You can't predict almost anything anyway. The relevant distribution for acting is a utility+risk distribution over various sets of predictions.
If you compute that, most of ML/AI isnt very useful for most of anyone. It's kinda interesting that generative AI finally achieved something here, given our 'distributions for action'.
Of course the popular conversation isnt there yet, people still think these things work. When more people find their utility hit by their failures, i think we'll arrive back to status-quo-ante-openai where people turn off siri.
Nevertheless -- there is an achivement here (finicial in paying for training; and legal in whitewashing copyright away) -- which will have some impact
Here is a pastebin: https://pastebin.com/DCqgxx8E
I'd only add that certainty requirements for real-world target applications can differ substantially. I.e. engineering vs art.
A toy box that gives magic answers 85% of the time is incredibly useful in some scenarios -- e.g. seeding the beginning of a manual research process with initial topics.
Which seems a general rule of thumb for deploying GenAI into production these days: find use cases where there are minimal consequences to being occasionally wrong.
In my head, that's the Netflix recommendation test. What's the impact if Netflix gives me a bad recommendation? Consequently, that's how aggressive they can be with their models.