Python
Kitchen table
This is the language people use to write instructions for the machine, and it was designed to read almost like English – 'if the picture is a cat, put it in the cat folder'. It is not the fastest language, but it is one of the friendliest, which is why scientists who never planned to be programmers use it every day. Think of it as the handwriting of AI: nearly everything on the shelves above was scribbled in it first.
Whiteboard
Slow in the loop, unbeatable as the glue. The entire scientific stack assumes it, and the heavy lifting happens in C, C++ and CUDA underneath. Your Python is mostly orchestrating compiled kernels, which is why 'Python is slow' is true and rarely matters. The version-and-environment story remains a mild embarrassment (venvs, lockfiles, the annual packaging drama), so treat environment pinning as seriously as you treat code review. The language is the easy part. The ecosystem hygiene is the job.
Boardroom
The default skill on any AI team. If you are hiring for AI, you are hiring Python, and the libraries matter far more than the language itself. Its dominance is a network effect a decade deep, which makes it a safe standardisation bet: tooling, talent and vendor support all point the same way. The hidden cost is not licence fees (there are none) but dependency governance – knowing what open-source code your systems actually run and who keeps it patched.
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