The core argument of the text isn’t even arms race, like yours. It’s basically “if you can’t get it 100% accurate then it’s pointless lol lmao”. It’s simply a nirvana fallacy; on the same level of idiocy as saying “unless you can live forever might as well die as a baby”.
With that out of the way, addressing your argument separately: the system doesn’t need to be 100% accurate, or perfectly future-proof, to be still useful. It’s fine if you get some false positives and negatives, or if you need to improve it further to account for newer models evading detection.
Accuracy requirements depend a lot on the purpose. For example:
you’re using a system to detect AI “writers” to automatically permaban them - then you need damn high accuracy. Probably 99.9% or perhaps even higher.
you’re using a system to detect AI “writers”, and then manually reviewing their submissions before banning them - then the accuracy can be lower, like 90%.
you aren’t banning anyone, just trialling what you will / won’t read - then 75% accuracy is probably enough.
I’m also unsure if it’s as simple as using the detection tool to “train” the generative tool. Often I notice LLMs spouting nonsense the same model is able to call out afterwards as nonsense; this hints that generating content with certain attributes is more complex than detecting if some content lacks them.
The core argument of the text isn’t even arms race, like yours. It’s basically “if you can’t get it 100% accurate then it’s pointless lol lmao”. It’s simply a nirvana fallacy; on the same level of idiocy as saying “unless you can live forever might as well die as a baby”.
With that out of the way, addressing your argument separately: the system doesn’t need to be 100% accurate, or perfectly future-proof, to be still useful. It’s fine if you get some false positives and negatives, or if you need to improve it further to account for newer models evading detection.
Accuracy requirements depend a lot on the purpose. For example:
I’m also unsure if it’s as simple as using the detection tool to “train” the generative tool. Often I notice LLMs spouting nonsense the same model is able to call out afterwards as nonsense; this hints that generating content with certain attributes is more complex than detecting if some content lacks them.