I don't think any of this indicates a fundamental property of the tech itself. AI companies post-train their models to sound like what people like to read better. There's a reason that engagement farmers have converged on the tone that these LLMs imitate, namely its something that people prefer. Maybe not you, but it's the same thing that gives us YouTube face on thumbnails etc.

It takes some prompting to nudge the model out of that default voice because post training reinforced it. They will likely shift it once these AI-isms are known and recognized widely. I'd assume the nextgem models under training now will get negative feedback from the human evaluators for talking too AI-like and then there will be new AI smells to calibrate to.

I'm not sure this invalidates anything I'm saying. The tools currently produce terrible-quality output unless actively prompted to stop producing terrible-quality output. To me, that's a bug, and I don't think post-training and popular preference excuses the tool's behavior. There's no value in normalizing slop if it's so easy to fix.

Should Youtube "fix" the proliferation of exaggerated faces in thumbnails?

People prefer the slop, at least until they collectively notice the AI smell, at which point the post training will likely train it out of models and slop will have new characteristics that take a while for the mainstream to detect.

This is like saying people shouldn't need to be trained for a job.

There's no reason to expect a general purpose model to know what you want when you've not given it any training in what to do for your specific case.

And in this case, the models do far better than humans: Most humans can't just switch to copy arbitrary tone, just by giving them a page worth of text. We don't even need to actually train/fine-tune these models further - we just need to actually fully specify the task we give them to get them to write well.