this post was submitted on 15 Sep 2024
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With you there. The workload on developers is reduced with these features, to a degree. But, instead of saved effort then getting directed to working on gameplay mechanics and such, to me it feels like many devs just see it as time/money saved, producing a game that looks and plays like one from 10 years ago, but runs like it's cutting edge.
For instance, Abiotic Factor. That game on my RX 6800 XT runs at 40-50fps when at 100% resolution scaling at 1440p. Why? It's got the fidelity of Half Life 1, why does it need temporal upscaling to run better? (I adore that game btw, Abiotic Factor is so much fun and worth getting even if playing alone!)
Not saying that's how every dev is, I know there are plenty of games coming out nowadays that look and run great with creators that care. Just feels like there are too many games that rely on these machine learning based features too heavily, resulting in blurriness, smearing, shimmering, on top of poorer performance.
Just hoping the expectation that something like an RTX 4090 does not become the default cost-of-entry in order to play PC games because of this. It would be unfortunate for the ability of game developers to create and tune by-hand to become a lost art.
As a (non-game) developer, AI isn't even that great at reducing my burden.
The organization is enthusiastic about AI, so we set up the Gitlab Copilot plugin for our development tools.
Even as "spicy autocomplete" only about one time in 4 or so it makes a useful suggestion.
There's so much hallucination, trying to guess the next thing I want and usually deciding on something that came out of its shiny metal ass. It actually undermines the tool's non-AI features, which pre-index the code to reliably complete fields and function names that actually exist.
I know this is a bit late, but copilot is only ok if used for code completion. I switched to the free tier of supermaven a month ago and it's been way more helpful, as it can handle context better. Probably cuts coding in half and takes away a third of debugging.
Asking chatgpt for code has also become better, but imo still not reliable enough to regularly use. Just had some docker code written and it got it wrong 3 times so I gave up on that.
I get your point, AI can only save time if you know exactly what you're doing and it will only be helpful sometimes. But when it is, it's such a time saver.
Mostly it really is just a fancier auto-complete. It is most useful for situations where you want to essentially do the equivalent of copy&paste and then make changes in a few predictable places in each copy.
It is total crap at writing code itself to the point where you need to read the code and understand it to know it hasn't screwed up, something that takes much, much longer than just writing it yourself.