When it was created, PyPy did not seek to optimise startup performance. Instead, its aim was steady-state performance of JITed code, and it succeeded handsomely. A conceptually simple fix would be to optimise traces in one processor core, while the interpreter runs in another. Once when the trace is finalised, does interpretation stop and switch to native execution. But PyPy is currently a sequential system and making it parallel, while conceptually easy, is a lot of work and needs a rewrite of the whole system. But, for better or worse, the PyPy team does not have the resources to do this.

The other of PyPy's big performance issues (Python code using a lot of FFI calls via a suboptimal FFI) is also, in principle, easily fixable. But this is a social problem: the Python community would have to move towards better FFIs.

In principle, it would also be easy to fix PyPy's resource problem. I am not sure about the social problem ...