These numbers are pretty ugly. You always expect new tech to operate at a loss initially but the structure of their losses is not something one easily scales out of. In fact it gets more painful as they scale. Unless something fundamentally changes and fast this is gonna get ugly real quick.
they could keep the current model in chatGPT the same forver and 99% of users wouldnt know or care, and unless you think hardware isnt going to improve, the cost of that will basically decrease to 0.
I didn't understand how bad it was until this weekend when I sat down and tried GPT-5, first without the thinking mode and then with the thinking mode, and it misunderstood sentences, generated crazy things, lost track of everything-- completely beyond how bad I thought it could possibly be.
I've fiddled with stories because I saw that LLMs had trouble, but I did not understand that this was where we were in NLP. At first I couldn't even fully believe it because the things don't fail to follow instructions when you talk about programming.
This extends to analyzing discussions. It simply misunderstands what people say. If you try to do this kind of thing you will realise the degree to which these things are just sequence models, with no ability to think, with really short attention spans and no ability to operate in a context. I experimented with stories set in established contexts, and the model repeatedly generated things that were impossible in those contexts.
When you do this kind of thing their character as sequence models that do not really integrate things from different sequences becomes apparent.