm. I would say that Python has many "specialised" JIT engines and it seems to work great for the community. Don't get me wrong, Julia is interesting, I can't deny that but I expect a huge adoption period for it. It can find its niche as C++ did for extremely high performance computing or Scala for Big Data (though Java is starting to replace many use cases). If you ask me now, I would say that the world converges around Java, C++ and Python when it comes to data, the old trio and it will remain this way for at least another decade.