one thing this era of #genAI keeps showing us, is that often, the thing we care about isn't *just* the literal end product, but also in the process of how we get there.

with art, turns out we do care about things like context, and what it has to say, and not just about the physical presence of something nice to look at or listen to. and while the creation of individual pieces of art can, to a degree, successfully be imitated (often with some artifacts, but it isn't always easy to tell), the larger cultural conversation that takes place through art can't.

with coding, even if LLMs produced flawless code, having humans write code (aside from personal satisfaction etc) produces humans understanding the codebase, institutional knowledge becomes an incredibly important resource when things break, or the software needs to be adapted to changing requirements. and because code produced by LLMs is not any more flawless than organic human code, things *will* break, and we *will* need that institutional knowledge.

with mathematics, a significant part of the value in solving major problems has often not just been the solution itself, but also the techniques and insights gained from trying. group theory mainly exists as a byproduct from the effort of proving that the quintic is unsolvable. lie theory came from an attempt to solve differential equations, but is now largely applied elsewhere. even failed or incomplete attempts can go on to have a second life with a different purpose.

and while AI companies advocate for us to simply consume more end product, they are also deliberately insulating us from how incredibly resource-hungry LLMs are, as if that *didn't* drive the scale of the hype entirely ad absurdum.