> All you need to do is establish what the output means and you can fine-tune an LLM final layer for this task

Yeah, that was the original idea with GPT, Generative Pre-trained Transformer, and earlier open pre-trained transformers.

Today people use AI via APIs rather then fine-tuning models by themselves and when someone provides this as an API they got excited.

No it wasn't. Those were models trained from scratch, required large scale data, and the nontrivial parts involved training at scale and the autoregressive task which no one expected to work as well as it does. It is the difference between developing a foundation model, and using one. I believe Jev falls in the latter category, because the task itself is no different, only the output.