Issues around the performance of Python and programs
written in it have far wider consequences than startup
time. During all the time any Python program is running,
its host machine is consuming power that typically depends
on pumping CO2 into the atmosphere. If most of that power is
wasted, the effects go far beyond extra money to buy
it, or to operate extra servers, or users who wait a
little longer. The carbon footprint of a Python program
that runs throughout a data center, or many data centers,
adds up.
I wonder how quantifiable implementation decisions like the ones exhibited by namedtuples in Python, which one might argue is an unfortunate/accidental side-effect vis-a-vis energy consumption, versus ones like proof-of-work, which I would argue are explicitly designed to be expensive.
And if anything should come of that quantification, namely, does optimizing code really become a moral imperative, and if so are there some usability and refactorability metrics that are often held in high regard that we ought to consider abandoning in the name of "energy efficient" software.
Obviously, this isn't a simple tradeoff, software that is difficult to write because it is highly optimized is difficult to maintain, and it might be the case that performance derived energy savings are outweighed by the energy cost of maintenance (literally, the energy cost of debugging and testing).