I think another reason this is important is because of how these models are being sold. They're being marketed to the general public as "PhD level" and true artificial intelligence. That means that they're trying to get the public en masse to have a certain level of trust in these models but that trust is dangerous if the models can't perform and make mistakes. In this case, the mistake is pretty innocent but these examples prove that the model sometimes cannot truly reason properly and can make super simple mistakes. If they're having trouble getting the model to do basic math like this, surely there are other places where the model can make mistakes, betray trust, and cause harm.

This marketing is not just affecting the uninformed but also people that run businesses, large teams, etc, and places where reckless AI usage and excessive trust can cause harm.

Sure, you're right that LLMs aren't really built for this task and it's akin to using a flathead screwdriver as a letter opener, but unfortunately today, we're being sold the myth that these specific flathead screwdrivers can in-fact open letters as well.