The actions of a few companies does not invalidate the entire category. There are open models, trained on previously aggregated datasets (which, for what its worth, nobody had a problem with being collected a decade ago!), doing research to make training and usage more efficient.

The technology is here. I think your assessment on its relevance is not informed by actual usage, your frame of its origins is black/white (rather than understanding the actual landscape of different model approaches), and that your lack of interest in using it does nothing to change the absolutely massive shift that is happening in the nature of work. I'm a Product Manager, and the Senior Engineer I work with has been reviewing my PRs before they get merged - 60%+ were merged without much comment, and his bar is high. I did half of our last release, while also doing my day job. Safe to say, his opinion has changed based on that.

Were they massive changes? No. But these are absolutely impactful in the decision calculus that goes into what it takes to build and maintain software.

The premise of my argument is that what you see as "fatal flaws" are an illusion created by bias (which bleeds into the second-hand perspectives you cite just as readily as it does the media), rather than your direct and actual validation that those flaws exist.

My suggestion is to be an objective scientist -- use the best model released (regardless of origins) with minor research into 'best practices' to see what is possible, and then ask yourself if the 'origins' issue were addressed by a responsible open-source player in 6-12 months, whether it would change anything about your views on the likely impact of this technology and your willingness to adopt it.

I believe that it's coming, not because the hype machine tells me so (and it's WAY hyped) - But because I've used it, seen its flaws and strengths, and forecast how quickly it will change the work that I've been doing for over a decade even if it stopped getting better (and it hasn't stopped yet)