this post was submitted on 17 Jul 2026
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Programming
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Depending on the project, development speed is often critical. Lots of data science stuff is really ad hoc and doesn't get reused much, just some math, biology etc. graduates doing number crunching on specific datasets. Sure the script might take a day to execute, but if it's only actually getting used a single or double digit number of times, it doesn't make sense to break out more low level languages.
With commercial projects, it's often an issue of whether the higher hardware requirements of a project done in a scripting language are offset by the longer development time and higher wages for sufficiently competent programmers to do it in more low-level languages. This does have environment implications, but commercial projects don't typically give a damn about socialized costs (despite the lip service that many had adopted for a while).
AI seems mental, though, unless Python is just some glue for calling functions or libraries that are implemented in lower-level languages, which is definitely also a thing.
Most of the work is done by parallel compute hardware on most systems, not the CPU running Python.
As a beginner, these AI frameworks are written and C++ and you can do it there, but nobody does it and all the books out there or video courses are specifically for Python.