It's not right to say that ML output isn't good at practical tasks. It is and it's already in use and has been for ages. The conversation about these is guided by the relatively anecdotal fact that chatbots and image generation got good so this stuff went viral, but ML models are being used for a bunch of practical uses, from speeding up repetitive, time consuming tasks (e.g. cleaning up motion capture, facial modelling or lip animation in games and movies) or specialized tasks (so much science research is using ML tools these days).

Now, a lot of those are done using fully owned datasets, but not all, and the ramifications there are also important. People dramatically overestimate the impact of trash product flooding channels (which is already the case, as you say) and dramatically underestimate the applications of the underlying tech beyond the couple of viral apps they only got access to recently.