Lol they will the second they get hit with that “you need to get parental consent” screen, that’s how it happened to us all.
The normie services are increasingly tied to real world identities, through verification methods that involve phone numbers and often government-issued IDs. As the regulatory requirements tighten on these services, it’ll be increasingly more difficult to create anonymous/alt accounts. Just because it was easy to anonymously create a new Gmail or a new Instagram account 10 years ago doesn’t mean it’s easy today. It’s a common complaint that things like an Oculus requires a Meta account that requires some tedious verification.
I don’t think it’ll ever be perfect, but it will probably be enough for the network effects of these types of services to be severely dampened (and then a feedback loop where the difficult-to-use-as-a-teen services have too much friction and aren’t being used, so nobody else feels it is worth the effort to set up). Especially if teens’ parent-supervised accounts are locked to their devices, in an increasingly cloud-reliant hardware world.
Because it’s not actually always true that garbage in = garbage out. DeepMind’s Alpha Zero trained itself from a very bad chess player to significantly better than any human has ever been, by simply playing chess games against itself and updating its parameters for evaluating which chess positions were better than which. All the system needed was a rule set for chess, a way to define winners and losers and draws, and then a training procedure that optimized for winning rather than drawing, and drawing rather than losing if a win was no longer available.
Face swaps and deep fakes in general relied on adversarial training as well, where they learned how to trick themselves, then how to detect those tricks, then improve on both ends.
Some tech guys thought they could bring that adversarial dynamic for improving models to generative AI, where they could train on inputs and improve over those inputs. But the problem is that there isn’t a good definition of “good” or “bad” inputs, and so the feedback loop in this case poisons itself when it starts optimizing on criteria different from what humans would consider good or bad.
So it’s less like other AI type technologies that came before, and more like how Netflix poisoned its own recommendation engine by producing its own content informed by that recommendation engine. When you can passively observe trends and connections you might be able to model those trends. But once you start actually feeding back into the data by producing shows and movies that you predict will do well, the feedback loop gets unpredictable and doesn’t actually work that well when you’re over-fitting the training data with new stuff your model thinks might be “good.”