Well. To each their own, use cases differ so wildly it's hard to compare them.
The key audience for conda is ML/DS space, where most if not all packages come from either C/C++/Rust/Fortran and have to be compiled, while also requiring a consistent set of external C libraries like libblas, etc. As I said, some of those packages are a completely nightmare to build locally. Conda simplifies this by a lot in that you can just 'conda create -n myenv some=1.0 crazy=2.0 deps=2.0' and in a few seconds (if you use mamba and not conda) you have a working Python environment so off you go; no dockers, no local builds etc.
Honestly, I've found that conda has made operationalizing code very difficult. We've found it much easier to simply switch back to using pip, poetry, docker, and the standard OS package management tools rather than conda. Conda's dependency resolution is also quite slow and causes our builds & CI to timeout unless we drop in mamba.
The key audience for conda is ML/DS space, where most if not all packages come from either C/C++/Rust/Fortran and have to be compiled, while also requiring a consistent set of external C libraries like libblas, etc. As I said, some of those packages are a completely nightmare to build locally. Conda simplifies this by a lot in that you can just 'conda create -n myenv some=1.0 crazy=2.0 deps=2.0' and in a few seconds (if you use mamba and not conda) you have a working Python environment so off you go; no dockers, no local builds etc.