There is no serious debate that vectorized Python remains excellent for exploratory research. The debate is about where its performance ceiling becomes unacceptable for production-scale experimentation.
The Tradeoff
Python frameworks minimize user friction. You can express an idea quickly, sweep parameters, and inspect plots with almost no ceremony. That workflow is hard to beat.
Where Compiled Engines Enter
When teams move from dozens of runs to thousands, interpreter overhead, object churn, and memory pressure become more visible. At that point, compiled loops and pre-allocated state stop looking like premature optimization and start looking like necessary infrastructure.
The Split Market
The most likely steady state is coexistence: Python for idea formation, compiled engines for repeatable large-scale execution. Product sites and docs are increasingly framing themselves around that division of labor.
