> I stress test commercially deployed LLMs like Gemini and Claude with trivial tasks: sports trivia, fixing recipes, explaining board game rules, etc. It works well like 95% of the time. That's fine for inconsequential things. But you'd have to be deeply irresponsible to accept that kind of error rate on things that actually matter.

95% is not my experience and frankly dishonest.

I have ChatGPT open right now, can you give me examples where it doesn't work but some other source may have got it correct?

I have tested it against a lot of examples - it barely gets anything wrong with a text prompt that fits a few pages.

> The most intellectually honest way to evaluate these things is how they behave now on real tasks

A falsifiable way is to see how it is used in real life. There are loads of serious enterprise projects that are mostly done by LLMs. Almost all companies use AI. Either they are irresponsible or you are exaggerating.

Lets be actually intellectually honest here.

>95% is not my experience and frankly dishonest.

Quite frankly, this is exactly like how two people can use the same compression program on two different files and get vastly different compression ratios (because one has a lot of redundancy and the other one has not).