That specific advice seems to narrowly apply to web applications because they never deploy on windows . Microsoft has a Linux cloud and WSL/etc is a sandbox for developing and testing cloud software to deploy on azure, containers, etc. I don’t think WSL is intended for high performance local production use quite yet.
So yes, if you are developing web apps to deploy to the cloud use WSL. If you are running other stuff, the packages are in conda, and want gpus locally, etc then don’t. Conditional on having a working conda installation and not accidentally breaking it, everything works fine on windows.
There are constant stream of problems in conda. If you think that is bad, try to come up with cross platform setup instructions for pytorch that always work for GPUs.
There are many great things about the python ecosystem (especially pytorch, pytorch lightning, pandas, and friends) and even the language due to its insane level of dynamic flexibility. But with manifests, binary builder, etc Julia is in a different class entirely for reproducibility since 1.3. It is an enormous advantage.
So I agree with the spirit of what you are saying about python’s package managers and their fragility being difficult to support (even in python only setups). I just think you are misreading the WSL advice.