At this point, this criticism has become a "thing" that no one really expands on or enumerates as if it's a given. It's not a given, and at the very least is nuanced and complicated.
No doubt that Python is sometimes fast enough (e.g., vanilla CRUD apps that do all of the heavy lifting in Postgres), but sometimes it’s not and you’re left with really crumby optimization options. And since we rarely can know with certainty on the outset of a project whether or not the bottlenecks will be amenable to the optimizations afforded by Python, it’s a dangerous game. I would even go so far as to say that other languages have become quite good at many of the things that Python is good at (namely pace of development) while being much better at the things that it’s not good at (performance, package management, etc), so I actually wouldn’t recommend starting new projects in Python except for certain niches, like scientific computing (and who knows if Python will even retain its dominance there).
As for scientific computing domain, I would start a new project in Julia rather than Python.
[1] https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
[2] https://github.com/frol/completely-unscientific-benchmarks
[3] https://www.techempower.com/benchmarks/#section=data-r19&hw=...