Longtime PyPy developer Antonio Cuni has a lengthy blog post that describes his talk at the recently completed 2025 CPython Core Dev Sprint, held at Arm in Cambridge, UK. The talk, entitled "Tracing JIT and real world Python — aka: what we can learn from PyPy" was meant to try to pass on some of his experiences "optimizing existing code for PyPy at a high-frequency trading firm" to the developers working on the CPython JIT compiler. His goal was to raise awareness of some of the problems he encountered:

Until now CPython's performance has been particularly predictable, there are well established "performance tricks" to make code faster, and generally speaking you can mostly reason about the speed of a given piece of code "locally".

Adding a JIT completely changes how we reason about performance of a given program, for two reasons:

1. JITted code can be very fast if your code conforms to the heuristics applied by the JIT compiler, but unexpectedly slow(-ish) otherwise;
2. the speed of a given piece of code might depend heavily on what happens elsewhere in the program, making it much harder to reason about performance locally.

The end result is that modifying a line of code can significantly impact seemingly unrelated code. This effect becomes more pronounced as the JIT becomes more sophisticated.

Cuni also gave a talk on Python performance, which LWN covered, at EuroPython 2025 in July.