The GIL means that a single Python interpreter process can execute at most one Python thread at a time, regardless of the number of CPUs or CPU cores available on the host machine. The GIL also introduces overhead which affects the performance of code using Python threads; how much you're affected by it will vary depending on what your code is doing. I/O-bound code tends to be much less affected, while CPU-bound code is much more affected.
All of this dates back to design decisions made in the 1990s which presumably seemed reasonable for the time: most people using Python were running it on machines with one CPU which had one core, so being able to take advantage of multiple CPUs/cores to schedule multiple threads to execute simultaneously was not necessarily a high priority. And most people who wanted threading wanted it to use in things like network daemons, which are primarily I/O-bound. Hence, the GIL and the set of tradeoffs it makes. Now, of course, we carry multi-core computers in our pockets and people routinely use Python for CPU-bound data science tasks. Hindsight is great at spotting that, but hindsight doesn't give us a time machine to go back and change the decisions.
Anyway. This is not the same thing as "multithreading is impossible". This is the same thing as "multithreading has some limitations, and for some cases the easiest way to work around them will be to use Python's C extension API". Which is what the parent comment seemed to be saying.
