Years back I came to the conclusion that conda using bzip2 for compression was a big mistake.
Back then if you wanted to use a particular neural network it was meant for a certain version of Tensorflow which expected you to have a certain version of the CUDA libs.
If you had to work with multiple models the "normal" way to do things was use the developer unfriendly [1][2] installers from NVIDIA to install a single version of the libs at a time.
Turned out you could have many versions of CUDA installed as long as you kept them in different directories and set the library path accordingly, it made sense to pack them up for conda and install them together with everything else.
But oh boy was it slow to unpack those bzip2 packages! Since conda had good caching, if you build environments often at all you could be paying more in decompress time than you pay in compression time.
If you were building a new system today you'd probably use zstd since it beats gzip on both speed and compression.
[1] click... click... click...
[2] like they're really going to do something useful with my email address