It depends on what kind of performance you need. For CPU intensive tasks I would agree with you. But for network I/O intensive tasks, even though Python is slow it's still more than fast enough to keep up with a large request volume since network I/O latency is so much longer than CPU/memory latency.

And for data science /ML things it works great these days, even though those applications are 100% performance-bound.