I agree with your comment but don't expect improvements to Python runtimes because the high-performance libraries people depend on are tied up with the guts of

I agree with your comment but don't expect improvements to Python runtimes because the high-performance libraries people depend on are tied up with the guts of cpython. Edit: Actually, for alphazero specifically, it would not be enough to make cpython run as fast as V8. I have implemented the alphazero training pipeline in torch, which uses a dynamic language runtime with performance comparable to V8. After spending weeks optimizing my MCTS, rules engine, and game state encoding and decoding, I still found that the CPU time consumed by these things caused substantial underutilization of the GPU. But at V8 levels of performance this is more like "it takes 2x as long to train" rather than "it is not viable to train." If I had done a better job of writing my above comment I would have made it clearer that this is not mostly about python-for-data-science. Most "right tools for the job" for other jobs are also points in tradeoff space that give up a lot of expressive power, ability to ensure correctness, performance, or all 3 in exchange for benefits that may only make sense over very short time horizons.