I’m just adding on to this.

Scale here comes in multiple layers, including the capacity/speed at which these technologies can be deployed, as well as the breadth of domains/fields/applications they touch upon, not to mention the unintended consequences when at scale. It’s not only they are much faster, cheaper, but they come almost all at once and have the potential to affect so many fields. Heck, “GP” means general purpose in GPT. Plus the effects of scale can be extremely unpredictable that we should not underestimate (disinformation campaigns now come much cheaper and easier, trust erodes even further).

I don’t know much about history so please correct me, but photography “replacing” painting may be quite specific, that painters could probably have adapted or switched to another professions. I think one commenter stated that the transition was “smoother”. In the case of these generative techs, this affects the livelihood of a whole bunch more of people (possibly both in absolute and per-capita number) that will need to grapple with what they’re going to do with their life, and have to do it fast.

One branch of the arguments I’ve been seeing is about capability comparison, sometimes even anthropomorphizing tech/companies. While I find that interesting and valuable intellectually, I personally think the conversations need to be more about the labor aspects.

Learning takes time and people need to eat. In the name of progress, society sometimes forgets or brushes over the “casualties” it leaves behind. I think many would benefit from this tech, but let’s hope they have a meal on their table doing so.

We don’t want a dystopian where they use these techs to generate the illusions of enjoying a feast over a big hotpot, while in reality it’s just a can of tomato soup for a family of 5.