The interpretation overhead is real but is being actively addressed. CPython 3.11's Faster CPython project added adaptive specialization -- the VM detects "hot" bytecodes and replaces them with type-specialized versions, skipping some of the dispatch. It helped (~1.4x). CPython 3.13 went further with an experimental copy-and-patch JIT compiler -- a lightweight JIT that stitches together pre-compiled machine code templates instead of generating code from scratch. It's not a full optimizing JIT like V8's TurboFan or a tracing JIT like PyPy's; it's designed to be small and fast to start, avoiding the heavyweight JIT startup cost that has historically kept CPython from going this route. Early results in 3.13 show no improvement on most benchmarks, but the infrastructure is now in place for more aggressive optimizations in future releases. JavaScript's V8 achieves much better JIT results, but V8 also had a large dedicated team and a single-threaded JavaScript execution model that makes speculative optimization easier. (For more on the "why doesn't CPython JIT" question, see Anthony Shaw's "Why is Python so slow?".)
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